Introduction

Chronic obstructive pulmonary disease (COPD) is the fourth leading cause of death in the United States, with high associated costs. Most of the cost burden results from acute exacerbations of COPD (AE-COPD), events associated with heightened symptoms and mortality. Cellular mechanisms underlying AE-COPD are poorly understood, likely because they arise from dysregulation of complex immune networks across multiple tissue compartments.

Methods

To gain systems-level insight into cellular environments relevant to exacerbation, we applied data-driven modeling approaches to measurements of immune factors (cytokines and flow cytometry) measured previously in two different human tissue environments (sputum and peripheral blood) during the stable and exacerbated state.

Results

Using partial least squares discriminant analysis, we identified a unique signature of cytokines in serum that differentiated stable and AE-COPD better than individual measurements. Furthermore, we found that models integrating data across tissue compartments (serum and sputum) trended towards being more accurate. The resulting paracrine signature defining AE-COPD events combined elevations of proteins associated with cell adhesion (sVCAM-1, sICAM-1) and increased levels of neutrophils and dendritic cells in blood with elevated chemoattractants (IP-10 and MCP-2) in sputum.

Conclusions

Our results supported a new hypothesis that AE-COPD is driven by immune cell trafficking into the lung, which requires expression of cell adhesion molecules and raised levels of innate immune cells in blood, with parallel upregulated expression of specific chemokines in pulmonary tissue. Overall, this work serves as a proof-of-concept for using data-driven modeling approaches to generate new insights into cellular processes involved in complex pulmonary diseases.

中文翻译：

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与 COPD 急性加重相关的痰和外周血细胞免疫环境的推断。

介绍

慢性阻塞性肺病 (COPD) 是美国第四大死亡原因，相关费用很高。大部分成本负担来自 COPD 急性加重 (AE-COPD)、与症状加重和死亡率相关的事件。对 AE-COPD 的细胞机制知之甚少，可能是因为它们源于跨多个组织隔间的复杂免疫网络的失调。

方法

为了获得对与恶化相关的细胞环境的系统级洞察，我们应用数据驱动的建模方法来测量免疫因子（细胞因子和流式细胞术），这些方法是先前在稳定和恶化期间在两种不同的人体组织环境（痰液和外周血）中测量的状态。

结果

使用偏最小二乘判别分析，我们确定了血清中细胞因子的独特特征，该特征比单独测量更好地区分稳定和 AE-COPD。此外，我们发现整合跨组织隔室（血清和痰液）数据的模型趋向于更准确。由此产生的定义 AE-COPD 事件的旁分泌特征结合了与细胞粘附相关的蛋白质（sVCAM-1、sICAM-1）的升高以及血液中中性粒细胞和树突状细胞水平的升高以及痰中化学引诱物（IP-10 和 MCP-2）升高.

结论

我们的研究结果支持了一个新的假设，即 AE-COPD 是由免疫细胞进入肺部驱动的，这需要细胞粘附分子的表达和血液中先天免疫细胞水平的升高，同时肺组织中特定趋化因子的表达平行上调。总体而言，这项工作可作为使用数据驱动的建模方法对涉及复杂肺部疾病的细胞过程产生新见解的概念验证。