Variants in the Atherogenic ALOX5AP, THBD, and KNG1 Genes Potentiate the Risk of Ischemic Stroke via a Genetic Main Effect and Epistatic Interactions in a Chinese Population

Mediators of the causal associations between protein ratios and ischemic stroke: a two-step Mendelian randomization study

BACKGROUND

Proteomics has revealed links between plasma proteins and ischemic stroke (IS), but the relationship between protein ratios, IS, and the effects of blood cells and serum uric acid (SUA) is underexplored.

METHODS

Using two-sample Mendelian randomization (MR), we assessed causal relationships between 2,821 protein ratios, 91 blood phenotypes, SUA, and IS subtypes. FDR correction was applied specifically to protein ratio analyses to account for multiple comparisons in the primary MR step. Significant associations were further validated through co-localization analysis, which assessed shared genetic architecture between exposure and outcome loci. This analysis used GWAS data from MEGASTROKE, GISCOME, minimizing confounding bias and reverse causation. Additionally, the total effects of protein ratio levels on IS were decomposed into direct and indirect effects mediated through multiple pathways. Sensitivity analyses ensured robustness.

RESULTS

The CD34/ITGAV ratio exhibited distinct effects on stroke risk, showing 34.9% increased odds of LAS (OR=1.349, 95% CI=1.097-1.658) while demonstrating protective effects against IS outcome progression (OR=0.564, 95% CI=0.380-0.838). Bayesian co-localization analysis revealed complete genetic overlap (PPH4 = 1) for key protein ratio-stroke subtype pairs: AIS with TGFBR2/THBD ratio, LAS with LGALS8/VWF ratio, CES with BST2/CEACAM1 and CD209/CLEC4G ratios. In mediation pathways, neutrophil parameters accounted for 54.4% of the prognosis effect in the ABHD14B/STAMBP-IS association, whereas SUA mediated only 1.3% of the PODXL2/SDC1 ratio-IS relationship.

CONCLUSIONS

Our MR study combined with co-localization analysis identifies causal links between protein interactions and IS, highlighting potential targets to disrupt pathways connecting protein ratio changes to IS incidence and outcomes, offering promising intervention avenues.

Network pharmacology-and molecular docking-based investigation of Danggui blood-supplementing decoction in ischaemic stroke

Danggui blood-supplementing decoction (DBsD) is an herbal preparation treating several diseases including stroke. The present study sought to investigate the potential mechanism of DBsD in ischaemic stroke (IS) using network pharmacology, molecular docking, and cell experiment. Based on the protein-protein (PPI) network analysis, MAPK1 (0.51, 12), KNG1 (0.57, 28), and TNF (0.64, 39) were found with relatively good performance in degree and closeness centrality. The functional enrichment analysis revealed that DBsD contributed to IS-related biological processes, molecule function, and presynaptic/postsynaptic cellular components. Pathway enrichment indicated that DBsD might protect IS by modulating multi-signalling pathways including the sphingolipid signalling pathway. Molecular docking verified the stigmasterol-KNG1, bifendate-TNF, and formononetin-MAPK1 pairs. Cell experiments confirmed the involvement of KNG1 and sphingolipid signalling pathway in hippocampal neuronal cell apoptosis. This study showed that DBsD can protect neuronal cell injury after IS through multiple components, multiple targets, and multiple pathways.

Multi-phenotype analyses of hemostatic traits with cardiovascular events reveal novel genetic associations

BACKGROUND

Multi-phenotype analysis of genetically correlated phenotypes can increase the statistical power to detect loci associated with multiple traits, leading to the discovery of novel loci. This is the first study to date to comprehensively analyze the shared genetic effects within different hemostatic traits, and between these and their associated disease outcomes.

OBJECTIVES

To discover novel genetic associations by combining summary data of correlated hemostatic traits and disease events.

METHODS

Summary statistics from genome wide-association studies (GWAS) from seven hemostatic traits (factor VII [FVII], factor VIII [FVIII], von Willebrand factor [VWF] factor XI [FXI], fibrinogen, tissue plasminogen activator [tPA], plasminogen activator inhibitor 1 [PAI-1]) and three major cardiovascular (CV) events (venous thromboembolism [VTE], coronary artery disease [CAD], ischemic stroke [IS]), were combined in 27 multi-trait combinations using metaUSAT. Genetic correlations between phenotypes were calculated using Linkage Disequilibrium Score Regression (LDSC). Newly associated loci were investigated for colocalization. We considered a significance threshold of 1.85 × 10-9 obtained after applying Bonferroni correction for the number of multi-trait combinations performed (n = 27).

RESULTS

Across the 27 multi-trait analyses, we found 4 novel pleiotropic loci (XXYLT1, KNG1, SUGP1/MAU2, TBL2/MLXIPL) that were not significant in the original individual datasets, were not described in previous GWAS for the individual traits, and that presented a common associated variant between the studied phenotypes.

CONCLUSIONS

The discovery of four novel loci contributes to the understanding of the relationship between hemostasis and CV events and elucidate common genetic factors between these traits.

Ischemic Stroke and Sleep: The Linking Genetic Factors

This review summarizes the available data about genetic factors which can link ischemic stroke and sleep. Sleep patterns (subjective and objective measures) are characterized by heritability and comprise up to 38-46%. According to Mendelian randomization analysis, genetic liability for short sleep duration and frequent insomnia symptoms is associated with ischemic stroke (predominantly of large artery subtype). The potential genetic links include variants of circadian genes, genes encoding components of neurotransmitter systems, common cardiovascular risk factors, as well as specific genetic factors related to certain sleep disorders.

Cryptogenic Stroke in the Young: Role of Candidate Gene Polymorphisms in Indian Patients with Ischemic Etiology

CONTEXT

Strokes that remain without a definite cause even after an extensive workup, termed cryptogenic strokes, constitute up to 30-40% of ischemic strokes (ISs) in the young. Some of them can have a genetic basis. However, the well-established genetic causes account for only a small percentage of these cases.

AIM

To evaluate the association of cryptogenic young IS with 16 candidate gene polymorphisms.

SETTINGS AND DESIGN

A case-control study with cryptogenic young IS patients (South and North Indians; n = 105) and age, sex, and ethnicity-matched controls (n = 215).

SUBJECTS AND METHODS

Genotyping was carried out by PCR-RFLP method using DNA extracted from the blood.

STATISTICAL ANALYSIS USED

Association of the genotypes with the disease was studied using Chi-square test.

RESULTS

MTHFR rs1801133 and KNG1 rs710446 showed significant statistical association with cryptogenic young IS (P = 0.0261 and 0.0157, respectively) in the Indian population. Significant association of KNG1 rs710446 (P 0.0036) and FXII rs1801020 (P 0.0376) with cryptogenic young stroke in South Indian males, SERPINC1 rs2227589 in South Indian female patients (P = 0.0374), and CYP4V2 rs13146272 in North Indian males (P = 0.0293) was observed.

CONCLUSIONS

Our study indicates that in the Indian population MTHFR rs1801133, KNG rs710446, FXII rs1801020, SERPINC1 rs2227589, CYP4V2 rs13146272, and FXIII V34L may be significant risk factors for cryptogenic IS in the young. In addition, ethnicity and gender play a significant role. Further studies with larger sample size are required to completely establish these polymorphisms as risk factors for cryptogenic IS in young Indians.

Identification of candidate biomarkers and therapeutic agents for heart failure by bioinformatics analysis

INTRODUCTION

Heart failure (HF) is a heterogeneous clinical syndrome and affects millions of people all over the world. HF occurs when the cardiac overload and injury, which is a worldwide complaint. The aim of this study was to screen and verify hub genes involved in developmental HF as well as to explore active drug molecules.

METHODS

The expression profiling by high throughput sequencing of GSE141910 dataset was downloaded from the Gene Expression Omnibus (GEO) database, which contained 366 samples, including 200 heart failure samples and 166 non heart failure samples. The raw data was integrated to find differentially expressed genes (DEGs) and were further analyzed with bioinformatics analysis. Gene ontology (GO) and REACTOME enrichment analyses were performed via ToppGene; protein-protein interaction (PPI) networks of the DEGs was constructed based on data from the HiPPIE interactome database; modules analysis was performed; target gene-miRNA regulatory network and target gene-TF regulatory network were constructed and analyzed; hub genes were validated; molecular docking studies was performed.

RESULTS

A total of 881 DEGs, including 442 up regulated genes and 439 down regulated genes were observed. Most of the DEGs were significantly enriched in biological adhesion, extracellular matrix, signaling receptor binding, secretion, intrinsic component of plasma membrane, signaling receptor activity, extracellular matrix organization and neutrophil degranulation. The top hub genes ESR1, PYHIN1, PPP2R2B, LCK, TP63, PCLAF, CFTR, TK1, ECT2 and FKBP5 were identified from the PPI network. Module analysis revealed that HF was associated with adaptive immune system and neutrophil degranulation. The target genes, miRNAs and TFs were identified from the target gene-miRNA regulatory network and target gene-TF regulatory network. Furthermore, receiver operating characteristic (ROC) curve analysis and RT-PCR analysis revealed that ESR1, PYHIN1, PPP2R2B, LCK, TP63, PCLAF, CFTR, TK1, ECT2 and FKBP5 might serve as prognostic, diagnostic biomarkers and therapeutic target for HF. The predicted targets of these active molecules were then confirmed.

CONCLUSION

The current investigation identified a series of key genes and pathways that might be involved in the progression of HF, providing a new understanding of the underlying molecular mechanisms of HF.

Characterization of differentially expressed plasma proteins in patients with acute myocardial infarction

Acute myocardial infarction (AMI) remains a leading cause of morbidity and mortality worldwide. Novel biomarkers are needed to identify NSTEMI in AMI patients. The study objective was to use proteomics to identify novel plasma biomarkers for STEMI and NSTEMI patients. iTRAQ analysis was performed on pooled samples from 8 healthy controls and 12 STEMI and 12 NSTEMI patients. Bioinformatics analysis identified 95 differentially expressed proteins that were differentially expressed in the plasma of AMI patients and healthy controls; 28 of these proteins were found in STEMI/Con (22 upregulated and 6 downregulated), 48 in NSTEMI/Con (12 upregulated and 36 downregulated), and 44 in NSTEMI/STEMI (11 upregulated and 33 downregulated). Protein network analysis was then performed using STRING software. Functional analysis revealed that the identified plasma proteins were mainly involved with carbon metabolism, toll-like receptor signaling pathway, and hypertrophic cardiomyopathy. Nine of the proteins (SSA1, MDH1, FCN2, GPI, S100A8, LBP, vinculin, VDBP, and RBP4) that changed levels during AMI progression were further validated by ELISA. The constructed plasma proteome could reflect the AMI pathogenesis molecular mechanisms and provide a method for the early identification of NSTEMI in AMI patients. SIGNIFICANCE: The aim of this study was to use proteomics to identify novel predictive plasma biomarkers for patients with acute myocardial infarction (AMI), which would allow for either identification of individuals at risk of an infarction, and early identification of NSTEMI in patients with AMI. Using an approach that combined iTRAQ with LC-MS/MS, we found 95 proteins that showed significant differences in expression levels among the AMI patients and healthy controls. The proteins SSA1, MDH1, FCN2, GPI, S100A8, LBP, vinculin, VDBP, and RBP4 were found to play crucial roles in the pathogenesis of AMI. Using bioinformatics analysis, we found that dysregulation of carbon metabolism, toll-like receptor signaling pathway, and hypertrophic cardiomyopathy may be the major driving forces for cardiac damage during myocardial infarction. However, further investigations are needed to verify the mechanisms involved in the development of AMI especially NSTEMI. Taken together, our findings lay the foundation for understanding the molecular mechanisms underlying the pathogenic processes of AMI, and suggest potential applications for specific biomarkers in early diagnosis and determination of prognosis.

Genetic effects of BDKRB2 and KNG1 on deep venous thrombosis after orthopedic surgery and the potential mediator

Deep venous thrombosis (DVT) is a common complication of orthopedic surgery. Genetic risk factors and high heritability carried a substantial risk of DVT. In this study, we aimed to investigate the potential association in the Han Chinese population between the polymorphisms of BDKRB2 and KNG1 and DVT after orthopedic surgery (DVTAOS). A total of 3,010 study subjects comprising 892 DVT cases and 2,118 controls were included in the study, and 39 single nucleotide polymorphisms (SNPs) in total (30 for BDKRB2 and 9 for KNG1) were chosen for genotyping. Two SNPs, rs710446 (OR = 1.27, P = 0.00016) and rs2069588 (OR = 1.29, P = 0.00056), were identified as significantly associated with DVTAOS. After adjusting for BMI, the significance of rs2069588 decreased (P = 0.0013). Haplotype analyses showed that an LD block containing rs2069588 significantly correlated with the DVTAOS risk. Moreover, bioinformatics analysis indicated that hsa-miR-758-5p and BDKRB2 formed miRNA/SNP target duplexes if the rs2069588 allele was in the T form, suggesting that rs2069588 may alter BDKRB2 expression by affecting hsa-miR-758-5p/single-nucleotide polymorphism target duplexes. Our results demonstrate additional evidence supporting that there is an important role for the KNG1 and BDKRB2 genes in the increased susceptibility of DVTAOS.

Genome-wide association study with additional genetic and post-transcriptional analyses reveals novel regulators of plasma factor XI levels

Coagulation factor XI (FXI) has become increasingly interesting for its role in pathogenesis of thrombosis. While elevated plasma levels of FXI have been associated with venous thromboembolism and ischemic stroke, its deficiency is associated with mild bleeding. We aimed to determine novel genetic and post-transcriptional plasma FXI regulators.We performed a genome-wide association study (GWAS) for plasma FXI levels, using novel data imputed to the 1000 Genomes reference panel. Individual GWAS analyses, including a total of 16,169 European individuals from the ARIC, GHS, MARTHA and PROCARDIS studies, were meta-analysed and further replicated in 2,045 individuals from the F5L family, GAIT2 and MEGA studies. Additional association with activated partial thromboplastin time (aPTT) was tested for the top SNPs. In addition, a study on the effect of miRNA on FXI regulation was performed using in silico prediction tools and in vitro luciferase assays.Three loci showed robust, replicating association with circulating FXI levels: KNG1 (rs710446, P-value = 2.07 × 10-302), F11 (rs4253417, P-value  = 2.86 × 10-193), and a novel association in GCKR (rs780094, P-value  = 3.56 ×10-09), here for the first time implicated in FXI regulation. The two first SNPs (rs710446 and rs4253417) also associated with aPTT. Conditional and haplotype analyses demonstrated a complex association signal, with additional novel SNPs modulating plasma FXI levels in both the F11 and KNG1 loci. Finally, eight miRNAs were predicted to bind F11 mRNA. Over-expression of either miR-145 or miR-181 significantly reduced the luciferase activity in cells transfected with a plasmid containing FXI-3'UTR.These results should open the door to new therapeutic targets for thrombosis prevention.