A Comprehensive Analysis of Immune Constituents in Blood and Bronchoalveolar Lavage Allows Identification of an Immune Signature of Severe Asthma in Children

Abstract

Background: Targeted approaches may not account for the complexity of inflammation involved in children with severe asthma (SA), highlighting the need to consider more global analyses. We aimed to identify sets of immune constituents that distinguish children with SA from disease-control subjects through a comprehensive analysis of cells and immune constituents measured in bronchoalveolar lavage (BAL) and blood.

Methods: Twenty children with SA and 10 age-matched control subjects with chronic respiratory disorders other than asthma were included. Paired blood and BAL samples were collected and analyzed for a large set of cellular (eosinophils, neutrophils, and subsets of lymphocytes and innate lymphoid cells) and soluble (chemokines, cytokines, and total antibodies) immune constituents. First, correlations of all immune constituents between BAL and blood and with demographic and clinical data were assessed (Spearman correlations). Then, all data were modelled using supervised multivariate analyses (partial least squares discriminant analysis, PLS-DA) to identify immune constituents that significantly discriminate between SA and control subjects. Univariate analyses were performed (Mann-Whitney tests) and then PLS-DA and univariate analyses were combined to identify the most discriminative and significant constituents.

Results: Concentrations of soluble immune constituents poorly correlated between BAL and blood. Certain constituents correlated with age or body mass index and, in asthmatics, with clinical symptoms, such as the number of exacerbations in the previous year, asthma control test score, or forced expiratory volume. Multivariate supervised analysis allowed construction of a model capable of distinguishing children with SA from control subjects with 80% specificity and 100% sensitivity. All immune constituents contributed to the model but some, identified by variable-important-in-projection values > 1 and p < 0.1, contributed more strongly, including BAL Th1 and Th2 cells and eosinophilia, CCL26 (Eotaxin 3), IgA and IL-19 concentrations in blood. Blood concentrations of IL-26, CCL13, APRIL, and Pentraxin-3 may also help in the characterization of SA.

Conclusions: The analysis of a large set of immune constituents may allow the identification of a biological immune signature of SA. Such an approach may provide new leads for delineating the pathogenesis of SA in children and identifying new targets for its diagnosis, prediction, and personalized treatment.

Keywords: children; immune signature; pathogenesis; precision medicine; severe asthma.

Figure 1

Non-supervised Principal component analysis (PCA, (A) and heatmap of Spearman correlations (B) for all soluble components analyzed in BAL and plasma from SA and NA (n= 30). Positive correlations are shown in red, the absence of a correlation in white, and negative correlations in blue. The intensity of the color indicates the intensity of the correlation. The black square indicates a core of plasma cytokines that more highly correlated between themselves (comprising IFNα2, IFNβ, IFNγ, IFNλ1, IFNλ2, IL-2, IL-11, IL-12p28, IL-12p40, IL-19, IL-20, IL-32, IL-34, IL-35, TSLP, MMP1, MMP3, and Pentraxin-3).

Figure 2

Modelling of all immune constituents measured to discriminate between children with SA and control (NA). (A) Graph of all individuals obtained by PLS-DA modelling. SA patients are indicated in green, and NA patients in blue. (B) Specificity and sensitivity of the patient classification provided by the PLS-DA modelling. (C) VIP x p values plot of all analyzed immune constituents and selection of the most discriminating and significant ones to distinguish between SA and NA patients (red rectangle: VIP > 1, p values < 0.05). The variables that largely participated in the PLS-DA model are shown in the grey region of the graph, i.e., those that belong to the set of variables that allow discrimination between asthmatic and non-asthmatics but show p > 0.05 in the MW test.

Figure 3

Discriminant immune constituents (VIP > 1) that show significant (p < 0.05) differences or trends towards (0.05 < p < 0.1, indicated in italics) differences between children with SA (grey bars) and NA controls (clear bars). (A) Cellular immune constituents. (B) Cytokines and IgG concentrations in BAL. (C) CCL26, IgA, and IL-19 concentrations in plasma. Exact p values (MW test) are indicated for each constituent.

Figure 4

Preliminary analysis to define a set of blood parameters that allow discrimination between children with SA and disease-control children. This model is biased, as the same cohort and parameters used to construct the initial PLS-DA were used to identify the discriminant variables. These results will need to be confirmed on an independent validation cohort.