Type-2 (T2) immune responses in airway epithelial cells (AECs) classifies mild-moderate asthma into a T2-high phenotype. We examined whether currently available clinical biomarkers can predict AEC-defined T2-high phenotype within the U-BIOPRED cohort.The transcriptomic profile of AECs obtained from brushings of 103 patients with asthma and 44 healthy controls was obtained and gene set variation analysis used to determine the relative expression score of T2 asthma using a signature from interleukin (IL)-13-exposed AECs.37% of asthmatics (45% nonsmoking severe asthma, n=49; 33% of smoking or ex-smoking severe asthma, n=18; and 28% mild-moderate asthma, n=36) were T2-high using AEC gene expression. They were more symptomatic with higher exhaled nitric oxide fraction (F eNO) and blood and sputum eosinophils, but not serum IgE or periostin. Sputum eosinophilia correlated best with the T2-high signature. F eNO (≥30 ppb) and blood eosinophils (≥300 cells·µL-1) gave a moderate prediction of T2-high asthma. Sputum IL-4, IL-5 and IL-13 protein levels did not correlate with gene expression.T2-high severe asthma can be predicted to some extent from raised levels of F eNO, blood and sputum eosinophil counts, but serum IgE or serum periostin were poor predictors. Better bedside biomarkers are needed to detect T2-high.
Multilevel data integration is becoming a major area of research in systems biology. Within this area, multi-'omics datasets on complex diseases are becoming more readily available and there is a need to set standards and good practices for integrated analysis of biological, clinical and environmental data. We present a framework to plan and generate single and multi-'omics signatures of disease states.METHODS:
The framework is divided into four major steps: dataset subsetting, feature filtering, 'omics-based clustering and biomarker identification.RESULTS:
We illustrate the usefulness of this framework by identifying potential patient clusters based on integrated multi-'omics signatures in a publicly available ovarian cystadenocarcinoma dataset. The analysis generated a higher number of stable and clinically relevant clusters than previously reported, and enabled the generation of predictive models of patient outcomes.CONCLUSIONS:
This framework will help health researchers plan and perform multi-'omics big data analyses to generate hypotheses and make sense of their rich, diverse and ever growing datasets, to enable implementation of translational P4 medicine.
Genome wide association studies (GWAS) in asthma have been successful in identifying disease susceptibility genes, however to date these have focused on mild disease. The genetic risk factors for moderate-severe asthma remain unclear.Aim:
To identify common genetic variants affecting susceptibility to develop moderate-severe asthma.Methods:
We identified asthma cases and controls from UK Biobank and additional cases from the Genetics of Asthma Severity & Phenotypes (GASP) cohort. A genome-wide association study was undertaken in 5,135 European ancestry individuals with moderate-severe asthma based on British Thoracic Society criteria 3 or above and 25,675 controls free from lung disease, allergic rhinitis and atopic dermatitis. After imputation (UK10K + 1000 genomes Phase 3) and standard quality control measures, the association of 33,771,858 single nucleotide polymorphisms (SNPs) were tested. A logistic model of association of asthma status with imputed genotype dose was fitted using SNPTEST adjusted for ancestry principal components.Results:
We identified 22 loci showing association (P < 5 × 10(-8)) including novel signals in or near D2HGDH, STAT6, HLA-B, CD247, GATA3, PDCD1LG2, ZNF652, RPAP3, MUC5AC and BACH2. Previously described asthma loci where replicated including signals in or near HLA-DQB1, TSLP, IL1RL1/IL18R1, CLEC16A, GATA3, IL33, SMAD3, SLC22A5/IL13, C11orf30, ZBTB10, IKZF3-ORMDL3 and IKZF4.Conclusion:
The largest genome-wide association study of moderate-severe asthma to date was carried out and multiple novel loci where identified. These findings may provide new insight into the molecular mechanisms underlying this difficult to treat population.
Stratification of asthma at the molecular level, especially using accessible biospecimens, could greatly enable patient selection for targeted therapy.OBJECTIVES:
To determine the value of blood analysis to identify transcriptional differences between clinically defined asthma and nonasthma groups, identify potential patient subgroups based on gene expression, and explore biological pathways associated with identified differences.METHODS:
Transcriptomic profiles were generated by microarray analysis of blood from 610 patients with asthma and control participants in the U-BIOPRED (Unbiased Biomarkers in Prediction of Respiratory Disease Outcomes) study. Differentially expressed genes (DEGs) were identified by analysis of variance, including covariates for RNA quality, sex, and clinical site, and Ingenuity Pathway Analysis was applied. Patient subgroups based on DEGs were created by hierarchical clustering and topological data analysis.MEASUREMENTS AND MAIN RESULTS:
A total of 1,693 genes were differentially expressed between patients with severe asthma and participants without asthma. The differences from participants without asthma in the nonsmoking severe asthma and mild/moderate asthma subgroups were significantly related (r = 0.76), with a larger effect size in the severe asthma group. The majority of, but not all, differences were explained by differences in circulating immune cell populations. Pathway analysis showed an increase in chemotaxis, migration, and myeloid cell trafficking in patients with severe asthma, decreased B-lymphocyte development and hematopoietic progenitor cells, and lymphoid organ hypoplasia. Cluster analysis of DEGs led to the creation of subgroups among the patients with severe asthma who differed in molecular responses to oral corticosteroids.CONCLUSIONS:
Blood gene expression differences between clinically defined subgroups of patients with asthma and individuals without asthma, as well as subgroups of patients with severe asthma defined by transcript profiles, show the value of blood analysis in stratifying patients with asthma and identifying molecular pathways for further study. Clinical trial registered with www.clinicaltrials.gov (NCT01982162).KEYWORDS:
biomarker; immune cell; microarray
Topological Data Analysis (TDA) network models can represent continuous variation in the shape of disease pathology. We generated a TDA network model of asthma using 498 gene expression profiles of peripheral blood from asthma and healthy participants. The TDA network model was characterised by a core region with increased prevalence of healthy participants and connected routes to increased prevalence of severe asthma associated with increases in circulating inflammatory cells and modulated expression of inflammatory genes. However, stratified medicine requires discretisation of disease populations for targeted treatments. Therefore, a discrete Morse theory algorithm was developed and applied, identifying nine clusters, BC1-9, representing molecular phenotypes with discrete profiles of immune cell populations and activation of Type-1, 2 & 17 cytokine inflammatory pathways. The TDA network model was also characterised by differential activity of glucocorticoid receptor signalling associated with different expression profiles of glucocorticoid receptor (GR), according to microarray probesets targeted to the start or end of the GR mRNA’s 3’ UTR; suggesting differential GR mRNA processing as a possible driver of asthma phenotypes including steroid insensitivity.
Asthma is a heterogeneous disease driven by diverse immunologic and inflammatory mechanisms.OBJECTIVES:
Using transcriptomic profiling of airway tissues, we sought to define the molecular phenotypes of severe asthma.METHODS:
The transcriptome derived from bronchial biopsies and epithelial brushings of 107 subjects with moderate to severe asthma were annotated by gene set variation analysis using 42 gene signatures relevant to asthma, inflammation, and immune function. Topological data analysis of clinical and histologic data was performed to derive clusters, and the nearest shrunken centroid algorithm was used for signature refinement.MEASUREMENTS AND MAIN RESULTS:
Nine gene set variation analysis signatures expressed in bronchial biopsies and airway epithelial brushings distinguished two distinct asthma subtypes associated with high expression of T-helper cell type 2 cytokines and lack of corticosteroid response (group 1 and group 3). Group 1 had the highest submucosal eosinophils, as well as high fractional exhaled nitric oxide levels, exacerbation rates, and oral corticosteroid use, whereas group 3 patients showed the highest levels of sputum eosinophils and had a high body mass index. In contrast, group 2 and group 4 patients had an 86% and 64% probability, respectively, of having noneosinophilic inflammation. Using machine learning tools, we describe an inference scheme using the currently available inflammatory biomarkers sputum eosinophilia and fractional exhaled nitric oxide levels, along with oral corticosteroid use, that could predict the subtypes of gene expression within bronchial biopsies and epithelial cells with good sensitivity and specificity.CONCLUSIONS:
This analysis demonstrates the usefulness of a transcriptomics-driven approach to phenotyping that segments patients who may benefit the most from specific agents that target T-helper cell type 2-mediated inflammation and/or corticosteroid insensitivity.KEYWORDS:
T-helper type 2; corticosteroid insensitivity; exhaled nitric oxide; gene set variation analysis; severe asthma
An overview of the science and processes being used in the project.
The U-BIOPRED consortium is an EU-wide collective of academics, pharmaceutical companies and other organisations working collaboratively to better understand severe asthma. The U-BIOPRED proteomics platform has been developed to discover novel protein biomarkers for disease stratification and to enhance our understanding of this disease at the molecular level via high throughput analysis of a variety of clinical sample types. Using innovative sample processing, mass spectrometry and data-mining methodologies, we report here the most comprehensive quantitative proteomic analysis of human induced sputum todate.
Inflammatory lung diseases are highly complex in the way they develop, interact with other diseases, and respond to treatment. Sophisticated large-scale methods of analysis can be used to look the different structures in the lungs, blood and urine. These different structures and their study are known as ‘omics and are important in indentifying biomarkers for disease. Using ‘omics approaches researchers hope to improve diagnosis and better understand molecular pathways. This article reviews the ‘omics technologies available to study biomarkers of lung disease. The contributions of the different ‘omics methods of analysis are summarised and their potential contribution to medicine-based studies of lung disease discussed.
A guide to disseminating material produced by U-BIOPRED, outlining the principles, when and how the project and consortium members should be referenced
Information about the project to recruit volunteers
The U-BIOPRED Patient Input Platform (PIP) is a voluntary group tasked with providing input from the patient's perspective on questions that may rise from the different work packages and activities of U-BIOPRED.
The Safety Monitoring Board (SMB) is concerned with clinical endpoints of mortality or major morbidity. A clinical study within U-BIOPRED may be of an observational nature, or may evaluate a treatment intervention for which the safety record is not established.
The U-BIOPRED project Ethics Board (EB) identifies, examines and provides advice on ethical aspects of research as well as good scientific conduct taking place in the context of U-BIOPRED.
Patients and carers of someone with severe asthma have been involved across U-BIOPRED's activities via the project's Patient Input Platform (PIP). Members of the PIP have worked with the paper's author, Dominic Shaw, and project lead, Peter Sterk, to produce a lay abstract for the paper, to ensure that the findings of the study are also accessible to patients and the public.
Recent research has shown that severe asthma is more than simply a worsening of mild asthma, and needs a different approach to diagnosis and treatment. Over 30 million adults and children in Europe suffer from asthma; it is estimated that of these, four percent (roughly 1.2 million people) have severe asthma. These patients have daily symptoms despite maximal medical treatment and are more likely to be admitted to hospital and to require emergency healthcare. It is estimated that these patients contribute over half of the total health care burden for this disease, and that each patient with uncontrolled asthma costs healthcare systems approximately 2,280 Euros per year.
In order to better understand severe asthma and identify new treatment targets the U-BIOPRED (Unbiased BIOmarkers in PREDiction of respiratory disease outcomes) project was set up in 2008 with public-private funding obtained from the Innovative Medicines Initiative (IMI) via the European Commission and the European Federation of Pharmaceutical Industries and Associations. U-BIOPRED is a collaboration of 20 academic institutions across 11 European countries with involvement of 11 pharmaceutical companies, 3 small to medium enterprises and 1 multinational industry, and 6 patient and care organizations.
U-BIOPRED aims to discover new subtypes of severe asthma by measuring and characterising metabolites (substances produced by the cells in a human body) in biological samples using the latest research technologies. This will lead to the identification of new treatment targets (what drugs work on), so treatment can eventually be tailored for the patient.
The adult U-BIOPRED study recruited four groups of adult participants and followed them up for over a year; 311 non-smokers with severe asthma, 110 smokers and ex-smokers with severe asthma, 88 patients with milder asthma and 101 healthy volunteers. As part of the study, blood, urine and sputum (mucus) samples were collected and specialised assessments of lung function and anatomy were performed.
The U-BIOPRED project has already revealed that, when compared to milder asthma, patients with severe asthma have significantly worse symptoms, more attacks, and higher levels of anxiety and depression. Patients with severe asthma also have more nasal polyps (small growths in the nose), acid indigestion and poorer lung function. One key finding is that although patients with severe asthma take greater amounts of anti-inflammatory treatment, including inhaled and oral steroids, they still have higher levels of inflammation in their airways.
Research is ongoing to identify the mechanisms involved in severe asthma and to identify subtypes, or clusters, that respond differently to treatment. This work will lead to swifter and more accurate diagnosis and, by targeting the mechanisms that are driving severe asthma, personalised treatments to lessen the burden of this chronic and debilitating disease.
To report on the baseline characteristics of the paediatric cohorts and factors which contribute to the burden of asthma, as measured by quality of life (QoL).
Transcriptomics is increasingly used to capture gene-expression profiles of airway samples in asthma. The optimal method for processing bronchial epithelial cells for RNA extraction has not yet been fully established. Our aim was to compare the RNA yield and quality achieved by two methods: (i) bronchoscopic brushes placed in PBS, spun for cell count and re-suspended in 1 ml RNAlater (Method 1) and (ii) direct suspension into 5 ml of RNAlater (Method 2).
Eosinophils play an important role in the pathophysiology of asthma being implicated in airway epithelial damage and airway wall remodeling. We determined the genes associated with airway remodeling and eosinophilic inflammation in patients with asthma.METHODS:
We analyzed the transcriptomic data from bronchial biopsies of 81 patients with moderate-to-severe asthma of the U-BIOPRED cohort. Expression profiling was performed using Affymetrix arrays on total RNA. Transcription binding site analysis used the PRIMA algorithm. Localization of proteins was by immunohistochemistry.RESULTS:
Using stringent false discovery rate analysis, MMP-10 and MET were significantly overexpressed in biopsies with high mucosal eosinophils (HE) compared to low mucosal eosinophil (LE) numbers. Immunohistochemical analysis confirmed increased expression of MMP-10 and MET in bronchial epithelial cells and in subepithelial inflammatory and resident cells in asthmatic biopsies. Using less-stringent conditions (raw P-value < 0.05, log2 fold change > 0.5), we defined a 73-gene set characteristic of the HE compared to the LE group. Thirty-three of 73 genes drove the pathway annotation that included extracellular matrix (ECM) organization, mast cell activation, CC-chemokine receptor binding, circulating immunoglobulin complex, serine protease inhibitors, and microtubule bundle formation pathways. Genes including MET and MMP10 involved in ECM organization correlated positively with submucosal thickness. Transcription factor binding site analysis identified two transcription factors, ETS-1 and SOX family proteins, that showed positive correlation with MMP10 and MET expression.CONCLUSION:
Pathways of airway remodeling and cellular inflammation are associated with submucosal eosinophilia. MET and MMP-10 likely play an important role in these processes.
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