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Systematic analysis of SARS-CoV-2 Omicron subvariants’ impact on B and T cell epitopes
SARS-CoV-2 Omicron subvariant’s impact on epitopes
Al Khalaf Ruba Data curation Formal analysis Investigation Software Writing – original draft
https://orcid.org/0000-0001-8016-5750
Bernasconi Anna Conceptualization Funding acquisition Methodology Project administration Supervision Writing – original draft Writing – review & editing *
https://orcid.org/0000-0001-9786-2851
Pinoli Pietro Conceptualization Methodology Supervision Writing – review & editing
Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB), Politecnico di Milano, Milano, Italia
Raju Nagarajan Editor
Emory University, UNITED STATES OF AMERICA
Competing Interests: The authors have declared that no competing interests exist.

* E-mail: anna.bernasconi@polimi.it
2024
19 9 2024
19 9 e030787322 3 2024
14 7 2024
© 2024 Al Khalaf et al
2024
Al Khalaf et al
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Introduction

Epitopes are specific structures in antigens that are recognized by the immune system. They are widely used in the context of immunology-related applications, such as vaccine development, drug design, and diagnosis / treatment / prevention of disease. The SARS-CoV-2 virus has represented the main point of interest within the viral and genomic surveillance community in the last four years. Its ability to mutate and acquire new characteristics while it reorganizes into new variants has been analyzed from many perspectives. Understanding how epitopes are impacted by mutations that accumulate on the protein level cannot be underrated.

Methods

With a focus on Omicron-named SARS-CoV-2 lineages, including the last WHO-designated Variants of Interest, we propose a workflow for data retrieval, integration, and analysis pipeline for conducting a database-wide study on the impact of lineages’ characterizing mutations on all T cell and B cell linear epitopes collected in the Immune Epitope Database (IEDB) for SARS-CoV-2.

Results

Our workflow allows us to showcase novel qualitative and quantitative results on 1) coverage of viral proteins by deposited epitopes; 2) distribution of epitopes that are mutated across Omicron variants; 3) distribution of Omicron characterizing mutations across epitopes. Results are discussed based on the type of epitope, the response frequency of the assays, and the sample size. Our proposed workflow can be reproduced at any point in time, given updated variant characterizations and epitopes from IEDB, thereby guaranteeing to observe a quantitative landscape of mutations’ impact on demand.

Conclusion

A big data-driven analysis such as the one provided here can inform the next genomic surveillance policies in combatting SARS-CoV-2 and future epidemic viruses.

Ministero dell’Università e della Ricerca (IT) P2022CNN2J https://orcid.org/0000-0001-8016-5750
Bernasconi Anna This work has been funded by Ministero dell’Università della Ricerca (PRIN PNRR 2022 “SENSIBLE” project, n. P2022CNN2J), Principal Investigator: Anna Bernasconi. Data AvailabilityThe analyzed data, code workflow (as a Jupyter Notebook), and supplementary materials are available on https://zenodo.org/doi/10.5281/zenodo.10514577.
OutbreaksCOVID-19
Data Availability

The analyzed data, code workflow (as a Jupyter Notebook), and supplementary materials are available on https://zenodo.org/doi/10.5281/zenodo.10514577.
==== Body
pmcIntroduction

Specific sequences of amino acid residues in a viral protein, called epitopes, can be recognized by antibodies or B/T cell receptors as part of the induction of T cell-dependent cellular immune response [1] or B cell-dependent humoral immune response from the host organism [2]. Studying SARS-CoV-2 epitopes is essential, especially in vaccine design; notably, a great number of epitopes available for the Spike protein of SARS-CoV-2 are used in the design of COVID-19 vaccines [3], following multi-epitopes designs [4]. Within the Immune Epitope Database (IEDB, [5]), epitopes have been deposited for many viral species. The ones initially designed for SARS-CoV-2 were derived from SARS [6]; the first 283 epitopes for the virus were deposited by August 2020, reaching -to date- almost 6K units. Candidates for next-generation COVID-19 vaccines can be revealed by identifying targets of broadly neutralizing antibody responses and immunodominant T cell epitopes [7, 8]. Observing epitope variability has applications in disease monitoring, diagnostic settings, as well as drug design [9]. For both B and T cell epitope-based vaccine design [10, 11], it is also important to study epitopes’ conservation with respect to mutations accumulated through evolution by the virus. Notably, a mutation occurring on the specific epitope range may affect the recognition of the epitope.

Starting in late November 2021, the Omicron variant (B.1.1.529)—with its 40 non-synonymous mutations in the Spike protein – captured the international community’s attention as a potentially highly critical SARS-CoV-2 variant. On November 26th, 2021, Omicron was labeled a Variant Of Concern (VOC) by the World Health Organization (WHO) and many of its descending lineages were considered VOCs too. On March 15th, 2023 [12] the WHO updated its definitions and criteria for the classification of SARS-CoV-2 variants, respectively of Variants of Interest (VOI) and Variants Under Monitoring (VUM) [13]. According to the statement, Omicron has been considered the most divergent VOC observed thus far. At the time of writing, five lineages descending from Omicron are considered VOIs and five VUMs.

Since its appearance, Omicron viruses have undergone genetic and antigenic changes, giving rise to an increasing array of sublineages. All sublineages share common traits, such as the ability to evade the immunity present in the population and a predilection for infecting the upper respiratory tract rather than the lower respiratory tract [14], distinguishing them from variants of concern that emerged before Omicron. Compared to other SARS-CoV-2 variants, Omicron is considered to have a higher non-synonymous mutation rate [15], a lower disease severity [16], and higher transmission [17, 18], which are all features that might lead a virus to quickly evolve into several sublineages. In the PANGO nomenclature [19], sublineages have been named using the BA alias; four are the sublineages descending from B.1.1.529 that are circulating (i.e., BA.1, BA.2, BA.4, and BA.5). During the pandemic wave happening in the winter of 2021, BA.1 was the dominant lineage; it was then replaced with BA.2 and BA.2.12.1, which were -in turn- replaced by BA.4 and BA.5 during the summer of 2022 [8].

As part of convergent evolution [20], genetic recombination has been observed as a genetic event within the Omicron genome [21, 22]. According to Shiraz and Tripathi [23], there was an extraordinary increase in the emergence of SARS-CoV-2 recombinant lineages during the Omicron waves; also, they noted the enrichment of certain amino acids in the Spike protein of recombinant lineages, which have been reported to confer immune escape from neutralizing antibodies and increase the binding to the host’s angiotensin-converting enzyme 2 receptor in some cases.

The emergence and rapid spread of the Omicron variant has highlighted that the COVID-19 pandemic is still ongoing, even if the virus may evolve toward an endemic seasonal upper respiratory tract infection. The importance of vaccination persists as a fundamental element of the strategy to manage the pandemic, even in the face of variants that can evade immunity [24]. Genomic surveillance, the study of the evolution of a pathogen through the sequencing of its genome, was universally recognized as a first line of defense to combat the pandemic [25].

Along with continuous monitoring of new sequences (performed with tools such as ViruSurf [26]), their mutations’ effects (that we categorized in the CoV2K model [27]), and how they aggregate into new variants [28, 29], it is crucial to observe how these impact epitopes [30]. To this end, we first proposed to study epitopes in the context of genomic surveillance in our EpiSurf tool [31], which allows user-friendly testing of epitope conservancy within selected populations of interest. Specifically, EpiSurf computes the number of mutations on epitopes for a specific population of sequences; these can be visualized through VirusViz [32].

It is important to understand how the currently employed set of epitopes is impacted by currently circulating variants. In this contribution, we select the time in the SARS-CoV-2 pandemic when a new generation of variants—led by the ‘Omicron’ name—emerged and we produce a database-wide data analysis on how these variants behaved on the epitopes locations. We collected all linear SARS-CoV-2 B cell and T cell epitopes on the different proteins, curated by the Immune Epitope Database and Analysis Resource and information on variants and characterizing mutations from CoVariants.org [33], building up on our preliminary observations made in the Virological.org forum post [34], reported as soon as the first samples of Omicron (B.1.1.529) were made available on public databases (Nov. 30th, 2021). Our big data study allows us to draw conclusions on the quantitative relationship between epitopes and evolving SARS-CoV-2 variability, contributing to informing the next health policy-making strategies.

Materials and methods

We carried out a complete data science pipeline comprising Data retrieval from publicly available databases, Data integration for achieving information interoperability, Data aggregation to consider epitope-level information, and Data analysis applying statistical tests. The data analysis steps have been repeated on a full epitopes dataset and also on a restricted dataset with only high-frequncy epitopes. Fig 1 presents the workflow used in this research. The analysis has been performed in Python (Version 3.9.13), using classical data science libraries, i.e., Pandas (Version 1.4.4) for data extraction and aggregation, Scipy (Version 1.9.1) for statistical analysis, Seaborn (Version 0.11.2) and Matplotlib (Version 3.5.2) for data visualization.

10.1371/journal.pone.0307873.g001 Fig 1 Analysis workflow, divided into four phases.

Each block corresponds to a code module; arrows indicate the use of input (incoming arrow) calculated in previous steps (outgoing arrow). Note that RF stands for Response Frequency, indicating the number of positively responding subjects over the total number of tested subjects.

Data retrieval

Epitopes datasets

The epitopes datasets tcell_full_v3 and bcell_full_v3 were retrieved as zip files from the Immune Epitope Database and Analysis Resource (IEDB) [5] on August 20th, 2023 from [35]. Note that IEDB only includes candidates with a maximum length of 50 amino acids and a maximum non-peptidic structure of 5K Daltons [36], experimentally tested for binding to an adaptive immune receptor (T cell receptor (TCR), antibody or B cell receptor (BCR), or major histocompatibility complex (MHC)) or with a receptor recognized to be epitope-specific.

The dataset on T cell epitopes contains 504,901 entries, each corresponding to one assay performed to identify an epitope. Such entries are described by 160 manually curated metadata fields (organized into 21 sections); 22,774 of them have been derived from Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes. We focus on the 22,752 ones (99.90%) that refer to linear peptides, while only 22 (0.09%) refer to discontinuous peptides. Overall, assays refer to 5,927 unique linear epitopes, which are considered in our analysis. Epitopes are derived from 14 SARS-CoV-2 proteins and are obtained from three host species, i.e., Homo sapiens (human), Mus musculus (mouse), and Macaca mulatta (rhesus macaque). Table 1 shows the counts of T cell (left) and B cell (right) linear epitopes over the SARS-CoV-2 proteins and their average length; the highest amount of linear T cell epitopes (2,794) is derived from the Spike protein, which is to be expected since Spike is an immunogenic protein that mediates host cell entry.

10.1371/journal.pone.0307873.t001 Table 1 Overview of quantitative charactristics of T cell and B cell linear epitopes considered in the input datasets.

Protein	T cell linear epitopes	B cell linear epitopes	
Name	Length	Count	Min length	Max length	Avg length	Count	Min. length	Max length	Avg length	
S	1273	2794	7	43	15.17	4323	4	48	16.05	
N	419	603	7	38	13.95	1101	4	47	16.14	
M	222	326	8	41	13.90	397	10	32	15.89	
E	75	86	8	21	12.80	123	10	23	15.74	
ORF1ab	7079	1724	8	29	11.22	13326	11	48	15.48	
ORF3a	275	171	5	38	13.63	466	7	34	15.32	
ORF6	61	29	9	31	13.86	87	11	16	15.14	
ORF7a	121	69	8	35	13.20	206	11	17	15.04	
ORF7b	43	9	8	25	11.44	35	12	16	15.63	
ORF8	121	72	8	37	13.86	248	11	22	14.83	
ORF9b	97	18	8	20	13.94	1	42	42	42.00	
ORF9c	73	10	9	20	15.60	0	0	0	0	
ORF10	38	14	9	22	12.21	68	12	24	14.44	

Instead, the dataset on B cell epitopes contains 1,387,901 entries regarding assays, described by 131 manually-curated metadata fields (organized into 11 sections); 90,344 entries are related to the SARS-CoV-2 species. Out of these, 79,364 (87.84%) refer to linear peptides (on which we focus), 10,679 (11.82%) to discontinuous peptides, and 301 (0.33%) to discontinuous multi-chain peptides. Assays refer to 20,388 unique epitopes, which we consider in the analysis. Here, epitopes are derived from 13 SARS-CoV-2 proteins. They are obtained from 15 host species, of which the three most presented ones are Homo sapiens (human), Macaca mulatta (rhesus macaque), and Mus musculus (mouse). In Table 1, we observe that the highest number of linear B cell epitopes is derived from the polyprotein ORF1ab (13,326 epitopes)—not surprisingly, as ORF1ab accounts for two-thirds of the genome. The ORF1ab of SARS-CoV-2 is considered a potential drug target because it encodes a large polyprotein that is subsequently cleaved into various non-structural proteins (NSPs) essential for viral replication.

In the datasets, each entry represents one assay performed to analyze or devise an epitope: the same epitope appears in many assays (i.e., rows). Metadata is organized into sections: the “Reference” section contains the metadata of the journal publications where the related assays have been studied and reported; it includes fields such as IRI, Type, PMID, submission ID, and Authors. The “Epitope” section contains the metadata of the epitopes, including fields such as the IEDB IRI, Object Type, Name, Starting Position, and Ending Position. The “Related Object” section describes the epitopes’ structure (i.e., analog, mimotope, neoepitope, or another structure). The “Host” section contains the metadata of the organism whose T/B cell response is being measured, including Name, IRI, Geolocation, etc. The “1st in vivo process” section contains the metadata of the in vivo process by means of which the organism generating T/B cells was exposed to a relevant immunogen in vivo; it includes fields such as Process Type and Disease. The “Assay” section contains the metadata of the experimental assay including fields Method, Response measured, Units, Qualitative Measurement, Measurement Inequality, Quantitative measurement, Number of Subjects Tested, Number of Subjects Positive, and Response Frequency (%).

In this work, we focus on metadata derived from the “Reference”, “Epitope” and “Assay” sections. In addition to metadata as is we extend the dataset by calculating the number of positive and negative assays and the Response Frequency aggregating results by epitope; this serves the purpose of exploring how often each protein region has been studied in different immune assays and in how many assays the immune response was positive or negative. Note that, due to the heterogeneity of samples and the complexity of the immune response, the response varies among studies and assays.

Variants information

We selected all Omicron subvariants indicated on CoVariants.org [33] as of August 29th, 2023. The 14 variants are shown in Table 2, where BA.2 has the highest number of collected sequences from GISAID [37]. Specifically, we restrict to all the clades that descend from the clade 21M (according to Nextstrain [38]), called B.1.1.529 within Pangolin [19] and Omicron from the WHO. The set of considered Omicron variants is next referred to as OV. Then, the list Mv = m1, …, mn, denoted list of characterizing mutations is considered for each variant v in OV, where mi is such that at least 75% of viral genomes in v exhibit this mutation. We extract Mv for each variant in OV as they are reported in OutbreakInfo.org [39]. Fig 2 visually reports the distribution of mutations Mv on Spike for each v in OV and for the four VOCs observed previously in the pandemic, namely Alpha (B.1.1.7), Beta (B.1.351), Delta (B.1.617.2), and Gamma (P.1).

10.1371/journal.pone.0307873.g002 Fig 2 Overview of Spike protein characterizing mutations for all the variants included in OV and previous VOCs Alpha, Beta, Delta, and Gamma.

Blue squares indicate substitutions of the reference amino acid residue with the one indicated by the letter; red squares indicate deletions.

10.1371/journal.pone.0307873.t002 Table 2 Omicron subvariants described by 1) Pango name (note that all the lineages descending from XBB.1 descendent lineages—including EG.5.1—are recombinants of BA.2.10.1 and BA.2.75 sublineages, i.e., BJ.1 and BM.1.1.1 [41]); 2) Nextstrain clade; 3) Common WHO-given name; 4) Number of characterizing mutations (established by CoVariants.org [33]); 5) Date of first detection, last detection, and number of GISAID sequences (as retrieved from OutBreakInfo.com on 29th of August 2023); 6) Classifications of these variants according to the WHO (retrieved at the end of August 2023)—the ones marked with * were classified as VOCs before 15 March 2023.

Variant	Clade	Common name	# Char. Mut.	First detected	Last detected	# Sequences	Classification	
BA.1	21K		56	5-Jan-20	27-Jul-23	440,101	*	
BA.2	21L		59	28-Mar-20	10-Aug-23	1,240,833	*	
BA.4	22A		66	2-Jul-20	16-Jun-23	38,786	*	
BA.5	22B		61	4-Jul-20	4-Jul-23	25,233	*	
BA.2.12.1	22C		61	15-Apr-20	29-Jul-23	292,096	-	
BA.2.75	22D	Centaurus	57	8-Aug-20	8-Apr-23	6,264	VUM	
BQ.1	22E	Typhon	68	11-Jan-22	9-May-23	24,572	-	
XBB	22F	Gryphon	73	29-Jun-21	15-Aug-23	28,607	VUM	
XBB.1.5	23A	Kraken	75	4-Apr-20	15-Aug-23	189,754	VOI	
XBB.1.16	23B	Arcturus	78	23-Feb-22	23-Aug-23	27,509	VOI	
CH.1.1	23C	Orthrus	77	9-Jan-22	27-Jul-23	23,879	VUM	
XBB.1.9	23D		75	12-Oct-22	20-Jul-23	1,581	VUM	
XBB.2.3	23E		76	24-Feb-22	21-Aug-23	5,604	VUM	
EG.5.1	23F	Eris	82	24-Mar-23	23-Aug-23	5,727	VOI	

We note that there are 15 mutations in the Spike protein shared in all the selected variants in OV; out of these N764K, D796Y, N856K, Q954H, and N969K are located in the S2 unit of the Spike protein. These five mutations have never been detected in previous VOCs [40]. The difference between the density of mutations in the first rows of the figure is immediately apparent w.r.t. the last four rows (representing previous variants).

Data integration

Three datasets (two for epitopes and one for variants characterizing mutations) were assessed and integrated, by mapping the characterizing mutations of each variant onto the epitopes using the corresponding amino acid coordinates. In this process, we used two intermediate data representations.

First, we built a coverage table to record the number of T or B cell epitopes that stand on each position of a given protein of SARS-CoV-2. For explanation purposes, Table 3 shows a small excerpt of the Spike protein, where amino acid residues are located in positions p1—p1273. As an example, position p498 of the Spike protein may exhibit the mutation Q498R; this is a widespread mutation that is considered a characterizing mutation of all the 14 variants of OV. According to Table 3, this mutation is affecting, respectively, 48 and 83 T cell and B cell linear epitopes. Instead, only 7 T cell (and 5 B cell) epitopes are on p1 and 2 T cell (and 9 B cell) on p1273 of the Spike, as a lower density of epitopes was identified on the borders of the protein.

10.1371/journal.pone.0307873.t003 Table 3 Excerpt of the T cell and B cell linear epitopes coverage table (only Spike protein), including seven centrally-located amino acid positions.

Each cell contains the number of T cell and B cell linear epitopes covering the pn-th position on the observed protein.

	p 1	…	p 497	p 498	p 499	p 500	p 501	p 502	p 503	…	p 1273	
T cell epitopes	7	…	51	48	46	45	45	43	45	…	2	
B cell epitopes	5	…	87	83	82	78	78	75	75	…	9	

Second, we computed an array of percentages of linear T and B cell epitopes affected by each variant in OV. Computations are repeated separately for each protein. To achieve so, for a given variant v¯ in OV, we count the distinct epitopes that are affected by at least one mutation m in the set Mv¯ of its characterizing mutations; this corresponds to checking that the position of m is included between the start and end position of the epitope. The obtained counts (one for each protein) are finally divided by the total of T cell (respectively, B cell) epitopes. We thus obtained a matrix of percentages for 14 variants by 13 SARS-CoV-2 proteins for T cell epitopes and 12 proteins for B cell epitopes. For instance, T cell epitopes affected by characterizing mutations of BA.1 on the Spike protein are 11.52% of all T cell epitopes on Spike.

Data aggregation

Each epitope e in IEDB has typically been studied in at least one bibliographic reference, as a result of at least one experimental assay. Thus, many records in the tcell_full_v3 and bcell_full_v3 datasets—each representing one assay—refer to the same epitope (i.e., an epitope in the same protein, with the same start-end positions, and therefore the same sequence of amino acid residues), reporting information on different assays that were performed to retrieve the same result.

In our datasets, information that was heterogeneous across the different assays and enclosing references of the same epitope e was aggregated. For a new aggregated record, we considered three columns in the original file, where records represent assays, grouped by reference:

the “Assay.Qualitative Measurement” (indicating the field Qualitative Measurements in the “Assay” metadata section), representing the qualitative outcome of the assay, which could be described as ‘positive’, ‘positive low’, ‘positive intermediate’, ‘positive high’, or ‘negative’,

the “Assay.Number of Subjects Tested” (called N), representing the total number of tested subjects;

the “Assay.Number of Subjects Positive” (called R), representing the total number of subjects that are positive for this assay.

In accordance with the IEDB documentation [42], if an assay lacks information on the number of subjects who responded or tested, we filled in the missing values following the “Assay.Qualitative Measurement” indication: when an assay outcome is positive, then N = 1 and R = 1; when an assay outcome is negative, then N = 1 and R = 0.

For each T cell and B cell linear epitope of the Spike protein, we calculated: the number of positive assays; the number of negative assays; and the response frequency RF (i.e., the number of positively responding subjects divided by the total number of those tested) with its 95% confidence interval (CI). The latter was retrieved according to the following steps:

For each reference r regarding e, Nr and Rr are the highest values from any existing positive assay of r; if there is no positive assay, Nr is the highest N of the negative assays of r and Rr is set to zero.

For each epitope e, Ne and Re are the sum of the Nr and Rr of all references regarding e.

For each epitope e, the Response Frequency equals Re/Ne and its 95% confidence intervals is computed as 95%CI=p^±1.96p^(1-p^)/n (as it is distributed as a binomial distribution).

Data analysis on all epitopes

Two versions of a Monte Carlo-based test were employed to estimate the significance of the placement of characterizing mutations of variants in OV within the set of epitopes. In both cases, for 1,000 repetitions, we randomly repositioned the set of the T cell and B cell linear epitopes extracted from the Spike protein, by sampling their start position on the protein from a uniform distribution. In the first version (called naive), for each repetition i, we compute ci as the count of repositioned epitopes affected by at least one mutation of at least one variant in OV, thus obtaining the set C = {c1, …, c1000}. Then, we calculated the corresponding p-values as: p=|c∈C:c≥co|1000

where co is the observed number of affected epitopes in the real data.

However, multiple mutations can appear in the same epitope and epitopes vary in length. Thus, we also implemented a second version of the experiment, where these aspects are weighted. Considering a set of epitopes E, for each e ∈ E (either real or repositioned), we compute Me as the count of characterizing mutations of all the variants affecting the epitope—i.e., observed in positions included in the epitope range—and we divide it by the length (number of amino acids) of e. Then, for each iteration i, we compute the overall factor fi, as the sum of the weighted counts across all the epitopes: fi=∑e∈EMelen(e).

Again the p-value is computed as: p=|fi:fi≥fo,i=1,…1000|1000

being fo the normalized count computed on the real data.

Data analysis on highly-responsive epitopes

The same approach was re-applied on a smaller set of T cell and B cell linear Spike epitopes, using an RF threshold of 0.75, which allows us to target only Spike’s highly responsive T cell and B cell linear epitopes. The threshold was selected experimentally; we started with a more strict one and then relaxed it gradually until we got a substantial set of epitopes with high response frequency. S1 Fig presents a histogram of the epitopes RFs.

Results

As explained in the Methods’ Section “Data Integration”, we build a coverage table that contains all the proteins included in the epitope datasets. For each amino acid of each protein, we count the number of linear epitopes including that specific position. The coverage of linear epitopes over the Spike protein is shown in the dot plot in Fig 3, where the blue profile reflects B cell epitopes and the orange one reflects T cell epitopes. For each position on the protein (x-axis) we show the counts (on y-axis) of linear epitopes that overlap that position. As expected, the coverage drops in correspondence to the protein’s boundaries, and B cell epitopes have a higher coverage. The Spike protein is cleaved into two subunits, S1 and S2, during the process of viral entry. The S1 subunit contains the N-terminal domain (NTD), which plays a crucial regulatory role in the conformational changes of the Spike [43], and the receptor-binding domain (RBD), which binds to the host cell receptor ACE2, while the S2 subunit facilitates the fusion of the viral and host cell membranes. Various diagnostics, therapeutic agents, and vaccines have been proposed against the Spike protein, with multiple targets, including Spike (RBD, S1-NTD, and less often the S2 subunit) since they are the immunogenic domains of the Spike [44, 45]; however, Omicron subvariants have a high number of characterizing mutations positioned in these regions (especially in the RBD) which makes them more likely to escape immunity.

10.1371/journal.pone.0307873.g003 Fig 3 Coverage of B cell (blue) and T cell (orange) linear epitopes over the Spike protein.

Vertical lines indicate positions of mutations from Mv for all v ∈ OV; the darker the green color, the higher the number of variants with a mutation at that position. The bottom legend indicates the two Spike subunits; we denote NTD: N-terminal Domain (13–303), RBD: Receptor Binding Domain (319–541), and S1/S2 cleavage site (685–686). The full dataset to generate the figure is available as S1 File.

From Fig 3 it can be noted that both T cell and B cell epitopes are condensed in the RBD region (located in the 319–541 range). In the figure, vertical lines indicate the positions of the characterizing mutations of the selected variants—darker colors indicate that a given mutation is present in multiple variants. A noticeable bundle of characterizing mutations is also affecting the RBD region, hence, affecting the epitopes derived from that region. Also, it can be noted that the NTD region (located in the 13–303 range) has a high number of both T cell and B cell linear epitopes, but it is affected by a wide set of characterizing mutations some of which are deletions (we refer the reader back to Fig 2). Here, two small regions contain peaks of T cell linear epitopes that exceed the counts of B cell linear epitopes; the 142–147 contains many characterizing mutations, whereas the 265–276 range holds none.

The triple mutations H655Y, N679K, and P681H, found in all Omicron subvariants, are located near the furin cleavage site of the Spike protein and might accelerate the S1-S2 cleavage, translating to more efficient viral entry into the host cell and enhanced infectivity. Another peak of B cell linear epitopes (in the 550–580 range) is located after the RBD region and is not affected by any mutations of Omicron subvariants.

The S2 domain (starting from the 685th amino acid) is quite conserved across all the 14 Omicron subvariants. Two regions in the S2 domain can be noted where many B cell linear epitopes are located (see the two peaks at 800–827 and 1139–1171). Regarding T cell linear epitopes, the counts fluctuate across the S2 domain with a noticeable peak near the C-terminus of the protein (i.e., 1198–1221 range).

Table 4 shows the coverage of T cell and B cell linear epitopes per amino acid per protein. All the structural proteins (S, N, M, and E) have been completely covered with T cell and B cell epitopes, whereas the open reading frames are less dense in this respect. Both B cell and T cell epitopes contribute to the overall immune response and help the immune system effectively combat a wide range of pathogens. However, B cell epitopes are observed to be more abundant than T cell epitopes. This is might be due to the way each type of immune response is functioning (T cell-mediated immunity vs B cell-mediated immunity).

10.1371/journal.pone.0307873.t004 Table 4 Statistics of B and T cell linear epitopes coverage per amino acid (AA), divided by protein.

Protein	T cell epitopes per AA	B cell epitopes per AA	
Min.	Max.	Average	Median	Min.	Max.	Average	Median	
S	2	63	33.12	32	5	92	54.34	52	
N	4	34	19.53	18	9	56	42.21	42	
M	3	38	20.28	20	7	35	28.41	29	
E	3	25	14.68	15	5	33	25.81	29	
ORF1ab	0	18	2.71	2	2	44	29.08	29	
ORF3a	3	18	8.48	8	3	37	25.96	25	
ORF6	0	14	6.59	6	2	30	21.59	25	
ORF7a	1	15	7.45	7	3	36	25.60	27	
ORF7b	0	4	2.40	3	2	18	12.72	15	
ORF8	3	12	8.25	8	4	43	30.40	32	
ORF9b	0	5	2.59	2	0	1	0.43	0	
ORF9c	0	5	2.14	2	0	0	0.00	0	
ORF10	0	12	4.50	2	6	43	25.21	27.5	

Coverage dot plots of other relevant proteins (N, E, M, and ORF1ab) can be found in S2–S5 Figs, respectively. The Spike has the highest coverage per amino acid compared to all other proteins, which makes it the central target of our study. The other immunogenic protein, the Nucleocapsid, has high coverage per amino acid (covered at least by 4 T cell linear epitopes and 9 B cell linear epitopes). Also M and E proteins, which are notoriously poor in immunogenic activity, are completely covered with both T cell and B cell linear epitopes with considerably high medians compared to other proteins. ORF1ab contains many B cell linear epitopes (median of 29 B cell epitopes covering its amino acids), while it has a very low T cell epitope coverage per amino acid (median of 2 epitopes per amino acid—some regions are not covered with any T cell linear epitopes).

To show the effect of the selected variants over the whole available SARS-CoV-2 set of linear T cell and B cell epitopes, we performed the same analysis on previously circulating VOCs—for these same variants, in Fig 2 we had previously portrayed characterizing mutations of the Spike protein. Quantitative statistics of the analysis are shown in Table 5. Characterizing mutations of Alpha, Beta, Delta, and Gamma only impacted 9–14% of B/T cell epitopes. Instead, subvariants of Omicron impact 24–34% of B/T cell epitopes. Strikingly, the EG.5.1 (VOI) variant with 44 mutations on Spike impacts 894 (32%) T cell epitopes and 1,438 (33.23%) B cell epitopes, while the XBB.2.3 (VUM) variant with 43 mutations on Spike impacts 896 (32.07%) T cell epitopes and 1,454 (33.63%) B cell epitopes. When compared to the impact of Omicron mutations, percentages of previous Variants of Concern are substantially lower.

10.1371/journal.pone.0307873.t005 Table 5 Comparative overview of the Spike protein epitopes’ coverage of different variants (from previous VOCs and in OV).

Lineage	# Mutations on Spike	# T cell epitopes	% T cell epitopes	# B cell epitopes	% B cell epitopes	
B.1.1.7	10	322	11.52	513	11.87	
B.1.351	10	280	10.02	499	11.54	
B.1.617.2	9	256	9.16	423	9.78	
P.1	12	361	12.92	618	14.30	
BA.1	33	731	26.16	1095	25.33	
BA.2	31	695	24.87	1116	25.82	
BA.4	34	776	27.77	1220	28.22	
BA.5	34	776	27.77	1220	28.22	
BA.2.12.1	33	771	27.59	1216	28.13	
BA.2.75	30	722	25.84	1189	27.50	
BQ.1	36	791	28.31	1253	28.98	
XBB	41	838	29.99	1339	30.97	
XBB.1.5	42	863	30.89	1395	32.27	
XBB.1.16	43	870	31.14	1403	32.45	
CH.1.1	41	841	30.10	1340	31.00	
XBB.1.9	40	838	29.99	1339	30.97	
XBB.2.3	43	896	32.07	1454	33.63	
EG.5.1	44	894	32.00	1438	33.26	

As seen in Fig 2, Omicron subvariants shared some characterizing mutations with previous VOCs (e.g., K417N, T478K, and N501Y) even though Omicron is far from these lineages from the phylogenetic point of view. All previously mentioned mutations were related to the ability to destabilize the antibody-binding affinity [46]. Also, Omicron subvariants kept mutating at the same position of previous VOCs but using different amino acid substitutions, e.g., E484K in Beta and Gamma becomes E484A in all Omicron subvariants. Liu et al. [47] found that substitutions at E484 of the Spike were associated with relative resistance to neutralization by several antibodies. However, E484A exhibited higher resistance to more antibodies than other substitutions at the same position (including E484K).

Fig 4 draws the distribution of the linear epitopes of the four structural proteins over the number of variants that exhibit at least one mutation in each epitope. Here, we observe that, out of 3,809 T cell and 5,944 B cell linear epitopes, 1,330 (34.91%) T cell epitopes and 2,178 (36.64%) B cell epitopes have at least one characterizingmutation of any of the selected variants. Instead, 584 (15.33%) T cell epitopes and 941 (15.83%) B cell epitopes are mutated in all 14 selected Omicron subvariants.

10.1371/journal.pone.0307873.g004 Fig 4 Distribution of linear T cell (on the left) and B cell (on the right) epitopes, according to the number of variants affecting them.

On the x-axis, we show the number of variants in OV whose characterizing mutations (at least one) are exhibited on the observed epitope range. Values regarding different proteins are stacked on the same variant-count bar.

On average, T cell linear epitopes on Spike were studied in 1.92 assays, the epitope YLQPRTFLL in the range 269–277 being the one studied the most (found in 49 assays, 44 positive and 5 negative assays). On average, B cell linear epitopes on Spike were studied in 1.44 assays. Three epitopes were the most studied ones: ECDIPIGAGICASYQ in the 661–675 range studied in 9 positive and 1 negative assays; NGVEGFNCYFPLQSY in the 481–495 range studied in 6 positive and 4 negative assays; and YNYKLPDDFTGCVIA in the 421–435 range studied in 5 positive and 5 negative assays.

When aggregating different assays and references’ data of different epitopes using their positional amino acid coordinates, we found that T cell linear epitopes of the Spike protein have an average of 27 positive and 37 negative assays per amino acid. Differently, there are 42 positive and 34 negative assays for B cell linear epitopes per amino acid. Fig 5 presents the counts of positive and negative assays for each amino acid of the Spike protein. This visual representation allows us to specify where the regions that have been tested more are located, distinguishing between their positive and negative results. We computed the differences between the positive and negative assay counts per amino acid; in the figure, we show a selection of interesting areas, where a considerable difference between their results is found (i.e., at least 25 assays).

10.1371/journal.pone.0307873.g005 Fig 5 Counts of positive and negative assays experimental outcome performed on T cell epitopes (top panel) and B cell epitopes (bottom panel) in the Spike protein.

Selected regions with large differences between positive and negative assays are highlighted with green (favoring positive assays) or red (favoring red assays) vertical bands. The full dataset to generate the figure is available as S2 File.

Considering T cell epitopes, a distinctive peak of positive assays is located on 266–279 with the highest value of 146 positive assays specifically located at the 269th amino acid (in line with the peak of the coverage of T cell epitopes mentioned earlier). Also, two regions (593–607 and 1230–1263) were noticed to have positive assays close to zero with a considerably high number of negative assays. In addition, negative assays were more likely to be achieved in the RBD domain.

Considering B cell epitopes, we noticed that the assays’ results were comparable over the Spike protein with few exceptions where the count of positive assays overcomes the count of negative assays. A peak of positive assays is located on 785–828 with the highest value of 111 positive assays at the 815-th amino acid and the other two at 555–584 and 1144–1175. These regions correspond to the two peaks found in the coverage plot (see Fig 3), indicating that many B cell epitopes are located in these regions and exhibit positive results when tested; the same was noticed in some ranges of the RBD. The regions 1059–1070 and 1216–1244 have low numbers of positive assays with considerably high numbers of negative assays.

Finally, we calculated the RF and its 95% CI for each T cell and B cell linear epitope. The average RFs per amino acid of the Spike protein are 0.18 (0.13–0.24 95% CI) and 0.21 (0.16–0.26 95% CI) for T cell and B cell epitopes, respectively. The results are shown in Fig 6: here, we observe that the two profiles are relatively distinct; for B cell epitopes five peaks (> 0.40 RF) arise around ranges 464–488, 555–584, 793–825, 1145–1162, and 1254–1270. Three of these peaks were also retrieved during the previous steps, meaning that the regions [555–584] in the S1 subunit and [793–825] and [1145–1162] in the S2 subunit of the Spike are rich with B cell linear epitopes (without any Omicron subvariants characterizing mutations) found in many positive assays and linked to high response frequencies. Instead, T cell epitopes have higher RF in the S1 subunit of the Spike protein and lower RF in the S2 subunit. Average RFs are forming a peak hitting 0.47 in the range of 259–280, which does not contain any characterizing mutations of the selected variants. T cell and B cell linear epitopes have diverse RFs across the RBD. Considering B cell epitopes, a wide region with considerably high average RFs (404–505 range) can be noticed; for T cell epitopes, two peaks in the RBD domain are detected (348–364 and 443–486 ranges).

10.1371/journal.pone.0307873.g006 Fig 6 Average response frequency (RF) flanked by its 95% confidence intervals (CI) of the epitopes computed at each position of the Spike protein.

Regions with high average RF (i.e., peaks > 0.40) are highlighted with red (for T cell) or blue (for B cell) vertical bands. The full dataset to generate the figure is available as S3 File.

Note that, the integration of the intervals detected in Figs 5 and 6, allows us to identify regions in the Spike protein with both a high number of positive assays and high response frequencies; such a preliminary filter could be a recommended first step when selecting the target region of antibody during its design/development.

Monte Carlo simulation results

Next, we employed the Monte Carlo simulation approach with the objective of determining the significance of identifying 1,163 T cell epitopes and 1,840 B cell epitopes affected by at least one of the characterizing mutations, among a total of 2,794 and 4,323 epitopes, respectively.

For 1,000 times we randomly re-positioned all the Spike linear epitopes by sampling their start position on the Spike protein from a uniform distribution. In the naive version, for each configuration, we compute the count of epitopes that are affected by at least one characterizing mutation of at least one Omicron subvariant and retrieve the distribution of such counts. Fig 7 presents two histograms illustrating the distributions of the counts of impacted T cell and B cell epitopes across the 1,000 simulations. We compared those distributions with the observed count (see red line). We compute the p-values as the number of random conformation having a count greater or equal than the observed one, obtaining p-values <0.001 for both T cell and B cell linear epitopes.

10.1371/journal.pone.0307873.g007 Fig 7 Monte Carlo-based test results, considering the naive test version.

We plot the density distribution of T-cell (left) or B-cell (right) simulated epitopes affected by at least one sampled mutation (randomly selected from all Omicron subvariants characterizing mutations). The red vertical lines (at 1,163 T cell and 1,840 B cell epitopes) represent the observed count of linear epitopes from the Spike protein that have been mutated.

In the weighted version, we weigh the count of the epitopes by the number of mutations affecting each epitope, normalized by the epitope length. Similarly to the first case, also here p-values are < 0.001 for T cell and B cell Spike linear epitopes. The related plot is provided in the S6 Fig.

Highly-responsive epitopes results

Finally, we focused on a dataset of highly responsive epitopes, derived from the original Spike linear epitopes dataset, using RF > 0.75 as a threshold for responsiveness; here, we retained 370 and 706 T cell and B cell epitopes, respectively. For this specific set, we first show the distribution of the selected epitopes over the number of variants that have at least one mutation in each epitope (see Fig 8). Their positive and negative assay counts and average RFs per amino acid of the Spike protein are provided in S7 and S8 Figs. We observe that 162 (43.78%) selected T cell epitopes and 335 (47.45%) selected B cell epitopes have at least one characterizing mutation of any of the selected variants, meaning that they appear in the 1–14 positions in the barplot. Instead, 67 (18.10%) selected T cell epitopes and 155 (21.95%) selected B cell epitopes are mutated in all 14 Omicron subvariants (OV).

10.1371/journal.pone.0307873.g008 Fig 8 Distribution of highly-responsive epitopes (i.e., with RF >0.75) according to the number of variants that affect them.

Monte Carlo simulation results

When running the Monte Carlo-based test, on data with simulated positions, using the naive version (plain counts of mutated re-positioned epitopes), we obtained p-values equal to 0.037 and < 0.001 for the subsets of T cell and B cell epitopes, respectively. The related plot is provided in the S9 Fig. In the weighted version (weighted counts of mutated re-positioned epitopes), we obtained p-values equal to 0.004 and < 0.001 for the subsets of T cell and B cell epitopes, respectively (see Fig 9). A slight increase in the (still significant) p-values was observed when the test was performed on smaller T cell epitopes subsets, possibly due to the considerable decrease in the number of tested epitopes.

10.1371/journal.pone.0307873.g009 Fig 9 Monte Carlo-based test results, simulating epitopes with RF > 0.75 and employing the weighted test version.

We plot the density distribution of T-cell (left) or B-cell (right) simulated epitopes’ factors (see Methods for details), affected by at least one sampled mutation. The red vertical lines (at 203.15 T cell and 493.75 B cell epitopes) represent the observed sum of computed factors for all the epitopes in the selected subset.

Analysis reproducibility: Focusing on the most recent variants

The proposed analysis workflow is completely reproducible. The code (fully disclosed and documented on our Zenodo public repository [48]) can be rerun at any point in time in the future. To demonstrate this, we reran the pipeline by considering SARS-CoV-2 variants that have risen at a later time than the original analysis run in August 2023. Assuming that the IEDB resource will be maintained for a long time, by downloading updated input datasets, any reader is able to regenerate all the numerical and visual artifacts of the previous analysis.

Here, we describe a selected portion of the most interesting results. Four additional Omicron subvariants (XBB.1.5.70, HK.3, BA.2.86, and JN.1) were tested over the IEDB T cell and B cell datasets (retrieved on 28, May 2024). It can be observed that the JN.1 and its ancestor BA.2.86 (with their 60 and 58 characterizing mutations in the Spike protein, respectively), both affect 41,29% and 43,07% of the Spike T-cell and B-cell linear epitopes, respectively. Instead, HK.3 (with its 45 characterizing mutations) affects 32,81% and 33,59% of the Spike T-cell and B-cell linear epitopes, respectively. Lastly, XBB.1.5.70 has 44 characterizing mutations on the Spike protein and affects 32,00% and 32,66% of the Spike T-cell and B-cell linear epitopes, respectively.

It is worth mentioning that both JN.1 and BA.2.86 are classified as VOIs by WHO (as of 3 May 2024); also, both share 15 distinct Spike characterizing mutations (e.g., S50L in NTD and N450D in the RBD which are thought to mediates resistance to some monoclonal antibodies [49]), which were not detected in any other omicron subvariants listed in our analysis. But, with its additional Spike mutation L455S, JN.1 rapidly achieved extensive resistance across antibodies targeting the RBD of the Spike and showed higher immune evasion compared to BA.2.86 [50]. On the other hand, both variants XBB.1.5.70 and HK.3 have L455F and F456L characterizing mutations on their RBD of the Spike protein, which is known as the ‘FLip’ genotype, so-called because of the abbreviations of the amino acids involved and the fact that the location of the F and L amino acids is switched; the ‘FLip’ genotype is associated with immune escape properties too [51–53].

Discussion

Comparison with related work. Several tools for epitope prediction have been studied in the past [54]; a series of projects were dedicated specifically to SARS-CoV-2 epitopes. COVIEdb [55] targets pan-coronavirus vaccine development; here, SARS-CoV-2 database entries were predicted by using tools hosted by IEDB, exploiting the similarity of other viruses. DBCOVP [56] and the COVID profiler [57] provide companion vaccine design tools, with a focus on prediction, with light integration with IEDB data. A small number of tools are specifically dedicated to the analysis of epitopes conservancy. The IEDB Epitope Conservancy Analysis (ECA) tool [58] requires inputs from users for both epitopes and mutations. COVIDep [59] is an integrative effort joining IEDB epitopes with GISAID sequences, with a “Population coverage analysis” providing quantifications of “conservation” and “population coverage” for each epitope. Our tool EpiSurf [31] also allows for conservancy and population coverage analysis, with the possibility to select a user-defined sequence population. Unfortunately, both COVIDep and EpiSurf have discontinued the updates of new sequenced in September 2021 due to the huge growth of the GISAID database. The Virus Pathogen Database and Analysis Resource (ViPR) [60] connects both predicted and experimentally derived epitopes and proteins of sequences deposited on GenBank. Finally, the COG-UK Mutation Explorer (COG-UK-ME) [9] presents a UK-centred interface to UK genomes and variation (also in the context of T cell epitopes reported by experimental studies). COVIDep, ViPR, and COG-UK-ME currently offer a curated list of epitopes, respectively predicted from SARS-CoV, predicted with NetCTL [61], and manually extracted from experimental studies.

Regrettably, none of these tools were fit for the purpose targeted in this analysis, which had to be completely re-engineered with an ad hoc data science pipeline. Here, we proposed to study the entire database of SARS-CoV-2 epitopes curated by IEDB to understand how epitopes are distributed. Specifically, we did not make any assumptions on the possible effects of the studied viral mutations on the immune response itself, but we separated the analysis for each type of immune cell; T cell (including both CD4+ and CD8+ cells) corresponds to cellular immunity and B cell corresponds to humoral immunity.

Contributions.

Overall, our key contributions are:

Epitopes’ distribution analysis. We provide insights into the coverage of SARS-CoV-2 viral proteins by T cell and B cell epitopes (Table 4), highlighting the regions with high-density mutations (Fig 3 and S2–S5 Figs), and aiding in identifying conserved epitopes crucial for vaccine design.

Impact of omicron characterizing mutations. Our study details how mutations in various Omicron (and non-Omicron) variants impact T cell and B cell epitopes (Table 5 and Fig 4). This information is essential for understanding the evolving landscape of SARS-CoV-2 and its implications for immune recognition.

Key Spike protein region identification. By analyzing the response frequency of assays related to specific epitopes, we identify key regions in the Spike protein (Figs 5 and 6). These include the S2 domain and specific segments of the S1 subunit, that remain conserved across multiple Omicron subvariants. These regions could be promising targets for therapeutic antibodies and vaccine development.

We finally confirmed our results by performing a Monte Carlo simulation on the entire dataset (Fig 7) and on high RF epitopes (Figs 8 and 9). Our pipeline is completely reproducible, as shown in a dedicated subsection, updating our results to May 2024.

Limitations

Our study also presents a number of limitations. Regarding the employed datasets: even though IEDB is currently the largest open-access database for reporting epitopes, their datasets only represent the epitopes studied in the literature. This may result in a bias towards regions of the viral proteins that have been more intensively studied, potentially overlooking epitopes in less examined areas. Also, the variability in sample sizes across different studies could affect the robustness of our results. Moreover, the immune response data is subject to assay biases. Variations in assay sensitivity, specificity, and the conditions under which they were performed can influence the detection of epitopes and response frequencies. This may impact the generalizability of our findings to broader populations and different experimental settings. In addition, we chose to include epitopes from any species, as our aim is to indicate how the already experimentally studied epitopes are affected by the currently circulating variants regardless of their host or the immune response pathway that these epitopes are involved in.

Significance

When designing vaccines, researchers aim to target epitopes that are highly immunogenic and conserved across different pathogen strains. This helps ensure the vaccine will be effective in a broad population and provide long-lasting protection against the target pathogen. High response frequency to a specific epitope suggests that the epitope is a good candidate for inclusion in a vaccine, as it is more likely to generate a strong and effective immune response in a larger portion of the population. This can contribute to developing more effective vaccines against various infectious diseases. Since Omicron has been heavily studied for its ability to permit evasion of vaccine-induced immunity [24, 62, 63]; in our study, we aimed to identify the regions where a high number of positively detected highly responsive epitopes are found, which might be good vaccine/therapeutic target candidates. For example, we observed that the S2 domain is more conserved across all the 14 Omicron subvariants, suggesting it could be a good target for therapeutic antibodies and vaccines, which is in line with what Guo et al. have suggested [64]. Three distinctive regions (555–584, 800–825, and 1145–1162) of the S2 subunit with high B cell epitopes, linked to many positive assays and high RF were noticed without any characterizing mutations of any of the selected Omicron subvariants (see Fig 6).

Interestingly, within the NTD region of the S1 subunit (specifically, 259–280 range) a high number of T cell epitopes were found and studied in a high number of positive assays (resulting in high RFs); this small region does not have any characterizing mutations of the Omicron subvariants.

Omicron is continuously evolving under immune pressure [65], and selection pressure may drive viral mutations for enhanced immune evasion [66]. Compared to previous VOCs, selective immune pressure was noticed by Duerr et al. [63] with the additional booster vaccine shots against the Delta variant during the period of an evolutionary transition from Delta to Omicron BA.1-BA.5. But, some of the variants included in the considered OV set (BA.2 and BA.5) were found to have a selective advantage under booster vaccination pressure, contributing to the evolution of BA.2 and BA.5 subvariants and recombinant forms that predominate in 2023 in which multiple sites underwent strong positive selection with a particular focus on the receptor-binding motif (RBM), including sites 417, 440, 444, 452, 486, and 493 that represent the most immune evasive mutation sites according to Duerr et al. [63], most of those mutations are found in almost all of the variants in our OV (see Fig 2). Other cases of selective immune pressure were also found in other omicron variants included in OV [67–69].

Conclusions

We conducted a database-wide analysis on the impact of lineages’ characterizing mutations on all T cell and B cell linear epitopes collected in the Immune Epitope Database (IEDB) for SARS-CoV-2 focusing on the omicron subvariants. We presented a workflow of this analysis and confirmed our results by performing a Monte Carlo simulation on both the entire dataset and selected epitopes with high response frequencies. Our pipeline delivers a comprehensive analysis of how mutations in omicron subvariants impact T cell and B cell epitopes. This approach focuses on epitopes distribution and mutation impact without pre-assumptions on immune response effects and extends to study the response frequency and assays of these epitopes.

The identification of highly immunogenic and conserved epitopes can inform the design of vaccines that offer broad and long-lasting protection. Our findings suggest specific regions of the Spike protein that are optimal for inclusion in vaccine formulations.

We note that three of the variants with the highest percentages of affected T cell and B cell Spike epitopes are XBB.1.5, XBB.1.16, and EG.5.1 (see Table 5). These variants are the only ones harboring G252V mutation (see Fig 2), which is linked to increased neutralization resistance of these variants [70]. Also, F486 on the Spike protein seems to be a hotspot for mutations with different AA alternatives depending on the variants (F486V in BA.4, BA.5, and BQ.1; F486S in XBB and CH.1.1; and F486P in XBB.1.5, XBB.1.16, XBB.2.3, and EG.5.1). Our analysis allowed us to pinpoint these locations and suggest that these locations are carefully studied before epitopes including them are designed.

The WHO has revised the nomenclature in October 2023 [13]. All variants included in OV are currently considered VOIs (XBB.1.5 and XBB.1.16) or VUMs, or de-escalated variants meaning that, at some point, they were considered as VOCs or VOIs by WHO, CDC, and/or ECDC. According to ECDC, a variant could be de-escalated based on at least one of the following criteria: (1) the variant is no longer circulating, (2) the variant has been circulating for a long time without any impact on the overall epidemiological situation, (3) scientific evidence demonstrates that the variant is not associated with any concerning properties. Designations may continue changing, contributing to making this effort even more relevant as continued monitoring becomes necessary to understand the implications of variants on epitopes. Then, SARS-CoV-2 warning systems could include not only the ability to recognize novel variants at their early stages [28, 29] but also a report on which epitopes are impacted by the arising mutations and suggested implications, in the form that we proposed in this study. Automatization of these analyses is expected in our upcoming work.

Supporting information

S1 Fig Histograms showing the counts of epitopes (y-axis) with calculated RFs (x-axis), for T cell (left) and B cell (right) Spike linear epitopes.

The vertical red line (RF = 0.75) is the selected threshold for filtering both cases.

(TIF)

S2 Fig Coverage of B cell (blue) and T cell (orange) linear epitopes over the Nucleocapsid protein.

Vertical lines indicate positions of mutations from Mv for all v ∈ OV; the darker the green color, the higher the number of variants with a mutation at that position.

(TIF)

S3 Fig Coverage of B cell (blue) and T cell (orange) linear epitopes over the Envelope protein.

Vertical lines indicate positions of mutations from Mv for all v ∈ OV; the darker the green color, the higher the number of variants with a mutation at that position.

(TIF)

S4 Fig Coverage of B cell (blue) and T cell (orange) linear epitopes over the Membrane protein.

Vertical lines indicate positions of mutations from Mv for all v ∈ OV; the darker the green color, the higher the number of variants with a mutation at that position.

(TIF)

S5 Fig Coverage of B cell (blue) and T cell (orange) linear epitopes over the ORF1ab polyprotein.

Vertical lines indicate positions of mutations from Mv for all v ∈ OV; the darker the green color, the higher the number of variants with a mutation at that position.

(TIF)

S6 Fig Monte Carlo-based test results, considering the weighted test version.

We plot the density distribution of T-cell (left) or B-cell (right) simulated epitopes’ factors (see Methods for details), affected by at least one sampled mutation. The red vertical lines (at 1353 T cell and 2071 B cell epitopes) represent the observed sum of the computed factors of all linear epitopes from the Spike protein that have been mutated.

(TIF)

S7 Fig Counts of positive and negative assays performed on T cell (top) and B cell (bottom) Spike highly responsive epitopes, derived from the original Spike linear epitopes dataset, using RF > 0.75 as a threshold for responsiveness.

(TIF)

S8 Fig Average response frequency (RF) flanked by its 95% confidence intervals (CI) of the highly responsive epitopes computed at each position of the Spike protein.

(TIF)

S9 Fig Monte Carlo-based test results, simulating epitopes with RF > 0.75 and considering the naive test version.

We plot the density distribution of T-cell (left) or B-cell (right) simulated epitopes affected by at least one sampled mutation. The red vertical lines (at 162 T cell and 335 B cell epitopes) represent the observed count of linear epitopes from the epitopes in the selected subset of Spike protein that have been mutated.

(TIF)

S1 File Dataset used to generate Fig 3.

For each amino acid of the spike protein, it reports T cell and B cell linear epitope counts and the number of omicron subvariants from the OV set with at least a characterizing mutation affecting that amino acid.

(XLSX)

S2 File Dataset used to generate Fig 5.

For each amino acid of the spike protein, it reports the positive and negative assay experimental counts performed on T cell and B cell spike linear epitopes and calculated as the sum of overlapping linear epitopes with the observed position.

(XLSX)

S3 File Dataset used to generate Fig 6.

For each amino acid of the spike protein, it reports the average RF values and 95% CI (lower and upper bound) of the linear T cell and B cell epitopes.

(XLSX)

The authors would like to thank Prof. Stefano Ceri and Prof. Matteo Chiara for the fruitful discussions that inspired this work.

10.1371/journal.pone.0307873.r001
Decision Letter 0
Raju Nagarajan Academic Editor
© 2024 Nagarajan Raju
2024
Nagarajan Raju
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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15 May 2024

PONE-D-24-11657Systematic analysis of SARS-CoV-2 Omicron subvariants’ impact on B and T cell epitopesPLOS ONE

Dear Dr. Bernasconi,

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Academic Editor

PLOS ONE

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Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The scientific rationale and investigations are sound. Monte Carlo analysis was used as a statistical test of significance. It appears that a complete upload of underlying data and workflow is present, but I suggest upload of the data from one figure to aid potential analysis by future readers (see review comment 4). While the data is presented and described free of spelling and grammatical errors, improvement in the clarity and presentation of the article is needed before acceptance (review comments 1-3, 5).

Reviewer #2: The study entitled “Systematic analysis of SARS-CoV-2 Omicron subvariants’ impact on B and T cell epitopes” presents an extensive analysis of the potential impact of mutations within Omicron subvariants on B- and T-cell epitopes, which is crucial for vaccine and monoclonal antibody development. While this paper makes a significant contribution to the field, it is essential that the information and evidence are well-structured and articulated. Therefore, I believe it is suitable for publication in PLOS ONE.

Some considerations regarding the following minor points:

- Change the short title, as it is identical to the main title.

- The abstract provides relevant introductory information; however, no results are mentioned. Please include the main findings of the paper and any future direction.

- Line 21: “as a candidate to become the most dangerous variant of SARS-CoV-2” appears to be a vague statement. Please rephrase for clarity or consider removing it.

- Check acronyms for consistency. When using an acronym, introduce the full term on first mention, followed by the acronym in brackets, to be used thereafter.

- I am concerned about the timing of retrieving epitope datasets and variant information, which dates back to August 2023, resulting in a lag of over 8 months so far. Could you provide any updates, if possible, at least on the current variants (e.g., JN.1 and its subvariants)?

- Considering the length of the materials and methods section, please include only the relevant information in the main text. Add to supplementary.

- In the discussion, provide additional context and background on the role of immunological pressure and proofreading activity on the epitopes impacted by Omicron mutations.

- What is the implication of the study's finding on antibody recognition and monoclonal antibody development?

- What is the added value of this paper in the current context?

- As the conclusion is integrated within the discussion, please briefly highlight your perspective and the potential value of the paper's findings.

Reviewer #3: This study presents a comprehensive analysis of how mutations in the Omicron variants of SARS-CoV-2 impact T cell and B cell linear epitopes. Utilizing data from the Immune Epitope Database (IEDB), and tried to develop robust analysis pipeline to investigate the distribution of epitopes affected by Omicron-characterizing mutations. The findings highlight novel insights into the coverage of viral proteins by epitopes, the distribution of mutated epitopes across Omicron variants, and the impact of Omicron mutations on epitope recognition. This study somehow provid a detailed understanding of the interplay between viral mutations and immune epitopes, contributes valuable information for future genomic surveillance and epidemic response strategies. The manuscript is acceptable in current form howler it might be advantage to state and discuss following issues with extra detail

1- It would be helpful to provide more context on the significance of the findings in relation to vaccine development and immune response.

2- Consider discussing potential limitations of the study, such as sample size variations or assay biases, and how they may have influenced the results.

**********

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Reviewer #1: No

Reviewer #2: Yes: Mohammad Alkhatib

Reviewer #3: No

**********

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Attachment Submitted filename: Khalaf_et_al_reviewer_comments.docx

10.1371/journal.pone.0307873.r002
Author response to Decision Letter 0
Submission Version1
26 Jun 2024

Please see the "response to reviewers.pdf" file.

Dear Dr. Nagarajan Raju,

We thank you for coordinating the review process for our manuscript, and the three reviewers for their time and efforts in reading and evaluating our work while providing their comments and suggestions. We are glad that the presented analysis was, in general, positively received.

Please find attached the revised manuscript (where changes are marked in red color). Note that our key revisions include the following.

As required by Reviewer #1:

1. Complete revision of all Tables and Figures’ captions to make them more self-explanatory;

2. Change in the structure of data analysis section, where first the complete epitopes dataset is employed, then we restrict our analysis to only highly-responsive epitopes;

3. Explicit mention of what parts of the manuscript use real data and what parts employ simulated data;

4. Provision of three new Supplementary Files for regenerating Figures 3, 5, and 6.

As required by Reviewer #2:

5. More explicit contribution in the abstract;

6. New section on reproducibility and results on recent variants;

7. Reorganization of the Discussion section, highlighting our proposed work’ impact and value.

As required by Reviewer #3:

8. In the discussion, brief hint on implications on vaccine development and immune response;

9. In the discussion, new paragraph on our approach limitations.

Below, we provide a detailed point-by-point response to each reviewer's comment.

We believe that our paper, after careful and complete revision, has greatly improved and deserves publication in PLOS ONE. Thank you for the opportunity to revise and resubmit our manuscript. We appreciate the valuable feedback provided by the reviewers and the editorial team. We look forward to hearing from you regarding the status of our submission.

With best regards,

Anna Bernasconi

(corresponding author)

Reviewer #1

>>>Reviewer #1 (text from doc)

The submitted research article by Khalaf et al. explores the localization of SARS-CoV-2 Omicron variant amino acid mutations within T cell and B cell epitopes collected in the Immune Epitope Database. The work leverages a data-science approach and provides a nice demonstration of analyzing and curating publicly available data. The analyses are appropriate and performed well, but some of their presentation led to confusion. I mark all these issues as ‘minor,’ because I don’t believe any new experiments or re-analysis will need to be performed. Nevertheless, I believe that modification/clarification of the text is critical for publication and to allow the hard work of this group to be clearly presented.

>>>Our response

We thank the Reviewer for positively evaluating our analyses and providing suggestions for better presenting our work. In the revised manuscript, we tried our best efforts to remove any source of confusion and clarify all the relevant steps.

>>>Reviewer #1 (text from doc)

1. Figure legends need to be addressed. Figure legends should enable the figure to allow the reader to reasonably understand the data independent of the main text. Figure 9 is the biggest example. I will not clarify every point, but most figures (and supplementary figures) would benefit from improved description. A couple of specific points:

a. Figure 8, also clarifying this was from simulated data (is it? See below), to prevent confusion with data from Figure 4.

b. Figure 5 (and methodology/main text [Line 340]): what defines “considerable differences” that warrants highlighting

>>>Our response

Thank you for highlighting this issue. In the revised version of the manuscript, we extended most of the figures’ captions (specifically, Figures 3-9, Tables 1, 4-5, and all Supplementary figures), using a more comprehensive style; we believe that now figures’ captions are more self-contained and understandable by readers.

Considering point (a), the data in Figure 8 is real data (not simulated): it was generated using a filter on the calculated response frequency (as it is now highlighted in the caption). Thanks to the Reviewer’s comment, we realized that the organization of the Results section could lead to some confusion. We now first report all the Results related to the full original epitopes dataset (up to Figure 6), validated with a Monte Carlo simulation (i.e., Fig. 7), up to Page 14, line 398.

After, we separately discuss the same analysis restricted to a smaller dataset, only including highly-responsive epitopes (see new subsection ’Highly-responsive epitopes results’ marked on Page 14, lines 399), where we first provide a description of the data distribution (Figure 8), then, we run a Monte Carlo test on simulated data (shown in Figure 9) -- this paragraph concludes on Page 15, line 420.

Considering point (b), we calculated the difference between the assays counts per Spike amino acids and detected the peaks in the difference (i.e., where positive and negative counts differ by at least 25 assays in absolute number). We reported in the manuscript only a relevant selection of these areas. The full dataset to generate the figure is now made available as Supplementary File S2. In the revised version of the manuscript, we rephrased this aspect for better clarity (see Page 12, lines 343-345). Similarly, we now make available Supplementary File S3, used to generate Figure 6.

>>>Reviewer #1 (text from doc)

2. Calling the two different Monte Carlo methodologies “variants” is confusing, since variant is used so frequently used to refer to Omicron lineages. Would suggest finding another phrase to describe the two simulations.

>>>Our response

We understand the concern of the reviewer and apologize for the confusion. We consequently opted for using a different term (i.e., “version”) when referring to the two Monte Carlo methodologies.

>>>Reviewer #1 (text from doc)

3. Lines 391-400. I am confused by this section, and Figure 8. I can’t tell if this is ‘real’ data that has been included in the Monte Carlo simulation section, or simulated data that is not clearly described, or something else that I am misunderstanding. Is this data effectively the data from Figure 4, but using only RF>0.75 epitopes? I think the transition from simulated data to real data within the same section is creating some confusion.

>>>Our response

We apologize for the confusion in this section. Please note that Figure 8 shows the distribution of the real Spike linear highly responsive epitopes (with RF > 0.75) according to the number of selected Omicron subvariants with at least one characterizing mutation affecting the epitope range. As mentioned in the response to point (1a) of this Reviewer, we re-organized the Results for avoiding this confusion..

More specifically, first, in the Results we included all the plots and descriptions related to the full original epitopes dataset (validated with a Monte Carlo simulation, i.e., Fig. 7).

Separately, we discuss the restriction to a smaller dataset, only including highly-responsive epitopes (see new subsection, on Pages 14-15, lines 399-410). Here, we first provide a description of the data distribution (Figure 8), then we briefly comment on the following results (which, for space concerns, have been moved to the Supplementary Figures 7, 8 and 9). Also in this case, we produce a Monte Carlo test on simulated data (shown in Figure 9).

This structure has been explained and reinforced in the first paragraph of the Materials and Methods section (see Page 3, lines 81-82). We trust that, with the new organization, the steps will be clearer.

>>>Reviewer #1 (text from doc)

4. If possible, including XY coordinate values of Figure 3 as an Excel/CSV file in the supplemental data may be helpful. This could allow researchers to quickly manipulate the data and find regions with high epitope coverage more easily.

>>>Our response

We appreciate the suggestion of the reviewer and now make available coordinate values to generate Figure 3 as a CSV file (Supplementary File S1), to allow readers to investigate further details on Spike linear epitopes coverage. Following the same reasons, we now make available Supplementary Files S2 and S3, used to generate Figures 5 and 6, respectively.

>>>Reviewer #1 (text from doc)

5.Line 55: Change “contrast” to “combat”

>>>Our response

Thank you for suggesting this replacement. We duly applied it in the revised version of the manuscript.

>>> Reviewer #1 (text from form)

The scientific rationale and investigations are sound. Monte Carlo analysis was used as a statistical test of significance. It appears that a complete upload of underlying data and workflow is present, but I suggest upload of the data from one figure to aid potential analysis by future readers (see review comment 4). While the data is presented and described free of spelling and grammatical errors, improvement in the clarity and presentation of the article is needed before acceptance (review comments 1-3, 5).

>>>Our response

We are very grateful to the Reviewer, who provide all on-spot comments (comments 1, 2, 3, and 5), which guided us in making the revised manuscript better organized and clear than its previous version. We confirm that also comment 4 has been addressed, by providing additional Supplementary Files with the dataset for generating Figure 3 and 4.

Reviewer #2

>>>Reviewer #2

The study entitled “Systematic analysis of SARS-CoV-2 Omicron subvariants’ impact on B and T cell epitopes” presents an extensive analysis of the potential impact of mutations within Omicron subvariants on B- and T-cell epitopes, which is crucial for vaccine and monoclonal antibody development. While this paper makes a significant contribution to the field, it is essential that the information and evidence are well-structured and articulated. Therefore, I believe it is suitable for publication in PLOS ONE.

>>>Our response

We thank the Reviewer for recognizing the significant contribution that this manuscript conveys. We appreciate the valuable suggestions, which allow us to better articulate our work. In the revised manuscript, we made our best efforts for correcting mistakes and improving the structure as indicated.

>>>Reviewer #2

Some considerations regarding the following minor points:

-Change the short title, as it is identical to the main title.

>>>Our response

We agree with the reviewer that selected editorial metadata entries would benefit from a shorter title, in which case we propose “SARS-CoV-2 Omicron subvariant’s impact on epitopes” to convey a strong message to the readers.

Unfortunately, we did not find an appropriate location for specifying a short title in the Latex template of this Journal. We will inquire with the editorial team if it is possible to add such a shorter title.

>>>Reviewer #2

-The abstract provides relevant introductory information; however, no results are mentioned. Please include the main findings of the paper and any future direction.

>>>Our response

We agree with the reviewer that the previous version of the abstract could be improved. Here, we clarified our contribution of a workflow and another sentence on the reproducibility of the code, which can be re-run on demand to inform genomic surveillance policy making.

>>>Reviewer #2

-Line 21: “as a candidate to become the most dangerous variant of SARS-CoV-2” appears to demand be a vague statement. Please rephrase for clarity or consider removing it.

>>>Our response

Thank you for noting this. We rephrased the sentence (see Page 2, lines 20-21).

>>>Reviewer #2

- Check acronyms for consistency. When using an acronym, introduce the full term on first mention, followed by the acronym in brackets, to be used thereafter.

>>>Our response

We carefully revised the whole manuscript; we believe that in the revised version, all the acronyms are properly introduced on their first mention. We apologize for leaving behind cases that are not properly introduced.

>>>Reviewer #2

-I am concerned about the timing of retrieving epitope datasets and variant information, which dates back to August 2023, resulting in a lag of over 8 months so far. Could you provide any updates, if possible, at least on the current variants (e.g., JN.1 and its subvariants)?

>>>Our response

We would like to thank the reviewer for making this highly relevant point. We completely share the reviewer's concern and believe that a short description of an updated analysis would largely benefit the manuscript and make it more interesting for the potential readers.

To this end, we added a new section in the manuscript, named “Analysis reproducibility: focusing on the most recent variants”. Here, we reinforce the message that our analysis workflow is completely repeatable, with openly shared code. As it can be run at any point in time, we did so at the end of May, 2024. The most relevant result is that – for four important new variants (XBB.1.5.70, HK.3, BA.2.86, and JN.1) that – to date – are considered the new variants to be monitored, the percentage of impacted epitopes is even higher than with previous variants. Details are in the revised manuscript on Page 15-16, lines 421-449.

We trust that, with this comment, the reviewer did not imply to completely transform our manuscript with the new analysis, as this would require to regenerate all figures, descriptions, and layout.

>>>Reviewer #2

-Considering the length of the materials and methods section, please include only the relevant information in the main text. Add to supplementary.

>>>Our response

We understand the reviewer’s concern. However, we feel that the description of our analysis workflow, given in the Materials and Methods section, provides a clear view of our main contribution in this manuscript: a reproducible analysis pipeline, which can be re-run at any point in time using open data and simple analysis metrics. With this motivation, we would prefer to leave all the relevant text in this section; however, we are open to editing if this is required also on the editorial side.

>>>Reviewer #2

- In the discussion, provide additional context and background on the role of immunological pressure and proofreading activity on the epitopes impacted by Omicron mutations.

>>>Our response

We would like to thank the reviewer for highlighting this point. In the revised version of the manuscript, we discussed shortly (on Page 18, lines 535-547) the role of immunological pressure on the immune evading ability of Omicron variants, by providing an example that compares it to Delta variant.

Instead, while we understand the importance of the impact of the proofreading activity on the epitope's regions, in this study, we did not analyze directly such an effect since it is the activity of the Nsp14 exoribonuclease of SARS-CoV-2 which usually repairs errors during replication. Instead, we performed a quantitative and qualitative analysis of the distribution of the T cell and B cell linear epitopes per specific proteins focusing on the Spike.

>>>Reviewer #2

- What is the implication of the study's finding on antibody recognition and monoclonal antibody development?

>>>Our response

We understand the reviewer’s interest in this highly relevant aspect. Please note that, in this study, we did not analyze directly the effects of specific mutations over the antibody recognition and monoclonal antibody. Instead, we gathered the characterizing mutations of Omicron subvariants and quantitatively assessed the epitopes listed in IEDB that have been affected by these mutations.

On Page 11, lines 312-321, we listed noteworthy Omicron characterizing mutations reported in the literature for decreasing the binding affinity to antibodies or being associated with relative resistance to antibodies.

Moreover, in Figure 5 (discussed on Pages 12-13, lines 336-360), for each amino acid of the Spike protein we count the number of assays that has been performed over the epitopes containing that amino acid and we distinguished between the positive and negative ones.

A further step is taken in Figure 6 (discussed on Pages 12-14, lines 361-376), where the average values of the response frequency are calculated

Attachment Submitted filename: response to reviewers.pdf

10.1371/journal.pone.0307873.r003
Decision Letter 1
Raju Nagarajan Academic Editor
© 2024 Nagarajan Raju
2024
Nagarajan Raju
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submission Version1
15 Jul 2024

Systematic analysis of SARS-CoV-2 Omicron subvariants’ impact on B and T cell epitopes

PONE-D-24-11657R1

Dear Dr. Bernasconi,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Nagarajan Raju

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

Reviewer #3: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: (No Response)

Reviewer #2: I appreciate the authors for adequately addressing all comments and I believe the manuscript is suitable for publication in its current form in PLOS ONE.

Reviewer #3: This study presents a comprehensive workflow for analyzing the impact of mutations in SARS-CoV-2 Omicron variants on T cell and B cell epitopes, using data from the Immune Epitope Database, providing valuable insights for genomic surveillance and future epidemic response strategies.

the manuscript has been improved and the comments have been adequately addressed by the authors.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: Yes: Mohammad Alkhatib

Reviewer #3: No

**********

10.1371/journal.pone.0307873.r004
Acceptance letter
Raju Nagarajan Academic Editor
© 2024 Nagarajan Raju
2024
Nagarajan Raju
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
17 Jul 2024

PONE-D-24-11657R1

PLOS ONE

Dear Dr. Bernasconi,

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on behalf of

Dr. Nagarajan Raju

Academic Editor

PLOS ONE
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