Eth meeting switzerland _2015_carlos lara romero

AdAptA projectLocal adaptation in marginal

alpine populations: an integrated perspective

Carlos Lara-Romero

ETH. April 2015.

• Alpine environments are highly vulnerable to global warming

•Main response of alpine plants Upward range shifts trancking their current climatic niche

Theoretical background

Paulí et al 2012 Science, Marris 2007 Nature, Dullinger et al 2012 Glob. Ecol Biogeogr, Lara-Romero et al 2014 Plos One



•Mediterranean alpine plants Upward migration is not an option (The scalator effect)





•Mediterranean alpine plants Upward migration is not an option (The scalator effect)

• Adaptation and phenotypic plasticity are the main response against new environmental conditions



Objectives & Study species

OBJETIVES

[1] To assess the main limitations on reproductive performance of Mediterranean alpineplants and to test whether local adaptation at small spatial scales has a significant effect on theirfitness.

Silene ciliata Pourret (A Mediterranean alpine specialist)

Objectives & Study species


OBJETIVES

[1] To assess the main limitations on reproductive performance of Mediterranean alpineplants and to test whether local adaptation at small spatial scales has a significant effect on theirsuccess.


Results

• Significant variation in vegetative and reproductive traits

between low and high elevations

Giménez-Benavides et al 2007 Anals of Botany, García-Fernández et al 2012 OIKOS, Lara-Romero et al 2014 Plos One


Results



• Summer drought Selective pressure at low elevations

P (mm)

T (ºC)

Elevation



Results




• Seedling establishment Demographic bottleneck


P (mm)

T (ºC)

Elevation


Results




• Seedling establishment Demographic bottleneck

• Local adaptation at seedling stage Drought tolerance


Objectives

Prof. Alex Widmer Dr. Niklaus Zemp

OBJETIVES

[1] To assess the main limitations on reproductive performance of Mediterranean alpineplants and to test whether local adaptation at small spatial scales has a significant effect on theirfitness.

[2] To identify genes expressed during the development of S. ciliata seedlings and selectcandidate genes that may be involved in adaptation processes.

Mountain 3Mountain 2Mountain 1

Transcriptome comparisons between high and low populations during the seedling stage

Genomic data

6 seedlings

3 High vs 3 Low

1 seedling per population (n = 6)

RNA extraction and Illumina sequencing

Seed collection &Greenhouse sowing

Work flow. Genomic data

Reference-based transcriptome assembly

BWA

Silene latifolia Reference Genome

T G T C G G T C TT G T C G G T C T

T G T C A G T C TT G T C A G T C T

SNP calling – Reads2SNP

High

Low

Differential expression

Candidate Genes

Candidate Genes

High

Low

Functional annotation&

Enrichment analysis





BWA





High

Low


Candidate Genes

Candidate Genes

Optimal

Marginal


Enrichment analysis

The novo transcriptome

assembly





BWA





High

Low


Candidate Genes

Candidate Genes

High

Low


Enrichment analysis

Genomic data

Pilot study

Study design (n=6) limits detection of outlier SNPs

Impossibility of implementing classical approaches (e.g., pairwise Fst)

How can candidate genes be detected based on single individual per population?

Differential expression analysis

Comparison of expression levels (RPKM) between high and low elevations

RPKM (Reads per kilobase per million mapped reads)

Differential expression analysis

129 contigs differentially expressed

GO term & Enrichment analysis

• 114 contigs annotated

• Response to extracellular stimulus (n=9) & external stimulus (n=19) overrepresented

Comparison of expression levels (RPKM) between high and low elevations

RPKM (Reads per kilobase per million mapped reads)

SNP calling & outlier detection

Reads2SNP

• 7 reads needed to infer genotype• Deletion of paralogous SNPs• Biallelic SNPs with no missing data

• Depth of coverage and posterior probability did not affect outlier detection.

147 118 SNPs & 12 688 contigs(mean =13.7)


Reads2SNP

• 7 reads needed to infer genotype• Deletion of paralogous SNPs• Biallelic SNPs with no missing data

• Depth of coverage and posterior probability did not affect outlier detection.

147 118 SNPs & 12 688 contigs(mean =13.7)

Strategies for selection of candidate genes

[1] Contingency table and Pearson’s Chi-square test (X2)

[2] Dispersal parameter (m, Muller et al 2010 Evolutionary Applications)

[3] Allelic frequency differentials (AFDs)


High Low Expected

A1 14 3 9

A2 4 15 9

Contingency table and Pearson’s Chi-square test (X2)

A1 A1 A1 A1 A1 A1 Plant #1 2 400 mA1 A1 A2 A1 A1 A1 Plant #2 2 370 mA1 A2 A1 A1 A1 A2 Plant #3 2 450 m

A2 A2 A2 A2 A2 A2 Plant #4 1 750 mA2 A2 A2 A1 A1 A2 Plant #5 1 650 mA1 A2 A2 A2 A2 A2 Plant #6

Gene i with 3 SNPsSNP #1 SNP #2 SNP #3 Environmental variable

High

Low


Selection Candidate genes

• Outlier: p value < 0.05 after FDR correction

• 646 genes (contigs) selected

• Enrichment analysis (GO-Term - Biolog. processes)

• Single-organism metabolic processes (n = 155)

Contingency table and Pearson’s Chi-square test (X2)


A2 A2 A2 A2 A2 A2 Plant #4 1 750 mA2 A2 A2 A1 A1 A2 Plant #5 1 650 mA1 A2 A2 A2 A2 A2 Plant #6


High

Low

High Low Expected

A1 14 3 9

A2 4 15 9





Dispersal parameter (mx)

Muller et al 2010 Evolutionary Applications

High

Low


A2

A2 A2 A2

High

Low

ββ = 1937.5 m






High

Low


A2

A2 A2 A2

β

mi1

mi2

mi3

mi4


• Dispersion of each allele ( mx ) Average distance of the allele to β






High

Low

High

Low


A2 A2

A2 A2

β mi1

mi2

mi3

mi4



• Outlier: permutations to detect alleles more geographically clustered

than expected at random






High

Low

High

Low


A2 A2

A2 A2

β mi1

mi2

mi3

mi4



• Outlier: permutations to detect alleles more geographically clustered

than expected at random

• 486 candidate genes

• Enrichment analysis (Biolog. process)

• Lipid metabolic process (n = 53)• Single-organism metabolic processes (n = 59)• Generation of precursor metabolites and energy (n = 31)






High

Low

High

Low


Minor allele frequency differentials (AFDs) between high and low elevations

AFD

1 0.5 0 0.5 1

Freq

uen

cy

Turner et al 2010 Nature; Stölting et al 2015 New Phytologist


AFD

-3 -2 -1 0 +1 +2 +3

Freq

uen

cy


• Outlier: AFDs > 3 SDs the genome-wide average (p-value < 0.001)

• 1222 SNPS & 419 candidate genes

• Enrichment analysis (Biolog. process)

• Carbohydrate metabolic process

Turner et al 2010 Nature; Stölting et al 2015, New Phytologist

Minor allele frequency differentials (AFDs) between high and low elevations


336

20

606

124

6

13

275

Dispersal param. Allele freq.

AFD

SNP overlap among different selection approaches

Venn diagrams showing the extent of overlap among selection approaches based on allele frequencies

6 genes overlapped among three approaches

GO TERM: response to stress & metabolic process

163 genes overlapped among two approaches

• 143 annotated genes

• Enrichment analysis (before FDR correction)

- Response to abiotic stimulus (n = 53)- Response to stress (n = 59)- Several additional terms related to metabolic processes and response to stimulus

Thanks for your attention

Prof. Jose M. IriondoGroup leader

Javier Morente-LópezPh.D student

Luisa RubioPh.D student

Dr. Alfredo García-Fernández

Eth meeting switzerland _2015_carlos lara romero

Science

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