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.

Universal Dynamics of Microbial Communities in Full-Scale Textile Wastewater Treatment Plants and System Prediction by Machine Learning.

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.

Microbial Communities in Full-Scale Wastewater Treatment Systems Exhibit Deterministic Assembly Processes and Functional Dependency over Time.

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.