Prior Viral Infections Including H3N2 influenza Can Cause COVID-19 Severity Due to Antigenic Interference


A new study findings indicate that antigenic interference or AIN from a previous viral infection could underlie some cases of COVID-19 disease severity.
The study findings were published in the peer reviewed journal: PLoS ONE.

Assays measured levels of αEp9 IgGs and IgMs from αEp9(+) patients whose plasma was collected at various times post-symptom onset (PSO). Consistent with the hallmarks of AIN tracing a prior infection, αEp9 IgG levels appeared elevated as early as one day PSO in one patient. Similar IgG levels were observed in the patient population over >4 weeks (one-way ANOVA, p = 0.321); thus, αEp9 IgG started high and remained high. Levels of αEp9 IgMs amongst patients at various times PSO were also similar (one-way ANOVA, p = 0.613).

The signals measured for αEp9 IgM levels were significantly lower than the equivalent αEp9 IgG levels (t-test, p = 0.0181) (S1 Fig); this difference could reflect lower IgM affinity, quantity, or both. Since the study focuses on identifying epitope binding by Abs upregulated in SARS-CoV-2 positive patients, we cannot discern between a single Ab or a population of Abs with the same binding profile. Additionally, the observation that the Ep9 epitope is targeted by both IgG and IgM antibodies suggests that multiple antibodies with similar binding profiles may exist in SARS-CoV-2 patients. Therefore, we refer to the anti-Ep9 paratopes as belonging to a population of Abs in sera.

Searches for sequence and structural homologs of Ep9 using pBLAST [11] and VAST [12] databases suggested candidate primary antigens. A structural homolog from betaherpesvirus 6A and 14 other Ep9 sequence homologs were identified. Additionally, Ep9-orthologous regions from six human coronaviruses (SARS-CoV, MERS, OC43, HKU-1, NL63, 229E) were chosen for subsequent assays (Fig 1A and S1 Table).

To expedite the binding measurements, the potential AIN epitope regions were subcloned into phagemids encoding the sequences as fusions to the M13 bacteriophage P8 coat protein. DNA sequencing and ELISA experiments demonstrated successful cloning and consistent phage display, respectively. Two epitopes failed to display on phage and were omitted from subsequent investigation (S2 Table and S2A Fig).

Fig 1. Potential OAS epitopes for binding αEp9 Abs suggested by bioinformatics and tested by phage ELISA.
(A) Cladogram depicting sequence homology of the Ep9 sequence from SARS-CoV-2 to the bioinformatics-identified, closest homologs. Sequence alignments used pBLAST and VAST, and the cladogram was generated by iTOL [13]. (B) Structures of SARS-CoV-2 NP RNA binding domain (PDB: 6M3M) and the influenza virus (Infz) A 2014 H3N2 NA protein (modeled by SWISS-Model [14]). SARS-CoV-2 NP highlights Ep9 residues (light and dark blue) and the region homologous region to EpNeu (dark blue). The depicted model of Infz A 2014 H3N2 NA highlights the EpNeu putative antigen (pink). (C) ELISAs examined binding of phage-displayed potential OAS epitopes to total Ig from three sets of pooled plasma from five αEp9(+) patients, or five αEp9(−) patients. Pooled plasma from healthy individuals was an additional negative control. The colors of the heat map represent the mean binding signal normalized to phage background negative controls (signal from phage without a displayed peptide). (D) Expansion of data from panel C shows ELISA signals from the independently assayed individual pools shows results from the individual pools (p <0.0001 for a two-way ANOVA comparing binding of phage-displayed epitopes listed in panel C to different groups of pooled plasma, ad hoc Tukey test). (E) Amino acid sequence alignment of the closely related Infz A NA homologs of EpNeu from pBLAST [11]. Blue and orange residues represent conserved and mismatched amino acids, respectively, relative to Ep9. Bolded residues are important for epitope recognition by αEp9 Abs. (F) Using EpNeu as the search template to generate homologous sequences (shown in panel E), ELISAs examined EpNeu homologs’ binding to pooled plasma from αEp9(+), αEp9(−), or healthy individuals. The data are represented as described in panel C (p <0.0001 for two-way ANOVA c phage-displayed epitopes, ad hoc Tukey and Dunnett’s test as shown).

Since patient samples were collected at different time points during the patients’ infection, Ab levels varied significantly between patients. Thus, patients’ samples were pooled for the initial assays to minimize outlier concentrations and best capture the average Ab population in patients. The pooled sample data were first used to screen for cross-reactivity against multiple possible epitopes (Fig 1C and 1F). These results were then re-examined with assays of samples from individual patients (Fig 2A).

In these experiments, phage ELISAs tested binding by Ep9 homologs to αEp9 Abs. An average response within the patient population was assessed using pooled plasma from three sets of five αEp9(+) and five αEp9(−) COVID-19 patients coated onto ELISA plates. Plasma from healthy individuals provided an additional negative control. Confirming previously reported results [10], SARS-COV-2 Ep9 and a homologous epitope from SARS-CoV-1 (90% similarity) bound only to plasma from αEp9(+) patients. The αEp9 Ab affinity for SARS-CoV-1 is unlikely to drive SARS-CoV-2 AIN due to the former’s limited spread in the US [15]

Fig 2. Cross-reactive Ab binding to both Ep9 and EpNeu, and EpNeu epitope prediction.

(A) Phage ELISA using 29 previously tested αEp9(+) COVID-19 patients. The ELISA demonstrated binding of patient plasma Abs to SARS-CoV-2 epitope, Ep9, or the influenza A neuraminidase epitope, EpNeu. Plasma Abs from 16 out of 29 patients Ep9(+) patients showed significant binding to EpNeu. (****p<0.0001, ***p<0.001, p<0.01, *p<0.05, two-way ANOVA ad hoc Tukey test shown) Significant differences in epitope binding in comparison to the no peptide displayed phage signals are denoted as blue for Ep9 and orange for EpNeu. (B) Comparing normalized levels of phage-displayed Ep9 and EpNeu binding to plasma-coated wells from individual αEp9(+) patients (n = 29). A strong correlation is observed, as shown by the depicted statistics. Each point in panels A through C represents data from individual patients. (C) A schematic diagram of the sandwich ELISA to examine cross-reactivity of αEp9 Abs. The assay tests for bivalent Ab binding to both Ep9 and EpNeu. Pooled plasma from five αEp9(+) patients or five αEp9(−) patients with other αNP Abs was tested for bivalent binding to both eGFP-fused Ep9 and phage-displayed EpNeu. Healthy patient plasma was used as a negative control. For additional negative controls, phage-FLAG and eGFP-FLAG replaced Ep9 and EpNeu, respectively (**p <0.0001 one-way ANOVA, ad hoc Tukey and Dunnett’s test shown, with healthy plasma in the presence of EpNeu and Ep9 as negative control). Error bars represent SD. Individual points on bar graph represent technical replicates. (D) Linear and structural B-cell epitope prediction tools Bepipred 2.0 [16] and Discotope 2.0 [17] suggested an extended, linear epitope region from the influenza virus A H3N2 2014 NA, including the eight residues of Ep9 Neu (light blue) with an additional ten, C-terminal residues (dark blue). This extended, predicted epitope is termed EpPred. Structural epitope predictions are underlined. Residues on EpNeu that are not aligned with Ep9 are depicted in orange. (E) Structural model depicting the influenza A H3N2 2014 NA. The model was generated using SWISS-Model based on the NA structure from influenza A H3N2 Tanzania 2010 (PDB: 4GZS). The NA structure highlights the EpNeu region (light blue), the extended residues in EpPred (dark blue), potential glycosylation sites (light pink), and the residues S141 and K142 (red), which are important for αEp9 Ab recognition. (F) Dose-dependent ELISA comparing binding of αEp9 Abs to Ep9, EpNeu and EpPred. Pooled plasma from five αEp9(+) patients and five αEp9(−) patients were tested in triplicates with varying concentrations of eGFP-fused epitopes. The data demonstrates the strongest interactions occurred between αEp9 Abs and Ep9 with an approximately 2-fold decrease in αEp9 Abs binding affinity for EpNeu. EpPred bound slightly stronger to αEp9 Abs than EpNeu; the difference in trend lines of EpNeu and EpPred are statistically significant (p<0.0001, Comparison of Fits). Trend lines represent non-linear regression fit with Hill slope analysis.

The panel of potential epitopes revealed a candidate epitope from the neuraminidase (NA) protein of an H3N2 influenza A strain, which circulated in 2014 (A/Para/128982-IEC/2014, Accession No. AIX95025.1), termed EpNeu here. The plasma from three different pools of αEp9(+) patients, but not αEp9(−) patients nor healthy individuals, bound EpNeu (p<0.0001, two-way ANOVA ad hoc Tukey test) (Fig 1C and 1D). Additionally, the combined technical replicates from two independent experiments of the same pooled samples also demonstrate significant increases in EpNeu binding signal from αEp9(+) plasma Abs, but not in αEp9(−) patients (EpNeu with p<0.0001, two-way ANOVA ad hoc Tukey test) (S1 Fig). Though Ep9 and EpNeu share 38% amino acid sequence similarity, other candidate epitope regions with significantly higher homology failed to bind to αEp9(+) plasma (S1 Table).

Next, the specificity of αEp9 Abs binding to NA from different viral strains was explored. EpNeu provided a template for further homolog searches in sequence databases. Closely aligned NA sequences isolated from human, avian, and swine hosts in North America were chosen for further analysis (Fig 1E, S1 Table). The sequences were phage-displayed as before. Despite their close similarity to EpNeu (up to 92.3% similarity or only one residue difference), none of the EpNeu homologs bound to Abs from αEp9(+) patients (Fig 1F). A single EpNeu amino acid substitution, K142N (numbering from full-length NA, Accession No. AID57909.1) in an H1N2 swine flu (2016) dramatically decreased binding affinity to Abs from αEp9(+) patients (p<0.0001 one-way ANOVA ad hoc Tukey). An epitope of H9N4 avian influenza A virus (2010) missing residue S141, but including conserved K142, also greatly reduced binding to Abs from αEp9(+) patients (p<0.0001 one-way ANOVA ad hoc Tukey) (Fig 1E and 1F). Therefore, S141 and K142 are critical for binding to αEp9 Abs.

We further examine whether Ep9 and EpNeu epitopes bind the same Abs. Data from 29 αEp9(+) patients demonstrated a strong, highly significant correlation between levels of Abs binding to Ep9 and EpNeu epitopes in patient plasma (Fig 2A and 2B). Cross-reactivity was confirmed by a sandwich-format assay requiring bivalent, simultaneous binding to both eGFP-fused Ep9 and phage-displayed EpNeu (Figs 2C, S4A and S4B). Cross-reactive Ab binding both Ep9 and EpNeu epitopes in pooled plasma from αEp9(+) patients, but not in αEp9(−) patients with other αNP Abs or healthy donors was demonstrated. Thus, we conclude that αEp9 Abs also recognize the EpNeu epitope. The bivalent ELISA was conducted using pooled patient plasma because epitope concentrations coated in wells of the microtiter plate required optimization to adjust for different levels of Abs from each individual patient and to allow bivalent binding to each type of epitope (S4A Fig). Therefore, it was speculated that the average amount of Abs in each pool would be similar and that the repeated optimization would not be required (S4B Fig).

We then investigated whether EpNeu could present a viable antigen during infection with 2014 H3N2 (NCBI: txid1566483). Linear epitope analysis of full-length NA protein (Bepipred 2.0) [16] predicted a candidate antigen with eight residues from EpNeu, including S141 and K142, and ten additional residues (146–155). This predicted epitope region, termed EpPred, includes the conserved catalytic NA residue D151 targeted for viral neutralization by the immune system [18] (Figs 2D and S5A). A model structure of 2014 H3N2 NA from Swiss-Model [14, 19] and structural epitope prediction (Discotope 2.0) [17] also identified potential epitopes within EpPred (Figs 2D and 2E and S5B).

eGFP-fused EpPred (S2B Fig) was assayed with pooled plasma from five αEp9(+) patients. Controls included EpNeu and Ep9 (positive) and eGFP FLAG (negative). The αEp9 Abs bound to Ep9 with ≈2-fold stronger apparent affinity than for EpNeu (Fig 2E). The increased binding strength of Ep9 could result from additional rounds of Ab affinity maturation after the primary infection [3]. The longer length EpPred appears to modestly improve upon binding of EpNeu to αEp9 Abs (Fig 2F). Thus, while αEp9 Abs may target a larger epitope of H3N2 2014 NA beyond regions homologous to Ep9, the known balkiness of full-length NA’s to overexpression makes this hypothesis difficult to test [20]. Additionally, the bacterially overexpressed epitopes assayed here do not include post-translational modifications. Taken together, the results are consistent with the hypothesis that αEp9 Abs found in severe COVID-19 can result from AIN with H3N2 influenza A virus.

Unfortunately, patient histories typically do not include influenza infections and vaccinations. Isolated from Para, Brazil, the H3N2 2014 strain has unknown spread in North America. However, a severe outbreak of influenza A was recorded in 2014 [21, 22]. Since only hemagglutinin was sequenced for strain identification in 2014 [22], the candidate AIN strain from the current investigation could not be effectively traced as only its NA sequence was available. Notably, the EpNeu homolog from the 2014 vaccine H3N2 strain (identical to influenza A 2015 H3N2 NA, Accession No. ANM97445.1) does not bind αEp9 Abs (Fig 1E and 1F). Therefore, αEpNeu Abs must have been generated against a primary influenza virus infection, not the vaccine.

Next, we analyzed Ep9 and EpNeu binding by αEp9 IgGs relative to days PSO (S6A Fig). Cross reactive αEp9 IgGs were observed within one day PSO. The observation is consistent with the imprinting hypothesis, whereby mature IgGs from a previous infection would be present early in the course of the infection. Though low levels of early αEp9 IgGs bound without EpNeu cross reactivity were observed in one patient at one day PSO, this observation could result from EpNeu binding below the level of detection; αEp9 Ab binds at lower affinities to EpNeu, for example.

Analysis of αEp9 IgG cross reactivity and disease severity demonstrated that cross reactive antibodies were observed in patients presenting with all levels of severity (asymptomatic, outpatient, inpatient, ICU admittance, or deceased) (S6B Fig). While EpNeu binding in most patients was drastically lower than binding to Ep9, a subset of hospitalized or ICU admitted patients demonstrated αEp9 Abs binding to EpNeu and Ep9 at comparable levels (>50%). Such similar Ab binding levels to both Ep9 and EpNeu are not observed in patients with less severe outcomes (i.e., patients who were asymptomatic or experienced only outpatient visits). However, 86% of the samples tested in this study were from hospitalized and admitted to the ICU patients. Similar levels of Ab binding to both Ep9 and EpNeu in the subset of hospitalized and ICU-admitted patients could suggest impaired affinity maturation in patients with more severe outcomes. Impaired Ab affinity maturation have been previously shown to correlate with COVID-19 severity [23, 24]. While multiple factors may lead to disease severity during COVID-19, our results suggest that a reliance on high levels of imprinted influenza Abs by a subset of COVID-19 patients could be indicative of a less effective immune response and consequently more severe disease outcomes.

This report suggests a possible molecular mechanism for AIN underlying the high-rate of severe COVID-19 in αEp9(+) patients. Specifically, we demonstrate cross-reactive binding between αEp9 Abs and a predicted NA epitope from a 2014 influenza A virus strain. Future studies could examine correlation between a country’s rate of the H3N2 2014 influenza virus and severe COVID-19. Additionally, correlation could be tested using health systems that record influenza infections. Examining epitope conservation and Ab cross-reactivity could predict AIN-based immune responses and disease outcomes in future infections. Identifying detrimental, benign, or beneficial AIN pathways could also guide vaccine design.



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