Abstract
Highly prevalent respiratory diseases such as asthma and allergy remain a pressing health challenge. Currently, there is an unmet need for precise diagnostic tools capable of predicting the great heterogeneity of these illnesses. In a previous study of 94 asthma/respiratory allergy biomarker candidates, we defined a group of potential biomarkers to distinguish clinical phenotypes (i.e. nonallergic asthma, allergic asthma, respiratory allergy without asthma) and disease severity. Here, we analyze our experimental results using complex algorithmic approaches that establish holistic disease models (systems biology), combining these insights with information available in specialized databases developed worldwide. With this approach, we aim to prioritize the most relevant biomarkers according to their specificity and mechanistic implication with molecular motifs of the diseases. The Therapeutic Performance Mapping System (Anaxomics’ TPMS technology) was used to generate one mathematical model per disease: allergic asthma (AA), non-allergic asthma (NA), and respiratory allergy (RA), defining specific molecular motifs for each. The relationship of our molecular biomarker candidates and each disease was analyzed by artificial neural networks (ANNs) scores. These analyses prioritized molecular biomarkers specific to the diseases and to particular molecular motifs. As a first step, molecular characterization of the pathophysiological processes of AA defined 16 molecular motifs: 2 specific for AA, 2 shared with RA, and 12 shared with NA. Mechanistic analysis showed 17 proteins that were strongly related to AA. Eleven proteins were associated with RA and 16 proteins with NA. Specificity analysis showed that 12 proteins were specific to AA, 7 were specific to RA, and 2 to NA. Finally, a triggering analysis revealed a relevant role for AKT1, STAT1, and MAPK13 in all three conditions and for TLR4 in asthmatic diseases (AA and NA). In conclusion, this study has enabled us to prioritize biomarkers depending on the functionality associated with each disease and with specific molecular motifs, which could improve the definition and usefulness of new molecular biomarkers.
Highlights
Chronic inflammatory respiratory diseases, including allergic diseases and asthma, are common, complex, and heterogeneous diseases in which the clinical course and treatment response are often challenging to predict
Here we found a good biomarker that can discriminate between asthma with a very good AUC (Table 7B), mainly between the most severe clinical phenotypes
A number of studies have investigated the disease modules associated with specific phenotypes for diseases such as asthma, diabetes, and cancer, for which a disease module would mainly be detected (68, 69)
Summary
Chronic inflammatory respiratory diseases, including allergic diseases and asthma, are common, complex, and heterogeneous diseases in which the clinical course and treatment response are often challenging to predict. Allergic mechanisms have been implicated in 50 to 80% of asthmatic patients and in approximately 50% of those with severe disease. Allergic asthma, characterized by an early onset, is the most common asthma phenotype and is traditionally considered as an eosinophilic mediated disease characterized by overexpression and activation of T helper type 2 (Th-2) cells (5). There are many clinical conditions where there is a combination of inflammatory cells (eosinophils/neutrophils) and no clear type of respiratory inflammation. Between 10 and 33% of subjects with asthma are not associated with allergy (non-allergic asthma) and exhibit non-type 2 inflammation (non-T2 or T2-low endotype) (6), in many cases with a prevalence of neutrophils; understanding of the immune mechanisms implicated is less developed (7–11)
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