With the appearance of the SARS-CoV-2 pandemic, numerous complications of COVID-19 disease followed, raising suspicion that severe forms of the disease and even deaths could partly be associated with air pollution (1). Some authors have even suggested that atmospheric factors can influence COVID-19 transmission and death rate (why is it so different around the world, even in the same country?) (2). As the World Health Organization’s (WHO) new Global Air Quality Guidelines (AQGs) provide convincing evidence of the harm caused by air pollution to human health, especially with regard to sulphur dioxide (SO2), carbon monoxide (CO), particulate matter (PMx), nitrogen dioxide (NO2), and ozone (O3) (3, 4), the aim of our study was to determine the relationships between these key air pollutants and COVID-19 complications using
Toxicogenomics combines the measurement of different biological molecules with both bioinformatics and traditional toxicology to find the exact relationships between genes and environmental stress in disease pathogenesis (5, 6, 7). As a result, it may be used to predict gene functions and genomic biomarkers in specific biochemical pathways (8, 9). It also provides combined evaluation methodologies that take into account all the conceivable chemical-gene-disease interactions that may be essential in generating combined toxicities (5).
In this study we used four freely available tools, each with a specific purpose: Comparative Toxicogenomic Database (CTD) to obtain a set of genes interrelated with air pollutants and COVID-19 complications, GeneMANIA to construct a network between the obtained gene set and related genes, ToppGene Suite to identify the most important biological processes and molecular pathways, and DisGeNET to search for the top gene-disease pairs. Figure 1 shows a flow chart detailing each step of our analysis.
The CTD database (
GeneMANIA is one of the most reliable free online tools (with its Cytoscape plugin available at
The ToppGene Suite is a publicly available online tool (
DisGeNET (
As expected, of the five investigated air pollutants PMx interacted with the highest number of genes (11,573), followed by O3 (3,578). Eighteen from the first group (
Table 1 shows how each pollutant interacts with each of the genes from the common set. To better understand the obtained set of shared genes (
Influence of air pollutants (SO2, CO, PMx, NO2 and O3) on protein secretion, mRNA expression, and protein expression of genes related to these air pollutants and COVID-19 disease complications (CTD Database;
Air pollutant | Interaction | ||||
---|---|---|---|---|---|
Protein secretion | ↑ | ||||
mRNA expression | |||||
Protein expression | ↑ | ↑ | ↑ | ||
Protein secretion | ↑ | ||||
mRNA expression | ↑ | ||||
Protein expression | ↑ | ↑ | ↑↓ | ↑↓ | |
Protein secretion | ↑↓ | ↑ | ↑ | ↑ | |
mRNA expression | ↑↓ | ↑↓ | ↑↓ | ||
Protein expression | ↑↓ | ↑ | ↑↓ | ↑↓ | |
Protein secretion | ↑ | ↓ | |||
mRNA expression | ↑ | ↑↓ | ↓ | ↑ | |
Protein expression | ↑ | ↑ | ↑ | ↑ | |
Protein secretion | ↑ | ↑ | |||
mRNA expression | ↓ | ↑ | ↑↓ | ↓ | |
Protein expression | ↓ | ↑ | ↑↓ | ↑↓ |
↑– induction; ↓– inhibition; ↑↓ – induction/inhibition
The ToppFun function at the ToppGene Suite identified top 10 molecular functions behind resulting COVID-19 complications that are connected to the investigated set of 24 genes (molecular functions and biological processes) as follows: cytokine binding, cytokine receptor binding, growth factor receptor binding, growth factor binding, cytokine receptor activity, immune receptor activity, interleukin-1 binding, signalling receptor binding, cytokine activity, and interleukin-6 receptor binding. The top 10 biological processes included cytokine-mediated signalling, cellular response to cytokine stimulus, response to cytokine, inflammatory response, defence response, regulation of inflammatory response, interleukin-6 production, cytokine production, regulation of defence response, and regulation of cytokine production.
The DisGeNET database provided the linkage between various diseases and genes from the set (Table 2). The highest score was obtained for the link between
Top 10 gene-disease pairs for genes related to both COVID-19 complications and air pollutants, along with the 20 predicted genes (DisGeNET database;
Gene | Disease | Disease class (DisGeNET) | Score |
---|---|---|---|
acute myelocytic leukaemia | neoplasms | 1.000 | |
TNF receptor-associated periodic fever syndrome (TRAPS) | pathological conditions, signs and symptoms; congenital, hereditary, and neonatal diseases and abnormalities; skin and connective tissue diseases | 1.000 | |
familial Behcet-like autoinflammatory syndrome | / | 0.710 | |
rheumatoid arthritis | skin and connective tissue diseases; musculoskeletal diseases; immune system diseases | 0.700 | |
inflammatory bowel diseases | digestive system diseases | 0.700 | |
systemic lupus erythematosus, systemic | skin and connective tissue diseases diseases; immune system | 0.700 | |
Crohn's disease | digestive system diseases | 0.700 | |
fever | pathological conditions, signs and symptoms | 0.700 | |
systemic lupus erythematosus | skin and connective tissue diseases; immune system diseases | 0.700 | |
amyotrophic lateral sclerosis | nutritional and metabolic diseases diseases; nervous system | 0.700 |
Our study has singled out cytokine binding and activation as the most important molecular functions for the extracted gene set. Cytokine release caused by air pollutants could, among other factors, lie behind the so-called “cytokine storm” (21), an excessive production of proinflammatory cytokines found to induce acute respiratory distress syndrome which has also been reported after SARS-CoV-2 infects the upper and lower respiratory tract (22, 23). A significant concentration of cytokines has also been reported in the plasma of severely sick patients infected with SARS-CoV-2, probably due to the “cytokine storm” (24).
Inflammatory response and defence mechanisms were also among the identified molecular functions, which was expected, as cytokines and chemokines recruit and mobilise immune cells such as macrophages, neutrophils, and T-cells to the site of infection (25, 26). Pro-inflammatory cytokines such as interleukins like IL-1, IL-6, and TNF play a key part in the early response, whereas anti-inflammatory molecules such as IL-10 are generated during longterm infection to keep inflammation under control and preserve immunological homeostasis (26). We found that all the investigated air pollutants apart from SO2 are capable of increasing TNF protein expression. Furthermore, all air pollutants increase TNF protein secretion, with the exception of CO. All the investigated air pollutants can also increase protein expression of
Finally, all air pollutants were found to increase the expression of
Our results have also revealed strong connections between certain genes affected by the investigated air pollutants and other diseases, namely acute myelocytic leukaemia and TNF receptor-associated periodic fever syndrome. This implies that individuals suffering from these diseases might additionally be affected by air pollutants in case of SARS-CoV-2 infection.
Our findings corroborate the assumption that air pollution could aggravate COVID-19 and significantly increase the rate of infection, disease severity, and fatality, most likely by affecting the expression of genes responsible for increased immune response, “cytokine storm” in particular. People living in urban areas, who are constantly exposed to air pollutants, are therefore more susceptible COVID-19 complications. However, it is important to acknowledge that this type of research has some limitations. It does not consider dose-response relationships, duration of exposure, or individual sensitivity. Even so, its application has grown strongly in recent years, as it can generate testable hypotheses and identify knowledge that could guide future