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U2AF1-S34F and U2AF1-Q157R induce distinct RNA splicing and hematopoietic phenotypes in vivo

GSE282060 Mus musculus (mouse) RNA-seq (gene expression) 9 samples Submitted 2026/05/10 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 9 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
9 samples: 3 × WT_KL_Native vs 3 × S34F_KL_Native vs 3 × Q157R_KL_NativeGroups 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)
Recurrent somatic mutations in a subset of spliceosome genes (SF3B1, SRSF2, and U2AF1) are frequently identified in patients with myeloid malignancies such as myelodysplastic syndromes (MDS). These heterozygous and mutually exclusive mutations are enriched in hotspot codons in these 3’ splicing factor proteins resulting in aberrant alternative mRNA splicing in hematopoietic cells.
Read the full summary ↓
However, each mutant protein predominantly affects a distinct set of alternatively spliced downstream target genes suggesting that global mRNA splicing alterations, rather than specific splicing targets, may be responsible for MDS phenotypes, including dysplasia, ineffective hematopoiesis, and cytopenias. U2AF1 provides a unique opportunity to address this question because it has two hotspot positions (serine 34 [S34] and glutamine 157 [Q157]) that are each commonly mutated in MDS and are associated with unique mRNA splicing consequences. In addition, U2AF1 S34 and Q157 codon mutations co-occur with mutations in different genes (e.g., BCOR and ASXL1, respectively) and patients with these mutations may have different hematopoietic phenotypes—highlighting that these mutations may induce distinct phenotypes. We asked if the splicing differences resulting from S34F and Q157R mutations were thus associated with different or similar effects on hematopoiesis. To do so, we engineered a new Cre/lox conditional mouse model with the Q157R mutation knocked-in to the endogenous U2af1 locus based on our established conditional S34F knock-in mouse model and utilized both models to directly study the hematopoietic phenotype, transcriptional, and mRNA splicing consequences of individual U2AF1 gene mutations in vivo. Our results indicate that the two mutations induce distinct hematopoietic phenotypes in mice, suggesting that the U2af1S34F and U2af1Q157R mutations should not be conflated as they may impact disease pathogenesis differently in patients. Mice expressing U2af1S34F have a more severe reduction in their PB and BM cell counts, and reduced HSPCs repopulating ability, compared to mice expressing U2af1Q157R. The expression and splicing of the majority of target genes are unique between the mutations, in both mouse and human samples, potentially driving the phenotypic differences induced by the two mutations. The two mutations co-occur with different gene mutations and are not equally represented in various myeloid neoplasms, suggesting that multiple mechanisms are likely to drive the pathogenesis of U2AF1 mutant myeloid diseases. Collectively, our results support that U2AF1S34F and U2AF1Q157R mutations induce distinct hematopoietic, gene expression, and RNA splicing phenotypes in vivo, suggesting that larger prospective studies are needed to understand if patients with these mutations should be classified differently.
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 GSE282060_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 PRJNA1186736 and SRA study SRP545523. Searching any of these in the dataset finder brings you back here.

Study design
3 × WT_KL_Native vs 3 × S34F_KL_Native vs 3 × Q157R_KL_Native

Supports a between-group comparison across 9 samples.

How we read this: 3 replicated groups read from 9 sample titles; they account for 9 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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