The potential of baicalin to enhance neuroprotection and mitochondrial function in a human neuronal cell model.

Baicalin is a flavone glycoside derived from flowering plants belonging to the Scutellaria genus. Previous studies have reported baicalin's anti-inflammatory and neuroprotective properties in rodent models, indicating the potential of baicalin in neuropsychiatric disorders where alterations in numerous processes are observed. However, the extent of baicalin's therapeutic effects remains undetermined in a human cell model, more specifically, neuronal cells to mimic the brain environment in vitro. As a proof of concept, we treated C8-B4 cells (murine cell model) with three different doses of baicalin (0.1, 1 and 5 µM) and vehicle control (DMSO) for 24 h after liposaccharide-induced inflammation and measured the levels of TNF-α in the medium by ELISA. NT2-N cells (human neuronal-like cell model) underwent identical baicalin treatment, followed by RNA extraction, genome-wide mRNA expression profiles and gene set enrichment analysis (GSEA). We also performed neurite outgrowth assays and mitochondrial flux bioanalysis (Seahorse) in NT2-N cells. We found that in C8-B4 cells, baicalin at ≥ 1 µM exhibited anti-inflammatory effects, lowering TNF-α levels in the cell culture media. In NT2-N cells, baicalin positively affected neurite outgrowth and transcriptionally up-regulated genes in the tricarboxylic acid cycle and the glycolysis pathway. Similarly, Seahorse analysis showed increased oxygen consumption rate in baicalin-treated NT2-N cells, an indicator of enhanced mitochondrial function. Together, our findings have confirmed the neuroprotective and mitochondria enhancing effects of baicalin in human-neuronal like cells. Given the increased prominence of mitochondrial mechanisms in diverse neuropsychiatric disorders and the paucity of mitochondrial therapeutics, this suggests the potential therapeutic application of baicalin in human neuropsychiatric disorders where these processes are altered.

Network-based drug repurposing for schizophrenia.

Despite recent progress, the challenges in drug discovery for schizophrenia persist. However, computational drug repurposing has gained popularity as it leverages the wealth of expanding biomedical databases. Network analyses provide a comprehensive understanding of transcription factor (TF) regulatory effects through gene regulatory networks, which capture the interactions between TFs and target genes by integrating various lines of evidence. Using the PANDA algorithm, we examined the topological variances in TF-gene regulatory networks between individuals with schizophrenia and healthy controls. This algorithm incorporates binding motifs, protein interactions, and gene co-expression data. To identify these differences, we subtracted the edge weights of the healthy control network from those of the schizophrenia network. The resulting differential network was then analysed using the CLUEreg tool in the GRAND database. This tool employs differential network signatures to identify drugs that potentially target the gene signature associated with the disease. Our analysis utilised a large RNA-seq dataset comprising 532 post-mortem brain samples from the CommonMind project. We constructed co-expression gene regulatory networks for both schizophrenia cases and healthy control subjects, incorporating 15,831 genes and 413 overlapping TFs. Through drug repurposing, we identified 18 promising candidates for repurposing as potential treatments for schizophrenia. The analysis of TF-gene regulatory networks revealed that the TFs in schizophrenia predominantly regulate pathways associated with energy metabolism, immune response, cell adhesion, and thyroid hormone signalling. These pathways represent significant targets for therapeutic intervention. The identified drug repurposing candidates likely act through TF-targeted pathways. These promising candidates, particularly those with preclinical evidence such as rimonabant and kaempferol, warrant further investigation into their potential mechanisms of action and efficacy in alleviating the symptoms of schizophrenia.

Use of gene regulatory network analysis to repurpose drugs to treat bipolar disorder.

BACKGROUND: Bipolar disorder (BD) presents significant challenges in drug discovery, necessitating alternative approaches. Drug repurposing, leveraging computational techniques and expanding biomedical data, holds promise for identifying novel treatment strategies. METHODS: This study utilized gene regulatory networks (GRNs) to identify significant regulatory changes in BD, using network-based signatures for drug repurposing. Employing the PANDA algorithm, we investigated the variations in transcription factor-GRNs between individuals with BD and unaffected individuals, incorporating binding motifs, protein interactions, and gene co-expression data. The differences in edge weights between BD and controls were then used as differential network signatures to identify drugs potentially targeting the disease-associated gene signature, employing the CLUEreg tool in the GRAND database. RESULTS: Using a large RNA-seq dataset of 216 post-mortem brain samples from the CommonMind consortium, we constructed GRNs based on co-expression for individuals with BD and unaffected controls, involving 15,271 genes and 405 TFs. Our analysis highlighted significant influences of these TFs on immune response, energy metabolism, cell signalling, and cell adhesion pathways in the disorder. By employing drug repurposing, we identified 10 promising candidates potentially repurposed as BD treatments. LIMITATIONS: Non-drug-naïve transcriptomics data, bulk analysis of BD samples, potential bias of GRNs towards well-studied genes. CONCLUSIONS: Further investigation into repurposing candidates, especially those with preclinical evidence supporting their efficacy, like kaempferol and pramocaine, is warranted to understand their mechanisms of action and effectiveness in treating BD. Additionally, novel targets such as PARP1 and A2b offer opportunities for future research on their relevance to the disorder.

Effects of antipsychotic drugs on energy metabolism.

Schizophrenia (SCZ) is a complex neuropsychiatric disorder associated with altered bioenergetic pathways and mitochondrial dysfunction. Antipsychotic medications, both first and second-generation, are commonly prescribed to manage SCZ symptoms, but their direct impact on mitochondrial function remains poorly understood. In this study, we investigated the effects of commonly prescribed antipsychotics on bioenergetic pathways in cultured neurons. We examined the impact of risperidone, aripiprazole, amisulpride, and clozapine on gene expression, mitochondrial bioenergetic profile, and targeted metabolomics after 24-h treatment, using RNA-seq, Seahorse XF24 Flux Analyser, and gas chromatography-mass spectrometry (GC-MS), respectively. Risperidone treatment reduced the expression of genes involved in oxidative phosphorylation, the tricarboxylic acid cycle, and glycolysis pathways, and it showed a tendency to decrease basal mitochondrial respiration. Aripiprazole led to dose-dependent reductions in various mitochondrial function parameters without significantly affecting gene expression. Aripiprazole, amisulpride and clozapine treatment showed an effect on the tricarboxylic acid cycle metabolism, leading to more abundant metabolite levels. Antipsychotic drug effects on mitochondrial function in SCZ are multifaceted. While some drugs have greater effects on gene expression, others appear to exert their effects through enzymatic post-translational or allosteric modification of enzymatic activity. Understanding these effects is crucial for optimising treatment strategies for SCZ. Novel therapeutic interventions targeting energy metabolism by post-transcriptional pathways might be more effective as these can more directly and efficiently regulate energy production.

Use of a gene expression signature to identify trimetazidine for repurposing to treat bipolar depression.

OBJECTIVES: The aim of this study was to repurpose a drug for the treatment of bipolar depression. METHODS: A gene expression signature representing the overall transcriptomic effects of a cocktail of drugs widely prescribed to treat bipolar disorder was generated using human neuronal-like (NT2-N) cells. A compound library of 960 approved, off-patent drugs were then screened to identify those drugs that affect transcription most similar to the effects of the bipolar depression drug cocktail. For mechanistic studies, peripheral blood mononuclear cells were obtained from a healthy subject and reprogrammed into induced pluripotent stem cells, which were then differentiated into co-cultured neurons and astrocytes. Efficacy studies were conducted in two animal models of depressive-like behaviours (Flinders Sensitive Line rats and social isolation with chronic restraint stress rats). RESULTS: The screen identified trimetazidine as a potential drug for repurposing. Trimetazidine alters metabolic processes to increase ATP production, which is thought to be deficient in bipolar depression. We showed that trimetazidine increased mitochondrial respiration in cultured human neuronal-like cells. Transcriptomic analysis in induced pluripotent stem cell-derived neuron/astrocyte co-cultures suggested additional mechanisms of action via the focal adhesion and MAPK signalling pathways. In two different rodent models of depressive-like behaviours, trimetazidine exhibited antidepressant-like activity with reduced anhedonia and reduced immobility in the forced swim test. CONCLUSION: Collectively our data support the repurposing of trimetazidine for the treatment of bipolar depression.

Metformin is Protective Against the Development of Mood Disorders.

INTRODUCTION: Mood disorders are a major cause of disability, and current treatment options are inadequate for reducing the burden on a global scale. The aim of this project was to identify drugs suitable for repurposing to treat mood disorders. METHODS: This mixed-method study utilized gene expression signature technology and pharmacoepidemiology to investigate drugs that may be suitable for repurposing to treat mood disorders. RESULTS: The transcriptional effects of a combination of drugs commonly used to treat mood disorders included regulation of the steroid and terpenoid backbone biosynthesis pathways, suggesting a mechanism involving cholesterol biosynthesis, and effects on the thyroid hormone signaling pathway. Connectivity Map analysis highlighted metformin, an FDA-approved treatment for type 2 diabetes, as a drug having global transcriptional effects similar to the mood disorder drug combination investigated. In a retrospective cohort study, we found evidence that metformin is protective against the onset of mood disorders. DISCUSSION: These results provide proof-of-principle of combining gene expression signature technology with pharmacoepidemiology to identify potential novel drugs for treating mood disorders. Importantly, metformin may have utility in the treatment of mood disorders, warranting future randomized controlled trials to test its efficacy.

Effects of Psychotropic Drugs on Ribosomal Genes and Protein Synthesis.

Altered protein synthesis has been implicated in the pathophysiology of several neuropsychiatric disorders, particularly schizophrenia. Ribosomes are the machinery responsible for protein synthesis. However, there remains little information on whether current psychotropic drugs affect ribosomes and contribute to their therapeutic effects. We treated human neuronal-like (NT2-N) cells with amisulpride (10 µM), aripiprazole (0.1 µM), clozapine (10 µM), lamotrigine (50 µM), lithium (2.5 mM), quetiapine (50 µM), risperidone (0.1 µM), valproate (0.5 mM) or vehicle control for 24 h. Transcriptomic and gene set enrichment analysis (GSEA) identified that the ribosomal pathway was altered by these drugs. We found that three of the eight drugs tested significantly decreased ribosomal gene expression, whilst one increased it. Most changes were observed in the components of cytosolic ribosomes and not mitochondrial ribosomes. Protein synthesis assays revealed that aripiprazole, clozapine and lithium all decreased protein synthesis. Several currently prescribed psychotropic drugs seem to impact ribosomal gene expression and protein synthesis. This suggests the possibility of using protein synthesis inhibitors as novel therapeutic agents for neuropsychiatric disorders.

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.

Integrative Analyses of Transcriptomes to Explore Common Molecular Effects of Antipsychotic Drugs.

There is little understanding of the underlying molecular mechanism(s) involved in the clinical efficacy of antipsychotics for schizophrenia. This study integrated schizophrenia-associated transcriptional perturbations with antipsychotic-induced gene expression profiles to detect potentially relevant therapeutic targets shared by multiple antipsychotics. Human neuronal-like cells (NT2-N) were treated for 24 h with one of the following antipsychotic drugs: amisulpride, aripiprazole, clozapine, risperidone, or vehicle controls. Drug-induced gene expression patterns were compared to schizophrenia-associated transcriptional data in post-mortem brain tissues. Genes regulated by each of four antipsychotic drugs in the reverse direction to schizophrenia were identified as potential therapeutic-relevant genes. A total of 886 genes were reversely expressed between at least one drug treatment (versus vehicle) and schizophrenia (versus healthy control), in which 218 genes were commonly regulated by all four antipsychotic drugs. The most enriched biological pathways include Wnt signaling and action potential regulation. The protein-protein interaction (PPI) networks found two main clusters having schizophrenia expression quantitative trait loci (eQTL) genes such as PDCD10, ANK2, and AKT3, suggesting further investigation on these genes as potential novel treatment targets.

Biological Mechanism(s) Underpinning the Association between Antipsychotic Drugs and Weight Gain.

Weight gain and consequent metabolic alterations are common side-effects of many antipsychotic drugs. Interestingly, several studies have suggested that improvement in symptoms and adverse metabolic effects are correlated. We used next generation sequencing data from NT-2 (human neuronal) cells treated with aripiprazole, amisulpride, risperidone, quetiapine, clozapine, or vehicle control, and compared with the Pillinger P-score (ranked from 0 to 1, indicating greater increase in weight gain and related metabolic parameters) to identify the genes most associated with the drugs' propensity to cause weight gain. The top 500 genes ranked for their correlation with the drugs' propensity to cause weight gain were subjected to pathway analysis using DAVID (NIH). We further investigated transcription factors (TFs) that are more likely to regulate the genes involved in these processes using the prediction tool of key TFs from TRRUST. The results suggest an enrichment for genes involved in lipid biosynthesis and metabolism, which are of interest for mechanisms underpinning weight-gain. The list of genes involved in the lipid pathways that correlated with weight gain was enriched for genes transcriptionally regulated by SREBF1 and SREBF2. Furthermore, quetiapine significantly increased the expression of SREBF1 and SREBF2 in NT-2 cells. Our results suggest that the effects of these antipsychotic drugs on lipid metabolism may be mediated, at least in part, via regulation of SREBF1/SREBF2 expression, with evidence of a direct effect of quetiapine on the expression of SREBF1/2. The effects of antipsychotic drugs on lipid metabolism may influence white matter structure (therapeutic effect) and the risk of weight gain, lipid disturbances, and, consequently, metabolic syndrome (adverse effects). Understanding the different molecular effects of these drugs could inform a personalized medicine approach in treating patients with schizophrenia.

Transcriptional Modulation of the Hippo Signaling Pathway by Drugs Used to Treat Bipolar Disorder and Schizophrenia.

Recent reports suggest a link between positive regulation of the Hippo pathway with bipolar disorder (BD), and the Hippo pathway is known to interact with multiple other signaling pathways previously associated with BD and other psychiatric disorders. In this study, neuronal-like NT2 cells were treated with amisulpride (10 µM), aripiprazole (0.1 µM), clozapine (10 µM), lamotrigine (50 µM), lithium (2.5 mM), quetiapine (50 µM), risperidone (0.1 µM), valproate (0.5 mM), or vehicle control for 24 h. Genome-wide mRNA expression was quantified and analyzed using gene set enrichment analysis (GSEA), with genes belonging to Hippo, Wnt, Notch, TGF- ß, and Hedgehog retrieved from the KEGG database. Five of the eight drugs downregulated the genes of the Hippo pathway and modulated several genes involved in the interacting pathways. We speculate that the regulation of these genes, especially by aripiprazole, clozapine, and quetiapine, results in a reduction of MAPK and NFκB pro-inflammatory signaling through modulation of Hippo, Wnt, and TGF-ß pathways. We also employed connectivity map analysis to identify compounds that act on these pathways in a similar manner to the known psychiatric drugs. Thirty-six compounds were identified. The presence of antidepressants and antipsychotics validates our approach and reveals possible new targets for drug repurposing.

Transcriptional Effects of Psychoactive Drugs on Genes Involved in Neurogenesis.

Although neurogenesis is affected in several psychiatric diseases, the effects and mechanisms of action of psychoactive drugs on neurogenesis remain unknown and/or controversial. This study aims to evaluate the effects of psychoactive drugs on the expression of genes involved in neurogenesis. Neuronal-like cells (NT2-N) were treated with amisulpride (10 µM), aripiprazole (0.1 µM), clozapine (10 µM), lamotrigine (50 µM), lithium (2.5 mM), quetiapine (50 µM), risperidone (0.1 µM), or valproate (0.5 mM) for 24 h. Genome wide mRNA expression was quantified and analysed using gene set enrichment analysis, with the neurogenesis gene set retrieved from the Gene Ontology database and the Mammalian Adult Neurogenesis Gene Ontology (MANGO) database. Transcription factors that are more likely to regulate these genes were investigated to better understand the biological processes driving neurogenesis. Targeted metabolomics were performed using gas chromatography-mass spectrometry. Six of the eight drugs decreased the expression of genes involved in neurogenesis in both databases. This suggests that acute treatment with these psychoactive drugs negatively regulates the expression of genes involved in neurogenesis in vitro. SOX2 and three of its target genes (CCND1, BMP4, and DKK1) were also decreased after treatment with quetiapine. This can, at least in part, explain the mechanisms by which these drugs decrease neurogenesis at a transcriptional level in vitro. These results were supported by the finding of increased metabolite markers of mature neurons following treatment with most of the drugs tested, suggesting increased proportions of mature relative to immature neurons consistent with reduced neurogenesis.