He Shiman, Hu Xuejiao, Zhu Jiajun, Wang Weiteng, Ma Chi, Ran Peng, Chen Oudi, Chen Fanyu, Qing Hongkun, Ma Jianhong, Zeng Danni, Wang Yunzhi, Liu Weijiang, Feng Jinwen, Gan Lixi, Qin Zhaoyu, Tan Subei, Tian Sha, Ding Chen, Jian Xuhua, Gu Bing
Clinical Research Center for Cell-based Immunotherapy of Shanghai Pudong Hospital, State Key Laboratory of Genetics and Development of Complex Phenotypes, School of Life Sciences, Human Phenome Institute, Fudan University, Shanghai, China.
Department of Laboratory Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
Nat Commun. 2025 May 30;16(1):5052. doi: 10.1038/s41467-025-60184-8.
Infective endocarditis, a life-threatening condition, poses challenges for early diagnosis and personalized treatment due to insufficient biomarkers and limited understanding of its pathophysiology. Here, we performed proteomic profiling of plasma and vegetation samples from 238 patients with infective endocarditis and 100 controls, with validation in two external plasma cohorts (n = 328). We developed machine learning-based diagnostic and prognostic models for infective endocarditis, with area under the curve values of 0.98 and 0.87, respectively. Leucine-rich alpha-2-glycoprotein 1 and NADH:ubiquinone oxidoreductase subunit B4 are potential biomarkers associated with infection severity. Pathologically, protein networks characterized by glycometabolism, amino acid metabolism, and adhesion are linked to adverse events. Liver dysfunction may exacerbate the condition in patients with severe heart failure. Neutrophil extracellular traps emerge as promising therapeutic targets in Streptococcus or Staphylococcus aureus infections. Our findings provide insights into biomarker discovery and pathophysiological mechanisms in infective endocarditis, advancing early diagnosis and personalized medicine.
感染性心内膜炎是一种危及生命的疾病，由于生物标志物不足以及对其病理生理学的了解有限，给早期诊断和个性化治疗带来了挑战。在此，我们对238例感染性心内膜炎患者和100例对照者的血浆和赘生物样本进行了蛋白质组分析，并在两个外部血浆队列（n = 328）中进行了验证。我们开发了基于机器学习的感染性心内膜炎诊断和预后模型，曲线下面积值分别为0.98和0.87。富含亮氨酸的α-2-糖蛋白1和NADH：泛醌氧化还原酶亚基B4是与感染严重程度相关的潜在生物标志物。在病理上，以糖代谢、氨基酸代谢和黏附为特征的蛋白质网络与不良事件相关。肝功能障碍可能会加重重症心力衰竭患者的病情。中性粒细胞胞外陷阱成为链球菌或金黄色葡萄球菌感染中有前景的治疗靶点。我们的研究结果为感染性心内膜炎的生物标志物发现和病理生理机制提供了见解，推动了早期诊断和个性化医疗的发展。
