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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↗
PubMed2026

Dental wastewater reveals a hidden reservoir of oral bacteriophage diversity.

Bacteriophages (phages) are being explored as alternatives or complements to antibiotics because of their ability to selectively kill bacterial pathogens. However, phages that infect many oral bacteria remain undiscovered. Here, we discovered that dental wastewater harbors previously underexplored phage diversity. Viral particles concentrated from dental wastewater displayed diverse morphologies, including abundant filamentous phage-like particles. Deep long-read metagenomic sequencing of concentrated viral particles generated 7.4 billion bases of sequence data and yielded 255 medium- to high-quality viral operational taxonomic units (vOTUs), including 46 predicted complete genomes. Comparison with large phage databases revealed that 63 of these 255 vOTUs had no detectable match, indicating that extensive sequencing of dental wastewater substantially expands the number of potential bacteriophages associated with the human oral microbiome. Host prediction linked many vOTUs to oral-associated bacterial taxa, including species with few or no previously reported phages, such as Porphyromonas gingivalis, Tannerella forsythia, and Candidatus Saccharibacteria. Functional annotation identified diverse genes associated with antiphage defense systems within a subset of vOTUs, suggesting that oral phages may contribute to the movement of genes encoding bacterial immune functions within the oral microbiome. Together, these findings expand the known oral phageome and show that dental wastewater contains a largely untapped diversity of phages.IMPORTANCEThe human oral cavity contains a diverse microbial community, but the bacteriophages (phages) that infect many oral bacteria remain poorly characterized. This gap limits our understanding of how phages shape oral microbial communities. Here, we show that dental wastewater is an underexplored source of oral phage diversity. Deep long-read metagenomic sequencing revealed 255 medium- to high-quality phage operational taxonomic units, many of which are not present in existing oral phage databases. These genomes include predicted phages of periodontal disease-associated bacteria and other oral taxa with few or no known phages. Dental wastewater therefore expands the known human oral phageome and reveals candidate phages linked to bacteria associated with oral health and disease.

Bacteriophages↗
PubMed2026

Causal Relationships Between Oral Microbiota and Inflammatory Skin Diseases.

INTRODUCTION AND AIMS: The oral microbiome has been increasingly linked to systemic inflammation and immune dysregulation, but whether specific oral bacteria causally contribute to inflammatory skin diseases remains unclear due to confounding and reverse causation. This study aimed to assess the causal effects of 43 oral microbiota taxa on the risk of five inflammatory skin diseases using a Mendelian randomization (MR) approach. METHODS: We performed a two-sample MR analysis using genetic instruments for oral microbiota derived from publicly available genome-wide association studies and outcome data from the FinnGen consortium. Causal effects of oral taxa on systemic lupus erythematosus, vitiligo, pemphigus, localized scleroderma, and dermatitis herpetiformis were estimated. The inverse-variance weighted method served as the primary analysis, complemented by sensitivity analyses to evaluate horizontal pleiotropy, heterogeneity, and reverse causality. RESULTS: MR analyses identified several putative causal associations between oral microbiota and inflammatory skin diseases. Genus Granulicatella and an unknown Streptococcus species (ASV0009) showed causal effects on systemic lupus erythematosus. Family Lachnospiraceae_[XIV] and an unknown Rothia species (ASV0016) were associated with vitiligo. Five oral microbiota taxa demonstrated causal associations with pemphigus. Actinomyces species micronuciformis was linked to localized scleroderma. Order Fusobacteriales and an unknown Neisseria species (ASV0004) were associated with dermatitis herpetiformis. No significant heterogeneity or horizontal pleiotropy was detected in sensitivity analyses. CONCLUSION: This MR study provides genetic evidence supporting a causal role of specific oral bacteria in the development of several inflammatory skin diseases, highlighting the oral microbiome as a potential contributor to cutaneous autoimmunity and inflammation. CLINICAL RELEVANCE: Our findings highlight the putative role of the oral microbiome as a plausible candidate for mechanistic and clinical investigations into the prevention or adjunctive management of selected inflammatory skin diseases. However, oral hygiene improvement, targeted antimicrobials, and other microbiota-directed interventions were not directly tested in this MR study and remain hypothetical strategies requiring validation in experimental and clinical studies.

Humans↗
PubMed2025

Doblin: inferring dominant clonal lineages from high-resolution DNA barcoding time series.

MOTIVATION: The lineage dynamics and history of cells in a population reflect the interplay of evolutionary forces they experience, including mutation, drift, and selection. When the population is polyclonal, lineage dynamics also manifest the extent of clonal competition among co-existing mutational variants. If the population exists in a community of other species, the lineage dynamics could also reflect the population's ecological interaction with the rest of the community. Recent advances in high-resolution lineage tracking via DNA barcoding, coupled with next-generation sequencing of bacteria, yeast, and mammalian cells, allow for precise quantification of clonal dynamics in these organisms. RESULTS: In this work, we introduce Doblin, an R suite for identifying dominant barcode lineages based on high-resolution lineage tracking data. We first benchmarked Doblin's accuracy using lineage data from evolutionary simulations, showing that it recovers the clones' identity and relative fitness in the simulation. Next, we applied Doblin to analyze clonal dynamics in laboratory evolutions of Escherichia coli populations undergoing antibiotic treatment and in colonization experiments of the gut microbial community. Doblin's versatility allows it to be applied to lineage time-series data across different experimental setups. AVAILABILITY AND IMPLEMENTATION: Doblin is available on CRAN (https://CRAN.R-project.org/package=doblin) and Github (https://github.com/dagagf/doblin).

DNA Barcoding, Taxonomic↗
PubMed2025

Challenges and Opportunities in Analyzing Cancer-Associated Microbiomes.

The study of cancer-associated microbiomes has gained significant attention in recent years, spurred by advances in high-throughput sequencing and metagenomic analysis. Microbiome research holds promise for identifying noninvasive biomarkers and possibly new paradigms for cancer treatment. In this review, we explore the key computational challenges and opportunities in analyzing cancer-associated microbiomes (in tumor/normal tissues and other body sites, e.g., gut, oral, and skin), focusing on sequencing-driven strategies and associated considerations for taxonomic and functional characterization. The discussion covers the strengths and limitations of current analysis tools for identifying contamination, determining compositional bias, and resolving species and strains, as well as the statistical, metabolic, and network inferences that are essential to uncover host-microbiome interactions. Several key considerations are required to guide the choice of databases used for metagenomic analysis in such studies. Recent advances in spatial and single-cell technologies have provided insights into cancer-associated microbiomes, and Artificial Intelligence-driven protein function prediction might enable rapid advances in this field. Finally, we provide a perspective on how the field can evolve to manage the ever-growing size of datasets and generate robust and testable hypotheses. This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI .

Humans↗
PubMed2025

Deciphering microbial and metabolic influences in gastrointestinal diseases-unveiling their roles in gastric cancer, colorectal cancer, and inflammatory bowel disease.

INTRODUCTION: Gastrointestinal disorders (GIDs) affect nearly 40% of the global population, with gut microbiome-metabolome interactions playing a crucial role in gastric cancer (GC), colorectal cancer (CRC), and inflammatory bowel disease (IBD). This study aims to investigate how microbial and metabolic alterations contribute to disease development and assess whether biomarkers identified in one disease could potentially be used to predict another, highlighting cross-disease applicability. METHODS: Microbiome and metabolome datasets from Erawijantari et al. (GC: n = 42, Healthy: n = 54), Franzosa et al. (IBD: n = 164, Healthy: n = 56), and Yachida et al. (CRC: n = 150, Healthy: n = 127) were subjected to three machine learning algorithms, eXtreme gradient boosting (XGBoost), Random Forest, and Least Absolute Shrinkage and Selection Operator (LASSO). Feature selection identified microbial and metabolite biomarkers unique to each disease and shared across conditions. A microbial community (MICOM) model simulated gut microbial growth and metabolite fluxes, revealing metabolic differences between healthy and diseased states. Finally, network analysis uncovered metabolite clusters associated with disease traits. RESULTS: Combined machine learning models demonstrated strong predictive performance, with Random Forest achieving the highest Area Under the Curve(AUC) scores for GC(0.94[0.83-1.00]), CRC (0.75[0.62-0.86]), and IBD (0.93[0.86-0.98]). These models were then employed for cross-disease analysis, revealing that models trained on GC data successfully predicted IBD biomarkers, while CRC models predicted GC biomarkers with optimal performance scores. CONCLUSION: These findings emphasize the potential of microbial and metabolic profiling in cross-disease characterization particularly for GIDs, advancing biomarker discovery for improved diagnostics and targeted therapies.

Humans↗
PubMed2025

PLSKO: a robust knockoff generator to control false discovery rate in omics variable selection.

MOTIVATION: Integrating the knockoff framework with any variable-selection method delivers stringent false discovery rate (FDR) control without recourse to p-values, offering a powerful alternative for differential expression analysis of high-throughput omics datasets. However, existing knockoff generators rely on restrictive modelling assumptions or coarse approximations that often inflate the FDR when applied to real-world data. RESULTS: We introduce Partial Least Squares Knockoff (PLSKO), an efficient, assumption-free generator that remains robust across diverse omics platforms. Our extensive simulations show that PLSKO is the only method to maintain FDR control with sufficient power in complex non-linear settings. Our semi-simulation studies drawn from RNA-seq, proteomics, metabolomics, and microbiome experiments confirm PLSKO generates valid knockoff variables. In pre-eclampsia multi-omics case studies, we combine PLSKO with Aggregation Knockoff to address the randomness of knockoffs and improve power, and demonstrate the method's ability to recover biologically meaningful features. AVAILABILITY AND IMPLEMENTATION: Our proposed algorithm is available on Github (https://github.com/guannan-yang/PLSKO) and Zenodo (https://doi.org/10.5281/zenodo.16879594).

Algorithms↗
PubMed2006

Community structure of actively growing bacterial populations in plant pathogen suppressive soil.

The bacterial community in soil was screened by using various molecular approaches for bacterial populations that were activated upon addition of different supplements. Plasmodiophora brassicae spores, chitin, sodium acetate, and cabbage plants were added to activate specific bacterial populations as an aid in screening for novel antagonists to plant pathogens. DNA from growing bacteria was specifically extracted from the soil by bromodeoxyuridine immunocapture. The captured DNA was fingerprinted by terminal restriction fragment length polymorphism (T-RFLP). The composition of the dominant bacterial community was also analyzed directly by T-RFLP and by denaturing gradient gel electrophoresis (DGGE). After chitin addition to the soil, some bacterial populations increased dramatically and became dominant both in the total and in the actively growing community. Some of the emerging bands on DGGE gels from chitin-amended soil were sequenced and found to be similar to known chitin-degrading genera such as Oerskovia, Kitasatospora, and Streptomyces species. Some of these sequences could be matched to specific terminal restriction fragments on the T-RFLP output. After addition of Plasmodiophora spores, an increase in specific Pseudomonads could be observed with Pseudomonas-specific primers for DGGE. These results demonstrate the utility of microbiomics, or a combination of molecular approaches, for investigating the composition of complex microbial communities in soil.

Bacteria↗
PubMed2026

Investigating mechanisms of divergent feed efficiency in dairy cows.

Objectives were to investigate the associations between residual DMI (RFI), calculated as the difference between observed minus predicted DMI, with rumen microbiome, digestion, behavior, and metabolism that might explain the differences in RFI in lactating cows. One hundred 50 genotyped Holstein cows in 3 cohorts were used in this cohort study in which exposure was RFI. Rumen microbiota from 114 cows were sequenced, and a subset of 30 cows was used for hepatic mitochondrial respiration analysis. Cows were ranked by RFI and grouped into quartiles (Q1, most efficient, to Q4, least efficient) according to phenotypic (pQ) or genomic (gQ) quartiles of RFI for data presentation. Statistical models fitted the linear and quadratic RFI as continuous explanatory variables. Increasing efficiency, i.e., from larger to smaller RFI values, whether phenotypic or genomic, were associated with reduced DMI, a 3.0 kg/d difference between Q4 and Q1 according to phenotypic RFI (pRFI) and 1.9 kg/d according to genomic RFI (gRFI) without compromising ECM or body tissue reserves. These differences between Q4 and Q1 of pRFI and gRFI resulted in increased feed conversion ratio by an additional 200 and 100 g of ECM/kg DMI, respectively. Both pRFI and gRFI were associated with FA profiles in milk fat, with decreasing proportions of de novo and mixed FA and increasing proportions of pre-formed FA, particularly monounsaturated FA, as efficiency improved. Additionally, pRFI and gRFI were moderately correlated (r = 0.48) and ranking of cows was consistent across the 2 grouping methods (ρ = 0.44). Reducing RFI was associated with less total rumination time, but greater rumination time per kg of DMI by 2.0 and 1.7 min/kg between the extreme quartiles of pRFI and gRFI, respectively. Phenotypically and genomically more efficient cows were associated with less microbial α diversity based on inverse Simpson index. A total of 57 amplicon sequence variant groups were differentially abundant between Q1 and Q4 classified based on pRFI and gRFI, with Prevotella and Succinivibrionaceae shared between phenotypic and genomic RFI classifications. Increasing phenotypic and genomic efficiency was associated with an increased concentration of ruminal NH3-N. Genomically more efficient cows tended to have reduced ruminal pH (gQ1 to gQ4; 6.42 vs. 6.47 vs. 6.43 vs. 6.53) despite eating less. Decreasing pRFI was associated with reduced microbial N yield whereas, it tended to increase microbial N yield relative to the amount of N intake. Collectively, phenotypic and genomic RFI have a moderate degree of agreement matching the estimated heritability of the trait, and mechanisms underlying improved feed efficiency were linked with differences in ruminal microbiota and fermentation, and with increased rumination per kg of DM rather than total-tract digestibility or hepatic mitochondrial respiration.

dairy cow↗
PubMed2024

Assessment of antibiotic resistance genes in soils polluted by chemical and technogenic ways with poly-aromatic hydrocarbons and heavy metals.

Anthropogenic activities are leaving lots of chemical footprints on the soil. It alters the physiochemical characteristics of the soil thereby modifying the natural soil microbiome. The prevalence of antimicrobial-resistance microbes in polluted soil has gained attention due to its obvious public health risks. This study focused on assessing the prevalence and distribution of antibiotic-resistance genes in polluted soil ecosystems impacted by industrial enterprises in southern Russia. Metagenomic analysis was conducted on soil samples collected from polluted sites using various approaches, and the prevalence of antibiotic-resistance genes was investigated. The results revealed that efflux-encoding pump sequences were the most widely represented group of genes, while genes whose products replaced antibiotic targets were less represented. The level of soil contamination increased, and there was an increase in the total number of antibiotic-resistance genes in proteobacteria, but a decrease in actinobacteria. The study proposed an optimal mechanism for processing metagenomic data in polluted soil ecosystems, which involves mapping raw reads by the KMA method, followed by a detailed study of specific genes. The study's conclusions provide valuable insights into the prevalence and distribution of antibiotic-resistance genes in polluted soils and have been illustrated in heat maps.

Soil Pollutants↗
PubMed2026

Gut microbiota dynamics and metabolic pathways associated with bleomycin-induced pulmonary fibrosis progression.

BACKGROUND: Pulmonary fibrosis (PF) is a progressive respiratory disease characterized by epithelial injury, aberrant repair and excessive extracellular matrix deposition. Although the gut-lung axis is increasingly implicated in respiratory disorders, stage-resolved characterization of gut microbiota taxonomic and functional potential during PF development is limited. METHODS: We established a bleomycin-induced murine PF model and performed cross-sectional shotgun metagenomic sequencing of fecal samples from separate cohorts at three defined stages: baseline (control), day 7 (early fibrosis; M7), and day 14 (established fibrosis; M14). Microbial taxonomy, alpha/beta diversity, and predicted functional capacity were inferred using Kyoto Encyclopedia of Genes and Genomes (KEGG) and Carbohydrate-Active enZymes (CAZy) annotations; associations were assessed using Procrustes and Spearman correlation analyses. RESULTS: Histopathology and immunohistochemistry confirmed progressive fibrogenesis with increased TGF-β1 and α-SMA expression. Compared with baseline, bleomycin-treated groups exhibited stage-specific shifts in gut microbial composition, including depletion of mucin-associated taxa (e.g., Prevotella, Akkermansia muciniphila) and expansion of Muribaculaceae- and Clostridiaceae-affiliated taxa. Alpha and beta diversity metrics differed across groups. KEGG/CAZy-based annotations revealed predicted, stage-dependent changes in microbial metabolic potential, including early reductions in pathways related to amino acid and glycan metabolism (M7) and later increases in predicted starch/sucrose catabolism, phosphotransferase system (PTS) representation, and secondary bile acid biosynthesis (M14). Correlation analyses linked compositional shifts to these predicted functional changes. CONCLUSION: In a stage-resolved, cross-sectional study, bleomycin-associated pulmonary fibrosis was accompanied by compositional and predicted functional alterations in the gut microbiota. These data identify candidate taxa and predicted pathways for follow-up mechanistic testing, but functional (metabolomic) and causality experiments are required to confirm whether and how microbial changes contribute to PF pathogenesis.

Animals↗
PubMed2026

A culturomics approach reveals cross-feeding capacity of intestinal pig bacteria upon release of inositol from phytate.

BACKGROUND: Phytate is the primary phosphorus storage molecule of plants and plays a major role in animal nutrition. To enhance phosphate availability and absorption in livestock, and to reduce eutrophication by liquid manure, bacterial phytases are often added to animal feed. The dephosphorylated form of phytate, the polyol myo-inositol (myo-Ins) with multiple functions in eukaryotes, is metabolized by approximately 30% of all bacterial species. RESULTS: Here, we employed a culturomics approach to identify possible metabolic interactions between phytase-producing and myo-Ins degrading bacteria in intestinal samples from pigs. Selective cultivation revealed an unexpectedly high abundance of myo-Ins degrading bacteria, suggesting substantial phytate dephosphorylation in the pig gut. Phytase activity assays performed on gut isolates showed a high degree of variability, suggesting the presence of a diverse set of phytases yet to be characterized. Furthermore, using supernatants of phytase-positive gut strains cultivated in the presence of phytate, we observed cross-feeding of myo-Ins from phytase producers to phytase-negative strains, including the pathogen Salmonella enterica serovar Typhimurium. CONCLUSIONS: The data demonstrate that a wide range of commensal bacteria can potentially benefit from phytase activity by utilizing myo-Ins, released through phytate hydrolysis, as a growth substrate. Video Abstract.

Animals↗
PubMed2026

Common xenobiotics modulate gut microbial responses to low‑calorie sweeteners in vitro.

The gut microbiota is implicated in adverse effects associated with low-calorie sweeteners. Yet, the direct impact of sweeteners on gut bacteria remains largely uncharacterized. Here, we report interactions between 25 phylogenetically diverse gut bacterial strains and 39 commercially used sweeteners. We tested these sweeteners individually and in combination with four commonly co-consumed compounds, viz., advantame, caffeine, vanillin, and duloxetine. Three-quarters of the tested sweeteners individually impacted the growth of at least one tested bacterial strain. Further, over 100 interactions were found between sweeteners and the four co-consumed compounds. Isosteviol, a commonly used sweetener-component, and duloxetine, an antidepressant, synergistically inhibited Roseburia intestinalis, a bacterium previously linked to glucose homeostasis, and Parabacteroides merdae, a prevalent commensal linked to healthy microbiota. Proteomic, metabolomic, and genetic analyses indicate altered small molecule transport underpinning this sweetener-drug synergy. The isosteviol-duloxetine combination also modulated metabolism of a synthetic gut bacterial community, leading to increased toxicity to HeLa cells and altered secretion of inflammation-modulatory cytokines IL-6 and IL-8 by Caco-2 cells. Our data warrant further studies on interactions between low-calorie sweeteners and common xenobiotics.

Humans↗
PubMed2026

Coral color morphs exhibit distinct microbial and proteomic profiles linked to stress and immune mechanisms in a changing ocean.

BACKGROUND: Coral phenotypic plasticity facilitates acclimation and adaptation to environmental variability. Coral species often display a variety of color morphs, yet key biological and ecological implications of such phenotypic variation remain underexplored. Here, we present the first proteomic and untargeted lipidomic and metabolomic survey to explore the biological characteristics and potential ecological significance of different color morphs (pink and brown) of healthy Pocillopora verrucosa sampled along a latitudinal gradient. RESULTS: Our multi-omic approach elucidated distinct mechanisms associated with these dominant color morphs. We discovered bacterial indicators specific to each morph: putative pathogens such as Salmonella, Escherichia-Shigella, and carotenoid-producing Gemmatimonas were notably associated with the pink morph, whereas the brown morph was associated with potentially beneficial bacteria, such as Lysobacter, Acinetobacter, and Endozoicomonas. Despite these microbiome differences, the lipidome and metabolome of P. verrucosa were surprisingly homogeneous across colors and locations, suggesting similar metabolic performances during summer conditions. Key polar and apolar lipid classes, such as fatty acids, glycerophosphocholines, and retinoids, were prevalent. Notably, our proteomic analysis revealed morph-specific expressions, with pink morphs exhibiting enhanced levels of GFP-like proteins, Ankyrin, and the enzyme pullulanase, suggesting novel putative protective roles. In contrast, the brown morphs showed a higher abundance of heat shock proteins, indicating putative differential stress response capabilities. CONCLUSION: This comprehensive study provides the first proteomic survey of P. verrucosa and identifies key physiological pathways and trade-offs linked to color morphs, which can further contribute to enhancing our understanding of coral resilience in the face of climate change. SIGNIFICANCE STATEMENT: Understanding the phenotypic plasticity of corals is crucial for uncovering mechanisms of resilience in warming oceans, yet the biological significance of coral color morphs still needs to be explored. Using an innovative multi-omic approach (proteomics, lipidomics, and metabolomics), we provide the first comprehensive analysis of differences between pink and brown morphs of Pocillopora verrucosa. Our data reveal key taxa, potentially pathogenic or beneficial, associated with each morph, and suggest different strategies for each color morph to cope with heat stress, either expressing proteins involved in UV protection and heterotrophic activity or enhanced levels of heat stress resilience and DNA repair. These findings offer insights into the phenotypic plasticity of coral color morphs and their differential responses to climate change. Video Abstract.

Anthozoa↗
PubMed2026

Meta2DB: curated shotgun metagenomic feature sets and metadata for health state prediction.

SUMMARY: Meta2DB is a curated metagenomic and metadata database that provides structurally consistent microbiome taxonomy feature count tables for 13 897 samples across 84 studies, 23 disease states, and 34 geographical locations. All samples were uniformly processed using a streamlined metagenomic classification pipeline that employs a unique and comprehensive reference database indexed to contain all sequences across all kingdoms of life that were present in the NCBI Nucleotide (nt) database retrieved on 4 January 2023. This pipeline leverages high-performance computing (HPC) resources at Lawrence Livermore National Laboratory and was used to process 50TB of publicly available raw metagenomic sequence data. Extensive metadata curation was carried out through a combination of manual curation and automated parsing, producing a consistent inter-study metadata table specifically structured to facilitate training of ML models for prediction of human health. AVAILABILITY: Data is available at https://gdo-meta2db.llnl.gov/ and https://zenodo.org/records/17315984.

Metadata↗
PubMed2026

Sulfated mannan of diatoms selects host-specific microbiota in the sunlit ocean.

BACKGROUND: Diatoms, a keystone phylum in Earth's ecosystems, are responsible for substantial oxygen production and the fixation of carbon dioxide in the form of carbohydrates that fuel global food webs. They host diverse prokaryotes, yet how diatoms preferentially recruit those with complementary metabolic traits remains unknown. RESULTS: We discovered that diatoms exude a C6-sulfated α-1,3-mannan that serves as a selective carbon source for adapted Polaribacter. Its structure was resolved using NMR spectroscopy, chromatography, chemical synthesis, and enzymatic dissection. Biochemical, physiological, and structural analyses demonstrated, that specialized Bacteroidota employ a four-enzyme pathway to metabolize this glycan. Metagenomic and transcriptomic data revealed that sulfated mannan utilization loci are globally abundant and actively expressed in surface ocean bacterioplankton. Because this mannan provides only carbon, oxygen, sulfur, and hydrogen, bacteria must obtain other essential elements elsewhere, reinforcing metabolic interdependence. CONCLUSIONS: Together, these results define a chemically specific interaction between diatoms and specialized bacteria that is mediated by a single sulfated polysaccharide and a dedicated four-enzyme degradation pathway. Presence of this pathway in marine metagenomes and transcriptomes indicates that a sulfated mannan from diatoms exerts selection pressure in the sunlit ocean microbiome. Video Abstract.

Diatoms↗
PubMed2026

Probiotic supplementation increases fecal TLR4 agonists without improving disease activity in juvenile idiopathic arthritis: a randomized placebo-controlled trial.

Gut dysbiosis has been implicated in the pathogenesis of juvenile idiopathic arthritis (JIA), suggesting that microbiota-targeted interventions may influence immune signalling during early immune development. We conducted the PERMAJI multicentre randomized, double-blind, placebo-controlled trial to evaluate the effects of probiotic supplementation (VSL#3) on host-microbiota immune interactions and disease activity in children with oligoarticular or RF-negative polyarticular JIA. Participants were randomly assigned (1:1) to receive VSL#3 or placebo for 3 months in addition to standard therapy. Stool and serum samples collected at baseline and month 3 were used to assess gut microbiota composition, fecal innate immune agonists, intestinal permeability, and systemic cytokines. The primary clinical endpoint was the proportion achieving an ACR Pedi 30 response at 3 months. Forty-four children were enrolled between September 2017 and July 2022. Clinical responses did not differ between groups (ACR Pedi 30: 47% with VSL#3 vs 63% with placebo; p = 0.33), and conservative worst-case assumptions for missing data suggested lower response rates with VSL#3 (36% vs 68%; p = 0.03). Probiotic supplementation significantly increased fecal Toll-like receptor 4 (TLR4) agonist activity, whereas gut microbiota diversity, intestinal permeability, and systemic cytokine levels remained unchanged. These findings indicate that probiotic supplementation can modify microbial innate immune signalling without detectable changes in microbial community diversity and may increase exposure to pro-inflammatory microbial stimuli in early-life autoimmune disease. The results highlight the complexity of host-microbiota immune interactions and underscore the need for careful evaluation of microbiome-targeted therapies in paediatric autoimmune disorders.

Humans↗
PubMed2026

Integrative analysis of rumen microbiota activity and host metabolism following methanogenesis inhibition in dairy cattle.

Enteric methane emission from dairy cattle is an environmental challenge. The most efficient mitigation strategies nowadays include the use of methanogenesis inhibitors that specifically target the rumen methanogens. Specific inhibitors, such as 3-nitrooxypropanol (3-NOP), reduce methane emissions without negative effects on the products of fermentation that serve as energy metabolites for the host. However, the concomitant effects of methanogenesis inhibition on rumen microbiota and host metabolism are poorly characterized. Thus, the objective of this study was to explore the association between rumen microbiota and host metabolism when methanogenesis is inhibited. Thirteen dairy cows were used as controls, and 12 were supplemented with 3-NOP for 6 weeks. Rumen microbiota composition and activity were characterized using metagenomics and metatranscriptomics. The host metabolism was assessed in a previous publication by a metabolomic analysis of the plasma. Microbiota data were used as explanatory variables of the metabolome data in a multiblock sparse partial least squares analysis. Overall, the association between rumen microbiota and host metabolism was moderate. Notwithstanding this, a few downregulated transcripts related to glycolysis, hydrogen transfer, and protein synthesis, together with a decrease in the proportion of taxa of the Oscillospirales order, showed a correlation with host one-carbon metabolites (|r| > 0.6). These associations raised novel hypotheses that remain to be elucidated, especially with regard to the effects of dihydrogen on the accumulation of microbial glycolysis and methanogenesis metabolite intermediates.IMPORTANCEDairy cattle produce a substantial amount of methane, a potent greenhouse gas. Several strategies have been designed to reduce methane production by targeting the rumen microbiota. One such strategy specifically inhibits methanogens with a molecule called 3-nitrooxypropanol. This study uses an integrative data analysis approach, combining rumen microbiota and host metabolome information, to explore the consequences of inhibiting methanogenesis on the holobiont. This provides additional holistic insight into the effect of methane mitigation strategies on dairy cattle.

Animals↗