Divison of Pulmonary, Critical Care & Sleep Medicine. University of South Florida, Morsani College of Medicine, Tampa, FL 33602, USA.
Division of Epidemiology and Biostatistics, University of Illinois at Chicago, Chicago, IL, USA.
EBioMedicine. 2021 Jul;69:103439. doi: 10.1016/j.ebiom.2021.103439. Epub 2021 Jun 20.
COVID-19 has been associated with Interstitial Lung Disease features. The immune transcriptomic overlap between Idiopathic Pulmonary Fibrosis (IPF) and COVID-19 has not been investigated.
we analyzed blood transcript levels of 50 genes known to predict IPF mortality in three COVID-19 and two IPF cohorts. The Scoring Algorithm of Molecular Subphenotypes (SAMS) was applied to distinguish high versus low-risk profiles in all cohorts. SAMS cutoffs derived from the COVID-19 Discovery cohort were used to predict intensive care unit (ICU) status, need for mechanical ventilation, and in-hospital mortality in the COVID-19 Validation cohort. A COVID-19 Single-cell RNA-sequencing cohort was used to identify the cellular sources of the 50-gene risk profiles. The same COVID-19 SAMS cutoffs were used to predict mortality in the IPF cohorts.
50-gene risk profiles discriminated severe from mild COVID-19 in the Discovery cohort (P = 0·015) and predicted ICU admission, need for mechanical ventilation, and in-hospital mortality (AUC: 0·77, 0·75, and 0·74, respectively, P < 0·001) in the COVID-19 Validation cohort. In COVID-19, 50-gene expressing cells with a high-risk profile included monocytes, dendritic cells, and neutrophils, while low-risk profile-expressing cells included CD4, CD8 T lymphocytes, IgG producing plasmablasts, B cells, NK, and gamma/delta T cells. Same COVID-19 SAMS cutoffs were also predictive of mortality in the University of Chicago (HR:5·26, 95%CI:1·81-15·27, P = 0·0013) and Imperial College of London (HR:4·31, 95%CI:1·81-10·23, P = 0·0016) IPF cohorts.
50-gene risk profiles in peripheral blood predict COVID-19 and IPF outcomes. The cellular sources of these gene expression changes suggest common innate and adaptive immune responses in both diseases.
This work was supported in part by National Institute for Health Research Clinician Scientist Fellowship NIHR: CS-2013-13-017 (TMM); Action for Pulmonary Fibrosis Mike Bray fellowship (PLM); The National Heart, Lung, and Blood Institute (NHLBI) through award K01-HL-130704 (AJ); The University of South Florida (USF) Academic Support Fund and the USF Foundation, Ubben Fibrosis Fund (JHM).
COVID-19 与间质性肺病特征有关。特发性肺纤维化（IPF）和 COVID-19 之间的免疫转录组重叠尚未得到研究。
我们分析了三个 COVID-19 和两个 IPF 队列中已知可预测 IPF 死亡率的 50 个基因的血液转录水平。应用分子亚表型评分算法（SAMS）来区分所有队列中的高风险与低风险特征。从 COVID-19 发现队列中得出的 SAMS 截止值用于预测 COVID-19 验证队列中的重症监护病房（ICU）状态、机械通气需求和住院死亡率。使用 COVID-19 单细胞 RNA 测序队列来确定 50 个基因风险特征的细胞来源。相同的 COVID-19 SAMS 截止值用于预测 IPF 队列的死亡率。
50 个基因风险特征在发现队列中区分了严重与轻度 COVID-19（P=0.015），并预测了 ICU 入院、机械通气需求和住院死亡率（AUC：0.77、0.75 和 0.74，分别，P<0.001）在 COVID-19 验证队列中。在 COVID-19 中，高风险特征表达的 50 个基因的细胞包括单核细胞、树突状细胞和中性粒细胞，而低风险特征表达的细胞包括 CD4、CD8 T 淋巴细胞、产生 IgG 的浆母细胞、B 细胞、NK 和γ/δ T 细胞。相同的 COVID-19 SAMS 截止值也可预测芝加哥大学（HR：5.26，95%CI：1.81-15.27，P=0.0013）和伦敦帝国理工学院（HR：4.31，95%CI：1.81-10.23，P=0.0016）的 IPF 队列的死亡率。
外周血中的 50 个基因风险特征可预测 COVID-19 和 IPF 的结果。这些基因表达变化的细胞来源表明两种疾病中存在共同的先天和适应性免疫反应。
这项工作得到了部分支持美国国立卫生研究院临床科学家奖学金 NIH：CS-2013-13-017（TMM）；肺纤维化行动迈克布雷奖学金（PLM）；美国国立心脏、肺和血液研究所（NHLBI）通过授予 K01-HL-130704（AJ）；南佛罗里达大学（USF）学术支持基金和 USF 基金会，Ubben 纤维化基金（JHM）。
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