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.

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 2–5).

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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).

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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.

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

1. 1. Aitken J
   2. Ambrose K
   3. Barrell S
   4. Beale R
   5. Bineva-Todd G
   6. Biswas D
   7. Byrne R
   8. Caidan S
   9. Cherepanov P
   10. Churchward L
   11. Clark G
   12. Crawford M
   13. Cubitt L
   14. Dearing V
   15. Earl C
   16. Edwards A
   17. Ekin C
   18. Fidanis E
   19. Gaiba A
   20. Gamblin S
   21. Gandhi S
   22. Goldman J
   23. Goldstone R
   24. Grant PR
   25. Greco M
   26. Heaney J
   27. Hindmarsh S
   28. Houlihan CF
   29. Howell M
   30. Hubank M
   31. Hughes D
   32. Instrell R
   33. Jackson D
   34. Jamal-Hanjani M
   35. Jiang M
   36. Johnson M
   37. Jones L
   38. Kanu N
   39. Kassiotis G
   40. Kirk S
   41. Kjaer S
   42. Levett A
   43. Levett L
   44. Levi M
   45. Lu WT
   46. MacRae JI
   47. Matthews J
   48. McCoy LE
   49. Moore C
   50. Moore D
   51. Nastouli E
   52. Nicod J
   53. Nightingale L
   54. Olsen J
   55. O'Reilly N
   56. Pabari A
   57. Papayannopoulos V
   58. Patel N
   59. Peat N
   60. Pollitt M
   61. Ratcliffe P
   62. Reis E Sousa C
   63. Rosa A
   64. Rosenthal R
   65. Roustan C
   66. Rowan A
   67. Shin GY
   68. Snell DM
   69. Song OR
   70. Spyer MJ
   71. Strange A
   72. Swanton C
   73. Turner JMA
   74. Turner M
   75. Wack A
   76. Walker PA
   77. Ward S
   78. Wong WK
   79. Wright J
   80. Wu M
   81. Crick COVID-19 Consortium

   (2020) Scalable and robust SARS-CoV-2 testing in an academic center

   Nature Biotechnology 38:927–931.

   https://doi.org/10.1038/s41587-020-0588-y

   • PubMed
   • Google Scholar
2. Preprint

   1. Brown JC
   2. Goldhill DH
   3. Zhou J
   4. Peacock TP
   5. Frise R
   6. Goonawardane N
   7. Baillon L
   8. Kugathasan R
   9. Pinto A
   10. McKay PF
   11. Hassard J
   12. Moshe M
   13. Singanayagam A
   14. Burgoyne T
   15. Barclay WS

   (2021) Increased transmission of Sars-Cov-2 lineage b.1.1.7 (Voc 2020212/01) Is not accounted for by a replicative advantage in primary airway cells or antibody escape

   bioRxiv.

   https://doi.org/10.1101/2021.02.24.432576

   • Google Scholar
3. 1. Cele S
   2. Gazy I
   3. Jackson L
   4. Hwa SH
   5. Tegally H
   6. Lustig G
   7. Giandhari J
   8. Pillay S
   9. Wilkinson E
   10. Naidoo Y
   11. Karim F
   12. Ganga Y
   13. Khan K
   14. Bernstein M
   15. Balazs AB
   16. Gosnell BI
   17. Hanekom W
   18. Moosa MS
   19. Lessells RJ
   20. de Oliveira T
   21. Sigal A
   22. Network for Genomic Surveillance in South Africa
   23. COMMIT-KZN Team

   (2021) Escape of SARS-CoV-2 501Y.V2 from neutralization by convalescent plasma

   Nature 593:142–146.

   https://doi.org/10.1038/s41586-021-03471-w

   • PubMed
   • Google Scholar
4. 1. Diamond M
   2. Chen R
   3. Xie X
   4. Case J
   5. Zhang X
   6. VanBlargan L
   7. Liu Y
   8. Liu J
   9. Errico J
   10. Winkler E
   11. Suryadevara N
   12. Tahan S
   13. Turner J
   14. Kim W
   15. Schmitz A
   16. Thapa M
   17. Wang D
   18. Boon A
   19. Pinto D
   20. Presti R

   (2021) Sars-Cov-2 variants show resistance to neutralization by many monoclonal and Serum-Derived polyclonal antibodies

   Research Square 5:228079.

   https://doi.org/10.21203/rs.3.rs-228079/v

   • Google Scholar
5. Preprint

   1. Edara VV
   2. Norwood C
   3. Floyd K
   4. Lai L
   5. Davis-Gardner ME
   6. Hudson WH
   7. Mantus G
   8. Nyhoff LE
   9. Adelman MW
   10. Fineman R
   11. Patel S
   12. Byram R
   13. Gomes DN
   14. Michael G
   15. Abdullahi H
   16. Beydoun N
   17. Panganiban B
   18. McNair N
   19. Hellmeister K
   20. Pitts J

   (2021) Reduced binding and neutralization of infection- and Vaccine-Induced antibodies to the b.1.351 (South african) Sars-Cov-2 variant

   bioRxiv.

   https://doi.org/10.1101/2021.02.20.432046

   • Google Scholar
6. 1. Elbe S
   2. Buckland-Merrett G

   (2017) Data, disease and diplomacy: gisaid's innovative contribution to global health

   Global Challenges 1:33–46.

   https://doi.org/10.1002/gch2.1018

   • PubMed
   • Google Scholar
7. 1. Emary KRW
   2. Golubchik T
   3. Aley PK
   4. Ariani CV
   5. Angus B
   6. Bibi S
   7. Blane B
   8. Bonsall D
   9. Cicconi P
   10. Charlton S
   11. Clutterbuck EA
   12. Collins AM
   13. Cox T
   14. Darton TC
   15. Dold C
   16. Douglas AD
   17. Duncan CJA
   18. Ewer KJ
   19. Flaxman AL
   20. Faust SN
   21. Ferreira DM
   22. Feng S
   23. Finn A
   24. Folegatti PM
   25. Fuskova M
   26. Galiza E
   27. Goodman AL
   28. Green CM
   29. Green CA
   30. Greenland M
   31. Hallis B
   32. Heath PT
   33. Hay J
   34. Hill HC
   35. Jenkin D
   36. Kerridge S
   37. Lazarus R
   38. Libri V
   39. Lillie PJ
   40. Ludden C
   41. Marchevsky NG
   42. Minassian AM
   43. McGregor AC
   44. Mujadidi YF
   45. Phillips DJ
   46. Plested E
   47. Pollock KM
   48. Robinson H
   49. Smith A
   50. Song R
   51. Snape MD
   52. Sutherland RK
   53. Thomson EC
   54. Toshner M
   55. Turner DPJ
   56. Vekemans J
   57. Villafana TL
   58. Williams CJ
   59. Hill AVS
   60. Lambe T
   61. Gilbert SC
   62. Voysey M
   63. Ramasamy MN
   64. Pollard AJ
   65. COVID-19 Genomics UK consortium
   66. AMPHEUS Project
   67. Oxford COVID-19 Vaccine Trial Group

   (2021) Efficacy of ChAdOx1 nCoV-19 (AZD1222) vaccine against SARS-CoV-2 variant of concern 202012/01 (B.1.1.7): an exploratory analysis of a randomised controlled trial

   The Lancet 397:1351–1362.

   https://doi.org/10.1016/S0140-6736(21)00628-0

   • PubMed
   • Google Scholar
8. 1. Frampton D
   2. Rampling T
   3. Cross A
   4. Bailey H
   5. Heaney J
   6. Byott M
   7. Scott R
   8. Sconza R
   9. Price J
   10. Margaritis M
   11. Bergstrom M
   12. Spyer MJ
   13. Miralhes PB
   14. Grant P
   15. Kirk S
   16. Valerio C
   17. Mangera Z
   18. Prabhahar T
   19. Moreno-Cuesta J
   20. Arulkumaran N
   21. Singer M
   22. Shin GY
   23. Sanchez E
   24. Paraskevopoulou SM
   25. Pillay D
   26. McKendry RA
   27. Mirfenderesky M
   28. Houlihan CF
   29. Nastouli E

   (2021) Genomic characteristics and clinical effect of the emergent SARS-CoV-2 b.1.1.7 lineage in London, UK: a whole-genome sequencing and hospital-based cohort study

   The Lancet. Infectious Diseases 12:00170-5.

   https://doi.org/10.1016/S1473-3099(21)00170-5

   • Google Scholar
9. 1. Gaebler C
   2. Wang Z
   3. Lorenzi JCC
   4. Muecksch F
   5. Finkin S
   6. Tokuyama M
   7. Cho A
   8. Jankovic M
   9. Schaefer-Babajew D
   10. Oliveira TY
   11. Cipolla M
   12. Viant C
   13. Barnes CO
   14. Bram Y
   15. Breton G
   16. Hägglöf T
   17. Mendoza P
   18. Hurley A
   19. Turroja M
   20. Gordon K
   21. Millard KG
   22. Ramos V
   23. Schmidt F
   24. Weisblum Y
   25. Jha D
   26. Tankelevich M
   27. Martinez-Delgado G
   28. Yee J
   29. Patel R
   30. Dizon J
   31. Unson-O’Brien C
   32. Shimeliovich I
   33. Robbiani DF
   34. Zhao Z
   35. Gazumyan A
   36. Schwartz RE
   37. Hatziioannou T
   38. Bjorkman PJ
   39. Mehandru S
   40. Bieniasz PD
   41. Caskey M
   42. Nussenzweig MC

   (2021) Evolution of antibody immunity to SARS-CoV-2

   Nature 591:639–644.

   https://doi.org/10.1038/s41586-021-03207-w

   • Google Scholar
10. Preprint

    1. Grant PR
    2. Turner MA
    3. Shin GY
    4. Nastouli E
    5. Levett LJ

    (2020) Extraction-Free Covid-19 (Sars-Cov-2) Diagnosis by Rt-Pcr to increase capacity for national testing programmes during a pandemic

    bioRxiv.

    https://doi.org/10.1101/2020.04.06.028316

    • Google Scholar
11. 1. Houlihan CF
    2. Vora N
    3. Byrne T
    4. Lewer D
    5. Kelly G
    6. Heaney J
    7. Gandhi S
    8. Spyer MJ
    9. Beale R
    10. Cherepanov P
    11. Moore D
    12. Gilson R
    13. Gamblin S
    14. Kassiotis G
    15. McCoy LE
    16. Swanton C
    17. Hayward A
    18. Nastouli E
    19. Crick COVID-19 Consortium
    20. SAFER Investigators

    (2020) Pandemic peak SARS-CoV-2 infection and seroconversion rates in London frontline health-care workers

    The Lancet 396:e6–e7.

    https://doi.org/10.1016/S0140-6736(20)31484-7

    • PubMed
    • Google Scholar
12. 1. ICONIC Consortium
    2. Harvala H
    3. Frampton D
    4. Grant P
    5. Raffle J
    6. Ferns RB
    7. Kozlakidis Z
    8. Kellam P
    9. Pillay D
    10. Hayward A
    11. Nastouli E

    (2017) Emergence of a novel subclade of influenza A(H3N2) virus in London, December 2016 to January 2017

    Eurosurveillance 22:30466.

    https://doi.org/10.2807/1560-7917.ES.2017.22.8.30466

    • PubMed
    • Google Scholar
13. 1. Katoh K
    2. Standley DM

    (2013) MAFFT multiple sequence alignment software version 7: improvements in performance and usability

    Molecular Biology and Evolution 30:772–780.

    https://doi.org/10.1093/molbev/mst010

    • PubMed
    • Google Scholar
14. 1. Kirby T

    (2021) New variant of SARS-CoV-2 in UK causes surge of COVID-19

    The Lancet Respiratory Medicine 9:e20–e21.

    https://doi.org/10.1016/S2213-2600(21)00005-9

    • PubMed
    • Google Scholar
15. 1. Liu C
    2. Ginn HM
    3. Dejnirattisai W
    4. Supasa P
    5. Wang B
    6. Tuekprakhon A
    7. Nutalai R
    8. Zhou D
    9. Mentzer AJ
    10. Zhao Y
    11. Duyvesteyn HME
    12. López-Camacho C
    13. Slon-Campos J
    14. Walter TS
    15. Skelly D
    16. Johnson SA
    17. Ritter TG
    18. Mason C
    19. Costa Clemens SA
    20. Naveca FG

    (2021a) Reduced neutralization of Sars-Cov-2 b.1.617 by vaccine and convalescent serum

    Cell 187:1–17.

    https://doi.org/10.1016/j.cell.2021.06.020

    • Google Scholar
16. 1. Liu Y
    2. Liu J
    3. Xia H
    4. Zhang X
    5. Fontes-Garfias CR
    6. Swanson KA
    7. Cai H
    8. Sarkar R
    9. Chen W
    10. Cutler M
    11. Cooper D
    12. Weaver SC
    13. Muik A
    14. Sahin U
    15. Jansen KU
    16. Xie X
    17. Dormitzer PR
    18. Shi PY

    (2021b) Neutralizing activity of BNT162b2-Elicited serum

    New England Journal of Medicine 384:1466–1468.

    https://doi.org/10.1056/NEJMc2102017

    • PubMed
    • Google Scholar
17. 1. Long QX
    2. Liu BZ
    3. Deng HJ
    4. Wu GC
    5. Deng K
    6. Chen YK
    7. Liao P
    8. Qiu JF
    9. Lin Y
    10. Cai XF
    11. Wang DQ
    12. Hu Y
    13. Ren JH
    14. Tang N
    15. Xu YY
    16. Yu LH
    17. Mo Z
    18. Gong F
    19. Zhang XL
    20. Tian WG
    21. Hu L
    22. Zhang XX
    23. Xiang JL
    24. Du HX
    25. Liu HW
    26. Lang CH
    27. Luo XH
    28. Wu SB
    29. Cui XP
    30. Zhou Z
    31. Zhu MM
    32. Wang J
    33. Xue CJ
    34. Li XF
    35. Wang L
    36. Li ZJ
    37. Wang K
    38. Niu CC
    39. Yang QJ
    40. Tang XJ
    41. Zhang Y
    42. Liu XM
    43. Li JJ
    44. Zhang DC
    45. Zhang F
    46. Liu P
    47. Yuan J
    48. Li Q
    49. Hu JL
    50. Chen J
    51. Huang AL

    (2020) Antibody responses to SARS-CoV-2 in patients with COVID-19

    Nature Medicine 26:845–848.

    https://doi.org/10.1038/s41591-020-0897-1

    • PubMed
    • Google Scholar
18. Preprint

    1. Moyo-Gwete T
    2. Madzivhandila M
    3. Makhado Z
    4. Ayres F
    5. Mhlanga D
    6. Oosthuysen B
    7. Lambson BE
    8. Kgagudi P
    9. Tegally H
    10. Iranzadeh A
    11. Doolabh D
    12. Tyers L
    13. Chinhoyi LR
    14. Mennen M
    15. Skelem S
    16. Marais G
    17. Wibmer CK
    18. Bhiman JN
    19. Ueckermann V
    20. Rossouw T

    (2021) Sars-Cov-2 501y.V2 (B.1.351) Elicits Cross-Reactive neutralizing antibodies

    bioRxiv.

    https://doi.org/10.1101/2021.03.06.434193

    • Google Scholar
19. 1. Ng KW
    2. Faulkner N
    3. Cornish GH
    4. Rosa A
    5. Harvey R
    6. Hussain S
    7. Ulferts R
    8. Earl C
    9. Wrobel AG
    10. Benton DJ
    11. Roustan C
    12. Bolland W
    13. Thompson R
    14. Agua-Doce A
    15. Hobson P
    16. Heaney J
    17. Rickman H
    18. Paraskevopoulou S
    19. Houlihan CF
    20. Thomson K
    21. Sanchez E
    22. Shin GY
    23. Spyer MJ
    24. Joshi D
    25. O'Reilly N
    26. Walker PA
    27. Kjaer S
    28. Riddell A
    29. Moore C
    30. Jebson BR
    31. Wilkinson M
    32. Marshall LR
    33. Rosser EC
    34. Radziszewska A
    35. Peckham H
    36. Ciurtin C
    37. Wedderburn LR
    38. Beale R
    39. Swanton C
    40. Gandhi S
    41. Stockinger B
    42. McCauley J
    43. Gamblin SJ
    44. McCoy LE
    45. Cherepanov P
    46. Nastouli E
    47. Kassiotis G

    (2020) Preexisting and de novo humoral immunity to SARS-CoV-2 in humans

    Science 370:1339–1343.

    https://doi.org/10.1126/science.abe1107

    • PubMed
    • Google Scholar
20. 1. Nguyen LT
    2. Schmidt HA
    3. von Haeseler A
    4. Minh BQ

    (2015) IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies

    Molecular Biology and Evolution 32:268–274.

    https://doi.org/10.1093/molbev/msu300

    • PubMed
    • Google Scholar
21. 1. Planas D
    2. Bruel T
    3. Grzelak L
    4. Guivel-Benhassine F
    5. Staropoli I
    6. Porrot F
    7. Planchais C
    8. Buchrieser J
    9. Rajah MM
    10. Bishop E
    11. Albert M
    12. Donati F
    13. Prot M
    14. Behillil S
    15. Enouf V
    16. Maquart M
    17. Smati-Lafarge M
    18. Varon E
    19. Schortgen F
    20. Yahyaoui L
    21. Gonzalez M
    22. De Sèze J
    23. Péré H
    24. Veyer D
    25. Sève A
    26. Simon-Lorière E
    27. Fafi-Kremer S
    28. Stefic K
    29. Mouquet H
    30. Hocqueloux L
    31. van der Werf S
    32. Prazuck T
    33. Schwartz O

    (2021) Sensitivity of infectious SARS-CoV-2 b.1.1.7 and b.1.351 variants to neutralizing antibodies

    Nature Medicine 27:917–924.

    https://doi.org/10.1038/s41591-021-01318-5

    • PubMed
    • Google Scholar
22. 1. Skelly DT
    2. Harding AC
    3. Gilbert-Jaramillo J
    4. Knight Michael L
    5. Longet S
    6. Brown A
    7. Adele S
    8. Adland E
    9. Brown H
    10. Medawar Laboratory T
    11. Tipton T
    12. Stafford L
    13. Johnson SA
    14. Amini A
    15. Group OC
    16. Kit Tan T
    17. Schimanski L
    18. Huang K-YA
    19. Rijal PR
    20. Group PS

    (2021) Vaccine-Induced immunity provides more robust heterotypic immunity than natural infection to emerging Sars-Cov-2 variants of concern

    Research Square 2021:226857.

    https://doi.org/10.21203/rs.3.rs-226857/v1

    • Google Scholar
23. 1. Wall EC
    2. Wu M
    3. Harvey R
    4. Kelly G
    5. Warchal S
    6. Sawyer C
    7. Daniels R
    8. Adams L
    9. Hobson P
    10. Hatipoglu E
    11. Ngai Y
    12. Hussain S
    13. Ambrose K
    14. Hindmarsh S
    15. Beale R
    16. Riddell A
    17. Gamblin S
    18. Howell M
    19. Kassiotis G
    20. Libri V
    21. Williams B
    22. Swanton C
    23. Gandhi S
    24. Bauer DL

    (2021a) AZD1222-induced neutralising antibody activity against SARS-CoV-2 Delta VOC

    The Lancet 398:207–209.

    https://doi.org/10.1016/S0140-6736(21)01462-8

    • PubMed
    • Google Scholar
24. 1. Wall EC
    2. Wu M
    3. Harvey R
    4. Kelly G
    5. Warchal S
    6. Sawyer C
    7. Daniels R
    8. Hobson P
    9. Hatipoglu E
    10. Ngai Y
    11. Hussain S
    12. Nicod J
    13. Goldstone R
    14. Ambrose K
    15. Hindmarsh S
    16. Beale R
    17. Riddell A
    18. Gamblin S
    19. Howell M
    20. Kassiotis G
    21. Libri V
    22. Williams B
    23. Swanton C
    24. Gandhi S
    25. Bauer DLV

    (2021b) Neutralising antibody activity against SARS-CoV-2 VOCs b.1.617.2 and b.1.351 by BNT162b2 vaccination

    The Lancet 397:2331–2333.

    https://doi.org/10.1016/S0140-6736(21)01290-3

    • Google Scholar
25. Preprint

    1. Wang P
    2. Nair MS
    3. Liu L
    4. Iketani S
    5. Luo Y
    6. Guo Y
    7. Wang M
    8. Yu J
    9. Zhang B
    10. Kwong PD
    11. Graham BS
    12. Mascola JR
    13. Chang JY
    14. Yin MT
    15. Sobieszczyk M
    16. Kyratsous CA
    17. Shapiro L
    18. Sheng Z
    19. Huang Y
    20. Dd H

    (2021) Antibody resistance of Sars-Cov-2 variants b.1.351 and b.1.1.7

    bioRxiv.

    https://doi.org/10.1101/2021.01.25.428137

    • Google Scholar
26. Preprint

    1. Wibmer CK
    2. Ayres F
    3. Hermanus T
    4. Madzivhandila M
    5. Kgagudi P
    6. Lambson BE
    7. Vermeulen M
    8. van den Berg K
    9. Rossouw T
    10. Boswell M
    11. Ueckermann V
    12. Meiring S
    13. von Gottberg A
    14. Cohen C
    15. Morris L
    16. Bhiman JN
    17. Moore PL

    (2021) Sars-Cov-2 501y.V2 escapes neutralization by south african Covid-19 donor plasma

    bioRxiv.

    https://doi.org/10.1101/2021.01.18.427166

    • Google Scholar
27. 1. Zhou D
    2. Dejnirattisai W
    3. Supasa P
    4. Liu C
    5. Mentzer AJ
    6. Ginn HM
    7. Zhao Y
    8. Duyvesteyn HME
    9. Tuekprakhon A
    10. Nutalai R
    11. Wang B
    12. Paesen GC
    13. Lopez-Camacho C
    14. Slon-Campos J
    15. Hallis B
    16. Coombes N
    17. Bewley K
    18. Charlton S
    19. Walter TS
    20. Skelly D
    21. Lumley SF
    22. Dold C
    23. Levin R
    24. Dong T
    25. Pollard AJ
    26. Knight JC
    27. Crook D
    28. Lambe T
    29. Clutterbuck E
    30. Bibi S
    31. Flaxman A
    32. Bittaye M
    33. Belij-Rammerstorfer S
    34. Gilbert S
    35. James W
    36. Carroll MW
    37. Klenerman P
    38. Barnes E
    39. Dunachie SJ
    40. Fry EE
    41. Mongkolsapaya J
    42. Ren J
    43. Stuart DI
    44. Screaton GR

    (2021) Evidence of escape of SARS-CoV-2 variant b.1.351 from natural and vaccine-induced sera

    Cell 184:2348–2361.

    https://doi.org/10.1016/j.cell.2021.02.037

    • PubMed
    • Google Scholar

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

   "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

   "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

    "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

    "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

    "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

    "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

    "This ORCID iD identifies the author of this article:" 0000-0002-8457-2633

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