MétaCan
Menu
Back to cohort

Development of a droplet digital PCR for pertussis toxin locus copy number determination in a genetically-modified Bordetella pertussis strain

2023· article· en· W4368348217 on OpenAlexaff
Raphaël Esson, Stéphanie Falque, Éric Abachin, Steve George, Nolwenn Nougarede

Bibliographic record

VenueBiologicals · 2023
Typearticle
Languageen
FieldImmunology and Microbiology
TopicBacterial Infections and Vaccines
Canadian institutionsSanofi (Canada)
FundersSanofi Pasteur
KeywordsLocus (genetics)Bordetella pertussisPertussis toxinBiologyGeneStrain (injury)Digital polymerase chain reactionGeneticsPolymerase chain reactionBacteriaReceptorG protein

Abstract

fetched live from OpenAlex

To improve pertussis toxin (PT) yield in B. pertussis strains for vaccine production a genetically-engineered strain (gdPT 191-134 strain) with a second copy of the genetically detoxified PT (gdPT) locus was developed. The consistency of the production and genetic stability of the strain when used for vaccine production must be established. We developed two simplex ddPCR assays with PCR systems for ptxA, the target gene present in two copies, and pgm, the reference gene present as a single copy. The ddPCR assay had sufficient precision to discriminate the copy number of the PT locus accurately in two B. pertussis strains: one copy in the parent, non-genetically-engineered strain and two copies in the gdPT 191-134 strain. Using the ddPCR assays, we were able to show that the ratio of the ptxA to pgm genes decreased during serial culture passages, due to the loss of PT locus, which in turn, resulted in lower levels of PT production over time. We were then able to assess culture conditions that improved the stability of the double locus, as shown by non-significant reduction in gdPT toxin yield.

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)0.002
metaresearch head score (Gemma)0.003
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: none
Teacher disagreement score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.042
GPT teacher head0.302
Teacher spread0.260 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueBiologicalsSame topicBacterial Infections and VaccinesFrench-language works237,207