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Mycobacterial metabolic model development for drug target identification.

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Peer-reviewed

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Article

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Abstract

Antibiotic resistance is increasing at an alarming rate, and three related mycobacteria are sources of widespread infections in humans. According to the World Health Organization, Mycobacterium leprae, which causes leprosy, is still endemic in tropical countries; Mycobacterium tuberculosis is the second leading infectious killer worldwide after COVID-19; and Mycobacteroides abscessus, a group of non-tuberculous mycobacteria, causes lung infections and other healthcare-associated infections in humans. Due to the rise in resistance to common antibacterial drugs, it is critical that we develop alternatives to traditional treatment procedures. Furthermore, an understanding of the biochemical mechanisms underlying pathogenic evolution is important for the treatment and management of these diseases. In this study, metabolic models have been developed for two bacterial pathogens, M. leprae and My. abscessus, and a new computational tool has been used to identify potential drug targets, which are referred to as bottleneck reactions. The genes, reactions, and pathways in each of these organisms have been highlighted; the potential drug targets can be further explored as broad-spectrum antibacterials and the unique drug targets for each pathogen are significant for precision medicine initiatives. The models and associated datasets described in this paper are available in GigaDB, Biomodels, and PatMeDB repositories.

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Keywords

3207 Medical Microbiology, 32 Biomedical and Clinical Sciences, 31 Biological Sciences, Emerging Infectious Diseases, Antimicrobial Resistance, Orphan Drug, Biodefense, Infectious Diseases, Rare Diseases, Tuberculosis, 5 Development of treatments and therapeutic interventions, 5.1 Pharmaceuticals, 2.1 Biological and endogenous factors, 2 Aetiology, 2.2 Factors relating to the physical environment, Infection, 3 Good Health and Well Being

Journal Title

GigaByte

Conference Name

Journal ISSN

2709-4715
2709-4715

Volume Title

2023

Publisher

GigaScience Press