Gut microbiota and health

The paper introduces a latent graphical model to identify microbial associations while accounting for environmental and technical factors, showing its effectiveness across simulated and gut microbiome datasets.

This study identifies microbiome determinants of hippurate, a metabolite linked to metabolic health, and demonstrates its protective effects in mice through benzoate-hippurate pathway analysis.

The paper compares correlation/proportionality methods with MMvec for identifying microbe-metabolite interactions and finds MMvec performs better on real data due to its handling of sparsity.

The paper argues that simple linear statistics can outperform the mmvec neural network in analyzing microbe-metabolite correlations.

This study examines the gut microbiome's role in stress resilience among first-year medical students over 4 months, finding correlations between microbiome composition and psychological resilience.

This paper develops a graph convolutional neural network for multiclass disease classification from microbial metagenomes, achieving high accuracy and AUC across 19 diseases.

This study shows that gut microbiota changes induced by chronic stress affect depressive-like behaviors and adult hippocampal neurogenesis through the endocannabinoid system in mice.

This study identifies age-related sex differences in gut microbiota composition and functionality across two population cohorts, showing women have more diverse and stable microbiota than men, with sex differences diminishing in older adults.

The paper develops a computational approach to analyze metabolic potential in gut microbiomes of IBD patients with dysbiosis, finding distinct metabolite profiles compared to healthy controls and non-dysbiotic patients.

The paper identifies human gut microbes that undergo consistent changes after transplantation into germ-free mice, highlighting challenges in replicating human microbiota phenotypes in mouse models.

The paper identifies prokaryotes involved in indole metabolism using KEGG and BLAST integration, and provides resources for further validation.

The paper shows that stress alters gut microbiota, which can induce depression and reduce neurogenesis, but this effect is blocked by vagus nerve disruption.

The paper introduces a method to identify multidimensional patterns in microbial communities using 2,380 Human Microbiome Project samples, detecting significant 2D and 3D relationships between organisms.

MAGMA infers microbial association networks by accounting for microbiota data features like zeros, overdispersion, and covariates, outperforming existing methods in simulations and revealing new biology in real data.

This study shows that high-fat diet during pregnancy alters maternal gut microbiota and barrier function, leading to placental hypoxia and fetal gut developmental issues.

This study characterizes the human blood microbiome using DNA and RNA analyses, culture methods, and comparative genomics, finding a core microbiome likely originating from oral or skin communities.

This paper investigates butyrate production by F. prausnitzii and B. thetaiotaomicron in monoculture and coculture using experiments and simulations, finding enhanced butyrate production in coculture and complex metabolic strategies.

This study investigates host genetic associations with gut microbiota traits in HIV-1-infected subjects but finds no significant genetic associations after multiple comparison correction.

The paper develops an integrated computational model to explore how diet and gut microbiota interact in autism, showing probiotics reduce oxidative stress and microbiome-diet interactions affect host metabolism.

BEEM improves ecological modeling of microbial communities using longitudinal metagenome data by integrating biomass estimation and model inference without requiring additional experimental data.

The paper develops a multi-method approach to analyze longitudinal intervention effects on bacterial communities, showing treatment impacts on microbiota and tumor load.

The paper identifies specific bacterial species associated with gut microbiome recovery after antibiotic use and shows their role in promoting microbial diversity and preventing pathogen colonization.

This study uses whole genome sequencing to analyze gut microbiota changes after Helicobacter pylori eradication therapy, finding reduced alpha-diversity, shifts in metabolic potential, and increased antibiotic resistance genes.

The paper uses personalized microbial community models to analyze bile acid metabolism in healthy and IBD individuals, revealing distinct metabolic potentials and strain contributions.

The paper introduces q2-longitudinal, a QIIME 2 plugin for analyzing longitudinal and paired microbiome data, offering methods like linear mixed effects models and volatility analysis.

This paper compares DNA extraction kits and sequence analysis methods for microbial community profiling in human stool, finding DADA2 outperforms QIIME 1 and DNA stabilization reagents have a greater impact than extraction kits.

The paper links infant weight gain trajectories to oral microbiome composition and shows associations with gut microbiome and diet.

This paper evaluates a topic model approach for analyzing microbiome data, showing it captures dynamic behavior and identifies gut configurations in time-series data.

The paper introduces a method using smoothing splines to analyze longitudinal microbial marker-gene data, identifying time periods of differential abundance across sample groups.