鉴定正乙酰-L-半胱氨酸和异丙肾上腺素诱导心脏肥大重塑的血清预测因子。

Turkish journal of biology = Turk biyoloji dergisi Pub Date : 2021-06-23 eCollection Date: 2021-01-01 DOI:10.3906/biy-2101-56
Dharaniyambigai Kuberapandian, Victor Arokia Doss
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摘要

心肌肥大(CH)导致心力衰竭的原因是细胞应激过程中发生的慢性代谢改变。除了已知的氧化应激与心肌肥大之间的关系外,还存在还原性应激导致心肌肥大的影响。本研究试图利用谷胱甘肽激动剂 n-乙酰-L-半胱氨酸(NAC)建立基于还原应激的 CH 大鼠模型,并与典型的异丙肾上腺素(ISO)诱导的 CH 模型进行比较。研究的主要目的是确定血清代谢物,这些代谢物可作为慢性心肌梗死的七个常规临床和诊断参数的有效预测因子:3-羟丁酸(3-HB)、乳酸(LA)、尿素和心电图-慢性心肌梗死参数(QRS 波群、R 波幅、R-R 间期、心率),本研究假设这些参数是代谢重塑的基础。2 周后,使用心电图、肥厚指数和组织病理学分析(H&E 染色)对两个心室的 CH 进行评估。气相色谱-质谱分析(GC-MS)确定了独特的代谢物指纹图谱。相关性和模式分析显示,特定代谢物与本研究参数(皮尔逊评分大于 0.7)之间存在密切关系。对与七个参数(因变量)中每个参数密切相关的代谢物(自变量)进行多元回归分析(MRA),确定了后者的重要预测因子,即 NAC 模型中的果糖、缬氨酸和丁酸,以及 ISO 模型中的胆固醇、红糖和异亮氨酸,而脯氨酸和琥珀酸则是这两个模型的共同预测因子。对这些重要预测因子(p < 0.05)进行的代谢物集富集分析(MSEA)显示,丁酸代谢是 NAC 中影响较大的途径,精氨酸-脯氨酸代谢和支链氨基酸(BCAA)降解是两个模型的共同途径,从而为 CH 发病机制中的初始代谢重塑提供了新的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Identification of serum predictors of n-acetyl-l-cysteine and isoproterenol induced remodelling in cardiac hypertrophy.

Cardiac hypertrophy (CH), leading to cardiac failure is due to chronic metabolic alterations occurring during cellular stress. Besides the already known relationship between oxidative stress and CH, there are implications of reductive stress leading to CH. This study attempted to develop reductive stress-based CH rat model using n-acetyl-L-cysteine (NAC), a glutathione agonist that was compared with typical isoproterenol (ISO) induced CH model. The main objective was to identify serum metabolites that can serve as potent predictors for seven routine clinical and diagnostic parameters in CH: 3-hydroxybutyrate (3-HB), lactic acid (LA), urea, and ECG-CH parameters (QRS complex, R-amplitude, R-R interval, heart rate) that were hypothesized to underlie metabolic remodelling in this study. CH was assessed using electrocardiography, hypertrophic index and histopathological analysis (H&E stain) in both ventricles after 2 weeks. Gas chromatography mass spectroscopy analysis (GC-MS) identified unique metabolite finger-prints. Correlation and pattern analysis revealed strong relationships between specific metabolites and parameters (Pearson's score > 0.7) of this study. Multiple regression analysis (MRA) for the strongly related metabolites (independent variables) with each of the seven parameters (dependent variables) identified significant predictors for the latter namely fructose, valine, butanoic acid in NAC and cholesterol, erythrose, isoleucine in ISO models, with proline and succinic acid as common for both models. Metabolite set enrichment analysis (MSEA) of those significant predictors (p < 0.05) mapped butyrate metabolism as highly influential pathway in NAC, with arginine-proline metabolism and branched chain amino acid (BCAA) degradation as common pathways in both models, thus providing new insights towards initial metabolic remodeling in the pathogenesis of CH.

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