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https://doi.org/10.1038/s41591-021-01413-7
1The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel. 2Edmond J. Safra
Center for Bioinformatics, Tel Aviv University, Tel Aviv, Israel. 3Clalit Research Institute, Innovation Division, Clalit Health Services, Ramat Gan, Israel.
4Clalit Health Services, Central Laboratories, Haifa and Western Galilee, Nesher, Israel. 5Progenin Laboratories, Jerusalem District, Clalit Health Services,
Tel Aviv, Israel. 6Microbiology lab, Rabin Medical Center, Beilinson Hospital, Petah Tiqva, Israel. 7Central Laboratory, Clalit Health Services, Tel Aviv, Israel.
8Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben Gurion University of the Negev, Beersheba, Israel. 9Microbiology
Laboratory, Emek Medical Center, Afula, Israel. 10Laboratory of Clinical Virology, Soroka University Medical Center, Beersheba, Israel. 11Faculty of Health
Sciences, Ben Gurion University of the Negev, Beersheba, Israel. 12The Bio-statistical and Bio-mathematical Unit, The Gertner Institute for Epidemiology
and Health Policy Research, Chaim Sheba Medical Center, Tel HaShomer, Ramat Gan, Israel. 13The Sackler Faculty of Medicine, Tel-Aviv University, Tel Aviv,
Israel. 14Clalit Health Services, Tel Aviv, Israel. 15These authors contributed equally: Talia Kustin, Noam Harel, Doron Nezer, Shay Ben-Shachar.
✉e-mail: shayb@clalit.org.il; sternadi@tauex.tau.ac.il
Mass vaccination against severe acute respiratory syndrome
coronavirus 2 (SARS-CoV-2) is currently underway world-
wide, providing hope that the coronavirus disease (2019)
COVID-19 pandemic may soon be mitigated. In Israel, vaccination
commenced on 20 December 2020, primarily with the BNT162b2
mRNA vaccine, and by mid-March 2021, more than 80% of the eli-
gible population (all individuals 16 years old and above) were vac-
cinated with at least one dose. In clinical trials, the BNT162b2 mRNA
vaccine was shown to be 95% efficacious in preventing symptomatic
disease; a similarly high protective effectiveness has also been found
in real-world settings in Israel1,2. However, concerns have emerged
regarding the effectiveness of vaccines against various SARS-CoV-2
strains. In particular, three strains have recently been defined as
VOCs by the WHO (World Health Organization): the B.1.1.7 strain
(first detected in the UK), the B.1.351 strain (first detected in South
Africa) and the P.1 strain (first detected in Brazil). Accumulating evi-
dence suggests that the B.1.1.7 strain spreads more rapidly than the
original circulating strain and leads to substantially more infections3,4.
Concerns have emerged that the B.1.351 and P.1 strains are able to
overcome previous immunity to SARS-CoV-2 (refs.
5,6), yet the evi-
dence has been mixed. Using engineered viruses and/or sequences,
laboratory studies have shown that neutralization of B.1.1.7 by
BNT162b2-vaccine-elicited sera was either similar to or slightly
reduced as compared to neutralization of early circulating isolates7–12
with or without the globally dominant D614G alteration13. Conversely,
a significant reduction in neutralization of B.1.351 was observed7–12,
while other studies suggested neutralization remained relatively high
against both B.1.1.7 and B.1.351 (ref. 14). T cell responses, which are
not captured by neutralization studies, were also shown to remain
stable against these variants following vaccination15. Thus, it remains
unknown whether VOCs can mediate BNT162b2 vaccine break-
through in real-world settings, in which the vaccine elicits persistent
antibody and T cell responses. Here, we tested the hypothesis that the
B.1.1.7 and B.1.351 strains are able to overcome BNT162b2 mRNA
vaccine protection, by comparing their distributions in infected vac-
cinated individuals and in infected non-vaccinated individuals.
Evidence for increased breakthrough rates of
SARS-CoV-2 variants of concern in BNT162b2-
mRNA-vaccinated individuals
Talia Kustin 1,2,15, Noam Harel1,2,15, Uriah Finkel3, Shay Perchik3, Sheri Harari1,2, Maayan Tahor1,
Itamar Caspi1, Rachel Levy1, Michael Leshchinsky3, Shifra Ken Dror4, Galit Bergerzon4, Hala Gadban4,
Faten Gadban4, Eti Eliassian5, Orit Shimron5, Loulou Saleh6, Haim Ben-Zvi6, Elena Keren Taraday7,
Doron Amichay7,8, Anat Ben-Dor7, Dana Sagas9, Merav Strauss9, Yonat Shemer Avni10,11,
Amit Huppert12,13, Eldad Kepten3, Ran D. Balicer3, Doron Netzer14,15, Shay Ben-Shachar 3,13,15 ✉ and
Adi Stern 1,2 ✉
The BNT162b2 mRNA vaccine is highly effective against SARS-CoV-2. However, apprehension exists that variants of concern
(VOCs) may evade vaccine protection, due to evidence of reduced neutralization of the VOCs B.1.1.7 and B.1.351 by vaccine sera
in laboratory assays. We performed a matched cohort study to examine the distribution of VOCs in infections of BNT162b2
mRNA vaccinees from Clalit Health Services (Israel) using viral genomic sequencing, and hypothesized that if vaccine effec-
tiveness against a VOC is reduced, its proportion among breakthrough cases would be higher than in unvaccinated controls.
Analyzing 813 viral genome sequences from nasopharyngeal swabs, we showed that vaccinees who tested positive at least
7 days after the second dose were disproportionally infected with B.1.351, compared with controls. Those who tested positive
between 2 weeks after the first dose and 6 days after the second dose were disproportionally infected by B.1.1.7. These findings
suggest reduced vaccine effectiveness against both VOCs within particular time windows. Our results emphasize the impor-
tance of rigorously tracking viral variants, and of increasing vaccination to prevent the spread of VOCs.
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Results
Study population. We began by identifying the relatively rare vac-
cinees with documented SARS-CoV-2 infection—symptomatic or
asymptomatic—among members of Clalit Health Services (CHS),
the largest health care organization in Israel, which insures 4.7 mil-
lion patients (53% of the population). We divided these individuals
into two categories: individuals who had a positive PCR test that
was performed between 14 days after the first dose and 6 days after
the second dose (denoted as the dose1 group); and individuals who
had a positive PCR test that was performed at least 7 days after
the second vaccine dose (denoted as the dose2 group). The defi-
nitions of these two categories were chosen to match the original
BNT162b2 vaccine efficacy study2, as well as our ensuing real-world
effectiveness study in Israel1, both of which revealed very high vac-
cine protection using these particular criteria. Each vaccinee (case)
was matched with an unvaccinated infected individual (control)
who tested positive on a similar date (±3 days) and had similar
demographic characteristics (age, sex, ethnic sector and geographic
location) to reduce bias associated with differential exposure
(Methods). Next, we obtained RNA from the nasopharyngeal swabs
sampled for PCR and performed complete viral genome sequencing
for 813 samples from different individuals, consisting of 149 pairs of
dose2–controls, 247 pairs of dose1–controls and additional samples
whose match did not undergo successful sequencing (see below;
Table 1, Supplementary Table 1 and Supplementary Fig. 1).
Analysis of variant distributions. We next used a stringent method
of lineage assignment for each viral sequence (Methods). Aside
from B.1.1.7 and B.1.351, no other VOCs or variants of interest, as
defined by the WHO, were found in our sample (Supplementary
Fig. 2). We hence collectively denoted all non-B.1.1.7 and
non-B.1.351 lineages found as wild type (WT). All of these WT
lineages bore the D614G alteration, in line with the very high fre-
quency of this alteration across the globe13. We did not find evi-
dence for the increased presence of any additional alterations that
are not lineage-defining alterations of B.1.1.7 or B.1.351.
When examining lineage frequency across time, we noted that
B.1.1.7 was the predominant strain of the virus in Israel over the
entire sampling period (712/813 sequences), increasing in frequency
over time (Fig. 1a). Conversely, B.1.351 was at an overall frequency
of 1.6% in our sample of both vaccinated and non-vaccinated indi-
viduals (13/813 sequences) (Fig. 1b), similar to previous reports of
B.1.351 frequency in Israel from January 202116.
On the basis of previous results from neutralization assays, we
hypothesized that B.1.1.7 may be slightly vaccine resistant as com-
pared to WT, whereas B.1.351 may be more vaccine resistant when
compared to both B.1.1.7 and WT. Under this hypothesis of ordered
resistance, we performed our statistical analyses first on the B.1.1.7
strain, while excluding B.1.351 sequences (to avoid obscuring a
potential signal), and then compared the B.1.351 with the B.1.1.7
and WT sequences combined (Fig. 2). We use the McNemar test on
our paired vaccinees–controls to examine discordant pairs, defined
as pairs where a different variant was found in the vaccinee as com-
pared to its matched control. The McNemar test is particularly
useful for comparing paired proportions in retrospective cohorts,
where each case is paired with a control, as in the study herein. The
null model of this test was that under a hypothesis of equal effective-
ness of the vaccine against all variants, the different variants should
be evenly distributed across the discordant pairs (see Fig. 2 for a
more elaborate explanation).
No statistically significant difference was observed in the dis-
cordant rates of B.1.1.7 infection in dose2 cases versus unvacci-
nated controls (McNemar odds ratio (OR) of 6:4; one-sided exact
McNemar test, P = 0.38), but a significantly higher proportion of
B.1.351 was observed in dose2 cases versus unvaccinated controls
(McNemar OR of 8:1; one-sided exact McNemar test, P = 0.02).
Of note, about half of dose2 cases tested positive on days 7–13 after
the second dose, and about half tested positive 14 days or more
after the second dose (Table 1). However, seven out of eight B.1.351
dose2 cases were isolated 7–13 days after the second dose and the
eighth B.1.351 dose2 case was isolated exactly 14 days after the
second dose (Fig. 3).
On the other hand, a significantly higher rate of B.1.1.7 was
observed in dose1 cases versus unvaccinated controls (McNemar
OR of 26:10; one-sided exact McNemar test, P = 0.006). For B.1.351
in the dose1 category, the sparsity of data (one infection in each
category) precluded statistical analysis (Fig. 2). A conditional logis-
tic regression was further performed on the dose1 B.1.1.7 data (as
more data were available in this category), supporting the previous
analysis: an OR of 2.4 was observed (95% confidence interval of 1.2
to 5.1). Age was included in the regression and was found to be a
nonsignificant confounder, suggesting that its possible role in pro-
pensity for infection by a specific VOC was corrected through our
matching scheme.
Testing for biases and inclusion of missing data. To test whether
our sampling scheme was biased, we reconstructed a phylogenetic
tree of all the sequenced samples together with additional available
sequences from Israel, and observed that vaccinated and unvac-
cinated samples were highly interspersed along the tree (Fig. 4),
ruling out strong biases in sampling. Moreover, we focused on the
nine dose2 B.1.351 samples (eight cases and one control), and noted
that they were from seven different municipalities spread across
the geography of Israel. When examining the phylogenetic struc-
ture of B.1.351 sequences in Israel in general, we noted that most
sequences belonged to one clade whose isolation dates ranged from
28 December 2020 until 9 February 2021, and accordingly most
sequences in this clade were quite similar (Supplementary Fig. 5A).
Nevertheless, most pairs of dose2 sequences were separated by one
to six alterations (Supplementary Fig. 5B). Combined with the data
on different municipalities, we conclude that it is unlikely (but not
impossible) that these dose2 B.1.351 sequences were part of the
same direct transmission chain.
Finally, we noted an additional two B.1.351 sequences, consist-
ing of one dose2 case and one dose1 control, where the sequencing
of the matched pair did not undergo successful sequencing, most
often due to a high cycle threshold (Ct) value (low viral load (VL)).
An additional dose1 control sequence was also ambiguously classi-
fied (Methods, Supplementary Fig. 3). Importantly, these sequences
would either leave our conclusions regarding B.1.351 unchanged, or
would increase the McNemar OR in favor of B.1.351 in the dose2
category (Supplementary Fig. 3), strengthening the results reported
above. With regards to B.1.1.7, we found a total of 28 non-paired
sequences, once again because a control or case yielded unreliable
sequencing. These sequences might change the significance of our
results with regards to B.1.1.7 but would not change the trend we
found for this variant (Supplementary Fig. 3).
Discussion
Our results show that there is an increased proportion of VOCs in
vaccine breakthrough infections that occurs within two particular
windows of time. An increased proportion of B.1.351 was found in
individuals fully vaccinated with BNT162b2, 7–14 days after the
second dose, as compared to the matched unvaccinated controls.
Furthermore, an increased proportion of B.1.1.7 was found in par-
tially vaccinated individuals, 14 days after the first dose until 6 days
after the second dose, as compared to the matched unvaccinated
control, yet we find no evidence for increased breakthrough rates
of B.1.1.7 a week or more after the second dose (Figs. 2 and 3). Not
enough data were available to assess vaccine breakthrough of B.1.351
in the dose1 category. These results are generally aligned with those
from in vitro neutralization assays that have shown a large reduction
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in neutralization against B.1.351, and little to no reduction against
B.1.1.7 in fully vaccinated individuals7–11,17. Overall, our data also
suggest that serum-based neutralization studies may provide a good
proxy for real-life protection in the case of SARS-CoV-2 (ref.
18).
Although this remains to be tested in a more widespread manner,
it suggests that neutralization studies may be valid as a prompt first
step before the establishment of real-world studies in the case of the
emergence of new SARS-CoV-2 VOCs.
The power of our approach stems from the combination of
real-world evaluation with the stringent case–control matching
strategy employed, allowing us to rule out that a high proportion
of a given variant was due to a confounding effect. For example,
an outbreak of B.1.351 in a given city would have led to spurious
identification of infected vaccinees there, yet by matching them
with unvaccinated individuals, this confounding effect is controlled
for. However, it is still possible that other confounding effects were
present and were not controlled for, such as various behavioral
effects among vaccinees. Additionally, sequencing limitations pre-
vented us from sequencing very low-VL samples (Methods), and
thus the focus of our study was on vaccinees who generated higher
VLs. However, it has been shown that cases with a low VL may be a
lesser concern from a public health perspective, as they are associ-
ated with fewer symptoms and lower risk of transmission19. Finally,
our dose2 cohort is based on infections documented 7 or more days
after the second vaccine dose (Table 1). Some individuals in this
cohort may have been infected before the immunity from the boost
was fully established, and it is thus possible that enhanced immunity
from the boost, which develops over time20, may more effectively
prevent infection with the B.1.351 variant. Notably, when focus-
ing on the eight B.1.351 cases in the dose2 group, all tested positive
during days 7–14 after the second dose, and none tested positive
more than 14 days after the second dose. This observation suggests
Table 1 | Demographic statistics on paired cases and controls sequenced herein
Control dose2 vaccinee
(n = 149) dose2 vaccinee
(n = 149) Control dose1 vaccinee
(n = 247) dose1 vaccinee (n = 247)
Age group
0–19 2 (1.3) 4 (1.6) 1 (0.4)
20–29 20 (13.4) 5 (3.4) 31 (12.6) 31 (12.6)
30–39 32 (21.5) 12 (8.1) 59 (23.9) 48 (19.4)
40–49 33 (22.1) 31 (20.8) 59 (23.9) 64 (25.9)
50–59 22 (14.8) 24 (16.1) 55 (22.3) 53 (21.5)
60–69 24 (16.1) 30 (20.1) 25 (10.1) 32 (13.0)
70–79 9 (6.0) 24 (16.1) 10 (4.0) 13 (5.3)
80–89 7 (4.7) 22 (14.8) 4 (1.6) 5 (2.0)
90+1 (0.7)
Sex
Female 87 (58.4) 81 (54.4) 152 (61.5) 152 (61.5)
Male 62 (41.6) 68 (45.6) 95 (38.5) 95 (38.5)
District
Dan 25 (16.8) 23 (15.4) 22 (8.9) 22 (8.9)
South 3 (2.0) 3 (2.0) 5 (2.0) 5 (2.0)
Haifa 45 (30.2) 45 (30.2) 70 (28.3) 70 (28.3)
Jerusalem 29 (19.5) 29 (19.5) 71 (28.7) 71 (28.7)
Center 23 (15.4) 23 (15.4) 28 (11.3) 28 (11.3)
North 7 (4.7) 7 (4.7) 22 (8.9) 22 (8.9)
Sharon-Shomron 7 (4.7) 9 (6.0) 19 (7.7) 19 (7.7)
Tel Aviv 10 (6.7) 10 (6.7) 10 (4.0) 10 (4.0)
Sector
General Jewish 117 (78.5) 117 (78.5) 163 (66.0) 163 (66.0)
Jewish Orthodox 11 (7.4) 11 (7.4) 28 (11.3) 28 (11.3)
Non-Jewish 21 (14.1) 21 (14.1) 56 (22.7) 56 (22.7)
Vaccine status
Non-vaccinated 149 (100.0) 247 (100.0)
14–20 days from first dose 133 (53.8)
21–28 days from first dose 95 (38.5)
28+ days from first dose 19 (7.7)
7–13 days from second dose 73 (49.0)
14–20 days from second dose 30 (20.1)
21+ days from second dose 46 (30.9)
Absolute counts are shown; relative proportions are in brackets.
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that increased breakthrough of B.1.351 in our cohort occurs mainly
in a limited time window post vaccination.
The main caveat of our study was the small sample size of both
the WT and B.1.351 variants. These small samples sizes are a prod-
uct of: the dramatic increase in frequency of the B.1.1.7 variant, first
detected in Israel in mid-December 2020, and reaching an overall
frequency of ~90% or higher during the period of this study (Fig. 1a);
and the low frequency of the B.1.351 variant in Israel at the time
of writing16. In fact, in our latest samples obtained in late February
and early March 2021, we noted fixation of the B.1.1.7 variant,
but this interpretation requires caution as our sample size was low
(Fig. 1a). Furthermore, caution is required from overinterpreting the
McNemar ORs obtained, for two reasons: statistically, they do not
necessarily represent the OR of breakthrough; and the absolute num-
bers we found, in particular for B.1.351 infections, are very small.
Our study design was not intended to deduce vaccine effective-
ness against either variant, as we observe VOCs conditioned on
infection. In other words, our focus is only on infected individuals;
we ignore non-infected individuals, and do not measure absolute
infection rates in the vaccinated or control population. Thus, we
can only cautiously speculate on vaccine effectiveness against the
B.1.1.7 and B.1.351 strains. Previous real-world work has shown
a very high effectiveness of the BNT162b2 vaccine starting a week
after the second dose in a large-scale study performed in Israel
among CHS patients1. During the period of that study, B.1.1.7 rose
to a high frequency in Israel, suggesting that the high vaccine effec-
tiveness observed in the study included high effectiveness against
this strain as well. However, our current study may suggest a lower
protection against B.1.1.7 in the first weeks after the first vaccine
dose. As some countries opt to increase the gap between the first
and the second BNT162b2 vaccine from the recommended 3 weeks
to a longer period21, it is important to carefully assess whether this
delay impacts vaccine effectiveness against the B.1.1.7 strain among
individuals who received only the first dose. In our data we do not
observe increased breakthrough of either the B.1.1.7 or the B.1.351
strain 2 weeks after the second dose, yet we note our data are rel-
atively limited in this period (76 cases, Table 1). Our results are
overall aligned with recent results that have shown slightly reduced
vaccine effectiveness against the B.1.1.7 and B.1.351 variants as
compared to WT, 14 days after the second dose22.
We conclude by discussing mechanistic explanations for why we
see increased breakthrough rates at very particular and different
time windows following vaccination for B.1.1.7 and B.1.351. From
a biological point of view, the breakthrough cases observed in this
dose2
n = 149
dose1
n = 247
0
24 Jan
27 Jan
30 Jan
2 Feb
5 Feb
8 Feb
11 Feb
14 Feb
17 Feb
20 Feb
23 Feb
26 Feb
01 Mar
4 Mar
7 Mar
10
20
30
40
50
60
70
80
90
100
0
10
20
30
40
50
Date
Cases
89.9%
89.5% 83.4%
16.2%
0.4%
10.1%
0.4%
4.7%
92.6%
6.7%
0.7%
5.4%
Controls
Cases Controls
Percentage of variant
Number of samples
Variant
B.1.1.7
B.1.1.7
B.1.351
WT
Variant
B.1.351
WT
Number of
samples
a
b
Fig. 1 | Variant frequencies of SARS-CoV-2-positive samples. a, Variant frequencies across the time of the study, including the number of samples
collected throughout the study. All values were calculated by averaging over a sliding window of 7 days. All samples sequenced in this study are included
herein, including unpaired samples. b, Breakdown of variant frequencies based on the four groups of this study. The pie charts display the proportion of
each variant (B.1.1.7, B.1.351 and WT) for paired vaccinated cases versus non-vaccinated controls separated by dosage (as defined in the main text), with
cases on the left and their associated control on the right. Only paired samples are shown in the figure.
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study might be due to immune evasion, mediated by particular
alterations present in these strains9,23–27. Alternatively, it has been
previously reported that B.1.1.7 is associated with lower Ct values,
corresponding to higher VLs3, which may be sufficient to overcome
the less potent immune response elicited by the vaccine before its
augmentation by a second dose. We note that in this study we did not
observe higher VL in B.1.1.7 infections as compared to other vari-
ants (see also ref. 28), yet we did note higher VL in B.1.351 infections,
while noting lower VL in vaccinees as compared to unvaccinated
individuals (Supplementary Fig. 6)29,30. We stress that these findings
are preliminary and may be affected by various behavioral biases.
In particular, vaccinees in Israel were exempt from quarantine and
testing following exposure to a positive patient, and this may affect
when and how they chose to be tested. Moreover, this created a
bias in their symptomatic status—most vaccinees were likely tested
only when they were symptomatic. For this reason, we refrain from
reporting the rate of symptoms in our cohort.
We were reassured to observe the low frequency of B.1.351 across
time (Fig. 1a)16. Of note, both B.1.1.7 and B.1351 were first detected
in Israel in late December, at the time vaccination commenced. Our
sampling began during a peak of epidemic growth, during increas-
ing rates of vaccination, into a phase of epidemic contraction (Fig. 1
and Supplementary Fig. 4). Owing to these complex dynamics, we
can only speculate that selection does not strongly favor the B.1.351
dose2
dose1
B.1.1.7
60
0
1
1
245
8
1140
24
10 14
128
195 26
B.1.1.7 B.1.351
B.1.351
B.1.351
Other
Other
B.1.1.7
B.1.1.7
WT
WT
WT
Control Control
B.1.1.7 WT
Control
Case
Case
B.1.351
B.1.351
Other
Other
Control
Case
Case
Fig. 2 | Results of matched vaccinated cases and non-vaccinated controls separated by effectiveness and VOC. In each table, a cell reflects the number
of pairs concordant (upper left and lower right) or discordant (upper right or lower left) for a given variant. The left panel focuses on the comparison
between B.1.1.7 and WT (pairs with B.1.351 were removed), whereas the right panel focuses on comparing B.1.351 and either WT or B.1.1.7 (denoted
collectively as ‘other’). Of note, the McNemar test focuses on a comparison of only discordant samples. Under a null hypothesis of equal vaccine
effectiveness against all variants, we expect an equal number of discordant pairs in the upper right cell and the lower left cell, in each of the tables.
133 pairs
24 Jan–3 Mar 2021
95 pairs
24 Jan–7 Mar 2021
19 pairs
25 Jan–23 Feb 2021
73 pairs
27 Jan–7 Mar 2021
30 pairs
27 Jan–4 Mar 2021
46 pairs
3 Feb–7 Mar 2021
14–20 days
from first dose
21–28 days
from first dose
28+ days
from first dose
7–13 days
from second dose
14–20 days
from second dose
21+ days
from second dose
Case Control Case Control Case Control Case Control Case Control Case Control
0
50
100
150
Count
Variant
B.1.1.7
B.1.351
WT
Fig. 3 | Breakdown of SARS-CoV-2 variant distribution during windows of weeks post vaccination. The first three panels correspond to the dose1 group
and the last three panels correspond to the dose2 group. The number of pairs and the isolation date range of the samples are noted for each panel. The
dose2 B.1.351 case that is shown in the 14–20 days category was isolated exactly 14 days after the second dose.
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variant in the particular conditions in Israel, despite the increased
rate of vaccination. This may be due to its limited ability to evade
vaccine-elicited immunity, mainly during days 7–13 after the second
dose. Alternatively, from an evolutionary point of view, it is possible
that immune evasion alterations incur a fitness cost in the form of
reduced transmissibility, especially as compared to the highly trans-
missible B.1.1.7 (ref.
3). More research is required to further under-
stand the evolutionary pressures operating on VOCs, in Israel, and
around the world. At the time of revision of this paper, May 2021, we
note that case counts in Israel have dramatically dropped to around
35 cases per day in a population of ~9 million (ref. 31), suggesting that
while vaccine breakthrough infections at particular windows of time
may be more frequent with the VOCs B.1.1.7 and B.1.351, mass vac-
cination with two doses controls and contains their spread.
Online content
Any methods, additional references, Nature Research reporting sum-
maries, source data, extended data, supplementary information,
acknowledgements, peer review information; details of author contri-
butions and competing interests; and statements of data and code avail-
ability are available at https://doi.org/10.1038/s41591-021-01413-7.
Received: 30 April 2021; Accepted: 26 May 2021;
Published online: 14 June 2021
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© The Author(s) 2021
Sample group
Control
dose1
dose2
Other Israeli sequences
B.1.1.7
WT
B.1.351
Fig. 4 | A maximum-likelihood phylogenetic tree of Israeli SARS-CoV-2
samples including those sequenced herein. Vaccinees are colored in violet
or green, non-vaccinees are colored in brown, and black sequences are
publicly available sequences from Israel (marked as ‘other’, Supplementary
Table 2). Clades composed of the B.1.1.7, B.1.351 and WT sequences are
encircled in blue, orange and gray, respectively.
NATURE MEDICINE | VOL 27 | AUGUST 2021 | 1379–1384 | www.nature.com/naturemedicine
1384
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NATuRe MedICINe
Methods
Ethics statement. e study was approved by the CHS institutional review board
(no. 0016-21-COM2) and was exempt from the requirement for informed consent.
e study was further approved by the Tel Aviv University ethics committee
(0002706-1).
Sample matching. Data for this study were obtained from CHS’s data
repositories (Supplementary Fig. 1). The study population consisted of
individuals who tested positive for SARS-CoV-2 by PCR with reverse
transcription from six major CHS testing laboratories located throughout Israel.
Individuals with a positive PCR test were then classified into one of two groups:
controls who were not vaccinated before the positive PCR result; and cases that
were vaccinated at least 14 days before the PCR result. Cases were further divided
into two additional subgroups: individuals who had a positive PCR test that was
performed between 14 days after the first dose and 6 days after the second dose
were denoted as the dose1 group, and individuals who had a positive PCR test
that was performed at least 7 days after the second vaccine dose were denoted as
the dose2 group. Next, each case was matched to a control using six parameters:
date of sampling for PCR (±3 days), sex, age (±10 years), municipality of
residence, geographical district of residence and sector. If two or more controls
were available, one was chosen at random. In preliminary analyses, we noted that
matching often failed for dose2 samples due to their small sample size, as well as
due to increasing proportions of vaccinated individuals in older age categories
across time, in line with the vaccine rollout policy in Israel. To increase dose2
matching, we enforced matching on the date of PCR sampling, but allowed
for four out of five matches in the remaining parameters, while prioritizing
municipality, then sector, then age, and then the additional criteria. We found
that the failed parameter match was most often age, sex or municipality. We
note that ten control samples served as controls for both a dose1 and a dose2
sample. Table 1 summarizes statistics on the parameters used for matching and
other parameters for the various groups of our sample. We also note that some
vaccinees (7.7%, Table 1) received the second vaccine dose more than 28 days
after the first vaccine, yet were still annotated as described above.
Obtaining RNA samples and sequencing. Following matching, RNA from
cases and controls was obtained from the main testing laboratories of CHS, with
one major limitation: only samples with Ct values of 33 or lower were collected.
Ct values were averaged over all genes tested (per laboratory). The dates of the
samples ranged from 23 January 2021 to 7 March 2021 (Supplementary Fig. 4).
Full-genome sequencing of SARS-CoV-2 was performed based on the ARTIC
protocol with a V3 primer set (https://artic.network/ncov-2019), with slight
modifications detailed below. Briefly, reverse transcription and multiplex PCR was
performed in two amplicon pools, and NEBNext Multiplex Oligos for Illumina
were ligated to allow for sequencing. All samples were run on an Illumina MiSeq
using 250-cycle V2 kits at either the Technion Genome Center (Israel) or at the
Genomics Research Unit at Tel Aviv University (Israel). We and others have
previously noted amplicon dropout of amplicons 74 and 76 (ref. 32), both of which
cover the spike gene, and in particular some of the lineage-defining alterations of
B.1.1.7 and B.1.351 (such as E484K and N501Y). To increase the sequencing yield
of these amplicons, we doubled the primer concentrations of both amplicons in our
primer pool and lowered the annealing–extension temperature to 63 °C.
Bioinformatic analysis and lineage assignment. Sequencing reads were trimmed
using pTrimmer v1.3.1, a multiplexing primer trimming tool33, and then aligned
to the reference genome of SARS-CoV-2 (GenBank ID MN908947) using our
AccuNGS V1 pipeline34 that is based on BLAST35, using a particular stringent
e-value of 10−30. We then set out to determine the consensus sequence of each
sample. Typically studies report a majority-rule consensus sequence; that is, the
consensus base at each position in the genome is the base that most reads (>50%)
support. However, the biological meaning of variable positions where more than
one base is observed is complex, especially if such positions are abundant: they
may indicate within-host variation, they may indicate multiple genotype infection,
they may indicate sample contamination, and they may indicate sequencing errors.
To overcome these limitations, we constructed two consensus sequences for each
sample, one based on majority rule, and a more strict consensus sequence where
we required at least 80% of reads to support a given base. Bases with lower support
were assigned an N (ambiguous base). We also noted some regions with fluctuating
ambiguity: if a continuous subsequence of length 20 or lower was flanked on both
ends by ambiguous bases, we masked out this entire subsequence by assigning it
with N, under the assumption it represents unreliable sequencing. In both types
of consensus sequencing assignments, we required sequencing coverage of at
least ten reads. Finally, we used the Pangolin v2.2.2 software (https://github.com/
cov-lineages/pangolin) to assign lineages for each consensus sequence using the
Pango nomenclature (pangoLEARN 2021-02-06)36, which requires that at least
50% of bases sequenced are unambiguous. After verifying the type of lineages
we obtained, we labeled all consensus sequences as either B.1.1.7, B.1.351 or WT.
Samples for which Pangolin labels of the strict and majority-rule consensuses
did not coincide were discarded. Thirty-two pairs in which one sample did not
undergo successful sequencing were discarded from the paired analyses (but see
Supplementary Fig. 3). The unpaired successful samples were, however, included in
the variant frequencies across time analysis (Fig. 1a).
Following classification by Pangolin, we noted that one dose1 control sequence,
originally classified as WT (B.1.235), was located within the B.1.351 clade on the
phylogenetic tree. Its pair was classified as B.1.1.7, and we included this pair in
our extreme scenarios analysis (Supplementary Fig. 3). This is in line with recent
concerns regarding misclassifications of Pangolin37, and led us to manually verify
the phylogenetic location of all sequences in our study.
R v4.0.4, Python v3.7.4, pandas v0.24.2 (ref. 38), Matplotlib v3.2.1 (ref. 39),
Seaborn v0.10.1 and ggplot2 (ref. 40) were used during the data analysis and
visualization.
Statistical analysis. For all primary analyses, a one-sided paired (exact)
McNemar’s test was used to compare breakthrough of a variant in partially or fully
vaccinated individuals. For the analysis of B.1.351, all other variants were defined
as the reference group, while for the B.1.1.7 analysis, we excluded any paired
observation that included B.1.3.5 (assuming ordinality of breakthrough), while
any other variant was defined as the reference. A conditional logistic regression
was used as a sensitivity analysis to include age as a possible confounder in case
that matching was not sufficient, under the assumption that it was sometimes
only partially mediated through matching. The regression was performed only on
the dose1 B.1.1.7 analysis, as not enough data were available in other categories.
All analyses were conducted with R software version 4.03 and the survival and
exact2x2 packages.
Phylogenetic analysis. All Israeli sequences available on GISAID (https://www.
gisaid.org/) from August onwards were downloaded, focusing on high-quality
sequences with 10% or less ambiguous sites. Of these sequences, owing to
computational limitations, we sampled the most distant 100 WT sequences and
50 B.1.1.7 sequences, and included all available B.1.351 sequences (Supplementary
Table 2). The reference genome sequence (MN908947.3) was added on as well,
and these sequences were combined with sequences from this study that contained
at most 10% ambiguous sites. Alignment was performed using Mafft v7.300b
(ref. 41) with default parameters. Next, a maximum-likelihood phylogeny was
reconstructed using PhyML v3.0_360-500M (ref. 42) with default parameters as
well, and the tree was rooted using the MN908947.3 sequence from the original
outbreak first detected in Wuhan. The ggtree v2.5.1 package was used to visualize
the phylogenetic trees.
Reporting Summary. Further information on research design is available in the
Nature Research Reporting Summary linked to this article.
Data availability
All sequences were uploaded to GISAID and the accession numbers are stated in
Supplementary Table 1. The raw sequencing reads were deposited in the National
Center for Biotechnology Information Sequence Read Archive database under
BioProject accession number PRJNA728463.
Code availability
Code used to generate consensus sequences as described in the section entitled
Bioinformatic analysis and lineage assignment is available at https://github.com/
SternLabTAU/COVID19-VACC.
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Articles NATuRe MedICINe
Acknowledgements
We thank R. Milo, N. Kopelman, Y. Woodbridge, D. Burstein and T. Hagai for their
advice and discussions, S. Krispin for editing support, and O. Tirosh and M. Meir for
extensive technical support. We also thank the Technion Genome Center for rapid
sequencing services. This publication is part of a project that has received funding
from the European Research Council (ERC) under the European Union’s Horizon 2020
research and innovation programme (grant agreement No. 852223, RNAVirFitness).
This study was further supported by an Israeli Science Foundation grant (3963/19)
and by kind donations from the Milner and AppsFlyer foundations. This study was
supported in part by fellowships to T.K., N.H. and S.H. from the Edmond J. Safra Center
for Bioinformatics at Tel Aviv University. The funders had no role in study design, data
collection and analysis, decision to publish or preparation of the manuscript.
Author contributions
T.K. and N.H. performed the bioinformatics analyses. S.P. U.F., E.K. and M.L. performed
the clinical data analyses. A.H. provided consultation on study design and statistical
analyses. U.F., S.P., I.C., T.K., N.H. and A.S. performed the statistical analyses. T.K.,
N.H., U.F., S.P., E.K., S.B.-S. and A.S. drafted the paper. T.K., N.H., S.H., M.T and R.L.
coordinated clinical sample collection and sequencing. S.H. and M.T. performed library
preparation for sequencing. S.K.D., G.B., H.G., F.G., E.E., O.S., L.S., H.B.-Z., E.K.T.,
D.A., A.B.-D., D.S., M.S. and Y.S.A. contributed clinical samples. T.K., N.H., S.P., E.K.,
D.N., R.D.B., S.B.-S. and A.S. conceived and coordinated the study. A.S., D.N. and S.B.-S.
supervised and led the study.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information The online version contains supplementary material
available at https://doi.org/10.1038/s41591-021-01413-7.
Correspondence and requests for materials should be addressed to S.B.-S. or A.S.
Peer review information Nature Medicine thanks the anonymous reviewers for their
contribution to the peer review of this work. Alison Farrell was the primary editor on
this article and managed its editorial process and peer review in collaboration with the
rest of the editorial team.
Reprints and permissions information is available at www.nature.com/reprints.
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