Mapping the proteo-genomic convergence of human diseases

The effect of histo-blood group ABO system transferase (BGAT) on pregnancy related outcomes：A Mendelian randomization study

Protein level of Histo-Blood Group ABO System Transferase (BGAT) has been reported to be associated with cardiometabolic diseases. But its effect on pregnancy related outcomes still remains unclear. Here we conducted a two-sample Mendelian randomization (MR) study to ascertain the putative causal roles of protein levels of BGAT in pregnancy related outcomes. Cis-acting protein quantitative trait loci (pQTLs) robustly associated with protein level of BGAT (P < 5 ×10-8) were used as instruments to proxy the BGAT protein level (N = 35,559, data from deCODE), with two additional pQTL datasets from Fenland (N = 10,708) and INTERVAL (N = 3301) used as validation exposures. Ten pregnancy related diseases and complications were selected as outcomes. We observed that a higher protein level of BGAT showed a putative causal effect on venous complications and haemorrhoids in pregnancy (VH) (odds ratio [OR]=1.19, 95% confidence interval [95% CI]=1.12-1.27, colocalization probability=91%), which was validated by using pQTLs from Fenland and INTERVAL. The Mendelian randomization results further showed effects of the BGAT protein on gestational hypertension (GH) (OR=0.97, 95% CI=0.96-0.99), despite little colocalization evidence to support it. Sensitivity analyses, including proteome-wide Mendelian randomization of the cis-acting BGAT pQTLs, showed little evidence of horizontal pleiotropy. Correctively, our study prioritised BGAT as a putative causal protein for venous complications and haemorrhoids in pregnancy. Future epidemiology and clinical studies are needed to investigate whether BGAT can be considered as a drug target to prevent adverse pregnancy outcomes.

Polygenic markers of survival and longevity in the antioxidant genes PON1, PON2, MTHFR, MSRA, SOD1, NQO1, and CAT in a 20-year follow-up study in the population from the Volga-Ural region

BACKGROUND

Genetic background of healthy or pathological styles of aging and human lifespan is determined by joint gene interactions. Lucky combinations of antioxidant gene polymorphisms can result in a highly adaptive phenotype, providing a successful way to interact with external triggers. Our purpose was to identify the polygenic markers of survival and longevity in the antioxidant genes among elderly people with physiological and pathological aging.

METHODS

In a 20-year follow-up study of 2350 individuals aged 18-114 years residing in the Volga-Ural region of Russia, sex-adjusted association analyses of MTHFR rs1801133, MSRA rs10098474, PON1 rs662, PON2 rs7493, SOD1 rs2070424, NQO1 rs1131341 and CAT rs1001179 polymorphic loci with longevity were carried out. Survival analysis was subsequently performed using the established single genes and gene-gene combinations as cofactors.

RESULTS

The PON1 rs662*G allele was defined as the main longevity marker in women (OR = 1.44, p = 3E-04 in the log-additive model; HR = 0.77, p = 1.9E-04 in the Cox-survival model). The polymorphisms in the MTHFR, MSRA, PON2, SOD1, and CAT genes had an additive effect on longevity. A strong protective effect of combined MTHFR rs1801133*C, MSRA rs10098474*T, PON1 rs662*G, and PON2 rs7493*C alleles against mortality was obtained in women (HR = 0.81, p = 5E-03). The PON1 rs662*A allele had a meaningful impact on mortality for both long-lived men with cerebrovascular accidents (HR = 1.76, p = 0.027 for the PON1 rs662*AG genotype) and women with cardiovascular diseases (HR = 1.43, p = 0.002 for PON1 rs662*AA genotype). The MTHFR rs1801133*TT (HR = 1.91, p = 0.036), CAT rs1001179*TT (HR = 2.83, p = 0.031) and SOD1 rs2070424*AG (HR = 1.58, p = 0.018) genotypes were associated with the cancer mortality.

CONCLUSION

In our longitudinal 20-year study, we found the combinations of functional polymorphisms in antioxidant genes involved in longevity and survival in certain clinical phenotypes in the advanced age.

Understanding the genetic complexity of puberty timing across the allele frequency spectrum

Pubertal timing varies considerably and is associated with later health outcomes. We performed multi-ancestry genetic analyses on ~800,000 women, identifying 1,080 signals for age at menarche. Collectively, these explained 11% of trait variance in an independent sample. Women at the top and bottom 1% of polygenic risk exhibited ~11 and ~14-fold higher risks of delayed and precocious puberty, respectively. We identified several genes harboring rare loss-of-function variants in ~200,000 women, including variants in ZNF483, which abolished the impact of polygenic risk. Variant-to-gene mapping approaches and mouse gonadotropin-releasing hormone neuron RNA sequencing implicated 665 genes, including an uncharacterized G-protein-coupled receptor, GPR83, which amplified the signaling of MC3R, a key nutritional sensor. Shared signals with menopause timing at genes involved in DNA damage response suggest that the ovarian reserve might signal centrally to trigger puberty. We also highlight body size-dependent and independent mechanisms that potentially link reproductive timing to later life disease.

Plasma Proteins Associated with Chronic Histopathologic Lesions on Kidney Biopsy

Key Points

Background

The severity of chronic histopathologic lesions on kidney biopsy is independently associated with higher risk of progressive CKD. Because kidney biopsies are invasive, identification of blood markers that report on underlying kidney histopathology has the potential to enhance CKD care.

Methods

We examined the association between 6592 plasma protein levels measured by aptamers and the severity of interstitial fibrosis and tubular atrophy (IFTA), glomerulosclerosis, arteriolar sclerosis, and arterial sclerosis among 434 participants of the Boston Kidney Biopsy Cohort. For proteins significantly associated with at least one histologic lesion, we assessed renal arteriovenous protein gradients among 21 individuals who had undergone invasive catheterization and assessed the expression of the cognate gene among 47 individuals with single-cell RNA sequencing data in the Kidney Precision Medicine Project.

Results

In models adjusted for eGFR, proteinuria, and demographic factors, we identified 35 proteins associated with one or more chronic histologic lesions, including 20 specific for IFTA, eight specific for glomerulosclerosis, and one specific for arteriolar sclerosis. In general, higher levels of these proteins were associated with more severe histologic score and lower eGFR. Exceptions included testican-2 and NELL1, which were associated with less glomerulosclerosis and IFTA, respectively, and higher eGFR; notably, both of these proteins demonstrated significantly higher levels from artery to renal vein, demonstrating net kidney release. In the Kidney Precision Medicine Project, 13 of the 35 protein hits had cognate gene expression enriched in one or more cell types in the kidney, including podocyte expression of select glomerulosclerosis markers (including testican-2) and tubular expression of several IFTA markers (including NELL1).

Conclusions

Proteomic analysis identified circulating proteins associated with chronic histopathologic lesions, some of which had concordant site-specific expression within the kidney.

Identification of novel therapeutic targets for chronic kidney disease and kidney function by integrating multi-omics proteome with transcriptome

BACKGROUND

Chronic kidney disease (CKD) is a progressive disease for which there is no effective cure. We aimed to identify potential drug targets for CKD and kidney function by integrating plasma proteome and transcriptome.

METHODS

We designed a comprehensive analysis pipeline involving two-sample Mendelian randomization (MR) (for proteins), summary-based MR (SMR) (for mRNA), and colocalization (for coding genes) to identify potential multi-omics biomarkers for CKD and combined the protein-protein interaction, Gene Ontology (GO), and single-cell annotation to explore the potential biological roles. The outcomes included CKD, extensive kidney function phenotypes, and different CKD clinical types (IgA nephropathy, chronic glomerulonephritis, chronic tubulointerstitial nephritis, membranous nephropathy, nephrotic syndrome, and diabetic nephropathy).

RESULTS

Leveraging pQTLs of 3032 proteins from 3 large-scale GWASs and corresponding blood- and tissue-specific eQTLs, we identified 32 proteins associated with CKD, which were validated across diverse CKD datasets, kidney function indicators, and clinical types. Notably, 12 proteins with prior MR support, including fibroblast growth factor 5 (FGF5), isopentenyl-diphosphate delta-isomerase 2 (IDI2), inhibin beta C chain (INHBC), butyrophilin subfamily 3 member A2 (BTN3A2), BTN3A3, uromodulin (UMOD), complement component 4A (C4a), C4b, centrosomal protein of 170 kDa (CEP170), serologically defined colon cancer antigen 8 (SDCCAG8), MHC class I polypeptide-related sequence B (MICB), and liver-expressed antimicrobial peptide 2 (LEAP2), were confirmed. To our knowledge, 20 novel causal proteins have not been previously reported. Five novel proteins, namely, GCKR (OR 1.17, 95% CI 1.10-1.24), IGFBP-5 (OR 0.43, 95% CI 0.29-0.62), sRAGE (OR 1.14, 95% CI 1.07-1.22), GNPTG (OR 0.90, 95% CI 0.86-0.95), and YOD1 (OR 1.39, 95% CI 1.18-1.64,) passed the MR, SMR, and colocalization analysis. The other 15 proteins were also candidate targets (GATM, AIF1L, DQA2, PFKFB2, NFATC1, activin AC, Apo A-IV, MFAP4, DJC10, C2CD2L, TCEA2, HLA-E, PLD3, AIF1, and GMPR1). These proteins interact with each other, and their coding genes were mainly enrichment in immunity-related pathways or presented specificity across tissues, kidney-related tissue cells, and kidney single cells.

CONCLUSIONS

Our integrated analysis of plasma proteome and transcriptome data identifies 32 potential therapeutic targets for CKD, kidney function, and specific CKD clinical types, offering potential targets for the development of novel immunotherapies, combination therapies, or targeted interventions.

Identifying proteomic risk factors for overall, aggressive, and early onset prostate cancer using Mendelian Randomisation and tumour spatial transcriptomics

BACKGROUND

Understanding the role of circulating proteins in prostate cancer risk can reveal key biological pathways and identify novel targets for cancer prevention.

METHODS

We investigated the association of 2002 genetically predicted circulating protein levels with risk of prostate cancer overall, and of aggressive and early onset disease, using cis-pQTL Mendelian randomisation (MR) and colocalisation. Findings for proteins with support from both MR, after correction for multiple-testing, and colocalisation were replicated using two independent cancer GWAS, one of European and one of African ancestry. Proteins with evidence of prostate-specific tissue expression were additionally investigated using spatial transcriptomic data in prostate tumour tissue to assess their role in tumour aggressiveness. Finally, we mapped risk proteins to drug and ongoing clinical trials targets.

FINDINGS

We identified 20 proteins genetically linked to prostate cancer risk (14 for overall [8 specific], 7 for aggressive [3 specific], and 8 for early onset disease [2 specific]), of which the majority replicated where data were available. Among these were proteins associated with aggressive disease, such as PPA2 [Odds Ratio (OR) per 1 SD increment = 2.13, 95% CI: 1.54-2.93], PYY [OR = 1.87, 95% CI: 1.43-2.44] and PRSS3 [OR = 0.80, 95% CI: 0.73-0.89], and those associated with early onset disease, including EHPB1 [OR = 2.89, 95% CI: 1.99-4.21], POGLUT3 [OR = 0.76, 95% CI: 0.67-0.86] and TPM3 [OR = 0.47, 95% CI: 0.34-0.64]. We confirmed an inverse association of MSMB with prostate cancer overall [OR = 0.81, 95% CI: 0.80-0.82], and also found an inverse association with both aggressive [OR = 0.84, 95% CI: 0.82-0.86] and early onset disease [OR = 0.71, 95% CI: 0.68-0.74]. Using spatial transcriptomics data, we identified MSMB as the genome-wide top-most predictive gene to distinguish benign regions from high grade cancer regions that comparatively had five-fold lower MSMB expression. Additionally, ten proteins that were associated with prostate cancer risk also mapped to existing therapeutic interventions.

INTERPRETATION

Our findings emphasise the importance of proteomics for improving our understanding of prostate cancer aetiology and of opportunities for novel therapeutic interventions. Additionally, we demonstrate the added benefit of in-depth functional analyses to triangulate the role of risk proteins in the clinical aggressiveness of prostate tumours. Using these integrated methods, we identify a subset of risk proteins associated with aggressive and early onset disease as priorities for investigation for the future prevention and treatment of prostate cancer.

FUNDING

This work was supported by Cancer Research UK (grant no. C8221/A29017).

Plasma Proteomics of Exercise Blood Pressure and Incident Hypertension

Importance

Blood pressure response during acute exercise (exercise blood pressure [EBP]) is associated with the future risk of hypertension and cardiovascular disease (CVD). Biochemical characterization of EBP could inform disease biology and identify novel biomarkers of future hypertension.

Objective

To identify protein markers associated with EBP and test their association with incident hypertension.

Design, Setting, and Participants

This study assayed 4977 plasma proteins in 681 healthy participants (from 763 assessed) of the Health, Risk Factors, Exercise Training and Genetics (HERITAGE; data collection from January 1993 to December 1997 and plasma proteomics from January 2019 to January 2020) Family Study at rest who underwent 2 cardiopulmonary exercise tests. Individuals were free of CVD at the time of recruitment. Individuals with resting SBP ≥160 mm Hg or DBP ≥100 mm Hg or taking antihypertensive drug therapy were excluded from the study. The association between resting plasma protein levels to both resting BP and EBP was evaluated. Proteins associated with EBP were analyzed for their association with incident hypertension in the Framingham Heart Study (FHS; n = 1177) and validated in the Jackson Heart Study (JHS; n = 772) and Multi-Ethnic Study of Atherosclerosis (MESA; n = 1367). Proteins associated with incident hypertension were tested for putative causal links in approximately 700 000 individuals using cis-protein quantitative loci mendelian randomization (cis-MR). Data were analyzed from January 2023 to January 2024.

Exposures

Plasma proteins.

Main Outcomes and Measures

EBP was defined as the BP response during a fixed workload (50 W) on a cycle ergometer. Hypertension was defined as BP ≥140/90 mm Hg or taking antihypertensive medication.

Results

Among the 681 participants in the HERITAGE Family Study, the mean (SD) age was 34 (13) years; 366 participants (54%) were female; 238 (35%) were self-reported Black and 443 (65%) were self-reported White. Proteomic profiling of EBP revealed 34 proteins that would not have otherwise been identified through profiling of resting BP alone. Transforming growth factor β receptor 3 (TGFBR3) and prostaglandin D2 synthase (PTGDS) had the strongest association with exercise systolic BP (SBP) and diastolic BP (DBP), respectively (TGFBR3: exercise SBP, β estimate, -3.39; 95% CI, -4.79 to -2.00; P = 2.33 × 10-6; PTGDS: exercise DBP β estimate, -2.50; 95% CI, -3.29 to -1.70; P = 1.18 × 10-9). In fully adjusted models, TGFBR3 was inversely associated with incident hypertension in FHS, JHS, and MESA (hazard ratio [HR]: FHS, 0.86; 95% CI, 0.75-0.97; P = .01; JHS, 0.87; 95% CI, 0.77-0.97; P = .02; MESA, 0.84; 95% CI, 0.71-0.98; P = .03; pooled cohort, 0.86; 95% CI, 0.79-0.92; P = 6 × 10-5). Using cis-MR, genetically predicted levels of TGFBR3 were associated with SBP, hypertension, and CVD events (SBP: β, -0.38; 95% CI, -0.64 to -0.11; P = .006; hypertension: odds ratio [OR], 0.99; 95% CI, 0.98-0.99; P < .001; heart failure with hypertension: OR, 0.86; 95% CI, 0.77-0.97; P = .01; CVD: OR, 0.84; 95% CI, 0.77-0.92; P = 8 × 10-5; cerebrovascular events: OR, 0.77; 95% CI, 0.70-0.85; P = 5 × 10-7).

Conclusions and Relevance

Plasma proteomic profiling of EBP identified a novel protein, TGFBR3, which may protect against elevated BP and long-term CVD outcomes.

Genetic drivers and cellular selection of female mosaic X chromosome loss

Mosaic loss of the X chromosome (mLOX) is the most common clonal somatic alteration in leukocytes of female individuals1,2, but little is known about its genetic determinants or phenotypic consequences. Here, to address this, we used data from 883,574 female participants across 8 biobanks; 12% of participants exhibited detectable mLOX in approximately 2% of leukocytes. Female participants with mLOX had an increased risk of myeloid and lymphoid leukaemias. Genetic analyses identified 56 common variants associated with mLOX, implicating genes with roles in chromosomal missegregation, cancer predisposition and autoimmune diseases. Exome-sequence analyses identified rare missense variants in FBXO10 that confer a twofold increased risk of mLOX. Only a small fraction of associations was shared with mosaic Y chromosome loss, suggesting that distinct biological processes drive formation and clonal expansion of sex chromosome missegregation. Allelic shift analyses identified X chromosome alleles that are preferentially retained in mLOX, demonstrating variation at many loci under cellular selection. A polygenic score including 44 allelic shift loci correctly inferred the retained X chromosomes in 80.7% of mLOX cases in the top decile. Our results support a model in which germline variants predispose female individuals to acquiring mLOX, with the allelic content of the X chromosome possibly shaping the magnitude of clonal expansion.

AI-based mining of biomedical literature: Applications for drug repurposing for the treatment of dementia

Neurodegenerative pathologies such as Alzheimer's disease, Parkinson's disease, Huntington's disease, Amyotrophic lateral sclerosis, Multiple sclerosis, HIV-associated neurocognitive disorder, and others significantly affect individuals, their families, caregivers, and healthcare systems. While there are no cures yet, researchers worldwide are actively working on the development of novel treatments that have the potential to slow disease progression, alleviate symptoms, and ultimately improve the overall health of patients. Huge volumes of new scientific information necessitate new analytical approaches for meaningful hypothesis generation. To enable the automatic analysis of biomedical data we introduced AGATHA, an effective AI-based literature mining tool that can navigate massive scientific literature databases, such as PubMed. The overarching goal of this effort is to adapt AGATHA for drug repurposing by revealing hidden connections between FDA-approved medications and a health condition of interest. Our tool converts the abstracts of peer-reviewed papers from PubMed into multidimensional space where each gene and health condition are represented by specific metrics. We implemented advanced statistical analysis to reveal distinct clusters of scientific terms within the virtual space created using AGATHA-calculated parameters for selected health conditions and genes. Partial Least Squares Discriminant Analysis was employed for categorizing and predicting samples (122 diseases and 20889 genes) fitted to specific classes. Advanced statistics were employed to build a discrimination model and extract lists of genes specific to each disease class. Here we focus on drugs that can be repurposed for dementia treatment as an outcome of neurodegenerative diseases. Therefore, we determined dementia-associated genes statistically highly ranked in other disease classes. Additionally, we report a mechanism for detecting genes common to multiple health conditions. These sets of genes were classified based on their presence in biological pathways, aiding in selecting candidates and biological processes that are exploitable with drug repurposing.

Author Summary

This manuscript outlines our project involving the application of AGATHA, an AI-based literature mining tool, to discover drugs with the potential for repurposing in the context of neurocognitive disorders. The primary objective is to identify connections between approved medications and specific health conditions through advanced statistical analysis, including techniques like Partial Least Squares Discriminant Analysis (PLSDA) and unsupervised clustering. The methodology involves grouping scientific terms related to different health conditions and genes, followed by building discrimination models to extract lists of disease-specific genes. These genes are then analyzed through pathway analysis to select candidates for drug repurposing.

Genome-wide association studies (GWAS) and post-GWAS analyses of impulsivity: A systematic review

Impulsivity is related to a host of mental and behavioral problems. It is a complex construct with many different manifestations, most of which are heritable. The genetic compositions of these impulsivity manifestations, however, remain unclear. A number of genome-wide association studies (GWAS) and post-GWAS analyses have tried to address this issue. We conducted a systematic review of all GWAS and post-GWAS analyses of impulsivity published up to December 2023. Available data suggest that single nucleotide polymorphisms (SNPs) in more than a dozen of genes (e.g., CADM2, CTNNA2, GPM6B) are associated with different measures of impulsivity at genome-wide significant levels. Post-GWAS analyses further show that different measures of impulsivity are subject to different degrees of genetic influence, share few genetic variants, and have divergent genetic overlap with basic personality traits such as extroversion and neuroticism, cognitive ability, psychiatric disorders, substance use, and obesity. These findings shed light on controversies in the conceptualization and measurement of impulsivity, while providing new insights on the underlying mechanisms that yoke impulsivity to psychopathology.

Unraveling the role of plasma proteins in dementia: insights from two cohort studies in the UK, with causal evidence from Mendelian randomization

Population-based proteomics offer a groundbreaking avenue to predict dementia onset. This study employed a proteome-wide, data-driven approach to investigate protein-dementia associations in 229 incident all-cause dementia (ACD) among 3,249 participants from the English Longitudinal Study of Ageing (ELSA) over a median 9.8-year follow-up, then validated in 1,506 incident ACD among 52,745 individuals from the UK Biobank (UKB) over median 13.7 years. NEFL and RPS6KB1 were robustly associated with incident ACD; MMP12 was associated with vascular dementia in ELSA. Additional markers EDA2R and KIM1 (HAVCR1) were identified from sensitivity analyses. Combining NEFL and RPS6KB1 with other factors yielded high predictive accuracy (area under the curve (AUC)=0.871) for incident ACD. Replication in the UKB confirmed associations between identified proteins with various dementia subtypes. Results from reverse Mendelian Randomization also supported the role of several proteins as early dementia biomarkers. These findings underscore proteomics' potential in identifying novel risk screening targets for dementia.

Plasma levels of complement components C5 and C9 are associated with thrombin generation

BACKGROUND

The thrombin generation assay (TGA) evaluates the potential of plasma to generate thrombin over time, providing a global picture of an individual's hemostatic balance.

OBJECTIVES

This study aimed to identify novel biological determinants of thrombin generation using a multiomics approach.

METHODS

Associations between TGA parameters and plasma levels of 377 antibodies targeting 236 candidate proteins for cardiovascular risk were tested using multiple linear regression analysis in 770 individuals with venous thrombosis from the Marseille Thrombosis Association (MARTHA) study. Proteins associated with at least 3 TGA parameters were selected for validation in an independent population of 536 healthy individuals (Etablissement Français du Sang Alpes-Méditerranée [EFS-AM]). Proteins with strongest associations in both groups underwent additional genetic analyses and in vitro experiments.

RESULTS

Eighteen proteins were associated (P < 1.33 × 10⁻4) with at least 3 TGA parameters in MARTHA, among which 13 demonstrated a similar pattern of associations in EFS-AM. Complement proteins C5 and C9 had the strongest associations in both groups. Ex vivo supplementation of platelet-poor plasma with purified C9 protein had a significant dose-dependent effect on TGA parameters. No effect was observed with purified C5. Several single nucleotide polymorphisms associated with C5 and C9 plasma levels were identified, with the strongest association for the C5 missense variant rs17611, which was associated with a decrease in C5 levels, endogenous thrombin potential, and peak in MARTHA. No association of this variant with TGA parameters was observed in EFS-AM.

CONCLUSION

This study identified complement proteins C5 and C9 as potential determinants of thrombin generation. Further studies are warranted to establish causality and elucidate the underlying mechanisms.

A genome-wide association study of mass spectrometry proteomics using the Seer Proteograph platform

Genome-wide association studies (GWAS) with proteomics are essential tools for drug discovery. To date, most studies have used affinity proteomics platforms, which have limited discovery to protein panels covered by the available affinity binders. Furthermore, it is not clear to which extent protein epitope changing variants interfere with the detection of protein quantitative trait loci (pQTLs). Mass spectrometry-based (MS) proteomics can overcome some of these limitations. Here we report a GWAS using the MS-based Seer Proteograph™ platform with blood samples from a discovery cohort of 1,260 American participants and a replication in 325 individuals from Asia, with diverse ethnic backgrounds. We analysed 1,980 proteins quantified in at least 80% of the samples, out of 5,753 proteins quantified across the discovery cohort. We identified 252 and replicated 90 pQTLs, where 30 of the replicated pQTLs have not been reported before. We further investigated 200 of the strongest associated cis-pQTLs previously identified using the SOMAscan and the Olink platforms and found that up to one third of the affinity proteomics pQTLs may be affected by epitope effects, while another third were confirmed by MS proteomics to be consistent with the hypothesis that genetic variants induce changes in protein expression. The present study demonstrates the complementarity of the different proteomics approaches and reports pQTLs not accessible to affinity proteomics, suggesting that many more pQTLs remain to be discovered using MS-based platforms.

Novel genetic association between obesity, colorectal cancer, and inflammatory bowel disease

Purpose

Obesity/overweight is an important risk factor for CRC and IBD. The aim of this study was to investigate the role of common genetic factors and haplotypes associated with obesity, CRC and IBD.

Methods

Significant GWAS variants associated with CRC, IBD or obesity were extracted from the GWAS catalog. The common variants between CRC-IBD, CRC-obesity or IBD-obesity were identified. Finally, the haplotypic structure between these diseases was identified, and SNP function analysis, gene-gene expression, protein-protein interactions, gene survival analysis and pathway analysis were performed with the results.

Results

While the results showed several common variants between CRC and IBD, IBD and obesity, and CRC and obesity identified in previous GWAS, rs3184504 was the only common variant for CRC-IBD-obesity (P ≤ 5E-8). The result also identified a haplotypic block AGCAGT (r2 ≥ 0.8 and D'≥0.08) associated with the common variants of CRC-IBD-obesity. These variants are located on the SH2B3 gene, whose expression level decreases in both colon and rectal cancers (P ≤ 1E-3) and which has protein-protein interaction with inflammation- and cancer-associated genes.

Conclusion

The rs3184504 variant and the novel haplotype AGCAGT co-occurred in CRC, IBD, obesity, and inflammation. This novel haplotype could potentially be used in genetic panels to identify CRC/IBD susceptibility in obese patients.

Promises and Challenges of populational Proteomics in Health and Disease

Advances in proteomic assay technologies have significantly increased coverage and throughput, enabling recent increases in the number of large-scale population-based proteomic studies of human plasma and serum. Improvements in multiplexed protein assays have facilitated the quantification of thousands of proteins over a large dynamic range, a key requirement for detecting the lowest-ranging, and potentially the most disease-relevant, blood-circulating proteins. In this perspective, we examine how populational proteomic datasets in conjunction with other concurrent omic measures can be leveraged to better understand the genomic and non-genomic correlates of the soluble proteome, constructing biomarker panels for disease prediction, among others. Mass spectrometry workflows are discussed as they are becoming increasingly competitive with affinity-based array platforms in terms of speed, cost, and proteome coverage due to advances in both instrumentation and workflows. Despite much success, there remain considerable challenges such as orthogonal validation and absolute quantification. We also highlight emergent challenges associated with study design, analytical considerations, and data integration as population-scale studies are run in batches and may involve longitudinal samples collated over many years. Lastly, we take a look at the future of what the nascent next-generation proteomic technologies might provide to the analysis of large sets of blood samples, as well as the difficulties in designing large-scale studies that will likely require participation from multiple and complex funding sources and where data sharing, study designs, and financing must be solved.

KAT8 beyond Acetylation: A Survey of Its Epigenetic Regulation, Genetic Variability, and Implications for Human Health

Lysine acetyltransferase 8, also known as KAT8, is an enzyme involved in epigenetic regulation, primarily recognized for its ability to modulate histone acetylation. This review presents an overview of KAT8, emphasizing its biological functions, which impact many cellular processes and range from chromatin remodeling to genetic and epigenetic regulation. In many model systems, KAT8's acetylation of histone H4 lysine 16 (H4K16) is critical for chromatin structure modification, which influences gene expression, cell proliferation, differentiation, and apoptosis. Furthermore, this review summarizes the observed genetic variability within the KAT8 gene, underscoring the implications of various single nucleotide polymorphisms (SNPs) that affect its functional efficacy and are linked to diverse phenotypic outcomes, ranging from metabolic traits to neurological disorders. Advanced insights into the structural biology of KAT8 reveal its interaction with multiprotein assemblies, such as the male-specific lethal (MSL) and non-specific lethal (NSL) complexes, which regulate a wide range of transcriptional activities and developmental functions. Additionally, this review focuses on KAT8's roles in cellular homeostasis, stem cell identity, DNA damage repair, and immune response, highlighting its potential as a therapeutic target. The implications of KAT8 in health and disease, as evidenced by recent studies, affirm its importance in cellular physiology and human pathology.

Targeted plasma proteomic analysis uncovers a high-performance biomarker panel for early diagnosis of gastric cancer

BACKGROUND

Gastric cancer (GC) is characterized by high morbidity, high mortality and low early diagnosis rate. Early diagnosis plays a crucial role in radically treating GC. The aim of this study was to identify plasma biomarkers for GC and early GC diagnosis.

METHODS

We quantified 369 protein levels with plasma samples from discovery cohort (n = 88) and validation cohort (n = 50) via high-throughput proximity extension assay (PEA) utilizing the Olink-Explore-384-Cardiometabolic panel. The multi-protein signatures were derived from LASSO and Ridge regression models.

RESULTS

In the discovery cohort, 13 proteins (GDF15, ITIH3, BOC, DPP7, EGFR, AMY2A, CCDC80, CD163, GPNMB, LTBP2, CTSZ, CCL18 and NECTIN2) were identified to distinguish GC (Stage I-IV) and early GC (HGIN-I) groups from control group with AUC of 0.994 and AUC of 0.998, severally. The validation cohort yielded AUC of 0.930 and AUC of 0.818 for GC and early GC, respectively.

CONCLUSIONS

This study identified a multi-protein signature with the potential to benefit clinical GC diagnosis, especially for Asian and early GC patients, which may contribute to the development of a less-invasive, convenient, and efficient early screening tool, promoting early diagnosis and treatment of GC and ultimately improving patient survival.

Genetics of environmental sensitivity to psychiatric and neurodevelopmental phenotypes: evidence from GWAS of monozygotic twins

Individual sensitivity to environmental exposures may be genetically influenced. This genotype-by-environment interplay implies differences in phenotypic variance across genotypes. However, environmental sensitivity genetic variants have proven challenging to detect. GWAS of monozygotic twin differences is a family-based variance analysis method, which is more robust to systemic biases that impact population-based methods. We combined data from up to 21,792 monozygotic twins (10,896 pairs) from 11 studies to conduct the largest GWAS meta-analysis of monozygotic phenotypic differences in children and adolescents/adults for seven psychiatric and neurodevelopmental phenotypes: attention deficit hyperactivity disorder (ADHD) symptoms, autistic traits, anxiety and depression symptoms, psychotic-like experiences, neuroticism, and wellbeing. The SNP-heritability of variance in these phenotypes were estimated (h2: 0% to 18%), but were imprecise. We identified a total of 13 genome-wide significant associations (SNP, gene, and gene-set), including genes related to stress-reactivity for depression, growth factor-related genes for autistic traits and catecholamine uptake-related genes for psychotic-like experiences. Monozygotic twins are an important new source of evidence about the genetics of environmental sensitivity.

SharePro: an accurate and efficient genetic colocalization method accounting for multiple causal signals

MOTIVATION

Colocalization analysis is commonly used to assess whether two or more traits share the same genetic signals identified in genome-wide association studies (GWAS), and is important for prioritizing targets for functional follow-up of GWAS results. Existing colocalization methods can have suboptimal performance when there are multiple causal variants in one genomic locus.

RESULTS

We propose SharePro to extend the COLOC framework for colocalization analysis. SharePro integrates linkage disequilibrium (LD) modeling and colocalization assessment by grouping correlated variants into effect groups. With an efficient variational inference algorithm, posterior colocalization probabilities can be accurately estimated. In simulation studies, SharePro demonstrated increased power with a well-controlled false positive rate at a low computational cost. Compared to existing methods, SharePro provided stronger and more consistent colocalization evidence for known lipid-lowering drug target proteins and their corresponding lipid traits. Through an additional challenging case of the colocalization analysis of the circulating abundance of R-spondin 3 GWAS and estimated bone mineral density GWAS, we demonstrated the utility of SharePro in identifying biologically plausible colocalized signals.

AVAILABILITY AND IMPLEMENTATION

SharePro for colocalization analysis is written in Python and openly available at https://github.com/zhwm/SharePro_coloc.

Identification of Circulating Inflammatory Proteins Associated with Calcific Aortic Valve Stenosis by Multiplex Analysis

Calcific aortic valve stenosis (CAVS) is characterized by increasing inflammation and progressive calcification in the aortic valve leaflets and is a major cause of death in the aging population. This study aimed to identify the inflammatory proteins involved in CAVS and provide potential therapeutic targets. We investigated the observational and causal associations of 92 inflammatory proteins, which were measured using affinity-based proteomic assays. Firstly, the case-control cohort identified differential proteins associated with the occurrence and progression of CAVS. Subsequently, we delved into exploring the causal impacts of these associated proteins through Mendelian randomization. This involved utilizing genetic instruments derived from cis-protein quantitative loci identified in genome-wide association studies, encompassing a cohort of over 400,000 individuals. Finally, we investigated the gene transcription and protein expression levels of inflammatory proteins by single-cell and immunohistochemistry analysis. Multivariate logistic regression and spearman's correlation analysis showed that five proteins showed a significant positive correlation with disease severity. Mendelian randomization showed that elevated levels of two proteins, namely, matrix metallopeptidase-1 (MMP1) and sirtuin 2 (SIRT2), were associated with an increased risk of CAVS. Immunohistochemistry and single-cell transcriptomes showed that expression levels of MMP1 and SIRT2 at the tissue and cell levels were significantly higher in calcified valves than in non-calcified control valves. These findings indicate that MMP1 and SIRT2 are causally related to CAVS and open up the possibility for identifying novel therapeutic targets.

Harnessing the power of proteomics in precision diabetes medicine

Precision diabetes medicine (PDM) aims to reduce errors in prevention programmes, diagnosis thresholds, prognosis prediction and treatment strategies. However, its advancement and implementation are difficult due to the heterogeneity of complex molecular processes and environmental exposures that influence an individual's disease trajectory. To address this challenge, it is imperative to develop robust screening methods for all areas of PDM. Innovative proteomic technologies, alongside genomics, have proven effective in precision cancer medicine and are showing promise in diabetes research for potential translation. This narrative review highlights how proteomics is well-positioned to help improve PDM. Specifically, a critical assessment of widely adopted affinity-based proteomic technologies in large-scale clinical studies and evidence of the benefits and feasibility of using MS-based plasma proteomics is presented. We also present a case for the use of proteomics to identify predictive protein panels for type 2 diabetes subtyping and the development of clinical prediction models for prevention, diagnosis, prognosis and treatment strategies. Lastly, we discuss the importance of plasma and tissue proteomics and its integration with genomics (proteogenomics) for identifying unique type 2 diabetes intra- and inter-subtype aetiology. We conclude with a call for action formed on advancing proteomics technologies, benchmarking their performance and standardisation across sites, with an emphasis on data sharing and the inclusion of diverse ancestries in large cohort studies. These efforts should foster collaboration with key stakeholders and align with ongoing academic programmes such as the Precision Medicine in Diabetes Initiative consortium.

Genetic assessment of efficacy and safety profiles of coagulation cascade proteins identifies Factors II and XI as actionable anticoagulant targets

Aims

Anticoagulants are routinely used by millions of patients worldwide to prevent blood clots. Yet, problems with anticoagulant therapy remain, including a persistent and cumulative bleeding risk in patients undergoing prolonged anticoagulation. New safer anticoagulant targets are needed.

Methods and results

To prioritize anticoagulant targets with the strongest efficacy [venous thromboembolism (VTE) prevention] and safety (low bleeding risk) profiles, we performed two-sample Mendelian randomization and genetic colocalization. We leveraged three large-scale plasma protein data sets (deCODE as discovery data set and Fenland and Atherosclerosis Risk in Communities as replication data sets] and one liver gene expression data set (Institut Universitaire de Cardiologie et de Pneumologie de Québec bariatric biobank) to evaluate evidence for a causal effect of 26 coagulation cascade proteins on VTE from a new genome-wide association meta-analysis of 44 232 VTE cases and 847 152 controls, stroke subtypes, bleeding outcomes, and parental lifespan as an overall measure of efficacy/safety ratio. A 1 SD genetically predicted reduction in F2 blood levels was associated with lower risk of VTE [odds ratio (OR) = 0.44, 95% confidence interval (CI) = 0.38-0.51, P = 2.6e-28] and cardioembolic stroke risk (OR = 0.55, 95% CI = 0.39-0.76, P = 4.2e-04) but not with bleeding (OR = 1.13, 95% CI = 0.93-1.36, P = 2.2e-01). Genetically predicted F11 reduction was associated with lower risk of VTE (OR = 0.61, 95% CI = 0.58-0.64, P = 4.1e-85) and cardioembolic stroke (OR = 0.77, 95% CI = 0.69-0.86, P = 4.1e-06) but not with bleeding (OR = 1.01, 95% CI = 0.95-1.08, P = 7.5e-01). These Mendelian randomization associations were concordant across the three blood protein data sets and the hepatic gene expression data set as well as colocalization analyses.

Conclusion

These results provide strong genetic evidence that F2 and F11 may represent safe and efficacious therapeutic targets to prevent VTE and cardioembolic strokes without substantially increasing bleeding risk.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

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

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

Introduction

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

Methods

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

Results

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

Conclusion

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

TOPMed imputed genomics enhances genomic atlas of the human proteome in brain, cerebrospinal fluid, and plasma

Comprehensive expression quantitative trait loci studies have been instrumental for understanding tissue-specific gene regulation and pinpointing functional genes for disease-associated loci in a tissue-specific manner. Compared to gene expressions, proteins more directly affect various biological processes, often dysregulated in disease, and are important drug targets. We previously performed and identified tissue-specific protein quantitative trait loci in brain, cerebrospinal fluid, and plasma. We now enhance this work by analyzing more proteins (1,300 versus 1,079) and an almost twofold increase in high quality imputed genetic variants (8.4 million versus 4.4 million) by using TOPMed reference panel. We identified 38 genomic regions associated with 43 proteins in brain, 150 regions associated with 247 proteins in cerebrospinal fluid, and 95 regions associated with 145 proteins in plasma. Compared to our previous study, this study newly identified 12 loci in brain, 30 loci in cerebrospinal fluid, and 22 loci in plasma. Our improved genomic atlas uncovers the genetic control of protein regulation across multiple tissues. These resources are accessible through the Online Neurodegenerative Trait Integrative Multi-Omics Explorer for use by the scientific community.

The broad impact of cell death genes on the human disease phenome

Cell death mediated by genetically defined signaling pathways influences the health and dynamics of all tissues, however the tissue specificity of cell death pathways and the relationships between these pathways and human disease are not well understood. We analyzed the expression profiles of an array of 44 cell death genes involved in apoptosis, necroptosis, and pyroptosis cell death pathways across 49 human tissues from GTEx, to elucidate the landscape of cell death gene expression across human tissues, and the relationship between tissue-specific genetically determined expression and the human phenome. We uncovered unique cell death gene expression profiles across tissue types, suggesting there are physiologically distinct cell death programs in different tissues. Using summary statistics-based transcriptome wide association studies (TWAS) on human traits in the UK Biobank (n ~ 500,000), we evaluated 513 traits encompassing ICD-10 defined diagnoses and laboratory-derived traits. Our analysis revealed hundreds of significant (FDR < 0.05) associations between genetically regulated cell death gene expression and an array of human phenotypes encompassing both clinical diagnoses and hematologic parameters, which were independently validated in another large-scale DNA biobank (BioVU) at Vanderbilt University Medical Center (n = 94,474) with matching phenotypes. Cell death genes were highly enriched for significant associations with blood traits versus non-cell-death genes, with apoptosis-associated genes enriched for leukocyte and platelet traits. Our findings are also concordant with independently published studies (e.g. associations between BCL2L11/BIM expression and platelet & lymphocyte counts). Overall, these results suggest that cell death genes play distinct roles in their contribution to human phenotypes, and that cell death genes influence a diverse array of human traits.

Identification of prospective aging drug targets via Mendelian randomization analysis

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

Genetic determinants of plasma protein levels in the Estonian population

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

Drug target Mendelian randomization supports apolipoprotein C3-lowering for lipoprotein-lipid levels reductions and cardiovascular diseases prevention

BACKGROUND AND AIMS

Inhibitors of apolipoprotein C-III (apoC3) are currently approved for the reduction of triglyceride levels in patients with Familial Chylomicronemia Syndrome. We used drug target Mendelian randomization (MR) to assess the effect of genetically predicted decrease in apoC3 blood protein levels on cardiometabolic traits and diseases.

METHODS

We quantified lifelong reductions in apoC3 blood levels by selecting all genome wide significant and independent (r2<0.1) single nucleotide polymorphisms (SNPs) in the APOC3 gene region ±1 Mb, from three genome-wide association studies (GWAS) of apoC3 blood protein levels (deCODE, n = 35,378, Fenland, n = 10,708 and ARIC, n = 7213). We included the largest GWASes on 18 cardiometabolic traits and 9 cardiometabolic diseases as study outcomes.

RESULTS

A one standard deviation lowering in apoC3 blood protein levels was associated with lower triglycerides, apolipoprotein B, low-density lipoprotein cholesterol, alanine aminotransferase, and glomerular filtration rate as well as higher high-density lipoprotein cholesterol levels. ApoC3 lowering was also associated with lower risk of acute pancreatitis (odds ratio [OR] = 0.91 95% CI = 0.82 to 1.00), aortic stenosis (OR = 0.82 95% CI = 0.73 to 0.93), and coronary artery disease (OR = 0.86 95% CI = 0.80 to 0.93), and was associated with increased parental lifespan (0.06 95% CI = 0.03-0.09 years). These results were concordant across robust MR methods, the three protein datasets and upon adjustment for APOA1, APOA4 and APOA5 using a multivariable MR framework.

CONCLUSIONS

These results provide evidence that apoC3 lowering could result in widespread benefits for cardiometabolic health and encourage the launch of trials on apoC3 inhibition for coronary artery disease prevention.

Systemic proteome adaptions to 7-day complete caloric restriction in humans

Surviving long periods without food has shaped human evolution. In ancient and modern societies, prolonged fasting was/is practiced by billions of people globally for religious purposes, used to treat diseases such as epilepsy, and recently gained popularity as weight loss intervention, but we still have a very limited understanding of the systemic adaptions in humans to extreme caloric restriction of different durations. Here we show that a 7-day water-only fast leads to an average weight loss of 5.7 kg (±0.8 kg) among 12 volunteers (5 women, 7 men). We demonstrate nine distinct proteomic response profiles, with systemic changes evident only after 3 days of complete calorie restriction based on in-depth characterization of the temporal trajectories of ~3,000 plasma proteins measured before, daily during, and after fasting. The multi-organ response to complete caloric restriction shows distinct effects of fasting duration and weight loss and is remarkably conserved across volunteers with >1,000 significantly responding proteins. The fasting signature is strongly enriched for extracellular matrix proteins from various body sites, demonstrating profound non-metabolic adaptions, including extreme changes in the brain-specific extracellular matrix protein tenascin-R. Using proteogenomic approaches, we estimate the health consequences for 212 proteins that change during fasting across ~500 outcomes and identified putative beneficial (SWAP70 and rheumatoid arthritis or HYOU1 and heart disease), as well as adverse effects. Our results advance our understanding of prolonged fasting in humans beyond a merely energy-centric adaptions towards a systemic response that can inform targeted therapeutic modulation.

Phenomewide Association Study of Health Outcomes Associated With the Genetic Correlates of 25 Hydroxyvitamin D Concentration and Vitamin D Binding Protein Concentration

While it is known that vitamin D deficiency is associated with adverse bone outcomes, it remains unclear whether low vitamin D status may increase the risk of a wider range of health outcomes. We had the opportunity to explore the association between common genetic variants associated with both 25 hydroxyvitamin D (25OHD) and the vitamin D binding protein (DBP, encoded by the GC gene) with a comprehensive range of health disorders and laboratory tests in a large academic medical center. We used summary statistics for 25OHD and DBP to generate polygenic scores (PGS) for 66,482 participants with primarily European ancestry and 13,285 participants with primarily African ancestry from the Vanderbilt University Medical Center Biobank (BioVU). We examined the predictive properties of PGS25OHD, and two scores related to DBP concentration with respect to 1322 health-related phenotypes and 315 laboratory-measured phenotypes from electronic health records. In those with European ancestry: (a) the PGS25OHD and PGSDBP scores, and individual SNPs rs4588 and rs7041 were associated with both 25OHD concentration and 1,25 dihydroxyvitamin D concentrations; (b) higher PGS25OHD was associated with decreased concentrations of triglycerides and cholesterol, and reduced risks of vitamin D deficiency, disorders of lipid metabolism, and diabetes. In general, the findings for the African ancestry group were consistent with findings from the European ancestry analyses. Our study confirms the utility of PGS and two key variants within the GC gene (rs4588 and rs7041) to predict the risk of vitamin D deficiency in clinical settings and highlights the shared biology between vitamin D-related genetic pathways a range of health outcomes.

Large-scale circulating proteome association study (CPAS) meta-analysis identifies circulating proteins and pathways predicting incident hip fractures

Hip fractures are associated with significant disability, high cost, and mortality. However, the exact biological mechanisms underlying susceptibility to hip fractures remain incompletely understood. In an exploratory search of the underlying biology as reflected through the circulating proteome, we performed a comprehensive Circulating Proteome Association Study (CPAS) meta-analysis for incident hip fractures. Analyses included 6430 subjects from two prospective cohort studies (Cardiovascular Health Study and Trøndelag Health Study) with circulating proteomics data (aptamer-based 5 K SomaScan version 4.0 assay; 4979 aptamers). Associations between circulating protein levels and incident hip fractures were estimated for each cohort using age and sex-adjusted Cox regression models. Participants experienced 643 incident hip fractures. Compared with the individual studies, inverse-variance weighted meta-analyses yielded more statistically significant associations, identifying 23 aptamers associated with incident hip fractures (conservative Bonferroni correction 0.05/4979, P < 1.0 × 10-5). The aptamers most strongly associated with hip fracture risk corresponded to two proteins of the growth hormone/insulin growth factor system (GHR and IGFBP2), as well as GDF15 and EGFR. High levels of several inflammation-related proteins (CD14, CXCL12, MMP12, ITIH3) were also associated with increased hip fracture risk. Ingenuity pathway analysis identified reduced LXR/RXR activation and increased acute phase response signaling to be overrepresented among those proteins associated with increased hip fracture risk. These analyses identified several circulating proteins and pathways consistently associated with incident hip fractures. These findings underscore the usefulness of the meta-analytic approach for comprehensive CPAS in a similar manner as has previously been observed for large-scale human genetic studies. Future studies should investigate the underlying biology of these potential novel drug targets.

Proteome-wide Mendelian randomization identifies therapeutic targets for ankylosing spondylitis

Background

Ankylosing Spondylitis (AS) is a chronic inflammatory disorder which can lead to considerable pain and disability. Mendelian randomization (MR) has been extensively applied for repurposing licensed drugs and uncovering new therapeutic targets. Our objective is to pinpoint innovative therapeutic protein targets for AS and assess the potential adverse effects of druggable proteins.

Methods

We conducted a comprehensive proteome-wide MR study to assess the causal relationships between plasma proteins and the risk of AS. The plasma proteins were sourced from the UK Biobank Pharma Proteomics Project (UKB-PPP) database, encompassing GWAS data for 2,940 plasma proteins. Additionally, GWAS data for AS were extracted from the R9 version of the Finnish database, including 2,860 patients and 270,964 controls. The colocalization analysis was executed to identify shared causal variants between plasma proteins and AS. Finally, we examined the potential adverse effects of druggable proteins for AS therapy by conducting a phenome-wide association study (PheWAS) utilizing the extensive Finnish database in version R9, encompassing 2,272 phenotypes categorized into 46 groups.

Results

The findings revealed a positive genetic association between the predicted plasma levels of six proteins and an elevated risk of AS, while two proteins exhibited an inverse association with AS risk (P fdr < 0.05). Among these eight plasma proteins, colocalization analysis identified AIF1, TNF, FKBPL, AGER, ALDH5A1, and ACOT13 as shared variation with AS(PPH3+PPH4>0.8), suggesting that they represent potential direct targets for AS intervention. Further phenotype-wide association studies have shown some potential side effects of these six targets (P fdr < 0.05).

Conclusion

Our investigation examined the causal connections between six plasma proteins and AS, providing a comprehensive understanding of potential therapeutic targets.

Genetic imputation of kidney transcriptome, proteome and multi-omics illuminates new blood pressure and hypertension targets

Genetic mechanisms of blood pressure (BP) regulation remain poorly defined. Using kidney-specific epigenomic annotations and 3D genome information we generated and validated gene expression prediction models for the purpose of transcriptome-wide association studies in 700 human kidneys. We identified 889 kidney genes associated with BP of which 399 were prioritised as contributors to BP regulation. Imputation of kidney proteome and microRNAome uncovered 97 renal proteins and 11 miRNAs associated with BP. Integration with plasma proteomics and metabolomics illuminated circulating levels of myo-inositol, 4-guanidinobutanoate and angiotensinogen as downstream effectors of several kidney BP genes (SLC5A11, AGMAT, AGT, respectively). We showed that genetically determined reduction in renal expression may mimic the effects of rare loss-of-function variants on kidney mRNA/protein and lead to an increase in BP (e.g., ENPEP). We demonstrated a strong correlation (r = 0.81) in expression of protein-coding genes between cells harvested from urine and the kidney highlighting a diagnostic potential of urinary cell transcriptomics. We uncovered adenylyl cyclase activators as a repurposing opportunity for hypertension and illustrated examples of BP-elevating effects of anticancer drugs (e.g. tubulin polymerisation inhibitors). Collectively, our studies provide new biological insights into genetic regulation of BP with potential to drive clinical translation in hypertension.

Genetically Determined Circulating Lactase/Phlorizin Hydrolase Concentrations and Risk of Colorectal Cancer: A Two-Sample Mendelian Randomization Study

Previous research has found that milk is associated with a decreased risk of colorectal cancer (CRC). However, it is unclear whether the milk digestion by the enzyme lactase-phlorizin hydrolase (LPH) plays a role in CRC susceptibility. Our study aims to investigate the direct causal relationship of CRC risk with LPH levels by applying a two-sample Mendelian Randomization (MR) strategy. Genetic instruments for LPH were derived from the Fenland Study, and CRC-associated summary statistics for these instruments were extracted from the FinnGen Study, PLCO Atlas Project, and Pan-UK Biobank. Primary MR analyses focused on a cis-variant (rs4988235) for LPH levels, with results integrated via meta-analysis. MR analyses using all variants were also undertaken. This analytical approach was further extended to assess CRC subtypes (colon and rectal). Meta-analysis across the three datasets illustrated an inverse association between genetically predicted LPH levels and CRC risk (OR: 0.92 [95% CI, 0.89-0.95]). Subtype analyses revealed associations of elevated LPH levels with reduced risks for both colon (OR: 0.92 [95% CI, 0.89-0.96]) and rectal cancer (OR: 0.92 [95% CI, 0.87, 0.98]). Consistency was observed across varied analytical methods and datasets. Further exploration is warranted to unveil the underlying mechanisms and validate LPH's potential role in CRC prevention.

Investigation of the acute pathogenesis of spondyloarthritis/HLA-B27-associated anterior uveitis based on genome-wide association analysis and single-cell transcriptomics

BACKGROUND

Patients with spondyloarthritis (SpA)/HLA-B27-associated acute anterior uveitis (AAU) experience recurring acute flares, which pose significant visual and financial challenges. Despite established links between SpA and HLA-B27-associated AAU, the exact mechanism involved remains unclear, and further understanding is needed for effective prevention and treatment.

METHODS

To investigate the acute pathogenesis of SpA/HLA-B27-associated AAU, Mendelian randomization (MR) and single-cell transcriptomic analyses were employed. The MR incorporated publicly available protein quantitative trait locus data from previous studies, along with genome-wide association study data from public databases. Causal relationships between plasma proteins and anterior uveitis were assessed using two-sample MR. Additionally, colocalization analysis was performed using Bayesian colocalization. Single-cell transcriptome analysis utilized the anterior uveitis dataset from the Gene Expression Omnibus (GEO) database. Dimensionality reduction, clustering, transcription factor analysis, pseudotime analysis, and cell communication analysis were subsequently conducted to explore the underlying mechanisms involved.

RESULTS

Mendelian randomization analysis revealed that circulating levels of AIF1 and VARS were significantly associated with a reduced risk of developing SpA/HLA-B27-associated AAU, with AIF1 showing a robust correlation with anterior uveitis onset. Colocalization analysis supported these findings. Single-cell transcriptome analysis showed predominant AIF1 expression in myeloid cells, which was notably lower in the HLA-B27-positive group. Pseudotime analysis revealed dendritic cell terminal positions in differentiation branches, accompanied by gradual decreases in AIF1 expression. Based on cell communication analysis, CD141+CLEC9A+ classic dendritic cells (cDCs) and the APP pathway play crucial roles in cellular communication in the Spa/HLA-B27 group.

CONCLUSIONS

AIF1 is essential for the pathogenesis of SpA/HLA-B27-associated AAU. Myeloid cell differentiation into DCs and decreased AIF1 levels are also pivotal in this process.

Proteomic Associations of Adverse Outcomes in Human Heart Failure

BACKGROUND

Identifying novel molecular drivers of disease progression in heart failure (HF) is a high-priority goal that may provide new therapeutic targets to improve patient outcomes. The authors investigated the relationship between plasma proteins and adverse outcomes in HF and their putative causal role using Mendelian randomization.

METHODS AND RESULTS

The authors measured 4776 plasma proteins among 1964 participants with HF with a reduced left ventricular ejection fraction enrolled in PHFS (Penn Heart Failure Study). Assessed were the observational relationship between plasma proteins and (1) all-cause death or (2) death or HF-related hospital admission (DHFA). The authors replicated nominally significant associations in the Washington University HF registry (N=1080). Proteins significantly associated with outcomes were the subject of 2-sample Mendelian randomization and colocalization analyses. After correction for multiple testing, 243 and 126 proteins were found to be significantly associated with death and DHFA, respectively. These included small ubiquitin-like modifier 2 (standardized hazard ratio [sHR], 1.56; P<0.0001), growth differentiation factor-15 (sHR, 1.68; P<0.0001) for death, A disintegrin and metalloproteinase with thrombospondin motifs-like protein (sHR, 1.40; P<0.0001), and pulmonary-associated surfactant protein C (sHR, 1.24; P<0.0001) for DHFA. In pathway analyses, top canonical pathways associated with death and DHFA included fibrotic, inflammatory, and coagulation pathways. Genomic analyses provided evidence of nominally significant associations between levels of 6 genetically predicted proteins with DHFA and 11 genetically predicted proteins with death.

CONCLUSIONS

This study implicates multiple novel proteins in HF and provides preliminary evidence of associations between genetically predicted plasma levels of 17 candidate proteins and the risk for adverse outcomes in human HF.

Genetic inhibition of angiopoietin-like protein-3, lipids, and cardiometabolic risk

BACKGROUND AND AIMS

RNA-based, antibody-based, and genome editing-based therapies are currently under investigation to determine if the inhibition of angiopoietin-like protein-3 (ANGPTL3) could reduce lipoprotein-lipid levels and atherosclerotic cardiovascular disease (ASCVD) risk. Mendelian randomisation (MR) was used to determine whether genetic variations influencing ANGPTL3 liver gene expression, blood levels, and protein structure could causally influence triglyceride and apolipoprotein B (apoB) levels as well as coronary artery disease (CAD), ischaemic stroke (IS), and other cardiometabolic diseases.

METHODS

RNA sequencing of 246 explanted liver samples and genome-wide genotyping was performed to identify single-nucleotide polymorphisms (SNPs) associated with liver expression of ANGPTL3. Genome-wide summary statistics of plasma protein levels of ANGPTL3 from the deCODE study (n = 35 359) were used. A total of 647 carriers of ANGPTL3 protein-truncating variants (PTVs) associated with lower plasma triglyceride levels were identified in the UK Biobank. Two-sample MR using SNPs that influence ANGPTL3 liver expression or ANGPTL3 plasma protein levels as exposure and cardiometabolic diseases as outcomes was performed (CAD, IS, heart failure, non-alcoholic fatty liver disease, acute pancreatitis, and type 2 diabetes). The impact of rare PTVs influencing plasma triglyceride levels on apoB levels and CAD was also investigated in the UK Biobank.

RESULTS

In two-sample MR studies, common genetic variants influencing ANGPTL3 hepatic or blood expression levels of ANGPTL3 had a very strong effect on plasma triglyceride levels, a more modest effect on low-density lipoprotein cholesterol, a weaker effect on apoB levels, and no effect on CAD or other cardiometabolic diseases. In the UK Biobank, the carriers of rare ANGPTL3 PTVs providing lifelong reductions in median plasma triglyceride levels [-0.37 (interquartile range 0.41) mmol/L] had slightly lower apoB levels (-0.06 ± 0.32 g/L) and similar CAD event rates compared with non-carriers (10.2% vs. 10.9% in carriers vs. non-carriers, P = .60).

CONCLUSIONS

PTVs influencing ANGPTL3 protein structure as well as common genetic variants influencing ANGPTL3 hepatic expression and/or blood protein levels exhibit a strong effect on circulating plasma triglyceride levels, a weak effect on circulating apoB levels, and no effect on ASCVD. Near-complete inhibition of ANGPTL3 function in patients with very elevated apoB levels may be required to reduce ASCVD risk.

Proteomic insights into modifiable risk of venous thromboembolism and cardiovascular comorbidities

BACKGROUND

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSION

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

Oxonium ion scanning mass spectrometry for large-scale plasma glycoproteomics

Protein glycosylation, a complex and heterogeneous post-translational modification that is frequently dysregulated in disease, has been difficult to analyse at scale. Here we report a data-independent acquisition technique for the large-scale mass-spectrometric quantification of glycopeptides in plasma samples. The technique, which we named 'OxoScan-MS', identifies oxonium ions as glycopeptide fragments and exploits a sliding-quadrupole dimension to generate comprehensive and untargeted oxonium ion maps of precursor masses assigned to fragment ions from non-enriched plasma samples. By applying OxoScan-MS to quantify 1,002 glycopeptide features in the plasma glycoproteomes from patients with COVID-19 and healthy controls, we found that severe COVID-19 induces differential glycosylation in IgA, haptoglobin, transferrin and other disease-relevant plasma glycoproteins. OxoScan-MS may allow for the quantitative mapping of glycoproteomes at the scale of hundreds to thousands of samples.

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

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

Identification of gene biomarkers for brain diseases via multi-network topological semantics extraction and graph convolutional network

BACKGROUND

Brain diseases pose a significant threat to human health, and various network-based methods have been proposed for identifying gene biomarkers associated with these diseases. However, the brain is a complex system, and extracting topological semantics from different brain networks is necessary yet challenging to identify pathogenic genes for brain diseases.

RESULTS

In this study, we present a multi-network representation learning framework called M-GBBD for the identification of gene biomarker in brain diseases. Specifically, we collected multi-omics data to construct eleven networks from different perspectives. M-GBBD extracts the spatial distributions of features from these networks and iteratively optimizes them using Kullback-Leibler divergence to fuse the networks into a common semantic space that represents the gene network for the brain. Subsequently, a graph consisting of both gene and large-scale disease proximity networks learns representations through graph convolution techniques and predicts whether a gene is associated which brain diseases while providing associated scores. Experimental results demonstrate that M-GBBD outperforms several baseline methods. Furthermore, our analysis supported by bioinformatics revealed CAMP as a significantly associated gene with Alzheimer's disease identified by M-GBBD.

CONCLUSION

Collectively, M-GBBD provides valuable insights into identifying gene biomarkers for brain diseases and serves as a promising framework for brain networks representation learning.

Sex-biased genetic regulation of inflammatory proteins in the Dutch population

BACKGROUND

Significant differences in immune responses, prevalence or susceptibility of diseases and treatment responses have been described between males and females. Despite this, sex-differentiation analysis of the genetic architecture of inflammatory proteins is largely unexplored. We performed sex-stratified meta-analysis after protein quantitative trait loci (pQTL) mapping using inflammatory biomarkers profiled using targeted proteomics (Olink inflammatory panel) of two population-based cohorts of Europeans.

RESULTS

Even though, around 67% of the pQTLs demonstrated shared effect between sexes, colocalization analysis identified two loci in the males (LINC01135 and ITGAV) and three loci (CNOT10, SRD5A2, and LILRB5) in the females with evidence of sex-dependent modulation by pQTL variants. Furthermore, we identified pathways with relevant functions in the sex-biased pQTL variants. We also showed through cross-validation that the sex-specific pQTLs are linked with sex-specific phenotypic traits.

CONCLUSION

Our study demonstrates the relevance of genetic sex-stratified analysis in the context of genetic dissection of protein abundances among individuals and reveals that, sex-specific pQTLs might mediate sex-linked phenotypes. Identification of sex-specific pQTLs associated with sex-biased diseases can help realize the promise of individualized treatment.

Identifying novel proteins for suicide attempt by integrating proteomes from brain and blood with genome-wide association data

Genome-wide association studies (GWASs) have identified risk loci for suicide attempt (SA), but deciphering how they confer risk for SA remains largely unknown. This study aims to identify the key proteins and gain insights into SA pathogenesis. We integrated data from the brain proteome (N = 376) and blood proteome (N = 35,559) and combined it with the largest SA GWAS summary statistics to date (N = 518,612). A comprehensive set of methods was employed, including Mendelian randomization (MR), Steiger filtering, Bayesian colocalization, proteome‑wide association studies (PWAS), transcript-levels, cell-type specificity, correlation, and protein-protein interaction (PPI) network analysis. Validation was performed using other protein datasets and the SA dataset from FinnGen study. We identified ten proteins (GLRX5, GMPPB, B3GALTL, FUCA2, TTLL12, ADCK1, MMAA, HIBADH, ACP1, DOC2A) associated with SA in brain proteomics. GLRX5, GMPPB, and FUCA2 showed strong colocalization evidence and were supported by PWAS and transcript-level analysis, and were predominantly expressed in glutamatergic neuronal cells. In blood proteomics, one significant protein (PEAR1) and three near-significant proteins (NDE1, EVA1C, B4GALT2) were identified, but lacked colocalization evidence. Moreover, despite the limited correlation between the same protein in brain and blood, the PPI network analysis provided new insights into the interaction between brain and blood in SA. Furthermore, GLRX5 was associated with the GSTP1, the target of Clozapine. The comprehensive analysis provides strong evidence supporting a causal association between three genetically determined brain proteins (GLRX5, GMPPB, and FUCA2) with SA. These findings offer valuable insights into SA's underlying mechanisms and potential therapeutic approaches.

Nanoparticle enrichment mass-spectrometry proteomics identifies protein-altering variants for precise pQTL mapping

Proteogenomics studies generate hypotheses on protein function and provide genetic evidence for drug target prioritization. Most previous work has been conducted using affinity-based proteomics approaches. These technologies face challenges, such as uncertainty regarding target identity, non-specific binding, and handling of variants that affect epitope affinity binding. Mass spectrometry-based proteomics can overcome some of these challenges. Here we report a pQTL study using the Proteograph™ Product Suite workflow (Seer, Inc.) where we quantify over 18,000 unique peptides from nearly 3000 proteins in more than 320 blood samples from a multi-ethnic cohort in a bottom-up, peptide-centric, mass spectrometry-based proteomics approach. We identify 184 protein-altering variants in 137 genes that are significantly associated with their corresponding variant peptides, confirming target specificity of co-associated affinity binders, identifying putatively causal cis-encoded proteins and providing experimental evidence for their presence in blood, including proteins that may be inaccessible to affinity-based proteomics.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Prognostic and predictive value of angiogenesis-associated serum proteins for immunotherapy in esophageal cancer

BACKGROUND

Immune checkpoint inhibitors (ICIs) have significantly improved patient survival in multiple cancers. However, therapy response in esophageal cancer is limited to subgroups of patients and clinically useful predictive biomarkers are lacking.

METHODS

We collected a series of plasma samples from 91 patients with esophageal cancer before and after ICI treatment. The Olink Immuno-Oncology panel (92 proteins) with proximity extension assays was used to detect the dynamic changes in plasma and potential biomarkers associated with treatment outcomes. We screened all survival-related proteins and established a risk score model to better predict the prognosis and treatment response in patients with esophageal cancer immunotherapy.

RESULTS

We found that 47 out of 92 quantified proteins had significant changes in plasma levels during ICI treatment (p<0.050), and these changed proteins were involved in immune-related reactions, such as intercellular adhesion and T-cell activation. Notably, the baseline levels of three angiogenesis-related proteins (IL-8, TIE2, and HGF) were significantly associated with the survival outcomes of patients treated with ICIs (p<0.050). According to these prognostic proteins, we established an angiogenesis-related risk score, which could be a superior biomarker for ICI response prediction. In addition, antiangiogenic therapy combined with ICIs significantly improved overall survival compared with ICI monotherapy (p=0.044).

CONCLUSIONS

An angiogenesis-related risk score based on three proteins (IL-8, TIE2, and HGF) could predict ICI response and prognosis in patients with esophageal cancer, which warrants verification in the future. Our study highlights the potential application of combining ICIs and antiangiogenic therapy and supports Olink plasma protein sequencing as a liquid biopsy method for biomarker exploration.