Application of 'omics technologies to biomarker discovery in inflammatory lung diseases.
Karolinska Institutet1, University of Southampton2, University of Rome Tor Vergata3, Semmelweis University4, Institute for Systems Biology5, National Institutes of Health6, Catholic University of the Sacred Heart7, Lancashire Teaching Hospitals NHS Foundation Trust8, Johnson & Johnson Pharmaceutical Research and Development9, University of Amsterdam10
TL;DR: The technologies presently available to study biomarkers of lung disease within the ’omics field are reviewed and the contributions of the individual �’omics analytical platforms to the field of respiratory diseases are summarised, with the goal of providing background on their respective abilities to contribute to systems medicine-based studies of Lung disease.
Abstract: Inflammatory lung diseases are highly complex in respect of pathogenesis and relationships between inflammation, clinical disease and response to treatment. Sophisticated large-scale analytical methods to quantify gene expression (transcriptomics), proteins (proteomics), lipids (lipidomics) and metabolites (metabolomics) in the lungs, blood and urine are now available to identify biomarkers that define disease in terms of combined clinical, physiological and patho-biological abnormalities. The aspiration is that these approaches will improve diagnosis, i.e. define pathological phenotypes, and facilitate the monitoring of disease and therapy, and also, unravel underlying molecular pathways. Biomarker studies can either select predefined biomarker(s) measured by specific methods or apply an "unbiased" approach involving detection platforms that are indiscriminate in focus. This article reviews the technologies presently available to study biomarkers of lung disease within the 'omics field. The contributions of the individual 'omics analytical platforms to the field of respiratory diseases are summarised, with the goal of providing background on their respective abilities to contribute to systems medicine-based studies of lung disease.
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University of Nottingham1, University of Manchester2, National Institutes of Health3, University of Southampton4, St Mary's Hospital5, AstraZeneca6, Imperial College London7, St John's Innovation Centre8, University of Amsterdam9, Institute for Systems Biology10, University of Copenhagen11, University of Catania12, Aix-Marseille University13, Karolinska University Hospital14, Karolinska Institutet15, University of Stirling16, Boston Children's Hospital17, Semmelweis University18, Fraunhofer Society19, Novartis20, Genentech21, Catholic University of the Sacred Heart22, Jagiellonian University Medical College23, GlaxoSmithKline24, Umeå University25, Boehringer Ingelheim26, University Hospital of South Manchester NHS Foundation Trust27, Janssen Pharmaceutica28
TL;DR: U-BIOPRED is characterised by poor symptom control, increased comorbidity and airway inflammation, despite high levels of treatment, and is well suited to identify asthma phenotypes using the array of "omic" datasets that are at the core of this systems medicine approach.
Abstract: U-BIOPRED is a European Union consortium of 20 academic institutions, 11 pharmaceutical companies and six patient organisations with the objective of improving the understanding of asthma disease mechanisms using a systems biology approach.This cross-sectional assessment of adults with severe asthma, mild/moderate asthma and healthy controls from 11 European countries consisted of analyses of patient-reported outcomes, lung function, blood and airway inflammatory measurements.Patients with severe asthma (nonsmokers, n=311; smokers/ex-smokers, n=110) had more symptoms and exacerbations compared to patients with mild/moderate disease (n=88) (2.5 exacerbations versus 0.4 in the preceding 12 months; p<0.001), with worse quality of life, and higher levels of anxiety and depression. They also had a higher incidence of nasal polyps and gastro-oesophageal reflux with lower lung function. Sputum eosinophil count was higher in severe asthma compared to mild/moderate asthma (median count 2.99% versus 1.05%; p=0.004) despite treatment with higher doses of inhaled and/or oral corticosteroids.Consistent with other severe asthma cohorts, U-BIOPRED is characterised by poor symptom control, increased comorbidity and airway inflammation, despite high levels of treatment. It is well suited to identify asthma phenotypes using the array of "omic" datasets that are at the core of this systems medicine approach.
407 citations
National Institutes of Health1, University of Amsterdam2, Uppsala University3, University of Gothenburg4, Innsbruck Medical University5, University of Padua6, Semmelweis University7, Maastricht University8, University of Turin9, University of Foggia10, University of Parma11, Radboud University Nijmegen12, Erasmus University Rotterdam13, Paris Descartes University14, Maastricht University Medical Centre15, University of Manchester16, Hochschule Hannover17, Fraunhofer Society18, Philips19, National and Kapodistrian University of Athens20, University of Tampere21, Karolinska Institutet22, Catholic University of the Sacred Heart23, Università Campus Bio-Medico24, Friedrich Loeffler Institute25, European Respiratory Society26
TL;DR: Application of breath biomarker measurement in a standardised manner will provide comparable results, thereby facilitating the potential use of these biomarkers in clinical practice, and highlighting future research priorities in the field.
Abstract: Breath tests cover the fraction of nitric oxide in expired gas (FENO), volatile organic compounds (VOCs), variables in exhaled breath condensate (EBC) and other measurements. For EBC and for FENO, official recommendations for standardised procedures are more than 10 years old and there is none for exhaled VOCs and particles. The aim of this document is to provide technical standards and recommendations for sample collection and analytic approaches and to highlight future research priorities in the field. For EBC and FENO, new developments and advances in technology have been evaluated in the current document. This report is not intended to provide clinical guidance on disease diagnosis and management. Clinicians and researchers with expertise in exhaled biomarkers were invited to participate. Published studies regarding methodology of breath tests were selected, discussed and evaluated in a consensus-based manner by the Task Force members. Recommendations for standardisation of sampling, analysing and reporting of data and suggestions for research to cover gaps in the evidence have been created and summarised. Application of breath biomarker measurement in a standardised manner will provide comparable results, thereby facilitating the potential use of these biomarkers in clinical practice.
384 citations
TL;DR: Sensors based breath analysis is expected to dramatically extend the diagnostic capabilities enabling the screening of large populations for the early diagnosis of pathologies and solid-state sensors are considered the natural complement to breath analysis.
289 citations
TL;DR: In this article, the authors propose guidelines for minimum reporting standards for a SIMCA-based workflow, in terms of data preprocessing (e.g., normalization, scaling) and model statistics (number of components, R2, Q2, and CV-ANOVA p-value).
Abstract: Respiratory diseases are multifactorial heterogeneous diseases that have proved recalcitrant to understanding using focused molecular techniques. This trend has led to the rise of ‘omics approaches (e.g., transcriptomics, proteomics) and subsequent acquisition of large-scale datasets consisting of multiple variables. In ‘omics technology-based investigations, discrepancies between the number of variables analyzed (e.g., mRNA, proteins, metabolites) and the number of study subjects constitutes a major statistical challenge. The application of traditional univariate statistical methods (e.g., t-test) to these “short-and-wide” datasets may result in high numbers of false positives, while the predominant approach of p-value correction to account for these high false positive rates (e.g., FDR, Bonferroni) are associated with significant losses in statistical power. In other words, the benefit in decreased false positives must be counterbalanced with a concomitant loss in true positives. As an alternative, multivariate statistical analysis (MVA) is increasingly being employed to cope with ‘omics-based data structures. When properly applied, MVA approaches can be powerful tools for integration and interpretation of complex ‘omics-based datasets towards the goal of identifying biomarkers and/or subphenotypes. However, MVA methods are also prone to over-interpretation and misuse. A common software used in biomedical research to perform MVA-based analyses is the SIMCA package, which includes multiple MVA methods. In this opinion piece, we propose guidelines for minimum reporting standards for a SIMCA-based workflow, in terms of data preprocessing (e.g., normalization, scaling) and model statistics (number of components, R2, Q2, and CV-ANOVA p-value). Examples of these applications in recent COPD and asthma studies are provided. It is expected that readers will gain an increased understanding of the power and utility of MVA methods for applications in biomedical research.
257 citations
TL;DR: NMR and EPR Spectroscopy papers presented at Varian's Third Annual Workshop on Nuclear Magnetic Resonance and Electron Paramagnetic Resonance, held at Palo Alto, California as mentioned in this paper.
Abstract: NMR and EPR Spectroscopy Papers presented at Varian's Third Annual Workshop on Nuclear Magnetic Resonance and Electron Paramagnetic Resonance, held at Palo Alto, California. By the NMR–EPR Staff of Varian Associates. Pp. viii + 288. (London and New York: Pergamon Press, 1960.) 80s. net.
250 citations
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TL;DR: Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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32,980 citations
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TL;DR: Although >90% of uniquely mapped reads fell within known exons, the remaining data suggest new and revised gene models, including changed or additional promoters, exons and 3′ untranscribed regions, as well as new candidate microRNA precursors.
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TL;DR: The RNA-Seq approach to transcriptome profiling that uses deep-sequencing technologies provides a far more precise measurement of levels of transcripts and their isoforms than other methods.
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11,528 citations