PT  - JOURNAL ARTICLE
AU  - Angelina Volkova
AU  - Kelly V. Ruggles
TI  - Predictive Metagenomic Analysis of Autoimmune Disease Identifies Robust Autoimmunity and Disease Specific Signatures
AID  - 10.1101/779967
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 779967
4099  - http://biorxiv.org/content/early/2019/09/24/779967.short
4100  - http://biorxiv.org/content/early/2019/09/24/779967.full
AB  - Within the last decade, numerous studies have demonstrated changes in the gut microbiome associated with specific autoimmune diseases. Due to differences in study design, data quality control, analysis and statistical methods, the results of these studies are inconsistent and incomparable. To better understand the relationship between the intestinal microbiome and autoimmunity, we have completed a comprehensive re-analysis of 29 studies focusing on the gut microbiome in nine autoimmune diseases to identify a specific microbial signature predictive of autoimmune disease using both 16S rRNA sequencing data and shotgun metagenomics data. Despite the heterogeneity of our data set, our approach has allowed us to build robust predictive models for general autoimmunity, as well as models for individual autoimmune diseases. Through this, we identified a number of common features predictive of autoimmune diseases including deficiency in Alistipes and Lachnobacterium, in addition to 9 inflammatory bowel disease, 7 multiple sclerosis and 7 rheumatoid disease predictive taxa consistently identified across multiple cohort comparison machine learning models. Lastly, we assessed potential metabolomic alterations based on metagenomic/metabolomic correlation analysis, identifying 114 metabolites associated with autoimmunity-predictive taxa.