Polymorphism of TLR genes and the course of COVID-19 bilateral pneumonia

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BACKGROUND: COVID-19 is a disease which course depends on a number of factors, including genetic ones, among which the genes of the innate immune system receptors – TLR (toll-like receptors), which play a central role in the development of innate immunity reactions, are of particular interest. The SARS-CoV-2 virus structure includes, in addition to the nucleocapsid, a protein-lipid membrane envelope, which determines the recognition of virus components by different TLRs, including TLR2 subfamily receptors (TLR1, 6, 10), which genetic polymorphisms occur with different frequencies in different human populations and affect not only the functional activity of the innate immunity but also determine the quality of the adaptive immune response.

AIM: The study aimed to determine the association of polymorphisms of toll-like receptor genes TLR1, TLR6 and TLR10 with the severity of coronavirus infection (COVID-19) in the Russian population of the Chelyabinsk region.

MATERIALS AND METHODS: The study included 86 patients from COVID-departments of hospitals in Chelyabinsk with a diagnosis of bilateral pneumonia with a degree of severity: moderate (M-BLP, n = 36) or severe (S-BLP, n = 50). The control group consisted of 100 healthy individuals from the register of the Chelyabinsk regional hemotransfusion station (“Control”). All the individuals studied belonged to the Russian ethnic group. Polymorphisms 1805T>G of TLR1 gene, 745C>T of TLR6 gene and 721A>C of TLR10 gene were determined using polymerase chain reaction with restriction fragment length polymorphism. The analysis of the association between genotypes and the status of individuals was carried out using the correspondence analysis and the Monte Carlo method.

RESULTS: It was revealed that the differences between the studied groups are completely determined by TLR1 genotypes. The GG genotype with statistical significance is more often detected in the “Control” group compared to M-BLP and S-BLP (p < 0.001, OR = 12.94) and can be assessed as protective in relation to the development of bilateral pneumonia in COVID-19. The TT genotype can be considered as predisposing to the development of a severe form of bilateral pneumonia in COVID-19 (p = 0.022): the TT genotype is significantly less common (OR = 0.20) in the M-BLP group compared to S-BLP.

CONCLUSIONS: It can be assumed that the genetic variant 1805*G of the TLR1 gene, which provides a moderate pro-inflammatory response and predominates in European populations, gives an advantage to its owners, preventing the development of complicated conditions in COVID-19 infection.

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Alexander V. Evdokimov

Chelyabinsk State University

Author for correspondence.
Email: avdax@yandex.ru
ORCID iD: 0000-0002-7011-368X
SPIN-code: 9092-4429
Scopus Author ID: 56946405800
ResearcherId: ABA-8628-2021

Cand. Sci. (Biol.), Assistant Professor at the Department of Microbiology, Immunology and General Biology of the Biology Faculty

Russian Federation, 129, Bratiev Kashirinykh Str., Chelyabinsk, 454001

Tatyana A. Suslova

Chelyabinsk State University; Chelyabinsk Regional Hemotransfusion Station

Email: hla_chel@mail.ru
ORCID iD: 0000-0002-7028-6839
SPIN-code: 2869-1066

MD, Cand. Sci. (Med.), Assistant Professor, Head of the Laboratory of Immunological Research, Assistant Professor at the Department of Microbiology, Immunology and General Biology of the Biology Faculty

Russian Federation, 129, Bratiev Kashirinykh Str., Chelyabinsk, 454001; Chelyabinsk

Svetlana V. Belyaeva

Chelyabinsk State University; Chelyabinsk Regional Hemotransfusion Station

Email: shshvetlana@gmail.com
SPIN-code: 9485-3361

Cand. Sci. (Biol.), biologist of the Laboratory of Immunological Research, Assistant Professor at the Department of Microbiology, Immunology and General Biology of the Biology Faculty

Russian Federation, 129, Bratiev Kashirinykh Str., Chelyabinsk, 454001; Chelyabinsk

Alexandra L. Burmistrova

Chelyabinsk State University

Email: burmal@csu.ru
ORCID iD: 0000-0001-6462-9500
SPIN-code: 2374-7309

MD, Dr. Sci. (Med.), Professor, Head of the Department of Microbiology, Immunology and General Biology of the Biology Faculty

Russian Federation, 129, Bratiev Kashirinykh Str., Chelyabinsk, 454001

Darya S. Stashkevich

Chelyabinsk State University

Email: stashkevich_dary@mail.ru
SPIN-code: 6592-1469

Cand. Sci. (Biol.), Assistant Professor, Dean of the Biology Faculty

Russian Federation, 129, Bratiev Kashirinykh Str., Chelyabinsk, 454001


  1. WHO Coronavirus (COVID-19) Dashboard [Internet]. Available from: https://covid19.who.int/. Accessed: Nov 29, 2021.
  2. Pinheiro DS, Santos RS, Jardim PCBV, et al. The combination of ACE I/D and ACE2 G8790A polymorphisms revels susceptibility to hypertension: A genetic association study in Brazilian patients. PLoS One. 2019;14(8):e0221248. doi: 10.1371/journal.pone.0221248
  3. Gemmati D, Tisato V. Genetic hypothesis and pharmogenetics side of Renin-Angiotensin-System in COVID-19. Genes (Basel). 2020;11(9):1044. doi: 10.3390/genes11091044
  4. Iwasaki A, Medzhitov R. Control of adaptive immunity by the innate immune system. Nat Immunol. 2015;16(4):343–353. doi: 10.1038/ni.3123
  5. Beutler B, Jiang Z, Georgel P, et al. Genetic analysis of host resistance: toll-like receptor signaling and immunity at large. Annu Rev Immunol. 2006;24:353–389. doi: 10.1146/annurev.immunol.24.021605.090552
  6. Mercier BC, Cottalorda A, Coupet CA, et al. TLR2 engagement on CD8 T cells enables generation of functional memory cells in response to a suboptimal TCR signal. J Immunol. 2009;182(4):1860–1867. doi: 10.4049/jimmunol.0801167
  7. Enard D, Depaulis F, Crollius HR. Human and non-human primate genomes share hotspots of positive selection. PLoS Genet. 2010;6(2):e1000840. doi: 10.1371/journal.pgen.1000840
  8. Barreiro LB, Quintana-Murci L. From evolutionary genetics to human immunology: how selection shapes host defense genes. Nat Rev Genet. 2010;11(1):17–30. doi: 10.1038/nrg2698
  9. Casanova JL, Abel L, Quintana-Murci L. Human TLRs and IL-1Rs in host defense: natural insights from evolutionary, epidemiological, and clinical genetics. Annu Rev Immunol. 2011;29:447–491. doi: 10.1146/annurev-immunol-030409-101335
  10. Fumagalli M, Sironi M, Pozzoli U, et al. Signatures of environmental genetic adaptation pinpoint pathogens as the main selective pressure through human evolution. PLoS Genet. 2011;7(11):e1002355. doi: 10.1371/journal.pgen.1002355
  11. Karlsson EK, Kwiatkowski DP, Sabeti PC. Natural selection and infectious disease in human populations. Nat Rev Genet. 2014;15(6):379–393. doi: 10.1038/nrg3734
  12. Pickrell JK, Coop G, Novembre J, et al. Signals of recent positive selection in a worldwide sample of human populations. Genome Res. 2009;19(5):826–837. doi: 10.1101/gr.087577.108
  13. Choudhury A, Mukherjee S. In silico studies on the comparative characterization of the interactions of SARS-CoV-2 spike glycoprotein with ACE-2 receptor homologs and human TLRs. J Med Virol. 2020;92(10):2105–2113. doi: 10.1002/jmv.25987
  14. Gadanec LK, McSweeney KR, Qaradakhi T, et al. Can SARS-CoV-2 virus use multiple receptors to enter host cells? Int J Mol Sci. 2021;22(3):992. doi: 10.3390/ijms22030992
  15. Patel S. Danger-Associated Molecular Patterns (DAMPs): The derivatives and triggers of inflammation. Curr Allergy Asthma Rep. 2018;18(11):63. doi: 10.1007/s11882-018-0817-3
  16. Komai K, Shichita T, Ito M, et al. Role of scavenger receptors as damage-associated molecular pattern receptors in Toll-like receptor activation. Int Immunol. 2017;29(2):59–70. doi: 10.1093/intimm/dxx010
  17. Matzinger P. The danger model: a renewed sense of self. Science. 2002;296(5566):301–305. doi: 10.1126/science.1071059
  18. Leoratti FM, Farias L, Alves FP, et al. Variants in the toll-like receptor signalling pathway and clinical outcomes of malaria. J Infect Dis. 2008;198(5):772–780. doi: 10.1086/590440
  19. Mailaparambil B, Krueger M, Heinze J, et al. Polymorphisms of toll-like receptors in the genetics of severe RSV associated diseases. Dis Markers. 2008;25(1):59–65. doi: 10.1155/2008/619595
  20. Hope ACA. A simplified Monte Carlo significance test procedure. Journal of the Royal Statistical Society Series B. 1968;30: 582–598. doi: 10.1111/j.2517-6161.1968.tb00759.x
  21. Benjamini Y, Yekutieli D. The control of the false discovery rate in multiple testing under dependency. Annals of Statistics. 2001;29(4):1165–1188. doi: 10.1214/aos/1013699998
  22. Clopper C, Pearson ES. The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika. 1934;26:404–413. doi: 10.1093/BIOMET/26.4.404
  23. R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria [Internet]. Available from: http://www.R-project.org/index.html. Accessed: Nov 29, 2021.
  24. Hawn TR, Misch EA, Dunstan SJ, et al. A common human TLR1 polymorphism regulates the innate immune response to lipopeptides. Eur J Immunol. 2007;37(8):2280–2289. doi: 10.1002/eji.200737034
  25. Bramanti B, Stenseth NC, Walløe L, Lei X. Plague: a disease which changed the path of human civilization. Adv Exp Med Biol. 2016;918:1–26. doi: 10.1007/978-94-024-0890-4_1
  26. Buntgen U, Ginzler C, Esper J, et al. Digitizing historical plague. Clin Infect Dis. 2012;55(11):1586–1588. doi: 10.1093/cid/cis723
  27. Schmid BV, Buntgen U, Easterday WR, et al. Climate-driven introduction of the Black Death and successive plague reintroductions into Europe. Proc Natl Acad Sci USA. 2015;112(10):3020–3025. doi: 10.1073/pnas.1412887112
  28. Evdokimov AV. Geneticheskie patterny klastera TLR10–TLR1–TLR6 populyatsiy Chelyabinskoy oblasti (russkie, bashkiry, nagaybaki) v sopostavlenii s nekotorymi evraziyskimi populyatsiyami [dissertation]. Chelyabinsk; 2016. (In Russ.)

Supplementary files

Supplementary Files
1. Fig. 1. Contribution of different TLR1/TLR6/TLR10 genotypes combinations to the association with the BLP form. The dashed line marks the average level above which the contribution is considered significant

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2. Fig. 2. Biplot of correspondence analysis. The graph simultaneously shows both combinations of genotypes (only those that have made the greatest contribution to the association with the studied groups are depicted) and the groups themselves. M-BLP — moderate bilateral pneumonia; S-BLP — severe bilateral pneumonia

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Copyright (c) 2021 Evdokimov A.V., Suslova T.A., Belyaeva S.V., Burmistrova A.L., Stashkevich D.S.

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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