D3.482 - Harnessing the Baseline Nasal Microbiome to Predict Pollen Related Symptom Expression

An often-overlooked subgroup in seasonal allergic rhinitis (SAR) research constitutes of healthy individuals without allergen sensitisation albeit experiencing symptoms during the pollen season. We previously published that a defined nasal biomarker combination predicts symptom severity prior to pollen season onset. Notably, this predictive signature extended beyond SAR patients: non-allergic individuals displayed pollen-associated low-grade symptom dynamics closely following pollen kinetics (Goekkaya et al. JACI, 2020).

Computational analysis of their nasal microbiome revealed a similar clustering in the post‑pollen season, which was confirmed in an independent cohort. We investigated here whether baseline microbial composition predicts symptom burden during the pollen season and aimed to identify species with potential protective roles - addressing the research question: what constitutes a healthy nasal microbiome?

Nasal microbiome data, local immune measurements, defined pollen season dates and daily symptom scores from two years (2016, 2018) were integrated. Post-pollen samples were used as baseline.

Unsupervised analyses included Principal Component Analysis (PCA) and Principal Coordinate Analysis (PCoA; Bray–Curtis), focusing on abundant taxa. Species driving separation between symptomatic and asymptomatic individuals were identified.

Baseline microbial composition differed between symptomatic and asymptomatic non‑allergic subjects. PCA showed separation along Principal-Component-1, driven by four key species: two putative protective species associated with the asymptomatic cluster and two putative driver species associated with the symptomatic cluster. PCoA supported these findings, with the same species contributing to sample dissimilarity.

Pairwise analysis of two primary candidates revealed a mutually exclusive pattern, where the protective species were present when the symptom‑associated species were absent, and vice versa - suggesting competitive dynamics potentially relevant to inflammation and, consequently, symptom expression.

The observed microbial patterns highlight candidate species with protective or symptom‑associated roles.

We have initiated ALI‑culture experiments to examine microbiome-inflammation-axis following stimulation with identified species. This approach aims to bridge microbial signatures with local inflammatory responses and support the development of predictive frameworks for SAR symptom burden, and early allergic trajectories.