Proteome-wide mendelian randomization identifies causal plasma proteins in venous thromboembolism development

Whole genome sequencing identifies pathogenic genetic variants in Han Chinese patients with familial venous thromboembolism

Genetic factors play a pivotal role in determining venous thromboembolism (VTE) risk, particularly in cases of unprovoked early-onset VTE and those with a family history. While genome-wide association studies (GWAS) has advanced our understanding, high-quality whole-genome sequencing (WGS) from family-based studies is essential to elucidate the role of rare variants. In this study, we performed WGS on 216 individuals from 35 Han Chinese VTE pedigrees and validated findings in 99 high-heritability VTE cases using whole-exome sequencing. Functional impact was assessed via qPCR and Western Blot in HEK293T cells. Classical genes explained partial familial inheritance (20/35), while non-classical genes showed comparable effects on VTE recurrence and CTEPH. From 36 rare variants, 34 pedigrees (97%) were interpreted, with 29 variants reported for the first time. Notably, three novel variants, GP6 (c.G1094A:p.R365H), TET2 (c.G3451T:p.E1151X), and JAK2 (c.G380A:p.G127D), shared in two unrelated pedigrees each and are classified as low frequency in East Asians. Functional analyses revealed significant changes in GP6 and TET2 expression compared to the wild type. These findings provide novel insights into the genetic architecture of VTE and highlight GP6, TET2, and JAK2 as potential risk factors in East Asian populations, underscoring the clinical relevance of rare variants in VTE pathogenesis.

Identification of novel IL17-related genes as prognostic and therapeutic biomarkers of psoriasis using comprehensive bioinformatics analysis and machine learning

Psoriasis is a common chronic skin disorder with a polygenic background. It is widely acknowledged that Th17/IL-17A axis plays a key role in the pathogenesis of psoriasis. However, numerous regulatory genes upstream of the pathway remain undiscovered, creating a knowledge gap in our understanding of the genetic aspects of the Th17/IL-17A axis. In this study, we employed machine learning algorithms to identify three target genes associated with psoriasis: CCR7, IL2RG, and PLEK. The validation of these genes was carried out in specimens from psoriatic patients. In vivo, investigations assessed the relationship between these three genes and IL-17A-related inflammation and their connection to psoriatic phenotypes. To further confirm the significance of the newly discovered gene, PLEK, we performed experiments involving the blockade of its expression. Our bioinformatics analysis revealed three novel genes closely linked to psoriasis: CCR7, IL2RG, and PLEK. These genes exhibited upregulated expression in psoriasis, consistently aligning with the Th17/IL-17A axis. Inhibition of PLEK expression reduced Th17/IL-17A-related inflammation and alleviated psoriatic phenotypes. CCR7, IL2RG, and PLEK show potential as three novel biomarkers for psoriasis, with PLEK being reported for the first time in this context. These genes contribute to pathogenesis by associating with the Th17/IL-17A signaling pathway.

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.

Transcriptomic profiling of lncRNAs and mRNAs in a venous thrombosis mouse model

This study explores the role of lncRNAs and mRNAs in venous thromboembolism (VTE) using an inferior vena cava (IVC) mouse model. RNA sequencing identified differentially expressed lncRNAs and mRNAs between model and control groups. Enrichment analyses revealed significant pathways, including HIF-1α signaling, glycolysis/gluconeogenesis, and platelet activation. A lncRNA-miRNA-mRNA network highlighted key regulatory interactions. Validation using qRT-PCR confirmed the RNA-seq findings. These results provide insights into the molecular mechanisms of VTE and suggest potential biomarkers and therapeutic targets for thrombosis.

Plasma Proteomic Signatures of Physical Activity Provide Insights into Biological Impacts of Physical Activity and its Protective Role Against Dementia

Physical activity (PA), including sedentary behavior, is associated with many diseases, including Alzheimer's disease and all-cause dementia. However, the specific biological mechanisms through which PA protects against disease are not entirely understood. To address this knowledge gap, we first assessed the conventional observational associations of three self-reported and three device-based PA measures with circulating levels of 2,911 plasma proteins measured in the UK Biobank (nmax=39,160) and assessed functional enrichment of identified proteins. We then used bi-directional Mendelian randomization (MR) to further evaluate the evidence for causal relationships of PA with protein levels. Finally, we performed mediation analyses to identify proteins that may mediate the relationship of PA with incident all-cause dementia. Our findings revealed 41 proteins consistently associated with all PA measures and 1,027 proteins associated with at least one PA measure. Both conventional observational and MR study designs converged on proteins that appear to increase as a result of PA, including integrin proteins such as ITGAV and ITGAM, as well as MXRA8, CLEC4A, CLEC4M, GFRA1, and ADGRG2; and on proteins that appear to decrease as a result of PA such as LEP, LPL, INHBC, CLMP, PTGDS, ADM, OGN, and PI3. Functional enrichment analyses revealed several relevant processes, including cell-matrix adhesion, integrin-mediated signaling, and collagen binding. Finally, several proteins, including GDF15, ITGAV, HPGDS, BCAN, and MENT, were found to mediate the relationship of PA with all-cause dementia, implicating processes such as synaptic plasticity, neurogenesis and inflammation, through which PA protects against dementia. Our results provide insights into how PA may affect biological processes and protect from all-cause dementia, and provide avenues for future research into the health-promoting effects of PA.

Exploring susceptibility and therapeutic targets for kidney stones through proteome-wide Mendelian randomization

Given the high recurrence rate of kidney stones, surgical lithotripsy and stone removal are not the ultimate treatments for kidney stones. There's an urgent need to explore the genetic mechanisms behind the susceptibility to kidney stones and to identify potential targets for prevention, to reduce the renal damage caused by recurrent stone formation. In this study, we screened 4548 circulating proteins using proteome-wide Mendelian Randomization (MR) to find proteins with a causal relationship to kidney stone risk. Additionally, proteome-wide association study (PWAS) and colocalization analysis were used to validate and prioritize candidate proteins. Moreover, downstream analyses including single-cell analysis, enrichment analysis, protein-protein interaction (PPI), and druggability analysis were conducted on the proteins causally related to kidney stones, to further explore the genetic mechanisms of susceptibility and the potential of proteins as drug targets. Ultimately, 22 target proteins associated with the risk of kidney stones were identified. Six plasma proteins (COLGALT1, CLMP, LECT1, ITIH1, CDHR3, CPLX2) were negatively correlated with kidney stone risk, while the genetic overexpression of 16 target proteins (GJA1, STOM, IRF9, F9, TMPRSS11D, ADH1B, SPINK13, CRYBB2, TNS2, DOCK9, OXSM, MST1, IL2, LMAN2, ITIH3, KLRF1) increased the risk of kidney stones. Based on the PWAS and colocalization analysis results, the 22 target proteins were classified into 3 tiers: IL2, CPLX2, and LMAN2 as tier 1 proteins with the most compelling evidence, MST1, ITIH1, and ITIH3 as tier 2 proteins, and the rest as tier 3 proteins. Enrichment analysis and PPI showed that target proteins mainly affect the occurrence of kidney stones through leukocyte activation and cell junction assembly. Druggability analysis suggested that IL2, MST1, and ITIH1 have potential as drug targets, and potential drugs were evaluated through molecular docking. In summary, this study employed multiple analytical methods to screen plasma proteins related to susceptibility to kidney stones, providing new insights into the genetic mechanisms of kidney stones and potential targets for treatment and prevention.

Sodium-glucose cotransporter protein 2 inhibition, plasma proteins, and ischemic stroke: A mediation Mendelian randomization and colocalization study

PURPOSE

To determine the effect of the sodium-glucose cotransporter protein 2 (SGLT2) inhibition on ischemic stroke (IS) and investigate the circulating proteins that mediate the effects of SGLT2 inhibition on IS.

METHODS

The effects of SGLT2 inhibition on IS were evaluated using two-sample Mendelian randomization (MR) analyses. The 4,907 circulating proteins from the plasma proteome were assessed to identify potential mediators. Sensitivity, colocalization, and external validation analyses were conducted to validate critical findings. MR analyses were also used to evaluate the associations of SGLT2 inhibition with magnetic resonance imaging (MRI)-based biomarkers and functional prognoses post-IS.

RESULTS

SGLT2 inhibition was significantly associated with decreased risks of IS (odds ratio (OR): 0.39, 95 % confidence interval (CI): 0.25-0.61, p = 3.53 × 10-5) and cardioembolic stroke (OR: 0.16, 95 % CI: 0.07-0.37, p = 1.82 × 10-5); the effect of SGLT2 inhibition on IS was indirectly mediated through pathways involving tryptophanyl-transfer RNA synthetase (WARS) (β:0.08, 95 % CI:0.15 - -0.01, p = 0.034) and matrix metalloproteinase 12 (MMP12) (β:0.06, 95 % CI:0.12 - -0.01, p = 0.016), with mediation proportions of 8.2 % and 6.8 %, respectively. The external validation confirmed the WARS mediating effect. In addition, the sensitivity and colocalization analyses and MR analyses of MRI biomarker-based and functional prognostic outcomes supported these results.

CONCLUSION

In this study, we demonstrated from a genetic perspective that SGLT2 inhibitors prevent the development of IS and improve functional prognostic outcomes and brain microstructural integrity. WARS and MMP12 may act as potential mediators, presenting a novel approach for IS intervention.

A scoping review of statistical methods to investigate colocalization between genetic associations and microRNA expression in osteoarthritis

Background

Genetic colocalization analysis is a statistical method that evaluates whether two traits (e.g., osteoarthritis [OA] risk and microRNA [miRNA] expression levels) share the same or distinct genetic association signals in a locus typically identified in genome-wide association studies (GWAS). This method is useful for providing insights into the biological relevance of genetic association signals, particularly in intergenic regions, which can help to elucidate disease mechanisms in OA and other complex traits.

Objectives

To review the existing literature on genetic colocalization methods, assess their suitability for studying OA, and investigate their capacity to integrate miRNA data, while bearing in view their statistical assumptions.

Design

We followed scoping review methodology and used Covidence software for data management. Search terms for colocalization, GWAS, and genetic or statistical models were used in the databases MEDLINE and EMBASE, searched till March 4, 2024.

Results

Our search returned 546 peer-reviewed papers, of which 96 were included following title/abstract and full-text screening. Based on both cumulative and annual publication counts, the most cited method for colocalization analysis was coloc. Four papers examined OA-related phenotypes, and none examined miRNA. An approach to colocalization analysis using miRNA was postulated based on further hand-searching.

Conclusions

Colocalization analysis is a largely unexplored method in OA. Many of the approaches to colocalization analysis identified in this review, including the integration of GWAS and miRNA data, may help to elucidate genetic and epigenetic factors implicated in OA and other complex traits.

Serum proteome profiling reveals HGFA as a candidate biomarker for pulmonary arterial hypertension

BACKGROUND

Identification and validation of potential biomarkers could facilitate the identification of pulmonary arterial hypertension (PAH) and thus aid to study their roles in the disease process.

METHODS

We used the isobaric tag for relative and absolute quantitation approaches to compare the protein profiles between the serum of PAH patients and the controls. Bioinformatics analyses and enzyme-linked immunosorbent assay (ELISA) identification of PAH patients and the controls were performed to identify the potential biomarkers. The receiver operating characteristic curve (ROC) analysis was used to evaluate the diagnostic performance of these potential biomarkers. Mendelian randomization (MR) analysis further clarified the relationship between the potential biomarkers and PAH. Additionally, the expression levels of the potential biomarkers were further validated in two PAH animal models (monocrotaline-PH and Sugen5416 plus hypoxia-PH) using ELISA and reverse transcription-quantitative PCR (RT-qPCR).

RESULTS

We identified significant changes in three proteins including heparanase (HPSE), gelsolin (GSN), and hepatocyte growth factor activator (HGFA) in PAH patients. The ROC analysis showed that the areas under the curve of HPSE, GSN, and HGFA in differentiating PAH patients from controls were 0.769, 0.777, and 0.964, respectively. HGFA was correlated with multiple parameters of right ventricular functions in PAH patients. Besides proteomic analysis, we also used MR method to demonstrate the causal link between genetically reduced HGFA levels and an increased risk of PAH. In subsequent validation study in PAH animal models, the mRNA expression levels of HGFA in the lung tissues were significantly lower in PAH rat models than in controls. In the rat models, serum levels of HGFA were lower compared to the control group and showed a negative correlation with right ventricular systolic pressure.

CONCLUSION

The study demonstrated that HGFA might be a promising biomarker for noninvasive detection of PAH.

Identification of hsa-miR-193a-5p-SURF4 axis related to the gut microbiota-metabolites- cytokines in lung cancer based on Mendelian randomization study and bioinformatics analysis

BACKGROUND

Lung cancer is a significant disease that affects people's physical and mental health. Currently, the treatment outcomes still do not meet clinical needs, and the causes of the disease are still unclear, therefore further exploration is needed.

METHODS

We analyzed the exposure factors of lung cancer, including gut microbiota, serum metabolites, and cytokines, through Mendelian randomization studies and bioinformatics analysis. We identified common SNPs and performed gene annotation, leading to the discovery of the key gene SURF4, which may affect the onset of lung cancer. We validated the oncogenic function and mechanism of SURF4 through public data analysis using GO and KEGG, and constructed a ceRNA network, revealing the lung cancer oncogenic pathway involving lncRNA/pseudogene-microRNA-SURF4.

RESULTS

We first conducted a Mendelian randomization analysis on 418 gut microbiota, 1400 serum metabolites, and 41 cytokines in relation to lung cancer. We found that 16 gut microbiota, 29 serum metabolites, and 2 cytokines were closely associated with lung cancer. Further comparison of all differential SNPs revealed that rs550057 on chromosome 9 was a common SNP among these three exposure factors, indicating its crucial role in lung cancer formation. Through gene functional annotation using R language, we found that the expression of 15 genes, including SURF4, was influenced by rs550057. By querying these 15 genes from public databases for their differential expression and prognosis in lung cancer, we found significant differences in SURF4, MED22, and RPL7A. Furthermore, by querying the expression and correlation coefficients of upstream microRNAs of these three genes through the starBase website, we found that hsa-miR-193a-5p-SURF4 had the most significant effect on lung cancer. Through GO and KEGG analysis of SURF4-related genes, we identified the molecular pathways associated metabolic synthesis and microbial infection related to the promotion of lung cancer by SURF4. This validated the results of the previous Mendelian randomization study. Furthermore, we constructed a ceRNA network for SURF4 and identified two upstream differentially expressed pseudogenes and nine lncRNAs, confirming the functionality of the pseudogene/lncRNA-microRNA-SUFR4 pathway.

CONCLUSIONS

In summary, we have elucidated the regulatory role of the pseudogene/lncRNA-microRNA-SUFR4 pathway in the progression of lung cancer, combining the research hotspots of gut microbiota-serum metabolites-cytokines. We have also confirmed the pathway and mechanism through SURF4 and its related genes promoting lung cancer formation. This may provide effective therapeutic methods for lung cancer and serve as a potential prognostic marker.

Genome-wide association analysis reveals potential genetic correlation and causality between circulating inflammatory proteins and amyotrophic lateral sclerosis

BACKGROUND

Amyotrophic Lateral Sclerosis (ALS), a fatal neurodegenerative disease, continues to elude complete comprehension of its pathological underpinnings. Recent focus on inflammation in ALS pathogenesis prompts this investigation into the genetic correlation and potential causal relationships between circulating inflammatory proteins and ALS.

METHODS

Genome-wide association study (GWAS) data encompassing 91 circulating inflammatory protein measures from 14,824 individuals of European ancestry, alongside records from 27,205 ALS cases and 110,881 controls, were employed. Assessment of genetic correlation and overlap utilized LD score regression (LDSC), high-definition likelihood (HDL), and genetic analysis integrating pleiotropy and annotation (GPA) methodologies. Identification of shared genetic loci involved pleiotropy analysis, functional mapping and annotation (FUMA), and co-localization analysis. Finally, Mendelian randomization was applied to probe causal relationships between inflammatory proteins and ALS.

RESULTS

Our investigation revealed significant genetic correlation and overlap between ALS and various inflammatory proteins, including C-C motif chemokine 28, Interleukin-18, C-X-C motif chemokine 1, and Leukemia inhibitory factor receptor (LIFR). Pleiotropy analysis uncovered shared variations at specific genetic loci, some of which bore potential harm. Mendelian randomization analysis suggested that alterations in specific inflammatory protein levels, notably LIFR, could impact ALS risk.

CONCLUSIONS

Our findings uncover a genetic correlation between certain circulating inflammatory proteins and ALS, suggesting their possible causal involvement in ALS pathogenesis. Moreover, the identification of LIFR as a crucial protein may yield new insights into ALS pathomechanisms and offer a promising avenue for therapeutic interventions. These discoveries provide novel perspectives for advancing the comprehension of ALS pathophysiology and exploring potential therapeutic avenues.

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.

Investigating potential biomarkers of acute pancreatitis in patients with a BMI>30 using Mendelian randomization and transcriptomic analysis

BACKGROUND

Acute pancreatitis (AP) has become a significant global health concern, and a high body mass index (BMI) has been identified as a key risk factor exacerbating this condition. Within this context, lipid metabolism assumes a critical role. The complex relationship between elevated BMI and AP, mediated by lipid metabolism, markedly increases the risk of complications and mortality. This study aimed to accurately define the correlation between BMI and AP, incorporating a comprehensive analysis of the interactions between individuals with high BMI and AP.

METHODS

Mendelian randomization (MR) analysis was first applied to determine the causal relationship between BMI and the risk of AP. Subsequently, three microarray datasets were obtained from the GEO database. This was followed by an analysis of differentially expressed genes and the application of weighted gene coexpression network analysis (WGCNA) to identify key modular genes associated with AP and elevated BMI. Functional enrichment analysis was then performed to shed light on disease pathogenesis. To identify the most informative genes, machine learning algorithms, including Random Forest (RF), Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and Least Absolute Shrinkage and Selection Operator (LASSO), were employed. Subsequent analysis focused on the colocalization of the Quantitative Trait Loci (eQTL) data associated with the selected genes and Genome-Wide Association Studies (GWAS) data related to the disease. Preliminary verification of gene expression trends was conducted using external GEO datasets. Ultimately, the diagnostic potential of these genes was further confirmed through the development of an AP model in mice with a high BMI.

RESULTS

A total of 21 intersecting genes related to BMI>30, AP, and lipid metabolism were identified from the datasets. These genes were primarily enriched in pathways related to cytosolic DNA sensing, cytokine‒cytokine receptor interactions, and various immune and inflammatory responses. Next, three machine learning techniques were utilized to identify HADH as the most prevalent diagnostic gene. Colocalization analysis revealed that HADH significantly influenced the risk factors associated with BMI and AP. Furthermore, the trend in HADH expression within the external validation dataset aligned with the trend in the experimental data, thus providing a preliminary validation of the experimental findings.The changes in its expression were further validated using external datasets and quantitative real-time polymerase chain reaction (qPCR).

CONCLUSION

This study systematically identified HADH as a potential lipid metabolism-grounded biomarker for AP in patients with a BMI>30.

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.

Serpin peptidase inhibitor, clade E, member 2 in physiology and pathology: recent advancements

Serine protease inhibitors (serpins) are the most numerous and widespread multifunctional protease inhibitor superfamily and are expressed by all eukaryotes. Serpin E2 (serpin peptidase inhibitor, clade E, member 2), a member of the serine protease inhibitor superfamily is a potent endogenous thrombin inhibitor, mainly found in the extracellular matrix and platelets, and expressed in numerous organs and secreted by many cell types. The multiple functions of serpin E2 are mainly mediated through regulating urokinase-type plasminogen activator (uPA, also known as PLAU), tissue-type plasminogen activator (tPA, also known as PLAT), and matrix metalloproteinase activity, and include hemostasis, cell adhesion, and promotion of tumor metastasis. The importance serpin E2 is clear from its involvement in numerous physiological and pathological processes. In this review, we summarize the structural characteristics of the Serpin E2 gene and protein, as well as its roles physiology and disease.

Elucidating the susceptibility to breast cancer: an in-depth proteomic and transcriptomic investigation into novel potential plasma protein biomarkers

Objectives: This study aimed to identify plasma proteins that are associated with and causative of breast cancer through Proteome and Transcriptome-wide association studies combining Mendelian Randomization. Methods: Utilizing high-throughput datasets, we designed a two-phase analytical framework aimed at identifying novel plasma proteins that are both associated with and causative of breast cancer. Initially, we conducted Proteome/Transcriptome-wide association studies (P/TWAS) to identify plasma proteins with significant associations. Subsequently, Mendelian Randomization was employed to ascertain the causation. The validity and robustness of our findings were further reinforced through external validation and various sensitivity analyses, including Bayesian colocalization, Steiger filtering, heterogeneity and pleiotropy. Additionally, we performed functional enrichment analysis of the identified proteins to better understand their roles in breast cancer and to assess their potential as druggable targets. Results: We identified 5 plasma proteins demonstrating strong associations and causative links with breast cancer. Specifically, PEX14 (OR = 1.201, p = 0.016) and CTSF (OR = 1.114, p < 0.001) both displayed positive and causal association with breast cancer. In contrast, SNUPN (OR = 0.905, p < 0.001), CSK (OR = 0.962, p = 0.038), and PARK7 (OR = 0.954, p < 0.001) were negatively associated with the disease. For the ER-positive subtype, 3 plasma proteins were identified, with CSK and CTSF exhibiting consistent trends, while GDI2 (OR = 0.920, p < 0.001) was distinct to this subtype. In ER-negative subtype, PEX14 (OR = 1.645, p < 0.001) stood out as the sole protein, even showing a stronger causal effect compared to breast cancer. These associations were robustly supported by colocalization and sensitivity analyses. Conclusion: Integrating multiple data dimensions, our study successfully pinpointed plasma proteins significantly associated with and causative of breast cancer, offering valuable insights for future research and potential new biomarkers and therapeutic targets.