Gut microbiota and health

MICOM models gut microbiota metabolism to predict individual-specific short chain fatty acid production and interactions, showing differences between healthy and diabetic subjects.

The paper identifies host-microbiome interactions using systems genetics in mice, revealing mechanisms of microbial involvement in disease.

This study uses conditional generative adversarial networks to simulate microbiome data, improving disease prediction through synthetic data augmentation in inflammatory bowel disease.

mAML is an automated ML pipeline for microbial classification in human diseases, achieving high performance on 13 benchmark datasets and providing a repository of 12,429 metagenomic and 38,643 amplicon samples.

This study uses nanopore sequencing to rapidly profile preterm infant gut microbiota, enabling pathogen detection and antimicrobial resistance profiling within one hour.

The paper introduces a topic modeling approach to analyze 16S rRNA amplicon data, linking microbial co-occurrence patterns to functional and environmental features.

This study examines the effects of enrofloxacin and tulathromycin on dairy calves' fecal microbiota composition and function, finding minimal long-term impacts on microbial communities.

This study shows high fat diet causes EEC silencing, a nutrient-insensitive state linked to ER stress, which is microbiota-dependent in zebrafish.

The paper uses small RNA sequencing data from The Cancer Genome Atlas to analyze the cancer-associated microbiome in colorectal samples, identifying microbial genera linked to cancer stages.

This study examines how maternal prenatal stressors and distress relate to infant and maternal faecal bacterial profiles in a South African cohort.

The paper identifies vitamin B12 biosynthesis in Akkermansia strains through genomic analysis and experimental validation, revealing distinct functional potentials across phylogroups.

The paper introduces a framework for integrating microbiome longitudinal data with other data types to analyze relationships and associations across different biological and clinical variables.

Viomega is a robust, automated stool metatranscriptomic method for population-scale studies, demonstrating stability and consistency across 10,000 samples.

This study uses a multiomics approach to show that fecal microbiota transplantation can restore gut microbiota balance and alleviate obesity-related dysbiosis in rats.

This study identifies non-genetic factors associated with gut microbiome diversity in a cohort of healthy individuals, finding no genome-wide significant genetic associations.

The paper shows gut microbiota influences host gene regulation, identifying specific taxa and genes, and demonstrates microbiome manipulation can alter gene expression profiles.

MITRE predicts host status from microbiome time-series data, performing on par or better than black-box methods while maintaining interpretability.

The paper identifies a blood metabolome signature that robustly predicts gut microbiome α-diversity across health and disease conditions.

FlashWeave infers direct microbial interactions from large-scale metagenomic data, outperforming existing methods in speed and accuracy across diverse benchmarks.

This study identifies genetic loci associated with gut microbiota composition and bile acid profiles in mice using QTL analysis and validation experiments.

VOLARE is a web tool that visualizes microbiome-immune system relationships through interactive regression analysis, enabling domain experts to evaluate candidate results for further experimentation.

This study uses 16S rRNA gene sequencing to examine gut microbial changes in 6-OHDA-induced Parkinson's disease mice and finds no significant changes in diversity but altered abundance of specific bacterial species.

The paper introduces MicroPro, a metagenomic analysis tool that improves disease association predictions by including reads from unknown organisms, and identifies new microbial and viral associations with colorectal cancer, type-2 diabetes, and liver cirrhosis.

The paper introduces a computational method to cluster microbial genes by co-abundance, identifying reproducible gene groups linked to colorectal cancer and inflammatory bowel disease.

This study performs a meta-analysis of gut metagenomes to identify microbiome features associated with health and disease across multiple conditions.

This study investigates the association between APOE genotype and gut microbiome composition in humans and APOE-TR mice, finding specific microbial and metabolic differences linked to APOE alleles.

The paper shows that human AMY1 gene copy number affects oral and gut microbiome composition and function, with low-AMY1 associated with enhanced complex carbohydrate breakdown and high-AMY1 linked to resistant starch fermentation and higher adiposity.

ENIGMA identifies microbial associations with disease by estimating enterotype-like clusters and environmental effects using a probabilistic model.

The paper uses metaproteomics to analyze cecum contents in CKD rats with and without resistant starch, revealing shifts in gut microbiota and host proteins associated with improved CKD outcomes.