Browse > Article
http://dx.doi.org/10.14348/molcells.2021.2249

Regional TMPRSS2 V197M Allele Frequencies Are Correlated with COVID-19 Case Fatality Rates  

Jeon, Sungwon (Korean Genomics Center (KOGIC), Ulsan National Institute of Science and Technology (UNIST))
Blazyte, Asta (Korean Genomics Center (KOGIC), Ulsan National Institute of Science and Technology (UNIST))
Yoon, Changhan (Korean Genomics Center (KOGIC), Ulsan National Institute of Science and Technology (UNIST))
Ryu, Hyojung (Korean Genomics Center (KOGIC), Ulsan National Institute of Science and Technology (UNIST))
Jeon, Yeonsu (Korean Genomics Center (KOGIC), Ulsan National Institute of Science and Technology (UNIST))
Bhak, Youngjune (Korean Genomics Center (KOGIC), Ulsan National Institute of Science and Technology (UNIST))
Bolser, Dan (Geromics, Ltd.)
Manica, Andrea (Department of Zoology, University of Cambridge)
Shin, Eun-Seok (Division of Cardiology, Department of Internal Medicine, Ulsan Medical Center)
Cho, Yun Sung (Clinomics, Inc.)
Kim, Byung Chul (Clinomics, Inc.)
Ryoo, Namhee (Department of Laboratory Medicine, Keimyung University School of Medicine)
Choi, Hansol (Korean Genomics Center (KOGIC), Ulsan National Institute of Science and Technology (UNIST))
Bhak, Jong (Korean Genomics Center (KOGIC), Ulsan National Institute of Science and Technology (UNIST))
Abstract
Coronavirus disease, COVID-19 (coronavirus disease 2019), caused by SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), has a higher case fatality rate in European countries than in others, especially East Asian ones. One potential explanation for this regional difference is the diversity of the viral infection efficiency. Here, we analyzed the allele frequencies of a nonsynonymous variant rs12329760 (V197M) in the TMPRSS2 gene, a key enzyme essential for viral infection and found a significant association between the COVID-19 case fatality rate and the V197M allele frequencies, using over 200,000 present-day and ancient genomic samples. East Asian countries have higher V197M allele frequencies than other regions, including European countries which correlates to their lower case fatality rates. Structural and energy calculation analysis of the V197M amino acid change showed that it destabilizes the TMPRSS2 protein, possibly negatively affecting its ACE2 and viral spike protein processing.
Keywords
allele frequency; case fatality rate; COVID-19; SARS-CoV-2; TMPRSS2;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Altschul, S.F., Madden, T.L., Schaffer, A.A., Zhang, J., Zhang, Z., Miller, W., and Lipman, D.J. (1997). Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389-3402.   DOI
2 Asselta, R., Paraboschi, E.M., Mantovani, A., and Duga, S. (2020). ACE2 and TMPRSS2 variants and expression as candidates to sex and country differences in COVID-19 severity in Italy. Aging (Albany N.Y.) 12, 10087-10098.   DOI
3 Broad Institute (2020). Picard toolkit. Retrieved May 7, 2020, from http://broadinstitute.github.io/picard/
4 Cai, G. (2020a). Bulk and single-cell transcriptomics identify tobacco-use disparity in lung gene expression of ACE2, the receptor of 2019-nCov. MedRxiv, https://doi.org/10.1101/2020.02.05.20020107
5 Capriotti, E., Fariselli, P., Rossi, I., and Casadio, R. (2008). A three-state prediction of single point mutations on protein stability changes. BMC Bioinformatics 9 Suppl 2, S6.
6 Pettersen, E.F., Goddard, T.D., Huang, C.C., Couch, G.S., Greenblatt, D.M., Meng, E.C., and Ferrin, T.E. (2004). UCSF Chimera--a visualization system for exploratory research and analysis. J. Comput. Chem. 25, 1605-1612.   DOI
7 Pires, D.E., Ascher, D.B., and Blundell, T.L. (2014a). DUET: a server for predicting effects of mutations on protein stability using an integrated computational approach. Nucleic Acids Res. 42(Web Server issue), W314-W319.   DOI
8 Prado-Martinez, J., Sudmant, P.H., Kidd, J.M., Li, H., Kelley, J.L., Lorente-Galdos, B., Veeramah, K.R., Woerner, A.E., O'Connor, T.D., Santpere, G., et al. (2013). Great ape genetic diversity and population history. Nature 499, 471-475.   DOI
9 Redelman-Sidi, G. (2020). Could BCG be used to protect against COVID-19? Nat. Rev. Urol. 17, 316-317.   DOI
10 Shen, M.Y. and Sali, A. (2006). Statistical potential for assessment and prediction of protein structures. Protein Sci. 15, 2507-2524.   DOI
11 Sonn, J.W., Kang, M., and Choi, Y. (2020). Smart city technologies for pandemic control without lockdown. Int. J. Urban Sci. 24, 149-151.   DOI
12 Williams, C.J., Headd, J.J., Moriarty, N.W., Prisant, M.G., Videau, L.L., Deis, L.N., Verma, V., Keedy, D.A., Hintze, B.J., Chen, V.B., et al. (2018). MolProbity: more and better reference data for improved all-atom structure validation. Protein Sci. 27, 293-315.   DOI
13 Sharma, S., Singh, I., Haider, S., Malik, M.Z., Ponnusamy, K., and Rai, E. (2020). ACE2 homo-dimerization, human genomic variants and interaction of host proteins explain high population specific differences in outcomes of COVID19. BioRxiv, https://doi.org/10.1101/2020.04.24.050534
14 Yang, Y. and Zhou, Y. (2008). Specific interactions for ab initio folding of protein terminal regions with secondary structures. Proteins 72, 793-803.   DOI
15 Cheng, J., Randall, A., and Baldi, P. (2006). Prediction of protein stability changes for single-site mutations using support vector machines. Proteins 62, 1125-1132.   DOI
16 Cocca, M., Barbieri, C., Concas, M.P., Robino, A., Brumat, M., Gandin, I., Trudu, M., Sala, C.F., Vuckovic, D., Girotto, G., et al. (2020). A bird's-eye view of Italian genomic variation through whole-genome sequencing. Eur. J. Hum. Genet. 28, 435-444.   DOI
17 Williams, F.M.K., Freidin, M.B., Mangino, M., Couvreur, S., Visconti, A., Bowyer, R.C.E., Le Roy, C.I., Falchi, M., Sudre, C., Davies, R., et al. (2020). Self-reported symptoms of covid-19 including symptoms most predictive of SARS-CoV-2 infection, are heritable. MedRxiv, https://doi.org/10.1101/2020.04.22.20072124
18 Das, R. and Ghate, S.D. (2020). Investigating the likely association between genetic ancestry and COVID-19 manifestations. MedRxiv, https://doi.org/10.1101/2020.04.05.20054627
19 Dehouck, Y., Kwasigroch, J.M., Gilis, D., and Rooman, M. (2011). PoPMuSiC 2.1: a web server for the estimation of protein stability changes upon mutation and sequence optimality. BMC Bioinformatics 12, 151.   DOI
20 Yuan, F.F., Velickovic, Z., Ashton, L.J., Dyer, W.B., Geczy, A.F., Dunckley, H., Lynch, G.W., and Sullivan, J.S. (2014). Influence of HLA gene polymorphisms on susceptibility and outcome post infection with the SARS-CoV virus. Virol. Sin. 29, 128-130.   DOI
21 Fu, Q., Posth, C., Hajdinjak, M., Petr, M., Mallick, S., Fernandes, D., Furtwangler, A., Haak, W., Meyer, M., Mittnik, A., et al. (2016). The genetic history of Ice Age Europe. Nature 534, 200-205.   DOI
22 Herter, S., Piper, D.E., Aaron, W., Gabriele, T., Cutler, G., Cao, P., Bhatt, A.S., Choe, Y., Craik, C.S., Walker, N., et al. (2005). Hepatocyte growth factor is a preferred in vitro substrate for human hepsin, a membrane-anchored serine protease implicated in prostate and ovarian cancers. Biochem. J. 390, 125-136.   DOI
23 Hoffmann, M., Kleine-Weber, H., Schroeder, S., Kruger, N., Herrler, T., Erichsen, S., Schiergens, T.S., Herrler, G., Wu, N.H., Nitsche, A., et al. (2020). SARS-CoV-2 cell entry depends on ACE2 and TMPRSS2 and is blocked by a clinically proven protease inhibitor. Cell 181, 271-280.e8.   DOI
24 Ji, Y., Ma, Z., Peppelenbosch, M.P., and Pan, Q. (2020). Potential association between COVID-19 mortality and health-care resource availability. Lancet Glob. Health 8, e480.   DOI
25 European Centre for Disease Prevention and Control (2020). COVID-19 statistics worldwide. Retrieved May 23, 2020, from https://www.ecdc.europa.eu/en/covid-19-pandemic
26 Mjaess, G., Karam, A., Aoun, F., Albisinni, S., and Roumeguere, T. (2020). COVID-19 and the male susceptibility: the role of ACE2, TMPRSS2 and the androgen receptor. Prog. Urol. 30, 484-487.   DOI
27 Kenyon, C. (2020). Why has COVID-19 spread more extensively in Europe than Asia? Preprints, https://doi.org/10.20944/preprints202005.0200.v1
28 Liu, X., Wu, C., Li, C., and Boerwinkle, E. (2016). dbNSFP v3.0: a one-stop database of functional predictions and annotations for human nonsynonymous and splice-site SNVs. Hum. Mutat. 37, 235-241.   DOI
29 Matsuyama, S., Nao, N., Shirato, K., Kawase, M., Saito, S., Takayama, I., Nagata, N., Sekizuka, T., Katoh, H., Kato, F., et al. (2020). Enhanced isolation of SARS-CoV-2 by TMPRSS2-expressing cells. Proc. Natl. Acad. Sci. U. S. A. 117, 7001-7003.   DOI
30 Miller, A., Reandelar, M.J., Fasciglione, K., Roumenova, V., Li, Y., and Otazu, G.H. (2020). Correlation between universal BCG vaccination policy and reduced morbidity and mortality for COVID-19. MedRxiv, https://doi.org/10.1101/2020.03.24.20042937
31 Ou, X., Liu, Y., Lei, X., Li, P., Mi, D., Ren, L., Guo, L., Guo, R., Chen, T., Hu, J., et al. (2020). Characterization of spike glycoprotein of SARS-CoV-2 on virus entry and its immune cross-reactivity with SARS-CoV. Nat. Commun. 11, 1620.   DOI
32 Paniri, A., Hosseini, M.M., and Akhavan-Niaki, H. (2021). First comprehensive computational analysis of functional consequences of TMPRSS2 SNPs in susceptibility to SARS-CoV-2 among different populations. J. Biomol. Struct. Dyn. 39, 3576-3593.   DOI
33 Kim, D.E., Chivian, D., and Baker, D. (2004). Protein structure prediction and analysis using the Robetta server. Nucleic Acids Res. 32(Web Server issue), W526-W531.   DOI
34 Cao, Y., Li, L., Feng, Z., Wan, S., Huang, P., Sun, X., Wen, F., Huang, X., Ning, G., and Wang, W. (2020). Comparative genetic analysis of the novel coronavirus (2019-nCoV/SARS-CoV-2) receptor ACE2 in different populations. Cell Discov. 6, 11.   DOI
35 Lai, C.C., Shih, T.P., Ko, W.C., Tang, H.J., and Hsueh, P.R. (2020). Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease-2019 (COVID-19): the epidemic and the challenges. Int. J. Antimicrob. Agents 55, 105924.   DOI
36 Pires, D.E., Ascher, D.B., and Blundell, T.L. (2014b). mCSM: predicting the effects of mutations in proteins using graph-based signatures. Bioinformatics 30, 335-342.   DOI
37 Urnikyte, A., Flores-Bello, A., Mondal, M., Molyte, A., Comas, D., Calafell, F., Bosch, E., and Kucinskas, V. (2019). Patterns of genetic structure and adaptive positive selection in the Lithuanian population from high-density SNP data. Sci. Rep. 9, 9163.   DOI
38 Jeon, S., Bhak, Y., Choi, Y., Jeon, Y., Kim, S., Jang, J., Jang, J., Blazyte, A., Kim, C., Kim, Y., et al. (2020). Korean Genome Project: 1094 Korean personal genomes with clinical information. Sci. Adv. 6, eaaz7835.   DOI
39 Zhang, C., Gao, Y., Ning, Z., Lu, Y., Zhang, X., Liu, J., Xie, B., Xue, Z., Wang, X., Yuan, K., et al. (2019). PGG.SNV: understanding the evolutionary and medical implications of human single nucleotide variations in diverse populations. Genome Biol. 20, 215.   DOI
40 Zhou, L., Xu, Z., Castiglione, G.M., Soiberman, U.S., Eberhart, C.G., and Duh, E.J. (2020). ACE2 and TMPRSS2 are expressed on the human ocular surface, suggesting susceptibility to SARS-CoV-2 infection. Ocul. Surf. 18, 537-544.   DOI
41 McLaren, W., Gil, L., Hunt, S.E., Riat, H.S., Ritchie, G.R., Thormann, A., Flicek, P., and Cunningham, F. (2016). The Ensembl Variant Effect Predictor. Genome Biol. 17, 122.   DOI
42 Chang, C.C., Chow, C.C., Tellier, L.C., Vattikuti, S., Purcell, S.M., and Lee, J.J. (2015). Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7.   DOI
43 Souilmi, Y., Lauterbur, M.E., Tobler, R., Huber, C.D., Johar, A.S., and Enard, D. (2020). An ancient coronavirus-like epidemic drove adaptation in East Asians from 25,000 to 5,000 years ago. BioRxiv, https://doi.org/10.1101/2020.11.16.385401
44 Daneshkhah, A., Eshein, A., Subramanian, H., Roy, H.K., and Backman, V. (2020). The possible role of vitamin D in suppressing cytokine storm and associated mortality in COVID-19 patients. MedRxiv, https://doi.org/10.1101/2020.04.08.20058578
45 Dowd, J.B., Andriano, L., Brazel, D.M., Rotondi, V., Block, P., Ding, X., Liu, Y., and Mills, M.C. (2020). Demographic science aids in understanding the spread and fatality rates of COVID-19. Proc. Natl. Acad. Sci. U. S. A. 117, 9696-9698.   DOI
46 Han, S., Andres, A.M., Marques-Bonet, T., and Kuhlwilm, M. (2019). Genetic variation in Pan species is shaped by demographic history and harbors lineage-specific functions. Genome Biol. Evol. 11, 1178-1191.   DOI
47 Hussein, N.R. (2020). Possible factors associated with low case fatality rate of COVID-19 in Kurdistan Region, Iraq. J. Kermanshah Univ. Med. Sci. 24, e103393.
48 Karczewski, K.J., Francioli, L.C., Tiao, G., Cummings, B.B., Alfoldi, J., Wang, Q., Collins, R.L., Laricchia, K.M., Ganna, A., Birnbaum, D.P., et al. (2020). The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 581, 434-443.   DOI
49 Cai, H. (2020b). Sex difference and smoking predisposition in patients with COVID-19. Lancet Respir. Med. 8, e20.   DOI
50 Blake, L.E. and Garcia-Blanco, M.A. (2014). Human genetic variation and yellow fever mortality during 19th century U.S. epidemics. mBio 5, e01253-14.
51 Adzhubei, I.A., Schmidt, S., Peshkin, L., Ramensky, V.E., Gerasimova, A., Bork, P., Kondrashov, A.S., and Sunyaev, S.R. (2010). A method and server for predicting damaging missense mutations. Nat. Methods 7, 248-249.   DOI
52 Monticelli, M., Hay Mele, B., Benetti, E., Fallerini, C., Baldassarri, M., Furini, S., Frullanti, E., Mari, F., Andreotti, G., Cubellis, M.V., et al. (2021). Protective role of a TMPRSS2 variant on severe COVID-19 outcome in young males and elderly women. Genes (Basel) 12, 596.   DOI
53 Pandurangan, A.P., Ochoa-Montano, B., Ascher, D.B., and Blundell, T.L. (2017). SDM: a server for predicting effects of mutations on protein stability. Nucleic Acids Res. 45(W1), W229-W235.   DOI
54 Petersen, L., Andersen, P.K., and Sorensen, T.I. (2010). Genetic influences on incidence and case-fatality of infectious disease. PLoS One 5, e10603.   DOI
55 Segovia-Juarez, J., Castagnetto, J.M., and Gonzales, G.F. (2020). High altitude reduces infection rate of COVID-19 but not case-fatality rate. Respir. Physiol. Neurobiol. 281, 103494.   DOI
56 Song, J., Li, Y., Huang, X., Chen, Z., Li, Y., Liu, C., Chen, Z., and Duan, X. (2020). Systematic analysis of ACE2 and TMPRSS2 expression in salivary glands reveals underlying transmission mechanism caused by SARS-CoV-2. J. Med. Virol. 92, 2556-2566.   DOI
57 Yang, J., Yan, R., Roy, A., Xu, D., Poisson, J., and Zhang, Y. (2015). The I-TASSER Suite: protein structure and function prediction. Nat. Methods 12, 7-8.
58 Bhattacharyya, C., Das, C., Ghosh, A., Singh, A.K., Mukherjee, S., Majumder, P.P., Basu, A., and Biswas, N.K. (2020). Global spread of SARS-CoV-2 subtype with spike protein mutation D614G is shaped by human genomic variations that regulate expression of TMPRSS2 and MX1 genes. BioRxiv, https://doi.org/10.1101/2020.05.04.075911
59 Ng, P.C. and Henikoff, S. (2003). SIFT: predicting amino acid changes that affect protein function. Nucleic Acids Res. 31, 3812-3814.   DOI
60 Parthiban, V., Gromiha, M.M., and Schomburg, D. (2006). CUPSAT: prediction of protein stability upon point mutations. Nucleic Acids Res. 34(Web Server issue), W239-W242.   DOI