In the prospective, multicentre TEDDY study, distinct patterns of gut microbiome maturation are associated with differential risk of type 1 diabetes, with host genetics modifying the strength of this ...

The Eddie Goldenberg Research Chairs of Canada program supports institutions in attracting world-leading researchers

Recent work in Gut has highlighted multi-kingdom interactions between gut bacteria and bacteriophage (‘phage’) as contributors to Crohn’s disease.1 Whether similar alterations characterise colorectal ...

Nature Methods - Getting over ANOVA: estimation graphics for multi-group comparisons

Coupled with well-characterized host genetic and environmental risk factors, alterations of gut microbial communities contribute to risk and severity …

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Nogal et al. profile the gut microbiome and metabolome of individuals with a history of colorectal adenoma resection, revealing microbial and metabolic alterations detectable more than a decade after ...

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Gallego-del-Sol et al. show that arbitrium-coding phages can sense non-cognate peptide signals from other phages to regulate lysis-lysogeny decisions. This crosstalk affects lysis-lysogeny outcomes of phage infections, mixed lysogenic communities, and polylysogens. Our results demonstrate that crosstalk is an important mechanism that drives phage interactions in microbial communities.

A host-filtering and decontamination pipeline was benchmarked and applied to 16,369 tumor genomes, providing a high-resolution atlas of the cancer microbiome. Although most cancer types lacked a detectable microbiome, orodigestive cancers harbored complex multi-kingdom microbial communities that varied by site, subtype, and somatic mutation burden, linking the tumor microbiome to host phenotype and tumor genomic context.

We are delighted to welcome everyone to the eighth annual Harvard Chan Microbiome in Public Health Center (HCMPH) symposium. As microbial community science

The OPTIMISTICC team tackled the microbiota challenge: understanding how the trillions of microorganisms that live in and on our bodies influence cancer.

This Review explains various multi-omic data integration methods and aims to help microbiome researchers determine which approach is most suited to their particular research question.