Revisiting Antipsychotic Drug Actions Through Gene Networks Associated With Schizophrenia

Large Language Model-Based Natural Language Encoding Could Be All You Need for Drug Biomedical Association Prediction

Analyzing drug-related interactions in the field of biomedicine has been a critical aspect of drug discovery and development. While various artificial intelligence (AI)-based tools have been proposed to analyze drug biomedical associations (DBAs), their feature encoding did not adequately account for crucial biomedical functions and semantic concepts, thereby still hindering their progress. Since the advent of ChatGPT by OpenAI in 2022, large language models (LLMs) have demonstrated rapid growth and significant success across various applications. Herein, LEDAP was introduced, which uniquely leveraged LLM-based biotext feature encoding for predicting drug-disease associations, drug-drug interactions, and drug-side effect associations. Benefiting from the large-scale knowledgebase pre-training, LLMs had great potential in drug development analysis owing to their holistic understanding of natural language and human topics. LEDAP illustrated its notable competitiveness in comparison with other popular DBA analysis tools. Specifically, even in simple conjunction with classical machine learning methods, LLM-based feature representations consistently enabled satisfactory performance across diverse DBA tasks like binary classification, multiclass classification, and regression. Our findings underpinned the considerable potential of LLMs in drug development research, indicating a catalyst for further progress in related fields.

Larger cerebral cortex is genetically correlated with greater frontal area and dorsal thickness

Human cortical expansion has occurred non-uniformly across the brain. We assessed the genetic architecture of cortical global expansion and regionalization by comparing two sets of genome-wide association studies of 24 cortical regions with and without adjustment for global measures (i.e., total surface area, mean cortical thickness) using a genetically informed parcellation in 32,488 adults. We found 393 and 756 significant loci with and without adjusting for globals, respectively, where 8% and 45% loci were associated with more than one region. Results from analyses without adjustment for globals recovered loci associated with global measures. Genetic factors that contribute to total surface area of the cortex particularly expand anterior/frontal regions, whereas those contributing to thicker cortex predominantly increase dorsal/frontal-parietal thickness. Interactome-based analyses revealed significant genetic overlap of global and dorsolateral prefrontal modules, enriched for neurodevelopmental and immune system pathways. Consideration of global measures is important in understanding the genetic variants underlying cortical morphology.

Candidates for drug repurposing to address the cognitive symptoms in schizophrenia

In the protein-protein interactome, we have previously identified a significant overlap between schizophrenia risk genes and genes associated with cognitive performance. Here, we further studied this overlap to identify potential candidate drugs for repurposing to treat the cognitive symptoms in schizophrenia. We first defined a cognition-related schizophrenia interactome from network propagation analyses, and identified drugs known to target more than one protein within this network. Thereafter, we used gene expression data to further select drugs that could counteract schizophrenia-associated gene expression perturbations. Additionally, we stratified these analyses by sex to identify sex-specific pharmacological treatment options for the cognitive symptoms in schizophrenia. After excluding drugs contraindicated in schizophrenia, we identified 12 drug repurposing candidates, most of which have anti-inflammatory and neuroprotective effects. Sex-stratified analyses showed that out of these 12 drugs, four were identified in females only, three were identified in males only, and five were identified in both sexes. Based on our bioinformatics analyses of disease genetics, we suggest 12 candidate drugs that warrant further examination for repurposing to treat the cognitive symptoms in schizophrenia, and suggest that these symptoms could be addressed by sex-specific pharmacological treatment options.

The Polygenic Risk Score Knowledge Base offers a centralized online repository for calculating and contextualizing polygenic risk scores

The process of identifying suitable genome-wide association (GWA) studies and formatting the data to calculate multiple polygenic risk scores on a single genome can be laborious. Here, we present a centralized polygenic risk score calculator currently containing over 250,000 genetic variant associations from the NHGRI-EBI GWAS Catalog for users to easily calculate sample-specific polygenic risk scores with comparable results to other available tools. Polygenic risk scores are calculated either online through the Polygenic Risk Score Knowledge Base (PRSKB; https://prs.byu.edu ) or via a command-line interface. We report study-specific polygenic risk scores across the UK Biobank, 1000 Genomes, and the Alzheimer's Disease Neuroimaging Initiative (ADNI), contextualize computed scores, and identify potentially confounding genetic risk factors in ADNI. We introduce a streamlined analysis tool and web interface to calculate and contextualize polygenic risk scores across various studies, which we anticipate will facilitate a wider adaptation of polygenic risk scores in future disease research.

Interactome overlap between risk genes of epilepsy and targets of anti-epileptic drugs

Aanti-epileptic drugs have been used for treating epilepsy for decades, meanwhile, more than one hundred genes have been identified to be associated with risk of epilepsy; however, the interaction mechanism between anti-epileptic drugs and risk genes of epilepsy was still not clearly understood. In this study, we systematically explored the interaction of epilepsy risk genes and anti-epileptic drug targets through a network-based approach. Our results revealed that anti-epileptic drug targets were significantly over-represented in risk genes of epilepsy with 17 overlapping genes and P-value = 2.2 ×10 −16. We identified a significantly localized PPI network with 55 epileptic risk genes and 94 anti-epileptic drug target genes, and network overlap analysis showed significant interactome overlap between risk genes and drug targets with P-value = 0.04. Besides, genes from PPI network were significantly enriched in the co-expression network of epilepsy with 22 enriched genes and P-value = 1.3 ×10 −15; meanwhile, cell type enrichment analysis indicated genes in this network were significantly enriched in 4 brain cell types (Interneuron, Medium Spiny Neuron, CA1 pyramidal Neuron, and Somatosensory pyramidal Neuron). These results provide evidence for significant interactions between epilepsy risk genes and anti-epileptic drug targets from the perspective of network biology.

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.

Updated perspectives on the clinical significance of negative symptoms in patients with schizophrenia

INTRODUCTION

Negative symptoms in schizophrenia are associated with poor response to available treatments, poor quality of life, and functional outcome. Therefore, they represent a substantial burden for people with schizophrenia, their families, and health-care systems.

AREAS COVERED

In this manuscript, we will provide an update on the conceptualization, assessment, and treatment of this complex psychopathological dimension of schizophrenia.

EXPERT OPINION

Despite the progress in the conceptualization of negative symptoms and in the development of state-of-the-art assessment instruments made in the last decades, these symptoms are still poorly recognized, and not always assessed in line with current conceptualization. Every effort should be made to disseminate the current knowledge on negative symptoms, on their assessment instruments and available treatments whose efficacy is supported by research evidence. Longitudinal studies should be promoted to evaluate the natural course of negative symptoms, improve our ability to identify the different sources of secondary negative symptoms, provide effective interventions, and target primary and persistent negative symptoms with innovative treatment strategies. Further research is needed to identify pathophysiological mechanisms of primary negative symptoms and foster the development of new treatments.

Identification of potential therapeutic targets and mechanisms of COVID-19 through network analysis and screening of chemicals and herbal ingredients

After experiencing the COVID-19 pandemic, it is widely acknowledged that a rapid drug repurposing method is highly needed. A series of useful drug repurposing tools have been developed based on data-driven modeling and network pharmacology. Based on the disease module, we identified several hub proteins that play important roles in the onset and development of the COVID-19, which are potential targets for repositioning approved drugs. Moreover, different network distance metrics were applied to quantify the relationship between drug targets and COVID-19 disease targets in the protein-protein-interaction (PPI) network and predict COVID-19 therapeutic effects of bioactive herbal ingredients and chemicals. Furthermore, the tentative mechanisms of candidates were illustrated through molecular docking and gene enrichment analysis. We obtained 15 chemical and 15 herbal ingredient candidates and found that different drugs may play different roles in the process of virus invasion and the onset and development of the COVID-19 disease. Given pandemic outbreaks, our method has an undeniable immense advantage in the feasibility analysis of drug repurposing or drug screening, especially in the analysis of herbal ingredients.

Integrative omics of schizophrenia: from genetic determinants to clinical classification and risk prediction

Schizophrenia (SCZ) is a debilitating neuropsychiatric disorder with high heritability and complex inheritance. In the past decade, successful identification of numerous susceptibility loci has provided useful insights into the molecular etiology of SCZ. However, applications of these findings to clinical classification and diagnosis, risk prediction, or intervention for SCZ have been limited, and elucidating the underlying genomic and molecular mechanisms of SCZ is still challenging. More recently, multiple Omics technologies - genomics, transcriptomics, epigenomics, proteomics, metabolomics, connectomics, and gut microbiomics - have all been applied to examine different aspects of SCZ pathogenesis. Integration of multi-Omics data has thus emerged as an approach to provide a more comprehensive view of biological complexity, which is vital to enable translation into assessments and interventions of clinical benefit to individuals with SCZ. In this review, we provide a broad survey of the single-omics studies of SCZ, summarize the advantages and challenges of different Omics technologies, and then focus on studies in which multiple omics data are integrated to unravel the complex pathophysiology of SCZ. We believe that integration of multi-Omics technologies would provide a roadmap to create a more comprehensive picture of interactions involved in the complex pathogenesis of SCZ, constitute a rich resource for elucidating the potential molecular mechanisms of the illness, and eventually improve clinical assessments and interventions of SCZ to address clinical translational questions from bench to bedside.

Shaping the Trans-Scale Properties of Schizophrenia via Cerebral Alterations on Magnetic Resonance Imaging and Single-Nucleotide Polymorphisms of Coding and Non-Coding Regions

Schizophrenia is a complex mental illness with genetic heterogeneity, which is often accompanied by alterations in brain structure and function. The neurobiological mechanism of schizophrenia associated with heredity remains unknown. Recently, the development of trans-scale and multi-omics methods that integrate gene and imaging information sheds new light on the nature of schizophrenia. In this article, we summarized the results of brain structural and functional changes related to the specific single-nucleotide polymorphisms (SNPs) in the past decade, and the SNPs were divided into non-coding regions and coding regions, respectively. It is hoped that the relationship between SNPs and cerebral alterations can be displayed more clearly and intuitively, so as to provide fresh approaches for the discovery of potential biomarkers and the development of clinical accurate individualized treatment decision-making.

Network-based analysis revealed significant interactions between risk genes of severe COVID-19 and host genes interacted with SARS-CoV-2 proteins

Whether risk genes of severe coronavirus disease 2019 (COVID-19) from genome-wide association study could play their regulatory roles by interacting with host genes that were interacted with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) proteins was worthy of exploration. In this study, we implemented a network-based approach by developing a user-friendly software Network Calculator (https://github.com/Haoxiang-Qi/Network-Calculator.git). By using Network Calculator, we identified a network composed of 13 risk genes and 28 SARS-CoV-2 interacted host genes that had the highest network proximity with each other, with a hub gene HNRNPK identified. Among these genes, 14 of them were identified to be differentially expressed in RNA-seq data from severe COVID-19 cases. Besides, by expression enrichment analysis in single-cell RNA-seq data, compared with mild COVID-19, these genes were significantly enriched in macrophage, T cell and epithelial cell for severe COVID-19. Meanwhile, 74 pathways were significantly enriched. Our analysis provided insights for the underlying genetic etiology of severe COVID-19 from the perspective of network biology.

Major depressive disorder with mixed features and treatment response to lurasidone: A symptom network model

BACKGROUND

To investigate the symptom network structure of major depressive disorder (MDD) with mixed features and implications for treatment.

METHODS

In this post-hoc analysis of a previously reported randomized trial, patients meeting DSM-IV-TR criteria for MDD presenting with two or three manic symptoms (DSM-5 mixed features specifier) were randomized to 6 weeks of double-blind treatment with lurasidone 20-60 mg/d (N = 109) or placebo (N = 100). The network structure of symptoms at baseline and their treatment moderating effects were investigated.

RESULTS

Network analyses showed that both ``elevated mood'' (YMRS item 1) and ``increased motor activity-energy'' (YMRS item 2) were associated with ``sleep disturbance'' ("bridge" symptom) and the depressive symptom cluster. Presence of both "elevated mood" and "increased motor activity-energy" at baseline predicted significantly less improvement in MADRS and CGI-S score at week 6 with lurasidone (vs. placebo) compared to patients without these manic symptoms at baseline. The network model also showed "rapid/pressured speech" (YMRS item 6) at baseline predicted improvement in both manic and depressive symptoms with lurasidone vs. placebo treatment.

LIMITATIONS

This was a post-hoc analysis where findings need to be confirmed by prospective controlled studies.

CONCLUSIONS

This post-hoc analysis describes the symptom network structure of MDD with mixed features in a patient sample at study baseline. Specific manic symptoms were found to be linked to sleep disturbance (characterized as a "bridge" symptom), which in turn linked the manic and depressive symptom clusters. The presence (vs. absence) of the specific manic symptoms we identified moderated the antidepressant and antimanic effects of lurasidone in the treatment of MDD with mixed (subthreshold hypomanic) features.

Probing the Mechanism of Hepatotoxicity of Hexabromocyclododecanes through Toxicological Network Analysis

The prediction and mechanism analysis of hepatotoxicity of contaminants, because of their various phenotypes and complex mechanisms, is still a key problem in environmental research. We applied a toxicological network analysis method to predict the hepatotoxicity of three hexabromocyclododecane (HBCD) diastereoisomers (α-HBCD, β-HBCD, and γ-HBCD) and explore their potential mechanisms. First, we collected the hepatotoxicity related genes and found that those genes were significantly localized in the human interactome. Therefore, these genes form a disease module of hepatotoxicity. We also collected targets of α-, β-, and γ-HBCD and found that their targets overlap with the hepatotoxicity disease module. Then, we trained a model to predict hepatotoxicity of three HBCD diastereoisomers based on the relationship between the hepatotoxicity disease module and targets of compounds. We found that 593 genes were significantly located in the hepatotoxicity disease module (Z = 11.9, p < 0.001) involved in oxidative stress, cellular immunity, and proliferation, and the accuracy of hepatotoxicity prediction of HBCD was 0.7095 ± 0.0193 and the recall score was 0.8355 ± 0.0352. HBCD mainly affects the core disease module genes to mediate the adenosine monophosphate-activated kinase, p38MAPK, PI3K/Akt, and TNFα pathways to regulate the immune reaction and inflammation. HBCD also induces the secretion of IL6 and STAT3 to lead hepatotoxicity by regulating NR3C1. This approach is transferable to other toxicity research studies of environmental pollutants.

In silico hippocampal modeling for multi-target pharmacotherapy in schizophrenia

Treatment of schizophrenia has had limited success in treating core cognitive symptoms. The evidence of multi-gene involvement suggests that multi-target therapy may be needed. Meanwhile, the complexity of schizophrenia pathophysiology and psychopathology, coupled with the species-specificity of much of the symptomatology, places limits on analysis via animal models, in vitro assays, and patient assessment. Multiscale computer modeling complements these traditional modes of study. Using a hippocampal CA3 computer model with 1200 neurons, we examined the effects of alterations in NMDAR, HCN (Ih current), and GABAAR on information flow (measured with normalized transfer entropy), and in gamma activity in local field potential (LFP). We found that altering NMDARs, GABAAR, Ih, individually or in combination, modified information flow in an inverted-U shape manner, with information flow reduced at low and high levels of these parameters. Theta-gamma phase-amplitude coupling also had an inverted-U shape relationship with NMDAR augmentation. The strong information flow was associated with an intermediate level of synchrony, seen as an intermediate level of gamma activity in the LFP, and an intermediate level of pyramidal cell excitability. Our results are consistent with the idea that overly low or high gamma power is associated with pathological information flow and information processing. These data suggest the need for careful titration of schizophrenia pharmacotherapy to avoid extremes that alter information flow in different ways. These results also identify gamma power as a potential biomarker for monitoring pathology and multi-target pharmacotherapy.

Interactome overlap between schizophrenia and cognition

Cognitive impairments constitute a core feature of schizophrenia, and a genetic overlap between schizophrenia and cognitive functioning in healthy individuals has been identified. However, due to the high polygenicity and complex genetic architecture of both traits, overlapping biological pathways have not yet been identified between schizophrenia and normal cognitive ability. Network medicine offers a framework to study underlying biological pathways through protein-protein interactions among risk genes. Here, established network-based methods were used to characterize the biological relatedness of schizophrenia and cognition by examining the genetic link between schizophrenia risk genes and genes associated with cognitive performance in healthy individuals, through the protein interactome. First, network separation showed a profound interactome overlap between schizophrenia risk genes and genes associated with cognitive performance (S_AB = -0.22, z-score = -6.80, p = 5.38e-12). To characterize this overlap, network propagation was thereafter used to identify schizophrenia risk genes that are close to cognition-associated genes in the interactome network space (n = 140, of which 54 were part of the direct genetic overlap). Schizophrenia risk genes close to cognition were enriched for pathways including long-term potentiation and Alzheimer's disease, and included genes with a role in neurotransmitter systems important for cognitive functioning, such as glutamate and dopamine. These results pinpoint a subset of schizophrenia risk genes that are of particular interest for further examination in schizophrenia patient groups, of which some are druggable genes with potential as candidate targets for cognitive enhancing drugs.

Minds, Maps, Meanderings

In her book Mind Fixers: Psychiatry's Troubled Search for the Biology of Mental Illness, historian Anne Harrington challenges psychiatrists to embrace complexity. Her central message is that society has often projected a lot onto psychiatry, with our field far too eager to accept these and to fill an expertise void: at one point, with psychoanalytic grandiosity, later with overpromised, underperforming biological boasts and reified diagnostic labels. Echoing Foucault, Harrington invites us to avoid mistaking the name of the thing for the thing itself and to keep struggling for answers, embracing a slow science, marketing expertise humbly.² Although some may feel defensive in their reading of Mind Fixers, I found it an invitation to recognize that the successes and failures of psychiatry's past are our inheritance. It is up to us to learn from this past, to be humbled and encouraged by it, and to examine how we are promoting our field in the present. We need to advertise that although many aspects of neural functioning elude us and although we rely on many of the same therapeutic practices from the past, we are not without direction for the future. For example, an elegant roadmap was laid out a year ago by Karolina Kauppi and colleagues wherein they combined a data set for schizophrenia risk genes and another set of genes associated with antipsychotic drug targets.³ Using this "network biology" approach, they identified four schizophrenia risk genes that were also antipsychotic drug targets (GRM3, DRD2, CHRM4, and CYP2D6); they also found several molecular targets that are not currently connected to antipsychotic drug treatment, providing researchers with some potential targets for novel therapeutics.⁴.

Management of opioid use disorder in the USA: present status and future directions

Opioid use disorder is characterised by the persistent use of opioids despite the adverse consequences of its use. The disorder is associated with a range of mental and general medical comorbid disorders, and with increased mortality. Although genetics are important in opioid use disorder, younger age, male sex, and lower educational attainment level and income, increase the risk of opioid use disorder, as do certain psychiatric disorders (eg, other substance use disorders and mood disorders). The medications for opioid use disorder, which include methadone, buprenorphine, and extended-release naltrexone, significantly improve opioid use disorder outcomes. However, the effectiveness of medications for opioid use disorder is limited by problems at all levels of the care cascade, including diagnosis, entry into treatment, and retention in treatment. There is an urgent need for expanding the use of medications for opioid use disorder, including training of health-care professionals in the treatment and prevention of opioid use disorder, and for development of alternative medications and new models of care to expand capabilities for personalised interventions.

Enhanced Molecular Appreciation of Psychiatric Disorders Through High-Dimensionality Data Acquisition and Analytics

The initial diagnosis, molecular investigation, treatment, and posttreatment care of major psychiatric disorders (schizophrenia and bipolar depression) are all still significantly hindered by the current inability to define these disorders in an explicit molecular signaling manner. High-dimensionality data analytics, using large datastreams from transcriptomic, proteomic, or metabolomic investigations, will likely advance both the appreciation of the molecular nature of major psychiatric disorders and simultaneously enhance our ability to more efficiently diagnose and treat these debilitating conditions. High-dimensionality data analysis in psychiatric research has been heterogeneous in aims and methods and limited by insufficient sample sizes, poorly defined case definitions, methodological inhomogeneity, and confounding results. All of these issues combine to constrain the conclusions that can be extracted from them. Here, we discuss possibilities for overcoming methodological challenges through the implementation of transcriptomic, proteomic, or metabolomics signatures in psychiatric diagnosis and offer an outlook for future investigations. To fulfill the promise of intelligent high-dimensionality data-based differential diagnosis in mental disease diagnosis and treatment, future research will need large, well-defined cohorts in combination with state-of-the-art technologies.