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Tafazzin contributes to IgE‐mediated mast cell degranulation and cytokine secretion

2020· article· en· W3016812340 on OpenAlexaffabout
Aindriu R. R. Maguire, Robert W. E. Crozier, Paul J. LeBlanc, Adam J. MacNeil

Bibliographic record

VenueThe FASEB Journal · 2020
Typearticle
Languageen
FieldImmunology and Microbiology
TopicMast cells and histamine
Canadian institutionsBrock University
Fundersnot available
KeywordsDegranulationMast cellImmunologyImmunoglobulin EStem cell factorBiologyProgenitor cellCell biologyImmune systemGene knockdownCytokineStem cellReceptorAntibodyCell culture

Abstract

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Prevalence of allergic pathologies, such as food allergies, asthma, and atopic dermatitis, have been on the rise over the past several decades with approximately 40% of children in developed nations suffering one of these chronic inflammatory diseases. These pathologies, driven largely by sentinel immune cells known as mast cells, occur in tissues that interface with the external environment. Induction of the allergic response is initiated through the activation of FceRI by receptor‐bound allergen‐specific IgE. Subsequently, mast cells rapidly modulate various metabolic pathways to meet the energy demands associated with the processes directing both the early and late phases of the allergic response. The growing field of immunometabolism suggests that immune cell function can be modulated through effective regulation of metabolic processes. The purpose of this study was to assess the role of tafazzin, a mitochondrial cardiolipin remodeler, in modulating FceRI‐mediated mast cell activation. Primary mast cell cultures were established using fetal livers generated through breeding of heterozygotes carrying the doxycycline‐inducible TAZ shRNA knockdown cassette. Hematopoietic progenitors from homozygote and wild‐type fetal liver, were differentiated into mature mast cells under the direction of IL‐3, PGE 2 , and stem cell factor. TAZ knockdown was initiated by 1 μg/ml doxycycline (dox) for 5 days, resulting in a 99.9% reduction in tafazzin protein content relative to baseline expression and a 99.7% reduction relative to wild‐type control. Flow cytometric analysis of mast cell receptor expression demonstrated that FcɛRI was unimpacted by dox treatment, allowing for interrogation of induced signaling‐mediated responses. Following tafazzin knockdown, anti‐TNP IgE‐sensitized liver‐derived mast cells (LMC) were assessed in b‐hexosaminidase degranulation assays identifying a 31.4% reduction in degranulation (n = 4, p < 0.05) between the TAZ shRNA +/+ cells and the wild‐type LMCs activated with TNP‐BSA (allergen) with SCF potentiation. ELISA (n = 6) were conducted to analyze the secretion of de novo synthesized inflammatory mediators. TAZ shRNA +/+ cells secreted lower levels of CCL1 (p < 0.01), CCL2 (p < 0.05), and TNF (p < 0.001) when compared to wild‐type cells treated with dox. Induced transcription was analyzed using qPCR (n = 4) to assess if differences detected in mediator secretion were due to altered transcription. Transcript levels following a knockdown in tafazzin protein levels were significantly reduced at the 60‐minute timepoint for CCL1 (p < 0.05); while other genes analyzed, such as CCL2 and TNF , were not significantly impacted. These results suggest that tafazzin contributes to FceRI‐mediated mast cell secretory mechanisms in both the early (degranulation) and late phases (secretion of de novo synthesized mediators) of the allergic response. Collectively, this work supports the notion that regulation of mast cell metabolism is a potentially viable approach for ameliorating the severity of mast cell‐mediated pathologies. Support or Funding Information Supported by the Natural Sciences and Engineering Research Council of Canada (NSERC); Canada Foundation for Innovation (CFI); Government of Ontario; and, Brock University.

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 score0.004

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)0.0010.001

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 head0.011
GPT teacher head0.198
Teacher spread0.187 · 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".

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Citations0
Published2020
Admission routes2
Has abstractyes

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