A genome-wide association study of serum proteins reveals shared loci with common diseases

Genetic insights into circulating osteocalcin for cardiovascular diseases and the role of vascular calcification

BACKGROUND AND AIMS

Studies have suggested that osteocalcin (OCN) is implicated in vascular calcification and linked to cardiovascular diseases (CVDs), but it is unclear whether the relationships are causal. The aim of this study is to evaluate the causal relationship of circulating OCN with CVDs and the role of vascular calcification.

METHODS AND RESULTS

Bi-directional, mediation, and multivariable Mendelian randomization (MVMR) were performed using summary-level data for circulating OCN levels, coronary artery calcification (CAC), and CVDs, including coronary artery disease (CAD), myocardial infarction (MI), heart failure, atrial fibrillation, stroke and its subtypes. Pooled estimates from two independent datasets of OCN were calculated using the inverse variance weighted method with sensitivity analyses. The conservative Hochberg correction method adjusted the P-value for multiple comparisons. Genetically predicted higher OCN levels were linked to an increased risk of CAD (odds ratio [OR] = 1.069, 95%CI = 1.037-1.102, P < 0.001) and MI (OR = 1.099, 95%CI = 1.069-1.130, P < 0.001). In addition, elevated OCN levels were associated with higher CAC (β = 0.180, 95%CI = 0.101-0.258, P = 0.006), which was related higher risk of CAD (OR = 1.225, 95%CI = 1.132-1.325, P < 0.001) and MI (OR = 1.286, 95%CI = 1.203-1.375, P < 0.001), mediating 54.5 % and 48.3 % of the effect of OCN on CAD and MI, respectively. Meanwhile, MVMR results also validated the mediating role of CAC. In contrast, CAD and MI were associated with decreased levels of plasma OCN.

CONCLUSION

Our findings reveal that higher OCN concentrations are associated with an elevated risk of CAD and MI, which was partially mediated by CAC. Lower OCN levels found in previous observational studies might be due to reverse causation.

The Plasma Proteome and Risk of Future Venous Thromboembolism-Results from the HUNT Study

This study aimed to identify novel plasma proteins associated with first-lifetime venous thromboembolism (VTE) and molecular pathways involved in VTE pathogenesis.A case-cohort comprising incident VTE cases (n = 294) and a randomly sampled age- and sex-weighted subcohort (n = 1,066) was derived from the Trøndelag Health Study (HUNT3, n = 50,800). Blood samples were collected and stored at cohort inclusion (2006-2008), and participants were followed up to 5 years. Proteome-wide analyses was performed using the 7k SomaScan® proteomics platform, and weighted Cox-regression models adjusted for age, sex, and sample batch were conducted, with the Bonferroni method applied to account for multiple testing. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were applied on the top-ranked 200 proteins associated with VTE.Out of 7,288 human proteins, 7 proteins were significantly associated with higher VTE risk with p-value <6.9 × 10-6 (hazard ratios per 1 standard deviation increase in protein levels ranging from 1.39 to 1.86). Except for coagulation factor VIII and tumor necrosis factor soluble receptor II, these proteins were novel associations and included collagen alpha-3(VI):BPTI/Kunitz inhibitor, histo-blood group ABO system transferase, peroxidasin, human epididymis protein 4, and regulator of G protein signaling 3. KEGG analyses of the top-ranked 200 proteins revealed significant pathway enrichment of nine proteins in the complement (mainly lectin pathway) and coagulation (mainly intrinsic pathway) cascades.Our proteome-wide analysis led to discovery of five novel protein candidates associated with 5-year risk of future VTE. KEGG analyses supported an interplay between the complement and coagulation pathways in the pathogenesis of VTE.

CAUSAL ASSOCIATION BETWEEN SEPSIS AND FIBROBLAST GROWTH FACTORS AS WELL AS THEIR RECEPTORS LEVELS: A TWO-SAMPLE MENDELIAN RANDOMIZATION STUDY

ABSTRACT

Objective: The potential association between sepsis risk and circulating levels of fibroblast growth factors (FGFs) and their receptors (FGFRs) has been a focus of research; however, the causal relationship between them remains to be elucidated. We hypothesize a causal association between genetically predicted FGFs, FGFRs, and sepsis risk, and we conduct a Mendelian randomization (MR) study to validate this hypothesis. Methods: We utilized a two-sample MR design to assess the effect of genetic variants associated with various FGFs (FGF1, FGF2, FGF7, FGF16, FGF19, FGF21, FGF23, FGF5) and FGFRs (FGFR1, FGFR2, FGFR3, α-Klotho) on sepsis risk, using genome-wide association study summary statistics. Our MR analyses employed the inverse-variance weighted (IVW) method, along with weighted median, weighted mode, and MR-Egger regression, supplemented by sensitivity analyses to ensure robustness. Results: The MR analysis identified an unequal number of instrumental variables ranging from 2 to 17 for FGFs and FGFRs when sepsis was the outcome. No significant correlation was found between genetically determined FGF levels and sepsis risk by IVW analysis (all P > 0.05). Correspondingly, similar nonsignificant associations were observed for FGFRs (all P > 0.05). Other MR methods corroborated the IVW findings. Sensitivity analyses, including Cochran's Q test, MR-Egger, and MR pleiotropy residual sum and outlier, indicated no significant heterogeneity or pleiotropy in the relationships, with the exception of a nonsignificant correlation between FGFR1 and sepsis that persisted after the exclusion of an outlier (odds ratio, 0.84; P = 0.34). Conclusion: The analysis found no significant causal associations between FGFs, their receptors, and sepsis risk, indicating a need for further research on their complex interactions.

Histo-blood group ABO system transferase plasma levels and risk of future venous thromboembolism: the HUNT study

ABSTRACT

The non-O blood group is a well-established risk factor for venous thromboembolism (VTE). However, the association between plasma levels of the histo-blood group ABO system transferase (BGAT), the gene product of the ABO locus, and VTE risk remains unclear. We aimed to investigate the association between plasma BGAT levels and risk of future VTE, and whether this relationship was mediated by plasma von Willebrand factor (VWF) or coagulation factor VIII (FVIII), as VWF is glycosylated by BGAT. Incident VTE-cases (n = 294) and a randomly sampled age- and-sex-weighted subcohort (n = 1066) were derived from the third survey of the Trøndelag Health Study. Baseline plasma samples (2006-2008) were subjected to the SomaScan aptamer-based-7K platform for protein measurements. Weighted Cox regression was used to estimate hazard ratios (HRs) with 95% confidence intervals (CIs) across BGAT quartiles. We found that ABO haplotypes (A1/A2/B/O1/O2) explained ≈80% of the BGAT plasma variability. Participants with BGAT levels in the highest quartile had 2-fold higher VTE risk (HR, 2.12; 95% CI, 1.39-3.22) compared with those with BGAT in the lowest quartile in age-, sex-, and sample batch-adjusted models. The associations were particularly pronounced for unprovoked VTE (HR, 3.71; 95% CI, 1.79-7.67) and deep vein thrombosis (HR, 3.28; 95% CI, 1.63-6.59). The HRs were similar after further adjustment for body mass index, C-reactive protein, and estimated glomerular filtration rate, and moderately attenuated when adding VWF or FVIII plasma levels to the models. Our findings indicate that elevated BGAT plasma levels are associated with increased risk of future VTE beyond what is explained by VWF and FVIII.

Combining genetic proxies of drug targets and time-to-event analyses from longitudinal observational data to identify target patient populations

BACKGROUND

Human genetics is an important tool for identifying genes as potential drug targets, and the extensive genetic study of cardiovascular disease provides an opportunity to leverage genetics to match specific patient populations to specific drug targets to improve prioritization of patient selection for clinical studies.

METHODS

We selected well described genetic variants in the region of PCSK9 (rs11591147 and rs562556), ADRB1 (rs7076938), ACE (rs4968782 and rs4363), GLP1R (rs10305492) and ABCC8 (rs757110) for use as proxies for the effects of drugs. Time-to-event analyses were utilized to evaluate their effects on atrial fibrillation (AF) and heart failure (HF) death and/or re-hospitalization using real-world longitudinal dataset. To mitigate the effect of confounding factors for cardiovascular (CV) outcomes, we employed propensity score matching.

RESULTS

After matching, a genetic proxy for PCSK9 inhibition (rs11591147) improved survival from CV death/heart transplant in individuals following a diagnosis of ischemic heart disease (Hazard Ratio (HR) 0.78, P = 0.03). A genetic proxy for beta-blockade (rs7076938) improved freedom from rehospitalization or death in individuals with AF (HR 0.92, P = 0.001), and a genetic proxy of ACE inhibition (rs7076938) improved freedom from rehospitalization for HF or death (HR 0.8, P = 0.017) and AF (HR 0.85, P = 0.0014). A protective variant in GLP1R (rs10305492) showed decreased risk of developing HF or CV death after diagnosis of ischemic heart disease (HR = 0.82, P = 0.031) and a protective variant in ABCC8 (rs757110) showed decreased risk of CV mortality since ischemic disease diagnosis (HR = 0.88, P = 0.04) and decreased risk of AF in diabetic patients with ischemic heart disease (HR = 0.68, P = 0.001). Notably, despite smaller cohort sizes after matching, we often observed numerically smaller HRs and reduced P, indicating more pronounced effects and increased statistical association. However, not all genetic proxies replicated known treatment effects.

CONCLUSIONS

Genetic proxies for well-known drugs corroborate findings from clinical trials in cardiovascular disease. Our results demonstrate a useful analytical approach that leverages genetic evidence from a large cohort with longitudinal outcomes data to effectively select patient populations where specific drug targets may be most effective.

Exploring the causal association between fatty acid-binding proteins and anaphylactic shock due to adverse reactions to medications: A two-sample Mendelian randomization study

Previous studies have identified a relationship between fatty acid-binding proteins (FABPs) and immune diseases. This study aimed to investigate whether a causal relationship exists between FABPs and anaphylactic shock resulting from adverse drug reactions. Single nucleotide polymorphisms associated with FABPs were utilized as instrumental variables, sourced from the National Human Genome Research Institute-European Bioinformatics Institute Catalog of human genome-wide association studies. Data on anaphylactic shock due to adverse effects of correctly administered drugs were obtained from the FinnGen database, which includes genomic and health data from 500,000 Finnish biobank donors. A two-sample Mendelian randomization analysis was conducted to explore the causality between FABPs and anaphylactic shock due to adverse drug reactions. The analysis revealed a negative causal relationship between FABP5 (odds ratio [OR] = 0.40; 95% confidence interval [CI] = 0.17-0.92; P = .032) and FABP12 (OR = 0.77; 95% CI = 0.63-0.94; P = .009) and anaphylactic shock due to adverse drug reactions. These findings were corroborated by Mendelian randomization-Egger, weighted median, and weighted mode methods. This study provides robust evidence supporting a protective relationship between FABP5 and FABP12 and anaphylactic shock due to adverse drug reactions. Further experimental studies are warranted to elucidate the causal mechanisms and associations between FABP5, FABP12, and anaphylactic shock in the context of adverse drug reactions.

Multiomic Underpinnings of Drug Targets for Intracranial Aneurysm: Evidence From Diversified Mendelian Randomization

AIMS

The absence of pharmaceutics poses challenges in preventing intracranial aneurysm (IA) progression and rupture. This research emphasized identifying drug targets for IA through a druggable genome-wide Mendelian randomization (MR) analysis.

METHODS

A two-sample MR analysis was performed leveraging cis-expression quantitative trait loci in the blood (n = 31,684) and arteries (n = 584) aligned with 5883 druggable genes as exposure and the largest IA summary statistics (n = 7495) as outcome. Bayesian colocalization analysis, plasma cis-protein quantitative trait loci (n = 35,559), and external IA cohorts (FinnGen, n = 2582; Zhou, n = 380) were used for validation. A phenome-wide MR (Phe-MR) incorporating 783 diseases uncovered side effects. Multivariable MR addressed unmeasured pleiotropy.

RESULTS

Five druggable genes in blood and one in the coronary artery showed significant association with IA risk (p-FDR ≤ 0.05). NT5C2, PRCP, and CRMP1 shared a common variant with IA (PPH4 ≥ 0.8). The external validation cohorts confirmed the effects of NT5C2 on IA (FinnGen cohort, Odds Ratio [OR], 0.81, 95% Confidential Interval [95% CI] 95% CI, 0.707-0.930; p = 0.003; Zhou cohort, OR, 0.68, 95% CI, 0.469-0.983; p = 0.041). The genetically predicted protein level of PRCP validated an inverse association with IA risk (OR, 0.734; 95% CI, 0.561-0.959; p = 0.023). The Phe-MR revealed insignificance for NT5C2 or PRCP. Direct causal effects on IA were 0.60 (95% CI, 0.457-0.797; p = 1.36E-05) for PRCP and 0.67 (95% CI, 0.527-0.860; p = 0.002) for NT5C2 after adjusting for IA modifiable risk factors.

CONCLUSIONS

NT5C2 and PRCP were identified as potential drug targets for IA, with effects independent of known modifiable risk factors. Targeting NT5C2 and PRCP appeared exclusively effective and safe.

Genetics of circulating proteins in newborn babies at high risk of type 1 diabetes

Type 1 diabetes is a chronic, autoimmune disease characterized by the destruction of insulin-producing β-cells in the pancreas. Early detection can facilitate timely intervention, potentially delaying or preventing disease onset. Circulating proteins reflect dysregulated biological processes and offer insights into early disease mechanisms. Here, we construct a genome-wide pQTL map of 1985 proteins in 695 newborn babies (median age 2 days) at increased genetic risk of developing Type 1 diabetes. We identify 535 pQTLs (352 cis-pQTLs, 183 trans-pQTLs), 62 of which characteristic of newborns. We show colocalization of pQTLs for CTRB1, APOBR, IL7R, CPA1, and PNLIPRP1 with Type 1 diabetes GWAS signals, and Mendelian randomization causally implicates each of these five proteins in the aetiology of Type 1 diabetes. Our study illustrates the utility of newborn molecular profiles for discovering potential drug targets for childhood diseases of significant concern.

Identification and Experimental Validation of NETosis-Mediated Abdominal Aortic Aneurysm Gene Signature Using Multi-omics, Machine Learning, and Mendelian Randomization

Abdominal aortic aneurysm (AAA) is a life-threatening disorder with limited therapeutic options. Neutrophil extracellular traps (NETs) are formed by a process known as "NETosis" that has been implicated in AAA pathogenesis, yet the roles and prognostic significance of NET-related genes in AAA remain poorly understood. This study aimed to identify key AAA- and NET-related genes (AAA-NETs-RGs), elucidate their potential mechanisms in contributing to AAA, and explore potential therapeutic compounds for AAA therapy. Through bioinformatics analysis of multiomics and machine learning, we identified six AAA-NETs-RGs: DUSP26, FCN1, MTHFD2, GPRC5C, SEMA4A, and CCR7, which exhibited strong diagnostic potential for predicting AAA progression, were significantly enriched in pathways related to cytokine-cytokine receptor interaction and chemokine signaling. Immune infiltration analysis revealed a causal association between AAA-NETs-RGs and immune cell infiltration. Cell-cell communication analysis indicated that AAA-NETs-RGs predominantly function in smooth muscle cells, B cells, T cells, and NK cells, primarily through cytokine and chemokine signaling. Gene profiling revealed that CCR7 and MTHFD2 exhibited the most significant upregulation in AAA patients compared to non-AAA controls, as well as in in vitro AAA models. Notably, genetic depletion of CCR7 and MTHFD2 strongly inhibited Ang II-induced phenotypic switching, functional impairment, and senescence in vascular smooth muscle cells (VSMCs). Based on AAA-NETs-RGs, molecular docking analysis combined with the Connectivity Map (CMap) database identified mirdametinib as a potential therapeutic agent for AAA. Mirdametinib effectively alleviated Ang II-induced phenotypic switching, biological dysfunction, and senescence. These findings provide valuable insights into understanding the pathophysiology of AAA and highlight promising therapeutic strategies targeting AAA-NETs-RGs.

Identification of CD209 as an Intervention Target for Type 2 Diabetes After COVID-19 Infection: Insights From Proteome-Wide Mendelian Randomization

ARTICLE HIGHLIGHTS

Increasing evidence links coronavirus disease 2019 (COVID-19) infection with heightened type 2 diabetes (T2D) risk; however, the mechanisms underlying this relationship remain poorly understood. We aimed to identify mediating proteins linking COVID-19 infection with T2D, elucidating how COVID-19 might heighten T2D risk. Protein CD209 and central obesity potentially play a crucial role between COVID-19 susceptibility and T2D. Our results highlight CD209 as a potential intervention target for T2D prevention following COVID-19 infection.

Genetic insights of lipid metabolism and lipid-lowering drugs with Lewy body dementia risk: Evidence from Mendelian randomization

BACKGROUND

Lewy body dementia (LBD) is the second common dementia, with unclear mechanisms and limited treatment options. Dyslipidemia has been implicated in LBD, but the role of lipid-lowering drugs remains underexplored. This study aims to investigate the association between lipid traits, drug targets, and LBD risk using Mendelian Randomization (MR) analysis.

METHODS

We performed univariable and multivariable MR analyses to evaluate the causal effects of lipid traits on the risk of LBD. Then, drug-target MR analysis and subtype analysis were conducted to evaluate the effects of lipid-lowering therapies on LBD.

RESULTS

In univariable MR, genetically predicted low-density lipoprotein cholesterol (LDL-C) and remnant cholesterol (RC) levels were associated with an increased risk of LBD. Mediation analysis suggested a potential interaction between LDL-C and RC in influencing LBD risk. Drug-target MR analysis identified significant associations between genetically proxied inhibition of ANGPTL3, CETP, and HMGCR and LBD risk.

CONCLUSION

This MR analysis provided evidence that elevated LDL-C and RC may increase the risk of LBD. Additionally, targeting ANGPTL3, CETP, and HMGCR may represent potential therapeutic strategies for the prevention or treatment of LBD.

Screening and analysis of programmed cell death related genes and targeted drugs in sepsis

OBJECTIVE

This study employed bioinformatics techniques to identify diagnostic genes associated with programmed cell death (PCD) and to explore potential therapeutic agents for the treatment of sepsis.

METHODS

Gene expression profiles from sepsis patients were analyzed to identify differentially expressed genes (DEGs) and hub genes through Weighted Gene Co-expression Network Analysis (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted to elucidate the functions of the DEGs. PCD-related genes were cross-referenced with the identified DEGs. Diagnostic genes were selected using Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest (RF) methodologies. Single-cell RNA sequencing was utilized to assess gene expression in blood cells, while CIBERSORT was employed to evaluate immune cell infiltration. A transcription factor (TF)-microRNA (miRNA)-hub gene network was constructed, and potential therapeutic compounds were predicted using the Drug Gene Interaction Database (DGIdb). Mendelian Randomization (MR) methods were applied to analyze genome-wide association study (GWAS) data for S100A9, TXN, and GSTO1.

RESULTS

The analysis revealed 2156 PCD-related genes, 714 DEGs, and 1198 hub genes, with 88 genes enriched in immune and cell death pathways. Five pivotal PCD-related genes (IRAK3, S100A9, TXN, NFATC2, and GSTO1) were identified, leading to the construction of a network comprising six transcription factors and 171 microRNAs. Additionally, seven drugs targeting S100A9, TXN, and NFATC2 were identified. MR analysis suggested that a decrease in GSTO1 levels is associated with an increased risk of sepsis, and that sepsis influences the levels of S100A9, TXN, and GSTO1.

CONCLUSIONS

Through bioinformatics approaches, this study successfully identified five genes (IRAK3, S100A9, TXN, NFATC2, and GSTO1) associated with programmed cell death in the context of sepsis. This research identified seven candidate drugs for sepsis treatment and established a methodological framework for predicting biomarkers and drug targets that could be applicable to other diseases.

Plasma proteome variation and its genetic determinants in children and adolescents

Our current understanding of the determinants of plasma proteome variation during pediatric development remains incomplete. Here, we show that genetic variants, age, sex and body mass index significantly influence this variation. Using a streamlined and highly quantitative mass spectrometry-based proteomics workflow, we analyzed plasma from 2,147 children and adolescents, identifying 1,216 proteins after quality control. Notably, the levels of 70% of these were associated with at least one of the aforementioned factors, with protein levels also being predictive. Quantitative trait loci (QTLs) regulated at least one-third of the proteins; between a few percent and up to 30-fold. Together with excellent replication in an additional 1,000 children and 558 adults, this reveals substantial genetic effects on plasma protein levels, persisting from childhood into adulthood. Through Mendelian randomization and colocalization analyses, we identified 41 causal genes for 33 cardiometabolic traits, emphasizing the value of protein QTLs in drug target identification and disease understanding.

Mendelian Randomization Analysis of the Possible Causal Relationships Between Neurodevelopment-Related Proteins and Bipolar Disorder

BACKGROUND

Bipolar disorder (BD) is a complex mental condition of which the mechanism of onset remains unclear. Mendelian randomization (MR) allows evaluation of the causal effects of biomarkers by minimizing the risks of reverse causation and confounding factors. In this study, MR was used to assess the causal relationships between neurodevelopment-related proteins and BD, thereby providing potential evidence for the neurodevelopmental hypothesis of this mental disorder.

METHODS

Leveraging data from large-scale genome-wide association studies (GWASs), the associations between six neurodevelopment-related proteins and BD were analyzed using five MR approaches; namely, inverse-variance weighted, weighted median, MR-Egger, simple mode, and weighted mode methods. The neurodevelopment-related proteins were selected in the study with 5368 European descents. GWAS of BD come from the Psychiatric Genomics Consortium (NCase = 41,917, NControl = 371,549).

RESULTS

The analyses identified robust causal relationships between BD and the proteins inter-alpha-trypsin inhibitor heavy chain (ITIH)5 (OR = 1.08, 95% CI = 1.00-1.17, p = 0.04) and neurofascin (NFASC) (OR = 0.96, 95% CI = 0.92-1.00, p = 0.042). Initial findings for ITIH1 and ITIH3 were deemed unreliable due to pleiotropy (ITIH1: MR-Egger intercept p = 0.025) or heterogeneity (ITIH3: Cochran's Q p = 0.001). Furthermore, the MR analyses failed to yield evidence supporting a causal effect of liability to BD on neurodevelopment-related proteins.

CONCLUSION

The MR analysis indicated potential causal relationships between two neurodevelopment-related proteins (NFASC and ITIH5) and BD. Further studies are required to validate these results and elucidate the specific functions of these proteins in the development of this mental disorder.

Assessing the Causal Relationship Between Plasma Proteins and Pulmonary Fibrosis: A Systematic Analysis Based on Mendelian Randomization

Pulmonary fibrosis (PF) is a chronic interstitial lung disease characterized by the destruction of alveolar structures, the abnormal accumulation of extracellular matrix (ECM), and ultimately respiratory failure. Although previous studies have shown that plasma proteins play an important role in the onset and progression of PF, there is currently a lack of systematic studies on causal relationships. To address the identified gap, the study employs the MR method to identify potential drug targets associated with PF. Plasma protein data (pQTL, exposure) were sourced from Ferkingstad et al. (n = 35,559), and PF-related summary statistics were obtained from the GWAS database (n = 469,126). The study integrates enrichment analysis, protein-protein interaction (PPI) networks, drug prediction, molecular docking, and single-cell sequencing to further evaluate the biological functions and pharmacological potential of the identified targets. In the MR analysis, 64 genetic loci were significantly associated with the occurrence of PF. Further reverse Mendelian analysis revealed a positive causal relationship between PF and genes such as NPTX1, IL31, and CTSE, suggesting that these proteins may play a promotive role in the onset and progression of pulmonary fibrosis. The PPI network analysis identified core genes such as CDH1, CRP, VTN, COL1A1, and MAPK8, which are involved in the key pathological processes of PF, including cell signaling, ECM remodeling, and immune responses. The drug prediction analysis identified potential drugs such as sorafenib, vitamin C, and vitamin E, and the molecular docking analysis showed good binding between the drugs and the proteins. The single-cell sequencing results showed that core genes were highly expressed in fibroblasts and alveolar type II cells, confirming their potential role in the pathogenesis of PF. This study successfully identifies 64 potential drug targets for PF, with 10 core targets considered particularly promising for clinical trials. These findings offer valuable insights into the molecular mechanisms underlying PF and open new avenues for the development of targeted therapies. This research may accelerate the development of effective PF treatments and reduce drug development costs by providing more precise and personalized approaches to managing the disease.

Mendelian randomization analysis to identify potential drug targets for osteoarthritis

BACKGROUND

Osteoarthritis (OA) is a prevalent chronic joint disease for which there is a lack of effective treatments. In this study, we used Mendelian randomization analysis to identify circulating proteins that are causally associated with OA-related traits, providing important insights into potential drug targets for OA.

METHOD

Causal associations between 1553 circulating proteins and five OA-related traits were assessed in large-scale two-sample MR analyses using Wald ratio or inverse variance weighting, and the results were corrected for Bonferroni. In addition, sensitivity analyses were performed to validate the reliability of the MR results, including reverse MR analysis and Steiger filtering to ensure the causal direction between circulating proteins and OA; Bayesian co-localization and phenotypic scanning were used to eliminate confounding effects and horizontal pleiotropy. External validation was performed to exclude incidental findings using novel plasma protein quantitative trait loci. Finally, the online analysis tool Enrichr was utilized to screen drugs and molecular docking was performed to predict binding modes and energies between proteins and drugs to identify the most stable and likely binding modes and drugs.

RESULT

Four proteins were ultimately found to be reliably and causally associated with three OA-related features: DNAJB12 and USP8 were associated with knee OA, IL12B with spinal OA, and RGMB with thumb OA. The ORs for the above proteins were 1.51 (95% CI, 1.26-1.81), 1.72 (95% CI, 1.42-2.08), 0.87 (95% CI, 0.81-0.92), and 0.59 (95% CI, 0.47-0.75), respectively. Drug-predicting small molecules (doxazosin, XEN 103, and montelukast) that simultaneously target three proteins, DNAJB12, USP8, and IL12B, docked well.

CONCLUSION

Based on our comprehensive analysis, we can draw the conclusion that there is a causal relationship between the genetic levels of DNAJB12, USP8, IL12B, and RGMB and the risk of respective OA.They may be potential options for OA screening and prevention in clinical practice. They can also serve as candidate molecules for future mechanism exploration and drug target selection.

Association Analysis of the Circulating Proteome With Sarcopenia-Related Traits Reveals Potential Drug Targets for Sarcopenia

BACKGROUND

Sarcopenia severely affects the physical health of the elderly. Currently, there is no specific drug available for sarcopenia. This study aims to identify pathogenic proteins and druggable targets for sarcopenia through Mendelian randomization (MR)-based analytical framework.

METHODS

A sequential stepwise screening method that includes two-sample MR, Steiger filtering test and colocalization (MRSC) was applied to identify causal proteins associated with sarcopenia-related traits. In the MR analyses, 4372 circulating proteins with valid instrumental variables (IVs) from eight proteomic genome-wide association studies were utilized as exposures, and nine sarcopenia-related traits were utilized as outcomes. IVs were classified into cis-protein quantitative trait loci (pQTLs) and trans-pQTLs based on their positions. We conducted cis-only MRSC analyses and cis + trans MRSC analyses using cis-pQTLs and cis + trans pQTLs as IVs, respectively. Post-MRSC analyses were conducted on the prioritized findings of MRSC, including annotation of protein-altering variants (PAVs), assessment of overlap between pQTLs and expression quantitative trait loci (eQTLs), protein-protein interaction (PPI) analysis, pathway enrichment analysis and annotation of drug targets. Utilizing data from the UK Biobank, we performed an observational study to explore the associations between baseline circulating protein levels and the longitudinal changes in nine sarcopenia-related traits.

RESULTS

A total of 181 causal associations for 65 proteins were prioritized by the cis-only MRSC analyses and 227 associations for 91 proteins were prioritized by the cis + trans MRSC analyses. Among the prioritized proteins, the majority of them employed non-PAVs as IVs and most of their cis-pQTLs overlapped with corresponding eQTLs and exhibited consistent directionality, with only one trans-pQTL overlapping with an eQTL. The PPI network of cis-only MRSC-prioritized proteins (p = 4.04 × 10-4) and cis + trans MRSC-prioritized proteins (p = 8.76 × 10-5) showed significantly more interactions than expected. Reactome, KEGG and GO pathway enrichment analyses for cis-only MRSC-prioritized proteins identified 52, 12 and 79 enriched pathways, respectively (adjusted p < 0.05). For proteins identified by cis + trans MRSC analyses, only 15 pathways were enriched through the GO pathway enrichment analyses. In the observational study, 197 circulating proteins were identified to be associated with one or more sarcopenia-related traits (p < 0.05/2923). Among them, the significant associations of CTSB (negative association) and ASGR1 (positive association) with sarcopenia-related traits were observed to have consistent directional associations in both MR-based studies and observational studies. Drug target annotations suggested that 52 MRSC-prioritized proteins and 145 biomarkers are drug targets or druggable.

CONCLUSIONS

This study identified 89 potential pathogenic proteins and 197 candidate biomarkers for sarcopenia, providing valuable clues for the development of therapeutic drugs for sarcopenia.

Identification of Circulating Proteins Associated With Blood Pressure

BACKGROUND

Circulating proteins in blood are involved in various physiological processes, but their contributions to blood pressure regulation remain partially understood. In traditional observational studies, identifying circulating proteins causally associated with blood pressure is challenging because of potentially unmeasured confounding and possible reverse causality.

METHODS

Two-sample Mendelian randomization analyses were conducted to estimate the causal effects of 2270 circulating proteins (data sourced from 8 genome-wide association studies) on diastolic blood pressure, systolic blood pressure, and pulse pressure. Colocalization analyses were then used to investigate whether the circulating proteins and blood pressure traits shared causal genetic variants. To further verify the findings, we subsequently performed Steiger filtering analyses, annotation of protein-altering variants, assessment of overlap between protein quantitative trait loci and expression quantitative trait loci, protein-protein interaction and functional enrichment analyses, and drug target evaluation. To provide more potential biomarkers, we further evaluated the epidemiological associations of 2923 circulating proteins with blood pressure and hypertension by cross-sectional and longitudinal analyses using individual data in the UK Biobank.

RESULTS

Mendelian randomization and colocalization analyses identified 121 circulating proteins with putative causal effects on at least 1 blood pressure trait. Many of the identified proteins are enriched in the pathways relevant to blood pressure regulation, and a majority of these proteins are either known drug targets or druggable candidates.

CONCLUSIONS

This study has uncovered numerous circulating proteins potentially causally associated with blood pressure, providing insights into the regulatory mechanisms of blood pressure and potential therapeutic targets to facilitate blood pressure management.

A meta-analysis of genome-wide studies of resilience in the German population

Resilience is the capacity to adapt to stressful life events. As such, this trait is associated with physical and mental functions and conditions. Here, we aimed to identify the genetic factors contributing to shape resilience. We performed variant- and gene-based meta-analyses of genome-wide association studies from six German cohorts (N = 15822) using the 11-item version of the Resilience Scale (RS-11) as outcome measure. Variant- and gene-level results were combined to explore the biological context using network analysis. In addition, we conducted tests of correlation between RS-11 and the polygenic scores (PGSs) for 12 personality and mental health traits in one of these cohorts (PROCAM-2, N = 3879). The variant-based analysis found no signals associated with resilience at the genome-wide level (p < 5 × 10-8), but suggested five genomic loci (p < 1 × 10-5). The gene-based analysis identified three genes (ROBO1, CIB3 and LYPD4) associated with resilience at genome-wide level (p < 2.48 × 10-6) and 32 potential candidates (p < 1 × 10-4). Network analysis revealed enrichment of biological pathways related to neuronal proliferation and differentiation, synaptic organization, immune responses and vascular homeostasis. We also found significant correlations (FDR < 0.05) between RS-11 and the PGSs for neuroticism and general happiness. Overall, our observations suggest low heritability of resilience. Large, international efforts will be required to uncover the genetic factors that contribute to shape trait resilience. Nevertheless, as the largest investigation of the genetics of resilience in general population to date, our study already offers valuable insights into the biology potentially underlying resilience and resilience's relationship with other personality traits and mental health.

The Causal Relationships and Therapeutic Targets of Plasma Proteins in Ankylosing Spondylitis

Objective: The purpose of this study was to assess the causal effects of circulating plasma proteins on ankylosing spondylitis (AS) and to explore potential therapeutic targets. Methods: The study used protein quantitative trait loci (pQTLs) for thousands of plasma proteins from nine genome-wide association studies (GWAS) as instrumental variables. The relationship between genetically predicted plasma proteins and AS was assessed through Mendelian randomization (MR) analysis. Further analyses, including colocalization analysis, Steiger filtering analysis, protein-altering variant assessment, protein-protein interaction (PPI), and pathway enrichment analysis, were conducted to validate the robustness and causal direction of the results, as well as to investigate the protein functions and potential drug targets. Results: Nine unique proteins were found to have strong causal associations with AS. Steiger filtering analysis confirmed that all associations identified by MR analysis have a direct causal link from the proteins to AS. Colocalization analysis identified four unique proteins-Interleukin-6 receptor alpha (IL-6Rα), Interleukin-23 receptor (IL-23R), Thrombospondin-2 (THBS2), and Interleukin-1 receptor type 2 (IL-1R2)-that share the same causal variants with AS. PPI and pathway enrichment analysis revealed the potential roles of these proteins in inflammatory responses and immune regulation. Moreover, these proteins were valuable drug targets or considered druggable. Conclusions: This study has identified multiple plasma proteins associated with AS, revealing the important roles of these proteins in the pathogenesis of AS and providing potential therapeutic targets for AS.

MeQTL Mapping in African American Hepatocytes Reveals Shared Genetic Regulators of DNA Methylation and Gene Expression

Methylation quantitative trait loci (meQTL) mapping can provide insight into the genetic architecture underlying the epigenome by identifying single-nucleotide polymorphisms (SNPs) associated with differential methylation at methylation sites (CpGs) across the genome. Given that the epigenetic architecture underlying differences in gene expression can vary across racial populations, performing epigenomic studies in African Americans is crucial for understanding the interplay between genetic variation, DNA methylation, and gene expression in this understudied group. By performing cis-meQTL mapping in African American hepatocytes, we identified 410,186 cis-meQTLs associated with methylation at 24,425 CpGs in the liver. Through colocalization analysis, we found that 18,206 of these meQTLs are also colocalized with known liver eQTLs. Additionally, we found that using African American eQTL data results in an increased ability to detect additional colocalized variants that exhibit strong differences in allele frequency between people of European and African ancestry. Furthermore, the presence of smaller linkage disequilibrium blocks in African Americans allows us to identify narrower genomic regions of potentially causal variants compared to when data from Europeans is used. Importantly, these colocalized SNPs are significantly enriched for genetic associations with lipid and inflammatory traits in the GWAS catalog, suggesting that DNA methylation may contribute to the etiologies of these diseases. Furthermore, while it is generally presumed that the genetic regulation of DNA methylation is shared between blood and liver, we found that only 5.4% of African American liver meQTLs colocalize with blood meQTLs. Overall, our results reveal that studying African American populations results in the identification of additional genetic and epigenetic factors that may regulate gene expression in the liver, thereby expanding our understanding of gene regulation in African Americans.

Plasma proteins are integral to gene-regulatory networks acting within and across blood cells, the arterial wall and major metabolic organs

The plasma proteome is the future for diagnostic markers for common diseases, like cardiometabolic disorders (CMDs) and coronary artery disease (CAD). The reliability of these markers requires identifying their source-organ(s). We profiled 974 plasma proteins in 532 CAD-patients of the STARNET study with arterial wall, major metabolic organ, and blood transcriptomic data. 144 plasma cis-pQTLs colocalized with tissue eQTLs including eight CMD/CAD GWAS genes. 262 plasma proteins correlated with their corresponding tissue "seed" genes whereof 101 in the liver. 851 plasma proteins were strongly associated with the activity of gene-regulatory networks (GRNs), particularly cross-tissue GRNs. The Adipose-Liver Plasma LeptIN-regulating GRN78 with the top key driver UCHL1 in fat stood out. Beyond genetics, explaining up to 20% of plasma protein variation, and a limited number of mostly hepatic seed genes, plasma proteins are integral to GRNs acting within and across blood cells, the arterial wall and major metabolic organs.

Causal relationship between matrix metalloproteinase and pulmonary embolism: a bidirectional two-sample Mendelian randomization study

This study evaluated the causal relationship between matrix metalloproteinases (MMPs) and pulmonary embolism using data from the genome-wide association study (GWAS) of pulmonary embolism from the UK Biobank and a GWAS dataset of MMPs based on 5,457 Icelanders aged 65 years and older. MR-Egger, MR-PRESSO, Cochran's Q, and leave-one-out were used for sensitivity analysis. The Mendelian randomization (MR) analysis, based on the IVW analysis, indicated an elevated risk for pulmonary embolism in association with MMP19 (OR = 1.0009, 95%CI: 1-1.0017, P = 0.041), consistent with the weighted median method results (P = 0.015). In addition, despite the negative result from the IVW method (P = 0.554), the weighted median analysis suggested a reduced risk for pulmonary embolism related to MMP12 (OR = 0.9992, 95%CI: 0.9984-1, P = 0.038). No causal associations were found for the other MMPs (including MMP1, MMP2, MMP3, MMP7, MMP8, MMP9, MMP10, MMP13, MMP14, MMP16, MMP17, and MMP20) on pulmonary embolism (all P > 0.05). The reverse MR analysis revealed no causal associations between pulmonary embolism as exposure and MMPs as outcomes. Sensitivity analyses confirmed the robustness of these findings. In conclusion, this MR analysis revealed the potential causal relationship between MMPs and pulmonary embolism, suggesting that measuring MMPs could help identify people at higher risk of pulmonary embolism, but further research is needed.

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

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

ARTICLE HIGHLIGHTS

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

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

C. Calado C. Early Mortality Prediction in Intensive Care Unit Patients Based on Serum Metabolomic Fingerprint

Predicting mortality in intensive care units (ICUs) is essential for timely interventions and efficient resource use, especially during pandemics like COVID-19, where high mortality persisted even after the state of emergency ended. Current mortality prediction methods remain limited, especially for critically ill ICU patients, due to their dynamic metabolic changes and heterogeneous pathophysiological processes. This study evaluated how the serum metabolomic fingerprint, acquired through Fourier-Transform Infrared (FTIR) spectroscopy, could support mortality prediction models in COVID-19 ICU patients. A preliminary univariate analysis of serum FTIR spectra revealed significant spectral differences between 21 discharged and 23 deceased patients; however, the most significant spectral bands did not yield high-performing predictive models. By applying a Fast-Correlation-Based Filter (FCBF) for feature selection of the spectra, a set of spectral bands spanning a broader range of molecular functional groups was identified, which enabled Naïve Bayes models with AUCs of 0.79, 0.97, and 0.98 for the first 48 h of ICU admission, seven days prior, and the day of the outcome, respectively, which are, in turn, defined as either death or discharge from the ICU. These findings suggest FTIR spectroscopy as a rapid, economical, and minimally invasive diagnostic tool, but further validation is needed in larger, more diverse cohorts.

Cross-sectional and Mendelian randomization study of fibroblast growth factor 19 reveals causal associations with metabolic diseases

BACKGROUND AND AIM

Fibroblast growth factor 19 (FGF19) is an intestinal-derived factor that plays a role in metabolic diseases. We performed a differential study of circulating FGF19 levels and investigated the causal effects of FGF19 on metabolic diseases using Mendelian randomization (MR).

METHODS

Firstly, 958 subjects were included in the physical examination center of affiliated hospital from January 2019 to January 2021. Dividing the subjects into different subgroups to compare FGF19 levels. We conducted a two-sample MR analysis of genetically predicted circulating FGF19 in relation to alcohol, cardiovascular and metabolic biomarkers and diseases, and liver function biomarkers using publicly available genome-wide association study summary statistics data.

RESULTS

The circulating FGF19 levels in nonalcoholic fatty liver disease (NAFLD) patients were lower than those without NAFLD (P < 0.001). The FGF19 levels in participants with obese were lower than those without obese (P < 0.001). In two-sample MR analyses, genetically predicted higher circulating FGF19 levels was significantly associated with lower aspartate aminotransferase, γ-glutamyltransferase, triglycerides, total cholesterol, low-density lipoprotein, and C-reactive protein concentrations (P < 0.05) and a negative correlation with cardiovascular disease and cirrhosis whereas a positive association with type 2 diabetes mellitus (P < 0.05).

CONCLUSIONS

Our study found that circulating FGF19 levels were lower in NAFLD and obese populations. Additionally, our MR research results support the causal effects of FGF19 on improved liver function, lipids, and reduced the occurrence of inflammation, cardiovascular disease, and cirrhosis. We found a positive correlation with diabetes, which may indicate a compensatory increase in regulating above FGF19 resistance states in humans.

Exploring novel drug targets for erectile dysfunction through plasma proteome with genome

Background

Currently, the treatment and prevention of erectile dysfunction (ED) remain highly challenging.

Aim

This study conducted a systematic druggable genome-wide Mendelian randomization (MR) analysis to identify potential therapeutic targets for ED.

Methods

A proteome-wide MR approach was employed to investigate the causal effects of plasma proteins on ED. Subsequently, summary data-based MR (SMR) analysis was performed to identify potential drug targets for ED. Enrichment analysis and protein-protein interaction (PPI) networks revealed the functional characteristics and biological relevance of these potential therapeutic targets. Drug prediction and molecular docking studies were conducted to validate the pharmacological activity of these identified targets. Finally, a systematic MR analysis was conducted to assess upstream intervention factors, such as lifestyles and diseases, associated with these targets, providing insights for the prevention and treatment of ED.

Outcomes

This study identified several potential therapeutic targets for ED.

Results

Proteome-wide MR analysis revealed that 126 genetically predicted plasma proteins were causally associated with ED. SMR analysis indicated that TMEM9 was associated with an increased risk of ED, while MDH1, NQO1, QDPR, ARL4D, TAGLN2, and PPP1R14A were associated with a decreased risk of ED. These potential targets were primarily enriched in metabolic and redox-related biological processes. Molecular docking indicated that the predicted drugs had favorable binding affinities with the proteins, further confirming the pharmacological value of these targets. Finally, 6 plasma proteins (MDH1, NQO1, QDPR, ARL4D, TAGLN2, and TMEM9) could be modulated by lifestyle- and disease-related factors.

Clinical Implications

This study provides new insights into the etiology and potential drug targets of ED and contributes to the development of more effective treatments for ED and reducing the cost of drug development.

Strengths and Limitations

This is a systematic and extensive study exploring the causal relationship between plasma proteins and ED, which helps to provide a comprehensive perspective to understand the role of potential targets in ED. However, we did not conduct this study in different types of ED or different stages of ED progression.

Conclusion

In summary, this study identified 7 plasma proteins causally associated with ED and provided new insights into the etiology and potential drug targets for ED.

Investigating the causal effect of various metabolites on postherpetic neuralgia: a Mendelian randomization study

Background

Common side effect of Herpes Zoster, postherpetic neuralgia (PHN), causes persistent pain that seriously affects quality of life. Lack of dependable biomarkers makes the clinical diagnosis and treatment of PHN difficult, so complicating the assessment of therapeutic efficacy. Blood metabolites are becoming more and more well known as significant disease markers. With an aim to find possible biomarkers for diagnosis and treatment, this work investigates the causal link between blood metabolites and PHN using Mendelian randomization.

Methods

This work evaluated causal relationships between PHN and 1,091 plasma metabolites using Mendelian randomization (MR). Complementing MR-Egger and weighted median approaches, the main causality analysis was done using inverse variance weighted (IVW) and Wald ratio (WR) approaches. Robustness was checked using sensitivity analyses including CAUSE, Cochran's Q tests, leave-one-out analysis, MR-PRESSO, and MR-Egger intercept analysis. Reverse MR analysis and linkage disequilibrium score regression (LDSC) was used to assess significant correlations as well. Two-step MR analysis was also used to look at the mediating function of positively correlated metabolites in the causal pathway.

Results

The results of this study indicated a significant association between N-acetyl-aspartyl-glutamate (NAAG) and PHN, with an odds ratio (OR) of 0.83 (95% CI: 0.76-0.91, p = 2.68E-05). Moreover, five potential associated metabolites were identified: Gamma-glutamylthreonine (OR = 1.60, 95% CI: 1.16-2.20, p = 0.004), 3-hydroxyphenylacetoylglutamine (OR = 1.43, 95% CI: 1.00-2.05, p = 0.048), Caprate (10:0) (OR = 1.86, 95% CI: 1.11-3.12, p = 0.018), X-12013 (OR = 1.64, 95% CI: 1.03-2.60, p = 0.035), and X-17328 (OR = 1.50, 95% CI: 1.04-2.18, p = 0.032). Additionally, NAAG likely acts as a complete mediator between FOLH1(CGPII) and postherpetic neuralgia in the causal pathway.

Conclusion

The results of this study indicated a significant association between N-acetyl-aspartyl-glutamate (NAAG) and PHN, with an odds ratio (OR) of 0.83 (95% CI: 0.76-0.91, p = 2.68E-05). Furthermore five possible related metabolites were found: Glutamylthreonine gamma-wise (OR = 1.60, 95% CI: 1.16-2.20, p = 0.004), 3-hydroxyphenylacetoylglutamine (OR = 1.43, 95% CI: 1.00-2.05, p = 0.048), Caprate (10:0) (OR = 1.86, 95% CI: 1.11-3.12, p = 0.018), X-12013 (OR = 1.64, 95% CI: 1.03-2.60, p = 0.035), and X-17328 (OR = 1.50, 95% CI: 1.04-2.18, p = 0.032). Furthermore, in the causal pathway NAAG most certainly serves as a complete mediator between FOLH1(CGPII) and postherpetic neuralgia.

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

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

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

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

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

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Validation of candidate protein biomarkers previously identified by genetic instruments for prostate cancer risk: A prospective cohort analysis of directly measured protein levels in the ARIC study

BACKGROUND

Multiple novel protein biomarkers have been shown to be associated with prostate cancer risk using genetic instruments. This study aimed to externally validate the associations of 30 genetically predicted candidate proteins with prostate cancer risk using aptamer-based levels in US Black and White men in the Atherosclerosis Risk in Communities (ARIC) study. Plasma protein levels were previously measured by SomaScan® using the blood collected in 1990-1992.

METHODS

Among 4864 eligible participants, we ascertained 667 first primary prostate cancer cases through 2015. Hazard ratios (HRs) of prostate cancer and 95% confidence intervals (CIs) were estimated using Cox proportional hazards regression for tertiles of each protein. We adjusted for age, race, and other risk factors.

RESULTS

Of the 30 proteins and considering a nominal p trend < 0.05, two were positively associated with prostate cancer risk-RF1ML (tertile 3 vs. 1: HR = 1.23; 95% CI 1.02-1.48; p trend = 0.037) and TPST1 (1.28, 95% CI 1.06-1.55; p trend = 0.0087); two were inversely associated-ATF6A (HR = 0.80, 95% CI 0.65-0.98; p trend = 0.028) and SPINT2 (HR = 0.74, 95% CI 0.61-0.90; p trend = 0.0025). One protein, KDEL2, which was nonlinearly associated (test-for-linearity: p < 0.01) showed a statistically significant lower risk in the second tertile (HR = 0.79, 95% CI 0.65-0.95). Of these five, four proteins-ATF6A, KDEL2, RF1ML, and TPST1-were consistent in the direction of association with the discovery studies.

CONCLUSION

This study validated some pre-diagnostic protein biomarkers of the risk of prostate cancer.

Single-nucleus sequencing unveils heterogeneity in renal cell carcinomas microenvironment: Insights into pathogenic origins and treatment-responsive cellular subgroups

BACKGROUND

Different individuals with renal cell carcinoma (RCC) exhibit substantial heterogeneity in histomorphology, genetic alterations in the proteome, immune cell infiltration patterns, and clinical behavior.

OBJECTIVES

This study aims to use single-nucleus sequencing on ten samples (four normal, three clear cell renal cell carcinoma (ccRCC), and three chromophobe renal cell carcinoma (chRCC)) to uncover pathogenic origins and prognostic characteristics in patients with RCC.

METHODS

By using two algorithms, inferCNV and k-means, the study explores malignant cells and compares them with the normal group to reveal their origins. Furthermore, we explore the pathogenic factors at the gene level through Summary-data-based Mendelian Randomization and co-localization methods. Based on the relevant malignant markers, a total of 212 machine-learning combinations were compared to develop a prognostic signature with high precision and stability. Finally, the study correlates with clinical data to investigate which cell subtypes may impact patients' prognosis.

RESULTS & CONCLUSION

Two main origin tumor cells were identified: Proximal tubule cell B and Intercalated cell type A, which were highly differentiated in epithelial cells, and three gene loci were determined as potential pathogenic genes. The best malignant signature among the 212 prognostic models demonstrated high predictive power in ccRCC: (AUC: 0.920 (1-year), 0.920 (3-year) and 0.930 (5-year) in the training dataset; 0.756 (1-year), 0.828 (3-year), and 0.832 (5-year) in the testing dataset. In addition, we confirmed that LYVE1+ tissue-resident macrophage and TOX+ CD8 significantly impact the prognosis of ccRCC patients, while monocytes play a crucial role in the prognosis of chRCC patients.

Crossing the Halfway Point: Aptamer-Based, Highly Multiplexed Assay for the Assessment of the Proteome

Measuring responses in the proteome to various perturbations improves our understanding of biological systems. The value of information gained from such studies is directly proportional to the number of proteins measured. To overcome technical challenges associated with highly multiplexed measurements, we developed an affinity reagent-based method that uses aptamers with protein-like side chains along with an assay that takes advantage of their unique properties. As hybrid affinity reagents, modified aptamers are fully comparable to antibodies in terms of binding characteristics toward proteins, including epitope size, shape complementarity, affinity and specificity. Our assay combines these intrinsic binding properties with serial kinetic proofreading steps to allow highly effective partitioning of stable specific complexes from unstable nonspecific complexes. The use of these orthogonal methods to enhance specificity effectively overcomes the severe limitation to multiplexing inherent to the use of sandwich-based methods. Our assay currently measures half of the unique proteins encoded in the human genome with femtomolar sensitivity, broad dynamic range and exceptionally high reproducibility. Using machine learning to identify patterns of change, we have developed tests based on measurement of multiple proteins predictive of current health states and future disease risk to guide a holistic approach to precision medicine.

Novel targets for the treatment and prevention of Alzheimer's disease in the European population, inspiration from amyloid beta and tau protein

Alzheimer's disease (AD) is a gradual neurodegenerative ailment that lacks any disease-modifying intervention. Our objective was to pinpoint pharmacological targets with a focus on amyloid beta (Aβ) and tau to treat and prevent AD in the European population. A proteome-wide Mendelian randomization (MR) analysis was carried out to estimate the associations between proteins and cerebrospinal fluid (CSF) Aβ-42 and phosphorylated Tau (p-Tau). We utilized colocalization and MR analysis to investigate whether the identified proteins were associated with the risk of AD. Additionally, we expanded our investigation to include non-AD phenotypes by conducting a phenome-wide MR analysis of 1646 disease traits based on the FinnGen and UK Biobank databases to explore potential side effects. We identified 11 proteins that were genetically associated with both CSF Aβ-42 and p-Tau levels. The genetically predicted levels of three proteins, GAL3ST2, POLR1C, and BIN1, were found to be associated with an increased risk of AD with high colocalization. In the phenome-wide MR analysis, two out of the three biomarkers were associated with at least one disease, except for GAL3ST2, which was not associated with any disease under the threshold of FDR <0.1. POLR1C was found to be associated with the most disease traits, and all disease associations with genetically inhibited BIN1 were protective. The proteome-wide MR investigation revealed 11 proteins that were associated with the level of CSF Aβ-42 and p-Tau. Among them, GAL3ST2, POLR1C, and BIN1 were identified as potential therapeutic targets for AD and warrant further investigation.

A genome-wide association study identifies genetic determinants of hemoglobin glycation index with implications across sex and ethnicity

Introduction

We investigated the genetic determinants of variation in the hemoglobin glycation index (HGI), an emerging biomarker for the risk of diabetes complications.

Methods

We conducted a genome-wide association study (GWAS) for HGI in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial (N = 7,913) using linear regression and additive genotype encoding on variants with minor allele frequency greater than 3%. We conducted replication analyses of top findings in the Atherosclerosis Risk in Communities (ARIC) study with inverse variance-weighted meta-analysis. We followed up with stratified GWAS analyses by sex and self-reported race.

Results

In ACCORD, we identified single nucleotide polymorphisms (SNPs) associated with HGI, including a peak with the strongest association at the intergenic SNP rs73407935 (7q11.22) (P = 5.8e-10) with a local replication in ARIC. In black individuals, the variant rs10739419 on chromosome 9 in the Whirlin (WHRN) gene formally replicated (meta-P = 2.2e-9). The SNP-based heritability of HGI was 0.39 (P< 1e-10). HGI had significant sex-specific associations with SNPs in or near GALNT11 in women and HECW2 in men. Finally, in Hispanic participants, we observed genome-wide significant associations with variants near USF1 and NXNL2/SPIN1.

Discussion

Many HGI-associated SNPs were distinct from those associated with fasting plasma glucose or HbA1c, lending further support for HGI as a distinct biomarker of diabetes complications. The results of this first evaluation of the genetic etiology of HGI indicate that it is highly heritable and point to heterogeneity by sex and race.

Integrating metabolomics and proteomics to identify novel drug targets for heart failure and atrial fibrillation

BACKGROUND

Altered metabolism plays a role in the pathophysiology of cardiac diseases, such as atrial fibrillation (AF) and heart failure (HF). We aimed to identify novel plasma metabolites and proteins associating with cardiac disease.

METHODS

Mendelian randomisation (MR) was used to assess the association of 174 metabolites measured in up to 86,507 participants with AF, HF, dilated cardiomyopathy (DCM), and non-ischemic cardiomyopathy (NICM). Subsequently, we sourced data on 1567 plasma proteins and performed cis MR to identify proteins affecting the identified metabolites as well as the cardiac diseases. Proteins were prioritised on cardiac expression and druggability, and mapped to biological pathways.

RESULTS

We identified 35 metabolites associating with cardiac disease. AF was affected by seventeen metabolites, HF by nineteen, DCM by four, and NCIM by taurine. HF was particularly enriched for phosphatidylcholines (p = 0.029) and DCM for acylcarnitines (p = 0.001). Metabolite involvement with AF was more uniform, spanning for example phosphatidylcholines, amino acids, and acylcarnitines. We identified 38 druggable proteins expressed in cardiac tissue, with a directionally concordant effect on metabolites and cardiac disease. We recapitulated known associations, for example between the drug target of digoxin (AT1B2), taurine and NICM risk. Additionally, we identified numerous novel findings, such as higher RET values associating with phosphatidylcholines and decreasing AF and HF. RET is targeted by drugs such as regorafenib which has known cardiotoxic side-effects. Pathway analysis implicated involvement of GDF15 signalling through RET, and ghrelin regulation of energy homeostasis in cardiac pathogenesis.

CONCLUSIONS

This study identified 35 plasma metabolites involved with cardiac diseases and linked these to 38 druggable proteins, providing actionable leads for drug development.

Application of large-scale and multicohort plasma proteomics data to discover novel causal proteins in gastric cancer

PURPOSES

Gastric cancer (GC) is one of the most common malignant tumors threatening human beings and has a poor prognosis. Therefore, exploring unveiled biomarkers or therapeutic targets for the diagnosis and treatment of GC is crucial.

METHODS

A total of 5772 protein quantitative trait loci (pQTL) were aggregated from four latest large-scale proteomics cohorts. Two-sample Mendelian randomization (two-sample MR) was utilized to identify the causal effect of blood plasma proteins on GC. Heterogeneity, pleiotropy, and directionality analyses were employed to evaluate proteins identified via two-sample MR. The robustness of results was further validated via colocalization. The drug targets of proteins were evaluated to reveal the compounds that can interfere with these proteins.

RESULTS

Ten proteins with potential causations in relation to GC were identified: LY6D, SLURP1, MLN, PSCA, THSD1, CFTR, PPM1B, KDM3A, TSC1, and HCG22. Among these proteins, LY6D, SLURP1, and THSD1 were considered as the most reliable biomarkers of GC due to their significant H4 posterior probabilities in colocalization analysis and absence of pleiotropy. Compound 35, nitrosamide, and 0175029-0000 were potential drugs or small molecules targeting LY6D, SLURP1, and THSD1, respectively.

CONCLUSION

This study identified several plasma proteins as potential biomarkers of GC and provided data support and new insights into the early diagnosis, intervention, and therapeutic targets of GC.

Integrative Multiomics in the Lung Reveals a Protective Role of Asporin in Pulmonary Arterial Hypertension

BACKGROUND

Integrative multiomics can elucidate pulmonary arterial hypertension (PAH) pathobiology, but procuring human PAH lung samples is rare.

METHODS

We leveraged transcriptomic profiling and deep phenotyping of the largest multicenter PAH lung biobank to date (96 disease and 52 control) by integration with clinicopathologic data, genome-wide association studies, Bayesian regulatory networks, single-cell transcriptomics, and pharmacotranscriptomics.

RESULTS

We identified 2 potentially protective gene network modules associated with vascular cells, and we validated ASPN, coding for asporin, as a key hub gene that is upregulated as a compensatory response to counteract PAH. We found that asporin is upregulated in lungs and plasma of multiple independent PAH cohorts and correlates with reduced PAH severity. We show that asporin inhibits proliferation and transforming growth factor-β/phosphorylated SMAD2/3 signaling in pulmonary artery smooth muscle cells from PAH lungs. We demonstrate in Sugen-hypoxia rats that ASPN knockdown exacerbated PAH and recombinant asporin attenuated PAH.

CONCLUSIONS

Our integrative systems biology approach to dissect the PAH lung transcriptome uncovered asporin as a novel protective target with therapeutic potential in PAH.

Causal relationship between beta-2 microglobulin and B-cell malignancies: genome-wide meta-analysis and a bidirectional two-sample Mendelian randomization study

Background

Beta-2 microglobulin (β2M) is acknowledged as a prognostic biomarker for B-cell malignancies. However, insights into the impact of β2M on B-cell malignancy risk, and vice versa, are limited.

Methods

We conducted a genome-wide meta-analysis (GWMA), bidirectional two-sample Mendelian randomization (TSMR) analysis, and pathway enrichment analysis to explore the causal relationship between β2M and B-cell malignancies and the underlying biological processes.

Results

The GWMA identified 55 lead SNPs across five genomic regions (three novel: WDR72, UMOD, and NLRC5) associated with β2M. In the UKB, genetically predicted β2M showed a positive association with diffuse large B-cell lymphoma (DLBCL; odds ratio [OR]: 1.742 per standard deviation increase in β2M; 95% confidence interval [CI]: 1.215-2.498; P = 3.00 × 10-3; FDR = 7.50× 10-3) and Hodgkin lymphoma (HL; OR: 2.270; 95% CI: 1.525-3.380; P = 5.15 × 10-5; FDR =2.58 × 10-4). However, no associations were found with follicular lymphoma (FL), chronic lymphoid leukemia (CLL), or multiple myeloma (MM). Reverse TSMR analysis revealed no association between genetically predicted B-cell malignancies and β2M. In FinnGen, β2M was found to be associated with an increased risk of DLBCL (OR: 2.098; 95% CI: 1.358-3.242; P = 8.28 × 10-4; FDR = 4.14 × 10-3), HL (OR: 1.581; 95% CI: 1.167-2.142; P = 3.13 × 10-3; FDR = 5.22 × 10-3), and FL (OR: 2.113; 95% CI: 1.292-3.455; P = 2.90 × 10-3; FDR = 5.22 × 10-3). However, no association was found with CLL or MM. Reverse TSMR analysis indicated that genetically predicted DLBCL, FL, and MM may perturb β2M levels. Pathway enrichment analysis suggested that the innate immune system represents a convergent biological process underlying β2M, DLBCL, and HL.

Conclusions

Our findings suggested that elevated levels of β2M were associated with an increased risk of DLBCL and HL, which is potentially linked to dysfunction of the innate immune system.

Translation of genome-wide association study: from genomic signals to biological insights

Since the turn of the 21st century, genome-wide association study (GWAS) have successfully identified genetic signals associated with a myriad of common complex traits and diseases. As we transition from establishing robust genetic associations with diverse phenotypes, the central challenge is now focused on characterizing the underlying functional mechanisms driving these signals. Previous GWAS efforts have revealed multiple variants, each conferring relatively subtle susceptibility, collectively contributing to the pathogenesis of various common diseases. Such variants can further exhibit associations with multiple other traits and differ across ancestries, plus disentangling causal variants from non-causal due to linkage disequilibrium complexities can lead to challenges in drawing direct biological conclusions. Combined with cellular context considerations, such challenges can reduce the capacity to definitively elucidate the biological significance of GWAS signals, limiting the potential to define mechanistic insights. This review will detail current and anticipated approaches for functional interpretation of GWAS signals, both in terms of characterizing the underlying causal variants and the corresponding effector genes.

Proteomic biomarkers of long-term lung function decline in textile workers: a 35-year longitudinal study

BACKGROUND

Occupational exposures contribute significantly to obstructive lung disease among textile workers. However, biomarkers associated with such declines are not available.

OBJECTIVES

We conducted a large-scale proteomic study to explore protein biomarkers potentially associated with long-term lung function decline.

METHODS

Shanghai Textile Workers Cohort was established in 1981 with 35 years of follow-up, assessing textile workers' lung functions every five years. Quantitative serum proteomics was performed on all 453 workers at 2016 survey. We employed four distinct models to examine the association between forced expiratory volume in one second (FEV1) and proteins, and consolidated the findings using an aggregated Cauchy association test. Furthermore, proteomic data of UK Biobank (UKB) was used to explore the associations of potential protein markers and decline of FEV1, and the interactions of these proteins were examined through STRING database. Associations were also externally validated using two-sample Mendelian randomizations (MR).

RESULTS

15 of 907 analyzed proteins displayed potential associations with long-term FEV1 decline, including two hemoglobin subunits: hemoglobin subunit beta (HBB, FDR-qACAT = 0.040), alpha globin chain (HBA2, FDR-qACAT = 0.045), and four immunoglobulin subunits: immunoglobulin kappa variable 3-7 (IGKV3-7, FDR-qACAT = 0.003), immunoglobulin heavy chain variable region (IgH, FDR-qACAT = 0.011). Five proteins were significantly associated with the rate of decline of FEV1 in UKB, in which RAB6A, LRRN1, and BSG were also found to be associated with proteins identified in Shanghai Textile Workers Cohort using STRING database. MR indicated bidirectional associations between HBB and FEV1 (P < 0.05), while different immunoglobulin subunits exhibited varying associations with FEV1.

IMPACT STATEMENT

We performed a large-scale proteomic study of the longest-follow-up pulmonary function cohort of textile workers to date. We discovered multiple novel proteins associated with long-term decline of FEV1 that have potential for identifying new biomarkers associated with long-term lung function decline among occupational populations, and may identify individuals at risk, as well as potential pharmaceutical targets for early intervention.

Serum proteomics reveal APOE-ε4-dependent and APOE-ε4-independent protein signatures in Alzheimer's disease

A deeper understanding of the molecular processes underlying late-onset Alzheimer's disease (LOAD) could aid in biomarker and drug target discovery. Using high-throughput serum proteomics in the prospective population-based Age, Gene/Environment Susceptibility-Reykjavik Study (AGES) cohort of 5,127 older Icelandic adults (mean age, 76.6 ± 5.6 years), we identified 303 proteins associated with incident LOAD over a median follow-up of 12.8 years. Over 40% of these proteins were associated with LOAD independently of APOE-ε4 carrier status, were implicated in neuronal processes and overlapped with LOAD protein signatures in brain and cerebrospinal fluid. We identified 17 proteins whose associations with LOAD were strongly dependent on APOE-ε4 carrier status, with mostly consistent associations in cerebrospinal fluid. Remarkably, four of these proteins (TBCA, ARL2, S100A13 and IRF6) were downregulated by APOE-ε4 yet upregulated due to LOAD, a finding replicated in external cohorts and possibly reflecting a response to disease onset. These findings highlight dysregulated pathways at the preclinical stages of LOAD, including those both independent of and dependent on APOE-ε4 status.

Identifying genetic variants that influence the abundance of cell states in single-cell data

Disease risk alleles influence the composition of cells present in the body, but modeling genetic effects on the cell states revealed by single-cell profiling is difficult because variant-associated states may reflect diverse combinations of the profiled cell features that are challenging to predefine. We introduce Genotype-Neighborhood Associations (GeNA), a statistical tool to identify cell-state abundance quantitative trait loci (csaQTLs) in high-dimensional single-cell datasets. Instead of testing associations to predefined cell states, GeNA flexibly identifies the cell states whose abundance is most associated with genetic variants. In a genome-wide survey of single-cell RNA sequencing peripheral blood profiling from 969 individuals, GeNA identifies five independent loci associated with shifts in the relative abundance of immune cell states. For example, rs3003-T (P = 1.96 × 10-11) associates with increased abundance of natural killer cells expressing tumor necrosis factor response programs. This csaQTL colocalizes with increased risk for psoriasis, an autoimmune disease that responds to anti-tumor necrosis factor treatments. Flexibly characterizing csaQTLs for granular cell states may help illuminate how genetic background alters cellular composition to confer disease risk.

Ancestrally diverse genome-wide association analysis highlights ancestry-specific differences in genetic regulation of plasma protein levels

Fully characterizing the genetic architecture of circulating proteins in multi-ancestry populations provides an unprecedented opportunity to gain insights into the etiology of complex diseases. We characterized and contrasted the genetic associations of plasma proteomes in 9,455 participants of European and African (19.8%) ancestry from the Atherosclerosis Risk in Communities Study. Of 4,651 proteins, 1,408 and 2,565 proteins had protein-quantitative trait loci (pQTLs) identified in African and European ancestry respectively, and twelve unreported potentially causal protein-disease relationships were identified. Shared pQTLs across the two ancestries were detected in 1,113 aptamer-region pairs pQTLs, where 53 of them were not previously reported (all trans pQTLs). Sixteen unique protein-cardiovascular trait pairs were colocalized in both European and African ancestry with the same candidate causal variants. Our systematic cross-ancestry comparison provided a reliable set of pQTLs, highlighted the shared and distinct genetic architecture of proteome in two ancestries, and demonstrated possible biological mechanisms underlying complex diseases.

Investigating the association of the effect of genetically proxied PCSK9i with mood disorders using cis-pQTLs: A drug-target Mendelian randomization study

PCSK9-inhibitors (PCSK9i) are new drugs recently approved to lower LDL-cholesterol levels. However, due to the lack of long-term clinical data, the potential adverse effects of long-term use are still unknown. The PCSK9 genetic locus has been recently implicated in mood disorders and hence we wanted to assess if the effect of PCSK9i that block the PCSK9 protein can lead to an increase in the incidence of mood disorders. We used genetically-reduced PCSK9 protein levels (pQTLs) in plasma, serum, cerebrospinal fluid as a proxy for the effect of PCSK9i. We performed Mendelian randomization analyses using PCSK9 levels as exposure and mood disorder traits major depressive disorder, mood instability, and neuroticism score as outcomes. We find no association of PCSK9 levels with mood disorder traits in serum, plasma, and cerebrospinal fluid. We can conclude that genetically proxied on-target effect of pharmacological PCSK9 inhibition is unlikely to contribute to mood disorders.

Proteogenomic analysis integrated with electronic health records data reveals disease-associated variants in Black Americans

BACKGROUNDMost GWAS of plasma proteomics have focused on White individuals of European ancestry, limiting biological insight from other ancestry-enriched protein quantitative loci (pQTL).METHODSWe conducted a discovery GWAS of approximately 3,000 plasma proteins measured by the antibody-based Olink platform in 1,054 Black adults from the Jackson Heart Study (JHS) and validated our findings in the Multi-Ethnic Study of Atherosclerosis (MESA). The genetic architecture of identified pQTLs was further explored through fine mapping and admixture association analysis. Finally, using our pQTL findings, we performed a phenome-wide association study (PheWAS) across 2 large multiethnic electronic health record (EHR) systems in All of Us and BioMe.RESULTSWe identified 1,002 pQTLs for 925 protein assays. Fine mapping and admixture analyses suggested allelic heterogeneity of the plasma proteome across diverse populations. We identified associations for variants enriched in African ancestry, many in diseases that lack precise biomarkers, including cis-pQTLs for cathepsin L (CTSL) and Siglec-9, which were linked with sarcoidosis and non-Hodgkin's lymphoma, respectively. We found concordant associations across clinical diagnoses and laboratory measurements, elucidating disease pathways, including a cis-pQTL associated with circulating CD58, WBC count, and multiple sclerosis.CONCLUSIONSOur findings emphasize the value of leveraging diverse populations to enhance biological insights from proteomics GWAS, and we have made this resource readily available as an interactive web portal.FUNDINGNIH K08 HL161445-01A1; 5T32HL160522-03; HHSN268201600034I; HL133870.

Integrative multi-omics summary-based mendelian randomization identifies key oxidative stress-related genes as therapeutic targets for atrial fibrillation and flutter

Background

Atrial fibrillation (AF) is a prevalent cardiac arrhythmia associated with substantial morbidity and mortality. Oxidative stress (OS) has been implicated in the pathogenesis of AF, suggesting that targeting OS-related genes could offer novel therapeutic opportunities. This study aimed to identify causal OS-related genes contributing to AF through a comprehensive multi-omics Summary-based Mendelian Randomization (SMR) approach.

Methods

This study integrated data from genome-wide association studies (GWAS) with methylation quantitative trait loci (mQTL), expression QTL (eQTL), and protein QTL (pQTL) to explore the relationships between oxidative stress-related (OS-related) genes and AF risk. Genes associated with oxidative stress and AF were obtained from the Nielsen et al. study (discovery) and the FinnGen study (replication). The SMR analysis and HEIDI test were utilized to assess causal associations, followed by Bayesian co-localization analysis (PPH4 > 0.5) to confirm shared causal variants. Multi-omics data were employed to analyze the associations within mQTL-eQTL pathways. A two-sample MR analysis was conducted for sensitivity verification. The significance of findings was determined using a false discovery rate (FDR) < 0.05 and p_HEIDI > 0.01.

Results

At the DNA methylation level, 19 CpG sites near 7 unique genes were found to have causal effects on AF and strong co-localization evidence support (PPH4 > 0.70). At the gene expression level, six oxidative stress-related genes from eQTLGen and three from GTEx (v8), including TNFSF10, CDKN1A, ALOX15, TTN, PTK2, ALB, KCNJ5, and CASQ2, were found to have causal effects on AF in the sensitivity and co-localization analyses (PPH4 > 0.50). At the circulating protein level, both ALAD (OR 0.898, 95% CI 0.845-0.954, PPH4 = 0.67) and APOH (OR 0.896, 95% CI 0.844-0.952, PPH4 = 0.93) were associated with a lower risk of AF, and APOH was validated in the replication group. After integrating the multi-omics data between mQTL and eQTL, we identified two oxidative stress-related genes, TTN and CASQ2. The methylation of cg09915519 and cg10087519 in TTN was associated with higher expression of TTN and a lower risk of AF, which aligns with the negative effect of TTN gene expression on AF risk. TTN may play a protective role in AF.

Conclusion

This study identified several OS-related genes, particularly TTN, as having causal roles in AF, which were verified across three-omics pathways. The findings underscore the importance of these genes in AF pathogenesis and highlight their potential as therapeutic targets. The integration of multi-omics data provides a comprehensive understanding of the molecular mechanisms underlying AF, paving the way for targeted therapeutic strategies.

Genome-wide association study meta-analysis of neurofilament light (NfL) levels in blood reveals novel loci related to neurodegeneration

Neurofilament light chain (NfL) levels in circulation have been established as a sensitive biomarker of neuro-axonal damage across a range of neurodegenerative disorders. Elucidation of the genetic architecture of blood NfL levels could provide new insights into molecular mechanisms underlying neurodegenerative disorders. In this meta-analysis of genome-wide association studies (GWAS) of blood NfL levels from eleven cohorts of European ancestry, we identify two genome-wide significant loci at 16p12 (UMOD) and 17q24 (SLC39A11). We observe association of three loci at 1q43 (FMN2), 12q14, and 12q21 with blood NfL levels in the meta-analysis of African-American ancestry. In the trans-ethnic meta-analysis, we identify three additional genome-wide significant loci at 1p32 (FGGY), 6q14 (TBX18), and 4q21. In the post-GWAS analyses, we observe the association of higher NfL polygenic risk score with increased plasma levels of total-tau, Aβ-40, Aβ-42, and higher incidence of Alzheimer's disease in the Rotterdam Study. Furthermore, Mendelian randomization analysis results suggest that a lower kidney function could cause higher blood NfL levels. This study uncovers multiple genetic loci of blood NfL levels, highlighting the genes related to molecular mechanism of neurodegeneration.