High temporal resolution systems profiling reveals distinct patterns of interferon response after Covid-19 mRNA vaccination and SARS-CoV2 infection

  1. Darawan Rinchai
  2. Sara Deola
  3. Gabriele Zoppoli
  4. Basirudeen Syed Ahamed Kabeer
  5. Sara Taleb
  6. Igor Pavlovski
  7. Selma Maacha
  8. Giusy Gentilcore
  9. Mohammed Toufiq
  10. Lisa Mathew
  11. Li Liu
  12. Fazulur Rehaman Vempalli
  13. Ghada Mubarak
  14. Stephan Lorenz
  15. Irene Sivieri
  16. Gabriella Cirmena
  17. Chiara Dentone
  18. Paola Cuccarolo
  19. Daniele Roberto Giacobbe
  20. Federico Baldi
  21. Alberto Garbarino
  22. Benedetta Cigolini
  23. Paolo Cremonesi
  24. Michele Bedognetti
  25. Alberto Ballestrero
  26. Matteo Bassetti
  27. Boris P. Hejblum
  28. Tracy Augustine
  29. Nicholas Van Panhuys
  30. Rodolphe Thiebaut
  31. Ricardo Branco
  32. Tracey Chew
  33. Maryam Shojaei
  34. Kirsty Short
  35. Carl Feng
  36. PREDICT-19 consortium
  37. Susu M. Zughaier
  38. Andrea De Maria
  39. Benjamin Tang
  40. Ali Ait Hssain
  41. Davide Bedognetti
  42. Jean-Charles Grivel
  43. Damien Chaussabel

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

Knowledge of the mechanisms underpinning the development of protective immunity conferred by mRNA vaccines is fragmentary. Here we investigated responses to COVID-19 mRNA vaccination via ultra-low-volume sampling and high-temporal-resolution transcriptome profiling (23 subjects across 22 timepoints, and with 117 COVID-19 patients used as comparators). There were marked differences in the timing and amplitude of the responses to the priming and booster doses. Notably, we identified two distinct interferon signatures. The first signature (A28/S1) was robustly induced both post-prime and post-boost and in both cases correlated with the subsequent development of antibody responses. In contrast, the second interferon signature (A28/S2) was robustly induced only post-boost, where it coincided with a transient inflammation peak. In COVID19 patients, a distinct phenotype dominated by A28/S2 was associated with longer duration of intensive care. In summary, high-temporal-resolution transcriptomic permitted the identification of post- vaccination phenotypes that are determinants of the course of COVID-19 disease.

Article activity feed

  1. Version published to 10.1101/2021.12.12.472257v2 on bioRxiv
  2. ScreenIT

    SciScore for 10.1101/2021.12.12.472257: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    EthicsConsent: The protocol was approved by Sidra Hospital IRB (IRB number 1670047-6), and all participants gave written informed consent.
    IRB: Martino, Genoa, Italy (Ethics Committee of the Liguria Region (N.
    Field Sample Permit: Sampling protocol: COVAX Cohort: For transcriptomics applications for the COVAX study, after puncturing the skin with a finger stick, 50 µl of blood was collected in a capillary/microfuge tube assembly supplied by KABE Labortechnik (Numbrecht, Germany) containing 100 µl of tempus RNA- stabilizing solution aliquoted from a regular-sized tempus tube (designed for the collection of 3 ml of blood and containing 6 ml of solution; ThermoFisher, Waltham, MA, USA).
    Sex as a biological variableSamples were also collected from control subjects who were adults and did not: 1) present with an infectious syndrome during the last 90 days, 2) experience extreme physical stress within the last week, 3) received during the last 90 days a treatment based on: antivirals; antibiotics; antiparasitic; antifungals; 4) received within the last 15 days, a treatment based on non- steroidal anti-inflammatory drugs; 5) received during the last 24 months a treatment based on: immunosuppressive therapy; corticosteroids; therapeutic antibodies; chemotherapy and 6) a person with history of: innate or acquired immune deficiency; hematological disease; solid tumor; severe chronic disease; surgery or hospitalization within the last 2 years; pregnancy within the last year; participation to a phase I clinical assay during the last year; participation to a phase I clinical assay during the last year; pregnant or breastfeeding women; person with restricted liberty or under legal protection.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    This method is described in detail in an earlier report (5), and the collection procedure is illustrated in an uploaded video: https://www.youtube.com/watch?v=xnrXidwg83I.
    https://www.youtube.com/watch
    suggested: (GENEtics Video, RRID:SCR_004770)
    RNA sequencing: COVAX & IMPROVISE Cohorts: mRNA-sequencing was performed using QuantSeq 3’ mRNA-
    QuantSeq
    suggested: None
    , INSDC Assembly GCA_000001405.28, Dec 2013) using STAR 2.6.1d and featureCounts v2.0.0 was used to generate the raw counts.
    STAR
    suggested: (STAR, RRID:SCR_004463)
    featureCounts
    suggested: (featureCounts, RRID:SCR_012919)
    Transcriptome profiling data were deposited, along with detailed sample information, into a public repository, the NCBI Gene Expression Ominibus (GEO), with accession ID GSE190001 and BioProject ID: PRJNA785113 PREDICT-19 Cohort: Total RNA was isolated from whole blood lysate using the Tempus Spin Isolation kit (Applied Biosystems) according to the manufacturer’s instructions.
    Gene Expression Ominibus
    suggested: None
    BioProject
    suggested: (NCBI BioProject, RRID:SCR_004801)
    BAM files were converted to a raw count’s expression matrix using HTSeq (https://github.com/Sydney-Informatics-Hub/RNASeq-DE).
    HTSeq
    suggested: (HTSeq, RRID:SCR_005514)
    Raw count data was normalized using DEseq2.
    DEseq2
    suggested: (DESeq2, RRID:SCR_015687)

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    There were several limitations to our study. While the sample size was adequate for an initial discovery phase, a larger study cohort would help to better resolve inter-individual variations. The dataset we generated, however, has been made available for reuse, and it should be possible to integrate and consolidate this dataset with those generated in follow- on studies by us and others (13). Follow on studies would need to be purposedly designed to formally address specific questions, for instance, comparing responses in individuals who had previously been exposed to SARS-CoV-2 with those in naïve individuals. It would also be interesting to compare responses elicited by the Pfizer/BioNTech and Moderna vaccines, which was not possible in our study due to the small numbers of individuals that received the Moderna vaccine. Indeed, although we hoped it would be possible to obtain more balanced sample sizes for a more detailed comparison, the speed at which the vaccinations were rolled out among our target population of healthcare workers meant we had very little control over the number of volunteers that received the different types of vaccines or their status as naïve or previously exposed individuals. It would also have been particularly interesting to enroll patients from different age categories, especially the elderly population, but this again proved impossible. In conclusion, a considerable number of COVID-19 vaccines have already been approved for use in humans, and oth...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • No funding statement was detected.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2021.12.12.472257v1 on bioRxiv