Complex systems in pulmonary medicine: a systems biology approach to lung disease

Models and Data Sources Used in Systems Medicine

Background: Systems medicine is a new approach for the development and selection of treatment strategies for patients with complex diseases. It is often referred to as the application of systems biology methods for decision making in patient care. For systems medicine computer applications, many different data sources have to be integrated and included into models. This is a challenging task for Medical Informatics since the approach exceeds traditional systems like Electronic Health Records. To prioritize research activities for systems medicine applications, it is necessary to get an overview over modelling methods and data sources already used in this field.

Objectives: We performed a systematic literature review with the objective to capture current use of 1) modelling methods and 2) data sources in systems medicine related research projects.

Methods: We queried the MEDLINE and ScienceDirect databases for papers associated with the search term systems medicine and related terms. Papers were screened and assessed in full text in a two-step process according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement guidelines.

Results: The queries returned 698 articles of which 34 papers were finally included into the study. A multitude of modelling approaches such as machine learning and network analysis was identified and classified. Since these approaches are also used in other domains, no methods specific for systems medicine could be identified. Omics data are the most widely used data types followed by clinical data. Most studies only include a rather limited number of data sources.

Conclusions: Currently, many different modelling approaches are used in systems medicine. Thus, highly flexible modular solutions are necessary for systems medicine clinical applications. However, the number of data sources included into the models is limited and most projects currently focus on prognosis. To leverage the potential of systems medicine further, it will be necessary to focus on treatment strategies for patients and consider a broader range of data.

Exposure Memory and Lung Regeneration

Many acute and chronic lung diseases could benefit from improved regeneration therapy. In development and throughout life, genetically encoded exposure memory systems allow environmental exposures, diet, and infectious agents to direct subsequent phenotypic adaptation and responses. The impact of such memory systems on lung regeneration is currently unknown. This article provides a brief overview of advances in redox biology and medicine as a framework for elucidating exposure memory and delineating spatiotemporal responses in lung regeneration. New imaging and omics methods enable precise definition to advance knowledge of development and the cumulative changes in lung biochemistry, structure, and cell populations occurring from prior and ongoing exposures. Importantly, conditioning steps may be needed to reverse exposure memory and enable effective regeneration. Thus, to complement developmental biology and regenerative medicine, research programs are needed to gain systematic knowledge of how lifelong exposures impact lung biology and support transition of lung regeneration from hypothetical to practical medicine.

Redox theory of aging: implications for health and disease

Genetics ultimately defines an individual, yet the phenotype of an adult is extensively determined by the sequence of lifelong exposures, termed the exposome. The redox theory of aging recognizes that animals evolved within an oxygen-rich environment, which created a critical redox interface between an organism and its environment. Advances in redox biology show that redox elements are present throughout metabolic and structural systems and operate as functional networks to support the genome in adaptation to environmental resources and challenges during lifespan. These principles emphasize that physical and functional phenotypes of an adult are determined by gene-environment interactions from early life onward. The principles highlight the critical nature of cumulative exposure memories in defining changes in resilience progressively during life. Both plasma glutathione and cysteine systems become oxidized with aging, and the recent finding that cystine to glutathione ratio in human plasma predicts death in coronary artery disease (CAD) patients suggests this could provide a way to measure resilience of redox networks in aging and disease. The emerging concepts of cumulative gene-environment interactions warrant focused efforts to elucidate central mechanisms by which exposure memory governs health and etiology, onset and progression of disease.

Systems Biology and Clinical Practice in Respiratory Medicine. The Twain Shall Meet

Respiratory diseases are highly complex, being driven by host-environment interactions and manifested by inflammatory, structural, and functional abnormalities that vary over time. Traditional reductionist approaches have contributed vastly to our knowledge of biological systems in health and disease to date; however, they are insufficient to provide an understanding of the behavior of the system as a whole. In this Pulmonary Perspective, we discuss systems biology approaches, especially but not limited to the study of the lung as a complex system. Such integrative approaches take into account the large number of dynamic subunits and their interactions found in biological systems. Borrowing methods from physics and mathematics, it is possible to study the collective behavior of these systems over time and in a multidimensional manner. We first examine the physiological basis for complexity in the respiratory system and its implications for disease. We then expand on the potential applications of systems biology methods to study complex systems, within the context of diagnosis and monitoring of respiratory diseases including asthma, chronic obstructive pulmonary disease (COPD), and critical illness. We summarize the significant advances made in recent years using systems approaches for disease phenotyping, applied to data ranging from the molecular to clinical level, obtained from large-scale asthma and COPD networks. We describe new studies using temporal complexity patterns to characterize asthma and COPD and predict exacerbations as well as predict adverse outcomes in critical care. We highlight new methods that are emerging with this approach and discuss remaining questions that merit greater attention in the field.

Brazilian studies on pulmonary function in COPD patients: what are the gaps?

BACKGROUND

COPD is a major cause of death and morbidity worldwide, and is characterized by persistent airflow obstruction. The evaluation of obstruction is critically dependent on sensitive methods for lung-function testing. A wide body of knowledge has been accumulated in recent years showing that these methods have been significantly refined and seems promising for detection of early disease.

OBJECTIVES

This review focuses on research on pulmonary function analysis in COPD performed in Brazil during this century.

MATERIALS AND METHODS

The literature was searched using a systematic search strategy limited to English language studies that were carried out in Brazil from the year 2000 onward, with study objectives that included a focus on lung function.

RESULTS

After we applied our inclusion and exclusion criteria, 94 articles addressed our stated objectives. Among the new methods reviewed are the forced-oscillation technique and the nitrogen-washout test, which may provide information on small-airway abnormalities. Studies investigating the respiratory muscles and thoracoabdominal motion are also discussed, as well as studies on automatic clinical decision-support systems and complexity measurements. We also examined important gaps in the present knowledge and suggested future directions for the cited research fields.

CONCLUSION

There is clear evidence that improvements in lung-function methods allowed us to obtain new pathophysiological information, contributing to improvement in our understanding of COPD. In addition, they may also assist in the diagnosis and prevention of COPD. Further investigations using prospective and longitudinal design may be of interest to elucidate the use of these new methods in the diagnosis and prevention of COPD.

Fluticasone propionate in clinically suspected asthma patients with negative methacholine challenge test

INTRODUCTION

Despite reports of response to steroid inhaler in some clinically suspected asthma patients with negative methacholine challenge test (CSA/MCT-), treatment in these patients has not been prospectively studied.

OBJECTIVE

We studied the role of a 12 week high dose inhaled fluticasone trial in CSA/MCT- patients.

METHODS

After a 2 week run-in period, CSA/MCT-patients were treated with 12 weeks of Fluticasone propionate 1000 µg/day. The Asthma Control Test (ACT), numeric cough score (NCS) and bronchodilator use were compared with their pretreatment values.

RESULTS

Thirty-four of 42 CSA/MCT-patients completed the study. Mean pretreatment ACT score (pACT) was significantly increased after treatment (14.7 ± 3.37 to 20.9 ± 3.1, P < 0.001). Posttreatment values of daytime (1.0 ± 1.0) and night-time (0.6 ± 0.9) NCS decreased compared to their pretreatment values (2.8 ± 1.1 and 1.9 ± 1.3, respectively; P < 0.001). ACT score change (ΔACT) were significantly greater in those with pACT < 15 than in those ≥15 (P < 0.001) . Fifteen of 21 patients with ΔACT > 5 did not need to use bronchodilator for their symptom relief. Wheeze disappeared in all six patients with ΔACT > 5 after the trial. Six months after the study, steroid inhaler continued to be used by 72.2% of patients.

CONCLUSION

A significant portion of CSA/MCT- (especially those with pretreatment ACT score <15) respond to high dose fluticasone inhaler in terms of symptoms relief, disappearance of wheeze and need to bronchodilator use. ΔACT could not be predicted with any individual symptoms or signs before MCT, % FEV1 decline or symptoms during MCT and exhaled nitric oxide.

Disease models of chronic inflammatory airway disease: applications and requirements for clinical trials

PURPOSE OF REVIEW

This review will discuss methodologies and applicability of key inflammatory models of respiratory disease in proof of concept or proof of efficacy clinical studies. In close relationship with these models, induced sputum and inflammatory cell counts will be addressed for phenotype-directed drug development. Additionally, important regulatory aspects regarding noninvestigational medicinal products used in bronchial challenges or clinical inflammatory models of respiratory disease will be highlighted.

RECENT FINDINGS

The recognition of an ever increasing number of phenotypes and endotypes within conditions such as asthma and chronic obstructive pulmonary disease urges phenotyping of study populations already in early clinical phases of drug development. Apart from the choice of a relevant disease model, recent studies show that especially targeted therapies need to be tested in well defined disease subsets for adequate efficacy assessment. Noninvasive biomarkers, especially sputum inflammatory cell counts, aid phenotyping and are useful outcome measures for novel, targeted therapies.

SUMMARY

Disease phenotyping becomes increasingly important for efficient and cost-effective drug development and subsequent disease management. Inflammatory models of respiratory disease combined with sputum biomarkers are important tools in this approach.

Systems biology approach for subtyping asthma; where do we stand now?

PURPOSE OF REVIEW

The purpose of this review is to discuss the present systems biology approach to asthma and how it is helping to define asthma subtypes. Although the general concept of systems biology will be discussed, the article will focus on recent developments in the field related to asthma.

RECENT FINDINGS

The most recent work in systems biology and asthma has occurred in the area of genomics (e.g., pharmacogenomics and gene-environment interactions), protein interaction networks [e.g., interleukin (IL)-33/IL-1 receptor-like 1 signaling], cluster analysis of asthma patients (e.g., application of severe asthma research program clusters to a general urban asthma population), and multiscale approaches to asthma encompassing data from the molecule to whole organ (e.g., modeling of airways hyperresponsiveness).

SUMMARY

The results of recent work in this area have led to new insight into gene-cytokine and protein-protein networks involved in asthma, a better determination of key clinical factors associated with asthma subtypes, and the beginning of sophisticated multiscale approaches to modeling, understanding and predicting the behavior of the asthmatic lung.

Integrating omics technologies to study pulmonary physiology and pathology at the systems level

Assimilation and integration of "omics" technologies, including genomics, epigenomics, proteomics, and metabolomics has readily altered the landscape of medical research in the last decade. The vast and complex nature of omics data can only be interpreted by linking molecular information at the organismic level, forming the foundation of systems biology. Research in pulmonary biology/medicine has necessitated integration of omics, network, systems and computational biology data to differentially diagnose, interpret, and prognosticate pulmonary diseases, facilitating improvement in therapy and treatment modalities. This review describes how to leverage this emerging technology in understanding pulmonary diseases at the systems level -called a "systomic" approach. Considering the operational wholeness of cellular and organ systems, diseased genome, proteome, and the metabolome needs to be conceptualized at the systems level to understand disease pathogenesis and progression. Currently available omics technology and resources require a certain degree of training and proficiency in addition to dedicated hardware and applications, making them relatively less user friendly for the pulmonary biologist and clinicians. Herein, we discuss the various strategies, computational tools and approaches required to study pulmonary diseases at the systems level for biomedical scientists and clinical researchers.

Airflow pattern complexity during resting breathing in patients with COPD: effect of airway obstruction

We investigated the influence of airway obstruction in the complexity of the airflow pattern in COPD and its use as a marker of disease activity. The sample entropy (SampEnV') and the variability (SDV') of the airflow pattern were measured in a group of 88 subjects with various levels of airway obstruction. Airway obstruction resulted in a reduction in the SampEnV' (p<0.0001) that was significantly correlated with spirometric indices of airway obstruction (R=0.50, p<0.001). The early adverse effects in mild airway obstruction were detected by the SampEnV' with an accuracy of 84%. SDV' increased with airway obstruction (p<0.002). We conclude that (1) the airflow patterns in COPD exhibit reduced complexity compared with healthy subjects; (2) this reduction in complexity is proportional to airway obstruction; and (3) the evaluation of SampEnV' may provide novel respiratory biomarkers suitable to facilitate the diagnosis of respiratory abnormalities in COPD.

Fluctuation analysis of respiratory impedance waveform in asthmatic patients: effect of airway obstruction

Fluctuation analysis has great potential to contribute to pulmonary clinical science and practice. We evaluated the relationship between asthma and the respiratory impedance recurrence period density entropy (RPDEnZrs) and the variability (SDZrs). A non-invasive and simple protocol for assessing respiratory mechanics during spontaneous breathing was used in a group of 74 subjects with various levels of airway obstruction. Airway obstruction resulted in a reduction in the RPDEnZrs that was significantly correlated with both spirometric indices of airway obstruction (R = 0.48, p < 0.0001) and mean respiratory impedance (R = -0.83, p < 0.0001). These results suggest that the impedance pattern becomes less complex in asthmatic patients, which may explain the reduction in respiratory systems' adaptability to daily life activities. Preliminary evaluations indicate that RPDEnZrs may contribute to the asthma diagnosis, presenting accuracies of 82 and 87 % in patients with moderate and severe airway obstruction, respectively. On the other hand, SDZrs increased with obstruction (p < 0.0001) and was inversely correlated with spirometric indices of obstruction (R = -0.42, p = 0.0003) and directly associated with mean impedance (R = 0.88, p < 0.0001). This analysis contributes to elucidate previous studies and identified respiratory changes in patients with moderate and severe obstruction with an adequate accuracy (85 and 87 %, respectively).

Trials and tribulations in identifying new biologic treatments for asthma

Drugs used to treat asthma have a long history, beginning with the bronchodilators and evolving into compounds that suppress airway inflammation. Guidelines for treatment of asthma are largely based on disease severity and control, rather than underlying mechanisms. However, identification of biomarkers in the causal pathways of asthma is enabling responders to be differentiated from nonresponders. Initial efforts have focused on biomarkers of the T helper (Th)2 pathway because this is a target of novel therapeutics. A concerted effort is now needed to substratify asthma beyond Th2 pathways, and using appropriate biomarkers, to target only those patients likely to respond to a specific biologic. To achieve this goal, a different type of relationship is needed between academia and industry, and also within industry, to promote collaboration in the precompetitive space.

Urinary metabolomics in newborns and infants

Metabolomics is a new approach based on the systematic study of the full complement of metabolites in a biological sample. This technology consists of two sequential steps: (1) an experimental technique, based on nuclear magnetic resonance (NMR) spectroscopy or mass spectrometry, designed to profile low-molecular-weight compounds, and (2) multivariate data analysis. The metabolomic analysis of biofluids or tissues has been successfully used in the fields of physiology, diagnostics, functional genomics, pharmacology, toxicology, and nutrition. Recent studies have evaluated how physiological variables or pathological conditions can affect metabolomic profiles of different biofluids in pediatric populations. The overall metabolic status of the neonate is little known. If more information on perinatal/neonatal maturational processes and their metabolic background were available, the management of sick or preterm newborns might be improved. Currently, the use of metabolomics in neonatology is still in the pioneering phase. Meaningful diagnostic information and simple, noninvasive collection techniques make urine a particularly suitable biofluid for metabolomic approach in neonatal medicine, although blood has also been investigated. Different fields of neonatology such as postnatal maturation, asphyxia/hypoxia, inborn errors of metabolism, nutrition, nephrouropathies, nephrotoxicity, cardiovascular diseases, and other conditions have been investigated using a metabolomic approach. Together with genomics and proteomics, metabolomics appears to be a promising tool in neonatology for the monitoring of postnatal metabolic maturation, the identification of biomarkers as early predictors of outcome, the diagnosis and monitoring of various diseases, and the "tailored" management of neonatal disorders.

Airflow pattern complexity and airway obstruction in asthma

The scientific and clinical value of a measure of complexity is potentially enormous because complexity appears to be lost in the presence of illness. The authors examined the effect of elevated airway obstruction on the complexity of the airflow (Q) pattern of asthmatic patients analyzing the airflow approximate entropy (ApEnQ). This study involved 11 healthy controls, 11 asthmatics with normal spirometric exams, and 40 asthmatics with mild (14), moderate (14), and severe (12) airway obstructions. A significant (P < 0.02) reduction in the ApEnQ was observed in the asthmatic patients. This reduction was significantly correlated with spirometric indexes of airway obstruction [FEV(1) (%): R = 0.31, P = 0.013] and the total respiratory impedance (R = -0.39; P < 0.002). These results are in close agreement with pathophysiological fundamentals and suggest that the airflow pattern becomes less complex in asthmatic patients, which may reduce the adaptability of the respiratory system to perform the exercise that is associated with daily life activities. This analysis was able to identify respiratory changes in patients with mild obstruction with an adequate accuracy (83%). Higher accuracies were obtained in patients with moderate and severe obstructions. The analysis of airflow pattern complexity by the ApEnQ was able to provide new information concerning the changes associated with asthma. In addition, this analysis was also able to contribute to the detection of the adverse effects of asthma. Because these measurements are easy to perform, such a technique may represent an alternative and/or a complement to other conventional exams to help the clinical evaluations of asthmatic patients.