Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Deep Mutational Scanning of HIV tat and rev in a non-overlapped context

ABSTRACT: 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

INSTRUMENT(S): Illumina MiSeq

ORGANISM(S): HIV-1 vector pNL4-3

SUBMITTER: Jason Fernandes

PROVIDER: E-MTAB-5154 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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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 expression of the HIV-1 Tat protein. Examine changes in gene expression in HIV-1 Tat transduced cells compared with cells transduced with an antisense Tat sequence used as a control.

Dataset Information

Deep Mutational Scanning of HIV tat and rev in a non-overlapped context

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