key: cord-0818595-792szmz1
authors: Bose, T.; Pant, N.; Pinna, N. K.; Bhar, S.; Dutta, A.; Mande, S. S.
title: Does immune recognition of SARS-CoV2 epitopes vary between different ethnic groups?
date: 2021-05-26
journal: nan
DOI: 10.1101/2021.05.24.21257707
sha: c47a0b9497d4e9f1ac4ea9736f2a5eecc53be11e
doc_id: 818595
cord_uid: 792szmz1

The SARS-CoV2 mediated Covid-19 pandemic has impacted humankind at an unprecedented scale. While substantial research efforts have focused towards understand the mechanisms of viral infection and developing vaccines/ therapeutics, factors affecting the susceptibility to SARS-CoV2 infection and manifestation of Covid-19 remain less explored. Given that the Human Leukocyte Antigen (HLA) system is known to vary among ethnic populations, it is likely to affect the recognition of the virus, and in turn, the susceptibility to Covid-19. To understand this, we used bioinformatic tools to probe all SARS-CoV2 peptides which could elicit T-cell response in humans. We also tried to answer the intriguing question of whether these potential epitopes were equally immunogenic across ethnicities, by studying the distribution of HLA alleles among different populations and their share of cognate epitopes. We provide evidence that the newer mutations in SARS-CoV2 are unlikely to alter the T-cell mediated immunogenic responses among the studied ethnic populations. The work presented herein is expected to bolster our understanding of the pandemic, by providing insights into differential immunological response of ethnic populations to the virus as well as by gauging the possible effects of mutations in SARS-CoV2 on efficacy of potential epitope-based vaccines through evaluating ~40000 viral genomes.

The ongoing outbreak of severe acute respiratory syndrome (SARS), commonly known as Covid-19, has already infected over 157 million and has led to the death of 3,200,000 people worldwide ("WHO Coronavirus Disease (COVID-19) Dashboard," n.d.). SARS-CoV2, a newly identified lineage B Betacoronavirus has been held responsible for this global pandemic (Letko et al., 2020) .

While the current pandemic is unprecedented in scale as compared to the earlier coronavirus outbreaks (Pathan et al., 2020) , they are quite alike considering the ongoing quest for an effective preventive/ treatment regime to counter the disease (Ayukekbong et al., 2020) . A significant amount of research has been directed at understanding various facets to the pathogenesis. These include the viral attachment and entry (Hoffmann et al., 2020; Letko et al., 2020) , formation of the coronavirus replication/ transcription complex Krichel et al., 2020) , replication, transcription and translation of viral proteins (Hillen et al., 2020; Satarker and Nampoothiri, 2020; Wang et al., 2020) , virion assembly and release (Kumar et al., 2020; Li et al., 2020) and the commonalities and differences of this disease with seasonal flu and previous SARS infections (Song et al., 2020; Xu et al., 2020) . In spite of these efforts, factors affecting susceptibility to infection of SARS-CoV2 and manifestation of Covid-19 are yet to be properly understood and demands more attention. It may be noted that SARS-CoV2's rate of genomic mutation is similar to most RNA viruses, (Pathan et al., 2020) , and recent evidences seem to suggest that the mortality rates among Covid-19 patients is strongly associated with the mutation profile of the infecting virus (Toyoshima et al., 2020 ; "WHO | Variant analysis of SARS-CoV-2 genomes," n.d.). While a pre-existing medical condition is likely to increase the risk of severity of Covid-19 infection (CDC, 2020 ), yet another factor influencing the susceptibility to the disease could be the genetic makeup of an individual. The Human Leukocyte Antigens (HLA) system, a major determinant of our ability to detect and neutralize an invading pathogen, is encoded by the Major Histocompatibility Complex (MHC) genes located on chromosome-6. MHCs, which are further categorized into classes-I and II, are highly polymorphic and are known to vary significantly among individuals of different ethnicities. The outcome of an infection event is therefore dependent on both the genotype of the virus as well as the host cell surface (MHC) molecules destined to present viral antigenic peptides to the human T-cell receptor (TCR) of Tlymphocytes (also called killer T-cells or CD8-positive cytotoxic T-cells) and T-helper cells (also called CD4-positive T cells) (Murray and McMichael, 1992; van Montfoort et al., 2014) .

In this work, we have investigated how the genetic variations across ethnicities is likely to influence the ability of their immune system in timely recognition of the virus, and in turn, their susceptibility to . To this end, state-of-the art bioinformatic tools were used to (a) identify the probable antigenic peptides on the SARS-CoV2 proteomes, and (b) identify HLA alleles which could recognize and present these epitopes to the T-cells, along with the prevalence of these alleles in different ethnic groups. In addition, a large set of fully sequenced SARS-CoV2 genomes were analyzed to probe the possible effect of viral genetic variations on antigenic recognition. Whether the variations in the viral genome over time are likely to change the susceptibility of an ethnic group to Covid-19 infection was evaluated. In this context it may be noted that, given the computational challenges of identifying B-cell epitopes with adequate confidence, the current study did not focus on this aspect of human immunity. Results presented in this work also assumes additional importance when viewed in the context of a recent publication which highlighted the prospect and benefits of considering non-spike proteins for future Covid-19 vaccine designs (Peng et al., 2020) .

One of the key mechanism of identification of an invading pathogen by the host immune system involves MHC class-I and MHC class-II proteins presenting the pathogenic protein fragments (epitopes) on the surface of the CD8-positive cytotoxic T-cells and CD4-positive T-helper cells respectively. Given this, efforts were first made to identify SARS-CoV2 epitopes that could be recognized by the HLA allelic variants. A total of 326 epitopic regions from all the proteins encoded by the SARS-CoV2 reference genome (GenBank Accession no. MN908947) were predicted to bind to 130 HLA allelic variants (Supplementary Table 1 ) with reasonable confidence (see Materials and Methods) . This comprised of 276 CD8 (MHC class-I) and 50 CD4 (MHC class-II) epitopes. From the list of predicted epitopes and the corresponding HLA alleles, it was observed that some of the HLA alleles could recognize higher number of SARS-CoV2 epitopes and thus might play a more significant role in immune response. Of the 108 MHC class-I associated HLA alleles, that were predicted to be involved in the antigenic recognition of SARS-CoV2 proteins in (CD8-positive) cytotoxic T-lymphocyte cells, the highest number of epitopes were identified by HLA-A*02:11, HLA.A*26:02, HLA.B*15:17, HLA.A*24: 03 and HLA.B*35.41 (50, 32, 32, 28 and 19 epitopes respectively). In contrast, among the MHC class-II associated 22 HLA alleles involved in the antigen recognition of SARS-CoV2 proteins in (CD4-positive) T-helper cells, HLA-DRB1*01:01, HLA.DRB*115:01, HLA.DRB*115:06 and HLA.DRB*101:02 were predicted to present the highest number of epitopes (16, 13, 13, and 7 epitopes respectively). It was also noted that few of the epitopes could be recognized by multiple HLA alleles (Supplementary Table 1 ). It therefore appeared that the potential of a population/ ethnic group to cope with Covid-19 infection could be determined by their MHC gene pool. HLA.DRB*115:06) with high antigen recognition capability were seen to be less common across ethnicities and in most cases were also sparsely distributed even among samples from same ethnicity. In summary, noticeable variability in the potential of the HLA alleles to recognize and present the SARS-CoV2 epitopes to the immune cells was observed. We subsequently investigated if this could have a bearing on the level of antigenic recognition among different ethnic groups.

The overall trend observed with respect to diversity of the predicted CD8 and CD4 epitopes were comparable to those of their corresponding alleles (MHC-I and MHC-II alleles respectively) across ethnicities ( Fig. 1) . However, certain subtle differences, specifically with respect to their relative richness, were observed. For example, the change in relative richness (number of observed alleles or epitopes for an ethnicity) among African (AFR) to East Asian (EAS) super-populations in the plots associated with MHC-I alleles and the corresponding CD8 epitopes was apparent. Similarly, a change in richness of MHC-II alleles and CD4 epitopes in case of Ad Mixed American (AMR)

to EAS super-populations was also visible. The richness of the antigenic recognition potential among European (EUR) ethnicities was seen to be quite diverse, particularly for CD8 epitopes (Supplementary Table 2 Table 3) , 149 CD8 and 49 CD4 epitopes were found to be common across five super-populations. In contrast to the MHC-I alleles (wherein SAS and EAS had the highest commonalities), SAS and AMR were found to have the highest overlap in terms of recognizing CD8 epitopes. Additionally, AMR and EAS were found to have the potential to identify a significant number of epitopes which were not identified by the MHC-I complexes of the other super-populations. Most notably, the CD4 epitopes were seen to be equally recognized among all super-populations. The trends in recognition of epitopes by different alleles All rights reserved. No reuse allowed without permission.

(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. Figures 3 and 4) . The antigenic peptide FLLPSLATV (Epitope 3 from nsp6) appeared to be the most recognized CD8specific epitope across ethnicities. Notably, this epitope could be recognized by 14 HLA allelic variants, the highest among all the CD8-specific SARS-CoV2 antigens recognized in this study (Supplementary Figure 3 and 

The potential of each of the individual subjects (comprising the 26 ethnic groups and five superpopulations) to recognize the SARS-CoV2 epitopes was determined (Supplementary Table 4 ). and Africans (AFR) reported low potentials to recognize both CD8 and CD4 epitopes. In contrast, ethnicities comprising the European (EUR) and South Asian (SAS) super-populations, showed high potentials to recognize all forms of SARS-CoV2 epitopes. The Peruvians from Lima (PEL) exhibited an interesting pattern with extremely low CD4, but very high CD8 epitope recognition capacity. Based on the above observations, we further probed if there were any statistically significant differences between the epitope recognition potential among the various ethnicities and super-populations (see Materials and Methods). The p-values for the Kruskal-Wallis test among all the super-populations and ethnic groups were seen to be 0.005257 and 0.004022 at 95% confidence interval, thereby implying significant difference in epitope recognition potential in at least one of the five super-populations and 26 ethnic groups. Indeed, the results obtained from Wilcoxon signed-rank test (see Materials and Methods) indicated substantial differences in the recognition potentials of both CD8 and CD4 epitopes among super-populations as well as ethnicities except for AMR super-population (Supplementary Table 5 ). Further, to check for any major differences in the epitope recognition potentials between individuals of different ethnicities, Dunn's test was performed (see Materials and Methods). Most notably, among the SAS ethnicities, GIH -Gujarati Indian from Houston, Texas and PJL -Punjabi from Lahore, Pakistan showed significant mean rank differences from STU -Sri Lankan Tamil from the UK (Table 1 and   Supplementary Table 6 ). PUR -Puerto Ricans from Puerto Rico and PEL -Peruvians from Lima, Peru (among AMR) and JPT -Japanese in Tokyo, Japan and CHB -Han Chinese in Beijing, China (among EAS) also demonstrated appreciable differences. In contrast, despite diverse origins, the mean rank differences between certain ethnic groups (such as, SAS_STU versus EUR_IBS) were found to be extremely low (Table 1 and Supplementary Table 6 ). While the prevalence of specific HLA alleles in the gene pool of a population or an ethnic group provides an overall idea about how well recognized the SARS-CoV2 epitopes would be in that population, the immune response of each of the individuals would be independently governed by their own allelic make-up. The above results provide an average estimate of (CD4 and CD8 cell mediated) immune sensitivity to SARS-CoV2 for individuals belonging to population and highlights inter-ethnic differences therein.

In addition to the HLA makeup, yet another factor which could determine the fate of the infection process is the genetic variation in the SARS-CoV2 genomes. Genetic variation could lead to epitope modifications, which in turn, might alter the binding affinity/ recognition of the viral epitopes by MHC-I/II alleles. To accommodate for this factor, the epitope signature of 40,342 SARS-CoV2 genomes, were generated (see Materials and Methods) based on the 326 epitopes predicted for the reference genome and their 2989 variants (Table 2 and Supplementary Table 7) .

Apart from the original 326 epitopes, only 12 of the 2989 epitope variants were seen to be present in more than 0.5% of the studied SARS-CoV2 genomes. Moreover, some of the variants were found to exclusively co-occur among a sub-set of strains isolated from certain geographies, most prominently among those isolated from India or United Kingdom. Even more significantly, a particular epitope variant (SEVGPEHSL at position 376 on ORF1a) was noted to be exclusively absent among the SARS-CoV2 genome sequences isolated and sequenced in Iran (Supplementary All rights reserved. No reuse allowed without permission.

(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted May 26, 2021. ; Table 7 ). It was further observed that while the more frequent epitope variants (those observed in more than 0.5% of the studied SARS-CoV2 genomes) exhibited a mixed pattern of altered immunological behaviors, the human MHC-I/II alleles did not have a high binding affinity/ recognition potential for a large proportion (1346 out of 2651) of the lesser frequent SARS-CoV2 epitope variants (Table 2) .

While it would be interesting to understand the effect of these variations in the viral genome on the antigenic recognition potentials among different ethnicities no major effects were expected, given that only 12 epitope variants were present in at least 0.5% of the studied SARS-CoV2 genomes. Even in the hypothetical scenario, wherein every population/ ethnicity is simultaneously exposed to all the 40,342 viral variants, the individual antigenic recognition potential would be driven by the originally identified 326 epitopes, and not by the lesser frequent epitope variants.

Considering the continuously evolving genome of SARS-CoV2, which entails selection/ retention of specific epitope variants over time in the newly emerging genomes, an analysis was performed to check if there were any temporal changes in the susceptibility to Covid-19 in any of the ethnicities (see Materials and Methods). Even in this case (Fig. 4) , it was noted that temporal variations in the SARS-CoV2 genome, while resulting in certain changes in its epitope signature, did not appreciably alter the epitope recognition ability among ethnic groups, at least at a population level (also see Supplementary Figure 7 ). Since ethnic groups (and people from same geography) are known to predominantly encoded for certain HLA gene variations, it may be perceived that the SARS-CoV2 epitope recognition potential of an ethnic group is not likely to change owing to the temporal variations in the viral genome. Further, it will be appreciated that although the said changes in the viral genomes might alter the biological functioning of the viral genes, yet they remain largely inconsequential with respect to immune determination in the host.

There have been three major coronavirus associated SARS outbreaks in the last 20 years. While there have been extensive research regarding the pathophysiology of these viruses Hillen et al., 2020; Hoffmann et al., 2020; Krichel et al., 2020; Kumar et al., 2020; Letko et al., 2020; Li et al., 2020; Satarker and Nampoothiri, 2020; Song et al., 2020; Wang et al., 2020;  All rights reserved. No reuse allowed without permission.

(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. Xu et al., 2020) , as yet we have limited knowledge into the factors affecting susceptibility to SARS-CoV2 infection and manifestation of Covid-19. Some scientists have opined that the mortality rate in Covid-19 could be linked to the genomic profile of the infecting virus (Toyoshima et al., 2020 ; "WHO | Variant analysis of SARS-CoV-2 genomes," n.d.) which seem to mutate at a rate similar to most RNA viruses. Further, an underlying/ pre-existing medical condition might be considered to be an added risk towards increasing the severity/ complications associated with Covid-19 infection (CDC, 2020) . In addition, the host genetic makeup could play a decisive role in disease manifestation. In this context, polymorphic genes like the Human Leukocyte Antigens (HLA) system which is known to vary significantly among individuals of different ethnicities assumes importance. Thus, the outcome of an infection event is multi-factorial and at least dependent on (a) genotype of the virus and (b) host cell surface (MHC) molecules destined to present viral antigenic peptides to the human immune cells.

To understand this, the antigenic peptides from over 40,000 SARS-CoV2 genomes were predicted for all major HLA alleles, as documented in the 1000 genomes project (ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/HLA_types/). The state-of-the-art tools NetMHCpan 4.1 and NetMHCIIpan 4.0 were used for the purpose in the current work (Reynisson et al., 2020) . It may be noted that most of other available tools and web-services for predicting MHC-I and MHC-II epitopes (Jespersen et al., 2017; Paul et al., 2016; Saha and Raghava, 2004; Zhang et al., 2009) were not equipped to handle the entire HLA allelic set that had been reported in the 1000 genomes project data. Further, most of the other tools also did not have a provision to identify variable length MHC-I epitopes. Consequently, some of the earlier studies associate with in silico identification of SARS-CoV2 epitopes (Abdelmageed et al., 2020; Dong et al., 2020; Lin et al., 2020; Naz et al., 2020; TOPUZOĞULLARI et al., 2020) are likely to have suffered from the mentioned limitations associated with these tools (Jespersen et al., 2017; Paul et al., 2016; Saha and Raghava, 2004; Zhang et al., 2009) . In other words, these studies might not have arrived at a list of epitopes as comprehensive as the one mentioned in the current study.

Further, studies following a combinatorial approach (H.-Z. Zaheer et al., 2020) are also expected to miss some of the epitopes reported in this study. It may however be noted that like most previous studies, the current study refrained from predicting potential B-cell epitopes, due to the computational complexities involved in the process and may be considered a limitation All rights reserved. No reuse allowed without permission.

(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted May 26, 2021. ;  of this exercise. Most B-cell epitopes are conformational in nature and comprise of discontinuous amino acid stretches which are difficult to be identified (with adequate confidence) from genomic information alone (Wang et al., 2011) .

A total of 276 CD8 (involving 108 MHC class-I alleles) and 50 CD4 (involving 22 MHC class-II alleles) SARS-CoV2 epitopes were predicted in the current study. The highest number of SARS- Table 2 ) comprising these super-populations. While the super-populations were seen to cluster in terms of the MHC-I genes encoded in the genome, such patterns could not be observed for MHC-II genes ( Supplementary Figures 1-2 ). In addition, certain HLA alleles were found to be more efficient in recognizing SARS-CoV2 epitopes as compared to others. Given the skewed distribution of these HLA alleles among a few of the ethnic groups, contrasting characteristics with respect to the ratio of the number of epitopes to the number of HLA alleles involved in their recognition, among each of the ethnic groups were observed, even among ethnicities from the same super-population (Fig. 3) . For example, GIH and PJL showed significant mean rank differences from STU among the ethnicities from the SAS super-population (Table 1 and Supplementary Table 6 ). Conversely, in a few cases, the mean rank differences between certain ethnic groups from different super-populations (such as, SAS_STU versus EUR_IBS) were found to be extremely low (Table 1 and Supplementary Table 6 ).

In this study, an attempt was further made to gauge the variations in the SARS-CoV2 epitope regions resulting from temporal changes in the viral genome and its effect on the epitope recognition potential across the ethnicities worldwide. The SARS-CoV2 genomic variation data which was obtained for different geographies over a six month period indicated that although the SARS-CoV2 genomic variations altered the overall predicted SARS-COV2 epitope signature profile (Table 2 and Supplementary Table 7) , most of the alternate epitopes were not as common and occurred in ≤ 0.5% of the analyzed genomes. Further, the overall capacity of an ethnic group All rights reserved. No reuse allowed without permission.

(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted May 26, 2021. ;  to recognize SARS-CoV2 did not seem to change either (a) on encountering a new viral strain ( Fig. 4) or (b) over the course of the pandemic (Supplementary Figure 7) . Moreover, a minimal set of SARS-CoV2 epitopes was identified, which can be recognized by the HLA repertoire of individuals from all ethnicities (Table 3) . Candidates from this set of predicted antigenic peptides could provide an opportunity to design newer vaccines against Covid-19 (refer to Supplementary File 1). While most of the currently available Covid-19 vaccines are designed to target the viral spike protein, a recent study while inspecting T-cell memory from patients recovering from Covid-19 infection commented on the prospect of considering non-spike proteins within future Covid-19 vaccine designs (Peng et al., 2020) . Moreover, the outcome of this study can also aid in disease prognosis and designing of personalized therapy regimes for Covid-19 patients. The idea of providing personalized therapy to Covid-19 patients, especially those requiring critical care is already under clinical consideration (Cacciapuoti et al., 2020; Fang and Schooley, 2020; Garcia-Vidal et al., 2020) . In addition, results presented herein could also prove beneficial in understanding/ countering a future flu/ pneumonia outbreak involving a similar lineage B

Betacoronavirus. Given that the world has already witnessed two such major outbreaks in less than 20 years, it would be prudent to prepare blueprints of a more potent coronavirus vaccine, particularly against the lineage B Betacoronavirus, before the next outbreak.

The context and results of the presented work is somewhat aligned to another intriguing aspect of the prevailing pandemic with respect to disease severity and case fatality rate (CFR). Appreciable spatial and temporal variations in CFR have been observed across geographies and suitable explanation(s) for these variations have eluded researchers, given the wide array of possible confounding factors. A key observation in this regard pertains to the economic status of a country.

Death rates due to Covid-19 has been observed to be positively associated with GDP (per-capita) in multiple studies (Cao et al., 2020; Sorci et al., 2020) . Higher death rates despite (expected) better access to healthcare in high-income populations seems outright counter intuitive. While some scientists have tried to explain these observations citing higher life-expectancy and consequently an older population in high-income countries, who would be more susceptible to others have hinted at the possibility of the "so called hygiene-hypothesis" at play (Bloomfield et al., 2016; Chatterjee et al., 2021) . The results of the current study unravel yet another interesting aspect in this context pertaining to the genetic diversity. Severe cases of Covid-19 disease have been seen All rights reserved. No reuse allowed without permission.

(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted May 26, 2021. ; https://doi.org/10.1101 https://doi.org/10. /2021 to be characterized by higher levels of inflammatory cytokines and CD8+ T cell exhaustion . Reports have also indicated that in case of milder Covid-19 infections, not leading to death and other complications, higher proportions of SARS-CoV2 specific CD8+ T cells have been identified (Peng et al., 2020) . Our results indicate that the heavily affected European population (EUR) tends to harbor a larger fraction of MHC-II alleles in their gene-pool, which are specific to SARS-CoV2 epitopes. On the other hand, the South Asian (SAS) population, having a relatively lower fatality rate, exhibited a relatively larger proportion of MHC-I alleles specific to the SARS-CoV2 epitopes. It is worth further investigation whether a larger pool of MHC-I alleles presenting SARS-CoV2 antigens to CD8+ T cells in the SAS population indeed can be linked to the apparently lower fatality rate in this region. A recent study while reporting an inverse associations between MHC-I epitopes and mortality rates also indicated at this possibility (Wilson et al., 2021) . Moreover, any possible relationship of the larger pool of the MHC-II alleles in the EUR population with CD4+ T Cell activation, cytokine production leading to a disproportionate immune response (or a so called "cytokine-storm") will also be intriguing to explore. While the current work is limited by the number of representative individuals genotyped for each population in the 1000 genomes project, the results nonetheless provide an overview of possible trends in protective immunity and T-cell responses against SARS-CoV2 across different geographies/ ethnicities.

Overall, to our knowledge, this is the first ever account to capture the effects of the evolution of SARS-CoV2 genome on its potential interactions with the human HLA gene products in a global perspective. This assumes immense importance in the context of vaccine development and its efficacy against the newer lineages of SARS-CoV2. It is however important to note that the nature of the findings was determined by (a) the availability of data in public repositories and (b) the efficiency of the available bioinformatic tools. As a result, insights into some of the aspects of immune response, such as, if there are any B-cell specific conformational epitopes in SARS-CoV2, expression levels of the MHC alleles in response to Covid-19 infection and its variations across ethnicities, age groups, co-morbidities, etc., if any, could not be obtained. Nonetheless, the observations presented in this study are expected to highlight the need of understanding the crosstalk of the pathogen with the components of HLA system. This, we believe, would have far All rights reserved. No reuse allowed without permission.

(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted May 26, 2021. ; https://doi.org/10.1101/2021.05.24.21257707 doi: medRxiv preprint reaching consequences in our coexistence with SARS-CoV2 and other RNA viruses which we may encounter in future.

Full length sequences of all SARS-CoV2 proteins (reference sequences) were obtained from NCBI (GenBank Accession no. MN908947). Further, individual non-structural protein sequences were also obtained from NCBI (NCBI Accession no. YP_009725300.1 to YP_009725312.1). These protein sequences were used for predicting antigenic peptides (epitopes) on the viral proteome.

The human HLA allelic profiles were obtained from the 1000 genomes project McCauley, 2017). Genomic data and corresponding metadata for 40342 genomes (which were deposited to GISAID till 11 th June 2020) have been used in this analysis (see Supplementary Table   10 for details).

NetMHCpan 4.1 (http://www.cbs.dtu.dk/services/NetMHCpan/) and NetMHCIIpan 4.0 (http://www.cbs.dtu.dk/services/NetMHCIIpan/) were used with default parameters for identifying strong and weak binders (i.e., 0.5 and 2 for NetMHCpan and 1 and 5 for NetMHCIIpan) (Reynisson et al., 2020) to generate epitopes from SARS-CoV2 protein sequences against 226 MHC-I and 75 MHC-II alleles reported in 1000 genomes project. Variable length (8 to 14-mer) CD8 epitopes (mapping to MHC-I alleles) and 15-mer CD4 epitopes (corresponding to MHC-II alleles) were thus obtained which were further filtered for subsequent use. These epitopes were then filtered based on the prediction scores. In case multiple overlapping epitopes were recognized by the same HLA allele, the epitope HLA allele pairs with the higher prediction score was retained. All rights reserved. No reuse allowed without permission.

(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. Next, all predictions with scores < 0.95 were discarded to retain only epitope HLA allele pairs with high binding affinity (recognition capacity). This list comprised of 326 (276 CD8 and 50 CD4) epitopes having high prediction scores for 130 (108 MHC-I and 22 MHC-II) HLA alleles and was used for all further analysis.

Based on the data obtained from the 1000 genomes project, the overall distribution of MHC-I and MHC-II alleles among the 26 ethnic groups and five super-populations were determined. In addition, the distribution of CD8 and CD4 epitopes recognized by individuals (Samples) across these ethnicities and super-populations were also ascertained from the filtered epitope prediction results (Supplementary Tables 4 and 5 ). These were used to compute various diversity indices (viz.

CD4 epitopes. While the phyloseq package (from R version 3.6) was used to calculate diversity, the ggplot2 package in R version 3.6 was used for the visualization. The data was also used to generate Euler plots to depict the distribution of alleles across super-populations as well as the epitopes which would be recognized by these alleles. For this purpose, an online Euler diagram generation tool (http://bioinformatics.psb.ugent.be/webtools/Venn/) was used. Moreover, heatmaps to visually inspect the frequency of distribution of HLA alleles (both MHC-I and MHC-II) and the identified epitopes across the 26 ethnic groups were created using the heatmap.2 function of gplots library in R version 3.6 ( Supplementary Figures 1-4) . For this purpose, 'frequency' of an allele (or an identified epitope) in a given ethnicity was computed as number of individuals (Samples) from that ethnicity which encoded for the allele (or could recognize the epitope) divided by the total number of individuals (Samples) representing the ethnic group. The heatmaps were further augmented with a colour-key along the vertical axis depicting (a) the number of epitopes recognized by the HLA alleles ( Supplementary Figures 1 and 2) , and (b) the number of HLA alleles which could identify a given epitope ( Supplementary Figures 3 and 4) according to the data provided in Supplementary Table 2. In addition, the data represented in the heat maps were also used to hierarchically cluster the ethnic groups based on the HLA alleles they encode and the epitopes they could identify ( Supplementary Figures 1-4) . The hclust package in R version 3.6 was used for this purpose. All rights reserved. No reuse allowed without permission.

(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted May 26, 2021. ; https://doi.org/10.1101 https://doi.org/10. /2021 Statistical Analysis:

In order to probe for any statistical differences between the number of epitopes recognized by the individuals (epitope recognition potential) among the various ethnicities and super-populations, Kruskal-Wallis test was conducted after grouping the individuals (Samples) into (a) superpopulations and (b) ethnic levels. Additionally, Wilcoxon signed-rank test was also performed to test whether mean ranks of the epitope recognition potential of a super-population (and/or ethnicity) differed with respect to that of all other super-populations (and/or ethnicities) combined (Supplementary Table 5 ). The above tests were conducted both for the CD8 and CD4 epitopes individually as well as in combination. The p-values were corrected for multiple testing using Benjamini-Hochberg (BH) correction method. Further, the specific super-population/ ethnic group pairs with significant differences were identified using Dunn's test with Benjamini-Hochberg (BH) correction (Supplementary Table 6 ). The DescTools package in R version 3.6 was used for this purpose.

Nextstrain/augur pipeline (accessed on 21 st April, 2020 from https://github.com/nextstrain/ncov) was used to align 40342 SARS-CoV2 genome sequences downloaded from GISAID using Wuhan-Hu-1/2019, as a reference sequence using MAFFT (Katoh and Standley, 2013) . Conforming to the Nextstrain protocol, 130 nucleotides from 5'-end and 50 nucleotides from the 3'-end as well as single nucleotide positions 18529, 29849, 29851, 29853 were masked from multiple sequence alignment (MSA). An initial maximum likelihood (raw) phylogenetic tree (GTR model) was built using IQ-TREE (Nguyen et al., 2015) and further refined using default parameters in the pipeline.

The tree was then processed to construct a TimeTree having the ancestor of the following two genomes, Wuhan-Hu-1/2019 and Wuhan/WH01/2019, at its root. Finally, using augur's translation step, a translated MSA of all 14 SARS-CoV2 proteins were retrieved. The 326 epitopic regions (w.r.t. reference genome Wuhan/Hu-1/2019) were cropped out from the translated MSA, and amino acid variations occurring in each epitope across 40342 SARS-CoV2 isolates were obtained. A total of 2989 variations could be identified in the 326 reference epitopes.

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(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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A 'weighted' epitope recognition potential for every individual was calculated using the cumulated value of the prediction scores (≥ 0.95) of the epitopes present in SARS-CoV2 interacting with alleles of the individual. This was calculated using both the CD8 epitopes interacting with MHCI alleles and CD4 epitopes interacting with MHCII alleles. Subsequently, we calculated the 'weighted' epitope recognition potential for all the 2693 individuals against 40342 variants of SARS-CoV2 genomes in the present study. To evaluate any possible effect of mutation occurring/accumulating in the SARS-CoV2 genome over-time on immune recognition of the virus among different ethnicities, the mean 'weighted' epitope recognition potential for each individual belonging to an ethnic group was calculated with epitopes predicted from the first 10,000 and last 10,000 SARS-CoV2 genomes arranged in the chronological order of their collection dates (Fig.   4 ). Further, we also tried to estimate the variations of 'weighted' epitope recognition potential within each ethnic group for all the 40342 variants of SARS-CoV2 genomes ( Supplementary   Figure 7) , where the mean 'weighted' epitope recognition potential (of all the individuals belonging to an ethnic group) for each genome was calculated and arranged in the chronological order of their collection dates (in months).

TB, AD and SSM conceptualized the work and designed the protocol. NP, NKP and SB performed all the analyses. TB, NP, and AD wrote the manuscript. SSM supervised the project and reviewed the manuscript. All authors read and approved the final version of the manuscript.

The presented work is based on SARS-CoV2 genome sequence data retrieved from the GISAID repository (www.gisaid.org). We also gratefully acknowledge the originating laboratories responsible for obtaining the specimens and the submitting laboratories where genetic sequence data were generated and shared via the GISAID Initiative (Supplementary Table 10 ). All submitters of data may be contacted directly via GISAID. All rights reserved. No reuse allowed without permission.

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The copyright holder for this preprint this version posted May 26, 2021. ; https://doi.org/10.1101/2021.05.24.21257707 doi: medRxiv preprint Tables   Table 1: Dunn's test results depicting variation in the SARS-CoV2 epitope recognition potential between different ethnicities. Differences in the epitope recognition potential among individuals of different ethnic groups have been presented in terms of mean rank differences and p-values of the test (see Supplementary Table 6 for complete table with all the ethnic groups) . Populations (or ethnic groups) exhibiting significant differences (with p-values < 0.05) have been highlighted. Positive mean rank difference indicates the first ethnic group in a compared pair has higher epitope recognition potential and vice-versa.

Ethnic group belonging to same super-population exhibiting high mean rank difference ( AMR_PEL vs AFR_ASW 21.65 8.83E-01 Note: Super-population names are abbreviated as AFR-Africans; AMR-Ad mixed Americans; EAS-East Asians; EUR-Europeans; SAS-South east Asians, and prefixed to each of the population names in the table. The abbreviations used for names of different populations/ ethnic groups are in accordance with those originally reported by the 1000 genomes project and the International Genome Sample Resource (IGSR) (https://www.internationalgenome.org/faq/which-populations-are-part-your-study/).

Summary of the different parameters associated with the epitope variance with the 40342 SARS-CoV2 strains analyzed in the study has been tabulated.

Number of genomes analyzed 40342 Number of epitopes in the reference genome (NCBI Genbank accession no: MN908947) 326

Number of additional epitopes (variants) identified among the analyzed genomes 2663

Total number of epitopes (reference epitopes + variants) 2989

Epitope variants present in at-least 0.5% of the studied genomes -more frequent epitope variants 12

Epitope variants present in less than 0.5% of the studied genomes -less frequent epitope variants 2651

Epitope variants with no change in epitope recognition by HLA alleles with respect to the reference epitopes 896

Epitope variants recognized by higher number of HLA alleles with respect to the reference epitopes 417

Epitope variants recognized by fewer number of HLA alleles with respect to the reference epitopes 406

Epitope variants not recognized by HLA system 944

Number of less frequent epitope variants not well recognized by HLA system when compared to the reference epitope 1346

Number of more frequent epitope variants not well recognized by HLA system when compared to the reference epitope 4

Number of more frequent epitope variants better recognized by HLA system when compared to the reference epitope 4 (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted May 26, 2021. ; https://doi.org/10.1101 https://doi.org/10. /2021 Fig. 1 : Diversity of HLA alleles and cognate epitopes across super-populations. Richness (total observed numbers), Shannon diversity and Simpson index indicating the heterogeneity of (A) MHC-I and (B) MHC-II alleles encoded by gene pool of studied super-populations as well as the corresponding (C) CD8-specific and (D) CD4-specific SARS-CoV2 epitopes recognized by the HLA-system in these super-populations. The super-populations are abbreviated as AFR-Africans; AMR-Ad mixed Americans; EAS-East Asians; EUR-Europeans; SAS-South east Asians. All rights reserved. No reuse allowed without permission.

(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted May 26, 2021. ; https://doi.org/10.1101 https://doi.org/10. /2021 Mirror-plots indicating the richness of MHC alleles in different populations (along the left half of the plots) along with the richness of SARS-CoV2 epitopes predicted to be recognized by these HLA alleles (along the right half of the plots). (A) MHC-I alleles and predicted cognate CD8specific epitopes and (B) MHC-II alleles and predicted cognate CD4-specific epitopes, across 26 different populations (ethnic groups). In each of the plots, the ethnicities were sorted along the vertical axis based on the ratio of the richness of MHC alleles to the richness of epitopes recognized. Super-population names are abbreviated as AFR-Africans; AMR-Ad mixed Americans; EAS-East Asians; EUR-Europeans; SAS-South east Asians, and prefixed to each of the population names in the plots, as well as represented with horizontal bars of specific colors. The abbreviations used for names of different populations/ ethnic groups are in accordance with those originally reported by the 1000 genomes project and the International Genome Sample Resource (IGSR) (https://www.internationalgenome.org/faq/which-populations-are-part-yourstudy/).

Representation of the average of predicted SARS-CoV2 'weighted' epitope recognition potential of individuals from different populations (ethnic groups), considering different SARS-CoV2 genome variants observed across geographies. For each population (ethnic-group), there are two box-plots indicating their average 'weighted' epitope recognition potential (see Materials and Methods). For each population, the color-filled box on the left corresponds to the epitopes predicted from the first 10,000 SARS-CoV2 genomes (out of a total 40,342 genomes analyzed in the study) when arranged in the chronological order of their collection dates. The corresponding unfilled boxes on the right corresponds to the epitopes predicted from the last 10,000 SARS-CoV2 genomes based on their collection dates. Although the SARS-CoV2 genomes may be evolving over time, no observable variation in the way its epitope repertoire is recognized by individuals at an overall population level could be noted. Out identification capacity among different ethnic groups. Each data-point on the plots represents the average of the number of epitopes in a given SARS-CoV2 genome that could be identified by all the individuals in an ethnic population. Dark black lines, for each of the ethnic groups, indicate the mean value for these data-points for the studied 40,342 SARS-CoV2 genomes. Super-population names are abbreviated as AFR-Africans; AMR-Ad mixed Americans; EAS-East Asians; EUR-Europeans; SAS-South east Asians, and prefixed to each of the population names in the plots, as well as represented with boxes drawn in specific colors. The abbreviations used for names of different populations/ ethnic groups are in accordance with those originally reported by the 1000 genomes project and the International Genome Sample Resource (IGSR) (https://www.internationalgenome.org/faq/which-populationsare-part-your-study/). Table 6 : Pair-wise comparison of epitope recognition potential between different ethnic groups and super populations. (A) Differences in epitope recognition potentials between individuals representing any two specific ethnic groups are represented in terms of mean rank differences. (B) Differences in epitope recognition potentials between individuals representing any two specific super-populations are represented in terms of mean rank differences. Significant differences have been highlighted (Dunn's test p-values < 0.05). Populations (or ethnic groups) exhibiting significant differences (with p-values < 0.05) have been highlighted. Superpopulation names are abbreviated as AFR-Africans; AMR-Ad mixed Americans; EAS-East Asians; EUR-Europeans; SAS-South east Asians, and prefixed to each of the population names in the table. The abbreviations used for names of different populations/ ethnic groups are in accordance with those originally reported by the 1000 genomes project and the International Genome Sample Resource (IGSR) (https://www.internationalgenome.org/faq/which-populationsare-part-your-study/). (B) CD4 specific SARS-CoV2 epitopes that may be considered for designing multi-epitope peptide vaccines. The super population(s) where the epitope have been predicted to be well recognized as well as the VaxiJen scores for the respective epitopes are indicated. Epitopes (and their variants) which were found to be present in atleast 10 studied SARS-CoV2 genomes and were recognized by the HLA-system of at least 5% of individuals constituting the five super-populations are presented in the table. The presence of these peptides in SARS coronavirus and MERS coronavirus proteomes is also indicated. Table. GISAID Accession IDs of the SARS-CoV2 genomes, along with the Originating laboratories responsible for obtaining the specimens and the Submitting laboratories where genetic sequence data were generated and shared via the GISAID Initiative, on which this research is based.

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(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. Intensity of the colour indicates frequency of a particular allele in an ethnic group. Both the ethnic groups and the MHC-II alleles have been hierarchically clustered. An additional colour-key along the vertical axis indicates the number of SARS-CoV2 epitopes recognized by the HLA allele. Along the horizontal axis ethnic groups have been tagged with different colours based on their affiliations to respective super-populations. Figure 3 : Distribution of CD8-specific epitopes recognized by the HLAsystem among the ethnic groups. Heat-map depicting the distribution of CD8-specific epitopes that could be recognized by the HLA alleles prevalent among the 26 ethnic groups involved in this study. Both the ethnic groups and the CD8-specific epitopes have been hierarchically clustered. An additional colour-key along the vertical axis indicates the number of human HLA-types capable of recognizing the epitope. Along the horizontal axis ethnic groups have been tagged with different colours based on their affiliations to respective super-populations. Figure 4 : Distribution of CD4-specific epitopes recognized by the HLAsystem among the ethnic groups. Heat-map depicting the distribution of CD4-specific epitopes that could be recognized by the HLA alleles prevalent among the 26 ethnic groups involved in this study. Both the ethnic groups and the CD4-specific epitopes have been hierarchically clustered. An additional colour-key along the vertical axis indicates the number of human HLA-types capable of recognizing the epitope. Along the horizontal axis ethnic groups have been tagged with different colours based on their affiliations to respective super-populations.

Average count of the number of SARS-CoV2 epitopes identified by the samples comprising each of the super-populations: (A) total epitopes, (B) CD8-specific epitopes and (C) CD4-specific epitopes. 95% confidence interval of the computed means are indicated in the bar plot. Bars marked with asterisk (*) represents that mean counts of epitopes identified by the particular super population is significantly different from rest of the populations combined together.

Average count of the number of SARS-CoV2 epitopes identified by the samples comprising each of the ethnic groups: (A) total epitopes, (B) CD8-specific epitopes and (C) CD4-specific All rights reserved. No reuse allowed without permission.

(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted May 26, 2021. ; https://doi.org/10.1101/2021.05.24.21257707 doi: medRxiv preprint epitopes. 95% confidence interval of the computed means are indicated in the bar plot. Bars marked with asterisk (*) represents that mean counts of epitopes identified by the particular ethnic group is significantly different from rest of the ethnic groups combined together.

Representation of the mean SARS-CoV2 epitope identification capacity among different ethnic groups with respect to SARS-Cov2 genomes sequenced till 11 th June 2020, as obtained from the GISAID (https://www.gisaid.org/). Each data-point on the plots represents the average of the number of epitopes in a given SARS-CoV2 genome that could be identified by all the individuals in an ethnic population. Dark black lines, for each of the ethnic groups, indicate the mean value for the SARS-CoV2 genomes isolated, sequenced and deposited to GISAID (A) in December 2019, (B) in January 2020, (C) in February 2020, (D) in March 2020, (E) in April 2020 and (F) in May-June 2020. Figure 8 : Distribution of epitopes shortlisted as vaccine peptides across super-populations. Euler representation of (a) CD8-specific and (b) CD4-specific SARS-CoV2 epitopes recognized by the HLA alleles in the five super-populations. Epitopes observed in at least 10 SARS-CoV2 genomes and those potentially recognized by at least 5% of the individuals representing a super-population have been considered. The CD8-specific and CD4-specific epitopes at the intersection of the five super-populations could serve as potential vaccine candidates.

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The copyright holder for this preprint this version posted May 26, 2021. ; https://doi.org/10.1101 https://doi.org/10. /2021 

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Supplementary Table 1: Epitopes predicted from the SARS-CoV2 reference proteome. 326 epitopes predicted by NetMHCpan and NetMHCIIpan from the SARS-CoV2 reference proteome along with corresponding HLA alleles involved in their recognition are presented in the table. Only those epitopes identified with prediction scores ≥ 0.95 are reported. For each epitope the number of alleles interacting with it, along with the best scoring allele and the corresponding prediction score are also mentioned. Table 2 : Observed richness of HLA alleles and epitopes. Table denoting observed richness of MHC-I and MHC-II alleles among the 26 ethnic populations belonging to five super-populations, as well as the observed richness of CD8-specific and CD4-specific SARS-CoV2 epitopes recognized by the respective HLA alleles. 

in the epitope recognition potential of a specific ethnic group w.r.t. the overall epitope recognition potential of all other ethnic groups taken together are depicted. Significant differences are highlighted (Wilcoxon signed rank test BH corrected p-value < 0.05). (B) Differences in the epitope recognition potential of a specific super-population w.r.t. the overall epitope recognition potential of all other super populations taken together are depicted. Significant differences are highlighted (Wilcoxon signed rank test BH corrected p-value < 0.05). The p-values obtained from Wilcoxon signed rank test were corrected for multiple testing using Benjamini-Hochberg (BH) method.