Integration of proteomic and metabolomic characterization in atrial fibrillation-induced heart failure.
Integration of proteomic and metabolomic characterization in atrial fibrillation-induced heart failure.
复制标题
房颤诱发心力衰竭中蛋白质组学和代谢组学特征的整合
DOI：
10.1186/s12864-022-09044-z
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发表时间：
2022-12-01
期刊：
影响因子：
4.4
通讯作者：
中科院分区：
文献类型：
--
作者：
心房颤动（AF）诱发心力衰竭（HF）的确切机制仍不清楚。本研究将蛋白质组学和代谢组学结合起来，描述 AF 患者失调的蛋白质和代谢物，比较无心力衰竭的患者与心力衰竭患者。通过多组学平台对 20 名无心力衰竭患者和另外 20 名心力衰竭患者的血浆样本进行了分析。蛋白质组学采用基于数据独立采集的液相色谱-串联质谱 (LC-MS/MS) 进行，代谢组学则采用 LC-MS/MS 平台进行。使用单变量统计方法、多变量统计方法或机器学习模型分别分析和整合蛋白质组学和代谢组学结果。我们发现，与没有心力衰竭的房颤患者相比，合并心力衰竭的房颤患者中有 35 个上调和 15 个下调的差异表达蛋白 (DEP)。此外，与无心力衰竭的房颤患者相比，心力衰竭患者中发现了 121 种上调和 14 种下调的差异表达代谢物 (DEM)。蛋白质组学和代谢组学的综合分析揭示了几种显着富集的途径，包括糖酵解或糖异生、酪氨酸代谢和磷酸戊糖途径。总共 10 个 DEP 和 DEM 被选为潜在生物标志物，提供了出色的预测性能，AUC 为 0.94。此外，基于代谢组学对心力衰竭分类进行亚组分析，得到了9个可以区分心力衰竭和心力衰竭的DEM，用于心力衰竭分类。这项研究为理解 AF 诱导的 HF 进展机制以及通过代谢组学和蛋白质组学分析识别 AF 合并 HF 预后的新生物标志物提供了新的见解。在线版本包含可在 10.1186/s12864-022-09044-z 获取的补充材料。
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. 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. 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. 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. The online version contains supplementary material available at 10.1186/s12864-022-09044-z.
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影响因子：
5.4
作者：
McManus DD;Hsu G;Sung SH;Saczynski JS;Smith DH;Magid DJ;Gurwitz JH;Goldberg RJ;Go AS;Cardiovascular Research Network PRESERVE Study
通讯作者：
Cardiovascular Research Network PRESERVE Study
影响因子：
14.9
作者：
Kanehisa M;Furumichi M;Sato Y;Ishiguro-Watanabe M;Tanabe M
通讯作者：
Tanabe M
线粒体丙酮酸载体丰度介导病理心脏肥大。
影响因子：
20.8
作者：
Fernandez-Caggiano M;Kamynina A;Francois AA;Prysyazhna O;Eykyn TR;Krasemann S;Crespo-Leiro MG;Vieites MG;Bianchi K;Morales V;Domenech N;Eaton P
通讯作者：
Eaton P
影响因子：
5.6
作者：
Chaanine AH;Higgins L;Markowski T;Harman J;Kachman M;Burant C;Navar LG;Busija D;Delafontaine P
通讯作者：
Delafontaine P
影响因子：
24
作者：
Mayr, Manuel;Yusuf, Shamil;Camm, A. John
通讯作者：
Camm, A. John