Gut microbiota and health

The paper examines how taxonomic bias affects differential-abundance analysis in microbiome studies and proposes methods to mitigate its effects.

The paper uses multiomics and quantitative modeling to determine how diet, host, and microbiota contribute to the host metabolome in gnotobiotic mice.

The paper introduces MIND, a platform for comparing and integrating microbial interaction networks from diverse datasets.

This study compares gut microbiota development in premature and term infants at six weeks and two years, finding initial differences that converge over time but with lasting associations with clinical factors and allergic conditions.

The paper identifies microbiome and metabolic vulnerabilities linked to oxycodone self-administration escalation and shows SCFAs have a causal role in addiction-like behaviors.

This paper introduces LIVE modeling to integrate microbiome multi-omics data in IBD, reducing feature correlations and identifying novel disease mechanisms.

The paper introduces GMHI-webtool, an online tool that computes and visualizes gut microbiome health indices from stool metagenomic data, enabling easier interpretation and comparison with reference datasets.

The paper identifies five bacterial strains associated with Crohn's disease using a novel method combining machine learning and assembly graphs for metagenomic analysis.

This study shows that the Neuroligin-3 R451C mutation in mice increases mucus density and alters bacterial distribution near the ileum, potentially contributing to microbial dysbiosis in ASD.

Evident calculates effect sizes and performs power analysis for microbiome community differences, scaling to large datasets with many metadata covariates.

The paper introduces PStrain-tracer, a software package to track microbial strain engraftment during FMT using shotgun metagenomic data, revealing strain-specific engraftment patterns and failure determinants.

The paper evaluates trait-based taxonomic enrichment methods for human microbiome data, finding they improve disease prediction and interpretability.

The paper benchmarks differential abundance methods in microbiome studies, showing that only a few methods control false discoveries well and that confounding variables can lead to spurious associations.

This study compares the effects of four chemotherapy drugs on gut microbiota and intestinal injury in mice, finding that L-OHP causes the most severe microbiota disruption and NODs/RIP2/NF-κB pathway activation.

The paper introduces a compositional data analysis algorithm for inferring dynamic microbial signatures in longitudinal studies using infant microbiome data.

The paper develops random-effects copula models to identify temporally conserved microbial co-variation networks in longitudinal microbiome data, showing that antibiotic treatment alters network stability.

This study identifies associations between bile acid profiles and symptoms of depression and anxiety in MDD patients, finding higher anxiety correlates and treatment failure predictors.

The paper identifies reproducible stool metagenomic biomarkers associated with positive melanoma immunotherapy outcomes across multiple studies.

The paper uses single-cell transcriptomics to analyze how the microbiota affects cell types across the entire body of zebrafish, showing cell-type-specific responses and promoting developmental plasticity.

The paper tests the biological clock hypothesis in the human gut microbiome by analyzing 11 datasets, finding that larger sample sizes are needed for consistent age-related microbial associations.

This paper develops a model-guided framework to design high-diversity synthetic human gut communities by optimizing culture conditions and leveraging ecological models.

The paper develops new statistical methods to test microbiome associations with censored survival outcomes at both community and individual taxon levels, showing improved performance over existing methods.

This study shows that kismet mutants in Drosophila have altered gut mechanics, microbiota, and behavior, linking gut physiology to the gut-brain axis.

The paper introduces a new analytical pipeline for metatranscriptomic analysis and applies it to study gut microbiome changes in a Down syndrome mouse model under high-fat diet.

This study identifies genetic links between gut microbiota, lipids, and host transcripts in mice, revealing key roles for Akkermansia muciniphila and ornithine lipids in microbe-host interactions.

The paper shows that improving gut microbiota with alginate oligosaccharide enhances semen quality in type 1 diabetes by increasing n-3 PUFA and improving metabolic and immune functions.

The paper introduces LDM-med, a new mediation analysis method for microbiome data that controls type I error and FDR, outperforming existing methods in power and flexibility.

GutMicroNet provides an interactive platform to explore gut microbiome interaction networks using data from the Human Gut Microbiome Atlas.

This paper introduces a scalable method using Variational Autoencoders to estimate microbial co-occurrence networks from high-dimensional microbiome data.

This study analyzes gut microbiota changes in COVID-19 patients using metagenomic and metatranscriptomic sequencing, identifying correlations between microbial composition, gene function, and disease severity.