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Circadian Clock Gene Modulation and Selective Reprogramming in Response to Copy-back Viral Genomes During Infection

GSE337458 Homo sapiens (human) Single-cell RNA-seq 12 samples Submitted 2026/07/07 Platform GPL34281
At a glance

Single-cell RNA-seq · human · 12 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Homo sapiens (human)
Samples
12 samples: 3 × CTRL vs 3 × CTRL_SEV vs 3 × BMAL2 vs 3 × BMAL2_SEVGroups 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)
The circadian clock regulates fundamental cellular processes that include the host response to infection. However, its intersection with the mechanisms driving antiviral immunity remains poorly explored. Copy-back viral genomes (cbVGs) generated during virus replication strongly stimulate immunity during infection with negative-sense RNA viruses.
Read the full summary ↓
Here, we demonstrate that the model lung epithelial cells A549 are circadian-competent, and by tracking the circadian clock at single-cell resolution across an active infection timeline, we show that infection with Sendai virus progressively disrupts the molecular clock architecture, driven primarily by cbVGs. Furthermore, we show that MAVS signaling is essential for the induction of the circadian clock activating component ARNTL2, while PKR signaling is required for the cbVG-specific induction of the repressive components NR1D1, NR1D2, and PER1. A similar cbVG-specific signature was observed in cells infected with a cbVG-high respiratory syncytial virus stock, demonstrating that cbVG-driven clock reprogramming extends to phylogenetically distinct negative-sense RNA viruses. In addition, we established through gain- and loss-of-function experiments that ARNTL2 is functionally required for amplifying the transcriptional response to infection, with selective effects on the expression of specific antiviral genes including CCL5. Together, these findings demonstrate that the host clock is actively disrupted by a subset of viral genomes produced during virus replication and specific clock genes are repurposed to enhance the host response to infection.
This dataset
Open GSE337458 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 GSE337458_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1489224 and SRA study SRP714834. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
3 × CTRL vs 3 × CTRL_SEV vs 3 × BMAL2 vs 3 × BMAL2_SEV

Supports a between-group comparison across 12 samples.

How we read this: 4 replicated groups read from 12 sample titles; they account for 12 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.