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
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.  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. Overall design: To investigate the effects of opioid use disorder and overdose on the human OFC transcriptome, we conducted RNA-seq on post-mortem OFC tissue from heroin users and controls. We then conducted machine learning experiments to identify genes predictive of experimental group. To translate these findings, we used a rat heroin self-administration model to test the effects of Shisa7 (identified as predictive in the human data) overexpression in the OFC, and conducted RNA-seq on OFC tissue from these animals