Soil type‐dependent effects of a potential biocontrol … pyrosequence analysis of samples from...

R E S EA RCH AR T I C L E

Soil type-dependent effects of a potential biocontrol inoculanton indigenous bacterial communities in the rhizosphere of

field-grown lettuce

Susanne Schreiter1,2, Guo-Chun Ding1,3, Rita Grosch2, Siegfried Kropf4, Kai Antweiler4 & KorneliaSmalla1

1Julius K€uhn-Institut, Federal Research Centre for Cultivated Plants (JKI), Institute for Epidemiology and Pathogen Diagnostics, Braunschweig,

Germany; 2Department Plant Health, Leibniz Institute of Vegetable and Ornamental Crops Großbeeren/Erfurt e.V., Großbeeren, Germany;3College of Resources and Environmental Sciences, China Agricultural University, Beijing, China; and 4Department for Biometrics and Medical

Informatics, Otto von Guericke University, Magdeburg, Germany

Correspondence: Kornelia Smalla, Julius

K€uhn-Institut, Institute for Epidemiology and

Pathogen Diagnostics, Messeweg 11-12,

38104 Braunschweig, Germany.

Tel.: +49 531 2993814;

fax: +49 531 2993013;

e-mail: [email protected]

Received 9 July 2014; revised 9 September

2014; accepted 14 September 2014. Final

version published online 31 October 2014.

DOI: 10.1111/1574-6941.12430

Editor: Wietse de Boer

Keywords

16S rRNA gene; biocontrol; denaturing

gradient gel electrophoresis; Pseudomonas

jessenii RU47; pyrosequencing; total

community DNA.

Abstract

Bacterial biocontrol strains used as an alternative to chemical fungicides may

influence bacterial communities in the rhizosphere and effects might differ

depending on the soil type. Here we present baseline data on the effects of

Pseudomonas jessenii RU47 on the bacterial community composition in the rhi-

zosphere of lettuce grown in diluvial sand, alluvial loam and loess loam at the

same field site. 16S rRNA gene fragments amplified from total community

DNA were analyzed by denaturing gradient gel electrophoresis (DGGE) and

pyrosequencing. DGGE fingerprints revealed that in three consecutive years

(2010–2012) RU47 had a slight but statistically significant effect on the bacte-

rial community composition in one (2010), two (2011) or all the three soils

(2012). However, these effects were much less pronounced compared with the

influence of soil types. Additional pyrosequence analysis of samples from 2011

showed that significant changes in bacterial community compositions in

response to RU47 inoculation occurred only in alluvial loam. Different taxo-

nomic groups responded to the RU47 application depending on the soil type.

Most remarkable was the increased relative abundance of OTUs belonging to

the genera Bacillus and Paenibacillus in alluvial loam. Pyrosequencing allowed

side-effects of the application of bacterial inoculants into the rhizosphere to be

identified.

Introduction

The soilborne pathogen Rhizoctonia solani AG1-IB can be

responsible for losses of up to 70% in field production of

lettuce (Davis et al., 1997). The pathogen is difficult to

control because of the long persistence of its sclerotia in

soil (Ogoshi, 1996). Efficient control strategies against

R. solani such as the soil fumigant methyl bromide were

phased out because of their stratospheric ozone depletion

potential and their ability to contaminate groundwater

(Guns, 1989). Additionally, the pathogen carries a high

acute toxicity risk for humans due to its carcinogenicity

and neurotoxicity (Barry et al., 2012; Bulathsinghala &

Shaw, 2014). The use of bacterial strains represents an

environmentally friendly control strategy for chemical

substances (Martin, 2003). In combination with other

management activities such as crop rotation, biofumiga-

tion and removal of infected plant tissue, use of biocon-

trol inoculants could be an important component of

integrated pest management (Barriere et al., 2014).

In the last decade a number of potential strains for bio-

control of R. solani AG1-IB have been isolated and charac-

terized under greenhouse and field conditions (Adesina

et al., 2009; Chowdhury et al., 2013). Furthermore, the

ability of inoculants to colonize the rhizosphere, termed

rhizocompetence, has been the focus of several studies

(G€otz et al., 2006; Adesina et al., 2009; Lugtenberg & Ka-

milova, 2009; Chowdhury et al., 2013; Xue et al., 2013). In

FEMS Microbiol Ecol 90 (2014) 718–730ª 2014 Federation of European Microbiological Societies.Published by John Wiley & Sons Ltd. All rights reserved

MIC

ROBI

OLO

GY

EC

OLO

GY

view of legislation on biocontrol strains, information on

potential effects on the soil microbiota should be

considered, as soil microbes are increasingly recognized as

important drivers for soil functions related to plant health

and growth (Berendsen et al., 2012; Pieterse et al., 2014).

With the advent of 16S rRNA gene-based molecular finger-

printing, detailed monitoring of rhizosphere microbial

community changes in response to inoculation became fea-

sible (Adesina et al., 2009; Grosch et al., 2012). To assess

the extent of shifts caused in response to inoculants in

comparison with other factors shaping soil and rhizosphere

bacterial diversity such as the soil type, plant species, culti-

var or plant growth stage, an appropriate experimental

design is needed (Berg & Smalla, 2009). Furthermore,

effects of inoculants on the microbial community in the

rhizosphere might differ depending on the soil type. How-

ever, these effects are difficult to assess under field condi-

tions, as many parameters such as cropping history,

agricultural management practice and weather conditions

influence the soil bacterial community composition (Costa

et al., 2006; Berg et al., 2014). For the first time, a unique

experimental plot system with three soil types under identi-

cal agricultural management practice and weather condi-

tions for more than 10 years (R€uhlmann & Ruppel, 2005)

has created the opportunity to study the effect of soil type

on the bacterial community composition in the bulk soil

and in the rhizosphere under field conditions.

Different analyses of 16S rRNA have shown that soil

types not only strongly shape the bacterial community

composition in the rhizosphere, but also influence the

extent of the so-called rhizosphere effect. In response to

the growing lettuce plant, several genera of bacteria signif-

icantly increased in the rhizosphere in comparison with

the corresponding bulk soil (Schreiter et al., 2014a).

Remarkably, the effects of the soil types on the rhizocom-

petence and biocontrol activity of two potential biocontrol

inoculants of R. solani AG1-IB, namely Pseudomonas jesse-

nii RU47 and Serratia plymuthica 3Re4-18, were negligible

(Schreiter et al., 2014b). The root exudate composition of

lettuce, which was recently shown by Neumann et al.

(2014) to differ only quantitatively between the soil types,

seemed to support the successful establishment of the

inoculant strains. DGGE analysis of 16S rRNA gene frag-

ments amplified from total community DNA (TC-DNA)

of lettuce rhizosphere samples taken 2 weeks after planting

(2WAP) in 2011 indicated a weak effect of the inoculant

strains on the bacterial community composition. The

effect of inoculants on the bacterial community composi-

tion was clearly stronger for 3Re4-18 than for RU47 (Sch-

reiter et al., 2014b). Based on the less pronounced effect

of RU47 and the reliable soil type-independent rhizocom-

petence and biocontrol activity, it was decided to focus

the present study specifically on the effects of RU47.

In the present study, the effect of the inoculant RU47

on the rhizosphere bacterial community of field-grown

lettuce was monitored for three consecutive years. We

hypothesized that the effects of the inoculant on the bac-

terial community composition in the rhizosphere of let-

tuce would be influenced by the soil type. Lettuce

rhizosphere pellets were obtained after destructive sam-

pling and recovery from a subset of the complete root

system of three plants. DGGE analysis of 16S rRNA gene

fragments was performed for all samples and subjected to

statistical analysis. Pyrosequence analysis was done for the

rhizosphere samples collected in 2011, the year when the

best biocontrol effects by RU47 were observed.

Materials and methods

Design of field experiments

Lettuce (Lactuca sativa L.) was selected as a model plant

to evaluate the effect of repeated applications of the inoc-

ulant P. jessenii RU47 on the bacterial community com-

positions in the rhizosphere in three soil types: diluvial

sand (DS), alluvial loam (AL) and loess loam (LL). The

three soils were stored at the same field site in a unique

experimental plot system, each arranged in separate

blocks at the Leibniz Institute of Vegetable and Ornamen-

tal Crops (IGZ, Großbeeren, Germany, 52°330N, 13°220E)in three consecutive years. Each block consists of 24 plots

sized 2 9 2 m with a depth of 75 cm (R€uhlmann & Rup-

pel, 2005). All three soil types were characterized by the

same crop history. Following crops were cultivated on

each block in the experimental plot system in the seasons

before the start of the experiment: pumpkin, nasturtium,

pumpkin, amaranth, wheat, wheat, pumpkin, nasturtium,

wheat, wheat, and lettuce. Four plots with lettuce with

and without RU47 inoculation were established in each

soil type from 2010 to 2012.

Lettuce seeds (cv. Tizian; Syngenta, Bad Salzuflen, Ger-

many) were sown in seedling trays filled with the respec-

tive soil type and incubated at 12 °C for 48 h and

transferred to the greenhouse to grow at about 20/15 °C(day/night). All seedling trays were watered daily and fer-

tilized weekly (0.2% Wuxal TOP N; Wilhelm Haug

GmbH & Co. KG, D€usseldorf, Germany) to maintain the

substrate moisture. Lettuce seedlings were planted at the

3–4 leaf stage (4 weeks after sowing) in six rows per plot

(36 plants per plot) with a within-row and intra-row dis-

tance of 30 cm each. Fertilizer (Kalkamon, 27% N;

TDGmbH Lommatzsch, Germany) was added to each

plot, based on a chemical analysis of the soil type before

planting. The same nitrogen amount of 157 kg ha�1 was

adjusted to each soil type. Lettuce was overhead-irrigated

based on the computer program BEREST (Gutezeit et al.,

FEMS Microbiol Ecol 90 (2014) 718–730 ª 2014 Federation of European Microbiological Societies.Published by John Wiley & Sons Ltd. All rights reserved

Effect of inoculant RU47 on rhizosphere bacterial community 719

1993) as described in detail by Schreiter et al. (2014a).

Each treatment included four replicates or plots.

Bacterization of lettuce

The rifampicin-resistant inoculant P. jessenii RU47 was

retrieved from the Julius K€uhn-Institut strain collection

by restreaking the stock culture stored in Luria–Bertanibroth (ROTH, Karlsruhe, Germany) with 20% glycerol at

�80 °C on King agar B plates (Merck KGaA, Darmstadt,

Germany) supplemented with rifampicin (75 lg mL�1).

For lettuce seed treatment a single colony of RU47 was

resuspended and spread on King agar B plates (Merck

KGaA) supplemented with rifampicin (75 lg mL�1).

Cells were scraped off from the bacterial lawn on the

King B agar and were suspended in 15 mL sterile 0.3%

NaCl solution with a density corresponding to

108 CFU mL�1 adjusted in a spectrometer. A total of

c. 100 lettuce seeds were coated with 250 lL of the bacte-

rial suspension or 0.3% NaCl (control seeds).

For young lettuce plant inoculation, RU47 was grown

in nutrient broth (NB II; SIFIN GmbH, Berlin, Ger-

many) supplemented with rifampicin (75 lg mL�1) on a

rotary shaker (90 r.p.m.) for 16 h at 29 °C. The cells

were harvested by centrifugation at 13 000 g for 5 min

and resuspended in sterile 0.3% NaCl solution and

adjusted to a density corresponding to 107 or

108 CFU mL�1. Each lettuce seedling was treated with

20-mL bacterial solutions of RU47 (107 CFU mL�1)

1 week before transplanting into the field and with

30 mL (108 CFU mL�1) at the 4-leaf stage 2 days after

planting. The seedlings of the control were drenched

with 0.3% NaCl, respectively.

Sampling and DNA extraction

Lettuce rhizosphere samples were collected 3 weeks after

planting (3WAP) in 2010 and 2 weeks after planting

(2WAP) in 2011 and 2012. The complete root system of

three lettuce plants with adhering soil was combined per

replicate (four replicates per treatment and soil type).

Loosely adhering soil was removed from the root by vig-

orous shaking but in the following years an additional

root wash was applied by dipping the lettuce roots of

each replicate briefly into 300 mL sterile tap water, fol-

lowed by a Stomacher blending step repeated three times.

To obtain the microbial rhizosphere/rhizoplane pellets,

roots were treated as described by Schreiter et al.

(2014b). TC-DNA was extracted from the pellets obtained

with the FastDNA� SPIN Kit for Soil (MP Biomedicals,

Heidelberg, Germany) according to the manufacturer’s

protocol after a harsh lysis step with the FastPrep�-24

Instrument (MP Biomedicals). The TC-DNA was purified

with GENECLEAN SPIN� Kit (MP Biomedicals) and

afterwards diluted 1 : 10 with 10 mM Tris HCl.

Analysis of 16S rRNA gene fragments by DGGE

PCR reactions with TC-DNA of rhizosphere samples were

performed for amplification of 16S rRNA gene fragments

using the bacterial primers F984-GC and R1378 as

described by Heuer et al. (1997) but using GoTaq� Flexi

(Promega, Mannheim, Germany) in 2012, instead of the

Taq DNA polymerase (Stoffel fragment; ABI, Darmstadt,

Germany) used in 2010 and 2011. To analyze the Actino-

bacteria, Alpha- and Betaproteobacteria in the 2010 and

2012 samples, a nested primer approach was used. The

PCR product of the first PCR was used as a template for

the bacterial primers F984 and R1378. All primers from

this study are summarized in Supporting Information,

Table S1. The PCR products were analyzed by DGGE as

described by Weinert et al. (2009), and silver staining was

performed according to Heuer et al. (2001).

Bacterial DGGE fingerprints were evaluated with GEL-

COMPAR II version 6.5 (Applied Maths, Sint-Martens-

Latem, Belgium). The normalization and background

subtraction was performed on the basis of each DGGE

gel image (Schreiter et al., 2014a). The Pearson correla-

tion coefficient as a curve-based method was chosen to

obtain the similarity matrices. These were used for con-

struction of a dendrogram by an unweighted pair group

method with arithmetic mean (UPGMA) as well as for

statistical analysis by the permutation test, where the

d-value was calculated as average overall correlation coef-

ficients within the groups minus the average overall cor-

relation coefficients between samples from different

groups (Kropf et al., 2004) and displayed in percentages.

Analysis of 16S rRNA gene fragments by

pyrosequencing

TC-DNA from rhizosphere samples (RU47 treatments

and controls) obtained 2WAP from the field experiment

2011 were sent to the Biotechnology Innovation Center

(BIOCANT, Cantanhede, Portugal) for barcoded pyrose-

quencing. PCR reactions were performed in 40-lL vol-

umes with Advantage Taq (Clontech) using 0.2 M of

both primers 338F and 802R (Table S1), 0.2 mM dNTPs,

19 polymerase mix and 6% DMSO. The PCR conditions

were 94 °C for 4 min, followed by 25 cycles of 94 °C for

30 s, 44 °C for 45 s and 68 °C for 60 s and a final elon-

gation step at 68 °C for 10 min. The amplicons were

quantified by fluorimetry with PicoGreen (Invitrogen,

Carlsbad, CA), pooled at equimolar concentrations and

sequenced in the A direction with GS 454 FLX Titanium

chemistry, according to the manufacturer’s instructions


720 S. Schreiter et al.

(Roche, 454 Life Sciences, Branford, CT). The raw pyro-

sequence reads (fasta files) were processed using an auto-

matic pipeline implemented at BIOCANT. In a first step,

sequencing reads were assigned to the appropriate sam-

ples based on the respective barcode. Then, reads were

quality filtered to minimize the effects of random

sequencing errors by elimination of sequence reads with

< 100 bp and sequences that contained more than two

undetermined nucleotides. Sequences were additionally

cut for the reverse primer if present.

The prefiltered pyrosequence data provided by BIO-

CANT were analyzed according to Ding et al. (2012a).

Briefly, only those sequences matching the barcode and

forward primer were selected for BLASTN analysis against a

SILVA 16S rRNA gene database to truncate the unpaired

regions for each sequence, only sequences with a length

of more than 200 bp were included. Operational taxo-

nomic unit (OTU) was generated with the following

steps: sequences were assigned to OTUs (defined at

> 97% sequences identity) with the program MOTHUR 1.21

software (Schloss et al., 2009). Na€ıve Bayesian Classifier

(Wang et al., 2007) was used to classify the sequences.

The OTU assignment and the classification of each

sequence were loaded into a MySQL-database for produc-

ing the taxonomic OTU report.

Based on the OTU report, a modified principal compo-

nent test, termed PCUniRot, was performed (Ding et al.,

2012b). This test supports multifactorial comparisons

with high-dimensional data including the investigation of

interactions of the factors after an upstream log transfor-

mation of the relative abundance in & of the OTUs after

adding ‘1’. It is exact under the assumption of multivari-

ate normal data. In a further development of the basic

principal component test (L€auter et al., 1996, 1998), the

modification was directed towards a more effective use of

the principal components in the case of small samples

and a very large number of variables were included.

For the detection of OTUs determining the overall shift

corresponding to the application of RU47, a multiple per-

mutation test as suggested by Westfall & Young (1989)

with the described pre-transformation of the data was used.

With this test an overall family-wise error rate based on all

detected OTUs of 5% was not possible. For this reason,

OTUs occurring in a minimum of 10 samples were selected

for further analysis. With this pre-filtering, the most abun-

dant 901 OTUs were used for significance test.

Furthermore, the ‘R’ (version 2.14) add-on package

‘vegan’ was used to analyze the community composition

and calculate the rarefaction curves as well as Pielous’s

evenness indices. A Tukey’s honestly significant difference

test, abbreviated Tukey test, under a generalized linear

model via a logistic function for binomial data with the

package multcomp (Hothorn et al., 2008) was performed

to identify the discriminative taxa between inoculation

with RU47 and control in the different soil types without

a logarithmic pre-transformation of the data. Bonferroni

adjustment was used for the P-value < 0.05. To visualize

the microbial community composition between treat-

ments, a non-metric multidimensional scaling (NMDS)

was used based on Bray–Curtis similarity. Pyrosequence

data were deposited at the NCBI Sequence Read Archive

under the study accession number SRP029944.

Results

DGGE revealed minor effects of RU47 on the

bacterial community in the rhizosphere of

lettuce

To assess the effects of the inoculant RU47 on the bacterial

community composition in the rhizosphere of lettuce

grown in three soil types in three consecutive years, com-

parative DGGE analysis was done. Fingerprints generated

from 16S rRNA gene fragments amplified from TC-DNA

of the four replicate samples per treatment showed little

within- or between-treatment variability in each of the

three soil types. No separate clusters between inoculated

and control treatments were found (Figs S1–S17), but a

separate clustering depending on the soil types was

observed for all 3 years. The calculated d-values (Table 1)

indicated low differences between the bacterial community

compositions in the rhizosphere of RU47-inoculated let-

tuce plants and the corresponding control plants. However,

for DS soils these differences were significant in all 3 years.

In 2011 the differences were also significant for lettuce

grown in LL soil, whereas in 2012 significant differences

between inoculated and control treatments were observed

for all three soil types. The differences between the bacterial

communities in the rhizosphere of inoculated and control

treatments increased in all three soils, with the highest d-

values (except for LL) observed in 2012.

The differences in bacterial community compositions

caused by soil types were much higher than the differ-

ences caused by the application of RU47 (Table 1). In

2010, the differences between the soil types were rather

similar to d-values, ranging from 18.8% to 19.9%; the

differences of DS vs. LL and AL vs. LL soil increased

strongly over time. The highest differences were recorded

between DS and LL soil, with 46.6% in 2012 (Table 1).

In comparison, the bacterial community compositions in

the rhizosphere of lettuce grown in DS and AL soil

became more similar with time, as the lowest d-value was

observed in 2012.

To reveal soil type-dependent effects of the RU47

treatment on less abundant populations in the rhizo-

sphere of lettuce, DGGE fingerprints were generated for



Actinobacteria, Alpha- and Betaproteobacteria from sam-

ples taken in 2010 and 2012 (Table 1). UPGMA analysis

revealed soil type-dependent clusters for the taxonomic

groups analyzed in both years (Figs S8, S10, S16 and

S18). Interestingly, Betaproteobacteria rhizosphere finger-

prints of lettuce grown in DS and AL soils in 2012

showed higher similarities with each other compared to

the 2010 LL rhizosphere fingerprints (Fig. S14). In con-

trast to this, the Actinobacteria fingerprints of AL and LL

rhizospheres were more similar to each other than those

of DS rhizospheres in 2010 and 2012 (Figs S16 and S18).

The effect of RU47 on the Betaproteobacteria community

composition was only significant for lettuce grown in AL

in 2010, and in all soils the d-values decreased in 2012

compared with 2010 (Table 1), whereas the RU47 inocu-

lation significantly influenced the Actinobacteria commu-

nity composition in the rhizosphere of lettuce grown in

DS soil in both years. In the two loamy soils, rather

strong effects of RU47 on the Actinobacteria community

composition were only detected in 2012. Overall,

increased d-values indicating higher differences between

the Actinobacteria communities in the rhizosphere of

inoculated and control treatments were recorded for all

three soils in 2012.

The analysis of the Actinobacteria, Alpha- and Betaproteo-

bacteria confirmed that the effect of RU47 was minor com-

pared with the effects of different soil types. However, it

also became clear that both the RU47 and the soil type

effects differed for the three taxonomic groups analyzed.

Pyrosequence data

To determine the taxonomic composition of bacterial

communities in the rhizosphere of lettuce and to identify

major responders to RU47 inoculation, pyrosequencing of

16S rRNA gene fragments amplified from a total of 24

rhizosphere samples of control and RU47 treatments

from the three soil types in 2011 was carried out (four

replicates per soil type and treatment). Altogether,

103 178 sequences with a sequence length of more than

200 bp were used for the analysis. The sequences were

classified into 18 phyla, 52 classes, 83 orders, 175 families

and 394 genera, and clustered to 7076 OTUs based on

97% sequence identity. In both treatments and all three

soil types, the dominant phyla (with more than 1%

relative abundance) were the Proteobacteria, followed by

Actinobacteria, Bacteroidetes, Firmicutes and Acidobacteria

(Table 2). The classes within the Proteobacteria with the

highest relative abundance were the Betaproteobacteria,

followed by Alphaproteobacteria; the Gamma- and Delta-

proteobacteria were less abundant (Table 2). Based on the

Tukey test, statistically significant differences between

RU47-treated and control samples at the phylum level

were observed only for the Firmicutes in the AL rhizos-

pere. A 1.5-fold increase in the relative abundance of the

phylum Firmicutes was observed in response to the RU47

inoculation. None of the other phyla and classes analyzed

changed significantly in their relative abundance in

response to the RU47 inoculation.

Rarefaction analysis was performed to compare bacte-

rial richness between soil types and treatments. The result

revealed that bacterial richness varied between soil type,

with the lowest richness for DS soil and the highest for

LL soil. However, the influence of RU47 on richness was

not pronounced for the three soil types (Fig. 1a). Inter-

estingly, the evenness indicated by Pielous’s indices was

lower in the control than in the RU47-treated samples for

the two loamy soils (Fig. 1b). But in the DS soil the

evenness was comparable between control samples and

RU47-treated samples.

Table 1. Differences (d-values in %) between taxonomic groups obtained by denaturing gradient gel electrophoresis analysis of rhizosphere

samples of lettuce grown in three soil types at the experimental plot system in Großbeeren (Germany) with and without application of the

inoculant RU47

Taxonomic group Sampling year Sampling time Figure

Differences caused by RU47

inoculation

Differences caused by

soil type

DS AL LL DS-AL DS-LL AL-LL

Bacteria 2010 3WAP S1 2.9* 2.1 2.0 18.8* 18.9* 19.9*

Bacteria 2011 2WAP S3 3.9* 5.8 4.5* 21.9* 36.8* 22.9*

Bacteria 2012 2WAP S5 8.9* 9.1* 3.2* 15.7* 46.6* 27.0*

Alphaproteobacteria 2010 3WAP S7 1.1 2.5 2.7* 27.6* 25.1* 37.9*

Alphaproteobacteria 2012 2WAP S9 0.4 0.9 5.1* 13.6* 24.0* 8.0*

Betaproteobacteria 2010 3WAP S11 3.5 7.8* 11.4 38.5* 51.4* 57.2*

Betaproteobacteria 2012 2WAP S13 0 0 3.4 6.8 29.9* 16.0*

Actinobacteria 2010 3WAP S15 2.4* 1.1 0 29.0* 28.5* 20.0*

Actinobacteria 2012 2WAP S17 8.1* 15.7* 14.4* 40.0* 35.4* 42.8*

AL, alluvial loam; DS, diluvial sand; LL, loess loam.

*Significant differences acquired by the permutation test as suggested by Kropf et al. (2004).



The comparison of the bacterial community composi-

tions performed by NMDS using Bray–Curtis similarities

confirmed that the RU47- and control treatments did not

cluster separately, although soil type-dependent clusters

were observed (Fig. 2). Using the PCUniRot for each soil

type separately, a statistically significant effect of the

RU47 inoculation on the bacterial community composi-

tion was observed in the AL rhizospere only. Two OTUs

responsible for this effect could be identified as OTU 402

and OTU 5304. These OTUs shared 98% sequence iden-

tity with Pseudomonas brassicacearum (EU391388) and

Pseudomonas reinekei (AM293556), respectively. Both of

these OTUs also had a high sequence identity of 98–99%to the applied biocontrol strain RU47.

Table 2. Relative abundance (in %) of dominant phyla and classes from the rhizosphere of lettuce grown in three soil types in 2011 in the

control plot and the plot inoculated with Pseudomonas jessenii RU47 obtained by pyrosequencing

Phylum Class

DS AL LL

Control RU47 Control RU47 Control RU47

Proteobacteria 63.8 � 4 67.3 � 3 63.2 � 7 58.9 � 4 60.7 � 6 59.0 � 4

Alphaproteobacteria 25.8 � 6 27.7 � 3 22.2 � 2 21.8 � 4 18.9 � 1 19.8 � 2

Betaproteobacteria 26.7 � 1 30.0 � 3 30.8 � 9 23.6 � 6 31.0 � 7 26.5 � 2

Gammaproteobacteria 8.2 � 5 6.8 � 2 7.2 � 2 10.1 � 6 6.6 � 1 8.0 � 1

Deltaproteobacteria 1.8 � 0 1.8 � 0 1.8 � 0 2.0 � 0 2.6 � 1 3.2 � 0

Actinobacteria 18.5 � 2 15.4 � 1 15.1 � 4 16.8 � 2 15.7 � 2 16.3 � 2

Bacteroidetes 4.8 � 1 5.9 � 1 6.1 � 1 5.6 � 1 6.6 � 0 6.9 � 1

Firmicutes 4.8 � 2 4.1 � 1 5.8 � 1 8.6 � 1* 6.6 � 2 7.9 � 2

Acidobacteria 3.0 � 1 2.7 � 0 2.91 � 3.2 � 1 3.0 � 1 2.9 � 0

AL, alluvial loam; DS, diluvial sand; LL, loess loam.

*Significantly enriched taxa in the rhizosphere between control and RU47-treated samples in each soil type, identified by Tukey test under a gen-

eralized linear model via logistic function for binomial data.

(a)

(b)

Fig. 1. Rarefaction analysis (a) based on OTUs

(> 97% sequence identity) and Pielous’s

evenness indices (b) for lettuce rhizosphere

samples treated with RU47 or untreated

control (con) from three soils (DS, AL, LL) in

2011.



Several genera with significant changes in relative abun-

dance in response to the RU47 inoculation were identi-

fied by the Tukey test. Most importantly, the genera with

significantly changed relative abundance in the lettuce

rhizosphere differed among the soil types. The most

abundant responders to RU47 inoculation in AL rhizo-

sphere were the genera Paenibacillus and Bacillus

(Table 3). In addition, the less abundant genus Bdellovib-

rio showed a twofold increase in relative abundance in

response to RU47 (Table 3). In DS rhizosphere, the gen-

era Methylophilus, Fluviicola and Cytophaga significantly

increased in relative abundance (two- and threefold;

Table 3). In contrast, the genera Nocardioides and Mar-

moricola, belonging to the Actinobacteria, decreased in rel-

ative abundance in DS rhizosphere of RU47 treatments

compared with the control (Table 3). In the LL soil only

the genus Bdellovibrio displayed a significantly changed

relative abundance in response to the RU47 treatment.

Similar to the AL rhizospere, the relative abundance of

Bdellovibrio doubled in LL rhizosphere in response to

RU47 inoculation (Table 3).

The 50 most abundant OTUs were selected and their

relative abundance was visualized in a heatmap (Fig. 3).

Among the 50 dominant OTUs, 42 were affiliated to the

Proteobacteria, with the majority being Betaproteobacteria

(22), followed by Alpha- (10) and Gammaproteobacteria

(10). The remaining dominant OTUs belonged to the Fir-

micutes (4), Actinobacteria (3) and Bacteroidetes (1). With

a few exceptions, most dominant OTUs displayed rather

high sequence identities (97–100%) to 16S rRNA gene

sequences of isolates. Significant changes in the relative

abundance of dominant OTUs in response to the applica-

tion of RU47 were only observed for six OTUs. Among

them were three OTUs with high sequence identity to

P. reinekei and P. brassicacearum which were detected in

the lettuce rhizosphere in all three soils. The fourth OTU

identified as Pseudomonas graminis did not show this

treatment-dependent response. In contrast to the few

OTUs responding to the RU47 inoculation, approxi-

mately two-thirds of the dominant OTUs showed a clear

soil type-dependent relative abundance (Fig. 3).

Discussion

Various factors such as soil type, plant species, cultivars,

plant growth stage, cropping history and agricultural

management systems were recently reported to shape the

bacterial community composition in the rhizosphere of

crops, as reviewed by Berg & Smalla (2009), Buee et al.

(2009) and Berg et al. (2014). In the present study, we

tested the hypothesis that the effects of potential biocon-

trol inoculants such as P. jessenii RU47 on the bacterial

community composition depend on soil type, as the inoc-

ulant is not only exposed to different soil physico-chemi-

cal conditions but also interacts with different indigenous

bacterial communities. Effects of the inoculant RU47 on

the bacterial communities in the rhizosphere were

assessed for lettuce grown under field conditions in three

different soils in three consecutive years and sampled at

the same plant growth stage. The experimental design

and the methods used not only allowed the evaluation of

shifts in the bacterial community composition in response

to the inoculant application in each year, but also made

it possible to reveal differences due to the repeated

growth of lettuce with and without RU47 application and

to identify responders to the inoculant. Bacterial DGGE

fingerprints revealed that the differences in the bacterial

community composition between RU47 and control treat-

ments were minor in all three soil types compared with

the differences observed between soil types (Table 1).

This observation was made for all soil types in the three

consecutive years. DGGE fingerprints indicated that the

differences in the bacterial and actinobacterial communi-

ties between RU47 and control treatments increased in

2012 in all three soils, indicating that the repeated appli-

cation of RU47 in successive lettuce cropping at the same

field site likely increased the effect of the inoculants

(Table 1). However, we cannot exclude that other factors

such as the weather conditions might have also contrib-

uted to these increased differences. Interestingly, the d-

values for Alpha- and Betaproteobacteria were lower in

2012 than in 2010, indicating a weaker effect of RU47

after repeated lettuce growth which might well be caused

Fig. 2. NMDS based on the Bray–Curtis similarity for lettuce

rhizosphere samples treated with RU47 or untreated control (con)

from three soils (DS, AL, LL) in 2011.



Table

3.Relativeab

undan

ce(in

%)oftaxonomic

groupsin

therhizosphereoflettuce,grownin

thethreesoiltypes

respondingto

thetreatm

entwiththeinoculants

RU47

Phylum

Class

Order

Family

Gen

us

DS

AL

LL

Control

RU47

Control

RU47

Control

RU47

Actinobacteria

Actinobacteria

Actinomycetales

Nocardioidaceae

Nocardioides

1.4

�0*

0.9

�0

1.9

�0

1.5

�0

2.3

�0

2.6

�0

Actinobacteria

Actinobacteria

Actinomycetales

Nocardioidaceae

Marmoricola

0.1

�0*

0.0

�0

0.1

�0

0.1

�0

0.1

�0

0.0

�0

Bacteroidetes

Flavobacteria

Flavobacteriales

Cryomorphaceae

0.1

�0

0.2

�0*

0.3

�0

0.4

�0

0.3

�0

0.2

�0

Bacteroidetes

Sphingobacteria

Sphingobacteriales

Cytophag

aceae

Cytophag

a0.1

�0

0.3

�0*

0.1

�0

0.0

�0

0.0

�0

0.0

�0

Bacteroidetes

Flavobacteria

Flavobacteriales

Cryomorphaceae

Fluviicola

0.1

�0

0.2

�0*

0.2

�0

0.4

�0

0.3

�0

0.2

�0

Firm

icutes

4.8

�2

4.1

�1

5.8

�1

8.6

�1*

6.6

�2

7.9

�2

Firm

icutes

Bacilli

4.4

�2

3.5

�1

5.0

�1

7.6

�1*

5.9

�1

6.8

�2

Firm

icutes

Bacilli

Bacillales

4.4

�2

3.5

�1

5.0

�1

7.6

�1*

5.9

�1

6.8

�2

Firm

icutes

Bacilli

Bacillales

Bacillaceae

2.3

�1

1.6

�0

2.1

�0

3.4

�1*

3.0

�0

3.6

�1

Firm

icutes

Bacilli

Bacillales

Bacillaceae

Bacillus

1.3

�0

1.2

�0

1.6

�0

2.4

�1*

1.5

�0

1.7

�0

Firm

icutes

Bacilli

Bacillales

Paen

ibacillaceae

1.7

�0

1.3

�0

2.0

�0

3.3

�1*

2.4

�1

2.7

�1

Firm

icutes

Bacilli

Bacillales

Paen

ibacillaceae

Paen

ibacillus

1.3

�0

1.0

�0

1.6

�0

3.0

�1*

2.0

�1

2.3

�1

Proteobacteria

Betap

roteobacteria

Methylophilales

1.3

�0

2.4

�0*

1.3

�0

1.1

�0

2.8

�1

2.8

�0

Proteobacteria

Betap

roteobacteria

Methylophilales

Methylophilaceae

1.3

�0

2.4

�0*

1.3

�0

1.1

�0

2.8

�1

2.8

�0

Proteobacteria

Betap

roteobacteria

Methylophilales

Methylophilaceae

Methylophilus

1.1

�0

2.2

�0*

0.5

�0

0.4

�0

1.6

�0

1.8

�0

Proteobacteria

Deltaproteobacteria

Bdellovibrionales

Bdellovibrionaceae

0.1

�0

0.1

�0

0.1

�0

0.2

�0*

0.1

�0

0.2

�0*

Proteobacteria

Deltaproteobacteria

Bdellovibrionales

Bdellovibrionaceae

Bdellovibrio

0.1

�0

0.1

�0

0.1

�0

0.2

�0*

0.1

�0

0.2

�0*

AL,

alluvial

loam

;DS,

diluvial

sand;LL,loessloam

.

*Significantlyen

riched

taxa

intherhizospherebetweencontrolan

dRU47-treated

samplesin

each

soiltype,

iden

tified

byTu

keytest

under

agen

eralized

linearmodel

vialogisticfunctionforbino-

mialdata.



by the strong enrichment of Beta- and Alphaproteobacteria

in the rhizosphere in consecutive lettuce cultivations

(Schreiter et al., 2014a). The enrichment of Beta- and Al-

phaproteobacteria in the rhizosphere in previous lettuce

crops may increase the relative abundance of these popu-

lations in bulk soil.

Pyrosequence analyses were done for the samples from

2011 when the best biocontrol effects by RU47 were

observed and the data largely confirmed the findings

obtained by DGGE. Both methods showed distinct soil

type-dependent bacterial community composition in the

rhizosphere of lettuce, whereas only minor differences

between the bacterial community compositions of the

RU47-inoculated and control treatments were detected.

However, statistical analysis of pyrosequence data showed

a significant effect of RU47 in the AL rhizosphere,

whereas the d-value of the same sample set was not sig-

nificantly different for DGGE fingerprints, despite the

highest d-values being observed for the AL rhizosphere.

The soil type-dependent responders to RU47 inocula-

tion identified through pyrosequence analysis further con-

firmed our hypothesis that the effects of RU47 were soil

type-specific. Although the effects of RU47 on the bacte-

rial community composition detected by DGGE finger-

prints and pyrosequence data were much less pronounced

compared with the effects caused by the soil types,

changes in relative abundance of a few genera in response

to RU47 application were observed by statistical analysis

of pyrosequence data. The significant increase of the

already abundant genera Bacillus and Paenibacillus in the

AL rhizospere was particularly interesting, as isolates

belonging to these genera often confer traits that are ben-

eficial for plant growth and health (Beneduzi et al.,

2008a, b). Strains belonging to these genera are already

used as biocontrol agents, for example Bacillus subtilis

CA32 and Bacillus amyloliquefaciens FZB42.

In comparison with the composition of dominant

OTUs in the rhizosphere of lettuce grown in the same

soils in 2010 (Schreiter et al., 2014a), a higher proportion

of the 50 dominant OTUs from the present study (2011)

belonged to the Betaproteobacteria (Acidovorax, Burkholde-

ria, Massilia, Methylophilus, Duganella, Naxibacter,

Piscinibacter, Rubrivivax, Variovorax) and Gammaproteo-

bacteria (Acinetobacter, Pantoea, Alkanindiges, Pseudo-

monas). This finding might be due to the modification

in the protocol for the extraction of the microbial pellet

from the roots. The additional root wash step was

introduced to avoid large clumps of soil adhering to the

root, which was observed in particular for the soils with

high clay content (AL, LL). Thus the rhizosphere commu-

nity composition of samples from 2011 and 2012 mainly

represent the fraction of soil adhering very tightly to the

root and the rhizoplane. Our data indicate that, in partic-

ular, Betaproteobacteria were more closely attached to the

roots of lettuce. In comparison, Alphaproteobacteria dom-

inated in the rhizosphere of lettuce samples from 2010,

which were analyzed without root wash, suggesting that

they adhered less firmly to the roots (Schreiter et al.,

2014a). Although the relative abundance of six dominant

OTUs changed significantly in response to RU47, only

three OTUs strongly increased under the RU47 treatment.

They displayed 98% sequence identity to P. brassicacea-

rum and P. reinekei. We cannot exclude that the inocu-

lant RU47 contributed to the OTU 5304, OTU 1077 and

OTU 402, which showed a sequence identity of > 98%,

respectively, to the 16S rRNA gene sequence of RU47.

Thus the heatmap data should not be overinterpreted, as

a partial 16S rRNA gene sequence clearly does not allow

a reliable taxonomic identification at the species level.

Screening the abundance of functional genes involved in

antibiotic production (prnD, PCA, DAPG) in 2010 did

not indicate any differences between controls and RU47

treatments (data not shown).

The present study is unique, being the first to report

on the assessment of the effects of a potential biocontrol

inoculant on the bacterial community composition in the

rhizosphere in three soil types at the same field site in

three consecutive years. Despite a good and soil type-

independent rhizocompetence of RU47 in the different

experiments (Schreiter et al., 2014b) the effects on the

bacterial community composition in the rhizosphere were

minor compared with the soil type, and likely transient.

Effects of the inoculant treatment might be comparable

to the effects of genetically modified potato plants, which

were within the range of natural cultivar variability as

reported by Weinert et al. (2009). Previous studies with

other inoculants (Scherwinski et al., 2008; Grosch et al.,

2012; Chowdhury et al., 2013), all of them based on fin-

gerprinting of 16S rRNA gene fragments [single-strand

conformation polymorphism (SSCP), DGGE and terminal

restriction fragment length polymorphism (T-RFLP)],

also reported only minor effects of inoculants. In the

study by Erlacher et al. (2014), the gammaproteobacterial

microbiome in the rhizosphere and phyllosphere of

Fig. 3. Relative abundance of the most dominant OTUs for lettuce rhizosphere samples detected 2 weeks after planting in 2011. The heatmap

indicates differences by an asterisk in the relative abundance of OTUs between RU47 treatment and untreated control. The lettuce was grown in

three soil types (DS, AL, LL). Each vertical column represents one sample and each horizontal row depicts one OTU. The color code grades from

black (not detected), to yellow (low abundance), to orange (medium abundance) to red (high abundance). Numbers in brackets indicate the NCBI

GenBank accession number of the sequence that was most similar to the consensus sequence of each OTU.



lettuce grown under greenhouse conditions was analyzed

by pyrosequencing. Those authors found that the highest

impact on the indigenous gammaproteobacterial commu-

nities due to the R. solani AG1-IB attack was reduced in

the treatments with the biocontrol strain B. amylolique-

faciens. A direct metagenome sequencing approach was

used recently to assess the effects of the inoculant B. amy-

loliquefaciens FZB42 on the bacterial community in the

rhizosphere of lettuce at three different plant growth

stages (Kr€ober et al., 2014). FZB42 was found to have

almost no effect on the bacterial community composition

in the rhizosphere of lettuce, whereas the growth stage

had a more pronounced impact. However, the absence of

replicates also prevented a more rigorous testing for sig-

nificant differences. The same lettuce cultivar was used by

Kr€ober et al. (2014) and, with a few exceptions, the me-

tagenome sequencing revealed a similar taxonomic affilia-

tion of the abundant classes, orders and genera as found

by amplicon sequencing of 16S rRNA gene fragments in

the present study. The most striking difference was the

high abundance of Mycobacteria revealed by direct me-

tagenome sequencing, which might be due to a different

bacterial community composition of the AL soil used in

that study. In the present study, we demonstrated the

potential of next generation sequencing technologies to

reveal populations changing in relative abundance in

response to RU47 inoculation. With increasing sequenc-

ing depth and decreasing costs of next generation

sequencing, the sensitivity of the methods and feasibility

to analyze large numbers of samples will provide an

important tool to assess the effects of inoculants on the

soil microbiota. Despite the limitations of the 16S rRNA

gene-based analysis, by providing limited taxonomic and

functional information, this is a big step forward in our

ability to detect even more subtle changes in the bacterial

community composition in response to inoculant applica-

tion. It is important to note that changes of the bacterial

community composition are not per se negative but could

well be beneficial, e.g. through the increased relative

abundance of plant beneficial bacteria or the decreased

abundance of plant pathogens. Considering functional

resilience in soils, we assume that the inoculation of

RU47 did not affect soil functions, or did so only tran-

siently. Changes in the bacterial community composition

could well contribute to the improved plant health in

inoculant treatments previously observed by Schreiter

et al. (2014b).

Acknowledgements

The authors acknowledge that the project SM59/11-1/

GR568121 was funded by the Deutsche Forschungsge-

meinschaft (DFG). We would also like to thank Petra

Zocher, Ute Zimmerling, Sabine Breitkopf and Angelika

Fandrey for their skilled technical assistance and Ilse-

Marie Jungkurth for her helpful comments on the manu-

script.

References

Adesina MF, Grosch R, Lembke A, Vatchev TD & Smalla K

(2009) In vitro antagonists of Rhizoctonia solani tested on

lettuce: rhizosphere competence, biocontrol efficiency and

rhizosphere microbial community response. FEMS Microbiol

Ecol 69: 62–74.Barriere V, Lecompte F, Nicot PC, Maisonneuve B,

Tchamitchian M & Lescourret F (2014) Lettuce cropping

with less pesticides. A review. Agron Sustain Dev 34: 175–198.

Barry KH, Koutros S, Lubin JH et al. (2012) Methyl bromide

exposure and cancer risk in the Agricultural Health Study.

Cancer Causes Control 23: 807–818.Beneduzi A, Peres D, da Costa PB, Bodanese Zanettini MH &

Pereira Passaglia LM (2008a) Genetic and phenotypic

diversity of plant-growth-promoting bacilli isolated from

wheat fields in southern Brazil. Res Microbiol 159: 244–250.Beneduzi A, Peres D, Vargas LK, Bodanese-Zanettini MH &

Passaglia LMP (2008b) Evaluation of genetic diversity and

plant growth promoting activities of nitrogen-fixing bacilli

isolated from rice fields in South Brazil. Appl Soil Ecol 39:

311–320.Berendsen RL, Pieterse CMJ & Bakker PAHM (2012) The

rhizosphere microbiome and plant health. Trends Plant Sci

17: 478–486.Berg G & Smalla K (2009) Plant species and soil type

cooperatively shape the structure and function of microbial

communities in the rhizosphere. FEMS Microbiol Ecol 68: 1–13.

Berg G, Grube M, Schloter M & Smalla K (2014) Unraveling

the plant microbiome: looking back and future perspectives.

Front Microbiol 5: 158.

Buee M, De Boer W, Martin F, van Overbeek L & Jurkevitch E

(2009) The rhizosphere zoo: an overview of plant-associated

communities of microorganisms, including phages, bacteria,

archaea, and fungi, and of some of their structuring factors.

Plant Soil 321: 189–212.Bulathsinghala AT & Shaw IC (2014) The toxic chemistry of

methyl bromide. Hum Exp Toxicol 33: 81–91.Chowdhury SP, Dietel K, R€andler M, Schmid M, Junge H,

Borriss R, Hartmann A & Grosch R (2013) Effects of

Bacillus amyloliquefaciens FZB42 on lettuce growth and

health under pathogen pressure and its impact on the

rhizosphere bacterial community. PLoS ONE 8: e68818.

Costa R, G€otz M, Mrotzek N, Lottmann J, Berg G & Smalla K

(2006) Effects of site and plant species on rhizosphere

community structure as revealed by molecular analysis of

microbial guilds. FEMS Microbiol Ecol 56: 236–249.Davis R, Subbarao K, Raid R & Kurtz E (1997) Compendium

of Lettuce Diseases. APS Press, St. Paul, MN.



Ding G-C, Heuer H & Smalla K (2012a) Dynamics of bacterial

communities in two unpolluted soils after spiking with

phenanthrene: soil type specific and common responders.

Front Microbiol 3: 290.

Ding G-C, Smalla K, Heuer H & Kropf S (2012b) A new

proposal for a principal component-based test for

high-dimensional data applied to the analysis of PhyloChip

data. Biom J 54: 94–107.Erlacher A, Cardinale M, Grosch R, Grube M & Berg G (2014)

The impact of the pathogen Rhizoctonia solani and its

beneficial counterpart Bacillus amyloliquefaciens on the

indigenous lettuce microbiome. Front Microbiol 5: 175.

G€otz M, Gomes NCM, Dratwinski A, Costa R, Berg G, Peixoto

R, Mendonc�a-Hagler L & Smalla K (2006) Survival of

gfp-tagged antagonistic bacteria in the rhizosphere of

tomato plants and their effects on the indigenous bacterial

community. FEMS Microbiol Ecol 56: 207–218.Grosch R, Dealtry S, Schreiter S, Berg G, Mendonc�a-Hagler L &

Smalla K (2012) Biocontrol of Rhizoctonia solani: complex

interaction of biocontrol strains, pathogen and indigenous

microbial community in the rhizosphere of lettuce shown by

molecular methods. Plant Soil 361: 343–357.Guns MF (1989) Monitoring of the groundwater bromine

content and plant residues in methyl bromide fumigated

glasshouses. Acta Hortic (Wageningen) 255: 337–346.Gutezeit B, Herzog FN & Wenkel KO (1993) Das

Beregnungsbedarfssystem f€ur Freilandgem€use. Gem€use 29:

106–108.Heuer H, Krsek M, Baker P, Smalla K & Wellington EMH

(1997) Analysis of actinomycete communities by specific

amplification of genes encoding 16S rRNA and

gel-electrophoretic separation in denaturing gradients. Appl

Environ Microbiol 63: 3233–3241.Heuer H, Wieland G, Sch€onfeld J, Sch€onw€alder A, Gomes

NCM & Smalla K (2001) Bacterial community profiling

using DGGE or TGGE analysis. Environmental Molecular

Microbiology: Protocols and Applications. Horizon Press Inc.,

New York, NY and Wymondham.

Hothorn T, Bretz F & Westfall P (2008) Simultaneous

inference in general parametric models. Biom J 50: 346–363.Kr€ober MS, Wibberg D, Grosch R, Eikmeyer FG, Verwaaijen

B, Chowdhury SP, Hartmann A, P€uhler A & Schl€uter A

(2014) Effect of the biocontrol strain Bacillus

amyloliquefaciens FZB42 on the microbial community in the

rhizosphere of lettuce under field conditions analyzed by

whole metagenome sequencing. Front Microbiol 5: 252.

Kropf S, Heuer H, Gr€uning M & Smalla K (2004) Significance

test for comparing complex microbial community

fingerprints using pairwise similarity measures. J Microbiol

Methods 57: 187–195.L€auter J, Glimm E & Kropf S (1996) New multivariate tests

for data with an inherent structure. Biom J 38: 5–23.L€auter J, Glimm E & Kropf S (1998) Multivariate tests based

on left-spherically distributed linear scores. Ann Stat 26:

1972–1988.

Lugtenberg B & Kamilova F (2009) Plant-growth-promoting

rhizobacteria. Annu Rev Microbiol 63: 541–556.Martin FN (2003) Development of alternative strategies for

management of soilborne pathogens currently controlled

with methyl bromide. Annu Rev Phytopathol 41: 325–350.Neumann G, Bott S, Ohler M, Mock H, Lippman R, Grosch R

& Smalla K (2014) Root exudation and root development of

lettuce (Lactuca sativa L. cv. Tizian) as affected by different

soils. Front Microbiol 5: 2.

Ogoshi A (1996) Introduction: the genus Rhizoctonia.

Rhizoctonia Species: Taxonomy, Molecular Biology, Ecology,

Pathology and Disease Control. Kluwer Academic Publishers,

Dordrecht and Norwell.

Pieterse CMJ, Zamioudis C, Berendsen RL, Weller DM, Wees

SCMV & Bakker PAHM (2014) Induced systemic resistance

by beneficial microbes. Annu Rev Phytopathol 52: 347–375.R€uhlmann J & Ruppel S (2005) Effects of organic amendments

on soil carbon content and microbial biomass – results of

the long-term box plot experiment in Grossbeeren. Arch

Agron Soil Sci 51: 163–170.Scherwinski K, Grosch R & Berg G (2008) Effect of bacterial

antagonists on lettuce: active biocontrol of Rhizoctonia

solani and negligible, short-term effects on nontarget

microorganisms. FEMS Microbiol Ecol 64: 106–116.Schloss PD, Westcott SL, Ryabin T et al. (2009) Introducing

mothur: open-source, platform-independent,

community-supported software for describing and comparing

microbial communities. Appl Environ Microbiol 75: 7537–7541.Schreiter S, Ding G-C, Heuer H, Neumann G, Sandmann M,

Grosch R, Kropf S & Smalla K (2014a) Effect of the soil

type on the microbiome in the rhizosphere of field-grown

lettuce. Front Microbiol 5: 144.

Schreiter S, Sandmann M, Smalla K & Grosch R (2014b) Soil

type dependent rhizosphere competence and biocontrol of

two bacterial inoculant strains and their effects on the

rhizosphere microbial community of field-grown lettuce.

PLoS ONE 9: e103726.

Wang Q, Garrity GM, Tiedje JM & Cole JR (2007) Na€ıve

Bayesian classifier for rapid assignment of rRNA sequences

into the new bacterial taxonomy. Appl Environ Microbiol 73:

5261–5267.Weinert N, Meincke R, Gottwald C, Heuer H, Gomes NCM,

Schloter M, Berg G & Smalla K (2009) Rhizosphere

communities of genetically modified

zeaxanthin-accumulating potato plants and their parent

cultivar differ less than those of different potato cultivars.

Appl Environ Microbiol 75: 3859–3865.Westfall PH & Young SS (1989) P-value adjustments for

multiple tests in multivariate binomial models. J Am Stat

Assoc 84: 780–786.Xue Q-Y, Ding G-C, Li S-M, Yang Y, Lan C-Z, Guo J-H &

Smalla K (2013) Rhizocompetence and antagonistic activity

towards genetically diverse Ralstonia solanacearum strains –an improved strategy for selecting biocontrol agents. Appl

Microbiol Biotechnol 97: 1361–1371.



Supporting Information

Additional Supporting Information may be found in the

online version of this article:

Fig. S1. Bacterial DGGE-fingerprints of 16S rRNA gene

fragments amplified from TC-DNA obtained from the

rhizosphere without inoculation (control) and with inoc-

ulation of P. jessenii RU47 3 weeks after planting lettuce

into three soil types (DS, AL, LL) with four replicates (a–d) in 2010. M: Marker.

Fig. S2. UPGMA dendrogram based on Pearson correla-

tion indices obtained from bacterial DGGE-fingerprints

three weeks after planting lettuce into three soil types

(DS, AL, LL) with four replicates (a–d) in 2010.




ulation of P. jessenii RU47 two weeks after planting let-

tuce into three soil types (DS, AL, LL) with four

replicates (a–d) in 2011.



2 weeks after planting lettuce into three soil types (DS,

AL, LL) with four replicates (a–d) in 2011.




ulation of P. jessenii RU47 2 weeks after planting lettuce

into three soil types (DS, AL, LL) with four replicates (a–d) in 2012. M: Marker.



2 weeks after planting lettuce into three soil types (DS,


Fig. S7. Alphaproteobacterial DGGE-fingerprints of 16S

rRNA gene fragments amplified from TC-DNA obtained

from the rhizosphere without inoculation (control) and

with inoculation of P. jessenii RU47 3 weeks after plant-

ing lettuce into three soil types (DS, AL, LL) with four

replicates (a–d) in 2010. M: Marker.


tion indices obtained from alphaproteobacterial DGGE-

fingerprints 3 weeks after planting lettuce into three soil

types (DS, AL, LL) with four replicates (a–d) in 2010.

Fig. S9. Alphaproteobacterial DGGE-fingerprints of 16S







tion indices obtained from alphaproteobacterial DGGE-

fingerprints 2 weeks after planting lettuce into three soil


Fig. S11. Betaproteobacterial DGGE-fingerprints of 16S







tion indices obtained from betaproteobacterial DGGE-fin-

gerprints 3 weeks after planting lettuce into three soil


Fig. S13. Betaproteobacterial DGGE-fingerprints of 16S







tion indices obtained from betaproteobacterial DGGE-fin-

gerprints 2 weeks after planting lettuce into three soil


Fig. S15. Actinobacterial DGGE fingerprints of 16S rRNA

gene fragments amplified from TC-DNA obtained from

the rhizosphere without inoculation (control) and with

inoculation of P. jessenii RU47 (RU47) 2 weeks after

planting lettuce into three soil types (DS, AL, LL) with

four replicates (a–d) in 2010. M: Marker.


tion indices obtained from actinobacterial DGGE-finger-

prints 3 weeks after planting lettuce into three soil types

(DS, AL, LL) with four replicates (a–d) in 2010.

Fig. S17. Actinobacterial DGGE-fingerprints of 16S rRNA

gene fragments amplified from TC-DNA obtained from

the rhizosphere without inoculation (control) and with

inoculation of P. jessenii RU47 (RU47) 2 weeks after

planting lettuce into three soil types (DS, AL, LL) with

four replicates (a–d) in 2012. M: Marker.


tion indices obtained from actinobacterial DGGE-finger-

prints 2 weeks after planting.

Table S1. Primers used in this study for the PCR amplifi-

cation followed by separation on DGGE-fingerprints and

for amplicon pyrosequen lettuce into three soil types (DS,




Soil type‐dependent effects of a potential biocontrol … pyrosequence analysis of samples from...

Documents

Transcript of Soil type‐dependent effects of a potential biocontrol … pyrosequence analysis of samples from...