Mucosal immune dynamics in young children with recurrent respiratory tract infections
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".