Dataset Information

Specific modulation of HIV RNA splicing and upregulation of anti-inflammatory miR-124 by the new drug candidate ABX464

ABSTRACT: Purpose: To ensure that ABX464 acted specifically on HIV splicing and did not significantly or globally affect the splicing events of human genes, we used a high-throughput RNAseq approach. Many genome-wide expression studies of HIV infection are based on analyses of total peripheral blood mononuclear cells (PBMCs), which consist of over a dozen cell subsets, including T cells, B cells, NK cells and monocytes Methods: The CD4 T cells were uninfected or infected with the YU2 strain and were untreated or treated for 6 days with ABX464, followed by high-throughput RNAseq. Each raw dataset of the samples contained between 44 and 105 million single-end reads (50 bp), with an average of approximately 60 million raw reads per sample Results: Approximately 98% of the total raw reads were mapped to the human genome sequence (GRCh38), giving an average of 60 million human reads per sample for further analyses. The reads that were correctly mapped (approximately 98% of total input reads) to the gene and transcript locations (GTF annotation file) Conclusions: The MDS of our gene expression data showed, without any outliers, that the different donors segregated well and distributed into the DMSO (untreated) and ABX464 treatments that were infected or uninfected. The displayed variance was donor-dependent (clustered by donor) but treatment-independent (no data structure related to the different treatments), which suggests that the ABX464 molecule did not induce a major difference in CD4 T cell gene expression.

ORGANISM(S): Homo sapiens

PROVIDER: GSE116073 | GEO | 2018/06/22

REPOSITORIES: GEO

ACCESS DATA

Similar Datasets

Project description:Purpose: Next-generation sequencing (NGS) has revolutionized systems-based analysis of cellular pathways. The objective of this study was to compare transcriptome analysis (RNA-seq) with microarray and quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) for HIV-1-infected and uninfected MDMs and to evaluate the optimal protocol for high-throughput data analysis Methods: The mRNA profiles of HIV-1-infected and uninfected MDMs were generated by deep sequencing, in triplicate, using Illumina GAIIx. The sequence reads that passed quality filters were analyzed at the transcript isoform level with two methods: Burrows–Wheeler Aligner (BWA) followed by ANOVA (ANOVA) and TopHat followed by Cufflinks. qRT–PCR validation was performed using TaqMan and SYBR Green assays. Results: Using an optimized data analysis workflow, we mapped about 20 million sequence reads per sample to the human genome and identified 60505 transcripts in the HIV-1-infected or uninfected MDMs. RNA-seq data confirmed stable expression of 15 known housekeeping genes, and 10 of these were validated with qRT–PCR. RNA-seq data had a linear relationship with qRT–PCR for more than four orders of magnitude and a goodness of fit (R2) of 0.8953. Approximately 10% of the transcripts showed differential expression between the HIV-1-infected or uninfected MDMs, with a fold change ≥1.5 and p value <0.05. Altered expression of 48 genes was confirmed with qRT–PCR, demonstrating the high degree of sensitivity of the RNA-seq method. Conclusions: Our study represents the detailed analysis of HIV-1-infected macrophages transcriptomes, with biologic replicates, generated by RNA-seq technology. Our results show that NGS offers a comprehensive and more accurate quantitative and qualitative evaluation of mRNA content within a cell or tissue. We conclude that RNA-seq based transcriptome characterization would expedite genetic network analyses and permit the dissection of complex biologic functions.

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.

Dataset Information

Specific modulation of HIV RNA splicing and upregulation of anti-inflammatory miR-124 by the new drug candidate ABX464

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure