蛋白质组学对高血压相关性白蛋白尿病理生理学的启示:南非队列试点研究。

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
Melanie A Govender, Stoyan H Stoychev, Jean-Tristan Brandenburg, Michèle Ramsay, June Fabian, Ireshyn S Govender
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引用次数: 0

摘要

背景:高血压是一项重要的公共卫生优先事项,在非洲的发病率很高。它也是导致肾功能衰竭的一个独立风险因素。我们的目的是通过评估合并高血压和白蛋白尿的南非黑人参与者的尿液蛋白质组图谱,确定与高血压相关的白蛋白尿有关的潜在蛋白质和通路,并与两者均无高血压和白蛋白尿的参与者进行比较:研究对象包括 24 名同时患有高血压和白蛋白尿的南非病例,以及 49 名既无高血压也无白蛋白尿的对照组参与者。从尿液样本中提取蛋白质,并使用超高效液相色谱法和质谱法进行分析。数据使用数据独立采集(DIA)生成,并使用 Spectronaut™ 15 进行处理。统计和功能数据注释在 Perseus 和 Cytoscape 上进行,以识别和注释差异丰富的蛋白质。使用 OmicLearn 平台对数据集进行了机器学习:总体而言,对照组和病例组分别量化了平均 1,225 个和 915 个蛋白质。共构建了 332 个差异丰度蛋白网络。与这些差异丰度蛋白相关的通路包括免疫系统(q 值[错误发现率] = 1.4 × 10-45)、先天免疫系统(q = 1.1 × 10-32)、细胞外基质(ECM)组织(q = 0.03)和基质金属蛋白酶活化(q = 0.04)。对高血压和慢性肾脏病具有高疾病评分(置信度为 76-100% )的蛋白质包括血管紧张素原 (AGT)、白蛋白 (ALB)、脂蛋白 L1 (APOL1) 和尿调节蛋白 (UMOD)。机器学习方法能够识别一组 20 种蛋白质,区分病例和对照组:尿液蛋白质组数据与机器学习方法相结合,能够对疾病状态进行分类,并确定与高血压相关性白蛋白尿有关的蛋白质和通路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Proteomic insights into the pathophysiology of hypertension-associated albuminuria: Pilot study in a South African cohort.

Background: Hypertension is an important public health priority with a high prevalence in Africa. It is also an independent risk factor for kidney outcomes. We aimed to identify potential proteins and pathways involved in hypertension-associated albuminuria by assessing urinary proteomic profiles in black South African participants with combined hypertension and albuminuria compared to those who have neither condition.

Methods: The study included 24 South African cases with both hypertension and albuminuria and 49 control participants who had neither condition. Protein was extracted from urine samples and analysed using ultra-high-performance liquid chromatography coupled with mass spectrometry. Data were generated using data-independent acquisition (DIA) and processed using Spectronaut™ 15. Statistical and functional data annotation were performed on Perseus and Cytoscape to identify and annotate differentially abundant proteins. Machine learning was applied to the dataset using the OmicLearn platform.

Results: Overall, a mean of 1,225 and 915 proteins were quantified in the control and case groups, respectively. Three hundred and thirty-two differentially abundant proteins were constructed into a network. Pathways associated with these differentially abundant proteins included the immune system (q-value [false discovery rate] = 1.4 × 10- 45), innate immune system (q = 1.1 × 10- 32), extracellular matrix (ECM) organisation (q = 0.03) and activation of matrix metalloproteinases (q = 0.04). Proteins with high disease scores (76-100% confidence) for both hypertension and chronic kidney disease included angiotensinogen (AGT), albumin (ALB), apolipoprotein L1 (APOL1), and uromodulin (UMOD). A machine learning approach was able to identify a set of 20 proteins, differentiating between cases and controls.

Conclusions: The urinary proteomic data combined with the machine learning approach was able to classify disease status and identify proteins and pathways associated with hypertension-associated albuminuria.

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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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