Microbial Phosphorus Transformation Pathways in Piggery ...€¦ · covered anaerobic piggery...
Transcript of Microbial Phosphorus Transformation Pathways in Piggery ...€¦ · covered anaerobic piggery...
Microbial Phosphorus Transformation Pathways in Piggery Waste Treatment
Systems
MLMAW Weerasekara
M.Sc. in Applied Biology, Saga University, Japan, 2009 M.Sc. in Environmental Soil Science, Postgraduate Institute of Agriculture,
University of Peradeniya, Sri Lanka, 2007 B.Sc. in Agriculture (Spp), University of Peradeniya, Sri Lanka, 2005
This thesis is presented for the degree of
Doctor of Philosophy at The University of Western Australia,
School of Earth and Environment,
Faculty of Science
2015
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DEDICATION
I dedicate this thesis to my loving husband, son,
mother, father, and the family whose love is boundless.
This work is the recompense for you standing beside me
like a pillar of strength, giving me warm
encouragement on the way to success.
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DECLARATIONS
I, MLMAW Weerasekara, declare that this thesis was composed by me and the research
detailed was conducted by me, except for the instances detailed and quoted in the text
and acknowledgments.
MLMAW Weerasekara
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ACKNOWLEDGEMENT
The entire work of this study has endowed me with much knowledge and experience.
Completing this was a testing task as I was in an entirely new environment outside my
home country, Sri Lanka. I believe this work would not have been a reality without the
help and support of many individuals along the way. So take this opportunity to
acknowledge everyone for their contributions with great pleasure.
I wish to express my first and foremost gratitude to my supervisors, Winthrop Professor
Lynette Abbott, Dr. Sasha Jenkins, and Winthrop Professor Anthony O'Donnell, for
offering me a scholarship to conduct my postgraduate studies under their supervision. I
am forever indebted to them for formulating and framing a very useful research theme,
and also for their intellectual suggestions, precious advice, constant supervision,
encouragement and most importantly for offering their valuable time during this study,
despite their crowded schedules. I also owe a depth of gratitude to Professor Abbott and
Dr Jenkins for the kindness and enormous help they have extended towards me in
making my life in Australia welcome and comfortable. My deepest appreciation is
extended to Winthrop Professor Andy Whiteley, who helped me to meet the challenges
posed by bioinformatics, thesis corrections and manuscript preparation. Also, I am
forever indebted to him for his intellectual suggestions, advice, encouragement, and
most importantly for offering his valuable time during this study.
I owe a special word of gratitude to Professor Richard Allcock and his research group
for performing sequencing and providing me facilities for analysing bioinformatics.
Special thanks to Dr Ela Eroglu for her valuable comments on my thesis. I deeply
acknowledge the support and time devoted by Ian Waite, my laboratory scientific
officer in helping me understand the molecular techniques, carrying out DNA library
preparations, assisting with field sampling, and other laboratory work. I owe a special
word of gratitude to the staff at the Centre for Microscopy at the University of Western
Australia, especially T Lee-Pullen, P Rigby, Irma Larma, and M Linden for their
assistance with flow cytometry, and epi-fluorecence microscopic analysis. I would also
like to acknowledge Andy Gulliver and John Barton at the Cwise for their advice and
providing me compost.
My sincere appreciation also goes to Dr Zakaria Solaiman, Dr Falko Mathes, Dr Yoshi
Sawada, Dr Suman Geroge, Dr.Matthias Leopold, Associate Professor Louise Barton
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and Professor Dan Murphy for their advice during various events throughout my
research.
I am grateful for the sponsorship provided by The University of Western Australia
through the International Research Fees (SIRF) programme for my studies in Australia
and to the people of this lovely university for their kindness towards me. I would also
like to acknowledge the Australian Pork Limited (APL) for partially funding my study.
I owe a special word of gratitude to Michael Smirk, Darryl Roberts, Kim Duffecy, for
their valuable time on technical assistance. I thank administration staff in the Soil
Science office of the School of Earth and Environment (SEE), Margaret Pryor, Gail
Ware, Karen Newnham, and Julia Carless for their assistances during my study. I would
also like to thank the Soil Biology and Molecular Ecology group members SEE. I
extend my heartfelt appreciation to my colleagues in SEE, especially Dr Vanesa Glez-
quiñones, Dr Basu Dev Regmi, Dr Khalil Kariman, Dr Hazal Gaza, Laila Harvard,
Joginder Gill, Bede Mickan, and Jing-wei Fan for their kindness and help. I especially
thank Lalith and Tamara and all my Sri Lankans friends for making my life in Perth
welcome and comfortable for my family. Special thanks also to my friends, Shezmin
Zavahir and Nilusha Henakaarchchi.
I owe my heartfelt gratitude to my parents, son, sisters, brother, and father-in-law for
their support, encouragement and for being with me to share my happiness and sorrow
at all times. I owe a special word of gratitude to my mother and father for their endless
support, help and love throughout my studies. Last but not least, I am lost for words to
thank my husband, Kandula, for his everlasting love, encouragement and support.
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Abstract
Agricultural wastewaters arising from the pork industry are often high in phosphorus
(P) and require treatment to control environmental loading of soluble P and to improve
efficiency of its re-use in agriculture. Knowledge of the taxa mediating P transformation
pathways and the factors that regulate bacterial activities in the piggery wastewater
treatment processes can be used to optimise the recovery of P form wastewater.
However, current knowledge is limited concerning P transformation pathways in
covered anaerobic piggery wastewater treatment systems. This thesis sought to
characterise taxa involved in P transformation pathways (i.e. P mineralisation, P
solubilisation, and polyphosphate accumulation) and their metabolic functions in a
model covered anaerobic piggery wastewater treatment systems using novel molecular
techniques. Further, the effect of value added products from piggery waste remediation
on plant growth, soil nutrient improvement and fungal-bacterial community
composition in soil was demonstrated.
The first objective was the baseline characterisation of all compartments involved in a
model covered anaerobic piggery wastewater treatment system in terms of physico-
chemical properties, microbial community composition and P cycling potential
(Chapter 3). Physico-chemical characteristics of samples taken from all the
compartments (pit, holding tank, covered anaerobic pond digester, and aerobic
pond/evaporation pond) were done. Bacterial community composition of the whole
system was assessed using 16S rRNA Ion Tag sequencing and putative genetic potential
of P metabolisms in terms of P mineralisation, P solubilisation, and polyphosphate
(polyP) accumulation was assessed by assigning functional annotations to shotgun
metagenomic sequences. This study identified the key components of the bacterial
community involved in the whole process of piggery waste treatment system. Both 16S
rRNA Ion Tag sequencing and metagenome analyses showed that bacterial community
composition of the initial facultative anaerobic stages (i.e. pits and holding tank) and the
covered anaerobic digester was relatively similar but remarkably varied to that of the
aerobic stage (i.e. evaporation pond). Resource availability and environmental factors
between the anaerobic stages and the aerobic stage were the key drivers in shaping the
bacterial community dynamics among these compartments of piggery wastewater
treatment system. Piggery wastewater was high in both organic and soluble P and its
distribution varied among the stages. Genes responsible for P mineralisation were
highest in the covered anaerobic pond digester and polyP accumulation was greatest in
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the treated piggery wastewater contained within the aerobic pond/evaporation pond.
These findings identified the critical treatment stages for further study to understand P
solubilisation, P mineralisation and polyP accumulation in the model piggery
wastewater treatment process.
In order to assess population and cellular level processes within key treatment stages,
microscopy, cell sorting and high throughput DNA sequencing approaches were used to
determine the extent to which P mineralisation contributed to P cycling within the
piggery wastewater treatment system (Chapter 4). P mineralisation was comparatively
higher in anaerobic ponds, where organic P was higher, when compared to the aerobic
pond. Bacteroidales, Clostridiales, Campylobacterales, and Synergistales were the most
dominant groups of P mineralising bacteria in each stage of the wastewater treatment
process occupying stable community compositions, with different degrees of
abundance, along the waste treatment process. The knowledge gained from the
composition of P mineralising microbial community serve as a basis for controlling
their function in the piggery waste treatment system.
The third objective was to identify key microbes involved in polyP accumulation and
the potential for its enhancement under imposed acidic treatments, as a novel strategy
for enhanced biological P removal (Chapter 5). Abundance, identity and functionality
of active polyP accumulating organisms (PAOs) under two pH environments (pH 5.5
and 8.5) were assessed using a range of high throughput single cell and next generation
sequencing methods. Significantly higher polyP accumulation was observed at pH 5.5
compared to pH 8.5, with enrichment of polyphosphate kinase and exopolyphosphatase
genes at pH 5.5. Functionally active polyP accumulating bacteria were identified as
Aeromonas hydrophila, Aeromonas salmonicida, Acinetobacter baumannii, Bordetella
pertussis, Citrobacter koseri, Escherichia coli, Enterobacter sp. Klebsiella,
Pseudomonas aeruginosa, Salmonella enterica, and Shigella flexneri. These findings
serve as a basis to understand and manipulate PAOs community diversity and
functionality to enhance P uptake by altering the pH in the waste treatment process.
The fourth objective was to demonstrate the use of value added products from piggery
waste remediation through addition of pelletised piggery compost within the root zone.
Pelletised fertilisers derived from remediated piggery waste were applied with a low
rate of inorganic fertiliser to assess plant growth, soil nutrient improvement and fungal-
bacterial community composition (Chapter 6). Banding of a pelletized composted
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piggery waste (Balance®) with a granulated inorganic fertiliser (Agras®) was most
effective on wheat growth and soil fertility compared to the other soil amendments
tested. The combined Balance-Agras soil amendment application resulted in an increase
in soil available P, plant P uptake, and shoot and root dry weights and decrease
percentage colonisation of arbuscular mycorrhizal (AM) fungi. Those positive effects
are most likely to reflect the plants and bacterial community responses to changes in
soil nutrient levels due to the application of soil amendments.
Ultimately, this research demonstrated that knowledge of the taxonomic and functional
identities of P mediating bacteria at each stage in the piggery wastewater treatment
process, together with exploitation of P mineralising bacteria and polyphosphate
accumulating organisms, provided a novel strategy for improving the waste treatment
process and developing value added fertilisers for land application. Critically, this
research impacts directly upon sustainable agricultural practices by a more effective
management of phosphorus resources through the potential development of piggery
waste by-products (e.g. pelletised piggery compost). From an environmental
perspective, the recycling of nutrients from piggery waste and adoption of new practices
(e.g. covered anaerobic pond digesters) will reduce waste accumulation and minimise
nutrient leaching, both of which are significant risk factors which currently contribute to
global eutrophication of water bodies and increased greenhouse gas emissions.
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TABLE OF CONTENTS Page
DEDICATION II
DECLARATIONS III
ACKNOWLEDGEMENT IV
ABSTRACT VI
TABLE OF CONTENTS IX
LIST OF FIGURES XV
LIST OF TABLES
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1 GENERAL INTRODUCTION 1
1.1 Background, research gaps, and expected outcomes 1
1.2 Thesis objectives and hypotheses 6
1.2 Thesis structure 7
2 LITERATURE REVIEW 10
2.1 Overview 10
2.2 Recycling piggery waste: general background 11
2.2.1 Risks and benefits of re-using pig waste 11
2.2.2 Recycling piggery waste by-product: Best management practices for agriculture
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- Anaerobic digestion 14
- Composting and pelletising 16
- Removal of excess P in piggery waste 17
2.3 Enhanced Biological P removal (EBPR) for removing excess P in piggeries
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2.4 P transformation in piggery waste and knowledge gaps in P cycling in wastewater
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2.4.1 P mineralisation 23
2.4.2 P precipitation/ P solubilisation 24
2.5 Role of P mediating microorganisms and their diversity in soil 25
2.6 Current molecular and microscopy techniques for identifying P cycling microbes and their advantages and limitations
27
2.7 New advances in molecular and microscopy technology to resolve problems encounter with P cycling microorganisms
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2.7.1 Enzyme-labeled fluorescence (ELF) coupled to epi-fluorescent microscopy, flow cytometry, and cell sorting
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2.7.2 Ion Torrent sequencing 39
2.7.3 Community metagenomic 39
2.8 Rationale 40
3 MICROBIAL COMMUNITY COMPOSITION AND PHOSPHORUS CYCLING POTENTIAL WITHIN A COVERED ANAEROBIC POND SYSTEM TREATING PIGGERY WASTE
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3.0 Abstract 42
3.1 Introduction 43
3.2 Material and Methods 45
3.2.1 Farm description and sampling 45
3.2.2 Physico-chemical characterization of pig waste samples 46
3.2.3 Isolation and identification of P mineralising bacteria and P solubilising bacteria
47
3.2.4 DNA extraction and 16S rRNA Ion Tag sequencing 48
3.2.5 Whole-genome-shotgun sequencing 49
3.2.6 Multivariate statistical analyses 49
3.3 Results 50
3.3.1 Physico-chemical characteristics of a piggery waste treatment system
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3.3.2 Isolation and identification of P mineralising bacteria (PMB) and P solubilising bacteria (PSB)
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3.3.3 Dynamics of bacterial populations in different stages of waste treatment
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3.3.4 Whole-genome-shotgun sequencing 57
3.3.5 Functional hierarchical classification analysis 61
3.3.6 Distribution of metabolic functions in relation to P cycling 62
3.3.6.1 Distribution of metabolic functions in relation to P mineralisation
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3.3.6.2 Distribution of metabolic functions in relation to P solubilisation
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3.3.6.3 Distribution of metabolic functions in relation to polyP accumulation
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3.4 Discussion 68
3.4.1 Characterisation of microbial community composition and diversity in the wastewater treatment process
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3.4.2 P mineralising and solubilising potential as revealed by the culture dependant detection
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3.4.3 Distribution of metabolic functions in relation to P mineralisation
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3.4.4 Distribution of metabolic functions in relation to polyp accumulation
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3.4.5 Distribution of metabolic functions in relation to P solubilisation
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3.4.6 Recycling potential of the piggery wastewater 73
3.5 Conclusions 75
4 PHOSPHORUS MINERALISING BACTERIA FOR NUTRIENT RECOVERY FROM HIGH PHOSPHORUS PIGGERY WASTEWATER EFFLUENTS
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4.0 Abstract 76
4.1 Introduction 77
4.2 Materials and Methods 79
4.2.1 Field sample collection and preparation 79
4.2.2 Culture conditions and ELF staining 79
4.2.3 Optimisation of incubation time necessary for ELF labelling 80
4.2.4 Field Sample preparation for epi-fluorescence microscopy 80
4.2.5 Field Sample preparation for flow cytometry 80
4.2.6 Cell sorting 82
4.2.7 Data analysis 82
4.2.8 DNA extraction and 16S rRNA tag sequencing 83
4.3 Results 84
4.3.1 Assessment of PO4ase activity of pure cultures using ELF®97 phosphate
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4.3.2 Optimisation of incubation time necessary for ELF-labeling 84
4.3.3 Optimisation of dual staining protocol for epi-fluorescence microscopic and flow cytometric detection of ELFA labeled cells
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4.3.4 Accuracy of ELF labeling and defining the gating strategy with ELF+SYTO9
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4.3.5 In situ applications 90
4.3.6 Community structure of PMBs within the piggery waste treatment process
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4.4 Discussion
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4.4.1 Optimisation of incubation time necessary for ELF labeling 94
4.4.2 Optimisation of dual staining protocol in epi-fluorescence microscopy and flow cytometric detection of ELFA labeled cells
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4.4.3 In situ applications 95
4.4.4 Community structure of PMB within the piggery waste treatment process
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4.5 Conclusions 97
5 ANALYSIS OF POLYPHOSPHATE ACCUMULATING ORGANISMS IN HIGH PHOSPHORUS PIGGERY WASTEWATER
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5.0 Abstract 99
5.1 Introduction 100
5.2 Materials and Methods 102
5.2.1 Sampling site and lab-scale incubation experiment 102
5.2.2 Bacterial Strain, Culture Conditions 102
5.2.3 Sample preparation for epi-fluorescence microscopy and flow cytometry
103
5.2.4 Titration of DAPI concentration, epi-fluorescence microscopy, and flow cytometry
104
5.2.5 DNA extraction and 16S rRNA tag sequencing 105
5.2.6 Whole-genome-shotgun sequencing 105
5.3 Results 106
5.3.1 Titration of DAPI concentration required for epi-fluorescence and flow analyses of polyP accumulation
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5.3.2 PolyP accumulation in high Pi loaded lab microcosm experiments
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5.3.3 Community structure of PAOs in piggery waste 111
5.3.4 Metagenomic analysis of piggery wastewater samples treated at pH 5.5
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5.4. Discussion 122
5.5 Conclusions 125
6 EFFECT OF LOW RATE APPLICATION OF BANDED PELLETISED PIG COMPOST ON PLANT GROWTH AND SOIL MICROBIAL COMMUNITY COMPOSITION
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6.0 Abstract 126
6.1 Introduction
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6.2. Materials and Methods 129
6.2.1 Experimental design 129
6.2.2 Soil collection and potting 132
6.2.3 Soil and plant analyses 132
6.2.4 Determination of root length and arbuscular mycorrhizal (AM) colonisation
133
6.2.5 DNA extraction and Ion Tag sequencing 133
6.2.6 ANOVA and multivariate statistical analysis 135
6.3. Results 136
6.3.1 Effect of soil amendments on plant growth, P uptake and AM colonization
136
6.3.2 Effect of different soil amendments on soil properties 139
6.3.3 Effect of soil amendments on rhizosphere and root colonising bacterial population dynamics
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6.3.3.1 Changes in the rhizosphere bacterial community profile of the different treatments to the measured plant and soil variables
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6.3.3.2 Changes in the root colonising bacterial community profile of the different treatments to the measured plant and soil variables
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6.4 Discussion 155
6.4.1 Effects of soil amendments on plant growth and soil fertility 155
6.4.2 Effect of soil amendments on beneficial bacterial associated with rhizosphere soil and root surface
156
6.4.3 Effect of soil amendments on AM fungal colonisation 158
6.5 Conclusions 159
7 GENERAL DISCUSSION AND CONCLUSION 160
7.1 Summary of the work performed 160
7.1.1 Overview 160
7.1.2 Specific objectives 160
7.2. Key factors driving the P cycling bacterial diversity and activity in the piggery waste treatment process
161
7.2.1. Abiotic factors 164
7.2.2. Biotic factors 166
7.2.3 Management practices 167
7.3 Methodological Considerations 167
7.3.1 Sampling strategy
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7.3.2. Methodological considerations in fluorescence staining, flow cytometry, and cell sorting
169
7.3.3 Methodological considerations to 16S rRNA Ion Tag sequencing
171
7.3.4. Discrepancy of degree of P mineralisation as revealed by ELF coupled to flow cytometry and metagenomics
172
7.3.5 Limitations in the pot trial 173
7.4 Underlying mechanisms in P cycling and proposed pathways for the piggery waste system
173
7. 5 Research Perspectives 177
7.5.1 Relevance to scientific community 177
7.5.2 Relevance to small scale and large scale pig farmers 178
7.6 Future research directions 178
7.6.1 Research directions for methodological development in tracking P cycling in environments
179
7.6.2 Research directions for improving the current piggery waste treatment process
180
7.6.3 Research direction for enhancing the low rate application of pelletised pig compost
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APPENDICES 182
REFERENCES 189
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LIST OF FIGURES Page
1.1 Approach for minimising environment loading of inorganic P (Pi) in piggery waste effluent through manipulation of microbial activities to improve its recycling potential.
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1.2 Structure of the thesis and the relationship between chapters. 8
2.1 (a) Growth of pork industry over the last decade (b) and meat production by type (Source FAO, 2013).
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2.2 A process of best management practices for piggery waste. 13
2.3 The process of anaerobic digestion. Modified from Batstone et al. (2000). 15
2.4 Covered anaerobic pond (CAP) (a) at initiation, and (b) under operation, which captures biogas produced for odour and GHG emission control at Medina Research Station, Department of Agriculture and Food, Western Australia (DAFWA).
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2.5 Metabolism of PolyP accumulating organisms under anaerobic and aerobic conditions and resulting by-products. Modified from Kulakovskaya et al. (2012).
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2.6 Probable P transformation pathways in piggery waste. 23
2.7 Proposed integrated approach for understanding P cycling pathways. 38 3.1 Ability of P mineralization and P solubilisation among isolates from the
waste treatment system at Medina Research Station. Ability of P solubilisation and mineralisation was assessed based on diameter of the clear zones around the colonies. a) high and low P mineralising ability, b) low solubilising ability(+), and c) high solubilising ability(+++).
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3.2 Alpha diversity rarefaction plots of observed species for different stages in the wastewater samples (a). Microbial diversity indicated by Shannon’s index (b) (Calculation of richness and diversity estimators was based on OTU tables rarified to the same sequencing depth, the lowest one of total sequencing reads; 7340).
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3.3 Identities and % composition of the bacteria, at class level, from stages in the waste treatment system at Medina Research Station.
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3.4 CCA biplot showing the relationship between a) microbial community composition or b) individual bacterial taxa and environmental variables in each sampling point of piggery wastewater treatment process. Plots on the graph represent the community composition at each sampling point () and individual taxa (▲). Arrows represent the environmental variables (EC, VS, TN, TC, Pi, C:N ratio, TS, TP, OP, Ca, Mg, K, pH, Ammonia).
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3.5 Community DNA composition of piggery waste treatment process based upon functional gene phylogenies (a). Microbial community composition obtained by taxonomic identity linked to functional gene content by MG-RAST analysis (b).
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3.6 Relationships between (a) the abundance of alkaline phosphatase gene involved in regulation of P mineralisation and the respective organic P concentration, and (b) the abundance of alkaline phosphatase gene and organic P concentration.
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LIST OF FIGURES continued Page
3.7 Abundance of gene involved in PolyP synthesis (polyphosphate kinase) and hydrolysis (exopolyphosphatase) at the different stages of the piggery waste treatment process.
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4.1 Preparation of the piggery waste effluent samples for flow cytometry. 81
4.2 Emission and excitation spectrums of ELF, DAPI and PI and filter settings for the Flow Cytometry (BD Influx). ELF97 was excited by 355nm UV laser, and detected using 550LP and 585/29BP filters. DAPI, Syto9 and PI were excited by UV 355nm, 488nm Blue and 561nm Yellow-Green lasers and emission collected with 450/50BP, 520/15BP, 670/30BP filters respectively.
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4.3 The Pseudomonas sp. (positive strain of P mineralization) grown in the P-limited PSM liquid medium was able to form clear zone around the colonies on PSM solid medium confirming their ability to mineralise organic P in the selective medium (a) whereas no E. coli (negative strain of P mineralization) colonies appeared on PSM solid medium (b). Epi-fluorescence microscopic images of DAPI stained cells of Pseudomonas sp. grown in P-limited PSM liquid medium (c) and that of ELFA stained cells (d).
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4.4 Ratio of ELF-labeled cells (%) with respect to the incubation time (min). Error bars represent the standard deviation between triplicate measuerements.
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4.5 Detection of PO4ase activity of piggery effluent using epi-fluorescence microscopy (a, b, and c), and flow cytometry (d, e, and f) after staining with DAPI (a and d), SYTO9 (b, and e), and PI (c, and f). Sample was gated on single cells and deployed is the percentage of ELF+ cells to the total bacteria. X Axes of the cytograms are ELF, DAPI, SYTO9 or PI fluorescence intensity in arbitrary units (a.u.).
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4.6 Flow cytograms showing (a) cells pre-fixed with paraformaldehyde and ELF-stained, (b) cells pre-fixed with paraformaldehyde and ELF + SYTO9, (c) unstained sample, (d) first single stained sample (SYTO9 only), (e) second single stained sample (ELF only), and (f) dual stained sample (ELF + SYTO9). Y axis represents the fluorescence intensity of ELFA, while X axis shows the fluorescence intensity of SYTO9.
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4.7 The percentages of ELF+ve cells (▲) and respective Pi levels (grey columns) at different stages of piggery waste treatment process.
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4.8 (a) Alpha diversity rarefaction plots of OTUs for different wastewater samples. (b) Microbial diversity indicated by Shannon diversity. (Calculation of richness and diversity estimators was based on OTU tables rarified to the same sequencing depth, the lowest one of total sequencing reads; 7396).
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4.9 Diversity of PMB communities within the piggery waste treatment process at (a) Phylum level, and (b) Order level.
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5.1 Microcosm set-up and subsequent sample preparation for epi-fluorescence microscopy, and flow cytometry.
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LIST OF FIGURES continued Page
5.2 DAPI staining of pure culture of Pseudomonas syringe cells for polyP analysed by epi-fluorescence microscopy (a-f) and flow cytometry (g-l). Cells were subsequently stained with (a/ g) 0.25; (b/ h) 0.5; (c/ i) 1; (d/ j) 5; (e, k) 7; and (f/ l) 15 µg/mL of DAPI. In epi-fluorescence micrograms (a-f), intracellular polyP granules form DAPI-polyP complexes appear yellow-green, whilst DAPI bound to DNA appears blue. In flow cytograms (g-l), sample was gated on single cells and deployed is the percentage of cells with (DAPI-polyP) and without accumulated polyP (DAPI-DNA).
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5.3 Aerobic pond samples stained for polyP. Cells were incubated with (a) 1 mg/L-P, (b) 10 mg/L-P, and (c) 50 mg/L-P; and were stained with 15 µg/L of DAPI followed by the flow cytometric analysis. Sample was gated on single cells and deployed is the percentage of cells with (DAPI-polyP) and without accumulated polyp (DAPI-DNA).
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5.4 Overall phosphate removals from the pond water at different pH treatments (3a). Percentage of the cellular content in the form of DAPI-PolyP and DAPI-DNA complex at pH 5.5 and 8.5 (control), for both filtered and unfiltered samples (3b).
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5.5 PolyP stained cells from aerobic pond at pH 5.5 and 8.5 for filtered (a and b, respectively) and unfiltered (c and d, respectively) samples viewed under epi-fluorescence microscopy. Intracellular polyP granules form DAPI-polyP complexes appear yellow-green, whilst DAPI bound to DNA appears blue. Flow cytograms of polyP stained cells from aerobic pond at pH 5.5 and 8.5 for filtered (e and f, respectively), and unfiltered (g and h, respectively) samples.
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5.6 (a) Alpha diversity rarefaction plots of phylogenetic diversity of 3 EBPR systems. (b) Microbial diversity indicated by Shannon diversity. (Calculation of richness and diversity estimators was based on OTU tables rarefied to the same sequencing depth, the lowest one of total sequencing reads; 5200).
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5.7 Identities and relative abundance (%) of the bacteria in 3 EBPR systems (a) at class level. Composition of the main polyP accumulators, Gammaproteobacteria under (b) pH 5.5 unfiltered, and (c) pH 5.5 filtered samples.
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5.8 Abundance of genes involved in polyP synthesis (polyphosphate kinase) and hydrolysis (exopolyphosphatase) in the three EBPR systems (pH 5.5 filtered, pH 5.5 un-filtered, and pH 8.5 un-filtered).
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6.1 Effect of treatments on (a) shoot dry weight, (b) root dry weight, and (c) root length from 3 harvests (4, 6 and 8weeks) in soil amended with (1) Agras100 (2) Balance100 (3) Balance50/Agras50 (4) control. All treatments were done in triplicate and error bars indicate the standard error where n=3.
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6.2 Effect of treatments on (a) P uptake (mg/pot), and (b) P concentration (%) from 3 harvests (4, 6 and 8weeks) in soil amended with (1) Agras100 (2) Balance100 (3) Balance50/Agras50 (4) control. All treatments were done in triplicate and error bars indicate the standard error where n=3.
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6.3 Effect of treatments on (a) arbuscular mycorrhizal fungi colonised root length (m/pot), and their colonisation (%) from 3 harvests (4, 6 and 8weeks) in soil amended with (1) Agras100 (2) Balance100 (3) Balance50/Agras50 (4) control. All treatments were done in triplicate and error bars indicate the standard error where n=3.
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6.4 Relationship between (a) soil available P (mg/kg) and plant P uptake (mg/kg), and (b) soil available P (mg/kg) and AM fungal colonization (%). All treatments were done in triplicate and error bars indicate the standard error where n=3.
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6.5 Alpha diversity rarefaction plots of phylogenetic diversity for (a) rhizosphere soil bacteria, and (b) root colonising bacteria. Value represents the mean of triplicate determinations.
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6.6 Relative abundance of (a) rhizosphere bacteria and, (b) root colonised bacteria at phylum level by different soil amendments. Value represents the mean of triplicate determinations. (Relative abundance <1% is ignored).
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6.7 Canonical correspondence analysis (CCA) biplot showing the relationship between (a) different soil amendment and measured plant and soil variables b) individual taxa distributions with measured plant and soil variables (b) for rhizosphere soil taken from pot experiment under different fertiliser treatments () at 6 weeks. Arrows represent the measured variables [pH, NH3, Colwell P, Plant P uptake, electrical conductivity (EC), Shoot and root DW, AM colonised root length (RL), and AM colonisation %]. Triangles (▲) on the graph (b) represent individual bacterial taxa. Taxonomic identities for the bacterial sequences are given in Table 6.10.
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6.8 Canonical correspondence analysis (CCA) biplot showing the relationship between (a) different soil amendment and measured plant and soil variables b) individual taxa distributions with measured plant and soil variables (b) for root colonising bacteria in soil taken from pot experiment under different fertiliser treatments () at 6 weeks. Arrows represent the measured variables [pH, NH3, Colwell P, Plant P uptake, electrical conductivity (EC), Shoot and root DW, AM colonised root length (RL), and AM colonisation %]. Triangles (▲) on the graph (b) represent individual bacterial taxa. Taxonomic identities for the bacterial sequences are given in Table 6.11.
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7.1 General diagram showing some of the factors influence of the P cycling microbial diversity and activity in wastewater treatment plants.
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7.2 Probable mechanisms of P transformations in the CAP digester and Evaporation Pond under its natural states (a). A proposed method for improvement of the current waste treatment process (b).
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LIST OF TABLES Page
2.1 Examples of microorganisms involved in polyP accumulation and their optimal conditions.
21
2.2 Examples of microorganisms involved in P transformation in soil. 28
2.3 Common methods used in identifying P mediating microorganisms highlighting their advantages and disadvantages.
32
2.4 Enzymes and their encoding genes in P metabolism. 37
3.1 Physical and chemical characteristics of different wastewater treatment compartments at Medina Research Station, Western Australia.
50
3.2 Genetic characterisation of the isolated P mineralising and P solubilising bacteria.
53
3.3 Taxonomic identities for the CCA biplot showing the relationship between measured variables and individual taxa distributions for different stages of waste treatment system.
58
3.4 Comparison of relative abundance (%) of the top 10 most abundant bacterial groups within the CAP-Bottom and Evaporation Pond as revealed by tag sequencing and metagenomic analyses.
60
3.5 Metabolic profiles based upon metagenomic functional classification within different compartments of waste treatment process
61
3.6 P mineralising potentials at different stages of piggery waste treatment process.
63
3.7 P solubilising potentials at different stages of piggery waste treatment process.
65
3.8 PolyP accumulating potentials at different stages of piggery waste treatment process.
67
5.1 Summary of the analysis of MG-RAST of the 3 EBPR systems (pH 5.5 filtered, pH 5.5 un-filtered, and pH 8.5 un-filtered).
114
5.2 Phylogenetic taxonomic composition of 3 EBPR systems based on metagenomics analysis (pH 5.5 filtered, pH 5.5 un-filtered, and pH 8.5 un-filtered)
116
5.3 Most abundant gene sequences involved in P metabolism in 3 EBPR systems (pH 5.5 filtered, pH 5.5 un-filtered, and pH 8.5 un-filtered).
120
5.4 Functional affiliations of PAOs in 3 EBPR systems (pH 5.5 filtered, pH 5.5 un-filtered, and pH 8.5 un-filtered).
121
6.1 Soil amendments used in this experiment and their corresponding abbreviations.
130
6.2 Typical characteristics of the pelletised compost, Balance®. 131
6.3 Typical analysis of the granulated fertiliser, Agras®. 131
6.4 Relative N and P application rates of each 3 fertiliser treatments applied to wheat. Rates are shown in both kg ha-1 and mg/pot basis.
131
6.5 Soil properties at the field sampling site, Pingelly.
132
xx
LIST OF TABLES continued Page
6.6 Effect of different soil amendments on measured plant properties (shoot and root dry weight, total root length, shoot P concentration, AM colonised root length, and AM colonisation (%)) after each harvesting time. Values presented are means ± standard error of the mean, n = 3.
137
6.7 Effect of different soil amendments on soil physico-chemical parameters after each harvesting time (4, 6, and 8 weeks). Values presented are means ± standard error of the mean, n = 3.
142
6.8 Bacterial diversity of rhizosphere soil bacteria and plant roots colonising bacteria indicated by phylogenetic diversity, Chao1 richness, and Shannon’s index. (Calculation of richness and diversity estimators was based on OTU tables rarefied to the same sequencing depth; the lowest one of total sequencing reads: 5000).
146
6.9 Relative abundance of (a) rhizosphere bacteria and, (b) root colonising bacteria up to genus level by different soil amendments. Value represents the mean of triplicate determinations. (Relative abundance <1% is ignored).
149
6.10 Taxonomic identities for the CCA biplot showing the relationship between measured variables and individual taxa distributions for rhizosphere bacteria.
152
6.11 Taxonomic identities for the CCA biplot showing the relationship between measured variables and individual taxa distributions for root colonising bacteria.
154
7.1 The specific contributions of this thesis in relation to the P transformation in the model piggery waste treatment process.
162
Chapter 1: General Introduction
1
CHAPTER 1
General Introduction
1.1 Background, research gaps, and expected outcomes
The demand on agriculture to feed the world’s population continues to increase with
speculations of the global population rising to 8.9 billion by 2050 (Alexandratos et al.
2006). Australia, a major wheat producing country, contributes to food security for
future generations with 80% of the wheat it exports (Asseng et al. 2011). This
productivity however, is constrained by phosphorus (P) availability in Australian soils,
particularly those in the wheatbelt of Western Australia, which are among the most P
deficient soils in the World in their natural state (Guppy and McLaughlin 2009; Kirono
et al. 2011). Globally, P plays an important role as a primary plant-growth limiting
nutrient in both natural and agricultural systems (Hammond et al. 2004; Guppy and
McLaughlin 2009; Clair and Lynch 2010).
The limited availability of P in soil to plants is mainly due to inorganic P (Pi) adsorption
to soil surfaces, precipitation with soil minerals, and incorporated into the microbial
biomass (Guppy and McLaughlin 2009: Clair and Lynch 2010; Stamm et al. 2011).
Generally, less than 1% of total P is immediately available for plant uptake as
dihydrogen phosphate (H2PO4-) and hydrogen phosphate (HPO4
2-) (Richardson and
Simpson 2011). This has necessitated regular applications of P fertilisers to achieve and
maintain high levels for crop productivity and profitability. Phosphate rock is a finite
resource (Hammond et al. 2004), highlighting the need for more sustainable P fertiliser
use without compromising crop performance. P resources can be conserved through two
major processes – by recycling waste materials, and by more efficient use of inorganic P
fertilisers in agriculture.
The possibility of recycling livestock wastes, which are characteristically high in P, is
gaining increased attention as an alternative P source for agriculture (Güngör and
Karthikeyan 2008). Animal waste can be manipulated to form valuable by-products
such as liquid P-fertilisers (digested effluent), algal biomass, slow release P-fertilisers
(e.g. struvite) and soil stabilisers (compost, digestate, sludge) (Westerman et al. 2010).
Chapter 1: General Introduction
2
Recycling of waste by-products also helps to reduce the environmental burden of waste
accumulation caused by intensive meat production (FAO, 2013). Crop performance, soil
quality and microbial activity can be enhanced by the application of animal waste by-
products (Colvan et al. 2001; Jenkins et al. 2009). However, significant risks associated
with their direct application to soil such as odour, greenhouse gas emissions, leaching,
toxicity and pathogen survival may counterbalance the benefits provided by these waste
by-products (Westerman et al. 2010). An opportunity to reduce these risks can be
achieved through anaerobic digestion of the waste prior to application on land. This can
be achieved using anaerobic digestion processes that help to reduce odour, greenhouse
gas emissions and pathogens, and stabilizes organic solids. This process also serves to
improve the versatility and quality of by-products including biogas (renewable energy),
P-fertilisers and soil improvers (Supaphol et al. 2011). However, the high expense
associated with installation and operation of anaerobic digestion technologies, together
with the lack of guaranteed return, has prevented this technology from being widely
adopted by the agricultural sector (Supaphol et al. 2011).
Low cost anaerobic digestion facilities, in the form of covered anaerobic pond digesters,
are increasing in popularity among Australian livestock industries (e.g. dairies and
piggeries) for treatment of slurry, biogas capture and recycling of nutrients (Davidson et
al. 2013). A covered anaerobic pond digester is a pond covered with an impermeable
cover (geosynthetic material) which captures the biogas produced (carbon dioxide and
methane) and maintains the anaerobic environment. This technology offers the
possibility for reduced odour and greenhouse gas (GHG) emission, pathogen removal,
and generation of biogas. However, the recovery of nutrients for production of value-
added products from these systems is largely unexplored.
Agricultural wastewater arising from piggeries is often high in P (Poulsen 2000).
Therefore, piggery waste by-products derived from covered anaerobic digesters and
composting can be effective sources of P nutrients for crop production. However, the
forms of P in these by-products have to be considered before their application in
agriculture. For example, the concentration of soluble P in treated piggery effluent is
often too high to permit its direct reuse in agriculture as liquid fertilisers (Obaja et al.
2003). One reason for the high P concentration in pig waste is likely to be due to the
high abundance of phytate-bound P (phytate-P). Phytate, found in many cereal grains, is
commonly included in pig feed (Selle and Ravindran 2008) and pigs are unable to fully
digest phytate in phosphorus-enriched food supplements. Both phytate and excess
Chapter 1: General Introduction
3
phosphorus from supplements become concentrated in animal manure, increasing the
potential for eutrophication. On the other hand, obligatory anaerobic treatment of
wastewater releases large amounts of phosphorus and nitrogen into wastewater, the
major agents of eutrophication (De-Bashan and Bashan 2004). The high level of P in
anaerobically digested piggery waste would be a problem in sandy-textured soils which
accelerate P loss to surface- and ground-water bodies. Thus, there is justification for
reducing the concentration of soluble P in piggery waste by-products before it is used as
liquid fertiliser, or otherwise disposed of in the environment. The expected outcome of
this research is to develop an environmentally sound approach for minimising
environment loading of soluble P in piggery waste effluent through microbial activities,
while recovering more stable and effective P fertilisers for use in agriculture.
Effluent or slurry arising from the piggery waste treatment process consists of both
inorganic (orthophosphates, mineral phosphates such as stuvite) and organic forms of P
(phytates, polyphosphates and microbially-derived P such as phospholipid and
nucleotides). Microorganisms are involved in P transformation by P mineralisation, P
solubilisation and P accumulation. These processes play an important role in
determining the quantity and forms of P present in piggery waste by-products. P
mineralisation microorganisms are involved in degradation of organic P compound into
orthophosphates and P solubilising microorganisms are involved in solubilising mineral
P compounds into orthophosphates. Further, some microorganisms are capable of
accumulating excess orthophosphates inside their cells as chains of phosphate ions
(PolyP). Despite the importance of these P cycling processes, there is little information
related to taxonomy and functional identity of P cycling microorganisms in piggery
waste treatment processes. Knowledge of the taxa that mediate P transformation
pathways, and the factors that regulate their activities in piggery waste treatment
processes, could be used to optimise the recovery of P for more effective use in
fertilisers while reducing the environmental loading of soluble P. However, current
understanding of P transformation in piggery waste treatment processes is not sufficient
for full implementation.
This research focused on understanding the abundance, diversity and metabolic function
of P solubilising, P accumulating, and P mineralising microorganism in a model
covered anaerobic pond digester system utilised for treating piggery waste. The model
piggery waste treatment process consisted of several stages (pit, holding tank, covered
anaerobic pond digester and aerobic pond) and was located in south-western Australia
Chapter 1: General Introduction
4
(Appendix 1). A detailed description of this system is found in the Chapter 2, Section
2.2.2. Knowledge of the taxa mediating P transformation pathways can be used in
planning cost effective and environmentally sound means for removing excess P from
piggery waste effluents (Figure 1.1) by enhancing the activity of P solubilising bacteria,
P mineralising bacteria, and P accumulating bacteria. The treated effluent with low
soluble P can then be used as liquid fertiliser with irrigation water or by mixing with
separated solids/slurry for preparation of novel soil improvers such as pelletised
compost. Pelletised compost can supply both organic and inorganic forms of P when
applied to land. However, the effect these have on soil and plant nutrition is not well
documented. Guppy and McLaughlin (2009) stated that the relative importance of each
pool depends on the soil microbial community. Therefore, it is crucial to gain a more
comprehensive understanding of the taxa involved and how they function in mediating
P cycling in soil.
The main objective was to characterise taxa involved in P transformation pathways (P
mineralisation, P solubilisation, and polyphosphate accumulation) and their specific
functions in pig waste by-products. In addition, soil amendment with piggery by-
products was investigated in association with mycorrhizal fungi. While knowledge of
the diversity, abundance and activity of microorganisms involved in P transformation is
critical, it has been constrained by the methods used to date. Therefore, emphasis was
placed on developing more effective approaches for characterising microorganisms
involved in the P cycle. To this end, a combined approach using epi-fluorescence
microscopy, flow cytometry, cell sorting and next generation sequencing was used to
quantify the abundance, taxonomic and functional diversity of P cycling
microorganisms in piggery waste.
Chapter 1: General Introduction
5
Figure 1.1 Approach for minimising environment loading of inorganic P (Pi) in piggery waste effluent through manipulation of microbial activities to improve its recycling potential.
Chapter 1: General Introduction
6
1.2 Thesis objectives and hypotheses
The aims and hypotheses were as follows:
Chapter 3
Objective: To characterise the piggery waste treatment process in terms of
physico-chemical properties, bacterial community composition, and P cycling
potential.
Chapter 4
Objective: To quantify the abundance and diversity of P mineralising bacteria
(the fraction of cells that expressed phosphatase activity) during the piggery waste
treatment process by developing an integrated approach using the enzyme-labeled
fluorescence technique coupled with epi-fluorescence microscopy, cell sorting,
and next generation sequencing (16S rRNA ion tag sequencing).
Hypothesis: A diverse and highly abundant P mineralising bacterial community
will be observed in piggery wastes which are high in organic P substrate.
Chapter 5
Objective: To identify key microbes involved in polyphosphate accumulation and
its enhancement under acidic conditions for assessing the efficacy of enhanced
biological P removal technology applied in high P loaded waste remediation.
Hypothesis: Under acidic conditions in a high inorganic P system there will be
an increase in abundance of polyP accumulating bacteria and a concomitant
increase in polyphosphate accumulation by these bacteria.
Chapter 6
Objective: To demonstrate the impact of application of pelletised piggery
compost to soil on plant growth, soil nutrient improvement, and changes in
bacterial and fungal community composition when banded with a reduced rate of
synthetic fertiliser.
Hypotheses:
1. Banding pelletised piggery compost at low rates in combination with inorganic
fertiliser in the root zone of wheat facilitates nutrient uptake by plant roots in a
P deficient agricultural soil, alters the abundance and community composition
Chapter 1: General Introduction
7
of bacterial involved in increasing P availability in soil, and enhances plant
growth.
2. The increase in P in soil following application of inorganic P fertiliser, in the
presence or absence of compost, will decrease the percentage of root length
colonised by arbuscular mycorrhizal (AM) fungi but increase the length of root
colonised by AM fungi grown in this soil in line with the availability of soil P
and root growth.
1.3 Thesis Structure
The structure of the thesis and the relationship between chapters is shown in the Figure
1.2.
Chapter 1 introduces the background, justification, research questions and aims.
Chapter 2 reviews the literature associated with recycling piggery waste for sustainable
agriculture and highlights knowledge gaps and constraints in microbial P cycling in
piggery waste treatment and soils amended with piggery wastes. Application of novel
molecular and microscopy approaches were identified to fill the gaps in detecting P
cycling microorganisms.
Chapter 3 is the first experimental chapter, and it characterises a model piggery waste
treatment process in terms of physico-chemical properties, bacterial community
composition, and P cycling potentials using P chemistry, conventional plate culturing,
and next generation sequencing approaches.
Chapter 4 is the second experimental chapter, and it determines the abundance, activity
and diversity of P mineralising bacteria during the pig waste treatment process by
integrating an enzyme labelled fluorescence technique coupled with epi-fluorescence
microscopy, cell sorting, and next generation sequencing (16 S taq sequencing and
community metagenomics).
Chapter 5 is the third experimental chapter, and it identifies key microbes involved in
polyP accumulation and its enhancement under acidic conditions for assessing the
efficacy of enhanced biological P removal technology applied to high P loading waste
remediation.
Chapter 1: General Introduction
8
Figure 1.2 Structure of the thesis and the relationship between chapters.
CHAPTER 1 General Introduction
-Background, research gaps, and expected outcomes -Thesis objectives and hypotheses -Thesis structure
CHAPTER 2 Literature Review
- Recycling piggery waste - Gaps and constraints in microbial
P cycling in piggery wastewater - Molecular and microscopy techniques for P cycling microbes
CHAPTER 3 Assessment of P cycling potential during the piggery
wastewater treatment process (P mineralisation, P solubilisation, and polyphosphate accumulation)
CHAPTER 4 Identify key microbes involved in P mineralisation and their abundance, diversity during the waste treatment process
CHAPTER 5 Identify key microbes involved in polyphosphate accumulation and its enhancement under acidic conditions
CHAPTER 6 Demonstrate the applicability of treated piggery waste by-product as a pelletised pig compost at low rate and its effect on plant growth, soil and plant P nutrient improvement, and soil microbial community composition (a pot experiment)
P transformation during the piggery waste treatment process
CHAPTER 7 GENERAL DISCUSSION
Summary of the thesis, conclusions, and recommendations
Applicability of research and knowledge transfer
Chapter 1: General Introduction
9
Chapter 6 is the fourth experimental chapter, and it investigates the application of
pelletised piggery compost to soil and its impact on plant growth, soil nutrient
improvement, and changes in bacterial and fungal community composition when
banded with a lower rate of synthetic fertiliser.
Chapter 7 is a general discussion of the research, and identifies areas for future
research.
The four experimental chapters (Chapter 3, 4, 5, and 6) are presented in the format of
scientific papers that can be read individually or as a part of the whole thesis. This
‘thesis as a series of papers’ format results in some unavoidable repetition, especially in
the Materials and Methods sections of each experimental chapter. I have tried to keep
such repetition to a minimum.
Chapter 2:Literature Review
10
CHAPTER 2
Literature Review
2.1 Overview
Recycling of piggery waste through best management practices such as anaerobic
digestion, composting and removal of excess nutrients aids in minimising negative
environmental impacts associated with direct use of untreated piggery wastewater.
Recovery of phosphorus (P) from piggery wastewater is gaining increased attention as
an effective source of P fertiliser for agriculture. Piggery wastewater are generally high
in both organic and inorganic forms of P and treated piggery waste by-products can be
manipulated as liquid P-fertilisers (digested effluent), slow release P-fertilisers (e.g.
struvite), and soil stabilisers for crop production (Yang et al. 2006; Chen et al. 2009;
Westerman et al. 2010).
The concentration of soluble P in treated piggery effluent is often too high to be used as
liquid fertiliser in free draining sandy textured soils (Obaja et al. 2003). Failure to
reduce the amount of soluble P during the waste treatment process can result in
increased soil P runoff and leaching if effluent is used directly by irrigation (Jaiswal
2010; Nielsen et al. 2010). In order to reduce the concentration of P in wastewater and
to maximise the recovery of P with solid waste by-products, knowledge of
microorganisms that govern the P cycling is important. An understanding how
management practices can be altered to make the conditions more favourable to enhance
their activities is also vital to optimisation of P nutrient management in piggeries. This
requires insight into microbial P cycling pathways.
This review introduces current piggery waste treatment systems, risks and benefits of
using piggery waste, management of excess soluble P, by-products arising from piggery
waste for agriculture, and knowledge gaps and constraints in microbial P cycling in
piggeries and in soils amended with piggery wastes. Current molecular and microscopy
techniques for identifying P cycling microbes and their advances to fill the gaps are
further discussed.
Chapter 2:Literature Review
11
2.2 Recycling piggery waste: general background
2.2.1 Risks and benefits of re-using pig waste
The pork industry is increasing rapidly and has become the most popular meat type
around the World (Figure 2.1 a and b). Most pig farms generate large quantities of
waste, generally in the form of wastewater. Typical pig waste is characterised by a high
content of suspended solids and organic matter, high biochemical oxygen demand, and
high phosphorus (P) and nitrogen (N) contents, odours caused by gases produced by
decomposing waste, as well as high levels of microbial populations including human
pathogens (Chelme-Ayala et al. 2011). Ineffective recycling has created environmental
problems such as odour generation, greenhouse gas emissions (GHG), pathogenicity,
leaching and runoff of N and P nutrients into water bodies (Giusti 2009; Westerman et
al. 2010; Chelme-Ayala et al. 2011). Unprocessed piggery waste is also a reservoir of
human pathogenic microorganisms (Chelme-Ayala et al. 2011). Some of the
predominant pathogens in piggery waste are Cryptosporidium, Campylobacter,
Salmonella, Escherichia, Enterococcus and spore-forming bacteria, Bacillus and
Clostridium (Chelme-Ayala et al. 2011; Mc Carthy et al. 2011). Faecal coliform
bacteria can persist in soils amended with untreated pig slurry especially at higher
application rates (Rufete et al. 2006). In addition to pathogens, piggery feed is often
supplemented with antimicrobial resulting in manures containing antimicrobials and
antimicrobial-resistant microorganisms. If the manure is applied to soil, this could lead
to the prevalence of antibiotic resistance genes in the environment (Zhou et al. 2010;
Graves et al. 2011).
Piggery manure has been shown to increase salinity and there is a possibility of
phytotoxicity though Cu and Zn contamination in cultivated soils following application
of pig slurry (Asada et al. 2010; Doelsch et al. 2010; Shi et al. 2011). Direct addition of
fresh or immature manure to soil can also give rise to plant toxicity problems, such as
those derived from nitrogen-rich feed-stocks which are often high in ammonium and
can be toxic to plant growth (Bernal et al. 2009). Nutrient leaching (especially N and P
leaching) into ground- and surface water systems through runoff from manure storage
facilities is another concern caused by improper pig manure management (Chelme-
Ayala et al. 2011). It has also been shown that under organic management, the amount
Chapter 2:Literature Review
12
Figure 2.1 (a) Growth of pork industry over the last decade (b) and meat production by type (Source FAO, 2013)
of mobile organic P lost from soil through leaching and run-off was greater than the
amount of inorganic P (Ross et al. 1999) which could potentially lead to groundwater
contamination and eutrophication of waterways, especially in sandy textured soils
(Edmeades, 2003; O' Flynn et al. 2011). Therefore, fresh pig slurry is a form of animal
manure which requires treatment to minimise negative effects on soil, water and crop
associated with bacterial and heavy metal contamination, nitrite accumulation, gaseous
losses, nutrient leaching and crop wilting.
2.2.2 Recycling piggery waste by-products: Best management practices for
agriculture
The disposal of piggery waste without pathogenic contamination, odour generation,
greenhouse gas emission, and excessive release of nutrients into the environment is
essential. This requires more environmentally-sound methods for treating and disposing
of piggery waste (Girard et al. 2009), the best management practices for piggery waste
are summarised in Figure 2.2. The major form of piggery waste is slurry that comprises
a mixture of faeces, residual foodstuff and undigested feed items, pig urine,
antimicrobial drug residues and washing-down water. Separation of solids from liquid
Chapter 2:Literature Review
13
waste is a common practice; the resulting liquid faction is usually treated through
anaerobic digestion and the solid faction is treated through composting (Vanotti et al.
2008). After anaerobic digestion, further processes are required to remove excess P
(also N) and to enhance the quality of any by-products derived from the piggery wastes.
The treated liquid faction can then be used as liquid fertiliser/irrigation water and for
on-farm recycling. The separated solids can be composted and further processed into
pellets for use in agriculture.
Figure 2.2 A process for best management practices for piggery waste.
(Mixture of faeces, residual foodstuff, urine, and washdown water)
Liquid fraction Separated Solids
Anaerobic digestion Composting
Solid liquid
separation
Excess P removal
On farm uses
Agricultural
application Treated waste water
Sludge
Irrigate as liquid fertilisers
Pelletalised
compost
Chapter 2:Literature Review
14
Anaerobic digestion
Anaerobic digestion is an effective method of treating piggery wastes which are high in
suspended solids, organic matter, biochemical oxygen demand, odours and human
pathogens. It involves microbial break down of complex waste materials into simple
forms in the absence of oxygen (Carrère et al. 2010) and the liquid waste can be
converted into a biogas and digestate (Figure 2.3). Anaerobic digestion offers the
possibility of reducing risks of using pig waste, through the reduction of odour,
destroying pathogens, stabilization of P, while recovering valuable by-products such as
biogas, manures and soil improvers (Heubeck and Craggs 2010; Supaphol et al. 2011).
However, anaerobic digestion technologies have not been widely adopted by the
agricultural sector primarily because of the high cost associated with installation and
operation without a guaranteed return (Supaphol et al. 2011). As a low-cost alternative,
anaerobic digestion systems such as covered anaerobic pond (CAP) digesters are being
practiced by Australian livestock industries (e.g. dairies and piggeries) for the treatment
of slurry, biogas capture, and recycling of nutrients (Davidson et al. 2013).
A covered anaerobic pond is an anaerobic pond covered with an impermeable cover
which maintains anaerobic conditions and captures biogases and thus controls odour
and GHG emissions (Figure 2.4). Waste treatment systems with covered anaerobic
ponds generally combine pre- (solid settlement and mechanical solid separation) and
post processes (aerobic digestion) to accelerate the waste treatment process. A model
covered anaerobic digestion system is shown in Appendix 1. This system is separated
into 5 stages: pits in the pig shed, solid separation screens, holding tank, covered
anaerobic pond and finally a secondary evaporation pond (aerobic pond) (Appendix 1).
First, the waste is washed down from the pig shed and collected in the pits. The waste is
collected in pits is then pumped over a static run-down screen (solid separator) that
removes about 10-15% the total solids. The remaining wastewater is transferred to the
holding tank prior to being pumped into the covered anaerobic pond (CAP) digester on
a weekly basis (75,000 L/week). The wastewater enters the CAP at one end and the
treated effluent is removed at the other end (CAP-outlet). The biogas is collected and
transported through pipes. After anaerobic digestion, the treated waste is collected in an
aeration pond, which is the final stage of waste treatment process under aerobic
conditions whilst also providing a dewatering step by evaporation.
Chapter 2:Literature Review
15
Figure 2.3 The process of anaerobic digestion. Modified from Batstone et al. (2000).
Figure 2.4 Covered anaerobic pond (CAP) (a) at initiation, and (b) under operation, which captures biogas produced for odour and GHG emission control at Medina Research Station, Department of Agriculture and Food, Western Australia (DAFWA).
Anaerobic digested effluents are not recommended for direct use in agriculture as they
are too wet and may contain a significant amount of volatile fatty acids which may
cause phytotoxicity and, if the digestion is incomplete, the finished product may not be
hygienic (Mata-Alvarez et al. 2000). Therefore, post processes (i.e aerobic digestion)
after anaerobic digestion are necessary to obtain a high-quality product. By-products
arising from the covered anaerobic waste treatment process can be a valuable resource
a b
Chapter 2:Literature Review
16
for renewable energy production and a source of nutrients for agriculture after post
treatment processes. The gaseous end products of anaerobic digestion, in the forms of
CH4 and CO2, can be processed into renewable natural gas (energy source for electricity
or heat) and transportation fuels (Holm-Nielsen et al. 2009). The digested solids at the
bottom of a covered anaerobic pond (sludge) is generally high in a precipitated form of
P, mainly as stuvite (NH4MgPO4·6H2O), and can be processed as compost (Bustamante
et al. 2013) or slow releasing P fertiliser for agriculture (De-Bashan and Bashan 2004).
The treated effluent in the evaporation pond can be recycled as a liquid fertiliser via
irrigation or for onsite non-potable uses such as cleaning. Overall, the covered anaerobic
technology is promising because the liquid and solid P phases can be easily and
inexpensively separated, allowing for the recovery of valuable by-products such as,
liquid P-fertilisers (digested effluent), slow release P-fertilisers (struvite) and soil
stabilisers (compost, digestate, sludge) (Westerman et al. 2010). These on-farm waste
management practices can help farmers recycle the waste while making profits via
production of cost effective fertilisers and reducing negative environmental impacts.
Composting and pelletising: Pig farms generate large quantities of manure, rich in
organic matter and nutrients (Imbeah 1998). Composting has potential as an effective
method of treating pig manure prior to land application (Imbeah 1998; Ros et al. 2006).
Pig manure generally includes a considerable amount of water; if it is too wet to be
composted directly, a solid separation prior to composting facilitates the composting
process (Imbeah 1998). The solid and colloidal parts of the digested slurry can be
inexpensively separated from the wastewater by mechanical screening (Hjorth et al.
2010). The separated solids can be further processed as compost which can be used as
valuable fertilisers for the farm and the domestic potting mix market (Bloxham and
Colclough 1996; Atiyeh et al. 2001; Rao et al. 2007). Co-composting of pig manure
with other biodegradable wastes such as saw dust (Zhang and He 2006), leaves (Huang
et al. 2001), rice straw (Zhu 2007), mushroom waste (Lee et al. 2010) has been reported.
Compost can be used in a defined agricultural management program to improve plant
growth, soil health, soil structure and water holding capacity. Fully matured compost
has been used as an alternative to chemical fertilizers, primarily due to positive effects
of compost on plant growth and soil quality, increased soil biological activity, increased
water holding capacity, suppression of plant pathogens, and reducing the risk of
Chapter 2:Literature Review
17
environmental damage and human health (Bibi et al. 2010; Ojeda et al. 2010; Cytryn et
al. 2011; Martínez-Blanco et al. 2013). Compost can also stimulate root growth (Bibi et
al. 2010), leading to increased soil exploration for water and nutrients and increased
root exudates, which further stimulates soil biological processes (Broeckling et al.
2008). As for other animal wastes, composted pig waste contains useful nutrients which
can be recycled into agricultural land (Choudhary et al. 1996). Pig manure
vermicomposts with a range in concentrations (0%, 5%, 10%, 25%, 50% and 100% by
volume) increased bulk density, electrical conductivity, overall microbial activity and
nitrate concentrations of potting medium and increased the root growth of tomato
seedlings (Atiyeh et al. 2001).
Compost is considered to be an uneconomical soil amendment in some countries mainly
due to difficulties in transport and cost of application at the rates required (Quilty and
Cattle 2011). The usual method for applying compost is to spread it on the soil surface
or incorporate it into the top layers of soil (Stieg et al. 1997). This can result in large
amounts of compost that can be removed by water and/or wind erosion, intercepted by
weeds or otherwise lost before it reaches the root zone (Halvorson et al. 1997). These
problems may be overcome by precision placement of compost in the root zone such as
through pelletising (Blackshaw et al. 2005) enabling easy access by roots. Compost
pellets have been investigated (Yan et al. 2001; Rao et al. 2007) and several businesses
are producing pelletised compost in Australia (Quilty and Cattle, 2011), including the
use of piggery waste by-products (http://www.cwise.com.au/). Banding of compost or
the Placement of fertiliser/compost in a concentrated band beneath the seed at sowing
could also allow a reduction in the application rate of compost to an economically
viable level for broad-acre agriculture.
Removal of excess P in piggery waste: P management is an integral part of recycling
pig waste. The concentration of Pi in treated piggery effluent is often too high to permit
its direct re-use as a liquid fertiliser (such as during irrigation) (Obaja et al. 2003)
especially in areas where free-draining sandy-textured soils are common. This justifies
removal of Pi to a more appropriate level before it is used in agriculture or otherwise
released into the environment. There are several ways to reduce the concentration of P
in wastewater; they involve both chemical and biological processes, and are used at
either a large or small scale. Chemical removal of P is the most common and reliable
Chapter 2:Literature Review
18
method and involves use of ferric, ferrous, aluminium, or calcium salts (Yeoman et al.
1988). However, the high cost and disposal of metal contamination reduces the
economic and environmental sustainability (Powell et al. 2008). Chemical precipitation
techniques (e.g. stuvite crystallisation) have been used to remove P from animal waste
including piggery waste (Çelen et al. 2007, Huang et al. 2011) but are not economically
feasible for low P concentration waste streams (<50 mg-P/L) (Wong et al. 2013). In
contrast, biological P removal methods known as enhanced biological P removal
(EBPR) can be economically viable (Günther et al. 2009).
2.3 Enhanced Biological P removal (EBPR) for removing excess P in
piggeries Enhance biological phosphorus removal (EBPR), on the basis of the accumulation of
excess Pi as polyP in bacterial cells, is extensively practiced for biological P removal
from wastewater (De-Bashan and Bashan 2004; Oehmen et al. 2007; Gebremariam et al.
2011; Sun et al. 2014; Zheng et al. 2014; Chen et al. 2015). Some microorganisms are
capable of taking up soluble phosphate in excess of their normal metabolic
requirements, and accumulate it as intracellular polyphosphate (McGrath and Quinn
2000; Blackall et al. 2002). EBPR is a P removal method based on the selective
enrichment of those P microorganisms that accumulate inorganic polyphosphate in their
microbial biomass. The EBPR processes in wastewater treatment have received
increased attention because EBPR processes afford the following benefits
(Kawaharasaki et al. 1999; Blackall et al. 2002):
1. Reduce operating costs
2. Lower sludge production and reduce energy costs
3. Minimise effluent salinity problems experienced during chemical processing
4. Enable significantly higher reuse potential of sludge than do conventional
chemical processes.
EBPR favours the initially carbon-rich, strictly anaerobic incubation, followed by
carbon-poor, aerobic incubation (De-Bashan and Bashan 2004) and is documented in
Figure 2.5. During the anaerobic incubation, microorganisms deplete organic matter and
carbon and accumulate biopolymers such as PHAs and glycogen. This requires the
energy released during the degradation of polyp, which in turn leads to the release of Pi
Chapter 2:Literature Review
19
from the sludge. During the aerobic phase, PHAs and glycogen serve as energy and
carbon sources for taking up a larger amount of Pi than the amount released during the
anaerobic phase, leaving phosphate-reduced conditions in the aeration pond (De-Bashan
and Bashan 2004). The P enriched bacteria and microalgae biomass is then separated
from the treated effluent wastewater, and the separated biomass can be further used as
slow releasing P fertilisers, while remaining effluent with lower Pi concentration can be
re-used as a liquid fertilizer (Yoon et al. 2004).
Figure 2.5 Metabolism of PolyP accumulating organisms under anaerobic and aerobic conditions and resulting by-products. Modified from Kulakovskaya et al. (2012).
Many countries set 1 mg/L and 2 mg/L as the limit for total P concentrations in
discharges of wastewater treatment plants (Jiang et al. 2004) where influents from
domestic wastewaters are in the range 10 – 15 mg/L (Blackall et al. 2002; Powell et al.
2008). If no particular P removal methods are applied, growth of activated sludge
microorganisms usually removes 1–2 mg/L of influent P, leaving more than 10 mg/L in
the effluent. EBPR processes can accomplish P levels as low as 0.1–0.2 mg/L.
However, EBPR processes are difficult to control and are sometimes ineffective in
phosphate removal (Kawaharasaki et al. 1999). It has been reported that P removal in
waste stabilisation ponds is highly variable with more effective P uptake occurring at
high temperature (25 °C) and low light intensity (60 μE/m2s) (Powell et al. 2008), and
Chapter 2:Literature Review
20
low pH (5.5) (McGrath and Quinn 2000; McGrath et al. 2001; Mullan et al. 2002b).
This demonstrates that the abundance and community structure of polyP accumulating
microorganisms can be highly variable with environmental conditions, serving to
increase or decrease the performance of the EBPR system. Accumulation of polyP in a
wide variety of microorganisms has been reported and the conditions favouring their
activity differ markedly, examples of some microorganisms involved in polyP
accumulation and their optimal conditions are highlighted in Table 2.1.
Although enhanced removal of biological P from wastewater has been widely studied,
an understanding of the microbial and environmental factors affecting enhanced P
accumulation efficiency is less well understood mainly due to the lack of understanding
of the microbiology of EBPR (Gebremariam et al. 2011; Chen et al. 2015; Sun et al.
2014; Zheng et al. 2014). Furthermore, the metabolic capacity of polyphosphate
accumulating organisms is still not adequately understood (Sun et al. 2014) due to the
absence of any isolates of the key agents of polyphosphate accumulating organisms, and
the lack of tools for their quantification and cellular level parameters in the EBPR
system. This can be overcome by using a combination of culture-dependent and culture-
independent techniques to characterize microbial composition and quantitative
evaluation of intracellular functional polymers in key populations of polyphosphate
accumulating organisms (Gebremariam et al. 2011; Majed et al. 2012). Therefore,
further research is essential for a more thorough understanding of P accumulation,
microbial identity and functionality under different environmental settings.
Ch
ap
ter
2:L
iter
atu
re R
evie
w
21
Tab
le 2
.1 E
xam
ples
of m
icro
orga
nism
s inv
olve
d in
pol
yP a
ccum
ulat
ion
and
thei
r opt
imal
con
ditio
ns
Bac
teri
al g
ener
a or
spec
ies
Evi
denc
e of
pol
yP a
ccum
ulat
ion
un
der
diff
eren
t con
ditio
ns
Ref
eren
ce
Esch
eric
hia
coli
Exte
nsiv
e ac
cum
ulat
ion
of p
olyP
in re
spon
se to
osm
otic
stre
ss
or to
nut
ritio
nal s
tress
(R
ao e
t al.
1998
)
Pseu
dom
onas
aer
ugin
osa
Intra
cellu
lar p
olyP
acc
umul
atio
n in
resp
onse
to p
hosp
hate
and
am
ino
acid
lim
itatio
ns
(Kim
et a
l. 19
98)
Ephy
datia
mue
lleri
(fre
shw
ater
spon
ge)
Poly
P ac
cum
ulat
ion
upon
exp
osur
e to
som
e or
gani
c po
lluta
nts
(Im
siec
ke e
t al.
1996
)
Can
dida
hum
icol
a G
-1 (e
nviro
nmen
tal
yeas
t) Po
lyP
accu
mul
atio
n as
a c
onse
quen
ce o
f gro
wth
at a
cid
pH
(McG
rath
and
Qui
nn 2
000)
Kle
bsie
lla a
erog
enes
Po
lyP
accu
mul
atio
n by
cel
ls o
f Kle
bsie
lla a
erog
enes
und
er
acid
con
ditio
ns (p
H 4
.0–5
.0)
(Dug
uid
et a
l. 19
54; D
ugui
d an
d W
ilkin
son
1956
)
Burk
hold
eria
cep
acia
R
emov
al o
f pho
spha
te a
nd a
ccum
ulat
ion
of p
olyP
occ
urre
d at
pH
5.5
(M
ulla
n et
al.
2002
a)
Acin
etob
acte
r St
rain
s acc
umul
ate
poly
phos
phat
e an
d PH
As u
nder
aer
obic
co
nditi
ons
Play
an
impo
rtant
role
in th
e EB
PR p
roce
sses
bas
ed o
n cu
lture
m
edia
gro
wth
. Gen
eral
ly c
ultu
re m
edia
con
ditio
ns a
re fa
vour
ed
(Min
o et
al.
1998
; Kaw
ahar
asak
i et
al. 1
999)
Mic
rolu
natu
s pho
spho
voru
s
Can
dida
tus A
ccum
ulib
acte
r pho
spha
tis
Bac
teria
acc
umul
ate
larg
e am
ount
s of p
olyp
hosp
hate
und
er
aero
bic
cond
ition
s.
EBPR
was
ach
ieve
d at
rel
ativ
ely
high
tem
pera
ture
s of
24
°C,
28 °C
and
32
°C (P
rem
oval
eff
icie
ncy
of >
95%
)
(Esc
henh
agen
et a
l. 20
03)
(Ong
et a
l. 20
14)
Chapter 2:Literature Review
22
2.4 P transformation in piggery waste and knowledge gaps in P
cycling in wastewater In the context of P removal from wastewater, major emphasis has been placed on the
processes of EBPR that are governed by the polyP accumulating microorganisms.
However, there are other important P transformation processes (i.e. P mineralisation, P
solubilisation, and P precipitation) which directly or indirectly affect the efficiency of P
removal processes from wastewater. The content of soluble P in wastewater is a net
effect of all of those P transformation pathways. Hypothesised P transformation
pathways in piggery waste are shown in Figure 2.6.
Typically, piggery waste can be characterized as high in organic matter and soluble P
(Chelme-Ayala et al. 2011), one reason for a high level of P in pig waste being the
abundance of phytate-bound P (phytate-P). Phytate, the mixed salt of phytic acid (myo-
inositol hexaphosphate) is one of the major organic P forms derived from plant-sourced
feed ingredients and is commonly present in piggery feed (Selle and Ravindran 2008).
However, phytate-P is only partially utilised by pigs because they do not generate
sufficient endogenous phytase activity. Therefore, farmers often add inorganic P to the
feed to improve animal health but this further increases the P level in pig waste. The
excess phytate returned to the environment can be a major organic P substrate for P
mineralising microorganisms (Kerovuo et al. 1998; Cho et al. 2005), with bacteria
mineralising phytate to release soluble P (Golterman et al. 1997). Obligatory anaerobic
treatment of wastewater releases large amounts of phosphorus (P) and nitrogen (N) into
wastewater (De-Bashan and Bashan 2004), another major pathway for liberation of
soluble P in wastewater. Fundamental to the design of an efficient P removal process
from high organic P loading systems such as the pig industry is knowledge of the
identity and functionality of polyP accumulating microorganisms and knowledge of
other P transformation pathways. However current knowledge is limited in these P
transformation pathways in wastewater treatment streams. The following sections focus
on the main P transformation pathways and current knowledge and gaps in determining
microbial mediated P cycling in the environment.
Chapter 2:Literature Review
23
Figure 2.6 Probable P transformation pathways in piggery waste.
2.4.1 P mineralisation
P mineralisation can be defined as the hydrolysis of Pi from organic or other complex P
compounds (e.g. polyP), soluble or particulate, in which the hydrolysed Pi is released
outside the cells (Kloeke and Geesey 1999). Most biologically-mediated P
transformations in activated sludge are carried out by bacteria (Kloeke and Geesey
1999) and are mediated by phosphomonoesterase and phosphodiesterase activity
(Anupama et al. 2008). Phosphomonoesterases are classified as either alkaline (pH>7;
EC 3.1.3.1) or acid (pH<6; EC 3.1.3.2) phosphatases depending upon their pH optima
(Kloeke and Geesey 1999; Anupama et al. 2008). Phosphatase (PO4ase) is a unique
extracellular, hydrolytic enzyme which catalyses the hydrolysis of Pi from organic
bound form of P (Kloeke and Geesey 1999; Anupama et al. 2008). The phosphatase
enzyme is also known to hydrolyze inorganic polyphosphates (polyP), the linear
polymers of phosphate stored by P accumulating microorganisms (Chrost 1991;
Golterman et al. 1997), which is known for Pi regeneration in wastewater. While
considerable work has been done on EBPR (Blackall et al. 2002; Malamis et al. 2013),
little is known about P mineralisation or Pi regeneration in wastewater which could
Chapter 2:Literature Review
24
affect the overall efficiency or control of the P removal process (Kloeke and Geesey
1999; Li and Chróst 2006; Whiteley et al. 2002).
Phosphatase activity has been detected in activated sludge (Lemmer et al. 1994; Kloeke
and Geesey 1999; Li and Chróst 2006) and anaerobic reactors (Whiteley et al. 2002;
Anupama et al. 2008). Furthermore, PO4ase activity can be used as a rapid biochemical
test for anaerobic digester instability (Ashley and Hurst 1981; Zhenglan et al. 1990;
Yamaguchi et al. 1991). Therefore, monitoring PO4ase activity is important for planning
cost-effective and sustainable P removal systems in wastewater. There is limited
information on PO4ase activity in complex and diverse environments such as piggery
wastewater effluent. This is mainly due to methodological limitations in detecting P
mineralisation in highly diverse environments such as piggery waste. Consequently, P
mineralising bacteria in piggery waste treatment process are poorly characterised and
little is known of their diversity, abundance or activity, making it difficult to fully
optimise the process. Moreover, microbes involved in P mineralisation, the molecular
mechanisms controlling phosphorus metabolism and the ecological interactions
controlling mineralisation process and rates in wastewater are poorly characterised
(McMahon and Read 2013). Therefore, adequate information on identification,
classification, enrichment, and metabolic capacity of P mineralising microorganisms in
piggery wastewater is required.
2.4.2 P precipitation and solubilisation
When wastewater is rich in soluble P and in the presence of some ions, soluble P tends
to precipitate as insoluble Ca/Al/Fe/Mg-phosphates or other P complexes (e.g. Struvite).
These precipitates are often deposited in sludge and reduce phosphate availability in the
wastewater (Mehta and Batstone 2013). Therefore, recovery of soluble P (PO43-) in
waste as precipitated mineral P is commonly practiced using chemical methods
(Yeoman et al. 1988) or air stripping CO2 from anaerobic effluents (Kalyuzhnyi et al.,
2000) followed by removal of sludge.
Solubilisation of mineral phosphates can be mediated by P-solubilising microorganisms.
These microorganisms have the capability to produce organic acids which are strong
enough to dissolve less-soluble mineral phosphate products (e.g. struvite and
hydroxyapatite) recovered from wastewater. Surprisingly, almost no research has been
Chapter 2:Literature Review
25
focused on P solubilising bacteria in wastewater where mineral phosphate precipitates
are highly abundant. P solubilising bacteria (Xanthobacter, Kluyvera and
Chryseomonas) have been isolated from an arid mangrove ecosystem in Mexico
(Holguin et al. 2001). Potentially they play an important role in releasing P that is
largely unavailable to plants in mangroves where precipitated forms of P are common
due to the higher abundance of cations (Holguin et al. 2001). The authors predicted that
root oxygen translocation plays an important role in solubilizing phosphate by bacteria
near the roots in mangroves where sediments are not always completely anoxic. This
could be the reason for lack of research on biological P solubilising activities in
wastewater treatment plant where the environment is basically completely anaerobic.
Alternatively it has been proposed that P solubilising bacteria and fungi can be
employed for solubilising precipitated phosphate recovered from wastewater (De-
Bashan and Bashan 2004). Finding suitable P solubilising microorganisms (bacteria,
fungi, or archea) which can be employed to solubilise precipitated forms of P (eg.
struvite and hydroxyapatite) in situ in anaerobic digesters would be breakthrough in P
removal from effluents and sludge. The activities of such microorganisms are also
beneficial in reducing the frequency of sludge withdrawal and blocking of wastewater
pipes with mineral P crystals. This would be a significant economic and environmental
advantage for the water management industries.
2.5 Role of P mediating microorganisms and their diversity in soil Microorganisms play a central role in cycling P in soil. They contribute to P
solubilisation, mineralisation, and immobilisation, thus controlling the plant available P
pool (Richardson and Simpson 2011). Plants can use P only in the forms of soluble
orthophosphate ions (e.g. H2PO4-1, HPO4
-2 and PO4-3) and the type of orthophosphate
ion present in the soil depends upon the soil environment, mainly pH and the form of
organic or inorganic P present. The P cycling microbes have the ability to convert
insoluble phosphorus (P) to an accessible form, such as orthophosphate, and this is an
important trait for increasing plant yields. However, their widespread utilisation remains
limited by a poor understanding of the microbial ecology and population dynamics in
soil, and by inconsistent performances over a range of environments (Richardson 2001).
Chapter 2:Literature Review
26
Inorganic P in soil is present in the form of insoluble Ca/Al/Fe phosphates. The form of
inorganic P in soil will depend on the type of soil (alkaline, acidic, or organic-rich)
which dictates the variability of Fe-P, Al-P, and Ca-P (Bashan et al. 2013). P
solubilising microorganisms solubilize these insoluble P forms and liberate more P than
their cellular requirement, rendering surplus P for uptake by plants. Solubilisation of
Ca–P complexes is a dominant process among P solubilising microbes, whereas the
release of P by Fe–P or Al–P is very scarce (Fatima 2014). The major mechanism used
by P solubilising microorganisms are based on decreasing pH (Zhu et al. 2011), through
the synthesis of low molecular weight organic acids (gluoconic, malate, acetate, citrate,
oxalate, and lactate) (Rodriguez et al. 2004; Ogut et al. 2010) or indirectly by the
production of exo-polysaccharides (Fatima 2014). The dominant P solubilising
microorganisms belong to the genera Bacillus, Pseudomonas, Rhizobium, Burkholderia,
Enterobacter, Streptomyces, Penicillium and Aspergillus (Rodríguez and Fraga 1999;
Tao et al. 2008).
Organic P in soil is present mainly in form of inositol phosphate (soil phytate),
phosphomonoesters, phosphodiesters and phosphotriester (Jorquera et al. 2008a;
Richardson and Simpson 2011). Some microorganisms can hydrolyse Pi from organic
forms, and this is carried out by diverse groups of enzymes including
phosphomonoesterases, phosphodiesterases, nucleases, nucleotidases, and phytases
(Mackey and Paytan 2009). Bacteria such as Bacillus megaterium, Burkholderia
caryophylli, and Pseudomonas syringae demonstrate P mineralization activity (Jorquera
et al. 2008b; Jorquera et al. 2013).
Microorganisms that are capable of removal and sequestration of reactive P from the
environment for a period of time are known as P immobilising microorganisms. This
can occur as cellular assimilation or through intracellular phosphorus containing
minerals such as polyP granules. However, knowledge of P immobilising
microorganisms in soil is limited compared to P mineralising microorganisms and P
solubilising microorganisms.
Another important group of P mediating soil organisms are arbuscular mycorrhizal
(AM) fungi. AM fungi form symbioses with roots of nearly all vascular plants (Roy-
Bolduc and Hijri 2011). In exchange for plant assimilated carbon, AM fungi provide a
range of benefits (Roy-Bolduc and Hijri 2011) such as contributing to soil structure,
Chapter 2:Literature Review
27
exploration of soil pores too small for plant roots and increasing the total volume of soil
explored for both nutrients and water (Al-Karaki 1998; Kaya et al. 2003; Rillig et al.
2003; Garg and Chandel 2010). AM fungi are well known to improve the efficiency of
plant P uptake and a range of other nutrients, including organic N (Hodge and Storer
2015). An increase in mycorrhiza formation when P is limiting can result in increases in
plant growth (Ryan et al. 2000) whereas when P is not limiting, an increase in AM
fungal colonisation of roots may lead to a reduction in growth due to an increased cost
in photosynthesis by the plant for little return from the fungi (Graham 2000; Jakobsen et
al. 2002).
Mechanisms for the increase in uptake of P by mycorrhizal plants can be characterised
as exploration of a larger soil volume, faster movement of P into mycorrhizal hyphae,
and solubilisation of soil phosphorus (Bolan 1991). AM fungi increase the explored
volume of soil by decreasing the distance that P ions must diffuse to plant roots and by
increasing the surface area for absorption of nutrients (particularly P) in exchange for
photosynthates (Garg and Chandel 2010; Hodge et al. 2010). Solubilisation of soil P by
mycorrhizal fungi is achieved by the release of organic acids and phosphatase enzymes.
Mycorrhizal plants have been shown to increase the uptake of poorly soluble P sources,
such as iron and aluminium phosphate and rock phosphates (Bolan 1991). Overall, P
mediating microorganisms could be manipulated for more efficient use of plant poorly-
available forms of P in soil (Gyaneshwar et al. 2002; Richardson 2001) and there is a
wide range of soil bacteria and fungi involved in P mineralisation and solubilisation
(Table 2.2).
2.6 Current molecular and microscopy techniques for identifying P
cycling microbes and their advantages and limitations A better understanding of the diversity, abundance and function of P cycling
microorganisms (P solubilisation, immobilisation, and mineralisation) is vital to
increasing their capacity to mobilize P to plants in piggery waste and in soil to which
piggery by-products have been applied.
Ch
ap
ter
2:L
iter
atu
re R
evie
w
28
Tab
le 2
.2 E
xam
ples
of m
icro
orga
nism
s inv
olve
d in
P tr
ansf
orm
atio
n in
soil.
Rol
e M
icro
orga
nism
s R
emar
ks a
nd r
efer
ence
s Ph
ytat
e-de
grad
ing
mic
roor
gani
sms
Pseu
dom
onal
spp.
, Bac
illus
subt
ilis,
Kleb
siel
la sp
p, E
sche
rich
ia c
oli,
Mits
uoke
lla sp
p., A
sper
gillu
s, Pe
nici
llium
spp.
, Arth
roba
cter
, St
aphy
loco
ccus
From
a v
arie
ty o
f env
ironm
ent
(Hill
et a
l. 20
07)
Org
anic
P m
iner
alis
ing
mic
roor
gani
sm
Pseu
dom
onas
fluo
resc
ens,
Pseu
dom
onas
sp. B
urkh
olde
ria
cepa
cia,
En
tero
bact
er a
erog
enes
, Ent
erob
acte
r clo
acae
, Citr
obac
ter f
reun
di,
Prot
eus m
irab
alis
, Ser
ratia
mar
cens
cens
, Bac
illus
subt
ilis,
Pseu
dom
onas
put
ida,
Pse
udom
onas
men
doci
na, B
acill
us
liche
nifo
rmis
, Kle
bsie
lla a
erog
enes
Phos
phat
e m
iner
aliz
atio
n fr
om
P-su
bstra
tes b
y so
me
soil
bact
eria
l spe
cies
(Rod
rígue
z an
d Fr
aga
1999
)
P so
lubi
lisin
g m
icro
orga
nism
s Ba
cillu
s meg
ater
ium
, Bur
khol
deri
a ca
ryop
hylli
, Pse
udom
onas
ci
chor
ii, P
seud
omon
as sy
ring
ae, B
acill
us, P
seud
omon
as, E
rwin
ia,
Agro
bact
eriu
m, S
erra
tia, F
lavo
bact
eriu
m, E
nter
obac
ter,
Mic
roco
ccus
, Azo
toba
cter
, Bra
dyrh
izob
ium
, Sal
mon
ella
, Alc
alig
enes
, C
hrom
obac
teri
um, A
rthr
obac
ter,
Stre
ptom
yces
,Thi
obac
illus
, Es
cher
ichi
a, P
enic
illiu
m, A
sper
gillu
s, Rh
izop
us, F
usar
ium
, Sc
lero
tium
, Ent
erob
acte
r, Pa
ntoe
a, a
nd K
lebs
iella
Isol
ated
from
soil
(Tao
et a
l. 20
08)
From
a v
arie
ty o
f env
ironm
ent
(Zha
o an
d Li
n 20
01)
From
rhiz
osph
e (C
hung
et a
l. 20
05)
(Rod
rígue
z an
d Fr
aga
1999
)
Chapter 2:Literature Review
29
The diversity of P mediating microorganisms varies with environment and only about
1% of can be cultured successfully in the laboratory (Wasaki and Maruyama 2011).
Furthermore, cultivation of AM fungi in artificial media remains difficult since they are
obligate symbionts (Franz Lang and Hijri 2009). Findings derived from culture
dependant techniques are likely to provide a biased and inaccurate assessment of P
cycling microbes (Torsvik and Ovreas, 2002). Therefore, culture-independent methods
are generally required to study the function and ecology of microorganisms.
The majority of culture independent techniques are polymerase chain reaction (PCR)
dependant. With the discovery of PCR, a more rapid and better representation of
microbial community structure and diversity was obtained from a variety of
environments (Amann, 1995). DNA ‘fingerprinting’ technologies such as denaturing
gradient gel electrophoresis (DGGE) and terminal restriction fragment length
polymorphism (T-RFLP) were used to assess how microbial communities respond to
changes (eg. environmental parameters) (Fromin et al. 2002; Jenkins et al. 2009;
Jenkins et al. 2010). Analysing clone libraries with traditional Sanger sequencing
methods provide better resolution and accuracy in molecular sequencing as it is capable
to provide longer sequencing reads (Medvedev et al. 2009). However this method is not
always practicable, depending on the scale of a study due to the effort, time and costs
involved. Quantitative PCR (qPCR) has also been shown to be important for monitoring
gene amplification in real-time because it provides abundance of gene copy number of a
specific target microbial group in the environment (Jenkins et al. 2009; Jenkins et al.
2010; Supaphol et al. 2011).
While these molecular techniques provide considerable input into discovery of
microbial ecology, understanding of microbial diversity and function in the environment
has been further expanded with the introduction of next generation sequencing. Next
generation sequencing (NGS) approaches provide higher throughput solutions for
investigations of microbial ecology than ever before, allowing scientists to understand
the microbial ecology and function at greater depth. NGS techniques include
pyrosequencing (Margulies et al., 2005), barcoding and multiplex analyses (Hamady et
al. 2008), ultra-high-throughput sequencing (Bartram et al. 2011; Caporaso et al. 2012)
and improved storage, computational processing and sequence analysis (Lozupone and
Knight, 2005; Meyer et al. 2008; Caporaso et al. 2010).
Chapter 2:Literature Review
30
In addition to PCR-based approaches, metagenomic sequencing (the analysis of whole
community genomes extracted from natural environments) is a more powerful approach
for characterising microbial community structure and its putative metabolic potential
based on genes, without a PCR bias (Meyer et al. 2008). Shotgun metagenomic
sequencing also provides information on both taxonomic and functional identity of
microbial community even although some taxa are in low abundance (Martin et al.
2006). While metagenomic sequencing is useful in identifying genes that could be
involved in a given metabolic pathway, metatranscriptomics (the collective RNA from
all the microorganisms in a community) provides functionally relevant groups or
individuals more precisely by assessing the expression of RNA transcripts (Bastida et
al. 2009). To date such studies have identified novel microorganisms and functional
genes encoding for metabolic pathways from a variety of environments (Martin et al.
2006; Xu, 2006), expanding the current databases for microbial ecology analysis.
Greater insight to visualisation, cellular location and characterisation of microorganisms
in the environment can be obtained when molecular tools are combined with
microscopy/flow cytometry. For example, fluorescence in situ hybridisation (FISH) can
be used to detect nucleic acid sequences by a fluorescently labelled probe that
hybridizes specifically to its complementary target sequence within intact cells. Various
probes have been used for specific detection of diverse levels of phylogenic groups
(Amann et al. 1990; Glöckner et al. 1999). FISH has been widely applied for
investigating bacterial population dynamics as well as community analysis in a range of
ecosystems without the need for isolation. This is a great advantage over most other
molecular detection techniques. Moreover, fluorescent probes can be labelled with dyes
of different emission wavelengths, enabling detection of several target sequences within
a single hybridization step (Moter and Göbel, 2000). Therefore, FISH is a powerful
technique for not only visualization and identification of individual microbial cells, but
also for investigating bacterial community compositions within their natural micro-
habitat. However, microscopy techniques are time-consuming and can be biased in
quantification. Thus, fluorescence techniques can be coupled to flow cytometry to
obtain higher accuracy and faster analysis.
Flow cytometers can be used to quantify fluorescence intensity data and provide
distinctive information about microbial cell populations (Schwartz and Fernandez-
Chapter 2:Literature Review
31
Repollet 2001). This process is less time-consuming than epi-fluorescence microscopy
with automatic image analysis, and a larger number of cells and samples can be
analysed (Nedoma et al. 2003).
The application of molecular techniques and some nucleic acid fluorescence staining
techniques in detecting P cycling microbes, their advantages and disadvantages are
listed in Table 2.3. The majority of molecular methods described in Table 2.3 are PCR-
dependant and primers and probes targeting P mediating microbes are limited, non-
specific or poorly developed (Wasaki and Maruyama 2011). For example, primer sets
based on published Bacillus phytase genes successfully amplified the positive control
but failed to detect these genes in isolated phytate-mineralizing Bacillus strains (Hill et
al. 2007). This suggests that more genes are involved in P mineralization/solubilisation
and that the current databases are too small to provide adequate coverage (Jorquera et
al. 2008b; Lim et al. 2007). Therefore, the application of molecular methods to P
transformation is further limited by the availability of sequences in the current databases
for genes involved in P mineralization/solubilisation (Lim et al. 2007; Wasaki and
Maruyama 2011).
Owing to the complexity of their life cycles, it is difficult to study AM fungi in either
soil or roots, and conventional methods of assessing AM fungi diversity rely on
morphological identification of spores, or fungi in roots extracted directly from the field
or trap cultures (Leal et al. 2009; Oehl et al. 2009). Molecular assays for measuring the
abundance and identity of AM fungi have also been limited by suitability of specific
primers (Redecker 2000). Consequently, there are gaps in understanding P
transformations in biogeochemical P cycling.
Cha
pter
2:L
itera
ture
Rev
iew
32
Tab
le 2
.3 C
omm
on m
etho
ds u
sed
in id
entif
ying
P-m
edia
ting
mic
roor
gani
sms h
ighl
ight
ing
thei
r adv
anta
ges a
nd d
isad
vant
ages
.
Mol
ecul
ar
tech
niqu
es
Des
crip
tion
Adv
anta
ges
Dis
adva
ntag
es
App
licat
ion
to P
cy
clin
g 1.
FIS
H
(Flu
ores
cenc
e in
si
tu h
ybrid
isat
ion)
FISH
use
s flu
ores
cent
ly la
bele
d ol
igon
ucle
otid
e pr
obes
to d
etec
t an
d lo
caliz
e w
hole
-bac
teria
l cel
ls o
r th
e pr
esen
ce o
f tar
get D
NA
se
quen
ce.
-Loc
aliz
atio
n of
spec
ific
mic
robe
s or
gene
s can
be
visu
aliz
ed
-Allo
ws d
etec
tion
and
spat
ial
dist
ribut
ion
of m
ore
than
one
spec
ies
at th
e sa
me
time.
-Non
-spe
cific
labe
lling
or
auto
-fluo
resc
ence
of
mic
roor
gani
sms
-Am
plifi
catio
n of
sign
als i
s re
quire
d fo
r fun
ctio
nal g
enes
-A
ccur
acy
and
relia
bilit
y is
hi
ghly
dep
ende
nt o
n sp
ecifi
city
of p
robe
s.
(Cro
cetti
et a
l. 20
00;
Kon
g et
al.
2005
)
2. E
LF
(Enz
yme-
labe
led
fluor
esce
nce)
A m
etho
d ut
ilize
s ELF
®97
ph
osph
ate,
whi
ch y
ield
an
inte
nsel
y flu
ores
cent
yel
low
-gre
en p
reci
pita
te
at th
e si
te o
f pho
spha
tase
act
ivity
.
-Loc
aliz
atio
n of
pho
spha
tase
-T
aggi
ng m
etho
d fo
r pho
spha
tase
ac
tivity
.
-App
licat
ion
is sp
ecifi
c.
(Duh
amel
et a
l. 20
08;
Was
aki e
t al.
2008
; D
uham
el e
t al.
2009
)
3. F
low
cyt
omet
ry
Flow
cyt
omet
ry is
a la
ser-
base
d,
biop
hysi
cal t
echn
olog
y em
ploy
ed
in c
ell c
ount
ing,
cel
l sor
ting,
bi
omar
ker d
etec
tion
and
prot
ein
engi
neer
ing,
by
susp
endi
ng c
ells
in
a st
ream
of f
luid
and
pas
sing
them
by
an
elec
troni
c de
tect
ion
appa
ratu
s.
-Qua
ntify
P c
yclin
g m
icro
orga
nism
s -S
imul
tane
ous u
mul
tipar
amet
ric
anal
ysis
of t
he p
hysi
cal a
nd c
hem
ical
ch
arac
teris
tics o
f up
to th
ousa
nds o
f pa
rticl
es p
er se
cond
. -T
arge
ted
cells
can
be
sepa
rate
d fr
om
the
non-
targ
et c
ells
and
the
sorte
d ce
lls c
an b
e us
ed fo
r dow
nstre
am
mol
ecul
ar a
naly
sis.
-App
licat
ion
is sp
ecifi
c (D
uham
el e
t al.
2008
; D
uham
el e
t al.
2009
; G
ünth
er e
t al.
2009
)
Cha
pter
2:L
itera
ture
Rev
iew
33
Tab
le 2
.3 C
omm
on m
etho
ds u
sed
in id
entif
ying
P-m
edia
ting
mic
roor
gani
sms h
ighl
ight
ing
thei
r adv
anta
ges a
nd d
isad
vant
ages
(con
tinue
d….).
Mol
ecul
ar
tech
niqu
es
Des
crip
tion
Adv
anta
ges
Dis
adva
ntag
es
App
licat
ion
to P
cy
clin
g 4.
Phos
phat
e re
porte
r bac
teria
Q
uant
itativ
e co
lorim
etric
ess
ay o
f β-
gala
ctos
idas
e, in
dica
tes w
heth
er
the
bact
eria
hav
e be
en g
row
ing
unde
r pho
spha
te-li
miti
ng o
r su
ffic
ient
con
ditio
ns.
-Can
be
used
to a
sses
s whe
ther
su
ffic
ient
pho
spha
te is
ava
ilabl
e to
th
e ba
cter
ia.
-Use
ful t
ool f
or st
udyi
ng th
e pl
ant–
mic
robe
inte
ract
ions
invo
lved
in P
cy
clin
g.
-App
licat
ion
is sp
ecifi
c.
(De
Weg
er e
t al.
1994
; K
rage
lund
et a
l. 19
97)
5.PL
FA
(pho
spho
lipid
fatty
ac
id)
All
mic
robe
s hav
e m
embr
anes
w
hich
con
sist
mai
nly
of
phos
phol
ipid
fatty
aci
ds (P
LFA
). PL
FA a
naly
sis i
s a te
chni
que
wid
ely
used
for e
stim
atio
n of
the
tota
l bio
mas
s and
to o
bser
ve b
road
ch
ange
s in
the
com
mun
ity
com
posi
tion
of th
e liv
ing
mic
robe
s in
soil
and
aque
ous e
nviro
nmen
ts.
-Acc
urat
e qu
antif
icat
ion,
rapi
d an
d se
nsiti
ve m
etho
d to
det
ect c
hang
es in
th
e m
icro
bial
com
mun
ity
-Inex
pens
ive
and
repr
oduc
ible
-P
LFA
is D
NA
or R
NA
inde
pend
ent,
-Use
ful i
nfor
mat
ion
on th
e dy
nam
ics o
f via
ble
bact
eria
.
-Los
ses o
f pho
spho
lipid
s du
ring
the
sepa
ratio
n st
ep
and
durin
g m
ethy
latio
n -T
ime
cons
umin
g -L
ow n
umbe
r of s
ampl
es c
an
be tr
eate
d at
the
sam
e tim
e.
(Tsc
herk
o et
al.
2004
; H
e et
al.
2013
)
6. q
-PC
R
(qua
ntita
tive
poly
mer
ase
chai
n re
actio
n)
q-PC
R is
com
mon
ly u
sed
to
quan
tify
the
targ
eted
gen
e us
ing
spec
ific
prim
ers.
-The
met
hod
is u
sefu
l for
func
tiona
l ge
nes t
hat a
re d
irect
ly in
volv
ed in
P
cycl
ing
such
as p
hosp
hata
ses,
phyt
ases
, and
nuc
leas
es
-Qua
ntifi
catio
n of
spec
ific
gene
s -Q
uick
, acc
urat
e an
d hi
ghly
se
nsiti
ve m
etho
d fo
r seq
uenc
e qu
antif
icat
ion.
-Can
onl
y be
use
d fo
r ta
rget
ing
of k
now
n se
quen
ces.
-L
ack
of sp
ecifi
c pr
imer
s ca
use
for h
inde
ring
its w
ide
appl
icat
ions
-D
NA
impu
ritie
s and
ar
tifac
ts m
ay c
reat
e fa
lse-
posi
tives
or i
nhib
it am
plifi
catio
n.
(Alk
an e
t al.
2006
)
Cha
pter
2:L
itera
ture
Rev
iew
34
Tab
le 2
.3 C
omm
on m
etho
ds u
sed
in id
entif
ying
P-m
edia
ting
mic
roor
gani
sms h
ighl
ight
ing
thei
r adv
anta
ges a
nd d
isad
vant
ages
(con
tinue
d….).
Mol
ecul
ar
tech
niqu
es
Des
crip
tion
Adv
anta
ges
Dis
adva
ntag
es
App
licat
ion
to P
cyc
ling
7. D
GG
E/TG
GE
(den
atur
ant
grad
ient
gel
el
ectro
phor
esis
)
DG
GE
is fr
eque
ntly
app
lied
for
com
parin
g th
e m
icro
bial
co
mm
uniti
es o
f var
ious
en
viro
nmen
ts. T
arge
t gen
es
ampl
ified
by
PCR
are
sepa
rate
d on
po
lyac
ryla
mid
e ge
ls c
onta
inin
g gr
adie
nts o
f eith
er a
tem
pera
ture
or
chem
ical
to d
enat
ure
the
DN
A a
s it
mov
es a
cros
s an
acry
lam
ide
gel.
-Diff
eren
t sam
ples
can
be
com
pare
d w
ithou
t DN
A se
quen
ce
dete
rmin
atio
n -B
ands
can
be
iden
tifie
d by
clo
ning
an
d se
quen
cing
. The
refo
re D
GG
E ca
n vi
sual
ize
diff
eren
ces u
p to
sp
ecie
s lev
els.
-At h
ighe
r div
ersi
ty, t
he
band
s sep
arat
ed in
the
DG
GE
are
poor
ly re
solv
ed.
-Uns
uita
ble
for d
etec
tion
of
diff
eren
ces a
t the
gen
us o
r an
y hi
gher
leve
ls.
-Tim
e co
nsum
ing
-Onl
y fo
r sho
rt fr
agm
ents
-M
ultip
le b
ands
for a
sing
le
spec
ies c
an b
e ge
nera
ted
due
to m
icro
-het
erog
enei
ty.
(Onu
ki e
t al.
2002
; W
asak
i et a
l. 20
05;
Wan
g et
al.
2009
)
8. C
lone
Lib
rarie
s Se
quen
cing
of c
lone
libr
arie
s is
mos
t com
mon
met
hod
for t
he
anal
ysis
of t
arge
t gen
e se
quen
ces.
The
sequ
ence
pro
vide
s use
ful
info
rmat
ion
on th
e ph
ylog
enet
ic
posi
tion
of th
e m
icro
be.
-Info
rmat
ion
of D
NA
sequ
ence
for
each
clo
ne b
ecom
es a
vaila
ble
-T
he se
quen
ce p
rovi
des u
sefu
l in
form
atio
n on
the
phyl
ogen
etic
po
sitio
n of
the
mic
robe
.
Rel
ativ
ely
high
exp
ense
Ti
me
cons
umin
g.
(He
et a
l. 20
06;
Kim
et a
l. 20
10)
9. R
FLP
(R
estri
ctio
n fr
agm
ent l
engt
h po
lym
orph
ism
)
RFL
P vi
sual
izes
mic
robi
al
com
mun
ities
as p
atte
rns o
f re
stric
tion
frag
men
t len
gth.
The
re
stric
tion
frag
men
ts a
re se
para
ted
by e
lect
roph
ores
is.
RFL
P is
a u
sefu
l met
hod
for
dete
ctin
g re
lativ
ely
subs
tant
ial
diff
eren
ces (
high
er th
an
genu
s lev
el).
RFL
P ca
nnot
reso
lve
smal
l di
ffer
ence
s bet
wee
n th
e se
quen
ces c
ompa
red.
(Kaw
ahar
asak
i et a
l. 20
02; S
late
r et a
l. 20
10)
10. S
IP (s
tabl
e is
otop
e pr
obin
g)
Sepa
ratio
n an
d m
olec
ular
ana
lysi
s of
labe
led
nucl
eic
acid
s (13
C- o
r 15
N-la
bele
d) re
veal
s phy
loge
netic
an
d fu
nctio
nal i
nfor
mat
ion
abou
t th
e m
icro
orga
nism
s res
pons
ible
for
the
met
abol
ism
of a
par
ticul
ar
subs
trate
.
-Hig
h se
nsiti
vity
-P
rovi
des e
vide
nce
on th
e fu
nctio
n of
mic
roor
gani
sms i
n a
cont
rolle
d ex
perim
enta
l set
up
- SIP
is a
met
hod
for i
dent
ifyin
g ac
tive
mic
robe
s in
the
envi
ronm
ent.
Stab
le is
otop
e fo
r P is
ab
sent
. 13
C-S
IP c
ould
be
appl
ied
for s
tudy
ing
the
effe
cts o
f ro
ot e
xuda
tes o
n m
icro
bes
invo
lved
in P
cyc
ling
but
not y
et e
luci
date
d (W
asak
i and
Mar
uyam
a 20
11)
Cha
pter
2:L
itera
ture
Rev
iew
35
Tab
le 2
.3 C
omm
on m
etho
ds u
sed
in id
entif
ying
P-m
edia
ting
mic
roor
gani
sms h
ighl
ight
ing
thei
r adv
anta
ges a
nd d
isad
vant
ages
(con
tinue
d….).
Mol
ecul
ar
tech
niqu
es
Des
crip
tion
Adv
anta
ges
Dis
adva
ntag
es
App
licat
ion
to P
cyc
ling
11. M
icro
arra
y A
pow
erfu
l tec
hnol
ogy
can
be u
sed
to m
easu
re th
e le
vel o
f act
ivity
of
thou
sand
s gen
es si
mul
tane
ousl
y.
The
amou
nt o
f mR
NA
for e
ach
gene
in a
giv
en sa
mpl
e ca
n be
m
easu
red.
-Hig
h-th
roug
hput
, -L
arge
-sca
le,
-Gen
omic
-sca
le
-Allo
ws f
or th
e co
mpa
rison
of
thou
sand
s of g
enes
at o
nce.
-Exp
ensi
ve,
-Lim
ited
by th
e pr
esen
ce o
f pr
obes
on
the
arra
y Is
sues
w
ith R
NA
ext
ract
ion
from
so
il, u
nive
rsal
mic
robe
ar
rays
are
not
yet
app
lied.
(Was
aki e
t al.
2003
; Zhu
et
al.
2010
)
12. N
ext
gene
ratio
n se
quen
cing
(NG
S)
NG
S is
a P
CR
bas
ed h
igh-
thro
ughp
ut se
quen
cing
tech
nolo
gy
perf
orm
ed b
y us
ing
diff
eren
t m
oder
n se
quen
cing
tech
nolo
gies
in
clud
ing,
Illu
min
a (S
olex
a)
sequ
enci
ng, R
oche
454
sequ
enci
ng,
Ion
torr
ent:
Prot
on /
PGM
se
quen
cing
, SO
LiD
sequ
enci
ng.
-Mul
tiple
ena
bles
larg
e sa
mpl
e nu
mbe
rs to
be
sim
ulta
neou
sly
sequ
ence
d du
ring
a si
ngle
ex
perim
ent.
-T
his a
dvan
ce e
nabl
es ra
pid
sequ
enci
ng.
-Diff
icul
ty g
ettin
g th
roug
h ho
mop
olym
ers
-Rel
ativ
ely
expe
nsiv
e -C
ompu
tatio
nal i
nten
sive
-T
ime
cons
umin
g in
term
s of
dat
a an
alys
is.
(And
erso
n et
al.
2011
; A
lber
tsen
et a
l. 20
13)
13. M
etag
enom
ics
An
appr
oach
of d
irect
sequ
enci
ng
of e
nviro
nmen
tal D
NA
with
out
PCR
am
plifi
catio
ns. M
etag
enom
ics
will
pro
vide
the
taxo
nom
ic
dive
rsity
of t
he w
hole
mic
roflo
ra in
an
env
ironm
ent,
toge
ther
with
the
func
tions
of t
he o
rgan
ism
s inv
olve
d an
d th
e is
olat
ion
of b
enef
icia
l gen
es
from
unc
ultu
red
mic
roor
gani
sms.
-Rev
eals
the
pres
ence
of t
hous
ands
of
mic
robi
al g
enom
es
sim
ulta
neou
sly
-Pro
vide
s inf
orm
atio
n ab
out t
he
func
tions
of m
icro
bial
com
mun
ities
in
a g
iven
env
ironm
ent
-Und
erst
andi
ng th
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Chapter 2:Literature Review
36
2.7 New advances in molecular and microscopy technology to resolve
problems encounter with P cycling microorganisms
There is increasing interest in developing PCR-independent molecular tools in relation
to P cycling. P-cycling microbes are known to produce a range of enzymes, including
phytases and phosphoesterases (phosphatases), which release inorganic P (Pi) from
organic P sources and dehydrogenases, which release inorganic P from mineral P
sources via production of organic acids such as, gluconic acid (Lim et al. 2007; Jorquera
et al. 2008a). Genes controlling the expression of these enzymes are potential candidates
for molecular biomarkers and the development of monitoring tools (Rondon et al. 2000;
Torsvik and Øvreås 2002; Cardon and Gage, 2006) (Table 2.4). Expressed protein-
encoding genes (‘functional genes’) as molecular genetic indicators of relevant P
mediating microorganisms can serve as indicators for their specific role in ecosystems
(Gamper et al. 2010). A number of mineral phosphate solubilising genes and organic P
mineralising genes involved in the turnover of phosphonates, phosphoesters and phytic
acid have been identified in specific bacterial or fungal strains, providing new
opportunities to target them to designing more reliable primers (Rodriguez et al. 2006).
For phosphoester mineralizing microorganisms primers can be designed to target a
range of genes including the phytase genes (phyA) alkaline phosphoesterase genes
(phoA/X) and acid phosphoesterase genes (acpA). The production of organic acids is
considered the principal mechanism for mineral phosphate solubilisation and any gene
involved in organic acid synthesis can be used for designing molecular markers.
Therefore, for mineral phosphate solubilising microorganisms, primers can be
developed to target the genes involved in P solubilizing pathways (e.g. pqq genes)
involved in gluconic acid, glucose dehydrogenase, malate dehydrogenase genes, any
genes involved in production of acids that solubilise mineral P.
The other option for understanding P cycling microbial diversity and functional
activities in detail is integration of suitable techniques listed in Table 2.3. For example,
a combined approach of epi-fluorescence microscopy (for co-location), flow cytometry
(quantification), cell sorting (separation of target cells) and Ion Tag sequencing (for
taxonomy), and community metagenomic (for putative functional) can be used to get
more reliable and more detailed understanding of P cycling microorganisms in
environmental samples. The schematic representation of the proposed integrated
approach is shown in Figure 2.7 and application of these techniques individually or in
Chapter 2:Literature Review
37
combinations in detecting P cycling bacteria in a piggery waste treatment process is
assessed throughout this thesis. Here, details of each method are discussed briefly.
Table 2.4 Enzymes and their encoding genes in P metabolism.
Function Enzyme Genes PolyP accumulation -uptake of inorganic phosphate and its transport across the cytoplasmic membrane
-PolyP synthesis
-PolyP utilisation
1) The low affinity Pit (phosphate inorganic transport) system, 2) The high affinity Pst (phosphate specific transport) system 3) Pi linked antiport systems of sn-glycerol-3-phosphate 4) Glucose-6-phosphate
1) polyphosphate kinase polyP dependent nucleoside diphosphate kinase 2) polyP dependent nucleoside diphosphate kinase
1) Exopolyphosphatase
PitA PstB GlpT UhpT PPK1 PPK2 PPX
P mineralisation -phosphate synthesis
-phosphate starvation
-inducible
-Phytase, alkaline phosphoesterase, acid phosphoesterase -Phosphate sensor PHO regulon activator, -Polyanionic specific outer membrane porin
-High and low affinity phosphate specific transport and phosphate inorganic transport
phyA, phoA/X, acpA PhoR PhoB Pst, Pit
P solubilisation Glucose dehydrogenase and its co-factor, pyrroloquinoline quinone, gluconic acid, malate dehydrogenase genes, and other genes involved in production of acids that solubilise mineral P
GHD, pqq, mps, gabY, pkkY, pk1M10, PKG3791, pcc, and gcd
2.7.1 Enzyme-labeled fluorescence (ELF) coupled to epi-fluorescent microscopy,
flow cytometry, and cell sorting
Enzyme-labeled fluorescence (ELF) or fluorescence labeled enzyme assay (FLEA) has
been used to co-locate phosphatase activity at the single-cell level in a range of
environments (Duhamel et al. 2008). The principle behind ELF is that the fluorogenic
substrate, ELF®97 phosphate (C14H7Cl2N2Na2O5P, Invitrogen or Molecular Probes,
E6589) reacts with cell surface-bound phosphatases and is cleaved into inorganic P (Pi)
and fluorescent product called ELF alcohol (ELFA), thereby forming a fluorescent
Chapter 2:Literature Review
38
precipitate at or near the site of phosphatase activity. ELFA fluorescent signal is bright
and photochemically stable, allowing a sensitive quantification of PO4ase activity either
by epi-fluorescent microscopy or rapid flow cytometry (Nedoma et al. 2003; Dignum et
al. 2004).
Figure 2.7 Proposed integrated approach for understanding P cycling pathways.
The detection of the ELFA signal by epi-fluorescence microscopy has been shown to be
efficient for collocating phosphatases activity in phytoplankton (Nedoma et al. 2003),
and bacterioplankton (Nedoma and Vrba 2006), aquatic bacteria (Duhamel et al. 2008).
Compared to epi-fluorescence microscopic techniques, flow cytometry is fast, and
highly accurate in quantification analysis. Therefore both techniques are often used
together to identify, visualise, and quantify PO4ase activity in microorganisms using
ELF®97 phosphate. ELF has been applied in various environments such as marine
(Nedoma et al. 2003; Dignum et al. 2004; Meseck et al. 2009), fresh water (Duhamel et
al. 2009), activated sludge (Kloeke and Geesey, 1999). To date, there is limited
information on PO4ase activity in complex and diverse environments such as piggery
Chapter 2:Literature Review
39
wastewater effluent due to lack of methods and limitations of applying ELF in these
diverse microbial habitats.
Many questions remain unresolved while more than ten years of research on ELF
application using flow cytometry and epi-fluorescence microscopy has brought new
understanding about P mineralisation by microorganisms, (Duhamel et al. 2009),
particularly in relation to the identity and function of P mineralising bacteria. Therefore,
downstream molecular sequencing (Ion Tag sequencing and community metagenomic)
of sorted phosphatase active cells obtained from flow cytometric analysis allows to
understand identity and function of P mineralising bacteria even they are low in
abundance in an environment.
2.7.2 Ion Torrent sequencing
Recently, a light independent, Ion sequencing method has been developed and
commercialised with the introduction of the Ion Torrent Personal Genome Machine
(PGM) by the Life Technologies. During Ion sequencing, DNA sequence composition
is determined by measuring slight changes in pH as hydrogen ions are released when
nucleotides are incorporated during DNA strand synthesis (Rothberg et al. 2011).
Compared to pyrosequencing, Ion sequencing is a more affordable sequencing option
due to the substantially reduced costs since a light detection system and associated
reagents are not required as other sequencing analysis (Glenn, 2011). Utilising different
output PGM chips, an average of ca. 350,000 (314 PGM chip) or 1.2 million reads (316
PGM chip) or more (318 PGM chip) van be generated within 8 h and the output is
satisfactory in quality for downstream data analysis pipelines such as QIIME (Caporaso
et al. 2010). Recently, it was demonstrated that the Ion Torrent platform was a suitable
low cost next generation sequencing platform for studying microbial community
dynamics and function associated with a covered anaerobic piggery waste treatment
system (Whiteley et al. 2012). Whiteley et al. (2012) developed Ion Torrent protocols
for both PCR amplified 16S rRNA or metagenomic community sequencing analysis and
used these protocols to assess community structure, temporal stability and key taxa
during the waste treatment process. This included the development of Golay barcoded
Ion Tags for multiplex analyses of microbial communities which allow sequencing of
large number of sample at low cost.
2.7.3 Community metagenomics
Chapter 2:Literature Review
40
Metagenomic analysis, the analysis of whole community genomes, provides
information on the whole microbial community in a given environment, the functions of
the organisms involved in their metabolic pathways, and even isolation of beneficial
genes from uncultured microorganisms (Wasaki and Maruyama 2011). Metagenomic
analysis helps to reveal the abundance of gene copies of functional genes as putative
molecular genetic indicators of relevant P mediating microorganisms and their specific
role in an environment (Table 2.4). Metagenomic analysis has been used for screening
functional pathways in relation to novel phosphonate utilization pathways in marine
bacteria (Martinez et al. 2010), key metabolic processes involved in soil phytic acid
utilization (Unno and Shinano 2013), distribution and diversity of phytate-mineralizing
bacteria (Lim et al. 2007), and community structure and genetic potential of EBPR
(Albertsen et al. 2012; Albertsen et al. 2013). With the progression of such studies of
functional details of a community (such as metatranscriptomics, metagenomic,
proteomics, metabolomics), a considerable number of sequences encoding for P cycling
genes are now available and these can be used to design molecular monitoring tools for
P cycling in environment.
2.8 Rationale It is well known that AM fungi, P solubilising, P accumulating and P mineralising
microorganism play crucial roles in P cycling via direct and indirect mechanisms. Given
that the most piggery wastes are high in both organic and inorganic forms of P, it can be
hypothesised that these systems are more diverse and abundant in P solubilising
microbial communities, P mineralising microbial communities, and P uptake/
immobilising microbial communities. However, despite the importance of these
processes for sustaining plant growth in natural and agricultural systems, little is known
about the specific microbes responsible for these transformation processes in piggery
waste treatment processes. It is therefore important to assess microbial abundance,
diversity and activity in piggery waste as a basis for recovery of environmentally and
economically sound P fertilisers. Knowledge gained can be applied to improving crop
production by amending soils with piggery waste by-products. In terms of
methodological advances in detecting P cycling microorganisms, further improvement
of available methods and the combination of molecular approaches is crucial for more
comprehensive understanding of microbes involved in P transformations.
Chapter 2:Literature Review
41
Finally, key gaps in knowledge for P cycling in the piggery waste management can be
summarised as:
(1) Lack of understanding of the diversity and metabolic function of P
solubilising, P accumulating, and P mineralising microorganism in piggery
waste.
2) Lack of methods to unravel both functional and taxonomical identities of P
cycling microorganism in piggery waste.
3) Lack of application of efficient methodologies for reducing the level of
inorganic P in piggeries using low cost enhanced biological P accumulation.
4) Lack of understanding of interactions between P cycling microbes in soils
amended with piggery waste for an optimised P reutilisation efficiency.
Chapter 3: Characterisation of the piggery waste treatment process
42
CHAPTER 3 Microbial Community Composition and Phosphorus Cycling
Potential within a Covered Anaerobic Pond System Treating
Piggery Waste
3.0 Abstract
Uncovering the taxonomic composition and functional capacity of phosphorus (P)
cycling bacteria within the piggery wastewater treatment process is of great importance
for developing effective strategies for P recovery from piggeries. The primary goal of
this study was baseline characterisation of all compartments involved in a model
covered anaerobic piggery wastewater treatment plant according to physico-chemical
properties, microbial community composition and P cycling potential. Sampling was
carried out at all stages of the treatment process (pit, holding tank, covered anaerobic
pond digester, and aerobic pond/evaporation pond). 16S rRNA Tag sequencing was
used to determine bacterial community composition. As revealed by high throughput
16S rRNA Ion Tag sequencing, culturing techniques and metagenomic analysis,
bacterial community composition was spatially distributed among different stages of the
piggery waste treatment process. Overall, there were clear shifts in bacterial community
composition between the anaerobic and aerobic stage. Total populations of the covered
anaerobic digester mainly comprised Bacteroidia, Clostridia, Cloacamonae and
Synergistia. The aerobic pond was dominated by Proteobacteria and Actinobacteria.
Chemical characterisation highlighted the need to reduce soluble P concentration in the
piggery wastewater before either its use in agriculture or disposal back into
environment. Functional analysis in relation to P cycling revealed that genes responsible
for P mineralisation were higher in number in the covered anaerobic digester, and polyP
accumulation was greater in the treated piggery wastewater in the aerobic pond. These
findings aid in identifying the key processing stages in relation to reducing soluble P
and recovering valuable by-products for re-use in agriculture.
Chapter 3: Characterisation of the piggery waste treatment process
43
3.1 Introduction
Intensive pork production has created serious waste disposal problems, notably in
relation to the environmental loading of soluble phosphorus (orthophosphates), a major
agent of eutrophication. Piggery wastewater are generally high in inorganic P (e.g.
orthophosphates), mineral P (e.g. struvite and hydroxyapatite), and a variety of organic
P forms (e.g. phytates, polyphosphates, and microbial derived P such as phospholipid
and nucleotides) (Güngör and Karthikeyan 2008; Westerman et al. 2010). Although the
occurrence of high concentrations of soluble P in piggery waste is unavoidable, it can be
reduced if sound recycling strategies are developed and applied.
Recycling of piggery waste can yield a wide range of P fertilizers; liquid P-fertilisers
(digested effluent), algal biomass (separated biomass), slow release P-fertilisers
(struvite) and soil stabilisers (compost, digestate, sludge) (Westerman et al. 2010).
Moreover, it has also been previously proposed that precipitated phosphate recovered
from wastewater (e.g. stuvite and hydroxyl apatite) can be manipulated as efficient P
fertiliser when applied together with common P solubilising bacteria and fungi (De-
Bashan and Bashan 2004). On the other hand, P recovered from enhanced biological P
removal (EBPR) from waste sludge (i.e. P accumulated in microbial cells as chains of
phosphate called polyP granules) can be used as raw material in the fertilizer industry
(Hirota et al. 2010). Recent development of bioprocesses for expanded use of polyP has
been reviewed by Hirota et al. (2010) illustrating the wider application of polyP in
industry, agriculture and medicine while reducing the threats of water pollution via
eutrophication. Nevertheless, further research is needed to enhance the efficiency and
consistency of developing these by-products as P-fertilisers, and to ensure their
application is cost effective, environmental sound and practical from an operations
perspective.
Understanding P cycling in high P loaded waste streams like piggeries will assist
development of effective strategies for P nutrient management. P cycling in wastewater
is often determined by the activity of microbial communities present (McMahon and
Read 2013). They play a central role in transforming one form P to another by P uptake
and immobilisation in biomass (P immobilisation), they liberate orthophosphates from
organic matter (P mineralisation), and they alter redox conditions that affect solubility
of mineral P (P solubilisation). Understanding these pathways is the key to control
excess orthophosphate in piggery waste and to recycle them as valuable by-products for
Chapter 3: Characterisation of the piggery waste treatment process
44
agriculture. Consequently, monitoring P transformation within piggeries is useful for
making decisions about P removal strategies in a cost effective manner.
To date, the major emphasis on microbial mediated P removal in wastewater was put on
the process of EBPR which is governed by polyP accumulating microorganisms. The
most common explanation for P transformation pathways in wastewater is the
occurrence of polyP degradation during the anaerobic digestion and poly P formation
during the subsequent aerobic digestion (McGrath and Quinn, 2003; De-Bashan and
Bashan, 2004). During the anaerobic conditions, microorganisms deplete organic matter
and carbon and accumulate biopolymers such as polyhydroxyalkanoate (PHA) and
glycogen using the energy released during the degradation of polyP which in turn leads
to release of Pi from the sludge. During the aerobic phase, these biopolymers serve as
energy and carbon sources for taking up larger amounts of Pi than the amount released
during the anaerobic phase, leaving phosphate-reduced conditions in the aeration pond
(De-Bashan and Bashan 2004). However, there are other important P transformation
pathways (i.e. P mineralisation, P solubilisation) which directly or indirectly affect the
efficiency of P removal processes from wastewater. Organic forms of P in piggery
waste are transformed by P mineralising microorganism (PMM), liberating soluble P.
When wastewater is rich in soluble P in the presence of some ions (e.g.
Ca,Al,Fe,Mg,NH4), soluble P tends to precipitate as insoluble Ca/Al/Fe/Mg-phosphates
or other P complexes (e.g. struvite; MgNH4PO4.6H2O). These precipitates are often
deposited in sludge and reduce phosphate availability in the wastewater. Solubilisation
of mineral phosphates can be mediated by microorganisms known as P solubilising
microorganisms (PSM). Therefore, the amount of soluble P in wastewater is the gross
balance of all of those P transformation pathways (Figure 2.6). However, these
processes are not adequately defined in wastewater treatment processes and further
research on efficient P removal from the wastewater is required. In particular,
uncovering the taxonomic composition, diversity and functional capacity of
microorganisms mediating P transformation and how this community influences P
mineralisation, polyP formation and solubilisation requires further investigation during
the recycling of piggery waste.
The limited information available on P transformation in waste treatment processes
could be due to methodological constraints. Effective methodological approaches are
needed to identify P cycling bacteria and to identify their physiological role in
controlling P metabolism and factors controlling P cycling in the piggery wastewater
Chapter 3: Characterisation of the piggery waste treatment process
45
treatment processes. Recently, sequence analysis of the 16S rRNA Ion Tag sequencing
has shed new light on the diversity and composition of microbial communities within a
covered anaerobic pond of piggery waste treatment processes over a period of time at a
lower cost (Whiteley et al. 2012). 16S rRNA gene-based techniques reveal high
microbial diversity. However, this approach offers only limited information about the
functional role of microorganisms within a given environment. In this context, shotgun
metagenomic analysis is a powerful technique for revealing the putative metabolic
potential within different environments, while providing information on diversity and
composition of microorganisms. Community metagenomics, the direct genetic analysis
of genomes in an environmental sample, is increasingly being used to reveal genetic
diversity, population structure and ecological role of microorganisms. Metagenomic
technology has been successfully applied to studying, functional gene expression in
microbial systems (Wilmes and Bond, 2006), functional capacity of the swine gut
(Lamendella et al. 2011), subcellular location of marine bacterial alkaline phosphatases
and bacterial (Luo et al. 2009) and archaeal community dynamics in a covered
anaerobic pond (Whiteley et al. 2012). While characterisation of microbial diversity in
anaerobically treated piggery waste has been reported (Cardinali-Rezende et al. 2012;
Whiteley et al. 2012; Tuan et al. 2014), the functional genetic potential of P cycling of
piggery water treatment processes has not been studied using metagenomics.
The primary goal of this study was to characterize the piggery waste treatment process
in terms of physico-chemical properties, microbial community composition and P
cycling potentials. It was expected that this would identify where reduction in soluble P
in piggery waste and its recovery as by-products for agriculture could be achieved.
Based on this characterisation, the objectives and hypothesis for the rest of this thesis
were developed.
3.2 Material and Methods
3.2.1 Farm description and sampling
Piggery waste by-products were collected from a covered anaerobic pond digester
system at Medina Research Station, Department of Agriculture and Food Western
Australia (GPS geocoder: latitude -32.223000, Longitude 115.805801). The layout of
the sampling site and the process of wastewater treatment are shown in Appendix 1.
Piggery waste had been treated in five consecutive stages in this waste management
Chapter 3: Characterisation of the piggery waste treatment process
46
system: pits in the pig shed; solid separation screens, holding tank, the covered
anaerobic pond (CAP) and finally a secondary evaporation pond. Effluents generated
from pig pens were collected into a pit tank. Solids were then separated by mechanical
screening and the remaining waste effluent was collected into a storage holding tank.
Afterwards, wastewater was transferred to a covered anaerobic pond (CAP) at a rate of
75,000 L per week for downstream biological remediation. Finally, the anaerobically
treated wastewater was pumped into an aeration pond, as the final stage of waste
treatment under aeration conditions while also yielding a dewatering stage by
evaporation. Sampling was done during Spring 2012.
Sampling points in the piggery waste treatment process were: 1) pit (effluent; facultative
anaerobic), 2) holding tank (effluent; facultative anaerobic), 3) CAP-Bottom (slurry;
anaerobic), 4) CAP-Top (digested effluent: anaerobic), and 5) evaporation pond (treated
wastewater; aerobic). Sampling from the covered anaerobic pond was performed by
suction collection using a 12 V marine grade bilge pump connected to a PVC hosepipe.
The hosepipe was placed into the access port of the covered anaerobic pond and was run
for 5 min to flush the sampling line, followed by a collection of the sample (1 L) into
autoclaved containers. Samples from CAP were collected from the surface (0.5 m) and
bottom (4 m). Samples from other places were also collected using the suction collection
method. There were no true replicates for each sample as there was only one pond for
the each waste treatment stage at that particular time. Therefore, samples from each
point were collected into several sampling bottles and then corresponding samples were
mixed together to make a composite sample for each stage of the waste treatment
process.
3.2.2 Physico-chemical characterization of pig waste samples
Physico-chemical parameters monitored were phosphorus (total P, organic P, and
orthophosphate), total carbon (TC), total nitrogen (TN), ammonia (NH4+), total solids
(TS), volatile solids (VS), electrical conductivity (EC), temperature and pH. All
chemical analyses were assessed according to standard methods for examination of
water and wastewater (American Public Health Association 2005). The NH4+ content,
EC (salinity) and pH were measured on a Thermo Scientific Orion 5 Star meter
(Thermo Fisher Scientific Australia Pty Ltd, Vic 3179) using specific probes for each
variable measurement, following the manufactures protocol. Total carbon (TC) and total
nitrogen (TN) were determined by combustion using elementar (vario Macro CNS;
Chapter 3: Characterisation of the piggery waste treatment process
47
Elementar, Germany). TS and VS were determined for a 50 mL sample. An evaporation
dish was cleaned in an acid wash and placed in a furnace oven at 550°C for 30 minutes
to burn off any volatiles. The dish was then weighed prior to and after the sample was
added allowing for the weight of the sample to be calculated by subtraction. The
evaporation dish was placed in a drying room at 45ºC overnight and then transferred to
into an oven at 105 ºC for 3 hours. Afterwards, the dish was placed in a desiccator to
cool before being reweighed. The weight of total solids in the sample was then
recorded. To measure the amount of volatile solids in the samples, the evaporation dish
containing the TS was placed in a furnace at 550°C for twenty minutes and then cooled
in a desiccator. Once cooled, the final weight of each sample was subtracted from the
TS weight (after 105ºC) to determine the VS of the sample.
3.2.3 Isolation and identification of P mineralising bacteria and P solubilising
bacteria
P mineralising bacteria (PMB) and P solubilising bacteria (PSB) were selectively
isolated at each stage of the piggery waste treatment process compartments, using a
selective medium supplemented with phytate and tri-calcium phosphate respectively
(Kerovuo et al. 1998; Jorquera et al. 2008). Each waste sample was diluted in sterile
phosphate-buffered saline (PBS) and plated in triplicate on agar medium. Two media
were used, one containing phytate for selective isolation of PMB (10 g/L D-glucose, 4
g/L Na-phytate, 2 g/L CaCl2, 5 g/L NH4NO3, 0.5 g/L KCl, 0.5 g/L MgSO4·7H2O, 0.01
g/L FeSO4·7H2O, 0.01 g/L MnSO4·H2O, 15 g/L agar) and the second as tri calcium
phosphate for PSB (10 g/L D-glucose, 5 g/L Ca3(PO4)2, 5 g/L MgCl2·6H2O, 0.25 g/L
MgSO4·7H2O, 0.2 g/L KCl, 0.1 g/L,(NH4)2SO4, 15 g/L agar). Plates were incubated for
4 days at 27°C and any colonies that formed clear zones around them on their respective
plates were selected and purified as PMB or PSB.
Each recovered PMB or PSB isolate was characterized to genus level using partial
sequencing of the 16S ribosomal RNA gene. The DNA was extracted from pure
cultures by the phenol/chloroform method (Griffiths et al. 2000). PCR was carried out
using a primer set pA (5'-AGA GTT TGA TCC TGG CTC AG-3') and pH (5'-
AAGGAGGTG ATC CAG CCG CA-3') (Edwards et al. 1989). The 30 μL PCR
mixtures contained: 0.75 μL of each primer (10 µM), 0.8 μL of 10 mM dNTP’s, 3 μL of
10X buffer (contains 1.5 mM MgCl2), 2 μL of Dynazyme EXT DNA Polymerase (1 U/
Chapter 3: Characterisation of the piggery waste treatment process
48
μL -Thermo Scientific), 2 μL of template DNA and 20.7 μL molecular grade water. The
reaction conditions were 94 °C for 10 min (initial denaturation) followed by 30 cycles
of 93°C 1 min (denaturation); 58 °C, 1 min (annealing); 72 °C, 2 min (extension); and a
final extension step at 72°C for 10 min using a thermocycler (Techgene, Techne Inc,
New Jersey, USA). After the reaction, 8 μL of the PCR reaction was analysed on a 1.5%
agarose gels containing 1 μg mL−1 of ethidium bromide to ascertain PCR fragment size
and quality. Suitable quality PCR products were sent to Macrogen Inc, Korea for
sequencing (Sanger sequencing) and sequences obtained in return were analysed by
BLASTn to find the closest related bacterial sequences within the publicly available
database.
3.2.4 DNA extraction and 16S rRNA Ion Tag sequencing
Genomic DNA of piggery waste samples from each sampling point was extracted using
the MoBio Powersoil DNA isolation kit (Geneworks, Australia), utilising beat beating
and column purification, according to the manufacturer's guidelines. Extracted DNA
was quantified and checked for purity at A260/280 nm (Nanodrop, Thermo Fisher
Scientific, USA) prior to storage at −20 °C. Bacterial 16S ribosomal RNA genes were
amplified by polymerase chain reaction (PCR) from the DNA samples using
oligonucleotide primers specific for bacteria. All forward primers were modified by the
addition of an Ion Torrent sequencing adaptor, ‘GT’ spacer and unique error correcting
Golay barcode (Hamady et al. 2008), to allow multiplex analyses. Multiplexed samples
were subject to 16S ribosomal RNA gene amplification by PCR using Golay barcoded
primers 341F and 518R (Muyzer et al. 1993, Whiteley et al., 2012) with amplification
conditions described previously (Jenkins et al. 2010; Supaphol et al. 2011).
Following amplification, all PCR products were checked for size and specificity by
electrophoresis on 2.5% w/v agarose, gel purified and adjusted to 10 ng/μL using
molecular grade water and pooled equally for subsequent sequencing. Sequencing was
performed on the Ion Torrent Personal Genome Machine (Life technologies, USA)
using 200 base-pair chemistry as described in Whiteley et al. (2012). All the PGM
quality filtered data were exported as FastQ files and the results were split into fasta and
qual files and analysed using the QIIME pipeline (Caporaso et al. 2010). Assigning the
multiplexed reads to samples was performed using default parameters (minimum
quality score = 25, minimum/maximum length = 130/220, no ambiguous base calls,
remove reverse primers and no mismatches allowed in the forward and reverse primer
Chapter 3: Characterisation of the piggery waste treatment process
49
sequences). Chimera checking was done using usearch61 and only non-chimeric
sequences were considered for assigning operational taxonomic units (OTUs,
Greengenes (GG) reference database clustered at 97% identity). Singletons were
removed and taxonomy for the sample sequences was assigned to the representative
sequence of each OTU.
3.2.5 Whole-genome-shotgun sequencing
In order to gain an insight into the microbial functional capacities within different
compartments of the waste treatment process, genomic DNA derived from each
compartment was used for PCR independent whole genome shotgun sequencing on the
Life technologies Proton system. Approximately 150 ng of DNA was used to generate a
whole genome shotgun library using a NEBnext Ultra library preparation kit (New
England Biosciences). Fragments of 320-330bp were selected from the final library by
gel-excision and sequenced for 520 flows on an Ion Torrent Proton sequencer (Life
Technologies), yielding reads of 230-240bp modal length. Quality filtering and
trimming were performed on the instrument using TorrentSuite 4.0. Sequencing data
sets were uploaded to the Metagenome Rapid Annotation using Subsystem Technology
(MG-RAST) server (http://metagenomics.nmpdr.org/). Sequences were aligned to
sequences stored in a number of public databases (Appendix 2). Metagenomic data sets
for samples derived from pits, holding tank, CAP-Bottom, CAP-Top, CAP-Outlet and
evaporation pond are publicly available in the MG-RAST system under project
identifiers 4553572.3, 4553568.3, 4553570.3, 4553571.3, 4553569.3, and 4553566.3
respectively. Assignment of metabolic function and phylogenetic identification were
performed as described previously (Meyer et al. 2008). Functional classifications were
computed by using SEED FIGfams and subsystems. The Min. % Identity Cutoff was
60%, Min. Alignment Length Cutoff was 15 nucleotides and the Max. e-Value Cutoff
1e-5.
3.2.6 Multivariate statistical analyses
Canonical Correspondence Analysis (CCA) was used to model the changes in the
bacteria community profile of the wastewater treatment compartments to the measured
environmental variables (Jongman et al. 1995) to explore how the microbial community
is structured relative to the environmental variables at each stage of wastewater
treatment process. A sequencing data matrix of relative taxon abundances and
Chapter 3: Characterisation of the piggery waste treatment process
50
corresponding matrix of the environmental variables (Section 3.2.2) for each waste
treatment point was prepared as the method described previously (Jenkins et al. 2010).
Canonical correspondence analysis (CCA) was performed using the software package
Canoco v4.55 (Plant Research International © 2006). The data were analysed to
ascertain which covariates best explained changes in bacterial community profiles.
3.3 Results
3.3.1 Physico-chemical characteristics of a piggery waste treatment system
The chemical composition of waste in different stages varied in pH (6.9-8.1), EC (3.8-
6.9 mS/cm), Total Solids (TS: 0.13- 2.61 %), Volatile Solids (VS: 25.8-76.1 %), Total
N (TN: 6.3-7.9 %), Total C (TC: 45.2-46.5 %), Total P (TP: 13.6-147.6 mg/ L) and
orthophosphate (Pi: 12.2-26.3 mg/L) (Table 3.1).
Table 3.1 Physical and chemical characteristics of different piggery wastewater treatment compartments at Medina Research Station, Western Australia.
Sample Pits Holding Tank
CAP- Bottom
CAP- Top
CAP- Outlet
Evaporation Pond
pH 7.1 6.9 7.2 7.1 7.6 8.1 EC (mS/cm) 3.8 3.7 6.8 6.9 6.6 5.2 TS % 0.3 0.9 2.6 0.3 0.4 0.1 VS % 35.8 75.4 76.1 39.6 60.1 25.9 TN% 4.3 4.3 6.1 7.9 6.8 NT TC% 45.0 45.9 46.5 45.2 45.9 NT C:N ratio 10.5 10.7 7.6 5.8 6.7 NT Ammonia (ppb) 180.0 241.0 348.0 403.0 410.0 440 Total P (mg/L) 41 42.9 147.6 40.9 34.1 13.6 Orthophosphate (mg/L) 26.3 25.1 10.8 21.8 20.5 12.2 Organic P (mg/L) 14.7 17.8 136.8 19.1 13.6 1.3 Ca (mg/L) 112 58 1666 62 NT 21 Fe (mg/L) 0.6 1 205.2 1.6 NT 0.1 K (mg/L) 167 202 289 269 NT 681 Mg (mg/L) 62 46 144 58 NT 148 Cd (mg/L) 0.0 0.0 0.0 0.0 NT 0.0 Co (mg/L) 0.0 0.0 0.3 0.0 NT 0.0 Cu (mg/L) 0.1 0.1 56.1 0.4 NT 0.1 Pb (mg/L) 0.0 0.0 0.3 0.0 NT 0.0 Cr (mg/L) 0.0 0.0 0.4 0.0 NT 0 Zn (mg/L) 0.3 0.3 159.6 1.1 NT 0 VS is shown as a percent of TS. NT stands for not tested.
Chapter 3: Characterisation of the piggery waste treatment process
51
The highest TP/OP and the lowest orthophosphate were observed in CAP-Bottom slurry
at 147.6/136.8 mg/L and 10.8 mg/L respectively showing that the majority of P in the
CAP-Bottom was organic or insoluble forms of P (Table 3.1). In contrast to CAP-
Bottom, orthophosphate was high in CAP-Top (21.8 mg/L) implying that the
orthophosphate generate during the anaerobic digestion is released to the CAP Top
effluent (digestate) (Table 3.1). The level of orthophosphate in the CAP-Outlet was 20.5
mg/L and when it was transferred to the aerobic stage, the level of orthophosphate
decreased to 12.2 mg/L. Considerably higher amounts of ammonia were observed in
CAP-Bottom slurry (348 ppb) and CAP-Top effluents (403 ppb) and increased in
concentration towards the latter part of the waste treatment processes, reaching the
highest levels in the treated wastewater in the evaporation pond (440 ppb).
3.3.2 Isolation and identification of P mineralising bacteria (PMB) and P
solubilising bacteria (PSB)
Bacteria capable of producing a clearing zone due to the mineralisation of organic P
(phytate) and the inorganic P (tri-calcium phosphate) during agar isolations were
recovered as a basic isolation strategy to characterise the cultivable PMB and PSB
fraction of the community (Figure 3.1). Genetic characterisation of the isolated PMB
and PSB, based on partial sequencing of the 16S ribosomal RNA gene, are documented
in Table 3.2. The ability of P mineralization or solubilisation among isolates was
assessed based upon the diameter of the clear zones formed around the colonies as
shown in Figure 3.1 (+++ indicates high, ++ indicates medium, and + indicates low).
The majority of isolates showed both P solubilising and mineralising abilities and
belonged to the genera Pseudomonas, Enterobacter, Escherichia coli, Bacillus, and
Cronobacter. Amongst them, Pseudomonas aeruginosa and Enterobacter were
dominant, both of which often isolated at all the stages of the waste treatment process.
Chapter 3: Characterisation of the piggery waste treatment process
52
Figure 3.1 Ability of P mineralization and P solubilisation among isolates from the waste treatment system at Medina Research Station. Ability of P solubilisation and mineralisation was assessed based on diameter of the clear zones around the colonies. a) high and low P mineralising ability, b) low solubilising ability (+), and c) high solubilising ability (+++). 3.3.3 Bacterial community structure by culture independent means within different
stages of the waste treatment system
Bacterial communities assessed by culture independent methods comprised of a range
of taxa at each stage of the waste treatment system (Figure 3.2). According to the
rarefaction analyses (Figure 3.2a), the overall qualitative operational units (OTUs; 97%
sequence similarity) richness of each wastewater treatment stage appeared to be high as
rarefaction curves did not reach asymptote, even after 7000 sequences were examined.
This implies that the wastewater bacteria community is highly diverse. Microbial
diversity assessed by Shannon’s Index (Figure 3.2b) indicated that the species richness
and diversity of the bacterial populations was greatest in the facultative
anaerobic/anaerobic stages (Pit effluent, Holding Tank effluent, CAP-Bottom slurry,
CAP-Top digested effluent, and CAP-Outlet effluents) and lowest in the aerobic stage
(Evaporation Pond treated wastewater).
In terms of diversity during waste remediation as revealed by 16S rRNA Tag
sequencing, samples taken from facultative anaerobic/anaerobic ponds (Pit effluent,
Holding Tank effluent, CAP-Bottom slurry, CAP-Top digested effluent, CAP-Outlet
effluents) were considerably different from those of the aerobic pond (Evaporation Pond
treated wastewater) (Figure 3.3). The facultative anaerobic/anaerobic ponds maintained
a relatively stable community composition, as assessed by mean abundances of
dominant community members of these special sampling points e.g. Bacteroidia
(43±7.0), Clostridia (19.7±2.5), Cloacamonae (6.8±3.8), and Synergistia (6.3±4.8)
Chapter 3: Characterisation of the piggery waste treatment process
53
(Figure 3.3). The other minor taxa were Epsilonproteobacteria, Alphaproteobacteria,
Betaproteobacteria, Gammaproteobacteria, Deltaproteobacteria, Mollicutes,
Spirochaetes, Tenericutes, Firmicutes, Cyanobacteria and Bacilli.
Table 3.2 Genetic characterisation of the isolated P mineralising bacteria and P solubilising bacteria at the different stages of piggery was treatment process.
Genus Accession no%
identityAbility of P
mineralisation*Ability of P
solubilisation*
Pits Enterobacter ludwigii JQ308612.1 94% ₊ ₊
Pseudomonas aeruginosa JQ579643.1 99% ₊₊₊ ₊₊₊
Enterobacter sp. JF690889.1 95% ₊₊ ₊₊₊
Enterobacter sp. GU086159.1 99% ₊ ₊
Escherichia coli AB548580.1 99% ₊ noHolding Tank Cronobacter sakazakii GU727682.1 99% ₊ ₊₊₊
Escherichia coli EF560783.1 98% ₊₊ noBacillus subtilis JF905698.1 99% ₊₊₊ ₊₊
Pseudomonas aeruginosa JQ659909.1 99% ₊₊₊ ₊₊
Cronobacter turicensis FN401357.1 99% ₊ ₊
Escherichia fergusonii HQ259940.1 99% ₊₊₊ noPseudomonas aeruginosa JN999891.1 99% ₊₊₊ ₊₊₊
Pseudomonas aeruginosa JQ659966.1 100% ₊₊₊ ₊₊₊
Escherichia coli EF560776.1 98% ₊₊₊ noEnterobacter sp. FN401343.1 98% ₊ ₊₊₊
CAP-Top Pseudomonas citronellolis JQ659858.1 98% ₊₊₊ noPseudomonas aeruginosa JQ579643.1 99% ₊₊₊ ₊₊₊
Pseudomonas aeruginosa JN999830.1 99% ₊₊₊ ₊₊₊
Escherichia fergusonii HQ259940.1 99% ₊₊₊ noPseudomonas aeruginosa JQ659909.1 99% ₊₊₊ ₊₊₊
CAP-Bottom Pseudomonas sp. JQ595498.1 92% ₊₊₊ noPseudomonas aeruginosa JN999891.1 99% ₊₊₊ ₊₊₊
Klebsiella pneumoniae HQ907956.1 99% ₊₊₊ ₊₊
Escherichia coli JN811622.1 98% ₊₊ noEvapotation Pond Enterobacter sp. JQ398852.1 94% ₊₊₊ ₊₊
Enterobacter asburiae JQ659874.1 94% ₊₊₊ ₊₊
Enterobacter sp EU430753.1 92% ₊₊ ₊₊
Enterobacter asburiae JQ659874.1 99% ₊₊₊ ₊₊
Enterobacter sp. EU430753.1 91% ₊₊ ₊₊
Enterobacter sp. JQ398852.1 95% ₊₊₊ ₊₊
Pseudomonas sp JQ595498.1 92% ₊₊₊ noBacterium B28 FJ628394.1 92% ₊₊ ₊₊₊
Enterobacter sp. JQ398852.1 95% ₊₊ ₊
Pseudomonas aeruginosa JQ579643.1 99% ₊₊₊ ₊₊
Pseudomonas aeruginosa JQ659909.1 99% ₊₊₊ ₊₊
Enterobacter sp. HM107175.1 91% ₊₊ no
*Ability of P mineralization or solubilisation (+++ indicates high, ++ indicates medium, and + indicates low)
Chapter 3: Characterisation of the piggery waste treatment process
54
Figure 3.2 Alpha diversity rarefaction plots of observed species for different stages in the piggery wastewater samples. (a) Microbial diversity indicated by Shannon’s index, (b) Calculation of richness and diversity estimators was based on OTU tables rarified to the same sequencing depth, the lowest one of total sequencing reads; 7340).
Chapter 3: Characterisation of the piggery waste treatment process
55
Figure 3.3 Identities and % composition of the bacteria, at class level, from stages in the piggery waste treatment system at Medina Research Station.
Conversely, the community composition of the aerobic pond (evaporation pond) was
markedly different from that of the anaerobic/anaerobic ponds (Figure 3.3). The
evaporation pond was dominated by Actinobacteria (50.7%), Betaproteobacteria
(19.9%) with other taxa including Erysipelotrichi (8.2 %), TM7 (5.1%),
Gammaproteobacteria (2.9%), Sphingobacteriia (1.5%), Flavobacteriia (1.3%),
Alphaproteobacteria (0.9%), Clostridia (0.7%), OD1 (0.7%), Epsilonproteobacteria
(0.7%), and Acidimicrobiia (0.6%).
Multivariate statistical analyses revealed that the bacterial communities showed a
unimodal response to the physicochemical parameters (Table 3.1) making these data
suitable for analysis using CCA. The first two axes of the CCA analysis explained 85 %
of the total variance for the bacterial communities (Figure 3.4) indicating a good sample
separation along the axis. For construction of the bacterial CCA plots, the sample scores
(community structure) and environmental variable (arrows) were plotted. Using these
analyses, the evaporation pond and CAP-Outlet were distinct from the other sampling
points of the water treatment process. Differences in the bacterial community structure
(Figure 3.4a) between pits, holding tank, CAP-Bottom and CAP-Top were mainly
Chapter 3: Characterisation of the piggery waste treatment process
56
Figure 3.4. CCA biplot showing the relationship between, a) microbial community composition or b) individual bacterial taxa and environmental variables in each sampling point of piggery wastewater treatment process. Plots on the graph represent the community composition at each sampling point () and individual taxa (▲). Arrows represent the environmental variables (EC, VS, TN, TC, Pi, C:N ratio, TS, TP, OP, Ca, Mg, K, pH, Ammonia).
Chapter 3: Characterisation of the piggery waste treatment process
57
related to differences in C:N ratio, Total Solids (TS), Total P (TP), Organic P (OP), and
Ca. Conversely, differences in bacterial community structure in the aerobic evaporation
pond were mainly related to differences in pH, ammonium nitrogen, Mg and K.
A second biplot (Figure 3.4b) was constructed using the individual taxa scores
(phylogenetic identities of the taxa are shown in Table 3.3 to assess the contribution of
individual taxa (▲) to the scatter seen in Figure 3.4a. This enabled the key components
of the bacterial communities responsible for driving waste degradation process to be
identified. High taxa scores (length of the arrow) indicated the importance of
environmental variables in determining the community composition whilst the angle
between arrows indicates the degree of correlation between the variables. The position
of points (taxa) relative to the arrows indicates the environmental conditions at each
sample site. Thus, there were marked changes in the relative abundance of some
bacterial taxa between waste treatment points whose distributions and responses were
particularly closely correlated with the environmental conditions of those waste
treatment points (Figure 3.4b, Table 3.3).
Some taxa, whose distributions and responses were particularly closely correlated with
the environmental variables (pH, NH4+, Mg and K) within the evaporation pond were:
Aeromonadaceae (#45), Flavobacteriia (#14), Fluviicola (#13), Xanthomonadaceae
(#48), Rhodocyclaceae (# 40), Actinomycetales (#1), Microbacteriaceae (#2),
Agrococcus (#3), Candidatus (#4), Leucobacter (#5), Pedobacter (#15),
Erysipelotrichaceae (#30), OD1 (#32), TM6 (#54), Alcaligenaceae (#37) and TM7-1
(#55).
Distribution and response of Bacteroides (#8), TM7-3 (#56), Porphyromonadaceae
(#10), Lachnospiraceae (#24), Comamonadaceae (#38), Pseudomonadaceae (#47)
were closely correlated to the environmental variables of CAP-Outlet. The other taxa
presented in Table 3.3 were distributed amongst the pits, holding tank, CAP-Bottom,
CAP-Top where TP, OP, TS, C:N ratio and Ca were higher.
3.3.4 Whole-genome-shotgun sequencing
Phylogenetic reconstruction, based upon functional gene phylogenies within MGRAST,
revealed that community DNA within the different stages of the piggery waste treatment
process was dominated by bacterial DNA (97.2%- 82.2%) followed by Archaeal (0.4%-
16.6%) and Eukaryotes (0.3%-0.2%) (Figure 3.5a). Furthermore, metagenome analyses
Chapter 3: Characterisation of the piggery waste treatment process
58
indicated that considerably higher DNA was attributed to the archaea in the CAP-top
(6.8 %) and CAP-bottom (15.6%), with Methanomicrobia being more prevalent and, in
general, dominated by members of the Methanosarcinaceae (data not shown).
Table 3.3 Taxonomic identities for the CCA biplot showing the relationship between measured variables and individual taxa distributions for different stages of the piggery waste treatment system.
Codes Taxa Codes Taxa1 Actinobacteria; Actinomycetales 32 Bacteria; OD12 Actinobacteria; Microbacteriaceae 33 Alphaproteobacteria;Other3 Actinobacteria; Agrococcus 34 Alphaproteobacteria; BD7-34 Actinobacteria; Candidatus Aquiluna 35 Alphaproteobacteria; Acetobacteraceae5 Actinobacteria; Leucobacter 36 Alphaproteobacteria; Rickettsiales6 Bacteroidetes; Bacteroidales 37 Betaproteobacteria; Alcaligenaceae7 Bacteroidetes; Bacteroidaceae 38 Betaproteobacteria; Comamonadaceae8 Bacteroidetes; Bacteroides 39 Betaproteobacteria; MWH-UniP19 Bacteroidetes; Marinilabiaceae 40 Betaproteobacteria; Rhodocyclaceae10 Bacteroidetes; Porphyromonadaceae 41 Deltaproteobacteria; Geobacter11 Bacteroidetes; Rikenellaceae 42 Deltaproteobacteria; Syntrophaceae12 Bacteroidetes; SB-1 43 Deltaproteobacteria; Syntrophus13 Bacteroidetes; Fluviicola 44 Epsilonproteobacteria; Helicobacteraceae14 Bacteroidetes; Flavobacteriia 45 Gammaproteobacteria; Aeromonadaceae15 Bacteroidetes; Pedobacter 46 Gammaproteobacteria; Methylomonas16 Chlorobi; OPB56 47 Gammaproteobacteria; Pseudomonadaceae17 Chloroflexi; Dehalococcoidaceae 48 Gammaproteobacteria; Xanthomonadaceae18 Cyanobacteria 49 Spirochaetes; PL-11B1019 Fibrobacteres 50 Spirochaetes; Sphaerochaeta20 Firmicutes; Lactobacillales 51 Spirochaetes; Treponema21 Firmicutes; Clostridiales 52 Synergistetes; Dethiosulfovibrionaceae22 Firmicutes; Clostridium 53 Synergistetes; Synergistaceae23 Firmicutes; Proteiniclasticum 54 TM624 Firmicutes; Lachnospiraceae 55 TM7; TM7-125 Firmicutes; Ruminococcaceae 56 TM7; TM7-326 Firmicutes; Syntrophomonas 57 Tenericutes;Acholeplasmataceae27 Firmicutes; Veillonellaceae 58 Tenericutes; Mollicutes28 Firmicutes; Mogibacteriaceae 59 Tenericutes; ML615J-2829 Firmicutes; Tissierellaceae 60 Verrucomicrobia;Puniceicoccaceae30 Firmicutes; Erysipelotrichaceae 61 Verrucomicrobia; Pedosphaerales31 Lentisphaerae; Victivallaceae 62 WWE1; Cloacamonales
Chapter 3: Characterisation of the piggery waste treatment process
59
Figure 3.5 Community DNA composition of the piggery waste treatment process based upon functional gene phylogenies (a) Microbial community composition obtained by taxonomic identity linked to functional gene content by MG-RAST analysis (b).
Bacterial identity derived from functional gene phylogenies (Figure 3.5b) further
supported the stability of the CAP system and revealed that Clostridia, Bacteroidia,
Actinobacteria, Deltaproteobacteria, Bacilli, Gammaproteobacteria, Flavobacteriia,
Chapter 3: Characterisation of the piggery waste treatment process
60
Alphaproteobacteria, Cytophagia, and Betaproteobacteria, were the 10 most abundant
bacterial groups within the CAP-Bottom. In contrast, Betaproteobacteria,
Actinobacteria, Gammaproteobacteria, Alphaproteobacteria, Clostridia, Bacilli,
Deltaproteobacteria, Flavobacteriia, Sphingobacteriia, and Bacteroidia were more
prevalent community members in the evaporation pond.
The diversity of the bacterial community profile derived by assignment of protein-
encoding genes via metagenomic analysis was closely matched with PCR-based 16S
rRNA diversity. However, their relative abundance varied markedly between the two
methods. For example, comparison of relative abundance using the two methods
indicated that the diversity of the bacterial profile was similar but their relative
abundance considerably varied between for both CAP-Bottom (anaerobic pond) and
evaporation pond (aerobic pond) (Table 3.4).
Table 3.4 Comparison of relative abundance (%) of the top 10 most abundant bacterial groups within the CAP-Bottom and evaporation pond as revealed by tag sequencing and metagenomic analyses.
PCR dependant tag sequencing PCR independent (metagenomic) CAP-Bottom
Bacteroidia 40.3 Clostridia 18.1 Clostridia 18.5 Bacteroidia 15.2 Synergistia 11.7 Actinobacteria 6.6 WWE1; [Cloacamonae] 9.2 Deltaproteobacteria 5.7 Deltaproteobacteria 2.0 Bacilli 5.0 Mollicutes 1.9 Gammaproteobacteria 4.9 Gammaproteobacteria 1.7 Flavobacteriia 3.6 Epsilonproteobacteria 1.6 Alphaproteobacteria 2.4 RF3 1.3 Cytophagia 2.0 Erysipelotrichi 1.2 Betaproteobacteria 1.9
Evaporation Pond Actinobacteria 50.7 Betaproteobacteria 39.6 Betaproteobacteria 19.9 Actinobacteria 27.1 Erysipelotrichi 8.2 Gammaproteobacteria 10.0 TM7-1 5.1 Alphaproteobacteria 6.3 Gammaproteobacteria 2.9 Clostridia 2.3 Sphingobacteriia 1.5 Bacilli 1.8 Flavobacteriia 1.3 Deltaproteobacteria 1.7 Alphaproteobacteria 0.9 Flavobacteriia 1.3 Clostridia 0.7 Sphingobacteriia 0.8 OD1 0.7 Bacteroidia 0.7
Chapter 3: Characterisation of the piggery waste treatment process
61
3.3.5 Hierarchical classification analysis of functional genes
Distribution of metabolic functions showed that most of the piggery microbiome linked
to known subsystems, including metabolism of organic macromolecules such as
proteins, carbohydrates and nucleic acids (Table 3.5). Clustering-based subsystems was
the most abundant SEED subsystem representing 14.6 %-16.1% of the total sequences
amongst the different stages of piggery waste process followed by genes associated with
protein metabolism (10.8-12.0%), carbohydrates (9.3-11.1%), miscellaneous (7.4-
7.7%), amino acids and derivatives (7.1-8.3%), DNA metabolism (6.0-6.4%) and RNA
metabolism (4.8-5.4%) were the most abundant metabolic functions in these systems
(Table 3.5). The high levels of genes associated with protein and nucleic acids
metabolism suggested that the cells in piggery waste were highly active. Carbohydrate
metabolism was particularly elevated in the CAP-Bottom compared to other points of
the waste treatment system, indicating that breakdown of carbohydrate is a predominant
function within CAP-Bottom.
Table 3.5 Metabolic profiles based upon metagenomic functional classification within
different compartments of waste treatment process
Metabolic potential Pits Holding Tank
CAP-Bottom
CAP-Top
CAP-outlet
Evaporation Pond
Clustering-based subsystems 16.0 16.1 14.6 16.0 16.2 15.6Protein Metabolism 10.8 12.0 10.8 11.7 12.0 10.6Carbohydrates 9.3 9.8 11.1 10.5 9.8 9.3Miscellaneous 7.4 7.7 7.6 7.5 7.5 7.4Amino Acids and Derivatives 7.1 7.7 8.3 8.2 8.2 8.6DNA Metabolism 6.4 6.7 6.1 6.0 6.1 5.0Cofactors, Vitamins, Prosthetic Groups, Pigments 5.4 5.1 5.9 5.3 5.0 6.9RNA Metabolism 5.2 5.4 4.8 5.0 5.0 4.4Phages, Prophages, Transposable elements, Plasmids 4.4 2.2 1.9 2.2 2.4 3.3Nucleosides and Nucleotides 3.5 3.3 3.5 3.5 3.5 3.5Cell Wall and Capsule 3.4 3.7 3.4 3.3 3.5 3.6Virulence, Disease and Defense 3.0 2.7 2.7 2.6 2.8 2.5Membrane Transport 2.9 2.7 2.9 3.1 3.1 2.6Respiration 2.5 2.8 3.6 3.0 2.8 3.1Stress Response 2.5 2.4 2.2 2.3 2.3 2.3Fatty Acids, Lipids, and Isoprenoids 2.1 1.9 2.0 1.9 1.9 2.5Cell Division and Cell Cycle 1.6 1.8 1.7 1.7 1.7 1.7Regulation and Cell signaling 1.2 1.1 1.2 1.2 1.2 1.2Metabolism of Aromatic Compounds 0.9 0.7 0.7 0.6 0.6 0.9Nitrogen Metabolism 0.9 0.9 1.1 1.0 0.9 1.4Sulfur Metabolism 0.8 0.7 0.9 0.7 0.7 0.7Iron acquisition and metabolism 0.7 0.4 0.6 0.4 0.4 0.3Phosphorus Metabolism 0.6 0.7 0.8 0.7 0.7 1.0Motility and Chemotaxis 0.6 0.5 0.6 0.5 0.6 0.4Dormancy and Sporulation 0.4 0.4 0.3 0.4 0.4 0.2Secondary Metabolism 0.3 0.3 0.4 0.3 0.3 0.4Potassium metabolism 0.2 0.2 0.3 0.2 0.2 0.2Photosynthesis 0.1 0.0 0.0 0.1 0.1 0.3
Chapter 3: Characterisation of the piggery waste treatment process
62
3.3.6 Distribution of metabolic functions in relation to P cycling
Community shotgun metagenomic analysis was used to identify the functionality of
microorganisms involved in P cycling in the each compartments of the piggery
wastewater treatment process. Functional assignments of P metabolisms were
conducted through the SEED subsystems of the MG-RAST, using an e-value of
minimum 1 e-5. The analyses showed that genes linked to P metabolism ranged among
the compartments from 0.6 % of the total sequences to 1.0 % in the pits and evaporation
pond respectively (data not shown). Putative genetic potential of P metabolisms in terms
of P mineralisation, P solubilisation, and polyP accumulation was assessed based on the
enrichment of the functional gens involved in these P transformation pathways by
assigning functional annotations to shotgun metagenomic sequences.
3.3.6.1 Distribution of metabolic functions in relation to P mineralisation
The genetic potential for P metabolism showed enrichment of genes involved in
assimilation and regulation of phosphatase metabolism within the piggery waste
treatment process (Table 3.6). Mainly the abundance of gene sequence numbers for
alkaline phosphatase, which is the primary enzyme responsible for P mineralisation,
was relatively higher in CAP-Bottom followed by CAP-Top, holding tank, pits and
evaporation pond stages. The alkaline phosphatase is regulated by PHO regulon and the
PHO regulon is central to assimilation of phosphate and regulation of phosphate
metabolism. Metagenomic analysis of the piggery waste treatment process revealed the
presence of a number of PHO regulon such as phosphate starvation-inducible genes
(PhoR, PhoB, Pst, Pit). This suggests that the piggery waste treatment stages are
fluctuating in their activity for the assimilation and regulation of phosphate metabolism.
The alkaline phosphatase gene sequence number was assessed to compare the
fluctuation of P mineralisation physiology within different compartments (Figure 3.6a).
The alkaline phosphatase gene sequence number fluctuated within the compartments
and corresponding organic P concentration fluctuated in the similar fashion. This
implies that P mineralisation was gradually increased during the early stage of waste
degradation and reached to the maximum at CAP-Bottom where the highest organic P
was available. Thereafter, the P mineralisation gradually decreased towards the end of
the waste treatment where the lowest level of organic P was available.
Chapter 3: Characterisation of the piggery waste treatment process
63
Table 3.6 P mineralising potentials at the different stages of the piggery waste treatment process.
Sample point function Abundance # hitsPits Phosphate starvation-inducible protein PhoH 71 49
Alkaline phosphatase (EC 3.1.3.1) 48 31Predicted ATPase related to phosphate starvation-inducible protein PhoH 48 24Phosphate regulon sensor protein PhoR (SphS) (EC 2.7.13.3) 40 33Phosphate regulon transcriptional regulatory protein PhoB (SphR) 40 32Phosphate transport system regulatory protein PhoU 23 18Phosphate transport regulator (distant homolog of PhoU) 14 12Phosphate starvation-inducible ATPase PhoH with RNA binding motif 10 9Alkaline phosphatase synthesis transcriptional regulatory protein PhoP 4 3
Holding Tank Phosphate starvation-inducible protein PhoH 93 55Alkaline phosphatase (EC 3.1.3.1) 65 44Phosphate regulon transcriptional regulatory protein PhoB (SphR) 60 41Phosphate regulon sensor protein PhoR (SphS) (EC 2.7.13.3) 45 40Exopolyphosphatase (EC 3.6.1.11) 39 21Phosphate transport system regulatory protein PhoU 33 20Phosphate transport regulator (distant homolog of PhoU) 21 12Alkaline phosphatase like protein 19 5response regulator in two-component regulatory system with PhoQ 4 4Alkaline phosphatase synthesis transcriptional regulatory protein PhoP 3 3
CAP-Bottom Alkaline phosphatase (EC 3.1.3.1) 137 48Phosphate starvation-inducible protein PhoH 101 62Phosphate starvation-inducible protein PhoH, predicted ATPase 101 62Predicted ATPase related to phosphate starvation-inducible protein PhoH 88 29Phosphate transport system regulatory protein PhoU 79 28Phosphate regulon sensor protein PhoR (SphS) (EC 2.7.13.3) 60 44Phosphate regulon transcriptional regulatory protein PhoB (SphR) 55 52Phosphate transport regulator (distant homolog of PhoU) 33 14Alkaline phosphatase like protein 16 4Alkaline phosphatase synthesis transcriptional regulatory protein PhoP 8 8PhoQ 6 6
CAP-Top Phosphate starvation-inducible protein PhoH 139 75Phosphate starvation-inducible protein PhoH, predicted ATPase 139 75Phosphate transport system regulatory protein PhoU 84 34Phosphate regulon transcriptional regulatory protein PhoB (SphR) 77 65Phosphate regulon sensor protein PhoR (SphS) (EC 2.7.13.3) 68 50Alkaline phosphatase (EC 3.1.3.1) 66 28Alkaline phosphatase synthesis transcriptional regulatory protein PhoP 11 8PhoQ 9 7Alkaline phosphatase like protein 8 5response regulator in two-component regulatory system with PhoQ 7 5secreted alkaline phosphatase 2 1
CAP-Outlet Phosphate starvation-inducible protein PhoH 109 57Phosphate transport system regulatory protein PhoU 49 27Phosphate regulon sensor protein PhoR (SphS) (EC 2.7.13.3) 46 34Phosphate regulon transcriptional regulatory protein PhoB (SphR) 44 36Predicted ATPase related to phosphate starvation-inducible protein PhoH 33 19Alkaline phosphatase (EC 3.1.3.1) 24 21Phosphate transport regulator (distant homolog of PhoU) 15 13Phosphate starvation-inducible ATPase PhoH with RNA binding motif 12 10Alkaline phosphatase synthesis transcriptional regulatory protein PhoP 7 7
Evaporation pond Predicted ATPase related to phosphate starvation-inducible protein PhoH 254 66Phosphate regulon transcriptional regulatory protein PhoB (SphR) 151 51Phosphate regulon sensor protein PhoR (SphS) (EC 2.7.13.3) 111 43Phosphate starvation-inducible protein PhoH 108 50Phosphate transport system regulatory protein PhoU 84 28Alkaline phosphatase (EC 3.1.3.1) 14 13PhoQ 7 7Alkaline phosphatase synthesis transcriptional regulatory protein PhoP 1 1
Chapter 3: Characterisation of the piggery waste treatment process
64
Figure 3.6 Relationships between (a) the abundance of alkaline phosphatase gene involved in regulation of P mineralisation and the respective organic P concentration, and (b) the abundance of alkaline phosphatase gene and organic P concentration.
A positive correlation (R2= 85) was observed between organic P availability and
alkaline phosphatase gene sequence number (Figure 3.6b). This confirms that organic P
availability is one of the key drivers for the P mineralising capacity in the piggery waste
treatment process. Furthermore, abundance of those genes involved in phosphatase
metabolism and the presence of microorganisms assigning the alkaline PO4ase activity
suggests that both bacteria and archaea (mainly Methanosarcina sp.) play an important
role in the piggery waste treatment process during P mineralisation (data not shown).
Chapter 3: Characterisation of the piggery waste treatment process
65
The most abundant PMB identified using 16S rRNA Ion Tag sequencing of sorted cells
(Bacteroidales, Clostridiales, Campylobacterales, Synergistales) were also commonly
found in the metagenomic analysis of gene encoding for alkaline phosphatase
(Bacterioides, Parabacteroides, Flavobacterium, Clostridium, Desulfitobacterium).
3.3.6.2 Distribution of metabolic functions in relation to P solubilisation
Genes involved in the P solubilizing pathways (e.g. pqq genes involved in gluconic
acid, glucose dehydrogenase, malate dehydrogenase genes and genes involved in
production of acids) gave an indication of the putative function of mineral phosphate
solubilising microbial activity. Table 3.7 shows the relative abundance of some of those
potential genes within the piggery waste treatment process. Glucose dehydrogenase,
pqq-dependent (EC 1.1.5.2), the primary enzyme that governs the P solubilisation
pathway, was not detected except at very low abundance within the collection pits (7
copies), CAP-Bottom (2 copies) and outlet (1 copy). However, genes involved in
production of acids such as citrate synthase (si) (EC 2.3.3.1) and malate dehydrogenase
(EC 1.1.1.37) were commonly found at all the stages of the waste treatment with
markedly higher abundance in CAP-Bottom and the evaporation pond, suggesting that
P solubilisation could potentially occur in those stages. Apart from this, there was no
direct functional gene evidence to demonstrate categorically P solubilising capacity,
except for culture isolates obtained from tri calcium selective media.
Table 3.7 P solubilising potentials at the different stages of the piggery waste treatment process.
Sample function abundance # hitsPits Citrate synthase (si) (EC 2.3.3.1) 139 82
Malate dehydrogenase (EC 1.1.1.37) 95 51Glucose dehydrogenase, PQQ-dependent (EC 1.1.5.2) 7 4Gluconate dehydratase (EC 4.2.1.39) 11 9
Holding Tank Citrate synthase (si) (EC 2.3.3.1) 159 59Malate dehydrogenase (EC 1.1.1.37) 101 41
CAP-Bottom Citrate synthase (si) (EC 2.3.3.1) 226 79Malate dehydrogenase (EC 1.1.1.37) 135 48Glucose dehydrogenase, PQQ-dependent (EC 1.1.5.2) 2 2
CAP-Top Citrate synthase (si) (EC 2.3.3.1) 217 96Malate dehydrogenase (EC 1.1.1.37) 71 43
CAP-Outlet Citrate synthase (si) (EC 2.3.3.1) 139 65Malate dehydrogenase (EC 1.1.1.37) 46 31Glucose dehydrogenase, PQQ-dependent (EC 1.1.5.2) 1 1
Evaporation Pond Citrate synthase (si) (EC 2.3.3.1) 355 82Malate dehydrogenase (EC 1.1.1.37) 216 41
Chapter 3: Characterisation of the piggery waste treatment process
66
3.3.6.3 Distribution of metabolic functions in relation to polyP accumulation
The abundance of genes of polyphosphate kinase and exopolyphosphatase, the primary
enzymes involved in polyP synthesis and degradation respectively, was assessed to
compare the activities of the polyP accumulation physiology within different
compartments (Figure 3.7). Enrichment of both polyphosphate kinase and
exopolyphosphatase was observed in the evaporation pond (aerobic pond) compared to
other stages (facultative anaerobic/anaerobic). This implies that under the aerobic
condition, poly P synthesis and degradation is highly active compared to the facultative/
anaerobic conditions.
Figure 3.7 Abundance of gene involved in polyP synthesis (polyphosphate kinase) and hydrolysis (exopolyphosphatase) at the different stages of the piggery waste treatment process.
High affinity Pst (phosphate specific transport) systems (PstA, PstB, and PstC) are
involved in the uptake and transport of Pi across the cytoplasmic membrane and
enrichment of those genes is an indication of phosphate assimilation to cells. The
genetic potential for P metabolism also showed enrichment of those genes in the
metagenome of piggery waste treatment process (Table 3.8). The higher abundance of
polyphosphate kinase and exopolyphosphatase further confirmed that assimilated
orthophosphate had subsequently converted to polyp granules. In general, the highest
community representation of genes that regulate polyP metabolism was found in the
evaporation pond. This implies that the reduction of orthophosphate at the evaporation
Chapter 3: Characterisation of the piggery waste treatment process
67
pond was due to polyP formation and not by just P assimilation inside the microbial
biomass.
Table 3.8 PolyP accumulating potentials at the different stages of piggery waste treatment process.
Pits function abundance # hitsPhosphate transport ATP-binding protein PstB (TC 3.A.1.7.1) 79 57Polyphosphate kinase (EC 2.7.4.1) 77 51Phosphate transport system permease protein PstC (TC 3.A.1.7.1) 67 49Sodium-dependent phosphate transporter 62 31Probable low-affinity inorganic phosphate transporter 59 33Phosphate transport system permease protein PstA (TC 3.A.1.7.1) 52 44Phosphate ABC transporter, periplasmic phosphate-binding protein PstS (TC 3.A.1.7.1) 43 38Exopolyphosphatase (EC 3.6.1.11) 28 18
Holding Tank Phosphate transport ATP-binding protein PstB (TC 3.A.1.7.1) 126 87Sodium-dependent phosphate transporter 119 36Probable low-affinity inorganic phosphate transporter 113 40Phosphate transport system permease protein PstC (TC 3.A.1.7.1) 107 63Polyphosphate kinase (EC 2.7.4.1) 91 45Phosphate ABC transporter, periplasmic phosphate-binding protein PstS (TC 3.A.1.7.1) 86 54Phosphate transport system permease protein PstA (TC 3.A.1.7.1) 83 43Exopolyphosphatase (EC 3.6.1.11) 39 21Low-affinity inorganic phosphate transporter 3 3
CAP-Bottom Probable low-affinity inorganic phosphate transporter 179 65Phosphate transport ATP-binding protein PstB (TC 3.A.1.7.1) 172 113Polyphosphate kinase (EC 2.7.4.1) 158 69Phosphate ABC transporter, periplasmic phosphate-binding protein PstS (TC 3.A.1.7.1) 135 54Phosphate transport system permease protein PstA (TC 3.A.1.7.1) 121 62Phosphate transport system permease protein PstC (TC 3.A.1.7.1) 119 65Sodium-dependent phosphate transporter 103 38Exopolyphosphatase (EC 3.6.1.11) 59 17Low-affinity inorganic phosphate transporter 4 4
CAP-Top Phosphate transport ATP-binding protein PstB (TC 3.A.1.7.1) 179 111Phosphate ABC transporter, periplasmic phosphate-binding protein PstS (TC 3.A.1.7.1) 161 69Phosphate transport system permease protein PstA (TC 3.A.1.7.1) 158 66Sodium-dependent phosphate transporter 148 44Phosphate transport system permease protein PstC (TC 3.A.1.7.1) 136 71Probable low-affinity inorganic phosphate transporter 114 36Polyphosphate kinase (EC 2.7.4.1) 110 55Exopolyphosphatase (EC 3.6.1.11) 45 17Putative periplasmic phosphate-binding protein PstS (Catenulisporaceae type) 1 1
CAP-Outlet Phosphate transport ATP-binding protein PstB (TC 3.A.1.7.1) 138 93Phosphate ABC transporter, periplasmic phosphate-binding protein PstS (TC 3.A.1.7.1) 147 76Phosphate transport system permease protein PstA (TC 3.A.1.7.1) 116 57Phosphate transport system permease protein PstC (TC 3.A.1.7.1) 91 53Sodium-dependent phosphate transporter 142 36Probable low-affinity inorganic phosphate transporter 74 41Polyphosphate kinase (EC 2.7.4.1) 91 53Exopolyphosphatase (EC 3.6.1.11) 16 13
Evaporation Pond Pyrophosphate-energized proton pump (EC 3.6.1.1) 557 80Polyphosphate kinase (EC 2.7.4.1) 453 92Phosphate transport ATP-binding protein PstB (TC 3.A.1.7.1) 251 86Phosphate transport system permease protein PstA (TC 3.A.1.7.1) 205 47Phosphate ABC transporter, periplasmic phosphate-binding protein PstS (TC 3.A.1.7.1) 168 42Inorganic pyrophosphatase (EC 3.6.1.1) 155 38Phosphate transport system permease protein PstC (TC 3.A.1.7.1) 137 38Exopolyphosphatase (EC 3.6.1.11) 127 40
Chapter 3: Characterisation of the piggery waste treatment process
68
3.4 Discussion
3.4.1 Characterisation of microbial community composition and diversity in the
wastewater treatment process
This study identified the key components of the bacterial community involved in the
whole process of piggery waste degradation in a model covered anaerobic pond digester
system. Both 16S rRNA Ion Tag sequencing and metagenome analyses showed that
bacterial community composition of the initial facultative anaerobic stages (i.e. pits and
holding tank) and the covered anaerobic digester (i.e. CAP-Bottom and CAP-Top) was
relatively similar but remarkably varied to that of the aerobic stage (i.e. evaporation
pond) (Figure 3.3 and 3.5b). Physico-chemical characterisation (Table 3.1) and CCA
analysis (Figure 3.4) supported that both resource availability and environmental factors
between the anaerobic stages and the aerobic stage are the key drivers in shaping the
bacterial community dynamics among these compartments of piggery wastewater
treatment system.
16S rRNA Ion Tag sequencing indicated that the dominant bacteria within the
facultative anaerobic/anaerobic ponds were Bacteroidia, Clostridia, Cloacamonae, and
Synergistia, with considerable fluctuation in their abundance between compartments.
These taxa are commonly found in piggery waste treatment systems and other anaerobic
digesters (Cook et al. 2010; Patil et al. 2010; Talbot et al. 2010; Kampmann et al. 2012;
Supaphol et al. 2011; Whiteley et al. 2012) and known to participate in one or more of
stage of the AD process (hydrolysis, acidogenesis, acetogenesis) (Müller et al. 2010;
Supaphol et al. 2011). For example, previous studies have highlighted that the
Firmicutes phylum (Clostridia) is of significant importance in cellulose degradation
within biogas generating microbial communities. Further, members of the Bacteriodia
are common where degradable organic material is to be found and the Clostridia are
noted for their highly effective cellulose degradation potential (Wirth et al. 2012). It has
been reported that Clostridia are important components of the swine gut community and
play significant roles in fermenting lipids, sugars and amino acids (Zhu 2000).
Therefore, it appeared that the microbial communities in facultative anaerobic/anaerobic
stages are likely to be ubiquitous in all AD systems and play an important role in waste
degradation and biogas production.
In contrast to the facultative anaerobic/anaerobic stages, Proteobacteria and
Actinobacteria were the most abundant taxa in the aerobic stage, They are commonly
Chapter 3: Characterisation of the piggery waste treatment process
69
found in industrial wastewater evaporation ponds (Ben‐Dov et al. 2008) and industrial
waste gas biofilter systems (Friedrich et al. 2003). CCA analyses indicated that the
community composition in the evaporation pond is shaped mainly by specific
environmental conditions (pH, K, Mg, ammonium). Previously, factors such as pH,
Mg2+ and K+ were shown to be important for polyP formation (McGrath et al. 2001;
Günther et al. 2009). This together with the significant reduction of orthophosphate in
the evaporation pond suggests that the microbial community in the evaporation pond
may play an important role in P removal via polyP formation.
Using metagenomic analyses to reconstruct diversity assessments, a higher abundance
of archaea was observed in CAP-Bottom (16.7 %) and CAP-Top (8%) compared to the
other sampled compartments. A previous study has shown that about 10% of the
identified microbes in the biogas producing community belong to the Archaea (Wirth et
al. 2012) and, here, we can also assume that archaea play an important role in the
methanogenic biogas production in the CAP.
When comparing the 16S rRNA Ion Tag sequencing with phylogenies derived from
metagenomic analysis, apparent differences were observed between the bacterial
community compositions (Table 3.4). Nevertheless, there was reasonably good
agreement between the approaches with many of the same groups recovered in both the
PCR based and metagenome analyses, including all classes of Clostridia, Bacteroidia,
Actinobacteria, Gammaproteobacteria, Betaproteobacteria, Alphaproteobacteria,
Sphingobacteriia, and Flavobacteriia. However, the observed differences are likely to
be attributed to bias in the both methods (e.g. unequal amplification of 16S rRNA genes
of bacteria in PCR based sequencing). However, it is still premature to make
conclusions about similarity or dissimilarity of data between metagenomic and PCR-
based assessments for microbial diversity because few studies have directly addressed
the issue. While some studies have showed that these two approaches give largely
similar species profiles (Kalyuzhnaya et al. 2008a, 2008b) others have observed
significantly different community structures (Shah et al. 2011). With the availability of
low cost sequencing platform such as Ion Toront PGM, it is now possible to minimise
the any misinterpretation of microbial community analyses by generating co-incident
datasets for both MG-RAST phylogenetic based analyses and by extracting 16S rRNA
sequences directly from the metagenome (Whiteley et al. 2012).
Chapter 3: Characterisation of the piggery waste treatment process
70
3.4.2 P mineralising and solubilising potential as revealed by the culture dependant
detection
P cycling potential was based on both taxonomic identity and functionality during the
waste treatment process. It was assessed by comparing sequences of isolates of P
mineralising bacteria (PMB) and P solubilising bacteria (PSB) with community
metagenome analyses. Genetic analyses of 16S rDNA sequences derived from the
isolated PMB and PSB revealed a high similarity with bacteria belonging to the genus
Pseudomonas, Enterobacter, Escherichia coli, Bacillus and Cronobacter. These
findings were consistent with previous studies, where the genera Pseudomonas,
Enterobacter and Bacillus have previously been identified as PMB (Rodríguez and
Fraga, 1999; Barik et al. 2001; Konietzny and Greiner 2004; Jorquera et al. 2008).
Members of the Bacillus spp. are also an important group in the mineralization of P in
aquatic and terrestrial environments (Hill et al. 2007) and play an important role in
organic P degradation (Kerovuo et al. 1998). Furthermore, the genera Pseudomonas,
Enterobacter and Bacillus have previously been identified as PSB (Chung et al. 2005;
Tao et al. 2008; Zhu et al. 2011). Therefore the finding of this study is in consistency
with the previous studies. However, these isolates only represented the minor taxa (<0.1
%) recovered from the PCR and metagenome approaches. The lower diversity observed
for PMB and PSB by culturing compared to diversity estimates obtained by PCR and
metagenomic approaches, reflects the inefficiency of culture methods to assess
functional diversity. Although culture-dependant techniques are useful for describing
the cultivable fraction of the population with defined physiologies (Whitehead et al.
2005), they often underestimate and/or bias total diversity estimates (Iannotti et al.
1982). For example, activities observed under culture conditions and in situ may not
reflect the same activity, as there are a large number of factors that affect phosphatase
activity (Barik et al. 2001).
3.4.3 Distribution of metabolic functions in relation to P mineralisation
Assessment of putative metabolic potential of P cycling in this study using
metagenomic analysis seemed to be a good approach for screening the P cycling genetic
potential in situ in the highly diverse environment of piggery wastewater. Putative
genetic potential of P metabolisms in terms of P mineralisation, P solubilisation, and
polyP accumulation was assessed based on the enrichment of the functional gens
involved in these P transformation pathways.
Chapter 3: Characterisation of the piggery waste treatment process
71
The putative functional potential in relation to P mineralising indicated that activity in
CAP-Bottom was dominant compared to other stages where the availability of substrate
(organic P) for P mineralising bacteria was high. Alkaline phosphatase is regulated by
PHO regulon (Willsky and Malamy 1976) and the PHO regulon is central to
assimilation of phosphate and regulation of phosphate metabolism (Wanner and
Metcalf, 1992; Wanner, 1993). Considerable abundance of these genes was found at all
the stages of the piggery waste treatment system with a higher abundance in CAP-
Bottom and CAP-Top (Table 3.6). This was further supported by the observation of
higher abundance of Pi at the CAP-Top (ca. 21.8 mg/L) indicating that P mineralising
capacity in CAP digester was high. P mineralisation can be defined as the hydrolysis of
Pi from organic P or other complex P compounds (e.g. polyP), in which the hydrolysed
Pi is released outside the cells (Kloeke and Geesey 1999). This process is catalysed by
acid or alkaline phosphatases (Kloeke and Geesey 1999; Anupama et al. 2008). There
was a direct relationship between the number of alkaline phosphatase gene sequences
and organic P concentration in the piggery waste treatment system (Figure 3.6b). The
higher abundance of alkaline phosphatase gene sequences at the higher organic P
availability led to the hypothesis that a diverse P mineralisation bacterial community
can be found in piggery wastes which are characteristically high in organic P which
acts as a substrate for P mineralising microorganisms.
Uncovering the taxonomic identity of PMB is important for enhancing P mineralisation
in the CAP digester, so that low carbon digestates can be produced for removal of
soluble P post processing (within the evaporation pond) via Enhanced Biological P
Removal as polyP accumulation. Other culture independent techniques, i.e Enzyme
labelled fluorescence (ELF) labelling of single cells of PMB, coupled with rapid flow
cytometric analyses, cell sorting and next generation sequencing approaches to assign
phylogenetic and functional affiliations of PMB in the piggery waste treatment system
were investigated in Chapter 4.
3.4.4 Distribution of metabolic functions in relation to polyP accumulation
The community polyP accumulation potential was assessed in terms of the abundance
of genes involved in polyP formation to understand the capacity to enhance biological P
removal during the piggery wastewater treatment process. A remarkable enrichment of
Polyphosphate kinase and exopolyphosphatase was observed in the evaporation pond
(aerobic pond) compared to other stages (facultative anaerobic/ anaerobic). This verified
that the evaporation pond is a considerable site of activity for polyP formation.
Chapter 3: Characterisation of the piggery waste treatment process
72
In general, polyP formation favours carbon-rich, strictly anaerobic conditions, followed
by carbon-poor, aerobic incubation (De-Bashan and Bashan 2004). These data
suggested that wastewater in the evaporation pond has already undergone suitable
anaerobic processing which meets carbon-low, aerobic conditions for effective polyP
accumulation within the evaporation pond. The reduction of the Pi level in the
evaporation pond (12.2 mg/L), compared to the outlet of the CAP digestion (20.5
mg/L), verifies that a considerable degree of Pi immobilisation happens within the
evaporation pond. This could be either due to the consumption of Pi for cellular
requirements or to accumulation of Pi as polyP inside the microbial population.
Enrichment of polyphosphate kinase verified that the reduction of orthophosphate is
mainly due to the formation of polyP granules inside the microbial biomass and not
solely due to the consumption of Pi for cellular requirements.
The reduction of Pi in the evaporation pond down to 12.2 mg/L under this natural
condition is not sufficient to recycle the treated waste with the irrigation waste as a
liquid fertiliser, especially for sandy soils in south-western Australia where Pi leaching
and subsequent Pi pollution of water bodies can occur, and therefore EBPR technology
is required. Biological P removal process known as ‘enhanced biological P removal
(EBPR)’ is more efficient and economically viable (Majed 2011). There is evidence that
the EPBR process could be enhanced under acidic conditions (McGrath et al. 2001;
Mullan et al. 2002; Moriarty et al. 2006). Therefore, it was hypothesised that under
acidic conditions in a high inorganic P system there will be an increase in abundance of
polyP accumulating bacteria and a concomitant increase in polyphosphate
accumulation by these bacteria facilitating further reduction of Pi level in the
evaporation pond.
As polyP accumulation was dominant in the evaporation pond /aerobic pond and,
Chapter 5 investigated a mechanistic understanding of polyP accumulation dynamics,
and the organisms involved under acidic pH compared to the natural pH level in the
evaporation pond, by applying single-cell analyses coupled with next generation
sequencing approaches.
3.4.5 Distribution of metabolic functions in relation to P solubilisation
While polyP accumulation was predominant under aerobic conditions (evaporation
pond), and P mineralisation was high in anaerobic ponds (CAP-Bottom and CAP-Top),
there was no direct functional evidence of P solubilising activities in this system, apart
Chapter 3: Characterisation of the piggery waste treatment process
73
from the culture dependant identification of PSB. The main organic acids known to
solubilise P are gluconic, 2-ketogluconic, oxalic, citric, malic and succinic acid (Patel et
al. 2008; Panhwar et al. 2014). Only genes involved in the production of acids such as
citrate synthase (EC 2.3.3.1) and malate dehydrogenase (EC 1.1.1.37) were found in
this system and P solubilisation could potentially occurs in those stages. Evidence
obtained from P solubilising activity on the tri-calcium selective media confirmed that
Enterobacter sp. (facultative aerobes) and Pseudomonas aeruginosa (aerobic organism
or facultative anaerobe) were the main two groups that could potentially perform P
solubilising activities from a cultivable standpoint.
P solubilising bacteria have been previously isolated from mangroves which represent
an anaerobic environment (Vazquez et al. 2000). However, it has been predicted that
root oxygen translocation plays an important role in solubilizing phosphate by bacteria
near roots in mangroves where sediments are not always completely anoxic (Holguin et
al. 2001). This implies that oxygen translocation plays an important role in P
solubilisation and could be the reason a higher abundance of functional genes related to
P solubilising activities was not observed in the wastewater treatment plant where the
environment is completely anaerobic. Further studies are needed to confirm this. By
mimicking this, it is worth investigating the P solubilising activity in the sludge of an
aeration pond where the precipitated form of P is high (e.g. stuvite and hydroxyl apatite)
under oxygen bubbling condition.
3.4.6 Recycling potential of the piggery wastewater
Based on the physicochemical and microbial community characterisation, and the P
cycling potential, it appears that by-products arising from the CAP digester (CAP-
Bottom sludge) and the evaporation pond (pond bottom sludge and treated effluent)
could potentially enhance soil quality and crop productivity in terms of the nutritional
supply value, beneficial microbes, and lower risk of pathogenicity and heavy metal
content. Recycling these piggery by-products, could potentially introduce some
beneficial bacteria to the soil such as Actinobacteria, Bacteriodetes and Synergistetes
that have a role in nutrient recycling (Jenkins et al. 2009; Mclnerney et al. 2009).
Furthermore, those piggery wastewater samples did not contain significant quantities of
heavy metals and therefore soil contamination can be avoided following their
application to land. Any risk associated with recycling of piggery waste can be further
reduced using suitable management practices as discussed below.
Chapter 3: Characterisation of the piggery waste treatment process
74
The wastewater samples had relatively high EC levels which imply the possibility of
creating high salinity in the field. Accumulation of salt in soils could reduce the water
availability and limit plant growth (Munns and Termaat 1986; Munns 2002). Hence, it
is recommended that the wastewater is diluted before application as liquid fertiliser or
for direct irrigation to saline tolerant cultivars of cereals (e.g. barley, wheat).
The pH of the wastewater samples was slightly basic and could provide buffering
capacity in acid soils. High amounts of more soluble forms of N and P were found in
the piggery wastewater samples. However, any potential runoff or leaching can be
avoided by selecting appropriate application rates based on crop demand and avoiding
application at times when heavy rainfall is forecast.
The presence of pathogenic bacteria was relatively low with the anaerobic digestion and
has a great potential to recycle the by-product arising from piggeries (e.g. CAP-sludge
and treated wastewater). However, a number of bacteria involved in methane, nitrous
oxide and odour emission were found in some stages of the waste treatment process
(e.g. CAP-Bottom sludge). Therefore, it is recommended that aerobic conditions are
maintained during storage and land application to avoid greenhouse gas emission or
odour generation which occur largely under anaerobic conditions.
Any risk associated with recycling of piggery waste can be further reduced by
composting, pelletising and avoiding application at times when heavy rainfall is
forecast. If managed well, by-products could improve soil fertility by supplying
beneficial soil microorganisms and crops with plant nutrients either singly or in
combination with synthetic fertilisers thereby reducing reliance on fertilisers.
Furthermore, this study has shown that piggery waste by-products formed at different
stages of waste management can be high in both inorganic and organic P forms and
microorganism harbouring in piggery waste could potentially play an important role in
P-mineralisation, P-solubilisation, and P-immobilisation which are generally assumed to
be the main contributor of P turnover in soils. This led to the hypothesis that pelletised
piggery compost at low rates in combination with inorganic fertiliser in the root zone of
wheat facilitates nutrient uptake by plant roots in a P deficient agricultural soil, alters
the abundance and community composition of bacterial involved in increasing P
availability in soil, and enhances plant growth. Chapter 6 examined this hypothesis and
using application of pelletised piggery compost to soil and its impact on plant growth
promotion, soil nutrient improvement, and changes in bacterial and fungal community
composition.
Chapter 3: Characterisation of the piggery waste treatment process
75
3.5 Conclusions
This study identified the key microbial community composition in stages of waste
degradation and their putative metabolic potential in terms of P transformation within a
covered anaerobic pond system treating piggery waste. Bacterial community
composition was spatially distributed among the different stages of piggery waste
treatment process and there were clear shifts in bacterial community composition
between the anaerobic and aerobic stages. The piggery waste water treatment system
was dominated by both soluble and organic form of P. Therefore, P cycling potential in
terms of P mineralisation and polyP accumulation was highly evident. Finally,
microbial community can be manipulated in the piggery waste treatment system to
enable efficient P nutrient re-use. From the economic and environmental perspectives,
this knowledge can contribute to increasing the efficiency of recycling of P from
piggery waste. Reduction in piggery waste accumulation and minimisation of nutrient
leaching could help avoid eutrophication of water bodies.
Chapter 4: Phosphorus mineralising bacteria
76
CHAPTER 4
Phosphorus Mineralising Bacteria for Nutrient Recovery from
High Phosphorus Piggery Wastewater Effluents
4.0 Abstract
Phosphorus mineralising bacteria (PMB) play an important role in phosphorus (P)
mineralisation or P regeneration within high P containing wastewater treatment such as
piggery waste remediation, but little is known of their diversity, abundance or activity in
these treatment systems. PMB are the cells that express phosphatase (PO4ase) activity.
Enzyme-labeled fluorescence (ELF) is a tool for detecting PO4ase activity, and thereby
P mineralisation at a single-cell level. Developing an integrated approach after coupling
the ELF-labeling technique with cell sorting and next generation sequencing methods
allowed enumeration and identify the active fraction of PMB expressing the phosphatase
(PO4ase) activity within the piggery waste remediation process. The ELF-labeling
protocol was optimised for flow cytometric detection of ELF-labeled cells in piggery
waste. A small fraction of total bacterial cells (5.5 %-0.3 % v/v) in wastewater samples
displayed PO4ase activity and the respective inorganic P (Pi) levels were high. Sorted
ELF-labeled cells were used for downstream 16S rRNA Ion Tag Sequencing to assign
phylogenetic identity of PMB. Sequence data revealed that ELF-labeled cells mainly
belonged to Bacteroidales followed by Clostridiales, Campylobacterales and
Synergistales, occupying stable community compositions, with differences in abundance
along the waste treatment process. Coincident single cell and population approaches
used in this study are promising for determining the taxonomic identity of active PMB in
piggery wastewater. Knowledge of the composition of the PMB microbial community
facilitates understanding of their function in P mineralisation and the development of
effective strategies for P management within high P containing wastes.
Chapter 4: Phosphorus mineralising bacteria
77
4.1 Introduction
With global population expected to reach between 8 and 10.5 billion by 2050, there is
increasing pressure to develop innovative wastewater treatment technologies for
effective bioenergy, water and nutrient recovery. Agricultural wastewaters arising from
cropping, livestock and meat processing are often high in phosphorus. Recently, new
low-cost anaerobic digestion systems offer the possibility for reduced odour and
greenhouse gas (GHG) emission, pathogen removal and generation of biogas but the
recovery of water and nutrients is largely unexplored. Physical separation methods
(membrane separation) are often economically unfeasible for smaller operations or low
P waste treatment systems. Enhanced biological P removal (EBPR) systems are
attractive low-cost alternative where phosphorus and carbon can be readily accumulated
and separated as polyphosphate (poly-P) and polyhydroxyalkanoates. Another possible
way of reducing high levels of Pi in this system is to reduce or control the rate of P
mineralisation or regeneration of Pi especially in the last stage of the piggery waste
treatment process. While considerable work has been done on EBPR (Blackall et al.
2002; Malamis et al. 2013), little is known about P mineralisation or Pi regeneration in
wastewater which could affect the overall efficiency or control of the P removal process
(Kloeke and Geesey 1999; Whiteley et al. 2002; Li and Chróst 2006).
P regeneration is the hydrolysis of Pi from organic or other complex P compounds (e.g.
polyphosphates), either in soluble or particulate forms, in which the hydrolysed Pi is
released from the cells (Kloeke and Geesey 1999). Microorganisms play an important
role in P mineralisation in both terrestrial and aquatic systems. Most of the biologically
mediated P transformations that occur during activated sludge processes are carried out
by bacteria (Kloeke and Geesey 1999). Organic-P mineralisation is mediated by PMB
via activity of phosphomonoesterase and phosphodiesterases (Anupama et al. 2008).
Phosphomonoesterases are classified as either alkaline (pH>7; EC 3.1.3.1) or acidic
(pH<6; EC 3.1.3.2) phosphatases depending upon their optimum pH (Kloeke and
Geesey 1999; Anupama et al. 2008). Phosphatase (PO4ase) is a unique extracellular,
hydrolytic enzyme which catalyses the hydrolysis of Pi from organic bound form of P
(Kloeke and Geesey 1999; Anupama et al. 2008). Therefore, the role of bacterial
phosphatase should also be considered for increasing the P removal efficiency from
wastewater. Phosphatase activity has been previously detected in activated sludge
(Lemmer et al. 1994; Kloeke and Geesey 1999; Li and Chróst 2006) and anaerobic
reactors (Whiteley et al. 2002; Anupama et al. 2008). Furthermore, PO4ase activity can
Chapter 4: Phosphorus mineralising bacteria
78
be used as a rapid biochemical test for detecting instabilities in anaerobic digesters
(Ashley and Hurst 1981; Zhenglan et al. 1990; Yamaguchi et al. 1991). Therefore,
monitoring PO4ase activity is an essential step for the development of cost-effective and
sustainable P removal systems from high P loaded wastes such as piggery wastewater.
To date, information about PO4ase activity in these systems is lacking, mainly due to
methodological limitations for detecting P mineralisation in a highly diverse
environment such as piggery waste. Consequently, PMB involved in P mineralisation in
piggery waste treatment processes are poorly characterised and little is known of their
diversity, abundance or activity, so it is difficult to fully optimise the process.
Moreover, microbes involved in P mineralisation, molecular mechanisms controlling
phosphorus metabolism, ecological interactions, and factors controlling mineralisation
process and rates in wastewater are poorly characterised (McMahon and Read 2013).
It may be possible to enhance P mineralisation and removal through targeted
management by manipulating the resident microbial pathways. It was expected that
advances in anaerobic digestion technologies and waste treatment that encompass the
manipulation of microbial community dynamics will enable facilitate nutrient re-use.
However, microorganisms involved must first be identified so that the environmental
conditions can be modified for effective management of waste treatment systems.
Recent improvements in next generation sequencing, including the use of the low-cost
Ion Torrent Personal Genome Machine (PGM), provides high throughput analysis of
community structure and function linking microbial ecology with process stability and
efficiency (Whiteley et al. 2012). Enzyme-labeled fluorescence (ELF) is a useful tool for
detecting PO4ase activity, and therefore the detection of P mineralisation at the single-
cell level. Combined with flow cytometry and cell sorting approaches followed by
downstream molecular sequencing of sorted phosphatase active cells, this would enable
more precise assessment of the identity and function of PMB.
It was hypothesised that a diverse and highly abundant P mineralising bacterial
community can be found in piggery wastes which are characteristically high in organic
P substrate. The aim was to quantify the abundance, diversity, and putative metabolic
potential of P mineralising bacteria (the fraction of cells that expressed phosphatase
activity) during the pig waste treatment process by developing an integrated approach
using the enzyme-labeled fluorescence technique coupled with epi-fluorescence
microscopy, cell sorting, and next generation sequencing (16S rRNA Ion Tag
sequencing and community metagenomics). The investigation also sought to understand
Chapter 4: Phosphorus mineralising bacteria
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and identify the key mechanisms, anaerobic digestion pathways and microorganisms
involved. A greater understanding of microbial P cycling and the factors that regulate it
in wastewater treatment will assist in capturing P from piggery wastewater for use as
fertiliser.
4.2 Materials and Methods
4.2.1 Field sample collection and preparation
Piggery waste samples were collected from waste treatment tanks at Medina Research
Station as described in Chapter 3 (see Table 3.1 for physical and chemical characteristics
of the different piggery waste treatment compartments). Samples for ELF were prepared
immediately after sampling from the field in Spring 2012. In order to facilitate the
filtration process, effluent wastewater samples (500 mL) were consecutively filtered
through 100 µm, 60 µm and 3 µm mesh filters, and the final extract was used for
analysis.
4.2.2 Culture conditions and ELF staining
Effective PMB previously isolated and taxonomically identified from different stages of
waste samples (Chapter 3) were used as positive controls (Pseudomonas sp.), while E.
coli K12 was used as a negative control to assess the PO4ase activity using ELF®97
phosphate. These strains were grown in P-limited conditions by reducing Na-phytate
level up to ¼ (i.e. 0.5 g/L) of its initial value (i.e. 2 g/L) in PSM liquid medium. Cultures
were grown in autoclaved 250 mL filter cap cell culture flasks in 50 mL P-limited PSM
at 27oC and 150 rpm in a rotary incubator until the cell number reached to 108. P
mineralising ability was tested on agar medium containing phytate, which was used for
selective isolation of PMB in Chapter 3 (Kerovuo et al. 1998).
For ELF-P staining, an aliquot (1 mL) of culture flask was taken and centrifuged at 3000
x g for 5 min to form a cell pellet (performed in triplicates). The pellets were washed 3
times with autoclaved distilled water by centrifuging at 3000 x g for 5 min. ELF staining
was done after some modifications to the previously described method (Duhamel et al.
2008). Briefly, cell pellets were incubated with 100 µL of ELF-P (ELF®97 phosphate,
Invitrogen, E6589, 20 µM final concentration) in the dark for 2 h at room temperature
and ELF activity was stopped by adding 4 % (w/v) paraformaldehyde. The fixed cells
were washed with phosphate buffer saline (PBS) and then with distilled water. Fifty
microliter (50 µL) of cells was placed in the middle of a microscope slide and the cells
Chapter 4: Phosphorus mineralising bacteria
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were air dried. Just prior to the microscopic examination the slides were stained with
DAPI (2.5 µg/µL) and cells were visualised at x100 magnification using a Zeiss
Axioplan epi-fluorescence microscope under UV excitation (DAPI filter block) and blue
light (ELF).
4.2.3 Optimisation of incubation time necessary for ELF labeling
The incubation time necessary to reach a stable percentage of ELFA-labeled cells was
checked by labeling piggery effluent with ELF-P substrate over an incubation period of
210 min. A 1 mL aliquot of each filtered piggery effluent sample was placed in 2 mL
Eppendorf tubes and centrifuged at 16,000 x g for 20 min to have a concentrated cell
pellet. To find the optimum incubation time for ELF-labeling of field samples, cell
pellets were incubated with 100 µL of ELF-P (ELF®97 phosphate, Invitrogen, E6589, 20
µM final concentration) in the dark for a series of time intervals (0 min to up to 210 min)
at room temperature. The reaction was stopped by adding 4 % (w/v) paraformaldehyde
(PFA) overnight at 4 oC and percentage of ELF+ve cells were analysed using flow
cytometry. Based on the results, 2 h of incubation time for ELF-labeling was chosen for
the rest of analysis.
4.2.4 Field sample preparation for epi-fluorescence microscopy
A 1 mL aliquot of each filtered piggery effluent sample was placed in 2 mL Eppendorf
tubes and centrifuged at 16,000 x g for 20 min to concentrate cells. Incubation of
ELF®97 phosphates and preparation of slides for the epi-fluorescence microscopic
analysis was as described in Section 4.2.2. Slides were stained with DAPI (2.5 µg/µL),
PI (1 µg/µL) or SYTO9 (1 µg/µL) and placed in the dark for 10 min. The slides were
then rinsed with autoclaved distilled water and air dried. Cells were visualised at x100
magnification using the Zeiss Axioplan epi-fluorescence microscope.
4.2.5 Field sample preparation for flow cytometry
Piggery waste effluent sample preparation for flow cytomery is shown in Figure 4.1. For
each samples, positive and negative controls were used to gate ELFA-labelled bacteria
(ELF+) from negative bacteria (ELF-) and also avoid any background noise. The final
batches include (1) unstained control cells (cells not stained with ELF or DNA-binding
dyes), (2) single stained control cells (cells stained with only ELF), (3) single stained
control cells (cells stained with only DNA-binding dye), and (4) dual stained control
cells (cells stained with both ELF and DAPI/PI/SYTO9). As for the abiotic controls, a
Chapter 4: Phosphorus mineralising bacteria
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filtered piggery waste effluent sample was fixed with 4 % (w/v) paraformaldehyde prior
to ELF staining.
Figure 4.1 Preparation of the piggery waste effluent samples for flow cytometry.
Before flow cytometric analysis, overnight fixed samples were passed through 3 µm
filter mesh to prevent cell clumps which could otherwise congest the BD Influx. Each
sample was diluted 10 times and DAPI staining was applied where necessary (2.5
Chapter 4: Phosphorus mineralising bacteria
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µg/µL), and then incubated for 15 min prior to flow analysis. Other than DAPI, PI (1
µg/µL) and SYTO9 (1 µg/µL) were evaluated to find the most suitable dyes in
separating ELF+ cell clusters from the ELF- cells. Analysis was carried out on a BD
Influx cell sorter at the Centre for Microscopy, Characterisation and Analysis (CMCA)
at The University of Western Australia. The BD Influx cell sorter was equipped with
355 nm, 488 nm, 561 nm and 640 nm excitation lasers, 12 fluorescence parameters, and
a small particle detector for the detection of particles with sizes of 200 nm – 60 µM, and
allows simultaneous separation of up to four pure populations from a heterogeneous
suspension sample at a speed of up to 90,000 cells per second. ELF and DAPI were both
excited by UV and were separated by using different filter configurations (Figure 4.2).
ELF97 was excited by 355nm UV laser, and detected using 550LP and 585/29BP filters.
DAPI, Syto9 and PI were excited by UV 355nm, 488nm Blue and 561nm Yellow-Green
lasers and emission collected with 450/50BP, 520/15BP, 670/30BP filters respectively.
Dual stained samples (cells stained with both ELF and DAPI/PI/SYTO9) were used in
triplicate to quantify ELF+ cells (%) in each stage of the piggery waste treatment
process.
4.2.6 Cell sorting
For cell sorting, drop drive frequency was set at approximately 27 kHz, 3 drops were
simultaneously deflected, and droplet delay was set between 12 and 15. PBS was used as
a sheath fluid. Sorting criteria were defined as gating ELF+ and ELF- microbial
communities. ELF+ cells were sorted after keeping the flow rate lower than 3000 events
per second to avoid doublets and cell clumps. Triplicates of each sample, used for the
aforementioned flow analysis, were pooled and minimum of 50,000 ELF+ cells were
sorted in 100 µL of PBS, and then stored at -20oC until the extraction of DNA.
4.2.7 Data analysis
Flow cytometry data were analysed using Flow Jo software (version 7.6.5). Three
replicates per sample were taken for each analysis. Compensation was applied where
necessary to avoid the fluorescence spillover (spectral overlap) between ELFA and
DNA binding dyes. Data are presented as mean ± standard deviation. ANOVA was
performed using the Statistical Analysis System (SAS) version 9.2 software package
(SAS Institute, Inc. Cary, NC, USA). Means were separated using least significant
difference (LSD) at 5 % significance level.
Chapter 4: Phosphorus mineralising bacteria
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Figure 4.2 Emission and excitation spectrums of ELF, DAPI and PI and filter settings for the Flow Cytometry (BD Influx). ELF97 was excited by 355nm UV laser, and detected using 550LP and 585/29BP filters. DAPI, Syto9 and PI were excited by UV 355nm, 488nm Blue and 561nm Yellow-Green lasers and emission collected with 450/50BP, 520/15BP, 670/30BP filters respectively.
4.2.8 DNA extraction and 16S rRNA Ion Tag sequencing
Sorted ELF+ve cells were pre-treated prior to DNA extraction to facilitate the DNA
extraction process from the PFA fixed cells. The sorted cells were concentrated by
centrifuging them at 16,000 x g for 20 min, and removing the supernatant carefully. The
remaining cells in the Eppendorf tubes were pre-incubated with freshly prepared 10 %
SDS (70 µL) plus 20 mg mL-1 proteinase K (10 µL) for 1 hr at 65oC inside a waterbath.
The treated cells were used for DNA extraction using the MoBio UltraClean® Microbial
DNA Isolation Kit (Geneworks, Australia), utilising beat beating and column
purification, in accordance with the manufacturer’s guidelines. The extracted DNA was
quantified and checked for its purity at A260/280 nm (Nanodrop, Thermofisher
Scientific, USA) prior to its storage at -20oC. A nested PCR approach was used to
amplify 16S rRNA genes due to low target abundance of the DNA extract. The first
round PCR was performed using pA and pH primers and the PCR conditions described
in 2.2 section. One microliter (1 μL) aliquot from the first round PCR was used as the
template for the second round PCR. The second round PCR was performed using Golay
barcode and Ion Torrent adapter modified core primers 341F and 518R (Whiteley et al.
2012). Following the amplification, all PCR products were checked for their sizes and
specificities by electrophoresis on 2.5% w/v agarose, gel purified and adjusted to 10
ng/μL using molecular grade water and pooled equally for subsequent sequencing. The
Chapter 4: Phosphorus mineralising bacteria
84
sequencing was performed on a PGM (Life technologies, USA) using 200 base-pair
chemistry as described in Whiteley et al. (2012). Analyses of the taxonomic identity and
abundance were done as described in the Chapter 3 (section 3.2.4).
4.3 Results
4.3.1 Assessment of PO4ase activity of pure cultures using ELF®97 phosphate
The tested positive controls (Pseudomonas sp.) formed clear zones around the colonies
(Figure 4.3a) proving their ability to mineralise organic P in the selective medium. In
contrast, no colonies of the negative control (E. coli K 12) formed clear zones on the
selective medium (Figure 4.3b) confirming their inability to mineralise organic P in the
selective medium. Also, the tested positive controls exhibited ELFA fluorescence when
the cells were grown in phosphate depleted medium, whereas no signals were detected
for the negative controls (data not shown). Epi-fluorescence microscopic images of
DAPI stained cells (blue) show the total cells of Pseudomonas sp. (Figure 4.3c), whereas
ELFA stained cells (yellow/green) show the ELF+ cells of Pseudomonas sp. (Figure
4.3d). Comparison of the epi-fluorescence microscopic images of DAPI stained cells of
Pseudomonas sp. (Figure 4.3c) and that of ELFA stained cells (Figure 4.3d) shows that
nearly all of the cells grown in P-limited conditions expressed PO4ase activity. These
data indicated the successful detection of ELFA stained cells of ELF positive cultures at
the single cell level.
4.3.2 Optimisation of incubation time necessary for ELF-labeling
The incubation time necessary to reach a stable percentage of ELFA-labeled cells within
the wastewater samples was checked by labelling piggery effluent with the ELF-P
substrate over an incubation period of 210 min at room temperature. Percentages of
ELFA-labeled cells linearly increased with incubation time (Figure 4.4), until reaching
an approximate plateau after the 120th min of incubation. Statistical differences between
samples incubated for different times (P < 0.05) were tested and no significant
difference was found after 120 min. Therefore, 120 min of incubation period was
adopted for ELF-labeling of the targeted piggery waste effluents.
Chapter 4: Phosphorus mineralising bacteria
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Figure 4.3 The Pseudomonas sp. (positive strain of P mineralization) grown in the P-limited PSM liquid medium was able form clear zone around the colonies on PSM solid medium confirming their ability to mineralise organic P in the selective medium (a) whereas no E. coli (negative strain of P mineralization) colonies appeared on PSM solid medium (b). Epi-fluorescence microscopic images of DAPI stained cells of Pseudomonas sp. grown in P-limited PSM liquid medium (c) and that of ELFA stained cells (d).
Chapter 4: Phosphorus mineralising bacteria
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Figure 4.4 Ratio of ELF-labeled cells (%) with respect to the incubation time (min). Error bars represent the standard deviation between triplicate measurements.
4.3.3 Optimisation of dual staining protocol for epi-fluorescence microscopic and
flow cytometric detection of ELFA-labeled cells
ELF-labeled piggery waste samples were examined for the presence of ELFA
precipitates using epi-fluorescence microscopy to co-locate PO4ase activity at each
sampling point within the piggery waste treatment process. Three different DNA binding
dyes (DAPI/SYTO9/PI) with different excitations/emissions were tested to distinguish
ELF+ cells (ELFA-labeled cells) from ELF- cells (cells only stained by
DAPI/SYTO9/PI). Dual stained samples with ELF+DAPI/SYTO9/PI taken from CAP-
Top were compared (Figure 4.5a,b,c). ELFA crystals produced a green/yellow
fluorescence in areas where PO4ase activity had occurred. The cells that did not express
PO4ase activity were distinguished in blue (only stained by DAPI; Figure 4.5a), green
(only stained by SYTO9; Figure 4.5b), and red (only stained by PI; Figure 4.5c) colours
for dual strained samples. While having different colour contrasts, all three DNA-
binding dyes can be used to discriminate ELFA labelled cells from the ELFA non-
labeled cells by epi-fluorescence microscopy.
Chapter 4: Phosphorus mineralising bacteria
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Figure 4.5 Detection of PO4ase activity of piggery effluent using epi-fluorescence microscopy (a, b, and c), and flow cytometry (d, e, and f) after staining with DAPI (a and d), SYTO9 (b, and e), and PI (c, and f). Sample was gated on single cells and deployed is the percentage of ELF+ cells to the total bacteria. X Axes of the cytograms are ELF, DAPI, SYTO9 or PI fluorescence intensity in arbitrary units (a.u.).
In order to obtain a clear separation between ELF+ cells from ELF- cells in flow
cytometric analysis, DAPI, SYTO9, and PI were further evaluated. Based on the spectral
set-up in our flow cytometer, a significant spectral spillover of DAPI and PI into the
ELF emission detector was observed initially. Spectral spillover of DNA binding dyes
caused a difficulty in separating ELF+ cells from other nucleated cells (i.e ELF- cells)
and noise. Therefore, software compensation was applied for separating the signals of
DAPI and PI from ELFA signals. There were no significant differences in the proportion
of ELF+ cells (p<0.05) when stained with ELF+DAPI, ELF+SYTO9 and ELF+PI
(Figure 4.5d,e,f respectively) with spectral compensation. While these three DNA
binding dyes provided usable data, the separation between ELFA-labeled cells from both
ELF-non-labeled cells and background was more easily achieved using the SYTO9
dyes, even without applying spectral compensation (Figure 4.5e). Therefore, the
ELF+SYTO9 dual staining protocol was chosen for defining the gating strategy for
quantifying and sorting ELFA-labeled cells.
Chapter 4: Phosphorus mineralising bacteria
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4.3.4 Accuracy of ELF-labeling and defining the gating strategy with ELF+SYTO9
Replicate subsamples were assessed for defining the abiotic contributions to the
fluorescence reactions of the ELF-P substrate by pre-fixing piggery waste pond samples
with 4 % (w/v) paraformaldehyde (1h) before their ELF-labeling, and analysing by flow
cytometry (abiotic control). This allowed determination of the false positive ELFA
fluorescence due to non-biological reactions. As expected, no ELFA signals were
observed in the abiotic control samples, and suggested that there is no background of
ELF-labeling within non-living particles (Figure 4.6a,b). Several background tests were
also performed to check the signals derived from the chemicals used during ELF
labeling protocol (PBS, paraformaldehyde, distilled water), also did not affect the
signals of ELFA and SYTO9 (data not shown). The signals derived from the background
samples fell into the typical noise area of the cytogram (i.e. 0 to 10 arbitrary units of
fluorescence).
Four controls were used for the gating of ELFA+ cells (Figure 4.6 c,d,e,f): (i) unstained
control cells (cells not stained with ELF or DNA-binding dyes); (ii) Single stained
control cells (cells stained with only ELF); (iii Single stained control (cells stained with
only DNA-binding dye); and (iv) Dual stained control cells (cells stained with both ELF
and DAPI/PI/SYTO9). For the unstained sample (Figure 4.6c), the signals were located
in the area corresponding to the noise (ELF <10 a.u.; SYTO9 < 10 a.u.). There were no
signals in ELF channel (ELF >10 A.U) or SYTO9 channel (SYTO9 >10 a.u.),
suggesting that there was no interference from noise upon ELF or SYTO9 fluorescence
signals. For the single stained sample labelled with SYTO9 (Figure 4.6d), two
distinguished populations were observed in the area corresponding to the noise and
SYTO +ve region (SYTO9 > 10 A.U).
On the other hand, for the single stained samples labelled with only ELF (Figure 4.6e),
two distinguished populations were observed in the area corresponding to the noise and
ELF +ve region (ELF >10 A.U). For the dual stained samples with ELF + SYTO9
(Figure 4.6f), three distinguished populations were observed in the area corresponding to
the noise, ELF+ (confirming that ELF+ cells were being labeled both by SYTO9 and
ELFA) and SYTO9+ region (confirms that ELF- cells are being labeled only with
SYTO9). Therefore, instrument set-up was found to be sufficient to separate ELF+ cells
from the ELF- and the background noise for the quantitative detection of ELFA-labeled
cells in these waste samples. Dual staining configurations for each waste pond samples
Chapter 4: Phosphorus mineralising bacteria
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were then used to differentiate ELF+ population from the ELF- population in order to
quantify and categorize the ELF-labeled cells.
Figure 4.6 Flow cytograms showing (a) cells pre-fixed with paraformaldehyde and ELF-stained, (b) cells pre-fixed with paraformaldehyde and ELF + SYTO9, (c) unstained sample, (d) first single stained sample (SYTO9 only), (e) second single stained sample (ELF only), and (f) dual stained sample (ELF + SYTO9). Y axis represents the fluorescence intensity of ELFA, while X axis shows the fluorescence intensity of SYTO9.
Chapter 4: Phosphorus mineralising bacteria
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4.3.5 In situ applications
The developed method was used to quantify the distribution of alkaline-phosphate
activity of the waste treatment process (1 mL of effluents from each stage in triplicates)
using ELF®97 phosphate. The percentage (%) abundance of PMB (ELF+ cells) was
determined as a proportion of the total bacterial cells in 1 mL effluent samples. The
percentages of ELF+ cells and respective Pi levels at different stages of piggery waste
treatment process are shown in Figure 4.7. It was observed that the percentages of ELF+
cells in the evaporation pond (5.5 %), and pits (1.9%) were significantly higher (p<
0.05) than the other sampling points. The lowest percentages of ELF+ cells (Ca. 0.4%)
were observed for the samples taken from holding tank, CAP-Top and CAP-Bottom,
where no significant difference (p< 0.05) was observed between those samples. Overall,
small fractions of the total bacterial cells in the waste samples displayed PO4ase activity
(0.3 %- 5.5 %), which could be due to the potential inhibition of this enzyme at high Pi
levels (10.8 - 26.3 mg/L). However, there was no direct relationship (coefficient of
determination, R² = 0.15) between ELF activity and Pi level among the samples (data
not shown). Due to the complex nature of the waste samples, variations observed in
ELF+ cell percentages and Pi levels among the different waste treatment stages are not
easy to elucidate. The level of PO4ase activity among different stages of the waste
treatment could be a cumulative effect of both biological and physicochemical dynamics
at each waste treatment stage.
Figure 4.7 The percentages of ELF+ve cells (▲) and respective Pi levels (grey columns) at different stages of piggery waste treatment process.
Chapter 4: Phosphorus mineralising bacteria
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4.3.6 Community structure of PMBs within the piggery waste treatment process
Genomic DNA was extracted from the sorted ELF+ cells and the V3 region of the 16S
rRNA gene was amplified and sequenced. Averages of 50,000 reads were obtained after
QIIME quality filtering and library splitting and all the samples were normalised to a
sequence number of 7396. The overall qualitative operational taxonomic unit (OTUs;
97% sequence similarity) richness from the ELF+ cells versus number of sequences per
sample were plotted and rarefaction curves for each sampling points were obtained. The
rarefaction curves appear to be reached to a plateau after 7000 sequences per sample
(Figure 4.8a), indicating that an overall excellent OTU coverage obtained for all the
samples.
The Shannon’s index indicated that all the stages of piggery wastewater treatment were
generally comprised of a higher diversity of PMB (Figure 4.8b). Comparison of 16S
rRNA gene sequences rarefaction curves showed that the α-diversity was lower at
Evaporation Pond and higher at CAP-Bottom.
The 16S rRNA Ion Tag sequencing revealed the contribution of the abundant ELF+
bacterial communities to each stage of waste treatment process at phylum level (Figure
4.9a) and class level (Figure 4.9b). Bacterial community composition of ELF+ bacteria
were dominated by the phyla Bacteroidetes (38.4-69.3 %), Firmicutes (13.1-28.7%),
Proteobacteria (3.1-21.9), Synergistetes (1.9-14.8), Tenericutes (0.7-4.9), Spirochaetes
(0.3-1.9), Cyanobacteria (0.3-1.4), Chloroflexi (0.2-0.8%) and Actinobacteria (0.1-0.5
%) with a minimum and maximum abundance between stages (Figure 4.9a). Across all
the stages, the most abundant classes of the ELF+ bacteria were Bacteroidales, followed
by Clostridiales, Campylobacterales, and Synergistales (Figure 4.9b). However, the
relative abundance of these groups differed between each stage of the wastewater
treatment process.
Chapter 4: Phosphorus mineralising bacteria
92
Figure 4.8 (a) Alpha diversity rarefaction plots of OTUs for different wastewater samples. (b) Microbial diversity indicated by Shannon diversity. (Calculation of richness and diversity estimators was based on OTU tables rarified to the same sequencing depth, the lowest one of total sequencing reads; 7396).
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Chapter 4: Phosphorus mineralising bacteria
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4.4 Discussion
4.4.1 Optimisation of incubation time necessary for ELF-labeling
The optimum incubation time (2 hours) found in this study is in good agreement with
previous time course studies in aquatic systems (Duhamel et al. 2009). Previous studies
using epi-fluorescence microscopy to detect ELFA-labeled cells highlighted the
difficulty in determining a threshold between ELFA-labeled and non-ELFA-labeled
cells (Dyhrman and Ruttenberg 2006; Duhamel et al. 2008; Wambeke et al. 2008).
Kloeke and Geesey (1999) suggested that time kinetics of ELF-P incubation is required
for each environment as a prerequisite for the estimation of PO4ase activity at the single
cell level. Two hours (2 h) of incubation was sufficient for the piggery waste samples for
labeling all of the phosphatase active cells, and also for observing a consistency in
ELFA signals.
4.4.2 Optimisation of dual staining protocol in epi-fluorescence microscopy and
flow cytometric detection of ELFA-labeled cells
Enzymatic dephosphorylation of the ELF®-phosphate (ELF-P) yielded a highly
green/yellow water insoluble product called ELF®97alchohol (ELFA) at the site of
enzymatic activity. It is necessary to choose a proper DNA-binding dye for
counterstaining the ELFA-labeled cells as the majority of bacteria (e.g. heterotrophic
bacteria) are not naturally fluorescent. However, with counterstaining, flow cytometry is
a promising tool to quantify the ELF-labeling of the heterotrophic bacteria expressing
PO4ase activity (Duhamel et al. 2008). Detection of ELFA-labeled bacteria in
environmental samples has generally been tested for naturally fluorescent
phytoplankton, which does not require any extra counterstaining steps (Gonzalez-Gil et
al. 1998; Dyhrman and Palenik 1999; Meseck et al. 2009). For non-auto-fluorescent
microorganisms, DAPI usage has been most common (Kloeke and Geesey 1999;
Duhamel et al. 2008), whereas acridine orange, SYTO9 (Kloeke and Geesey 1999), and
PI (Kloeke and Geesey 1999; Duhamel et al. 2008) were used less often. Applying epi-
fluorescence microscopy or flow cytometric detection of ELFA-labeled cells into a
complex and highly diverse environment such as piggery waste effluent, indicated
difficulties for discriminating ELF+ cells from the ELF- cells and debris. Therefore, it is
important to experimentally rationalise the best protocol for each environment type,
using the most appropriate nucleic-acid-binding dye to stain ELF- bacteria and
discriminate them from the ELF+ cells and debris (noise). Excitation and emission
wavelength of the counterstaining dye should be within the range of the excitation and
Chapter 4: Phosphorus mineralising bacteria
95
emission wave length of the ELF97® (345-530 nm) with minimum spectral spillover
between each fluorescence channels. In order to capture the maximum fluorescence
from each channel, appropriate filters were selected according to the type of the flow
cytometer (explained in Figure 4.2). To obtain a clear separation between ELF+ cells
(cells expressed phosphatase activity) and ELF- cells (other nucleated cells) in flow
cytometric analysis, 3 suitable fluorescent dyes, DAPI (358-461 nm), SYTO9 (485-498
nm), and PI (535-616 nm) were evaluated. SYTO9 was the most appropriate dye based
on the spectral set-up used. Additionally, SYTO9 has very low excitation efficiency by
UV and therefore exhibits low spectral cross talk in the ELF emission channel. It has
been shown that SYTO9 is one of the most appropriate dyes for bacterial enumeration in
non-saline waters and can be applied for both live and dead bacteria in activated sludge
flocs (Lebaron et al. 1998; Kloeke and Geesey 1999).
For other stains tested, the separation between nucleated and non-nucleated cells using
PI was less satisfactory, possibly due to its poor penetration into cells. Many bacteria
may not have been permeabilised adequately and this can affect the determination of
ELF+ cells as a frequency of nucleated cells, especially for membrane impermeable
stains. Further, DAPI and SYTO9 have advantages over PI to discriminate DNA stained
cells from naturally fluorescent microbial cells. Propidium iodide, as for chlorophyll a,
emits in the red wavelength making the discrimination of autotrophic cells from
heterotrophic ones almost impossible when PI is used in complex ecosystems
harbouring autotrophs (Duhamel et al. 2008).
4.4.3 In situ applications
As revealed by ELF detection, community differences in PO4ase activity along the
piggery waste treatment process is likely to be due to the cumulative effect of biological
and physicochemical dynamics of each waste treatment stage. For example, alkaline
PO4ase synthesis and activity in aquatic bacteria appeared to be controlled by the
concentrations and types of external organic-P, temperature, ionic strength, pH, presence
of any metal ions (Güngör and Karthikeyan 2008), internal N:P ratio, P demand of the
cell (Espeland and Wetzel 2001), and composition of wastewater (Li and Chróst 2006).
Moreover, it has been shown that starvation, salinity, presence of primary substrate, pH,
and volatile fatty acids (VFA) caused different expression of total PO4ase activity in
anaerobic sludge (Anupama et al. 2008).
There was no direct relationship between the percentages of ELF+ cells (0.3-5.5 %) and
Pi levels (10.8-26.3 mg/L). It is generally understood that elevated Pi concentrations
Chapter 4: Phosphorus mineralising bacteria
96
inhibit the PO4ase activity (Dignum et al. 2004). However, alkaline PO4ase synthesis in
many bacteria was not inhibited by elevated Pi (MH and HJ 1961; Chrost et al. 1986;
Kloeke and Geesey 1999). PO4ase in activated sludge has been attributed to microbial
cells (cell bound), extra cellular polymeric substance (EPS) and bulk liquid (cell-free
form) of sludge (Anupama et al. 2008). The ELF®97 phosphate provides cell surface-
associated PO4ase activity (Kloeke and Geesey 1999), and it has been reported that cell
surface-associated PO4ase was not regulated by environmental PO4 level (Braibant
2001). Therefore, the observed PO4ase activities in this study were more likely to
represent the fraction of cells that are not inhibited by higher Pi levels. Furthermore,
there might be some species-specific differences in association with Pi and PO4ase
activity. Meseck et al. (2009) explained that expression of PO4ase activity at a highly
soluble reactive P could be attributed to the ratio of DNA to protein or more of an
individual response, rather than a population response. This implies that further research
is necessary to elucidate the mechanisms responsible for PO4ase activity and subsequent
Pi regeneration under different environmental conditions.
This is the first study to demonstrate fluctuation and distribution of phosphatase activity
in the entire process of piggery waste treatment by using an integrated approach using
the enzyme labeled fluorescence technique coupled with epi-fluorescence microscopy,
cell sorting, and next generation sequencing. Previous investigation of phosphatase
activity in wastewater treatment systems is limited but phosphatase activity has been
detected in activated sludge (Lemmer et al. 1994; Kloeke and Geesey 1999; Li and
Chróst 2006) and anaerobic reactors (Ashley and Hurst 1981; Zhenglan et al. 1990;
Yamaguchi et al. 1991; Whiteley et al. 2002; Anupama et al. 2008).
4.4.4 Community structure of PMB within the piggery waste treatment process
Cell sorting of ELFA-labeled bacteria from the piggery waste provided efficient
separation of P mineralising bacteria with a high degree of purity. Therefore, molecular
sequencing of sorted cells represented the fraction of P mineralising bacteria that
expressed the phosphate activity, demonstrating the link between microbial identity and
P mineralisation activity. The most abundant P mineralising bacteria identified using
16S rRNA tag sequencing of sorted cells were represented by Bacteroidales,
Clostridiales, Campylobacterales and Synergistales. The dominant PMB found in this
study were consistent with the total bacterial community diversity for the initial stages
of piggery waste treatment process as revealed by the 16 S rRNA Ion Tag sequencing in
Chapter 3. Therefore, the identified PMB bacterial diversity was ecologically significant
Chapter 4: Phosphorus mineralising bacteria
97
and has been previously identified in piggery waste treatment systems (Cook et al. 2010;
Patil et al. 2010). Furthermore, the identified PMB also matched some of the homologs
identified in the metagenomics analysis (Bacterioides, Parabacteroides,
Flavobacterium, Clostridium, Desulfitobacterium) in Chapter 3 in relation to the
presence of the alkaline-phosphatase genes. This confirmed that the taxonomic identity
of PMB in this study is linked to the functional identity of the community P mineralising
bacteria in this piggery waste treatment process (Chapter 3).
The abundance of alkaline phosphatase, as revealed by using metagenomics (Chapter 3),
was not positively correlated to the % of ELF+ bacteria revealed by ELF. For example,
the highest % of PMB in the CAP digesters was expected to correspond with genes
encoding for alkaline phosphatase which was the highest in the CAP digester (Chapter
3). However, the % of ELF+ bacteria in the CAP digester was low according to the ELF
analysis. This observed discrepancy might be because the number of alkaline
phosphatase reads found in anaerobic ponds was mainly associated with activity of
Methanosarcina, an anaerobic methanogen, which was not expected to be detected by
either ELF or bacterial 16S rRNA Tag sequencing. This discrepancy may indicate that
archaea are playing an important role for the PO4ase activity in the wastewater treatment
process. Anupama et al. (2008) showed that both archaea and bacteria played equal roles
for PO4ase activity in anaerobic bioreactors. Therefore, further studies are required to
understanding the contribution of archaea, or specifically methanogens, in
mineralisation of P in waste treatment process as this environment significantly favoured
anaerobic microorganisms. Overall, ELF followed by cell sorting and sequencing used
in this study is reliable for determining the taxonomic identity of active PMB in piggery
waste. Although the identified PMB have been widely recovered from wastewater
plants, their role in piggery waste process and in P transformations in particular, has not
been elucidated until now.
4.5 Conclusions
This study aimed to determine the P mineralising bacterial abundance in a piggery waste
treatment process and their taxonomic and functional identity. ELF was useful in direct
localisation and quantification of P mineralising microbes at a single cell level by
applying epi-fluorescence microscopy and flow cytometric analysis in piggery waste
after 2 h of incubation. The importance of discrimination between ELFA-labeled cells
from non-labelled cells and background when the PO4ase activity was represented as a
Chapter 4: Phosphorus mineralising bacteria
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frequency of the total number of bacteria in the samples was demonstrated. Moreover,
detection of PO4ase activity at a single cell level by flow cytometry followed by cell
sorting allowed an intended and defined selection of phosphatase active cells even at
their low abundance. Subsequent downstream next generation sequencing provided
insight into the microbial identity of active P mineralisers in the piggery waste.
Therefore, coincident single cell and population approaches used in this study are
promising for determining the taxonomic identity of active phosphorus mineralising
bacteria in piggery waste. Further studies should include investigation of detailed
approaches for flow cytometric detection of phosphatase activity, and monitoring of P
generation capacity at different operational or environmental conditions. Routine
analysis of PMB in the piggery waste stream will be useful for managing P levels in
piggeries by adding phytase enzyme to the pig feeds in order to reduce the accumulation
of organic P sources in the system. On the other hand, knowledge gained in taxa
mediating P mineralisation will aid for controlling the P generation in the system by
altering the P mineralising bacterial community. For example, the identified PMB could
employ as potential inocula (“seeds”) for enhancing the P mineralisation in the early
stages of piggery waste treatment process and reducing the Pi in the system at the later
stages by applying enhanced biological P removal (EBPR).
Chapter 5: PolyP accumulating bacteria in piggery waste
99
CHAPTER 5
Analysis of Polyphosphate Accumulating Organisms in
High Phosphorus Piggery Wastewater Effluents
5.0 Abstract
Phosphorus is a key agent of environmental impact in liquid fertilisers, its removal
being possible through enhanced biological phosphorus removal (EBPR) technologies
applied during wastewater treatment. Using a range of high throughput single cell and
next generation sequencing methods, active polyphosphate accumulating organisms
(PAOs) were identified and functionally analysed in two pH environments (pH 5.5 and
8.5) to assess the efficacy of EBPR technology applied to high P loading waste
remediation. A significant positive effect on polyphosphate accumulation was observed
at pH 5.5 compared to pH 8.5, with significant enrichment of polyphosphate kinase and
exopolyphosphatase genes at pH 5.5. Functionally active PAO accumulators at pH 5.5
were identified as Aeromonas hydrophila, Aeromonas salmonicida, Acinetobacter
baumannii, Bordetella pertussis, Citrobacter koseri, Escherichia coli, Enterobacter sp.
Klebsiella, Pseudomonas aeruginosa, Salmonella enterica and Shigella flexneri. These
findings serve as a basis to understand and manipulate PAOs community diversity and
functionality to enhance P uptake by altering the pH for improving the EBPR waste
treatment process and develop high value/low environmental risk products from a range
of effluents.
Chapter 5: PolyP accumulating bacteria in piggery waste
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5.1 Introduction
Anaerobic treatment of wastewater streams releases large amounts of phosphorus (P)
and nitrogen (N) compounds into the wastewater bodies, both being major agents of
eutrophication problem in their surrounding environment (De-Bashan and Bashan
2004). For example, failure to remove soluble P (Pi) during the wastewater treatment
process can result in increased soil P runoff and leaching during irrigation if treated
effluents are used as liquid fertilisers (Jaiswal 2010; Nielsen et al. 2010). In high P-
producing waste production systems, such as pig husbandry (Poulsen 2000), the
concentration of Pi in treated effluents is often too high to permit its re-use as a liquid
fertiliser for agricultural soils (Obaja et al. 2003), unless effective P removal systems
can be applied to reduce Pi loading.
Traditionally, Pi is removed from wastewater treatment plants by chemical precipitation
techniques before land application, but this can be both expensive and not
environmentally friendly (Günther et al. 2009). Specifically, chemical precipitation
techniques (e.g. stuvite crystallisation) are not economically feasible for low P
concentration wastewater streams (< 50 mg-P/L)(Wong et al. 2013), which can increase
the volume of sludge by up to 20 % (Cooper et al. 1994; Cooper et al. 1995).
Alternatively, Pi can be removed using a biological process called enhanced biological
phosphorus removal (EBPR) (Oehmen et al. 2007). In this process specific bacteria and
microalgae, called polyphosphate accumulating organisms (PAOs), accumulate large
quantities of P within their cells and are selectively enriched within the community. The
enriched bacteria and microalgae biomass is then separated from the treated effluent
wastewater, as the separated biomass can be further used as slow releasing P fertilisers,
whilst the remaining effluent can be re-used as a liquid fertilizer (Yoon et al. 2004;
Hirota et al. 2010).
Fundamental to the design of effective P removal systems is the knowledge of the
identity and functionality of PAO accumulating microorganisms (Nielsen et al. 2010;
Mehlig et al. 2013). Although the enhanced biological P removal from wastewater has
been widely studied, an understanding of the microbial identity and environmental
factors affecting enhanced P accumulation efficiency of PAO is less understood. Also,
this information is lacking in systems associated with high P-loaded piggery industry.
Specifically, enhanced biological phosphorus removal processes can be difficult to
control and sometimes ineffective (Kawaharasaki et al. 1999; Oehmen et al. 2007)
Chapter 5: PolyP accumulating bacteria in piggery waste
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mainly due to the competition amongst microorganisms within the community, or
variation in parameters such as temperature, light intensity, pH, redox potential, and
biological activity (Crocetti et al. 2000; Oehmen et al. 2007). Therefore, for a consistent
performance, a better understanding of the types of microorganisms involved, their
interactions, optimum process conditions for their activity, and the possible causes of
environmental stresses is required.
Low-pH stimulated polyphosphate accumulation due to acid stress on environmental
microorganisms was first observed by McGrath and Quinn (2000). The study reported
10.5-fold increase in intracellular polyphosphate accumulation in Candida humicola G-
1, grown at pH 5.5 in a medium containing glucose as the carbon source, compared to
pH 7.5. Phosphate uptake from culture medium with activated sludge inocula increased
between 50% and 143% when pH was 5.5 rather than 7.5 (McGrath et al. 2001).
Approximately 34% of the activated sludge microflora was capable of acid-stimulated
luxury phosphate uptake. This is evidence that the EPBR process could be enhanced
under acidic conditions (McGrath et al. 2001; Mullan et al. 2002a; Moriarty et al. 2006)
and it has been claimed to be economically feasible (Mullan et al. 2006) for low P
concentration wastewater streams (< 50 mg-P/L). However, information on what
microbial communities in wastewater respond to acidic conditions and their
physiological role with respect to polyP accumulation is less well studied.
It was hypothesised that there would be an increase in polyphosphate uptake,
abundance, and functional activities of PAOs under acidic conditions in an aerobic
pond within a piggery waste treatment system. In order to address this hypothesis,
coincident single cell and population approaches were used to quantify and identify
functionally relevant polyphosphate accumulating organisms (PAOs) under two
different pH environments (pH 5.5 and pH 8.5). Epi-fluorescence microscopy, flow
cytometry, cell sorting and next-generation sequencing approaches were combined in
order to better link variation of environmental conditions and taxonomic/functional
capacities of PAO populations. Ultimately, this approach provides a ‘cell to population’
mechanistic understanding to enhance the consistency of EBPR systems.
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5.2 Materials and Methods
5.2.1 Sampling site and lab-scale incubation experiment
Samples for laboratory incubation experiments for EBPR were collected at the final
stage of waste treatment pond (i.e. the aerobic pond) from the covered anaerobic pond
digester system at Medina Research Station as described in Chapter 3. Initially the
wastewater had a pH of 8.5 and phosphate concentration (Pi) of 12.2 mg/L. Half of the
samples were filtered (3 µm filtered) and the rest was left unfiltered. The Pi
concentration was adjusted to 25 mg/L using KH2PO4 (25mM) to simulate a high P
loaded wastewater system for both filtered and unfiltered samples. The experimental
design comprised either filtered or unfiltered samples over five different pH treatments
(5.5, 6.0, 6.5, 7.0 and 8.5 [control]) in triplicate to determine the best pH level for polyp
accumulation. Filtered or unfiltered pond samples (300 mL) were put into autoclaved
jam jars (500 mL) and left under aerobic conditions at room temperature (25oC) for 48 h
under the natural light/dark illumination cycle of the laboratory conditions. Samples
from each triplicated microcosm were combined to get a composite sample for
downstream analysis. Phosphate concentrations at the beginning and after the 48th h of
incubation were determined according to standard methods described previously (Eaton
et al. 2005). The microcosm set-up and subsequent sample preparation for epi-
fluorescence microscopy, and flow cytometry are schematically shown in Figure 5.1.
5.2.2 Optimisation of polyP staining: bacterial strain, culture conditions
A bacterial culture of Pseudomonas syringae grown in P-limited medium was used for
the titration of DAPI (4',6-diamidino-2-phenylindole) stain concentration required for
detecting and quantifying PAOs using epi-fluorescence microscopy and flow cytometry.
P-limited medium was prepared by modifying a previously reported PSM medium by
Jorquera et al. (2008b), by replacing Na-phytate with 0.41 ml of 0.1M K2HPO4 (42
uM) as the sole source of P. The cultures were grown in UV sterilized filter cap cell
culture flasks (180 mL) in 50 mL P-limited PSM by mixing at a rate of at 150 rpm in an
orbital shaking incubator at 27oC until the cell number reached approximately 108.
Subsequently, aliquots of cultured cells were supplemented with 1 ppm, 10 ppm or 50
ppm of K2HPO4 as a P pulse and incubated for 48 h. After incubation, cells were
prepared for epi-fluorescence and flow cytometric detection of polyP accumulation as
discussed below.
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Figure 5.1 Microcosm set-up and subsequent sample preparation for epi-fluorescence microscopy, and flow cytometry. 5.2.3 Sample preparation for epi-fluorescence microscopy and flow cytometry
Aliquots (1 mL) of each pure culture sample were incubated at different P levels (1, 10,
and 50 mg/L) and collected by centrifugation at 3000 x g for 5 min. The cell pellet was
washed with phosphate buffered saline (PBS) and then resuspended in PBS medium and
fixed with 4% (w/v) paraformaldehyde fixative solution (PFA) and incubated for 1 h at
room temperature. PFA fixed cells were subsequently washed with PBS and finally with
distilled water, before being resuspended in 1 mL distilled water prior to the DAPI
Chapter 5: PolyP accumulating bacteria in piggery waste
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staining for investigation of polyP cellular inclusion and further analysis at the Centre
for Microscopy, Characterisation and Analysis (CMCA) at the University of Western
Australia.
5.2.4 Titration of DAPI concentration, epi-fluorescence microscopy, and flow
cytometry
To verify the reliability of polyP staining, the stained pure cultures and environmental
samples were observed using epi-fluorescence microscopy. Fifty microliter (50 µL) of
cell culture was placed in the middle of a microscope slide and the cells were air dried.
Slides were stained with DAPI immediately prior to microscopic examination. For the
optimisation of polyP staining, pure culture samples were stained with 0.25, 0.5, 1, 5,
7.5 and 15 ug/mL of DAPI for 20 mins in the dark. Based on the DAPI titration results
of the pure culture, a 15 ug/mL DAPI concentration was selected for polyP staining of
the environmental samples. Apart from the pure cultures, the staining efficiency of
polyP-DAPI for environmental wastewater samples was also demonstrated under 3
levels of polyP accumulation using the wastewater samples incubated at three different
concentrations of P (1 mg/L, 10 mg/L, and 50 mg/L). The slides were then rinsed with
distilled water and air dried. Cells were visualised at x100 magnification using a Zeiss
Axioplan epifluorescence microscope under UV excitation (DAPI filter block). DAPI
stains both DNA (present in all microbial cells), and polyP (found in just PAO cells).
Both DAPI-DNA and DAPI-PolyP complexes are excited by UV light (355 nm) but
emit light at different wavelengths for PolyP (570-600 nm, yellow-green light) and
DNA (435-485 nm, blue light) (Kulakova et al. 2011a).
Flow cytometric analyses were performed using a BD Influx cell sorter. DAPI was
excited with a 355 nm (UV) laser, the standard DAPI emission was collected with a
460/50 nm band pass filter, and the DAPI-PolyP emission was collected with a 585/29
bandpass filter. Measurements for DAPI and polyP were acquired on a logarithmic scale
and post-acquisition analysis was performed using Flow Jo software version 7.6.5.
Briefly, single cells were gated on forward scatter area (FSC-A) vs forward scatter
height (FSC-H) to exclude any doublets, and DAPI-DNA and DAPI-polyP were gated
to determine proportions of bacteria accumulating polyP.
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5.2.5 DNA extraction and 16S rRNA tag sequencing
DNA from the microcosm experiments was extracted using the MoBio Powersoil DNA
isolation kit (Geneworks, Australia), utilising bead beating and column purification
following the manufacturer's guidelines. Extracted DNA was quantified and checked for
its purity at A260/280 nm (Nanodrop, Thermo Fisher Scientific, USA) prior to storage
at −20°C. Fragments of the 16S ribosomal RNA gene were amplified by polymerase
chain reaction (PCR) from the DNA samples using Golay barcode and Ion Torrent
adapter modified core primers 341F and 518R (Muyzer et al. 1993), using amplification
conditions described previously (Jenkins et al. 2010). All PCR products were checked
for size and specificity by electrophoresis on 2.5% w/v agarose. Samples were purified
by gel excision and adjusted to a concentration of 10 ng/μL in molecular grade water,
and then pooled equally for sequencing. Sequencing was performed using an Ion
Torrent Personal Genome Machine (Life technologies, USA) using 200 base-pair
chemistry as described in Whiteley et al. (2012). All the PGM quality filtered data were
exported as FastQ files and split into fasta and qual files and analysed using the QIIME
pipeline (Caporaso et al. 2010). Sequencing data analyses performed as described in the
Chapter 3 (section 3.2.4).
5.2.6 Whole genome shotgun sequencing
DNA was extracted from the microcosm experiments (pH 5.5 filtered, pH 5.5 un-
filtered and pH 8.5 unfiltered) using the MoBio Powersoil DNA isolation kit
(Geneworks, Australia), by following the manufacturer's guidelines. Sequencing of the
genomic DNA derived from these samples was done by whole genome shotgun
sequencing. Approximately 150 ng of DNA-preparation was used to generate a whole
genome shotgun library using a NEBnext Ultra library preparation kit (New England
Biosciences). Fragments of 320-330bp were selected from the final library by gel-
excision and sequenced for 520 flows on an Ion Torrent Proton sequencer (Life
Technologies), yielding reads of 230-240bp modal length. Quality filtering and
trimming were performed on instrument using TorrentSuite 4.0. Metagenomic data sets
are publicly available in the MG-RAST system under project identifiers 4553565.3,
4553566.3, and 4553565.3. Assignment of metabolic function and phylogenetic
identification were done as described previously (Meyer et al. 2008).
Chapter 5: PolyP accumulating bacteria in piggery waste
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5.3. Results
5.3.1 Titration of DAPI concentration required for epi-fluorescence and flow
analyses of polyP accumulation
Pseudomonas syringae cells grown in P-limited medium were stained with increasing
concentrations of DAPI (0.25, 0.5, 1, 5, 7 and 15 ug/mL) to ascertain the best
concentration for an effective staining of polyP granules by flow analyses and for
defining polyP gates within flow cytograms. Both DAPI-DNA and DAPI-PolyP
complexes were excited by UV light (355 nm) but emit light at different wavelengths
for PolyP (570-600 nm, yellow-green light) and DNA (435-485 nm, blue light).
Polyphosphate accumulating organisms with polyP granules (excitation/emission:
415/550 nm; DAPI-polyP) appeared bright yellow-green and were easily differentiated
from non-accumulating bacteria which stained blue due to DAPI-DNA binding
(excitation/emission: 358/461 nm; DAPI-DNA) under epi-fluorescence microscopy
(Figure 5.2a-f) and within two dimensional cytograms (Figure 5.2g-l). According to the
epi-fluorescence microscopy, the staining efficiency of polyP was increased from the
lowest (at 0.25 ug/mL- DAPI) to the highest (at 15 ug/mL- DAPI) within the range of
DAPI concentration tested on the Pseudomonas syringae (0.25, 0.5, 1, 5, 7 and 15
ug/mL) (Figure 5.2a-f). The culture condition was P limited and it can be assumed that
there is a higher affinity of cells to accumulate Pi as polyP granules due to the long-time
incubation of cells in the P limited medium. This was proven further by quantitative
analysis of the samples for the abundance of polyP cells by using flow cytometry
(Figure 5.2g-l). The lowest abundance of polyP cells (64.5 %) was observed at 0.25
ug/mL-DAPI as two clusters of cells (DAPI-polyP and DAPI-DNA) (Figure 5.2g) due
to the poor staining efficiency attributed to the lower DAPI concentration. The highest
abundance of polyP cells (99.9 %) was observed at 15 ug/mL-DAPI (Figure 5.2l),
where all the cells were concentrated to a single cluster (DAPI-polyP).
PolyP accumulation was detected in almost all cells when a minimum DAPI
concentration of 5 ug/mL (Figure 5.2j) was used, which was also selected to be the
optimum concentration for the pure cultures of Pseudomonas syringae that would yield
good signal to noise ratios. Having seen that majority of cells contained polyP granules
at a DAPI concentration of 15 ug/mL, we can assume that it is important to choose
appropriate concentrations of DAPI for staining the polyP granule to avoid partial or
Chapter 5: PolyP accumulating bacteria in piggery waste
107
poor staining of polyP granules as we observed under lower DAPI concentrations
(Figure 5.2g-h).
Figure 5.2 DAPI staining of pure culture of Pseudomonas syringe cells for polyP analysed by epi-fluorescence microscopy (a-f) and flow cytometry (g-l). Cells were subsequently stained with (a/ g) 0.25; (b/ h) 0.5; (c/ i) 1; (d/ j) 5; (e, k) 7; and (f/ l) 15 µg/mL of DAPI. In epi-fluorescence micrograms (a-f), intracellular polyP granules form DAPI-polyP complexes appear yellow-green, whilst DAPI bound to DNA appears blue. In flow cytograms (g-l), sample was gated on single cells and deployed is the percentage of cells with (DAPI-polyP) and without accumulated polyP (DAPI-DNA).
Although 5 ug/mL was found to be the optimum concentration in pure cultures of
Pseudomonas syringae for good signal to noise ratios, due to the heterogeneous matrix
of natural samples, 15 ug/mL of DAPI was selected as the optimum concentration for
analysing the wastewater samples by using flow cytometry.
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The efficiency of 15 ug/mL of DAPI concentration for the staining of polyP
accumulation in the diverse groups of bacteria was further assessed. Samples taken from
the aerobic pond were incubated in 1, 10, 50 mg/L P containing media, before
investigating the degree of polyP accumulation. Specifically, the titration of P
availability against polyP accumulation, using 15 ug/mL DAPI, revealed significantly
lower polyP accumulation at 1 mg/L-P (0.59 %, Figure 5.3a), compared to those of 10
and 50 mg/L-P (56.6 and 64%, Figure 5.3b and 5.3c, respectively). These results
confirmed that 15 ug/mL of DAPI was sufficient for staining of polyP accumulation in
diverse groups of microorganisms at high levels of available P which increased polyP
accumulation within the cellular biomass.
Figure 5.3 Aerobic pond samples stained for polyP. Cells were incubated with (a) 1 mg/L-P, (b) 10 mg/L-P, and (c) 50 mg/L-P; and were stained with 15 µg/L of DAPI followed by the flow cytometric analysis. Sample was gated on single cells and deployed is the percentage of cells with (DAPI-polyP) and without accumulated polyp (DAPI-DNA).
5.3.2 PolyP accumulation in high Pi loaded lab microcosm experiments
Phosphate concentrations at the beginning and after the 48th h of incubation were
determined and percentage P removal was calculated. A greater P removal (63 %) was
observed at pH 5.5 with respect to pH 8.5 (44 %) (Figure 5.4a). Based on this
preliminary observations, both filtered and unfiltered samples at pH 5.5 and pH 8.5 (pH
in the aerobic pond) were selected for assessing the polyP formation using flow
cytometry and epi-fluorescence microscopy. There was an increase in polyP formation
under acidic conditions for both filtered and unfiltered samples (Figure 5.4b). The
highest percentage content of the DAPI-polyP positive population was observed in pH
5.5 unfiltered samples and was almost 2 times greater than the pH 8.5 unfiltered
Chapter 5: PolyP accumulating bacteria in piggery waste
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samples. The greatest polyP formation observed in the unfiltered samples at pH 5.5
suggested microalgae could be a key component of the polyP accumulating population
in piggery effluent ponds.
Figure 5.4 Overall phosphate removals from the pond water at different pH treatments (3a). Percentage of the cellular content in the form of DAPI-PolyP and DAPI-DNA complex at pH 5.5 and 8.5 (control), for both filtered and unfiltered samples (3b).
Under epi-fluorescence microscopy, DAPI staining confirmed the presence of
intracellular polyP granules inside bacterial and microalgal cells at pH 5.5 for both
filtered (Figure 5.5 a), and unfiltered samples (Figure 5.5c).
Chapter 5: PolyP accumulating bacteria in piggery waste
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Figure 5.5 PolyP stained cells from aerobic pond at pH 5.5 and 8.5 for filtered (a and b, respectively) and unfiltered (c and d, respectively) samples viewed under epi-fluorescence microscopy. Intracellular polyP granules form DAPI-polyP complexes appear yellow-green, whilst DAPI bound to DNA appears blue. Flow cytograms of polyP stained cells from aerobic pond at pH 5.5 and 8.5 for filtered (e and f, respectively), and unfiltered (g and h, respectively) samples.
In contrast, there were less polyP granules within cells maintained at pH 8.5 for both
filtered (Figure 5.5b), and unfiltered (Figure 5.5d) samples. These data suggest the
presence of intracellular polyP granules inside bacterial and microalgal cells at pH 5.5
implying the increased uptake of P in these samples (Figure 5.4 a) is due to the activity
Chapter 5: PolyP accumulating bacteria in piggery waste
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of polyphosphate accumulating organisms and not just P assimilation. In the unfiltered
samples the microalgal cells were the predominant PAOs, suggesting that they
outcompete bacterial PAOs for phosphate. Filtering the aerobic pond sample prior to pH
adjustment appears to favour the growth of bacterial PAOs in the absence of
microalgae.
Flow cytograms (Figure 5.5e-h) provide further verification that two distinct DAPI
populations are formed: a DAPI-bound PolyP cell population (polyP positive) that
forms a distinct cluster in the PolyP 570-600 nm range and DAPI bound DNA cell
population (polyP negative) that forms a cluster in the DAPI 435-465 nm range. In the
pH 5.5 filtered samples these two populations were well defined showing the presence
of bacterial PAO (ca. 52 %) (Figure 5.5e). In the pH 5.5 unfiltered samples the PolyP
stained cells increased to 70 % (Figure 5.5g). Under more alkaline conditions (pH 8.5)
for both the filtered (Figure 5.5f) and unfiltered (Figure 5.5h) samples, the polyP
population significantly declined to 15 % and 36 %, respectively.
5.3.3 Community structure of PAOs in piggery waste
Based on these data above, only three systems of EBPR (pH 5.5 filtered, pH 5.5
unfiltered, and pH 8.5 unfiltered) were selected for downstream molecular analysis.
DNA extracted from incubated microcosms was used to assess the abundance and
phylogenetic affiliation of bacterial taxa present under the 3 microcosms. The V3 region
of 16S rRNA gene was assessed by sequencing on a PGM seminconductor sequencer.
An average of 42,000 reads after QIIME quality filtering and library splitting were
suitable for subsequent phylogenetic analysis. All the samples were normalised and
rarefied to 5200 sequences and the rarefaction curve analysis showed that the overall
bacterial diversity was well represented (Figure 5.6a). Microbial diversity indicated by
Shannon’s index (Figure 5.6b) showed that the richness of species and their diversity
were highest in the control (pH 8.5, unfiltered) followed by pH 5.5 unfiltered and pH
5.5 filtered microcosms. These data also showed that the diversity of the microbial
community at pH 5.5 was less diverse compared to the unfiltered sample at pH 8.5
(natural pH level of the aerobic pond), indicating that only specific microorganisms
favour the acidic pH of 5.5.
Bacterial community compositions under control conditions (pH 8.5, unfiltered) were
dominated by Actinobacteria (51.3 %), followed by Betaproteobacteria (19.9 %),
Firmicutes (8.2 %), TM7 (5.1 %), Gammaproteobacteria (2.9 %), Bacteroidetes (3.1%),
Chapter 5: PolyP accumulating bacteria in piggery waste
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Alphaproteobacteria (0.9 %), Epsilonproteobacteria (0.7 %), Tenericutes (0.4 %), and
TM6 (0.4 %).
Figure 5.6 (a) Alpha diversity rarefaction plots of phylogenetic diversity of 3 EBPR systems. (b) Microbial diversity indicated by Shannon diversity. (Calculation of richness and diversity estimators was based on OTU tables rarefied to the same sequencing depth, the lowest one of total sequencing reads; 5200). In comparison, Gammaproteobacteria taxa dominated both filtered and unfiltered
microcosms maintained at pH 5.5, with relatively high abundances in both filtered and
unfiltered microcosms (ca. 93 % and 89 %, respectively) when compared to their
abundance under alkaline conditions (ca. 2.9 %) (Figure 5.7a). Within the acidic
microcosms, the Gammaproteobacteria, in filtered (Figure 5.7b) samples were
dominated by the genus Alteromonadales (59 %) followed by Aeromonadaceae (26 %),
Shewanella (9 %), Pseudomonas (3 %), Enterobacteriaceae (1 %) and Citrobacter (1
%). Conversely, in unfiltered samples (Figure 5.7c), Gammaproteobacteria community
members were aligned to the genus Aeromonadaceae (73 %) followed by
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Enterobacteriaceae (16 %), Alteromonadales (4 %), Citrobacter (3 %), Pseudomonas
(3 %), and Acinetobacter (1 %).
Figure 5.7 Identities and relative abundance (%) of the bacteria in 3 EBPR systems as revealed by 16S rRNA Tag sequencing at class level (a). Composition of the main polyP accumulators, Gammaproteobacteria under (b) pH 5.5 unfiltered, and (c) pH 5.5 filtered samples.
5.3.4 Metagenomic analysis of piggery wastewater samples treated at pH 5.5
Shotgun metagenomics was used to confirm community diversity and the presumptive
functional genes involved in polyP accumulation. Whole genome shotgun libraries were
sequenced and normalised to 3,000,000 reads per sample for each of the three EBPR
systems. The data were analysed using the “Metagenome Rapid Annotation using a
Subsystem Technology” (MG-RAST) server (http://metagenomics.nmpdr.org/). A
summary of the sequencing analyses are shown in Table 5.1.
Cha
pter
5: P
oly
P a
ccu
mu
lati
ng
ba
cter
ia in
pig
ge
ry w
ast
e
114
Tab
le 5
.1 S
umm
ary
of th
e an
alys
es o
f MG
-RA
ST o
f the
3 E
BPR
sys
tem
s (pH
5.5
filte
red,
pH
5.5
un-
filte
red,
and
pH
8.5
un-
filte
red)
.
Sam
ple
char
acte
ristic
s pH
5.5
filte
red
pH 5
.5 u
n-fil
tere
d pH
8.5
un-
filte
red
U
ploa
d: b
p C
ount
53
8,37
0,13
0 bp
53
9,59
3,63
9 bp
53
1,18
0,68
4 bp
U
ploa
d: S
eque
nces
Cou
nt
3000
000
3000
000
3000
000
Upl
oad:
Mea
n Se
quen
ce L
engt
h 17
9 ±
58 b
p 17
9 ±
57 b
p 17
7 ±
59 b
p U
ploa
d: M
ean
GC
per
cent
54
± 9
%
51 ±
10
%
47 ±
11
%
Arti
ficia
l Dup
licat
e R
eads
: Seq
uenc
e C
ount
23
0711
33
7432
32
4612
Po
st Q
C: b
p C
ount
22
4,08
3,38
2 bp
20
2,10
4,70
5 bp
19
4,62
9,42
7 bp
Po
st Q
C: S
eque
nces
Cou
nt
1978
095
1806
120
1773
619
Post
QC
: Mea
n Se
quen
ce L
engt
h 11
3 ±
44 b
p 11
1 ±
44 b
p 10
9 ±
44 b
p Po
st Q
C: M
ean
GC
per
cent
54
± 9
%
51 ±
10
%
48 ±
11
%
Proc
esse
d: P
redi
cted
Pro
tein
Fea
ture
s 15
3741
0 13
8204
1 13
1965
4 Pr
oces
sed:
Pre
dict
ed rR
NA
Fea
ture
s 40
5435
40
7414
41
0162
A
lignm
ent:
Iden
tifie
d Pr
otei
n Fe
atur
es
8396
93
7410
95
2651
92
Alig
nmen
t: Id
entif
ied
rRN
A F
eatu
res
1604
2 16
533
1463
A
nnot
atio
n: Id
entif
ied
Func
tiona
l Cat
egor
ies
7573
51
6617
55
2292
15
Chapter 5: PolyP accumulating bacteria in piggery waste
115
Taxonomic analysis of metagenomic reads of all three EBPR systems (Table 5.2)
indicated that most of the sequences belonged to unclassified bacteria indicating that our
knowledge of polyP accumulating taxa under acid stimulation is lacking from a
taxonomic standpoint. However, amongst the known bacteria within the MG-RAST
database, taxonomic identity of the pH 5.5 filtered and unfiltered samples were
relatively similar to each other when compared to that of the control. Similar to the 16S
rRNA sequence dataset, the most dominant group in pH 5.5 samples (both filtered and
unfiltered) was Gammaproteobacteria (eg. Aeromonas, Pseudomonas, Xanthomonas,
Enterobacter, Klebsiella, Acinetobacter, Castellaniella, Pantoea, Edwardsiella,
Escherichia, Shewanella, Tolumonas, Citrobacter, Flavobacterium, Burkholderia,
Cronobacter, Pasteurella). In contrast, whole community shotgun sequencing showed
that the community composition of the control microcosms was comprised of
Glaciibacter, Candidatus Aquiluna, Candidatus Rhodoluna, Burkholderia,
Microbacterium and Agrococcus. Metabolic phosphorus potential of the community, in
terms of the abundance of genes involved in polyP metabolism, were compared by
assigning functional annotations to metagenomic sequences with subsequent sequence
assignment to subsystems. The genetic potential for P metabolism showed enrichment
of Polyphosphate kinase (EC 2.7.4.1) and Exopolyphosphatase (EC 3.6.1.11) genes,
which are essential in polyP synthesis and hydrolysis respectively (Figure 5.8).
Figure 5.8 Abundance of genes involved in polyP synthesis (polyphosphate kinase) and hydrolysis (exopolyphosphatase) in the three EBPR systems (pH 5.5 filtered, pH 5.5 un-filtered, and pH 8.5 un-filtered).
Cha
pter
5: P
oly
P a
ccu
mu
lati
ng
ba
cter
ia in
pig
ge
ry w
ast
e
116
Tab
le 5
.2 P
hylo
gene
tic ta
xono
mic
com
posi
tion
of 3
EB
PR s
yste
ms
base
d on
met
agen
omic
s an
alys
is (p
H 5
.5 fi
ltere
d, p
H 5
.5 u
n-fil
tere
d,
and
pH 8
.5 u
n-fil
tere
d).
Cla
ss
Ord
er
Fam
ily
Gen
us
Abu
ndan
ce
Avg
%
iden
tity
# Hits
pH
5.5
filte
red
uncl
assi
fied
(Bac
teri
a)
uncl
assi
fied
un
clas
sifie
d
uncl
assi
fied
19
94
100.
0 25
1 G
amm
apro
teob
acte
ria
Aero
mon
adal
es
Aero
mon
adac
eae
Aero
mon
as
655
99.8
88
G
amm
apro
teob
acte
ria
Pseu
dom
onad
ales
Ps
eudo
mon
adac
eae
Pseu
dom
onas
11
5 99
.9
33
Gam
map
rote
obac
teri
a Xa
ntho
mon
adal
es
Xant
hom
onad
acea
e Xa
ntho
mon
as
96
100.
0 6
unas
sign
ed
unas
sign
ed
unas
sign
ed
unas
sign
ed
89
100.
0 36
G
amm
apro
teob
acte
ria
uncl
assi
fied
un
clas
sifie
d
uncl
assi
fied
72
99
.9
13
Gam
map
rote
obac
teri
a En
tero
bact
eria
les
Ente
roba
cter
iace
ae
Ente
roba
cter
55
99
.9
29
Gam
map
rote
obac
teri
a En
tero
bact
eria
les
Ente
roba
cter
iace
ae
Kle
bsie
lla
55
100.
0 23
G
amm
apro
teob
acte
ria
Pseu
dom
onad
ales
M
orax
ella
ceae
Ac
inet
obac
ter
53
100.
0 2
Beta
prot
eoba
cter
ia
Burk
hold
eria
les
Alca
ligen
acea
e C
aste
llani
ella
33
10
0.0
4 G
amm
apro
teob
acte
ria
Ente
roba
cter
iale
s En
tero
bact
eria
ceae
Pa
ntoe
a 30
99
.4
15
Gam
map
rote
obac
teri
a En
tero
bact
eria
les
Ente
roba
cter
iace
ae
Edw
ards
iella
25
10
0.0
3 G
amm
apro
teob
acte
ria
Ente
roba
cter
iale
s En
tero
bact
eria
ceae
Es
cher
ichi
a 23
10
0.0
10
Beta
prot
eoba
cter
ia
uncl
assi
fied
un
clas
sifie
d
uncl
assi
fied
20
10
0.0
10
Gam
map
rote
obac
teri
a Al
tero
mon
adal
es
Shew
anel
lace
ae
Shew
anel
la
20
99.7
4
Gam
map
rote
obac
teri
a Ae
rom
onad
ales
Ae
rom
onad
acea
e To
lum
onas
19
99
.9
3 G
amm
apro
teob
acte
ria
Ente
roba
cter
iale
s En
tero
bact
eria
ceae
C
itrob
acte
r 19
99
.9
10
Flav
obac
teri
ia
Flav
obac
teri
ales
Fl
avob
acte
riac
eae
Flav
obac
teri
um
16
99.9
9
Beta
prot
eoba
cter
ia
Burk
hold
eria
les
Burk
hold
eria
ceae
Bu
rkho
lder
ia
16
99.9
6
Gam
map
rote
obac
teri
a En
tero
bact
eria
les
Ente
roba
cter
iace
ae
Cro
noba
cter
15
99
.9
9 G
amm
apro
teob
acte
ria
Past
eure
llale
s Pa
steu
rella
ceae
Pa
steu
rella
15
10
0.0
1 Th
ese
data
are
bas
ed o
n R
DP
(min
imum
alig
nmen
t len
gth,
≥50
bp;
E-v
alue
cut
off
s, 0.
01).
Num
ber o
f seq
uenc
e fe
atur
es w
ith a
hit
no le
ss
than
20
is ig
nore
d.
Cha
pter
5: P
oly
P a
ccu
mu
lati
ng
ba
cter
ia in
pig
ge
ry w
ast
e
117
Tab
le 5
.2 P
hylo
gene
tic ta
xono
mic
com
posi
tion
of 3
EB
PR sy
stem
s bas
ed o
n m
etag
enom
ics a
naly
sis (
cont
inue
d….)
Cla
ss
Ord
er
Fam
ily
Gen
us
Abu
ndan
ce
Avg
%
iden
tity
# H
its
pH 5
.5 u
nfilt
ered
un
clas
sifie
d (B
acte
ria)
un
clas
sifie
d
uncl
assi
fied
uncl
assi
fied
19
17
99.3
22
4 G
amm
apro
teob
acte
ria
Aero
mon
adal
es
Aero
mon
adac
eae
Aero
mon
as
475
99.8
85
G
amm
apro
teob
acte
ria
uncl
assi
fied
uncl
assi
fied
un
clas
sifie
d
391
99.9
13
G
amm
apro
teob
acte
ria
Pseu
dom
onad
ales
Ps
eudo
mon
adac
eae
Pseu
dom
onas
21
1 99
.8
41
unas
sign
ed
unas
sign
ed
unas
sign
ed
unas
sign
ed
178
100.
0 43
G
amm
apro
teob
acte
ria
Aero
mon
adal
es
Aero
mon
adac
eae
Tolu
mon
as
156
99.8
4
Gam
map
rote
obac
teri
a En
tero
bact
eria
les
Ente
roba
cter
iace
ae
Ente
roba
cter
10
1 10
0.0
19
Gam
map
rote
obac
teri
a Xa
ntho
mon
adal
es
Xant
hom
onad
acea
e Xa
ntho
mon
as
86
100.
0 5
Gam
map
rote
obac
teri
a Al
tero
mon
adal
es
Shew
anel
lace
ae
Shew
anel
la
56
99.8
9
Gam
map
rote
obac
teri
a En
tero
bact
eria
les
Ente
roba
cter
iace
ae
Kle
bsie
lla
51
99.9
25
G
amm
apro
teob
acte
ria
Ente
roba
cter
iale
s En
tero
bact
eria
ceae
Sa
lmon
ella
42
99
.9
6 G
amm
apro
teob
acte
ria
Ente
roba
cter
iale
s En
tero
bact
eria
ceae
Pa
ntoe
a 35
10
0.0
20
Gam
map
rote
obac
teri
a En
tero
bact
eria
les
Ente
roba
cter
iace
ae
Man
grov
ibac
ter
29
100.
0 1
Gam
map
rote
obac
teri
a Pa
steu
rella
les
Past
eure
llace
ae
Past
eure
lla
28
100.
0 1
Gam
map
rote
obac
teri
a Ps
eudo
mon
adal
es
Mor
axel
lace
ae
Acin
etob
acte
r 27
10
0.0
1 G
amm
apro
teob
acte
ria
Ente
roba
cter
iale
s En
tero
bact
eria
ceae
Es
cher
ichi
a 19
10
0.0
7 G
amm
apro
teob
acte
ria
Ente
roba
cter
iale
s En
tero
bact
eria
ceae
Ed
war
dsie
lla
18
100.
0 2
Beta
prot
eoba
cter
ia
Burk
hold
eria
les
Com
amon
adac
eae
Com
amon
as
17
100.
0 3
Thes
e da
ta a
re b
ased
on
RD
P (m
inim
um a
lignm
ent l
engt
h, ≥
50 b
p; E
-val
ue c
ut o
ffs,
0.01
). N
umbe
r of s
eque
nce
feat
ures
with
a h
it no
less
th
an 2
0 is
igno
red.
Cha
pter
5: P
oly
P a
ccu
mu
lati
ng
ba
cter
ia in
pig
ge
ry w
ast
e
118
Tab
le 5
.2 P
hylo
gene
tic ta
xono
mic
com
posi
tion
of 3
EB
PR sy
stem
s bas
ed o
n m
etag
enom
ics a
naly
sis (
cont
inue
d….)
Cla
ss
Ord
er
Fam
ily
Gen
us
Abu
ndan
ce
Avg
%
iden
tity
# H
its
pH 8
.5 u
nfilt
ered
un
clas
sifie
d (B
acte
ria)
un
clas
sifie
d
uncl
assi
fied
un
clas
sifie
d
185
100.
0 89
Be
tapr
oteo
bact
eria
un
clas
sifie
d
Beta
prot
eoba
cter
ia)
uncl
assi
fied
47
99
.9
10
Actin
obac
teri
a (c
lass
) Ac
tinom
ycet
ales
M
icro
bact
eria
ceae
G
laci
ibac
ter
41
100.
0 2
Actin
obac
teri
a (c
lass
) Ac
tinom
ycet
ales
M
icro
bact
eria
ceae
C
andi
datu
s Aqu
iluna
35
99
.8
3 Ac
tinob
acte
ria
(cla
ss)
Actin
omyc
etal
es
Mic
roba
cter
iace
ae
Can
dida
tus R
hodo
luna
27
10
0.0
3 Be
tapr
oteo
bact
eria
Bu
rkho
lder
iale
s Bu
rkho
lder
iace
ae
Burk
hold
eria
22
99
.8
8 Ac
tinob
acte
ria
(cla
ss)
Actin
omyc
etal
es
Mic
roba
cter
iace
ae
Mic
roba
cter
ium
18
99
.3
7 Ac
tinob
acte
ria
(cla
ss)
Actin
omyc
etal
es
Mic
roba
cter
iace
ae
Agro
cocc
us
16
100.
0 4
Thes
e da
ta a
re b
ased
on
RD
P (m
inim
um a
lignm
ent l
engt
h, ≥
50 b
p; E
-val
ue c
ut o
ffs,
0.01
). N
umbe
r of s
eque
nce
feat
ures
with
a h
it no
less
th
an 2
0 is
igno
red.
Chapter 5: PolyP accumulating bacteria in piggery waste
119
Abundances of these genes within these datasets were higher in pH 5.5 filtered and
unfiltered systems compared to the control microcosms at pH 8.5 (unfiltered). In both
pH 5.5 filtered and unfiltered, polyphosphate kinase (EC 2.7.4.1) was the most abundant
gene sequence compared to other genes involved in P metabolism (Table 5.3). Before
polyP synthesis, Pi is required to be taken up and transported across the cytoplasmic
membrane. We found that high affinity Pst (phosphate specific transport) systems
(PstA, PstB, and PstC), which are involved in the uptake and transport of Pi across the
cytoplasmic membrane, were also highly abundant under pH 5.5 compared to pH 8.5
(Table 5.3). These data indicate that a selective enrichment of Pi uptake and polyP
synthesis pathways were more pronounced at an acidic pH of 5.5 compared to pH 8.5.
In terms of phylogenetic and functional diversity, the presence of the ppk genes were
mainly related to homologs from Aeromonas hydrophila, Aeromonas salmonicida,
Enterobacter sp, Pseudomonas aeruginosa, Klebsiella variicola, Citrobacter koseri,
Salmonella enterica, and Bordetella parapertussis (Table 5.4). Burkholderia like ppk
sequences were dominant among the bacteria expressing Polyphosphate kinase at pH
5.5, whereas under pH 8.5 conditions homologs relating to Bordetella parapertussis,
Bordetella avium, Burkholderia mallei, Kribbella flavida, Kineococcus radiotolerans,
Cellulomonas flavigena and Chromobacterium were the most abundant (Table 5.4).
Chapter 5: PolyP accumulating bacteria in piggery waste
120
Table 5.3 Most abundant gene sequences involved in P metabolism in 3 EBPR systems (pH 5.5 filtered, pH 5.5 un-filtered, and pH 8.5 un-filtered).
Function abundance
Polyphosphate kinase (EC 2.7.4.1) 762Phosphate transport system permease protein PstC (TC 3.A.1.7.1) 511Alkaline phosphatase (EC 3.1.3.1) 460NAD(P) transhydrogenase subunit beta (EC 1.6.1.2) 435Phosphate transport ATP-binding protein PstB (TC 3.A.1.7.1) 433NAD(P) transhydrogenase alpha subunit (EC 1.6.1.2) 380Low-affinity inorganic phosphate transporter 366Phosphate transport system permease protein PstA (TC 3.A.1.7.1) 347Predicted ATPase related to phosphate starvation-inducible protein PhoH 344Phosphate ABC transporter, periplasmic phosphate-binding protein PstS (TC 3.A.1.7.1) 342Guanosine-5'-triphosphate,3'-diphosphate pyrophosphatase (EC 3.6.1.40) 340Exopolyphosphatase (EC 3.6.1.11) 326Phosphate regulon sensor protein PhoR (SphS) (EC 2.7.13.3) 272Inorganic pyrophosphatase (EC 3.6.1.1) 240PhoQ 237Sodium-dependent phosphate transporter 226Probable low-affinity inorganic phosphate transporter 222
Polyphosphate kinase (EC 2.7.4.1) 671NAD(P) transhydrogenase subunit beta (EC 1.6.1.2) 447Phosphate transport system permease protein PstC (TC 3.A.1.7.1) 436NAD(P) transhydrogenase alpha subunit (EC 1.6.1.2) 415Low-affinity inorganic phosphate transporter 397Phosphate transport ATP-binding protein PstB (TC 3.A.1.7.1) 374Phosphate ABC transporter, periplasmic phosphate-binding protein PstS (TC 3.A.1.7.1) 365Guanosine-5'-triphosphate,3'-diphosphate pyrophosphatase (EC 3.6.1.40) 347Alkaline phosphatase (EC 3.1.3.1) 333Phosphate transport system permease protein PstA (TC 3.A.1.7.1) 323Predicted ATPase related to phosphate starvation-inducible protein PhoH 317Exopolyphosphatase (EC 3.6.1.11) 299Inorganic pyrophosphatase (EC 3.6.1.1) 271Phosphate regulon sensor protein PhoR (SphS) (EC 2.7.13.3) 255Phosphate regulon transcriptional regulatory protein PhoB (SphR) 229Phosphate transport system regulatory protein PhoU 222
Pyrophosphate-energized proton pump (EC 3.6.1.1) 557Polyphosphate kinase (EC 2.7.4.1) 453NAD(P) transhydrogenase subunit beta (EC 1.6.1.2) 347NAD(P) transhydrogenase alpha subunit (EC 1.6.1.2) 290Predicted ATPase related to phosphate starvation-inducible protein PhoH 254Phosphate transport ATP-binding protein PstB (TC 3.A.1.7.1) 251Number of sequence less than 200 is ignored
pH 5.5 filtered
pH 8.5 unfiltered
pH 5.5 unfiltered
Chapter 5: PolyP accumulating bacteria in piggery waste
121
Table 5.4 Functional affiliations (homologs) of PAOs in 3 EBPR systems (pH 5.5 filtered, pH 5.5 un-filtered, and pH 8.5 un-filtered) based on the presence of the ppk genes
Organism Reference Seq. ID Identity
Aeromonas hydrophila subsp. hydrophila ATCC 7966 YP_857333.1 61/61 (100%)Aeromonas salmonicida subsp. salmonicida A449 YP_001141341.1 71/71 (100%)Enterobacter sp. 638 YP_001177705.1 79/79 (100%)Pseudomonas aeruginosa 2192 ZP_04937198.1 31/31 (100%)Klebsiella variicola (strain At-22) YP_003438168.1 75/75 (100%)Citrobacter koseri ATCC BAA-895 YP_001451886.1 31/31 (100%)Salmonella enterica subsp. enterica serovar Typhi Ty2 ZP_06546958.1 64/64 (100%)Bordetella avium 197N YP_785463.1 36/41 (88%)Bordetella parapertussis 12822 NP_884330.1 26/27 (96%)Burkholderia YP_559732.1 49/65 (75%)
Aeromonas hydrophila subsp. hydrophila ATCC 7966 YP_857333.1 73/73 (100%)Aeromonas salmonicida subsp. salmonicida A449 YP_001141341.1 22/22 (100%)Pseudomonas aeruginosa 2192 ZP_04937198.1 49/49 (100%)Enterobacter sp. 638 YP_001177705.1 38/38 (100%)Shigella flexneri 2457T (serotype 2a) NP_838046.1 29/29 (100%)Klebsiella variicola (strain At-22) YP_003438168.1 39/39 (100%)Salmonella enterica subsp. enterica serovar Typhi Ty2 ZP_06546958.1 28/28 (100%)Bordetella avium 197N YP_785463.1 50/58 (86%)Bordetella parapertussis 12822 NP_884330.1 44/50 (88%)Citrobacter koseri ATCC BAA-895 YP_001451886.1 74/74 (100%)
Bordetella parapertussis 12822 NP_884330.1 57/61 (93%)Bordetella avium 197N YP_785463.1 26/29 (90%)Burkholderia mallei NCTC 10247 YP_001026565.1 27/32 (84%)Kribbella flavida DSM 17836 YP_003378870.1 44/50 (88%)Kineococcus radiotolerans SRS30216 YP_001360629.1 29/35 (83%)Cellulomonas flavigena DSM 20109 Unclassified. YP_003637899.1 66/72 (92%)Chromobacterium violaceum ATCC 12472 Unclassified NP_903027.1 45/57 (79%)Dechloromonas aromatica RCB YP_286024.1 38/42 (90%)Beutenbergia cavernae DSM 12333 Unclassified. YP_002883432.1 61/74 (82%)Jonesia denitrificans DSM 20603 YP_003162175.1 57/63 (90%)
pH 5.5 filtered
pH 5.5 unfiltered
pH 8.5 unfiltered (control)
Chapter 5: PolyP accumulating bacteria in piggery waste
122
5.4. Discussion
This study provides a comprehensive understanding of key PAOs and their diversity in
identity and functionality under two different pH conditions (pH 5.5 and pH 8.5) of high
P loaded wastewater. The results obtained using coincident single cell and population
approaches verified that maintaining pH of the wastewater at 5.5 would be a good
strategy to remove P from the high P loaded wastewater systems.
Quantification of the polyP formation seems to be a more accurate way for the
assessment of the performances of EBPR systems. The presence of intracellular polyP
granules inside PAOs is a good marker to prove the reduction of Pi level after EBPR,
due to the activity of PAOs rather than by the P assimilation alone. DAPI staining at a
higher concentration allows the quantification of intracellular polyP without the
requirement for prior polyP biopolymer isolation (Kulakova et al. 2011).
Using both epi-fluorescence microscopy and flow cytometry methods, we demonstrated
that 15 ug/mL concentration of DAPI was sufficient to obtain good signal to noise ratios
for the wastewater samples tested here when staining for the detection of polyP
accumulation. This concentration provided a good cytometric discrimination of polyP
containing bacteria from other non-target bacteria and is in good agreement with
previous findings (Kawaharasaki et al. 1999). Due to the fact that the environmental
matrices vary depending upon sample type, impurities, cellular abundance, and machine
sensitivity, we also suggest performing titration analyses of DAPI for the new
environments to obtain the optimum cytometric discrimination. Moreover, among the
different ways of quantitative visualization of polyP granules in microorganisms
(Serafim et al. 2002; Günther et al. 2009), DAPI staining is easily adaptable for a direct
application on environmental samples.
In order to test the hypothesis of enhanced P uptake at reduced pH (pH 5.5) in high P
environments, we applied the optimised polyP staining protocols to acidified and
control microcosms and subsequently challenged them with phosphorus amendments.
Specifically, we observed enhanced P accumulation, as measured by numbers of
accumulating cells, and determined around 70% of cells under acidic conditions (pH
5.5, unfiltered) were actively accumulating P, almost twice the number under control
(pH 8.5, unfiltered) (ca. 36%). Therefore, our findings support the hypothesis that
polyP accumulation in high P containing systems, such as piggery wastewater, can be
significantly enhanced by acidic manipulation. This result is consistent with previous
Chapter 5: PolyP accumulating bacteria in piggery waste
123
findings in other treatment systems, where growth of PAOs were enhanced and the
aerobic uptake of phosphate reached a maximum at pH 5.5 (McGrath et al. 2001;
Mullan et al. 2002a; Moriarty et al. 2006). Epi-fluorescence microscopy revealed that
polyP accumulating microalgae were highly abundant in unfiltered samples treated at
pH 5.5 with flow cytometry also confirming the differences of polyP accumulation
between filtered and unfiltered samples as ca. 20 %, presumably due to the additional
uptake by microalgae within the community. The formation of polyP during growth at
pH 5.5 could infer that pH regulates intracellular phosphate levels in bacteria and
microalgae (McGrath et al., 2001). Therefore, we surmise that, in addition to bacterial
PAOs, a number of microalgae play a significant role in polyP accumulation in this
waste system, an observation that is also consistent with previous findings (Powell et al.
2011; Powell et al. 2008).
In addition, here we addressed the question of identity and function of key PAO
diversity and functionality under two different pH conditions using next generation
sequencing approaches applied to the EBPR systems. We observed that both 16S rRNA
gene and shotgun metagenomic approaches yielded uniform taxonomic identities of the
key PAO organisms within the EBPR systems tested. Specifically, 16S rRNA Ion Tag
sequencing for microcosms at pH 5.5 indicated different phylogenetic composition of
the communities when compared to pH 8.5. PolyP accumulating communities
maintained at pH 5.5 were dominated by Gammaproteobacteria, represented by the
Aeromonadaceae, Enterobacteriaceae, Alteromonadales, Citrobacter, Pseudomonas,
Acinetobacter and, Shewanella. These taxa are known polyphosphate accumulating
bacteria and have been detected in other wastewater treatment systems (Sidat et al.
1999; Nielsen et al. 2010), with polyP synthesis being widely studied in Escherichia
coli, Pseudomonas aeruginosa and Acinetobacter spp. (Kornberg 1999). Indeed, based
upon the current literature, a broad spectrum of microbial phyla is able to accumulate
polyP including Actinobacteria, Bacteroidetes, and Proteobacteria
(Alphaproteobacteria, Betaproteobacteria, and Gammaproteobacteria), indicating that
PAOs are ubiquitous, with only relative proportions varying upon the differences in the
types of wastewater treatment plant (Mehlig et al. 2013).
Although substantial progress has been made towards identifying the species of PAOs
involved in EBPR, knowledge gaps still exist in understanding the linked functional and
phylogenetic gene content of EBPRs, especially for high P remediating systems. In
order to address this, we performed shotgun sequencing of total DNA and grouped
Chapter 5: PolyP accumulating bacteria in piggery waste
124
sequences at the 97% sequence similarity level. Using a PCR independent approach,
we found that the Gammaproteobacteria were at a consistently higher abundance when
employing shotgun sequence analysis from microcosms at pH 5.5. Specifically, both
filtered and unfiltered microcosms comprised of Aeromonas, Pseudomonas,
Xanthomonas, Enterobacter, Klebsiella, Acinetobacter, Castellaniella, Pantoea,
Edwardsiella, Escherichia, Shewanella, Tolumonas, Citrobacter, Flavobacterium,
Burkholderia, Cronobacter and Pasteurella.
PolyP synthesis is catalyzed by a polyphosphate kinase in prokaryotes, whilst
hydrolysis of the terminal phosphate residues from polyP to form orthophosphate is
catalyzed by exo- and endo-polyphosphatases. Exo-polyphosphatases are considered as
the central regulatory enzymes in polyP metabolism (Espiau et al. 2006), with the
bacterial ppk1 gene, encoding for the enzyme polyphosphate kinase responsible for
polyP synthesis in many bacteria (Mielczarek et al. 2013). For this reason, they can be
used as reliable indicators for the assessment of the performances of EBPR systems.
Comparing the functional genes involved in polyP metabolism, we observed a higher
abundance of genes involved in phosphorus metabolism under pH 5.5, indicating a
specific environmental selection for more polyP genes under acidic conditions. We
found that high affinity Pst (phosphate specific transport) systems (PstA, PstB, and
PstC) which are involved in the uptake and transportation of Pi across the cytoplasmic
membrane were highly abundant under pH 5.5 compared to pH 8.5. The exact
physiological role of the polyP accumulation in sludge microorganisms at pH 5.5 is
currently unknown (McGrath et al. 2001). However, it was reported that phosphate is
transported by the Pst system in the form of H2PO4- and HPO4
2- and the proportion of
H2PO4- increases with decreasing pH (van Veen et al. 1994). Therefore, we could
assume that under pH 5.5, uptake of Pi is enhanced and subsequently increases the
accumulation of Pi in cells as polyP. McGrath et al. (2001) have suggested that growth
at an external pH close to the phosphate transport optimum (pH 5.0-6.5) may well result
in an increased phosphate uptake and the elevation of intracellular phosphate
concentrations.
Functional affiliations of the ppk gene suggested that Aeromonas hydrophila,
Acinetobacter baumannii, Bordetella pertussis, Escherichia coli, Klebsiella,
Pseudomonas aeruginosa, Salmonella enterica, Shigella flexneri type species were
responsible for enhancing the polyP activity of the acid induced EBPR systems. The
revealed functional diversity was also in good agreement with a recent listing of species
Chapter 5: PolyP accumulating bacteria in piggery waste
125
possessing sequences homologous to polyphosphate kinase (ppk) (Rao et al. 1998).
Oehmen et al. (2007) have also showed the involvement of ppk in poly-P synthesis
within EBPRs, but there are still few studies which demonstrate the genetic potential of
the PAO community by metagenomic analysis (Martin et al. 2006; Temperton et al.
2011; Albertsen et al. 2012) or by chemical fingerprinting (Majed et al. 2012). We
believe this is the first study that investigates taxonomic and functional identity of
PAOs in acid induced EBPR system by DNA based metagenomic analyses.
5.5 Conclusions
In essence, this study has developed a practical and consistent approach to identify
polyP accumulating bacteria populations in EBPR processes in piggery waste effluent
and resolved key diversity and pathways present within a high P wastewater treatment
system. Specifically, flow cytometric and tag sequencing data supported our hypothesis
that there is an increase in polyP uptake and a greater activity, and abundance of PAOs
under more acidic conditions.
These findings indicate that polyphosphate accumulation within EBPRs could be
enhanced for P removal in piggery waste effluent and other EBPR systems where the
treatment of effluent wastewater is a problem. The information gained here by altering
the pH provides a basis of a novel strategy for improving the waste treatment process
and developing high value fertilisers for land application. Subsequent investigations
should therefore focus on assessing the economic feasibility of incorporating EBPR
systems into existing water treatment systems by lowering the pH of the aerobic pond.
Secondly, new methods and technologies should be explored for enhancing
polyphosphate accumulating organisms. Choosing one of the established separation
technologies (such as filtration, sedimentation, or air flotation) for the subsequent
harvesting stages would then remove the phosphorus rich accumulated microorganisms
from the system, which can further be used as slow releasing P fertilisers, and leave a
treated effluent with lower P concentration in the system. The application of an
integrated EBPR to the on-farm waste treatment systems, including covered anaerobic
ponds (CAPs), would also give the farmers an opportunity to recycle this treated
effluent during irrigation.
Chapter 6: Application of piggery waste compost to soil
126
CHAPTER 6
Effect of Low Rate Application of Banded Pelletised Pig
Compost on Plant Growth and Soil Microbial Community
Composition
6.0 Abstract
Recycling composted piggery waste, which is generally high in phosphorus (P), is
gaining interest to augment inorganic P fertiliser. Precise placement of compost in the
root zone by pelletising and mixing with a low rate of inorganic fertiliser could support
application of compost at an economically viable level in broad-acre agriculture and
horticulture. This study compared the effects of a low application rate (50 kg ha-1) of
banded pelletised pig compost (Balance®) in combination with an inorganic fertiliser
(Agras®) with single applications applied at a commercial rate (100 kg ha-1) on plant
growth, soil fertility and changes in bacterial and fungal communities. Wheat was
grown in pots with 4 treatments: (1) Agras® 100 kg ha-1, (2) Balance® 100 kg ha-1, (3)
Balance® 50 kg ha-1 + Agras® 50 kg ha-1, and (4) a nil application. The experiment was
harvested 4, 6, and 8 weeks from sowing. Shoot and root dry weights, plant P uptake,
and arbuscular mycorrhizal (AM) colonisation of roots were assessed. Soils were
analysed for electrical conductivity (EC), pH, total carbon (TC), total nitrogen (TN),
available P, nitrate nitrogen (NH3-N), and (ammonium-nitrogen) NH4+-N. The diversity
of bacteria was analysed using 16 S rRNA Ion Tag sequencing for both rhizosphere soil
and plant root colonising bacteria. There was a positive correlation between soil
available P and plant P uptake, and a strong negative relationship between soil available
P level and AM colonisation, irrespective of the source of P. Banding of Balance®50 kg
ha-1+Agras®50 kg ha-1 was the most effective treatment for wheat growth and soil
fertility. Changes in bacterial community composition for this soil amendment were
associated with an increase in soil available P, plant P uptake, and shoot and root dry
weights. The blend of the reduced rate of inorganic P fertiliser and pelletised piggery
compost (50 kg ha-1 each) placed in close proximity to seeds at sowing could be more
effective than application of fertiliser applied alone at a rate of 100 kg ha-1.
Chapter 6: Application of piggery waste compost to soil
127
6.1 Introduction
Potential difficulties in maintaining food security are linked to future shortages of
phosphorus (P) fertiliser due to limitations in energy and resources (Hammond et al.
2004; Shafiee and Topal 2010; Beardsley 2011). Lower quality ores and a reduction in
the supply of new deposits will further impede P extraction (Beardsley 2011; Cordell et
al. 2011). Thus, there is an ongoing interest in finding more sustainable P fertiliser
supplies. Two major opportunities exist for conserving the world's phosphate resources:
(1) recycling waste materials, and (2) more efficient use of inorganic P fertilisers in
agriculture (Cordell et al. 2009; Güngör and Karthikeyan 2008; Beddington 2010;
Childers et al. 2011).
By-products arising from treated animal waste can be a valuable resource for renewable
energy production and an economical source of P for agriculture (De-Bashan and
Bashan 2004; Güngör and Karthikeyan 2008; Westerman et al. 2010). It has been
previously shown that the treatment of animal waste using anerobic digestion recovers
wide range of P fertilizers; liquid P-fertilisers (digested effluent), slow release P-
fertilisers (struvite) and soil stabilisers (digestate, sludge) (Westerman et al. 2010).
Animal waste, including piggery waste, can be high in P (Poulsen 2000; Güngör and
Karthikeyan 2008) and the application of animal manure to soil can enhance plant P
nutrition, crop performance, soil quality and microbial activity (Greaves et al. 1999;
Motavalli and Miles 2002; Parham et al. 2002). However, despite these benefits there
are considerable risks associated with application of animal manure to soil such as,
odour, greenhouse gas emissions (GHG), leaching, toxicity, and pathogen survival.
Composting waste prior to land application offers the possibility of reducing these risks
(Vanotti et al. 2009).
Composting has not been widely adopted by the agricultural sector in Australia
primarily because it is uneconomical and impractical in relation to transport and its
application to larger land areas. It is not very common in Australia, as the low gross
margins per hectare, large farms and relatively long distances from compost suppliers
generally make compost an uneconomical soil amendment (Quilty and Cattle 2011).
Preparation of compost as a pellet is of interest due to the ease of handling and the
opportunity to band compost at seeding using air-seeding equipment. Placement of
fertiliser in a concentrated band beneath the seed at sowing is more efficient than
Chapter 6: Application of piggery waste compost to soil
128
surface broadcasting of fertiliser which can be removed by water and/or wind erosion,
intercepted by weeds or otherwise lost before it reaches the root zone (Halvorson et al.
1997). Application of pelletised compost using air-seeding equipment allows placement
of compost in the root zone for efficient nutrient supply for crops (Barton et al.
unpublished).
The second opportunity for conserving the world's phosphate resources is more efficient
use of synthetic P fertilisers in agriculture. A range of soil amendments based on
organic materials has been used successfully to decrease the amount of synthetic
fertiliser used per yield. Biochar is an example of a soil amendment which has been
banded in this manner (Blackwell et al. 2010; Solaiman et al. 2010). Banding biochar in
broad-acre crops allowed the application rate to be reduced from a range of 20-60 t/ha
for wheat (Castaldi et al. 2011; Prendergast-Miller et al. 2011) to 1 t/ha (Blackwell et al.
2010) for wheat. Banding compost could produce similar reductions in average broad-
acre rates of application which are 5 t/ha in Australia (Quilty and Cattle 2011). These
rates can be reduced even further by pelletising the compost for efficacy (Barton et al.
unpublished). Pelletising allows compost materials to be mixed with granulated
synthetic fertilisers and sown directly into the fertiliser band, while compost cannot be
used in conventional seeding equipment used in broad-acre agriculture when it is in its
natural loose form as it can block the delivery tubes. In this manner, a sufficient level of
compost could be achieved in the root zone with significantly less applied overall.
Pelletised compost has been trialled (Rao et al. 2007; Yan et al. 2001), and several
businesses produce this form of compost in Australia (Quilty and Cattle 2011).
Nevertheless, further research is needed to develop these pellets as P-fertilisers and to
ensure their application is economical, sustainable and practical from an operations
perspective.
The present study investigated whether piggery compost as pellets in combination with
a low rate of synthetic P fertilisers could contribute to plant growth with more
sustainability use of P fertiliser. Pelletised compost derived from remediated piggery
waste was applied with a low rate of inorganic fertiliser to assess the effect on plant
growth, soil nutrient improvement and fungal-bacterial community composition in soil.
Accurate placement of compost in the root zone following pelletising and mixing with
fertiliser when sowing cereal crops has been observed a in recent trial to allow an order
of magnitude reduction in compost and a 50% reduction in phosphorus fertiliser rates
Chapter 6: Application of piggery waste compost to soil
129
(Barton et al. unpublished). However, the mechanisms involved in increasing yield with
small inputs of compost and lower fertiliser inputs are not fully explained. On the other
hand, it has been shown throughout this thesis that piggery wastes can be high in both
inorganic and organic P forms and P cycling microorganisms (Chapters 3 to 5).
Recycling piggery by-products as pelletised compost could potentially introduce some
beneficial bacteria into the soil and they could play an important role in P turnover in
soils. However, the identity of the microorganisms and mechanisms involved in P
transformation is largely unresolved in soils amended with piggery compost. Without
this knowledge the potential to further increase yield or reduce fertiliser application may
be overlooked.
In order to understand the effect of the reduced rate of organic-synthetic combination in
agriculture the following hypothesis were tested.
1. Banding pelletised piggery compost at low rates in combination with inorganic
fertiliser in the root zone of wheat facilitates nutrient uptake by the roots of the
plant in a P deficient agricultural soil alters the abundance and community
composition of bacteria involved in increasing P availability in soil, and
enhances plant growth.
2. The increase in P in soil following application of synthetic P fertiliser, in the
presence or absence of compost, will decrease the percentage of root length
colonised by arbuscular mycorrhizal (AM) fungi but increase the length of root
colonised by AM fungi grown in this soil in line with the availability of soil P
and root growth.
6.2. Materials and Methods
6.2.1 Experimental design
Wheat (Triticum aestivum L. cv. Wyalkatchem) was grown in pots (1 kg soil) for 8
weeks (from the 12th of October 2013 to 7th of December 2013) in a glasshouse at The
University of Western Australia, Crawley, Australia. The experiment was set up in a
complete randomised design with 4 treatments and 3 replicates were carried out for each
batch. Two different fertilisers were used (organic and synthetic). A commercial
pelletised compost product was used as the organic fertiliser. The pelletised compost
Chapter 6: Application of piggery waste compost to soil
130
product, Balance® (http://www.cwise.com.au), was derived from a blend of piggery
and other wastes, fully composted to meet ‘maturity index 3’ in the AS4454 (2012)
standard for composts (http://www.recycledorganics.com/lab/). Balance® was used in
combination with nitrogen/phosphorus/sulphur (NPS) synthetic fertiliser (Agras®)
(http://www.csbp-fertilisers.com.au). The application rate for the experiment was
decided on the basis of a previous observation (Barton et al. unpublished). Treatments
(Table 6.1) were applied to pots at the following equivalent rates: (1) Agras® 100 kg ha-
1, (2) Balance® 100 kg ha-1, (3) Balance® 50 kg ha-1+Agras® 50 kg ha-1 (4) control (no
nutrients were added, nil application). Treatment 1 simulates seeding practice of
conventional N and P chemical fertiliser (100 kg ha-1) in Western Australian wheat
growing regions. Agras® has been used as a fertiliser under Western Austrian conditions
for more than 30 years and adequate levels of nitrogen are supplied at seeding for
situations requiring low total nitrogen inputs. Agras® has relatively high nitrogen to
phosphorus ratio and is claimed to be an ideal starter fertiliser for canola and cereals
(http://www.csbp-fertilisers.com.au). Treatment 2 simulates the seeding practice
recommended by Cwise (http://www.cwise.com.au/) when using their Balance®
product. Treatment 3 simulates the reduction in the amount of fertiliser recommended
by Cwise compared to the conventional practice. Treatment 4 is a control (no nutrients
or compost were added). Fertilisers were placed beneath the soil surface in close
proximity to the seed to simulate application through an air-seeder. Typical
characteristics of Balance® and Agras®, and comparative nutrient contents of each
treatment, are shown in Table 6.2, Table 6.3, and Table 6.4 respectively.
Table 6.1 Soil amendments used in this experiment and their corresponding abbreviations. Treatments Abbreviations
Agras® 100 kg ha-1 Agras100
Balance® 100 kg ha-1 Balance100
Balance® 50 kg ha-1 + Agras® 50 kg ha-1 Balance50/Agras50
Control (a nil application) Control
Chapter 6: Application of piggery waste compost to soil
131
Table 6.2 Typical characteristics of the pelletised compost, Balance® . pH 8.1
Mg (mg/kg) 3500
EC (dS/m) 8
Mn (mg/kg) 280
TC (%) 37.1
Mo (mg/kg) 3.6
Organic C (%) 34.6
NO3-N (mg/kg) 24
TN (%) 2.55
NH4-N (mg/kg) 205
C:N 15
Na (mg/kg) 4200
TP (mg/kg) 7300
S (mg/kg) 10000
Ca (mg/kg) 360000
Se (mg/kg) 1
Co (mg/kg) 3.2
Cl (mg/kg) 0.95
B (mg/kg) 18
Zn (mg/kg) 330
Fe (mg/kg) 5100
Si (mg/kg) <0.1
K (mg/kg) 11000 CEC (cmol (+)/kg) 45
Source: Barton et al. unpublished
Table 6.3 Typical analysis of the granulated fertiliser, Agras®.
N (w/w %)
16.1
P (w/w %)
9.1
S (w/w %)
14.3
Ca (w/w %)
0.5
Zn (w/w %) 0.06
Source: http://www.csbp-fertilisers.com.au
Table 6.4 Relative N and P application rates of three fertiliser treatments applied to wheat. Rates are shown in both kg ha-1 and mg/pot basis. Treatments*
1 2 3
N (kg ha-1) 16.1 2.6 9.3
N (mg/pot) 28.5 4.5 16.5
P (kg ha-1) 9.1 0.7 4.9
P (mg/pot) 16.1 1.3 8.7
*1) Agras® 100 kg ha-1 2) Balance® 100 kg ha-1 3) Balance® 50 kg ha-1 + Agras® 50 kg ha-1
Chapter 6: Application of piggery waste compost to soil
132
6.2.2 Soil collection and potting
Soil with low available P was collected for the experiment (0-10 cm depth) from a
broadacre cereal farm at The University of Western Australia Farm, Pingelly WA
(UTM 50H. 498440 m E, 6406561 m S). The soil properties are shown in Table 6.5. At
the time of sampling vegetation was dominated by subterranean clover and Wimmera
ryegrass, with low input management. The soil was sieved to 2 mm before potting. Six
wheat seeds were planted in each pot at a depth of 30 mm and seedlings were thinned to
3 per pot after germination. The pots were watered to and maintained at 80 % water
holding capacity.
Table 6.5 Soil properties at the field sampling site, Pingelly.
Soil characteristics Texture class Loamy Sand C (%) 1.2 N (%) 0.06 C/N ratio 19 pH (in water) 6.26 pH (in CaCl2) 5.06 EC (mS/m) 7.42 Exchangeable ions (meq/100g) CEC 4 Ca 1.6 Mg 1.6 Na 0.6 P retention index 3.7 Bicarbonate extractable P (mgP/kg) 9.53 Bicarbonate extractable K (mgK/kg) 126
6.2.3 Soil and plant analyses
At the end of each harvest (4, 6, and 8 weeks), roots were carefully lifted out of the soil
and shaken vigorously to remove loose adhering soil. The tightly adhering rhizosphere
soil was collected and used for subsequent soil analyses. Fresh shoot weight was taken
and oven-dried at 60°C for 72 h and total shoot dry weights per pot for each treatment
was calculated. The roots were washed well with water to remove the remaining
adhering soil particles, blotted dry, weighed, cut into 1 cm segments and mixed
thoroughly. Known weights of subsamples were taken for DNA extraction and root
staining (AM colonisation). The root fragments for DNA extractions were further cut
Chapter 6: Application of piggery waste compost to soil
133
into segments several mm long at the time of harvesting and stored at -80oC for
molecular analysis. The remaining roots were oven-dried at 60°C for 72 h and total root
dry weights per pot for each treatment was calculated taking to consideration the weight
taken for DNA extraction and root staining.
Oven-dried shoots were ground and digested (HNO3–HClO4) and the P concentration in
the digest was measured by the molybdovanadophosphate method. Basic soil chemical
parameters were measured (EC, pH, soil available P, total carbon and total nitrogen,
NH4+ and NO3
–). Available P from the soil was extracted with 0.5 M aqueous NaHCO3-
(pH 8.5) and measured colorimetrically (Murphy and Riley 1962). The soil EC was
measured in water at 1: 5 (w/v) ratios. Soil pH was also measured in CaCl2 at 1:5 (w/v)
ratios. Total carbon and total N in ground soil and plant were assessed using combustion
analysis using an Elementar analyser (vario Macro CNS; Elementar, Germany). Soil
NH4+ and NO3
– were measured by extracting 20 g with 80 mL 0.5 M K2SO4 and
analysing the extracts colorimetrically for NH4+ using the salicylate–nitroprusside
method (Searle 1984) and NO3– concentration using the hydrazine reduction method
(Kempers and Luft 1988) on an automated flow injection Skalar AutoAnalyser (San
plus, Skalar Analytical, The Netherlands). All measurements were completed in
triplicate.
6.2.4 Determination of root length and arbuscular mycorrhizal (AM) colonisation
The root sub-samples (0.20-0.50 g fresh weight) taken for staining were used to assess
AM fungal colonisation. Roots were cleared in 10% KOH, acidified, and stained with
Trypan blue (0.05%) in lactoglycerol (1:1:1.2 lactic acid:glycerol:water) and de-stained
in lactoglycerol (Abbott and Robson 1981). Root length and root length colonised by
AM fungi were assessed by using the gridline intercept method scoring more than 100
intercepts per pot under a microscope at 100× magnification (Giovannetti and Mosse
1980).
6.2.5 DNA extraction and Ion Tag sequencing
DNA was extracted from both rhizosphere soil and roots at the second harvest (at 6
weeks) and used for subsequent sequencing for rhizosphere bacteria and root colonising
bacteria. DNA was extracted from 0.5 g of rhizosphere soil taken form each soil
amendments using the MoBio Powersoil DNA isolation kit (Geneworks, Australia),
utilising beat beating and column purification, according to the manufacturer's
Chapter 6: Application of piggery waste compost to soil
134
guidelines. All the extractions were done in triplicates. Extracted DNA was quantified
and checked for purity at A260/280 nm (Nanodrop,Thermo Fisher Scientific, USA)
prior to storage at -20 °C.
Root DNA was extracted in triplicates using 50-100 mg of roots collected randomly
from well-mixed root fragments stored at -80 oC for the molecular analysis. Total DNA
was extracted from roots using the PowerPlant® pro DNA isolation kit (MO BIO, USA)
according to the manufacturer's guidelines. Extracted DNA was quantified and checked
for purity at A260/280 nm (Nanodrop,Thermo Fisher Scientific, USA) prior to storage
at −20 °C. Both rhizosphere soil and roots DNA samples were further diluted to 1ng/uL
for PCR amplification.
For the sequencing of both rhizosphere and root colonised bacteria, PCR was performed
on the 16S rRNA genes using V4/5 Domain specific primers (Appendix 3). Each
individual DNA sample had a unique Golay barcode added to the primer. In brief,
primers were labelled according to whether they have sequencing adaptors or not. For
example 806R_BACT is the untagged reverse bacterial V4/5 primer, whilst
806R_BACT_P1 is the tagged version. 515F-BACT is the forward untagged Bacterial
V4/5 primer. Whilst, 515_BACT_A_xx (barcode) is the forward tagged primer. For the
untagged and the reverse primers, a mixture was made (universal primer mix). The
universal primer mix was made in low Tris-EDTA (TE) buffer to obtain the desired
primer concentrations as 806R_BACT_P1 (final concentration: 4uM), 515F_BACT
(final concentration: 0.44 uM), 806R_BACT (final concentration: 0.44 uM), and there
was no barcoded forward tagged primer added in this mixture. The PCR was set up in
total volume of 20 μL. In brief, 18 uL of master mix per reaction [(H2O; 8.56 μL, BSA-
non-acetylated (50 ug uL-1); 0.24 μL, universal primer mix (4 uM); 1.20 μL, 5PRIME
HOT MasterMix (2.5x); 8 μL] was added with 1uL barcoded forward primer (5 uM)
and 1 uL DNA template (1.0 ng uL-1) to each reaction. One uL H2O was used for NTC
(no template control) and any barcode. The reaction conditions were 94 °C for 2 min
(initial denaturation) followed by 25 cycles of 94 oC, 45 S at (denaturation); 50 °C, 60 S
(annealing); 65 °C, 90 min (extension) and another 2 cycles of 94 oC, 45 S
(denaturation); 65 °C, 90 S (annealing/ extension) and a final extension step at 65°C for
10 min using a thermocycler (Techgene, Techne Inc, New Jersey, USA). All PCR
products (5 uL) were checked for size and specificity by electrophoresis on a 2%
agarose gel with a HyperLadder 1. Concentration of 1 uL of each reaction was
Chapter 6: Application of piggery waste compost to soil
135
measured using Qubit including the NTC. Baseline acceptable concentration was
established using the NTC, to determine pass/failure performance metrics (e.g. set
minimum concentration to 2x higher concentration than the NTC). The highest
concentration in the batch of amplicons to be pooled was determined. One uL of that
sample was used for pooling (e.g. 1 uL of 10.5 ng uL-1 reaction), and an equivalent
amount (in ng) of all the other reactions (e.g. 2uL of a 5.25ng uL-1 reaction).
Purification was done (LSBFG uses Ampure XP reagent, at a ratio of 1.2x volume), to
remove excess primers; elute in Low TE (10mM Tris, 0.1mM EDTA) or 10mM Tris-
HCl. The pool was quantified using Qubit to get an approximate concentration, which
were used to dilute the pool to the appropriate concentration range for the Agilent
Bioanalyzer. The sequencing was performed using 400 base-pair chemistry in
accordance with the manufactures protocol using the OneTouch Ion sphere particle
(ISP) emulsion and recovery. The samples were then washed and enriched prior to been
loaded onto the Semi-conductor chip. The enriched ISP was then sequenced using the
Ion Torrent Personal Genome Machine. The results were split into fasta and qual files,
and analysed using the QIIME pipeline (Caporaso et al. 2010). Assigning the
multiplexed reads to samples was done using default parameters (minimum quality
score = 25, minimum/maximum length = 130/220, no ambiguous base calls, remove
reverse primers and no mismatches allowed in the forward and reverse primer
sequences). The rest of the analysis was done as described in the Chapter 3 (section
3.2.4).
6.2.6 ANOVA and multivariate statistical analysis
All environmental variables were analysed by two-way ANOVA using the R statistics
package (V 2.13.0 © 2011 The R Foundation for Statistical Computing). Separation of
means was done using the least significant difference (LSD) method. Canonical
Correspondence Analysis (CCA) was used to model the changes in the community
profile of the different treatments to the measured variables (Jongman et al. 1995) to
explore which of these parameters best explained the differences in bacterial
communities between treatments. Triplicated samples for each treatment from the
second harvest (at 6 weeks) were used to generate a sequencing data matrix of relative
taxon abundances for both of the soil and root colonised bacteria. A corresponding
matrix of the plant variables (Table 6.5) and soil variables (Table 6.6) for each treatment
was also prepared in triplicates. Canonical correspondence analysis (CCA) performed
Chapter 6: Application of piggery waste compost to soil
136
using the software package Canoco v4.55 (Plant Research International © 2006). The
results were analysed to ascertain which covariates best explained the changes in
bacterial community profiles (Jenkins et al. 2009).
6.3. Results
6.3.1. Effect of soil amendments on plant growth, P uptake and AM colonization
The soil amendments showed significant differences in measured plant properties (Table
6.6). The shoot dry weight (DW) for all the soil amendments increased significantly
(P<0.001) with time and the highest shoot DW was observed in Balance50/Agras50 for
all the 3 harvests (Figure 6.1a; Table 6.6). The unamended soil (control) had the least
shoot DW for all the 3 harvests. Shoot and root DW following application of Agras100
was significantly lower at weeks 4 and 6 (Table 6.6) and had increased significantly
(P<0.001) at the third harvest (8 weeks). Root DW showed similar trends as shoot DW
except that application of Agras100 had the highest root dry weight at 8 weeks
compared to the other treatments (Figure 6.1b; Table 6.6). The total root length (m per
pot) for all the soil amendments increased significantly (P<0.001) over time and
significantly greater (P<0.001) total root length was observed for Balance50/Agras50 in
comparison with other soil amendments (Figure 6.1c). Adding pelletised compost in
combination with synthetic fertiliser at a low rate (50 kg ha-1) resulted in better plant
shoot and root growth compared to their single application at higher rates (100 kg ha-1).
There was better plant establishment and faster plant growth with Balance50/Agras50
compared to other treatments at each harvest. In contrast, poor plant establishment was
observed for the soil amended with Agras100, Balance100, and the control until the
second harvest (at 6 weeks). Thereafter, application of Agras100 increased tillering and
panicle formation compared to application of Balance100 and the control. At the last
harvest (8 weeks), the greatest plant growth was observed for the treatments with
application of Balance50/Agras50 followed by Agras100 (Appendix 4).
Cha
pter
6: A
pplic
atio
n of
pig
gery
was
te c
ompo
st to
soil
137
Tab
le 6
.6 E
ffec
t of d
iffer
ent s
oil a
men
dmen
ts o
n m
easu
red
plan
t pro
perti
es (s
hoot
and
root
dry
wei
ght,
tota
l roo
t len
gth,
sho
ot P
con
cent
ratio
n, A
M
colo
nise
d ro
ot le
ngth
, and
AM
col
onis
atio
n (%
)) a
fter e
ach
harv
estin
g tim
e. V
alue
s pre
sent
ed a
re m
eans
± st
anda
rd e
rror
of t
he m
ean,
n =
3.
Trea
tmen
ts
Har
vest
Sh
oot d
ry
wei
ght
Roo
t dry
w
eigh
t To
tal r
oot
leng
th
Plan
t P
Plan
t P/p
ot
Plan
t P
AM
co
loni
satio
n A
M C
olon
ised
ro
ot le
ngth
(g /p
lant
) (g
/ pla
nt)
(m/p
ot)
(g/k
g)
(mg/
pot)
(%)
(%)
(m/p
ot)
Agr
as
4 w
eeks
0.
10 ±
0.0
1g 0.
11 ±
0.0
2fg
5±0.
1f 14
54 ±
49.
2d 0.
13±0
.010
ef
1.45
±0.0
5d 23
±1.2
d 1±
0.1d
6
wee
ks
0.74
± 0
.14ef
0.
49 ±
0.0
4de
73±8
.8de
31
44 ±
62.
4bc
2.34
±0.4
9c
3.14
±0.0
6bc
16±3
.1e
11±1
.9d
8
wee
ks
3.44
± 0
.08b
1.98
± 0
.20a
182±
13.8
ab
2761
± 2
09.0
c 9.
50±0
.82a
2.76
±0.2
1c 2.
0±0.
1g 4±
0.3d
Bal
ance
4
wee
ks
0.11
± 0
.01g
0.17
± 0
.01fg
10
±0.7
f 13
42±
187.
1d 0.
15±0
.02ef
1.
34±0
.19d
50±4
.5c
5±0.
3d
6
wee
ks
0.34
± 0
.01fg
0.
32 ±
0.0
1ef
61±4
.1e
1560
± 1
75.4
d 0.
52±0
.05ef
1.
56±0
.18d
69±0
.4b
42±2
.8c
8
wee
ks
1.17
± 0
.15cd
0.
80 ±
0.0
6c 14
1±9.
2bc
1473
± 2
18.8
d 1.
76±0
.38cd
1.
47±0
.22d
73±3
.0b
103±
9.9a
Bal
ance
+Agr
as
4 w
eeks
0.
28 ±
0.0
2g 0.
29 ±
0.0
1ef
7.3±
0.7a
3954
± 3
70.9
a 1.
11±0
.10de
f 3.
96±0
.37a
11±2
.0ef
1±0.
5d
6
wee
ks
1.57
± 0
.04c
1.35
± 0
.04b
184±
11.8
a 35
95±
167.
0ab
5.66
±0.4
1b 3.
59±0
.17ab
4±
0.3fg
8±
0.2d
8
wee
ks
4.75
± 0
.41a
1.41
± 0
.17b
193±
35.7
f 18
91 ±
209
.3d
8.90
±0.8
2a 1.
89±0
.21d
6±
0.7g
12±2
.9d
Con
trol
4 w
eeks
0.
1 0
± 0.
01g
0.18
± 0
.01fg
8±
0.5cd
70
5 ±
48.8
e 0.
07±0
.01f
0.71
±0.0
5e 51
±2.7
c 4±
0.4d
6
wee
ks
0.48
± 0
.05fg
0.
56 ±
0.1
7cde
110±
26.7
de
1568
± 2
33.7
d 0.
74±0
.04de
f 1.
57±0
.23d
53
±2.3
c 57
±11.
1b
8
wee
ks
0.91
± 0
.17de
0.
60 ±
0.0
4cd
86±3
.7f
1363
± 1
23.3
d 1.
27±0
.30cd
e 1.
36±0
.12d
81
±1.3
a 69
±1.9
b
P va
lue
Tre
atm
ent
<
0.00
1 <
0.00
1 <
0.00
1 <
0.00
1 <
0.00
1 <
0.00
1 <
0.00
1 <
0.00
1
Har
vest
< 0.
001
< 0.
001
< 0.
001
< 0.
001
< 0.
001
< 0.
001
<0.0
1 <
0.00
1
Trea
tmen
t x H
arve
st
<
0.00
1 <
0.00
1 <
0.00
1 <
0.00
1 <
0.00
1 <
0.00
1 <
0.00
1 <
0.00
1 LS
D
T
reat
men
t
0.24
2 0.
157
24.4
00
324.
550
0.68
8 0.
325
3.76
8 7.
66
H
arve
st
0.
210
0.13
6 21
.130
28
1.06
9 0.
596
0.28
2 3.
263
6.63
0
T
reat
men
t x H
arve
st
0.
420
0.27
2 42
.260
56
2.13
8 1.
192
0.56
4 6.
527
13.2
7 M
eans
in th
e sa
me
colu
mn
follo
wed
by
the
sam
e le
tter a
re n
ot si
gnifi
cant
ly d
iffer
ent (
P= 0
.05)
.
Chapter 6: Application of piggery waste compost to soil
138
Figure 6.1 Effect of treatments on (a) shoot dry weight, (b) root dry weight, and (c) root length from 3 harvests (4, 6 and 8weeks) in soil amended with (1) Agras100 (2) Balance100 (3) Balance50/Agras50 (4) control. All treatments were done in triplicate and error bars indicate the standard error where n=3.
Chapter 6: Application of piggery waste compost to soil
139
The highest total P uptake (mg per pot) and P concentration (%) was observed for
Balance50/Agras50 (P<0.001) (Figure 6.2a and Figure 6.2b respectively). The total P
uptake increased over time for all the soil amendments (Figure 6.2a; Table 6.6), the
shoot P concentration (%) declined over time for the Balance50/Agras50 treatment
(Figure 6.2 b; Table 6.6).
AM colonised root length (m per pot) increased over the time and was highest for the
Balance100 treatment (P<0.001) followed by the control (Figure 6.3a). Conversely, AM
colonised root length was significantly lower with the application of either
Balance50/Agras50 or Agras100 (P<0.001) treatments, while they showed a slightly
increasing trend (during the 3 harvests). The AM colonisation % was higher in soil
amended with Balance100 (P<0.001) and in the unamended soil, while it was
considerably lower in soil amended with Balance50/Agras50, and Agras100 (Figure
6.3b; Table 6.6). Pelletised piggery waste alone enhanced AM colonisation % but
reduced it in combination with synthetic fertiliser or when synthetic fertiliser was
applied alone. Photos of AM colonization of roots of wheat under the different soil
amendments at the first harvest (4 weeks) are shown in Appendix 5.
6.3.2 Effect of different soil amendments on soil properties
Soil amendments showed significant differences in soil nutrients (Table 6.7). Soil
available P (Colwell P) was significantly higher (P<0.001) with application of
Balance50/Agras50 and decreased over time compared to the other soil amendments
(Table 6.7). Available P in the soil amended with Agras100 increased progressively
over time. On the other hand, available P in the soil amended with Balance100, and the
control were significantly lower compared to the other treatments. Furthermore, soil
available P (mg kg-1) was positively correlated with plant P uptake (mg kg-1) (R2 =0.83)
and negatively correlated with AM colonization (%) (R2 =0.59) (Figure 6.4a and Figure
6.4b respectively) and soil available P had a positive effect on plant P uptake and
negative effect on AM colonisation (%).
Chapter 6: Application of piggery waste compost to soil
140
Figure 6.2 Effect of treatments on (a) P uptake (mg/pot), and (b) P concentration (%) from 3 harvests (4, 6 and 8weeks) in soil amended with (1) Agras100 (2) Balance100 (3) Balance50/Agras50 (4) control. All treatments were done in triplicate and error bars indicate the standard error where n=3.
Chapter 6: Application of piggery waste compost to soil
141
Figure 6.3 Effect of treatments on (a) arbuscular mycorrhizal fungi colonised root length (m/pot), and their colonisation (%) from 3 harvests (4, 6 and 8weeks) in soil amended with (1) Agras100 (2) Balance100 (3) Balance50/Agras50 (4) control. All treatments were done in triplicate and error bars indicate the standard error where n=3.
Cha
pter
6: A
pplic
atio
n of
pig
gery
was
te c
ompo
st to
soil
142
Tab
le 6
.7 E
ffec
t of d
iffer
ent s
oil a
men
dmen
ts o
n so
il ph
ysic
o-ch
emic
al p
aram
eter
s af
ter e
ach
harv
estin
g tim
e (4
, 6, a
nd 8
wee
ks).
Val
ues
pres
ente
d ar
e m
eans
± st
anda
rd e
rror
of t
he m
ean,
n =
3.
Trea
tmen
ts
Har
vest
So
il P
Soil
TC
Soil
TN
Nitr
ate-
N
Am
mon
ium
-N
pH
EC
(mg
/ kg)
(%
) (%
) (µ
g/ g
soil)
(µ
g/ g
soil)
(C
aCl 2)
(µ
S/cm
)
Agr
as10
0 4
wee
ks
37.4
9 ±
1.33
e 4.
17±
0.13
ab
0.25
± 0
.01ab
cd
54.4
1 ±
3.00
b 10
4.64
± 1
1.24
a 4.
86±0
.03cd
39
0.33
±21.
79b
6
wee
ks
47.7
8 ±
1.13
c 3.
92 ±
0.0
5b 0.
26 ±
0.0
1abc
72.1
6 ±
2.19
a 11
0.71
± 4
.22a
4.77
±0.0
1de
434.
67±1
1.68
a
8
wee
ks
55.1
2 ±
2.02
b 3.
90 ±
0.0
8b 0.
26 ±
0.0
1ab
48.6
8 ±
0.72
c 63
.87
± 1.
60c
4.71
±0.0
1e 39
2.67
± 3.
93b
Bal
ance
100
4 w
eeks
34
.60
± 0.
48ef
4.03
± 0
.13b
0.24
± 0
.01bc
d 13
.63
± 1.
15e
1.58
± 0
.29d
4.96
±0.0
2bc
75.1
0±2.
34ef
6
wee
ks
37.7
6 ±
0.57
e 3.
91 ±
0.0
5b 0.
23 ±
0.0
1d 3.
82 ±
0.3
9f 0.
94 ±
0.1
9d 5.
06±0
.02a
99.1
0±2.
28e
8
wee
ks
35.5
4 ±
0.88
ef
4.10
± 0
.06ab
0.
25 ±
0.0
1abcd
0.
79 ±
0.0
5f 2.
59 ±
0.2
5d 5.
00±0
.01a
b 13
5.57
±15.
40d
Bal
ance
50/A
gras
50
4 w
eeks
64
.59
± 0.
76a
4.03
± 0
.11b
0.26
± 0
.01ab
c 47
.17
± 1.
93c
77.6
6 ±
1.79
b 4.
81±0
.01de
24
2.07
±6.7
8ef
6
wee
ks
51.3
7 ±
1.11
c 3.
98 ±
0.1
4b 0.
24 ±
0.0
1bcd
27.5
2 ±
2.42
d 8.
11 ±
1.2
3d 4.
83±0
.08d
248.
47±3
.18e
8
wee
ks
48.3
9 ±
0.84
c 4.
00 ±
0.1
1b 0.
24 ±
0.0
1bcd
12.2
1 ±
1.42
f 3.
71 ±
0.6
2d 4.
80±0
.08de
23
1.40
±10.
54d
Con
trol
4 w
eeks
42
.02
± 1.
15d
3.96
± 0
.04b
0.24
± 0
.01cd
14
.03
± 0.
51e
1.54
± 0
.25d
4.94
±0.0
1bc
67.9
3±2.
44f
6
wee
ks
37.1
2 ±
1.72
e 4.
33 ±
0.1
4a 0.
27 ±
0.0
1a 1.
62 ±
0.1
8f 1.
20 ±
0.1
2d 4.
99±0
.02ab
10
0.20
±3.3
8e
8
wee
ks
33.2
3 ±
1.84
f 4.
14 ±
0.0
7ab
0.26
± 0
.01ab
c 1.
23 ±
0.2
6f 0.
63 ±
0.1
4d 4.
93±0
.03bc
93
.17±
1.60
ef
P
valu
e T
reat
men
t
< 0.
001
0.24
5*
0.11
6*
< 0.
001
< 0.
001
< 0.
001
< 0.
001
Har
vest
< 0.
05
0.97
8*
0.28
8*
< 0.
001
< 0.
001
0.07
7*
< 0.
01
T
reat
men
t x H
arve
st
<
0.00
1 0.
073*
0.
099*
<
0.00
1 <
0.00
1 0.
127*
<
0.01
LSD
T
reat
men
t
2.10
9 0.
168
0.01
3 2.
574
5.10
0 0.
004
15.8
30
Har
vest
1.82
7 0.
145
0.01
1 2.
229
5.19
4 0.
051
13.7
20
T
reat
men
t x H
arve
st
3.
653
0.29
1 0.
022
4.45
9 10
.390
0.
102
27.4
30
Mea
ns in
the
sam
e co
lum
n fo
llow
ed b
y th
e sa
me
lette
r are
not
sign
ifica
ntly
diff
eren
t (P=
0.0
5).
Chapter 6: Application of piggery waste compost to soil
143
Figure 6.4 Relationship between (a) soil available P (mg/kg) and plant P uptake (mg/kg), and (b) soil available P (mg/kg) and AM fungal colonization (%). All treatments were done in triplicate and error bars indicate the standard error where n=3.
There was no significant difference in soil TC and soil TN content among the different
fertilizer amendments and harvesting time (Table 6.7). Both NO3--N and NH4
+-N (µg/g
soil) differed among soil amendments and harvesting time. NO3--N and NH4
+-N levels
in soil after each harvest were significantly higher in soil amended with Agras100
(P<0.001) and tended to increase up to 6 weeks and thereafter decreased. The second
highest NO3--N and NH4
+-N levels in soil after each harvest were observed with
Chapter 6: Application of piggery waste compost to soil
144
application of Balance50/Agras50 and generally decreased over time with plant
maturity. Compared to Agras100 and Balance50/Agras50, lower NO3--N and NH4
+-N
levels were observed for both Balance100, and control for each harvest. There was a
significant difference (P<0.001) between soil amendments on soil pH but no significant
difference with harvesting time. Further, addition of Balance100 slightly increased soil
pH compared to the Agras100 treatment. Soil amended with Agras100 had the highest
impact on EC. The second highest EC level for each harvest was observed with
application of Balance50/Agras50. The significant difference between soil amendments
for EC was more pronounced (P<0.001) than harvesting time (P<0.01).
6.3.3 Effect of soil amendments on rhizosphere and root colonising bacterial
population dynamics
The rhizosphere and root colonising bacterial population dynamics were determined for
the second harvest (6 weeks from sowing) for each soil amendment. Alpha rarefaction
(the distribution of number of sequences per sample) was performed using the observed
species metrics for both rhizosphere bacteria and plant root colonising bacteria (Figures
6.5a and 6.5b respectively). All the samples were normalised to a sequence number of
5000 where the samples were getting parallel with the x axis, revealing that the overall
bacterial diversity was well represented for both rhizosphere and root colonised bacteria
for all samples. However, at a sequence number of 5000, overall bacterial diversity of
rhizosphere was higher (OTUs 1044-1064) than for root colonising bacteria (OTUs 324-
415) for all soil amendments.
Bacterial diversity indicated by phylogenetic diversity chao1 richness and Shannon’s
index for both rhizosphere and root colonising bacteria is shown in Table 6.8. Overall,
species richness and diversity of the bacterial population were higher in the unamended
soil than for the amended soils. Furthermore, addition of pelletised piggery compost
alone (Balance100) altered the species richness and diversity for both rhizosphere and
root colonising bacteria considerably more than for the synthetic fertiliser alone
amendment. However, pelletised compost applied in combined with synthetic fertiliser
caused a loss in bacterial diversity for both rhizosphere bacteria and root colonising
bacteria but not as much as when soils received only synthetic fertiliser.
Relative abundance of bacterial community composition in rhizosphere bacteria and
root colonising bacteria for different soil amendments is shown in Figure 6.6.
Chapter 6: Application of piggery waste compost to soil
145
Figure 6.5 Alpha diversity rarefaction plots of phylogenetic diversity for (a) rhizosphere soil bacteria, and (b) root colonising bacteria. Value represents the mean of triplicate determinations. Relative mean abundance between different soil amendments for rhizosphere bacteria
was fairly stable (Figure 6.6a) and dominated by phyla Proteobacteria (26±0.3%),
Acidobacteria (17±1.8%), Actinobacteria (14±1.0%), Gemmatimonadetes (5±0.4%),
Verrucomicrobia (6±1.0%), Bacteroidetes (5±0.4%), Chloroflexi (3±0.5%), and
Firmicutes (3±0.5%). The observed slight differences between the soil amendments
were mainly associated with the phyla Acidobacteria (by 1.8%) and Actinobacteria (by
1.0%) in soil amended with Balance100 implying that that adding pelletised compost
alone (Balance100) influenced in a slight increase in the abundance of Acidobacteria
and Actinobacteria in the rhizosphere but there was little change associated with the
other soil amendments for the rhizosphere bacteria.
Chapter 6: Application of piggery waste compost to soil
146
Table 6.8 Bacterial diversity of rhizosphere soil bacteria and plant roots colonising bacteria indicated by phylogenetic diversity, Chao1 richness, and Shannon’s index. (Calculation of richness and diversity estimators was based on OTU tables rarefied to the same sequencing depth; the lowest one of total sequencing reads: 5000).
Treatments Phylogenetic diversity Chao richness Shannon’s
index Rhizosphere soil bacteria Balance100 74.93±0.55 2104.71±198.92 8.51±0.03
Agras100 72.64±1.51 1966.14±126.61 8.48±0.08
Balance50/Agras50 70.92±1.01 1781.40±75.30 8.41±0.04
Control 78.75±3.12 2156.04±30.31 8.64±0.12 Root colonised bacteria Balance100 26.34±5.39 633.09±182.88 5.34±0.41
Agras100 20.01±3.99 511.94±117.24 4.41±0.49
Balance50/Agras50 21.66±1.28 529.95±17.36 4.54±0.27
Control 27.37±1.00 758.64±71.48 5.56±0.36
In contrast to the rhizosphere bacteria, the relative mean abundance between different
soil amendments for root colonising bacteria was altered markedly (Figure 6.6a) by soil
amendments and was dominated by the phyla Proteobacteria (29±4.5%),
Cyanobacteria/Chloroplast (12±4.6%), Bacteroidetes (11±5.9%), Actinobacteria
(9±3.0%), and Acidobacteria (1±0.1%) (Figure 6.6b). The increase in Proteobacteria
(5%), Bacteroidetes (6%), and Actinobacteria (3.0%), and decrease of
Cyanobacteria/Chloroplast (5%) were mainly associated with Balance100.
A higher abundance of Cyanobacteria/Chloroplast (16%) was observed for Agras100
and Balance50/Agras50 compared to both Balance100 (7%) and control (9%).
Actinobacteria and Bacteroidetes were relatively less abundant in Agras100 (8% and
3% respectively) and Balance50/Agras50 (5% and 11%) with respect to Balance100
(12% and 17%) and control (11% and 13%). Adding synthetic fertiliser alone or in
combination with pelletised compost increased the abundance of
Cyanobacteria/Chloroplast and decreased the abundance of Actinobacteria and
Bacteroidetes on roots. Comparison of Figures 6.6a and 6.6b shows that the bacterial
community composition and diversity in the rhizosphere soil bacteria and root
colonising bacteria appeared to differ significantly within and between the soil
amendments.
Chapter 6: Application of piggery waste compost to soil
147
Figure 6.6 Relative abundance of (a) rhizosphere bacteria and, (b) root colonised bacteria at phylum level by different soil amendments. Value represents the mean of triplicate determinations. (Relative abundance <1% is ignored)
Chapter 6: Application of piggery waste compost to soil
148
Relative changes in the bacterial abundance and diversity in both rhizosphere soil and
root colonising bacteria up to genus level are shown in Table 6.9. According to the
mean abundance of bacteria between soil amendments for the rhizosphere soil bacteria,
the bacterial communities between different treatments were very similar and the most
dominant groups (>5%) across all soil amendments were Acidobacteria (21±1.6%),
Acidimicrobiales (13±0.9%), Gemmatimonas (5±0.5%), and Sphingomonadaceae
(5±0.2%). In contrast, root colonising bacteria showed a considerable fluctuation
between soil amendments mainly by Cyanobacteria/Chloroplast (5%), Pseudomonas
(4%), Sphingobacteriales (5%). Cyanobacteria/Chloroplast was the most dominant root
colonising bacteria in both Agras100 and Balance50/Agras50 compared to other soil
amendments. Pseudomonas was the second most dominant class of root colonised
bacteria among soil amendments and their relative abundance was higher in
Balance50/Agras50 (15±3%). Pseudonocardiacea and Sphingobacteria were markedly
lower in soil amended with both Agras100 and Balance50/Agras50 compared to other
soil amendments. Overall, the effect of soil amendments on root colonising bacterial
population dynamics was more prominent than on rhizosphere bacteria.
6.3.3.1 Changes in the rhizosphere bacterial community profile of the different
treatments to the measured plant and soil variables
Apart from the changes in the major taxa, there were some changes in the abundance of
minor taxa up to genus level associated with the changes in the environmental factors
among different soil amendments. To explore this further, CCA analysis was used to
examine the relationship between plant and soil variables and species composition of
both rhizosphere bacteria and root colonising bacteria (Figure 6.7 and Figure 6.8).
Bacterial community composition for each treatment, or individual taxa distribution for
soil or plant variables are given in Figures 6.7a, 6.8a and Figures 6.7b, 6.8b
respectively. Differences in bacterial composition of rhizosphere bacteria between soil
amendments by differences in plant and soil variables are shown in Figure 6.7a. The
first 2 axes of the CCA analysis explained 46 % of the total variance. Triplicate samples
of Balance100 and control are grouped closer to each other and positioned along a
vector associated with positive correlations with pH, AM colonisation % and the
colonised root length (Figure 6.7a).
Cha
pter
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atio
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149
Tab
le 6
.9 R
elat
ive
abun
danc
e of
(a) r
hizo
sphe
re b
acte
ria a
nd, (
b) ro
ot c
olon
isin
g ba
cter
ia u
p to
gen
us le
vel b
y di
ffer
ent s
oil a
men
dmen
ts.
Val
ue re
pres
ents
the
mea
n of
trip
licat
e de
term
inat
ions
. (R
elat
ive
abun
danc
e <1
% is
igno
red)
Rhi
zosp
here
soi
l bat
eria
Agr
as10
0*B
alan
ce10
0*B
alan
ce50
+Agr
as50
*C
ontr
ol*
Mea
n**
SD**
Bact
eria;
Aci
doba
cter
ia;A
cido
bact
eria
2222
1921
211.
6Ba
cter
ia;A
ctin
obac
teria
;Act
inob
acte
ria;A
cidi
mic
robi
ales
1412
1413
130.
9Ba
cter
ia;G
emm
atim
onad
acea
e;G
emm
atim
onas
65
65
50.
5Ba
cter
ia;Pr
oteo
bact
eria;
Alp
hapr
oteo
bact
eria;
Sphi
ngom
onad
ales;
Sphi
ngom
onad
acea
e5
55
55
0.2
Bact
eria;
Prot
eoba
cter
ia;A
lpha
prot
eoba
cter
ia;O
ther
43
34
30.
5Ba
cter
ia;Pr
oteo
bact
eria;
Alp
hapr
oteo
bact
eria;
Rhi
zobi
ales
43
34
40.
5Ba
cter
ia;V
erru
com
icro
bia
46
64
51.
0Ba
cter
ia;Ba
cter
oide
tes;
Sphi
ngob
acte
ria;S
phin
goba
cter
iales
44
45
40.
5Ba
cter
ia;Pr
oteo
bact
eria;
Beta
prot
eoba
cter
ia3
13
22
0.9
Bact
eria;
Prot
eoba
cter
ia;G
amm
apro
teob
acte
ria;P
seud
omon
adale
s;Ps
eudo
mon
adac
eae;
Pseu
dom
onas
34
52
41.
2Ba
cter
ia;Pr
oteo
bact
eria;
Gam
map
rote
obac
teria
;Oth
er2
12
22
0.2
Bact
eria;
Chl
orof
lexi;K
tedo
noba
cter
ia2
23
22
0.3
Bact
eria;
Firm
icut
es;B
acilli
;Bac
illales
22
33
20.
5Ba
cter
ia;Pr
oteo
bact
eria;
Delt
apro
teob
acte
ria1
31
32
0.8
Roo
t con
loni
sed
bact
eria
Agr
as10
0*B
alan
ce10
0*B
alan
ce50
+Agr
as50
*C
ontr
ol*
Mea
n**
SD**
Bact
eria;
Cya
noba
cter
ia/C
hlor
oplas
t;Chl
orop
last;C
hlor
oplas
t;Chl
orop
last;S
trept
ophy
ta16
716
912
5Ba
cter
ia;Pr
oteo
bact
eria;
Pseu
dom
onas
1314
156
124
Bact
eria;
Aci
doba
cter
ia;A
cido
bact
eria
41
11
21
Bact
eria;
Prot
eoba
cter
ia;G
amm
apro
teob
acte
ria;X
anth
omon
adale
s3
14
23
1Ba
cter
ia;Pr
oteo
bact
eria;
Beta
prot
eoba
cter
ia;Bu
rkho
lder
iales
34
36
42
Bact
eria;
Bact
eroi
dete
s;Sp
hing
obac
teria
;Sph
ingo
bact
erial
es2
134
107
5Ba
cter
ia;A
ctin
obac
teria
;Act
inob
acte
ria;A
ctin
omyc
etale
s;St
rept
omyc
etac
eae
22
23
21
Bact
eria;
Prot
eoba
cter
ia;A
lpha
prot
eoba
cter
ia;Sp
hing
omon
adale
s;Sp
hing
omon
adac
eae;
Sphi
ngom
onas
23
12
21
Bact
eria;
Act
inob
acte
ria;A
ctin
obac
teria
;Act
inom
ycet
ales;
Pseu
dono
card
iacea
e1
71
53
3Ba
cter
ia;Pr
oteo
bact
eria;
Alp
hapr
oteo
bact
eria;
Rhi
zobi
ales;
Rhi
zobi
acea
e;R
hizo
bium
14
35
32
Bact
eria;
Prot
eoba
cter
ia;A
lpha
prot
eoba
cter
ia;R
hizo
biale
s;Br
adyr
hizo
biac
eae;
Brad
yrhi
zobi
um0
21
11
1Ba
cter
ia;Ba
cter
oide
tes;
Flav
obac
teria
;Flav
obac
teria
les;F
lavob
acte
riace
ae;C
hrys
eoba
cter
ium
03
62
33
*ab
unda
nce
of e
ach
soil
amen
dmen
t rep
rese
nts
mea
n va
lue
of tr
iplic
ate
dete
rmin
atio
ns**
mea
ns a
nd s
tand
ard
devi
atio
n be
twee
n th
e di
ffere
nt s
oil a
men
dmen
ts
Cha
pter
6: A
pplic
atio
n of
pig
gery
was
te c
ompo
st to
soil
150
Fi
gure
6.7
Can
onic
al c
orre
spon
denc
e an
alys
is (C
CA
) bip
lot s
how
ing
the
rela
tions
hip
betw
een
(a) d
iffer
ent s
oil a
men
dmen
t and
mea
sure
d pl
ant
and
soil
varia
bles
b)
indi
vidu
al t
axa
dist
ribut
ions
with
mea
sure
d pl
ant
and
soil
varia
bles
(b)
for
rhi
zosp
here
soi
l ta
ken
from
pot
ex
perim
ent u
nder
diff
eren
t fer
tilis
er tr
eatm
ents
(
) at
6 w
eeks
. A
rrow
s re
pres
ent t
he m
easu
red
varia
bles
[pH
, NH
3, C
olw
ell P
, Pla
nt P
up
take
, ele
ctric
al c
ondu
ctiv
ity (E
C),
Shoo
t and
root
DW
, AM
col
onis
ed ro
ot le
ngth
(RL)
, and
AM
col
onis
atio
n %
]. Tr
iang
les
(▲) o
n th
e gr
aph
(b) r
epre
sent
indi
vidu
al b
acte
rial t
axa.
Tax
onom
ic id
entit
ies f
or th
e ba
cter
ial s
eque
nces
are
giv
en in
Tab
le 6
.10.
Chapter 6: Application of piggery waste compost to soil
151
Balance50/Agras50 was positively associated with shoot DW, root DW, soil available
P, and plant P uptake and negatively associated with pH, AM colonisation %, and AM
colonised root length. Also it showed that treatment of Agras100 was positively
correlated with soil nitrate-N, ammonium-N, and EC. A second biplot (Figure 6.7b) was
constructed using the individual taxa scores to assess the contribution of individual taxa
to the scatter seen in Figure 6.7a (phylogenetic identities of the taxa are shown in Table
6.10). There were marked changes in the relative abundance of some minor bacterial
taxa between soil amendments whose distributions and responses were particularly
closely correlated with the plant and soil variables (Figure 6.7b, Table 6.10). The minor
taxa belonging to Edaphobacter (#1), Georgenia (#4), Arthrobacter (#9), Actinoplanes
(#10), Mycobacterium (#12), Actinomycetales (#15), Streptomycetaceae (#17),
Rhizobium (#38), Oxalobacteraceae (#47), and Nitrosospira (#48) responded to the
addition of Balance50/Agras50 whilst Janthinobacterium (#46), Burkholderia (#44),
Xanthomonadaceae (#56), Rhizomicrobium (#33), were more influenced by Agras100.
Moreover, Nocardioides (#14), Skermanella (#40), Steroidobacter (#55), Myxococcales
(#51), Acidimicrobiales (#3) had responded to the treatments of both Balance100 and
the control.
6.3.3.2 Changes in the root colonising bacterial community profile of the different
treatments to the measured plant and soil variables
The CCA biplot for plant root colonising bacteria (Figure 6.8a) showed an opposite
trend to the CCA biplot of soil rhizosphere bacteria (Figure 6.7b). The first 2 axes of the
CCA analysis explained 67 % of the total variance. More clear treatment separation was
observed for root colonising bacterial taxa between soil amendments by differences in
plant and soil variables (Figure 6.8a). In addition to the effect of soil amendments on the
relative abundance of major phyla (Figure 6b) for root colonising bacteria, considerable
influence was observed on some minor bacterial taxa (Figure 6.8b, Table 6.11). For
example Edaphobacter (#1), Pseudomonas (#41), Cyanobacteria/Chloroplast (#24),
Xanthomonadaceae (#45) responded by the addition of Balance50/Agras50.
Conversely, Acidobacteria group 2 (#3), Microbacteriaceae (#6), Bacillales (#25),
Burkholderia (#37), Acidobacteria group 1 (#2), Dyella (#43), Rhodanobacter (#46)
responded by the addition of Agras100.
Chapter 6: Application of piggery waste compost to soil
152
Table 6.10 Taxonomic identities for the CCA biplot showing the relationship between measured variables and individual taxa distributions for rhizosphere bacteria Code Taxa Code Taxa
1 Acidobacteria; Edaphobacter 26 Gemmatimonadetes; Gemmatimonas
2 Acidobacteria; group 1 27 Bacteria;Other
3 Acidobacteria;group 2 28 Alphaproteobacteria;Caulobacteraceae
4 Acidobacteria; Group 3 29 Alphaproteobacteria; Bosea
5 Actinobacteria;Catenulispora 30 Alphaproteobacteria; Bradyrhizobium
6 Actinobacteria;Microbacteriaceae 31 Proteobacteria; Devosia
7 Actinobacteria;Micromonosporaceae 32 Alphaproteobacteria; Rhizobiales
8 Actinobacteria;Mycobacterium 33 Alphaproteobacteria; Phyllobacteriaceae
9 Actinobacteria;Kribbella 34 Alphaproteobacteria; Rhizobium
10 Actinobacteria;Amycolatopsis 35 Alphaproteobacteria; Inquilinus
11 Actinobacteria;Kutzneria 36 Alphaproteobacteria; Sphingomonadaceae
12 Actinobacteria;Pseudonocardiaceae 37 Betaproteobacteria; Burkholderia
13 Actinobacteria; Pseudonocardia 38 Betaproteobacteria; Ralstonia
14 Actinobacteria;Saccharothrix 39 Betaproteobacteria; Comamonadaceae
15 Actinobacteria;Streptomyces 40 Betaproteobacteria; Oxalobacteraceae
16 Actinobacteria; Nonomuraea 41 Gammaproteobacteria; Pseudomonas
17 Actinobacteria; Thermomonosporaceae 42 Gammaproteobacteria; Dokdonella
18 Actinobacteria; Solirubrobacterales 43 Gammaproteobacteria; Dyella
19 Bacteroidetes; Chryseobacterium 44 Gammaproteobacteria; Luteibacter
20 Bacteroidetes; Chitinophagaceae 45 Gammaproteobacteria; Xanthomonadaceae
21 Bacteroidetes; Dyadobacter 46 Gammaproteobacteria; Rhodanobacter
22 Bacteroidetes; Sphingobacteriaceae 47 Gammaproteobacteria; Stenotrophomonas
23 Chloroflexi; Ktedonobacter 48 Bacteria; TM7
24 Cyanobacteria/Chloroplast 49 Verrucomicrobia
25 Firmicutes; Bacillales
The distribution of Kribbella (#9), Saccharothrix (#14), Bradyrhizobium (#30),
Comamonadaceae (#39), Bosea (#29), Phyllobacteriaceae (#33), Rhizobium (#34) was
influenced by the addition of Balance100. Overall, CCA analyses of both rhizosphere
bacteria and root colonising bacteria indicated that addition of soil amendment has
changed the abundance and community composition of bacterial taxa which in turn has
caused to change plant and soil conditions.
Cha
pter
6: A
pplic
atio
n of
pig
gery
was
te c
ompo
st to
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153
Fi
gure
6.8
Can
onic
al c
orre
spon
denc
e an
alys
is (C
CA
) bip
lot s
how
ing
the
rela
tions
hip
betw
een
(a) d
iffer
ent s
oil a
men
dmen
t and
mea
sure
d pl
ant a
nd s
oil v
aria
bles
b) i
ndiv
idua
l tax
a di
strib
utio
ns w
ith m
easu
red
plan
t and
soi
l var
iabl
es (b
) for
root
col
onis
ing
bact
eria
in s
oil t
aken
fr
om p
ot e
xper
imen
t und
er d
iffer
ent f
ertil
iser
trea
tmen
ts (
) at 6
wee
ks.
Arr
ows
repr
esen
t the
mea
sure
d va
riabl
es [p
H, N
H3,
Col
wel
l P,
Plan
t P u
ptak
e, e
lect
rical
con
duct
ivity
(EC
), Sh
oot a
nd ro
ot D
W, A
M c
olon
ised
root
leng
th (R
L), a
nd A
M c
olon
isat
ion
%].
Tria
ngle
s (▲
) on
the
grap
h (b
) rep
rese
nt in
divi
dual
bac
teria
l tax
a. T
axon
omic
iden
titie
s for
the
bact
eria
l seq
uenc
es a
re g
iven
in T
able
6.1
1.
Chapter 6: Application of piggery waste compost to soil
154
Table 6.11 Taxonomic identities for the CCA biplot showing the relationship between measured variables and individual taxa distributions for root colonising bacteria
Codes Taxa Codes Taxa 1 Acidobacteria;Edaphobacter 31 Bacteria;Other 2 Acidobacteria 32 Planctomycetes;Zavarzinella 3 Actinobacteria;Acidimicrobiales 33 Alphaproteobacteria;Rhizomicrobium 4 Actinobacteria;Georgenia 34 Alphaproteobacteria;Phenylobacterium 5 Actinobacteria;Catenulispora 35 Alphaproteobacteria;Other 6 Actinobacteria;Fodinicola 36 Alphaproteobacteria;Rhizobiales 7 Actinobacteria;Geodermatophilaceae 37 Alphaproteobacteria;Mesorhizobium 8 Actinobacteria;Microbacteriaceae 38 Alphaproteobacteria;Rhizobium 9 Actinobacteria;Arthrobacter 39 Alphaproteobacteria;Rhodospirillales 10 Actinobacteria;Actinoplanes 40 Alphaproteobacteria;Skermanella 11 Actinobacteria;Micromonosporaceae 41 Alphaproteobacteria;Porphyrobacter 12 Actinobacteria;Mycobacterium 42 Alphaproteobacteria;Novosphingobium 13 Actinobacteria;Kribbella 43 Alphaproteobacteria;Sphingomonadaceae 14 Actinobacteria;Nocardioides 44 Betaproteobacteria;Burkholderia 15 Actinobacteria;Actinomycetales 45 Betaproteobacteria;Burkholderiales 16 Actinobacteria;Pseudonocardiaceae 46 Betaproteobacteria;Janthinobacterium 17 Actinobacteria;Streptomycetaceae 47 Betaproteobacteria;Oxalobacteraceae 18 Actinobacteria;Streptomyces 48 Betaproteobacteria;Nitrosospira 19 Actinobacteria;Thermomonosporaceae 49 Betaproteobacteria;Other 20 Actinobacteria;Actinobacteria other 50 Betaproteobacteria;Cystobacteraceae 21 Actinobacteria;Solirubrobacterales 51 Betaproteobacteria;Myxococcales 22 Armatimonadetes;Armatimonadetes 52 Deltaproteobacteria;Other 23 Bacteroidetes 53 Gammaproteobacteria;Other 24 Chloroflexi 54 Gammaproteobacteria;Pseudomonas 25 Bacteria;Cyanobacteria/Chloroplast 55 Gammaproteobacteria;Steroidobacter 26 Firmicutes;Bacilli 56 Gammaproteobacteria;Xanthomonadaceae 27 Firmicutes;Clostridium 57 Proteobacteria;Other 28 Firmicutes;Turicibacter 58 TM7 29 Gemmatimonadetes;Gemmatimonas 59 Verrucomicrobia 30 Nitrospira;Nitrospira
Chapter 6: Application of piggery waste compost to soil
155
6.4. Discussion
6.4.1 Effects of soil amendments on plant growth and soil fertility
Application of pelletised piggery compost (Balance®), in combination with synthetic P
fertiliser (Agras®) at a lower than commercially recommended rate (50 kg ha-1 each) on
plant growth and soil nutrient improvement was evaluated in comparison to their single
application at the commercial application rate (100 kg ha-1). Balance50/Agras50 was the
most effective soil amendment on wheat growth and soil fertility compared to their
single applications (100 kg ha-1). Adding Balance50/Agras50 resulted in significant P
uptake in plants and P availability in soils and showed the best plant growth in terms of
shoot and root dry weight.
The reason for achieving a better growth with Balance50/Agras50, in comparison with
Agras100 alone, could be associated with the increased early uptake of nutrients and
presence of adequate nutrient supply over time in the soil amended with
Balance50/Agras50. Whereas, Agras100 showed poor seeding establishment and low
growth until 6 weeks and thereafter a rapid growth was observed. Agras® contains
relatively high concentrations of ammonium nitrogen (http://www.csbp-
fertilisers.com.au) and here it was observed higher amounts of NH4+-N after adding
Agras at a higher rate (100 kg ha-1). It has been shown that root growth is sensitive to
excess NH4+-N and its toxic effects are common in higher plants (Krupa 2003; Li et al.
2010). Hence, slow growth at early stages of the Agras100 could be attributed to the
excess supply of NH4+-N, which might be toxic for immature plant roots due to the
close proximity of fertilizer to the seed. However in this study the effect was diluted
over time with the plant maturity and tended to perform well after 6 weeks, leading
Agras100 as the second best soil amendment.
Wide range of studies investigated combined nutrient management by recycling animal
manures or crop residues as the organic portions of the organic-inorganic blend
(MacDonald et al. 2011; Sommer et al. 2013). The application of an organic-inorganic
compound fertilizer can slowly release nutrients into soil, promote plant growth and
increase crop yield (Shi et al. 2003; Zhao et al. 2014). The Balance50/Agras50 seemed
to be act as a slow releasing fertiliser in which the nutrients are gradually make
available for the growing plants. The differences in biomass yield between the different
soil amendments in this study are most likely to correspond to the differences in nutrient
release from the applied soil amendments. Therefore, the blend of the reduced rate of
Chapter 6: Application of piggery waste compost to soil
156
synthetic P fertiliser and pelletised piggery compost (50 kg ha-1 each) in close proximity
to seeds during planting would be a more effective soil amendment than applying the
synthetic P fertiliser alone or pelletised piggery compost alone at 100 kg ha-1.
6.4.2 Effect of soil amendments on beneficial bacteria associated with rhizosphere
soil and root surface
Favourable alterations in the bacterial community diversity and abundance associated
with rhizosphere soil and root surface by the Balance50/Agras50 is an attractive
outcome of the use of pelletised piggery compost in combination with reduced rate of
synthetic P fertilisers. Few studies have investigated bacterial colonisation of the root
surface of wheat influenced by different soil/plant or environmental factors (Germida et
al. 1998; Rengel et al. 1998; Turnbull et al. 2001; Meyer et al. 2013). Here, it was
observed that the bacterial community composition of both root colonising bacteria and
rhizosphere bacteria was associated with soil amendment influences on the soil
available P, plant P uptake, and shoot and root dry weights. Soil available P is an
important covariate in determining the distribution of bacterial taxa (Figure 6.7a and
6.8a). The bacterial community composition under the Balance50/Agras50 could have
involved in increasing the P nutrient in soil and improving their accessibility to plant
uptake. Pseudomonas, a known plant growth-promoting bacteria and one of the best
root colonizers (Lugtenberg et al. 2001) were found to be highly abundant on the roots
of wheat plant treated with Balance50/Agras50 comparted to the rhizosphere soil of the
same treatment. It is possible that Pseudomonas could play an important role in
increasing soil available P (via solubilising and mineralising P), and facilitating plant P
uptake (via root colonisation) in this soil amendment. P-solubilising and P-mineralising
capacity of Pseudomonas were also reported previously (Richardson and Hadobas 1997;
Jorquera et al. 2008b; Tao et al. 2008). Apart from the major taxa, some minor taxa
identified in this study (Arthrobacter, Rhizobium, Streptomycetaceae, Actinoplanes)
responded to the addition of Balance50/Agras50. These groups have previously been
shown to be involved in P transformation. For example, Arthrobacter (Rodrı́guez and
Fraga 1999; Chen et al. 2006), Rhizobium (Rodrı́guez and Fraga 1999; Chen et al. 2006;
Harvey et al. 2009), Streptomycetaceae (Ragot et al. 2013), Actinoplanes (El-Tarabily et
al. 2008) are known P-solubilisers. Compost amendment was also enhanced by
phosphate solubilizing bacteria Arthrobacter (Wickramatilake et al. 2011). This implies
that there is potential for P mineralisation and solubilisation in soils ameneded with
pelletised piggery compost in combination with low rate of synthetic P fertilisers and
Chapter 6: Application of piggery waste compost to soil
157
therefore more P available to plants which may account for the increased shoot growth.
Thus, bacteria associated with rhizosphere soil and colonising roots under the
Balance50/Agras50 appear to be involved in increasing P availability in soil, and plant P
uptake, thereby enhancing plant growth, which is consistent with the first hypothesis.
The bacterial community composition of rhizosphere soil bacteria only slightly shifted
compared to root colonising bacteria by the soil amendments. Some differences in
composition and diversity were also observed between the rhizosphere bacteria and root
colonising bacteria within and between the soil amendments. The relationship between
plant and soil variables and species composition for different soil amendments showed
that the bacterial community composition was positively linked to the root growth,
indicating an expected link between bacterial populations and root exudates. The most
dominant classes (>5%) of the root associated bacteria, across all soil amendments were
Cyanobacteria/Chloroplast, Gammaproteobacteria, Actinobacteria, and
Alphaproteobacteria. The reason for the higher abundance of
Cyanobacteria/Chloroplast and Gammaproteobacteria, associated with roots of both
Agras100 and Balance50/Agras50 could be related to the fast growing response of those
groups (Copiotrophic bacteria) in soils with high nutrients availability in rhizosphere.
Apart from the changes in these major classes, there were some changes in the
abundance of minor taxa on the genus level due to the variations on soil amendments.
This indicates that minor taxa may have more sensitive responses to different soil
amendments due to their own characteristics (Zhao et al. 2014).
Slightly higher pH was observed when soil was amended with pelletised pig compost
(Balance100) or in unamended soil compared to chemical fertiliser alone (Agras100) or
in combination with the pelletised pig compost (Balance50/Agras50). Some chemical
fertilizers are already known to acidify the soil by accumulating hydrogen cations
(Barak et al. 1997; van Diepeningen et al. 2006). Changes in soil pH could alter the
bacterial community composition (Lauber et al. 2009) and percentage of AM
colonisation (Coughlan et al. 2000). The difference between soil amendments in terms
of rhizosphere soil bacteria was mainly observed for Balance100, which was mainly
associated with the slight increase of Acidobacteria (by 1.8 %), and Verrucomicrobia
(by 1.0 %), and decrease of Actinobacteria (1.0%). In contrast, marked increase of
Proteobacteria (5 %), Bacteroidetes (6 %), Actinobacteria (3.0 %), and decrease of
Cyanobacteria/Chloroplast (5 %) in root associated bacteria were observed for soil
amended with Balance100 compare to the other soil amendments. These findings
Chapter 6: Application of piggery waste compost to soil
158
roughly correspond with previous studies demonstrating that the bacterial community
composition significantly correlated with differences in soil pH and largely driven by
changes in the relative abundances of Acidobacteria, Actinobacteria, and Bacteroidetes
across the range of soil pHs (Lauber et al. 2009). In addition, it was found that
Proteobacteria and Actinobacteria were more sensitive to pH variation (Li et al. 2012)
and were generally predominant in organic farming systems (Fließbach et al. 2007; Li et
al. 2012). It has been previously reported that some members of Actinobacteria such as
Streptomyces, Acidimicrobium, Actinospica, Arthrobacter, Norcardia, Micrococcus and
Mycobacterium were particularly associated with manures and organic compost added
soils (Atagana 2004; Jenkins et al. 2009). Inorganic fertiliser had a major impact on the
Actinobacteria community structure, since relative amount of some Actinobacteria
groups were significantly reduced in soils amended with inorganic N (Jenkins et al.
2009). Similar results were observed on the significant reduction of Actinobacteria with
the addition of synthetic fertiliser, as the abundance of Actinobacteria was
comparatively low in Balance50/Agras50 and Agras100 amendments compared to
Balance100 and control.
6.4.3 Effect of soil amendments on AM fungal colonisation
Although the addition of Balance50/Agras50 linked to the increases in the relative
abundance of some major and minor taxa that are previously recognised as P
mineralisers and P solubilisers, it reduced the colonisation percentage of AM fungi as a
consequence. This could be due to the high availability of P in this treatment which
leads to reduce the AM colonisation. Mycorrhizal fungi prefer low- nutrient soils (P and
N) or in soils receiving slow release fertilisers (e.g. pelletised compost). AM
colonisation (%) was shown to be decreased by both increasing N and P (Gazey et al.
2004). There was a strong negative relationship to soil available P level and AM
colonisation. The trend of low AM fungal colonisation (%) under sufficient P level for
plant growth in soil was consistent with previous studies (Graham and Abbott 2000;
Raiesi and Ghollarata 2006). Relationship between plant and soil variables and species
compositions between soil amendments (Figure 6.7a and 6.8a) revealed that the
presence of adequate P and N in soil causes a reduction in AM colonisation (%).
Therefore, the hypothesis that statement on the increase of P in soil by fertilisers that
would lead to a decrease in the percentage AM colonisation (%) was accepted. On the
other hand, the corresponding hypothesis statement on increasing the length of root
colonised by AM fungi was rejected.
Chapter 6: Application of piggery waste compost to soil
159
6.5. Conclusions
Application of pelletised piggery compost in combination with inorganic P fertiliser at
low rates enhanced wheat growth and soil fertility compared to their conventional
application rates. The bacterial community composition for this soil amendment was
associated with an increase in soil available P, plant P uptake, and shoot and root dry
weights. These responses were most likely to be the reactions of plant and bacterial
communities to the changes in soil nutrient levels by effective blending of pelletised
piggery compost with inorganic fertilisers.
Banding pelletised piggery compost with chemical fertiliser at a low rate with no yield
reduction is an attracting outcome and an effective strategy for sustainable nutrient
management in agriculture. Improved understanding of these interactions can be also
use to optimise P-use efficiency by identifying suitable blends of other animal waste
products for soil amendment. The reduced volumes and associated transport and
spreading costs should provide budget savings. Therefore, application of low rates of
fertilisers would be financially and technically viable for the farmers. The potential for
using piggery waste as a component of P fertiliser could also provide an additional
income for pig farmers while reducing the amount of on-farm waste accumulation. As
for a future direction, field trials are necessary to verify the data for other conditions,
develop strategies for the efficient and practical management of nutrient resources, and
expand on the interpretation that causes the observed effects.
Chapter 7: General Discussion
160
CHAPTER 7
General Discussion
7.1 Summary of the work performed
7.1.1 Overview
This thesis sought to explore bacteria involved in P transformation in a model piggery
waste treatment process (a detail treatment process is found in Appendix 1). The main
objective was to characterise taxa involved in P transformation pathways (P
mineralisation, P solubilisation, and polyP accumulation) and their specific functions in
piggery waste by-products, and in association with mycorrhizal fungi in soils amended
with piggery waste. Knowledge of the diversity, abundance, and activity of
microorganisms involved in P transformation in the piggery waste treatment process is
critical but it has been constrained by the methods used to date. Therefore, particular
emphasis was placed on applying methodologies for characterising microorganisms
involved in P transformation in the piggery waste treatment process. The emphasis was
on applying an integrated approach using epi-fluorescence microscopy, flow cytometry,
cell sorting, and next generation sequencing.
7.1.2 Specific objectives
1. To characterise the piggery waste treatment process in terms of physico-
chemical properties, bacterial community composition, and P cycling potential
(Chapter 3).
2. To quantify the abundance, and diversity of P mineralising bacteria (the fraction
of cells that expressed phosphatase activity) during the pig waste treatment
process by developing an integrated approach using the enzyme labelled
fluorescence technique coupled with epi-fluorescence microscopy, cell sorting,
and next generation sequencing (Chapter 4).
3. To identify key microbes involved in polyP accumulation and its enhancement
under acidic conditions for assessing the efficacy of enhanced biological P
removal technology applied in high P loaded waste remediation (Chapter 5).
Chapter 7: General Discussion
161
4. To demonstrate the impact of application of pelletised piggery compost to soil
on plant growth, soil nutrient improvement, and changes in bacterial and fungal
community composition when banded with a reduced rate of synthetic fertiliser
(Chapter 6).
7.2. Key factors driving the P cycling bacterial diversity and activity in
the piggery waste treatment process The main contributions of this thesis in relation to the P transformation in the model
piggery waste treatment process are summarised in Table 7.1. Understanding how P
cycling bacterial community diversity and activity changes in response to
environmental factors is essential for understanding P cycling pathways in wastewater.
Here, it was observed that community composition of P cycling microorganisms
fluctuated across sequential stages in the waste treatment process as a response to
prevailing environmental conditions (pH, total organic C and P, total solids and volatile
solids, C:N ratio) (Chapter 3). The proportion of P mineralising bacteria present at
stages of the piggery waste treatment process fluctuated, and this could be due to the
differences in organic P level (Chapter 3 and Chapter 4). It was found that pH was a
major factor influencing the community diversity and species richness for polyP
accumulating microorganisms (Chapter 5).
Previous studies have shown that alkaline PO4ase synthesis and activity (i.e. P
mineralisation) for aquatic bacteria appear to be controlled by organic-P, temperature,
ionic strength, pH, and the presence of metal ions (Güngör and Karthikeyan, 2008),
internal N:P ratio, P demand of the cell (Espeland and Wetzel, 2001), composition of
wastewater (Li and Chróst, 2006). Starvation, salinity, presence of primary substrate,
pH, and volatile fatty acids (VFAs) had different expressions of total PO4ase activity of
anaerobic sludge (Anupama et al. 2008).
Cha
pter
7: G
ener
al D
iscu
ssio
n
162
Tab
le 7
.1. T
he sp
ecifi
c co
ntrib
utio
ns o
f thi
s the
sis i
n re
latio
n to
the
P tra
nsfo
rmat
ion
in th
e m
odel
pig
gery
was
te tr
eatm
ent p
roce
ss.
Cha
pter
s A
im
Spec
ific
cont
ribu
tions
of t
his t
hesi
s in
rela
tion
to th
e P
tran
sfor
mat
ion
in
pigg
erie
s and
soil
amen
ded
with
pig
gery
was
te
Cha
pter
3
To c
hara
cter
ise
the
pigg
ery
was
te t
reat
men
t pr
oces
s in
te
rms
of
phys
ico-
chem
ical
pr
oper
ties,
bact
eria
l co
mm
unity
com
posi
tion,
an
d P
cycl
ing
pote
ntia
l
1)
Pi
gger
y w
aste
was
hig
h in
bot
h or
gani
c an
d so
lubl
e P
and
its d
istri
butio
n va
ries
amon
g th
e di
ffer
ent s
tage
s of
the
was
te tr
eatm
ent p
roce
ss. A
lso
the
cove
red
anae
robi
c po
nd b
otto
m sl
udge
was
app
aren
tly h
igh
in m
iner
al P
. 2)
Th
e hi
gher
leve
l of s
olub
le P
in th
e en
d pr
oduc
t of t
he w
aste
wat
er e
fflu
ent
impl
ies
the
requ
irem
ent o
f rem
oval
of s
olub
le P
up
to a
low
er le
vel b
efor
e be
ing
used
in a
gric
ultu
re o
r dis
posa
l bac
k to
env
ironm
ent.
3)
Occ
urre
nce
of P
min
eral
isat
ion
was
hig
her
in a
naer
obic
pon
ds a
nd p
olyP
ac
cum
ulat
ion
was
gra
ter
in t
he t
reat
ed w
aste
wat
er a
t ev
apor
atio
n po
nd/
aero
bic
pond
. 4)
O
rgan
ic P
ava
ilabi
lity
is o
ne o
f th
e ke
y dr
iver
s fo
r th
e P
min
eral
isin
g ca
paci
ty in
the
pigg
ery
was
te tr
eatm
ent p
roce
ss.
Cha
pter
4
To q
uant
ify th
e ab
unda
nce,
and
div
ersi
ty o
f P
min
eral
isin
g ba
cter
ia (
the
frac
tion
of c
ells
that
ex
pres
sed
phos
phat
ase
activ
ity)
durin
g th
e pi
g w
aste
tre
atm
ent
proc
ess
by
deve
lopi
ng
an
inte
grat
ed a
ppro
ach
usin
g th
e en
zym
e la
belle
d flu
ores
cenc
e te
chni
que
coup
led
with
ep
i-flu
ores
cenc
e m
icro
scop
y, c
ell s
ortin
g, a
nd n
ext
gene
ratio
n se
quen
cing
1)
An
inte
grat
ed a
ppro
ach
was
dev
elop
ed fo
r ide
ntify
ing
func
tiona
lly a
ctiv
e fr
actio
n of
P m
iner
alis
ing
bact
eria
in w
aste
wat
er.
2)
P m
iner
alis
atio
n w
as c
ompa
rativ
ely
high
er in
ana
erob
ic p
onds
com
pare
d to
the
aero
bic
pond
. 3)
Ba
cter
oida
les,
Clo
stri
dial
es, C
ampy
loba
cter
ales
, and
Syn
ergi
stal
es w
ere
the
mos
t dom
inan
t gro
ups
of P
min
eral
isin
g ba
cter
ia in
eac
h st
age
of th
e w
aste
trea
tmen
t pro
cess
. 4)
Th
e id
entif
ied
P m
iner
alis
ing
bact
eria
co
uld
empl
oy
as
pote
ntia
l in
ocul
um/‘s
eeds
”(es
tabl
ishe
d m
icro
bial
com
mun
ity)
for
enha
ncin
g th
e P
min
eral
isat
ion
in th
e ea
rly st
ages
of p
igge
ry w
aste
trea
tmen
t pro
cess
Cha
pter
7: G
ener
al D
iscu
ssio
n
163
Tab
le 7
.1.
The
spec
ific
cont
ribut
ions
of
this
the
sis
in r
elat
ion
to t
he P
tra
nsfo
rmat
ion
in t
he m
odel
pig
gery
was
te t
reat
men
t pr
oces
s
(con
tinue
d….)
Cha
pter
s A
im
Spec
ific
cont
ribu
tions
of t
his t
hesi
s in
rela
tion
to th
e P
tran
sfor
mat
ion
in
pigg
erie
s and
soil
amen
ded
with
pig
gery
was
te
Cha
pter
5
To i
dent
ify k
ey m
icro
bes
invo
lved
in
poly
P ac
cum
ulat
ion
and
its e
nhan
cem
ent u
nder
aci
dic
cond
ition
s fo
r as
sess
ing
the
effic
acy
of
enha
nced
bi
olog
ical
P
rem
oval
te
chno
logy
ap
plie
d in
hig
h P
load
ed w
aste
rem
edia
tion
1)
A s
igni
fican
t hig
her p
olyp
hosp
hate
acc
umul
atio
n w
as o
bser
ved
at p
H 5
.5
com
pare
d to
pH
8.5
, with
sig
nific
ant e
nric
hmen
t of p
olyp
hosp
hate
kin
ase
and
exop
olyp
hosp
hata
se g
enes
at p
H 5
.5.
2)
Func
tiona
lly a
ctiv
e PA
O a
ccum
ulat
ors
wer
e id
entif
ied
as A
erom
onas
hy
drop
hila
, Ae
rom
onas
sa
lmon
icid
a,
Acin
etob
acte
r ba
uman
nii,
Bord
etel
la p
ertu
ssis
, Citr
obac
ter
kose
ri,
Esch
eric
hia
coli,
Ent
erob
acte
r sp
. K
lebs
iella
, Ps
eudo
mon
as
aeru
gino
sa,
Salm
onel
la
ente
rica
, an
d Sh
igel
la fl
exne
ri.
3)
Ther
efor
e, th
ose
spec
ific
bact
eria
l gro
ups
can
be m
anip
ulat
ed u
nder
pH
5.
5 fo
r im
prov
ing
the
EBPR
was
te t
reat
men
t pr
oces
s an
d de
velo
p hi
gh
valu
e an
d lo
w e
nviro
nmen
tal r
isk
liqui
d fe
rtilis
ers.
Cha
pter
6
To d
emon
stra
te t
he i
mpa
ct o
f ap
plic
atio
n of
pe
lletis
ed p
igge
ry c
ompo
st t
o so
il on
pla
nt
grow
th, s
oil n
utrie
nt im
prov
emen
t, an
d ch
ange
s in
bac
teria
l and
fung
al c
omm
unity
com
posi
tion
whe
n ba
nded
with
a r
educ
ed r
ate
of s
ynth
etic
fe
rtilis
er
1)
Ban
ding
of
pelle
tised
pig
gery
com
post
in
com
bina
tion
with
syn
thet
ic
ferti
liser
at a
low
er r
ate
to s
oil i
ncre
ased
the
avai
labl
e P
in s
oil,
plan
t P
upta
ke, a
nd sh
oot a
nd ro
ot d
ry w
eigh
t.
2)
Abo
ve re
spon
ses
are
mos
t lik
ely
to re
flect
pla
nt a
nd b
acte
rial c
omm
unity
re
spon
ses
to c
hang
es i
n so
il nu
trien
t le
vels
due
to
the
appr
opria
te
blen
ding
of b
oth
pelle
tised
pig
gery
com
post
and
inor
gani
c P
ferti
liser
s 3)
D
ata
indi
cate
that
ban
ding
of
pelle
tised
pig
gery
com
post
in c
ombi
natio
n w
ith s
ynth
etic
ferti
liser
at a
low
er ra
te w
ould
be
an a
ltern
ativ
e P
ferti
liser
fo
r agr
icul
ture
Chapter 7: General Discussion
164
For enhanced biological P removal by PAOs, environmental parameters such as pH,
COD, availability of ions (magnesium, calcium, and potassium), sludge retention time,
temperature, excessive aeration, redox potential, and light intensity influenced the
efficiency of P removal from the wastewater.
Although a major focus of this thesis was on identification of P cycling bacteria in a
piggery waste treatment process, it was also found that both abiotic factors (physico-
chemical variables) and biotic factors (community diversity and its interaction) had a
role in shaping P cycling bacterial communities in association with different stages of
waste treatment process.
Based on the finding of this thesis and previous findings, the key factors driving the P
cycling microbial diversity and activity in piggery waste treatment process can be
categorised as abiotic (e.g. pH, C:N ratio, P availability, temperature, P availability,
volatile fatty acids), biotic (microbial community diversity and its interaction with
bacterial P functional groups), and management (e.g. loading rate, storage conditions,
hydraulic retention, feed type etc.). Figure 7.1 shows the key factors influencing P
cycling bacterial diversity and activity in wastewater treatment plants.
7.2.1. Abiotic factors
Among different abiotic factors effecting P cycling microorganisms, pH and availability
of P in wastewater were most influential in the piggery waste treatment process.
The effect of pH
The pH of the piggery waste treatment system is a major driver of community structure
and activity of P cycling bacteria (e.g. polyP accumulating organisms, P mineralising
bacteria). CCA analysis showed that pH was higher in the Evaporation pond compared
to early stages in piggery waste treatment process (Chapter 3) and the community
composition of Evaporation pond was different from the other waste treatment ponds.
Hence, pH was a major factor influencing community diversity and spatial distribution
of microorganisms present within the piggery waste treatment process. This was
illustrated as an effect on the percentage removal of P from the wastewater (Chapter 5,
Figure 5.4 a) and also on the relative abundance and diversity of PAOs community
under changed pH conditions (Chapter 5, Figure 5.7). The effect of acidic pH in
enhancing P removal in EBPR has previously been observed (McGrath et al. 2001;
Mullan et al. 2002; Moriarty et al. 2006b). It is clear that pH drives community diversity
and activity of P mineralising microorganisms in the piggery waste treatment process.
Chapter 7: General Discussion
165
Hydrolysis of Pi from organic or other complex P compounds (i.e. P mineralisation) is
mediated by phosphomonoesterase and phosphodiesterase activity (Anupama et al.
2008). The activity of phosphomonoesterases is dependent on pH and they are classified
as either alkaline (pH>7; EC 3.1.3.1) or acid (pH<6; EC 3.1.3.2) phosphatases
depending upon their pH optimum level (Geesey 1999 and Anupama et al. 2008;
Kloeke and). The observed P mineralisation capacity in the piggery waste treatment
process was assumed to be related to the alkaline phosphatases (pH>7; EC 3.1.3.1)
activity as the pH of the system was above 7 (Chapter 3, Table 3.1).
Figure 7.1 General diagram showing some of the factors influence of the P cycling microbial diversity and activity in wastewater treatment plants.
P cycling bacterial diversity and activity
in wastewater
Abiotic factors
pH, C:N ratio, P availability, temperature, P availability, volatile fatty acids
Biotic factors
Interactions of bacterial P cycling groups with other microbial
communities such as microalgae, fungi, and phytoplankton
Management practices in animal husbandry
Waste loading rate, storage conditions, hydraulic retention, feed type etc.
Chapter 7: General Discussion
166
P availability
Organic P seemed to be an intrinsic factor for the microbial community composition
(Chapter 3, Figure 3.4) as it was found in the earlier stages of the waste treatment
process. This indicated that P mineralisers play an important role in degrading organic P
at the early stages of the treatment process. Extracellular phosphatases (such as alkaline
PO4ase) catalyse the liberation of Pi from various organic P compounds. Alkaline
PO4ase synthesis and activity in aquatic bacteria appear to be controlled by the levels of
specific forms of external organic-P, type, and by the concentration of the substrate and
the enzyme (Güngör and Karthikeyan, 2008).
Inorganic P concentration in the environment either directly or indirectly influences the
P mineralisation. A negative correlation between PO4ase activity and inorganic P has
been reported widely, indicating algal and bacterial PO4ase activity is inhibited by
elevated Pi concentrations (Dignum et al., 2004). However, few other studies have
reported that alkaline PO4ase synthesis in many bacteria is not inhibited by elevated Pi
(MH and HJ, 1961; Chrost et al., 1986; Kloeke and Geesey, 1999). A small fraction of
total bacterial cells in the waste samples displayed PO4ase activity (0.3 %- 5.5 %),
(Chapter 4, Figure 4.7), which could be due to potential inhibition of this enzyme at
high Pi levels (10.8 - 26.3 mg/L). However, there was no direct relationship between
ELF activity and Pi level among the samples as was found in some other studies.
Therefore, there might be species-specific differences in the relationship between Pi and
PO4ase activity. Meseck et al. (2009) explained that expression of PO4ase activity at a
high soluble reactive P concentration could be attributed to the ratio of DNA to protein
or more of an individual response, rather than a population response. Concerning these
observations, the level of PO4ase activity among different stages of waste treatment
could be a cumulative effect of both biological and physicochemical dynamics of the
each waste treatment stage.
7.2.2. Biotic factors
Theinfluence of biotic factors on polyP accumulating microorganisms was illustrated in
Chapter 5. Microscopy observations of polyP granules in both microalgae and bacteria
(Chapter 5, Figure 5.5) show the likelihood of competition for soluble P between the
two groups. It was further confirmed that the microbial community composition in both
unfiltered and unfiltered samples maintained at pH 5.5 greatly affected the abundance of
polyP accumulating bacteria (Chapter 5; Figure 5.7). For example, a marked difference
Chapter 7: General Discussion
167
in relative abundance of Alteromonadales and Aeromonadaceae in wastewater
maintained at pH 5.5 was observed between the filtered (Alteromonadales 59 %;
Aeromonadaceae 26 %) and unfiltered (Alteromonadales 4 %; Aeromonadaceae 73 %)
samples. The accumulation of polyP in both bacteria and microalgae was previously
observed under pH 5.5 (McGrath et al., 2001). It has also been observed that two
species of microalgae (Chlorella vulgaris and Scenedesmus dimorphus) were capable of
removing up to 55% of the phosphates from an agroindustrial wastewater system
associated with dairy and pig farming wastewaters (Gonzalez et al. 1997). Apart from
microalgae, some other primary producers, such as phytoplankton have the ability to
assimilate both organic and inorganic P fractions (Withers and Jarvie 2008) and thereby
influence bacterially mediated P cycling in wastewater. Heterotrophic bacteria and
biofilms (mixtures of microbes, algae, and particulate matter within a polysaccharide
matrix) are recognised as the dominant sites for accumulation of microbial biomass,
rapid P cycling and grazing activity in natural water bodies (Withers and Jarvie 2008)
indicating the effect of biotic factors on the P cycling bacterial diversity and abundance.
7.2.3 Management practices
Management practices such as loading rates, hydraulic retention time, and composition
of pig feeds influence methane yield of digestate (Menardo et al. 2011). Thus,
management practices lead to changes in microbial community composition in the early
stages of the waste treatment process, and hence on organic P mineralisers. Diversity
and abundance of P mineralising bacteria at early stages in waste treatment and CAP
digesters seem to be controlled by organic P, total solids (TS), volatile solids (VS)
(Chapter 3, Figure 3.4). Loading rates of piggery waste cause changes in total solids
(TS) and volatile solids (VS) in waste. The composition of pig feeds will also affect the
amount of energy and nutrients (e.g. carbon, nitrogen, phosphorus, potassium, organic
nutrients) available for microbial growth. Furthermore, phytate in pig feed is a substrate
for P mineralising bacteria (Lim et al. 2007; Baxter et al. 2003) and therefore the
amount of phytate in pig feed directly influences P mineralising bacteria in waste.
7.3 Methodological Considerations This research has shown that bacterial communities and their functional P components
(P mineralisation, polyP accumulation, and P solubilisation) in the piggery waste
treatment system investigated are spatially separated, as was confirmed by two
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168
independent approaches (i.e.16S rRNA Ion Tag sequencing, and community
metagenomic analysis) (Chapters 3 to 5). Overall, the dual approach was advantageous
when studying wastewater bacterial P communities using molecular methods targeting
the 16S rRNA gene and community metagenomic analysis, because a variety of
community descriptors, including abundance, composition, species richness, alpha and
beta diversity indicators, were able to be investigated simultaneously. According to the
findings of this study (Chapters 3 to 5) these molecular techniques were generally
compatible. Therefore, the methods applied should be applicable to detecting taxonomic
and functional diversity of P cycling bacteria in similar studies. Chapters 4 and 5
reported the application of epi-fluorescence microscopy, flow cytometry, cell sorting,
and 16S rRNA Ion Tag sequencing approach which linked microbial identity with
activity of P cycling microorganisms. Epi-fluorescence microscopy and flow cytometry
approaches further assisted in co-locating and quantifying P mineralising bacteria
(Chapter 4), and polyP accumulating microorganisms (Chapter 5). One of major
obstacles in detecting P microbial communities is the lack of primers targeting P cycling
microorganisms (see Chapter 2). This was addressed by using a combined approach in
this study.
7.3.1 Sampling strategy
In order to reduce sampling biases, samples from each pond were collected into several
sampling bottles and corresponding samples were mixed to make a composite sample
per each stage of waste treatment process. Additionally, technical replicates were also
used for the majority of analyses, except for next generation sequencing approaches due
to the high cost of sequencing. Nevertheless, the sequencing data were reliable and
reproducible for identifying P mineralisers (Chapter 4), and polyP accumulators
(Chapter 5) were representative members of the community in this piggery waste
treatment process (Chapter 3) as the three studies were done independently to each
other. The bacterial populations in the covered anaerobic digester were similar to the
taxonomic identity of the bacterial community in the same CAP digester studied
previously (Whiteley et al. 2012). Samples from the piggery wastewater treatment
process were collected at one time during the year to maintain consistency but seasonal
variation (which was not assessed) could follow changes in environmental conditions in
the waste ponds over time. Nevertheless, it has been previously shown that this CAP
digester has a relatively stable community composition over a period of more than 10
months (Whiteley et al. 2012).
Chapter 7: General Discussion
169
7.3.2. Methodological considerations in fluorescence staining, flow cytometry, and
cell sorting
Single-cell-based methods such as fluorescence microscopy and flow cytometry are
appropriate for selective analysis of specific microorganisms (such as PAOs) within
complex communities (Günther et al. 2009). However, there are some limitations which
need to be addressed when applying these fluorescence techniques to complex
environmental samples comprising high microbial diversity and debris. Auto
fluorescence derived from debris can interfere with fluorescence signals from stained
cells which are present in different sizes, granularity and shapes making it difficult to
assign accurate gating strategy for discrimination of cells from auto fluorescence.
Piggery wastewater effluent and slurry also comprises complex microbial communities
and large amounts of debris (e.g. organic matter and clay minerals). Therefore,
optimisation of the staining protocol for ELF-stained cells (Chapter 3), and polyP
granules (Chapter 4) was a challenging task.
In Chapter 3, the selection of a fluorescent dye for counter staining of ELF-stained cell
was done carefully. Excitation and emission wavelength of the counterstaining dye
should be within the range of the excitation and emission wave length of the ELF97®
(345-530 nm) with minimum spectral spillover between each fluorescence channels. In
order to capture the maximum fluorescence from each channel, appropriate filters were
selected according to the type of the flow cytometer (as explained in Figure 4.2). In
brief, to obtain a clear separation between ELF+ cells (cells expressed phosphatase
activity) and ELF- cells (other nucleated cells) in flow cytometric analysis, three
potentially suitable fluorescent dyes, DAPI (358-461 nm), SYTO9 (485-498 nm), and
PI (535-616 nm) were evaluated. Based on the spectral set-up in the flow cytometry
used, a significant spectral spillover of DAPI and PI into the ELF emission detector was
initially nobserved. Spectral spillover of DNA binding dyes is caused by difficulty in
separating ELF+ cells from other nucleated cells (i.e ELF- cells) and noise. In contrast,
SYTO9 was the best fluorescent dye in separating ELFA-labelled cells from both ELF-
non-labelled cells (other nucleated cells) and background (auto-fluorescence) (Chapter
4, Figure 4.5b).
In chapter 5, optimisation of DAPI was done to assess the best concentration of DAPI to
stain the accumulated polyP granules in the piggery waste microbial communities. In
general, DAPI at higher concentration (5-50 µg/ mL) is recommended for staining
polyP granules (Günther et al. 2009, Klauth et al. 2006) and low concentrations of
Chapter 7: General Discussion
170
DAPI fluorescence (0.24-5 µM) is related to bacterial DNA. However, an optimum
concentration is needed for staining polyP granule and this appears to vary with the type
of environmental samples, complexity of microbial community, and size of the polyP
granules. The other problem associated with DAPI is unspecific fluorescence derived
from other cellular constituents (such as lipids) when DAPI is applied at high
concentrations (180 μM) (Streichan et al. 1990). Based on this study (Chapter 5, Figure
5.2), a 15 µg/ mL DAPI concentration was sufficient for staining polyP granules in the
tested piggery wastewater samples. Another study showed that dual staining of
fluorescence antibiotic tetracycline (TC) and DAPI can also be used for reliable and
accurate detection and quantification of PAOs (Günther et al. 2009). This dual staining
has the added advantage that it is a quantitative method for PAO detection and DNA
content analysis (such as bacterial growth rate) (Günther et al. 2009). Apart from
quantification of polyP granules and information on bacterial growth rates, the activity
states of PAOs would be useful for the bioengineering aspect of EBPR. As for further
methodological improvement, application of a dual staining protocol for TC and DAPI
for detection and quantification of PAOs in piggery wastewater is proposed.
Cell sorting coupled to flow cytometry was an efficient and accurate way to separate P
mineralising bacteria in a complex diverse environment such as piggery waste treatment
ponds. Nevertheless, the DNA concentration recovered from sorted cells (from 106
cells) was low and not sufficient for downstream next generation sequencing. In
particular, it was not sufficient for PCR independent metagenomic analysis. The reason
for the low recovery of DNA from the sorted cell could be mainly due to the
stabilisation of sorted cells by cross-links formed due to paraformaldehyde fixation and
this stabilization hampered the release of DNA from the fixed cells. Piggery wastes
harbour pathogenic microorganisms and therefore fixation with an appropriate cell
fixative is a compulsory step prior to handling and also to ensure no contamination
occurs in the flow cytometry instrument. On the other hand, fixation is important to
maintain the cell structure for microscopy and flow cytometric analysis. The need for
purity of cell sorting from complex environmental samples like piggery waste means it
is very time consuming to retrieve an adequate amount of cells (>109) for DNA
extraction, especially when handling a large number of samples. Even if a large number
of cells are sorted, most of DNA remains inside the fixed cells. This could bias the PCR
amplification because of inefficiency of DNA extraction and could lead to deceptive
microbial composition for a given environment. Negative effects of formaldehyde
Chapter 7: General Discussion
171
fixation on the efficacy and fidelity of PCR amplification of DNA have been described
previously (Wallner et al. 1997).
In order to address these issues, sorted ELF+ve cells were pre-treated prior to DNA
extraction to facilitate the DNA extraction from the PFA fixed cells (Chapter 4). The
sorted cells were concentrated by centrifuging at 16,000 g for 20 min and the
supernatant was carefully removed. The remaining cells in the Eppendorf tubes were
pre-incubated with freshly prepared 10 % SDS (70 µL) plus 20 mg mL-1 proteinase K
(10 µL) for 1 hr at 65 oC followed by DNA extraction was done. The treated cells were
used for DNA extraction using the MoBio UltraClean® Microbial DNA Isolation Kit
(Geneworks, Australia), utilising beat beating and column purification, according to the
manufacturer’s guidelines. The DNA recovered in this way and the subsequent nested
PCR yielded an adequate concentration of DNA with a good purity for 16S rRNA Ion
Tag library preparation. On the other hand, other fixation methods could be tested
which were not evaluated in this study. For example, 10% sodium azide, as a fixative,
has been applied successfully to determine the abundance and identity of polyP
accumulating microorganisms in a wastewater treatment plant using fluorescence
labelling of polyP, cell sorting, and denaturing gradient gel electrophoresis (DGGE)
(Mehlig et al. 2013).
7.3.3 Methodological considerations to 16S rRNA Ion Tag sequencing
The Ion Torrent Personal Genome platform has proven to be an effective tool to assess
microbial community structure, temporal stability and key taxa in the same CAP
digester with appreciable levels of sequence outputs at low cost (Whiteley et al. 2012).
The protocol explained by Whiteley et al. (2012) was used with Golay barcoded Ion
Tags for multiplex analyses of microbial communities to determine the microbial
community structure in the piggery waste treatment process (Chapter 3), P mineralising
bacteria (Chapter 4), and PAOs (Chapter 5). This was based on amplification of a
standard 200 b.p. V3 region of bacterial 16S rRNA using Golay barcode and Ion
Torrent adapter modified core primers 341F and 518R (Muyzer et al. 1993).
However, there are some limitations that have to be considered when estimating the
relative abundance of microbial community structure derived from PCR amplification
using the 200 b.p. V3 region. For example, the relative abundance derived from the 200
bp could be relatively low and bias can occur when assigning taxonomic identity based
on a short sequence length of 16S rRNA. Ion Torrent sequencing has some inherent
Chapter 7: General Discussion
172
biases such as low quality scores and chimera sequences. Nonetheless, efforts were
made to reduce the effects of low quality sequences retrieved from the Ion Torrent
platform and also the possibility of miss-assigning taxa to the wrong group using the
QIIME software package (Caporaso et al. 2010). For example, quantity filtering was
done by defining appropriate sequence selection criteria (Chapter 3-6). Possibility of
miss-assigning taxa to wrong group was further avoided by removing chimeras and
singletons followed by picking OTUs at 97% similarity cut off.
7.3.4. Discrepancy of degree of P mineralisation as revealed by ELF coupled to
flow cytometry and metagenomics
Having seen that the number of gene encoding for alkaline phosphatase was the highest
in the CAP-Bottom (Chapter 3), it was assumed that the highest abundance of ELF+
cells could be found in CAP-Bottom when the ELF was coupled to flow cytometry.
However, there a lower % of ELF+ bacteria in the CAP-Bottom was revealed by ELF
coupled to flow cytometry. The observed discrepancy might be due to a number of
alkaline phosphatase reads found in CAP-Bottom being associated with the activity of
Methanosarcina spp., anaerobic methanogen which were not accounted for by ELF and
bacterial 16S rRNA tag sequencing. The discrepancy in abundance of alkaline
phosphatase gene revealed by metagenomic analysis, and the % of ELF + bacteria
revealed by ELF, also indicated that archea might be playing an important role in
PO4ase activity in the waste treatment process in addition to activity of bacteria.
Anupama et al. (2008) showed that both archaea and bacteria played equal roles in
PO4ase activity in anaerobic bioreactors. Therefore, further studies are required to
understanding the contribution of archaea in the mineralisation of organic P in this
piggery waste treatment process, especially as this environment is significantly favoured
by anaerobic microorganisms.
The second reason for the discrepancy of degree of P mineralisation revealed by ELF
coupled to flow cytometry and metagenomics could be associated with variable cell
extraction from the environmental samples during the sample preparation for ELF
staining. For example, the majority of bacterial cells in CAP-bottom can be attached to
the clay/silt and organic particulates and fewer cells can be expected when compared to
extraction from the evaporation pond effluent which was very low in clay and organic
particulate. When the number of cells and purity is low, interferences caused by
impurities lead for underestimation of total bacteria/ELF + cells in a sample. This
happens when the numbers of ELF + cells present as a percentage of the total bacteria
Chapter 7: General Discussion
173
cells. This is one of the disadvantages of quantification of ELF +cells in environmental
samples using flow cytometric analyses. In this study, fluorescence interferences were
minimised by assigning a proper gating strategy (Chapter 4, Figure 4.6). As for further
optimisation to the current ELF protocol, different extraction methods can be tested. For
example, interference of clay minerals and organic matter while recovering higher
number of cells from sludge samples or lake sediments can be achieved by using
advance cell recovering procedures such as Nycodenz gradient centrifugation (Poté et
al. 2010).
7.3.5 Limitations in the pot trial
Effect of pelletised piggery compost (alone or in combination with inorganic fertiliser)
on plant growth, soil nutrients, and soil bacterial and AM fungal community was
assessed up to 8 weeks at 3 independent harvests (at 4, 6, and 8 weeks) to investigate
the effect of temporal variation on the above parameters. However, the discontinuation
of the pot experiment (at 8 weeks) before obtaining the yield limits conclusions about
the best-performed soil amendment. Differences in responses to soil amendments
observed over time. For example, some fertiliser amendments (e.g. inorganic fertiliser
alone) started to perform well only after 6 weeks (inorganic P fertilisers alone).
In addition to PCR-based sequencing approaches used in Chapter 6, PCR independent
metagenomic sequencing is a more powerful approach for identifying the P cycling
microbial community structure together with their putative metabolic potential based on
genes and pathways. Therefore, the integration of these methods could provide a greater
insight into the microbial diversity and functional activities in soil amended with both
pelletised piggery compost and inorganic P fertilizers and proposed for future studies.
7.4 Underlying mechanisms in P cycling and proposed pathways for
the piggery waste system
More complete understanding of the underlying mechanisms of the P transformation in
the piggery waste treatment process would aid both reduction of environmental loading
of inorganic P and recovery of valuable by-products. According to this study (Table 7.1)
and related literature (De-Bashan and Bashan 2004, Kulakovskaya et al. 2012, Yoon et
al. 2004), the probable mechanism of P transformations in the model piggery waste
treatment process is illustrated in Figure 7.2a.
Chapter 7: General Discussion
174
In the CAP digester of the piggery waste treatment system, P transformation can occur
via three main pathways (P mineralisation, polyP degradation, and crystallisation). The
anaerobic environment is electron acceptor deficient and carbon rich. Therefore, it is
proposed that PolyP is degraded to Pi (orthophosphate) from microbial sludge for
energy. The energy is used for uptake of acetate and form microbial biopolymers such
as polyhydroxyalkanoate (PHA) and consequently orthophosphates is excreted from
cells to the digestate (Figure 7.2a). On the other hand, organic P mineralisation (the
process of hydrolysis of Pi) also releases orthophosphate into the digestate. The higher
abundance of genes encode for alkaline phosphatases (Chapter 3, Figure 3.6a), in the
CAP digester confirmed that P mineralisation is primarily occurring at this stage. Other
P transformation occurring in the CAP digester is chemical precipitation of P. With the
availability of some cations, the orthophosphate in digestate becomes progressively less
soluble with the formation of crystallised P forms (Stuvite;MgNH4PO4.6H2O,
Ca3(PO4)2, Mg3(PO4)2, Fe3(PO4)2). Chemical precipitation of orthophosphate as
crystallised P forms has been documented in anaerobic digestion of piggery waste
(Mehta and Batstone 2013). Solubilisation of precipitated forms of P is governed by P
solubilising microorganisms. Although there was no direct evidence of in situ P
solubilisation activity in the CAP-Bottom sludge, P solubilisation could possibly occur
in the CAP-Bottom where precipitated forms of P are high.
The end product of anaerobic digestion (digestate) is generally rich in orthophosphate
compared to the starting wastewater (De-Bashan and Bashan 2004). The same trend was
observed in this study (Chapter 3, Table 1). For example, the concentration of
orthophosphate in the CAP-inlet (i.e. holding tank: 25.1 Pi-mg/L) was higher than the
CAP-outlet (20.5 Pi-mg/L). Orthophosphate concentration in the anaerobic digesters is
a result of the net effect of P mineralisation, polyP degradation, and P crystallisation.
Thus, orthophosphate concentration varies among different anaerobic digesters, and
clearly, microbial communities in CAP digesters play an important role in P
transformation.
Chapter 7: General Discussion
175
Figure 7.2 Probable mechanisms of P transformations in the CAP digester and Evaporation Pond under its natural states (a). A proposed method for improving the current wastewater treatment process (b).
The anaerobically treated wastewater is collected into an evaporation pond which is in
natural aerobic state. There are two main P transformations taking place in this last
stage of piggery waste treatment process (polyP accumulation and P solubilisation).
This environment is generally electron acceptor rich but carbon deficient. It has been
proposed that PHA is degraded and PolyP is synthesized from the ATP generated from
the PHA metabolism. The removal of P in EBPR as polyP is well documented and an
anaerobic condition followed by aerobic condition is a prerequisite for the activity of
PAOs. Therefore, conditions in the aerobic pond are more favourable for polyP
accumulation (Chapter 3 Figure 3b) and less favourable for P mineralisation (Chapter 3
Figure 3a). Therefore, polyP accumulation under EBPR could potentially be an
important mechanism for P removal in the piggery waste treatment systems where high
P in wastewater is a problem. As more orthophosphate is taken up during the aerobic
phase than released during the anaerobic phase, reduction of orthophosphate
Chapter 7: General Discussion
176
concentration in aerobic pond is expected. There was a lower concentration of
orthophosphates in the aerobic pond (12.2 mg/L) compared to the outlet of the
anaerobic pond (i.e. CAP-outlet; 20.5 Pi-mg/L). However, the reduction in
orthophosphate in the evaporation pond/aerobic pond up to 12.2 mg/L under its natural
state is not at the required standard for recycling the treated wastewater as the irrigation
water. In particular, irrigation of sandy soils with the recycled water leads to phosphate
leaching and subsequent pollution of waste bodies (Obaja et al. 2003). Therefore,
further improvement is necessary to reduce the soluble P (i.e. orthophosphate) in the
aerobic pond before they are used as liquid fertilisers on sandy soils.
A proposed method for improving the current waste treatment process is illustrated in
Figure 7.2b. P removal in EBPR can be enhanced by providing intermittent anaerobic
and aerobic conditions (De-Bashan and Bashan 2004). Therefore, circulation of
wastewater between the evaporation/aerobic pond and the anaerobic pond is proposed.
Furthermore, the knowledge gained about how to manipulate and exploit polyphosphate
accumulating organisms to enhance P uptake by altering the pH (Chapter 5) provides
the basis of a novel strategy for improving the piggery waste treatment process and
developing high value liquid fertilisers for land application. Subsequent investigations
should therefore focus on assessing the economic feasibility of incorporating EBPR
systems into existing piggery waste treatment systems by lowering the pH of the aerobic
pond. Acidification of the aerobic pond using acids in its current state (larger volume)
would not be economically feasible and could result pond failure due to the breakdown
of other microbial pathways resulting in unexpected consequences. Therefore, the
introduction of another two ponds for acidification of wastewater (i.e. acidification
pond) and purification of water (a sedimentation tank) is advisable. The size of the
secondary acidification pond, loading rate and frequency can be decided based on the
daily requirement of the irrigation water for the farm land. Inclusion of a separate
acidification pond is an added advantage for producing only the required volume of
wastewater for subsequent on farm irrigation and also for easy maintenance. Efficiency
can be further improved by enriching the acidification pond with acid loving PAOs
(identified in the Chapter 5). The acid treated wastewater can then be passed through a
membrane bioreactor which is made of polyethylene fibre where the PAO bacteria and
other microbes attach forming a biofilms. Therefore, the membrane bioreactor facilitates
the recovery of phosphate-depleted cleaner effluent (Yoon et al. 2004) which can be
used for irrigation or other farm activities. The sediment accumulated in the bottom of
Chapter 7: General Discussion
177
the sedimentation tank over time can be further processed as compost/pelletised forms
which are considered to be high in P form (biomass P, mineral P, and organic P).
CAP-bottom sludge can be high in mineral P sources and can be recycled in slow
releasing P fertilisers (Figure 7.2b). P in these products is expected to be present mainly
in crystallised and organic P forms and appeared to be an effective slow releasing P
fertiliser for plants with a low risk for eutrophication. Gradual release of plant available
P can be expected in the root zone of plant with the activities P-solubilising and P-
mineralising bacteria, which are generally assumed to be the main contributor of P
turnover in soils.
7.5 Research Perspectives
This research will be beneficial to different stakeholders (including the scientific
community, pork industry, environmentalist, public, farmers). This section highlights
some of implications for these groups.
7.5.1 Relevance to scientific community
This thesis involves the development of single-cell-based methods (i.e. fluorescence
microscopy and flow cytometry) coupled to next generations sequencing approach for
characterising P mineralising bacteria and PAOs in a piggery wastewater treatment
system. The findings help to track P transformations in piggeries through the microbial
community to provide greater insight into P cycling in the piggery waste management.
These techniques could be modified and adapted for different systems in natural,
agricultural, and engineered environments for understanding P cycling pathways.
Knowledge of the taxa mediating these P transformations pathways means possibility of
developing molecular biomarkers (i.e. probes/primers) for monitoring P cycling bacteria
in different environmental settings. Knowledge of P mineralising microorganisms and
the factors affecting their activities would provide the opportunity to optimise organic P
degradation during the anaerobic digestion processes to yield high biogas production.
Efficiency and reliability of EBPR can be achieved by discovering highly efficient
PAOs (e.g. bioengineering).
Chapter 7: General Discussion
178
7.5.2 Relevance to small scale and large scale pig farmers
Economic Benefits
Adoption of CAPs and sustainable re-use of animal by-products will benefit both small
scale and large scale commercial farmers by reducing the cost of production through on
farm energy production (e.g. biogas generation), increasing profitability of crop
production (e.g. low cost P fertilisers), and recycling of water (e.g. treated wastewater
for irrigation).
On the other hand, pelletised piggery compost would be economically more favourable
because it can be applied through an air seeder and its application beneath the soil
surface allows the rate of application to be significantly reduced, while simultaneously
reducing dependence on inorganic fertilisers. The reduced volumes would facilitate
transport and spreading costs leading to greater financial and technical viability for the
farmer. This would also be beneficial to pig farmers since it increases marketability of
piggery manures outside their farms.
Environmental Benefits
From an environmental perspective, recycling or re-use of piggery waste will reduce the
waste accumulation on piggery farms which in turn leads to reduced cost of waste
management and land required for waste disposal. Reduction of P in wastewater
through EBPR reduces the P leaching and subsequent eutrophication.
Social Benefits
Dissemination of the findings of this study through on-farm demonstrations and training
will encourage farmers to adapt new sustainable farming practices for reducing on farm
waste accumulation, efficient biogas generation and recovery of P for re-use as
fertilizer. Adoption of CAPs and sustainable re-use of by-product will reduce odour
emissions, pathogenicity, and GHG emission leads for improving the wellbeing of pig
farmers and neighbouring communities.
7.6. Future research directions
As summarized above, the research study generated important fundamental knowledge
relating to the P transformation in the piggery waste treatment process. This research
study also identified a number of issues that require further investigation to enhance the
practical application of the findings to the real environment as summarised below.
Chapter 7: General Discussion
179
7.6.1 Research directions for methodological development in tracking P cycling
in environments
Primer design of sequencing retrieved
- Retrieved sequences from the identified taxa in this study and other related work can
be exploited in design of highly specific oligonucleotide primers for screening P
cycling bacteria. As discussed in Chapter 2, primers and probes targeting P mediating
microbes are limited, non-specific or poorly developed. Also, the application of
molecular methods to P transformation is limited (Wasaki and Maruyama 2011) by the
availability of sequences in the current databases for genes involved in P mineralization
/solubilisation / polyP accumulation. With the advancement of novel molecular
techniques which provides functional details of a community (such as
metatranscriptomics, metagenomic, proteomics, metabolomics), a considerable number
of sequences encoding for P cycling genes (Chapter 2. Table 2.4) are now available and
these can be used to design molecular monitoring tools such as primers and probes for
exposing the role of P mediating microbes in a given environment. Therefore, sequence
retrieved from this study and available P cycling gene sequences in current databases
would be a basis for design of new primers.
Stable isotope approaches linking diversity with function of P cycling
pathways
- The combined molecular microscopy approaches in Chapters 4 and 5 were successful
when applied to the P mineralising and polyp accumulating microorganisms. However,
there are a number of alternative strategies that could be used in situ, which may offer
new opportunities for detecting functional gene sequences at a more detailed level,
linking functional capacity with the diversity of those P cycling microorganisms. One
promising way is use of stable isotope techniques, SIP-CHIP approaches and
NanoSIMs (Read and Whiteley 2010; Wasaki and Maruyama 2011). These novel
methods can be used to track P partitioning into different P pools and to identify the key
microorganisms involved in P transformations (P immobilisation, P mineralisation, and
P solubilisation) in a given environment. Development of stable isotope probing
techniques for determining the impact of piggery waste by-products (such as pelletised
piggery compost or Eco-shelter manure) on microbial P cycling in soil would assist in
unravelling the partitioning of P into different soil P fractions in soils receiving organic
and inorganic P inputs. This can be achieved by tracing the fate of 18O-labelled organic
Chapter 7: General Discussion
180
P and 18O-labelled inorganic P inputs into the soil microbial community. By identifying
the taxa involved in the decomposition and/or transformations of the organic and
inorganic P the relative importance of the bacteria and fungi in P-mineralisation and P-
immobilisation can be determined.
Metagenomics / metatranscriptomics approaches for linking diversity with
function of P cycling pathways
- It is recommended that metagenomic analysis be used as a part of a risk and benefit
analysis when a new manure management practice or land application method is being
introduced. Therefore, study of P transformation genes in the rhizosphere and root
colonised bacteria and fungi using metagenomics / metatranscriptomics (Bastida et al.
2009) which in turn provide reliable validation for the effect of P mediating
microorganisms in soils amended with piggery waste by-products with a proper control
is proposed.
- Further research should be focus on understanding P solubilising activities in piggery
waste treatment process using both culture dependant and independent approaches. As
noted in Chapter 2, finding suitable P solubilising microorganisms (bacteria, fungi, or
archaea) which can be employed for solubilisation of precipitated forms of P (e.g.
struvite and hydroxyapatite) in situ under strictly anoxic environment like anaerobic
digesters would be a highly beneficial in P removal from effluents and sludge.
- Optimisation of the cell fixation protocol and cell sorting to maximise DNA yield and
improve the success of PCR amplifications would maximise the quality and reliability
of sequencing data.
7.6.2 Research directions for improving the current piggery waste treatment
process
- Investigation of the role of microalgae in P cycling in piggery waste treatment
process. As microalgae play an important role in polyP accumulation in the piggery
waste treatment process, further research is proposed to understand their taxonomic
identity, function and factors that affect their activity. Understanding the interaction
between polyP accumulating bacteria and microalgae would help improve P removal in
these systems and would enable improvements in engineering for current piggery waste
treatment processes.
Chapter 7: General Discussion
181
- Detection of changes of P transformation in piggeries with respect to the variability of
environment, diet composition, and management practices.
- Evaluation of enhanced P uptake by altering the pH under on-farm conditions.
7.6.3 Research directions for enhancing the low rate application of pelletised
piggery compost
- Larger and longer-term field trials combined with laboratory assessment are necessary
to verify positive effects of low rate application of organic and inorganic soil
amendment (Balance50/Agras50) under field conditions.
- Identifying other compatible organic or inorganic fertilisers to blend with pelletised
piggery compost to increase profitability and technical viability for the farmers.
- Canonical correspondence analysis (CCA) was used to explore the relationship
between environmental variability and individual bacteria taxa (Chapter 3 and Chapter
5). These data can also be analysed using Structural Equation Modelling (SEM), a new
modelling approaches used to test hypothesised pathways, links, and then identify the
most reliable model that explains the observed data. In this way, SEM can be used to
evaluate the impact of different P-fertilisers on P cycling by allowing P partitioning and
transformation to be modelled and predicted.
Appendices
182
Appendix 1
Appendix 1. (a) Location of the study site. (b) The piggery waste treatment process at Medina Research Station, Department of Agriculture and Food, Western Australia (DAFWA) for treating piggery effluent waste and capture bioenergy.
Appendices
183
Sampling site description
The piggery wastewater treatment process at Medina Research Station can be described
as several stages as shown above. Medina Research Station has a pig shed which can
accommodate maximum 400 pigs at a time. The pig shed has four storage pits (1.2 m
deep x 1.8 m) under the slatted areas of the pens that are discharged every two weeks.
Bore water is used to flush piggery waste from the pens into the pits. The waste
treatment process is separated into 5 stages: pits in the pig shed; solid separation
screens, holding tank, the covered anaerobic pond (CAP) and finally a secondary
evaporation pond. Effluent from the pig pens is collected in the pits and held there until
pits are ¾ full and then released into a 100,000 L underground tank from where it is
pumped over a static run-down screen (solid separator) that removes about 10-15% the
total solids (TS). The remaining wastewater is transferred to the holding tank prior to
being pumped into the covered anaerobic pond (CAP) (ca. 25m x25m x5m) digester on
a weekly basis (75,000 L/wk). Treated effluent is then transferred to the secondary
pond (ca. 50m x50m x5m) in aerobic state, where the treated waste water evaporates.
The biogas produced from the CAP is removed through a perforated pipe system placed
around the perimeter of the pond. A small centrifugal fan draws the gas off and the flow
of biogas is measured using a domestic gas meter. The gas is currently ignited using a
biogas flare and converted to carbon dioxide (CO2), a less potent Greenhouse gas
(GHG).
Appendices
184
Appendix 2
Appendix 2. Source hit distribution of CAP-Bottom metagenome that were annotated by the different databases. Bars represent annotated reads, which are colored according to their e-value range.
Appendices
185
Appendix 3
Primer sequences
A1 different for each sample
Universal Primer Mix:
Vol (uL) Stock Conc Final Conc
515F_BACT_A_xx (barcode) 0 5uM - 806R_BACT_P1 10 100uM 4uM
515F_BACT 1.1 100uM 0.44uM
806R_BACT 1.1 100uM 0.44uM
Low TE 237.8 0 0
TOTAL: 250
PCR reactions
BACT v4/5 - Reaction Setup Component Vol (1x) MM (n) Final H2O 8.56 239.68 BSA (50ug/uL) 0.24 6.72 0-600ng/uL Univ. primer pool (4uM) 1.20 33.60 0.2uM Barcoded Fwd primer (5uM) 1.00 - 0.2uM DNA Template (0.25ng/uL) 1.00 - 5PRIME HOT MM (2.5x) 8 224.00 20uL 504.00 Aliquot 18uL MM per rxn; Added 1uL Barcoded Forward primer to each sample; Added 1uL DNA template to each reaction and Used 1uL H2O for NTC (no template control) and any barcode
Oligo Name Length
515F_BACT GTGCCAGCMGCCGCGGTAA 19
806R_BACT GGACTACHVGGGTWTCTAAT 20
806R_BACT_P1 CCTCTCTATGGGCAGTCGGTGATCCGGACTACHVGGGTWTCTAAT 45
515F_BACT_A_1 CCATCTCATCCCTGCGTGTCTCCGACTCAGTCCCTTGTCTCCGTGTGCCAGCMGCCGCGGTAA 63
Sequence (5' to 3')
Appendices
186
PCR Conditions:
BACT v4/5 - Amplification Conditions Stage Temp Time
Denature 94° 2m Denature 94° 45s
x 25 cycles Anneal 50° 60s Extend 65° 90s
Denature 94° 45s x 2 cycles
Anneal/Extend 65° 90s Final Extension 65° 10m
Hold 4° ∞
Appendices
187
Appendix 4
Appendix 4 (a) Wheat plant growth in soils receiving different soil amendments from left to right: Balance50/Agras50, Control, and Agras100 at 4 weeks after sowing.
Appendix 4. Plant growth performances at 8 weeks. (a) Agras100 (b) Balance100 (c) Balance50/Agras50 (d) control (nothing added).
Appendices
188
Appendix 5
Appendix 5. AM colonization of roots of wheat at the first harvest (4 weeks). High colonisation % was observed for Balance® 100 kg ha-1 and control and low colonisation % was observed for Agras® 100 kg ha-1 and Balance® 50 kg ha-1+Agras® 50 kg ha-1).
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