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Transcriptomics DNAJC15 and DNAJC19 knockdown in HeLa cells

GSE299431 Homo sapiens (human) RNA-seq (gene expression) 14 samples Submitted 2025/12/19 Platform GPL34284
At a glance

RNA-seq (gene expression) · human · 14 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
14 samples: 5 × HeLa cells, esiScrambled vs 5 × HeLa cells, esiDNAJC19 vs 4 × HeLa cells, esiDNAJC15Groups 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
Stress adaptation of mitochondrial protein import by OMA1-mediated degradation of DNAJC15Nature structural & molecular biology · 2026
Summary (written by the submitters)
To better understand how OMA1 affects mitochondrial proteostasis and stress responses, we performed a proteomic survey of OMA1-deficient cells for proteolytic substrates. We demonstrate that OMA1 cleaves the mitochondrial chaperone DNAJC15 facilitating its degradation by the mitochondrial m-AAA protease. The loss of DNAJC15 alters protein import by TIM23 protein translocases in the IM and limits the accumulation of OXPHOS-related mitochondrial matrix and IM proteins.
Read the full summary ↓
Non-imported mitochondrial preproteins accumulate at the endoplasmic reticulum (ER) and trigger an ATF6-related unfolded protein response. These results demonstrate that OMA1 allows to adapt mitochondrial protein biogenesis to stress and reveal an intricate network of cellular stress responses to proteostasis disturbances.
Published in
Stress adaptation of mitochondrial protein import by OMA1-mediated degradation of DNAJC15
Kroczek L, Nolte H, Lasarzewski Y et al. · Nature structural & molecular biology 2026 · PMID 41760807 · doi:10.1038/s41594-026-01756-0
This dataset
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.

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 GSE299431_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1274417 and SRA study SRP591044. Searching any of these in the dataset finder brings you back here.

Study design
5 × HeLa cells, esiScrambled vs 5 × HeLa cells, esiDNAJC19 vs 4 × HeLa cells, esiDNAJC15

Supports a between-group comparison across 14 samples.

How we read this: 3 replicated groups read from 14 sample titles; they account for 14 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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