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Paradoxical regulation of IGF2 in promoting lipid metabolism in adipose tissues

GSE273546 Mus musculus (mouse) RNA-seq (gene expression) 10 samples Submitted 2024/08/08 Platform GPL24247
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: 3 × Epididymal white adipose tissue, Ad-GFP adenovirus overexpression, Ad-GFP vs 3 × Epididymal white adipose tissue, NC-RNAi lentivirus knockdown, NC-RNAi vs 2 × Epididymal white adipose tissue, Ad-IGF2 adenovirus overexpression, Ad-IGF2 vs 2 × Epididymal white adipose tissue, IGF2-RNAi lentivirus knockdown, IGF2-RNAiGroups 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
Paradoxical regulation of IGF2 in promoting lipid metabolism in adipose tissuesCommunications biology · 2025
Summary (written by the submitters)
Background and aims: Metabolic diseases, including diabetes, obesity, and dyslipidemia, are significant public health concerns worldwide. The insulin-like growth factor 2 (IGF2) gene have been implicated in various physiological processes, but its specific role in lipid metabolism remains unclear. We aim to elucidate the role of IGF2 in regulating lipid metabolism in adipose tissues and its association with metabolic syndrome (MetS).
Read the full summary ↓
Methods: The research employed a multidisciplinary approach to investigate the role of IGF2 in lipid metabolism. We investigated the correlation between genetic variations within the IGF2 gene and metabolic parameters. We conduct a cross-sectional human study to evaluate relationships between varying IGF2 serum concentrations and the incidence of MetS. Additionally, manipulation of IGF2 expression levels in mouse and cell models via overexpression and knockdown to assess impacts on lipid metabolism in adipose tissue, specifically lipid accumulation, insulin resistance, and the balance between lipogenesis and lipolysis. Furthermore, the study employs metabolomics techniques to scrutinize the broader metabolic profiles in adipose tissues in response to IGF2 modulation. Results: Multiple SNP loci in the IGF2 gene were significantly associated with BMI, HbA1c, and diabetes. Insufficient or excessive expression of IGF2 was identified as a risk factor for hyperlipidemia, low HDL-c, and central obesity in MetS. We observed that IGF2 was mainly concentrated in adipose tissues and adipocytes. Enhanced IGF2 expression stimulated adipogenesis and lipid accumulation, whereas IGF2 knockdown hindered lipolysis, exacerbating ectopic lipid accumulation and insulin resistance. There is a substantial enlargement of pancreatic tissue and heightened insulin generation in mice deficient in IGF2. Activation of the PI3K/Akt pathway through IGF1R in IGF2 excess or INSR in conditions of IGF2 scarcity, along with inhibition of AMPK, implies a common downstream process that favors lipid accumulation and metabolic reprogramming in adipocytes. Conclusions: Our study demonstrated that upregulation of IGF2 enhanced adipogenesis and lipogenesis, while knockdown of IGF2 inhibited lipolysis, which resulting in accelerating lipid accumulation through PI3K/Akt-AMPK pathway.
Published in
Paradoxical regulation of IGF2 in promoting lipid metabolism in adipose tissues
Zhou C, Gui W, Zhu W et al. · Communications biology 2025 · PMID 40634529 · doi:10.1038/s42003-025-08458-1
This dataset
Open GSE273546 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 GSE273546_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 PRJNA1142079 and SRA study SRP523245. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40634529) ↗
Study design
3 × Epididymal white adipose tissue, Ad-GFP adenovirus overexpression, Ad-GFP vs 3 × Epididymal white adipose tissue, NC-RNAi lentivirus knockdown, NC-RNAi vs 2 × Epididymal white adipose tissue, Ad-IGF2 adenovirus overexpression, Ad-IGF2 vs 2 × Epididymal white adipose tissue, IGF2-RNAi lentivirus knockdown, IGF2-RNAi

Supports a between-group comparison across 10 samples.

How we read this: 4 replicated groups read from 10 sample titles; they account for 10 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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