Background

Patients at high risk of severe forms of COVID-19 frequently suffer from chronic diseases, but other risk factors may also play a role. Environmental stressors, such as endocrine disrupting chemicals (EDCs), can contribute to certain chronic diseases and might aggravate the course of COVID-19.

Objectives

To explore putative links between EDCs and COVID-19 severity, an integrative systems biology approach was constructed and applied.

Methods

As a first step, relevant data sets were compiled from major data sources. Biological associations of major EDCs to proteins were extracted from the CompTox database. Associations between proteins and diseases known as important COVID-19 comorbidities were obtained from the GeneCards and DisGeNET databases. Based on these data, we developed a tripartite network (EDCs-proteins-diseases) and used it to identify proteins overlapping between the EDCs and the diseases. Signaling pathways for common proteins were then investigated by over-representation analysis.

Results

We found several statistically significant pathways that may be dysregulated by EDCs and that may also be involved in COVID-19 severity. The Th17 and the AGE/RAGE signaling pathways were particularly promising.

Conclusions

Pathways were identified as possible targets of EDCs and as contributors to COVID-19 severity, thereby highlighting possible links between exposure to environmental chemicals and disease development. This study also documents the application of computational systems biology methods as a relevant approach to increase the understanding of molecular mechanisms linking EDCs and human diseases, thereby contributing to toxicology prediction.

中文翻译：

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内分泌干​​扰化学物质与 COVID-19 关系：一种计算系统生物学方法

背景

患有严重 COVID-19 的高风险患者经常患有慢性疾病，但其他风险因素也可能起作用。环境压力因素，例如内分泌干扰物 (EDC)，可导致某些慢性疾病，并可能加剧 COVID-19 的病程。

目标

为了探索 EDC 与 COVID-19 严重性之间的假定联系，构建并应用了一种综合系统生物学方法。

方法

第一步，从主要数据来源汇编相关数据集。从 CompTox 数据库中提取主要 EDC 与蛋白质的生物学关联。从 GeneCards 和 DisGeNET 数据库中获得了被称为重要 COVID-19 合并症的蛋白质与疾病之间的关联。基于这些数据，我们开发了一个三方网络（EDCs-蛋白质-疾病），并用它来识别 EDCs 和疾病之间重叠的蛋白质。然后通过过度表达分析研究常见蛋白质的信号通路。

结果

我们发现了几个可能被 EDC 失调并且也可能与 COVID-19 严重性有关的统计显着途径。Th17 和 AGE/RAGE 信号通路特别有希望。

结论

途径被确定为 EDC 的可能目标和 COVID-19 严重性的贡献者，从而突出了暴露于环境化学品与疾病发展之间的可能联系。这项研究还记录了计算系统生物学方法作为相关方法的应用，以增加对连接 EDC 和人类疾病的分子机制的理解，从而有助于毒理学预测。