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Record W2888990405 · doi:10.1016/j.ymthe.2018.08.023

Recombinant BCG Overexpressing phoP-phoR Confers Enhanced Protection against Tuberculosis

2018· article· en· W2888990405 on OpenAlexafffund
Sang Kyun Ahn, Vanessa Tran, Andrea S. Leung, Mark Ng, Ming Li, Jun Liu

Bibliographic record

VenueMolecular Therapy · 2018
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsMycobacterium bovisImmunogenicityComplementationMycobacterium tuberculosisTuberculosis vaccinesVirologyBiologyTuberculosisRecombinant DNAMicrobiologyBCG vaccineVaccinationGeneMutantGeneticsImmune systemMedicine

Abstract

fetched live from OpenAlex

The live tuberculosis vaccine Mycobacterium bovis BCG (Bacille Calmette-Guérin) comprises a number of genetically distinct substrains. In BCG-Prague, phoP of the PhoP-PhoR two-component system is a pseudogene due to a single insertion mutation. We hypothesized that this mutation partially accounts for the low immunogenicity of BCG-Prague observed in the 1970s. In this study, we showed that complementation with the M. bovis allele of phoP restored BCG-Prague's immunogenicity. Furthermore, we showed that overexpression of the M. bovis allele of phoP-phoR in BCG-Japan, a strain already containing a copy of phoP-phoR , further enhanced immunogenicity and protective efficacy. Vaccination of C57BL/6 mice with the recombinant strain rBCG-Japan/PhoPR induced higher levels of interferon-γ (IFN-γ) production by CD4 + T cells than that with the parental BCG. Guinea pigs vaccinated with rBCG-Japan/PhoPR were better protected against challenge with Mycobacterium tuberculosis than those immunized with the parental BCG, showing significantly longer survival time, reduced bacterial burdens, and less severe pathology. Taken together, our study has identified a genetic modification that could be generally applied to generate new recombinant BCG 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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.108
Threshold uncertainty score0.787

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.035
GPT teacher head0.326
Teacher spread0.291 · 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 teacher head, 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

Citations24
Published2018
Admission routes2
Has abstractyes

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