title: Emergent biosynthetic capacity in simple microbial communities.
abstract: Microbes have an astonishing capacity to transform their environments. Yet, the metabolic capacity of a single species is limited and the vast majority of microorganisms form complex communities and join forces to exhibit capabilities far exceeding those achieved by any single species. Such enhanced metabolic capacities represent a promising route to many medical, environmental, and industrial applications and call for the development of a predictive, systems-level understanding of synergistic microbial capacity. Here we present a comprehensive computational framework, integrating high-quality metabolic models of multiple species, temporal dynamics, and flux variability analysis, to study the metabolic capacity and dynamics of simple two-species microbial ecosystems. We specifically focus on detecting emergent biosynthetic capacity--instances in which a community growing on some medium produces and secretes metabolites that are not secreted by any member species when growing in isolation on that same medium. Using this framework to model a large collection of two-species communities on multiple media, we demonstrate that emergent biosynthetic capacity is highly prevalent. We identify commonly observed emergent metabolites and metabolic reprogramming patterns, characterizing typical mechanisms of emergent capacity. We further find that emergent secretion tends to occur in two waves, the first as soon as the two organisms are introduced, and the second when the medium is depleted and nutrients become limited. Finally, aiming to identify global community determinants of emergent capacity, we find a marked association between the level of emergent biosynthetic capacity and the functional/phylogenetic distance between community members. Specifically, we demonstrate a "Goldilocks" principle, where high levels of emergent capacity are observed when the species comprising the community are functionally neither too close, nor too distant. Taken together, our results demonstrate the potential to design and engineer synthetic communities capable of novel metabolic activities and point to promising future directions in environmental and clinical bioengineering.
authors.affiliation: Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America.
authors.forename: Hsuan-Chao
authors.initials: HC
authors.lastname: Chiu
authors.affiliation: Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America.
authors.forename: Roie
authors.initials: R
authors.lastname: Levy
authors.affiliation: Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America; Department of Computer Science and Engineering, University of Washington, Seattle, Washington, United States of America; Santa Fe Institute, Santa Fe, New Mexico, United States of America.
authors.forename: Elhanan
authors.initials: E
authors.lastname: Borenstein
mesh_terms: D019295:Computational Biology; D017753:Ecosystem; D008660:Metabolism / Q000502:physiology*; D059013:Microbial Consortia / Q000502:physiology*; D008954:Models, Biological