MétaCan
Menu
Retour à la cohorte
Enregistrement W7115812274

B CELL RESPONSES IN ALLERGIC ASTHMA

2017· dissertation· en· W7115812274 sur OpenAlexaboutno aff

Notice bibliographique

RevueMacSphere (McMaster University) · 2017
Typedissertation
Langueen
DomaineImmunology and Microbiology
ThématiqueMast cells and histamine
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésDegranulationImmunoglobulin EAsthmaRegulatory B cellsAllergic asthmaAllergic inflammationAllergenInhalation
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Global prevalence of allergic diseases has been on the rise for the last 30 years. In Canada, this upward trend in allergic diseases has resulted in over 3 million Canadians being affected by allergic asthma. Allergic asthma is triggered by inhalation of environmental allergens resulting in bronchial constriction and inflammation, which leads to clinical symptoms such as wheezing, coughing and difficulty breathing. Asthmatic airway inflammation is initiated by the release of inflammatory mediators (-eg- histamine) released by granulocytic cells (-eg- mast cells and basophils). However, immunoglobulin E (IgE) antibody is also necessary for the initiation of the allergic cascade, and IgE is produced and released exclusively by memory B cells and plasma cells. Allergen crosslinking of IgE:FcεRI complexes on the surface of mast cells and basophils causes degranulation of pro-inflammatory mediators. Acute allergen exposure has also been shown to increase IgE levels in the airways of patients diagnosed with allergic asthma; however, more studies are needed to better understand local airway inflammation. Our group's work, in accordance with the literature, has shown an increase of IgE in the airways of subjects with mild allergic asthma following allergen inhalation challenge. Although regulatory B cells (Bregs) have been shown to modulate IgE-mediated inflammatory processes in allergic asthma pathogenesis, particularly in mouse models of allergic airway disease, the levels and function of these IgE+ B cells and Bregs remain to be elucidated in human models of asthma. The overall objective for this dissertation was to investigate the biology of B cells in allergic asthma pathogenesis, specifically investigating the frequency of IgE+ B cells and Bregs in allergic asthma, and the kinetics of these cells after allergen exposure. First, we characterized IgE+ B cells in the blood and sputum of allergic asthmatics and healthy controls with and without allergies (Chapter 2). We showed that IgE+ B cell levels were higher in sputum, but not blood, of allergic asthmatics compared to controls. We further demonstrated that these findings were consistent across airway IgE+ B cell subsets, which include IgE+ memory B cells and IgE+ plasma cells. Additionally, IgE+ B cells in sputum positively correlated with sputum eosinophils, total IgE and B cell activating factor (BAFF) measured in sputum fluid phase. These findings highlight the association of airway IgE+ B cells with allergic asthma, and suggest that local IgE+ B cell functions contribute to the pathogenesis of asthma. Second, we measured the trafficking of IgE+ B cells in periphery (blood, bone marrow and tonsil) and locally (sputum) in allergic asthmatics following whole lung allergen challenge (Chapter 3). IgE+ B cells only increased in the airways of allergic asthmatics following allergen inhalation challenge; there were no allergen-induced changes in IgE+ B cell levels in blood, bone marrow and tonsil. In addition, we showed allergen-induced increases in BAFF and total IgE, but not allergen-specific IgE in sputum fluid phase. Taken together, chapters 2 and 3 show that allergic asthmatics have elevated levels of IgE+ B cells in the airways, that can be further increased after allergen exposure. Therefore, local B cell production of IgE in the lungs may be an important source of IgE for initiation of acute inflammatory responses in allergic airways. Third, we evaluated the levels of Bregs in allergic asthmatics compared to controls, and examined the kinetics, function and distribution (bone marrow, blood and sputum) of Bregs following allergen inhalation challenge (Chapter 4). We showed that Bregs were 2-fold lower in the blood of allergic asthmatics compared to controls, highlighting a possible dysregulation of this regulatory cell type in allergic asthmatics, which may contribute to disease pathology. Furthermore, after whole lung allergen challenge Bregs decreased in the bone marrow with a co-incident increase in the blood and sputum of allergic asthmatics. This pattern reflects potential trafficking of these cells from bone marrow to the airways after exposure to allergic stimuli. Lastly, we stimulated CD19+ B cells purified from blood of allergic asthmatic with IL-4 in vitro. IL-4 is a type 2 cytokine known to isotype-switch B cells to IgE+ B cells, as well as differentiates naïve T cells into Th2 cells, thus propagating the allergic cascade. We found that IL-4 promoted higher proportions of IL-10+ and FoxP3+ Bregs, which demonstrates that Bregs may have a role in dampening IgE-mediated inflammation in a type 2 environment. However, further functional studies are warranted. Taken together, the findings of this dissertation highlight the local compartmental changes in IgE+ B cells and Bregs following allergen challenge of allergic airways. Better understanding the temporal and compartmental shifts in B cell subpopulations, particularly IgE+ B cells and Bregs, may aid in future development of therapeutics.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,003
Score d'incertitude au seuil0,011

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0000,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0030,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,010
Tête enseignante GPT0,205
Écart entre enseignants0,195 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2017
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueMacSphere (McMaster University)Même sujetMast cells and histamineTravaux en français237 207