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.

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.

metaSpectraST: an unsupervised and database-independent analysis workflow for metaproteomic MS/MS data using spectrum clustering.

BACKGROUND: The high diversity and complexity of the microbial community make it a formidable challenge to identify and quantify the large number of proteins expressed in the community. Conventional metaproteomics approaches largely rely on accurate identification of the MS/MS spectra to their corresponding short peptides in the digested samples, followed by protein inference and subsequent taxonomic and functional analysis of the detected proteins. These approaches are dependent on the availability of protein sequence databases derived either from sample-specific metagenomic data or from public repositories. Due to the incompleteness and imperfections of these protein sequence databases, and the preponderance of homologous proteins expressed by different bacterial species in the community, this computational process of peptide identification and protein inference is challenging and error-prone, which hinders the comparison of metaproteomes across multiple samples. RESULTS: We developed metaSpectraST, an unsupervised and database-independent metaproteomics workflow, which quantitatively profiles and compares metaproteomics samples by clustering experimentally observed MS/MS spectra based on their spectral similarity. We applied metaSpectraST to fecal samples collected from littermates of two different mother mice right after weaning. Quantitative proteome profiles of the microbial communities of different mice were obtained without any peptide-spectrum identification and used to evaluate the overall similarity between samples and highlight any differentiating markers. Compared to the conventional database-dependent metaproteomics analysis, metaSpectraST is more successful in classifying the samples and detecting the subtle microbiome changes of mouse gut microbiomes post-weaning. metaSpectraST could also be used as a tool to select the suitable biological replicates from samples with wide inter-individual variation. CONCLUSIONS: metaSpectraST enables rapid profiling of metaproteomic samples quantitatively, without the need for constructing the protein sequence database or identification of the MS/MS spectra. It maximally preserves information contained in the experimental MS/MS spectra by clustering all of them first and thus is able to better profile the complex microbial communities and highlight their functional changes, as compared with conventional approaches. tag the videobyte in this section as ESM4 Video Abstract.

Emerging investigator series: aqueous photooxidation of live bacteria with hydroxyl radicals under cloud-like conditions: insights into the production and transformation of biological and organic matter originating from bioaerosols.

Live bacteria in clouds are exposed to free radicals such as the hydroxyl radical (˙OH), which is the main driver of many photochemical processes. While the ˙OH photooxidation of organic matter in clouds has been widely studied, equivalent investigations on the ˙OH photooxidation of bioaerosols are limited. Little is known about the daytime encounters between ˙OH and live bacteria in clouds. Here we investigated the aqueous ˙OH photooxidation of four bacterial strains, B. subtilis, P. putida, E. hormaechei B0910, and E. hormaechei pf0910, in microcosms composed of artificial cloud water that mimicked the chemical composition of cloud water in Hong Kong. The survival rates for the four bacterial strains decreased to zero within 6 hours during exposure to 1 × 10-16 M of ˙OH under artificial sunlight. Bacterial cell damage and lysis released biological and organic compounds, which were subsequently oxidized by ˙OH. The molecular weights of some of these biological and organic compounds were >50 kDa. The O/C, H/C, and N/C ratios increased at the initial onset of photooxidation. As the photooxidation progressed, there were few changes in the H/C and N/C, whereas the O/C continued to increase for hours after all the bacterial cells had died. The increase in the O/C was due to functionalization and fragmentation reactions, which increased the O content and decreased the C content, respectively. In particular, fragmentation reactions played key roles in transforming biological and organic compounds. Fragmentation reactions cleaved the C-C bonds of carbon backbones of higher molecular weight proteinaceous-like matter to form a variety of lower molecular weight compounds, including HULIS of molecular weight <3 kDa and highly oxygenated organic compounds of molecular weight <1.2 kDa. Overall, our results provided new insights at the process level into how daytime reactive interactions between live bacteria and ˙OH in clouds contribute to the formation and transformation of organic matter.

Secondary aerosol formation in incense burning particles by O3 and OH oxidation via single particle mixing state analysis.

Incense burning is a common religious activity that emits abundant gaseous and particulate pollutants into the atmosphere. During their atmospheric lifetime, these gases and particles are subjected to oxidation, leading to the formation of secondary pollutants. We examined the oxidation of incense burning plumes under O3 exposure and dark condition using an oxidation flow reactor connected to a single particle aerosol mass spectrometer (SPAMS). Nitrate formation was observed in incense burning particles, mainly attributable to the ozonolysis of nitrogen-containing organic compounds. With UV on, nitrate formation was significantly enhanced, likely due to HNO3/HNO2/NOx uptake triggered by OH chemistry, which is more effective than ozone oxidation. The extent of nitrate formation is insensitive to O3 and OH exposure, possibly due to the diffusion limitation on interfacial uptake. The O3-UV-aged particles are more oxygenated and functionalized than O3-Dark-aged particles. Oxalate and malonate, two typical secondary organic aerosol (SOA) components, were found in O3-UV-aged particles. Our work reveals that nitrate, accompanied by SOA, can rapidly form in incense-burning particles upon photochemical oxidation in the atmosphere, which could deepen our understanding of air pollution caused by religious activities.

Skin microbiome differentiates into distinct cutotypes with unique metabolic functions upon exposure to polycyclic aromatic hydrocarbons.

BACKGROUND: The effects of air pollutants, particularly polycyclic aromatic hydrocarbons (PAHs), on the skin microbiome remain poorly understood. Thus, to better understand the interplay between air pollutants, microbiomes, and skin conditions, we applied metagenomics and metabolomics to analyze the effects of PAHs in air pollution on the skin microbiomes of over 120 subjects residing in two cities in China with different levels of air pollution. RESULTS: The skin microbiomes differentiated into two cutotypes (termed 1 and 2) with distinct taxonomic, functional, resistome, and metabolite compositions as well as skin phenotypes that transcended geography and host factors. High PAH exposure was linked to dry skin and cutotype 2, which was enriched with species with potential biodegradation functions and had reduced correlation network structure integrity. The positive correlations identified between dominant taxa, key functional genes, and metabolites in the arginine biosynthesis pathway in cutotype 1 suggest that arginine from bacteria contributes to the synthesis of filaggrin-derived natural moisturizing factors (NMFs), which provide hydration for the skin, and could explain the normal skin phenotype observed. In contrast, no correlation with the arginine biosynthesis pathway was observed in cutotype 2, which indicates the limited hydration functions of NMFs and explains the observed dry skin phenotype. In addition to dryness, skin associated with cutotype 2 appeared prone to other adverse conditions such as inflammation. CONCLUSIONS: This study revealed the roles of PAHs in driving skin microbiome differentiation into cutotypes that vary extensively in taxonomy and metabolic functions and may subsequently lead to variations in skin-microbe interactions that affect host skin health. An improved understanding of the roles of microbiomes on skin exposed to air pollutants can aid the development of strategies that harness microbes to prevent undesirable skin conditions. Video Abstract.

Highly host-linked viromes in the built environment possess habitat-dependent diversity and functions for potential virus-host coevolution.

Viruses in built environments (BEs) raise public health concerns, yet they are generally less studied than bacteria. To better understand viral dynamics in BEs, this study assesses viromes from 11 habitats across four types of BEs with low to high occupancy. The diversity, composition, metabolic functions, and lifestyles of the viromes are found to be habitat dependent. Caudoviricetes species are ubiquitous on surface habitats in the BEs, and some of them are distinct from those present in other environments. Antimicrobial resistance genes are identified in viruses inhabiting surfaces frequently touched by occupants and in viruses inhabiting occupants' skin. Diverse CRISPR/Cas immunity systems and anti-CRISPR proteins are found in bacterial hosts and viruses, respectively, consistent with the strongly coupled virus-host links. Evidence of viruses potentially aiding host adaptation in a specific-habitat manner is identified through a unique gene insertion. This work illustrates that virus-host interactions occur frequently in BEs and that viruses are integral members of BE microbiomes.

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.

Contribution of soil bacteria to the atmosphere across biomes.

The dispersion of microorganisms through the atmosphere is a continual and essential process that underpins biogeography and ecosystem development and function. Despite the ubiquity of atmospheric microorganisms globally, specific knowledge of the determinants of atmospheric microbial diversity at any given location remains unresolved. Here we describe bacterial diversity in the atmospheric boundary layer and underlying soil at twelve globally distributed locations encompassing all major biomes, and characterise the contribution of local and distant soils to the observed atmospheric community. Across biomes the diversity of bacteria in the atmosphere was negatively correlated with mean annual precipitation but positively correlated to mean annual temperature. We identified distinct non-randomly assembled atmosphere and soil communities from each location, and some broad trends persisted across biomes including the enrichment of desiccation and UV tolerant taxa in the atmospheric community. Source tracking revealed that local soils were more influential than distant soil sources in determining observed diversity in the atmosphere, with more emissive semi-arid and arid biomes contributing most to signatures from distant soil. Our findings highlight complexities in the atmospheric microbiota that are relevant to understanding regional and global ecosystem connectivity.

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.

Selective single-bacteria extraction based on capture and release of microemulsion droplets.

Human host-associated microbial communities in body sites can reflect health status based on the population distribution and specific microbial properties in the heterogeneous community. Bacteria identification at the single-cell level provides a reliable biomarker and pathological information for clinical diagnosis. Nevertheless, biosamples obtained from some body sites cannot offer sufficient sample volume and number of target cells as required by most of the existing single-cell isolation methods such as flow cytometry. Herein we report a novel integrated microfluidic system, which consists of a microemulsion module for single-bacteria encapsulation and a sequential microdroplet capture and release module for selectively extracting only the single-bacteria encapsulated in microdroplets. We optimize the system for a success rate of the single-cell extraction to be > 38%. We further verify applicability of the system with prepared cell mixtures (Methylorubrum extorquens AM1 and Methylomicrobium album BG8) and biosamples collected from human skin, to quantify the population distribution of multiple key species in a heterogeneous microbial community. Results indicate perfect viability of the single-cell extracts and compatibility with downstream analyses such as PCR. Together, this research demonstrates that the reported single-bacteria extraction system can be applied in microbiome and pathology research and clinical diagnosis as a clinical or point-of-care device.

Per- and polyfluoroalkyl substances in the atmosphere of waste management infrastructures: Uncovering secondary fluorotelomer alcohols, particle size distribution, and human inhalation exposure.

Per- and polyfluoroalkyl substances (PFAS) have been applied in numerous industrial and consumer products, the majority of which flow into waste management infrastructures (WMIs) at the end of their life cycles, but little is known about atmospheric releases of PFAS from these facilities. In this study, we addressed this key issue by investigating 49 PFAS, including 23 ionic and 26 neutral and precursor PFAS, in the potential sources (n = 4; within or adjacent to WMIs) and reference sites (n = 2; coastal and natural reserve sites) in urban and rural areas of Hong Kong, China. Duplicate samples of air and size-segregated particulate matter were collected for 48 h continuously using a 11-stage Micro-Orifice Uniform Deposit Impactor (MOUDI). In general, fluorotelomer alcohols (FTOHs) and perfluoroalkane sulfonamides were the predominant PFAS classes found across sampling sites. We also demonstrated the release of several less frequently observed semivolatile intermediate products (e.g., secondary FTOHs) during waste treatment. Except for perfluorooctane sulfonate, the size-segregated distributions of particulate PFAS exhibited heterogeneity across sampling sites, particularly in the WMIs, implying combined effects of sorption affinity and emission sources. A preliminary daily air emission estimation revealed that landfill was a relatively important source of PFAS relative to the wastewater treatment plant. A simplified International Commission on Radiological Protection model was used to estimate lung depositional fluxes, and the results showed that inhaled particulate PFAS were mainly deposited in the head airway while fine and ultrafine particles carried PFAS deeper into the lung alveoli. The cumulative daily inhalation dose of gaseous and particulate PFAS ranged from 81.9 to 265 pg/kg/d. In-depth research is required to understand the health effect of airborne PFAS on workers at WMIs.

Fomite Transmission Follows Invasion Ecology Principles.

The invasion ecology principles illustrated in many ecosystems have not yet been explored in the context of fomite transmission. We hypothesized that invaders in fomite transmission are trackable, are neutrally distributed between hands and environmental surfaces, and exhibit a proximity effect. To test this hypothesis, a surrogate invader, Lactobacillus delbrueckii subsp. bulgaricus, was spread by a root carrier in an office housing more than 20 participants undertaking normal activities, and the microbiotas on skin and environmental surfaces were analyzed before and after invasion. First, we found that the invader was trackable. Its identity and emission source could be determined using microbial-interaction networks, and the root carrier could be identified using a rank analysis. Without prior information, L. bulgaricus could be identified as the invader emitted from a source that exclusively contained the invader, and the probable root carrier could be located. In addition to the single-taxon invasion by L. bulgaricus, multiple-taxon invasion was observed, as genera from sputum/saliva exhibited co-occurrence relationships on skin and environmental surfaces. Second, the invader had a below-neutral distribution in a neutral community model, suggesting that hands accrued heavier invader contamination than environmental surfaces. Third, a proximity effect was observed on a surface touch network. Invader contamination on surfaces decreased with increasing geodesic distance from the hands of the carrier, indicating that the carrier's touching behaviors were the main driver of fomite transmission. Taken together, these results demonstrate the invasion ecology principles in fomite transmission and provide a general basis for the management of ecological fomite transmission. IMPORTANCE Fomite transmission contributes to the spread of many infectious diseases. However, pathogens in fomite transmission typically are either investigated individually without considering the context of native microbiotas or investigated in a nondiscriminatory way from the dispersal of microbiotas. In this study, we adopted an invasion ecology framework in which we considered pathogens as invaders, the surface environment as an ecosystem, and human behaviors as the driver of microbial dispersal. With this approach, we assessed the ability of quantitative ecological theories to track and forecast pathogen movements in fomite transmission. By uncovering the relationships between the invader and native microbiotas and between human behaviors and invader/microbiota dispersal, we demonstrated that fomite transmission follows idiosyncratic invasion ecology principles. Our findings suggest that attempts to manage fomite transmission for public health purposes should focus on the microbial communities and anthropogenic factors involved, in addition to the pathogens.

Integrative Genome-Scale Metabolic Modeling Reveals Versatile Metabolic Strategies for Methane Utilization in Methylomicrobium album BG8.

Methylomicrobium album BG8 is an aerobic methanotrophic bacterium with promising features as a microbial cell factory for the conversion of methane to value-added chemicals. However, the lack of a genome-scale metabolic model (GEM) of M. album BG8 has hindered the development of systems biology and metabolic engineering of this methanotroph. To fill this gap, a high-quality GEM was constructed to facilitate a system-level understanding of the biochemistry of M. album BG8. Flux balance analysis, constrained with time-series data derived from experiments with various levels of methane, oxygen, and biomass, was used to investigate the metabolic states that promote the production of biomass and the excretion of carbon dioxide, formate, and acetate. The experimental and modeling results indicated that M. album BG8 requires a ratio of ∼1.5:1 between the oxygen- and methane-specific uptake rates for optimal growth. Integrative modeling revealed that at ratios of >2:1 oxygen-to-methane uptake flux, carbon dioxide and formate were the preferred excreted compounds, while at ratios of <1.5:1 acetate accounted for a larger fraction of the total excreted flux. Our results showed a coupling between biomass production and the excretion of carbon dioxide that was linked to the ratio between the oxygen- and methane-specific uptake rates. In contrast, acetate excretion was experimentally detected during exponential growth only when the initial biomass concentration was increased. A relatively lower growth rate was also observed when acetate was produced in the exponential phase, suggesting a trade-off between biomass and acetate production. IMPORTANCE A genome-scale metabolic model (GEM) is an integrative platform that enables the incorporation of a wide range of experimental data. It is used to reveal system-level metabolism and, thus, clarify the link between the genotype and phenotype. The lack of a GEM for Methylomicrobium album BG8, an aerobic methane-oxidizing bacterium, has hindered its use in environmental and industrial biotechnology applications. The diverse metabolic states indicated by the GEM developed in this study demonstrate the versatility in the methane metabolic processes used by this strain. The integrative GEM presented here will aid the implementation of the design-build-test-learn paradigm in the metabolic engineering of M. album BG8. This advance will facilitate the development of a robust methane bioconversion platform and help to mitigate methane emissions from environmental systems.

Microbial ecology of the atmosphere.

The atmosphere connects habitats across multiple spatial scales via airborne dispersal of microbial cells, propagules and biomolecules. Atmospheric microorganisms have been implicated in a variety of biochemical and biophysical transformations. Here, we review ecological aspects of airborne microorganisms with respect to their dispersal, activity and contribution to climatic processes. Latest studies utilizing metagenomic approaches demonstrate that airborne microbial communities exhibit pronounced biogeography, driven by a combination of biotic and abiotic factors. We quantify distributions and fluxes of microbial cells between surface habitats and the atmosphere and place special emphasis on long-range pathogen dispersal. Recent advances have established that these processes may be relevant for macroecological outcomes in terrestrial and marine habitats. We evaluate the potential biological transformation of atmospheric volatile organic compounds and other substrates by airborne microorganisms and discuss clouds as hotspots of microbial metabolic activity in the atmosphere. Furthermore, we emphasize the role of microorganisms as ice nucleating particles and their relevance for the water cycle via formation of clouds and precipitation. Finally, potential impacts of anthropogenic forcing on the natural atmospheric microbiota via emission of particulate matter, greenhouse gases and microorganisms are discussed.

Annotating unknown species of urban microorganisms on a global scale unveils novel functional diversity and local environment association.

In urban ecosystems, microbes play a key role in maintaining major ecological functions that directly support human health and city life. However, the knowledge about the species composition and functions involved in urban environments is still limited, which is largely due to the lack of reference genomes in metagenomic studies comprises more than half of unclassified reads. Here we uncovered 732 novel bacterial species from 4728 samples collected from various common surface with the matching materials in the mass transit system across 60 cities by the MetaSUB Consortium. The number of novel species is significantly and positively correlated with the city population, and more novel species can be identified in the skin-associated samples. The in-depth analysis of the new gene catalog showed that the functional terms have a significant geographical distinguishability. Moreover, we revealed that more biosynthetic gene clusters (BGCs) can be found in novel species. The co-occurrence relationship between BGCs and genera and the geographical specificity of BGCs can also provide us more information for the synthesis pathways of natural products. Expanded the known urban microbiome diversity and suggested additional mechanisms for taxonomic and functional characterization of the urban microbiome. Considering the great impact of urban microbiomes on human life, our study can also facilitate the microbial interaction analysis between human and urban environment.