Multi-Level Integrated Analysis of Chronic Obstructive Pulmonary Disease (COPD) heterogeneity

[eng] Non-Communicable Diseases (NCDs), including cancer, cardiovascular (heart diseases or stroke), respiratory (COPD or asthma) and metabolic diseases (diabetes) are chronic conditions that represent a major global health problem of the 21st century. All of them, however, are the end-result of a complex set of gene-environment interactions that develop over years and often lead to several NCDs co-existing in the same individual (multi-morbidity). Multi-level integrated analysis has the potential to uncover the heterogeneity of NCDs by conceptualizing them as emergent properties of a complex, non-linear, dynamic and multilevel biological system, or network of biological and environmental interactions. Chronic Obstructive Pulmonary Disease (COPD) is a NCD of increasing prevalence worldwide that is projected to be by 2020 the third leading cause of death worldwide. It is currently viewed as a broad diagnostic term that encompass a continuum of subtypes each characterized by distinct functional or pathobiological mechanisms (endotypes) and is characterized by persistent respiratory symptoms and airflow limitation. The underlying hypothesis of this PhD Thesis is that multi-level integrated analysis can help us understand highly heterogeneous respiratory diseases such as COPD. Specifically, the following two aspects of COPD heterogeneity will be addressed: 1) Exacerbations of COPD (ECOPD): ECOPD are episodes of worsening of the symptoms whose pathogenesis and biology are not entirely understood. They are heterogeneous events of non-specific diagnosis. Biomarkers analysis and networks medicine were used to uncover novel pathobiological information from the comparison of the multi-level (i.e., clinical, physiological, biological, imaging and microbiological) correlation networks determined during ECOPD and clinical recover. We concluded that ECOPD are characterised by disruption of network homeokinesis that exists during convalescence and can be identified objectively by using a panel of three biomarkers (dyspnoea, circulating neutrophils and CRP levels) frequently determined in clinical practice. 2) Early low lung function and health in later life: In 2015 Lange P. et al. showed that low peak lung function in early adulthood is associated with the diagnosis of COPD later in life. We assessed in three general population cohorts the prevalence of low peak lung function and its association with other clinical or biological parameters - specifically respiratory, cardiovascular, and metabolic abnormalities – as well as incidence of comorbid diseases during follow-up. We concluded that low peak lung function in early adulthood is common in the general population and could identify a group of individuals at risk of early (cardiovascular, metabolic and systemic) comorbidities and premature death

Lung immune signatures define two groups of end-stage IPF patients

BackgroundThe role of the immune system in the pathobiology of Idiopathic Pulmonary Fibrosis (IPF) is controversial.MethodsTo investigate it, we calculated immune signatures with Gene Set Variation Analysis (GSVA) and applied them to the lung transcriptome followed by unbiased cluster analysis of GSVA immune-enrichment scores, in 109 IPF patients from the Lung Tissue Research Consortium (LTRC). Results were validated experimentally using cell-based methods (flow cytometry) in lung tissue of IPF patients from the University of Pittsburgh (n = 26). Finally, differential gene expression and hypergeometric test were used to explore non-immune differences between clusters.ResultsWe identified two clusters (C#1 and C#2) of IPF patients of similar size in the LTRC dataset. C#1 included 58 patients (53%) with enrichment in GSVA immune signatures, particularly cytotoxic and memory T cells signatures, whereas C#2 included 51 patients (47%) with an overall lower expression of GSVA immune signatures (results were validated by flow cytometry with similar unbiased clustering generation). Differential gene expression between clusters identified differences in cilium, epithelial and secretory cell genes, all of them showing an inverse correlation with the immune response signatures. Notably, both clusters showed distinct features despite clinical similarities.ConclusionsIn end-stage IPF lung tissue, we identified two clusters of patients with very different levels of immune signatures and gene expression but with similar clinical characteristics. Weather these immune clusters differentiate diverse disease trajectories remains unexplored

Multi-level immune response network in mild-moderate Chronic Obstructive Pulmonary Disease (COPD)

Background: Chronic Obstructive Pulmonary Disease (COPD) is associated with an abnormal pulmonary and systemic immune response to tobacco smoking. Yet, how do immune cells relate within and between these two biological compartments, how the pulmonary infiltrate influences the lung transcriptome, and what is the role of active smoking vs. presence of disease is unclear. Methods: To investigate these questions, we simultaneously collected lung tissue and blood from 65 individuals stratified by smoking habit and presence of the disease. The immune cell composition of both tissues was assessed by flow cytometry, whole lung transcriptome was determined with Affymetrix arrays, and we used Weighted Gene Co-expression Network Analysis (WGCNA) to integrate results. Results: Main results showed that: (1) current smoking and the presence of COPD were both independently associated with a reduction in the proportion of lung T cells and an increase of macrophages, specifically those expressing CD80 + CD163+; (2) changes in the proportion of infiltrating macrophages, smoking status or the level of airflow limitation were associated to different WGCNA modules, which were enriched in iron ion transport, extracellular matrix and cilium organization gene ontologies; and, (3) circulating white blood cells counts were correlated with lung macrophages and T cells. Conclusions: Mild-moderated COPD lung immune infiltrate is associated with the active smoking status and presence of disease; is associated with changes in whole lung tissue transcriptome and marginally reflected in blood

Bone marrow characterization in COPD: a multi-level network analysis

BACKGROUND: Bone marrow (BM) produces hematopoietic and progenitor cells that contribute to distant organ inflammation and repair. Chronic obstructive pulmonary disease (COPD) is characterized by defective lung repair. Yet, BM composition has not been previously characterized in COPD patients. METHODS: In this prospective and controlled study, BM was obtained by sternum fine-needle aspiration in 35 COPD patients and 25 healthy controls (10 smokers and 15 never-smokers). BM cell count and immunophenotype were determined by microscopy and flow cytometry, respectively. Circulating inflammatory (C-reactive protein, IL-6, IL-8) and repair markers (HGF, IGF, TGF-β, VEGF) were quantified by ELISA. Results were integrated by multi-level network correlation analysis. RESULTS: We found that: (1) there were no major significant pair wise differences between COPD patients and controls in the BM structural characteristics; (2) multi-level network analysis including patients and controls identifies a relation between immunity, repair and lung function not previously described, that remains in the COPD network but is absent in controls; and (3) this novel network identifies eosinophils as a potential mediator relating immunity and repair, particularly in patients with emphysema. CONCLUSIONS: Overall, these results suggest that BM is activated in COPD with impaired repair capacity in patients with more emphysema and/or higher circulating eosinophils

Do sputum or circulating blood samples reflect the pulmonary transcriptomic differences of COPD patients? A multi-tissue transcriptomic network META-analysis

BACKGROUND: Previous studies have identified lung, sputum or blood transcriptomic biomarkers associated with the severity of airflow limitation in COPD. Yet, it is not clear whether the lung pathobiology is mirrored by these surrogate tissues. The aim of this study was to explore this question. METHODS: We used Weighted Gene Co-expression Network Analysis (WGCNA) to identify shared pathological mechanisms across four COPD gene-expression datasets: two sets of lung tissues (L1 n = 70; L2 n = 124), and one each of induced sputum (S; n = 121) and peripheral blood (B; n = 121). RESULTS: WGCNA analysis identified twenty-one gene co-expression modules in L1. A robust module preservation between the two L datasets was observed (86%), with less preservation in S (33%) and even less in B (23%). Three modules preserved across lung tissues and sputum (not blood) were associated with the severity of airflow limitation. Ontology enrichment analysis showed that these modules included genes related to mitochondrial function, ion-homeostasis, T cells and RNA processing. These findings were largely reproduced using the consensus WGCNA network approach. CONCLUSIONS: These observations indicate that major differences in lung tissue transcriptomics in patients with COPD are poorly mirrored in sputum and are unrelated to those determined in blood, suggesting that the systemic component in COPD is independently regulated. Finally, the fact that one of the preserved modules associated with FEV1 was enriched in mitochondria-related genes supports a role for mitochondrial dysfunction in the pathobiology of COPD

From systems biology to P4 medicine: applications in respiratory medicine

Human health and disease are emergent properties of a complex, nonlinear, dynamic multilevel biological system: the human body. Systems biology is a comprehensive research strategy that has the potential to understand these emergent properties holistically. It stems from advancements in medical diagnostics, “omics” data and bioinformatic computing power. It paves the way forward towards “P4 medicine” (predictive, preventive, personalised and participatory), which seeks to better intervene preventively to preserve health or therapeutically to cure diseases. In this review, we: 1) discuss the principles of systems biology; 2) elaborate on how P4 medicine has the potential to shift healthcare from reactive medicine (treatment of illness) to predict and prevent illness, in a revolution that will be personalised in nature, probabilistic in essence and participatory driven; 3) review the current state of the art of network (systems) medicine in three prevalent respiratory diseases (chronic obstructive pulmonary disease, asthma and lung cancer); and 4) outline current challenges and future goals in the field

The EASI model: A first integrative computational approximation to the natural history of COPD

The natural history of chronic obstructive pulmonary disease (COPD) is still not well understood. Traditionally believed to be a self-inflicted disease by smoking, now we know that not all smokers develop COPD, that other inhaled pollutants different from cigarette smoke can also cause it, and that abnormal lung development can also lead to COPD in adulthood. Likewise, the inflammatory response that characterizes COPD varies significantly between patients, and not all of them perceive symptoms (mostly breathlessness) similarly. To investigate the variability and determinants of different "individual natural histories" of COPD, we developed a theoretical, multi-stage, computational model of COPD (EASI) that integrates dynamically and represents graphically the relationships between exposure (E) to inhaled particles and gases (smoking), the biological activity (inflammatory response) of the disease (A), the severity (S) of airflow limitation (FEV1) and the impact (I) of the disease (breathlessness) in different clinical scenarios. EASI shows that the relationships between E, A, S and I vary markedly within individuals (through life) and between individuals (at the same age). It also helps to delineate some potentially relevant, but often overlooked concepts, such as disease progression, susceptibility to COPD and issues related to symptom perception. In conclusion, EASI is an initial conceptual model to interpret the longitudinal and cross-sectional relationships between E, A, S and I in different clinical scenarios. Currently, it does not have any direct clinical application, thus it requires experimental validation and further mathematical development. However, it has the potential to open novel research and teaching alternatives

Bone marrow characterization in COPD: a multi-level network analysis

Abstract Background Bone marrow (BM) produces hematopoietic and progenitor cells that contribute to distant organ inflammation and repair. Chronic obstructive pulmonary disease (COPD) is characterized by defective lung repair. Yet, BM composition has not been previously characterized in COPD patients. Methods In this prospective and controlled study, BM was obtained by sternum fine-needle aspiration in 35 COPD patients and 25 healthy controls (10 smokers and 15 never-smokers). BM cell count and immunophenotype were determined by microscopy and flow cytometry, respectively. Circulating inflammatory (C-reactive protein, IL-6, IL-8) and repair markers (HGF, IGF, TGF-β, VEGF) were quantified by ELISA. Results were integrated by multi-level network correlation analysis. Results We found that: (1) there were no major significant pair wise differences between COPD patients and controls in the BM structural characteristics; (2) multi-level network analysis including patients and controls identifies a relation between immunity, repair and lung function not previously described, that remains in the COPD network but is absent in controls; and (3) this novel network identifies eosinophils as a potential mediator relating immunity and repair, particularly in patients with emphysema. Conclusions Overall, these results suggest that BM is activated in COPD with impaired repair capacity in patients with more emphysema and/or higher circulating eosinophils

Lung immune signatures define two groups of end-stage IPF patients

Abstract Background The role of the immune system in the pathobiology of Idiopathic Pulmonary Fibrosis (IPF) is controversial. Methods To investigate it, we calculated immune signatures with Gene Set Variation Analysis (GSVA) and applied them to the lung transcriptome followed by unbiased cluster analysis of GSVA immune-enrichment scores, in 109 IPF patients from the Lung Tissue Research Consortium (LTRC). Results were validated experimentally using cell-based methods (flow cytometry) in lung tissue of IPF patients from the University of Pittsburgh (n = 26). Finally, differential gene expression and hypergeometric test were used to explore non-immune differences between clusters. Results We identified two clusters (C#1 and C#2) of IPF patients of similar size in the LTRC dataset. C#1 included 58 patients (53%) with enrichment in GSVA immune signatures, particularly cytotoxic and memory T cells signatures, whereas C#2 included 51 patients (47%) with an overall lower expression of GSVA immune signatures (results were validated by flow cytometry with similar unbiased clustering generation). Differential gene expression between clusters identified differences in cilium, epithelial and secretory cell genes, all of them showing an inverse correlation with the immune response signatures. Notably, both clusters showed distinct features despite clinical similarities. Conclusions In end-stage IPF lung tissue, we identified two clusters of patients with very different levels of immune signatures and gene expression but with similar clinical characteristics. Weather these immune clusters differentiate diverse disease trajectories remains unexplored