Genome-driven evolutionary game theory helps understand the rise of metabolic interdependencies in microbial communities

Metabolite exchanges in microbial communities give rise to ecological interactions that govern ecosystem diversity and stability. It is unclear, however, how the rise of these interactions varies across metabolites and organisms. Here we address this question by integrating genome-scale models of metabolism with evolutionary game theory. Specifically, we use microbial fitness values estimated by metabolic models to infer evolutionarily stable interactions in multi-species microbial “games”. We first validate our approach using a well-characterized yeast cheater-cooperator system. We next perform over 80,000 in silico experiments to infer how metabolic interdependencies mediated by amino acid leakage in Escherichia coli vary across 189 amino acid pairs. While most pairs display shared patterns of inter-species interactions, multiple deviations are caused by pleiotropy and epistasis in metabolism. Furthermore, simulated invasion experiments reveal possible paths to obligate cross-feeding. Our study provides genomically driven insight into the rise of ecological interactions, with implications for microbiome research and synthetic ecology.We gratefully acknowledge funding from the Defense Advanced Research Projects Agency (Purchase Request No. HR0011515303, Contract No. HR0011-15-C-0091), the U.S. Department of Energy (Grants DE-SC0004962 and DE-SC0012627), the NIH (Grants 5R01DE024468 and R01GM121950), the national Science Foundation (Grants 1457695 and NSFOCE-BSF 1635070), MURI Grant W911NF-12-1-0390, the Human Frontiers Science Program (grant RGP0020/2016), and the Boston University Interdisciplinary Biomedical Research Office ARC grant on Systems Biology Approaches to Microbiome Research. We also thank Dr Kirill Korolev and members of the Segre Lab for their invaluable feedback on this work. (HR0011515303 - Defense Advanced Research Projects Agency; HR0011-15-C-0091 - Defense Advanced Research Projects Agency; DE-SC0004962 - U.S. Department of Energy; DE-SC0012627 - U.S. Department of Energy; 5R01DE024468 - NIH; R01GM121950 - NIH; 1457695 - national Science Foundation; NSFOCE-BSF 1635070 - national Science Foundation; W911NF-12-1-0390 - MURI; RGP0020/2016 - Human Frontiers Science Program; Boston University Interdisciplinary Biomedical Research Office ARC)Published versio

Improving the iMM904 S. cerevisiae metabolic model using essentiality and synthetic lethality data

<p>Abstract</p> <p>Background</p> <p><it>Saccharomyces cerevisiae </it>is the first eukaryotic organism for which a multi-compartment genome-scale metabolic model was constructed. Since then a sequence of improved metabolic reconstructions for yeast has been introduced. These metabolic models have been extensively used to elucidate the organizational principles of yeast metabolism and drive yeast strain engineering strategies for targeted overproductions. They have also served as a starting point and a benchmark for the reconstruction of genome-scale metabolic models for other eukaryotic organisms. In spite of the successive improvements in the details of the described metabolic processes, even the recent yeast model (i.e., <it>i</it>MM904) remains significantly less predictive than the latest <it>E. coli </it>model (i.e., <it>i</it>AF1260). This is manifested by its significantly lower specificity in predicting the outcome of grow/no grow experiments in comparison to the <it>E. coli </it>model.</p> <p>Results</p> <p>In this paper we make use of the automated GrowMatch procedure for restoring consistency with single gene deletion experiments in yeast and extend the procedure to make use of synthetic lethality data using the genome-scale model <it>i</it>MM904 as a basis. We identified and vetted using literature sources 120 distinct model modifications including various regulatory constraints for minimal and YP media. The incorporation of the suggested modifications led to a substantial increase in the fraction of correctly predicted lethal knockouts (i.e., specificity) from 38.84% (87 out of 224) to 53.57% (120 out of 224) for the minimal medium and from 24.73% (45 out of 182) to 40.11% (73 out of 182) for the YP medium. Synthetic lethality predictions improved from 12.03% (16 out of 133) to 23.31% (31 out of 133) for the minimal medium and from 6.96% (8 out of 115) to 13.04% (15 out of 115) for the YP medium.</p> <p>Conclusions</p> <p>Overall, this study provides a roadmap for the computationally driven correction of multi-compartment genome-scale metabolic models and demonstrates the value of synthetic lethals as curation agents.</p

OptCom: A Multi-Level Optimization Framework for the Metabolic Modeling and Analysis of Microbial Communities

Microorganisms rarely live isolated in their natural environments but rather function in consolidated and socializing communities. Despite the growing availability of high-throughput sequencing and metagenomic data, we still know very little about the metabolic contributions of individual microbial players within an ecological niche and the extent and directionality of interactions among them. This calls for development of efficient modeling frameworks to shed light on less understood aspects of metabolism in microbial communities. Here, we introduce OptCom, a comprehensive flux balance analysis framework for microbial communities, which relies on a multi-level and multi-objective optimization formulation to properly describe trade-offs between individual vs. community level fitness criteria. In contrast to earlier approaches that rely on a single objective function, here, we consider species-level fitness criteria for the inner problems while relying on community-level objective maximization for the outer problem. OptCom is general enough to capture any type of interactions (positive, negative or combinations thereof) and is capable of accommodating any number of microbial species (or guilds) involved. We applied OptCom to quantify the syntrophic association in a well-characterized two-species microbial system, assess the level of sub-optimal growth in phototrophic microbial mats, and elucidate the extent and direction of inter-species metabolite and electron transfer in a model microbial community. We also used OptCom to examine addition of a new member to an existing community. Our study demonstrates the importance of trade-offs between species- and community-level fitness driving forces and lays the foundation for metabolic-driven analysis of various types of interactions in multi-species microbial systems using genome-scale metabolic models

Down-Regulation of HtrA1 Activates the Epithelial-Mesenchymal Transition and ATM DNA Damage Response Pathways

Expression of the serine protease HtrA1 is decreased or abrogated in a variety of human primary cancers, and higher levels of HtrA1 expression are directly related to better response to chemotherapeutics. However, the precise mechanisms leading to HtrA1 down regulation during malignant transformation are unclear. To investigate HtrA1 gene regulation in breast cancer, we characterized expression in primary breast tissues and seven human breast epithelial cell lines, including two non-tumorigenic cell lines. In human breast tissues, HtrA1 expression was prominent in normal ductal glands. In DCIS and in invasive cancers, HtrA1 expression was greatly reduced or lost entirely. HtrA1 staining was also reduced in all of the human breast cancer cell lines, compared with the normal tissue and non-tumorigenic cell line controls. Loss of HtrA1 gene expression was attributable primarily to epigenetic silencing mechanisms, with different mechanisms operative in the various cell lines. To mechanistically examine the functional consequences of HtrA1 loss, we stably reduced and/or overexpressed HtrA1 in the non-tumorigenic MCF10A cell line. Reduction of HtrA1 levels resulted in the epithelial-to-mesenchymal transition with acquisition of mesenchymal phenotypic characteristics, including increased growth rate, migration, and invasion, as well as expression of mesenchymal biomarkers. A concomitant decrease in expression of epithelial biomarkers and all microRNA 200 family members was also observed. Moreover, reduction of HtrA1 expression resulted in activation of the ATM and DNA damage response, whereas overexpression of HtrA1 prevented this activation. Collectively, these results suggest that HtrA1 may function as a tumor suppressor by controlling the epithelial-to-mesenchymal transition, and may function in chemotherapeutic responsiveness by mediating DNA damage response pathways

Schematic illustration of OptCom.

<p>(A) The multi-level optimization structure of the OptCom. A separate biomass maximization problem is defined for each species as inner problems. These inner problems are then integrated in the outer stage through the inter-organism flow constraint to optimize a community-level objective function. (B) Structure of the Descriptive OptCom to determine the optimality level of each species (<i>c^k</i>), given a set of experimental data. The available experimental data for the entire community and the individual species are described using constraints in the outer and inner problems, respectively, whereas, sub- or super-optimal behavior of each microorganism is captured by using a constraint for the respective inner problem.</p

Comparison of the predicted fluxes by OptCom with estimates in the proposed model of [<b>47</b>].

<p>The total predicted acetate and CO₂ production rates by OptCom are in good agreement with experimental measurements by Miller <i>et al </i><a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002363#pcbi.1002363-Miller1" target="_blank">[47]</a>. Note that it was not possible to determine experimentally how much of the total acetate or CO₂ available in the supernatant of the bioreactor is produced by which microorganism (the values provided by Miller <i>et al </i><a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002363#pcbi.1002363-Miller1" target="_blank">[47]</a> for the acetate and CO₂ production by each species as well as all inter-organism flow rates are estimates and not experimental measurements). The values associated with the biomass of each microorganism represent fluxes (1/h) for OptCom predictions and concentrations (M) for experimental measurements <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002363#pcbi.1002363-Miller1" target="_blank">[47]</a>.</p

Pictorial illustration of the customized OptCom for various types of interactions.

<p>OptCom (top panel) can be readily customized for each type of interaction through properly adjusting the inter-organism flow constraints as demonstrated for a typical microbial community composed of two interacting members.</p

Optimality levels for the SYN and FAP guilds and their effect on the total community biomass.

<p>Optimality levels for (A) SYN and (B) FAP as a function of the SYN/FAP biomass ratio across different values of the O₂/CO₂ flux ratio (C) Comparison of the predicted total community biomass (1/h) for the case when SYN grows sub-optimally and when it grows optimally. Note that, to compute the total community biomass when SYN grows optimally only O₂/CO₂ flux ratio was fixed at values in the experimentally determined range (i.e., 0.03 to 0.07), whereas for all other cases, in addition to O₂/CO₂ flux ratio, SYN/FAP biomass ratio was also fixed at values measured experimentally (i.e., 1.5 to 3.5). Lower and upper dashed lines in (C) represent the maximum and minimum predicted community biomass (when SYN grows sub-optimally) across various SYN/FAP biomass ratios.</p

k-OptForce: Integrating Kinetics with Flux Balance Analysis for Strain Design

<div><p>Computational strain design protocols aim at the system-wide identification of intervention strategies for the enhanced production of biochemicals in microorganisms. Existing approaches relying solely on stoichiometry and rudimentary constraint-based regulation overlook the effects of metabolite concentrations and substrate-level enzyme regulation while identifying metabolic interventions. In this paper, we introduce k-OptForce, which integrates the available kinetic descriptions of metabolic steps with stoichiometric models to sharpen the prediction of intervention strategies for improving the bio-production of a chemical of interest. It enables identification of a minimal set of interventions comprised of both enzymatic parameter changes (for reactions with available kinetics) and reaction flux changes (for reactions with only stoichiometric information). Application of k-OptForce to the overproduction of L-serine in <i>E. coli</i> and triacetic acid lactone (TAL) in <i>S. cerevisiae</i> revealed that the identified interventions tend to cause less dramatic rearrangements of the flux distribution so as not to violate concentration bounds. In some cases the incorporation of kinetic information leads to the need for additional interventions as kinetic expressions render stoichiometry-only derived interventions infeasible by violating concentration bounds, whereas in other cases the kinetic expressions impart flux changes that favor the overproduction of the target product thereby requiring fewer direct interventions. A sensitivity analysis on metabolite concentrations shows that the required number of interventions can be significantly affected by changing the imposed bounds on metabolite concentrations. Furthermore, k-OptForce was capable of finding non-intuitive interventions aiming at alleviating the substrate-level inhibition of key enzymes in order to enhance the flux towards the product of interest, which cannot be captured by stoichiometry-alone analysis. This study paves the way for the integrated analysis of kinetic and stoichiometric models and enables elucidating system-wide metabolic interventions while capturing regulatory and kinetic effects.</p></div

Comparison of the concentrations (in mM) of metabolites in <i>I^kin</i> for the engineered and reference strain of <i>S. cerevisiae</i> for overproduction of TAL.

<p>The first value indicates the overproducing strain concentration, while the second value refers to the reference strain concentration.</p