SAB presentation to Jim narrated

Ecological and evolutionary functional genomics:

finding & studying genes that matter

Christopher W. Wheat

Ecological and Evolutionary Functional Genomics

• Integrative Research– Ecology (Hanski) + Molecular Biology (Frilander) +

Evolution Genetics (Wheat) + Physiology (Marden)

• Need genomic tools– Access to the coding genes

– 1000’s of SNPs &/or 100’s of microsatellites

– Microarrays for gene expression

– QTL, association mapping, and outlier analyses

• Need to sequence the genes

C. Vera and C. Wheat, Fescemyer, Frilander, Crawford, Hanski, Marden 2008

Rapid transcriptomecharacterization for a nonmodel organism using

454 pyrosequencing

48k contigs

SNP’s

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First de novo transcriptome assembly using 454

pyrosequencing

Annotation of 45,000 contigs + singletons

Predicted genes:D. melanogaster = 13,379B. mori = 18,510

Estimated coverage:70% D. mel estimate50% B. mori estimate

Lower estimate 50% genes

Upper estimate of 70% = 13,142 genes

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glycolysis &related pathways

citric acid cycle fatty acidmetabolism

purinemetabolism

pyrimidinemetabolism

average oxidativephosphorylation

ribosomalproteins

M. cinxia

B. mori

Metabolic Map Comparison

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Bombyx mori with WGS M. cinxia with 454 seq.

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Linear Fit

Polynomial Fit Degree=2

ContigLength = 192.47394 + 4.8993402 NumOfEsts

RSquareRSquare AdjRoot Mean Square ErrorMean of ResponseObservations (or Sum Wgts)

0.6756970.673347163.9744327.8357

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Summary of Fit

ModelErrorC. Total

Source 1

138 139

DF 7730931 3710490

11441421

Sum of Squares 7730931

26888

Mean Square287.5276

F Ratio

<.0001Prob > F

Analysis of Variance

InterceptNumOfEsts

Term192.473944.8993402

Estimate15.993120.288933

Std Error 12.03 16.96

t Ratio<.0001<.0001

Prob>|t|

Parameter Estimates

Linear Fit

RSquareRSquare AdjRoot Mean Square ErrorMean of ResponseObservations (or Sum Wgts)

0.8172030.814534123.5563327.8357

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Summary of Fit

ModelErrorC. Total

Source 2

137 139

DF 9349959 2091462

11441421

Sum of Squares 4674980

15266

Mean Square306.2318

F Ratio

<.0001Prob > F

Analysis of Variance

InterceptNumOfEsts(NumOfEsts-27.6286)^2

Term145.091498.4811567 -0.02242

Estimate12.89942 0.41033

0.002177

Std Error 11.25 20.67

-10.30

t Ratio<.0001<.0001<.0001

Prob>|t|

Parameter Estimates

Polynomial Fit Degree=2

Bivariate Fit of ContigLength By NumOfEsts

Increasing 454 sequencing (Aland + China & France)

• Find & cover more genes – Get full length contigs

• SNPs – Reconfirm previous – Identify population specific SNPs– Map genes & compare with other

species (synteny)– Finding genes of interest

• QTL and association mapping• Outlier analyses (Fst)

– Demographics• Population structure effects on genetic

diversity• Colonization history

Custom Designed Microarrays (Agilent) :

• Using 13,780 assembled contigs • Used validated probes (selected best of 6 per

contig)• 60 bp probes randomly printed at least in

triplicate • 44,000 feature array, with 4 arrays per slide.

• 2 dyes hybridized per array• randomized across population type• amplified RNA indirectly labeled using Alexiflour

dyes (555 & 647) coupled to aaUTP incorporated during synthesis.

• Scanned using Genepix 4000B

Wheat et al., in prep.

Population age: P = 0.02

• 2 day old females, 25 families from 25 different local populations (n = 65)

• Common garden reared for 2 generations

Assessing expression variation across 12,000 genes in :

Abdomen (n = 20)Thorax (n = 34)

Head (n = 18) analysis in progress

What are the expression differences:

1. between new and old populations?2. across PMR performance variation?

Popage fixed effects: dye popage bodymass popage*bodymass

PMR fixed effect:dye PMR

Random effects:slide array(slide) spot spot*array individual

(FDR threshold = 5%) Using JMP Genomics (SAS)

Mixed Model Analysis : popage & peak metabolic rate

Mixed model analysis of Abdomen ANOVA normalized

(Multiple test correction used FDR @ 5% = 3.16 -log10P)

221 genes differentially expressed

(≈ 11 false positives) Horizontal reference line draw n at -log10(p) = 3.16

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Diff of PopAge = (new )-(old)

Diff of PopAge = (new)-(old)

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-12 -10 -8 -6 -4 -2 0 1 2 3 4 5 6 7 8 9 11

Estimate of popage: new - old

Estimate of popage: new - old

-log 1

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Significantly different genes(FDR threshold = 5%)

623 3

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188

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Peak Metabolic

Rate

Continuous

Population Age

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Categorical

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Wheat et al., in prep.

Abdomen:• 33 genes overlap population age and PMR

• Egg development• Ecdysteroid 22-phosphate• 3-dehydroecdysone 3alpha-reductase

• Metabolism• Fructose 1,6-bisphosphate aldolase• Gallerin• Tyrosine aminotransferase

• Only 12 of 33 with homology inferred function

Thorax: No sig. popage effects expression

Biological validation:vitellogenin

• Egg yolk precursor protein• Higher protein = more eggs

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• Protein levels – new > old (P < 0.05)

• Microarray (mRNA levels)– new > old (P = 0.02)

Treatments Hot Standard Cold

Temperature maximum

35oC 26oC 20oC

Temperature treatments

Last instar larvaeReared in lab

Study gene expression

H vs. S C vs. S

11405

1066835 302

-0.95 -0.90 -0.85 -0.80 -0.75 -0.70

Comp1

HotStandardCold

Kvist et al., in prep.

Summary• We’ve built, and are improving, our genomic tools :

– Sequenced transcriptome & aiming for full length, better coverage– 1000’s of SNPs identified and being prep’d for high throughput analysis– Microarray validated and used to address ecological hypotheses

• Using tools to test hypotheses about expression differences– between population types?– larval temperature experience?

• Preliminary findings:– Metapopulation dynamics sort/maintain expression variation– Larval thermal experience matters, some shared responses to cold and heat

• Dispersal variation appears to arise from– abdomen fuel supply variation rather than thorax structural variation