Mucosal immune dynamics in young children with recurrent respiratory tract infections
Notice bibliographique
Résumé
Recurrent respiratory tract infections (rRTIs) often affect young children. While generally limited to the respiratory mucosa, rRTIs can damage developing respiratory tissue, potentially leading to hearing loss, chronic obstructive pulmonary disease (COPD), and lung damage later in life with increased risk of premature death from respiratory diseases. Despite serum antibody measurements being a standard diagnostic tool, underlying immune deficiencies are identified in only a minority of children with rRTIs. This thesis investigates clinical characteristics, antibody levels, and microbiota dynamics in children with rRTIs, aiming to improve diagnostics and treatment. Section 1: IgA Deficiency and Intestinal Microbiota Children with rRTIs had a higher prevalence of IgA deficiency compared to healthy controls, implicating the importance of IgA in rRTI susceptibility. The intestinal microbiota composition differed between children with and without IgA deficiency, suggesting that the microbiota could play a role in IgA production. Using a germ-free mouse model, two bacterial clusters were associated with IgA production. Among these, Bifidobacterium correlated with IgA levels in serum, gut, and lung, as well as lower RTI symptom burden. These findings suggest that Bifidobacterium or its metabolites may enhance mucosal immunity against RTIs through the gut-lung axis. Section 2: Serum Antibodies and rRTIs Antibody deficiencies were prevalent in 56% of children with rRTIs, with IgA and IgG subclass deficiencies being the most common. These deficiencies were linked to premature birth and family history of rRTIs but not with increased RTI severity. However, 18% of children with antibody deficiencies progressed to a more severe phenotype over time. Serum antibody levels, while valuable for identifying deficiencies, did not predict RTI outcomes such as symptom frequency or severity. Thus, prophylactic interventions should not vary based on serum antibody deficiencies alone. Section 3: Mucosal Immune Markers Respiratory pathogens primarily infect the mucosal tract, making mucosal antibodies critical in rRTI diagnostics. High carriage rates of Mycoplasma pneumoniae suggested household transmission, while Haemophilus influenzae was strongly associated with RTI severity and M. pneumoniae carriage. Importantly, polyreactive salivary antibodies, comprising 28% of total salivary antibodies, were associated with reduced RTI severity, lower viral burden, and decreased H. influenzae abundance. Polyreactive IgA levels increased with age and after influenza vaccination, highlighting their potential as a diagnostic marker for rRTI susceptibility. Intestinal Microbiota and IgA Production Fecal transplants in germ-free mice from children with and without IgA deficiency showed distinct microbiota compositions. A Bifidobacterium-associated cluster correlated with elevated IgA levels in feces, serum, and the lungs. In children, Bifidobacterium abundance was linked to reduced RTI symptom severity in winter, suggesting its therapeutic potential in promoting IgA production. Key Diagnostic Insights While serum antibody levels help identify deficiencies, they do not predict RTI outcomes. Instead, polyreactive salivary IgA levels and H. influenzae abundance emerged as key diagnostic markers. Salivary polyreactivity, predominantly a mucosal immune property, accounted for 28% of salivary antibodies but only 2% in serum. High H. influenzae abundance strongly predicted RTI severity and was associated with increased proinflammatory cytokine levels. Conclusions and Future Directions This thesis highlights the importance of exploring systemic and mucosal immunity in children with rRTIs. Polyreactive salivary antibodies and H. influenzae abundance are promising markers for identifying children at risk of severe RTIs. Future interventions could include influenza vaccinations to boost polyreactive salivary IgA, pre- and probiotics targeting respiratory microbiota health, and targeted treatments to reduce H. influenzae load. Probiotic supplementation with Bifidobacterium may increase IgA levels, strengthening mucosal immunity. Mucosal screening at the start of winter could help identify high-risk children and enable personalized interventions for managing paediatric rRTIs. This research underscores the potential of novel diagnostic markers and targeted treatments in mitigating the burden of rRTIs in children.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».