Bridging physiological and evolutionary time‐scales in a gene regulatory network

Abstract

SummaryGene regulatory networks (GRNs) 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 (ABA) 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 (F ST ) 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 the 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.

Causal Queries from Observational Data in Biological Systems via Bayesian Networks: An Empirical Study in Small Networks

“…A vast number of methods were developed to circumvent this difficulty with difference performance guarantees [16,140,49]. A second obstacle is concerned with the non-linearity of the relationships [100], sometimes immersed in intricate temporal responses [124,111,83]. Another feature of modern biological data sets is that of missing observations, either due to technical fault or be-cause all relevant variables cannot be monitored [19]; adequate techniques need be implemented to deal with this [8,22,44,117].…”

“…The material in this chapter slowly hatched after many discussions with a multitude of quantitative field colleagues and biologists during some works we were involved in, e.g. [141,138,83], but most of our inspiration stems from other works we read about and discussed (e.g. [21,63,112,45,137] to cite only a few).…”

Insights into the Ecology and Evolution of Polyploid Plants through Network Analysis

“…A recent example from Helianthus exemplifies what can be learned from appropriately tailoring the experimental data to the biological question. Marchand et al (2014) utilized gene expression data in H. annuus under nine hormonal treatments from seven time points to build a gene regulatory network (GRN) for drought stress in sunflowers, with a focus on an informed set of candidate genes. From this analysis, they: (i) uncovered hub genes for the drought stress GRN; (ii) discovered a role for nitrate transporters in regulating transpiration; and (iii) connected the abscisic acid-dependent and abscisic acid-independent pathways.…”

Validation and characterisation of a wheat GENIE3 network using an independent RNA-Seq dataset

“…GENIE3 was able to recapitulate known genetic regulatory networks in Escherichia coli when first tested. Since its introduction, the GENIE3 algorithm has been used to identify tissue-specific gene regulatory networks in maize [10] and key regulatory genes in glaucoma [11], as well as to study the drought response in sunflower [12]. Previous studies have integrated the GENIE3 network predictions with ChIP-Seq and other proteomic and transcriptomic data and found that the GENIE3 predictions do correspond with independent biological datasets [10, 13].…”