Mapping the proteo-genomic convergence of human diseases

Multi-omics reveal the role of nociception-related genes TNXB, CTNND1 and CBL in depression

BACKGROUND

Recent studies have suggested a link between nociception and depression. However, the specific genes involved remain unclear.

METHODS

This study investigates this genetic link using multi-omics data. We collected nociception-related genes from the GeneCards database and integrated quantitative trait loci (mQTLs, eQTLs and pQTLs) data for gene expression, DNA methylation and protein expression. GWAS data from the IEU database served as the discovery cohort for depression, with FinnGen and GWAS Catalog data used for validation. Summary data-based Mendelian Randomization (SMR) analysis was employed to examine the interactions between nociception-related genes and depression, and colocalization analysis identified shared causal variants. The associations between depression and target gene expression in specific tissues and specific cell types were assessed using the GTEx v8 dataset and single-cell eQTL data.

RESULTS

SMR analysis revealed 215 mQTLs, 12 eQTLs, and 1 pQTL associated with depression in the discovery cohort. By integrating multi-omics evidence, we found that the hypermethylation of the TNXB gene (cg02272968, cg02432444, cg27624229) and the hypomethylation of the CTNND1 gene (cg16127573) and the P2RY6 gene (cg12889420) were found to upregulate their expression, potentially increasing the risk of depression. GTEx eQTL analysis confirmed CBL expression in the substantia nigra positively correlates with depression risk. However, none of the key genes were confirmed in the single-cell eQTL analysis.

CONCLUSIONS

Our study emphasizes the importance of nociception-related genes, particularly TNXB, CTNND1 and CBL in the pathogenesis of depression. Future research should build on these findings for potential prevention and treatment strategies.

Mendelian randomization analysis identifies ERAP1 and IL23R as potential drug targets for ankylosing spondylitis

BACKGROUND

Ankylosing spondylitis (AS) is a chronic inflammatory disease primarily affecting the spine and pelvis, leading to ankylosis, stiffness, and reduced quality of life. Despite therapeutic advances, identifying novel drug targets remains critical.

METHODS

This Mendelian randomization (MR) study leveraged proteome-wide analysis, utilizing data from five genome-wide association studies (GWAS) for proteomics exposure. Outcome data included 3162 European ancestry cases and 294,770 controls from FinnGen R10, and replication analyses used two plasma proteomic datasets (ARIC: 4657 proteins in 7213 individuals; UK Biobank: 4907 proteins in 35,559 participants) and UK Biobank AS GWAS (1344 cases, 324,074 controls). Sensitivity analyses, including Bayesian co-localization and Steiger's direction test, were conducted to ensure robust findings. Protein-protein interaction (PPI) networks were constructed to explore interactions between identified proteins and known AS drug targets.

RESULTS

After FDR adjustment, circulating ERAP1 (OR = 1.30, 95 % CI: 1.21-1.39, P = 8.18 × 10-14) and IL23R (OR = 2.21, 95 % CI: 1.61-3.03, P = 9.47 × 10-7) were genetically linked to increased AS risk, while IL1RL2 showed a protective effect (OR = 0.70, 95 % CI: 0.58-0.85, P = 3.22 × 10-4). Steiger's test confirmed directionality (P < 1.70 × 10-15), and Bayesian co-localization supported shared causal variants (PPH4 > 0.75 for IL23R/IL1RL2). PPI networks revealed interactions between ERAP1, IL23R, and known AS targets. Replication validated ERAP1 and IL23R associations but not IL1RL2.

CONCLUSION

Genetically determined levels of ERAP1 and IL23R are robustly associated with AS risk, highlighting their potential as therapeutic targets. This study demonstrates the utility of MR in identifying drug targets for complex diseases, providing a foundation for further clinical investigation into their therapeutic potential.

Identification of potential drug targets for four site-specific cancers by integrating human plasma proteome with genome

Drug targets supported by genetic evidence with a several-fold higher probability of success in clinical trials. We performed a comprehensive proteome-wide Mendelian randomization (MR) analysis to identify causal proteins and potential therapeutic targets for four site-specific cancers. A total of 13,248 protein quantitative trait loci for 4853 plasma proteins were utilized for proteome-wide MR analysis. Identification of cancer causal proteins in the discovery cohort and further validation in the replication cohort. Colocalization, summary-data-based MR (SMR) analysis, and transcriptome‑wide association studies (TWAS) were performed to check the accuracy of the candidate proteins. Two-step MR analysis was used to explore the effects of plasma protein-mediated 248 modifiable factors on cancer. Phenome-wide MR (Phe-MR) analysis, druggability evaluation, and single-cell type expression analysis further assessed the potential of causal proteins. Combining the results of the meta-analysis of MR estimates from the two cohorts, 21, 2, 24 and 1 causal proteins were identified in breast, lung, prostate and stomach cancers, respectively. Evidence from colocalization, SMR analysis, and TWAS highlighted CD36, DNPH1, and PLXND1 as the most promising drug targets for breast cancer, and ZNF175 for prostate cancer. 1 new potential biomarker (PLXND1) for breast cancer, 2 new promising targets (RELL1, DEFB119) for lung cancer, and 8 new circulating biomarkers (ARFIP2, CCN6, CTRB2, HTR7, MRPL33, TNFRSF6B, VAMP5, ZNF175) for prostate cancer were firstly reported. Some plasma proteins may mediate the association of these cancers with other systemic diseases. Additionally, genetically predicted higher BMI and overweight may reduce breast cancer risk by altering CASP8, ADM, PLXND1, TNFRSF9, ULK3 and VSIG4 protein levels. Causal proteins of breast and prostate cancer were expressed predominantly on macrophages in cancerous tissues. This study genetically identified several cancer causal proteins which provided new perspectives for the understanding of the etiology and development of novel targeted drugs for cancer.

Variant-to-function approaches for adipose tissue: Insights into cardiometabolic disorders

Genome-wide association studies (GWASs) have identified thousands of genetic loci associated with cardiometabolic disorders. However, the functional interpretation of these loci remains a daunting challenge. This is particularly true for adipose tissue, a critical organ in systemic metabolism and the pathogenesis of various cardiometabolic diseases. We discuss how variant-to-function (V2F) approaches are used to elucidate the mechanisms by which GWAS loci increase the risk of cardiometabolic disorders by directly influencing adipose tissue. We outline GWAS traits most likely to harbor adipose-related variants and summarize tools to pinpoint the putative causal variants, genes, and cell types for the associated loci. We explain how large-scale perturbation experiments, coupled with imaging and multi-omics, can be used to screen variants' effects on cellular phenotypes and how these phenotypes can be tied to physiological mechanisms. Lastly, we discuss the challenges and opportunities that lie ahead for V2F research and propose a roadmap for future studies.

Sex differences in the genetic regulation of the human plasma proteome

Mechanisms underlying sex differences in the development and prognosis of many diseases remain largely elusive. Here, we systematically investigated sex differences in the genetic regulation of plasma proteome (>5800 protein targets) across two cohorts (30,307 females; 26,058 males). Plasma levels of two-thirds of protein targets differ significantly by sex. In contrast, genetic effects on protein targets are remarkably similar across sexes, with only 103 sex-differential protein quantitative loci (sd-pQTLs; for 2.9% and 0.3% of protein targets from antibody- and aptamer-based platforms, respectively). A third of those show evidence of sexual discordance, i.e., effects observed in one sex only (n = 30) or opposite effect directions (n = 1 for CDH15). Phenome-wide analyses of 365 outcomes in UK Biobank did not provide evidence that the identified sd-pQTLs accounted for sex-differential disease risk. Our results demonstrate similarities in the genetic regulation of protein levels by sex with important implications for genetically-guided drug target discovery and validation.

Data Interoperability and Harmonization in Cardiovascular Genomic and Precision Medicine

Despite advances in cardiovascular care and improved outcomes, fragmented healthcare systems, nonequitable access to health care, and nonuniform and unbiased collection and access to healthcare data have exacerbated disparities in healthcare provision and further delayed the technological-enabled implementation of precision medicine. Precision medicine relies on a foundation of accurate and valid omics and phenomics that can be harnessed at scale from electronic health records. Big data approaches in noncardiovascular healthcare domains have helped improve efficiency and expedite the development of novel therapeutics; therefore, applying such an approach to cardiovascular precision medicine is an opportunity to further advance the field. Several endeavors, including the American Heart Association Precision Medicine platform and public-private partnerships (such as BigData@Heart in Europe), as well as cloud-based platforms, such as Terra used for the National Institutes of Health All of Us, are attempting to temporally and ontologically harmonize data. This state-of-the-art review summarizes best practices used in cardiovascular genomic and precision medicine and provides recommendations for systems' requirements that could enhance and accelerate the integration of these platforms.

Integrating analysis of multi-omics summary data identifies novel plasma protein biomarkers and drug targets for bladder cancer

The plasma proteins are an important source of therapeutic targets. This study aims to address the diagnostic and therapeutic challenges of bladder cancer (BC) by using Mendelian randomization (MR) with a large sample size from multiple centers to identify the plasma proteins which are causally related to the pathogenesis of BC. Followed by merging nine plasma protein datasets from six studies, a total of 5538 plasma proteins and three BC datasets (ieu-b-4874, ukb-b-8193, FinnGen_R11_C3_ BLADDER_EXALL) were used to perform proteome‑wide MR to estimate the contribution of plasma proteins to BC, separately. To ensure the robustness of the results, Veen intersection operation on MR results revealed that 14 meaningful candidate pathogenic plasma proteins (ANKRD27, BIN1, FAHD1, IL17RB, MRPL21, PPT1, PSCA, SLC16A3, SLURP1, SPON2, TACSTD2, TMEM87B, YWHAB) were obtain from three datasets. Then, we validated these proteins through various methods, including meta-analysis, reverse MR, Bayesian co-localization analysis and summary-data-based MR (SMR), and pathogenic plasma proteins were divided into three layers according to the validation confidence. We then performed single-cell transcriptome analysis (Registration number: GSE222315), which showed that 13/14 candidate plasma proteins were expressed and 12 proteins were differentially expressed in at least one cell type. Finally, protein-protein interactions (PPI) analysis and druggability evaluation were performed to explore the relationship between the interaction of plasma protein markers and existing cancer drug targets. Summarily, our research uncovered 14 plasma protein biomarkers linked to BC risk, offering novel perspectives on the etiology and potential targets for developing screening biomarkers and therapeutic drugs for BC.

Genome-wide Mendelian randomization mapping the influence of plasma proteome on major depressive disorder

Plasma proteins play critical roles in a series of biological processes and represent a major source of translational biomarkers and drug targets. In this study, we performed Mendelian randomization (MR) to explore potential causal associations of protein quantitative trait loci (pQTL, n = 54,219) with major depressive disorder (MDD) using summary statistics from the PGC (n = 143,265) and further replicated in FinnGen cohort (n = 406,986). Subsequently, gene expression quantitative trait loci (eQTL) of identified proteins were leveraged to validate the primary findings in both PGC and FinnGen cohorts. We implemented reverse causality detection using bidirectional MR analysis, Steiger test, Bayesian co-localization and phenotype scanning to further strengthen the MR findings. In primary analyses, MR analysis revealed 2 plasma protein significantly associated with MDD risk at Bonferroni correction (P < 3.720 × 10-5), including butyrophilin subfamily 2 member A1 (BTN2A1, OR = 0.860; 95 % CI, 0.825-0.895; P = 1.79 × 10-5) and butyrophilin subfamily 3 member A2 (BTN3A2, OR = 1.071; 95 % CI, 1.056-1.086; P = 3.89 × 10-6). Both the identified proteins had no reverse causality. Bayesian co-localization indicated that BTN2A1 (coloc.abf-PPH4 = 0.620) and BTN3A2 (coloc.abf-PPH4 = 0.872) exhibited a shared variant with MDD, a finding that was subsequently validated by HEIDI test. In the replication stage, BTN2A1 and BTN3A2 were successfully validated in the FinnGen cohort. This study genetically determined BTN2A1 and BTN3A2 were associated with MDD and these findings may have clinical implications for MDD prevention.

Polygenic overlap with granulocyte counts identifies novel loci for clozapine metabolism and clozapine-induced agranulocytosis

While clozapine is the most effective antipsychotic drug, its use is limited due to hematological adverse effects involving the reduction of granulocyte counts with potential life-threatening agranulocytosis. It is not yet possible to predict or prevent the risk of agranulocytosis, and the mechanisms are unknown but likely related to clozapine metabolism. Genome-wide association studies (GWASs) of clozapine metabolism and clozapine-induced agranulocytosis have identified few genetic loci. We used the largest available GWAS summary statistics of clozapine metabolism (clozapine-to-norclozapine ratio) and clozapine-induced agranulocytosis, applying the conditional false discovery rate (condFDR) method to increase power for genetic discovery by conditioning on granulocyte counts variants. To investigate potential causal effects of shared loci, we performed Mendelian Randomization analyses. After conditioning on granulocyte counts, we identified two novel loci associated with clozapine-to-norclozapine ratio. These loci were significantly associated with clozapine metabolism in a validation sample of 392 clozapine-treated individuals. For clozapine-induced agranulocytosis, five loci were identified after conditioning on granulocyte counts. These five loci were significantly associated with reduced granulocyte counts in a small independent sample of clozapine-treated individuals. Genetic liability to slow clozapine metabolism (high clozapine-to-norclozapine ratio) showed evidence of a causal effect on reduced neutrophil counts, and genetic liability to low neutrophil counts exhibited weak evidence of a causal effect on clozapine-induced agranulocytosis. Our findings of shared genetic variants associated with clozapine metabolism and granulocyte counts may form the basis for developing prediction models for clozapine-induced agranulocytosis.

Potential drug targets for Neuromyelitis optica spectrum disorders (NMOSD): A Mendelian randomization analysis

BACKGROUND

Certain peripheral proteins are involved in the development of Neuromyelitis optica spectrum disorders (NMOSD), such as IL-6, complement proteins, and MHC class II molecules. However, the roles of other new protein biomarkers are unclear. Current NMOSD treatments (e.g., intravenous pulse methylprednisolone, or satralizumab for IL-6 receptor inhibition) can only manage symptoms, necessitating the identification of new drug targets to treat NMOSD. The objective of this study is to identify potential drug targets for NMOSD through Mendelian randomization (MR) analysis, thereby addressing the limitations of current treatments and providing better clinical options for patients.

METHODS

NMOSD potential drug targets were evaluated via MR. Data was obtained from a genome-wide association study (GWAS) with 132 individuals with AQP4-IgG-positive NMOSD and 1244 controls. Genetic instruments for plasma and cerebrospinal fluid (CSF) proteins were identified. Sensitivity analyses were conducted using Bayesian co-localization, reverse causality testing and phenotype scanning. Additionally, a comparison and analysis of protein-protein interactions (PPI) were conducted to identify potential causal proteins. The implications of these findings were further explored by evaluating existing NMOSD drugs and their respective targets.

RESULTS

Four proteins were identified at the FDR correction via MR analysis (p < 0.05). Higher levels of PF4V1 (OR = 0.47; 95% CI, 0.29-0.78; p = 3.39 × 10-3) and FAM3B (OR = 0.12; 95% CI, 0.03-0.45; p = 1.65 × 10-3) were associated with a reduced risk of NMOSD, whereas elevated SERPINA1 (OR = 2.28; 95% CI, 1.29-4.04; p= 4.71 × 10-3) and CLEC11A (OR = 13.45; 95% CI, 1.29-4.04; p = 4.71 × 10-3) were related to an increased risk of NMOSD. Bayesian co-localization showed that the protein-related genes shared the same mutation as NMOSD (all PPH4>0.80). Reverse causality testing showed no evidence of NMOSD-driven protein changes (all p > 0.05). PPI analysis revealed SERPINA1 interacts with PF4V1 (combined score = 0.72). Drug evaluation identified Mercaptoethanol and Ferrous gluconate as repurposing candidates.

CONCLUSION

Increased levels of plasma CLEC11A and SERPINA1 are correlated with an elevated risk of NMOSD, whereas elevated levels of plasma PF4V1 and CSF FAM3B are associated with a decreased risk of NMOSD. The opposing effects of risk or protective proteins suggest synergistic targeting could improve efficacy beyond current immunosuppressive regimens. Nonetheless, clinical trials are required to confirm the findings.

Genetic determinants of proteomic aging

Changes in the proteome and its dysregulation have long been known to be a hallmark of aging. We derived a proteomic aging trait using data on 1459 plasma proteins from 44,435 UK Biobank individuals measured using an antibody-based assay. This metric is strongly associated with four age-related disease outcomes, even after adjusting for chronological age. Survival analysis showed that one-year older proteomic age, relative to chronological age, increases all-cause mortality hazard by 13 percent. We performed a genome-wide association analysis of proteomic age acceleration (proteomic aging trait minus chronological age) to identify its biological determinants. Proteomic age acceleration showed modest genetic correlations with four epigenetic clocks (Rg = 0.17 to 0.19) and telomere length (Rg = -0.2). Once we removed associations that were explained by a single pQTL, we were left with three signals mapping to BRCA1, POLR2A and TET2 with apparent widespread effects on plasma proteomic aging. Genetic variation at these three loci has been shown to affect other omics-related aging measures. Mendelian randomisation analyses showed causal effects of higher BMI and type 2 diabetes on faster proteomic age acceleration. This supports the idea that obesity and other features of metabolic syndrome have an adverse effect on the processes of human aging.

Multi-ancestry sequencing-based genome-wide association study of C-reactive protein in 513,273 genomes

C-reactive protein (CRP) serves as a pivotal marker of systemic inflammation, yet its genetic architecture has predominantly been explored within European populations. Our multi-ancestry sequencing-based genome-wide association study (seqGWAS) meta-analysis encompasses 447,369 Europeans, 10,389 Africans, 9685 Asians, and 9200 Hispanics in the discovery set, and 23,521 Europeans, 7160 Africans, 771 Asians, and 5178 Hispanics in the replication set. We identify 113 independent association signals (Pdiscovery ≤ 5 × 10-9 and Preplication ≤ 0.05), including 21 loci that passed the conditional analysis, among which 3 are European-specific. Cross ancestry fine-mapping pinpoints 19 of 113 independent signals within the 95% credible set. Functional annotation reveals significant enrichment in blood tissue, H3K27me3 histone marks, and exonic regions. Leveraging the Polygenic Priority Score (PoPS) and gene-based analyses, we implicate 151 genes as potential regulators of CRP levels, 55 of which have not been previously reported. Among these, 17 genes and four proteins show causal evidence or strong colocalization with CRP-related pathologies.

Combining GWAS Summary Data and Proteomics Identified Potential Drug Targets in Dementia

Due to progressive cognitive loss and subsequent incapability of daily life, the development of novel therapeutics is urgently needed for dementia patients. We performed a two-sample bi-directional Mendelian randomization (MR) analysis using summary-level statistics to identify causality between peripheral and cerebrospinal fluid (CSF) proteins and the risk of dementia. Genetic variants were subtracted from the Genome-Wide Association Studies (GWAS) results. Wald ratio (WR) and inverse-variance weighted (IVW) ratio were utilized to estimate the causal effects of plasma and CSF proteins on dementia. Reverse MR, Steiger filtering, Bayesian co-localization phenotype scanning, and external validation were integrated to strengthen the robustness of primary MR results. After sensitivity analysis, six circulating proteins were identified in three dementia classifications, whereas no causality was found in frontotemporal dementia (FTD). Elevated levels of circulating C1R protein increased the odds of developing Alzheimer's disease (AD), while PILRA and CELA2A were estimated to protect against the pathogenesis of AD; genetically predicted increase of α-synuclein and APOE elevated the occurrence of Dementia of Lewy Bodies (DLB); elevated level of circulating CRP was assessed to increase the onset of vascular dementia (VD). Our MR analyses identified a genetically predicted association between circulating C1R, PILRA, and CELA2A and the risk of AD, causal estimates between α-syn, APOE protein, and the onset of DLB, and a robust correlation between CRP and the etiology of VD. This study might guide the discovery of disease etiology and build up a novel disease-modifying paradigm of dementia.

Genetic determinants and clinical significance of circulating and tumor-specific levels of insulin-like growth factor binding protein 7 (IGFBP7) in a Swedish breast cancer cohort

Previous research indicates that insulin-like growth factor binding protein 7 (IGFBP7) protein levels in breast cancer tissue and blood are prognostic. However, genetic determinants of IGFBP7 in breast cancer remain largely unexplored. We examined IGFBP7 in a cohort of 1701 patients with first breast cancer from Sweden, enrolled prior to surgery 2002-16 and followed for up to 15 years. Genotyping was performed on blood samples using OncoArray. Tumor-specific protein levels of IGFBP7, insulin receptor (InsR), and IGF-I receptor (IGFIR) were assessed on tumor tissue microarrays in 964 patients. Furthermore, 275 patients had plasma IGFBP7 levels measured. A genetic proxy marker for circulating IGFBP7 levels was constructed from five candidate single-nucleotide polymorphisms (SNPs) (rs6852762, rs1714014, rs9992658, rs10004910, and rs4865180) based on number of recessive genotypes. Age-adjusted linear regression was used to evaluate SNPs and tumor-specific IGFBP7 levels in relation to circulating IGFBP7 levels. Cox regression adjusted for age, tumor characteristics, and adjuvant treatments was used to assess associations with clinical outcomes. Circulating and tumor-specific IGFBP7 levels were significantly positively correlated. High circulating and genetically predicted IGFBP7 levels were associated with increased risk for distant metastasis and all-cause mortality. A significant interaction between high tumor-specific IGFBP7 levels and membrane-bound InsR resulted in a four-fold increased risk of breast cancer events and distant metastases. Both measured and genetically predicted IGFBP7 levels were independent prognostic biomarkers in breast cancer.

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.

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

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

Identification of novel biomarkers for gastric adenocarcinoma through two-sample Mendelian randomization analysis of the human plasma proteome

BACKGROUND

Papillary gastric adenocarcinoma (PGC), a histological subtype of gastric cancer (GC), is characterized by malignant potential and poor prognosis. Therefore, identifying novel biomarkers is urgently needed to enhance PGC diagnosis and treatment outcomes.

METHODS

This study utilized two-sample Mendelian randomization (MR) to explore potential causal relationships between human blood plasma proteins and GC. Heterogeneity testing, pleiotropy assessment, and directionality analyses were performed to evaluate identified plasma proteins. Additionally, pathway enrichment analysis was conducted to elucidate the molecular mechanisms underlying the causal associations between plasma proteins and GC development.

RESULTS

MR analysis of 4,907 plasma proteins related to GC risk identified 90 proteins with potential causal relationships. The findings revealed that DNAJB9, CHCHD10, and suppressor of cytokine signaling 3 exhibited protective effects against GC, while Syntaxin-8, alcohol dehydrogenase 7, and UDP-glucose 4-epimerase were associated with increased GC risk at the genetic level.

CONCLUSION

In the present study, the six plasma proteins identified through comprehensive MR analysis may serve as potential biomarkers for GC, offering new insights for future molecular diagnosis and therapeutic strategies.

GWAS identifies genetic loci, lifestyle factors and circulating biomarkers that are risk factors for sarcoidosis

Sarcoidosis is a complex inflammatory disease with a strong genetic component. Here, we perform a genome-wide association study in 9755 sarcoidosis cases to identify risk loci and map associated genes. We then use transcriptome-wide association studies and enrichment analyses to explore pathways involved in sarcoidosis and use Mendelian randomization to examine associations with modifiable factors and circulating biomarkers. We identify 28 genomic loci associated with sarcoidosis, with the C1orf141-IL23R locus showing the largest effect size. We observe gene expression patterns related to sarcoidosis in the spleen, whole blood, and lung, and highlight 75 tissue-specific genes through transcriptome-wide association studies. Furthermore, we use enrichment analysis to establish key roles for T cell activation, leukocyte adhesion, and cytokine production in sarcoidosis. Additionally, we find associations between sarcoidosis and genetically predicted body mass index, interleukin-23 receptor, and eight circulating proteins.

Biomarker identification for Alzheimer's disease through integration of comprehensive Mendelian randomization and proteomics data

BACKGROUND

Alzheimer's disease (AD) is the main cause of dementia with few effective therapies. We aimed to identify potential plasma biomarkers or drug targets for AD by investigating the causal association between plasma proteins and AD by integrating comprehensive Mendelian randomization (MR) and multi-omics data.

METHODS

Using two-sample MR, cis protein quantitative trait loci (cis-pQTLs) for 1,916 plasma proteins were used as an exposure to infer their causal effect on AD liability in individuals of European ancestry, with two large-scale AD genome-wide association study (GWAS) datasets as the outcome for discovery and replication. Significant causal relationships were validated by sensitivity analyses, reverse MR analysis, and Bayesian colocalization analysis. Additionally, we investigated the causal associations at the transcriptional level with cis gene expression quantitative trait loci (cis-eQTLs) data across brain tissues and blood in European ancestry populations, as well as causal plasma proteins in African ancestry populations.

RESULTS

In those of European ancestry, the genetically predicted levels of five plasma proteins (BLNK, CD2AP, GRN, PILRA, and PILRB) were causally associated with AD. Among these five proteins, GRN was protective against AD, while the rest were risk factors. Consistent causal effects were found in the brain for cis-eQTLs of GRN, BLNK, and CD2AP, while the same was true for PILRA in the blood. None of the plasma proteins were significantly associated with AD in persons of African ancestry.

CONCLUSIONS

Comprehensive MR analyses with multi-omics data identified five plasma proteins that had causal effects on AD, highlighting potential biomarkers or drug targets for better diagnosis and treatment for AD.

Multi-omics study on autophagic dysfunction molecular network in the pathogenesis of rheumatoid arthritis

BACKGROUND

Autophagy is associated with the development of rheumatoid arthritis (RA), but its genetic pathological mechanisms remain incompletely understood. In this study, we employed summary-data-based Mendelian randomization (SMR) and co-localization analysis to systematically investigate the relationship between autophagy-related genes and RA.

METHODS

We obtained summary data on blood methylation (mQTL), gene expression (eQTL), and protein abundance (pQTL) from respective quantitative trait locus (QTL) studies. Genetic association data for RA were primarily derived from the FinnGen database, with validation performed using the UK Biobank (UKB) and GWAS Catalog databases. SMR analysis was conducted to evaluate the association between molecular characteristics of autophagy-related genes and RA. Subsequently, co-localization analysis was performed to determine whether the identified signals share the same causal genetic variants.

RESULTS

After integrating mQTL-eQTL multi-omics data, we identified two key autophagy genes, BCL2L1 and RAF1, which may have a causal relationship with RA. Significant associations were found for BCL2L1 (cg12873919, cg13989999) and RAF1 (cg26432171) in the SMR analysis of autophagy-related mQTL, eQTL, and GWAS data (p SMR < 0.05). In the integrated mQTL-eQTL SMR analysis, cg12873919 (p SMR = 1.40E-07, OR = 0.82, 95% CI [0.76-0.88]), cg13989999 (p SMR = 1.43E-06, OR = 0.78, 95% CI [0.71-0.87]), and cg26432171 (p SMR = 9.18E-09, OR = 1.83, 95% CI [1.49-2.25]) were all significantly validated. Methylation of cg12873919 and cg13989999 in BCL2L1 was associated with increased BCL2L1 expression, consistent with their negative impact on RA risk. Conversely, the cg26432171 site in RAF1 showed a positive correlation between gene methylation and expression. In the eQTL-GWAS SMR analysis, MAPK3 expression (p SMR = 7.24E-05, OR = 0.91, 95% CI [0.87-0.95]) was negatively correlated with RA risk, a finding supported by co-localization analysis (PPH4 > 0.5), suggesting that this gene may inhibit RA pathogenesis by regulating the autophagy process. Furthermore, protein level analysis also supported the protective role of MAPK3 (p SMR = 7.53E-05, OR = 0.89, 95% CI [0.84-0.94]).

CONCLUSION

We identified that autophagy-related genes BCL2L1 and RAF1 may be associated with RA risk, providing strong evidence from multi-omics data. This study identifies autophagy genes related to RA, potentially offering new insights into the pathogenesis of RA.

Mendelian randomization of plasma proteomics identifies novel ALS-associated proteins and their GO enrichment and KEGG pathway analyses

BACKGROUND

Amyotrophic lateral sclerosis (ALS) is a progressive and fatal neurological disorder with an increasing incidence rate. Despite advances in ALS research over the years, the precise etiology and pathogenic mechanisms remain largely elusive.

OBJECTIVE

To identify novel plasma proteins associated with ALS through Mendelian randomization methods in large-scale plasma proteomics and to provide potential biomarkers and therapeutic targets for ALS treatment.

METHODS

This study employed a large-scale plasma proteomic Mendelian randomization approach using genetic data from 80,610 individuals of European ancestry (including 20,806 ALS patients and 59,804 controls) derived from a genome-wide association study (GWAS). Protein quantitative trait loci (pQTLs) data were obtained from Ferkingstad et al. (2021), which measured 4,907 proteins in 35,559 Icelandic individuals. Multiple Mendelian randomization (MR) techniques were utilized, including weighted median, MR-Egger, Wald ratio, inverse-variance weighting (IVW), basic model, and weighted model. Heterogeneity was evaluated using Cochran's Q test. Horizontal pleiotropy was assessed through the MR-Egger intercept test and MR-PRESSO outlier detection. Sensitivity analysis was performed via leave-one-out analysis.

RESULTS

MR analysis revealed potential causal associations between 491 plasma proteins and ALS, identifying 19 novel plasma proteins significantly linked to the disease. Proteins such as C1QC, UMOD, SLITRK5, ASAP2, TREML2, DAPK2, ARHGEF10, POLM, SST, and SIGLEC1 showed positive correlations with ALS risk, whereas ADPGK, BTNL9, COLEC12, ADGRF5, FAIM, CRTAM, PRSS3, BAG5, and PSMD11 exhibited negative correlations. Reverse MR analyses confirmed that ALS negatively correlates with ADPGK and ADGRF5 expression. Enrichment analyses, including Gene Ontology (GO) functional analysis, indicated involvement in critical biological processes such as external encapsulating structure organization, extracellular matrix organization, chemotaxis, and taxis. KEGG pathway analysis highlighted significant enrichment in the PI3K-Akt signaling pathway, cytokine-cytokine receptor interactions, and axon guidance.

CONCLUSION

This study enhances the understanding of ALS pathophysiology and proposes potential biomarkers and mechanistic insights for therapeutic development. Future research should explore the clinical translation of these findings to improve ALS patient outcomes and quality of life.

Unraveling the role of proteins in dementia: insights from two UK cohorts with causal evidence

Population-based proteomics offers a groundbreaking avenue to predict future disease risks, enhance our understanding of disease mechanisms, and discover novel therapeutic targets and biomarkers. The role of plasma proteins in dementia, however, requires further exploration. This study investigated 276 protein-dementia associations in 229 incident all-cause dementia, 89 Alzheimer's disease, and 41 vascular dementia among 3249 participants (55% women, 97.2% white ethnicity) from the English Longitudinal Study of Ageing (ELSA) over a median 9.8-year follow-up. We used Cox proportional hazard regression for the analysis. Receiver operating characteristic analyses were conducted to assess the precision of the identified proteins from the fully adjusted Cox regression models in predicting incident all-cause dementia, both individually and in combination with demographic predictors, APOE genotype, and memory score, to estimate the area under the curve. Additionally, the eXtreme Gradient Boosting machine learning algorithm was used to identify the most important features predictive of future all-cause dementia onset. These associations were then validated in 1506 incident all-cause dementia, 732 Alzheimer's disease, 281 vascular dementia, and 111 frontotemporal dementia cases among 52 745 individuals (53.9% women, 93.3% White ethnicity) from the UK Biobank over a median 13.7-year follow-up. Two-sample bi-directional Mendelian randomization and drug target Mendelian randomization were further employed to determine the causal direction between protein concentration and dementia. NEFL (hazard ratio [HR] [95% confidence intervals (CIs)]: 1.54 [1.29, 1.84]) and RPS6KB1 (HR [95% CI]: 1.33 [1.16, 1.52]) were robustly associated with incident all-cause dementia; MMP12 (HR [95% CI]: 2.06 [1.41, 2.99]) was associated with vascular dementia in ELSA, after correcting for multiple testing. Additional markers EDA2R and KIM1 were identified from subgroup and sensitivity analyses. Combining NEFL and RPS6KB1 with other predictors yielded high predictive accuracy (area under the curve = 0.871) for incident all-cause dementia. The eXtreme Gradient Boosting machine learning algorithm also identified RPS6KB1, NEFL, and KIM1 as the most important protein features for predicting future all-cause dementia. Sex difference was evident for the association between RPS6KB1 and all-cause dementia, with stronger association in men (P for interaction = 0.037). Replication in the UK Biobank confirmed the associations between the identified proteins and various dementia subtypes. The results from Mendelian randomization in the reverse direction indicated that several proteins serve as early markers for dementia, rather than being direct causes of the disease. These findings provide insights into putative mechanisms for dementia. Future studies are needed to validate the findings on RPS6KB1 in relation to dementia risk.

Genome-wide association study and Mendelian randomization analyses reveal insights into bladder cancer etiology

BACKGROUND

The causes of bladder cancer are not completely understood. Our objective was to identify blood proteins and modifiable causal risk factors for bladder cancer by combining genome-wide association study (GWAS) and Mendelian randomization (MR) analyses.

METHODS

We first performed a GWAS meta-analysis of 6984 bladder cancer case patients and 708 432 control individuals from 3 European databases. Next, we conducted 2-sample MR and colocalization analyses using data from the present GWAS and published GWAS meta-analyses on plasma proteins and modifiable factors.

RESULTS

Genome-wide association study meta-analysis uncovered 17 bladder cancer susceptibility loci, of which 3 loci were novel. Genes were enriched in pathways related to the metabolic and catabolic processes of xenobiotics and cellular detoxification. Proteome-wide MR analysis based on cis-acting genetic variants revealed that higher plasma levels of glutathione S-transferases were strongly associated with a reduced risk of bladder cancer. There is strong evidence of colocalization between GSTM1 and bladder cancer. Finally, multivariable MR analyses of suspected risk factors for bladder cancer revealed independent causal associations between smoking and adiposity, particularly abdominal obesity, and risk of bladder cancer.

CONCLUSIONS

Findings from this large-scale GWAS and multivariable MR analyses highlight the key role of detoxification processes, particularly glutathione S-transferase 1, as well as smoking and abdominal obesity in bladder cancer etiology.

Mendelian randomization identifies proteins involved in neurodegenerative diseases

Proteins are involved in multiple biological functions. High-throughput technologies have allowed the measurement of thousands of proteins in population biobanks. In this study, we aimed to identify proteins related to Alzheimer's disease, Parkinson's disease, multiple sclerosis and amyotrophic lateral sclerosis by leveraging large-scale genetic and proteomic data. We performed a two-sample cis Mendelian randomization study by selecting instrumental variables for the abundance of >2700 proteins measured by either Olink or SomaScan platforms in plasma from the UK Biobank and the deCODE Health Study. We also used the latest publicly available genome-wide association studies for the neurodegenerative diseases of interest. The potentially causal effect of proteins on neurodegenerative diseases was estimated based on the Wald ratio. We tested 13 377 protein-disease associations, identifying 169 associations that were statistically significant (5% false discovery rate). Evidence of co-localization between plasma protein abundance and disease risk (posterior probability > 0.80) was identified for 61 protein-disease pairs, leading to 50 unique protein-disease associations. Notably, 23 of 50 protein-disease associations corresponded to genetic loci not previously reported by genome-wide association studies. The two-sample Mendelian randomization and co-localization analysis also showed that APOE abundance in plasma was associated with three subcortical volumes (hippocampus, amygdala and nucleus accumbens) and white matter hyper-intensities, whereas PILRA and PILRB abundance in plasma was associated with caudate nucleus volume. Our study provided a comprehensive assessment of the effect of the human proteome that is currently measurable through two different platforms on neurodegenerative diseases. The newly associated proteins indicated the involvement of complement (C1S and C1R), microglia (SIRPA, SIGLEC9 and PRSS8) and lysosomes (CLN5) in Alzheimer's disease; the interleukin-6 pathway (CTF1) in Parkinson's disease; lysosomes (TPP1), blood-brain barrier integrity (MFAP2) and astrocytes (TNFSF13) in amyotrophic lateral sclerosis; and blood-brain barrier integrity (VEGFB), oligodendrocytes (PARP1), node of Ranvier and dorsal root ganglion (NCS1, FLRT3 and CDH15) and the innate immune system (CR1, AHSG and WARS) in multiple sclerosis. Our study demonstrates how harnessing large-scale genomic and proteomic data can yield new insights into the role of the plasma proteome in the pathogenesis of neurodegenerative diseases.

Assessing Causality Between Plasma Brain-Derived Neurotrophic Factor With Major Depression Disorder: A Bidirectional Mendelian Randomization Study

PURPOSE

This study employed a two-sample Mendelian randomization (MR) approach to investigate the bidirectional relationship between brain-derived neurotrophic factor (BDNF) and major depressive disorder (MDD), addressing gaps left by prior observational studies.

METHODS

We utilized Genome-Wide Association Study (GWAS) datasets, including MDD information from the Psychiatric Genomics Consortium (PGC) and the UK Biobank (N = 500,199), along with plasma BDNF measurements from the FinnGen Consortium (N = 619). In a subsequent phase, we analyzed MDD data from FinnGen (N = 448,069) with plasma BDNF data from three additional GWAS sources: UK Biobank (N = 33,924), deCODE (N = 35,353), and INTERVAL (N = 3301). Multiple MR methods were applied to ensure a robust analysis.

RESULTS

The inverse variance weighted (IVW) method revealed no significant association between plasma BDNF levels and the risk of developing MDD (IVW odds ratio [OR] = 1.00, 95% confidence interval [CI] = 0.99-1.01, p = 0.769). Similarly, no causal effect of the BDNF gene on MDD was identified (OR = 0.91, CI = 0.23-3.56, p = 0.893). Furthermore, there was no evidence supporting a causal link between MDD and plasma BDNF levels (OR = 0.99, CI = 0.89-1.09, p = 0.783). The second phase of analysis confirmed the absence of bidirectional causal relationships.

CONCLUSION

This bidirectional MR analysis provides no evidence of a causal association between plasma BDNF levels and MDD. These findings prompt a re-evaluation of plasma BDNF as a biomarker for MDD and emphasize the need for further investigation into its functional role within the plasma as well as its levels and activity in the brain and cerebrospinal fluid.

The contribution of genetic determinants of blood gene expression and splicing to molecular phenotypes and health outcomes

The biological mechanisms through which most nonprotein-coding genetic variants affect disease risk are unknown. To investigate gene-regulatory mechanisms, we mapped blood gene expression and splicing quantitative trait loci (QTLs) through bulk RNA sequencing in 4,732 participants and integrated protein, metabolite and lipid data from the same individuals. We identified cis-QTLs for the expression of 17,233 genes and 29,514 splicing events (in 6,853 genes). Colocalization analyses revealed 3,430 proteomic and metabolomic traits with a shared association signal with either gene expression or splicing. We quantified the relative contribution of the genetic effects at loci with shared etiology, observing 222 molecular phenotypes significantly mediated by gene expression or splicing. We uncovered gene-regulatory mechanisms at disease loci with therapeutic implications, such as WARS1 in hypertension, IL7R in dermatitis and IFNAR2 in COVID-19. Our study provides an open-access resource on the shared genetic etiology across transcriptional phenotypes, molecular traits and health outcomes in humans ( https://IntervalRNA.org.uk ).

Identification of Candidate Lung Function-Related Plasma Proteins to Pinpoint Drug Targets for Common Pulmonary Diseases: A Comprehensive Multi-Omics Integration Analysis

The genome-wide association studies (GWAS) of lung disease and lung function indices suffer from challenges to be transformed into clinical interventions, due to a lack of knowledge on the molecular mechanism underlying the GWAS associations. A proteome-wide association study (PWAS) was first performed to identify candidate proteins by integrating two independent largest protein quantitative trait loci datasets of plasma proteins and four large-scale GWAS summary statistics of lung function indices (forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC), FEV1/FVC and peak expiratory flow (PEF)), followed by enrichment analysis to reveal the underlying biological processes and pathways. Then, with a discovery dataset, we conducted Mendelian randomization (MR) and Bayesian colocalization analyses to select potentially causal proteins, followed by a replicated MR analysis with an independent dataset. Mediation analysis was also performed to explore the possible mediating role of these indices on the association between proteins and two common lung diseases (chronic obstructive pulmonary disease, COPD and Asthma). We finally prioritized the potential drug targets. A total of 210 protein-lung function index associations were identified by PWAS, and were significantly enriched in the pulmonary fibrosis and lung tissue repair. Subsequent MR and colocalization analysis identified 59 causal protein-index pairs, among which 42 pairs were replicated. Further mediation analysis identified 3 potential pathways from proteins to COPD or asthma mediated by FEV1/FVC. The mediated proportion ranges from 68.4% to 82.7%. Notably, 24 proteins were reported as druggable targets in Drug Gene Interaction Database, among which 8 were reported to interact with drugs, including FKBP4, GM2A, COL6A3, MAPK3, SERPING1, XPNPEP1, DNER, and FER. Our study identified the crucial plasma proteins causally associated with lung functions and highlighted potential mediating mechanism underlying the effect of proteins on common lung diseases. These findings may have an important insight into pathogenesis and possible future therapies of lung disorders.

Identifying Circulating Protein Mediators in the Link Between Smoking and Abdominal Aortic Aneurysm: An Integrated Analysis of Human Proteomic and Genomic Data

Background

Smoking is a well-established risk factor for abdominal aortic aneurysm (AAA). However, the molecular pathways underlying this relationship remain poorly understood. This study aimed to identify circulating protein mediators that may explain the association between smoking and AAA.

Methods

We conducted a network Mendelian randomization (MR) study utilizing summary-level data from the largest available genome-wide association studies. Our primary smoking exposure was the lifetime smoking index, with smoking initiation and cigarettes per day included as supplementary traits. The AAA dataset comprised 39,221 cases and 1,086,107 controls. Protein data were sourced from two large cohorts: UKB-PPP, where proteins were measured using the Olink platform in 54,219 individuals, and deCODE, where proteins were measured using the SomaScan platform in 35,559 individuals. Two-sample MR was employed to estimate the association between smoking and AAA (βtotal) and between smoking and circulating protein levels (β1). Summary data-based MR was then used to assess the association between smoking-related proteins and AAA risk (β2). Mediation pathways were identified based on the directionality of effect estimates, and the corresponding mediation effects were quantified.

Results

Genetically predicted smoking traits were consistently associated with an increased risk of AAA. The lifetime smoking index was associated with the levels of 543 out of 5,764 unique circulating proteins, with 470 of these associations replicated in supplementary analyses using additional smoking traits and protein sources. Among the smoking-related proteins, genetically predicted levels of 22 were associated with AAA risk. Eight mediation pathways were identified accounting for 42.7% of the total smoking-AAA association and with mediation effects >4% for ADAMTS15, IL1RN, MMP12, PGF, PCSK9, and UXS1.

Conclusion

This study identified numerous circulating proteins potentially causally linked to smoking, and eight of these proteins were found to mediate the association between smoking and AAA risk.

Large-scale multi-omics identifies drug targets for heart failure with reduced and preserved ejection fraction

Heart failure (HF) has limited therapeutic options. In this study, we differentiated the pathophysiological underpinnings of the HF subtypes-HF with reduced ejection fraction (HFrEF) and HF with preserved ejection fraction (HFpEF)-and uncovered subtype-specific therapeutic strategies. We investigated the causal roles of the human proteome and transcriptome using Mendelian randomization on more than 420,000 participants from the Million Veteran Program (27,799 HFrEF and 27,579 HFpEF cases). We created therapeutic target profiles covering efficacy, safety, novelty, druggability and mechanism of action. We replicated findings on more than 175,000 participants of diverse ancestries. We identified 70 HFrEF and 10 HFpEF targets, of which 58 were not previously reported; notably, the HFrEF and HFpEF targets are non-overlapping, suggesting the need for subtype-specific therapies. We classified 14 previously unclassified HF loci as HFrEF. We substantiated the role of ubiquitin-proteasome system, small ubiquitin-related modifier pathway, inflammation and mitochondrial metabolism in HFrEF. Among druggable genes, IL6R, ADM and EDNRA emerged as potential HFrEF targets, and LPA emerged as a potential target for both subtypes.

Biobanking with genetics shapes precision medicine and global health

Precision medicine provides patients with access to personally tailored treatments based on individual-level data. However, developing personalized therapies requires analyses with substantial statistical power to map genetic and epidemiologic associations that ultimately create models informing clinical decisions. As one solution, biobanks have emerged as large-scale, longitudinal cohort studies with long-term storage of biological specimens and health information, including electronic health records and participant survey responses. By providing access to individual-level data for genotype-phenotype mapping efforts, pharmacogenomic studies, polygenic risk score assessments and rare variant analyses, biobanks support ongoing and future precision medicine research. Notably, due in part to the geographical enrichment of biobanks in Western Europe and North America, European ancestries have become disproportionately over-represented in precision medicine research. Herein, we provide a genetics-focused review of biobanks from around the world that are in pursuit of supporting precision medicine. We discuss the limitations of their designs, ongoing efforts to diversify genomics research and strategies to maximize the benefits of research leveraging biobanks for all.

Improving the Sensitivity of a Multiplexed Digital Immunoassay Based on Extremely High Bead Analysis Efficiency

The development of a detection methodology with high sensitivity, stability, and user-friendliness for quantification of proteins at subfemtogram levels is essential for clinical applications such as early screening, disease diagnosis, and monitoring disease progression. A traditional micropartition-based digital enzyme-linked immunosorbent assay (dELISA) results in significant bead loss due to intricate partitioning based on Poisson distribution, multistep reaction operations, nonglobal signal recognition, and reading modes, which have not yet achieved the ultimate detection sensitivity. This study introduces an ultrasensitive multiplexed digital immunoassay with extremely high bead analysis efficiency (HiBeA) through integrating the bead transfer strategy in multistep immunoreaction processing and flow cytometry detection mode. Typically, a bead analysis ratio over 95% was achieved, ensuring high sensitivity, efficiency, and stability of the established HiBeA using as few as only 5,000 beads. As a proof of concept, HiBeA was utilized for the multiplexed detection of IL-10 and IL-6, achieving detection limits of 5.9 and 8.8 fg/mL, respectively. This signifies a 3- to 4-fold enhancement in detection sensitivity under the same reaction time while using only 1% of the assay bead number compared to the commercial single-molecule array (SiMoA) system. HiBeA presents ultrasensitivity, robust detection stability based on tailored, multistep operation of immune-reaction, and the ability to perform multiplexed detection, thereby offering substantial prospects for the advancement of ultrasensitive clinical diagnostics.

Exploring Potential Drug Targets in Multiple Cardiovascular Diseases: A Study Based on Proteome-Wide Mendelian Randomization and Colocalization Analysis

Background: Cardiovascular diseases (CVDs) encompass a group of diseases that affect the heart and/or blood vessels, making them the leading cause of global mortality. In our study, we performed proteome-wide Mendelian randomization (MR) and colocalization analyses to identify novel therapeutic protein targets for CVDs and evaluate the potential drug-related protein side effects. Methods: We conducted a comprehensive proteome-wide MR study to assess the causal relationship between plasma proteins and the risk of CVDs. Summary-level data for 4907 circulating protein levels were extracted from a large-scale protein quantitative trait loci (pQTL) study involving 35,559 individuals. Additionally, genome-wide association study (GWAS) data for CVDs were extracted from the UK Biobank and the Finnish database. Colocalization analysis was utilized to identify causal variants shared between plasma proteins and CVDs. Finally, we conducted a comprehensive phenome-wide association study (PheWAS) using the R10 version of the Finnish database. This study was aimed at examining the potential drug-related protein side effects in the treatment of CVDs. A total of 2408 phenotypes were included in the analysis, categorized into 44 groups. Results: The research findings indicate the following associations: (1) In coronary artery disease (CAD), the plasma proteins A4GNT, COL6A3, KLC1, CALB2, KPNA2, MSMP, and ADH1B showed a positive causal relationship (p-fdr < 0.05). LAYN and GCKR exhibited a negative causal relationship (p-fdr < 0.05). (2) In chronic heart failure (CHF), PLG demonstrated a positive causal relationship (p-fdr < 0.05), while AZGP1 displayed a negative causal relationship (p-fdr < 0.05). (3) In ischemic stroke (IS), ALDH2 exhibited a positive causal relationship (p-fdr < 0.05), while PELO showed a negative causal relationship (p-fdr < 0.05). (4) In Type 2 diabetes (T2DM), the plasma proteins MCL1, SVEP1, PIP4K2A, RFK, HEXIM2, ALDH2, RAB1A, APOE, ANGPTL4, JAG1, FGFR1, and MLN demonstrated a positive causal relationship (p-fdr < 0.05). PTPN9, SNUPN, VAT1, COMT, CCL27, BMP7, and MSMP displayed a negative causal relationship (p-fdr < 0.05). Colocalization analysis conclusively identified that AZGP1, ALDH2, APOE, JAG1, MCL1, PTPN9, PIP4K2A, SNUPN, and RAB1A share a single causal variant with CVDs (PPH3 + PPH4 > 0.8). Further phenotype-wide association studies have shown some potential side effects of these nine targets (p-fdr < 0.05). Conclusions: This study identifies plasma proteins with significant causal associations with CVDs, providing a more comprehensive understanding of potential therapeutic targets. These findings contribute to our knowledge of the underlying mechanisms and offer insights into potential avenues for treatment.

An integrated multi-omics analysis identifies protein biomarkers and potential drug targets for psoriatic arthritis

Psoriatic arthritis (PsA) is a complex, chronic immune-mediated inflammatory arthropathy that currently lacks definitive biomarkers and treatment targets. Identifying biomarkers and treatment targets is urgently needed for effectively managing PsA. Here, we conducted a multi-omics approach to identify protein biomarkers and potential drug targets for psoriatic arthritis. Proteome-wide Mendelian randomization (MR) analysis revealed seven plasma protein biomarkers significantly associated with PsA. Specifically, genetically predicted lower levels of NEO1 were linked to an increased PsA risk, whereas the remaining six proteins (IL23R, ERAP2, IFNLR1, KIR2DL3, CLSTN3, and POLR2F) exhibited a positive association with PsA risk. PPI analysis further supported these findings. Notably, druggability assessment revealed that scopoletin and esculetin were the two most significant drugs associated with ERAP2. Single-cell RNA-seq analysis revealed expression of IL23R, ERAP2, CLSTN3, and POLR2F in distinct T-cell subgroups of PBMCs derived from PsA patients. Furthermore, phenome-wide association studies (PheWAS) analysis assessed the potential side effects and safety as potential drug targets. Interestingly, experimental evidence showed that IFNLR1 expression is significantly upregulated under simulated inflammatory conditions. This study employed proteome-wide mendelian randomization to identify seven plasma proteins associated with PsA, including IL23R, ERAP2 and IFNLR1, offering potential insights for personalized PsA treatment strategies.

Cross-Ancestry Comparison of Aptamer and Antibody Proteomics Measures

Measures from affinity-proteomics platforms often correlate poorly, challenging interpretation of protein associations with genetic variants (pQTL) and phenotypes. Here, we examined 2,157 proteins measured on both SomaScan 7k and Olink Explore 3072 across 1,930 participants with genetic similarity to European, African, East Asian, and Admixed American ancestry references. Inter-platform correlation coefficients for these 2,157 proteins followed a bimodal distribution (median r=0.30). Protein measures from each platform were associated with genetic variants (pQTLs), and one-third of the pQTL signals were driven by protein-altering variants (PAVs). We highlight 80 proteins that correlate differently across ancestry groups likely due to differing PAV frequencies by ancestry. Furthermore, adjustment for PAVs with opposite directions of effect by platform improved inter-platform protein measure correlation and resulted in more concordant genetic and phenotypic associations. Hence, PAVs need to be accounted for across ancestries to facilitate platform-concordant and accurate protein measurement.

Proteome-Wide Mendelian Randomization Identifies Therapeutic Targets for Abdominal Aortic Aneurysm

BACKGROUND

The proteome is a key source of therapeutic targets. We conducted a comprehensive Mendelian randomization analysis across the proteome to identify potential protein markers and therapeutic targets for abdominal aortic aneurysm (AAA).

METHODS AND RESULTS

Our study used plasma proteomics data from the UK Biobank, comprising 2923 proteins from 54 219 individuals, and from deCODE Genetics, which measured 4907 proteins across 35 559 individuals. Significant proteomic quantitative trait loci were used as instruments for Mendelian randomization. Genetic associations with AAA were sourced from the AAAgen consortium, a large-scale genome-wide association study meta-analysis involving 37 214 cases and 1 086 107 controls, and the FinnGen study, which included 3869 cases and 381 977 controls. Sequential analyses of colocalization and summary-data-based Mendelian randomization were performed to verify the causal roles of candidate proteins. Additionally, single-cell expression analysis, protein-protein interaction network analysis, pathway enrichment analysis, and druggability assessments were conducted to identify cell types with enriched expression and prioritize potential therapeutic targets. The proteome-wide Mendelian randomization analysis identified 34 proteins associated with AAA risk. Among them, 2 proteins, COL6A3 and PRKD2, were highlighted by colocalization analysis, summary-data-based Mendelian randomization, and the heterogeneity in an independent instrument test, providing the most convincing evidence. These protein-coding genes are primarily expressed in macrophages, smooth muscle cells, and mast cells within abdominal aortic aneurysm tissue. Several causal proteins are involved in pathways regulating lipid metabolism, immune responses, and extracellular matrix organization. Nine proteins have already been targeted for drug development in diabetes and other cardiovascular diseases, presenting opportunities for repurposing as AAA therapeutic targets.

CONCLUSIONS

This study identifies causal proteins for AAA, enhancing our understanding of its molecular cause and advancing the development of therapeutics.

Pathophysiological insights into HFpEF from studies of human cardiac tissue

Heart failure with preserved ejection fraction (HFpEF) is a major, worldwide health-care problem. Few therapies for HFpEF exist because the pathophysiology of this condition is poorly defined and, increasingly, postulated to be diverse. Although perturbations in other organs contribute to the clinical profile in HFpEF, altered cardiac structure, function or both are the primary causes of this heart failure syndrome. Therefore, studying myocardial tissue is fundamental to improve pathophysiological insights and therapeutic discovery in HFpEF. Most studies of myocardial changes in HFpEF have relied on cardiac tissue from animal models without (or with limited) confirmatory studies in human cardiac tissue. Animal models of HFpEF have evolved based on theoretical HFpEF aetiologies, but these models might not reflect the complex pathophysiology of human HFpEF. The focus of this Review is the pathophysiological insights gained from studies of human HFpEF myocardium. We outline the rationale for these studies, the challenges and opportunities in obtaining myocardial tissue from patients with HFpEF and relevant comparator groups, the analytical approaches, the pathophysiological insights gained to date and the remaining knowledge gaps. Our objective is to provide a roadmap for future studies of cardiac tissue from diverse cohorts of patients with HFpEF, coupling discovery biology with measures to account for pathophysiological diversity.

Integrative proteogenomic analysis identifies COL6A3-derived endotrophin as a mediator of the effect of obesity on coronary artery disease

Obesity strongly increases the risk of cardiometabolic diseases, yet the underlying mediators of this relationship are not fully understood. Given that obesity strongly influences circulating protein levels, we investigated proteins mediating the effects of obesity on coronary artery disease, stroke and type 2 diabetes. By integrating two-step proteome-wide Mendelian randomization, colocalization, epigenomics and single-cell RNA sequencing, we identified five mediators and prioritized collagen type VI α3 (COL6A3). COL6A3 levels were strongly increased by body mass index and increased coronary artery disease risk. Notably, the carboxyl terminus product of COL6A3, endotrophin, drove this effect. COL6A3 was highly expressed in disease-relevant cell types and tissues. Finally, we found that body fat reduction could reduce plasma levels of COL6A3-derived endotrophin, indicating a tractable way to modify endotrophin levels. In summary, we provide actionable insights into how circulating proteins mediate the effects of obesity on cardiometabolic diseases and prioritize endotrophin as a potential therapeutic target.

Integrating plasma proteome with genome reveals novel protein biomarkers in colorectal cancer

BACKGROUND

Biomarkers for colorectal cancer (CRC) can complement population screening methods, but so far, few plasma proteins have been identified as biomarkers for CRC. This study aims to identify potential protein biomarkers and therapeutic targets for CRC within the proteome range.

METHODS

We extracted summary-level data of circulating protein from 7 published genome-wide association studies (GWASs) of plasma proteome for Mendelian randomization (MR), summary-data-based MR (SMR), and co-localization analyses to screen and validate proteins with causal effects in CRC. In addition, we further conducted druggability evaluation, prognosis analysis at the transcriptional level, and enrichment expression at the single-cell level, highlighting the important role of these plasma protein biomarkers in CRC.

RESULTS

We identified 117 plasma protein biomarkers associated with CRC risk, with 9 proteins showing stronger genetic correlations in Bayesian co-localization (PP.H4 > 0.70). Further, we found 26 protein-coding genes already used in targeted drug development and may potentially become therapeutic targets for CRC. In prognosis analysis, the encoding genes of plasma proteins exhibited consistent effects with MR analysis and can serve as prognostic biomarkers for CRC. Additionally, we also found that the differentially expressed proteins are mainly expressed in fibroblasts, endothelial cells, macrophages, and T cells.

CONCLUSION

Our study has identified plasma protein biomarkers associated with CRC risk, which may complement population screening methods for CRC and achieve more precise treatment for patients.

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

BACKGROUND

Genome-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.

METHODS

Through 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.

RESULTS

PWAS 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.

CONCLUSIONS

Our 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.

Genetics of constant and severe pain in the NAPS2 cohort of recurrent acute and chronic pancreatitis patients

Recurrent acute and chronic pancreatitis (RAP, CP) are complex, progressive inflammatory diseases with variable pain experiences impacting patient function and quality of life. The genetic variants and pain pathways in patients contributing to most severe pain experiences are unknown. We used previously genotyped individuals with RAP/CP from the North American Pancreatitis Study II (NAPS2) of European Ancestry for nested genome-wide associated study (GWAS) for pain-severity, chronicity, or both. Lead variants from GWAS were determined using FUMA. Loci with p<1e-5 were identified for post-hoc candidate identification. Transcriptome-wide association studies (TWAS) identified loci in cis and trans to the lead variants. Serum from phenotyped individuals with CP from the PROspective Evaluation of Chronic Pancreatitis for EpidEmiologic and Translational StuDies (PROCEED) was assessed for BDNF levels using Meso Scale Discovery Immunoassay. We identified four pain systems defined by candidate genes: 1) Pancreas-associated injury/stress mitigation genes include: REG gene cluster, CTRC, NEURL3 and HSF22. 2) Neural development and axon guidance tracing genes include: SNPO, RGMA, MAML1 and DOK6 (part of the RET complex). 3) Genes linked to psychiatric stress disorders include TMEM65, RBFOX1, and ZNF385D. 4) Genes in the dorsal horn pain-modulating BDNF/neuropathic pathway included SYNPR, NTF3 and RBFOX1. In an independent cohort BDNF was significantly elevated in patients with constant-severe pain. Extension and expansion of this exploratory study may identify pathway- and mechanism-dependent targets for individualized pain treatments in CP patients. PERSPECTIVE: Pain is the most distressing and debilitating feature of chronic pancreatitis. Yet many patients with chronic pancreatitis have little or no pain. The North American Pancreatitis Study II (NAPS2) includes over 1250 pancreatitis patients of all progressive stages with all clinical and phenotypic characteristics carefully recorded. Pain did not correlate well with disease stage, inflammation, fibrosis or other features. Here we spit the patients into groups with the most severe pain and/or chronic pain syndromes and compared them genetically with patients reporting mild or minimal pain. Although some genetic variants associated with pain were expressed in cells (1) of the pancreas, most genetic variants were linked to genes expressed in the nervous system cells associated with (2) neural development and axon guidance (as needed for the descending inhibition pathway), (3) psychiatric stress disorders, and (4) cells regulating sensory nerves associated with BDNF and neuropathic pain. Similar and overlapping genetic variants in systems 2 -4 are also seen in pain syndromes form other organs. The implications for treating pancreatic pain are great in that we can no longer focus on just the pancreas. Furthermore, new treatments designed for pain disorders in other tissues may be effective in some patient with pain syndromes from the pancreas. Further research is needed to replicate and extend these observations so that new, genetics-guided rational treatments can be developed and delivered.

Network Mendelian randomisation analysis deciphers protein pathways linking type 2 diabetes and gastrointestinal disease

AIMS

The molecular mechanisms underlying the association between type 2 diabetes (T2D) and gastrointestinal (GI) disease are unclear. To identify protein pathways, we conducted a two-stage network Mendelian randomisation (MR) study.

MATERIALS AND METHODS

Genetic instruments for T2D were obtained from a large-scale summary-level genome-wide meta-analysis. Genetic associations with blood protein levels were obtained from three genome-wide association studies on plasma proteins (i.e. the deCODE study as the discovery and the UKB-PPP and Fenland studies as the replication). Summary-level data on 10 GI diseases were derived from genome-wide meta-analysis of the UK Biobank and FinnGen. MR and colocalisation analyses were performed. Pathways were constructed according to the directionality of total and indirect effects, and corresponding proportional mediation was estimated. Druggability assessments were conducted across four databases to prioritise protein mediators.

RESULTS

Genetic liability to T2D was associated with 69 proteins in the discovery protein dataset after multiple testing corrections. All associations were replicated at the nominal significance level. Among T2D-associated proteins, genetically predicted levels of nine proteins were associated with at least one of the GI diseases. Genetically predicted levels of SULT2A1 (odds ratio = 1.98, 95% CI 1.80-2.18), and ADH1B (odds ratio = 2.05, 95% CI 1.43-2.94) were associated with cholelithiasis and cirrhosis respectively. SULT2A1 and cholelithiasis (PH4 = 0.996) and ADH1B and cirrhosis (PH4 = 0.931) have strong colocalisation support, accounting for the mediation proportion of 72.8% (95% CI 45.7-99.9) and 42.9% (95% CI 15.5-70.4) respectively.

CONCLUSIONS

The study identified some proteins mediating T2D-GI disease associations, which provided biological insights into the underlying pathways.

Identification of potential druggable targets for endometriosis through Mendelian randomization analysis

Introduction

Endometriosis (EM) is a widely recognized disorder in gynecological endocrinology. Although hormonal therapies are frequently employed for EM, their side effects and outcome limitations underscore the need to explore the genetic basis and potential drug targets for developing innovative therapeutic approaches. This study aimed to identify both cerebrospinal fluid (CSF) and plasma protein markers as promising therapeutic targets for EM.

Methods

We utilized Mendelian randomization (MR) analysis to explore potential disease-causing proteins, utilizing genetic datasets from genome-wide association studies (GWAS) and protein quantitative trait loci (pQTL) analyses. We applied a range of validation techniques, including reverse causality detection, phenotype scanning, Bayesian co-localization (BC) analysis, and external validations to substantiate our findings. Additionally, we conducted a protein-protein interaction (PPI) network as well as functional enrichment analyses to unveil potential associations among target proteins.

Results

MR analysis revealed that a decrease of one standard deviation (SD) in plasma R-Spondin 3 (RSPO3) level had a protective effect on EM (OR = 1.0029; 95% confidence interval (95% CI): 1.0015-1.0043; P = 3.2567e-05; Bonferroni P < 5.63 × 10-5). BC analysis showed that RSPO3 shared the same genetic variant with EM (coloc.abf-PPH4 = 0.874). External validation further supported this causal association. Galectin-3 (LGALS3; OR = 0.9906; 95% CI: 0.9835-0.9977; P = 0.0101), carboxypeptidase E (CPE; OR = 1.0147; 95% CI: 1.0009-1.0287; P = 0.0366), and alpha-(1,3)-fucosyltransferase 5 (FUT5; OR = 1.0053; 95% CI: 1.0013-1.0093; P = 0.002) were detected as potential targets for EM in CSF. PPI analysis showed that fibronectin (FN1) had the highest combined score. Furthermore, several EM-linked proteins were involved in the glycan degradation pathway.

Discussion

In conclusion, this comprehensive study offers valuable insights into potential drug targets for EM, with RSPO3 emerging as a promising candidate. Additionally, mechanistic roles of FN1, glycan degradation pathway, LGALS3, CPE, and FUT5 in EM warrant further investigation.

Proteome-wide mendelian randomization identifies causal plasma proteins in interstitial lung disease

Interstitial lung disease (ILD) has shown limited treatment advancements, with minimal exploration of circulating protein biomarkers causally linked to ILD and its subtypes beyond idiopathic pulmonary fibrosis (IPF). In this study, we aimed to identify potential drug targets and circulating protein biomarkers for ILD and its subtypes. We utilized the most recent large-scale plasma protein quantitative trait loci (pQTL) data detected from the antibody-based method and ILD and its subtypes' GWAS data from the updated FinnGen database for Mendelian randomization analysis. To enhance the reliability of causal associations, we conducted external validation and sensitivity analyses, including Bayesian colocalization and bidirectional Mendelian randomization analysis. Our study identified eight plasma proteins genetically associated with ILD or its subtypes. Among these, three proteins-CDH15 (Cadherin-15), LTBR (Lymphotoxin-beta receptor), and ADAM15 (A disintegrin and metalloproteinase 15)-emerged as priority biomarkers and potential therapeutic targets, demonstrating more reliable associations by passing a series of sensitivity analyses compared to the others. Based on these findings, we propose for the first time that CDH15, ADAM15, and LTBR hold promise as novel potential circulating protein biomarkers and therapeutic targets for the diagnosis and treatment of ILD, IPF, and sarcoidosis, respectively, especially ADAM15, and these findings have the potential to provide new perspectives for advancing the research on the heterogeneity of ILD.

The landscape of plasma proteomic links to human organ imaging

Plasma protein levels provide important insights into human disease, yet a comprehensive assessment of plasma proteomics across organs is lacking. Using large-scale multimodal data from the UK Biobank, we integrated plasma proteomics with organ imaging to map their phenotypic and genetic links, analyzing 2,923 proteins and 1,051 imaging traits across multiple organs. We uncovered 5,067 phenotypic protein-imaging associations, identifying both organ-specific and organ-shared proteomic relations, along with their enriched protein-protein interaction networks and biological pathways. By integrating external gene expression data, we observed that plasma proteins associated with the brain, liver, lung, pancreas, and spleen tended to be primarily produced in the corresponding organs, while proteins associated with the heart, body fat, and skeletal muscle were predominantly expressed in the liver. We also mapped key protein predictors of organ structures and showed the effective stratification capability of plasma protein-based prediction models. Furthermore, we identified 8,116 genetic-root putative causal links between proteins and imaging traits across multiple organs. Our study presents the most comprehensive pan-organ imaging proteomics map, bridging molecular and structural biology and offering a valuable resource to contextualize the complex roles of molecular pathways underlying plasma proteomics in organ systems.

Regulation of protein abundance in normal human tissues

We report a systematic quantification of 10,841 unique proteins from over 700 GTEx samples, representing five human tissues. Sex, age and genetic factors are associated with variation in protein abundance. In total, 1981 cis-protein quantitative trait loci (cis-pQTL) are identified, of which a majority of protein targets have not been assayed in the recent plasma-based proteogenomic studies. Integrating transcriptomic information from matching tissues delineates concordant as well as discordant expression patterns at RNA and protein levels. Juxtaposition of data from different tissues indicates both shared and tissue-specific genetic architecture that underlie protein abundance. Complementing genomic annotation, RNA-based eQTL studies, as well as the recent establishment of plasma-based proteogenomic characterization, tissue-pQTLs shed light on biology underlying genotype-phenotype association of complex traits and diseases.

Revealing potential drug targets in schizophrenia through proteome-wide Mendelian randomization genetic insights

BACKGROUND

Schizophrenia (SCZ) is a severe, chronic mental disorder with no current cure. Identifying novel pharmacological targets is crucial for developing more effective treatments.

METHODS

We performed two-sample Mendelian randomization (MR) analyses to estimate the associations between cerebrospinal fluid (CSF) containing 154 proteins and plasma containing 734 proteins and risk of SCZ. Bidirectional MR analysis, steiger filtering, bayesian colocalization, phenotypic scanning, and validation analysis were examined to validate the assumptions of MR. For proteins significantly associated with SCZ identified by MR, we explored their potential impact on brain structures, including cortical surface area (SA), thickness (TH), and the volume of subcortical structures.

RESULTS

MR analysis identified 13 protein-SCZ pairs at Bonferroni significance (P < 5.63 × 10-5). Notably, the genetically proxied protein level of neuromedin B (NMB) was associated with an increased risk for SCZ (odds ratio [OR] = 1.41; 95 % CI, 1.27 to 1.58; P = 6.68 × 10-10). Bayesian colocalization suggested that NMB shares genetic variations with SCZ. Further, NMB interacts with target proteins of current SCZ drugs and was validated in the UK Biobank. The genetically proxied NMB was positively associated with an increase in the surface area (SA) of the parahippocampal gyrus (β = 8.93 mm2, 95 % CI, 1.58 to 16.3, P = .02). Additionally, an increase in the genetically proxied SA of the parahippocampal gyrus was inversely associated with the risk of SCZ (OR = 0.996, 95 % CI, 0.993 to 0.999, P = .04).

CONCLUSIONS

The findings suggest that NMB may represent a promising target for pharmacological intervention in SCZ. This warrants further investigation into the specific constituents involved, which could have potential for follow-up studies.

Development and Validation of a Diagnostic Model for Stanford Type B Aortic Dissection Based on Proteomic Profiling

Purpose

Stanford Type B Aortic Dissection (TBAD), a critical aortic disease, has exhibited stable mortality rates over the past decade. However, diagnostic approaches for TBAD during routine health check-ups are currently lacking. This study focused on developing a model to improve the diagnosis in a population.

Patients and Methods

Serum biomarkers were investigated in 88 participants using proteomic profiling combined with machine learning. The findings were validated using ELISA in other 80 participants. Subsequently, a diagnostic model for TBAD integrating biomarkers with clinical indicators was developed and assessed using machine learning.

Results

Six differentially expressed proteins (DEPs) were identified through proteomic profiling and machine learning in discovery and derivation cohorts. Five of these (GDF-15, IL6, CD58, LY9, and Siglec-7) were further verified through ELISA validation within the validation cohort. In addition, ten blood-related indicators were selected as clinical indicators. Combining biomarkers and clinical indicators, the machine learning-based models performed well (AUC of the biomarker model = 0.865, AUC of the clinical model = 0.904, and AUC of the combined model = 0.909) using relative quantitation. The performance of the three models was verified (AUC of biomarker model = 0.866, AUC of clinical model = 0.868, and AUC of combined model = 0.886) using absolute quantitation. Crucially, the combined models outperformed individual biomarkers and clinical models, demonstrating superior efficacy.

Conclusion

Using proteomic profiling, we identified serum IL-6, GDF-15, CD58, LY9, and Siglec-7 as TBAD biomarkers. The machine-learning-based diagnostic model exhibited significant potential for TBAD diagnosis using only blood samples within the population.

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

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