Bioinformatic identification of Endemic Coronaviruses’ epitopes in SARS-CoV-2 genomes isolated in Kenya

Bioinformatic identification of Endemic Coronaviruses’ epitopes in SARS-CoV-2 genomes isolated in Kenya | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Bioinformatic identification of Endemic Coronaviruses’ epitopes in SARS-CoV-2 genomes isolated in Kenya Elius Mbogori, Stanslaus Musyoki, Richard Biegon, Kirtika Patel This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4402197/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Identification of SARS-CoV-2 genome regions with similarity to epitopes for endemic coronaviruses is crucial for understanding cross-immunity and designing broad-spectrum vaccines. Research has highlighted that several epitopes exhibit homology or cross-reactivity between SARS-CoV-2 and various endemic coronaviruses. To identify these shared epitopes, annotated proteins from SARS-CoV-2 genomes isolated in Moi Teaching and Referral Hospital, Kenya were aligned with Epitopes for four endemic coronaviruses using BlastP. Additionally, the overlapping epitopes were aligned with SARS-CoV-2 immunodominant epitopes. 321 epitopes from HCoV-OC43, 206 epitopes from HCoV-HKU1, 136 epitopes from HCoV-NL63, and 182 epitopes from HCoV-229E exhibited similarities with regions on SARS-CoV-2 genomes. Of these, ten HCoV-OC43 epitopes; thirteen HCoV-HKU1 epitopes; one HCoV-NL63 epitope; and three HCoV-229E spike epitopes exhibited similarity with the SARS-CoV-2 genomes. Seven immunodominant epitopes had notable similarities with the epitopes from endemic coronaviruses. This discovery holds great importance as it implies the existence of potential cross-reactivity and shared immune responses among these coronaviruses, thereby potentially impacting the comprehension of immunity and the development of vaccines against SARS-CoV-2. Epitopes Endemic Coronaviruses Cross-Reactivity SARS-CoV-2 Immunodominant Genome Introduction In recent years, there has been significant attention on coronaviruses due to the emergence of new strains. Amid the attention on emerging coronaviruses like SARS-CoV-2, which is behind the COVID-19 pandemic, it is crucial not to overlook the endemic coronaviruses that have existed in human populations for years. Endemic coronaviruses, such as HCoV-229E, HCoV-NL63, HCoV-OC43, and HCoV-HKU1, have been causing mild symptoms in humans for decades 1 , 2 . These viruses entered human populations between 500 to 55 years ago, and their evolutionary dynamics provide insights into the potential future trajectories of emerging coronaviruses like SARS-CoV-2 3 . Understanding the emergence, immunity and evolution of both endemic and emerging coronaviruses is essential for comprehensive preparedness and response strategies to combat the ongoing and potential future threats posed by these viruses 4 , 5 . Immunity in the context of endemic coronaviruses and SARS-CoV-2 presents a complex interplay between pre-existing immunity, the emergence of new variants, and the efficacy of vaccines. Studies have shown that pre-existing immunity to endemic coronaviruses may confer some degree of cross-protection against SARS-CoV-2, potentially influencing the severity of COVID-19 outcomes and mortality rates 6 . This cross-reactivity is attributed to similarities in protein sequences between endemic coronaviruses and SARS-CoV-2, leading to the hypothesis that exposure to endemic coronaviruses could generate cross-reactive antibodies and T-cells 7 . However, experimental evidence in mouse models suggests that pre-existing immunity to endemic coronaviruses spike antigens does not significantly alter the immune response elicited by COVID-19 vaccines, indicating that vaccines can provide robust immunity regardless of prior endemic coronaviruses exposure 8 . Epitopes are domains on the surface of a pathogen that are recognized by the immune system. These short amino acid sequences define the antigen signature to which an antibody or T cell receptor binds, playing a crucial role in the immune response against pathogens 9 , 10 . The identification of shared epitopes between SARS-CoV-2 and endemic coronaviruses is crucial for understanding cross-immunity and designing broad-spectrum vaccines. Research has highlighted several epitopes that exhibit homology or cross-reactivity between SARS-CoV-2 and various endemic coronaviruses. One significant finding is the identification of a highly conserved SARS-CoV-2 sequence S 811–831, with two overlapping epitopes presented by common MHC-II proteins HLA-DQ5 and HLA-DP4, recognized by CD4 + T cells from convalescent COVID-19 donors, mRNA vaccine recipients, and even uninfected donors, indicating a potential memory response to prior HCoV infections 11 . Additionally, the S2 domain epitope present only in the prefusion core of β-coronaviruses, specifically SARS-CoV-2 S2 apex residues 980–1006, has been identified as a conserved epitope across coronavirus lineages, suggesting its role in cross-reactive immune responses 12 . Moreover, studies have shown that the spike (S) glycoprotein harbours immunodominant epitope regions that are highly conserved among variants of concern (VOCs) and variants of interest (VOIs), indicating a potential for cross-protection 13 . Cross-reactive T-cell epitopes have also been identified between SARS-CoV-2, HCoVs, and live attenuated vaccines (LAVs), further emphasizing the potential for pre-existing immunity to influence COVID-19 outcomes 14 . This is due to their closeness in their molecular structures 15 . Analysis of known epitope sequences from SARS-CoV, SARS-CoV-2, and other Coronaviridae has revealed conserved epitopes between SARS-CoV-2 and SARS-CoV, particularly in the nucleoprotein and spike glycoprotein, suggesting shared antigenic sites that could be targeted for vaccine development 16 , 17 . Additionally, the identification of a conserved SARS-CoV-2 S2 spike epitope that elicited cross-reactive antibody responses to related coronaviruses and host bacteria underscores the potential for broad coronavirus therapeutics and vaccines 18 . Therefore these shared epitopes between SARS-CoV-2 and endemic coronaviruses highlight the potential for cross-immunity and the development of vaccines that could offer broad protection against current and future coronavirus outbreaks 19 – 21 . Bioinformatics has been crucial in identifying potential cross-reactivity between epitopes of SARS-CoV-2 and those from endemic coronaviruses 21 . Studies have utilized bioinformatic approaches to predict candidate targets for immune responses to SARS-CoV-2, identifying potential B and T cell epitopes 22 . By aligning SARS-CoV-2-derived peptides with sequences from endemic coronaviruses, researchers have identified cross-reactive epitopes, shedding light on T-cell recognition induced by COVID-19 and heterologous responses 23 . Studies have further identified potential cross-reactive T-cell epitopes between SARS-CoV-2 and endemic coronaviruses, highlighting the significance of sequence homology in generating cross-reactive T-cell responses 24 . Additionally, bioinformatic analyses have identified regions of potential T cell cross-reactivity within the SARS-CoV-2 N protein and equivalent positions in human alpha and beta coronaviruses, further underlining the relevance of cross-reactivity in immune responses 25 . Studies have highlighted the presence of cross-reactive T cells resulting from sequence homology between SARS-CoV-2 and endemic coronaviruses, emphasizing the role of cross-reactivity in immune recognition 26 . Thus, bioinformatics-driven research has been instrumental in uncovering potential cross-reactivity between epitopes of SARS-CoV-2 and endemic coronaviruses, providing valuable insights into immune responses and aiding in the development of effective vaccination strategies. Methods Informed consent was obtained from 44 patients at Moi Teaching and Referral Hospital whose samples were used in the study. These samples, taken from both the nasopharynx and oropharynx, were collected and handled according to guidelines outlined by the Center for Disease Control 27 to isolate SARS-CoV-2 genomes. The proteins of the viruses were then annotated and translated. Epitopes for each endemic coronavirus strain, including HCoV-HKU1 (NCBITaxon_290028), HCoV-OC43 (NCBITaxon_31631), HCoV-NL63 (NCBITaxon_277944), and HCoV-229E (NCBITaxon_11137) were downloaded from Immune Epitope Database (IEDB). Using BlastP, an alignment analysis was conducted, wherein the annotated proteins of SARS-CoV-2 were aligned with the epitopes obtained from endemic coronaviruses. This comparative analysis aimed to elucidate regions of similarity and potential cross-reactivity between the endemic coronaviruses and SARS-CoV-2. Regions demonstrating significant similarities were singled out for further examination, with an emphasis on their potential implications for immune recognition and cross-reactivity. Finally, the aligned epitopes were documented alongside the corresponding SARS-CoV-2 annotated proteins. Additionally, the overlapping epitopes were aligned with SARS-CoV-2 immunodominant epitopes that were downloaded from a previous article 28 to estimate the impact of those epitopes. Results 3.1 Overlapping epitopes The genomes of SARS-CoV-2 isolated from Moi Teaching and Referral Hospital were found to have regions similar to epitopes from endemic coronaviruses as shown in Table 1 . The analysis revealed 321 epitopes from HCoV-OC43, 206 epitopes from HCoV-HKU1, 136 epitopes from HCoV-NL63, and 182 epitopes from HCoV-229E exhibited similarities with regions on SARS-CoV-2 genomes. Specifically, 3 HCoV-OC43 epitopes displayed similarities with the Membrane gene of SARS-CoV-2, indicating a potential for HCoV-OC43 and SARS-CoV-2 cross-reactivity at this gene, a phenomenon not seen in other HCoVs. On the Nucleoprotein gene, only 2 HCoV-OC43 and one HCoV-HKU1 epitopes showed similarity with the SARS-CoV-2 genomes, while no correspondences were identified for either HCoV-NL63 or HCoV-229E. A notable level of similarity exists between regions on the SARS-CoV-2 ORF1ab gene and all four endemic coronaviruses epitopes. Specifically, the total tally amounts to 306 HCoV-OC43 epitopes; 192 HCoV-HKU1 epitopes; 135 HCoV-NL63 epitopes; and HCoV-229E epitopes align with regions in the SARS-CoV-2 genomes. Concerning the Spike gene, ten HCoV-OC43 epitopes; thirteen HCoV-HKU1 epitopes; one HCoV-NL63 epitope; and three HCoV-229E exhibited similarity with the SARS-CoV-2 genomes. Table 1 Number of epitopes from endemic coronaviruses found on SARS-COV-2 genome isolated in Moi Teaching and Referral Hospital Gene HCoV-OC43 HCoV-HKU1 HCoV-NL63 HCoV-229E Membrane 3 0 0 0 Nucleoprotein 2 1 0 0 ORF1ab 306 192 135 179 Spike 11 13 1 3 Total 322 206 136 182 3.2 Cross-Reactive endemic coronaviruses’ Spike Epitopes Spike epitopes derived from HCoV-OC43, HCoV-NL63, HCoV-HKU1, and HCoV-229E were analyzed, shedding light on potential similarities in immune recognition across different coronaviruses. Specifically, epitopes from HCoV-OC43 were found to exhibit similarity to certain SARS-COV-2 epitopes, as evidenced by shared amino acid sequences. Notably, epitopes such as "ECSKASSRSAIEDLLFDKVKLSDV" and "TSIPNLPDFKEELDQWFKNQTSVA" from HCoV-OC43 showed notable resemblance to sequences found in SARS-COV-2 epitopes. The analysis further revealed diverse epitope sequences recognized by various immune cell types, including B cells and T cells presenting epitopes via major histocompatibility complex class 2 molecules (T cells-MHC2) as detailed in Table 2 . Table 2 The cross-reacting spike epitope from endemic coronaviruses IEDB ID Virus Immune cell Epitope Sequence 77848 HCoV-OC43 Bcell ECSKASSRSAIEDLLFDKVKLSDV 84110 HCoV-OC43 Bcell YYVKWPWYVWLL 77858 HCoV-OC43 Bcell TSIPNLPDFKEELDQWFKNQTSVA 84513 HCoV-OC43 Bcell DLICVQSYKGIKVLPP 85237 HCoV-OC43 Bcell ILSRLDALEAEAQIDR 85634 HCoV-OC43 Bcell LICVQSYKGIKVLPPL 85650 HCoV-OC43 Bcell LKDIGTYEYYVKWPWY 85739 HCoV-OC43 Bcell LSRLDALEAEAQIDRL 86254 HCoV-OC43 Bcell RDLICVQSYKGIKVLP 86533 HCoV-OC43 Bcell SRLDALEAEAQIDRLI 87237 HCoV-OC43 Bcell TSIPNLPDFKEELDQWFKNQTSVAP 1656676 HCoV-NL63 Bcell FENYIKWPWWVWLIIS 1310951 HCoV-HKU1 Tcell-MHC2 YEMYVKWPWYVWLLI 1386658 HCoV-HKU1 Bcell PHCGSSSRSFFEDLLFDKVKLSDVGF 1644870 HCoV-HKU1 Bcell ALEAQVQIDRLINGRL 1649596 HCoV-HKU1 Bcell DALEAQVQIDRLINGR i1667205 HCoV-HKU1 Bcell ILSRLDALEAQVQIDR 1673904 HCoV-HKU1 Bcell LEAQVQIDRLINGRLT 1675295 HCoV-HKU1 Bcell LKDIGTYEMYVKWPWY 1677933 HCoV-HKU1 Bcell LSRLDALEAQVQIDRL 1691286 HCoV-HKU1 Bcell RSFFEDLLFDKVKLSD 1695319 HCoV-HKU1 Bcell SRLDALEAQVQIDRLI 1708895 HCoV-HKU1 Bcell YEMYVKWPWYVWLLIS 1869240 HCoV-HKU1 Tcell-MHC2 SFFEDLLFDKVKLSDVGFVE 2180202 HCoV-HKU1 Bcell SSRSLLEDLLFNKVKLSDV 1655637 HCoV-229E Bcell ETYIKWPWWVWLCISV 1683464 HCoV-229E Bcell NRVETYIKWPWWVWLC 1702665 HCoV-229E Bcell VETYIKWPWWVWLCIS 3.3 Immunodominant epitopes The impact of spike epitopes from the various endemic coronaviruses was assessed by checking their similarities with SARS-CoV-2 immunodominant epitopes that were published earlier. Seven immunodominant epitopes were found to have notable similarities with the epitopes from endemic coronaviruses as shown in Table 3 . Specifically, for immunodominant peptide "NNLDSKVGGNYNYLYRLFRK," the analysis reveals one epitope from HCoV-OC43 that shares similarity, suggesting a potential shared antigenic region between these coronaviruses. Moving to "PTNGVGYQPYRVVVLSFELL," two epitopes from HCoV-HKU1 overlap, hinting at an interaction between the immune responses to these viruses. In the case of "LPKGFYAEGSRGGSQASSRS," one epitope from HCoV-OC43 and two epitopes from HCoV-HKU1 demonstrate overlap, indicating a convergence in immunogenicity. Further analysis of "ASSRSSSRSRNSSRNSTPGS" reveals overlap with two epitopes from HCoV-OC43 and four epitopes from HCoV-HKU1, suggesting a significant potential for shared antigenic properties. For "GNGGDAALALLLLDRLNQLE," six epitopes from HCoV-OC43 and seven epitopes from HCoV-HKU1 exhibit overlap, indicating substantial convergence in immunogenicity between these coronaviruses. In the case of "RRIRGGDGKMKDLSPRWYFY," one epitope from HCoV-NL63 demonstrates overlap, suggesting a potential interaction between immune responses to these viruses. Similarly, "GDQELIRQGTDYKHWPQIAQ" shows overlap with one epitope from HCoV-NL63, indicating a shared antigenic region between these coronaviruses. Table 3 Number of epitopes from endemic coronaviruses overlapping with each SARS-CoV-2 Immunodominant Epitope Immunodominant epitope peptide HCoV-OC43 HCoV-NL63 HCoV-HKU1 HCoV-229E NNLDSKVGGNYNYLYRLFRK 1 0 0 0 PTNGVGYQPYRVVVLSFELL 0 0 2 0 LPKGFYAEGSRGGSQASSRS 1 0 2 0 ASSRSSSRSRNSSRNSTPGS 2 0 4 0 GNGGDAALALLLLDRLNQLE 6 0 7 0 RRIRGGDGKMKDLSPRWYFY 1 0 0 0 GDQELIRQGTDYKHWPQIAQ 0 1 0 0 Discussion The analysis carried out has uncovered a substantial quantity of epitopes that are common among various human coronaviruses and SARS-CoV-2 genomes. This discovery holds great importance as it implies the existence of potential cross-reactivity and shared immune responses among these coronaviruses, thereby potentially impacting the comprehension of immunity and the development of vaccines against SARS-CoV-2. Studies have extensively explored the sequence similarity between SARS-CoV-2 and endemic coronaviruses. Research has shown that SARS-CoV-2 shares significant sequence homology with endemic coronaviruses, leading to potential cross-reactivity of immune responses 29 . The literature further suggests that preexisting immunity to HCoV-OC43 can influence the durability and cross-reactivity of the humoral response to SARS-CoV-2 vaccination, indicating a potential link between prior exposure to common coronaviruses and vaccine responses 30 . Viruses classified as betacoronaviruses share great homology with SARS-CoV-2 as indeed demonstrated in this study where HCoV-HKU1 alongside HCoV-OC43 showed more cross-reactive epitopes than HCoV-229E and HCoV-NL63 Research has shown that antibodies and memory B-cell responses to the spike protein in SARS-CoV-2 can be influenced by pre-existing immunity to endemic coronaviruses 31 . Additionally, pre-existing humoral immunity to common cold coronaviruses has been found to negatively impact the protective antibody response to SARS-CoV-2 32 . The immune overlaps at the spike region by B cell epitopes as shown in this study may be responsible for the immune memory. Additionally, in silico studies have indicated the possibility of T-cell cross-reactivity between less dangerous coronaviruses and SARS-CoV-2, further supporting the concept of shared immune responses 33 . This study puts more weight on other investigations that have indicated that cross-reactive T cells are likely a result of sequence homology between SARS-CoV-2 and endemic common cold coronaviruses, suggesting a potential role of pre-existing immunity in the immune response to SARS-CoV-2 26 . The comparison of spike epitopes between various endemic coronaviruses and SARS-CoV-2 immunodominant epitopes has been a focus of this research. Seven immunodominant epitopes had notable similarities with the epitopes from endemic coronaviruses. An earlier study by Mcnaughton et al. found that fatal outcomes in COVID-19 patients were linked to an antibody response targeting epitopes shared with endemic coronaviruses, suggesting a potential association between the immune response to endemic coronaviruses and severe COVID-19 cases 34 Conclusion To conclude, there are several epitopes which are common to many endemic coronaviruses and the SARS-CoV-2 genome suggesting the possibility of cross-reactivity and a shared immune response. This will have far-reaching implications for our understanding of immunity and the design of SARS-CoV-2 vaccines. More specifically, pre-existing immunity to HCoV-OC43 and HCoV-HKU1 associated with long-lasting serological responses to COVID-19 vaccination as well as its cross-reactivity implying that pre exposure to other common corona-virus may play a role in vaccine responses. Besides, comparisons between spike epitopes in various endemic coronaviruses and SARS-CoV-2 immunodominant epitopes have shown some interesting likeness. These findings necessitate further investigations into the mechanisms driving cross-reactivity and immune responses against coronaviruses thereby guiding future public health interventions regarding vaccine development targeting COVID-19 etiologies. Declarations Ethics approval and consent to participate Ethic Committee Name: Moi University / Moi Teaching and Referral Hospital - Institutional Research and Ethics Committee Approval Code: 0004002 Approval Date: 28th October 2021 Consent for publication Not Applicable Availability of data and materials The SARS-CoV-2 genome sequences analysed during the current study are available in the Global Initiative on Sharing All Influenza Data (GISAID) repository database with accession numbers: EPI_ISL_19004000 to EPI_ISL_19004016; and EPI_ISL_19004981 to EPI_ISL_19005007 (https://gisaid.org/) The epitopes for endemic coronaviruses are available in the Immune Epitope Database (IEDB) repository (https://www.iedb.org/) Competing interests Authors declare no conflict of interest Funding The research was not funded Authors' contributions Kirtika Patel contributed in designing the project; Elius Mbogori contributed in executing the bioinformatics and overall project execution; Richard Biegon participated in analysis of results; and Stanslaus Musyoki participated in writing the manuscript Acknowledgements Binhua Liang, University of Manitoba; Elijah Songok, Kenya Medical Research Institute Authors' information Elius Mbogori is a Researcher and Principal Medical Laboratory Officer at Moi Teaching and Referral Hospital Stanslaus Musyoki is a Senior lecturer in South Eastern Kenya University in the Department of Medical Laboratory Science Richard Biegon is a Senior Lecturer of Immunology in Moi University Kirtika Patel is a Professor of Immunology in Moi University References Islam, A. et al. 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In Silico Studies Suggest T-Cell Cross-Reactivity Between SARS-CoV-2 and Less Dangerous Coronaviruses. (2020) doi:10.21203/rs.3.rs-73773/v1. McNaughton, A. L. et al. Fatal COVID-19 outcomes are associated with an antibody response targeting epitopes shared with endemic coronaviruses. JCI insight 7 , (2022). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4402197","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":305640358,"identity":"ba5eb4d7-d1b7-4d5a-b0d8-4e8d99d3fd13","order_by":0,"name":"Elius Mbogori","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYDACdsYGIHkAzD7wAUgYQJh4tDAjaTk4A6iBCC1IKph5iNHCz8zcJvkz546cvHvvw8O2OX/yzRnYLz5gOGODU4tkM2ObNO+2Z8aGZ44bHM7dZmC5s4Gn2IDhRhpOLQaHgVoYtx1O3DgjjQGkxcDgAE+aBMOHwzi12AO1SP6EabFEaPmP2xZmxjYJXqCW+RJALYxgLezHJBhu4Pa/xGHGZmuQXwx4jjEc7N1mbGBwmIfZIOFMMk4t/O3tD2/+3AYMsfY25g8/t8kZGBxvf/jgwzE7nFoQLoS7hJnHgCGBsAYGBvkGOJP9ATEaRsEoGAWjYOQAAMT3WaJ+Z7bFAAAAAElFTkSuQmCC","orcid":"","institution":"Moi Teaching and Referral Hospital","correspondingAuthor":true,"prefix":"","firstName":"Elius","middleName":"","lastName":"Mbogori","suffix":""},{"id":305640362,"identity":"64bbc33d-9dc8-418b-9af2-2da0de088e8c","order_by":1,"name":"Stanslaus Musyoki","email":"","orcid":"","institution":"South Eastern Kenya University","correspondingAuthor":false,"prefix":"","firstName":"Stanslaus","middleName":"","lastName":"Musyoki","suffix":""},{"id":305640364,"identity":"ce52a95e-d214-4fb2-9bfe-054aebde3b23","order_by":2,"name":"Richard Biegon","email":"","orcid":"","institution":"Moi University","correspondingAuthor":false,"prefix":"","firstName":"Richard","middleName":"","lastName":"Biegon","suffix":""},{"id":305640365,"identity":"f155f6d4-d642-46ae-9f7d-2093dc5e8128","order_by":3,"name":"Kirtika Patel","email":"","orcid":"","institution":"Moi University","correspondingAuthor":false,"prefix":"","firstName":"Kirtika","middleName":"","lastName":"Patel","suffix":""}],"badges":[],"createdAt":"2024-05-10 18:18:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4402197/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4402197/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60130329,"identity":"540d14c0-dbf7-46b5-ba6c-50ee90af174a","added_by":"auto","created_at":"2024-07-12 07:00:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":505539,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4402197/v1/a2d33c40-9b66-4a0a-8356-f6c321db17cb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bioinformatic identification of Endemic Coronaviruses’ epitopes in SARS-CoV-2 genomes isolated in Kenya","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn recent years, there has been significant attention on coronaviruses due to the emergence of new strains. Amid the attention on emerging coronaviruses like SARS-CoV-2, which is behind the COVID-19 pandemic, it is crucial not to overlook the endemic coronaviruses that have existed in human populations for years. Endemic coronaviruses, such as HCoV-229E, HCoV-NL63, HCoV-OC43, and HCoV-HKU1, have been causing mild symptoms in humans for decades \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. These viruses entered human populations between 500 to 55 years ago, and their evolutionary dynamics provide insights into the potential future trajectories of emerging coronaviruses like SARS-CoV-2 \u003csup\u003e3\u003c/sup\u003e. Understanding the emergence, immunity and evolution of both endemic and emerging coronaviruses is essential for comprehensive preparedness and response strategies to combat the ongoing and potential future threats posed by these viruses \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eImmunity in the context of endemic coronaviruses and SARS-CoV-2 presents a complex interplay between pre-existing immunity, the emergence of new variants, and the efficacy of vaccines. Studies have shown that pre-existing immunity to endemic coronaviruses may confer some degree of cross-protection against SARS-CoV-2, potentially influencing the severity of COVID-19 outcomes and mortality rates \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. This cross-reactivity is attributed to similarities in protein sequences between endemic coronaviruses and SARS-CoV-2, leading to the hypothesis that exposure to endemic coronaviruses could generate cross-reactive antibodies and T-cells \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. However, experimental evidence in mouse models suggests that pre-existing immunity to endemic coronaviruses spike antigens does not significantly alter the immune response elicited by COVID-19 vaccines, indicating that vaccines can provide robust immunity regardless of prior endemic coronaviruses exposure \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eEpitopes are domains on the surface of a pathogen that are recognized by the immune system. These short amino acid sequences define the antigen signature to which an antibody or T cell receptor binds, playing a crucial role in the immune response against pathogens \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. The identification of shared epitopes between SARS-CoV-2 and endemic coronaviruses is crucial for understanding cross-immunity and designing broad-spectrum vaccines. Research has highlighted several epitopes that exhibit homology or cross-reactivity between SARS-CoV-2 and various endemic coronaviruses. One significant finding is the identification of a highly conserved SARS-CoV-2 sequence S 811\u0026ndash;831, with two overlapping epitopes presented by common MHC-II proteins HLA-DQ5 and HLA-DP4, recognized by CD4\u0026thinsp;+\u0026thinsp;T cells from convalescent COVID-19 donors, mRNA vaccine recipients, and even uninfected donors, indicating a potential memory response to prior HCoV infections \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Additionally, the S2 domain epitope present only in the prefusion core of β-coronaviruses, specifically SARS-CoV-2 S2 apex residues 980\u0026ndash;1006, has been identified as a conserved epitope across coronavirus lineages, suggesting its role in cross-reactive immune responses \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Moreover, studies have shown that the spike (S) glycoprotein harbours immunodominant epitope regions that are highly conserved among variants of concern (VOCs) and variants of interest (VOIs), indicating a potential for cross-protection \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Cross-reactive T-cell epitopes have also been identified between SARS-CoV-2, HCoVs, and live attenuated vaccines (LAVs), further emphasizing the potential for pre-existing immunity to influence COVID-19 outcomes \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. This is due to their closeness in their molecular structures \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Analysis of known epitope sequences from SARS-CoV, SARS-CoV-2, and other Coronaviridae has revealed conserved epitopes between SARS-CoV-2 and SARS-CoV, particularly in the nucleoprotein and spike glycoprotein, suggesting shared antigenic sites that could be targeted for vaccine development \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Additionally, the identification of a conserved SARS-CoV-2 S2 spike epitope that elicited cross-reactive antibody responses to related coronaviruses and host bacteria underscores the potential for broad coronavirus therapeutics and vaccines \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Therefore these shared epitopes between SARS-CoV-2 and endemic coronaviruses highlight the potential for cross-immunity and the development of vaccines that could offer broad protection against current and future coronavirus outbreaks \u003csup\u003e\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBioinformatics has been crucial in identifying potential cross-reactivity between epitopes of SARS-CoV-2 and those from endemic coronaviruses \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Studies have utilized bioinformatic approaches to predict candidate targets for immune responses to SARS-CoV-2, identifying potential B and T cell epitopes \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. By aligning SARS-CoV-2-derived peptides with sequences from endemic coronaviruses, researchers have identified cross-reactive epitopes, shedding light on T-cell recognition induced by COVID-19 and heterologous responses \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Studies have further identified potential cross-reactive T-cell epitopes between SARS-CoV-2 and endemic coronaviruses, highlighting the significance of sequence homology in generating cross-reactive T-cell responses \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Additionally, bioinformatic analyses have identified regions of potential T cell cross-reactivity within the SARS-CoV-2 N protein and equivalent positions in human alpha and beta coronaviruses, further underlining the relevance of cross-reactivity in immune responses \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Studies have highlighted the presence of cross-reactive T cells resulting from sequence homology between SARS-CoV-2 and endemic coronaviruses, emphasizing the role of cross-reactivity in immune recognition \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Thus, bioinformatics-driven research has been instrumental in uncovering potential cross-reactivity between epitopes of SARS-CoV-2 and endemic coronaviruses, providing valuable insights into immune responses and aiding in the development of effective vaccination strategies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e \u003cstrong\u003eInformed consent\u003c/strong\u003e \u003cp\u003ewas obtained from 44 patients at Moi Teaching and Referral Hospital whose samples were used in the study. These samples, taken from both the nasopharynx and oropharynx, were collected and handled according to guidelines outlined by the Center for Disease Control \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e to isolate SARS-CoV-2 genomes. The proteins of the viruses were then annotated and translated. Epitopes for each endemic coronavirus strain, including HCoV-HKU1 (NCBITaxon_290028), HCoV-OC43 (NCBITaxon_31631), HCoV-NL63 (NCBITaxon_277944), and HCoV-229E (NCBITaxon_11137) were downloaded from Immune Epitope Database (IEDB). Using BlastP, an alignment analysis was conducted, wherein the annotated proteins of SARS-CoV-2 were aligned with the epitopes obtained from endemic coronaviruses. This comparative analysis aimed to elucidate regions of similarity and potential cross-reactivity between the endemic coronaviruses and SARS-CoV-2. Regions demonstrating significant similarities were singled out for further examination, with an emphasis on their potential implications for immune recognition and cross-reactivity. Finally, the aligned epitopes were documented alongside the corresponding SARS-CoV-2 annotated proteins.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eAdditionally, the overlapping epitopes were aligned with SARS-CoV-2 immunodominant epitopes that were downloaded from a previous article \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e to estimate the impact of those epitopes.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Overlapping epitopes\u003c/h2\u003e \u003cp\u003eThe genomes of SARS-CoV-2 isolated from Moi Teaching and Referral Hospital were found to have regions similar to epitopes from endemic coronaviruses as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The analysis revealed 321 epitopes from HCoV-OC43, 206 epitopes from HCoV-HKU1, 136 epitopes from HCoV-NL63, and 182 epitopes from HCoV-229E exhibited similarities with regions on SARS-CoV-2 genomes. Specifically, 3 HCoV-OC43 epitopes displayed similarities with the Membrane gene of SARS-CoV-2, indicating a potential for HCoV-OC43 and SARS-CoV-2 cross-reactivity at this gene, a phenomenon not seen in other HCoVs. On the Nucleoprotein gene, only 2 HCoV-OC43 and one HCoV-HKU1 epitopes showed similarity with the SARS-CoV-2 genomes, while no correspondences were identified for either HCoV-NL63 or HCoV-229E.\u003c/p\u003e \u003cp\u003eA notable level of similarity exists between regions on the SARS-CoV-2 ORF1ab gene and all four endemic coronaviruses epitopes. Specifically, the total tally amounts to 306 HCoV-OC43 epitopes; 192 HCoV-HKU1 epitopes; 135 HCoV-NL63 epitopes; and HCoV-229E epitopes align with regions in the SARS-CoV-2 genomes. Concerning the Spike gene, ten HCoV-OC43 epitopes; thirteen HCoV-HKU1 epitopes; one HCoV-NL63 epitope; and three HCoV-229E exhibited similarity with the SARS-CoV-2 genomes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNumber of epitopes from endemic coronaviruses found on SARS-COV-2 genome isolated in Moi Teaching and Referral Hospital\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHCoV-OC43\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHCoV-HKU1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHCoV-NL63\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHCoV-229E\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMembrane\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNucleoprotein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORF1ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e179\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpike\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e182\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Cross-Reactive endemic coronaviruses\u0026rsquo; Spike Epitopes\u003c/h2\u003e \u003cp\u003eSpike epitopes derived from HCoV-OC43, HCoV-NL63, HCoV-HKU1, and HCoV-229E were analyzed, shedding light on potential similarities in immune recognition across different coronaviruses. Specifically, epitopes from HCoV-OC43 were found to exhibit similarity to certain SARS-COV-2 epitopes, as evidenced by shared amino acid sequences. Notably, epitopes such as \"ECSKASSRSAIEDLLFDKVKLSDV\" and \"TSIPNLPDFKEELDQWFKNQTSVA\" from HCoV-OC43 showed notable resemblance to sequences found in SARS-COV-2 epitopes. The analysis further revealed diverse epitope sequences recognized by various immune cell types, including B cells and T cells presenting epitopes via major histocompatibility complex class 2 molecules (T cells-MHC2) as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe cross-reacting spike epitope from endemic coronaviruses\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIEDB ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVirus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eImmune cell\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEpitope Sequence\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e77848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHCoV-OC43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBcell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eECSKASSRSAIEDLLFDKVKLSDV\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e84110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHCoV-OC43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBcell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYYVKWPWYVWLL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e77858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHCoV-OC43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBcell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTSIPNLPDFKEELDQWFKNQTSVA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e84513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHCoV-OC43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBcell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDLICVQSYKGIKVLPP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e85237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHCoV-OC43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBcell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eILSRLDALEAEAQIDR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e85634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHCoV-OC43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBcell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLICVQSYKGIKVLPPL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e85650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHCoV-OC43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBcell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLKDIGTYEYYVKWPWY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e85739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHCoV-OC43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBcell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLSRLDALEAEAQIDRL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e86254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHCoV-OC43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBcell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRDLICVQSYKGIKVLP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e86533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHCoV-OC43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBcell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSRLDALEAEAQIDRLI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e87237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHCoV-OC43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBcell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTSIPNLPDFKEELDQWFKNQTSVAP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1656676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHCoV-NL63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBcell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFENYIKWPWWVWLIIS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1310951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHCoV-HKU1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTcell-MHC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYEMYVKWPWYVWLLI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1386658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHCoV-HKU1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBcell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePHCGSSSRSFFEDLLFDKVKLSDVGF\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1644870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHCoV-HKU1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBcell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eALEAQVQIDRLINGRL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1649596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHCoV-HKU1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBcell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDALEAQVQIDRLINGR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ei1667205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHCoV-HKU1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBcell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eILSRLDALEAQVQIDR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1673904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHCoV-HKU1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBcell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLEAQVQIDRLINGRLT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1675295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHCoV-HKU1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBcell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLKDIGTYEMYVKWPWY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1677933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHCoV-HKU1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBcell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLSRLDALEAQVQIDRL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1691286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHCoV-HKU1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBcell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRSFFEDLLFDKVKLSD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1695319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHCoV-HKU1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBcell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSRLDALEAQVQIDRLI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1708895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHCoV-HKU1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBcell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYEMYVKWPWYVWLLIS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1869240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHCoV-HKU1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTcell-MHC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSFFEDLLFDKVKLSDVGFVE\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2180202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHCoV-HKU1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBcell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSSRSLLEDLLFNKVKLSDV\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1655637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHCoV-229E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBcell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eETYIKWPWWVWLCISV\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1683464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHCoV-229E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBcell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNRVETYIKWPWWVWLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1702665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHCoV-229E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBcell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVETYIKWPWWVWLCIS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Immunodominant epitopes\u003c/h2\u003e \u003cp\u003eThe impact of spike epitopes from the various endemic coronaviruses was assessed by checking their similarities with SARS-CoV-2 immunodominant epitopes that were published earlier. Seven immunodominant epitopes were found to have notable similarities with the epitopes from endemic coronaviruses as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Specifically, for immunodominant peptide \"NNLDSKVGGNYNYLYRLFRK,\" the analysis reveals one epitope from HCoV-OC43 that shares similarity, suggesting a potential shared antigenic region between these coronaviruses. Moving to \"PTNGVGYQPYRVVVLSFELL,\" two epitopes from HCoV-HKU1 overlap, hinting at an interaction between the immune responses to these viruses. In the case of \"LPKGFYAEGSRGGSQASSRS,\" one epitope from HCoV-OC43 and two epitopes from HCoV-HKU1 demonstrate overlap, indicating a convergence in immunogenicity. Further analysis of \"ASSRSSSRSRNSSRNSTPGS\" reveals overlap with two epitopes from HCoV-OC43 and four epitopes from HCoV-HKU1, suggesting a significant potential for shared antigenic properties. For \"GNGGDAALALLLLDRLNQLE,\" six epitopes from HCoV-OC43 and seven epitopes from HCoV-HKU1 exhibit overlap, indicating substantial convergence in immunogenicity between these coronaviruses. In the case of \"RRIRGGDGKMKDLSPRWYFY,\" one epitope from HCoV-NL63 demonstrates overlap, suggesting a potential interaction between immune responses to these viruses. Similarly, \"GDQELIRQGTDYKHWPQIAQ\" shows overlap with one epitope from HCoV-NL63, indicating a shared antigenic region between these coronaviruses.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNumber of epitopes from endemic coronaviruses overlapping with each SARS-CoV-2 Immunodominant Epitope\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmunodominant epitope peptide\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHCoV-OC43\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHCoV-NL63\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHCoV-HKU1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHCoV-229E\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNNLDSKVGGNYNYLYRLFRK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTNGVGYQPYRVVVLSFELL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLPKGFYAEGSRGGSQASSRS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASSRSSSRSRNSSRNSTPGS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGNGGDAALALLLLDRLNQLE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRRIRGGDGKMKDLSPRWYFY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDQELIRQGTDYKHWPQIAQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe analysis carried out has uncovered a substantial quantity of epitopes that are common among various human coronaviruses and SARS-CoV-2 genomes. This discovery holds great importance as it implies the existence of potential cross-reactivity and shared immune responses among these coronaviruses, thereby potentially impacting the comprehension of immunity and the development of vaccines against SARS-CoV-2. Studies have extensively explored the sequence similarity between SARS-CoV-2 and endemic coronaviruses. Research has shown that SARS-CoV-2 shares significant sequence homology with endemic coronaviruses, leading to potential cross-reactivity of immune responses \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe literature further suggests that preexisting immunity to HCoV-OC43 can influence the durability and cross-reactivity of the humoral response to SARS-CoV-2 vaccination, indicating a potential link between prior exposure to common coronaviruses and vaccine responses \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Viruses classified as betacoronaviruses share great homology with SARS-CoV-2 as indeed demonstrated in this study where HCoV-HKU1 alongside HCoV-OC43 showed more cross-reactive epitopes than HCoV-229E and HCoV-NL63\u003c/p\u003e \u003cp\u003eResearch has shown that antibodies and memory B-cell responses to the spike protein in SARS-CoV-2 can be influenced by pre-existing immunity to endemic coronaviruses \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Additionally, pre-existing humoral immunity to common cold coronaviruses has been found to negatively impact the protective antibody response to SARS-CoV-2 \u003csup\u003e32\u003c/sup\u003e. The immune overlaps at the spike region by B cell epitopes as shown in this study may be responsible for the immune memory. Additionally, in silico studies have indicated the possibility of T-cell cross-reactivity between less dangerous coronaviruses and SARS-CoV-2, further supporting the concept of shared immune responses \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. This study puts more weight on other investigations that have indicated that cross-reactive T cells are likely a result of sequence homology between SARS-CoV-2 and endemic common cold coronaviruses, suggesting a potential role of pre-existing immunity in the immune response to SARS-CoV-2 \u003csup\u003e26\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe comparison of spike epitopes between various endemic coronaviruses and SARS-CoV-2 immunodominant epitopes has been a focus of this research. Seven immunodominant epitopes had notable similarities with the epitopes from endemic coronaviruses. An earlier study by Mcnaughton et al. found that fatal outcomes in COVID-19 patients were linked to an antibody response targeting epitopes shared with endemic coronaviruses, suggesting a potential association between the immune response to endemic coronaviruses and severe COVID-19 cases \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eTo conclude, there are several epitopes which are common to many endemic coronaviruses and the SARS-CoV-2 genome suggesting the possibility of cross-reactivity and a shared immune response. This will have far-reaching implications for our understanding of immunity and the design of SARS-CoV-2 vaccines. More specifically, pre-existing immunity to HCoV-OC43 and HCoV-HKU1 associated with long-lasting serological responses to COVID-19 vaccination as well as its cross-reactivity implying that pre exposure to other common corona-virus may play a role in vaccine responses. Besides, comparisons between spike epitopes in various endemic coronaviruses and SARS-CoV-2 immunodominant epitopes have shown some interesting likeness. These findings necessitate further investigations into the mechanisms driving cross-reactivity and immune responses against coronaviruses thereby guiding future public health interventions regarding vaccine development targeting COVID-19 etiologies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthic Committee Name: Moi University / Moi Teaching and Referral Hospital - Institutional Research and Ethics Committee\u003c/p\u003e\n\u003cp\u003eApproval Code: 0004002\u003c/p\u003e\n\u003cp\u003eApproval Date: 28th October 2021\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe SARS-CoV-2 genome sequences analysed during the current study are available in the Global Initiative on Sharing All Influenza Data (GISAID) repository database with accession numbers: EPI_ISL_19004000 to EPI_ISL_19004016; and EPI_ISL_19004981 to EPI_ISL_19005007 (https://gisaid.org/)\u0026nbsp;\u003cbr\u003e\u0026nbsp;The epitopes for endemic coronaviruses are available in the Immune Epitope Database (IEDB) repository (https://www.iedb.org/)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors declare no conflict of interest\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research was not funded\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKirtika Patel contributed in designing the project; Elius Mbogori contributed in executing the bioinformatics and overall project execution; Richard Biegon participated in analysis of results; and Stanslaus Musyoki participated in writing the manuscript\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBinhua Liang, University of Manitoba; Elijah Songok, Kenya Medical Research Institute\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eElius Mbogori is a Researcher and Principal Medical Laboratory Officer at Moi Teaching and Referral Hospital\u003c/p\u003e\n\u003cp\u003eStanslaus Musyoki is a Senior lecturer in South Eastern Kenya University in the Department of Medical Laboratory Science\u003c/p\u003e\n\u003cp\u003eRichard Biegon is a Senior Lecturer of Immunology in Moi University\u003c/p\u003e\n\u003cp\u003eKirtika Patel is a Professor of Immunology in Moi University\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eIslam, A. \u003cem\u003eet al.\u003c/em\u003e Evolutionary Dynamics and Epidemiology of Endemic and Emerging Coronaviruses in Humans, Domestic Animals, and Wildlife. \u003cem\u003eViruses\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 1908 (2021).\u003c/li\u003e\n\u003cli\u003eLomeli, F. 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L. \u003cem\u003eet al.\u003c/em\u003e Fatal COVID-19 outcomes are associated with an antibody response targeting epitopes shared with endemic coronaviruses. \u003cem\u003eJCI insight\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, (2022).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Epitopes, Endemic Coronaviruses, Cross-Reactivity, SARS-CoV-2, Immunodominant, Genome","lastPublishedDoi":"10.21203/rs.3.rs-4402197/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4402197/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIdentification of SARS-CoV-2 genome regions with similarity to epitopes for endemic coronaviruses is crucial for understanding cross-immunity and designing broad-spectrum vaccines. Research has highlighted that several epitopes exhibit homology or cross-reactivity between SARS-CoV-2 and various endemic coronaviruses. To identify these shared epitopes, annotated proteins from SARS-CoV-2 genomes isolated in Moi Teaching and Referral Hospital, Kenya were aligned with Epitopes for four endemic coronaviruses using BlastP. Additionally, the overlapping epitopes were aligned with SARS-CoV-2 immunodominant epitopes. 321 epitopes from HCoV-OC43, 206 epitopes from HCoV-HKU1, 136 epitopes from HCoV-NL63, and 182 epitopes from HCoV-229E exhibited similarities with regions on SARS-CoV-2 genomes. Of these, ten HCoV-OC43 epitopes; thirteen HCoV-HKU1 epitopes; one HCoV-NL63 epitope; and three HCoV-229E spike epitopes exhibited similarity with the SARS-CoV-2 genomes. Seven immunodominant epitopes had notable similarities with the epitopes from endemic coronaviruses. This discovery holds great importance as it implies the existence of potential cross-reactivity and shared immune responses among these coronaviruses, thereby potentially impacting the comprehension of immunity and the development of vaccines against SARS-CoV-2.\u003c/p\u003e","manuscriptTitle":"Bioinformatic identification of Endemic Coronaviruses’ epitopes in SARS-CoV-2 genomes isolated in Kenya","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-23 13:45:50","doi":"10.21203/rs.3.rs-4402197/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bf0aa516-1221-4cd2-8ce0-e1fd13cf20c9","owner":[],"postedDate":"May 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-12T06:52:36+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-23 13:45:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4402197","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4402197","identity":"rs-4402197","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}