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PubMed2026

Integrative multi-omics profiling of insomnia-related molecular features reveals microbiome, immune, and therapy-relevant heterogeneity in colorectal cancer.

Emerging evidence implicates insomnia as a potential risk factor in carcinogenesis, potentially involving systemic inflammation, circadian disruption, and microbiome alterations. However, the molecular associations linking insomnia-related features to colorectal cancer (CRC), particularly with respect to tumor biology, immune microenvironmental states, and therapy-relevant phenotypes, remain largely unexplored. Multi-omics integration of genomic, transcriptomic, and microbiome data from 3,026 CRC patients across seven independent cohorts, including a large, well-annotated Clinical Omics study of Colorectal Cancer in China (COCC) cohort, enabled insomnia-based molecular classification through unsupervised non-negative matrix factorization (NMF) clustering. The insomnia subtype (IS) was biologically characterized via pathway enrichment, immune deconvolution, microbial profiling, and single-cell transcriptomics. Furthermore, an insomnia score (ISscore) was developed and validated in multiple cohorts for risk stratification and assessment of treatment-response-related indicators in CRC. Unsupervised clustering revealed two distinct molecular subtypes (IS1/IS2), with IS2 demonstrating significantly poorer survival. IS2 exhibited marked activation of EMT/angiogenesis pathways versus cell cycle activation in IS1. The IS2 microenvironment showed increased immunosuppression-related infiltration and exhausted T cell signatures, together with intratumoral microbiome variation characterized by depletion of Ruminococcaceae UCG-002 and enrichment of Hungatella/Selenomonas. The ISscore system stratified survival risk and was associated with computational indicators of immunotherapy response. Single-cell analysis nominated PPIA-BSG as a potential cell-cell communication signal involving high-ISscore tumor cells, CXCL12+ endothelial cells, and CLEC9A+ dendritic cell subsets. This multi-omics characterization of insomnia-CRC interplay suggests that insomnia-related molecular features are associated with an immunologically distinct and microbiome-altered tumor ecosystem. The ISscore provides a reproducible framework for capturing insomnia-related molecular heterogeneity, supporting risk stratification and future evaluation of therapy-relevant phenotypes.IMPORTANCEChronic insomnia affects millions, but it is not typically considered a cancer risk factor. Our study, analyzing vast biological data from over 3,000 colorectal cancer patients, uncovers a potential link between a person's predisposition to insomnia and their risk of developing this disease. This suggests that the biological pathways related to sleep may play a role in cancer development. Understanding this connection opens up new avenues for identifying individuals at higher risk and developing novel prevention strategies for colorectal cancer.

colorectal cancer↗

PubMed2026

Metagenomic-based quantification of Pseudomonas aeruginosa burden links microbiome collapse to mortality in severe community-acquired pneumonia.

BACKGROUND: Severe community-acquired pneumonia (sCAP) remains a major cause of mortality in critically ill patients, Pseudomonas aeruginosa (P. aeruginosa) is a frequent pathogen associated with poor prognosis in this population. While metagenomic next-generation sequencing (mNGS) is widely used for pathogen detection, its value in quantifying pathogen abundance and linking it to lung microbiome alterations remains unclear. OBJECTIVES: This study investigated the association between P. aeruginosa abundance quantified by mNGS and lung microbiome alterations and clinical outcomes in sCAP patients. METHODS: This multicenter retrospective study included 130 patients with sCAP caused by P. aeruginosa from five hospitals (September 2021-June 2025). Patients were stratified into low, medium, and high abundance groups according to mNGS-derived reads per ten million (RPTM) values of P. aeruginosa. Lung microbiome diversity and community structure were analyzed, and differences between groups were assessed using appropriate statistical methods. The association between P. aeruginosa abundance and clinical outcomes was evaluated using correlation analysis, sankey diagram, receiver operating characteristic curve, grey zone analysis and logistic regression. RESULTS: A total of 130 patients with sCAP due to P. aeruginosa were stratified into low, medium, and high abundance groups based on mNGS-derived RPTM value. Microbial diversity decreased progressively with increasing abundance, and community structures differed significantly among groups (all P < 0.05). P. aeruginosa became increasingly dominant, accounting for up to 95.99% of the microbiota in the high abundance group. Higher P. aeruginosa abundance was associated with increased disease severity, including longer mechanical ventilation, prolonged hospital stay, and higher 28-day mortality. Sankey diagram showed a progressive decline in treatment effectiveness and an increase in mortality with increasing P. aeruginosa abundance. P. aeruginosa_RPTM showed moderate predictive value for mortality (AUC = 0.761, Sens = 69.40%, Spec = 75.30%, cutoff: 41122, grey zone: 2287-220339) and remained independently associated with 28-day mortality in multivariable analysis [2.219 (1.509 to 3.262), P < 0.001]. CONCLUSION: In patients with sCAP, higher P. aeruginosa_RPTM measured by mNGS was associated with reduced lung microbiome diversity and unfavorable clinical outcomes. RPTM-based risk stratification may help identify patients at increased risk of poor prognosis.

Humans↗

PubMed2026

SimpleMicrobiome: An integrated web-based platform for streamlined microbiome data analysis and visualization.

Microbiome studies require multiple analytical steps after initial sequence processing. These steps commonly include data harmonization, preprocessing, taxonomic profiling, diversity analysis, differential abundance testing, predictive modeling, network inference, and preparation of publication-ready outputs. Although robust packages are available for many of these tasks, routine use often depends on command-line workflows, repeated data reformatting, and method-specific scripting. These requirements can limit accessibility for experimental researchers and complicate consistent analysis across interdisciplinary teams. We developed SimpleMicrobiome, a web-based R Shiny platform that integrates established microbiome analysis methods into a single interactive downstream workflow. The application accepts standard abundance, taxonomy, and metadata tables, supports interactive preprocessing and sample filtering, and provides modules for taxa profile visualization, alpha and beta diversity analysis, ANCOM-BC2 and MaAsLin2 differential abundance testing, Random Forest modeling with SHAP-based interpretation, microbial association network inference using SparCC and SPIEC-EASI through NetCoMi, correlation heatmaps, and dbRDA/CAP-style association biplots. The platform is implemented as a modular Shiny application so that preprocessing choices are propagated across downstream analyses, results can be exported as figures and tables, and the same application can be run through the public server, source-code installation, or a Docker image. SimpleMicrobiome consolidates major downstream microbiome analysis tasks in an accessible browser-based environment while retaining links to established analytical frameworks. The platform may reduce technical barriers for non-programming users, improve consistency across exploratory and reporting-oriented analyses, and support collaborative microbiome research. The public application is available at https://simplemicrobiome.mglab.org, the source code is available at https://github.com/yjcho2252/SimpleMicrobiome, and a Docker image for local deployment is available at https://hub.docker.com/r/mglab2252/simplemicrobiome.

differential abundance↗

PubMed2026

The effects of cold temperature on the development, microbiome, and transcriptome of the sea anemone Nematostella vectensis.

Thermal conditions impact essentially all aspects of the physiology for ectotherms. While the effects of high temperatures have been widely studied, cold temperature effects on aquatic invertebrates and their microbial communities have been poorly characterized. To determine the diverse effects of exposure to cold temperatures, we assessed acute and long-term impacts of ecologically relevant low temperatures on the development, microbiome, and gene expression of the sea anemone Nematostella vectensis. Two hours post fertilization, embryos were exposed to temperatures from 4°C to 35°C and development rate to the juvenile stage was quantified. We found temperature impacts the development rate of embryos, where lower temperatures extended development time and resulted in mortality below 10°C. For both microbiome and host transcriptomic responses, anemones were held at 20°C, 10°C, and 0°C and compared at 24 hours and 7 days. Extended exposures to colder temperatures caused restructuring of the host-associated microbiome, with the loss of common taxonomic groups from the class Bacteroidia and Bacilli. Lastly, cold stress induced significant changes in gene expression, which were more pronounced at the 10°C than 0°C but showed little change over time in each temperature. Interestingly, expression of genes associated with innate immunity were among the most differentially expressed genes including heat shock proteins and innate immune genes providing a potential host-imposed mechanism to explain the shift in the microbiome. Overall, cold temperatures have broad effects on many facets of this sea anemone and its microbial community and indicate the importance of cold temperature events when characterizing how ectotherms acclimate to thermal variation.

Animals↗

PubMed2026

Integrating multi-omics technologies to decipher microbiome functions.

Multi-omics approaches have revolutionized our understanding of microbial communities by enabling simultaneous interrogation of genomic, transcriptomic, proteomic, and metabolomic data. The systematic integration and analysis of these deep datasets help decipher the functional roles of microbiomes, providing critical insights into microbial activities, interactions, and dynamics across diverse environments. Biological complexity makes multi-omics analysis of a single, isolated organism demanding but highly informative, yet this complexity increases further when samples comprise hundreds to thousands of individual species. As microbiome research continues to expand into clinical, environmental, and engineered systems, standardized workflows, benchmarked datasets, and community-driven initiatives are essential to ensure reproducibility, standardization and interpretability. Establishing and disseminating best practices for experimental design, data processing, and integrative analyses will be critical for maximizing comparability and scientific rigor across studies. This perspective highlights recent advances in multi-omics microbiome research, outlines key obstacles in data integration and metadata harmonization, and proposes a collaborative roadmap for scalable, FAIR-compliant multi-omics investigations and potentially disruptive Artificial Intelligence (AI) advances comparable to those of AlphaFold in the field of microbiome science.

Multiomics↗

PubMed2025

Identification of Sample Processing Errors in Microbiome Studies Using Host Genetic Profiles.

In microbiome studies, sample processing errors are frequent and difficult to detect, especially in large studies involving multiple sites, personnel, and sample types. We present two complementary approaches to identify such errors using host DNA profiled via metagenomic sequencing of microbiome samples. The first approach compares host SNPs inferred from metagenomics to independently obtained genotypes (e.g., microarray genotypes) to match samples to their donors, while the second method compares metagenomics-inferred SNPs between samples to identify samples supplied by the same donor. Furthermore, we demonstrate that combining these methods with experimental metadata provides greater confidence in the identification of errors. Analyzing a longitudinal vaginal microbiome dataset, we demonstrate the ability of our approach to identify mislabeled samples. Using subsampling, we further show that our methods are robust to low sequencing coverage. Overall, our analysis highlights the frequency of processing errors in microbiome studies. We therefore recommend applying error-detection methods in all studies with suitable data.

Journal Article↗

PubMed2025

Breaking the reproducibility barrier with standardized protocols for plant-microbiome research.

Inter-laboratory replicability is crucial yet challenging in microbiome research. Leveraging microbiomes to promote soil health and plant growth requires understanding underlying molecular mechanisms using reproducible experimental systems. In a global collaborative effort involving five laboratories, we aimed to help advance reproducibility in microbiome studies by testing our ability to replicate synthetic community assembly experiments. Our study compared fabricated ecosystems constructed using two different synthetic bacterial communities, the model grass Brachypodium distachyon, and sterile EcoFAB 2.0 devices. All participating laboratories observed consistent inoculum-dependent changes in plant phenotype, root exudate composition, and final bacterial community structure, where Paraburkholderia sp. OAS925 could dramatically shift microbiome composition. Comparative genomics and exudate utilization linked the pH-dependent colonization ability of Paraburkholderia, which was further confirmed with motility assays. The study provides detailed protocols, benchmarking datasets, and best practices to help advance replicable science and inform future multi-laboratory reproducibility studies.

Plants↗

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

Unveiling novel antimicrobial peptides from the ruminant gastrointestinal microbiomes: A deep learning-driven approach yields an anti-MRSA candidate.

INTRODUCTION: Antimicrobial peptides (AMPs) present a promising avenue to combat the growing threat of antibiotic resistance. The ruminant gastrointestinal microbiome serves as a unique ecosystem that offers untapped potential for AMP discovery. OBJECTIVES: The aims of this study are to develop an effective methodology for the identification of novel AMPs from ruminant gastrointestinal microbiomes, followed by evaluating their antimicrobial efficacy and elucidating the mechanisms underlying their activity. METHODS: We developed a deep learning-based model to identify AMP candidates from a dataset comprising 120 metagenomes and 10,373 metagenome-assembled genomes derived from the ruminant gastrointestinal tract. Both in vivo and in vitro experiments were performed to examine and validate the antimicrobial activities of the AMP candidates that were selected through bioinformatic analysis and subsequently synthesized chemically. Additionally, molecular dynamics simulations were conducted to explore the action mechanism of the most potent AMP candidate. RESULTS: The deep learning model identified 27,192 potential secretory AMP candidates. Following bioinformatic analysis, 39 candidates were synthesized and tested. Remarkably, all synthesized peptides demonstrated antimicrobial activity against Staphylococcus aureus, with 79.5% showing effectiveness against multiple pathogens. Notably, Peptide 4, which exhibited the highest antimicrobial activity against methicillin-resistant Staphylococcus aureus (MRSA), confirmed this effect in a mouse model with wound infection, exhibiting a low propensity for resistance development and minimal cytotoxicity and hemolysis towards mammalian cells. Molecular dynamics simulations provided insights into the mechanism of Peptide 4, primarily its ability to disrupt bacterial cell membranes, leading to cell death. CONCLUSION: This study highlights the power of combining deep learning with microbiome research to uncover novel therapeutic candidates, paving the way for the development of next-generation antimicrobials like Peptide 4 to combat the growing threat of MRSA would infections. It also underscores the value of utilizing ruminant microbial resources.

Animals↗

PubMed2026

Host immunogenetic variation and gut microbiome functionality in a wild vertebrate population.

BACKGROUND: The gut microbiome (GM) -important for host health and survival- is partially shaped by host immunogenetics. However, to date, no study has investigated the influence of host Major Histocompatibility Complex (MHC) genes on gut microbiome functionality in a wild population. Here we use a natural population of the Seychelles warbler (Acrocephalus sechellensis) to assess the effects of MHC genes on GM taxonomy and functionality using shotgun metagenomics. RESULTS: Our results show that taxonomic GM composition was associated with MHC-II diversity and the presence of one specific MHC-I allele (Ase-ua 7). Specifically, MHC-II diversity was associated with decreased Lactococcus lactis and increased Staphylococcus lloydii abundance, while Ase-ua 7 was linked to reduced Enterococcus casselifavus and Gordonia sp OPL2 but increased Escherichia coli and Vulcaniibacterium thermophilum. These taxonomic changes may reflect differences in MHC-mediated microbial recognition. In contrast, functional GM composition was significantly associated with increasing individual MHC-I diversity but not MHC-II diversity. In particular, increasing MHC-I diversity was associated with an increased prevalence of microbial defence genes but a reduced prevalence of microbial metabolism genes. Analysis also revealed that functional GM networks were more fragmented in high compared to low MHC-I diversity hosts. CONCLUSION: These results suggest that MHC variation (particularly at MHC-I) plays an important role in shaping both the taxonomy and function of the GM in wild vertebrates. In the Seychelles warbler, this results in trade-offs whereby there is an increase in microbial defence and a reduction in GM metabolic potential in individuals with higher MHC-I diversity. Thus, this work sheds light on the possible costs and benefits of maintaining a healthy microbiome, which is essential for understanding how the GM and immune system co-evolve. Video Abstract.

Animals↗

PubMed2026

Impacts of host genetics on gut microbiome composition in Alzheimer's disease.

BACKGROUND: Host-microbiome interactions play essential roles in the development of Alzheimer's disease (AD), yet the host genetic impacts on gut microbial alterations in AD remain poorly understood. RESULTS: Here, we simultaneously profiled host genotype and gut microbiome in 252 Chinese individuals with varying degrees of cognitive disability. Using the latent Dirichlet allocation topic model, we identified the Anaerostipes-enriched enterosignature (ES-Ana) at the microbial subgroup level as significantly negatively associated with cognitive disability, which could be recapitulated in external cohorts. With the whole-genome sequencing data, we performed microbiome genome-wide association studies for the ES-Ana relative abundance. We prioritized 41 lead genetic variants and confirmed that the high ES-Ana relative abundance showed a negative correlation with the polygenic risk score of AD, indicating its protective effect against AD. Furthermore, we identified 174 ES-Ana-associated genes, which are enriched in AD-related biological functions and phenotypes, and exhibite pervasive underexpression in glial cells during brain aging. CONCLUSIONS: In summary, our study reveals the complex genetic effects on the gut microbiota in AD, and provides novel evidence for the roles of the gut-brain axis in AD. Video Abstract.

Alzheimer Disease↗

PubMed2026

Mulberry-derived endophytic Bacillus velezensis suppresses gray mold and promotes mulberry growth via reshaping the root metabolism and microbiome.

INTRODUCTION: Gray mold is an important fungal disease caused by Botrytis cinerea which threatens global agriculture. As chemical control faces limitations, biological control using Bacillus has gained attention for its environmental friendliness and growth promotion. However, their ecological basis and application potential in mulberry gray mold control remain insufficiently understood. OBJECTIVE: This study aimed to evaluate the biocontrol efficacy of the mulberry derived endophytic strain Bacillus velezensis ZJU_268 and to investigate its associated effects on plant growth, root-associated microbiomes, and metabolic profiles. METHODS: Greenhouse assays were combined with genomic and comparative genomic analyses, amplicon sequencing, non-targeted metabolomics, and functional validation of isolated microbes and metabolites to assess the effects of ZJU_268 and its cell free supernatant (CFS) on mulberry seedlings. RESULTS: This study isolated a mulberry derived endophytic bacterium, B. velezensis ZJU_268, which exhibits strong antifungal activity and reduces the incidence of gray mold in mulberry seedlings. Whole-genome sequencing and comparative genomic analyses revealed strain-specific regions and genes associated with root colonization, stress adaptation, and antimicrobial biosynthesis. Both live cells and CFS significantly promoted seed germination, seedling growth, and biomass accumulation in a dose dependent manner. Amplicon sequencing showed that ZJU_268 and its supernatant reshaped the mulberry root microbiome, enriching beneficial bacterial and fungal taxa while reducing potentially pathogenic members. Cultivable members of the enriched microbiota displayed strong antifungal activity against B. cinerea and promoted mulberry growth. Metabolomic profiling further showed that ZJU_268 and its supernatant were associated with marked metabolic shifts in mulberry roots, accompanied by the accumulation of selected metabolites that supported the growth of representative enriched isolates. CONCLUSIONS: This study demonstrates that ZJU_268 suppresses gray mold and promotes mulberry growth in association with direct antagonistic activity, microbiome restructuring, and holobiont-level metabolic shifts, providing a promising biological strategy for sustainable mulberry disease management.

Bacillusvelezensis↗

PubMed2026

Ecological Restoration of the Soil-Like Function in the Bauxite Residue: Natural Microbiomes Mediated Molecular Transformation of Dissolved Organic Matter.

Soilization of bauxite residues offers a scalable route for long-term carbon management and ecological restoration. However, the microbial processes that transform exogenous organic inputs into stable soil-like carbon pools remain poorly resolved. Here, we combined cross-ecosystem meta-analysis, machine-learning prediction, native synthetic community (SynCom) construction, 13C-labeled straw microcosms, field validation, Fourier transform ion cyclotron resonance mass spectrometry, and genome-resolved metagenomics to unravel microbiome-mediated carbon transformation at the dissolved organic matter (DOM) molecular scale. Our meta-analysis revealed that alkaline industrial wastes retained soil-like DOM signatures but were enriched in microbial humic- and protein-like components, indicating active yet incomplete carbon processing. Guided by these patterns, native SynCom inoculation increased 13C incorporation into total organic carbon (TOC) and dissolved organic carbon (DOC), enlarged biodegradable and adsorbable DOC fractions, and shifted DOM from recalcitrant aromatic pools toward oxygenated carbohydrate-, tannin-, and phenolic-like molecular classes. Genome-resolved analyses linked this transformation to complementary polymer degradation and nutrient-cycling functions across fungal and bacterial guilds, including enriched carbohydrate-active enzymes in straw-carbon-utilizing metagenome-assembled genomes. Null model and thermodynamic analyses further showed that microbial communities were constrained by homogeneous selection, whereas DOM molecules were diversified through variable selection and redox-dependent transformation. Field-scale validation confirmed that SynCom promoted TOC and DOC accumulation and humic-like, high-density DOM fractions under alkaline conditions. Together, these findings establish a mechanistic framework in which functional microbiomes couple plant carbon depolymerization, DOM molecular diversification, and mineral-interactive carbon stabilization, providing a microbiome-guided strategy for carbon sequestration and soilization in the bauxite residue.

Soil↗

PubMed2026

Prevalence and chronology of colibactin-associated mutational processes and their microbiome spectra in Japanese colorectal cancer.

The incidence of colorectal cancer (CRC) has risen in recent decades, with a disproportionate increase observed among younger individuals in Japan and other countries. The etiological contribution of the gut microbiota to CRC pathogenesis is recognized, yet the mechanisms involved remain to be fully clarified. Here we integrated whole-genome sequencing (WGS) and transcriptome profiling of CRC with whole-genome metagenomic sequencing of fecal samples to interrogate host-microbiome interactions at high resolution. Application of interpretable artificial intelligence enabled the stratification of CRC into four distinct microbiome-informed subtypes. WGS analysis identified mutational signatures SBS88 and ID18, linked to colibactin exposure, as early clonal events detected in 44.8% of non-hypermutated patients. Notably, these signatures were significantly more frequent among patients born after the 1960s. Microbiome-based subclassification revealed subtype-specific clinical and molecular features. Collectively, our findings indicate that colibactin exposure constitutes a prevalent and potentially modifiable risk factor for CRC in the Japanese population.

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

Effects of commonly used antibiotics on children's developing gut microbiomes and resistomes in peri-urban Lima, Peru.

BACKGROUND: The effects of antibiotic use on children's gut microbiomes and resistomes are not well characterized in middle-income countries, where antibiotic consumption is exceptionally common. OBJECTIVES: We characterized the effects of antibiotics commonly used by Peruvian children (i.e. amoxicillin, azithromycin, cefalexin, trimethoprim/sulfamethoxazole) on the α-diversity, β-diversity and abundance of gut genera and antibiotic resistance genes (ARGs) from 3 to 16 months. METHODS: This study included 54 children from a prospective cohort of enteric infections in peri-urban Lima, 2016-19. Stools collected at 3, 6, 7, 9, 12 and 16 months underwent DNA extraction and short-read metagenomic sequencing. We profiled the taxonomy of stool metagenomes and assessed ARG abundance by aligning reads to the ResFinder database. We used daily surveillance data (40 662 observations) to tabulate the number of antibiotic courses consumed in the 30 days prior to stool sampling. Using linear mixed models, we examined associations of recent antibiotic use with richness, diversity and abundance of gut genera and ARGs over time. RESULTS: Each additional recent antibiotic course decreased Bifidobacterium and Dialister abundance and increased Veillonella abundance, although gut richness and diversity were not affected. Recent use of amoxicillin, azithromycin, cefalexin or trimethoprim/sulfamethoxazole, specifically, did not impact gut microbiome measures. Amoxicillin, azithromycin and trimethoprim/sulfamethoxazole significantly enriched multiple ARGs and amoxicillin use significantly increased total ARGs. CONCLUSIONS: Common antibiotics like amoxicillin and azithromycin appear to be key drivers of the paediatric gut resistome. Resistome perturbations appeared to be stronger, or persist for longer, than gut microbiome effects in this middle-income country setting.

Humans↗

PubMed2026

Identifying fundamental gaps in functional metagenomics: a step towards unlocking microbiome research potential.

Incomplete functional annotation limits biological interpretation in microbiome studies and their translational potential. Poor annotation arises from multiple causes, with incomplete gene-protein-reaction mapping being one tractable yet under-examined contributor. We address this gap by developing a comprehensive hierarchical framework that systematically integrates gene families in UniRef, proteins in UniProt, and metabolic reactions in MetaCyc and BioCyc through UniProtKB accession, EC number, and Pfam-domain matching. Applied to a human gut metagenome dataset via HUMAnN3, our MetaCyc-based mapping recovers up to 2.3-fold more unique reaction identifiers than the default pipeline and increases reaction prevalence across samples from ≈32% to 52% core reactions, addressing the data sparsity that limits statistical and machine-learning applications in microbiome research. Biological plausibility for the tested functions was supported by positive and negative controls: gut-microbial hormone-metabolism reactions previously linked to this dataset were recovered, while vertebrate-specific hormone-metabolism reactions remained correctly undetected. These gains derive from systematic database integration alone, without predictive algorithms, indicating that a tractable, mapping-related component of functional dark matter and data sparsity in microbiome studies is directly addressable. Because Pfam- and BioCyc-derived mappings trade specificity for coverage, confidence in any individual reaction assignment depends on the supporting evidence tier and source database.

Humans↗

PubMed2024

Comprehensive Assessment of the Intrinsic Pancreatic Microbiome.

OBJECTIVE: To sought comprehensively profile tissue and cyst fluid in patients with benign, precancerous, and cancerous conditions of the pancreas to characterize the intrinsic pancreatic microbiome. BACKGROUND: Small studies in pancreatic ductal adenocarcinoma (PDAC) and intraductal papillary mucinous neoplasm (IPMN) have suggested that intrapancreatic microbial dysbiosis may drive malignant transformation. METHODS: Pancreatic samples were collected at the time of resection from 109 patients. Samples included tumor tissue (control, n = 20; IPMN, n = 20; PDAC, n = 19) and pancreatic cyst fluid (IPMN, n = 30; serous cystadenomas, n = 10; mucinous cystic neoplasm, n = 10). Assessment of bacterial DNA by quantitative polymerase chain reaction and 16S ribosomal RNA gene sequencing was performed. Downstream analyses determined the relative abundances of individual taxa between groups and compared intergroup diversity. Whole-genome sequencing data from 140 patients with PDAC in the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium were analyzed to validate findings. RESULTS: Sequencing of pancreatic tissue yielded few microbial reads regardless of diagnosis, and analysis of pancreatic tissue showed no difference in the abundance and composition of bacterial taxa between normal pancreas, IPMN, or PDAC groups. Low-grade and high-grade dysplasia IPMN were characterized by low bacterial abundances with no difference in tissue composition and a slight increase in Pseudomonas and Sediminibacterium in high-grade dysplasia cyst fluid. Decontamination analysis using the Clinical Proteomic Tumor Analysis Consortium database confirmed a low-biomass, low-diversity intrinsic pancreatic microbiome that did not differ by pathology. CONCLUSIONS: Our analysis of the pancreatic microbiome demonstrated very low intrinsic biomass that is relatively conserved across diverse neoplastic conditions and thus unlikely to drive malignant transformation.

Humans↗