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Next Generation Sequencing Facilitates Quantitative Analysis of HIV-1 Latency in Central Memory T Cells

ABSTRACT: Purpose: Next-generation sequencing (NGS) has become a powerful microscope to study cell models of HIV. The goals of this study is to analyze latent HIV infection in the TCM model of HIV latency described in Martins et al 2015 and to evaluate potential markers for HIV infection. Methods: Peripheral blood mononuclear cells were collected from 4 healthy donors. Naive CD4 T cells were isolated and utilized to generate a model of latent HIV infection. During model generation, cells were sorted and collected for RNA extraction 10 days post infection along with their uninfected counterparts. 2 days after this, cells were activated with CD3/CD28 stimulation and collected for study for a total of 16 samples from 4 donors. After cell collection, RNA was extracted from infected cells and their uninfected counterparts for deep sequencing by Expression Analysis. Sequence reads that passed quality filters were mapped using Tophat and counted using HTSeq. In addition to human transcripts, we utilized 92 ERCC spikes as negative controls. Any human gene which did not achieve at least 1 count per million reads in at least 4 samples or any ERCC that did not achieve at least 5 reads in at least 4 samples was discarded. Differential expression for activated samples was not performed, but rather used to demonstrate upregulation of T-cell activation markers along with changing to the type and abundance of HIV transcripts produced. Results: Using an custom built data analysis pipeline, 82 million reads per sample to the human genome (build hg38) and identified 13,534 human transcripts 67 ERCC spike in transcripts, and HIV NL43 transcripts were identified with the Tophat/HTSeq workflow. Differential expression analysis was performed between uninfected and latently infected cells. 826 genes were found to be differentially expressed, 275 downregulated and 551 upregulated (FDR < 0.05) with EdgeR. GO and Pathway analysis of differential expressed genes revealed significant dysregulation of genes involved in the p53 signaling pathway. Subsequent studies with pifithrin demonstrated a reduction in latently infected cell during model generation. Conclusions: This study represents the first detailed analysis of HIV latency with this cell model using next generations sequencing. These results demonstrate that the TCM model of HIV latency of Martins et al 2015 is truly reflective of HIV latency. This study also provides a framework for which to analyze future cell models of HIV latency using next generation sequencing. Finally, this work demonstrates that the p53 signaling pathway is a key pathway dysregulated in latency for this cell model with several genes dysregulated in HIV latency.

ORGANISM(S): Homo sapiens

PROVIDER: GSE81810 | GEO | 2016/11/01

SECONDARY ACCESSION(S): PRJNA322599

REPOSITORIES: GEO

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Project description:Purpose: The goal of this study is to identify host genes whose expression is perturbed in primary CD4+ T cells by histone deacetylase (HDAC) inhibitors (HDACi) SAHA and RMD, which have different potencies and specificities for various HDACs. The study aims to evaluate the effects of SAHA and RMD that may promote or inhibit reactivation of HIV provirus out of latency. Methods: Peripheral blood mononuclear cells were collected from 4 HIV-seronegative donors. CD4+ T cells were isolated and utilized to generate an in vitro model of latent HIV infection (model developed in the Spina laboratory and previously described in Spina et al., 2013). Mock-infected cells were cultured in parallel to evaluate effects of SAHA and RMD that may be dependent on the exposure of cells to virus. Following generation of the model, cells were treated with SAHA, RMD or their solvent dimethyl sulfoxide (DMSO) for 24 hours. Mock-infected cells were treated in parallel. The experiment had 4 biological replicates, 6 conditions for each, for a total of 24 samples. ERCC spikes (Thermo Fisher Scientific, Inc.) were added to cell lysates based on cell number in each sample (10 ul of 1:800 dilution per million cells). Mix 1 was used for DMSO- and mix 2 for SAHA- and RMD-treated cells. After all samples were collected, RNA was extracted and subjected to deep sequencing by Expression Analysis, Inc. Sequence reads that passed quality filters were mapped using Tophat (human genome) or Bowtie (ERCC spikes and HIV) and counted using HTSeq. ERCC spikes with the same concentration in mixes 1 and 2 were utilized to remove unwanted technical variation. Any human gene which did not achieve at least 1 count per million reads in at least 4 samples or any ERCC that did not achieve at least 5 reads in at least 4 samples was discarded. Differential gene expression analysis was performed using library EdgeR in Bioconductor R. National Center for Biotechnology Information (NCBI) HIV-1 Human Interaction Database was then searched for genes that have been implicated in controlling HIV latency. EdgeR output was used to extract expression information of the genes of interest from the NCBI database to identify genes implicated in HIV latency that were modulated by SAHA and RMD. The resulting lists were manually curated to verify relevance to HIV latency, using the Description column of the NCBI database, as well as available PubMed references. Results: Using a custom built data analysis pipeline, ~100 million reads per sample were mapped to the human genome (build hg38). After applying filtering criteria, 16058 human transcripts, 19 ERCC spikes transcripts, and HIV NL4-3 transcripts were identified with the Tophat/Bowtie and HTSeq workflow. Differential expression analysis was performed between SAHA or RMD-treated and DMSO-treated cells. In addition, differential modulation of gene expression by SAHA and RMD in the model of HIV latency and mock-infected cells was assessed using EdgeR. In mock-infected cells, SAHA upregulated 3,971 genes and downregulated 2,940 genes; RMD upregulated 5,068 genes and downregulated 4,050 genes. In the model of HIV latency, SAHA upregulated 3,498 genes and downregulated 2,904 genes; RMD upregulated 5,116 genes and downregulated 4,053 genes (FDR < 0.05). SAHA modulated 6, and RMD 11 genes differentially between mock-infected cells and the model of HIV latency. Following search of the NCBI HIV-1 Human Interaction Database, 27 genes upregulated and 29 downregulated in common between SAHA and RMD were found to be relevant to regulation of HIV latency; 31 were up- and 32 downregulated by RMD only; and 6 were up- and 2 were downregulated by SAHA only. Conclusions: This study demonstrates that SAHA and RMD, which have different potencies and specificities for HDACs, modulate a set of overlapping genes implicated in regulation of HIV latency. Some of these genes may be explored as additional host targets for improving the outcomes of “shock and kill” strategies.

Project description:Suppressive HAART does not eradicate HIV-1 and viral DNA persists as a stably integrated form in the absence of viral particle production. As a consequence, latent reservoirs are refractory to antiretroviral drugs and invisible to immune surveillance. The largest latent reservoir consists of resting memory CD4+ T cells. These cells can resume viral infection when activated through antigen recognition, causing bursts of viremia (blips). Current therapies targeting latent HIV-1 have focused primarily on the M-bM-^@M-^\shock and killM-bM-^@M-^] approach, which employs M-bM-^@M-^\anti-latencyM-bM-^@M-^] drugs M-bM-^@M-^S most notably histone deacetylase (HDAC) inhibitors M-bM-^@M-^S to reactivate and flush latent provirus from its cellular reservoirs in the absence of global T cell activation. This approach is predicated on the notions that viral reactivation will lead to the demise of the infected cell, and that HAART will prevent spreading of the infection. On the contrary, recent evidence indicates that latently infected CD4+ T cells of HIV-1 patients on HAART survive in vitro viral reactivation with the HDAC inhibitor, SAHA, even when co-cultured with autologous CD8+ cytotoxic T lymphocytes (CTL). Moreover, it remains to be addressed the impact of anti-latency drugs on viral reservoirs undergoing low-level ongoing replication, inherently more resistant to the cytopathic effects of HIV-1 and residing in anatomical sites hard to reach for some antiretroviral drugs (e.g. macrophages). As a consequence, there is a need to develop alternative therapeutic approaches aimed at eliminating or decreasing the latent reservoir. Progress in that direction has been hindered by the lack of biomarkers uniquely or differentially expressed on latently infected compared to their uninfected counterparts. To gain insight into the cellular mechanisms that take place in the context of latency, and with the goal of identifying distinctive markers that distinguish latently infected CD4+ T cells, we have used an in vitro model developed in our laboratory to study the expression profile of latently infected CD4+ T cells by microarray analysis. We have used a culture system, previously established in our laboratory, to generate and isolate quiescent latently infected CD4+ T cells in vitro. In this in vitro HIV-1 latency model, CD4+ T cells are activated, infected with full length, replication competent HIV-1, and then returned to quiescence in the presence of IL-7, yielding a culture of quiescent latently infected and uninfected cells. We showed that HIV-1 p24gag expressed during viral replication persists in the cytoplasm of latently infected cells for several days before being degraded. Therefore, we exploited the presence of cytoplasmic p24gag to sort latently infected from uninfected cells by FACS from the same initial cell culture. Total RNA was isolated from sorted latently infected and uninfected cells generated from CD4+ T cells of four different donors. Paired RNA samples from infected and uninfected cells were labeled with Cy3 and Cy5 to allow dual-color competitive hybridization. Moreover, to control for the dye bias in our experiments, we implemented a dye swap protocol (reciprocal labeling) for paired RNA samples from 2 donors. Samples were analyzed by dual-color competitive hybridization on the Agilent whole human genome microarrays (41,000 unique probes). This is the first comparative genomic profiling of primary latently infected resting memory CD4+ T cells versus their uninfected counterparts sorted from the same culture. Microarray analyses performed in this study revealed profound differences between latently infected and uninfected cells. Of relevance are genes involved, not only in previously described pathways related with transcriptional and post-transcriptional regulation, but affecting proliferation, survival, cell cycle progression and cell metabolism. This could explain why latently infected cells have been resistant to reactivation with current anti-latency approaches. Thus, targeting of more downstream steps, such as the ones identified in this study, may be able to enhance viral flushing from refractory latent reservoirs. In addition, we identified a panel of surface makers differentially expressed in latently infected cells, which seem worth investigating for their potential use as biomarkers. Indeed, they might allow the enrichment of this latent reservoir for molecular in depth studies, for monitoring the size of the latent reservoir in the clinical setting, as well as for the development of new therapeutic strategies aimed at eradicating this reservoir.

Project description:Background: The cellular reservoir of latent HIV infection remains the main barrier to cure this virus. Elimination of this reservoir would be possible, if molecular identity of latently infected cells were fully elucidated. Biomarkers proposed previously were able to capture only a relatively small fraction of all reservoir cells. In the present study, we set out to conduct comprehensive molecular profiling, at the protein and RNA levels, of CD4+ T cells latently infected with HIV in vitro, using liquid chromatography-mass spectrometry (LC-MS) and RNA sequencing (RNA-Seq), respectively. Protein-based methods such as quantitative proteomic profiling using LC-MS may be more beneficial due to direct transferability of results to antibody-based approaches to capture latently infected cells. Integrated analysis of proteomic and transcriptomic data adds a level of validation and increases confidence in identified biomarkers. Flow cytometry and integrated HIV DNA assay were further used to enrich for latently infected cells with antibodies against selected biomarker proteins. Results: Using quantitative proteomics, we identified a total of 10,886 proteins (peptide level FDR < 0.05), of which 673 were up- and 780 down-regulated in latently infected compared to mock-infected cells in vitro (p < 0.05). Among these proteins, 21 were dysregulated at the RNA level in the same direction. Pathway analysis identified p53, mTOR, Wnt and NOTCH signaling, demonstrating that our in vitro model reflects known mechanisms of latency establishment and maintenance. Comparison of identified proteins with other proteomics studies revealed that identified molecular signatures of latency depend on technology and cell types used; however, a subset of proteins were identified both in the present, and at least one other study. Antibodies against selected protein markers, CEACAM1 and PLXNB2, could enrich for latently infected cells from mixed cell population 3-10 fold (5.8 fold average, p < 0.001). Conclusion: Two new molecules, CEACAM1 and PLXNB2, were identified as biomarkers for HIV latency. However, the level of enrichment for latently infected cells compared to biomarkers proposed previously was not improved. These results are consistent with the idea that each proposed biomarker defines only a subset of latently infected cells, and that a combined biomarker will be required to capture or target the latent HIV reservoir represented by different cell types.

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Next Generation Sequencing Facilitates Quantitative Analysis of HIV-1 Latency in Central Memory T Cells

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