title: Ecological modeling from time-series inference: insight into dynamics and stability of intestinal microbiota.
abstract: The intestinal microbiota is a microbial ecosystem of crucial importance to human health. Understanding how the microbiota confers resistance against enteric pathogens and how antibiotics disrupt that resistance is key to the prevention and cure of intestinal infections. We present a novel method to infer microbial community ecology directly from time-resolved metagenomics. This method extends generalized Lotka-Volterra dynamics to account for external perturbations. Data from recent experiments on antibiotic-mediated Clostridium difficile infection is analyzed to quantify microbial interactions, commensal-pathogen interactions, and the effect of the antibiotic on the community. Stability analysis reveals that the microbiota is intrinsically stable, explaining how antibiotic perturbations and C. difficile inoculation can produce catastrophic shifts that persist even after removal of the perturbations. Importantly, the analysis suggests a subnetwork of bacterial groups implicated in protection against C. difficile. Due to its generality, our method can be applied to any high-resolution ecological time-series data to infer community structure and response to external stimuli.
authors.affiliation: Computational Biology Program, Sloan-Kettering Institute, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America.
authors.forename: Richard R
authors.initials: RR
authors.lastname: Stein
authors.affiliation: Computational Biology Program, Sloan-Kettering Institute, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America.
authors.forename: Vanni
authors.initials: V
authors.lastname: Bucci
authors.affiliation: Immunology Program, Sloan-Kettering Institute, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America.
authors.forename: Nora C
authors.initials: NC
authors.lastname: Toussaint
authors.affiliation: Immunology Program, Sloan-Kettering Institute, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America.
authors.forename: Charlie G
authors.initials: CG
authors.lastname: Buffie
authors.affiliation: Computational Biology Program, Sloan-Kettering Institute, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America.
authors.forename: Gunnar
authors.initials: G
authors.lastname: Rätsch
authors.affiliation: Immunology Program, Sloan-Kettering Institute, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America.
authors.forename: Eric G
authors.initials: EG
authors.lastname: Pamer
authors.affiliation: Computational Biology Program, Sloan-Kettering Institute, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America.
authors.forename: Chris
authors.initials: C
authors.lastname: Sander
authors.affiliation: Computational Biology Program, Sloan-Kettering Institute, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America.
authors.forename: João B
authors.initials: JB
authors.lastname: Xavier
mesh_terms: D000818:Animals; D016360:Clostridioides difficile / Q000302:isolation & purification*; D004463:Ecology; D007422:Intestines / Q000382:microbiology*; D051379:Mice; D023421:Models, Animal; D008962:Models, Theoretical; D060888:Real-Time Polymerase Chain Reaction