Unravelling Microbial Dynamics of Inferred Pollutant Degradation through Integrated Metagenomic and Species Interaction Modelling Approaches

Riverine microbial communities play a critical role in contaminant attenuation, yet the ecological and functional mechanisms governing this process along longitudinal river gradients remain poorly understood. Here, we investigated microbial community structure, assembly processes and inferred metabolic potential across the River Ganges and its headwater tributaries using high-resolution whole-genome shotgun (WGS) metagenomics coupled with Hierarchical Modelling of Species Communities (HMSC). Multi-domain profiling revealed distinct elevation-linked patterns of bacteria, fungi, archaea and viruses with turbidity emerging as the dominant environmental driver followed by electrical conductivity and pH. Taxonomic profiling revealed signatures of bacteria ( Rhodoferax, Curvibacter, Methyloversatilis, Caulobacter, Aeromicrobium, Azospirillum ), fungi ( Fusarium ), archaea ( Candidatus Nitrosotenuis ) and viruses ( Caudovirales ) in a clear elevation-linked spatial pattern. The upstream communities enriched in taxa associated with cold-adapted metabolic functions, midstream regions by nutrient-cycling taxa and downstream regions enriched in taxa linked to stress adaptation and putative detoxification pathways. Functional analyses identified enrichment of enzyme-coding gene families and metabolic pathways involved in xenobiotic degradation, aromatic hydrocarbon breakdown, energy metabolism and cellular stress responses with site-specific taxa contributing to pollutant transformation. These functional attributes represent inferred metabolic potential derived from metagenomic data. HMSC further confirmed turbidity as the primary determinant of community variation while EC, pH and elevation contributed secondary effects. Negative residual species associations of fungal and archaeal guilds in the HMSC analysis suggested that both biotic interactions and environmental filtering may influence community structure. Overall, this study provides an integrative framework combining metagenomics and joint species distribution modelling to link microbial community with environmental gradients and inferred contaminant transformation potential. These findings offer ecological insights into inferred natural purification processes with implications for river restoration, risk management and bioremediation.