An Integrative Systems Biology Approach to Understanding Pulmonary Diseases FREE TO VIEW

A wealth of biologic and clinical information is  available on chronic pulmonary diseases such as asthma, COPD, or pulmonary fibrosis. However, the primary causes and paths of disease development remain largely unknown, preventing the development of more efficient diagnosis and treatment. The fundamental reason for this situation is that these complex diseases involve a large number and diverse types of molecular and cellular components interacting through complex networks in nonlinear dynamic modes. Thus they impact in specific ways upon biologic processes involved in multiple human disease mechanisms, such as inflammation, immunity, cell cycle, apoptosis, or metabolism. Equally important, these biologic networks are closely linked to the clinical, phenotypic expression of these diseases. Adding to this complexity, the slow developmental and disease processes occurring at the organ and organism levels and fast subcellular molecular events can influence each other through upward and downward causation chains operating across multiple levels of biologic organization. Indeed, molecular and signal transduction pathways can be switched on or off depending on physical location within the cell or organ. Thus, complex inflammatory diseases like asthma are characterized by the functional redundancy of the mediators involved, such that inhibition of single mediators is highly unlikely to be effective. Because they can only partially be accounted for by classic approaches targeting individual components, it is necessary to investigate them through more integrated systems approaches that take advantage of functional genomics tools.

In Europe a novel platform is being established for a more efficient exploration, understanding, diagnosis, and treatment of prevalent pulmonary and other complex diseases through implementation of an integrative systems biology approach. It is envisaged that this global collaboration between the academic, industrial, and patient organization partners of the consortium Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes will produce a step-change in respiratory medicine, and avenues for better treatment of the patients. This precompetitive research will link with that of other large consortia, such as the Severe Asthma Research Program funded by the National Institutes of Health, by making its data and discoveries public according to established European Union policy (www.imi.europa.eu), thus providing opportunities to others for further exploration. This review outlines the essential principles for such an approach but also highlights the challenges that are likely to be encountered in its implementation.

Current treatment strategies using a combination of inhaled corticosteroids and long-acting β-agonists are effective in patients with nonsevere asthma, but appear to be insufficient to control severe asthma characterized by frequent and severe exacerbations and/or irreversible airflow limitation, despite the use of higher drug doses or oral corticosteroid therapy.^1-4 Moreover, about one-third of patients with severe asthma are relatively corticosteroid insensitive; they have persistent symptoms and abnormal lung function, accompanied by irreversible airflow limitation, neutrophilic inflammation, ongoing mediator release, and a reduced association with atopy.^5,6 In addition, new drug entities for asthma, such as the leukotriene receptor antagonists and the anti-IgE antibody, omalizumab, thus far have not resolved this treatment resistance in the majority of patients with difficult-to-treat asthma.

Little information is available on the natural history and progression of disease in patients diagnosed with severe asthma. Independently of corticosteroid sensitivity, a large proportion of patients with severe asthma develop significant persistent and often irreversible airflow obstruction, associated with more severe bronchial hyperresponsiveness, elevated markers of airway inflammation, and abnormalities suggestive of airway remodeling.^3,7,8 Frequent exacerbations are associated with comorbid factors, including severe nasal sinus disease, gastroesophageal reflux, recurrent respiratory infections, psychologic dysfunctioning, and obstructive sleep apnea.⁹ All these features add to the complexity of severe asthma and raise the need for careful clinical and cellular phenotyping of these patients.^3,10

Severe refractory asthma is a heterogeneous, multifaceted condition that can be subdivided into different phenotypes.¹¹ Traditionally, it has been classified using a number of features, including atopy, trigger factors, and severity of exacerbations. More recently, several important clinical phenotypes have been recognized: frequent exacerbations including near-fatal episodes, those with partially fixed airway obstruction, and those with oral corticosteroid dependency or resistance.¹² Assessment of airway inflammation led to the identification of persistent eosinophilic and neutrophilic asthma. Other features, such as age of onset of the symptoms, or onset following respiratory tract infection, may represent interesting additional phenotypes. Therefore, the time is now ripe to approach asthma, particularly the defined severe asthma paradigm, as a disease entity of different phenotypes.¹³

Preliminary studies have begun to take an unbiased approach toward examining biomarker expression in severe asthma, including hierarchical clustering of bronchial and sputum inflammatory mediators^10,14 and transcriptome analysis.^15,16 The analysis of volatile organic components of exhaled breath using an electronic nose and the transcriptomic analysis of bronchial biopsies have supported the high-dimensional background severe asthma phenotypes.^17,18 Targeting nodal points in these clusters may highlight potential novel sites for drug intervention.

Rodent models are able to mimic many aspects of asthma, such as T-helper 2-driven allergic responses, eosinophilia, and airway hyperresponsiveness, and have been used as an essential part of drug development because they provide an intact immune and respiratory system and are extremely useful to understand toxicology.¹⁹ Currently, the consensus is that chronic models of allergen exposure in mice are more reflective of human asthma than acute allergen challenges, and that addition of challenges such as viral infection or ozone may provide a model of disease exacerbation.²⁰ However, a number of drugs that have been shown to have efficacy in these animal models have shown limited clinical benefit in humans with asthma.^21-23 Hence there is a need for improved linkage of animal disease models to human disease phenotypes, in order to increase knowledge of pathways driving exacerbations, to facilitate identification of novel therapeutic agents, and to identify relevant biomarkers of inflammation, airway remodeling, and physiology.

Human experimental models of asthma exacerbations have given a major contribution to understanding the links between cellular mechanisms and clinical responses. They aim at mimicking environmental exposures that are relevant for loss of asthma control or exacerbations in a controlled laboratory setting using a prospective study design and challenges usually accompanied with worsening in airway hyperresponsiveness and inflammation. Thus far, the field has largely relied on allergen challenges, based on the assumption that allergen exposure drives the episodic worsening of asthma. However, there is good evidence that viral infections, predominantly by rhinoviruses, are by far the most important inducers of asthma exacerbations, involving various pathways of inflammatory and resident cells. This has driven the development of a validated, human in vivo model of experimental rhinovirus infection, which has demonstrated the limitations of current asthma therapy in controlling virus-induced airways inflammation.²⁴ It has also successfully been linked to human ex vivo bronchial cell culture studies and to rhinovirus mouse models, allowing the combination of clinical and preclinical challenge studies.

Taken together, phenotyping severe asthma can help to better understand the pathophysiology and identify individuals who are at greater risk for more severe disease and mortality.²⁵ It may also guide current therapy, as well as better link genotypes and disease phenotypes. Although categorizing patients with severe refractory asthma into dichotomous groups has increased our understanding of the underlying mechanisms, it is obvious that this approach does not reflect the true complexity of the disease. A better approach is to determine asthma phenotypes using more complex statistical approaches such as cluster or factor analysis that use multiple parameters to describe multidimensional phenotypes. The advent of new technologies to better image the lungs, to measure multiple inflammatory markers in blood,²⁶ exhaled breath,¹⁷ or induced sputum, and to assess genetic polymorphisms for disease predisposition and early onset^27,28 makes it possible to use complex statistical models to better identify severe asthma phenotypes.

The application of comprehensive and unbiased functional genomics methods, combined with focused approaches that draw upon decades of research into disease pathways, offers a more integrated approach to better disease understanding and definition of novel disease phenotypes. Because the “omics” technologies make no a priori assumptions about the type of component that may be associated with a particular disease, they have the potential to discover new and unsuspected links between processes and pathways perturbed in disease. Transcriptomic and proteomic analyses provide essential direct readouts of how gene expression is regulated, and analysis of posttranslation modification of proteins gives an indication of biochemical activity regulation.^29,30 It is the metabolic products of such biochemical activity, which can be directly affected by environmental and nutritional elements or exercise, that modulate physiologic responses in health and disease.

Omics technologies are particularly powerful when integrated and correlated, because of the orthologous nature of the data produced by each method.³¹ For example, the detection of increased levels of a metabolite in addition to upregulation of a linked enzyme strengthens the individual observations. Differences between the data sets can also be informative: up-regulation of a protein in the absence of a corresponding increase in its mRNA transcript provides evidence suggestive of a post-transcriptional regulatory mechanism. Proteins are the ultimate effectors of most cellular processes and quantitative and qualitative changes can often indicate that a particular process is in operation even in the absence of changes at the mRNA level: proteolytic processing or reversible post-translational modifications such as phosphorylation are frequently used as a surrogates for the direct measurement of cellular activity.

Metabolomics is a global approach to understanding regulation of metabolic pathways and metabolic networks of a biologic system,³² which can be applied to the discovery of novel mechanisms/phenotypes of severe asthma through analysis of blood or exhaled air (breatheomics), providing useful tools for monitoring of disease for clinical diagnosis and clinical trials. Metabolites result from the interaction of the genome with its environment and are not merely the end product of gene expression but also form part of the regulatory system in an integrated manner. The lipidome is a critically important component of metabolic analysis.³³ Membrane phospholipid structures provide the appropriate environment for activity of all membrane proteins and act as substrates for a wide array of lipid-signaling molecules, and their oxidation underlies many pathologic processes. Moreover, as cell lipid compositions are regulated by the interactions of enzyme activities and specificities, lipid nutrition, and cellular activation states, detailed lipidomic analysis can generate insights into the interactions of genetic and environmental factors on disease processes.

In order to gain a deeper understanding of the functional and regulatory pathways that play central roles in the behavior of complex biologic systems, integrative systems biology approaches have been developed to combine experimental approaches with mathematical and computational methods for modeling and simulation of molecular, subcellular, cellular, and organ-level structures and processes.³⁴ Integrative systems biology approaches are being increasingly applied in medicine (“systems medicine”) to unravel the bases of human diseases and to overcome the limitations faced in diagnostic and drug development.^35,36 They are based on the concept that models of molecular and cellular networks encode information on mechanisms underlying cellular function and enable the formulation of hypotheses predicting the behavior of the system, ideally as quantitative changes in levels of transcripts, proteins, and metabolites.^37,38 It is expected that computer simulation can reveal patterns that are hidden by system complexity or are counterintuitive. Modeling requires the formulation of explicit hypotheses, identifying critical points where understanding of the biologic system is weak or absent. It helps to generate novel hypotheses that can be tested in further experiments.

To enable such modeling and simulation to be performed efficiently, high-dimensional transcriptome, proteome, and metabolome data sets of high quality are collected from cross-sectional and longitudinal patient cohorts covering 30 months of follow-up with high-throughput measurement platforms, integrated, analyzed, and validated.^33,39,40 An essential aspect for the success in detecting to the maximum extent true differences, while maintaining false positives to a minimum, is the need for rigorous experimental design. Ascertainment of the optimal sample size for each type of study is performed through statistical power calculations, based on pilot studies that measure platform technical performance and biologic variation in the study population.^29,41,42 It is also important to collect omics data sets according to standard operating procedures and data formats,^43,44 so that they can be efficiently analyzed using a variety of bioinformatics data and text mining tools in the context of reference metabolic, signaling, and regulatory networks available in public repositories.⁴⁵

Typically, in a first stage, each type of omics data is collected on patient or animal/human disease model samples and analyzed separately to identify differences in expression, associations, and correlations of sets of elementary and network components in relation to disease phenotypes. Omics fingerprint signatures are obtained using various methods and bioinformatics tools for univariate and multivariate statistical analysis and clustering. In a second stage, the different omics data sets are integrated together to inform predictive models ranging usually from the molecular to the cellular levels.^38,46 This enables the establishment of complex network “handprints” that are developed through integration of molecular fingerprints obtained from transcriptomic, proteomic, or lipidomics data, and characterization of the different disease phenotypes in a more extensive fashion than the individual fingerprint biomarkers. The power of this integrated approach is illustrated by the advances gained, for example, in breast cancer: although the initial expression biomarkers derived by several groups proved useful for tumor classification,^47,48 they were poorly overlapping and difficult to replicate across patient cohorts. Integration with protein-protein interaction data has significantly improved the classifiers and also provided novel hypotheses for pathways involved in tumor progression that are potential targets for drug development.⁴⁹

In the next stage, simple graphical and mathematical models of gene expression, regulation, signal transduction, and metabolic networks are used initially, as they can enable answering biologic or clinical questions when they are defined precisely. Since the validity of the molecular models is most often constrained by stoichiometric and kinetic parameter estimation, careful attention must be paid to populate them using only the statistically most significant and reliable quantitative data; indeed, those collected in time series and longitudinal studies are best suited to decipher causal relationships between key biologic processes and identify disease mechanisms. Functional and structural models defined at the molecular and cellular levels can then be integrated with organ-level models developed in the context of the Physiome Project.⁵⁰

In the systems biology approach, working hypotheses are formulated on the mechanisms and pathways involved in the disease states in order to assess the predictive value of the observed fingerprints and handprints. The observed responses are then compared with those predicted by the initial models, which are refined iteratively so that they account better for the results. Whenever applicable, novel perturbation experiments are designed and tested both computationally and experimentally in order to arbitrate between competing hypotheses. The process is repeated until the derived knowledge allows a better classification of the disease phenotypes as compared with related diseases using complex biomarkers, and the design of novel diagnostic and therapeutic interventions targeting multiple components of the molecular and cellular pathways involved that can be tested and validated in prospective clinical trials (Table 1).

The case of severe asthma enables the establishment of links between disease models (eg, experimental virus-induced exacerbation models both in animals and humans in vivo) and patient cohorts (natural exacerbations) in order to identify individual biomarkers or preferably whole pathways. These can then be used to improve the disease models and make them more predictive of real, natural disease and more relevant to drug discovery. For example, current ovalbumin challenge models of asthma in mice are not predictive of drug efficacy in human disease.^21-23 A murine model that uses chronic exposure to an aerosolized antigen (eg, house dust mite) may provide a model more reflective of human asthma. Overlaying this model with a standardized infection challenge may generate a model that more accurately reflects the lung physiology, pathology, and remodeling characteristics of severe asthma, particularly during exacerbations. A similar iterative approach analyzing the effect of various inflammatory stimuli and steroid responsiveness on primary cells and tissues from patients with severe asthma may reveal key signaling pathways involved in this severe asthma phenotype. It is likely that several iterations of the models will be needed to achieve optimal results.

It will also be important and challenging to relate the observations made in patient/healthy volunteer cohorts to environmental influences (eg, pollution, cigarette smoking), which are known to have an enormous influence on genetic determinants of disease.⁵¹ With the full implementation of the systems approach described here, it is expected that the fingerprints and phenotype handprints generated will reflect on molecular- and cellular-level events occurring on short time scales and their relationships with appearance or progression of disease at the organ and body levels over long time scales. In the longer term, such computable multiscale mathematical models should enable virtual iterative simulation experiments that could ultimately replace unethical, overly expensive or impossible-to-perform experiments.

Financial/nonfinancial disclosures: The authors have reported to CHEST the following conflicts of interest: Dr Auffray has done work on systems approaches to pulmonary diseases supported by the French government through CNRS and by the Association Nationale pour les Traitements A Domicile, les Innovations et la Recherche, and is a member of Scientific Advisory Board, Mérieux Alliance (unrelated to the topic of this review). Drs Adcock, Chung, and Djukanovic have done work on asthma and COPD supported by grants from UK research agencies, charities, and industry. Dr Pison has done work on asthma and COPD supported by the French government through Inserm and by grants from charities and industry, and has done work on systems approaches to pulmonary diseases supported by the Association Nationale pour les Traitements A Domicile, les Innovations et la Recherche. Dr Sterk has done work on asthma and COPD supported by grants from the Dutch government, charities, and industry.

Other contributions: We thank the Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes Consortium partners for their valuable insights.