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Record W2927161796 · doi:10.3389/fimmu.2019.00615

Necroptotic Cell Death Promotes Adaptive Immunity Against Colonizing Pneumococci

2019· article· en· W2927161796 on OpenAlexfundno aff
Ashleigh N. Riegler, Terry Brissac, Norberto González-Juarbe, Carlos J. Orihuela

Bibliographic record

VenueFrontiers in Immunology · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammasome and immune disorders
Canadian institutionsnot available
FundersNational Institute of Allergy and Infectious DiseasesNational Institute on AgingNational Institutes of HealthSchool of Medicine, New York UniversityYork UniversityUniversity of AlabamaUniversity of Alabama at Birmingham
KeywordsNecroptosisPneumolysinBiologyImmunologyImmunityMicrobiologyStreptococcus pneumoniaeImmune systemProgrammed cell deathAntibioticsApoptosis

Abstract

fetched live from OpenAlex

Pore-forming toxin (PFT) induced necroptosis exacerbates pulmonary injury during bacterial pneumonia. However, its role during asymptomatic nasopharyngeal colonization and toward the development of protective immunity was unknown. Using a mouse model of Streptococcus pneumoniae (Spn) asymptomatic colonization, we determined that nasopharyngeal epithelial cells (nEC) died of pneumolysin (Ply)-dependent necroptosis. Mice deficient in MLKL, the necroptosis effector, or challenged with Ply-deficient Spn showed less nEC sloughing, increased neutrophil infiltration, and altered IL-1α, IL-33, CXCL2, IL-17 and IL-6 levels in nasal lavage fluid (NALF). Activated MLKL correlated with increased presence of CD11c+ antigen presenting cells in Spn-associated submucosa. Colonized MLKL KO mice and wildtype mice colonized with Ply-deficient produced less antibody against the Spn protein antigen PspA, were delayed in bacterial clearance, and were more susceptible to a lethal secondary Spn challenge. We conclude that PFT-induced necroptosis is instrumental in the natural development of protective immunity against opportunistic PFT-producing bacterial pathogens.

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 categoriesMeta-epidemiology (narrow)
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 score0.099
Threshold uncertainty score1.000

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 head0.006
GPT teacher head0.200
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 teacher head, not a consensus.

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

Citations39
Published2019
Admission routes1
Has abstractyes

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