Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases

MTHFR Polymorphisms, Homocysteine Elevation, and Ischemic Stroke Susceptibility in East Asian and European Populations

BACKGROUND AND OBJECTIVES

Methylenetetrahydrofolate reductase (MTHFR) is a key enzyme that regulates folate and homocysteine metabolism. Genetic variation in MTHFR has been implicated in cerebrovascular disease risk, although research in diverse populations is lacking. We thus aimed to investigate the effect of genetically predicted MTHFR activity on risk of ischemic stroke (IS) and its main subtypes using a multiancestry Mendelian randomization (MR) approach.

METHODS

We proxied reduced MTHFR function using the C677T missense variant that impairs MTHFR function and consequently increases levels of total plasma homocysteine (tHcy) in both East Asian and European populations. Summary data for IS and its subtypes (small vessel stroke [SVS], large artery stroke [LAS], and cardioembolic stroke [CES]) were obtained from the largest available genome-wide association studies. MR estimates were calculated using the Wald ratio and random-effects inverse-variance-weighted methods. We performed sensitivity analyses to evaluate for confounding due to linkage disequilibrium.

RESULTS

Genetically downregulated MTHFR activity, associated with a consequent SD increase in tHcy levels, was associated with an increased risk of SVS in both East Asian (odds ratio [OR] 1.20, 95% CI 1.08-1.34, p = 8.58 × 10-4) and European populations (OR 1.62, 95% CI 1.24-2.12; p = 3.73 × 10-4). There was no evidence that genetically perturbed MTHFR activity influenced risk of CES or LAS. These findings were consistent in sensitivity analyses.

DISCUSSION

Our findings provide genetic evidence that reduced MTHFR activity was selectively associated with an increased risk of SVS in both East Asian and European populations. These findings warrant further investigation of genotype-guided nutritional supplementation for the prevention of SVS.

Assessing Association Between Circulating Bilirubin Levels and the Risk of Frailty: An Observational and Mendelian Randomization Study

BACKGROUND

Bilirubin is a by-product of haemoglobin breakdown and has been reported to be a potent antioxidant recently. While elevated levels of bilirubin have been linked to a reduced risk of various diseases, their role remains unknown in frailty. This study aims to explore the relationship between serum bilirubin levels and the risk of frailty.

METHODS

This cohort study included 442 223 White British participants (aged 39 to 73 years) with an available frailty index at baseline (2006 to 2010) from the UK Biobank. The associations of total/direct bilirubin levels with the continuous frailty index were analysed by multivariable linear regression, and multivariable logistic regression was used after classifying frailty outcomes into non-frailty, pre-frailty and frailty. A Mendelian randomization (MR) analysis was applied to evaluate the association of genetically predicted bilirubin levels with frailty risk.

RESULTS

The prevalence rates of both pre-frailty and frailty were 46.17% and 12.49%, respectively, with higher rates observed in women than in men (pre-frailty: 47.33% vs. 44.79%, frailty: 13.64% vs. 11.13%, respectively). There was a non-linear negative association between total bilirubin levels and frailty indexes (p < 0.0001). Mildly elevated total bilirubin levels had protective effects against pre-frailty (OR = 0.863, 95% CI: 0.849 to 0.879, p < 0.001) and frailty (OR = 0.660, 95% CI: 0.641 to 0.679, p < 0.001). Increased total bilirubin levels were more beneficial for women with frailty risk (percent changes per SD μmol/L = -0.37%, 95% CI: -0.40% to -0.34%). The MR analysis revealed a negative association between genetically predicted total/direct bilirubin levels and frailty risk (both p < 0.0001).

CONCLUSIONS

Circulating total/direct bilirubin levels were negatively associated with frailty risk in White British individuals. Mildly elevated total bilirubin levels were more beneficial for women subpopulation.

Low CD3 level is a risk factor for amyotrophic lateral sclerosis: a Mendelian randomization study

Amyotrophic lateral sclerosis (ALS) is a progressive and fatal disease characterized by neuronal degeneration of the spinal cord and brain and believed to be related to the immune system. In this study, our aim is to use Mendelian randomization (MR) to search for immune markers related to ALS. A total of 731 immune cell traits were included in this study. MR analysis was used to identify the causality between 731 immune cell traits (with 3,757 Europeans) and ALS (with 138,086 Europeans). Colocalization analysis was used to verify the found causality, protein-protein interaction prediction was used to look for the interacting proteins that are known to be involved in ALS. We found low expression levels of CD3 on central memory CD8+ T cell is risk factor for ALS (OR = 0.90, 95% CI: 0.86-0.95, P = 0.0000303). CD3 can interact with three ALS-related proteins: VCP, HLA-DRA and HLA-DRB5, which are associated with adaptive immune response. Our study reported for the first time that low-level CD3 is a risk factor for ALS and the possible mechanism, which could provide a potential strategy for ALS diagnosis and therapy.

Deciphering the genetic underpinnings of neuroticism: A Mendelian randomization study of druggable gene targets

BACKGROUND

Neuroticism, known for its association with a greater risk of psychiatric conditions such as depression and anxiety, is a critical focus of research.

METHODS

Cis-expression quantitative trait loci (eQTLs) from 31,684 whole blood samples provided by the eQTLGen Consortium, alongside data from a large neuroticism cohort, were analyzed to identify genes causally linked to neuroticism. To further explore the influence of gene expression changes on neuroticism, colocalization analysis was conducted. Identified drug targets were assessed for potential side effects using a phenome-wide association study (PheWAS). Additionally, we utilized multiple databases to explore the interactions between drugs and genes for drug prediction and assess the current medications for drug repurposing.

RESULTS

The analysis involved a total of 4473 druggable genes, with two-sample Mendelian randomization (MR) identifying 186 genes that are causally linked to neuroticism. Colocalization analysis highlighted 11 genes (TLR4, MMRN1, EP300, BRAF, ORM1, ACVR1B, LRRC17, NOS2, ADAMTS6, GPX1, and VCL) with a posterior probability of colocalization (PPH4) >0.8. PheWAS revealed that drugs targeting BRAF, LRRC17, ADAMTS6, and GPX1 were also associated with other traits. Notably, six of these genes (TLR4, MMRN1, BRAF, ACVR1B, NOS2, and GPX1) are already being explored for drug development in psychiatric and other diseases.

CONCLUSION

This study pinpointed six genes as promising therapeutic targets for neuroticism. The repurposing and development of drugs targeting these genes hold potential for managing neuroticism and associated psychiatric disorders.

Genetic insights into the gut microbiota-duodenal diseases interplay: A Mendelian randomization and Bayesian weighting study

BACKGROUND

Many observational studies have shown a close association between gut microbiota and the risk of various duodenal diseases. Therefore, we urgently explore the potential causal relationship between gut microbiota and some duodenal diseases, mainly including duodenal ulcers and duodenitis.

METHODS

We conducted Mendelian randomization (MR) studies using genetic instrumental variables for gut microbiota from GWAS and duodenal disease datasets. Causal relationships were examined using multiple MR methods with Bonferroni correction. Bayesian Weighted Mendelian Randomization (BWMR) assessed causal relationships, employing ELBO and weighted data. Reverse MR analysis was conducted on microbiota showing significant causal relationships with duodenal diseases.

RESULTS

Through MR analysis, we identified three gut microbiota that promote the occurrence of duodenal ulcers (family. Coriobacteriaceae: OR = 1.003; 95 % CI = 1.0005-1.0056; p = 0.016, genus.RuminococcaceaeUCG003: OR = 1.006; 95 % CI = 1.002-1.007; p = 0.002, order. Coriobacteriales: OR = 1.003; 95 % CI = 1.0005-1.0056; p = 0.016), one microbiota that inhibits the occurrence of duodenitis (family. Acidaminococcaceae: OR = 0.994; 95 % CI = 0.988-0.999; p = 0.046), and one microbiota that promotes the occurrence of duodenitis (genus.Eubacteriumcoprostanoligenesgroup: OR = 1.006; 95 % CI = 1.0005-1.013; p = 0.033). Further confirmation of the occurrence of duodenal ulcers and the production of family.Coriobacteriaceae and order.Coriobacteriales microbiota was obtained through reverse MR analysis, indicating that the occurrence of duodenal ulcers also promotes the growth of these microbiota.

CONCLUSION

Our study employs Mendelian randomization techniques to demonstrate a causal relationship between specific gut microbiota and duodenal ulcers and duodenitis. Additionally, our analysis suggests that duodenal ulcer occurrence promotes the growth of certain microbiota, emphasizing the intricate interplay between gut microbiota composition and these diseases.

Study on the activity and mechanism of herbal formula anti-infection powder (AIP) against influenza-virus-induced pneumonia through genetic susceptibility genes

ETHNOPHARMACOLOGICAL RELEVANCE

Anti-infection powder (AIP), a patented Chinese herbal formulation, is used traditionally in the treatment of upper respiratory tract infections. In this study, an ethanol extract of AIP was demonstrated to inhibit influenza A virus (IAV) infection and IAV-induced pneumonia (IVP), both in vitro and in vivo, highlighting its potential mechanism of action.

AIM OF THE STUDY

To determine the anti-IAV activity of AIP and to explore the possible mechanisms of inhibiting IAV-induced pneumonia.

MATERIALS AND METHODS

An ethanol extract was extracted from AIP and its major ingredients were determined by high-performance liquid chromatography (HPLC). An IAV-infected A549 cell model and an IAV-induced mouse pneumonia model were established to evaluate the therapeutic effects of AIP on IVP in vivo and in vitro. The mice were respectively administered AIP at high- and low-dose in different groups. The anti-IAV activity of AIP was evaluated by detecting viral load, lung lesion, lung index, suvival time, inflammatory cytokines and transcriptomic analysis in the lung tissue. The potential pathways and targets that involved in AIP against IVP were predicted by network pharmacology. Mendelian randomization (MR), colocalization analysis, and molecular docking were employed to identify novel therapeutic targets for IVP. Polymerase chain reaction (PCR) and Western blot (WB) techniques were used to confirm the effect of AIP on the expression of risk target genes in the lungs of IVP mice.

RESULTS

In A549 cell line, AIP effectively inhibited IAV infection with IC50 values of 65.49 μg/mL. The anti-IAV activity of AIP was mainly determined by chlorogenic acid, forsythiarin, puerarin, paeoniflorin and prim-o-glucosylcimigin. Moreover, AIP inhibited the neuraminidase activity and the M gene expression in vitro. In vivo, oral administration of AIP significantly reduced viral load and improved lung tissue lesions. AIP decreased the concentration of pro-inflammatory factors such as IL-1β, TNF-α, and IFN-γ, and significantly increased the concentration of the anti-inflammatory factor IL-4. According to network pharmacology analysis, toll-like receptor signaling pathway, chemokine signaling pathway, and TNF signaling pathway may be the possible mechanisms by which AIP inhibits IVP and regulates excessive inflammatory response.Two new genes, LRG1 and PSMA4, associated with genetic susceptibility to influenza and pneumonia, predicted as potential IVP drug target genes by MR and colocalization analysis. The antiviral mechanism of AIP may be to inhibit the expression levels of LRG1 and PSMA4 in lungs of mouse IVP.

CONCLUSIONS

AIP exhibited anti-IAV activities both in vitro and in vivo. AIP had a protective effect against pneumonia caused by influenza virus and can inhibit the progression of inflammation. This effect may be associated with its ability to inhibit the expression levels of genetic susceptibility genes (LRG1 and PSMA4) in lungs of mouse IVP. The findings of this study enhance our understanding of the role and mechanisms of AIP in the treatment of IVP.

Exploring potential drug targets for SLE through Mendelian randomization and network pharmacology

BACKGROUND

Systemic lupus erythematosus (SLE) is a complex and incurable autoimmune disease, so several drug remission for SLE symptoms have been developed and used at present. However, treatment varies by patient and disease activity, and existing medications for SLE were far from satisfactory. Novel drug targets to be found for SLE therapy are still needed.

METHODS

Mendelian randomization (MR), an observational study way, was performed to explore potential drug targets for SLE using protein quantitative trait loci (pQTL) from recently published genome-wide association studies (GWAS) of cerebrospinal fluid (CSF) and plasma proteins, which obtained genetic instruments for 154 CSF proteins of 971 participants, and 734 plasma proteins of 23591 participants. Bidirectional Mendelian randomization analysis, colocalization analysis, and phenotype scanning were performed to find key proteins for SLE. In addition, external data verification was implemented to further consolidate the Mendelian randomization findings. Candidate proteins as targets to find drugs and discuss the druggability. Finally, Network pharmacology and molecular docking methods were used to verify the effects of Voclosporin and Cyclosporine on SLE targets. Protein-protein interaction (PPI) and core target analysis of candidate drugs and SLE overlapping targets were performed to identify potential hub targets and interactions. The affinity between drug targets and SLE targets was confirmed by molecular docking.

RESULTS

In the preliminary analysis, we identified four key proteins as possible drug targets in CSF and plasma proteins, included ICAM-1(P = 4.62E-05, OR = 0.90(0.86, 0.95)), sICAM-1(P = 4.62E-05, OR = 0.49(0.35, 0.69)), FCG2B (P = 7.63E-11, OR = 0.57(0.48, 0.67)), PPP3CA; PPP3R1 (P = 5.47E-07, OR = 0.66(0.57, 0.78)). Among them, ICAM1 was detected in both CSF and plasma proteins. By excluding reverse causality, confounding factors, and linkage disequilibrium (LD), we identified PPP3CA; PPP3R1 as novel drug targets for SLE, including Voclosporin and Cyclosporine. Finally, the Drugbank database shows that novel drugs contain 33 targets for treating SLE. PPI suggested that SIRT1, ACE, PTGS2, and BACE1 were pivotal targets for SLE treatment. In addition, the molecular docking showed that the bioactive molecules of Voclosporin and Cyclosporine had a good affinity with the target of SLE.

CONCLUSIONS

Our integrative analysis suggested that levels of circulating PPP3CA; PPP3R1 had causal effects on SLE risk and served as potential treatment targets. Moreover, this study provides new evidence for Voclosporin as an SLE treatment through Mendelian randomization and Network pharmacology, and warrants further clinical investigation.

Potential drug targets for asthma identified through mendelian randomization analysis

BACKGROUND

The emergence of new molecular targeted drugs marks a breakthrough in asthma treatment, particularly for severe cases. Yet, options for moderate-to-severe asthma treatment remain limited, highlighting the urgent need for novel therapeutic drug targets. In this study, we aimed to identify new treatment targets for asthma using the Mendelian randomization method and large-scale genome-wide association data (GWAS).

METHODS

We utilized GWAS data from the UK Biobank (comprising 56,167 patients and 352,255 control subjects) and the FinnGen cohort (including 23,834 patients and 228,085 control subjects). Genetic instruments for 734 plasma proteins and 154 cerebrospinal fluid proteins were derived from recently published GWAS. Bidirectional Mendelian randomization analysis, Steiger filtering, colocalization, and phenotype scanning were employed for reverse causal inference detection, further substantiating the Mendelian randomization results. A protein-protein interaction network was also constructed to reveal potential associations between proteins and asthma medications.

RESULTS

Under Bonferroni significance conditions, Mendelian randomization analysis revealed causal relationships between seven proteins and asthma. In plasma, we observed that an increase of one standard deviation in IL1R1[1.30 (95% CI 1.20-1.42)], IL7R[1.07 (95% CI 1.04-1.11)], ECM1[1.03 (95% CI 1.02-1.05)], and CD200R1[1.18 (95% CI 1.09-1.27)] were associated with an increased risk of asthma, while an increase in ADAM19 [0.87 (95% CI 0.82-0.92)] was found to be protective. In the brain, each 10-fold increase in IL-6 sRa [1.29 (95% CI 1.15-1.45)] was associated with an increased risk of asthma, while an increase in Layilin [0.61 (95% CI 0.51-0.73)] was found to be protective. None of the seven proteins exhibited a reverse causal relationship. Colocalization analysis indicated that ECM1 (coloc.abf-PPH4 = 0.953), IL-6 sRa (coloc.abf-PPH4 = 0.966), and layilin (coloc.abf-PPH4 = 0.975) shared the same genetic variation as in asthma.

CONCLUSION

A causal relationship exists between genetically determined protein levels of IL1R1, IL7R, ECM1, CD200R1, ADAM19, IL-6 sRa, and Layilin (LAYN) and asthma. Moreover, the identified proteins may serve as attractive drug targets for asthma, especially ECM1 and Layilin (LAYN). However, further research is required to comprehensively understand the roles of these proteins in the occurrence and progression of asthma.

Integrative bioinformatics approach identifies novel drug targets for hyperaldosteronism, with a focus on SHMT1 as a promising therapeutic candidate

Primary aldosteronism (PA), characterized by autonomous aldosterone overproduction, is a major cause of secondary hypertension with significant cardiovascular complications. Current treatments mainly focus on symptom management rather than addressing underlying mechanisms. This study aims to discover novel therapeutic targets for PA using integrated bioinformatics and experimental validation approaches. We employed a systematic approach combining: gene identification through transcriptome-wide association studies (TWAS); causal inference using summary data-based Mendelian randomization (SMR) and two-sample Mendelian randomization (MR) analyses; additional analyses included phenome-wide association analysis, enrichment analysis, protein-protein interaction (PPI) networks, drug repurposing, molecular docking and clinical validation through aldosterone-producing adenomas (APAs) tissue. Through systematic screening and prioritization, we identified 163 PA-associated genes, of which seven emerged as potential drug targets: CEP104, HIP1, TONSL, ZNF100, SHMT1, and two long non-coding RNAs (AC006369.2 and MRPL23-AS1). SHMT1 was identified as the most promising target, showing significantly elevated expression in APAs compared to adjacent non-tumorous tissues. Drug repurposing analysis identified four potential SHMT1-targeting compounds (Mimosine, Pemetrexed, Leucovorin, and Irinotecan), supported by molecular docking studies. The integration of multiple bioinformatics methods and experimental validation successfully identified novel drug targets for hyperaldosteronism. SHMT1, in particular, represents a promising candidate for future therapeutic development. These findings provide new opportunities for developing causative treatments for PA, though further clinical validation is warranted.

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.

Identification of Drug-Targetable Genes for Eczema and Dermatitis Using Integrated Genomic and Proteomic Approaches

Background: Eczema and dermatitis are common inflammatory skin conditions with significant morbidity. Identifying drug-targetable genes can facilitate the development of effective treatments. Methods: This study analyzed data obtained by meta-analysis of 2 genome-wide association studies on eczema/dermatitis (57,311 cases and 896,779 controls, European ancestry). We identified drug-targetable genes from the Drug-Gene Interaction Database and Finan et al's findings. Cis-expression quantitative trait loci (eQTL) data from human blood and skin tissues were used for Mendelian randomization (MR) analysis. Bayesian colocalization, proteomic MR, and meta-analysis validated the causal relationships. Finally, protein-protein interactions (PPIs) and correlation analysis of potential drug targets and cytokines were performed. Results: We identified 2532 drug-targetable genes; 3378 Single Nucleotide Polymorphism (SNPs) were associated with 1531 genes in blood cis-eQTLs, 664 SNPs with 667 genes in sun-exposed skin eQTLs, and 572 SNPs with 574 genes in nonsun-exposed skin eQTLs. Five genes (SLC22A5, NOTCH4, AGER, HLA-DRB5, and EHMT2) showed causal relationships with eczema/dermatitis across multiple datasets. Single-variable and multi-variable Mendelian randomization (SMR) and multi-SNP SMR analysis identified 8 genes (PIK3R4, DHODH, CXCR2, Interleukin (IL)18, LGALS9, RPS6KB2, SLC22A5, and AGER) across all tissues. Functional Summary Information for Variants in the Online Network (FUSION) analysis confirmed associations for SLC22A5 and AGER. Bayesian colocalization indicated AGER (PPH4: 0.95) as a shared causal variant. Proteomic MR and meta-analysis showed that increased AGER protein levels were associated with a lower risk of eczema or dermatitis (odds ratio: 0.995, 95% confidence interval: 0.997-0.993, P = 0.0002). A PPI network revealed interactions of AGER with NOTCH4 and multiple cytokines, whereas SLC22A5 showed no cytokine interactions. Conclusions: This study identified potential drug-targetable genes, with AGER showing strong potential as a target for reducing eczema/dermatitis risk. These findings provide a basis for developing targeted therapies.

Atlas of the plasma proteome in health and disease in 53,026 adults

Large-scale proteomics studies can refine our understanding of health and disease and enable precision medicine. Here, we provide a detailed atlas of 2,920 plasma proteins linking to diseases (406 prevalent and 660 incident) and 986 health-related traits in 53,026 individuals (median follow-up: 14.8 years) from the UK Biobank, representing the most comprehensive proteome profiles to date. This atlas revealed 168,100 protein-disease associations and 554,488 protein-trait associations. Over 650 proteins were shared among at least 50 diseases, and over 1,000 showed sex and age heterogeneity. Furthermore, proteins demonstrated promising potential in disease discrimination (area under the curve [AUC] > 0.80 in 183 diseases). Finally, integrating protein quantitative trait locus data determined 474 causal proteins, providing 37 drug-repurposing opportunities and 26 promising targets with favorable safety profiles. These results provide an open-access comprehensive proteome-phenome resource (https://proteome-phenome-atlas.com/) to help elucidate the biological mechanisms of diseases and accelerate the development of disease biomarkers, prediction models, and therapeutic targets.

Causal Inference and Annotation of Phosphoproteomics Data in Multi-omics Cancer Studies

Protein phosphorylation plays a crucial role in regulating diverse biological processes. Perturbations in protein phosphorylation are closely associated with downstream pathway dysfunctions, while alterations in protein expression could serve as sensitive indicators of pathological status. However, there are currently few methods that can accurately identify the regulatory links between protein phosphorylation and expression, given issues like reverse causation and confounders. Here, we present Phoslink, a causal inference model to infer causal effects between protein phosphorylation and expression, integrating prior evidence and multi-omics data. We demonstrated the feasibility and advantages of our method under various simulation scenarios. Phoslink exhibited more robust estimates and lower FDR than commonly used Pearson and Spearman correlations, with better performance than canonical IV selection methods for Mendelian randomization. Applying this approach, we identified 345 causal links involving 109 phosphosites and 310 proteins in 79 lung adenocarcinoma (LUAD) samples. Based on these links, we constructed a causal regulatory network and identified 26 key regulatory phosphosites as regulators strongly associated with LUAD. Notably, 16 of these regulators were exclusively identified through phosphosite-protein causal regulatory relationships, highlighting the significance of causal inference. We explored potentially druggable phosphoproteins and provided critical clues for drug repurposing in LUAD. We also identified significant mediation between protein phosphorylation and LUAD through protein expression. In summary, our study introduces a new approach for causal inference in phosphoproteomics studies. Phoslink demonstrates its utility in potential drug target identification thereby accelerating the clinical translation of cancer proteomics and phosphoproteomic data.

Metformin and the risk of malignant tumors of digestive system: a mendelian randomization study

BACKGROUND

Observational studies suggest that metformin may reduce the risk of malignant tumors of the digestive system (MTDS), but these findings are often confounded by bias and unmeasured variables. Recent meta-analyses have questioned these associations, emphasizing the need for robust causal inference.

METHODS

Mendelian randomization (MR) was used to evaluate the causal relationship between metformin and MTDS. Genetic variants associated with metformin's molecular targets were selected from GTEx, eQTLGen, and UK Biobank and validated using genetic colocalization to ensure instrument validity. GWAS summary statistics for MTDS, encompassing up to 314,193 controls and 6,847 colorectal cancer cases, were obtained from FinnGen and EBI. The primary analysis employed the inverse-variance weighted (IVW) method, supplemented by MR-Egger, weighted median, and weighted mode analyses. Bonferroni correction was applied to adjust for multiple testing across 14 cancer types.

RESULTS

Genetically proxied metformin use was associated with an increased risk of colorectal cancer (OR = 2.38, 95%CI = 1.38-4.09, P = 0.0018) and related subtypes. No causal relationship was found for hepatocellular carcinoma, gastric cancer, pancreatic cancer, or other digestive system cancers. The robustness of these findings was supported by sensitivity analyses, which indicated no significant pleiotropy, and leave-one-out tests.

CONCLUSION

This study provides robust genetic evidence that metformin use increases the risk of colorectal cancer, challenging its role as a preventive agent for digestive cancers. These findings emphasize the need for clinicians to carefully evaluate the risks and benefits of metformin, particularly in populations at higher risk for colorectal cancer. Future research should focus on delineating the mechanisms underlying this association to optimize the use of metformin in clinical practice.

Exploration of potential drug targets for Glaucoma by plasma proteome screening

BACKGROUND

Glaucoma is the leading cause of irreversible blindness. However, the current available treatment methods are still unsatisfactory. Therefore, the exploration of new drug targets for the treatment of glaucoma is of paramount importance.

METHODS

We conducted two-sample Mendelian randomization (MR) using plasma protein quantitative trait loci (pQTL) data from two datasets (n = 734, n = 4907) and their instrumental variables to investigate the causal relationship between plasma proteins and glaucoma. The analysis was validated by replacing the exposure and outcome cohorts. Additionally, we utilized protein-protein interaction networks to assess the associations between these potential drug targets and existing drug targets.

RESULTS

Through two-sample Mendelian randomization analysis, we identified causal relationships between Glaucoma and the following proteins: AZU1, OBP2B, ENPP5, INPP5B, KREMEN1, LYPLAL1, and PTPRJ. External validation confirmed the protective effect of LYPLAL1 on Glaucoma, while ENPP5, KREMEN1, and PTPRJ increased the risk of Glaucoma. Reverse MR and Steiger filtering did not indicate any reverse causal associations of the aforementioned proteins with Glaucoma.

CONCLUSION

Our study demonstrates a causal impact of ENPP5, KREMEN1, PTPRJ, and LYPLAL1 on the risk of Glaucoma. These findings suggest that these four proteins may serve as promising drug targets for Glaucoma treatment.

SIGNIFICANCE

Currently, the pharmacological treatment of glaucoma primarily focuses on lowering intraocular pressure, which has its limitations. Targeted therapy is a personalized treatment approach that aims to inhibit or block the development and progression of diseases such as cancer and inflammation by selectively acting on specific biomolecules or signaling pathways. Our research employs a two-sample Mendelian randomization (MR) method, integrating a large amount of GWAS and pQTL data to perform MR analysis. This has enabled us to explore several plasma proteins as potential drug targets for glaucoma, providing direction and a research foundation for future investigations into glaucoma drug targets.

Identification of biomarkers for chronic lymphocytic leukemia risk: a proteome-wide Mendelian randomization study

BACKGROUND

Chronic lymphocytic leukemia (CLL) is a common hematologic malignancy. Although previous research has explored associations between plasma proteins and CLL, the causal relationships remain unclear. This study used Mendelian randomization (MR) to investigate the causal relationship between 7156 plasma proteins and CLL risk.

METHODS

A two-sample MR analysis assessed the impact of specific plasma proteins on CLL risk, using data from the Finngen Proteomics project (analyzing 828 participants) and the UK Biobank. Additional analyses included colocalization, phenomenon-wide MR, and protein-protein interaction networks.

RESULTS

The study identified nine plasma proteins significantly associated with CLL risk. Increased levels of Peptidyl-prolyl cis-trans isomerase E (PPIE) (OR = 1.66, 95% CI 1.22-2.27, P = 0.001) were associated with an increased risk of developing CLL, whereas Protein O-Mannosyltransferase 2 (POMGNT2) (OR = 0.62, 95% CI 0.41-0.91, P = 0.017) and C-C Motif Chemokine Ligand 14(CCL14) (OR = 0.80, 95% CI 0.67-0.94, P = 0.010) were associated with a reduced risk of CLL. Colocalization analysis suggested that PPIE may share pathogenic variants with CLL (PP.H4 = 0.758). Phenomenon-wide MR analysis of PPIE also indicated associations with other clinical features, including rheumatic diseases and type 2 diabetes. Protein-protein interaction and drug-gene interaction analyses highlighted CDC5L and SNW1 as potential therapeutic targets.

CONCLUSION

This study identifies nine plasma proteins linked to CLL risk, with PPIE offering new insights into the disease's pathogenesis. Further research is needed to validate these findings and explore their potential as therapeutic targets.

Identification of potential therapeutic targets for Alzheimer's disease from the proteomes of plasma and cerebrospinal fluid in a multicenter Mendelian randomization study

BACKGROUND

Certain peripheral proteins are believed to be involved in the development of Alzheimer's disease (AD), but the roles of other new protein biomarkers are still unclear. Current treatments aim to manage symptoms, but they are not effective in stopping the progression of the disease. New drug targets are needed to prevent Alzheimer's disease.

METHODS

We used Mendelian randomization (MR) to study drug targets for Alzheimer's disease. We analyzed data from the European Alzheimer's and Dementia Biobank consortium and replicated our findings in GWAS data from IGAP and FinnGen cohorts. We identified genetic instruments for plasma and cerebrospinal fluid (CSF) proteins and conducted sensitivity analyses using various methods. Additionally, a comparison and analysis of protein-protein interactions (PPI) were conducted to identify potential causal proteins. The implications of these findings were further explored through an examination of existing AD drugs and their respective targets.

RESULTS

Through MR analysis, 10 protein AD pairs were identified as statistically significant at the Bonferroni level (P < 6.35 × 10-5). The specific findings indicate that elevated levels of plasma cathepsin H (CTSH) (OR = 1.06, 95%CI: 1.03-1.08, p = 6.12 × 10-6), plasma signal regulatory protein alpha (SIRPA) (OR = 1.03, 95%CI: 1.02-1.05, p = 1.37 × 10-5), plasma TMEM106B (OR = 1.16, 95%CI: 1.09-1.23, p = 1.92 × 10-6), and CSF bone sialoprotein (BSP) (OR = 1.33, 95%CI: 1.17-1.51, p = 9.34 × 10-6), CSF Interleukin-34 (IL-34) (OR = 2.13, 95%CI: 1.51-3.01, p = 1.85 × 10-5), CSF immunoglobulin-like transcript 2 (ILT-2) (OR = 1.33, 95%CI: 1.17-1.51, p = 9.34 × 10-6) are associated with an increased risk of AD, while increased levels of plasma progranulin gene (GRN) (OR = 0.79, 95%CI: 0.74-0.84, p = 2.19 × 10-12), plasma triggering receptor expressed on myeloid cells 2 (TREM2) (OR = 0.67, 95%CI: 0.58-0.78, p = 6.95 × 10-8), plasma sialic acid-bind immunoglobulin-like lectins (SIGLEC)-9 (OR = 0.67, 95%CI: 0.58-0.78, p = 6.95 × 10-8), and CSF SIGLEC7 (OR = 0.42, 95%CI: 0.28-0.64, p = 4.30 × 10-5) are associated with a decreased risk of AD. Bayesian colocalization found that the above protein-related genes shared the same mutation as AD.

CONCLUSION

Increased levels of plasma CTSH, SIRPA, TMEM106B, CSF BSP, CSF IL-34, and CSF ILT-2 have been found to be correlated with an elevated risk of AD, whereas elevated levels of plasma GRN, TREM2, SIGLEC9, and CSF SIGLEC7 are associated with a decreased risk of developing AD. Further investigation through clinical trials is needed.

Evaluating the Causal Effect of Circulating Proteome on Glycemic Traits: Evidence From Mendelian Randomization

Exploring the mechanisms underlying abnormal glycemic traits is important for deciphering type 2 diabetes and characterizing novel drug targets. This study aimed to decipher the causal associations of circulating proteins with fasting glucose (FG), 2-h glucose after an oral glucose challenge (2hGlu), fasting insulin (FI), and glycated hemoglobin (HbA1c) using large-scale proteome-wide Mendelian randomization (MR) analyses. Genetic data on plasma proteomes were obtained from 10 proteomic genome-wide association studies. Both cis-protein quantitative trait loci (pQTLs) and cis + trans-pQTLs MR analyses were conducted. Bayesian colocalization, Steiger filtering analysis, assessment of protein-altering variants, and mapping expression QTLs to pQTLs were performed to investigate the reliability of the MR findings. Protein-protein interaction, pathway enrichment analysis, and evaluation of drug targets were performed. Thirty-three proteins were identified with causal effects on FG, FI, or HbA1c but not 2hGlu in the cis-pQTL analysis, and 93 proteins had causal effects on glycemic traits in the cis + trans-pQTLs analysis. Most proteins were either considered druggable or drug targets. In conclusion, many novel circulating protein biomarkers were identified to be causally associated with glycemic traits. These biomarkers enhance the understanding of molecular etiology and provide insights into the screening, monitoring, and treatment of diabetes.

ARTICLE HIGHLIGHTS

Identifying Potential Drug Targets for Keloid: A Mendelian Randomization Study

Keloids are a skin fibrosis disease characterized by troublesome symptoms, a varying degree of recurrence and inevitable side effects from treatments. Thus, identifying their drug targets is necessary. A 2-sample Mendelian randomization analysis was conducted using proteins from the intersection of the deCODE database and "The Druggable Genome and Support for Target Identification and Validation in Drug Development" as the exposure variable. The outcome variable was based on recently published GWAS of keloids. Summary data-based Mendelian randomization and colocalization analysis was employed to distinguish pleiotropy from linkage. Candidate targets underwent drug target analysis. The primary findings were validated through single-cell RNA-sequencing data, Western Blot and immunofluorescence staining on keloids. Seven proteins were identified as potential drug targets for keloids. Among these proteins, Hedgehog-interacting protein, neurotrimin [NTM], KLKB1, and CRIPTO showed positive correlations with keloids, while PLXNC1, SCG3 and PDGFD exhibited negative correlations. Combined with the single-cell RNA-sequencing data, NTM, PLXNC1, and PDGFD were found highly expressed in the fibroblasts. NTM showed a significant increase in keloids as compared to normal scars. In accordance with the analysis, higher levels of protein expression of NTM in keloids compared to normal skin was observed. The identified proteins may be appealing drug targets for keloids treatment with a special emphasis on NTM.

Circulating plasma protein identified as a therapeutic target for intracranial aneurysm through Mendelian Randomization analysis

BACKGROUND

Intracranial aneurysms are the main cause of subarachnoid hemorrhage (SAH), a severe stroke with devastating effects. However, there are no existing medications for intracranial aneurysms (IAs) and novel therapeutic targets are required.

METHODS

We performed a summary data-based Mendelian Randomization (MR) analysis to explore the causal association between circulating plasma proteins and the risk of IAs and SAH. Colocalization analysis was conducted to identify shared causal variants between circulating plasma proteins and IAs, as well as SAH. Finally, mediation MR analyses were conducted to clarify the role of potential plasma proteins in aneurysm formation.

RESULTS

Proteome-wide MR analysis showed that FGF5 (fibroblast growth factor 5) had a causal effect on IA and SAH risk (Pfdr < 0.05). Moreover, genetic variants affecting FGF5 expression levels showed strong evidence of colocalization with IA risk (PPH4 = 0.993) and SAH risk (PPH = 0.988), suggesting that this protein represents a potential direct target for IA intervention. Mediation analysis using two-step MR showed that systolic blood pressure and diastolic blood pressure mediate the effects of FGF5 on IA and SAH.

CONCLUSION

Our investigation identified a causal connection between FGF5 and IAs.

Integrated Mendelian randomization and single-cell RNA-sequencing analyses identified OAS1 as a novel therapeutic target for erectile dysfunction via targeting fibroblasts

Clinically, phosphodiesterase type 5 inhibitors (PDE5-Is) remain the first-line therapy for erectile dysfunction (ED) patients; however, approximately 35% of these patients are still failing to respond to the therapeutic effects. So, urgent needs are required to identify novel therapeutic targets for ED. Hence, in this report, it was the first time for us to integrate single-cell RNA-sequencing (scRNA-Seq), mendelian randomization (MR) analysis with expression quantitative trait loci (eQTL), and protein quantitative trait loci (pQTL) data to find new treatment targets for ED. Disease-causing changes were revealed by MR analysis, and it showed that the OAS1 eQTL/cis-eQTL/cis-pQTL was causally related to ED, significantly reducing its risks (all P < 0.05). Disease-induced changes were revealed by scRNA-Seq, and it suggested that OAS1 mainly played its role in ED via targeting fibroblasts. We further concluded that the positive regulation of OAS1 gene expression could lead to the vicious circle of ED. As a result, drugs targeting OAS1 in the future might provide more potential opportunities and flexibility for treating ED. In conclusion, our study identified OAS1 as a gene of interest in the context of ED via targeting fibroblasts through integrated MR and scRNA-Seq analyses. While these findings highlighted the potential of OAS1 as a therapeutic target, further experimental and clinical studies were still required to validate its functional role and therapeutic relevance in ED pathology.

Identifying potential drug targets for myocardial infarction through Mendelian randomization

BACKGROUND

This study explored the associations between plasma and cerebrospinal fluid (CSF) proteins and myocardial infarction (MI) risk. Identifying specific proteins as biomarkers for MI could enhance our understanding of disease mechanisms and inform clinical practice.

METHODS

We combined protein quantitative trait loci (pQTL) data for plasma and CSF proteins with genome-wide association study (GWAS) summary statistics for MI. Mendelian Randomization (MR) analyses were conducted to establish causal relationships, supported by Bayesian colocalization and Spearman correlation analyses. For plasma proteins, we used pQTL data from Cheng et al. to select 738 cis-acting SNPs associated with 734 proteins. The "TwoSampleMR" method and inverse-variance weighted MR were applied for evaluations.

RESULTS

In plasma, CD8A and HDHD2 were identified as protective factors against MI, while DPEP1 was linked to increased risk. In CSF, CD30 Ligand was associated with MI risk. Bayesian colocalization supported the association for CD8A in plasma. No significant correlation was found between plasma and CSF results, suggesting distinct mechanisms for these biomarkers.

CONCLUSION

Our study identified several plasma and CSF proteins linked to MI risk, offering new insights into the disease's biological underpinnings. These findings could guide future research on MI biomarkers and contribute to improved prevention and treatment strategies.

Systematic druggable genome-wide Mendelian randomization identifies therapeutic targets for hyperemesis gravidarum

BACKGROUND

Hyperemesis gravidarum (HG), excessive vomiting in pregnancy, occurs in 0.3-10.8% of pregnancies and is associated with maternal and fetal morbidity. Despite the existence of several off-label treatment options that have shown clinical effectiveness in managing HG symptoms, the variability in treatment response highlights the need for more effective therapies. Our study aims to identify novel therapeutic targets that could lead to the development of additional, more effective treatment options.

METHODS

A two-sample Mendelian randomization (MR) analysis was performed to estimate the causal effects of blood-druggable genes on HG. Summary statistics for HG were obtained from the FinnGen study and UK Biobank. Cis-expression quantitative trait loci (cis-eQTL) for blood druggable genes were obtained from the eQTLGen Consortium and used as genetic instrumental variables. Another MR method, summary level mendelian randomization (SMR), was used to further confirm our results. We also used eQTL data of other vomiting-related tissues, brain regions, and esophagus, to validate our MR results. Finally, the potential side effects of the druggable genes for HG treatment were assessed using a phenome-wide MR.

RESULTS

Overall, 2499 unique druggable genes were gathered. Two blood drug targets (OVGP1 and LGALS1) showed significant MR results in two independent datasets. No significant heterogeneity of instrumental variables or pleiotropy was detected. In addition, SMR analysis further confirmed the significance of these two prior druggable genes in the brain and esophagus tissues. Further phenome-wide MR analysis revealed no association between genetic proxies of OVGP1, and LGALS1 has been detected in increasing the risk of adverse pregnancy outcomes and other common diseases.

CONCLUSIONS

This study provides genetic evidence that targeting two druggable genes for HG has potential therapeutic advantages. This information is of considerable value in guiding and prioritizing the development of more effective therapies for HG.

Proteome-Wide Mendelian Randomization Analysis to Identify Potential Plasma Biomarkers and Therapeutic Targets for Epithelial Ovarian Cancer Subtypes

Background

Epithelial ovarian cancer (EOC) remains an unmet medical challenge due to its insidious onset, atypical symptoms, and increasing resistance to conventional chemotherapeutic agents. It is imperative to explore novel biomarkers and generate innovative target drugs.

Methods

To identify potential proteins with causal association to EOC subtypes, we conducted a Mendelian Randomization (MR) analysis using 15,419 protein quantitative trait loci (pQTLs) associated with 2015 proteins. Bayesian colocalization analysis, Summary-data-based MR, and Heterogeneity in Dependent Instruments tests were employed for validation. Enrichment and druggability analyses were performed to assess the biological significance and therapeutic potential of identified proteins.

Results

Our analysis identified 455 unique proteins associated with at least one EOC subtype, with 14 protein-cancer associations confirmed by further validation. Ten proteins were prioritized as potential therapeutic targets, including α1B-glycoprotein (A1BG) and ephrin-A1 (EFNA1), which interact with the known drug targets human epidermal growth factor receptor 2 (HER2) and vascular endothelial growth factor receptor (VEGFR).

Conclusion

This study elucidated the plasma proteins causally associated with EOC subtypes, potentially offering easily detectable biomarkers and promising therapeutic targets. A1BG and EFNA1 were identified as druggable targets and confirmed to correspond with current pharmacological targets. Targeting these proteins in drug development potentially offers an avenue for innovative treatment strategies.

Systematic proteome-wide Mendelian randomization to prioritize causal plasma proteins for skin cancers

Skin cancer is one of the most common cancers worldwide. Some risk factors including sun exposure and MC1R variants are recognized; however, the identification of additional genetic factors is essential for the development of novel therapeutic strategies. Here, we conducted a proteome-wide Mendelian randomization (MR) using plasma protein quantitative trait loci (pQTLs) from a published study and the UK Biobank genome-wide association study (GWAS) of skin cancers. We replicated the published result of ASIP, which was significantly associated with increased risks of basal cell carcinoma (BCC) and malignant melanoma. Moreover, we newly identified CTSS, which was significantly associated with a decreased risk of BCC. A series of replication analyses using the DeCODE pQTLs and the FinnGen GWAS, and sensitivity analyses including Steiger filtering, reverse MR, and Bayesian colocalization, supported our primary results. Our findings highlighted the possibility of prioritizing proteins for novel therapeutic or preventive targets and biomarkers for skin cancers.

Co-expression-wide association studies link genetically regulated interactions with complex traits

Transcriptome- and proteome-wide association studies (TWAS/PWAS) have proven successful in prioritizing genes and proteins whose genetically regulated expression modulates disease risk, but they ignore potential co-expression and interaction effects. To address this limitation, we introduce the co-expression-wide association study (COWAS) method, which can identify pairs of genes or proteins whose genetically regulated co-expression is associated with complex traits. COWAS first trains models to predict expression and co-expression conditional on genetic variation, and then tests for association between imputed co-expression and the trait of interest while also accounting for direct effects from each exposure. We applied our method to plasma proteomic concentrations from the UK Biobank, identifying dozens of interacting protein pairs associated with cholesterol levels, Alzheimer's disease, and Parkinson's disease. Notably, our results demonstrate that co-expression between proteins may affect complex traits even if neither protein is detected to influence the trait when considered on its own. We also show how COWAS can help disentangle direct and interaction effects, providing a richer picture of the molecular networks that mediate genetic effects on disease outcomes.

Unveiling Novel Protein Biomarkers for Psoriasis Through Integrated Analysis of Human Plasma Proteomics and Mendelian Randomization

Background

Current pharmacological treatments for psoriasis are generally non-specific and have significant limitations, particularly in the realm of targeted biologic therapies. There is an urgent need to identify and develop new therapeutic targets to improve treatment options.

Objective

The aim of this study was to explore the proteome associated with psoriasis in large population cohorts to discover novel biomarkers that could guide therapy.

Methods

We analyzed data from 54,306 participants enrolled in the UK Biobank Pharmacological Proteomics Project (UKB-PPP). We investigated the relationship between 2923 serum proteins and the risk of psoriasis using multivariate Cox regression models initially. This was complemented by two-sample Mendelian randomization (TSMR), Summary-data-based Mendelian Randomization (SMR), and coloc colocalization studies to identify genetic correlations with protein targets linked to psoriasis. A protein scoring system was created using the Cox proportional hazards model, and cumulative risk curves were generated to analyze psoriasis incidence variations.

Results

Our study pinpointed 62 proteins significantly linked to the risk of developing psoriasis. Further analysis through TSMR narrowed these down to ten proteins with strong causal relationships to the disease. Additional deep-dive analyses such as SMR, colocalization, and differential expression studies highlighted four critical proteins (MMP12, PCSK9, PRSS8, and SCLY). We calculated a protein score based on the levels of these proteins, with higher scores correlating with increased risk of psoriasis.

Conclusion

This study's integration of proteomic and genetic data from a European adult cohort provides compelling evidence of several proteins as viable predictive biomarkers and potential therapeutic targets for psoriasis, facilitating the advancement of targeted treatment strategies.

Proteome-wide Mendelian randomization and functional studies uncover therapeutic targets for polycystic ovarian syndrome

Polycystic ovarian syndrome (PCOS) is an endocrine syndrome that affects a large portion of women worldwide. This proteogenomic and functional study aimed to uncover candidate therapeutic targets for PCOS. We comprehensively investigated the causal association between circulating proteins and PCOS using two-sample Mendelian randomization analysis. Cis-protein quantitative trait loci were derived from six genome-wide association studies (GWASs) on plasma proteome. Genetic associations with PCOS were obtained from a large-scale GWAS meta-analysis, FinnGen cohort, and UK Biobank. Colocalization analyses were performed to prioritize the causal role of candidate proteins. Protein-protein interaction (PPI) and druggability evaluation assessed the druggability of candidate proteins. We evaluated the enrichment of tier 1 and 2 candidate proteins in individuals with PCOS and a mouse model and explored the potential application of the identified drug target. Genetically predicted levels of 65 proteins exhibited associations with PCOS risk, with 30 proteins showing elevated levels and 35 proteins showing decreased levels linked to higher susceptibility. PPI analyses revealed that FSHB, POSTN, CCN2, and CXCL11 interacted with targets of current PCOS medications. Eighty medications targeting 20 proteins showed their potential for repurposing as therapeutic targets for PCOS. EGLN1 levels were elevated in granulosa cells and the plasma of individuals with PCOS and in the plasma and ovaries of dehydroepiandrosterone (DHEA)-induced PCOS mouse model. As an EGLN1 inhibitor, administration of roxadustat in the PCOS mouse model elucidated the EGLN1-HIF1α-ferroptosis axis in inducing PCOS and validated its therapeutic effect in PCOS. Our study identifies candidate proteins causally associated with PCOS risk and suggests that targeting EGLN1 provides a promising treatment strategy.

Identification of Causal Genes and Potential Drug Targets for Restless Legs Syndrome: A Comprehensive Mendelian Randomization Study

Background: Restless legs syndrome (RLS) is a common sensorimotor sleep disorder that affects sleep quality of life. Much effort has been made to make progress in RLS pharmacotherapy; however, patients with RLS still report poor long-term symptom control. Methods: Comprehensive Mendelian randomization (MR) was performed to search for potential causal genes and drug targets using the cis-pQTL and RLS GWAS data. Robustness was validated using the summary-based Mendelian randomization (SMR) method and co-localization analysis. Further evidence of pleiotropy of the target genes and their potential side effects was provided by phenome-wide MR analysis (MR-PheWAS). Finally, molecular docking simulations were conducted on drug candidates corresponding to these targets, which revealed promising binding affinities and interaction patterns and underscored the druggable potential of the target gene. All of the analyses above were conducted in the context of Homo sapiens. Results:MAN1A2 showed a statistically significant result in the MR analysis, which was validated through SMR and co-localization analysis. The MR-PheWAS showed a low probability of pleiotropy and prospective side effects. Molecular docking was used to visualize the binding structure and fine affinity for MAN1A2 and the drugs predicted by DSigDB. Conclusions: Our study provides comprehensive evidence supporting MAN1A2 as a promising causal gene and therapeutic target for RLS, offering insights into the underlying molecular mechanisms and paving the way for future drug development efforts.

A scoping review of statistical methods to investigate colocalization between genetic associations and microRNA expression in osteoarthritis

Background

Genetic colocalization analysis is a statistical method that evaluates whether two traits (e.g., osteoarthritis [OA] risk and microRNA [miRNA] expression levels) share the same or distinct genetic association signals in a locus typically identified in genome-wide association studies (GWAS). This method is useful for providing insights into the biological relevance of genetic association signals, particularly in intergenic regions, which can help to elucidate disease mechanisms in OA and other complex traits.

Objectives

To review the existing literature on genetic colocalization methods, assess their suitability for studying OA, and investigate their capacity to integrate miRNA data, while bearing in view their statistical assumptions.

Design

We followed scoping review methodology and used Covidence software for data management. Search terms for colocalization, GWAS, and genetic or statistical models were used in the databases MEDLINE and EMBASE, searched till March 4, 2024.

Results

Our search returned 546 peer-reviewed papers, of which 96 were included following title/abstract and full-text screening. Based on both cumulative and annual publication counts, the most cited method for colocalization analysis was coloc. Four papers examined OA-related phenotypes, and none examined miRNA. An approach to colocalization analysis using miRNA was postulated based on further hand-searching.

Conclusions

Colocalization analysis is a largely unexplored method in OA. Many of the approaches to colocalization analysis identified in this review, including the integration of GWAS and miRNA data, may help to elucidate genetic and epigenetic factors implicated in OA and other complex traits.

Innovate therapeutic targets for autoimmune diseases: insights from proteome-wide mendelian randomization and Bayesian colocalization

BACKGROUND

Despite growing knowledge regarding the pathogenesis of autoimmune diseases (ADs) onset, the current treatment remains unsatisfactory. This study aimed to identify innovative therapeutic targets for ADs through various analytical approaches.

RESEARCH DESIGN AND METHODS

Utilizing Mendelian randomization, Bayesian co-localization, phenotype scanning, and protein-protein interaction network, we explored potential therapeutic targets for 14 ADs and externally validated our preliminary findings.

RESULTS

This study identified 12 circulating proteins as potential therapeutic targets for six ADs. Specifically, IL12B was judged to be a risk factor for ankylosing spondylitis (p = 1.61E - 07). TYMP (p = 6.28E - 06) was identified as a protective factor for ulcerative colitis. For Crohn's disease, ERAP2 (p = 4.47E - 14), HP (p = 2.08E - 05), and RSPO3 (p = 6.52E - 07), were identified as facilitators, whereas FLRT3 (p = 3.42E - 07) had a protective effect. In rheumatoid arthritis, SWAP70 (p = 3.26E - 10), SIGLEC6 (p = 2.47E - 05), ISG15 (p = 3.69E - 05), and FCRL3 (p = 1.10E - 10) were identified as risk factors. B4GALT1 (p = 6.59E - 05) was associated with a lower risk of Type 1 diabetes (T1D). Interestingly, CTSH was identified as a protective factor for narcolepsy (p = 1.58E - 09) but a risk factor for T1D (p = 7.36E - 11), respectively. External validation supported the associations of eight of these proteins with three ADs.

CONCLUSIONS

Our integrated study identified 12 potential therapeutic targets for ADs and provided novel insights into future drug development for ADs.

Multi-omics combined to investigate potential druggable therapeutic targets for stroke: A systematic Mendelian randomization study and transcriptome verification

OBJECTIVE

Stroke is a highly prevalent and disabling disease whose disease mechanisms are not fully understood. The discovery of disease-associated proteins with genetic evidence of pathogenicity provides an opportunity to identify new therapeutic targets.

METHOD

We examined the observed and causal associations of thousands of plasma and inflammatory proteins that were measured using affinity-based proteomic assays. First, we pooled >3000 relevant proteins using a fixed-effects meta-analysis of 2 population-based studies involving 48,383 participants, then investigated the causal effects of stroke and its subtype-associated proteins by forward Mendelian randomization using cis-protein quantitative locus genetic tools identified from genome-wide association studies of these >48,000 individuals. To improve the accuracy of causal estimation, we implemented a systematic Mendelian randomization model that accounts for cascading imbalances between instruments and tested the robustness of causal estimation through multi-method analyses. To further validate the hypothesis that ginsenoside Rg1 monomer acts on the five protein targets screened for drug-targeted regulation, we conducted a comparative analysis of the mRNA (gene) expression levels of a limited number of genes in the brain tissues of different groups of SD rats. The druggability of the candidate proteins was investigated and the mechanism of action and potential targeting side effects were explored by Phenome-wide MR.

RESULTS

Six circulating proteins were identified to have a significant genetic association with stroke (PFDR < 0.05). For example, in patients with cardioembolic stroke, higher genetically predicted APRT was associated with a lower risk of cardioembolic stroke (ORivw [95 % CI] = 0.641 [0.517, 0.795]; P = 5.25 × 10-5, ORSMR [95 % CI] = 0.572, [0.397, 0.825], PSMR = 0.003). Mediation analyses suggested that atrial fibrillation, angina pectoris, and heart failure may mediate the association of CD40L, LIFR, and UPA with stroke. Molecular docking revealed promising interactions between the identified proteins and glycosides. Transcriptomic sequencing in animal models indicated that ginsenoside Rg1 may act through APRT, IL15RA, and VSIR pathways, with APRT showing significant variability in mRNA sequencing expression. Phenome-wide MR of the six target proteins showed an overwhelming predominance of PFDR > 0.05, indicating less toxicity.

CONCLUSIONS

The present study provides genetic evidence to support the potential efficacy of targeting the three druggable protein targets for the treatment of stroke. This is achieved by triangulating population genomic and proteomic data. Furthermore, the study validates the pathway mechanisms by which APRT, IL15RA, and VSIR dock ginsenoside Rg1 in animal models. This will help to prioritize stroke drug development.

MIMOSA: a resource consisting of improved methylome prediction models increases power to identify DNA methylation-phenotype associations

Although DNA methylation (DNAm) has been implicated in the pathogenesis of numerous complex diseases, from cancer to cardiovascular disease to autoimmune disease, the exact methylation sites that play key roles in these processes remain elusive. One strategy to identify putative causal CpG sites and enhance disease etiology understanding is to conduct methylome-wide association studies (MWASs), in which predicted DNA methylation that is associated with complex diseases can be identified. However, current MWAS models are primarily trained using the data from single studies, thereby limiting the methylation prediction accuracy and the power of subsequent association studies. Here, we introduce a new resource, MWAS Imputing Methylome Obliging Summary-level mQTLs and Associated LD matrices (MIMOSA), a set of models that substantially improve the prediction accuracy of DNA methylation and subsequent MWAS power through the use of a large summary-level mQTL dataset provided by the Genetics of DNA Methylation Consortium (GoDMC). Through the analyses of GWAS (genome-wide association study) summary statistics for 28 complex traits and diseases, we demonstrate that MIMOSA considerably increases the accuracy of DNA methylation prediction in whole blood, crafts fruitful prediction models for low heritability CpG sites, and determines markedly more CpG site-phenotype associations than preceding methods. Finally, we use MIMOSA to conduct a case study on high cholesterol, pinpointing 146 putatively causal CpG sites.

Proteogenomic analysis of human cerebrospinal fluid identifies neurologically relevant regulation and implicates causal proteins for Alzheimer's disease

The integration of quantitative trait loci (QTLs) with disease genome-wide association studies (GWASs) has proven successful in prioritizing candidate genes at disease-associated loci. QTL mapping has been focused on multi-tissue expression QTLs or plasma protein QTLs (pQTLs). We generated a cerebrospinal fluid (CSF) pQTL atlas by measuring 6,361 proteins in 3,506 samples. We identified 3,885 associations for 1,883 proteins, including 2,885 new pQTLs, demonstrating unique genetic regulation in CSF. We identified CSF-enriched pleiotropic regions on chromosome (chr)3q28 near OSTN and chr19q13.32 near APOE that were enriched for neuron specificity and neurological development. We integrated our associations with Alzheimer's disease (AD) through proteome-wide association study (PWAS), colocalization and Mendelian randomization and identified 38 putative causal proteins, 15 of which have drugs available. Finally, we developed a proteomics-based AD prediction model that outperforms genetics-based models. These findings will be instrumental to further understand the biology and identify causal and druggable proteins for brain and neurological traits.

Proteomics appending a complementary dimension to precision oncotherapy

Recent advances in high-throughput proteomic profiling technologies have facilitated the precise quantification of numerous proteins across multiple specimens concurrently. Researchers have the opportunity to comprehensively analyze the molecular signatures in plentiful medical specimens or disease pattern cell lines. Along with advances in data analysis and integration, proteomics data could be efficiently consolidated and employed to recognize precise elementary molecular mechanisms and decode individual biomarkers, guiding the precision treatment of tumors. Herein, we review a broad array of proteomics technologies and the progress and methods for the integration of proteomics data and further discuss how to better merge proteomics in precision medicine and clinical settings.

The effect of histo-blood group ABO system transferase (BGAT) on pregnancy related outcomes：A Mendelian randomization study

Protein level of Histo-Blood Group ABO System Transferase (BGAT) has been reported to be associated with cardiometabolic diseases. But its effect on pregnancy related outcomes still remains unclear. Here we conducted a two-sample Mendelian randomization (MR) study to ascertain the putative causal roles of protein levels of BGAT in pregnancy related outcomes. Cis-acting protein quantitative trait loci (pQTLs) robustly associated with protein level of BGAT (P < 5 ×10-8) were used as instruments to proxy the BGAT protein level (N = 35,559, data from deCODE), with two additional pQTL datasets from Fenland (N = 10,708) and INTERVAL (N = 3301) used as validation exposures. Ten pregnancy related diseases and complications were selected as outcomes. We observed that a higher protein level of BGAT showed a putative causal effect on venous complications and haemorrhoids in pregnancy (VH) (odds ratio [OR]=1.19, 95% confidence interval [95% CI]=1.12-1.27, colocalization probability=91%), which was validated by using pQTLs from Fenland and INTERVAL. The Mendelian randomization results further showed effects of the BGAT protein on gestational hypertension (GH) (OR=0.97, 95% CI=0.96-0.99), despite little colocalization evidence to support it. Sensitivity analyses, including proteome-wide Mendelian randomization of the cis-acting BGAT pQTLs, showed little evidence of horizontal pleiotropy. Correctively, our study prioritised BGAT as a putative causal protein for venous complications and haemorrhoids in pregnancy. Future epidemiology and clinical studies are needed to investigate whether BGAT can be considered as a drug target to prevent adverse pregnancy outcomes.

Novel Drug Targets for Atrial Fibrillation Identified Through Mendelian Randomization Analysis of the Blood Proteome

PURPOSE

Novel, effective, and safe preventive therapy targets for AF are still needed. Circulating proteins with causal genetic evidence are promising candidates. We aimed to systematically screen circulating proteins for AF drug targets and determine their safety and efficacy using genetic methods.

METHODS

The protein quantitative trait loci (pQTL) of up to 1949 circulating proteins were retrieved from nine large genome-proteome-wide association studies. Two-sample Mendelian Randomization (MR) and colocalization analyses were used to estimate the causal effects of proteins on the risk of AF. Furthermore, phenome-wide MR was conducted to depict side effects and the drug-target databases were searched for drug validation and repurposing.

RESULTS

Systematic MR screen identified 30 proteins as promising AF drug targets. Genetically predicted 12 proteins increased AF risk (TES, CFL2, MTHFD1, RAB1A, DUSP13, SRL, ANXA4, NEO1, FKBP7, SPON1, LPA, MANBA); 18 proteins decreased AF risk (PMVK, UBE2F, SYT11, CHMP3, PFKM, FBP1, TNFSF12, CTSZ, QSOX2, ALAD, EFEMP1, FLRT2, LRIG1, OLA1, SH3BGRL3, IL6R, B3GNT8, FCGR2A). DUSP13 and TNFSF12 possess strong colocalization evidence. For the proteins that were identified, extended phe-MR analysis was conducted to assess their side-effect profiles, while drug-target databases provided information on their approved or investigated indications.

CONCLUSION

We identified 30 circulating proteins as potential preventive targets for AF.

Understanding the Genetic Architecture of Vitamin Status Biomarkers in the Genome-Wide Association Study Era: Biological Insights and Clinical Significance

Vitamins play an intrinsic role in human health and are targets for clinical intervention through dietary or pharmacological approaches. Biomarkers of vitamin status are complex traits, measurable phenotypes that arise from an interplay between dietary and other environmental factors with a genetic component that is polygenic, meaning many genes are plausibly involved. Studying these genetic influences will improve our knowledge of fundamental vitamin biochemistry, refine estimates of the effects of vitamins on human health, and may in future prove clinically actionable. Here, we evaluate genetic studies of circulating and excreted biomarkers of vitamin status in the era of hypothesis-free genome-wide association studies (GWAS) that have provided unprecedented insights into the genetic architecture of these traits. We found that the most comprehensive and well-powered GWAS currently available were for circulating status biomarkers of vitamin A, C, D, and a subset of the B vitamins (B9 and B12). The biology implicated by GWAS of measured biomarkers of each vitamin is then discussed, both in terms of key genes and higher-order processes. Across all major vitamins, there were genetic signals revealed by GWAS that could be directly linked with known vitamin biochemistry. We also outline how genetic variants associated with vitamin status biomarkers have been already extensively used to estimate causal effects of vitamins on human health outcomes, which is particularly important given the large number of randomized control trials of vitamin related interventions with null findings. Finally, we discuss the current evidence for the clinical applicability of findings from vitamin GWAS, along with future directions for the field to maximize the utility of these data.

Identification of Novel Proteins Mediating Causal Association Between Smoking and Essential Hypertension: A Mendelian Randomization Study

BACKGROUND

Smoking is a factor for hypertension. We aim to reveal novel plasma proteins mediating the relationship of smoking with hypertension and identify potential drug targets for hypertension on the basis of Mendelian randomization design.

METHODS AND RESULTS

Data for smoking were selected from the largest genome-wide association study meta-analysis performed by the Genome-Wide Association Study and Sequencing Consortium of Alcohol and Nicotine Use. Data for plasma proteins were selected from the deCODE Health study and the UK Biobank Pharma Proteomics Project. Data for hypertension were extracted from the FinnGen Study. Moreover, proteome-wide Mendelian randomization and colocalization analyses, 2-step Mendelian randomization, and gene function and network prediction, as well as druggability assessment were performed. We finally identified 8 proteins (ANXA4 [annexin A4], DLK1 [protein delta homolog 1], KLB [β-klotho], MMP8 [matrix metallopeptidase 8], PLAT [tissue-type plasminogen activator], POSTN [periostin], SAT2 [thialysine N-ε-acetyltransferase], and IFNLR1 [interferon λ receptor 1]) mediating association of smoking with hypertension. PLAT and IFNLR1 were identified to be involved in the complement and coagulation cascades and the Janus kinase/signal transducer and activator of transcription signaling pathway. ANXA4, KLB, MMP8, PLAT, and IFNLR1 had druggability. Moreover, IFNLR1 had strong evidence of genetic colocalization, because the posterior probability for H4 of IFNLR1 was 91.3%.

CONCLUSIONS

This study identified the 8 proteins that mediate causal association between smoking and essential hypertension. Interferon λ receptor agonist targeting IFNLR1 may open a new avenue for treating hypertension. Our discoveries provide new insights into protein pathogenesis of hypertension and to better guide hypertension prevention and treatment among smokers.

Unveiling therapeutic targets for spinal stenosis from genetic insights: a Mendelian randomization analysis

Spinal stenosis is a commonly chronic spinal degenerative disease, which is a major cause of pain and dysfunction in the elderly. Mendelian randomization (MR) has been widely applied to repurpose licensed drugs and identify novel therapeutic targets. Consequently, we intended to identify new therapeutic targets for spinal stenosis and to analyze their possible mechanisms and potential side effects.We conducted the Mendelian randomization analysis to identify potential drug targets for the management of spinal stenosis. Cis-expressed quantitative trait loci (cis-eQTL) data as genetic instrumental variables were acquired from the eQTLGen consortium. The summary statistics for single nucleotide polymorphism (SNP) associations of spinal stenosis were obtained from the FinnGen study(20,807 cases and 294,770 controls). Co-localization analysis was performed to determine whether there was shared causal variation between the SNPs associated with spinal stenosis as well as the eQTL. Multiple external validations were performed to reinforce the reliability and stability of the findings utilizing the cis-eQTL from the GTEx portal, the Ferkingstad et al. pQTL dataset, and the Sun et al. pQTL dataset. The viability of the identified drug targets for future clinical applications was elucidated through the phenome-wide association study and drug candidate prediction. Three drug targets (BMP6, DLK1, and GFPT1) exhibited significant causal associations with spinal stenosis in the eQTLGen cohort by MR analysis, which was strongly supported by the results of the co-localization analysis. The causal association of DLK1 and GFPT1 with spinal stenosis remained remarkable with multiple external validations. Multivariate MR and phenome-wide association study analysis indicated that both targets were not associated with other traits. In addition, phenome-wide association study analysis and drug prediction analysis demonstrated the potential of these two targets for future clinical applications. In this study, DLK1 and GFPT1 were identified as promising novel therapeutic targets for spinal stenosis, providing initial genetic insights for drug development in spinal stenosis.

Identification of novel drug targets for liver cirrhosis and its potential side-effects by human plasma proteome

Liver cirrhosis, a common liver disease, currently lacks specific targeted therapies. This study investigates the potential therapeutic effects of serum circulating proteins on cirrhosis from a genetic perspective, and identified six associated plasma proteins (SERPINA1, PSG5, NCAN, APOE, ADH1B, GM2A). To search for therapeutic drugs associated with circulating proteins, databases such as DrugBank and DGIdb are utilized. Phenome-wide Mendelian Randomization analysis of the six significantly associated proteins revealed that GM2A exhibited no notable side effects as a therapeutic target for cirrhosis, SERPINA1 may offer additional therapeutic benefits for cholelithiasis and emphysema. ADH1B serves as a potential drug target that could simultaneously reduce the risk of alcohol-related disorders and hypertension. Furthermore, PSG5 and APOE might increase the risk of cardiovascular and neurological diseases, and NCAN has the potential to additionally reduce the risk of developing non-alcoholic fatty liver disease NAFLD. In conclusions, this study substantiates, from a genetic perspective, the potential therapeutic target role of six plasma proteins in cirrhosis, while comprehensively evaluating their side effects.

Genetic variation perspective reveals potential drug targets for subtypes of endometrial cancer

The study aims to identify potential drug targets for endometrial cancer (EC) subtypes through a Mendelian randomization (MR) approach, assessing their clinical relevance. We utilized genetic instruments for 4,907 plasma proteins from the deCODE Genetics study dataset, and data with EC (n = 12,906) from a genome-wide study (GWAS) meta-analysis in European populations for MR analyses. Complementary analyses included protein-protein interactions (PPI) network analysis, therapeutic efficacy evaluation, differential gene expression assessment, and prognosis evaluation. The expression levels of key drug targets were quantitatively measured at both the transcriptional and translational stages utilizing reverse transcription quantitative PCR (RT-qPCR) and immunohistochemistry (IHC). Additionally, we analyzed various clinicopathological features. Five drug targets for EC (CBR3, GSTO1, HHIP, IGF2R, and MMP10), seven for endometrioid subtypes (ACAP2, CBR3, GSTO1, HHIP, IGF2R, MMP10, and TLR2), and seven for non-endometrioid subtypes (CST3, DNAJB14, FSTL5, GMPR2, IFI16, MAPK9, and NEO1) were identified. Among these, IGF2R (OR = 1.165; 95% CI 1.067-1.272; p = 1.046 × 10- 2) and CST3 (OR = 0.523; 95% CI 0.339-0.804; p = 7.010 × 10- 3) were highlighted as key drug targets with causal evidence both at transcriptional and translational levels. This study preliminarily confirms that IGF2R and CST3 may serve as novel targets for the treatment of EC, providing a foundational reference for innovative clinical approaches to this disease.

Circulating Metabolite Abundances Associated With Risks of Bipolar Disorder, Schizophrenia, and Depression: A Mendelian Randomization Study

BACKGROUND

Preventive measures and treatments for psychiatric disorders are limited. Circulating metabolites are potential candidates for biomarker and therapeutic target identification, given their measurability and essential roles in biological processes.

METHODS

Leveraging large-scale genome-wide association studies, we conducted Mendelian randomization analyses to assess the associations between circulating metabolite abundances and the risks of bipolar disorder, schizophrenia, and depression. Genetic instruments were selected for 94 metabolites measured in the Canadian Longitudinal Study on Aging cohort (N = 8299). We repeated Mendelian randomization analyses based on the UK Biobank, INTERVAL, and EPIC (European Prospective Investigation into Cancer)-Norfolk studies.

RESULTS

After validating Mendelian randomization assumptions and colocalization evidence, we found that a 1 SD increase in genetically predicted circulating abundances of eicosapentaenoate and docosapentaenoate was associated with odds ratios of 0.72 (95% CI, 0.65-0.79) and 0.63 (95% CI, 0.55-0.72), respectively, for bipolar disorder. Genetically increased Ω-3 unsaturated fatty acids abundance and Ω-3-to-total fatty acids ratio, as well as genetically decreased Ω-6-to-Ω-3 ratio, were negatively associated with the risk of bipolar disorder in the UK Biobank. Genetically increased circulating abundances of 3 N-acetyl-amino acids were associated with an increased risk of schizophrenia with a maximum odds ratio of 1.31 (95% CI, 1.18-1.44) per 1 SD increase. Furthermore, a 1 SD increase in genetically predicted circulating abundance of hypotaurine was associated with an odds ratio of 0.85 (95% CI, 0.78-0.93) for depression.

CONCLUSIONS

The biological mechanisms that underlie Ω-3 unsaturated fatty acids, NAT8-catalyzed N-acetyl-amino acids, and hypotaurine warrant exploration to identify new biomarkers and potential therapeutic targets.

Identification of Potential Biomarkers and Therapeutic Targets for Periodontitis

BACKGROUND

Periodontitis is a chronic and multifactorial inflammatory disease. However, existing medications often lack sufficient therapeutic effects. The aim is to identify potential biomarkers and efficient therapeutic targets using Mendelian randomisation (MR) and single-cell analysis.

METHODS

MR analysis was conducted based on the cis-expression quantitative trait loci (cis-eQTLs) extracted from the eQTLGen Consortium and genome-wide association study (GWAS) data of periodontitis sourced from the Gene Lifestyle Interactions in Dental Endpoints (GLIDE) consortium (17,353 cases, 28,210 controls). Subsequently, colocalisation analysis was employed to detect whether genes and periodontitis shared the same casual variant. Finally, enrichment analysis, protein-protein interaction (PPI) networks, drug prediction, phenome-wide association study (PheWAS), molecular docking, and single-cell analysis were conducted to validate the significance of target genes.

RESULTS

Fourteen drug targets were significant related with periodontitis in MR analysis. Following the colocalisation and summary-data-based MR (SMR) analysis, 3 targets (S100A12, S100A9, and S100A8) were classified into tier 1 with strong evidence, 6 therapeutic targets (ADAM12, ADHFE1, BLK, HEBP1, SERPINE2, and TEK) were classified into tier 2 with moderate evidence, and 5 therapeutic targets (LY86, MMEL1, S100B, SPP1, and TRIB3) were classified into tier 3 with convincing evidence. PheWAS analysis showed that only TEK and SPP1 in tier 2 may induce side effects, including cardiometabolic and oncological issues. Molecular docking demonstrated strong binding between drugs and their respective protein targets. In the single-cell analysis, 5 target genes (HEBP1, LY86, S100A8, S100A9, and S100A12) exhibited enrichment in monocytes, while BLK and LY86 were primarily enriched in B cells.

CONCLUSION

The study identified 14 potential therapeutic targets for periodontitis. Among these, 3 therapeutic targets (S100A12, S100A9, and S100A8) demonstrated robust and well-supported results. Drugs designed to target these genes have a higher possibility of success in clinical trials, which are hopeful for prioritising periodontitis drug development.

CCN3/NOV as a potential therapeutic target for diverticular disease: A proteome-wide Mendelian randomization study

Genome-wide association studies (GWAS) identified over 100 susceptibility loci and candidate causal genes for diverticular disease (DD) at the transcriptional level. However, effective therapeutics or preventions based on underlying disease mechanisms remain to be elucidated. In this study, we explored potential causal genes for DD at the protein level. We used 2 GWAS summary statistics of DD; 1 was obtained from the United Kingdom Biobank (UKBB) with 31,917 cases and 419,135 controls, and the other from the FinnGen consortium with 30,649 cases and 301,931 controls. For the primary analysis, we employed proteome-wide Mendelian randomization (MR) studies using 738 cis-acting protein quantitative trait loci (pQTLs) for 735 plasma proteins from the 5 published studies. For external validation, we conducted 2-sample MR analyses using plasma pQTLs of the screened proteins from another study by deCODE genetics. Moreover, we performed a series of sensitivity analyses including reverse MR and Bayesian colocalization tests. The primary MR identified 4 plasma proteins that were associated with DD risk including CCN3/NOV (odds ratio [OR], 0.98; 95% confidence interval [CI], 0.97-0.99; P = 1.2 × 10-11 for UKBB. OR, 0.73; 95% CI, 0.66-0.81; P = 7.2 × 10-10 for FinnGen). The validation MR well replicated the primary result of CCN3/NOV (OR, 0.95; 95% CI, 0.93-0.96; P = 1.9 × 10-11 for UKBB. OR, 0.43; 95% CI, 0.33-0.56; P = 7.0 × 10-10 for FinnGen). Sensitivity analyses supported the causal association. We prioritized plasma CCN3/NOV protein as a protective factor for DD for follow-up functional studies to elucidate the disease mechanisms and therapeutics.

Investigating potential drug targets for IgA nephropathy and membranous nephropathy through multi-queue plasma protein analysis: a Mendelian randomization study based on SMR and co-localization analysis

BACKGROUND

Membranous nephropathy (MN) and IgA nephropathy (IgAN) pose challenges in clinical treatment with existing therapies primarily focusing on symptom relief and often yielding unsatisfactory outcomes. The search for novel drug targets remains crucial to address the shortcomings in managing both kidney diseases.

METHODS

Utilizing GWAS data for MN (ncase = 2150, ncontrol = 5829) and IgAN (ncase = 15587, ncontrol = 462197), instrumental variables for plasma proteins were derived from recent GWAS. Sensitivity analysis involved bidirectional Mendelian randomization analysis, MR Steiger, Bayesian co-localization, and Phenotype scanning. The SMR analysis using eQTL data from the eQTLGen Consortium was conducted to assess the availability of selected protein targets. The PPI network was constructed to reveal potential associations with existing drug treatment targets.

RESULTS

The study, subjected to the stringent Bonferroni correction, revealed significant associations: four proteins with MN and three proteins with IgAN. In plasma protein cis-pQTL data from two cohorts, an increase in one standard deviation in PLA2R1 (OR = 2.01, 95%CI = 1.83-2.21), AIF1 (OR = 9.04, 95%CI = 4.69-17.41), MLN (OR = 3.79, 95%CI = 2.12-6.78), and NFKB1 (OR = 29.43, 95%CI = 7.73-112.0) was associated with an increased risk of MN. Additionally, in plasma protein cis-pQTL data, a standard deviation increase in FCGR3B (OR = 1.15, 95%CI = 1.09-1.22) and BTN3A1 (OR = 4.05, 95%CI = 2.65-6.19) correlated with elevated IgAN risk, while AIF1 (OR = 0.58, 95%CI = 0.46-0.73) exhibited IgAN protection. Bayesian co-localization indicated that PLA2R1 (coloc.abf-PPH4 = 0.695), NFKB1 (coloc.abf-PPH4 = 0.949), FCGR3B (coloc.abf-PPH4 = 0.909), and BTN3A1 (coloc.abf-PPH4 = 0.685) share the same variants associated with MN and IgAN. The SMR analysis indicated a causal link between NFKB1 and BTN3A1 plasma protein eQTL in both conditions, and BTN3A1 was validated externally.

CONCLUSION

Genetically influenced plasma levels of PLA2R1 and NFKB1 impact MN risk, while FCGR3B and BTN3A1 levels are causally linked to IgAN risk, suggesting potential drug targets for further clinical exploration, notably BTN3A1 for IgAN.

Mendelian Randomization Estimates the Effects of Plasma and Cerebrospinal Fluid Proteins on Intelligence, Fluid Intelligence Score, and Cognitive Performance

Observational studies have revealed associations between levels of plasma and cerebrospinal fluid (CSF) proteins and cognition-related traits. However, these associations may be influenced by confounding factors inherent in observational research. This study aims to identify plasma and CSF proteins associated with intelligence, fluid intelligence score, and cognitive performance through the application of Mendelian randomization (MR). Proteomic quantitative trait locus (pQTL) data for plasma and CSF proteins were sourced from existing genome-wide association study (GWAS). Intelligence, fluid intelligence score, and cognitive performance GWAS summary statistics provided comprehensive data for two-sample MR analysis. Extensive sensitivity analyses, including Steiger testing, reverse MR analysis, and Bayesian co-localization, were conducted to validate associations and identify shared genetic variants. Phenotype scanning explored potential pleiotropic effects. MR analysis identified several proteins in plasma and CSF significantly associated with intelligence, fluid intelligence scores, and cognitive performance. For intelligence, negatively associated proteins in plasma include endoplasmic reticulum aminopeptidase 2 (ERAP2) and secretogranin III (SCG3), while positively associated proteins are myeloperoxidase (MPO), signal regulatory protein alpha (SIRPA), regulator of microtubule dynamics 1 (RMDN1), and endoplasmic reticulum lectin 1 (ERLEC1). In CSF, C1-esterase inhibitor and carboxypeptidase E (CBPE) both exhibited positive associations with intelligence. For fluid intelligence scores, negatively associated proteins in plasma are copine 1 (CPNE1) and SCG3, while positively associated proteins are nudix hydrolase 12 (NUDT12) and RMDN1. In CSF, Macrophage Stimulating Protein (MSP) demonstrated a significant negative impact. For cognitive performance, negatively associated proteins in plasma include ERAP2, tyrosine kinase with immunoglobulin-like and EGF-like domains 1 (TIE1), and SCG3, while positively associated proteins are NUDT12, RMDN1, ERLEC1, and ectonucleotide pyrophosphatase/phosphodiesterase family member 5 (ENPP5). In CSF, C1-esterase inhibitor was positively associated, while MSP and soluble tyrosine kinase with immunoglobulin-like and EGF-like domains 1(sTie-1) showed a negative association. Bayesian co-localization analysis revealed significant genetic overlaps between SIRPA, RMDN1, and ERLEC1 in plasma with intelligence; NUDT12 and SCG3 in plasma with fluid intelligence scores; and TIE1, NUDT12, RMDN1, ERLEC1, and ENPP5 in plasma with cognitive performance. Additionally, significant co-localization was identified between C1-esterase inhibitor and CBPE in CSF with intelligence, as well as between C1-esterase inhibitor and sTie-1 in CSF with cognitive performance. Reverse causality analysis confirmed the causal direction from proteins to cognitive traits. This study identifies specific plasma and CSF proteins that significantly impact intelligence, fluid intelligence scores, and cognitive performance. These proteins could serve as biomarkers and targets for future research and therapeutic interventions aimed at sustaining cognitive abilities and reducing impairment risks.

Potential Drug Targets for Diabetic Retinopathy Identified Through Mendelian Randomization Analysis

Purpose

This study aimed to investigate the causal effect of plasma proteins on diabetic retinopathy (DR) risk and identify potential drug targets for this disease.

Methods

Two-sample Mendelian randomization was performed to explore potential drug targets for DR. A total of 734 proteins were selected as instrumental variables. The Steiger filtering test and colocalization analysis were conducted to determine the causal direction and genetic pleiotropy. Plasma proteins from the decode study were used to validate the findings.

Results

Eleven plasma proteins were associated with DR risk. Genetically predicted high levels of CCL3L1 (odds ratio [OR] = 0.582; 95% confidence interval [CI], 0.343-0.986; P = 0.044), PAM (OR = 0.782; 95% CI, 0.652-0.937; P = 0.008), GP1BA (OR = 0.793; 95% CI, 0.632-0.994; P = 0.044), GALNT16 (OR = 0.832; 95% CI, 0.727-0.952; P = 0.008), POGLUT1 (OR = 0.836; 95% CI = 0.703-0.995; P = 0.043), and DKK3 (OR = 0.859; 95% CI, 0.777-0.950; P = 0.003) have the protective effect on DR risk. Genetically predicted high levels of GFRA2 (OR = 1.104; 95% CI, 1.028-1.187; P = 0.007), PATE4 (OR = 1.405; 95% CI, 1.060-1.860; P = 0.018), GSTA1 (OR = 1.464; 95% CI, 1.163-1.842; P = 0.001), SIRPG (OR = 1.600, 95% CI, 1.244-2.057; P = 2.51E-04), and MAPK13 (OR = 1.731; 95% CI, 1.233-2.431; P = 0.002) were associated with an increased risk of DR. However, the colocalization analysis results suggested that SIRPG and GP1BA have a shared causal variant with DR.

Conclusions

CCL3L1, PAM, GALNT16, POGLUT1, DKK3, GFRA2, PATE4, GSTA1, and MAPK13 were associated with DR risk and were identified as potential drug targets for DR.

Translational Relevance

The present study has highlighted the role of CCL3L1, PAM, GALNT16, POGLUT1, DKK3, GFRA2, PATE4, GSTA1, and MAPK13 in the development of DR.

Assessing the causal relationship between plasma proteins and osteoporosis: novel insights into pathological mechanisms and therapeutic implications

Identifying dysregulated plasma proteins in osteoporosis (OP) progression offers insights into prevention and treatment. This study found 8 such proteins associated with OP, suggesting them as therapy targets. This discovery may cut drug development costs and improve personalized treatments.

PURPOSE

This study aims to identify potential therapeutic targets for OP using summary data-based Mendelian randomization (SMR) and colocalization analysis methods. Furthermore, we seek to explore the biological significance and pharmacological value of these drug targets.

METHODS

To identify potential therapeutic targets for OP, we conducted SMR and colocalization analysis. Plasma protein (pQTL, exposure) data were sourced from the study by Ferkingstad et al. (n = 35,559). Summary statistics for bone mineral density (BMD, outcome) were obtained from the GWAS Catalog (n = 56,284). Additionally, we utilized enrichment analysis, protein-protein interaction (PPI) network analysis, drug prediction, and molecular docking to further analyze the biological significance and pharmacological value of these drug targets.

RESULTS

In the SMR analysis, while 20 proteins showed significance, only 8 potential drug targets (GCKR, ERBB3, CFHR1, GPN1, SDF2, VTN, BET1L, and SERPING1) received support from colocalization (PP.H4 > 0.8). These proteins are closely associated with immune function in terms of biological significance. Molecular docking also demonstrated favorable binding of drugs to proteins, consistent with existing structural data, further substantiating the pharmacological value of these targets.

CONCLUSIONS

The study identified 8 potential drug targets for OP. These prospective targets are believed to have a higher chance of success in clinical trials, thus aiding in prioritizing OP drug development and reducing development costs.

Transcriptome-wide Mendelian randomization during CD4+ T cell activation reveals immune-related drug targets for cardiometabolic diseases

Immunity has shown potentials in informing drug development for cardiometabolic diseases, such as type 2 diabetes (T2D) and coronary artery disease (CAD). Here, we performed a transcriptome-wide Mendelian randomization (MR) study to estimate the putative causal effects of 11,021 gene expression profiles during CD4+ T cells activation on the development of T2D and CAD. Robust MR and colocalization evidence was observed for 162 genes altering T2D risk and 80 genes altering CAD risk, with 12% and 16% respectively demonstrating CD4+ T cell specificity. We observed temporal causal patterns during T cell activation in 69 gene-T2D pairs and 34 gene-CAD pairs. These genes were eight times more likely to show robust genetic evidence. We further identified 25 genes that were targets for drugs under clinical investigation, including LIPA and GCK. This study provides evidence to support immune-to-metabolic disease connections, and prioritises immune-mediated drug targets for cardiometabolic diseases.