Candidate pharmacological treatments for substance use disorder and suicide identified by gene co-expression network-based drug repositioning

Integrating genetics and transcriptomics to study major depressive disorder: a conceptual framework, bioinformatic approaches, and recent findings

Major depressive disorder (MDD) is a complex and heterogeneous psychiatric syndrome with genetic and environmental influences. In addition to neuroanatomical and circuit-level disturbances, dysregulation of the brain transcriptome is a key phenotypic signature of MDD. Postmortem brain gene expression data are uniquely valuable resources for identifying this signature and key genomic drivers in human depression; however, the scarcity of brain tissue limits our capacity to observe the dynamic transcriptional landscape of MDD. It is therefore crucial to explore and integrate depression and stress transcriptomic data from numerous, complementary perspectives to construct a richer understanding of the pathophysiology of depression. In this review, we discuss multiple approaches for exploring the brain transcriptome reflecting dynamic stages of MDD: predisposition, onset, and illness. We next highlight bioinformatic approaches for hypothesis-free, genome-wide analyses of genomic and transcriptomic data and their integration. Last, we summarize the findings of recent genetic and transcriptomic studies within this conceptual framework.

Repurposing Drugs via Network Analysis: Opportunities for Psychiatric Disorders

Despite advances in pharmacology and neuroscience, the path to new medications for psychiatric disorders largely remains stagnated. Drug repurposing offers a more efficient pathway compared with de novo drug discovery with lower cost and less risk. Various computational approaches have been applied to mine the vast amount of biomedical data generated over recent decades. Among these methods, network-based drug repurposing stands out as a potent tool for the comprehension of multiple domains of knowledge considering the interactions or associations of various factors. Aligned well with the poly-pharmacology paradigm shift in drug discovery, network-based approaches offer great opportunities to discover repurposing candidates for complex psychiatric disorders. In this review, we present the potential of network-based drug repurposing in psychiatry focusing on the incentives for using network-centric repurposing, major network-based repurposing strategies and data resources, applications in psychiatry and challenges of network-based drug repurposing. This review aims to provide readers with an update on network-based drug repurposing in psychiatry. We expect the repurposing approach to become a pivotal tool in the coming years to battle debilitating psychiatric disorders.

Exploring biomarkers for ischemic stroke through integrated microarray data analysis

Stroke is the third leading cause of disability-adjusted life years worldwide, and drugs available for its treatment are limited. This study aimed to explore high-confidence candidate genes associated with ischemic stroke (IS) through bioinformatics analysis and identify potential diagnostic biomarkers and gene-drug interactions. Weighted gene coexpression network analysis (WGCNA) and differentially expressed genes (DEGs) were integrated to identify overlapping genes. Then, high-confidence candidate genes were screened by least absolute shrinkage and selection operator (LASSO) regression. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic value of high-confidence candidate genes as biomarkers for IS. The NetworkAnalyst database was used to construct the TF-gene network and miRNA-TF regulatory network of the high-confidence candidate genes. The DGIdb database was used to identified gene-drug interactions. Through the comprehensive analysis of GSE58294 and GSE16561, 10 high-confidence candidate genes were identified by LASSO regression: ARG1, LY96, ABCA1, SLC22A4, CD163, TPM2, SLC25A42, ID3, FAM102A and CD79B. FAM102A had the highest diagnostic value, and the area under curve (AUC), sensitivity and specificity values were 0.974, 0.919 and 0.936, respectively. The HPA database demonstrated that 10 high-confidence candidate genes were expressed in the brain and blood in normal humans. Finally, DGIdb database analysis identified 8 gene-drug interactions. We identified IS-related diagnostic biomarkers and gene-drug interactions that potentially provide new insights into the diagnosis and treatment of IS.