1. Epidemiology and Global Health
  2. Microbiology and Infectious Disease
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Reduced antibody cross-reactivity following infection with B.1.1.7 than with parental SARS-CoV-2 strains

  1. Nikhil Faulkner
  2. Kevin W Ng
  3. Mary Y Wu
  4. Ruth Harvey
  5. Marios Margaritis
  6. Stavroula Paraskevopoulou
  7. Catherine Houlihan
  8. Saira Hussain
  9. Maria Greco
  10. William Bolland
  11. Scott Warchal
  12. Judith Heaney
  13. Hannah Rickman
  14. Moria Spyer
  15. Daniel Frampton
  16. Matthew Byott
  17. Tulio de Oliveira
  18. Alex Sigal
  19. Svend Kjaer
  20. Charles Swanton
  21. Sonia Gandhi
  22. Rupert Beale
  23. Steve J Gamblin
  24. John W McCauley
  25. Rodney Stuart Daniels
  26. Michael Howell
  27. David Bauer
  28. Eleni Nastouli
  29. George Kassiotis  Is a corresponding author
  1. Retroviral Immunology, United Kingdom
  2. National Heart and Lung Institute, Imperial College London, United Kingdom
  3. High Throughput Screening STP, United Kingdom
  4. Worldwide Influenza Centre, United Kingdom
  5. Advanced Pathogen Diagnostics Unit UCLH NHS Trust, United Kingdom
  6. Division of Infection and Immunity, United Kingdom
  7. RNA Virus Replication Laboratory, United Kingdom
  8. Department of Population, Policy and Practice, United Kingdom
  9. School of Laboratory Medicine and Medical Sciences, University of KwaZulu-Natal, South Africa
  10. KwaZulu-Natal Research Innovation and Sequencing Platform, South Africa
  11. Centre for the AIDS Programme of Research in South Africa, South Africa
  12. Department of Global Health, University of Washington, United States
  13. Africa Health Research Institute, South Africa
  14. Max Planck Institute for Infection Biology, Germany
  15. Structural Biology STP, United Kingdom
  16. Cancer Evolution and Genome Instability Laboratory, United Kingdom
  17. Neurodegradation Biology Laboratory, United Kingdom
  18. Cell Biology of Infection Laboratory, United Kingdom
  19. Structural Biology of Disease Processes Laboratory, The Francis Crick Institute, United Kingdom
  20. Department of Infectious Disease, St Mary's Hospital, Imperial College London, United Kingdom
Research Article
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Cite this article as: eLife 2021;10:e69317 doi: 10.7554/eLife.69317

Abstract

Background:

The degree of heterotypic immunity induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) strains is a major determinant of the spread of emerging variants and the success of vaccination campaigns, but remains incompletely understood.

Methods:

We examined the immunogenicity of SARS-CoV-2 variant B.1.1.7 (Alpha) that arose in the United Kingdom and spread globally. We determined titres of spike glycoprotein-binding antibodies and authentic virus neutralising antibodies induced by B.1.1.7 infection to infer homotypic and heterotypic immunity.

Results:

Antibodies elicited by B.1.1.7 infection exhibited significantly reduced recognition and neutralisation of parental strains or of the South Africa variant B.1.351 (Beta) than of the infecting variant. The drop in cross-reactivity was significantly more pronounced following B.1.1.7 than parental strain infection.

Conclusions:

The results indicate that heterotypic immunity induced by SARS-CoV-2 variants is asymmetric.

Funding:

This work was supported by the Francis Crick Institute and the Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg.

Introduction

Mutations in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants that arose in the United Kingdom (UK) (B.1.1.7; Alpha) or in South Africa (B.1.351; Beta) reduce recognition by antibodies elicited by natural infection with the parental reference (Wuhan) strain and the subsequent D614G variant (Cele et al., 2021; Diamond et al., 2021; Edara et al., 2021; Emary et al., 2021; Liu et al., 2021b; Planas et al., 2021; Skelly et al., 2021; Wang et al., 2021; Wibmer et al., 2021; Zhou et al., 2021). Such reduction in cross-reactivity also impinges the effectiveness of current vaccines based on the Wuhan strain (Diamond et al., 2021; Edara et al., 2021; Emary et al., 2021; Liu et al., 2021b; Skelly et al., 2021; Wang et al., 2021; Zhou et al., 2021), prompting consideration of alternative vaccines based on the new variants. However, the immunogenicity of the latter or, indeed, the degree of heterotypic immunity the new variants may afford remains to be established.

Results and Discussion

The B.1.1.7 variant is thought to have first emerged in the UK in September 2020 and has since been detected in over 50 countries (Kirby, 2021). To examine the antibody response to B.1.1.7, we collected sera from 29 patients, admitted to University College London Hospitals (UCLH) for unrelated reasons (Supplementary file 1), who had confirmed B.1.1.7 infection. The majority (23/29) of these patients displayed relatively mild COVID-19 symptoms and a smaller number (6/29) remained COVID-19-asymptomatic. As antibody titres may depend on the severity of SARS-CoV-2 infection, as well as on time since infection (Gaebler et al., 2021; Long et al., 2020), we compared B.1.1.7 sera with sera collected during the first wave of D614G variant spread in London from hospitalised COVID-19 patients (Ng et al., 2020) (n=20) and mild/asymptomatic SARS-CoV-2-infected health care workers (Houlihan et al., 2020) (n=17) who were additionally sampled 2 months later.

IgG, IgM, and IgA antibodies to the spikes of the Wuhan strain or of variants D614G, B.1.1.7, or B.1.351, expressed on HEK293T cells, were detected by a flow cytometry-based method (Figure 1; Figure 1—figure supplement 1; Ng et al., 2020). Titres of antibodies that bound the parental D614G spike largely correlated with those that bound the B.1.1.7 or B.1.351 spikes (Figure 1a–c), consistent with the high degree of similarity. Similar correlations were observed for all three Ig classes also between the Wuhan strain and the three variant spikes and between the B.1.1.7 and B.1.351 spikes (Figure 1—figure supplements 25).

Figure 1 with 6 supplements see all
Recognition of distinct severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike glycoproteins by antibodies in D614G and B.1.1.7 sera.

(a-c) Correlation of IgG (a), IgM (b), and IgA (c) antibody levels to D614G and B.1.1.7 or B.1.351 spikes in the indicated groups of donors infected either with the D614G or B.1.1.7 strains. Each symbol represents an individual sample and levels are expressed as a percentage of the positive control. Black lines denote complete correlation and grey lines a 25% change in either direction. (d-f) Comparison of IgG (d), IgM (e), and IgA (f) antibody levels to the indicated spikes in groups of donors acutely infected either with the D614G or B.1.1.7 strains. Connected symbols represent individual donors. Numbers above the plots denote the average binding to each spike, expressed as a percentage of binding to the infecting spike.

Comparison of sera from acute D614G and B.1.1.7 infections revealed stronger recognition of the infecting variant than of other variants. Although B.1.1.7 sera were collected on average earlier than D614G sera (Supplementary file 1), titres of antibodies that bound the homotypic spike or neutralised the homotypic virus, as well as the relation between these two properties, were similar in D614G and B.1.1.7 sera (Figure 1—figure supplement 6a–c), suggesting comparable immunogenicity of the two variants. Moreover, levels of binding and neutralising antibodies were not statistically significantly different in sera from mild or asymptomatic B.1.1.7 infection, although they were, on average, lower in the latter (Figure 1—figure supplement 6d).

Recognition of heterotypic spikes was reduced by a small, but statistically significant degree for both D614G and B.1.1.7 sera and for all three Ig classes (Figure 1d–f). IgM or IgA antibodies in both D614G and B.1.1.7 sera were less cross-reactive than IgG antibodies (Figure 1d–f). The direction of cross-reactivity was disproportionally affected for some combinations, with IgA antibodies in D614G sera retaining on average 81% of recognition of the B.1.1.7 spike and IgA antibodies in B.1.1.7 sera retaining on average 30% of recognition of the D614G spike (Figure 1f). Similarly, recognition of the B.1.351 spike by IgM antibodies was retained, on average, to 71% in D614G sera and to 46% in B.1.1.7 sera (Figure 1f). Measurable reduction in polyclonal antibody binding to heterotypic spikes was unexpected, given >98% amino acid identity between them. Furthermore, mutations selected for escape from neutralising antibodies, which target the receptor binding domain more frequently, should not directly affect binding of non-neutralising antibodies to other domains of the spike. Indeed, we found that the reduction in heterotypic binding was less pronounced than the reduction in heterotypic neutralisation. However, reduction in serum antibody binding has also been observed for the receptor binding domain of the B.1.351 spike (Edara et al., 2021). Together, these findings suggested that either the limited number of mutated epitopes were targeted by a substantial fraction of the response (Diamond et al., 2021; Skelly et al., 2021; Wang et al., 2021; Zhou et al., 2021) or allosteric effects or conformational changes affecting a larger fraction of polyclonal antibodies.

To examine a functional consequence of reduced antibody recognition, we measured the half maximal inhibitory concentration (IC50) of D614G and B.1.1.7 sera using in vitro neutralisation of authentic Wuhan or B.1.1.7 and B.1.351 viral isolates (Figure 2a–b). Titres of neutralising antibodies correlated most closely with levels of IgG binding antibodies for each variant (Figure 1—figure supplement 5). Neutralisation of B.1.1.7 by D614G sera was largely preserved at levels similar to neutralisation of the parental Wuhan strain (fold change −1.3; range 3.0 to −3.8, p=0.183) (Figure 2b), consistent with other recent reports, where authentic virus neutralisation was tested (Brown et al., 2021; Diamond et al., 2021; Planas et al., 2021; Skelly et al., 2021; Wang et al., 2021). Thus, D614G infection appeared to induce substantial cross-neutralisation of the B.1.1.7 variant. However, the reverse was not true. Neutralisation of the parental Wuhan strain by B.1.1.7 sera was significantly reduced, compared to neutralisation of the infecting B.1.1.7 variant (fold change −3.4; range −1.20 to −10.6, p<0.001) (Figure 2b), and the difference in cross-neutralisation drop was also significant (p<0.001). Both D614G and B.1.1.7 sera displayed significantly reduced neutralisation of the B.1.351 variant with a fold change of −8.2 (range −1.7 to −33.5) and −7.7 (range −3.4 to −17.9), respectively (Figure 2b).

Figure 2 with 5 supplements see all
Neutralisation of distinct severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) strains by antibodies in D614G and B.1.1.7 sera.

(a) Correlation of neutralising antibody levels (IC50) against the Wuhan, B.1.1.7, or B.1.351 strains in the indicated groups of donors infected either with the D614G or B.1.1.7 strains. Each symbol represents an individual sample. Black lines denote complete correlation and grey lines a 50% (twofold) change in either direction. (b) Comparison of neutralising antibody levels (IC50) to the indicated SARS-CoV-2 strains in groups of donors acutely infected with either the D614G or B.1.1.7 strains. Connected symbols represent individual donors. Numbers above the plots denote the average IC50 against each strain, expressed as a percentage of IC50 against the infecting strain. Grey horizontal lines denote the lower and upper limit of detection.

Although B.1.1.7 infection appeared to induce limited heterotypic immunity, relative to D614G infection, differences in both the severity of infection with each variant and the time since infection may have affected the degree of antibody cross-reactivity observed. For example, higher SARS-CoV-2-neutralising antibody titres are found in infections leading to severe COVID-19 than in mild/asymptomatic infection (Long et al., 2020) and these higher titres may include broader antibody diversity. Similarly, a longer time since infection may permit broader antibody diversity through somatic hypermutation and affinity maturation (Gaebler et al., 2021), potentially increasing cross-reactivity. However, the stronger heterotypic recognition of B.1.1.7 by D614G sera was independent of severity of infection and was, in fact, more pronounced in mild/asymptomatic than in severe D614G infection, when the two were considered separately, with sera from severe and mild/asymptomatic D614G infection retaining 52% and 85% neutralisation of B.1.1.7 (Figure 2—figure supplement 1). Moreover, the ability of sera from mild/asymptomatic D614G to neutralise B.1.1.7 did not change over time (Figure 2—figure supplement 2). Indeed, whilst binding antibody titres were significantly reduced for all three Ig classes in D614G sera in the 2 months of follow-up, neutralising antibody titres remained comparable for the Wuhan and B.1.1.7 strains and were undetectable at both time-points for the B.1.351 strain (Figure 2—figure supplement 2). Lastly, to adjust for potentially confounding differences in both the severity of infection and time since infection with each variant, we compared a subset of 11 seropositive samples from D614G or B.1.1.7 infection. These were selected for comparable disease outcome (all mild/asymptomatic) and for time since confirmed infection (on average, 24.0 and 19.5 days, respectively, p=0.37). Analysis of these comparable subsets further supported the notion that B.1.1.7 infection elicited reduced heterotypic immunity, with D614G and B.1.1.7 sera retaining 87% and 42% neutralisation of B.1.1.7 and D614G, respectively, and much lower neutralisation of B.1.351 (Figure 2—figure supplement 3).

Together, these results argue that natural infection with each SARS-CoV-2 strain induces antibodies that recognise the infecting strain most strongly, with variable degrees of cross-recognition of the other strains. Importantly, antibodies induced by B.1.1.7 infection were less cross-reactive with other dominant SARS-CoV-2 strains than those induced by the parental strain. Similar findings were recently obtained independently by Brown et al., who found that B.1.1.7 convalescent sera neutralised the parental strain significantly less than the infecting B.1.1.7 strain (Brown et al., 2021). Conversely, sera from D614G infection retained full neutralisation of the B.1.1.7 strain (Brown et al., 2021). This unidirectional pattern of cross-reactivity argues that emergence of B.1.1.7 is unlikely to have been driven by antibody escape. In support of this premise, B.1.1.7 and D614G viruses were equally sensitive to neutralisation by BNT162b2 or AZD1222 vaccination-induced antibodies, although they were both approximately twofold less sensitive than the Wuhan strain (Wall et al., 2021a; Wall et al., 2021b).

In contrast to the results reported here and by Brown et al., Liu et al. recently reported that B.1.1.7 convalescent sera recognised significantly stronger the Victoria strain (a Wuhan related strain) than homotypic B.1.1.7 virus, and retained stronger heterotypic recognition of other variants of concern (VOCs) than sera from infection with D614G, B.1.351, or with variant B.1.1.28 (Gamma) first emerged in Brazil (Liu et al., 2021a). Methodological differences notwithstanding, it is possible that donor selection may be responsible for the reported differences in antibody levels and cross-reactivity. Of note, neutralising antibody titres in B.1.1.7 sera were two to three times higher than those in sera from any other infection in Liu et al., suggesting higher immunogenicity of the B.1.1.7 infection compared with all other strains (Liu et al., 2021a). In contrast, overall antibody titres induced by B.1.1.7 infection were comparable with those induced by parental strain infection in this study (Figure 1—figure supplement 6a–c) and in Brown et al., when tested against the homotypic strains (Brown et al., 2021). Nevertheless, it is possible that the higher viral loads achieved during B.1.1.7 infection than D614G infection (Frampton et al., 2021) also induce higher antibody levels in B.1.1.7 sera than in D614G sera. Consequently, even though, relative to recognition of the infecting strain, B.1.1.7 sera may be less cross-reactive than D614G sera, they may still harbour higher antibody titres than D614G sera against other strains in absolute terms. Indeed, our comparison of B.1.1.7 and D614G sera from donors we attempted to match for severity and time of serum collection since infection indicated that B.1.1.7 sera contained higher absolute levels of neutralising antibodies than D614G sera against the infecting variant (p=0.003) and against the B.1.351 variant (p=0.006). Although analysis of larger numbers of samples will be required to conclusively determine if B.1.1.7 infection is more immunogenic than D614G infection, the current data highlight the effect of the severity of infection on resulting antibody titres and the importance of controlling for such confounding factors.

In addition to the emergence and global spread of the B.1.1.7 variant, several other variants have emerged, such as variant B.1.617.2 (Delta), first emerged in India, that has now replaced variant B.1.1.7 in the UK. Assessment of the extent of heterotypic immunity induced by new variants will be critical for understanding of the degree of infection-induced immunity against other variants and for adapting current vaccines. A recent comparison of sera from infection with B.1.351 or the parental strain B.1.1.117 in South Africa also observed stronger neutralisation of the infecting strain (Cele et al., 2021). In contrast to B.1.1.7 infection, however, B.1.351 infection induced substantial cross-neutralisation of the parental strain, as well as of the B.1.1.28 variant, whereas parental strain B.1.1.117 infection induced significantly lower B.1.351 neutralisation (Cele et al., 2021; Moyo-Gwete et al., 2021). Therefore, heterotypic immunity in the case of B.1.351 and the parental strain B.1.1.117 was also asymmetrical, but reversed.

The B.1.351, B.1.1.28, and B.1.617.2 VOCs appear comparably sensitive to antibodies induced by the BNT162b2 and AZD1222 vaccines, which are both based on the Wuhan sequence (Liu et al., 2021a; Wall et al., 2021a; Wall et al., 2021b). However, infection with the B.1.351 or the B.1.1.28 variant may induce lower cross-neutralisation of the other variant than itself (Liu et al., 2021a), likely owing to spike sequence divergence between them (Figure 2—figure supplement 4). The cross-reactivity of antibodies induced by B.1.617.2 infection is currently unknown, but spike sequence divergence considerations would predict an even lower degree of heterotypic immunity. Indeed, whereas the spike proteins of all current VOCs harbour between 10 and 12 amino acid changes from the Wuhan reference spike sequence, they harbour between 12 and 21 amino acid changes between them, with B.1.617.2 being the most divergent at present (Figure 2—figure supplement 4). It stands to reason that the more divergent their spike sequences become, the lower the degree of heterotypic immunity the variants induce. This degree of heterotypic immunity should be an important consideration in the choice of spike variants as vaccine candidates. The antigenic variation associated with SARS-CoV-2 evolution may instead necessitate the use of multivalent vaccines.

Materials and methods

Key resources table
Reagent type
(species) or
resource
DesignationSource or
reference
IdentifiersAdditional
information
AntibodyBV421 anti-human IgG (monoclonal)BiolegendRRID:AB_2562176; Cat# 409318FACS (1:200)
AntibodyAPC anti-human IgM (monoclonal)BiolegendRRID:AB_493011; Cat# 314510FACS (1:200)
AntibodyPE anti-human IgA (monoclonal)Miltenyi BiotechRRID:AB_2733860; Cat# 130-114-002FACS (1:200)
AntibodyAnti-SARS-CoV-2 S2 clone D001 (monoclonal)SinoBiologicalRRID:AB_2857932; Cat# 40590-D001FACS
AntibodyAlexa488 anti-SARS-CoV-2 nucleoprotein (monoclonal)Produced in-houseCR3009IF
Recombinant DNA reagentpcDNA3-SARS-CoV-2_WT spikeDr Massimo Pizzato, University of Trento, ItalyWuhan spike sequenceTransfected construct
Recombinant DNA reagentpcDNA3-SARS-CoV-2_D614G spikeDr Massimo Pizzato, University of Trento, ItalyWuhan spike sequence with D614G mutation and cytoplasmic tail deletionTransfected construct
Recombinant DNA reagentpcDNA3-SARS-CoV-2_B.1.1.7 spikeThis paperB.1.1.7 spike sequenceTransfected construct
Recombinant DNA reagentpcDNA3-SARS-CoV-2_ B.1.351 spikeThis paperB.1.351 spike sequenceTransfected construct
Cell line (Homo sapiens)HEK293TCell Services facility at the Francis Crick InstituteRRID:CVCL_0063; CVCL_0063
Cell line
(Chlorocebus sp.)
Vero E6Dr Björn Meyer, Institut Pasteur, Paris, FranceCRL-1586
Cell line
(Chlorocebus sp.)
Vero V1Prof. Steve Goodbourn, St. George’s, University of London, London, UKCCL-81
OtherSARS-CoV-2hCoV-19/England/02/2020Respiratory Virus Unit, Public Health England, UKWuhan strain
OtherSARS-CoV-2hCoV-19/England/204690005/2020Public Health England (PHE), UK, through Prof. Wendy Barclay, Imperial College London, London, UKB.1.1.7 strain
OtherSARS-CoV-2501Y.V2.HV001
Cele et al., 2021
B.1.351 strain

Donor and patient samples and clinical data

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Serum or plasma samples from D614G infection were obtained from UCLH (REC ref: 20/HRA/2505) COVID-19 patients (n=20, acute D614G infection, COVID-19 patients) as previously described (Ng et al., 2020), or from UCLH health care workers (n=17, acute D614G infection, mild/asymptomatic), as previously described (Houlihan et al., 2020; Supplementary file 1). These samples were collected between March 2020 and June 2020. Serum or plasma samples from B.1.1.7 infection were obtained from patients (n=29, acute B.1.1.7 infection, mild/asymptomatic) admitted to UCLH (REC ref: 20/HRA/2505) for unrelated reasons, between December 2020 and January 2021, who then tested positive for SARS-CoV-2 infection by RT-qPCR, as part of routine testing (Supplementary file 1). Infection with B.1.1.7 was confirmed by sequencing of viral RNA, covered from nasopharyngeal swabs. A majority of these patients (n=23) subsequently developed mild COVID-19 symptoms and six remained asymptomatic. All serum or plasma samples were heat-treated at 56°C for 30 min prior to testing. No statistical methods were used to compute sample size for a pre-determined effect size. All patients/participants who had consented and were available at the time of the study were included.

Diagnosis of SARS-CoV-2 infection by RT-qPCR and next-generation sequencing

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SARS-CoV-2 nucleic acids were detected in nasopharyngeal swabs from hospitalised patients by a diagnostic RT-qPCR assay using custom primers and probes (Grant et al., 2020). Assays were run by Health Services Laboratories (HSL), London, UK. Diagnostic RT-qPCR assays for SARS-CoV-2 infection in health care workers was run at the Francis Crick Institute, as previously described (Aitken et al., 2020). SARS-CoV-2 RNA-positive samples (RNA amplified by Aptima Hologic) were subjected to real-time whole-genome sequencing at the UCLH Advanced Pathogen Diagnostics Unit. RNA was extracted from nasopharyngeal swab samples on the QiaSymphony platform using the Virus Pathogen Mini Kit (Qiagen). Libraries were prepared using the Illumina DNA Flex library preparation kit and sequenced on an Illumina MiSeq (V2) using the ARTIC protocol for targeted amplification (primer set V3). Genomes were assembled using an in-house pipeline (ICONIC Consortium et al., 2017) and aligned to a selection of publicly available SARS-CoV-2 genomes (Elbe and Buckland-Merrett, 2017) using the MAFFT alignment software (Katoh and Standley, 2013). Phylogenetic trees were generated from multiple sequence alignments using IQ-TREE (Nguyen et al., 2015) and FigTree (http://tree.bio.ed.ac.uk/software/figtree), with lineages assigned (including B.1.1.7 calls) using pangolin (http://github.com/cov-lineages/pangolin), and confirmed by manual inspection of alignments.

Cells lines and plasmids

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HEK293T cells were obtained from the Cell Services facility at the Francis Crick Institute, verified as mycoplasma-free and validated by DNA fingerprinting. Vero E6 and Vero V1 cells were kindly provided by Dr Björn Meyer, Institut Pasteur, Paris, France, and Prof. Steve Goodbourn, St. George’s, University of London, London, UK, respectively. Cells were grown in Iscove’s Modified Dulbecco’s Medium (Sigma-Aldrich) supplemented with 5% fetal bovine serum (Thermo Fisher Scientific), L-glutamine (2 mM, Thermo Fisher Scientific), penicillin (100 U/ml, Thermo Fisher Scientific), and streptomycin (0.1 mg/ml, Thermo Fisher Scientific). For SARS-CoV-2 spike expression, HEK293T cells were transfected with an expression vector (pcDNA3) carrying a codon-optimised gene encoding the wild-type full-length SARS-CoV-2 reference spike (referred to here as Wuhan spike, UniProt ID: P0DTC2) or a variant carrying the D614G mutation and a deletion of the last 19 amino acids of the cytoplasmic tail (referred to here as D614G spike) (both kindly provided by Massimo Pizzato, University of Trento, Italy). Similarly, HEK293T cells were transfected with expression plasmids (pcDNA3) encoding the full-length B.1.1.7 spike variant (D614G, Δ69–70, Δ144, N501Y, A570D, P681H, T716I, S982A, and D1118H) or the full-length B.1.351 spike variant (D614G, L18F, D80A, D215G, L242H, R246I, K417N, E484K, N501Y, A701V) (both synthesised and cloned by GenScript). All transfections were carried out using GeneJuice (EMD Millipore) and transfection efficiency was between 20% and 54% in separate experiments.

SARS-CoV-2 isolates

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The SARS-CoV-2 reference isolate (referred to as the Wuhan strain) was the hCoV-19/England/02/2020, obtained from the Respiratory Virus Unit, Public Health England, UK (GISAID EpiCov accession EPI_ISL_407073). The B.1.1.7 isolate was the hCoV-19/England/204690005/2020, which carries the D614G, Δ69–70, Δ144, N501Y, A570D, P681H, T716I, S982A, and D1118H mutations (Brown et al., 2021; Figure 2—figure supplement 4), obtained from Public Health England (PHE), UK, through Prof. Wendy Barclay, Imperial College London, London, UK. The B.1.351 virus isolate was the 501Y.V2.HV001, which carries the D614G, L18F, D80A, D215G, Δ242–244, K417N, E484K, N501Y, A701V mutations (Cele et al., 2021; Figure 2—figure supplement 4). However, sequencing of viral genomes isolated following further passage in Vero V1 cells identified the Q677H and R682W mutations at the furin cleavage site, in approximately 50% of the genomes. All viral isolates were propagated in Vero V1 cells.

Flow cytometric detection of antibodies to spike glycoproteins

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HEK293T cells were transfected to express the different SARS-CoV-2 spike variants. Two days after transfection, cells were trypsinised and transferred into V-bottom 96-well plates (20,000 cells/well). Cells were incubated with sera (diluted 1:50 in PBS) for 30 min, washed with FACS buffer (PBS, 5% BSA, 0.05% sodium azide), and stained with BV421 anti-IgG (clone HP6017, Biolegend), APC anti-IgM (clone MHM-88, Biolegend), and PE anti-IgA (clone IS11-8E10, Miltenyi Biotech) for 30 min (all antibodies diluted 1:200 in FACS buffer). Expression of SARS-CoV-2 spike was confirmed by staining with the D001 antibody (40590-D001, SinoBiological). Cells were washed with FACS buffer and fixed for 20 min in CellFIX buffer (BD Bioscience). Samples were run on a Ze5 analyzer (Bio-Rad) running Bio-Rad Everest software v2.4 or an LSR Fortessa with a high-throughput sampler (BD Biosciences) running BD FACSDiva software v8.0, and analyzed using FlowJo v10 (Tree Star Inc) analysis software, as previously described (Ng et al., 2020). All runs included three positive control samples, which were used for normalisation of mean fluorescence intensity (MFI) values. To this end, the MFI of the positively stained cells in each sample was expressed as a percentage of the MFI of the positive control on the same 96-well plate. The results shown are from one of one to two independent experiments.

SARS-CoV-2 neutralisation assay

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SARS-CoV-2 variant neutralisation was tested using an in-house developed method (Figure 2—figure supplement 5). Heat-inactivated serum samples in QR coded vials (FluidX/Brooks) were assembled into 96-well racks along with foetal calf serum-containing vials as negative controls and SARS-CoV-2 spike RBD-binding nanobody (produced in-house) vials as positive controls. A Viaflo automatic pipettor fitted with a 96-channel head (Integra) was used to transfer serum samples into V-bottom 96-well plates (Thermo 249946) prefilled with Dulbecco’s modified eagle medium to achieve a 1:10 dilution. The Viaflo was then used to serially dilute from the first dilution plate into three further plates at 1:4 to achieve 1:40, 1:160, and 1:640. Next, the diluted serum plates were stamped into duplicate 384-well imaging plates (Greiner 781091) pre-seeded the day before with 3000 Vero E6 cells per well, with each of the four dilutions into a different quadrant of the final assay plates to achieve a final working dilution of samples at 1:40, 1:160, 1:640, and 1:2560. Assay plates were then transferred to containment level 3 (CL3) where cells were infected with the indicated SARS-CoV-2 viral strain, by adding a pre-determined dilution of the virus prep using a Viaflo fitted with a 384 head with tips for the no-virus wells removed. Plates were incubated for 24 hr at 37°C, 5% CO2 and then fixed by adding a concentrated formaldehyde solution to achieve a final concentration of 4%. Assay plates were then transferred out of CL3 and fixing solution washed off, cells blocked, and permeabilised with a 3% BSA/0.2% Triton-X100/PBS solution, and finally immunostained with DAPI and an Alexa488-conjugated anti-nucleoprotein monoclonal antibody (clone CR3009; produced in-house). Automated imaging was carried out using an Opera Phenix (Perkin Elmer) with a 5× lens and the ratio of infected area (Alexa488-positive region) to cell area (DAPI-positive region) per well calculated by the Phenix-associated software Harmony. A custom automated script runs plate normalisation by background subtracting the median of the no-virus wells and then dividing by the median of the virus-only wells before using a three-parameter dose-response model for curve fitting and identification of the dilution which achieves 50% neutralisation for that particular serum sample (IC50). The results shown are from one of two to three independent experiments.

Statistical analyses

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Data were analysed and plotted in SigmaPlot v14.0 (Systat Software). Parametric comparisons of normally distributed values that satisfied the variance criteria were made by paired or unpaired Student’s t-tests or one-way analysis of variance tests. Data that did not pass the variance test were compared with Wilcoxon signed rank tests.

Data availability

All data generated or analysed during this study are included in the manuscript and supporting files.

References

Decision letter

  1. Bavesh D Kana
    Senior and Reviewing Editor; University of the Witwatersrand, South Africa

Our editorial process produces two outputs: i) public reviews designed to be posted alongside the preprint for the benefit of readers; ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Acceptance summary:

This study describes reduced antibody cross-reactivity between the SARS-CoV-2 B.1.1.7 variant and the parental strain or the B.1.351 variant. Asymmetric antibody responses and reduced neutralizing antibodies against heterogeneous variants have been demonstrated in multiple studies. The current study reports reduction of B.1.1.7 COVID-19 sera against the SARS-CoV-2 parental strain and B.1.351. This observation is interesting and could be useful for future vaccine development. The work is of interests to virologists and infectious disease specialists.

Decision letter after peer review:

Thank you for submitting your article "Reduced antibody cross-reactivity following infection with B.1.1.7 than with parental SARS-CoV-2 strains" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Bavesh Kana as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential Revisions:

1. As the authors are aware, the B.1.617.2 VOC has become highly topical and is currently circulating around the world. Recent studies have shown similar results for B.1.1.7 and also B.1.617.2 (for example, https://www.cell.com/cell/fulltext/S0092-8674(21)00755-8). These other studies should be cited and discussed in the manuscript to better contextualise this study's findings.

2. In sup. Figure S9 the authors present their most controlled (like-for-like) comparison of sera from WT and B.1.1.7 infected individuals. They conclude that B.1.1.7 infection leads to nAbs with lower cross reactivity. But this is only true for the fold drop. Looking at the absolute level of nAbs in these two groups, it seems to indicate the opposite conclusion is plausible. That is, if one gets an asymptomatic B.1.1.7 infection, it seems they may have a higher neutralisation against B.1.1.7 and WT (and even B.1.351) compared with someone who receives an asymptomatic WT infection. This would seem to indicate that a B.1.1.7 infection leads to a higher overall response including a higher cross-reactive response. This interpretation is the opposite of the authors, and the difference relies on whether absolute level or fold-drop is counted as the better measure of cross-reactivity. It may be that B.1.1.7 infections tend to be asymptomatic with higher viral loads and so lead to higher overall Ab responses with asymptomatic infection. It is not clear which of these two opposite interpretations of the data is the "correct" interpretation and the authors approach is very reasonable one – it's just not clear which is the most meaningful at this stage. Therefore, the authors should include some discussion that another interpretation is possible when looking at the absolute level of nAbs in B.1.1.7 infected individuals and offer a balanced justification for why they favour the interpretation that B.1.1.7 leads to lower cross-reactivity instead of higher cross reactivity.

3. Do the expressed B.1.1.7 and B.1.351 spikes have the cytoplasmic tail removed?

Editorial/clarification:

1. Figure 1a and Figure 1c should have x axis labelled.

2. A schematic or figure showing the mutations for each variant might be useful to see how different they are from each other.

3. Currently available vaccines are based on the Wuhan sequence and sera collected in this study are from D614G infected patients. Can the authors comment on the effects D614G has on antigenicity?

4. It would be worth showing the median IC50s along with % of infecting variant. For example, in Figure S7 the IC50s of sera from severe D614G infection are still high despite a 50% decrease from the infecting variant.

4. It is interesting that IgG binding is largely retained across all variant spikes yet neutralisation drops significantly against B.1.351. Do the authors have any explanations for this?

5. If asymptomatic patients are considered, are there any differences with binding or neutralisation when compared to mild?

6. Typo on page 4 – seconds last line "IgG biding antibodies".

Reviewer #3 (Recommendations for the authors):

I have several questions and comments.

1. Do the expressed B.1.1.7 and B.1.351 spikes have the cytoplasmic tail removed?

2. Figure 1a and Figure 1c should have x axis labelled.

3. A schematic or figure showing the mutations for each variant might be useful to see how different they are from each other.

4. Currently available vaccines are based on the Wuhan sequence and sera collected in this study are from D614G infected patients. Can the authors comment on the effects D614G has on antigenicity?

5. I think it would be worth showing the median IC50s along with % of infecting variant. For example, in Figure S7 the IC50s of sera from severe D614G infection are still high despite a 50% decrease from the infecting variant.

6. It is interesting that IgG binding is largely retained across all variant spikes yet neutralisation drops significantly against B.1.351. Do the authors have any explanations for this?

7. If you look at asymptomatic patients are there any differences with binding or neutralisation when compared to mild?

8. Is there a reason D614G mild/asymptomatic 3 month data is not included in some of the figures?

9. Typo on page 4 – seconds last line "IgG biding antibodies".

https://doi.org/10.7554/eLife.69317.sa1

Author response

Essential Revisions (for the authors):

1. As the authors are aware, the B.1.617.2 VOC has become highly topical and is currently circulating around the world. Recent studies have shown similar results for B.1.1.7 and also B.1.617.2 (for example, https://www.cell.com/cell/fulltext/S0092-8674(21)00755-8). These other studies should be cited and discussed in the manuscript to better contextualise this study's findings.

Testament to how quickly SARS-CoV-2 (and our understanding of it) evolves, since submission of this work, B.1.617.2 started spreading in the UK, replacing B.1.1.7, and we and others have since reported the reduced sensitivity of this variant to vaccine-induced antibodies (Wall et al., 2021a, Wall et al., 2021b in the revised manuscript). The B.1.617.2 variant was not available when the experiments reported here were performed.

We have now discussed the new variant and new studies published during review of this work. The consensus is that each variant induces the strongest response to itself and the more the variants diverge, the lower the heterotypic immunity they induce. There are, however, notable differences too. For example, the new study from Oxford (Liu et al., 2021a) uniquely finds that B.1.1.7 sera recognise better the Victoria strain (a Wuhan related strain) than B.1.1.7 itself and these differences, as well as the implications for vaccine adaptation, have now been discussed.

2. In sup. Figure S9 the authors present their most controlled (like-for-like) comparison of sera from WT and B.1.1.7 infected individuals. They conclude that B.1.1.7 infection leads to nAbs with lower cross reactivity. But this is only true for the fold drop. Looking at the absolute level of nAbs in these two groups, it seems to indicate the opposite conclusion is plausible. That is, if one gets an asymptomatic B.1.1.7 infection, it seems they may have a higher neutralisation against B.1.1.7 and WT (and even B.1.351) compared with someone who receives an asymptomatic WT infection. This would seem to indicate that a B.1.1.7 infection leads to a higher overall response including a higher cross-reactive response. This interpretation is the opposite of the authors, and the difference relies on whether absolute level or fold-drop is counted as the better measure of cross-reactivity. It may be that B.1.1.7 infections tend to be asymptomatic with higher viral loads and so lead to higher overall Ab responses with asymptomatic infection. It is not clear which of these two opposite interpretations of the data is the "correct" interpretation and the authors approach is very reasonable one – it's just not clear which is the most meaningful at this stage. Therefore, the authors should include some discussion that another interpretation is possible when looking at the absolute level of nAbs in B.1.1.7 infected individuals and offer a balanced justification for why they favour the interpretation that B.1.1.7 leads to lower cross-reactivity instead of higher cross reactivity.

We thank the Reviewer for raising this important issue. Despite our best efforts to match all other variables in the comparison of B.1.1.7 and parental strain convalescent sera in our collection, confounding differences still remain. We, therefore, compared each the fold drop of the response to other variants, only in relation to the response to the infecting variant in each donor (internally controlled), which is independent of all other variables. We conclude that, for a given response to the infecting variant, relative recognition of parental strains is lower for B.1.1.7 sera than of B.1.1.7 strain for parental strain sera.

Nevertheless, we fully agree with the Reviewer that, given the enormous variability in the antibody response to any variant between individuals, cross-reactivity to another variant may well be higher in absolute terms for at least some B.1.1.7 sera than for parental strain sera, even if it’s lower in relative terms. Also, in light of our previous findings (Frampton et al., 2021) that, on average, B.1.1.7 infection results in increased viral loads than parental strain infection, it could be argued that heterotypic immunity following B.1.1.7 infection will be, on average, higher than following parental strain infection in absolute terms. The discussion has now been revised to reflect these comments.

3. Do the expressed B.1.1.7 and B.1.351 spikes have the cytoplasmic tail removed?

With the exception of the D614G spike, all other spike variants were full-length and contained the intact cytoplasmic domain. This has now been clarified in the Methods section.

Editorial/clarification:

1. Figure 1a and Figure 1c should have x axis labelled.

Labels have now been added to all x and y axes in Figure 1a-c. For consistency, the x-axis label has been added also to Figure 2a.

2. A schematic or figure showing the mutations for each variant might be useful to see how different they are from each other.

We have now included a figure (new Figure 2—figure supplement 10) depicting the distance of the spike protein sequences, as well as shared and unique mutations among the variants, and discussed such divergence in the context of cross-reactivity.

3. Currently available vaccines are based on the Wuhan sequence and sera collected in this study are from D614G infected patients. Can the authors comment on the effects D614G has on antigenicity?

During review of this manuscript, we were able to directly compare the effect of the D614G mutation on antigenicity and sensitivity to antibody recognition, using sera also from vaccinated donors. The results of this comparison (Wall et al., 2021a, Wall et al., 2021b) show that the D614G mutation reduces antigenicity in comparison with the Wuhan strain by a factor of ~2.3. However, sensitivity of the D614G and B.1.1.7 variants to vaccinee sera was indistinguishable. These findings are now discussed also in this manuscript.

4. It would be worth showing the median IC50s along with % of infecting variant. For example, in Figure S7 the IC50s of sera from severe D614G infection are still high despite a 50% decrease from the infecting variant.

As per Reviewer’s suggestion, median IC50 values have been added to all the plots.

4. It is interesting that IgG binding is largely retained across all variant spikes yet neutralisation drops significantly against B.1.351. Do the authors have any explanations for this?

As only a fraction of binding antibodies are neutralising, it should be expected that binding is less affected than neutralisation by mutations in emerging variants. There may be little selection pressure to propagate or fix mutations that affect binding of non-neutralising antibodies. In contrast, the selection pressure for mutation that affect neutralisation will be greater in an increasingly immune population. The relatively small number of mutations seen in B.1.351 and other variants of concern (with the exception of the B.1.1.7 variant) appear to be neutralising antibody escape mutations. However, these mutations affect less than 2% of the overall spike sequence and, therefore, binding of antibodies to the unmutated regions of the spike that may not be relevant for neutralisation should be retained. This potential explanation has now been added to the text.

5. If asymptomatic patients are considered, are there any differences with binding or neutralisation when compared to mild?

We have now plotted binding and neutralising antibody levels in B.1.1.7-infected donors according to symptoms (new Figure 1—figure supplement 6d). Although levels of all antibodies were lower in asymptomatic cases than in mild cases, differences were not statistically significant with this small number of asymptomatic cases.

6. Typo on page 4 – seconds last line "IgG biding antibodies".

We thank the Reviewer for spotting this error, which has now been corrected.

https://doi.org/10.7554/eLife.69317.sa2

Article and author information

Author details

  1. Nikhil Faulkner

    1. Retroviral Immunology, London, United Kingdom
    2. National Heart and Lung Institute, Imperial College London, London, United Kingdom
    Contribution
    Formal analysis, Investigation
    Contributed equally with
    Kevin W Ng, Mary Y Wu and Ruth Harvey
    Competing interests
    No competing interests declared
  2. Kevin W Ng

    Retroviral Immunology, London, United Kingdom
    Contribution
    Formal analysis, Investigation
    Contributed equally with
    Nikhil Faulkner, Mary Y Wu and Ruth Harvey
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1635-6768
  3. Mary Y Wu

    High Throughput Screening STP, London, United Kingdom
    Contribution
    Formal analysis, Investigation
    Contributed equally with
    Nikhil Faulkner, Kevin W Ng and Ruth Harvey
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2074-6171
  4. Ruth Harvey

    Worldwide Influenza Centre, London, United Kingdom
    Contribution
    Formal analysis, Investigation
    Contributed equally with
    Nikhil Faulkner, Kevin W Ng and Mary Y Wu
    Competing interests
    No competing interests declared
  5. Marios Margaritis

    Advanced Pathogen Diagnostics Unit UCLH NHS Trust, London, United Kingdom
    Contribution
    Resources
    Competing interests
    No competing interests declared
  6. Stavroula Paraskevopoulou

    Advanced Pathogen Diagnostics Unit UCLH NHS Trust, London, United Kingdom
    Contribution
    Resources
    Competing interests
    No competing interests declared
  7. Catherine Houlihan

    1. Advanced Pathogen Diagnostics Unit UCLH NHS Trust, London, United Kingdom
    2. Division of Infection and Immunity, London, United Kingdom
    Contribution
    Resources
    Competing interests
    No competing interests declared
  8. Saira Hussain

    1. Worldwide Influenza Centre, London, United Kingdom
    2. RNA Virus Replication Laboratory, London, United Kingdom
    Contribution
    Formal analysis, Investigation
    Competing interests
    No competing interests declared
  9. Maria Greco

    RNA Virus Replication Laboratory, London, United Kingdom
    Contribution
    Formal analysis, Investigation
    Competing interests
    No competing interests declared
  10. William Bolland

    Retroviral Immunology, London, United Kingdom
    Contribution
    Formal analysis, Investigation
    Competing interests
    No competing interests declared
  11. Scott Warchal

    High Throughput Screening STP, London, United Kingdom
    Contribution
    Formal analysis, Investigation
    Competing interests
    No competing interests declared
  12. Judith Heaney

    Advanced Pathogen Diagnostics Unit UCLH NHS Trust, London, United Kingdom
    Contribution
    Resources
    Competing interests
    No competing interests declared
  13. Hannah Rickman

    Advanced Pathogen Diagnostics Unit UCLH NHS Trust, London, United Kingdom
    Contribution
    Resources
    Competing interests
    No competing interests declared
  14. Moria Spyer

    1. Advanced Pathogen Diagnostics Unit UCLH NHS Trust, London, United Kingdom
    2. Department of Population, Policy and Practice, London, United Kingdom
    Contribution
    Resources
    Competing interests
    No competing interests declared
  15. Daniel Frampton

    Division of Infection and Immunity, London, United Kingdom
    Contribution
    Resources
    Competing interests
    No competing interests declared
  16. Matthew Byott

    Advanced Pathogen Diagnostics Unit UCLH NHS Trust, London, United Kingdom
    Contribution
    Resources
    Competing interests
    No competing interests declared
  17. Tulio de Oliveira

    1. School of Laboratory Medicine and Medical Sciences, University of KwaZulu-Natal, Durban, South Africa
    2. KwaZulu-Natal Research Innovation and Sequencing Platform, Durban, South Africa
    3. Centre for the AIDS Programme of Research in South Africa, Durban, South Africa
    4. Department of Global Health, University of Washington, Seattle, United States
    Contribution
    Resources
    Competing interests
    No competing interests declared
  18. Alex Sigal

    1. School of Laboratory Medicine and Medical Sciences, University of KwaZulu-Natal, Durban, South Africa
    2. Africa Health Research Institute, Durban, South Africa
    3. Max Planck Institute for Infection Biology, Berlin, Germany
    Contribution
    Resources
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8571-2004
  19. Svend Kjaer

    Structural Biology STP, London, United Kingdom
    Contribution
    Resources
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9767-8683
  20. Charles Swanton

    Cancer Evolution and Genome Instability Laboratory, London, United Kingdom
    Contribution
    Supervision
    Competing interests
    No competing interests declared
  21. Sonia Gandhi

    Neurodegradation Biology Laboratory, London, United Kingdom
    Contribution
    Supervision
    Competing interests
    No competing interests declared
  22. Rupert Beale

    Cell Biology of Infection Laboratory, London, United Kingdom
    Contribution
    Resources
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6705-8560
  23. Steve J Gamblin

    Structural Biology of Disease Processes Laboratory, The Francis Crick Institute, London, United Kingdom
    Contribution
    Supervision
    Competing interests
    No competing interests declared
  24. John W McCauley

    Worldwide Influenza Centre, London, United Kingdom
    Contribution
    Supervision
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4744-6347
  25. Rodney Stuart Daniels

    Worldwide Influenza Centre, London, United Kingdom
    Contribution
    Supervision
    Competing interests
    No competing interests declared
  26. Michael Howell

    High Throughput Screening STP, London, United Kingdom
    Contribution
    Supervision
    Competing interests
    No competing interests declared
  27. David Bauer

    RNA Virus Replication Laboratory, London, United Kingdom
    Contribution
    Supervision
    Competing interests
    No competing interests declared
  28. Eleni Nastouli

    1. Retroviral Immunology, London, United Kingdom
    2. Advanced Pathogen Diagnostics Unit UCLH NHS Trust, London, United Kingdom
    3. Department of Population, Policy and Practice, London, United Kingdom
    Contribution
    Supervision
    Competing interests
    No competing interests declared
  29. George Kassiotis

    1. Retroviral Immunology, London, United Kingdom
    2. Department of Infectious Disease, St Mary's Hospital, Imperial College London, London, United Kingdom
    Contribution
    Conceptualization, Supervision, Writing - original draft
    For correspondence
    george.kassiotis@crick.ac.uk
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8457-2633

Funding

Francis Crick Institute

  • Nikhil Faulkner
  • Kevin W Ng
  • Mary Y Wu
  • Ruth Harvey
  • Saira Hussain
  • Maria Greco
  • William Bolland
  • Scott Warchal
  • Svend Kjaer
  • Charles Swanton
  • Sonia Gandhi
  • Rupert Beale
  • Steve j Gamblin
  • John W McCauley
  • Rodney Stuart Daniels
  • Michael Howell
  • David Bauer
  • George Kassiotis

Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg

  • Alex Sigal

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We are grateful for assistance from the Flow Cytometry and Cell Services facilities at the Francis Crick Institute and to Mr Michael Bennet and Mr Simon Caidan for training and support in the high-containment laboratory. We wish to thank the Public Health England (PHE) Virology Consortium and PHE field staff, the ATACCC (Assessment of Transmission and Contagiousness of COVID-19 in Contacts) investigators, the G2P-UK (Genotype to Phenotype-UK) National Virology Consortium, and Prof. Wendy Barclay, Imperial College London, London, UK, for the B.1.1.7 viral isolate. This work was supported by the Francis Crick Institute, which receives its core funding from Cancer Research UK, the UK Medical Research Council, and the Wellcome Trust. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Ethics

Human subjects: Serum or plasma samples were obtained from University College London Hospitals (UCLH) (REC ref: 20/HRA/2505).

Senior and Reviewing Editor

  1. Bavesh D Kana, University of the Witwatersrand, South Africa

Publication history

  1. Preprint posted: March 1, 2021 (view preprint)
  2. Received: April 11, 2021
  3. Accepted: July 26, 2021
  4. Accepted Manuscript published: July 29, 2021 (version 1)
  5. Version of Record published: August 9, 2021 (version 2)

Copyright

© 2021, Faulkner et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Further reading

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    Background: Identifying environmentally responsive genetic loci where DNA methylation is associated with coronary heart disease (CHD) may reveal novel pathways or therapeutic targets for CHD. We conducted the first prospective epigenome-wide analysis of DNA methylation in relation to incident CHD in the Asian population.

    Methods: We did a nested case-control study comprising incident CHD cases and 1:1 matched controls who were identified from the 10-year follow-up of the China Kadoorie Biobank. Methylation level of baseline blood leukocyte DNA was measured by Infinium Methylation EPIC BeadChip. We performed the single cytosine-phosphate-guanine (CpG) site association analysis and network approach to identify CHD-associated CpG sites and co-methylation gene module.

    Results: After quality control, 982 participants (mean age 50.1 years) were retained. Methylation level at 25 CpG sites across the genome was associated with incident CHD (genome-wide false discovery rate [FDR] < 0.05 or module-specific FDR <0.01). One SD increase in methylation level of identified CpGs was associated with differences in CHD risk, ranging from a 47% decrease to a 118% increase. Mediation analyses revealed 28.5% of the excessed CHD risk associated with smoking was mediated by methylation level at the promoter region of ANKS1A gene (P for mediation effect = 0.036). Methylation level at the promoter region of SNX30 was associated with blood pressure and subsequent risk of CHD, with the mediating proportion to be 7.7% (P = 0.003) via systolic blood pressure and 6.4% (P = 0.006) via diastolic blood pressure. Network analysis revealed a co-methylation module associated with CHD.

    Conclusions: We identified novel blood methylation alterations associated with incident CHD in the Asian population and provided evidence of the possible role of epigenetic regulations in the smoking- and BP-related pathways to CHD risk.

    Funding: This work was supported by National Natural Science Foundation of China (81390544 and 91846303). The CKB baseline survey and the first re-survey were supported by a grant from the Kadoorie Charitable Foundation in Hong Kong. The long-term follow-up is supported by grants from the UK Wellcome Trust (202922/Z/16/Z, 088158/Z/09/Z, 104085/Z/14/Z), grant (2016YFC0900500, 2016YFC0900501, 2016YFC0900504, 2016YFC1303904) from the National Key and Program of China, and Chinese Ministry of Science and Technology (2011BAI09B01).