Innate and adaptive T cells in asthmatic patients: Relationship to severity and disease mechanisms

Abstract

Background: Asthma is a chronic inflammatory disease involving diverse cells and mediators whose interconnectivity and relationships to asthma severity are unclear.

Objective: We performed a comprehensive assessment of TH17 cells, regulatory T cells, mucosal-associated invariant T (MAIT) cells, other T-cell subsets, and granulocyte mediators in asthmatic patients.

Methods: Sixty patients with mild-to-severe asthma and 24 control subjects underwent detailed clinical assessment and provided induced sputum, endobronchial biopsy, bronchoalveolar lavage, and blood samples. Adaptive and invariant T-cell subsets, cytokines, mast cells, and basophil mediators were analyzed.

Results: Significant heterogeneity of T-cell phenotypes was observed, with levels of IL-13-secreting T cells and type 2 cytokines increased at some, but not all, asthma severities. TH17 cells and γδ-17 cells, proposed drivers of neutrophilic inflammation, were not strongly associated with asthma, even in severe neutrophilic forms. MAIT cell frequencies were strikingly reduced in both blood and lung tissue in relation to corticosteroid therapy and vitamin D levels, especially in patients with severe asthma in whom bronchoalveolar lavage regulatory T-cell numbers were also reduced. Bayesian network analysis identified complex relationships between pathobiologic and clinical parameters. Topological data analysis identified 6 novel clusters that are associated with diverse underlying disease mechanisms, with increased mast cell mediator levels in patients with severe asthma both in its atopic (type 2 cytokine-high) and nonatopic forms.

Conclusion: The evidence for a role for TH17 cells in patients with severe asthma is limited. Severe asthma is associated with a striking deficiency of MAIT cells and high mast cell mediator levels. This study provides proof of concept for disease mechanistic networks in asthmatic patients with clusters that could inform the development of new therapies.

Keywords: Asthma; T lymphocytes; T(H)17; T(H)2; cytokines; endotype; mast cells; mucosal-associated invariant T-cell; phenotype; regulatory T.

Fig 1

Frequencies of CD3⁺CD4⁺ T cells expressing IL-13 (T_H2 cells; A), IL-17 (T_H17 cells; B), and FOXP3 (Treg cells; C) in PBMCs, sputum, BAL fluid, and bronchial biopsy specimens as a percentage of live CD3⁺CD4⁺ T cells or, for endobronchial biopsy specimens, a percentage of CD3⁺CD8⁻ T cells. Horizontal lines show medians. Left columns, Healthy control subjects versus asthmatic patients with Mann-Whitney U P values. Right 3 columns, Stratified by disease severity with Kruskal-Wallis P values (P < .05). *P < .05 and **P < .01, post hoc Dunn test compared with healthy subjects.

Fig 2

MAIT cells (Vα7.2⁺CD161⁺) as proportions of CD3⁺ T cells in blood, sputum, BAL fluid, and endobronchial biopsy specimens in healthy subjects and asthmatic patients (A) and stratified by disease severity (B). Horizontal lines show medians. Unpaired t tests were used for log-transformed data. MAIT cell deficiency correlates with severity by linear trends across groups using residuals on log-transformed data (where P < .05). *P < .05, **P < .01, and ***P < .001, post hoc Dunnett test compared with healthy subjects.

Fig 3

Bayesian belief network showing the strongest interactions between pathobiologic parameters across a range of clinical severities of asthma or health. Nodes without strong interactions are excluded. Line thickness represents strength of interaction (Euclidean distance). Line colors: green, positive associations; red, negative associations; black, nonlinear associations. Asthma severity is based on overall physician's assessment at enrollment (see Table E1). BMI, Body mass index; T_C1, CD8⁺IFN-γ⁺ T cells; T_C2, CD8⁺IL-13⁺ T cells.

Fig 4

A, Multidimensional clinicopathobiologic clusters in asthmatic patients and healthy subjects. Topological network analysis of clinical and pathobiologic features generates 1 healthy (blue) and 6 distinct clinicopathobiologic asthma clusters (1-6). The network is colored by disease severity (GINA classification), with patients with the most severe disease in red and patients with the milder forms in varying shades of orange, yellow, and green. B, The same network as Fig 4, A, overlaid with distribution of neutrophilic (sputum neutrophils >61%, green) or eosinophilic (sputum eosinophils >3%, red) asthma. C, Frequencies of MAIT cells. D, The network is colored based on average concentrations of the type 2 cytokines IL-4, IL-5, and IL-13 in serum, sputum, and BAL fluid. E, The network is colored based on concentrations of mast cell tryptase in sputum and BAL fluid. In Fig 4, B-E, the colors represent concentrations or frequencies, ranging from low (blue) to high (red) concentrations. The TDA used 62 subjects with most complete data. The variance normalized Euclidean metric was used. The lenses used were principal and secondary singular value decomposition (resolution, 32; gain, 4.0/3.5×; equalized). Node size is proportional to the number of subjects in the node.

Fig 5

Analyses generated from the clinicopathobiologic TDA network in Fig 4 show concentrations of IL-5 averaged across serum, sputum, and BAL fluid (A); eNO concentrations (B); mast cell tryptase levels in BAL fluid and sputum (C); and MAIT cells in blood, sputum, BAL fluid, and bronchial biopsy specimens (D). Box and whisker plots show medians, IQRs, and ranges. Statistical tests indicate 1-way ANOVA, with post hoc t tests compared with healthy subjects by using the Bonferroni correction. *P < .05, **P < .01, and ***P < .001.