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.

Artificial intelligence-based prediction of indoor bioaerosol concentrations from indoor air quality sensor data.

Exposure to bioaerosols in indoor environments, especially public venues that have a high occupancy and poor ventilation, is a serious public health concern. However, it remains challenging to monitor and determine real-time or predict near-future concentrations of airborne biological matter. In this study, we developed artificial intelligence (AI) models using physical and chemical data from indoor air quality sensors and physical data from ultraviolet light-induced fluorescence observations of bioaerosols. This enabled us to effectively estimate the bioaerosol (bacteria-, fungi- and pollen-like particle) and 2.5-µm and 10-µm particulate matter (PM2.5 and PM10) on a real-time and near-future (≤60 min) basis. Seven AI models were developed and evaluated using measured data from an occupied commercial office and a shopping mall. A long short-term memory model required a relatively short training time and gave the highest prediction accuracy of âˆ¼ 60 %-80 % for bioaerosols and âˆ¼ 90 % for PM on the testing and time series datasets from the two venues. This work demonstrates how AI-based methods can leverage bioaerosol monitoring into predictive scenarios that building operators can use for improving indoor environmental quality in near real-time.

City-Scale Meta-Analysis of Indoor Airborne Microbiota Reveals that Taxonomic and Functional Compositions Vary with Building Types.

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.

Metagenomic insights into the microbial communities of inert and oligotrophic outdoor pier surfaces of a coastal city.

BACKGROUND: Studies of the microbiomes on surfaces in built environment have largely focused on indoor spaces, while outdoor spaces have received far less attention. Piers are engineered infrastructures commonly found in coastal areas, and due to their unique locations at the interface between terrestrial and aquatic ecosystems, pier surfaces are likely to harbor interesting microbiology. In this study, the microbiomes on the metal and concrete surfaces at nine piers located along the coastline of Hong Kong were investigated by metagenomic sequencing. The roles played by different physical attributes and environmental factors in shaping the taxonomic composition and functional traits of the pier surface microbiomes were determined. Metagenome-assembled genomes were reconstructed and their putative biosynthetic gene clusters were characterized in detail. RESULTS: Surface material was found to be the strongest factor in structuring the taxonomic and functional compositions of the pier surface microbiomes. Corrosion-related bacteria were significantly enriched on metal surfaces, consistent with the pitting corrosion observed. The differential enrichment of taxa mediating biodegradation suggests differences between the metal and concrete surfaces in terms of specific xenobiotics being potentially degraded. Genome-centric analysis detected the presence of many novel species, with the majority of them belonging to the phylum Proteobacteria. Genomic characterization showed that the potential metabolic functions and secondary biosynthetic capacity were largely correlated with taxonomy, rather than surface attributes and geography. CONCLUSIONS: Pier surfaces are a rich reservoir of abundant novel bacterial species. Members of the surface microbial communities use different mechanisms to counter the stresses under oligotrophic conditions. A better understanding of the outdoor surface microbiomes located in different environments should enhance the ability to maintain outdoor surfaces of infrastructures. Video Abstract.