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PubMed2025

Phyllosphere microbiomes in grassland plants harbor a vast reservoir of novel antimicrobial peptides and biosynthetic diversity.

INTRODUCTION: The phyllosphere microorganisms colonizing plant surface harbor capacities to synthesize diverse specialized metabolites that mediate communication and interactions with environment and host. However, most known metabolites are derived from a few culturable microorganisms, and the genomic diversity and biosynthetic potential of the vast majority of bacteria associated with plants remain largely unexplored. OBJECTIVES: Here, we aim to explore the genome architecture, biosynthetic ability, and host specific adaptability of grassland ecosystems, uncovering new perspectives on grassland phyllosphere microbial resources. METHODS: We employed ultra-deep metagenomic sequencing, functional analysis, host-associated characterization, and bioactivity assays to explore the phyllosphere microbiome across 221 grassland plant samples representing 45 families. This approach revealed host preference in biosynthetic gene clusters (BGCs) and validated the antimicrobial efficacy of phyllosphere-derived antimicrobial peptides (AMPs). RESULTS: Grassland plant phyllosphere microbiomes encode diverse BGCs. We identified 885,396 potential AMPs from over 68 million non-redundant gene sequences. Then, we reconstructed hundreds of near-complete genomes from phyllosphere metagenomes, and 32.61 % of reconstructed genomes were identified as unclassified genomes, primarily within Pseudomonadota, Actinomycetota, Bacillota and Bacteroidota phyla. Of the near-complete genomes, 91.97 % of the BGCs and 99.76 % of the identified AMPs were previously uncharacterized. Host phylogenetic analysis revealed functional divergence. Poaceae-associated Pseudomonas genomes contain an average of 28 BGCs, significantly higher than those in Asteraceae-associated genomes (mean = 14.76, P = 0.033). Similarly, Poaceae-associated Pantoea genomes carried an average of 9 BGCs, exhibiting significant enrichment compared to genomes from Asteraceae (mean = 7.13, P = 6.1e-05), Lamiaceae (mean = 7, P = 0.015), Ranunculaceae (mean = 8.22, P = 0.0053), and Rosaceae (mean = 7.75, P = 0.00069). ParaFit analyses further confirmed that host phylogeny significantly structures microbial functional repertoires, with intra-family hosts sharing more KEGG pathways than inter-family hosts. These results suggest that host evolutionary relationships are associated with metabolic specialization in phyllosphere microbiomes. All 13 AMPs synthesized via solid-phase peptide synthesis demonstrated antimicrobial activity, inhibiting the growth of at least one tested bacterial strain. CONCLUSION: This study demonstrates the promise of grassland plant phyllosphere microbiome as a rich source for novel antimicrobial agents.

Antimicrobial Peptides↗

PubMed2026

KG-Microbe: Building modular and scalable knowledge graphs for microbiome and microbial sciences.

BACKGROUND: The integration of many disparate forms of data is essential for understanding the microbial world and its interaction with the environment and human health. Doing so is particularly challenging in the context of microbe-host and microbe-microbe interactions that contribute to health or environmental outcomes. There are thousands of relevant microbial species, and millions of interactions among those microbes and with their environment or host. Integrated information (e.g., about host and microbial physiology, genetics, and metabolism) facilitates deeper understanding of complex mechanisms and helps interpret correlative results. RESULTS: The KG-Microbe construction framework is a novel approach to harmonizing bacterial and archaeal data in the form of a findable, accessible, interoperable, reusable and AI-ready knowledge graph (KG). Starting from a core KG with organismal traits, environments, and growth preferences and the integration of established ontologies, the framework generates a hierarchy of related KGs targeting specific use cases, including the human microbiome in the context of disease, or environmental microbiomes. The framework supports customizable taxa subsets representing communities or clades of interest. Evaluations of the KG-Microbe KGs through a series of competency questions demonstrate the accuracy and effectiveness of the data harmonization, and the utility of the resulting KGs in studies of inflammatory bowel disease and Parkinson's disease. Finally, the predictive and environmental capabilities of the KGs are demonstrated by predicting growth preferences using graph features. CONCLUSIONS: The KG-Microbe framework unifies microbial contexts in a single resource to support integrative analyses across biomedical, host, and environmental domains. KG-Microbe is a flexible, modular enabling technology for humans and machine learning methods to uncover candidate mechanistic explanations of microbial associations.

Microbiota↗

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↗

PubMed2026

MegaPX: fast and space-efficient peptide assignment method using IBF-based multi-indexing.

MOTIVATION: A central problem for metaproteomic analysis is the often-unknown taxonomic composition of the analyzed microbiomes. Using a database search, the standard approach requires prior knowledge of which proteins and taxa to include in the protein reference database or to use tailored metagenome-derived databases, which are expensive and error-prone in their generation. A possible strategy to circumvent this database search issue is de novo sequencing, where peptide sequences are directly identified from mass spectra. However, these sequences must still be mapped back to potentially extensive databases. Here, alignment-based approaches enable robust and precise results, with the potential drawback of high memory usage and long run times. RESULTS: We present MegaPX, a software for rapidly classifying de novo peptide sequences against large protein databases. MegaPX implemented as a C++-based tool, uses an alignment-free, k-mer approach as a taxonomic classification method with the possibility of generating mutated reference databases for error-tolerant searching. It uses various algorithms, including interleaved Bloom filters, to efficiently compute approximate membership queries, ensuring fast processing times while querying and indexing large databases in a multi-indexing fashion. We demonstrate the potential of MegaPX by analyzing different samples, including metaproteomics, against extensive reference databases, highlighting its use as a fast screening tool.

Software↗

PubMed2026

In silico analysis and comparison of the metabolic capabilities of different organisms by reducing metabolic complexity.

BACKGROUND: Understanding how metabolic capabilities diverge across microbial species is essential for deciphering community function, ecological interactions, and the design of synthetic microbiomes. Despite shared core pathways, microbial phenotypes can differ markedly due to evolutionary adaptations and metabolic specialization. Genome-scale metabolic models (GEMs) provide a systems-level framework to explore these differences; however, their complexity hinders direct comparison. RESULTS: We introduce NIS (Neidhardt-Ingraham-Schaechter), a computational workflow that integrates the redGEM, lumpGEM, and redGEMX algorithms to systematically reduce genome-scale models into biologically interpretable modules. This approach enables direct, quantitative comparison of fueling pathways, biomass biosynthetic routes, and environmental exchange processes while retaining essential metabolic information. We first demonstrate the utility of NIS by analyzing Escherichia coli and Saccharomyces cerevisiae, which revealed both conserved and divergent strategies in central metabolism, biosynthetic cost, and substrate utilization. We then applied NIS to the core honeybee gut microbiome, uncovering distinct metabolic traits, functional redundancy, and complementarity that help explain auxotrophy, cross-feeding interactions, and microbial coexistence. CONCLUSIONS: NIS provides an automated, scalable, and reproducible framework for dissecting microbial metabolic networks beyond gene content or taxonomy. By linking metabolism to ecological function, NIS offers new opportunities to interpret microbial community dynamics and to support the rational design of microbiomes in health, agriculture, and environmental applications. Video Abstract.

Metabolic Networks and Pathways↗

PubMed2026

Linking visceral fat accumulation to gut microbiota: key bacterial taxa and their roles in the glycogen synthesis pathway.

Obesity, marked by visceral fat accumulation, has a complex relationship with the gut microbiome that impacts body weight and fat accumulation. However, previous studies did not account for fat distribution, reflecting only overall fat mass, leaving specifics of this relationship partially understood. Here we analyzed the mechanistic links between visceral fat and the microbiome in a large cohort of healthy Koreans. Using permutational multivariate analysis of variance and prediction modeling, we examined associations between microbial profiles and metabolic variables including insulin, triglycerides, waist circumference and visceral fat. The strongest correlations were noted with specific enterotypes. Shotgun sequencing revealed that visceral fat is linked to the glycogen synthesis pathway influenced by Dorea longicatena and Bifidobacterium adolescentis. This suggests that these specific microbial signatures and their associated functional potential play a role in visceral fat-related obesity. To validate these findings, we conducted an in vivo study using diet-induced obesity mouse model. Oral administration of D. longicatena or B. adolescentis significantly promoted body weight gain and fat mass expansion and induced hepatic lipogenic gene upregulation. The prevalence of these strains in Korean and American populations highlights their global relevance, contributing to the development of personalized treatments and advanced health strategies.

Journal Article↗

PubMed2026

Exploring the hypothetical role of Bacteroides species in depression progression: insights from metagenomic analysis.

Depression, a psychiatric disorder with significant morbidity and mortality, has a complex etiology. Recent advances in microbiome research have highlighted the potential role of fecal microbiota in depression pathogenesis. This study utilized shotgun metagenomic sequencing to compare the fecal microbiota of 28 depression patients and 26 healthy individuals. Significant differences in fecal microbiota composition were observed between the two groups. We generated 350 non-redundant high-quality metagenome-assembled genomes (MAGs) by binning and conducted comparisons between the depression and control groups. Notably, we found that the MAGs enriched in people with depression mostly belonged to Bacteroides, indicating a close link between Bacteroides abundance and the development of depression, suggesting that Bacteroides might be a potential culprit for depression. In the depression group, we found that the module of nitric oxide synthesis was remarkably enriched, and all Bacteroides MAGs contained genes annotated as nitric oxide synthase, suggesting that increased levels of Bacteroides may contribute to elevated nitric oxide synthesis. A distinct microbial signature consisting of Arthrobacter sp._U41, Bacillus cereus, Campylobacter rectus, and Pasteurella dagmatis accurately discriminates between depressed individuals and healthy controls, achieving an average area under the receiver operating characteristic curve of 0.950. This research sheds light on the potential role of fecal microbiota in depression and highlights specific metabolic pathways and microbial markers for further investigation.IMPORTANCEThis research highlighted significant differences in the composition and function of fecal microbiota between individuals with depression and healthy individuals, particularly the enrichment of Bacteroides metagenome-assembled genomes (MAGs) in depression patients. The upregulation of the nitric oxide synthesis pathway associated with these MAGs belonging to Bacteroides in the gut of depression patients had also been observed. The selected bacterial biomarkers reliably differentiate depression cases from healthy controls with high diagnostic accuracy (mean area under the receiver operating characteristic curve = 0.950). Our results suggest the importance of exploring microbial markers as potential diagnostic and therapeutic targets in managing depression.

Humans↗

PubMed2026

Human DNA levels in feces reflect gut inflammation and associate with presence of gut species in IBD patients across the age spectrum.

BACKGROUND: Feces represent a complex biological matrix that provides valuable information about intestinal physiology and gut microbial activity. Comprehensive fecal DNA sequencing is mostly utilized as a non-invasive way to profile the gut microbiome, and both clinical practice and research on inflammatory bowel diseases (IBD) would greatly benefit from accurate and non-invasive methods to monitor gut inflammation in IBD patients. In IBD, excessive immune cell recruitment and epithelial cell shedding in the gut increase the amount of human DNA in feces, making fecal DNA profiling a desirable approach to monitor gut inflammation dynamics. METHODS: We used a combination of sequencing techniques to comprehensively characterize the fecal DNA diversity in a newly established cohort of pediatric IBD patients and controls (Pediatric cohort, N = 134 children, Israel). We performed methylation-based human cell-specific profiling together with shotgun metagenomics to characterize the human and the microbial DNA content in feces, respectively. Moreover, we included a large complementary external cohort including adult IBD patients and controls (Adult cohort, N = 689 adults, the Netherlands), not only to compare microbial patterns across the age spectrum, but also to extend our findings from the methylation-based profiling to the more broadly-available quantification of human DNA in metagenomic sequencing. RESULTS: We found that neutrophil DNA dominates fecal human DNA content in IBD patients, and our measurements were highly correlated with fecal calprotectin levels. Combining neutrophil and other cell type DNA fractions in one metric was able to distinguish between remissive and active cases of IBD. Human reads percentage by metagenomics was well correlated with disease severity and species richness, which had distinct trends in CD and UC over time. We used a combination of species richness, human DNA percentage, and microbiome composition data to predict IBD and distinguish CD from UC in both adult and pediatric IBD cohorts. CONCLUSIONS: The comprehensive characterization of human and microbiome fecal DNA is a useful approach to track immune response level and investigate the interaction that the immune system has with gut microbiome richness and composition over time, enriching opportunities for better disease monitoring and thus better treatment of IBD patients. Video Abstract.

Humans↗

PubMed2026

Resistant starch types 2 and 4 induce distinct and reversible changes in the human gut microbiome.

Resistant starch (RS) can confer benefits for the gut microbiome and host cardiometabolic health. However, different types of resistant starch can differentially affect gut microbiome composition and functional capacity, especially given interindividual variability in responses, thus limiting the application of resistant starch in dietary strategies. We used shotgun metagenomics to perform a secondary analysis of samples collected during a previously reported randomized clinical trial to determine the effects of dietary supplementation with two types of resistant starch (RS2 and RS4) and a digestible starch (control) on the gut microbiome. Both resistant starch types induced distinct but transient alterations in the gut microbial community. RS2 enriched the keystone degrader, Ruminococcus bromii, and Blautia glucerasea, whereas RS4 favored Parabacteroides distasonis and known but uncharacterized microbial species such as a Lachnospiraceae bacterium. Moreover, we detected strain-level differences in the response of Bifidobacterium adolescentis to resistant starch. Microbial functional profiling revealed an enhanced capacity for complex carbohydrate utilization following resistant starch intake, including increased abundance of specific α-amylases, glycoside hydrolases, starch utilization systems, and other currently uncharacterized genes. Identifying the bacterial strains and genes that respond to different RS types will help to more accurately predict who will benefit from a given RS type. Our findings demonstrate that RS2 and RS4 differentially shape microbial ecology and metabolic capacity and provide a foundation for microbiome-informed personalization of resistant starch-based dietary interventions.IMPORTANCEDietary intake influences human health by modulating metabolism, partly by shaping the microbiota inhabiting the gut. Resistant starch (RS), a dietary fiber, is associated with metabolic improvements. While previous research has explored how RS alters the gut microbiome, RS comprises five types with differing physical and chemical characteristics, and the distinct impacts of each type on the microbiome and host health have not been fully characterized, particularly using high-resolution approaches such as shotgun metagenomics. In this secondary analysis of samples from a longitudinal crossover intervention study, we link dietary supplementation with RS2 and RS4 with distinct and transient changes in the composition and functional potential of the human gut microbiome. Specifically, we identify species that increase in abundance with each RS type, accompanied by increases in genes and pathways involved in complex carbohydrate utilization. The findings support the development of precision nutrition strategies utilizing RS supplementation to improve metabolic health.This study is registered with ClinicalTrials.gov as NCT05743790.

Humans↗

PubMed2026

MetagenomicKG: a knowledge graph for metagenomic applications.

MOTIVATION: The sheer volume and variety of genomic content within microbial communities makes metagenomics a field rich in biomedical knowledge. To traverse these complex communities and their vast unknowns, metagenomic studies often depend on distinct reference databases, such as the Genome Taxonomy Database (GTDB), the Kyoto Encyclopedia of Genes and Genomes (KEGG), and the Bacterial and Viral Bioinformatics Resource Center (BV-BRC), for various analytical purposes. These databases are crucial for the genetic and functional annotation of microbial communities. Nevertheless, the inconsistent nomenclature or identifiers of these databases present challenges for effective integration, representation, and utilization. Knowledge graphs (KGs) offer an appropriate solution by organizing biological entities from different databases to standardized identifiers, allowing their interrelations to be captured into a cohesive network regardless of the naming conventions used in each source. The graph structure not only facilitates the unveiling of hidden patterns but also enriches our biological understanding with deeper insights. Despite KGs having shown potential in various biomedical fields, their application in metagenomics remains underexplored. RESULTS: We present MetagenomicKG, a novel knowledge graph specifically tailored for metagenomic analysis. MetagenomicKG integrates taxonomic, functional, and pathogenesis-related information on the human microbiome sourced from various databases, and further connects these with existing biomedical KGs to expand the biological network. Through various case studies involving the human microbiome, we demonstrate its utility in enabling hypothesis generation regarding the relationships between microbes and diseases, generating sample-specific graph embeddings, and providing robust pathogen prediction. CODE AVAILABILITY: The source code and technical details for constructing the MetagenomicKG and reproducing all analyses are available on GitHub at https://github.com/KoslickiLab/MetagenomicKG. The data used in this manuscript, including the pre-built files and use case input data, are archived on Zenodo with DOI: 10.5281/zenodo.17546861.

Metagenomics↗

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

Effects of aerobic exercise on inflammation and gut microbiota in obese mice: a metagenomic and metabolomic analysis.

BACKGROUND: Aerobic exercise can ameliorate insulin resistance (IR). However, the mechanism by which aerobic exercise regulates the gut microbiome to ameliorate IR and obesity remains unexplored. METHODS: Obese models were established by feeding C57BL/6 male mice a high-fat diet. A total of 26 mice were randomly divided into control group (group A, N = 8) and high-fat diet group (HFD group, N = 18). Successfully modeled mice were further assigned to model group (group B, N = 8) and exercise group (group C, N = 8). Group C underwent a 6-week treadmill exercise program (12 m/min, 60 min per day, 5 days per week). After intervention, colon tissue morphology was observed through hematoxylin-eosin staining, serum lipids and inflammatory indicators levels were detected by ELISA. The changes in the intestinal microbiota of the mice were also examined using metagenomic sequencing and UPLC-MS non-targeted metabolomics. RESULTS: Compared with the group A, the body weight, TC, TG, LDL-C, blood glucose, insulin, and IR in the group B significantly increased (P < 0.01), while the levels of pro-inflammatory cytokines TXNIP, TNF-α, NLRP3, IL-1β, and IL-18 significantly increased (P < 0.05 or P < 0.01). Compared with the group B, aerobic exercise reduced the body weight, TC, blood glucose, insulin, IR, TXNIP, TNF-α and other indicators in obese mice (P < 0.05 or P < 0.01). Moreover, aerobic exercise can regulate the imbalance of the intestinal flora in obese mice and ameliorate the disorder of metabolites. The metabolic pathways including arachidonic acid metabolism and histidine metabolism showed the most significant differences after the intervention of aerobic exercise. CONCLUSIONS: In conclusion, aerobic exercise can ameliorate glucose and lipid metabolism, IR, inflammatory response, and regulate the intestinal microecology and metabolic disorders in obese mice. The mechanism may be closely related to enhancing the diversity of intestinal flora, regulating the metabolism of arachidonic acid and histidine.

Animals↗

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↗

PubMed2024

Comprehensive analyses of a large human gut Bacteroidales culture collection reveal species- and strain-level diversity and evolution.

Species of the Bacteroidales order are among the most abundant and stable bacterial members of the human gut microbiome, with diverse impacts on human health. We cultured and sequenced the genomes of 408 Bacteroidales isolates from healthy human donors representing nine genera and 35 species and performed comparative genomic, gene-specific, metabolomic, and horizontal gene transfer analyses. Families, genera, and species could be grouped based on many distinctive features. We also observed extensive DNA transfer between diverse families, allowing for shared traits and strain evolution. Inter- and intra-species diversity is also apparent in the metabolomic profiling studies. This highly characterized and diverse Bacteroidales culture collection with strain-resolved genomic and metabolomic analyses represents a valuable resource to facilitate informed selection of strains for microbiome reconstitution.

Humans↗

PubMed2026

Exploring the role of gut microbiota in coronary atherosclerosis through lipoprotein-mediated cholesterol transport and distribution: A Mendelian randomization analysis.

We employed Mendelian randomization (MR) to explore causal relationships between gut microbiota (GM), coronary atherosclerotic heart disease (CAHD), and potential metabolic mediators. We utilized summary statistics from genome-wide association studies (GWAS), encompassing data on 473 GM traits from comprehensive microbiome GWAS, 61 lipoprotein-mediated cholesterol transport and distribution data from large-scale metabolic biomarker studies, and coronary atherosclerosis (CA) data from the GWAS catalog (study accession GCST90043957) involving 456,348 European participants. Bidirectional MR analyses were conducted to investigate the causal relationships between GM and CA. Two-sample Mendelian randomization analyses were performed to identify potential mediating metabolites and quantify the mediation proportion. Ultimately, the GM GCA-900066755, identified through MR as having a potential causal relationship, was selected to investigate its potential effects on CA by influencing cholesterol transport and distribution. Our results indicated that GCA-900066755 was positively associated with an increased risk of CA (odds ratio = 1.156). CA did not significantly affect the levels of GCA-900066755 (odds ratio = 1.009). GCA-900066755 was negatively correlated with total cholesterol levels in medium high-density lipoprotein, which reduced CA risk, and was positively correlated with total cholesterol levels in low-density lipoprotein (LDL), large LDL, medium LDL, and small LDL, which were positively associated with CA. Mediation analysis showed 7 data points mediating the association between GCA-900066755 and CA. Our MR study supports a causal relationship between specific GM groups and the risk of CAHD, highlighting that cholesterol traits are not merely outcomes associated with the relationship between GM and CAHD, but are important mediating factors. Understanding the biological mechanisms of these traits can provide a concrete foundation for future targeted interventions.

Mendelian Randomization Analysis↗

PubMed2026

Symbiotic interactions and climate change implications of the octocoral microbiome.

Octocorals are vital components of tropical, temperate, and cold-water benthic marine ecosystems. Their associated microbiomes, comprising microeukaryotes, prokaryotes, and viruses, are increasingly recognised as central to host health, nutrient cycling, and chemical defence. Metagenomics and amplicon sequencing have uncovered taxonomic and functional complexity within these microbial communities, revealing patterns of host specificity and health status, along with seasonality and geographic structuring. However, anthropogenic stressors, particularly those associated with global climate change, exert intense pressure on coral-dominated ecosystems, leading to complex and poorly understood local and regional patterns of octocoral expansion and mortality. Microbial interactions may be a main driver of these contrasting outcomes by mediating the ecological resilience of octocorals to environmental stress. We synthesise the current state of research on the diversity, organisation, and function of the octocoral microbiome, and identify critical knowledge gaps on octocoral holobionts relative to scleractinian corals. Our meta-analysis of 79 publicly available bacterial genomes from octocorals reveals group-specific specialisation in denitrification and nitrate assimilation, along with widespread capacities for essential amino acid, cofactor, and vitamin production, suggesting important contributions to nutrient cycling in the holobiont. While sampling efforts between cultured and uncultured lineages are even, our genomic survey reveals strong sampling bias toward the Atlantic Ocean, temperate gorgonians, and healthy host states, whereas bacterial genomes representing the pathobiome, tropical and/or deep-sea regions, and other octocoral taxa remain underrepresented. Accordingly, we propose future research directions to advance understanding of octocoral microbiome ecology and its role in the resilience of tropical, temperate and cold-water coral reefs.

Endozoicomonadaceae↗

PubMed2026

A hybrid and cost-efficient barcoding strategy for full-length 16S rRNA gene nanopore sequencing of environmental samples.

BACKGROUND: Accurate species-level identification of bacteria in complex environmental samples is essential for applications in biotechnology, ecological monitoring, and clinical diagnostics. Short-read platforms such as Illumina frequently truncate the 16S rRNA gene, limiting taxonomic resolution. In this work, we applied Oxford Nanopore Technology (ONT) long-read sequencing to full-length 16S rRNA amplicon in samples from natural soil amended with lignocellulosic biomass and a simplified microbial community derived from cultures grown on selective and differential carboxymethyl cellulose (CMC)-based substrates, with the aim to evaluate the difference in performance between a real, complex community and a less complex system. To reduce consumable costs, we substituted the standard ONT Barcoding kits with an in-house hybrid barcoding workflow. Specifically, PacBio PCR-based barcoding protocol was used for sample indexing, followed by library preparation using the ONT Ligation Sequencing Kit. This simplified approach retained compatibility with MinION and Flongle flow cells and supported accurate downstream demultiplexing while lowering barcode costs substantially. Additionally, a new bioinformatic workflow tailored to ONT data was implemented. RESULTS: Overall, the hybrid protocol significantly reduced per-sample barcoding costs while preserving high sequencing quality and throughput. The sequencing run yielded over 5 Gb of quality-filtered data (Q-score ≥ 10). Furthermore, the new bioinformatic workflow allowed taxonomic assignment at the species level for 49.38% of annotated taxa, compared to just 4.59% using Illumina NovaSeq sequencing of the V3-V4 region. ONT also recovered 2.3 times more genera and 1.3 times more families. Although 16S rRNA gene sequencing often cannot distinguish between closely related species, particularly within taxonomically complex groups, in this work, full-length reads substantially improved both taxonomic resolution and database matching. CONCLUSIONS: These results show that full-length 16S rRNA sequencing with ONT, paired with a low-cost barcoding strategy, enhanced taxonomic resolution compared to short-read workflows. This approach also offers a scalable and cost-effective option for high-resolution microbiome profiling in research and applied settings.

RNA, Ribosomal, 16S↗

PubMed2026

Cerium dioxide nanoparticle exposure attenuates mobility-linked antibiotic resistome signatures across the soil-lettuce continuum.

Antibiotic resistance genes (ARGs) are contaminants of emerging concern in agricultural microbiomes. Their association with mobile genetic elements (MGEs) can enhance dissemination across soil-plant interfaces, creating potential environmental and food-chain exposure risks. However, how engineered nanoparticles modulate relative ARG abundance and mobility-linked resistome features in plant-associated microbiomes remains poorly understood. Here, we examined the effects of graded, experimentally elevated cerium dioxide nanoparticle (CeO2 NP) loadings in a soil-lettuce system by integrating compartment-resolved metagenomics, ARG-MGE co-occurrence analysis, putative host-reservoir profiling, transcriptomics, and functional assays. Metagenomic profiling identified 16 ARG types and 125 subtypes and revealed niche-dependent microbiome restructuring under CeO2 NP exposure. Rhizosphere relative ARG abundance showed a negative dose-associated trend, although overall inter-group differences were not significant, whereas leaf endophytes showed a weaker response. Relative MGE abundance decreased significantly in both compartments, and lower assembly-level ARG-MGE co-occurrence reflected fewer ARGs detected in MGE-associated genomic contexts, whereas fewer multi-ARG contigs suggested reduced ARG clustering and potential co-selection. Putative host-reservoir analysis associated key efflux determinants with bacterial families whose relative representation declined following CeO2 NP exposure. Transcriptomic profiling of representative putative ARG hosts revealed host-specific responses, including downregulation of genes involved in central metabolism and Sec-dependent trafficking. Complementary host assays showed reduced apparent envelope permeability and lower recovery of tetracycline-resistant recipient-identity colonies in the plasmid-associated host system. Together, under the tested elevated-loading conditions, CeO2 NP exposure was associated with lower relative ARG signals and weaker mobility-linked resistome features across the soil-lettuce continuum, providing mechanistic insight into nanoparticle-resistome interactions in soil-plant systems.

ARG dissemination↗