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PubMed2025

Improved detection of microbiome-disease associations via population structure-aware generalized linear mixed effects models (microSLAM).

Microbiome association studies typically link host disease or other traits to summary statistics measured in metagenomics data, such as diversity or taxonomic composition. But identifying disease-associated species based on their relative abundance does not provide insight into why these microbes act as disease markers, and it overlooks cases where disease risk is related to specific strains with unique biological functions. To bridge this knowledge gap, we developed microSLAM, a mixed-effects model and an R package that performs association tests that connect host traits to the presence/absence of genes within each microbiome species, while accounting for strain genetic relatedness across hosts. Traits can be quantitative or binary (such as case/control). MicroSLAM is fit in three steps for each species. The first step estimates population structure across hosts. Step two calculates the association between population structure and the trait, enabling detection of species for which a subset of related strains confer risk. To identify specific genes whose presence/absence across diverse strains is associated with the trait, step three models the trait as a function of gene occurrence plus random effects estimated from step two. Applying microSLAM to 710 gut metagenomes from inflammatory bowel disease (IBD) samples, we discovered 56 species whose population structure correlates with IBD, meaning that different lineages are found in cases versus controls. After controlling for population structure, 20 species had genes significantly associated with IBD. Twenty-one of these genes were more common in IBD patients, while 32 genes were enriched in healthy controls, including a seven-gene operon in Faecalibacterium prausnitzii that is involved in utilization of fructoselysine from the gut environment. The vast majority of species detected by microSLAM were not significantly associated with IBD using standard relative abundance tests. These findings highlight the importance of accounting for within-species genetic variation in microbiome studies.

Humans↗

PubMed2026

Microbial diversity: the essential foundation for life on our planet.

The biological basis of life on Earth is microbial diversity that ensures human health, agricultural productivity, ecological balance, and ecosystem functioning. Microorganisms enable ecosystem restoration through bioremediation, maintain soil fertility, support plant growth, manage vital biogeochemical cycles, and contribute to climate resilience. Precision probiotics, postbiotics, faecal microbiota transplantation, and personalized microbiome medicine are the examples of emerging microbiome-based therapies that offer promising therapeutic opportunities. In humans, the gut microbial community is essential for immune regulation, metabolism, and disease prevention. In terrestrial ecological systems, interactions between plants, fungi, bacteria, and other soil microorganisms improve carbon sequestration, nutrient cycling, stress resilience, and sustainable agricultural productivity in the given effects of climate change. Emerging uses in agriculture, environmental restoration, and medicine are made possible by advancements in multi-omic techniques, synthetic microbial genomes, microbiome engineering, and artificial intelligence. Considering these developments, issues with ecological complexity, long-term validation, standardization, and field scale application still exist. Therefore, preserving microbial diversity is important for conserving ecological resilience and strengthening the One Health framework, which highlights the mutual dependance of health of animal, human, plant, and environment. This review summarizes what has been discovered about ecological and biomedical relevance of microbiome, identifies important research gaps, highlighting emerging technologies, and evaluates potential future directions for using microbiome to support planetary sustainability.

Bioremediation↗

PubMed2025

Diverse defense systems and prophages in human-associated Bifidobacterium species reveal coevolutionary "arms race" dynamics.

Bacteria of the genus Bifidobacterium are pivotal for human health, especially in early life, where they dominate the gut microbiome in healthy infants. Bacteriophages, as drivers of gut bacterial composition, can affect bifidobacterial abundance. Here, we use a bioinformatics approach to explore direct interactions between human-associated Bifidobacterium spp. and prophages, as evidenced by their genomes. Analysis of 1,086 bifidobacterial genomes reveals the presence of complex systems that prevent viral invasion, with 34 defense systems and 56 subtypes detected, including several different CRISPR-Cas systems. CRISPR spacers target almost three-quarters of bifidobacteria-derived prophages, indicating dynamic interactions. At least one prophage is present in ∼67% of strains, with phages exhibiting high genomic diversity and evidence of historical recombination. These prophages encode various defense and anti-defense systems, such as anti-CRISPR genes and restriction-modification mechanisms. Overall, this investigation reveals that coevolutionary "arms race" dynamics drive genomic diversity in both bifidobacteria and their phages.

Prophages↗

PubMed2026

Spatial scaling of metagenomic diversity reveals ecological disruption in the gut microbiome of gout patients.

Gout, a painful inflammatory arthritis, is characterized by hyperuricemia and monosodium urate crystal deposition, with growing evidence linking its pathogenesis to gut microbiome dysbiosis. However, traditional diversity metrics fail to capture the complex spatial organization of microbial communities. This study addresses this gap by applying the novel metagenomic Diversity-Area Relationship (m-DAR) model to investigate scaling laws in the gout microbiome-quantifying how metagenomic diversity changes with the number of individuals sampled. Our analysis of gut microbiomes from gout patients and healthy controls revealed fundamental ecological disruptions. We found that gout microbiomes exhibited significantly altered scaling patterns: they showed greater inter-individual dissimilarity (higher z-values) at the level of rare genes (q = 0), but weaker scaling of dominant genes (q = 1-3) compared to healthy controls. Crucially, the maximal accrual diversity (MAD) was substantially lower in gout patients, indicating a severely constrained potential for total microbial gene diversity. Furthermore, profiling of metagenomic functional gene clusters (MFGCs) uncovered widespread functional perturbations, including increased diversity scaling for carbohydrate-active enzymes (CAZy) but decreased scaling in essential metabolic pathways (KEGG, KO). These results demonstrate that the gout gut microbiome is defined by a loss of ecological structure, featuring reduced homogeneity in dominant taxa, expanded rare biosphere variation, and an overall collapsed diversity capacity. This work introduces an ecological framework for characterizing dysbiosis in gout that complements traditional diversity metrics and may inform the development of microbiome-based therapeutic strategies. Further research is needed to translate these ecological patterns into clinical applications.

Humans↗

PubMed2026

Pilot metaproteomic profiling reveals bacterial diversity and potential medical and veterinary relevance of tick microbiomes in northern Algeria.

Ticks are major ectoparasites and vectors of pathogens affecting humans, livestock, and wildlife. They harbor diverse microbial communities that may influence tick biology and interactions with microorganisms; however, functional information on tick-associated microbiomes remains limited, particularly in North Africa. In this pilot study, we applied a metaproteomic approach based on high-resolution tandem mass spectrometry to characterize bacterial communities associated with three tick species collected in Algeria: Rhipicephalus sanguineus sensu lato, Hyalomma aegyptium, and Hyalomma dromedarii. Peptide spectra were assigned to taxa using a two-step database search strategy based on NCBInr, and bacterial composition and relative abundance were compared across tick species and sampling locations. A total of 40 bacterial genera belonging to 32 families and four phyla were identified. Microbiome composition differed significantly between tick genera and collection locations, suggesting an influence of species-specific and geographical factors on microbial community structure. Dominant genera included Streptomyces, Bacillus, Clostridium, Escherichia, Flavobacterium, Paenibacillus, and Providencia. Peptides related to Coxiella spp. were frequently detected, consistent with previous reports of Coxiella-like endosymbionts in ticks. This pilot study provides a first metaproteomic characterization of tick-associated communities in Algeria. The results reveal species- and location-associated differences in microbial composition and highlight the potential of metaproteomics for exploring tick-associated microbiomes in North Africa.

Animals↗

PubMed2025

Gut microbiota: a hidden player in polycystic ovary syndrome.

Polycystic ovary syndrome (PCOS) is an endocrine disorder that affects reproductive-aged women worldwide, causing hormonal imbalances and ovarian dysfunction. PCOS affects metabolic health and increases the risk of obesity, insulin resistance, and cardiovascular disease, in addition to infertility. This review delves deeper into the connections of gut microbiota with PCOS pathophysiology, particularly into its impact on hormone metabolism, obesity, inflammation, and insulin resistance by way of short-chain fatty acids, lipopolysaccharides, and gut-brain axis. Studies also show that changes in the metabolic processes and immune responses are seen in changes in the gut microbiota in PCOS subjects, such as changes in the Bacteroidetes and Firmicutes groups. Some bacteria, like Escherichia and Shigella, have been associated with dysbiosis in patients with PCOS, leading to systemic inflammation and changed hormone levels, which further worsen the clinical symptoms. Therapeutic interventions targeting the gut microbiota comprise probiotics, prebiotics, and fecal microbiota transplantation; these have potential to alleviate the symptoms of PCOS. Other precision microbiome-based therapies include postbiotics, and CRISPR-Cas9 genome editing, which are relatively new avenues toward precision treatment. This complex interlink of gut microbiota and PCOS pathophysiology will open the avenues for possible treatments for hormonal imbalances and metabolic problems that characterize these complex disorders. The review here focuses on the requirement of further studies to be able to elucidate the specific pathways relating gut microbiota dysregulation to PCOS and, thus, improve microbiome-based therapies for better clinical outcomes in affected individuals.

Humans↗

PubMed2025

Specificities of chemosensory receptors in the human gut microbiota.

The human gut is rich in metabolites and harbors a complex microbial community, yet surprisingly little is known about the spectrum of chemical signals detected by the large variety of sensory receptors present in the gut microbiome. Here, we systematically mapped the ligand specificities of selected extracytoplasmic sensory domains from twenty members of the human gut microbiota, with a primary focus on the abundant and physiologically important class of Clostridia. Twenty-five metabolites from different chemical classes-including amino acids, nucleobase derivatives, amines, indole, and carboxylates-were identified as specific ligands for fifteen sensory domains from nine bacterial species, which represent all three major functional classes of transmembrane receptors: chemotaxis receptors, histidine kinases, and enzymatic sensors. We have further characterized the specificity and evolution of ligand binding to Cache superfamily sensors specific for lactate, dicarboxylic acids, and for uracil and short-chain fatty acids (SCFAs). Structural and biochemical analysis of the dCache sensor of uracil and SCFAs revealed that its two different ligand types bind at distinct sensory modules. Overall, combining experimental identification with computational analyses, we were able to assign ligands to approximately half of the Cache-type chemotaxis receptors found in the eleven gut commensal genomes from our set, with carboxylic acids representing the largest ligand class. Among these, the most commonly found ligand specificities were for lactate and formate, indicating a particular importance of these metabolites in the human gut microbiota and consistent with their observed growth-promoting effects on selected bacterial commensals.

Humans↗

PubMed2026

Human Milk Oligosaccharides Modulate Nitrogen Utilization in Lactobacillus crispatus in a Glucose-Dependent Manner.

The vaginal microbiome's transition to a dysbiotic state increases susceptibility to pathogens like group B Streptococcus. While human milk oligosaccharides are established prebiotics in the neonatal gut, their impact on the vaginal niche remains largely unexplored. This study investigated the effects of pooled human milk oligosaccharides on the growth and physiology of vaginal (Lactobacillus crispatus, Lactobacillus gasseri, Lactobacillus iners) and gut-derived (Lactobacillus reuteri, Lactobacillus rhamnosus) commensals. Growth analyses revealed that human milk oligosaccharides significantly and selectively stimulated growth across all vaginal strains tested, whereas gut commensals exhibited variable or inhibited growth. Carbohydrate utilization assays and comparative genomics against Bifidobacterium infantis showed that Lactobacillus crispatus and Lactobacillus reuteri lack the canonical metabolic machinery to catabolize human milk oligosaccharides. Instead, nitrogen utilization assays identified a glucose-dependent pathway where human milk oligosaccharides are associated with the depletion of primary amines and amino acids in Lactobacillus crispatus supernatants. These results suggest that human milk oligosaccharides act as noncatabolic modulators of vaginal lactobacilli. Collectively, these in vitro findings may warrant investigation of human milk oligosaccharides as modulators of vaginal commensal physiology in more complex experimental systems.

Humans↗

PubMed2025

Understanding disease-associated metabolic changes in human colonic epithelial cells using the iColonEpithelium metabolic reconstruction.

The colonic epithelium plays a key role in the host-microbiome interactions, allowing uptake of various nutrients and driving important metabolic processes. To unravel detailed metabolic activities in the human colonic epithelium, our present study focuses on the generation of the first cell-type-specific genome-scale metabolic model (GEM) of human colonic epithelial cells, named iColonEpithelium. GEMs are powerful tools for exploring reactions and metabolites at the systems level and predicting the flux distributions at steady state. Our cell-type-specific iColonEpithelium metabolic reconstruction captures genes specifically expressed in the human colonic epithelial cells. iColonEpithelium is also capable of performing metabolic tasks specific to the colonic epithelium. A unique transport reaction compartment has been included to allow for the simulation of metabolic interactions with the gut microbiome. We used iColonEpithelium to identify metabolic signatures associated with inflammatory bowel disease. We used single-cell RNA sequencing data from Crohn's Diseases (CD) and ulcerative colitis (UC) samples to build disease-specific iColonEpithelium metabolic networks in order to predict metabolic signatures of colonocytes in both healthy and disease states. We identified reactions in nucleotide interconversion, fatty acid synthesis and tryptophan metabolism were differentially regulated in CD and UC conditions, relative to healthy control, which were in accordance with experimental results. The iColonEpithelium metabolic network can be used to identify mechanisms at the cellular level, and we show an initial proof-of-concept for how our tool can be leveraged to explore the metabolic interactions between host and gut microbiota.

Humans↗

PubMed2026

Human skin microbiota and postpartum depression: A bidirectional Mendelian randomization study.

Postpartum depression (PPD) is a common mental health disorder after childbirth. Although microbiome research in PPD has mainly focused on the gut, the role of skin microbiota remains unclear. We used Mendelian randomization (MR) to assess potential causal associations between skin microbiota and PPD. A bidirectional 2-sample MR analysis used genome-wide association study (GWAS) summary statistics. Genetic instruments for skin microbial features were obtained from a published skin microbiota GWAS, and PPD data were derived from 67,205 mothers (7604 cases, 59,601 controls). Instruments were selected at P <1 × 10-5, linkage disequilibrium-clumped, harmonized, and filtered for weak instruments (F statistic <10). Because this microbiome threshold is exploratory, Benjamini-Hochberg false discovery rate correction was applied within taxonomic levels. The inverse-variance weighted method was primary, complemented by weighted median and mode-based methods. Heterogeneity, pleiotropy, and outliers were assessed using Cochran Q, MR-Egger intercept, and MR-PRESSO. Three skin microbial taxa showed nominal associations with PPD. Higher genetically predicted Acinetobacter on the dorsal forearm (dry skin; 9 single nucleotide polymorphisms [SNPs]; mean F = 22.12) and Proteobacteria in the antecubital fossa (moist skin; 6 SNPs; mean F = 23.44) were associated with increased PPD risk, whereas Betaproteobacteria in the antecubital fossa (11 SNPs; mean F = 21.54) was associated with decreased risk. Associations were directionally consistent, with no substantial heterogeneity or horizontal pleiotropy. After multiple-testing assessment, the findings were exploratory rather than definitive. Reverse MR did not support an effect of PPD on the identified skin microbiota. This MR study provides exploratory genetic evidence linking specific skin microbial features to PPD risk. The findings extend microbiota-related hypotheses beyond the gut microbiome but require validation in larger microbiome GWAS datasets, longitudinal cohorts, and mechanistic studies before clinical or causal conclusions are drawn.

Humans↗

PubMed2025

Impact of the maternal microbiome on neonatal immune development.

Historically, multigenerational health and disease transmission have primarily focused on genetic inheritance. However, the discovery that beneficial microorganisms known as commensal microbiota outnumber human genes tenfold has reshaped this perspective, highlighting their critical role in maintaining homeostasis and protecting against pathogens. Unlike the human genome, commensal microbiota is not genetically inherited but is acquired anew with each generation. with initial gut colonization playing a pivotal role in shaping an infant's immune system, neurodevelopment, and long-term health, all heavily influenced by maternal factors. In this review, we examine emerging research on maternal microbial influences on the fetus beginning in utero. We provide an updated overview of the current insights into the impact of the vaginal microbiome during parturition on offspring immunity and discuss the potential long-term health implications for infants born via cesarean section. We explore the advantages and limitations of techniques designed to mitigate these effects, such as vaginal seeding and emphasize that the development of the neonatal immune system is a dynamic process influenced by maternal factors beyond birth, including the transfer of microbiota through breast milk and skin contact. Finally, we present gaps in current research and propose future research directions to deepen our understanding of the impacts of the maternal microbiome on her child. Together, these insights demonstrate how maternal influence on offspring health and immunity extends beyond genetic factors, encompassing the transmission of microbiota, which, in turn, has profound long-term implications for health and disease resilience, offering a novel perspective on intergenerational health dynamics.

Humans↗

PubMed2026

Dealcoholized muscadine wine improved skin elasticity and oxidative stress biomarkers without affecting gut microbiome in women over 40 in a randomized controlled trial.

Muscadine wine has a unique polyphenol profile distinct from that of common wine, and limited research exists on its health benefits. This study aimed to investigate the effects of intake of dealcoholized muscadine wine (DMW) on skin health, oxidative stress, inflammatory biomarkers, and the gut microbiome. Seventeen healthy women were randomly assigned to consume 300 mL of DMW or a placebo daily for 6 weeks, separated by a 3-week washout period, in a randomized, single-blinded, crossover design. Skin health parameters were measured on the face and forearm. Oxidative stress and inflammatory biomarkers were assessed in plasma. Fecal bacterial DNA was sequenced using shotgun sequencing. DMW did not affect UVB-induced erythema compared to placebo. However, it significantly decreased transepidermal water loss and increased facial gross elasticity. Skin elasticity significantly improved on the forearm, whereas other skin parameters were not affected. DMW significantly decreased plasma levels of matrix metalloproteinase-9 and advanced glycation end products compared with placebo. However, the abundance, diversity, and functions of the gut microbiome were not affected. Polyphenol-rich DMW administered for six weeks improved certain skin health parameters and reduced oxidative and inflammatory stress, without affecting the gut microbiome in healthy women.

Humans↗

PubMed2025

Reduction of Bacteroides fragilis in Gut Microbiome of Chronic Hepatitis B Patients Promotes Liver Injury.

In chronic hepatitis B (CHB) patients under antiviral treatment, liver injury, as evidenced by elevated alanine transaminase (ALT), is associated with unfavorable outcomes and needs effective treatment. The interaction between gut microbiota and liver injury in CHB patients remains unclear. Using a case-control design, 28 cases with elevated ALT and 28 matched controls with normal ALT were randomly selected from CHB patients with viral control. Clinical characteristics were comparable between groups. Metagenomic sequencing revealed that Bacteroides fragilis was decreased in cases and exhibited the greatest disparity between cases and controls. Mice colonized by gut microbiota from cases exhibited more severe liver damage in both LPS-induced and MCD diet-induced liver injury models, and had a lower abundance of B. fragilis compared to mice colonized by gut microbiota from controls. Oral gavage of B. fragilis improved both LPS-induced and MCD diet-induced liver injury. Metabolomics analysis revealed that the levels of 7-Ketolithocholic acid (7-Keto-LCA) were positively correlated with B. fragilis and significantly increased in the cultural supernatant of B. fragilis. Consistently, 7-Keto-LCA exerted protective effects against both LPS-induced and MCD diet-induced liver damage. Targeting gut microbiota might be a promising therapeutic treatment for alleviation residual liver inflammation in CHB patients with viral control.

Humans↗

PubMed2026

Integrative machine learning models to unravel gut microbial dysbiosis and functional disruption in polycystic ovary syndrome.

OBJECTIVE: To study gut microbial diversity and metabolic pathway disruptions in women with PolyCystic Ovary Syndrome (PCOS) compared with healthy controls, and to evaluate the diagnostic potential of microbiome-driven machine learning models. DESIGN: Case-controlled metagenomic data analysis SUBJECTS: Gut metagenomic data from women diagnosed with PCOS and age-matched healthy female controls EXPOSURE: Presence of PCOS MAIN OUTCOME MEASURES: The primary outcome measures will include gut microbial alpha and beta diversity indices, microbial taxon abundance, functional pathway profiles, predicted metabolite levels, microbe-functional pathway-metabolite interaction networks, and the diagnostic accuracy of microbiome-based machine learning models. RESULTS: Alpha and beta diversity analyses revealed marked gut microbial dysbiosis in women with PCOS, despite comparable species richness to healthy controls. Differential abundance analysis identified 41 significantly altered microbial species, including enrichment of proinflammatory taxa, such as Bacteroides vulgatus and Ruminococcus gnavus, and depletion of beneficial commensals, including Roseburia hominis and Prevotella copri. These compositional shifts indicate a proinflammatory microbial community structure in PCOS. Functional profiling demonstrated the upregulation of pathways involved in nucleotide turnover, lipid and carbohydrate metabolism, and neurotransmitter synthesis, potentially contributing to metabolic and neuroendocrine disruption. Network analysis revealed fragmented and unstable microbial-metabolite associations in PCOS compared with cohesive networks in controls. Microbiome-based machine learning models achieved a diagnostic accuracy of 84.25% (area under the curve 0.93), underscoring their predictive potential. CONCLUSION: The gut microbiome in PCOS is characterized by a proinflammatory community structure and disrupted metabolic pathways. These findings demonstrate the diagnostic potential of microbiome-based models and underscore the gut microbiome as a promising target for therapeutic interventions in the management of PCOS.

Polycystic Ovary Syndrome↗

PubMed2026

Probiotic potential of Parabacteroides johnsonii in mitigating age-related ovarian functional decline.

The gut microbiota is increasingly recognized as a regulator of reproductive health, yet its role in ovarian aging remains unclear. Here, we combine Mendelian randomization (MR) analysis with experimental validation to investigate the causal relationship between gut microbiota and ovarian aging. MR analysis identifies four microbial taxa significantly associated with age at natural menopause. In mouse models, germ-free mice exhibit accelerated ovarian functional decline, including reduced ovarian reserve and impaired folliculogenesis. Fecal microbiota transplantation (FMT) from young donors alleviates ovarian aging phenotypes, whereas FMT from aged donors exacerbates functional decline. Metagenomic analysis reveals species-level differences between young and ovarian-aging mice, with Parabacteroides johnsonii (P. johnsonii) enriched in young mice. Administration of P. johnsonii to middle-aged mice improves ovarian reserve, reduces follicular atresia, enhances granulosa cell proliferation, and decreases systemic inflammation. These findings highlight a causal role of the gut microbiota in ovarian aging and support microbiota-targeted interventions as a potential strategy to preserve ovarian function.

Female↗

PubMed2026

Targeting the Microbiota-Gut-Brain Axis: Emerging Nanomedicine Approaches for Neurodegenerative Diseases.

The microbiota-gut-brain axis (MGBA) is a bidirectional relationship between the gut microbiota (GM) and the brain, where the GM affects the gastrointestinal tract (GIT) and the central nervous system (CNS), and vice versa. Microbiotas are important for several vital body processes, including metabolism, immunity, and homeostasis. The MGBA has three main pathways: the vagal nerve mechanism, the immune-related mechanism, and the neuroendocrine mechanism. GM imbalance, known as dysbiosis, affects the GIT, the brain, and the CNS. Furthermore, dysbiosis is linked to several neurological disorders such as Alzheimer's (AD), Parkinson's (PD), depression, autism spectrum disorder (ASD), and multiple sclerosis (MS). Studying MGBA gives researchers new therapeutic ideas using microbiota. Using special diets rich in fiber and probiotics, in addition to fecal microbiota transplantation (FMT), is being studied as a new therapy for MGBA. From the point of view that these therapeutic interventions maintain microbiota imbalance, which in turn will affect the brain and can relieve the neurological disorders caused by dysbiosis and MGBA.

Humans↗

PubMed2024

The causal effect of gut microbiota on hepatic encephalopathy: a mendelian randomization analysis.

BACKGROUND: There is growing evidence for a relationship between gut microbiota and hepatic encephalopathy (HE). However, the causal nature of the relationship between gut microbiota and HE has not been thoroughly investigated. METHOD: This study utilized the large-scale genome-wide association studies (GWAS) summary statistics to evaluate the causal association between gut microbiota and HE risk. Specifically, two-sample Mendelian randomization (MR) approach was used to identify the causal microbial taxa for HE. The inverse variance weighted (IVW) method was used as the primary MR analysis. Sensitive analyses were performed to validate the robustness of the results. RESULTS: The IVW method revealed that the genus Bifidobacterium (OR = 0.363, 95% CI: 0.139-0.943, P = 0.037), the family Bifidobacteriaceae (OR = 0.359, 95% CI: 0.133-0.950, P = 0.039), and the order Bifidobacteriales (OR = 0.359, 95% CI: 0.133-0.950, P = 0.039) were negatively associated with HE. However, no causal relationship was observed among them after the Bonferroni correction test. Neither heterogeneity nor horizontal pleiotropy was found in the sensitivity analysis. CONCLUSION: Our MR study demonstrated a potential causal association between Bifidobacterium, Bifidobacteriaceae, and Bifidobacteriales and HE. This finding may provide new therapeutic targets for patients at risk of HE in the future.

Mendelian Randomization Analysis↗

PubMed2025

Versatile wastewater monitoring of pathogens and antimicrobial resistance enabled by metatranscriptomics and long-read metagenomics.

Widespread interest in the development of population-wide pathogen and antimicrobial resistance (AMR) monitoring has revealed wastewater's microbial footprint as a marker of public health. Near-source wastewater remains a difficult sample type for microbiome analyses but represents a closer link to human health than the downstream products of its treatment. Few studies integrate methods for non-targeted monitoring applications, and critically, current methods cannot connect AMR genes to species, nor resolve full genomes. We address these challenges by developing a pipeline that enables untargeted metagenomics, metatranscriptomics, and novel long-read metagenomics (LRG). We achieve untargeted pathogen detection, limited by highly abundant resident species, while retaining microbial information with near-source sampling. Furthermore, LRG identifies antibiotic resistance gene-containing microbes and enables assembly of culture-independent genomes with previously unreported AMR genes. We establish an integrated approach to broadly monitor pathogens in wastewater, while demonstrating the importance of LRG to illuminate microbial AMR at the species level.

Journal Article↗