Reverse Engineering: A Key Component of Systems Biology to Unravel Global Abiotic Stress Cross-Talk

Friedel, Swetlana; Sreenivasulu, Nese; von Wirén, Nicolaus; Usadel, Björn

doi:10.3389/fpls.2012.00294

Journal Article

FZJ-2013-01142

Reverse Engineering: A Key Component of Systems Biology to Unravel Global Abiotic Stress Cross-Talk

Friedel, S. (Corresponding author) ; Usadel, B.FZJ* ; von Wirén, N. ; Sreenivasulu, N.

2012
Frontiers Media83580 Lausanne

Frontiers in Plant Physiology 3(294), 1-16 (2012) [10.3389/fpls.2012.00294]

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Please use a persistent id in citations: http://hdl.handle.net/2128/4999 doi:10.3389/fpls.2012.00294

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.

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