An airway epithelial IL-17A response signature identifies a steroid-unresponsive COPD patient subgroup

BACKGROUND. Chronic obstructive pulmonary disease (COPD) is a heterogeneous smoking-related disease characterized by airway obstruction and inflammation. This inflammation may persist even after smoking cessation and responds variably to corticosteroids. Personalizing treatment to biologically similar "molecular phenotypes" may improve therapeutic efficacy in a COPD. IL-17A is involved in neutrophilic inflammation and corticosteroid resistance, and thus may be particularly important in a COPD molecular phenotype. METHODS. We generated a gene expression signature of IL-17A response in bronchial airway epithelial brushings from smokers with and without COPD (n = 238) , and validated it using data from 2 randomized trials of IL-17 blockade in psoriasis. This IL-17 signature was related to clinical and pathologic characteristics in 2 additional human studies of COPD: (a) SPIROMICS (n = 47), which included former and current smokers with COPD, and (b) GLUCOLD (n = 79), in which COPD participants were randomized to placebo or corticosteroids. RESULTS. The IL-17 signature was associated with an inflammatory profile characteristic of an IL-17 response, including increased airway neutrophils and macrophages. In SPIROMICS the signature was associated with increased airway obstruction and functional small airways disease on quantitative chest CT. In GLUCOLD the signature was associated with decreased response to corticosteroids, irrespective of airway eosinophilic or type 2 inflammation. CONCLUSION. These data suggest that a gene signature of IL-17 airway epithelial response distinguishes a biologically, radiographically, and clinically distinct COPD subgroup that may benefit from personalized therapy

An airway epithelial IL-17A response signature identifies a steroid-unresponsive COPD patient subgroup

© 2019 American Society for Clinical Investigation. All rights reserved. BACKGROUND. Chronic obstructive pulmonary disease (COPD) is a heterogeneous smoking-related disease characterized by airway obstruction and inflammation. This inflammation may persist even after smoking cessation and responds variably to corticosteroids. Personalizing treatment to biologically similar "molecular phenotypes" may improve therapeutic efficacy in COPD. IL-17A is involved in neutrophilic inflammation and corticosteroid resistance, and thus may be particularly important in a COPD molecular phenotype. METHODS. We generated a gene expression signature of IL-17A response in bronchial airway epithelial brushings from smokers with and without COPD (n = 238), and validated it using data from 2 randomized trials of IL-17 blockade in psoriasis. This IL-17 signature was related to clinical and pathologic characteristics in 2 additional human studies of COPD: (a) SPIROMICS (n = 47), which included former and current smokers with COPD, and (b) GLUCOLD (n = 79), in which COPD participants were randomized to placebo or corticosteroids. RESULTS. The IL-17 signature was associated with an inflammatory profile characteristic of an IL-17 response, including increased airway neutrophils and macrophages. In SPIROMICS the signature was associated with increased airway obstruction and functional small airways disease on quantitative chest CT. In GLUCOLD the signature was associated with decreased response to corticosteroids, irrespective of airway eosinophilic or type 2 inflammation. CONCLUSION. These data suggest that a gene signature of IL-17 airway epithelial response distinguishes a biologically, radiographically, and clinically distinct COPD subgroup that may benefit from personalized therapy

Multi-omics approach to understand the role of plasma proteins in cognitive ageing and dementia

The global burden of age-related cognitive decline and dementia will continue to rise in tandem with our ageing population. This necessitates the discovery of novel biomarkers and candidate drug targets to combat cognitive dysfunction. Blood proteins are important drug targets, and blood samples can be acquired routinely in clinical settings and epidemiological studies. Whereas hundreds of blood proteins are associated with cognitive ability and dementia, we do not understand whether these associations represent correlation or causation. Genome-wide association studies (GWAS) are required to define variants that are associated with blood protein levels. These variants can proxy for candidate disease-markers and assess their causal associations with health outcomes in analysis methods such as Mendelian randomisation. DNA methylation is an epigenetic mechanism that regulates gene expression and is influenced by genetic and environmental factors. Studying the relationship between DNA methylation and protein levels could reveal whether genetic variation or environmental factors likely mediate associations between blood proteins and disease states. The first aim of this thesis is to conduct GWAS and epigenome-wide association studies (EWAS, using DNA methylation) on plasma levels of 422 unique proteins. Using these data, I apply causal inference approaches to determine whether blood proteins are causally associated with Alzheimer’s disease risk. Several strategies have been proposed to estimate biological age by leveraging inter-individual variation in DNA methylation profiles. Epigenetic measures of ageing correlate strongly with chronological age. Recently, a novel epigenetic measure of ageing termed ‘DNAm GrimAge’ was developed to predict one’s risk of mortality. DNAm GrimAge is a composite biomarker that incorporates methylation-based predictors of seven blood protein levels and smoking. The relationship between this biomarker of ageing and cognitive decline or dementia is not known. Therefore, the second aim of this thesis is to examine whether DNAm GrimAge associates with measures of brain health and Alzheimer’s disease. To conduct these aims, I utilise data from two cohort studies: the Lothian Birth Cohort 1936 (n ≤ 906, LBC1936) and Generation Scotland (n ≤ 9,537, GS). In Chapters 1-3, I provide an overview of cognitive ageing and dementia. I describe GWAS and EWAS on blood protein levels and the development of DNAm GrimAge. In Chapter 4, I detail the population cohorts and main methodologies that are used in this thesis. In Chapter 5, I conduct GWAS and EWAS on plasma levels of 92 neurology-related proteins (n ≤ 750, LBC1936). I identified 41 independent genetic and 26 epigenetic loci that associate with 33 and 9 proteins, respectively. I showed that an immune-related protein, poliovirus receptor (PVR), is causally associated with Alzheimer’s disease risk. In Chapter 6, I use a novel Bayesian framework termed BayesR+ to perform an integrated GWAS/EWAS on plasma levels of 70 inflammation-associated proteins (n = 876, LBC1936). Many GWAS and EWAS use linear models, which examine every measured genetic or epigenetic site in isolation. BayesR+ accounts for intercorrelations among genetic and epigenetic sites and the reciprocal influences of these data types. I estimated the contribution of genetic and epigenetic variation towards inter-individual differences in inflammatory protein levels, considered alone and together. There was no evidence for causal associations between blood inflammatory proteins and the risk of Alzheimer’s disease. In Chapter 7, I perform a systematic literature review to identify known blood protein correlates of Alzheimer’s disease. I then use BayesR+ to conduct an integrated GWAS and EWAS on plasma levels of 282 Alzheimer’s disease-associated proteins (n ≤ 1,064, GS). I observed strong evidence for causal associations between two proteins, TBCA and TREM2, and Alzheimer’s disease risk. In Chapter 8, I examine associations between DNAm GrimAge and measures of brain health (n ≤ 709, LBC1936). A higher-than-expected DNAm GrimAge associated with poorer performance on cognitive tasks and neurostructural correlates of dementia at age 73. I observed weak evidence to suggest that DNAm GrimAge assessed at age 70 predicts cognitive decline up to age 79. In Chapter 9, I assess whether DNAm GrimAge and other measures of epigenetic ageing predict the prevalence and incidence of common disease states, including Alzheimer’s disease (n ≤ 9,537, GS). Epigenetic ageing measures did not predict the prevalence or incidence of Alzheimer’s disease. In Chapter 10, I discuss the major findings from this thesis in light of their limitations. The work presented in this thesis helps to detail the molecular regulation of 422 plasma protein levels and their causal associations with Alzheimer’s disease. This work also highlights the performance of DNAm GrimAge in predicting indices of cognitive performance and common disease states. By incorporating genetic, epigenetic and protein data in two large-scale epidemiological studies, my findings inform our understanding of relationships between blood proteins and cognitive ageing and dementia

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

An integrated cell atlas of the lung in health and disease

Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1 + profibrotic monocyte-derived macrophages in COVID-19, pulmonary fibrosis and lung carcinoma. Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas. </p

An integrated cell atlas of the lung in health and disease

Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1+ profibrotic monocyte-derived macrophages in COVID-19, pulmonary fibrosis and lung carcinoma. Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas

Pulmonary hypertension and chronic lung disease: where are we headed?

Pulmonary hypertension related to chronic lung disease, mainly represented by COPD and idiopathic pulmonary fibrosis, is associated with a worse outcome when compared with patients only affected by parenchymal lung disease. At present, no therapies are available to reverse or slow down the pathological process of this condition and most of the clinical trials conducted to date have had no clinically significant impact. Nevertheless, the importance of chronic lung diseases is always more widely recognised and, along with its increasing incidence, associated pulmonary hypertension is also expected to be growing in frequency and as a health burden worldwide. Therefore, it is desirable to develop useful and reliable tools to obtain an early diagnosis and to monitor and follow-up this condition, while new insights in the therapeutic approach are explored

An airway epithelial IL-17A response signature identifies a steroid-unresponsive COPD patient subgroup

BACKGROUND. Chronic obstructive pulmonary disease (COPD) is a heterogeneous smoking-related disease characterized by airway obstruction and inflammation. This inflammation may persist even after smoking cessation and responds variably to corticosteroids. Personalizing treatment to biologically similar "molecular phenotypes" may improve therapeutic efficacy in COPD. IL-17A is involved in neutrophilic inflammation and corticosteroid resistance, and thus may be particularly important in a COPD molecular phenotype. METHODS. We generated a gene expression signature of IL-17A response in bronchial airway epithelial brushings from smokers with and without COPD (n = 238), and validated it using data from 2 randomized trials of IL-17 blockade in psoriasis. This IL-17 signature was related to clinical and pathologic characteristics in 2 additional human studies of COPD: (a) SPIROMICS (n = 47), which included former and current smokers with COPD, and (b) GLUCOLD (n = 79), in which COPD participants were randomized to placebo or corticosteroids. RESULTS. The IL-17 signature was associated with an inflammatory profile characteristic of an IL-17 response, including increased airway neutrophils and macrophages. In SPIROMICS the signature was associated with increased airway obstruction and functional small airways disease on quantitative chest CT. In GLUCOLD the signature was associated with decreased response to corticosteroids, irrespective of airway eosinophilic or type 2 inflammation. CONCLUSION. These data suggest that a gene signature of IL-17 airway epithelial response distinguishes a biologically, radiographically, and clinically distinct COPD subgroup that may benefit from personalized therapy

Non-invasive Biomarkers in Asthma: Promises and Pitfalls

The asthma concept has evolved throughout the years: one major step in asthma management is the recognition of the chronic (airway) inflammation; another major step is further understanding of asthma heterogeneity and subsequent development of targeted therapies. While the concept of chronic inflammation, airway structural changes and their variability over time are widely accepted, their measurement and monitoring have gone through many hardships