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Ablation of PKCα phosphorylation cascades by CRISPR-Cas9 Base Editing Rescues Heart Failure

GSE294246 Mus musculus (mouse) RNA-seq (gene expression) 12 samples Submitted 2026/03/04 Platform GPL19057
At a glance

RNA-seq (gene expression) · mouse · 12 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
Mus musculus (mouse)
Samples
12 samples: 12 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.
Paper
Ablation of PKCα Phosphorylation by CRISPR-Cas9 Base Editing Rescues Heart FailureCirculation research · 2026
Summary (written by the submitters)
BACKGROUND: The global prevalence of heart failure is increasing significantly, and the prognosis for patients with heart failure remains poor, highlighting the urgent need for novel therapeutic strategies. Protein kinase C alpha (PKCα) plays a central role in heart failure progression. Phosphorylation of PRKCA at T497 triggers a series of phosphorylation cascades, leading to its activation.
Read the full summary ↓
Ablation of T497 phosphorylation leads to reduced protein stability and activity. However, there is no therapeutic strategy for heart failure targeting PKCα T497. METHODS: We employed CRISPR-Cas9 adenine base editing to ablate the phosphorylation site T497 of PKCα. We generated mice harboring a PKCα phosphoresistant T497A mutation in the germline and administered adeno-associated virus 9 (AAV9) harboring base editor of PrkcaT497A to postnatal wild-type (WT) mice to explore its clinical feasibility. To model for heart failure, mice were subjected to transverse aortic constriction (TAC). Cardiac function, hypertrophy, fibrosis, and transcriptional changes were evaluated by echocardiography, wheat germ agglutinin staining, Masson’s trichrome staining, and RNA sequencing, respectively. The editing efficiency of PrkcaT497A was assessed using Sanger sequencing and deep amplicon sequencing. Cellular Ca2+ homeostasis was analyzed using epifluorescence microscopy in Fura-2–loaded human induced pluripotent stem cells-derived cardiomyocytes (iPSC-CMs) carrying the PRKCAT497A mutation under chronic angiotensin II (AngII) stimulation. RESULTS: The T497A mutation in PKCα prevented subsequent phosphorylation and led to PKCα degradation. Four weeks after TAC surgery, WT mice showed dramatically impaired cardiac function, cardiac remodeling, and increased lung weight, whereas PKCα phosphoresistant mice were protected from these effects. PKCα phosphoresistant mice also showed protection against heart failure-related aberrant changes in cardiac gene expression, cardiac hypertrophy, and fibrosis. Likewise, mice administrated with AAV9-base editors exhibited similar cardioprotective effects against TAC-induced heart failure. In vitro, PKCα-edited iPSC-CMs were protected from AngII-induced impairments in contractility and Ca2+ transients observed in WT iPSC-CMs. CONCLUSIONS: The editing of PRKCAT497A through adenine base editing may potentially provide a promising therapeutic approach for human cardiac diseases.
Published in
Ablation of PKCα Phosphorylation by CRISPR-Cas9 Base Editing Rescues Heart Failure
Tadokoro T, Li H, Gan P et al. · Circulation research 2026 · PMID 41717698 · doi:10.1161/CIRCRESAHA.125.326738
This dataset
Open GSE294246 in finder → Find more mouse 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 GSE294246_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 PRJNA1248868 and SRA study SRP577666. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41717698) ↗
Study design
12 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 12 sample titles: 12 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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