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Research Article
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Stability of gut microbiome after COVID-19 vaccination in healthy and immuno-compromised individuals

View ORCID ProfileRebecca H Boston, View ORCID ProfileRui Guan, View ORCID ProfileLajos Kalmar, View ORCID ProfileSina Beier, View ORCID ProfileEmily C Horner, View ORCID ProfileNonantzin Beristain-Covarrubias, View ORCID ProfileJuan Carlos Yam-Puc, Pehuén Pereyra Gerber, Luisa Faria, Anna Kuroshchenkova, View ORCID ProfileAnna E Lindell, View ORCID ProfileSonja Blasche, Andrea Correa-Noguera, Anne Elmer, Caroline Saunders, Areti Bermperi, Sherly Jose, Nathalie Kingston, CITIID-NIHR COVID-19 BioResource Collaboration, Sofia Grigoriadou, Emily Staples, View ORCID ProfileMatthew S Buckland, Sara Lear, Nicholas J Matheson, View ORCID ProfileVladimir Benes, Christine Parkinson, View ORCID ProfileJames ED Thaventhiran  Correspondence email, View ORCID ProfileKiran R Patil  Correspondence email
Rebecca H Boston
1Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK
Roles: Data curation, Formal analysis, Validation, Investigation, Visualization, Writing—original draft, Writing—review and editing
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Rui Guan
1Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK
Roles: Data curation, Software, Formal analysis, Visualization, Writing—original draft, Writing—review and editing
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  • ORCID record for Rui Guan
Lajos Kalmar
1Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK
Roles: Software, Formal analysis
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  • ORCID record for Lajos Kalmar
Sina Beier
1Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK
Roles: Software, Formal analysis
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  • ORCID record for Sina Beier
Emily C Horner
1Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK
Roles: Investigation
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  • ORCID record for Emily C Horner
Nonantzin Beristain-Covarrubias
1Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK
Roles: Investigation
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  • ORCID record for Nonantzin Beristain-Covarrubias
Juan Carlos Yam-Puc
1Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK
Roles: Investigation
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  • ORCID record for Juan Carlos Yam-Puc
Pehuén Pereyra Gerber
2Cambridge Institute of Therapeutic Immunology and Infectious Disease, University of Cambridge, Cambridge, UK
3Department of Medicine, University of Cambridge, Cambridge, UK
Roles: Resources
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Luisa Faria
1Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK
Roles: Investigation, Methodology
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Anna Kuroshchenkova
1Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK
Roles: Investigation, Methodology
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Anna E Lindell
1Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK
Roles: Investigation, Methodology
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  • ORCID record for Anna E Lindell
Sonja Blasche
1Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK
Roles: Investigation, Methodology
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  • ORCID record for Sonja Blasche
Andrea Correa-Noguera
4Department of Clinical Immunology, Cambridge University NHS Hospitals Foundation Trust, Cambridge, UK
Roles: Resources
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Anne Elmer
5NIHR Cambridge Clinical Research Facility, Cambridge, UK
Roles: Resources
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Caroline Saunders
5NIHR Cambridge Clinical Research Facility, Cambridge, UK
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Areti Bermperi
5NIHR Cambridge Clinical Research Facility, Cambridge, UK
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Sherly Jose
5NIHR Cambridge Clinical Research Facility, Cambridge, UK
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Nathalie Kingston
6NIHR BioResource, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
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Sofia Grigoriadou
7Department of Clinical Immunology, Barts Health, London, UK
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Emily Staples
1Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK
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Matthew S Buckland
7Department of Clinical Immunology, Barts Health, London, UK
8UCL GOSH Institute of Child Health Division of Infection and Immunity, Section of Cellular and Molecular Immunology, London, UK
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Sara Lear
4Department of Clinical Immunology, Cambridge University NHS Hospitals Foundation Trust, Cambridge, UK
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Nicholas J Matheson
2Cambridge Institute of Therapeutic Immunology and Infectious Disease, University of Cambridge, Cambridge, UK
3Department of Medicine, University of Cambridge, Cambridge, UK
9NHS Blood and Transplant, Cambridge, UK
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Vladimir Benes
10European Molecular Biology Laboratory, Heidelberg, Germany
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Christine Parkinson
4Department of Clinical Immunology, Cambridge University NHS Hospitals Foundation Trust, Cambridge, UK
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James ED Thaventhiran
1Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK
4Department of Clinical Immunology, Cambridge University NHS Hospitals Foundation Trust, Cambridge, UK
Roles: Conceptualization, Resources, Funding acquisition, Writing—review and editing
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  • For correspondence: jedt2@mrc-tox.cam.ac.uk
Kiran R Patil
1Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK
Roles: Conceptualization, Resources, Supervision, Funding acquisition, Methodology, Writing—original draft, Writing—review and editing
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  • For correspondence: kp533@mrc-tox.cam.ac.uk
Published 5 February 2024. DOI: 10.26508/lsa.202302529
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    Figure S1. Participant gut microbiome sampling across three doses of COVID-19 vaccinations (V1, vaccine dose 1; V2, vaccine dose 2; and V3, vaccine dose 3) from the different cohorts: healthy control, immune-checkpoint therapy-treated cancer patients, or patients with primary immunodeficiencies.
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    Figure 1. The composition of the gut microbiome remains unaltered after COVID-19 vaccination.

    (A) 59 patients were recruited for longitudinal analysis of the effect of the vaccines against COVID-19. Samples were assigned to one of three cohorts, healthy control, immune-checkpoint therapy treated cancer patients (ICP), or patients with primary immunodeficiencies (PID). Blood samples were analysed for their live-virus neutralisation capacity and quantifying the amount of anti-spike IgG antibodies, whilst fecal samples were analysed with shotgun metagenomics for taxonomic and functional annotations. (B) Diversity measures of chao1 and Shannon assessed in fecal samples taken from different vaccine timepoints, from within healthy control, ICP and patients with PID. Statistical testing performed using Wilcoxon test and adjusted for multiple testing using Bonferroni correction. (C) Principal component (PC) analysis at the operational taxonomic unit level. Each dot represents a unique sample from within each cohort (shapes) taken at unique timepoints after vaccination (colours). (D) Relative abundance at the phyla taxonomic level depicted by colours of each of the bars, from samples taken from each of the cohorts (HC, ICP, and PID), separated by the vaccine timepoints from which the sample was taken; PD, Pre-Dose, Acute, and Late. (E) Relative abundance of the six most prevalent phyla in patient samples from within each of the cohorts and separated by the vaccine timepoint from which the sample was taken. Statistical testing performed using Wilcoxon test and adjusted for multiple testings using Bonferroni correction. (N = 43 HC, 160 ICP, and 36 PID).

  • Figure S2.
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    Figure S2. Gut microbiome compositional differences are evident between cohorts but not vaccine timepoints.

    (A) Alpha diversity measures of chao1 and Shannon diversity in samples from different cohorts, healthy control, immune-checkpoint therapy-treated cancer patients (ICP), or patients with primary immunodeficiencies (PID) (N = 43 HC, 160 ICP, and 36 PID). (B) Paired analysis of the alpha diversity measures of patient samples taken from different vaccine timepoints from each of our cohorts (N = 40 HC, 153 ICP, and 31 PID). (C) Reported P-values of the linear model comparing baseline model of fixed patient effects on the explained variance, to the model using the vaccine timepoints of the patient samples as random effects when using the principal components (PC) (healthy controls [HCs], ICP, PID). (D) Relative abundance of the six most prevalent phyla in our patient samples from within each of our patient cohorts and separated by the vaccine timepoint from which the sample was taken (N = 43 HC, 160 ICP, and 36 PID). (E) Paired analysis of the six most prevalent phyla in our patient samples taken from different vaccine timepoints from each of our cohorts (N = 40 HC, 153 ICP, and 31 PID). (F) Volcano plot of the paired relative phylum abundance between two timepoints across all pair combinations unique to different vaccine doses and different cohorts. Colours represent the significance indicated in the legend. Statistical testing within figures was performed using Wilcoxon test and adjusted for multiple testing using bonferonni, paired where appropriate.

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    Figure 2. Bacterial species demonstrate minimal change attributable to the COVID-19 vaccines.

    (A) Differential abundance analysis using DESeq2 of relative abundance of the top 35 differential species between samples taken at pre-dose (N = 29) and acutely (N = 69) after vaccination. (B) Log2 fold-change of the significant differential abundant species taken from the DESeq2 analysis. (C) Relative abundance of Klebsiella pneumoniae in ICP cohort samples. (D) Relative abundance of Butyrivibrio crossotus in ICP cohort samples. (E) Relative abundance of the top 15 abundant species within the ICP cohort taken at each of the vaccine timepoints (N = 43 HC, 160 ICP, and 36 PID). (F, G, H) Relative abundance of various bacterial species correlated with immune-related diseases: Faecalibacterium prausnitzii (F), Akkermansia muciniphila (G), and Escherichia coli (H) within patient samples taken at each vaccine timepoint. Statistical testing performed using Wilcoxon test and adjusted for multiple testing using Bonferroni correction. (N = 43 HC, 160 ICP, and 36 PID).

  • Figure S3.
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    Figure S3. Differential abundance analysis of HC and PID cohort samples taken at pre-dose and acutely after COVID-19 vaccination.

    (A, B, C, D) DESeq analysis of HC cohort (N = 11 Pre-Dose, 16 Acute) and PID cohort (N = 5 Pre-Dose, 13 Acute) (B), along with corresponding log2FoldChange of significantly different bacterial species between pre-dose and acute samples in HC samples (C) and PID samples (D), colours represent different phyla. (E) Relative abundance of Enterobacter sp. in our cohorts.

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    Figure 3. Vaccine efficacy is not correlated with the gut microbiome diversity.

    Live-virus neutralisation capacity (NT) assessed against Shannon diversity of fecal samples, each point represents a different sample taken at one of the three vaccine timepoints. Colours represent cohorts, within healthy control, immune-checkpoint therapy-treated cancer patients (ICP) and patients with primary immunodeficiencies (PID). Correlated vaccine response through neutralisation capacity of patient serum taken at the peak of the second dose (v2D21) (N = 9 HC, 57 ICP, and 15 PID). (A) or third dose (v3D28) (N = 33 HC, 54 ICP, and 5 PID). (B) rho and P-values from Spearman’s Rank correlation testing displayed.

  • Figure S4.
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    Figure S4. Absence of correlation between vaccine efficacy and gut microbiome composition.

    Second dose anti-spike IgG antibody levels assessed against Shannon diversity of fecal samples; each point represents a different sample taken at one of the three vaccine timepoints. Colours represent cohorts, within healthy control, ICP and patients with PID. rho and P-values from Spearman correlation testing displayed (N = 9 HC, 35 ICP, and 15 PID).

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    Figure 4. Functional capacity of microbiome samples are not altered by the COVID-19 vaccines.

    (A) The relative abundance of the highest functional annotation level using the EGGNOG database within patient samples at different vaccine timepoints in each of our patient cohorts. (B) Functional composition depicted by colours of each of the bars, from samples taken from each of the cohorts (HC, ICP, and PID), separated by the vaccine timepoints from which the sample was taken; PD, Pre-Dose, Acute, and Late. (C) Relative abundance of the three most abundant functional annotations in our patient samples from within each of our patient cohorts and separated by the vaccine timepoint from which the sample was taken. Statistical testing performed using Wilcoxon test and adjusted for multiple testing using Bonferroni correction (N = 43 HC, 160 ICP, and 36 PID).

  • Figure S5.
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    Figure S5. Functional annotation differences between cohorts, but not between vaccine timepoints.

    (A) The relative abundance of the highest functional annotation level within patient samples from different patient cohort (HC, ICP, and PID), separated by the vaccine timepoints from which the sample was taken. (B) Relative abundance of the remaining 19 out of a possible 22 functional annotations in our patient samples from within each of our patient cohorts and separated by the vaccine timepoint from which the sample was taken. Statistical testing performed using Wilcoxon test and adjusted for multiple testing using FDR (N = 43 HC, 160 ICP, and 36 PID).

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    Table 1.

    Characteristics of the participants in this study.

    CohortParticipantsAgeVaccineConditionTreatment
    Healthy controls6F 9M28–59Mean = 43.7Vaccine doses, n = 20 90% Pfizer doses10% Moderna dosesNANA
    Immune checkpoint treated cancer patients (ICP)9F 26M39–86Mean = 61.7Vaccine doses, n = 7097% Pfizer doses3% Moderna doses11 Metastatic Melanoma10 Adjuvant Melanoma5 Melanoma controls6 Metastatic Renal3 Renal controls3 Nivolumab, 13 Pembrolizumab, 10 Ipilimumab + Nivolumab, 1 Ipilimumab + Pembrolizumab
    Primary immunodeficient patients (PID)4F 5M19–61Mean = 41.1Vaccine doses, n = 1995% Pfizer doses5% AstraZeneca doses1 CD40L deficiency2 CTLA4 deficiency4 NFKB1 deficiency2 Undiagnosed condition5 intravenous immunoglobulin3 Antibiotics
    • Participants enrolled in the study are split into one of three cohorts: healthy controls, immune checkpoint treated cancer patients and primary immunodeficient patients. F, female; M, male.

Supplementary Materials

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  • Table S1 Paired sample alpha diversity analysis from different patient cohorts (healthy controls, immune checkpoint therapy-treated cancer patients, and primary immunodeficient patients).

  • Table S2 PERMANOVA analysis describing the influence of study variables on the variance seen in the composition of the gut microbiome samples from our patients.

  • Table S3 Paired sample analysis of the relative abundance of phylum from different patient cohorts (healthy controls, immune checkpoint therapy-treated cancer patients, and primary immunodeficient patients).

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Microbiome stability after vaccination
Rebecca H Boston, Rui Guan, Lajos Kalmar, Sina Beier, Emily C Horner, Nonantzin Beristain-Covarrubias, Juan Carlos Yam-Puc, Pehuén Pereyra Gerber, Luisa Faria, Anna Kuroshchenkova, Anna E Lindell, Sonja Blasche, Andrea Correa-Noguera, Anne Elmer, Caroline Saunders, Areti Bermperi, Sherly Jose, Nathalie Kingston, CITIID-NIHR COVID-19 BioResource Collaboration, Sofia Grigoriadou, Emily Staples, Matthew S Buckland, Sara Lear, Nicholas J Matheson, Vladimir Benes, Christine Parkinson, James ED Thaventhiran, Kiran R Patil
Life Science Alliance Feb 2024, 7 (4) e202302529; DOI: 10.26508/lsa.202302529

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Microbiome stability after vaccination
Rebecca H Boston, Rui Guan, Lajos Kalmar, Sina Beier, Emily C Horner, Nonantzin Beristain-Covarrubias, Juan Carlos Yam-Puc, Pehuén Pereyra Gerber, Luisa Faria, Anna Kuroshchenkova, Anna E Lindell, Sonja Blasche, Andrea Correa-Noguera, Anne Elmer, Caroline Saunders, Areti Bermperi, Sherly Jose, Nathalie Kingston, CITIID-NIHR COVID-19 BioResource Collaboration, Sofia Grigoriadou, Emily Staples, Matthew S Buckland, Sara Lear, Nicholas J Matheson, Vladimir Benes, Christine Parkinson, James ED Thaventhiran, Kiran R Patil
Life Science Alliance Feb 2024, 7 (4) e202302529; DOI: 10.26508/lsa.202302529
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Volume 7, No. 4
April 2024
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