RESUMO
In dealing with Mycobacterium tuberculosis, the causative agent of the deadliest human disease-tuberculosis (TB)-utilization of cholesterol as a carbon source indicates the possibility of using cholesterol catabolic genes/proteins as novel drug targets. However, studies on cholesterol catabolism in mycobacterial species are scarce, and the number of mycobacterial species utilizing cholesterol as a carbon source is unknown. The availability of a large number of mycobacterial species' genomic data affords an opportunity to explore and predict mycobacterial species' ability to utilize cholesterol employing in silico methods. In this study, comprehensive comparative analysis of cholesterol catabolic genes/proteins in 93 mycobacterial species was achieved by deducing a comprehensive cholesterol catabolic pathway, developing a software tool for extracting homologous protein data and using protein structure and functional data. Based on the presence of cholesterol catabolic homologous proteins proven or predicted to be either essential or specifically required for the growth of M. tuberculosis H37Rv on cholesterol, we predict that among 93 mycobacterial species, 51 species will be able to utilize cholesterol as a carbon source. This study's predictions need further experimental validation and the results should be taken as a source of information on cholesterol catabolism and genes/proteins involved in this process among mycobacterial species.
Assuntos
Proteínas de Bactérias/genética , Colesterol/metabolismo , Genes Bacterianos , Mycobacterium/genética , Animais , Proteínas de Bactérias/metabolismo , Colesterol/química , Genes Essenciais , Macrófagos/metabolismo , Macrófagos/microbiologia , Redes e Vias Metabólicas , Camundongos , Viabilidade Microbiana/genética , Mycobacterium/crescimento & desenvolvimento , Infecções por Mycobacterium/genética , Infecções por Mycobacterium/microbiologia , Especificidade da EspécieRESUMO
Mycobacterium ulcerans, the causative agent of Buruli ulcer, is the third most common mycobacterial disease after tuberculosis and leprosy. The present treatment options are limited and emergence of treatment resistant isolates represents a serious concern and a need for better therapeutics. Conventional drug discovery methods are time consuming and labor-intensive. Unfortunately, the slow growing nature of M. ulcerans in experimental conditions is also a barrier for drug discovery and development. In contrast, recent advancements in complete genome sequencing, in combination with cheminformatics and computational biology, represent an attractive alternative approach for the identification of therapeutic candidates worthy of experimental research. A computational, comparative genomics workflow was defined for the identification of novel therapeutic candidates against M. ulcerans, with the aim that a selected target should be essential to the pathogen, and have no homology in the human host. Initially, a total of 424 genes were predicted as essential from the M. ulcerans genome, via homology searching of essential genome content from 20 different bacteria. Metabolic pathway analysis showed that the most essential genes are associated with carbohydrate and amino acid metabolism. Among these, 236 proteins were identified as non-host and essential, and could serve as potential drug and vaccine candidates. Several drug target prioritization parameters including druggability were also calculated. Enzymes from several pathways are discussed as potential drug targets, including those from cell wall synthesis, thiamine biosynthesis, protein biosynthesis, and histidine biosynthesis. It is expected that our data will facilitate selection of M. ulcerans proteins for successful entry into drug design pipelines.
Assuntos
Descoberta de Drogas/métodos , Genes Essenciais/genética , Genoma Bacteriano/genética , Redes e Vias Metabólicas/genética , Mycobacterium ulcerans/genética , Enzimas/genética , Genômica , Mycobacterium ulcerans/metabolismoRESUMO
Experiences in the application of Boolean logic to the clusters of orthologous groups of proteins (COGs) database for target selection in the Mycobacterium tuberculosis genome are described.