Protein Quantitative Trait Loci Research Articles

Plasma Glutaminyl-Peptide Cyclotransferase Mediates Glucosamine-Metabolism-Driven Protection Against Hypertension: A Mendelian Randomization Study

Hypertension is one of the major risk factors for morbidity and mortality worldwide. In this study, Mendelian randomization was utilized to investigate how dietary supplement intake can impact hypertension based on circulating plasma metabolite genome-wide association study (GWAS) datasets, protein quantitative trait loci (pQTLs) of plasma proteins, and multiple public summary-level GWAS data. Pathway enrichment analysis combined with the results of inverse variance weighted Mendelian randomization revealed that a lower risk of hypertension was associated with the dietary intake of glucosamine, an anti-inflammatory supplement: odds ratio (OR) (95% CI): 0.888 (0.824–0.958). Additionally, glucosamine 6-phosphate N-acetyltransferase was identified as a protective factor against hypertension, OR (95% CI): 0.995 (0.992–0.998), shedding light on the potential protective mechanism of glucosamine. Mediation Mendelian randomization indicated that the protective effect of glucosamine metabolism was mediated by glutaminyl-peptide cyclotransferase, with a mediation proportion of 12.1% (5.9–18.2%), p < 0.05. This study offers new insights into preventive strategies for individuals with hypertension risk.

EPCO-58. INTEGRATIVE PROTEOGENOMIC ANALYSES REVEAL INSIGHTS INTO SUBTYPE-SPECIFIC GLIOMA RISK

Abstract Gliomas are heterogeneous brain tumors with prognostically-significant subtypes and few risk factors. IDH-wildtype glioblastoma, the most common subtype, carries a dismal prognosis. Using the largest GWAS dataset with histological (11,304 cases, 304,523 controls) and molecular (3,418 cases, 8,156 controls) subtypes, we analyzed the plasma and brain proteome to uncover proteins associated with glioma susceptibility. Using protein quantitative trait loci (pQTL) for 1,776 proteins in brain tissue, we identified 21 candidate proteins, including 9 associations with high probability (>70%) of colocalization (shared causal genetic variants for glioma risk and protein abundance). We confirmed known risk genes such as EGFR (p=1.9×10-35) and D2HGDH (p=1.1×10-5). We also identified novel risk proteins, including ENPP6, a choline phosphodiesterase overexpressed in glioma, for IDH-wildtype glioblastoma (OR=0.62, p=7.6×10-5), and galectin-3, a lectin encoded by a tumor fitness gene in CRISPR screens of glioma proliferation and a clinical drug target, for glioma overall (OR=1.37, p=1.3×10-5). Analyses of the plasma proteome using pQTLs for 1,362 proteins from 3 cohorts (N=88,838) identified 9 colocalized associations in whole blood. Most risk signals were subtype-specific, such as CRELD1 (p=2.5×10-5) for triple-negative glioblastoma. We found three candidate risk proteins for IDH-mutant 1p19q-intact glioma: immunoglobulin glycoprotein BCAM (p=9.6×10-8), neurotrophin receptor SorCS2 (p=3.6×10-5) and immune checkpoint PD-1 (p=1.1×10-5). Interestingly, genetically-predicted PD-1 levels had a larger effect on IDH-mutant 1p19q-intact (OR=2.75, 95% CI=1.61-4.69) than IDH-wildtype (OR=0.73, 95% CI=0.51-1.05) tumors, suggesting germline differences in immune checkpoint mechanisms among subtypes. For all colocalized candidates, genetically-predicted protein abundance was correlated with cis-regulated transcriptional activity in brain tissue. For example, genetically-predicted SORCS2 gene expression in brain tissue was associated with plasma SorCS2 levels (Z=16.1, p=1.1×10-68). Our analysis identified candidate proteins involved in immune response and neuronal proliferation, which may enable prioritization of drug targets and provide insight into disease mechanisms for glioma.

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

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

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.

Network medicine informed multiomics integration identifies drug targets and repurposable medicines for Amyotrophic Lateral Sclerosis

Amyotrophic Lateral Sclerosis (ALS) is a devastating, immensely complex neurodegenerative disease by lack of effective treatments. We developed a network medicine methodology via integrating human brain multi-omics data to prioritize drug targets and repurposable treatments for ALS. We leveraged non-coding ALS loci effects from genome-wide associated studies (GWAS) on human brain expression quantitative trait loci (QTL) (eQTL), protein QTL (pQTL), splicing QTL (sQTL), methylation QTL (meQTL), and histone acetylation QTL (haQTL). Using a network-based deep learning framework, we identified 105 putative ALS-associated genes enriched in known ALS pathobiological pathways. Applying network proximity analysis of predicted ALS-associated genes and drug-target networks under the human protein-protein interactome (PPI) model, we identified potential repurposable drugs (i.e., Diazoxide and Gefitinib) for ALS. Subsequent validation established preclinical evidence for top-prioritized drugs. In summary, we presented a network-based multi-omics framework to identify drug targets and repurposable treatments for ALS and other neurodegenerative disease if broadly applied.

Genomics-driven integrative analysis highlights immune-related plasma proteins for psychiatric disorders

BackgroundGenome-wide association studies (GWAS) have identified numerous variants associated with psychiatric disorders. However, it remains largely unknown on how GWAS risk variants contribute to psychiatric disorders. MethodsThrough integrating two largest, publicly available, independent protein quantitative trait loci datasets of plasma protein and nine large-scale GWAS summary statistics of psychiatric disorders, we first performed proteome-wide association study (PWAS) to identify psychiatric disorders-associated plasma proteins, followed by enrichment analysis to reveal the underlying biological processes and pathways. Then, we conducted Mendelian randomization (MR) and Bayesian colocalization (COLOC) analyses, with both discovery and parallel replication datasets, to further identify protein-disorder pairs with putatively causal relationships. We finally prioritized the potential drug targets using Drug Gene Interaction Database. ResultsPWAS totally identified 112 proteins, which were significantly enriched in biological processes relevant to immune regulation and response to stimulus including regulation of immune system process (adjusted P = 1.69 × 10−7) and response to external stimulus (adjusted P = 4.13 × 10−7), and viral infection related pathways, including COVID-19 (adjusted P = 2.94 × 10−2). MR and COLOC analysis further identified 26 potentially causal protein-disorder pairs in both discovery and replication analysis. Notably, eight protein-coding genes were immune-related, such as IRF3, CSK, and ACE, five among 16 druggable genes were reported to interact with drugs, including ACE, CSK, PSMB4, XPNPEP1, and MICB. ConclusionsOur findings highlighted the immunological hypothesis and identified potentially causal plasma proteins for psychiatric disorders, providing biological insights into the pathogenesis and benefit the development of preventive or therapeutic drugs for psychiatric disorders.

Proteogenomic analysis in population biobanks identifies new therapeutic targets across heart failure subtypes

Abstract Background Heart failure (HF) represents a multifactorial clinical syndrome with differential response to existing therapies across its subtypes. Proteogenomic analysis of genetic variants associated with changes in circulating protein level and risk of heart failure subtypes provides an opportunity for a systematic identification of proteins with subtype-specific causal effects as novel therapeutic targets. Purpose To identify proteins as potential therapeutic targets for specific heart failure subtypes by leveraging proteomics and genomics data in population biobanks. Methods We evaluated the causal effects of 2,773 proteins on 5 HF-related outcomes, including overall HF, non-ischaemic HF (ni-HF), ni-HF with reduced / preserved ejection fraction (ni-HFrEF / ni-HFpEF), and dilated cardiomyopathy (DCM). For each protein-phenotype pair, we performed a two-sample Mendelian randomisation (MR) analysis using protein-specific genetic association estimates derived from protein quantitative trait locus (pQTL) studies in 114,145 individuals from deCODE, UK Biobank, Fenland study, and SCALLOP consortium and genome-wide association studies (GWAS) of HF outcomes in 145,795 individuals by HERMES Consortium. We further prioritised candidate protein targets based on replication of results across datasets, model parameters, assays, and colocalization of causal genetic variants. Finally, we profiled the causal effects of identified proteins on 32 related cardiac traits and risk factors to explore potential mechanisms of actions and on-target side effects. Results We identified 493 proteins associated with at least one HF outcome at a false discovery rate (FDR) < 1% in the primary MR analysis. We further prioritised 44 proteins with concordant effect direction across datasets, model parameters, and assays or colocalization in at least one dataset. All 44 identified proteins showed cross association with at least one of 35 tested traits other than HF or cardiomyopathies at FDR <1%, including coronary artery disease (18 proteins), body mass index (11 proteins), type 2 diabetes (6 proteins), systolic / diastolic blood pressure (13 proteins), and chronic kidney disease (9 proteins). Conclusion We characterised the causal associations of 2,773 circulating proteins across HF-related outcomes and prioritised 44 proteins by integrating proteomics and genomics data in population biobanks. These findings extend our understanding of the molecular mechanism underlying different forms of HF and may inform the development of new therapeutic strategies.Study design

Protein quantitative trait loci identifies genetic biomarkers of proteins associated with measures of myocardial fibrosis in new-onset, treatment naive rheumatoid arthritis

Abstract Introduction The excess risk of cardiovascular disease (CVD) in patients with rheumatoid arthritis (RA) compared to the general population is attributed to chronic inflammation. Identifying the underlying mechanisms and shared inflammatory pathways more precisely would enable tailored treatment. Purpose To identify genetic variants associated with the cardio-metabolic-inflammatory proteomic signature in patients with early, treatment-naïve RA and low CVD risk. Methods The Coronary Artery Disease Evaluation in RA (CADERA) study was a bolt-on to a RCT of patients with early, treatment-naïve RA and low CVD risk profile (maximum of 1 traditional CVD risk factor and no diabetes mellitus). Normalised expression of 334 proteins from 75 CADERA patients who underwent cardiovascular magnetic resonance imaging (CMR) was measured using the Olink Proximity Extension Immunoassay across Inflammation, Cardiovascular II, III and Cardiometabolic panels. Genome-wide single nucleotide polymorphism (SNP) data were generated using the Illumina Global Screening Array and was imputed and checked for data quality using standard approaches. Proteins associated with CMR parameters were identified using Bayesian mixed effects linear regression. Protein quantitative trait locus (pQTL) analysis was then performed using linear regression models adjusted for age, gender, and systolic blood pressure. Cis-pQTLs were defined as SNPs within 1Mb of the protein-encoding gene, and significance threshold was set at 1e-5. Due to sample size, trans-pQTLs were not considered in this analysis. Linkage disequilibrium (LD) clumping was performed using PLINK software to identify index SNPs defined as the most significant variant in LD (r2 <0.5) within a region of +/- 250Kb. Results Following quality control 318 proteins and 5,971,781 SNPs were available for analysis in 75 CADERA patients. The median age was 51 years [inter-quartile range (IQR) 40 - 61] with 52/75 (69%) females, 40/75 (53%) current/ex-smokers, and median body mass index (BMI) 26.4kg/m2 (IQR 23.3 - 28). Participants had a median RA symptom duration of 21.9 weeks (IQR 14.4 - 32.7). Only 8/75 (10%) reported a history of hypertension, hypercholesteroaemia or family history of CVD. 54 out of 318 proteins were associated with CMR measures of myocardial fibrosis and/or vascular stiffness. 460 cis-pQTLs were identified for 20 out of the 318 proteins. LD clumping revealed 40 cis-pQTLs independently associated with these 20 proteins. Of these, NOTCH3 (rs34763630) was positively associated with extracellular volume, and SELPLG (rs7980427) was negatively associated with late gadolinium enhancement as detected on CMR (table 1). No other proteins with genetic support were associated with CMR measures. Conclusion This study for the first time, implicates genetic markers associated with two proteins as putative candidate biomarkers of CMR-identified myocardial fibrosis in new-onset RA that now warrant validation in an independent cohort.

Causal association of serum calcium, phosphate, vitamin D, parathyroid hormone, and FGF23 with the risk of aortic stenosis

Abstract Aim Disorders of mineral metabolism, including elevated levels of serum calcium, phosphate, 25-hydroxyvitamin D (25OH-VitD), parathyroid hormone (PTH), and fibroblast growth factor 23 (FGF23), have been reported in patients with calcific aortic valve stenosis (CAVS). However, evidence of the causal role of mineral metabolism in CAVS is still lacking. We aimed to investigate the causality between mineral metabolism and CAVS. Methods A systematic pipeline combining Mendelian randomization (MR), Steiger directionality test, colocalization analysis, protein-protein network, and enrichment analysis was applied to investigate the causal effect. Genome-wide association study (GWAS) and protein quantitative trait loci data for mineral metabolism markers were extracted from large-scale meta-analyses. Summary statistics for CAVS were obtained from two independent GWAS datasets as discovery and replication cohorts (n=374,277 and 653,867). Results In MR analysis, genetic mimicry of serum FGF23 elevation was associated with increased CAVS risk [ORdiscovery=3.081 (1.649-5.760), Pdiscovery=4.21×10-4; ORreplication=2.280 (1.461-3.558), Preplication=2.82×10-4] without evidence of reverse causation (Psteiger=7.21×10-98). Strong colocalisation association with CAVS was observed for FGF23 expression in the blood (PP.H4 = 0.96). Additionally, we identified some protein-protein interactions between FGF23 and known CAVS causative genes. Serum calcium, phosphate, 25OH-VitD, and PTH failed to show causal effects on CAVS at Bonferroni-corrected significance (all P>0.05/5=0.01). Conclusions Elevated serum FGF23 level is a causal risk factor for CAVS, and its mechanism of action in CAVS development may be independent of its function in regulating mineral metabolism. Hence, FGF23 may serve as a circulating marker and a promising preventive target for CAVS, warranting further investigation.

Systematic identification of therapeutic targets for coronary artery calcification: an integrated transcriptomic and proteomic Mendelian randomization.

Coronary artery calcification (CAC) is associated with an increased risk of mortality and cardiovascular events. However, none therapeutic drugs have been proven effective for CAC treatment. The objective of this study was to identify potential therapeutic targets for CAC through the utilization of Mendelian randomization (MR) and colocalization analysis. The expression quantitative trait loci (eQTLs) of 16,943 genes from the eQTLGen consortium and protein quantitative trait loci (pQTLs) of 4,412 proteins from a plasma proteome were utilized as genetic instruments. Genetic associations with CAC were derived from a GWAS meta-analysis of 26,909 individuals. The MR and colocalization analysis were utilized to identify potential target genes. A total of 671 genes were found to be significantly associated with the risk of CAC based on transcriptomic MR analysis at a false discovery rate <0.05, while proteomic MR analysis identified 15 genes with significant associations with CAC at the same threshold. With robust evidence from colocalization analysis, we observed positive associations between CWF19L2, JARID2, and MANBA and the risk of CAC, while KLB exhibited an inverse association. In summary, our study identified 23 potential therapeutic targets for CAC. Further downstream analysis revealed IGFBP3, ABCC6, ULK3, DOT1L, KLB and AMH as promising candidates for repurposing in the treatment of CAC. The integrated MR analysis of transcriptomic and proteomic data identified multiple potential drug targets for the treatment of CAC. ULK3, DOT1L, and AMH were recognized as novel targets for drug repurposing for CAC and deserve further investigation.

Bidirectional two-sample Mendelian randomization analysis unveils causal association between inflammatory cytokines and the risk of diabetic nephropathy

ObjectivePrevious observational studies have indicated associations between various inflammatory cytokines and diabetic nephropathy (DN) caused by type 2 diabetes mellitus (T2DM). However, the causality remains unclear. We aimed to further evaluate the causal association between 91 inflammatory cytokines and DN using bidirectional two-sample Mendelian randomization (MR) analysis.MethodSummary statistics for DN were obtained from a publicly available genome-wide association study (GWAS) analysis. Data pertaining to inflammatory cytokines were derived from a GWAS protein quantitative trait locus (pQTL) study. The primary analytical approach employed the inverse variance weighted (IVW) method, complemented by MR-Egger regression, weighted mode (WM), and weighted median (WME) methods to evaluate the causal association between inflammatory cytokines and DN. Sensitivity analyses were conducted to validate the robustness of the findings.ResultAmong individuals of European ancestry, the IVW method results revealed a positive causal association between the gene expression of tumor necrosis factor ligand superfamily member 14 (TNFSF14), and TNF-related activation-induced cytokine (TRANCE) with DN. Conversely, a negative causal association was observed between the gene expression of interleukin-1-alpha (IL-1α), and transforming growth factor-alpha (TGF-α) with DN. Among individuals of East Asian ancestry, the IVW method results indicated a negative causal association between the gene expression of glial cell line-derived neurotrophic factor (GDNF) and DN. Notably, these findings persisted without evidence of horizontal pleiotropy or heterogeneity, ensuring their robustness and reliability.ConclusionThe MR analysis underscores a causal association between inflammatory cytokines and DN, providing an important reference and evidence for the study of DN.

Polygenic proxies of age-related plasma protein levels reveal TIMP2 role in cognitive performance.

Background While numerous studies have identified blood proteins that modulate brain aging in mice, the direct translation of these findings to human health remains a substantial challenge. Bridging this gap is critical for developing interventions that can effectively target human brain aging and associated diseases. Methods We first identified 12 proteins with aging or rejuvenating properties in murine brains through a systematic review. Using protein quantitative trait loci data for these proteins, we developed polygenic scores to predict plasma protein levels, which we then validated in two independent human cohorts. We employed association models to explore the association between these genetically predicted protein levels and cognitive performance, focusing specifically on their interaction with key genetic markers such as sex, APOE -ε4 and Aβ42 status. Results Predicted plasma levels of Tissue Inhibitor of Metalloproteinases 2 (TIMP2) were significantly associated with improved global cognition and memory performance in humans, also when the models were stratified by sex, APOE -ε4, and Aβ42 status. Conclusions This finding aligns with TIMP2's brain-rejuvenating role in murine models, suggesting it as a promising therapeutic target for brain aging and age-related brain diseases in humans.

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

PurposesGastric 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.MethodsA 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.ResultsTen 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.ConclusionThis 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.

Evaluating the causal effect of circulating proteome on the 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 ten proteomic genome-wide association studies (GWAS). Both cis- and cis+trans-protein quantitative trait loci (pQTLs) MR analyses were conducted. Bayesian colocalization, Steiger filtering analysis, assessment of protein-altering variants, and mapping expression quantitative trait loci to protein quantitative trait loci were performed to investigate the reliability of the MR findings. Protein-protein interaction, pathway enrichment analysis, and evaluation of drug targets were performed. Thirty-three proteins were identified with causal effects on FG, FI, or HbA1c but not 2hGlu in the cis-pQTLs 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.

Genetically Supported Drug Targets and Dental Traits: A Mendelian Randomization Study

Current interventions for oral/dental diseases heavily rely on operative/surgical procedures, while the discovery of novel drug targets may enable access to noninvasive pharmacotherapy. Therefore, this study aims to leverage large-scale data and Mendelian randomization (MR) techniques, utilizing genetic variants as instruments, to identify potential therapeutic targets for oral and dental diseases supported by genetic evidence. By intersecting 4,302 druggable genes with expression quantitative trait loci from 31,684 blood samples, we identified 2,580 druggable targets as exposures. Single nucleotide polymorphisms associated with dental disease/symptom traits were collected from FinnGen R9, the Gene–Lifestyle Interactions in Dental Endpoints consortium, and the UK Biobank to serve as outcomes for both discovery and replication purposes. Through MR analysis, we identified 43 druggable targets for various dental disease/symptom traits. To evaluate the viability of these targets, we replicated the analysis using circulating protein quantitative trait loci as exposures. Additionally, we conducted sensitivity, colocalization, Gene Ontology/Kyoto Encyclopedia of Genes and Genomes annotation, protein–protein interaction analyses, and validated dental trait–associated druggable gene expression in animal models. Among these targets, IL12RB1 (odds ratio [OR], 1.01; 95% confidence interval [CI], 1.01–1.01) and TNF (OR, 0.98; 95% CI, 0.97–0.99) exhibited therapeutic promise for oral ulcers, whereas CXCL10 (OR, 0.84; 95% CI, 0.76–0.91) was for periodontitis. Through a rigorous quality control and validation pipeline, our study yields compelling evidence for these druggable targets, which may enhance the clinical prognosis by developing novel drugs or repurposing existing ones.

Heterogeneity-aware integrative regression for ancestry-specific association studies.

Ancestry-specific proteome-wide association studies (PWAS) based on genetically predicted protein expression can reveal complex disease etiology specific to certain ancestral groups. These studies require ancestry-specific models for protein expression as a function of SNP genotypes. In order to improve protein expression prediction in ancestral populations historically underrepresented in genomic studies, we propose a new penalized maximum likelihood estimator for fitting ancestry-specific joint protein quantitative trait loci models. Our estimator borrows information across ancestral groups, while simultaneously allowing for heterogeneous error variances and regression coefficients. We propose an alternative parameterization of our model that makes the objective function convex and the penalty scale invariant. To improve computational efficiency, we propose an approximate version of our method and study its theoretical properties. Our method provides a substantial improvement in protein expression prediction accuracy in individuals of African ancestry, and in a downstream PWAS analysis, leads to the discovery of multiple associations between protein expression and blood lipid traits in the African ancestry population.

Association between lipid-lowering agents with intervertebral disc degeneration, sciatica and low back pain: a drug-targeted mendelian randomized study and cross-sectional observation

BackgroundAbnormal lipid metabolism is linked to intervertebral disc degeneration (IVDD), sciatica, and low back pain (LBP), but it remains unclear whether targeted interventions can prevent these issues. This study investigated the causal effects of lipid-lowering drug use on IVDD, sciatica, and LBP development.MethodsSingle-nucleotide polymorphisms (SNPs) linked to total cholesterol (TC), low-density-lipoprotein cholesterol (LDL-C), and non-high-density-lipoprotein cholesterol (non-HDL-C) were obtained from the Global Lipids Genetics Consortium’s genome-wide association study (GWAS). Genes near HMGCR, PCSK9, and NPC1L1 were selected to represent therapeutic inhibition targets. Using Mendelian randomization (MR) focusing on these drug targets, we identified causal effects of PCSK9, HMGCR, and NPC1L1 on the risk of developing IVDD, sciatica, and LBP, with coronary heart disease risk serving as a positive control. Using summary data from Mendelian randomization (SMR) analysis, we evaluated potential therapeutic targets for IVDD, sciatica, and LBP through protein quantitative trait loci (pQTL). The genetic associations with IVDD, sciatica, LBP, and coronary heart disease were derived from FinnGen (discovery) and UK Biobank (replication). Additionally, a cross-sectional observational study was performed using data from the National Health and Nutrition Examination Survey (NHANES) to further investigate the connection between LBP and statin use, with a sample size of 4343 participants. Odds ratios (ORs) and corresponding 95% confidence intervals (CIs) were calculated to assess the outcomes.ResultsThe NHANES-based cross-sectional study indicated that non-statin use was associated with an increased risk of developing LBP (OR = 1.29, 95% CI [1.04, 1.59], P = 0.019). Moreover, Inverse-variance weighting (IVW) analysis revealed that NPC1L1-mediated reductions in TC, LDL-C, and non-HDL-C concentrations were associated with a decreased risk of developing IVDD (P = 9.956E-03; P = 3.516E-02; P = 1.253E-04). Similarly, PCSK9-mediated reductions in LDL-C and TC concentrations were linked to a lower risk of developing sciatica (P = 3.825E-02; P = 2.709E-02). Sensitivity analysis confirmed the stability and reliability of the MR results. MST1 (macrophage stimulating 1) levels was inversely associated with IVDD, sciatica, and LBP risks.ConclusionThe results of cross-sectional study suggested that non-use of statins was positively correlated with LBP. The results of Mendelian randomization study suggest that NPC1L1 could lower the risk of developing IVDD by reducing TC, LDL-C, and non-HDL-C levels. Additionally, PCSK9 may reduce the risk of developing sciatica by lowering LDL-C and TC levels. In contrast, HMGCR appears to have no significant effect on IVDD, sciatica, or LBP development. Nonetheless, further research is needed to verify these preliminary results. MST1 warrants further exploration as a potential therapeutic target. It is necessary to do further research to validate these findings.

The critical involvement of monocytes/macrophages in the pathogenesis of primary Sjögren's syndrome: New evidence from Mendelian randomization and single-cell sequencing

BackgroundPrimary Sjögren's syndrome (pSS) stands as a chronic autoimmune disease characterized by an elusive pathogenesis. The synergy of single-cell RNA sequencing and Mendelian randomization (MR) analysis provides an opportunity to comprehensively unravel the contributory role of monocytes/macrophages in the intricate pathogenesis of pSS. MethodsDifferentially expressed genes (DEGs) of various types of immune cells were analyzed after annotating single-cell RNA sequencing (scRNA-seq) data. MR analysis of expression quantitative trait loci (eQTL) and protein quantitative trait loci (pQTL) was conducted to search for key pathogenic genes and proteins. Cellular localization of pathogenic genes was performed based on scRNA-seq data. Variations in signaling pathways between immune cells were further analyzed. ResultsA total of 1434 significant DEGs were identified. Among these, 60 genes exhibited strong relevance to the occurrence of pSS, of which 32 genes differentially expressed in monocytes/macrophages. CTSS was found to be a significant risk protein with a p-value of 0.001 and an odds ratio of 1.384 (1.147–1.669), showing pronounced expression in monocytes/macrophages. Furthermore, monocytes/macrophages displayed heightened expression levels of MXD1, AMPD2, TNFSF10, FTL, UBXN11, CSF3R, and LILRA5. The analysis of intercellular signaling revealed increased signal intensity in both incoming and outgoing signals in monocytes/macrophages. The signaling interactions between monocytes/macrophages, B cells, and T cells exhibited varying degrees of deviation. ConclusionsThis study highlights the significant involvement of monocytes/macrophages in the pathogenesis of pSS, as evidenced by MR analysis and scRNA-seq analysis. This suggests monocytes/macrophages as a focal point for pathogenesis research and potential therapeutic targeting in pSS.

Exploration of protein and genetic targets causing atrioventricular block: mendelian-randomization analyses based on eQTL data and pQTL data

BackgroundAtrioventricular block (AVB) is a heterogeneous group of arrhythmias. AVB can lead to sudden arrest of the heart and subsequent syncope or sudden cardiac death. Few scholars have investigated the underlying molecular mechanisms of AVB. Finding molecular markers can facilitate understanding of AVB and exploration of therapeutic targets.MethodsTwo-sample Mendelian randomization (MR) analysis was undertaken with inverse variance weighted (IVW) model and Wald ratio as the primary approach. Reverse MR analysis was undertaken to identify the associated protein targets and gene targets. Expression quantitative trait loci (eQTL) data from the eQTLGen database and protein quantitative trait loci (pQTL) data from three previous large-scale proteomic studies on plasma were retrieved as exposure data. Genome-wide association study (GWAS) summary data (586 cases and 379,215 controls) for AVB were retrieved from the UK Biobank database. Colocalization analyses were undertaken to identify the effect of filtered markers on outcome data. Databases (DrugBank, Therapeutic Target, PubChem) were used to identify drugs that interacted with targets.ResultsWe discovered that 692 genes and 42 proteins showed a significant correlation with the AVB phenotype. Proteins (cadherin-5, sTie-1, Notch 1) and genes (DNAJC30, ABO) were putative molecules to AVB. Drug–interaction analyses revealed anticancer drugs such as tyrosine-kinase inhibitors and TIMD3 inhibitors could cause AVB. Other substances (e.g. toxins, neurological drugs) could also cause AVB.ConclusionsWe identified the proteins (cadherin-5, sTie-1, Notch 1) and gene (DNAJC30, ABO) targets associated with AVB pathogenesis. Anticancer drugs (tyrosine-kinase inhibitors, TIMD3 inhibitors), toxins, or neurological drugs could also cause AVB.

DMRdb: a disease-centric Mendelian randomization database for systematically assessing causal relationships of diseases with genes, proteins, CpG sites, metabolites and other diseases.

Exploring the causal relationships of diseases with genes, proteins, CpG sites, metabolites and other diseases is fundamental to the life sciences. However, large-scale research using Mendelian randomization (MR) analysis is currently lacking. To address this, we introduce DMRdb (http://www.inbirg.com/DMRdb/), a disease-centric Mendelian randomization database, designed to systematically assess causal relationships of diseases with genes, proteins, CpG sites, metabolites and other diseases. The database consists of three main components: (i) 6640 high-quality disease genome-wide association studies (GWASs) from public sources that were subjected to rigorous quality filtering and standardization; (ii) over 497 billion results from MR analyses involving 6640 disease GWAS datasets, 16 238 expression quantitative trait loci (eQTLs) data, 2564 protein quantitative trait loci (pQTLs) data, 12 000 methylation quantitative trait locus (meQTLs) data and 825 metabolites data and (iii) over 380 000 causal relationship pairs from 1223 literature sources relevant to MR analyses. A user-friendly online database was developed to allow users to query, search, and download all the results. In summary, we anticipate that DMRdb will be a valuable resource for advancing our understanding of disease mechanisms and identifying new biomarkers and therapeutic targets.