Besides affecting the respiratory system and causing persistent respiratory conditions (1), the coronavirus disease 2019 (COVID-19) can lead to complications affecting the cardiovascular, renal, and neurological systems (2,3,4). Conversely, conditions like cardiovascular diseases, obesity, asthma, chronic obstructive pulmonary disease, diabetes, tumours, renal, neurological diseases, inflammation, coagulation disorders, and factors such as age, sex, and lifestyle also contribute to the severity of COVID-19 (5,6,7). Finally, the severity and resulting complications are believed to be aggravated by exposure to various environmental pollutants, toxic metals in particular (6, 8,9,10,11). However, the extent of their contribution remains uncertain.
According to the Agency for Toxic Substances and Disease Registry (ATSDR), the top list of these pollutants includes arsenic (As), cadmium (Cd), lead (Pb), and mercury (Hg) (9, 10). Moreover, the International Agency for Research on Cancer (IARC) has classified As, Cd, nickel (Ni), and chromium (Cr) as human carcinogens, and all four metals have been associated with the development of lung cancer (11,12,13,14).
The aim of our study was to take a look at likely molecular mechanisms underlying COVID-19 complications in the context of exposure to six toxic metals (As, Cd, Pb, Hg, Ni, and Cr) by employing toxicogenomic research databases that investigate the interplay between the genes involved in environmental stress and disease development (15). This kind of approach can help to identify and predict gene functions within specific pathways. Furthermore, it provides methods to examine diverse interactions involving chemicals, genes, proteins, and metabolites and to identify networks that could play a crucial role in causing specific harmful effects (16).
The relationships between the selected toxic metals and COVID-19 disease complications were investigated using the Comparative Toxicogenomics Database (CTD) (
All results presented in this article are based on data available at the time of analysis (September 2023), and the number of genes associated with the selected metals and COVID-19 complications may have changed since, as the database is updated constantly.
The gene set identified with the CTD analysis was then entered into the GeneMANIA prediction tool (
To further explore the molecular mechanisms associated with exposure to toxic metals and development of COVID-19 complications we used the ToppGene ToppFun function (p<0.05, false discovery rate-corrected) (22). The input gene set comprised the five common genes obtained with the CTD analysis and 20 related genes obtained with the GeneMANIA tool. Information on molecular functions, biological processes, molecular pathways, and diseases potentially contributing to the development of COVID-19 complications was ranked based on the p-value. The number of genes involved in each process was determined, and the top five molecular functions, biological processes, molecular pathways, and diseases were listed. A detailed flow chart of the process is presented in Figure 1.
Our CTD research revealed five genes – namely
Genes associated with COVID-19 and the investigated metals (
Arsenic (As) | 4997 | |
Cadmium (Cd) | 5281 | |
Lead (Pb) | 3278 | |
Mercury (Hg) | 635 | |
Nickel (Ni) | 7665 | |
Chromium (Cr) | 2353 | |
Tumour necrosis factor (TNF) is another important inflammatory cytokine associated with excessive immune response (25) and inflammatory lung conditions such as COVID-19. It triggers inflammatory and proteolytic pathways and interferes with processes responsible for controlling inflammatory response. Both serum TNF and IL6 levels are predictors of survival in COVID-19 patients. An overactive immune response caused by TNF, IL1B, and IL6 is the most common cause of COVID-19 complications and death (26).
Interleukin 10 (IL10) is an anti-inflammatory cytokine that, while safeguarding against various pathological conditions, paradoxically promotes lung fibrosis (27).
Interleukin 8, also known as CXCL8, is a neutrophil-attracting chemokine. High levels have been reported in bronchial epithelial cells of patients infected with the corona virus (28).
COVID-19 is marked by elevated cytokine levels in both blood and epithelial lining fluid obtained with bronchoalveolar lavage, reflecting the immune response to the virus. Alveolar macrophages and monocytes produce IL6, TNF, IL8, and IL1B, while neutrophils produce ROS, lipid mediators, and proteases, all of which are toxic to the virus but also to the lungs. Furthermore, IL6, TNF, IL8, IL1B, and IL10 levels have been reported to correlate with the clinical severity of COVID-19 (29, 30).
Table 2 details the interactions between each toxic metal and the five shared genes. Each of the six metals appears to upregulate
Effects of As, Cd, Pb, Hg, Ni, and Cr on common genes
As | Expression of mRNA | N/A | ↓ | ↓↑ | ↑ | ↓ |
Protein expression | ↑ | ↑ | ↓↑ | ↑ | ↓↑ | |
Cd | Expression of mRNA | ↑ | ↓ | ↓↑ | ↓↑ | ↓↑ |
Protein expression | ↑ | ↑ | ↑ | ↑ | ↑ | |
Pb | Expression of mRNA | ↑ | N/A | ↑ | N/A | ↑ |
Protein expression | ↑ | ↓↑ | ↑ | ↑ | ↑ | |
Hg | Expression of mRNA | N/A | ↑ | ↑ | N/A | ↑ |
Protein expression | ↑ | N/A | ↑ | ↑ | ↑ | |
Ni | Expression of mRNA | ↑ | ↑ | ↑ | ↑ | ↑ |
Protein expression | ↑ | N/A | ↑ | ↑ | ↑ | |
Cr | Expression of mRNA | ↑ | N/A | N/A | N/A | ↑ |
Protein expression | N/A | N/A | N/A | ↓ | ↑ |
↑ – upregulation; ↓ – downregulation; ↑↓ – upregulation and downregulation. N/A – no data available in CTD;
Figure 2 shows the interactions between all 25 genes identified by CTD and GeneMANIA (five input and 20 related genes) by interaction type: physical interactions (77.64 %), co-expression (8.01 %), server-predicted interactions (5.37 %), co-localisation (3.63 %), genetic interactions (2.87 %), shared pathway (1.88 %), and occurrences of shared protein domains (0.60 %). Physical interactions means that the genes are related if their proteins interact. Co-expression means that the genes are related if their expression is similar under test conditions. Server-predicted interactions means that extracted protein interactions are predicted based on known interactions between orthologous genes in other organisms. If two proteins interact in one organism (e.g. human), their orthologues are expected to interact in another (e.g. mouse). Co-localisation means that genes are expressed or their proteins are found in the same tissue. Genetic interactions means that a change in one gene triggers changes in another. Pathway interactions means that proteins coded by two genes participate in the same reaction pathway. Shared protein domains means that proteins coded by two genes have similar domains (19,20,21).
Figure 3 details interactions between genes linked to each metal separately and the nature of interactions among them. The dominant type of interaction is co-expression, whereas the interactions between the genes common to all investigated metals are mostly physical.
Interactions between the obtained sets of genes reveal how the molecular dynamics of these genes contribute to the exacerbation of COVID-19. We have chosen to present both the interactions between genes with which individual metals interact and the interactions between genes with which all the investigated metals interact. This decision was made with the understanding that examining interactions between genes and individual metals provide insights into metal-specific interactions. On the other hand, studying interactions with all investigated metals collectively sheds light on common interactions affected by combined metal exposure. This highlights the complex functions of genes shared among all the investigated metals and those unique to each metal, providing insights into the diverse molecular processes that may contribute to the severity of COVID-19 complications.
Table 3 shows the correlations between the 25 identified genes and various biological processes, molecular functions, pathways, and diseases contributing to the development of COVID-19 complications. It also lists the number of genes involved in each biological process, molecular function, pathway, and disease.
Correlation of the 25 identified genes (five common and 20 related) with biological processes, molecular functions, pathways, and diseases associated with COVID-19 complications
Molecular function | cytokine binding | GO:0019955 | 1.341E-17 | 11 |
cytokine receptor binding | GO:0005126 | 2.170E-17 | 13 | |
cytokine activity | GO:0005125 | 3.580E-15 | 11 | |
signalling receptor binding | GO:0005102 | 1.187E-11 | 16 | |
receptor ligand activity | GO:0048018 | 1.952E-11 | 11 | |
Biological process | inflammatory response | GO:0006954 | 4.830E-28 | 23 |
cytokine-mediated signalling pathway | GO:0019221 | 1.992E-25 | 19 | |
cellular response to cytokine stimulus | GO:0071345 | 1.277E-20 | 19 | |
response to cytokine | GO:0034097 | 9.706E-20 | 19 | |
leukocyte migration | GO:0050900 | 8.016E-19 | 15 | |
Pathway | KEGG cytokine receptor interaction | M9809 | 1.039E-30 | 20 |
reactome interleukin 10 signalling | M27605 | 6.459E-22 | 11 | |
reactome signalling by interleukins | M874 | 4.294E-18 | 16 | |
WP SARSCOV2 innate immunity evasion and cell-specific immune response | M40067 | 9.207E-18 | 10 | |
WP overview of proinflammatory and profibrotic mediators | M42533 | 1.091E-16 | 11 | |
Disease | susceptibility to HIV infection | 609423 | 6.577E-6 | 3 |
malaria, susceptibility to | cv:C1836230 | 6.577E-6 | 3 | |
white blood cell count quantitative trait locus 1 | 611162 | 5.893E-4 | 2 | |
WHIM syndrome 2 | cv:C1970028 | 5.893E-4 | 2 | |
Graft-versus-host disease, susceptibility to | 611862 | 2.333E-3 | 1 |
Number of genes involved in each category. KEGG – Kyoto Encyclopedia of Genes and Genomes; WHIM – warts, hypogammaglobulinaemia, infections, and myelokathexis syndrome type 2; WP – WikiPathways
Relying on gene ontology and pathway analysis, our findings provide a comprehensive understanding of the intricate molecular and cellular mechanisms behind the interplay between toxic metal exposure and COVID-19 severity. Three molecular functions stand out: cytokine binding, cytokine binding to receptors, and cytokine activity. Similarly, the most common biological processes involve inflammatory response, cytokine-mediated signalling pathway, and cellular response to cytokine stimulation. All this suggests that the common genes for As, Cd, Pb, Hg, Ni, and Cr, as well as the 20 related genes are involved in the cytokine storm, a key event in the development of COVID-19 complications. Cytokine-cytokine receptor interaction, the IL10 signalling pathway, and interleukin signalling are the most important pathways in which the investigated genes are involved. The involvement of IL10, along with IL6, in cytokine-cytokine receptor interaction has already been reported in patients with severe clinical symptoms (31).
In patients with severe clinical manifestations of the disease, uncontrolled systemic inflammation and cytokine storm occur as a result of the excessive production of pro-inflammatory cytokines, which damages many tissues and can lead to their insufficiency, acute respiratory distress syndrome, sepsis-induced shock, and death. The cytokine storm can cause T-lymphocyte apoptosis or necrosis, weakening the organism’s defences against the pathogen (32). Cytokines facilitate communication between the immune and hematopoietic cells by binding to receptors on the target cell surface. Notably, a single cytokine can carry out diverse biological functions across different tissues and cell types (33). Patients with more severe clinical manifestations often have comorbidities such as diabetes, hypertension, and cardiovascular disease, which may weaken their capacity to tolerate systemic cytokines (34). Furthermore, IL1B and IL6 produced by infected tissue are involved in megakaryocyte function and platelet production, the consequence of which is hypercoagulation (35).
IL6 can affect cells via two signalling pathways: cis and trans. The cis signalling pathway involves IL6 binding to its immune cell membrane receptor complexed with
The existing evidence indicates that individuals with pre-existing health conditions are more susceptible to contracting COVID-19 and experiencing complications. This, in particular, concerns patients with HIV infection, whose HIV antiretroviral therapy has proved ineffective against SARS-CoV-2 infection (37, 38). In contrast, COVID-19 has a lower prevalence in endemic malarial areas. One of the reasons is that the residents of these low-income areas have fewer opportunities to test for SARS-CoV 2 infection due to limited resources, but also that COVID-19 may be misdiagnosed for malaria due to symptom similarities (39). It has been shown that the
Our study highlights the value of free online toxicogenomic data mining and analysis tools, such as CTD, ToppGene Suite, and GeneMANIA. However, they come with certain limitations, as they may not include all available information and miss specific interaction data (19). Furthermore, while these tools can statistically identify associations between genes affected by chemicals and those involved in environmental diseases such as SARS-CoV-2, they do not establish dose-response relationships or take into account various exposure factors which could affect the final outcome (16). Even so, they provide indirect access to literature, allowing users to gather information on factors like duration of exposure, dose, and exposure period (41). In other words,
By applying parameters of interest, our study has revealed how exposure to toxic metals can aggravate COVID-19 and its complications, primarily by inducing changes in gene expression. Interactions between the five genes shared by all the investigated metals and COVID-19 (