RT Journal Article
SR Electronic
T1 Meta analysis of microbiome studies identifies shared and disease-specific patterns
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 134031
DO 10.1101/134031
A1 Claire Duvallet
A1 Sean Gibbons
A1 Thomas Gurry
A1 Rafael Irizarry
A1 Eric Alm
YR 2017
UL http://biorxiv.org/content/early/2017/05/08/134031.abstract
AB Hundreds of clinical studies have been published that demonstrate associations between the human microbiome and a variety of diseases. Yet, fundamental questions remain on how we can generalize this knowledge. For example, if diseases are mainly characterized by a small number of pathogenic species, then new targeted antimicrobial therapies may be called for. Alternatively, if diseases are characterized by a lack of healthy commensal bacteria, then new probiotic therapies might be a better option. Results from individual studies, however, can be inconsistent or in conflict, and comparing published data is further complicated by the lack of standard processing and analysis methods.Here, we introduce the MicrobiomeHD database, which includes 29 published case-control gut microbiome studies spanning ten different diseases. Using standardized data processing and analyses, we perform a comprehensive crossdisease meta-analysis of these studies. We find consistent and specific patterns of disease-associated microbiome changes. A few diseases are associated with many individual bacterial associations, while most show only around 20 genus-level changes. Some diseases are marked by the presence of pathogenic microbes whereas others are characterized by a depletion of health-associated bacteria. Furthermore, over 60% of microbes associated with individual diseases fall into a set of “core” health and disease-associated microbes, which are associated with multiple disease states. This suggests a universal microbial response to disease.