蛋白质组学和代谢组学特征在心房颤动引起的心力衰竭中的整合。
Integration of proteomic and metabolomic characterization in atrial fibrillation-induced heart failure.
机构信息
Key Laboratory of Cardiovascular Disease Acousto-Optic Electromagnetic Diagnosis and Treatment in Heilongjiang Province, the First Affiliated Hospital, Harbin Medical University, 23 Youzheng Street, Nangang District, Harbin, 150001, China.
Department of Cardiology, the First Affiliated Hospital, Harbin Medical University, 23 Youzheng Street, Nangang District, Harbin, 150001, China.
出版信息
BMC Genomics. 2022 Dec 1;23(1):789. doi: 10.1186/s12864-022-09044-z.
BACKGROUND
The exact mechanism of atrial fibrillation (AF)-induced heart failure (HF) remains unclear. Proteomics and metabolomics were integrated to in this study, as to describe AF patients' dysregulated proteins and metabolites, comparing patients without HF to patients with HF.
METHODS
Plasma samples of 20 AF patients without HF and another 20 with HF were analyzed by multi-omics platforms. Proteomics was performed with data independent acquisition-based liquid chromatography-tandem mass spectrometry (LC-MS/MS), as metabolomics was performed with LC-MS/MS platform. Proteomic and metabolomic results were analyzed separately and integrated using univariate statistical methods, multivariate statistical methods or machine learning model.
RESULTS
We found 35 up-regulated and 15 down-regulated differentially expressed proteins (DEPs) in AF patients with HF compared to AF patients without HF. Moreover, 121 up-regulated and 14 down-regulated differentially expressed metabolites (DEMs) were discovered in HF patients compared to AF patients without HF. An integrated analysis of proteomics and metabolomics revealed several significantly enriched pathways, including Glycolysis or Gluconeogenesis, Tyrosine metabolism and Pentose phosphate pathway. A total of 10 DEPs and DEMs selected as potential biomarkers provided excellent predictive performance, with an AUC of 0.94. In addition, subgroup analysis of HF classification was performed based on metabolomics, which yielded 9 DEMs that can distinguish between AF and HF for HF classification.
CONCLUSIONS
This study provides novel insights to understanding the mechanisms of AF-induced HF progression and identifying novel biomarkers for prognosis of AF with HF by using metabolomics and proteomics analyses.
背景
心房颤动（AF）引起心力衰竭（HF）的确切机制仍不清楚。本研究整合了蛋白质组学和代谢组学，以描述 AF 患者失调的蛋白质和代谢物，将无 HF 的患者与有 HF 的患者进行比较。
方法
使用基于数据非依赖性采集的液相色谱-串联质谱（LC-MS/MS）进行蛋白质组学分析，使用 LC-MS/MS 平台进行代谢组学分析，对 20 名无 HF 的 AF 患者和另外 20 名有 HF 的 AF 患者的血浆样本进行分析。蛋白质组学和代谢组学结果分别进行分析，并使用单变量统计方法、多变量统计方法或机器学习模型进行整合。
结果
与无 HF 的 AF 患者相比，HF 患者的 AF 患者中发现了 35 个上调和 15 个下调的差异表达蛋白（DEPs）。此外，与无 HF 的 AF 患者相比，HF 患者中发现了 121 个上调和 14 个下调的差异表达代谢物（DEMs）。蛋白质组学和代谢组学的综合分析揭示了几个显著富集的途径，包括糖酵解或糖异生、酪氨酸代谢和戊糖磷酸途径。作为潜在生物标志物选择的 10 个 DEP 和 DEM 提供了出色的预测性能，AUC 为 0.94。此外，根据代谢组学对 HF 分类进行了亚组分析，得出了 9 个可用于 HF 分类的区分 AF 和 HF 的 DEM。
结论
本研究通过代谢组学和蛋白质组学分析，为理解 AF 引起的 HF 进展的机制提供了新的见解，并为具有 HF 的 AF 的预后确定了新的生物标志物。
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