Metagenomics analysis of sediments of river Ganga, India for bacterial diversity, functional genomics, antibiotic resistant genes and virulence factors

IF 3.6 Q2 BIOTECHNOLOGY & APPLIED MICROBIOLOGY
Ajaya Kumar Rout , Partha Sarathi Tripathy , Sangita Dixit , Dibyajyoti Uttameswar Behera , Bhaskar Behera , Basanta Kumar Das , Bijay Kumar Behera
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Abstract

The river Ganges in India has faced considerable issues due to water quality degradation caused by various anthropogenic activities. This study employs metagenomic analysis to comprehensively characterize bacterial communities, explore functional genomics, and investigate the prevalence of virulence factors and antibiotic resistant genes (ARGs) within the sediment environment of the river Ganges. Taxonomic profiling revealed that Proteobacteria were the most dominating phyla found in all samples, whereas the abundance of Pseudomonas at the genus level was the highest in all the samples. Functional annotation and pathway analysis uncover the genomic potential of sediment associated bacteria, shedding light on metabolic pathways, biogeochemical processes, human diseases and adaptive mechanisms within the riverine ecosystem. Moreover, identifying the highest number of genes related to virulence factors was observed in K1 samples (3), and the highest number of genes related to ARGs found in K3 (25) samples emphasizes the need to understand potential pathogenicity in these environments. Characterization of ARGs provides crucial insights into the prevalence of resistance determinants, their genetic contexts, and potential sources of antibiotic resistance in this vital aquatic ecosystem. Overall metagenomics analysis in different sampling sites of river Ganga observed nearly the same OTUs at microbial communities at the taxonomic level but not at a functional level. This research can be a critical foundation for assessing the ecological implications of microbial communities, functional genomics, and the resistome in the river Ganges sediments. The findings underscore the importance of metagenomic approaches in elucidating the intricate microbial ecology and the prevalence of genetic elements relevant to environmental health and antimicrobial resistance in aquatic ecosystems. Further investigation would be required to understand the underlying cause behind the restoration of microbial functional profiles, including ARGs and VFs, to unravel the rejuvenation aspects of this unique ecosystem.

Abstract Image

对印度甘加河沉积物进行元基因组学分析,研究细菌多样性、功能基因组学、抗生素耐药基因和毒力因子
由于各种人为活动造成的水质恶化,印度恒河面临着相当大的问题。本研究采用元基因组分析法全面描述恒河沉积物环境中细菌群落的特征,探索功能基因组学,并调查毒力因子和抗生素耐药基因(ARGs)的流行情况。分类学分析表明,变形菌是所有样本中最主要的菌门,而假单胞菌属在所有样本中的丰度最高。功能注释和通路分析揭示了沉积物相关细菌的基因组潜力,揭示了河流生态系统中的代谢通路、生物地球化学过程、人类疾病和适应机制。此外,在 K1 样本(3 个)中发现了最多的与毒力因子相关的基因,而在 K3 样本(25 个)中发现了最多的与 ARGs 相关的基因,这强调了了解这些环境中潜在致病性的必要性。ARGs 的特征描述为了解抗药性决定因素的普遍性、其遗传背景以及这一重要水生生态系统中潜在的抗生素抗药性来源提供了重要信息。在恒河的不同取样点进行的元基因组学分析在分类水平上观察到微生物群落中几乎相同的 OTUs,但在功能水平上却没有发现。这项研究为评估恒河沉积物中微生物群落、功能基因组学和抗药性组的生态影响奠定了重要基础。研究结果强调了元基因组学方法在阐明错综复杂的微生物生态学以及水生生态系统中与环境健康和抗菌药耐药性相关的遗传元素流行方面的重要性。要了解微生物功能特征(包括 ARGs 和 VFs)恢复背后的根本原因,以揭示这一独特生态系统的年轻化方面,还需要进一步的调查。
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来源期刊
Current Research in Biotechnology
Current Research in Biotechnology Biochemistry, Genetics and Molecular Biology-Biotechnology
CiteScore
6.70
自引率
3.60%
发文量
50
审稿时长
38 days
期刊介绍: Current Research in Biotechnology (CRBIOT) is a new primary research, gold open access journal from Elsevier. CRBIOT publishes original papers, reviews, and short communications (including viewpoints and perspectives) resulting from research in biotechnology and biotech-associated disciplines. Current Research in Biotechnology is a peer-reviewed gold open access (OA) journal and upon acceptance all articles are permanently and freely available. It is a companion to the highly regarded review journal Current Opinion in Biotechnology (2018 CiteScore 8.450) and is part of the Current Opinion and Research (CO+RE) suite of journals. All CO+RE journals leverage the Current Opinion legacy-of editorial excellence, high-impact, and global reach-to ensure they are a widely read resource that is integral to scientists' workflow.
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