Scalable, methanol‐free manufacturing of the SARS‐CoV‐2 receptor‐binding domain in engineered Komagataella phaffii

  1. Neil C. Dalvie
  2. Andrew M. Biedermann
  3. Sergio A. Rodriguez‐Aponte
  4. Christopher A. Naranjo
  5. Harish D. Rao
  6. Meghraj P. Rajurkar
  7. Rakesh R. Lothe
  8. Umesh S. Shaligram
  9. Ryan S. Johnston
  10. Laura E. Crowell
  11. Seraphin Castelino
  12. Mary K. Tracey
  13. Charles A. Whittaker
  14. J. Christopher Love

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Abstract

Prevention of COVID‐19 on a global scale will require the continued development of high‐volume, low‐cost platforms for the manufacturing of vaccines to supply ongoing demand. Vaccine candidates based on recombinant protein subunits remain important because they can be manufactured at low costs in existing large‐scale production facilities that use microbial hosts like Komagataella phaffii ( Pichia pastoris ). Here, we report an improved and scalable manufacturing approach for the SARS‐CoV‐2 spike protein receptor‐binding domain (RBD); this protein is a key antigen for several reported vaccine candidates. We genetically engineered a manufacturing strain of K. phaffii to obviate the requirement for methanol induction of the recombinant gene. Methanol‐free production improved the secreted titer of the RBD protein by >5X by alleviating protein folding stress. Removal of methanol from the production process enabled to scale up to a 1200 L pre‐existing production facility. This engineered strain is now used to produce an RBD‐based vaccine antigen that is currently in clinical trials and could be used to produce other variants of RBD as needed for future vaccines.

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  1. Version published to 10.1002/bit.27979
  2. ScreenIT

    SciScore for 10.1101/2021.04.15.440035: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Transcripts were quantified with Salmon version 1.3.0 (Patro et al., 2017) and selective alignment using a target consisting of the K. phaffii transcripts, the RBD, and selectable marker transgene sequences and the K.
    Salmon
    suggested: (Salmon, RRID:SCR_017036)
    Expression values were summarized with tximport version 1.12.3 (Soneson et al., 2016) and edgeR version 3.26.8 (McCarthy et al., 2012; Robinson et al., 2009).
    tximport
    suggested: (tximport, RRID:SCR_016752)
    edgeR
    suggested: (edgeR, RRID:SCR_012802)
    Gene set enrichment analysis (GSEA) was performed with GSEA 4.1.0 using Wald statistics calculated by DESeq2 (Love et al., 2014) and gene sets from yeast GO Slim (Subramanian et al., 2005).
    Gene set enrichment analysis
    suggested: (Gene Set Enrichment Analysis, RRID:SCR_003199)
    GSEA
    suggested: (SeqGSEA, RRID:SCR_005724)
    DESeq2
    suggested: (DESeq, RRID:SCR_000154)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    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.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    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 10.1101/2021.04.15.440035v1 on bioRxiv