Pinpointing Novel Plasma and Brain Proteins for Common Ocular Diseases: A Comprehensive Cross-Omics Integration Analysis.

The pathogenesis of ocular diseases (ODs) remains unclear, although genome-wide association studies (GWAS) have identified numerous associated genetic risk loci. We integrated protein quantitative trait loci (pQTL) datasets and five large-scale GWAS summary statistics of ODs under a cutting-edge systematic analytic framework. Proteome-wide association studies (PWAS) identified plasma and brain proteins associated with ODs, and 11 plasma proteins were identified by Mendelian randomization (MR) and colocalization (COLOC) analyses as being potentially causally associated with ODs. Five of these proteins (protein-coding genes ECI1, LCT, and NPTXR for glaucoma, WARS1 for age-related macular degeneration (AMD), and SIGLEC14 for diabetic retinopathy (DR)) are newly reported. Twenty brain-protein-OD pairs were identified by COLOC analysis. Eight pairs (protein-coding genes TOM1L2, MXRA7, RHPN2, and HINT1 for senile cataract, WARS1 and TDRD7 for AMD, STAT6 for myopia, and TPPP3 for DR) are newly reported in this study. Phenotype-disease mapping analysis revealed 10 genes related to the eye/vision phenotype or ODs. Combined with a drug exploration analysis, we found that the drugs related to C3 and TXN have been used for the treatment of ODs, and another eight genes (GSTM3 for senile cataract, IGFBP7 and CFHR1 for AMD, PTPMT1 for glaucoma, EFEMP1 and ACP1 for myopia, SIRPG and CTSH for DR) are promising targets for pharmacological interventions. Our study highlights the role played by proteins in ODs, in which brain proteins were taken into account due to the deepening of eye-brain connection studies. The potential pathogenic proteins finally identified provide a more reliable reference range for subsequent medical studies.

Plasma Proteomic Insights for Identification of Novel Predictors and Potential Drug Targets in Atrial Fibrillation: A Prospective Cohort Study and Mendelian Randomization Analysis.

BACKGROUND: Currently, there are no reliable methods for predicting and preventing atrial fibrillation (AF) in its early stages. This study aimed to identify plasma proteins associated with AF to discover biomarkers and potential drug targets. METHODS: The UK Biobank Pharma Proteomics Project examined 2923 circulating proteins using the Olink platform, forming the basis of this prospective cohort study. The UK Biobank Pharma Proteomics Project included a randomly selected discovery cohort and the consortium-selected replication cohort. The study's end point was incident AF, identified using International Classification of Diseases, Tenth Revision codes. The association between plasma proteins and incident AF was evaluated using Cox proportional hazard models in both cohorts. Proteins present in both cohorts underwent Mendelian randomization analysis to delineate causal connections, utilizing cis-protein quantitative trait loci as genetic tools. The predictive efficacy of the identified proteins for AF was assessed using the area under the receiver operating characteristic curve, and their druggability was explored. RESULTS: Data from 38 784 participants were included in this study. Incident AF cases were identified in the discovery cohort (1894; 5.5%) within a median follow-up of 14.5 years and in the replication cohort (451; 10.6%) within a median follow-up of 14.4 years. Twenty-one proteins linked to AF were identified in both cohorts. Specifically, COL4A1 (collagen IV α-1; odds ratio, 1.11 [95% CI, 1.04-1.19]; false discovery rate, 0.016) and RET (proto-oncogene tyrosine-protein kinase receptor Ret; odds ratio, 0.96 [95% CI, 0.94-0.98]; false discovery rate, 0.013) demonstrated a causal link with AF, and RET is druggable. COL4A1 improved the short- and long-term predictive performance of established AF models, as evidenced by significant enhancements in the area under the receiver operating characteristic, integrated discrimination improvement, and net reclassification index, all with P values below 0.05. CONCLUSIONS: COL4A1 and RET are associated with the development of AF. RET is identified as a potential drug target for AF prevention, while COL4A1 serves as a biomarker for AF prediction. Future studies are needed to evaluate the effectiveness of targeting these proteins to reduce AF risk.

Proteo-genomic analyses in relatively lean Chinese adults identify proteins and pathways that affect general and central adiposity levels.

Adiposity is an established risk factor for multiple diseases, but the causal relationships of different adiposity types with circulating protein biomarkers have not been systematically investigated. We examine the causal associations of general and central adiposity with 2923 plasma proteins among 3977 Chinese adults (mean BMI = 23.9 kg/m²). Genetically-predicted body mass index (BMI), body fat percentage (BF%), waist circumference (WC), and waist-to-hip ratio (WHR) are significantly (FDR < 0.05) associated with 399, 239, 436, and 283 proteins, respectively, with 80 proteins associated with all four and 275 with only one adiposity trait. WHR is associated with the most proteins (n = 90) after adjusting for other adiposity traits. These associations are largely replicated in Europeans (mean BMI = 27.4 kg/m²). Two-sample Mendelian randomisation (MR) analyses in East Asians using cis-protein quantitative trait locus (cis-pQTLs) identified in GWAS find 30/2 proteins significantly affect levels of BMI/WC, respectively, with 10 showing evidence of colocalisation, and seven (inter-alpha-trypsin inhibitor heavy chain H3, complement factor B, EGF-containing fibulin-like extracellular matrix protein 1, thioredoxin domain-containing protein 15, alpha-2-antiplasmin, fibronectin, mimecan) are replicated in separate MR using different cis-pQTLs identified in Europeans. These findings identified potential novel mechanisms and targets, to our knowledge, for improved treatment and prevention of obesity and associated diseases.

Causal inference of the effect of plasma proteins on the incidence of oral cancer: two-sample Mendelian randomization.

OBJECTIVE: This study is aimed to investigate the causal relationship between plasma proteins and oral cancer risk using two-sample MR (Mendelian randomization). METHODS: Summary-level GWAS (genome-wide association study) data on plasma protein levels (4,907 proteins) and oral cancer (6,034 cases, 6,585 controls) of European ancestry were utilized. SNPs (single nucleotide polymorphisms) associated with proteins at genome-wide significance were selected as instrumental variables. Multiple MR methods including IVW (inverse-variance weighted), MR-Egger, weighted median, simple mode and weighted mode were applied to estimate causal effects. Sensitivity analyses were conducted. RESULTS: Eight plasma proteins (CCDC167, MID2, NDRG4, PEAR1, PIAS4, RCAN1, SAMHD1 and TNMD) were identified to have significant causal associations with oral cancer risk. NDRG4, RCAN1, SAMHD1 and TNMD were associated with increased oral cancer risk while PEAR1 was associated with decreased risk. The causal estimates were consistent across different methods. Sensitivity analyses indicated the results were robust without significant heterogeneity or horizontal pleiotropy. Multivariable MR adjusting for smoking, alcohol intake and periodontal disease showed CCDC167, MID2, NDRG4, PEAR1, PIAS4 and SAMHD1 still had direct effects on oral cancer. CONCLUSION: This two-sample MR study provides evidence for potentially causal effects of several plasma proteins on oral cancer risk. The identified proteins may serve as biomarkers and shed light on biological mechanisms underlying oral carcinogenesis. Further research is warranted to validate and extend these findings.

Proteome-wide Mendelian randomization identified potential drug targets for migraine.

BACKGROUND: Migraine is a highly prevalent and complex neurovascular disease. However, the currently available therapeutic drugs often fall to adequately meet clinical needs due to limited effectiveness and numerous undesirable side effects. This study aims to identify putative novel targets for migraine treatment through proteome-wide Mendelian randomization (MR). METHODS: We utilized MR to estimate the causal effects of plasma proteins on migraine and its two subtypes, migraine with aura (MA) and without aura (MO). This analysis integrated plasma protein quantitative trait loci (pQTL) data with genome-wide association studies (GWAS) findings for these migraine phenotypes. Moreover, we conducted a phenome-wide MR assessment, enrichment analysis, protein-protein interaction networks construction, and mediation MR analysis to further validate the pharmaceutical potential of the identified protein targets. RESULTS: We identified 35 protein targets for migraine and its subtypes (p < 8.04 × 10-6), with prioritized targets showing minimal side effects. Phenome-wide MR identified novel protein targets-FCAR, UBE2L6, LATS1, PDCD1LG2, and MMP3-that have no major disease side effects and interacted with current acute migraine medication targets. Additionally, MMP3, PDCD1LG2, and HBQ1 interacted with current preventive migraine medication targets. The causal effects of plasma protein on migraine were partly mediated by plasma metabolites (proportion of mediation from 3.8% to 21.0%). CONCLUSIONS: A set of potential protein targets for migraine and its subtypes were identified. These proteins showed rare side effects and were responsible for biological mechanisms involved in migraine pathogenesis, indicating priority for the development of migraine treatments.

Proteomic Mendelian randomization to identify protein biomarkers of telomere length.

Shortening of telomere length (TL) is correlated with many age-related disorders and is a hallmark of biological aging. This study used proteome-wide Mendelian randomization to identify the protein biomarkers associated with telomere length. Protein quantitative trait loci (pQTL) were derived from two studies, the deCODE Health study (4907 plasma proteins) and the UK Biobank Pharma Proteomics Project (2923 plasma proteins). Summary data from genome-wide association studies (GWAS) for TL were obtained from the UK Biobank (472,174 cases) and GWAS Catalog (418,401 cases). The association between proteins and TL was further assessed using colocalization and summary data-based Mendelian randomization (SMR) analyses. The protein-protein network, druggability assessment, and phenome-wide MR were used to further evaluate the potential biological effects, druggability, and safety of the target proteins. Proteome-wide MR analysis identified 22 plasma proteins that were causally associated with telomere length. Five of these proteins (APOE, SPRED2, MAX, RALY, and PSMB1) had the highest evidence of association with TL and should be prioritized. This study revealed telomere length-related protein biomarkers, providing new insights into the development of new treatment targets for chronic diseases and anti-aging intervention strategies.

Potential Plasma Proteins (LGALS9, LAMP3, PRSS8 and AGRN) as Predictors of Hospitalisation Risk in COVID-19 Patients.

Background: The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, has posed unprecedented challenges to healthcare systems worldwide. Here, we have identified proteomic and genetic signatures for improved prognosis which is vital for COVID-19 research. Methods: We investigated the proteomic and genomic profile of COVID-19-positive patients (n = 400 for proteomics, n = 483 for genomics), focusing on differential regulation between hospitalised and non-hospitalised COVID-19 patients. Signatures had their predictive capabilities tested using independent machine learning models such as Support Vector Machine (SVM), Random Forest (RF) and Logistic Regression (LR). Results: This study has identified 224 differentially expressed proteins involved in various inflammatory and immunological pathways in hospitalised COVID-19 patients compared to non-hospitalised COVID-19 patients. LGALS9 (p-value < 0.001), LAMP3 (p-value < 0.001), PRSS8 (p-value < 0.001) and AGRN (p-value < 0.001) were identified as the most statistically significant proteins. Several hundred rsIDs were queried across the top 10 significant signatures, identifying three significant SNPs on the FSTL3 gene showing a correlation with hospitalisation status. Conclusions: Our study has not only identified key signatures of COVID-19 patients with worsened health but has also demonstrated their predictive capabilities as potential biomarkers, which suggests a staple role in the worsened health effects caused by COVID-19.

Enhanced prediction of atrial fibrillation risk using proteomic markers: a comparative analysis with clinical and polygenic risk scores.

BACKGROUND: Proteomic biomarkers have shown promise in predicting various cardiovascular conditions, but their utility in assessing the risk of atrial fibrillation (AF) remains unclear. This study aimed to develop and validate a protein-based risk score for predicting incident AF and to compare its predictive performance with traditional clinical risk factors and polygenic risk scores in a large cohort from the UK Biobank. METHODS: We analysed data from 36 129 white British individuals without prior AF, assessing 2923 plasma proteins using the Olink Explore 3072 assay. The cohort was divided into a training set (70%) and a test set (30%) to develop and validate a protein risk score for AF. We compared the predictive performance of this score with the HARMS2-AF risk model and a polygenic risk score. RESULTS: Over an average follow-up of 11.8 years, 2450 incident AF cases were identified. A 47-protein risk score was developed, with N-terminal prohormone of brain natriuretic peptide (NT-proBNP) being the most significant predictor. In the test set, the protein risk score (per SD increment, HR 1.94; 95% CI 1.83 to 2.05) and NT-proBNP alone (HR 1.80; 95% CI 1.70 to 1.91) demonstrated superior predictive performance (C-statistic: 0.802 and 0.785, respectively) compared with HARMS2-AF and polygenic risk scores (C-statistic: 0.751 and 0.748, respectively). CONCLUSIONS: A protein-based risk score, particularly incorporating NT-proBNP, offers superior predictive value for AF risk over traditional clinical and polygenic risk scores, highlighting the potential for proteomic data in AF risk stratification.

Measured and genetically predicted protein levels and cardiovascular diseases in UK Biobank and China Kadoorie Biobank.

Several large-scale studies have measured plasma levels of the proteome in individuals with cardiovascular diseases (CVDs)1-7. However, since the majority of such proteins are interrelated2, it is difficult for observational studies to distinguish which proteins are likely to be of etiological relevance. Here we evaluate whether plasma levels of 2,919 proteins measured in 52,164 UK Biobank participants are associated with incident myocardial infarction, ischemic stroke or heart failure. Of those proteins, 126 were associated with all three CVD outcomes and 118 were associated with at least one CVD in the China Kadoorie Biobank. Mendelian randomization and colocalization analyses indicated that genetically determined levels of 47 and 18 proteins, respectively, were associated with CVDs, including FGF5, PROCR and FURIN. While the majority of protein-CVD observational associations were noncausal, these three proteins showed evidence to support potential causality and are therefore promising targets for drug treatment for CVD outcomes.

Systematic discovery of gene-environment interactions underlying the human plasma proteome in UK Biobank.

Understanding how gene-environment interactions (GEIs) influence the circulating proteome could aid in biomarker discovery and validation. The presence of GEIs can be inferred from single nucleotide polymorphisms that associate with phenotypic variability - termed variance quantitative trait loci (vQTLs). Here, vQTL association studies are performed on plasma levels of 1463 proteins in 52,363 UK Biobank participants. A set of 677 independent vQTLs are identified across 568 proteins. They include 67 variants that lack conventional additive main effects on protein levels. Over 1100 GEIs are identified between 101 proteins and 153 environmental exposures. GEI analyses uncover possible mechanisms that explain why 13/67 vQTL-only sites lack corresponding main effects. Additional analyses also highlight how age, sex, epistatic interactions and statistical artefacts may underscore associations between genetic variation and variance heterogeneity. This study establishes the most comprehensive database yet of vQTLs and GEIs for the human proteome.

Proteomic aging clock predicts mortality and risk of common age-related diseases in diverse populations.

Circulating plasma proteins play key roles in human health and can potentially be used to measure biological age, allowing risk prediction for age-related diseases, multimorbidity and mortality. Here we developed a proteomic age clock in the UK Biobank (n = 45,441) using a proteomic platform comprising 2,897 plasma proteins and explored its utility to predict major disease morbidity and mortality in diverse populations. We identified 204 proteins that accurately predict chronological age (Pearson r = 0.94) and found that proteomic aging was associated with the incidence of 18 major chronic diseases (including diseases of the heart, liver, kidney and lung, diabetes, neurodegeneration and cancer), as well as with multimorbidity and all-cause mortality risk. Proteomic aging was also associated with age-related measures of biological, physical and cognitive function, including telomere length, frailty index and reaction time. Proteins contributing most substantially to the proteomic age clock are involved in numerous biological functions, including extracellular matrix interactions, immune response and inflammation, hormone regulation and reproduction, neuronal structure and function and development and differentiation. In a validation study involving biobanks in China (n = 3,977) and Finland (n = 1,990), the proteomic age clock showed similar age prediction accuracy (Pearson r = 0.92 and r = 0.94, respectively) compared to its performance in the UK Biobank. Our results demonstrate that proteomic aging involves proteins spanning multiple functional categories and can be used to predict age-related functional status, multimorbidity and mortality risk across geographically and genetically diverse populations.

Exploring novel drug targets for atopic dermatitis through plasma proteome with genome.

Atopic dermatitis (AD) is a chronic inflammatory disease with a complex and heterogeneous clinical presentation, leading to treatment limitations. Therefore, there is an urgent demand for new therapeutic drug targets. This study utilized Summary-data-based Mendelian randomization (SMR) to identify potential drug targets for AD. Summary statistics for 2,940 human plasma proteins were obtained from the UK Biobank, while AD statistics came from the Early Genetics and Epidemiology of Life Processes consortium and the FinnGen consortium. Furthermore, subsequent colocalization analyses confirmed the causal roles of candidate proteins. Moreover, Phenome-Wide Association Studies (PheWAS), protein-protein interaction (PPI), enrichment analysis, and single cell-type expression analysis provided additional insights. Additionally, drug prediction, druggability prediction, and molecular docking informed the discovery of novel drug targets. SMR analysis showed that eight plasma proteins were causally associated with AD: PVALB and TST were associated with a reduced risk of AD, while CA14, ECM1, IL22, IL6R, IL18R1, and MMP12 were associated with an increased risk of AD. Colocalization analysis confirmed significant associations for TST, IL22, and CA14. PheWAS further revealed that candidate drug targets were mainly linked to other allergic diseases. The corresponding protein-coding genes are predominantly expressed in melanocytes, T cells, and macrophages in skin tissue. Importantly, these proteins were identified to be involved in cytokine-cytokine receptor interaction, Th17 cell differentiation, and the JAK-STAT signaling pathway. All of these proteins are druggable, and six of them show great potential as drug targets. In conclusion, this study identified eight plasma proteins causally associated with AD and provided new insights into the etiology and potential drug targets for AD.

Evaluating the causal effect of circulating proteome on the risk of inflammatory bowel disease-related traits using Mendelian randomization.

Objective: This study sought to identify circulating proteins causally linked to Inflammatory Bowel Disease (IBD) traits through a Mendelian Randomization (MR) analytical framework. Methods: Using a large-scale, two-sample MR approach, we estimated the genetic links of numerous plasma proteins with IBD and its subtypes, leveraging information from the Inflammatory Bowel Disease Genetics Consortium. To assess the robustness of MR findings, methods like Bayesian colocalization, and Steiger filtering analysis, evaluation of protein-altering variants. Further insights into IBD's underlying mechanisms and therapeutic targets were gleaned from single-cell sequencing analyses, protein-protein interaction assessments, pathway enrichment analyses, and evaluation of drug targets. Results: By cis-only MR analysis, we identified 83 protein-phenotype associations involving 27 different proteins associated with at least one IBD subtype. Among these proteins, DAG1, IL10, IL12B, IL23R, MST1, STAT3 and TNFRSF6B showed overlapping positive or negative associations in all IBD phenotypes. Extending to cis + trans MR analysis, we further identified 117 protein-feature associations, including 44 unique proteins, most of which were not detected in the cis-only analysis. In addition, by performing co-localization analysis and Steiger filtering analysis on the prioritized associations, we further confirmed the causal relationship between these proteins and the IBD phenotype and verified the exact causal direction from the protein to the IBD-related feature. Conclusion: MR analysis facilitated the identification of numerous circulating proteins associated with IBD traits, unveiling protein-mediated mechanisms and promising therapeutic targets.

Investigating potential novel therapeutic targets and biomarkers for ankylosing spondylitis using plasma protein screening.

Background: Ankylosing spondylitis (AS) is a chronic inflammatory disease affecting the spine and sacroiliac joints. Recent genetic studies suggest certain plasma proteins may play a causal role in AS development. This study aims to identify and characterize these proteins using Mendelian randomization (MR) and colocalization analyses. Methods: Plasma protein data were obtained from recent publications in Nature Genetics, integrating data from five previous GWAS datasets, including 738 cis-pQTLs for 734 plasma proteins. GWAS summary data for AS were sourced from IGAS and other European cohorts. MR analyses were conducted using "TwoSampleMR" to assess causal links between plasma protein levels and AS. Colocalization analysis was performed with the coloc R package to identify shared genetic variants. Sensitivity analyses and protein-protein interaction (PPI) network analyses were conducted to validate findings and explore therapeutic targets. We performed Phenome-wide association study (PheWAS) to examine the potential side effects of drug protein on AS treatment. Results: After FDR correction, eight significant proteins were identified: IL7R, TYMP, IL12B, CCL8, TNFAIP6, IL18R1, IL23R, and ERAP1. Elevated levels of IL7R, IL12B, CCL8, IL18R1, IL23R, and ERAP1 increased AS risk, whereas elevated TYMP and TNFAIP6 levels decreased AS risk. Colocalization analysis indicated that IL23R, IL7R, and TYMP likely share causal variants with AS. PPI network analysis identified IL23R and IL7R as potential new therapeutic targets. Conclusions: This study identified eight plasma proteins with significant associations with AS risk, suggesting IL23R, IL7R, and TYMP as promising therapeutic targets. Further research is needed to explore underlying mechanisms and potential for drug repurposing.

Plasma proteins and inflammatory dermatoses: proteome-wide Mendelian randomization and colocalization analyses.

Current genome-wide association studies (GWAS) of plasma proteomes provide additional possibilities for finding new drug targets for inflammatory dermatoses. We performed proteome-wide Mendelian randomization (MR) and colocalization analyses to identify novel potential drug targets for inflammatory dermatoses. We performed MR and colocalization analysis using genetic variation as instrumental variables to determine the causal relationship between circulating plasma proteins and inflammatory dermatoses. 5 plasma proteins were found to be causally associated with dermatitis eczematosa, SLE, urticaria and psoriasis using cis-pQTLs as instrumental variables, but not found in AD and LP. 19 candidate genes with high colocalization evidence were identified. These potential drug targets still require more research and rigorous validation in future trials.

The causal relationship between plasma protein-to-protein ratios and type 2 diabetes and its complications: Proteomics mendelian randomization study.

AIM: In recent years, proteomics research has surged, with numerous observational studies identifying associations between plasma proteins and type 2 diabetes. However, research specifically focusing on the ratios of plasma proteins in type 2 diabetes remains relatively scarce. METHODS: This study primarily employed a two-sample, two-step Mendelian randomization (MR) approach, leveraging genetic data from several large, publicly accessible genome-wide association studies, wherein single nucleotide polymorphisms served as proxies for exposures and diseases. Within this framework, we applied two-sample MR to assess the associations between the 2821 plasma protein-to-protein ratios and type 2 diabetes along with its complications and utilized reverse MR to confirm the unidirectionality of these causal relationships. In addition, we employed two-step MR to investigate the potential mediating role of body mass index in these associations. To augment the robustness of our findings, we systematically implemented a series of sensitivity analyses. RESULTS: The results gleaned from the inverse-variance weighted method elucidated that a cumulative sum of 23 protein-to-protein ratios bore a causal nexus with type 2 diabetes across both sample cohorts. With each incremental elevation of 1 standard deviation in the genetically anticipated protein-to-protein ratio, the susceptibility to type 2 diabetes oscillated from 0.93 (95% confidence interval: 0.87, 1.00) for the CNTN3/NCSS1 protein level ratio to 1.13 (1.06, 1.22) for the DBNL/NCK2 protein level ratio. Moreover, a tally of eight protein-to-protein ratios correlated with a minimum of one complication linked to type 2 diabetes. Diverse sensitivity analyses corroborated the robustness of these observations. CONCLUSIONS: The outcomes of our investigation unveiled correlations between 23 plasma protein-to-protein ratios and type 2 diabetes, with eight of these ratios entwined with complications of type 2 diabetes. These discoveries offer novel perspectives on the diagnosis and management of type 2 diabetes and its associated complications.

Galectin-3 contributes to pathogenesis of IgA nephropathy.

IgA nephropathy (IgAN) is the most common type of glomerulonephritis that frequently progresses to kidney failure. However, the molecular pathogenesis underlying IgAN remains largely unknown. Here, we investigated the role of galectin-3 (Gal-3), a galactoside-binding protein in IgAN pathogenesis, and showed that Gal-3 expression by the kidney was significantly enhanced in patients with IgAN. In both TEPC-15 hybridoma-derived IgA-induced, passive, and spontaneous "grouped" ddY IgAN models, Gal-3 expression was clearly increased with disease severity in the glomeruli, peri-glomerular regions, and some kidney tubules. Gal-3 knockout (KO) in the passive IgAN model had significantly improved proteinuria, kidney function and reduced severity of kidney pathology, including neutrophil infiltration and decreased differentiation of Th17 cells from kidney-draining lymph nodes, despite increased percentages of regulatory T cells. Gal-3 KO also inhibited the NLRP3 inflammasome, yet it enhanced autophagy and improved kidney inflammation and fibrosis. Moreover, administration of 6-de-O-sulfated, N-acetylated low-molecular-weight heparin, a competitive Gal-3 binding inhibitor, restored kidney function and improved kidney lesions in passive IgAN mice. Thus, our results suggest that Gal-3 is critically involved in IgAN pathogenesis by activating the NLRP3 inflammasome and promoting Th17 cell differentiation. Hence, targeting Gal-3 action may represent a new therapeutic strategy for treatment of this kidney disease.

Associations between circulating proteins and cardiometabolic diseases: a systematic review and meta-analysis of observational and Mendelian randomisation studies.

BACKGROUND: Integration of large proteomics and genetic data in population-based studies can provide insights into discovery of novel biomarkers and potential therapeutic targets for cardiometabolic diseases (CMD). We aimed to synthesise existing evidence on the observational and genetic associations between circulating proteins and CMD. METHODS: PubMed, Embase and Web of Science were searched until July 2023 for potentially relevant prospective observational and Mendelian randomisation (MR) studies investigating associations between circulating proteins and CMD, including coronary heart disease, stroke, type 2 diabetes, heart failure, atrial fibrillation and atherosclerosis. Two investigators independently extracted study characteristics using a standard form and pooled data using random effects models. RESULTS: 50 observational, 25 MR and 10 studies performing both analyses were included, involving 26 414 160 non-overlapping participants. Meta-analysis of observational studies revealed 560 proteins associated with CMD, of which 133 proteins were associated with ≥2 CMDs (ie, pleiotropic). There were 245 potentially causal protein biomarkers identified in MR pooled results, involving 23 pleiotropic proteins. IL6RA and MMP12 were each causally associated with seven diseases. 22 protein-disease pairs showed directionally concordant associations in observational and MR pooled estimates. Addition of protein biomarkers to traditional clinical models modestly improved the accuracy of predicting incident CMD, with the highest improvement for heart failure (ΔC-index ~0.2). Of the 245 potentially causal proteins (291 protein-disease pairs), 3 pairs were validated by evidence of drug development from existing drug databases, 288 pairs lacked evidence of drug development and 66 proteins were drug targets approved for other indications. CONCLUSIONS: Combined analyses of observational and genetic studies revealed the potential causal role of several proteins in the aetiology of CMD. Novel protein biomarkers are promising targets for drug development and risk stratification. PROSPERO REGISTRATION NUMBER: CRD42022350327.

Plasma proteins and psoriatic arthritis: a proteome-wide Mendelian randomization study.

Background: Previous epidemiological studies have identified a correlation between serum protein levels and Psoriatic Arthritis (PsA). However, the precise nature of this relationship remains uncertain. Therefore, our objective was to assess whether circulating levels of 2,923 plasma proteins are associated with the risk of PsA, utilizing the Mendelian randomization (MR) approach. Methods: Two-sample MR analysis was performed to assess the causal impact of proteins on PsA risk. Exposure data for plasma proteins were sourced from a genome-wide association study (GWAS) conducted within the UK Biobank Pharma Proteomics Project, which encompassed 2,923 unique plasma proteins. The outcome data for PsA were sourced from the FinnGen study, a large-scale genomics initiative, comprising 3,537 cases and 262,844 controls. Additionally, colocalization analysis, Phenome-wide MR analysis, and candidate drug prediction were employed to identify potential causal circulating proteins and novel drug targets. Results: We thoroughly assessed the association between 1,837 plasma proteins and PsA risk, identifying seven proteins associated with PsA risk. An inverse association of Interleukin-10 (IL-10) with PsA risk was observed [odds ratio (OR)=0.45, 95% confidence interval (CI), 0.28 to 0.70, P FDR=0.072]. Additionally, Apolipoprotein F (APOF) has a positive effect on PsA risk (OR=2.08, 95% CI, 1.51 to 2.86, P FDR=0.005). Subsequently, we found strong evidence indicating that IL-10 and APOF were colocalized with PsA associations (PP.H4 = 0.834 for IL-10 and PP.H4 = 0.900 for APOF). Phenome-wide association analysis suggested that these two proteins may have dual effects on other clinical traits (P FDR<0.1). Conclusion: This study identified 7 plasma proteins associated with PsA risk, particularly IL-10 and APOF, which offer new insights into its etiology. Further studies are needed to assess the utility and effectiveness of these candidate proteins.

Associations between genetically predicted concentrations of plasma proteins and the risk of prostate cancer.

BACKGROUND: Prostate cancer (PCa) is a leading cause of cancer-related death in men. Understanding the proteomic landscape associated with PCa risk can provide insights into its molecular mechanisms and pave the way for potential therapeutic interventions. METHODS: A proteome-wide Mendelian randomization (MR) analysis was employed to determine associations between genetically predicted protein concentrations in plasma and PCa risk. From an initial list of 4,364 proteins, significant associations were identified and validated. Multiple sensitivity analyses were also conducted to enhance the robustness of our findings. RESULTS: Of the 4,364 genetically predicted proteins, 308 exhibited preliminary associations with PCa risk. After rigorous statistical refinement, genetically predicted concentrations of 14 proteins showed positive associations with PCa risk, with odds ratios spanning from 1.55 (95% CI 1.28-1.87) for ATG4B to 2.67 (95% CI 1.94-3.67) for HCN1. In contrast, genetically predicted concentrations of ATG7, B2M, MSMB, and TMEM108 demonstrated inverse associations with PCa. The replication analysis further substantiated positive associations for MDH1 and LSM1, and a negative one for MSMB with PCa. A meta-analysis harmonizing primary and replication data mirrored these findings. Furthermore, the MVMR analysis pinpointed B2M and MSMB as having significant associations with PCa risk. CONCLUSION: The genetic evidence unveils a refined set of proteins associated with PCa risk. The findings underscore the potential of these proteins as molecular markers or therapeutic targets for PCa, calling for deeper mechanistic studies and exploration into their translational relevance.