A combination of gene expression ranking and co-expression network analysis increases discovery rate in large-scale mutant screens for novel Arabidopsis thaliana abiotic stress genes

作者：Ran**otyn Vanessa; Yeger Lotem Esti; Basha Omer; Acuna Tania; Verduyn Christoph; Gordon Michal; Chalifa Caspi Vered; Hannah Matthew A; Barak Simon^*

来源：Plant Biotechnology Journal, 2015, 13(4): 501-513.

DOI：10.1111/pbi.12274

• Arabidopsis thaliana
• abiotic stress
• systems biology
• co-expression network
• mutant screen
• transcriptome

As challenges to food security increase, the demand for lead genes for improving crop production is growing. However, genetic screens of plant mutants typically yield very low frequencies of desired phenotypes. Here, we present a powerful computational approach for selecting candidate genes for screening insertion mutants. We combined ranking of Arabidopsis thaliana regulatory genes according to their expression in response to multiple abiotic stresses (Multiple Stress [MST] score), with stress-responsive RNA co-expression network analysis to select candidate multiple stress regulatory (MSTR) genes. Screening of 62 T-DNA insertion mutants defective in candidate MSTR genes, for abiotic stress germination phenotypes yielded a remarkable hit rate of up to 62%; this gene discovery rate is 48-fold greater than that of other large-scale insertional mutant screens. Moreover, the MST score of these genes could be used to prioritize them for screening. To evaluate the contribution of the co-expression analysis, we screened 64 additional mutant lines of MST-scored genes that did not appear in the RNA co-expression network. The screening of these MST-scored genes yielded a gene discovery rate of 36%, which is much higher than that of classic mutant screens but not as high as when picking candidate genes from the co-expression network. The MSTR co-expression network that we created, AraSTressRegNet is publicly available at . This systems biology-based screening approach combining gene ranking and network analysis could be generally applicable to enhancing identification of genes regulating additional processes in plants and other organisms provided that suitable transcriptome data are available.

• 出版日期2015-5