Immuno-profiles of COVID-19 uniquely differentiated from other viruses: A machine learning investigation of multiplex immunoassay data

Ashneet Kaur, Viswanathan V Krishnan
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Abstract

Cytokines and chemokines are vital in maintaining a healthy state by efficiently controlling invading microbes. In addition, the dysregulation of these immune mediators can contribute to viral infection pathology. We comprehensively analyzed the profiles of host immunomodulators in response to infections with members of several virus families, particularly if the SARS-CoV-2 infection produces a unique immune profile compared with other viral infections. Multiplex microbead immunoassay results from 219 data sets of with a range of viruses were curated systematically. The curated immunoassay data, obtained using the Luminex technology, includes 35 different viruses in 18 different viral families; this analysis also incorporated data from studies performed in seven different cell model systems with 28 different sample types. A multivariate statistical analysis was performed with a specific focus involving many investigations (> 10), which include viral families of Coronaviridae, Orthomyxoviridae, Retroviridae, Flaviviridae, and Hantaviridae. A random forest-based classification of the profiles indicates that IL1-RA, CXCL9, CCL4, IFN-λ1, IP-10, and IL-27 are the top immunomodulators among human samples. Similar approaches only between Coronaviridae (COVID-19) and Orthomyxoviridae (Influenza A/B) indicated that TGF-β, IFN-λ1, IL-9, and eotaxin-1 are important features. In particular, the IFN-λ1 protein was implicated as one of the significant immunomodulators differentiating viral family infection. It is evident that Coronaviridae infection, including SARS-CoV-2, induces a unique cytokine/chemokine profile and can lead to specific immunoassays for diagnosing and prognosis viral diseases based on host immune responses. It is also essential to note that meta-analysis-based predictions must be appropriately validated before clinical implementation.
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将 COVID-19 与其他病毒独特区分开来的免疫特征:对多重免疫测定数据的机器学习研究
细胞因子和趋化因子能有效控制入侵的微生物,对维持健康状态至关重要。此外,这些免疫介质的失调也会导致病毒感染的病理变化。我们全面分析了宿主免疫调节剂对几种病毒家族成员感染的反应谱,特别是与其他病毒感染相比,SARS-CoV-2 感染是否会产生独特的免疫谱。研究人员对 219 组病毒感染数据中的多重微珠免疫测定结果进行了系统整理。这些利用 Luminex 技术获得的免疫测定数据包括 18 个不同病毒科的 35 种不同病毒;这项分析还纳入了在 7 个不同细胞模型系统中进行的 28 种不同样本类型的研究数据。多变量统计分析的具体重点涉及多项研究(10 项),其中包括冠状病毒科、正粘病毒科、逆转录病毒科、黄病毒科和汉坦病毒科等病毒科。基于随机森林的分类表明,在人类样本中,IL1-RA、CXCL9、CCL4、IFN-λ1、IP-10 和 IL-27 是最主要的免疫调节剂。仅在冠状病毒科(COVID-19)和正粘病毒科(甲型/乙型流感)之间采用的类似方法表明,TGF-β、IFN-λ1、IL-9 和 eotaxin-1 是重要的特征。特别是,IFN-λ1 蛋白被认为是区分病毒科感染的重要免疫调节剂之一。很明显,冠状病毒科感染(包括 SARS-CoV-2)会诱导一种独特的细胞因子/趋化因子谱,并可根据宿主的免疫反应开发出用于诊断和预后病毒性疾病的特异性免疫测定方法。还必须指出的是,在临床应用之前,必须对基于荟萃分析的预测进行适当的验证。
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