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Bacterial metabolism and pathogenesis intimate intertwining: time for metabolic modelling to come into action
research-article
Publication date (Electronic):
21 October 2021
Journal:
Microbial Biotechnology
Publisher:
John Wiley and Sons Inc.
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Most cited references63
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What is flux balance analysis?
Jeffrey Orth, Ines Thiele, Bernhard Palsson (2010)
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Current status and applications of genome-scale metabolic models
Changdai Gu, Gi Kim, Won Jun Kim … (2019)
Genome-scale metabolic models (GEMs) computationally describe gene-protein-reaction associations for entire metabolic genes in an organism, and can be simulated to predict metabolic fluxes for various systems-level metabolic studies. Since the first GEM for Haemophilus influenzae was reported in 1999, advances have been made to develop and simulate GEMs for an increasing number of organisms across bacteria, archaea, and eukarya. Here, we review current reconstructed GEMs and discuss their applications, including strain development for chemicals and materials production, drug targeting in pathogens, prediction of enzyme functions, pan-reactome analysis, modeling interactions among multiple cells or organisms, and understanding human diseases. Electronic supplementary material The online version of this article (10.1186/s13059-019-1730-3) contains supplementary material, which is available to authorized users.
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MEMOTE for standardized genome-scale metabolic model testing
Christian Lieven, Moritz Beber, Brett Olivier … (2020)
To the Editor — Reconstructing metabolic reaction networks enables the development of testable hypotheses of an organism’s metabolism under different conditions 1 . State-of-the-art genome-scale metabolic models (GEMs) can include thousands of metabolites and reactions that are assigned to subcellular locations. Gene–protein–reaction (GPR) rules and annotations using database information can add meta-information to GEMs. GEMs with metadata can be built using standard reconstruction protocols 2 , and guidelines have been put in place for tracking provenance and enabling interoperability, but a standardized means of quality control for GEMs is lacking 3 . Here we report a community effort to develop a test suite named MEMOTE (for metabolic model tests) to assess GEM quality. Incompatible description formats and missing annotations 4 limit GEM reuse. Moreover, numerical errors 5 and omission of essential cofactors 6 in a single biomass objective function can have substantial impact on the predictive performance of a GEM. Failure to make checks for flux cycles and imbalances can render model predictions untrustworthy 7 . Every year, increasing numbers of manually curated and automatically generated GEMs are published, including those for human and cancer tissue models 8 . We believe that it is essential to optimize GEM reproducibility and reuse. Researchers need models that are software-agnostic, with components that have standardized, database-independent identifiers. Default conditions and mathematically specified modeling formulations must be precisely defined to allow reproduction of the original model predictions. Models must produce feasible phenotypes under various conditions. Finally, data used to build any model must be made available in a reusable format. A dual approach could be used to improve GEM reuse and reproducibility. First, we advocate adoption of the latest version of the Systems Biology Markup Language (SBML) level 3 flux balance constraints (SBML3FBC) package 9 as the primary description and exchange format. The SBML3FBC package adds structured, semantic descriptions for domain-specific model components such as flux bounds, multiple linear objective functions, GPR rules, metabolite chemical formulas, charge and annotations. The SBML and constraint-based modeling communities collaboratively develop this package, updating it based on user input. It has been adopted by a wide range of constraint-based modeling software and public model repositories (http://cbmpy.sourceforge.net/ and refs. 10–15 ), and should therefore be considered the standard for encoding GEMs. Second, we present MEMOTE (/’mi:moʊt/ in international phonetic alphabet notation), an open-source Python software that represents a unified approach to ensure the formally correct definition of SBML3FBC and provides quality control and continuous quality assurance of metabolic models with tools and best practices already used in software development 16,17 . MEMOTE accepts stoichiometric models encoded in SBML3FBC and previous versions as input. In addition to structural validation analogous to the SBML validator 18 , MEMOTE benchmarks metabolic models using consensus tests from four general areas: annotation, basic tests, biomass reaction and stoichiometry. Annotation tests check that a model is annotated according to community standards with minimum information required in annotation of models (MIRIAM)-compliant cross-references 19 , that all primary identifiers belong to the same namespace rather than being fractured across several namespaces, and that components are described using Systems Biology Ontology (SBO) terms 20 . A lack of explicit, standardized annotations complicates the use, comparison and extension of GEMs, and thus strongly hampers collaboration 3,4 . Basic tests check the formal correctness of a model and verify the presence of components such as metabolites, compartments, reactions and genes. These tests also check for metabolite formula and charge information, and GPR rules. General quality metrics, such as the degree of metabolic coverage representing the ratio of reactions and genes 21 , are also checked. A model is tested for production of biomass precursors in different conditions, for biomass consistency, for nonzero growth rate and for direct precursors. The biomass reaction is based on the biomass composition of the modeled organism and expresses its ability to produce the necessary precursors for in silico cell growth and maintenance. Thus, an extensive, well-formed biomass reaction is crucial for accurate predictions with a GEM 6 . Stoichiometric inconsistency, erroneously produced energy metabolites 7 and permanently blocked reactions are identified by MEMOTE. Errors in stoichiometries may result in the production of ATP or redox cofactors from nothing 2 and are detrimental to the performance of the model when using flux-based analysis 4 . MEMOTE enables a quick comparison of any two given models, in which individual test results are quantified and condensed to calculate an overall score (Supplementary Note 1). In addition to these consensus tests, researchers can supply experimental data from growth and gene perturbation studies in a range of input formats (.csv, .tsv, .xls or .xslx) in MEMOTE. To support reproducibility, researchers can configure MEMOTE to recognize specific data types as input to predefined experimental tests for model validation (Supplementary Note 2). There are two main workflows for MEMOTE (Fig. 1a and Supplementary Figs. 1–3). For peer review, MEMOTE can produce either a ‘snapshot report’ or a ‘diff report’ that display MEMOTE test results of one single or multiple models, respectively. For model reconstruction, MEMOTE helps users to create a version-controlled repository of the model and to activate continuous integration toward building a ‘history report’ that records the results of each tracked edit of the model. Although a model repository can be used offline, we encourage community collaboration via distributed version control development platforms, such as GitHub (https://github.com), GitLab (https://gitlab.com/) or BioModels 12 (http://wwwdev.ebi.ac.uk/biomodels/). MEMOTE is tightly integrated with GitHub. Models generated and versioned in MEMOTE can easily be uploaded to GitLab and BioModels. Collaborative model reconstruction with MEMOTE as benchmark can occur using all three software platforms (Fig. 1b). Fig. 1 Graphical summary of MEMOTE. a, Graphical representation of the two principal workflows in detail. For peer review, MEMOTE serves as a benchmark tool generating a comprehensive, human-readable report, which quantifies the model’s performance (Supplementary Figs. 1 and 2). With this information, a definitive assessment of model quality can be made by editors, reviewers and users. This workflow is accessible through a web interface (https://memote.io) or locally through a command line interface. For model reconstruction, MEMOTE helps users to create a version-controlled repository for the model (indicated by the blue asterisk), and to activate continuous integration. The model is tested using MEMOTE’s library of test cases, the results are saved, and an initial report of the model is generated. This constitutes the first iteration of the development cycle. Now, users may edit the model using their preferred reconstruction tool and subsequently export it to SBML3FBC, thus creating a new version (indicated by +n). This will restart the cycle by running the tests automatically, saving the results for each version and including them incrementally in a report on the entire history of results. This serves as a guide toward a functional, high-quality GEM (Supplementary Fig. 3). This workflow is accessible through the command line only. b, Both, GitHub and GitLab support a branching strategy, which model builders could use to curate different parts of the model simultaneously or to invite external experts to improve specific model features. MEMOTE further enables model authors to act as gatekeepers, choosing to accept only high-quality contributions. Identification of functional differences happens in the form of a comparative ‘diff’ report, whereas for file-based discrepancies MEMOTE capitalizes on the platform’s ability to show the line-by-line changes between different versions of a model. For this purpose, the model is written in a sorted YAML format 28 after every change. Bold blue text denotes actions performed by MEMOTE. We validated MEMOTE using models from seven GEM collections (Fig. 2, Supplementary Table 1 and Supplementary Methods), that comprise manually and (semi)-automatically reconstructed GEMs (10,780 models in total). Most GEM collections have already made models available in SMBL format. A nonlinear dimensional reduction of the normalized test results (Supplementary Methods) using t-distributed stochastic neighbor embedding (t-SNE; Fig. 2a) indicates that models from the same source are generally more similar to each other than to models from other sources. Nevertheless, several model sources reveal internal subgroupings (Fig. 2a). With the exception of Path2Models 22 , which relies on pathway resources that contain problematic reaction information on stoichiometry and directionality 23 , automatically reconstructed GEMs were stoichiometrically consistent (Fig. 2b) and mass-balanced (Supplementary Fig. 4). Of the manually reconstructed GEMs we tested, most models in BiGG 13 are stoichiometrically consistent, but there is wide variation among published models, with ~70% of models having at least one stoichiometrically unbalanced metabolite. Stoichiometrically inconsistent models cannot be mass-balanced, but missing formula annotations, from which molecular masses are calculated, further contribute to reactions being counted as unbalanced. The problems that we identified in published models underpin the need for application of MEMOTE during peer-review process (but ideally before submission) of GEMs. Fig. 2 Quality of manually reconstructed GEMs from collections without quality control or quality assurance. a, Depicted is a t-SNE two-dimensional reduction of models using normalized test features as input. Only GEMs from the BiGG collection form a single albeit small cluster. Models from all other collections are grouped in several fragmented but distinct clusters. b–d, SinaPlots 29 of each collection overlaid with box and whisker plots to indicate 25%, 50% (median) and 75% quantiles. GEMs from collections built in a modern automated pipeline (AGORA, CarveMe, KBase) are stoichiometrically consistent, whereas models from the older Path2Models collection are up to 50% stoichiometrically inconsistent (b). Manually reconstructed models (BiGG, Ebrahim et al. 30 , OptFlux models) contain varying degrees of inconsistent GEMs. GPR rules are essential for in silico knockout studies, but also serve to justify the presence of a reaction (c). Generally, the fraction of reactions without GPR rules is low (~15%). Yet a distinct group of models from the collections of Ebrahim et al. and OptFlux lack GPR rules for >75% of their reactions. Most models from the CarveMe and Path2Models collections contain very few blocked reactions, whereas for models from the other collections the number of blocked reactions lies mostly between 10% and 30% (d). Again, models from the collections of Ebrahim et al. and the Optflux models show the largest variance. Source Data During GEM reconstruction, metabolic reactions are defined based on functional gene annotations, and this information is output as GPR rules. We found that ~15% of reactions in models we tested are not annotated with GPR rules (Fig. 2c). For published models, subgroups of models contain up to 85% of reactions without GPR rules. This could be due to a large number of modeling-specific reactions, spontaneous reactions 24 and known reactions with undiscovered genes, or if GPR rules were annotated in nonstandard ways. CarveMe 25 and Path2Models 22 have a very low fraction of universally blocked reactions, whereas models from AGORA 26 and KBase 14 contain ~30% blocked reactions, and BiGG 13 models and OptFlux 15 models contain ~20% blocked reactions (Fig. 2d). Similarly, orphan and dead-end metabolites (Supplementary Figs. 5 and 6) are also present in all of these published collections. We note that blocked reactions and dead-end metabolites are not indicators of low-quality models but that a large proportion (for example, >50%) of universally blocked reactions can indicate problems in reconstruction that need solving. AGORA, KBase and BiGG are the only collections with SBML-compliant metabolite and reaction annotations. Gene annotations are only present in KBase models and selected BiGG models (Supplementary Figs. 7–9). Each collection uses its own system of identifiers for each model component, but there is some overlap between all three (Supplementary Figs. 10 and 11), and partial overlaps for models from KBase and BiGG (Supplementary Figs. 12–16), or AGORA and BiGG (Supplementary Figs. 17 and 18), but not KBase and AGORA. BiGG is the only collection with models using MetaNetX 27 annotations (Supplementary Fig. 19). MetaNetX consolidates biochemical namespaces by establishing a mapping between them through a set of unique identifiers. Hence, knowing the MetaNetX identifier for a given entity often means also knowing the identifiers for other databases (Supplementary Methods). MEMOTE tests cover semantic and conceptual requirements, which are fundamental to SBML3FBC and constraint-based modeling, respectively. They are extensible to allow the validation of a model’s performance against experimental data and can be executed as a stand-alone tool or integrated into existing reconstruction pipelines. Capitalizing on robust workflows established in modern software development, MEMOTE promotes openness and collaboration by granting the community tangible metrics to support their research and to discuss assumptions or limitations openly. Application of a set of defined metabolic model tests is not dependent on implementation in MEMOTE, and for some users it may be more desirable to implement each test separately to streamline the user experience. We propose that an independent, central library of tests and a tool to run them offers an unbiased approach to quality control because the tests are continuously reviewed by the community. This resource will be maintained under stewardship of Nikolaus Sonnenschein by the openCOBRA consortium (https://github.com/opencobra). To encourage integration as opposed to duplication, MEMOTE provides a Python application programing interface (API) as well as being available as a web service. MEMOTE has already been integrated in several services and tools (Supplementary Note 3). We discuss alternatives and future perspectives of MEMOTE in Supplementary Notes 4 and 5, respectively. We recommend that MEMOTE users reach out to GEM authors to report any errors and thereby enable community improvement of models as resources. Using inconsistent GEMs for hypothesis generation could lead researchers down blind alleys, so we weighed the influence of ‘consistency’ and ‘stoichiometric consistency’ and SBO terms higher than tests for metabolite, reaction and gene annotations. We are committed to keeping MEMOTE open to support community principles. Robust benchmarking will only work if it is actively supported by the whole community, and we call on any interested experts to join this endeavor and enable its continual improvement. Reporting Summary Further information on research design is available in the Nature Research Reporting Summary linked to this article. Supplementary information Supplementary Materials Supplementary Figs. 1–162, Notes 1–5, Methods, and Tables 1 and 2 Reporting Summary
Author and article information
Contributors
Juan Nogales:
ORCID: https://orcid.org/0000-0002-4961-0833
j.nogales@csic.es
Junkal Garmendia:
ORCID: https://orcid.org/0000-0002-7440-2737
juncal.garmendia@csic.es
Journal
Journal ID (nlm-ta): Microb Biotechnol
Journal ID (iso-abbrev): Microb Biotechnol
Journal ID (doi): 10.1111/(ISSN)1751-7915
Journal ID (publisher-id): MBT2
Title:
Microbial Biotechnology
Publisher:
John Wiley and Sons Inc.
(Hoboken
)
ISSN
(Electronic):
1751-7915
Publication date
(Electronic):
21
October
2021
Publication date Collection:
January
2022
Volume: 15
Issue: 1
,
Special Issue on Microbial Biotechnology is 15!
(
doiID:
10.1111/mbt2.v15.1
)
Pages: 95-102
Affiliations
[
2
]
Interdisciplinary Platform for Sustainable Plastics towards a Circular Economy‐Spanish
National Research Council (SusPlast‐CSIC)
Madrid
Spain
Author notes
[*]
[*
]
For correspondence. *E‐mail
juncal.garmendia@
123456csic.es
; Tel. 00 34 948168484; Fax 00 34 948232191. **E‐mail
jnogales@
123456cnb.csic.es
; Tel: 00 34 915854557; Fax 00 34 915854506.
Author information
Juan Nogales
https://orcid.org/0000-0002-4961-0833
Junkal Garmendia
https://orcid.org/0000-0002-7440-2737
Article
Publisher ID: MBT213942DOI: 10.1111/1751-7915.13942
PMC ID: 8719832
PubMed ID: 34672429
SO-VID: 67ce67b8-327b-4a63-bd2f-3aaf6f69a951
Copyright © © 2021 The Authors.
Microbial Biotechnology published by Society for Applied Microbiology and John Wiley & Sons Ltd.
License:
This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
History
Date
received
: 20
September
2021
Date
accepted
: 25
September
2021
Page count
Figures: 2,
Tables: 0,
Pages: 102,
Words: 5358
Funding
Funded by: MINECO
Award ID: RTI2018‐096369‐B‐I00
Funded by: SEPAR
Award ID: 875/2019
Funded by: Gobierno de Navarra
Award ID: PC150‐151‐152
Funded by: MICIU
Award ID: PID2019‐108458RB‐I00
Funded by: Instituto de Salud Carlos III
Categories
Subject:
Special Issue Article
Subject:
Special Issue Articles
Custom metadata
source-schema-version-number 2.0
cover-date January 2022
details-of-publishers-convertor Converter:WILEY_ML3GV2_TO_JATSPMC version:6.7.0 mode:remove_FC converted:31.12.2021
ScienceOpen disciplines: Biotechnology
Data availability:
ScienceOpen disciplines: Biotechnology