Expected immune recognition of COVID-19 virus by memory from earlier infections with common coronaviruses in a large

Abstract SARS-CoV-2 is the coronavirus agent of the COVID-19 pandemic causing high mortalities. In contrast, the widely spread human coronaviruses OC43, HKU1, 229E, and NL63 tend to cause only mild symptoms. The present study shows, by analysis, that these common human viruses are in silico expected to induce immune memory against SARS-CoV-2 by sharing protein fragments (antigen epitopes) for presentation to the immune system by MHC class I. A list of such epitopes is provided. The number of these epitopes and the prevalence of the common coronaviruses suggest that a large part of the world population has some degree of specific immunity against SARS-CoV-2 already, even without having been infected by that virus. For inducing protection, booster vaccinations enhancing existing immunity are less demanding than primary vaccinations against new antigens. Therefore, for the discussion on vaccination strategies against COVID-19, the available immune memory against related viruses should be part of the consideration.


SARS-CoV-2 and other human coronaviruses
Prevalence and associated disease of the common human coronaviruses 229E, NL63, OC43, and HKU1 The Centers for Disease Control and Prevention (CDC; https:// www.cdc.gov/coronavirus/general-information.html) states: "Common human coronaviruses, including types 229E, NL63, OC43, and HKU1, usually cause mild to moderate upper-respiratory tract illnesses, like the common cold. Most people get infected with one or more of these viruses at some point in their lives." The same agency lists the common symptoms caused by these viruses as runny nose, sore throat, headache, fever, cough, and general feeling of being unwell, but also explains that they occasionally cause lower-respiratory tract illnesses, such as pneumonia or bronchitis. The viruses 229E and OC43 have been known since the 1960s (reviewed in Kahn & McIntosh, 2005), but NL63 (van der Hoek et al., 2004) and HKU1 (Woo et al., 2005) were only (conclusively) identified following the rise in interest in coronaviruses in the wake of the SARS epidemy. These common coronaviruses are believed to be the second most common cause of the common cold (Mäkelä et al., 1998). In the U.S.A., a 3-year RT-PCR surveillance of respiratory samples of patients revealed that the four viruses 229E, NL63, OC43, and HKU1 were present at levels varying by season and region, with all individual viruses peaking at >3% prevalence in each investigated region (Midwest, Northeast, South, West); co-infection with other coronaviruses was found in only ~2% of infected cases, but co-infection with another respiratory virus was found in a substantial ~30% of infected cases (Killerby et al., 2018). This pattern was reminiscent of findings in the United Kingdom Since immune memory protection can be induced by related pathogens, as exemplified by the eradication of human smallpox virus (Variola) by immunization with a related "cowpox" virus (Vaccinia) (Plotkin & Plotkin, 2018), it is interesting to consider whether common human coronavirus infections may have induced some level of protection against SARS-CoV-2.
The possibility of matching linear epitopes between SARS-CoV-2 and the common human coronaviruses that may stimulate the immune system through MHC class I presentation The two major arms of immune memory concern antibody secretion by B cells and killing of infected cells by CD8 + T cells. For a coronavirus infection in mouse, both immune responses were needed to efficiently control the virus (reviewed by Weiss & Navas-Martin, 2005). Based on theoretical considerations alone, it is difficult to predict effective B cell memory across different virus species (Qiu et al., 2020), and very recent experiments concluded that sera from people that likely had been infected with the common human coronaviruses 229E, NL63, OC43, and/or HKU1, possessed no or negligible cross-reactivity with SARS-CoV-2 virus S protein (Amanat et al., 2020) and thus probably possess no neutralizing antibodies. However, for inducing CD8 + T cell memory, the core requirement is merely that an identical peptide is presented by major histocompatibility complex (MHC) class I (MHC-I) molecules. MHC-I molecules present peptide fragments from intracellular proteins, thus also from viral proteins, at the cell surface for screening by CD8 + cytotoxic T cells (Neefjes et al., 2011). CD8 + T cells recognize the combination of MHC-I molecule with peptide by T cell receptors (TCR) that are unique per T cell clone, and if stimulated these clones can proliferate, kill the presenting (virus-infected) cell, and produce memory cells. MHC-I molecules are polymorphic in that they are represented by many diverse allelic forms that differ between human populations and individuals (Robinson et al., 2020), and mostly bind 9 amino acids (aa) length in their binding groove which is closed at either end (Bjorkman et al., 1987;Rammensee et al., 1995;Schellens et al., 2015).
In the present study, we analyzed whether there are linear 9 aa epitopes that are identical between proteins encoded by SARS-CoV-2 and one or more of the common human coronaviruses. We found many of such epitopes indeed, and, by using prediction software, found that some are expected to bind well to certain MHC-I alleles. We therefore expect that common human coronaviruses can induce some level of CD8 + T cell-mediated immune memory recognizing SARS-CoV-2, and consider the possibility of enhancing that immune memory by vaccination. ) by performing BLAST homology searches at the NCBI database (https://blast.ncbi.nlm.nih.gov/ Blast.cgi) and by making multiple sequence alignments using CLUSTALW software (https://www.genome.jp/tools-bin/clustalw); continuous stretches of 9 aa acids identical between SARS-CoV-2 and one of the other viruses were identified manually. All these shared 9 aa epitopes were screened by ANN 4.0 software at IEDB Analysis Resource (http://tools.immuneepitope. org/mhci/) for prediction of their affinity to a set of representative human MHC-I alleles. Table 1 lists the 9 aa epitopes that are identical between proteins encoded by SARS-CoV-2 and one or more of the common human coronaviruses. Many identical >9 aa stretches were found with ORF1ab encoded polyprotein, one such identical stretch (of 12 aa) was found with the N protein of the other two type II coronaviruses HCoV-OC43 and HCoV-HKU1, and no such stretches were found when comparing with any of the other gene products; ORF1ab-derived mature proteins with such stretches, expected from cleavage of the polyprotein precursor (Wu et al., 2020), were the transmembrane protein nonstructural protein 4 (NSP4), 3C-like cysteine protease NSP5, RNA binding protein NSP9, RNA dependent RNA polymerase NSP12, helicase NSP13, 3'-to-5' exonuclease NSP14, nidoviral endoribonuclease specific for U NSP15, and S-adenosylmethionine-dependent ribose 2'-O-methyltransferase NSP16 (Table 1). Sequence alignment figures of the ORF1ab and N proteins are shown in Extended data (Dijkstra, 2020) with highlighting of the interesting epitopes. It is of note that the S protein, which is the prime candidate for inducing neutralizing antibodies (Cohen, 2020), is not suitable for inducing an MHC-I-restricted immune memory across the investigated viral species as between S protein of SARS-CoV-2 and S proteins of the common human coronaviruses there are no 9 aa or even 8 aa matches (not shown). Table 1 shows that there are >200 linear epitopes of 9 aa that are identical between SARS-CoV-2 and at least one of the common human coronaviruses, most of them with OC43 and HKU1 which, like SARS-CoV-2, belong to the group II coronaviruses. In a simplified model, if people would have been exposed to many of these epitopes through common HCoV infections, this kind of equals immunization by a small intracellular protein under natural viral infection conditions. Whereas live virus is commonly considered the gold standard in regard to inducing strong immunity, unless the virus has some tricks up its sleeve to manipulate the immune system, which for common human coronaviruses is not well investigated, a research grant proposal suggesting this as a vaccination strategy would probably fail. Reviewers of such proposal would righteously point out that the strategy would not induce neutralizing antibodies, which for combating some viral infections can be very important, and that for inducing MHC-I-restricted cellmediated cytotoxicity memory, ideally, a much larger protein or more proteins should be taken. Those reviewers would conclude that for such small intracellular protein to induce strong immune memory it would need too much luck in regard to immunogenicity and it would be too dependent on the MHC alleles of the immunized person as different alleles bind different peptides. Nevertheless, those reviewers would probably also agree that in most persons thus vaccinated some (small) level of immune memory protection would be established, even with such small non-surface protein (e.g. Polakos et al., 2001). Regardless of that this obviously is not the ideal way to induce a population-wide strong protective immunity (see the spread of COVID-19), together with other factors such as health and the number of encountered viruses (the strength of the viral challenge), the induced immune memory could make a difference for whether a person gets sick; at the population scale, it so may somewhat reduce the virus reproduction number. Importantly, by stimulating this HCoV-derived MHC-I restricted immune memory by vaccination (see below), it could become a more significant helper in fighting COVID-19.

Software predictions of MHC-I-binding epitopes
Based on combinations of experimental results and computer learning, various software has been created that with some degree of reliability can predict how efficiently peptides can bind to the grooves of various MHC-I alleles. In the present study we used the artificial neural network ( Table 1 shows these expected affinities for twelve MHC-I alleles that are rather representative for sets of MHC-I alleles with similar binding properties (supertypes) and so represent a large part of the human MHC-I binding repertoire  2008) found that a SARS-CoV-1 15 aa peptide sequence (their "Replicase 4701-4715" peptide) encompassing the SARS-CoV-2/HCoVshared ORF1ab4725 and ORF1ab4726 epitopes that are predicted to bind well to the MHC-I alleles HLA-A*0201 and HLA-B*3901 (see our Table 1) was associated with a CD8 + T cell response against SARS-CoV-1 in humans. However, Li et al. (2008) also found such CD8 + T cell response associated with a SARS-CoV-1 15 aa peptide (their "Nucleocapsid 106-120" peptide) encompassing the SARS-CoV-2/HCoV-shared N 106, N 107, N 108, an N 109 epitopes for which our analyses did not predict MHC-I binding (see our Table 1).
The MHC-I binding affinity is considered the most selective in determining which peptides are presented, but also steps in the Table 1. Stretches of 9 consecutive amino acids that are identical between SARS-CoV-2 and at least one of the common human coronaviruses.   Nucleocapsid YFYYLGTGP peptide processing and loading pathways may play selective roles which are difficult to capture in prediction software (Nielsen et al., 2005). We argue that, if such steps would be selective for presentation, in most cases they would probably not differentiate between the 9 aa epitope in the SARS-CoV-2 context versus the respective HCoV context, since most of those epitopes are within stretches that also show many similarities in the neighboring residues (Extended data).
Not all stable complexes of MHC-I with non-self peptides elicit a strong immune response, but "immunogenicity" features are hard to predict with meaningful reliability by in silico analysis (Calis et al., 2013), and in the present study we refrain from such predictions. Table 1 should, foremost, be understood as evidence of principle and a list of promising peptides, whereas only future experiments can prove MHC-I-mediated immune memory involving these or other peptides.
In regard to SARS-CoV-2 recognition, the common human coronaviruses may also induce some MHC-II-mediated immune memory by CD4 + helper T cells (for shared epitope use by different coronaviruses see Zhao et al., 2016). CD4 + helper T cells can help stimulate cells involved in antibody or cellmediated cytotoxic immune responses (Neefjes et al., 2011). However, for this topic we refrained from detailed (software) predictions because comparison of MHC-II epitopes across different viruses is harder than for MHC-I epitopes. Namely, although the core of MHC-II bound peptides is also only 9 aa, the surrounding amino acids are also part of the bound peptide that tends to be 12-25 aa (Brown et al., 1993;Rammensee et al., 1995;Stern & Wiley, 1994) and can affect how the peptide interacts with the receptors on the CD4 + helper T cells (Arnold et al., 2002).

Vaccination potential
Immune memory means that a secondary immune response, upon renewed encounter with the same pathogen, is faster and stronger than the primary immune response during the first encounter with the pathogen. This is based on expansion of specific B and T cell clones, which specifically recognize pathogen(-derived) epitopes, with some of those cells becoming memory cells (Paul, 2013). This principle also causes that for a booster vaccination/immunization the requirements for efficiently inducing an immune response are lower than for a first vaccination/immunization (e.g. Goding, 1996). Especially in elderly people, who have a decreased ability to mount adaptive immune responses against new antigens, vaccination that stimulates an immune memory response may be beneficial (Reber et al., 2012). As discussed above, people's past infections with common coronaviruses probably did not induce a B cell memory for making antibodies that can neutralize SARS-CoV-2. However, as the current study shows by analysis of linear 9 aa epitopes, these common human coronaviruses are expected to induce CD8 + T cells that may potentially kill SARS-CoV-2-infected cells and so can help eradicate the virus. There are several possible ways to exploit this probable immune memory. For example, if using RNA for immunization (Cohen, 2020), it may be best to also include SARS-CoV-2 genes that encode MHC-I epitopes that match those of the common coronaviruses. Alternatively, delivery of these epitopes to the MHC-I presentation system may be tried by peptide or protein based vaccines (e.g. Kohyama et al., 2009;Slingluff, 2011;van Montfoort et al., 2014;Yadav et al., 2014), possibly in combination with some of the strategies that are currently being explored for non-specific stimulation of the immune system against COVID-19 (Kupferschmidt & Cohen, 2020). Protein (-coding) vaccines, for example encompassing a large part of the SARS-CoV-2 ORF1ab product, would have an advantage over peptide-vaccines by including multiple possible MHC-I and also MHC-II epitopes, and be less dependent on MHC-allele matching and the quality of software predictions. Naturally, as for any new vaccine strategy, it should be carefully assessed whether the benefits of the induced type of immunity outweigh the potential deleterious health effects caused by an increased inflammation response (Cohen, 2020; Weingart et al., 2004). Important questions are also whether the history of previousespecially recent-infections with common coronaviruses, or people's MHC alleles, affect people's resistance to SARS-CoV-2. Most definitely, if discussing possible strategies for vaccination against SARS-CoV-2, pre-existing MHC-I-based immunity derived from previous infections with common coronaviruses should be part of the consideration.

Notification
Although we were not aware of this at the time of writing, a recent preprint appeared with overlapping contents (Nguyen et al., 2020). I think the Table could be made quite a lot smaller and thus more valuable to the reader. The source proteins could be indicated as an abbreviation provided in the legend as could the various seasonal strains. The boxes could then be quite small, and either be positive or negative. In any case, an effort should be made to condense this table.

Is the work clearly and accurately presented and does it cite the current literature? Yes
Is the study design appropriate and is the work technically sound? Yes

If applicable, is the statistical analysis and its interpretation appropriate? Yes
Are all the source data underlying the results available to ensure full reproducibility? Yes

Are the conclusions drawn adequately supported by the results? Partly
No competing interests were disclosed. 1.
As authors already mentioned in their manuscript the similar study by Nguyen A. et al (JVI, 2020 ) demonstrated the HLA binding affinity of all possible 8-to 12-mers from SARS-CoV-2 proteome. This group found that HLA-B15:03 type has the greatest capacity to present highly conserved peptides which are shared among coronaviruses suggesting a cross-protective T cell immune response. In current manuscript using different prediction software authors identified and showed the sequence of epitopes which bind well to similar HLA type, HLA-B15:01. Interestingly, one of the epitopes (YLRKHFSM) can be bound by 4 different HLA types. The obvious strength of this study is the demonstration that certain epitopes, which are identical between SARS-CoV-2 and the common human coronaviruses are being predicted as high affinity binders in multiple HLA-A and B types.
Overall, the work reports important new details about SARS-CoV-2 epitopes theoretically being recognized by human CD8+ T cells. Undeniably, future experiments can prove if generated memory immune responses are specific to the proposed epitopes.
There are some suggestions: The analysis of p/MHCI binding for HLA-C type (if available) would certainly complete the list of presented epitopes The introduction part subtitled: "The possibility of matching linear epitopes…" has missing information about previously published reports regarding T cell response in individuals infected with coronaviruses, either common or SARS-CoV.
In the discussion part readers may wonder why the authors did not discuss their findings with those already published (although they may not have been out at the time of submission) but should be included in the revision.