Metagenomics-Based Microbial Ecological Community Threshold and Indicators of Anthropogenic Disturbances in Estuarine Sediments.

Assessing the impacts of cumulative anthropogenic disturbances on estuarine ecosystem health is challenging. Using spatially distributed sediments from the Pearl River Estuary (PRE) in southern China, which are significantly influenced by anthropogenic activities, we demonstrated that metagenomics-based surveillance of benthic microbial communities is a robust approach to assess anthropogenic impacts on estuarine benthic ecosystems. Correlational and threshold analyses between microbial compositions and environmental conditions indicated that anthropogenic disturbances in the PRE sediments drove the taxonomic and functional variations in the benthic microbial communities. An ecological community threshold of anthropogenic disturbances was identified, which delineated the PRE sediments into two groups (H and L) with distinct taxa and functional traits. Group H, located nearshore and subjected to a higher level of anthropogenic disturbances, was enriched with pollutant degraders, putative human pathogens, fecal pollution indicators, and functional traits related to stress tolerance. In contrast, Group L, located offshore and subjected to a lower level of anthropogenic disturbances, was enriched with halotolerant and oligotrophic taxa and functional traits related to growth and resource acquisition. The machine learning random forest model identified a number of taxonomic and functional indicators that could differentiate PRE sediments between Groups H and L. The identified ecological community threshold and microbial indicators highlight the utility of metagenomics-based microbial surveillance in assessing the adverse impacts of anthropogenic disturbances in estuarine sediments, which can assist environmental management to better protect ecosystem health.

Niche differentiation in microbial communities with stable genomic traits over time in engineered systems.

Microbial communities in full-scale engineered systems undergo dynamic compositional changes. However, mechanisms governing assembly of such microbes and succession of their functioning and genomic traits under various environmental conditions are unclear. In this study, we used the activated sludge and anaerobic treatment systems of four full-scale industrial wastewater treatment plants as models to investigate the niches of microbes in communities and the temporal succession patterns of community compositions. High-quality representative metagenome-assembled genomes revealed that taxonomic, functional, and trait-based compositions were strongly shaped by environmental selection, with replacement processes primarily driving variations in taxonomic and functional compositions. Plant-specific indicators were associated with system environmental conditions and exhibited strong determinism and trajectory directionality over time. The partitioning of microbes in a co-abundance network according to groups of plant-specific indicators, together with significant between-group differences in genomic traits, indicated the occurrence of niche differentiation. The indicators of the treatment plant with rich nutrient input and high substrate removal efficiency exhibited a faster predicted growth rate, lower guanine-cytosine content, smaller genome size, and higher codon usage bias than the indicators of the other plants. In individual plants, taxonomic composition displayed a more rapid temporal succession than functional and trait-based compositions. The succession of taxonomic, functional, and trait-based compositions was correlated with the kinetics of treatment processes in the activated sludge systems. This study provides insights into ecological niches of microbes in engineered systems and succession patterns of their functions and traits, which will aid microbial community management to improve treatment performance.

Landscape of global urban environmental resistome and its association with local socioeconomic and medical status.

Antimicrobial resistance (AMR) poses a critical threat to global health and development, with environmental factors-particularly in urban areas-contributing significantly to the spread of antibiotic resistance genes (ARGs). However, most research to date has been conducted at a local level, leaving significant gaps in our understanding of the global status of antibiotic resistance in urban environments. To address this issue, we thoroughly analyzed a total of 86,213 ARGs detected within 4,728 metagenome samples, which were collected by the MetaSUB International Consortium involving diverse urban environments in 60 cities of 27 countries, utilizing a deep-learning based methodology. Our findings demonstrated the strong geographical specificity of urban environmental resistome, and their correlation with various local socioeconomic and medical conditions. We also identified distinctive evolutionary patterns of ARG-related biosynthetic gene clusters (BGCs) across different countries, and discovered that the urban environment represents a rich source of novel antibiotics. Our study provides a comprehensive overview of the global urban environmental resistome, and fills a significant gap in our knowledge of large-scale urban antibiotic resistome analysis.