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Record W3159229445 · doi:10.1101/2021.04.29.442077

Design of the recombinant influenza neuraminidase antigen is crucial for protective efficacy

2021· preprint· en· W3159229445 on OpenAlexafffund
Jin Gao, Laura Klenow, Lisa M. Parsons, Tahir Malik, Je‐Nie Phue, Zhizeng Gao, Stephen G. Withers, John F. Cipollo, Robert Daniels, Hongquan Wan

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2021
Typepreprint
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsUniversity of British Columbia
FundersCenters for Disease Control and PreventionUniversity of British ColumbiaSt. Jude Children's Research Hospital
KeywordsRNAEctodomainNeuraminidaseRecombinant DNAChemistryAntigenicityHeterologousPhosphoproteinRational designBiochemistryEnzymeCell biologyVirologyBiologyAntigenPhosphorylationGeneticsGeneReceptor

Abstract

fetched live from OpenAlex

ABSTRACT Supplementing influenza vaccines with recombinant neuraminidase (rNA) remains a promising approach for improving the suboptimal efficacy. However, correlations among rNA designs, properties, and protection have not been systematically investigated. Here, we performed a comparative analysis of several rNAs produced from different construct designs using the baculovirus/insect cell system. The rNAs were designed with different tetramerization motifs and NA domains from a recent H1N1 vaccine strain (A/Brisbane/02/2018) and were analyzed for enzymatic properties, antigenicity, thermal and size stability, and protection in mice. We found that rNAs containing the NA head-domain versus the full-ectodomain possess distinct enzymatic properties and that the molecular size stability is tetramerization domain-dependent, whereas protection is more contingent on the combination of the tetramerization and NA domains. Following single-dose immunizations, a rNA possessing the full-ectodomain, non-native enzymatic activity, and the tetramerization motif from the human vasodilator-stimulated phosphoprotein provided substantially higher protection than a rNA possessing the head-domain, native activity and the same tetramerization motif. In contrast, these two rNAs provided comparable protection when the tetramerization motif was exchanged with the one from the tetrabrachion protein. These findings demonstrate that the rNA design is crucial for the protective efficacy and should be thoroughly evaluated for vaccine development, as the unpredictable nature of the heterologous domain combination can result in rNAs with similar key attributes but vastly differ in protection. IMPORTANCE For several decades it has been proposed that influenza vaccines could be supplemented with recombinant neuraminidase (rNA) to improve the efficacy. However, some key questions for manufacturing stable and immunogenic rNA remain to be answered. We show here that the tetramerization motifs and NA domains included in the rNA construct design can have a profound impact on the biochemical, immunological and protective properties. We also show that the single-dose immunization regimen is more informative for assessing the rNA immune response and protective efficacy, which is surprisingly more dependent on the specific combination of NA and tetramerization domains than common attributes for evaluating NA. Our findings may help to optimize the design of rNAs that can be used to improve or develop influenza vaccines.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.085
GPT teacher head0.329
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2021
Admission routes2
Has abstractyes

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