Regulation of Endogenous Retroviruses and Cellular Genes by HIV-1 Rev [RNA-seq]
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE160606 | GEO | 2023/10/31
REPOSITORIES: GEO
Similar Datasets
Project description:A combination of bioinformatics, RNAseq, and cell-based functional assays were used to study the changes in host cell gene expression when HIV-1 Rev, Tat, Tat and Rev or HERV-K Rec was expressed. These results suggest that HIV Rev is capable of modulating gene and mRNA isoform expression, during HIV-1 infection.
2023-12-07 | GSE162852 | GEO
Project description:A combination of bioinformatics, RNAseq, and cell-based functional assays were used to study the changes in host cell gene expression when HIV-1 Rev was expressed. These results suggest that HIV Rev is capable of modulating gene and mRNA isoform expression, during HIV-1 infection.
2023-08-28 | GSE157046 | GEO
Project description:A combination of bioinformatics, RNAseq, and cell-based functional assays were used to study the changes in host cell gene expression when HIV-1 Rev or Nef was expressed. These results suggest that HIV Rev and Nef are capable of modulating gene and mRNA isoform expression, during HIV-1 infection.
2023-12-22 | GSE163714 | GEO
Project description:A combination of bioinformatics, RNAseq, and cell-based functional assays were used to study the changes in host cell gene expression when HIV-1 Rev or Nef was expressed. These results suggest that HIV Rev and Nef are capable of modulating gene and mRNA isoform expression, during HIV-1 infection.
2023-12-22 | GSE163713 | GEO
Project description:This dataset provides allele counts and raw fastqs for deep mutational scanning of the HIV-1 genes tat and rev when not-overlapped with one another (placed in the nef locus) as described in Fernandes et al. Functional segregation of overlapping genes in HIV Cell 2016 (in revision). Preselection (input) and post selection (replicate 1/2) files for every possible point mutant of these two HIV proteins from the NL4-3 background are given.Tab delimited files including codon counts across the amplicons are also included and are probably the most useful thing to most researchers. The data here was used to generate Figures 3 and 4 and 7 and might be of general use for people interested in deep mutational scanning, looking for signatures of epistasis in rev or tat, or reanalyzing and mining the data. FAQ: Why do the ends of each amplicon have such variation? In order to increase diversity across the flowcell, I pooled standard primers with N, NN, and NNN extensions to throw amplicons out of phase. When aligning you should trim the ends or ignore them. This means that the overlap between PE's can vary by 3 nt. Why are the filenames not easy to deal with? The filenames are tied to separate MiSeq runs. I hope to clean up the nomenclature and update this entry in the future while preserving the run information. You can get a sense of that as different residues will vary in Q-score, and that is mostly tied to the run they were pooled on and not any interesting biology. While this is makes it a little harder to follow, I think it's good to get a sense that doing this kind of analysis in high-throughput fashion leads to a reasonable amount of failure (i.e. RNA isolation, RT, fail) that led to repetition until we had good data for every position. Can you help me deal with this dataset? Yes. Please email me at jferna10@ucsc.edu, or contact me on twitter @jdf_ev. For reagents please contact Alan Frankel at frankel@cgl.ucsf.edu. Do you have the analysis scripts you used to process the data? Yes they are on github. https://github.com/nbstrauli/allele_frequency_trajectory_sim
2016-10-30 | E-MTAB-5154 | biostudies-arrayexpress
Project description:Purpose: In an HIV cure setting, Vorinostat may provide the “shock” capable of flushing HIV out of the persistent reservoir, while antiretroviral therapy is used to prevent new infections. However, this drug may modulate the expression of human endogenous retroviruses (HERVs). This study demonstrates significant modulation of HERVs and suggests that they should be considered as off-target effects of this drug treatment. Methods: Peripheral blood mononuclear cells were collected from 4 healthy donors. Naive CD4 T cells were isolated and utilized to assess HERV dysregulation after treatment with Vorinostat. Cells were collected for study and exposed to a dose of Vorinostat (10uM dose over a 24 hour period). After this, RNA was extracted from cells and their untreated paired counterparts for deep sequencing by Expression Analysis. Sequence reads that passed quality filters were mapped to the HERVd reference database using Bowtie and counted using HTSeq. Any HERV which did not achieve at least 1 count per million mapped and counted reads in at least 4 samples was discarded. Differential expression was performed upon the filtered dataset with edgeR and using TMM normalization. Results: Using an custom built data analysis pipeline, ~100 million reads per sample were mapped to the HERVd reference database and identified 10784 distinct dysregulated elements Bowtie/HTSeq workflow. Differential expression analysis was performed between vorinostat treated and untreated conditions which demonstrated 2102 dysregulated HERV elements with 1007 downregulated and 1095 upregulated (FDR < 0.05) with EdgeR. Results were further subset with a log2FC ± 3 which resulted in 451 upregulated and 363 downregulated HERV elements from 81 and 82 distinct families respectively. HERV elements from the LTR12 family were by far the most dramatically upregulated in family frequency and fold change magnitude. Further confirmation with droplet digital PCR confirmed upregulation of LTR12 elements and demonstrated an exponential dose response curve with HERV expression found at even moderate doses (334nM) of vorinostat treatment. Conclusions: This study represents the first detailed analysis of HERV dysregulation following vorinostat treatment using the untargeted approach of total RNA-Seq. These results demonstrate that vorinostat dysregulates multiple HERV families with a propensity to upregulate members of the LTR12 HERV family. This study also provides a methodology to analyze HERV dysregulation in future treatments with vorinostat or other drugs by providing a mechanism to choose primer and probe sets which will properly represent HERV dysregulation as expression across an HERV is not uniform or continuous. Finally, this work suggests that HERV dysregulation by vorinostat treatment should be considered an off-target effect of this drug and HERV elements, such as LTR12, should be monitored as biomarkers during shock and kill clinical trials with HDAC inhibitors and that trial subjects should be screened to explore further HIV:HERV interactions.
2018-05-01 | GSE102187 | GEO
Project description:Deep Mutational Scanning of HIV tat and rev in a non-overlapped context
| PRJEB16306 | ENA
Project description:We report HERV-K rec iCLIP-seq binding data, ribosome profiling data, and RNA-seq from ELF1 naïve hESC and RNA-seq from NCCIT cells. HERV-K Rec iCLIP-seq: 2 replicates in NCCIT. Ribosome profiling: 4 replicates each of Rec-overexpressing NCCIT vs. control NCCIT; RNAseq: 3 replicates each of HERV-K Rec siRNA vs. control siRNA in NCCIT; RNA-seq: 3 replicates each of ELF1 naïve hESC vs. primed hESC.
2015-04-21 | E-GEOD-63570 | biostudies-arrayexpress
Project description:HIV-1 Tat protein is essential for virus production. RNA-binding proteins that facilitate Tat production may be absent or downregulated in resting CD4+ T-cells, the main reservoir of latent HIV. In this study, we examined the role of Tat RNA-binding proteins on the expression of Tat and control of latent and productive infection.
2021-06-14 | PXD025782 | Pride
Project description:Our previous work demonstrated that HIV-1 infection progressively reduces TCR/CD3 expression due to a defect in CD3g gene transcripts. We further found that knocking down expression of the viral tat and/or nef genes was correlated with CD3g transcript and TCR/CD3 surface receptor levels on HIV-1 infected cells. This study was undertaken to investigate the direct effect of HIV-1 Tat expression on the TCR/CD3 machinery. Progressive downregulation from TCR/CD3hi to TCR/CD3lo to TCR/CD3â was observed on Tat expressing cells in a manner that emulated HIV-1 infection, with a lack of CD3g transcripts again responsible for the defect. When Tat cell cultures containing a mixture of TCR/CD3 surface densities were separated into TCR/CD3hi and TCR/CD3lo/â populations, they quickly reverted to a mixed CD3 phenotype. Thus, the progression TCR/CD3hi to TCR/CD3lo to TCR/CD3â is an active, reversible process with receptor levels fluctuating in response to intracellular dynamics. Examination of tat mutants found that the regions involved in Tat-mediated transactivation and TAR binding are required for TCR/CD3 downregulation while the lysine at position 28 and Tat exon 2 are dispensable. Global gene expression, assessed in association with TCR/CD3 downregulation in HIV-1 infected and Tat expressing cells, detected broad suppression of TCR/CD3 signaling, co-stimulation and negative regulatory genes along with target transcription factors, ligands and receptors. A significant subset of the genes altered in HIV-1 infected cells was specifically targeted by Tat in association with TCR/CD3 loss. Our finding that Tat negatively regulates many facets of the TCR/CD3 machinery has important implications for disease pathogenesis. We used microarrays to investigate changes in CD4+ T cell gene expression induced by HIV-1 infection for comparison with the Tat expressing cells. Examine changes in gene expression in TCR/CD3 negative HIV-1 infected cells compared to TCR/CD3 positive uninfected controls.
2015-12-31 | E-GEOD-33706 | biostudies-arrayexpress