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Protein tyrosine phosphatase receptor kappa regulates glycolysis, de novo lipogenesis and promotes hepatocyte metabolic reprogramming in obesity

GSE247670 Mus musculus (mouse) RNA-seq (gene expression) 12 samples Submitted 2024/09/26 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 12 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
12 samples
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
PTPRK regulates glycolysis and de novo lipogenesis to promote hepatocyte metabolic reprogramming in obesityNature communications · 2024
Summary (written by the submitters)
Metabolic dysfunction within the liver is a major cause of human disease worldwide. Fat accumulation, de novo lipogenesis, and glycolysis are key drivers of hepatocyte reprogramming and the consequent metabolic dysfunction-associated steatotic liver disease (MASLD). The underpinning molecular mechanisms affected by steatosis and inflammation in the obese states remain unknown.
Read the full summary ↓
Here we report that obesity leads to dysregulated expression of protein-tyrosine phosphatases (PTPs) in the liver. Protein Tyrosine Phosphatase Receptor Kappa (PTPRK) expression was increased in hepatocytes during steatosis and inflammation in humans and mice, and positively correlates with PPARγ-induced lipogenic signalling. Supporting this, PTPRK knockout mice displayed reduced fat accumulation in adipose tissue and liver after exposure to an obesogenic diet. Phosphoproteomic analysis in primary hepatocytes and hepatic metabolomics identified specific phosphotyrosine residues in fructose-1,6 bisphosphatase-1 and glycolysis regulation as targets of PTPRK. The changes in glycolysis and de novo lipogenesis revealed PTPRK was a driving force for metabolic reprogramming in hepatocytes. Moreover, hepatoma cell lines showed reduced colony-forming ability after PTPRK silencing in vitro, and PTPRK knockout mice developed smaller tumours after diethylnitrosamine-induced hepatocarcinogenesis in vivo. Through computational modelling, we identified selective PTPRK inhibitors. These compounds decreased glycolytic rates in hepatoma cell lines, PPARγ expression in primary hepatocytes and steatosis in obese mice. In conclusion, our study defines a novel mechanism for the development of MASLD, revealing a key role of PTPRK on hepatic glycolysis regulation with implications in lipid metabolism, and liver tumour development. We propose PTPRK as a potential target for metabolic liver dysfunction, and the identified inhibitors may represent promising candidates for therapy in obesity-associated liver diseases.
Published in
PTPRK regulates glycolysis and de novo lipogenesis to promote hepatocyte metabolic reprogramming in obesity
Gilglioni EH, Li A, St-Pierre-Wijckmans W et al. · Nature communications 2024 · PMID 39496584 · doi:10.1038/s41467-024-53733-0
This dataset
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.

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 GSE247670_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1040139 and SRA study SRP471736. Searching any of these in the dataset finder brings you back here.

Samples in this study

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+ 12 more — browse all 12 samples with per-sample file links →

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