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Abstract
The etiopathogenesis of diverticulitis, among the most common gastrointestinal diagnoses, remains largely unknown. By leveraging stool collected within a large prospective cohort, we performed shotgun metagenomic sequencing and untargeted metabolomics profiling among 121 women diagnosed with diverticulitis requiring antibiotics or hospitalizations (cases), matched to 121 women without diverticulitis (controls) according to age and race. Overall microbial community structure and metabolomic profiles differed in diverticulitis cases compared to controls, including enrichment of pro-inflammatory Ruminococcus gnavus, 1,7-dimethyluric acid, and histidine-related metabolites, and depletion of butyrate-producing bacteria and anti-inflammatory ceramides. Through integrated multi-omic analysis, we detected covarying microbial and metabolic features, such as Bilophila wadsworthia and bile acids, specific to diverticulitis. Additionally, we observed that microbial composition modulated the protective association between a prudent fiber-rich diet and diverticulitis. Our findings offer insights into the perturbations in inflammation-related microbial and metabolic signatures associated with diverticulitis, supporting the potential of microbial-based diagnostics and therapeutic targets.
Here, the authors present a multi-omics examination of stool samples obtained from individuals with diverticulitis and controls, uncovering disruptions in the balance of microbial composition and metabolites, as well as co-occurring microbe-metabolite associations relevant to the disease.
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1 Massachusetts General Hospital and Harvard Medical School, Clinical and Translational Epidemiology Unit, Boston, USA (GRID:grid.32224.35) (ISNI:0000 0004 0386 9924); Massachusetts General Hospital and Harvard Medical School, Division of Gastroenterology, Boston, USA (GRID:grid.32224.35) (ISNI:0000 0004 0386 9924)
2 Harvard T.H. Chan School of Public Health, Department of Biostatistics, Boston, USA (GRID:grid.38142.3c) (ISNI:000000041936754X); Broad Institute of MIT and Harvard, Cambridge, USA (GRID:grid.66859.34) (ISNI:0000 0004 0546 1623)
3 Harvard T.H. Chan School of Public Health, Department of Biostatistics, Boston, USA (GRID:grid.38142.3c) (ISNI:000000041936754X)
4 Massachusetts General Hospital and Harvard Medical School, Clinical and Translational Epidemiology Unit, Boston, USA (GRID:grid.32224.35) (ISNI:0000 0004 0386 9924); Massachusetts General Hospital and Harvard Medical School, Division of Gastroenterology, Boston, USA (GRID:grid.32224.35) (ISNI:0000 0004 0386 9924); Harvard T.H. Chan School of Public Health, Department of Epidemiology, Boston, USA (GRID:grid.38142.3c) (ISNI:000000041936754X); Harvard T.H. Chan School of Public Health, Department of Nutrition, Boston, USA (GRID:grid.38142.3c) (ISNI:000000041936754X)
5 Broad Institute of MIT and Harvard, Cambridge, USA (GRID:grid.66859.34) (ISNI:0000 0004 0546 1623)
6 University of Washington School of Medicine, Division of Gastroenterology, Seattle, USA (GRID:grid.34477.33) (ISNI:0000000122986657)
7 Massachusetts General Hospital and Harvard Medical School, Clinical and Translational Epidemiology Unit, Boston, USA (GRID:grid.32224.35) (ISNI:0000 0004 0386 9924); Massachusetts General Hospital and Harvard Medical School, Division of Gastroenterology, Boston, USA (GRID:grid.32224.35) (ISNI:0000 0004 0386 9924); Broad Institute of MIT and Harvard, Cambridge, USA (GRID:grid.66859.34) (ISNI:0000 0004 0546 1623); Harvard T.H. Chan School of Public Health, Department of Immunology and Infectious Diseases, Boston, USA (GRID:grid.38142.3c) (ISNI:000000041936754X); Massachusetts General Hospital, Cancer Center, Boston, USA (GRID:grid.32224.35) (ISNI:0000 0004 0386 9924); Brigham and Women’s Hospital and Harvard Medical School, Channing Division of Network Medicine, Department of Medicine, Boston, USA (GRID:grid.62560.37) (ISNI:0000 0004 0378 8294)