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MiR-30d-5p modulation induces expression changes related to β-cell function, survival and regeneration

GSE316801 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2026/05/21 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 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
Homo sapiens (human)
Samples
12 samples: 12 samples, each with a distinct title — no replicated groupsGroups 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)
Type 1 diabetes is an autoimmune disease characterized by the destruction of insulin-producing β-cells, leading to hyperglycemia. A temporary partial remission (PR) phase often follows diagnosis, marked by improved glycemic control and reduced autoimmunity. This study investigates the role of miR-30d-5p, a microRNA highly expressed during PR, on beta-cell recovery and regeneration.
Read the full summary ↓
Remarkably, human pancreatic slices were successfully transfected with oligonucleotides (miR-30d-5p inhibitor or mimic), enabling exploration of post-transcriptional regulation in a human context. Bulk RNA sequencing on human pancreatic slices from 3 non-diabetic donors confirmed the regulation of gene networks linked to both β-cell function and regeneration. PCA revealed clustering by donor, indicating strong inter-donor variability; this was corrected using ComBat, allowing clearer separation of treatment groups. Differential expression analysis identified 273 DEGs for miR-30d-5p inhibition, 518 for overexpression, and 480 between the two treatments. Key gene changes reflected biological roles aligned with the miRNA’s proposed regulatory function: inhibition of miR-30d-5p upregulated genes linked to stress, immune activation, and metabolism (e.g., GLUT6, TGFB3, CYP4F8), while overexpression enhanced β-cell function and insulin secretion genes (e.g., HOOK1, ISL1, TTR, CACNB3). GO analysis showed that miR-30d-5p inhibition enriched pathways related to protein stress and impaired differentiation, whereas overexpression activated processes involved in ion homeostasis, morphogenesis, and β-cell maintenance. Transcription factor analysis highlighted FOXO3, NOTCH3, JUND, and NR1H2 as potential regulators. Overall, miR-30d-5p promotes β-cell survival, insulin synthesis, and immunoregulation.
This dataset
Open GSE316801 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 GSE316801_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 PRJNA1404233 and SRA study SRP663764. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
12 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 12 sample titles: 12 distinct titles with little repetition. 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

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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.