Genomics as a potential tool to unravel the rhizosphere microbiome interactions on plant health

根际 基因组 蛋白质组 微生物群 背景(考古学) 生物 可持续农业 生物技术 农业 数据科学 计算生物学 生态学 计算机科学 生物信息学 生物化学 基因 遗传学 古生物学 细菌
作者
Piyush Priya,Balakrishna Pillai Aneesh,K. Harikrishnan
出处
期刊:Journal of Microbiological Methods [Elsevier BV]
卷期号:185: 106215-106215 被引量:24
标识
DOI:10.1016/j.mimet.2021.106215
摘要

Intense agricultural practices to meet rising food demands have caused ecosystem perturbations. For sustainable crop production, biological agents are gaining attention, but exploring their functional potential on a multi-layered complex ecosystem like the rhizosphere is challenging. This review explains the significance of genomics as a culture-independent molecular tool to understand the diversity and functional significance of the rhizosphere microbiome for sustainable agriculture. It discusses the recent significant studies in the rhizosphere environment carried out using evolving techniques like metagenomics, metatranscriptomics, and metaproteomics, their challenges, constraints infield application, and prospective solutions. The recent advances in techniques such as nanotechnology for the development of bioformulations and visualization techniques contemplating environmental safety were also discussed. The need for development of metagenomic data sets of regionally important crops, their plant microbial interactions and agricultural practices for narrowing down significant data from huge databases have been suggested. The role of taxonomical and functional diversity of soil microbiota in understanding soil suppression and part played by the microbial metabolites in the process have been analyzed and discussed in the context of 'omics' approach. 'Omics' studies have revealed important information about microbial diversity, their responses to various biotic and abiotic stimuli, and the physiology of disease suppression. This can be translated to crop sustainability and combinational approaches with advancing visualization and analysis methodologies fix the existing knowledge gap to a huge extend. With improved data processing and standardization of the methods, details of plant-microbe interactions can be successfully decoded to develop sustainable agricultural practices.

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