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Unraveling the role of CACNA1F, RASAL1, GARIN4, and TRIM56 in common opioid side effects via transcriptomic analysis of differentiated neuron-like SH-SY5Y cells

GSE306403 Homo sapiens (human) RNA-seq (gene expression) 18 samples Submitted 2025/08/27 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 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
Homo sapiens (human)
Samples
18 samples: 6 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.
Summary (written by the submitters)
Opioids remain indispensable for pain management but their use is limited by significant side effects, including respiratory depression, constipation, tolerance, addiction, and immunosuppression. Although much is known about their mechanism of action, the effects of acute opioid exposure on transcriptional responses have not yet been fully characterized.
Read the full summary ↓
We performed a transcriptomic analysis of differentiated neuron-like SH-SY5Y cells exposed to five opioid ligands, namely, morphine, TRV130, metamorphine, β-endorphin, and naloxone. After incubation for 15 minutes, the cells were harvested and processed for RNA sequencing via the Illumina NovaSeq 6000 platform. Differential gene expression analysis was performed with DESeq2, and pathway enrichment was conducted via GO, KEGG and Reactome. Individual comparisons between each opioid-treated group and the control group revealed no statistically significant transcriptional changes. However, when all the agonist-treated samples were pooled and compared with the control samples, we identified several significantly downregulated genes (adjusted p < 0.05). Specifically, we observed alterations in the expression of the genes CACNA1F, RASAL1, GARIN4, and TRIM56, which are involved in calcium signaling, synaptic plasticity, the immune response, and reproductive function. CACNA1F downregulation may affect neuronal excitability and retinal signaling; RASAL1 suppression could impact synaptic maturation and memory; GARIN4 is associated with sperm morphology; and TRIM56 downregulation has an immunomodulatory effect, all of which aligns with known opioid-induced side effects. Our findings demonstrate that even short-term opioid exposure can initiate subtle but functionally relevant transcriptional changes. These early responses highlight the potential of transcriptomic profiling to uncover the molecular mechanisms underlying opioid pharmacodynamics and side effects. This approach offers deeper insights into opioid action and supports the development of safer analgesics with fewer systemic adverse effects.
This dataset
Open GSE306403 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 GSE306403_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 PRJNA1310585 and SRA study SRP612321. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
6 conditions, mostly in triplicate
Differentiated SH-SY5Y cells treated with Con… ×3 Differentiated SH-SY5Y cells treated with Mor… ×3 Differentiated SH-SY5Y cells treated with Met… ×3 Differentiated SH-SY5Y cells treated with TRV… ×3 Differentiated SH-SY5Y cells treated with β-E… ×3 Differentiated SH-SY5Y cells treated with Nal… ×3

Supports a between-group comparison across 18 samples.

How we read this: 6 replicated groups read from 18 sample titles; they account for 18 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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