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Record W4311793123 · doi:10.3389/fimmu.2022.1026052

Exploring in vitro expression and immune potency in mice using mRNA encoding the Plasmodium falciparum malaria antigen, CelTOS

2022· article· en· W4311793123 on OpenAlexaff
Ishita N. Waghela, Katherine L. Mallory, Justin A. Taylor, Cosette G. Schneider, Tatyana Savransky, Chris J. Janse, Paulo J.C. Lin, Ying K. Tam, Drew Weissman, Evelina Angov

Bibliographic record

VenueFrontiers in Immunology · 2022
Typearticle
Languageen
FieldImmunology and Microbiology
TopicInvertebrate Immune Response Mechanisms
Canadian institutionsAcuitas Therapeutics (Canada)
FundersLeids Universitair Medisch CentrumUniversiteit LeidenUnited States Agency for International Development
KeywordsBiologyPlasmodium falciparumImmune systemImmunogenicityAntigenMalaria vaccinePeripheral blood mononuclear cellVirologyAntibodyMolecular biologyIn vitroImmunologyMalaria

Abstract

fetched live from OpenAlex

The secreted malarial protein, Cell-Traversal protein for Ookinetes and Sporozoites (CelTOS), is highly conserved among Plasmodium species, and plays a role in the invasion of mosquito midgut cells and hepatocytes in the vertebrate host. CelTOS was identified as a potential protective antigen based on a proteomic analysis, which showed that CelTOS stimulated significant effector T cells producing IFN-γ in peripheral blood mononuclear cells (PBMCs) from radiation attenuated sporozoite-immunized, malaria-naïve human subjects. In a rodent malaria model, recombinant full-length CelTOS protein/adjuvant combinations induced sterile protection, and in several studies, functional antibodies were produced that had hepatocyte invasion inhibition and transmission-blocking activities. Despite some encouraging results, vaccine approaches using CelTOS will require improvement before it can be considered as an effective vaccine candidate. Here, we report on the use of mRNA vaccine technology to induce humoral and cell-mediated immune responses using this antigen. Several pfceltos encoding mRNA transcripts were assessed for the impact on protein translation levels in vitro. Protein coding sequences included those to evaluate the effects of signal sequence, N-glycosylation on translation, and of nucleoside substitutions. Using in vitro transfection experiments as a pre-screen, we assessed the quality of the expressed CelTOS target relative to the homogeneity, cellular localization, and durability of expression levels. Optimized mRNA transcripts, which demonstrated highest protein expression levels in vitro were selected for encapsulation in lipid nanoparticles (LNP) and used to immunize mice to assess for both humoral and cellular cytokine responses. Our findings indicate that mRNA transcripts encoding pfceltos while potent for inducing antigen-specific cellular cytokine responses in mice, were less able to mount PfCelTOS-specific antibody responses using a two-dose regimen. An additional booster dose was needed to overcome low seroconversion rates in mice. With respect to antibody fine specificities, N-glycosylation site mutated immunogens yielded lower immune responses, particularly to the N-terminus of the molecule. While it remains unclear the impact on CelTOS antigen as immunogen, this study highlights the need to optimize antigen design for vaccine development.

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)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.030
GPT teacher head0.224
Teacher spread0.194 · 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

Citations9
Published2022
Admission routes1
Has abstractyes

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