Gene-level metagenomic architectures across diseases yield high-resolution microbiome diagnostic indicators

• Published in: Nature communications Vol. 12; no. 1; p. 2907
• Main Authors: Tierney, Braden T, Tan, Yingxuan, Kostic, Aleksandar D, Patel, Chirag J
• Format: Journal Article
• Language: English
• Published: England Nature Publishing Group 18.05.2021; Nature Publishing Group UK; Nature Portfolio
• Subjects: Bowel disease; Cancer; Cardiovascular disease; Cirrhosis; Colorectal cancer; Colorectal carcinoma; Coronary artery; Coronary artery disease; Diabetes mellitus (non-insulin dependent); Diagnostic systems; Genes; Heart diseases; Heterogeneity; Indicators; Inflammatory bowel diseases; Intestinal microflora; Intestine; Liver cirrhosis; Liver diseases; Mathematical models; Metagenomics; Microbiomes; Microorganisms; Phenotypes; Pipeline design; Robustness; Species; Strain analysis
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-021-23029-8

We propose microbiome disease "architectures": linking >1 million microbial features (species, pathways, and genes) to 7 host phenotypes from 13 cohorts using a pipeline designed to identify associations that are robust to analytical model choice. Here, we quantify conservation and heterogeneity in microbiome-disease associations, using gene-level analysis to identify strain-specific, cross-disease, positive and negative associations. We find coronary artery disease, inflammatory bowel diseases, and liver cirrhosis to share gene-level signatures ascribed to the Streptococcus genus. Type 2 diabetes, by comparison, has a distinct metagenomic signature not linked to any one specific species or genus. We additionally find that at the species-level, the prior-reported connection between Solobacterium moorei and colorectal cancer is not consistently identified across models-however, our gene-level analysis unveils a group of robust, strain-specific gene associations. Finally, we validate our findings regarding colorectal cancer and inflammatory bowel diseases in independent cohorts and identify that features inversely associated with disease tend to be less reproducible than features enriched in disease. Overall, our work is not only a step towards gene-based, cross-disease microbiome diagnostic indicators, but it also illuminates the nuances of the genetic architecture of the human microbiome, including tension between gene- and species-level associations.