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Nuclear class 3 PI3K co-activates fasting-specific chromatin remodeling

GSE290682 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 63 samples Submitted 2026/06/02 Platform GPL28457Platform GPL28038
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 63 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
63 samples: 19 conditions, mostly in duplicateGroups 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)
Transcriptional remodeling is essential for stress adaptation such as in fasting. While the core transcriptional machinery and its regulators are known, how information regarding energy availability is relayed to transcriptional complexes remains unclear. Surprisingly, we find that class 3 Phosphatidyl inositol-3 kinase (PI3K-3) lipid kinase, a master regulator of cytosolic autophagy, co-activates epigenetic writers for fasting-induced transcription.
Read the full summary ↓
To this end, chromatin distribution of PI3K-3 significantly overlapped with transcriptionally engaged RNA Pol II phosphorylated on Ser5 (RNAPII-S5p) and transcriptionally permissive H3K4me3. Mechanistically, nuclear PI3K-3 interacted with both RNAPII and the Set1a/COMPASS methyltransferase of H4K4me3. Moreover, decreased binding between RNAPII and Set1a/COMPASS was found in PI3K-3 depleted cells. Accordingly, PI3K-3 deletion led to lower H3K4me3 and RNAPII-S5p enrichment at selective loci. Conversely, PI3K-3 overexpression co-activated acetyltransferase p300/CBP in luciferase assays while its chromatin targeting promoted H3K4me3 deposition. In starved cells, PI3K-3 induced transcription of autophagy-related genes, and in fasted liver, it was critical for transcriptional remodeling of metabolism, such as activation of lipid degradation. Thus, PI3K-3 couples nutrient availability to chromatin remodeling for transcriptional activation, enabling metabolic adaptation in fasting.
This dataset
Open GSE290682 in finder → Find more 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 GSE290682_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 PRJNA1229530 and SRA study SRP566832. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
19 conditions, mostly in duplicate
RNA Pol II S5P, liver, Fed Vps15LKO, ×3 RNA Pol II S5P, liver, Fast Vps15LKO, ×3 Vps15, liver, Fast wt, ×3 RNA Pol II S5P, liver, Fed wt, ×3 RNA Pol II S5P, liver, Fast wt, ×3 RNA Pol II S5P, MEFs, basal methionine, ×3 RNA Pol II S5P, MEFs, -4h methionine, ×3 RNA Pol II S5P, MEFs, -4h+1h methionine, ×3 +28 more

Supports a case/control comparison: 9 samples read as cases, 23 as controls.

How we read this: 19 replicated groups read from 63 sample titles; they account for 46 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

+ 23 more — browse all 63 samples with per-sample file links →

Datasets on a similar topic

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