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An HSF1-JMJD6-HSP feedback circuit promotes cell adaptation to proteotoxic stress [RNA-Seq]

GSE270672 Homo sapiens (human) RNA-seq (gene expression) 17 samples Submitted 2024/07/18 Platform GPL18573
At a glance

RNA-seq (gene expression) · human · 17 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
17 samples: 3 × MA_PC3M_siNT vs 3 × MA_PC3M_siNT_HS vs 3 × MA_PC3M_siJMJD6_HSGroups 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
An HSF1-JMJD6-HSP feedback circuit promotes cell adaptation to proteotoxic stressProceedings of the National Academy of Sciences of the United States of America · 2024
Summary (written by the submitters)
Heat Shock Factor 1 (HSF1) is best known as the master transcriptional regulator of the heat-shock response (HSR), a conserved adaptive mechanism critical for protein homeostasis (proteostasis). Combining a genome-wide RNAi library with an HSR reporter, we identified JMJD6 as an essential mediator of HSF1 activity. In follow-up studies, we found that JMJD6 is itself a non-canonical transcriptional target of HSF1 which acts as a critical regulator of proteostasis.
Read the full summary ↓
In a positive feedback circuit, HSF1 binds and promotes JMJD6 expression, which in turn reduces HSP70 R469 monomethylation to disrupt HSP70-HSF1 repressive complexes resulting in enhanced HSF1 activation. Thus, JMJD6 is intricately wired into the proteostasis network where it plays a critical role for cellular adaptation to proteotoxic stress.
Published in
An HSF1-JMJD6-HSP feedback circuit promotes cell adaptation to proteotoxic stress
Alasady MJ, Koeva M, Takagishi SR et al. · Proceedings of the National Academy of Sciences of the United States of America 2024 · PMID 38985769 · doi:10.1073/pnas.2313370121
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 GSE270672_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 PRJNA1127665 and SRA study SRP515692. Searching any of these in the dataset finder brings you back here.

Study design
3 × MA_PC3M_siNT vs 3 × MA_PC3M_siNT_HS vs 3 × MA_PC3M_siJMJD6_HS

Supports a between-group comparison across 9 samples.

How we read this: 3 replicated groups read from 17 sample titles; they account for 9 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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