A transcriptome meta-analysis identifies the response of plant to stresses. Etienne Delannoy, Rim Zaag, Guillem Rigaill, Marie-Laure Martin-Magniette

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1 A transcriptome meta-analysis identifies the response of plant to es Etienne Delannoy, Rim Zaag, Guillem Rigaill, Marie-Laure Martin-Magniette

2 Biological context Multiple biotic and abiotic es impacting plant growth Coordinated response to es in general Na + Cd 2+ Numerous single transcriptomic data sets available

3 Gene co-expression Study patterns of relative gene expression across several conditions

4 Gene co-expression using mixture models Global modelling of the entire gene population Genes are assumed to come from different subpopulations Rigorous framework for parameters estimation and the choice of the number of subpopulations Each gene is assigned to a subpopulation with a probability.

5 Gaussian Mixture Model Coexpression analyses of 18 responses : 387 comparisons in 18 categories: 9 biotic and 9 abiotic Matrix { genes x log-ratios} by Zaag R et al., Nucleic Acids Res, Mathematical Criterion to select the cluster number (BIC) Classification rule based on conditional probabilities ~700 Clusters of coexpressed genes Stress category Sample_nb Gene_nb Cluster_nb Drought Gamma ray Heavy metals Nitrogen Oxidative Salt Temperature UV Other abiotic Fungi Biotrophic bacteria Necrotrophic bacteria Nematodes Oomycetes Rhodococcus Stifenia Virus Other biotic

6 From coexpression clusters to coregulation gene network Integration Coexpression clusters for each category of Occurrence of pairs of coexpressed genes conserved in several es among the 18 considered categories Coexpression network 1) Compared with random networks, only edges providing a FDR<1% were kept Filters Coregulation network 2) Only genes involved in triangles were considered as co-regulated

7 Arabidopsis co-regulation network 4475 genes and co-regulation links 86% of the co-regulation links are supported by both types of Scale-free network Density = Transitivity= 0.54 Biological network Presence of gene clusters

8 Identification of communities within the network Communities are defined by their probabilities of interaction (within-community and between communities) Synthetic representation: a node is a community An edge is a probability of interaction between two communities

9 Identification of gene communities within the network 52 communities of 21 to 351 genes Stability of the communities

10 Identification of gene communities within the network 52 communities of 21 to 351 genes Stability measured with a cross-validation procedure For each category, create a network from the 17 others Find communities using mixture of graphs Comparison of these 18 results with the network built from all the categories

11 Identification of the common response to es 2674 genes in 43 communities describe the common response to es Cross-validation procedure For each category, create a network from the 17 others Find communities using mixture of graphs Comparison of these 18 results with the network built from all the categories

12 Identification of the common response to es Most communities with GO enrichments ER Cell wall DNA rep transcription transport

13 Identification of the common response to es Functional validation of the communities AT5G14910 in Pulido et al. (june 2018) Supports plastid translation under associated with 30S subunit ER NA AT1G01230 in Yang et al. (dec 2018) Associated to ER and autophagy of FLS2 Cell wall Chloroplast DNA rep transcription transport

14 Identification of the common response to es Functional validation of the communities ER Cell wall DNA rep transcription transport Phenotyping of T-DNA mutants of 12 genes with unknown function

15 Identification of the common response to es Functional validation of MITO the communities ER Cell wall DNA rep MITO transcription transport PLASTID

16 Stresses impact the energy metabolism ER Cell wall DNA rep transcription transport In addition to, predominance of, and clusters A common point to es is their impact on the energy metabolism

17 The organisation of the response The functional modules are not randomly located and interacting in the network

18 The organisation of the response + a shortest path analysis The functional modules are not randomly located and interacting in the network 4 biological functions in 5 groups

19 + a shortest path analysis The backbone of plant response

20 The backbone of plant response + a shortest path analysis + GO enrichment STRESS CHLOROPLAST MITOCHONDRION RIBOSOME

21 The backbone of plant response STRESS Energy PHOTOSYNTHESIS source ATP SYNTHESIS conversion TRANSLATION sink

22 The backbone of plant response STRESS RESPONSES Energy PHOTOSYNTHESIS source Post-transcriptional ATP SYNTHESIS conversion TRANSLATION sink

23 Conclusions Transcriptome meta-analysis Identification of a common response to Involvement of the energy organelles and translation Hierarchical organization along the energy gradient Statistical modelling: a relevant approach to formulate biological hypotheses

24 Perspectives Network validation

25 Perspectives Biological Network validation

26 Perspectives Biological Network validation NODES validation of communities = validation of genes

27 Perspectives Biological Network validation NODES EDGES

28

29 Genes with orthologs The actors of the plant reponse are conserved Based on Murat et al using similarity + synteny to identify orthologs among 38 Angiosperm species + C. reinhardii + P. patens 100% 55% The genes of the clusters are significantly less conserved than the other genes

30 Functional validation

31 Functional validation T-DNA mutants for 12 genes of unknown function

32 phenotypes Sur la base des Phenotypes du papier Ahmed et al. Nature comm AGI with curated phenotypes 648 AGI dans le réseau après CV Léger enrichissement en AGI essential et conditional C est difficile de faire la même analyse par communauté car seules 10 communautés ont >20 gènes avec phenotype

33 Plant response 86% of the edges are supported by both types of es Homogenous distribution of edges Large overlap between biotic and abiotic responses Analysis of the gene modules identified by the mixture model