The application of constraint-based modeling to functionally analyze metagenomic data has been limited so far, partially due to the absence of suitable toolboxes.
To address this gap, we created a comprehensive toolbox to model i) microbe-microbe and host-microbe metabolic interactions, and ii) microbial communities using microbial genome-scale metabolic reconstructions and metagenomic data. The Microbiome Modeling Toolbox extends the functionality of the COBRA Toolbox.
The Microbiome Modeling Toolbox and the tutorials at https://git.io/microbiomeModelingToolbox

Baldini, Federico

Fleming, Ronan

Heinken, Almut Katrin

Heirendt, Laurent

Magnusdottir, Stefania

Thiele, Ines

peer reviewedThe application of constraint-based modeling to functionally analyze metagenomic data has been limited so far, partially due to the absence of suitable toolboxes.
To address this gap, we created a comprehensive toolbox to model i) microbe-microbe and host-microbe metabolic interactions, and ii) microbial communities using microbial genome-scale metabolic reconstructions and metagenomic data. The Microbiome Modeling Toolbox extends the functionality of the COBRA Toolbox.
The Microbiome Modeling Toolbox and the tutorials at https://git.io/microbiomeModelingToolbox

Open Repository and Bibliography - Luxembourg

Bioinformaticsdoi.10.1093/bioinformatics/xxxxxxAdvance Access Publication Date: Day Month YearManuscript CategorySystems biologyThe Microbiome Modeling Toolbox: from microbialinteractions to personalized microbial communitiesFederico Baldini 1, Almut Heinken 1, Laurent Heirendt 1, StefaniaMagnusdottir 1, Ronan M.T. Fleming 2, and Ines Thiele 1∗1Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette, Luxembourg and2Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Faculty of Science, LeidenUniversity, Leiden, The Netherlands∗To whom correspondence should be addressed.Associate Editor: XXXXXXXReceived on XXXXX; revised on XXXXX; accepted on XXXXXAbstractMotivation: The application of constraint-based modeling to functionally analyze metagenomic data hasbeen limited so far, partially due to the absence of suitable toolboxes.Results: To address this gap, we created a comprehensive toolbox to model i) microbe-microbe and host-microbe metabolic interactions, and ii) microbial communities using microbial genome-scale metabolicreconstructions and metagenomic data. The Microbiome Modeling Toolbox extends the functionality ofthe COBRA Toolbox.Availability: TheMicrobiomeModeling Toolbox and the tutorials at https://git.io/microbiomeModelingToolbox.Contact: ines.thiele@gmail.com1 IntroductionMicrobial community sequencing data are increasingly available fornumerous environmental niches (Mitchell, 2017). The analysis of this dataoften relies on investigating which microbes are present in a given sample.However, to further our understanding of the functional contribution ofindividual microbes in a community as well as the overall functionaldifferences between communities, advanced analysis approaches, suchas computational modeling, are required.One possible approach is the constraint-based reconstruction andanalysis (COBRA) approach, which builds genome-scale reconstructionsof an organism and enables the prediction of, e.g., phenotypic properties(Palsson, 2006). Through the application of condition-specific constraints,an organism’s metabolic reconstruction can be converted into manycondition-specific models, which can be analyzed using availabletoolboxes, such as the Matlab (Mathworks, Inc.) based COBRA Toolbox(Heirendt et al., 2017a). Metabolic reconstructions have been assembledfor many organisms, including hundreds of gut microbes (Magnúsdóttiret al., 2017) and human (Brunk et al., 2018). While the COBRA Toolboxencapsulates many tools developed by the community for biotechnologicaland biomedical applications, it is currently focused on modeling singleorganisms or cells. Here, we present the Microbiome Modeling Toolbox,which enables the generation, simulation, and interpretation of 1.pairwise microbe-microbe and host-microbe interactions, and 2. sample-specific microbial community models. By integrating sample-specificmetagenomic data, the Microbiome Modeling Toolbox facilitates itsanalysis in the context of microbial reconstructions.2 FeaturesPairwise interactions: The pairwise interaction analysis determinesmetabolic exchange between two metabolic reconstructions. A jointmatrix of two individual genome-scale reconstructions is generated, whichenables them to freely exchange metabolites (Figure 1A). Defined nutrientinput, e.g., a particular medium formulation, can be applied via the sharedcompartment using the corresponding exchange reactions. The pairwisemicrobial models can be investigated for six possible interaction types (i.e.,competition, parasitism, amensalism, neutralism, commensalism, andmutualism) and Pareto optimality frontiers can be calculated. The tutorialsMicrobeMicrobeInteractions and HostMicrobeInteractions illustrate theimplemented functionalities.Microbial community modeling: Metagenomic data can be analyzedusing mgPipe (Figure 1B), which requires microbe identification and© The Author(s) 2018. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com Downloaded from https://academic.oup.com/bioinformatics/advance-article-abstract/doi/10.1093/bioinformatics/bty941/5194337 by guest on 23 November 20182 Baldini et al.Relative organism abundances  MDS 1MDS2Multidimensional scaling (MDS) on reaction contentMicrobiota sizeUnique reactionsMetabolicdiversity  Data mapping  Metabolic reconstructions Community model with lumen, diet, and fecal compartmentsCommunity model setup Diet integration  Personalized model setup Simulations Predicted metabolic profile MDS 1MDS2Comparison of the predicted metabolic profiles Flux variability analysis on diet and fecal exchange reactionsgetMappingInfo.m | plotMappingInfo.m fastSetupCreator.m createPersonalizedModel.m microbiotaModelSimulator.m mgSimResCollect.mPopulation Healthy  Diseased  B mgPipe.m mgPipe.m mgPipe.m mgPipe.mLumenDietFecalLumenDietFecal112233Pairwise modeling  Metabolic reconstructions  Diet integration Tradeoff Interaction types Interactions by taxa Pareto frontier of the two organisms' objectives 9%27%18% 2%44%11%36%13%40%AmensalismCommensalismMutualismNeutralismParasitism012345678joinModelsPairwiseFromList.m  computeParetoOptimality.m  simulatePairwiseInteractions.m  calculateInteractionsByTaxon.mPAIRWISE MODELING MICROBIAL COMMUNITY MODELINGA Biomass of species 1Biomass of species 2LumenDietFecalV1V2V3TaxaInteraction typeWith oxygenWith no oxygenFig. 1. Overview of the Microbiome Modeling Toolbox. A. Pairwise modeling of microbe-microbe and host-microbe interactions. B. Microbial community modeling.relative abundance data for each sample, obtained with bioinformatictools, such as QiIME 2(Caporaso et al., 2010) and MetaPhlAn (Segataet al., 2012). mgPipe is divided into three parts: 1. the analysis ofindividuals’ specific microbes abundances, including metabolic diversityand classical multidimensional scaling of the reactions in the identifiedmicrobes. 2. Construction of a personalized microbial communitymodel using the identified microbes and their relative abundance data.For each personalized (or sample-specific) model, the correspondingmicrobial reconstructions are joined by adding reactions to each microbialreconstruction transporting metabolites from the extracellular space tothe common lumen compartment. Metabolites present in the lumencompartment are connected to a diet and fecal compartment, enablingthe uptake and secretion from/to the environment, respectively. Hundredsof reconstructions can be combined and modeled with using staticparallelization. In each microbial community model, the communitybiomass reaction is personalized using the relative abundance data. Finally,coupling constraints (Heinken et al., 2013) are applied to couple the fluxthrough each microbial reaction to its corresponding biomass reaction flux.And 3. simulation of the personalized microbial community models underdifferent diet regimes, e.g., using flux variability analysis (Heirendt et al.,2017b). The differences between maximal uptake and secretion fluxesprovide a metabolic profile for each microbial community sample, whichcan be analyzed using classical multidimensional scaling analyses. Diet-specific constraints (e.g., obtained from https://vmh.life/#nutrition) can beapplied to the corresponding diet exchange reactions.3 ImplementationThe Microbiome Modeling Toolbox is written in MATLAB (Mathworks,Inc.) and accompagnied with comprehesive documentation and tutorials.The toolbox allows for the integrative analysis of any number ofreconstructions, including the human metabolic reonstruction (Brunket al., 2018). Metabolic reconstructions can be obtained from, e.g., theVHM (https://vmh.life), BioModels (https://www.ebi.ac.uk/biomodels-main/), and the KBase (https://kbase.us/). A uniform nomenclatureof reaction and metabolite abbreviations across the reconstructions isrequired. The implemented diet constraints require VMH abbreviations. Touse higher taxonomical levels create pan-reconstructions (createPanModels).For larger datasets and/or bigger microbial community models, werecommend the use of the MATLAB command line or .m files and ofa high-performance computing cluster.4 DiscussionThe Microbiome Modeling Toolbox enables the user to investigatemicrobial interactions at a large scale (Heinken et al., 2013; Magnúsdóttiret al., 2017). Moreover, metagenomically-derived data can be integratedwith microbial metabolic reconstructions permitting the prediction ofaltered functional assessment of different microbial communities, e.g.,in health and disease (Heinken et al., 2018; Thiele et al., 2018).FundingThis study received funding from the Luxembourg National Research Fund(FNR), through the ATTRACT programme (FNR/A12/01), and the OPENgrant (FNR/O16/11402054), as well as the European Research Council(ERC) under the European Union’s Horizon 2020 research and innovationprogramme (grant agreement No 757922).ReferencesBrunk, E., et al. (2018). Recon3d enables a three-dimensional view of gene variationin human metabolism. Nat. Biotech., 36(3), 272.Caporaso, J. G., et al. (2010). Qiime allows analysis of high-throughput communitysequencing data. Nat. Methods, 7(5), 335.Heinken, A., et al. (2013). Systems-level characterization of a host-microbemetabolic symbiosis in the mammalian gut. Gut microbes, 4(1), 28–40.Heinken, A. et al. (2018). Personalized modeling of the human gut microbiomereveals distinct bile acid deconjugation and biotransformation potential in healthyand IBD individuals. bioRxiv, doi.org/10.1101/229138.Heirendt, L., et al. (2017a). Creation and analysis of biochemical constraint-basedmodels: the COBRA ToolBox v3. 0. arXiv:1710.04038.Heirendt, L., et al. (2017b). DistributedFBA. jl: High-level, high-performance fluxbalance analysis in julia. Bioinformatics, 33(9), 1421–1423.Downloaded from https://academic.oup.com/bioinformatics/advance-article-abstract/doi/10.1093/bioinformatics/bty941/5194337 by guest on 23 November 2018short Title 3Magnúsdóttir, S., et al. (2017). Generation of genome-scale metabolicreconstructions for 773 members of the human gut microbiota. Nat Biotech., 35(1),81.Mitchell, A. L. et al. (2017). EBI Metagenomics in 2017: enriching the analysis ofmicrobial communities, from sequence reads to assemblies. Nucl Ac Res., 46(D1):D726-D735.Palsson, B. (2006). Systems biology: properties of reconstructed networks.Cambridge: Cambridge Univ Pr.Segata, N. et al. (2012). Metagenomic microbial community profiling using uniqueclade-specific marker genes. Nat Meth., 9: 811-814.Thiele, I. et al. (2018). When metabolism meets physiology: Harvey and Harvetta.bioRxiv, doi.org/10.1101/255885. Downloaded from https://academic.oup.com/bioinformatics/advance-article-abstract/doi/10.1093/bioinformatics/bty941/5194337 by guest on 23 November 2018

The Microbiome Modeling Toolbox: from microbial interactions to personalized microbial communities

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The Microbiome Modeling Toolbox: from microbial interactions to personalized microbial communities

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