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Ketohexokinase deletion on liver and small intestine transcriptome

GSE331146 Mus musculus (mouse) RNA-seq (gene expression) 32 samples Submitted 2026/06/11 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 32 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
32 samples: 8 conditions, mostly with about 4 replicates eachGroups 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)
Dietary fructose is a major risk factor driving the progression of metabolic dysfunction-associated steatohepatitis (MASH) and hepatocellular carcinoma (HCC). However, the underlying fructose-induced nutrient-sensing pathway has remained unclear. Here, we report that fructose facilitates iron absorption through the (Ketohexokinase) KHK/PKM2/HIF-2α axis, driving MASH/HCC development.
Read the full summary ↓
Fructose aberrantly stabilizes intestinal HIF-α compared to glucose; this effect is abrogated by a KHK inhibitor and genetic Khk deletion. Interestingly, reduced HIF-α stability in Khk deficient mice is rescued by a small-molecule inhibitor of pyruvate kinase M2 (PKM2). Conversely, Khk deficient mice exhibit spontaneous systemic iron deficiency, characterized by hypochromic anemia, and markedly reduced blood neutrophil numbers. Moreover, Khk deficiency inhibits iron absorption in a HIF-2α-dependent manner. Finally, fructose promotes MASH/HCC progression in an iron-dependent manner. This study reveals a unique nutrient-sensing pathway by dietary fructose that is therapeutically targetable.
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 GSE331146_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 PRJNA1466962 and SRA study SRP700997. Searching any of these in the dataset finder brings you back here.

Study design
8 conditions, mostly with about 4 replicates each
Liver-WT ×4 Liver-Khk KO ×4 Jejunum-WT ×4 Jejunum-Khk KO ×4 Ileum-WT ×4 Ileum-Khk KO ×4 Duodenum-WT ×4 Duodenum-Khk KO ×4

Supports a between-group comparison across 32 samples.

How we read this: 8 replicated groups read from 32 sample titles; they account for 32 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
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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.