Víctor A. López-Agudelo,Tom A. Mendum,Emma Laing,HuiHai Wu,Andres Baena,Luis F. Barrera,Dany J. V. Beste , Rigoberto Rios-Estepa
Metabolism underpins the pathogenic strategy of the causative agent of TB, Mycobacterium tuberculosis (Mtb), and therefore metabolic pathways have recently re-emerged as attractive drug targets. A powerful approach to study Mtb metabolism as a whole, rather than just individual enzymatic components, is to use a systems biology framework, such as a Genome-Scale Metabolic Network (GSMN) that allows the dynamic interactions of all the components of metabolism to be interrogated together. Several GSMNs networks have been constructed for Mtb and used to study the complex relationship between the Mtb genotype and its phenotype. However, the utility of this approach is hampered by the existence of multiple models, each with varying properties and performances. Here we systematically evaluate eight recently published metabolic models of Mtb-H37Rv to facilitate model choice. The best performing models, sMtb2018 and iEK1011, were refined and improved for use in future studies by the TB research community.
Mycobacterium tuberculosis (Mtb) is the causative bacterial agent of the global tuberculosis (TB) epidemic, which is now the biggest infectious disease killer worldwide, causing 1.6 million deaths in 2017 alone . Mtb is an unusual bacterial pathogen, as it is able to cause both acute life threatening disease and a clinically latent infections that can persist for the lifetime of the human host [2,3]. Metabolic reprogramming in response to the host niche during both the acute and the chronic phase of TB infections is a crucial determinant of virulence [4–7]. With the worldwide spread of multi- and extensively-resistant strains of Mtb thwarting the control of this global emergency, new drugs against Mtb are urgently needed and metabolic pathways present attractive and potentially powerful targets
All simulations were conducted on a laptop running Windows 10 (Microsoft) using MATLAB 2016a (MathWorks Corporation, Natick, Massachusetts, USA), COBRA Toolbox version 3.0 , RAVEN 2.0  and Gurobi Optimizer version 7.5.2 (Gurobi Optimization, Inc., Houston, Texas, USA). All code written for this study is available in supplementary information (S1–S8 Files). Genome-scale models of Mtb Models were obtained from supplementary information of published papers and modified as follows:
• GSMN-TB1.1 –from  supplementary info.
• iOSDD890 –from  supplementary info.
• sMtb–from  supplementary info. Modification included were the addition of exchange reactions to allow constraints by growth medium components.
• iCG760 –from  supplementary info.
• iSM810 –from  supplementary info.
• iNJ661v_modified–from  supplementary info.
• sMtb2018 –from  supplementary info.
• iEK1011 –from  supplementary info.
Results and discussion
Descriptive evaluation of the models
Each of the GSMNs analysed in this study (Fig 1, S1 Appendix) combine knowledge from genome annotations, literature and measured biochemical compositions of Mtb. The complex linkage between genotype and phenotype is made by gene-protein-reaction (GPR) associations, implemented as Boolean rules in order to connect gene functions to enzyme complexes, isozymes or promiscuous enzymes, and finally to biochemical reactions . Using set theory, we computed the intersection between all sets of the models’ genes (Fig 2, and S1 Table). In accordance with expectations, the pairwise matrix (Fig 2) demonstrates that Mtb models constructed from the same ancestor (iNJ661 or GSMN-TB), are more similar (Fig 1, Fig 2). By contrast the consolidated models iEK1011 and sMtb2018 share gene similarities (>60%, <85% for iEK1011; and >60%, <98.4%) with all the other models demonstrating an independence from iNJ661 and GSMN-TB.
Víctor A. López-Agudelo thank Msc. Laura P. Pedraza-Palacios for valuable discussions and suggestions.
Citation: López-Agudelo VA, Mendum TA, Laing E, Wu H, Baena A, Barrera LF, et al. (2020) A systematic evaluation of Mycobacterium tuberculosis Genome-Scale Metabolic Networks. PLoS Comput Biol 16(6): e1007533. https://doi.org/10.1371/journal.pcbi.1007533
Editor: Anders Wallqvist, US Army Medical Research and Materiel Command, UNITED STATES
Received: November 5, 2019; Accepted: May 8, 2020; Published: June 15, 2020
Copyright: © 2020 López-Agudelo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the manuscript and its Supporting Information files
Funding: This work was supported by COLCIENCIAS – Colombia, Grant 1115-5693-3520 (https://minciencias.gov.co/). DJVB received funds (MR/K01224X/1) from Medical Research Council (https://mrc.ukri.org/). VALA received funds from COLCIENCIAS (National PhD scholarship Conv. 727-2015) (https://www.minciencias.gov.co/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript
Competing interests: The authors have declared that no competing interests exist.