Identifying drug targets for neurological and psychiatric disease via genetics and the brain transcriptome

Exploration oxidative stress underlying gastroesophageal reflux disease and therapeutic targets identification: a multi-omics Mendelian randomization study

INTRODUCTION

Gastroesophageal reflux disease (GERD) is a chronic inflammatory gastrointestinal disease, which has no thoroughly effective or safe treatment. Elevated oxidative stress is a common consequence of chronic inflammatory conditions.

METHODS

We employed Summary-data based MR (SMR) analysis to assess the associations between gene molecular characteristics and GERD. Exposure data were the summary-level data on the levels of DNA methylation, gene expression, and protein expression, which obtained from related methylation, expression, and protein quantitative trait loci investigations (mQTL, eQTL, and pQTL). Outcome data, Genome-wide association study (GWAS) summary statistics of GERD, were extracted from the Ong's study (discovery), the Dönertaş's study (replication), and the FinnGen study (replication). Colocalization analysis was performed to determine if the detected signal pairs shared a causative genetic mutation. Oxidative stress related genes and druggable genes were imported to explore oxidative stress mechanism underlying GERD and therapeutic targets of GERD. The Drugbank database was utilized to conduct druggability evaluation.

RESULTS

After multi-omics SMR analysis and colocalization analysis, we identified seven key genes for GERD, which were SUOX and SERPING1, DUSP13, SULT1A1, LMOD1, UBE2L6, and PSCA. SUOX was screened out to be the mediator, which suggest that GERD is related to oxidative stress. SERPING1, SULT1A1, and PSCA were selected to be the druggable genes.

CONCLUSIONS

These findings offered strong support for the identification of GERD treatment targets in the future as well as for the study of the oxidative stress mechanism underlying GERD.

A comprehensive review of neurotransmitter modulation via artificial intelligence: A new frontier in personalized neurobiochemistry

The deployment of artificial intelligence (AI) is revolutionizing neuropharmacology and drug development, allowing the modulation of neurotransmitter systems at the personal level. This review focuses on the neuropharmacology and regulation of neurotransmitters using predictive modeling, closed-loop neuromodulation, and precision drug design. The fusion of AI with applications such as machine learning, deep-learning, and even computational modeling allows for the real-time tracking and enhancement of biological processes within the body. An exemplary application of AI is the use of DeepMind's AlphaFold to design new GABA reuptake inhibitors for epilepsy and anxiety. Likewise, Benevolent AI and IBM Watson have fast-tracked drug repositioning for neurodegenerative conditions. Furthermore, we identified new serotonin reuptake inhibitors for depression through AI screening. In addition, the application of Deep Brain Stimulation (DBS) settings using AI for patients with Parkinson's disease and for patients with major depressive disorder (MDD) using reinforcement learning-based transcranial magnetic stimulation (TMS) leads to better treatment. This review highlights other challenges including algorithm bias, ethical concerns, and limited clinical validation. Their proposal to incorporate AI with optogenetics, CRISPR, neuroprosthesis, and other advanced technologies fosters further exploration and refinement of precision neurotherapeutic approaches. By bridging computational neuroscience with clinical applications, AI has the potential to revolutionize neuropharmacology and improve patient-specific treatment strategies. We addressed critical challenges, such as algorithmic bias and ethical concerns, by proposing bias auditing, diverse datasets, explainable AI, and regulatory frameworks as practical solutions to ensure equitable and transparent AI applications in neurotransmitter modulation.

Network pharmacology approach to unravel the neuroprotective potential of natural products: a narrative review

Aging is a slow and irreversible biological process leading to decreased cell and tissue functions with higher risks of multiple age-related diseases, including neurodegenerative diseases. It is widely accepted that aging represents the leading risk factor for neurodegeneration. The pathogenesis of these diseases involves complex interactions of genetic mutations, environmental factors, oxidative stress, neuroinflammation, and mitochondrial dysfunction, which complicate treatment with traditional mono-targeted therapies. Network pharmacology can help identify potential gene or protein targets related to neurodegenerative diseases. Integrating advanced molecular profiling technologies and computer-aided drug design further enhances the potential of network pharmacology, enabling the identification of biomarkers and therapeutic targets, thus paving the way for precision medicine in neurodegenerative diseases. This review article delves into the application of network pharmacology in understanding and treating neurodegenerative disorders such as Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, Huntington's disease, and spinal muscular atrophy. Overall, this article emphasizes the importance of addressing aging as a central factor in developing effective disease-modifying therapies, highlighting how network pharmacology can unravel the complex biological networks associated with aging and pave the way for personalized medical strategies.

Cerebellar pathology in forensic and clinical neuroscience

Recent research underscores the cerebellum's growing importance in forensic science and neurology, showing its functions extend beyond motor control, especially in identifying causes of death. Critical neuropathological markers including alpha-synuclein and tau protein aggregates, cellular degeneration, inflammation, and vascular changes are vital for identifying neurodegenerative diseases, injuries, and toxic exposures. Advanced forensic methods, such as Magnetic resonance imaging (MRI), immunohistochemistry, and molecular analysis, have greatly improved the accuracy of diagnoses. Promising new therapies, including neuroprotective agents like resveratrol and transcranial magnetic stimulation (TMS), offer potential in treating cerebellar disorders. The cerebellum's vulnerability to toxins, drugs, and traumatic brain injuries (TBIs) highlights its forensic relevance. Moreover, advancements in genetic diagnostics, such as next-generation sequencing and CRISPR-Cas9, are enhancing the understanding and treatment of genetic conditions like Joubert syndrome and Dandy-Walker malformation. These findings emphasize the need for further research into cerebellar function and its broader significance in both forensic science and neurology.

GPNMB and ATP6V1A interact to mediate microglia phagocytosis of multiple types of pathological particles

Pronounced elevation of glycoprotein non-metastatic melanoma B (GPNMB) is a common phenomenon in a variety of brain diseases, but the expression patterns, functions, and molecular signaling of GPNMB have not been well studied. Here, we showed that pathological factors, including neuronal degeneration caused by seizures, caspase-3-induced neuronal apoptosis, neuronal debris, and β-amyloid, induced "on-demand" GPNMB expression in hippocampal microglia. Genetic ablation of GPNMB did not affect acute seizures but worsened chronic epileptogenesis. We found that GPNMB functioned in phagocytosis, deficiency of which resulted in defects in both phagocytic engulfment and degradation. GPNMB could be internalized into cells, where it wrapped engulfed pathogenic particles and presented them to lysosomes through interaction with lysosomal vacuolar-type proton ATPase catalytic subunit A (ATP6V1A). Activating ATP6V1A was able to rescue GPNMB-deficiency-caused phagocytosis impairment. Thus, microglial GPNMB-ATP6V1A might be a common treatment target of a batch of chronic neurological disorders, and clearing the degenerative neurons might be more valuable than reserving them to protect the brain.

Summarizing attributable factors and evaluating risk of bias of Mendelian randomization studies for Alzheimer's dementia and cognitive status: a systematic review and meta-analysis

BACKGROUND

No effective treatment is available to delay or reverse the onset and progression of Alzheimer's dementia (AD). Mild cognitive impairment, a clinical state between normal aging and AD, may offer the proper window for AD intervention and treatment. This systematic review aimed to summarize evidence from Mendelian randomization (MR) studies exploring factors attributable to AD and related cognitive status and to assess its credibility.

METHODS

We searched PubMed, Embase, MEDLINE, and the Cochrane Library to identify MR studies investigating the associations between any factor and AD and related cognitive status. The risk of bias in MR studies was evaluated using nine signaling questions tailored to identify potential biases based on the STROBE-MR guidelines.

RESULTS

A total of 125 eligible publications were examined, including 106 AD-related MR studies reporting 674 records and 28 cognition-related MR studies reporting 141 records. We identified 185 unique causal risk factors for AD and 49 for cognitive status. More than half of the MR studies reporting AD or cognitive status outcomes exhibited poor methodological quality, with a high risk of bias observed in 59% of the AD-related studies and 64% of the cognitive-related studies.

CONCLUSIONS

This systematic review summarized modifiable factors and omics signatures, providing a database of MR studies on AD and related cognitive status. The evaluation of bias risk in MR studies serves to raise awareness and improve overall quality. A critical appraisal checklist for assessing the risk of bias may pave the way for the development of a standardized tool.

SYSTEMATIC REVIEW REGISTRATION

The review protocol was registered with the Prospective Register of Systematic Reviews (PROSPERO) under the registration number CRD42023213990.

Mendelian randomization identifies proteins involved in neurodegenerative diseases

Proteins are involved in multiple biological functions. High-throughput technologies have allowed the measurement of thousands of proteins in population biobanks. In this study, we aimed to identify proteins related to Alzheimer's disease, Parkinson's disease, multiple sclerosis and amyotrophic lateral sclerosis by leveraging large-scale genetic and proteomic data. We performed a two-sample cis Mendelian randomization study by selecting instrumental variables for the abundance of >2700 proteins measured by either Olink or SomaScan platforms in plasma from the UK Biobank and the deCODE Health Study. We also used the latest publicly available genome-wide association studies for the neurodegenerative diseases of interest. The potentially causal effect of proteins on neurodegenerative diseases was estimated based on the Wald ratio. We tested 13 377 protein-disease associations, identifying 169 associations that were statistically significant (5% false discovery rate). Evidence of co-localization between plasma protein abundance and disease risk (posterior probability > 0.80) was identified for 61 protein-disease pairs, leading to 50 unique protein-disease associations. Notably, 23 of 50 protein-disease associations corresponded to genetic loci not previously reported by genome-wide association studies. The two-sample Mendelian randomization and co-localization analysis also showed that APOE abundance in plasma was associated with three subcortical volumes (hippocampus, amygdala and nucleus accumbens) and white matter hyper-intensities, whereas PILRA and PILRB abundance in plasma was associated with caudate nucleus volume. Our study provided a comprehensive assessment of the effect of the human proteome that is currently measurable through two different platforms on neurodegenerative diseases. The newly associated proteins indicated the involvement of complement (C1S and C1R), microglia (SIRPA, SIGLEC9 and PRSS8) and lysosomes (CLN5) in Alzheimer's disease; the interleukin-6 pathway (CTF1) in Parkinson's disease; lysosomes (TPP1), blood-brain barrier integrity (MFAP2) and astrocytes (TNFSF13) in amyotrophic lateral sclerosis; and blood-brain barrier integrity (VEGFB), oligodendrocytes (PARP1), node of Ranvier and dorsal root ganglion (NCS1, FLRT3 and CDH15) and the innate immune system (CR1, AHSG and WARS) in multiple sclerosis. Our study demonstrates how harnessing large-scale genomic and proteomic data can yield new insights into the role of the plasma proteome in the pathogenesis of neurodegenerative diseases.

Cell state-dependent allelic effects and contextual Mendelian randomization analysis for human brain phenotypes

Gene expression quantitative trait loci are widely used to infer relationships between genes and central nervous system (CNS) phenotypes; however, the effect of brain disease on these inferences is unclear. Using 2,348,438 single-nuclei profiles from 391 disease-case and control brains, we report 13,939 genes whose expression correlated with genetic variation, of which 16.7-40.8% (depending on cell type) showed disease-dependent allelic effects. Across 501 colocalizations for 30 CNS traits, 23.6% had a disease dependency, even after adjusting for disease status. To estimate the unconfounded effect of genes on outcomes, we repeated the analysis using nondiseased brains (n = 183) and reported an additional 91 colocalizations not present in the larger mixed disease and control dataset, demonstrating enhanced interpretation of disease-associated variants. Principled implementation of single-cell Mendelian randomization in control-only brains identified 140 putatively causal gene-trait associations, of which 11 were replicated in the UK Biobank, prioritizing candidate peripheral biomarkers predictive of CNS outcomes.

Leveraging pleiotropy for the improved treatment of psychiatric disorders

Over 90% of drug candidates fail in clinical trials, while it takes 10-15 years and one billion US dollars to develop a single successful drug. Drug development is more challenging for psychiatric disorders, where disease comorbidity and complex symptom profiles obscure the identification of causal mechanisms for therapeutic intervention. One promising approach for determining more suitable drug candidates in clinical trials is integrating human genetic data into the selection process. Genome-wide association studies have identified thousands of replicable risk loci for psychiatric disorders, and sophisticated statistical tools are increasingly effective at using these data to pinpoint likely causal genes. These studies have also uncovered shared or pleiotropic genetic risk factors underlying comorbid psychiatric disorders. In this article, we argue that leveraging pleiotropic effects will provide opportunities to discover novel drug targets and identify more effective treatments for psychiatric disorders by targeting a common mechanism rather than treating each disease separately.

Linking Genome-Wide Association Studies to Pharmacological Treatments for Psychiatric Disorders

Importance

Large-scale genome-wide association studies (GWAS) should ideally inform the development of pharmacological treatments, but whether GWAS-identified mechanisms of disease liability correspond to the pathophysiological processes targeted by current pharmacological treatments is unclear.

Objective

To investigate whether functional information from a range of open bioinformatics datasets can elucidate the relationship between GWAS-identified genetic variation and the genes targeted by current treatments for psychiatric disorders.

Design, Setting, and Participants

Associations between GWAS-identified genetic variation and pharmacological treatment targets were investigated across 4 psychiatric disorders-attention-deficit/hyperactivity disorder, bipolar disorder, schizophrenia, and major depressive disorder. Using a candidate set of 2232 genes listed as targets for all approved treatments in the DrugBank database, each gene was independently assigned 2 scores for each disorder-one based on its involvement as a treatment target and the other based on the mapping between GWAS-implicated single-nucleotide variants (SNVs) and genes according to 1 of 4 bioinformatic data modalities: SNV position, gene distance on the protein-protein interaction (PPI) network, brain expression quantitative trail locus (eQTL), and gene expression patterns across the brain. Study data were analyzed from November 2023 to September 2024.

Main Outcomes and Measures

Gene scores for pharmacological treatments and GWAS-implicated genes were compared using a measure of weighted similarity applying a stringent null hypothesis-testing framework that quantified the specificity of the match by comparing identified associations for a particular disorder with a randomly selected set of treatments.

Results

Incorporating information derived from functional bioinformatics data in the form of a PPI network revealed links for bipolar disorder (P permutation [P-perm] = 7 × 10-4; weighted similarity score, empirical [ρ-emp] = 0.1347; mean [SD] weighted similarity score, random [ρ-rand] = 0.0704 [0.0163]); however, the overall correspondence between treatment targets and GWAS-implicated genes in psychiatric disorders rarely exceeded null expectations. Exploratory analysis assessing the overlap between the GWAS-identified genetic architecture and treatment targets across disorders identified that most disorder pairs and mapping methods did not show a significant correspondence.

Conclusions and Relevance

In this bioinformatic study, the relatively low degree of correspondence across modalities suggests that the genetic architecture driving the risk for psychiatric disorders may be distinct from the pathophysiological mechanisms currently used for targeting symptom manifestations through pharmacological treatments. Novel approaches incorporating insights derived from GWAS based on refined phenotypes including treatment response may assist in mapping disorder risk genes to pharmacological treatments in the long term.

Investigating the shared genetic links between hypothyroidism and psychiatric disorders: a large-scale genomewide cross-trait analysis

BACKGROUND

Associations between thyroid diseases and psychiatric disorders have been mainly described before. However, the genetic mechanism behind hypothyroidism and psychiatric disorders remains unexplained.

METHODS

We examined the genetic architecture of hypothyroidism and 8 psychiatric disorders. Firstly, the global and local genetic relationship between the paired traits was explored. Secondly, cross-trait analysis was performed to investigate the genomic loci and genes between psychiatric disorders and hypothyroidism. Thirdly, the significant expression of these genes and the causal relationships were investigated. Lastly, enrichment analysis was conducted on these genes to explore their biological mechanisms.

RESULTS

We observed significant positive genetic correlations between psychiatric disorders and hypothyroidism. The cross-trait meta-analysis identified 62 shared genetic loci between hypothyroidism and psychiatric disorders. The colocalization analysis additionally revealed 15 potential pleiotropic loci with a posterior probabilities.H4 (PP·H4) value >0.7. We also found 2308 genes shared between both traits, which were highly enriched in biological pathways such as immune cell differentiation and autoimmune diseases, as well as in tissue structures like the frontal cortex and cerebral cortex. Especially, many pleiotropic genes were significantly expressed for multiple pairwise traits, such as BCL11B, RERE, and SUOX. Lastly, the Latent causal variable model (LCV) analysis did not find any causal components in the genetic structure between them.

LIMITATIONS

The limitations of this study include that the conclusions were drawn from a European population.

CONCLUSIONS

These findings not only deepens our understanding of their biological mechanisms but also has significant implications for the intervention and treatment of these diseases.

Revealing potential drug targets in schizophrenia through proteome-wide Mendelian randomization genetic insights

BACKGROUND

Schizophrenia (SCZ) is a severe, chronic mental disorder with no current cure. Identifying novel pharmacological targets is crucial for developing more effective treatments.

METHODS

We performed two-sample Mendelian randomization (MR) analyses to estimate the associations between cerebrospinal fluid (CSF) containing 154 proteins and plasma containing 734 proteins and risk of SCZ. Bidirectional MR analysis, steiger filtering, bayesian colocalization, phenotypic scanning, and validation analysis were examined to validate the assumptions of MR. For proteins significantly associated with SCZ identified by MR, we explored their potential impact on brain structures, including cortical surface area (SA), thickness (TH), and the volume of subcortical structures.

RESULTS

MR analysis identified 13 protein-SCZ pairs at Bonferroni significance (P < 5.63 × 10-5). Notably, the genetically proxied protein level of neuromedin B (NMB) was associated with an increased risk for SCZ (odds ratio [OR] = 1.41; 95 % CI, 1.27 to 1.58; P = 6.68 × 10-10). Bayesian colocalization suggested that NMB shares genetic variations with SCZ. Further, NMB interacts with target proteins of current SCZ drugs and was validated in the UK Biobank. The genetically proxied NMB was positively associated with an increase in the surface area (SA) of the parahippocampal gyrus (β = 8.93 mm2, 95 % CI, 1.58 to 16.3, P = .02). Additionally, an increase in the genetically proxied SA of the parahippocampal gyrus was inversely associated with the risk of SCZ (OR = 0.996, 95 % CI, 0.993 to 0.999, P = .04).

CONCLUSIONS

The findings suggest that NMB may represent a promising target for pharmacological intervention in SCZ. This warrants further investigation into the specific constituents involved, which could have potential for follow-up studies.

Potential Functions and Causal Associations of GNLY in Primary Open-Angle Glaucoma: Integration of Blood-Derived Proteome, Transcriptome, and Experimental Verification

Purpose

Genome-wide association studies (GWAS) have identified multiple genetic loci associated with primary open-angle glaucoma (POAG). However, the mechanisms by which these loci contribute to POAG progression remain unclear. This study aimed to identify potential causative genes involved in the development of POAG.

Methods

We utilized multi-dimensional high-throughput data, integrating proteome-wide association study(PWAS), transcriptome-wide association study (TWAS), and summary data-based Mendelian randomization (SMR) analysis. This approach enabled the identification of genes influencing POAG risk by affecting gene expression and protein concentrations in the bloodstream. The key gene was validated through enzyme-linked immunosorbent assay (ELISA) analysis.

Results

PWAS identified 86 genes associated with altered blood protein levels in POAG patients. Of these, eight genes (SFTPD, CSK, COL18A1, TCN2, GZMK, RAB2A, TEK, and GNLY) were identified as likely causative for POAG (P SMR < 0.05). TWAS revealed that GNLY was significantly associated with POAG at the gene expression level. GNLY-interacting genes were found to play roles in immune dysregulation, inflammation, and apoptosis. Clinical and cell-based validation confirmed reduced GNLY expression in POAG groups.

Conclusion

This study reveals GNLY as a significant potential therapeutic target for managing primary open-angle glaucoma.

Identification of novel risk genes for Alzheimer's disease by integrating genetics from hippocampus

Alzheimer's disease (AD) stands as the most prevalent neurodegenerative ailment, presently lacking a definitive cure. Given that primary medications for AD patients in the early or middle stages demonstrate optimal efficacy, it becomes crucial to delve into the identification of risk genes associated with early onset. In our study, we compiled and integrated three transcriptomics datasets (GSE48350, GSE36980, GSE5281) originating from the hippocampus of 37 AD patients and 66 healthy controls (CTR) for comprehensive bioinformatics analysis. Comparative analysis with CTR revealed 25 up-regulated genes and 291 down-regulated genes in AD. Those down-regulated genes were notably enriched in processes related to the transmission and transport of synaptic signals. Intriguingly, 27 differentially expressed genes implicated in AD were also correlated with the Braak stage, establishing a connection with various immune cell types that exhibit differences in AD, including cytotoxic T cells, neutrophils, CD4 T cells, Th1, Th2, and Tfh. Significantly, a Cox model, constructed using nine feature genes, effectively stratified AD samples (HR = 2.72, 95% CI 1.94 ~ 3.81, P = 3.6e-10), highlighting their promising potential for risk assessment. In conclusion, our investigation sheds light on novel genes intricately linked to the onset and progression of AD, offering potential biomarkers for the early detection of this debilitating condition. This study contributes valuable insights toward enhancing the strategies for preventing and treating AD.

Mendelian randomization analysis using GWAS and eQTL data to investigate the relationship between chronotype and neuropsychiatric disorders and their molecular basis

Chronotype is a proxy sleep measure that has been associated with neuropsychiatric disorders. By investigating how chronotype influences risk for neuropsychiatric disorders and vice versa, we may identify modifiable risk factors for each phenotype. Here we used Mendelian randomization (MR), to explore causal effects by (1) studying the causal relationships between neuropsychiatric disorders and chronotype and (2) characterizing the genetic components of these phenotypes. Firstly, we investigated if a causal role exists between five neuropsychiatric disorders and chronotype using the largest genome-wide association studies (GWAS) available. Secondly, we integrated data from expression quantitative trait loci (eQTLs) to investigate the role of gene expression alterations on these phenotypes. Evening chronotype was causal for increased risk of schizophrenia and autism spectrum disorder and schizophrenia was causal for a tendency toward evening chronotype. We identified 12 eQTLs where gene expression changes in brain or blood were causal for one of the phenotypes, including two eQTLs for SNX19 in hippocampus and hypothalamus that were causal for schizophrenia. These findings provide important evidence for the complex, bidirectional relationship that exists between a sleep-based phenotype and neuropsychiatric disorders, and use gene expression data to identify causal roles for genes at associated loci.

Identifying novel risk genes in intracranial aneurysm by integrating human proteomes and genetics

Genome-wide association studies (GWAS) have become increasingly popular for detecting numerous loci associated with intracranial aneurysm (IA), but how these loci function remains unclear. In this study, we employed an integrative analytical pipeline to efficiently transform genetic associations and identify novel genes for IA. Using multidimensional high-throughput data, we integrated proteome-wide association studies (PWAS), transcriptome-wide association studies (TWAS), Mendelian randomization (MR) and Bayesian co-localization analyses to prioritize genes that can increase IA risk by altering their expression and protein abundances in the brain and blood. Moreover, single-cell RNA sequencing (scRNA-seq) of the circle of Willis was performed to enrich filtered genes in cells, and gene set enrichment analysis (GSEA) was conducted for each gene using bulk RNA-seq data for IA. No significant genes with cis-regulated plasma protein levels were proven to be associated with IA. The protein abundances of five genes in the brain were found to be associated with IA. According to cellular enrichment analysis, these five genes were expressed mainly in the endothelium, fibroblasts and vascular smooth muscle cells. Only three genes, CNNM2, GPRIN3 and UFL1, passed MR and Bayesian co-localization analyses. While UFL1 was not validated in confirmation PWAS as it was not profiled, it was validated in TWAS. GSEA suggested these three genes are associated with the cell cycle. In addition, the protein abundance of CNNM2 was found to be associated with IA rupture (based on PWAS, MR and co-localization analyses). Our findings indicated that CNNM2, GPRIN3 and UFL1 (CNNM2 correlated with IA rupture) are potential IA risk genes that may provide a broad hint for future research on possible mechanisms and therapeutic targets for IA.

Progress and Implications from Genetic Studies of Bipolar Disorder

With the advancements in gene sequencing technologies, including genome-wide association studies, polygenetic risk scores, and high-throughput sequencing, there has been a tremendous advantage in mapping a detailed blueprint for the genetic model of bipolar disorder (BD). To date, intriguing genetic clues have been identified to explain the development of BD, as well as the genetic association that might be applied for the development of susceptibility prediction and pharmacogenetic intervention. Risk genes of BD, such as CACNA1C, ANK3, TRANK1, and CLOCK, have been found to be involved in various pathophysiological processes correlated with BD. Although the specific roles of these genes have yet to be determined, genetic research on BD will help improve the prevention, therapeutics, and prognosis in clinical practice. The latest preclinical and clinical studies, and reviews of the genetics of BD, are analyzed in this review, aiming to summarize the progress in this intriguing field and to provide perspectives for individualized, precise, and effective clinical practice.

Lithium response in bipolar disorder: Epigenome-wide DNA methylation signatures and epigenetic aging

Lithium (Li) is the first-line treatment for bipolar disorder (BD) even though only 30 % of BD patients are considered excellent responders. The mechanisms by which Li exerts its action are not clearly understood, but it has been suggested that specific epigenetic mechanisms, such as methylation processes, may play a role. In this regard, DNA methylation patterns can be used to estimate epigenetic age (EpiAge), which is accelerated in BD patients and reversed by Li treatment. Our first aim was to compare the DNA methylation profile in peripheral blood between BD patients categorized as excellent responders to Li (Ex-Rp) and non-responders (N-Rp). Secondly, EpiAge was estimated to detect differential age acceleration between the two groups. A total of 130 differentially methylated positions (DMPs) and 16 differentially methylated regions (DMRs) between Ex-Rp (n = 26) and N-Rp (n = 37) were identified (FDR adjusted p-value < 0.05). We found 122 genes mapping the DMPs and DMRs, nine of which (HOXB6, HOXB3, HOXB-AS3, TENM2, CACNA1B, ANK3, EEF2K, CYP1A1, and SORCS2) had previously been linked to Li response. We found genes related to the GSK3β pathway to be highly represented. Using FUMA, we found enrichment in Gene Ontology Cell Component for the synapse. Gene network analysis highlighted functions related to the cell cycle, nervous system development and function, and gene expression. No significant differences in age acceleration were found between Ex-Rp and N-Rp for any of the epigenetic clocks analysed. Our findings indicate that a specific methylation pattern could determine the response to Li in BD patients. We also found that a significant portion of the differentially methylated genes are closely associated with the GSK3β pathway, reinforcing the role of this system in Li response. Future longitudinal studies with larger samples will help to elucidate the epigenetic mechanisms underlying Li response.

ENPP2/Autotaxin: The potential drug target for alcoholic liver disease identified through Mendelian randomization analysis

BACKGROUND AND AIMS

At present, there is still a lack of radical drug targets for intervention in alcoholic liver disease (ALD), and drug discovery through randomized controlled trials is a lengthy, risky, and expensive undertaking, so we aimed to identify effective drug targets based on human genetics.

METHODS

We used Mendelian randomization (MR) and Bayesian colocalization analysis to investigate 2639 genes encoding druggable proteins and examined the causal effects on ALD (PMID 34737426: 456348 European with 451 cases and 455 897 controls). In addition, we conducted the mediation analysis to explore the potential mechanism using the genome-wide association study (GWAS) data of blood biomarkers as mediators.

RESULTS

We finally identified the drug target: ENPP2/Autotaxin and genetically proxied ENPP2/Autotaxin was causally associated with the risk of ALD (OR = 2.28, 95% CI: 1.64 to 3.16, p = 7.49E-7). In addition, we found that the effect of ENPP2/Autotaxin on ALD may be partly mediated by effector memory CD8+ T cell (the proportion of mediation effect: 8.49%).

CONCLUSIONS

Our integrative analysis suggested that genetically determined levels of circulating ENPP2/Autotaxin have a causal effect on ALD risk and are a promising drug target.

Optimising the use of electronic medical records for large scale research in psychiatry

The explosion and abundance of digital data could facilitate large-scale research for psychiatry and mental health. Research using so-called "real world data"-such as electronic medical/health records-can be resource-efficient, facilitate rapid hypothesis generation and testing, complement existing evidence (e.g. from trials and evidence-synthesis) and may enable a route to translate evidence into clinically effective, outcomes-driven care for patient populations that may be under-represented. However, the interpretation and processing of real-world data sources is complex because the clinically important 'signal' is often contained in both structured and unstructured (narrative or "free-text") data. Techniques for extracting meaningful information (signal) from unstructured text exist and have advanced the re-use of routinely collected clinical data, but these techniques require cautious evaluation. In this paper, we survey the opportunities, risks and progress made in the use of electronic medical record (real-world) data for psychiatric research.

Integrating human endogenous retroviruses into transcriptome-wide association studies highlights novel risk factors for major psychiatric conditions

Human endogenous retroviruses (HERVs) are repetitive elements previously implicated in major psychiatric conditions, but their role in aetiology remains unclear. Here, we perform specialised transcriptome-wide association studies that consider HERV expression quantified to precise genomic locations, using RNA sequencing and genetic data from 792 post-mortem brain samples. In Europeans, we identify 1238 HERVs with expression regulated in cis, of which 26 represent expression signals associated with psychiatric disorders, with ten being conditionally independent from neighbouring expression signals. Of these, five are additionally significant in fine-mapping analyses and thus are considered high confidence risk HERVs. These include two HERV expression signatures specific to schizophrenia risk, one shared between schizophrenia and bipolar disorder, and one specific to major depressive disorder. No robust signatures are identified for autism spectrum conditions or attention deficit hyperactivity disorder in Europeans, or for any psychiatric trait in other ancestries, although this is likely a result of relatively limited statistical power. Ultimately, our study highlights extensive HERV expression and regulation in the adult cortex, including in association with psychiatric disorder risk, therefore providing a rationale for exploring neurological HERV expression in complex neuropsychiatric traits.

Exploration of the potential causative genes for inflammatory bowel disease: Transcriptome-wide association analysis, Mendelian randomization analysis and Bayesian colocalisation

Background

Inflammatory bowel disease (IBD) poses a complex challenge due to its intricate underlying mechanisms, and curative treatments remain elusive. Consequently, there is an urgent need to identify genes causally associated with IBD.

Methods

We extracted blood eQTL data from the GTExv8.ALL.Whole_Blood database, genome-wide association studies (GWAS) summary statistics of IBD from the IEU GWAS database, and performed a three-fold analysis protocol, including transcriptome-wide association analysis, Mendelian randomisation analysis, Bayesian colocalisation, and subsequent potential therapeutic agents identification.

Results

We identified four pathogenic genes, namely CARD9, RTEL1, STMN3 and ARFRP1, that promote the development of IBD, encompassing both ulcerative colitis (UC) and Crohn's disease (CD). Notably, ARFRP1 exhibited the ability to suppress IBD (encompassing UC and CD) development. Regarding drug prediction, cyclophosphamide emerged as a promising novel therapeutic option for IBD, encompassing UC and CD.

Conclusion

We identified several potential genes related to IBD (UC and CD), including CARD9, RTEL1, STMN3 and ARFRP1, warranting further investigation in functional studies to elucidate underlying disease mechanisms. Additionally, clinical studies exploring the potential of cyclophosphamide as a treatment avenue for IBD are warranted.

A review and analysis of key biomarkers in Alzheimer's disease

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects over 50 million elderly individuals worldwide. Although the pathogenesis of AD is not fully understood, based on current research, researchers are able to identify potential biomarker genes and proteins that may serve as effective targets against AD. This article aims to present a comprehensive overview of recent advances in AD biomarker identification, with highlights on the use of various algorithms, the exploration of relevant biological processes, and the investigation of shared biomarkers with co-occurring diseases. Additionally, this article includes a statistical analysis of key genes reported in the research literature, and identifies the intersection with AD-related gene sets from databases such as AlzGen, GeneCard, and DisGeNet. For these gene sets, besides enrichment analysis, protein-protein interaction (PPI) networks utilized to identify central genes among the overlapping genes. Enrichment analysis, protein interaction network analysis, and tissue-specific connectedness analysis based on GTEx database performed on multiple groups of overlapping genes. Our work has laid the foundation for a better understanding of the molecular mechanisms of AD and more accurate identification of key AD markers.

omicSynth: An open multi-omic community resource for identifying druggable targets across neurodegenerative diseases

Treatments for neurodegenerative disorders remain rare, but recent FDA approvals, such as lecanemab and aducanumab for Alzheimer disease (MIM: 607822), highlight the importance of the underlying biological mechanisms in driving discovery and creating disease modifying therapies. The global population is aging, driving an urgent need for therapeutics that stop disease progression and eliminate symptoms. In this study, we create an open framework and resource for evidence-based identification of therapeutic targets for neurodegenerative disease. We use summary-data-based Mendelian randomization to identify genetic targets for drug discovery and repurposing. In parallel, we provide mechanistic insights into disease processes and potential network-level consequences of gene-based therapeutics. We identify 116 Alzheimer disease, 3 amyotrophic lateral sclerosis (MIM: 105400), 5 Lewy body dementia (MIM: 127750), 46 Parkinson disease (MIM: 605909), and 9 progressive supranuclear palsy (MIM: 601104) target genes passing multiple test corrections (pSMR_multi < 2.95 × 10-6 and pHEIDI > 0.01). We created a therapeutic scheme to classify our identified target genes into strata based on druggability and approved therapeutics, classifying 41 novel targets, 3 known targets, and 115 difficult targets (of these, 69.8% are expressed in the disease-relevant cell type from single-nucleus experiments). Our novel class of genes provides a springboard for new opportunities in drug discovery, development, and repurposing in the pre-competitive space. In addition, looking at drug-gene interaction networks, we identify previous trials that may require further follow-up such as riluzole in Alzheimer disease. We also provide a user-friendly web platform to help users explore potential therapeutic targets for neurodegenerative diseases, decreasing activation energy for the community.

Investigating the Shared Genetic Etiology Between Parkinson's Disease and Depression

Background

Depression is a common symptom in Parkinson's disease (PD), resulting from underlying neuropathological processes and psychological factors. However, the extent to which shared genetic risk factors contribute to the relationship between depression and PD is poorly understood.

Objective

To examine the effects of common genetic variants influencing the etiology of PD and depression risk at the genome-wide and local genomic regional level.

Methods

We comprehensively investigated the genetic relationship between PD and depression using genome-wide association studies data. First, we estimated the genetic correlation at the genome-wide level using linkage-disequilibrium score regression, followed by local genetic correlation analysis using the GWAS-pairwise method and functional annotation to identify genes that may jointly influence the risk for both traits. Also, we performed Latent Causal Variable, Latent Heritable Confounder Mendelian Randomization, and traditional Mendelian Randomization analyses to investigate the potential causal relationship.

Results

Although the genetic correlation between PD and depression was not statistically significant at the genome-wide level, GWAS-pairwise analyses identified 16 genomic segments associated with PD and depression, implicating nine genes. Further analyses revealed distinct patterns within individual genes, suggesting an intricate pattern. These genes involve various biological processes, including neurotransmitter regulation, senescence, and nucleo-cytoplasmic transport mechanisms. We did not observe genetic evidence of causality between PD and depression.

Conclusions

Our findings did not support a genome-wide genetic correlation or a causal association between both conditions. However, we identified genomic segments but identified genomic segments linked to distinct biological pathways influencing their etiology.Further research is needed to understand their functional consequences.

Ensuring Stakeholder Feedback in the Design and Conduct of Clinical Trials for Rare Diseases: ISCTM Position Paper of the Orphan Disease Working Group

The 1983 Orphan Drug Act in the United States (US) changed the landscape for development of therapeutics for rare or orphan diseases, which collectively affect approximately 300 million people worldwide, half of whom are children. The act has undoubtedly accelerated drug development for orphan diseases, with over 6,400 orphan drug applications submitted to the US Food and Drug Administration (FDA) from 1983 to 2023, including 350 drugs approved for over 420 indications. Drug development in this population is a global and collaborative endeavor. This position paper of the International Society for Central Nervous System Clinical Trials and Methodology (ISCTM) describes some potential best practices for the involvement of key stakeholder feedback in the drug development process. Stakeholders include advocacy groups, patients and caregivers with lived experience, public and private research institutions (including academia and pharmaceutical companies), treating clinicians, and funders (including the government and independent foundations). The authors articulate the challenges of drug development in orphan diseases and propose methods to address them. Challenges range from the poor understanding of disease history to development of endpoints, targets, and clinical trials designs, to finding solutions to competing research priorities by involved parties.

Exploration of the Noncoding Genome for Human-Specific Therapeutic Targets-Recent Insights at Molecular and Cellular Level

While it is well known that 98-99% of the human genome does not encode proteins, but are nevertheless transcriptionally active and give rise to a broad spectrum of noncoding RNAs [ncRNAs] with complex regulatory and structural functions, specific functions have so far been assigned to only a tiny fraction of all known transcripts. On the other hand, the striking observation of an overwhelmingly growing fraction of ncRNAs, in contrast to an only modest increase in the number of protein-coding genes, during evolution from simple organisms to humans, strongly suggests critical but so far essentially unexplored roles of the noncoding genome for human health and disease pathogenesis. Research into the vast realm of the noncoding genome during the past decades thus lead to a profoundly enhanced appreciation of the multi-level complexity of the human genome. Here, we address a few of the many huge remaining knowledge gaps and consider some newly emerging questions and concepts of research. We attempt to provide an up-to-date assessment of recent insights obtained by molecular and cell biological methods, and by the application of systems biology approaches. Specifically, we discuss current data regarding two topics of high current interest: (1) By which mechanisms could evolutionary recent ncRNAs with critical regulatory functions in a broad spectrum of cell types (neural, immune, cardiovascular) constitute novel therapeutic targets in human diseases? (2) Since noncoding genome evolution is causally linked to brain evolution, and given the profound interactions between brain and immune system, could human-specific brain-expressed ncRNAs play a direct or indirect (immune-mediated) role in human diseases? Synergistic with remarkable recent progress regarding delivery, efficacy, and safety of nucleic acid-based therapies, the ongoing large-scale exploration of the noncoding genome for human-specific therapeutic targets is encouraging to proceed with the development and clinical evaluation of novel therapeutic pathways suggested by these research fields.

Massively parallel functional dissection of schizophrenia-associated noncoding genetic variants

Schizophrenia (SCZ) is a highly heritable mental disorder with thousands of associated genetic variants located mostly in the noncoding space of the genome. Translating these associations into insights regarding the underlying pathomechanisms has been challenging because the causal variants, their mechanisms of action, and their target genes remain largely unknown. We implemented a massively parallel variant annotation pipeline (MVAP) to perform SCZ variant-to-function mapping at scale in disease-relevant neural cell types. This approach identified 620 functional variants (1.7%) that operate in a highly developmental context and neuronal-activity-dependent manner. Multimodal integration of epigenomic and CRISPRi screening data enabled us to link these functional variants to target genes, biological processes, and ultimately alterations of neuronal physiology. These results provide a multistage prioritization strategy to map functional single-nucleotide polymorphism (SNP)-to-gene-to-endophenotype relations and offer biological insights into the context-dependent molecular processes modulated by SCZ-associated genetic variation.

Expanding causal genes for Parkinson's disease via multi-omics analysis

Genome‑wide association studies (GWASs) have revealed numerous loci associated with Parkinson's disease (PD). However, some potential causal/risk genes were still not revealed and no etiological therapies are available. To find potential causal genes and explore genetically supported drug targets for PD is urgent. By integrating the expression quantitative trait loci (eQTL) and protein quantitative trait loci (pQTL) datasets from multiple tissues (blood, cerebrospinal fluid (CSF) and brain) and PD GWAS summary statistics, a pipeline combing Mendelian randomization (MR), Steiger filtering analysis, Bayesian colocalization, fine mapping, Protein-protein network and enrichment analysis were applied to identify potential causal genes for PD. As a result, GPNMB displayed a robust causal role for PD at the protein level in the blood, CSF and brain, and transcriptional level in the brain, while the protective role of CD38 (in brain pQTL and eQTL) was also identified. We also found inconsistent roles of DGKQ on PD between protein and mRNA levels. Another 9 proteins (CTSB, ARSA, SEC23IP, CD84, ENTPD1, FCGR2B, BAG3, SNCA, FCGR2A) were associated with the risk for PD based on only a single pQTL after multiple corrections. We also identified some proteins' interactions with known PD causative genes and therapeutic targets. In conclusion, this study suggested GPNMB, CD38, and DGKQ may act in the pathogenesis of PD, but whether the other proteins involved in PD needs more evidence. These findings would help to uncover the genes underlying PD and prioritize targets for future therapeutic interventions.

Genome-wide census of ATF4 binding sites and functional profiling of trait-associated genetic variants overlapping ATF4 binding motifs

Activating Transcription Factor 4 (ATF4) is an important regulator of gene expression in stress responses and developmental processes in many cell types. Here, we catalogued ATF4 binding sites in the human genome and identified overlaps with trait-associated genetic variants. We probed these genetic variants for allelic regulatory activity using a massively parallel reporter assay (MPRA) in HepG2 hepatoma cells exposed to tunicamycin to induce endoplasmic reticulum stress and ATF4 upregulation. The results revealed that in the majority of cases, the MPRA allelic activity of these SNPs was in agreement with the nucleotide preference seen in the ATF4 binding motif from ChIP-Seq. Luciferase and electrophoretic mobility shift assays in additional cellular models further confirmed ATF4-dependent regulatory effects for the SNPs rs532446 (GADD45A intronic; linked to hematological parameters), rs7011846 (LPL upstream; myocardial infarction), rs2718215 (diastolic blood pressure), rs281758 (psychiatric disorders) and rs6491544 (educational attainment). CRISPR-Cas9 disruption and/or deletion of the regulatory elements harboring rs532446 and rs7011846 led to the downregulation of GADD45A and LPL, respectively. Thus, these SNPs could represent examples of GWAS genetic variants that affect gene expression by altering ATF4-mediated transcriptional activation.

Multiomic prioritisation of risk genes for anorexia nervosa

BACKGROUND

Anorexia nervosa (AN) is a psychiatric disorder associated with marked morbidity. Whilst AN genetic studies could identify novel treatment targets, integration of functional genomics data, including transcriptomics and proteomics, would assist to disentangle correlated signals and reveal causally associated genes.

METHODS

We used models of genetically imputed expression and splicing from 14 tissues, leveraging mRNA, protein, and mRNA alternative splicing weights to identify genes, proteins, and transcripts, respectively, associated with AN risk. This was accomplished through transcriptome, proteome, and spliceosome-wide association studies, followed by conditional analysis and finemapping to prioritise candidate causal genes.

RESULTS

We uncovered 134 genes for which genetically predicted mRNA expression was associated with AN after multiple-testing correction, as well as four proteins and 16 alternatively spliced transcripts. Conditional analysis of these significantly associated genes on other proximal association signals resulted in 97 genes independently associated with AN. Moreover, probabilistic finemapping further refined these associations and prioritised putative causal genes. The gene WDR6, for which increased genetically predicted mRNA expression was correlated with AN, was strongly supported by both conditional analyses and finemapping. Pathway analysis of genes revealed by finemapping identified the pathway regulation of immune system process (overlapping genes = MST1, TREX1, PRKAR2A, PROS1) as statistically overrepresented.

CONCLUSIONS

We leveraged multiomic datasets to genetically prioritise novel risk genes for AN. Multiple-lines of evidence support that WDR6 is associated with AN, whilst other prioritised genes were enriched within immune related pathways, further supporting the role of the immune system in AN.

Genetic modification of inflammation- and clonal hematopoiesis-associated cardiovascular risk

Clonal hematopoiesis of indeterminate potential (CHIP) is associated with an increased risk of cardiovascular diseases (CVDs), putatively via inflammasome activation. We pursued an inflammatory gene modifier scan for CHIP-associated CVD risk among 424,651 UK Biobank participants. We identified CHIP using whole-exome sequencing data of blood DNA and modeled as a composite, considering all driver genes together, as well as separately for common drivers (DNMT3A, TET2, ASXL1, and JAK2). We developed predicted gene expression scores for 26 inflammasome-related genes and assessed how they modify CHIP-associated CVD risk. We identified IL1RAP as a potential key molecule for CHIP-associated CVD risk across genes and increased AIM2 gene expression leading to heightened JAK2- and ASXL1-associated CVD risk. We show that CRISPR-induced Asxl1-mutated murine macrophages had a particularly heightened inflammatory response to AIM2 agonism, associated with an increased DNA damage response, as well as increased IL-10 secretion, mirroring a CVD-protective effect of IL10 expression in ASXL1 CHIP. Our study supports the role of inflammasomes in CHIP-associated CVD and provides evidence to support gene-specific strategies to address CHIP-associated CVD risk.

Mendelian Randomization Using the Druggable Genome Reveals Genetically Supported Drug Targets for Psychiatric Disorders

BACKGROUND AND HYPOTHESIS

Psychiatric disorders impose a huge health and economic burden on modern society. However, there is currently no proven completely effective treatment available, partly owing to the inefficiency of drug target identification and validation. We aim to identify therapeutic targets relevant to psychiatric disorders by conducting Mendelian randomization (MR) analysis.

STUDY DESIGN

We performed genome-wide MR analysis by integrating expression quantitative trait loci (eQTL) of 4479 actionable genes that encode druggable proteins and genetic summary statistics from genome-wide association studies of psychiatric disorders. After conducting colocalization analysis on the brain MR findings, we employed protein quantitative trait loci (pQTL) data as genetic proposed instruments for intersecting the colocalized genes to provide further genetic evidence.

STUDY RESULTS

By performing MR and colocalization analysis with eQTL genetic instruments, we obtained 31 promising drug targets for psychiatric disorders, including 21 significant genes for schizophrenia, 7 for bipolar disorder, 2 for depression, 1 for attention deficit and hyperactivity (ADHD) and none for autism spectrum disorder. Combining MR results using pQTL genetic instruments, we finally proposed 8 drug-targeting genes supported by the strongest MR evidence, including gene ACE, BTN3A3, HAPLN4, MAPK3 and NEK4 for schizophrenia, gene NEK4 and HAPLN4 for bipolar disorder, and gene TIE1 for ADHD.

CONCLUSIONS

Our findings with genetic support were more likely to be to succeed in clinical trials. In addition, our study prioritizes approved drug targets for the development of new therapies and provides critical drug reuse opportunities for psychiatric disorders.

omicSynth: an Open Multi-omic Community Resource for Identifying Druggable Targets across Neurodegenerative Diseases

Treatments for neurodegenerative disorders remain rare, although recent FDA approvals, such as Lecanemab and Aducanumab for Alzheimer's Disease, highlight the importance of the underlying biological mechanisms in driving discovery and creating disease modifying therapies. The global population is aging, driving an urgent need for therapeutics that stop disease progression and eliminate symptoms. In this study, we create an open framework and resource for evidence-based identification of therapeutic targets for neurodegenerative disease. We use Summary-data-based Mendelian Randomization to identify genetic targets for drug discovery and repurposing. In parallel, we provide mechanistic insights into disease processes and potential network-level consequences of gene-based therapeutics. We identify 116 Alzheimer's disease, 3 amyotrophic lateral sclerosis, 5 Lewy body dementia, 46 Parkinson's disease, and 9 Progressive supranuclear palsy target genes passing multiple test corrections (pSMR_multi < 2.95×10-6 and pHEIDI > 0.01). We created a therapeutic scheme to classify our identified target genes into strata based on druggability and approved therapeutics - classifying 41 novel targets, 3 known targets, and 115 difficult targets (of these 69.8% are expressed in the disease relevant cell type from single nucleus experiments). Our novel class of genes provides a springboard for new opportunities in drug discovery, development and repurposing in the pre-competitive space. In addition, looking at drug-gene interaction networks, we identify previous trials that may require further follow-up such as Riluzole in AD. We also provide a user-friendly web platform to help users explore potential therapeutic targets for neurodegenerative diseases, decreasing activation energy for the community [https://nih-card-ndd-smr-home-syboky.streamlit.app/].

Integrating GWAS and proteome data to identify novel drug targets for MU

Mouth ulcers have been associated with numerous loci in genome wide association studies (GWAS). Nonetheless, it remains unclear what mechanisms are involved in the pathogenesis of mouth ulcers at these loci, as well as what the most effective ulcer drugs are. Thus, we aimed to screen hub genes responsible for mouth ulcer pathogenesis. We conducted an imputed/in-silico proteome-wide association study to discover candidate genes that impact the development of mouth ulcers and affect the expression and concentration of associated proteins in the bloodstream. The integrative analysis revealed that 35 genes play a significant role in the development of mouth ulcers, both in terms of their protein and transcriptional levels. Following this analysis, the researchers identified 6 key genes, namely BTN3A3, IL12B, BPI, FAM213A, PLXNB2, and IL22RA2, which were related to the onset of mouth ulcers. By combining with multidimensional data, six genes were found to correlate with mouth ulcer pathogenesis, which can be useful for further biological and therapeutic research.

Cell type and condition specific functional annotation of schizophrenia associated non-coding genetic variants

Schizophrenia (SCZ) is a highly polygenic disease and genome wide association studies have identified thousands of genetic variants that are statistically associated with this psychiatric disorder. However, our ability to translate these associations into insights on the disease mechanisms has been challenging since the causal genetic variants, their molecular function and their target genes remain largely unknown. In order to address these questions, we established a functional genomics pipeline in combination with induced pluripotent stem cell technology to functionally characterize ~35,000 non-coding genetic variants associated with schizophrenia along with their target genes. This analysis identified a set of 620 (1.7%) single nucleotide polymorphisms as functional on a molecular level in a highly cell type and condition specific fashion. These results provide a high-resolution map of functional variant-gene combinations and offer comprehensive biological insights into the developmental context and stimulation dependent molecular processes modulated by SCZ associated genetic variation.

Genome-wide association studies on Northern Italy isolated populations provide further support concerning genetic susceptibility for major depressive disorder

OBJECTIVES

Major depressive disorder (MDD) is a psychiatric disorder with pathogenesis influenced by both genetic and environmental factors. To date, the molecular-level understanding of its aetiology remains unclear. Thus, we aimed to identify genetic variants and susceptibility genes for MDD with a genome-wide association study (GWAS) approach.

METHODS

We performed a meta-analysis of GWASs and a gene-based analysis on two Northern Italy isolated populations (cases/controls n = 166/472 and 33/320), followed by replication and polygenic risk score (PRS) analyses in Italian independent samples (cases n = 464, controls n = 339).

RESULTS

We identified two novel MDD-associated genes, KCNQ5 (lead SNP rs867262, p = 3.82 × 10^-9) and CTNNA2 (rs6729523, p = 1.25 × 10^-8). The gene-based analysis revealed another six genes (p < 2.703 × 10^-6): GRM7, CTNT4, SNRK, SRGAP3, TRAPPC9, and FHIT. No replication of the genome-wide significant SNPs was found in the independent cohort, even if 14 SNPs around CTNNA2 showed association with MDD and related phenotypes at the nominal level of p (<0.05). Furthermore, the PRS model developed in the discovery cohort discriminated cases and controls in the replication cohort.

CONCLUSIONS

Our work suggests new possible genes associated with MDD, and the PRS analysis confirms the polygenic nature of this disorder. Future studies are required to better understand the role of these findings in MDD.

Integrative Post-Genome-Wide Association Study Analyses Relevant to Psychiatric Disorders: Imputing Transcriptome and Proteome Signals

Background

The genome-wide association study (GWAS) is a common tool to identify genetic variants associated with complex traits, including psychiatric disorders (PDs). However, post-GWAS analyses are needed to extend the statistical inference to biologically relevant entities, e.g., genes, proteins, and pathways. To achieve this goal, researchers developed methods that incorporate biologically relevant intermediate molecular phenotypes, such as gene expression and protein abundance, which are posited to mediate the variant-trait association. Transcriptome-wide association study (TWAS) and proteome-wide association study (PWAS) are commonly used methods to test the association between these molecular mediators and the trait.

Summary

In this review, we discuss the most recent developments in TWAS and PWAS. These methods integrate existing "omic" information with the GWAS summary statistics for trait(s) of interest. Specifically, they impute transcript/protein data and test the association between imputed gene expression/protein level with phenotype of interest by using (i) GWAS summary statistics and (ii) reference transcriptomic/proteomic/genomic datasets. TWAS and PWAS are suitable as analysis tools for (i) primary association scan and (ii) fine-mapping to identify potentially causal genes for PDs.

Key Messages

As post-GWAS analyses, TWAS and PWAS have the potential to highlight causal genes for PDs. These prioritized genes could indicate targets for the development of novel drug therapies. For researchers attempting such analyses, we recommend Mendelian randomization tools that use GWAS statistics for both trait and reference datasets, e.g., summary Mendelian randomization (SMR). We base our recommendation on (i) being able to use the same tool for both TWAS and PWAS, (ii) not requiring the pre-computed weights (and thus easier to update for larger reference datasets), and (iii) most larger transcriptome reference datasets are publicly available and easy to transform into a compatible format for SMR analysis.

Genome-wide Mendelian randomization identifies actionable novel drug targets for psychiatric disorders

Psychiatric disorders impose tremendous economic burden on society and are leading causes of disability worldwide. However, only limited drugs are available for psychiatric disorders and the efficacy of most currently used drugs is poor for many patients. To identify novel therapeutic targets for psychiatric disorders, we performed genome-wide Mendelian randomization analyses by integrating brain-derived molecular quantitative trait loci (mRNA expression and protein abundance quantitative trait loci) of 1263 actionable proteins (targeted by approved drugs or drugs in clinical phase of development) and genetic findings from large-scale genome-wide association studies (GWASs). Using transcriptome data, we identified 25 potential drug targets for psychiatric disorders, including 12 genes for schizophrenia, 7 for bipolar disorder, 7 for depression, and 1 (TIE1) for attention deficit and hyperactivity. We also identified 10 actionable drug targets by using brain proteome data, including 4 (HLA-DRB1, CAMKK2, P2RX7, and MAPK3) for schizophrenia, 1 (PRKCB) for bipolar disorder, 6 (PSMB4, IMPDH2, SERPINC1, GRIA1, P2RX7 and TAOK3) for depression. Of note, MAPK3 and HLA-DRB1 were supported by both transcriptome and proteome-wide MR analyses, suggesting that these two proteins are promising therapeutic targets for schizophrenia. Our study shows the power of integrating large-scale GWAS findings and transcriptomic and proteomic data in identifying actionable drug targets. Besides, our findings prioritize actionable novel drug targets for development of new therapeutics and provide critical drug-repurposing opportunities for psychiatric disorders.

Collaboration and knowledge integration for successful brain therapeutics - lessons learned from the pandemic

Brain diseases are a major cause of death and disability worldwide and contribute significantly to years of potential life lost. Although there have been considerable advances in biological mechanisms associated with brain disorders as well as drug discovery paradigms in recent years, these have not been sufficiently translated into effective treatments. This Special Article expands on Keystone Symposia's pre- and post-pandemic panel discussions on translational neuroscience research. In the article, we discuss how lessons learned from the COVID-19 pandemic can catalyze critical progress in translational research, with efficient collaboration bridging the gap between basic discovery and clinical application. To achieve this, we must place patients at the center of the research paradigm. Furthermore, we need commitment from all collaborators to jointly mitigate the risk associated with the research process. This will require support from investors, the public sector and pharmaceutical companies to translate disease mechanisms into world-class drugs. We also discuss the role of scientific publishing in supporting these models of open innovation. Open science journals can now function as hubs to accelerate progress from discovery to treatments, in neuroscience in particular, making this process less tortuous by bringing scientists together and enabling them to exchange data, tools and knowledge effectively. As stakeholders from a broad range of scientific professions, we feel an urgency to advance brain disease therapies and encourage readers to work together in tackling this challenge.

The missing link between genetic association and regulatory function

The genetic basis of most traits is highly polygenic and dominated by non-coding alleles. It is widely assumed that such alleles exert small regulatory effects on the expression of cis-linked genes. However, despite the availability of gene expression and epigenomic datasets, few variant-to-gene links have emerged. It is unclear whether these sparse results are due to limitations in available data and methods, or to deficiencies in the underlying assumed model. To better distinguish between these possibilities, we identified 220 gene-trait pairs in which protein-coding variants influence a complex trait or its Mendelian cognate. Despite the presence of expression quantitative trait loci near most GWAS associations, by applying a gene-based approach we found limited evidence that the baseline expression of trait-related genes explains GWAS associations, whether using colocalization methods (8% of genes implicated), transcription-wide association (2% of genes implicated), or a combination of regulatory annotations and distance (4% of genes implicated). These results contradict the hypothesis that most complex trait-associated variants coincide with homeostatic expression QTLs, suggesting that better models are needed. The field must confront this deficit and pursue this 'missing regulation.'

Mendelian randomization and genetic colocalization infer the effects of the multi-tissue proteome on 211 complex disease-related phenotypes

BACKGROUND

Human proteins are widely used as drug targets. Integration of large-scale protein-level genome-wide association studies (GWAS) and disease-related GWAS has thus connected genetic variation to disease mechanisms via protein. Previous proteome-by-phenome-wide Mendelian randomization (MR) studies have been mainly focused on plasma proteomes. Previous MR studies using the brain proteome only reported protein effects on a set of pre-selected tissue-specific diseases. No studies, however, have used high-throughput proteomics from multiple tissues to perform MR on hundreds of phenotypes.

METHODS

Here, we performed MR and colocalization analysis using multi-tissue (cerebrospinal fluid (CSF), plasma, and brain from pre- and post-meta-analysis of several disease-focus cohorts including Alzheimer disease (AD)) protein quantitative trait loci (pQTLs) as instrumental variables to infer protein effects on 211 phenotypes, covering seven broad categories: biological traits, blood traits, cancer types, neurological diseases, other diseases, personality traits, and other risk factors. We first implemented these analyses with cis pQTLs, as cis pQTLs are known for being less prone to horizontal pleiotropy. Next, we included both cis and trans conditionally independent pQTLs that passed the genome-wide significance threshold keeping only variants associated with fewer than five proteins to minimize pleiotropic effects. We compared the tissue-specific protein effects on phenotypes across different categories. Finally, we integrated the MR-prioritized proteins with the druggable genome to identify new potential targets.

RESULTS

In the MR and colocalization analysis including study-wide significant cis pQTLs as instrumental variables, we identified 33 CSF, 13 plasma, and five brain proteins to be putative causal for 37, 18, and eight phenotypes, respectively. After expanding the instrumental variables by including genome-wide significant cis and trans pQTLs, we identified a total of 58 CSF, 32 plasma, and nine brain proteins associated with 58, 44, and 16 phenotypes, respectively. For those protein-phenotype associations that were found in more than one tissue, the directions of the associations for 13 (87%) pairs were consistent across tissues. As we were unable to use methods correcting for horizontal pleiotropy given most of the proteins were only associated with one valid instrumental variable after clumping, we found that the observations of protein-phenotype associations were consistent with a causal role or horizontal pleiotropy. Between 66.7 and 86.3% of the disease-causing proteins overlapped with the druggable genome. Finally, between one and three proteins, depending on the tissue, were connected with at least one drug compound for one phenotype from both DrugBank and ChEMBL databases.

CONCLUSIONS

Integrating multi-tissue pQTLs with MR and the druggable genome may open doors to pinpoint novel interventions for complex traits with no effective treatments, such as ovarian and lung cancers.

Identifying novel proteins underlying schizophrenia via integrating pQTLs of the plasma, CSF, and brain with GWAS summary data

BACKGROUND

Schizophrenia (SCZ) is a chronic and severe mental illness with no cure so far. Mendelian randomization (MR) is a genetic method widely used to explore etiologies of complex traits. In the current study, we aimed to identify novel proteins underlying SCZ with a systematic analytical approach.

METHODS

We integrated protein quantitative trait loci (pQTLs) of the brain, cerebrospinal fluid (CSF), and plasma with the latest and largest SCZ genome-wide association study (GWAS) via a systematic analytical framework, including two-sample MR analysis, Steiger filtering analysis, and Bayesian colocalization analysis.

RESULTS

The genetically determined protein level of C4A/C4B (OR = 0.70, p = 1.66E-07) in the brain and ACP5 (OR = 0.42, p = 3.73E-05), CNTN2 (OR = 0.62, p = 2.57E-04), and PLA2G7 (OR = 0.71, p = 1.48E-04) in the CSF was associated with a lower risk of SCZ, while the genetically determined protein level of TIE1 (OR = 3.46, p = 4.76E-05), BCL6 (OR = 3.63, p = 1.59E-07), and MICB (OR = 4.49, p = 2.31E-11) in the CSF were associated with an increased risk for SCZ. Pathway enrichment analysis indicated that genetically determined proteins suggestively associated with SCZ were enriched in the biological process of the immune response.

CONCLUSION

In conclusion, we identified one protein in the brain and six proteins in the CSF that showed supporting evidence of being potentially associated with SCZ, which could provide insights into future mechanistic studies to find new treatments for the disease. Our results also supported the important role of neuroinflammation in the pathogenesis of SCZ.

Crosstalk of organelles in Parkinson's disease - MiT family transcription factors as central players in signaling pathways connecting mitochondria and lysosomes

Living organisms constantly need to adapt to their surrounding environment and have evolved sophisticated mechanisms to deal with stress. Mitochondria and lysosomes are central organelles in the response to energy and nutrient availability within a cell and act through interconnected mechanisms. However, when such processes become overwhelmed, it can lead to pathologies. Parkinson's disease (PD) is a common neurodegenerative disorder (NDD) characterized by proteinaceous intracellular inclusions and progressive loss of dopaminergic neurons, which causes motor and non-motor symptoms. Genetic and environmental factors may contribute to the disease etiology. Mitochondrial dysfunction has long been recognized as a hallmark of PD pathogenesis, and several aspects of mitochondrial biology are impaired in PD patients and models. In addition, defects of the autophagy-lysosomal pathway have extensively been observed in cell and animal models as well as PD patients' brains, where constitutive autophagy is indispensable for adaptation to stress and energy deficiency. Genetic and molecular studies have shown that the functions of mitochondria and lysosomal compartments are tightly linked and influence each other. Connections between these organelles are constituted among others by mitophagy, organellar dynamics and cellular signaling cascades, such as calcium (Ca²⁺) and mTOR (mammalian target of rapamycin) signaling and the activation of transcription factors. Members of the Microphthalmia-associated transcription factor family (MiT), including MITF, TFE3 and TFEB, play a central role in regulating cellular homeostasis in response to metabolic pressure and are considered master regulators of lysosomal biogenesis. As such, they are part of the interconnection between mitochondria and lysosome functions and therefore represent attractive targets for therapeutic approaches against NDD, including PD. The activation of MiT transcription factors through genetic and pharmacological approaches have shown encouraging results at ameliorating PD-related phenotypes in in vitro and in vivo models. In this review, we summarize the relationship between mitochondrial and autophagy-lysosomal functions in the context of PD etiology and focus on the role of the MiT pathway and its potential as pharmacological target against PD.

Identifying causal genes for depression via integration of the proteome and transcriptome from brain and blood

Genome-wide association studies (GWASs) have identified numerous risk genes for depression. Nevertheless, genes crucial for understanding the molecular mechanisms of depression and effective antidepressant drug targets are largely unknown. Addressing this, we aimed to highlight potentially causal genes by systematically integrating the brain and blood protein and expression quantitative trait loci (QTL) data with a depression GWAS dataset via a statistical framework including Mendelian randomization (MR), Bayesian colocalization, and Steiger filtering analysis. In summary, we identified three candidate genes (TMEM106B, RAB27B, and GMPPB) based on brain data and two genes (TMEM106B and NEGR1) based on blood data with consistent robust evidence at both the protein and transcriptional levels. Furthermore, the protein-protein interaction (PPI) network provided new insights into the interaction between brain and blood in depression. Collectively, four genes (TMEM106B, RAB27B, GMPPB, and NEGR1) affect depression by influencing protein and gene expression level, which could guide future researches on candidate genes investigations in animal studies as well as prioritize antidepressant drug targets.

Combining evidence from Mendelian randomization and colocalization: Review and comparison of approaches

Mendelian randomization and colocalization are two statistical approaches that can be applied to summarized data from genome-wide association studies (GWASs) to understand relationships between traits and diseases. However, despite similarities in scope, they are different in their objectives, implementation, and interpretation, in part because they were developed to serve different scientific communities. Mendelian randomization assesses whether genetic predictors of an exposure are associated with the outcome and interprets an association as evidence that the exposure has a causal effect on the outcome, whereas colocalization assesses whether two traits are affected by the same or distinct causal variants. When considering genetic variants in a single genetic region, both approaches can be performed. While a positive colocalization finding typically implies a non-zero Mendelian randomization estimate, the reverse is not generally true: there are several scenarios which would lead to a non-zero Mendelian randomization estimate but lack evidence for colocalization. These include the existence of distinct but correlated causal variants for the exposure and outcome, which would violate the Mendelian randomization assumptions, and a lack of strong associations with the outcome. As colocalization was developed in the GWAS tradition, typically evidence for colocalization is concluded only when there is strong evidence for associations with both traits. In contrast, a non-zero estimate from Mendelian randomization can be obtained despite only nominally significant genetic associations with the outcome at the locus. In this review, we discuss how the two approaches can provide complementary information on potential therapeutic targets.

Identifying causal genes for stroke via integrating the proteome and transcriptome from brain and blood

Background

Genome-wide association studies (GWAS) have revealed numerous loci associated with stroke. However, the underlying mechanisms at these loci in the pathogenesis of stroke and effective stroke drug targets are elusive. Therefore, we aimed to identify causal genes in the pathogenesis of stroke and its subtypes.

Methods

Utilizing multidimensional high-throughput data generated, we integrated proteome-wide association study (PWAS), transcriptome-wide association study (TWAS), Mendelian randomization (MR), and Bayesian colocalization analysis to prioritize genes that contribute to stroke and its subtypes risk via affecting their expression and protein abundance in brain and blood.

Results

Our integrative analysis revealed that ICA1L was associated with small-vessel stroke (SVS), according to robust evidence at both protein and transcriptional levels based on brain-derived data. We also identified NBEAL1 that was causally related to SVS via its cis-regulated brain expression level. In blood, we identified 5 genes (MMP12, SCARF1, ABO, F11, and CKAP2) that had causal relationships with stroke and stroke subtypes.

Conclusions

Together, via using an integrative analysis to deal with multidimensional data, we prioritized causal genes in the pathogenesis of SVS, which offered hints for future biological and therapeutic studies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03377-9.

Improving mouse models for the study of Alzheimer's disease

Alzheimer's disease (AD) is a complex neurodegenerative disease whose risk is influenced by genetic and environmental factors. Although a number of pathological hallmarks have been extensively studied over the last several decades, a complete picture of disease initiation and progression remains unclear. We now understand that numerous cell types and systems are involved in AD pathogenesis, and that this cellular profile may present differently for each individual, making the creation of relevant mouse models challenging. However, with increasingly diverse data made available by genome-wide association studies, we can identify and examine new genes and pathways involved in genetic risk for AD, many of which involve vascular health and inflammation. When developing mouse models, it is critical to assess (1) an aging timeline that represents onset and progression in humans, (2) genetic variants and context, (3) environmental factors present in human populations that result in both neuropathological and functional changes-themes that we address in this chapter.

Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application

Alzheimer's disease (AD) is the most common form of dementia, characterized by progressive cognitive impairment and neurodegeneration. Extensive clinical and genomic studies have revealed biomarkers, risk factors, pathways, and targets of AD in the past decade. However, the exact molecular basis of AD development and progression remains elusive. The emerging single-cell sequencing technology can potentially provide cell-level insights into the disease. Here we systematically review the state-of-the-art bioinformatics approaches to analyze single-cell sequencing data and their applications to AD in 14 major directions, including 1) quality control and normalization, 2) dimension reduction and feature extraction, 3) cell clustering analysis, 4) cell type inference and annotation, 5) differential expression, 6) trajectory inference, 7) copy number variation analysis, 8) integration of single-cell multi-omics, 9) epigenomic analysis, 10) gene network inference, 11) prioritization of cell subpopulations, 12) integrative analysis of human and mouse sc-RNA-seq data, 13) spatial transcriptomics, and 14) comparison of single cell AD mouse model studies and single cell human AD studies. We also address challenges in using human postmortem and mouse tissues and outline future developments in single cell sequencing data analysis. Importantly, we have implemented our recommended workflow for each major analytic direction and applied them to a large single nucleus RNA-sequencing (snRNA-seq) dataset in AD. Key analytic results are reported while the scripts and the data are shared with the research community through  GitHub. In summary, this comprehensive review provides insights into various approaches to analyze single cell sequencing data and offers specific guidelines for study design and a variety of analytic directions. The review and the accompanied software tools will serve as a valuable resource for studying cellular and molecular mechanisms of AD, other diseases, or biological systems at the single cell level.

MicroRNA-21 Is a Versatile Regulator and Potential Treatment Target in Central Nervous System Disorders

MicroRNAs (miRNAs) are a class of endogenous, non-coding, single-stranded RNAs with a length of approximately 22 nucleotides that are found in eukaryotes. miRNAs are involved in the regulation of cell differentiation, proliferation, invasion, apoptosis, and metabolism by regulating the expression of their target genes. Emerging studies have suggested that various miRNAs play key roles in the pathogenesis of central nervous system (CNS) disorders and may be viable therapeutic targets. In particular, miR-21 has prominently emerged as a focus of increasing research on the mechanisms of its involvement in CNS disorders. Herein, we reviewed recent studies on the critical roles of miR-21, including its dysregulated expression and target genes, in the regulation of pathophysiological processes of CNS disorders, with a special focus on apoptosis and inflammation. Collectively, miR-21 is a versatile regulator in the progression of CNS disorders and could be a promising biomarker and therapeutic target for these diseases. An in-depth understanding of the mechanisms by which miR-21 affects the pathogenesis of CNS disorders could pave the way for miR-21 to serve as a therapeutic target for these conditions.