A gene-phenotype network based on genetic variability for drought responses reveals key physiological processes in controlled and natural environments.
Identifying the connections between molecular and physiological processes underlying the diversity of drought stress responses in plants is key for basic and applied science. Drought stress response involves a large number of molecular pathways and subsequent physiological processes. Therefore, it constitutes an archetypical systems biology model. We first inferred a gene-phenotype network exploiting differences in drought responses of eight sunflower (Helianthus annuus) genotypes to two drought stress scenarios. Large transcriptomic data were obtained with the sunflower Affymetrix microarray, comprising 32423 probesets, and were associated to nine morpho-physiological traits (integrated transpired water, leaf transpiration rate, osmotic potential, relative water content, leaf mass per area, carbon isotope discrimination, plant height, number of leaves and collar diameter) using sPLS regression. Overall, we could associate the expression patterns of 1263 probesets to six phenotypic traits and identify if correlations were due to treatment, genotype and/or their interaction. We also identified genes whose expression is affected at moderate and/or intense drought stress together with genes whose expression variation could explain phenotypic and drought tolerance variability among our genetic material. We then used the network model to study phenotypic changes in less tractable agronomical conditions, i.e. sunflower hybrids subjected to different watering regimes in field trials. Mapping this new dataset in the gene-phenotype network allowed us to identify genes whose expression was robustly affected by water deprivation in both controlled and field conditions. The enrichment in genes correlated to relative water content and osmotic potential provides evidence of the importance of these traits in agronomical conditions.
INRA, Laboratoire des Interactions Plantes-Microorganismes (LIPM), UMR441, Castanet-Tolosan, France.
David
D
Rengel
Sandrine
S
Arribat
Pierre
P
Maury
Marie-Laure
ML
Martin-Magniette
Thibaut
T
Hourlier
Marion
M
Laporte
Didier
D
Varès
Sébastien
S
Carrère
Philippe
P
Grieu
Sandrine
S
Balzergue
Jérôme
J
Gouzy
Patrick
P
Vincourt
Nicolas B
NB
Langlade
D000222:Adaptation, Physiological / Q000187:drug effects / Q000235:genetics*; D000704:Analysis of Variance; D055864:Droughts; D017753:Ecosystem; D004780:Environment, Controlled; D018506:Gene Expression Regulation, Plant / Q000187:drug effects; D053263:Gene Regulatory Networks; D014644:Genetic Variation; D005838:Genotype; D006368:Helianthus / Q000187:drug effects / Q000235:genetics* / Q000502:physiology; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D010641:Phenotype; D059467:Transcriptome; D014867:Water / Q000378:metabolism / Q000494:pharmacology
D014867:Water