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3 September 2022

Association between Mutations in Papain-like Protease (PLpro) of SARS-CoV-2 with COVID-19 Clinical Outcomes

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1
School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
2
The Fourth People’s Hospital of Foshan City, Foshan 528000, China
3
Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China
4
Huangpu District Center for Disease Control and Prevention, Guangzhou 510700, China
Pathogens2022, 11(9), 1008;https://doi.org/10.3390/pathogens11091008
This article belongs to the Section Emerging Pathogens
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Abstract

Papain-like protease (PLpro) is important for the replication and transcription of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This study aimed to reveal the PLpro mutations associated with the clinical outcomes of patients. Due to the importance of the S protein in the pathogenicity of SARS-CoV-2, the mutation of the S protein was also analyzed in this study. After downloading the data from the Global Initiative on Sharing Avian Influenza Data (GISAID) database, samples were divided into two groups on the basis of patient status, namely, recovered and dead groups. This study performed a univariate analysis and further explored the association of mutations with patient outcomes through multivariate logistic regression analysis. A total of 138,492 samples were used for analysis. The patients had a mean age of 43.66 ± 21.56 years, and 51.3% of them were female. Multivariate logistic regression results showed that, compared with men, women had a lower risk of dying from coronavirus disease 2019 (COVID-19) (OR = 0.687, 95%CI: 0.638–0.740). Compared with patients aged 17 years and younger, patients aged 18–64 years (OR = 2.864, 95%CI: 1.982–4.139) and patients over 65 years old (OR = 19.135, 95%CI: 13.280–27.572) had a higher risk of death after infection. Compared with the wild type, P78L (OR = 5.185, 95%CI: 2.763–9.730) and K233Q (OR = 5.154, 95%CI: 1.442–18.416) in PLpro were associated with an increased risk of death. A synergistic interaction existed between age and mutations A146D and P78L. The results of the multivariate logistic regression analysis of the data on vaccinated patients demonstrated that, compared with the wild type, the P78L (OR = 3.376, 95%CI: 2.040–5.585) mutation was associated with an increased risk of death. In conclusion, compared with the wild-type PLpro protein, the P78L and K233Q mutations may increase the risk of death in infected individuals. In addition, a synergistic effect existed between age and P78L and K233Q that increased the risk of death in older patients.

1. Introduction

Coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has triggered a worldwide pandemic and spread to more than 200 countries since it was first reported in 2019. Data from the World Health Organization (WHO) show that as of 31 May 2022 at 5:09 pm Central European Time (CET), 526,558,033 cases and 6,287,117 deaths have been confirmed globally [1]. SARS-CoV-2 has a positive and single-stranded RNA genome that is approximately 30,000 bases in length [2,3] and that contains multiple open reading frames (ORF). ORF1a and ORF1b can encode continuous polypeptides, which can generate 16 non-structural proteins (NSPs) after cleavage [4].
Papain-like protease (PLpro), which contains 319 amino acids at positions 745–1063 of NSP3 of SARS-CoV-2 [5], is responsible for the cleavage of NSP1–NSP3 from polyproteins and is essential for viral replication [6]. It can also modulate host immune functions by binding to human ubiquitin-like protein, interferon-stimulated gene 15 (ISG15), and ubiquitin A-52 (UBA52) [5,7]. This ability has long been one of the popular coronavirus-related research topics. Although the PLpros of SARS-CoV-2 and SARS-CoV share 83% sequence identity, their substrate preferences in the host and deubiquitinating and deISGylating (deISG) activities differ considerably [8,9]. Coronaviruses mutate as they spread, and their mutations can lead to changes in structure or function. For example, the Q233E mutation in PLpro of SARS-CoV resulted in reduced deubiquitinase activity [10]. Considering that PLpro is an important enzyme of SARS-CoV-2, its mutations require further research attention.
Different mutations in SARS-CoV-2 may have different effects on the infectivity, virulence, or immune resistance of the virus. Many previous studies focused on the effects of mutations in the Spike (S) protein. The D614G mutation in the S protein can enhance the replication, transmission, and infectivity of SARS-CoV-2 [11,12]. The V367F mutation in the S protein receptor-binding domain (RBD) can also enhance viral infectivity [13]. A study that included 12,343 SARS-CoV-2 genome sequences found that the frequencies of ORF1ab P4715L (i.e., P323L in NSP12) and S protein 614G variants were positively associated with case fatality [14]. A work identified an N501Y mutation in the Spike protein associated with enhanced SARS-CoV-2 infection and transmission [15]. An investigation in the United States found that the E484K mutation in the S protein was associated with immune escape [16].
Genome-wide surveillance is important for focusing on viral mutations. The Global Initiative on Sharing Avian Influenza Data (GIASID) [17,18] is the world’s largest repository of SARS-CoV-2 sequences. As of April 2022, 10 million SARS-CoV-2 sequences have been accumulated in this database. In this study, by using data from GISAID, we focused on the PLpro of SARS-CoV-2 to identify mutations associated with the clinical outcomes of patients. At the same time, we evaluated the potential co-occurrence of mutations in Spike proteins and NSP12_P323L that are known to affect the risk of death in patients with those in PLpro proposed in this study.

2. Results

2.1. Sociodemographic Characteristics of Patients

We accessed GISAID (https://www.gisaid.org/) [18] on 18 January 2022 and downloaded patient status metadata uploaded between 1 January 2021 and 31 December 2021. We obtained 166,339 entries after the first filtering, with filters including “Host = Human,” “Complete,” “High Coverage,” “Exclude Low Coverage,” “Patient Status,” and “Complete on Collection Date.” We included entries with defined gender, age, and patient status and containing mutations located in NSP3 (n = 138,492) after a second filter.
Of the 138,492 entries uploaded by laboratories in 117 countries on six continents, 54.7% (75,707/138,492) were from Europe and 25.3% (34,989/138,492) were from North America.
Out of the 138,492 patients, 135,479 recovered and 3013 died. A total of 51.3% of the cases in the dataset were female. Patients aged 18–64 years represented the largest proportion (69.7%) of the cases, with an average age of 43.66 ± 21.56 for all cases. Univariate analysis revealed that women had a lower risk of death than men. Patients aged 18–64 years and patients aged 65 years and older had a higher risk of death than patients aged 17 years and younger (Table 1).
Table 1. Sociodemographic characteristics of cases in patient metadata.

2.2. Mutations

On average, each sample contained 3.26 mutations in the NSP3 protein, with an average of 3.27 mutations in the “Recovered” group and an average of 2.76 in the “Dead” group.
Univariate analysis showed that compared with wild-type SARS-CoV-2, mutations P78L, K233Q, or K93N in PLpro may increase the risk of death in patients, whereas the mutation E162D may reduce the risk of death. However, mutations identified by previous studies, such as Spike_D614G and NSP12_P323L, that may affect the clinical outcomes of patients were not statistically significant in the univariate analysis performed in this study. Among the fifty-four mutations in the S protein, all of them except for D614G, T716I, V70del, H69del, S982A, D1118H, A570D, V1264L, L5F, A243del, and S98F may be related to the clinical outcomes of patients. The detailed results are shown in Table 2.
Table 2. Univariate analysis of mutation in PLpro and Spike protein.

2.3. Age-Subgroup Univariate Analysis

The univariate analysis of PLpro mutations in different age subgroups yielded inconsistent results. Pairwise comparisons through the Breslow-Day test with adjusted statistical significance levels indicated that only the OR values of the K233Q, K93N, and E162D mutations were homogeneous among the three age groups.
The A146D mutation may be associated with an increased risk of mortality in patients aged 18–64 years. In all age groups, the P78L, K93N, and K233Q mutations may be associated with an increased risk of death. Although the E162D mutation had a low frequency, it may be associated with a reduced risk of death in patients. The results of the detailed age-subgroup univariate analysis are shown in Table 3.
Table 3. Result of age-subgroup univariate analysis of mutations.

2.4. Multivariate Logistic Regression Analysis

Gender, age, mutations with p < 0.1 in univariate analysis, and mutations identified by previous studies that may affect the clinical outcomes of patients, such as Spike_D614G and NSP12_P323L, were included in the multiple logistic regression analysis. The detailed results are shown in Table A1.
Given the heterogeneity of the OR values of A146D, P78L, and K233Q among different age groups, we also constructed a logistic regression model that included interaction terms. The difference between the two models was statistically significant (p < 0.001), and the inclusion of interaction terms was reasonable.
The results of logistic regression including interaction terms revealed that, with other factors being equal, the risk of death in female patients was 0.687 (95%CI: 0.638–0.740) times that in males. When infected with the wild-type virus, patients aged 18–64 years (OR = 2.864, 95%CI: 1.982–4.139) and those aged 65 years and older (OR = 19.135, 95%CI: 13.280–27.572) had a significantly higher risk of death than patients aged 17 years and younger. The K233Q and P78L mutations may increase the risk of death by 5.154 (95%CI: 1.442–18.416) and 5.185 (95%CI: 2.763–9.730) times, respectively, compared with the wild type. Unexpectedly, Spike_D614G, Spike_E484K, Spike_N501Y, and NSP12_P323L were not statistically significant. The A701V, D950N, E1258D, E156G, G142D, P26S, R346K, T732A, and V1176F mutations in the S protein may increase the risk of death by 2.048 (95%CI: 1.246–3.366), 1.587 (95%CI: 1.247–2.021), 1.718 (95%CI: 1.29–2.288), 5.658 (95%CI: 3.199–10.006), 1.637 (95%CI: 1.452–1.844), 1.772 (95%CI: 1.162–2.703), 2.405 (95%CI: 1.341–4.312), 2.485 (95%CI: 1.616–3.821), and 1.771 (95%CI: 1.194–2.628) times, respectively, compared with the wild type. The D1259H, F157del, L18F, N1074S, Q677H, T19R, T20N, T240I, T478K, and V1104L mutations in the S protein may reduce the risk of death by 0.474 (95%CI: 0.318–0.707), 0.209 (95%CI: 0.088–0.494), 0.375 (95%CI: 0.252–0.560), 0.135 (95%CI: 0.069–0.262), 0.501 (95%CI: 0.334–0.753), 0.385 (95%CI: 0.243–0.612), 0.185 (95%CI: 0.098–0.35), 0.059 (95%CI: 0.019–0.184), 0.644 (95%CI: 0.434–0.955), and 0.435 (95%CI: 0.289–0.656) times, respectively, compared with the wild type. The multiplicative interactions between age and mutations A146D, P78L, and K233Q were not statistically significant. The detailed results are shown in Table 4.
Table 4. Result of the logistic regression model with interactions.
The results of additive interaction demonstrated the existence of synergistic interactions between age and mutations A146D or P78L. The risk of death in patients aged 18–64 years infected with the virus with the A146D mutation was 3.571 times as high as the sum of the risks in patients exposed to only a single risk factor. The risk of death in patients aged ≥ 65 years infected with the A146D mutant virus was 3.388 times higher than that in patients exposed to only a single risk factor combined. The risk of death in patients aged 18–64 years infected with the virus with the P78L mutation was 2.137 times as high as the sum of the risks in patients exposed to only a single risk factor. Patients over the age of 65 infected with the P78L mutant virus had a 2.761-times higher risk of death than those exposed to only a single risk factor combined. The detailed results are provided in Table 5.
Table 5. The results of additive interaction metrics.

2.5. Analysis of Vaccinated Patient Data

Of the 138,492 entries, 3569 were reported to have been vaccinated. Among the vaccinated patients, 3456 recovered and 113 died. We performed univariate and multivariate analyses on the data on vaccinated patients with breakthrough infections. The results of the univariate analysis are shown in Table 6.
Table 6. Results of univariate analysis of vaccinated patients.
Variables with p < 0.1 in univariate analysis and mutations previously found to be potentially associated with patient clinical outcomes were included in further logistic regression. The multivariate logistic regression results showed that women had a lower risk of death than men (OR = 0.589, 95%CI: 0.389–0.893). The P78L mutation may increase the risk of death by 3.376 (95%CI: 2.040–5.585) times compared with the wild type (Table 7). The latter result is similar to the logistic regression results of the total metadata. In addition, the Spike_D950N mutation may increase the risk of death by 6.123 (95%CI: 1.147–32.677) times compared with the wild type, whereas the P681R and V1264L mutations in the S protein may reduce the risk of death by 0.045 (95%CI: 0.005–0.387) and 0.118 (95%CI: 0.015–0.922) times, respectively, compared with the wild type.
Table 7. Results of logistic regression model for vaccinated patients.

3. Discussion

Studies conducted early in the pandemic highlighted similar substitution rates for most genes in SARS-CoV-2. For example, the replacement rate of ORF1ab and Spike is approximately 3.5 × 10−4 per site per year [19]. The PLpro coding sequence of interest in this study was located in ORF1ab. Since the start of the pandemic, many mutation sites have been found, and some of them have high mutation frequencies. Considering the possible co-occurrence of other mutations in the Spike protein, which is widely recognized to affect patient outcomes, our study identified several mutations in PLpro that may affect the risk of death in patients.
The results of multivariate logistic regression on 138,492 items showed that, compared with the reference sequence, the K233Q and P78L mutations were associated with an increased risk of death in patients. The mutations identified in this study that may affect the patient’s risk of death have occurred in variants previously considered to be variants of concern (VOC) [20], such as the P78L mutation in the Delta VOC and the K233Q mutation in the Gamma VOC. Available evidence suggests that the Beta, Delta, and Gamma VOCs significantly increase the risk of death in patients compared with wild-type SARS-CoV-2 [21,22,23]. These mutations may explain some of the pathogenicity changes in VOCs. No dual P78L and K233Q mutations in PLpro were detected in VOCs, and even among the 138,492 entries included in this study, the frequency of double mutations was less than 0.1%, and triple mutations of the above mutations were absent.
Whether the mutation of SARS-CoV-2 affects clinical outcomes is an issue of wide concern. A recent study found that the frequencies of the D614G mutation in the S protein and the P323L mutation in NSP12 were positively correlated with patient mortality [14]. In this study, in addition to the mutation located in PLpro, we included other mutations that may be related to the clinical outcomes of patients in the multivariate logistic regression model. The results of this study revealed that Spike_D614G, Spike_E484K, Spike_N501Y, and NSP12_P323L mutations, which have been shown to be associated with outcomes, were not statistically different between the “Recovery” group and the “Death” group, demonstrating that these mutations were balanced in the two groups. Furthermore, the larger sample size of this study (n = 138,492) compared to the above studies enhances its representativeness.
The results of additive interaction analysis indicated the existence of a significant synergistic interaction between age grouping and the A146D and P78L mutations. The risk of death in patients exposed to the factors of older age and infection with the mutated virus was higher than the sum of the risks in patients exposed to only a single factor. In 1976, Rothman developed the sufficient-component casual model, which may be used to explain the variation in the effects of viral mutations with patient age. In addition to SARS-CoV-2 mutation and patient age, other unrecognized factors that affect patient outcomes exist. Thus, further research is needed to reveal possible complementary etiologies and prevent death outcomes.
Moreover, given that studies have shown that existing immunity can reduce the risk of death after breakthrough infection in patients [24,25], immunity is one of the important factors affecting the risk of death in patients. Therefore, in this study, we selected items related to patients who reported having been vaccinated against COVID-19 for further analysis to corroborate our previous findings. Similar to the results obtained from the analysis of 138,492 items in this study, the results of multivariate analysis revealed that the P78L mutation may increase the risk of death. These findings also confirmed the reliability of our results.
Although our study focused on the pathogenicity of SARS-CoV-2 and revealed mutations in PLpro that may be associated with the clinical outcomes of patients, their underlying mechanisms were not elucidated. PLpro has been widely accepted to modulate immune responses by affecting ubiquitination in host cells [5,7]. Notably, position 233 of PLpro (position 1795 of replica polyprotein 1ab) has been identified as one of the ubiquitination sites of the SARS-CoV-2 protein [5,26]. Therefore, the K233Q mutation in PLpro may suppress host immune responses by regulating the ubiquitination of important proteins, thereby affecting the clinical outcomes of patients. However, whether the 78th position of PLpro has special biological functions remains unclear. Further exploration of the functional, structural, and biological changes associated with the P78L and K233Q mutations in PLpro would be meaningful to reveal the mechanisms that affect the risk of death in patients.
In addition, the data used in this study did not meet random sampling requirements. Therefore, sampling bias may exist. However, we still found mutations associated with clinical outcomes in the PLpro gene of SARS-CoV-2. Our findings could provide evidence for early responses to mutations that could lead to clinically fatal outcomes.

4. Materials and Methods

4.1. Data Collection and Filtering

The GISAID [18] database (https://www.gisaid.org/) was accessed on 18 January 2022 by using filter conditions that included “Host = Human,” “Complete,” “High coverage,” “Low coverage excluded,” “With patient status,” and “Collection date complete”, and patient status metadata (n = 166,339) collected between 1 January 2021 and 31 December 2021 were downloaded. Data containing mutations located in NSP3 with defined gender, age, and patient status (n = 138,492) were obtained after applying a second filter.

4.2. Classification of Patient Status

According to the data provided, 138,492 entries were divided into two categories.
Entries that included “Dead,” “Death,” “Deceased,” “Demise,” “Died,” “Exitus,” “Expired,” or “Fatal” in the patient status were classified into the “Death” group.
Entries that included “Admitted,” “Alive,” “Ambulatory,” “Asymptomatic,” “Discharge,” “Home,” “Hospitalized,” “Inpatient,” “Live,” “Mild,” “Outpatient,” “Paucisymtpmatic,” “Recovery,” “Symptomatic,” or any of their combinations in the patient status were classified into the “Recovery” group.

4.3. Mutation

Mutations were assessed by using “AA substitutions” from the GISAID database. Mutations with frequencies below 1% were discarded. The official reference sequence used by GISAID is hCoV-19/Wuhan/WIV04/2019 (WIV04) with the accession ID EPI_ISL_402124. Given that PLpro is not one of the individual proteins displayed in the AA substitutions, mutations in PLpro are presented in the GISAID database as mutations at positions 745–1063 of NSP3.

4.4. Statistical Analysis

Categorical variables were described as frequencies (percentages). Chi-squared test or Fisher’s exact test were used to compare categorical variables. Given that age differences can lead to significant differences in the risk of death after infection with SARS-CoV-2, age-subgroup univariate analysis was performed to explore whether the effects of mutations differed by age. Variables with p < 0.1 in univariate analysis and mutations found in previous studies that may affect the clinical outcomes of patients were further included in multivariate logistic regression to explore the effect of patient gender, age, and mutation on mortality. The R package “epiR” was used to calculate the indices of additive interaction: the relative excess risk (RERI), the attributable proportion (AP), and the synergy index (S). The synergy index was used as a summary measure of additive interaction [27]. In all statistical analyses, p-values less than 0.05 were considered statistically significant. IBM SPSS statistics 25.0 software, R Statistical Software 4.0.1, and RStudio 1.4.1717 were utilized for statistical analysis.

5. Conclusions

Compared with the wild type, the P78L and K233Q mutations in PLpro increased the risk of death in infected individuals. A synergistic effect existed between age and P78L and A146D. This effect increased the risk of death in older patients.

Author Contributions

Conceptualization, D.Z.; methodology, L.G.; formal analysis, P.H.; data curation, J.T. and Z.W.; writing—original draft preparation, J.T. and Z.W; visualization, Y.W.; funding acquisition, D.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Foshan Scientific and Technological Key Project for COVID-19, grant number 2020001000430.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of the School of Public Health at Sun Yat-sen University (protocol code L202001, dated 4 February 2020).

Data Availability Statement

The datasets used and analyzed in this study are available from the corresponding authors upon reasonable request.

Acknowledgments

The authors would like to thank GISAID and the originating laboratories that submitted the sequences as well as recognize the support of the Foshan Scientific and Technological Key Project for COVID-19.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table
Table A1. Results of the logistic regression model without interactions.
Table A1. Results of the logistic regression model without interactions.
Predictive Variables OR (95%CI)p
GenderMaleReference
Female0.688 (0.638–0.741)<0.001
Age, years≤17Reference<0.001
18–643.023 (2.310–3.957)<0.001
≥6518.285 (13.988–23.902)<0.001
A890DNoReference
Yes3.171 (1.040–9.674)0.043
P822LNoReference
Yes4.103 (3.399–4.952)<0.001
K977QNoReference
Yes2.689 (1.074–6.733)0.035
K837NNoReference
Yes1.218 (0.736–2.013)0.443
E906DNoReference
Yes0.31 (0.044–2.175)0.239
Spike_D614GNoReference
Yes3.755 (0.501–28.114)0.198
Spike_E484KNoReference
Yes0.731 (0.510–1.047)0.088
Spike_N501YNoReference
Yes0.984 (0.606–1.597)0.946
NSP12_P323LNoReference
Yes1.065 (0.720–1.576)0.751
Spike_A222VNoReference
Yes0.852 (0.709–1.025)0.090
Spike_A243delNoReference
Yes1.398 (0.047–42.055)0.847
Spike_A27SNoReference
Yes0.908 (0.637–1.293)0.592
Spike_A570DNoReference
Yes1.829 (0.589–5.679)0.297
Spike_A701VNoReference
Yes2.001 (1.219–3.285)0.006
Spike_D1118HNoReference
Yes0.557 (0.239–1.301)0.177
Spike_D1259HNoReference
Yes0.473 (0.317–0.706)<0.001
Spike_D138YNoReference
Yes1.636 (0.962–2.785)0.069
Spike_D215GNoReference
Yes0.572 (0.210–1.558)0.275
Spike_D80ANoReference
Yes2.076 (0.571–7.553)0.268
Spike_D950NNoReference
Yes1.585 (1.244–2.018)<0.001
Spike_E1258DNoReference
Yes1.736 (1.304–2.311)<0.001
Spike_E156GNoReference
Yes5.737 (3.248–10.133)<0.001
Spike_F157delNoReference
Yes0.207 (0.087–0.490)<0.001
Spike_G142DNoReference
Yes1.639 (1.454–1.848)<0.001
Spike_H655YNoReference
Yes1.194 (0.627–2.272)0.590
Spike_H69delNoReference
Yes0.978 (0.274–3.496)0.973
Spike_K417NNoReference
Yes1.073 (0.534–2.158)0.843
Spike_K417TNoReference
Yes0.796 (0.460–1.377)0.414
Spike_K97ENoReference
Yes1.795 (0.965–3.341)0.065
Spike_L18FNoReference
Yes0.377 (0.253–0.563)<0.001
Spike_L242delNoReference
Yes0.723 (0.326–1.605)0.426
Spike_L244delNoReference
Yes0.705 (0.023–21.188)0.840
Spike_L452RNoReference
Yes1.313 (0.949–1.816)0.100
Spike_N1074SNoReference
Yes0.131 (0.067–0.255)<0.001
Spike_P251LNoReference
Yes0.91 (0.152–5.444)0.918
Spike_P26SNoReference
Yes1.772 (1.160–2.707)0.008
Spike_P681HNoReference
Yes0.744 (0.541–1.024)0.070
Spike_P681RNoReference
Yes0.788 (0.572–1.087)0.147
Spike_Q677HNoReference
Yes0.498 (0.332–0.747)0.001
Spike_R158delNoReference
Yes0.646 (0.315–1.327)0.234
Spike_R158SNoReference
Yes1.074 (0.448–2.574)0.874
Spike_R190SNoReference
Yes1.558 (0.704–3.448)0.274
Spike_R346KNoReference
Yes2.422 (1.355–4.330)0.003
Spike_S982ANoReference
Yes0.303 (0.087–1.062)0.062
Spike_T1027INoReference
Yes1.605 (0.861–2.991)0.136
Spike_T19RNoReference
Yes0.376 (0.237–0.597)<0.001
Spike_T20NNoReference
Yes0.179 (0.093–0.343)<0.001
Spike_T240INoReference
Yes0.061 (0.019–0.189)<0.001
Spike_T478KNoReference
Yes0.647 (0.437–0.959)0.030
Spike_T732ANoReference
Yes2.497 (1.626–3.833)<0.001
Spike_T95INoReference
Yes1.018 (0.890–1.166)0.792
Spike_V1104LNoReference
Yes0.436 (0.289–0.657)<0.001
Spike_V1176FNoReference
Yes1.783 (1.204–2.641)0.004
Spike_V70delNoReference
Yes0.649 (0.180–2.334)0.508
Spike_Y144delNoReference
Yes1.439 (1.013–2.043)0.042
Spike_Y160F and Spike_V159LNone or only oneReference
Yes0.651 (0.224–1.889)0.430
Note: p < 0.05 was considered statistically significant and is highlighted in bold.

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