Chris Newby

Biological clustering supports both “Dutch” and “British” hypotheses of asthma and chronic obstructive pulmonary disease

Background Asthma and chronic obstructive pulmonary disease (COPD) are heterogeneous diseases. Objective We sought to determine, in terms of their sputum cellular and mediator profiles, the extent to which they represent distinct or overlapping conditions supporting either the “British” or “Dutch” hypotheses of airway disease pathogenesis. Methods We compared the clinical and physiological characteristics and sputum mediators between 86 subjects with severe asthma and 75 with moderate-to-severe COPD. Biological subgroups were determined using factor and cluster analyses on 18 sputum cytokines. The subgroups were validated on independent severe asthma (n = 166) and COPD (n = 58) cohorts. Two techniques were used to assign the validation subjects to subgroups: linear discriminant analysis, or the best identified discriminator (single cytokine) in combination with subject disease status (asthma or COPD). Results Discriminant analysis distinguished severe asthma from COPD completely using a combination of clinical and biological variables. Factor and cluster analyses of the sputum cytokine profiles revealed 3 biological clusters: cluster 1: asthma predominant, eosinophilic, high TH2 cytokines; cluster 2: asthma and COPD overlap, neutrophilic; cluster 3: COPD predominant, mixed eosinophilic and neutrophilic. Validation subjects were classified into 3 subgroups using discriminant analysis, or disease status with a binary assessment of sputum IL-1β expression. Sputum cellular and cytokine profiles of the validation subgroups were similar to the subgroups from the test study. Conclusions Sputum cytokine profiling can determine distinct and overlapping groups of subjects with asthma and COPD, supporting both the British and Dutch hypotheses. These findings may contribute to improved patient classification to enable stratified medicine.

Statistical Cluster Analysis of the British Thoracic Society Severe Refractory Asthma Registry: Clinical Outcomes and Phenotype Stability

Background Severe refractory asthma is a heterogeneous disease. We sought to determine statistical clusters from the British Thoracic Society Severe refractory Asthma Registry and to examine cluster-specific outcomes and stability. Methods Factor analysis and statistical cluster modelling was undertaken to determine the number of clusters and their membership (N = 349). Cluster-specific outcomes were assessed after a median follow-up of 3 years. A classifier was programmed to determine cluster stability and was validated in an independent cohort of new patients recruited to the registry (n = 245). Findings Five clusters were identified. Cluster 1 (34%) were atopic with early onset disease, cluster 2 (21%) were obese with late onset disease, cluster 3 (15%) had the least severe disease, cluster 4 (15%) were the eosinophilic with late onset disease and cluster 5 (15%) had significant fixed airflow obstruction. At follow-up, the proportion of subjects treated with oral corticosteroids increased in all groups with an increase in body mass index. Exacerbation frequency decreased significantly in clusters 1, 2 and 4 and was associated with a significant fall in the peripheral blood eosinophil count in clusters 2 and 4. Stability of cluster membership at follow-up was 52% for the whole group with stability being best in cluster 2 (71%) and worst in cluster 4 (25%). In an independent validation cohort, the classifier identified the same 5 clusters with similar patient distribution and characteristics. Interpretation Statistical cluster analysis can identify distinct phenotypes with specific outcomes. Cluster membership can be determined using a classifier, but when treatment is optimised, cluster stability is poor.

Relationship between lung function and quantitative computed tomographic parameters of airway remodeling, air trapping, and emphysema in patients with asthma and chronic obstructive pulmonary disease: A single-center study

Background There is a paucity of studies comparing asthma and chronic obstructive pulmonary disease (COPD) based on thoracic quantitative computed tomographic (QCT) parameters. Objectives We sought to compare QCT parameters of airway remodeling, air trapping, and emphysema between asthmatic patients and patients with COPD and explore their relationship with airflow limitation. Methods Asthmatic patients (n = 171), patients with COPD (n = 81), and healthy subjects (n = 49) recruited from a single center underwent QCT and clinical characterization. Results Proximal airway percentage wall area (%WA) was significantly increased in asthmatic patients (62.5% [SD, 2.2]) and patients with COPD (62.7% [SD, 2.3]) compared with that in healthy control subjects (60.3% [SD, 2.2], P < .001). Air trapping measured based on mean lung density expiratory/inspiratory ratio was significantly increased in patients with COPD (mean, 0.922 [SD, 0.037]) and asthmatic patients (mean, 0.852 [SD, 0.061]) compared with that in healthy subjects (mean, 0.816 [SD, 0.066], P < .001). Emphysema assessed based on lung density measured by using Hounsfield units below which 15% of the voxels lie (Perc15) was a feature of COPD only (patients with COPD: mean, −964 [SD, 19.62] vs asthmatic patients: mean, −937 [SD, 22.7] and healthy subjects: mean, −937 [SD, 17.1], P < .001). Multiple regression analyses showed that the strongest predictor of lung function impairment in asthmatic patients was %WA, whereas in the COPD and asthma subgrouped with postbronchodilator FEV1 percent predicted value of less than 80%, it was air trapping. Factor analysis of QCT parameters in asthmatic patients and patients with COPD combined determined 3 components, with %WA, air trapping, and Perc15 values being the highest loading factors. Cluster analysis identified 3 clusters with mild, moderate, or severe lung function impairment with corresponding decreased lung density (Perc15 values) and increased air trapping. Conclusions In asthmatic patients and patients with COPD, lung function impairment is strongly associated with air trapping, with a contribution from proximal airway narrowing in asthmatic patients.

Emphysema- and airway-dominant COPD phenotypes defined by standardised quantitative computed tomography

EvA (Emphysema versus Airway disease) is a multicentre project to study mechanisms and identify biomarkers of emphysema and airway disease in chronic obstructive pulmonary disease (COPD). The objective of this study was to delineate objectively imaging-based emphysema-dominant and airway disease-dominant phenotypes using quantitative computed tomography (QCT) indices, standardised with a novel phantom-based approach. 441 subjects with COPD (Global Initiative for Chronic Obstructive Lung Disease (GOLD) stages 1–3) were assessed in terms of clinical and physiological measurements, laboratory testing and standardised QCT indices of emphysema and airway wall geometry. QCT indices were influenced by scanner non-conformity, but standardisation significantly reduced variability (p<0.001) and led to more robust phenotypes. Four imaging-derived phenotypes were identified, reflecting “emphysema-dominant”, “airway disease-dominant”, “mixed” disease and “mild” disease. The emphysema-dominant group had significantly higher lung volumes, lower gas transfer coefficient, lower oxygen (PO2) and carbon dioxide (PCO2) tensions, higher haemoglobin and higher blood leukocyte numbers than the airway disease-dominant group. The utility of QCT for phenotyping in the setting of an international multicentre study is improved by standardisation. QCT indices of emphysema and airway disease can delineate within a population of patients with COPD, phenotypic groups that have typical clinical features known to be associated with emphysema-dominant and airway-dominant disease. Standardisation of quantitative CT improves delineation of emphysema and airway phenotypes in a multicentre study http://ow.ly/10zjhV

Bidirectional Counterregulation of Human Lung Mast Cell and Airway Smooth Muscle β2 Adrenoceptors

Human lung mast cells (HLMCs) play a central role in asthma pathogenesis through their relocation to the airway smooth muscle (ASM) bundles. β2 adrenoceptor (β2-AR)-agonists are used to relieve bronchoconstriction in asthma, but may reduce asthma control, particularly when used as monotherapy. We hypothesized that HLMC and human ASM cell (HASMC) responsiveness to β2-AR agonists would be attenuated when HLMCs are in contact with HASMCs. Cells were cultured in the presence of the short-acting β2-agonist albuterol, and the long-acting β2-agonists formoterol and olodaterol. Constitutive and FcεRI-dependent HLMC histamine release, HASMC contraction, and β2-AR phosphorylation at Tyr350 were assessed. Constitutive HLMC histamine release was increased in HLMC–HASMC coculture and this was enhanced by β2-AR agonists. Inhibition of FcεRI-dependent HLMC mediator release by β2-agonists was greatly reduced in HLMC–HASMC coculture. These effects were reversed by neutralization of stem cell factor (SCF) or cell adhesion molecule 1 (CADM1). β2-AR agonists did not prevent HASMC contraction when HLMCs were present, but this was reversed by fluticasone. β2-AR phosphorylation at Tyr350 occurred within 5 min in both HLMCs and HASMCs when the cells were cocultured, and was inhibited by neutralizing SCF or CADM1. HLMC interactions with HASMCs via CADM1 and Kit inhibit the potentially beneficial effects of β2-AR agonists on these cells via phosphorylation of the β2-AR. These results may explain the potentially adverse effects of β2-ARs agonists when used for asthma therapy. Targeting SCF and CADM1 may enhance β2-AR efficacy, particularly in corticosteroid-resistant patients.

Myeloid-derived suppressor cell-like fibrocytes are increased and associated with preserved lung function in chronic obstructive pulmonary disease

The role of fibrocytes in chronic obstructive pulmonary disease (COPD) is unknown. We sought to enumerate blood and tissue fibrocytes in COPD and determine the association of blood fibrocytes with clinical features of disease.

A model of human lung fibrogenesis for the assessment of anti-fibrotic strategies in idiopathic pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial lung disease with limited therapeutic options. KCa3.1 ion channels play a critical role in TGFβ1-dependent pro-fibrotic responses in human lung myofibroblasts. We aimed to develop a human lung parenchymal model of fibrogenesis and test the efficacy of the selective KCa3.1 blocker senicapoc. 2 mm3 pieces of human lung parenchyma were cultured for 7 days in DMEM ± TGFβ1 (10 ng/ml) and pro-fibrotic pathways examined by RT-PCR, immunohistochemistry and collagen secretion. Following 7 days of culture with TGFβ1, 41 IPF- and fibrosis-associated genes were significantly upregulated. Immunohistochemical staining demonstrated increased expression of ECM proteins and fibroblast-specific protein after TGFβ1-stimulation. Collagen secretion was significantly increased following TGFβ1-stimulation. These pro-fibrotic responses were attenuated by senicapoc, but not by dexamethasone. This 7 day ex vivo model of human lung fibrogenesis recapitulates pro-fibrotic events evident in IPF and is sensitive to KCa3.1 channel inhibition. By maintaining the complex cell-cell and cell-matrix interactions of human tissue, and removing cross-species heterogeneity, this model may better predict drug efficacy in clinical trials and accelerate drug development in IPF. KCa3.1 channels are a promising target for the treatment of IPF.

Investigation into effectiveness of rough sets in prediction of enzyme and protein structure classes

Among various methods in protein function prediction, rough set has recently been applied to prediction of protein structural classes. However, this was a blind application on a single but small data set of high homology, which did not consider investigation of various parameters in the rough set. The aim of this paper is therefore to study rough set in the area through comprehensive and consistent analysis and then to present a practical strategy in the rough set-based protein function prediction. To achieve this aim, three different data sets were considered: the first data set for prediction of six main enzyme classes, and other two for prediction of structural classes. Boolean reasoning, Entropy scaling and Equal frequency binning were used for discretization along with two methods for producing reducts and rules, genetic and Johnsons algorithms. It can be seen that the predictive accuracies were poor for the enzyme dataset whereas it performed better at prediction of the protein structural classes. It is also observed that the dataset with low homology produced poor accuracies than the dataset with high homology. Furthermore, various parameters and methods used in the rough set were sensitive to the problems in the area, as well as the data sets of low and high homology and different number of the features. The results appear to indicate that the equal frequency-based approach combined with genetic algorithm yields higher prediction. However, other methods such as Boolean reasoning with the genetic algorithm are also found to be promising. Further investigation will provide a practical strategy that can be used in the rough set-based protein function prediction as well as other areas of Bioinformatics.

Synergistic activity of polarised osteoblasts inside condensations cause their differentiation

Condensation of pre-osteogenic, or pre-chondrogenic, cells is the first of a series of processes that initiate skeletal development. We present a validated, novel, three-dimensional agent-based model of in vitro intramembranous osteogenic condensation. The model, informed by system heterogeneity and relying on an interaction-reliant strategy, is shown to be sensitive to ‘rules’ capturing condensation growth and can be employed to track activity of individual cells to observe their macroscopic impact. It, therefore, makes available previously inaccessible data, offering new insights and providing a new context for exploring the emergence, as well as normal and abnormal development, of osteogenic structures. Of the several stages of condensation we investigate osteoblast ‘burial’ within the osteoid they deposit. The mechanisms underlying entrapment – required for osteoblasts to differentiate into osteocytes – remain a matter of conjecture with several hypotheses claiming to capture this important transition. Computational examination of this transition indicates that osteoblasts neither turn off nor slow down their matrix secreting genes – a widely held view; nor do they secrete matrix randomly. The model further reveals that osteoblasts display polarised behaviour to deposit osteoid. This is both an important addition to our understanding of condensation and an important validation of the model’s utility.