M. R. Olm

The paper introduces MIG-Seq to analyze IgA binding to gut microbiome strains and finds that microbial genes, especially mucus degradation genes, predict IgA binding levels.

The paper shows that Prevotella copri requires plant-derived microbiota-accessible carbohydrates for persistence in mice, unlike Bacteroides thetaiotaomicron.

The paper identifies metabolic diversity in commensal protists and shows how it influences intestinal immunity and trans-kingdom competition with bacteria.

This study compares infant gut microbiome assembly in Hadza hunter-gatherers and industrialized infants, finding distinct microbial trajectories and functional differences.

The paper uses ultra-deep metagenomic sequencing to analyze the gut microbiome of Hadza hunter-gatherers, revealing many microbes absent from existing datasets and highlighting differences in microbial dynamics compared to industrialized populations.

This study identifies contamination in metagenomics data using strain-resolved analysis, finding that contamination is more common in low-biomass samples and adjacent extraction plate positions.

This study identifies genetic and behavioral adaptations of C. parapsilosis in the gut microbiome of hospitalized infants using in situ genomics, transcriptomics, and proteomics.

The paper investigates microbial metabolic potential in floodplain soils along a river corridor, finding that meander-bound regions predict biogeochemical capacities at the watershed scale.

The paper investigates factors influencing gut microbiome strain persistence in infants, finding maternal origin and specific genetic functions are associated with strain survival.

InStrain analyzes metagenomic data to detect microbial strain diversity and finds siblings share more strains than unrelated infants, but identical twins do not share more than fraternal siblings.

The paper evaluates metagenome-derived metrics to define bacterial species boundaries and finds ribosomal proteins are more effective than 16S rRNA genes for species discrimination.

The paper identifies major clades of large phage genomes across diverse ecosystems and highlights their expanded genetic repertoires and potential functional roles.

The paper identifies microbial features predictive of necrotizing enterocolitis (NEC) in premature infants using metagenomic analysis and machine learning.

This study investigates how gestational age affects the functional potential and species richness of the gut microbiome in premature infants using genome-resolved metagenomics.

The paper identifies large phage (Lak phage) in gut microbiomes that infect Prevotella and are widespread, especially in developing-world individuals.

The paper analyzes metagenomic data from premature infants and NICUs to identify fungal strains and their genetic diversity, revealing high similarity between infant and hospital-associated fungi.

The paper investigates how the gut microbiome of premature infants shapes the microbial composition of NICU rooms over time.

This study examines microbial colonization in premature infants, finding distinct proteomic profiles among related bacterial strains and correlations with NEC development.

The paper uses machine learning on genome-resolved metagenomics data to identify antibiotic resistance genes in the infant gut microbiome that influence bacterial survival and response to antibiotics.