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HBV-driven host chromatin accessibility changes affect liver metabolic pathways, iron homeostasis and promote a preneoplastic phenotype [RNA-seq]

GSE239860 Homo sapiens (human) RNA-seq (gene expression) 18 samples Submitted 2025/05/29 Platform GPL24676Platform GPL18573
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: 18 samples, each with a distinct title — no replicated groupsGroups 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
HBV-driven host chromatin accessibility changes affect liver metabolic pathways, iron homeostasis and promote a preneoplastic phenotypeJournal of experimental & clinical cancer research : CR · 2025
Summary (written by the submitters)
Background and Aims: Complex host-virus interactions account for HBV-specific T and B cells dysfunction and the persistence of the viral cccDNA mini-chromosome, key features of HBV chronicity and challenges for HBV cure. The extent of HBV impact on liver transcriptome remains controversial. Transcriptional activation in eukaryotic cells has been tightly linked with disruption of nucleosome organization at accessible genomic sites of remodeled chromatin.
Read the full summary ↓
We used ATAC-seq (Assay for Transposase Accessible Chromatin followed by high throughput sequencing) to detect early changes in chromatin accessibility in HBV-infected Primary Human Hepatocytes (PHHs). Results. ATAC-seq analysis revealed that after HBV infection an increasing number of genomic sites change their chromatin accessibility over time with a prevalent global reduction of accessibility (that is) partially prevented by inhibiting HBV transcription and replication. Motif Enrichment Analysis identified several TF involved in liver cell identity, glucose and lipid metabolism as potential regulators of differentially accessible genes 72h post-infection, when cccDNA transcription and viral replication are established. ATAC-seq and RNA-seq integration confirmed HBV infection impact on liver metabolism, the early establishment of a premalignant phenotype and identified a 39-genes liver iron signature modulated by chromatin accessibility changes. The upregulation of iron uptake genes translates in a significant increase of iron content in HBV-infected PHHs whereas iron chelation results in a drastic inhibition of cccDNA transcription and viral replication. Conclusions. Altogether our results show that HBV infection impacts on host cell chromatin landscape and specific transcriptional programs and that HBV modulates intracellular available iron to boost its replication.
Published in
HBV-driven host chromatin accessibility changes affect liver metabolic pathways, iron homeostasis and promote a preneoplastic phenotype
Alfano V, Pascucci GR, Corleone G et al. · Journal of experimental & clinical cancer research : CR 2025 · PMID 40380227 · doi:10.1186/s13046-025-03414-7
This dataset
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.

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 GSE239860_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1001287 and SRA study SRP452927. Searching any of these in the dataset finder brings you back here.

Study design
18 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 18 sample titles: 18 distinct titles with little repetition. 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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