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Interaction between long-range chromatin regulators Nipbl & Isl1 synergistically drives heart defects in mice

GSE278563 Mus musculus (mouse) RNA-seq (gene expression) 37 samples Submitted 2025/05/19 Platform GPL34290
At a glance

RNA-seq (gene expression) · mouse · 37 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
37 samples: 10 × Nipbl +/+; Isl1 +/+ vs 9 × Isl1 +/ vs 9 × Nipbl +/ vs 9 × Nipbl +/-; Isl1 +/Groups 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
Interaction between long-range chromatin regulators Nipbl & Isl1 synergistically drives heart defects in micebioRxiv : the preprint server for biology · 2025
Summary (written by the submitters)
Congenital heart defects (CHDs) are frequently observed in the most common form of Cornelia de Lange Syndrome (CdLS), which is caused by haploinsufficiency for NIPBL, a gene involved in chromatin looping and cis-regulatory control of gene expression. Here, we surveyed cardiac defects in mice made Nipbl-haploinsufficient in the second heart field using two Cre drivers: Mef2c-Cre and Isl1-Cre.
Read the full summary ↓
Only Isl1-Cre-driven Nipbl-haploinsufficiency resulted in CHDs – a finding we traced to the additional contribution of Isl1-haploinsufficiency caused by the Isl1-Cre allele. To test whether combined reduction of Nipbl and Isl1 cause CHDs, we made mice globally haploinsufficient for both genes. Indeed, Nipbl+/-; Isl1+/- mice exhibited a substantially higher frequency and severity of CHDs than mice haploinsufficient for either gene alone. As a member of the LIM-homeodomain transcription factor family, Isl1 is involved in chromatin looping and enhancer-promoter communication via a mechanism distinct from that of Nipbl. Nevertheless, when we performed RNA sequencing on E10.5 hearts from wildtype, Nipbl+/-, Isl1+/-, and Nipbl+/-; Isl1+/- embryos, we observed that combined haploinsufficiency resulted in largely additive gene expression changes, including dysregulation of known cardiac regulators (Irx4, Tbx1, Foxo6, Heyl, Bnc1, Sox17) and novel candidates (Gbx1, Csdc2, Myrf, Pou6f1, Zfp579, ad Zfp763). A subset of additive changes arose from opposing regulatory influences in single mutants that restored gene expression to WT levels in Nipbl⁺/⁻; Isl1⁺/⁻ hearts. For example, Hoxc4, Pitx2, Isl1 itself, and Pax6 (a known target of Isl1), were upregulated in Nipbl+/- hearts, downregulated in Isl1+/- hearts, but expressed at WT levels in Nipbl⁺/⁻; Isl1⁺/⁻ hearts. Since loss of Isl1 upregulation from Nipbl+/- to Nipbl+/-; Isl1+/- hearts coincided with a marked increase in CHDs, we propose that Isl1 upregulation compensates for the loss of cis-regulatory interactions due to Nipbl-haploinsufficiency, and protects hearts from severe CHD risk. Supporting this model, other LIM-homeodomain transcription factors (Lhx2, Lhx3, Lhx9) were also upregulated in Nipbl+/- hearts, with Lhx3 and Lhx9 showing even greater upregulation in Nipbl+/-; Isl1+/- hearts. Despite this, CHDs resulting from the combined loss of Nipbl and Isl1 were particularly severe. These findings suggest that heart development is exquisitely sensitive to small change in gene expression, leading to synergistic phenotypic interactions when relatively modest gene expression changes are combined.
Published in
Interaction between long-range chromatin regulators Nipbl & Isl1 synergistically drives heart defects in mice
Chea S, Santos R, Lopez-Burks ME et al. · bioRxiv : the preprint server for biology 2025 · PMID 40475661 · doi:10.1101/2025.05.15.654123
This dataset
Open GSE278563 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 GSE278563_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 PRJNA1167790 and SRA study SRP536107. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40475661) ↗
Study design
10 × Nipbl +/+; Isl1 +/+ vs 9 × Isl1 +/ vs 9 × Nipbl +/ vs 9 × Nipbl +/-; Isl1 +/

Supports a between-group comparison across 37 samples.

How we read this: 4 replicated groups read from 37 sample titles; they account for 37 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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