Table_1_Single-cell RNA sequencing reveals common and unique gene expression profiles in primary CD4+ T cells latently infected with HIV under different conditions.xlsx

BackgroundThe latent HIV reservoir represents the major barrier to a cure. One curative strategy is targeting diseased cells for elimination based on biomarkers that uniquely define these cells. Single-cell RNA sequencing (scRNA-seq) has enabled the identification of gene expression profiles associated with disease at the single-cell level. Because HIV provirus in many cells during latency is not entirely silent, it became possible to determine gene expression patterns in a subset of cells latently infected with HIV.ObjectiveThe primary objective of this study was the identification of the gene expression profiles of single latently infected CD4+ T cells using scRNA-seq. Different conditions of latency establishment were considered. The identified profiles were then explored to prioritize the identified genes for future experimental validation.MethodsTo facilitate gene prioritization, three approaches were used. First, we characterized and compared the gene expression profiles of HIV latency established in different environments: in cells that encountered an activation stimulus and then returned to quiescence, and in resting cells that were infected directly via cell-to-cell viral transmission from autologous activated, productively infected cells. Second, we characterized and compared the gene expression profiles of HIV latency established with viruses of different tropisms, using an isogenic pair of CXCR4- and CCR5-tropic viruses. Lastly, we used proviral expression patterns in cells from people with HIV to more accurately define the latently infected cells in vitro.ResultsOur analyses demonstrated that a subset of genes is expressed differentially between latently infected and uninfected cells consistently under most conditions tested, including cells from people with HIV. Our second important observation was the presence of latency signatures, associated with variable conditions when latency was established, including cellular exposure and responsiveness to a T cell receptor stimulus and the tropism of the infecting virus.ConclusionCommon signatures, specifically genes that encode proteins localized to the cell surface, should be prioritized for further testing at the protein level as biomarkers for the ability to enrich or target latently infected cells. Cell- and tropism-dependent biomarkers may need to be considered in developing targeting strategies to ensure that all the different reservoir subsets are eliminated.</p

Table_3_Single-cell RNA sequencing reveals common and unique gene expression profiles in primary CD4+ T cells latently infected with HIV under different conditions.xlsx

BackgroundThe latent HIV reservoir represents the major barrier to a cure. One curative strategy is targeting diseased cells for elimination based on biomarkers that uniquely define these cells. Single-cell RNA sequencing (scRNA-seq) has enabled the identification of gene expression profiles associated with disease at the single-cell level. Because HIV provirus in many cells during latency is not entirely silent, it became possible to determine gene expression patterns in a subset of cells latently infected with HIV.ObjectiveThe primary objective of this study was the identification of the gene expression profiles of single latently infected CD4+ T cells using scRNA-seq. Different conditions of latency establishment were considered. The identified profiles were then explored to prioritize the identified genes for future experimental validation.MethodsTo facilitate gene prioritization, three approaches were used. First, we characterized and compared the gene expression profiles of HIV latency established in different environments: in cells that encountered an activation stimulus and then returned to quiescence, and in resting cells that were infected directly via cell-to-cell viral transmission from autologous activated, productively infected cells. Second, we characterized and compared the gene expression profiles of HIV latency established with viruses of different tropisms, using an isogenic pair of CXCR4- and CCR5-tropic viruses. Lastly, we used proviral expression patterns in cells from people with HIV to more accurately define the latently infected cells in vitro.ResultsOur analyses demonstrated that a subset of genes is expressed differentially between latently infected and uninfected cells consistently under most conditions tested, including cells from people with HIV. Our second important observation was the presence of latency signatures, associated with variable conditions when latency was established, including cellular exposure and responsiveness to a T cell receptor stimulus and the tropism of the infecting virus.ConclusionCommon signatures, specifically genes that encode proteins localized to the cell surface, should be prioritized for further testing at the protein level as biomarkers for the ability to enrich or target latently infected cells. Cell- and tropism-dependent biomarkers may need to be considered in developing targeting strategies to ensure that all the different reservoir subsets are eliminated.</p

Table_2_Single-cell RNA sequencing reveals common and unique gene expression profiles in primary CD4+ T cells latently infected with HIV under different conditions.xlsx

BackgroundThe latent HIV reservoir represents the major barrier to a cure. One curative strategy is targeting diseased cells for elimination based on biomarkers that uniquely define these cells. Single-cell RNA sequencing (scRNA-seq) has enabled the identification of gene expression profiles associated with disease at the single-cell level. Because HIV provirus in many cells during latency is not entirely silent, it became possible to determine gene expression patterns in a subset of cells latently infected with HIV.ObjectiveThe primary objective of this study was the identification of the gene expression profiles of single latently infected CD4+ T cells using scRNA-seq. Different conditions of latency establishment were considered. The identified profiles were then explored to prioritize the identified genes for future experimental validation.MethodsTo facilitate gene prioritization, three approaches were used. First, we characterized and compared the gene expression profiles of HIV latency established in different environments: in cells that encountered an activation stimulus and then returned to quiescence, and in resting cells that were infected directly via cell-to-cell viral transmission from autologous activated, productively infected cells. Second, we characterized and compared the gene expression profiles of HIV latency established with viruses of different tropisms, using an isogenic pair of CXCR4- and CCR5-tropic viruses. Lastly, we used proviral expression patterns in cells from people with HIV to more accurately define the latently infected cells in vitro.ResultsOur analyses demonstrated that a subset of genes is expressed differentially between latently infected and uninfected cells consistently under most conditions tested, including cells from people with HIV. Our second important observation was the presence of latency signatures, associated with variable conditions when latency was established, including cellular exposure and responsiveness to a T cell receptor stimulus and the tropism of the infecting virus.ConclusionCommon signatures, specifically genes that encode proteins localized to the cell surface, should be prioritized for further testing at the protein level as biomarkers for the ability to enrich or target latently infected cells. Cell- and tropism-dependent biomarkers may need to be considered in developing targeting strategies to ensure that all the different reservoir subsets are eliminated.</p

Table_4_Single-cell RNA sequencing reveals common and unique gene expression profiles in primary CD4+ T cells latently infected with HIV under different conditions.xlsx

BackgroundThe latent HIV reservoir represents the major barrier to a cure. One curative strategy is targeting diseased cells for elimination based on biomarkers that uniquely define these cells. Single-cell RNA sequencing (scRNA-seq) has enabled the identification of gene expression profiles associated with disease at the single-cell level. Because HIV provirus in many cells during latency is not entirely silent, it became possible to determine gene expression patterns in a subset of cells latently infected with HIV.ObjectiveThe primary objective of this study was the identification of the gene expression profiles of single latently infected CD4+ T cells using scRNA-seq. Different conditions of latency establishment were considered. The identified profiles were then explored to prioritize the identified genes for future experimental validation.MethodsTo facilitate gene prioritization, three approaches were used. First, we characterized and compared the gene expression profiles of HIV latency established in different environments: in cells that encountered an activation stimulus and then returned to quiescence, and in resting cells that were infected directly via cell-to-cell viral transmission from autologous activated, productively infected cells. Second, we characterized and compared the gene expression profiles of HIV latency established with viruses of different tropisms, using an isogenic pair of CXCR4- and CCR5-tropic viruses. Lastly, we used proviral expression patterns in cells from people with HIV to more accurately define the latently infected cells in vitro.ResultsOur analyses demonstrated that a subset of genes is expressed differentially between latently infected and uninfected cells consistently under most conditions tested, including cells from people with HIV. Our second important observation was the presence of latency signatures, associated with variable conditions when latency was established, including cellular exposure and responsiveness to a T cell receptor stimulus and the tropism of the infecting virus.ConclusionCommon signatures, specifically genes that encode proteins localized to the cell surface, should be prioritized for further testing at the protein level as biomarkers for the ability to enrich or target latently infected cells. Cell- and tropism-dependent biomarkers may need to be considered in developing targeting strategies to ensure that all the different reservoir subsets are eliminated.</p

DataSheet_1_Single-cell RNA sequencing reveals common and unique gene expression profiles in primary CD4+ T cells latently infected with HIV under different conditions.docx

BackgroundThe latent HIV reservoir represents the major barrier to a cure. One curative strategy is targeting diseased cells for elimination based on biomarkers that uniquely define these cells. Single-cell RNA sequencing (scRNA-seq) has enabled the identification of gene expression profiles associated with disease at the single-cell level. Because HIV provirus in many cells during latency is not entirely silent, it became possible to determine gene expression patterns in a subset of cells latently infected with HIV.ObjectiveThe primary objective of this study was the identification of the gene expression profiles of single latently infected CD4+ T cells using scRNA-seq. Different conditions of latency establishment were considered. The identified profiles were then explored to prioritize the identified genes for future experimental validation.MethodsTo facilitate gene prioritization, three approaches were used. First, we characterized and compared the gene expression profiles of HIV latency established in different environments: in cells that encountered an activation stimulus and then returned to quiescence, and in resting cells that were infected directly via cell-to-cell viral transmission from autologous activated, productively infected cells. Second, we characterized and compared the gene expression profiles of HIV latency established with viruses of different tropisms, using an isogenic pair of CXCR4- and CCR5-tropic viruses. Lastly, we used proviral expression patterns in cells from people with HIV to more accurately define the latently infected cells in vitro.ResultsOur analyses demonstrated that a subset of genes is expressed differentially between latently infected and uninfected cells consistently under most conditions tested, including cells from people with HIV. Our second important observation was the presence of latency signatures, associated with variable conditions when latency was established, including cellular exposure and responsiveness to a T cell receptor stimulus and the tropism of the infecting virus.ConclusionCommon signatures, specifically genes that encode proteins localized to the cell surface, should be prioritized for further testing at the protein level as biomarkers for the ability to enrich or target latently infected cells. Cell- and tropism-dependent biomarkers may need to be considered in developing targeting strategies to ensure that all the different reservoir subsets are eliminated.</p

Functional analysis of genes modulated in HIV-1 latency.

<p>(A) Protein interaction network (PIN) generated using differentially expressed genes (N = 826) after a log₂FC > 0.5 filter was applied (N = 64). Any single nodes not attached to a main hub were removed for clarity. Abbreviations for the type of interaction are as follows: <i>B</i>–Binding, <i>C</i>–Cleavage, <i>CM</i>–Covalent Modification, <i>GR</i>–Group Relation, <i>+P</i>–Phosphorylation, <i>TR</i>–Transcription Regulation, <i>T</i>–Transformation. Edge coloring of Green indicates activation, Red indicates inhibition, Black indicates unspecified, while Blue is utilized for a group relation. Arrows indicate the direction of interactions. The color gradient of log₂FC for (A) is included with red indicating upregulation and blue indicating downregulation. (B) RT-qPCR validation of a panel of p53 related genes modulated in HIV-1 latency. Fold change is plotted on the log₂ scale with error bars representing standard deviation. Significance for RT-qPCR results was determined with a paired <i>t</i>-test (*p<0.05, **p<0.01, ***p<0.001) and all RNA-Seq results were significant (EdgeR’s FDR corrected <i>p</i>-value < 0.05). (C) Dot plots of gene expression data (i.e., counts per million) from RNA-Seq results for <i>TNFRSF10B</i> (DR5) and <i>FAS</i> (CD95). Abbreviations: <i>UI</i>, uninfected; <i>LI</i>, latently infected. (D) Histograms of DR5 (<i>TNFRSF10B</i>) and CD95 (<i>FAS</i>) protein surface expression from a representative donor. Red line indicates UI and blue line indicates LI. Geometric mean fluorescence intensity (gMFI) is indicated at the right top corner of the histogram and the grey histogram represents unstained control. (E) Surface expression was measured by FACS for DR5 (6 donors) or CD95 (5 donors). Significance was determined with a paired <i>t</i>-test (<i>p</i>-values provided).</p

Differentially expressed genes from the p53 signaling pathway during HIV-1 latency.

<p>Gene expression changes overlaid upon the KEGG <i>p53 signaling pathway</i> for all differentially expressed genes with an FDR corrected p-value <0.05. The shade of red indicates the degree of upregulation while the shade of blue indicates the degree of downregulation.</p

Transcriptomic Analysis Implicates the p53 Signaling Pathway in the Establishment of HIV-1 Latency in Central Memory CD4 T Cells in an In Vitro Model

<div><p>The search for an HIV-1 cure has been greatly hindered by the presence of a viral reservoir that persists despite antiretroviral therapy (ART). Studies of HIV-1 latency <i>in vivo</i> are also complicated by the low proportion of latently infected cells in HIV-1 infected individuals. A number of models of HIV-1 latency have been developed to examine the signaling pathways and viral determinants of latency and reactivation. A primary cell model of HIV-1 latency, which incorporates the generation of primary central memory CD4 T cells (T_CM), full-length virus infection (HIV_NL4-3) and ART to suppress virus replication, was used to investigate the establishment of HIV latency using RNA-Seq. Initially, an investigation of host and viral gene expression in the resting and activated states of this model indicated that the resting condition was reflective of a latent state. Then, a comparison of the host transcriptome between the uninfected and latently infected conditions of this model identified 826 differentially expressed genes, many of which were related to p53 signaling. Inhibition of the transcriptional activity of p53 by pifithrin-α during HIV-1 infection reduced the ability of HIV-1 to be reactivated from its latent state by an unknown mechanism. In conclusion, this model may be used to screen latency reversing agents utilized in shock and kill approaches to cure HIV, to search for cellular markers of latency, and to understand the mechanisms by which HIV-1 establishes latency.</p></div

Genes responsive to p53 activity.

<p>Genes responsive to p53 activity.</p

Effect of pifithrin-α upon establishment of latency.

<p>(A) Time-line of experimental procedures for pifithrin-α treatment of the latently infected (LI) condition. For the majority of experiments pifithrin-α was maintained in culture from day 10 to 17. (B) CD4 and p24 Gag staining from a representative donor throughout the experiment. Numbers in dot-plots represent percentages. (C) p24 Gag positive CD4 negative cells from 5 independent donors at day 13 and day 17 in cells treated (purple symbols) or untreated (black symbols) with pifithrin-α. (D) p24 Gag positive CD4 negative cells at day 19 following reactivation of LI cells with αCD3/αCD28 beads at day 17 in cells previously treated (purple symbols) or untreated (black symbols) with pifithrin-α (day 10 to 17). As a negative control, p24 Gag positive CD4 negative cells were also assessed in unstimulated cells treated with pifithrin-α in a similar manner. (E) In a deviation from the time-line presented in (A), pifithrin-α was added only during the reactivation period from day 17 to 19. p24 positive CD4 negative cells were then determined at day 19 following reactivation of LI cells that were treated (salmon symbols) or untreated (black symbols) with pifithrin-α. As a negative control, p24 Gag positive CD4 negative cells were also assessed in unstimulated cells treated with pifithrin-α in a similar manner. (F) HIV-1 integration analysis in LI cells at day 17 using Alu-PCR with 250 ng of genomic DNA. As a negative control, HIV-1 integration was also assessed in uninfected cells. (G) Correlation of integrated HIV-1 with the percentage of p24 Gag positive CD4 negative cells after reactivation with αCD3/αCD28 beads. Correlation was determined using the Pearson correlation coefficient. Black symbols represent untreated samples and purple symbols represent cells treated with pifithrin-α from day 10 to 17. (H) Reactivation index calculated as the percentage of cells expressing p24 (day 19) after αCD3/αCD28 reactivation divided by the number of integrated copies before reactivation (day 17). Throughout this figure each donor is represented with a different symbol and significance was determined with a paired <i>t</i>-test (<i>p</i>-values provided).</p