A. Crits-Christoph

MicrobeMod identifies prokaryotic methylation and restriction-modification systems using nanopore sequencing, revealing 5mC and 6mA methylation patterns in diverse bacterial and archaeal strains.

The paper identifies Borgs, large extrachromosomal elements in Methanoperedens archaea, which enhance methane oxidation capacity and may influence greenhouse gas emissions.

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 expands genomic sampling of Ca. Angelobacter, showing they are common in soil communities and enriched in biosynthetic gene clusters for specialized metabolites.

The paper identifies novel archaea in soils and sediments with divergent CuMMO enzymes, suggesting they contribute to nitrogen cycling through ammonia oxidation.

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

This paper develops a method to edit specific bacteria within microbial communities using ET-Seq and DART systems, enabling genetic manipulation in complex environments.

The paper identifies large freshwater phages encoding the pmoC gene, which may enhance bacterial methane oxidation and influence greenhouse gas emissions.

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 examines bacterial secondary metabolite biosynthetic potential in soil, finding variations linked to phylum, depth, and vegetation type.

The paper examines genetic variation in soil bacterial populations across a meadow, finding that recombination and gene-specific selection shape local genetic diversity.

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