Striatal miR-183-5p inhibits methamphetamine-induced locomotion by regulating glucocorticoid receptor signaling

Identification of the circRNA-miRNA-mRNA network for treating methamphetamine-induced relapse and behavioral sensitization with cannabidiol

AIMS

This study aims to investigate the pharmacological effects and the underlying mechanism of cannabidiol (CBD) on methamphetamine (METH)-induced relapse and behavioral sensitization in male mice.

METHODS

The conditioned place preference (CPP) test with a biased paradigm and open-field test were used to assess the effects of CBD on METH-induced relapse and behavioral sensitization in male mice. RNA sequencing and bioinformatics analysis was employed to identify differential expressed (DE) circRNAs, miRNAs, and mRNAs in the nucleus accumbens (NAc) of mice, and the interaction among them was predicted using competing endogenous RNAs (ceRNAs) network analysis.

RESULTS

Chronic administration of CBD (40 mg/kg) during the METH withdrawal phase alleviated METH (2 mg/kg)-induced CPP reinstatement and behavioral sensitization in mice, as well as mood and cognitive impairments following behavioral sensitization. Furthermore, 42 DEcircRNAs, 11 DEmiRNAs, and 40 DEmRNAs were identified in the NAc of mice. The circMeis2-miR-183-5p-Kcnj5 network in the NAc of mice is involved in the effects of CBD on METH-induced CPP reinstatement and behavioral sensitization.

CONCLUSIONS

This study constructed the ceRNAs network for the first time, revealing the potential mechanism of CBD in treating METH-induced CPP reinstatement and behavioral sensitization, thus advancing the application of CBD in METH use disorders.

Uncovering transcriptomic biomarkers for enhanced diagnosis of methamphetamine use disorder: a comprehensive review

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.

Identification of Potential Biomarkers for Diagnosis of Patients with Methamphetamine Use Disorder

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.