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DPP4 inhibition affects metabolism and inflammation associated pathways in hiPSC-derived steatotic HLCs

GSE310214 Homo sapiens (human) RNA-seq (gene expression) 24 samples Submitted 2026/03/18 Platform GPL30173
At a glance

RNA-seq (gene expression) · human · 24 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
24 samples: 8 conditions, mostly in triplicateGroups 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
DPP4 inhibition affects metabolism and inflammation associated pathways in hiPSC-derived steatotic HLCsFrontiers in cell and developmental biology · 2026
Summary (written by the submitters)
Background Metabolic dysfunction-associated steatotic liver disease (MAFLD) has a high prevalence and high co-morbidity for other diseases. Due to the complexity of this multifactorial disease, therapy options are still rather limited. We employed an in vitro pluripotent stem cell-based model to decipher potential disease-associated molecular pathways and to study the mode of action of prospective drugs.
Read the full summary ↓
Dipeptidyl peptidase 4 (DPP4) or Cluster of differentiation 26 (CD26) is involved in inflammation, infections, immune disorders, type 2 diabetes, kidney disease and cancer. Methods We induced the steatosis phenotype in human induced pluripotent stem cell (iPSC) derived hepatocyte-like cells (HLCs) by oleic acid (OA)-feeding and confirmed regulation of clinically relevant pathways by NGS-based global transcriptomic analyses. Analysis of the secretome of steatotic HLCs revealed DPP4 as a potential key mediator of the disease. To further elucidate its role in the development of MAFLD, we inhibited DPP4 activity with Vildagliptin (VILDA) and analyzed the global transcriptome changes as well as specific gene and protein expression of steatosis-associated genes with and without DPP4 inhibition. Results MAFLD-associated pathways such as PPAR- and TNF signaling were differentially regulated in hiPSC-derived steatotic HLCs. We found increased hepatic DPP4 activity and secretion upon OA. Gene expression of fatty acid and purine metabolism and inflammation-associated pathways were regulated upon DPP4 inhibition. Conclusions Our HLC-model confirmed association of DPP4 with metabolism and inflammation which foster the development of MAFLD. Inhibiting DPP4 with VILDA partially relieved the steatotic phenotype on a global transcriptomic level. Impact and implications Given the difficulties of identifying suitable anti-MAFLD drugs, novel model systems are urgently needed. Our in vitro HLC-model reproduced DPP4-dependent aspects of the disease and responded positively to Vildagliptin treatment. Further elucidation of the role of DPP4 in the etiology of MAFLD and other diseases is warranted.
Published in
DPP4 inhibition affects metabolism and inflammation associated pathways in hiPSC-derived steatotic HLCs
Loerch C, Wruck W, Wittich A et al. · Frontiers in cell and developmental biology 2026 · PMID 41836294 · doi:10.3389/fcell.2026.1686556
This dataset
Open GSE310214 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 GSE310214_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 PRJNA1365309. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41836294) ↗
Study design
8 conditions, mostly in triplicate
UJ_Cntrl_n ×3 UJ_OA_n ×3 CO2_Cntrl_n ×3 CO2_OA_n ×3 S08_Cntrl_n ×3 S08_OA_n ×3 S11_Cntrl_n ×3 S11_OA_n ×3

Supports a between-group comparison across 24 samples.

How we read this: 8 replicated groups read from 24 sample titles; they account for 24 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
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