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Suicide remains a critical global public health issue, accounting for nearly one million deaths annually and imposing profound societal and economic burdens. Despite its urgency, the lack of diagnostic and predictive biomarkers continues to hinder the development of effective prevention and treatment strategies. This study presents a comprehensive meta-analysis that integrates publicly available postmortem brain transcriptomic datasets and a domestic cohort, encompassing 16 cohorts. The transcriptomic data, sourced from the Gene Expression Omnibus repository, were generated using various techniques, including traditional RNA sequencing, microarray methods, and single-cell RNA sequencing. Differential expression analyses were performed across multiple brain regions, with meta-analyses stratified by cortical regions, the dorsolateral prefrontal cortex (DLPFC), and combined. We further analyzed whether covariates may affect the identified genes. Three meta-analytic approaches were employed, complemented by pathway and cell-set enrichment analyses. The unadjusted meta-analysis consistently identified several genes with altered expression, including upregulated P2RY12, CX3CR1, and GPR34, and downregulated SOX9 and PMP2, all at nominal significance. Additionally, multiple genes encoding long non-coding RNAs (lncRNAs) exhibited nominally altered expression in suicide, including RP5-837J1.4, AC159540.14, DNM1P47, AC004158.2, EEF1A1P30, and RP11-339B21.8. Several alternative strategies to run meta-analysis were performed and moderators were investigated. Cell-type-specific expression deconvolution and meta-analysis identified several genes overlapping with bulk expression meta-analysis, and genes were attributed to neuronal lineages. These findings highlight plausible molecular targets for future validation studies, suggesting the involvement of microglia (P2RY12 and CX3CR1), astrocytes (SOX9), immune responses (GPR34), myelin regulation (PMP2), and epigenetic modulation via lncRNAs. This research advances the understanding of the molecular architecture of suicide and provides a foundation for future studies focused on targeted prevention and therapeutic interventions.

G protein-coupled receptors (GPCRs) form one of the largest drug target families, reflecting their involvement in numerous pathophysiological processes. In this Review, we analyse drug discovery trends for the GPCR superfamily, covering compounds, targets and indications that have reached regulatory approval or that are being investigated in clinical trials. We find that there are 516 approved drugs targeting GPCRs, making up 36% of all approved drugs. These drugs act on 121 GPCR targets, one-third of all non-sensory GPCRs. Furthermore, 337 agents targeting 133 GPCRs, including 30 novel targets, are being investigated in clinical trials. Notably, 165 of these agents are approved drugs being tested for additional indications and novel agents are increasingly allosteric modulators and biologics. Remarkably, diabetes and obesity drugs targeting GPCRs had sales of nearly US $30 billion in 2023 and the numbers of clinical trials for GPCR modulators in the metabolic diseases, oncology and immunology areas are increasing strongly. Finally, we highlight the potential of untapped target-disease associations and pathway-biased signalling. Overall, this Review provides an up-to-date reference for the drugged and potentially druggable GPCRome to inform future GPCR drug discovery and development.

Aims Type 2 diabetes (T2D) is a highly heterogeneous disease characterised by subtypes with variations in aetiology, disease progression, and risk of complications. However, potential drug targets for these subtypes have not been explored. This study aims to investigate potential drug targets by integrating proteomics.Materials and methods Summary-level data of circulating proteins were extracted from the UK Biobank and the deCODE Health Study. Genetic associations with five diabetes subtypes were obtained from Swedish All New Diabetics in Scania and Malm & ouml; Diet and Cancer cohort, including severe autoimmune diabetes (SAID), severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD), and mild age-related diabetes (MARD). The associations between circulating proteins and diabetes subtypes were assessed through Mendelian randomisation, followed by multiple sensitivity and colocalization analyses. Additionally, tissue-specific, pathway and functional enrichment analysis, assessment of protein druggability, and the protein-protein interaction (PPI) networks were used to further explore biological mechanisms and therapeutic potential.Results Genetically predicted levels of 2, 2, 9, 3, and 5 circulating proteins were associated with SIRD, SIDD, MARD, MOD, and SAID, respectively. Colocalization analyses further revealed links between GRN with MARD/SIRD, LILRB5 with SIDD/MARD, CR1 with MARD, TNFSF12 with MOD, and DAPK2 with SAID. Enrichment analysis suggested that these proteins were mainly enriched in blood and adipose tissues and involved in immune and inflammatory related pathways. PPI analysis revealed GRN, TNFSF12, and DAPK2 are associated with known T2D targets.Conclusions Our study identified several potential drug targets for different subtypes of diabetes using an integrated genetic approach, yielding new insights for precision medicine of diabetes.

Depression is a very prevalent disorder affecting between 2 to 21% of the world population. This thesis extends the knowledge on the biological aspects of depression, aiming to identify and validate markers of genetic, epigenetic, and gene expression origin.

In Study I, the main focus was depression-related gene MAD1L1 that was previously linked to depression by SNPs and frequently mentioned as a stress-related marker. We identified that depression-related SNPs in MAD1L1 affect DNA methylation levels at cg02825527, cg18302629, and cg19624444 that were associated with depressive phenotypes in independent cohorts.

In Study II, we investigated whether GWAS catalog depression SNPs located in Olink-detectable genes could be replicated in a UKBiobank cohort and whether these associations are supported by DNA methylation and transcriptome. We validated eight depression SNPs and found very weak evidence that TNXB may be related to depression.

Study III was based on comparison of different depression -OMIC layers, including genetics, DNA methylation, and transcriptome. We explored how the identified genes from different -OMICs overlap, are functionally related and if they could show patterns in drugs and clinical trials. Only three genes were supported by evidence at all three -OMIC levels and included: FOXP1, VPS41, and AKTIP. Different -OMIC levels showed involvement of multiple systems in depression.

In Study IV, we used the Neuro Exploratory panel (Olink) to identify depression proteomic changes in blood. We took antidepressant intake into the account and validated associations in the independent datasets. We identified several proteins that showed nominally different levels between depression risk groups in the adolescent cohort. Validation of identified markers yielded that only PPP3R1 was also differentially expressed in prefrontal cortex and whole blood in the independent open-access cohorts with matching association directions.

In Study V, we used the entire blood DNA methylation as a depression marker. We investigated stability of DNA-methylation in eight independent datasets with meta-analysis and compared common machine learning and deep learning strategies for the depression detection purposes. We found 1987 CpG sites related to depression in both mega- and meta-analysis at the nominal level. Random forest classifiers achieved the best performance in identifying depression based on DNA methylation data in blood (AUC 0.73 and 0.76) in CV and hold-out tests respectively on the batch-level processed data.

Overall, the thesis supports multiple depression genetic, epigenetic, and expression markers. However, identified individual and systemic depression changes show high variability, which is in agreement with previous studies and observations.

The causes of depression are complex, and the current diagnosis methods rely solely on psychiatric evaluations with no incorporation of laboratory biomarkers in clinical practices. We investigated the stability of blood DNA methylation depression signatures in six different populations using six public and two domestic cohorts (n = 1942) conducting mega-analysis and meta-analysis of the individual studies. We evaluated 12 machine learning and deep learning strategies for depression classification both in cross-validation (CV) and in hold-out tests using merged data from 8 separate batches, constructing models with both biased and unbiased feature selection. We found 1987 CpG sites related to depression in both mega- and meta-analysis at the nominal level, and the associated genes were nominally related to axon guidance and immune pathways based on enrichment analysis and eQTM data. Random forest classifiers achieved the highest performance (AUC 0.73 and 0.76) in CV and hold-out tests respectively on the batch-level processed data. In contrast, the methylation showed low predictive power (all AUCs < 0.57) for all classifiers in CV and no predictive power in hold-out tests when used with harmonized data. All models achieved significantly better performance (>14% gain in AUCs) with pre-selected features (selection bias), with some of the models (joint autoencoder-classifier) reaching AUCs of up to 0.91 in the final testing regardless of data preparation. Different algorithmic feature selection approaches may outperform limma, however, random forest models perform well regardless of the strategy. The results provide an overview over potential future biomarkers for depression and highlight many important methodological aspects for DNA methylation-based depression profiling including the use of machine learning strategies.

Major depressive disorder (MDD), commonly known as depression, affects over 300 million people worldwide as of 2018 and presents a wide range of clinical symptoms. The international clinical trials registry platform (ICTRP) introduced by WHO includes aggregated data from ClinicalTrials.gov and 17 other national registers, making it the largest clinical trial platform. Here we analysed data in ICTRP with the aim of providing comprehensive insights into clinical trials on depression. Applying a novel hidden duplicate identification method, 10,606 depression trials were identified in ICTRP, with ANZCTR being the largest non- ClinicalTrials.gov database at 1031 trials, followed by IRCT with 576 trials, ISRCTN with 501 trials, CHiCTR with 489 trials, and EUCTR with 351 trials. The top four most studied drugs, ketamine, sertraline, duloxetine, and fluoxetine, were consistent in both groups, but ClinicalTrials.gov had more trials for each drug compared to the non-ClinicalTrials.gov group. Out of 9229 interventional trials, 663 unique agents were identified, including approved drugs (74.5%), investigational drugs (23.2%), withdrawn drugs (1.8%), nutraceuticals (0.3%), and illicit substances (0.2%). Both ClinicalTrials.gov and non-ClinicalTrials.gov databases revealed that the largest categories were antidepressive agents (1172 in ClinicalTrials.gov and 659 in non-ClinicalTrials.gov) and nutrients, amino acids, and chemical elements (250 in ClinicalTrials.gov and 659 in non-ClinicalTrials.gov), indicating a focus on alternative treatments involving dietary supplements and nutrients. Additionally, 26 investigational antidepressive agents targeting 16 different drug targets were identified, with buprenorphine (opioid agonist), saredutant (NK2 antagonist), and seltorexant (OX2 antagonist) being the most frequently studied. This analysis addresses 40 approved drugs for depression treatment including new drug classes like GABA modulators and NMDA antagonists that are offering new prospects for treating MDD, including drug-resistant depression and postpartum depression subtypes.

Introduction Depression is a major global burden with unclear pathophysiology and poor treatment outcomes. Diagnosis of depression continues to rely primarily on behavioral rather than biological methods. Investigating tools that might aid in diagnosing and treating early-onset depression is essential for improving the prognosis of the disease course. While there is increasing evidence of possible biomarkers in adult depression, studies investigating this subject in adolescents are lacking.Methods In the current study, we analyzed protein levels in 461 adolescents assessed for depression using the Development and Well-Being Assessment (DAWBA) questionnaire as part of the domestic Psychiatric Health in Adolescent Study conducted in Uppsala, Sweden. We used the Proseek Multiplex Neuro Exploratory panel with Proximity Extension Assay technology provided by Olink Bioscience, followed by transcriptome analyses for the genes corresponding to the significant proteins, using four publicly available cohorts.Results We identified a total of seven proteins showing different levels between DAWBA risk groups at nominal significance, including RBKS, CRADD, ASGR1, HMOX2, PPP3R1, CD63, and PMVK. Transcriptomic analyses for these genes showed nominally significant replication of PPP3R1 in two of four cohorts including whole blood and prefrontal cortex, while ASGR1 and CD63 were replicated in only one cohort.Discussion Our study on adolescent depression revealed protein-level and transcriptomic differences, particularly in PPP3R1, pointing to the involvement of the calcineurin pathway in depression. Our findings regarding PPP3R1 also support the role of the prefrontal cortex in depression and reinforce the significance of investigating prefrontal cortex-related mechanisms in depression.

BACKGROUND: Trigeminal neuralgia (TN) is a severe facial pain condition often associated with a neurovascular conflict. However, neuroinflammation has also been implicated in TN, as it frequently co-occurs with multiple sclerosis (MS).

METHODS: We analysed protein expression levels of TN patients compared to MS patients and controls. Proximity Extension Assay technology was used to analyse the levels of 92 proteins with the Multiplex Neuro-Exploratory panel provided by SciLifeLab, Uppsala, Sweden. Serum and CSF samples were collected from TN patients before (n = 33 and n = 27, respectively) and after (n = 28 and n = 8, respectively) microvascular decompression surgery. Additionally, we included samples from MS patients (n = 20) and controls (n = 20) for comparison.

RESULTS: In both serum and CSF, several proteins were found increased in TN patients compared to either MS patients, controls, or both, including EIF4B, PTPN1, EREG, TBCB, PMVK, FKBP5, CD63, CRADD, BST2, CD302, CRIP2, CCL27, PPP3R1, WWP2, KLB, PLA2G10, TDGF1, SMOC1, RBKS, LTBP3, CLSTN1, NXPH1, SFRP1, HMOX2, and GGT5. The overall expression of the 92 proteins in postoperative TN samples seems to shift towards the levels of MS patients and controls in both serum and CSF, as compared to preoperative samples. Interestingly, there was no difference in protein levels between MS patients and controls.

CONCLUSIONS: We conclude that TN patients showed increased serum and CSF levels of specific proteins and that successful surgery normalizes these protein levels, highlighting its potential as an effective treatment. However, the similarity between MS and controls challenges the idea of shared pathophysiology with TN, suggesting distinct underlying mechanisms in these conditions.

SIGNIFICANCE: This study advances our understanding of trigeminal neuralgia (TN) and its association with multiple sclerosis (MS). By analysing 92 protein biomarkers, we identified distinctive molecular profiles in TN patients, shedding light on potential pathophysiological mechanisms. The observation that successful surgery normalizes many protein levels suggests a promising avenue for TN treatment. Furthermore, the contrasting protein patterns between TN and MS challenge prevailing assumptions of similarity between the two conditions and point to distinct pathophysiological mechanisms.

Trigeminal neuralgia (TN) is a severe facial pain disease of uncertain pathophysiology and unclear genetic background. Although recent research has reported a more important role of genetic factors in TN pathogenesis, few candidate genes have been proposed to date. The present study aimed to identify independent genetic variants in the protein-coding genes associated with TN. We focused on genes previously linked to TN based on the results of four proteomic studies conducted by our research team. The goal was to validate these findings on the genetic level to enhance our understanding of the role of genetics in TN. The study is based on the participants from UK Biobank cohort. Following quality control, 175 independent single nucleotide polymorphisms (SNPs) in 17 genes were selected. The study sample comprised of diagnosed TN cases (N = 555) and randomly matched controls (N = 6245) based on specific criteria. Two SNPs corresponding to C8B rs706484 [odds ratio (OR) (95% confidence interval (CI)): 1.357 (1.158–1.590); p: 0.00016] and MFG-E8 rs2015495 [OR (95% CI): 1.313 (1.134–1.521); p: 0.00028] showed significant positive association with TN, indicating a positive effect of the SNP alleles on gene expression and disease risk. Interestingly, both SNPs are Expression Quantitative Trait Loci (eQTLs), and are associated with changes in the expression activity of their corresponding gene. Our findings suggest novel genetic associations between C8B, a key component of the complement system, and MFG-E8, which plays a role in regulating neuroinflammation, in relation to TN. The identified genetic variations may help explain why some individuals develop TN while others do not, indicating a potential genetic predisposition to the condition.

Background Understanding the role of circulating proteins in prostate cancer risk can reveal key biological pathways and identify novel targets for cancer prevention. Methods We investigated the association of 2002 genetically predicted circulating protein levels with risk of prostate cancer overall, and of aggressive and early onset disease, using cis- pQTL Mendelian randomisation (MR) and colocalisation. Findings for proteins with support from both MR, after correction for multiple -testing, and colocalisation were replicated using two independent cancer GWAS, one of European and one of African ancestry. Proteins with evidence of prostate -speci fi c tissue expression were additionally investigated using spatial transcriptomic data in prostate tumour tissue to assess their role in tumour aggressiveness. Finally, we mapped risk proteins to drug and ongoing clinical trials targets. Findings We identi fi ed 20 proteins genetically linked to prostate cancer risk (14 for overall [8 speci fi c], 7 for aggressive [3 speci fi c], and 8 for early onset disease [2 speci fi c]), of which the majority replicated where data were available. Among these were proteins associated with aggressive disease, such as PPA2 [Odds Ratio (OR) per 1 SD increment = 2.13, 95% CI: 1.54 - 2.93], PYY [OR = 1.87, 95% CI: 1.43 - 2.44] and PRSS3 [OR = 0.80, 95% CI: 0.73 - 0.89], and those associated with early onset disease, including EHPB1 [OR = 2.89, 95% CI: 1.99 - 4.21], POGLUT3 [OR = 0.76, 95% CI: 0.67 - 0.86] and TPM3 [OR = 0.47, 95% CI: 0.34 - 0.64]. We con fi rmed an inverse association of MSMB with prostate cancer overall [OR = 0.81, 95% CI: 0.80 - 0.82], and also found an inverse association with both aggressive [OR = 0.84, 95% CI: 0.82 - 0.86] and early onset disease [OR = 0.71, 95% CI: 0.68 - 0.74]. Using spatial transcriptomics data, we identi fi ed MSMB as the genome-wide top -most predictive gene to distinguish benign regions from high grade cancer regions that comparatively had fi ve -fold lower MSMB expression. Additionally, ten proteins that were associated with prostate cancer risk also mapped to existing therapeutic interventions. Interpretation Our fi ndings emphasise the importance of proteomics for improving our understanding of prostate cancer aetiology and of opportunities for novel therapeutic interventions. Additionally, we demonstrate the added bene fi t of in-depth functional analyses to triangulate the role of risk proteins in the clinical aggressiveness of prostate tumours. Using these integrated methods, we identify a subset of risk proteins associated with aggressive and early onset disease as priorities for investigation for the future prevention and treatment of prostate cancer.