Abstract

Understanding the global abiotic stress response is an important stepping stone for the development of universal stress tolerance in plants in the era of climate change. Although co-occurrence of several stress factors (abiotic and biotic) in nature is found to be frequent, current attempts are poor to understand the complex physiological processes impacting plant growth under combinatory factors. In this review article, we discuss the recent advances of reverse engineering approaches that led to seminal discoveries of key candidate regulatory genes involved in cross-talk of abiotic stress responses and summarized the available tools of reverse engineering and its relevant application. Among the universally induced regulators involved in various abiotic stress responses, we highlight the importance of (i) abscisic acid (ABA) and jasmonic acid (JA) hormonal cross-talks and (ii) the central role of WRKY transcription factors (TF), potentially mediating both abiotic and biotic stress responses. Such interactome networks help not only to derive hypotheses but also play a vital role in identifying key regulatory targets and interconnected hormonal responses. To explore the full potential of gene network inference in the area of abiotic stress tolerance, we need to validate hypotheses by implementing time-dependent gene expression data from genetically engineered plants with modulated expression of target genes. We further propose to combine information on gene-by-gene interactions with data from physical interaction platforms such as protein–protein or TF-gene networks.

Highlights

  • During their growth and development plants are constantly exposed to various kinds of environmental stimuli and stresses due to their sessile lifestyle

  • Among the most prominent physiological alterations in response to abiotic stress is a substantial induction of the universal stress hormone abscisic acid (ABA; Sreenivasulu et al, 2012), which is involved in the cross-talk between abiotic and biotic stresses (Chan, 2012)

  • We briefly summarize the existing resources and advancement that has been made in the development of suitable software required to analyze gene regulatory networks and we exemplify this by a case study of plant responses to abiotic stress

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Summary

INTRODUCTION

During their growth and development plants are constantly exposed to various kinds of environmental stimuli and stresses due to their sessile lifestyle. As a part of systems biology approaches, several methods have been developed to construct genome-wide gene networks using heuristic cluster chiseling algorithms and correlation coefficients These networks have been derived from whole-genome coexpression networks in Arabidopsis and six other important crop plants (Lee et al, 2009; Mutwil et al, 2011), which allows cross-species comparisons based on time series networks. Gene networks in Arabidopsis (curated at AtGenExpress) derived from genome-wide gene expression data using the Affymetrix platform as well tiling arrays designed for the assessment of plant responses to environmental perturbations, help to understand the fundamental stochastic processes underlying individual cell and tissue typespecific responses to various abiotic stress factors (Kilian et al, 2007; Zeller et al, 2009). The gene regulation at transcriptional level could be associated with transcription factor (TF)-target www.frontiersin.org

Weighted correlation
Linear model fitting
Boolean Networks
Dynamic Bayesian network
Mutual information
The TF tool retrieves regulatory interactions from AGRIS and from
Metabolic network
Genevestigator Coexpression
Any species where high throughput data is available
Evidence network
Biomolecular interaction based on textmining
Bayesian network
Findings
Mutual Information
Full Text
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