Bronchoconstriction and Airway Biology:Potential Impact and Therapeutic Opportunities

Recent work has demonstrated that mechanical forces occurring in the airway as a consequence of bronchoconstriction are sufficient to not only induce symptoms but also influence airway biology. Animal and human in vitro and in vivo work demonstrates that the airways are structurally and functionally altered by mechanical stress induced by bronchoconstriction. Compression of the airway epithelium and mechanosensing by the airway smooth muscle trigger the activation and release of growth factors, causing cell proliferation, extracellular matrix protein accumulation, and goblet cell differentiation. These effects of bronchoconstriction are of major importance to asthma pathophysiology and appear sufficient to induce remodeling independent of the inflammatory response. We review these findings in detail and discuss previous studies in light of this new evidence regarding the influence of mechanical forces in the airways. Furthermore, we highlight potential impacts of therapies influencing mechanical forces on airway structure and function in asthma

Bronchoconstriction and Airway Biology:Potential Impact and Therapeutic Opportunities

Recent work has demonstrated that mechanical forces occurring in the airway as a consequence of bronchoconstriction are sufficient to not only induce symptoms but also influence airway biology. Animal and human in vitro and in vivo work demonstrates that the airways are structurally and functionally altered by mechanical stress induced by bronchoconstriction. Compression of the airway epithelium and mechanosensing by the airway smooth muscle trigger the activation and release of growth factors, causing cell proliferation, extracellular matrix protein accumulation, and goblet cell differentiation. These effects of bronchoconstriction are of major importance to asthma pathophysiology and appear sufficient to induce remodeling independent of the inflammatory response. We review these findings in detail and discuss previous studies in light of this new evidence regarding the influence of mechanical forces in the airways. Furthermore, we highlight potential impacts of therapies influencing mechanical forces on airway structure and function in asthma.</p

Exogenous IFN-? has antiviral and anti-inflammatory properties in primary bronchial epithelial cells from asthmatic subjects exposed to rhinovirus

Background: Rhinoviruses are the major cause of asthma exacerbations. Previous studies suggest that primary bronchial epithelial cells (PBECs) from asthmatic subjects are more susceptible to rhinovirus infection because of deficient IFN-? production. Although augmenting the innate immune response might provide a novel approach for treatment of virus-induced asthma exacerbations, the potential of IFN-? to modulate antiviral and proinflammatory responses in asthmatic epithelium is poorly characterized.Objectives: We sought to compare responses of PBECs from nonasthmatic and asthmatic subjects to exogenous IFN-? and test the inflammatory effects of IFN-? in response to rhinovirus infection.Methods: PBECs were treated with IFN-? and infected with a low inoculum of human rhinovirus serotype 1B to simulate a natural viral infection. Expression of interferon-responsive genes and inflammatory responses were analyzed by using reverse transcription–quantitative real-time PCR, cytometric bead arrays, or both; viral titers were assessed by using the 50% tissue culture infection dose.Results: Expression of IFN-?–stimulated antiviral genes was comparable in PBECs from nonasthmatic or asthmatic donors. Exogenous IFN-? significantly protected PBECs from asthmatic donors against rhinovirus infection by suppressing viral replication. Interferon-inducible protein 10 (IP-10), RANTES, and IL-6 release in response to rhinovirus infection was triggered only in PBECs from asthmatic donors. Although exogenous IFN-? alone stimulated some release of IP-10 (but not IL-6 or RANTES), it significantly reduced rhinovirus-induced IP-10, RANTES, and IL-6 expression when tested in combination with rhinovirus.Conclusions: PBECs from asthmatic donors have a normal antiviral response to exogenous IFN-?. The ability of IFN-? to suppress viral replication suggests that it might limit virus-induced exacerbations by shortening the duration of the inflammatory response

Persistent induction of goblet cell differentiation in the airways:Therapeutic approaches

Dysregulated induction of goblet cell differentiation results in excessive production and retention of mucus and is a common feature of several chronic airways diseases. To date, therapeutic strategies to reduce mucus accumulation have focused primarily on altering the properties of the mucus itself, or have aimed to limit the production of mucus-stimulating cytokines. Here we review the current knowledge of key molecular pathways that are dysregulated during persistent goblet cell differentiation and highlights both pre-existing and novel therapeutic strategies to combat this pathology

Predicting pulmonary function from the analysis of voice: A machine learning approach

Introduction: To self-monitor asthma symptoms, existing methods (e.g. peak flow metre, smart spirometer) require special equipment and are not always used by the patients. Voice recording has the potential to generate surrogate measures of lung function and this study aims to apply machine learning approaches to predict lung function and severity of abnormal lung function from recorded voice for asthma patients.Methods: A threshold-based mechanism was designed to separate speech and breathing from 323 recordings. Features extracted from these were combined with biological factors to predict lung function. Three predictive models were developed using Random Forest (RF), Support Vector Machine (SVM), and linear regression algorithms: (a) regression models to predict lung function, (b) multi-class classification models to predict severity of lung function abnormality, and (c) binary classification models to predict lung function abnormality. Training and test samples were separated (70%:30%, using balanced portioning), features were normalised, 10-fold cross-validation was used and model performances were evaluated on the test samples.Results: The RF-based regression model performed better with the lowest root mean square error of 10·86. To predict severity of lung function impairment, the SVM-based model performed best in multi-class classification (accuracy = 73.20%), whereas the RF-based model performed best in binary classification models for predicting abnormal lung function (accuracy = 85%).Conclusion: Our machine learning approaches can predict lung function, from recorded voice files, better than published approaches. This technique could be used to develop future telehealth solutions including smartphone-based applications which have potential to aid decision making and self-monitoring in asthma.</p