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Asymmetric crosstalk between the BMP and TGFβ pathways resolves signaling ambiguity

GSE254418 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 25 samples Submitted 2024/12/30 Platform GPL19057Platform GPL24247
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 25 samples

What it is
ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism
Mus musculus (mouse)
Samples
25 samples: 7 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)
The BMP and TGFβ signaling pathways control cellular fate decisions in diverse biological contexts, often playing opposing roles. Despite extensive knowledge of these pathways, understanding how cells respond to environments containing these opposing cues remains a challenge. Here, we systematically analyze the activation of these pathways under combinatorial signaling environments.
Read the full summary ↓
We find that TGFβ ligands inhibit BMP signaling, while BMP ligands enhance TGFβ transcriptional response across concentrations, ligand variants, and cell types. This asymmetric crosstalk results in the activation of a TGFβ-biased transcriptional response, even under mixed signaling conditions, effectively reducing signal ambiguity, with implications for processes such as EMT. We show that this crosstalk originates downstream of SMAD proteins phosphorylation. Using mathematical models, we predict, and experimentally verify, that promiscuous interactions between SMAD proteins provide the mechanism for the observed crosstalk. Our findings challenge the canonical models, suggesting an active role for mediator proteins in determining biological responses.
This dataset
Open GSE254418 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 GSE254418_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1070349 and SRA study SRP486638. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
7 conditions, mostly with about 4 replicates each
C2 cells, BMP4 added, 6h, biol ×4 C2 cells, control- no ligand added, 6h, biol ×4 C2 cells, TGFb1 added, 6h, biol ×4 C2 cells, added with TGFb1 and BMP4, 6h, biol ×4 NMuMG cells transfected with SMAD1-Mnase, con… ×3 NMuMG cells transfected with SMAD1-Mnase, add… ×3 NMuMG cells transfected with SMAD1-Mnase, add… ×3

Supports a between-group comparison across 25 samples.

How we read this: 7 replicated groups read from 25 sample titles; they account for 25 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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