Antiretroviral therapy resistance mutations among HIV infected people in Kazakhstan

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HIV incidence in Kazakhstan increased by 73% between 2010 and 2020, with an estimated 35,000 people living with HIV (PLHIV) in 2020. The development of antiretroviral drug resistance is a major threat to effective antiretroviral therapy (ART), yet studies on the prevalence of drug resistance in Kazakhstan are sparse. In this study on the molecular epidemiology of HIV in Kazakhstan, we analyzed 968 partial HIV-1 pol sequences that were collected between 2017 and 2020 from PLHIV across all regions of Kazakhstan, covering almost 3% of PLHIV in 2020. Sequences predominantly represented subtypes A6 (57%) and CRF02_AG (41%), with 32% of sequences exhibiting high-level drug resistance. We further identified distinct drug-resistant mutations (DRMs) in the two subtypes: subtype A6 showed a propensity for DRMs A62V, G190S, K101E, and D67N, while CRF02_AG showed a propensity for K103N and V179E. Codon usage analysis revealed that different mutational pathways for the two subtypes may explain the difference in G190S and V179E frequencies. Phylogenetic analysis highlighted differences in the timing and geographic spread of both subtypes within the country, with A62V-harboring subtype A6 sequences clustering on the phylogeny, indicative of sustained transmission of the mutation. Our findings suggest an HIV epidemic characterized by high levels of drug resistance and differential DRM frequencies between subtypes. This emphasizes the importance of drug resistance monitoring within Kazakhstan, together with DRM and subtype screening at diagnosis, to tailor drug regimens and provide effective, virally suppressive ART.

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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High-Level Drug-Resistant Mutations among HIV-1 Subtype A6 and CRF02_AG in Kazakhstan

Sanaubarova

Pujol-Hodge

Dzissyuk

et al. 2023

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“…The selection pressure predicted using the FUBAR method performed separately for each group indicated four distinct DRM sites under positive selection pressure, including DRM site 138 in the ART-naïve group, DRM site 188 in the ART-experienced group, and DRM sites 103 and 115 in the unknown status group, while all other DRM sites were under negative selection. Further analysis of DRM sites between the 3 groups showed DRM sites 69, 101, 179, 184, and 190 (−0.99, −0.95, −0.97, −0.93, and −0.99, respectively) to be under high negative selection pressure in the ART-naïve group; DRM sites 10, 30,41,54,65,100,101,190,221, and 230 (−1.00, −1.00, −0.94, −0.99, −0.97, −0.99, −0.87, −0.99, −0.95, and −0.93, respectively) to be under high negative selection pressure in the ART-experienced group; while DRM sites 46, 47, 54, 106, and 138 (−0.95, −0.90, −0.99, −0.99, and −0.98, respectively) to be under high negative selection pressure in the unknown status group. Moreover, DRM site 103 was observed under positive selection in the ART-experienced group (Supplementary Table S1).…”

Section: Genomic Variability and Selection Pressure On Drm Sitesmentioning

confidence: 98%

Human Immunodeficiency Virus Type-1 Genetic Diversity and Drugs Resistance Mutations among People Living with HIV in Karachi, Pakistan

Rashid,

Kang,

et al. 2024

The human immunodeficiency virus type-1 epidemic in Pakistan has significantly increased over the last two decades. In Karachi, Pakistan, there is a lack of updated information on the complexity of HIV-1 genetic diversity and the burden of drug resistance mutations (DRMs) that can contribute to ART failure and poor treatment outcomes. This study aimed to determine HIV-1 genetic diversity and identify drug-resistance mutations among people living with HIV in Karachi. A total of 364 HIV-positive individuals, with a median age of 36 years, were enrolled in the study. The HIV-1 partial pol gene was successfully sequenced from 268 individuals. The sequences were used to generate phylogenetic trees to determine clade diversity and also to assess the burden of DRMs. Based on the partial pol sequences, 13 distinct HIV-1 subtypes and recombinant forms were identified. Subtype A1 was the most common clade (40%), followed by CRF02_AG (33.2%). Acquired DRMs were found in 30.6% of the ART-experienced patients, of whom 70.7%, 20.7%, and 8.5% were associated with resistance to NNRTIs, NRTIs, and PIs, respectively. Transmitted DRMs were found in 5.6% of the ART-naïve patients, of whom 93% were associated with resistance against NNRTIs and 7% to PIs. The high prevalence of DRMs in ART-experienced patients poses significant challenges to the long-term benefits and sustainability of the ART program. This study emphasizes the importance of continuous HIV molecular epidemiology and drug resistance surveillance to support evidence-based HIV prevention, precise ART, and targeted AIDS care.

“…This plays an important role in ensuring treatment adherence and selecting the most effective combinations of antiretroviral drugs to minimize resistance. For various reasons some patients demonstrate high viral loads even when taking antiretroviral drugs due to development of HIV drug resistance [9][10][11]. Therefore, the development of methods for the prediction of HIV drug resistance is of high importance.…”

Section: Introductionmentioning

confidence: 99%

Web Service for HIV Drug Resistance Prediction Based on Analysis of Amino Acid Substitutions in Main Drug Targets

Paremskaia,

Rudik,

Filimonov

et al. 2023

Predicting viral drug resistance is a significant medical concern. The importance of this problem stimulates the continuous development of experimental and new computational approaches. The use of computational approaches allows researchers to increase therapy effectiveness and reduce the time and expenses involved when the prescribed antiretroviral therapy is ineffective in the treatment of infection caused by the human immunodeficiency virus type 1 (HIV-1). We propose two machine learning methods and the appropriate models for predicting HIV drug resistance related to amino acid substitutions in HIV targets: (i) k-mers utilizing the random forest and the support vector machine algorithms of the scikit-learn library, and (ii) multi-n-grams using the Bayesian approach implemented in MultiPASSR software. Both multi-n-grams and k-mers were computed based on the amino acid sequences of HIV enzymes: reverse transcriptase and protease. The performance of the models was estimated by five-fold cross-validation. The resulting classification models have a relatively high reliability (minimum accuracy for the drugs is 0.82, maximum: 0.94) and were used to create a web application, HVR, for the prediction of HIV drug resistance to protease inhibitors and nucleoside and non-nucleoside reverse transcriptase inhibitors based on the analysis of the amino acid sequences of the appropriate HIV proteins from clinical samples.