Gut microbiota and health

This study benchmarks differential abundance analysis methods for correlated microbiome data, finding linear model-based methods more robust than generalized linear model-based ones.

The paper introduces a microbiome analysis toolbox with methods for analyzing and visualizing longitudinal microbiome data, including trajectory prediction and anomaly detection.

This study investigates the gut microbiome's role in Parkinson's disease using metagenomics, finding widespread dysbiosis and associations with disease mechanisms.

The paper analyzes the genetic and microbiome contributions to blood metabolite variation across 2,049 individuals, finding that over 60% of metabolites are associated with either genetics or the microbiome, with some showing interaction effects.

The paper links gut microbiome composition and its metabolic traits to lipoprotein distribution profiles in healthy individuals.

This study compares fecal and colonic lavage samples to assess their ability to represent the human gut microbiome, finding that colonic lavage reveals higher biodiversity and detects mucosa-associated microbes.

The paper performs a meta-analysis of microbial association networks to identify dysbiosis patterns in diseased gut microbiomes, revealing network features that distinguish healthy from disease states.

The paper introduces a joint model with matching and regularization to detect OTU-specific compositional trajectories predictive of disease onset in metagenomic data.

MDITRE predicts host status from microbiome time-series data using interpretable deep learning, outperforming existing methods in speed and accuracy.

The paper develops a multiscale model of infant gut microbiota succession, showing that oxygen levels influence the transition from facultative to strict anaerobes like Bifidobacterium.

The paper quantifies nutrient preferences of gut bacteria in mice using isotope tracing and finds dietary fiber, protein, and host-derived metabolites drive microbial growth.

SCNIC generates correlation networks and identifies modules of correlated features in compositional data, increasing statistical power and revealing microbial relationships.

The paper analyzes 1,119 metagenome-assembled genomes of Akkermansia to identify new phylogroups and functional variations across human gut populations.

Namco provides a user-friendly R Shiny application for comprehensive microbiome analysis, covering data processing to complex tasks like machine learning and network analysis.

LOCOM is a logistic regression method for differential abundance testing in microbiome data that controls FDR and is robust to experimental biases.

This study identifies germline variants associated with gut microbiota composition in CRC patients and shows they influence both microbiota and inflammatory gene expression, potentially affecting CRC risk.

MB-SupCon integrates microbiome and metabolomics data to improve prediction accuracy in microbiome-only datasets, achieving high accuracy in predicting insulin resistance, sex, and race.

The paper introduces a non-stochastic PERMANOVA extension for microbiome presence-absence analyses to address library size confounding without rarefaction, showing improved performance over resampling-based methods.

The paper uses genetic mapping in hybrid mice to show host genetics shape gut microbiome traits, identifies 443 loci influencing 123 taxa, and finds disease-related genes.

The paper extends PERMANOVA to test microbiome mediation effects by incorporating exposure and outcome as covariates, showing improved power and error control over existing methods.

The paper uses LSTM to model synthetic human gut microbiome dynamics, outperforming traditional models and identifying key microbial drivers of metabolite production.

The paper analyzes E. coli genomes from IBD patients to identify disease-specific genomic adaptations and their functional implications.

The paper investigates the association between amylase gene copy numbers and diabetes, obesity, and gut microbiome function in a northern Japanese population, finding a positive link between higher amylase copy numbers and these conditions.

The paper introduces a causal inference framework to assess environmental exposure effects on the gut microbiome using observational data from the KORA cohort.

The paper develops a neural network framework to identify microbial markers associated with Type 2 Diabetes using metagenomic data, finding fasting blood glucose is a significant factor.

This study shows that the light-dark cycle modulates gut microbiota via the ipRGC-sympathetic circuit in mice.

This study compares the gut mycobiome of RRMS patients to healthy controls, finding altered fungal communities and increased fungal-to-bacterial richness.

This study shows that fecal microbiota from Alzheimer's mice worsens brain injury outcomes in healthy mice.

This paper introduces MVIB, a deep learning model that combines gut microbiome data with metabolomic data to predict disease, achieving high performance across multiple disease cohorts.

This study examines how pharmacologically induced weight loss in obese rats affects their gut microbiome, identifying specific microbial taxa and pathways associated with weight loss.