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PubMed2025

Comparison of a long-read amplicon sequencing approach to short-read amplicons for microbiome analysis.

Most microbiome studies to date rely on sequencing short amplicons of the 16S rRNA gene on Illumina's platforms. Because of the short read length, sequences often can be identified reliably only to the family or genus levels. Long read sequencing with whole-length 16S rRNA sequencing can improve taxonomic resolution, but often only to the species level. StrainID is an alternative approach that amplifies a large segment of the ribosomal operon, including the entire 16S rRNA gene, internal transcribed spacer, and a portion of the 23S rRNA gene. This longer amplicon is designed to allow ribotype-level classification. Although studies have demonstrated the utility of StrainID for several sample types, it has not yet been validated for saliva. Here, we compared the performance of StrainID to short read amplicons with saliva samples as well as a synthetic mock DNA community and human and mouse fecal samples. Short reads were amplified with primer pairs appropriate for the corresponding sample type, and were classified with two different taxonomic databases. For both saliva and fecal samples, we found that StrainID performed similarly to short reads overall and demonstrated a key benefit with phylogenetic-based beta diversity tests and taxonomic classification. Our results further build on establishing StrainID as a valid method and specifically validate its use with saliva samples.

Journal Article↗

PubMed2025

Uncertainty Modeling Outperforms Machine Learning for Microbiome Data Analysis.

Microbiome sequencing measures relative rather than absolute abundances, providing no direct information about total microbial load. Normalization methods attempt to compensate, but rely on strong, often untestable assumptions that can bias inference. Experimental measurements of load (e.g., qPCR, flow cytometry) offer a solution, but remain costly and uncommon. A recent high-profile study proposed that machine learning could bypass this limitation by predicting microbial load from sequencing data alone. To evaluate this claim, we assembled mutt, the largest public database of paired sequencing and load measurements, spanning 35 studies and over 15,000 samples. Using mutt, we show that published machine learning models fail to generalize: on average they perform worse than a naive baseline that always predicted the training set mean. These failures stem from covariate shift-limited shared taxa between studies, differences in community composition, and differences in preprocessing pipelines-that silently derail model inputs. In contrast, Bayesian partially identified models do not attempt to impute microbial load, but instead propagate scale uncertainty through downstream analyses. Across 30 benchmark datasets, Bayesian partially identified models consistently outperformed normalization and machine learning approaches, providing a principled and reproducible foundation for microbiome inference.

16S rRNA-seq↗

PubMed2025

Impact of RNA extraction on respiratory microbiome analysis using third-generation sequencing.

BACKGROUND: The respiratory microbiome, which comprises bacteria, fungi, and viruses, plays a crucial role in respiratory health and disease. However, its study is limited by the low microbial biomass in respiratory samples and the dominance of host RNA. Metatranscriptomics offers comprehensive insights into active microbial communities and their interactions with the host but requires optimized RNA extraction protocols for robust and unbiased analysis. This study evaluated two RNA extraction kits—one employing chemical lysis (CL) and another combining chemical and mechanical lysis (CML)—to determine their effectiveness for metatranscriptomic analysis of respiratory samples. RESULTS: The CML protocol significantly increased double-stranded DNA (dsDNA) library yields, leading to higher sequencing read counts for both sample types (p < 0.0001). The read length was unaffected by the lysis protocol for the BAL and NPS samples. Taxonomic profiling revealed that CML enhanced the detection of robust microorganisms, such as gram-positive bacteria and fungi, without compromising viral detection. CONCLUSIONS: The CML protocol demonstrated superior recovery of genetic material, particularly for fungi and gram-positive bacteria, making it better suited for comprehensive metatranscriptomic analyses. These findings underscore the need for tailored RNA extraction strategies on the basis of sample type and research objectives. Optimized metatranscriptomic protocols are pivotal for advancing our understanding of the respiratory microbiome and its role in health and disease.

Microbiota↗

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↗

PubMed2025

MetaflowX: a scalable and resource-efficient workflow for multi-strategy metagenomic analysis.

Microbiomes play crucial roles in diverse ecosystems, spanning environmental, agricultural, and human health domains. However, in-depth metagenomic data analysis presents significant technical and resource challenges, particularly at scale. Existing computational pipelines are typically limited to either reference-based or reference-free approaches and exhibit inefficiencies in process large datasets. Here, we introduce MetaflowX (https://github.com/01life/MetaflowX), an open-resource workflow integrating both analytical paradigms for enhanced metagenomic investigations. This modular framework encompasses short-read quality control, rapid microbial profiling, hybrid contig assembly and binning, high-quality metagenome-assembled genome (MAG) identification, as well as bin refinement and reassembly. Benchmarking tests showed that MetaflowX completed full metagenomic analyses up to 14-fold faster and with 38% less disk usage than existing workflows. It also recovered the highest number of high-quality and taxonomically diverse MAGs. A dedicated reassembly module further improved MAG quality, increasing completeness by 5.6% and reducing contamination by 53% on average. Functional annotation modules enable detection of key features, including virulence and antibiotic resistance genes. Designed for extensibility, MetaflowX provides an efficient solution addressing current and emerging demands in large-scale metagenomic research.

Metagenomics↗

PubMed2026

Multi-level aggregation analysis of microbiome composition and host gene expression reveals associations with systemic and local immunity.

The human gut microbiome plays a critical role in immune regulation, yet the molecular links between microbiome composition and host gene expression remain incompletely understood. We analyzed associations between host gene expression and microbiome composition in a cohort of 315 healthy individuals, integrating microarray-based gene expression data from three intestinal sites (ileum, transverse colon, and rectum) and six immune cell types with microbiome sequencing data. Using a hierarchical feature aggregation strategy combining principal component analysis, clustering, and covariate correction, we discovered significant associations primarily related to immunity. While microbial profiles were similar across the three intestinal sites, the transverse colon yielded the most "microbiome-host gene expression" associations. Among the immune cell types, CD8+ cells showed the highest number of associations. The first principal component of microbiome composition, reflecting a gradient from commensals (e.g., Ruminococcaceae and Christensenellaceae) to proinflammatory taxa ([Ruminococcus] gnavus and Lachnoclostridium), correlated with the expression of TNF-α-linked genes (HMOX1, CPI17, HSD3B2, and SLC5A1). Among individual genera, Catenibacterium abundance was associated with gene expression in both intestinal and immune cells, including negative associations with MRPS21 (related to mitochondrial function) in the transverse colon and with CD8+ gene programs related to T cell differentiation. These findings align with emerging evidence implicating mitochondrial dysfunction in intestinal inflammation. Our results identify multi-level associations between the gut microbiome and host gene expression, suggesting potential mechanisms by which microbiota shape local and systemic immunity and vice versa. The implicated genes and taxa represent candidates for experimental validation to improve understanding of host-microbiome homeostasis and its disruption in disease.IMPORTANCEThe gut microbiome and immune system are engaged in a complex interplay throughout human life. While most associative studies focus on case-control comparisons-typically examining patients with conditions such as inflammatory bowel disease or metabolic diseases-less is known about the molecular links between the microbiome and immune system in healthy individuals. In this study of a large cohort of healthy individuals, we addressed this gap by applying multiscale modeling to tackle the high dimensionality of host-microbiome data. We identified multi-level associations between microbiome composition and host gene expression in both intestinal tissues and immune cells. These findings offer a valuable reference for understanding baseline host-microbiome communication and highlight molecular candidates-such as TNF-α-related genes and mitochondrial pathways-for future experimental validation.

Humans↗

PubMed2026

Scale reliant mixed effects models enhance microbiome data analysis.

Linear models, including those used for differential abundance analyses, are frequently used in microbiome research to assess how experimental conditions (e.g., disease state or age) affect microbial abundance. Linear mixed-effects models (MEMs) extend linear models to accommodate complex designs, such as longitudinal sampling or hierarchical study structures. However, when applied to microbiome data, existing MEM approaches suffer from high false positive and false negative rates because sequence counts are compositional - they reflect relative rather than absolute abundances. Current methods attempt to overcome this limitation through normalization, but these approaches rely on strong, often unrealistic assumptions about the unmeasured biological scale (e.g., total microbial load). Here we introduce scale-reliant mixed-effects models (SR-MEM), which extend our earlier scale-reliant inference framework by explicitly modeling uncertainty in the unmeasured scale via user-defined probability distributions. By treating scale as a latent variable rather than fixing it through normalization, SR-MEM enables robust inference for complex experimental designs. SR-MEM can incorporate external scale measurements (e.g., flow cytometry, qPCR) or leverage scale information from independent studies to further improve inference. Across simulations and multiple real-world case studies, SR-MEM consistently controls the false discovery rate while maintaining comparable or higher power than standard approaches relying on normalization or bias correction. In reanalyses of published datasets, SR-MEM yields results that are more reproducible across studies and more consistent with known biological and pharmacological effects. SR-MEM provides a principled and practical framework for mixed-effects modeling of microbiome sequence count data in the presence of unmeasured biological scale. By avoiding normalization-based assumptions and instead propagating scale uncertainty through inference, SR-MEM improves error control and reproducibility in longitudinal and hierarchical studies. An accessible implementation is provided in the ALDEx3 R package.

Microbiota↗

PubMed2025

SPARKI: a tool for the statistical analysis of pathogen identification results.

MOTIVATION: Many pathogen identification and microbiome analysis tools have been developed in recent years, with Kraken 2 being one of the most popular. While tools downstream of Kraken 2 can assist in the interpretation of its outputs, a statistical framework to assess the likelihood that a taxon/organism is present in a single sample alongside an automated end-to-end analysis pipeline has not yet been fully implemented. RESULTS: Here, we introduce SPARKI, an R package that performs statistical analysis of Kraken 2 outputs and aids in the identification of pathogens present in next-generation sequencing samples. SPARKI adds to the field by bringing a probabilistic view to Kraken 2 data, serving as a discovery tool and complementing other methods such as KrakenTools, Bracken, and Pavian. AVAILABILITY AND IMPLEMENTATION: SPARKI code is available on GitHub at https://github.com/team113sanger/sparki. SPARKI is also part of an end-to-end pathogen identification pipeline, sparki-nf, which is available at https://github.com/team113sanger/sparki-nf. An additional pipeline for further exploration and validation of SPARKI results is also available at https://github.com/team113sanger/map-to-genome.

Software↗

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↗

PubMed2026

Challenges and future directions in AI-driven biomaterials for microbiome-associated oral infectious diseases: A systematic review.

Oral biofilm-induced antimicrobial resistance is the core pathogenic mechanism of microbiome-associated oral infectious diseases (dental caries, periodontitis, peri-implantitis, and endodontic infection). Traditional therapies and biomaterials are limited by poor biofilm penetration, drug resistance induction, single functionality, and inadequate adaptation to dynamic oral microenvironmental changes (e.g., pH fluctuations, salivary rinsing, masticatory stimulation). Artificial intelligence (AI) has transformed the field by integrating materials science, microbiology, and stomatology data. Via machine learning, deep learning, and multi-physics simulation, AI optimizes biomaterial physicochemical properties, decodes microenvironmental signals, constructs precise sensing-response loops, and supports the full chain of material design, performance prediction, and action simulation, advancing treatment from empirical intervention to precision regulation. This systematic review retrieved literature from PubMed, Embase, and Web of Science (January 2016-January 2026) using keywords across three dimensions: AI, biomaterials, and oral microbiome. Following inclusion/exclusion criteria, 99 articles were included. It elaborates on five core mechanisms of AI-driven oral biomaterials (precise oral microbiome analysis, targeted material design/optimization, performance prediction/simulation, targeted delivery/intervention, effect evaluation/dynamic regulation), analyzes their applications in microbiome-targeted biomaterial research and development (R&D) and clinical practice for the four major oral infectious diseases, addresses technical bottlenecks (insufficient targeting specificity and precision of biomaterials, poor stability and durability in complex oral microenvironments, inadequate biofilm disruption capacity, and clinical translation obstacles), and proposes future directions (multimodal design to enhance targeting specificity, structural and component optimization to improve stability/durability, development of multi-mechanism synergistic biofilm disruption strategies, strengthening translational research for clinical application, and deep integration of AI in the full chain of biomaterial R&D). This work provides comprehensive theoretical and practical support for the R&D, optimization, and clinical translation of AI-driven microbiome-targeted oral biomaterials.

Humans↗

PubMed2025

Mapping the oral microbiome opens links to periodontitis.

Many microbiome analysis techniques can only detect the microbes present in the reference genome database used. In this issue of Cell Host & Microbe, Cha et al. establish an improved genome database of the human oral microbiome, which they use to discover a connection between periodontitis and an enigmatic bacterial phylum.

Humans↗

PubMed2026

Lactiplantibacillus plantarum SLpl116 attenuates OVA-induced food allergy with ecological restoration of the gut microbiota and immune rebalancing.

Gut dysbiosis is increasingly recognized as a key contributor to food allergy, yet probiotic strains capable of restoring allergic microbiota and rebalancing host immunity remain limited. Here, we identified Lactiplantibacillus plantarum SLpl116 through a multi-criteria screening pipeline integrating anti-allergic activity, safety, and processing stability, and evaluated its efficacy in a prophylactic ovalbumin (OVA)-induced murine food allergy model. SLpl116 significantly attenuated allergic symptoms, including diarrhea and hypothermia, and suppressed serum IgE, IgG1, OVA-specific immunoglobulins, and mucosal mast cell protease-1. It was also associated with suppression of Th2-related responses and enhancement of systemic Th1-associated signaling, indicating restoration of Th1/Th2 immune balance. Microbiome analysis showed that SLpl116 was associated with ecological restoration of the dysbiotic gut community, including suppression of allergy-associated taxa such as Alistipes finegoldii and Bacteroides and enrichment of beneficial commensals, particularly Lachnospiraceae. Correlation analysis supported an association between microbial reconfiguration and immune rebalancing, while PICRUSt2-based functional prediction suggested enriched butyrate-associated metabolic potential in the effective strain groups. Comparative genome-informed analysis further indicated that SLpl116 possessed distinctive phenotype-linked features, providing a plausible molecular rationale for its favorable phenotype. Together, these findings identify SLpl116 as a promising strain-level probiotic candidate associated with direct immune rebalancing and microbiome-associated ecological restoration.

Journal Article↗

PubMed2026

Isoquinoline alkaloids enhance growth performance through multifaceted modulation of the bacterial-fungal microbiome, CAZyme profiles, gut health, and neuroendocrine function in broilers.

The bacterial-fungal microbiome and its carbohydrate-active enzyme (CAZyme) capacity play critical roles in regulating gut health and growth performance in broiler chickens. This study evaluated the effects of dietary isoquinoline alkaloids (IQ) on growth performance, gut microbiome composition, CAZyme profiles, and the microbiome-gut-neuroendocrine axis in broilers. A total of 400 Ross 308 (1-day-old) chicks were randomly assigned to either a Basal diet (CON) or IQ supplemented diet (IQ). Dietary IQ supplementation significantly increased final body weight and cumulative body weight gain (P < 0.0001) and improved feed conversion ratio (P < 0.05). Intestinal permeability was reduced (lower FITC-dextran; P < 0.05), accompanied by increased serotonin and serotonin-to-corticosterone ratio and decreased corticosterone (P < 0.05). Expression of inflammatory genes (TNF-α, NF-κB, IL-4, and TLR-1) was downregulated (P < 0.05). Microbiome analysis showed increased α-diversity (P < 0.05) and clear β-diversity separation (PERMANOVA, P < 0.001), with enrichment of beneficial bacteria (Akkermansia muciniphila, Lactobacillus salivarius, Turicibacter sanguinis, Bacillus subtilis) and suppression of fungal taxa (Aspergillus, Penicillium). CAZyme-related pathways involved in lignin and carbohydrate degradation were increased (P < 0.05). Microbial diversity was negatively correlated with inflammation and gut permeability, whereas network analysis identified 164 significant associations (|ρ| ≥ 0.50), revealing strong negative correlations between beneficial bacteria and inflammatory markers (ρ = -0.65 to -0.78) and positive associations for fungal taxa (ρ = 0.62-0.81). Serotonin was positively associated with microbial diversity (ρ = 0.63-0.70). In conclusion, IQ supplementation promotes a bacteria-dominant and metabolically active microbiome, reduces inflammation and intestinal permeability, and improves neuroendocrine balance, collectively enhancing gut health and growth performance in broiler chickens.

Bacteriome↗

PubMed2025

Temporal stability and lack of variance in microbiome composition and functionality in fit recreational athletes.

Human gut microbiome composition and function is influenced by environmental and lifestyle factors, including exercise and fitness. We studied the composition and functionality of the faecal microbiome of recreational (non-elite) runners (n = 62) with serial shotgun metagenomics, at 4 time points over a 7-week period. Gut microbiome composition and function was stable over time. Grouping of samples on the basis of their fitness level (fair, good, excellent, and superior) or habitual training (low (4-6 h/week), medium (7-9 h/week), high (10-12 h/week), and extreme (13 + hours/week)) revealed no significant microbiome-related differences. Overall, the species Faecalibacterium prausnitzii, Blautia wexlerae, and Prevotella copri were the most abundant members of the gut microbiome. Analysis of co-abundance groups (CAGs) revealed no significant relationship between CAGs and fitness levels or training subgroups. Functional pathways were similar across all samples and timepoints with no clustering based on associated metadata. The most abundant genes identified within samples corresponded to pathways for nucleoside and nucleotide biosynthesis, amino acid biosynthesis, and cell wall biosynthesis. Collectively, these results describe the microbiome of active recreational runners and note temporal stability amongst participants.

Humans↗

PubMed2026

Effects of Lactiplantibacillus plantarum KABP051 Probiotic on Body Composition, Microbiome and Mood in Healthy Overweight Adults.

Obesity and mental health disorders are among the greatest public health challenges of the 21st century. Interestingly, an altered microbiome profile has been associated with both conditions. The aim of this randomized, double-blind, placebo-controlled clinical trial was to evaluate the effects of dietary supplementation with a specific probiotic strain (Lactiplantibacillus plantarum KABP051) on body composition and gut microbiome balance, together with measures of mood state, in a population of healthy overweight subjects. Sixty healthy, moderately stressed, nondepressed and overweight or obese volunteers were supplemented for 12 weeks with probiotic (L. plantarum KABP051; 1 billion colony forming units/day) or placebo (microcrystalline cellulose). The KABP051 group experienced significantly greater improvements compared with placebo on body composition measurements, including a reduction in body weight and waist circumference, which decreased in 1.97 ± 0.77 (mean ± SE) kg and 2.15 ± 0.81 (mean ± SE) cm versus placebo at the end of the intervention (both P < .05, mixed model for repeated measures [MMRM] and post-hoc analysis). Microbiome composition improved in KABP051 group, with significant increase in the relative abundance of Lactiplantibacillus spp. versus placebo. Body fat percentage, profile of mood states fatigue, and confusion sub-scores showed a global trend toward improvement compared with placebo, with the change at 12 weeks being significant in the three measurements in post-hoc analysis (P = .015, P = .014, and P = .016, respectively). No serious adverse events were registered during the intervention period. These results suggest that a specific strain of probiotic bacteria (L. plantarum KABP051) may have both metabolic and psychobiotic effects and may be beneficial for enhancing weight loss and body composition, improving energy (less fatigue) and mood levels while embarking on a healthy lifestyle regimen. ClinicalTrials.gov identifier: NCT06808061.

Humans↗

PubMed2024

Genetic modification of the shikimate pathway to reduce lignin content in switchgrass (Panicum virgatum L.) significantly impacts plant microbiomes.

UNLABELLED: Switchgrass (Panicum virgatum L.) is considered a sustainable biofuel feedstock, given its fast-impact growth, low input requirements, and high biomass yields. Improvements in bioenergy conversion efficiency of switchgrass could be made by reducing its lignin content. Engineered switchgrass that expresses a bacterial 3-dehydroshikimate dehydratase (QsuB) has reduced lignin content and improved biomass saccharification due to the rerouting of the shikimate pathway towards the simple aromatic protocatechuate at the expense of lignin biosynthesis. However, the impacts of this QsuB trait on switchgrass microbiome structure and function remain unclear. To address this, wild-type and QsuB-engineered switchgrass were grown in switchgrass field soils, and samples were collected from inflorescences, leaves, roots, rhizospheres, and bulk soils for microbiome analysis. We investigated how QsuB expression influenced switchgrass-associated fungal and bacterial communities using high-throughput Illumina MiSeq amplicon sequencing of ITS and 16S rDNA. Compared to wild-type, QsuB-engineered switchgrass hosted different microbial communities in roots, rhizosphere, and leaves. Specifically, QsuB-engineered plants had a lower relative abundance of arbuscular mycorrhizal fungi (AMF). Additionally, QsuB-engineered plants had fewer Actinobacteriota in root and rhizosphere samples. These findings may indicate that changes in the plant metabolism impact both AMF and Actinobacteriota similarly or potential interactions between AMF and the bacterial community. This study enhances understanding of plant-microbiome interactions by providing baseline microbial data for developing beneficial bioengineering strategies and by assessing nontarget impacts of engineered plant traits on the plant microbiome. IMPORTANCE: Bioenergy crops provide an important strategy for mitigating climate change. Reducing the lignin in bioenergy crops could improve fermentable sugar yields for more efficient conversion into bioenergy and bioproducts. In this study, we assessed how switchgrass engineered for low lignin impacted aboveground and belowground switchgrass microbiome. Our results show unexpected reductions in mycorrhizas and actinobacteria in belowground tissues, raising questions on the resilience and function of genetically engineered plants in agricultural systems.

Panicum↗

PubMed2025

Decreased intestinal abundance of Akkermansia muciniphila is associated with metabolic disorders among people living with HIV.

BACKGROUND: Previous studies have shown changes in gut microbiota after human immunodeficiency virus (HIV) infection, but there is limited research linking the gut microbiota of people living with HIV (PLWHIV) to metabolic diseases. METHODS: A total of 103 PLWHIV were followed for 48 weeks of anti-retroviral therapy (ART), with demographic and clinical data collected. Gut microbiome analysis was conducted using metagenomic sequencing of fecal samples from 12 individuals. Nonalcoholic fatty liver disease (NAFLD) was diagnosed based on controlled attenuation parameter (CAP) values of 238 dB/m from liver fibro-scans. Participants were divided based on the presence of metabolic disorders, including NAFLD, overweight, and hyperlipidemia. Akkermansia abundance in stool samples was measured using RT-qPCR, and Pearson correlation and logistic regression were applied for analysis. RESULTS: Metagenomic sequencing revealed a significant decline in gut Akkermansia abundance in PLWHIV with NAFLD. STAMP analysis of public datasets confirmed this decline after HIV infection, while KEGG pathway analysis identified enrichment of metabolism-related genes. A prospective cohort study with 103 PLWHIV followed for 48 weeks validated these findings. Akkermansia abundance was significantly lower in participants with NAFLD, overweight, and hyperlipidemia at baseline, and it emerged as an independent predictor of NAFLD and overweight. Negative correlations were observed between Akkermansia abundance and both CAP values and body mass index (BMI) at baseline and at week 48. At the 48-week follow-up, Akkermansia remained a predictive marker for NAFLD. CONCLUSIONS: Akkermansia abundance was reduced in PLWHIV with metabolic disorders and served as a predictive biomarker for NAFLD progression over 48 weeks of ART.

Humans↗

PubMed2026

Successful Live Birth Following Treatment of Persistent Endometrial Dysbiosis and Recurrent Chronic Endometritis: A Case Report.

CASE: To study the cause of recurrent endometritis, which recurred after standard antibiotic therapy, we report the case of a 40-year-old woman with a history of recurrent pregnancy, preterm birth, and CE. The endometritis recurred following a standard antibiotic regimen. OUTCOME: Microbiome analysis via 16S rRNA gene sequencing revealed persistent dysbiosis in both vaginal and endometrial samples despite antibiotic regimens. Whole-exome sequencing (WES) identified rare variants in TRPV3 and CD36, potentially associated with epithelial barrier dysfunction. Following an extended course of antibiotic therapy, the woman gave birth to a healthy baby at GA 32 weeks. CONCLUSIONS: This case highlights the possibility that barrier gene variants may be associated with persistent endometrial dysbiosis and recurrence of CE. An intensive antibiotic regimen may help achieve a viable pregnancy in patients with recurrent CE following standard antibiotic therapy.

antibiotics↗