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Lysine Methylpropionylation, a Hybrid Post-Translational Modification Mediated by p300/PCAF Couples Metabolic Dynamics with Protein Function

GSE295019 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 16 samples Submitted 2025/04/30 Platform GPL29480
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 16 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
Homo sapiens (human)
Samples
16 samples: 16 samples, each with a distinct title — no replicated groupsGroups 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)
Lysine alkylation and acylation serve as crucial links between cellular metabolism and a wide range of biological functions. However, hybrid modifications that combine features of both alkylation and acylation remain largely unexplored. Here, we report the discovery and in-depth characterization of lysine methylpropionylation (Kpm), a hybrid post-translational modification (PTM) defined by the propionylation of monomethyllysine residues.
Read the full summary ↓
Genome-wide chromatin immunoprecipitation coupled with transcriptomic profiling revealed that Kpm exhibits distinct genomic distribution compared to canonical Kac and Kpr, with preferential enrichment at promoters of genes involved in metabolic pathways. Together, our findings establish Kpm as a dual-feature PTM that integrates metabolic cues with both chromatin regulation and non-histone protein function, offering a mechanistic framework for understanding metabolism-proteome crosstalk.
This dataset
Open GSE295019 in finder → Find more human ChIP / ATAC / CUT&Tag 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 GSE295019_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 PRJNA1252466 and SRA study SRP579640. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
16 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 16 sample titles: 16 distinct titles with little repetition. 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.