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Transcriptomic analysis of Jurkat T cells upon transient expression of HTLV-1a, -1b, -1c and STLV-1mac Tax proteins

GSE279439 Homo sapiens (human) RNA-seq (gene expression) 30 samples Submitted 2026/08/03 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 30 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
30 samples: 6 conditions, mostly with about 5 replicates eachGroups 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.
Summary (written by the submitters)
Molecular analyses of Human T-cell Leukemia Virus type 1 (HTLV-1) have historically focused on the Japanese strain HTLV-1aB-ATK. However, with the identification of endemic genotypes such as the transcontinental HTLV-1aTC, the major African subtype HTLV-1b, and the Australo-Melanesian subtype HTLV-1c, emerging clinical evidence have suggested that these genotypes may be associated with distinct risks of developing HTLV-1-associated diseases.
Read the full summary ↓
Tax1a, one of the major viral oncoprotein encoded by HTLV-1a, displays pleiotropic functions, including the manipulation of signalling pathways and the transcriptomic reprogramming of infected cells. We hypothesize that Tax proteins from distinct viral genotypes might share some common transcriptional properties, but might also induce some specific transcriptional landscapes in host cells. In this study, we conducted a comparative transcriptomic analysis of Jurkat T cells expressing HTLV-1a, -1b, -1c and STLV-1mac Tax proteins and highlighted commonalities and specificities in Tax-induced transcriptomic reprogramming.
This dataset
Open GSE279439 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 GSE279439_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 PRJNA1172706. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
6 conditions, mostly with about 5 replicates each
Jurkat cells, pEF-myc, ×5 Jurkat cells, pEF-Tax1aB-ATK-myc, ×5 Jurkat cells, pEF-Tax1b-SF26-myc, ×5 Jurkat cells, pEF-Tax1c-EM5-myc, ×5 Jurkat cells, pEF-Tax1c-AusCSK-myc, ×5 Jurkat cells, pEF-TaxMac-Mra18C9, ×5

Supports a between-group comparison across 30 samples.

How we read this: 6 replicated groups read from 30 sample titles; they account for 30 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
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.