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Profiling the RNA-Binding Proteome of KSHV-Infected Cells Uncovers a Viral Strategy for Hijacking Host Signaling via RNA-Dependent Interactions.

GSE308077 Homo sapiens (human) RNA-seq (gene expression) 18 samples Submitted 2026/08/07 Platform GPL34281

At a glance

RNA-seq (gene expression) · human · 18 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: 18 samples: 6 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.

Summary (written by the submitters)

Viruses encode RNA-binding proteins (RBPs) to manipulate host processes and promote infection. However, due to limited genomic space, many viral proteins are multifunctional and may engage RNA through non-canonical domains, making their identification difficult using traditional sequence-based approaches. Kaposi’s sarcoma-associated herpesvirus (KSHV) is the etiological agent of many AIDS-associated cancers.

Read the full summary ↓

Here, leveraging an unbiased proteomic approach we identified all RBPs in KSHV-infected primary effusion lymphoma (PEL) cells, uncovering 7 previously unrecognized KSHV-encoded RBPs. Among these is viral Interferon Regulatory Factor 1 (vIRF1), which we found is both necessary and sufficient to activate oncogenic RAS/MAPK signaling through an RNA-dependent interaction with host G3BP proteins. Importantly, a KSHV vIRF1 mutant lacking the ability to bind RNA exhibits a B cell-specific replication defect, highlighting the essential role of RNA interactions in viral gene regulation and pathogenesis. These findings reveal a novel strategy by which KSHV hijacks RNA-mediated signaling to support its lifecycle.

This dataset

Open GSE308077 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 GSE308077_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 PRJNA1328701 and SRA study SRP620475. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

6 conditions, mostly in triplicate

BJAB-BAC16 WT latent ×3 BJAB-BAC16 REV latent ×3 BJAB-BAC16 AAA latent ×3 BJAB-BAC16 WT lytic ×3 BJAB-BAC16 REV lytic ×3 BJAB-BAC16 AAA lytic ×3

Supports a between-group comparison across 18 samples.

How we read this: 6 replicated groups read from 18 sample titles; they account for 18 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

GSM9237549 BJAB-BAC16 WT latent 1 BJAB-BAC16 WT latent
GSM9237550 BJAB-BAC16 WT latent 2 BJAB-BAC16 WT latent
GSM9237551 BJAB-BAC16 WT latent 3 BJAB-BAC16 WT latent
GSM9237552 BJAB-BAC16 REV latent 1 BJAB-BAC16 REV latent
GSM9237553 BJAB-BAC16 REV latent 2 BJAB-BAC16 REV latent
GSM9237554 BJAB-BAC16 REV latent 3 BJAB-BAC16 REV latent
GSM9237555 BJAB-BAC16 AAA latent 1 BJAB-BAC16 AAA latent
GSM9237556 BJAB-BAC16 AAA latent 2 BJAB-BAC16 AAA latent
GSM9237557 BJAB-BAC16 AAA latent 3 BJAB-BAC16 AAA latent
GSM9237558 BJAB-BAC16 WT lytic 1 BJAB-BAC16 WT lytic
GSM9237559 BJAB-BAC16 WT lytic 2 BJAB-BAC16 WT lytic
GSM9237560 BJAB-BAC16 WT lytic 3 BJAB-BAC16 WT lytic
GSM9237561 BJAB-BAC16 REV lytic 1 BJAB-BAC16 REV lytic
GSM9237562 BJAB-BAC16 REV lytic 2 BJAB-BAC16 REV lytic
GSM9237563 BJAB-BAC16 REV lytic 3 BJAB-BAC16 REV lytic
GSM9237564 BJAB-BAC16 AAA lytic 1 BJAB-BAC16 AAA lytic
GSM9237565 BJAB-BAC16 AAA lytic 2 BJAB-BAC16 AAA lytic
GSM9237566 BJAB-BAC16 AAA lytic 3 BJAB-BAC16 AAA lytic

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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.