GEO series

Unveiling the Molecular Legacy of Transient Insulin Resistance: Implications for Hepatic Metabolic Adaptability [ChIP-Seq]

GSE249841 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 19 samples Submitted 2025/07/17 Platform GPL21103

At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 19 samples

What it is: ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism: Mus musculus (mouse)
Samples: 19 samples: 6 × K4m vs 6 × K27m vs 5 × Ac vs 2 × InpGroups 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: Unveiling the molecular legacy of transient insulin resistance: Implications for hepatic metabolic adaptabilityJournal of hepatology · 2025

Summary (written by the submitters)

Metabolic flexibility (MetF) refers to an organism's capacity to adapt to changes in metabolic supplies and energy demands. MetF involves various organs, such as skeletal muscle, white adipose tissues, and the liver, and plays a critical role in altering fuel selection. Insulin acts as a central coordinator of MetF, regulated at the molecular level through various mechanisms, including signaling cascades, transcriptional responses, and circadian regulation.

Read the full summary ↓

Insulin resistance (IR) can hinder MetF, leading to the development of type 2 diabetes and obesity. These conditions are primarily linked to long-term, chronic exposure to sedentary lifestyles, unbalanced diets, and disruptions in circadian rhythms. However, transient episodes of IR significantly elevate the risk of later-in-life development of type 2 diabetes, as seen in cases like gestational diabetes and stress-induced hyperglycemia. Despite their significance to human health, the molecular processes underlying metabolic dysregulation after these transient episodes remain poorly understood. In our study, we demonstrate that a temporary and fully reversible insulin receptor blockade in young mice results in later-in-life hepatic abnormalities that impair the PPARα-mediated response to a high-fat diet. This impairment is associated with increased histone repressive marks at PPARα-centered loci and decreased hepatic content of PPARα endogenous ligands. Transient insulin receptor blockade temporarily disrupts the liver's circadian rhythm, and this disruption specifically propagates to high-fat diet-fed animals. Our findings reveal that PPARα ligand responsiveness is critically influenced by the circadian clock, adding another layer of (dys)regulation to hepatic homeostasis due to early transient events. In summary, our data suggest that seemingly reversible and unnoticed metabolic challenges in early adulthood may predispose the liver to exacerbated metabolic dysfunctions when confronted with chronic challenges later in life.

Published in

Unveiling the molecular legacy of transient insulin resistance: Implications for hepatic metabolic adaptability

Berthier A, Gheeraert C, Vinod M et al. · Journal of hepatology 2025 · PMID 39947330 · doi:10.1016/j.jhep.2025.02.004

This dataset

Open GSE249841 in finder → Find more mouse ChIP / ATAC / CUT&Tag 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 GSE249841_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1050999 and SRA study SRP477257. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39947330) ↗

Study design

6 × K4m vs 6 × K27m vs 5 × Ac vs 2 × Inp

Supports a between-group comparison across 19 samples.

How we read this: 4 replicated groups read from 19 sample titles; they account for 19 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

GSM7965644 K4m-1 K4m
GSM7965645 K4m-2 K4m
GSM7965646 K4m-3 K4m
GSM7965647 K4m-4 K4m
GSM7965648 K4m-5 K4m
GSM7965649 K4m-6 K4m
GSM7965650 K27m-1 K27m
GSM7965651 K27m-2 K27m
GSM7965652 K27m-3 K27m
GSM7965653 K27m-4 K27m
GSM7965654 K27m-5 K27m
GSM7965655 K27m-6 K27m
GSM7965656 Ac-1 Ac
GSM7965657 Ac-2 Ac
GSM7965658 Ac-3 Ac
GSM7965659 Ac-4 Ac
GSM7965660 Ac-5 Ac
GSM7965661 Inp 1 Inp
GSM7965662 Inp 2 Inp

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GSE286275 Insulin Resistance Increases TNBC Aggressiveness and Brain Metastasis via Adipocyte-derived Exosomes 38 samples shares resistance, insulin
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