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Scientific Reports volume  12 , Article number:  13207 ( 2022 ) Cite this article

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Sputnik-V (Gam-COVID-Vac) is a heterologous, recombinant adenoviral (rAdv) vector-based, COVID-19 vaccine now used in > 70 countries. Yet there is a shortage of data on this vaccine's performance in diverse populations. Here, we performed a prospective cohort study to assess the reactogenicity and immunologic outcomes of Sputnik-V vaccination in Kazakhstan. COVID-19-free participants (n = 82 at baseline) were followed at day 21 after Sputnik-V dose 1 (rAd5) and dose 2 (rAd26). Self-reported local and systemic adverse events were captured using questionnaires. Blood and nasopharyngeal swabs were collected to perform SARS-CoV-2 diagnostic and immunologic assays. We observed that most of the reported adverse events were mild-to-moderate injection site or systemic reactions, no severe or potentially life-threatening conditions were reported, and dose 1 appeared to be more reactogenic than dose 2. The seroconversion rate was 97% post-dose 1, remaining the same post-dose 2. The proportion of participants with detectable virus neutralization was 83% post-dose 1, increasing to 98% post-dose 2, with the largest relative increase observed in participants without prior COVID-19 exposure. Dose 1 boosted nasal S-IgG and S-IgA, while the boosting effect of dose 2 on mucosal S-IgG, but not S-IgA, was only observed in subjects without prior COVID-19. Systemically, vaccination reduced serum levels of growth regulated oncogene (GRO), which correlated with an elevation in blood platelet count. Overall, Sputnik-V dose 1 elicited both blood and mucosal SARS-CoV-2 immunity, while the immune boosting effect of dose 2 was minimal. Thus, adjustments to the current vaccine dosing regimen are necessary to optimize immunization efficacy and cost-effectiveness. While Sputnik-V reactogenicity is similar to that of other COVID-19 vaccines, the induced alterations to the GRO/platelet axis warrant investigation of the vaccine’s effects on systemic immunology.


Sputnik-V, also known as Gam-COVID-Vac, is a recombinant adenovirus vector-based vaccine against COVID-19, developed in Russia 1 , 2 . The vaccine has a heterologous prime-boost regimen and is typically administered in two doses containing rAd26 as a prime and rAd5 as a boost, with a 21-day interval between the doses. The initial clinical trials demonstrated that Sputnik-V vaccination resulted in complete prevention of severe COVID-19 and was 91.6% effective against symptomatic SARS-CoV-2 infection 1 , 2 , while more recent studies indicate that the vaccine is effective against symptomatic infection by the SARS-CoV-2 variants such as delta and omicron 3 , 4 , 5 .

Sputnik-V has been approved for use in over 70 countries 6 ; despite such broad geographic distribution, there is still a dearth of data describing the performance of the vaccine. This lack of rigorous data has precluded major regulatory agencies and the World Health Organization from granting Sputnik-V emergency use approval 7 . The studies conducted after the original Phase I-III trials have indicated that, like the COVID-19 vaccines approved in the West, Sputnik-V is safe and elicits robust titers of binding and neutralizing antibodies (Ab) against SARS-CoV-2 5 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 . However, more detailed data on the reactogenicity and immunogenicity of Sputnik-V are urgently needed to not only facilitate vaccine deployment, but also to guide population-specific guidelines regarding matters such as the timing of prime-boost doses, which remains a focus of debate 19 , 20 , 21 . Importantly, no data are currently available on the antibody isotypes, other than IgG, elicited by Sputnik-V in the blood or mucosa. A recent study of mRNA vaccination, for example, indicates that breakthrough COVID-19 is associated with lower serologic titers of S/RBD-specific IgA, but not IgG 22 , thus emphasizing the need to include IgA in vaccine immunogenicity assessments.

The first confirmed cases of COVID-19 in Kazakhstan were identified in mid-March 2020 23 . Subsequent rapid community spread led to substantial morbidity and mortality in the country 23 , 24 , 25 . Sputnik-V vaccination roll-out in Kazakhstan began in February 2021, with several other COVID-19 vaccines later added to the national vaccination portfolio. Here, results of a prospective study of Sputnik-V-associated self-reported adverse reactions and immunologic responses are presented for a cohort with and without prior history of COVID-19.

Study population and design

This registered clinical trial ( #NCT04871841, registered on 04/05/2021) was conducted in Karaganda, the capital of the Karaganda region located in Central Kazakhstan, where serologically confirmed SARS-CoV-2 exposure exceeds 63% 26 . The initial participant screening occurred in April–May 2021 at the Karaganda Medical University COVID-19 vaccination site. Consenting, asymptomatic adults, who had not previously received a COVID-19 vaccine, were invited to participate. Exclusion criteria were presence of respiratory symptoms or laboratory-confirmed COVID-19 diagnosis within two weeks prior to the study. Short questionnaires addressing the participants' demographic background and recent history of COVID-19 exposure were administered. At follow-up, participants were screened for respiratory symptoms and tested for COVID-19 breakthrough infections using SARS-CoV-2 PCR and nucleocapsid protein (NCP) ELISA. Participants, who were not eligible due to the presence of respiratory conditions, were referred to the clinic physician for counselling and treatment. We used earlier studies 2 , 19 , 21 as reference point when establishing the study sample size, which was ultimately constrained by the study budget.

Vaccination procedures

The vaccine was administered after collection of biological samples according to the National Ministry of Healthcare guidelines by the clinic staff. Specifically, a 0.5 ml dose of Sputnik-V (sourced from Moscow, Russia) containing 1 ± 0.5 × 10 11 rAd particles encoding native Spike protein was injected into the deltoid muscle. Doses 1 and 2 consisted of rAd26 and rAd5, respectively, administered 21 days apart.

Adverse event data collection and reporting

Standardized questionnaires regarding the solicited adverse events (AE), pre-defined as local (at the injection site) or systemic were administered at the 21-day post-vaccination follow-up visits. Solicited local AEs included injection site pain, redness, swelling, itching. Solicited systemic AEs included fatigue, headache, myalgia, chills, fever, joint pain, nausea, vomiting, diarrhea, abdominal pain, rash external to injection site. We used the Food and Drug Administration AE grading scale 27 to classify the severity of self-reported AE.

Sample collection and processing

Samples were collected during the baseline visit, and at follow-up, 21 days post-dose 1 and -dose 2. Two nasopharyngeal swabs (Med Ams, Russia) were collected following the national guidelines for COVID-19 testing by insertion of the swab into the nasopharynx at a 5–6 cm depth (~ distance from the nostrils to the outer opening of the ear) for 10 s and rotated during slow removal; one swab was then stored for subsequent SARS-CoV-2 PCR testing in DNA/RNA shield media (Zymo Research, Irvine, US), and another in 500 µl of PBS (for nasal Ab measurements). Blood (5 ml) was collected by venipuncture into EDTA tubes (Improvacuter, Gel & EDTA.K2, Improve Medical Instruments, Guangzhou, China). Full blood counts were acquired using a BC3200 automated hematology analyzer (Shenzhen Mindray Bio-Medical Electronics Co., China). Blood plasma was isolated by centrifugation at 2,000 × g for 10 min. All samples were stored at − 80 °C prior to analyses. All readouts were performed using commercially available, well-validated assays deployable in a clinical lab setting 28 , 29 , 30 , 31 , 32 . To avoid the bias of inter-run variation, paired (baseline-dose 1-dose 2) samples were examined on the same plate for immunoglobulin, neutralization, and cytokine assays.


Total RNA was isolated from nasopharyngeal swabs by magnetic bead-based nucleic acid extraction (RealBest Sorbitus, Vector-Best, Novosibirsk, Russia) and used for SARS-CoV-2 real-time RT-PCR testing by the Real-Best RNA SARS-CoV-2 kit (Vector-Best, Novosibirsk, Russia) targeting the SARS-CoV-2 RdRp and N loci, following the manufacturer's protocol.

Immunoglobulin assays

SARS-CoV-2 S1 IgG, IgA and NCP IgG ELISAs (Euroimmun Medizinische Labordiagnostika AG, Lübeck, Germany) were performed on the Evolis 100 ELISA reader (Bio-Rad) according to the manufacturers’ protocols. Optical density (OD) ratios were calculated as ratio of the OD reading for each sample to the reading of the kit calibrator at 450 nm. For the serologic assays, we used the manufacturer-recommended threshold (0.8) and considered all samples with OD ratios < 0.8 and >  = 0.8 as “negative” and "positive", respectively. The manufacturer-reported sensitivity and specificity for IgG ELISA was 90% and 100%, respectively, while the sensitivity and specificity of IgA ELISA were 85% and 98%, respectively. To set the mucosal assay thresholds, we calculated the means and standard deviations of the OD450 ratios of samples from the "no prior COVID-19" subjects at baseline. We then empirically assumed that ∼ 99.7% of IgG- and IgA-negative samples would fall within 3 standard deviations of the calculated means, i.e. within OD450 ratios of 0.11 and 3.03 for S-IgG and S-IgA, respectively.

SARS-CoV-2 surrogate virus neutralization assay

Surrogate neutralization assays (cPass SARS-CoV-2 Neutralization Antibody Detection Kit, #L00847-C, GenScript Biotech Co., Nanjing City, China) were performed by assessing inhibition of the interaction between the recombinant SARS-CoV-2 receptor binding domain (RBD) fragment and the human ACE2 receptor protein (hACE2) 32 . The test manufacturer reported the assay performance to be comparable with the WHO Plaque Reduction Neutralization Test, indicating no cross-reactivity seen to a panel of tested infections and some cross reactivity with SARS-CoV-1 (specificity = 96.7%). Briefly, plasma samples and manufacturer-provided controls were pre-incubated with the horseradish peroxidase (HRP)-conjugated RBD at 37 °C for 30 min, and then added to the hACE-2 pre-coated plate for incubation at 37 °C for 15 min. After washing and incubation with the tetramethylbenzidine substrate, absorbance was measured at 450 nm using the Evolis 100 ELISA reader (Bio-Rad). Quality control was performed following the manufacturer's recommendations. Neutralization % was calculated by subtracting the negative control-normalized absorbance of the samples from 1 and multiplying it by 100%; a manufacturer-recommended cut-off of 30% was used for detectable SARS-CoV-2 neutralization activity.

Multiplex cytokine ELISA

Blood plasma was analyzed using the Milliplex Map Human Magnetic Bead Panel for cytokines and chemokines (HCYTMAG-60 K-PX41) according to the manufacturer’s protocol on a Bio-Plex 3D instrument (Bio-Rad) as done previously 33 . The ELISA assay details, and analyte classification are given in Appendix Table 1 .


We defined the "no prior COVID-19" subjects as negative for both S-IgG and -IgA (IgG-, IgA-) and the "prior COVID-19" subjects as positive for either or both IgG and/or IgA (IgG+/−, IgA+/−) at baseline using the serologic S-IgG and S-IgA assay threshold. Based on both medical records and self-reported respiratory illness 26 , we estimated that in most "prior COVID-19" participants SARS-CoV-2 infection occurred ~ 8–12 months prior to Sputnik-V vaccination.

Statistical analysis

We hypothesised that Sputnik-V vaccination in this cohort from Kazakhstan safely boosts both serologic and mucosal SARS-CoV-2-reactive immunity. The primary reactogenicity outcomes were vaccine-elicited self-reported AE. The primary immunogenicity outcomes were vaccine-elicited S-IgG, S-IgA and neutralizing Ab changes. All other clinical and immunologic findings were exploratory outcomes. All analyses were performed in the IBM SPSS V.26 and R V. 4.1.2 (R Core Team, 2021) software. We used the two-sided Mann–Whitney U, Pearson χ2, or Fisher's exact tests to compare differences between groups, as appropriate and 95% confidence intervals (CI) were calculated using the binomial "exact" method. Reactogenicity between vaccine doses was compared by McNemar's test. Fold changes were calculated as ratios of geometric mean OD450 ratios or % neutralization, for the serologic Ab-binding and neutralization assays, respectively. Correlations among variables were explored using the Spearman rank test. To facilitate the graphing of geometric means and confidence intervals for the neutralization assay data, “zero” values were substituted with “1”. Cytokine and FBC heatmaps were made by first calculating “follow-up/baseline” ratios of cytokine concentrations for each participant, then by deriving geometric means of individual ratios for each parameter. To minimize the bias contributed by missing data, we focused our analyses on subjects with complete data for at least dose 1 follow-up (N = 73).

Role of the funding source

The funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. All authors had full access to all the data in the study and the lead authors (SY, IK, DB) had final responsibility for the decision to submit manuscript for publication.

Ethics statement

All study procedures were approved by the Research Ethics Board of the Karaganda Medical University. Written informed consent was obtained from all participants. Authors confirm that all methods were performed in accordance with the relevant guidelines and regulations.

Study participants

We recruited 82 COVID-19-negative adults scheduled to receive their first two Sputnik-V doses (Fig.  1 ). Diagnostic testing was done to identify breakthrough SARS-CoV-2 infection at follow-up visits, since S-specific IgG and IgA tests can not differentiate between immunity acquired by vaccination or natural infection. As a result, there were 4 cases of breakthrough COVID-19 among the participants post-dose 1; no breakthrough infection was detected post-dose 2 (Fig.  1 ). Cumulatively, 73 and 70 participants were sampled during the first and second post-vaccination follow-up visits, respectively (Fig.  1 , Table 1 ). The participants were stratified by prior SARS-CoV-2 exposure based on serologically confirmed presence of S-IgG and/or S-IgA [see Methods]. Apart from ethnicity, whereby ethnic Kazakh subjects were over-represented in the “prior COVID-19” group, there were no differences between the subgroups regarding their basic biometric and demographic characteristics (Table 1 ).

figure 1

Screening and recruitment flow chart. See the Methods for detailed study inclusion criteria. rAd recombinant adenovirus, i.m. intramuscular, NCP nucleoprotein.

Sputnik-V-associated adverse events

The self-reported AE associated with Sputnik-V vaccination, a pre-defined primary trial endpoint, were assessed in all participants at 21 days post-dose 1 and at 21 days post-dose 2. All self-reported AE were mild to moderate in nature; we did not detect any severe (grade 3) or potentially life-threatening (grade 4) AE. Most participants reported an injection site (IS) reaction (dose 1: 58%; dose 2: 49%) or a systemic reaction (dose 1: 71%; dose 2: 61%) (Fig.  2 ). The most frequent local and systemic reactions were IS pain (dose 1: 54%; dose 2: 47%), fatigue (dose 1: 53%; dose 2: 47%), myalgia (dose 1: 40%; dose 2: 31%), chills (dose 1: 33%; dose 2: 26%), joint pain (dose 1: 33%; dose 2: 23%), headache (dose 1: 33%; dose 2: 26%) and fever (dose 1: 31%; dose 2: 23%) (Fig.  2 ). Although overall dose 2 appeared to be less reactogenic (Fig.  2 ), there was not a significant difference in reactogenicity between the two doses. In the analysis stratified by prior COVID-19 exposure (Fig.  2 ), fatigue (35% vs. 65%, p  = 0.016) and headache (44% vs. 17%, p  = 0.022) were more frequent post-dose 1 in participants with prior COVID-19, while nausea was more common in the “no prior COVID-19” group after dose 2 (14% vs. none, p  = 0.026). We also noted that participants reporting any IS reactions post-dose ‘1 were more likely to report an IS reaction post-dose ‘2 (Chi square, p  < 0.001); this was not observed for systemic reactions ( p  = 0.18).

figure 2

Solicited adverse events (AE) associated with Sputnik-V vaccination. Bars and percentages represent the proportions of participants reporting an AE. Stars represent significant differences ( p  < 0.05) in AE presentation between the Prior and No Prior COVID groups. IS injection site.

Sputnik-V-elicited serologic responses

Titers of Sputnik-V vaccination-induced serologic SARS-CoV-2 S-specific IgG (S-IgG) and IgA (S-IgA), another pre-defined primary trial endpoint, were assessed in all participants at 21 days post-dose 1 and at 21 days post-dose 2 (Fig.  3 A). S-IgG levels increased 7.7-fold post-dose 1 ( p  < 0.001) and remained high post-dose 2 in all participants (Fig.  3 A). Compared to dose 1, dose 2 did not further boost S-IgG ( p  = 0.495, Fig.  3 A). Overall, 90% (66/73) and 91% (64/70) of participants were above the threshold of positivity for S-IgG after dose 1 and dose 2, respectively. Post-dose 1, S-IgA levels increased fivefold ( p  < 0.001) and remained high post-dose 2 in all participants (Fig.  3 B); there was no further change after dose 2 compared to dose 1 (Fig.  3 A). Overall, 93% (68/73) and 89% (62/70) of participants were above the threshold of positivity for S-IgA post-dose 1 and -dose 2, respectively. The combined seroconversion rate based on both S-IgG and S-IgA after dose 1 was 97% (71/73), remaining at 97% (68/70) post-dose 2.

figure 3

Effects of Sputnik-V vaccination on blood and mucosal SARS-CoV-2 antibodies in all participants. ( A ) Blood Spike-reactive IgG titers at baseline and after vaccination. ( B ) Blood Spike-reactive IgA titers at baseline and after vaccination. ( C ) SARS-CoV-2 neutralization at baseline and after vaccination. ( D ) Mucosal Spike-reactive IgG titers at baseline and after vaccination. ( E ) Mucosal Spike-reactive IgA titers at baseline and after vaccination. ( F ) Correlation plots of mucosal and blood IgG (top) and IgA (bottom) log2-transformed post-dose 2/baseline titer ratios (denoted by “ Δ ”). The Spearman coefficients (r) and their statistical significance (p) are shown. In panels A-E: brackets represent geometric means and 95% confidence intervals; p values indicate the statistical significance assessed by the Mann–Whitney U test. In panels A–E: dotted lines represent the thresholds of assay positivity, defined as OD450 ratio = 0.8 for blood S-IgG and S-IgA, SARS-CoV-2 neutralization = 30% and OD450 ratio = 0.11 and 3.03 for mucosal S-IgG and S-IgA, respectively.

Sputnik-V-elicited SARS-CoV-2 neutralizing capacity

The effects of Sputnik-V vaccination on the serologic titers of SARS-CoV-2 neutralizing antibodies were next assessed (Fig.  3 C). Due to limited reagent availability, the assay was performed on a randomly sampled subset of samples (n = 54). Virus neutralization increased 8.3-fold post-dose 1 ( p  < 0.001) and remained high post-dose 2 in all participants (Fig.  3 C). Compared to dose 1, dose 2 did not further increase virus neutralization ( p  = 0.226, Fig.  3 C). Overall, 45/54 (83%) and 53/54 (98%) of tested participants were above the threshold of positivity for neutralization after dose 1 and dose 2, respectively.

Sputnik-V-elicited mucosal SARS-CoV-2 antibody responses

Mucosal immunity is thought to be important for protection against COVID-19 34 . Therefore, we next assessed the S-IgG and S-IgA profiles in nasopharyngeal swab-derived secretions, a secondary trial endpoint, to determine the impact of Sputnik-V on mucosal immunity. Nasal S-IgG levels increased 25-fold after dose 1 ( p  < 0.001) and remained high after dose 2 in all participants (Fig.  3 D). Compared to dose 1, dose 2 did not further increase IgG levels ( p  = 0.626, Fig.  3 D). Nasal IgA levels increased 16.5-fold after dose 1 ( p  < 0.001) and remained high after dose 2 in all participants (Fig.  3 E). Compared to dose 1, dose 2 did not further increase IgA levels ( p  = 0.609, Fig.  3 E). The magnitudes of S-IgG and S-IgA responses did not correlate between the blood and mucosa (Figs.  3 F and 4 F), while the ratios of mucosal/blood immune responses were boosted by dose 1 but not by dose 2 (Fig. S1 ).

figure 4

Effects of Sputnik-V vaccination on blood and mucosal SARS-CoV-2 antibodies in participants stratified by prior exposure to COVID-19. ( A ) Blood Spike-reactive IgG titers at baseline and after vaccination. ( B ) Blood Spike-reactive IgA titers at baseline and after vaccination. ( C ) SARS-CoV-2 neutralization at baseline and after vaccination. ( D ) Mucosal Spike-reactive IgG titers at baseline and after vaccination. ( E ) Mucosal Spike-reactive IgA titers at baseline and after vaccination. ( F ) Correlation plots of mucosal and blood IgG (top) and IgA (bottom) log2-transformed post-dose 2/baseline titer ratios (denoted by “ Δ ”). The Spearman coefficients (r) and their statistical significance (p) are shown. In panels A-E: brackets represent geometric means and 95% confidence intervals; p values indicate the statistical significance assessed by the Mann–Whitney U test. In panels A–E: dotted lines represent the thresholds of assay positivity, defined as OD450 ratio = 0.8 for blood S-IgG and S-IgA, SARS-CoV-2 neutralization = 30% and OD450 ratio = 0.11 and 3.03 for mucosal S-IgG and S-IgA, respectively.

Effects of prior COVID-19 exposure on Sputnik-V-elicited immunity in blood

Earlier studies have that individuals with prior COVID-19 exposure generated a stronger immune response after a single vaccine dose than naïve individuals 19 , 20 , 21 , 35 . To test whether this held true in our cohort, we compared Sputnik-V-elicited SARS-CoV-2 immune responses in the ‘prior COVID-19” and “no prior COVID-19″ groups in the blood (Fig.  4 A–C) and mucosa (Fig.  4 D–E).

After each vaccine dose, the prior COVID-19 group had higher levels of all measured humoral immune responses compared to the no prior COVID-19 group (Fig.  4 A–C). While dose 1 increased the titres of serologic S-IgG and S-IgA, dose 2 did not have a perceivable boosting effect on either blood S-IgG or S-IgA (Fig.  4 A–B). Compared to the “prior COVID-19” subjects post-dose 1, S-binding Ab titres were lower in the “no prior COVID-19” group post-dose 2 (1.8- and fivefold, p  < 0.001 for IgG and IgA, respectively, Fig.  4 A,B).

Whereas neutralization was robustly boosted by dose 1 in both the "prior" and "no prior COVID-19" groups, dose 2 was associated with an increase in neutralization only in the “no prior COVID-19” group (2.4-fold; p  = 0.038) (Fig.  4 C). Notably, compared to the "prior COVID-19” subjects post-dose 1, neutralizing antibody titers were 1.2-fold higher in the “no prior COVID-19″ group ( p  = 0.005) post-dose 2 (Fig.  4 C). Thus, overall, in this cohort prior COVID-19 exposure was associated with stronger vaccine-elicited serologic responses, with effects on the binding abs slightly more pronounced compared to neutralizing Ab titres.

Effects of prior COVID-19 exposure on Sputnik-V-elicited immunity in mucosa

Subjects with prior COVID-19 exposure had higher mucosal S-IgG compared to the “no prior COVID-19” group ( p  = 0.004) (Fig.  4 D). Vaccination-elicited boost of mucosal IgG in the "prior COVID" participants was higher in magnitude (45.8 and 31.2-fold for dose 1 and 2’, respectively) compared to the "no prior COVID-19" subjects (8.4 and 26.2-fold for dose 1 and 2’, respectively, Fig.  4 D).

Baseline mucosal S-IgA titres also tended to be higher in the “prior COVID-19” participants, although this difference was not significant ( p  = 0.079, Fig.  4 E). Vaccination-elicited mucosal S-IgA boost after each dose was similar in magnitude in participants with prior COVID-19 (16.6 and 23.5-fold for dose 1 and 2’, respectively) compared to those without prior COVID-19 (16.4 and 18.2-fold for dose 1 and 2’, respectively, Fig.  4 E). Consistent with the unstratified analysis, the magnitudes of S-IgG and S-IgA responses did not correlate between the blood and mucosa in the "prior" vs. "no prior" COVID groups (Fig.  4 F), while the ratios of mucosal/blood immune responses were mainly boosted by dose 1 (Fig. S1 ).

After each vaccine dose, the prior COVID-19 group had higher levels of both mucosal S-IgG and S-IgA compared to the no prior COVID-19 group (Fig.  4 D,E). Compared to the “no prior COVID-19” subjects post-dose 2, both mucosal S-IgG and S-IgA titres were higher (4.0 and 1.7-fold, p  = 0.001 and p  = 0.008, respectively) in the “prior COVID-19” group after a single Sputnik-V dose (Fig.  4 D,E). Overall, in this cohort Sputnik-V had a perceivable impact on nasal immune responses and prior COVID-19 exposure was associated with a stronger vaccine-elicited mucosal immunity.

Effects of Sputnik-V on systemic immunology

Lastly, we explored the impact of Sputnik-V vaccination on cytokine changes in the blood using a multiplex assay targeting 41 analytes (see Appendix for complete list). We observed that the only analyte significantly affected by vaccination was growth regulated oncogene (GRO), the concentration of which post-dose 1 and 2’ was reduced by 2.0- ( p  = 0.007) and 2.2-fold ( p  = 0.003), respectively (Fig.  5 A,B), while notable trends were also seen for some chemokines (eotaxin, RANTES and IP-10) and proinflammatory cytokines TNF-a and IL-17 in association with dose 2 (Fig.  5 A). Changes in GRO concentration did not correlate with S-IgG or S-IgA changes (Appendix Fig. S2 ). We therefore hypothesized that Sputnik-V-elicited reduction in GRO concentration might be related to alterations in some circulating cells type(s). To test this hypothesis, we first analyzed full blood counts (FBC), which were available at both baseline and post-vaccination follow-up for a subset of participants (N = 34, see Appendix Table S2 ), and then assessed whether the GRO change was associated with FBC alterations. Counts of all assessed cell types, including granulocytes, lymphocytes, monocytes, platelets, and erythrocytes, were elevated 21 days after both vaccine doses (Fig.  5 C). Absolute platelet counts were inversely correlated with Sputnik-V-induced GRO reduction (Fig.  5 D). Notably, at baseline 24% (8/34) of the subjects had mild thrombocytopenia (median platelet count = 122 × 10 9 cells/L), while at follow-up no thrombocytopenia was detected and one subject had thrombocythemia (511 × 10 9 cells/L), highlighting a clinically meaningful increase to circulating platelet counts elicited by Sputnik-V.

figure 5

Systemic cytokine and cellular changes associated with Sputnik-V vaccination. ( A ) Blood cytokine changes represented as the geometric mean of fold change of each cytokine post-vaccination (after dose 1 and 2) over pre-vaccination level in the No Prior (N = 21) and Prior (N = 36) COVID-19 groups. Cytokines were plotted in alphabetical order (see the Methods and Appendix for the details). ( B ) Blood growth regulated oncogene (GRO) concentration at baseline and after Sputnik-V vaccination in the No Prior (N = 21) and Prior (N = 36) COVID-19 groups. Brackets represent geometric means and 95% confidence intervals; p values indicate the statistical significance assessed by the Mann–Whitney U test. ( C ) Changes in full blood counts (FBC) represented as the geometric mean of fold change of each cell sub-type post-vaccination (after dose 1 and 2) over pre-vaccination count in the No Prior (N = 10) and Prior (N = 25) COVID-19 groups. ( D ) Correlation plots of blood GRO (y-axis) and FBC-derived counts of major blood cell subtypes (x-axis) in all participants (n = 30). Post-dose 2/baseline ratios were log2-transformed (denoted by “ Δ ”). The Spearman coefficients (r) and their statistical significance ( p ) are shown. In panels A, C: Scale denotes fold changes, whereby 1.0 = “no change”. Stars represent statistically significant differences before and after vaccination at p  < 0.05.

Here we assessed the reactogenicity and immunogenicity of Sputnik-V in a prospective clinical trial involving subjects with and without prior COVID-19 exposure in Kazakhstan. The observed reactogenicity was similar to that reported by earlier studies of Sputnik-V 1 , 2 , 8 , 9 , 10 , 11 , 12 and other COVID-19 vaccines 36 . Consistently, systemic side-effects were more frequent in subjects with prior COVID-19 history. The high SARS-CoV-2 seroconversion rates observed post-vaccination also align well with studies of both Sputnik-V and other COVID-19 vaccines 1 , 2 , 7 , 10 , 11 , 13 , 14 , 15 , 18 , 19 , 20 , 21 . We further show that both serologic and nasal S-IgG and S-IgA are effectively boosted by the Sputnik-V prime shot. Overall, after dose 2, all assessed humoral immune responses were significantly higher in individuals with prior natural exposure to COVID-19 than in those without. Importantly, the effects of dose 2 were limited to neutralizing abs and mucosal S-IgG only in participants without prior COVID-19 history, consistent with prior studies 19 , 21 , 37 . The Sputnik-V-elicited alterations to the GRO/platelet axis call for further investigation of Sputnik V effects on systemic immunology.

The current deployment of Sputnik-V is occurring despite the scarcity of research on the safety and immunologic characteristics across different populations. Although this was justifiable at the early stages of the pandemic, more detailed data are urgently needed to inform the immunization programs across different nations in the wake of the rapidly evolving COVID-19 dynamics. Notably, the unique, heterologous design of Sputnik-V was proposed by the vaccine creators to avoid immunogenicity issues stemming from anti-vector immunity 1 , 2 . Our findings support the earlier reports that Sputnik-V has a similar safety and immunogenicity profile to other approved AdV- and mRNA-based vaccines. Furthermore, our finding that prior COVID-19 history is linked to a higher likelihood of vaccination-elicited systemic adverse events is also in keeping with other COVID-19 vaccine studies 36 .

The high seroconversion rate, calculated based on both S-IgG and S-IgA in our cohort, is similar to earlier studies of Sputnik-V 2 , 10 , 19 , 20 , 21 , and consistently, there was little benefit of Sputnik-V dose 2—limited to neutralizing Ab and mucosal S-IgG in participants without prior COVID-19. We also observed that Sputnik-V dose 1 boosted both S-IgA and S-IgG in blood and mucosa. This is remarkable given the emerging evidence that serologic S/RBD-specific IgA was associated with protection against breakthrough COVID-19 in mRNA vaccinees 22 . Consistent with the latter, not only did Sputnik-V dose 2 not boost systemic or mucosal S-IgA, but S-IgA also tended to decline post-dose 2.

Our data suggest that adjustments to the current vaccination regimen interval may be necessary to optimize the immune effects of Sputnik-V dose 2. In support of this, recent studies of mRNA and AstraZeneca vaccines have demonstrated that extending the interval between the doses results in more efficient boosting of antibody titres 38 , 39 , 40 . Hence, a similar increase of the inter-dose interval would be expected to result in a stronger immunity elicited by the Sputnik-V second dose.

We observed that in the mucosa Sputnik-V elicited a substantial Ab boost, with a more prominent effect on S-IgG compared to S-IgA. Even in the absence of prior COVID-19 exposure, participants developed substantial levels of both mucosal IgG and IgA suggesting that intramuscular vaccination could induce protective mucosal immunity against SARS-CoV-2 infection. This finding corroborates the recent data that intramuscularly administered COVID-19 vaccines can impact mucosal immunity 22 , 37 , 41 , 42 . Thus, comparisons of mRNA and inactivated virus (CoronaVac) vaccines, indicated that mRNA-based Comirnaty (Pfizer-BioNTech) but not CoronaVac induced S-IgG and -IgA in the nasal epithelial lining fluid 41 , while mRNA vaccines also boosted S-abs in the saliva 22 , 37 , 42 . Furthermore, mRNA vaccinees with prior COVID-19 exposure mounted stronger salivary S-IgA responses compared to COVID-19-naive vaccinees 37 . Although the implications of this for protection against COVID-19 are unclear, nasal IgA contributes to protection against human influenza 43 , 44 and SARS-CoV-2 in mice 45 . Similar to our recent findings in influenza-vaccinated subjects 46 , in this cohort we did not observe a correlation between Ab responses in the blood and nasal mucosa, suggesting that vaccine-elicited systemic immunity may not be assumed to accompany mucosal immunity, and emphasizing a potential benefit of mucosal sampling in clinical trials.

Our systemic cytokine analysis indicated subtle effects of Sputnik-V on most of the assessed soluble mediators in blood at 21 days post-vaccination, consistent with the data indicating that mRNA-based Comirnaty-altered cytokine responses also return to the baseline pre-vaccination levels by day 22 post-vaccination 47 . In the light of this, the sustained reduction of systemic GRO sustained for at least 21 days after Sputnik-V vaccination seen in our study was somewhat unexpected. The GRO family of chemokines consists of GRO-α, GRO-β and GRO-γ or chemokine (C-X-C motif) ligands (CXCL) 1, 2 and 3, respectively, with GRO-α representing the bulk of circulating GRO (~ 80%) 48 . All three chemokines share one receptor, CXCR2 (C-X-C Motif Chemokine Receptor 2), and have been implicated in responses to viral infection, hematopoietic malignancies and vascular disease 49 , 50 , 51 , 52 . Innate immune cells, including monocytes 52 and neutrophils 48 , and platelets 53 have been identified as major GRO producers. Post-vaccination elevations of major blood cell subtypes is not surprising, however, it is intriguing that vaccine-induced GRO reduction was correlated with elevated platelet counts. Alterations in platelet functionality have been proposed to play a role in the rare immune thrombocytopenia events associated with COVID-19 vaccines (but has not been reported for Sputnik-V) 7 . Therefore, the observed alterations to the GRO/platelet axis in the context of viral vectored vaccines could provide insight into the mechanism of vaccine-driven alterations of platelet dynamics and mobilization of specific cell sub-types.

Important limitations of our study are the relatively small sample size and short duration of post-vaccination follow-up, although these limitations were partially overcome by the longitudinal design with a high retention (> 85%) of participants throughout the study follow-up. Our cohort was also female-dominated, which may have biased the results of both the SAE and immunologic comparisons. Interestingly, we observed that one participant in the “no prior COVID-19” group had a detectable neutralization titer prior to vaccination. Although this finding is somewhat unexpected, we speculate that this participant may have had an early-onset SARS-CoV-2 infection accompanied by generation of neutralizing IgM, which is known to be responsible for a substantial amount of virus neutralization in the absence of other Ab subtypes 54 . Another, less likely, possibility is that past exposure to infections other than SARS-CoV-2 and/or having autoimmune conditions may have led to the generation of broadly neutralizing abs in this participant.

In summary, our study contributes to the growing body of evidence that Sputnik-V vaccination safely elicits robust immune responses, both serologically and mucosally. Our findings also suggest that the current Sputnik-V dosing interval guidelines need urgent adjustment given the minimal benefit observed after dose 2. Finally, the impact of Sputnik-V on platelets merits further investigation to better our understanding of rare adverse events related to thrombosis and thrombocytopenia that have been reported for COVID-19 vaccines.

Data availability

The source data and R code used for all analyses are available through . Any additional data from this study will be made available, wherever possible, upon appropriate request to the corresponding author.

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We thank all the participants and clinical and laboratory staff, who have been involved in the study.

The study was funded by the Ministry of Education and Science of the Republic of Kazakhstan (MESRK, AP09259123) to IK and, in part, by a Faculty Development Competitive Research Grant (COVID) of Nazarbayev University #280720FD1902 to GH, MESRK grant #AP08053387 to YB and MESRK grant #AP09260233 and NU CRP grant #091019CRP2111 to BM. SY was supported, in part, by a M.G. DeGroote Postdoctoral Fellowship. YB was supported, in part, by a SEDS/SSH Nazarbayev University doctoral fellowship. All authors had full access to all the data in the study and the lead authors (SY, IK, DB) had final responsibility for the decision to submit manuscript for publication.

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These authors contributed equally: Sergey Yegorov and Irina Kadyrova.

Authors and Affiliations

Department of Biochemistry and Biomedical Sciences, Michael G. DeGroote Institute for Infectious Disease Research, McMaster Immunology Research Centre, McMaster University, Hamilton, ON, Canada

Sergey Yegorov & Matthew S. Miller

School of Sciences and Humanities, Nazarbayev University, Nur-Sultan, Kazakhstan

Sergey Yegorov, Baurzhan Negmetzhanov, Yeldar Baiken & Gonzalo H. Hortelano

Research Centre, Karaganda Medical University, Karaganda, Kazakhstan

Irina Kadyrova, Yevgeniya Kolesnikova, Svetlana Kolesnichenko, Ilya Korshukov & Dmitriy Babenko

National Laboratory Astana, Centre for Life Sciences, Nazarbayev University, Nur-Sultan, Kazakhstan

Baurzhan Negmetzhanov, Yeldar Baiken & Bakhyt Matkarimov

School of Engineering and Digital Sciences, Nazarbayev University, Nur-Sultan, Kazakhstan

Yeldar Baiken

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Conceptualization, S.Y., I.K., D.B. Data curation, S.Y., I.l.K., D.B. Investigation and formal analysis, S.Y., I.K., B.N., Y.K., S.K., I.l.K., Y.B., B.M., M.S.M., M.S.M., G.H., D.B. Clinical and laboratory site supervision I.K., B.N., B.M., G.H. Writing—original draft, S.Y., I.K., M.S.M., D.B. Writing—review and editing, S.Y., I.K., B.N., Y.K., S.K., I.l.K., Y.B., B.M., M.S.M., M.S.M., G.H., D.B. Funding acquisition, I.K., B.M., G.H., D.B.

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Yegorov, S., Kadyrova, I., Negmetzhanov, B. et al. Sputnik-V reactogenicity and immunogenicity in the blood and mucosa: a prospective cohort study. Sci Rep 12 , 13207 (2022).

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Retrospective Cohort Study of the Effectiveness of the Sputnik V and EpiVacCorona Vaccines against the SARS-CoV-2 Delta Variant in Moscow (June–July 2021)

Olga matveeva.

1 Sendai Viralytics LLC, 23 Nylander Way, Acton, MA 01720, USA

Alexander Ershov

2 Medusa Project SIA, Krisjana Barona iela 5-2, LV-1050 Rīga, Latvia; [email protected]

Associated Data

The data used for the calculations made in this work are available in Supplementary Table S1 . In addition, all input data, calculations and the code are available in GitHub [ 59 ]. The content of the manuscript has previously appeared online in ResearchSquare preprint [ 60 ].

The goal of this study was to evaluate the epidemiological effectiveness of the Sputnik V and EpiVacCorona vaccines against COVID-19. This work is a retrospective cohort study of COVID-19 patients. The cohort created by the Moscow Health Department included more than 300,000 infected people who sought medical care in June and July 2021. Analysis of data revealed a tendency for the increase in the Sputnik V vaccine effectiveness (VE) as the severity of the disease increased. Protection was the lowest for mild disease, and it was more pronounced for severe disease. We also observed a decrease in VE with increasing age. For the youngest group (18–50 years old), the estimated VE in preventing death in June 2021 was 95% (95% CI 64–100), and for the older group (50+ years old), it was 74% (95% CI 67–87). The estimated protection against a severe form of the disease in the 18–50-year-old group was above 81% (CI 95% 72–93), and in the 50+ years-old group, it was above 68% (CI 95% 65–82). According to our analysis, EpiVacCorona proved to be an ineffective vaccine and therefore cannot protect against COVID-19.

1. Introduction

A wide range of effective vaccines for the prevention of COVID-19 are now available worldwide. The efficacy and effectiveness of these vaccines have been demonstrated in both clinical and retrospective studies [ 1 , 2 , 3 , 4 ]. However, the SARS-CoV-2 viral genomes continue to evolve, giving the virus an increasing number of advantages in evading the immune response. In this regard, the effectiveness of vaccines based on antigens of the ancestral variant needs to be re-evaluated for new viral variants of concern (VOCs) that can cause new epidemic waves.

There are two vaccines, the effectiveness of which is analyzed in this study. Both have been approved for use in Russia. One of them is a viral vector vaccine (Sputnik V), and the other is a peptide-based vaccine (EpiVacCorona).

Sputnik V (Gam-COVID-Vac) is based on a recombinant human replication-defective adenovirus of two types: Ad26 (serotype 26) and Ad5 (serotype 5). Both Ad26 and Ad5 are used as vectors for the expression of the SARS-CoV-2 spike protein (S-protein). Immunization with Sputnik V occurs with two doses of the vaccine, one of which is a primer (rAd26) and the other of which is a booster (rAd5).

There is another vaccine that resembles Sputnik V (primer dose), which was developed by Janssen/Johnson & Johnson. The vaccine Ad26COV2 is based on the rAd26 adenovirus vector. It encodes a full length, stabilized S protein of SARS-CoV-2 and is used in a one-time, single-dose immunization regimen [ 5 ].

According to a randomized, double-blind, placebo-controlled, multicenter phase 3 trial that took place in Russia, the protective efficacy of the Sputnik V vaccine is 91.6% [ 6 ]. In Hungary, a large retrospective cohort study was carried out to compare the effectiveness of vector-based and mRNA-based vaccines. In this study, almost all the data were gathered before the spread of the Delta VOC. Hungarian researchers estimated the protective effectiveness of Sputnik V in preventing symptomatic infection at 86% and its ability to prevent deaths associated with COVID-19 at 97% [ 7 ]. Analysis of data from retrospective case-control studies conducted in Bahrain shows that the ratio of the risks of death in the vaccinated and unvaccinated groups was 1/15. Consequently, the vaccine effectiveness in preventing deaths exceeds 90% [ 8 ]. Unfortunately, the publication indicated that during the time when the study was conducted in the country, the dominant variants of the virus changed, so it is not possible to link the estimated vaccine effectiveness to a specific variant of SARS-CoV-2.

Russia has also developed another variant of the Sputnik V vaccine, called Sputnik-Light. This is a single-dose version of Sputnik V, which includes only a primer dose for immunization. The effectiveness of the similar vaccine Ad26COV2. (Janssen/Johnson & Johnson) against COVID-19 infection, based on the results of phase 3 clinical trials, is like Sputnik Light at 67% (95% CI 59–73). The effectiveness of the vaccine against severe COVID-19 is slightly higher at 85% (95% CI 54–97) [ 5 ]. However, these trials were conducted before the Delta VOC became dominant. It can be assumed that in a situation with the dominant viral variant Delta or Omicron, the result would be different.

The vaccine representing the second platform in Russia is the peptide vaccine EpiVacCorona. It consists of three peptides of SARS-CoV-2 S-protein conjugated to a carrier protein. The carrier in EpiVacCorona is a two-part chimeric protein, one of which is a viral nucleocapsid protein (N-protein), and the other a bacterial maltose-binding protein. The carrier protein is expressed in E. coli , and peptides are also expressed in E. coli or chemically synthesized. The three peptides of the vaccine have the following amino acid sequences: CRLFRKSNLKPFERDISTEIYQAGS, CKEIDRLNEVAKNLNESLIDLQE, and CKNLNESLIDLQELGKYEQYIK [ 9 ]. It is worth noting that these three peptides do not overlap with the mapped antigenic linear epitopes of the S-protein of SARS-CoV-2 [ 10 , 11 , 12 , 13 , 14 , 15 ].

EpiVacCorona received a registration certificate on 14 October 2020 [ 16 ]. In Russia, the certificate provides emergency use authorization. By the time large-scale immunization with this vaccine began (11 December 2020), even a Phase I clinical trial had not been completed. The representative of the State Research Center “Vector” told the reporter of RIA-Novosti (Russian state media outlet) on 22 January 2021: “Clinical trials of Phases I-II have not yet been completed. There are only intermediate results” [ 17 ]. Later, preclinical, and clinical phase I/II trial results were published in Russian journals that are not referenced in PubMed [ 9 , 18 ].

However, these publications and the design of the vaccine itself have come under serious criticism from the scientific community. For example, the lack of important controls in published experiments was noted. In addition, the vaccine has been criticized for the lack of overlap between the three peptides and the experimentally determined linear antigenic epitopes of SARS-CoV-2 S-protein reactive B-cells [ 19 ].

After an injection of EpiVacCorona, a vaccinated person can develop antibodies not only to the S-protein peptides of the coronavirus, the protective function of which has not been established, but also to the chimeric protein antigens present in the vaccine to the viral N-protein and bacterial maltose-binding protein. The antiviral immune protective function of the latter has not been demonstrated either.

Independent studies, the results of which were presented on the preprint server in Russia, showed the absence of neutralizing antibodies in the plasma of those vaccinated with EpiVacCorona [ 20 ].

Only 3000 participants were enrolled in the EpiVacCorona Phase III clinical trial [ 21 ]. It was planned that 25% (750 of 3000) of the participants would receive a placebo. The total number of participants was very small. Therefore, it is difficult to imagine that any statistically significant information can be extracted from the trial data. The trial was registered with on 3 March 2021.

At the time of this writing (May 2022), the results of Phase III clinical trials showing the epidemiological effectiveness of the vaccine have not been published. To the best of our knowledge, the first data on the effectiveness of vaccines are presented below.

Delta (B.1.617.2) VOCs were first detected in patient samples from India but quickly spread and became dominant in other countries [ 22 ]. This viral variant can circulate efficiently at current vaccination levels in most countries [ 23 , 24 , 25 ]. In addition, the effectiveness of vaccines against the disease caused by the SARS-CoV-2 Delta variant was shown to be reduced [ 26 , 27 , 28 ].

In Moscow, the Delta variant replaced all other variants and became dominant in the summer of 2021 [ 26 , 29 , 30 ]. In June, it averaged more than half of COVID-19 infections, and in July, its share was already more than 90%. However, most of the cases in June corresponded to the second half of the month, when the Delta VOC became dominant. Among the variants of B.1.617.2, the following representatives, AY.4, AY.5, AY.6, AY.10, AY.12, AY.20, AY.23, and AY.24, were encountered in Moscow. However, two variants prevailed: B.1.617.2 and AY.12 [ 29 , 31 ]. The genomes of these variants have several characteristic mutations in the S-protein, which significantly reduce the neutralizing potential of antiviral antibodies directed to this protein [ 32 ].

It is of interest to conduct studies that can reveal vaccine effectiveness against different dominant virus variants in Russia. Performing such studies is a new challenge at present since vaccines were developed against one (Wuhan) ancestral variant of SARS-CoV-2, whereas they should protect against other rapidly appearing variants with different antigenic properties. The effectiveness of vaccines registered for use in Russia, especially those with no published results from Phase III clinical trials, such as EpiVacCorona, needs to be evaluated.

One method that can help answer the question of whether a vaccine against the new dominant variant of SARS-CoV-2 is effective is a retrospective cohort data analysis. We performed such a study based on data collected in June and July 2021 in Moscow, Russia. During this period, the viral Delta variant dominated [ 26 , 29 , 30 ].

2. Materials and Methods

2.1. dataset of covid-19-infected individuals.

We conducted our analysis using the dataset created by the Moscow Health Department that included people who sought medical care for COVID-19 in Moscow, Russian Federation, in June and July 2021. The dataset was published in the Telegram group “COVID-19 Vaccine News: 2021” and is in the public domain [ 33 ].

In this dataset, the number of COVID-19 cases is divided into categories according to patient age and vaccination status. All vaccinated cases were further divided into subcategories according to the type of vaccine (Sputnik V, EpiVacCorona, and CoviVac) and the number of doses (one or two) patients received. In addition, COVID-19 cases are categorized according to the severity of patients’ illnesses or deaths. The severe form of the disease patient group includes those with severe pneumonia, in which more than 75% of the lungs are affected. The moderately severe patient group included those who had less lung damage but still needed oxygen support. Hospitalized patients not requiring oxygen supplementation or outpatients constitute the mild disease group, and PCR+ patients without COVID-19 symptoms constitute the asymptomatic group.

In June, Moscow residents were mainly immunized with Sputnik V. At the end of the month, the number of those immunized with other vaccines was less than 3% of the total number. In July, there were more of those immunized with EpiVacCorona or Covivac. In our work, only those vaccinated with two doses of each vaccine were counted.

A less detailed analysis of Sputnik V data, as well as a comparison of its effectiveness with EpiVacCorona vaccine was carried out for data obtained in July. At the same time, a more detailed analysis of the VE of Sputnik V was carried out using the data obtained in June. We were unable to perform a more detailed analysis of VE values for July data since the number of people vaccinated and diagnosed with COVID-19 for July was available in the database only for the severe COVID-19 case category.

Russian vaccination protocols do not recommend mixing vaccines. Therefore, the entire vaccinated population received only one type of vaccine.

In our work, a few methods were used to count the number of people in a control group. From the total number of city residents in each age group, we subtracted (1) the number of vaccinated individuals or (2) the number of seropositive individuals. The first control group was formed by subtracting vaccinated city residents from the total population of the same age group. The second control group was formed by subtracting seropositive city residents from the total population of the same age group.

To calculate the number of those fully vaccinated by the time the number of cases was estimated, we used data from the Ministry of Health registry created for the registration and issuance of vaccine certificates in Russia [ 34 ]. A representative sample of the registry contents was generated by computer polling of vaccine certificate issuance service URL addresses from the space of all possible unique registry record numbers. Data from this registry on those who received Sputnik V or EpiVacCorona vaccines were grouped into the same age categories used to estimate COVID-19 cases.

The 2021 demographics were used to normalize the data and estimate the total number of Moscow residents in different control age groups [ 35 ]. The study also used estimates of the total number of vaccinated and COVID-19-infected city residents at various dates in June and July 2021 [ 36 , 37 ].

We calculated the number of seropositive city residents in each age group based on the Moscow Department of Health data in the published preprint [ 29 ]. The details are shown below in the following section.

2.2. Estimation of the Percentage of Moscow Residents with Antibodies

The monitoring of a representative sample of Moscow residents demonstrated that just under half of them had antibodies to SARS-CoV-2 by the end of June 2021. This number is based on the results of continuous serological studies. For this seroprevalence study, samples were from patients who were admitted to the hospital for routine treatment for a disease not related to COVID-19. The average number of patients tested for SARS-CoV-2 antibodies was 10,000 per week. The presence of IgG antibodies in the venous blood serum was evaluated in all patients using a Mindray Medical International Limited (China) immunochemiluminescent analyzer. The results of sero-monitoring were made public in the preprint publication [ 29 ]. This estimation was used by us to form a second control group by subtracting seropositive city residents from the total population.

2.3. Method of VE Calculation

The calculations were based on published algorithms [ 38 , 39 ]. The significance values for VE estimations were calculated using the chi-square method [ 40 ]. If the outcome in the study population is rare, as with COVID-19 cases among vaccinated or unvaccinated individuals, the odds ratio obtained accurately estimates the risk ratio (RR).

The odds ratio (OR) was calculated as follows:


Vaccine effectiveness (VE) % was calculated as follows:

The data for the calculations were obtained from the following sources:

The total number of Moscow residents in each age group was calculated based on the city’s 2021 demographics [ 35 ].

a and c—[ 33 ] and Supplementary Table S1 .

2.4. Confidence Intervals for Vaccine Effectiveness Estimates

In this paper, we estimate vaccine effectiveness using two types of assumptions about the control group. Because of this, we can only estimate a vaccine’s effectiveness value as a value within a confidence interval whose width is determined by our assumptions. Thus, the lower end of the interval for estimating effectiveness can be used as the minimum among all estimates derived from assumptions about the size of the control group, and the upper end can be used as the maximum. The confidence intervals for the effectiveness of the Sputnik V vaccine in preventing severe disease, calculated using this algorithm, are shown in the last column of Table 1 .

Efficiency of the Sputnik V vaccine to prevent severe forms of COVID-19 with confidence intervals. Data obtained in Moscow in July 2021.

VE, vaccine effectiveness; CI, confidence interval. The EpiVacCorona vaccine is not effective.

3.1. The Effectiveness of Sputnik V and EpiVacCorona against Severe COVID-19 and Death

The results of the VE analysis of the Sputnik V and EpiVacCorona vaccines against severe COVID-19 are shown in Figure 1 a. A comparison of the results in the upper and lower bar charts of Figure 1 a suggests that, regardless of the definition of the control group, the estimated VE of Sputnik V to prevent severe COVID-19 is high, and its value is statistically significant. The bar graphs also show the presence of an age-related decrease in vaccine efficiency. The older the person is, the lower the estimated VE is.

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Characterization of immune protection by COVID-19 vaccines. Data obtained in Moscow for the summer of 2021. ( a ) The estimated effectiveness values of the Sputnik V and EpiVacCorona vaccines in preventing severe forms of COVID-19. The analysis is based on data from July 2021. The upper chart shows the results of the analysis performed with a control group of unvaccinated Moscow residents, and the lower chart corresponds to the data analysis performed with a control group of seronegative individuals. The VE estimates for the Sputnik V vaccine were positive and highly significant ( p < 0.001) by the chi-square test for both age groups. The VE values for EpiVacCorona vaccines were negative and nonsignificant ( p > 0.05) for the 18–50 age group and negative and significant for the 50+ age group ( p < 0.001). ( b ) The panel shows the number of deaths and severe cases of COVID-19 among fully vaccinated or seronegative Moscow residents, normalized per 10,000 person-years.

Estimates of the number of deaths and severe COVID-19 among vaccinated or seronegative people in Moscow are plotted on the bar graphs shown in Figure 1 b. We clearly see that the risk of either outcome is drastically reduced in individuals vaccinated with Sputnik V. Among those who received the vaccine but were older than 51, the number of deaths and serious illnesses was significantly higher compared to those who were younger than 50.

We also created a graphical illustration showing the Sputnik V VE in preventing COVID-19-related deaths ( Figure 2 , left panel). A comparison of the upper and lower charts in the left panel of Figure 2 shows that the estimate of VE in preventing COVID-19 deaths is almost independent of how the control group is defined.

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Protective effectiveness of the Sputnik V vaccine against COVID-19 disease of varying severity or death (%). The upper bar charts represent calculations with control group 1, and the lower bar charts represent calculations with control group 2. All positive VE estimates in all charts are highly significant according to the chi-square test, p < 0.001. Data obtained in Moscow in June 2021.

More detailed information on the age-related VE of Sputnik V is presented in Table 1 . Because any control group definition is not completely satisfactory in all necessary aspects of VE estimation, it may be appropriate to use a combination of calculated 95% confidence intervals to determine the upper and lower limits of VE. The combination obtained from the two control groups is presented in the last column under the title “CI 95% of both control groups” in Table 1 . Based on this 95% CI combination, we can say that the vaccine protects against severe forms of the disease with an effectiveness of over 81% in the group under 50 years of age and over 32% in the group over 70 years of age.

In Figure 1 a, instead of positive estimates of EpiVacCorona vaccine effectiveness, negative estimates are shown. Despite the wide confidence intervals of these negative values, we can confidently state that the EpiVacCorona vaccine cannot protect against severe COVID-19.

3.2. Comparative Effectiveness of the Sputnik V Vaccine in Preventing COVID-19 of Varying Severity in Different Age Groups

Table 2 demonstrates the calculated odds ratios of COVID-19 outcomes among vaccinated or seronegative individuals. Although all calculations were based on data collected in one month (June or July), we introduced a time parameter into our estimates to compare our results with those of other studies that use time normalization.

Estimation of deaths and severe COVID-19 cases. Data obtained in Moscow in June 2021.

Figure 1 and Figure 2 show a tendency for the calculated VE values to decrease as age increases. This trend was independent of which control group was used for the analysis. According to this estimation, breakthrough infections occur more frequently in the older age group.

In addition, Figure 2 also revealed the following trend: the more severe the disease, the better the vaccine protection observed. We noticed a tendency for vaccine effectiveness to increase as the severity of the disease increases, so that the vaccine protects particularly well against the most severe form of the disease or even death.

4. Discussion

4.1. limitations of our study.

Our retrospective cohort study has several limitations. We must deal with a dataset that lacks important information about those who received COVID-19. We do not know the demographics, except for the age of COVID-19 patients. We do not know the timing of vaccination, and we cannot connect each vaccinated and unvaccinated person so that their characteristics match each other. Commercial test systems that have been used to determine the presence of antibodies in the Moscow population have not been able to distinguish antibodies acquired through natural infection from those acquired through vaccination against SARS-CoV-2. Therefore, evidence of prior infection was not available for the studied population. However, estimates of the proportion of the Moscow population with antibodies capable of recognizing viral proteins were available (at the end of June). Nevertheless, it was unclear whether these antibodies recognized viral N-protein, which arise because of natural infection, or S-protein, which can also arise as a result of vaccination.

4.2. EpiVacCorona Is an Inefficient Vaccine

To date, the developers of EpiVacCorona have not published the results of their phase III study. The results of this analysis, along with an earlier analysis [ 41 ], indicate a lack of protective VE. Our study showed the negative efficacy of EpiVacCorona. What could this be due to? We assume that the active or even aggressive advertising campaign in Moscow for this particular vaccine as the most sparing for health, i.e., the vaccine with the least number of side effects, led to a greater number of people with morbid conditions choosing this particular vaccine. Thus, it is highly likely that there were many more chronically ill people in the vaccination group who were prone to more severe COVID-19. As a result of this bias, the vaccinated group has more incidents of severe disease and deaths compared to unvaccinated groups.

Of course, one can also try to explain the negative effectiveness of EpiVacCorona by biological factors. For example, immune imprinting may be to blame. The vaccine contains a large amount of viral N-protein. Thus, when those who have been immunized with EpiVacCorona encounter a real virus, their memory B-cell clones produce mainly IgG antibodies targeting the viral N-protein. These antibodies cannot bind the virus, since only antibodies targeting the viral S-protein are able to do that and induce immune protection. Therefore, the immunized person’s body produces the wrong ineffective antibodies after SARS-CoV-2 invasion.

The developers of EpiVacCorona tried, but failed, to equip the vaccine with S-protein fragments (in the form of three peptides), which can trigger an antibody response to the virus. This failure is explained by a lack of overlap between the three protein fragments they chose as vaccine peptides and the experimentally mapped linear antigenic epitopes of the S-protein of SARS-CoV-2 [ 10 , 11 , 12 , 13 , 14 , 15 ].

4.3. Perhaps There Are More People with Comorbid Chronic Conditions in the Vaccinated Elderly Groups

It may be assumed that the vaccinated elderly groups had many more people with comorbid chronic conditions than the other groups, simply because people with chronic conditions were more motivated to get vaccinated and received the vaccine more often. As a result, despite vaccination, groups of older people with more comorbid conditions had more infections than they would have had if people with chronic conditions were not there. This effect can significantly decrease the VE in certain age groups of people or even make it negative. Perhaps for this reason, and because there are more people with comorbid chronic diseases in the vaccinated elderly groups than in other groups, morbidity after EpiVacCorona is somewhat more common among vaccinated individuals and results in negative vaccine efficiency values. It is possible that this may also explain the low or even negative efficiency values of Sputnik V for preventing non severe forms of COVID-19 disease in the older age groups.

4.4. Biological Factors May Contribute to the Decrease in VE in Older Age Groups

In addition to social and behavioral factors, features of the immune system of the elderly may contribute to the age-related decline in VE. Below is a detailed description of the specific functioning of the immune system in the elderly.

4.4.1. Protection against SARS-CoV-2 Reinfection Is Lower in the Older Age Group

Interestingly, the ability of the human immune system to protect against COVID-19 reinfection also appears to depend on age. For example, a study of 4 million RT-PCR-positive cases in the first and second wave of infections in Denmark showed that protection against reinfection was stronger in the younger generation: 81% in the under-65 group versus 47% in the over-65 group [ 42 ].

4.4.2. The Rate of Decrease in the Level of Antibodies Is Higher in the Older Age Group

Our retrospective cohort analysis revealed a consistent decline in the estimated vaccine effectiveness with age. Although we attribute this decline to the assumption that there are more people with comorbidities among the elderly vaccinated compared to the unvaccinated of the same age, we do not rule out that in the elderly, vaccine-induced protective immunity can weaken more quickly because their antibody levels might decrease faster, as shown in a number of publications for different vaccines [ 43 , 44 , 45 , 46 ]. After vaccination with BNT162b2, the level of antiviral IgG antibodies decreased faster in older vaccine recipients (≥50). Interestingly, no such effect was observed after mRNA-1273 vaccination for six months [ 47 ].

4.5. Comparison with Retrospective Studies of Vaccine Effectiveness Estimates in Different Countries

In general, our VE estimate of the Sputnik V vaccine in preventing COVID-19-related deaths or severe disease is comparable to the VE estimates of vaccines from other developers. However, the VE that we estimated decreased as the age of the individual increased. The reasons for this trend may be related to deficiencies in the cohort study design that does not consider large differences in the characteristics of people in the observation groups. In addition, differences in the immunological and behavioral characteristics of people in different age cohorts may contribute to this effect. Does the same tendency appear in the studies of other authors? As seen in Table 3 , the results are variable but most frequently are consistent with our own.

Studies demonstrating the negative correlation between VE and age.

4.6. Perspectives. How Long Does Vaccine Protection Last?

How long can Sputnik V protect against severe COVID-19 and death? When should the boosting dose be given? There are no open databases that can be analyzed to answer these questions. However, studies have been performed with other vaccines.

An interesting retrospective cohort study of the duration of vaccine protection against infection was conducted in Sweden [ 49 ]. It was found that the effectiveness of the vaccine in protecting against COVID-19 infection decreased faster among men and the elderly. The observations of this study, in terms of decreasing vaccine efficacy with age, are consistent with our findings.

In the Swedish study, however, special emphasis was placed on examining the duration of the protective effect of the vaccine. It was shown that, on average, over a period slightly longer than six months, vaccine efficiency in preventing symptomatic COVID-19 for the BNT162b2 vaccine dropped from 92% to zero. Additionally, over an even shorter period (four months), the efficiency of the ChAdOx1 nCoV-19 vaccine dropped from 66% to zero. Slightly more stable was the efficiency of the mRNA-1273 vaccine (Moderna). It dropped from 96% to 59% in half a year [ 49 ]. However, the average efficiency of all vaccines to prevent symptomatic COVID-19 for those under age 50 was still positive, although not high (~34%) even after half a year. In contrast, for those over 50 years of age, vaccine efficiency in just over half a year waned to zero [ 49 ].

Interestingly, in a retrospective cohort study in the United States, the results showed a slightly slower decline in the effectiveness of the BNT162b2 vaccine compared to the decline found by scientists in Sweden. The protective effectiveness (against infection with the Delta variant of SARS-CoV-2) in the United States fell from 93% to 53% in five months [ 53 ] and not to zero as in Sweden [ 49 ]. However, the VE against Delta-related hospitalizations was overall high at 93% and did not decline for at least six months. VE was age dependent and was higher for younger adults [ 53 ].

Similar results were obtained in the UK in a case-control study. VE against symptomatic disease caused by the Delta viral variant dropped to 70% for BNT162b24 and to 47% for ChAdOx1 nCoV-19 over 20 weeks but did not fall as much for hospitalizations. The decline in the efficiency of these vaccines was more noticeable in people over 65. The authors conclude that the VE declines much faster in older people than in younger people [ 50 ].

British researchers performed a retrospective cohort design study [ 52 ] and presented an analysis of a large database obtained mainly from the United States. In this database, the researchers identified more than 10,000 vaccine breakthrough COVID-19 cases, which were matched with those of unvaccinated controls. The work showed that the vaccine protected those under 60 years of age from death and intensive care unit hospitalization significantly better than those who were older. In fact, the authors of the study did not detect any positive effects of vaccination for those over 60 years of age [ 52 ]. Unfortunately, the type of vaccine was not specified in the study.

Finally, an Israeli study demonstrated that immunity against SARS-CoV-2 infection waned in all age groups a few months after vaccination. However, after the same period following the second dose of the vaccine, severe COVID-19 was more common among those over 60 years of age [ 54 ].

In summary, it is unlikely that the Sputnik V vaccine protects longer than the vaccines mentioned above, so a boosting dose can be useful six months after the first vaccination. This recommendation is most applicable to the elderly.

5. Concluding Remarks

Our retrospective study shows that those vaccinated with EpiVacCorona have no advantage over unvaccinated or seronegative individuals in their chances of contracting COVID-19. The vaccine was introduced into civilian circulation without sufficient testing, has not proven itself, and should therefore be withdrawn from production and distribution. At the same time, all those immunized with this poorly tested vaccine preparation should be given the opportunity to be vaccinated with a modern, effective vaccine.

We find that the estimated VE of the Sputnik V vaccine to prevent deaths and severe forms of disease caused by the Delta variant of SARS-CoV-2 is comparable to the estimated VE of this vaccine against COVID-19 caused by other viral variants that have previously appeared in circulation. Our observations are consistent with other studies that have evaluated the VE of Sputnik V in clinical trials [ 6 ] or case-control studies [ 41 ]. However, due to several factors, and primarily due to the limitations of the database with which we worked, it was impossible to directly compare the estimated VE values in our work with VE estimates from published papers. Our database does not allow us to normalize the analyzed groups by the number of people with chronic diseases, as well as by other important demographic characteristics. If there are more people with comorbidities among the vaccinated elderly than among the unvaccinated, then the VE calculations will underestimate the effectiveness of the vaccine.

We found that the estimated VE of the Sputnik V vaccine decreases with age and reaches a minimum in the age cohort over age 70. We do not know the exact reasons for this decrease in effectiveness, and several hypotheses have been discussed above. Among them are those based on (a) limitations in the design of our database, (b) biological factors, and (c) social factors.

Biological hypotheses relate to the immune characteristics of the elderly. These characteristics may manifest as lower antibody levels that appear after vaccination in the elderly and/or a more rapid decline in antibody levels. The protective effect of the vaccine may thus be shorter. These hypotheses are partially supported by the literature [ 43 , 44 , 45 , 57 ].

A hypothesis based on social factors links the decrease in vaccine effectiveness to behaviors leading to additional risks of COVID-19 infection predominantly in the elderly group. Members of this group might feel more protected and spend more time in public places where the risk of infection is higher, such as on public transportation. The opposite hypothesis can also be considered. It may be that young people, because of their busier social lives compared to older people, are more likely to be infected with COVID-19 in an asymptomatic form, and after vaccination they develop hybrid immunity, which protects better than just vaccine immunity in older people. Hybrid immunity forms in those who have had the disease and have been vaccinated [ 51 ].

In any case, we and others have found a pattern of decreasing estimated VE with increasing age. Similar observations for other vaccines have been made in various countries, including Sweden [ 49 ], the UK [ 50 , 51 , 58 ], the United States [ 52 , 53 ], Israel [ 54 ], and Qatar [ 56 ]. Regardless of how the nature of the detected effect is explained, it might indicate that elderly people get sick more often than younger people, which means that they need to protect themselves to a greater extent, e.g., by booster doses of vaccines and, in addition, by masks, social distance, and all other means.

6. Conclusions

In summary, we can state the following:


We express deep gratitude to the organizers of social networks: “Project, chat systems about vaccinations” and to particularly Max Popov for pointing out the data that became the basis for the analysis in this work. We are also very grateful to Andrei Krinitsky, the administrator of the Telegram chat room, who organized the COVID-19 Grais “” civic vaccination group, and the members of this group. One member of the group, who is a medical informatics specialist and who preferred to remain anonymous, helped a great deal with the algorithmic part of the study and the verification of all the calculations. His help was extremely important and greatly accelerated our work. Special thanks to Alexander Chepurnov for the experimental data that inspired the study and to Alena Makarova for our multiple and highly valuable discussions. We also greatly appreciate the input of Anatoly Alstein, Mikhail Gelfand, Ilya Yasny, Anton Chugunov, Andrey Panov, Dmitry Kuznets, Denis Lagutkin and Margarita Romanenko for their help in formulating the ideas that provided the basis for this article. It is important to mention that the “COVID-19 Watch” social network, created, and moderated on Facebook by data analyst Alexei Kouprianov, has become a useful forum for regular additional discussion and critical assessment of our research findings, a discussion that significantly speeded up our work. Finally, thanks to Alexander Ljubimov for careful proofreading of the manuscript, as well as to Eugene Koonin for motivating us to publish the study.

Supplementary Materials

The following supporting information can be downloaded at: , Table S1: COVID-19 cases in Moscow categorized according to age and vaccination status of patients (June–July 2021).

Funding Statement

This research received no external funding.

Author Contributions

O.M. and A.E. participated in data analysis, writing, and editing. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

The study does not fall within the regulatory definition of research involving human subjects. Current research involves only secondary data analysis of already publicly available datasets, which contain no information that can identify subjects, directly or through identifiers linked to the subjects.

Informed Consent Statement

Not applicable.

Data Availability Statement

Conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Safety and efficacy of an rAd26 and rAd5 vector-based heterologous prime-boost COVID-19 vaccine: an interim analysis of a randomised controlled phase 3 trial in Russia

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Original research article, memory b cells induced by sputnik v vaccination produce sars-cov-2 neutralizing antibodies upon ex vivo restimulation.

sputnik vaccine research paper

The development of effective vaccines against SARS-CoV-2 remains a global health priority. Despite extensive use, the effects of Sputnik V on B cell immunity need to be explored in detail. We performed comprehensive profiling of humoral and B cell responses in a cohort of vaccinated subjects (n = 22), and demonstrate that Sputnik vaccination results in robust B cell immunity.

We show that B memory cell (MBC) and antibody responses to Sputnik V were heavily dependent on whether the vaccinee had a history of SARS-CoV-2 infection or not. 85 days after the first dose of the vaccine, ex vivo stimulated MBCs from the vast majority of Sputnik V vaccinees produced antibodies that robustly neutralized the Wuhan Spike-pseudotyped lentivirus. MBC-derived antibodies from all previously infected and some of the naïve vaccine recipients could also cross-neutralize Beta (B.1.351) variant of SARS-CoV-2.

Virus-neutralizing activity of MBC-derived antibodies correlated well with that of the serum antibodies, suggesting the interplay between the MBC and long-lived plasma cell responses. Thus, our in-depth analysis of MBC responses in Sputnik V vaccinees complements traditional serological approaches and may provide important outlook into future B cell responses upon re-encounter with the emerging variants of SARS-CoV-2.


Presently, the therapeutic options for COVID-19 patients remain limited, emphasizing the necessity of concerted mass vaccination campaigns to counteract the pandemic. An ideal vaccine must induce long-lasting protective cellular and humoral immunity, which should translate into reduced rates of infection and mortality. Importantly, an ideal vaccine should, in addition, retain activity against emerging viral lineages. Three anti-SARS-CoV-2 vaccines, Moderna mRNA-1273, BioNTech BNT162b2, and Janssen Ad26.COV2.S, are now being extensively used around the world and have received the most public attention and validation ( 1 , 2 ), while less is known about immunity after Sputnik V vaccination ( 3 ). Although humoral responses to Sputnik V have recently been reported for a limited number of study participants ( 4 , 5 ), data regarding the B cell response in Sputnik V-vaccinated subjects are presently lacking. Clearly, these data are central to the comprehensive assessment of current vaccines ( 1 ), provide important clues to the development of new vaccines, and impact the epidemiological models of immunity.

In late 2020, multiple SARS-CoV-2 lineages were reported across the globe, of which Alpha (В.1.1.7), Beta (В.1.351), and Delta (В.1.617) are now referred to as variants of concern (VOCs) ( 6 , 7 ). Beta and Delta display profound resistance to most of the approved highly potent neutralizing monoclonal antibodies, as well as to the polyclonal antisera induced by infection with the ancestral SARS-CoV-2 and by all the vaccines developed to date ( 8 – 11 ). It is generally believed that it was the emergence and rapid spread of VOCs that are largely responsible for the documented cases of SARS-CoV-2 re-infection ( 12 , 13 ). Specifically, post-vaccination antisera from Moderna and BioNTech vaccinees were 6.5–40-fold less potent against the Вeta VOC, but most typically neutralization was reduced 3–8-fold compared to that of the ancestral Wuhan-1 SARS-CoV-2 strain ( 14 ). So far, qualitative and quantitative data on the VOC neutralization by Sputnik V-induced antisera have been very limited ( 4 , 15 ).

Notably, in contrast to Moderna mRNA-1273, BioNTech BNT162b2, and Janssen Ad26.COV2.S vaccines that were designed to present the SARS-CoV-2 Spike protein in its pre-fusion conformation, Sputnik V is based on a native Spike protein lacking such modifications. This, in turn, may underlie distinct immune responses, upon cross-platform comparisons, and warrants in-depth analysis ( 16 ). The level of virus binding and virus neutralizing serum antibodies is the most studied parameter of the B cell response to SARS-CoV-2 in both convalescents and vaccinated subjects. Much less is known about the antibodies that MBCs will secrete during the secondary immune responses. To address this question, quantification of the levels of RBD-specific and virus neutralizing antibodies in cultures of polyclonally ex vivo stimulated B cells is needed, as it may provide a measure of B memory cell immunity and predict the outcome of infection.

In this study, we aimed to determine i) whether Sputnik V vaccination is efficient in inducing durable memory B cell (MBC) immunity, ii) whether stimulated MBCs are capable of secreting virus-neutralizing antibodies, and if so, iii) whether Sputnik-induced serum and MBC-derived antibodies are protective against one of the most neutralization-resistant SARS-CoV-2 viral variants Beta B.1.351 or not. To date, these issues have not been explored in detail and addressing these research gaps should be instrumental for the development of next-generation SARS-CoV-2 vaccines.

Twenty-two healthy subjects were recruited during the winter/spring of 2021 to receive two doses of the Gam-COVID-Vac (Sputnik V) vaccine ( Figures 1A , S1 ). The demographic characteristics of this cohort are provided in Table S1 . The age of the volunteers ranged from 25 to 70 years (median age, 60.0 years; an interquartile range (IQR), 49.8–63.0 years; 64% female). Five individuals recruited in October-November 2020 had experienced mild COVID-19 symptoms prior to vaccination (53–120 days). Although no virus-containing samples were available for these patients, it must be noted that the SARS-CoV-2 viral lineage Alfa (B.1.1.7) was predominant in Moscow at that time and it was only in March 2021 that the first Beta-associated infections were reported in Moscow ( 15 ). Before vaccination, no nucleocapsid (N)- or receptor-binding domain (RBD)-specific IgGs were detected in the sera of naïve individuals without prior COVID-19 symptoms. In contrast, all recovered recipients with self-reported COVID-19 symptoms had both N- and RBD-specific IgG prior to vaccination ( Figure S2 ). In order to investigate the plasmablast and MBC responses, the following study design was used: blood samples were collected before vaccination (T0), one week after the first and second doses (T1 and T2, correspondingly), and on day 85 after the first dose ( Figure 1A , Figure S1 ).

Figure 1 Virus-binding and virus-neutralizing activity of sera from Sputnik V-vaccinated individuals. (A) Study design. (B) Serum anti-RBD IgG (top row), IgA (middle row) or IgM (bottom row) levels for all Sputnik V-vaccinated individuals, measured by ELISA. IgA and IgM levels are shown as a relative units (RU) against a standard convalescent serum. (C) Representative neutralization curves of vaccine-induced sera from one recovered and one naïve individual at T3 time point. (D) Paired analysis of neutralization titers (ID 50 ) against WA1 strain and Beta variant at T3 time point. (E) Analysis of hillslopes of virus neutralization curves for sera from vaccinated individuals. (F) Spearman’s correlation between serum virus neutralization half-maximal inhibitory serum dilution (ID 50 ) values and the serum levels of anti-RBD IgG (left panel), IgA (middle panel) and IgM (right panel). Blue and red symbols indicate naïve (n = 17) and recovered (n = 5) participants. Symbols connected by solid lines represent time points considered for each individual. Data are presented as median ± IQR. The dotted lines indicate the threshold for positivity. Statistics were calculated using Mann-Whitney test (comparisons between WA1 strain and Beta variant) or the Kruskal–Wallis test (comparisons between time points and naïve and recovered, *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001, ns, not significant. GMT, geometric mean titer; ID 50 , half-maximal inhibitory dilution; IQR, interquartile range; RBD, receptor-binding domain; RU, relative units.

Serum Antibody Responses to Sputnik V Vaccination

Plasma samples from the vaccinated individuals were tested for IgG, IgA and IgM RBD-specific antibodies using ELISA. Longitudinal analysis of circulating serum antibodies showed that the levels of anti-RBD IgG and IgA increased markedly after vaccination ( Figure 1B ). In naive vaccine recipients, the RBD-specific IgG levels mainly increased after the second vaccine dose (T1 vs. T2, P = 0.0156). In recovered vaccine recipients, the levels of RBD-specific IgG were higher at baseline and displayed a more pronounced increase after the first vaccine dose (T0 vs. T1, P = 0.0374). Both in recovered and naïve vaccine recipients, the serum RBD-specific IgG levels achieved after the second dose remained stable until day 85. A similar trend was observed for RBD-specific IgA antibodies; however, the overall increase was not as strong. The anti-RBD IgM response in vaccinated individuals was low. The increase in RBD-specific IgM levels was most pronounced in naïve individuals at T2 (T0 vs. T2, P < 0.0001), consistent with the primary nature of their immune response. Taken together, these results are consistent with those of previous reports exploring antibody responses to mRNA vaccines ( 1 , 17 ).

Next, we investigated if plasma from Sputnik V-vaccinated subjects, with or without prior COVID-19 history, was active in terms of virus neutralization against the wild-type SARS-CoV-2 strain WA1 and Beta VOC. To address this question, a SARS-CoV-2 Spike-pseudotyped virus-neutralization test (pVNT) was used to analyze the sera collected at T3 ( Figure 1C ). Beta VOC is known to be one of the most neutralization-resistant viral variants ( 18 ) and encompasses multiple Spike substitutions (of which three, K417N, E484K, and N501Y, are in the RBD). All the 22 serum samples were able to neutralize WA1, although neutralization potency varied broadly with ID 50 ranging from 36–1690 [geometric mean titer (GMT) 215, Figures 1C, D ).

Compared to the sera from the naïve group of vaccinees, samples obtained from the recovered group demonstrated significantly higher neutralization activity against both WA1 (ID 50 GMT 913 vs. 149, Р = 0.0019), and Вeta (ID 50 GMT 190.7 vs. 49.8, Р = 0.0111). Importantly, the GMT values for Beta were 2.9-fold lower than those for WA1 in naïve recipients (P = 0.0038) ( Figure 1D ). All T3 sera from the five recovered subjects displayed 90% neutralization of Вeta at a 1:20 dilution ( Figure S3 ). In contrast, in the naïve group, only one out of the total 17 samples (subject 19) displayed similar neutralization potency. Nonetheless, the undiluted sera could achieve 90% neutralization in all but one of the naïve samples. The neutralization activity in that exceptional sample (subject 22) could not be reliably measured. However, the neutralization titers we measured in the cohort of naïve Sputnik V vaccinees were significantly higher than those reported by Ikegame et al. ( 4 ). Specifically, they were three-fold higher for the WA1 Spike (ID 50 GMT 149; 95% confidence interval (CI) - 85.7–229.8) and seven-fold higher for Вeta (ID 50 GMT 49.8; 95% CI - 28.67–83.49). Overall, neutralizing activities of the sera against WA1 and Beta displayed pronounced positive correlation (Spearman’s r = 0.8647, P < 0.0001; Figure S4 ), а neutralization curve shape comparison did not reveal significant differences between the Hill slopes for WA1 and Beta (P = 0.1497 and P = 0.5476 for naïve and recovered individuals, respectively) ( Figure 1E ), indicating that the neutralization abilities of sera towards WA1 and Beta vary more in quantitative rather than in qualitative terms.

Finally, the levels of WA1 virus neutralization were strongly correlated with the level of RBD-specific IgG and modestly correlated with that of IgA and IgM (Spearman’s r = 0.8464, P < 0.0001; r = 0.5833, P = 0.0044; r = 0.5302, P = 0.0111, for IgG, IgA, and IgM, respectively) ( Figure 1F ), suggesting that IgG antibodies are the most potent neutralizing component of the sera.

Total and RBD-Specific Plasmablast Response

One of the earliest manifestations of the B cell response is the emergence of circulating total and antigen-specific plasmablasts, which peak around the seventh day post-immunization ( 19 ). Plasmablasts were defined here as CD3 − CD16 − CD19 + IgD − CD27 hi CD38 hi cells ( 19 ) ( Figure 2A , left panel). Before vaccination, the plasmablast frequencies were the same as those in normal donors but increased markedly after the primary immunization (T0 vs. T1, P = 0.0076 and P = 0.0289 for naïve and recovered individuals, respectively) ( Figure 2B ). Booster immunization resulted in an insignificant increase in the percentage of total plasmablasts compared to the baseline level. Positive plasmablast response was detected in 81% (17/21) of cases at T1 and only in 40% (8/20) of cases at T2. Subsequently, as can be seen from the measurements at T3, plasmablasts completely disappeared from the circulation.

Figure 2 Quantification of total and RBD-specific plasmablasts in blood samples from Sputnik V-vaccinated individuals. (A) Representative flow cytometry dot plots showing the gating strategy for measuring the percentage of total (left panel) and RBD + (middle panel) plasmablasts. As a negative control, sample stained with an irrelevant protein Bet v 1 is shown (right panel). Numbers inside the plots indicate the percentage of events specific to respective gates. (B, C) Dynamic changes in total (B) and RBD + (C) plasmablast frequencies in samples collected at different time points. (D) Representative ELISpot images for measuring the frequencies of circulating total (left), anti-RBD (middle) or anti-S (right) IgG ASCs. Numbers below the wells represent the frequencies of ASCs relative to the total number of cells in the well. (E) Frequencies of circulating IgG (left column), IgA (middle column) or IgM (right column) ASCs specific for RBD (upper row) or S (bottom row) antigens per 10 6 PBMCs collected from vaccinated individuals at different time points. The dotted lines indicate the threshold for positive antigen-specific ASC responses. (F) Spearman’s correlation between RBD-specific (IgG + IgA + IgM) ASCs and the levels of RBD + plasmablasts at T1 (left) and T2 (right) time points. Results are shown for individual samples (symbols) from naïve (n = 17) and recovered (n = 5) recipients. Data are presented as median ± IQR. Asterisks indicate significant difference between groups determined using the Kruskal–Wallis test, *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001, ns, not significant. ACS, antibody-secreting cell; IQR, interquartile range; RBD, receptor-binding domain.

The dynamics of SARS-CoV-2-specific plasmablasts during vaccination were of special interest to us. Since virus-neutralizing antibodies are known to predominantly target the RBD ( 8 ), when detecting antigen-specific B cells, we focused on detecting RBD-binding (RBD + ) cells. Antigen-specific plasmablasts were detected by double staining using RBD-PE and RBD-APC ( Figure 2A , middle panel). As a negative control, we used the samples stained with an irrelevant PE-labeled protein Bet v 1, which is the major birch allergen ( Figure 2A , right panel) (none of the study participants had birch pollen allergy). Based on this negative control, the cut-off value for RBD-binding plasmablasts was set at 0.01%. In contrast to COVID-19 patients sampled in the acute phase of the disease ( 20 ), the frequencies of RBD + plasmablasts in vaccinated individuals were low ( Figure 2C ). The RBD + plasmablast response was detectable in 18 subjects (86%) after the first dose of Sputnik V and only in eight participants (40%) after the booster immunization. In only one subject from the recovered subgroup, it exceeded 1%, which is still significantly lower than what was observed in patients with moderate COVID-19 ( 20 , 21 ).

Since plasmablasts are antibody-secreting cells (ASCs), it is logical to detect and enumerate them using an enzyme-linked immunosorbent spot (ELISpot) assay. Representative ELISpot images of circulating Spike- and RBD-specific IgG ASCs are presented in Figure 2D . Historic control wells showed only rare spontaneous total ASCs and no SARS-CoV-2-specific ASCs. The magnitude of the IgG ASC response was the largest in the recovered group after the first vaccine dose, when RBD- and S-specific IgG ASCs were detected in all vaccine recipients (median 69, IQR 27.33–772.8 and median 459, IQR 318–1439 for RBD- and S-specific ASCs, respectively) ( Figure 2E ). In contrast, no RBD- or S-specific IgG ASCs were found in all but one naïve subject (recipient 8) after the first dose. After the second vaccination, 7 and 11 naive participants (n = 15) had RBD- and S-specific IgG ASCs above the baseline, respectively. The fact that after the first vaccine dose, the frequencies of anti-RBD IgG ASCs were higher in SARS-CoV-2-recovered individuals than in individuals in the naïve group is consistent with the idea that, in the former cohort, this increase is due to the re-activation of MBCs. As for the IgA ASCs, the most notable difference was that both RBD- and S-specific ASCs were significantly overrepresented in the SARS-CoV-2-recovered (median 76, IQR 8.375–186.9 and median 62.22, IQR 19.75–394.2 for RBD- and S-specific ASCs, respectively) vs naïve (median 1, IQR 1–4.5 and median 2.2, IQR 1–4 for RBD- and S-specific ASCs, respectively) individuals. The SARS-CoV-2-specific IgM responses mediated by the circulating ASCs were generally lower in their magnitude than the IgG responses and we did not observe significant differences between the naive and recovered samples, nor between the T1 and T2 time points.

Plasmablasts are a heterogeneous population of cells that are usually subdivided into early and later plasmablasts based on their ability to express the B cell receptor (BCR) on their surface and secrete antibodies ( 22 ). For the most part, plasmablasts express membrane bound BCR and simultaneously secrete antibodies. However, surface BCR expression is more a characteristic of early plasmablasts, while antibody-secreting capacity is more associated with later plasmablasts. Accordingly, we found a modest correlation between the frequencies of RBD + plasmablasts and RBD-specific Ig (IgG + IgA + IgM) circulating ASCs (Spearman’s r = 0.6505, P = 0.0468 at T1; r = 0.5574, P = 0.0162 at T2) ( Figure 2F ).

SARS-CoV-2-Specific Memory B Cell Response After Sputnik V Vaccination

To investigate whether Sputnik V-vaccinated individuals developed antigen-specific MBCs, we used two complimentary approaches, namely, flow cytometry of RBD-binding circulating cells ( 1 , 23 , 24 ) and quantification of SARS-CoV-2-specific ASCs induced by in vitro interleukin 21 (IL-21)/CD40L stimulation ( 20 ). The RBD + MBCs were defined as CD19 + CD27 + CD38 - and double-positive for the fluorescently labeled RBD-PE and RBD-APC following exclusion of IgD + B cells ( Figure 3A ). The frequencies of RBD + MBCs measured at different time points are shown in Figure 3B . Even before vaccination, the recovered individuals had a noticeable number of RBD + MBCs, which was higher than that of naive subjects (P = 0.0362), and above the level of the negative control defined by staining with an irrelevant Bet v 1 protein (0.01%). Until day 85, the level of RBD + MBCs in recovered individuals remained on average stable. In naïve individuals, baseline frequencies of RBD+ MBCs were observed at T0 and T1. At T2, RBD + MBCs were detected above the threshold in 37.5% of naïve individuals, displayed a further increase at T3 (T1 vs. T3, P = 0.0023), and approached the level observed in recovered individuals.

Figure 3 Analysis of the MBC response in Sputnik V-vaccinated individuals. (A) Representative flow cytometry dot plots showing double discrimination of RBD + MBCs. Numbers inside the plots indicate the percentage of events specific to the respective gates. (B) RBD + MBCs as a percentage of all memory B cells (CD19 + CD27 + CD38 - IgD - ). (C) Representative ELISpot showing SARS-CoV-2-specific MBC-derived ASCs. Purified B cells were stimulated with IL-21/CD40L for 7 days and then incubated in ELISpot plates for 16 h to detect ASCs secreting total (left column), RBD-(middle column) or S-specific (right column) IgG at T2 time point (upper row), T3 (middle row) or in historic control samples (bottom row). The numbers indicated below the wells represent positive dots and the total number of cells in the well. (D) RBD- (upper row) and S-specific (bottom row) MBC-derived ASCs per 10 6 B cells from blood samples of naïve (n = 17) or recovered (n = 5) vaccinated individuals at different time points. Data for IgG (left column), IgM (middle column) and IgA (right column) ASCs are presented. The dotted lines indicate the threshold for a positive antigen-specific ASC response calculated with pre-pandemic samples per 10 6 B cells. Results are shown for individual samples (symbols) from naïve (n = 17) and recovered (n = 5) recipients. Data are presented as median ± IQR. Asterisks indicate significant difference between groups determined using the Kruskal–Wallis test, *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001, ns, not significant. ASC, antibody-secreting cell; IQR, interquartile range; RBD, receptor-binding domain.

Measurements of the SARS-CoV-2-specific circulating MBC numbers were supplemented with a more functional ELISpot assay. In contrast to plasmablasts, MBCs are resting cells and do not secrete antibodies without stimulation. For MBC activation and induction of antibody secretion, immunomagnetically purified B cells were stimulated in vitro with IL-21/CD40L. After stimulation for 7 days, the frequencies of S- and RBD-specific ASCs were evaluated using an ELISpot assay. As shown in the representative ELISpot images ( Figure 3C ), our protocol for polyclonal B cell activation was highly efficient and resulted in the secretion of both total and SARS-CoV-2-specific antibodies. In pre-pandemic control samples, the frequencies of SARS-CoV-2-specific IgG ASCs were below 200 and 215 spots per million B cells were seeded in Spike- and RBD-coated wells. These values served as a cut-off for positivity. The MBC-derived ASC numbers were measured at two time points, 28 (T2) and 85 (T3) days after the first dose of the vaccine, when MBCs become detectable (T2) and undergo maturation (T3).

MBC-derived IgG ASCs displayed the strongest response. At T2, in all recovered (5/5) and in some naive subjects (7/17) the SARS-CoV-2-specific MBC-derived ASC numbers were above the baseline; however, recovered subjects had a higher level of ASCs than naïve subjects (P = 0.034 and P = 0.0373 for RBD- and S-specific ASCs, respectively) ( Figure 3D ). At T3, the numbers of ASCs in recovered vaccinees remained stable, while in naïve subjects they increased 14-fold and approached the recovered group level (in the naïve group T2 vs. T3, P < 0.0001 and P = 0.0002 for RBD- and S-specific ASCs, respectively). On average, at the peak of the response, approximately 3,000 RBD-specific IgG ASCs per million B cells (0.3% of total B cells) were detected, which was similar to the number of RBD + MBCs detected using flow cytometry in the recovered group (median 0.23%) ( Figure 3B ). The dynamics of the RBD-specific IgG ASC response was also consistent with the flow cytometry data.

The frequencies of IgA MBC-derived ASCs targeting the Spike and RBD followed a similar pattern, namely, recovered subjects responded more quickly than naïve subjects, but the responses of both groups were roughly comparable at T3. Although the anti-SARS-CoV-2 IgM ASC responses were above the threshold, no significant differences were found between the naive and recovered groups, or between T2 and T3. Thus, our results indicate that in naive recipients, the maximum number of vaccine-induced MBCs is reached only 85 days after the first vaccination, and in terms of kinetics, this process significantly lags behind the formation of serum antigen-specific antibodies. Thus, on day 85 after the first dose of Sputnik V, almost all the vaccinees developed RBD- and S-specific MBCs.

MBC-Derived SARS-CoV-2 Antibody Reactivity

In most SARS-CoV-2-related studies, virus-binding and virus-neutralizing activities are measured in the serum samples, i.e. antibodies secreted by the long-lived plasma cells are primarily assayed. In contrast, we aimed at measuring the quantity and quality of antibodies that will be secreted by MBCs upon ex vivo re-stimulation. To do so, we used 7-day cultures of IL-21/CD40L-stimulated B cells as a source of MBC-derived antibodies. These conditions mimic the germinal center environment in which B cells differentiate into plasma cells ( 25 ). Indeed, approximately 95% of MBCs become plasmablasts (CD19 + CD20 - CD27 hi CD38 hi ) over 7 day culture with CD40L and IL-21 ( Figure S5 ). Virus-specific antibody levels in the culture supernatants were assessed with ELISA on RBD-precoated plates. The stimulated MBCs from all recovered individuals secreted a significant amount of anti-RBD IgG both at T2 and T3 ( Figure 4A ). In the naïve group at T3, the level of anti-RBD IgG secretion was five- to six-fold reduced compared to that of the recovered group. Stimulated MBCs produced anti-RBD IgA and IgM less efficiently than IgG, and the difference between samples from naïve and recovered subjects was less pronounced. We observed an apparent discrepancy between the ELISpot ( Figure 3D ) and ELISA data for MBC-derived antibodies ( Figure 4A ). Whereas ELISpot quantifies the secreting cell frequencies in B cell populations, ELISA provides the estimate of a total level of the secreted antibodies, which in turn is the product of the number of ASCs multiplied by the IgG secretion rate. This in turn underlies the importance of using a more comprehensive set of approaches to assess MBC responses, which in our work includes flow cytometry of RBD-binding circulating cells, ELISpot-based quantification of SARS-CoV-2-specific ASC frequencies, and ELISA measurement of the overall levels of MBC-derived antibodies.

Figure 4 Analysis of MBC-derived antibody response in supernatants of CD40L/IL-21 stimulated B cells from Sputnik V-vaccinated individuals. (A) Production of RBD-specific IgG (left panel), IgA (middle panel) or IgM (right panel) in cultures of IL-21/CD40L-stimulated B cells from Sputnik V-vaccinated individuals evaluated using ELISA. (B) Virus-neutralizing activity of MBC-derived antibodies against WA1 strain (left panel) and Beta variant (right panel) at T2 and T3 time points. (C) Paired analysis of virus-neutralizing activity of MBC-derived antibodies against WA1 strain and Beta variant at T3 time point. (D) Spearman’s correlation between virus neutralization (%) and the levels of anti-RBD IgG in supernatants of IL-21/CD40L-stimulated B cells obtained from Sputnik V-vaccinated individuals at T3 time point. (E) Spearman’s correlation between the virus-neutralizing activity of plasma and MBC-derived antibodies. Each symbol represents half-maximal inhibitory plasma dilution (ID 50 ) values and % of virus neutralization by supernatant of IL-21/CD40L-stimulated B cells. (F) Heatmap and hierarchical clustering of Sputnik V recipients. Columns denote Sputnik V recipients. Rows correspond to immune response variables. Dendrograms on the top illustrate the clustering of Sputnik V recipients. Immune response measurement values are color-coded according to the key shown in the upper left. (G) Principal component analysis of Sputnik V recipients. Recipient IDs are shown. Two distinct clusters are indicated by the ovals. Results are shown for individual samples (symbols) from naïve (n = 17) and recovered (n = 5) recipients. Data are presented as median ± IQR. Asterisks indicate significant difference between groups determined using the Kruskal–Wallis test, statistics in (C) panel were calculated using Mann-Whitney test. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001, ns, not significant. IQR, interquartile range; RBD, receptor-binding domain.

Next, we tested the virus-neutralizing activity of MBC-derived antibodies using a pVNT assay. Since the concentration of antibodies in the supernatants was approximately two orders of magnitude lower than that in the plasma, undiluted supernatants were used. First, we ran pVNT assay against the WA1 Spike-pseudotyped lentiviral particles. MBC-derived antibodies from recovered individuals inhibited pseudovirus entry in the range of 42–99% at both time points T2 and T3 ( Figure 4B , left panel, Figure S6 ). At T2, 10/17 individuals from the naïve group were also responders in the pVNT assay (median neutralization 16.3%, IQR 1.0–31.8%). At T3, all naïve subjects demonstrated virus neutralization above the threshold ( Figures 4B , S6 ).

Next, we proceeded to measurements of the cross-neutralizing potential of MBC-derived antibodies using the Beta variant Spike-pseudotyped lentivirus ( Figure 4B , right panel, Figure S6 ). Similarly to the serum antibodies, MBC-derived antibodies displayed weaker cross-neutralization of Beta VOC, which was further reduced in the samples from naïve vaccinees (median fold-change 1.8, P = 0.0066; 2.6, P = 0.0317 for naïve and recovered, respectively). Despite the overall reduction in cross-neutralization of the Beta VOC, about half of the vaccinees (15/22) displayed appreciable virus neutralization at T3 ( Figure 4C ). Also, much as was observed for the serum antibodies, strong correlations were observed between the virus-neutralization of MBC-derived antibodies and the level of ex vivo anti-RBD IgG secretion both at T2 (Spearman’s r = 0.9127, P < 0.0001) ( Figure S7 ) and T3 (Spearman’s r = 0.8419, P < 0.0001) ( Figure 4D ). The virus-neutralizing activity of plasma and MBC-derived antibodies at T3 were modestly correlated (Spearman’s r = 0.5610, P = 0.0081; r = 0.5195, P = 0.0158, for WA1 and Beta, respectively) ( Figure 4E ), pointing at the association between MBC and long-lived plasma cell responses. Based on these findings, it may be possible to assess the quality of antibodies that MBCs will produce upon re-exposure to an antigen.

We observed that naïve and recovered vaccine recipients differed in several key parameters of humoral and B cell immunity. To investigate if study participants could be divided into subgroups, we carried out hierarchical cluster and principal component analyses. Based on the 14 humoral and B cell response measurements, these analyses, which included 15 naïve and five recovered vaccine recipients indicated the existence of two well-separated clusters ( Figures 4F, G ) . The first compact cluster encompassed exclusively naïve vaccine recipients; the second, somewhat loose cluster included all the recovered and three naïve vaccine recipients. Thus, when several humoral and B cell parameters were taken into consideration, the distinct immune profiles of the naïve and recovered vaccine recipients became clearly apparent. Most interestingly, our comprehensive B cell profiling analysis has uncovered the existence of two categories of vaccine recipients, namely, the high- and low-responders. This highlights the underappreciated heterogeneity of the human immune response to Sputnik V, thereby warranting a systematic identification of predictors and modifiers of this response.

Presently, Sputnik V is used in several countries including Russia, Argentina, India, and Brazil. Nonetheless, the protective properties of this vaccine have been debated and the information available has been somewhat ambivalent ( 3 – 5 , 15 , 16 , 26 ). In the present study, a comprehensive analysis of B cell immunity was performed in a cohort of 22 vaccinated subjects, among whom 5 had previously recovered from mild COVID-19. Several parameters were analyzed, namely, (i) the serum antibody titers to RBD, (ii) activity of serum antibodies in the pVNT assay with wild-type SARS-CoV-2 and its mutant variant Beta В1.351, (iii) detection of RBD-specific plasmablasts and MBCs, (iv) enumeration of circulating and MBC-derived ASCs, and the (v) virus-binding and -neutralizing activity of MBC-derived antibodies. Most of these parameters were measured during vaccination with the final time point being 2 months after the first vaccine dose. Even though the study group was rather limited, our in-depth analysis has allowed us to compare the dynamics and magnitude of B cell immune responses in naïve and COVID-19-recovered vaccine recipients, which reflects the real-world epidemiological situation.

Taken together, our results demonstrate faster and more robust B cell responses to Sputnik V in COVID-19-recovered individuals than in individuals without prior infection. Considering many parameters, recovered individuals achieved the maximum immune response after the first vaccine dose. This is in line with the results obtained in mRNA vaccine studies ( 1 , 23 , 27 – 29 ). Importantly, we, for the first time, demonstrate this effect at the level of both plasmablasts and MBCs rather than just via quantification of antibody responses and assessment of their neutralization activity. Combined with previously reported data ( 5 , 30 ), our results indicate that a single dose of the adenovirus-based anti-SARS-CoV-2 vaccine, like mRNA-based vaccines, may be sufficient for protective immunity, when used in subjects with pre-existing immunity to SARS-CoV-2.

Among naive vaccine recipients, the response to Sputnik V vaccination was overall slower than that of recovered vaccine recipients, and a fraction of vaccinees who received both doses of Sputnik V never displayed the concentration and neutralization potency of antibodies that would match the levels observed in recovered individuals. Importantly, after the second dose of Sputnik V, the antisera of naïve vaccine recipients showed virus-neutralizing activity against the wild-type virus in all but one individual (recipient 22). On average, the antisera from naïve Sputnik V recipients were less potent in terms of their neutralizing ability as compared to the antisera from the mRNA vaccines ( 31 – 33 ), yet they were on par with the numbers reported for other adenovirus-based vaccines ( 2 , 34 , 35 ).

Furthermore, we wanted to investigate if Sputnik V vaccination may efficiently neutralize mutant SARS-CoV-2 variants. Whereas detailed studies are available for the Pfizer and Moderna vaccines, data for Sputnik V are limited. Recently, it was reported that antisera from naïve Sputnik V recipients were 3.1-, 2.8-, and 2.5-fold less potent against the Beta, and Delta variants, respectively ( 15 ). Another recent study references a median 6.1-fold reduction in the GMT against Beta ( 4 ) with a notable comment that when extrapolated to full serum strength, half of the serum samples failed to achieve an ID 80 and only one out of 12 achieved an ID 90 against Beta. Intriguingly, sera from Sputnik V-vaccinated individuals have been reported to neutralize Omicron variant quite well ( 36 ). In this study, we found that the GMT of sera from recovered and naïve recipients exhibited five-fold reduction against Beta compared to that against the wild-type virus in a pVNT assay, but the neutralizing activity of all but one samples was sufficient to achieve an ID 90 against when extrapolated to full serum strength. The neutralizing potency of the antisera from recovered subjects against the wild-type variant was more than four-fold higher than that of the naive group. Predictably, these samples were also much more active against the Вeta variant. This is in excellent agreement with the data reported for mRNA vaccines ( 1 ) and is indicative of the higher level of protection against emerging viral variants in the SARS-CoV-2 pre-exposed vaccinees.

It is interesting to compare the B cell immunity elicited by Sputnik V vaccination with that induced by natural SARS-CoV-2 infection. The dynamics of plasmablast numbers is of special interest because it can be used as a predictor of successful humoral immunity ( 37 ). We found low levels of RBD + plasmablasts and circulating RBD-specific ASCs in vaccinated naive individuals compared to those observed in the acute phase in COVID-19 patients ( 20 , 21 ). Perhaps these differences are associated with the extrafollicular pathway of B cell activation in the acute phase of COVID-19, which is characterized by massive plasmablast expansion ( 38 ). It has been suggested that this pathway may contribute to the pathogenesis of acute COVID-19. From this standpoint, the modest plasmablast response observed during vaccination can be viewed as beneficial.

Unlike the plasmablast response, the MBC response was well-pronounced, and the MBC numbers observed are comparable to those found in acute COVID-19 patients. This is important because vaccine-induced MBCs are known to be central to the longevity of immune memory and are among the first cells to produce massive amounts of antibodies upon antigen re-encounter. Although the MBC numbers are informative descriptors of B cell immunity, the functional activity of the antibodies that will be produced during secondary immune responses is key to our understanding of vaccine-induced protection. To address this question, antigen-specific MBCs were isolated followed by single-cell sequencing of Ig genes and expression of recombinant MBC-derived antibodies ( 8 , 39 , 40 ). Alternatively, antibody secretion can be induced in cultures of polyclonally stimulated B cells. Seven-day cultures of IL-21/CD40L-stimulated MBCs isolated from the blood of Sputnik V vaccinees secreted anti-RBD IgG at approximately the same level as MBCs from acute COVID-19 patients. Moreover, MBC-derived antibodies from the vast majority of Sputnik V vaccinees could neutralize the ancestral variant of SARS-CoV-2, with antibodies from some of the Sputnik V vaccinees displaying cross-neutralization of the Beta variant.

Virus-neutralizing activity of antibodies is a composite of their quantity and specificity, yet it is also critically dependent on their affinity, which is known to increase during maturation ( 41 ). In the acute phase of COVID-19, near-germline clonotypes featuring low mutation numbers are typically formed ( 38 ), and no correlation between the activities of plasma and MBC-derived antibodies is observed ( 20 ). This is consistent with the idea that MBC and plasma cells may differ in their level of maturation and breadth of reactivity with viral variants ( 42 ). In fact, affinity maturation occurs several months following infection or vaccination, and is accompanied with progressive accumulation of up multiple somatic mutations in RBD-specific antibodies ( 24 ). Interestingly, two months after vaccination with Sputnik V, modest correlation between the virus-neutralizing activities of plasma and MBC-derived antibodies was observed in our cohort. This is indicative of the comparable degrees of affinity maturation that the antibodies produced by MBCs and long-lived plasma cells undergo during this period ( 43 ).

Two months following vaccination, the differences between the naïve and recovered vaccine recipient cohorts in terms of MBC numbers and their ability to differentiate into ASCs that secrete RBD-specific and virus-neutralizing antibodies, became less pronounced. Comparison of WA1- and Beta variant-neutralizing activities at T3 by serum- and MBC-derived antibodies is indicative of the gradual maturation and continued evolution of the MBC population. Thus, we show that in vitro stimulation of virus-specific MBCs can considerably extend the traditional serological analysis of vaccinated donors. This will allow the study of the dynamics and longevity of antigen-specific MBCs in the course of infection and vaccination. Using this approach, we provide experimental evidence indicating that both recovered and naïve vaccinees accumulate similar numbers of virus-specific MBCs at 2 months after the second dose of Sputnik V. Upon antigen stimulation, these cells differentiate into ASCs that secrete virus-specific antibodies, of which a significant proportion is virus-neutralizing. Based on these data, we conclude that the MBCs elicited by Sputnik vaccination, both in their number and productivity, are comparable to those generated during natural infection.

As in the scenario of natural infection, some individuals, classified as low responders, fail to mount robust immune response upon Sputnik V vaccination. The reasons underlying the poor vaccine immunogenicity in these subjects are presently unknown but are likely related to individual features of the immune system. However, our study group was rather limited; 2 months after vaccination, one recipient with the lowest levels of virus-neutralizing antibodies and other indicators of poor B cell immunity developed PCR-confirmed COVID-19 (to be published). Currently, the minimum levels of serum virus-binding and virus-neutralizing activity that are protective against vaccine breakthrough infections remain to be determined. Hence, additional studies are required to establish this minimum level of immunity.

Material And Methods

A cohort of 22 Sputnik V recipients was enrolled in December 2020 at the National Research Center Institute of Immunology of The Federal Medical Biological Agency of Russia. None of the participants were pregnant, immunodeficient, or receiving immunosuppressive treatment. Subjects were immunized by intramuscular injection into the deltoid muscle with a 21-day interval between the doses. All subjects received two doses of Gam-COVID-Vac (Sputnik V) vaccine. None of the volunteers had experienced serious adverse events after vaccination. Written informed consent was obtained from each of the study participants before performing any study procedures. The study protocol was reviewed and approved by the Medical Ethical Committee of Institute of Immunology (#12-1, December 29, 2020).

Blood Sample Collection and Processing

Whole-blood samples were collected into heparinized vacutainer tubes (Sarstedt, Cat. No. 04.1927) four times: one day before vaccination, on day 7 after the first and the second doses of vaccine, and on day 85 from the start of vaccination (T0, T1, T2, and T3 time points, respectively) ( Figure S1 ). PBMCs were isolated by density gradient centrifugation. Plasma samples were stored at -80°C. B cells were purified from PBMCs by negative selection using the Dynabeads Untouched human B cells kit (Thermo Fisher Scientific, Cat. No. 11351D).

Immunomagnetically separated B cells were cultured in complete DMEM/F12 medium (Cat. No. C470p) supplemented with 10% FBS (Cat. No. SV30160.03), 2 mM L-glutamine (Cat. No. F032), 24 µg/mL gentamicin (Cat. No. A011p), 1 mM sodium pyruvate (Cat. No. F023), and 10 mM HEPES (Cat. No. F134) (all from Paneko). To obtain MBC-derived antibody-secreting cells (ASCs), B cells were stimulated with 25 ng/mL interleukin-21 (IL-21; PeproTech, Cat. No. 200-21) in the presence of mitomycin-treated feeder A549 cells stably expressing CD40L (A549-CD40L, 1 x 10 5 cells/well) for 7 days at a density of 5 × 10 3 B cells/well in 96-well plates at 37°C in 5% CO 2 . Stimulated B cells were harvested 7 days later and used in ELISpot assay. In parallel, supernatants from IL-21/CD40L-stimulated B cells were also collected on day 7 of co-culture for measuring the levels of secreted antibodies in ELISA or virus neutralization assays.

The level of SARS-CoV-2 receptor binding domain (RBD)-specific antibodies was measured using ELISA Quantitation Kit (Xema Co., Cat. No. K153G). Plasma samples from vaccinated individuals or supernatants from IL-21/CD40L stimulated B cells were 2-fold serially diluted from 1:20 to 1:12500 and 1:2 to 1:200 respectively in blocking buffer. Plates were incubated with samples for 1 hour at room temperature. After washing, the plates were additionally incubated for 1 hour with anti-human IgG, IgM or IgA secondary antibody conjugates with horseradish peroxidase (Jackson Immuno Research, Cat. No. 109-036-088, 109-035-129, and 109-035-011) diluted 1:5,000 in blocking buffer. ELISA plates were washed 7 times and developed for 10 min with 100 μL of TMB chromogen solution. The reaction was stopped by adding 50 μl 1 M H 2 SO 4 and optical density at 450 nm was measured using the iMark microplate absorbance reader (Bio-Rad, Cat. No. 1681130). Each sample was measured in triplicate. To determine the concentration of IgG, a serial dilution of anti-SARS-CoV-2 RBD-specific human monoclonal antibody iB12 was included on each plate, a calibration curve was built and IgG levels were calculated (μg/mL). When determining the levels of IgM and IgA, we used high-titer convalescent serum as a standard and antibody levels were expressed as relative units (RU).

Flow Cytometry

Freshly isolated PBMCs were stained with the following antibodies: CD3 FITC (clone TB3), CD16 FITC (clone LNK16), CD19 PE (clone LT19), CD27 PECy5.5 (clone LT27), CD38 PECy7 (clone LT38) [all were produced in-house earlier ( 20 )]; CD14 FITC (clone MEM-15, Exbio, Cat. No. ED7028); anti-human IgG APC (clone M1310G05, Biolegend Cat. No. 410720) and anti-human IgM APC-Fire750 (clone MHM-88, Biolegend Cat. No. 314546). RBD-specific B cells were detected using double staining with phycoerythrin- and allophycocyanin-labelled RBD (RBD-PE and RBD-APC). Production of recombinant RBD (isolate Wuhan-Hu-1) or Bet v 1 conjugated to PE or APC was described earlier (Byazrova et al., 2021). Cells were analyzed on a CytoFLEX S flow cytometer (Beckman Coulter). Up to 10 × 10 6 cells were acquired per sample. Data were analyzed using FlowJo Software (version 10.6.1., Tree Star).


Quantification of SARS-CoV-2-specific ASCs was performed by enzyme-linked immunosorbent spot (ELISpot) assay as described previously ( 20 ). Briefly, sterile clear 96-well Multiscreen HTS Filter Plates with 0.45 μm pore size hydrophobic polyvinylidene difluoride membrane (Merck Millipore, Cat. No. MSIPS4510) were stripped with 70% ethanol for 2 min, washed and coated with 10 μg/mL of recombinant RBD or native ectodomain S protein from SARS-CoV-2 (isolate Wuhan-Hu-1). In-house production of recombinant SARS-CoV-2 proteins was described earlier (Byazrova et al. , 2021). To capture the total immunoglobulin (IgG, IgM or IgA) produced by ASCs, wells were coated with 10 µg/ml of rabbit anti-human IgG or IgM (R&D Systems, Cat. No. SELB002, SELB003), or goat anti-human IgA antibodies (SouthernBiotech, Cat. No. 2050-01).

Freshly purified PBMCs or IL-21/CD40L stimulated B cells were used for quantification of circulating ASCs or MBC-derived ASCs, respectively. Cells were resuspended in complete DMEM/F12 medium and plated at a density of 250000–3000000 of PBMCs or 100–30000 of purified B cells per well in duplicate. After incubation for 16 h at 37°C, 5% CO 2 , the cells were thoroughly removed with washing buffer (0.05% Tween 20 in PBS). Isotype-specific ASCs were detected using IgG- or IgM-specific biotinylated rabbit antibodies (R&D Systems, Cat. No. SELB002, SELB003) or IgA-specific biotinylated goat antibodies (SouthernBiotech, Cat. No. 2052-08).

After five sequential washes with 0.05% Tween-20/PBS, streptavidin alkaline phosphatase conjugate (R&D Systems, Cat. No. SEL002) was added at a 1:60 dilution and the plates were further incubated for 2 hours at room temperature. After several washes, the colorimetric reaction was developed by the addition of Substrate Reagent from B Cell ELISpot Development Module (R&B Systems, Cat. No. SEL002) until clear distinct spots appeared. The reaction was stopped by rinsing the plate with tap water. ELISpot images were acquired using the CTL ImmunoSpot ® analyzer (CTL). Spots were counted using ImmunoSpot ® software. Wells coated with an irrelevant protein Bet v 1 served as negative controls.

Pseudotyped Virus Neutralization (pVNT) Assay

For titration of the neutralizing activity of plasma samples, lentiviral particles pseudotyped with the SARS-CoV-2 S-protein of the WA1 strain, or Beta VOC were used. Lentiviral particles were produced as follows. HEK293T cells were transfected with plasmids psPax2 (kind gift from Dr. Didier Trono), pLV-eGFP (was a gift from Pantelis Tsoulfas, (Addgene, Cat. No. 36083)), and the pCAGGS-SΔ19 plasmid encoding wild-type or Beta S protein (see below). Transfection was performed by the calcium phosphate method; 72 hours after transfection, the supernatants were filtered through a 0.45 µm filter, concentrated 20-fold on Amicon ® Ultra-15 ultrafiltration cells with a 100 kDa cutoff (Merck, Cat. No. UFC910008). Concentrated supernatant was further centrifuged at 20,000 g, 8°C for 90 min. The pellet was resuspended in Opti-MEM medium. Then the viral particles were immediately used in neutralization tests or stored at -70°C for no more than a month. Viral yield was quantified using titration on HEK293T-hACE2 cells.

Prior to analysis, plasma samples were heated for 30 min at 56°C to inactivate complement. After that, serial two-fold plasma dilutions ranging 1:10 - 1:1280 were prepared in a 96-well plate and 20,000 lentiviral particles were added in an equal volume of Opti-Mem supplemented with 2.5% of heat-inactivated FBS. Plasma and viral particles were co-incubated for 30 min at 37°C, and added to HEK293T-hACE2 cells. 72 hours following transduction, the percentage of transduced cells was measured in the cultures using flow cytometry. Half-maximal inhibitory dilution (ID 50 ) was determined by non-linear regression as the serum dilution that neutralized 50% of the pseudotyped lentivirus.

Cloning of the Beta Spike Variant

The construct pCAGGS-SΔ19 carrying a codon-optimized cassette encoding a SARS-CoV-2 Spike protein (identical in the reference Wuhan-Hu-1 and WA1 isolates) lacking 19 C-terminal residues, which has been shown to boost the viral titers ( 44 ), has been described ( 45 ).To obtain pCAGGS-SΔ19_Beta, sets of complementary mutagenic primers (27 nt each) centered at the desired site were used to sequentially introduce the individual mutations (L18F, D80A, D215G, del241-243, R246I, K417N, E484K, N501Y, D614G, A701V) into the coding sequence of SΔ19. Sequence identity of the resulting plasmid was confirmed by Sanger sequencing.

Statistical Analysis

Statistical analysis was performed using Graph Pad Prism (version 8.4.3 GraphPad Software, La Jolla California). The Kruskal–Wallis H test was used for comparison between multiple groups. The Mann-Whitney test was used for comparison between two groups. P < 0.05 was considered statistically significant. Calculation of 95% confidence intervals (CI) was based on the t-distribution of the log-transformed titers, then back-transformed to the original scale. The correlation between two groups was determined by Spearman rank test. A normalized non-linear regression was performed using GraphPad Prism software (Sigmoidal, 4PL). Heatmap generation and principal component analysis were performed with Clustvis ( 46 ) using normalized data. Data are presented as median ± IQR. Asterisks indicate significant difference between groups, *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001, ns = not significant.

Data Availability Statement

The original contributions presented in the study are included in the article/ Supplementary Material , further inquiries can be directed to the corresponding author.

Ethics Statement

The studies involving human participants were reviewed and approved by the Medical Ethical Committee of National Research Center Institute of Immunology of Federal Medical Biological Agency of Russia, Moscow, Russia (#12-1, December 29, 2020). The patients/participants provided their written informed consent to participate in this study.

Author Contributions

MB, SK, AG, AT, and AF contributed to study design, data collection, data analysis, data interpretation, literature search, and the writing of this report. MB, SK, EA, TB, GE, AC, IK, and AG performed the experiments, contributed to data analysis, and data interpretation. MB, EA, and AF contributed to individual recruitment. All authors contributed to the article and approved the submitted version.

This work was supported by the Russian Science Foundation (Project 21-15-00286) and the Russian Fund for Basic Research (20–04–60527).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The authors thank all the individuals in the study for the kind donation of both their time and biological material. We thank Gaukhar Yusubalieva, Vladimir Baklaushev, Yuri Lebedin and Rudolf Valenta for their kind help with experiments and for providing reagents. Figure 1A and Supplementary Figure 1 were created with the help of

Supplementary Material

The Supplementary Material for this article can be found online at:

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Keywords: memory B cells, Sputnik V vaccine, COVID-19, SARS-CoV-2, vaccination

Citation: Byazrova MG, Kulemzin SV, Astakhova EA, Belovezhets TN, Efimov GA, Chikaev AN, Kolotygin IO, Gorchakov AA, Taranin AV and Filatov AV (2022) Memory B Cells Induced by Sputnik V Vaccination Produce SARS-CoV-2 Neutralizing Antibodies Upon Ex Vivo Restimulation. Front. Immunol. 13:840707. doi: 10.3389/fimmu.2022.840707

Received: 21 December 2021; Accepted: 07 February 2022; Published: 24 February 2022.

Reviewed by:

Copyright © 2022 Byazrova, Kulemzin, Astakhova, Belovezhets, Efimov, Chikaev, Kolotygin, Gorchakov, Taranin and Filatov. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Alexander V. Filatov, [email protected]

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sputnik vaccine research paper

Two leading researchers who have raised questions about the reliability of Sputnik V’s vaccine efficacy ratings across age groups shared their concerns with Health Policy Watch. One called the results “impossible” and “very concerning”.

More than 70 countries have approved the use of Sputnik V, Russia’s COVID-19 vaccine, based on the reported 91.6% efficacy across nearly all age groups in the first part of its Phase III trial published by The Lancet in 2021. 

But a number of scientists have since raised a red flag about the reliability of the published data. 

In exclusive interviews, two of the leading critics told Health Policy Watch that such consistent efficacy is not only nearly impossible but also unmatched by any other vaccines. 

These findings “may have substantial implications for the utility of the vaccine and thus regulatory approval and the ongoing use to prevent COVID-19,” wrote Dr Kyle Sheldrick, author of the most recent report, published in   American Therapeutics .

In a recent interview with Health Policy Watch , Sheldrick said that data on the Sputnik vaccine as reviewed in independent analyses, such as one published by the Mexican Ministry of Health, is “very reassuring that this is a vaccine that works.” 

Another vocal critic, the Italian researcher Enrico M. Bucci, echoed that, telling Health Policy Watch in an interview that Sputnik is essentially a combination of two proven vaccine delivery platforms, the Chinese CanSino vaccine and Johnson & Johnson dose.    

But the question still remains, the researchers said: does the Sputnik vaccine work as well as its developers have claimed? 

Concerns about transparency and homogeneity of results 

Concerns about a lack of transparency with regards to aspects of the clinical trials for Sputnik V, as well as the unusual homogeneity of results among different age groups, have been raised about Sputnik since Russia first approved the jab back in August 2020. At that time, it was the first vaccine to receive a green light from any country’s regulatory agency. 

To date, the World Health Organization, European Medicines Agency and the US Food and Drug Administration have not authorized the vaccine, despite repeated statements by Russian officials and representatives of the Russian Direct Investment Fund (RDIF) that funds and sells it that all necessary data has been submitted to those bodies. 

In Sheldrick’s recent paper, he asserts that “the results contained with the phase-III RCT of Sputnik by Logunov et al showed a distribution [among age groups] inconsistent with what would be expected from genuine experimental data.” 

He was referring to the fact that the reported Sputnik results showed equal efficacy amongst all age groups. 

“It is our opinion that it is not possible for a journal or reader to have confidence in the results, and the article should be thoroughly investigated, including immediate release of anonymized individual patient data to an unbiased statistical expert. If the authors are not willing to do this, the paper should be retracted.”

Sputnik results ‘too perfect’

sputnik vaccine research paper

Specifically, Sheldrick and his team performed a simulation study that assessed the statistical probability that the results for different age subgroups participating would fall in the results ranges reported for the Sputnik vaccine in The Lancet article. They compared their findings for Sputnik with those of other US and Australia-approved vaccines, including AstraZeneca, Johnson and Johnson, Moderna and Pfizer. 

To do this, they ran statistical simulations for all of the vaccines 1,000 and then 50,000 times – so as to yield probability factors for the likelihood that results by age subgroup would line up exactly as they did in the real, reported results of the trials. 

“We used study-wide efficacy and infection rate for all age groups,” the paper explained. “We recorded the observed vaccine efficacies in each age group and summated how many simulations had all observed efficacies fall within the range of efficacies described in the relevant article.

“We calculated how many times, on average, a trial would need to be repeated to obtain results that fell within the range of efficacies from the relevant published article,” the authors reported. 

Their findings: The simulations showed that each of the vaccines except Sputnik has a range of efficacy results, by age subgroup, which is not unexpected, when considering the prevailing infection rates in the country at the time,  number of participants, and reported study-wise efficacy.  

“You would only have to repeat the studies two or three times to get a similar result for these other vaccines,” Sheldrick explained. “For Sputnik, you would have to repeat it 3,800 times to get results as perfect as they claimed.”

For instance, in the 1,000-trial simulation for the AstraZeneca vaccine, in 23.8% of simulated trials, the observed efficacies of all age subgroups fell within the efficacy bounds for age subgroups in the published article, according to the report. For J&J: 44.7%, Moderna 51.1%, Pfizer 30.5%. For Sputnik, the result was 0.0%. 

“In 50,000 simulated trials of the Sputnik vaccine, 0.026% had all age subgroups fall within the limits of the efficacy estimates described by the published article, whereas 99.974% did not,” it said in the report. 

Australian scientist: ‘Very uncommon to raise accusations against Lancet’

Sheldrick said that it is improbable that the Russian researchers “just got very lucky,” a notion that has been seconded by a number of other scientists in the US, Italy , Europe and Australia. 

“It is very uncommon to have accusations such as this raised against papers in journals as big as The Lancet ,” Sheldrick told Health Policy Watch , “maybe because concerns are rare or because we don’t have a culture of double-checking. At first I thought I was just being suspicious.”

But after completing his study: The combination of the rapid approval timeline, the lack of availability of the data and the unexpected results “are very concerning to me.” Although he said he still believes that the Russian vaccine is likely to be at least somewhat effective against fighting COVID.

“Just because the data is not genuine, does not mean the vaccine does not work at all,” he stressed. 

Time and political pressures could have led to shortcuts 

Russia, which only offered its citizens Sputnik, is experiencing a slight increase in cases like the rest of the world, but at a much slower pace, according to official data.

The Reuters COVID-19 tracker , for instance, reported only 15 infections per 100K people in the last seven days. That as compared to 385.23 infections and 324.38 infections per one million people in nearby European countries of Estonia and Latvia.

But Sheldrick said that given the lack of transparency seen in other areas, he is unsure whether one can trust the daily cases as reported by the Russian government.  

He also stressed that he and his colleagues had been attempting to raise its concerns about the Russian researcher in private long before the country’s invasion of Ukraine, so the recent paper has nothing to do with the war.

“We raised these concerns with The Lancet before the invasion occurred,” he told Health Policy Watch . Some people like to link the two. I try to stay out of the political side.”

So why does he think that the Russian scientists would have fudged the data, assuming that they did? Time, Sheldrick said. 

“There was internal pressure to produce results before other vaccines,” he said. “Also, the vaccine was already being given to the general public. If you had by random chance found low results in one group, this would have looked particularly bad.”

Not the first to raise concerns

Sheldrick is not the first to raise concerns over Sputnik’s Phase III data. 

Within days of  Sputnik publishing its Phase I/II data in The Lancet , Italian researcher Enrico M. Bucci published a “ note of concern ” on his website about the data. 

Bucci and his team also were concerned that the homogeneity of results for different age groups, as reported in the Sputnik V trials was improbably high. They estimated that  the probability of results falling within the ranges reported in The Lancet study was less than 0.1%. But they had concerns beyond that as well. 

“We noted several gross inconsistencies, including several data points from different experiments which look identical, as well as inconsistencies in the number of enrolled patients and some more problematic data,” Bucci explained to Health Policy Watch . 

“With 15 other colleagues from several international institutions, we signed a letter asking for access to the full data set documenting the results purportedly obtained in the Phase I/II study,” Bucci recounted to Health Policy Watch . 

“In disregard of our and others’ requests, access to the data set used for the study publication was never granted.”

A version of that letter was ultimately published by The  Lancet in September 2020 under the title “Safety and efficacy of the Russian COVID-19 vaccine: more information needed.” 

In it, Bucci stressed that “while the research described in this study is potentially significant, the presentation of the data raises several concerns which require access to the original data to fully investigate.”

Sputnik: All has data been double-checked

sputnik vaccine research paper

Sputnik immediately responded to Bucci’s points, stressing in a formal letter that the data had all been “double-checked.”

“Some data that repeated itself was what elicited the greatest number of questions from Bucci and his colleagues: nine of the volunteers from day 21 to 28 in the vaccination process registered antibody indicators that were completely identical,” Sputnik wrote on its website. “Logunov stated that in small-sized groups of test subjects this possibility ‘cannot be ruled out.’

“‘It is possible that immune system indicators could reach the kind of plateau that we observed during the research,’ the letter states. Logunov also stated that all of the data obtained by scientists during the experiment was double-checked.”

But Bucci said that it was not only the Phase I/II study that raised eyebrows. 

There have now been three Lancet articles on Sputnik, all of which were problematic and subjected to several partial corrections after the errors were flagged, including the most recent Phase III study . 

Errata were published by The Lancet on 22 September  2020; 7 January 7 2021, 9 January 2021, 20 February , 2022 and finally on 11 June , 2022 – the latter in response to an Argentinian paper on Sputnik.  

‘Doubts about the reliability of data

“The published corrections did not address most of the flagged problems,” Bucci contended. “This is why several criticisms were raised in several scientific journals by researchers from all over the world.”

In May 2021, for instance, Vasiliy Vlassov, vice president of the Society for Evidence-Based Medicine in Moscow, published a piece on the BMJ blog in which he stated that “the quality of the reports and secrecy over trial data raise deep concerns about research integrity.”

Another paper titled, “Controversy surrounding the Sputnik V vaccine,” was published in October 2021 by independent researchers in the journal Respiratory Medicine in which they, too, stated there are “doubts about the reliability of the [Phase III] study and that while “Sputnik V could meet the need to provide equitable access to COVID-19 vaccines for people living in low- and middle-income countries …there are still many concerns regarding the use of this vaccine.”

Still… Sputnik V vaccine should work 

Because The Lancet left the paper online and no action was taken, Bucci ultimately put out yet another assessment , this time in March 2022. In this report, he stressed once again his belief that the vaccine works, but that the data was incorrect. 

From the early days of vaccine roll-outs , the Sputnik vaccine has received considerable uptake in many low- as well as middle and even upper-middle income countries, particularly in Latin America – where it was more widely available and affordable than mRNA options. 

“The Russian Sputnik V vaccine should work much like any others,” Bucci wrote. “I am convinced of this because Sputnik V basically can be seen as a combination of a Chinese adenoviral vaccine and a Johnson & Johnson dose . Such vaccines use disabled adenoviruses (cold viruses) to deliver an inactive fragment of the SARS-CoV2 spike protein into the body, prompting an immune reaction.   

“The [Sputnik] production problems are linked precisely to the fact of combining two different vaccines in one, with what follows for the simplicity of the process and the quality control; but this does not mean that the idea, from a scientific point of view, is not valid, or that once the vials are obtained, they should not be useful.”

Bucci also said he had observed instances of sloppiness in data reporting, which nonetheless made it to press. For instance in the original online version of an article on Sputnik’s rollout in Argentina, published in The Lancet on 15 March, 2022, one chart showed that in the 60-69 age group,  49.7% of vaccine recipients were women and 80.7% were men – obviously impossible. The currently available version online shows the data as 49.7% women and 50.3% men -although it acknowledges that a correction version was posted on 9 June. 

sputnik vaccine research paper

The final results of Sputnik’s Phase III trials have not yet been published by the Russian Direct Investment Fund (RDIF), which developed the vaccine together with the Gamaleya Institute – although their completion was announced by the Russian Health Ministry in September 2021. 

At the time, the Health Ministry explained to the Russian media agency Kommersant that the results would not be published at all, since “the results of clinical trials are a trade secret.”

RDIF: ‘Best vaccine in the world’

This has not stopped RDIF from continually stressing how good the vaccine is. 

The Gamaleya Institute told Health Policy Watch in an email that “the efficacy of the Sputnik V vaccine has been documented  in more than 50 real-world and scientific studies in 10 countries, involving more than 12 million people.” 

It highlighted studies in the United Arab Emirates, Bahrain, San Marino, Argentina, Iran, Belarus and Paraguay. 

 “A computer simulation is not the way to check the quality of clinical trials, as opposed to the multiple independent studies in different countries,” the Gamaleya Institute added in its reply to Health Policy Watch . 

“In international science, computer modeling is not a recognized method to cast doubt over outcomes of clinical trials and real-world studies whose peer reviewed results were published in respected medical journals.” 

In his own interviews , RDIF CEO Kirill Dmitriev has stated boldly that “Sputnik is the best vaccine in the world, we have proven it.” He said any challenges were “bureaucratic obstacles.”

Kirill Dmitriev, CEO of the Russian Direct Investment Fund

In November 2021, RDIF also disclosed real world data of the Health Ministry of the Republic of San Marino on the Russian Sputnik V vaccine – which it said demonstrated that Sputnik V was 80% effective against COVID-19 infection up to eight months after receiving the second dose – a much higher percentage of efficacy more months after receipt than Pfizer or Moderna.

“Sputnik team believes that adenoviral vaccines provide for longer efficacy than mRNA vaccines due to longer antibody and T-cell response,” a release by Sputnik stated.

Ultimately, the vaccine received approval for use in 71 countries, RDIF reported, and more than 100 million people outside of Russia have received a Sputnik vaccine.

As of 31 January, “over 400 million Sputnik vaccine doses have been supplied worldwide including Russia to date to fight COVID-19 pandemic,” Sputnik tweeted less than a month before the war. “Over 800,000 doses of 1-shot Sputnik Light, standalone vaccine & universal booster effective against #Omicron & other mutations arrived in Turkmenistan.”

‘Science is based on trust’

Bucci said that the most important point at this stage is to address the potential shortcomings in  proper data checking and editorial controls by The Lancet , as well as its failure to press harder for more detailed data clarifications from the authors of the Sputnik studies when questions arose.  

“Science is based on trust stemming from the possibility of checking and reproducing published results,” he told Health Policy Watch . “When the scientific community is denied access to the data needed to reproduce the findings from a specific research group, we are no longer dealing with science but with unsubstantiated claims.”

In response to queries from Health Policy Watch , The Lancet Group said that it “takes issues relating to scientific misconduct extremely seriously and follows best practice guidelines set by the Committee on Publication Ethics (COPE)”. 

“We acknowledge the questions raised about the validity of Sputnik vaccine data published in The Lancet and we have invited the authors of the Lancet paper to respond to these latest questions,” it added.

Image Credits: RDIF , Sputnikvaccine/Twitter , The Lancet .

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Sputnik light and sputnik v vaccination is effective at protecting medical personnel from covid-19 during the period of delta variant dominance.

sputnik vaccine research paper

1. Introduction

2. materials and methods, 2.1. database preparation for epidemiological analysis, 2.2. statistical analysis, 2.3. characteristics of the genetic landscape of sars-cov-2 lines, 3.1. circulating lines sars-cov-2 virus, 3.2. vaccination and cases of the disease among the medical center personnel, 3.3. effectiveness of sputnik v in the medical personnel, 4. discussion, 5. conclusions, supplementary materials, author contributions, institutional review board statement, informed consent statement, conflicts of interest.

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Sukhikh, G.T.; Priputnevich, T.V.; Ogarkova, D.A.; Pochtovyi, A.A.; Kustova, D.D.; Zlobin, V.I.; Logunov, D.Y.; Gushchin, V.A.; Gintsburg, A.L. Sputnik Light and Sputnik V Vaccination Is Effective at Protecting Medical Personnel from COVID-19 during the Period of Delta Variant Dominance. Vaccines 2022 , 10 , 1804.

Sukhikh GT, Priputnevich TV, Ogarkova DA, Pochtovyi AA, Kustova DD, Zlobin VI, Logunov DY, Gushchin VA, Gintsburg AL. Sputnik Light and Sputnik V Vaccination Is Effective at Protecting Medical Personnel from COVID-19 during the Period of Delta Variant Dominance. Vaccines . 2022; 10(11):1804.

Sukhikh, Gennady T., Tatiana V. Priputnevich, Darya A. Ogarkova, Andrei A. Pochtovyi, Daria D. Kustova, Vladimir I. Zlobin, Denis Y. Logunov, Vladimir A. Gushchin, and Alexander L. Gintsburg. 2022. "Sputnik Light and Sputnik V Vaccination Is Effective at Protecting Medical Personnel from COVID-19 during the Period of Delta Variant Dominance" Vaccines 10, no. 11: 1804.

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Cell Reports Medicine

Report sputnik v vaccine elicits seroconversion and neutralizing capacity to sars-cov-2 after a single dose.

First dose of Sputnik V results in 94% seroconversion rate in naive individuals

A second dose greatly increases antibody titers and neutralizing capacity

One dose in seropositive individuals elicits higher titers than two doses in naive

There is no evident benefit of using a second dose in previously infected individuals

Massive vaccination offers great promise for halting the global COVID-19 pandemic. However, the limited supply and uneven vaccine distribution create an urgent need to optimize vaccination strategies. We evaluate SARS-CoV-2-specific antibody responses after Sputnik V vaccination of healthcare workers in Argentina, measuring IgG anti-spike titers and neutralizing capacity after one and two doses in a cohort of naive or previously infected volunteers. By 21 days after receiving the first dose of the vaccine, 94% of naive participants develop spike-specific IgG antibodies. A single Sputnik V dose elicits higher antibody levels and virus-neutralizing capacity in previously infected individuals than in naive ones receiving the full two-dose schedule. The high seroconversion rate after a single dose in naive participants suggests a benefit of delaying administration of the second dose to increase the number of people vaccinated. The data presented provide information for guiding public health decisions in light of the current global health emergency.

Graphical abstract

sputnik vaccine research paper

Data and code availability

Datasets generated in this study have been uploaded to at .

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These authors contributed equally

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Sputnik vaccine

Source: © Sefa Karacan/Anadolu Agency/Getty Images

New questions raised over Sputnik Covid vaccine results point to fraudulent practices

Anthony King

An analysis of Sputnik V vaccine data has concluded that the published results were likely faked. The vaccine was authorised in Russia before any clinical studies were published , and subsequent trial results proved controversial.

An Australian–Singapore group casts fresh doubt on Sputnik data after investigating the almost identical efficacy for every age group from a phase 3 trial reported in  The Lancet in February 2021 . They found that the efficacies were closer to each other than would be expected, given the small number of patients and infections, and high vaccine efficacy. ‘Such a result would be expected in fewer than 1 in 1000 trials,’ the team wrote.

The concern is ‘that it was wildly more perfect than you would expect given the number of infections in the study’, says Kyle Sheldrick at the University of New South Wales, Australia, who led the research. ‘They got virtually identical results for every single age group, and that just doesn’t happen in real data.’

This is not the first time Sputnik data has been questioned. Enrico Bucci at Temple University, in Philadelphia, US, also flagged concerns in September 2020 . He subsequently reported on data discrepancies and substandard reporting from the phase 3 trial.

He noted vaccine efficacy at 91.9% among adults aged 18 to 30 years, 90% among those 31 to 40 years, 91.3% among those 41 to 50 years, 92.7% among those 51 to 60 years, and 91.8% in those older than 60. A statistical analysis concluded a low probability of observing such homogeneity.

The new analysis ran simulations that took the same number of patients and assumed that vaccine efficacy was correct and identical in every age group. It then randomly assigned the 21,000 patients into vaccinated or unvaccinated pools, assuming the same infection rate as for the control group.

In 50,000 simulated trials of the Sputnik V vaccine, 0.026% of them had all age subgroups fall within the efficacy figures reported for the phase 3 trial. The same approach taken for the AstraZeneca, Janssen, Moderna and Pfizer vaccines yielded results between 24% and 51%.

‘Our simulation shows that, even if everything they claim is true, the chance of getting the results they published is so incredibly small that it just can’t be accepted as real data,’ says Sheldrick. ‘This fits a pattern of overly similar results that we’ve seen across multiple studies with this vaccine.’

The analysis reversed the approach taken by Bucci and others. ‘The authors took at face value the claims made by the Russians on the vaccine efficacy, instead of showing they were highly improbable as we did,’ noted Bucci. ‘Once you do so, you will never get the results described in the original paper by running a trial the way it was.’

He and other critics say that problems are compounded by secrecy around trial protocols and statistical analysis. ‘Access to the original data is paramount to evaluating the actual consistency of Sputnik V efficacy,’ according to Bucci.

The Australian-led group called for a thorough investigation of the article, as well as the immediate release of anonymised individual patient data to an unbiased statistical expert. Without such steps, the paper should be retracted by The Lancet.

‘The Lancet Group take issues relating to scientific misconduct extremely seriously and follow best practice guidelines set by the Committee on Publication Ethics,’ a spokesperson for The Lancet Group said in statement. ‘We recognise the concerns about the validity of Sputnik vaccine data published in The Lancet and we will be inviting the authors of The Lancet paper to respond to these latest questions.’

Nonetheless, the trial problems do not mean that the vaccine doesn’t work, says Sheldrick, ‘but it does mean that we cannot rely on this trial, which is the largest to date’. He forwarded his analysis to the European Medicines Agency, which previously announced a review of Sputnik V .

‘It is worth noting that the vaccine was used in many countries who have since published their own findings, and they have all been positive,’ notes virologist Ian Jones at the University of Reading, who wrote a comment for The Lancet on the February 2021 results. He adds that ‘Sputnik may have been talked up, especially for older age groups, but there is no doubt it works’.

KA Sheldrick et al , Am. J. Ther. , 2022, DOI: 10.1097/MJT.0000000000001528

Anthony King

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