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HIV-1 transcription dominates over host gene activity at the HIV-1 integration site

GSE302199 Homo sapiens (human) RNA-seq (gene expression) 123 samples Submitted 2025/10/21 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 123 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Samples
123 samples: 42 conditions, mostly in triplicateGroups are read from the sample names, not from the paper. Check the list below.
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Paper
HIV-1 transcription dominates over host gene activity at the HIV-1 integration sitemBio · 2026
Summary (written by the submitters)
Despite effective antiretroviral therapy (ART), HIV-1 persists as an integrated DNA provirus in the genome of infected cells. Host cells can regulate HIV-1 transcription at the HIV-1 integration site dependent on the location (actively transcribed genes vs repressive chromatin) and the orientation (in the same vs opposite orientation of the host gene transcription) of HIV-1 integration.
Read the full summary ↓
Presumably, HIV-1 follows the host gene transcriptional activity at the HIV-1 integration site. We interrogated HIV-1-host gene transcriptional interactions at the HIV-1 integration site using CRISPR-mediated activation and inhibition of the host genes (in which HIV-1 was integrated) in seven HIV-1-infected Jurkat T cell clones with known HIV-1 integration sites in the introns of actively transcribed genes and a non-genic region. Using ATAC-seq and strand-specific RNA-seq to examine chromatin accessibility and RNA transcription levels, we found that host gene activation did not increase HIV-1 transcription, while host gene inhibition did not decrease HIV-1 transcription. HIV-1 drove high levels of aberrant host RNA transcription regardless of the HIV-1 integration orientation. HIV-1-driven aberrant host RNA transcription was inhibited by CRISPR-mediated HIV-1 inhibition but not by CRISPR-mediated host gene activation or inhibition. When HIV-1 was integrated into a non-genic region, HIV-1 increased host chromatin accessibility and drove high levels of aberrant host RNA transcription. Overall, HIV-1 LTR promoter-driven transcriptional activity dominated over the host promoter activity. HIV-1 transcription does not passively follow host gene activity. Our results highlight that despite effective ART, silencing HIV-1 promoter is required to inhibit HIV-1-driven aberrant host gene expression and chronic immune activation.
Published in
HIV-1 transcription dominates over host gene activity at the HIV-1 integration site
Weissman S, Yeh Y-HJ, Viazmenski M et al. · mBio 2026 · PMID 41277870 · doi:10.1128/mbio.02755-25
This dataset
Open GSE302199 in finder → Find more human RNA-seq datasets →
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE302199_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1289183 and SRA study SRP599694. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41277870) ↗
Study design
42 conditions, mostly in triplicate
Jurkat_1A8_EEF2K_CRISPRa_geneTargeting ×3 Jurkat_1A8_EEF2K_CRISPRi_geneTargeting ×3 Jurkat_1A8_EEF2K_CRISPRa_nontargeting ×3 Jurkat_1A8_EEF2K_CRISPRi_nontargeting ×3 Jurkat_1A8_EEF2K_CRISPRa_HIVTargeting ×3 Jurkat_1A8_EEF2K_CRISPRi_HIVTargeting ×3 Jurkat_8B10_VAV1_CRISPRa_geneTargeting ×3 Jurkat_8B10_VAV1_CRISPRi_geneTargeting ×3 +34 more

Supports a between-group comparison across 123 samples.

How we read this: 42 replicated groups read from 123 sample titles; they account for 123 of them. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study

+ 83 more — browse all 123 samples with per-sample file links →

Datasets on a similar topic

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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.