Proteomic Analysis of COVID-19 Plasma Reveals Dysregulated TREM-1, I-17, and Tumor Microenvironment Pathways Associated with Disease Severity

C. Cosgriff, H. Giannini, D. Mathew, B. J. Anderson, T. Jones, C. Ittner, A. Weisman, A. Baxter, L. Kuri-Cervantes, M. Pampena, K. D’Andrea, R. Agyekum, T. Dunn, J. Reilly, M. Betts, E. Wherry, M. Shashaty, N. Meyer
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Abstract

Rationale: To utilize high-dimensional proteomic data to identify dysregulated pathways that are associated with COVID-19 disease severity and suggest potential therapeutic targets. Methods: We enrolled 161 COVID-19 inpatients admitted at two tertiary care hospitals. Plasma samples collected within 48 hours of admission were analyzed with the Olink Proximity Extension Assay;713 unique proteins were assayed. The WHO COVID-19 ordinal severity scale at enrollment was dichotomized into moderate (levels 3-4) and severe (levels 5-7). Normalized protein expression (NPX) values were generated in relation to a common pooled control plasma on each plate. The association between NPX values and disease severity on admission was estimated with logistic regression (LR) after adjustment for age, sex, race, and select comorbidities. Ingenuity Pathway Analysis (IPA) was employed after application of the Benjamini-Hochberg procedure with a false discovery rate of 5% to all proteins for which the NPX difference was +/-0.8 between groups. Predictive models of disease severity on hospital day 7 using all proteins as potential features were fit using elastic net LR (ENLR) and gradient boosting (GBM). Performance was estimated on a held-out test set (40% of the data) with area under the receiveroperator characteristic curve (AUROC). Results: Of 161 subjects, 85 (53%) were classified as having severe COVID-19. A total of 552 proteins were differentially expressed (Figure 1), and 31 of these proteins met criteria for inclusion in pathway analysis. IPA identified the triggering receptor expressed on myeloid cells 1 (TREM-1) signaling pathway (4 members, p=3.8E-3), the tumor microenvironment (TME) pathway (5 members, p=4.1E-3), and the interleukin 17 (IL-17) signaling pathway (4 members, p=1.8E-2). Interleukin 1 receptor-like 1, a member of the TREM-1 pathway, was the protein most associated with disease severity (OR=3.18, p=1.82E-08). Tumor necrosis factor ligand superfamily member 11 (TNFSF11), a member of the IL-17 signaling pathway was the only factor whose enrichment was associated with less severe disease (OR=0.39, p=2.3E-05). ENLR and GBM predicted disease severity on day 7 with AUROC values of 0.908 (0.828, 0.968) and 0.882 (0.788, 0.957), respectively. Conclusion: We identified pathways differentially expressed between patients with severe and nonsevere COVID-19 associated with immune function and angiogenesis. Several agents currently being investigated to treat severe COVID-19 act on these dysregulated pathways, and future investigations could test whether these proteins act as enrichment markers or response indicators. Integrating protein expression with cellular immune phenotype may help explain COVID-19 pathophysiology.
COVID-19血浆蛋白质组学分析揭示与疾病严重程度相关的TREM-1、I-17和肿瘤微环境通路异常
目的:利用高维蛋白质组学数据识别与COVID-19疾病严重程度相关的失调通路,并提出潜在的治疗靶点。方法:我们纳入了两所三级医院的161例COVID-19住院患者。入院48小时内收集的血浆样本用Olink接近延伸法进行分析,检测713种独特的蛋白质。入组时WHO COVID-19严重程度分级分为中度(3-4级)和重度(5-7级)。标准化蛋白表达(NPX)值与每个平板上的共同对照血浆相关。在调整年龄、性别、种族和选择的合并症后,用logistic回归(LR)估计入院时NPX值与疾病严重程度之间的关系。应用Benjamini-Hochberg程序后,采用独创性途径分析(Ingenuity Pathway Analysis, IPA),对组间NPX差异为+/-0.8的所有蛋白质的错误发现率为5%。使用弹性网LR (ENLR)和梯度增强(GBM)拟合以所有蛋白质为潜在特征的住院第7天疾病严重程度预测模型。性能是在一个固定测试集(40%的数据)上进行评估的,该测试集的面积在接收器操作员特征曲线(AUROC)下。结果:161例患者中,85例(53%)为重症。共有552个蛋白存在差异表达(图1),其中31个蛋白符合纳入途径分析的标准。IPA鉴定出髓样细胞1 (TREM-1)信号通路(4个成员,p=3.8E-3)、肿瘤微环境(TME)信号通路(5个成员,p=4.1E-3)和白细胞介素17 (IL-17)信号通路(4个成员,p=1.8E-2)上表达的触发受体。TREM-1通路的成员白介素1受体样1是与疾病严重程度最相关的蛋白(OR=3.18, p=1.82E-08)。肿瘤坏死因子配体超家族成员11 (TNFSF11)是IL-17信号通路的成员,是唯一富集与较轻疾病相关的因子(OR=0.39, p=2.3E-05)。ENLR和GBM预测第7天疾病严重程度的AUROC值分别为0.908(0.828,0.968)和0.882(0.788,0.957)。结论:我们确定了重症和非重症COVID-19患者与免疫功能和血管生成相关的差异表达途径。目前正在研究治疗严重COVID-19的几种药物作用于这些失调的途径,未来的研究可以测试这些蛋白质是作为富集标记还是反应指标。整合蛋白表达与细胞免疫表型可能有助于解释COVID-19的病理生理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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