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

Alexander V. Evdokimov , Tatyana A. Suslova , Svetlana V. Belyaeva , Alexandra L. Burmistrova , Darya S. Stashkevich

Medical academic journal ›› 2021, Vol. 21 ›› Issue (4) : 57 -66.

PDF
Medical academic journal ›› 2021, Vol. 21 ›› Issue (4) : 57 -66. DOI: 10.17816/MAJ90324
Original research
research-article

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

Author information +
History +
PDF

Abstract

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.

Keywords

COVID-19 / TLR / genetic polymorphism / bilateral pneumonia / immunogenetics

Cite this article

Download citation ▾
Alexander V. Evdokimov, Tatyana A. Suslova, Svetlana V. Belyaeva, Alexandra L. Burmistrova, Darya S. Stashkevich. Polymorphism of TLR genes and the course of COVID-19 bilateral pneumonia. Medical academic journal, 2021, 21(4): 57-66 DOI:10.17816/MAJ90324

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

WHO Coronavirus (COVID-19) Dashboard [Internet]. Available from: https://covid19.who.int/. Accessed: Nov 29, 2021.

[2]

WHO Coronavirus (COVID-19) Dashboard [Электронный ресурс]. Режим доступа: https://covid19.who.int/. Дата обращения: 29.11.2021.

[3]

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

[4]

Pinheiro D.S., Santos R.S., Jardim P.C.B.V. 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. Vol. 14, No. 8. P. e0221248. DOI: 10.1371/journal.pone.0221248

[5]

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

[6]

Gemmati D., Tisato V. Genetic hypothesis and pharmogenetics side of Renin-Angiotensin-System in COVID-19 // Genes (Basel). 2020. Vol. 11, No. 9. P. 1044. DOI: 10.3390/genes11091044

[7]

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

[8]

Iwasaki A., Medzhitov R. Control of adaptive immunity by the innate immune system // Nat. Immunol. 2015. Vol. 16, No. 4. P. 343–353. DOI: 10.1038/ni.3123

[9]

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

[10]

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. Vol. 24. P. 353–389. DOI: 10.1146/annurev.immunol.24.021605.090552

[11]

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

[12]

Mercier B.C., Cottalorda A., Coupet C.A. et al. TLR2 engagement on CD8 T cells enables generation of functional memory cells in response to a suboptimal TCR signal // J. Immunol. 2009. Vol. 182, No. 4. P. 1860–1867. DOI: 10.4049/jimmunol.0801167

[13]

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

[14]

Enard D., Depaulis F., Crollius H.R. Human and non-human primate genomes share hotspots of positive selection // PLoS Genet. 2010. Vol. 6, No. 2. P. e1000840. DOI: 10.1371/journal.pgen.1000840

[15]

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

[16]

Barreiro L.B., Quintana-Murci L. From evolutionary genetics to human immunology: how selection shapes host defense genes // Nat. Rev. Genet. 2010. Vol. 11, No. 1. P. 17–30. DOI: 10.1038/nrg2698

[17]

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

[18]

Casanova J.L., 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. Vol. 29. P. 447–491. DOI: 10.1146/annurev-immunol-030409-101335

[19]

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

[20]

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. Vol. 7, No. 11. P. e1002355. DOI: 10.1371/journal.pgen.1002355

[21]

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

[22]

Karlsson E.K., Kwiatkowski D.P., Sabeti P.C. Natural selection and infectious disease in human populations // Nat. Rev. Genet. 2014. Vol. 15, No. 6. P. 379–393. DOI: 10.1038/nrg3734

[23]

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

[24]

Pickrell J.K., Coop G., Novembre J. et al. Signals of recent positive selection in a worldwide sample of human populations // Genome Res. 2009. Vol. 19, No. 5. P. 826–837. DOI: 10.1101/gr.087577.108

[25]

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

[26]

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. Vol. 92, No. 10. P. 2105–2113. DOI: 10.1002/jmv.25987

[27]

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

[28]

Gadanec L.K., McSweeney K.R., Qaradakhi T. et al. Can SARS-CoV-2 virus use multiple receptors to enter host cells? // Int. J. Mol. Sci. 2021. Vol. 22, No. 3. P. 992. DOI: 10.3390/ijms22030992

[29]

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

[30]

Patel S. Danger-Associated Molecular Patterns (DAMPs): The derivatives and triggers of inflammation // Curr. Allergy Asthma Rep. 2018. Vol. 18, No. 11. P. 63. DOI: 10.1007/s11882-018-0817-3

[31]

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

[32]

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. Vol. 29, No. 2. P. 59–70. DOI: 10.1093/intimm/dxx010

[33]

Matzinger P. The danger model: a renewed sense of self. Science. 2002;296(5566):301–305. DOI: 10.1126/science.1071059

[34]

Matzinger P. The danger model: a renewed sense of self // Science. 2002. Vol. 296, No. 5566. P. 301–305. DOI: 10.1126/science.1071059

[35]

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

[36]

Leoratti F.M., Farias L., Alves F.P. et al. Variants in the toll-like receptor signalling pathway and clinical outcomes of malaria // J. Infect. Dis. 2008. Vol. 198, No. 5. P. 772–780. DOI: 10.1086/590440

[37]

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

[38]

Mailaparambil B., Krueger M., Heinze J. et al. Polymorphisms of toll-like receptors in the genetics of severe RSV associated diseases // Dis. Markers. 2008. Vol. 25, No. 1. P. 59–65. DOI: 10.1155/2008/619595

[39]

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

[40]

Hope A.C.A. A simplified Monte Carlo significance test procedure // Journal of the Royal Statistical Society Series B. 1968. Vol. 30. P. 582–598. DOI: 10.1111/j.2517-6161.1968.tb00759.x

[41]

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

[42]

Benjamini Y., Yekutieli D. The control of the false discovery rate in multiple testing under dependency // Annals of Statistics. 2001. Vol. 29, No. 4. P. 1165–1188. DOI: 10.1214/aos/1013699998

[43]

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

[44]

Clopper C., Pearson E.S. The use of confidence or fiducial limits illustrated in the case of the binomial // Biometrika. 1934. Vol. 26. P. 404–413. DOI: 10.1093/BIOMET/26.4.404

[45]

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.

[46]

R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria [Электронный ресурс]. Режим доступа: http://www.R-project.org/index.html. Дата обращения: 29.11.2021.

[47]

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

[48]

Hawn T.R., Misch E.A., Dunstan S.J. et al. A common human TLR1 polymorphism regulates the innate immune response to lipopeptides // Eur. J. Immunol. 2007. Vol. 37, No. 8. P. 2280–2289. DOI: 10.1002/eji.200737034

[49]

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

[50]

Bramanti B., Stenseth N.C., Walløe L., Lei X. Plague: a disease which changed the path of human civilization // Adv. Exp. Med. Biol. 2016. Vol. 918. P. 1–26. DOI: 10.1007/978-94-024-0890-4_1

[51]

Buntgen U, Ginzler C, Esper J, et al. Digitizing historical plague. Clin Infect Dis. 2012;55(11):1586–1588. DOI: 10.1093/cid/cis723

[52]

Buntgen U., Ginzler C., Esper J. et al. Digitizing historical plague // Clin. Infect. Dis. 2012. Vol. 55, No. 11. P. 1586–1588. DOI: 10.1093/cid/cis723

[53]

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

[54]

Schmid B.V., Buntgen U., Easterday W.R. et al. Climate-driven introduction of the Black Death and successive plague reintroductions into Europe // Proc. Natl. Acad. Sci. USA. 2015. Vol. 112, No. 10. P. 3020–3025. DOI: 10.1073/pnas.1412887112

[55]

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.)

[56]

Евдокимов А.В. Генетические паттерны кластера TLR10–TLR1–TLR6 популяций Челябинской области (русские, башкиры, нагайбаки) в сопоставлении с некоторыми евразийскими популяциями: автореф. дис. ... канд. биол. наук. Челябинск, 2016.

RIGHTS & PERMISSIONS

Evdokimov A.V., Suslova T.A., Belyaeva S.V., Burmistrova A.L., Stashkevich D.S.

AI Summary AI Mindmap
PDF

69

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/