Objectives: Antiretroviral (ARV) drugs have played a vital role in controlling the HIV-1 epidemic; however, some challenges remain. ARV drugs vary in their ability to control HIV infection, displaying differences in treatment-limiting factors and genetic barriers to resistance. The current report assesses the prevalence of HIV-1 drug resistance mutations (DRMs) among patients who failed first-line antiretroviral therapy (ART) and evaluates the genetic barrier of different regimens.Methods: The study cohort ( n = 271) included HIV-infected individuals who visited the Novosibirsk, Russia, HIV/AIDS clinic in 2018-2022. All patients received first-line ART prior to virological failure. Sociode-mographic and HIV-related data were collected from medical records and self-reported questionnaires. HIV-1 pol gene sequences were generated, and the presence of HIV-1 DRM was assessed. The genetic barrier to resistance was assessed by combining treatment regimen and adherence data.Results: Nonoptimal ART adherence was identified in 48.3% of patients and correlated with male sex, PWID, unemployment, and rural area residence. Most of the patients with high-level adherence were identified among those who were on TDF + 3TC + DTG. HIV-1 DRMs were identified in 54.6% of the patients. The analysis of HIV-1 DRM, ART regimen, and adherence data classified TDF + 3TC + DTG and TDF + 3TC + LPV/r as treatment regimens with a high genetic barrier, whereas EFV-containing ART was classified as a regimen with a low genetic barrier.Conclusions: The current study delivers results on the efficacy of HIV-1 ART and treatment adherence in real-world practice settings. This report suggests that ART regimens with a high genetic barrier to resistance combined with improved treatment adherence may reduce the transmission of HIV-1 resistant variants.(c) 2023 The Author(s). Published by Elsevier Ltd on behalf of International Society for Antimicrobial Chemotherapy.This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )

   Aim. To research the molecular epidemiological features of the territorial spread of HIV infection and the influence of recombination variability on the formation of the HIV‐1 population in the Primorsky Krai, Russia.   Materials and Methods. A comprehensive analysis of the molecular genetic characteristics of HIV‐1 and clinical‐epidemiological data of 127 HIV‐infected residents of Primorsky Krai was performed. The nucleotide sequences of the pol gene region encoding protease, reverse transcriptase and integrase of HIV‐1 were obtained by the sequencing of amplified fragments of the virus. Genotyping, phylogenetic, and mutational analyses were performed using specialised software resources.   Results. In the samples studied of HIV‐1 isolated in the Primorsky Krai, the dominance of HIV‐1 subtype A6 was detected as 40.9 %. The prevalence of subtype B viruses is high compared to other Russian regions – 29.9 %. The HIV‐1 subtype C was found in 6.3 % of the samples studied; circulating recombinant forms CRF63_02A6 – 6.3 %, CRF02_AG – 0.8 %. Unique viruses resulting from the recombination of HIV subtypes A6 and B, A6 and C, C and B were found in 15.7 % of cases (URF_A6C, URF_CB, URF_BA6). Of the 18 HIV‐1 URFs described, 14 were variants of URF_A6C (11 %). Analysis of the near full‐length genome (NFLG) of URF_A6C made it possible to find and describe a new HIV genovariant spreading in Primorye ‐ CRF157_A6C.   Conclusion. The analysis of HIV‐1 circulating in the Primorsky Krai confirmed that a specific molecular epidemic situation is developing in the region, which is characterized by active transmission of viruses not only of subtype A6, but also of subtypes B and C, which is not observed in other regions of Russia. For the first time in the Far East, a high frequency of HIV‐1 new unique recombinant forms occurrence has been described, some variants of which are beginning to spread among residents of the Primorsky Krai. An increase in the genetic heterogeneity of the viral population due to recombination variability of HIV is a negative prognostic factor for the territorial epidemic progress of HIV infection in the Far East.

Studies of HIV-1 molecular epidemiology describe significant differences in HIV infection spread across geographical areas. We examined 80 HIV-1 samples from the Jewish Autonomous Region of Russia in 2024. HIV-1 genome sequences for 12 samples revealed a novel HIV-1 called CRF173_63A6. HIV-1 CRF173_63A6 was found to have arisen through recombination between a specific Russian A6 subtype and the recombinant virus CRF63_02A6, which is responsible for the PWID-associated HIV outbreak in the Siberian region of Russia. Phylogenetic analysis of pol sequences previously deposited in Genbank showed that the CRF173_63A6 samples we described are grouped into a common phylogenetic cluster that includes 54 HIV-1 samples isolated in the JAR and other areas of the Russian Far East, indicating a wide distribution of this virus genovariant. This study once again proves the significant contribution of the key PWID group not only to the development of local Russian HIV epidemics, but also to the change in the characteristics of the circulating virus population.

Against the backdrop of active efforts to combat HCV worldwide with the help of DAAs, knowledge of the genetic diversity of HCV in the general population and in groups most at risk of infection is becoming increasingly important. The aim of this study was to characterize the molecular genetic diversity of HCV among individuals with HIV in Armenia, Kyrgyzstan and the Krasnoyarsk Krai region of Russia. The study included residents of Armenia (n = 73), Kyrgyzstan (n = 180) and the Krasnoyarsk Territory (n = 141) with HIV/HCV co-infection who were under observation at AIDS centers in these countries, collected between 2021 and 2023. The Core/E1 gene fragments obtained were analyzed using the maximum likelihood method to create a phylogenetic tree. HCV subtype 3a was dominant in Armenia (56.2%) and Kyrgyzstan (51.4%). The circulation of HCV subtype 4a was detected for the first time in Armenia, while the spread of HCV genotype 2, represented by three different subtypes, was documented in Kyrgyzstan. The genetic diversity of HCV in Krasnoyarsk Krai is consistent with the findings of previous Russian studies. Phylogenetic analysis revealed the formation of HCV clusters with a high level of bootstrap support, suggesting shared transmission routes, predominantly among PWID. This suggests that there are common routes of HCV transmission between and within countries.

Determining HIV-1 tropism provides the prognosis of HIV infection and is required before prescribing maraviroc, an entry inhibitor that blocks the interaction between the viral gp120 and the CCR5 coreceptor. However, existing prediction algorithms have been developed primarily for the globally most prevalent subtypes (B, C, and CRF01_AE) and often show reduced performance for other HIV-1 genetic variants. Sub-subtype A6 and circulating recombinant form CRF63_02A6 dominate the HIV-1 epidemic in Russia and other Former Soviet Union (FSU) countries, yet the reliability of tropism prediction for these viruses remains virtually unexplored. We phenotypically determined the tropism of 25 clinical isolates (11 R5, 1 X4, and 7 dual-tropic R5/X4) using U87.CD4.CCR5 and U87.CD4.CXCR4 cell lines and performed a comparative analysis of eight existing genotypic tools (Geno2pheno, WebPSSM, T-CUP 2.0, the Delobel/Garrido rules, and others) or their modifications on a combined dataset that included Los Alamos National Laboratory (LANL) reference sequences (subtypes A, B, C, CRF01_AE, and CRF02_AG) and our laboratory-derived isolates. Most models achieved high accuracy for globally prevalent subtypes (≈95% for B, C, and CRF01_AE) but showed markedly reduced performance for sub-subtype A6 (best accuracy among existing models, 85%) and CRF63_02A6 (best accuracy, 72%), with a poor balance between sensitivity and specificity. To address this problem, we developed HIV-V3Augur, an ensemble stacking model based on the Random Forest and Support Vector Machine (SVM) machine learning algorithms, trained on Pseudo Amino Acid Composition (PseAAC) and Relative Synonymous Codon Usage (RSCU) features with 10-fold stratified cross-validation. HIV-V3Augur achieved an accuracy of 77%, sensitivity of 79%, and specificity of 79% on sub-subtype A6, and on CRF63_02A6 it reached an accuracy of 95%, sensitivity of 87%, and specificity of 100%. Cross-validation demonstrated that HIV-V3Augur represents a balanced genotypic tropism prediction tool for understudied HIV-1 variants circulating in the FSU region. HIV-V3Augur can be used locally through a graphical user interface.