Literature DB >> 35811452

Analysis of ACE2 and TMPRSS2 coding variants as a risk factor for SARS-CoV-2 from 946 whole-exome sequencing data in the Turkish population.

Nilgun Duman1, Gulten Tuncel2, Atil Bisgin3,4, Sevcan Tug Bozdogan3,4, Sebnem Ozemri Sag5, Seref Gul6, Aslihan Kiraz7, Burhan Balta7, Murat Erdogan7, Bulent Uyanik8, Sezin Canbek9, Pinar Ata10, Bilgen Bilge Geckinli10, Esra Arslan Ates10, Ceren Alavanda10, Sevda Yesim Ozdemir11, Ozlem Sezer12, Gulay Oner Ozgon13, Hakan Gurkan14, Kubra Guler15, Ibrahim Boga3,4, Niyazi Kaya5, Adem Alemdar5, Murat Sayan2,16, Munis Dundar17, Mahmut Cerkez Ergoren2,18, Sehime Gulsun Temel5,18,19,20.   

Abstract

Heterogeneity in symptoms associated with COVID-19 in infected patients remains unclear. ACE2 and TMPRSS2 gene variants are considered possible risk factors for COVID-19. In this study, a retrospective comparative genome analysis of the ACE2 and TMPRSS2 variants from 946 whole-exome sequencing data was conducted. Allele frequencies of all variants were calculated and filtered to remove variants with allele frequencies lower than 0.003 and to prioritize functional coding variants. The majority of detected variants were intronic, only two ACE2 and three TMPRSS2 nonsynonymous variants were detected in the analyzed cohort. The main ACE2 variants that putatively have a protective or susceptibility effect on SARS-CoV-2 have not yet been determined in the Turkish population. The Turkish genetic makeup likely lacks any ACE2 variant that increases susceptibility to SARS-CoV-2 infection. TMPRSS2 rs75603675 and rs12329760 variants that were previously defined as common variants that have different allele frequencies among populations and may have a role in SARS-CoV-2 attachment to host cells were determined in the population. Overall, these data will contribute to the formation of a national variation database and may also contribute to further studies of ACE2 and TMPRSS2 in the Turkish population and differences in SARS-CoV-2 infection among other populations.
© 2022 Wiley Periodicals LLC.

Entities:  

Keywords:  ACE2; COVID-19; SARS-CoV-2; TMPRSS2; variant

Mesh:

Substances:

Year:  2022        PMID: 35811452      PMCID: PMC9349697          DOI: 10.1002/jmv.27976

Source DB:  PubMed          Journal:  J Med Virol        ISSN: 0146-6615            Impact factor:   20.693


INTRODUCTION

After the pneumonia cases of unknown cause were reported to the World Health Organization (WHO) in Wuhan Province of China in December 2019, the factor causing the disease was identified as a novel coronavirus strain. Cases were spread worldwide and the WHO declared a pandemic on March 11, 2020. The new coronavirus strain was named SARS‐CoV‐2 as they share a remarkable genetic identity with the known SARS‐CoV and the disease was referred to as coronavirus disease 2019 (COVID‐19). SARS‐CoV‐2, which has a much higher transmission rate than the known human coronavirus strains, damages the lung tissue, causing respiratory failure and leading to death. Individuals over 65 years old, smokers, and people with chronic diseases such as hypertension, diabetes, and kidney failure are more severely affected. Patients commonly show symptoms of dry cough, high fever, and shortness of breath, while some patients with abdominal pain, diarrhea, and headache are also reported. Some infected individuals, on the other hand, remain asymptomatic. As of the end of March 2021, the number of cases reported as SARS‐CoV‐2 positive worldwide has exceeded 128 million, and over 2 million deaths were reported to the WHO. Considering the cases worldwide, it was observed that SARS‐CoV‐2 was strangely and tragically selective. While only some infected people have been reported to be sick and most of the critical patients are elderly or people with chronic problems; some of those who die from the disease are individuals who do not have any chronic disease and are relatively young. A great variation in cases and mortality rates among countries were also detected. Along with factors including the number of tests performed, percentage of smokers, average age, and environmental factors, it is thought that genetic characteristics might also affect susceptibility to SARS‐CoV‐2 infection. The entry of enveloped viruses into cells is initiated by the binding of its spike (S) proteins to cell surface receptors. Previous reports indicated that angiotensin‐converting enzyme 2 (ACE2) is one of the host receptors for the novel coronavirus, SARS‐CoV‐2. , ACE2 is a transmembrane protein encoded by the ACE2 (MIM# 300335) gene on the Xp22.2 chromosome and has a transcript composed of 3339 bp and 21 exons. It is responsible for the conversion of angiotensin I to angiotensin 1−9 and angiotensin II to vasodilator angiotensin 1−7 and has roles in renal and cardiovascular function. In addition to cell surface receptors, another factor required for the entry of viruses into host cells is proteases. Proteases cleave and activate viral envelope glycoproteins and form domains catalyzing membrane melding, which is a process called priming. Transmembrane protease serine 2 (TMPRSS2) is shown to be involved in priming SARS‐CoV‐2 by cleaving the S protein at the S1/S2 and S2 sites. TMPRSS2 is encoded by TMPRSS2 (MIM# 602060) gene on chromosome 21q22.3, producing a 3250 bp‐long transcript with 14 exons according to the NCBI database. Expression levels and variations in ACE2 and TMPRSS2 in different individuals may facilitate or slow down the entrance of the virus into host cells and this might explain the dramatic variability of SARS‐CoV‐2 infection through individuals and populations. Likewise, variations in expression quantitative trait loci (eQTL) regions, known to regulate the ACE2 gene expression, may lead to changes in protein synthesis hence the course of infection. In a recent study, ACE2 and eQTL variation data from worldwide populations in ChinaMap, 1000 Genomes Project, and gnomAD databases were examined. Even though there is no direct evidence supporting the presence of ACE2 variations causing resistance to coronavirus S‐protein binding among populations, the study suggested that the eQTL variants associated with higher ACE2 expression have much higher allele frequencies in East Asian populations that may have an effect on different sensitivity or response from different populations to COVID‐19 under similar conditions. In the Italian population, where the disease caused more severe results compared to Asian and European countries, four TMPRSS2 variants were found to have significantly different allele frequencies. Furthermore, concerning the eQTL variants, population‐specific haplotypes were detected that are expected to upregulate TMPRSS2 gene expression. In light of these works, we conducted a retrospective comparative genome analysis of the ACE2 and TMPRSS2 gene variants in the Turkish population.

MATERIAL AND METHODS

Data collection and analysis

To investigate the allele frequencies of all functional coding variants of ACE2 and TMPRSS2, variation data from 946 unique individuals were collected from a total of 10 centers and hospitals around Turkey. As these individuals were randomly selected from centers located in various cities over the whole geographical parts of Turkey, we believe that the data represent the population of the country. This study was approved by the institutional review board (approval no: YDU/2020/79‐1103). The name of the center, sequencing platform, panels, and bioinformatic pipelines used are listed in Supporting Information: Table 1. Allele frequencies of all ACE2 and TMPRSS2 variants were calculated and then filtered to remove variants with allele frequencies lower than 0.003. Individuals with unknown gender (fetus) and without sufficient variant information were removed from the analysis. Public databases including Database of single‐nucleotide polymorphism (dbSNP), genome aggregation database (gnomAD v2.1.1), and Ensembl were used to prioritize functional coding variants and to obtain global and population‐based allele frequencies for comparison. , ,

In silico analysis

Crystal structures of ACE2‐Spike (PDB ID:6LZG) and TMPRSS2 (PDB ID: 7MEQ) were retrieved from the protein data bank. PyMol program (http://pymol.sourceforge.net) was used to visualize and generate in silico mutant proteins.

RESULTS

ACE2 gene variant analysis

A total of 2948 variants from 617 individuals were analyzed and 451 different variants were detected. Among the 451 variants, 9 of them were nonsynonymous. When the variants that have allele frequencies lower than 0.003 were removed, 70 variants remained and 2 of those were missense variants, one coding sequence synonymous variant, and the others were intronic variants. Details of the 70 variants, calculated allele frequencies in the Turkish population, and global and population‐based allele frequencies obtained from public databases are represented in Table 1.
Table 1

The list of ACE2 gene variant analysis from the whole‐exome sequencing data (only the variants ≥0.003 are listed)

dbSNP‐IDc.DNAAllele frequencyCoding consequenceAmino acid changeg1000GnomADGLOBALEUR
rs971249c.584‐71A>G0.581Intronic0.8030.6950.6440.609
rs113691336, rs4646158c.1297 + 68_1297 + 69insCTTAT0.455Intronic0.8340.7290.629
rs4646174c.1896 + 147G>C0.404Intronic0.6830.6210.6830.649
rs2285666c.439 + 4G>A0.326Intronic, splice donor0.3500.2730.3500.230
rs11340646, rs769765211, rs775397699c.1443‐97del0.128Intronic0.0016
rs4646156c.1071‐605T>A0.078Intronic0.8020.6990.8030.651
rs4646143c.900 + 1879A>G0.072Intronic0.8260.7270.8280.650
rs397822493c.187‐1538dup0.070Intronic0.8350.732
rs111691073c.1997 + 520_1997 + 527del0.060Intronic0.9710.940
rs35803318c.2247G>A0.060Coding sequence variant; synonymous0.0200.0380.0200.050
rs4646152c.1070 + 1320T>C0.058Intronic0.8320.7290.8320.651
rs879922c.1542‐361G>C0.056Intronic0.6820.6190.6820.646
rs4240157c.1897‐1015G>A0.056Intronic0.6820.6170.6820.641
rs397686765, rs398087648, rs4646131, rs869127567c.345 + 524delT0.053Intronic0.8030.701
rs233575c.211‐625C>T0.051Intronic0.8630.7710.8630.664
rs1514279c.802 + 101C>T0.048Intronic0.8030.6980.8030.646
rs2158083c.584‐807G>A0.047Intronic0.8080.7030.8080.648
rs2048683c.584‐920A>C0.046Intronic0.8030.6980.8030.648
rs4646153c.1071‐1397G>A0.044Intronic0.8310.7300.8320.649
rs2316904c.901‐1178G>A0.042Intronic0.6230.8270.8280.650
rs146122606, rs57823828, rs754565978c.346‐1077_346‐1070dupCCTTCCTT0.041IntronicNA
rs776459296, rs759499720, rs752472046c.584‐8dupA0.041IntronicNA
rs4646124c.186 + 2053A>G0.037Intronic0.8030.6990.8040.651
rs1978124c.186 + 786A>G0.035Intronic0.7940.6250.7950.529
rs4646127c.187‐2327T>C0.035Intronic0.8090.6920.8090.651
rs138373349, rs4646148c.901‐380_901‐379insTTAA0.035IntronicNA
rs11394305c.901‐1367dup0.034Intronic
rs2074192c.2115‐449G>A0.033Intronic0.3630.4240.3640.426
rs200672831c.1443‐187G>C0.032IntronicNA
rs2048684c.901‐702T>G0.032Intronic0.8320.7290.8320.651
rs11374008c.1298‐936dup0.032IntronicNA
rs34481900c.186 + 2745dup0.031Intronic0.809
rs1514280c.1897‐499T>C0.030Intronic0.8020.7190.8020.655
rs233574c.2115‐268A>G0.029Intronic0.8420.7480.8420.670
rs4646120c.186 + 1113C>T0.028Intronic0.7350.5670.7350.527
rs4646142c.900 + 534C>G0.028Intronic0.3570.2350.3590.238
rs199544436c.1443‐168G>C0.027Intronic0.0001
rs2023802c.187‐1019C>T0.027Intronic0.8040.7020.8040.651
rs4646147c.901‐1231A>T0.024Intronic0.8280.7240.8280.651
rs2316903c.901‐1761C>A0.019Intronic0.8270.7240.8280.651
rs2106809c.186 + 788T>C0.016Intronic0.3160.1910.3160.247
rs41303171c.2158A>G0.016Coding sequence missense variantp. Asn720Asp0.00450.0160.0230.018
rs714205c.2114 + 472G>C0.015Intronic0.3080.1840.3080.205
rs757066c.583 + 884G>A0.015Intronic0.8560.7500.8560.649
rs892503408c.1443‐200C>T0.007IntronicNA
rs1132186c.2309 + 6768T>G0.006Intronic0.6880.6290.6880.649
rs73195521c.346‐143A>T0.006Intronic0.00130.003030.0010.005
rs73195520c.439 + 24G>A0.006Intronic0.00130.003070.0010.005
rs542683073c.440‐133G>A0.005Intronic0.00014
c.1297 + 70_1297 + 71insTATGA0.004NA
rs146598386c.187‐1124C>A0.004Intronic0.00260.012350.0030.009
rs755489152c.2115‐274del0.004IntronicNA
rs4830542c.2309 + 5541G>T0.004Intronic0.6840.6230.6840.649
rs10551988c.2310‐701_2310‐696del0.004IntronicNo data
rs780782488c.584‐19T>A0.004Intronic0.000223
rs34161673c.697‐161del0.004Intronic0.00190.006380.0020.007
rs41297301c.900 + 90C>A0.004Intronic0.00370.014530.0040.012
rs4646188c.901‐1830T>C0.004Intronic0.04370.104050.0440.131
rs1043432251c.901‐1890del0.004IntronicNA
rs934301151c.901‐72C>T0.004Intronic0.000370.00034
c.*812C>A0.003NA
rs200260858c.1442 + 90_1442 + 91delCA0.003Intronic0.00740.0070.004
c.186 + 73G>A0.003NA
c.186 + 74G>A0.003NA
c.186 + 75G>A0.003NA
c.186 + 79T>A0.003NA
rs187959864c.186 + 80C>A0.003Intronic0.00030.000090.0002640.001
rs777042582c.2114 + 44CAA0.003Intronic0.000140.000130.00028
rs4646140c.802 + 24G>A0.003Intronic0.06010.03360.0600.001
rs4646116c.77A>G0.003Coding sequence missense variantp. Lys26Arg0.0020.0030.0000.010
The list of ACE2 gene variant analysis from the whole‐exome sequencing data (only the variants ≥0.003 are listed)

TMPRSS2 gene variant analysis

A total of 13 382 variants from 1072 individuals were analyzed and 490 different variants were detected. Among these variants, 9 were missense and 1 was deletion causing a frameshift. When the variants that have allele frequencies lower than 0.003 were removed, 192 variants remained. Three of those were missense variants, eight were coding sequence synonymous variants, seven were 3′UTR variants, and nine were upstream variants. Details of the 192 variants, calculated allele frequencies in the Turkish population, and global and population‐based allele frequencies obtained from public databases are represented in Table 2.
Table 2

The list of TMPRSS2 gene variant analysis from the whole‐exome sequencing data (only the variants ≥0.003 are listed)

dbSNP‐IDc.DNAAllele frequencyCoding consequenceAmino acid changeg1000GnomADGLOBALEUR
rs140530035c.795‐15_795‐14del, c.684‐15_684‐14del0.449Intronic0.810.900.8130.977
rs17854725c.768T>C0.302Coding sequence; synonymous variant0.360.470.3390.458
rs422471c.445 + 14G>A0.286Intronic0.550.610.5550.698
rs386416c.326‐45C>G0.267Intronic0.550.5550.700
rs464431c.1011‐52T>C0.242Intronic0.870.960.8740.980
rs112132031c.1076‐44_1076‐43insCCCGAGGCCTTAG0.211Intronic0.830.8300.979
rs75603675c.−57 + 99G>T, c.23G>T0.205Coding sequence; missense_variantp. Gly8Val0.240.360.2440.405
rs462321c.1172‐115A>G0.161Intronic0.580.680.5780.784
rs462326c.1172‐130C>G0.135Intronic0.570.680.5730.784
rs12329760c.478G>A, c.589G>A0.129Coding sequence; missense_variantp. Val197Met0.260.280.2610.236
rs2298659c.777C>T0.121Coding sequence; synonymous variant0.200.250.2090.230
rs458280c.1011‐144A>C0.120Intronic0.880.960.8790.980
rs455922c.1076‐164A>G0.112Intronic0.880.960.8780.981
rs9975014c.683 + 93T>C0.106Intronic0.260.250.2620.254
rs734056c.572 + 83G>T0.100Intronic0.280.370.2850.489
rs458213c.1011‐54A>T0.099Intronic0.230.320.2250.441
rs465576c.1076‐184G>T0.094Intronic0.830.930.8340.979
rs3787950c.225A>G0.083Coding sequence; synonymous variant0.160.110.1630.079
rs9974933c.683 + 122T>C0.066Intronic0.260.250.2620.254
rs429442c.325 + 102G>A0.063Intronic0.280.250.2800.232
rs7364083c.1011‐149C>T0.062Intronic0.640.620.6390.538
rs2838042c.238 + 176A>G0.058Intronic0.230.230.2330.243
rs455281c.1467 + 589C>A0.058Intronic0.740.890.7360.967
rs28524972c.1076‐101G>C0.054Intronic0.290.2870.303
rs2094881c.795‐288A>G, c.684‐288A>G0.053Intronic0.530.630.5300.748
rs4816720c.445 + 2877G>A0.053Intronic0.820.920.8200.977
rs9985159c.684‐137G>A0.053Intronic0.330.330.3350.230
rs386638c.238 + 1236G>A0.052Intronic0.840.950.8440.976
rs2298662c.727 + 389C>G0.050Intronic0.880.960.1230.021
rs365724c.445 + 1099C>G0.050Intronic0.560.620.5550.715
rs112132031, rs71951459c.1187‐43_1187‐42insCCGAGGCCTTAGT, c.1076‐44_1076‐43insCCCGAGGCCTTAG0.049Intronic0.830.8300.979
rs456016c.1076‐279A>G0.049Intronic0.870.960.8740.981
rs4818241c.445 + 2975T>C0.048Intronic0.870.960.8720.978
rs415731c.126 + 983T>C0.043Intronic0.710.700.7140.651
rs2298663c.727 + 317G>A0.043Intronic0.530.620.5250.747
rs417443c.239‐1658T>C0.043Intronic0.860.8650.978
rs457909c.1467 + 465C>T0.043Intronic0.990.990.994
rs2156300c.445 + 3565C>T0.042Intronic0.870.960.8720.978
rs138365638; rs557282706; rs869112255c.684‐358_684‐357del, c.573‐358_573‐357del0.042Intronic0.870.960.8740.979
rs2156301c.445 + 3372A>G0.041Intronic0.870.960.8710.978
rs402303c.238 + 1132A>G0.039Intronic0.520.580.5190.709
rs4818240c.445 + 3019A>G0.038Intronic0.820.920.8210.977
rs3787947c.326‐153G>A0.037Intronic0.310.340.3070.279
rs467375c.1075 + 168C>T0.036Intronic0.220.320.2230.441
rs7277080c.−57 + 3608G>A0.036Intronic0.230.350.2300.369
rs55964536c.728‐215G>A0.035Intronic0.240.350.2420.483
rs435877c.−56‐2781C>G0.033Intronic0.810.830.8120.852
rs2410430c.446‐3587T>C0.033Intronic0.870.960.8690.978
rs402197c.445 + 651A>G0.033Intronic0.870.960.8740.978
rs462448c.1172‐407A>G0.033Intronic0.880.970.8790.981
rs8131648c.684‐587A>G0.033Intronic0.560.640.5550.744
rs429524c.‐56‐1825C>G0.032Intronic0.860.870.8600.853
rs8131649c.684‐590A>G0.032Intronic0.560.640.5550.746
rs2104810c.795‐550C>T, c.684‐550C>T0.031Intronic0.530.630.5320.745
rs3819138c.326‐54G>C0.030Intronic0.0690.0680.151
rs2410429c.446‐3519T>G0.029Intronic0.620.740.6200.744
rs461194c.1467 + 362G>C0.029Intronic0.870.960.8690.969
rs55896064c.1468‐118C>T0.029Intronic0.0780.130.0780.133
rs5844077insA0.028Upstream variant0.780.740.7790.730
rs417888c.239‐1806T>C0.028Intronic0.620.620.6250.515
rs73905370c.1468‐58T>A0.028Intronic0.0780.130.0780.133
rs456298c.*1318A>T0.0273′UTR variant0.630.720.6280.831
rs462471c.*1593T>C0.0273′UTR variant0.630.740.6300.831
rs35899679c.239‐1800G>T0.026Intronic0.240.2380.463
rs381179c.445 + 2679A>G0.026Intronic0.001
rs392370c.238 + 2117T>G0.026Intronic0.300.260.3020.240
rs462574c.*1340T>C0.0263′UTR variant0.740.900.7430.966
rs8126497c.‐57 + 284C>T0.026Intronic0.100.130.1010.199
rs3980617690.026Intronic0.5550.715
rs9974589c.1171 + 452T>G0.025Intronic0.600.600.6040.536
rs383510c.445 + 1954A>G0.025Intronic0.600.570.6040.515
rs415918c.445 + 445G>A0.025intronic0.560.620.5580.713
rs61735794c.1155G>A, c.1266G>A0.025Coding sequence; synonymous variant0.0090.020.0090.030
rs2298661c.728‐219G>T0.023Intronic0.270.260.2680.230
rs365025c.445 + 1254C>G0.023Intronic0.560.620.5590.715
rs378616c.‐57 + 2466G>T0.023Intronic0.710.690.7140.717
rs8134203c.684‐695G>A0.023Intronic0.540.630.5360.744
rs375408c.445 + 717C>T0.023Intronic0.880.960.8770.979
rs456142c.*1573A>G0.0233′UTR variant0.630.730.6300.831
rs11701576c.56‐146T>C0.022Intronic0.160.0990.1620.106
rs2070788c.1282 + 587C>T, c.1171 + 587C>T0.021Intronic0.600.590.6030.536
rs4609760.021Intronic0.870.960.8720.968
rs4290734c.446‐554T>C0.020Intronic0.240.340.2450.487
rs4818239c.683 + 1024A>G0.020Intronic0.300.390.3020.502
rs8134216c.684‐711G>A0.020Intronic0.540.630.5360.745
rs9974995c.683 + 188G>A0.020Intronic0.260.250.2610.253
rs455045c.126 + 1158G>A0.020Intronic0.630.560.6270.493
rs2070787c.1282 + 446A>C, c.1171 + 446A>C0.020Intronic0.290.2930.308
rs34769294c.238 + 2137dup0.020Intronic0.240.2450.238
rs61170417; rs67617179c.1172‐773_1172‐772del0.020Intronic0.270.260.2660.308
rs35041537c.239‐1849G>A0.019Intronic0.240.340.2400.463
rs42835040.019Upstream variant0.870.890.8740.887
rs7279603c.1172‐759A>G0.019Intronic0.330.320.3340.310
rs2298664c.325 + 253C>G0.018Intronic0.320.3220.278
rs2298660c.728‐210G>A0.017Intronic0.260.270.2590.201
rs56097233c.445 + 1040_445 + 1041del0.017Intronic0.560.610.5550.715
rs62217531c.445 + 2420G>A0.017Intronic0.300.390.2980.478
rs430915c.238 + 1540T>C0.017Intronic0.620.620.6230.514
rs7275220c.238 + 959C>T0.016Intronic0.530.660.5290.750
rs139374762; rs75929377c.557‐671_557‐666delTGTCTG0.016Intronic0.250.340.2530.487
rs34624090c.1075 + 291dup0.015Intronic0.220.320.2240.442
rs2070793c.1282 + 998T>C, c.1171 + 998T>C0.015Intronic0.330.320.3320.309
rs57474639c.1468‐188G>A0.015Intronic0.080.140.0850.133
rs8129713c.‐57 + 3410A>G0.014Intronic0.110.130.1090.199
rs386818798c.1011‐54_1011‐52delACTinsTCC0.014IntronicNA
rs2070790c.1282 + 888C>G, c.1171 + 888C>G0.013Intronic0.290.290.2920.307
rs2070792c.1282 + 965C>T, c.1171 + 965C>T0.013Intronic0.330.320.3320.309
rs10154090c.239‐2203A>T0.013Intronic0.300.330.2980.273
rs11702475c.556 + 2753G>A, c.445 + 2753G>A0.013Intronic0.260.350.2590.491
rs2298857c.445 + 2340C>T0.013Intronic0.300.260.2990.236
rs915823c.573‐245T>G0.013Intronic0.160.220.1610.204
rs9976780c.446‐2706G>A0.013Intronic0.560.610.5610.723
rs928871c.239‐1011G>A0.012Intronic0.370.3720.279
rs9636988c.683 + 1054A>G0.012Intronic0.260.250.2610.257
rs34783969c.446‐2109T>A0.012Intronic0.260.350.2570.488
rs110885510.011Upstream variant0.270.360.2460.411
rs375760c.445 + 635C>A0.011Intronic0.220.200.2230.232
rs43037950.011Upstream variant0.250.390.2460.411
rs66575656c.727 + 569G>A0.011Intronic0.250.240.2450.257
rs43037940.010Upstream variant0.250.360.2460.411
rs6517669c.239‐1416T>C0.010Intronic0.370.390.3690.278
rs7364088c.1011‐222C>T0.010Intronic0.300.300.3040.263
rs364289c.445 + 1456C>T0.010Intronic0.300.260.3080.224
rs81280740.010Upstream variant0.870.890.8740.886
rs9305744c.1076‐318C>T0.010Intronic0.310.310.3130.233
rs9977234c.446‐3035C>A0.010Intronic0.200.190.2070.232
rs117696554c.437‐92C>T, c.326‐92C>T0.009Intronic0.0080.0190.0080.028
rs2257202c.445 + 2606A>G0.009Intronic0.220.190.2170.233
rs28548447c.1011‐330C>T0.009Intronic0.260.260.2640.303
rs34983238c.126 + 1049T>G0.009Intronic0.0580.080.0580.117
rs61735792c.300C>T, c.189C>T0.009Coding sequence variant; synonymous0.0050.0090.0050.017
rs73230068c.1010 + 85C>G0.009Intronic0.0130.0260.0130.039
rs10668560, rs150454800c.445 + 3305_445 + 3312del0.009IntronicNA
rs34561135c.683 + 92C>T0.009Intronic0.0170.0460.0170.053
rs3787946c.727 + 769C>G0.009Intronic0.280.280.2850.231
rs612991150.009Upstream variant0.250.360.2460.411
rs9305745c.238 + 2209G>A0.009Intronic0.290.330.2920.273
rs391099c.239‐2259A>G0.008Intronic0.300.270.3040.240
rs56695953c.126 + 311C>T0.008Intronic0.120.130.1080.200
rs9983252c.445 + 2999G>C0.008Intronic0.310.340.3130.253
rs401371c.238 + 1471C>G0.008Intronic0.200.170.1960.210
rs56066678c.445 + 3842G>A0.008Intronic0.290.260.2930.236
rs743542c.1425 + 151C>T0.008Intronic0.150.100.1490.063
rs918360768c.239‐480T>C0.008IntronicNA
rs145283231c.838 + 1237del. c.727 + 1237del0.007Intronic0.25
rs2070786c.1282 + 372A>G. c.1171 + 372A>G0.007Intronic0.300.290.2980.308
rs9983330c.683 + 846T>C0.007Intronic0.260.280.2610.235
rs112467088c.55 + 474T>A, c.‐57 + 605T>A0.007Intronic0.190.280.1860.281
rs11911394c.350‐755A>G0.007Intronic0.370.390.3690.279
rs61735793c.224C>T0.007Coding sequence; missense_variantp. Thr75Ile0.0030.0090.0030.008
rs62217525c.*221G>A0.0073′UTR variant0.020.0350.0210.055
rs144192191c.839‐422_839‐419dup, c.728‐422_728‐419dup0.007Intronic0.280.220.2830.263
rs62217527c.727 + 285G>A0.007Intronic0.0460.0780.0460.117
rs73230088c.55 + 273G>T0.007Intronic0.080.130.0800.157
rs124819840.006Upstream variant0.240.360.2400.406
rs2070789c.1282 + 771G>A, c.1171 + 771G>A0.006Intronic0.320.320.3150.229
rs28360562c.55 + 1751T>G0.006Intronic0.060.080.0570.117
rs34205539c.‐56‐1430dup0.006Intronic0.060.080.0570.117
rs55704664c.55 + 1266G>A0.006Intronic0.100.130.0570.117
rs2838043c.‐56‐1104G>A0.006Intronic0.110.130.1090.200
rs395584c.‐57 + 3561A>G0.006Intronic0.220.110.2240.019
rs55760462c.127‐1701A>G0.006Intronic0.160.240.1650.239
rs61735789c.540C>T0.006Coding sequence variant; synonymous0.0040.0100.0040.013
rs7283324c.1172‐364G>A0.006Intronic0.310.310.3120.224
rs73372166c.1467 + 623C>T0.006Intronic0.130.180.1330.136
rs2838039c.445 + 3777A>G0.005Intronic0.310.340.3080.254
rs75756279c.838 + 47G>A0.005Intronic0.0040.0040.0040.008
rs73372163c.1467 + 669C>T0.005Intronic0.130.180.1320.136
rs73905371c.1467 + 674G>C0.005Intronic0.080.140.0850.133
rs3761373c.56‐406G>A0.005Intronic0.160.100.1630.106
rs4607510.005Intronic0.830.920.8260.965
rs111220497c.838 + 1292C>T, c.727 + 1292C>T0.004Intronic0.31
rs1003030c.126 + 440T>C0.004Intronic0.160.100.1630.106
rs111220481c.838 + 1319C>G, c.727 + 1319C>G0.004Intronic0.28
rs143680939c.*1583del0.0043′UTR variant0.080.0820.133
rs201627185c.557‐2706delG0.004IntronicNA
rs2187238c.‐56‐2635A>G0.004Intronic0.110.140.1120.199
rs2838040c.238 + 1591T>C0.004Intronic0.330.360.3310.275
rs287075080.004Upstream variant0.230.340.2300.384
rs34256269c.126 + 1170C>T0.004Intronic0.080.130.0790.159
rs61728255c.727 + 1468T>C0.004Intronic0.880.920.8800.980
rs141788162c.759C>T0.003Coding sequence variant; synonymous0.0020.0040.0020.003
rs199824558c.210C>T0.003Coding sequence variant; synonymous0.0010.00020.001
rs422761c.‐56‐877C>T0.003Intronic0.220.110.2250.019
rs61459778c.1468‐343G>C0.003Intronic0.140.190.1360.136
rs7778603290.003IntronicNA
rs113506821c.795‐200G>A, c.684‐200G>A0.003Intronic0.020.050.0220.050
rs35871560c.445 + 2741del0.003Intronic0.540.5360.680
rs56136037c.445 + 185C>A0.003Intronic0.0140.040.0140.046
rs74749793c.55 + 4225C>A0.003Intronic0.160.0980.1580.103
rs75200570c.‐57 + 1396A>G0.003Intronic0.060.030.0560.011
rs76000363c.*1592C>T0.0033′UTR variant0.080.140.0820.133
The list of TMPRSS2 gene variant analysis from the whole‐exome sequencing data (only the variants ≥0.003 are listed)

In silico findings and functional predictions

The crystal structure of ACE2 (PDB ID: 6LZG) revealed that N‐linked glycan molecules are attached to Asn53, Asn90, and Asn322. Asn90 is a conserved amino acid in a number of bats in which coronaviruses cannot infect through ACE2. Glycosylation of this amino acid regulates the Spike−ACE2 interaction in bats. Glycosylation of Asn90 and its subsequent branching is suggested to decrease the ACE2−Spike binding affinity through steric effects. Lys26 of ACE2 generates critical polar and salt bridge interactions with sugar moiety and nearby amino acids, Glu22 and Asn90 (Figure 1A). To analyze the effect of Lys26Arg mutation we generated in silico mutant on the ACE2−Spike structure using the crystal structure having PDB ID of 6LZG. Since Arg has a larger side chain than Lys, the side chain of Arg cannot fit in the same space. The sterically most favorable orientation of in silico mutation showed that Arg side chain cannot generate polar interactions as in the case of Lys amino acid. Instead, Arg may generate a salt bridge with Asp30 (Figure 1B). Asp30 forms a salt bridge with Lys417 of Spike in the crystal structure (Figure 1). In Lys26Arg mutation, Arg can stabilize the Asp30−Lys417 interaction which may result in higher infectivity of the SARS‐CoV‐2.
Figure 1

Structure of ACE2‐Spike (6LZG). (A) ACE2 is shown as a cyan cartoon and Spike is shown on a white surface. At the zoom‐in structure, Lys26 interacting amino acids are shown. (B) In silico generated Lys26Arg ACE2 mutation and its proposed interaction scheme is shown.

Structure of ACE2‐Spike (6LZG). (A) ACE2 is shown as a cyan cartoon and Spike is shown on a white surface. At the zoom‐in structure, Lys26 interacting amino acids are shown. (B) In silico generated Lys26Arg ACE2 mutation and its proposed interaction scheme is shown. The TMPRRS2 has three regions: cytoplasmic, transmembrane, and extracellular. Val160 is found in the extracellular region of the protein. TMPRSS2 structure (PDB ID: 7MEQ) shows that Val160 is located on a beta‐strand structure and surrounded by hydrophobic residues Leu225, Val171, Leu158, and Val149 (Figure 2A). Mutation of Val160 to a less hydrophobic residue may disturb this interaction network. To analyze the effect of Val160Met on TMPRSS2, we mutated valine to methionine in silico. Molecular analysis of Val160Met shows that (i) hydrophobic network cannot be maintained and (ii) there is a steric clash between Met and nearby amino acids for example with Tyr222 which suggests that the mutation may destabilize the protein (Figure 2B). In addition to this analysis, we used DUET server to predict the effect of mutation on the TMPRSS2. DUET uses two previously developed approaches for predictions: knowledge‐based and graph‐based signature methods. All type of calculations in DUET predicts that Val160Met mutation destabilizes the protein (Table 3).
Figure 2

Structure of TMPRSS2 (7MEQ). (A) TMPRSS2 is shown as a blue cartoon representation. Val160 and its interacting amino acids are shown. (B) In silico generated Val160Met TMPRSS2 mutation is shown. To show a steric clash between mutant Met160 and a nearby Tyr222, amino acids are shown in red and blue spheres (proportional to their van der Waals radius) (at right).

Table 3

In silico analysis of TMPRSS2 Val160Met. ∆∆G < 0 indicates destabilization

DUET Results (kcal/mol)
mCSM (∆∆G)−0.847
SDM (∆∆G)−2.39
DUET (∆∆G)−1.251
Structure of TMPRSS2 (7MEQ). (A) TMPRSS2 is shown as a blue cartoon representation. Val160 and its interacting amino acids are shown. (B) In silico generated Val160Met TMPRSS2 mutation is shown. To show a steric clash between mutant Met160 and a nearby Tyr222, amino acids are shown in red and blue spheres (proportional to their van der Waals radius) (at right). In silico analysis of TMPRSS2 Val160Met. ∆∆G < 0 indicates destabilization

DISCUSSION

Many studies have demonstrated that the symptoms of COVID‐19 vary greatly among patients. Understanding the reason underlying this heterogeneity in risk of progression to a severe form has been a challenge since the start of the pandemic. There are many known factors that can potentially affect the severity of COVID‐19 infection including greater age, presence of co‐morbidities, smoking, and air pollution. , , In addition to these clinical and environmental factors, genetic variability can also account for the susceptibility to SARS‐CoV‐2 infection and the different clinical presentations observed in COVID‐19 patients. ACE2 and TMPRSS2 are transmembrane surface proteins that play critical roles in viral attachment and host cell entry for SARS‐CoV and SARS‐CoV‐2. SARS‐CoV‐2 binds to ACE2 through the receptor‐binding domain in spike proteins, which are then cleaved by TMPRSS2 to allow fusion with the host cell membrane. , Therefore, polymorphisms in genes encoding these proteins can affect the binding affinity of the viral spike protein to host cells as well as membrane fusion efficiency, modulating the host susceptibility to SARS‐CoV‐2. In this context, we investigated the genetic variability of ACE2 and TMPRSS2 in the Turkish population to show the existence of any enrichment of missense or indel variants in coding regions that may potentially affect the binding dynamics of the virus to host cells and also wanted to compare our results with previous epidemiological studies in different populations. For both ACE2 and TMPRSS2, majority of variants detected in the Turkish population were intronic. Only 2/70 of ACE2 variants (c.2158A>G;p.Asn720Asp; NM_021804.2 (rs41303171) and c.77A>G;p.Lys26Arg; NM_021804.2 (rs4646116)) (Table 1) and 3/192 of TMPRSS2 variants (c.23G>T;p.Gly8Val; NM_001135099.1 (rs75603675), c.589G>A;p.Val197Met; NM_001135099.1 (rs12329760) and c.224C>T;p.Thr75Ile; NM_005656.3 (rs61735793)) (Table 2) that have allele frequencies above 0.003 were identified as coding variant missense variations. The most frequent ACE2 variant was identified as rs971249 variant with an allele frequency of 0.581, followed by rs113691336 which has an allele frequency of 0.455 and the third most frequent ACE2 variant was found to be rs4646174 with an allele frequency of 0.404 in the Turkish population. All frequent variants that have allele frequencies above 0.06 were intronic. Considering the missense variants that potentially affect protein structure or function, ACE2 rs41303171 has a detected allele frequency of 0.016 in the Turkish population. Previous in silico structural analyses have demonstrated that this variation causes ACE2 protein to have a higher binding affinity to TMPRSS2 and may facilitate entry of the virus to the host cells. The global allele frequency of this variant is 0.023, 0.018 in European populations, and 0.001 in the Southern Asian population according to the dbSNP database. The variation was previously mentioned by different groups from Italy, India, and Iran. It was found to be frequent in the study where ACE2 variants in a cohort of SARS‐CoV‐2‐positive Italian patients were investigated. Likely, the variant was reported as a common missense change (AF 0.011) together with rs4646116 and c.631G>A; p.(Gly211Arg) variants in a study conducted with whole‐exome data of 6930 Italian control individuals, which are predicted to affect protein structure and stabilization. c.1051C>G;p.(Leu351Val) and c.1166C>A;p.(Pro389His) were the rare variants detected in this cohort predicted to interfere with the internalization process but were not present in our studied group. In the same study, WES data of 131 patients and 258 controls were compared. The allelic variability in the control group was detected to be statistically significant even though no single variant was significantly enriched between the two groups. c.1166C>A;p.(Pro389His) was one of the missense variants, along with c.1174A>C;p.(Lys392Gln), c.1178C>G;p.(Thr393Ser), and c.1312C>G;p.(Gln438Glu) that was listed as variants leading to an increase in interaction affinity between TMPRSS2 and SARS‐CoV‐2 S protein, where c.1409G>T;p.(Arg470Ile) and c.1247A>G;p.(Tyr416Cys) were found to cause a decrease in a recent study that performed molecular docking analyses. None of these variants were present in the Turkish population included in the present study. In a comprehensive retrospective study, c.1166C>A;p.(Pro389His) was stated to be present only in the Latino/Admixed American population, with an allele frequency of 0.015%. Intronic c.439+4G>A (rs2285666) and c.1888G>C;p.(Asp630His) variants were also detected in the Italian COVID‐19 patient cohort. p.(Asp630His) variant is not present in our study group. However, rs2285666 is the fourth common variant detected in the Turkish population with an allele frequency of 0.326. Allele frequency of the variant in EUR‐TSI (Italy) is 0.186, lower than other populations reported in dbSNP. Additionally, in another study, this variant was found to be the most frequent ACE2 variant detected among clinical exome data of 103 individuals from India. In the same study, the rs4646116 variant was detected in one individual. The variant was shown to potentially affect the binding affinity of SARS‐CoV‐2 spike protein to ACE2 receptor and is not frequent in the Turkish population (0.003) whereas it is not detected in the Italian population according to dbSNP variation data. Consistent with the previous analysis, our in silico model predicts that rs4646116 variation (p. Lys26Arg ACE2) may facilitate the SARS‐CoV‐2 infection via stronger Spike−ACE2 interaction (Figure 1). It was shown to be not associated with COVID‐19 clinical outcomes in Iranian patients. Synonymous exonic c.2247G>A;p.(Val749=) (rs35803318) variant was detected in groups of COVID‐19 patients with different clinical symptoms (mild, severe, and death) in the Iranian population, according to the same study. This variation is relatively common in the Turkish population (AF 0.06) compared to Global (AF 0.02). p.Arg708Trp, p.Arg710Cys, p.Arg710His, and p.Arg716Cys ACE2 variants that are located in the dimeric interface of ACE2 with TMPRSS2 were found to be present in European, Eastern, Asian, and Latino/Admixed American populations but not present in our Turkish study population in the present study. In a more recent study, in a total of 1378 whole‐exome sequences of individuals from the Middle Eastern populations (Iran, Qatar, and Kuwait), the prevalence of the rs41303171 was noted to be highest among Europeans (2.5%), Iranians (0.6%) when compared to Kuwaitis (0.3%), Qataris (0.2%), and other global populations (0.4%) and minör allele frequency of this variant significantly correlated with the case fatality rates (p < 0.0003) in the corresponding countries as of December 2020. In the same study, they also propose that the rs41303171 variant may enhance TMPRSS2 activation and subsequent viral entry. Cao et al. investigated allele frequency distributions of 1700 ACE2 variants among different populations. Uneven distribution of some variants between populations was observed in this study. For example, ACE2 rs4646127 intronic variant was shown to be associated with higher expression levels in East‐Asian populations with an allele frequency of 0.993 according to dbSNP data. This variant was also detected in the studied Turkish population with an allele frequency of 0.035. The most frequent TMPRSS2 variant detected in the Turkish population was the intronic rs140530035 variant with an allele frequency of 0.449. The second most frequent variant is a coding sequence synonymous variant, rs17854725. It has an allele frequency of 0.302. This variant was reported to be rare in the Latin American population and is frequent in the Eastern Asian populations according to the databases. The third most frequent variant in the studied population is the intronic rs422471 variant with the calculated allele frequency of 0.286. TMPRSS2 rs75603675 and rs12329760 were the missense variants detected in the Turkish population with allele frequencies of 0.205 and 0.129 respectively. Both were within the 10 most frequent variants detected in the studied population. In a recent study, these variants were referred to as variants whose allele frequencies vary by ancestry and geography, differing between East Asians and other populations. Importantly, rs12329760 was predicted to be deleterious by SIFT, PolyPhen‐2, and PROVEAN which suggest altered protein function. Our in silico analyses suggest that the rs12329760 variant (p.Val160Met TMPRSS2) may disrupt the hydrophobic interaction core of TMPRSS2 and destabilize the protein (Figure 2, Table 3). It is in a highly conserved exonic splicing enhancer region of the gene and is strongly associated with TMPRSS2‐ERG fusion translocation in prostate cancer due to the increased risk of exon skipping. Rs75603675, on the other hand, was considered deleterious only by PolyPhen‐2 software. Both could potentially affect the function of TMPRSS2 in facilitating SARS‐CoV‐2 cell entry and therefore may possess a protective role. It was noted in a study that the rs12329760‐T variant allele may have altered the highly conserved scavenger receptor cysteine‐rich (SRCR) domain of TMPRSS2 and also decreased protein stability thus impairing the processing of the spike protein of the SARS‐CoV‐2 A2a subtype. , This may result in the protection of East Asians from the SARS‐CoV‐2 A2a subtype as the variant has a higher allele frequency in that region compared to others and also the Turkish population. Rs12329760 was reported in 4.85% of individuals studied in India as well. Rs383510, rs2298662, and rs2070788 are three variants, that are known to increase susceptibility to Influenza A (H7N9) and may also affect COVID‐19 infectivity was reported to have low allele frequencies in the Indian population as well as in the Turkish population in our study. A very recent study, which analyzed the association between the rs12329760 and COVID‐19 severity in 2244 critically ill patients with COVID‐19 from the UK intensive care units has shown that the T allele of rs12329760 is associated with a reduced likelihood of developing severe COVID‐19. Results of this study further identified TMPRSS2 protein as a promising drug target, with a potential role for camostat mesylate, which is a drug approved for the treatment of postoperative reflux esophagitis and chronic pancreatitis, in COVID‐19 treatment. In another study among Italian COVID‐19 patients, the rare rs114363287; p.Gly111Arg TMPRSS2 variant was detected with a higher frequency compared to other populations. This variant is missing in our cohort. On the other hand, rs75603675 and rs12329760 which are among frequent TMPRSS2 variants in the general Turkish population were detected in lower frequencies in the COVID‐19 patients, which supports the possible protective role of these two variants against COVID‐19. The other TMPRSS2 missense variant detected in the Turkish population was rs61735793 with an allele frequency of 0.007. The variant has low allele frequencies in all reported populations in the dbSNP database. No studies are associating this variant with COVID‐19 susceptibility or disease severity in any population. Irham et. al. investigated TMPRSS2 variants affecting expression among populations from different continents. They identified four variants: rs464397, rs469390, rs2070788, and rs383510 that influence TMPRSS2 protein expression in the lungs. Rs464397 and rs469390 variants were not detected in the studied cohort of the Turkish population, whereas rs2070788 and rs383510 were detected with frequencies of 0.021 and 0.025 respectively. These frequencies are lower than other studied populations. Considering the large population size of Turkey, the sample size may be a limitation in our study. Additionally, we conducted this analysis on the general population. A study with a larger sample size that will include COVID‐19 infected and control groups can be designed for further analysis of alleles affecting susceptibility and disease severity.

CONCLUSION

Overall, our data suggests enrichment of the rs4646116 ACE2 functional allele in the Turkish population, which was demonstrated to potentially enhance the binding of the SARS‐CoV‐2 to the receptor by in silico modelling. The two TMPRSS2 missense variants, rs12329760 and rs75603675, that were detected in the Turkish population and have differential frequency distributions in dbSNP may have a role in population‐specific outcomes in COVID‐19 severity. To conclude, new SARS‐CoV‐2 variants and their potentially different transmission abilities, as well as ACE2 and TMPRSS2 gene variants should be considered while developing therapeutics for COVID‐19 disease.

AUTHOR CONTRIBUTIONS

Conceived and designed the analysis: Gulten Tuncel, Mahmut Cerkez Ergoren, Sehime Gulsun Temel. Collected the data: Nilgun Duman, Atil Bisgin, Sevcan Tug Bozdogan, Sebnem Ozemri Sag, Aslihan Kiraz, Burhan Balta, Murat Erdogan, Bulent Uyanik, Sezin Canbek, Pinar Ata, Bilgen Bilge Geckinli, Esra Arslan Ates, Ceren Alavanda, Sevda Yesim Ozdemir, Ozlem Sezer, Gulay Oner Ozgon, Hakan Gurkan, Kubra Guler, Ibrahim Boga, Niyazi Kaya, Adem Alemdar, Murat Sayan, Munis Dundar, Sehime Gulsun Temel. Contributed data or analysis tools: Nilgun Duman, Gulten Tuncel, Atil Bisgin, Sevcan Tug Bozdogan, Sebnem Ozemri Sag, Seref Gul, Aslihan Kiraz, Burhan Balta, Murat Erdogan, Bulent Uyanik, Sezin Canbek, Pinar Ata, Bilgen Bilge Geckinli, Esra Arslan Ates, Ceren Alavanda, Sevda Yesim Ozdemir, Ozlem Sezer, Gulay Oner Ozgon, Hakan Gurkan, Kubra Guler, Ibrahim Boga, Niyazi Kaya, Adem Alemdar, Murat Sayan, Munis Dundar, Mahmut Cerkez Ergoren, Sehime Gulsun Temel. Performed analysis: Nilgun Duman, Gulten Tuncel, Atil Bisgin, Sevcan Tug Bozdogan, Sebnem Ozemri Sag, Seref Gul, Aslihan Kiraz, Burhan Balta, Murat Erdogan, Bulent Uyanik, Sezin Canbek, Pinar Ata, Bilgen Bilge Geckinli, Esra Arslan Ates, Ceren Alavanda, Sevda Yesim Ozdemir, Ozlem Sezer, Gulay Oner Ozgon, Hakan Gurkan, Kubra Guler, Ibrahim Boga, Niyazi Kaya, Adem Alemdar, Murat Sayan, Munis Dundar, Mahmut Cerkez Ergoren, Sehime Gulsun Temel. Wrote the paper: Gulten Tuncel, Seref Gul, Mahmut Cerkez Ergoren, Sehime Gulsun Temel. Read and revised the paper: Nilgun Duman, Gulten Tuncel, Atil Bisgin, Sevcan Tug Bozdogan, Sebnem Ozemri Sag, Seref Gul, Aslihan Kiraz, Burhan Balta, Murat Erdogan, Bulent Uyanik, Sezin Canbek, Pinar Ata, Bilgen Bilge Geckinli, Esra Arslan Ates, Ceren Alavanda, Sevda Yesim Ozdemir, Ozlem Sezer, Gulay Oner Ozgon, Hakan Gurkan, Kubra Guler, Ibrahim Boga, Niyazi Kaya, Adem Alemdar, Murat Sayan, Munis Dundar, Mahmut Cerkez Ergoren, Sehime Gulsun Temel.

CONFLICT OF INTEREST

The authors declare no conflict of interest.

ETHICS STATEMENT

All procedures performed in this study were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards (approval number: YDU/2020/78‐1055). Supplementary information. Click here for additional data file. Supplementary information. Click here for additional data file.
  34 in total

1.  dbSNP: a database of single nucleotide polymorphisms.

Authors:  E M Smigielski; K Sirotkin; M Ward; S T Sherry
Journal:  Nucleic Acids Res       Date:  2000-01-01       Impact factor: 16.971

2.  Evidence for ACE2-utilizing coronaviruses (CoVs) related to severe acute respiratory syndrome CoV in bats.

Authors:  Ann Demogines; Michael Farzan; Sara L Sawyer
Journal:  J Virol       Date:  2012-03-21       Impact factor: 5.103

3.  A pneumonia outbreak associated with a new coronavirus of probable bat origin.

Authors:  Peng Zhou; Xing-Lou Yang; Xian-Guang Wang; Ben Hu; Lei Zhang; Wei Zhang; Hao-Rui Si; Yan Zhu; Bei Li; Chao-Lin Huang; Hui-Dong Chen; Jing Chen; Yun Luo; Hua Guo; Ren-Di Jiang; Mei-Qin Liu; Ying Chen; Xu-Rui Shen; Xi Wang; Xiao-Shuang Zheng; Kai Zhao; Quan-Jiao Chen; Fei Deng; Lin-Lin Liu; Bing Yan; Fa-Xian Zhan; Yan-Yi Wang; Geng-Fu Xiao; Zheng-Li Shi
Journal:  Nature       Date:  2020-02-03       Impact factor: 69.504

4.  Genetic variants that influence SARS-CoV-2 receptor TMPRSS2 expression among population cohorts from multiple continents.

Authors:  Lalu Muhammad Irham; Wan-Hsuan Chou; Marcus J Calkins; Wirawan Adikusuma; Shie-Liang Hsieh; Wei-Chiao Chang
Journal:  Biochem Biophys Res Commun       Date:  2020-06-08       Impact factor: 3.575

5.  Structural analysis of ACE2 variant N720D demonstrates a higher binding affinity to TMPRSS2.

Authors:  Anwar Mohammad; Sulaiman K Marafie; Eman Alshawaf; Mohamed Abu-Farha; Jehad Abubaker; Fahd Al-Mulla
Journal:  Life Sci       Date:  2020-08-05       Impact factor: 5.037

6.  Analysis of factors associated with disease outcomes in hospitalized patients with 2019 novel coronavirus disease.

Authors:  Wei Liu; Zhao-Wu Tao; Lei Wang; Ming-Li Yuan; Kui Liu; Ling Zhou; Shuang Wei; Yan Deng; Jing Liu; Hui-Guo Liu; Ming Yang; Yi Hu
Journal:  Chin Med J (Engl)       Date:  2020-05-05       Impact factor: 2.628

7.  Factors associated with COVID-19-related death using OpenSAFELY.

Authors:  Elizabeth J Williamson; Alex J Walker; Krishnan Bhaskaran; Seb Bacon; Chris Bates; Caroline E Morton; Helen J Curtis; Amir Mehrkar; David Evans; Peter Inglesby; Jonathan Cockburn; Helen I McDonald; Brian MacKenna; Laurie Tomlinson; Ian J Douglas; Christopher T Rentsch; Rohini Mathur; Angel Y S Wong; Richard Grieve; David Harrison; Harriet Forbes; Anna Schultze; Richard Croker; John Parry; Frank Hester; Sam Harper; Rafael Perera; Stephen J W Evans; Liam Smeeth; Ben Goldacre
Journal:  Nature       Date:  2020-07-08       Impact factor: 49.962

8.  COVID-19 and Genetic Variants of Protein Involved in the SARS-CoV-2 Entry into the Host Cells.

Authors:  Andrea Latini; Emanuele Agolini; Antonio Novelli; Paola Borgiani; Rosalinda Giannini; Paolo Gravina; Andrea Smarrazzo; Mario Dauri; Massimo Andreoni; Paola Rogliani; Sergio Bernardini; Manuela Helmer-Citterich; Michela Biancolella; Giuseppe Novelli
Journal:  Genes (Basel)       Date:  2020-08-27       Impact factor: 4.096

9.  Analysis of ACE2 genetic variants in 131 Italian SARS-CoV-2-positive patients.

Authors:  Antonio Novelli; Michela Biancolella; Paola Borgiani; Dario Cocciadiferro; Vito Luigi Colona; Maria Rosaria D'Apice; Paola Rogliani; Salvatore Zaffina; Francesca Leonardis; Andrea Campana; Massimiliano Raponi; Massimo Andreoni; Sandro Grelli; Giuseppe Novelli
Journal:  Hum Genomics       Date:  2020-09-11       Impact factor: 4.639

Review 10.  Human genetic factors associated with susceptibility to SARS-CoV-2 infection and COVID-19 disease severity.

Authors:  Cleo Anastassopoulou; Zoi Gkizarioti; George P Patrinos; Athanasios Tsakris
Journal:  Hum Genomics       Date:  2020-10-22       Impact factor: 4.639

View more
  1 in total

1.  Analysis of ACE2 and TMPRSS2 coding variants as a risk factor for SARS-CoV-2 from 946 whole-exome sequencing data in the Turkish population.

Authors:  Nilgun Duman; Gulten Tuncel; Atil Bisgin; Sevcan Tug Bozdogan; Sebnem Ozemri Sag; Seref Gul; Aslihan Kiraz; Burhan Balta; Murat Erdogan; Bulent Uyanik; Sezin Canbek; Pinar Ata; Bilgen Bilge Geckinli; Esra Arslan Ates; Ceren Alavanda; Sevda Yesim Ozdemir; Ozlem Sezer; Gulay Oner Ozgon; Hakan Gurkan; Kubra Guler; Ibrahim Boga; Niyazi Kaya; Adem Alemdar; Murat Sayan; Munis Dundar; Mahmut Cerkez Ergoren; Sehime Gulsun Temel
Journal:  J Med Virol       Date:  2022-07-22       Impact factor: 20.693

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.