Rhinovirus as a driver of airway T cell dynamics in children with treatment-refractory recurrent wheeze

Abstract

Severe asthma in children is notoriously difficult to treat, and its immunopathogenesis is complex. In particular, the contribution of T cells and relationships to antiviral immunity remain enigmatic. Here, we coupled deep phenotyping with machine learning methods to elucidate the dynamics of T cells in the lower airways of children with treatment-refractory recurrent wheeze, and examine rhinovirus (RV) as a driver. Our strategy revealed a T cell landscape dominated by type 1 and type 17 CD8+ signatures. Interrogation of phenotypic relationships coupled with trajectory mapping identified T cell migratory and differentiation pathways spanning the blood and airways that culminated in tissue residency, and involved transitions between type 1 and type 17 tissue-resident types. These dynamics were reflected in cytokine polyfunctionality. Use of machine learning tools to cross-compare T cell populations that were enriched in the airways of RV-positive children with those induced in the blood following experimental RV challenge precisely pinpointed RV-responsive signatures that contributed to T cell migratory and differentiation pathways. Despite their rarity, these signatures were also detected in the airways of RV-negative children. Together, our results underscore the aberrant nature of type 1 immunity in the airways of children with recurrent wheeze, and implicate an important viral trigger as a driver.

Keywords: Immunology; Infectious disease; T cells; Th1 response.

Figure 1. Mixtures of type 1 and type 17 signatures dominate the T cell landscape in the lower airways of children with recurrent wheeze.

(A) T cell frequencies in matched blood and BAL, as a percentage of CD3⁺ cells. (B) CD4⁺/CD8⁺ T cell ratio in blood and BAL. (C) CD4⁺/CD8⁺ T cell ratio in the BAL of patients with (n = 17) or without (n = 15) a respiratory pathogen. (D) Frequencies of naive (CD45RO^–CCR7⁺), central memory (CD45RO⁺CCR7⁺, Tcm), effector memory (CD45RO⁺CCR7^–, Tem), and terminal effector memory–like (CD45RO^–CCR7^–CD27^–, TEMRA-like) T cells in blood and BAL, as a percentage of CD4⁺ and CD8⁺ T cells. (E) Frequencies of marker-positive memory (Tcm, Tem, and TEMRA-like) CD4⁺ and CD8⁺ T cells in blood and BAL. (F) Frequencies of type 1, type 2, and type 17 subsets in BAL. Black symbols denote atopic patients (n = 20). (G) Frequencies of CD4⁺ and CD8⁺ tissue-resident memory T cells in blood and BAL (CD69⁺CD103^– and CD69⁺CD103⁺, Trm). (H) Histograms depicting the expression of select surface receptors on CD4⁺ and CD8⁺ Trm and non-Trm cells in BAL. Bars denote mean ± SEM (A–G). *P < 0.05, ****P ≤ 0.0001 by multiple Wilcoxon’s tests with Holm-Šidák correction (A, D, and E), Wilcoxon’s matched-pairs signed rank test (B and G), or Mann-Whitney test (C).

Figure 2. High-dimensional analysis reveals the complexity of the T cell landscape in recurrent wheeze.

(A) opt-SNE dimensionality reduction showing the distribution of T cells in matched blood and BAL from 32 children. (B) Expression of select phenotypic markers across all samples (n = 64). (C) PhenoGraph clusters for all samples. (D) Heatmap showing the median expression of each marker according to clusters generated by PhenoGraph. Blue/gray annotation on the left denotes the proportion of cells in each cluster derived from BAL or blood.

Figure 3. PHATE analysis captures transitions in T cell clusters in the lower airways.

(A) PHATE map generated on CD3⁺ cells from matched blood and BAL (64 samples), with heatmaps of select markers. (B) Pseudotime trajectories of CD4⁺, CD8⁺, and DN T cells. The red star denotes the starting point for wishbone analysis. (C–E) PhenoGraph clusters projected on PHATE map to show transitions within CD4⁺ T cells (C), CD8⁺ T cells (D), and DN T cells (E). Black boxes within heatmaps contain markers that are differentially expressed between related clusters. Blue/gray annotation on the left of each heatmap denotes the proportion of cells in each cluster derived from BAL or blood.

Figure 4. Respiratory pathogens are linked to increased T cell activation in the lower airways.

(A) Representative scatter plots showing the gating strategy for PD-1⁺ICOS⁺CD95⁺ T cells within total memory (Tcm, Tem, TEMRA-like) T cells. (B–E) Frequencies of PD-1⁺ICOS⁺CD95⁺ memory T cells in the BAL in relation to (B) any respiratory pathogen (– n = 15, + n = 17), (C) any virus (– n = 19, + n = 13), (D) any bacteria (– n = 22, + n = 10), and (E) coinfection (– n = 26, + n = 6). Mean ± SEM. Mann-Whitney test. P values in parentheses are adjusted for age and sex. *P < 0.05, **P ≤ 0.01.

Figure 5. T-REX analysis reveals activated T cell signatures linked to RV infection in the lower airways.

(A) T-REX plot comparing BAL T cells from RV^– (n = 22) and RV⁺ (n = 10) patients. Red populations are increased in the RV⁺ group and blue are increased in the RV^– group. Corresponding MEM labels are shown for 4 dominant populations increased in the RV⁺ group. Each marker in MEM labels is scored on a scale of 1-10 based on their enrichment in the population. (B) Frequencies of T-REX populations in RV^– and RV⁺ patients. Green symbols denote patients positive for other viruses (n = 3). Yellow symbols denote patients currently receiving a biologic (n = 4). Mean ± SEM. (C) Histograms comparing the expression of select markers on T-REX populations (n = 32). (D) Spearman’s correlations between frequencies of BAL neutrophils and T-REX populations in RV⁺ patients (n = 9). Lines denote linear regression. Mann-Whitney test (B). **P ≤ 0.01, ***P ≤ 0.001, ****P ≤ 0.0001.

Figure 6. RV-related signatures map to T cell differentiation pathways in the lower airways.

(A) Projection of T-REX populations 1–3 on a PHATE map generated on CD3⁺ cells from matched blood and BAL (64 samples). (B and C) PHATE maps with overlay of PhenoGraph clusters related to T-REX populations. Heatmaps show marker expression profiles for T-REX populations and related PhenoGraph clusters. Black squares on heatmaps contain markers expressed on all populations. Blue/gray annotation on the left of each heatmap denotes the proportion of cells in each population derived from BAL or blood. (D) Frequencies of PhenoGraph cluster related to T-REX 1–4 in the pediatric cohort. Colored symbols denote patients positive for other viruses (green, n = 3), RV⁺ patients (red, n = 10), patients negative for any virus (gray, n = 15), and patients who received a biologic (yellow, n = 4). Mean ± SEM.

Figure 7. T cell signatures enriched in the lower airways of RV⁺ children display RV-responsive hallmarks.

(A) opt-SNE maps showing expression of key markers across samples from adults with asthma (n=6) and healthy controls (n=3) during RV challenge. (B) T-REX plots comparing day 0 of RV challenge with days 4, 7, and 21 after challenge. (C) MEM signature of RV-induced T-REX A population in the blood. (D) Change in frequencies of T-REX A population during RV challenge, with average fold increase on day 7 versus day 0. Horizontal lines denote mean frequencies. (E) Histograms showing the expression of markers of interest for T-REX A (blood of RV-challenged adults, n = 6) and similar populations T-REX 1–3 that are enriched in the BAL of RV⁺ children (n = 32). Friedman’s test with Dunn’s multiple-comparison test (D). *P < 0.05.

Figure 8. Polyfunctional T cells populate the lower airways of children with recurrent wheeze.

(A) Frequencies of cytokine-positive cells in the BAL, as a percentage of CD4⁺ and CD8⁺ memory T cells (n = 6). (B) SPICE plots showing average cytokine signatures of CD4⁺ and CD8⁺ memory T cells in BAL. (C) Average cytokine signatures of CD4⁺ candidate T-REX populations. A non-Trm (CD69^–) counterpart of T-REX 3 is shown as a comparison. (D) Frequencies of polyfunctional signatures within each CD4⁺ candidate T-REX population. (E) Average cytokine signatures of CD8⁺ T-REX 4–like signature and its CD161⁺ counterpart. Mean ± SEM (A and D). Friedman’s test with Dunn’s multiple-comparison test (D). *P < 0.05.