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Transcriptomic profiling of micro RNAs in mock-infected cells and an in vitro model of HIV latency before and after activation

ABSTRACT: Purpose: The goal of this study is to identify host micro RNAs (miRNAs) whose expression is dysregulated during HIV latency, cell activation and productive infection. This information provides a mechanistic insight into an additional layer of miRNA-mediated regulation of gene expression and cellular pathways that are involved in HIV latency establishment and maintenance. Methods: An in vitro model of HIV latency and mock-infected cells before and after activation with antiCD3/antiCD28 coated magnetic beads was used for miRNA expression profiling using small RNA sequencing (smRNA-Seq). Results: Read counts of the 418 miRNAs survived filtering. Differential expression analysis identified 26 miRNAs dysregulated in latency. Messenger RNA (mRNA) targets and cellular pathways enriched for mRNA targets were identified using several analytical methods. Our analyses showed that many protein-coding genes and pathways targeted by dysregulated miRNAs have relevance to regulation of HIV expression or establishment of HIV latency. The p53 signalling pathway was found among pathways that were targeted by dysregulated miRNAs at a greater level than expected by chance. Conclusions: The present study identifies miRNAs dysregulated during HIV latency and provides a mechanistic insight into regulation of the p53 pathway via miRNAs that may contribute to latency establishment.

ORGANISM(S): Homo sapiens

PROVIDER: GSE153094 | GEO | 2020/11/02

REPOSITORIES: GEO

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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.

Project description: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.

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Transcriptomic profiling of micro RNAs in mock-infected cells and an in vitro model of HIV latency before and after activation

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