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Modulatory Role of Vitamin D in Stem Cell Factor‐Mediated Mast Cell TNF Expression

2018· article· en· W3173905554 on OpenAlexafffundabout
Aindriu R. R. Maguire, Colton J. F. Watson, Adam J. MacNeil

Bibliographic record

VenueThe FASEB Journal · 2018
Typearticle
Languageen
FieldImmunology and Microbiology
TopicMast cells and histamine
Canadian institutionsBrock University
FundersBrock UniversityGovernment of Ontario
KeywordsStem cell factorMast cellCalcitriol receptorBone marrowCalcitriolProgenitor cellStem cellVitamin D and neurologyImmunologyImmunoglobulin ETumor necrosis factor alphaChemistryBiologyCancer researchCell biologyEndocrinologyAntibody

Abstract

fetched live from OpenAlex

Prevalence of allergic pathologies, such as atopic dermatitis (eczema), have been on the rise over the past few decades in developed nations, with approximately 10–30% of children suffering from atopic dermatitis alone. These pathologies are driven largely by mast cells, a sentinel immune cell enriched in tissues that interface with the external environment, including skin. Stem cell factor (SCF), an endogenous growth factor for hematopoietic stem cells, and required for mature mast cell survival in the periphery, is capable of activating mast cells through the c‐kit (CD117) receptor, either alone, or by playing a synergistic role in the IgE‐FcɛRI allergic inflammatory pathway. Vitamin D, a secosteroid synthesized in the skin and processed in organs including the liver, is largely responsible for increasing mineral absorption in the body, but has also been shown to impact mast cell activation through the IgE‐FcɛRI pathway. Mast cells have been found to express vitamin D receptor (VDR), and their ability to directly participate in the metabolism and activation of vitamin D to the active metabolite calcitriol has been characterized. The purpose of this study was to assess the role that vitamin D plays in SCF‐mediated mast cell activation. Primary mast cell cultures were established using bone marrow isolated from the tibias and femurs of female C57BL/6 mice and by differentiating progenitor cells into mature bone marrow‐derived mast cells (BMMC) under the direction of IL‐3 and PGE 2 . To condition mast cells, BMMCs were pre‐treated with 1 μM calcitriol for 48 hours prior to activation with 100 ng/mL of SCF across a 5‐hour time course accounting for the early (minutes) and late (hours) phase of the allergic response. SCF‐induced mast cell activation was assessed through gene expression by qPCR (n=3) for a variety of cytokines and transcription factors involved in the allergic inflammatory response. A decrease in gene expression level was seen in TNF , but not IL6 nor IL13 . This prompted the investigation of a transcriptional mechanism behind the reduction in TNF expression. Expression of various transcription factors was impacted with significant decreases at various time points for Egr1 (p<0.05), Egr2 (p<0.01), and NFκB subunit 2 (p<0.01). Gene expression for components of the heterodimeric vitamin D receptor retinoid X receptor complex remained unchanged across the activation time course. SCF‐induced mast cell degranulation, measured by β‐hexosaminidase release (n=4), a hallmark event of the early phase of an allergic reaction, was not impacted by the 48‐hour vitamin D pretreatment. These results suggest that vitamin D may modulate the late phase of mast cell activation through TNF production via reductions in induced Egr1, Egr2, and NF‐κB2 transcription factors. Further work will elucidate the interaction between these transcription factors and the vitamin D retinoid X receptor complex at the promotor region for TNF . Collectively, this work supports an anti‐inflammatory role for vitamin D in the activation of mast cells with relevance in allergy and host defence. 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. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

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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)0.0010.000

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 head0.008
GPT teacher head0.194
Teacher spread0.186 · 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

Citations0
Published2018
Admission routes3
Has abstractyes

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