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Low physiological pH drives P300-mediated acetylation of PARP1 and promotes PARP inhibitor resistance [RNA-seq]

GSE314447 Homo sapiens (human) RNA-seq (gene expression) 18 samples Submitted 2026/07/13 Platform GPL30173
At a glance

RNA-seq (gene expression) · human · 18 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
Homo sapiens (human)
Samples
18 samples: 6 conditions, mostly in triplicateGroups 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)
Resistance to PARP inhibitors (PARPi) remains a major clinical obstacle in epithelial ovarian carcinoma (EOC), ultimately limiting therapeutic efficacy and contributing to patient mortality. Here, we demonstrate that the acidic tumor microenvironment drives a novel mechanism of PARPi resistance. Decreasing the pH is sufficient to reduce PARPi anti-tumor efficacy in multiple in vitro and in vivo EOC models.
Read the full summary ↓
Through three independent, epigenetically focused CRISPR/Cas9 screens conducted under low pH conditions, we identified p300 as a druggable target for overcoming pH-induced PARPi resistance. Mechanistically, in an unbiased functional proteomic evaluation we uncovered an ERK1/2–p300–PARP1 signaling axis activated under low pH, which alleviates PARPi-induced PARP1 trapping by promoting PARP1 K505 acetylation and thereby reducing replication fork collapse and associated DNA damage and driving PARPi resistance. Notably, in multiple in vivo patient-derived and syngeneic EOC models, two novel p300 bromodomain inhibitors, IACS-16559 and TT125-802, restore sensitivity to PARPi. Together, our findings establish p300 as a promising therapeutic target for overcoming pH-driven PARPi resistance in EOC.
This dataset
Open GSE314447 in finder → Find more human 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 GSE314447_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 PRJNA1391339 and SRA study SRP656682. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
6 conditions, mostly in triplicate
DMSO-pH7.4# ×3 DMSO-pH6.5# ×3 IACS16559-pH7.4# ×3 IACS16559-pH6.5# ×3 CCS1477-pH7.4# ×3 CCS1477-pH6.5# ×3

Supports a between-group comparison across 18 samples.

How we read this: 6 replicated groups read from 18 sample titles; they account for 18 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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