Factors Modulating COVID-19: A Mechanistic Understanding Based on the Adverse Outcome Pathway Framework

Abstract

Addressing factors modulating COVID-19 is crucial since abundant clinical evidence shows that outcomes are markedly heterogeneous between patients. This requires identifying the factors and understanding how they mechanistically influence COVID-19. Here, we describe how eleven selected factors (age, sex, genetic factors, lipid disorders, heart failure, gut dysbiosis, diet, vitamin D deficiency, air pollution and exposure to chemicals) influence COVID-19 by applying the Adverse Outcome Pathway (AOP), which is well-established in regulatory toxicology. This framework aims to model the sequence of events leading to an adverse health outcome. Several linear AOPs depicting pathways from the binding of the virus to ACE2 up to clinical outcomes observed in COVID-19 have been developed and integrated into a network offering a unique overview of the mechanisms underlying the disease. As SARS-CoV-2 infectibility and ACE2 activity are the major starting points and inflammatory response is central in the development of COVID-19, we evaluated how those eleven intrinsic and extrinsic factors modulate those processes impacting clinical outcomes. Applying this AOP-aligned approach enables the identification of current knowledge gaps orientating for further research and allows to propose biomarkers to identify of high-risk patients. This approach also facilitates expertise synergy from different disciplines to address public health issues.

Keywords: COVID-19; SARS-CoV-2 infection; adverse outcome pathway; age; co-morbidities; environment; lifestyle; modulating factors; pre-existing conditions; sex.

Figure 1

An AOP describes a sequential chain of causally linked events at different levels of biological organization that lead to an adverse health effect. Created with Biorender.com.

Figure 2

Factors modulating the clinical outcomes of COVID-19 investigated in this study, being representative of different categories: intrinsic (age, sex, and genetic factors), co-morbidities (dyslipidemia, obesity, pre-existing heart failure, and gut dysbiosis), lifestyle-related (vitamin D deficiency and diet). and environmental (air pollution and exposure to chemicals). Created with Biorender.com.

Figure 3

Simplified AOP network depicting COVID-19 pathogenesis, highlighting the biological key steps for evaluation of the mechanisms by which the eleven selected MFs affect the relationships between two KEs, namely the KERs (black arrows). Green boxes: initial events depicting the infectious process and ACE2 dysregulation. Orange boxes: central inflammatory events. Red boxes: AOs. Grey arrows and grey boxes: KERs and KEs identified in COVID-19 but not investigated in detail here. Created with Biorender.com.

Figure 4

Visualization of MFs within an AOP network. Individual MFs (blue triangle) might have an influence (dotted line) on one or several KERs within an AOP or within a network. In addition, a KE (blue triangle within the rectangle) can act as a MF for KER(s) in other AOP(s). Diagram adapted from US EPA. Created with Biorender.com.

Figure 5

Interplay between the MFs investigated in this study. Black arrows: negative effect (arrow width indicating approximative magnitude). Green inhibitor arrows: potential positive effect. Arrows in both directions: mutual effect. Created with Biorender.com.