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Abstract：
BACKGROUND: Programmed cell death (PCD) has recently been implicated in modulating the removal of neutrophils recruited in acute myocardial infarction (AMI). Nonetheless, the clinical significance and biological mechanism of neutrophil-related PCD remain unexplored.
METHODS: We employed an integrative machine learning-based computational framework to generate a predictive neutrophil-derived PCD signature (NPCDS) within five independent microarray cohorts from the peripheral blood of AMI patients. Non-negative matrix factorization was leveraged to develop an NPCDS-based AMI subtype. To elucidate the biological mechanism underlying NPCDS, we implemented single-cell transcriptomics on Cd45+ cells isolated from the murine heart of experimental AMI. We finally conducted a Mendelian randomization (MR) study and molecular docking to investigate the therapeutic value of NPCDS on AMI.
RESULTS: We reported the robust and superior performance of NPCDS in AMI prediction, which contributed to an optimal combination of random forest and stepwise regression fitted on nine neutrophil-related PCD genes (MDM2, PTK2B, MYH9, IVNS1ABP, MAPK14, GNS, MYD88, TLR2, CFLAR). Two divergent NPCDS-based subtypes of AMI were revealed, in which subtype 1 was characterized as inflammation-activated with more vibrant neutrophil activities, whereas subtype 2 demonstrated the opposite. Mechanically, we unveiled the expression dynamics of NPCDS to regulate neutrophil transformation from a pro-inflammatory phase to an anti-inflammatory phase in AMI. We uncovered a significant causal association between genetic predisposition towards MDM2 expression and the risk of AMI. We also found that lidoflazine, isotetrandrine, and cepharanthine could stably target MDM2.
CONCLUSIONS: Altogether, NPCDS offers significant implications for prediction, stratification, and therapeutic management for AMI.
METHODS: We employed an integrative machine learning-based computational framework to generate a predictive neutrophil-derived PCD signature (NPCDS) within five independent microarray cohorts from the peripheral blood of AMI patients. Non-negative matrix factorization was leveraged to develop an NPCDS-based AMI subtype. To elucidate the biological mechanism underlying NPCDS, we implemented single-cell transcriptomics on Cd45+ cells isolated from the murine heart of experimental AMI. We finally conducted a Mendelian randomization (MR) study and molecular docking to investigate the therapeutic value of NPCDS on AMI.
RESULTS: We reported the robust and superior performance of NPCDS in AMI prediction, which contributed to an optimal combination of random forest and stepwise regression fitted on nine neutrophil-related PCD genes (MDM2, PTK2B, MYH9, IVNS1ABP, MAPK14, GNS, MYD88, TLR2, CFLAR). Two divergent NPCDS-based subtypes of AMI were revealed, in which subtype 1 was characterized as inflammation-activated with more vibrant neutrophil activities, whereas subtype 2 demonstrated the opposite. Mechanically, we unveiled the expression dynamics of NPCDS to regulate neutrophil transformation from a pro-inflammatory phase to an anti-inflammatory phase in AMI. We uncovered a significant causal association between genetic predisposition towards MDM2 expression and the risk of AMI. We also found that lidoflazine, isotetrandrine, and cepharanthine could stably target MDM2.
CONCLUSIONS: Altogether, NPCDS offers significant implications for prediction, stratification, and therapeutic management for AMI.
摘要：
背景：最近，程序性细胞死亡（PCD）与调节急性心肌梗死（AMI）中招募的中性粒细胞的去除有关。尽管如此，中性粒细胞相关性PCD的临床意义和生物学机制尚待研究.
方法：我们采用基于机器学习的综合计算框架，在来自AMI患者外周血的五个独立微阵列队列中生成预测中性粒细胞衍生的PCD特征（NPCDS）。利用非负矩阵分解来开发基于NPCDS的AMI亚型。为了阐明NPCDS的生物学机制，我们对从实验性AMI小鼠心脏分离的Cd45+细胞实施了单细胞转录组学。最后，我们进行了孟德尔随机化（MR）研究和分子对接，以探讨NPCDS对AMI的治疗价值。
结果：我们报告了NPCDS在AMI预测中的稳健和优越的性能，这有助于在9个中性粒细胞相关PCD基因(MDM2，PTK2B，MYH9,IVNS1ABP,MAPK14,GNS,MYD88、TLR2、CFLAR)。揭示了两种不同的基于NPCDS的AMI亚型，其中亚型1的特征是炎症激活，中性粒细胞活动更活跃，而亚型2则相反。机械上,我们揭示了NPCDS在AMI中调节中性粒细胞从促炎阶段向抗炎阶段转化的表达动力学。我们发现MDM2表达的遗传易感性与AMI风险之间存在显著的因果关系。我们还发现了利多氟嗪,异防己碱,头孢嘌呤可以稳定地靶向MDM2。
结论：总而言之，NPCDS对预测具有重要意义，分层，和AMI的治疗管理。
方法：我们采用基于机器学习的综合计算框架，在来自AMI患者外周血的五个独立微阵列队列中生成预测中性粒细胞衍生的PCD特征（NPCDS）。利用非负矩阵分解来开发基于NPCDS的AMI亚型。为了阐明NPCDS的生物学机制，我们对从实验性AMI小鼠心脏分离的Cd45+细胞实施了单细胞转录组学。最后，我们进行了孟德尔随机化（MR）研究和分子对接，以探讨NPCDS对AMI的治疗价值。
结果：我们报告了NPCDS在AMI预测中的稳健和优越的性能，这有助于在9个中性粒细胞相关PCD基因(MDM2，PTK2B，MYH9,IVNS1ABP,MAPK14,GNS,MYD88、TLR2、CFLAR)。揭示了两种不同的基于NPCDS的AMI亚型，其中亚型1的特征是炎症激活，中性粒细胞活动更活跃，而亚型2则相反。机械上,我们揭示了NPCDS在AMI中调节中性粒细胞从促炎阶段向抗炎阶段转化的表达动力学。我们发现MDM2表达的遗传易感性与AMI风险之间存在显著的因果关系。我们还发现了利多氟嗪,异防己碱,头孢嘌呤可以稳定地靶向MDM2。
结论：总而言之，NPCDS对预测具有重要意义，分层，和AMI的治疗管理。
