Identification of shared and disease-specific host gene–microbiome associations across human diseases using multi-omic integration

Sambhawa Priya, Michael B Burns, Tonya Ward, Ruben A.T. Mars, Beth Adamowicz, Eric F. Lock, Purna C Kashyap, Dan Knights, Ran Blekhman

Research output: Contribution to journalArticlepeer-review

28 Scopus citations


While gut microbiome and host gene regulation independently contribute to gastrointestinal disorders, it is unclear how the two may interact to influence host pathophysiology. Here we developed a machine learning-based framework to jointly analyse paired host transcriptomic (n = 208) and gut microbiome (n = 208) profiles from colonic mucosal samples of patients with colorectal cancer, inflammatory bowel disease and irritable bowel syndrome. We identified associations between gut microbes and host genes that depict shared as well as disease-specific patterns. We found that a common set of host genes and pathways implicated in gastrointestinal inflammation, gut barrier protection and energy metabolism are associated with disease-specific gut microbes. Additionally, we also found that mucosal gut microbes that have been implicated in all three diseases, such as Streptococcus, are associated with different host pathways in each disease, suggesting that similar microbes can affect host pathophysiology in a disease-specific manner through regulation of different host genes. Our framework can be applied to other diseases for the identification of host gene–microbiome associations that may influence disease outcomes.

Original languageEnglish (US)
Pages (from-to)780-795
Number of pages16
JournalNature Microbiology
Issue number6
StatePublished - Jun 2022

Bibliographical note

Funding Information:
We thank the IBD HMP2 consortium for making the dataset publicly available; the Blekhman Lab members for their comments and suggestions on the manuscript; W. Wang and G. Al-Ghalith for their feedback. This work was supported by NIH grant R35-GM128716 (to R.B.), a Minnesota Partnership for Biotechnology and Genomics grant (to R.B.), a University of Minnesota Doctoral Dissertation Fellowship (to S.P.), NIH grant R01-GM130622 (to E.F.L.) and R01-DK114007 (to P.C.K.). This work was carried out in part by resources provided by the Minnesota Supercomputing Institute.

Publisher Copyright:
© 2022, The Author(s).

PubMed: MeSH publication types

  • Journal Article
  • Research Support, Non-U.S. Gov't
  • Research Support, N.I.H., Extramural


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