Multi‐omics atlas of combinatorial abiotic stress responses in wheat

生物 转录组 非生物胁迫 基因 计算生物学 基因调控网络 非生物成分 遗传学 基因表达 生物技术 生态学
作者
Letitia Da Ros,Venkatesh Bollina,Raju Soolanayakanahally,Shankar Pahari,Raëd Elferjani,Manoj Kulkarni,Neha Vaid,Eddy Risseuw,Dustin Cram,Asher Pasha,Eddi Esteban,David Konkin,Nicholas J. Provart,Eiji Nambara,Sateesh Kagale
出处
期刊:Plant Journal [Wiley]
卷期号:116 (4): 1118-1135 被引量:41
标识
DOI:10.1111/tpj.16332
摘要

SUMMARY Field‐grown crops rarely experience growth conditions in which yield can be maximized. Environmental stresses occur in combination, with advancements in crop tolerance further complicated by its polygenic nature. Strategic targeting of causal genes is required to meet future crop production needs. Here, we employed a systems biology approach in wheat ( Triticum aestivum L.) to investigate physio‐metabolic adjustments and transcriptome reprogramming involved in acclimations to heat, drought, salinity and all combinations therein. A significant shift in magnitude and complexity of plant response was evident across stress scenarios based on the agronomic losses, increased proline concentrations and 8.7‐fold increase in unique differentially expressed transcripts (DETs) observed under the triple stress condition. Transcriptome data from all stress treatments were assembled into an online, open access eFP browser for visualizing gene expression during abiotic stress. Weighted gene co‐expression network analysis revealed 152 hub genes of which 32% contained the ethylene‐responsive element binding factor‐associated amphiphilic repression (EAR) transcriptional repression motif. Cross‐referencing against the 31 DETs common to all stress treatments isolated TaWRKY33 as a leading candidate for greater plant tolerance to combinatorial stresses. Integration of our findings with available literature on gene functional characterization allowed us to further suggest flexible gene combinations for future adaptive gene stacking in wheat. Our approach demonstrates the strength of robust multi‐omics‐based data resources for gene discovery in complex environmental conditions. Accessibility of such datasets will promote cross‐validation of candidate genes across studies and aid in accelerating causal gene validation for crop resiliency.
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