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

Abstract

BackgroundTargeted 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.MethodsTwenty 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.ResultsConcentrations 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.ConclusionsThe 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.

Assessment of local and systemic signature of eosinophilic esophagitis (EoE) in children through multi-omics approaches

“…Upregulation of this cytokine has also been found in autoimmune inflammatory diseases, such as Crohn's disease or rheumatoid arthritis, and it can be produced by Th17 cells (45), a cell type that was enriched in our biopsies. In summary, increased frequency of various Th2 markers in biopsy, but also of Th17 and ILC1-IFNg+ cells and of a large panel of soluble factors underlined the complexity of EoE pathology, with involvement of various immune actors far beyond a simplistic T2 immune response, as recently observed for severe asthma (25). As observed for transcriptomics, our immune signature was almost not affected by PPI, which only affected ILC2 frequency, MMP1 concentrations and, within EoE patients, sTNFR2.…”

“…Accordingly, in a large prospective study, serum samples from adult EoE patients were analysed for a number of soluble immune factors; no difference between EoE patients and controls were found and the measured markers, alone or in combination, had little diagnostic value (14). Using a more comprehensive and untargeted approach, we first observed that almost all immune markers in affected tissue and in periphery were not correlated, as we also observed for severe asthma (25). Such an approach may then be valuable to identify a signature in periphery but not for a better understanding of pathophysiological mechanisms.…”

“…Compared to CT, we observed higher concentrations of IgE and CXCL12 associated with lower concentrations of a number of chemokines (CXCL8, CXCL9, CXCL16, CCL15, CCL19), cytokines (IL-17A, IL-28A, LIGHT, sTNFR1) and IgG1 in the blood of EoE patients. Plasma CXCL12 seems not increased in other eosinophil related pathology such as asthma ( 25 ), suggesting that it may be specific for EoE. Conversely, we did not observe increased concentrations of the IL-20 subfamily cytokines in plasma from our EoE children, on the opposite to recent observations in adults ( 37 ), nor increased concentrations of IL-4, IL-5, IL-13, IL-6, IL-8 or IL-1α as observed but not reproduced in ( 32 ).…”

“…Subtypes of helper T cells (Th) and innate lymphoid cells (ILC) were analysed in blood and biopsies as previously described ( 25 ). Activated mucosal-associated invariant T (MAIT) and invariant natural killer T (iNKT) cells were analysed in blood as previously described ( 26 ).…”

Standardized Whole Blood Assay and Bead-Based Cytokine Profiling Reveal Commonalities and Diversity of the Response to Bacteria and TLR Ligands in Cattle

“…Differences in pathogen-related cytokine profiles can be revealed by simple pairwise comparisons of absolute cytokine concentration across different diseases or stimulation conditions, but multidimensional data analysis often provides a more in-depth view ( 12 , 28 , 50 – 52 ). Among various statistical approaches, non-supervised Principal Component Analysis (PCA) has been the most extensively applied, while supervised approaches, namely, PLS-DA, Random Forest, and Support Vector Machine data analysis, have been less widely explored as a means to interpret cytokine profiling ( 45 , 53 – 56 ).…”