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Herbal terpenoids activate autophagy and mitophagy through modulation of bioenergetics and protect from metabolic stress, sarcopenia and epigenetic aging [Quantseq_cell]

GSE298195 Mus musculus (mouse) RNA-seq (gene expression) 12 samples Submitted 2025/06/01 Platform GPL30172
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: 6 × Thymol100 vs 6 × DMSOGroups 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)
Small molecular food components contribute to health benefits of diets rich in fruits, vegetables, herbs, and spices. The cellular mechanisms by which non-caloric bioactives promote healthspan are not well understood, limiting their use for disease prevention. Here, we deploy a whole organism, high-content screen in zebrafish to profile food-derived compounds for activation of autophagy, a cellular quality control mechanism promoting healthy aging.
Read the full summary ↓
We identify thymol and carvacrol as activators of autophagy and mitophagy through short-acting dampening of mitochondrial membrane potential. Chemical stabilization of thymol-induced mitochondrial depolarization blocks mitophagy activation, suggesting a mitochondrial membrane-originating mechanism. Supplementation of thymol prevents excess liver fat accumulation in a diet-induced obesity mouse model, improves pink1-dependent heat stress-resilience in Caenorhabditis elegans and slows decline of skeletal muscle performance and epigenetic aging in SAMP8 mice. Thus, terpenoids from common herbs promote autophagy during aging and metabolic overload, making them attractive for nutrition-based healthspan promotion.
This dataset
Open GSE298195 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 GSE298195_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 PRJNA1268502 and SRA study SRP588070. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
6 × Thymol100 vs 6 × DMSO

Supports a between-group comparison across 12 samples.

How we read this: 2 replicated groups read from 12 sample titles; they account for 12 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

Search all mouse RNA-seq datasets in GEO →

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