Regulation of Endogenous Retroviruses and Cellular Genes by HIV-1 Rev
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE157046 | GEO | 2023/08/28
REPOSITORIES: GEO
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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
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Project description:Deep Mutational Scanning of HIV tat and rev in a non-overlapped context
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Project description:Latency reversal and clearance strategies for HIV cure are beginning to employ IAP antagonists (IAPi) to induce unprecedented levels of latent reservoir expression without immunotoxicity during suppressive antiretroviral therapy (ART). However, full targeting of the reservoir may require combinatorial approaches. A Jurkat latency model screen for IAPi combination partners demonstrated synergistic latency reversal with Bromodomain and Extra-Terminal domain protein inhibitors (BETi). Mechanistic investigations using CRISPR-CAS9 and single cell-RNAseq informed comprehensive ex vivo evaluations of IAPi plus pan-BET, bromodomain (BD)-selective BET, or selective BET isoform targeting in CD4 T cells from ART-suppressed donors. IAPi+BETi treatment resulted in striking induction of cell-associated HIV gag RNA but lesser induction of fully elongated and tat-rev RNA, especially compared to T cell activation positive controls. IAPi/BETi resulted in HIV protein induction in bulk cultures of CD4 T cells using an ultrasensitive p24 assay, but did not translate to enhanced viral outgrowth frequency using a standard quantitative viral outgrowth assay. Overall, this study defines HIV transcriptional elongation and splicing as key barriers to latent HIV protein expression following latency reversal, delineates the roles of BET proteins and their bromodomains in HIV latency, and provides rationale for testing of IAPi+BETi in animal models of HIV latency.
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Project description:The question of how HIV 1 interfaces with cellular microRNA (miRNA) biogenesis and effector mechanisms has been highly controversial. Here, we first used deep sequencing of small RNAs present in two different infected cell lines (TZM-bl and C8166) and two types of primary human cells (CD4+ PBMCs and macrophages) to unequivocally demonstrate that HIV 1 does not encode any viral miRNAs. Perhaps surprisingly, we also observed that infection of T cells by HIV 1 has only a modest effect on the expression of cellular miRNAs at early times after infection. Comprehensive analysis of miRNA binding to the HIV 1 genome using the photoactivatable ribonucleoside-induced crosslinking and immunoprecipitation (PAR-CLIP) technique revealed several binding sites for cellular miRNAs, a subset of which were shown to be capable of mediating miRNA-mediated repression of gene expression. However, the main finding from this analysis is that HIV 1 transcripts are largely refractory to miRNA binding, most probably due to extensive viral RNA secondary structure. Together, these data demonstrate that HIV 1 neither encodes viral miRNAs nor strongly influences cellular miRNA expression, at least early after infection, and imply that HIV 1 transcripts have evolved to avoid inhibition by pre-existing cellular miRNAs by adopting extensive RNA secondary structures that occlude most potential miRNA binding sites. Small RNA profiling of HIV-1 infected cells through total small RNA deep sequencing and identification of RNA-induced silencing complex (RISC)-associated HIV-1 sequences through the RNP-immunoprecipitation technique of PAR-CLIP on RISC
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