Jack A. Gilbert

The paper analyzes gut microbiome data from Vietnamese adults to show two enterotypes reflecting industrialization stages, similar to global patterns.

This study shows that multi-species microbial inoculants enhance crop yields in Rwanda when combined with fertilizers, but not as effectively as fertilizers alone.

This study tracks gut microbiota changes in wild baboons across reproductive states, finding distinct microbial patterns linked to pregnancy and hormone levels.

This study examines the vaginal mycobiome in Hispanic women, identifying environmental fungi associated with cervical disease progression and reproductive stages.

The paper links social microbiome transmission to microbial specialization and host lifespan in wild baboons using metagenomic data.

This study shows that adding fermentable fiber to a Western diet reduces indole levels and improves gut microbiota diversity and metabolic health in mice.

This study shows that social transfer of pain in mice alters the gut microbiome of bystander animals, resembling changes seen in pain-injected mice.

This study analyzes the ISS microbiome over seven years, revealing stable core communities with localized microevolution and AMR/virulence dynamics that require functional validation.

This study identifies SETDB1's dual role in endometrial cancer by promoting tumor growth and enabling immune evasion through gene regulation and macrophage suppression.

This study compares gut and oral microbiota in women with breast cancer, ductal carcinoma in situ, and healthy controls, finding significant gut microbiota differences but not oral ones.

The paper develops a microbiome-based age predictor for wild baboons using 13,563 gut microbial profiles and finds that microbiome age reflects current social and environmental conditions but not developmental milestones or lifespan.

This paper introduces OGUs, a metagenome analysis method that uses sequence alignment hits to reference genomes for community analysis, showing better performance in predicting biological traits than taxonomic or 16S rRNA methods.

This paper quantifies well-to-well contamination in microbiome studies, showing it occurs mainly during DNA extraction and affects low-biomass samples, with implications for data interpretation.