Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.08.06.240655v1?rss=1 Authors: Wu, D., Jiao, N., Zhu, R., Zhang, Y., Gao, W., Fang, S., Li, Y., Cheng, S., Tian, C., Lan, P., Loomba, R., Zhu, L. Abstract: Objective: Keystone species are required for the integrity and stability of an ecological community, and therefore, are potential intervention targets for microbiome related diseases. Design: Here we describe an algorithm for the identification of keystone species from cross-sectional microbiome data of non-alcoholic fatty liver disease (NAFLD) based on causal inference theories and dynamic intervention modeling (DIM). Results: Eight keystone species in the gut of NAFLD, represented by P. loveana, A. indistinctus and D. pneumosintes, were identified by our algorithm, which could efficiently restore the microbial composition of the NAFLD toward a normal gut microbiome with 92.3% recovery. These keystone species regulate intestinal amino acids metabolism and acid-base environment to promote the growth of the butyrate-producing Lachnospiraceae and Ruminococcaceae species. Conclusion: Our method may benefit microbiome studies in the broad fields of medicine, environmental science and microbiology. Copy rights belong to original authors. Visit the link for more info