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C2orf74 Orchestrates Germ-Leydig Crosstalk to Reprogram White Adipocyte Identity II

GSE236434 Mus musculus (mouse) RNA-seq (gene expression) 12 samples Submitted 2026/04/06 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: 3 × C2orf74 KO mice,epWAT;C vs 3 × WT mice;epWAT;C vs 3 × C2orf74 KO mice,scWAT;C vs 3 × WT mice;scWAT;CGroups 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)
Gender differences in obesity is widely reported, however, identification of gender specific obese gene and characterization of its role in obesity remain elusive. Here, we identify a germ cell specifically expressed gene C2orf74 and demonstrate that C2ORF74 promotes high-fat diet (HFD) induced obesity in male but not female male. Loss of C2ORF74 protects male mice from obesity by boosting testosterone levels and enhancing energy expenditure and browning of white adipose tissues (WAT).
Read the full summary ↓
We demonstrate that spermatogenic C2ORF74 suppresses testosterone biogenesis in Leydig cells through germ-cell secreted BPIFA3, whose maturation and secretion is dependent on DPM1-mediated glycosylation. Collectively, these findings define a role of spermatogenic C2ORF74 in energy expenditure and adipose browning in a gender specific manner.
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 GSE236434_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 PRJNA990802 and SRA study SRP447181. Searching any of these in the dataset finder brings you back here.

Study design
3 × C2orf74 KO mice,epWAT;C vs 3 × WT mice;epWAT;C vs 3 × C2orf74 KO mice,scWAT;C vs 3 × WT mice;scWAT;C

Supports a between-group comparison across 12 samples.

How we read this: 4 replicated groups read from 12 sample titles; they account for 12 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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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.