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Machine learning analysis of the orbitofrontal cortex transcriptome of human opioid users identifies Shisa7 as a translational target relevant for heroin-seeking leveraging a male rat model

GSE280431 Rattus norvegicus (rat) RNA-seq (gene expression) 100 samples Submitted 2025/10/20 Platform GPL18694Platform GPL11154
At a glance

RNA-seq (gene expression) · rat · 100 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
Rattus norvegicus (rat)
Samples
100 samples: 100 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)
Background Identifying neurobiological targets predictive of the molecular neuropathophysiological signature of human opioid use disorder (OUD) could expedite new treatments. OUD is characterized by dysregulated cognition and goal-directed behavior mediated by the orbitofrontal cortex (OFC), and next-generation sequencing could provide insights regarding novel targets.
Read the full summary ↓
Methods Here, we used machine learning to evaluate human post-mortem OFC RNA-sequencing datasets from heroin-users and controls to identify transcripts predictive of heroin use. To determine a causal link to OUD-related behaviors, we examined the effects of overexpressing the top target gene in a translational rat model of heroin-seeking and behavioral updating. Additionally, we determined the effects of overexpression on the rat OFC transcriptome compared to that of human heroin users. Co-immunoprecipitation/mass-spectrometry from rat OFC elucidated the protein complex of the novel target. Results Our machine learning approach identified SHISA7 as predictive of human heroin users. Shisa7 is understudied but appears to be an auxiliary protein of GABAA or AMPA receptors. In rats, Shisa7 expression positively-correlated with heroin-seeking behavior. Overexpressing Shisa7 in the OFC augmented heroin-seeking and impaired behavioral updating for sucrose-based operant contingency. RNA-sequencing of rat OFC revealed gene co-expression networks regulated by Shisa7-overexpression similar to human heroin-users. Finally, co-immunoprecipitation/mass-spectrometry showed that heroin influences Shisa7 binding to glutamatergic and GABAergic receptor subunits. Both gene expression signatures and Shisa7 protein complex emphasized perturbations of neurodegenerative and neuroimmune processes. Conclusions Our findings suggest that OFC Shisa7 is a critical driver of neurobehavioral pathology related to drug-seeking behavior and behavioral updating, identifying a potential therapeutic target for OUD.
This dataset
Open GSE280431 in finder → Find more 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 GSE280431_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 PRJNA1178586 and SRA study SRP541575. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
100 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 100 sample titles: 100 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

+ 60 more — browse all 100 samples with per-sample file links →

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