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ABCC4 impairs the clearance of plasma LDL cholesterol through suppressing LDLR abundance on hepatic surface

GSE297526 Mus musculus (mouse) RNA-seq (gene expression) 15 samples Submitted 2025/08/19 Platform GPL17021
At a glance

RNA-seq (gene expression) · mouse · 15 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
Mus musculus (mouse)
Samples
15 samples: 5 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)
Low expression level of low-density lipoprotein receptor (LDLR) on the surface of hepatocytes contributes to hypercholesterolemia and ultimately is the primary determinant of atherosclerotic cardiovascular disease (ASCVD)，which is the leading cause of death in humans globally. Here, we report that inhibition of hepatocyte ABCC4, identified as a top hit from large-scale CRISPR/Cas9 screens, significantly increases hepatic LDLR abundance and LDL cholesterol clearance.
Read the full summary ↓
As a hepatic transporter for cAMP efflux, ABCC4 silencing alters its intracellular distribution and activates downstream Epac2/Rap1a signaling pathway, which ultimately downregulates PCSK9 protein expression, thereby blocking lysosomal degradation of LDLR. Furthermore, in both animal and cell models, we demonstrate that liver-specific disruption of ABCC4 and pharmacological inhibition of ABCC4 enhance hepatic plasma membrane LDLR expression and lower plasma LDL cholesterol level through ABCC4-cAMP-PCKS9 pathway. Collectively, genome-wide CRISPR screening offers a valuable resource for identifying LDLR modifiers, providing potential insights for therapeutic strategies in hypercholesterolemia and atherosclerosis.
This dataset
Open GSE297526 in finder → Find more mouse 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 GSE297526_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 PRJNA1265130 and SRA study SRP586461. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
5 conditions, mostly in triplicate
KO_1 ×3 KO_2 ×3 Rosa26 ×3 WT_C ×3 WT_T ×3

Supports a between-group comparison across 15 samples.

How we read this: 5 replicated groups read from 15 sample titles; they account for 15 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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