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Potential Genes Related to Levofloxacin Resistance in Mycobacterium tuberculosis Based on Transcriptome and Methylome Overlap Analysis.

ABSTRACT: Drug-resistant Mycobacterium tuberculosis (M. tuberculosis) has become an increasingly serious public health problem and has complicated tuberculosis (TB) treatment. Levofloxacin (LOF) is an ideal anti-tuberculosis drug in clinical applications. However, the detailed molecular mechanisms of LOF-resistant M. tuberculosis in TB treatment have not been revealed. Our study performed transcriptome and methylome sequencing to investigate the potential biological characteristics of LOF resistance in M. tuberculosis H37Rv. In the transcriptome analysis, 953 differentially expressed genes (DEGs) were identified; 514 and 439 DEGs were significantly downregulated and upregulated in the LOF-resistant group and control group, respectively. The KEGG pathway analysis revealed that 97 pathways were enriched in this study. In the methylome analysis, 239 differentially methylated genes (DMGs) were identified; 150 and 89 DMGs were hypomethylated and hypermethylated in the LOF-resistant group and control group, respectively. The KEGG pathway analysis revealed that 74 pathways were enriched in this study. The overlap study suggested that 25 genes were obtained. It was notable that nine genes expressed downregulated mRNA and upregulated methylated levels, including pgi, fadE4, php, cyp132, pckA, rpmB1, pfkB, acg, and ctpF, especially cyp132, pckA, and pfkB, which were vital in LOF-resistant M. tuberculosis H37Rv. The overlapping genes between transcriptome and methylome could be essential for studying the molecular mechanisms of LOF-resistant M. tuberculosis H37Rv. These results may provide informative evidence for TB treatment with LOF.

SUBMITTER: Li HC

PROVIDER: S-EPMC6989609 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundEmergence of drug resistant varieties of tuberculosis is posing a major threat to global tuberculosis eradication programmes. Although several approaches have been explored to counter resistance, there has been limited success due to a lack of understanding of how resistance emerges in bacteria upon drug treatment. A systems level analysis of the proteins involved is essential to gain insights into the routes required for emergence of drug resistance.ResultsWe derive a genome-scale protein-protein interaction network for Mycobacterium tuberculosis H37Rv from the STRING database, with proteins as nodes and interactions as edges. A set of proteins involved in both intrinsic and extrinsic drug resistance mechanisms are identified from literature. We then compute shortest paths from different drug targets to the set of resistance proteins in the protein-protein interactome, to derive a sub-network relevant to study emergence of drug resistance. The shortest paths are then scored and ranked based on a new scheme that considers (a) drug-induced gene upregulation data, from microarray experiments reported in literature, for the individual nodes and (b) edge-hubness, a network parameter which signifies centrality of a given edge in the network. High-scoring paths identified from this analysis indicate most plausible pathways for the emergence of drug resistance. Different targets appear to have different propensities for four drug resistance mechanisms. A new concept of 'co-targets' has been proposed to counter drug resistance, co-targets being defined as protein(s) that need to be simultaneously inhibited along with the intended target(s), to check emergence of resistance to a given drug.ConclusionThe study leads to the identification of possible pathways for drug resistance, providing novel insights into the problem of resistance. Knowledge of important proteins in such pathways enables identification of appropriate 'co-targets', best examples being RecA, Rv0823c, Rv0892 and DnaE1, for drugs targeting the mycolic acid pathway. Insights obtained about the propensity of a drug to trigger resistance will be useful both for more careful identification of drug targets as well as to identify target-co-target pairs, both implementable in early stages of drug discovery itself. This approach is also inherently generic, likely to significantly impact drug discovery.

Project description:Accurate and timely functional genome annotation is essential for translating basic pathogen research into clinically impactful advances. Here, through literature curation and structure-function inference, we systematically update the functional genome annotation of Mycobacterium tuberculosis virulent type strain H37Rv. First, we systematically curated annotations for 589 genes from 662 publications, including 282 gene products absent from leading databases. Second, we modeled 1,711 underannotated proteins and developed a semiautomated pipeline that captured shared function between 400 protein models and structural matches of known function on Protein Data Bank, including drug efflux proteins, metabolic enzymes, and virulence factors. In aggregate, these structure- and literature-derived annotations update 940/1,725 underannotated H37Rv genes and generate hundreds of functional hypotheses. Retrospectively applying the annotation to a recent whole-genome transposon mutant screen provided missing function for 48% (13/27) of underannotated genes altering antibiotic efficacy and 33% (23/69) required for persistence during mouse tuberculosis (TB) infection. Prospective application of the protein models enabled us to functionally interpret novel laboratory generated pyrazinamide (PZA)-resistant mutants of unknown function, which implicated the emerging coenzyme A depletion model of PZA action in the mutants' PZA resistance. Our findings demonstrate the functional insight gained by integrating structural modeling and systematic literature curation, even for widely studied microorganisms. Functional annotations and protein structure models are available at https://tuberculosis.sdsu.edu/H37Rv in human- and machine-readable formats. IMPORTANCE Mycobacterium tuberculosis, the primary causative agent of tuberculosis, kills more humans than any other infectious bacterium. Yet 40% of its genome is functionally uncharacterized, leaving much about the genetic basis of its resistance to antibiotics, capacity to withstand host immunity, and basic metabolism yet undiscovered. Irregular literature curation for functional annotation contributes to this gap. We systematically curated functions from literature and structural similarity for over half of poorly characterized genes, expanding the functionally annotated Mycobacterium tuberculosis proteome. Applying this updated annotation to recent in vivo functional screens added functional information to dozens of clinically pertinent proteins described as having unknown function. Integrating the annotations with a prospective functional screen identified new mutants resistant to a first-line TB drug, supporting an emerging hypothesis for its mode of action. These improvements in functional interpretation of clinically informative studies underscore the translational value of this functional knowledge. Structure-derived annotations identify hundreds of high-confidence candidates for mechanisms of antibiotic resistance, virulence factors, and basic metabolism and other functions key in clinical and basic tuberculosis research. More broadly, they provide a systematic framework for improving prokaryotic reference annotations.

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Potential Genes Related to Levofloxacin Resistance in Mycobacterium tuberculosis Based on Transcriptome and Methylome Overlap Analysis.

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