Department of Zoology, Dr. Harisingh Gour University (Central University), Sagar, 470003, MP, India; Metagenomics and Secretomics Research Laboratory, Department of Botany, Dr. Harisingh Gour University (Central University), Sagar, 470003, MP, India.
Metagenomics and Secretomics Research Laboratory, Department of Botany, Dr. Harisingh Gour University (Central University), Sagar, 470003, MP, India.
Environ Pollut. 2022 Apr 15;299:118851. doi: 10.1016/j.envpol.2022.118851. Epub 2022 Jan 24.
The overuse of pesticides for augmenting agriculture productivity always comes at the cost of environment, biodiversity, and human health and has put the land, water, and environmental footprints under severe threat throughout the globe. Underpinning and maximizing the microbiome functions in pesticide-contaminated environments has become a prerequisite for a sustainable environment and resilient agriculture. It is imperative to elucidate the metabolic network of the microbial communities and environmental variables at the contaminated site to predict the best strategy for remediation and soil microbe-pesticide interactions. High throughput next-generation sequencing and in silico analysis allow us to identify and discern the members and characteristics of core microbiomes at the contaminated site. Integration of modern high throughput multi-omics investigations and informatics pipelines provide novel approaches and pathways to capitalize on the core microbiomes for enhancing environmental functioning and mitigation. The role of eco-genomics tools in visualising the microbial network, taxonomy, functional potential, and environmental variables in contaminated habitats is discussed in this review. The integrated role of the potential microbe identification as individual or consortia, mechanistic approach for pesticide degradation, identification of responsible enzymes/genes, and in silico approach is emphasized for the prospects of the area.
过度使用农药来提高农业生产力,总是以环境、生物多样性和人类健康为代价,使全球土地、水和环境足迹受到严重威胁。在受农药污染的环境中,最大限度地发挥微生物组功能已成为可持续环境和弹性农业的前提条件。阐明污染场地中微生物群落和环境变量的代谢网络,以预测修复和土壤微生物-农药相互作用的最佳策略至关重要。高通量下一代测序和计算机分析使我们能够识别和区分污染场地中核心微生物组的成员和特征。整合现代高通量多组学研究和信息学管道,为利用核心微生物组增强环境功能和缓解提供了新的方法和途径。本文综述了生态基因组学工具在污染生境中可视化微生物网络、分类学、功能潜力和环境变量的作用。强调了潜在微生物鉴定为个体或联合体、农药降解的机制方法、负责酶/基因的鉴定以及计算机方法的综合作用,以展望该领域的前景。