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Bioinformation and deep-learning based model reveals norepinephrine inhibiting PRDX1 aggravates atherosclerosis

GSE282003 Mus musculus (mouse) RNA-seq (gene expression) 16 samples Submitted 2025/05/07 Platform GPL21273
At a glance

RNA-seq (gene expression) · mouse · 16 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
16 samples
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)
The regulation of metabolites comprises the fundamental biological processes of human. More new phenotypes of classic metabolites were revealed, while the devolution of functional targets and mechanisms of them still have many gaps due to diversity in structures and functions. Here, we report a bioinformation and attention mechanism based deep learning model leveraging abundant bioinformation of metabolites for functional target prediction of specific phenotypes.
Read the full summary ↓
Taking advantage of this model, the potential target database of 3,382 common human metabolites were resourced publicly. Norepinephrine was identified as a selective peroxidase inhibitor of peroxiredoxin 1 (PRDX1) for atherosclerosis aggravation in model validation experiments. Salvianolic acid A (SAA) was screened out as PRDX1 exogenous stabilization compound for rescuing norepinephrine’s aggravation effects in vivo. Besides, bulk RNA-Sequencing analysis indicated norepinephrine aggravates atherosclerosis through activating transcriptional activity of SREBP2 in a ROS dependent manner. Overall, our research provides a new computational regime for metabolites target identification besides experimental chemical proteomics and PRDX1 stabilization was validated a potential druggable target for cardiovascular diseases intervention.
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 GSE282003_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 PRJNA1186652 and SRA study SRP545500. Searching any of these in the dataset finder brings you back here.

Samples in this study

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+ 16 more — browse all 16 samples with per-sample file links →

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