An Atlas of Immune Cell Transcriptomes in Human Immunodeficiency Virus-Infected Immunological Non-Responders Identified Marker Genes That Control Viral Replication
Acquired immunodeficiency syndrome (AIDS), caused by the human immunodeficiency virus (HIV), is characterized by a severe decline in CD4+ T-lymphocyte counts, leading to substantial morbidity and mortality. Antiretroviral therapy (ART) effectively inhibits HIV replication and increases CD4+ T cell counts in the peripheral blood of HIV-infected patients. However, despite complete suppression of HIV replication with ART, 15–30% of patients fail to recover their CD4+ T-cell counts and do not achieve optimal recovery. These individuals are referred to as immunological non-responders (INRs). After 4–7 years of combined ART (cART), patients with CD4+ T-cell counts below 350–500 cells/mL are classified as INRs, while those with counts above 500 cells/mL are defined as immunological responders (IRs). INRs exhibit severe immune dysfunction and, compared with IRs, are at higher risk of developing AIDS and non-AIDS-related conditions, including metabolic syndrome, liver disease, renal disease, cardiovascular disease, cancer, and HIV-1-related neurocognitive deficits, resulting in increased morbidity and mortality.
The mechanisms underlying the incomplete recovery of CD4+ T-cell counts have been extensively investigated and are believed to involve decreased bone marrow hematopoiesis, reduced CD4+ T-cell production due to inadequate thymic and lymph node productivity, apoptosis, the depletion of CD4+ T cells due to residual viral replication, sustained immune activation, and disrupted cytokine levels. Nevertheless, the mechanisms responsible for immunological non-responsiveness in a subset of HIV-infected patients remain incompletely understood, and there is still a lack of understanding regarding the status of the peripheral immune system in INRs. To address this knowledge gap, several studies have compared the transcriptomic characteristics of peripheral blood mononuclear cells (PBMCs) between IRs and INRs, and have identified the differential expression of interferon-pathway genes, including IFI27, as likely playing a significant role in immune recovery. Nevertheless, current knowledge regarding the specific immune cell types in which these genes exhibit differential expression remains limited.
High-throughput, single-cell RNA sequencing (scRNA-seq) has emerged as a prominent tool in transcriptomic-based investigations of human diseases. This procedure enables the determination of transcriptional disparities in cell subpopulations and even the identification of novel cell subtypes at single-cell resolution. The utilization of scRNA-seq has been employed in transcriptomic studies related to HIV-1 replication and infection, latent HIV-1 reservoirs, and lymphoid tissues in HIV-infected individuals.
In this study, we performed scRNA-seq on 60,000 peripheral blood immune cells obtained from three HIV-1-infected IRs and three HIV-1-infected INRs. We further examined cell type and status, and compared the proportions of immune cell subsets between the two groups of patients. Subsequently, we analyzed the differences in gene expression in various cell subtypes, and compared the findings with those obtained from bulk transcriptomic analysis.
The patients in this study were enrolled in the HIV clinic at the Department of Infectious Diseases, Mengchao Hepatobiliary Hospital in Fujian Province, China. They underwent regular follow-up after receiving cART. Before their participation, the patients provided informed consent, and ethical approval was obtained from Mengchao Hepatobiliary Hospital’s Ethics Committee (No. KS_2021_031_01).
The inclusion criteria for defining INRs were as follows: individuals who tested positive for HIV; patients who had received cART for at least 4.0 years; patients who had maintained virologic control with a viral load of less than 20 copies/mL for more than 3.5 years; patients with persistently low CD4+ T-cell counts (<350 cells/mL). Notably, the first three criteria were also used for defining IRs; only criterion (4) differed, and referred to patients who had achieved the best CD4+ T-cell recovery, with the last five test results showing counts exceeding 500 cells/mL.
Peripheral venous blood (6 mL) was carefully drawn and transferred into heparinized tubes, followed by gentle shaking. An equal volume of room-temperature phosphate buffered saline (PBS) was added and mixed by gentle blowing. Next, 6 mL of Ficoll, which is a lymphocyte isolation solution in a 1:1 ratio with the original blood volume, was carefully injected into a 50-mL centrifuge tube. The centrifuge tube was tilted at a 45° angle, and all the diluted blood was added slowly along the tube wall, approximately 1 cm above the surface of the Ficoll, overlaying the Ficoll layer. The tube was subsequently centrifuged at 200 r/min for 30 min at 18–20°C, yielding four distinct layers: a bottom layer containing red blood cells and granulocytes, a Ficoll layer, a layer with mononucleated cells, and a top layer containing plasma. The cloudy layer at the plasma/Ficoll interface, which contained the PBMCs, was carefully extracted using a pipette and transferred into a new centrifuge tube, and PBS of at least three times the volume of the PBMCs was added. The tube was then centrifuged twice at 202 r/min for 10 min at 18–20°C. After discarding the supernatant, the PBMCs were resuspended, with gentle pipetting, in 1 mL of RPMI-1640 medium (Thermo Fisher Scientific Inc. Shanghai, China) containing 10% fetal bovine serum.
The scRNA-seq libraries were prepared using Matrix NEO Automated Single-Cell Processing System and the GEXSCOPE Single-Cell RNAseq Library Kit (Singleron biotechnology inc. Nanjing, China, http://cn.singleronbio.com/) following the manufacturers’ protocols. Library sequencing was performed by Singleron on the Illumina HiSeq 2500 platform with a sequencing depth of 40 G reads per sample and obtaining paired-end reads of 100 and 32 bp. Sequencing was performed to generate >50,000 reads per cell. The scRNA-seq dataset was aligned to the h19 human reference genome (https://www.ncbi.nlm.nih.gov/datasets/taxonomy/9606/) using SynEcoSys (Singleron biotechnology inc. Nanjing, China, http://cn.singleronbio.com/). Genome Reference Consortium Human Build 38 (GRCh38)-1.2.0 served as the gene model for alignment, providing a transcriptome reference. To generate single-cell gene counts for individual libraries, the SynEcoSys counting functionality within the SynEcoSys pipeline was employed. Additionally, the pipeline was utilized for comparison, filtering, and unique molecular identifier counts.
The transcriptome matrices obtained from sequencing were analyzed using the Seurat R package (v. 2.2.0, Satija lab, NY, USA) for single-cell genomics. Initially, cells with a large number of genes or a high amount of mitochondrial DNA were filtered out by applying thresholds of mitochondrial counts >5 and unique feature counts >5000. Subsequently, the data were normalized using the global-scaling normalization method “LogNormalize”. To identify genes with significant expression variation, the average expression of each gene was calculated, and the 2000 genes showing the greatest differences in expression were selected and scaled to remove background variation. The harmony R package (https://CRAN.R-project.org/package=harmony) was then employed to de-batch the data from different samples. A principal component analysis was undertaken on the identified variable genes and the principal components were determined using Jackstraw and Elbow plots, with principal component 20 primarily used as the cutoff for PBMC analysis. Cells were clustered by type using a graph-based clustering method with a resolution parameter of 0.8. Nonlinear dimensionality reduction (t-distributed stochastic neighbor embedding [t-SNE]) was employed for data visualization and cell-type labels were assigned to the plots. Unsupervised clustering of PBMCs and T cells was carried out using the Seurat Program. The average expression level of each gene in a specific cluster was compared to the median expression of the same gene in all the other clusters to identify genes enriched in that cluster. The Seurat Findclusters algorithm was utilized to identify cell clusters using a graph-based clustering method based on shared nearest neighbor modular optimization. Clusters were further characterized based on known markers. Cluster-specific markers were determined by finding differentially expressed genes (DEGs) for each cluster, with the top 10 genes considered. DEGs were defined based on an average log2 fold change (avg_log2 FC) >1 and the percentage of cells expressing a given gene >0.25. The SingleR (https://rdrr.io/bioc/SingleR/) algorithm was employed to annotate cell clusters by referencing multiple annotated libraries. Finally, cell proportions were obtained.
The bulk transcriptome data used in this study were obtained from the GSE143742 and GSE106792 datasets, which were downloaded from the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo), which is a repository of freely available high-throughput sequencing data and does not require permission from the original authors. For genes with multiple probe sets, the expression values were averaged before analysis. Statistical significance was determined based on an adjusted P value of 0.5.
To investigate the disparities in gene expression among immune cells in IRs and INRs after five years of cART following HIV infection, PBMCs were isolated from three IRs and three INRs donors and subjected to scRNA-seq analysis. Cells with mitochondrial reads accounting for more than 5% of the total were excluded. To mitigate batch effects among the six samples and ensure a standardized and consistent comparison, we employed the Harmony algorithm. Eventually, the cells were clustered based on differential gene expression. We identified a total of nine distinct cell clusters, with cluster 0 and cluster 1 representing the major cell subgroups, each accounting for more than 20% of the total number of cells. Conversely, cluster 6, cluster 7, and cluster 8 were considered rare cell subgroups, each comprising less than 5% of the total number of cells. Significant differences in the percentages of cell subpopulations were observed between the IRs and INRs groups, except for cluster 7 and cluster 8. Notably, cluster 1, cluster 2, and cluster 5 exhibited the greatest fold changes (IRs/INRs: 3.64, 0.07, and 0.03, respectively).
Next, we annotated the clustered cell subpopulations using the SingleR algorithm, which leveraged the transcriptional profiling of the cells. This analysis revealed seven major cell clusters—exhausted B cells, monocytes, CD16+ monocytes, natural killer (NK) cells, plasmacytoid dendritic cells, CD4+ T cells, and terminal effector CD8+ T cells—with CD16+ monocytes, monocytes, NK cells, CD4+ T cells, and terminal effector CD8+ T cells constituting the largest subgroups. Aside from plasmacytoid dendritic cells, significant differences in the percentages of cell subpopulations were observed between the IRs and INRs groups. Notably, the ratios of CD16+ monocytes, exhausted B cells, and monocytes exhibited the greatest differences, being 11.08-, 2.71-, and 39.79-fold higher in the INRs group than in the IRs group, respectively. In contrast, the CD4+ T-cell ratio was significantly decreased (0.39-fold change) in INRs compared with that in IRs. Similarly, the percentages of NK cells and terminal effector CD8+ T cells were also lower (0.37-fold and 0.27-fold, respectively) in the INRs group. However, no significant difference in the CD4+ T cells /CD8+ T cells ratio was recorded between the two groups, all around 1.5.
In our study, we first determined the differences in the ratios of the identified cell subpopulations between INRs and IRs, and then sought to detect marker genes associated with the differentially abundant cells. We identified 266 marker genes in NK cells, with GZMB, NKG7, FGFBP2, KLRF1, and GNLY being the most notable representatives; in CD16+ monocytes, we found 458 marker genes, with CDKN1C, MS4A7, HES4, FCGR3A, and SMIM25 being the most significant representatives; 478 putative marker genes were identified in monocytes, with LYZ, S100A8, S100A9, FCN1, and MNDA as the most prominent representatives; exhausted B cells exhibited the fewest potential marker genes, with 147 in total, the most significant representatives being MS4A1, CD79A, FCRLA, RALGPS2, and BANK1; CD4+ T cells had 358 marker genes, with RPS27, LTB, CCR7, RPL21, and RPL13A as the most notable representatives; terminal effector CD8+ T cells presented 242 marker genes, with CCL5, B2M, NKG7, CD8B, and CST7 as the most significant representatives; and plasmacytoid dendritic cells had 471 genes, among which SERPINF1, AL357143.1, LILRA4, CLEC4C, and LRRC26 were the most prominent representatives. In total, 1114 potential marker genes were identified among all the cell subpopulations.
An enrichment analysis of the marker genes using the HIV_INTERACTION_CATEGORY (https://www.ncbi.nlm.nih.gov/genome/viruses/retroviruses/hiv-1/interactions/) database showed that many of these genes were significantly associated with HIV-related processes, including interaction with viral proteins such as envelope surface glycoprotein gp120, Pr55(Gag), Nef, and Gag-Pol, as well as the regulatory effects of Vpu, Nef, and Vpr. Of particular interest, we identified 181 genes reported to be involved in the regulation of HIV proteins. For instance, HLA-B, HLA-C, HLA-A, SRSF5, and HLA-E were related to Vpu regulation, while EIF4A2, HLA-DRB5, STX7, CD3G, SAMHD1, CD3E, and CD3D were associated with the regulation of Pr55(Gag). Ten genes—IFITM3, CD63, IFITM2, CXCL8, CCL5, CCL4, CALM3, CALM1, CALM2, and MTDH—were linked to the process of envelope transmembrane glycoprotein gp41 inhibition. These findings suggested that the 181 genes were involved in the regulation of HIV replication.
Furthermore, we compared these results with those of previously undertaken bulk transcriptome analyses (GSE143742 and GSE106792 datasets), and identified 26 and 25 overlapping genes, respectively. Twelve of these genes—ISG15, IFITM3, PLSCR1, NFKBIA, FOS, HLA-DQB1, CCL3L1, IL1B, YBX1, CTSB, JUN, and DDX5—which are involved in the regulation of HIV replication, were differentially expressed across all seven cell types identified in this study. ISG15 and IFITM3 were identified as marker genes in CD4+ T cells and monocytes, while PLSCR1, HLA-DQB1, CCL3L1, and DDX5 were found to serve as marker genes in monocytes.
Next, we conducted an analysis of DEGs between IRs and INRs for all the cell clusters. We found a total of 114 DEGs in NK cells, which were particularly enriched in the Toll-like receptor signaling, interleukin (IL)-17 signaling, and T helper 17 (Th17) cell differentiation biological pathways. Meanwhile, 117 genes were found to be differentially expressed in monocytes, and these were enriched in pathways such as the hsa04620: Toll-like receptor signaling pathway, hsa04062: chemokine signaling pathway, hsa04657: IL-17 signaling pathway, hsa04625: C-type lectin receptor signaling pathway, and hsa04659: Th17 cell differentiation. Additionally, a total of 186 DEGs were identified in CD4+ T cells, and were enriched in hsa04658: Th1 and Th2 cell differentiation, hsa04657: IL-17 signaling pathway, hsa04659: Th17 cell differentiation, and various other pathways. Terminal effector CD8+ T cells exhibited a total of 148 DEGs, which were enriched in hsa04210: apoptosis, hsa04658: Th1 and Th2 cell differentiation, and hsa04659: Th17 cell differentiation, among other pathways. CD16+ monocytes had 139 DEGs, although no significant enrichment in biological pathways was observed for these DEGs. Additionally, only one gene was differentially expressed in both exhausted B cells and plasmacytoid dendritic cells (TMSB4XP4).
Among the 114 DEGs identified in NK cells, 50 were upregulated and 64 were downregulated in IRs compared with that in INRs. The top five upregulated genes were TMSB4XP4, MYOM2, KIR2DL1, GHITM, and WTAP, while the five most downregulated genes were HLA-DRA, CCL3L1, LST1, CCL4L2, and LYZ. Of the 114 DEGs, 56 served as marker genes. Enrichment analysis of the remaining 58 non-marker genes using the HIV_INTERACTION_CATEGORY database showed that 21 of them interacted with HIV. Notably, the genes HLA-DMA, VCAN, CXCL8, CCL3L1, IL1B, HLA-C, and G0S2, which are known to be regulated by HIV replication, were found to be associated with “Envelope transmembrane glycoprotein gp41 upregulates”. Meanwhile, the YWHAZ, HLA-C, PPP1CB, SRSF3, CXCL8, EIF4A2, IL1B, TPST2, CCL3L1, GRINA, and HLA-DMA genes were found to correspond to regulation on Vpr, Asp, Tat, Vpu, capsid, Pr55(Gag), gp120, and gp41. However, no statistical significance was observed in these associations.
In monocytes, we identified 117 DEGs, 83 of which were upregulated and 34 downregulated in IRs compared with that in INRs. The top five upregulated genes were TMSB4XP4, GNG11, ACRBP, TSC22D1, and PPBP, while AC103591.3, IER2, CCL3L1, CXCL8, and CCL3 were the top five downregulated genes. Among the 117 DEGs, 36 were identified as marker genes, 34 of which were downregulated. The remaining 81 non-marker genes were further investigated for their potential interaction with HIV through enrichment analysis in the HIV_INTERACTION_CATEGORY database. Interestingly, 16 of the 81 genes showed interaction with HIV, but none of these interactions was significant.
Regarding CD4+ T cells, we observed that 186 genes were differentially expressed between the two groups. Of these, 124 were upregulated and 62 downregulated in the IRs group relative to that in the INRs group. The five most upregulated genes were TMSB4XP4, MTND1P23, ABO, RPS26, and MORF4L1, while the five most downregulated genes were JUN, IER2, MTCO1P12, LYZ, and CCL3. Among these DEGs, 101 were identified as marker genes. Out of the 85 remaining non-marker DEGs, 71 were found to be upregulated. Enrichment analysis on these upregulated genes using the HIV_INTERACTION_CATEGORY database showed that 28 were involved in interactions with HIV; however, none of these interactions was significant.
We identified 148 DEGS in Terminal effector CD8+ T cells between the two groups of patients, 79 of which were upregulated and 69 downregulated in IRs compared with that in INRs. The top five upregulated genes were TMSB4XP4, GZMB, FCRL6, CTSW, and RPL41P5, while the top five downregulated genes were CXCL8, S100A8, FCN1, LYZ, and FOS. Of the DEGs, 71 were identified as marker genes. These marker genes play an important role in distinguishing the terminal effector CD8+ T cells in immune response compared to non-immune response. To further explore the functional relevance of the remaining 77 non-marker genes, we performed an enrichment analysis using the HIV_INTERACTION_CATEGORY database. Interestingly, we found that 28 genes from this subset showed interactions with HIV, but no significant enrichment was observed regarding HIV interaction.
In CD16+ monocytes, all 139 DEGs were upregulated in the IRs group compared with that in the INRs group. The top five upregulated genes among these DEGs were TMSB4XP4, SCCPDH, GATC, Z84492.1, and RN7SL138P. Additionally, only one gene was identified as a marker gene. To explore the functional implications of these upregulated genes, we conducted an enrichment analysis using the HIV_INTERACTION_CATEGORY database. Interestingly, 26 of the DEGs were found to have interactions with HIV. Again, however, no statistically significant enrichment was observed.
Patients with AIDS have shown viral suppression and clinical cure following cART. However, the mechanism behind the immunological non-responsiveness observed in a proportion of these patients remains a crucial question in clinical practice. Recent research has identified several factors that contribute to this non-responsiveness, including reduced bone marrow hematopoiesis, diminished CD4+ T-cell production due to inadequate thymus and lymph node functionality, CD4+ T-cell apoptosis and depletion resulting from residual viral replication, sustained immune activation, and disrupted cytokine levels. Bulk transcriptome studies have highlighted the involvement of interferon-pathway genes, notably IFI27, in immunological non-responsiveness, as well as their possible role in immunological recovery. Additionally, other bulk transcriptome investigations have identified genes related to mitochondrial and apoptotic pathways as key players in this phenomenon. These findings, combined with initial evidence supporting disturbances in cytokine levels, suggest a potential mechanism underlying immunological non-responsiveness.
Utilizing single-cell transcriptome sequencing, we identified 12 cytokine-related genes—ISG15, IFITM3, PLSCR1, NFKBIA, FOS, HLA-DQB1, CCL3L1, IL1B, YBX1, CTSB, JUN, and DDX5—as markers of PBMC subpopulations. The expression of these genes varied with cell numbers, consistent with the findings from other bulk transcriptome studies. Several of these genes have demonstrated importance in the regulation of HIV replication. Studies have shown that ISG15, along with MX1 and IFI27, is upregulated during acute and chronic HIV infection and is strongly associated with plasma HIV-1 RNA levels and the presence of TNF-a. ISG15 is likely to play a significant role in the immune response to HIV infection through its effects on viral replication and viral susceptibility. For instance, ISG15 deficiency has been linked to increased resistance to HIV-1 infection in interferon type I (IFN-I) -primed fibroblasts compared to healthy control fibroblasts, suggesting that ISG15 may contribute to viral susceptibility in specific cell types. Furthermore, it has been observed that secreted ISG15 enhances IFN-I production as well as HIV-1-specific CD8+ T-cell immune responses. IFITM3, an interferon-inducible transmembrane protein, has been found to exert an inhibitory effect against HIV and simian immunodeficiency virus infection during the host cell entry phase. This cytokine reduces HIV-1 infectivity through a mechanism that is not fully understood. Furthermore, IFITM proteins, and in particular IFITM3, can bind with HIV-1 viruses and inhibit their fusion and cell-to-cell transmission. These observations indicate that IFITM3 plays a critical role in controlling HIV infection. Studies have demonstrated that PLSCR1 directly interacts with the HIV-1 tat protein and acts as a negative regulator during HIV-1 infection by inhibiting the transcriptional activation of the HIV-1 long terminal repeat by tat. HLA-DQB106, a variant allele of the HLA-DQB1 gene, has been found to play a protective role against HIV-1 disease progression and the targeting of T cells by specific Nef epitopes, thereby contributing to the suppression of HIV-1 replication. Individuals with the strongest HIV-specific CD4+ T-cell responses commonly carry HLA class II gene-specific alleles, such as HLA-DRB113 and/or HLA-DQB106, which have previously been associated with non-progressive HIV infection phenotypes. This indicates that HLA-DQB106 plays an important and positive role in the regulation of the immune response to HIV-1 infection. Copy number variation in CCL3L1 has been identified as a significant indicator of susceptibility or immunity to HIV-1 infection and its consequences. This variation may influence susceptibility to HIV through a gene-dose effect. Indeed, CCL3L1 copy number is considered an important determinant of HIV seropositivity. These findings suggest that copy number variation in CCL3L1 may serve as a predictive factor for the risk of HIV infection and disease progression. The knockdown of DDX5 has been found to significantly reduce HIV RNA levels and viral production compared with that in control cells, with a two- to three-fold reduction as measured by p24 (CA) and infectivity. This suggests that DDX5 enhances HIV-1 replication by affecting Rev function and the Rev Response Element (RRE) pathway.
Additionally, among the genes investigated, we identified a total of 181 marker genes that were associated with the regulation of HIV replication. For instance, HLA-B, HLA-C, HLA-A, SRSF5, and HLA-E can be regulated by Vpu, while EIF4A2, HLA-DRB5, STX7, CD3G, SAMHD1, CD3E, and CD3D can be regulated by Pr55(Gag). Furthermore, IFITM3, CD63, IFITM2, CXCL8, CCL5, CCL4, CALM3, CALM1, CALM2, and MTDH were found to have the potential to regulate gp41. Notably, there were discrepancies between these results and those of the two bulk transcriptome analyses, which may be attributed to the clinical heterogeneity of patients, and also suggests that single-cell transcriptome studies can uncover additional factors. Notably, these identified genes are representative of immune cells. For example, as mentioned earlier, ISG15 and IFITM3 serve as marker genes for CD4+ T cells and monocytes, respectively. Similarly, PLSCR1, HLA-DQB1, CCL3L1, and DDX5 were found to serve as marker genes in monocytes. This finding indicates that monocytes may play a vital role in immunological non-responsiveness. One study demonstrated that HIV can cross the epithelial surface and interact with tissue mononuclear phagocytes (MNPs) beneath the anal–genital epithelium. The same study identified specific subpopulations of MNPs in human anal–genital and colonic tissues, which HIV may encounter during sexual transmission, revealing that CD14+CD1c+ monocyte-derived dendritic cells and langerin+ conventional dendritic cells 2 are more efficient at ingesting HIV, becoming infected, and transmitting the virus to CD4+ T cells. Additionally, it has been shown that blood samples from HIV-infected individuals undergoing long-term standard ART contain a single nucleotide capable of infecting neighboring monocytes with stabilized HIV DNA. Residual viral replication is a crucial factor influencing immunological non-responsiveness, and our findings suggest that monocytes and their marker genes, which affect HIV replication, may have an impact on overall immune reconstitution by influencing the replication of latent viruses in monocytes.
A limitation of this study is that the sample size is small, consisting of only three pairs of samples. However, in the field of single-cell research, such a sample size is still considered reasonable. Increasing the sample size could present challenges in terms of computational analysis. Despite the small sample size, the transcriptomic data of thousands of cells in this study demonstrate significant statistical relevance for specific features and transcriptional changes in individual cell types. Nevertheless, it is important to note that these conclusions may not be universally applicable to the entire patient population. Further research and validation are essential to generalize these findings.
In conclusion, in this study, we observed significant increases in the ratios of CD16+ monocytes, monocytes, and exhausted B cells in INRs compared to IRs, whereas the ratios of NK cells, CD4+ T cells, and terminal effector CD8+ T cells were significantly decreased. Furthermore, our analysis identified marker genes that are relatively specifically expressed in these cells, and are involved in regulating HIV replication. Among these marker genes, ISG15, IFITM3, PLSCR1, HLA-DQB1, CCL3L1, and DDX5 were found to be associated with monocytes, and have previously been shown to interact with HIV proteins and to affect HIV replication. Additionally, we found that the DEGs in NK cells were enriched in biological pathways associated with HIV replication. These findings provide preliminary insights into the intricate interplay between the immune system and HIV replication, which may play a critical role in cART non-responsiveness.
doi.org/10.1097/CM9.0000000000002918
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