A systematic evaluation of Mycobacterium tuberculosis Genome-Scale Metabolic Networks.

Víctor A López-Agudelo,Dany J V Beste,Luis F Barrera,Huihai Wu,Rigoberto Rios-Estepa,Andres Baena,Tom A Mendum,Emma Laing,Anders Wallqvist

doi:10.1371/journal.pcbi.1007533

Víctor A López-Agudelo, Dany J V Beste + Show 7 more

Open Access

https://doi.org/10.1371/journal.pcbi.1007533

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Abstract

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.

Highlights

Mycobacterium tuberculosis (Mtb) is the causative bacterial agent of the global tuberculosis (TB) epidemic, which is the biggest infectious disease killer worldwide, causing 1.6 million deaths in 2017 alone [1]
The pairwise matrix (Fig 2) demonstrates that Mtb models constructed from the same ancestor, are more similar (Fig 1, Fig 2)
By contrast the consolidated models iEK1011 and sMtb2018 share gene similarities (>60%, 60%,

Summary

Introduction

Mycobacterium tuberculosis (Mtb) is the causative bacterial agent of the global tuberculosis (TB) epidemic, which is the biggest infectious disease killer worldwide, causing 1.6 million deaths in 2017 alone [1]. Genome-scale constraint-based modelling has proved to be a powerful method to probe the metabolism of Mtb. The first Genome Scale Metabolic Networks (GSMNs) of Mtb were published in 2007 by Beste (GSMN-TB) [10] and Jamshidi (iNJ661) [10,11] and have been used as a platform for interrogating high throughput ‘omics’ data, by simulating bacterial growth, generating hypothesis and informing drug discovery. The first Genome Scale Metabolic Networks (GSMNs) of Mtb were published in 2007 by Beste (GSMN-TB) [10] and Jamshidi (iNJ661) [10,11] and have been used as a platform for interrogating high throughput ‘omics’ data, by simulating bacterial growth, generating hypothesis and informing drug discovery These two original models were iteratively improved to expand both their scope and accuracy [12,13,14,15,16,17,18,19,20], to give us a current total of 16 inter-related GSMN of Mtb (Fig 1)

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Results

Conclusion