Lung Tissue Multi-Layer Network Analysis Uncovers the Molecular Heterogeneity of COPD.

BACKGROUND: Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous condition. We hypothesized that the unbiased integration of different COPD lung omics using a novel multi-layer approach may unravel mechanisms associated with clinical characteristics. METHODS: We profiled mRNA, miRNA and methylome in lung tissue samples from 135 former smokers with COPD. For each omic (layer) we built a patient network based on molecular similarity. The three networks were used to build a multi-layer network, and optimization of multiplex-modularity was employed to identify patient communities across the three distinct layers. Uncovered communities were related to clinical features. RESULTS: We identified five patient communities in the multi-layer network which were molecularly distinct and related to clinical characteristics, such as FEV1 and blood eosinophils. Two communities (C#3 and C#4) had both similarly low FEV1 values and emphysema, but were molecularly different: C#3, but not C#4, presented B and T cell signatures and a downregulation of secretory (SCGB1A1/SCGB3A1) and ciliated cells. A machine learning model was set up to discriminate C#3 and C#4 in our cohort, and to validate them in an independent cohort. Finally, using spatial transcriptomics we characterized the small airway differences between C#3 and C#4, identifying an upregulation of T/B cell homing chemokines, and bacterial response genes in C#3. CONCLUSIONS: A novel multi-layer network analysis is able to identify clinically relevant COPD patient communities. Patients with similarly low FEV1 and emphysema can have molecularly distinct small airways and immune response patterns, indicating that different endotypes can lead to similar clinical presentation.

Similarity network fusion to identify phenotypes of small-for-gestational-age fetuses.

Fetal growth restriction (FGR) affects 5-10% of pregnancies, is the largest contributor to fetal death, and can have long-term consequences for the child. Implementation of a standard clinical classification system is hampered by the multiphenotypic spectrum of small fetuses with substantial differences in perinatal risks. Machine learning and multiomics data can potentially revolutionize clinical decision-making in FGR by identifying new phenotypes. Herein, we describe a cluster analysis of FGR based on an unbiased machine-learning method. Our results confirm the existence of two subtypes of human FGR with distinct molecular and clinical features based on multiomic analysis. In addition, we demonstrated that clusters generated by machine learning significantly outperform single data subtype analysis and biologically support the current clinical classification in predicting adverse maternal and neonatal outcomes. Our approach can aid in the refinement of clinical classification systems for FGR supported by molecular and clinical signatures.

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

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.

Optimized whole-genome CRISPR interference screens identify ARID1A-dependent growth regulators in human induced pluripotent stem cells.

Perturbing expression is a powerful way to understand the role of individual genes, but can be challenging in important models. CRISPR-Cas screens in human induced pluripotent stem cells (iPSCs) are of limited efficiency due to DNA break-induced stress, while the less stressful silencing with an inactive Cas9 has been considered less effective so far. Here, we developed the dCas9-KRAB-MeCP2 fusion protein for screening in iPSCs from multiple donors. We found silencing in a 200 bp window around the transcription start site in polyclonal pools to be as effective as using wild-type Cas9 for identifying essential genes, but with much reduced cell numbers. Whole-genome screens to identify ARID1A-dependent dosage sensitivity revealed the PSMB2 gene, and enrichment of proteasome genes among the hits. This selective dependency was replicated with a proteasome inhibitor, indicating a targetable drug-gene interaction. Many more plausible targets in challenging cell models can be efficiently identified with our approach.

Single cell RNA sequencing identifies IGFBP5 and QKI as ciliated epithelial cell genes associated with severe COPD.

BACKGROUND: Whole lung tissue transcriptomic profiling studies in chronic obstructive pulmonary disease (COPD) have led to the identification of several genes associated with the severity of airflow limitation and/or the presence of emphysema, however, the cell types driving these gene expression signatures remain unidentified. METHODS: To determine cell specific transcriptomic changes in severe COPD, we conducted single-cell RNA sequencing (scRNA seq) on n = 29,961 cells from the peripheral lung parenchymal tissue of nonsmoking subjects without underlying lung disease (n = 3) and patients with severe COPD (n = 3). The cell type composition and cell specific gene expression signature was assessed. Gene set enrichment analysis (GSEA) was used to identify the specific cell types contributing to the previously reported transcriptomic signatures. RESULTS: T-distributed stochastic neighbor embedding and clustering of scRNA seq data revealed a total of 17 distinct populations. Among them, the populations with more differentially expressed genes in cases vs. controls (log fold change >|0.4| and FDR = 0.05) were: monocytes (n = 1499); macrophages (n = 868) and ciliated epithelial cells (n = 590), respectively. Using GSEA, we found that only ciliated and cytotoxic T cells manifested a trend towards enrichment of the previously reported 127 regional emphysema gene signatures (normalized enrichment score [NES] = 1.28 and = 1.33, FDR = 0.085 and = 0.092 respectively). Among the significantly altered genes present in ciliated epithelial cells of the COPD lungs, QKI and IGFBP5 protein levels were also found to be altered in the COPD lungs. CONCLUSIONS: scRNA seq is useful for identifying transcriptional changes and possibly individual protein levels that may contribute to the development of emphysema in a cell-type specific manner.

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.

Smoking Impairs the Immunomodulatory Capacity of Lung-Resident Mesenchymal Stem Cells in Chronic Obstructive Pulmonary Disease.

Tobacco smoking is the main environmental risk factor for chronic obstructive pulmonary disease (COPD), but not all smokers develop the disease. A population of lung-resident mesenchymal stem cells (LR-MSCs) exist in healthy lungs, but how tobacco smoking affects them and their role in COPD have not been assessed yet. Using a sphere-based culture technique, we isolated LR-MSCs from lung tissue obtained from nonsmokers and current and former smokers with and without COPD (n = 53). The cells were characterized by flow cytometry and Affymetrix arrays. Their immunomodulatory capacity was assessed in vitro using cocultures with T cells and after preincubation with 2.5% and 5% cigarette smoke extract. We were able to isolate LR-MSCs expressing similar phenotypic markers in all of the study groups. LR-MSCs from current smokers with COPD expressed different levels of CX3CL1 and CCL5 cytokines, and were unable to modulate CD8+ T-cell proliferation. Preincubation of LR-MSCs with cigarette smoke extract reduced their immunomodulatory capacity. In conclusion, 1) LR-MSCs can be isolated in similar amounts from never-smokers and smokers with and without COPD; 2) their immunomodulatory capacity is impaired in current smokers with COPD, but not in those with normal lung function; and 3) this is reversible after smoking cessation and is reproducible in vitro.

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.

Distribution, temporal stability and association with all-cause mortality of the 2017 GOLD groups in the ECLIPSE cohort.

BACKGROUND: In 2017, the Global Initiative for Chronic Obstructive Lung Disease (GOLD) proposed a new classification of patients with chronic obstructive pulmonary disease (COPD). MATERIAL AND METHODS: We contrasted the distribution of COPD patients according to GOLD 2017 and 2011 classifications, the temporal stability of the 2017 groups during 3 years follow-up and their association with all-cause mortality in the ECLIPSE cohort. RESULTS: We found that GOLD 2017: (1) switched a substantial proportion of GOLD 2011C and D patients to A and B groups at recruitment; (2) about half of A, B and D patients remained in the same group at the end of follow-up, whereas 74% of C patients (the smallest group of all) changed, either because exacerbation rate decreased or dyspnea increased; and, (3) all-cause mortality by group was not significantly different between GOLD 2011 and 2017. Of note, mortality in B (16%) and D patients (18%) was similar, both with similar severity of airflow limitation, the best individual mortality risk factor. CONCLUSIONS: These results illustrate the cross-sectional and longitudinal effects of excluding FEV1 from GOLD 2017, and highlight both the clinical relevance of symptom assessment in the management of COPD and the prognostic capacity of FEV1.

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-ß, 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.

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.

Lung function in early adulthood and health in later life: a transgenerational cohort analysis.

BACKGROUND: Early life events can affect health in later life. We hypothesised that low lung function (FEV1 <80% predicted) in early adulthood (25-40 years) is associated with higher prevalence and earlier incidence of respiratory, cardiovascular, and metabolic abnormalities, and premature death. METHODS: In this cohort analysis, we tested this hypothesis using data from the Framingham Offspring Cohort (FOC) and validated our observations in CARDIA (an independent cohort) and GenIII (which includes the direct descendants of FOC participants). These were three general population cohorts that included men and women, who were regularly and prospectively followed up to collect extensive clinical, physiological, biological, and imaging information. Main outcomes were prevalence (in early adulthood) and incidence (during follow-up) of comorbidity, and all-cause mortality. χ2 test, unpaired t test, Fisher's exact test, and Cox proportional hazards models were used for data analysis. Differential dropout rates during follow-up were regarded as a potential source of bias. FINDINGS: We found that 111 (10%) of 1161 participants in FOC, 338 (13%) of 2648 participants in CARDIA, and 71 (4%) of 1912 participants in GenIII had FEV1 of less than 80% predicted at the age of 25-40 years. These individuals also had higher prevalence of respiratory, cardiovascular, and metabolic abnormalities in early adulthood; higher and earlier (about a decade) incidence of comorbidities during follow-up (39 years vs 47 years in FOC; 30 years vs 37 years in CARDIA, p<0·0001); and higher all-cause mortality than individuals with normal lung function in early adulthood (in FOC, hazard ratio 2·3 [95% CI 1·4-3·7], p=0·001), which was independent of, but additive with, cumulative smoking exposure. In GenIII, we observed that individuals with at least one parent stratified as having low lung function in early adulthood in FOC (n=115) had lower FEV1 in early adulthood (10% had FEV1 of less than 80% predicted; this proportion was 3% in those with both parents classified as normal in FOC [n=248]; p<0·0001); and early adulthood FEV1 of GenIII participants was related (R2=0·28, p<0·0001) to FOC parents' average FEV1 in early adulthood. INTERPRETATION: Low peak lung function in early adulthood is common in the general population and could identify a group of individuals at risk of early comorbidities and premature death. FUNDING: Fondo de Investigacion Sanitaria, Sociedad Española de Neumologia y Cirurgia Torácica, Formació Personal Investigador, Agencia de Gestió d'Ajuts de Recerca 2016, and AstraZeneca Foundation Young Researcher Award.

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.

Multi-level differential network analysis of COPD exacerbations.

Patients with chronic obstructive pulmonary disease (COPD) often suffer episodes of exacerbation (ECOPD) that impact negatively the course of their disease. ECOPD are heterogeneous events of unclear pathobiology and non-specific diagnosis. Network analysis is a novel research approach that can help unravelling complex biological systems. We hypothesised that the comparison of multi-level (i.e., clinical, physiological, biological, imaging and microbiological) correlation networks determined during ECOPD and convalescence can yield novel patho-biologic information.In this proof-of-concept study we included 86 patients hospitalised because of ECOPD in a multicentre study in Spain. Patients were extensively characterised both during the first 72 h of hospitalisation and during clinical stability, at least 3 months after hospital discharge.We found that 1) episodes of ECOPD are characterised by disruption of the network correlation observed during convalescence; and 2) a panel of biomarkers that include increased levels of dyspnoea, circulating neutrophils and C-reactive protein (CRP) has a high predictive value for ECOPD diagnosis (AUC 0.97).We conclude that ECOPD 1) are characterised by disruption of network homeokinesis that exists during convalescence; and 2) can be identified objectively by using a panel of three biomarkers (dyspnoea, circulating neutrophils and CRP levels) frequently determined in clinical practice.

Network Analysis of Lung Transcriptomics Reveals a Distinct B-Cell Signature in Emphysema.

RATIONALE: Chronic obstructive pulmonary disease (COPD) is characterized by chronic airflow limitation caused by a combination of airways disease (bronchiolitis) and parenchymal destruction (emphysema), whose relative proportion varies from patient to patient. OBJECTIVES: To explore and contrast the molecular pathogenesis of emphysema and bronchiolitis in COPD. METHODS: We used network analysis of lung transcriptomics (Affymetrix arrays) in 70 former smokers with COPD to compare differential expression and gene coexpression in bronchiolitis and emphysema. MEASUREMENTS AND MAIN RESULTS: We observed that in emphysema (but not in bronchiolitis) (1) up-regulated genes were enriched in ontologies related to B-cell homing and activation; (2) the immune coexpression network had a central core of B cell-related genes; (3) B-cell recruitment and immunoglobulin transcription genes (CXCL13, CCL19, and POU2AF1) correlated with emphysema severity; (4) there were lymphoid follicles (CD20(+)IgM(+)) with active B cells (phosphorylated nuclear factor-κB p65(+)), proliferation markers (Ki-67(+)), and class-switched B cells (IgG(+)); and (5) both TNFRSF17 mRNA and B cell-activating factor protein were up-regulated. These findings were by and large reproduced in a group of patients with incipient emphysema and when patients with emphysema were matched for the severity of airflow limitation of those with bronchiolitis. CONCLUSIONS: Our study identifies enrichment in B cell-related genes in patients with COPD with emphysema that is absent in bronchiolitis. These observations contribute to a better understanding of COPD pathobiology and may open new therapeutic opportunities for patients with COPD.