Association of Polymorphism CHRNA5 and CHRNA3 Gene in People Addicted to Nicotine

Effect of high mobility group box 1 pathway inhibition on gene expression in the prefrontal cortex of mice exposed to alcohol

INTRODUCTION

The high mobility group box 1 (HMGB1) pathway plays a pivotal role in the development of alcohol-induced brain injury. Glycyrrhizinic acid (GlyA) is widely regarded as an inhibitor of HMGB1. The objective is to investigate the impact on gene expression in the prefrontal cortex,we sequenced the transcriptome in control, alcohol-exposed, and HMGB1-inhibited groups of mice. We verified our findings by real-time quantitative PCR (qRT-PCR).

METHODS

An alcohol exposure model was established in mice by intraperitoneal injection of alcohol. Transcriptome sequencing and bioinformatics analyses were performed on prefrontal cortex tissue. Kyoto Encyclopedia of Genes and Genomes analysis was performed to identify pivotal pathways of differentially expressed genes. The role of relevant genes was verified by qRT-PCR.

RESULTS

Expression of genes involved in the neuroactive ligand-receptor interaction pathway exhibited an increase in mice from the alcohol-exposed group.However, there were no significant differences observed in the expression of these genes between control and those receiving an intraperitoneal injection of alcohol along with a HMGB1 inhibitor. Mice in the alcohol-exposed group showed increased gene expression of Cysltr2, Chrna6, Chrna3, Chrnb4, and Pmch. Expression of these genes was decreased in mice injected with HMGB1 inhibitor.

SIGNIFICANCE

Our study demonstrates that alcohol primarily influences gene expression in the prefrontal cortex of mice through the neuroactive ligand-receptor interaction pathway. HMGB1 inhibitor effectively inhibited the expression of this pathway. This study provides a novel route for drug development in alcohol-induced brain injury.

Investigating CHRNA5, CHRNA3, and CHRNB4 variants in the genetic landscape of substance use disorder in Jordan

BACKGROUND

Substance use disorder (SUD) is a complex illness that can be attributed to the interaction between environmental and genetic factors. The nicotinic receptor gene cluster on chromosome 15 has a plausible association with SUD, particularly with nicotine dependence.

METHODS

This study investigated 15 SNPs within the CHRNA5, CHRNA3, and CHRNB4 genes. Sequencing was used for genotyping 495 Jordanian males with SUD and 497 controls matched for age, gender, and descent.

RESULTS

Our findings revealed that none of the tested alleles or genotypes were correlated with SUD. However, our analysis suggests that the route of substance use was linked to rs1051730 (P value = 0.04), rs8040868 (P value = 0.01) of CHRNA3, and rs16969968 (P value = 0.03) of CHRNA5. Additionally, a correlation was identified between rs3813567 of the CHRNB4 gene and the age at substance use onset (P value = 0.04).

CONCLUSIONS

Variants in CHRNA5, CHRNA3, and CHRNB4 may interact with SUD features that can influence the development and progression of the disorder among Jordanians.

Neurotransmitter receptor-related gene signature as potential prognostic and therapeutic biomarkers in colorectal cancer

Background: Colorectal cancer is one of the most common malignant tumors worldwide. A various of neurotransmitter receptors have been found to be expressed in tumor cells, and the activation of these receptors may promote tumor growth and metastasis. This study aimed to construct a novel neurotransmitter receptor-related genes signature to predict the survival, immune microenvironment, and treatment response of colorectal cancer patients. Methods: RNA-seq and clinical data of colorectal cancer from The Cancer Genome Atlas database and Gene Expression Omnibus were downloaded. Neurotransmitter receptor-related gene were collected from publicly available data sources. The Weighted Gene Coexpression Network Analysis (WGCNA), Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression, Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and Random Forest (RF) algorithms were employed to construct the Neurotransmitter receptor-related gene prognostic signature. Further analyses, functional enrichment, CIBERSORTx, The Tumor Immune Single Cell Center (TISCH), survival analysis, and CellMiner, were performed to analyze immune status and treatment responses. Quantitative real-time polymerase chain reaction (qRT-PCR) assays were carried out to confirm the expression levels of prognostic genes. Results: By combining machine learning algorithm and WGCNA, we identified CHRNA3, GABRD, GRIK3, and GRIK5 as Neurotransmitter receptor-related prognostic genes signature. Functional enrichment analyses showed that these genes were enriched with cellular metabolic-related pathways, such as organic acid, inorganic acid, and lipid metabolism. CIBERSORTx and Single cell analysis showed that the high expression of genes were positively correlated with immunosuppressive cells infiltration, and the genes were mainly expressed in cancer-associated fibroblasts and endothelial cells. A nomogram was further built to predict overall survival (OS). The expression of CHRNA3, GABRD, GRIK3, and GRIK5 in cancer cells significantly impacted their response to chemotherapy. Conclusion: A neurotransmitter receptor-related prognostic gene signature was developed and validated in the current study, giving novel sights of neurotransmitter in predicting the prognostic and improving the treatment of CRC.

Implementing Core Genes and an Omnigenic Model for Behaviour Traits Prediction in Genomics

A high number of genome variants are associated with complex traits, mainly due to genome-wide association studies (GWAS). Using polygenic risk scores (PRSs) is a widely accepted method for calculating an individual's complex trait prognosis using such data. Unlike monogenic traits, the practical implementation of complex traits by applying this method still falls behind. Calculating PRSs from all GWAS data has limited practical usability in behaviour traits due to statistical noise and the small effect size from a high number of genome variants involved. From a behaviour traits perspective, complex traits are explored using the concept of core genes from an omnigenic model, aiming to employ a simplified calculation version. Simplification may reduce the accuracy compared to a complete PRS encompassing all trait-associated variants. Integrating genome data with datasets from various disciplines, such as IT and psychology, could lead to better complex trait prediction. This review elucidates the significance of clear biological pathways in understanding behaviour traits. Specifically, it highlights the essential role of genes related to hormones, enzymes, and neurotransmitters as robust core genes in shaping these traits. Significant variations in core genes are prominently observed in behaviour traits such as stress response, impulsivity, and substance use.