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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.

The performance of full-scale biological wastewater treatment plants (WWTPs) depends on the operational and environmental conditions of treatment systems. However, we do not know how much these conditions affect microbial community structures and dynamics across systems over time and predictability of the treatment performance. For over a year, the microbial communities of four full-scale WWTPs processing textile wastewater were monitored. During temporal succession, the environmental conditions and system treatment performance were the main drivers, which explained up to 51% of community variations within and between all plants based on the multiple regression models. We identified the universality of community dynamics in all systems using the dissimilarity-overlap curve method, with the significant negative slopes suggesting that the communities containing the same taxa from different plants over time exhibited a similar composition dynamic. The Hubbell neutral theory and the covariance neutrality test indicated that all systems had a dominant niche-based assembly mechanism, supporting that the communities had a similar composition dynamic. Phylogenetically diverse biomarkers for the system conditions and treatment performance were identified by machine learning. Most of the biomarkers (83%) were classified as generalist taxa, and the phylogenetically related biomarkers responded similarly to the system conditions. Many biomarkers for treatment performance perform functions that are crucial for wastewater treatment processes (e.g., carbon and nutrient removal). This study clarifies the relationships between community composition and environmental conditions in full-scale WWTPs over time.

Currently, there is a lack of understanding on the variations of the indoor airborne microbiotas of different building types within a city, and how operational taxonomic unit (OTU)- and amplicon sequence variant (ASV)-based analyses of the 16S rRNA gene sequences affect interpretation of the indoor airborne microbiota results. Therefore, in this study, the indoor airborne bacterial microbiotas between commercial buildings, residences, and subways within the same city were compared using both OTU- and ASV-based analytic methods. Our findings suggested that indoor airborne bacterial microbiota compositions were significantly different between building types regardless of the bioinformatics method used. The processes of ecological drift and random dispersal consistently played significant roles in the assembly of the indoor microbiota across building types. Abundant taxa tended to be more centralized in the correlation network of each building type, highlighting their importance. Taxonomic changes between the microbiotas of different building types were also linked to changes in their inferred metabolic function capabilities. Overall, the results imply that customized strategies are necessary to manage indoor airborne bacterial microbiotas for each building type or even within each specific building.

Metabolically active bacteria within built environments are poorly understood. This study aims to investigate the active airborne bacterial microbiota and compare the total and active microbiota in eight mechanically ventilated buildings over four consecutive seasons using the 16S rRNA gene (rDNA) and the 16S rRNA (rRNA), respectively. The relative abundances of the taxa of presumptive occupants and environmental origins were significantly different between the active and total microbiota. The Sloan neutral model suggested that ecological drift and random dispersal played a smaller role in the assembly of the active microbiota than the total microbiota. The seasonal nature of the active microbiota was consistent with that of the total microbiota in both indoor and outdoor environments, while only the indoor environment was significantly affected by geography. The relative abundances of the active and total taxa were positively correlated, suggesting that the high-abundance members were also the greatest contributors to the community-level metabolic activity. Based on the rRNA/rDNA ratio, the low-abundance members consistently had a higher taxon-level metabolic activity than the high-abundance members over seasons, suggesting that the low-abundance members may have the ability to survive and thrive in the indoor environment and their impact on the health of occupants cannot be overlooked.

Studies of the indoor airborne microbiome have mostly been confined to a single location and time point. Here, we characterized, over the course of a year, the geographic variation, building-function dependence, and dispersal characteristics of indoor and outdoor airborne microbiomes (bacterial members only) of eight mechanically ventilated commercial buildings. Based on the Sloan neutral model, airborne microbiomes were randomly dispersed in the respective indoor and outdoor environments and between the two environments during each season. The dominant taxa in the indoor and outdoor environments showed minor variations at each location among seasons. The airborne microbiomes displayed weak seasonality for both indoor and outdoor environments, while a weak geographic variation was found only for the indoor environments. Source tracking results show that outdoor air and occupant skin were major contributors to the indoor airborne microbiomes, but the extent of the contribution from each source varied within and among buildings over the seasons, which suggests variations in local building use. Based on 32 cases of indoor airborne microbiome data, we determined that the indoor/outdoor (I/O) ratio of PM2.5was not a robust indicator of the sources found indoors. Alternatively, the indoor concentration of carbon dioxide was more closely correlated with the major sources of the indoor airborne microbiome in mechanically ventilated environments.

Microbial communities constitute the core component of biological wastewater treatment processes. We conducted a meta-analysis based on the 16S rRNA gene of temporal samples obtained from diverse full-scale activated sludge and anaerobic digestion systems treating municipal and industrial wastewater (collected in this study and published previously) to investigate their community assembly mechanism and functional traits over time, which are not currently well understood. The influent composition was found to be the main driver of the microbial community’s composition, and relatively large proportions of specialist (26.1% and 18.6%) and transient taxa (67.2% and 68.1%) were estimated in both systems. Deterministic processes, especially homogeneous selection events (accounting for >53.8% of assembly events), were consistently identified as the dominant microbial community assembly mechanisms in both systems over time. Significant and strong correlations (Pearson’s r= 0.51–0.92) were detected between the dynamics of the temporal community and the functional compositions in both systems, which suggests functional dependency. In contrast, the occurrence of sludge bulking and foaming in the activated sludge system led to an increase in stochastic assembly processes (i.e., limited dispersal and undominated events), a shift toward functional redundancy and less community diversity, a decreased community niche breadth index, and a more compact co-association network. This study illustrates that the mechanism of microbial community assembly and functional traits over time can be used to diagnose system performance and provide information on potential system malfunction.