SEARCH · PubMed Health

Results for “Microbial community”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 recordsLinked to original sources

PubMed2026

Comparative metagenomic analysis of microbial communities: unravelling microbial communities from the great Rann of Kachchh and coastal saltpans, Gujarat, India.

Hypersaline environments exhibit extreme physiochemical conditions yet support diverse microbial communities. These communities are not only ecologically important but also possess substantial potential for biotechnological exploitation. In this study, we employed a comparative metagenomic approach to assess microbial diversity using two distinct methodologies: (1) direct DNA extraction from raw sediment, and (2) DNA extraction following halophilic enrichment in selective media. Sediment samples were collected from multiple sites and pooled together within the Rann of Kachchh and close-by saltpans and were analysed using 16S rRNA sequencing coupled with bioinformatics pipelines. The results revealed pronounced differences in microbial community composition between the two approaches. Raw sediment samples exhibited significantly higher alpha diversity, with dominant taxa including Halobacterota, Cyanobacteria, and Desulfobacterota, with a substantial proportion of unclassified genera. In contrast, enriched samples were dominated by fast-growing, culturable genera such as Halobacterium, Alkalibacillus, and Candidatus haloredivivus. Principal Coordinate Analysis (PCoA) of beta diversity demonstrated distinct clustering between raw and enriched communities, even within samples from the same sites, underscoring the selective bias introduced by enrichment procedures. These findings emphasise that the methodological choice strongly influences the observed microbial diversity. The aim of this study was to compare microbial community composition in raw hypersaline sediments and enrichment cultures using metagenomic sequencing, to evaluate how enrichment selectively favours specific halophilic taxa. This comparative approach allows identification of the microbial groups that rapidly proliferate under controlled hypersaline conditions, thereby complementing direct environmental sequencing. By integrating both direct and enrichment-based metagenomic approaches, a more comprehensive understanding of microbial community structure in hypersaline environments can be achieved.

India↗

PubMed2025

StrainR2 accurately deconvolutes strain-level abundances in synthetic microbial communities.

MOTIVATION: Synthetic microbial communities offer an opportunity to conduct reductionist research in tractable model systems. However, deriving abundances of highly related strains within these communities is currently unreliable. 16S rRNA gene sequencing does not resolve abundance at the strain level and other methods such as quantitative polymerase chain reaction (qPCR) scale poorly and are resource prohibitive for complex communities. We present StrainR2, which utilizes shotgun metagenomic sequencing to provide high accuracy strain-level abundances for all members of a synthetic community, provided their genomes. RESULTS: Both in silico, and using sequencing data derived from gnotobiotic mice colonized with a synthetic fecal microbiota, StrainR2 resolves strain abundances with greater accuracy and efficiency than other tools utilizing shotgun metagenomic sequencing reads. We demonstrate that StrainR2's accuracy is comparable to that of qPCR on a subset of strains resolved using absolute quantification. AVAILABILITY AND IMPLEMENTATION: Software is available at GitHub and implemented in C, R, and Bash. Software is supported on Linux and MacOS, with packages available on Bioconda or as a Docker container. The source code at the time of publication is also available on figshare at the doi: 10.6084/m9.figshare.29420780.

Mice↗

PubMed2026

Bioplastic biodegradability shapes microbial communities in a coastal brackish environment.

Microorganisms are metabolically versatile and central to marine ecosystems, yet the potential of marine microbial communities to degrade different bioplastics and the effect of environmental factors are poorly understood. Employing multi-seasonal in situ and in vitro experiments, we assessed the biodegradation of six commonly used bio-based bioplastic materials at a coastal site in the brackish Baltic Sea and characterized the associated microbial communities using metagenomics and metatranscriptomics. Cellulose acetate (CA), polybutylene succinate (PBS), and polyhydroxybutyrate/valerate (PHB) degraded at varying rates across materials, seasons, and experimental settings, with up to 28% weight attrition after 97 weeks in situ (CA) and 56% carbon loss as CO2 after 4 weeks in vitro (PBS). The three biodegraded plastics developed similar microbial communities that differed markedly from those on the other materials (cellulose acetate propionate, polyamide, and polyethylene) and in the water column. The main microbial populations on the biodegraded plastics included aerobic and facultative anaerobic heterotrophs with a broad capacity for carbohydrate metabolism. Populations with the potential for nitrogen fixation and denitrification were more prevalent on the biodegraded plastics, suggesting that bioplastic biodegradation is constrained by and coupled to the marine nitrogen cycle. Based on the metatranscriptomic signal of key genes involved in the initial hydrolysis of CA, PBS, and PHB, we identified diverse microbial populations that can potentially drive the biodegradation of these materials in the Baltic Sea, many of which encoded the potential to degrade multiple bioplastics. We propose the term 'bioplastisphere' to denote the distinctive microbial communities associated with biodegradable plastics.

Seawater↗

PubMed2024

Environmental stress mediates groundwater microbial community assembly.

Community assembly describes how different ecological processes shape microbial community composition and structure. How environmental factors impact community assembly remains elusive. Here we sampled microbial communities and >200 biogeochemical variables in groundwater at the Oak Ridge Field Research Center, a former nuclear waste disposal site, and developed a theoretical framework to conceptualize the relationships between community assembly processes and environmental stresses. We found that stochastic assembly processes were critical (>60% on average) in shaping community structure, but their relative importance decreased as stress increased. Dispersal limitation and 'drift' related to random birth and death had negative correlations with stresses, whereas the selection processes leading to dissimilar communities increased with stresses, primarily related to pH, cobalt and molybdenum. Assembly mechanisms also varied greatly among different phylogenetic groups. Our findings highlight the importance of microbial dispersal limitation and environmental heterogeneity in ecosystem restoration and management.

Phylogeny↗

PubMed2026

Increased precipitation decelerates temporal succession of grassland soil microbial communities.

Global precipitation regimes have been shifted in recent decades, imposing significant consequences in water-limited grassland ecosystems. However, the effects of increased precipitation on the succession of soil microbial communities remain unclear, mainly due to the scarcity of long-term experiments with time-series data. Here, we examined temporal succession of grassland soil microbial communities in a long-term increased precipitation experiment. Both soil microbial taxonomic and functional structures were significantly altered by increased precipitation. Increased precipitation significantly decelerated the succession rates of soil microbial functional structure (i.e. time-decay relationships). Consistent with the increased microbial decomposition and heterotrophic respiration, the abundances of soil microbial carbon decomposition genes were markedly enhanced by increased precipitation. Furthermore, increased precipitation stimulated genes involved in nutrient cycling processes, potentially promoting plant growth. Collectively, the contributions of stochastic processes in shaping microbial communities were increased under increased precipitation, suggesting that microbial successional trajectories may shift toward multiple alternative states characterized by greater stochasticity under future altered precipitation regimes.

Soil Microbiology↗

PubMed2022

Ecological dynamics of plasmid transfer and persistence in microbial communities.

Plasmids are a major driver of horizontal gene transfer in prokaryotes, allowing the sharing of ecologically important accessory traits between distantly related bacterial taxa. Within microbial communities, interspecies transfer of conjugative plasmids can rapidly drive the generation genomic innovation and diversification. Recent studies are starting to shed light on how the microbial community context, that is, the bacterial diversity together with interspecies interactions that occur within a community, can alter the dynamics of conjugative plasmid transfer and persistence. Here, I summarise the latest research exploring how community ecology can both facilitate and impose barriers to the spread of conjugative plasmids within complex microbial communities. Ultimately, the fate of plasmids within communities is unlikely to be determined by any one individual host, rather it will depend on the interacting factors imposed by the community in which it is embedded.

Bacteria↗

PubMed2026

Unveiling microbial communities and biogeochemical cycles in Antarctic colored snow.

Snow cover, the extensive terrestrial habitat in Antarctica, sometimes exhibits vivid coloration, yet the structure and function of its microbial communities remain poorly characterized. Using metagenomic sequencing of red snow (RS) and green snow (GS) from the Fildes Peninsula, we found that bacterial, eukaryotic, and archaeal relative abundances were 85.82%, 13.52% and 0.16%, respectively. β-Diversity differed significantly between RS and GS across these three domains (P < 0.05). Dominant bacterial phyla included Bacteroidota (RS: 62.61%; GS: 38.72%) and Pseudomonadota (RS: 32.80%; GS: 54.10%). Among eukaryotes, Chlorophyta (RS: 58.10%; GS: 52.98%) and Basidiomycota (RS: 14.80%; GS: 8.08%) were prevalent. Nanobdellota dominated archaea, with lower abundance in RS than GS. In the algal community, Sanguina, Gonium and Chloromonas were significantly enriched in red snow, while Chlorella and Micractinium were enriched in green snow (P < 0.05). Marker genes associated with carbon (C), nitrogen (N), phosphorus (P) and sulfur (S) cycles were identified in green and red snow. Aerobic respiration and phosphate regulation were significantly enriched in red snow, while CO oxidation, fermentation, and denitrification were significantly enriched in green snow. Key microbial genera associated with these functional pathways also varied. In the denitrification of red snow, Stutzerimonas was the most abundant genus, while Janthinobacterium was abundant in green snow. Nitrification-related genes were detected only in red snow based on the present metagenomic data. The network of the red snow microbial community was potentially more complex and resistant based on topology, which not only benefited its own long-term survival but might also have potentially influenced the positive feedback effect of snowmelt by maintaining a low-albedo snow surface. This provided an ecological implication under climate warming: the expansion of red snow patches showed the potential to the increase nitrate runoff export, which would affect nitrogen nutrient levels in coastal Antarctic waters. Overall, this study used metagenomics to compare the multidomain (bacteria, archaea and eukaryotes) composition and diversity between red snow and green snow, and directly linked key microbial taxa with functional genes of biogeochemical cycles. This study provided new insights into the biological characteristics and functional potential of Antarctic colored snow.

Snow↗

PubMed2026

Unraveling critical role of photosynthetic bacteria in sustaining aquatic microbial community stability and function through large-scale genomic data analyses.

The application of photosynthetic bacteria (PB) in water remediation has demonstrated exceptional advantages in terms of high efficiency and low-carbon benefits. However, the limited understanding of PB across natural aquatic environments has constrained the rational development of this strategy. Here, we analyzed 3198 genomic sequencing samples from seven types of natural aquatic ecosystems to investigate the distribution and functions of 42 PB genera commonly used in water remediation. The results showed that the average abundance of the targeted PB reached 9.83 %, with the highest value of 14.93 % observed in River, while Lake harbored the greatest PB genus diversity. PB genera exhibited high sensitivity to salinity, with Rhodoferax dominating freshwater habitats, whereas Rhodovulum was predominant in marine environments. Notably, co-occurrence network analysis revealed that PB were closely associated with microbial community stability and optimized interspecific interactions. Aquatic microbial communities with high PB abundance were characterized by efficient division-of-labor modules, accompanied by enhanced PB-associated functional potential for carbon fixation, denitrification, and sulfur oxidation. In summary, this study systematically elucidates the regional biogeographical patterns and ecological roles of PB in natural aquatic environments, providing a comprehensive scientific basis and theoretical guidance for the development and practical application of PB-based water remediation technologies.

Bacteria↗

PubMed2026

Metagenomics Reveals Microbial Community Shifts Associated With Contrasting Anthropogenic Impacts in Freshwater Sources of A Coastal Protected Area in Southeastern Brazil.

This study aimed to characterize freshwater microbial communities, environmental drivers, and anthropogenic impact patterns across three sites on Marambaia Island (southeastern Brazil) using metagenomics. Samples collected from freshwater sources used for human consumption were processed through concentration, nucleic acid extraction, and sequencing on the Illumina NextSeq 2000 platform. A total of 67.2 million reads were assembled into 89,230 bacterial contigs, mostly attributed to Gammaproteobacteria, Alphaproteobacteria, and Betaproteobacteria. Sites under lower anthropogenic influence exhibited higher microbial diversity, whereas impacted sites showed enrichment of opportunistic and fecal-associated genera. A heterogeneous anthropogenic impact profile was observed across sites, corroborated by the proposed Anthropogenic Impact Index (AII). Fourteen antimicrobial resistance genes conferring resistance to beta-lactams, quinolones, sulfonamides, tetracyclines, and macrolides were detected predominantly in sewage-impacted areas, indicating potential diffuse contamination. Redundancy analysis revealed that environmental variables explained 88.1% of microbial community variation, with conductivity, salinity, and turbidity as key drivers. These findings demonstrate the applicability of metagenomics as a powerful tool for assessing microbial diversity, ecological dynamics, and contamination risks in vulnerable freshwater systems.

Brazil↗

PubMed2025

A Graph Contrastive Learning Method for Enhancing Genome Recovery in Complex Microbial Communities.

Accurate genome binning is essential for resolving microbial community structure and functional potential from metagenomic data. However, existing approaches-primarily reliant on tetranucleotide frequency (TNF) and abundance profiles-often perform sub-optimally in the face of complex community compositions, low-abundance taxa, and long-read sequencing datasets. To address these limitations, we present MBGCCA, a novel metagenomic binning framework that synergistically integrates graph neural networks (GNNs), contrastive learning, and information-theoretic regularization to enhance binning accuracy, robustness, and biological coherence. MBGCCA operates in two stages: (1) multimodal information integration, where TNF and abundance profiles are fused via a deep neural network trained using a multi-view contrastive loss, and (2) self-supervised graph representation learning, which leverages assembly graph topology to refine contig embeddings. The contrastive learning objective follows the InfoMax principle by maximizing mutual information across augmented views and modalities, encouraging the model to extract globally consistent and high-information representations. By aligning perturbed graph views while preserving topological structure, MBGCCA effectively captures both global genomic characteristics and local contig relationships. Comprehensive evaluations using both synthetic and real-world datasets-including wastewater and soil microbiomes-demonstrate that MBGCCA consistently outperforms state-of-the-art binning methods, particularly in challenging scenarios marked by sparse data and high community complexity. These results highlight the value of entropy-aware, topology-preserving learning for advancing metagenomic genome reconstruction.

canonical correlation analysis↗

PubMed2026

Metagenomic insights into biogeochemical functional potential and resistome dynamics of PM2.5 microbial communities.

Atmospheric particulate matter harbors diverse microorganisms, yet their functional potential in biogeochemical cycling and the associated risks of resistome remain poorly understood. Here, we performed metagenomic sequencing on PM2.5 samples collected across four months to unravel the microbial genetic repertoire involved in methane, nitrogen, phosphorus, and sulfur cycling, as well as the resistome, and pathogen composition. A broad range of functional genes was detected for each biogeochemical cycle, with more than 65% of gene subtypes shared across all months, indicating conserved functional signatures. In contrast, more than 80% of the resistome showed temporal variation in abundance, with the lowest richness observed in March. Temporal shifts were also observed in resistome composition, with several resistance determinants reaching higher abundances in April and May. Network analysis indicated frequent co-occurrence among several pathogenic and opportunistic taxa. Contig-based profiling identified 51 potential pathogenic taxa, including 32 human- or animal-associated taxa. In addition, both PM10 and PM2.5 concentrations were associated with pathogen abundance and functional gene richness (e.g., antibiotic resistance genes and virulence factors). Together, this metagenomic survey suggests contrasting temporal patterns between conserved biogeochemical functional potential and more variable resistome-related traits in PM2.5 microbial communities. While constrained by limited temporal coverage and sample size, this study provides preliminary insights into the ecological and potential public health relevance of airborne microbial communities in urban environments.

Particulate Matter↗

PubMed2025

Reducing redundancy and enhancing accuracy through a phylogenetically-informed microbial community metabolic modeling approach.

MOTIVATION: Metabolic modeling has emerged as a powerful tool for predicting community functions. However, current modeling approaches face significant challenges in balancing the metabolic trade-offs between individual and community-level growth. In this study, we investigated the effect of metabolic relatedness among taxa on growth rate calculations by merging related taxa based on their metabolic similarity, introducing this approach as PhyloCOBRA. RESULTS: This approach enhanced the accuracy and efficiency of microbial community simulations by combining genome-scale metabolic models (GEMs) of closely related organisms, aligning with the concepts of niche differentiation and nestedness theory. To validate our approach, we implemented PhyloCOBRA within the MICOM and OptCom package (creating PhyloMICOM and PhyloOptCom, respectively), and applied it to metagenomic data from 186 individuals and four-species synthetic community (SynCom). Our results demonstrated significant improvement in the accuracy and reliability of growth rate predictions compared to the standard methods. Sensitivity analysis revealed that PhyloMICOM models were more robust to random noise, while Jaccard index calculations showed a reduction in redundancy, highlighting the enhanced specificity of the generated community models. Furthermore, PhyloMICOM reduced the computational complexity, addressing a key concern in microbial community simulations. This approach marks a significant advancement in community-scale metabolic modeling, offering a more stable, efficient, and ecologically relevant tool for simulating and understanding the intricate dynamics of microbial ecosystems. AVAILABILITY AND IMPLEMENTATION: PhyloCOBRA implementations are available as extensions to the MICOM packages and can be accessed at https://github.com/sepideh-mofidifar/PhyloCOBRA.

Phylogeny↗

PubMed2026

Decoding the spatiotemporal patterns of food spoilage microbial communities: Integrating multi-omics and artificial intelligence to enable precision preservation.

In the global food supply chain, food wastage caused by spoilage has resulted in significant economic losses, food shortages, and environmental pressure. This process is fundamentally driven by the spatiotemporal dynamics of microbial communities. However, traditional research methods struggle to elucidate the complex mechanisms of spatial heterogeneity, interspecies interactions, and functional succession. This limits the development of effective preservation strategies. This review systematically reviews the cutting-edge progress of integrating multi-omics technologies and artificial intelligence (AI) to study food spoilage microbial communities, breaking through this bottleneck. We propose an intelligent theoretical framework that could potentially analyze microbial metabolic activities and predict dynamic shelf life if implemented. The conceptual framework integrates multidimensional data, including spatial metabolomics, temporal metatranscriptomics, single-cell transcriptomics, and longitudinal metagenomics. It can also be combined with AI models, such as graph neural networks. The article elaborates on the principles and applications of spatio-temporal monitoring technologies, such as nano secondary ion mass spectrometry, hyperspectral imaging, and the Internet of Things sensing. Through illustrative cases of typical perishable foods, it also explores how such a multi-omics - AI system might be applied to spoilage warning and precise intervention. Additionally, the article addresses the current challenges in data coverage, model generalization, and federated learning implementation. Then the research further explores emerging areas such as engineered probiotics, edge AI, and microfluidic sensing. These areas are targeted at transforming food preservation from an empirical control approach to a data-driven, precise regulatory framework. This transformation provides theoretical support and technical approaches for developing a smart, sustainable food preservation system.

Multiomics↗

PubMed1976

Enzyme evolution in a microbial community growing on the herbicide Dalapon.

A seven-membered microbial community capable of utilising the herbicide Dalapon has been isolated by continuous-flow enrichment culture. The composition of this community has remained remarkably stable over thousands of hours in a Dalapon-limited chemostat. During this period, however, one member of the community, Pseudomonas putida, acquired the ability to grow on Dalapon through the evolution of an extant dehalogenase.

Biological Evolution↗

PubMed2025

Impacts of non-spherical polyethylene nanoplastics on microbial communities and antibiotic resistance genes in the rhizosphere of pea (Pisum sativum L.): An integrated metagenomic and metabolomic analysis.

The ecological effects of nanoplastics (NPs) has become a growing concern; however, the influence of non-spherical NPs-which better represent real-world morphologies-remains poorly understood. This study investigated the impact of non-spherical polyethylene (PE) NPs on the growth of pea (Pisum sativum L.) and its rhizosphere microenvironment across different concentration levels (0, 20, and 200 mg/kg) using integrated metagenomics and metabolomics. Results showed that high-dose (200 mg/kg) exposure significantly inhibited plant growth. Although soil physicochemical properties remained unchanged, the rhizosphere microbial communities experienced significant restructuring, characterized by a marked enrichment of Pseudomonas and a reduction in beneficial Rhizobium populations. Metagenomic analysis revealed a concurrent increase in the abundance and diversity of antibiotic resistance genes (ARGs) under non-spherical PE-NP stress. This was accompanied by a shift in bacterial host composition, with a trend toward a higher prevalence of potentially pathogenic taxa such as Pseudomonas aeruginosa. Metabolomics analysis further revealed that non-spherical PE-NPs altered the rhizosphere metabolite profile, thereby significantly driving the succession of ARG hosts. Our integrated analysis enhances the understanding of how non-spherical PE-NPs disrupt microbial communities and elevate the risks of ARGs in rhizosphere soil, highlighting the significance of incorporating environmentally relevant NPs into environmental risk assessments.

Pisum sativum↗

PubMed1975

Use of the adenylate energy charge ratio to measure growth state of natural microbial communities.

Measurement of the adenylate energy charge ratio is proposed as a means of determining the growth state of natural microbial communities and the effect of environmental changes on them. Observations on microbial cultures and on natural microbial populations from the Western North Atlantic Ocean water and from sediments of a costal salt marsh show that energy charge measurements do show the metabolic state of communities as well as species populations.

Adenine Nucleotides↗

PubMed2026

Optimizing eco-engineering pedogenesis of bauxite residues: Synergistic effects of humus and FeSO4/sulfur on microbial community and function.

Eco-engineered pedogenesis represents a promising approach for soil amelioration of bauxite residues (BRs) through exogenous organic matter. However, the role of humus in mediating this process remains poorly understood, significantly impeding the eco-engineering rehabilitation of BRs. In this study, we conducted pot experiments and subsequent microbial analysis to evaluate the individual improvement of humic acid (HA), fulvic acid (FA), and corn straw (SWZ) on the BRs' pedogenesis. High-throughput sequencing analysis revealed that both FA and SWZ were more effective than HA in steering microbial community assembly, as community diversity, dominant taxa enrichment, and species' interaction were all significantly higher (p < 0.05) in the FA/SWZ treatments than in HA treatments. Notably, the combination of FA with FeSO4 specifically enriched halophilic taxa, while FA coupled with sulfur (S) significantly improved the connectivity and complexity of the microbial network, as the average connection degree increasing from 1.008 to 1.113. Hydrolytic enzyme activity assays further indicated that FA, especially when combined with S, was the most effective treatment in restoring microbial function during BR pedogenesis. These findings highlight FA as a critical driver of microbial restructuring and functional recovery in BRs. Moreover, its efficacy can be enhanced by co-amendment with FeSO4 or S. This study provides important theoretical and practical insights for optimizing organic-inorganic amendment strategies to accelerate the eco-engineering pedogenesis of bauxite residues.

Humic Substances↗

PubMed2026

Unveiling phthalate esters biodegradation from microbial community to Pseudarthrobacter scleromae HL-1: Kinetics, genomic insights, pathways, toxicity assessment and environmental remediation.

Phthalate esters (PAEs) are ubiquitous synthetic plasticizer pollutants posing severe ecological and human health risks. This study compared microbial community structures and dibutyl phthalate (DBP) degradation kinetics of two consortia: 7-day enriched MC1 (50 mg/L DBP) and 6-cycle acclimated MC7 (50-1000 mg/L DBP), demonstrating directional DBP stress selection generated a low-diversity, highly specialized degradative community with a 25.4 mg/L/h maximum degradation rate (Vmax), 1.68-fold higher than MC1. Four dominant DBP-degrading strains were isolated from MC7; Pseudarthrobacter scleromae HL-1 showed the highest efficiency with 17.1 mg/L/h Vmax and complete 500 mg/L DBP removal within 72 h, broad substrate spectrum, and strong adaptability after optimization. Whole-genome sequencing and GC-MS/MS elucidated a dual-parallel DBP mineralization pathway, first reported in Pseudarthrobacter, integrating ester hydrolysis and side-chain β-oxidation. ECOSAR and Chlorella vulgaris bioassays confirmed progressive toxicity attenuation, with > 99% relative toxicity reduction after 72 h and no toxic intermediate accumulation. Natural lake water trials with trace background PAEs showed HL-1 successfully colonized aquatic environments, reshaped indigenous communities into synergistic degradative consortia, and achieved 99.2% DBP removal in 84 h. This work provides comprehensive insights into PAE biodegradation mechanisms from community to single strain and highlights HL-1 as a promising candidate for remediating PAE-polluted aquatic ecosystems.

Genomic analysis↗