Updated and standardized genome-scale reconstruction of Mycobacterium tuberculosis H37Rv, iEK1011, simulates flux states indicative of physiological conditions

Kavvas, Erol; Seif, Yara; Yurkovich, James T.; Norsigian, Charles J.; Poudel, Saugat; Greenwald, William W.; Ghatak, Sankha; Monk, Jonathan M.

doi:10.1186/s12918-018-0557-y

Cited by 62 publications

(81 citation statements)

References 69 publications

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“…For a comparative analysis of pathways shared between M. tuberculosis H37Rv, M. abscessus and M. leprae, we used the newly reconstructed M. tuberculosis model iEKVIII (30) and the draft BioCyc pathway databases of M. abscessus and M. leprae. There were 297 pathways in M. abscessus, 212 pathways in M. tuberculosis and 135 pathways in M. leprae (S1 Table).…”

Section: Pathway Analysesmentioning

confidence: 99%

“…We performed a comparative analysis of pathways in the networks of M. tuberculosis, M. abscessus and M. leprae by using the most recent reconstruction of Mycobacterium tuberculosis metabolic network (30) and the automated build of the pathway genome database for M. abscessus and M. leprae using the Pathway Tools software (60). To determine potential drug targets, we implemented a pipeline (Fig 1), which includes an in-house chokepoint algorithm in Python, the input of which is a metabolic network expressed in the Systems Biology Markup Language (SBML) format, and the output is a spreadsheet with the reactions that are either the only producers or the only consumers of the metabolites.…”

Section: Pathway / Chokepoint Analysis / Gene Essentialitymentioning

confidence: 99%

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Analysis of metabolic pathways in mycobacteria to aid drug-target identification

Bannerman

Vedithi

Júlvez

et al. 2019

Preprint

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Full Title: Comparative analyses of metabolic pathways in mycobacteria: Insights into the identification and characterisation of new drug targets in M. tuberculosis, M. leprae and M. abscessus Abstract Three related mycobacteria are the cause of widespread infections in man and are the focus of intense research and drug-discovery efforts in the face of growing antimicrobial resistance.Mycobacterium tuberculosis, the causative agent of tuberculosis, is currently one of the top ten causes of death in the world according to WHO; M. abscessus, a group of non-tuberculous mycobacteria causes lung infections and other opportunistic infections in humans; and M. leprae, the causative agent of leprosy, remains endemic in tropical countries. There is an urgent need to design alternatives to conventional treatment strategies, due to the increase in resistance to standard antibacterials. In this study, we present a comparative analysis of chokepoint and essentiality datasets that will provide insight into the development of new treatment regimes. We illustrate the key metabolic pathways shared between these three organisms and identify drug targets with a wide metabolic impact that are common to the three species. We demonstrate that 72% of the chokepoint enzymes are proteins essential to Mycobacterium tuberculosis. We show also that 78% of the drug targets, prioritized based on their presence in multiple paths on the metabolic network, are present in pathways shared by M. tuberculosis, M. leprae and M. abscessus, including biosynthesis of amino acids, carbohydrates, cell structures, fatty acid and lipid biosynthesis. A further 17% is found in the prioritised pathways shared between M. tuberculosis and M. abscessus. We have performed comparative structure modelling of potential drug targets identified using our analysis in order to assess druggability and demonstrate the importance of chokepoint analysis in terms of drug target identification. AUTHOR SUMMARYComputer simulation studies to design new drugs against mycobacteria

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Section: Pathway Analysesmentioning

confidence: 99%

Section: Pathway / Chokepoint Analysis / Gene Essentialitymentioning

confidence: 99%

Analysis of metabolic pathways in mycobacteria to aid drug-target identification

Bannerman

Vedithi

Júlvez

et al. 2019

Preprint

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“…We mapped the measured gene expression data against the most recent genome-scale metabolic network construction of M. tuberculosis H37Rv iEK1011 (Kavvas et al, 2018) using COBRApy (Ebrahim et al, 2013). We used the subsystem defintions defined in iEK1011 to explore pathway usage at the network level.…”

Section: Metabolic Pathway Analysismentioning

confidence: 99%

“…Using the gene expression data, we also analyzed changes in MTB metabolic pathways, as reconstructed by Kavvas et al (Kavvas et al, 2018), along the hypoxia time series. The expression of many metabolic pathways reiterated the state transitions described above (Fig 2C-F).…”

mentioning

confidence: 99%

“…The star symbol (*) indicates the most enriched term at the individual term level in the Resuscitation state (not present in any of the significant term clusters). (C-F) Log2 fold-change respect to normoxia (T0 and T1) of selected metabolic pathways reconstructed by Kavvas et al (Kavvas et al, 2018); numbers in parentheses indicated the number of genes in the relevant pathway. (Minch et al, 2015) were used to establish the connections between TFs.…”

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confidence: 99%

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Intricate genetic programs controlling dormancy inMycobacterium tuberculosis

Abidi

Peterson

Arrieta-Ortiz

et al. 2019

Preprint

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Mycobacterium tuberculosis (MTB), responsible for the deadliest infectious disease worldwide, displays the remarkable ability to transition in and out of dormancy, a hallmark of the pathogen's capacity to evade the immune system and opportunistically exploit immunocompromised individuals. Uncovering the gene regulatory programs that underlie the dramatic phenotypic shifts in MTB during disease latency and reactivation has posed an extraordinary challenge. We developed a novel experimental system to precisely control dissolved oxygen levels in MTB cultures in order to capture the chain of transcriptional events that unfold as MTB transitions into and out of hypoxia-induced dormancy. Using a comprehensive genomewide transcription factor binding location map and insights from network topology analysis, we identified regulatory circuits that deterministically drive sequential transitions across six transcriptionally and functionally distinct states encompassing more than three-fifths of the MTB genome. The architecture of the genetic programs explains the transcriptional dynamics underlying synchronous entry of cells into a dormant state that is primed to infect the host upon encountering favorable conditions. One Sentence Summary: High-resolution transcriptional time-course reveals six-state genetic program that enables MTB to enter and exit hypoxia-induced dormancy.Main Text: Mycobacterium tuberculosis (MTB) kills more people than any other infectious agent, causing ~10 million new cases of active tuberculosis (TB) disease and 1.7 million deaths each year (Murray et al., 2014). TB remains a major human public health burden, in large part due to the sizeable reservoir of latently infected individuals, who may relapse into active disease decades after acquiring the infection. MTB can persist in a stable, non-replicative (often termed dormant) state within the host for months or years without symptoms, and then revive to initiate the production of lesions and active TB disease. Moreover, dormant cells may be responsible for the slow treatment response of patients with active TB. Elucidation of the factors that affect treatment outcome, latency and activation requires a better characterization of functional states adopted by the pathogen during progression of the disease, as well as a mechanistic understanding of the genetic programs that orchestrate transitions between these states.Hypoxia, an environmental stress encountered by MTB within granulomas (Tsai et al., 2006), is sufficient to shift the pathogen into a defined non-growing survival form, which can be reversed upon aeration of the culture (Chao and Rubin, 2010). Therefore, hypoxia has been leveraged as an in vitro approximation to study MTB dormancy and the underlying genetic programs. However, previous transcriptional analyses under in vitro hypoxic conditions (via the Wayne model in which MTB cultures are sealed and gradually depleted of oxygen (Wayne and Hayes, 1996;Wayne and Sohaskey, 2001) or the defined hypoxia model in which nitrogen gas is flowed into...

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Comparative metabolic modeling of multiple sulfate‐reducing prokaryotes reveals versatile energy conservation mechanisms

Tang

Hao

Chen

2021

Biotech & Bioengineering

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Sulfate-reducing prokaryotes (SRPs) are crucial participants in the cycling of sulfur, carbon, and various metals in the natural environment and in engineered systems.Despite recent advances in genetics and molecular biology bringing a huge amount of information about the energy metabolism of SRPs, little effort has been made to link this important information with their biotechnological studies. This study aims to construct multiple metabolic models of SRPs that systematically compile genomic, genetic, biochemical, and molecular information about SRPs to study their energy metabolism. Pan-genome analysis was conducted to compare the genomes of SRPs, from which a list of orthologous genes related to central and energy metabolism was obtained. Twenty-four SRP metabolic models via the inference of pan-genome analysis were efficiently constructed. The metabolic model of the well-studied model SRP Desulfovibrio vulgaris Hildenborough (DvH) was validated via flux balance analysis (FBA). The DvH model predictions matched reported experimental growth and energy yields, which demonstrated that the core metabolic model worked successfully.Further, steady-state simulation of SRP metabolic models under different growth conditions showed how the use of different electron transfer pathways leads to energy generation. Three energy conservation mechanisms were identified, including menaquinone-based redox loop, hydrogen cycling, and proton pumping. Flavin-based electron bifurcation (FBEB) was also demonstrated to be an essential mechanism for supporting energy conservation. The developed models can be easily extended to other species of SRPs not examined in this study. More importantly, the present work develops an accurate and efficient approach for constructing metabolic models of multiple organisms, which can be applied to other critical microbes in environmental and industrial systems, thereby enabling the quantitative prediction of their metabolic behaviors to benefit relevant applications.

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Updated and standardized genome-scale reconstruction of Mycobacterium tuberculosis H37Rv, iEK1011, simulates flux states indicative of physiological conditions

Cited by 62 publications

References 69 publications

Analysis of metabolic pathways in mycobacteria to aid drug-target identification

Analysis of metabolic pathways in mycobacteria to aid drug-target identification

Intricate genetic programs controlling dormancy inMycobacterium tuberculosis

Comparative metabolic modeling of multiple sulfate‐reducing prokaryotes reveals versatile energy conservation mechanisms

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