GEO series

AF6 promotes lipid droplet fusion and aggravates MASH via C/EBPβ–CIDEA axis

GSE305662 Mus musculus (mouse) RNA-seq (gene expression) 10 samples Submitted 2026/02/18 Platform GPL34290

At a glance

RNA-seq (gene expression) · mouse · 10 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: 10 samples: 10 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.

Summary (written by the submitters)

Metabolic dysfunction-associated steatohepatitis (MASH) is a progressive liver disease characterized by steatosis, inflammation, and fibrosis. However, the mechanisms linking lipid droplet (LD) metabolism to MASH progression remain poorly understood. AF6 is a polarity protein known to regulate metabolic homeostasis, but its role in the development of MASH remains largely undefined.

Read the full summary ↓

Here, we identify AF6 as a critical regulator of hepatic lipid accumulation and inflammation in MASH. Hepatic AF6 expression is elevated in MASH patients and diet-induced mouse models. Liver-specific deletion of AF6 attenuated steatosis, inflammation, and fibrosis in both high-fat, high-cholesterol (HFHC) and choline-deficient, amino acid-defined high-fat diet (CDA-HFD)-induced MASH models. Mechanistically, lipid overload promotes nuclear translocation of AF6, which interacts with the transcription factor C/EBPβ and enhances its nuclear accumulation. This interaction increases transcription of the lipid droplet fusion protein CIDEA, promoting LD growth and lipid deposition. AF6 also exacerbates MASH by enhancing C/EBPβ-mediated proinflammatory signaling. Restoration of CIDEA expression in AF6-deficient mice reverses the protective effects. In human liver biopsies and iPSC-derived liver organoids, the AF6–C/EBPβ–CIDEA axis is upregulated and correlates with disease severity. Importantly, a CRISPR-Cas9–based lipid nanoparticle (LNP) therapy targeting AF6 effectively reduced lipid accumulation, inflammation, and fibrosis in MASH mice and organoids. AF6 promotes MASH progression by facilitating C/EBPβ nuclear localization and transcriptional activation of CIDEA. Targeting the AF6–C/EBPβ–CIDEA axis represents a promising therapeutic strategy for treating MASH.

This dataset

Open GSE305662 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 GSE305662_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 PRJNA1307434 and SRA study SRP610307. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

10 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 10 sample titles: 10 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

GSM9181568 HF_HM47rep1,HF_HM47rep2
GSM9181569 HF_HM51rep1,HF_HM51rep2
GSM9181570 HM_204rep1,HM_204rep2
GSM9181571 HM_273rep1,HM_273rep2
GSM9181572 HM_278rep1,HM_278rep2
GSM9181573 HF_KO52rep1,HF_KO52rep2
GSM9181574 KO_202rep1,KO_202rep2
GSM9181575 KO_274rep1,KO_274rep2
GSM9181576 KO_287rep1,KO_287rep2
GSM9181577 KO_289rep1,KO_289rep2

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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.