Patent Application
20230263883
Described herein are peptide epitopes identified in subjects infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and methods of use thereof for diagnosing, determining prognosis, and treating Coronavirus Disease 2019 (COVID-19), and developing prophylactic or therapeutic vaccines against SARS-CoV-2.
Coronaviruses comprise a large family of enveloped, positive-sense single-stranded RNA viruses that cause diseases in birds and mammals (1). Among the strains that infect humans are the alpha-coronaviruses HCoV-229E and HCoV-NL63 and the beta-coronaviruses HCoV-OC43 and HCoV-HKU1, which cause common colds (
As described herein, VirScan (see PCT/US2018/036663) was used to map a total of 3071 SARS-CoV-2 epitopes, including 813 unique epitopes, with unprecedented resolution. Kinetics of induction and variation in epitope selection were observed over time in recently-infected individuals. A machine learning model was developed, trained on VirScan data to detect SARS-CoV-2 exposure history with very high sensitivity and specificity. VirScan identified public epitopes that are specific to SARS-CoV-2, and we employed these in a rapid Luminex assay to distinguish recently-infected COVID-19 patients from controls. Finally, VirScan enabled us to examine the history of previous viral infections and to determine correlates of COVID-19 outcomes.
Described herein are high throughput anti-SARS-CoV-2 antibody detection methodologies, e.g., the exemplary COVID-19 Luminex assay, which facilitate accurate analyses of seroprevalence. The identification of binding sites of anti-SARS-CoV-2 antibodies provides a stepping stone to the isolation and functional dissection of both neutralizing antibodies and antibodies that might exacerbate patient outcomes through antibody-dependent enhancement (ADE). Finally, the data showed that H COVID-19 patients exhibited a higher incidence of prior infection with CMV and HSV-1 but had lower levels of antibodies to most common viruses, compared to the NH cohorts.
Thus, provided herein are methods for detecting the presence of antibodies that bind to SARS-CoV-2 in a sample. The methods include providing a sample comprising or suspected of comprising antibodies that bind to SARS-CoV-2; contacting the sample with one, two, or more, e.g., 1, 2, 3, 4, 5, 8, 10, 12, 15, 20, 25, 30, 50, 75, 80, 100, 150, 200, 250, 300, 350, 400, 500, 600, 700, 800, or more, peptides comprising 4 or more consecutive amino acids from a SARS-CoV-2 epitope sequence shown herein, e.g., in Table 1, Table 3, and/or Table 4 or SEQ ID NOs:13-1170, under conditions sufficient for binding of antibodies in the sample to the peptides; and detecting binding of antibodies in the sample to the peptides.
In some embodiments, the sample is from a subject, optionally a subject who is known or suspected of being infected with SARS-CoV-2. In some embodiments, the methods include identifying a subject who has antibodies that bind to SARS-CoV-2 as having been infected with SARS-CoV-2. In some embodiments, the methods further include administering a treatment for SARS-CoV-2 to the subject or monitoring the subject for later health consequences of infection with SARS-CoV-2. In some embodiments, the subject is a human subject. In some embodiments, the sample comprises whole blood, serum, saliva or plasma.
In some embodiments, the peptides comprise a detectable moiety, are conjugated to a bead, or are conjugated to a surface. In some embodiments, the detectable moiety is a fluorescent label. In some embodiments, the surface is a multiwell plate or glass coverslip. In some embodiments, the beads are magnetic.
In some embodiments, detecting comprises performing an immunoassay, multiplex immunoassay, protein-fragment complementation assay (PCA), or single molecule array.
Also provided herein are compositions or kits comprising one, two, or a plurality of antigenic peptides comprising 4 or more consecutive amino acids from epitope sequences shown herein, e.g., in Table 1, 3, or 4 or SEQ ID NOs:13-1170, e.g., from one of SEQ ID NOs: 1036-1050.
In some embodiments, at least one of the peptides comprises a detectable moiety, is conjugated to a bead, or is conjugated to a surface. In some embodiments, the detectable moiety is a fluorescent label. In some embodiments, the surface is a multiwell plate or glass coverslip. In some embodiments, the beads are magnetic. In some embodiments, the composition comprises a pharmaceutically acceptable carrier and optionally an adjuvant.
Also provided are the compositions for use in a method of treating or reducing risk of an infection with SARS-CoV-2 in a subject.
Further provided are methods of treating or reducing risk or severity of an infection with SARS-CoV-2 in a subject, the methods comprising administering a therapeutically of prophylactically effective amount of a composition as described herein, comprising one, two, or a plurality of antigenic peptides comprising 4 or more consecutive amino acids from epitope sequences shown herein, e.g., in Table 1, 3, or 4 or SEQ ID NOs:13-1170, e.g., from one of SEQ ID NOs: 1036-1050.
Additionally, provided are methods of generating an antibody to SARS-CoV-2, the method comprising administering the compositions, and optionally an adjuvant, to a mammal, and isolating antibodies from the mammal that bind to SARS-CoV-2.
In addition, provided herein are methods for identifying antibodies that bind to neutralizing or non-neutralizing epitopes of SARS-CoV-2. The methods include providing a sample comprising an antibody obtained, preferably cloned, from a human who has had a SARS-CoV-2 infection; contacting the antibody with peptides comprising one or more, e.g., 1, 2, 3, 4, 5, 8, 10, 12, 15, 20, 25, 30, 50, 75, 80, 100, 150, 200, 250, 300, 350, 400, 500, 600, 700, 800, or more, peptides comprising at least 4, e.g., 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more, consecutive amino acids from a SARS-CoV-2 epitope sequence shown herein, e.g., in Table 1, Table 3, and/or Table 4 or SEQ ID NOs:13-1170, wherein: (i) the peptides comprise non-neutralizing epitopes as shown herein, e.g., from one of SEQ ID NOs: 333-1035 or 1051-1155, and the contacting is performed under conditions to allow binding of the antibody on B cells to the peptides; and identifying the antibody as non-neutralizing if it binds to a peptide that comprises a non-neutralizing epitope; or (ii) the peptides comprise neutralizing epitopes shown herein, e.g., from one of SEQ ID NOs: 1036-1050, and the contacting is performed under conditions to allow binding of the antibody on B cells to the peptides; and identifying the antibody as neutralizing if it binds to a peptide that comprises a neutralizing epitope.
In some embodiments, the methods further include cloning one or more antibodies, wherein cloning the antibodies comprises providing a sample of B cells from a human who has had a SARS-CoV-2 infection; contacting the B cells with peptides including one, two, or more of the epitope sequences shown herein, e.g., in Table 1, Table 3, and/or Table 4, optionally one of one of SEQ ID NOs: 1036-1050; cloning and sequencing B cells encoding antibodies specific for one or more of the epitope sequences; and optionally testing these antibodies for neutralizing activity or Fc-mediated effector function (e.g., antibody-dependent cellular cytotoxicity, complement-dependent cytotoxicity, and antibody-dependent cellular phagocytosis).
In some embodiments, the methods further include formulating the optimized population of antibodies into a pharmaceutical composition by mixing the antibodies with a pharmaceutically acceptable carrier, e.g., to reduce or prevent the evolution of antibodies that are immunodominant but not protective.
In some embodiments, the methods further include administering a therapeutically effective amount of the pharmaceutical composition to a subject in need thereof.
In some embodiments, the methods further include cloning one or more antibodies identified as non-neutralizing into a pharmaceutical composition.
In some embodiments, the methods further include formulating the optimized population of antibodies into a pharmaceutical composition by mixing the antibodies with one or more of a pharmaceutically acceptable carrier, an adjuvant, and/or a SARS-CoV-2 vaccine comprising a SARS-CoV-2 protein, peptide, or nucleic acid encoding a SARS-CoV-2 protein or peptide.
In some embodiments, the methods further include administering a prophylactically effective amount of the pharmaceutical composition to a subject in need thereof.
Also provided herein are methods for selecting a vaccine composition for use in eliciting a prophylactic response to SARS-CoV-2 in a subject. The methods include administering a composition comprising a SARS-CoV-2 protein, peptide, or nucleic acid encoding a SARS-CoV-2 protein or peptide, to a test subject in an amount sufficient to elicit an immune response; obtaining a sample comprising antibodies obtained from the subject; contacting the sample with one or more, e.g., 1, 2, 3, 4, 5, 8, 10, 12, 15, 20, 25, 30, 50, 75, 80, 100, 150, 200, 250, 300, 350, 400, 500, 600, 700, 800, or more, peptides comprising at least 4, e.g., 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more, consecutive amino acids from a SARS-CoV-2 epitope sequence shown herein, e.g., in Table 1, Table 3, and/or Table 4 or SEQ ID NOs:13-1170, under conditions to allow binding of the antibody to the peptides; and detecting binding of antibodies in the sample to the peptides, wherein: (i) the composition of the vaccine excludes one or more epitopes that elicit non-protective antibodies; or (ii) the composition of the vaccine comprises epitopes that elicit protective (neutralizing) antibodies shown herein, e.g., one of SEQ ID NOs: 1036-1050; and selecting a vaccine composition that elicits neutralizing antibodies.
In some embodiments, the vaccine composition comprises one or more mutations in a non-neutralizing epitope.
Also provided are compositions comprising a SARS-CoV-2 protein, peptide, or nucleic acid encoding a SARS-CoV-2 protein or peptide, wherein the SARS-CoV-2 protein, peptide, or nucleic acid encoding a SARS-CoV-2 protein or peptide comprises a mutation in a non-neutralizing epitope sequences shown herein, e.g., in Table 3 or 4, and a pharmaceutically acceptable carrier, and optionally an adjuvant, and the use thereof in eliciting a prophylactic response in a subject.
Further, provided herein are methods for generating an antibody to SARS-CoV-2, the method comprising administering the compositions to a subject.
Additionally provided are methods for treating or reducing risk or severity of an infection with SARS-CoV-2 in a subject, the method comprising administering a therapeutically or prophylactically effective amount of the compositions to the subject. Also provided are kits comprising a composition as described herein, e.g., for use in a method of detecting the presence of antibodies that bind to SARS-CoV-2 in a sample, e.g., to diagnose a subject with COVID-19.
Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Methods and materials are described herein for use in the present invention; other, suitable methods and materials known in the art can also be used. The materials, methods, and examples are illustrative only and not intended to be limiting. All publications, patent applications, patents, sequences, database entries, and other references mentioned herein are incorporated by reference in their entirety. In case of conflict, the present specification, including definitions, will control.
Other features and advantages of the invention will be apparent from the following detailed description and figures, and from the claims.
(A) Phylogeny tree of 50 coronavirus sequences (13) constructed using MEGA X (14, 15). The scale bar indicates the estimated number of base substitutions per site (16). Coronaviruses included in the updated VirScan library are indicated.
(B) Schematic representation of the ORFs encoded by the SARS-CoV-2 genome (12, 17).
(C) Overview of the VirScan procedure (7-10). The coronavirus oligonucleotide library includes 56-mer peptides tiling every 28 amino acids across the proteomes of 10 coronavirus strains, and 20-mer peptides tiling every 5 amino acids across the SARS-CoV-2 proteome. Oligonucleotides were cloned into a T7 bacteriophage display vector and packaged into phage particles displaying the encoded peptides on their surface. The phage library was mixed with sera containing antibodies that bind to their cognate epitopes on the phage surface; bound phage were isolated by immunoprecipitation (IP) with either anti-IgG- or anti-IgA-coated magnetic beads. Lastly, PCR amplification and Illumina sequencing from the DNA of the bound phage revealed the peptides targeted by the serum antibodies.
(D) Detection of antibodies targeting coronavirus epitopes by VirScan. Heatmaps depict the humoral response from COVID-19 patients (n=232) and pre-COVID-19 era control samples (n=190). Each column represents a sample from a unique individual. The color intensity indicates the number of 56-mer peptides from the indicated coronaviruses significantly enriched by IgG antibodies in the serum sample.
(E) Boxplots illustrate the number of peptide hits from the indicated coronaviruses in COVID-19 patients and pre-COVID-19 era controls. The box indicates the interquartile range, with a line at the median. The whiskers represent 1.5 times the interquartile range.
(A) Antibodies targeting SARS-CoV-2 proteins. Each column represents a unique patient sample and each row represents a SARS-CoV-2 protein. The color intensity in each cell of the heatmap indicates the number of 56-mer peptides as in
(B) Boxplots as in
(C) Longitudinal analysis of the antibody response to SARS-CoV-2 for 23 patients with confirmed COVID-19. Days on which a sample was available for analysis are indicated with a black line. Each point represents the maximum antibody fold-change score per SARS-CoV-2 peptide in each sample, colored by protein target.
(A)Example response to S and N proteins from a single COVID-19 patient. The y-axis indicates the strength of enrichment (Z-Score, see methods) of each 56-mer or 20-mer peptide recognized by the IgG antibodies present in the serum sample.
(B) Common responses to S and N proteins across COVID-19 patients. The y-axis indicates the fraction of COVID-19 patient samples (n=348) enriching each 20-mer peptide with either IgG (top panel) or IgA (bottom panel) antibodies.
(C) Comparison of the IgA and IgG responses in individual COVID-19 patients. Each set of two rows represent the IgG and IgA antibody specificities of a single patient, with ten representative COVID-19 patients displayed. Numeric values indicate the degree of enrichment (Z-Score) of each peptide tiling across the S and N proteins.
(A) Gradient boosting machine learning models were trained on IgG and IgA VirScan data from 232 COVID-19 patients and 190 pre-COVID-19 era controls. Separate models were created for the IgG and IgA data, and then a third model (Ensemble) was trained to combine the outputs of the first two.
(B) The plot shows the predicted probability that each sample is positive for COVID-19; true COVID-19 positive samples are shown as darker grey dots, and true COVID-19 negative samples are shown as lighter grey dots. The corresponding confusion matrix for each model is shown below.
(C-D) SHAP analysis to identify the most discriminatory peptides informing the models in (B). The chart in (C) summarizes the relative importance of the most discriminatory peptides increased among COVID-19 patients identified by the IgG and IgA gradient boosting models. The enrichment (log 2(Fold Change) of the normalized read counts in the sample IP versus in no-serum control reactions) of each of these peptides across all samples is shown in (D).
(E) Luminex assay using highly discriminatory SARS-CoV-2 peptides identifies IgG antibody responses in COVID-19 patients but rarely in pre-COVID-19 era controls. Each column represents a COVID-19 individual (n=163) or pre-COVID-19 era control (n=165); each row is a SARS-CoV-2-specific peptide. Peptides containing public epitopes from Rhinovirus A, EBV, and HIV-1 served as positive and negative controls. The color-scale indicates the median fluorescent intensity (MFI) signals after background subtraction.
(F) Receiver operating characteristic (ROC) curve for the Luminex assay predicting SARS-CoV-2 infection history, evaluated by 10× cross-validation. The light grey lines indicate the ROC curve for each test set, the dark line indicates the average, the grey region reflects±1std. dev. The average area under the curve (AUC) is shown.
(G) Left, the predicted probability that each sample is positive for COVID-19 by the Luminex model as in (B). The dashed line indicates the model threshold. Right, confusion matrix for the Luminex model.
(A) Differential recognition of peptides from SARS-CoV-2 nucleoprotein and spike between COVID-19 non-hospitalized patients (n=131), hospitalized patients (n=101), and pre-COVID-19 era negative controls. Each column represents a unique patient and each row represents a peptide tile; tiles are labelled by amino acid start and end position and may be duplicated for intervals for which amino acid sequence diversity are represented in the library. Color intensity represents the degree of enrichment (Z-score) of each peptide in IgG samples. Peptides exhibiting a significant increase in recognition by sera from hospitalized versus non-hospitalized patients are indicated with an asterisk, Kolmogorov-Smirnov test, Bonferroni-corrected p-value thresholds of 0.001 for S and 0.0025 for N).
(B) SARS-CoV-2 Luminex assay identifies stronger IgG responses in hospitalized COVID-19 patients than in non-hospitalized COVID-19 patients. Each column represents either a non-hospitalized (n=32) or hospitalized (n=32) COVID-19+ patient or a pre-COVID-19 era control (n=32); each row represents a peptide in the Luminex assay. The color-scale indicates the median fluorescent intensity (MFI) signals after background subtraction.
(C) All peptides in the VirScan library are plotted by the fraction of non-hospitalized (x-axis) and hospitalized COVID-19 patient IgG samples (y-axis) in which they are recognized. A Z-score threshold of 3.5 was used as an enrichment cutoff to count a peptide as positive. Peptides that exhibit statistically significant associations with hospitalization status are colored by virus of origin (Fisher's exact test, Bonferroni-corrected p-value threshold of 8.52×10-7). All peptides that do not exhibit significant association with hospitalization status are shown in grey. The significant peptides shown are collapsed for high sequence identity.
(D) All peptides derived from CMV present in the VirScan library are plotted by median Z-score for the non-hospitalized (x-axis) and hospitalized COVID-19 patients (y-axis). The line y=x is shown as a dotted line.
(E) Reduced recognition of mild-associated antigens with age. The histogram shows the relative recognition in healthy donors at age 58 compared to age 42 for each unique antigen that was more strongly recognized by antibodies in non-hospitalized than hospitalized COVID-19 patients.
(A)Bar graphs depicting the average number of 56-mer peptides derived from SARS-CoV-2, SARS-CoV, and each of the 4 common HCoVs that are significantly enriched per sample (IgG IP). Error bars represent the 95% confidence interval.
(B) Analysis of cross-reactive epitopes for HCoV S proteins. The upper plot shows the similarity of each region of the SARS-CoV-2 S protein to the corresponding region in the four common HCoVs (see Methods). The frequency of peptide recognition is shown in the bottom two plots. Peptides from each virus are indicated by the colored lines: the length of each line along the x-axis indicates the corresponding region of the SARS-CoV-2 S protein covered by each peptide according to a pairwise protein alignment, and the height of each line corresponds to the fraction of samples in which that peptide scored in either the IgG or IgA IPs. The epitopes mapped in (C) and (D) are highlighted in pink.
(C,D) Mapping of recurrently recognized SARS-CoV-2 S IgG (C) and IgA (D) epitopes by triple-alanine scanning mutagenesis. Each plot represents a 20 amino acid region of the SARS-CoV-2 S protein within the regions highlighted in (B). Each column of the heatmap corresponds to an amino acid position, and each row represents a sample. The color intensity indicates the average enrichment of 56-mer peptides containing an alanine mutation at that site relative to the median enrichment of all mutants of that 56-mer in each sample. COVID-19 patients with a minimum relative enrichment below 0.6 in the specified window are shown. The amino acid sequence across each region of SARS-CoV-2 S, as well as an alignment of the corresponding sequences in the common HCoVs, is shown below each heatmap. Shown are
(A)Mapping of antibody epitopes in the SARS-CoV-2 S protein using triple-alanine scanning mutagenesis. Each column of the heatmap corresponds to an amino acid position, and each row represents a COVID-19+ patient. The color intensity indicates the average enrichment of three triple-alanine mutant 56-mer peptides containing an alanine mutation at that site, relative to the median enrichment of all mutants of that 56-mer. The upper panel shows the fraction of samples that recognized each region of S as mapped by the IgA 56mer versus the IgA and IgG triple-alanine scanning.
(B-C) Detailed plot of triple-alanine scanning mutagenesis in (A) to show the epitope complexity within two regions: S 766-835 (B) and S 406-520 (C). The amino acid sequence at each position is shown on the x-axis. In (B), the fusion peptide and predicted S2′ cleavage site are indicated below the sequence (27, 28); in (C) the unique epitopes identified by the HMM and clustering algorithms are depicted by colored bars. The black dots correspond to ACE2 contact residues in the crystal structure of the RBD receptor complex (6MOJ) (29). Epitopes in regions E9 and E10 were not picked up by the HMM classifier because of their short length; however, these regions scored in multiple samples and correspond to accessible regions in the crystal structure, suggesting they may be true epitopes. Shown are
(D) Cryo-electron microscopy (cryo-EM) structure of the partially-open SARS-CoV-2 spike trimer (6VSB) (30) highlighting the locations of the antibody epitopes mapped by triple-alanine scanning mutagenesis. The three spike monomers are depicted for the two closed and single open-conformation monomers respectively. The RBD of the open monomer is show in light grey. Three of the RBD epitopes from (C) that overlap ACE2 contact residues and are resolved in the cryo-EM structure (E2, E5, E6) are highlighted. The locations of additional public epitopes that were mapped in at least 10 samples across the IgG and IgA experiments are depicted.
(E-H) The locations of four of the epitope footprints mapped in (C) are shown in relation to the RBD-ACE2 binding interface. The upper image for each figure shows the structure (6MOJ) of SARS-2-CoV-2 RBD in complex with ACE2 (cyan). The E2, E5, E6 and E8 epitopes are highlighted. Below each image is the sequence alignment of the regions of the SARS-CoV-2 and the SARS-CoV S proteins encompassing each epitope. The bars indicate each epitope, the black dots indicate residues that directly interact with ACE2 in the crystal structure, and the shaded residues indicate conservation between SARS-CoV-2 and SARS-CoV. Shown are
(A) Alanine scanning mutagenesis data and the corresponding epitopes mapped in the HMM output for the full-length SARS-CoV-2 spike RBD (S334-528). Each column of the heatmap corresponds to an amino acid position, and each row represents a COVID-19+ sample. The second and fourth heatmaps from the top show the alanine-scanning data. The color intensity indicates the average enrichment of triple-alanine mutant 56-mer peptides containing an alanine mutation at that site, relative to the median enrichment of all mutants of that 56-mer in each sample. The first and third plots show the output of the HMM classification. Each position is classified as “no response”, “mapped epitope”, or “mapped critical region”. The top two heatmaps show the data for the IgG IPs; the bottom shows the data for the IgA IPs. Data is shown for samples with a minimum relative enrichment of 0.6 in the window. The row order is the same for each of the heatmaps. Unique epitopes mapped by the hierarchical clustering are shown below the sequence. Epitopes 9 and 10 were not identified by the HMM but the fact that these regions score in multiple samples and are located in surface exposed regions of the RBD structure suggest that they may be true epitopes. Black dots indicate residues that contact ACE2 in the crystal structure of the receptor-bound RBD (6V0J). Shown is
(B-C) Results of the HMM classification and the corresponding alanine scanning data as in (A) for SARS-CoV-2 N25-56
(A) Comparison of the positions of epitopes mapped by the HMM classifier (using the triple-alanine mutagenesis data as input) and the positions of the 20-mer and 56-mer peptides enriched in COVID-19 patient samples. For each plot, the y-axis shows different IgA serum samples and the x-axis shows the amino acid position along ORF1. Each heatmap is on a binary scale. In the top heatmap, the dark color indicates epitopes mapped to each location along the length of ORF1 for each serum sample. The second and third plots show the positions of 20-mer and 56-mer peptides, respectively, that scored with a Z-score>3.5 for each sample.
(B) Fraction of COVID-19 patient IgA samples that recognize each position in ORF1 (top) and S (bottom) as mapped by the 56mer library and the HMM classifier.
(A-F) Heatmaps showing the alanine-scanning profile of epitopes within specific hotspot clusters. IgA epitopes identified by the HMM classifier were clustered based on their start and stop positions into “hotspot” clusters that represent overlapping sets of related antibody footprints. Each heatmap in (A-F) shows the alanine-scanning data for epitopes that clustered into a particular hotspot. The y-axis shows the amino acid position in the SARS-CoV-2 Spike protein. Independent samples are depicted along the x-axis. The color intensity represents the relative enrichment for each residue, as in
(A) The total number of epitopes, unique epitopes, and hotspots mapped for IgG, IgA, and IgG plus IgA (combined) samples.
(B) Number of hotspots mapped in each SARS-CoV-2 ORF; only ORFs with at least one hotspot are shown.
(C)Number of hotspots recognized per patient.
(D)Distribution of the number of patients that recognized each hotspot among the 169 COVID-19+ samples analyzed.
(E) Length distribution of the unique epitopes. Epitopes smaller than 5 amino acids were not considered in the analysis.
(F) Distribution of the number of patients that recognized each unique epitope among the 169 COVID-19+ samples analyzed.
(G)Distribution of the number of epitopes mapped per patient.
(H)Distribution of the number of epitopes mapped per ORF.
(I) Distribution of the linear amino acid distance between epitopes within each protein. This was calculated using the combined IgG and IgA data for each of the 169 COVID-19 patient samples.
(A) Alanine scanning mutagenesis to map antibody epitopes in the SARS-CoV-2 N protein. Each column of the heatmap corresponds to an amino acid position, and each row represents a COVID-19-positive sample. The color intensity indicates the average enrichment of triple-alanine mutant 56-mer peptides containing an alanine mutation at that site, relative to the median enrichment of all mutants of that 56-mer in each sample. The top heatmap show shows the data for the IgG IPs; the bottom heatmap shows the data for IgA IPs.
(B-D) Detailed plot of alanine-scanning in (A) to show the epitope complexity within specified regions of the SARS-CoV-2 N protein
for COVID19-positive samples with a minimum relative-enrichment below 0.55 in the specified window. The x-axis shows the amino acid sequence at each position.
(A) Number of samples classified as positive for SARS-CoV-2 infection among the set of COVID-19 positive sera run on both the VirScan and the ELISA assays (n=45). The left panel shows the ELISA samples that scored above the 99% specificity threshold for at least one of the three single-antigen ELISAs (N, S, RBD). The right panel shows samples that scored for at least 2 of the three ELISAs. (B) Number of samples classified as positive for SARS-CoV-2 infection among the set of COVID-19 positive sera run on both the Luminex and the ELISA assays (n=107) as in (A). (C) Number of samples classified as positive for SARS-CoV-2 infection among the set of COVID-19 positive sera run on both VirScan and the Luminex assays (n=90). (D) Scatterplots showing the correlation between SARS-CoV-2 peptide seroreactivity in the VirScan and Luminex assays among the COVID-19 positive samples run on both assays (n=90). The y-axis shows the log-transformed Luminex MFI values. The x-axis shows the log of normalized VirScan Z-scores. The peptide N365-385 did not score well in VirScan, leading to a relatively weak correlation; however, the overlapping peptide N360-380 performed better in VirScan and showed greater correlation with the Luminex data (R=0.64).
All HSV-1 peptides in the VirScan library are plotted by median Z-score for the non-hospitalized (x-axis) and hospitalized COVID-19 patients (y-axis). The line y=x is shown as a dotted line.
(A) The design of the triple-alanine scanning mutagenesis library. For each wildtype 56-mer peptide we designed a set of mutant peptides containing three consecutive alanine mutations. In the first mutant the first three amino acids were mutated to alanine, and for each consecutive mutant peptide the starting position of the alanine mutations was moved one residue toward the C-terminus. This is repeated along the entire length of the 56mer. The complete triple-alanine scanning library contains peptides encoding triple alanine substitutions tiling across the entire length of every wildtype SARS-CoV-2 56mer. The relative enrichment at each position was calculated as the mean of the three peptides containing a mutation at that position (indicated in grey). Shown are SEQ ID NOs. 1171-1177, in order.
(B). Antibody footprint mapping by triple-alanine scanning. A hypothetical antibody epitope and its hypothetical critical antibody binding residues are shown. The top sequence shows the wild-type 56mer, the sequences in the middle represent the set of triple-alanine mutant peptides tiling across the region containing the hypothetical epitope. The mutant peptides expected to score with reduced relative enrichments based on this hypothetical epitope are indicated. The heatmap on the bottom depicts hypothetical relative enrichment values for this 56mer given the indicated epitope. Because each mutant peptide encodes three consecutive alanine substitutions, the antibody footprint mapped according to the relative enrichment values (bottom) begins two residues prior to the first critical binding residue and ends two residues after the last critical residue. Shown are SEQ ID NOs. 1171 and 1178-1189, in order
The clinical course of Coronavirus Disease 19 (COVID-19)—the disease resulting from SARS-CoV-2 infection—is notable for its extreme variability: while some individuals remain entirely asymptomatic, others experience fever, anosmia, diarrhea, severe respiratory distress, pneumonia, cardiac arrhythmia, blood clotting disorders, liver and kidney distress, enhanced cytokine release and, in a small percentage of cases, death (6). Understanding the factors influencing this spectrum of outcomes is therefore an intense area of research. Disease severity is correlated with advanced age, sex, ethnicity, socio-economic status, and co-morbidities including diabetes, cardiovascular disease, chronic lung disease, obesity, and reduced immune function (6). Additional relevant factors are likely to include the inoculum of virus at infection, the individual's genetic background and viral exposure history. The complex interplay of these elements also determines how individuals respond to therapies aimed at mitigating disease severity. One of the key aspects of human physiology that integrates many of these components is the functionality of the immune system. The immune system is the primary defense against the virus. The outcome of any individual's encounter with the virus is thus dependent on the functionality of the immune system, which depends on a number of factors including genetics, stress, age and the history of prior exposures. Detailed knowledge of the immune response to SARS-CoV-2 could improve our understanding of diverse outcomes and inform the development of improved diagnostics vaccines, and antibody-based therapies.
The first SARS-CoV-2 infection was first reported from Wuhan, China, in December 2019. The genome of the virus has been determined. The genome comprises or flab encoding or flab polyproteins, genes encoding structural proteins including surface (S), envelope (E), membrane (M), and nucleocapsid N proteins, and 6 accessory proteins, encoded by ORF3a, ORF6, ORF7a, ORF7b, and ORF8 genes (Khailany et al., Gene Rep. 2020 June; 19: 100682; Wang et al., J Med Virol. 2020 June; 92(6):667-674. Epub 2020 Mar 20); genomic information is available at the NCBI Severe acute respiratory syndrome coronavirus 2 database (nhc.gov.cn/jkj/s7915/202001/e4e2d5e6f01147e0a8df3f6701d49f33.shtml) and NGDC Genome Warehouse (bigd.big.ac.cn/gwh/).
Here we describe a detailed analysis of the humoral response in COVID-19 patients using VirScan, a programmable phage-display immunoprecipitation and sequencing (PhIP-Seq) technology we developed previously to explore antiviral antibody responses across the human virome (7-9). Cohorts of COVID-19 patients, pre-COVID-19 era negative controls, and longitudinal samples from COVID-19 patients over the course of infection enabled us to characterize SARS-CoV-2-specific antibodies as well as cross-reacting antibodies. These cross-reacting antibodies can confound serological diagnosis of COVID-19. VirScan can also identify virus-specific epitopes that allow one to discriminate between different coronavirus infections. We developed a machine learning model trained on VirScan data that detects SARS-CoV-2 exposure history with extremely high sensitivity and specificity, and we employed the most differentially-recognized SARS-CoV-2 peptides between COVID-19+ patients and pre-COVID-19 era controls in a Luminex assay to produce a fast and reliable diagnostic. We compared the anti-SARS-CoV-2 antibody response and virome-wide exposure history in COVID-19 patients who did or did not require hospitalization in order to identify correlates of disease severity. Finally, we used alanine-scanning mutagenesis coupled with VirScan to map epitopes across the SARS-CoV-2 proteome to single amino acid resolution; over a dozen of these epitopes are located in the receptor binding (RBD) of the spike, and 10 of these are located on the receptor binding motif (RBM) that directly contacts ACE2 and are likely targets of neutralizing antibodies.
Using VirScan, we were able to map a total of over 3,000 SARS-CoV-2 epitopes, including 813 unique epitopes, with unprecedented resolution. Further, we were able to investigate their cross-reactivity with other human and bat coronavirus epitopes.
Antibody profiling of sera from 232 COVID-19 patients and 190 pre-COVID-19 era controls revealed robust antibody recognition of peptides encoded by SARS-CoV-2 among COVID-19 patients compared with controls. These were primarily directed against the S and N proteins, with significant cross-reactivity to SARS-CoV, and milder cross-reactivity with the more distantly related MERS-CoV and the seasonal Human coronaviruses (HCoVs). Cross-reactive responses to SARS-CoV-2 ORF1 were frequently detected in pre-COVID-19 era controls, suggesting that these result from antibodies induced by other pathogens.
Examination of the response at the epitope level revealed the existence of public epitopes targeted by many COVID-19 patients. Using a combination of both 56-mer and 20-mer peptide tiles, together with the alanine scanning mutagenesis library, we mapped epitopes within SARS-CoV-2 at unprecedented resolution.
At the population level, most SARS-CoV-2 epitopes were recognized by both IgA and IgG antibodies. We found individuals often exhibited a “checkerboard” pattern, utilizing either IgG or IgA antibodies against a given epitope. This suggests that a given IgM clone often evolves into either an IgG or an IgA antibody, potentially influenced by local signals, and that, within an individual, there may often be a largely monoclonal response to a given epitope.
Examination of the humoral response to SARS-CoV-2 at the epitope level using the triple-alanine scanning mutagenesis library revealed 145 epitopes in S, 116 in N, and 562 across the remainder of the SARS-CoV-2 proteome (
Using machine learning models trained on VirScan data, we developed a classifier that predicts SARS-CoV-2 exposure history with 99% sensitivity and 98% specificity. We identified peptides frequently and specifically recognized by COVID-19 patients and used these to create a Luminex assay that predicted SARS-CoV-2 exposure with 90% sensitivity and 95% specificity. Remarkably, the Luminex assay only required three peptides to obtain performance comparable to full antigen ELISAs. This highlights the utility of VirScan-based serological profiling in the development of rapid and efficient diagnostic assays based on public epitopes.
The compositions and methods described herein can also be used to detect the presence of antibodies in a sample from a subject to determine whether a subject has been infected with SARS-CoV-2; the presence of antibodies that bind the epitopes indicates that the subject has had an infection with SARS-CoV-2. Thus provided herein are methods and kits for use in determining whether a subject has, or has had, SARS-CoV-2.
The methods can include providing a sample from a subject, e.g., a sample comprising whole blood, serum, saliva or plasma, that comprises antibodies from a subject. In some embodiments, the subject is suspected to have, or to have been exposed to, SARS-CoV-2. In some embodiments, the subject is a mammal, e.g., a human or non-human veterinary subject, e.g., a cat, dog, ferret, Syrian hamster, tiger, lion, mink, bat, or pangolin.
The sample is contacted with one or more, e.g., 1, 2, 3, 4, 5, 8, 10, 12, 15, 20, 25, 30, 50, 75, 80, 100, 150, 200, 250, 300, 350, 400, 500, 600, 700, 800, or more, peptides comprising at least 4, e.g., 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more, consecutive amino acids from a SARS-CoV-2 epitope sequence shown herein, e.g., in Table 1, Table 3, and/or Table 4 or SEQ ID NOs:13-1170, and binding of antibodies in the sample to the peptides (e.g., formation of antibody-epitope complexes) is detected. The presence of antibodies bound to the peptides indicates the presence of the virus in the subject. Preferably, the peptides are at least 4, e.g., 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, up to 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 amino acids long, with each number being an endpoint for a range of sizes.
The methods can include a purification step, in which un-bound epitope peptides, un-bound antibodies, or both, are removed from the sample, or in which bound complexes are isolated from the sample, before detection is performed.
Detection of binding of antibodies to the epitopes can be performed using methods known in the art. In some embodiments, multiplex immunoassays are used, e.g., assays in which the peptides are immobilized on beads (e.g., Luminex (e.g., x eAP@ Assay) Abcam's FirePlex®, or Cytometric Bead Array (CBA) from BD Biosciences) or on a surface (e.g., RayBiotech's Quantibody® glass chip-based array), wherein each species of peptide (i.e., a species is a set of peptides that all share the same sequence) is individually identifiable, e.g., each peptide species is associated with a different label. See, e.g., Fu et al., Clin Chem. 2010 February;56(2):314-8. In some embodiments, split enzymes reconstitution or protein-fragment complementation assays (PCAs) (e.g., as described in Shekhawat and Ghosh, Curr Opin Chem Biol. 2011 December; 15(6): 789-797; Sierecki, ACS Cent. Sci. 2019, 5, 11, 1744-1746; Jones et al., ACS Cent. Sci. 2019, 5, 11, 1768-1776; Li et al., J. Proteome Res. 2019, 18, 8, 2987-2998) or single molecule detection methods (e.g., single molecule array (SIMOA) can be used (Mora et al., AAPS J. 2014 November; 16(6): 1175-1184; Costa et al., PLoS One. 2018; 13(3): e0193670; Chang et al., J Immunol Methods. 2012 Apr. 30; 378(1-2): 102-115; Libre et al., J Vis Exp. 2018; (136): 57421).
In some embodiments, the methods can include quantitating a level of antibodies in a sample, e.g., by detecting a level of antibody/epitope complexes formed.
In some embodiments, the presence and/or level of antibodies that bind to one or more peptide epitopes is comparable to or above the presence and/or level of binding in the disease reference, and the subject has one or more symptoms associated with COVID-19, then the subject has COVID-19 (i.e., a positive result) or had it in the past. In some embodiments, the subject has no overt signs or symptoms of COVID-19, but the presence and/or level of binding to one or more of the peptide epitopes is comparable to or above the presence and/or level of binding in the disease reference, then the subject has or had COVID-19 (i.e., a positive result). In some embodiments, once it has been determined that a person has COVID-19, then a treatment, e.g., as known in the art or as described herein, can be administered.
The methods can also include contacting the samples with peptide epitopes specific for other pathogens, e.g., other viruses, e.g., Severe acute respiratory syndrome coronavirus (SARS-CoV, identified in 2003); cytomegalovirus (CMV); Rhinoviruses A and/or B; Influenza A and/or B, Enteroviruses A, B and/or C; HIV-1, Epstein-Barr virus (EBV), cytomegalovirus (CMV), and Herpes Simplex Virus 1 (HSV-1), or other Human coronaviruses (HCoVs) (e.g., MERS, SARS and other coronaviruses, including alphacoronaviruses (HCoV-229E and HCoV-NL63) and betacoronaviruses (HCoV-HKU1, HCoV-OC43, MERS-CoV, SARS-CoV, SARS-CoV-2)). See, e.g., U.S. Pat. No. 10,768,181. In some embodiments, epitope mapping is performed for the HCoVs to identify HCoV specific epitopes, and these are integrated into the methods described herein to reduce false positives, i.e., some response to SARS-CoV-2 peptides and a very strong response to peptides from another HCoV indicates the presence of an active high-titer response to the HCoV and that the SARS-CoV-2 response is a cross-reaction (and therefore a false positive for SARS-CoV-2).
In these methods, a single sample can be used to detect infection with a plurality of viruses.
In some embodiments, the reference level is the limit of detection of the assay, wherein detection of any level of antibodies that bind to one or more peptide epitopes is considered a positive result. In some embodiments, a reference value is chosen. Suitable reference values can be determined using methods known in the art, e.g., using standard clinical trial methodology and statistical analysis. The reference values can have any relevant form. In some cases, the reference comprises a predetermined value for a meaningful level of binding, e.g., a control reference level that represents a normal level of antibodies, e.g., a level in a subject who was previously exposed to a different coronavirus, and/or a disease reference that represents a level of binding associated with infection, e.g., a level in a subject who has or had a SARS-CoV-2 infection. In some embodiments, the reference value is a combined score that integrates antibody binding to multiple epitopes, determined using a machine learning model.
The predetermined level can be a single cut-off (threshold) value, such as a median or mean, or a level that defines the boundaries of an upper or lower quartile, tertile, or other segment of a clinical trial population that is determined to be statistically different from the other segments. It can be a range of cut-off (or threshold) values, such as a confidence interval. It can be established based upon comparative groups, such as where association with risk of developing disease or presence of disease in one defined group is a fold higher, or lower, (e.g., approximately 2-fold, 4-fold, 8-fold, 16-fold or more) than the risk or presence of disease in another defined group. It can be a range, for example, where a population of subjects (e.g., control subjects) is divided equally (or unequally) into groups, such as a low-risk group, a medium-risk group and a high-risk group, or into quartiles, the lowest quartile being subjects with the lowest risk and the highest quartile being subjects with the highest risk, or into n-quantiles (i.e., n regularly spaced intervals) the lowest of the n-quantiles being subjects with the lowest risk and the highest of the n-quantiles being subjects with the highest risk.
In some embodiments, the predetermined level is a level or occurrence in the same subject, e.g., at a different time point, e.g., an earlier time point.
Subjects associated with predetermined values are typically referred to as reference subjects. For example, in some embodiments, a control reference subject does not have COVID-19 and/or has not been exposed to COVID-19.
A disease reference subject is one who has (or has had) COVID-19.
Thus, in some cases the level of antibody binding to an epitope described herein in a subject being less than or equal to a reference level of binding is indicative of a clinical status (e.g., indicative of absence of infection). In other cases the level of binding in a subject being greater than or equal to the reference level of binding is indicative of the presence of infection or a past infection. In some embodiments, the amount by which the level in the subject is the less than the reference level is sufficient to distinguish a subject from a control subject, and optionally is a statistically significantly less than the level in a control subject. In cases where the level of binding in a subject being equal to the reference level of binding, the “being equal” refers to being approximately equal (e.g., not statistically different).
The predetermined value can depend upon the particular population of subjects (e.g., human subjects) selected. Accordingly, the predetermined values selected may take into account the category (e.g., sex, age, health, risk, presence of other diseases) in which a subject (e.g., human subject) falls. Appropriate ranges and categories can be selected with no more than routine experimentation by those of ordinary skill in the art.
In characterizing likelihood, or risk, numerous predetermined values can be established.
In some embodiments, once a subject has been diagnosed with COVID-19 using a method described herein, a treatment can be administered. Treatments for COVID-19 are known in the art and include quarantining the subject, administration of an antiviral medication (e.g., remdesivir, Favipiravir, MK-4482; Lopinavir and ritonavir); Recombinant ACE-2; Ivermectin; Oleandrin; bradykinin signaling blockers (e.g., icatibant, ecallantide lanadelumab); vasopressors; Vitamin D; steroids (e.g., Dexamethasone); Cytokine Inhibitors; Convalescent plasma/antibodies; Interferons; ventilation/respiratory support devices; Anticoagulants. Alternatively, if the active infection is past but it is found that infections can predispose an individual with other ailments such as heart or kidney disease or a predisposition for future strokes, they could be monitored more closely for those diseases later in their lives.
An important goal is to uncover serological elements that either correlate with, or predict the severity of, COVID-19 disease. To this end, we compared cohorts of COVID-19 patients who had (H) or had not (NH) required hospitalization. Using both VirScan and the COVID-19 Luminex assay, we noticed a striking and somewhat counterintuitive increase in recognition of peptides derived from the SARS-CoV-2 S and N proteins among the H group, with more extensive epitope spreading. Whether this is a cause or a consequence of severe disease is not clear. Individuals whose innate and adaptive immune responses are not able to quell the infection early may experience a higher viral antigen load, a prolonged period of antibody evolution and epitope spreading. Consequently, these patients might develop stronger and broader antibody responses to SARS-CoV-2 and could be more likely to have hyperinflammatory reactions such as cytokine storms that increase the probability of hospitalization. We noticed that hospitalized males had stronger antibody responses to SARS-CoV-2 than hospitalized females. This may indicate that males in this group are less able to control the virus soon after infection and is consistent with reported differences in disease outcomes for males and females. The presence of antibodies that bind to these epitopes (in the SARS-CoV-2 nucleoprotein) can be used to identify subjects who are likely to have a more severe response.
VirScan also allowed us to examine viral exposure history, which revealed two striking correlations. First, the seroprevalence of CMV and HSV-1 was much greater in the H group compared to the NH group. The demographic differences in our relatively small cohort of H versus NH COVID-19 patients make it impossible for us to determine with certainty if CMV or HSV-1 infection impacts disease outcome or is simply associated with other covariates such as age, race and socioeconomic status. While CMV prevalence does slightly increase with age after 40 (31), its prevalence also differs greatly among ethnic and socioeconomic groups (32). CMV is a herpes virus that exhibits latency within the host and is known to have a profound impact on the immune system; it can skew the naive T-cell repertoire (33), decrease T and B cell function (34), and is associated with higher systemic levels of inflammatory mediators (35, 36). CMV latency also results in inversion of CD4+ and CD8+ T-cell numbers, poor proliferation response of T-cells, low B cell numbers, and has been associated with increased mortality of people over 65 years of age (37). CMV's effects on the immune system could potentially impact the response to SARS-CoV-2 infection in an older population. The effects of CMV on the immune system could impact COVID-19 outcomes.
The second striking correlation we observed was a significant decrease in the levels of antibodies targeting ubiquitous viruses such as Rhinoviruses, Enteroviruses, and Influenza viruses, in COVID-19 H patients compared with NH patients. When we examined only the CMV+ or HSV-1+ individuals in the two groups, we found that the strength of the antibody response to CMV and HSV-1 peptides was also reduced in the H group. We examined the effects of age on viral antibody levels in a pre-COVID-19 era cohort and found a diminution with age in the antibody response against viral peptides differentially recognized between the H and NH groups, consistent with previous studies on the effects of aging on the immune system (38). This inferred reduced immunity during aging could impact the severity of COVID-19 outcomes. Thus, the presence of decreased levels antibodies to CMV and/or HSV-1 epitopes can be used to identify subjects who are likely to have a more severe response.
In correlative analyses such as these, it is difficult to draw strong conclusions about causality given the demographical differences in the NH vs H groups. The NH group is younger, has a higher percentage of Caucasian individuals, and has more females (average age 42, 66% female) versus H (average age 58, 42% female). This is consistent with the well-documented age, race and sex differences among the more severely affected individuals (25, 26). However, even if age and other demographic factors are covariates, the reduction in immune function with age and CMV status described here could still impact severity of infection.
The present methods can include identifying public and/or immunodominant epitopes that are the targets of non-protective antibodies and generating vaccines in which these epitopes are disrupted or removed, or delivering vaccines together with antibodies against these epitopes, with the goal of reducing the production of non-protective antibodies against these epitopes and boosting the production of more protective antibodies.
As demonstrated herein, certain epitopes are more likely to be associated with neutralizing antibodies, while others may be more likely to generate immunodominant non-neutralising antibodies. It is believed that the epitopes within the receptor binding domain (see Table 4, SEQ ID NOs. 1036-1050, are believed to be associated with neutralizing antibodies. Thus, provided herein are methods for generating vaccines that are less likely to generate antibodies that bind to non-neutralizing epitopes. The methods can include administering to a mammal, e.g., a rodent (e.g., rat or mouse), rabbit, ferret, hamster, or a human or non-human primate, a composition comprising a mutated version of SARS-CoV-2 proteins, or nucleic acids encoding mutated versions of SARS-CoV-2 proteins, wherein the mutated versions comprises one or more mutations that disrupt one or more non-neutralizing epitopes as described herein, allowing sufficient time for an immune response to occur in the mammal, and obtaining antibodies from the mammal, then screening the antibodies and identifying mutant viral proteins that produce higher titers of neutralizing antibodies, but produce fewer, or do not produce any, antibodies to non-neutralizing epitopes. These methods can be used to identify and select mutations that reduce the generation of antibodies to non-neutralizing epitopes. In some embodiments, the methods are used to reduce the possibility of inducing or increasing risk of post-viral syndrome in subjects vaccinated with an antibody vaccine.
Also provided herein are mutated versions of SARS-CoV-2 proteins, or nucleic acids encoding mutated versions of SARS-CoV-2 proteins, wherein the mutations remove one or more of the non-neutralizing epitopes described herein. The mutated nucleic acids or proteins can be used to generate vaccine compositions, wherein administration of the composition would result in generation of antibodies to neutralizing epitopes but with fewer antibodies to non-neutralizing epitopes.
Also provided herein are methods that can be used for identifying those antibodies that are most likely to induce a protective immune response. The methods include providing a sample comprising (or expected to comprise) antibodies to SARS-CoV-2 from a subject who has been administered a vaccine to SARS-CoV-2; contacting the sample with one or more peptides as described herein, and detecting binding of the sample to the peptides. Vaccines that produce antibodies that bind to epitopes associated with neutralizing antibodies are likely to induce a protective response, and can be selected for further development, while vaccines that produce an antibody response to non-neutralizing epitopes, or to both neutralizing and non-neutralizing epitiopes, may be less desirable.
The present methods can also include isolating and identifying protective and non-protective antibodies from SARS-CoV-2 patient samples. The methods can include providing a sample including B cells or antibodies to SARS-CoV-2, e.g., obtained from a human subject infected with SARS-CoV-2; contacting the sample with one or more peptides described herein, and isolating B cells or antibodies that bind these peptides. The antibodies may then be tested for protective function via neutralizing activity or Fc-mediated effector function.
The methods can further include formulating the antibodies that bind neutralizing epitopes for administration as a therapeutic, and administering the antibodies that bind neutralizing epitopes to a subject, e.g., a subject who has or is at risk of contracting an infection with SARS-CoV-2. In some embodiments, the methods include detecting binding to antibodies that bind non-neutralizing epitopes, and optionally removing antibodies that bind non-neutralizing epitopes. The methods can also include isolating antibodies that bind to the non-neutralizing epitopes, and adding those antibodies to non-neutralizing epitopes to a vaccine, such that the non-neutralizing epitopes are covered (not accessible), and thus can be eliminated from the response because they are covered and are not capable of eliciting an antibody response.
Also provided herein are methods for generating antibodies to SARS-CoV-2. Methods for making suitable antibodies are known in the art. One or more of the peptides listed in Tables 1, 3, and/or 4, e.g., SEQ ID NO: 1036-1050, can be used as an immunogen, or can be used to identify antibodies made with other immunogens, e.g., cells, membrane preparations, and the like, e.g., E rosette positive purified normal human peripheral T cells, as described in U.S. Pat. Nos. 4,361,549 and 4,654,210.
Methods for making monoclonal antibodies are known in the art. Basically, the process involves obtaining antibody-secreting immune cells (lymphocytes) from the spleen of a mammal (e.g., mouse) that has been previously immunized with the antigen of interest (e.g., a neutralizing epitope antigen) either in vivo or in vitro. The antibody-secreting lymphocytes are then fused with myeloma cells or transformed cells that are capable of replicating indefinitely in cell culture, thereby producing an immortal, immunoglobulin-secreting cell line. The resulting fused cells, or hybridomas, are cultured, and the resulting colonies screened for the production of the desired monoclonal antibodies. Colonies producing such antibodies are cloned, and grown either in vivo or in vitro to produce large quantities of antibody. A description of the theoretical basis and practical methodology of fusing such cells is set forth in Kohler and Milstein, Nature 256:495 (1975), which is hereby incorporated by reference.
Mammalian lymphocytes are immunized by in vivo immunization of the animal (e.g., a mouse) with a neutralizing epitope antigen. Such immunizations are repeated as necessary at intervals of up to several weeks to obtain a sufficient titer of antibodies. Following the last antigen boost, the animals are sacrificed and spleen cells removed.
Fusion with mammalian myeloma cells or other fusion partners capable of replicating indefinitely in cell culture is effected by known techniques, for example, using polyethylene glycol (“PEG”) or other fusing agents (See Milstein and Kohler, Eur. J. Immunol. 6:511 (1976), which is hereby incorporated by reference). This immortal cell line, which is preferably murine, but can also be derived from cells of other mammalian species, including but not limited to rats and humans, is selected to be deficient in enzymes necessary for the utilization of certain nutrients, to be capable of rapid growth, and to have good fusion capability. Many such cell lines are known to those skilled in the art, and others are regularly described.
Procedures for raising polyclonal antibodies are also known. Typically, such antibodies can be raised by administering the protein or polypeptide of the present invention subcutaneously to New Zealand white rabbits that have first been bled to obtain pre-immune serum. The antigens can be injected at a total volume of 100:1 per site at six different sites. Each injected material will contain synthetic surfactant adjuvant pluronic polyols, or pulverized acrylamide gel containing the protein or polypeptide after SDS-polyacrylamide gel electrophoresis. The rabbits are then bled two weeks after the first injection and periodically boosted with the same antigen three times every six weeks. A sample of serum is then collected 10 days after each boost. Polyclonal antibodies are then recovered from the serum by affinity chromatography using the corresponding antigen to capture the antibody. Ultimately, the rabbits are euthanized, e.g., with pentobarbital 150 mg/Kg IV. This and other procedures for raising polyclonal antibodies are disclosed in E. Harlow, et. al., editors, Antibodies: A Laboratory Manual (1988).
In addition to utilizing whole antibodies, the invention encompasses the use of binding portions of such antibodies. Such binding portions include Fab fragments, F(ab′)2 fragments, and Fv fragments. These antibody fragments can be made by conventional procedures, such as proteolytic fragmentation procedures, as described in J. Goding, Monoclonal Antibodies: Principles and Practice, pp. 98-118 (N.Y. Academic Press 1983).
The antibody can also be a single chain antibody. A single-chain antibody (scFV) can be engineered (see, for example, Colcher et al., Ann. N. Y. Acad. Sci. 880:263-80 (1999); and Reiter, Clin. Cancer Res. 2:245-52 (1996)). The single chain antibody can be dimerized or multimerized to generate multivalent antibodies having specificities for different epitopes of the same target protein. In some embodiments, the antibody is monovalent, e.g., as described in Abbs et al., Ther. Immunol. 1(6):325-31 (1994), incorporated herein by reference.
Also provided herein are compositions for use in eliciting a protective immune response to SARS-CoV-2 comprising one or more peptides as described herein that bind to a neutralizing epitope. The compositions can also include an adjuvant to increase T cell response. For example, nanoparticles that enhance T cell response can be included, e.g., as described in Stano et al., Vaccine (2012) 30:7541-6 and Swaminathan et al., Vaccine (2016) 34:110-9. See also Panagioti et al., Front. Immunol., 16 Feb. 2018; doi.org/10.3389/fimmu.2018.00276. Alternatively or in addition, an adjuvant comprising poly-ICLC (carboxymethylcellulose, polyinosinic-polycytidylic acid, and 25 poly-L-lysine double-stranded RNA), Imiquimod, Resiquimod (R-848), CpG oligodeoxynuceotides and formulations (IC31, QB10), AS04 (aluminium salt formulated with 3-O-desacyl-4′-monophosphoryl lipid A (MPL)), ASO1 (MPL and the saponin QS-21), MPLA, STING agonists, other TLR agonists, Candida albicans Skin Test Antigen (Candin), GM-CSF, Fms-like tyrosine kinase-3 ligand (Flt3L), 30 and/or IFA (Incomplete Freund's adjuvant) can also be used. See, e.g., Coffman et al., Immunity. 2010 Oct. 29; 33(4): 492-503. See, e.g., WO2006071896.
These compositions can be administered in a therapeutically effective amount to subjects who have, or in a prophylactically effective amount to subjects who are at risk of developing, an infection with SARS-CoV-2. In some embodiments, the methods include administering two or more doses of the composition (e.g., an initial dose and a booster dose), e.g., 1, 2, 3, 4, 5, 6, 7, or 8, 12, 18, 24, or 52 weeks apart. In some embodiments, the methods include administering annual doses of the compositions, e.g., a prophylactically effective amount.
The present compositions can be used prophylactically to induce anti-SARS-CoV-2 immunity, or therapeutically to treat a SARS-CoV-2 infection in a subject. The methods include administering one or more doses of the vaccine compositions described herein to a subject, e.g., a subject in need thereof.
A therapeutically effective amount as used herein is an amount sufficient to reduce one or more symptoms of a SARS-CoV-2 infection in a subject, or to reduce the length of time that the subject is infected or is symptomatic. A prophylactically effective amount as used herein is an amount sufficient to reduce risk of a subject developing a SARS-CoV-2 infection, or reduce the risk that the subject will experience severe morbidity or mortality associated with a SARS-CoV-2 infection.
Dosage, toxicity and therapeutic efficacy of the therapeutic compositions can be determined by standard pharmaceutical procedures in cell cultures or experimental animals, e.g., for determining the LD50 (the dose lethal to 50% of the population) and the ED50 (the dose therapeutically effective in 50% of the population). The dose ratio between toxic and therapeutic effects is the therapeutic index and it can be expressed as the ratio LD50/ED50. Compositions that exhibit high therapeutic indices are preferred. While compositions that exhibit toxic side effects may be used, care should be taken to minimize and reduce side effects.
The data obtained from cell culture assays and animal studies can be used in formulating a range of dosage for use in humans. The dosage of such compounds lies preferably within a range of circulating concentrations that include the ED50 with little or no toxicity. The dosage may vary within this range depending upon the dosage form employed and the route of administration utilized. For any compositions used in the methods described herein, the therapeutically effective dose can be estimated initially from cell culture assays. A dose may be formulated in animal models. Such information can be used to more accurately determine useful doses in humans.
Alternatively or in addition, peptides described herein as associated with a neutralizing response can be used to generate antibodies, e.g., for use in vaccines for inducing a protective response or for use in treating subjects. These methods include immunizing an animal, e.g., a mouse, rat, rabbit, guinea pig, goat, sheep, llama, or camel, with an amount of the peptides sufficient to induce an immune response. The antibodies can be isolated from the animals using known methods and formulated for administration as a therapeutic or prophylactic treatment as described herein. The antibodies can optionally be humanized or otherwise rendered less immunogenic before administration.
Also provided herein are kits and compositions comprising one or more of the peptides described herein. The peptides can be, e.g., labeled and/or conjugated to beads or surfaces for use in a method of screening as described herein. Beads useful in the present methods and compositions include magnetic beads, polystyrene beads, and agarose beads. Methods of conjugating the peptides to a bead or a surface are known and can include conjugations via carboxy, aldehyde, azide, or alkyne groups; avidin/streptavidin binding; or protein A/G binding. Exemplary beads include Luminex MAGPLEX Microspheres (carboxylated polystyrene micro-particles dyed into spectrally distinct sets) and DYNABEADS magnetic beads. Surfaces useful in the present methods and compositions include columns, culture dishes, assay plates such as multiwell assay plates, and coverslips, e.g., glass coverslips.
Also provided are pharmaceutical compositions, which typically include a pharmaceutically acceptable carrier. As used herein the language “pharmaceutically acceptable carrier” includes saline, solvents, dispersion media, coatings, antibacterial and antifungal agents, isotonic and absorption delaying agents, and the like, compatible with pharmaceutical administration.
Pharmaceutical compositions are typically formulated to be compatible with its intended route of administration. Examples of routes of administration include parenteral, e.g., intravenous, intradermal, subcutaneous, intratumoral, intramuscular or subcutaneous administration.
Methods of formulating suitable pharmaceutical compositions are known in the art, see, e.g., Remington: The Science and Practice of Pharmacy, 21st ed., 2005; and the books in the series Drugs and the Pharmaceutical Sciences: a Series of Textbooks and Monographs (Dekker, NY). For example, solutions or suspensions used for parenteral, intradermal, intramuscular, or subcutaneous application can include the following components: a sterile diluent such as water for injection, saline solution, fixed oils, polyethylene glycols, glycerine, propylene glycol or other synthetic solvents; antibacterial agents such as benzyl alcohol or methyl parabens; antioxidants such as ascorbic acid or sodium bisulfite; chelating agents such as ethylenediaminetetraacetic acid; buffers such as acetates, citrates or phosphates and agents for the adjustment of tonicity such as sodium chloride or dextrose. pH can be adjusted with acids or bases, such as hydrochloric acid or sodium hydroxide. The parenteral preparation can be enclosed in ampoules, disposable syringes or multiple dose vials made of glass or plastic.
Pharmaceutical compositions suitable for injectable use can include sterile aqueous solutions (where water soluble) or dispersions and sterile powders for the extemporaneous preparation of sterile injectable solutions or dispersion. For intravenous administration, suitable carriers include physiological saline, bacteriostatic water, Cremophor EL™ (BASF, Parsippany, N.J.) or phosphate buffered saline (PBS). In all cases, the composition must be sterile and should be fluid to the extent that easy syringability exists. It should be stable under the conditions of manufacture and storage and must be preserved against the contaminating action of microorganisms such as bacteria and fungi. The carrier can be a solvent or dispersion medium containing, for example, water, ethanol, polyol (for example, glycerol, propylene glycol, and liquid polyetheylene glycol, and the like), and suitable mixtures thereof. The proper fluidity can be maintained, for example, by the use of a coating such as lecithin, by the maintenance of the required particle size in the case of dispersion and by the use of surfactants. Prevention of the action of microorganisms can be achieved by various antibacterial and antifungal agents, for example, parabens, chlorobutanol, phenol, ascorbic acid, thimerosal, and the like. In many cases, it will be preferable to include isotonic agents, for example, sugars, polyalcohols such as mannitol, sorbitol, sodium chloride in the composition. Prolonged absorption of the injectable compositions can be brought about by including in the composition an agent that delays absorption, for example, aluminum monostearate and gelatin.
Sterile injectable solutions can be prepared by incorporating the active compound in the required amount in an appropriate solvent with one or a combination of ingredients enumerated above, as required, followed by filtered sterilization. Generally, dispersions are prepared by incorporating the active compound into a sterile vehicle, which contains a basic dispersion medium and the required other ingredients from those enumerated above. In the case of sterile powders for the preparation of sterile injectable solutions, the preferred methods of preparation are vacuum drying and freeze-drying, which yield a powder of the active ingredient plus any additional desired ingredient from a previously sterile-filtered solution thereof.
In one embodiment, the therapeutic compounds are prepared with carriers that will protect the therapeutic compounds against rapid elimination from the body, such as a controlled release formulation, including implants and microencapsulated delivery systems. Biodegradable, biocompatible polymers can be used, such as ethylene vinyl acetate, polyanhydrides, polyglycolic acid, collagen, polyorthoesters, and polylactic acid. Such formulations can be prepared using standard techniques, or obtained commercially, e.g., from Alza Corporation and Nova Pharmaceuticals, Inc. Liposomal suspensions (including liposomes targeted to selected cells with monoclonal antibodies to cellular antigens) can also be used as pharmaceutically acceptable carriers. These can be prepared according to methods known to those skilled in the art, for example, as described in U.S. Pat. No. 4,522,811.
The pharmaceutical compositions can be included in a container, pack, or dispenser together with instructions for administration.
The invention is further described in the following examples, which do not limit the scope of the invention described in the claims.
The following materials and methods were used in the Examples below.
Sources of serum used in this study
Cohort 1
Plasma samples were from volunteers recruited at Brigham and Women's Hospital who had recovered from a confirmed case of Corona Virus Infectious Disease 19 (COVID-19). All volunteers had a PCR-confirmed diagnosis of COVID-19 prior to being admitted to the study. Volunteers were invited to donate specimens after recovering from their illness and were required to be symptom free for a minimum of 7 days. Participants provided verbal and/or written informed consent and provided blood specimens for analysis. Clinical data including date of initial symptom onset, symptom type, date of diagnosis, date of symptom cessation, and severity of symptoms was recorded for all participants, as were results of COVID-19 molecular testing. Participation in these studies was voluntary and the study protocols have been approved by the respective Institutional Review Boards.
Cohort 2
Serum samples were provided by collaborators from University of Washington in patients with PCR-confirmed COVID-19 cases while admitted to the hospital. Residual clinical blood specimens were used, as well as patients who were actively enrolled into a prospective study of COVID-19 infection. Clinical data, including symptom duration and comorbidities were extracted from the medical records and from participant-completed questionnaires. All study procedures have been approved by the University of Washington Institutional Review Board.
Cohort 3
Plasma samples were provided by collaborators from Ragon Institute of MGH, MIT and Harvard and Massachusetts General Hospital from study participants in three settings: 1) PCR-confirmed COVID-19 cases while admitted to the hospital; 2) PCR-confirmed SARS-CoV-2 infected cases seen in an ambulatory setting; 2) PCR-confirmed COVID-19 cases in their convalescent stage. All study participants provided verbal and/or written informed consent. Basic data on days since symptom onset were recorded for all participants as were results of COVID-19 molecular testing. Participation in these studies was voluntary and the study protocols have been approved by the Partners Institutional Review Board.
Cohort 4
Patients were enrolled in the Emergency Department (ED) in Massachusetts General Hospital from 3/15/2020 to 4/15/2020 in Boston at the during the peak of the COVID-19 surge, with an institutional IRB-approved waiver of informed consent. These included patients 18 years or older with a clinical concern for COVID-19 upon ED arrival, and with acute respiratory distress with at least one of the following: 1) tachypnea≥22 breaths per minute, 2) oxygen saturation≤92% on room air, 3) a requirement for supplemental oxygen, or 4) positive-pressure ventilation. A blood sample was obtained in a 10 mL EDTA tube concurrent with the initial clinical blood draw in the ED. Day 3 and day 7 blood draws were obtained if the patient was still hospitalized at those times. Clinical course was followed to 28 days post-enrollment, or until hospital discharge if that occurred after 28 days.
Enrolled subjects who were SARS-CoV-2 positive were categorized into four outcome groups: 1) Requiring mechanical ventilation with subsequent death, 2) Requiring mechanical ventilation and recovered, 3) Requiring hospitalization on supplemental oxygen but not requiring mechanical ventilation, and 4) Discharge from ED and not subsequently readmitted with supplemental oxygen. Those who were SARS-CoV-2 negative were categorized as Controls.
Demographic, past medical and clinical data were collected and summarized for each outcome group, using medians with interquartile ranges and proportions with 95% confidence intervals, where appropriate.
Cohorts 5, 6
Longitudinal Hopkins Cohort: Remnant serum specimens were collected longitudinally from PCR confirmed COVID-19 patients seen at Johns Hopkins Hospital. Samples were de-identified prior to analysis, with linked time since onset of symptom information. Specimens were obtained and utilized in accordance with an approved IRB protocol.
Cohorts 7-8
Cohorts 7-8 were previously published (9, 10).
Cohort 9
Plasma samples were collected from consented participants of the Partner's Biobank program at BWH during the period from July to August 2016 from 37 female and 51 male individuals with ages ranging from 18 to 85 years old. Plasma was harvested after a 10 minutes 1200xg ficoll density centrifugation from blood that was diluted 1:1 in phosphate buffered saline. Samples were frozen at −30 C in 1 mL aliquots. All samples were collected with Partners Institutional Review Board (IRB) approval.
For cohorts 1-3: Blood samples were collected into EDTA (Ethylenediamine Tetraacetic Acid) tubes and spun for 15 minutes at 2600 rpm according to standard protocol. Plasma was aliquoted into 1.5 ml cryovials and stored in −80° C. until analyzed. Only de-identified plasma aliquots including metadata (e.g., days since symptom onset, severity of illness, hospitalization, ICU status, survival) were shared for this study. When appropriate for non-convelescent samples plasma/serum was also heat inactivated at 56° C. for 60 minutes, and stored at ≤20C until analyzed.
For cohort 4: Blood samples were collected in EDTA tubes, and processed no more than 3 hours post blood draw in a Biosafety Level 2+ laboratory on site. Whole blood was diluted with room temperature RPMI medium in a 1:2 ratio to facilitate cell separation for other analyses using the SepMate PBMC isolation tubes (STEMCELL) containing 16 ml of Ficoll (GE Healthcare). Diluted whole blood was centrifuged at 1200 rcf for 20 minutes at 20C. After centrifugation, plasma (5 mL) was pipetted into 15 mL conical tubes and placed on ice during PBMC separation procedures. Plasma was then centrifuged at 1000 rcf for 5 min at 4C, pipetted in 1.5 mL aliquots into 3 cryovials (4.5 mL total), and stored at −80C. For the current study samples (200 uL) were first randomly allocated onto a 96 well plate based on disease outcome grouping.
Multiple VirScan libraries were constructed and each peptide was encoded in two distinct ways so there were distinguishable duplicate peptides for each fragment described below. We created ˜200 nt oligos encoding peptide sequences 56 AAs in length, tiled with 28-amino acid overlap through the proteomes of all coronaviruses known to infect humans including HCov-NL63, HCoV-229E, HCoV-OC43, HCoV-HKU1, SARS-CoV, MERS and SARS-CoV-2 as well as three closely related bat viruses: BatCoV-Rp3, BatCoV-HKU3 and BatCoV-279. For SARS-CoV-2 we included a number of coding variants available in early sequencing of the viruses. For SARS-CoV-2 we additionally made a 20 AA tiling library with 15-AA overlap. Additionally, for SARS-CoV-2 we made triple-mutant sequences scanning through all 56-mer peptides. Non-alanine AAs were mutated to alanine, and alanines were mutated to glycine. We reverse-translated the peptide sequences into DNA sequences that were codon-optimized for expression in Escherichia coli, that lacked restriction sites used in downstream cloning steps (EcoRI and XhoI), and that were unique in the 50 nt at the 5′ end to allow for unambiguous mapping of the sequencing results. Then we added adapter sequences to the 5′ and 3′ ends to form the final oligonucleotide sequences. Adapter sequences were added to the 5′ and 3′ ends to facilitate downstream PCR and cloning steps. Different adapters were added to each sub-library so that they could be amplified separately. The resulting sequences were synthesized on a releasable DNA microarray (Agilent). We PCR-amplified the DNA oligo library with the primers shown below, digested the product with EcoRI and XhoI, and cloned it into the EcoRI/SalI site of the T7FNS2 vector (Larman et al., 2011). We packaged the resultant library into T7 bacteriophage using the T7 Select Packaging Kit (EMD Millipore) and amplified the library according to the manufacturer's protocol.
We performed phage immunoprecipitation and sequencing as described previously or with slight modifications (9). For the IgA and IgG chain isotype-specific immunoprecipitations, we substituted magnetic protein A and protein G Dynabeads (Invitrogen) with 6 μg Mouse Anti-Human IgG Fc-BIOT (Southern Biotech) or 4 μg Goat Anti-Human IgA-BIOT (Southern Biotech) antibodies. We added these antibodies to the phage and serum mixture and incubated the reactions overnight a 4° C. Next, we added 25 μL or 20 μL of Pierce Streptavidin Magnetic Beads (Thermo-Fisher) to the IgG or IgA reactions, respectively, and incubated the reactions for 4 h at room temperature, then continued with the washing steps and the remainder of the protocol, as previously described (9).
Gradient boosting classifier models were generated using the XGBoost algorithm. Classifier models were trained to discriminate either COVID-19+ and COVID-19—patients (n=232 and n=190 respectively) or severe disease and mild disease (n=101 hospitalized patients and n=131 non-hospitalized patients). Two models were generated in each case, one using the Z-scores for each VirScan peptide from the IgG immunoprecipitation as input features, and the other using the Z-scores for each VirScan peptide from the IgA immunoprecipitation as input features. Additionally, a third logistic regression classifier was trained on the output probabilities from the IgG and IgA models to generate a combined prediction. The performance of each of the three model was assessed using a 20-fold cross-validation procedure, whereby predictions for each 5% of the data points were generated from a model trained on the remaining 95%. The SHAP package was used to identify the top discriminatory peptide features from each of the XGBoost models.
To generate a single-amino acid resolution map of SARS-CoV-2 antibody epitopes triple-alanine scanning data from each 56-mer peptide were aggregated across each protein. For each position in the 56-mer, the relative enrichment for each amino-acid was calculated as the mean fold-change of the three mutant peptides containing an alanine-mutation at that location relative to the median fold-change of all alanine mutants for the 56-mer. Overlapping 56-mers were combined by taking the minimum value at each shared position to account for the possibility that an epitope is disrupted in one of the tiles by the peptide junction. To map epitopes from the alanine-scanning data for each sample we used the HMMlearn python package to develop a three-state Hidden-Markov model (HMM) assuming a gaussian distribution of relative-enrichment emissions for each state. Mapped epitopes smaller than 5 amino acids were removed from the subsequent analysis. Next, we performed two-step hierarchical clustering procedure to identify the number of unique epitopes. First, for each protein all of the epitopes identified across the 169 COVID-19+ patients were clustered based on the start and stop locations predicted by the HMM classifier to generate a set of positional clusters we refer to as hotspots. Next, to identify unique epitopes within each hotspot we performed an additional step of hierarchical clustering on the samples with epitopes within each hotspot based on the alanine-scanning relative-enrichment values within the hotspot region (
Pairwise alignments were generated for the S protein of SARS-CoV-2 and each of the four common HCoVs. Similarity scores were calculated separately for a 21-amino acid window centered at each position of the SARS-CoV-2 S protein. The mean similarity score between SARS-CoV-2 and the corresponding sequence of the other HCoV was calculated for each window using the BLOSUM62 substitution matrix with a gap opening and extending penalty of −10 and −1 respectively. The maximum similarity was score was calculated as the maximum value among the pairwise-similarity scores between SARS-CoV-2 and each of the four common HCoVs for the sliding window centered at each position.
Multiplexed SARS-CoV-2 peptide epitope assays were built using the peptides listed in Table 1. Peptides were synthesized by the Ragon Core Facility with a Proparglyglycine (Pra, X) (Fmok-Pra-OH) moiety in the amino terminus to facilitate crosslinking to Luminex beads using a “click” chemistry strategy as described (39). In brief, Luminex beads were first functionalized with amine-PEG4-azide and then reacted with the peptides to generate 20 different Luminex beads with attached peptides. Luminex bead-based serology assays were performed in 96-well U-bottom polypropylene plates using PBS+0.1% bovine serum albumin as the assay buffer. Bead washes were done using PBS+0.05% Triton X-100 by incubation for 1 minute on a strong magnetic plate (Millipore-Sigma, Burlington, Mass.). All assay incubation times were 20 minutes. In the first step, beads were incubated with 20 uL of plasma samples (1:300 dilution). Samples used for the classifier were diluted 1:100, samples used to compare disease severity were diluted 1:300. After a wash step, peptide bound IgA or IgG detection was performed by adding 40 μL of biotin-labeled anti-IgA or IgG antibodies at 0.1 μg/ml (Southern Biotechnology, Birmingham, Ala.). Bound IgA or IgG was detected by adding 40 μL of phycoerythrin (PE)-labeled streptavidin (0.2 μg/ml) (Biolegend, San Diego, Calif.). Assay plates were analyzed on a Luminex FLEXMAP 3D instrument (Luminex Corporation, Austin, Tex.) to generate median fluorescence intensity (MFI) values to quantify peptide-specific IgA or IgG levels.
ELISAs were performed separately using the SARS-CoV-2 N protein, S protein, or the S receptor-binding domain (RBD). 96-well plates were coated with antigen overnight. The plates were then blocked in PBS+3% BSA. After washing with PBS+0.05% Tween-20, the plasma sample were diluted 1:100, added to the plates and incubated overnight at 4° C. Following incubation, the plates were washed 3× with PBS+0.05% Tween-20. The bound IgG was detected by adding anti-Human IgG-alkaline phosphatase (Southern Biotech, Birmingham, Ala.) and incubating for 90 minutes at room temperature. The plates were washed an additional three times after which p-nitrophenyl phosphate solution (1.6 mg/mL in 0.1 M glycine, 1 mM ZnCl2, 1 mM MgCl2, pH 10.4) was added to each well and allowed to develop for 2 hours. Bound IgG was quantified by measuring the OD405, and the reported values were calculated as the fold change over the pre-COVID-19 controls.
Our existing VirScan phage-display platform is based on an oligonucleotide library encoding 56-amino acid (56-mer) peptides tiling every 28 amino acids across the proteomes of all known pathogenic human viruses (˜400 species and strains) plus many bacterial proteins (10). In order to interrogate the serological response to SARS-CoV-2 and other human coronaviruses (HCoVs), we supplemented this library with additional oligonucleotides encoding peptides that span the proteome of SARS-CoV-2 itself, plus the proteomes of the six human coronaviruses and the three bat coronaviruses that are most closely related to SARS-CoV-2 (
We used VirScan (
To measure immune responses to SARS-CoV-2, we compared VirScan profiles of serum samples from COVID-19 patients to those of controls obtained before the emergence of SARS-CoV-2 in 2019. These pre-COVID-19 era controls facilitate identification of (1) SARS-CoV-2 peptides encoding epitopes specific to COVID-19 patients and (2) SARS-CoV-2 peptides encoding epitopes that are cross-reactive with antibodies developed in response to the ubiquitous common-cold HCoVs. Sera from COVID-19 patients exhibited much more SARS-CoV-2 reactivity compared to pre-COVID-19 era controls (
COVID-19 patient sera also showed significant levels of cross-reactivity with the other highly pathogenic HCoVs, SARS-CoV and MERS-CoV, although less was observed against the more distantly-related MERS-CoV. Extensive cross-reactivity was also observed against peptides derived from the three bat coronaviruses that share the greatest sequence identity with SARS-CoV-2 (
COVID-19 patient sera also exhibited a significantly higher level of reactivity to seasonal HCoV peptides compared to pre-COVID-19 era controls (
Analysis of SARS-CoV-2 proteins targeted by COVID-19 patient antibodies revealed that the primary responses to SARS-CoV-2 are reactive with peptides derived from spike (S) and nucleoprotein (N) (
We also analyzed longitudinal samples from 23 COVID-19 patients. Most patients displayed an antibody response to peptides derived from the S or N in the second week after symptom onset, with many displaying an antibody response by the end of the first week (
To more precisely define the immunogenic regions of the SARS-CoV-2 proteome, we examined the specific 56-mer and 20-mer peptides that were detected by VirScan in COVID-19 patients compared to pre-COVID-19 era controls. An example IgG response from a single patient to the SARS-CoV-2 S and N is shown in
Next, we compared the protein regions recognized by IgG and IgA across COVID-19 patients (
To predict SARS-CoV-2 exposure history from VirScan data, we developed a gradient-boosting algorithm (XGBoost) that integrated both IgG and IgA data and predicted current or past COVID-19 disease with 99.1% sensitivity and 98.4% specificity (
We leveraged these insights to develop a simple, rapid Luminex-based diagnostic for COVID-19. We chose 12 SARS-CoV-2 peptides predicted by VirScan data and the machine-learning model to be highly indicative of SARS-CoV-2 exposure history (Table 1). These SARS-CoV-2 peptides, plus two positive control peptides from Rhinovirus A and Epstein-Barr virus (EBV) that are recognized in over 80% of seropositive individuals by VirScan (9), and a negative control peptide from HIV-1, were coupled to Luminex beads (39). We tested 163 COVID-19 patient samples and 165 pre-COVID-19 era controls for IgG reactivity to the Luminex panel. We detected clear responses to SARS-CoV-2 peptides in COVID-19 patient samples but rarely in the pre-COVID-19 era controls (
We next considered whether differences in the antibody response to SARS-CoV-2 or to other viruses might be associated with the severity of COVID-19 disease. We grouped the COVID-19 patients into two subsets: those who required hospitalization (n=101), and those who did not (n=131). We compared the responses to peptides derived from the SARS-CoV-2 S and N proteins between the hospitalized (H) and non-hospitalized (NH) groups, and found that the H group exhibited stronger and broader antibody responses to S and N peptides that might be due to epitope spreading (
VirScan also offers the opportunity to examine the history of previous viral infections and to determine correlates of COVID-19 outcomes. For example, prior viral exposure could provide some protection if cross-reactive neutralizing antibodies or T cell responses are stimulated upon exposure to SARS-CoV-2 (21, 22). Alternatively, cross-reactive antibodies to viral surface proteins could increase the risk of severe disease due to antibody-dependent enhancement (ADE), as has been observed for SARS-CoV (23, 24). Furthermore, exposure to certain viruses could impact the response to SARS-CoV-2 by altering the immune system. To examine these possibilities, we analyzed the virome-wide VirScan data and found that overall, the NH patients exhibited greater responses to individual peptides from common viruses such as Rhinoviruses, Influenza viruses, and Enteroviruses, while the H patients displayed greater responses to peptides from cytomegalovirus (CMV) and Herpes Simplex Virus 1 (HSV-1) (
We sought to understand whether the differential reactivity to CMV and HSV-1 between the H and NH patients was due to differences in the strength of antibody responses or the prevalence of infection (these viruses are common, but not ubiquitous as are Rhinoviruses, Enteroviruses and Influenza viruses). Using VirScan data, we found that the H group had a higher incidence of both CMV and HSV-1 infection: 82.2% (83/101) of the H group were positive for CMV versus 37.4% (49/131) of the NH group, while 92.1% (93/101) of the H group were positive for HSV-1 versus 45.8% (60/131) of the NH group. To examine the relative strength of the antibody responses, we considered only CIV or HSV-1 seropositive individuals from the NH and H groups: the antibody response to both CMV (
These striking differences led us to examine potential demographic covariates between the NH and H groups. We found that age, sex, and race were all significantly associated with COVID-19 severity, as has been reported (25, 26). Higher age, male sex, and non-white ethnicity groups were significantly overrepresented in the H group compared with the NH group. Furthermore, hospitalized males exhibited stronger responses to N than hospitalized females while non-hospitalized males and females did not exhibit differential responses to any SARS-CoV-2 proteins. Advanced age is a dominant risk factor for severe COVID-19 and is correlated with reduced immune function (38). In light of the age difference between the H (median age 58) and NH (median age 42) patients in our cohort, we reasoned that the antigens recognized more strongly in the NH group might reflect more general age-associated changes in humoral immunity. To test this hypothesis, we examined VirScan data for a cohort of 648 healthy, pre-pandemic donors. We characterized the recognition of each NH-associated peptide in subsets of the healthy donors representing different age groups and observed a general decline in recognition with age, including a median 19% reduction in recognition from age 42 to 58 (
We returned to the question of epitope cross-reactivity, this time examining antibody responses to the triple-alanine scanning library. For each 56-mer peptide spanning the SARS-CoV-2 proteome, this library contained a collection of scanning mutants: the first mutant peptide encoded 3 alanines instead of the first 3 residues, the second mutant peptide contained the 3 alanines moved one residue downstream, and so on (
With respect to cross-reactivity, IgG from COVID-19 patients recognized more 56-mer peptides from the common HCoVs HKU1, OC43, 299E, and NL63, than IgG from pre-COVID-19 era controls. This difference is primarily driven by a dramatic increase in recognition of S peptides from the HCoVs and is likely a result of cross-reactivity of antibodies developed during SARS-CoV-2 infection (
We mapped the position of all HCoV S peptides that display increased recognition in COVID-19 patient samples onto the SARS-CoV-2 S protein. This revealed four immunodominant regions recognized by >25% of COVID-19 patients (
Interestingly, we detected antibody responses to SARS-CoV-2 S 811-830 in 79% of COVID-19 patients, but we also saw responses to the corresponding peptides from OC43 and 229E in ˜20% of the pre-COVID-19 era controls and these responses seem to cross-react with SARS-CoV-2. It is possible that some patients have pre-existing antibodies to this region that cross-react and are expanded during SARS-CoV-2 infection. This might explain the remarkably high prevalence of antibody responses to this epitope, and suggests that anamnestic responses to seasonal coronaviruses may influence the antibody response to SARS-CoV-2. Interestingly, this region is located directly after the predicted S2′ cleavage site for SARS-CoV-2 and overlaps the fusion peptide. A recent study showed that adding an excess of the fusion peptide reduced neutralization, implying that an antibody that binds the fusion peptide might contribute to neutralization by interfering with membrane fusion (27, 29). Given the frequency of seroreactivity toward this epitope in COVID-19 patients, it will be important to determine if the antibodies recognizing this epitope are neutralizing in future studies.
We also used the triple-alanine scanning mutagenesis library to map antibody footprints across the entire SARS-CoV-2 proteome (
The SARS-CoV-2 epitope landscape includes regions recognized by a large fraction of COVID-19 patients (public epitopes) and regions recognized by one or a few individuals (private epitopes). For example, we mapped 6 distinct epitopes in the region spanning N 151-175 (
We also mapped at least 12 distinct epitopes in the SARS-CoV-2 RBD, including 5 in the receptor binding motif (RBM) that binds ACE2, the human receptor for SARS-CoV-2, and 5 that are directly adjacent to ACE2 binding sites (
Table 3 presents 303 peptide epitope clusters, and Table 4 presents 823 epitopes with their peptide sequences, with an indication of whether the peptide is believed to be the receptor binding domain (RBD) (True/False).
Immunol. 215, 108427 (2020).
It is to be understood that while the invention has been described in conjunction with the detailed description thereof, the foregoing description is intended to illustrate and not limit the scope of the invention, which is defined by the scope of the appended claims. Other aspects, advantages, and modifications are within the scope of the following claims.
This application claims the benefit of U.S. Provisional Patent Application Serial Nos. 63/049,359, filed on Jul. 8, 2020, and 63/083,607, filed on Sep. 25, 2020. The entire contents of the foregoing are hereby incorporated by reference.
This invention was made with Government support under Grant No. All 18633 awarded by the National Institutes of Health. The Government has certain rights in the invention.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/US21/40920 | 7/8/2021 | WO |
| Number | Date | Country | |
|---|---|---|---|
| 63049359 | Jul 2020 | US | |
| 63083607 | Sep 2020 | US |
