GEO series

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

GSE249858 Mus musculus (mouse) RNA-seq (gene expression) 18 samples Submitted 2025/07/17 Platform GPL28457

At a glance

RNA-seq (gene expression) · mouse · 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: Mus musculus (mouse)
Samples: 18 samples: 6 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.
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 GSE249858 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 GSE249858_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 PRJNA1051037 and SRA study SRP477288. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39947330) ↗

Study design

6 conditions, mostly in triplicate

DMSO_T0 to T24 ×3 Pema T0 to T24 ×3 DMSO_T14 to T20 ×3 Pema T14 to T20 ×3 DMSO_T24 to T30 ×3 Pema_T24 to t30 ×3

Supports a between-group comparison across 18 samples.

How we read this: 6 replicated groups read from 18 sample titles; they account for 18 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

GSM7966021 DMSO_T0 to T24 Rep1 DMSO_T0 to T24
GSM7966022 Pema T0 to T24 Rep 1 Pema T0 to T24
GSM7966023 DMSO_T14 to T20 Rep1 DMSO_T14 to T20
GSM7966024 Pema T14 to T20 Rep 1 Pema T14 to T20
GSM7966025 DMSO_T24 to T30 Rep1 DMSO_T24 to T30
GSM7966026 Pema_T24 to t30 Rep1 Pema_T24 to t30
GSM7966027 DMSO_T0 to T24 Rep2 DMSO_T0 to T24
GSM7966028 Pema T0 to T24 Rep 2 Pema T0 to T24
GSM7966029 DMSO_T14 to T20 Rep2 DMSO_T14 to T20
GSM7966030 Pema T14 to T20 Rep 2 Pema T14 to T20
GSM7966031 DMSO_T24 to T30 Rep2 DMSO_T24 to T30
GSM7966032 Pema_T24 to t30 Rep2 Pema_T24 to t30
GSM7966033 DMSO_T0 to T24 Rep3 DMSO_T0 to T24
GSM7966034 Pema T0 to T24 Rep 3 Pema T0 to T24
GSM7966035 DMSO_T14 to T20 Rep3 DMSO_T14 to T20
GSM7966036 Pema T14 to T20 Rep 3 Pema T14 to T20
GSM7966037 DMSO_T24 to T30 Rep3 DMSO_T24 to T30
GSM7966038 Pema_T24 to t30 Rep3 Pema_T24 to t30

Datasets on a similar topic

GSE249859 Unveiling the Molecular Legacy of Transient Insulin Resistance: Implications for Hepatic Metabolic Adaptability 82 samples shares implications, adaptability, resistance, unveiling, transient, insulin, hepatic
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GSE275407 Covalent Binding of Thioredoxin to TXNIP is Required for Diet-induced Hepatic Insulin Resistance in the Liver 15 samples shares resistance, insulin, hepatic
GSE307773 Oncogenic MAPK signalling induces epithelial cell state change and therapeutic resistance in vivo [RNA-Seq] 175 samples shares resistance, rna-seq
GSE290031 Very long-chain ceramides in skeletal muscle associate with systemic insulin resistance independent of obesity 92 samples shares resistance, insulin
GSE268774 Lymph node macrophages drive immune tolerance and resistance to cancer therapy by expression of the immune-regulatory cytokine IL-33 [RNA-seq] 48 samples shares resistance, rna-seq
GSE286275 Insulin Resistance Increases TNBC Aggressiveness and Brain Metastasis via Adipocyte-derived Exosomes 38 samples shares resistance, insulin
GSE254704 Skeletal muscle cystathionine γ-lyase deficiency promotes obesity and insulin resistance and results in hyperglycemia and skeletal muscle injury upon HFD in mice 32 samples shares resistance, insulin

Search all mouse RNA-seq datasets in GEO →

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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.