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Opioid use disorder is linked to over 350,000 fatalities each year globally. In an effort to deepen the understanding of the neurobiological aspects of addiction, researchers have identified a distinct molecular signature and specific genes in the orbitofrontal cortex that are associated with heroin-seeking behaviors. A preclinical study using rodent models indicated that a gene named Shisa7 serves as a crucial predictor in this context. A recently published study in Biological Psychiatry by Elsevier sheds light on the neurobiological mechanisms involved in heroin addiction and suggests potential strategies for addressing the ongoing opioid crisis.
Dr. Yasmin L. Hurd, who leads the research team at the Friedman Brain Institute and the Addiction Institute of Mount Sinai in New York, emphasized the goal of enhancing the understanding of the neurobiological factors fueling addiction. “Our research aims to unravel the intricate molecular signatures found in the brains of individuals with opioid use disorder, moving beyond acute reward mechanisms to identify new treatment pathways relevant to the core characteristics driving substance use,” she stated.
In their research, the team employed advanced machine learning techniques to differentiate the molecular signatures present in the orbitofrontal cortex, a critical area of the brain involved in impulse control, drug-seeking behaviors, and cognitive processes tied to substance use disorders.
The findings showed that the machine learning algorithm accurately identified the molecular signature associated with human heroin users and pinpointed the gene Shisa7 as the most predictive factor, a gene that had not been significantly studied in this field before. Further investigations demonstrated that manipulating the expression of this gene in the orbitofrontal cortex altered heroin-seeking behaviors and cognitive flexibility.
Dr. Hurd remarked, “When we experimentally increased the levels of Shisa7 in drug-naïve animals, it closely replicated the transcriptomic profile seen after repeated heroin exposure. Notably, this signature is associated with neurodegenerative diseases and neuroimmune processes. We also discovered that proteins interacting with Shisa7 are tied to GABA and glutamate receptor signaling, which are intricately linked to pathways involved in neurodegenerative diseases.”
Lead author Dr. Randall Ellis from the Friedman Brain Institute added, “Our use of cutting-edge machine learning methods opened up remarkable avenues for discovery, showcasing AI’s capability in navigating complex biological systems. This approach is invaluable for interpreting extensive datasets like those derived from RNA sequencing, helping to identify new gene expression patterns that can predict disease conditions. Our findings are particularly significant as they could inform innovative responses to the opioid epidemic, also highlighting the potential long-term risks for neurodegenerative diseases arising from opioid use.”
John Krystal, MD, the Editor of Biological Psychiatry, commented on the study’s implications, stating, “This research illustrates the intricate biology behind opioid use disorder. Thorough investigations of postmortem brain tissues, supported by AI-based analyses, are essential for pinpointing the molecular foundations of addiction. It’s noteworthy that a target like Shisa7 not only influences learning but also encourages opioid self-administration when its levels are heightened in animal models.”
Dr. Hurd summarized the study’s significance, stating, “Our findings underline the critical need for further examination of the human brain, which can unveil new biological systems that underlie opioid use disorder, potentially paving the way for new treatment options.”
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