Gut microbiota and health

The paper uses LICA to identify multivariate associations between gut microbiota and brain network connectivity in healthy females, finding specific microbial genera linked to brain network strength.

This study shows shrinkage estimation improves microbial association estimates across different normalization methods using the American Gut Project data.

MicroPhenoDB connects metagenomic data to pathogenic microbes, core genes, and disease phenotypes through curated associations and a score model.

The paper identifies gut microbiome alterations associated with recurrent atrial fibrillation and develops a predictive model based on microbial taxa.

This study examines age-related changes in the colonic mucosal microbiome and systemic inflammation in vervet monkeys, finding increased inflammation and altered microbial composition in older individuals.

This study shows that FMT from aged mice impairs spatial learning in young recipients by altering hippocampal proteins related to synaptic plasticity and neurotransmission.

The paper introduces a gut microbiome health index (GMHI) that predicts general health status using species-level profiling and validates its robustness across multiple datasets.

This study compares mucosal microbiota composition and function along the intestine using shotgun metagenomics, highlighting functional enrichments and antimicrobial resistance patterns.

This study shows that gut microbiota differences between mouse strains contribute to varied metabolic responses to high fructose consumption, with Akkermansia playing a key protective role.

The paper shows that gut microbes influence sympathetic neurons through a metabolite-mediated gut-brain circuit, with butyrate suppressing sympathetic activity.

The paper introduces a sparse neural encoder-decoder network to predict gut metabolites from microbiome data, showing improved accuracy and interpretability over traditional methods.

This study identifies three clusters of gut microbiota associated with Parkinson's disease, including overabundance of opportunistic pathogens.

This study uses read cloud sequencing to identify mobile elements in Bacteroides caccae that contribute to antibiotic resistance in a patient's microbiome over time.

This study identifies ergothioneine, a metabolite from Lactobacillus reuteri, as protective against stress-induced sleep disturbances in a depression rat model.

The paper analyzes mucosal microbiota and metabolome in non-human primates to identify location-specific microbial signatures and their relationship with host tissue metabolites.

The paper develops a deep learning model to predict human biological age from gut microbiome data and identifies key microbial biomarkers associated with aging.

PopPhy-CNN uses a phylogenetic tree embedded architecture to predict host phenotype from metagenomic data, outperforming traditional and baseline models.

This study characterizes gut eukaryotic communities in pigs and finds limited host genetic control over their diversity and composition, with some genetic variants associated with fungal diversity and Blastocystis abundance.

This paper introduces a method to analyze microbiome data using word-embedding techniques to capture community-level properties, showing improved predictive power for inflammatory bowel disease classification.

The paper identifies diverse gene clusters containing Fe-S flavoenzymes in gut microbiota, suggesting their broad role in metabolic pathways and potential impact on human health.

The paper introduces the linear decomposition model (LDM) to unify global and individual OTU hypothesis testing in microbiome studies while controlling FDR and offering flexibility for various analyses.

This study shows that JNK signaling disruption in intestinal tumors leads to barrier failure and microbiome imbalance, which can be reversed by JNK inhibition or microbiome depletion.

DeepMicro uses deep representation learning to predict diseases from microbiome data, outperforming existing methods and achieving faster training.

M2IA is a web server that integrates microbiome and metabolome data to identify pathways linked to obesity using TwinsUK cohort data.

The paper uses metabolomics to correct metadata classifications in cheetah microbiome studies, revealing antibiotic transfer via grooming and improving understanding of medication effects.

The paper develops interpretable metagenomics prediction models based on microbial taxon abundance ratios, additions, and subtractions, achieving performance comparable to or better than traditional machine learning methods.

This study examines early sexual dimorphism in the gut microbiome of northern elephant seal pups during a six-week post-weaning period, finding significant sex-based differences in microbial diversity and composition.

The paper introduces ExHiBITT, a colon multiomic cohort study of twins, describing its characteristics and initial findings on polyps, diverticulosis, and their associations with BMI and age.

This study shows that dietary fiber, not fat, drives gut microbiota changes when comparing refined diets to chow in mice, with sex and age effects observed.

The paper examines gut microbiome and host gene expression changes in CF patients, identifying CRC-related gene-microbe associations.