W849
Bridging Physiological and Evolutionary Time Scales in a Gene Regulatory Network

Date: Tuesday, January 13, 2015
Time: 11:30 AM
Room: Sunrise - Meeting House
Gwenaelle Marchand , INRA LIPM, Castanet-Tolosan, France
Vân Anh Huynh-Thu , University of Liège, Liège, Belgium
Nolan Kane , University of Colorado, Boulder, CO
Sandrine Arribat , INRA - URGV, Evry, France
Didier Varès , INRA LIPM, Castanet-Tolosan, France
David Rengel , INRA LIPM, Castanet-Tolosan, France
Sandrine Balzergue , INRA URGV, Evry, France
Loren H. Rieseberg , University of British Columbia, Vancouver, BC, Canada
Patrick Vincourt , INRA LIPM, Castanet-Tolosan, France
Pierre Geurts , University of Liège, Liège, Belgium
Matthieu Vignes , INRA MIAT, Castanet-Tolosan, France
Nicolas Langlade , Laboratoire des Interactions Plantes Micro-organismes (LIPM) - INRA/CNRS, TOULOUSE, France
Gene regulatory networks (GRN) govern phenotypic adaptations and reflect the trade-offs between physiological responses and evolutionary adaptation that act at different time scales. To identify patterns of molecular function and genetic diversity in GRNs, we studied the drought response of the common sunflower, Helianthus annuus, and how the underlying GRN is related to its evolution.

We examined the responses of 32,423 expressed sequences to drought and to abscisic acid and selected 145 co-expressed transcripts. We characterized their regulatory relationships in nine kinetic studies based on different hormones. From this, we inferred a GRN by meta-analyses of a Gaussian graphical model and a random forest algorithm and studied the genetic differentiation among populations (FST) at nodes.

We identified two main hubs in the network that transport nitrate in guard cells. This suggests that nitrate transport is a critical aspect of sunflower physiological response to drought. We observed that differentiation of the network genes in elite sunflower cultivars is correlated with their position and connectivity.

This systems biology approach combined molecular data at different time scales and identified important physiological processes. At the evolutionary level, we propose that network topology could influence responses to human selection and possibly adaptation to dry environments.