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Joint impact of key air pollutants on COVID-19 severity: prediction based on toxicogenomic data analysis Cover

Joint impact of key air pollutants on COVID-19 severity: prediction based on toxicogenomic data analysis

Archives of Industrial Hygiene and Toxicology
Volume 73 (2022): Issue 2 (June 2022)
By: ,  ,  ,  ,  ,  ,  ,  ,   and    
Open Access
|Jul 2022
Figures & tables

Figures & Tables

Figure 1

Flow chart of toxicogenomic analysis to identify relationships between air pollutants and COVID-19. CTD – Comparative Toxicogenomic Database (http://CTD.mdibl.org)

Figure 2

Network of genes associated with SO2, CO, PMx, NO2, and O3 exposure and COVID-19 disease complications (IL10, IL6, IL1B, and TNF) (black circles) and 20 related genes predicted by the GeneMANIA tool (http://geneMANIA.org/plug-in/) (grey circles). Panel A shows prevalence of interaction types (in percentages) and panel B networks for each interaction type

Table 1

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; http://CTD.mdibl.org)

Air pollutantInteractionIL10IL6IL1BTNF
SO2Protein secretion↑
mRNA expression
Protein expression↑↑↑
COProtein secretion↑
mRNA expression↑
Protein expression↑↑↑↓↑↓
PMxProtein secretion↑↓↑↑↑
mRNA expression↑↓↑↓↑↓
Protein expression↑↓↑↑↓↑↓
NO2Protein secretion↑↓
mRNA expression↑↑↓↓↑
Protein expression↑↑↑↑
O3Protein secretion↑↑
mRNA expression↓↑↑↓↓
Protein expression↓↑↑↓↑↓

[i] ↑– induction; ↓– inhibition; ↑↓ – induction/inhibition

Table 2

Top 10 gene-disease pairs for genes related to both COVID-19 complications and air pollutants, along with the 20 predicted genes (DisGeNET database; http://www.disgenet.org)

GeneDiseaseDisease class (DisGeNET)Score
CEBPAacute myelocytic leukaemianeoplasms1.000
TNFRSF1ATNF receptor-associated periodic fever syndrome (TRAPS)pathological conditions, signs and symptoms; congenital, hereditary, and neonatal diseases and abnormalities; skin and connective tissue diseases1.000
TNFAIP3familial Behcet-like autoinflammatory syndrome/0.710
TNFrheumatoid arthritisskin and connective tissue diseases; musculoskeletal diseases; immune system diseases0.700
IL10inflammatory bowel diseasesdigestive system diseases0.700
IL10systemic lupus erythematosus, systemicskin and connective tissue diseases diseases; immune system0.700
IL10Crohn's diseasedigestive system diseases0.700
IL6feverpathological conditions, signs and symptoms0.700
TNFAIP3systemic lupus erythematosusskin and connective tissue diseases; immune system diseases0.700
SQSTM1amyotrophic lateral sclerosisnutritional and metabolic diseases diseases; nervous system0.700
DOI: https://doi.org/10.2478/aiht-2022-73-3631 | Journal eISSN: 1848-6312 (formerly 0004-1254) | Journal ISSN: 0004-1254
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Language: English, Croatian
Page range: 119 - 125
Submitted on: Feb 1, 2022
Accepted on: May 1, 2022
Published on: Jul 7, 2022
Published by: Institute for Medical Research and Occupational Health
In partnership with: Paradigm Publishing Services
Keywords:
carbon monoxide,
cytokines,
disease complications,
in silico,
nitrogen dioxide,
ozone,
particulate matter,
SARS-CoV-2,
sulphur dioxide
Related subjects:
Medicine,
Basic medical science,
Basic medical science, other

© 2022 Danijela Đukić-Ćosić, Katarina Baralić, Teodora Filipović, Dragica Božić, Katarina Živančević, Evica Antonijević Miljaković, Aleksandra Buha Đorđević, Zorica Bulat, Biljana Antonijević, Marijana Ćurčić, published by Institute for Medical Research and Occupational Health
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 73 (2022): Issue 2 (June 2022)
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