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

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.

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.

C-reactive protein and cognitive impairment: A bidirectional Mendelian randomization study

OBJECTIVES

While C-reactive protein (CRP) has been solidly linked as a risk factor for cognitive impairment, observational research alone cannot definitively demonstrate a causal relationship. This study therefore sought to determine whether there was an association between CRP and the development of cognitive impairment.

METHODS

This study employed bidirectional Mendelian randomization (MR) to investigate the genetic association between CRP and cognitive impairment. genome-wide association studies (GWAS) summary statistics for both were sourced from IEU Open GWAS or prior reports. Cognitive GWAS's used were on tests designed to assess cognitive performance, fluid intelligence, prospective memory, and reaction time. The MR analysis applied several methods, including inverse variance-weighted (IVW), MR Egger, weighted median, simple mode, and weighted mode approaches, then use of MR sensitivity analyses to interrogate findings.

RESULTS

Forward MR analysis showed that genetically proxied CRP was associated with prospective memory (P = 0.009), whereas there is little evidence to support an association between CRP and other cognitive tests. Reverse MR analysis indicated a potential association between genetic proxy cognitive performance (P = 0.002) and fluid intelligence score (P = 0.019) with CRP levels. For genetically proxied CRP on prospective memory, the level of pleiotropy (P > 0.05) and no genetic variant heterogeneity (P > 0.05) made bias unlikely, and leave-one-out tests also confirmed robust associations.

CONCLUSIONS

The effect of genetically proxied CRP on prospective memory, with little evidence on other cognitive tests. The reverse MR shows some evidence of genetically proxied cognition (cognitive performance and fluid intelligence) on CRP levels.

From genetic associations to genes: methods, applications, and challenges

Genome-wide association studies (GWASs) have identified numerous genetic loci associated with human traits and diseases. However, pinpointing the causal genes remains a challenge, which impedes the translation of GWAS findings into biological insights and medical applications. In this review, we provide an in-depth overview of the methods and technologies used for prioritizing genes from GWAS loci, including gene-based association tests, integrative analysis of GWAS and molecular quantitative trait loci (xQTL) data, linking GWAS variants to target genes through enhancer-gene connection maps, and network-based prioritization. We also outline strategies for generating context-dependent xQTL data and their applications in gene prioritization. We further highlight the potential of gene prioritization in drug repurposing. Lastly, we discuss future challenges and opportunities in this field.

Unveiling potential therapeutic targets for diabetes-induced frozen shoulder through Mendelian randomization analysis of the human plasma proteome

INTRODUCTION

This study aimed to assess the causal relationship between diabetes and frozen shoulder by investigating the target proteins associated with diabetes and frozen shoulder in the human plasma proteome through Mendelian randomization (MR) and to reveal the corresponding pathological mechanisms.

RESEARCH DESIGN AND METHODS

We employed the MR approach for the purposes of establishing: (1) the causal link between diabetes and frozen shoulder; (2) the plasma causal proteins associated with frozen shoulder; (3) the plasma target proteins associated with diabetes; and (4) the causal relationship between diabetes target proteins and frozen shoulder causal proteins. The MR results were validated and consolidated through colocalization analysis and protein-protein interaction network.

RESULTS

Our MR analysis demonstrated a significant causal relationship between diabetes and frozen shoulder. We found that the plasma levels of four proteins were correlated with frozen shoulder at the Bonferroni significance level (p<3.03E-5). According to colocalization analysis, parathyroid hormone-related protein (PTHLH) was moderately correlated with the genetic variance of frozen shoulder (posterior probability=0.68), while secreted frizzled-related protein 4 was highly correlated with the genetic variance of frozen shoulder (posterior probability=0.97). Additionally, nine plasma proteins were activated during diabetes-associated pathologies. Subsequent MR analysis of nine diabetic target proteins with four frozen shoulder causal proteins indicated that insulin receptor subunit alpha, interleukin-6 receptor subunit alpha, interleukin-1 receptor accessory protein, glutathione peroxidase 7, and PTHLH might contribute to the onset and progression of frozen shoulder induced by diabetes.

CONCLUSIONS

Our study identified a causal relationship between diabetes and frozen shoulder, highlighting the pathological pathways through which diabetes influences frozen shoulder.

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 (MR) 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 of Aging (N=8,299) cohort. We repeated MR analyses based on the UK Biobank, INTERVAL, and EPIC-Norfolk studies.

RESULTS

After validating MR assumptions and colocalization evidence, we found that a one standard deviation (SD) increase in genetically predicted circulating abundances of eicosapentaenoate (EPA) and docosapentaenoate (n3 DPA) was associated with odds ratios (ORs) of 0.72 (95% CI: 0.65-0.79) and 0.63 (95% CI: 0.55-0.72) for bipolar disorder, respectively. 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 three N-acetyl-amino acids were associated with an increased risk of schizophrenia with a maximum OR of 1.31 (95% CI: 1.18-1.44) per one SD increase. Furthermore, a one SD increase in genetically predicted circulating abundance of hypotaurine was associated with an OR of 0.85 (95% CI: 0.78-0.93) for depression.

CONCLUSIONS

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

Nominating novel proteins for anxiety via integrating human brain proteomes and genome-wide association study

BACKGROUND

The underlying pathogenesis of anxiety remain elusive, making the pinpointing of potential therapeutic and diagnostic biomarkers for anxiety paramount to its efficient treatment.

METHODS

We undertook a proteome-wide association study (PWAS), fusing human brain proteomes from both discovery (ROS/MAP; N = 376) and validation cohorts (Banner; N = 152) with anxiety genome-wide association study (GWAS) summary statistics. Complementing this, we executed transcriptome-wide association studies (TWAS) leveraging human brain transcriptomic data from the Common Mind Consortium (CMC) to discern the confluence of genetic influences spanning both proteomic and transcriptomic levels. We further scrutinized significant genes through a suite of methodologies.

RESULTS

We discerned 14 genes instrumental in the genesis of anxiety through their specific cis-regulated brain protein abundance. Out of these, 6 were corroborated in the confirmatory PWAS, with 4 also showing associations with anxiety via their cis-regulated brain mRNA levels. A heightened confidence level was attributed to 5 genes (RAB27B, CCDC92, BTN2A1, TMEM106B, and DOC2A), taking into account corroborative evidence from both the confirmatory PWAS and TWAS, coupled with insights from mendelian randomization analysis and colocalization evaluations. A majority of the identified genes manifest in brain regions intricately linked to anxiety and predominantly partake in lysosomal metabolic processes.

LIMITATIONS

The limited scope of the brain proteome reference datasets, stemming from a relatively modest sample size, potentially curtails our grasp on the entire gamut of genetic effects.

CONCLUSION

The genes pinpointed in our research present a promising groundwork for crafting therapeutic interventions and diagnostic tools for anxiety.

Elevated ITGA1 levels in type 2 diabetes: implications for cardiac function impairment

AIMS/HYPOTHESIS

Type 2 diabetes mellitus is known to contribute to the development of heart failure with preserved ejection fraction (HFpEF). However, identifying HFpEF in individuals with type 2 diabetes early on is often challenging due to a limited array of biomarkers. This study aims to investigate specific biomarkers associated with the progression of HFpEF in individuals with type 2 diabetes, for the purpose of enabling early detection and more effective management strategies.

METHODS

Blood samples were collected from individuals with type 2 diabetes, both with and without HFpEF, for proteomic analysis. Plasma integrin α1 (ITGA1) levels were measured and compared between the two groups. Participants were further categorised based on ITGA1 levels and underwent detailed transthoracic echocardiography at baseline and during a median follow-up period of 30 months. Multivariable linear and Cox regression analyses were conducted separately to assess the associations between plasma ITGA1 levels and changes in echocardiography indicators and re-hospitalisation risk. Additionally, proteomic data for the individuals' left ventricles, from ProteomeXchange database, were analysed to uncover mechanisms underlying the change in ITGA1 levels in HFpEF.

RESULTS

Individuals with type 2 diabetes and HFpEF showed significantly higher plasma ITGA1 levels than the individuals with type 2 diabetes without HFpEF. These elevated ITGA1 levels were associated with left ventricular remodelling and impaired diastolic function. Furthermore, during a median follow-up of 30 months, multivariable analysis revealed that elevated ITGA1 levels independently correlated with deterioration of both diastolic and systolic cardiac functions. Additionally, higher baseline plasma ITGA1 levels independently predicted re-hospitalisation risk (HR 2.331 [95% CI 1.387, 3.917], p=0.001). Proteomic analysis of left ventricular myocardial tissue provided insights into the impact of increased ITGA1 levels on cardiac fibrosis-related pathways and the contribution made by these changes to the development and progression of HFpEF.

CONCLUSIONS/INTERPRETATION

ITGA1 serves as a biomarker for monitoring cardiac structural and functional damage, can be used to accurately diagnose the presence of HFpEF, and can be used to predict potential deterioration in cardiac structure and function as well as re-hospitalisation for individuals with type 2 diabetes. Its measurement holds promise for facilitating risk stratification and early intervention to mitigate the adverse cardiovascular effects associated with diabetes.

DATA AVAILABILITY

The proteomic data of left ventricular myocardial tissue from individuals with type 2 diabetes, encompassing both those with and without HFpEF, is available from the ProteomeXchange database at http://proteomecentral.proteomexchange.org .

​Comprehensive mendelian randomization analysis of plasma proteomics to identify new therapeutic targets for the treatment of coronary heart disease and myocardial infarction

BACKGROUND

Ischemic heart disease is one of the leading causes of mortality worldwide, and thus calls for development of more effective therapeutic strategies. This study aimed to identify potential therapeutic targets for coronary heart disease (CHD) and myocardial infarction (MI) by investigating the causal relationship between plasma proteins and these conditions.

METHODS

A two-sample Mendelian randomization (MR) study was performed to evaluate more than 1600 plasma proteins for their causal associations with CHD and MI. The MR findings were further confirmed through Bayesian colocalization, Summary-data-based Mendelian Randomization (SMR), and Transcriptome-Wide Association Studies (TWAS) analyses. Further analyses, including enrichment analysis, single-cell analysis, MR analysis of cardiovascular risk factors, phenome-wide Mendelian Randomization (Phe-MR), and protein-protein interaction (PPI) network construction were conducted to verify the roles of selected causal proteins.

RESULTS

Thirteen proteins were causally associated with CHD, seven of which were also causal for MI. Among them, FES and PCSK9 were causal proteins for both diseases as determined by several analytical methods. PCSK9 was a risk factor of CHD (OR = 1.25, 95% CI: 1.13-1.38, P = 7.47E-06) and MI (OR = 1.36, 95% CI: 1.21-1.54, P = 2.30E-07), whereas FES was protective against CHD (OR = 0.68, 95% CI: 0.59-0.79, P = 6.40E-07) and MI (OR = 0.65, 95% CI: 0.54-0.77, P = 5.38E-07). Further validation through enrichment and single-cell analysis confirmed the causal effects of these proteins. Moreover, MR analysis of cardiovascular risk factors, Phe-MR, and PPI network provided insights into the potential drug development based on the proteins.

CONCLUSIONS

This study investigated the causal pathways associated with CHD and MI, highlighting the protective and risk roles of FES and PCSK9, respectively. FES. Specifically, the results showed that these proteins are promising therapeutic targets for future drug development.

Identifying therapeutic targets for cancer among 2074 circulating proteins and risk of nine cancers

Circulating proteins can reveal key pathways to cancer and identify therapeutic targets for cancer prevention. We investigate 2,074 circulating proteins and risk of nine common cancers (bladder, breast, endometrium, head and neck, lung, ovary, pancreas, kidney, and malignant non-melanoma) using cis protein Mendelian randomisation and colocalization. We conduct additional analyses to identify adverse side-effects of altering risk proteins and map cancer risk proteins to drug targets. Here we find 40 proteins associated with common cancers, such as PLAUR and risk of breast cancer [odds ratio per standard deviation increment: 2.27, 1.88-2.74], and with high-mortality cancers, such as CTRB1 and pancreatic cancer [0.79, 0.73-0.85]. We also identify potential adverse effects of protein-altering interventions to reduce cancer risk, such as hypertension. Additionally, we report 18 proteins associated with cancer risk that map to existing drugs and 15 that are not currently under clinical investigation. In sum, we identify protein-cancer links that improve our understanding of cancer aetiology. We also demonstrate that the wider consequence of any protein-altering intervention on well-being and morbidity is required to interpret any utility of proteins as potential future targets for therapeutic prevention.

Identification of potential drug targets for insomnia by Mendelian randomization analysis based on plasma proteomics

Introduction

Insomnia, a common clinical disorder, significantly impacts the physical and mental well-being of patients. Currently, available hypnotic medications are unsatisfactory due to adverse reactions and dependency, necessitating the identification of new drug targets for the treatment of insomnia.

Methods

In this study, we utilized 734 plasma proteins as genetic instruments obtained from genome-wide association studies to conduct a Mendelian randomization analysis, with insomnia as the outcome variable, to identify potential drug targets for insomnia. Additionally, we validated our results externally using other datasets. Sensitivity analyses entailed reverse Mendelian randomization analysis, Bayesian co-localization analysis, and phenotype scanning. Furthermore, we constructed a protein-protein interaction network to elucidate potential correlations between the identified proteins and existing targets.

Results

Mendelian randomization analysis indicated that elevated levels of TGFBI (OR = 1.01; 95% CI, 1.01-1.02) and PAM ((OR = 1.01; 95% CI, 1.01-1.02) in plasma are associated with an increased risk of insomnia, with external validation supporting these findings. Moreover, there was no evidence of reverse causality for these two proteins. Co-localization analysis confirmed that PAM (coloc.abf-PPH4 = 0.823) shared the same variant with insomnia, further substantiating its potential role as a therapeutic target. There are interactive relationships between the potential proteins and existing targets of insomnia.

Conclusion

Overall, our findings suggested that elevated plasma levels of TGFBI and PAM are connected with an increased risk of insomnia and might be promising therapeutic targets, particularly PAM. However, further exploration is necessary to fully understand the underlying mechanisms involved.

Mendelian randomization and colocalization analysis reveal novel drug targets for myasthenia gravis

OBJECTIVE

Myasthenia gravis (MG) is a complex autoimmune disease affecting the neuromuscular junction with limited drug options, but the field of MG treatment recently benefits from novel biological agents. We performed a drug-targeted Mendelian randomization (MR) study to identify novel therapeutic targets of MG.

METHODS

Cis-expression quantitative loci (cis-eQTL), which proxy expression levels for 2176 druggable genes, were used for MR analysis. Causal relationships between genes and disease, identified by eQTL MR analysis, were verified by comprehensive sensitivity, colocalization, and protein quantitative loci (pQTL) MR analyses. The protein-protein interaction (PPI) analysis was also performed to extend targets, followed by enzyme-linked immunosorbent assay (ELISA) to explore the serum level of drug targets in MG patients. A phenome-wide MR analysis was then performed to assess side effects with a clinical trial review assessing druggability.

RESULTS

The eQTL MR analysis has identified eight potential targets for MG, one for early-onset MG and seven for late-onset MG. Further colocalization analyses indicated that CD226, CDC42BPB, PRSS36, and TNFSF12 possess evidence for colocalization with MG or late-onset MG. pQTL MR analyses identified the causal relations of TNFSF12 and CD226 with MG and late-onset MG. Furthermore, PPI analysis has revealed the protein interaction between TNFSF12-TNFSF13(APRIL) and TNFSF12-TNFSF13B(BLyS). Elevated TNFSF13 serum level of MG patients was also identified by ELISA experiments. This study has ultimately proposed three promising therapeutic targets (TNFSF12, TNFSF13, TNFSF13B) of MG.

CONCLUSIONS

Three drug targets associated with the BLyS/APRIL pathway have been identified. Multiple biological agents, including telitacicept and belimumab, are promising for MG therapy.

Potential therapeutic targets for membranous nephropathy: proteome-wide Mendelian randomization and colocalization analysis

Background

The currently available medications for treating membranous nephropathy (MN) still have unsatisfactory efficacy in inhibiting disease recurrence, slowing down its progression, and even halting the development of end-stage renal disease. There is still a need to develop novel drugs targeting MN.

Methods

We utilized summary statistics of MN from the Kiryluk Lab and obtained plasma protein data from Zheng et al. We performed a Bidirectional Mendelian randomization analysis, HEIDI test, mediation analysis, Bayesian colocalization, phenotype scanning, drug bank analysis, and protein-protein interaction network.

Results

The Mendelian randomization analysis uncovered 8 distinct proteins associated with MN after multiple false discovery rate corrections. Proteins related to an increased risk of MN in plasma include ABO [(Histo-Blood Group Abo System Transferase) (WR OR = 1.12, 95%CI:1.05-1.19, FDR=0.09, PPH4 = 0.79)], VWF [(Von Willebrand Factor) (WR OR = 1.41, 95%CI:1.16-1.72, FDR=0.02, PPH4 = 0.81)] and CD209 [(Cd209 Antigen) (WR OR = 1.19, 95%CI:1.07-1.31, FDR=0.09, PPH4 = 0.78)], and proteins that have a protective effect on MN: HRG [(Histidine-Rich Glycoprotein) (WR OR = 0.84, 95%CI:0.76-0.93, FDR=0.02, PPH4 = 0.80)], CD27 [(Cd27 Antigen) (WR OR = 0.78, 95%CI:0.68-0.90, FDR=0.02, PPH4 = 0.80)], LRPPRC [(Leucine-Rich Ppr Motif-Containing Protein, Mitochondrial) (WR OR = 0.79, 95%CI:0.69-0.91, FDR=0.09, PPH4 = 0.80)], TIMP4 [(Metalloproteinase Inhibitor 4) (WR OR = 0.67, 95%CI:0.53-0.84, FDR=0.09, PPH4 = 0.79)] and MAP2K4 [(Dual Specificity Mitogen-Activated Protein Kinase Kinase 4) (WR OR = 0.82, 95%CI:0.72-0.92, FDR=0.09, PPH4 = 0.80)]. ABO, HRG, and TIMP4 successfully passed the HEIDI test. None of these proteins exhibited a reverse causal relationship. Bayesian colocalization analysis provided evidence that all of them share variants with MN. We identified type 1 diabetes, trunk fat, and asthma as having intermediate effects in these pathways.

Conclusions

Our comprehensive analysis indicates a causal effect of ABO, CD27, VWF, HRG, CD209, LRPPRC, MAP2K4, and TIMP4 at the genetically determined circulating levels on the risk of MN. These proteins can potentially be a promising therapeutic target for the treatment of MN.

Refining the impact of genetic evidence on clinical success

The cost of drug discovery and development is driven primarily by failure1, with only about 10% of clinical programmes eventually receiving approval2-4. We previously estimated that human genetic evidence doubles the success rate from clinical development to approval5. In this study we leverage the growth in genetic evidence over the past decade to better understand the characteristics that distinguish clinical success and failure. We estimate the probability of success for drug mechanisms with genetic support is 2.6 times greater than those without. This relative success varies among therapy areas and development phases, and improves with increasing confidence in the causal gene, but is largely unaffected by genetic effect size, minor allele frequency or year of discovery. These results indicate we are far from reaching peak genetic insights to aid the discovery of targets for more effective drugs.

Clinically relevant plasma proteome for adiposity depots: evidence from systematic mendelian randomization and colocalization analyses

BACKGROUND

The accumulation of visceral and ectopic fat comprise a major cause of cardiometabolic diseases. However, novel drug targets for reducing unnecessary visceral and ectopic fat are still limited. Our study aims to provide a comprehensive investigation of the causal effects of the plasma proteome on visceral and ectopic fat using Mendelian randomization (MR) approach.

METHODS

We performed two-sample MR analyses based on five large genome-wide association study (GWAS) summary statistics of 2656 plasma proteins, to screen for causal associations of these proteins with traits of visceral and ectopic fat in over 30,000 participants of European ancestry, as well as to assess mediation effects by risk factors of outcomes. The colocalization analysis was conducted to examine whether the identified proteins and outcomes shared casual variants.

RESULTS

Genetically predicted levels of 14 circulating proteins were associated with visceral and ectopic fat (P < 4.99 × 10- 5, at a Bonferroni-corrected threshold). Colocalization analysis prioritized ten protein targets that showed effect on outcomes, including FST, SIRT2, DNAJB9, IL6R, CTSA, RGMB, PNLIPRP1, FLT4, PPY and IL6ST. MR analyses revealed seven risk factors for visceral and ectopic fat (P < 0.0024). Furthermore, the associations of CTSA, DNAJB9 and IGFBP1 with primary outcomes were mediated by HDL-C and SHBG. Sensitivity analyses showed little evidence of pleiotropy.

CONCLUSIONS

Our study identified candidate proteins showing putative causal effects as potential therapeutic targets for visceral and ectopic fat accumulation and outlined causal pathways for further prevention of downstream cardiometabolic diseases.

Integrated Pleiotropic Gene Set Unveils Comorbidity Insights across Digestive Cancers and Other Diseases

Comorbidities are prevalent in digestive cancers, intensifying patient discomfort and complicating prognosis. Identifying potential comorbidities and investigating their genetic connections in a systemic manner prove to be instrumental in averting additional health challenges during digestive cancer management. Here, we investigated 150 diseases across 18 categories by collecting and integrating various factors related to disease comorbidity, such as disease-associated SNPs or genes from sources like MalaCards, GWAS Catalog and UK Biobank. Through this extensive analysis, we have established an integrated pleiotropic gene set comprising 548 genes in total. Particularly, there enclosed the genes encoding major histocompatibility complex or related to antigen presentation. Additionally, we have unveiled patterns in protein-protein interactions and key hub genes/proteins including TP53, KRAS, CTNNB1 and PIK3CA, which may elucidate the co-occurrence of digestive cancers with certain diseases. These findings provide valuable insights into the molecular origins of comorbidity, offering potential avenues for patient stratification and the development of targeted therapies in clinical trials.

Systematic proteome-wide Mendelian randomization using the human plasma proteome to identify therapeutic targets for lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is the predominant histological subtype of lung cancer and the leading cause of cancer-related mortality. Identifying effective drug targets is crucial for advancing LUAD treatment strategies.

METHODS

This study employed proteome-wide Mendelian randomization (MR) and colocalization analyses. We collected data on 1394 plasma proteins from a protein quantitative trait loci (pQTL) study involving 4907 individuals. Genetic associations with LUAD were derived from the Transdisciplinary Research in Cancer of the Lung (TRICL) study, including 11,245 cases and 54,619 controls. We integrated pQTL and LUAD genome-wide association studies (GWASs) data to identify candidate proteins. MR utilizes single nucleotide polymorphisms (SNPs) as genetic instruments to estimate the causal effect of exposure on outcome, while Bayesian colocalization analysis determines the probability of shared causal genetic variants between traits. Our study applied these methods to assess causality between plasma proteins and LUAD. Furthermore, we employed a two-step MR to quantify the proportion of risk factors mediated by proteins on LUAD. Finally, protein-protein interaction (PPI) analysis elucidated potential links between proteins and current LUAD medications.

RESULTS

We identified nine plasma proteins significantly associated with LUAD. Increased levels of ALAD, FLT1, ICAM5, and VWC2 exhibited protective effects, with odds ratios of 0.79 (95% CI 0.72-0.87), 0.39 (95% CI 0.28-0.55), 0.91 (95% CI 0.72-0.87), and 0.85 (95% CI 0.79-0.92), respectively. Conversely, MDGA2 (OR, 1.13; 95% CI 1.08-1.19), NTM (OR, 1.12; 95% CI 1.09-1.16), PMM2 (OR, 1.35; 95% CI 1.18-1.53), RNASET2 (OR, 1.15; 95% CI 1.08-1.21), and TFPI (OR, 4.58; 95% CI 3.02-6.94) increased LUAD risk. Notably, none of the nine proteins showed evidence of reverse causality. Bayesian colocalization indicated that RNASET2, TFPI, and VWC2 shared the same variant with LUAD. Furthermore, NTM and FLT1 demonstrated interactions with targets of current LUAD medications. Additionally, FLT1 and TFPI are currently under evaluation as therapeutic targets, while NTM, RNASET2, and VWC2 are potentially druggable. These findings shed light on LUAD pathogenesis, highlighting the tumor-promoting effects of RNASET2, TFPI, and NTM, along with the protective effects of VWC2 and FLT1, providing a significant biological foundation for future LUAD therapeutic targets.

CONCLUSIONS

Our proteome-wide MR analysis highlighted RNASET2, TFPI, VWC2, NTM, and FLT1 as potential drug targets for further clinical investigation in LUAD. However, the specific mechanisms by which these proteins influence LUAD remain elusive. Targeting these proteins in drug development holds the potential for successful clinical trials, providing a pathway to prioritize and reduce costs in LUAD therapeutics.

Identification of prospective aging drug targets via Mendelian randomization analysis

Aging represents a multifaceted process culminating in the deterioration of biological functions. Despite the introduction of numerous anti-aging strategies, their therapeutic outcomes have often been less than optimal. Consequently, discovering new targets to mitigate aging effects is of critical importance. We applied Mendelian randomization (MR) to identify potential pharmacological targets against aging, drawing upon summary statistics from both the Decode and FinnGen cohorts, with further validation in an additional cohort. To address potential reverse causality, bidirectional MR analysis with Steiger filtering was utilized. Additionally, Bayesian co-localization and phenotype scanning were implemented to investigate previous associations between genetic variants and traits. Summary-data-based Mendelian randomization (SMR) analysis was conducted to assess the impact of genetic variants on aging via their effects on protein expression. Additionally, mediation analysis was orchestrated to uncover potential intermediaries in these associations. Finally, we probed the systemic implications of drug-target protein expression across diverse indications by MR-PheWas analysis. Utilizing a Bonferroni-corrected threshold, our MR examination identified 10 protein-aging associations. Within this cohort of proteins, MST1, LCT, GMPR2, PSMB4, ECM1, EFEMP1, and ISLR2 appear to exacerbate aging risks, while MAX, B3GNT8, and USP8 may exert protective influences. None of these proteins displayed reverse causality except EFEMP1. Bayesian co-localization inferred shared variants between aging and proteins such as B3GNT8 (rs11670143), ECM1 (rs61819393), and others listed. Mediator analysis pinpointed 1,5-anhydroglucitol as a partial intermediary in the influence LCT exhibits on telomere length. Circulating proteins play a pivotal role in influencing the aging process, making them promising candidates for therapeutic intervention. The implications of these proteins in aging warrant further investigation in future clinical research.

Genetic determinants of plasma protein levels in the Estonian population

The proteome holds great potential as an intermediate layer between the genome and phenome. Previous protein quantitative trait locus studies have focused mainly on describing the effects of common genetic variations on the proteome. Here, we assessed the impact of the common and rare genetic variations as well as the copy number variants (CNVs) on 326 plasma proteins measured in up to 500 individuals. We identified 184 cis and 94 trans signals for 157 protein traits, which were further fine-mapped to credible sets for 101 cis and 87 trans signals for 151 proteins. Rare genetic variation contributed to the levels of 7 proteins, with 5 cis and 14 trans associations. CNVs were associated with the levels of 11 proteins (7 cis and 5 trans), examples including a 3q12.1 deletion acting as a hub for multiple trans associations; and a CNV overlapping NAIP, a sensor component of the NAIP-NLRC4 inflammasome which is affecting pro-inflammatory cytokine interleukin 18 levels. In summary, this work presents a comprehensive resource of genetic variation affecting the plasma protein levels and provides the interpretation of identified effects.

Therapeutic Targets for Diabetic Kidney Disease: Proteome-Wide Mendelian Randomization and Colocalization Analyses

At present, safe and effective treatment drugs are urgently needed for diabetic kidney disease (DKD). Circulating protein biomarkers with causal genetic evidence represent promising drug targets, which provides an opportunity to identify new therapeutic targets. Summary data from two protein quantitative trait loci studies are presented, one involving 4,907 plasma proteins data from 35,559 individuals and the other encompassing 4,657 plasma proteins among 7,213 European Americans. Summary statistics for DKD were obtained from a large genome-wide association study (3,345 cases and 2,372 controls) and the FinnGen study (3,676 cases and 283,456 controls). Mendelian randomization (MR) analysis was conducted to examine the potential targets for DKD. The colocalization analysis was used to detect whether the potential proteins exist in the shared causal variants. To enhance the credibility of the results, external validation was conducted. Additionally, enrichment analysis, assessment of protein druggability, and the protein-protein interaction networks were used to further enrich the research findings. The proteome-wide MR analyses identified 21 blood proteins that may causally be associated with DKD. Colocalization analysis further supported a causal relationship between 12 proteins and DKD, with external validation confirming 4 of these proteins, and TGFBI was affirmed through two separate group data sets. These results indicate that targeting these four proteins could be a promising approach for treating DKD, and warrant further clinical investigations.

ARTICLE HIGHLIGHTS

Phenome-wide Mendelian randomisation analysis of 378,142 cases reveals risk factors for eight common cancers

For many cancers there are only a few well-established risk factors. Here, we use summary data from genome-wide association studies (GWAS) in a Mendelian randomisation (MR) phenome-wide association study (PheWAS) to identify potentially causal relationships for over 3,000 traits. Our outcome datasets comprise 378,142 cases across breast, prostate, colorectal, lung, endometrial, oesophageal, renal, and ovarian cancers, as well as 485,715 controls. We complement this analysis by systematically mining the literature space for supporting evidence. In addition to providing supporting evidence for well-established risk factors (smoking, alcohol, obesity, lack of physical activity), we also find sex steroid hormones, plasma lipids, and telomere length as determinants of cancer risk. A number of the molecular factors we identify may prove to be potential biomarkers. Our analysis, which highlights aetiological similarities and differences in common cancers, should aid public health prevention strategies to reduce cancer burden. We provide a R/Shiny app to visualise findings.

Integrative multi-omics analysis identifies genetically supported druggable targets and immune cell specificity for myasthenia gravis

BACKGROUND

Myasthenia gravis (MG) is a chronic autoimmune disorder characterized by fluctuating muscle weakness. Despite the availability of established therapies, the management of MG symptoms remains suboptimal, partially attributed to lack of efficacy or intolerable side-effects. Therefore, new effective drugs are warranted for treatment of MG.

METHODS

By employing an analytical framework that combines Mendelian randomization (MR) and colocalization analysis, we estimate the causal effects of blood druggable expression quantitative trait loci (eQTLs) and protein quantitative trait loci (pQTLs) on the susceptibility of MG. We subsequently investigated whether potential genetic effects exhibit cell-type specificity by utilizing genetic colocalization analysis to assess the interplay between immune-cell-specific eQTLs and MG risk.

RESULTS

We identified significant MR results for four genes (CDC42BPB, CD226, PRSS36, and TNFSF12) using cis-eQTL genetic instruments and three proteins (CTSH, PRSS8, and CPN2) using cis-pQTL genetic instruments. Six of these loci demonstrated evidence of colocalization with MG susceptibility (posterior probability > 0.80). We next undertook genetic colocalization to investigate cell-type-specific effects at these loci. Notably, we identified robust evidence of colocalization, with a posterior probability of 0.854, linking CTSH expression in TH2 cells and MG risk.

CONCLUSIONS

This study provides crucial insights into the genetic and molecular factors associated with MG susceptibility, singling out CTSH as a potential candidate for in-depth investigation and clinical consideration. It additionally sheds light on the immune-cell regulatory mechanisms related to the disease. However, further research is imperative to validate these targets and evaluate their feasibility for drug development.

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

BACKGROUND AND AIMS

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Genetics of chronic respiratory disease

Chronic respiratory diseases, such as chronic obstructive pulmonary disease (COPD), asthma and interstitial lung diseases are frequently occurring disorders with a polygenic basis that account for a large global burden of morbidity and mortality. Recent large-scale genetic epidemiology studies have identified associations between genetic variation and individual respiratory diseases and linked specific genetic variants to quantitative traits related to lung function. These associations have improved our understanding of the genetic basis and mechanisms underlying common lung diseases. Moreover, examining the overlap between genetic associations of different respiratory conditions, along with evidence for gene-environment interactions, has yielded additional biological insights into affected molecular pathways. This genetic information could inform the assessment of respiratory disease risk and contribute to stratified treatment approaches.

Insight into telomere regulation: road to discovery and intervention in plasma drug-protein targets

BACKGROUND

Telomere length is a critical metric linked to aging, health, and disease. Currently, the exploration of target proteins related to telomere length is usually limited to the context of aging and specific diseases, which limits the discovery of more relevant drug targets. This study integrated large-scale plasma cis-pQTLs data and telomere length GWAS datasets. We used Mendelian randomization(MR) to identify drug target proteins for telomere length, providing essential clues for future precision therapy and targeted drug development.

METHODS

Using plasma cis-pQTLs data from a previous GWAS study (3,606 Pqtls associated with 2,656 proteins) and a GWAS dataset of telomere length (sample size: 472,174; GWAS ID: ieu-b-4879) from UK Biobank, using MR, external validation, and reverse causality testing, we identified essential drug target proteins for telomere length. We also performed co-localization, Phenome-wide association studies and enrichment analysis, protein-protein interaction network construction, search for existing intervening drugs, and potential drug/compound prediction for these critical targets to strengthen and expand our findings.

RESULTS

After Bonferron correction (p < 0.05/734), RPN1 (OR: 0.96; 95%CI: (0.95, 0.97)), GDI2 (OR: 0.94; 95%CI: (0.92, 0.96)), NT5C (OR: 0.97; 95%CI: (0.95, 0.98)) had a significant negative causal association with telomere length; TYRO3 (OR: 1.11; 95%CI: (1.09, 1.15)) had a significant positive causal association with telomere length. GDI2 shared the same genetic variants with telomere length (coloc.abf-PPH 4 > 0.8).

CONCLUSION

Genetically determined plasma RPN1, GDI2, NT5C, and TYRO3 have significant causal effects on telomere length and can potentially be drug targets. Further exploration of the role and mechanism of these proteins/genes in regulating telomere length is needed.

Potential drug targets for gastroesophageal reflux disease and Barrett's esophagus identified through Mendelian randomization analysis

Gastroesophageal reflux disease (GERD) is a prevalent chronic ailment, and present therapeutic approaches are not always effective. This study aimed to find new drug targets for GERD and Barrett's esophagus (BE). We obtained genetic instruments for GERD, BE, and 2004 plasma proteins from recently published genome-wide association studies (GWAS), and Mendelian randomization (MR) was employed to explore potential drug targets. We further winnowed down MR-prioritized proteins through replication, reverse causality testing, colocalization analysis, phenotype scanning, and Phenome-wide MR. Furthermore, we constructed a protein-protein interaction network, unveiling potential associations among candidate proteins. Simultaneously, we acquired mRNA expression quantitative trait loci (eQTL) data from another GWAS encompassing four different tissues to identify additional drug targets. Meanwhile, we searched drug databases to evaluate these targets. Under Bonferroni correction (P < 4.8 × 10-5), we identified 11 plasma proteins significantly associated with GERD. Among these, 7 are protective proteins (MSP, GPX1, ERBB3, BT3A3, ANTR2, CCM2, and DECR2), while 4 are detrimental proteins (TMEM106B, DUSP13, C1-INH, and LINGO1). Ultimately, C1-INH and DECR2 successfully passed the screening process and exhibited similar directional causal effects on BE. Further analysis of eQTLs highlighted 4 potential drug targets, including EDEM3, PBX3, MEIS1-AS3, and NME7. The search of drug databases further supported our conclusions. Our study indicated that the plasma proteins C1-INH and DECR2, along with 4 genes (EDEM3, PBX3, MEIS1-AS3, and NME7), may represent potential drug targets for GERD and BE, warranting further investigation.

Proteomic insights into modifiable risk of venous thromboembolism and cardiovascular comorbidities

BACKGROUND

Venous thromboembolism (VTE) has been associated with several modifiable factors (MFs) and cardiovascular comorbidities. However, the mechanisms are largely unknown.

OBJECTIVES

We aimed to decipher proteomic pathways underlying the associations of VTE with MFs and cardiovascular comorbidities.

METHODS

A 2-stage network Mendelian randomization analysis was conducted to explore the associations between 15 MFs, 1151 blood proteins, and VTE using data from a genome-wide meta-analysis including 81 190 cases of VTE. We used protein data from 35 559 individuals as the discovery analysis, and from 2 independent studies including 10 708 and 54 219 participants as the replication analyses. Based on the identified proteins, we assessed the druggability and examined the cardiovascular pleiotropy.

RESULTS

The network Mendelian randomization analyses identified 10 MF-VTE, 86 MF-protein, and 34 protein-VTE associations. These associations were overall consistent in the replication analyses. Thirty-eight pathways with directionally consistent direct and indirect effects in the MF-protein-VTE pathway were identified. Low-density lipoprotein receptor-related protein 12 (LRP12: 34.3%-58.1%) and coagulation factor (F)XI (20.6%-39.6%) mediated most of the associations between 3 obesity indicators and VTE. Likewise, coagulation FXI mediated most of the smoking-VTE association (40%; 95% CI, 20%-60%) and insomnia-VTE association (27%; 95% CI, 5%-49%). Many VTE-associated proteins were highly druggable for thrombotic conditions. Five proteins (interleukin-6 receptor subunit alpha, LRP12, prothrombin, angiopoietin-1, and low-density lipoprotein receptor-related protein 4) were associated with VTE and its cardiovascular comorbidities.

CONCLUSION

This study suggests that coagulation FXI, a druggable target, is an important mediator of the associations of obesity, smoking, and insomnia with VTE risk.

Risk factors for prostate cancer: An umbrella review of prospective observational studies and mendelian randomization analyses

BACKGROUND

The incidence of prostate cancer is increasing in older males globally. Age, ethnicity, and family history are identified as the well-known risk factors for prostate cancer, but few modifiable factors have been firmly established. The objective of this study was to identify and evaluate various factors modifying the risk of prostate cancer reported in meta-analyses of prospective observational studies and mendelian randomization (MR) analyses.

METHODS AND FINDINGS

We searched PubMed, Embase, and Web of Science from the inception to January 10, 2022, updated on September 9, 2023, to identify meta-analyses and MR studies on prostate cancer. Eligibility criteria for meta-analyses were (1) meta-analyses including prospective observational studies or studies that declared outcome-free at baseline; (2) evaluating the factors of any category associated with prostate cancer incidence; and (3) providing effect estimates for further data synthesis. Similar criteria were applied to MR studies. Meta-analysis was repeated using the random-effects inverse-variance model with DerSimonian-Laird method. Quality assessment was then conducted for included meta-analyses using AMSTAR-2 tool and for MR studies using STROBE-MR and assumption evaluation. Subsequent evidence grading criteria for significant associations in meta-analyses contained sample size, P values and 95% confidence intervals, 95% prediction intervals, heterogeneity, and publication bias, assigning 4 evidence grades (convincing, highly suggestive, suggestive, or weak). Significant associations in MR studies were graded as robust, probable, suggestive, or insufficient considering P values and concordance of effect directions. Finally, 92 selected from 411 meta-analyses and 64 selected from 118 MR studies were included after excluding the overlapping and outdated studies which were published earlier and contained fewer participants or fewer instrument variables for the same exposure. In total, 123 observational associations (45 significant and 78 null) and 145 causal associations (55 significant and 90 null) were categorized into lifestyle; diet and nutrition; anthropometric indices; biomarkers; clinical variables, diseases, and treatments; and environmental factors. Concerning evidence grading on significant associations, there were 5 highly suggestive, 36 suggestive, and 4 weak associations in meta-analyses, and 10 robust, 24 probable, 4 suggestive, and 17 insufficient causal associations in MR studies. Twenty-six overlapping factors between meta-analyses and MR studies were identified, with consistent significant effects found for physical activity (PA) (occupational PA in meta: OR = 0.87, 95% CI: 0.80, 0.94; accelerator-measured PA in MR: OR = 0.49, 95% CI: 0.33, 0.72), height (meta: OR = 1.09, 95% CI: 1.06, 1.12; MR: OR = 1.07, 95% CI: 1.01, 1.15, for aggressive prostate cancer), and smoking (current smoking in meta: OR = 0.74, 95% CI: 0.68, 0.80; smoking initiation in MR: OR = 0.91, 95% CI: 0.86, 0.97). Methodological limitation is that the evidence grading criteria could be expanded by considering more indices.

CONCLUSIONS

In this large-scale study, we summarized the associations of various factors with prostate cancer risk and provided comparisons between observational associations by meta-analysis and genetically estimated causality by MR analyses. In the absence of convincing overlapping evidence based on the existing literature, no robust associations were identified, but some effects were observed for height, physical activity, and smoking.

Proteogenomic Data Integration Reveals CXCL10 as a Potentially Downstream Causal Mediator for IL-6 Signaling on Atherosclerosis

BACKGROUND

Genetic and experimental studies support a causal involvement of IL-6 (interleukin-6) signaling in atheroprogression. Although trials targeting IL-6 signaling are underway, any benefits must be balanced against an impaired host immune response. Dissecting the mechanisms that mediate the effects of IL-6 signaling on atherosclerosis could offer insights about novel drug targets with more specific effects.

METHODS

Leveraging data from 522 681 individuals, we constructed a genetic instrument of 26 variants in the gene encoding the IL-6R (IL-6 receptor) that proxied for pharmacological IL-6R inhibition. Using Mendelian randomization, we assessed its effects on 3281 plasma proteins quantified with an aptamer-based assay in the INTERVAL cohort (n=3301). Using mediation Mendelian randomization, we explored proteomic mediators of the effects of genetically proxied IL-6 signaling on coronary artery disease, large artery atherosclerotic stroke, and peripheral artery disease. For significant mediators, we tested associations of their circulating levels with incident cardiovascular events in a population-based study (n=1704) and explored the histological, transcriptomic, and cellular phenotypes correlated with their expression levels in samples from human atherosclerotic lesions.

RESULTS

We found significant effects of genetically proxied IL-6 signaling on 70 circulating proteins involved in cytokine production/regulation and immune cell recruitment/differentiation, which correlated with the proteomic effects of pharmacological IL-6R inhibition in a clinical trial. Among the 70 significant proteins, genetically proxied circulating levels of CXCL10 (C-X-C motif chemokine ligand 10) were associated with risk of coronary artery disease, large artery atherosclerotic stroke, and peripheral artery disease, with up to 67% of the effects of genetically downregulated IL-6 signaling on these end points mediated by decreases in CXCL10. Higher midlife circulating CXCL10 levels were associated with a larger number of cardiovascular events over 20 years, whereas higher CXCL10 expression in human atherosclerotic lesions correlated with a larger lipid core and a transcriptomic profile reflecting immune cell infiltration, adaptive immune system activation, and cytokine signaling.

CONCLUSIONS

Integrating multiomics data, we found a proteomic signature of IL-6 signaling activation and mediators of its effects on cardiovascular disease. Our analyses suggest the interferon-γ-inducible chemokine CXCL10 to be a potentially causal mediator for atherosclerosis in 3 vascular compartments and, as such, could serve as a promising drug target for atheroprotection.

Sex-stratified genome-wide association and transcriptome-wide Mendelian randomization studies reveal drug targets of heart failure

The prevalence of heart failure (HF) subtypes, which are classified by left ventricular ejection fraction (LVEF), demonstrate significant sex differences. Here, we perform sex-stratified genome-wide association studies (GWASs) on LVEF and transcriptome-wide Mendelian randomization (MR) on LVEF, all-cause HF, HF with reduced ejection fraction (HFrEF), and HF with preserved ejection fraction (HFpEF). The sex-stratified GWASs of LVEF identified three sex-specific loci that were exclusively detected in the sex-stratified GWASs. Three drug target genes show sex-differential effects on HF/HFrEF via influencing LVEF, with NPR2 as the target gene for the HF drug Cenderitide under phase 2 clinical trial. Our study highlights the importance of considering sex-differential genetic effects in sex-balanced diseases such as HF and emphasizes the value of sex-stratified GWASs and MR in identifying putative genetic variants, causal genes, and candidate drug targets for HF, which is not identifiable using a sex-combined strategy.

Proteome-wide Mendelian randomization identifies causal plasma proteins in lung cancer

Plasma proteins are promising biomarkers and potential drug targets in lung cancer. To evaluate the causal association between plasma proteins and lung cancer, we performed proteome-wide Mendelian randomization meta-analysis (PW-MR-meta) based on lung cancer genome-wide association studies (GWASs), protein quantitative trait loci (pQTLs) of 4,719 plasma proteins in deCODE and 4,775 in Fenland. Further, causal-protein risk score (CPRS) was developed based on causal proteins and validated in the UK Biobank. 270 plasma proteins were identified using PW-MR meta-analysis, including 39 robust causal proteins (both FDR-q < 0.05) and 78 moderate causal proteins (FDR-q < 0.05 in one and p < 0.05 in another). The CPRS had satisfactory performance in risk stratification for lung cancer (top 10% CPRS:Hazard ratio (HR) (95%CI):4.33(2.65-7.06)). The CPRS [AUC (95%CI): 65.93 (62.91-68.78)] outperformed the traditional polygenic risk score (PRS) [AUC (95%CI): 55.71(52.67-58.59)]. Our findings offer further insight into the genetic architecture of plasma proteins for lung cancer susceptibility.

Investigating the association between genetically proxied circulating levels of immune checkpoint proteins and cancer survival: protocol for a Mendelian randomisation analysis

INTRODUCTION

Compared with the traditional drug development pathway, investigating alternative uses for existing drugs (ie, drug repurposing) requires substantially less time, cost and resources. Immune checkpoint inhibitors are licensed for the treatment of certain breast, colorectal, head and neck, lung and melanoma cancers. These drugs target immune checkpoint proteins to reduce the suppression of T cell activation by cancer cells. As T cell suppression is a hallmark of cancer common across anatomical sites, we hypothesise that immune checkpoint inhibitors could be repurposed for the treatment of additional cancers beyond the ones already indicated.

METHODS AND ANALYSIS

We will use two-sample Mendelian randomisation to investigate the effect of genetically proxied levels of protein targets of two immune checkpoint inhibitors-programmed cell death protein 1 and programmed death ligand 1-on survival of seven cancer types (breast, colorectal, head and neck, lung, melanoma, ovarian and prostate). Summary genetic association data will be obtained from prior genome-wide association studies of circulating protein levels and cancer survival in populations of European ancestry. Various sensitivity analyses will be performed to examine the robustness of findings to potential violations of Mendelian randomisation assumptions, collider bias and the impact of alternative genetic instrument construction strategies. The impact of treatment history and tumour stage on the findings will also be investigated using summary-level and individual-level genetic data where available.

ETHICS AND DISSEMINATION

No separate ethics approval will be required for these analyses as we will be using data from previously published genome-wide association studies which individually gained ethical approval and participant consent. Results from analyses will be submitted as an open-access peer-reviewed publication and statistical code will be made freely available on the completion of the analysis.

Brain tissue- and cell type-specific eQTL Mendelian randomization reveals efficacy of FADS1 and FADS2 on cognitive function

Epidemiological studies suggested an association between omega-3 fatty acids and cognitive function. However, the causal role of the fatty acid desaturase (FADS) gene, which play a key role in regulating omega-3 fatty acids biosynthesis, on cognitive function is unclear. Hence, we used two-sample Mendelian randomization (MR) to estimate the gene-specific causal effect of omega-3 fatty acids (N = 114,999) on cognitive function (N = 300,486). Tissue- and cell type-specific effects of FADS1/FADS2 expression on cognitive function were estimated using brain tissue cis-expression quantitative trait loci (cis-eQTL) datasets (GTEx, N ≤ 209; MetaBrain, N ≤ 8,613) and single cell cis-eQTL data (N = 373), respectively. These causal effects were further evaluated in whole blood cis-eQTL data (N ≤ 31,684). A series of sensitivity analyses were conducted to validate MR assumptions. Leave-one-out MR showed a FADS gene-specific effect of omega-3 fatty acids on cognitive function [β = -1.3 × 10-2, 95% confidence interval (CI) (-2.2 × 10-2, -5 × 10-3), P = 2 × 10-3]. Tissue-specific MR showed an effect of increased FADS1 expression in cerebellar hemisphere and FADS2 expression in nucleus accumbens basal ganglia on maintaining cognitive function, while decreased FADS1 expression in nine brain tissues on maintaining cognitive function [colocalization probability (PP.H4) ranged from 71.7% to 100.0%]. Cell type-specific MR showed decreased FADS1/FADS2 expression in oligodendrocyte was associated with maintaining cognitive function (PP.H4 = 82.3%, respectively). Increased FADS1/FADS2 expression in whole blood showed an effect on cognitive function maintenance (PP.H4 = 86.6% and 88.4%, respectively). This study revealed putative causal effect of FADS1/FADS2 expression in brain tissues and blood on cognitive function. These findings provided evidence to prioritize FADS gene as potential target gene for maintenance of cognitive function.

Promising therapeutic targets for ischemic stroke identified from plasma and cerebrospinal fluid proteomes: a multicenter Mendelian randomization study

BACKGROUND

Ischemic stroke (IS) is more common every year, the condition is serious, and have a poor prognosis. New, efficient, and safe therapeutic targets are desperately needed as early treatment especially prevention and reperfusion is the key to lowering the occurrence of poorer prognosis. Generally circulating proteins are attractive therapeutic targets, this study aims to identify potential pharmacological targets among plasma and cerebrospinal fluid (CSF) proteins for the prevention and treatment of IS using a multicenter Mendelian randomization (MR) approach.

METHODS

First, the genetic instruments of 734 plasma and 151 CSF proteins were assessed for causative connections with IS from MEGASTROKE consortium by MR to identify prospective therapeutic targets. Then, for additional validation, plasma proteins from the deCODE consortium and the Fenland consortium, as well as IS GWAS data from the FinnGen cohort, the ISGC consortium and UK biobank, were employed. A thorough evaluation of the aforementioned possible pharmacological targets was carried out using meta-analysis. The robustness of MR results was then confirmed through sensitivity analysis using several techniques, such as bidirectional MR analysis, Steiger filtering, and Bayesian colocalization. Finally, methods like Protein-Protein Interaction (PPI) Networking were utilized to investigate the relationship between putative drug targets and therapeutic agents.

RESULTS

The authors discovered three proteins that may function as promising therapeutic targets for IS and meet the Bonferroni correction ( P <0.05/885=5.65×10 -5 ). Prekallikrein (OR=0.41, 95% CI: 0.27-0.63, P =3.61×10 -5 ), a protein found in CSF, has a 10-fold protective impact in IS, while the plasma proteins SWAP70 (OR=0.85, 95% CI: 0.80-0.91, P =1.64×10 -6 ) and MMP-12 (OR=0.92, 95% CI: 0.89-0.95, P =4.49×10 -6 ) of each SD play a protective role in IS. Prekallikrein, MMP-12, SWAP70 was replicated in the FinnGen cohort and ISGC database. MMP-12 (OR=0.93, 95% CI: 0.91-0.94, P <0.001), SWAP70 (OR=0.92, 95% CI: 0.90-0.94, P <0.001), and prekallikrein (OR=0.53, 95% CI: 0.33-0.72, P <0.001) may all be viable targets for IS, according to the combined meta-analysis results. Additionally, no evidence of reverse causality was identified, and Bayesian colocalization revealed MMP-12 (PPH 4 =0.995), SWAP70 (PPH 4 =0.987), and prekallikrein (PPH 4 =0.894) shared the same variant with IS, supporting the robustness of the aforementioned causation. Prekallikrein and MMP-12 were associated with the target protein of the current treatment of IS. Among them, Lanadelumab, a new drug whose target protein is a prekallikrein, may be a promising new drug for the treatment of IS.

CONCLUSION

The prekallikrein, MMP-12, and SWAP70 are causally associated with the risk of IS. Moreover, MMP-12 and prekallikrein may be treated as promising therapeutic targets for medical intervention of IS.

Causality-enriched epigenetic age uncouples damage and adaptation

Machine learning models based on DNA methylation data can predict biological age but often lack causal insights. By harnessing large-scale genetic data through epigenome-wide Mendelian randomization, we identified CpG sites potentially causal for aging-related traits. Neither the existing epigenetic clocks nor age-related differential DNA methylation are enriched in these sites. These CpGs include sites that contribute to aging and protect against it, yet their combined contribution negatively affects age-related traits. We established a new framework to introduce causal information into epigenetic clocks, resulting in DamAge and AdaptAge-clocks that track detrimental and adaptive methylation changes, respectively. DamAge correlates with adverse outcomes, including mortality, while AdaptAge is associated with beneficial adaptations. These causality-enriched clocks exhibit sensitivity to short-term interventions. Our findings provide a detailed landscape of CpG sites with putative causal links to lifespan and healthspan, facilitating the development of aging biomarkers, assessing interventions, and studying reversibility of age-associated changes.

Exploring noncoding variants in genetic diseases: from detection to functional insights

Previous studies on genetic diseases predominantly focused on protein-coding variations, overlooking the vast noncoding regions in the human genome. The development of high-throughput sequencing technologies and functional genomics tools has enabled the systematic identification of functional noncoding variants. These variants can impact gene expression, regulation, and chromatin conformation, thereby contributing to disease pathogenesis. Understanding the mechanisms that underlie the impact of noncoding variants on genetic diseases is indispensable for the development of precisely targeted therapies and the implementation of personalized medicine strategies. The intricacies of noncoding regions introduce a multitude of challenges and research opportunities. In this review, we introduce a spectrum of noncoding variants involved in genetic diseases, along with research strategies and advanced technologies for their precise identification and in-depth understanding of the complexity of the noncoding genome. We will delve into the research challenges and propose potential solutions for unraveling the genetic basis of rare and complex diseases.

Genetic determinants of complement activation in the general population

Complement is a fundamental innate immune response component. Its alterations are associated with severe systemic diseases. To illuminate the complement's genetic underpinnings, we conduct genome-wide association studies of the functional activity of the classical (CP), lectin (LP), and alternative (AP) complement pathways in the Cooperative Health Research in South Tyrol study (n = 4,990). We identify seven loci, encompassing 13 independent, pathway-specific variants located in or near complement genes (CFHR4, C7, C2, MBL2) and non-complement genes (PDE3A, TNXB, ABO), explaining up to 74% of complement pathways' genetic heritability and implicating long-range haplotypes associated with LP at MBL2. Two-sample Mendelian randomization analyses, supported by transcriptome- and proteome-wide colocalization, confirm known causal pathways, establish within-complement feedback loops, and implicate causality of ABO on LP and of CFHR2 and C7 on AP. LP causally influences collectin-11 and KAAG1 levels and the risk of mouth ulcers. These results build a comprehensive resource to investigate the role of complement in human health.

Potential disease biomarkers for diabetic retinopathy identified through Mendelian randomization analysis

Background

Diabetic retinopathy (DR), a leading cause of vision loss, has limited options for effective prevention and treatment. This study aims to utilize genomics and proteomics data to identify potential drug targets for DR.

Methods

We utilized plasma protein quantitative trait loci data from the Atherosclerosis Risk in Communities Study and the Icelandic Decoding Genetics Study for discovery and replication, respectively. Genetic associations with DR, including its subtypes, were derived from the FinnGen study. Mendelian Randomization (MR) analysis estimated associations between protein levels and DR risk, complemented by colocalization analysis to examine shared causal variants.

Results

Our MR analysis identified significant associations of specific plasma proteins with DR and proliferative DR (PDR). Elevated genetically predicted levels of WARS (OR = 1.16; 95% CI = 0.095-0.208, FDR = 1.31×10-4) and SIRPG (OR = 1.15; 95% CI = 0.071-0.201, FDR = 1.46×10-2) were associated with higher DR risk, while increased levels of ALDOC (OR = 1.56; 95% CI = 0.246-0.637, FDR = 5.48×10-3) and SIRPG (OR = 1.15; 95% CI = 0.068-0.208, FDR = 4.73×10-2) were associated with higher PDR risk. These findings were corroborated by strong colocalization evidence.

Conclusions

Our study highlights WARS, SIRPG, and ALDOC as significant proteins associated with DR and PDR, providing a basis for further exploration in drug development. Additional studies are needed to validate these proteins as disease biomarkers across diverse populations.

Mendelian randomization identifies circulating proteins as biomarkers for age at menarche and age at natural menopause

Age at menarche (AAM) and age at natural menopause (ANM) are highly heritable traits and have been linked to various health outcomes. We aimed to identify circulating proteins associated with altered ANM and AAM using an unbiased two-sample Mendelian randomization (MR) and colocalization approach. By testing causal effects of 1,271 proteins on AAM, we identified 22 proteins causally associated with AAM in MR, among which 13 proteins (GCKR, FOXO3, SEMA3G, PATE4, AZGP1, NEGR1, LHB, DLK1, ANXA2, YWHAB, DNAJB12, RMDN1 and HPGDS) colocalized. Among 1,349 proteins tested for causal association with ANM using MR, we identified 19 causal proteins among which 7 proteins (CPNE1, TYMP, DNER, ADAMTS13, LCT, ARL and PLXNA1) colocalized. Follow-up pathway and gene enrichment analyses demonstrated links between AAM-related proteins and obesity and diabetes, and between AAM and ANM-related proteins and various types of cancer. In conclusion, we identified proteomic signatures of reproductive ageing in women, highlighting biological processes at both ends of the reproductive lifespan.

The effect of metformin on urate metabolism: Findings from observational and Mendelian randomization analyses

AIM

To evaluate the effect of metformin on urate metabolism.

MATERIALS AND METHODS

Using the UK Biobank, we first performed association analyses of metformin use with urate levels, risk of hyperuricaemia and incident gout in patients with diabetes. To explore the causal effect of metformin on urate and gout, we identified genetic variants proxying the glycated haemoglobin (HbA1c)-lowering effect of metformin targets and conducted a two-sample Mendelian randomization (MR) utilizing the urate and gout genetic summary-level data from the CKDGen (n = 288 649) and the FinnGen cohort. We conducted two-step MR to explore the mediation effect of body mass index and systolic blood pressure. We also performed non-linear MR in the UK Biobank (n = 414 055) to show the results across HbA1c levels.

RESULTS

In 18 776 patients with type 2 diabetes in UK Biobank, metformin use was associated with decreased urate [β = -4.3 μmol/L, 95% confidence interval (CI) -7.0, -1.7, p = .001] and reduced hyperuricaemia risk (odds ratio = 0.87, 95% CI 0.79, 0.96, p = .004), but not gout. Genetically proxied averaged HbA1c-lowering effects of metformin targets, equivalent to a 0.62% reduction in HbA1c, was associated with reduced urate (β = -12.5 μmol/L, 95% CI -21.4, -4.2, p = .004). Body mass index significantly mediated this association (proportion mediated = 33.0%, p = .002). Non-linear MR results suggest a linear trend of the effect of metformin on urate reduction across various HbA1c levels.

CONCLUSIONS

The effect of metformin may reduce urate levels but not incident gout in the general population.

The association between ACE inhibitors and psoriasis based on the drug-targeted Mendelian randomization and real-world pharmacovigilance analyses

BACKGROUND

Although a growing number of observational studies suggest that angiotensin-converting enzyme inhibitors (ACEIs) intake may be a risk factor for psoriasis, evidence is still insufficient to draw definitive conclusions.

RESEARCH DESIGN AND METHODS

Drug-targeted Mendelian randomization (DTMR) was used to analyze the causality between genetic proxied ACEIs and psoriasis. Furthermore, we performed a disproportionality analysis based on the FDA adverse event reporting system (FAERS) database to identify more suspicious subclasses of ACEIs.

RESULTS

Using two kinds of genetic proxy instruments, the present DTMR research identified genetic proxied ACEIs as risk factors for psoriasis. Furthermore, our disproportionality analysis revealed that ramipril, trandolapril, perindopril, lisinopril, and enalapril were associated with the risk of psoriasis, which validates and refines the findings of the DTMR.

CONCLUSIONS

Our integrative study verified that ACEIs, especially ramipril, trandolapril, perindopril, lisinopril, and enalapril, tended to increase the risk of psoriasis statistically.

Potential drug targets for myocardial infarction identified through Mendelian randomization analysis and Genetic colocalization

Myocardial infarction (MI) is a major cause of death and disability worldwide, but current treatments are limited by their invasiveness, side effects, and lack of efficacy. Novel drug targets for MI prevention are urgently needed. In this study, we used Mendelian randomization to identify potential therapeutic targets for MI using plasma protein quantitative trait loci as exposure variables and MI as the outcome variable. We further validated our findings using reverse causation analysis, Bayesian co-localization analysis, and external datasets. We also constructed a protein-protein interaction network to explore the relationships between the identified proteins and known MI targets. Our analysis revealed 2 proteins, LPA and APOA5, as potential drug targets for MI, with causal effects on MI risk confirmed by multiple lines of evidence. LPA and APOA5 are involved in lipid metabolism and interact with target proteins of current MI medications. We also found 4 other proteins, IL1RN, FN1, NT5C, and SEMA3C, that may have potential as drug targets but require further confirmation. Our study demonstrates the utility of Mendelian randomization and protein quantitative trait loci in discovering novel drug targets for complex diseases such as MI. It provides insights into the underlying mechanisms of MI pathology and treatment.

Proteome-wide analysis reveals potential therapeutic targets for Colorectal cancer: a two-sample mendelian randomization study

BACKGROUND

Colorectal cancer (CRC) is a leading cause of cancer-related mortality, highlighting an unmet clinical need for more effective therapies. This study aims to evaluate the causal relationship between 4,489 plasma proteins and CRC to identify potential therapeutic targets for CRC.

METHODS

We conducted two-sample Mendelian randomization (MR) analysis to examine the causal effects of plasma proteins on CRC. Mediation analysis was performed to assess the indirect effects of plasma proteins on CRC through associated risk factors. In addition, we conducted a phenome-wide association study using the UK Biobank dataset to examine associations between these plasma proteins and other phenotypes.

RESULTS

Out of 4,489 plasma proteins, MR analysis revealed causal associations with CRC for 23 proteins, including VIMP, MICB, TNFRSF11B, C5orf38 and SLC5A8. Our findings also confirm the associations between reported risk factors and CRC. Mediation analysis identified mediating effects of proteins on CRC outcomes through risk factors. Furthermore, MR analysis identified 154 plasma proteins are causally linked to at least one CRC risk factor.

CONCLUSIONS

Our study evaluated the causal relationships between plasma proteins and CRC, providing a more complete understanding of potential therapeutic targets for CRC.

Evaluating the causal effect of circulating proteome on the risk of osteoarthritis-related traits

OBJECTIVES

This study aims to identify circulating proteins that are causally associated with osteoarthritis (OA)-related traits through Mendelian randomisation (MR)-based analytical framework.

METHODS

Large-scale two-sample MR was employed to estimate the effects of thousands of plasma proteins on 12 OA-related traits. Additional analyses including Bayesian colocalisation, Steiger filtering analysis, assessment of protein-altering variants and mapping expression quantitative trait loci to protein quantitative trait loci were performed to investigate the reliability of the MR findings; protein-protein interaction, pathway enrichment analysis and evaluation of drug targets were conducted to deepen the understanding and identify potential therapeutic targets of OA.

RESULTS

Dozens of circulating proteins were identified to have putatively causal effects on OA-related traits, and a majority of these proteins were either drug targets or considered druggable.

CONCLUSIONS

Through MR analysis, we have identified numerous plasma proteins associated with OA-related traits, shedding light on protein-mediated mechanisms and offering promising therapeutic targets for OA.

Genetic evidence supporting a causal role of Janus kinase 2 in prostate cancer: a Mendelian randomization study

BACKGROUND

Janus kinase-2 (JAK2) inhibitors are now being tried in basic research and clinical practice in prostate cancer (PCa). However, the causal relationship between JAK2 and PCa has not been uniformly described. Here, we examined the cause-effect relation between JAK2 and PCa.

METHODS

Two-sample Mendelian randomization (MR) analysis of genetic variation data of JAK2, PCa from IEU OpenGWAS Project was performed by inverse variance weighted, MR-Egger, and weighted median. Cochran's Q heterogeneity test and MR-Egger multiplicity analysis were performed to normalize the MR analysis results to reduce the effect of bias on the results.

RESULTS

Five instrumental variables were identified for further MR analysis. Specifically, combining the inverse variance-weighted (OR: 1.0009, 95% CI: 1.0001-1.0015, p = 0.02) and weighted median (OR: 1.0009, 95% CI: 1.0000-1.0017, p = 0.03). Sensitivity analysis showed that there was no heterogeneity (p = 0.448) and horizontal multiplicity (p = 0.770) among the instrumental variables.

CONCLUSIONS

We found JAK2 was associated with the development of PCa and was a risk factor for PCa, which might be instructive for the use of JAK2 inhibitors in PCa patients.

Integrated untargeted and targeted proteomics to unveil plasma prognostic markers for patients with acute paraquat poisoning: A pilot study

Paraquat (PQ) is a widely used but strongly toxic herbicide, which can induce multiple organ failure. The overall survival rate of the poisoned patients was only 54.4% due to lack of specific antidotes. Besides, the definite pathogenic mechanism of PQ is still not fully understood. In this pilot study, untargeted and targeted proteomics were integrated to explore the expression characteristics of plasma protein in PQ poisoned patients, and identify the differentially expressed proteins between survivors and non-survivors. A total of 494 plasma proteins were detected, and of which 47 were upregulated and 44 were downregulated in PQ poisoned patients compared to healthy controls. Among them, five differential plasma proteins (S100A9, S100A8, MB, ACTB and RAB11FIP3) were further validated by multiple reaction monitoring (MRM)-based targeted proteomic approach, and three of them (S100A9, S100A8 and ACTB) were confirmed to be correlated with PQ poisoning. Meanwhile, 84 dysregulated plasma proteins were identified in non-survivors compared with survivors. Moreover, targeted proteomic and ROC analysis suggested that ACTB had a good performance in predicting the prognosis of PQ poisoned patients. These findings highlighted the value of label-free and mass spectrometry-based proteomics in screening prognostic biomarkers of PQ poisoning and studying the mechanism of PQ toxicity.