Transcriptome and ecophysiological data of Populus nigra genotypes during drought stress

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HAL Id: hal-02800816

https://hal.inrae.fr/hal-02800816

Submitted on 5 Jun 2020

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Transcriptome and ecophysiological data of Populus nigra genotypes during drought stress

Marie Garavillon-Tournayre, Boris Fumanal, Aurelie Gousset, Jean-Stéphane Venisse, Romain de Oliveira, Pierrick Benoit, Benjamin Alary, Philippe Label

To cite this version:

Marie Garavillon-Tournayre, Boris Fumanal, Aurelie Gousset, Jean-Stéphane Venisse, Romain de Oliveira, et al.. Transcriptome and ecophysiological data of Populus nigra genotypes during drought stress. JOBIM 2015, Jul 2015, Clermont-Ferrand, France. pp.1, 2015. �hal-02800816�

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α = 0.90 AL L-29 SPM -28 SPM -40 STR -10 SPM -12 ER S-05 α = 0.95 1,5 MM sequences 30 M sequences/sample 47 samples

σ

CV =

µ

Levene’s test

Transcriptome and ecophysiological data of Populus nigra genotypes

during drought stress

Garavillon-Tournayre M., Fumanal B., Gousset-Dupont A., Venisse J. S., De Oliveira R., Benoit P., Alary B. and Label P.

[email protected]

stress

Ecole Doctorale

Sciences de la Vie, Santé, Agronomie et Environnement

Experimental design

Statistical analysis

Do poplars responses to drought stress depend

on plasticity?

Is the leaf transcriptome modified

by drought treatment?

Questions

Explained variables Normalisation Homoscedasticity

CV

matrix

Placticity response to drought

Ascendant Hierarchical Clustering

Distance: euclidian

Contrast: Ward

Clustering Statistic choice

Genotype effect

ANOVA 2

Boxcox’s

transformation

Shapiro-Wilk’s test

Phenotypic data Reference transcriptome Populus trichocarpa 73013 Populus nigra leaf 47739 > library("DESeq2")

Wald’s statistic

False Discovery Rate 0.1‰

Principal Component Analysis

N

AAAA < 60 pb

> 30% redundancy contaminants

Leaf transcriptome in drought

Transcriptional data Cleaning RNAseq paired-end Mapping Filtered reads 7%

Treatment impact on transcription

Multivariate analysis 9689 up 8836 down 29214 non-differential 0,9 1,1 -1,0 -0,5 0,0 0,5 1,0 Log RLD (RN As eq)

Log Relative Quantity (RT-qPCR)

Pearson’s correlation

Validation of aquaporins expression

A T C G G C

A G C

T C G

Issue

Global warming

Increase of drought in progress…

Treatment Genotype Biological

repetition WD SPM-28 R1 WD SPM-28 R2 WD SPM-28 R3 WD SPM-28 R4 WD SPM-40 R1 WD SPM-40 R2 WD SPM-40 R3 WD SPM-40 R4 WD STR-10 R1 WD STR-10 R2 WD STR-10 R3 WD STR-10 R4 WD ERS-05 R1 WD ERS-05 R2 WD ERS-05 R3 WD ERS-05 R4 WD SPM-12 R1 WD SPM-12 R2 WD SPM-12 R3 WD SPM-12 R4 WD ALL-29 R1 WD ALL-29 R2 WD ALL-29 R3 WD ALL-29 R4 WW SPM-28 R1 WW SPM-28 R2 WW SPM-28 R3 WW SPM-28 R4 WW SPM-40 R1 WW SPM-40 R2 WW SPM-40 R3 WW SPM-40 R4 WW STR-10 R1 WW STR-10 R2 WW STR-10 R3 WW STR-10 R4 WW ERS-05 R1 WW ERS-05 R2 WW ERS-05 R3 WW ERS-05 R4 WW SPM-12 R1 WW SPM-12 R2 WW SPM-12 R3 WW SPM-12 R4 WW ALL-29 R1 WW ALL-29 R2 WW ALL-29 R3 WW ALL-29 R4

6 levels

2 levels

Explanatory variables

4 levels

n = 48

Phenotypes Transcriptome V1 … V133 V1 … V73013 SPM-28 SPM-40 STR-10 ERS-05 SPM-12 ALL-29 V1 … Vx Selection of variables Dim 1 (29.96%) Dim 2 (14.69%) Density Curves Data D e n si ty 0 5 10 15 0 .0 0 0 .0 2 0 .0 4 0 .0 6 0 .0 8 0 .1 0 0 .1 2 Density Density Curves Data D e n si ty 0 100 200 300 400 0 .0 0 0 0 .0 0 5 0 .0 1 0 0 .0 1 5 Density WD WW