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VILMIR is a trans-acting long noncoding RNA that enhances the host interferon response in human epithelial cells

GSE305270 Homo sapiens (human) RNA-seq (gene expression) 18 samples Submitted 2025/12/02 Platform GPL23525
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.
Paper
VILMIR is a trans-acting long noncoding RNA that enhances the host interferon response in human epithelial cellsJournal of virology · 2026
Summary (written by the submitters)
Long noncoding RNAs (lncRNAs) have been found to play significant regulatory roles within antiviral and immune responses. We previously identified the novel lncRNA virus-inducible lncRNA modulator of interferon response (VILMIR), that was found to broadly regulate the host transcriptional response to interferon-beta (IFN-β) treatment in A549 human lung epithelial cells.
Read the full summary ↓
Here, we investigated the mechanism by which VILMIR regulates the host interferon response in trans by identifying interacting proteins and gene regulatory networks of VILMIR. Through an RNA pull-down assay, we found that VILMIR interacted with both nuclear and cytoplasmic proteins in vitro, including the transcriptional regulators FUBP1 and PUF60 in the nucleus, as well as the antiviral proteins IFIT1 and IFIT3 and the aminoacyl-tRNA synthetases QARS1 and KARS1 in the cytoplasm. In addition, we found that overexpression of VILMIR in A549 cells resulted in an overall enhancement of host interferon response genes and identified a core set of interferon-stimulated genes that were consistently regulated by VILMIR knockdown and overexpression. Finally, we proposed several possible mechanisms by which VILMIR may interact with the identified proteins to regulate the interferon response, such as by interacting with FUBP1 and PUF60 in the nucleus to regulate host transcription in trans or by interacting with the IFIT proteins and aminoacyl-tRNA synthetases in the cytoplasm to regulate translation.
Published in
VILMIR is a trans-acting long noncoding RNA that enhances the host interferon response in human epithelial cells
John K, Smith E, Istishin A et al. · Journal of virology 2026 · PMID 41378895 · doi:10.1128/jvi.01380-25
This dataset
Open GSE305270 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 GSE305270_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 PRJNA1304971 and SRA study SRP608441. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41378895) ↗
Study design
6 conditions, mostly in triplicate
A549 cells, Ctrl, mock, ×3 A549 cells, Ctrl, 1 ng/mL IFN, ×3 A549 cells, Ctrl, 10 ng/mL IFN, ×3 A549 cells, VILMIR, mock, ×3 A549 cells, VILMIR, 1 ng/mL IFN, ×3 A549 cells, VILMIR, 10 ng/mL IFN, ×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
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