Discovery of a novel inhibitor against urokinase-type plasminogen activator, a potential enzyme with a role in atherosclerotic plaque instability
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Causal correlations between inflammatory proteins and heart failure: A two‐sample Mendelian randomization analysis
AimsInflammation plays a critical role in both the development and progression of heart failure (HF), which is a leading cause of morbidity and mortality worldwide. However, the causality between specific inflammation‐related proteins and HF risk remains unclear. This study aims to investigate the genetically supported causality between inflammatory proteins and HF using a two‐sample Mendelian randomization (MR) analysis.Methods and resultsWe utilized genome‐wide association study (GWAS) data of 91 inflammation‐related proteins as exposures from the SCALLOP Consortium (14,824 participants), alongside outcome GWAS summary statistics from FinnGen (29,218 cases/381,838 controls) and HERMES (47,309 cases/930,014 controls) for HF, to conduct a two‐sample MR analysis. For each inflammatory protein, instrumental variables (IVs) were chosen following the three foundational assumptions of the MR analysis, requiring a minimum of three qualifying single nucleotide polymorphisms (SNPs) each with a P < 5e‐8. Associations between inflammatory proteins and HF were assessed through inverse‐variance weighted (IVW), MR‐Egger regression, weighted median and weighted mode analysis. The reliability and validity of the results were evaluated by examining heterogeneity, horizontal pleiotropy, leave‐one‐out analysis, meta‐analysis and reverse MR analysis. Heterogeneity refers to the variation in results across different genetic variants. Horizontal pleiotropy occurs when a genetic variant influences multiple traits through different biological pathways. Addressing both heterogeneity and horizontal pleiotropy is crucial for ensuring the reliability and interpretability of MR results. Our analysis identified associations between three inflammatory proteins and HF risk. Matrix metalloproteinase‐1 (MMP‐1) (OR, 1.09; 95% CI, 1.00–1.18; P = 0.04) and TNF‐beta (OR, 1.05; 95% CI, 1.01–1.09; P = 0.01) were positively associated with HF risk in FinnGen. In contrast, urokinase‐type plasminogen activator (uPA) was inversely associated with HF risk in both FinnGen (OR, 0.85; 95% CI, 0.78–0.92; P = 3.27e‐5) and HERMES (OR, 0.93; 95% CI, 0.87–0.99; P = 0.03). No evidence of heterogeneity and horizontal pleiotropy was observed in the MR analysis, indicating the robustness of our findings. A meta‐analysis further supported this association, indicating a reduced risk (OR, 0.89; 95% CI, 0.81–0.98; P = 0.02). No reverse causality was found between HF and these three inflammatory proteins (P > 0.05 for all).ConclusionsThis study provides genetically supported evidence of the causal association of specific inflammatory proteins with HF risk. The positive association of MMP‐1 and TNF‐beta with HF suggests their roles in disease pathogenesis, whereas the inverse association of the uPA indicates its potential protective effect. Our findings highlight the potential of targeting specific inflammatory pathways as a therapeutic strategy for HF.
Causal correlations between inflammatory proteins and heart failure: A two‐sample Mendelian randomization analysis
AimsInflammation plays a critical role in both the development and progression of heart failure (HF), which is a leading cause of morbidity and mortality worldwide. However, the causality between specific inflammation‐related proteins and HF risk remains unclear. This study aims to investigate the genetically supported causality between inflammatory proteins and HF using a two‐sample Mendelian randomization (MR) analysis.Methods and resultsWe utilized genome‐wide association study (GWAS) data of 91 inflammation‐related proteins as exposures from the SCALLOP Consortium (14,824 participants), alongside outcome GWAS summary statistics from FinnGen (29,218 cases/381,838 controls) and HERMES (47,309 cases/930,014 controls) for HF, to conduct a two‐sample MR analysis. For each inflammatory protein, instrumental variables (IVs) were chosen following the three foundational assumptions of the MR analysis, requiring a minimum of three qualifying single nucleotide polymorphisms (SNPs) each with a P < 5e‐8. Associations between inflammatory proteins and HF were assessed through inverse‐variance weighted (IVW), MR‐Egger regression, weighted median and weighted mode analysis. The reliability and validity of the results were evaluated by examining heterogeneity, horizontal pleiotropy, leave‐one‐out analysis, meta‐analysis and reverse MR analysis. Heterogeneity refers to the variation in results across different genetic variants. Horizontal pleiotropy occurs when a genetic variant influences multiple traits through different biological pathways. Addressing both heterogeneity and horizontal pleiotropy is crucial for ensuring the reliability and interpretability of MR results. Our analysis identified associations between three inflammatory proteins and HF risk. Matrix metalloproteinase‐1 (MMP‐1) (OR, 1.09; 95% CI, 1.00–1.18; P = 0.04) and TNF‐beta (OR, 1.05; 95% CI, 1.01–1.09; P = 0.01) were positively associated with HF risk in FinnGen. In contrast, urokinase‐type plasminogen activator (uPA) was inversely associated with HF risk in both FinnGen (OR, 0.85; 95% CI, 0.78–0.92; P = 3.27e‐5) and HERMES (OR, 0.93; 95% CI, 0.87–0.99; P = 0.03). No evidence of heterogeneity and horizontal pleiotropy was observed in the MR analysis, indicating the robustness of our findings. A meta‐analysis further supported this association, indicating a reduced risk (OR, 0.89; 95% CI, 0.81–0.98; P = 0.02). No reverse causality was found between HF and these three inflammatory proteins (P > 0.05 for all).ConclusionsThis study provides genetically supported evidence of the causal association of specific inflammatory proteins with HF risk. The positive association of MMP‐1 and TNF‐beta with HF suggests their roles in disease pathogenesis, whereas the inverse association of the uPA indicates its potential protective effect. Our findings highlight the potential of targeting specific inflammatory pathways as a therapeutic strategy for HF.
Background NOX-1 overexpression has been observed in various studies, persons with diabetes or cardiovascular conditions. NOX-1 orchestrates the disease pathogenesis of various cardiovascular conditions such as atherosclerotic plaque development and is a very crucial biomarker. Therefore, this study was carried out to deduce the three-dimensional modelled structure of NOX-1 using DeepMind AlphaFold-2 to find meaningful insight into the structural biology. Extensive in silico approaches have been used to determine the active pocket, virtually screen large chemical space to identify potential inhibitors. The role of the key amino acid residues was also deduced using alanine scanning mutagenesis contributing to the catalytic process and to the overall stability of NOX-1. Results The modelled structure of NOX-1 protein was validated using ERRAT. The ERRAT statistics with 9 amino acids sliding window have shown a confidence score of 96.937%. According to the Ramachandran statistics, 96.60% of the residues lie within the most favoured region, and 2.80% of residues lie in the additionally allowed region, which gives an overall of 99.4% residues in the three quadrants in the plot. GKT-831 which is a referral drug in this study has shown a GOLD interaction score of 62.12 with respect to the lead molecule zinc000059139266 which has shown a higher GOLD score of 78.07. Alanine scanning mutagenesis studies has shown that Phe201, Leu98 and Leu76 are found to be the key interacting residues in hydrophobic interactions. Similarly, Tyr324, Arg287 and Cys73 are major amino acid residues in the hydrogen bond interactions. Conclusions NOX-1 overexpression leads to heightened ROS production resulting in catastrophic outcomes. The modelled structure of NOX-1 has a good stereochemistry with respect to Ramachandran plot. The lead molecule zinc000059139266 has shown to have a very high interaction score of 78.07 compared to the referral drug GKT-831 with a score of 62.12. There is an excellent scope for the lead molecule to progress further into in vitro and in vivo studies.
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