metaGEM: reconstruction of genome scale metabolic models directly from metagenomes

AbstractAdvances in genome-resolved metagenomic analysis of complex microbial communities have revealed a large degree of interspecies and intraspecies genetic diversity through the reconstruction of metagenome assembled genomes (MAGs). Yet, metabolic modeling efforts still tend to rely on reference genomes as the starting point for reconstruction and simulation of genome scale metabolic models (GEMs), neglecting the immense intra- and inter-species diversity present in microbial communities. Here we present metaGEM (https://github.com/franciscozorrilla/metaGEM), an end-to-end highly scalable pipeline enabling metabolic modeling of multi-species communities directly from metagenomic samples. The pipeline automates all steps from the extraction of context-specific prokaryotic GEMs from metagenome assembled genomes to community level flux balance simulations. To demonstrate the capabilities of the metaGEM pipeline, we analyzed 483 samples spanning lab culture, human gut, plant associated, soil, and ocean metagenomes, to reconstruct over 14 000 prokaryotic GEMs. We show that GEMs reconstructed from metagenomes have fully represented metabolism comparable to the GEMs reconstructed from reference genomes. We further demonstrate that metagenomic GEMs capture intraspecies metabolic diversity by identifying the differences between pathogenicity levels of type 2 diabetes at the level of gut bacterial metabolic exchanges. Overall, our pipeline enables simulation-ready metabolic model reconstruction directly from individual metagenomes, provides a resource of all reconstructed metabolic models, and showcases community-level modeling of microbiomes associated with disease conditions allowing generation of mechanistic hypotheses.

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On the inconsistent treatment of gene-protein-reaction rules in context-specific metabolic models

10.1101/593277 ◽

2019 ◽

Author(s):

Miguel Ponce-de-León ◽

Iñigo Apaolaza ◽

Alfonso Valencia ◽

Francisco J. Planes

Keyword(s):

Gene Expression ◽

Metabolic Model ◽

Specific Model ◽

Omics Data ◽

Model Reconstruction ◽

Reconstruction Algorithms ◽

Reconstruction Methods ◽

Metabolic Models ◽

Context Specific ◽

Genome Scale

ABSTRACTWith the publication of high-quality genome-scale metabolic models for several organisms, the Systems Biology community has developed a number of algorithms for their analysis making use of ever growing –omics data. In particular, the reconstruction of the first genome-scale human metabolic model, Recon1, promoted the development of Context-Specific Model (CS-Model) reconstruction methods. This family of algorithms aims to identify the set of metabolic reactions that are active in a cell in a given condition using omics data, such as gene expression levels. Different CS-Model reconstruction algorithms have their own strengths and weaknesses depending on the problem under study and omics data available. However, after careful inspection, we found that all of these algorithms share common issues in the way GPR rules and gene expression data are treated. The first issue is related with how gapfilling reactions are managed after the reconstruction is conducted. The second issue concerns the molecular context, which is used to build the CS-model but neglected for posterior analyses. To evaluate the effect of these issues, we reconstructed ∼400 CS-Models of cancer cell lines and conducted gene essentiality analysis, using CRISPR–Cas9 essentiality data for validation purposes. Altogether, our results illustrate the importance of correcting the errors introduced during the GPR translation in many of the published metabolic reconstructions.

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Metabolic dependencies drive species co-occurrence in diverse microbial communities

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1421834112 ◽

2015 ◽

Vol 112 (20) ◽

pp. 6449-6454 ◽

Cited By ~ 286

Author(s):

Aleksej Zelezniak ◽

Sergej Andrejev ◽

Olga Ponomarova ◽

Daniel R. Mende ◽

Peer Bork ◽

...

Keyword(s):

Microbial Communities ◽

Resource Competition ◽

Critical Role ◽

Metabolic Modeling ◽

Cooperative Groups ◽

Cooperative Interactions ◽

A Genome ◽

Group Survival ◽

Metabolic Interactions ◽

Genome Scale

Microbial communities populate most environments on earth and play a critical role in ecology and human health. Their composition is thought to be largely shaped by interspecies competition for the available resources, but cooperative interactions, such as metabolite exchanges, have also been implicated in community assembly. The prevalence of metabolic interactions in microbial communities, however, has remained largely unknown. Here, we systematically survey, by using a genome-scale metabolic modeling approach, the extent of resource competition and metabolic exchanges in over 800 communities. We find that, despite marked resource competition at the level of whole assemblies, microbial communities harbor metabolically interdependent groups that recur across diverse habitats. By enumerating flux-balanced metabolic exchanges in these co-occurring subcommunities we also predict the likely exchanged metabolites, such as amino acids and sugars, that can promote group survival under nutritionally challenging conditions. Our results highlight metabolic dependencies as a major driver of species co-occurrence and hint at cooperative groups as recurring modules of microbial community architecture.

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StanDep: capturing transcriptomic variability improves context-specific metabolic models

10.1101/594861 ◽

2019 ◽

Cited By ~ 1

Author(s):

Chintan J. Joshi ◽

Song-Min Schinn ◽

Anne Richelle ◽

Isaac Shamie ◽

Eyleen J. O’Rourke ◽

...

Keyword(s):

Expression Profiles ◽

Heuristic Method ◽

Housekeeping Genes ◽

Extraction Methods ◽

Expression Data ◽

Matlab Toolbox ◽

Metabolic Models ◽

Using Data ◽

Context Specific ◽

Genome Scale

AbstractDiverse algorithms can integrate transcriptomics with genome-scale metabolic models (GEMs) to build context-specific metabolic models. These algorithms require identification of a list of high confidence (core) reactions from transcriptomics, but parameters related to identification of core reactions, such as thresholding of expression profiles, can significantly change model content. Importantly, current thresholding approaches are burdened with setting singular arbitrary thresholds for all genes; thus, resulting in removal of enzymes needed in small amounts and even many housekeeping genes. Here, we describe StanDep, a novel heuristic method for using transcriptomics to identify core reactions prior to building context-specific metabolic models. StanDep clusters gene expression data based on their expression pattern across different contexts and determines thresholds for each cluster using data-dependent statistics, specifically standard deviation and mean. To demonstrate the use of StanDep, we built hundreds of models for the NCI-60 cancer cell lines. These models successfully increased the inclusion of housekeeping reactions, which are often lost in models built using standard thresholding approaches. Further, StanDep also provided a transcriptomic explanation for inclusion of lowly expressed reactions that were otherwise only supported by model extraction methods. Our study also provides novel insights into how cells may deal with context-specific and ubiquitous functions. StanDep, as a MATLAB toolbox, is available at https://github.com/LewisLabUCSD/StanDep

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iEC7871 Quercus suber model: the first multi-tissue diel cycle genome-scale metabolic model of a woody tree

10.1101/2021.03.09.434537 ◽

2021 ◽

Author(s):

Emanuel Cunha ◽

Miguel Silva ◽

Ines Chaves ◽

Huseyin Demirci ◽

Davide Lagoa ◽

...

Keyword(s):

Metabolic Model ◽

Tissue Level ◽

Quercus Suber ◽

Biomass Composition ◽

Improve Model ◽

Transcriptomics Data ◽

Oak Tree ◽

Metabolic Models ◽

Genome Scale ◽

Metabolic Behaviour

AbstractIn the last decade, genome-scale metabolic models have been increasingly used to study plant metabolic behaviour at the tissue and multi-tissue level in different environmental conditions. Quercus suber (Q. suber), also known as the cork oak tree, is one of the most important forest communities of the Mediterranean/Iberian region. In this work, we present the genome-scale metabolic model of the Q. suber (iEC7871), the first of a woody plant. The metabolic model comprises 7871 genes, 6230 reactions, and 6481 metabolites across eight compartments. Transcriptomics data was integrated into the model to obtain tissue-specific models for the leaf, inner bark, and phellogen. Each tissue’s biomass composition was determined to improve model accuracy and merged into a diel multi-tissue metabolic model to predict interactions among the three tissues at the light and dark phases. The metabolic models were also used to analyze the pathways associated with the synthesis of suberin monomers. Nevertheless, the models developed in this work can provide insights about other aspects of the metabolism of Q. suber, such as its secondary metabolism and cork formation.

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Fast automated reconstruction of genome-scale metabolic models for microbial species and communities

10.1101/223198 ◽

2018 ◽

Cited By ~ 2

Author(s):

Daniel Machado ◽

Sergej Andrejev ◽

Melanie Tramontano ◽

Kiran Raosaheb Patil

Keyword(s):

Microbial Communities ◽

Single Species ◽

Model Organisms ◽

Universal Model ◽

Microbial Species ◽

Scale Models ◽

Metabolic Models ◽

User Friendly ◽

Genome Scale ◽

Automated Tool

AbstractGenome-scale metabolic models are instrumental in uncovering operating principles of cellular metabolism and model-guided re-engineering. Recent applications of metabolic models have also demonstrated their usefulness in unraveling cross-feeding within microbial communities. Yet, the application of genome-scale models, especially to microbial communities, is lagging far behind the availability of sequenced genomes. This is largely due to the time-consuming steps of manual cura-tion required to obtain good quality models and thus physiologically meaningful simulation results. Here, we present an automated tool – CarveMe – for reconstruction of species and community level metabolic models. We introduce the concept of a universal model, which is manually curated and simulation-ready. Starting with this universal model and annotated genome sequences, CarveMe uses a top-down approach to build single-species and community models in a fast and scalable manner. We build reconstructions for two model organisms, Escherichia coli and Bacillus subtillis, as well as a collection of human gut bacteria, and show that CarveMe models perform similarly to manually curated models in reproducing experimental phenotypes. Finally, we demonstrate the scalability of CarveMe through reconstructing 5587 bacterial models. Overall, CarveMe provides an open-source and user-friendly tool towards broadening the use of metabolic modeling in studying microbial species and communities.

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Methods for automated genome-scale metabolic model reconstruction

Biochemical Society Transactions ◽

10.1042/bst20170246 ◽

2018 ◽

Vol 46 (4) ◽

pp. 931-936 ◽

Cited By ~ 22

Author(s):

José P. Faria ◽

Miguel Rocha ◽

Isabel Rocha ◽

Christopher S. Henry

Keyword(s):

Next Generation Sequencing ◽

Sequence Data ◽

Metabolic Model ◽

Model Reconstruction ◽

Next Generation ◽

Genome Sequences ◽

Metabolic Models ◽

Genome Scale ◽

Generation Sequencing

In the era of next-generation sequencing and ubiquitous assembly and binning of metagenomes, new putative genome sequences are being produced from isolate and microbiome samples at ever-increasing rates. Genome-scale metabolic models have enormous utility for supporting the analysis and predictive characterization of these genomes based on sequence data. As a result, tools for rapid automated reconstruction of metabolic models are becoming critically important for supporting the analysis of new genome sequences. Many tools and algorithms have now emerged to support rapid model reconstruction and analysis. Here, we are comparing and contrasting the capabilities and output of a variety of these tools, including ModelSEED, Raven Toolbox, PathwayTools, SuBliMinal Toolbox and merlin.

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Genome-scale metabolic reconstruction of the stress-tolerant hybrid yeast Zygosaccharomyces parabailii

10.1101/373621 ◽

2018 ◽

Author(s):

Marzia Di Filippo ◽

Raúl A. Ortiz-Merino ◽

Chiara Damiani ◽

Gianni Frascotti ◽

Danilo Porro ◽

...

Keyword(s):

Laboratory Data ◽

Metabolic Model ◽

Cellular Systems ◽

Metabolic Reconstruction ◽

Genome Sequences ◽

Metabolic Potential ◽

Cell Factories ◽

Metabolic Models ◽

Constraint Based Modeling ◽

Genome Scale

Genome-scale metabolic models are powerful tools to understand and engineer cellular systems facilitating their use as cell factories. This is especially true for microorganisms with known genome sequences from which nearly complete sets of enzymes and metabolic pathways are determined, or can be inferred. Yeasts are highly diverse eukaryotes whose metabolic traits have long been exploited in industry, and although many of their genome sequences are available, few genome-scale metabolic models have so far been produced. For the first time, we reconstructed the genome-scale metabolic model of the hybrid yeast Zygosaccharomyces parabailii, which is a member of the Z. bailii sensu lato clade notorious for stress-tolerance and therefore relevant to industry. The model comprises 3096 reactions, 2091 metabolites, and 2413 genes. Our own laboratory data were then used to establish a biomass synthesis reaction, and constrain the extracellular environment. Through constraint-based modeling, our model reproduces the co-consumption and catabolism of acetate and glucose posing it as a promising platform for understanding and exploiting the metabolic potential of Z. parabailii.

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Towards creating an extended metabolic model (EMM) for E. coli using enzyme promiscuity prediction and metabolomics data

10.1101/536060 ◽

2019 ◽

Author(s):

Sara A. Amin ◽

Elizabeth Chavez ◽

Nikhil U. Nair ◽

Soha Hassoun

Keyword(s):

Metabolic Model ◽

Enzyme Promiscuity ◽

Metabolomics Data ◽

Structural Modifications ◽

E Coli ◽

Cellular Metabolic Activity ◽

Metabolic Models ◽

Genome Scale ◽

Primary Substrate ◽

Model Annotation

AbstractBackgroundMetabolic models are indispensable in guiding cellular engineering and in advancing our understanding of systems biology. As not all enzymatic activities are fully known and/or annotated, metabolic models remain incomplete, resulting in suboptimal computational analysis and leading to unexpected experimental results. We posit that one major source of unaccounted metabolism is promiscuous enzymatic activity. It is now well-accepted that most, if not all, enzymes are promiscuous – i.e., they transform substrates other than their primary substrate. However, there have been no systematic analyses of genome-scale metabolic models to predict putative reactions and/or metabolites that arise from enzyme promiscuity.ResultsOur workflow utilizes PROXIMAL – a tool that uses reactant-product transformation patterns from the KEGG database – to predict putative structural modifications due to promiscuous enzymes. Using iML1515 as a model system, we first utilized a computational workflow, referred to as Extended Metabolite Model Annotation (EMMA), to predict promiscuous reactions catalyzed, and metabolites produced, by natively encoded enzymes in E. coli. We predict hundreds of new metabolites that can be used to augment iML1515. We then validated our method by comparing predicted metabolites with the Escherichia coli Metabolome Database (ECMDB).ConclusionsWe utilized EMMA to augment the iML1515 metabolic model to more fully reflect cellular metabolic activity. This workflow uses enzyme promiscuity as basis to predict hundreds of reactions and metabolites that may exist in E. coli but have not been documented in iML1515 or other databases. Among these, we found that 17 metabolites have previously been documented in E. coli metabolomics studies. Further, 6 of these metabolites are not documented for any other E. coli metabolic model (e.g. KEGG, EcoCyc). The corresponding reactions should be added to iML1515 to create an Extended Metabolic Model (EMM). Other predicted metabolites and reactions can guide future experimental metabolomics studies. Further, our workflow can easily be applied to other organisms for which comprehensive genome-scale metabolic models are desirable.

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Enhancing Microbiome Research through Genome-Scale Metabolic Modeling

mSystems ◽

10.1128/msystems.00599-21 ◽

2021 ◽

Author(s):

Nana Y. D. Ankrah ◽

David B. Bernstein ◽

Matthew Biggs ◽

Maureen Carey ◽

Melinda Engevik ◽

...

Keyword(s):

Systems Biology ◽

Metabolic Modeling ◽

Ecological Processes ◽

Mechanistic Insight ◽

Microbiome Research ◽

Metabolic Models ◽

Genome Scale ◽

Insight Into

Construction and analysis of genome-scale metabolic models (GEMs) is a well-established systems biology approach that can be used to predict metabolic and growth phenotypes. The ability of GEMs to produce mechanistic insight into microbial ecological processes makes them appealing tools that can open a range of exciting opportunities in microbiome research.

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Metabolic Responses to Polymyxin Treatment in Acinetobacter baumannii ATCC 19606: Integrating Transcriptomics and Metabolomics with Genome-Scale Metabolic Modeling

mSystems ◽

10.1128/msystems.00157-18 ◽

2019 ◽

Vol 4 (1) ◽

Cited By ~ 8

Author(s):

Yan Zhu ◽

Jinxin Zhao ◽

Mohd Hafidz Mahamad Maifiah ◽

Tony Velkov ◽

Falk Schreiber ◽

...

Keyword(s):

Acinetobacter Baumannii ◽

Metabolic Modeling ◽

Metabolic Model ◽

Metabolic Responses ◽

Metabolomics Data ◽

Gram Negative ◽

Content Type ◽

Integrative Modeling ◽

Line Therapy ◽

Genome Scale

ABSTRACT Multidrug-resistant (MDR) Acinetobacter baumannii has emerged as a very problematic pathogen over the past decades, with a high incidence in nosocomial infections. Discovered in the late 1940s but abandoned in the 1970s, polymyxins (i.e., polymyxin B and colistin) have been revived as the last-line therapy against Gram-negative “superbugs,” including MDR A. baumannii. Worryingly, resistance to polymyxins in A. baumannii has been increasingly reported, urging the development of novel antimicrobial therapies to rescue this last-line class of antibiotics. In the present study, we integrated genome-scale metabolic modeling with multiomics data to elucidate the mechanisms of cellular responses to colistin treatment in A. baumannii. A genome-scale metabolic model, iATCC19606, was constructed for strain ATCC 19606 based on the literature and genome annotation, containing 897 genes, 1,270 reactions, and 1,180 metabolites. After extensive curation, prediction of growth on 190 carbon sources using iATCC19606 achieved an overall accuracy of 84.3% compared to Biolog experimental results. Prediction of gene essentiality reached a high accuracy of 86.1% and 82.7% compared to two transposon mutant libraries of AB5075 and ATCC 17978, respectively. Further integrative modeling with our correlative transcriptomics and metabolomics data deciphered the complex regulation on metabolic responses to colistin treatment, including (i) upregulated fluxes through gluconeogenesis, the pentose phosphate pathway, and amino acid and nucleotide biosynthesis; (ii) downregulated TCA cycle and peptidoglycan and lipopolysaccharide biogenesis; and (iii) altered fluxes over respiratory chain. Our results elucidated the interplay of multiple metabolic pathways under colistin treatment in A. baumannii and provide key mechanistic insights into optimizing polymyxin combination therapy. IMPORTANCE Combating antimicrobial resistance has been highlighted as a critical global health priority. Due to the drying drug discovery pipeline, polymyxins have been employed as the last-line therapy against Gram-negative “superbugs”; however, the detailed mechanisms of antibacterial killing remain largely unclear, hampering the improvement of polymyxin therapy. Our integrative modeling using the constructed genome-scale metabolic model iATCC19606 and the correlative multiomics data provide the fundamental understanding of the complex metabolic responses to polymyxin treatment in A. baumannii at the systems level. The model iATCC19606 may have a significant potential in antimicrobial systems pharmacology research in A. baumannii.

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