Riverine sediments serve as critical reservoirs of microbial diversity and functional genes, reflecting both natural ecological processes and anthropogenic impacts. In the present study, we employed a shotgun metagenomic approach to investigate microbial community composition, antimicrobial resistance (AMR) genes, and virulence factors in sediments collected from three environmentally distinct locations of the Yamuna River near Agra, India, representing BSA, TGY, and YEA. The sediment DNA was subjected to high-throughput Illumina sequencing, followed by quality control, assembly, and open reading frame prediction. Taxonomic classification and diversity analyses were performed using MEGAN6 and R-based statistical tools, while AMR genes were identified from predicted metagenomic proteins using the Resistance Gene Identifier (RGI) against the CARD database, with high-confidence perfect and strict hits retained; ARGs were interpreted independently of species-level host assignment. Virulence factors were assessed through presence–absence profiling of functionally relevant gene categories. The results revealed pronounced spatial heterogeneity in microbial communities, with increasing taxonomic diversity, functional complexity, and evenness from BSA to TGY and YEA. TGY and YEA composite samples showed greater observed representation of high-confidence AMR gene predictions spanning multiple drug classes and resistance mechanisms, alongside a diverse repertoire of virulence-associated genes linked to motility, adhesion, and secretion systems. In contrast, the BSA site harbored a comparatively simpler resistome and virulome. Overall, this study highlights Yamuna River sediments as important reservoirs of resistance and virulence determinants and underscores the need for long-term genomic surveillance to inform risk assessment, pollution control, and sustainable river management strategies.
A. K. Rout, P. Tripathy, S. Dey et al.· Frontiers in Microbiology· 0 citations
The global health crisis of antimicrobial resistance necessitates the discovery of new antibacterial agents. Underexplored marine microbiomes, particularly from the biodiverse Indian coast, represent a rich potential source of antimicrobial peptides (AMPs). Targeting the urgent threat of multidrug-resistant ESKAPE pathogens, the present study aimed to computationally identify novel, membrane-active AMPs from these unique metagenomic datasets, with a focus on inhibiting Gram-negative bacteria. In this study, we computationally mined Indian marine high-resolution shotgun metagenomic datasets through quality filtering, de novo assembly, and small open reading frame prediction. An ensemble of six machine learning-based AMP prediction tools identified over 51,000 high-confidence candidate AMPs. Subsequent filtering based on physicochemical properties and AlphaFold3-predicted structures prioritized ten peptides with favourable membrane-active characteristics. Two lead candidates, c_AMP_1 and c_AMP_2, were subjected to all-atom molecular dynamics simulations within Gram-negative membrane mimetic models of Pseudomonas aeruginosa, Acinetobacter baumannii, and Klebsiella pneumoniae. Our simulations indicated distinct membrane interaction modes: c_AMP_1 adopted a stable, surface-associated α-helical orientation, while c_AMP_2 displayed a more flexible, membrane-inserting orientation in the simulations. Analysis of the MD simulations revealed distinct predicted peptide-membrane interaction profiles, characterized by specific hydrogen bonding patterns, peptide tilt angles, and membrane thinning, which collectively suggest differing biophysical interaction modes. Taken together, our work suggests the Indian marine microbiome as a promising reservoir for novel AMP candidates and suggests that an integrated computational pipeline – combining machine learning, structural biology, and biophysical simulation – may help prioritize candidate peptides for future experimental validation against critical pathogens.
Sreelakshmi K V, Nasri Thaha, B. Dehury· PLoS ONE· 0 citations
