Microbial Agrogenomics 4/2/2015, UK-MX Workshop

Microbial AgrogenomicsWhere can it lead us?

Leighton PritchardInformation and Computational SciencesThe James Hutton Institute

Acceptable Use PolicyIntroduction Why Genomics? 2003-Now Implications Where Next? Conclusions

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These slides will be available on SlideShare.

Table of ContentsIntroduction Why Genomics? 2003-Now Implications Where Next? Conclusions

Introduction

Why Genomics?

2003-Now

Implications

Where Next?

Conclusions

The James Hutton InstituteIntroduction Why Genomics? 2003-Now Implications Where Next? Conclusions

Centres of ExpertiseIntroduction Why Genomics? 2003-Now Implications Where Next? Conclusions

http://www.hutton.ac.uk• Dundee Effector Consortium (DEC, with University of Dundee) [link]

• Centre for Research on Potato and Other Solanaceous Plants (CRPS) [link]

• Centre for Human and Animal Pathogens in the Environment (HAP-E) [link]

http://www.hutton.ac.uk

http://www.hutton.ac.uk/research/groups/cell-and-molecular-sciences/dundee-effector-consortium

http://www.hutton.ac.uk/research/groups/cell-and-molecular-sciences/crps

http://www.hutton.ac.uk/research/themes/controlling-weeds-pests-and-diseases/hape

Plant-Pathogen InteractionsIntroduction Why Genomics? 2003-Now Implications Where Next? Conclusions

Pathogens of barley (e.g. Rhynchosporium commune), and soft fruit

(e.g. Raspberry Leaf Blotch Virus (RLBV))

Plant-Pathogen InteractionsIntroduction Why Genomics? 2003-Now Implications Where Next? Conclusions

Potato pathogens, pests, and vectors.• soft-rot bacteria (Dickeya, Pectobacterium, Erwinia)

• blight (Phytophthora infestans)

• Potato Cyst Nematode (PCN) (Globodera)

• aphids (Myzus persicae)

Issue 1: Food SecurityIntroduction Why Genomics? 2003-Now Implications Where Next? Conclusions

• Economic cost and burden of crop disease• P. infestans: e1bn Europe; $4bn global

• Societal impact (human health, commodity prices; farming)

• Emerging pathogens (JIT supply chain; climate change)

• Plant-associated human pathogens

• Food fraud

http://www.sfsa-crops.org/

Issue 2: Environmental SustainabilityIntroduction Why Genomics? 2003-Now Implications Where Next? Conclusions

• Pesticide minimisation and withdrawal

• Durable resistance, soil-beneficial microbes, plantgrowth/nutritional enhancement

• Traditional breeding, GM, or engineering?

• Soils: rhizosphere interactions/soil diversity

• Farming practices (water run-off, rotation, equipment-cleaning- EU sulphuric acid ban)


Introduction

Why Genomics?

2003-Now

Implications

Where Next?

Conclusions

What Have Genomes Ever Done For Us?Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions

• Catalogue features (genes, regulatory elements, etc.) in anorganism.

Plant-microbe interactionsIntroduction Why Genomics? 2003-Now Implications Where Next? Conclusions

Gene products at the host-microbe interface

Dodds & Rathjen (2010) Nat. Rev. Genet. 11:539-548 doi:10.1038/nrg2812

http://dx.doi.org/10.1038/nrg2812

http://dx.doi.org/10.1038/nrg2812

Plant-Nematode InteractionsIntroduction Why Genomics? 2003-Now Implications Where Next? Conclusions

RNA-seq identification of 27 putative nematode effectors:Small proteins, expressed in gland cells during feeding stage only.

Cotton et al. (2014) Genome Biol. 15:R43 doi:10.1186/gb-2014-15-3-r43

http://dx.doi.org/10.1186/gb-2014-15-3-r43

Plant DefenceIntroduction Why Genomics? 2003-Now Implications Where Next? Conclusions

Prediction of NB-LRR genes (sequence capture).

Jupe et al. (2013) Plant J. 76:530-544 doi:10.1111/tpj.12307

Jupe et al. (2012) BMC Genomics 13:75 doi:10.1186/1471-2164-13-75

http://dx.doi.org/10.1186/1471-2164-13-75

http://dx.doi.org/10.1111/tpj.12307

http://dx.doi.org/10.1186/1471-2164-13-75



• If we have multiple genomes. . .• What common features associate with phenotype or

environment?


GWAS/QTLs/genotyping for plant breeding

http://ics.hutton.ac.uk/flapjack/

Milne et al. (2010) Bioinformatics 26:3133-3134 doi:10.1093/bioinformatics/btq580



http://dx.doi.org/10.1093/bioinformatics/btq580


Structural changes to genomes: repeat-driven expansion

duplication, mutation, recombination, epigenetic control of effectors . . .

Haas et al. (2009) Nature 461:393-398 doi:10.1038/nature08358

http://dx.doi.org/10.1038/nature08358

http://dx.doi.org/10.1038/nature08358


Structural changes to genomes: genome reductions

Buchnera, Serratia symbiotica - aphid symbionts, ‘random’ inactivationGil et al. (2002) Proc. Natl. Acad. Sci. USA 99:4454-4458 doi:10.1073/pnas.062067299

Burke & Moran (2011) Genome Biol. Evol. 99:4454-4458 doi:10.1093/gbe/evr002

http://dx.doi.org/10.1073/pnas.062067299

http://dx.doi.org/10.1093/gbe/evr002


http://dx.doi.org/10.1093/gbe/evr002


Lateral gene transfer (virulence-associated genes)

Bell et al. (2004) Proc. Natl. Acad. Sci. 101:11105-11110 doi:10.1073/pnas.0402424101




Closely-related bacteria, different host/environmental preference.Pectobacterium atrosepticum

Holden et al. (2009) FEMS Micro. Rev. 33:689-703 doi:10.1111/j.1574-6976.2008.00153.xToth et al. (2006) Annu. Rev. Phytopath. 44:305-336 doi:10.1146/annurev.phyto.44.070505.143444

http://dx.doi.org/10.1146/annurev.phyto.44.070505.143444

http://dx.doi.org/10.1111/j.1574-6976.2008.00153.x

http://dx.doi.org/10.1146/annurev.phyto.44.070505.143444



• If we have multiple genomes. . .• What common features associate with phenotype or

environment?• Epidemiology: spread and transmission

Historical OriginsIntroduction Why Genomics? 2003-Now Implications Where Next? Conclusions

Retracing 19th-century P.infestans pandemics

Yoshida et al. (2014) PLoS Pathog. 10:e1004028 doi:10.1371/journal.ppat.1004028

http://dx.doi.org/10.1371/journal.ppat.1004028




International EmergenceIntroduction Why Genomics? 2003-Now Implications Where Next? Conclusions

Distribution of Dickeya spp. in Europe• D.dianthicola; ◦ D.solani ; � Dickeya spp. on potato

Toth et al. (2011) Plant Pathol. 60:385-399 doi:10.1111/j.1365-3059.2011.02427.x

http://dx.doi.org/10.1111/j.1365-3059.2011.02427.x

http://dx.doi.org/10.1111/j.1365-3059.2011.02427.x

Host JumpsIntroduction Why Genomics? 2003-Now Implications Where Next? Conclusions

Movement of Dickeya from ornamental to crop plants

Parkinson et al. (2015) Eur. J. Plant Pathol. 141:63-70 doi:10.1007/s10658-014-0523-5

http://dx.doi.org/10.1007/s10658-014-0523-5

http://dx.doi.org/10.1007/s10658-014-0523-5

http://dx.doi.org/10.1007/s10658-014-0523-5

Diagnostic ToolsIntroduction Why Genomics? 2003-Now Implications Where Next? Conclusions

Quarantine and legislation require precise identification.Genomes enable rapid, robust RT-PCR diagnostics.

targets

V

IV

III

II

I

genomes

IIIIIIIVV

https://github.com/widdowquinn/find differential primersPritchard et al. (2013) Plant Pathol. 62:587-596 doi:10.1111/j.1365-3059.2012.02678.x

Pritchard et al. (2012) PLoS One 7:e34498 doi:10.1371/journal.pone.0034498

http://dx.doi.org/10.1371/journal.pone.0034498


https://github.com/widdowquinn/find_differential_primers

http://dx.doi.org/10.1111/j.1365-3059.2012.02678.x



Introduction

Why Genomics?

2003-Now

Implications

Where Next?

Conclusions

2003: E. carotovora subsp. atrosepticaIntroduction Why Genomics? 2003-Now Implications Where Next? Conclusions

• £250k collaboration between SCRI, University of Cambridge,WT Sanger Institute

• Single isolate: E. carotovora subsp. atroseptica SCRI1043

• First sequenced enterobacterial plant pathogen (32 authors!)

• Annotation: 6 people, for 6 months ≈ three person-years

• Result: single, complete 5Mbp circular chromosome (10.2X)

Bell et al. (2004) Proc. Natl. Acad. Sci. USA 101: 30:11105-11110. doi:10.1073/pnas.0402424101


2003: E. carotovora subsp. atrosepticaIntroduction Why Genomics? 2003-Now Implications Where Next? Conclusions

Compared against all 142 then-available bacterial genomes

Bell et al. (2004) Proc. Natl. Acad. Sci. USA 101: 30:11105-11110. doi:10.1073/pnas.0402424101


2013: Dickeya spp.Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions

Sequenced and annotated 25 isolates of Dickeya over two years

• Multiple sequencing methods: 454, Illumina (SE, PE)

• Automated annotation, limited manual correction

• Results: 12-237 fragments: 4.2-5.1Mbp/genome (6-84X)Pritchard et al. (2013) Genome Ann. 1 (4) doi:10.1128/genomeA.00087-12

Pritchard et al. (2013) Genome Ann. 1 (6) doi:10.1128/genomeA.00978-13

http://dx.doi.org/10.1128/genomeA.00087-12

http://dx.doi.org/10.1128/genomeA.00978-13


Whole genome-based species definitions: sp. nov. D. solani

van der Wolf et al. (2014) Int. J. Syst. Evol. Micr. 64:768-774 doi:10.1099/ijs.0.052944-0

http://dx.doi.org/10.1099/ijs.0.052944-0


Differences in metabolic capacity (but ≈ 20% orphan EC activities)

2014: E. coliIntroduction Why Genomics? 2003-Now Implications Where Next? Conclusions

Sequenced and annotated ≈ 190 isolates of E. coliAll bacteria environmental, sampled from lysimeters

• Illumina PE sequencing, cost ≈£11k

• Automated annotation: PROKKA

(w/ Fiona Brennan, Florence Abram, NUI Galway)

2014: E. coliIntroduction Why Genomics? 2003-Now Implications Where Next? Conclusions

Whole genome-based subspecies classification

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ANIm

0.9 0.92 0.94 0.96 0.98

Value

010

0020

0030

0040

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Color Keyand Histogram

Cou

nt

AB1B2CDEFUX

(w/ Fiona Brennan, Florence Abram, NUI Galway)

2014: Campylobacter spp.Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions

≈1034 clinical, animal, food-associated Campylobacter isolates

• Illumina PE sequencing, cost ≈£60k

• Automated annotation: PRODIGAL

(w/ Ken Forbes, Norval Strachan, University of Aberdeen)

2014: Campylobacter spp.Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions

• 15554 ‘gene families’ in 1034 isolates.

• Calculation: 4e12 pairwise protein comparisons!

(w/ Ken Forbes, Norval Strachan, University of Aberdeen)


Introduction

Why Genomics?

2003-Now

Implications

Where Next?

Conclusions

So what’s changed?Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions

Everything.

• Cost: £250k → £60 per genome.Now cheaper to sequence than analyse a genome!Offload work from people to software.

• Location: sequencing centre, to benchtop (Nanopore!)

• Speed: sequencing run time can be less than a day

• Data: massive volume increase

Predicting the future is hard. . .Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions

Su et al. attempted to do it, though:

10,000 prokaryotes in 2015 was an underestimate.http://sulab.org/2013/06/sequenced-genomes-per-year/

http://sulab.org/2013/06/sequenced-genomes-per-year/

So what’s changed?Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions

Everything.

• Cost: £250k → £60 per genome.

• Location: sequencing centre, to benchtop (Nanopore!)

• Speed: sequencing run time can be less than a day

• Data: massive volume increaseMore data ≈ better, but also more challenging.

• Software: more ( 6= better. . .) software for more things

• New experiments: genomes, exomes, variant calling,methylated sequences, STARR-seq, . . .

• New applications: diagnostics, epidemic tracking,metagenomics, . . .

Sequence first. . . ask questions, laterIntroduction Why Genomics? 2003-Now Implications Where Next? Conclusions

• “Why?” has sometimes been replaced by “What?”

http://dilbert.com/strip/2000-01-03

“The thesis is not hypothesis driven. Add a hypothesis and refer to it in subsequent

chapters.”



More isn’t always betterIntroduction Why Genomics? 2003-Now Implications Where Next? Conclusions

Deeper sequencing (more reads) 6= more information or betterassembly.

60-80X coverage the ‘sweet spot’ for bacterial genomes.More reps � more reads!Conway & Bromage (2011) Bioinformatics 27:479-486 doi:10.1093/bioinformatics/btq697



Are database annotations reliable?Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions

Automated annotation is essentialThe Critical Assessment of Function Annotation (CAFA) project.

Radivojac et al. (2013) Nat. Meth. 10:221-227 doi:10.1038/nmeth.2340

http://dx.doi.org/10.1038/nmeth.2340

Do biased database annotations matter?Introduction Why Genomics? 2003-Now Implications Where Next? Conclusions

Experimental annotations of proteins are incomplete. Is thatimportant?Tested by simulation, and following databases for three years.• Yes. It matters.

• Current large scale annotations are meaningful and almost surprisingly reliable.

• The nature and level of data incompleteness, and type of classification modelhave an effect.

• “Low precision, high recall” (i.e. less discriminating) tools most significantlyaffected.

Molecular function prediction is usually more reliable thanbiological process predictionJiang et al. (2014) Bioinformatics 30:i609-i616 doi:10.1093/bioinformatics/btu472

Cozzetto et al. (2013) BMC Bioinf. 14:S3-S1 doi:10.1186/1471-2105-14-S3-S1

http://dx.doi.org/10.1093/bioinformatics/btu472

http://dx.doi.org/10.1186/1471-2105-14-S3-S1

CAFA resultsIntroduction Why Genomics? 2003-Now Implications Where Next? Conclusions

The Critical Assessment of Function Annotation (CAFA) 2013results. (F-measure combines precision and recall)

• You can do better thanBLAST.

• Best-performing methods docomparably well.

• Best methods usedevolutionary relationships,structure, and expressiondata.

• Machine Learning methodswork best.

Radivojac et al. (2013) Nat. Meth. 10:221-227 doi:10.1038/nmeth.2340

http://dx.doi.org/10.1038/nmeth.2340

More Isn’t Always BetterIntroduction Why Genomics? 2003-Now Implications Where Next? Conclusions

Statistical inference on large datasets requires extra care.

Hypothesis tests may incorrectly reject null hypotheses (B-H)

http://xkcd.com/882/




More Isn’t Always BetterIntroduction Why Genomics? 2003-Now Implications Where Next? Conclusions

• More tests → random effect seems ’real’

• May be considering a large set of inferences simultaneously(and yet not notice!):“p-hacking”, “Researcher Degrees ofFreedom”“good scientists are skilled at looking hard enough and subsequently coming up

with good stories (plausible even to themselves, as well as to their colleagues

and peer reviewers) to back up any statistically-significant comparisons they

happen to come up with.” Gelman & Loken (2013) ”The Garden of Forking Paths”

(“Data-dredging”)

True for all large data analyses: genomics, metabolomics,proteomics, health screening, finding terrorists, etc.Xia et al. (2012) Metabolomics 9:280-299 doi:10.1007/s11306-012-0482-9Broadhurst & Kell (2006) Metabolomics 2:171-196 doi:10.1007/s11306-006-0037-z

http://www.stat.columbia.edu/~gelman/research/unpublished/p_hacking.pdf

http://dx.doi.org/10.1007/s11306-012-0482-9

http://dx.doi.org/10.1007/s11306-006-0037-z

Genome-Scale PredictionsIntroduction Why Genomics? 2003-Now Implications Where Next? Conclusions

• Imagine a paper describing a predictor for protein functionalclass (e.g. pathogen effector)

• The paper reports sensitivity = 0.95, FPR = 0.01

• We run the predictor on 20,000 proteins in an organism

• It predicts 130 members of the class. How many of them arelikely to be true positives?

• We need a baseline level of that class (fX ) in the genome todetermine this.

• Estimate ≈ 200 in gene complement, so fX = 0.01• fX = 0.01 =⇒ P(class|+ve) = 0.490 ≈ 0.5: 65 TP

Pritchard & Broadhurst (2014) Meth. Mol. Biol. 9:280-299 doi:10.1007/978-1-62703-986-4 4

http://dx.doi.org/10.1007/978-1-62703-986-4_4

Baserate FallacyIntroduction Why Genomics? 2003-Now Implications Where Next? Conclusions

http://bit.ly/1EFbzCI

http://armchairbiology.blogspot.co.uk/2014/07/the-baserate-fallacy-revisited.html

http://nbviewer.ipython.org/github/widdowquinn/Teaching-EMBL-Plant-Path-Genomics/blob/master/data/Effectors/pred_acc.ipynb



http://armchairbiology.blogspot.co.uk/2014/07/the-baserate-fallacy-revisited.html

A Literature ExampleIntroduction Why Genomics? 2003-Now Implications Where Next? Conclusions

Reported sensitivity ≈ 0.71, FPR ≈ 0.15

Arnold et al. (2009) PLoS Pathog. 5:e1000376 doi:10.1371/journal.ppat.1000376



Big Data: New ProblemsIntroduction Why Genomics? 2003-Now Implications Where Next? Conclusions

• Lots of high throughput experiments, and large datasets(but even more small datasets)

• Historically ill-formed data (sequences in Word documents,BLAST results pasted into notebooks).

• How do we connect all this data in a productive way?

This section influenced heavily by C. Titus Brown and Philip Bourne


• Data management. Too often:“Goodbye to the student is goodbye to the data”

• Persistence of data resources (link rot, database entropy)

http://www.phdcomics.com/comics/archive.php?comicid=382




• How reproducible are computational results?

• Software/data versions prevent exact reproduction: 280h toreproduce one paper approximately - in the same lab!Garijo et al. (2013) PLoS One doi:10.1371/journal.pone.0080278

http://www.slideshare.net/pebourne/sib0114




Maybe we can get away with all of this in a traditional model ofscience publishing. . .

http://www.slideshare.net/c.titus.brown/2015-baltiandbioinformatics



. . .but lots of biological data doesn’t make sense except in the lightof other biological data.



Big Data: New SolutionsIntroduction Why Genomics? 2003-Now Implications Where Next? Conclusions

Everyone could be better off with collaboration and data sharing.

What is winning: career progression, or feeding people?(still competing, but on analysis and insight, not on who holds what data. . .)




Data quality ≈ data trust:

• Sustainable: storage, archiving, maintenance

• Findable: “where is the dataset?”, “is it available?”

• Queryable: “is X in the dataset?”

• Analysable: metadata, annotation




Interoperable digital assets: datasets, software, lab books, etc.

• Uniquely identified (DOI, PMID, etc.)

• Provenance (version and access control)

• Open standards - what data to keep, how to organise it:MINSEQE (sequencing), MIAME (microarray), MIASE (simulation), MIAPE

(proteomics), MIARE (RNAi), SBML, GFF3, SAM/BAM/CRAM, etc.

• Sustainable infrastructure for biological information(ELIXIR, “The Commons” [US], RDF, Open Data)


https://pebourne.wordpress.com/2014/10/07/the-commons/

http://www.elixir-europe.org/





Too much software is difficult to use for experts, or unusable fornon-experts.Veretnik et al. (2008) PLoS Comp. Biol. doi:10.1371/journal.pcbi.1000136

http://dx.doi.org/10.1371/journal.pcbi.1000136


Workflows, pipelines, and service integrative frameworks

Cock et al. (2014) Methods Mol. Biol. 1127:3-15 doi:10.1007/978-1-62703-986-4 1Cock et al. (2013) PeerJ 1:e167 doi:10.7717/peerj.167

http://galaxy-community.org.uk/

http://dx.doi.org/10.1007/978-1-62703-986-4_1

http://dx.doi.org/10.7717/peerj.167

http://galaxy-community.org.uk/


Sometimes new software is needed.Writing good software is difficult, and expensive.

http://www.theregister.co.uk/2015/01/22/us military finds f35 software is a buggy mess/

http://www.theregister.co.uk/2015/01/22/us_military_finds_f35_software_is_a_buggy_mess/

http://www.theregister.co.uk/2015/01/22/us_military_finds_f35_software_is_a_buggy_mess/


Not enough software engineers to go round: train what we have.Programming literacy, computational thinking: versioned, readable,maintainable code.

http://www.software.ac.uk/http://software-carpentry.org/http://datacarpentry.org/

http://www.software.ac.uk/

http://software-carpentry.org/

http://datacarpentry.org/


Introduction

Why Genomics?

2003-Now

Implications

Where Next?

Conclusions

Cheap Sequencing In The FieldIntroduction Why Genomics? 2003-Now Implications Where Next? Conclusions

Diagnostics and epidemic tracking by sequencingGlobal Microbial Identifier (GMI) http://www.globalmicrobialidentifier.org:

Global system of databases for microbial/disease identification and diagnostics.

Quick et al. (2014) BMJ Open 11:e006278 doi:10.1136/bmjopen-2014-006278

http://www.globalmicrobialidentifier.org

http://dx.doi.org/10.1136/bmjopen-2014-006278

Sequencing In The FieldIntroduction Why Genomics? 2003-Now Implications Where Next? Conclusions

Live prediction for epidemiology?

(Peter Skelsey, JHI)

http://www.hutton.ac.uk/staff/peter-skelsey

Sequence Isn’t EverythingIntroduction Why Genomics? 2003-Now Implications Where Next? Conclusions

Organisms are dynamic, and multi-scale

• Context: epigenetics, tissue differentiation, mesoscale systems,symbiosis, etc.

• Phenotypic plasticity: responses to environment - stress,temperature, etc.

The PhytobiomeIntroduction Why Genomics? 2003-Now Implications Where Next? Conclusions

Phytobiome: the plant, and its associated microbial community

• American Phytopathological Society “Phytobiomes Intitative”

• “a complete systems approach that spans foundational to appliedscience focused on downstream application”

• We are not at war with all microbes. . .

https://www.apsnet.org/members/outreach/ppb/phytobiomes

https://www.apsnet.org/members/outreach/ppb/phytobiomes

Genomes Are Parts ListsIntroduction Why Genomics? 2003-Now Implications Where Next? Conclusions

We know (some of) the bits that make up the machinery. . .

http://dx.doi.org/10.1111/mpp.12210

Flux Balance AnalysisIntroduction Why Genomics? 2003-Now Implications Where Next? Conclusions

Flux Balance Analysis: constraint-based static representation ofmetabolism (RNA/ChIP-seq adds dynamics to models)

• Set upper, lower bounds to reaction rate, define objective phenotype(biomass, target flux profile)

• in silico knockouts; viable states; nutrient usage

• A basis for synthetic biology and engineering

Flux Balance AnalysisIntroduction Why Genomics? 2003-Now Implications Where Next? Conclusions

Dickeya: 29 × FBA, host range ≈ nutrient-dependent growthalso transposon mutant libraries

(w/ Sonia Humphris, Ian Toth, JHI)

http://dx.doi.org/10.6084/m9.figshare.767275

Plant-Microbe Interactions Are SystemsIntroduction Why Genomics? 2003-Now Implications Where Next? Conclusions

Components, interactions, dynamics etc. = systems biologyInteraction creates a third system from host and microbe

Pritchard & Birch (2014) Mol. Plant. Pathol. 15:865-870 doi:10.1111/mpp.12210

Pritchard & Birch (2011) Plant Sci. 180:584-603 doi:10.1016/j.plantsci.2010.12.008


http://dx.doi.org/10.1016/j.plantsci.2010.12.008

Plant-Microbe Interactions Are SystemsIntroduction Why Genomics? 2003-Now Implications Where Next? Conclusions

Components, interactions, dynamics etc. = systems biologyInteraction creates a third system from host and microbe

microbe(bulk)

microbe(local)

PRR PRR*

R protein

R protein*

ø

øøe�ector translocation

e�ector(internalised)

PAMP

ø

ø

cell wall

microbe approaches cell microbe leaves cell/is destroyed

microbe producesPAMP

microbe producese�ector

PAMP bindingactivates PRR

e�ector bindingactivates R protein

calloseproduction

calloseloss

e�ectorloss

e�ectorloss

PAMPloss

enhanced by callose (PTI)and R protein* (ETI)

enhanced byPRR* (PTI)

slowed by callose (PTI)

callose

e�ector(external)

enhanced bye�ector action

No Response PTI

PTI+ETS PTI+ETS+ETI

0.00

0.25

0.50

0.75

1.00

0.00

0.25

0.50

0.75

1.00

0 50 100 150 200 0 50 100 150 200Time

Arb

itrar

y un

its

variable

Callose

Pathogen

Pathogen, Callose timecourses by host type

Pritchard & Birch (2014) Mol. Plant. Pathol. 15:865-870 doi:10.1111/mpp.12210

Pritchard & Birch (2011) Plant Sci. 180:584-603 doi:10.1016/j.plantsci.2010.12.008


http://dx.doi.org/10.1016/j.plantsci.2010.12.008

Integrate Models and DataIntroduction Why Genomics? 2003-Now Implications Where Next? Conclusions

Integration of models and datasets still a challenge

• Models at different scales

• Kinetic, metabolomic, proteomic, transcriptomic, genomicdatasets

Hartmann & Schreiber (2014) Front. Bioeng. Biotechnol. 8:226-244 doi:10.3389/fbioe.2014.00091

http://dx.doi.org/10.3389/fbioe.2014.00091

http://dx.doi.org/10.3389/fbioe.2014.00091

Types of ModelIntroduction Why Genomics? 2003-Now Implications Where Next? Conclusions

• Combining data: models at different scales.

• Information required/produced depends on model type.

• Size/detail trade-off

Hartmann & Schreiber (2014) Front. Bioeng. Biotechnol. 8:226-244 doi:10.3389/fbioe.2014.00091/abstract

http://dx.doi.org/10.3389/fbioe.2014.00091/abstract

http://dx.doi.org/10.3389/fbioe.2014.00091/abstract

Synthetic BiologyIntroduction Why Genomics? 2003-Now Implications Where Next? Conclusions

Engineering new response modes into crops.

Gurr & Rushton (2005) Trends Biotech. 23:283-290 doi:10.1016/j.tibtech.2005.04.009

http://dx.doi.org/10.1016/j.tibtech.2005.04.009


Genome EditingIntroduction Why Genomics? 2003-Now Implications Where Next? Conclusions

TALENs and CRISPR/Cas9s

http://www.lifetechnologies.com/

http://www.umassmed.edu/xuelab





Trait StackingIntroduction Why Genomics? 2003-Now Implications Where Next? Conclusions

For resistance and other beneficial traits (yield, nutrients, biofuels)

Vanholme et al. (2010) Trends Biotechnol. 28:543-547 doi:10.1016/j.tibtech.2010.07.008



Engineering Soil-Beneficial MicrobesIntroduction Why Genomics? 2003-Now Implications Where Next? Conclusions

Refactoring of Klebsiella nitrogen fixation:

Temme et al. (2012) Proc. Natl. Acad. Sci. USA 10:763 doi:10.1073/pnas.1120788109



Engineering New BiologyIntroduction Why Genomics? 2003-Now Implications Where Next? Conclusions

dCas9 logic circuits, integrating with host regulation

Nielsen & Voigt (2014) Mol. Syst. Biol. 10:763 doi:10.15252/msb.20145735

http://dx.doi.org/10.15252/msb.20145735

http://dx.doi.org/10.15252/msb.20145735


Introduction

Why Genomics?

2003-Now

Implications

Where Next?

Conclusions

DataIntroduction Why Genomics? 2003-Now Implications Where Next? Conclusions

Sequencing is ever cheaper and more productive:

• Very large datasets

• More information (with good planning)

• Challenges for data storage and sharing

• Challenges for analysis (“why” vs. “what”)

• Challenges for software, accessibility (workflows,multidisciplinary training)

• Interdisciplinary collaboration and data integration willbe essential

Systems/SyntheticsIntroduction Why Genomics? 2003-Now Implications Where Next? Conclusions

A parts list only gets us so far:

• Cells are dynamic biophysical systems

• Organisms are dynamic cellular systems

• ‘Real’ plant systems include the phytobiome

• Systems biology essential to understand plant-microbeinteractions

• Synthetic biology promises to be a powerful tool to improveplant health, nutrition, etc.

• BUT: ethical issues around deployment of synthetic systems

ConclusionsIntroduction Why Genomics? 2003-Now Implications Where Next? Conclusions

AcknowledgementsIntroduction Why Genomics? 2003-Now Implications Where Next? Conclusions

James Hutton InstitutePaul BirchEmma CampbellPeter CockIngo HeinNicola HoldenSonia HumphrisFlorian JupeIan TothNUI GalwayFlorence AbramFiona BrennanUniversity of AberdeenKen ForbesNorval Strachan

University of AlbertaDavid BroadhurstSASAVincent MulhollandGerry SaddlerFeraValerie BertrandJohn ElphinstoneRachel GloverNeil ParkinsonUniversity of MunsterMartina BielaszewskaHelge KarchUniversity of SalfordNatalie FerryRyan Joynson

And many others!

Microbial Agrogenomics 4/2/2015, UK-MX Workshop

Science

Transcript of Microbial Agrogenomics 4/2/2015, UK-MX Workshop