High-resolution time-series transcriptomic and metabolomic profiling reveals the regulatory mechanism underlying salt tolerance in maize by Fei Zhang & Boming Ji & Si Wu & Jie Zhang & Hui Zhang & Fei Wang & Baoxing Song & Qing Sang & Wenjie Huang & ShijuanYan & Mustafa Bulut & Yariv Brotman & Mingqiu Dai instant download
Genome Biology,Abstract*Correspondence: Background: Soil salinization represents a critical global challenge to agricultural productivity, profoundly impacting crop yields and threatening food security. Plant
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[email protected] is complex and dynamic, making it challenging to fully elucidate salt 1 National Key Laboratory tolerance mechanism and leading to gaps in our understanding of how plants adapt of Crop Genetic Improvement, to and mitigate salt stress.Huazhong Agricultural University, Results: Here, we conduct high-resolution time-series transcriptomic and metaboWuhan 430070, China 9 School of Plant Sciences lomic profling of the extremely salt-tolerant maize inbred line, HLZY, and the salt-sensiand Food Security, Tel Aviv tive elite line, JI853. Utilizing advanced data mining techniques, we identify key factors University, Tel Aviv 69978, Israelunderlying the divergence in salt tolerance between these two lines and discover Full list of author information is available at the end of the articlea series of novel genes and metabolites essential for maize salt tolerance. Additionally, we develop an innovative decision algorithm that enabled the construction of a highconfdence gene regulatory network for important salt-responsive metabolites. Comprehensive genetic and molecular studies further reveal the pivotal role of a hub gene, ZmGLN2, in regulating metabolite biosynthesis and salt tolerance in maize.Conclusions: Our study provides the frst high-resolution transcriptomic and metabolomic dataset for crop salt response, uncovering novel maize salt-responsive genes and metabolites. These fndings demonstrate the efectiveness of high-resolution multi-omics in deciphering the mechanisms underlying complex crop traits. Furthermore, we develop a systematic analytical framework for mining time-series multi-omics data, which can be broadly applied to other species or traits.Keywords: Maize, Time series, Multi-omics,
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