Study Identifies Shisa7 Gene as Key Driver in Heroin Addiction

Opioid use disorder is associated with more than 350,000 deaths annually worldwide. Guided by the need for an increased understanding of critical neurobiological features of addiction, researchers have now found a unique molecular signature and genes in the orbitofrontal cortex associated with heroin-seeking behavior. A preclinical rodent model implicated a gene called Shisa7 as the key predictor. A new study opens in new tab/window in Biological Psychiatry opens in new tab/window, published by Elsevier, provides valuable insights into the neurobiological mechanisms underlying heroin addiction and may have implications for the development of innovative strategies to combat the ongoing opioid epidemic.

Lead investigator Yasmin L. Hurd, PhD, Departments of Neuroscience and Psychiatry, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, and Addiction Institute of Mount Sinai, New York, says, "My research team and I are driven to expand the neurobiological insights underlying addiction. By examining molecular signatures in the brains of human opioid users, we hope to gain insights into the neuropathology of opioid use disorder beyond acute reward mechanisms and to identify new pathways for treatments relevant to core phenotypes that perpetuate substance use.”

In this study, the research team applied machine learning to distinguish the molecular signature in the orbitofrontal cortex, a brain region critical for aspects of impulse control, drug-seeking behavior, and cognitive functions related to substance use disorders.

The investigators found that the machine learning algorithm was not only effective in identifying which signatures distinguished the brain of a human heroin user, but it also identified the gene most predictive of that molecular signature called Shisa7 that had not been explored previously in the field. Further investigation revealed that modulating this gene's expression in the orbitofrontal cortex influences heroin-seeking behavior and cognitive flexibility.

Dr. Hurd adds, "We also observed that when we overexpressed Shisa7 in drug-naïve animals, it completely mimicked the transcriptome signature observed with repeated heroin use. Interestingly, the Shisa7 signature related to neurodegenerative disease and neuroimmune processes. Moreover, we determined that the proteins that bind to Shisa7 were linked to both GABA (the primary inhibitory neurotransmitter in the central nervous system of mammals) and glutamate (the primary excitatory neurotransmitter in the central nervous system) receptor signaling, which are also highly related to neurodegenerative disease pathways."

Lead author of the study Randall Ellis, PhD, Department of Neuroscience and Department of Psychiatry, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, and Addiction Institute of Mount Sinai, New York, notes, "The use of our advanced machine learning approach led us down a very exciting path of discovery, which highlights the potential of AI in understanding complex biological systems. Applying machine learning in this way is exceptionally useful to better understand high-dimensional datasets, such as the thousands of genes captured by RNA sequencing, to uncover novel patterns in gene expression that can effectively predict disease states. This strategy in leveraging data from human opioid users is particularly important as we seek innovative strategies to combat the ongoing opioid epidemic. Moreover, our findings highlight the potential long-term risks of opioid use for future neurodegenerative disease."

John Krystal, MD, Editor of Biological Psychiatry, says, “This study highlights the complex biology of opioid use disorder. Careful studies of postmortem brain tissue, employing AI-guided analyses, are critical for identifying the molecular building blocks of addiction. It is interesting that a target identified in this process, Shisa7, also alters learning and promotes opioid self-administration when levels are increased in animals.”

Dr. Hurd concludes, "These translational findings highlight the importance of studying the human brain, which can help uncover novel biological systems underlying the disorder that could ultimately open up new treatment avenues."

Notes for editors

The article is "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,” by Randall J. Ellis, Jacqueline-Marie N. Ferland, Tanni Rahman, Joseph L. Landry, James E. Callens, Gaurav Pandey, TuKiet Lam, Jean Kanyo, Angus C. Nairn, Stella Dracheva, and Yasmin L. Hurd (https://doi.org/10.1016/j.biopsych.2024.12.007 opens in new tab/window). It appears online in Biological Psychiatry, published by Elsevier.

The article is openly available for 30 days at https://www.biologicalpsychiatryjournal.com/article/S0006-3223(24)01815-8/fulltext opens in new tab/window.

Copies of this paper and additional information are also available to credentialed journalists upon request; please contact Rhiannon Bugno at [email protected] opens in new tab/window. Journalists wishing to interview the authors should contact Yasmin L. Hurd, PhD, at [email protected] opens in new tab/window.

This work was supported by the National Institutes of Health (National Institute on Drug Abuse Grant Nos. R01DA051191 and F31DA051183) and Yale/National Institute on Drug Abuse Neuroproteomics Center (Grant No. DA018343).

The authors’ affiliations and disclosures of financial and conflicts of interests are available in the article.

John H. Krystal, MD, is Chairman of the Department of Psychiatry at the Yale University School of Medicine, Chief of Psychiatry at Yale-New Haven Hospital, and a research psychiatrist at the VA Connecticut Healthcare System. His disclosures of financial and conflicts of interests are available here opens in new tab/window.

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