Molecular pathways associated with blood pressure and hexadecanedioate levels

Investigating the relationship between blood metabolites and diabetic retinopathy using two-sample mendelian randomization and in vivo validation

We addressed fundamental questions about the influence of metabolites on the development of Diabetic retinopathy (DR), and explored the related pathological mechanism. Genome-wide association study (GWAS) database data for metabolites and DR were used to perform Mendelian randomization (MR) studies. The inverse variance weighting (IVW) was chosen as the primary analysis method. Sensitivity analysis was conducted using MR-PRESSO, leave-one-out and Cochran's Q test. Confounding factors were eliminated to ensure robustness. We also conducted metabolic pathway analysis. In vivo experimental validation was conducted using Sprague Dawley rats. The serum metabolites of the DR group rats and normal group rats were examined to evaluate the MR results. The screen identified eighteen metabolites associated with DR risk, twelve of which were known components. Seven metabolites were positively correlated with DR risk, while five could reduce it. Eight metabolites associated with proliferative DR (PDR) risk were identified, four of which are known components. Three of these were positively associated with PDR risk and one metabolite reduced PDR risk. Additionally, two possible metabolic pathways involved in the biological mechanism of DR were identified. The ELISA results showed that the serum levels of isoleucine and 4-HPA were significantly increased in DR rats, while the level of inosine was decreased. This study offers novel insights into the biological mechanisms underlying DR. Metabolites that are causally linked to DR may serve as promising biomarkers and therapeutic targets.

Mechanism of Mongolian mind-body interactive therapy in regulating essential hypertension through HTR2B: A metabolome- and transcriptome-based study

Essential hypertension is a psychosomatic disease associated with emotions and behaviors. Although Mongolian mind-body interactive therapy can help patients with essential hypertension reduce their systolic blood pressure (SBP), the mechanism is unclear. We assigned patients who underwent Mongolian mind-body interactive therapy to groups that were treated with (DT) or without (NDT) antihypertensive drugs (Clinical registration no: ChiCTR2000034918). We screened differentially expressed genes (DEGs) using targeted metabolic and transcriptomic analyses of blood samples before and after intervention. Sequenced data were analyzed using quantitative polymerase chain reaction (qPCR) and validated using enzyme-linked immunosorbent assays (ELISAs). Small interfering (Si)-RNA interference on key DEGs in human umbilical vein endothelial cells (HUVECs) was experimentally verified. Omics analysis identified 187 DEGS, including human 5-hydroxytryptamine (5-HT) receptor 2B (5-HTR2B), human endothelin receptor type B (EDNRB), and the metabolite N-acetylserotonin. The qPCR and transcriptome sequencing results were consistent. Post-intervention ELISA assays revealed significantly elevated 5-HT in the NDT group after intervention (P < 0.05). Interactions between 5-HTR2B and N-acetylserotonin differed between the groups. The cellular findings showed significantly reduced G protein-coupled receptor 82 (GPR82) and phospholipid phosphatase-related protein type 4 (PLPPR4), and significantly increased S100A2 protein expression in the Si-HTR2B group, compared with the controls (P < 0.05). The biochemical results uncovered significantly decreased nitric oxide (NO) and significantly increased malondialdehyde and NO synthetase concentrations compared with the models (P < 0.05). Mongolian mind-body interactive therapy might affect SBP in patients with essential hypertension by combining 5-HT with 5-HTR2B to mediate NO relaxation.

Causal association between blood metabolites and risk of hypertension: a Mendelian randomization study

Background

Previous studies have indicated a strong link between blood metabolites and hypertension, however the causality of metabolites and hypertension is unknown.

Methods

Two-sample Mendelian randomization (MR) analysis was performed to assess the causal relationship between 486 blood metabolites and essential hypertension (EHT). Blood metabolite GWAS data was utilized as the exposure, with EHT GWAS data as the outcome. To further verify the results, another different source of EHT GWAS data was repeatedly analyzed. The major MR analytic approach used to determine causality was inverse variance weighted (IVW), with MR-Egger, Weighted Median, and MR-PRESSO models serving as supplements. We used the Cochran Q test to examine heterogeneity. Horizontal pleiotropy was examined using MR-Egger intercept and MR-PRESSO global test. The MR Steiger test confirmed the causal relationship between blood metabolites and EHT.

Results

In this study, nine blood metabolites associated with EHT were preliminarily identified by MR analysis, including four known metabolites (N-acetylornithine, X-12510-2-aminooctanoic acid, creatine, hexadecanedioate) and five unknown metabolites. Then another source of EHT GWAS data was repeatedly analyzed for further verification, and two overlapped metabolites (N-acetylornithine, X-12510-2-aminooctanoic acid) were found. There was a negative correlation between N-acetylornithine and EHT (OR = 0.987, 95% CI = 0.980-0.993, P = 1.01 × 10-4), Cochran's Q test suggested there was no heterogeneity (Q = 31.7586, P = 0.1331), MR-Egger intercept and MR-PRESSO global test suggested there was no horizontal pleiotropy (P > 0.05), Leave-one-out analysis indicated that no single single-nucleotide polymorphism (SNP) had a significant effect on the results, and MR Steiger test confirmed that the direction of causality was correct (P < 0.001). There was a negative correlation between X-12510-2-aminooctanoic acid and EHT (OR = 0.982, 95% CI = 0.972-0.993, P = 0.0017), and there was no evidence of heterogeneity or pleiotropy in multiple sensitivity analyses.

Conclusion

The study discovered some blood metabolites causally linked to EHT, which might lead to new understandings of the pathophysiology of hypertension.

Metabolomic signatures for blood pressure from early to late adolescence: findings from a U.S. cohort

INTRODUCTION

Metabolite signatures for blood pressure (BP) may reveal biomarkers, elucidate pathogenesis, and provide prevention targets for high BP. Knowledge regarding metabolites associated with BP in adolescence remains limited.

OBJECTIVES

Investigate the associations between metabolites and adolescent BP, both cross-sectionally (in early and late adolescence) and prospectively (from early to late adolescence).

METHODS

Participants are from the Project Viva prospective cohort. During the early (median: 12.8 years; N = 556) and late (median: 17.4 years; N = 501) adolescence visits, we conducted untargeted plasma metabolomic profiling and measured systolic (SBP) and diastolic BP (DBP). We used linear regression to identify metabolites cross-sectionally associated with BP at each time point, and to assess prospective associations of changes in metabolite levels from early to late adolescence with late adolescence BP. We used Weighted Gene Correlation Network Analysis and Spearman's partial correlation to identify metabolite clusters associated with BP at each time point.

RESULTS

In the linear models, higher androgenic steroid levels were consistently associated with higher SBP and DBP in early and late adolescence. A cluster of 59 metabolites, mainly composed of androgenic steroids, correlated with higher SBP and DBP in early adolescence. A cluster primarily composed of fatty acid lipids was marginally associated with higher SBP in females in late adolescence. Multiple metabolites, including those in the creatine and purine metabolism sub-pathways, were associated with higher SBP and DBP both cross-sectionally and prospectively.

CONCLUSION

Our results shed light on the potential metabolic processes and pathophysiology underlying high BP in adolescents.

Application of omics in hypertension and resistant hypertension

Hypertension is a major modifiable risk factor that affects the global health burden. Despite the availability of multiple antihypertensive drugs, blood pressure is often not optimally controlled. The prevalence of true resistant hypertension in treated hypertensive patients is ~2-20%, and these patients are at higher risk for adverse events and poor clinical outcomes. Therefore, an in-depth dissection of the pathophysiological mechanisms of hypertension and resistant hypertension is needed to identify more effective targets for regulating blood pressure. Omics technologies, such as genomics, transcriptomics, proteomics, metabolomics, and microbiomics, can accurately present the characteristics of organisms at varying molecular levels. Integrative omics can further reveal the network of interactions between molecular levels and provide a complete dynamic view of the organism. In this review, we describe the applications, progress, and challenges of omics technologies in hypertension. Specifically, we discuss the application of omics in resistant hypertension. We believe that omics approaches will produce a better understanding of the pathogenesis of hypertension and resistant hypertension and improve diagnostic and therapeutic strategies, thus increasing rates of blood pressure control and reducing the public health burden of hypertension.

A two-stage computational approach to predict novel ligands for a chemosensory receptor

Olfactory receptor (OR) 1A2 is the member of largest superfamily of G protein-coupled receptors (GPCRs). OR1A2 is an ectopically expressed receptor with only 13 known ligands, implicated in reducing hepatocellular carcinoma progression, with enormous therapeutic potential. We have developed a two-stage screening approach to identify novel putative ligands of OR1A2. We first used a pharmacophore model based on atomic property field (APF) to virtually screen a library of 5942 human metabolites. We then carried out structure-based virtual screening (SBVS) for predicting the potential agonists, based on a 3D homology model of OR1A2. This model was developed using a biophysical approach for template selection, based on multiple parameters including hydrophobicity correspondence, applied to the complete set of available GPCR structures to pick the most appropriate template. Finally, the membrane-embedded 3D model was refined by molecular dynamics (MD) simulations in both the apo and holo forms. The refined model in the apo form was selected for SBVS. Four novel small molecules were identified as strong binders to this olfactory receptor on the basis of computed binding energies.

Genomic Determinants of Hypertension With a Focus on Metabolomics and the Gut Microbiome

Epidemiologic and genomic studies have progressively improved our understanding of the causation of hypertension and the complex relationship with diet and environment. The majority of Mendelian forms of syndromic hypotension and hypertension (HTN) have all been linked to mutations in genes whose encoded proteins regulate salt-water balance in the kidney, supporting the primacy of the kidneys in blood pressure regulation. There are more than 1,477 single nucleotide polymorphisms associated with blood pressure and hypertension and the challenge is establishing a causal role for these variants. Hypertension is a complex multifactorial phenotype and it is likely to be influenced by multiple factors including interactions between diet and lifestyle factors, microbiome, and epigenetics. Given the finite genetic variability that is possible in humans, it is likely that incremental gains from single marker analyses have now plateaued and a greater leap in our understanding of the genetic basis of disease will come from integration of other omics and the interacting environmental factors. In this review, we focus on emerging results from the microbiome and metabolomics and discuss how leveraging these findings may facilitate a deeper understanding of the interrelationships between genomics, diet, and microbial ecology in humans in the causation of essential hypertension.

Using Network Pharmacology to Explore Potential Treatment Mechanism for Coronary Heart Disease Using Chuanxiong and Jiangxiang Essential Oils in Jingzhi Guanxin Prescriptions

Background

To predict the active components and potential targets of traditional Chinese medicine and to determine the mechanism behind the curative effect of traditional Chinese medicine, a multitargeted method was used. Jingzhi Guanxin prescriptions expressed a high efficacy for coronary heart disease (CHD) patients of which essential oils from Chuanxiong and Jiangxiang were confirmed to be the most important effective substance. However, the interaction between the active components and the targets for the treatment of CHD has not been clearly explained in previous studies.

Materials and Methods

Genes associated with the disease and the treatment strategy were searched from the electronic database and analyzed by Cytoscape (version 3.2.1). Protein-protein interaction network diagram of CHD with Jiangxiang and Chuanxiong essential oils was constructed by Cytoscape. Pathway functional enrichment analysis was executed by clusterProfiler package in R platform.

Results

121 ingredients of Chuanxiong and Jiangxiang essential oils were analyzed, and 393 target genes of the compositions and 912 CHD-related genes were retrieved. 15 coexpression genes were selected, including UGT1A1, DPP4, RXRA, ADH1A, RXRG, UGT1A3, PPARA, TRPC3, CYP1A1, ABCC2, AHR, and ADRA2A. The crucial pathways of occurrence and treatment molecular mechanism of CHD were analyzed, including retinoic acid metabolic process, flavonoid metabolic process, response to xenobiotic stimulus, cellular response to xenobiotic stimulus, cellular response to steroid hormone stimulus, retinoid binding, retinoic acid binding, and monocarboxylic acid binding. Finally, we elucidate the underlying role and mechanism behind these genes in the pathogenesis and treatment of CHD.

Conclusions

Generally speaking, the nodes in subnetwork affect the pathological process of CHD, thus indicating the mechanism of Jingzhi Guanxin prescriptions containing Chuanxiong and Jiangxiang essential oils in the treatment of CHD.

A prospective study of serum metabolites and risk of ischemic stroke

OBJECTIVE

To identify promising blood-based biomarkers and novel etiologic pathways of disease risk, we applied an untargeted serum metabolomics profiling in a community-based prospective study of ischemic stroke (IS).

METHODS

In 3,904 men and women from the Atherosclerosis Risk In Communities study, Cox proportional hazard models were used to estimate the association of incident IS with the standardized level of 245 fasting serum metabolites individually, adjusting for age, sex, race, field center, batch, diabetes, hypertension, current smoking status, body mass index, and estimated glomerular filtration rate. Validation of results was carried out in an independent sample of 114 IS cases and 112 healthy controls.

RESULTS

Serum levels of 2 long-chain dicarboxylic acids, tetradecanedioate and hexadecanedioate, were strongly correlated (r = 0.88) and were associated with incident IS after adjusting for covariates (hazard ratio [95% confidence interval (CI)] 1.11 [1.06-1.16] and 1.12 [1.07-1.17], respectively; p < 0.0001). Analyses by IS subtypes suggested that these associations were specific to cardioembolic stroke (CES). Associations of tetradecanedioate and hexadecanedioate with IS were independently confirmed (odds ratio [95% CI] 1.76 [1.21; 2.56] and 1.60 [1.11; 2.32], respectively).

CONCLUSION

Two serum long-chain dicarboxylic acids, metabolic products of ω-oxidation of fatty acids, were associated with IS and CES independently of known risk factors. Pathways related to intracellular hexadecanedioate synthesis or those involved in its clearance from the circulation may mediate IS risk. These results highlight the potential of metabolomics to discover novel circulating biomarkers for stroke and to unravel novel pathways for IS and its subtypes.

Distinct Prognostic Values of Alcohol Dehydrogenase Family Members for Non-Small Cell Lung Cancer

BACKGROUND Non-small cell lung cancer (NSCLC) is a leading cause of cancer-related death worldwide. The relationships of alcohol dehydrogenase (ADH) enzymes, encoded by the genes ADH1 (1A), ADH1B (ADH2), ADH1C (ADH3), ADH4, ADH5, ADH6, and ADH7, with NSCLC have not been studied. The aim of this study was to explore the associations between NSCLC prognosis and the expression patterns of ADH family members. MATERIAL AND METHODS The online resource Metabolic gEne RApid Visualizer was used to assess the expression patterns of ADH family members in normal and primary lung tumor tissues. The GeneMANIA plugin of Cytoscape software and STRING website were used to evaluate the relationships of the 7 ADH family members at the gene and protein levels. Gene ontology enrichment analysis and KEGG pathway analysis were performed using DAVID. The online website Kaplan-Meier Plotter was used to construct survival curves between NSCLC and ADH isoforms. RESULTS The prognosis of patients with high expression levels of the ADH1B, ADH1C, ADH4, and ADH5 genes was better than those with low expression in adenocarcinoma and all (containing adenocarcinoma and squamous cell cancer) histological types (all P<0.05). Low expression of ADH7 was associated with a better prognosis in patients with both the adenocarcinoma and squamous cell cancer histological types (P=9e-05). Moreover, expression of ADH family members was associated with smoking status, clinical stage, and chemotherapy status. CONCLUSIONS ADH1B, ADH1C, ADH4, ADH5, and ADH7 appear to be useful biomarkers for the prognosis of NSCLC patients.