ADRb1 ELISA kit
- Known as:
- ADRb1 Enzyme-linked immunosorbent assay test reagent
- Catalog number:
- DL-ADRb1-Mu
- Product Quantity:
- 96T
- Category:
- Elisa Kits
- Supplier:
- WDSTD
- Gene target:
- ADRb1 ELISA kit
Related genes to: ADRb1 ELISA kit
- Gene:
- ADRB1 NIH gene
- Name:
- adrenoceptor beta 1
- Previous symbol:
- ADRB1R
- Synonyms:
- -
- Chromosome:
- 10q25.3
- Locus Type:
- gene with protein product
- Date approved:
- 1990-09-10
- Date modifiied:
- 2015-08-24
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- This case-control study investigated the association between β-adrenergic receptors, rs1801252 (A-to-G/Ser49Gly) and rs1042714 (C-to-G/Gln27Glu), polymorphisms and primary open-angle glaucoma (POAG) in a Saudi cohort. Genotyping of 418 participants (167 cases and 251 controls) was performed using real-time PCR assays. Analysis was performed among 412 participants with complete genotype data for both SNPs (163 cases and 249 controls). The rs1801252-G (Gly49) allele was significantly associated with increased POAG risk across allelic (adjusted odds ratio (OR) = 1.93; 95% confidence interval (CI) = 1.22-3.08; = 0.005), dominant (adjusted OR = 2.13; 95% CI = 1.23-3.70; = 0.007), and additive (adjusted OR per G allele = 1.93; 95% CI = 1.22-3.08; = 0.005) models, surviving Bonferroni correction (α = 0.025). In contrast, rs1042714 showed no significant association under any model. Combined two-locus allelic analysis suggested a nominally increased risk for the C-G combined profile (C + G; OR = 1.91; = 0.033). No significant genotype associations were observed with intraocular pressure or cup-to-disc ratio within the POAG cohort. These findings suggest that rs1801252 may contribute to POAG susceptibility in this population; replication in larger cohorts and targeted functional follow-up studies are warranted to elucidate pressure-independent mechanisms. - Source: PubMed
Publication date: 2026/07/26
Kondkar Altaf ASultan TahiraAzad Taif AOsman Essam AAlmobarak Faisal AAlsirhy Ehab YAl-Obeidan Saleh A - Aging is a fundamental driver of cardiovascular and cerebrovascular comorbidities, yet the mechanisms linking aging to concurrent heart and brain diseases remain incompletely understood. The neurovascular interface (NVI) refers to the structural and functional interface through which neural, vascular, and extracellular-matrix signals interact. In the CNS, this concept substantially overlaps with the classical neurovascular unit, whereas in the heart it refers to an emerging heart tissue-specific interface involving coronary microvessels, vascular cells, autonomic and sensory nerve fibers, cardiomyocytes, and extracellular-matrix components. Although direct experimental evidence that aging affects cardiovascular and cerebrovascular comorbidity through NVI is still accumulating, current evidence suggests that aging may disrupt shared neurovascular features. These changes are associated with hypertension, cerebral small vessel disease, heart failure, and related disorders. Moreover, genetic variants may act as susceptibility modifiers of neurovascular aging - from NOS3 regulating vascular tone to ADRB1 and COMT affecting autonomic balance. These variants are associated with cardiovascular and cerebrovascular disease risk and may represent upstream molecular determinants through which genetic susceptibility and aging-related stressors converge to alter NVI integrity and promote cardiovascular-cerebrovascular comorbidity. This review synthesizes current evidence on the structural and functional basis of the NVI, discusses how aging may disrupt its integrity, and evaluates its potential role as an integrative framework linking aging-related vascular, neural, inflammatory, and extracellular-matrix alterations to cardiovascular-cerebrovascular comorbidity. We also outline potential integrated strategies that may preserve interface function, including lifestyle interventions, senolytics, and gene-guided precision medicine. - Source: PubMed
Publication date: 2026/08/03
Chen KexinMa JunPeng Yong - Background and objective Antipsychotic-associated metabolic abnormalities, including weight gain, dyslipidemia, hyperglycemia, obesity, and metabolic syndrome, vary among drugs. Receptor pharmacology offers mechanistic clues, while transcriptome-wide association studies identify genetically regulated expression signals linked to lipid traits. These evidence streams are usually evaluated separately. This secondary computational sensitivity analysis used previously generated Ki-DDD-TWAS outputs and was not a primary discovery or clinical validation study. The primary hypothesis was that removing literature-derived metabolic receptor weights would preserve overall drug ranking, with Spearman ρ approximately 0.9, while shifting receptor-level contributions from histaminergic and serotonergic systems toward dopaminergic and low-prior TWAS signals. Secondary objectives were to identify low-weight genes with strong TWAS signals, assess discordance with an author-defined clinical-liability comparator, and test sensitivity to gene removal and TWAS scaling. Methods Previously generated outputs were analyzed. Ki, the inhibition constant, and DDD, the defined daily dose, served as affinity and exposure-proxy inputs. Receptor-level and per-drug risk-score files were analyzed for 52 antipsychotics across LDL, HDL, log-transformed triglycerides, non-HDL, and total cholesterol. Analyses included reconstruction validation, pooled receptor contributions, TWAS signal versus literature weight, clinical-liability discordance, leave-one-gene-out and TWAS-scaling sensitivity, and receptor co-occurrence in high-risk drug sets. No patient-level data, independent TWAS resources, longitudinal outcomes, or outcome-based validation were used. Results Reconstruction reproduced saved risk ranks with Spearman correlation 1.000 for all 10 mode-trait combinations. V4 rankings remained concordant after removing literature weights, with trait-specific correlations of 0.900-0.923 and a mean of 0.913. The weighted model was dominated by HRH1, HTR2A, and HTR2C, contributing 25.7%, 21.6%, and 10.3% of pooled signal. Under uniform weighting, DRD2, DRD3, and HTR2A led, accounting for 22.1%, 12.9%, and 12.6%. ADRB1 was the strongest high-priority low-weight TWAS candidate, with an absolute HDL z-score of 13.53 and literature weight of 0.08. Other candidates included CHRM4, DRD2, DRD4, SLC6A4, and ADRB2. The model most strongly under-ranked paliperidone versus the supplied 12-drug comparator, while ziprasidone rose under uniform weighting. Removing HRH1 produced the largest rank shifts, particularly for levomepromazine, promazine, and acepromazine. Conclusions Removing literature-derived weights preserved overall drug ranking but substantially changed receptor-level score allocation. The weighted model emphasized histaminergic and serotonergic receptors prioritized in metabolic-liability literature, whereas the uniform model revealed broader dopaminergic and low-prior contributions. ADRB1 is a computational hypothesis-generating candidate, not a confirmed mechanism or established metabolic-risk contributor. HDL-association direction was not analyzed. The framework supports dual reporting of clinically anchored weighted and discovery-oriented uniform models as an internal sensitivity strategy only. It is not externally validated, does not predict patient-level outcomes, and should not be interpreted as estimating clinical metabolic risk or establishing receptor-level causality. - Source: PubMed
Publication date: 2026/07/30
Cheung NgoCheung Hoi-KiYu Yee-WahTsang Yolanda Yuen-Ching - Natural products are a valuable source of antihypertensive agents; however, most studies have focused on multitarget effects or extract-level activity, limiting mechanism-specific interpretation and drug development. Here, a structure-driven screening strategy was applied to identify phytochemicals targeting CACNA1C, ADRB1, and AGTR1, key pharmacological targets of antihypertensive therapy. Quantitative structure-activity relationship models were developed using ChEMBL datasets (11,452 for CACNA1C, 612 for ADRB1, and 291 for AGTR1) and demonstrated robust predictive performance (AUC = 0.973), which was maintained under scaffold split and cross-validation. The SHAP-based interpretation revealed target-specific structural features, followed by pharmacophore profiling, chemical space analysis, and network-based prioritization. Overall, 10,586 natural compounds were screened and the top 5% per target (530 compound-target edges) were selected, revealing that 83.5% of the compounds were target-specific. Chemical space analysis revealed the predicted compounds occupied diverse and partially orthogonal regions compared with approved drugs, indicating scaffold diversification. Representative compounds selected based on docking, ADMET, and structural criteria predicted to form stable binding interactions in 100 ns molecular dynamics simulations, with consistent RMSD stabilization and favorable MM-PBSA binding free energies (-29.17 to -36.59 kJ/mol). Although source plants have reported antihypertensive effects, direct evidence in the literature linking individual compounds to specific pharmacological targets remains limited. Overall, this study presents a multilayer validation framework for the computational prioritization of antihypertensive phytochemicals, facilitating their potential integration of natural products into established pharmacological paradigms. This approach provides a strategy for prioritizing structurally diverse and computationally target-aligned candidates and may facilitate the translation of traditional herbal knowledge into modern target-based drug discovery. - Source: PubMed
Publication date: 2026/07/22
Park JunkyuShin SujinKim YoungminYoo JahyunLee KyungjinChoi Ho-Young - Longstanding observational work has associated Nonalcoholic Fatty Liver Disease (NAFLD) with hypertension and suggested that antihypertensive therapy may slow NAFLD progression. However, confounding and reverse causation in these studies obscure the effects on the risk for NAFLD. To tackle this problem, the study utilized Mendelian randomization (MR) to test the causal antihypertensive effects of drug targets on NAFLD and to evaluate safety. - Source: PubMed
Publication date: 2026/06/24
Zhang KanglongGuo LingGan YimingChen YihuaJiang ZhiweiYu ShuqinLei QiaoRan LinweiZheng JieHu Guoxin