Dataset Information

A Quantitative Method for the Study of HIV-1 and Mycobacterium tuberculosis Coinfection.

ABSTRACT: Mycobacterium tuberculosis and human immunodeficiency virus-1 (HIV-1) syndemic interactions are a major global health concern. Despite the clinical significance of coinfection, our understanding of the cellular pathophysiology and the therapeutic pharmacodynamic impact of coinfection is limited. Here, we use single-round infectious HIV-1 pseudotyped viral particles expressing green fluorescent protein alongside M. tuberculosis expressing mCherry to study pathogenesis and treatment. We report that HIV-1 infection inhibited intracellular replication of M. tuberculosis and demonstrate the therapeutic activity of antiviral treatment (efavirenz) and antimicrobial treatment (rifampicin). The described method could be applied for detailed mechanistic studies to inform the development of novel treatment strategies.

SUBMITTER: Donnellan S

PROVIDER: S-EPMC9978310 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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A Quantitative Method for the Study of HIV-1 and Mycobacterium tuberculosis Coinfection.

Donnellan Samantha S Pennington Shaun H SH Ruggiero Alessandra A Martinez-Rodriguez Carmen C Pouget Marion M Thomas Jordan J Ward Steve A SA Pollakis Georgios G Biagini Giancarlo A GA Paxton William A WA

The Journal of infectious diseases 20230301 5

Mycobacterium tuberculosis and human immunodeficiency virus-1 (HIV-1) syndemic interactions are a major global health concern. Despite the clinical significance of coinfection, our understanding of the cellular pathophysiology and the therapeutic pharmacodynamic impact of coinfection is limited. Here, we use single-round infectious HIV-1 pseudotyped viral particles expressing green fluorescent protein alongside M. tuberculosis expressing mCherry to study pathogenesis and treatment. We report tha ...[more]

PMID: 36537213

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Project description:Human immunodeficiency virus type-1 and hepatitis C virus (HIV/HCV) coinfection occurs when a patient is simultaneously infected with both human immunodeficiency virus type-1 (HIV-1) and hepatitis C virus (HCV), which is common today in certain populations. However, the treatment of coinfection is a challenge because of the special considerations needed to ensure hepatic safety and avoid drug-drug interactions. Multitarget inhibitors with less toxicity may provide a promising therapeutic strategy for HIV/HCV coinfection. However, the identification of one molecule that acts on multiple targets simultaneously by experimental evaluation is costly and time-consuming. In silico target prediction tools provide more opportunities for the development of multitarget inhibitors. In this study, by combining Naïve Bayes (NB) and support vector machine (SVM) algorithms with two types of molecular fingerprints, MACCS and extended connectivity fingerprints 6 (ECFP6), 60 classification models were constructed to predict compounds that were active against 11 HIV-1 targets and four HCV targets based on a multiple quantitative structure-activity relationships (multiple QSAR) method. Five-fold cross-validation and test set validation were performed to measure the performance of the 60 classification models. Our results show that the 60 multiple QSAR models appeared to have high classification accuracy in terms of the area under the ROC curve (AUC) values, which ranged from 0.83 to 1 with a mean value of 0.97 for the HIV-1 models and from 0.84 to 1 with a mean value of 0.96 for the HCV models. Furthermore, the 60 models were used to comprehensively predict the potential targets of an additional 46 compounds, including 27 approved HIV-1 drugs, 10 approved HCV drugs and nine selected compounds known to be active against one or more targets of HIV-1 or HCV. Finally, 20 hits, including seven approved HIV-1 drugs, four approved HCV drugs, and nine other compounds, were predicted to be HIV/HCV coinfection multitarget inhibitors. The reported bioactivity data confirmed that seven out of nine compounds actually interacted with HIV-1 and HCV targets simultaneously with diverse binding affinities. The remaining predicted hits and chemical-protein interaction pairs with the potential ability to suppress HIV/HCV coinfection are worthy of further experimental investigation. This investigation shows that the multiple QSAR method is useful in predicting chemical-protein interactions for the discovery of multitarget inhibitors and provides a unique strategy for the treatment of HIV/HCV coinfection.

Dataset Information

A Quantitative Method for the Study of HIV-1 and Mycobacterium tuberculosis Coinfection.

Publications

A Quantitative Method for the Study of HIV-1 and Mycobacterium tuberculosis Coinfection.

Similar Datasets

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