Chul-Ho Jeong

Substance use disorders, particularly drug addiction, are complex neurophysiological conditions characterized by cycles of compulsive drug use, withdrawal symptoms, and relapses. Methamphetamine (MA) addiction evolves through repeated exposure, altering brain circuits related to reward and neuroplasticity. The need for reliable biomarkers to diagnose and monitor MA addiction has become increasingly critical in clinical practice. In this study, we explored the time-dependent transcriptomic changes in the rat striatum immediately after short-term abstinence following MA self-administration. Using a rat model, we conducted RNA sequencing to analyze the transcriptomic alterations in the striatum immediately after the self-administration and short-term abstinence phases (12- and 24-h post-MA). Through protein–protein interaction (PPI) network analysis and gene expression pattern assessment, we identified key genes that demonstrated significant expression changes. These genes were strongly linked to reward mechanisms, synaptic plasticity, and memory processes, suggesting a role in mediating MA-associated behaviors. Understanding the expression dynamics of these genes provides valuable insights into the molecular mechanisms underlying MA addiction and offers a foundation for developing diagnostic tools and therapeutic strategies targeting addiction-related neural adaptations.

Dong-Hwan Kim, Dong Joon Kim, Seong-Jun Park, Won-Jun Jang, and Chul-Ho Jeong. J. Microbiol. Biotechnol. 2025;35:1-12. https://doi.org/10.4014/jmb.2412.12032

Persistent neurochemical and biological disturbances resulting from repeated cycles of drug reward, withdrawal, and relapse contribute to drug dependence. Methamphetamine (MA) is a psychostimulant with substantial abuse potential and neurotoxic effects, primarily affecting monoamine neurotransmitter systems in the brain. In this study, we aimed to explore the progression of drug dependence in rat models of MA self-administration, extinction, and reinstatement through targeted and non-targeted metabolomics analyses. Metabolic profiles were examined in rat plasma during the following phases: after 16 days of MA self-administration (Group M); after 16 days of self-administration followed by 14 days of extinction (Group MS); and after self-administration and extinction followed by a reinstatement injection of MA (Group MSM). Each group of MA self-administration, extinction, and reinstatement induces distinct changes in the metabolic pathways, particularly those related to the TCA cycle, arginine and proline metabolism, and arginine biosynthesis. Additionally, the downregulation of glycerophospholipids and sphingomyelins in Group MSM suggests their potential role in MA reinstatement. These alterations may signify the progressive deterioration of these metabolic pathways, possibly contributing to drug dependence following repeated cycles of drug reward, withdrawal, and relapse. These results provide valuable insights into the metabolic changes associated with MA use at various stages, potentially facilitating the discovery of early diagnostic biomarkers and therapeutic targets for MA use disorders.

Introduction: Methamphetamine use disorder (MUD) is a chronic relapsing disorder characterized by compulsive Methamphetamine (MA) use despite its detrimental effects on physical, psychological, and social well-being. The development of MUD is a complex process that involves the interplay of genetic, epigenetic, and environmental factors. The treatment of MUD remains a significant challenge, with no FDA-approved pharmacotherapies currently available. Current diagnostic criteria for MUD rely primarily on self-reporting and behavioral assessments, which have inherent limitations owing to their subjective nature. This lack of objective biomarkers and unidimensional approaches may not fully capture the unique features and consequences of MA addiction.Methods: We performed a literature search for this review using the Boolean search in the PubMed database.Results: This review explores existing technologies for identifying transcriptomic biomarkers for MUD diagnosis. We examined non-invasive tissues and scrutinized transcriptomic biomarkers relevant to MUD. Additionally, we investigated transcriptomic biomarkers identified for diagnosing, predicting, and monitoring MUD in non-invasive tissues.Discussion: Developing and validating non-invasive MUD biomarkers could address these limitations, foster more precise and reliable diagnostic approaches, and ultimately enhance the quality of care for individuals with MA addiction.

Seong-Jun Park, Naeun Lee, and Chul-Ho Jeong*. Korean J Physiol Pharmacol 2024;28:83-91. https://doi.org/10.4196/kjpp.2024.28.1.83

The current method for diagnosing methamphetamine use disorder (MUD) relies on self-reports and interviews with psychiatrists, which lack scientific rigor. This highlights the need for novel biomarkers to accurately diagnose MUD. In this study, we identified transcriptome biomarkers using hair follicles and proposed a diagnostic model for monitoring the MUD treatment process. We performed RNA sequencing analysis on hair follicle cells from healthy controls and former and current MUD patients who had been detained in the past for illegal use of methamphetamine (MA). We selected candidate genes for monitoring MUD patients by performing multivariate analysis methods, such as PCA and PLS-DA, and PPI network analysis. We developed a two-stage diagnostic model using multivariate ROC analysis based on the PLS-DA method. We constructed a two-step prediction model for MUD diagnosis using multivariate ROC analysis, including 10 biomarkers. The first step model, which distinguishes non-recovered patients from others, showed very high accuracy (prediction accuracy, 98.7%). The second step model, which distinguishes almost-recovered patients from healthy controls, showed high accuracy (prediction accuracy, 81.3%). This study is the first report to use hair follicles of MUD patients and to develop a MUD prediction model based on transcriptomic biomarkers, which offers a potential solution to improve the accuracy of MUD diagnosis and may lead to the development of better pharmacological treatments for the disorder in the future.