Associations of NADPH oxidase-related genes with blood pressure changes and incident hypertension: The GenSalt Study

NADPH oxidase family proteins: signaling dynamics to disease management

Reactive oxygen species (ROS) are pervasive signaling molecules in biological systems. In humans, a lack of ROS causes chronic and extreme bacterial infections, while uncontrolled release of these factors causes pathologies due to excessive inflammation. Professional phagocytes such as neutrophils (PMNs), eosinophils, monocytes, and macrophages use superoxide-generating NADPH oxidase (NOX) as part of their arsenal of antimicrobial mechanisms to produce high levels of ROS. NOX is a multisubunit enzyme complex composed of five essential subunits, two of which are localized in the membrane, while three are localized in the cytosol. In resting phagocytes, the oxidase complex is unassembled and inactive; however, it becomes activated after cytosolic components translocate to the membrane and are assembled into a functional oxidase. The NOX isoforms play a variety of roles in cellular differentiation, development, proliferation, apoptosis, cytoskeletal control, migration, and contraction. Recent studies have identified NOX as a major contributor to disease pathologies, resulting in a shift in focus on inhibiting the formation of potentially harmful free radicals. Therefore, a better understanding of the molecular mechanisms and the transduction pathways involved in NOX-mediated signaling is essential for the development of new therapeutic agents that minimize the hyperproduction of ROS. The current review provides a thorough overview of the various NOX enzymes and their roles in disease pathophysiology, highlights pharmacological strategies, and discusses the importance of computational modeling for future NOX-related studies.

Comprehensive Analysis to Identify Key Genes Involved in Advanced Atherosclerosis

Background

The study was aimed at finding accurate and effective therapeutic targets and deepening our understanding of the mechanisms of advanced atherosclerosis (AA).

Methods

We downloaded the gene expression datasets GSE28829, GSE120521, and GSE43292 from Gene Expression Omnibus. Weighted gene coexpression network analysis (WGCNA) was performed for GSE28829, and functional enrichment analysis and protein–protein interaction network analysis were conducted on the key module. Significant genes in the key module were analyzed by molecular complex detection, and genes in the most important subnetwork were defined as hub genes. Multiple dataset analyses for hub genes were conducted. Genes that overlapped between hub genes and differentially expressed genes (DEGs) of GSE28829 and GSE120521 were defined as key genes. Further validation for key genes was performed using GSE28829 and GSE43292. Gene set enrichment analysis (GSEA) was applied to key genes.

Results

A total of 77 significant genes in the key module of GSE28829 were screened out that were mainly associated with inflammation and immunity. The subnetwork was obtained from significant genes, and 18 genes in this module were defined as hub genes, which were related to immunity and expressed in multiple diseases, particularly systemic lupus erythematosus. Some hub genes were regulated by SPI1 and associated with the blood, spleen, and lung. After overlapping with DEGs of GSE28829 and GSE120521, a total of 10 genes (HCK, ITGAM, CTSS, TYROBP, LAPTM5, FCER1G, ITGB2, NCF2, AIF1, and CD86) were identified as key genes. All key genes were validated and evaluated successfully and were related to immune response pathways.

Conclusion

Our study suggests that the key genes related to immune and inflammatory responses are involved in the development of AA. This may deepen our understanding of the mechanisms of and provide valuable therapeutic targets for AA.

Identification of Potential Biomarkers Associated with Acute Myocardial Infarction by Weighted Gene Coexpression Network Analysis

Background

In the general population, acute myocardial infarction (AMI) represents a significant cause of mortality. This study is aimed at identifying novel diagnostic biomarkers to aid in treating and diagnosing AMI.

Methods

The Gene Expression Omnibus (GEO) database was explored to extract two microarray datasets, GSE66360 and GSE48060, which were subsequently merged into a single cohort. Both AMI and control samples were analyzed for differentially expressed genes (DEGs), which were subsequently subjected to weighed gene coexpression network analysis (WGCNA) to identify the most significant module. Gene Ontology (GO) and pathway analyses subsequently carried out the most significant gene modules along with construction of a protein-protein interaction network (PPI). Cytoscape plugin cytoHubba allowed for the prediction of the top 4 key genes according to the network maximal clique centrality (MCC) algorithm. The expression levels and diagnostic value of the four key genes were additionally verified in the GSE62646 dataset.

Results

A WCGNA analysis revealed 878 DEGs which were clustered into 6 modules. The module with the most significance in AMI was colored blue. Subsequent GO and KEGG pathway enrichment analysis on blue module genes revealed that they were primarily enriched in the inflammation-related pathways. These findings, in combination with PPI and coexpression networks, resulted in the identification of the top four genes by cytoHubba, which included leukocyte immunoglobulin-like receptor B2 (LILRB2), toll-like receptor 2 (TLR2), neutrophil cytosolic factor 2 (NCF2), and S100A9. Among them, LILRB2, NCF2, and S100A9 were validated in the GSE62646 dataset.

Conclusions

The results suggested that LILRB2, NCF2, and S100A9 could be potential gene biomarkers for AMI.

Pleiotropy of systemic lupus erythematosus risk alleles and cardiometabolic disorders: A phenome-wide association study and inverse-variance weighted meta-analysis

OBJECTIVES

To test the hypothesis that genetic predisposition to systemic lupus erythematosus (SLE) increases the risk of cardiometabolic disorders.

METHODS

Using 41 single nucleotide polymorphisms (SNPs) associated with SLE, we calculated a weighted genetic risk score (wGRS) for SLE. In a large biobank we tested the association between this wGRS and 9 cardiometabolic phenotypes previously associated with SLE: atrial fibrillation, ischemic stroke, coronary artery disease, type 1 and type 2 diabetes, obesity, chronic kidney disease, hypertension, and hypercholesterolemia. Additionally, we performed a phenome-wide association analysis (pheWAS) to discover novel clinical associations with a genetic predisposition to SLE. Findings were replicated in the Electronic Medical Records and Genomics (eMERGE) Network. To further define the association between SLE-related risk alleles and the selected cardiometabolic phenotypes, we performed an inverse variance weighted regression (IVWR) meta-analysis.

RESULTS

The wGRS for SLE was calculated in 74,759 individuals of European ancestry. Among the pre-selected phenotypes, the wGRS was significantly associated with type 1 diabetes (OR [95%CI] =1.11 [1.06, 1.17], P-value = 1.05x10-5). In the PheWAS, the wGRS was associated with several autoimmune phenotypes, kidney disorders, and skin neoplasm; but only the associations with autoimmune phenotypes were replicated. In the IVWR meta-analysis, SLE-related risk alleles were nominally associated with type 1 diabetes (P = 0.048) but the associations were heterogeneous and did not meet the adjusted significance threshold.

CONCLUSION

A weighted GRS for SLE was associated with an increased risk of several autoimmune-related phenotypes including type I diabetes but not with cardiometabolic disorders.

Oxidative Stress and Hypertension

A link between oxidative stress and hypertension has been firmly established in multiple animal models of hypertension but remains elusive in humans. While initial studies focused on inactivation of nitric oxide by superoxide, our understanding of relevant reactive oxygen species (superoxide, hydrogen peroxide, and peroxynitrite) and how they modify complex signaling pathways to promote hypertension has expanded significantly. In this review, we summarize recent advances in delineating the primary and secondary sources of reactive oxygen species (nicotinamide adenine dinucleotide phosphate oxidases, uncoupled endothelial nitric oxide synthase, endoplasmic reticulum, and mitochondria), the posttranslational oxidative modifications they induce on protein targets important for redox signaling, their interplay with endogenous antioxidant systems, and the role of inflammasome activation and endoplasmic reticular stress in the development of hypertension. We highlight how oxidative stress in different organ systems contributes to hypertension, describe new animal models that have clarified the importance of specific proteins, and discuss clinical studies that shed light on how these processes and pathways are altered in human hypertension. Finally, we focus on the promise of redox proteomics and systems biology to help us fully understand the relationship between ROS and hypertension and their potential for designing and evaluating novel antihypertensive therapies.

Construction and Validation of a New Model for the Prediction of Rupture in Patients with Intracranial Aneurysms

BACKGROUND

Despite progress in the detection of biological molecules that contribute to intracranial aneurysm (IA) development, many pathophysiological mechanisms remain unclear, particularly with regard to predicting IA rupture. In this study, we aimed to identify hub genes and construct a new model to predict IA rupture.

METHODS

Four datasets (62 ruptured IAs, 16 unruptured IAs, and 31 normal controls) were downloaded from the Gene Expression Omnibus. Differentially expressed genes (DEGs) were identified between the IAs and normal controls. All overlapping genes were analyzed using weighted gene co-expression network analysis. Functional enrichment analyses were performed using key modules. We then intersected the key module genes with DEGs. Protein-protein interaction networks were assessed to identify key hub genes. Least absolute shrinkage and selection operator logistic regression analysis was performed to construct a prediction model. A receiver operating characteristic curve was constructed to evaluate the reliability of the scoring system.

RESULTS

After intersection and normalization, 433 DEGs were identified and 15,388 genes were selected for weighted gene co-expression network analysis. The black module with 1145 genes exhibited the highest correlation with IA rupture. Many potential mechanisms are involved, such as the inflammatory response, innate immune response, extracellular exosome, and extracellular space. Thirty hub genes were selected from the protein-protein interaction, and 4 independent risk genes, TNFAIP6, NCF2, OSM, and IRAK3, were identified in the least absolute shrinkage and selection operator logistic regression model.

CONCLUSIONS

Our prediction model not only serves as a useful tool for assessing the risk of IA rupture, but the key genes identified herein could also serve as biomarkers and therapeutic targets.

Weighted Gene Co-expression Network Analysis Identifies Crucial Genes Mediating Progression of Carotid Plaque

Background

Surface rupture of carotid plaque can cause severe cerebrovascular disease, including transient ischemic attack and stroke. The aim of this study was to elucidate the molecular mechanism governing carotid plaque progression and to provide candidate treatment targets for carotid atherosclerosis.

Methods

The microarray dataset GSE28829 and the RNA-seq dataset GSE104140, which contain advanced plaque and early plaque samples, were utilized in our analysis. Differentially expressed genes (DEGs) were screened using the “limma” R package. Gene modules for both early and advanced plaques were identified based on co-expression networks constructed by weighted gene co-expression network analysis (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes Genomes (KEGG) analyses were employed in each module. In addition, hub genes for each module were identified. Crucial genes were identified by molecular complex detection (MCODE) based on the DEG co-expression network and were validated by the GSE43292 dataset. Gene set enrichment analysis (GSEA) for crucial genes was performed. Sensitivity analysis was performed to evaluate the robustness of the networks that we constructed.

Results

A total of 436 DEGs were screened, of which 335 were up-regulated and 81 were down-regulated. The pathways related to inflammation and immune response were determined to be concentrated in the black module of the advanced plaques. The hub gene of the black module was ARHGAP18 (Rho GTPase activating protein 18). NCF2 (neutrophil cytosolic factor 2), IQGAP2 (IQ motif containing GTPase activating protein 2) and CD86 (CD86 molecule) had the highest connectivity among the crucial genes. All crucial genes were validated successfully, and sensitivity analysis demonstrated that our results were reliable.

Conclusion

To the best of our knowledge, this study is the first to combine DEGs and WGCNA to establish a DEG co-expression network in carotid plaques, and it proposes potential therapeutic targets for carotid atherosclerosis.

NOX5-induced uncoupling of endothelial NO synthase is a causal mechanism and theragnostic target of an age-related hypertension endotype

Hypertension is the most important cause of death and disability in the elderly. In 9 out of 10 cases, the molecular cause, however, is unknown. One mechanistic hypothesis involves impaired endothelium-dependent vasodilation through reactive oxygen species (ROS) formation. Indeed, ROS forming NADPH oxidase (Nox) genes associate with hypertension, yet target validation has been negative. We re-investigate this association by molecular network analysis and identify NOX5, not present in rodents, as a sole neighbor to human vasodilatory endothelial nitric oxide (NO) signaling. In hypertensive patients, endothelial microparticles indeed contained higher levels of NOX5—but not NOX1, NOX2, or NOX4—with a bimodal distribution correlating with disease severity. Mechanistically, mice expressing human Nox5 in endothelial cells developed—upon aging—severe systolic hypertension and impaired endothelium-dependent vasodilation due to uncoupled NO synthase (NOS). We conclude that NOX5-induced uncoupling of endothelial NOS is a causal mechanism and theragnostic target of an age-related hypertension endotype. Nox5 knock-in (KI) mice represent the first mechanism-based animal model of hypertension.

The causes of hypertension are not understood; treatments are symptomatic and prevent only few of the associated risks. This study applies network medicine to identify a subgroup of patients with NADPH oxidase 5-induced uncoupling of nitric oxide synthase as the cause of age-related hypertension, enabling a first-in-class mechanism-based treatment of hypertension.

NCF2, MYO1F, S1PR4, and FCN1 as potential noninvasive diagnostic biomarkers in patients with obstructive coronary artery: A weighted gene co-expression network analysis

This study aims to explore the predictive noninvasive biomarker for obstructive coronary artery disease (CAD). By using the data set GSE90074, weighted gene co-expression network analysis (WGCNA), and protein-protein interactive network, construction of differentially expressed genes in peripheral blood mononuclear cells was conducted to identify the most significant gene clusters associated with obstructive CAD. Univariate and multivariate stepwise logistic regression analyses and receiver operating characteristic analysis were used to predicate the diagnostic accuracy of biomarker candidates in the detection of obstructive CAD. Furthermore, functional prediction of candidate gene biomarkers was further confirmed in ST-segment elevation myocardial infarction (STEMI) patients or stable CAD patients by using the datasets of GSE62646 and GSE59867. We found that the blue module discriminated by WGCNA contained 13 hub-genes that could be independent risk factors for obstructive CAD (P < .05). Among these 13 hub-genes, a four-gene signature including neutrophil cytosol factor 2 (NCF2, P = .025), myosin-If (MYO1F, P = .001), sphingosine-1-phosphate receptor 4 (S1PR4, P = .015), and ficolin-1 (FCN1, P = .012) alone or combined with two risk factors (male sex and hyperlipidemia) may represent potential diagnostic biomarkers in obstructive CAD. Furthermore, the messenger RNA levels of NCF2, MYO1F, S1PR4, and FCN1 were higher in STEMI patients than that in stable CAD patients, although S1PR4 showed no statistical difference (P > .05). This four-gene signature could also act as a prognostic biomarker to discriminate STEMI patients from stable CAD patients. These findings suggest a four-gene signature (NCF2, MYO1F, S1PR4, and FCN1) alone or combined with two risk factors (male sex and hyperlipidemia) as a promising prognostic biomarker in the diagnosis of STEMI. Well-designed cohort studies should be implemented to warrant the diagnostic value of these genes in clinical purpose.

Genetic polymorphisms associated with reactive oxygen species and blood pressure regulation

Hypertension is the most prevalent cause of cardiovascular disease and kidney failure, but only about 50% of patients achieve adequate blood pressure control, in part, due to inter-individual genetic variations in the response to antihypertensive medication. Significant strides have been made toward the understanding of the role of reactive oxygen species (ROS) in the regulation of the cardiovascular system. However, the role of ROS in human hypertension is still unclear. Polymorphisms of some genes involved in the regulation of ROS production are associated with hypertension, suggesting their potential influence on blood pressure control and response to antihypertensive medication. This review provides an update on the genes associated with the regulation of ROS production in hypertension and discusses the controversies on the use of antioxidants in the treatment of hypertension, including the antioxidant effects of antihypertensive drugs.