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The obesity-linked peptide SP16 regulates adipocytes through GIP and insulin receptor

GSE303737 Homo sapiens (human) RNA-seq (gene expression) 90 samples Submitted 2025/08/12 Platform GPL34281
At a glance

RNA-seq (gene expression) · human · 90 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
90 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)
Obesity has emerged as a global epidemic and represents a major public health concern due to its profound implications for metabolic diseases. While recent pharmacological interventions primarily aim to reduce food intake, enhancing energy expenditure in adipose tissue offers a promising complementary approach. In this study, we identify that SP16, a synthetic α1- antitrypsin derived peptide, is a novel GIP (gastric inhibitory peptide) receptor agonist.
Read the full summary ↓
SP16 promotes lipolysis via the cAMP pathway, resulting in increased mitochondrial oxygen consumption in adipocytes. Furthermore, SP16 binds to the insulin receptor, and at high concentrations, it attenuates insulin receptor signaling. In diet-induced obese (DIO) mice, acute SP16 treatment improved glucose clearance, elevated circulating free fatty acids, and decreased leptin levels. Ex vivo analyses of epididymal white adipose tissue corroborated these findings, demonstrating enhanced lipolysis in SP16-treated mice. In conclusion, SP16 emerges as a novel lipolytic peptide with beneficial metabolic effects.
This dataset
Open GSE303737 in finder → Find more human 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 GSE303737_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 PRJNA1297185 and SRA study SRP603704. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Samples in this study

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