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Young and undamaged recombinant albumin alleviates T2DM by improving hepatic glycolysis through EGFR and protecting islet β cells in mice

GSE217975 Mus musculus (mouse) RNA-seq (gene expression) 18 samples Submitted 2023/11/11 Platform GPL23479
At a glance

RNA-seq (gene expression) · mouse · 18 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
18 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)
Type 2 Diabetes Mellitus (T2DM) is a severe disease that has reached epidemic proportions. In this study, treatment of T2DM mice with young, undamaged, and ultrapure recombinant mouse serum albumin (rMSA) increased their serum albumin levels, which resulted in a reversal of the disease including reduced fasting blood glucose levels, improved glucose tolerance, increased glucose-stimulated insulin secretion, and alleviated islet atrophy.
Read the full summary ↓
At the cellular level, rMSA improved glucose uptake and glycolysis in hepatocytes. Mechanistically, rMSA reduced the binding between CAV1 and EGFR to increase EGFR activation leading to PI3K-AKT activation. Furthermore, rMSA extracellularly reduced the rate of fatty acid uptake by islet β-cells, which relieved the accumulation of intracellular ceramide, endoplasmic reticulum stress, and apoptosis. This study provided the first clear demonstration that injections of young, undamaged, and ultrapure recombinant serum albumin can alleviate T2DM in mice.
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 GSE217975_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 PRJNA901501 and SRA study SRP407920. Searching any of these in the dataset finder brings you back here.

Samples in this study

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