Arina Kiryakina

HIV-1 tropism toward the CCR5 and CXCR4 coreceptors is an important viral characteristic associated with cell entry, the potential use of specific entry inhibitors, studies of disease progression, and analyses of proviral reservoirs. The development of tools for genotypic tropism prediction also requires annotated sequence datasets with reliable phenotypic annotation. In this study, we describe a curated HIV-1 sequence dataset intended to complement, rather than replace, existing reference collections. Publications and GenBank records were searched manually using PubMed, Google Scholar, OpenAlex, cross-references, and records from the GenBank Nucleotide database. Sequences were included if a GenBank accession number could be linked to the V3 region of the HIV-1 env gene, or to a longer fragment containing this region, and to phenotypically determined tropism; records already present in LANL were removed. The final dataset comprises 1809 records associated with 32 unique PubMed-indexed publications. Subtype B predominated in the dataset; however, non-B variants accounted for 455 records (25.2%), including several Circulating Recombinant Forms (CRFs) and Unique Recombinant Forms (URFs). R5 variants accounted for 1528 records (84.5%), whereas X4 and R5/X4 variants accounted for 281 records (15.5%). The dataset includes 14 categories of phenotypic assays, geographic metadata, and sequences ranging from env gene fragments to near-complete HIV-1 genomes. This resource can be used as a complement to existing databases for the development, testing, and external validation of approaches for HIV-1 tropism prediction.

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.

We present the single-cell transcriptomic analysis of peripheral blood mononuclear cells (PBMC) from individuals during acute HIV-1 infection caused by viral strains circulating in Russia and the Former Soviet Union (FSU) countries. Using 10x Genomics single-cell RNA sequencing (scRNA-seq) on the Illumina NextSeq 550 platform, we have analyzed scRNA-seq data from three treatment-naive patients (viral load > 1 × 10⁶ copies/mL, estimated infection duration ≤ 4 weeks) and three healthy donors. Data integration (Seurat, Harmony), automated cell-type annotation (CellTypist), and GeneOntology (GO) enrichment analysis for highly expressed and low-expressed genes revealed a profound reorganization of transcriptional programs across key immune populations, including memory CD4⁺ and CD8⁺ T cells, non-classical monocytes and natural killer cells (NK-cells). We observed signatures of hyperactivation of pro-inflammatory pathways (NF-kB, TNF, and type I/II interferon signaling), upregulation of genes associated with cellular migration (CXCR4, CCR7) and metabolic adaptation (oxidative phosphorylation components), alongside a mixed pro- and anti-apoptotic expression profile. Notably, our data pointed to a pronounced dysregulation of the TGF-β and mTOR signaling cascades, disrupted intercellular communication networks—particularly between cytotoxic cells and their regulators—altered expression of genes implicated in disease progression (OLR1, SERPINB2, COPS9) and viral persistence control (NEAT1, NAF1). This work provides an initial single-cell transcriptional atlas characterizing early immune responses to HIV-1 sub-subtypes A6 and CRF63_02A6, the predominant drivers of the HIV epidemic across the FSU region.