基于机器学习的蛋白质学揭示了COPD患者获得的呼吸道上皮细胞在吸烟暴露后的费罗死
Jung-Ki Yoon1, Sungjoon Park2, Kyoung-Hee Lee3
1Division of Pulmonary, and Critical Care Medicine, Department of Medicine, Stanford University, Stanford, CA, USA.
Journal of Korean medical science
|July 25, 2023
概括
机器学习确定了铁亡作为慢性阻塞性肺病 (COPD) 皮质细胞暴露于香烟烟雾提取物 (CSE) 的关键途径. 慢性肺炎细胞表现出特别的脆弱性,突出了铁亡作为这种疾病的独特特征.
科学领域:
- 肺部医学 肺部医学
- 细胞生物学 细胞生物学
- 生物化学 生物化学
背景情况:
- 慢性阻塞性肺病 (COPD) 病原体的理解正在通过omics研究取得进展.
- 以前的蛋白质和基因组研究在确定关键受影响的途径方面存在局限性.
- 这项研究利用机器学习来研究COPD支气管上皮细胞中暴露于香烟烟雾提取物 (CSE) 的途径.
研究的目的:
- 在CSE暴露后从COPD患者培养的支气管上皮细胞中识别显著受影响的途径.
- 利用机器学习来识别COPD细胞蛋白质组中的独特模式.
- 用公开可用的单细胞RNA测序数据验证发现.
主要方法:
- 从COPD和非COPD个体收集了小气道上皮细胞.
- 细胞被培养并暴露在CSE中,然后通过质谱学进行蛋白质组分析.
- 基于机器学习的特征选择确定了差异性蛋白质组模式,并与单细胞RNA测序数据 (GSE136831) 进行了验证.
主要成果:
- 蛋白质组分析检测到5名COPD和5名非COPD患者的细胞中的7953种蛋白质.
- 在CSE暴露后,Ferroptosis在两组的上皮细胞中都得到了丰富.
- 机器学习突出显示铁亡是COPD细胞中最明显的反应 (调整P=4.172 × 10^-6),表明脆弱性增加.
结论:
- 对蛋白质组数据的机器学习分析揭示了铁化作为一个关键的区分特征.
- 慢性肺炎上皮细胞对CSE.表现出特别明显的铁死反应.
- 在COPD患者样本中,铁酶富含基底细胞,杯体细胞和球体细胞.
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