University of Groningen Chronic Mucus Hypersecretion in COPD And

University of Groningen

Chronic mucus hypersecretion in COPD and asthma Tasena, Hataitip

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Hataitip Tasena

531891-L-bw-Tasena Processed on: 6-6-2019 PDF page: 1 The studies presented in this thesis were performed within the framework of the Groningen University Institute for Drug Exploration (GUIDE), the Groningen Research Institute for Asthma and COPD (GRIAC), and U4 Ageing Lung Consortium. They are financially supported by the University of Groningen, Stichting Astma Bestrijding (SAB), and de Cock foundation.

Printing of this thesis was financially supported by the University of Groningen, the Graduate school of Medical Sciences Groningen (GSMS), and Stichting Astma Bestrijding (SAB).

ISBN (printed): 978-94-034-1704-2 ISBN (electronic): 978-94-034-1703-5

Cover design by Nick (fiverr.com/glitchfool) Lay out by Abubakar Abdullahi Dubagari and Hataitip Tasena Printed by Ipskamp Printing, Enschede

© 2019, Hataitip Tasena, The Netherlands All rights reserved. No part of this thesis may be reproduced or transmitted in any form or by any means without prior permission of the author or, when appropriate, of the publisher of the publication.

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PhD thesis

to obtain the degree of PhD at the University of Groningen on the authority of the Rector Magnificus prof. E. Sterken and in accordance with the decision by the College of Deans.

This thesis will be defended in public on

Wednesday 3 July 2019 at 12.45 hours

Hataitip Tasena

born on 23 April 1989 in Chiang Rai, Thailand

531891-L-bw-Tasena Processed on: 6-6-2019 PDF page: 3 Supervisors Prof. H.I. Heijink Prof. W. Timens

Co-supervisors Dr. C.A. Brandsma Dr. M. van den Berge

Assessment Committee Prof. K. Bracke Prof. G.H. Koppelman Prof. A. van den Berg

531891-L-bw-Tasena Processed on: 6-6-2019 PDF page: 4 Paranimfen Jennie Ong Roderick de Hilster

531891-L-bw-Tasena Processed on: 6-6-2019 PDF page: 5 In memory of my mom, Ketsuda.

531891-L-bw-Tasena Processed on: 6-6-2019 PDF page: 6 CONTENTS

CHAPTER 1 General introduction 9 CHAPTER 2 Role of microRNAs and exosomes in asthma 21 Opin Pulm Med 2019: 25(1):87-93 CHAPTER 3 MicroRNA-mRNA regulatory networks underlying 33 chronic mucus hypersecretion in COPD Eur Respir J 2018: 52(3): 1701556 CHAPTER 4 MiR-31-5p: a shared regulator of chronic 71 mucus hypersecretion in asthma and chronic obstructive pulmonary disease CHAPTER 5 Airway mucus secretion in COPD-derived 87 airway epithelial cells is promoted by fibroblast-epithelium crosstalk CHAPTER 6 Involvement of miRNAs associated with chronic 109 mucus hypersecretion in fibroblast-epithelium crosstalk in COPD CHAPTER 7 MiR-146a-5p plays an essential role in the aberrant 119 epithelial-fibroblast crosstalk in COPD Eur Respir J 2017: 49(5): 1602538 CHAPTER 8 Profiling of healthy and asthmatic airway smooth 141 muscle cells following IL-1β treatment: a novel role for CCL20 in chronic mucus hypersecretion Eur Respir J 2018: 52(2): 1800310 CHAPTER 9 Summary, general discussion and future perspectives 171 CHAPTER 10 Dutch summary | Nederlandse samenvatting 187 CHAPTER 11 Acknowledgements | Dankwoord 193 APPENDIX Curriculum vitae and list of publications 209

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General introduction

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Chronic mucus hypersecretion (CMH) is a feature observed in various chronic respiratory diseases, particularly chronic obstructive pulmonary disease (COPD)1,2 and asthma3,4. While COPD is one of the top leading causes of death worldwide especially in elderly5, asthma is the most common chronic disease among children6 and responsible for more than 400,000 deaths overall each year worldwide7. Both genetic and environmental factors contribute to COPD and asthma8–10, and cigarette smoke is widely recognized as a major cause of COPD10. COPD and asthma patients may experience similar symptoms, e.g. breathing difficulty, chest tightness, wheezing, and cough; however, these symptoms are driven by different—though partly overlapping—mechanisms11. In COPD, exposure to air pollutants or irritant particles (e.g. cigarette or wood smoke) triggers inflammatory responses characterized by infiltration of neutrophils, macrophages, T lymphocytes and innate lymphoid cells, which in the long-term causes damage to the lung12. Abnormal tissue repair is a key pathophysiological feature of COPD which leads to small airway wall thickening and/or destruction of alveolar tissue (emphysema)13, resulting in airflow limitation that is not fully reversible and accelerated lung function decline13,14. In asthma, the most common type is allergen-induced asthma characterized by chronic eosinophilic airway inflammation, airway remodeling with increased smooth muscle mass, subepithelial fibrosis and goblet cell hyperplasia14,15. In patients with allergic asthma, allergen-specific type-2 T-helper cells drive the airway inflammatory response that gives rise to clinical symptoms such as wheezing, variable airflow limitation and airway hyperresponsiveness16. As both are highly heterogeneous diseases, COPD and asthma patients can be categorized into different subgroups based on their clinical features. One of the important features shared by both COPD and asthma patients is CMH. CMH in COPD is often referred to as chronic bronchitis1,2. Here, it is associated with lower quality of life, an accelerated decline of lung function, more severe airflow obstruction and an increased risk of exacerbations and mortality17. In asthma, CMH is most prevalent in patients with severe asthma4 and associated with acute exacerbations18. Mucus plugging in the airways is found in the vast majority of fatal asthma cases and may be an important, but underappreciated, cause of respiratory failure19. Although some anti-inflammatory drugs, bronchodilators and antibiotics have been reported to reduce mucus production or improve mucus clearance, the results of the studies were either inconsistent or not yet proven to be beneficial in humans20. The lack of effective CMH-targeted therapy, while it represents a high burden, reflects an urgent need to better understand mechanisms underlying CMH pathophysiology in order to find better treatment options.

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Factors contributing to chronic mucus hypersecretion Patients with CMH suffer from chronic cough and sputum expectoration as a result of increased mucus accumulation in their airways, a process attributed to exaggerated 1 mucin secretion and/or impaired mucus clearance21. Increased mucin secretion in COPD and asthmatic airways likely results from increased numbers of goblet cells22–24 and enlargement of mucous glands25,26, increased mucin synthesis24,27–30 and/or increased degranulation of goblet cells31,32. Ineffective mucus clearance can be driven by defective cilia function33,34, changes in mucus composition35, mucus dehydration36 and/or higher viscoelasticity resulting from more mucin cross-linking or impaired mucin degradation37–39. All these factors are commonly used as markers for studying CMH, as summarized in figure 1. Mucins are heavily glycosylated proteins responsible for mucus viscoelasticity40. MUC5AC and MUC5B are the most common mucins present in the respiratory tract and they are associated with both COPD29,30 and asthma27,28. When external pathogens or noxious particles are inhaled, the airway epithelium is the first line of defense protecting the lungs from these harmful stimuli. It is composed of various epithelial cell types including basal cells and more differentiated cells, e.g. club cells, goblet cells and ciliated cells41. Mucins are synthesized by goblet cells as a constituent of the airway epithelium and by mucous cells in submucosal glands21. After being synthesized, mucins are stored in intracellular granules before being released into the airway lumen. Here, secreted mucins are mixed with lipids, antimicrobial proteins, electrolytes, and water to form a mucus layer40, which can then be transported from distal to proximal airways by the rhythmic beating of cilia before entering the larynx and being expectorated by a sudden opening of vocal cords42. Mucus which is accumulated in the airways requires effective clearance, a process that is often impaired in patients with COPD or asthma33,34,43. As described above, one of the factors contributing to ineffective mucus clearance is ciliary dysfunction. Cilia on airway epithelium of COPD smokers are shorter, more vulnerable and show disorderly beating33, while lower cilia beating frequency has been observed in asthmatic airways34. In addition, smoking causes squamous metaplasia in the airways, that even further worsens mucus clearance. The type of secreted mucins may also influence mucociliary clearance. A recent study suggested that coating of secreted MUC5B with MUC5AC may provide anchoring activity that slows down mucus transport44. In asthma, impaired mucus transport has been proposed to be caused by tethering of MUC5AC to the epithelium rather than by cilia dysfunction35. Other studies suggest a negative impact of airway surface dehydration on mucus clearance36,45,46. Even without an increase of mucin synthesis and goblet cell number, dehydration of constitutively secreted mucus alone is sufficient to cause mucus plugging36. Apart from dehydration, impaired cleavage of mucins or extracellular DNA can increase mucus viscosity and therefore impair mucus clearance37,45. In

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acute asthma, more cross-linking of mucin polymers is associated with reduced cough clearance, while mucin degradation is suppressed possibly due to inhibition of protease by excess plasma proteins39. These mechanisms can contribute to ineffective mucus clearance leading to CMH in asthma and COPD.

Figure 1. Factors contributing to CMH.

Molecular mechanisms involved in mucus production Various well-known transcription factors play a crucial role in mucin synthesis/ secretion and cilia function. The first one is SAM pointed domain-containing ETS transcription factor (SPDEF). Expression of SPDEF is dependent on Signal transducer and activator of transcription 6 (STAT6)47. Goblet cells are absent in SPDEF-deficient mice, while SPDEF overexpression in club cells leads to goblet cell differentiation without proliferation, suggesting that club cells can function as a progenitor for goblet cells in these mice48. The second transcription factor is Forkhead box protein A2 (FOXA2), a winged helix protein known to play an important role in embryonic development. FOXA2-deleted mice developed goblet cell hyperplasia and increased MUC5AC synthesis in the airways49. Upon mucociliary differentiation of airway epithelial cells, SPDEF is upregulated while FOXA2 is downregulated50, suggesting their opposite roles in mucociliary development. Another transcription factor involved in epithelial differentiation is Forkhead box protein J1 (FOXJ1) which plays an essential role in epithelial polarization51 and differentiation towards ciliated cells52 and is often used as a marker to assess ciliated cell differentiation in vitro. These molecular processes may be regulated by pro-inflammatory cytokines, which are also known to play a role in CMH pathophysiology. IL-13, for instance, is an important T cell-derived mediator of type 2 inflammatory responses in allergic asthma53, which is also upregulated in COPD54 and is well known for its central role in regulating mucus production through induction of SPDEF27,47,55. In addition,

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IL-1 family members, e.g. IL-1α and IL-1β, can promote mucus hypersecretion. Inhibition of IL-1 receptor 1 (IL-1R1) in βENaC-overexpressing (βENaC-Tg) mice, which develop spontaneous mucus plugging associated with cystic fibrosis/COPD- 1 like phenotypes, alleviates airway mucus obstruction56. IL-1β acts on IL-1R1 and has been shown to stimulate MUC5AC and MUC5B expression in human airway epithelial cells via NF-κB activation57,58.

Role of stromal cells in chronic mucus hypersecretion Recent reports suggest that stromal cells, such as airway smooth muscle cells (ASMCs) and fibroblasts, play a crucial role in epithelial homeostasis and differentiation59–62 and therefore may also be involved in CMH development. ASMCs respond to various pro-inflammatory cytokines, which are secreted by other cell types including epithelial cells, e.g. IL-1β and TGF-β63,64. Co-culturing tracheal epithelium with fibroblasts stimulates epithelial proliferation, mucin production and basement membrane formation in vitro65. It is still unknown which mechanisms mediate this crosstalk and whether these findings are relevant in the context of airway diseases. Our group previously observed that during epithelial-fibroblast co-culture, epithelial- derived IL-1α increases CXCL8 and IL-6 production by fibroblasts66. Furthermore, we observed a significant association between a polymorphism in the Frizzled (FZD) 8 region and CMH67. FZD8 is a receptor for WNT growth factors, which are involved in lung development and regeneration as well as pro-inflammatory responses68. Lung fibroblasts responded to epithelial-derived stimuli (IL-1 and EGF) by upregulating FZD8 expression and this upregulation was more pronounced in fibroblasts from patients with CMH than those without CMH69. Fibroblasts derived from COPD patients with CMH, compared to ones without CMH, secreted higher levels of CXCL8 and IL-6 cytokines, which have been shown to promote MUC5AC production in differentiated epithelial cells67. Together, these findings suggest a potential involvement of fibroblasts in CMH development. How abnormalities in the complex interplay between structural cells in the airways lead to CMH and which gene networks are involved is currently unknown.

Role of microRNAs in chronic mucus hypersecretion and stromal cell- epithelium crosstalk As microRNAs (miRNA) play a key role in many cellular processes and have been implicated in a variety of diseases, it is likely that they also are involved in CMH pathogenesis. miRNAs are small non-coding RNA molecules that regulate messenger RNA (mRNA) expression at post-transcriptional level by targeting the 3’ untranslated region (3’ UTR) of their mRNA target, leading to mRNA degradation or translational inhibition70. It is predicted that miRNAs regulate the expression of over 60% of all

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genes in mammalian genome71. Expression patterns of miRNAs in differentiated airway epithelial cells differ from those in basal cells72. Several miRNAs have been shown to be associated with asthma or COPD73. The expression of 7 miRNAs were higher and of 15 miRNAs were lower in bronchial brushings of asthma compared to healthy controls74. IL-13 stimulation alters expression of several miRNAs in this list, including suppression of miR-34/449 family, consistent with lower expression in asthma74. In addition, the expression of 5 miRNAs was higher and of 23 miRNAs, including miR-146a-5p, was lower in bronchial epithelium derived from current- smokers than from never-smokers75. miRNAs may also be involved in the crosstalk between epithelial cells and fibroblasts. The induction of miR-146a-5p by pro- inflammatory cytokines was lower in primary lung fibroblasts from COPD patients compared to those from healthy controls76. Interestingly, miR-146a-5p silencing promotes MUC5AC secretion by 16HBE cells77. Altogether, these findings led us to the hypothesis that abnormal stromal- epithelial crosstalk contributes to CMH pathophysiology and that miRNAs act as mediators of disturbed molecular mechanisms underlying abnormalities in mucociliary differentiation, pro-inflammatory responses, and stromal cell-epithelium crosstalk in CMH (figure 2).

Figure 2. Hypothesis overview.

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The scope of this thesis We started this thesis with a comprehensive review discussing the role of miRNAs and exosomes in asthma pathogenesis ( ). As to date no studies on chapter 2 1 differential miRNA expression profiles in relation to CMH or chronic bronchitis have been reported, we applied an unbiased approach to identify novel candidate miRNAs potentially involved in CMH pathophysiology. Moreover, since most in vitro studies of CMH focused solely on airway epithelial cells, we developed co-culture models using patient-derived cells that take cell-cell interactions into consideration to shed new light on our current understanding of CMH regulatory mechanisms. The aims of this thesis were to; 1) identify CMH-associated miRNAs and their associated biological pathways in COPD (Chapter 3) and asthma (Chapter 4) using miRNA and mRNA expression profiles of patient-derived bronchial biopsies; 2) to investigate whether and how COPD patient-derived fibroblasts promote mucin secretion and mucociliary differentiation by airway epithelial cells from COPD patients with CMH using a long-term air-liquid interface (ALI) co-culture model (Chapter 5); 3) to assess the expression of selected CMH-associated miRNAs identified in bronchial biopsies in COPD patient-derived airway fibroblasts and epithelial cells co-cultured at ALI (Chapter 6); 4) to elucidate the function of miR- 146a-5p in disturbed fibroblast-epithelial cell crosstalk in COPD using a submerged co-culture model of lung fibroblasts and airway epithelial cells from controls and COPD patients (Chapter 7), and 5) to compare gene expression profiles of asthma- and control-derived ASMCs and to determine the subsequent role of a selected soluble factor on mucin production using ALI-cultured Calu-3 bronchial adenocarcinoma cells and primary airway epithelial cells (Chapter 8).

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40. Bansil, R. & Turner, B. S. The biology of mucus: Composition, synthesis and organization. Adv. Drug Deliv. Rev. 124, 3–15 (2018). 41. Whitsett, J. A. Airway Epithelial Differentiation and Mucociliary Clearance. Ann. Am. Thorac. Soc. 15, S143– S148 (2018). 42. Ramos, F. L., Krahnke, J. S. & Kim, V. Clinical issues of mucus accumulation in COPD. Int. J. COPD 9, 139–150 (2014). 43. Smaldone, G. C. et al. Regional Impairment of Mucociliary Clearance in Chronic Obstructive Pulmonary Disease. Chest 103, 1390–1396 (1993). 44. Ermund, A. et al. The normal trachea is cleaned by MUC5B mucin bundles from the submucosal glands coated with the MUC5AC mucin. Biochem. Biophys. Res. Commun. 492, 331–337 (2017). 45. Mall, M. A., Danahay, H. & Boucher, R. C. Emerging Concepts and Therapies for Mucoobstructive Lung Disease. Ann. Am. Thorac. Soc. 15, S216–S226 (2018). 46. Mall, M. A. et al. Development of chronic bronchitis and emphysema in β-epithelial Na+ channel-overexpressing mice. Am. J. Respir. Crit. Care Med. 177, 730–742 (2008). 47. Park, K. et al. SPDEF regulates goblet cell hyperplasia in the airway epithelium. J. Clin. Invest. 117, 978–988 (2007). 48. Chen, G. et al. SPDEF is required for mouse pulmonary goblet cell differentiation and regulates a network of genes associated with mucus production. J. Clin. Invest. 119, 2914–2924 (2009). 49. Wan, H. et al. Foxa2 regulates alveolarization and goblet cell hyperplasia. Development 131, 953–964 (2004). 50. Song, J. et al. Aberrant DNA methylation and expression of SPDEF and FOXA2 in airway epithelium of patients with COPD. Clin. Epigenetics 9, 42 (2017). 51. Huang, T. et al. Foxj1 is required for apical localization of ezrin in airway epithelial cells. J Cell Sci 116, 4935–4945 (2003). 52. You, Y. et al. Role of f-box factor foxj1 in differentiation of ciliated airway epithelial cells. Am. J. Physiol. Cell. Mol. Physiol. 286, L650–L657 (2004). 53. Licona-Limón, P., Kim, L. K., Palm, N. W. & Flavell, R. A. TH2, allergy and group 2 innate lymphoid cells. Nat. Immunol. 14, 536–542 (2013). 54. Miotto, D. et al. Interleukin-13 and -4 expression in the central airways of smokers with chronic bronchitis. Eur. Respir. J. 22, 602–608 (2003). 55. Yu, H., Li, Q., Kolosov, V. P., Perelman, J. M. & Zhou, X. Interleukin-13 Induces Mucin 5AC Production Involving STAT6/SPDEF in Human Airway Epithelial Cells. Cell Commun. Adhes. 17, 83–92 (2010). 56. Fritzsching, B. et al. Hypoxic Epithelial Necrosis Triggers Neutrophilic Inflammation via IL-1 Receptor Signaling in Cystic Fibrosis Lung Disease. Am. J. Respir. Crit. Care Med. 191, 902–913 (2015). 57. Fujisawa, T. et al. NF-κB mediates IL-1β- and IL-17A-induced MUC5B expression in airway epithelial cells. Am. J. Respir. Cell Mol. Biol. 45, 246–252 (2011). 58. Chen, Y. et al. IL-1β induction of MUC5AC gene expression is mediated by CREB and NF-κB and repressed by dexamethasone. Am. J. Physiol. - Lung Cell. Mol. Physiol. 306, L797–L807 (2014). 59. Adamson, I. Y., Hedgecock, C. & Bowden, D. H. Epithelial cell-fibroblast interactions in lung injury and repair. Am. J. Pathol. 137, 385–392 (1990). 60. Sacco, O. et al. Epithelial cells and fibroblasts: Structural repair and remodelling in the airways. Paediatr.

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Respir. Rev. 5, (2004). 61. Albers, S., Thiebes, A. L., Gessenich, K. L., Jockenhoevel, S. & Cornelissen, C. G. Differentiation of respiratory epithelium in a 3-dimensional co-culture with fibroblasts embedded in fibrin gel. Multidiscip. Respir. Med. 11, 1 (2016). 62. Amrani, Y., Tliba, O., Krymskaya, V. P., Sims, M. W. & Panettieri, R. A. in Asthma and COPD: Basic Mechanisms and Clinical Management (eds. Barnes, P. J., Thomson, N. C., Drazen, J. M. & Rennard, S. I.) 225–239 (Academic Press, 2009). doi:10.1016/B978-0-12-374001-4.00018-3 63. Fan Chung, K. et al. Induction of eotaxin expression and release from human airway smooth muscle cells by IL-1β and TNFα: Effects of IL-10 and corticosteroids. Br. J. Pharmacol. 127, 1145–1150 (1999). 64. Fan Chung, K. et al. Effects of interleukin-1β, interleukin-13 and transforming growth factor-β on gene expression in human airway smooth muscle using gene microarrays. Eur. J. Pharmacol. 497, 255–265 (2004). 65. Kobayashi, K. et al. Effect of Fibroblasts on Tracheal Epithelial Regeneration in vitro. Tissue Eng. 12, 2619– 2628 (2006). 66. Osei, E. T. et al. Interleukin-1 α drives the dysfunctional cross-talk of the airway epithelium and lung fibroblasts in COPD. Eur. Respir. J. 48, 359–369 (2016). 67. Spanjer, A. I. R. et al. A pro-inflammatory role for the Frizzled-8 receptor in chronic bronchitis. Thorax 71, 312–322 (2016). 68. Baarsma, H. A. & Königshoff, M. ‘WNT-er is coming’: WNT signalling in chronic lung diseases. Thorax 72, 746–759 (2017). 69. Spanjer, A. I. R. et al. A pro-inflammatory role for the Frizzled-8 receptor in chronic bronchitis. Thorax 71, 312–322 (2016). 70. Engels, B. M. & Hutvagner, G. Principles and effects of microRNA-mediated post-transcriptional gene regulation. Oncogene 25, 6163–6169 (2006). 71. Friedman, R. C., Farh, K. K. H., Burge, C. B. & Bartel, D. P. Most mammalian mRNAs are conserved targets of microRNAs. Genome Res. 19, 92–105 (2009). 72. Martinez-anton, A. et al. Changes in microRNA and mRNA Expression with Differentiation of Human Bronchial Epithelial Cells. Am J Respir Cell Mol Biol 49, 384–395 (2013). 73. Osei, E. T. et al. Unravelling the complexity of COPD by microRNAs: It’s a small world after all. Eur. Respir. J. 46, 807–818 (2015). 74. Solberg, O. D. et al. Airway Epithelial miRNA Expression Is Altered in Asthma. Am. J. Respir. Crit. Care Med. 186, 965–974 (2012). 75. Schembri, F. et al. MicroRNAs as modulators of smoking-induced gene expression changes in human airway epithelium. Proc. Natl. Acad. Sci. U. S. A. 106, 2319–2324 (2009). 76. Sato, T. et al. Reduced miR-146a increases prostaglandin E₂in chronic obstructive pulmonary disease fibroblasts. Am. J. Respir. Crit. Care Med. 182, 1020–1029 (2010).

77. Zhong, T., Perelman, J. M., Kolosov, V. P. & Zhou, X. D. MiR-146a negatively regulates neutrophil elastase- induced MUC5AC secretion from 16HBE human bronchial epithelial cells. Mol. Cell. Biochem. 358, 249–255

(2011).

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Role of microRNAs and exosomes in asthma

Curr Opin Pulm Med 2019: 25(1):87-93

Maarten van den Berge1 Hataitip Tasena2

1University of Groningen, University Medical Center Groningen, Department of Pulmonary Diseases, Groningen, Box 30.001 NL-9700-RB, Groningen, The Netherlands. 2University of Groningen, University Medical Center Groningen, Department of Pathology and Medical Biology, Groningen, Box 30.001 NL-9700-RB, Groningen, The Netherlands.

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ABSTRACT

Purpose of this review: Numerous signaling pathways and inflammatory responses in cells and tissues are under microRNA (miRNA) control. In the present review, the role of miRNAs and exosomes in the pathogenesis of asthma will be discussed. Recent findings: miRNAs differentially expressed with asthma, e.g. miRNA-34/449, let-7, miRNA-19, miRNA-21, and miRNA-455, were identified in various cell types and tissues including epithelial cells, T cells, type 2 innate lymphoid cells, lung tissues and smooth muscles. Current data suggest the involvement of these miRNAs in epithelial differentiation, mucus production, airway remodeling, inflammation, etc. However, it is often difficult to predict which genes are targeted by a specific miRNA. We recently combined genome-wide miRNA analyses together with transcriptome in bronchial biopsies, in relation to chronic mucus hypersecretion, then performed a genome-wide miRNA-mRNA network analysis and identified the key miRNA regulators for chronic mucus hypersecretion. Summary: There is now growing evidence suggesting that miRNAs play critically important roles in asthma. Several asthma-associated miRNAs have already been identified. Although miRNAs are attractive targets for therapeutic intervention, a safe and effective delivery to target tissues and cells in humans remains a challenge.

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Introduction Asthma is a commonly occurring inflammatory airways disease characterized by variable airflow obstruction in association with symptoms of wheeze and dyspnea. The asthmatic inflammatory process is characterized by a complex interplay of resident cells (i.e. epithelial and dendritic cells, fibroblasts, nerves, endothelial cells) and inflammatory cells (eosinophils, mast cells, neutrophils, macrophages, T-lymphocytes). 2 At present, physicians have limited choice in anti-inflammatory and bronchodilator treatments for asthma and there is no cure available for the disease. Better insight into the underlying mechanisms that drive asthma is needed as a first step toward discovering new druggable targets. microRNAs (miRNAs) are recognized to play an important role in asthma. miRNAs are small non-coding RNA transcripts (18-25 nucleotides) that are highly conserved across species. They are involved in the posttranscriptional regulation of gene expression. Once formed, miRNAs bind to Argonaute-2 (AGO2) proteins, which are part of the RNA-induced silencing complex (RISC). In the RISC complex, miRNAs induce cleavage of their target mRNAs or inhibit their translation. A single miRNA can target hundreds of genes and it is estimated that as many as 60% of mRNAs are controlled by miRNAs1. It is now known that numerous cellular responses are under miRNA control enabling miRNAs to regulate signaling pathways and inflammatory responses in tissues. Some miRNAs are secreted into exosomes, cell-derived nano-sized vesicles reported to be involved in various human diseases including asthma2, 3. In the present review, the role of miRNAs and exosomes in the pathogenesis of asthma will be discussed.

MicroRNAs and asthma Several studies investigated miRNA expression levels both in vitro and in vivo in human and experimental models of asthma, a table with an overview can be found in reference 4. Although many studies have been performed in vitro and in animal models of asthma, studies on the role of miRNAs in humans are scarce. Williams et al compared the expression of 227 miRNAs in bronchial biopsies between eight corticosteroid-naïve patients with mild allergic asthma and eight healthy controls 5. No differences were found which may have been due to the fact that mild asthma patients were included or to the cellular heterogeneity within bronchial biopsies which may have masked differences in miRNA expression within specific cell types. Another possible explanation may simply be that the study was underpowered for an unbiased approach analyzing 227 miRNAs as only 16 subjects were included. Solberg et al compared the miRNA expression profile in isolated epithelial cells derived from bronchial brushings between 16 steroid-naïve patients with asthma and 12 healthy controls using microarrays 6. They identified 22 differentially expressed miRNAs that could be validated with PCR and showed that members of the miRNA-34/449 23

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family are downregulated in asthma. They validated their findings in air-liquid interface cultured epithelial cells demonstrating that exposure to interleukin(IL)13 represses miRNA-34/449 levels. This effect persisted after treating the cells with corticosteroids. These findings are of interest as miRNA-449 has previously been found to regulate differentiation of airway ciliated cells promoting centriole multiplication and multiciliogenesis, in part by targeting NOTCH1 mRNA7. It has been shown in animal models that an increase in NOTCH contributes to the airway mucous metaplasia frequently observed in asthma8. Taken together, the IL13-induced reduction in miRNA-34/449 in the bronchial epithelium, as observed by Solberg et al, may contribute to the alterations in epithelial differentiation that are often seen in asthma. Other miRNAs found to be downregulated in epithelial cells derived from steroid-naïve patients with asthma were members of the let-7 family (let-7a-5p, let- 7c, let-7f-5p, let-7g-5p, and let-7i-5p)6. Several studies have shown the let-7 family to be important in asthma. Polikepahad et al demonstrated that IL13 is a direct target of let-7 using a luciferase reporter system and inhibition of let-7a significantly upregulated IL13 expression in T cells 9, 10. In ovalbumin(OVA)-challenged murine model, expression of several let-7 family members decreased in allergic inflammatory lungs compared to healthy lungs 11. Intranasal delivery of a let-7 mimic improved airway hyperresponsiveness and mucus production and decreased inflammatory cell infiltration in this experimental model of asthma. These findings suggest that decreased levels of let-7 increases type 2 inflammation thus contributing to a more severe asthma. However, this could not be confirmed in a subsequent study by Polikepahad et al where an anti-let-7 locked nucleic acid (LNA) antagomir did not worsen the asthmatic inflammatory process in their OVA-challenged murine model, but contrastingly reduced inflammatory cell counts in bronchoalveolar lavage fluid and decreased levels of IL4, IL5 and IL13 9. Possible explanations for the discrepant findings between the studies could be different dose regimens or route of administration of the anti-let-7 antagomir used or the fact that the antagomir used by Polikepahad et al only inhibited four members of the let-7 family. In a recent study by Kim et al, exposure of human airway smooth muscle cells to β2-agonists increased let-7f by 2-3-fold together with a ~90% decrease in β2-receptors, while inhibition of let-7f reduced this downregulation by 50%12. These findings suggest that inhibition of let-7 may render airway smooth muscle more sensitive to bronchodilator effects of β2- agonists. In a study in COPD, we recently found increased let-7 in bronchial biopsies to be associated with chronic mucus hypersecretion (CMH)13. Whether this is also the case in asthma is currently under investigation. One miRNA can target multiple mRNAs, validation of its target genes is challenging since many miRNA-mRNA interactions are still unknown14. Several algorithms have been proposed to predict which genes are targeted by a specific miRNA, but there is large variability across

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different algorithms and the false positive rates are high15. Importantly, we analyzed both miRNA and mRNA data available from the same bronchial biopsies derived from COPD patients13, which allowed us to create miRNA-mRNA co-expression networks to directly evaluate the miRNA-mRNA interactions. This approach enabled us to identify the let-7 family with its CMH-associated targets including EDN1, NKD1, PDGFB, COL4A1, and COL4A2 as key important regulators of CMH in COPD. In a recent study, Martinez-Nunez et al performed miRNA sequencing 2 (miRNA-seq) in cultured bronchial epithelial cells derived from 8 severe asthma patients and 5 healthy controls. To determine the genome-wide relationship between differentially expressed miRNAs and their mRNA targets, they performed subcellular fractionation and RNA-seq (frac-seq) in the same samples allowing detection of both cytoplasmic mRNA as well as polyribosome-bound mRNA transcripts16. A total of 21 miRNAs were differentially expressed between severe asthma and healthy controls. Importantly, these miRNAs were found to preferentially target polyribosome- bound rather than cytoplasmic mRNA transcripts, suggesting a higher impact on translating mRNAs. Amongst the most differentially expressed miRNAs in the study by Martinez-Nunez was miRNA-19. This is in agreement with the findings of Haj-Salem et al who showed miRNA-19a to be specifically upregulated in cultured bronchial epithelial cells derived from patients with severe asthma compared to those with mild asthma and healthy controls17. Follow-up functional studies revealed that upregulation of miRNA-19a leads to an increased epithelial cell proliferation rate by targeting TGFB2 mRNA, thus possibly contributing to airway remodeling in severe asthma. In another study, miRNA-19, part of the 17~92 cluster (also including miRNA-17, miRNA-18, and miRNA-92), was found to play an important role in T cells regulating Th2 cell differentiation18. The miRNA was upregulated in CD3+ CD4+ T cells sorted from bronchoalveolar lavage fluid (BAL) fluid from asthma patients compared to healthy controls, independent of the use of steroids. In follow- up experiments, it was demonstrated that CD4 cells lacking the miRNA-17~92 cluster produced fewer Th2 cytokines like IL4 and IL13, whereas this cytokine defect could be completely restored after transfection with a miRNA-19a or miRNA-19b mimic, but not after transfection with a mimic for other members of the 17~92 cluster. In a recent study, miRNAs of the miRNA-17~92 cluster were also found to be critically important for normal type 2 innate lymphoid cell (ILC2) survival, another source of type 2 cytokines; and similar as in T cells, miRNA-19 was particularly involved in IL13 production19. Little is known about how miRNAs contribute to airway hyperresponsiveness and remodeling which are important features of asthma and COPD. Chiba et al reported that miR-133a inhibition in in vitro cultured human bronchial smooth muscle cells leads to an upregulation of RhoA, a key protein regulating contractility of smooth muscles20. Recently, we investigated how miRNAs contribute to airway remodeling,

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an important feature in asthma and COPD. To this end, we investigated how TGF-β influences miRNA expression in fibroblasts and how this affects their function 21. After exposure to TGF-β, miRNA-21 and miRNA-455 expression increased in cultured human lung fibroblasts. We then used the AGO2 ImmunoPrecipitation-gene Chip approach to identify miRNA targets on a large scale. With this approach the AGO2 protein is directly immunoprecipitated. Subsequently, the AGO2-associated mRNA transcripts were analyzed by microarray, to investigate the miRNA targetome (the whole miRNA-regulated gene set) in fibroblasts either or not exposed to TGF-β. We found that the predicted miRNA-455 and miRNA-21 targets present in the miRNA targetome of unstimulated and TGF-β-stimulated human lung fibroblasts are significantly enriched for TGF-β associated signaling processes. These findings show that there exists a cross-talk between the TGF-β pathway and the miRNAs, i.e. miRNA-445 and miRNA-21. In animal models of asthma, miRNA-21 has been shown to be upregulated in the airway wall22, 23. Using the OVA-model, Lu et al found that miRNA-21-deficient mice had reduced levels of eosinophilic inflammation and IL4 in their lungs and BAL fluid22, 23. A plausible implication of miRNA-455 was reported in a study by Garbacki et al who observed miR-455 upregulation in the lungs of allergen-exposed mice.24 Taken together, accumulating evidence points towards a potentially important role of several miRNAs in airway inflammation and remodeling in asthma, including miRNA-34/449, let-7, miRNA-19, miRNA-21, and miRNA-455, as summarized in figure 1.

Exosomes Exosomes are small (10-150 nm) membrane-bound vesicles. They can be released by e.g. inflammatory cells upon activation and are thought to play a crucial role in intercellular signaling and communication25. Genetic material, proteins, and lipid mediators can be packaged in exosomes and transferred to cells both locally and distally. Exosomes are found in a large variety of body fluids including blood, urine, BAL fluid, exhaled breath condensate, saliva and nasal lavage fluid. Exosomes also contain miRNAs and it has been shown that miRNAs packaged in exosomes can contribute to inflammation in many diseases including asthma. Sinha etal were able to demonstrate that exosomes are present in exhaled breath condensate from patients with asthma and healthy controls and contain the majority of the 634 miRNAs detectable in exhaled breath condensate26. Levänen et al demonstrated the presence of miRNAs in exosomes isolated from BAL fluid from 10 asthmatics and 10 healthy controls3. Despite the fact that patients with mild intermittent asthma were included, as many as 24 exosomal miRNAs were found to be differentially expressed, including let-7 which was downregulated in asthma. A total of 22 out of the 24 exosomal miRNAs in BAL fluid were previously also found to be altered in the airway epithelium of patients with asthma in the study by Solberg et al, with the

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same direction of effect6. Thus, exosomes can carry and transport miRNAs making intercellular communication possible and may play important roles in asthma.

Figure 1. Key miRNAs and their potential roles in asthma. The figure summarizes miRNAs of which expression is associated with asthma, cell types in which the differential expression was determined, and how the miRNAs possibly play a role in asthma. AHR is airway hyperresponsiveness; ILC2 is type 2 innate lymphoid cells.

MicroRNA-based treatment of asthma There is now increasing evidence that specific miRNAs play potentially important roles in asthma and they can be either inhibited or overexpressed. Therefore, miRNA- based therapeutics are an attractive area of investigation. Although there are several successful examples of treatments with miRNA

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inhibitors or mimics in animal models of asthma, safe and effective delivery to target tissues or cells in humans remains a challenge. The latter is important as miRNA targeting can affect multiple genes and therefore ‘off-target’ side-effects should be prevented as much as possible. A commonly used method to inhibit the function of a specific miRNA is to deliver oligonucleotides with a complementary sequence that bind and inactivate the miRNA. To prevent degradation of these oligonucleotides by RNases, chemical modifications can be used, including LNAs and addition of 2’-O-methyl groups.27 Further, addition of cholesterol tags may help to facilitate their uptake into target cells28. An alternative to chemically modified antisense oligonucleotides may be the introduction of a so-called ‘microRNA sponge’- encoding transgene into the cell, the expressed transcript being an engineered RNA molecule with multiple complementary binding sites to the target miRNA. Conversely, overexpression of miRNAs can be achieved with synthetic miRNAs that mimic natural miRNAs. These miRNA mimics often require liposomes, lipoprotein- based carriers, or nanoparticles as vehicles for their delivery29, 30. Currently, miRNA- based therapeutics are not (yet) in development for asthma, but there are successful examples that modulation of miRNAs can be treatment strategy. The best example so far is miravirsen, the world’s first miRNA therapeutic, which is a short LNA for miR-122 currently in phase II clinical trials for the treatment of hepatitis C virus (HCV) infection31. Other examples of miRNA therapeutics in clinical trials include antimiR-103/107, antimiR-155, miR-29 mimic, miR-16 mimic, and miR-34 mimic29. There are also several other miRNA therapeutics currently being investigated in preclinical models29.

Conclusions There is now increasing evidence that miRNAs play potentially important roles in asthma. Several miRNAs have already been identified including miRNA-34/449, let- 7, miRNA-19, miRNA-21, and miRNA-455. A limitation of studies performed so far is that they were mostly done in in vitro and in animal models of asthma and evidence in humans remains limited. Adequately powered studies leading to a better insight in the role of miRNAs in relevant human cells and tissues are now needed. miRNAs can play a central role in the regulation of inflammatory responses as one miRNA can target multiple genes. However, once a relevant miRNA has been identified, it is often difficult to predict which genes/pathways are actually regulated by that specific miRNA as target prediction software is far from perfect. Therefore, we have previously combined genome-wide miRNA analyses with transcriptome data from the same sample, in this case bronchial biopsies, in relation to chronic mucus hypersecretion. Using this approach, we examined direct and indirect miRNA-mRNA associations responsible for CMH. In addition, we were able to perform a genome-wide miRNA- mRNA network analysis to identify the key miRNA regulators (figure 2). Another

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approach to identify miRNA target genes is to perform AGO2-Immunoprecipation in cultured cells, i.e. with and without overexpression or inhibition of a miRNA of interest. With this technique, all genes regulated by miRNAs are captured and can then further be investigated using microarray or RNA-sequencing to identify the targetome of the miRNA of interest. The central role of miRNAs in regulation of inflammatory processes makes them attractive targets for therapeutic intervention and there have been several successful examples of treatments with miRNA inhibitors 2 or mimics in animal models of asthma. Although the safe and effective delivery to target tissues and cells in humans remains a challenge, improved technologies are emerging to facilitate the development of potential miRNA-based treatments in several indications including asthma.

Figure 2. miRNA-mRNA co-expression network showing the key regulatory miRNA and their correlated predicted target genes associated with chronic mucus hypersecretion in COPD. This analysis identified amongst other, the let-7 family with its CMH-associated targets including EDN1, NKD1, PDGFB, COL4A1, and COL4A2 as key important regulators. Red diamonds represent miRNAs higher expressed with CMH; blue diamonds represent miRNAs lower expressed with CMH; green circles represent predicted target genes negatively correlated with that miRNA; black circles represent predicted target genes negatively correlated with that miRNA whose expression was also associated with CMH. Line width correlates to degree of significance of the miRNA-mRNA correlation. Figure reproduced with permission from Tasena et al.13

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REFERENCES

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15. Sethupathy P, Megraw M, Hatzigeorgiou AG. A guide through present computational approaches for the identification of mammalian microRNA targets. Nat Methods 2006; 3: 881-886. 16. Martinez-Nunez RT, Rupani H, Plate M, Niranjan M, Chambers RC, Howarth PH, Sanchez-Elsner T. Genome- Wide Posttranscriptional Dysregulation by MicroRNAs in Human Asthma as Revealed by Frac-seq. J Immunol 2018; 201: 251-263. 17. Haj-Salem I, Fakhfakh R, Berube JC, Jacques E, Plante S, Simard MJ, Bosse Y, Chakir J. MicroRNA-19a enhances proliferation of bronchial epithelial cells by targeting TGFbetaR2 gene in severe asthma. Allergy 2 2015; 70: 212-219. 18. Simpson LJ, Patel S, Bhakta NR, Choy DF, Brightbill HD, Ren X, Wang Y, Pua HH, Baumjohann D, Montoya MM, Panduro M, Remedios KA, Huang X, Fahy JV, Arron JR, Woodruff PG, Ansel KM. A microRNA upregulated in asthma airway T cells promotes TH2 cytokine production. Nat Immunol 2014; 15: 1162-1170. 19. Singh PB, Pua HH, Happ HC, Schneider C, von Moltke J, Locksley RM, Baumjohann D, Ansel KM. MicroRNA regulation of type 2 innate lymphoid cell homeostasis and function in allergic inflammation. J Exp Med 2017; 214: 3627-3643. 20. Chiba Y, Tanabe M, Goto K, Sakai H, Misawa M. Down-regulation of miR-133a contributes to up-regulation of Rhoa in bronchial smooth muscle cells. Am J Respir Crit Care Med 2009; 180: 713-719. 21. Ong J, Timens W, Rajendran V, Algra A, Spira A, Lenburg ME, Campbell JD, van den Berge M, Postma DS, van den Berg A, Kluiver J, Brandsma CA. Identification of transforming growth factor-beta-regulated microRNAs and the microRNA-targetomes in primary lung fibroblasts. PLoS One 2017; 12: e0183815. 22. Lu TX, Hartner J, Lim EJ, Fabry V, Mingler MK, Cole ET, Orkin SH, Aronow BJ, Rothenberg ME. MicroRNA-21 limits in vivo immune response-mediated activation of the IL-12/IFN-gamma pathway, Th1 polarization, and the severity of delayed-type hypersensitivity. J Immunol 2011; 187: 3362-3373. 23. Mattes J, Collison A, Plank M, Phipps S, Foster PS. Antagonism of microRNA-126 suppresses the effector function of TH2 cells and the development of allergic airways disease. Proc Natl Acad Sci U S A 2009; 106: 18704-18709. 24. Garbacki N, Di Valentin E, Huynh-Thu VA, Geurts P, Irrthum A, Crahay C, Arnould T, Deroanne C, Piette J, Cataldo D, Colige A. MicroRNAs profiling in murine models of acute and chronic asthma: a relationship with mRNAs targets. PLoS One 2011; 6: e16509. 25. Sastre B, Canas JA, Rodrigo-Munoz JM, Del Pozo V. Novel Modulators of Asthma and Allergy: Exosomes and MicroRNAs. Front Immunol 2017; 8: 826. 26. Sinha A, Yadav AK, Chakraborty S, Kabra SK, Lodha R, Kumar M, Kulshreshtha A, Sethi T, Pandey R, Malik G, Laddha S, Mukhopadhyay A, Dash D, Ghosh B, Agrawal A. Exosome-enclosed microRNAs in exhaled breath hold potential for biomarker discovery in patients with pulmonary diseases. J Allergy Clin Immunol 2013; 132: 219-222. 27. Krutzfeldt J, Rajewsky N, Braich R, Rajeev KG, Tuschl T, Manoharan M, Stoffel M. Silencing of microRNAs in vivo with ‘antagomirs’. Nature 2005; 438: 685-689. 28. Ameres SL, Horwich MD, Hung JH, Xu J, Ghildiyal M, Weng Z, Zamore PD. Target RNA-directed trimming and tailing of small silencing RNAs. Science 2010; 328: 1534-1539. 29. Rupaimoole R, Slack FJ. MicroRNA therapeutics: towards a new era for the management of cancer and other diseases. Nat Rev Drug Discov 2017; 16: 203-222.

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30. Kreth S, Hubner M, Hinske LC. MicroRNAs as Clinical Biomarkers and Therapeutic Tools in Perioperative Medicine. Anesth Analg 2018; 126: 670-681. 31. Lindow M, Kauppinen S. Discovering the first microRNA-targeted drug. J Cell Biol 2012; 199: 407-412. *Describes discovery of first miRNA-based treatment for a human disease.

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Eur Respir J 2018: 52(3): 1701556

Hataitip Tasena1,2, Alen Faiz1,2,3, Wim Timens1,2, Jacobien Noordhoek1,2,3, Machteld N Hylkema1,2, Reinoud Gosens2,4, Pieter S Hiemstra5, Avrum Spira6, Dirkje S Postma2,3, Gaik W Tew7, Michele A Grimbaldeston8, Maarten van den Berge2,3, *Irene H Heijink1,2,3 and *Corry-Anke Brandsma1,2 (*both authors contributed equally)

1University of Groningen, University Medical Centre Groningen, Department of Pathology and Medical Biology, Groningen, the Netherlands. 2University of Groningen, University Medical Centre Groningen, Groningen Research nstitute for Asthma and COPD, Groningen, the Netherlands. 3University of Groningen, University Medical Centre Groningen, Department of Pulmonary Diseases, Groningen, the Netherlands. 4University of Groningen, Department of Molecular Pharmacology, Groningen, the therlands. 5Leiden University Medical Centre, Department of Pulmonology, Leiden, The Netherlands. 6Boston University Medical Centre, Department of Medicine, Division of Computational Biomedicine, Boston, Massachusetts, USA. 7Research & Early Development, Genentech Inc, South San Francisco, California, USA.

8OMNI-Biomarker Development, Genentech Inc, South San Francisco, California, USA.

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ABSTRACT

Chronic mucus hypersecretion (CMH) is a common feature in COPD and associated with worse prognosis and quality of life. This study aimed to identify microRNA (miRNA)-mRNA regulatory networks underlying CMH. miRNA and mRNA expression profiles in bronchial biopsies from 63 COPD patients were associated with CMH using linear regression. Potential mRNA targets of each CMH-associated miRNA were identified using Pearson correlations. GSEA and STRING analyses were used to identify key genes and pathways. Twenty miRNAs and 539 mRNAs were differentially expressed with CMH in COPD. The expression of 10 miRNAs was significantly correlated with the expression of one or more mRNAs. Of these, miR-134-5p, miR146a-5p and the let-7 family had the highest representation of CMH-associated mRNAs among their negatively correlated predicted targets. KRAS and EDN1 were identified as key regulators of CMH and were negatively correlated predicted targets of miR-134-5p and the let-7a/ d/f-5p, respectively. GSEA suggested involvement of MUC5AC-related genes and several other relevant gene sets in CMH. The lower expression of miR-134-5p was confirmed in primary airway fibroblasts from COPD patients with CMH. We identified miR-134-5p, miR-146a-5p and let-7 family, along with their potential target genes including KRAS and EDN1, as potential key miRNA-mRNA networks regulating CMH in COPD.

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INTRODUCTION

Chronic obstructive pulmonary disease (COPD) is a frequently occurring lung disease, associated with an abnormal inflammatory response to inhaled noxious particles and gases, including cigarette smoke. A substantial proportion of patients experience chronic cough with sputum production [1, 2] termed chronic bronchitis or chronic mucus hypersecretion (CMH) [3]. CMH in COPD is associated with lower quality of life, accelerated lung function decline, increased risk of exacerbations, and higher mortality [1, 2, 4]. Therefore, there is an urgent need for improved treatment of CMH in COPD patients. Unfortunately, our current understanding of the regulatory 3 mechanisms that drive CMH is still limited. Goblet cells within the airway epithelial layer together with mucous glands in the airway submucosa are responsible for the secretion of mucins, the principal components of mucus [3]. The most abundant gel-forming mucins found in human airways are MUC5AC and MUC5B [5], which are both increased in COPD [6]. Interestingly, recent in vitro studies suggest that fibroblasts play a role in the regulation of airway epithelial mucociliary differentiation and mucus production [7, 8]. microRNAs (miRNA) are small non-coding RNA molecules that target messenger RNA (mRNA), causing mRNA degradation or inhibition of protein translation [9]. Thus far, no studies have reported on differential miRNA expression in CMH, although several miRNAs have been implicated in the response to smoking and COPD [10], including miR-146a-5p [11]. The aim of this study was to identify key miRNA-mRNA interactions underlying CMH in bronchial biopsies from a well- defined COPD cohort.

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METHODS

Patient characteristics Baseline miRNA and mRNA expression was studied in bronchial biopsies from 63 COPD patients who participated in the Groningen Leiden Universities and Corticosteroids in Obstructive Lung Disease (GLUCOLD) study (clinicalTrials.gov NCT00158847). Details of the study including patient characteristics were previously described [12, 13]. Briefly, all patients had irreversible airflow limitation (post- bronchodilator forced expiratory volume in 1 second (FEV1) and FEV1/inspiratory vital capacity (IVC)<90% confidence interval (CI) of the predicted value) and chronic respiratory symptoms. All patients were stable, either current or ex-smokers, and not on corticosteroid therapy. The local medical ethics committee approved the study and all patients gave their written informed consent.

Definitions of chronic mucus hypersecretion (CMH) In our dataset, clinical questionnaires were used, providing information comparable to the most commonly used definition of CMH: symptoms of cough and phlegm on most days for more than three months during at least two consecutive years [4]. Thus, CMH was defined based on patient responses to the question: A) “How often did you cough up sputum during the last three months?” Since a patient’s response may vary over time, we decided to include another question B) “How often did you cough up sputum during the last week?”, to cover both the longer time frame (3 months) and shorter, more recent time frame (1 week), henceforth referred to as definition A and B, respectively. A full description of the response options to these questions is presented in the online supplement. For each question, patients were divided in three groups: no CMH, mild CMH, and moderate/severe CMH.

microRNAs and mRNA expression profiling The methods for mRNA and miRNA extraction from bronchial biopsies, for gene expression profiling using Affymetrix arrays, and for the RNA-sequencing are described in the online supplement.

Statistical analysis mRNA and miRNA analyses on bronchial biopsies were performed using R software (v3.2.5). A linear regression model was used to identify miRNAs and mRNAs that were differentially expressed in patients with mild or with moderate/severe CMH compared to those with no CMH. The model was corrected for age, gender, smoking

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history and RNA integrity number (RIN). Multiple testing correction was performed using Benjamini and Hochberg’s method. Definition A was applied to acquire the primary lists of candidate miRNAs and mRNAs that were associated with CMH using a false discovery rate adjusted p-value (FDR) cut-off<0.25. Definition B was then applied to further strengthen the primary findings using a nominal p-value cut- off<0.05. Subsequently, the final lists consisted of candidate miRNAs and mRNAs that were associated with CMH according to both definitions. Other statistical tests were performed using GraphPad Prism (v6). Differences in patient characteristics were compared using ANOVA. The correlation of the two CMH definitions was assessed using Spearman’s rank correlation coefficient. Mann-Whitney U test was 3 performed to determine significant differences in immunohistochemistry markers between CMH status and miRNA expression in vitro. The methods for miRNA- mRNA co-expression network analysis, Gene Set Enrichment Analysis (GSEA), and STRING interaction network analysis are described in the online supplement.

microRNA expression in human primary airway epithelial cells and fibroblasts The expression of candidate miRNAs was evaluated in air-liquid interface (ALI)- differentiated primary airway epithelial cells (PAECs) and primary airway fibroblasts (PAFs) obtained during lung transplantation procedures. PAECs were obtained from six stage IV COPD (table S1) explanted lungs and six non-COPD donor lungs as described before [14], of which the majority did not have CMH. PAFs were isolated from stage IV COPD patients (table S2) as previously described [15], of which eight had clinical CMH symptoms and eight had no clinical CMH symptoms. Cell culture procedures and RT-qPCR details are described in the online supplement.

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RESULTS

Patient characteristics Microarray data of sufficient quality was obtained from 63 patients for miRNA expression profiles and from 57 patients for mRNA expression profiles. Patient characteristics are shown in table 1. CMH definitions A and B were significantly correlated (r=0.651, p<0.0001, figure S1). There was no significant difference in smoking status, pack-years, age, BMI, or lung function among these three groups (table 1). See figure 1 for the study approach and flow diagram of the main findings.

Table 1. Patient characteristics

CMH Definition A CMH Definition B

No Mild Moderate/ No Mild Moderate/ CMH (n=22) severe CMH (n=25) severe (n=8) (n=33) (n=8) (n=30) Gender (male), 8(100.0) 18(81.8) 28(84.8) 8(100.0) 22(88.0) 24(80.0) n(%) Current smokers, 4(50.0) 14(63.6) 23(69.7) 4(50.0) 17(68.0) 20(67.0) n(%) 41.7 44.1 41.5 40.9 45.0 41.5 Pack-years* (23.6-50.3) (31.9-54.4) (35.9-53.5) (24.4-51.2) (31.2-55.8) (36.5-53.5) 64 59 60 56 59 62 Age, years* (57-69) (53-64) (57-66) (50-60) (55-63) (57-69) 25.8 24.2 24.0 25.1 24.4 24.4 BMI, kg/m2* (23.2-27.8) (22.8-27.2) (22.0-28.0) (23.8-27.1) (22.3-29.4) (21.6-28.0) FEV1, 69.9 63.3 64.8 66.6 65.6 63.9 %predicted* (48.5-71.8) (58.5-67.4) (58.2-69.8) (61.4-70.5) (58.5-71.9) (56.3-67.2)

0.54 0.49 0.50 0.56 0.50 0.49 FEV1/FVC* (0.42-0.60) (0.44-0.54) (0.44-0.56) (0.49-0.63) (0.44-0.56) (0.420.54)

2.6 2.7 2.6 2.7 2.7 2.6 RIN* (2.5-3.2) (2.3-4.7) (2.4-3.7) (2.5-4.4) (2.4-4.0) (2.4-3.4)

*Data are presented as median (interquartile range); BMI is body mass index; FEV1 is forced expiratory volume in 1 second; FVC is forced vital capacity; RIN is RNA integrity number.

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FIGURE 1. Flow diagram of the study approach. A linear regression model was used to identify chronic mucus hypersecretion (CMH) associated microRNAs (miRNAs) and CMH-associated mRNAs based on two CMH definitions. Pearson correlation was used to identify miRNA-correlated mRNAs. Gene set enrichment analysis (GSEA) was performed using gene sets of interest and ranked lists of miRNA-correlated and CMH-associated mRNAs. Interaction network analysis using STRING (search tool for the retrieval of interacting genes/proteins) was performed on the list of CMH-associated mRNAs correlated with at least one CMH-associated miRNA. The list of CMH-associated mRNAs and the lists of negatively correlated predicted targets of the candidate miRNAs were used to create miRNA–mRNA co-expression networks. Finally, the expression of candidate miRNAs was assessed in primary airway epithelial cells and primary airway fibroblasts.

CMH-associated miRNAs To identify miRNAs associated with CMH, we compared expression profiles of 230 miRNAs in patients with 1) mild and no CMH and 2) moderate/severe and no CMH. According to definition A, three miRNAs (miR-34b-3p, miR-92b-3p and miR-449b- 5p) were higher expressed with mild CMH, while miR-664a-5p was lower expressed. With moderate/severe CMH, we found 39 differentially expressed miRNAs, including the four that were associated with mild CMH (FDR<0.25, figure 2a and 2b). Twenty out of the 39 miRNAs were also associated with moderate/severe CMH and in the same direction according to definition B (p<0.05). Among these, miR-708-5p had the highest fold increase (2.06) and miR-134-5p had the strongest fold decrease (-2.15) (table S3). No miRNA was differentially expressed in mild CMH according to definition B.

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FIGURE 2. MicroRNAs (miRNAs) and mRNAs differentially expressed with chronic mucus hypersecretion (CMH). Patients were classified into three groups depending on their CMH status, as defined by two definitions, with miRNA profiles of patients with moderate/severe CMH being compared to those of patients with no CMH. Heat map [a] shows expression changes of 39 miRNAs in the patients with moderate/severe CMH (n=33) compared to those with no CMH (n=8) according to definition A. Bold labels indicate the 20 miRNAs whose expression was significant based on both definition A and definition B. Volcano plot [b] shows expression changes of 230 miRNAs in the patients with moderate/ severe CMH compared to those with no CMH according to definition A. Heat map [c] shows expression changes of 942 mRNAs in the patients with severe CMH (n=30) compared to those with no CMH (n=8) according to definition A. Volcano plot [d] shows expression changes of 19793 mRNAs in the patients with severe CMH compared to those with no CMH according to definition A. Blue indicates significant miRNAs/mRNAs lower expressed with CMH while red represents significant miRNAs/mRNAs higher expressed with CMH. Triangles represent miRNAs/mRNAs of which differential expression was significant based on both definitions. A false-discovery rate (FDR) <0.25 was a cut-off for definition A and a nominal p-value <0.05 was a cut-off for definition B.

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CMH-associated mRNAs To identify mRNAs associated with CMH, we compared expression profiles of 19,793 mRNAs in patients with 1) mild and no CMH and 2) moderate/severe and no CMH. According to CMH definition A, no differences in mRNA expression were found with mild CMH. The expression of 942 mRNAs differed with moderate/severe (FDR<0.25, figure 2c and 2d). Further, 539 out of 942 mRNAs were also associated with CMH in the same direction according to definition B (p<0.05, figure 2c and 2d). Among these, 264 mRNAs were higher and 275 mRNAs were lower expressed with moderate/severe CMH (table S4). The top 20 significant mRNAs are shown in table 2. To validate the findings from the microarray dataset, RNA-sequencing was 3 performed on bronchial biopsies from a subset of the patients in GLUCOLD cohort, with moderate/severe CMH (n=21) and without CMH (n=5). GSEA was performed to assess if the CMH-associated genes in our microarray dataset were enriched among the CMH-associated genes in our RNA-sequencing dataset. Indeed, we found a significant enrichment (figure S2, table S7). To ensure that the observed differences in miRNA and mRNA expression were not due to changes in cellular composition, we analysed available immunohistochemical data on the numbers of neutrophils, macrophages, eosinophils, CD3+/CD4+/CD8+ lymphocytes, mast cells, and epithelial cells in the bronchial biopsies previously described [13]. We observed that only CD8+ lymphocytes differed with CMH. When accounting for the CD8+ cell number in our linear regression model, all 20 CMH- associated miRNAs and 539 CMH-associated mRNAs remained significant.

Gene Set Enrichment Analysis (GSEA) of CMH-associated mRNAs To identify pathways, biological processes, and molecular functions in which the CMH-associated mRNAs may be involved, GSEA was performed. The top 20 significant gene sets enriched among mRNAs differentially expressed withCMH according to both definitions are shown in table 3. Among these were gene sets related to cilium development and function, neurohormonal signalling, ion channel activities, and extracellular matrix (ECM) structure. The complete list of significant gene sets (FDR<0.01) is reported in the online supplement (table S5). Furthermore, we investigated whether our CMH-associated mRNAs are involved in the mechanisms relevant to MUC5AC expression. Since MUC5AC was not expressed above background levels in our microarrays, we built further on the findings of Wang et al. [16] who previously identified 73 MUC5AC-associated core genes which were higher expressed in small airway epithelium from individuals with high versus those with low MUC5AC gene expression. Using this list, we found a strong enrichment of MUC5AC-associated genes among the genes higher expressed with CMH according to both definition A (enrichment score (ES)=0.40, p<0.001) and B (ES=0.44, p<0.001) (figure S3). 41

531891-L-bw-Tasena Processed on: 6-6-2019 PDF page: 41 CHAPTER 3 p-value 6.47E-03 4.34E-02 1.38E-02 1.50E-02 2.17E-03 5.30E-03 1.85E-02 1.20E-02 2.86E-02 3.91E-05 8.07E-03 2.91E-03 9.43E-04 3.03E-03 1.40E-02 8.38E-05 5.70E-03 3.87E-03 5.50E-03 4.10E-02 FC 1.227 1.194 1.124 1.316 1.173 1.522 1.462 1.359 1.213 -1.173 -1.198 -1.266 -1.270 -1.142 -1.167 -1.342 -1.296 -1.262 -1.180 -1.207 (definition B) Moderate/severe CMH vs No Moderate/severe 3.117 2.842 2.608 2.254 4.512 2.760 3.130 3.515 2.548 -2.072 -2.552 -2.518 -3.233 -2.915 -2.435 -4.282 -2.889 -3.030 -2.902 -2.098 t-value FDR 0.225 0.225 0.225 0.225 0.231 0.231 0.231 0.231 0.231 0.231 0.225 0.225 0.225 0.225 0.225 0.231 0.231 0.231 0.231 0.231 p-value 8.86E-05 9.36E-05 1.02E-04 1.02E-04 1.65E-04 1.84E-04 3.13E-04 3.02E-04 1.90E-04 1.61E-04 6.82E-05 5.64E-05 5.49E-05 4.10E-05 2.32E-05 2.02E-04 2.37E-04 2.62E-04 2.73E-04 3.02E-04 (definition A) (definition FC 1.289 1.271 1.194 1.264 1.238 1.671 1.538 1.487 1.355 -1.306 -1.312 -1.386 -1.328 -1.183 -1.266 -1.326 -1.355 -1.321 -1.223 -1.357 Moderate/severe CMH vs No Moderate/severe 3.873 3.884 4.030 4.082 4.345 4.402 4.410 4.498 4.667 -4.265 -4.249 -4.223 -4.222 -4.075 -4.039 -4.010 -3.961 -3.929 -3.916 -3.884 t-value Gene LOC100133388 NKD1 FAM115A C1QTNF1 RPL23AP64 WTIP LIMS2 MTF1 USP46 LIAS RQCD1 RC3H1 OSBPL3 ROD1 LRRC8B AIG1 BTN2A3 PDGFB C15orf60 EDN1 Table 2. Top 20 significant mRNAs differently expressed with CMH and their statistics with CMH and their expressed 20 significant mRNAs differently Top 2. Table FC is fold change, FDR false discovery rate.

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Identification of miRNA-mRNA co-expression networks contributing to CMH in COPD To identify mRNAs that are regulated by the CMH-associated miRNAs, we assessed positive and negative correlations between the expression of each miRNA and the mRNA expression profile in the matched biopsies (same patient). The expression levels of 10 out of 20 miRNAs, i.e., let-7a-5p, let-7d-5p, let-7f-5p, miR-31-5p, miR- 708-5p, miR-134-5p, miR-146a-5p, miR-193-5p, miR-500a-3p and miR-1207-5p were significantly correlated with at least one mRNA (FDR<0.25, table S6). To identify potential direct targets, the lists of miRNA-negatively correlated mRNAs were compared to the list of miRNAs’ predicted targets. mRNAs that 3 overlapped in both lists were used to generate miRNA-mRNA co-expression networks (figure 3a). let-7a-5p, let-7d-5p, let-7f-5p, miR-31-5p, and miR-708-5p, which were higher expressed with CMH, shared several potential targets, and as expected, the members of let-7 family clustered together through their shared target genes. Similarly, miR-134-5p, miR-146a-5p, miR-500a-3p and miR-1207-5p, which were lower expressed with CMH, shared several potential targets (figure 3a). In these networks, the interactions between miRNAs and negatively correlated predicted targets that were also associated with CMH (indicated in figure 3a) are of special interest as these are the potential key drivers of CMH. Among the higher expressed miRNAs, the let-7 cluster represents a key cluster with negative correlation with 16 CMH-associated potential targets. Among the lower expressed miRNAs, miR- 134-5p and miR-146a-5p are key miRNAs negatively correlating with 8 and 10 CMH-associated potential targets, respectively. The percentage of CMH-associated potential targets among the negatively correlated predicted targets was highest for miR-134-5p (figure 3a). To identify potential interactions among CMH-associated mRNAs, STRING interaction network analysis was performed (figure 3b). The networks point towards KRAS, EDN1, PRKAR2A, GSK3B and POLR2H as hub genes and potential regulators of CMH in our biopsies as they possessed most interactions with other genes. Interestingly, KRAS and EDN1 are potential targets of miR-134-5p and let- 7a/d/f-5p, respectively. There was also clear clustering of several collagen and other extracellular matrix (ECM) related genes.

531891-L-bw-Tasena Processed on: 6-6-2019 PDF page: 43 CHAPTER 3 core-enriched CMH-associated genes core-enriched SPAG1, TTLL5 SPAG1, IFT52, SPAG1, TTLL5 IFT52, SPAG1, - SPAG1, TTLL5 SPAG1, - - SPAG1 CLIP1, SPAG1, TTLL5 CLIP1, SPAG1, - SOD1 NEBL, TMOD3, SPAG1, TTLL5 NEBL, TMOD3, SPAG1, TTLL5 EZR, SPAG1, RC3H1, NEBL, WDR45L, CNOT7, MIS12, STAM2, WDR45L, CNOT7, MIS12, STAM2, RC3H1, NEBL, TMOD3, CHMP5, RAB11A, VTA1, EZR, ATXN2, TTLL5, DDX3X, PDCD6IP CHMP2B, SPAG1, USP38, OTUD4 USP46, USP42, USP10, USP9Y, - - CBFB, CBLB, RNF6, SKP2 DDX3X ATXN2, RC3H1, CNOT7, CDH7, RPP40, POLR2H, DHX32, PAPOLA HNRNPF, USP46, USP42, USP10, USP9Y, USP38, OTUD4 USP46, USP42, USP10, USP9Y, ψ genes 30 (33) 35 (41) 20 (24) 20 (24) 19 (19) 44 (63) 16 (22) 26 (43) 22 (28) 64 (97) 69 (112) 85 (195) 117 (171) 117 114 (246) 114 117 (240) 117 123 (187) 122 (229) 175 (452) 176 (385) 103 (231) #core-enriched #core-enriched 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 FDR* 7.94E-05 6.46E-05 7.38E-05 6.89E-05 6.08E-05 3.80 3.72 3.24 3.12 2.92 2.90 2.90 2.86 2.71 2.63 3.16 2.97 2.92 2.90 2.76 2.86 2.76 2.69 2.64 2.62 NES* CMH Gene sets enriched among genes higher expressed with expressed Gene sets enriched among genes higher GO Cilium organization GO Cilium morphogenesis GO Cilium movement GO Axoneme assembly GO GO Protein transport along microtubule GO Intraciliary transport GO Axonemal dynein complex assembly GO GO Microtubule bundle formation GO Nonmotile primary cilium assembly GO Microtubule based movement GO Cellular component assembly involved in morphogenesis GO Cell projection assembly assembly GO Organelle GO Protein modification by small protein removal KEGG Proteasome BIOCARTA Proteasome pathway BIOCARTA GO Ubiquitin like protein specific protease activity GO Ribonucleoprotein complex biogenesis splicing via transesterification reactions GO RNA GO Protein polyubiquitination Category Table 3. Top 20 significant gene sets enriched among CMH-associated mRNAs Top 3. Table Cilium/ microtubule development and function Cellular component assembly Proteolysis Central dogma

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3 core-enriched CMH-associated genes core-enriched PDGFB, GDNF COL5A1, COL8A2, COL4A2, COL4A1, EFEMP2 JPH2, SLC17A7 TMEM38A SLC17A7 TRPV2, JPH2, TMEM38A TRPV2, JPH2, ENPEP, MRC2, COL6A1 ENPEP, COL5A1, COL6A2, COL8A2, LEPRE1, COL4A2, COL4A1, PDE9A, ADCY1, NPPB PDE9A, ADCY1, NPPB F2RL3, RAMP1, OPRK1, NPFFR1 PDGFB, REG1A, IL34, GDF11, CLEC11A, NENF, PGF, PGF, NENF, CLEC11A, PDGFB, REG1A, IL34, GDF11, NRTN GDNF, ADCY1, OPRK1 CRH EDN1, ADRA2C, OPRK1 ADRA1A, ADCY1, GNAO1, ADRA2C, OPRK1 ADCY1, GNAO1, ADRA1A, F2RL3, ADRA1A, ADRA2C, OPRK1, NPFFR1, LPAR4, ADRA2C, OPRK1, NPFFR1, LPAR4, ADRA1A, F2RL3, PTGER1 ADRA1A, ADCY1, GNAO1, ADRA2C, OPRK1 ADCY1, GNAO1, ADRA1A, OPRK1, NPFFR1 EDN1, CRH, NPPB OR2B11 ψ

genes 22 (32) 46 (75) 59 (74) 45 (78) 34 (55) 23 (33) 45 (66) 25 (29) 16 (35) 73 (97) 65 (118) 81 (113) 84 (129) 95 (154) 84 (142) 171 (316) 158 (293) 103 (169) 183 (269) 257 (347) ore-enriched ore-enriched #c

0.0 0.0 0.0 0.0 0.0 0.0 0.0 FDR* 2.30E-04 2.60E-04 6.30E-04 2.60E-04 2.20E-04 1.50E-04 5.70E-04 2.20E-04 8.40E-04 2.50E-04 1.40E-04 1.30E-04 8.00E-05 -2.31 -2.31 -2.25 -2.30 -2.32 -2.36 -2.26 -2.31 -2.53 -2.23 -2.33 -2.36 -2.36 -2.37 -2.45 -2.48 -2.51 -2.52 -2.82 -2.84 NES* CMH Gene sets enriched among genes lower expressed with expressed Gene sets enriched among genes lower GO Metanephric nephron development GO Extracellular matrix structural constituent GO Gated channel activity GO Potassium channel activity GO Extracellular ligand gated ion channel activity GO Cation channel activity metabolic process GO Multicellular organismal macromolecule GO Multicellular organismal GO Cyclic nucleotide metabolic process GO Cyclic nucleotide biosynthetic process GO Peptide receptor activity GO Growth factor activity GO Adenylate cyclase inhibiting GPCR signaling pathway GO GO Neuropeptide hormone activity nucleotide second messenger GO Regulation of sensory perception pain GO GPCR signaling pathway coupled to cyclic KEGG Neuroactive ligand receptor interaction GO Adenylate cyclase modulating GPCR signaling pathway GO GO Neuropeptide signaling pathway GO Hormone activity GO Olfactory receptor activity Category

Others

Ion channel

processes Metabolic

signaling and cell *statistics based on mRNAs associated with CMH definition A. ψ(#total genes). NES is normalized enrichment score, FDR is false discovery rate. GPCR is G-protein coupled receptor. A. ψ(#total genes). NES is normalized enrichment score, FDR false discovery rate. GPCR G-protein coupled receptor. *statistics based on mRNAs associated with CMH definition Neuronal

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FIGURE 3. Potential networks underlying chronic mucus hypersecretion (CMH) in chronic obstructive pulmonary disease (COPD). MicroRNA (miRNA)–mRNA co-expression networks are shown in part [a]. Red diamonds represent miRNAs higher expressed with CMH while blue diamonds represent miRNAs lower expressed with CMH. Green circles represent predicted target genes negatively correlated with a particular miRNA while black circles represent negatively correlated predicted targets of the miRNAs whose expression was also associated with CMH. Line width correlates to degree of significance of the miRNA–mRNA correlation. The percentage of CMH-associated genes among the negatively correlated predicted targets of each miRNA is illustrated in the table. Predicted interactions

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among CMH-associated targets of CMH-associated miRNAs are shown in part [b]. The networks were created using the STRING (search tool for the retrieval of interacting genes/proteins) database. All mRNAs that were predicted to interact with each other are depicted in the interaction networks while mRNAs that were not predicted to interact with any other mRNA were excluded. Red text represents negatively correlated predicted targets of miRNAs. Large and bold text represents the top five genes with the most interactions to other genes. Line width correlates to interaction scores. The interactions were predicted based on the combined score of all active interaction sources with the minimum required interaction score of 0.700. Only connected nodes are shown.

3 Enrichment of CMH- and MUC5AC-associated genes among the miRNA- correlated genes GSEA revealed that genes higher expressed with CMH were enriched among the genes positively correlated with miRNAs higher expressed with CMH and among the genes negatively correlated with miRNAs lower expressed with CMH, and vice versa for the genes that were lower expressed with CMH (figure 4a, 4b and S4). We found that the MUC5AC-associated gene set [16] was significantly enriched among the genes positively correlated with miRNAs that were higher expressed with CMH and among the genes negatively correlated with miRNAs that were lower expressed with CMH, except for miR-193a-5p (figure 4c and S5).

Expression of candidate miRNAs and mRNAs in human primary airway epithelial cells and fibroblasts As bronchial biopsy specimens contain various cell types, including mucus producing epithelial cells, we investigated whether the CMH-associated miRNAs identified in biopsies were expressed in PAECs in vitro. ALI-differentiated PAECs expressed let-7a-5p (representative of let-7 family), miR-31-5p, miR-708-5p, miR-146a-5p and miR-193-5p (figure 5a), but not miR-134-5p, miR-500a-3p and miR-1207-5p (values>35 Ct). We did not find differences in miRNA expression between COPD and healthy control-derived PAECs (figure 5a), neither did we observe a significant difference in MUC5AC expression between these two groups (figure S6). As our previous report suggests involvement of fibroblasts in mucus production via crosstalk with epithelial cells [8], we additionally investigated candidate miRNA expression in PAFs derived from COPD patients with or without clinical CMH symptoms. All PAFs expressed let-7a-5p, miR-31-5p, miR-708-5p, miR-134-5p, miR-146a-5p and miR-193a-5p (figure 5b), but not miR-500a-3p and miR-1207-5p. Of interest, the expression of miR-134-5p (figure 5c), but not the other miRNAs, was significantly lower in PAFs derived from the patients with CMH, in line with the findings in our biopsies.

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FIGURE 4. Overrepresentation of chronic mucus hypersecretion (CMH) associated genes and MUC5AC-associated genes among microRNA (miRNA) correlated genes. Enrichment of genes higher expressed with CMH among the genes correlated with CMH-associated miRNAs is shown in part [a]. Enrichment of genes lower expressed with CMH among the genes correlated with CMH-associated miRNAs is shown in part [b]. Enrichment of MUC5AC-associated genes among the genes correlated with CMH-associated miRNAs is shown in part [c]. The enrichment plots of miR-134-5p are shown as an example. Asterisks represent significant enrichment. **: p<0.01; ***: p<0.001.

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FIGURE 5. Expression of candidate microRNAs (miRNAs) in primary human airway structural cells. miRNAs expressed in primary airway epithelial cells (PAECs) are shown in part [a]. PAECs were derived from chronic obstructive pulmonary disease (COPD) patients (n=6) and healthy controls (n=6) and grown at an air–liquid interface for 14 days. Cells were hormonally deprived overnight and RNA was isolated. miRNAs expressed in primary airway fibroblasts (PAFs) from COPD patients (n=16) are shown in part [b]. The differential expression of miR-134-5p in PAFs from COPD patients with chronic mucus hypersecretion (CMH) compared to those with no CMH is shown in part [c]. PAFs were derived from COPD patients with CMH (n=8) and without CMH (n=8) and grown until confluent. Cells were then serum deprived for 24 h and RNA was isolated. Expression of all miRNAs was normalised to RNU48. Relative expression levels (2-ΔCt, where ΔCt equals the cycle threshold (Ct) of the miRNA of interest minus the Ct of RNU48) are shown as median±interquartile range. *: p<0.05 between the indicated values (as assessed by the Mann–Whitney U-test).

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To assess whether the top 20 CMH-associated genes were expressed in ALI- differentiated PAECs , we used a publicly available gene expression profiles [17] and found that the majority of these genes were expressed in the ALI-differentiated PAECs (figure S7). LOC100133388, RPL23AP64, C15orf60 were not available in this dataset. Furthermore, using a publicly available gene expression profiles of primary human lung fibroblasts [18], we found that the majority of these genes were also expressed in primary human lung fibroblasts, except NKD1 and PDGFB (figure S8). LOC100133388 was not available in this dataset.

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DISCUSSION

We identified 20 miRNAs and 539 mRNAs associated with CMH in bronchial biopsies from COPD patients. Our data suggest that miR-134-5p, miR-146a-5p and the let-7 family may regulate CMH via their potential CMH-associated targets e.g. KRAS and EDN1. The relevance of our CMH signatures was supported by the enrichment of MUC5AC-associated genes [16] among our CMH-associated mRNAs and mRNAs that correlated with the CMH-associated miRNAs. Furthermore, our in vitro studies demonstrated that miR134-5p was lower expressed in primary airway fibroblasts from COPD patients with CMH compared to those without CMH, which 3 is in line with the findings in bronchial biopsies and supports a key role for mir134-5p in regulating CMH in COPD. Since we had both miRNA and mRNA data available from the same bronchial biopsies, we were able to create miRNA-mRNA co-expression networks. This analysis identified the let-7 family, miR-134-5p and miR-146a-5p with their CMH-associated targets to be the key regulators of CMH. miR-134-5p had the highest percentage of CMH-associated genes among its potential targets, of which KRAS was identified as a hub and potential regulatory gene for CMH. KRAS was previously found to be one of the MUC5AC-associated core genes [16]. Interestingly, the expression of miR- 134-5p was lower in fibroblasts from COPD patients with CMH than those without, in line with the finding in biopsies. It was not expressed in epithelial cells, suggesting an indirect regulation of CMH via fibroblasts. There are various explanations why the differential expression of miR-134-5p in fibroblasts can affect MUC5AC- associated genes identified in airway epithelium. Firstly, miRNAs can be secreted into exosomes [19] and thus transported from one cell type (e.g. fibroblasts) to the other (e.g. epithelium). Secondly, MUC5AC-associated genes are not only expressed by epithelial cells, but also by fibroblasts. We found that approximately 70% of the MUC5AC-associated genes identified in airway epithelium [16] are also expressed in the lung fibroblasts [18]. Finally, the MUC5AC-associated genes may be secondary targets of miR-134-5p. Our recent studies have shown that CXCL8 produced by lung fibroblasts promotes epithelial mucus production [8] and that the CXCL8 release is increased upon co-culture of fibroblasts and epithelial cells [20]. In line with that report, we observed in this study that CXCL8 is negatively correlated with miR-134- 5p (table S6). Another key miRNA identified in our miRNA-mRNA networks is miR-146a- 5p, which was lower expressed with CMH. miR-146a5p was previously reported to be involved in airway inflammation, associated with smoking and COPD [10], and to inhibit MUC5AC production in vitro [21]. We recently found miR-146a-5p to be higher expressed in non-COPD lung fibroblasts upon co-culture with bronchial epithelial cells, while there was significantly less increase in COPD fibroblasts, 51

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suggesting a role of miR-146a-5p in disturbed epithelial-fibroblast crosstalk in COPD [22]. Our present findings suggest that miR-146a-5p may also be involved in mucus hypersecretion via epithelial-fibroblast crosstalk, yet it is not clear via which target gene nor in which cell type it has its main effect on CMH. Further mechanistic studies using co-culture or lung organoid models could help elucidating the CMH-related function of miR-146-5p. The last key miRNA-mRNA cluster identified was the let-7 family, which was higher expressed in association with CMH. The members of the let-7 family shared several potential targets associated with CMH of which EDN1, NKD1, PDGFB, COL4A1 and COL4A2 are of particular interest. EDN1 is localized in submucosal glands and stimulates mucus secretion from serous and mucus cells in mucosal explant culture [23]. NKD1 is an antagonist of the WNT/β-catenin -signalling pathway[24], which plays an important role in respiratory epithelial differentiation [25] and of which specific components, e.g. LEF1, have been reported to regulate MUC5AC production [8, 26]. PDGFB is involved in airway remodelling [27] and epithelial-fibroblast crosstalk [28]. Further, COL4A1 and COL4A2 were also identified in a larger cluster of collagen genes in the CMH interaction networks and are essential components of basement membranes [29]. Basement membrane thickness is associated with an increase of submucosal glands and central airway remodelling in asthma [30], but its role linking airway remodelling with CMH in COPD remains to be investigated. Of note, various CMH-associated targets of let-7a/d/f-5p (EDA, LIX1L, MAPK11, NME4) have been validated with next-generation sequencing [31], supporting our findings. Apart from that, the CMH-associated genes we identified may be involved in several other biological processes, including cilium development and function, neurohormonal activities, cyclic nucleotide metabolism and signalling, and ion transport. Cilia function and movement is important for mucus clearance [32] and impaired cilia function was observed in COPD airways [33, 34]. The movement of cilia is dependent, at least partially, on cyclic nucleotides, in particular cAMPs [35]. Furthermore, active ion transport, such as that of Ca2+, Na+ and Cl-, plays an important role in regulating mucus viscosity and mucociliary clearance [36, 37], and both Ca2+ and Na+ can be transported through cyclic nucleotide-gated channels [38]. Besides, previous studies suggested that neurohormonal signalling regulates cGMP- induced mucin secretion [39] and the expression of GDNF, the gene encoding for a neurotrophic factor involving in lung development, is associated with CMH in COPD [40]. We also generated gene-interaction networks for CMH and identified KRAS, EDN1, PRKAR2A, GSK3B and POLR2H as hub genes and potential regulators of CMH. Next to KRAS and EDN1, GSK3B is also of interest as it is a pleiotropic signalling molecule that regulates WNT/β-catenin signalling – the pathway that has been shown to induce mucus cell metaplasia in a mouse model [25].

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One of the limitations of this study is the relatively small dataset that we used to explore the miRNA and mRNA changes associated with CMH and consequent use of a lenient FDR cut-off. To support our initial findings, several approaches were used. First, we used GSEA to demonstrate the enrichment of MUC5AC- associated genes [16] among the genes higher expressed with CMH. This confirms the relation between our CMH definitions and MUC5AC expression. In addition, we demonstrated significant enrichment of the CMH-associated genes identified in the microarray dataset among the CMH-associated genes identified in the RNA- sequencing dataset. The replication of our findings in a different patient cohort would be informative. However, to the best of our knowledge, there are no other datasets 3 in which both microRNA/mRNA profiles and CMH definitions are available which reflects an urgent need of more studies on this topic. For the in vitro studies, we decided to determine the expression of our candidate miRNAs in PAECs and PAFs as they are present in airway wall biopsies and important cells in the pathogenesis of COPD. As our findings were derived from biopsies, miRNA changes in other cell types may have contributed to our findings; for instance, inflammatory cells or smooth muscle cells. In fact, this could explain the lack of expression of miR-500a-3p and miR-1207-5p in PAECs and PAFs, and validation in other cell types and co-culture studies is warranted in future studies. Furthermore, it will be of interest for future investigations to compare PAECs from patients with CMH and without CMH, which was not possible in the current study. In addition, we focused on the 10 miRNAs that were significantly correlated with mRNA expression in the same subjects in this study. This does not imply that the other 10 miRNAs are not relevant to CMH, since miRNAs not only regulate gene translation by degradation but also by inhibiting the translation [9]. Collectively, we identified three key miRNA-mRNA clusters for CMH: miR- 134-5p, miR-146a-5p and the let-7 family, as well as their associated potential target genes. The let-7 and miR-134-5p clusters are connected to the CMH gene expression networks via the potential key regulatory genes KRAS and EDN1. Furthermore, we identified pathways and biological processes, including MUC5AC-associated genes, in which these key miRNA-mRNA clusters are likely to be involved. Future studies involving co-cultures of epithelial cells with fibroblast and other cell types are required to further unravel the functional role of these key genes and miRNAs, and to establish whether they represent potential therapeutic targets for CMH.

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ACKNOWLEDGEMENT

This study was funded by a non-restricted grant from Stichting Astma Bestrijding, the Netherlands Asthma Foundation. The authors would like to thank Dr. Anita Spanjer for providing RNA samples from human primary airway fibroblasts, Jeunard Glenn Boekhoudt for the analyses on the RNA-sequencing data, and Margaret Neighbors from Genentech for securing funding for the RNA-sequencing analyses.

CONTRIBUTIONS

HT, AF, WT, MNH, RG, DSP, MB, IHH, CAB contributed to the study concept and design. PSH, MB and WT coordinated patient inclusion and data collection of GLUCOLD. MB and AS organized and performed the microarrays. GWT and MAG organized and performed the RNA-sequencing. HT and JN conducted in vitro laboratory work under supervision of IHH and CAB. HT and AF performed the microarray data analysis under supervision of MB and CAB. HT, AF, MB, IHH and CAB analysed and interpreted data and drafted the manuscript. HT, AF, WT, JN, MNH, RG, PSH, AS, DSP, GWT, MAG, MB, IHH and CAB critically read and revised the manuscript. All authors have read, reviewed and approved the final manuscript.

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16. Wang G, Xu Z, Wang R, Al-Hijji M, Salit J, Strulovici-Barel Y, Tilley AE, Mezey JG, Crystal RG. Genes associated with MUC5AC expression in small airway epithelium of human smokers and non-smokers. BMC Med. Genomics 2012; 5: 21. 17. Ross AJ, Dailey LA, Brighton LE, Devlin RB. Transcriptional profiling of mucociliary differentiation in human airway epithelial cells. Am. J. Respir. Cell Mol. Biol. 2007; 37: 169–185. 18. Ong J, Timens W, Rajendran V, Algra A, Spira A, Lenburg ME, Campbell JD, Van Den Berge M, Postma DS, Van Den Berg A, Kluiver J, Brandsma CA. Identification of transforming growth factor-beta-regulated microRNAs and the microRNAtargetomes in primary lung fibroblasts. PLoS One 2017; 12: 1–24. 19. Zhang J, Li S, Mi S, Li L. Exosome and Exosomal MicroRNA : Trafficking , Sorting , and Function. Genomics. Proteomics Bioinformatics 2015; 13: 17–24. 20. Osei ET, Noordhoek JA, Hackett TL, Spanjer AIR, Postma DS, Timens W, Brandsma C, Heijink IH. Interleukin-1 α drives the dysfunctional cross-talk of the airway epithelium and lung fibroblasts in COPD. Eur. Respir. J. 2016; 48: 359–369. 21. Zhong T, Perelman JM, Kolosov VP, Zhou XD. MiR-146a negatively regulates neutrophil elastase-induced MUC5AC secretion from 16HBE human bronchial epithelial cells. Mol. Cell. Biochem. 2011; 358: 249–255. 22. Osei ET, Florez-Sampedro L, Tasena H, Faiz A, Noordhoek JA, Timens W, Postma DS, Hackett TL, Heijink IH, Brandsma C-AA. MiR-146a-5p plays an essential role in the aberrant epithelial-fibroblast crosstalk in COPD. Eur. Respir. J. MiR-146a-5p 2017; 49: 1602538. 23. Mullol J, Chowdhury BA, White M V, Ohkubo K, Rieves RD, Baraniuk J, Hausfeld JN, Shelhamer JH, Kaliner MA. Endothelin in Human Nasal Mucosa. Am. J. Respir. Cell Mol. BioI. 1993; 8: 393–402. 24. Van Raay TJ, Coffey RJ, Solnica-Krezel L. Zebrafish Naked1 and Naked2 antagonize both canonical and noncanonical Wnt signaling. Dev. Biol. 2007; 309: 151–168. 25. Mucenski ML, Nation JM, Thitoff AR, Besnard V, Xu Y, Wert SE, Harada N, Taketo MM, Stahlman MT, Whitsett JA. β-Catenin regulates differentiation of respiratory epithelial cellsin vivo. Am. J. Physiol. Cell. Mol. Physiol. 2005; 289: L971–L979. 26. Young HWJ, Williams OW, Chandra D, Bellinghausen LK, Pérez G, Suárez A, Tuvim MJ, Roy MG, Alexander SN, Moghaddam SJ, Adachi R, Blackburn MR, Dickey BF, Evans CM. Central role of Muc5ac expression in mucous metaplasia and its regulation by conserved 5’ elements. Am. J. Respir. Cell Mol. Biol. 2007; 37: 273–290. 27. Yamashita N, Sekine K, Miyasaka T, Kawashima R, Nakajima Y, Nakano J, Yamamoto T, Horiuchi T, Hirai K, Ohta K. Platelet-derived growth factor is involved in the augmentation of airway responsiveness through remodeling of airways in diesel exhaust particulate-treated mice. J. Allergy Clin. Immunol. 2001; 107: 135–142. 28. Li DQ, Tseng SC. Three patterns of cytokine expression potentially involved in epithelial-fibroblast interactions of human ocular surface. J. Cell. Physiol. 1995; 163: 61–79. 29. Pöschl E, Schlötzer-Schrehardt U, Brachvogel B, Saito K, Ninomiya Y, Mayer U. Collagen IV is essential for basement membrane stability but dispensable for initiation of its assembly during early development. Development 2004; 131: 1619–1628. 30. James AL, Maxwell PS, Pearce-Pinto G, Elliot JG, Carroll NG. The relationship of reticular basement membrane thickness to airway wall remodeling in asthma. Am. J. Respir. Crit. Care Med. 2002; 166: 1590–1595. 31. Helwak A, Kudla G, Dudnakova T, Tollervey D. Mapping the human miRNA interactome by CLASH reveals frequent noncanonical binding. Cell 2013; 153: 654–665.

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32. Knowles MR, Boucher RC. Innate defenses in the lung Mucus clearance as a primary innate defense mechanism for mammalian airways. J. Clin. Invest. 2002; 109: 571–577. 33. Rogers DF. Mucociliary dysfunction in COPD : effect of current pharmacotherapeutic options. Pulm. Pharmacol. Ther. 2005; 18: 1–8. 34. Smaldone GC, Foster MW, O’Riordan TG, Messina MS, Perry RJ, Langenback EG. Regional Impairment of Mucociliary Clearance in Chronic Obstructive Pulmonary Disease. Chest 1993; 103: 1390–1396. 35. Wyatt TA, Forge MA, Sisson JH. Ethanol Stimulates Ciliary Beating by Dual Cyclic Nucleotide Kinase Activation in Bovine Bronchial Epithelial Cells. Am. J. Pathol. 2003; 163: 1157–1166. 36. González C, Droguett K, Rios M, Cohen NA, Villalón M. TNFα Affects Ciliary Beat Response to Increased Viscosity in Human Pediatric Airway Epithelium. Biomed Res. Int. 2016; 2016: 3628501. 3 37. Tarran R, Grubb BR, Gatzy JT, Davis CW, Boucher RC. The Relative Roles of Passive Surface Forces and Active Ion Transport in the Modulation of Airway Surface Liquid Volume and Composition. J. Gen. Physiol. 2001; 118: 223–236. 38. Kaupp UB, Seifert R. Cyclic Nucleotide-Gated Ion Channels. Physiol. Rev. 2002; 82: 769–824. 39. Shelhamer JH, Marom ZVI, Kaliner M, Diseases A. Immunologic and Neuropharmacologic Stimulation of Mucous Glycoprotein Release from Human Airways In vitro. J. Clin. Investig. 1980; 66: 1400–1408. 40. Dijkstra AE, Boezen HM, Berge M Van Den, Vonk JM, Hiemstra PS, Barr RG, Burkart KM, Manichaikul A, Pottinger TD, Silverman EK, Cho MH, Crapo JD, Beaty TH, Bakke P, Gulsvik A, Lomas DA, Bossé Y, Nickle DC, Paré PD, Koning HJ De, Lammers J, Zanen P, Smolonska J, Wijmenga C, Brandsma C, Groen HJM, Postma DS, Study C. Dissecting the genetics of chronic mucus hypersecretion in smokers with and without COPD. Eur. Respir. J. 2015; 45: 60–75.

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SUPPLEMENTARY DATA

Definitions of chronic mucus hypersecretion (CMH) CMH was defined based on patient’s responses to clinical questionnaires including two questions: A) “How often did you cough up sputum during the last three months?” and B) “How often did you cough up sputum during the last week?” The latter was derived from the clinical COPD questionnaire (CCQ) [1]. Question A had four optional answers: i) never, ii) sometimes, iii) almost daily, and iv) only during a cold. Patients who responded i) or iv) were classified as group 1: no CMH. Patients who responded ii) were classified as group 2: mild CMH. Patients who responded iii) were classified as group 3: moderate/severe CMH. Question B had seven optional answers: i) never, ii) sometimes, iii) once in a while, iv) often, v) most of the times, vi) regularly, and vii) always. Patients who responded i) were classified as group 1: no CMH. Patients who responded ii) and iii) were classified as group 2: mild CMH. Patients who responded iv) to vii) were classified as group 3: moderate/severe CMH.

MicroRNA (miRNA) and messenger RNA (mRNA) profiling Both total mRNA and miRNA was extracted from bronchial biopsies at baseline (n=63) as part of the GLUCOLD study [2, 3]. mRNA profiling was performed at Boston University Microarray Resource Facility as described in GeneChip® Whole Transcript (WT) Sense Target Labeling Assay Manual (Affymetrix, Santa Clara, CA, current version available at www.affymetrix.com) as previously described [4]. Microarray hybridization was performed using Affymetrix Human Gene_ST v1.0 Arrays. Isolation of small fractions of RNA for miRNA profiling was done using the miRNeasy mini kit (Qiagen) and RNeasy MinElute Cleanup Kit (Qiagen) according to the manufacturer’s protocols. MiRNA profiling was performed at Boston University Microarray Resource Facility using FlashTag™ Biotin HSR Labeling Kit (Affymetrix, Santa Clara, CA, current version available at www.affymetrix. com). miRNAs and mRNAs that were expressed in less than 50% of the patients were excluded. The filtering of miRNAs was done using the standard Affymetrix Expression Console software, and of mRNAs was done using the Robust Multichip Average algorithm and the Entrez Gene Chip Definition File (CDF) v11.0.1[5]. As a result, the expression profiles used for all analyses included 230 miRNAs and 19,793 mRNAs. Both microarray datasets can be accessed at http://www.ncbi.nlm.nih.gov/ geo/ (series accession number GSE76774 for miRNA; GSE36221 for mRNA). To validate the findings from the microarrays, we used the mRNA profiles from the RNA-sequencing (RNA-seq) conducted on libraries belonging to biopsies obtained from 76 GLUCOLD patients. Total mRNA was extracted as described previously [2, 3]. The libraries were prepared using Ribo-Zero Gold kit and

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sequenced using 50bp single end read sequencing. The “FastQC” program version 0.11.5 was utilized to perform quality control checks on the raw sequence data from the RNA-Seq (http://www.bioinformatics.babraham.ac.uk). The sequences were trimmed using the java program “trimmomatic 0.33” [6] and the RNA-Seq mapping was conducted using the “Spliced Transcripts Alignment to a Reference (STAR)” program version 2.5.3a, which is an RNA-Seq aligner program [7]. We checked the quality of the libraries by calculating the raw count percentages of the forward reads. Libraries with a percentage lower than 90% were excluded from further analysis. Principal component analysis was also performed (using R) on the libraries in order to detect extreme outlier. After quality check, we decided to keep all samples for 3 further analyses. This dataset can be made available for future potential collaboration upon request. The R package “edgeR_3.16.5” was utilized to perform differential expression analysis on the samples taken from CMH patients at baseline (n=38). We compared mRNA profiles of the patients with moderate/severe CMH (n=22 for definition A and n=21 for definition B) with the patients with no CMH (n=5 for both definitions). These analyses were corrected for age, gender and smoking status.

Identification of CMH-associated miRNAs and mRNAs A linear regression model was used to identify miRNAs and mRNAs that were differentially expressed in patients with mild CMH or with moderate/severe CMH compared to those without CMH. The model was corrected for age, gender, smoking history and RNA integrity number scores (RIN) as described below, where Mei represents the log2 miRNA/gene expression value for a miRNA/gene in a sample from patient i, εi represents the error that is assumed to be normally distributed.

Mei=β0+ β1 XRIN-i+ β2 XSmoking history-i +β3 XAge-i + β4XGender-i+β5 XCMH status-i + εi

MiRNA-mRNA co-expression network analysis Pearson correlation analysis was used to determine the correlation between the expression of each CMH-associated miRNA and all mRNAs in the same patient based on False Discovery Rate (FDR)<0.25. The lists of miRNA’s predicted target genes based on TargetScan (v6.2), miRTarBase (v4.5) and miRDB (v5.0) were combined. Those predicted targets that were negatively correlated with the candidate miRNAs were used for creating miRNA-mRNA co-expression networks on Cytoscape (v3.4.0).

Gene Set Enrichment Analysis (GSEA) GSEA (v2.2.2) was used for the following purposes: 1) to identify enriched pathways (Kegg, v5.2; Biocarta, v5.2), biological processes (Gene Ontology, v5.2), and molecular functions (Gene Ontology, v5.2) in which CMH-associated mRNAs may

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be involved, 2) to determine enrichment of a previously published set of MUC5AC- associated genes [8] among mRNAs higher expressed with CMH, 3) to determine enrichment of CMH-associated mRNAs and MUC5AC-associated genes among miRNA-correlated mRNAs, and 4) to determine enrichment of CMH-associated mRNAs identified in the microarray dataset among the CMH-associated mRNAs identified in the RNA-seq dataset. Genes were ranked according to the strength of their t-statistic reflecting their association with CMH or their correlation with the miRNA of interest. Enrichment p-values were calculated after 1000 permutations were performed. Significant enrichment was determined by an FDR<0.01 for the first purpose and by p<0.05 for the second, third and fourth purposes.

STRING interaction network analysis Of all 539 CMH-associated mRNAs, expression of 538 was significantly correlated with one or more of the 10 CMH-associated miRNAs (table S6). Interactions among these miRNAs’ potential target genes were predicted using the STRING database (v10.0) and the interaction networks were created on Cytoscape (v3.4.0) with stringApp (v1.0.5) [9]. Sources of the interactions include text-mining, experiments, databases, co-expression, neighborhood, gene fusion and co-occurrence. Minimum required interaction score is 0.700.

Cell culture and RNA isolation To compare the expression of candidate miRNAs in primary airway epithelial cells (PAECs) from COPD and non-COPD subjects, PAECs were obtained from 6 GOLD stage IV COPD (table S1) explanted lungs and 6 non-COPD donor lungs as described before [10]. PAECs were used in passage 3, grown to confluence in a transwell system, air-exposed for 14 days in BEGM/DMEM supplemented with 15 ng/ml retinoic acid and hormonally and growth factor-deprived overnight as described previously [10]. To compare the expression of candidate miRNAs in primary airway fibroblasts (PAFs), a separate experiment was performed. PAFs were isolated from GOLD stage IV COPD patients (table S2) with (n=8) and without CMH (n=8) as described before [11] and used in passage 5/6 and cultured in Ham’s F12 medium supplemented with 10% (v/v) fetal bovine serum (FBS) until confluence. The cells were then serum deprived for 24 hours. The study protocol followed national ethical and professional guidelines (‘Code of conduct; Dutch federation of biomedical scientific societies’; http://www. federa.org) for all lung tissues and explant cell culture studies in Groningen.

Reverse transcription-quantitative PCR (RT-qPCR) Total RNA was isolated using Tri Reagent® according to the manufacturer’s protocol. For the miRNAs of interest RNA was converted to cDNA using the TaqMan microRNA

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reverse transcription kit (Life Technologies, Bleiswijk, Netherlands) and reverse transcription primers (Life Technologies) for let-7a-5p (assay id: 000377), miR-31- 5p (002279), miR-708-5p (002341), miR-134-5p (000459), miR-146a-5p (000468), miR-193a-5p (002281), miR-500a-3p (001046) and miR-1207-5p (241060_mat). As let-7d-5p and let-7f-5p were in the same cluster as let-7a-5p and these three miRNAs shared very similar results in the previous analyses, we only investigated let-7a-5p expression. qPCR was performed using the matched PCR primers (Life Technologies) and Eurogentec qPCR MasterMix Plus (05-QP2X-03; Eurogentech, Maastricht, Netherlands) on the LightCycler 480 II (Roche, Almere, Netherlands). Expression of the miRNAs of interest was normalized to the expression of the small nuclear 3 RNA, RNU48 (001006). To determine MUC5AC expression, RNA was converted to cDNA using iScript™ cDNA Synthesis Kit (BioRad). qPCR was then performed using MUC5AC Taqman® assay (Hs01365616_m1) and the Taqman® Master Mix according to the manufacturer’s guidelines (Applied Biosystems, Foster City, CA). MUC5AC expression was normalized to the expression of the reference genes: B2M (Hs99999907_m1) and PPIA (Hs99999904_m1).

Table S1. Characteristics of patients from which primary airway epithelial cells (PAECs) were obtained

COPD IV patients# (n=6)

Gender (male), n(%) 3(50.0) Ex-smokers, n(%) 6(100.0) 41.7 Pack-year* (23.6-50.3) 55 Age, year* (51-59) 18.8 FEV1, %predicted* (17.3-19.8) 0.24 FEV1/FVC* (0.22-0.25)

*Data are presented as median (interquartile range); FEV1 is forced expiratory volume in 1 second; FVC is forced vital capacity; #two patients were CMH-positive.

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Table S2. Characteristics of patients from which primary airway fibroblast (PAFs) were obtained

COPD patients no CMH (n=8) CMH (n=8) Gender (male), n(%) 2(25.0) 4(50.0) Pack-year* 38.0 30.0 (30.0-57.0) (28.8-36.3) Age, year* 58 58 (57-61) (50-59) FEV1, %predicted* 20.7 15.2 (17.3-22.6) (14.2-18.5) FEV1/FVC* 0.26 0.27 (0.24-0.31) (0.24-0.32)

*Data are presented as median (interquartile range); FEV1 is forced expiratory volume in 1 second; FVC is forced vital capacity.

Table S3. microRNAs differentially expressed with CMH and their statistics

Severe CMH vs No CMH Severe CMH vs No CMH

(definition A) (definition B) miRNA t-value FC p-value FDR t-value FC p-value let-7d-5p 2.928 1.263 4.927E-03 0.149 2.453 1.171 1.730E-02 miR-30c-5p 2.922 1.411 5.001E-03 0.149 3.365 1.423 1.400E-03 miR-708-5p 2.794 2.061 7.116E-03 0.149 2.930 2.060 4.900E-03 miR-31-5p 2.528 1.684 1.431E-02 0.182 2.566 1.613 1.300E-02 miR-27-3p 2.433 1.452 1.821E-02 0.182 3.420 1.730 1.200E-03 miR-30b-5p 2.339 1.475 2.291E-02 0.182 3.060 1.475 3.400E-03 let-7f-5p 2.227 1.700 2.996E-02 0.230 2.766 1.789 7.700E-03 let-7a-5p 2.133 1.261 3.731E-02 0.240 2.400 1.223 1.980E-02 miR-19a-3p 2.088 1.638 4.139E-02 0.244 2.463 1.529 1.690E-02 miR-134-5p -3.507 -2.152 9.002E-04 0.149 -2.433 -1.615 1.820E-02 miR-193a-5p -2.774 -1.785 7.519E-03 0.149 -2.545 -1.653 1.370E-02 miR-320c -2.681 -1.355 9.638E-03 0.171 -2.874 -1.316 5.700E-03 miR-339-3p -2.448 -1.342 1.754E-02 0.182 -2.138 -1.269 3.690E-02 miR-320a -2.419 -1.334 1.885E-02 0.182 -2.863 -1.320 5.900E-03 miR-320b -2.384 -1.309 2.054E-02 0.182 -2.873 -1.299 5.700E-03 miR-1207-5p -2.371 -1.547 2.120E-02 0.182 -3.727 -1.694 5.000E-04 miR-22-3p -2.183 -1.209 3.326E-02 0.239 -2.250 -1.230 2.840E-02 miR-500a-3p -2.151 -1.336 3.577E-02 0.240 -2.166 -1.239 3.460E-02 miR-146a-5p -2.117 -1.626 3.872E-02 0.240 -2.288 -1.569 2.590E-02 miR-320d -2.106 -1.259 3.969E-02 0.240 -2.288 -1.242 2.590E-02

FC is fold change; FDR is false discovery rate.

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Figure S1. Responses of patients (n=63) to two questions used for defining CMH were strongly correlated. Dot plot showing the patients’ answers to question A) “How often did you cough up sputum during the last three months?” and question B) “How often did you cough up sputum during the last week?”. 1 represents “never cough” or “only during a cold”; 2 represents “sometimes” or “once in a while”; 3 represents “almost daily” for question A) or “often”, “most of the times”, “regularly”, or “always” for question B). Spearman’s rank correlation coefficient was performed. r=0.651; p<0.0001.

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Figure S2. Gene set enrichment analysis (GSEA) on the RNA-seq dataset. Genes higher expressed with CMH in the microarray dataset were positively enriched based on [a] CMH definition A and [b] CMH definition B. Genes lower expressed with CMH in the microarray dataset were negatively enriched based on [c] CMH definition A and [d] CMH definition B. All enrichment was significant (p<0.001). Vertical black lines (Hits) represent position of 539 CMH-associated genes identified in the microarray dataset in the ranked gene list. From left to right, 19,075 genes were ranked by t-statistics for association with CMH in the RNA-seq dataset. Red and blue represent positive and negative association, respectively.

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Figure S3. Overrepresentation of MUC5AC-associated genes among CMH-associated mRNAs. Enrichment analysis was performed using the ranked list of CMH-associated mRNAs, based on definition A and B, and the list of 73 MUC5AC- associated genes [8]. Significant enrichment was observed for both definition A (enrichment score (ES)=0.40, p<0.001) and B (ES=0.44, p<0.001). ES was calculated by walking down the ranked list of genes – a running-sum statistic increases when a gene is in the gene set and decreases when the gene is not. On the left graph, leading edge genes associated with CMH in both definitions are shown in red.

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Figure S4. Enrichment of CMH-associated mRNAs among miRNA-correlated mRNAs. a) Enrichment of mRNAs higher expressed with CMH. b) Enrichment of mRNAs lower expressed with CMH. All enrichments were significant (p<0.001).

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Figure S5. Enrichment of MUC5AC-core genes among miRNA-correlated mRNAs. All enrichments were significant (p<0.005) except for miR-193-5p.

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Figure S6. Expression of MUC5AC in primary airway epithelial cells (PAECs). PAECs were derived from COPD patients (n=6) and healthy controls (n=6) and grown at air-liquid interface for 14 days. Cells were hormonally deprived overnight and RNA was isolated. The MUC5AC expression was normalized to B2M and PPIA. Relative expression levels are shown (median±interquartile range).

Figure S7. Expression of top CMH-associated genes in ALI-cultured primary airway epithelial cells (day 14)12. The microarray dataset is publicly available on www.ncbi.nlm.nih.gov/geo/ (series accession number GSE5264). Genes with microarray intensity above 5.0 were considered expressed.

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Figure S8. Expression of top CMH-associated genes in primary lung fibroblasts13. The microarray dataset is publicly available on www.ncbi.nlm.nih.gov/geo/ (series accession number GSE86183). Genes with microarray intensity above 5.0 were considered expressed.

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1. Molen T Van Der, Willemse BWM, Schokker S, Ht N, Postma DS, Juniper EF. Development , validity and responsiveness of the Clinical COPD Questionnaire. Health Qual. Life Outcomes 2003; 1: 1–10. 2. Lapperre TS, Postma DS, Gosman MME, Snoeck-Stroband JB, ten Hacken NHT, Hiemstra PS, Timens W, Sterk PJ, Mauad T. Relation between duration of smoking cessation and bronchial inflammation in COPD. Thorax 2006; 61: 115–121. 3. Lapperre TS, Sont JK, Schadewijk A Van, Gosman MME, Postma DS, Bajema IM, Timens W, Mauad T, Hiemstra PS, Group S. Smoking cessation and bronchial epithelial remodelling in COPD : a cross-sectional study. Respir. Res. 2007; 9: 1–9. 4. van den Berge M, Steiling K, Timens W, Hiemstra PS, Sterk PJ, Heijink IH, Liu G, Alekseyev YO, Lenburg ME, Spira A, Postma DS. Airway gene expression in COPD is dynamic with inhaled corticosteroid treatment and reflects biological pathways associated with disease activity. Thorax 2014; 69: 14–23. 5. Dai M, Wang P, Boyd AD, Kostov G, Athey B, Jones EG, Bunney WE, Myers RM, Speed TP, Akil H, Watson SJ, Meng F. Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data. Nucleic Acids Res. 2005; 33: 1–9.

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6. Bolger AM, Lohse M, Usadel B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 2014; 30: 2114–2120. 7. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR. STAR: Ultrafast universal RNA-seq aligner. Bioinformatics 2013; 29: 15–21. 8. Wang G, Xu Z, Wang R, Al-Hijji M, Salit J, Strulovici-Barel Y, Tilley AE, Mezey JG, Crystal RG. Genes associated with MUC5AC expression in small airway epithelium of human smokers and non-smokers. BMC Med. Genomics 2012; 5: 21. 9. Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, Simonovic M, Roth A, Santos A, Tsafou KP, Kuhn M, Bork P, Jensen LJ, Von Mering C. STRING v10: Protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 2015; 43: D447–D452. 10. Heijink IH, De Bruin HG, Dennebos R, Jonker MR, Noordhoek JA, Brandsma CA, Van Den Berge M, Postma DS. Cigarette smoke-induced epithelial expression of WNT-5B: Implications for COPD. Eur. Respir. J. 2016; 48: 504–515. 11. Brandsma C-A, Timens W, Jonker MR, Rutgers B, Noordhoek JA, Postma DS. Differential effects of fluticasone on extracellular matrix production by airway and parenchymal fibroblasts in severe COPD. Am. J. Physiol. Lung Cell. Mol. Physiol. 2013; 305: L582-9. 12. Ross AJ, Dailey LA, Brighton LE, Devlin RB. Transcriptional profiling of mucociliary differentiation in human airway epithelial cells. Am. J. Respir. Cell Mol. Biol. 2007; 37: 169–185. 13. Ong J, Timens W, Rajendran V, Algra A, Spira A, Lenburg ME, Campbell JD, Van Den Berge M, Postma DS, Van Den Berg A, Kluiver J, Brandsma CA. Identification of transforming growth factor-beta-regulated microRNAs and the microRNAtargetomes in primary lung fibroblasts. PLoS One 2017; 12: 1–24.

Additional supplementary materials can be found at the following link: https://erj.ersjournals.com/content/52/3/1701556.figures-only

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Submitted

Hataitip Tasena1, 2 Ilse M Boudewijn2, 3 Alen Faiz1, 2, 3 Wim Timens1, 2 Machteld N Hylkema1, 2 Marijn Berg1, 2 Nick H. T. ten Hacken2, 3 Corry-Anke Brandsma1, 2 Irene H Heijink1, 2, 3 Maarten van den Berge2, 3

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To the Editor:

Chronic mucus hypersecretion (CMH) is prevalent in both asthma1 and chronic obstructive pulmonary disease (COPD)2 and is associated with more severe symptoms.2–5 Mucus is composed of heavily glycosylated proteins called mucins, of which MUC5AC and MUC5B are the main mucins involved in asthma6,7 and COPD.8,9 As post-transcriptional regulators of gene expression, microRNAs (miRNAs) play a role in various human diseases.10 Recently, we identified several miRNAs associated with CMH in COPD.11 In the present study, we extended our analysis to CMH in asthma which is important as it would be efficient to develop the same therapy targeting CMH in both diseases if they share common regulatory mechanisms. Small RNA sequencing and RNA sequencing was performed on bronchial biopsies from 65 asthmatic patients.12 The patients either had never been treated with inhaled corticosteroid (ICS), had stopped ICS treatment for 6-8 weeks prior to the bronchoscopy, or were current ICS users. The study approach is illustrated in Fig.1A and detailed methods are described in the Online Repository. Patient characteristics are presented in Table E1. We first compared miRNA profiles between ICS-naïve asthmatic patients with (n=6) and without CMH (n=14) using linear regression adjusting for age, gender, smoking status and library preparation batch. We identified 17 differentially expressed miRNAs associated with CMH (FDR<.05; Fig.1B and E1). In asthmatic patients with CMH, the expression of miR-31-5p, miR-152-3p, and miR-155-5p was higher, while the expression of miR-15b-3p, miR-15b-5p, miR-16-2-3p, miR-25-3p, miR-92a-3p, miR-106b-3p, miR-185-5p, miR-223-3p, miR-3615, miR-423-5p, miR-425-5p, miR- 451a, miR-484 and miR-486-5p was lower compared to patients without CMH. The most strongly up-regulated miRNA was miR-31-5p (4.11 fold) and the most strongly down-regulated one was miR-486-5p (9.47 fold) (Table E2). Interestingly, miR-31- 5p was previously also found to be associated with CMH in COPD, with the same direction of effect.11 When including the current ICS users in the analysis, none of the miRNAs was significantly associated with CMH, reflecting an evident influence of corticosteroids on expression of these miRNAs. Next, we identified CMH-associated mRNAs with the same approach. Expression of MUC5AC, but not MUC5B, was significantly higher in the asthmatic patients with versus those without CMH (P<.0001, Fig.1C). When corrected for multiple tests (FDR<.05), we identified 2 mRNAs associated with CMH, i.e. Chromosome 3 Open Reading Frame 70 (C3orf70) and phosphatidylinositol transfer protein membrane associated 2 (PITPNM2). Using Gene Set Enrichment Analysis (GSEA), we assessed whether MUC5AC-associated core genes13 and CMH-associated genes in COPD11 were enriched among the genome-wide mRNAs,

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ranked based on the strength (fold change) of their association with CMH in ICS- naïve asthma patients as determined by linear regression. As expected, we found significant enrichment (P<.001) of both gene sets among the ranked list (Fig.1C-1D), suggesting that MUC5AC is associated with CMH and that CMH in asthma and COPD share common regulatory mechanisms. We then assessed whether any CMH-associated miRNA was correlated with MUC5AC or MUC5B using matched small RNA-sequencing and RNA-sequencing profiles in all asthmatic patients (n=65) (Table E3). There was a significant positive correlation between miR-16-2-3p and MUC5B (P=.0208) and a trend of positive correlation between miR-31-5p and MUC5AC (P=.0649). No correlation was observed between MUC5AC or MUC5B and other miRNAs, possibly due to the fact that CMH is attributed to several factors apart from higher mucin synthesis, such as more mucin secretion, impaired mucus clearance or more mucin cross-linking14,15. Similar to in asthma, miR-31-5p, which was also associated with CMH in COPD, 4 was not correlated with MUC5B in the COPD dataset (data not shown). Our previous report, however, showed that MUC5AC-associated core genes were enriched among miR-31-5p-correlated genes11 suggesting that miR-31-5p may regulate CMH, at least in part, via modulation of MUC5AC synthesis. Since we found miR-31-5p to be associated with CMH in both asthma and COPD, we assessed correlations between miR-31-5p and genome-wide mRNA expression using matched small RNA-sequencing and RNA-sequencing profiles in all asthmatic patients (n=65). As miRNAs inhibit translation of their mRNA targets partly by inducing mRNA degradation, we expected the mRNA targets of miR-31- 5p to be negatively correlated with this miRNA. Spearman’s correlation coefficient revealed 890 genes of which expression levels were negatively correlated with miR- 31-5p (FDR<.05). Among these 890 negatively correlated genes, 48 were predicted miR-31-5p targets according to TargetScan and miRDB (Fig.2A). Next, we applied the same approach on our previously published COPD bronchial biopsy dataset11 which resulted in 62 predicted targets negatively correlated with miR-31-5p (Fig.2A). When combining these 48 and 62 predicted targets, we found 17 overlapping negatively correlated predicted targets of miR-31-5p in asthma and COPD (Fig.2A, Table E4). To identify common mechanisms in which these 17 genes may be involved, we performed GO Enrichment Analysis16,17, but found no significant enrichment of GO terms likely because there were low number of genes representing several distinct biological processes. Future functional studies would help elucidating how these 17 genes contribute to shared mechanism underlying CMH in asthma and COPD. In addition, we determined whether COPD and asthma patients with CMH in this study shared eosinophilic endotype by performing principal component analysis on these 17 target genes using their expression profiles in the COPD cohort. No correlation was observed between the first principal component and blood eosinophil

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counts suggesting that eosinophilic endotype is not a common pathogenesis shared between COPD and asthma patients with CMH. Further studies on larger cohorts are encouraged to identify other endotypes that underlie this shared pathogenesis.

FIGURE 1. MiRNAs and associated genes potentially regulate CMH. [A] An overview of the study approach. [B] MiRNAs differentially expressed with CMH in asthma. Linear regression was performed on a group of ICS-naïve asthmatic patients (n=20). Only miRNAs of >100 counts were shown. Dot line indicates a significance of FDR<.05. C, Enrichment of MUC5AC-associated genes among CMH- positively associated genes (P<.001) and higher MUC5AC expression in asthmatic patients with CMH vs without CMH. ****P<.0001. D, Enrichment of CMH-associated genes in COPD among CMH- associated genes in asthma (P<.001). Height of each bar in the enrichment plot represents the enrichment score of each gene in the gene set of interest.

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Interestingly, of these 17 miR-31-5p correlated targets, ST3 Beta-Galactoside Alpha-2,3-Sialyltransferase 2 (ST3GAL2), PITPNM2 and Rho guanine nucleotide exchange factor 15 (ARHGEF15) showed significantly lower expression levels in patients with CMH compared to those without CMH in both diseases (Fig.2A, 2B, 2C). ST3GAL2 is a member of sialyltransferases. Previous studies showed that higher sialylation of airway mucins is present in bacterial infected patients with cystic fibrosis and with chronic bronchitis compared to non-infected patients.18 This sialylation, occurring during mucin biosynthesis, could influence host-microbial interaction during infection.19 Thus, ST3GAL2 may play a role in mucin glycosylation and it is worthwhile to investigate if and how this contributes to CMH. Little is known about the function of PITPNM2, while ARHGEF15 is a Rho guanine nucleotide exchange factor specifically expressed in endothelial cells.20 Notably, 12 out of these 17 genes (including ST3GAL2 and PITPNM2) are expressed in human airway epithelial cells differentiated upon air-liquid interface culture (Table E4).21 Other genes that are not 4 expressed by epithelial cells may regulate CMH via other cell types including stromal cells, e.g. fibroblasts.22,23 Since these findings are based on association analyses, future functional studies are required to elucidate whether and how these genes and miR-31-5p directly contribute to CMH. It is not yet conclusive whether higher miR-31-5p expression in CMH is a reflection of its stimulatory or protective role on mucus production. Previously, miR-31-5p expression was found to be slightly lower in asthmatic airway epithelial brushings compared to those of healthy controls but CMH status of these patients was not reported.24 Although bronchial biopsies contain various cell types apart from epithelial cells, we found no difference in numbers of inflammatory cells, i.e. T-cells, B-cells, neutrophils, macrophages, eosinophils, and mast cells, between the asthmatic patients with and without CMH suggesting that these findings were not driven by infiltration of different inflammatory cells.12 As our data is based on miRNA- mRNA correlations, future studies are warranted to confirm the direct interaction and functional consequences. Furthermore, these miRNAs and mRNAs were identified using a relatively small sample size and the patients with CMH were all males (Table E1). Therefore, similar analyses performed on a larger cohort with equal gender distribution would strengthen these findings. Besides, it would be useful to include other definitions of CMH in future analyses such as overall mucin concentration in sputum which is one of the important markers of CMH.

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FIGURE 2. Shared mechanisms of CMH in asthma and COPD likely modulated by miR-31- 5p. [A] MiR-31-5p-negatively correlated predicted targets. [B] Correlations of miR-31-5p and its CMH-associated targets in all asthmatic patients (n=65). C, Correlations of miR-31-5p and its CMH- associated targets in ICS-naïve asthmatic patients (n=24). Correlations of miR-31-5p and genome-wide mRNA were assessed in bronchial biopsies from asthmatic (n=65) and COPD (n=57)11 patients using Spearman’s correlation. Significance was defined by FDR<.05.

This is the first study to propose miR-31-5p as a shared regulator of CMH in both asthma and COPD. We identified several mRNAs potentially targeted by miR-31-5p, including ST3GAL2, PITPNM2, and ARHGEF15 that were negatively associated with CMH in both diseases. These findings provide novel and important insights on common CMH regulatory mechanisms and could contribute to the development of CMH-targeted therapy that efficiently benefits both asthma and COPD patients.

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Hataitip Tasena1, 2, MSc; Ilse M Boudewijn2, 3, MD; Alen Faiz1, 2, 3, PhD; Wim Timens1, 2, MD, PhD; Machteld N Hylkema1, 2, PhD; Marijn Berg1, 2, BSc; Nick H. T. ten Hacken2, 3, MD, PhD; Corry-Anke Brandsma1, 2, PhD; Irene H Heijink1, 2, 3, PhD; Maarten van den Berge2, 3, MD, PhD

1University of Groningen, University Medical Centre Groningen, Department of Pathology and Medical Biology, Groningen, the Netherlands.; 2University of Groningen, University Medical Centre Groningen, Groningen Research Institute for Asthma and COPD, Groningen, the Netherlands.; 3University of Groningen, University Medical Centre Groningen, Department of Pulmonary Diseases, Groningen, the Netherlands.

Acknowledgement of funding We thank the Dutch Lung Foundation and GlaxoSmithKline plc (GSK) for funding 4 this study.

A Conflict of Interest disclosure statement HT, IMB, AF, WT, MNH, NHTtH, CAB, and IHH report no conflict of interest. MvdB received a research grant from GSK.

A statement of contribution HT, AF, WT, MNH, CAB, IHH and MvdB contributed to the study concept and design. NHTtH and MvdB coordinated patient inclusion and data collection. NHTtH performed bronchoscopy. MvdB secured funding for the study. MvdB and IMB organized and performed the RNA- and small RNA-sequencing. HT, IMB, CAB, IHH and MvdB analysed and interpreted data. HT drafted the manuscript under supervision of CAB, IHH and MvdB. HT, AF, WT, MNH, CAB, IHH and MvdB critically read and revised the manuscript. All authors have read, reviewed and approved the final manuscript.

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REFERENCES

1. de Marco R, Marcon A, Jarvis D, Accordini S, Almar E, Bugiani M, et al. Prognostic factors of asthma severity: A 9-year international prospective cohort study. J Allergy Clin Immunol. 2006;117:1249–56. 2. Burgel PR. Chronic cough and sputum production: A clinical COPD phenotype? Eur Respir J. 2012;40:4–6. 3. Burgel P, Nesme-Meyer P, Chanez P, Caillaud D, Carre P, Perez T, et al. Cough and Sputum Production Are Associated With Frequent Exacerbations and Hospitalizations in COPO Subjects. Chest. 2009;135:975–82. 4. Fahy J V, Dickey BF. Airway Mucus Function and Dysfunction. N Engl J Med. 2010;363:2233–47. 5. Hays SR, Fahy J V. The role of mucus in fatal asthma. Am J Med. 2003;115:68–9. 6. Bonser L, Erle D. Airway Mucus and Asthma: The Role of MUC5AC and MUC5B. J Clin Med. 2017;6:112. 7. Lachowicz-Scroggins ME, Yuan S, Kerr SC, Dunican EM, Yu M, Carrington SD, et al. Abnormalities in MUC5AC and MUC5B Protein in Airway Mucus in Asthma. Am J Respir Crit Care Med. 2016;194:1296–9. 8. Caramori G, Di Gregorio C, Carlstedt I, Casolari P, Guzzinati I, Adcock IM, et al. Mucin expression in peripheral airways of patients with chronic obstructive pulmonary disease. Histopathology. 2004;45:477–84. 9. Kesimer M, Ford AA, Ceppe A, Radicioni G, Cao R, Davis CW, et al. Airway Mucin Concentration as a Marker of Chronic Bronchitis. N Engl J Med. 2017;377:911–22.

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SUPPLEMENTARY DATA

Methods

Patient cohort Bronchoscopy was performed to collect bronchial biopsies from persistent asthmatic patients at the University Medical Center Groningen between 2002 and 2012. Procedures for sample collection, spirometry, and provocation tests were previously described.12 These patients either used asthma medication (e.g. inhaled corticosteroid (ICS), beta-agonists and/or other) or experienced asthma symptoms (e.g. wheeze or asthma attacks during the last 3 years). A subset of the patients either have never been treated with ICS, have stopped using ICS for 6-8 weeks prior to the bronchoscopy, or are currently using ICS. Subjects with upper respiratory tract infection or used 4 antibiotics and/or oral corticosteroids within 2 months prior to the study were excluded. The local medical ethics committee approved the study and all patients gave their written informed consent.

MicroRNA (miRNA) and mRNA profiling Bronchial biopsy specimens were stored in Tissue-Tek (VWR, Radnor, PA, USA) at -80 °C until processing. Total RNA was extracted using AllPrep DNA/RNA/miRNA Universal kit (Qiagen, Venlo, The Netherlands) and the RNA quality was assessed using Nanodrop-1000 and Labchip GX (PerkinElmer, Waltham, MA, USA). Sample randomization was applied to avoid batch effects. MiRNA expression profiles were obtained from small RNA sequencing. Small RNA library was prepared using NEXTflex Small RNA-Seq Kit v3 (Bioo Scientific Corporation, Austin, TX, USA) according to the manufacturer’s protocol. Small RNA fraction was selected using NEXTflex Cleanup Beads and the size distribution of the final libraries were verified using Labchip GX (PerkinElmer, Waltham, MA, USA). The sequencing was performed using an Illumina HiSeq2500 sequencer with default parameters applied (single read 1x50bp). The quality control was performed using FastQC (v0.11.5) and adapter trimming was done using TrimGalore (v0.3.7). The sequencing data was processed using custom scripts documented in the GitHub repository.20 The reads were aligned and quantified using miRDeep2 (2.0.0.8)21 with Bowtie (v0.12.7).22 mRNA expression profiles were obtained from RNA sequencing. RNA library was prepared with TruSeq Stranded Total RNA Sample Preparation Kit (Illumina, San Diego, CA, USA) and the Caliper Sciclone NGS Workstation (PerkinElmer, Waltham, MA, USA). The removal of ribosomal RNA was done by Ribo-Zero Gold Kit (Illumina, San Diego, CA, USA). Paired-end sequencing was then performed using an Illumina HiSeq2500 sequencer. Quality control of the raw sequencing

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data was performed with FastQC (v0.11.3). The reads were aligned to build b37 of the human reference genome using HISAT (v0.1.5) allowing for 2 mismatches.23 The aligned reads were sorted with SAMtools (version 1.2)24 and quantified with HTSeq (v0.6.1p1) using Ensembl release 75 as gene annotation database.25 Quality control of aligned reads was performed using Picard-tools (v1.130). Furthermore, concordance between reported gender and gender-associated gene expression (XIST and Y-chromosomal genes) was assessed and we observed that all samples were concordant.

Identification of miRNAs and mRNAs associated with CMH The presence of CMH was defined according to the patients’ responses to the question: how often did you cough up sputum during the last week?, as part of the Clinical COPD Questionnaires (CCQ).26 Patients who responded never, seldom, or once in a while were categorized as “without CMH” and patients who responded regularly and very often were categorized as “with CMH”. No patients included in this study responded mostly or always. MiRNAs differentially expressed with the presence of CMH were identified using linear regression model adjusted for age, gender, smoking status and library preparation batch. To avoid ICS effects, only miRNA profiles of ICS-naïve asthmatic patients with (n=6) and without (n=14) CMH were included. mRNAs differentially expressed with CMH were identified using the same approach, but only mRNA profiles of 5 patients with CMH and 11 patients with CMH passed the quality control. Expression was normalized using DESeq2 package. RNA transcripts of less than 5 normalized counts in more than 50% of the samples were removed prior to the analyses. Only miRNAs and mRNAs with at least 100 normalized counts on average were reported in this study. Significance was determined by FDR<.05. All analyses were performed in R (v3.2.5).

Gene Set Enrichment Analysis (GSEA) Genome-wide mRNAs were ranked according to the strength of their association with CMH as assessed by the linear regression, from the most positive to the most negative associated mRNAs. GSEA (v2.2.2) was then performed on this ranked list using a MUC5AC-associated core gene set13 and CMH-associated gene sets11 previously reported. Enrichment p-value was calculated after 1000 permutations were performed. Significance was determined by p<.05.

Identification of miR-31-5p targets in asthma and COPD Correlations between miR-31-5p expression and genome-wide mRNAs were determined using Spearman’s rank correlation coefficient on matched miRNA and

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mRNA profiles of 65 asthmatic patients regardless of their ICS history. Significance was determined by FDR<.05. MiR-31-5p-correlated mRNAs in COPD biopsy dataset11 were identified using the same approach. Next, we compared miR-31- 5p-negatively correlated genes identified in both asthma and COPD datasets to miR-31-5p’s predicted targets reported on TargetScan (v7.1) and/or miRDB (v5.0). The overlapping genes were considered potential targets of miR-31-5p. We further determined whether any of these genes were significantly associated with CMH in both asthma and COPD using a nominal p<.05. The network illustrating these data was created on Cytoscape (v3.4.0). The graphs illustrating correlations between miR- 31-5p and selected genes were created on GraphPad Prism (v7).

Table E1. Patient characteristics ICS-naïve asthmatic group Asthmatic group 4 (CMH-associated miRNA and mRNA (miRNA-mRNA cor- analyses) relation analysis)

no CMH CMH Total patients 14 6 65 Gender (n, male/female) 6/8 6/0* 30/35 Age 50 51 49 (29-53) (41-53) (38-55) Smoking status (n, current-/non-smoker) 8/6 4/2 15/50 Pack-years 7.5 16.8 0.2 (0.4-13.6) (10.1-28.4) (0.0-10.0) BMI 26.8 28.5 26.8 (23.2-28.9) (25.6-30.5) (23.8-28.7) FEV1, % predicted 88.4 81.5 85.2 (75.5-97.5) (78.5-84.9) (75.6-96.4) FEV1/FVC 0.72 0.70 0.71 (0.62-0.78) (0.68-0.76) (0.66-0.77) Atopic (n, yes/no/not available) 11/3/0 3/2/1 48/13/4 Airway hyperresponsiveness 37.6 58.8 45.4 (PC20 AMP mg/ml) (13.7-529.3) (15.8-86.0) (5.9-640.0) ICS history (n, naive/withdrawn/active) 14/0/0 6/0/0 24/16/25

Data are presented as median (interquartile range) for age, pack-years, body mass index (BMI), forced expiratory volume in 1 second (FEV1), FEV1/forced vital capacity (FVC), and provocative concentration causing 20% fall in FEV1 of adenosine-59-monophosphate (PC20 AMP mg/ml). ICS is inhaled corticorsteroid. Statistical comparisons between CMH and no CMH groups were performed using Mann-Whitney U-test. *P < .05.

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Table E2. CMH-associated miRNAs in asthma

miRNA Mean count Fold change P value FDR miR-31-5p 458.74 4.11 9.06E-04 2.63E-02 miR-155-5p 1049.92 2.70 2.64E-03 4.88E-02 miR-152-3p 511.52 2.45 2.64E-03 4.88E-02 miR-425-5p 807.48 -2.53 2.59E-03 4.88E-02 miR-423-5p 1463.75 -2.86 2.53E-04 9.36E-03 miR-15b-5p 2637.20 -3.32 9.78E-04 2.65E-02 miR-3615 101.60 -3.36 1.96E-03 4.43E-02 miR-25-3p 5323.29 -3.87 4.55E-06 3.70E-04 miR-106b-3p 281.44 -3.87 5.95E-05 3.46E-03 miR-92a-3p 27689.78 -3.88 3.18E-06 3.24E-04 miR-223-3p 2149.10 -4.27 1.79E-04 7.28E-03 miR-185-5p 557.90 -4.47 7.98E-05 4.06E-03 miR-484 918.62 -5.30 2.24E-07 4.56E-05 miR-451a 51118.93 -5.92 5.13E-05 3.46E-03 miR-16-2-3p 417.70 -6.07 6.87E-07 9.32E-05 miR-15b-3p 124.79 -6.26 1.62E-04 7.28E-03 miR-486-5p 79385.72 -9.47 5.68E-08 2.31E-05

Table E3. Correlations between CMH-associated miRNAs and MUC5AC or MUC5B MUC5AC MUC5B rhoᶧ P valueᶧ rhoᶧ P valueᶧ miR-31-5p 0.2303 0.0649 -0.0622 0.6228 miR-155-5p -0.1009 0.4238 -0.0521 0.6800 miR-152-3p 0.0739 0.5587 -0.0422 0.7384 miR-425-5p 0.1397 0.2669 0.1583 0.2079 miR-423-5p 0.1687 0.1793 0.0149 0.9059 miR-15b-5p 0.0329 0.7949 0.1435 0.2542 miR-3615 0.1037 0.4110 -0.0594 0.6386 miR-25-3p 0.0328 0.7955 0.1185 0.3470 miR-106b-3p 0.0604 0.6327 0.0918 0.4671 miR-92a-3p 0.1553 0.2166 0.0446 0.7241 miR-223-3p 0.0058 0.9636 -0.0037 0.9766 miR-185-5p 0.0489 0.6988 0.1378 0.2735 miR-484 0.0972 0.4411 0.1243 0.3240 miR-451a 0.0007 0.9956 0.2153 0.0851 miR-16-2-3p -0.0670 0.5961 0.2862 0.0208 miR-15b-3p 0.0549 0.6638 0.1567 0.2125 miR-486-5p 0.1101 0.3828 0.0623 0.6219

Statistics from Spearman’s correlation performed on the asthmatic cohort (n=65)

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Table E4. MiR-31-5p-correlated predicted targets

Gene symbol Rhoᶧ P valueᶧ FDRᶧ expression in airway epithelial cells ARRB1 -0.4998 2.24E-05 2.71E-03 yes FZD4 -0.4949 2.78E-05 3.00E-03 yes ST3GAL2 ‡ -0.4632 1.02E-04 5.66E-03 yes EBF3 -0.4296 3.55E-04 1.11E-02 no APBB2 -0.3815 1.71E-03 2.64E-02 yes PRKCB -0.3747 2.10E-03 2.94E-02 no TMOD2 -0.3721 2.27E-03 3.05E-02 no SYDE1 -0.3686 2.52E-03 3.23E-02 yes KCNN3 -0.3683 2.54E-03 3.25E-02 yes TNS1 -0.3628 2.98E-03 3.52E-02 yes STARD13 -0.3617 3.07E-03 3.60E-02 yes ATP8A1 -0.3609 3.14E-03 3.65E-02 no 4 ZBTB20 -0.3557 3.64E-03 3.96E-02 yes PITPNM2# -0.3506 4.20E-03 4.28E-02 yes ARHGEF15# -0.3498 4.28E-03 4.35E-02 no SPARC -0.3441 5.01E-03 4.77E-02 yes SHC4 -0.342 5.30E-03 4.93E-02 yes

Statistics from Spearman’s correlation performed on the asthmatic cohort (n=65); ‡also significantly associated with CMH in asthma and COPD

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FIGURE E1. Heatmap showing CMH-associated miRNAs in asthma. Only miRNAs of > 100 counts were considered expressed. Linear regression corrected for age, gender and smoking status was performed on the ICS-naïve cohort (n = 20). FDR<.05.

Additional references

10. Li Y, Kowdley K V. MicroRNAs in Common Human Diseases. Genomics, Proteomics Bioinforma. 2012;10:295–301. 11. Tasena H, Faiz A, Timens W, Noordhoek J, Hylkema MN, Gosens R, et al. MicroRNA–mRNA regulatory networks underlying chronic mucus hypersecretion in COPD. Eur Respir J. 2018;52:13993003. 12. Broekema M, Timens W, Vonk JM, Volbeda F, Lodewijk ME, Hylkema MN, et al. Persisting remodeling and less airway wall eosinophil activation in complete remission of asthma. Am J Respir Crit Care Med. 2011;183:310–6. 13. Wang G, Xu Z, Wang R, Al-Hijji M, Salit J, Strulovici-Barel Y, et al. Genes associated with MUC5AC expression in small airway epithelium of human smokers and non-smokers. BMC Med Genomics. 2012;5:21. 14. Innes AL, Carrington SD, Thornton DJ, Kirkham S, Rousseau K, Dougherty RH, et al. Ex vivo sputum analysis reveals impairment of protease-dependent mucus degradation by plasma proteins in acute asthma. Am J Respir Crit Care Med. 2009;180:203–10.

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15. Ramos FL, Krahnke JS, Kim V. Clinical issues of mucus accumulation in COPD. Int J COPD. 2014;9:139–50. 16. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: Tool for the unification of biology. Nat Genet. 2000;25:25–9. 17. Mi H, Huang X, Muruganujan A, Tang H, Mills C, Kang D, et al. PANTHER version 11: Expanded annotation data from Gene Ontology and Reactome pathways, and data analysis tool enhancements. Nucleic Acids Res. 2017;45:D183–9. 18. Davril M, Degroote S, Humbert P, Galabert C, Dumur V, Lafitte JJ, et al. The sialylation of bronchial mucins secreted by patients suffering from cystic fibrosis or from chronic bronchitis is related to the severity of airway infection. Glycobiology. 1999;9:311–21. 19. Degroote S, Maes E, Humbert P, Delmotte P, Lamblin G, Roussel P. Sulfated oligosaccharides isolated from the respiratory mucins of a secretor patient suffering from chronic bronchitis. Biochimie. 2003;85:369–79. 20. Kusuhara S, Fukushima Y, Fukuhara S, Jakt LM, Okada M, Shimizu Y, et al. Arhgef15 Promotes Retinal Angiogenesis by Mediating VEGF-Induced Cdc42 Activation and Potentiating RhoJ Inactivation in Endothelial Cells. PLoS One. 2012;7:1–11. 4 21. Alevy YG, Patel AC, Romero AG, Patel DA, Tucker J, Roswit WT, et al. IL-13–induced airway mucus production is attenuated by MAPK13 inhibition. 2012;122:4555–68. 22. Spanjer AIR, Menzen MH, Dijkstra AE, van den Berge M, Boezen HM, Nickle DC, et al. A pro-inflammatory role for the Frizzled-8 receptor in chronic bronchitis. Thorax. 2016;71:312–22. 23. Albers S, Thiebes AL, Gessenich KL, Jockenhoevel S, Cornelissen CG. Differentiation of respiratory epithelium in a 3-dimensional co-culture with fibroblasts embedded in fibrin gel. Multidiscip Respir Med [Internet]. 2016;11. Available from: http://dx.doi.org/10.1186/s40248-016-0046-3 24. Solberg OD, Ostrin EJ, Love MI, Peng JC, Bhakta NR, Hou L, et al. Airway Epithelial miRNA Expression Is Altered in Asthma. Am J Respir Crit Care Med. 2012;186:965–74.

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Airway mucus secretion in COPD-derived airway epithelial cells is promoted by fibroblast-epithelium crosstalk

Submitted

Hataitip Tasena1,2 Maarten van den Berge2,3 Alen Faiz1,2,3 Marjan Reinders-Luinge1,2 Machteld N Hylkema1,2 Wim Timens1,2 *Corry-Anke Brandsma1,2 *Irene H Heijink1,2,3, (*both authors contributed equally)

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ABSTRACT

Chronic mucus hypersecretion (CMH) is a common feature in chronic obstructive pulmonary disease (COPD). One of the major factors contributing to CMH is higher mucin release from airway epithelium. We hypothesized that fibroblasts are involved in mucociliary differentiation and epithelial mucin production and release. Primary bronchial epithelial cells (PBECs) from COPD patients with CMH were cultured at air-liquid interface (ALI) with and without primary airway fibroblasts (PAFs). MUC5AC and MUC5B release was measured in apical wash fluid. IL-6, CXCL8, and CCL20 release was measured in basal supernatant by ELISA. mRNA expression of mucociliary differentiation markers and cytokines was assessed by qPCR. Cross-sections of paraffin-embedded inserts were stained and semi-quantitatively scored for mucus-positive and ciliated cells using Alcian blue and acetylated α-tubulin immunohistochemistry, respectively. Upon co-culture with PAFs, PBECs expressed higher MUC5B mRNA and secreted higher levels of MUC5AC and MUC5B proteins. These effects were accompanied by more pronounced mucous cell differentiation. Higher levels of IL-6 were secreted upon co-culture of PBECs and PAFs compared to mono-culture. PAFs, but not PBECs, expressed higher IL-6 mRNA upon co-culture. The use of anti-IL-6 neutralizing antibody in Calu-3 cultures abolished the increase in MUC5B mRNA upon co-culture with fibroblasts, while no change was observed for MUC5AC. This study demonstrates that fibroblasts are involved in the regulation of mucous cell differentiation and epithelial mucin release, and that specifically MUC5B expression can be mediated, at least in part, by fibroblast-derived IL-6. These findings provide support for the notion that fibroblast-epithelium crosstalk contributes to CMH in COPD.

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INTRODUCTION

Chronic mucus hypersecretion (CMH) is an important symptom in the patients with chronic obstructive pulmonary disease (COPD) and is associated with lower quality of life, an accelerated decline of lung function, more severe airflow obstruction, and an increased risk of exacerbations and mortality1. Currently, an effective CMH-targeted therapy is lacking, reflecting an urgent need to better understand the mechanisms underlying CMH pathophysiology. CMH is usually characterized by the presence of chronic inflammation, cough and sputum expectoration2,3. Goblet cells present in the airway mucosa as well as mucus glands in the airway submucosa secrete heavily glycosylated proteins called mucins which are principal components of mucus. The most abundant gel-forming mucins found in human airways are MUC5AC and MUC5B4. Both are increased in COPD6 and can be positively regulated by SAM Pointed Domain Containing ETS Transcription Factor (SPDEF)6–8. Furthermore, both goblet cell hyperplasia and ciliary dysfunction are thought to contribute to CMH in COPD9,10. Cilia on airway epithelium of COPD smokers are shorter than those of healthy smokers and non- 5 smokers9, which can lead to ineffective mucus clearance. While stromal cells in sub-epithelial layers do not produce mucus, they may play a critical role in CMH development by regulating differentiation of goblet or ciliated cells and/or regulation of mucus secretion. Various reports suggest that epithelial cells communicate with stromal cells, particularly fibroblasts, influencing the phenotypes of each other11–14. Previous studies using animal-derived cells suggest that fibroblasts can promote in vitro epithelial differentiation to closely resemble native tracheal epithelium14,15. In a rat model, more proliferation of basal cells as well as more differentiation into ciliated and MUC5AC-producing cells was observed upon co-culture with fibroblasts16. None of these studies assessed MUC5B production or identified the mediators driving this crosstalk. Moreover, it remains to be determined whether these findings are applicable to human cells and whether they are related to CMH in COPD. Recently, we reported that fibroblasts from COPD patients with CMH express higher levels of various receptors, including the IL-1 family receptor IL-1R1, which is involved in IL-6 and CXCL8 release17. These pro-inflammatory cytokines promote MUC5AC production in differentiated epithelial cells in vitro17. We demonstrated that CXCL8 secretion by fibroblasts can be induced by epithelial- derived IL-1α12 and that airway smooth muscle cells promote mucin secretion by bronchial epithelial cells via CCL2018. These findings led us to hypothesize that stromal cells, particularly airway fibroblasts, support the development of CMH in COPD by altering mucociliary differentiation and promoting epithelial mucin secretion.

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In this study, we investigated whether co-culture of airway epithelial cells derived from COPD patients with CMH with airway fibroblasts leads to alterations in epithelial cell polarization, mucociliary differentiation, as well as MUC5AC and MUC5B expression and release. In addition, we further evaluated the underlying mechanism by assessing the release of specific pro-inflammatory cytokines, i.e. IL-6, CXCL8, and CCL20, and by using neutralizing antibodies in a Calu-3 cell line model.

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METHODS

Isolation of primary bronchial epithelial cells and primary airway fibroblasts Primary bronchial epithelial cells (PBECs) were isolated from explanted lungs from 3 COPD stage IV patients with CMH undergoing lung transplantation, as previously described19. Primary airway fibroblasts (PAFs) were isolated from 9 COPD stage IV patients (6 with CMH and 3 without CMH) as previously published20. The patient characteristics are described in Table 1. The presence of CMH was defined by the patients’ clinical records. PBECs were grown in Keratinocyte Serum-Free Growth Medium (KSFM, Gibco, NY, USA) supplemented with 1% Penicillin (10,000 U/mL)/ Streptomycin (10,000 μg/mL) (P/S) (Gibco, California, USA), 1 μM isoproterenol, 25 ng/ml bovine pituitary extract (BPE, Gibco) and 2.5 µg/ml epidermal growth factor (EGF, Gibco). PAFs were grown in HAMs’ F12 medium supplemented with 1% pennicilin/streptomycin, 1% L-glutamine (Lonza, Switzerland) and 10% fetal bovine serum (FBS, Sigma-Aldrich, Germany). Cells were passaged when 90% confluent. In all experiments, we used PBECs in passage 3 and PAFs in passage 4-6. 5

Co-culture of PBECs and PAFs at air-liquid interface Air-liquid interface (ALI) culture of PBECs was performed as previously described17. Transwell inserts for 24-well plates (Corning®, NY, USA) were coated with 10 μg/ ml bovine serum albumin (BSA; Sigma-Aldrich, MO, USA), 10 μg/ml fibronectin (Sigma-Aldrich, MO, USA) and 30 μg/ml collagen (PureCol®, Advanced Biomatrix, San Diego, CA, USA) in Eagle’s Minimum Essential Medium (EMEM, Lonza, Walkersville, MD, USA) before being used to culture PBECs. PBECs from each donor were seeded at a density of 75,000 cells/insert in ALI culture medium prepared by mixing DMEM (LONZA BE12-709F) and BEBM (Clonetics CC-3171) in 1:1 ratio supplemented with a set of BEGM Single Quots (Clonetics CC-4175) and 1.5µg/ml BSA (Sigma-Aldrich) and15 ng/ml retinoic acid (Sigma-Aldrich). After 4-5 days, the cells were air-exposed for 14 days (day 0-14) during which the basal medium was refreshed every 2-3 days. PAFs seeded in 24-well plates in HAMs’ F12 medium until 70-80% confluent were co-cultured with PBECs on the inserts from day 7-14 using ALI culture medium (6 PAF donors per 1 PBEC donor). On day 0, day 7 and day 14, transepithelial electrical resistance (TEER) was measured using the Epithelial Volt-Ohm Meter (Millicel® ERS-2). At the end of day 14, 100 µl/insert of the wash medium was added to the apical compartment and incubated at room temperature for 5 minutes. This apical wash fluid was collected and incubated with 0.1% w/v Dithiothreitol (DTT) (Sputolysin®, Calbiochem, San Diego, CA, USA) at room temperature for 15-20 minutes before being centrifuged at 200 g for 5 minutes.

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The supernatant was then collected and stored at -20 °C for mucin measurement. Next, the basal medium was collected and centrifuged at 200 g for 5 minutes, and the supernatant was collected and stored at -20 °C for cytokine measurements. The cells were collected for RNA isolation. Each condition was performed in duplicate, except for one experiment which was performed in triplicate to harvest inserts for immunohistochemistry (IHC) staining. An overview of our long-term co-culture model and the experimental design is illustrated in figure 1.

Table 1. Patient characteristics donor age gender pack-years FEV1 FEV1/FVC CMH (%predicted) E1 49 m 11 20 0.22 yes E2 58 m 35 15 0.19 yes E3 60 f 40 18 0.19 yes F1 53 f 40 23 0.26 yes F2 48 f 30 12 0.26 yes F3 59 m 40 15 0.29 yes F4 61 f 30 16 0.19 yes F5 58 m 35 15 0.19 yes F6 59 m 47 15 0.21 yes F7 48 m 25 12 0.23 no F8 61 f 70 22 0.25 no F9 58 f 38 22 0.23 no

All patients are ex-smokers with COPD stage IV. E1-E3 are PBEC donors; F1-F9 are PAF donors; FEV1 is forced expiratory volume in 1 second; FVC is forced vital capacity; CMH is chronic mucus hypersecretion based on clinical records; m is male, f is female.

Blocking of IL-6 with neutralizing antibodies To study the effects of fibroblast-derived IL-6 on epithelial mucin secretion, we used Calu-3 lung adenocarcinoma cells (known to produce mucus upon air exposure21) as a bronchial epithelial cell model to keep the epithelial component consistent. Calu-3 cells (ATCC® HTB-55™) were seeded at 165,000 cells/insert density in Transwell inserts for 24-well plates and submerged-cultured in DMEM-F12 medium supplemented with 1% non-essential amono acid (NEAA), 1% pennicilin/ streptomycin, 1% L-glutamine and 10% FBS until confluent. The cells were then air- exposed on the apical side for 10 days (day 0-10) during which the basal medium was refreshed every 2 days. From day 4 to day 10, the cells were co-cultured with 70-80%

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Figure 1. Experimental design. [A] ALI-culture of PBECs. PBECs were submerged-cultured for 5 days before air-exposed for 2 weeks (day 0-14), during which the medium was refreshed every 2-3 days. On day 7, PAFs on basal compartments were co-cultured with PBECs for 7 days. On day 14, apical wash fluid, basal supernatant and RNA from both cell types were collected. [B] ALI-culture of Calu-3 cells. Calu-3 cells were submerged-cultured for 3 days before air-exposed for 10 days, during which the medium was refreshed every 2 days. On day 4, PAFs on basal compartments were co-cultured with Calu-3 cells for the next 6 days, with or without the present of anti-IL-6 neutralizing antibody and IgG control antibody. On day 10, apical wash fluid, basal supernatant and RNA from both cell types were collected.

confluent PAFs from 3 COPD patients with CMH seeded on 24-well plates in the DMEM-F12 medium with or without 1 μg/ml anti-IL-6 neutralizing antibody (mabg- hil6-3, Invivogen, France) or IgG controls (mabg1-ctrlm, Invivogen). The experiment was repeated with PAFs from 3 other COPD patients with CMH, thus 6 PAF donors in total. On day 10, 100 µl/insert of Hank’s Balanced Salt Solution (HBSS) (Lonza) was added to the apical compartment and incubated at room temperature for 5 minutes. This apical wash fluid was then collected and incubated with 0.1% w/v DTT at room

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temperature for 15-20 minutes before being centrifuged at 200 g for 5 minutes. The supernatant was then collected and stored at -20 °C for mucin measurement. The cells were collected for RNA isolation. Each condition was performed in duplicate.

Reverse transcription and quantitative PCR (RT-qPCR) Total RNA was collected and isolated using Tri Reagent® according to the manufacturer’s protocol. To assess mRNA expression, total RNA was converted to cDNA using iScript™ cDNA Synthesis Kit (BioRad). qPCR was then performed using Taqman® assays (MUC5AC, ID: Hs00873651_Mh; MUC5B, Hs00861588_m1; SPDEF, Hs01026050_m1; FOXA2, Hs00232764_m1; FOXJ1, Hs00230964_m1; IL-6, HsHs00174131_m1; CXCL8, Hs00174103_m1; CCL20, Hs01011368_m1; IL-33, Hs04931857_m1; IL1RL1, Hs00545033_m1) and LightCycler® 480 Probes Master according to the manufacturer’s guidelines (Roche, Switzerland). All mRNA expression was normalized to the expression of the reference genes: β2 microglobulin (B2M, Hs99999907_m1) and Peptidylprolyl Isomerase A (PPIA, Hs99999904_m1).

Enzyme-Linked Immunosorbent Assay (ELISA) Levels of MUC5AC and MUC5B proteins were measured in apical wash fluid using ELISA kits for MUC5AC (SEA756Hu, USCN, China) and MUC5B (SEA684Hu, USCN, China) according to the manufacturer’s protocols. Levels of IL-6, CXCL8, and CCL20 were measured in basal supernatants using ELISA kits (R&D Systems, Minnesota, USA) and C96 Maxisorp NUNC Immuno-plate (Sigma-Aldrich).

Immunohistochemical staining Transwell membranes from the ALI-cultured inserts were fixed with formalin and embedded cross-sectionally in paraffin. Paraffin-embedded membranes were cut and double-stained with Alcian blue (CLIN-TECH, CI 42780, UK) for mucus-positive cells and acetylated-α-tubulin (Sigma-Aldrich, T7451, MO, USA) for ciliated cells. At least 5 sections per insert and experimental condition were semi-quantitatively scored for mucus positivity and ciliary differentiation by two independent researchers in a blinded manner. The scores ranged from 1, 2, and 3 representing lowest, moderate, and highest mucus positivity/epithelial differentiation. A mean score for each sample was then calculated.

Statistical analyses One-sample Wilcoxon signed rank test was used to determine significant differences between mono-cultured PBECs or Calu-3 cells and co-cultured samples by assessing the fold change relative to mono-culture. Paired-samples Wilcoxon signed rank test was used to determine significant differences between mono-cultured PAFs and co-

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cultured samples as well as between different conditions in co-cultured Calu-3 cells. Mann-Whitney U test was used for comparisons between changes induced by PAFs from patients with CMH and without CMH. Significant difference in the change of TEER from day 7 to day 14 of mono-cultured and co-culture PBECs was determined by one-sample Wilcoxon signed rank test. All statistical analyses were performed on GraphPad PRISM v7.

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RESULTS

Fibroblasts promote mucous cell differentiation and epithelial mucus secretion To investigate whether fibroblast-epithelium crosstalk promotes epithelial mucin secretion and alter mucociliary differentiation, PBECs were cultured at ALI in the presence and absence of PAFs. Firstly, we observed that co-cultured PBECs secreted significantly higher levels of MUC5AC and MUC5B proteins compared to mono- cultured cells (figure 2A), although MUC5AC level was not detectable in one of the three PBEC donors. Next, we investigated whether the changes in mucin secretion observed above were regulated at the transcriptional level and found that the expression of MUC5B mRNA was significantly higher in co-cultured PBECs (figure 2B). The effect of co-culture on MUC5AC mRNA expression varied among different PBEC donors and no significant change was observed (figure 2B). Expression of SPDEF, a transcription factor known to induce MUC5AC and MUC5B synthesis22, was upregulated in co-cultured PBECs (figure 2B) and correlated with both MUC5AC and MUC5B mRNAs (figure S1). Expression of Forkhead box protein A2 (FOXA2), a negative suppressor of MUC5AC gene expression, was not detected in any of the samples. The expression of Forkhead box protein J1 (FOXJ1), a transcription factor required for ciliated cell differentiation, did not change upon co-culture with fibroblasts (figure 2B). To determine whether mucin upregulation by PAFsfrom COPD patients with CMH was stronger than by PAFs from patients without CMH, we co-cultured PBECs from one donor with PAFs from three donors with CMH and three without CMH and found no significant differences in mucin protein secretion and gene expression (figure S2). In addition, we determined whether the higher mucin secretion upon co-culture was accompanied by alterations in mucociliary differentiation. We assessed TEER (an indicator of epithelial cell polarization), the presence of mucus positive cells, ciliated cells and epithelial cell differentiation. Upon air-exposure, we observed that PBECs increased their TEER from day 0 to day 7 indicating barrier formation and polarization, after which TEER levels stabilized. TEER was not affected by co-culture (figure S3). Semi-quantitative histological assessment of mucus-producing and ciliated cells revealed that the number of mucous cells significantly increased upon co-culture (figure 2C). Ciliated cells were only occasionally observed in the co-cultured epithelial layers. Furthermore, co-cultured PBECs tended to show more maturation, i.e. being taller and more multi-layered, compared to mono-cultured cells (figure 2D).

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Figure 2. Fibroblasts promote epithelial mucin release and mucous cell differentiation. PBECs from each COPD patient with CMH were differentiated at ALI in the presence or absence of PAFs from 6 COPD patients. The experiment was repeated 3 times with different PBEC donors. [A] MUC5AC and MUC5B protein levels secreted by PBECs measured by ELISA. MUC5AC protein was not detectable in apical wash from PBEC donor 3, while MUC5B was not measured in PBEC donor 1 due to limited samples. Circles, diamonds, and triangles represent PBEC donor E1, E2, and E3, respectively. [B] mRNA expression of MUC5AC, MUC5B, SPDEF and FOXJ1 in PBECs measured by qPCR. Relative expression (2-dCt) normalized to the reference genes B2M and PPIA is shown. Expression of FOXJ1 in PBEC donor 1 was below detection limit. [C] Semi-quantitative analysis of mucus positivity determined by Alcian blue immunohistochemistry and [D] epithelial maturation determined by cell height and

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layers. [E] example of a ciliated cell stained by acetylated α-tubulin immunohistochemistry. The score 1, 2, and 3 represent lowest, moderate, and highest mucus positivity/epithelial maturation, respectively. Mean scores of ≥ 5 inserts scored by two independent observers are depicted. Median±IQR is depicted. All significant differences were determined by one-sample Wilcoxon signed rank test.

Epithelial cells stimulate pro-inflammatory cytokine production by fibroblasts To determine potential mediators of fibroblast-epithelium crosstalk, we assessed the concentration of IL-6, CXCL8 and CCL20 in basal supernatant samples. IL-6 secretion was increased upon co-culture when compared to both mono-cultured PBECs and mono-cultured PAFs (figure 3A). CXCL8 and CCL20 levels were higher upon co-culture when compared to mono-cultured PAFs but not compared to mono- cultured PBECs (figure 3A), suggesting that IL-6 is more likely to contribute to the changes observed upon co-culture in this model. To determine the source of IL-6 protein secreted upon co-culture, we assessed IL-6 mRNA expression and found that levels were higher in PAFs upon co-culture, but not in PBECs, indicating that PAFs are likely the source of the increased IL-6 levels upon co-culture (figure 3B). Therefore, we hypothesized that fibroblasts promote mucin expression and secretion by releasing IL-6.

IL-6 mediates the increase in MUC5B expression upon fibroblast- epithelium crosstalk To determine if higher mucin expression and secretion upon co-culture was driven by IL-6 release, we used neutralizing antibodies in a co-culture model of Calu-3 epithelial cells with PAFs. Similar to our co-culture model with PBECs and PAFs, co- culture of Calu-3 with PAFs resulted in higher MUC5B mRNA expression and protein secretion as well as MUC5AC expression (figure 4 and S4A), while Calu-3 cells did not secrete detectable MUC5AC protein. When compared to untreated co-culture, IL-6 neutralization significantly suppressed the upregulation of MUC5B mRNA, but not MUC5AC (data not shown), by Calu-3 cells upon co-culture (figure 4). No difference in MUC5B secretion was observed between the untreated and anti-IL-6 antibody-treated co-cultures (figure 4). Treatment with the IgG control had no effect on MUC5B mRNA and protein (figure S4A). When compared to the IgG control, upregulation of MUC5B mRNA upon co-culture was significantly suppressed by anti-IL-6 antibody with a similar a trend for MUC5B protein secretion (figure S4B). In addition, a positive correlation between MUC5B expression and MUC5B protein secretion was observed (figure S4C).

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Figure 3. Epithelial cells stimulate pro-inflammatory cytokine production by fibroblasts. PBECs from 3 COPD patients with CMH were differentiated at ALI in the presence or absence of PAFs from 5 9 COPD patients. Basal supernatant and total RNA was collected for measurement of cytokine release and gene expression, respectively. [A] IL-6, CXCL8, and CCL20 protein secretion measured by ELISA and [B] IL-6 mRNA expression in PBECS and PAFs measured by qPCR. Significant difference was determined by one-sample Wilcoxon signed rank test or paired-samples Wilcoxon signed rank test. iure

Figure 4. IL-6 neutralization suppresses fibroblast-induced MUC5B upregulation by epithelial cells. Calu-3 cells were co-cultured with PAFs from 6 COPD patients with CMH. MUC5B mRNA expression (left), MUC5B protein secretion (middle) and MUC5AC mRNA expression (right) is depicted comparing untreated mono-culture, untreated co-culture and anti-IL-6 treated co-culture. Significant difference was determined by paired-samples Wilcoxon signed rank test.

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DISCUSSION

This study demonstrates that mucous cell differentiation and epithelial mucin expression and release in airway epithelial cells from COPD patients with CMH is promoted by COPD-derived airway fibroblasts, and that the increase in MUC5B expression can be mediated by IL-6. Our findings indicate the involvement of airway fibroblasts in the development of CMH in COPD at least in part through pro- inflammatory IL-6 secretion. Our results show that co-culture with fibroblasts promotes mucous cell differentiation, contributing to increased production and release of mucins. With respect to morphology, the co-cultured epithelial cells appeared to be taller and more multilayered than the mono-cultured cells, which is in line with to the study by Goto et al.15, showing that guinea pig-derived fibroblasts promote tracheal epithelial differentiation. Our findings show that this effect is also true for patient-derived cells, without the need of direct cell-cell contact, suggesting involvement of soluble factors in this crosstalk. Indeed, we observed that IL-6 may mediate the increase in MUC5B expression seen upon co-culture. In contrast to MUC5B, our findings suggest that MUC5AC expression is not mediated by IL-6. This is somewhat surprising, since previous studies showed that airway epithelial stimulation with IL-6 leads to higher levels of both MUC5AC and MUC5B17,23. Although both MUC5AC and MUC5B have been shown to increase in COPD5, Kirkham et al. reported that MUC5B is the major mucin in sputum from COPD patients, with a higher MUC5B/MUC5AC protein ratio compared to smokers. MUC5B is also associated with lower lung function, suggesting that it is predominantly involved in COPD24. Therefore, especially the mechanisms involved in MUC5B secretion may be of interest for the treatment of CMH in COPD. Nevertheless, MUC5B is not as widely studied as MUC5AC and thus the molecular pathways regulating MUC5B production are less well known25. Both mucins share overlapping mechanisms such as those mediated by SPDEF26, IL-3327 and EGF28,29, but a pathway that uniquely regulates either of them remains to be explored. In this study, we show that MUC5B expression and protein release in both COPD-derived bronchial epithelial cells and Calu-3 lung adenocarcinoma cells can be promoted by fibroblasts. As for MUC5AC release, the timing in Calu-3 may not have been optimal, as we did not detect MUC5AC protein here. Similarly, the timing for MUC5AC mRNA levels in the co-cultured primary cells may not have been optimal, explaining the lack of effect on MUC5AC mRNA expression upon co- culture. It might have been upregulated earlier, but as mRNA expression is a transient process, this change might have been receded at the end of the experiments, leaving only the change in MUC5AC protein levels to be observed. Our current findings do not render it likely that CCL20 and CXCL8 contribute to fibroblast-induced mucin secretion in our model. Nevertheless, this does not exclude a role for these cytokines 100

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in CMH. In our other studies, we showed that CCL20 treatment increased mucus production in healthy airway epithelial cells (Chapter 8)18 and that epithelial cell- fibroblast co-culture resulted in higher CXCL8 levels over mono-cultured epithelial cells30. Increased MUC5AC and MUC5B production5,31 and goblet cell hyperplasia10 are features commonly observed in COPD patients with CMH. In this study, we showed that co-culture with fibroblasts upregulated the transcription factor SPDEF in bronchial epithelial cells, likely promoting mucin expression and release. In our model, the fibroblasts supported epithelial maturation into more cuboidal cells that produced more mucus than those cells without the fibroblast support. Epithelial barrier formation and polarization is thought to be a prerequisite for epithelial differentiation32. Accordingly, we observed that TEER increased upon air exposure especially during the early stage of differentiation, and stabilized or slowly declined over time, consistent with the observation in other studies21,29. The lack of effect of co-culture on TEER levels can be explained by the fact that fibroblasts were co-cultured with the epithelial cells in the last week, when barrier formation and polarization was already established. Ciliated cells were only observed scarcely in 5 co-cultured epithelial layers and no change in FOXJ1 expression was observed upon co-culture. There are various explanations for this lack of ciliary differentiation in our model. Firstly, the epithelial cells were air-exposed for 14 days before collecting RNA. This is the time when an increase in mucin expression is usually observed, but development of cilia is usually seen later, i.e. 28 days after air-exposure33. Secondly, we performed our experiments in a translational setting using PBECs from COPD patients with CMH, so deficiencies in the ability of these cells to differentiate towards ciliated cells may have existed. Lastly, it is possible that fibroblasts are more prone to drive differentiation into mucous cells rather than ciliated cells. Although fibroblast-epithelium crosstalk also exists in healthy physiological conditions, it may be altered in pathological conditions. As we were interested in the mechanisms involved in CMH in COPD, we used epithelial cells and fibroblasts derived from COPD patients with CMH. We also used fibroblasts from COPD patients without CMH but did not observe different effects. Firstly, this may be due to the fact that epithelial cells were from COPD patients who already developed CMH and may be more susceptible to signals from fibroblasts, thus responding equally well to fibroblasts from both groups of donors. The other way round, signals from these epithelial cells may have abolished differences between fibroblasts from donors with and without CMH as previously observed34. Finally, since the sample size of donors without CMH was small, future studies including more fibroblast donors without CMH and epithelial cell donors without CMH are needed to clarify this point. One of the major strengths of this study is that, for the first time, primary airway epithelial cells and fibroblasts derived from COPD patients were used for a

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long-term air-exposed co-culture model to evaluate how fibroblasts are involved in CMH development by promoting mucous cell differentiation and epithelial mucin release. It is the in vitro setting that is, so far, most translational to patients in a context of CMH in COPD. Our findings provide novel insights into the mechanisms that regulate CMH in COPD and this can be of relevance to the identification of novel therapeutic strategies in the future.

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REFERENCES

1. Burgel, P. R. Chronic cough and sputum production: A clinical COPD phenotype? Eur. Respir. J. 40, 4–6 (2012). 2. Ramos, F. L., Krahnke, J. S. & Kim, V. Clinical issues of mucus accumulation in COPD. Int. J. COPD 9, 139–150 (2014). 3. Burgel, P.-R. Chronic cough and sputum production: a clinical COPD phenotype? ´. Eur. Respir. J. 40, 4–6 (2012). 4. Kirkham, S., Sheehan, J. K., Knight, D., Richardson, P. S. & Thornton, D. J. Heterogeneity of airways mucus. Biochem. J. 546, 537–546 (2002). 5. Caramori, G. et al. Mucin expression in peripheral airways of patients with chronic obstructive pulmonary disease. Histopathology 45, 477–484 (2004). 6. Park, K. et al. SPDEF regulates goblet cell hyperplasia in the airway epithelium. J. Clin. Invest. 117, 978–988 (2007). 7. Yu, H., Li, Q., Kolosov, V. P., Perelman, J. M. & Zhou, X. Interleukin-13 Induces Mucin 5AC Production Involving STAT6/SPDEF in Human Airway Epithelial Cells. Cell Commun. Adhes. 17, 83–92 (2010). 8. Song, J. et al. Targeted epigenetic editing of SPDEF reduces mucus production in lung epithelial cells. Am. J. Physiol. Cell. Mol. Physiol. 312, L334–L347 (2017). 5 9. Hessel, J. et al. Intraflagellar transport gene expression associated with short cilia in smoking and COPD. PLoS One 9, (2014). 10. Saetta, M. et al. Goblet Cell Hyperplasia and Epithelial Inflammation in Peripheral Airways of Smokers with Both Symptoms of Chronic Bronchitis and Chronic Airflow Limitation. Am. J. Respir. Crit. Care Med. 161, 1016–1021 (2000). 11. Adamson, I. Y., Hedgecock, C. & Bowden, D. H. Epithelial cell-fibroblast interactions in lung injury and repair. Am. J. Pathol. 137, 385–392 (1990). 12. Osei, E. T. et al. Interleukin-1 α drives the dysfunctional cross-talk of the airway epithelium and lung fibroblasts in COPD. Eur. Respir. J. 48, 359–369 (2016). 13. Osei, E. T. et al. MiR-146a-5p plays an essential role in the aberrant epithelial-fibroblast crosstalk in COPD. Eur. Respir. J. 49, 1602538 (2017). 14. Albers, S., Thiebes, A. L., Gessenich, K. L., Jockenhoevel, S. & Cornelissen, C. G. Differentiation of respiratory epithelium in a 3-dimensional co-culture with fibroblasts embedded in fibrin gel. Multidiscip. Respir. Med. 11, (2016). 15. Goto, Y. et al. In vitro reconstitution of the tracheal epithelium. Am. J. Respir. Cell Mol. Biol. 20, 312–318 (1999). 16. Kobayashi, K. et al. Effect of Fibroblasts on Tracheal Epithelial Regeneration in vitro. Tissue Eng. 12, 2619– 2628 (2006). 17. Spanjer, A. I. R. et al. A pro-inflammatory role for the Frizzled-8 receptor in chronic bronchitis. Thorax 71, 312–322 (2016). 18. Faiz, A. et al. Profiling of healthy and asthmatic airway smooth muscle cells following interleukin-1β treatment: A novel role for CCL20 in chronic mucus hypersecretion. Eur. Respir. J. 52, (2018).

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19. Heijink, I. H. et al. Cigarette smoke-induced epithelial expression of WNT-5B: Implications for COPD. Eur. Respir. J. 48, 504–515 (2016). 20. Brandsma, C.-A. et al. Differential effects of fluticasone on extracellular matrix production by airway and parenchymal fibroblasts in severe COPD. Am. J. Physiol. Lung Cell. Mol. Physiol. 305, L582-9 (2013). 21. Grainger, C. I., Greenwell, L. L., Lockley, D. J., Martin, G. P. & Forbes, B. Culture of Calu-3 cells at the air interface provides a representative model of the airway epithelial barrier. Pharm. Res. 23, 1482–1490 (2006). 22. Lambrecht, B. N. & Hammad, H. The airway epithelium in asthma. Nat. Med. 18, 684–692 (2012). 23. Chen, Y. et al. Stimulation of Airway Mucin Gene Expression by Interleukin ( IL ) -17 through IL-6 Paracrine / Autocrine Loop *. 278, 17036–17043 (2003). 24. Kirkham, S. et al. MUC5B is the major mucin in the gel phase of sputum in chronic obstructive pulmonary disease. Am. J. Respir. Crit. Care Med. 178, 1033–1039 (2008). 25. Theodoropoulos, G. & Carraway, K. L. Molecular signaling in the regulation of mucins. J. Cell. Biochem. 102, 1103–1116 (2007). 26. Rajavelu, P. et al. Airway epithelial SPDEF integrates goblet cell differentiation and pulmonary Th2 inflammation. J. Clin. Invest. 125, 2021–2031 (2015). 27. Ishinaga, H. et al. Interleukin-33 induces mucin gene expression and goblet cell hyperplasia in human nasal epithelial cells. Cytokine 90, 60–65 (2017). 28. Zhu, L. et al. Rhinovirus-induced major airway mucin production involves a novel TLR3-EGFR-dependent pathway. Am. J. Respir. Cell Mol. Biol. 40, 610–619 (2009). 29. Shaykhiev, R. et al. EGF shifts human airway basal cell fate toward a smoking-associated airway epithelial phenotype. Proc. Natl. Acad. Sci. U. S. A. 110, 12102–7 (2013). 30. Osei, E. T. et al. Interleukin-1α drives the dysfunctional cross-talk of the airway epithelium and lung fibroblasts in COPD. Eur. Respir. J. 48, 359–369 (2016). 31. Kesimer, M. et al. Airway Mucin Concentration as a Marker of Chronic Bronchitis. N. Engl. J. Med. 377, 911–922 (2017). 32. Gordon, S. et al. Non-Animal Models of Epithelial Barriers ( Skin , Intestine and Lung ) in Research , Industrial Applications and Regulatory Toxicology. Altern. to Anim. Exp. 32, 327–378 (2015). 33. Song, J. et al. Aberrant DNA methylation and expression of SPDEF and FOXA2 in airway epithelium of patients with COPD. Clin. Epigenetics 9, 42 (2017). 34. Spanjer, A. I. R. et al. A pro-inflammatory role for the Frizzled-8 receptor in chronic bronchitis. Thorax 71, 312–322 (2016).

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SUPPLEMENTARY DATA

Figure S1. Correlations of mucin and SPDEF expression. PBECs from 3 COPD patients with CMH were differentiated at ALI in the presence or absence of PAFs from 9 COPD patients. Only co- cultured samples were analyzed. Significant correlation was determined by Spearman’s rank correlation coefficient.

Figure S2. No difference between PAFs isolated from COPD patients with CMH and without CMH. PBECs from 1 COPD patient with CMH were differentiated at ALI in the presence or absence of PAFs from 3 COPD patients with CMH and 3 without CMH. [A] MUC5B secretion by PBECs. [B] Expression of mucociliary markers in PBECs. MUC5AC protein was not detected from these experiments. Significant difference between PAFs from the patients with and without CMHwas determined by Mann-Whitney U test.

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Figure S3

Figure S3. Transepithelial electrical resistance (TEER) of ALI-differentiated PBECs.PBECs were submerged-cultured for 5 days before air-exposed for 2 weeks with or without the presence of PAFs on day 7-14. No difference was observed between mono-cultured PBECs (black solid line) and co-cultured PBECs (grey dash line). Significant difference between mono-culture and co-culture was determined by one-sample Wilcoxon signed rank test.

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Figure S4. IL-6 neutralization abates fibroblast-induced mucin upregulation by epithelial cells. Calu-3 cells were co-cultured with PAFs from 6 COPD patients with CMH. Apical wash fluid and total RNA was collected for measurement of mucin release and gene expression, respectively. Significant difference between untreated mono-culture and IgG-treated co-culture was determined by one-sample Wilcoxon signed rank test. Significant difference between IgG-treated and anti-IL-6-treated co-culture was determined by paired-samples Wilcoxon signed rank test. Significant correlation between MUC5B mRNA and protein was determined by Spearman’s rank correlation coefficient.

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Involvement of miRNAs associated with chronic mucus hypersecretion in fibroblast-epithelium crosstalk in COPD

Hataitip Tasena1, 2 Wim Timens1, 2 Maarten van den Berge2, 3 Machteld N Hylkema1, 2 *Irene H Heijink1, 2, 3 *Corry-Anke Brandsma1, 2 (*both authors contributed equally)

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ABSTRACT

We recently showed that airway fibroblasts derived from chronic obstructive pulmonary disease (COPD) patients promote epithelial mucin secretion and mucociliary differentiation, and thus speculate that this crosstalk may underlie chronic mucus hypersecretion (CMH) development in COPD (chapter 5). We hypothesized that microRNAs (miRNAs) are involved in this fibroblast-epithelium crosstalk. The present study aimed to identify which of our previously identified CMH- associated miRNAs are differentially expressed in primary bronchial epithelial cells (PBECs) and primary airway fibroblasts (PAFs) upon co-culture. PBECs from one stage IV COPD patient with CMH were cultured at air-liquid interface with and without PAFs from 6 stage IV COPD patients with CMH. Total RNA from both cell types was harvested separately and used for assessing the expression of selected candidate miRNAs with qPCR. The miRNAs positively associated with CMH, i.e. let-7a-5p, miR-31-5p, and miR-708-5p, were detected in both PBECs and PAFs. For miRNAs negatively associated with CMH, miR-146a-5p and miR-193-5p were detected in both PBECs and PAFs, while miR-134-5p was only detected in PAFs. In PAFs, let-7a-5p and miR- 146a-5p expression was significantly higher upon co-culture, and there was a trend for higher expression of miR-31-5p (p=0.0625). In PBECs, we observed a trend for higher miR-708-5p expression upon co-culture (p=0.0625). The significantly higher expression of let-7a-5p and miR-146a-5p in airway fibroblasts upon co-culture with airway epithelial cells from COPD patients with CMH suggests their involvement in fibroblast-epithelium crosstalk. These miRNAs may thus serve as candidates for future studies that aim to elucidate the mechanisms of this crosstalk in CMH.

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INTRODUCTION

One of the characteristics of chronic obstructive pulmonary disease (COPD) is chronic mucus hypersecretion (CMH), which is characterized by the presence of chronic inflammation, chronic cough and exaggerated sputum production1. microRNAs (miRNAs) are main regulators of many processes in cells and tissues and can be expected to also play a role in the pathogenesis of CMH. miRNAs are small non-coding RNA molecules consisting of approximately 22 nucleotides which post-transcriptionally regulate gene expression by inducing mRNA degradation or inhibiting protein translation2. Over 60% of mammalian genes are predicted to be targeted by miRNAs2. They have been reported to be involved in various respiratory diseases, including COPD3. Recently, we identified 10 miRNAs that are associated with CMH in COPD using miRNA gene expression profiles of bronchial biopsies4. Among these miRNAs, the expression of let-7a-5p, let-7d-5p, let-7f-5p, miR-31-5p, and miR-708-5p was higher with CMH and the expression of miR-134-5p, miR- 146a-5p and miR-193-5p, miR-500a-3p, and miR-1207-5p was lower with CMH. It is not yet clear in which cell types these miRNAs are active and how these miRNAs contribute to CMH. We recently demonstrated that the co-culture of primary bronchial epithelial cells (PBECs) with primary airway fibroblasts (PAFs) leads 6 to more mucociliary differentiation and more secretion of MUC5B and MUC5AC mucins (chapter 5). These findings suggest that aberrant fibroblast-epithelial cell crosstalk may contribute to CMH development in COPD. Thus, we hypothesized that CMH-associated miRNAs previously identified in bronchial biopsies4 are involved in aberrant fibroblast-epithelial cell crosstalk in CMH. This study aims to identify which CMH-associated miRNAs are involved in fibroblast-epithelium crosstalk in COPD using a long-term co-culture model in which PBECs are co-cultured with PAFs for 1 week during ALI differentiation.

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METHODS

Air-liquid interface (ALI) culture PBECs were isolated from explanted lungs of one COPD stage IV patient with CMH undergoing lung transplantation, as previously described5. PAFs were isolated from 6 COPD stage IV patients with CMH as previously published6. The patient characteristics are described in Table 1. The presence of CMH was defined by the patients’ clinical records. PBECs were grown in Keratinocyte Serum-Free Growth Medium (KSFM, Gibco, NY, USA) supplemented with 1% Penicillin (10,000 U/mL)/ Streptomycin (10,000 μg/mL) (P/S) (Gibco, California, USA), 1 μM isoproterenol, 25 ng/ml bovine pituitary extract (BPE, Gibco) and 2.5 µg/ml epidermal growth factor (EGF, Gibco). PAFs were grown in HAMs’ F12 medium supplemented with 1% pennicilin/streptomycin, 1% L-glutamine (Lonza, Switzerland) and 10% fetal bovine serum (FBS, Sigma-Aldrich, Germany). Cells were passaged at 90% confluence. In all experiments, we used PBECs in passage 3 and PAFs in passage 4-6. ALI culture of PBECs was performed as previously described7. Transwell inserts for 24-well plates (Corning®, NY, USA) were coated with 10 μg/ml bovine serum albumin (BSA; Sigma-Aldrich, MO, USA), 10 μg/ml fibronectin (Sigma-Aldrich, MO, USA) and 30 μg/ml collagen (PureCol®, Advanced Biomatrix, San Diego, CA, USA) in Eagle’s Minimum Essential Medium (EMEM, Lonza, Walkersville, MD, USA) before being used to culture PBECs. PBECs from each donor were seeded at a density of 75,000 cells/insert in ALI culture medium prepared by mixing DMEM (LONZA BE12-709F) and BEBM (Clonetics CC-3171) in 1:1 ratio supplemented with a set of BEGM Single Quots (Clonetics CC-4175) and 1.5µg/ml BSA (Sigma- Aldrich) and 15 ng/ml retinoic acid (Sigma-Aldrich). After 4-5 days, the cells were air-exposed for 14 days (day 0-14) during which the basal medium was refreshed every 2-3 days. PAFs seeded in 24-well plates in HAMs’ F12 medium until 70-80% confluent were co-cultured with PBECs on the inserts from day 7 to day 14 using ALI culture medium. At the end of day 14, total RNA from PBECs and PAFs was collected separately. This experimental design is demonstrated in figure 1.

RNA isolation and qPCR Total RNA was collected and isolated using Tri Reagent® according to the manufacturer’s protocol. To assess miRNA expression, total RNA was converted to cDNA using the TaqMan microRNA reverse transcription kit (Life Technologies, Bleiswijk, Netherlands) and reverse transcription primers (Life Technologies) for let- 7a-5p (assay id: 000377), miR-31-5p (002279), miR-134-5p (000459), miR-146a-5p (000468), miR-193a-5p (002281), and miR-708-5p (002341). qPCR was performed using LightCycler® 480 Probes Master according to the manufacturer’s guidelines

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(Roche, Switzerland). Expression of all miRNAs was normalized to the expression of small nuclear RNA, RNU48 (001006).

Table 1. Patient characteristics

donor age gender pack-years FEV1 (%predicted) FEV1/FVC E1 58 m 35 15 0.19 F1 53 f 40 23 0.26 F2 48 f 30 12 0.26 F3 59 m 40 15 0.29 F4 61 f 30 16 0.19 F5 58 m 35 15 0.19 F6 59 m 47 15 0.21 All patients are ex-smokers with COPD stage IV with CMH. E1 is PBEC donors; F1-F6 are PAF donors; FEV1 is forced expiratory volume in 1 second; FVC is forced vital capacity; CMH is chronic mucus hypersecretion defined by clinical records; m is male, f is female.

Figure 1. Co-culture of primary bronchial epithelial cells (PBECs) and primary airway fibroblasts (PAFs) at air-liquid interface (ALI). PBECs were submerged-cultured for 5 days before air-exposed for 2 weeks (day 0-14), during which the basal medium was refreshed every 2-3 days. On day 7, PAFs on basal compartments were co-cultured with PBECs for the next 7 days. On day 14, RNA from both cell types were collected separately for gene expression assessment.

Statistical analyses One-sample Wilcoxon signed rank test was used to determine significant differences between mono-cultured and co-cultured PBECs. Paired-samples Wilcoxon signed rank test was used for comparisons between mono-cultured and co-cultured PAFs. All statistical analyses were performed on GraphPad PRISM v7.

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RESULTS

To determine which of the 10 CMH-associated miRNAs previously identified in bronchial biopsies4 (Table 2) are likely involved in fibroblast-epithelial cell crosstalk, we assessed whether the expression of any of these miRNAs changes upon co-culture. Since let-7a-5p, let-7d-5p, and let-7f-5p are from the same miRNA cluster of which seed sequences are very similar, sharing several potential targets4, let-7a-5p was selected as a representative of the let-7 family. Since we previously observed that miR-500a-3p and miR-1207-5p were neither expressed in PBECs nor PAFs4, we did not include them in the current study.

Table 2. CMH-associated miRNAs identified in bronchial biopsies of COPD patients

miRNA positively associated with CMH miRNA negatively associated with CMH

let-7a-5p miR-134-5p let-7d-5p miR-146a-5p let-7f-5p miR-193a-5p miR-31-5p miR-500a-3p miR-708-5p miR-1207-5p

When assessing the expression of let-7a-5p, miR-31-5p, miR-134-5p, miR- 146a-5p, miR-193a-5p, and miR-708-5p in mono-cultured and co-cultured PBECs and PAFs, we observed that in PAFs, all miRNAs were expressed, with a significantly higher expression of let-7a-5p and miR-146a-5p upon co-culture with PBECs and a trend for higher expression of miR-31-5p (p=0.0625) (figure 2A). In PBECs, all miRNAs except miR-31-5p were detected. miR-708-5p expression tended to increase upon co-culture with PAFs (p=0.0625), while no change was observed for expression of other miRNAs (figure 2B).

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Figure 2. miRNA expression upon co-culture. Primary bronchial epithelial cells (PBECs) from a COPD patient with CMH were differentiated at air-liquid interface (ALI) for 14 days. From day7 to day 14, the cells were continued growing in mono-culture or in co-culture with primary airway fibroblasts (PAFs) from 6 COPD patients with CMH before total RNA was collected from each cell types separately. Each condition was performed in duplicates. [A] Expression in PAFs. [B] Expression in PBECs. Expression of all miRNAs was normalized to the expression of small nuclear RNA, RNU48. Significant difference was determined by paired-samples Wilcoxon signed rank test for the expression in PAFs and by one-sample Wilcoxon signed rank test for the expression in PBECs.

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DISCUSSION

In this study, we show that let-7a-5p and miR-146a-5p expression significantly increased in COPD-derived airway fibroblasts upon co-culture with COPD-derived airway epithelial cells, while miR-31-5p expression tended to increase as well. In the epithelial cells, miR-708-5p tended to increase after co-culturing with the fibroblasts, while no significant differences were observed for any of the other miRNAs. The increased expression of these CMH-associated miRNAs upon co-culture suggests that they are involved in fibroblast-epithelial cell crosstalk, which may be dysregulated in COPD patients with CMH. We previously demonstrated that COPD patient-derived airway fibroblasts promote and mucous cell differentiation and mucin secretion in COPD patient-derived bronchial epithelial cells during ALI culture (Chapter 5). Using the same co-culture model, our findings suggest that CMH-associated miRNAs of which expression increased in the co-cultured fibroblasts may be involved in epithelial differentiation into mucus-producing cells and/or the regulation of mucin secretion. The expression of let-7a-5p increased in the co-cultured fibroblasts, and its associations with CMH in bronchial biopsies was also positive (i.e. let-7a-5p levels were higher with moderate/ severe CMH compared to no-CMH controls) (Chapter 3)4. Little is known about the role of let-7a-5p in relation to CMH development and fibroblast-epithelial cell crosstalk. Kumar et al. showed that intranasal administration of let-7 miRNA mimic suppresses IL-13 and attenuates mucus production in bronchial biopsies (Chapter 3)8. It is possible that let-7a-5p upregulation in fibroblasts suppresses mucin secretion in an allergic mouse model, but a biological function of this miRNA was not shown in their report8. Since we did not observe increased expression of let-7a-5p in the co-cultured epithelial cells, let-7a-5p is more likely to be involved in a fibroblast response to the epithelial cells than directly in the increased epithelial mucus secretion observed upon co-culture with fibroblasts. Of interest, COL4A1 and COL4A2, crucial components of the basement membrane expressed by lung fibroblasts9, are predicted targets that were negatively correlated with CMH in bronchial biopsies (Chapter 3)4. It is possible that let-7a-5p upregulation in fibroblasts leads to lower collagen 4 production and this change in extracellular matrix (ECM) composition might consequently stimulate airway epithelium to secrete more mucus, or the other way around. This would require further investigation. In contrast to let-7a-5p, miR-146a-5p expression was lower with CMH. We also showed that miR-146a-5p was less upregulated upon co-culture in COPD compared to control fibroblasts and proposed that this miRNA functions as a negative feedback to suppress pro-inflammatory responses, i.e. IL-6 and CXCL8 secretion (Chapter 7)10. A similar role was also described in another study showing that miR- 146a-5p expression is upregulated by IL-1β and miR-146a-5p overexpression suppresses CXCL8 release11. Whether this miR-146 feedback mechanism is also

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impaired in CMH remains to be studied. In another study, we found that both IL-6 and CXCL8 promote mucin secretion by airway epithelial cells upon differentiation at ALI culture7, and in chapter 5 we demonstrate that IL-6 is involved in increased airway epithelial MUC5B expression upon co-culture with CMH-derived fibroblasts. Although there was just a trend of miR-31-5p upregulation in co-cultured fibroblasts and miR-708-5p upregulation in co-cultured epithelial cells, these two miRNAs are also of interest. miR-31-5p is positively associated with CMH in both asthma and COPD, suggesting that it may be a component of a shared mechanism between CMH in both diseases (Chapter 4). An interesting predicted target of miR- 31-5p is ST3 Beta-Galactoside Alpha-2,3-Sialyltransferase 2 (ST3GAL2), a member of sialyltransferases which may facilitate sialylation of the core mucin structure (Chapter 4) and consequently alter the viscosity of mucus. Higher mucus viscosity means worse clearance contributing to accumulation of mucus in the airways12. Since mucins are synthesized in epithelial cells, future experiments could evaluate whether fibroblasts secrete miR-31-5p to be taken up by the epithelial cells. The expression of miR-708-5p, on the other hand, was previously shown to be negatively associated with mucociliary differentiation at ALI13, suggesting that it may negatively regulate mucociliary differentiation. The set-up of this experiment included one epithelial cell donor and six 6 fibroblast donors which aimed at determining fibroblast-derived factors that influence the crosstalk to promote epithelial mucus secretion. To determine epithelial cell- derived factors influencing fibroblasts or being influenced by this crosstalk, more epithelial cell donors should be included in the future. Overall, this study provides candidate miRNAs that may be involved in fibroblast-epithelial cell crosstalk and contribute to CMH development in COPD.

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REFERENCES

1. Ramos, F. L., Krahnke, J. S. & Kim, V. Clinical issues of mucus accumulation in COPD. Int. J. COPD 9, 139–150 (2014). 2. riedman, R. C., Farh, K. K. H., Burge, C. B. & Bartel, D. P. Most mammalian mRNAs are conserved targets of microRNAs. Genome Res. 19, 92–105 (2009). 3. Sessa, R. & Hata, A. Role of microRNAs in lung development and pulmonary diseases. Pulm. Circ. 3, 315–28 (2013). 4. Tasena, H. et al. MicroRNA–mRNA regulatory networks underlying chronic mucus hypersecretion in COPD - Cover only. Eur. Respir. J. 52, 13993003 (2018). 5. Heijink, I. H. et al. Cigarette smoke-induced epithelial expression of WNT-5B: Implications for COPD. Eur. Respir. J. 48, 504–515 (2016). 6. Brandsma, C.-A. et al. Differential effects of fluticasone on extracellular matrix production by airway and parenchymal fibroblasts in severe COPD. Am. J. Physiol. Lung Cell. Mol. Physiol. 305, L582-9 (2013). 7. Spanjer, A. I. R. et al. A pro-inflammatory role for the Frizzled-8 receptor in chronic bronchitis. Thorax 71, 312–322 (2016). 8. Kumar, M. et al. Let-7 microRNA-mediated regulation of IL-13 and allergic airway inflammation. J. Allergy Clin. Immunol. 128, 1077–1085.e10 (2011). 9. Chambers, R. C., Leoni, P., Kaminski, N., Laurent, G. J. & Heller, R. A. Global Expression Profiling of Fibroblast Responses to Transforming Growth Factor- Beta1 Reveals the Induction of Inhibitor of Differentiation-1 and Provides Evidence of Smooth muscle Cell Phenotypic Switching. Am. J. Pathol. 162, 533–546 (2003). 10. Osei, E. T. et al. MiR-146a-5p plays an essential role in the aberrant epithelial-fibroblast crosstalk in COPD. Eur. Respir. J. 49, 1602538 (2017). 11. Perry, M. M. et al. Rapid Changes in MicroRNA-146a Expression Negatively Regulate the IL-1β-Induced Inflammatory Response in Human Lung Alveolar Epithelial Cells. J. Immunol. 180, 5689–5698 (2008). 12. Mall, M. A., Danahay, H. & Boucher, R. C. Emerging concepts and therapies for mucoobstructive lung disease. Ann. Am. Thorac. Soc. 15, S216–S226 (2018). 13. Martinez-anton, A. et al. Changes in microRNA and mRNA Expression with Differentiation of Human Bronchial Epithelial Cells. Am J Respir Cell Mol Biol 49, 384–395 (2013).

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MiR-146a-5p plays an essential role in the aberrant epithelial-fibroblast crosstalk in COPD

Eur Respir J 2017: 49(5): 1602538

Emmanuel T. Osei1,2,4 Laura Florez-Sampedro1,2 Hataitip Tasena1,2 Alen Faiz1,2 Jacobien Noordhoek1,2,3 Wim Timens1,2 Dirkje S. Postma2,3 Tillie L. Hacket4 *Irene H. Heijink1,2,3 *Corry-Anke Brandsma1,2 (*both authors contributed equally)

1University of Groningen, University Medical Center Groningen, Department of Pathology and Medical Biology; 2University of Groningen University Medical Center Groningen, GRIAC Research Institute; 3University of Groningen University Medical Center Groningen, Department of Pulmonology Groningen, the Netherlands 4University of British Columbia, Centre for Heart and Lung Innovation, Vancouver, Canada

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ABSTRACT

We previously reported that epithelial-derived IL-1α drives fibroblast-derived inflammation in the lung epithelial-mesenchymal trophic unit. Since miR-146a-5p has been shown to negatively regulate IL-1 signalling, we investigated the role of miR-146a-5p in the regulation of IL-1α-driven inflammation in chronic obstructive pulmonary disease (COPD). Human bronchial epithelial (16HBE14o-) cells were co-cultured with control and COPD -derived lung fibroblasts (PHLFs) and MiR-146a-5p expression was assessed with and without IL-1α neutralizing antibody. Genomic DNA was assessed for the presence of the Single Nucleotide Polymorphism (SNP) rs2910164. MiR- 146a-5p mimics were used for over-expression studies to assess IL-1α-induced signalling and IL-8 production by PHLFs. Co-culture of PHLFs with AECs significantly increased the expression of miR-146a-5p, and this induction was dependent on epithelial-derived IL-1α. MiR- 146a-5p over-expression decreased IL-1α-induced IL-8 secretion in PHLFs via down-regulation of IRAK-1. In COPD PHLFs, the induction of miR-146a-5p is significantly less compared to controls, and was associated with the SNP, rs2910164 (GG allele), in the miR-146a-5p gene. Our results suggest that induction of miR-146a-5p is involved in epithelial- fibroblast communication in the lungs, and negatively regulates epithelial-derived- IL-1α induction of IL-8 by fibroblasts. The decreased levels of miR-146a-5p in COPD fibroblasts may induce a more pro-inflammatory phenotype, contributing to chronic inflammation in COPD.

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INTRODUCTION

Chronic obstructive pulmonary disease (COPD) is a progressive disease in which chronic neutrophilic inflammation is associated with destruction of the lung parenchyma (emphysema) and small airway disease, which both contribute to airflow limitation and lung function decline1. The inhalation of noxious particles, specifically from cigarette smoke, is the most common risk factor for COPD and smoking is known to induce the recruitment of neutrophils1, 2. However, not all smokers develop COPD, indicating a genetic susceptibility to the disease process3. The inconsistency in replicating target genes related to COPD in different study populations points to an important role of epigenetic regulation4. Epigenetic mediators such as miRNAs can regulate the transcriptional activity of various genes involved in lung function and inflammation that are thought to be involved in the pathogenesis of COPD5. MicroRNAs (miRNAs) are small noncoding RNAs of approximately 19 to 25 nucleotides that cause post-transcriptional gene repression by increasing mRNA degradation or by inhibiting protein translation of specific mRNA targets6. They are involved in various biological processes and alterations in their expression can result in pathological conditions, including pulmonary diseases7. We recently provided an up-to-date review of studies showing the role of miRNA dysregulation in COPD and how it is associated with the various features of COPD5. In particular, it has been shown that exposure to cigarette smoke alone can change miRNA expression in the lungs8. Several studies have shown that various miRNAs are differentially expressed in whole lung tissue, serum and/or sputum of smoking COPD patients compared 7 to smokers without COPD9-12. Of these, miR-146a-5p has been shown to regulate the release of interleukin (IL)-1-induced inflammatory mediators from pulmonary epithelial cells, including the neutrophil chemo-attractant IL-813. We recently showed in a co-culture model that cigarette smoke extract (CSE)-induced IL-1α expression, is higher in airway epithelial cells from COPD patients compared to control-derived epithelial cells14. Further, the higher levels of epithelial IL-1α induce a stronger release of the pro-inflammatory cytokines including IL-8 from lung fibroblasts14. Interestingly, others have shown that when lung fibroblasts are stimulated with IL-1β and Tumour necrosis Factor-α, miR-146a-5p expression is induced to a lesser extent in lung fibroblasts from COPD patients when compared to control fibroblasts12. In this study, we hypothesized that a failure to upregulate miR-146a-5p in lung fibroblasts contributes to the disturbed communication within the epithelial- mesenchymal trophic unit in COPD, leading to increased inflammation in the disease. Therefore we studied the expression of miR-146a-5p in COPD and non-COPD control -derived primary human lung fibroblasts (PHLFs) in our co-culture model, and conducted functional assays to investigate the mechanism of regulation of pro- inflammatory activity by miR-146a-5p in pulmonary fibroblasts. 121

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METHODS

Subjects and cell culture conditions The human bronchial epithelial cell line 16HBE14o- was kindly donated by Dr D.C. Gruenert (University of California, San Francisco, CA) and cultured as previously described (1) in Eagle’s minimal essential medium (EMEM)/10% Fetal Calf Serum (Lonza, BioWhittaker®, Verviers, Belgium) on collagen/BSA-coated flasks. Fetal lung fibroblast cells (MRC-5, BioWhittaker) were cultured in EMEM/10% FCS on 24 well plates before experiments. Primary human lung fibroblasts (PHLFs) were isolated from peripheral parenchymal lung tissue of 8 non-COPD control donors undergoing tumour resection surgery and 12 COPD patients with severe disease undergoing lung transplantation using the explant technique as previously described [15, 16]. PHLFs from controls were isolated from histologically normal tissue taken as far away as possible from the tumour. The tissue was checked for cancerous abnormalities by an experienced pathologist and found to be cancer- free. Clinical information on the COPD patients and non-COPD control subjects is presented in table 1. The study protocol for this project was consistent with the Research Code of the University Medical Center Groningen (http://www.rug. nl/umcg/onderzoek/researchcode/index) and national ethical and professional guidelines (htttp://www.federa.org). PHLFs were cultured in HAMS-F12 medium/10% FCS (Lonza) on 24 well culture plates before experiments.

Co-culture model We used 16HBE14o- and MRC-5 co-cultures for the mechanistic studies. 16HBE14o- cells were co-cultured with COPD and control–derived PHLFs to determine the disease-specific effects. Briefly, 16HBE14o- cells were plated on coated 0.4µM pore 6.5mm transwell membranes (Costar, Corning Inc., New York, NY), while fibroblasts were cultured separately on a 24 well plate. After a confluent layer was obtained for both cell types, the transwell with 16HBE14o- cells (upper compartment) was placed in co-culture with lung fibroblasts in the 24 well-plate (lower compartment) and left for 72 hours in EMEM/10% FCS or HAMS-F12 medium/10% FCS (Lonza) for co-culture with MRC-5 cells or PHLFs respectively14 . Before experimentation, cells were serum- deprived overnight.

Cigarette smoke extract (CSE) preparation CSE experiments were performed in serum-free, hormone-supplemented media (Lonza). CSE was prepared before each experiment by bubbling 2 filter-less cigarettes through 25ml of media to make 100% CSE, which was then diluted to 20% CSE as

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previously described17.

Conditioned medium and neutralizing antibody experiments 16HBE14o- cells were serum-deprived overnight and stimulated for 6 hours with or without 20% CSE, which we have previously reported did not affect cell viability [14]. After stimulation, the CSE was thoroughly washed off and CSE-free conditioned medium (CM) was collected after a further 24 hour incubation. CSE-free CM was pre-incubated for 1 hour with or without 4µg/ml IL-1α neutralizing antibody (AB- 200-NA, MAB601, R&D Systems, Abingdon, UK) and was used to stimulate serum- deprived fibroblasts for 24 hours. For experiments with recombinant human (Rh) IL- 1α (R&D Systems), a 1ng/ml concentration of rhIL-1α (R&D Systems) was chosen which is comparable to levels previously reported in sputum of COPD patients [18]. Cell-free supernatants were collected and analyzed by ELISA and cell lysates were harvested with TRIreagent for RNA isolation.

MiR-146a-5p mimic transfection MRC-5 fibroblasts were transfected with the miR-146a-5p mimic at 25nM (MirVana miRNA mimic, assay ID= MC10722; Life Technologies) and scrambled small RNA at 25nM (AllStars Negative Control siRNA; QIAGEN) as a non-targeting control to assess the effects of miR-146a-5p induction on IL-1 signaling in lung fibroblasts. MiR-146a-5p expression was analyzed by qPCR, protein lysates were assessed by Western blot and IL-8 concentrations were determined by ELISA (R& D) in the cell- free supernatant as described in the online attachment. 7

SNP Genotyping Genomic DNA for genotyping was extracted from lung tissue of PHLF donors assessed for the presence of the SNP rs2910164. A detailed description is available on the online supplement.

Statistical analysis SPSS software was used for data analyses. Differences between COPD patients and non-COPD control subjects were analyzed with a Mann-Whitney U test. Differences between treatments within a group were analyzed using paired Students t-tests for the cell lines and Wilcoxon signed-rank tests for the primary cells. P<0.05 was considered statistically significant.

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Table 1. Characteristics of COPD patients and non-COPD controls from whom primary human lung fibroblasts (PHLFs) were obtained

No. Age Gender Smoking Pack-years FEV1 % FEV1/ Experiment (years) status predicted FVC %

Control-derived PHLFs

1 74 M ES 50 100 71 Co-culture 2 50 M ES 31 97 78 Co-culture 3 65 M ES 40 97 76 Co-culture&CM 4# 68 M ES 51 NA 74 Co-culture&CM 5 67 F NS 0 101 81 Co-culture& CM 6 65 F CS 38 98 76 Co-culture&CM 7 46 M ES 32 97 82 CM 8$ 60 M ES NA 85 NA CM

COPD-derived PHLFs

7 58 F ES 30 18 28 Co-culture 8 60 M ES 30 37 49 Co-culture 9 62 M ES 44 22 19 Co-culture 10 48 M ES 27 12 23 Co-culture 11 57 F ES 33 25 33 Co-culture 12 44 M ES 25 60 50 Co-culture 13 44 M ES 27 14 28 CM 14 56 M ES 38 23 35 CM 15 53 F ES 40 24 26 CM 16 52 M ES 20 18 62 CM 17 51 M ES 42 28 29 CM 18 59 F ES 37 21 21 CM

FEV1= Forced expiratory volume in 1 second, FVC= Forced Vital Capacity, F=female M= male CS= current smoker, ES= ex-smoker, NS= never smoker. #FEV1% Predicted not available, $Pack years and FEV1/FVC not available. CM= conditioned medium. All COPD donors were on inhaled or oral steroids before transplantation. There was a significant difference in FEV1%predicted (p=0.04) and FEV1/FVC% (p=0.04), as well as in age between the COPD patients and controls, with the COPD patients being slightly younger (p=0.03). There was no significant difference in pack-years between the groups.

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RESULTS

Epithelium-derived IL-1a is responsible for the increased miR-146a-5p expression in lung fibroblasts As our previous study showed that IL-1α derived from the airway epithelium (primary and 16HBE14o- cells) is an important regulator of pulmonary fibroblast-derived inflammation [14], we first determined whether epithelial-derived IL-1α causes an induction of miR-146a-5p in lung fibroblasts. We stimulated PHLFs from non- COPD controls and COPD patients with 16HBE14o- conditioned medium whose IL- 1α levels had been previously determined [14] in the presence or absence of the IL-1α neutralizing antibody. We found that the expression of miR-146a-5p was increased in PHLFs treated with conditioned media from 16HBE14o- cells, and this was further enhanced by pre-stimulation of the 16HBE14o- cells with cigarette smoke extract (CSE) (figure 1a). Furthermore, the induction of miR-146a-5p was completely abrogated by the addition of IL-1α neutralizing antibody (figure 1a). We also assessed the direct effects of rh-IL-1α stimulation on miR-146a-5p expression in PHLFs, and found as with the 16HBE14o- cell conditioned media, a significant increase in miR- 146a-5p expression in PHLFs (figure 1b). Lastly, stimulation with 1ng/ml (figure 1c) or 0.01ng/ml (supplemental Figure 1) rh-IL-1α led to a significant release of IL-8 concentration from PHLFs (figure 1c), which significantly correlated with the increased expression of miR-146a-5p in PHLFs (figure 1d). Neither the expression level of miR-146a-5p nor the release of IL-8 was significantly different between COPD and control-derived PHLFs after 16HBE14o- CM or rh-IL-1α stimulations. 7

MiR-146a-5p expression is decreased in primary lung fibroblasts from COPD patients in co-culture with 16HBE14o- cells We have previously shown in our co-culture model that COPD-derived airway epithelial cells, through higher induction of IL-1α, induce lung fibroblasts to be more pro-inflammatory [14]. Hence, we were interested in the regulation of miR- 146a-5p expression in control and COPD-derived PHLFs. In our co-culture model, 16HBE14o- cells significantly upregulated miR-146a-5p expression in PHLFs from both control and COPD donors. Interestingly, there was a significantly lower induction of miR-146a-5p in COPD-derived PHLFs compared to those from control donors upon co-culture with 16HBE14o- cells (figure 2).

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Figure 1. Epithelial-derived IL-1α is responsible for increased miR-146a-5p expression in lung fibroblasts. PHLFs from control donors (open triangles, n=6) and COPD patients (closed triangles, n=6) were grown to confluence and serum deprived overnight. [A] miR-146a-5p (with median) expression in PHLFs after stimulating with conditioned medium (CM) from 16HBE14o- cells pre-treated with or without cigarette smoke extract (CSE) in the presence or absence of 4 μg/ml IL-1α neutralizing antibody (NAb). [B] MiR-146a-5p expression levels in serum-deprived PHLFs after stimulation with/without 1ng/ml recombinant human IL-1α for 24 hours. MiR-146a-5p expression levels were related to the housekeeping non-coding RNA, RNU48 and expressed as 2-dCt. [C] IL-8 concentration (with median) released from PHLFs and [D] Correlation of IL-8 concentration released from PHLFs to miR-146a-5p expression in PHLFs after IL-1α stimulation. Data are shown in a Log scale. *** and ### =p<0.001 between the indicated values.

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Figure 2. Expression of miR-146a-5p in primary human lung fibroblasts upon co-culture with 16HBE14o- cells. Primary human lung fibroblasts from control donors or COPD patients were cultured alone or co-cultured with 16HBE14o- cells for 72 hours after which they were serum deprived. miR- 146a-5p expression in primary human lung fibroblasts from control donors (open triangles) and COPD patients (closed triangles) in mono-culture and co-culture with 16HBE14o- cells was related to the housekeeping non-coding RNA, RNU48 and expressed as 2-DCt. **=p<0.01 and *=p< 0.05 between the indicated values. 7 Lower induction of miR-146a-5p in COPD fibroblasts is associated with single nucleotide polymorphism (SNP) rs2910164 To explain the difference in miR-146a-5p induction between COPD and control fibroblasts we considered two possibilities. The first was a difference inRelB expression, a member of the nuclear factor (NF)-κB family of transcription factors, since it has been shown to be responsible for the up-regulation of miR-146a-5p in PHLFs [19]. The second possibility was a difference in the presence of the single nucleotide polymorphism (SNP) rs2910164 in the primary mir-146a-5p sequence as this SNP has been shown to cause a reduction in the expression of mature miR-146a- 5p [20]. First, we examined the expression of RelB in our PHLFs from the co-culture model and we found no significant difference in the expression of RelB between control and COPD-derived PHLFs (figure 3a). Secondly, we compared the genotypes for the rs2910164 SNP of the PHLFs used in our co-culture experiments and found that donors with the GG genotype had a lower miR-146a-5p induction after co-culture than those with the CG genotype (figure 3b).

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Figure 3. Effect of RelB expression and rs2910164 polymorphism on miR-146a-5p expression in co- culture. Primary human lung fibroblasts (PHLFs) from control donors or COPD patients were cultured alone or co-cultured with 16HBE14o- cells for 72 hours after which they were serum deprived. [A] RelB expression in PHLFs from control donors (open triangles) and COPD patients (closed triangles) in mono-culture and co-culture with 16HBE14o- cells. [B] Effect of the single nucleotide polymorphism rs2910164 on miR-146a-5p expression in PHLFs before and after co-culture with 16HBE14o- cells. mRNA levels were normalized to the housekeeping genes β2-microglobulinand protein phosphatase 1α, and expressed as 2−ΔCt. *=p<0.05 and **=p<0.01 between the indicated values.

MiR-146a-5p over-expression has anti-inflammatory effects on lung fibroblasts MiR-146a-5p has been reported to exert anti-inflammatory properties by targeting the proteins of interleukin 1 receptor-associated kinase (IRAK)-1 and TNF receptor- associated factor (TRAF)-6, which are key downstream mediators in the cellular response to IL-121. Hence, we were interested in examining the anti-inflammatory mechanism of miR-146a-5p in human lung fibroblasts. For over-expression of miR- 146a-5p experiments we used a human lung fetal fibroblast cell line (MRC-5). First, we examined the expression of miR-146a-5p in MRC-5 fibroblasts co-cultured with 16HBE14o- cells and found a similar induction of miR-146a-5p in MRC-5 fibroblasts upon co-culture (figure 4a) as in the PHLFs. Next, we successfully over-expressed miR-146a-5p levels in MRC-5 fibroblasts by treatment with the miR-146a-5p mimic compared to the scrambled non-targeting control mimic (figure 4b). We then assessed the protein expression levels IRAK-1 and TRAF-6 in MRC-5 fibroblasts after over-expressing miR-146a-5p. We found the protein expression of IRAK-1 was significantly reduced after miR-146a-5p over-expression compared to scrambled control, but TRAF-6 was unaffected (figures 4c & 4d). This indicates that miR-146a- 5p indeed regulates IL-1 receptor downstream signaling in human lung fibroblasts.

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Figure 4. miR-146a-5p has anti-inflammatory effects in lung fibroblasts. [A] MRC-5 fibroblasts were cultured alone or co-cultured with 16HBE14o- cells. miR-146a-5p expression in the fibroblasts was related to the housekeeping non-coding RNU48, expressed as 2-DCt (median indicated). [B] MRC-5 fibroblasts were seeded and immediately transfected with 25nM of miR-146a-5p mimic or the scrambled control for 48 hours. MiR-146a-5p expression in the fibroblasts was related to the housekeeping non-coding RNU48, expressed as 2-DCt, and presented as mean ± SEM (n=3/4). [C] TRAF-6 protein expression with representative blot and densitometry. [D] IRAK-1 protein expression with respective representative blot and densitometry in MRC-5 fibroblasts after transfection with miR- 146a-5p mimic or scrambled control. β-actin was used as the loading control for protein expression and data are represented mean ± SEM (n=3/4). N indicates the number of independent experiments. *=p<0.05 and **=p<0.01 between the indicated values.

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To determine the role of miR-146a-5p in the suppression of IL-1α-induced- IL-8 release, we treated MRC-5 fibroblasts with rh-IL-1α after miR-146a-5p overexpression (figure 5a). Here, we found a significant decrease of IL-8 release from MRC-5 fibroblasts when miR-146a-5p was over-expressed compared to cells treated with the scrambled control (figure 5b). Together our data show that miR-146a-5p regulates IL-1α-induced pro-inflammatory responses in lung fibroblasts (figure 6).

Figure 5. MiR-146a-5p reduces IL-1α-induced IL-8 release in lung fibroblasts. MRC-5 fibroblasts were seeded and immediately transfected with 25nM of miR-146a-5p mimic or the scrambled control for 48 hours. MRC-5 fibroblasts were then serum deprived overnight and stimulated with/without 1ng/ ml recombinant human IL-1α for 24 hours. [A] miR-146a-5p mRNA expression in MRC-5 fibroblasts was related to the housekeeping non-coding RNU48, expressed as 2-DCt, and presented as mean ± SEM. [B] IL-8 release from MRC-5 fibroblasts. Data is presented as mean ± SEM (n=3/4). N indicates the number of independent experiments. *=p<0.05 between the indicated values.

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Figure 6. Proposed role of miR-146a-5p in the crosstalk between airway epithelial cells and lung fibroblasts. [A] Epithelial-derived IL-1α causes an induction of miR-146a-5p expression as well as release of IL-8 from lung fibroblasts. MiR-146a-5p then binds to and down-regulates the expression of IRAK-1 down-stream of the IL-1 pathway in a feedback loop to dampen the NF-κB activation and the inflammatory effects of the epithelial-derived IL-1α on pulmonary fibroblasts.[B] In COPD, cigarette smoke exposure causes an increased IL-1α release from airway epithelial cells which further increases IL-8 release from fibroblasts. However in COPD -derived fibroblasts, the IL-1α induced increase in miR-146a-5p expression is lower compared to control-derived fibroblasts, which then contributes to lower feedback inhibition of the NF-κB activation and an exaggerated pro-inflammatory response.

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DISCUSSION

We investigated the role of miR-146a-5p in aberrant IL-1α signaling between the airway epithelium and lung fibroblasts in COPD. We found that co-culture of primary human lung fibroblasts with airway epithelial cells significantly increases the expression of miR-146a-5p, which is completely dependent on epithelial-derived IL-1α. We demonstrate that miR-146a-5p expression has an anti-inflammatory role, by down-regulating the expression of IRAK-1, which is downstream of the IL-1 pathway, and subsequently reduces IL-8 release from lung fibroblasts. Further, we shown that the induction of miR-146a-5p is significantly less in COPD fibroblasts and this was associated with the SNP, rs2910164r (GG allele), in the miR-146a-5p gene. MiR-146a-5p has been well studied as a regulator of cellular function in both innate and adaptive immunity22. Specifically, miR-146a-5p has been suggested to target various inflammatory pathways including Toll-like receptor and IL-1 receptor signaling22, 23. Over-expressing miR-146a-5p in the liver prevents the release of pro-inflammatory cytokines and protects mice from ischemia-reperfusion injury by targeting and reducing the protein expression of IRAK-1 and TRAF-6, which are downstream of the IL-1 pathway21. Perry and colleagues showed that miR-146a-5p over-expression reduces IL-1β-induced IL-8 production in mono-cultures of alveolar epithelial cells13. Additionally, Bhaumik and colleagues found a high expression of miR-146a-5p in senescent human neonatal foreskin fibroblasts compared to quiescent cells24. This high expression was shown to reflect a negative feedback mechanism that modulates the secretion of IL-1α-induced IL-6 and IL-8 release due to a robust senescent-associated secretory phenotype activity in fibroblasts24. In line with our present study, this effect was linked to the inhibition of IRAK1, but not TRAF-624. This finding is particularly important since senescence of various cell types such as epithelial cells and fibroblasts in the lung has been shown to contribute to COPD pathogenesis25. IL-1α is an important driver of innate immune responses26. This cytokine is constitutively present in the lung epithelium as part of the immune defense against inhaled particles and is responsible for the release of chemokines, such as IL-8, responsible for neutrophilic recruitment26, 27. In COPD, there is an increased release of IL-1α as indicated by the increased levels in sputum and broncho-aveolar lavage fluid compared to control subjects28. We have previously shown that exposure to CSE induces a stronger expression of IL-1α in airway epithelium from COPD patients compared to controls, leading to enhanced release of pro-inflammatory mediators such as IL-8 and IL-6 from lung fibroblasts upon their co-culture14. In addition, we showed that IL-1α was secreted at baseline and was responsible for a pro-inflammatory switch in lung fibroblasts14. 132

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In the present study, over-expression of miR-146a-5p reduced the IL-1α- induced IL-8 secretion from lung fibroblasts. Several regulatory mechanisms to modulate the effects of IL-1α are presentin vivo, such as the secretion of the naturally occurring IL-1 receptor antagonist (IL-1Ra) and the decoy IL-1R2 receptor26, 29. Apart from these mechanisms, miR-146a-5p has emerged as a crucial regulator of the IL-1 pathway24. In line with previous studies21, 24, we show that the induction of miR- 146a-5p is dependent on IL-1α stimulation and also causes a down-regulation in the protein expression of IRAK-1. IRAK-1 is an important serine/threonine kinase that associates with the IL-1R1 receptor complex upon stimulation30. This eventually leads to the activation of transcription factors such as activator protein (AP)-1 and nuclear factor (NF)-κB which subsequently leads to the induction and release of several inflammatory mediators, including 30IL-8 . Thus, we hypothesize that the IL-1-induced increase in miR-146a-5p acts in a negative feedback loop to regulate the observed lung fibroblast pro-inflammatory activity upon co-culture with airway epithelial cells. The induction of miR-146a-5p was further enhanced when epithelial cells were pre-stimulated with CSE. This indicates an increased demand for miR- 146a-5p induction as a negative feedback mechanism to counteract the enhanced release of IL-1α from the airway epithelium in smokers. Of interest, the observed increase in miR-146a-5p expression upon co-culture with epithelial cells was smaller in COPD-derived lung fibroblasts compared to control-derived lung fibroblasts. In line with our previous study [14], this reduced induction of miR-146a-5p may lead to an impaired feedback inhibition of the fibroblast-derived inflammation resulting from a higher production of IL-1α from 7 COPD-derived epithelium exposed to CSE (figure 6). Sato and colleagues additionally found less induction of miR-146a-5p in fibroblasts from COPD patients compared to healthy subjects after IL-1β/TNF-α stimulation12. This down-regulation was associated with increased expression of the COX-2 enzyme and an increased production of the inflammatory mediator PGE2 in the sputum of COPD patients12. The difference in the induction of miR-146a-5p in PHLFs from our model was only seen after 72 hours of co-culture, but not after the 24 hour stimulation of PHLFs with epithelial-CM. This suggests that prolonged periods of exposure to IL-1α are required to induce differential miR-146a-5p induction between COPD and control- derived fibroblasts which may be representative of the lung EMTU in COPD where there is a chronic exposure to cigarette smoke and the resultant epithelial-derived -IL- 1α14, 18, 31. This is also in line with Perry et al, who suggested a time and concentration dependent effect of IL-1 on miR-146a-5p expression13. To elucidate the underlying mechanism responsible for the lower induction of miR-146a-5p by COPD-derived PHLFs upon co-culture with epithelial cells, we investigated the possible involvement of RelB, a family member of the NF-B family, which has been shown to regulate miR-146a-5p expression19. Although Zago et al19

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reported a lower expression of RelB in PHLFs from smokers with and without COPD compared those from non-smokers, we did not find a difference in expression in RelB mRNA expression between non-COPD and COPD –derived PHLFs in our co-culture model. A common G>C SNP rs2910164 in the primary miR-146a-5p sequence has been associated with a reduction in the expression of mature miR-146a-5p [20]. Of interest we found that PHLFs from donors homozygous for the GG allele of SNP rs2910164 had a lower miR-146a-5p expression in co-culture than fibroblasts from donors heterozygous for this allele. This indicates that the expression of this SNP is associated with a lower miR-146a-5p induction in COPD-derived lung fibroblasts in our co-culture model. Of interest, Wang et al showed that this particular SNP is also associated with a lower miR-146a-5p expression in relation to COPD32. In conclusion, this study demonstrates that the pro-inflammatory phenotype of COPD lung fibroblasts resulting from a dysregulated epithelium-fibroblast interaction in our co-culture model may, at least in part, be due to the reduced ability of COPD- derived fibroblasts to up-regulate miR-146a-5p to counter-regulate pro-inflammatory activity. MiRNAs are likely to have therapeutic potential [5] with miRNA therapies recently making it through to clinical trials33. Hence our finding could provide a basis for further investigations to target chronic inflammation in COPD.

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REFERENCES

1. Decramer M, Janssens W, Miravitlles M. Chronic obstructive pulmonary disease. Lancet 2012: 379(9823): 1341-1351. 2. Hogg JC, Timens W. The pathology of chronic obstructive pulmonary disease. Annual review of pathology 2009: 4: 435-459. 3. Løkke A, Lange P, Vestbo J, Fabricius PG. [Developing COPD--25 years follow-up study of the general population]. Ugeskr Laeger 2006: 168(50): 4422-4424. 4. Sakao S, Tatsumi K. The importance of epigenetics in the development of chronic obstructive pulmonary disease. Respirology 2011: 16(7): 1056-1063. 5. Osei ET, Florez-Sampedro L, Timens W, Postma DS, Heijink IH, Brandsma CA. Unravelling the complexity of COPD by microRNAs: it’s a small world after all. Eur Respir J 2015: 46(3): 807-818. 6. Ha M, Kim VN. Regulation of microRNA biogenesis. Nat Rev Mol Cell Biol 2014: 15(8): 509-524. 7. Ezzie ME, Crawford M, Cho JH, Orellana R, Zhang S, Gelinas R, Batte K, Yu L, Nuovo G, Galas D, Diaz P, Wang K, Nana-Sinkam SP. Gene expression networks in COPD: microRNA and mRNA regulation. Thorax 2012: 67(2): 122-131. 8. Graff JW, Powers LS, Dickson AM, Kim J, Reisetter AC, Hassan IH, Kremens K, Gross TJ, Wilson ME, Monick MM. Cigarette smoking decreases global microRNA expression in human alveolar macrophages. PloS one 2012: 7(8): e44066. 9. Bosse Y, Postma DS, Sin DD, Lamontagne M, Couture C, Gaudreault N, Joubert P, Wong V, Elliott M, van den Berge M, Brandsma CA, Tribouley C, Malkov V, Tsou JA, Opiteck GJ, Hogg JC, Sandford AJ, Timens W, Pare PD, Laviolette M. Molecular signature of smoking in human lung tissues. Cancer research 2012: 72(15): 3753-3763. 7 10. Akbas F, Coskunpinar E, Aynaci E, Oltulu YM, Yildiz P. Analysis of serum micro-RNAs as potential biomarker in chronic obstructive pulmonary disease. Exp Lung Res 2012: 38(6): 286-294. 11. Van Pottelberge GR, Mestdagh P, Bracke KR, Thas O, van Durme YM, Joos GF, Vandesompele J, Brusselle GG. MicroRNA expression in induced sputum of smokers and patients with chronic obstructive pulmonary disease. American journal of respiratory and critical care medicine 2011: 183(7): 898-906. 12. Sato T, Liu X, Nelson A, Nakanishi M, Kanaji N, Wang X, Kim M, Li Y, Sun J, Michalski J, Patil A, Basma H, Holz O, Magnussen H, Rennard SI. Reduced miR-146a increases prostaglandin E(2)in chronic obstructive pulmonary disease fibroblasts. American journal of respiratory and critical care medicine 2010: 182(8): 1020- 1029. 13. Perry MM, Moschos SA, Williams AE, Shepherd NJ, Larner-Svensson HM, Lindsay MA. Rapid changes in microRNA-146a expression negatively regulate the IL-1beta-induced inflammatory response in human lung alveolar epithelial cells. Journal of immunology (Baltimore, Md: 1950) 2008: 180(8): 5689-5698. 14. Osei ET, Noordhoek JA, Hackett TL, Spanjer AI, Postma DS, Timens W, Brandsma CA, Heijink IH. Interleukin- 1α drives the dysfunctional crosstalk of the airway epithelium and lung fibroblasts in COPD. Eur Respir J 2016. 15. Noordhoek JA, Postma DS, Chong LL, Menkema L, Kauffman HF, Timens W, van Straaten JF, van der Geld YM. Different modulation of decorin production by lung fibroblasts from patients with mild and severe emphysema. Copd 2005: 2(1): 17-25.

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16. Brandsma CA, Timens W, Jonker MR, Rutgers B, Noordhoek JA, Postma DS. Differential effects of fluticasone on extracellular matrix production by airway and parenchymal fibroblasts in severe COPD. American journal of physiologyLung cellular and molecular physiology 2013: 305(8): L582-589. 17. Slebos DJ, Ryter SW, van der Toorn M, Liu F, Guo F, Baty CJ, Karlsson JM, Watkins SC, Kim HP, Wang X, Lee JS, Postma DS, Kauffman HF, Choi AM. Mitochondrial localization and function of heme oxygenase-1 in cigarette smoke-induced cell death. American journal of respiratory cell and molecular biology 2007: 36(4): 409-417. 18. Botelho MF, Bauer CMT, Finch D, Nikota JK, Zavitz JCC, Kelly A, Lambert NK, Piper S, Foster LM, Goldring JJP, Wedzincha JA, Basset J, Bramson J. IL-1 Alpha/ IL1-R1 Expression in Chronic Obstructive Pulmonary Disease and Mechanisic Relevance to Smoke-Induced Neutrophilia in Mice. PLoS one 2011: 6(12): e28457-e28457. 19. Zago M, Rico de Souza A, Hecht E, Rousseau S, Hamid Q, Eidelman DH, Baglole CJ. The NF-kappaB family member RelB regulates microRNA miR-146a to suppress cigarette smoke-induced COX-2 protein expression in lung fibroblasts. Toxicology letters 2014: 226(2): 107-116. 20. Jazdzewski K, Murray EL, Franssila K, Jarzab B, Schoenberg DR, de la Chapelle A. Common SNP in pre-miR- 146a decreases mature miR expression and predisposes to papillary thyroid carcinoma. Proc Natl Acad Sci U S A 2008: 105(20): 7269-7274. 21. Jiang W, Kong L, Ni Q, Lu Y, Ding W, Liu G, Pu L, Tang W. miR-146a ameliorates liver ischemia/reperfusion injury by suppressing IRAK1 and TRAF6. PLoS One 2014: 9(7): e101530. 22. Rusca N, Monticelli S. MiR-146a in Immunity and Disease. Mol Biol Int 2011: 2011: 437301. 23. Perry MM, Williams AE, Tsitsiou E, Larner-Svensson HM, Lindsay MA. Divergent intracellular pathways regulate interleukin-1beta-induced miR-146a and miR-146b expression and chemokine release in human alveolar epithelial cells. FEBS Lett 2009: 583(20): 3349-3355. 24. Bhaumik D, Scott GK, Schokrpur S, Patil CK, Orjalo AV, Rodier F, Lithgow GJ, Campisi J. MicroRNAs miR- 146a/b negatively modulate the senescence-associated inflammatory mediators IL-6 and IL-8. Aging 2009: 1(4): 402-411. 25. Tuder RM, Kern JA, Miller YE. Senescence in chronic obstructive pulmonary disease. Proc Am Thorac Soc 2012: 9(2): 62-63. 26. Garlanda C, Dinarello CA, Mantovani A. The interleukin-1 family: back to the future. Immunity 2013: 39(6): 1003-1018. 27. Martin TR, Frevert CW. Innate immunity in the lungs. Proceedings of the American Thoracic Society 2005: 2(5): 403-411. 28. Pauwels NS, Bracke KR, Dupont LL, Van Pottelberge GR, Provoost S, Vanden Berghe T, Vandenabeele P, Lambrecht BN, Joos GF, Brusselle GG. Role of IL-1alpha and the Nlrp3/caspase-1/IL-1beta axis in cigarette smoke-induced pulmonary inflammation and COPD. The European respiratory journal 2011: 38(5): 1019-1028. 29. Arend WP. The balance between IL-1 and IL-1Ra in disease. Cytokine Growth Factor Rev 2002: 13(4-5): 323-340. 30. Jain A, Kaczanowska S, Davila E. IL-1 Receptor-Associated Kinase Signaling and Its Role in Inflammation, Cancer Progression, and Therapy Resistance. Front Immunol 2014: 5: 553. 31. Suwara MI, Green NJ, Borthwick LA, Mann J, Mayer-Barber KD, Barron L, Corris PA, Farrow SN, Wynn

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TA, Fisher AJ, Mann DA. IL-1alpha released from damaged epithelial cells is sufficient and essential to trigger inflammatory responses in human lung fibroblasts. Mucosal immunology 2014: 7(3): 684-693. 32. Wang R, Li M, Zhou S, Zeng D, Xu X, Xu R, Sun G. Effect of a single nucleotide polymorphism in miR-146a on COX-2 protein expression and lung function in smokers with chronic obstructive pulmonary disease. Int J Chron Obstruct Pulmon Dis 2015: 10: 463-473. 33. Janssen HL, Reesink HW, Lawitz EJ, Zeuzem S, Rodriguez-Torres M, Patel K, van der Meer AJ, Patick AK, Chen A, Zhou Y, Persson R, King BD, Kauppinen S, Levin AA, Hodges MR. Treatment of HCV infection by targeting microRNA. N Engl J Med 2013: 368(18): 1685-1694.

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SUPPLEMENTARY DATA

miR-146a-5p mimic transfection MRC-5 fibroblasts were seeded at a density of 1x105 cells per well in 24-well plates in EMEM/10% FCS and transfected shortly after seeding, using the HiPerFect reagent (QIAGEN, Venlo, Netherlands) and the miR-146a-5p mimic at 25nM (MirVana miRNA mimic, assay ID= MC10722; Applied Biosystems, Carlsbad, CA, USA). A scrambled small RNA at 25nM (AllStars Negative Control siRNA; QIAGEN) was used as a control. After 48 hours, cells were washed and placed in serum-free medium, followed by 24 hour stimulation with or without 1ng/ml IL-1α (R&D Systems) and harvested with TriReagent for RNA isolation or Laemmli buffer for protein lysates preparation and cell-free supernatants were collected for IL-8 measurement.

rs2910164 Genotyping To investigate if the lower induction of miR-146a-5p by COPD-derived primary human lung fibroblasts (PHLFs) in co-culture was caused by a common G>C SNP rs2910164, genotyping for this SNP was performed. A subset of samples were previously genotyped on Illumina Human1M-Duo BeadChip array and imputed using MACH program for genotype imputation using HapMap release 22 template, as previously described [1]. Five samples which were not genotypes on arrays had their DNA extracted from lung tissues of the same subjects from which PHLFs were obtained, according to the standard salt-chloroform extraction method. The rs2910164 polymorphism was identified using TaqMan® SNP Genotyping assay (C_15946974_10) according to the manufacturer’s protocol. Each PCR was done in duplicates with 10 ng DNA template and 40 cycles of amplification was used. The PCR was carried out on a ABI7800HT machine (Applied Biosystems).

RNA isolation and qRT-PCR RNA was harvested from cells with the standard TRIreagent method. For miRNA expression analysis, miRNA-specific cDNA synthesis was done using the TaqMan microRNA reverse transcription kit (Life Technologies, Bleiswijk, Netherlands) together with reverse transcription primers (Life Technologies) for miR-146a (000468). qRT-PCR was performed on the LightCycler 480 II (Roche, Almere, Netherlands) and expression of the miRNAs of interest was normalized to the expression the small nuclear RNA, RNU48 (001006) as an endogenous control with approximately equal amplification efficiency. For expression analysis of the NF-κB family member RelB, the iScript cDNA kit (Biorad, Herts, UK) was used for cDNA synthesis, after which qRT-PCR was performed using the Taqman® Gene expression assay RelB (Hs00232399_m1) with the protein phosphatase 1, catalytic subunit,

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alpha isoenzyme (Hs00267568_m1) and β2- microglobulin (Hs00984230_m1) as the housekeeping genes. The reaction was followed for 40 cycles and the Ct value calculated using the LightCycler software. DeltaCt (ΔCt) values were calculated (Average triplicate Gene of Interest – average triplicate Housekeeping gene) and the data were represented as 2-ΔCt.

IL-8 ELISA and Western blotting Protein levels of IL-8 were measured with a sandwich ELISA (R&D Systems) according to the manufacturer’s instructions. Total cell lysates of fibroblasts were obtained by harvesting cells in 1X Laemmli buffer (containing 2% SDS, 10% glycerol, 2% β-mercaptol, 60mM Tris- Hcl (pH 6.8) and bromophenol blue) and boiling for 5 minutes after transfection. Samples were then blotted on a nitrocellulose membrane (Schleider and Schuell GmbH, Einbeck, Germany) after being subjected to SDS-PAGE. Expression of the interleukin 1 receptor associated kinase (IRAK)-1 and TNF receptor-associated factor (TRAF)-6 was analysed using mouse anti-human IRAK-1 (Sc-5288) and mouse anti- human TRAF-6 (Sc-8409) with goat anti-human β-Actin (all antibodies from, Santa Cruz Biotechnology, Santa Cruz, CA) as loading control as previously described2. Detection of bands were done by enhanced chemilumuniscence according to the manufacturer’s instruction (ECL, Amersham) and imaged with the ChemiDocTM MP system (Biorad, Veenendaal, Netherlands)

Figure S1. Recombinant human IL-1α caused IL-8 release from primary human lung fibroblasts (PHLFs). PHLFs from control donors (open triangles, n=6) and COPD patients (closed triangles, n=6) were grown to confluence and serum deprived overnight. IL-8 concentration (with median) as released after PHLFs IL-1α stimulation. *** = p<0.001

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References

1 Hao K, Bossé Y, Nickle DC, et al. Lung eQTLs to help reveal the molecular underpinnings of asthma. PLoS Genet 2012; 8: e1003029. 2 Wang R, Li M, Zhou S, et al. Effect of a single nucleotide polymorphism in miR-146a on COX-2 protein expression and lung function in smokers with chronic obstructive pulmonary disease. Int J Chron Obstruct Pulmon Dis 2015; 10: 463–473.

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Profiling of healthy and asthmatic airway smooth muscle cells following IL-1β treatment: a novel role for CCL20 in chronic mucus hypersecretion

Eur Respir J 2018: 52(2): 1800310

Alen Faiz1,2,3,4,5, Markus Weckmann1,6, Haitatip Tasena3,4,5, Corneel. J.Vermeulen3,4, Maarten Van den Berge3,4, Nick .H.T. ten Hacken3, Andrew J. Halayko7, Jeremy P.T. Ward8, Tak H. Lee8, Gavin Tjin1,2,9, Judith L. Black1,2,9, Cheng-Jian Xu4,10, Gregory .G. King1,2,11, Claude Farah1,2,12, Brian G. Oliver1,13, Irene H. Heijink3,4,5 and Janette K. Burgess1,2,4,9,5

1Woolcock Institute of Medical Research, The University of Sydney, Glebe, NSW/AU 2Sydney Medical School, The University of Sydney - Sydney, NSW/AU 3University of Groningen, University Medical Center Groningen, Department of Pulmonary Diseases, Groningen, The Netherlands 4University of Groningen, University Medical Center Groningen, GRIAC (Groningen Research Institute for Asthma and COPD), Groningen, The Netherlands 5University of Groningen, University Medical Center Groningen, Department of Pathology & Medical Biology, Groningen, The Netherlands 6Section for Pediatric Pneumology and Allergology, University Medical Center Schleswig-Holstein, Campus Centrum Luebeck, Airway Research Centre North (ARCN), Member of the German Centre of Lung Research (DZL), Germany. 7University of Manitoba/Manitoba Inst of Child Health - Winnipeg, MB/CA 8Kings College London - London/UK 9Discipline of Pharmacology, Faculty of Medicine, The University of Sydney, NSW 10University of Groningen, University Medical Center Groningen, Department of Pediatric Pulmonology and Pediatric Allergology, Beatrix Children’s Hospital, GRIAC Research Institute, Groningen, the Netherlands 11Department of Respiratory Medicine, Royal North Shore Hospital, St Leonards, NSW, Australia 12Department of Respiratory Medicine, Concord Hospital, Concord, NSW, Australia 13School of Medical and Molecular Biosciences, University of Technology Sydney NSW 2007 Australia

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ABSTRACT

Chronic mucus hyper-secretion (CMH) contributes to the morbidity and mortality of asthma and remains uncontrolled by current therapies in the subset of patients with severe, steroid resistant disease. Altered crosstalk between airway epithelium and airway smooth muscle cells (ASMCs), driven by pro-inflammatory cytokines such as IL-1β, provides a potential mechanism that influences CMH. This study investigated mechanisms underlying CMH by comparing IL- 1β-induced gene expression profiles between asthma and control-derived ASMCs and the subsequent paracrine influence on airway epithelial mucus production in- vitro. IL-1β-treated ASMCs from asthmatic and healthy donors were profiled using microarray analysis and ELISA. Air-liquid interface (ALI)-cultured CALU-3 and primary airway epithelial cells were treated with identified candidates and mucus production assessed. The IL-1β-induced CCL20 expression and protein release was increased in ASMCs from moderate compared to mild asthmatics and healthy controls. IL-1β induced lower MIR146A expression in asthma-derived ASMCs compared to controls. Decreased MIR146A expression was validated in-vivo in bronchial biopsies from 16 asthmatic versus 39 healthy donors. MiR-146a-5p overexpression abrogated CCL20 release in ASMCs. CCL20 treatment of ALI-cultured CALU-3 and primary airway epithelial cells induced mucus production, while CCL20 levels in sputum were associated with increased levels of CMH in asthmatic patients. Elevated CCL20 production by ASMC, possibly resulting from dysregulated expression of the anti-inflammatory miR-146a-5p, may contribute to enhanced mucus production in asthma.

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INTRODUCTION

Asthma is a chronic inflammatory disease affecting 300 million people worldwide1. Chronic mucus hyper-secretion (CMH) contributes to the morbidity and mortality of asthma2 and remains uncontrolled by current therapies. There is an urgent need to identify new therapeutic targets. Although mucus production increases in the airway epithelial layer during inflammation3, the underlying mechanism of CMH remains to be elucidated. Differentiation of airway epithelial cells into either ciliated or goblet cells is directed by other structural cells in the submucosa. The cross talk between the epithelial layer and airway smooth muscle cells (ASMC) may regulate mucus production, since the airway smooth muscle mass is enlarged in asthma4. ASMC have long been thought to have a passive role, but accumulating evidence suggests that these cells play an important role in the inflammatory process that underlies CMH, providing an active source of cytokines and chemokines via a number of pathways5. One of these inflammatory pathways know to be altered in asthma is the inflammasome, a multi-protein complex that plays an important role in the activation of pro-inflammatory cytokines, for example conversion of IL-1β from its pro-form into its active state6. The activity of the inflammasome is enhanced in neutrophilic asthma7, leading to increased levels of active IL-1β in sputum. IL-1β is a strong pro-inflammatory signaling molecule, the downstream mediators of which are associated with mucus production8. However, little is known about the influence of IL-1β on the pro-inflammatory response of airway structural cells, especially ASMC and the potential role in CMH in asthma. In this study, we identified CCL20 and MIR146Awhen comparing gene expression profiles between asthmatic and healthy ASMC in vitro in response to IL- 8 1β. Importantly, CCL20 had been shown to induce MUC5AC expression in epithelial cultures by binding to its only known receptor CCR69. Furthermore, in murine models, anti-CCL20-treatment significantly decreased virus-induced mucus production. While previously a SNP in miR-146a has been associated with the presence of asthma and other pro-inflammatory diseases11,12. Interestingly, we have recently shown that lower expression of miR-146a-5p in bronchial biopsies is inversely correlated with CMH in COPD13, highlighting miR-146a-5p as a regulator of mucus regulation in respiratory diseases. Based on CCL20 and miR-146a-5p known role in mucus production, we then investigated how these factors produced by ASMC influence mucus production in airway epithelial cells.

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METHODS

Human tissue Primary human ASMCs were obtained as described previously14,15 with ethical approval from The University of Sydney and participating hospitals (Concord Repatriation General Hospital, Sydney South West Area Health Service and Royal Price Alfred Hospital). All patients, or next of kin, provided written informed consent. An outline of the patients’ characteristics is shown in Table 1.

Microarray processing and analysis ASMCs were isolated from asthmatic patients (n=3) and healthy controls (n=3) and cultured as previously described14,15 (Dataset A). Cells were treated with 10 ng/ml IL-1β16 (R&D Systems) for 8 hours. Total cellular mRNA was isolated, labeled and run on an Affymetrix (Santa Clara, California, USA) GeneChip Human Gene 1.0 ST Array according to the manufacturer’s instructions (GSE63383)4. For independent validation, ASMCs derived from 2 asthmatic patients and 4 healthy donors (Dataset B) were grown and treated with IL-1β as above. Samples were labeled and run on an Affymetrix (Santa Clara, California, USA) Human U133Plus 2.0 Array according to the manufacturer’s instructions. Microarray analysis is outlined in the online supplement.

Pathway analysis Functional enrichment analysis to identify the overlapping genes altered by IL-1β treatment in Datasets A and B was performed using Gene Set Enrichment Analysis (GSEA) V.2.0.14. Protein Network analysis was conducted using String v10 on the overlapping genes. GSEA was also used to investigate pathways regulated in Dataset A using the Biocarta database. Transcript factor binding site analysis was conducted using gprofiler.

Miroarray candidate validation The validation of the microarray results was undertaken at a transcriptional (quantitative real-time PCR) and translational (ELISA) level as described in the online supplement.

Bronchial biopsies processing for quantification of MIR146A expression Bronchial biopsies were collected from respiratory healthy subjects[17] and current asthma patients18,19 with a previous doctor’s diagnosis of asthma, documented reversibility and AHR to histamine (PC20 histamine (using 30-s tidal breathing) < 32 mg/ml). The analysis was conducted on 39 healthy subjects and 16 asthmatics,

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who were all non-smokers and currently not taking inhaled corticosteroids (ICS). An outline of the patients’ characteristics is shown in Table S1. All study protocols were approved by the UMCG medical ethics committee and all subjects provided written informed consent. RNA was isolated and sequenced as described in the online supplement.

MiR-146a-5p predicted targets To identify downstream targets of miR-146a-5p, we used a publically available microarray dataset (GSE79340) of human hepatic Huh7.5.1 cells transfected with miR-146a-5p mimic (5 nM) compared to a negative control (n=3).

Functional analysis Immortalised ASMC (IASMC) were grown to 80-90% confluence and serum deprived before transfection with either a miR-146a-5p (100 nM) mimic or mimic control using RNAimax (Invitrogen).Twenty four hours later, cells were treated with IL-1β (10 ng/ml) or 0.1% BSA (control). Cell-free supernatants were collected at 24 hours and IL-8 levels assessed by ELISA. The human lung epithelial cell line CALU-3 and primary airway epithelial cells were grown at air-liquid interface (ALI), treated with CCL20 (10 ng/ml) for 48 hours and mucus assessed by Alcian Blue staining as described in the online supplement.

CCL20 levels in sputum Sputum was induced in a population of asthmatic patients (n=89), as previously described20,21, with and without CMH. CCL20 was measured by ELISA.

Definition of CMH 8 To define CMH asthmatic patients were asked to respond to a clinical questionnaire: “How often did you cough up sputum during the last week?”22 This question had seven possible answers: i) never, ii) sometimes, iii) once in a while, iv) often, v) most of the time, vi) regularly, and vii) always. Patients who responded i) were classified as no CMH; ii) and iii) moderate CHM; and iv) to viii) severe CMH. Of the asthmatics with available sputum 80 gave answers to the questionnaire and were analysed in this study.

Statistics Statistical tests and graph plotting were conducted using GraphPad Prism 6 (GraphPad Software, La Jolla, California USA). A probability (p) value of <0.05 was considered statistically significant.

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Table 1. Demographics of individual patients from whom samples were obtained. Experiments for FEV1% No. Diagnosis Age Gender Samples which sample predicted was used 1 Asthma 33 M Bronchoscopy N 1 2 Asthma 22 M Bronchoscopy N 1 3 Asthma 33 F Transplant N 1 4 Non diseased donor 31 M Bronchoscopy N 1 5 Non diseased donor 22 M Bronchoscopy N 1 6 Non diseased donor 27 F Bronchoscopy N 1 7 Asthma 20 M Bronchoscopy 65 2 8 Unknown F Transplant N 2 9 Asthma 19 F Bronchoscopy 97 2 10 Non diseased donor 20 M Bronchoscopy N 2 11 Non diseased donor 30 M Bronchoscopy N 2 12 Non diseased donor 21 F Bronchoscopy N 2 13 Non diseased donor 31 F Immortalised ASM cells 105 3,4 14 Non diseased donor 40 M Immortalised ASM cells 131 3 15 Non diseased donor 23 M Immortalised ASM cells 82 3,4 16 Non diseased donor 22 F Immortalised ASM cells 87 3,4 17 Asthma 39 M Immortalised ASM cells 84 3,4 18 Asthma 29 M Immortalised ASM cells 89 3,4 19 Asthma 21 M Immortalised ASM cells 108 3,4 20 Asthma 31 M Immortalised ASM cells 85 3,4 21 Asthma 27 F Immortalised ASM cells 78 3,4 22 Asthma 33 M Immortalised ASM cells 78 3,4 23 Non diseased donor 69 M Immortalised ASM cells N 4 24 Non diseased donor 22 F Immortalised ASM cells N 4 25 Non diseased donor Immortalised ASM cells N 6 26 Cystic fibrosis 22 F Paraffin embedded bronchus N 5 27 Non diseased donor 16 M Paraffin embedded bronchus N 5 28 COPD 56 F Paraffin embedded bronchus N 5 29 Pulmonary Fibrosis 53 F Paraffin embedded bronchus N 5

Abbreviations used M = male, F = female, N= not available, COPD= Chronic obstructive pulmonary disease 1= HuGene-1_0-st-v1 microarray (dataset A), 2= HG-U133_Plus_2 microarray (dataset B), 3= qReal Time-PCR validation, 4= CCL20 ELISA, 5= CCR6 IHC,6=miR-146A-5p functional work.

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RESULTS

Response of ASMC to IL-1β To evaluate if IL-1β is involved in the abnormal crosstalk between ASMC and airway epithelium, contributing to CMH in the asthmatic airway, we first examined the regulation of genes following IL-1β treatment. Asthmatics (n=3) and controls (n=3) were pooled to obtain sufficient power to determine the effect of IL-1β on gene expression (dataset A). Gene expression analysis identified 408 genes that were up-regulated and 143 genes downregulated upon IL-1β treatment compared to baseline (Fold change ± 2, FDR<0.05, Table S2). Figure 1A&B illustrate the genes significantly altered by IL-1β and a volcano plot, respectively. To validate these findings, we investigated a second independent dataset from 2 asthmatic and 4 healthy-derived ASMC cultures treated with IL-1β 10 ng/ml for 8 hours (dataset B). Gene expression analysis identified 377 genes that were upregulated and 98 genes down-regulated during IL-1β treatment compared to baseline (Fold change ± 2, FDR<0.05). Gene Set Enrichment Analysis (GSEA) of the two datasets revealed that upon IL-1β treatment, 255 of the significantly up- and 111 of the significantly down-regulated genes were core enriched in the same direction in the two datasets (Figure 1C). Pathway analysis of Dataset A revealed that the majority of pathways increased by IL-1β were pro-inflammatory, including the NF-κB, Interleukin-1 receptor (IL1R) and TID (Chaperones modulate interferon Signaling Pathway) pathways (Figure 1D). Protein network analysis (using STRING v10.0) showed that the increased IL-1β expression signature in ASMC was enriched for protein-protein interactions, indicating that the identified genes may have similar functions (Figure 1E). Three distinct clusters were identified: interferon related genes, NF-κB signaling related genes and pro-inflammatory cytokines. NF-κB and IRF1 were identified as central 8 hub proteins connecting a number of protein network clusters together, indicating a central role of these proteins during IL-1β stimulation in ASMC (Figure 1E). Six of the top ten genes up-regulated by IL-1β formed a clear individual cluster (cytokines), which included the CXCL family proteins CXCL8, a cytokine previously associated with neutrophilic airway inflammation in asthma23, CXCL10, an interferon regulated cytokine associated with mast cell migration24 and CCL20, a chemoattractant for CCR6+ immature dendritic cells, Th17 cells and neutrophils. Transcription factor binding analysis conducted on the overlapping genes between Dataset A&B using gprofiler identified that the up-regulated genes were enriched for NF-κB (FDR=7.84x10-10), RelA, a component of the NF-κB complex (FDR=7.18x10-13) and IRF1 (FDR=1.72x10-13) transcription factor binding sites, while the down-regulated genes were enriched for ETF (FDR=5.57x10-7) and EGR1 (FDR=5.64x10-5) transcription factor binding sites. These results again identify NF- κB and IRF1 as key regulators of IL-1β signaling in ASMC.

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Importantly, CCL20 has been shown to induce MUC5AC expression in epithelial cultures10,25. Furthermore, in murine models, anti-CCL20-treatment significantly decreased virus-induced mucus production. Based on its known role in mucus production, we selected CCL20 for further functional studies.

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Figure 1. Treatment of ASMC with IL-1β. [A] Heatmap and [B] Volcano plot of genes altered by IL-1β treatment for 8 hours in airway smooth muscle cells (ASMC), (FDR<0.05, FC>±2). [C] Gene set enrichment analysis (GSEA): enrichment of genes up- and down -regulated by IL-1 β treatment in ASMC comparing two independent datasets (GSEA FDR<0.05). [D] GSEA: Enrichment of genes involved with the in the NF-κB, IL1R and TID pathways with genes up-regulated by IL-1β treatment in ASMC (GSEA FDR<0.05). Coloured bar represents genes ranked based on their differential expression to treatment in ASMC. Vertical bars represent the running GSEA enrichment score and location (in the ranked gene list) of genes which are involved in the pathway being tested. [E] Protein interaction analysis

CCL20 protein release induced by IL-1β in ASMC To validate the microarray findings, CCL20 mRNA expression was measured following the stimulation of the asthmatic (mild and moderate) and healthy-derived ASMCs from Dataset A with IL-1β (10 ng/ml) for 8 hours. IL-1β significantly increased CCL20 expression, supporting the microarray results (Figure 2A). No differences were found in mRNA expression between asthmatic and healthy-derived ASMCs, nor between AMSCs from mild and moderate asthmatics. Next we confirmed our findings at the protein level and observed that IL-1β significantly increased CCL20 release from ASMC after 24 hours (Figure 2B). The levels of CCL20 were more strongly elevated in moderate asthmatic ASMCs compared to those from both healthy controls and mild asthmatics. Levels of CCL20 released from ASMCs at baseline were equivalent to levels previously reported to be released by epithelial cells20

MIR146A is decreased in asthma and regulates CCL20 Having seen that CCL20 protein was differentially regulated by IL-1β in asthmatic 8 compared to healthy-derived ASMC, we investigated other IL-1β-induced genes. This analysis was conducted on a subset of genes regulated by IL-1β treatment in Dataset A (Fold change±2, FDR<0.05). Only a single transcript, MIR146A had a significantly smaller increase in gene expression upon IL-1β treatment in asthmatic- derived ASMC compared to ASMC from healthy controls (Fold change±2, FDR<0.05, Figure 3A). MIR146A is the precursor transcript for miR-146a-3p and miR-146a-5p, the latter being a well-known anti-inflammatory miRNA, identified to be dysregulated in a number of inflammatory diseases[26]. To determine whether MIR146A expression was altered in asthmatics in-vivo, we investigated its expression in bronchial biopsies from 16 asthmatics and 39 healthy controls. MIR146A expression was decreased in asthmatic bronchial biopsies compared to healthy controls, reflecting the in-vitro results (Figure 3B). To identify the function of MIR146A, we focused on the known anti-

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inflammatory mature transcript miR-146a-5p and studied direct and indirect targets of this transcript using a publically available dataset of gene expression in Huh7.5.1 cells overexpressing miR-146a-5p. Gene expression analysis identified 5 genes (UBD, CXCL10, CXCL8, CCL20 and UCA1) that were down-regulated, but no genes were upregulated upon miR-146a-5p overexpression (Fold change>±2, FDR<0.05). A volcano plot is illustrated in Figure 3C, a table of significant genes in Table S3. One of the down-regulated genes, CCL20, is known to be modulated by miR-146a-5p[27]. Therefore we investigated whether miR-146a-5p negatively regulates IL-1β-induced CCL20 protein release in ASMCs. Overexpression of the miR-146a-5p mimic in immortalised ASMCs led to a significant down- regulation of IL-1β- induced CCL20 release (Figure 2C).

Figure 2. IL1β induced production of CCL20 mRNA by ASMC. ASMC were grown to confluence in growth media and quiesced for 72 hours and then treated with 0.1% BSA (control) or IL-1β (10ng/ml) for 8 or (mRNA) 24 hours (protein). [A] mRNA levels of CCL20 (healthy control n=4, mild asthma n=3 and moderate asthma n=3). [B] CCL20 protein levels were measured in cell-free supernatant (healthy control n=4, mild asthma n=3 and moderate asthma n=3). Data are expressed as a mean ± standard error of the mean. Statistical analysis used was paired Student’s t test and Student’s t test for paired and unpaired samples, respectively (*=p<0.05 compared to healthy control, # = p<0.05 compare treatment to IL1β). Abbreviations ASMC=airway smooth muscle cells, IL-1β= Interleukin-1beta.

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Figure 3. Function of miR-146a-5p in ASMC. [A] Microarray results of ASMC treated with IL-1β for 8 hours from asthmatics (n=3) and healthy controls (n=3). [B] MIR146A expression in bronchial biopsies from asthmatic and healthy control. [C] Volcano plot of Huh7.5.1 cells transfected with a miR- 146a-5p mimic (5nM) compared to a negative control (n=3). [D] CCL20 levels from ASMC treated with IL1β in the presence and absence of miR-146a-5p (mimic) over expression compared to mimic control (n=5). Data are expressed as a mean ± standard error of the mean. Statistical analysis used was paired 8 Student’s t test and Student t test for paired and unpaired samples, respectively (*=p<0.05, #=p<0.05 treatment compared to control).

CCL20 receptor CCR6 is present on structural cells of the airways and CCL20 induces mucus production by CALU-3 cells grown at air-liquid interface Having identified CCL20 as a mediator that is differentially expressed in asthmatic compared to healthy-derived ASMC, we next wanted to understand the functional consequences of increased CCL20 in the asthmatic airways. First, to determine whether structural cells in the airways are able to respond to CCL20, the expression of CCR6, its unique receptor, was investigated in airway cells. Initially, we performed realtime PCR in human primary ASMCs, IASMCs and CALU-3 cells, which showed

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detectable levels of CCR6 mRNA (Figure S1). Immunohistochemistry staining for CCR6 in human bronchial sections confirmed expression on both ASMCs and airway epithelium (Figure S2). Previously, in murine models, anti-CCL20-treatment significantly decreased mucus production in response to RSV infection, and CCL20 has been shown to induce MUC5AC expression in submerged culture[9, 10]. To determine whether CCL20 can directly promote mucus production in a model of differentiated epithelial cells, CALU-3 cells were grown at ALI and allowed to differentiate into mucus producing cells before being treated basolaterally with physiologically relevant levels of CCL20. CCL20 treatment of CALU-3 cells for 48 hours increased the production of mucus measured by alcian blue staining (Figure 4A). CCL20-induced mucus production was significantly reduced with the specific anti-CCL20 antibody in CALU-3 cells, while the isotype control had no significant effect (Figure 4B). A trend in the same direction was found by Alcian blue staining upon CCL20 treatment of ALI-differentiated primary human airway epithelial cultures (p=0.0625, Figure 4C-E). MUC5AC protein levels in apical were also found to be increased by CCL20 treatment (Figure 4F).

Sputum levels of CCL20 are associated with mucus hypersecretion in asthma Previous studies have shown that mesenchymal factors can cross the basal lamina propria and be found in lung fluids[28]. This phenomenon is thought to be enhanced in asthmatic patients due to the documented leaky nature of the epithelial layer[29], allowing trafficking to the mucosal layer, where it may induce mucus production. To determine whether CCL20 levels are associated with mucus production in asthmatics, we investigated sputum levels of CCL20 in asthmatic patients with and without CMH. Of the 80 patients included, 24 were classified with having no CMH, 32 with mild CMH, and 19 with moderate to severe CMH. CCL20 protein levels in sputum were found to be significantly increased comparing moderate-to-severe CMH with no CMH (p<0.05), and a trend towards an increase was observed between mild and no CMH (p=0.062, Figure 4G), further supporting the role of CCL20 in mucus production. As smoking may be a confounding factor, this analysis was repeated in non-smoking patients only, where CCL20 sputum levels were also significantly increased in moderate to severe CMH (n=11) compared with no CMH (n=21).

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Figure 4. CCL20 effect on mucus production in CALU-3 and primary airway epithelial cells grown at air-liquid interface. [A] Representative images of alcian blue staining of CALU-3 cells grown in air-liquid interface treated on day 5 with either complete DMEM (control), rabbit anti-human CCL20 antibody, Isotype control, CCL20 10ng/ml, rabbit anti-human CCL20 antibody + CCL20 10ng/ml or Isotype control + CCL20 10ng/ml for 48 hours (n=3) for each. [B] Densitometry analysis of Alcian blue staining in CALU-3 cells. Representative images alcian blue staining of primary airway epithelial cells grown in air-liquid interface (ALI)s treated on day 28 with either [C] PBS or [D] CCL20 10ng/ml for 48 8 hours and [E] Densitometry analysis (n=5). [F] MUC5AC protein measurement from ALI washes (n=5). [G] CCL20 levels is sputum from patients with i) No CMH, ii) mild CMH and ii) moderate-to-severe CMH. Statistical analysis used was paired Student’s t test and Student’s t test for pair and unpaired samples, respectively (*=p<0.05, #=p<0.05 compared to control). A Wilcoxon analysis was conducted on matches samples of primary airway epithelial cells grown at ALI. Data are expressed as a mean ± standard error of the mean. Abbreviations: DMEM= Dulbecco’s Modified Eagle Medium.

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allowing trafficking to the mucosal layer, where it may induce mucus production. To determine whether CCL20 levels are associated with mucus production in asthmatics, we investigated sputum levels of CCL20 in asthmatic patients with and without CMH. Of the 80 patients included, 24 were classified with having no CMH, 32 with mild CMH, and 19 with moderate to severe CMH. CCL20 protein levels in sputum were found to be significantly increased comparing moderate-to-severe CMH with no CMH (p<0.05), and a trend towards an increase was observed between mild and no CMH (p=0.062, Figure 4G), further supporting the role of CCL20 in mucus production. As smoking may be a confounding factor, this analysis was repeated in non-smoking patients only, where CCL20 sputum levels were also significantly increased in moderate to severe CMH (n=11) compared with no CMH (n=21). Our current findings indicate that IL1β produced by the airway epithelium following insult induces CCL20 production by the ASM mass which is increased in asthmatic ASMC. This CCL20 can then act on the airway epithelium by binding to CCR6, resulting in increased mucus production. CCL20 production is inhibited by miR-146a-5p expression, which is induced by NFκB activation. However this miR- 146a-5p induction is lower in asthmatic ASMC (Figure 5)

Figure 5. Summary of the crosstalk between the airway epithelium and airway smooth muscle cells (ASMC) in the asthmatic airway. Basolaterally secreted IL-1β produced by the damaged airway epithelium induces CCL20 production by ASM cells, which is increased in asthmatic ASMC. CCL20 can subsequently act on the airway epithelium by binding to its only known receptor CCR6, resulting in increased mucus production. CCL20 production is inhibited by miR-146a-5p expression, which is induced upon NF-κB activation. The miR-146a-5p induction is lower in asthmatic ASMC, leading to reduced suppression of CCL20. Red arrows = asthma, Blue arrows= healthy.

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DISCUSSION

In this study, we compared gene expression profiles between asthmatic and healthy ASMC in vitro in response to the NLRP3 inflammasome downstream mediator IL-1β. Through this analysis, we provide genome-wide evidence that ASMCs respond to the active form of IL-1β, levels of which are increased in the sputum of asthmatics7, providing a source of inflammatory chemokines and cytokines in the airways. Furthermore, we observed enhanced expression of CCL20 and MIR146A in response to IL-1β in ASMCs, with a lower increase in MIR146A in asthmatic ASMC. Furthermore, IL-1β induced a stronger increase in CCL20 protein secretion by ASMCs from moderate compared to mild asthmatics and healthy controls. Interestingly, CCL20 release was reduced following overexpression of miR-146a- 5p, providing evidence that this miRNA may be a dysregulated inhibitor of CCL20 production in ASMCs from asthmatics. Recombinant CCL20 directly induced mucus production from differentiated airway epithelial cells, indicating that CCL20 may contribute to CMH in asthma. The importance of this finding was corroborated by our observation that CCL20 levels in sputum were associated with increased levels of CMH in asthmatic patients. The current study reinforces the hypothesis that ASM is not a passive bystander in inflammation and CMH, but a key driver30,31. MiR-146a-5p has previously been found to regulate CCL20 production in skin keratinocytes after Toll-like receptor 2 (TLR2) stimulation, mirroring the results in this study27. However, this repression of inflammatory cytokines is not limited to CCL20, as the over-expression of miR-146a-5p led to the decrease of well-known NF-κB-regulated pro-inflammatory cytokines UBD, CXCL10 and CXCL8. Previous studies have identified miR146a-5p as an anti-inflammatory miRNA that inhibits NF-κB signaling by targeting the IL1R downstream signaling molecules IRAK1 and TRAF6 for degradation, key genes in the activation of the NF-κB pathway32. 8 In this study we found that induction of MIR146A in response to IL-1β was less in asthmatic ASMCs compared to healthy controls, which may thus be responsible for the increased secretion of CCL20 from these cells. Excitingly, we validated the lower MIR146A expression in asthmatics using bronchial biopsies from asthmatics and healthy controls. Similar findings have been reported in human inflammatory cells, where circulating CD4+ and CD8+ T cells of severe asthmatics expressed less miR- 146a-5p than healthy controls33. A likely rationale for the decrease of MIR146A in asthmatics may be the presence of the SNP (rs2910164), which is known to influence the levels of both the pre and mature MIR146A transcripts[34]. This SNP has previously been associated with the presence of asthma and other pro-inflammatory diseases11,12. Interestingly, we have recently shown that lower expression of miR-146a- 5p in bronchial biopsies is inversely correlated with CMH in COPD13, highlighting miR-146a-5p as a consistent regulator of mucus regulation in respiratory diseases. 155

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Although we observed increased CCL20 secretion from ASMCs derived from asthma patients, we did not find a significant difference in CCL20 gene expression between asthmatic and healthy controls. Furthermore, we observed a decrease in CCL20 gene expression following 72 hours miR-146a-5p overexpression. We postulate that only once the IL-1β-induced increase in miR-146a-5p starts to repress CCL20 expression, this leads to differences between the asthma and control groups, with an insufficient level of miR-146a-5p in asthma-derived ASMCs. The selected time point of 8 hours used in this study may have been too early to detect differences in CCL20 gene expression levels between ASMCs from asthmatic and healthy controls due to the absence of miR-146a-5p suppression. CCL20 was identified as a key chemokine for immature dendritic cells35, and has recently been described as an anti-microbial protein36 and regulator of mucus production9,25. Overall, the function of CCL20 appears to be pro-inflammatory in nature and it is up-regulated in the sputum in a number of inflammatory respiratory diseases including asthma20,37, COPD38 and cystic fibrosis39. Of interest, CMH is a feature of all of these diseases in at least a subset of patients40-42. In the current study we found that CCL20 promoted mucus production from an ALI-differentiated airway epithelial cell line. Furthermore there was a direct link between sputum CCL20 levels and mucus production in asthma patients. Of note, data from our group have shown that treatment with inhaled corticosteroids increases sputum levels of CCL2020, offering an explanation why current anti-inflammatory therapies are unable to revert mucus hypersecretion. The main strength of this study is the multidisciplinary approach used to identify a novel gene target using mass screen approaches including microarray analysis followed by the functional interrogation of the candidate using in vitro models. There are some limitations to this study, as we were unable to determine the origin of the CCL20 levels in the sputum of the asthmatic patients. A number of cell types in addition to ASMCs, including airway epithelial cells, produce this chemokine43. Despite this, the increased sensitivity and size of the muscle mass in asthmatic airways provides a potential reservoir of CCL20. Furthermore, due to remodeling of the airways, the ASM mass is in closer proximity to the epithelial layer in asthma, which may increase the crosstalk between epithelial cells and ASMCs. The measurement of MIR146A in the bronchial biopsies is reflective of its expression within a mixed population of cells in the asthmatic airways rather than a reflection of expression in the ASM alone. Finally, our finding that recombinant CCL20 increases mucin expression does not directly prove that airway smooth muscle-derived CCL20 drives e[epithelial mucus production in vivo. In future studies, we will use epithelium and smooth muscle co-cultures to further support our current findings. In conclusion, in this study we identified a novel pathway leading to mucus production in asthma, where increased CCL20 released from the enhanced ASM

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mass may contribute to the exaggerated mucus production by airway epithelium in asthmatic airways, due to reduced suppression by miR-146a-5p.

CONTRIBUTIONS

AF participated in project design, microarray analysis, in vitro cellular work, writing and proofreading of the manuscript. CJV, CX and MvdB participated in miRNA analysis writing and proofreading of the manuscript. NHTH, GGK, CF, AJH, THL and JPTW provided either IASMC samples or sputum samples, and participated in the proofreading of the manuscript. GT helped with IHC analysis. JKB, MW, JLB and BGO participated in project design, provided funding for project, writing and proofreading of the manuscript.

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REFERENCES

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respiratory syncytial virus and air pollution. American journal of respiratory cell and molecular biology 2011: 45(5): 1090-1100. 30. Damera G, Tliba O, Panettieri RA. Airway smooth muscle as an immunomodulatory cell. Pulmonary pharmacology & therapeutics 2009: 22(5): 353-359. 31. Faiz A, Burgess JK. How can microarrays unlock asthma? Journal of allergy 2012: 2012. 32. Taganov KD, Boldin MP, Chang K-J, Baltimore D. NF-κB-dependent induction of microRNA miR-146, an inhibitor targeted to signaling proteins of innate immune responses. Proceedings of the National Academy of Sciences 2006: 103(33): 12481-12486. 33. Tsitsiou E, Williams AE, Moschos SA, Patel K, Rossios C, Jiang X, Adams O-D, Macedo P, Booton R, Gibeon D. Transcriptome analysis shows activation of circulating CD8+ T cells in patients with severe asthma. Journal of Allergy and Clinical Immunology 2012: 129(1): 95-103. 34. Jazdzewski K, Murray EL, Franssila K, Jarzab B, Schoenberg DR, de La Chapelle A. Common SNP in pre- miR-146a decreases mature miR expression and predisposes to papillary thyroid carcinoma. Proceedings of the National Academy of Sciences 2008: 105(20): 7269-7274. 35. Schutyser E, Struyf S, Van Damme J. The CC chemokine CCL20 and its receptor CCR6. Cytokine & growth factor reviews 2003: 14(5): 409-426. 36. Yang D, Chen Q, Hoover DM, Staley P, Tucker KD, Lubkowski J, Oppenheim JJ. Many chemokines including CCL20/MIP-3α display antimicrobial activity. Journal of leukocyte biology 2003: 74(3): 448-455. 37. Pichavant M, Charbonnier A-S, Taront S, Brichet A, Wallaert B, Pestel J, Tonnel A-B, Gosset P. Asthmatic bronchial epithelium activated by the proteolytic allergen Der p 1 increases selective dendritic cell recruitment. Journal of Allergy and Clinical Immunology 2005: 115(4): 771-778. 38. Demedts IK, Bracke KR, Van Pottelberge G, Testelmans D, Verleden GM, Vermassen FE, Joos GF, Brusselle GG. Accumulation of dendritic cells and increased CCL20 levels in the airways of patients with chronic obstructive pulmonary disease. American journal of respiratory and critical care medicine 2007: 175(10): 998- 1005. 39. Starner TD, Barker CK, Jia HP, Kang Y, McCray Jr PB. CCL20 is an inducible product of human airway epithelia with innate immune properties. American journal of respiratory cell and molecular biology 2003: 29(5): 627-633. 40. Prescott E, Lange P, Vestbo J. Chronic mucus hypersecretion in COPD and death from pulmonary infection. European Respiratory Journal 1995: 8(8): 1333-1338. 41. Rogers DF. Airway mucus hypersecretion in asthma: an undervalued pathology? Current opinion in pharmacology 2004: 4(3): 241-250. 42. Rubin BK. Mucus structure and properties in cystic fibrosis. Paediatric respiratory reviews 2007: 8(1): 4-7. 43. Reibman J, Hsu Y, Chen LC, Bleck B, Gordon T. Airway epithelial cells release MIP-3α/CCL20 in response to cytokines and ambient particulate matter. American journal of respiratory cell and molecular biology 2003: 28(6): 648-654.

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SUPPLEMENTARY DATA

Microarray analysis Microarray analysis was conducted in two stages. First, asthmatics and controls were pooled to obtain sufficient power to determine the effect of IL-1β on gene expression in airway smooth muscle cells (ASMC) (n=6, dataset A). To validate these findings, we investigated a second independent dataset from 2 asthmatic and 4 healthy-derived ASMC cultures treated with IL-1β 10 ng/ml for 8 hours (dataset B). Where again asthmatics and controls were pooled to determine the effect of IL-1β on gene expression inASMC. Second, subjects in dataset A were separated based on health status and analysis was conducted on the subset of genes altered by IL-1β, comparing the change in expression to IL-1β in asthmatics to the change in expression of IL-1β in healthy controls (Fold change ± 2, FDR<0.05). All microarray analysis was performed using the computing environment R (R Development Core Team, 2013, version 3.02). Additional software packages (limma) were taken from the Bioconductor project, and normalised using Robust Multi-array Average.

Cell culture ASMC ASMC n=6 were isolated from asthmatic and healthy controls and cultured as previously described1, 2. At passage 3-4, ASMC were seeded at 1x104 cell/cm2 and grown in Dulbecco’s modification of Eagle’s medium (DMEM, Invitrogen, Carlsbad, CA, USA), 5% fetal bovine serum FBS and 1% antibiotics (Invitrogen) for 3-4 days to confluence. Cells were quiesced for 72 hours in DMEM with 0.1% bovine serum albumin (BSA, (Sigma Aldrich, St Louis, MO, USA) and then treated with vehicle (0.1% BSA) or, 10 ng/ml IL-1β (R&D Systems). Total cellular mRNA was 8 isolated using the Qiagen total RNA isolation kit (Qiagen, Doncaster, VIC, Australia) following 0 or 8 hours stimulation and finally stored at -80oC (dataset A). Total mRNA was collected for microarray analysis.

Immortalised ASMC For in vitro validation and functional studies, primary ASMC were immortalised using the hTERT over expression system, as previously described 3. Moderate asthmatic, mild asthmatic and healthy ASMC, defined by the Global Initiative for Asthma (GINA) guidelines were seeded at 1x104 cells per cm2 in 6 well plates and grown to confluence in DMEM(Invitrogen) supplemented with 10% FBS (JRH Biosciences) and 1% antibiotics (Invitrogen). Cells were quiesced in 0.1% BSA in DMEM for 3 days and subsequently treated with IL-1β (10 ng/ml) and control. mRNA was collected at 8 hours for realtime PCR while supernatants for CCL20

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ELISAs were collected at 24 hours.

CALU-3 cells CALU- 3 cells were grown to confluence in 75cm2 flasks in complete Dulbecco’s Modified Eagle’s medium (F-12 containing 10% (v/v) foetal bovine serum, 1% (v/v) nonessential amino acid solution and 1% (v/v) L-glutamine solution).

Microarray candidate validation Real-time PCR was conducted on total mRNA using Taqman primer CCL20 (Life Technologies, Mulgrave, VIC, AUS). Samples with no expression were given the value Ct=35. Samples were normalised to their untreated controls. CCL20 protein levels were accessed using CCL20/MIP-3α ELISA (R&D Systems). Samples below the detection limit were given the value 16 pg/ml the detection limit of the ELISA.

RNA extraction, Sample preparation and High-throughput sequencing Bronchial biopsies were taken from segmental divisions of the main bronchi. Biopsies frozen in Tissue-Tek (VWR, Radnor, PA) at -80°C were thawed at room temperature and cut from the blocks when they were semi-solid. Total RNA was extracted using AllPrep DNA/RNA Mini kit (Qiagen, Venlo, the Netherlands). Samples were lysed in 600 µl RLT-plus buffer using an IKA Ultra Turrax T10 Homogenizer, and RNA was purified according to the manufacturer’s instructions. RNA samples were dissolved in 30 µl RNAse free water. Concentrations and quality of RNA were checked using a Nanodrop-1000 and run on a Labchip GX (PerkinElmer, Waltham, MA). RNA samples were further processed using the TruSeq Stranded Total RNA Sample Preparation Kit (Illumina, San Diego, CA), using an automated procedure in a Caliper Sciclone NGS Workstation (PerkinElmer, Waltham, MA). In this procedure, all cytoplasmic and mitochondria rRNA was removed (RiboZero Gold kit). The obtained cDNA fragment libraries were loaded in pools of multiple samples unto an Illumina HiSeq2500 sequencer using default parameters for paired-end sequencing (2 × 100 bp).

Gene expression quantification The trimmed fastQ files where aligned to build b37 of the human reference genome using HISAT (version 0.1.5) allowing for 2 mismatches (Kim et al. 2015). Before gene quantification SAMtools (version 1.2) was used to sort the aligned reads (Li et al. 2009). The gene level quantification was performed by HTSeq (version 0.6.1p1) using Ensembl version 75 as gene annotation database.

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Quality Control Quality control (QC) metrics were calculated for the raw sequencing data, using the FastQC tool (version 0.11.3) (Andrews 2010). Alignments of 220 subjects were obtained. QC metrics were calculated for the aligned reads using Picard-tools (version 1.130) (http://picard.sourceforge.net) CollectRnaSeqMetrics, MarkDuplicates, CollectInsertSize-Metrics and SAMtools flagstat. We discarded 36 samples due to poor alignment metrics. In addition, we checked for concordance between sexlinked (XIST and Y-chromosomal genes) gene expression and reported sex. All samples were concordant. This resulted in high quality RNAseq data from 184 subjects.

Differential expression Raw counts of expressed features were analysed using the R-package DESeq2 (Love et al. 2014). Feature counts were set as the dependent variable, with asthma status as the predictor variable. Sex, current smoking, and age were entered as co-variables.

Immunohistochemistry Tissue staining for CCR6 with the rabbit anti-human CCR6 (R&D Systems) [0.2 μg/ ml] was conducted as previously described 3.

Air liquid interface CALU-3 To establish the air-liquid interface model CALU-3 cells were seeded onto Transwell polyester inserts (Sigma Aldrich) at a density of 5x105 cells/cm2 in 100 μL apical and 500 μL basolateral medium. The apical medium was removed 24 hours after seeding and cells were allowed to grow for 5-7 days, with basolateral medium changed at day 4. On day 5 of treatment the apical layer of the transwells was washed with HEPES 8 for 1 hour to remove any mucus. Cells were treated with either CCL20 (10ng/ml), rabbit anti-human CCL20 antibody (Abcam), isotype control (DakoCytomation), rabbit anti human CCL20 antibody + CCL20 10ng/ml, Isotype control + CCL20 (10ng/ml) and complete DMEM (control) in the basolateral side for 48 hours.

Primary airway epithelium Air liquid interface (ALI) cultured primary epithelial cells was conducted according to a previous publication 4. Briefly, primary epithelial cells obtained from the enzymatic digestion of bronchial tissue = were seeded at 75,000 in 200μl Bronchial Epithelial Cell Growth Medium (BEGM) in the apical part of the insert and 500μl BEGM at the basolateral part. When the cell-layer was confluent (3-5 days) the cells were exposed to air at the apical side and Dulbecco’s Modified Eagle Medium (DMEM) / BEBM (1:1) with retinoic acid (15ng/ml) added to the basolateral side (500μl). Media was

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refreshed every 3 days with DMEM / BEBM (1:1) with retinoic acid (15ng/ml). Cells were quiesed on day 28 with BEBM medium for 24H and then treated with CCL20 10ng/ml or PBS for 48H. ALI apical washes (BEBM medium 5 minutes) were conducted following treatment and MUC5AC was measured by ELISA (SEA756Hu, Cloud-Clone Corp., China).

MUC5AC ELISA MUC5AC levels in apical washes (2x dilution) were measured using the ELISA kit (SEA756Hu, Cloud-Clone Corp., China) according to the manufacturer’s protocol.

Alcian Blue Staining Following treatment transwells were washed with PBS. Cells were fixed with 4% (v/v) paraformaldehyde for 20 minutes. Transwells were then washed with PBS and stained using alcian blue (1% (w/v) alcian blue in 3% (v/v) acetic acid/water at pH 2.5) (Sigma-Aldrich) for 15 minutes. Following the staining the transwells were rinsed multiple times with PBS and allowed to air-dry overnight. The transwell’s filter was then cut out with a sharp point scalpel and mounted on a glass slide using Entellan new mounting medium (Merck Millipore). Sections were imaged on an Olympus BX60 microscope (Olympus, Hamburg, Germany) with manual light exposure and ‘one push’ white balance on a background region. Images were then taken using an attached DP71 camera (Olympus) at 20X magnification and recorded using Kodak software. Each image was analysed using Image J (v1.42q, NIH) with Colour deconvolution plugin. Images were separated based on alcian blue stain colours and densitometry mean was determined for 5 representative images of each insert and averaged.

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Figure S1. Gene expression of CCR6 in structural cells in the airways. CCR6 mRNA levels were measured by Real time PCR in quiesced cells (ASMC (n=4), IASMC (n=3), and CALU-3 cells (n=3)). Data are expressed as mean ± standard error of the mean. Abbreviations ASMC=airway smooth muscle cells, IASMC=immortalised airway smooth muscle cells, DMEM= Dulbecco’s Modified Eagle Medium, FBS= Foetal bovine serum and BSA= bovine serum albumin.

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Figure S2. Immunohistochemistry on paraffin embedded bronchus (>2mm) for CCR6. CCR6 immunohistochemistry was conducted on non-cancerous sections following lung resections (n= 4)(representative images). Specific staining was detected using a chemical chromophore DAB (brown) and cell nucleus was counterstained with haematoxylin (blue) (scale 100μm). Abbreviations M= Airway Smooth Muscle, L= Lumen, E = Epithelium

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Figure S3. Densitometry analysis of Alcian blue staining in ALI’s derived from primary airway epithelial cells treated with CCL20. Primary airway epithelial cells grown in air-liquid interface treated on day 28 with either C) PBS or CCL20 10ng/ml for 48 hours (n=1).

Table S1. Patient characteristics for RNA-Seq results

Asthma Healthy 8 N 16 39 Age (years) 44.72±12.93 38.95±18.9 Gender male n(%) 8 (50) 19(48.7) FEV1 % predicted 84.45±9.63** 101.95±11.67 PC20 218.61±296.29** 630.52±59.23

Data are presented as mean± SD unless stated otherwise. Differences in variables before and after treatment were analysed using a two-sided, Student's t test. **p<0.01 versus healthy. FEV1, forced expiratory volume in one second; PC20, provocative dose of Adenosine 5 ′ -Monophosphate (AMP) causing a 20% fall in FEV1

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Table S2. Top 50 genes altered by IL1β compared to baseline (FDR adjusted p value<0.05)

log Gene Symbol Gene Name p value FC CXCL10 chemokine (C-X-C motif) ligand 10 7.75 2.87E-12 IL8 interleukin 8 7.60 1.02E-14 CXCL1 chemokine (C-X-C motif) ligand 1 (melanoma growth stimulating 6.58 2.43E-14 activity, alpha) CXCL2 chemokine (C-X-C motif) ligand 2 5.82 2.43E-14 CXCL6 chemokine (C-X-C motif) ligand 6 5.48 1.50E-10 TNFAIP2 tumor necrosis factor, alpha-induced protein 2 5.33 4.56E-16 GBP4 guanylate binding protein 4 4.87 1.91E-11 IL6 interleukin 6 (interferon, beta 2) 4.80 2.28E-10 GCH1 GTP cyclohydrolase 1 4.79 2.17E-12 CCL20 chemokine (C-C motif) ligand 20 4.77 9.56E-12 CYP7B1 cytochrome P450, family 7, subfamily B, polypeptide 1 4.72 1.55E-16 BIRC3 baculoviral IAP repeat containing 3 4.64 1.17E-11 TNFAIP3 tumor necrosis factor, alpha-induced protein 3 4.61 1.44E-13 ELOVL7 ELOVL fatty acid elongase 7 4.19 2.69E-10 MFSD2A major facilitator superfamily domain containing 2A 3.92 2.53E-11 CFB complement factor B 3.86 7.73E-12 NFΚBIZ nuclear factor of kappa light polypeptide gene enhancer in B-cells 3.69 5.16E-12 inhibitor, zeta MARCH3 membrane-associated ring finger (C3HC4) 3, E3 ubiquitin protein ligase 3.60 1.46E-11 SLC39A14 solute carrier family 39 (zinc transporter), member 14 3.39 9.64E-12 C15orf48 chromosome 15 open reading frame 48 3.28 2.41E-10 IRAK2 interleukin-1 receptor-associated kinase 2 3.17 1.22E-10 NFΚBIA nuclear factor of kappa light polypeptide gene enhancer in B-cells 3.17 5.27E-14 inhibitor, alpha RIPK2 receptor-interacting serine-threonine kinase 2 3.15 3.17E-13 GBP3 guanylate binding protein 3 2.88 2.40E-10 ZC3H12A zinc finger CCCH-type containing 12A 2.83 5.27E-14 IRF1 interferon regulatory factor 1 2.81 7.16E-10 CXCL3 chemokine (C-X-C motif) ligand 3 2.81 1.24E-09 NFΚB2 nuclear factor of kappa light polypeptide gene enhancer in B-cells 2 2.56 5.27E-14 (p49/p100) PARP14 poly (ADP-ribose) polymerase family, member 14 2.49 9.02E-10 GBP2 guanylate binding protein 2, interferon-inducible 2.45 6.18E-11 SLC43A2 solute carrier family 43 (amino acid system L transporter), member 2 2.41 6.18E-11 RELB v-rel avian reticuloendotheliosis viral oncogene homolog B 2.40 8.08E-12 IKBKE inhibitor of kappa light polypeptide gene enhancer in B-cells, kinase 2.30 9.04E-13 epsilon

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TAP2 transporter 2, ATP-binding cassette, sub-family B (MDR/TAP) 2.19 6.11E-13 WTAP Wilms tumor 1 associated protein 2.16 1.34E-12 IFNAR2 interferon (alpha, beta and omega) receptor 2 2.14 7.85E-11 HIVEP3 human immunodeficiency virus type I enhancer binding protein 3 2.13 4.88E-10 TNIP1 TNFAIP3 interacting protein 1 2.10 1.58E-12 MT2A metallothionein 2A 2.10 3.05E-11 GPR37L1 G protein-coupled receptor 37 like 1 2.07 2.36E-10 WTAP Wilms tumor 1 associated protein 2.05 9.91E-13 MT2A metallothionein 2A 2.04 1.46E-11 IL32 interleukin 32 2.03 5.85E-10 MT1JP metallothionein 1J, pseudogene 2.02 6.53E-13 MT2A metallothionein 2A 1.99 2.23E-10 NINJ1 ninjurin 1 1.96 2.04E-12 BID BH3 interacting domain death agonist 1.77 1.54E-11 STX11 syntaxin 11 1.57 1.06E-09 TRAF3 TNF receptor-associated factor 3 1.28 4.79E-11 UXS1 UDP-glucuronate decarboxylase 1 1.08 1.46E-10

Table S3 Gene logFC p value FDR UBD -1.20299 2.19E-08 0.000419 UBD -1.24331 2.52E-08 0.000419 CXCL10 -1.4818 3.38E-07 0.003749 CXCL8 -1.48886 1.64E-06 0.010912 CCL20 -1.10357 3.76E-06 0.017906 UCA1 -1.14167 9.29E-06 0.028705

References

1. Faiz A, Tjin G, Harkness L, Weckmann M, Bao S, Black JL, et al. The expression and activity of cathepsins D, H and K in asthmatic airways. PloS one 2013; 8:e57245. 2. Faiz A, Donovan C, Nieuwenhuis MA, van den Berge M, Postma D, Yao S, et al. Latrophilin receptors: novel bronchodilator targets in asthma. Thorax 2017; 72:74-82. 3. Bossé Y, Paré PD, Seow CY. Airway wall remodeling in asthma: from the epithelial layer to the adventitia. Current allergy and asthma reports 2008; 8:357-66. 4. Hackett T-L, de Bruin HG, Shaheen F, van den Berge M, van Oosterhout AJ, Postma DS, et al. Caveolin-1 controls airway epithelial barrier function. Implications for asthma. American journal of respiratory cell and molecular biology 2013; 49:662-71.

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Summary, general discussion and future perspectives

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SUMMARY

Chronic mucus hypersecretion (CMH) is an important clinical feature in chronic obstructive pulmonary disease (COPD) and asthma. It is associated with lower quality of life, more severe symptoms, and an increased risk of exacerbations and mortality1–3. Important characteristics for CMH pathophysiology are exaggerated mucin secretion, increased goblet cell number, and impaired cilia function. In this thesis, we hypothesized that aberrant stromal cell-epithelium crosstalk contributes to CMH pathophysiology and that miRNAs are involved in CMH via this crosstalk or mechanisms underlying mucin secretion, mucociliary differentiation, and pro- inflammatory responses. We used bioinformatics approaches and in vitro models to investigate the involvement of miRNAs in CMH and the role of stromal cells in epithelial mucin secretion. In Chapter 2, we discussed the recent findings on the role of miRNAs and exosomes in asthma pathogenesis. In relation to CMH, specifically, miR-34/449 family—which is less expressed in asthma than in controls—has been shown to regulate ciliated cell differentiation; while let-7 family—which improves airway hyperresponsiveness and alleviates mucus production—is less expressed in exosomes isolated from asthma-derived bronchoalveolar lavage fluid compared to from healthy controls. Other miRNAs, e.g. miR-19, miR-21, miR-455, may be involved in airway remodeling in asthma but their role in CMH has not been reported. In Chapter 3, we identified 10 CMH-associated miRNAs in bronchial biopsies of COPD patients of which expression was significantly correlated with at least one mRNA. The miRNAs positively associated with CMH were let-7a-5p, let-7d-5p, let-5f-5p, miR-31-5p, and miR-708-5p; and miRNAs negatively associated with CMH were miR-134-5p, miR-146a-5p, miR-193a-5p, miR-500a-3p, and miR-1207- 5p. We created miRNA–mRNA co-expression networks based on miRNA-mRNA correlations which highlighted miR-134-5p, miR-146a-5p and the let-7 family as well as their correlated targets (e.g. KRAS and EDN1) as key regulators of CMH in COPD. Gene Set Enrichment Analyses (GSEA) revealed that molecular mechanisms related to MUC5AC expression likely contribute to CMH in COPD. Of the key miRNAs, miR-134-5p expression was lower in the fibroblasts derived from COPD patients with CMH compared to those without CMH, supporting our hypothesis that fibroblasts are involved in CMH pathophysiology. Our next aim was to determine whether miRNAs are also involved in CMH in asthma and whether CMH in asthma and COPD share common mechanisms mediated by miRNAs. In Chapter 4, we identified 17 miRNAs associated with CMH in bronchial biopsies from asthmatic patients. Among these miRNAs, miR-31-5p was the only miRNA associated with CMH in both asthma and COPD, so we propose that this miRNA may be involved in a shared mechanism underlying CMH in both

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diseases. miR-31-5p expression was higher with CMH and negatively correlated with several predicted targets including ST3GAL2, PITPNM2, and ARHGEF15 which were also negatively associated with CMH in both asthma and COPD. Similar to our findings in COPD, GSEA revealed that molecular mechanisms related to MUC5AC expression likely contribute to CMH in asthma. Moreover, significant enrichment of the CMH-associated gene set in COPD among CMH-associated genes in asthma suggests that molecular mechanisms underlying CMH in asthma and COPD are overlapping. In Chapter 5, we addressed the role of fibroblast-epithelium crosstalk in mucous cell differentiation and epithelial mucin secretion. We set up a long-term co- culture model of ALI-differentiated primary bronchial epithelial cells (PBECs) and primary airway fibroblasts (PAFs). We found more differentiation towards mucous cells and higher MUC5AC and MUC5B expression and secretion by PBECs upon co-culture with PAFs; and showed that the effect on MUC5B was (partly) mediated by fibroblast-derived IL-6. Overall, these findings support the hypothesis that stromal cells are involved in mucous cell differentiation and mucin secretion and indicate that MUC5B expression is modulated by fibroblast-derived IL-6, which may have implications for CMH in COPD. Since our data showed that fibroblasts support mucous cell differentiation and mucin secretion by epithelial cells, we further assessed whether any of the CMH- associated miRNAs identified in bronchial biopsies from COPD patients are involved in this crosstalk. In Chapter 6, we identified let-7a-5p and miR-146a-5p as potential candidates regulating the crosstalk in CMH via their effects in AFs.P In Chapter 7, we investigated the function of miR-146a-5p, which was positively associated with CMH in bronchial biopsies, in fibroblast-epithelium crosstalk in COPD. Since the focus of this study was on the role of fibroblasts in inflammatory response to epithelial damage, not mucociliary differentiation, we used 16HBE14o- immortalized bronchial epithelial cells as a model to keep the epithelial component stable. A submerged co-culture model of 16HBE14o- cells and primary 9 human lung fibroblasts showed that miR-146a-5p expression was upregulated in the fibroblasts upon co-culture with the epithelial cells and this upregulation was less pronounced in COPD-derived fibroblasts compared to non-COPD controls. Furthermore, we showed that the upregulation of miR-146a-5p was mediated by epithelial-derived IL-1α and proposed miR-146a-5p as an anti-inflammatory miRNA. In Chapter 8, we investigated how airway smooth muscle cells (ASMCs) contribute to CMH pathophysiology in asthma. We observed that ASMCs derived from asthmatic patients respond more strongly to IL-1β by upregulating several pro-inflammatory genes, including CCL20. ASMCs derived from moderate asthmatic patients secreted more CCL20 than those from mild asthmatic patients and healthy controls, which was accompanied by lower expression of MIR146A (a precursor for miR-146a-3p

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and miR-146a-5p). MiR-146a-5p overexpression suppressed IL-1β-induced CCL20 release, while CCL20 induced higher mucus production by Calu-3 and PBECs. Thus, miR-146a-5p may not only act as a negative regulator of pro-inflammatory signals but may also suppress mucus production upon crosstalk between stromal and epithelial cells.

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GENERAL DISCUSSION

In this thesis, we first used the available miRNA and mRNA expression profiles of bronchial biopsies derived from COPD and asthma patients to identify miRNA-mRNA networks that underlie CMH in both diseases (Chapter 3 and 4). Using an in vitro model of a long-term co-culture at ALI, we were able to demonstrate the involvement of fibroblasts in mucous cell differentiation and mucin secretion (Chapter 5). Here, we found support for various CMH-associated miRNAs potentially involved in this crosstalk (Chapter 6). The role of a selected miRNA, miR-146a-5p, in regulating stromal cell-epithelium crosstalk was further supported by our finding that miR- 146a-5p suppresses fibroblast pro-inflammatory responses upon epithelial-derived IL-1α and that this mechanism is impaired in COPD (Chapter 7). This suppression of IL-6 release by fibroblasts may have important consequences for mucus production, as IL-6 was shown to mediate epithelial MUC5B expression upon co-culture with fibroblasts Chapter( 5). Additionally, miR-146a-5p may regulate epithelial mucus production through suppression of IL-1β-induced CCL20 which can be suppressed by miR-146a-5p (Chapter 8). The overview of these findings is illustrated in figure 1. It is well known that goblet cell hyperplasia contributes to exaggerated mucin secretion, one of the markers of CMH, in COPD and asthma patients4–6. In this thesis, our in vitro findings suggest that stromal cells—such as fibroblasts and ASMCs—are also involved in CMH, supporting both MUC5AC and MUC5B secretion by epithelial cells as well as their differentiation towards mucus- producing cells (Chapter 5 and 8). Our findings indicate the involvement of pro- inflammatory signals from stromal cells in exaggerated mucin secretion in COPD. Notably, mucus obstruction itself can also trigger airway inflammation even in the absence of infection7. Without effective clearance, the epithelium is continually exposed to harmful particles that are trapped in sticky mucus. This chronic condition induces cellular stress and hypoxia7. In vitro studies showed that hypoxia induced 9 by mucus plugging leads to necrotic cell death and the release of IL-1α, similar to the damage induced by cigarette smoke8, and triggers IL-1 receptor 1 (IL-1R1)/ MyD88 signaling in neighboring cells. This subsequently leads to NF-κB activation and secretion of various cytokines and chemokines, resulting in infiltration of inflammatory cells7,9,10. As IL-1R1 signaling also increases a release of stromal pro- inflammatory cytokines, e.g. IL-6, CXCL8, and CCL20; in turn, mucus production may be enhanced in a vicious cycle resulting in chronic inflammation and CMH. miR-146a-5p may serve as regulatory mechanism to suppress this process, and this type of regulatory feedback may be impaired in CMH.

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Figure 1. Proposed mechanisms underlying CMH pathophysiology. The main hypothesis addressed in this thesis is that miRNAs and stromal cell-epithelium crosstalk are involved in mucus production and goblet cell differentiation in CMH. miR-708-5p may regulate epithelial mucin secretion intracellularly or in autocrine fashion as we observed that miR-708-5p mimics suppressed mucin secretion of A549 adenocarcinoma cells. miR-146a-5p in fibroblasts or ASMCs mediates a negative feedback loop to suppress inflammatory response induced by damaged epithelium. Other CMH-associated miRNAs, i.e. let-7 family, miR-31-5p, and miR-134-5p may be involved in CMH development by mediating mechanisms within or from fibroblasts as the changes in their expression were observed in this cell type but not epithelial cells. Various pro-inflammatory cytokines (i.e. IL-1 family, IL-6, CXCL8, and CCL20) mediate stromal cell-epithelium crosstalk which may contribute to aberrant mucin production and secretion.

Stromal cell-epithelium crosstalk also exists in healthy physiological conditions, but we speculate that especially the lack of negative feedback mechanisms may result in abnormal inflammatory responses and mucus secretion in COPD and asthma. For instance, we previously observed that IL-1α is critically involved in the upregulation of IL-6 and CXCL8 secretion by fibroblasts upon co-culture with epithelial cells11. In this thesis, we demonstrated that fibroblasts from COPD patients as well as ASMCs from asthma patients were less able to upregulate the anti-inflammatory miR-146a- 5p upon co-culture with epithelial cells or exposure to epithelial-derived cytokines (i.e. IL-1 family members) than control-derived cells, suggesting a hampered anti- inflammatory regulation in COPD and asthma resulting from aberrant crosstalk

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(Chapter 7)12. We also observed that miR-146a-5p expression was lower in bronchial biopsies of COPD patients with CMH than those without CMH but did not observe differential expression of miR-146a-5p in fibroblasts from patients with and without CMH at baseline (Chapter 3). Future studies should compare the levels of miR- 146a-5p upregulation in fibroblasts from both patient groups upon co-culture with epithelial cells or upon exposure to epithelium-derived signals. Notably, we observed that the expression of miR-134-5p, another miRNA negatively associated with CMH in bronchial biopsies, was lower in airway fibroblasts from the patients with CMH than those without CMH (Chapter 3). These findings suggest that a negative feedback loop—for instance, those mediated by miRNAs—may be impaired in the patients with CMH. Interestingly, we previously demonstrated that primary lung fibroblasts derived from COPD patients with CMH expressed higher IL-1R1 at baseline and higher FZD8 receptor upon stimulation with IL-1β compared to fibroblasts from COPD patients without CMH13. The increased expression of IL1R1 as well as FZD8 may lead to increased IL-6 and CXCL8 secretion by the fibroblasts, which in turn can promote mucin production in epithelial cells13. Together with the findings in this thesis, we propose that in COPD patients with CMH, airway fibroblasts express higher IL-1R1 leading to higher (FZD8-mediated) IL-6 secretion upon exposure to epithelial-derived stimuli, including IL-1α. Since miR-146a-5p expression was lower in CMH and less upregulated upon co-culture in COPD-derived fibroblasts, IL-6 may not be efficiently suppressed in COPD (and especially in CMH-derived fibroblasts) upon prolonged co-culture, leading to more mucin synthesis and release by epithelial cells (figure 2). In addition, airway epithelial cells from COPD patients with CMH may secrete more pro-inflammatory signals and alarmins, including IL-1 family members, to promote fibroblast responses. Future studies are needed to compare epithelial cells from COPD patients with and without CMH. Although our in vitro data suggest that miR-146a-5p also plays a role in abnormal ASMC-epithelium crosstalk in asthma patients, we did not identify miR-146a-5p as one of the CMH- associated miRNAs in asthma-derived bronchial biopsies (Chapter 4). It is possible 9 that this effect is specific to ASMCs and that the lower miR-146a-5p expression in ASMCs derived from asthma patients with CMH was negated by other cell types that are more abundantly present in bronchial biopsies.

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Figure 2. Proposed role of miR-146a-5p in stromal cell-epithelium crosstalk underlying chronic mucus hypersecretion. Upon damage, epithelial cells secrete alarmins (e.g. IL-1 family) which activates IL-1R1 signaling leading to upregulation of a WNT receptor, FZD8. This upregulation results in higher secretion of pro-inflammatory cytokines, e.g. IL-6, which upregulates anti-inflammatory miR- 146a-5p as a negative feedback mechanism to suppress IL-6-induced inflammation. However, this negative feedback may be impaired in CMH patients, allowing IL-6 to be continually released and trigger epithelial cells to differentiate to mucous cells and secrete more mucins.

Although only the function of miR-146a-5p was studied in more detail in this thesis, other CMH-associated miRNAs may also be involved in CMH and/or stromal cell-epithelium crosstalk. The majority of the CMH-associated miRNAs in COPD are expressed in both COPD-derived PBECs and COPD-derived PAFs (Chapter 3 and 6), indicating the potential role of these miRNAs in modulation of CMH via their effects in these cell types. For instance, let-7a-5p, which was higher expressed in bronchial biopsies derived from CMH patients (Chapter 3), was upregulated in PAFs upon co-culture with PBECs (Chapter 6). We found that the other two miRNAs from the same cluster, let-7d-5p and let-7f-5p, were also positively associated with CMH in COPD. The expression of these miRNAs was negatively correlated with predicted targets such as NKD1, a gene involved in WNT/β-catenin signaling pathway14 which plays a crucial role in airway epithelial differentiation15 and is suggested to be involved in CMH13. The enrichment of MUC5AC-associated genes among genes positively correlated with the let-7 family also supports a role for these miRNAs in

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MUC5AC expression (Chapter 3). Interestingly, intranasal delivery of let-7 family was reported to alleviate airway hyperresponsiveness and mucus production in an asthmatic mouse model16 (Chapter 2). let-7 miRNAs can inhibit the expression of IL-1316, an important T cell-derived mediator of type 2 inflammatory responses in allergic asthma17 which is also upregulated in COPD18 and is well known for its central role in regulating mucus production through induction of SPDEF19–21. Lower let-7 expression was found in exosomes isolated from asthma-derived bronchoalveolar lavage fluid than in healthy controls22 suggesting that they can act as mediators of cell-cell crosstalk (Chapter 2). Future studies should examine whether this miRNA family is secreted by stromal cells in order to act on epithelium. Another miRNA that should not be overlooked is miR-708-5p, which was higher expressed with CMH in bronchial biopsies (Chapter 3) and tended to be upregulated in the epithelial cells upon co-culture with the fibroblasts (Chapter 6). Interestingly, this miRNA was previously shown to be downregulated upon mucociliary differentiation at ALI23, suggesting that it may have a negative effect on this differentiation. Our preliminary experiments showed that overexpression of miR- 708-5p using miRNA mimics in MUC5AC-producing A549 alveolar adenocarcinoma epithelial cells resulted in lower MUC5AC secretion compared to treatment with negative control siRNA (data not shown). This finding needs to be validated in primary airway epithelial cells. We speculate that miR708-5p is a negative regulator of mucin secretion and is upregulated in COPD patients with CMH to compensate this feature. Last but not least, miR-31-5p may serve as a modulator or biomarker of CMH in both COPD and asthma as its expression in bronchial biopsies was higher with CMH in both diseases (Chapter 4). Little is reported about the link between this miRNA and CMH or relevant biological processes, apart from cigarette smoke condensate upregulates miR-31-5p expression in both normal human airway epithelial cells and lung cancer cells24. This thesis provided further clues for future functional studies of this miRNA. First of all, it is expressed in both control- and COPD-derived PBECs 9 and COPD-derived PAFs (Chapter 3) suggesting its potential role in both cell types. Secondly, its expression tends to be upregulated in PAFs upon co-culture with PBECs suggesting it may be involved in fibroblast-epithelium crosstalk (Chapter 6). Moreover, MUC5AC-associated genes are enriched among its positively correlated genes (Chapter 3) indicating its potential involvement in MUC5AC expression. Lastly, one of its negatively correlated targets is ST3 Beta-Galactoside Alpha-2,3- Sialyltransferase 2 (ST3GAL2), a member of sialyltransferases, which may play a role in post-translational modification of mucins and consequently affects mucus viscoelasticity (Chapter 4).

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FUTURE PERSPECTIVES

To the best of our knowledge, this is the first time an unbiased approach has been used to identify miRNAs associated with CMH in COPD (Chapter 3) and asthma (Chapter 4) which revealed several novel candidate miRNAs that could be involved in CMH pathophysiology. Since this is a cross-sectional study that identified associations, not causal effects, miRNAs of which expression was altered with CMH could be a propagator promoting CMH (i.e. their up- or down-regulation leads to higher mucin secretion or impaired mucus clearance), a suppressor of CMH (i.e. they are up- or down-regulated as a negative feedback loop to lower mucin secretion or increase clearance), or just a downstream molecule being affected by increased mucus levels in the airways. Therefore, whether these miRNA changes are a cause, an effect, or a bystander of CMH feature remains to be studied in the future. Even if it is just a bystander effect, this could be of relevance as these miRNAs could serve as a biomarker for CMH. Since CMH-associated miRNAs in this thesis were identified in bronchial biopsies, it raises at least two important questions: in which cell types are these miRNAs expressed and where do these miRNAs function? In this thesis, we determined the expression of CMH-associated miRNAs in COPD-derived PBECs and PAFs; whereas the expression of CMH-associated miRNAs in asthma, except miR-31-5p, remains to be studied in asthma-derived cells. Apart from stromal and epithelial cells, other cell types, particularly inflammatory cells (e.g. eosinophils, T-cells, neutrophils, and macrophages) which play a crucial role in both COPD and asthma25–27, could also be the sources and targets of these miRNAs and therefore should also be studied. In addition, future studies should consider unbiased screening for CMH-associated miRNAs in other patient materials, e.g. bronchial brushings of which more than 90% are epithelial cells28, or bronchoalveolar lavage fluid or sputum which contain extracellularly secreted miRNAs29,22 and inflammatory cells28,30. The latter are of interest to evaluate whether these are suitable to identify potential mediators of cell- cell crosstalk as these mediators including miRNAs (e.g. let-7 family) can be secreted into extra-vesicular exosomes (Chapter 2). However, as different lung compartments may show essential differences in this respect, it should be realized that also studies in the bronchial wall tissues are needed as that is the actual site of the cellular interplay and crosstalk. Furthermore, other techniques beyond qPCR could be used to assess miRNA expression. For example, in situ hybridization or single-cell sequencing can determine in which specific cell types the miRNAs of interest are expressed in the tissues. Apart from miRNAs, this thesis also reports mRNAs that are correlated with CMH-associated miRNAs and are associated with CMH itself (Chapter 3 and 4). As a future approach, it would be informative to also study proteomic data as miRNAs can alter protein translation without inducing mRNA degradation31 and the 180

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expression levels of coding mRNAs do not always represent the production levels of corresponding proteins. Various experimental approaches can be taken in order to investigate the functions of miRNAs and the involvement of stromal cell-epithelium crosstalk in CMH. In this thesis, it is shown that fibroblasts can promote epithelial mucus secretion and differentiation towards mucus-producing cells and thus proposed that aberrant crosstalk between the two cell types may occur in COPD patients with CMH, thus playing a role in CMH pathogenesis. Therefore, it would be important for the future co- culture models to compare the effects of airway fibroblasts derived from patients with and without CMH on airway epithelial cells, ideally of the same patients. Vice versa, it would also be informative to compare the epithelial cells derived from the patients with and without CMH since they signal differently to the fibroblasts. These studies will help to further validate the hypothesis that the aberrant fibroblast-epithelial cell crosstalk indeed is a main component in CMH pathophysiology in COPD. In asthma, we used mono-culture models to demonstrate potential mechanisms regulating epithelial mucus production via CCL20, a pro-inflammatory cytokine secreted by ASMCs which could also be modulated by miR-146a-5p. An in vitro model including co-culture between the two cell types would help to confirm this mechanism. Apart from ASMCs, future experiments should consider studying the crosstalk between epithelium and fibroblasts, as they are an important player in airway remodeling in asthma26 and another cell type expressing miR-146a-6p (Chapter 4 and 7), as well as inflammatory cells as they are drivers of allergic inflammation. Moreover, since we are looking at a specific clinical feature (i.e. CMH) in these diseases, it would be informative to compare the expression of candidate miRNAs in the cells derived from the patients with CMH and those from the patients without CMH both at baseline and upon relevant stimulators such as the IL-1 family members. Apart from in vitro models, animal models are also available for studying CMH features both in COPD32 and asthma33–35 and could be suitable for confirming functional roles of candidate signaling molecules in a more systemic manner in which different cell 9 types and components can interact in vivo. Overall, these studies will shed light into how abnormalities in the complex interplay between structural cells in the airways contribute to CMH and which gene networks are involved. This thesis focused on mucus secretion and mucous cell differentiation, but there are also other markers of CMH that were not studied. For instance, mucus clearance is known to be impaired in COPD and asthma36–38. Ciliated cells are the major cell type that helps facilitating mucus clearance and cilia dysfunction was observed in both diseases36,38. Besides, biological processes related to cilia development and functions are also enriched among CMH-associated mRNAs (Chapter 3). In this thesis, we cultured PBECs at ALI for 2 weeks which is the time point where mucin production could be observed but this could be too early for cilia

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development. Therefore, future experiments aiming to study cilia development should allow epithelial cells to differentiate for a longer time, i.e. 3-4 weeks39. Notably, it has been reported that miRNAs are involved in ciliated cell differentiation. miR- 449, for instance, has been shown to regulate this process via promoting centriole multiplication and multiciliogenesis partly part by targeting NOTCH140 (Chapter 2). Apart from cilia development and function, the changes in mucin composition41, mucus dehydration42 and/or higher viscoelasticity resulting from more mucin cross- linking or impaired mucin degradation43–45 could also contribute to less effective mucus clearance. In this respect, it would also be of interest to examine if a selected miRNA induces changes in mucin composition by comparing MUC5B:MUC5AC ratio produced by epithelial cells upon miRNA overexpression. Other markers such as mucus viscoelasticity can be attributed to higher secretion of extracellular DNA43,44 or impaired cleavage of mucins which can be caused by impaired or lower neutrophil elastase—both of which are the results of a complex interplay between different cell types and components and thus would require in vivo models to study them. For example, neutrophil elastase activity in bronchoalveolar lavage fluid collected from mice or sputum derived from patients can be assessed by Foerster-resonance energy transfer (FRET)–based neutrophil elastase reporter assays42. Including all above features in future studies would improve the understanding of CMH pathogenesis and pathophysiology from a more thorough perspective. At present, there are various drugs being developed to treat CMH but their efficacy remains debatable46. As the findings in this thesis suggests that mechanisms regulating CMH in COPD and asthma are overlapping and could involve miR-31-5p (Chapter 4), it would be worthwhile to look further into these mechanisms to identify promising therapeutic targets which may be effective for both groups of patients. Interestingly, a previous study on in vitro and in vivo models proposed pendrin—a transmembrane protein responsible for anion exchange—as a mediator of mucus production in both COPD and asthma47. Although we did not observe a correlation between SLC26A4 mRNA (a gene encoding for pendrin) and miR-31-5p in bronchial biopsies, this might be due to various cell types present in the samples or miR-31-5p only affects its protein production. Future studies should investigate whether miR- 31-5p is involved in molecular pathways related to pendrin protein production and activity. Moreover, it would be of interest to investigate whether post-translational modification of mucin by ST3GAL2 sialyltransferases leads to changes in mucus viscoelasticity and thus consequently contributes to CMH. If miR-31-5p is shown to be a possible regulator of CMH in both diseases, it might serve as a new therapeutic target for CMH treatment to which the use of an antagomir can be applied. To be successful, certain challenges related to safety and specific delivery as also described in Chapter 2 still require to be overcome. Overall, this thesis started from identifying candidate miRNAs and mRNAs

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that may be involved in CMH using clinical data (i.e. bed) and investigated the role of stromal cell-epithelium crosstalk as well as selected miRNAs in in vitro models (i.e. bench). We hypothesized that aberrant stromal cell-epithelium crosstalk contributes to CMH pathogenesis and pathophysiology and that miRNAs are involved in CMH via this crosstalk or molecular mechanisms underlying mucin secretion, mucociliary differentiation, and pro-inflammatory responses. We identified CMH-associated miRNAs in COPD and asthma, as well as demonstrated the involvement of fibroblast- and ASMC-epithelium crosstalk in inflammatory responses contributing to mucin expression and/or secretion and mucous cell differentiation by epithelial cells. With these findings, we improved our understanding of the molecular mechanisms involving CMH in both COPD and asthma. Ultimately, it would be a great perspective if in the next steps we could translate these discoveries back to the clinic (i.e. the bed) and provide insights that lead to the development of a new therapeutic strategy that could help the patients better than currently available options (figure 3).

Figure 3. Steps from bed to bench and back to bed leading towards CMH-targeted therapy. 9

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REFERENCES

1. Burgel, P. R. Chronic cough and sputum production: A clinical COPD phenotype? Eur. Respir. J. 40, 4–6 (2012). 2. de Marco, R. et al. Prognostic factors of asthma severity: A 9-year international prospective cohort study. J. Allergy Clin. Immunol. 117, 1249–1256 (2006). 3. Fahy, J. V & Dickey, B. F. Airway Mucus Function and Dysfunction. N. Engl. J. Med. 363, 2233–2247 (2010). 4. Saetta, M. et al. Goblet Cell Hyperplasia and Epithelial Inflammation in Peripheral Airways of Smokers with Both Symptoms of Chronic Bronchitis and Chronic Airflow Limitation. Am. J. Respir. Crit. Care Med. 161, 1016–1021 (2000). 5. Fahy, J. V. Goblet Cell and Mucin Gene Abnormalities in Asthma. Chest 122, 320S–326S (2002). 6. Ordonez, C. L. et al. Mild and Moderate Asthma Is Associated with Airway Goblet Cell Hyperplasia and Abnormalities in Mucin Gene Expression. Am. J. Respir. Crit. Care Med. 163, 517–523 (2001). 7. Mall, M. A., Danahay, H. & Boucher, R. C. Emerging Concepts and Therapies for Mucoobstructive Lung Disease. Ann. Am. Thorac. Soc. 15, S216–S226 (2018). 8. Osei, E. T. et al. Interleukin-1α drives the dysfunctional cross-talk of the airway epithelium and lung fibroblasts in COPD. Eur. Respir. J. 48, 359–369 (2016). 9. Fritzsching, B. et al. Hypoxic Epithelial Necrosis Triggers Neutrophilic Inflammation via IL-1 Receptor Signaling in Cystic Fibrosis Lung Disease. Am. J. Respir. Crit. Care Med. 191, 902–913 (2015). 10. Chen, C. J. et al. Identification of a key pathway required for the sterile inflammatory response triggered by dying cells. Nat. Med. 13, 851–856 (2007). 11. Osei, E. T. et al. Interleukin-1α drives the dysfunctional cross-talk of the airway epithelium and lung fibroblasts in COPD. Eur. Respir. J. 48, 359–369 (2016). 12. Osei, E. T. et al. MiR-146a-5p plays an essential role in the aberrant epithelial-fibroblast crosstalk in COPD. Eur. Respir. J. 49, 1602538 (2017). 13. Spanjer, A. I. R. et al. A pro-inflammatory role for the Frizzled-8 receptor in chronic bronchitis. Thorax 71, 312–322 (2016). 14. Van Raay, T. J., Coffey, R. J. & Solnica-Krezel, L. Zebrafish Naked1 and Naked2 antagonize both canonical and non-canonical Wnt signaling. Dev. Biol. 309, 151–168 (2007). 15. Mucenski, M. L. et al. β-Catenin regulates differentiation of respiratory epithelial cells in vivo. Am. J. Physiol. Cell. Mol. Physiol. 289, L971–L979 (2005). 16. Kumar, M. et al. Let-7 microRNA-mediated regulation of IL-13 and allergic airway inflammation. J. Allergy Clin. Immunol. 128, 1077–1085.e10 (2011). 17. Licona-Limón, P., Kim, L. K., Palm, N. W. & Flavell, R. A. TH2, allergy and group 2 innate lymphoid cells. Nat. Immunol. 14, 536–542 (2013). 18. Miotto, D. et al. Interleukin-13 and -4 expression in the central airways of smokers with chronic bronchitis. Eur. Respir. J. 22, 602–608 (2003). 19. Yu, H., Li, Q., Kolosov, V. P., Perelman, J. M. & Zhou, X. Interleukin-13 Induces Mucin 5AC Production Involving STAT6/SPDEF in Human Airway Epithelial Cells. Cell Commun. Adhes. 17, 83–92 (2010). 20. Park, K. et al. SPDEF regulates goblet cell hyperplasia in the airway epithelium. J. Clin. Invest. 117, 978–988 (2007).

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21. Bonser, L. & Erle, D. Airway Mucus and Asthma: The Role of MUC5AC and MUC5B. J. Clin. Med. 6, 112 (2017). 22. Levänen, B. et al. Altered microRNA profiles in bronchoalveolar lavage fluid exosomes in asthmatic patients. J. Allergy Clin. Immunol. 131, 18–23 (2013). 23. Martinez-anton, A. et al. Changes in microRNA and mRNA Expression with Differentiation of Human Bronchial Epithelial Cells. Am J Respir Cell Mol Biol 49, 384–395 (2013). 24. Xi, S. et al. Cigarette Smoke Induces C / EBP- b -Mediated Activation of miR-31 in Normal Human Respiratory Epithelia and Lung Cancer Cells. 5, (2010). 25. Aghasafari, P., George, U. & Pidaparti, R. A review of inflammatory mechanism in airway diseases. Inflamm. Res. 0, 0 (2018). 26. Jeffery, P. K. Remodeling and Inflammation of Bronchi in Asthma and Chronic Obstructive Pulmonary Disease. Proc. Am. Thorac. Soc. 1, 176–183 (2004). 27. Barnes, P. J. Inflammatory mechanisms in patients with chronic obstructive pulmonary disease. J. Allergy Clin. Immunol. 138, 16–27 (2016). 28. Hodge, S. J., Hodge, G. L., Holmes, M. & Reynolds, P. N. Flow cytometric characterization of cell populations in bronchoalveolar lavage and bronchial brushings from patients with chronic obstructive pulmonary disease. Cytom. Part B - Clin. Cytom. 61, 27–34 (2004). 29. Pottelberge, G. R. Van et al. MicroRNA Expression in Induced Sputum of Smokers and Patients with Chronic Obstructive Pulmonary Disease. doi:10.1164/rccm.201002-0304OC 30. Rossios, C. et al. Sputum transcriptomics reveal upregulation of IL-1 receptor family members in patients with severe asthma. J. Allergy Clin. Immunol. 141, 560–570 (2018). 31. Engels, B. M. & Hutvagner, G. Principles and effects of microRNA-mediated post-transcriptional gene regulation. Oncogene 25, 6163–6169 (2006). 32. Fricker, M., Deane, A. & Hansbro, P. M. Animal models of chronic obstructive pulmonary disease. Expert Opin. Drug Discov. 9, 629–645 (2014). 33. Chen, G. et al. Foxa3 Induces Goblet Cell Metaplasia and Inhibits Innate Antiviral Immunity. 189, 301–313 (2014). 34. Mushaben, E. M., Kramer, E. L., Brandt, E. B., Khurana Hershey, G. K. & Le Cras, T. D. Rapamycin Attenuates Airway Hyperreactivity, Goblet Cells, and IgE in Experimental Allergic Asthma. J. Immunol. 187, 5756–5763 (2011). 9 35. Zhen, G. et al. IL-13 and epidermal growth factor receptor have critical but distinct roles in epithelial cell mucin production. Am. J. Respir. Cell Mol. Biol. 36, 244–253 (2007). 36. Thomas, B. et al. Ciliary dysfunction and ultrastructural abnormalities are features of severe asthma. J. Allergy Clin. Immunol. 126, 722–729.e2 (2010). 37. Smaldone, G. C. et al. Regional Impairment of Mucociliary Clearance in Chronic Obstructive Pulmonary Disease. Chest 103, 1390–1396 (1993). 38. Hessel, J. et al. Intraflagellar transport gene expression associated with short cilia in smoking and COPD. PLoS One 9, (2014). 39. Song, J. et al. Aberrant DNA methylation and expression of SPDEF and FOXA2 in airway epithelium of patients with COPD. Clin. Epigenetics 9, 42 (2017).

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40. Marcet, B. et al. Control of vertebrate multiciliogenesis by miR-449 through direct repression of the Delta/ Notch pathway. Nat. Cell Biol. 13, 693–699 (2011). 41. Bonser, L. R., Zlock, L., Finkbeiner, W. & Erle, D. J. Epithelial tethering of MUC5AC-rich mucus impairs mucociliary transport in asthma. J. Clin. Invest. 126, 2367–2371 (2016). 42. Gehrig, S. et al. Lack of neutrophil elastase reduces inflammation, mucus hypersecretion, and emphysema, but not mucus obstruction, in mice with cystic fibrosislike lung disease. Am. J. Respir. Crit. Care Med. 189, 1082–1092 (2014). 43. Kim, K. C. et al. Human neutrophil elastase releases cell surface mucins from primary cultures of hamster tracheal epithelial cells. Proc. Natl. Acad. Sci. U. S. A. 84, 9304–9308 (1987). 44. Mall, M. A., Danahay, H. & Boucher, R. C. Emerging concepts and therapies for mucoobstructive lung disease. Ann. Am. Thorac. Soc. 15, S216–S226 (2018). 45. Innes, A. L. et al. Ex vivo sputum analysis reveals impairment of protease-dependent mucus degradation by plasma proteins in acute asthma. Am. J. Respir. Crit. Care Med. 180, 203–210 (2009). 46. Rogers, D. F. & Barnes, P. J. Treatment of airway mucus hypersecretion. Ann. Med. 38, 116–125 (2006). 47. Nakao, I. et al. Identification of Pendrin as a Common Mediator for Mucus Production in Bronchial Asthma and Chronic Obstructive Pulmonary Disease. J. Immunol. 180, 6262–6269 (2008).

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Dutch summary | Nederlandse samenvatting

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Nederlandse samenvatting

Chronische obstructieve longziekte (COPD) is een van de belangrijkste doodsoorzaken in de wereld en is een ziekte die vooral voorkomt op oudere leeftijd met als belangrijkste oorzaak blootstelling aan rook of toxische stoffen. Astma is de meest voorkomende chronische ziekte bij kinderen met elk jaar wereldwijd meer dan 400.000 sterfgevallen. Veel patiënten die lijden aan COPD en astma hebben last van chronische hoestklachten en overmatige productie van slijm in de luchtwegen. De combinatie van deze symptomen berust op zgn. chronische mucus hypersecretie (CMH). Zowel COPD als astma patiënten met CMH hebben een lagere kwaliteit van leven, meer moeite met ademhalen en een grotere kans op overlijden. Op dit moment bestaat er geen effectieve behandeling voor CMH en daarom is het van belang om meer inzicht te krijgen in de onderliggende mechanismen. De belangrijkste componenten van slijm in de luchtwegen zijn mucus eiwitten genaamd mucine type 5AC (MUC5AC) en mucine type 5B (MUC5B). De toename in slijm productie in de luchtwegen bij CMH kan veroorzaakt worden door 1) een toename in het aantal slijmproducerende cellen (slijmbekercellen) of een toegenomen slijmproductie per cel in de luchtweg zelf, 2) een toename in slijmproductie door slijmklieren, of 3) een defect in het afvoeren van overtollig slijm door een verminderde functie van de trilhaardragende cellen. De slijmproducerende en trilhaardragende cellen zijn onderdeel van het luchtwegepitheel, de bekleding van de luchtwegen. Dit luchtwegepitheel bestaat uit een laag van verschillende soorten epitheelcellen die de luchtwegen beschermen tegen invloeden van buitenaf. Het luchtwegepitheel kan communiceren met de onderliggende cellen in het bindweefsel van de luchtwegen, de stromale cellen, mogelijk doordat deze stromale cellen oplosbare factoren uitscheiden die de ontwikkeling en/of functie van het epitheel beïnvloeden. Wij veronderstellen dat deze stromale cellen een belangrijkere rol hebben in het aansturen van het luchtwegepitheel dan aanvankelijk gedacht, op basis van onze recente onderzoeksresultaten. MicroRNAs (miRNAs) zijn kleine RNA moleculen, die de productie/ aanwezigheid van eiwitten kunnen reguleren door specifiek te binden aan het erfelijk materiaal (de genen) die voor deze eiwitten coderen. Op deze manier kunnen miRNAs de aanwezigheid van heel veel eiwitten die aanwezig zijn in onze cellen en weefsels beïnvloeden, en spelen ze een belangrijke regulerende rol bij veel biologische en pathologische processen. In dit proefschrift is de hypothese getoetst dat miRNAs betrokken zijn bij CMH via 1) de regulering van het aantal slijmproducerende cellen en de mate van slijmproductie door deze cellen en 2) door het reguleren van de communicatie tussen de stromale cellen en de slijmproducerende cellen in het luchtwegepitheel.

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Om deze hypothese te toetsen hebben we onderzocht welke miRNAs betrokken zijn bij CMH in patiënten met COPD en astma door verschillen in de aanwezigheid van microRNAs te bepalen in luchtwegbiopten tussen patiënten met en zonder CMH. Verder, door gebruik te maken van kweekmodellen in het lab, is onderzocht wat de rol is van de stromale cellen bij de differentiatie van slijmproducerende epitheel cellen en de secretie van de MUC5AC en MUC5B eiwitten door deze cellen, en wat de rol is van miRNAs in de communicatie tussen stromale cellen en het luchtwegepitheel.

In hoofdstuk 2 is een literatuurstudie gedaan met betrekking tot de recente bevindingen over de rol van miRNAs in astma en de aanwezigheid van miRNAs in kleine partikels die uitgescheiden kunnen worden door het luchtwegepitheel, zogenaamde exosomen, en zo de functie van andere cellen kunnen beïnvloeden. In hoofdstuk 3 zijn 10 miRNAs geïdentificeerd in luchtwegbiopten die een sterk verband hebben met de aanwezigheid van CMH in patiënten met COPD. Van deze 10 miRNAs was de hoeveelheid van let-7a-5p, let-7d-5p, let-5f-5p, miR-31-5p en miR-708-5p hoger en de hoeveelheid van miR-134-5p, miR-146a-5p, miR-193a- 5p, miR-500a-3p en miR-1207-5p lager in de biopten van COPD patiënten met CMH vergeleken met de COPD patiënten zonder CMH. Door vervolgens te onderzoeken welke genen mogelijk beïnvloed worden door deze miRNAs is ontdekt dat miR- 134-5p, miR-146a-5p en de let-7-familie waarschijnlijk de belangrijkste rol spelen in de regulatie van CMH bij patiënten met COPD. Bovendien is ontdekt dat één van de manieren waarop deze miRNAs mogelijk bijdragen aan CMH de aanmaak van MUC5AC is. Als laatste is in gekweekte stromale cellen uit de luchtwegen van COPD patiënten met en zonder CMH bevestigd dat miR-134-5p verminderd aanwezig is in patiënten met CMH. Deze bevindingen ondersteunen onze hypothese dat miRNAs mogelijk betrokken zijn bij CMH via de interactie tussen stromale cellen en het luchtwegepitheel. Het volgende doel was om te bepalen of miRNAs ook betrokken zijn bij CMH in patiënten met astma en of er gemeenschappelijke oorzaken ten grondslag liggen aan CMH bij astma en COPD. In hoofdstuk 4 zijn 17 miRNAs geïdentificeerd die een verband vertoonden met CMH in luchtweg biopten van astma patiënten. Van deze miRNAs was miR-31-5p de enige die geassocieerd was met CMH in zowel 10 astma als COPD. In zowel astma als COPD patiënten vonden we dat dit miRNA geassocieerd was met 3 belangrijke genen die dit miRNA reguleert. Bovendien waren deze 3 genen ook geassocieerd met de aanwezigheid van CMH in patiënten met astma en COPD. Op basis van deze bevindingen is de conclusie dat miRNA-31 een gemeenschappelijke regulator is van CMH in astma en COPD. In hoofdstuk 5 is de invloed van stromale cellen uit de luchtwegen, luchtwegfibroblasten, op de ontwikkeling van slijmbekercellen en de slijmproductie door deze cellen onderzocht. Hiervoor is een kweekmodel ontwikkeld waarbij

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epitheelcellen uit konden groeien tot slijmbekercellen door ze bloot te stellen aan lucht. Tijdens deze ontwikkeling tot slijmbekercellen zijn de cellen 1 week samen met luchtwegfibroblasten gekweekt, terwijl de verschillende cellen van elkaar werden gescheiden door een doorlaatbaar membraan. Hierdoor kwamen de epitheelcellen in contact met oplosbare stoffen die uitgescheiden werden door de fibroblasten. De epitheelcellen ontwikkelden zich meer tot slijmbekercellen met meer MUC5AC en MUC5B productie in aanwezigheid van fibroblasten dan in afwezigheid van fibroblasten. Daarnaast zijn aanwijzingen gevonden dat met name het effectop MUC5B gereguleerd wordt door IL-6, een ontstekingsmediator die uitgescheiden wordt door de fibroblasten in ons model. In hoofdstuk 6 is vervolgens onderzocht welke van de miRNAs die betrokken zijn bij CMH in COPD mogelijk een rol spelen in de communicatie tussen stromale cellen en epitheelcellen en verantwoordelijk zouden kunnen zijn voor de toename in slijmbekercellen in ons model waarbij we epitheelcellen en fibroblasten 1 week samen kweekten. miR-146a-5p en let-7a-5p bleken de meest waarschijnlijke kandidaten die CMH kunnen reguleren via hun effecten op de luchtwegfibroblasten. In hoofdstuk 7 is de rol van miR-146a-5p onderzocht in de communicatie tussen luchtwegfibroblasten en het luchtwegepitheel. MiR-146a was 1 van de miRNAs die meer aanwezig was in luchtwegbiopten van COPD patiënten met CMH in vergelijking met patiënten zonder CMH. Bij deze studie hebben we ook gebruik gemaakt van een kweekmodel waarbij longfibroblasten samen met epitheelcellen gekweekt werden, maar nu voor een kortere periode, omdat we met name geïnteresseerd waren om te onderzoeken of miR-146 een rol heeft bij het onderdrukken van ontstekingsreacties. We hebben aangetoond dat de aanwezigheid van miRNA-146 sterk toenam in fibroblasten wanneer zij werden gekweekt in aanwezigheid van epitheelcellen, en dat deze toename minder sterk was in fibroblasten van COPD patiënten in vergelijking met controle fibroblasten. Bovendien vonden we dat deze toename van miR-146 in de fibroblasten gereguleerd werd door IL-1α, een ontstekingsmediator die uitgescheiden wordt door de epitheelcellen in ons model. Op basis van deze bevindingen veronderstellen we dat de toename van miR-146 functioneert als een beschermingsmechanisme tegen ontstekingsreacties die tot stand komen door communicatie tussen fibroblasten en beschadigd epitheel en dat dit mechanisme minder goed functioneert in patiënten met COPD, wat mogelijk leidt tot meer ontsteking. In hoofdstuk 8 is onderzocht hoe een ander type stromale cel, de gladde spiercel van de luchtwegen (GSC), bijdraagt aan CMH in astma. GSC cellen van astmapatiënten reageerden sterker op stimulatie met de ontstekingsmediator IL-1β en produceerden meer CCL20 dan controle GSC cellen. CCL20 is een ontstekingsmediator die de productie van mucus eiwitten kan stimuleren. Tegelijkertijd was de hoeveelheid miR- 146a lager in de astma GSC cellen in vergelijking met de control GSC cellen en lijkt dit microRNA ook in de GSC cellen een ontstekingsremmende rol te hebben.

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Concluderend zijn in dit proefschrift miRNAs en genen geïdentificeerd die mogelijk betrokken zijn bij CMH op basis van studies in luchtwegweefsel en cellen van patiënten met COPD en astma. Hierbij is gebruikgemaakt van relevante klinische gegevens wat betreft klachten van CMH en kweekmodellen van patiënt-afkomstige epitheelcellen en stromale cellen. Hierbij zijn miRNAs gevonden die specifiek een verband bleken te hebben met CMH in COPD of in astma, evenals 1 miRNA die bij beide een rol kan spelen. Een van de mogelijke mechanismen betrokken bij de ontwikkeling van CMH is de verminderde aanwezigheid van miR-146, een ontstekingsremmend miRNA. Onze kweekmodellen lieten zien dat dit miRNA een regulerende rol zou kunnen hebben bij CMH in zowel astma als COPD. Met deze bevindingen is meer inzicht verkregen in de moleculaire mechanismen die een rol spelen bij CMH in de context van zowel COPD als astma. Deze ontdekkingen met vertaling naar de kliniek leiden mogelijk tot de ontwikkeling van nieuwe therapeutische strategieën voor CMH.

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Acknowledgements | Dangwoord

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Acknowledgements | Dankwoord

“If you want to go fast, go alone. If you want to go far, go together.” - African proverb -

My PhD experience told me that the academic world is full of dilemmas. Often, we have to choose between speed vs. quality, staying safe vs. taking risks, collaboration vs. competition, oneself vs. others, etc. Deciding what has a higher priority at each moment is challenging, I believe, not only to me but to everyone. The last four years were not only an opportunity for me to learn new scientific skills, but also to observe how people in academia deal with these dilemmas. I was fortunate to be part of a highly collaborative environment in Groningen Research Institute of Asthma and COPD (GRIAC) and the U4 Ageing Lung consortium, allowing me to learn good examples from a number of collaborators and colleagues. Many times, I asked myself what I would have done if I were in their shoes; and when it came to my own decisions, I often used the benefit of hindsight to consider whether better choices could have been made. Taken together reflections of my own experiences and examples from others, both to which I agreed and disagreed, I slowly formed a concept of an ‘ideal scientific community’—which can be summarized in the African proverb above. A great scientific community, in my view, is not a world where everyone seeks personal successes but where we all work together to push—as far as possible—the boundaries of human knowledge. It is not about what we have achieved before we die, but how useful the things we leave behind to the next generations. When I just started my PhD, I always felt that everyone else was more capable and more knowledgeablethan I was. To them, I was likely one of the ‘next generations’ mentioned above. In this highly competitive world of academia, a lot of people have sacrificed parts of their valuable time to help me grow. Many did so without expecting anything in return. Here, I would like to express my gratitude to everyone who has contributed, knowingly or unknowingly, to my achievements today. The first two persons I would like to thank are my first supervisor,Prof. Irene H. Heijink, and my first co-supervisor, Dr. Corry-Anke Brandsma. Both of you were my daily supervisors throughout the past four years of my PhD. I remembered vividly when you two picked me up at the UMCG main entrance on 16 March 2015, the first day I arrived. I was amazed by how amiable and approachable you were. Throughout my PhD, you had shown me your patience and kindness in multiple occasions. We might not have agreed on every issue, but I never doubted your intention and I knew you always meant well to me—you wanted me to be successful. My favourite meeting with you was my annual review, where you always provided

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me helpful feedback on my performance and gave me tangible advice on how to improve myself. Over time, your willingness to listen helped growing our trust and understanding, which were important factors contributing to my positive experiences during this PhD. Irene, you are a role model in various aspects. As a group leader, you always tried to bring everyone together, listened to our problems and encouraged everyone to work towards solutions together. You paid attention to our group members not only on a professional level but also on a personal level (for instance, you knew I loved stroopwafels and found bitterballen quite ‘special’). Be it a lab meeting or a fun gathering outside work, you were almost always there with us. Regardless of how tight your schedule was, you always tried to be available for those who needed advice and remembered to give credits to those who deserve. You once made my day by passing me a big flower bouquet you received as a thank-you present from the ISCOMS organizer. When I asked if you didn’t like the flowers, you said you did but rather gave them to me since I was the one teaching the students. As a supervisor, you paid attention not only to my research data but also to my development as an independent researcher. Thanks to your previous guidance, I now can accept a journal’s invitation to review a manuscript independently with confidence. Your feedback on my writing was always in detail and, at the same time, triggered me to think deeper or broader. Often, you helped me see connections of the data I did not see. The question you often asked me “what do you think?” greatly helped training my scientific thought process and reminding me that one day I will have to make all decisions by myself. Of course, you did not leave me swimming alone hoping I would magically become independent. Amazingly, there were multiple occasions that you recognized I needed help and offered me your help even before I asked for it. Despite being extremely busy with many deadlines, you still sent me the information about the grant that I did not know of but might be helpful to my postdoctoral plan. The most torturous (yet most fun) moment I shared with you was during our five-hour bike trip in San Francisco. While we were cycling up countless of ‘mountains’ (at least I could not count how many there were) with the rest, you suddenly extended your hand to push me on the back just before I was going to give up. At that moment, I did not know whether to laugh or cry (‘laugh’ as I was happy feeling so touched by your kindness and amazed by your fitness, ‘cry’ as I was sad thinking my life might have been easier if I just got off my bike and walked instead). Looking back, I think this cycling trip is quite similar to my PhD journey: it was tough but fun, and you never left me alone 11 when I needed help most. Corry-Anke, I could not imagine going through this PhD without your support especially in the first two years and the last few weeks. Most of the times when I had urgent questions, you were the first person I ran to ask for advice. No matter how crazy or silly my questions were, you never once expressed your irritation

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and were always willing to provide me guidance. When I made mistakes, you never pushed me down but instead encouraged me to learn from them and think of how to avoid repeating them in the future. When I lost self-confidence (whether it was over an experiment, a presentation, writing, etc.), you often helped me restoring it. When it came to research planning, you taught me about priorities and focus. You reassured me that it was okay to take one step at a time and pick quality over quantity. When it came to writing, you taught me that good scientific articles do not have to be complicated; in fact, simplicity is often the best. The first manuscript was a huge challenge for me and I still remembered you spent a great amount of time giving me detailed suggestions on how to rewrite the discussion. I am glad you said my writing skills have improved and this thesis did not cause you as much headache. Occasionally, you have saved me from overthinking and taught me that certain things might not be as difficult as I thought—all I needed was to change my perspective. As a PhD student, working regularly with several principal investigators was quite a challenge and getting advice from multiple directions all at once often left me befuddled. Thus, when you recognized I was struggling trying to please everybody, you offered advice that helped me handling the situation more efficiently which I really appreciate. Stress is common during a PhD study, but I was lucky to have a caring and understanding supervisor as you showed me that you were there not to judge but to support. Your care also extended to my future after the PhD. Remembering my postdoctoral plan, you told me about the lecture you attended at a conference and advised me to contact the professor who gave that talk, later resulting in my decision to write grant proposals to join his lab. Apart from work, you sometimes also asked about my health, my family, or my home country. When I moved to a new house, you offered me a ride to buy new furniture to prepare for my housewarming. Although I rarely felt lonely living abroad, your kind actions had warmed my heart. The next person I would like to thank is Prof. Wim Timens, my second supervisor. Wim, I felt really fortunate to have you in my PhD promotor team. Your broad and deep knowledge as a pathologist and your long experience in the field had resulted in several helpful inputs for my project that I could not get from anywhere else. During my monthly meeting, you often reminded me not to get lost in small details but also look at a big picture and make sure our findings were relevant to the patients. Sometimes it was challenging to handle your critical questions but I never felt offended or discouraged by them. In contrast, I could feel that those inputs were given out of your good will and I always had fun thinking of how to best respond to them. Although you were one of the most senior principal investigators I worked with, both in terms of experiences and job appointments, I never felt intimidated while talking with you. I once heard that “a good leader is confident enough to be humble”, and I think your actions reflected this statement very well. Somehow, you made me feel comfortable asking you any question, telling you any of my viewpoint (even when

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it is different from yours), and knocking at your door without a prior appointment whenever I needed advice. The distance between you and us, PhD students, seemed quite far, yet you had a great understanding of the pressure and difficulties we were going through. With your empathy, I was allowed to focus my time and energy on getting each task done properly instead of wasting them on explaining why certain tasks could not be completed in a short period. By recognizing the difficulties we were facing, you also recognized the efforts we put in to overcome them. This recognition means a lot to us, PhD students, especially during stressful periods. Next, I would like to thank Dr. Maarten van den Berge, my second co- supervisor. Maarten, you made my PhD journey much more ‘colourful’. Your presence usually made our meetings more fun and more challenging in a way no one else could replace (and I mean it in a good way!). Often, I was baffled by how you seemed lost during our discussion but suddenly pointed out an important issue the rest of us had overlooked. Many questions you asked trained me to communicate more effectively and straight to the point as well as to think more clearly about the rationale of my study or the question I was trying to answer. You taught me that clearer communication is a result of clearer thinking. Furthermore, you were my role model in being practical. While discussing the project plan, you helped me distinguish what really mattered and what did not. I always felt free to be frank with you while discussing any issue as I was confident that you were objective and open-minded and thus not easily offended. This was significant in my view as it saved us time (a precious resource) and freed me from unnecessary worries over our communication. In the beginning, I hesitated to go to your office to ask questions as you always seemed busy. Seeing me being so apologetic while seeking your advice outside our weekly meetings, you told me something I never forgot: “Hataitip, my [office] door will always be opened for you. Don’t be afraid to come.” When I made a progress that I thought was insignificant, you always encouraged me to see its value and told me that I should be proud of it. Aside from your intellectual input, I also appreciate your humour. Once, I gave you a pack of dried seaweeds from Thailand as I thought it was a ‘healthier’ choice than the sweets I gave to others; you told me how much you appreciated it and that you would “never forget how it tastes”. My assumption is that there is a negative correlation between delicious food and healthy ones (the better the food tastes, the less healthy it is), so I hope the taste of the seaweeds represented how much I cared about your health. I would also like to express my gratitude to my PhD thesis assessment 11 committee, Prof. Ken Bracke, Prof. Gerard H. Koppelman, and Prof. Anke van den Berg, for their careful evaluation and approval of my thesis. Ken, thank you for always being so kind to me whenever we met. Initially, I was a bit nervous to visit your lab at Ghent University Hospital alone during my first year, but your warm hospitality and your kind support helped making that nervous feeling gone in a short

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time. Despite the negative results of our analyses, I learned a lot from that ten days in Ghent and it was one of the most memorable experiences I had during this PhD. Gerard, your presence at GRIAC meetings were always helpful to us, PhD students. Your lecture on Systems Biology was eye-opening and easy to follow. Your questions during scientific discussion trained us to think critically and look at our research from different perspectives. Your advice on research careers and related skills (e.g. writing, presentation, etc.) helped us developed as a professional scientist. Thank you for always supporting the young generation of GRIAC and encouraging us to be proud of ourselves and keep improving. Anke, even though I was not working under your supervision, you were always willing to share your knowledge and experiences with me whenever I went to seek your advice. Your expertise in microRNA studies and your critical input during my presentation at our department meeting were very helpful for my PhD research. Your kindness extended to students outside your group, which I appreciate very much in the past four years. The next person I would like to thank is Dr. Alen Faiz. Alen, I cannot thank you enough for your help, particularly during my first and second years. I started this PhD with nearly zero knowledge in R and microarray data analysis. You spent a great amount of time teaching me the basics of bioinformatics analyses and how to use R as well as other open source tools. Your enthusiasm in science was remarkable and you were a rare example of a scientist who could bridge both computational and experimental research together. When I faced difficult technical problems in the lab, you were often the one who helped me figure out the solution. When hearing I had a tough meeting at which you were not present, you came to my office specially to give me encouragement and support. Not only at UMCG, but you were also helpful to me outside work. After going out with the EXPIRE lab members late at night (I was still new to Groningen at that time), you cycled with me till I almost reached home to ensure I did not get lost along the way. Last summer, you taught me how to canoe at Paterswoldsemeer (that was the second time I rowed a boat in my life). After getting hit accidentally(?) by your paddle a few times, my rowing skill had improved significantly. I also remembered you were very good at mud walking. When I fell on the mud during our mud walk, you kindly came to support me to get up (though only after laughing with the rest for a few seconds). I cannot help but feel amazed by your various skills both in science and recreational activities. I think that was impressive and made it fun to have you as a colleague and a friend. Next, I would like to thank Dr. Machteld N Hylkema. Although you were not my direct supervisor, you always tried to be at every monthly meeting to support me and give input on my project progress. You were also very supportive and very approachable outside the meetings. Whenever I had questions, you never hesitated to give me advice. When I had a request, you always responded positively. Throughout the past four years, I very appreciate your encouragement and generosity. Once,

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during our meeting where we discussed the challenges I was facing, you told me something I would never forget: “we want to help you, but you must give us the opportunity”. This statement had woken me up. Suddenly, I understood that there might be many people who were willing to help us but did not have the chance to do so because we never told them about our problems. Thus, I must not keep waiting passively; but instead learn to ‘open the door’, allowing others to help. Apart from getting a PhD, another dream I had while staying in the Netherlands was to go sailing for the first time in my life and you helped making it come true. Thank youfor offering me a chance to go sailing on your boat. I am also thankful for Jan’s kindness while showing me how to sail and sharing with me his knowledge and experiences on sailing. I would also like to express my gratitude to Prof. Dirkje S. Postma. Although I only had a chance to work with you during my first year before you retired, it was an invaluable experience. My impression is that you are very critical and strict, yet very warm-hearted and compassionate at the same time—a rare balance that very few supervisors managed to find. It was always helpful to have you joined my monthly project meeting. Somehow, you could give very critical comments without causing the other party to feel pain. In contrast, your presence made the meeting more fun and livelier. After your retirement, I always felt delighted whenever we met again and you always greeted me with a broad, warm smile. To me, you are an inspiration and a role model. I wish you healthy and happy days ahead and hope I will still get to meet you again somewhere in the world after my PhD. I would like to thank Prof. Reinoud Gosens. At first, I hesitated to approach you as you were not my direct supervisor and you often seemed very busy. Later, I realized that you were very approachable and willing to help despite your tight schedule. Whenever I asked for help, you always responded positively. If you could not help directly, you would ask someone in your group to help me out without hesitation. Your support meant a lot to my PhD project, especially concerning cell culture experiments. Besides, your feedback on my first manuscript helped me interpret the data in a way I never thought of, resulting in significant improvement of the manuscript. I also appreciate your advice concerning the careers after PhD and your general support to GRIAC members, especially the PhD students. Next, I would like to thank all collaborators and members of the U4 Ageing Lung consortium. Prof. Guy Brusselle, thank you for setting aside your precious time to discuss my research plan and data during my visit at your research group at Ghent University Hospital four years ago. As I went there alone without any supervisor or 11 colleague from Groningen, your support meant a lot to me. I also appreciate your positive encouragement whenever I gave a presentation at the U4 meetings. It was always motivating to observe your remarkable passion for science and your enthusiasm to initiate or maintain fruitful collaborations among our universities. In the future, I hope I would become a scientist who is able to keep bringing positive impacts to the

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organization or community like how you have been doing. Prof. Tania Maes, thank you for your warm hospitality when I visited your lab in Ghent and your kind advice during the U4 meetings. Your inputs on my project were valuable and helped me see my data from a different perspective. Whenever we met, you were always willing to share your insight which helped broadening my knowledge especially on microRNAs and asthma. Dr. Leen Seys and Francisco Avila Cobos, thank you so much for your help in preparing the dataset and running the analyses on microarray data during my 10-day visit at your institute. I also enjoyed fun moments we spent together on a rainy day in Ghent city. Dr. Muriel Lizé and Dr. Merit Wildung, thank you for your hard work on our joint manuscript focusing on miR-34/449 family. I learned a lot from our collaboration, and it was a pleasure to work with you both. Dr. Lies Lahousse, I always appreciate your kind treatment whenever we met and greatly admire your enthusiasm to do research and to support the collaborations among the U4 members. Prof. Marike Boezen, I heard getting the university’s funding for this PhD position was a long journey and believe this achievement was largely attributed to your effort. I am grateful for your hard work which opened up an opportunity for me to pursue a PhD here in Groningen. I also appreciate your advice and humour during our long rides to Schiphol airport or to the U4 meetings overseas. Finally, I would also like to thank the rest of the U4 Ageing Lung consortium members for all the lively conversations we had during our annual meetings and all collaborations happening throughout the past years. Jacobien Noordhoek, I could not finish this PhD without thanking you for all your help and advice. I started working in the lab with little experience; you patiently taught me air-liquid interface culture and several technical tips that I was not aware of. You were a great example of how a researcher should plan and conduct experiments. You trained me to think ahead, keep good records, stick to protocols, and be aware of the underlying reasons why we do something in a particular way. I also appreciate your good will and fair treatment towards everyone in the group and cannot imagine how our labs will be like without your contribution. Marjan Reinders-Luinge, thank you for your help especially towards the end of my PhD research. It would be hard for me to complete my thesis in time without your help in the IHC staining on transwell inserts. Despite the challenges and sometimes negative results we faced, you did not give up but continued trying to make it work. I really appreciate your time, your effort, as well as your smile and laugh, especially during a very busy period near the end of my last year. I would also like to thank Dr. Emmanuel Osei. Emmanuel, most of our interactions happened online as I started my PhD when you were about to move to Vancouver, but it did not seem to be an obstacle for us. As the focus of our projects are overlapping, I often texted or emailed you to ask for advice and you always responded with enthusiasm and in detail, which I really appreciate. When we had a chance to meet, it was always fun (and funny) to hang out with you. Although it is

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unrealistic to expect the road in academia to be a smooth one, I hope you occasionally find roses along the way and never lose your confidence and optimism no matter what challenges you encounter in the future. Next, I would like to thank Dr. Ilse M. Boudewijn. Ilse, it was a pleasant experience to work with you even just for a short time. You were very organized and very good at communication which made working with you both enjoyable and efficient. To my impression, you are one of the most ideal co-workers one could wish for. Marijn Berg, we got to collaborate scientifically near the end of my PhD when you helped me with the analysis on the single-cell datasets, which I really appreciate. Before that, however, I am also thankful for your tips when I encountered problems about R. Besides work, I very much enjoyed having you as a companion during many fun activities, e.g. mud walk, canoeing, and most often, playing Go. Next, I am thankful for Prof. Peter J. Sterk, Prof. Pieter S Hiemstra, Prof. Avrum Spira, Dr Michele A. Grimbaldeston, Gaik W. Tew, and Dr. Nick H. T. ten Hacken for your contribution to my manuscripts, both directly and indirectly, as well as your kind and helpful feedback on my drafts before the submission. I would also like to thank Prof. Janette Burgess and Dr. Martijn Nawijn in the EXPIRE group. Janette, I am grateful for your advice not only concerning my research but also my future career, particularly in the Asia-Pacific region. I also remembered you spent a long time giving me feedback on how to write a good CV, which was eye-opening to me. When there was a review manuscript relevant to my research, you gave me an opportunity to critically review it and provided me constructive feedback afterwards. Admirably, I feel that you treat other PhD students as kindly as your own supervisees, and I know of some other PhD students who feel this way as well. Martijn, although you were not directly involved in my project, you had always been giving me helpful feedback both during and outside the meetings, which helped sharpening my critical thinking and broadening my perspectives. Despite your limited time and even without a prior appointment, you welcomed my questions and provided help, e.g. allow me to use your datasets, without hesitation. As a scientist and a supervisor, I very much admire your broad-mindedness, objectivity, and creativity, which were shown through your lectures, scientific discussion, as well as your treatment to students. I am also thankful for other colleagues in the EXPIRE group. Marlies Ketelaar, you are an amazing friend and colleague. Your hard work and kindness 11 are unparalleled (seriously). At work, you often asked good questions, gave helpful suggestions, and were always willing to do others a favour. Outside work, you introduced me to many fun activities I had never done before. You will be one of the people I miss the most after leaving the Netherlands. Dennis Kruk, I always enjoyed our conversations no matter on which topics. I was amazed by your broad

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knowledge which was likely a result of your high curiosity in science and the world. Often, the ideas you expressed challenged my traditional thoughts, which I found useful for developing my understanding about myself and others. I wish you the best for finishing up your PhD this year and hope you will find a happy path to continue your journey afterwards. Mirjam Roffel, it was impressive to see how you could get along well with anyone from any research group, and I too enjoyed talking, working, and hanging out with you. In the lab, you were a very helpful and well-organized colleague. Outside work, you were a fun and kind friend. I hope you will enjoy your two-year stay in Belgium and let us meet again in Thailand or Singapore someday. Marnix Jonker, thanks for taking care of my experiments when I was not around and for always ensuring me it was okay to ask for help. Djoke van Gosliga, thanks for always being helpful to me in the lab, for sharing your insight about local traditions or local festivals, and for giving me ‘lekker’ sugar bread from Friesland. Marissa Wisman, thanks for taking care of my cells when I was not around and teaching me Western Blot. You were a very active, fun, and helpful colleague, and I believe having you joined our group had made us better. Dr. Daan Pouwels, you were not only a very capable scientist but also a very helpful colleague. Whenever I sent you an email to ask questions, you usually responded quickly with clear answers. I admire your logical thinking and frank opinions whenever we discussed science; however, I still do not understand why you like boiled potatoes so much. Virinchi Kuchibhotla, thanks for all the (philosophical) conversations we had when you were in the Netherlands and for allowing me to be reminded of how wonderful ‘Life of Pi’ is. On a side note, I look forward to the day you change your view about relationship and marriage! Qing Chen, I always had fun going out with you and I am glad we decided to go to Santorini together. Do not be too sad that many of us are leaving; changes in our life look scary at first but they allow new positive things to happen to us if we keep our eyes open. Harold de Bruin, I am sure you now enjoy being a daddy although that probably causes you some sleepless nights. Thank you for helping me out in the lab and also for chicken eggs from your home which reminded me of my parents’ farm where I grew up. Uilke Brouwer, you will also become a daddy soon and I am sure you will be a wonderful one. I really appreciate your humour and optimism which often helped lighten the (sometimes heavy) atmosphere in the lab. Sharon Brouwer, it was fun having you as a colleague. Thanks for offering help and reminding me many times that I could always ask you questions when in doubt of anything (even without Snickers!). I hope someday you will get to try authentic Thai food in Thailand. Dr. Jian Jiang, I am glad we got to know each other although it was only for a short time. You reminded me that friendship is not about a complete understanding of what the other had gone through, but it is about trust that the other always means well to us and wishes to see us happy. I hope you always find a peace of mind no matter where you are in the world. Laura Hesse, you always gave me a cheerful greeting and asked

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how I was doing every time we met. You are one of a few people whom I never felt intimidated or scared to ask for advice whenever I had questions. Kirsten Muizer, we got to hang out together a lot despite a short period you joined our group. You were fun, kind, and brave. I hope you will soon discover the path that best fits with your dreams and values. To all current and previous members of the EXPIRE group, thanks for always answering my questions in the labs (or offices) and for all the fun times we spent together outside work. You guys had introduced me to another world I had not experienced back in Thailand and you guys played a significant role in making the last four years one of the most memorable periods of my life. Likewise, I would like to thank my colleagues in the Lung Pathology group. Wierd Kooistra, thank you for always helping me out in the labs especially when it came to cell culture and PCR. Whenever I had questions, you were always willing to give me advice and you did so as quick as possible. I also enjoyed the times when we shared our experiences and exchanged our opinions about science and life in general. Roy Woldhuis, although you were not a talkative person, you were always responsive when someone asked for help. Impressively, no matter what situation you were in, you always appeared to be calm as if everything was never out of your control. I wish you the best for finishing up your PhD next year and I believe you have a bright future waiting ahead. Khosbayar Lkhagvadorj and Zhijun Zeng, you guys were always sincere and polite. I enjoyed the past few years that we shared the same office and appreciate your suggestions whenever I asked for your opinion on my work or sought your advice in troubleshooting some computer’s problems. I wish you a lot of strength and positive data to finish up your PhD within your planned timeframe. Dr. Sabine Bartel, it was only a short while that you joined our group and office, but you had already been very helpful to me. Thank you for sharing with me your knowledge and frank perspectives both when it came to issues about my research and my future career. Minghui Li, your PhD journey just started not long ago and I am sure there are many challenges waiting ahead. One advice from others that I would like to pass to you is “do not be afraid to ask for help”. Besides, don’t forget to go out of your comfort zone whenever you can; I am sure it will pay off in a long run. I would like to express my special thanks to Jennie Ong and Roderick de Hilster for agreeing to become paranimfen for my promotion. Jen, it might be the fact that we started our PhD approximately at the same time, we both worked on microRNAs, both liked to stay in office until late in the evening, both are more like a listener than a talker, or any other reasons that helped forming our profound 11 friendship. No matter what led us here, I believe this friendship will last a lifetime. If you ever doubt your own value, I hope you will remember that you are a very special person. In the world where most people compete and focus on their own interest, you always put other people’s interest before your own. If everyone has 1/10 of your kindness, this planet would have been a much happier place. But only those

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who survive can continue helping others; I hope you will soon manage to find that healthy balance (for instance, by giving yourself a priority more often). No matter where you decide to settle down in the future, I wish you a fulfilling life and a happy family. Roderick, your friendly and outgoing personality made it easy for anyone to be friend with you in a short period of time. You might sometimes be worried that you were too talkative, but in fact, I appreciate all the conversations we had together and never once thought they were wasteful. Often, talking with you helped me reflect on what I had done and what I truly wanted from being a PhD student. I felt that I could share my sincere thoughts with you without any worry. I also very much appreciate the kindness you showed me even when you just recently joined our office. Once, you helped me took my bike to a repair shop when I (thought I) had lost my bike key. You also volunteered to accompany me to search for a location for my promotion party even though you were already quite busy. As you still have a long way to go before finishing your PhD, I wish you enjoy this road and embrace any challenge that appears with strong faith in your own capabilities and purposes. Next, I would like to thank my previous officemates. Dr. Maaike de Vries, you were of a great help to me when I started this PhD. As I was unfamiliar with the system and the culture of this workplace, you paid attention to how I was doing and offered help when I needed. I also appreciate that you often kindly offered me a ride when we were heading to the same meeting or event, as well as when I moved to a new house. Further, I would like to thank Dr. Karolin Meyer. Karo, I felt a huge change in our office atmosphere when you left, most likely because we often accompanied each other in late evenings especially during my second-third year. Whether it was a conversation about science or life in general, you were a rare partner who could help sharpening and broadening my perception. In my view, you were a very capable, persistent, and considerate person. No matter what had happened in the past and what will happen in the future, I have full faith in your capabilities and your values. Dr. Juan Song, thank you for always giving me advice both when we were still sharing the office and when you had already moved to Belgium. You were always responsive and clear in your answers when I sent you emails. Besides, I also appreciate the RNA samples and experimental data you passed to me which were very helpful for my research. Dr. Nato Teteloshvili and Dr. Rae Wu, although we were not working in the same research group, you two often gave me good advice and always treated me kindly, allowing me to be able to adapt to this new workplace in a short period of time. I would also like to thank my hard-working students who did master’s internships with me, Joy van Broekhuizen and Yovita Ariela. I appreciate your diligence, your perseverance, and your willingness to learn. No matter which career you choose after finishing your study, I wish you a great success and happiness in whatever you are doing.

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Next, I would like to thank Prof. Barbro Melgert. Your inputs during our research meetings, both when I and other students presented, were always helpful and often triggered me to think from a different perspective. In my view, you were a passionate and creative supervisor who was willing to think out of the box, and you were very supportive of all students. In addition, I would also like to thank all participants of the Lung Pathology meeting (10:30 AM Monday) for your questions and feedback during our discussion. I would like to specially mention Laura Florez-Sampedro, with whom I co-authored one of the manuscripts. Through your presentations and involvement in the discussion, I could sense your passion for science and your hard work which I admire very much. I wish you, as well as other group members who are doing PhD, a lot of joy and success finishing up your theses. I would like to thank all GRIAC principal investigators for your kind advice and feedback that helped grooming the young generations of our institute. In particular, I would like to express my gratitude to Dr. Judith Vonk for your precious advice concerning statistical analyses of my experimental data as well as the name lists of current GRIAC members and collaborators. Furthermore, I would also like to thank Dr. Anita Spanjer for providing me your insight on mucin study in air-liquid interface culture as well as your experimental data and RNA samples that were helpful for the start of my research, thank Dr. Loes Kistemaker for your valuable advice and support for my air-liquid interface culture, and thank Xinhui Wu, Mariska van den Berg and Pien Goldsteen for your warm friendship and the fun times we spent together in Berlin. I would also like thank Dr. Corneel Vermeulen specially for your help with the deposition of the RNA-sequencing dataset that was required for my manuscript. Lastly, I would like to thank all GRIAC PhD students, whose names I could not mention all, for your active participation in our regular meetings and your feedback that helped improving my presentations, my posters, and my abstracts. I would like to thank my neighbours at Pathology, particularly Dr. Joost Kluiver who was always willing to give me advice whenever I asked for your insight. Thanks, Dr. Lydia Visser, Marianne van der Bulthuis-Horst, Bea Rutgers, and all other staff members working in Pathology labs, for your general support and kind advice on any unknown I encountered. Thanks, Reeny Razak, Mathilde de Jong, Melanie Winkle, Yuan Ye, Fubiao Nui, Myra Langendonk, Jiacong Wei, and all the MicroRNA group members, for your kindness during work and fun evenings we occasionally spent together. I would also like to thank our secretaries, Annet Bouman-van der Jagt, Marijke Grimberg, Ellen Kuijpers, and Janine Boxem, 11 for all your kind assistance with administrative tasks which made my life at UMCG much easier. Likewise, I would like to thank my neighbours at Medical Biology, including PhD students, technicians, and all other staff members for your friendliness and fun moments we spent together in the coffee room or during the lab days. I would

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like to express my special thanks to Martin Beukema for many interesting (and occasionally humourous) conversations as well as the canoeing trip in Giethoorn, to Dandan Wu and Chengcheng Ren for a very nice home-cooked Chinese meal and your sincere friendship, to Pochanart Kanjan for the lovely times we spent talking about our home country, and to Désirée Goubert, Susanne Phi, Marloes Sol, Renate Akkerman, as well as other MB colleagues for your warm greetings whenever we met at the corridor. Then I would like to express my special thanks to Dr. Emine Cilek. Emine, you were here as a visiting PhD student and it was incredible how we became friends in just a short period of time. I still remembered the first time we met—while we were doing a DNA lab cleaning. After that, we started to realize that we shared many common interests (e.g. fictions, societal issues, humanities, etc.) and felt like we spoke the same language (even though Thai and Turkish are very distinct from each other). You are a very good listener and a compassionate friend. I hope this world will treat you as kindly as how you treat others. I could not end this section without mentioning all the research funds that contributed to this thesis. I am thankful for the University of Groningen, Stichting Astma Bestrijding (SAB), and de Cock foundation for funding my PhD research at UMCG. I am thankful for the European Cooperation in Science and Technology (COST) for funding my Short-Term Scientific Project at Ghent University Hospital. Importantly, I am also thankful for patients and general public who care to support medical research in any way you are able to, which reminds us—researchers—to do our best to deliver outcomes that are meaningful to your lives. I would also like to thank the Royal Government of Thailand for its full scholarships under the Development and Promotion of Science and Technology Talents (DPST) Project, which supported my bachelor’s study in Thailand and my master’s study in the UK. This earlier education led me to where I am today. I am also thankful for the opportunity to join the Global Undergraduate Exchange Program (UGRAD)—supported by the US Department of State and partly facilitated by Fulbright Thailand—which allowed me to study abroad (in this case, in the USA) for the first time, as an exchange student. It was an eye-opening experience and a big stepping stone that helped introduce me to the international scientific community later on. Last but not least, I would like to thank my family members and my fiancé for your support and patience while waiting for me to return home. During the past five years, we met each other less than five times. I always felt that it was tougher for you than it was for me. Mom, it was a pity I could only spend my first 13 years with you before you left this world, but I am very grateful for the love and effort you put in to take care of me during the most vulnerable period of my life. When you were ill and I was a teenager, I did not know how important it was to show you my

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gratitude. Thus, I dedicated this thesis to you, hoping to make up for my mistake that I regret till today. Dad, thank you for taking the best care of me when mom was no longer around regardless of how exhausted you were. You might not be the best man on earth, but you have been giving me the best of everything you had. Without you, I would not have been where I am today. To my step-mom, my brothers, my step- brother and step-sister, thank you for taking care of each other all these times when I was living more than 12 hours away from home by plane. Your well-being was extremely important to me as it allowed me to be able to focus on finishing my PhD without too much worry. Soon, I will go back to be near you all again. It took me only four years to complete this PhD thesis, but it took more than 20 years before I started it. Any achievement I made today is a result of several people’s effort, directly or indirectly. It may now look like a happy ending, but I know this is merely the beginning of the next episode of life. I will continue improving myself, so that I will be able to make a difference to the lives of others, just like how you all were able to make a difference to my life.

Hataitip Tasena

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Curriculum vitae and list of publications

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Curriculum vitae

Hataitip Tasena was born on 23 April 1989 in Chiang Rai, the northernmost province of Thailand. In 2007, she started her undergraduate study in Biology at Chiang Mai University with a full scholarship from the Royal Thai government. In 2011, she went to University of Wyoming, USA, for a 5-month exchange study fully sponsored by the US Department of State. She then received another Royal Thai government scholarship to complete her master’s study in Molecular Medicine at University of Sheffield, UK. In 2015, she came to Groningen to start her PhD research on molecular mechanisms related to chronic mucus hypersecretion in respiratory diseases, which was part of the U4 Ageing lung consortium involving four universities in Europe (Ghent university, Uppsala university, University of Gottingen, and University of Groningen). During her PhD, she also visited Ghent University Hospital for research collaboration, supervised two master’s theses, coached undergraduate assignments, presented her work at national and international conferences in respiratory fields, coordinated journal clubs for PhD students in GRIAC research institute, as well as volunteered to organize the PhD Day (a career conference sponsored by University of Groningen and opened to PhD students from all disciplines). Throughout her four years in Groningen, Hataitip had demonstrated that her passions lie not only in molecular medicine research, but also in education, public communication, and community services.

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Grants and awards

2017 ERS Young Scientist Sponsorship [€600] a travel grant for attending the ERS Congress, Italy

2016 Dutch Asthma Foundation Research Grant [€24,500] a grant for asthma and COPD research in the Netherlands

2016 De Cock Foundation Research Grant [€4,500] a grant for medical research lead by young scientists in Groningen

2015 Short-Term Scientific Mission [€1,540] a grant from the European Cooperation in Science and Technology for a 10-day visit at the Laboratory for Translational Research of Obstructive Pulmonary Diseases, Department of Respiratory Medicine, Ghent University Hospital, Belgium

2015 Young Investigator Symposium Best Presentation Award [€500] a prize offered by Netherlands Respiratory Society (NRS) for the best pitch talk

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List of publications

• van den Berge M, Tasena H. Role of microRNAs and exosomes in asthma. Curr Opin Pulm Med. 2019;25(1):87-93. • Tasena H, Faiz A, Timens W, Noordhoek J, Hylkema MN, Gosens R, Hiemstra PS, Spira A, Postma DS, van den Berge M, Heijink IH, Brandsma CA. MicroRNA- mRNA regulatory networks underlying chronic mucus hypersecretion in COPD. Eur Respir J. 2018;52:1701556. • Faiz A, Weckmann M, Tasena H, Vermeulen CJ, van den Berge M, ten Hacken NHT, Halayko AJ, Ward JPT, Lee TH, Tjin G, Black JL, Xu CJ, King GG, Farah C, Oliver BG, Heijink IH, Burgess JK. Profiling of healthy and asthmatic airway smooth muscle cells following IL-1β treatment: a novel role for CCL20 in chronic mucus hyper-secretion. Eur Respir J. 2018;52:1800310. • Osei ET, Florez-Sampedro L, Tasena H, Faiz A, Noordhoek JA, Timens W, Postma DS, Hackett TL, Heijink IH, Brandsma CA. miR-146a-5p plays an essential role in the aberrant epithelial-fibroblast cross-talk in COPD. Eur Respir J. 2017;49(5):1602538. • Lambert DW, Tasena H, Speight PM. “MicroRNA: Utility as Biomarkers and Therapeutic Targets in Squamous Cell Carcinoma”. In: Warnakulasuriya S., Khan Z. (eds) Squamous cell Carcinoma. Dordrecht: Springer, 2017. 205-215. Print. • Kabir TD, Leigh RJ, Tasena H, Mellone M, Coletta RD, Parkinson EK, Prime SS, Thomas GJ, Paterson IC, Zhou D, McCall J, Speight PM, Lambert DW. A miR-335/COX-2/PTEN axis regulates the secretory phenotype of senescent cancer-associated fibroblasts. Ageing. 2016;8(8):1608-1635.

Submitted manuscripts

• Tasena H, Boudewijn IM, Faiz A, Timens W, Hylkema MN, Berg M, Heijink IH, Brandsma CA, van den Berge M. miR-31-5p: a shared regulator of chronic mucus hypersecretion in both asthma and COPD. • Tasena H, Faiz A, Timens W, Hylkema MN, Gosens R, van den Berge M, Heijink IH, Brandsma CA. Influence of fibroblast-epithelial cell crosstalk on epithelial mucociliary differentiation. • Wildung M, Herr C, Riedel D, Cors M, Tasena H, Alevra M, Wiedwald C, Movsisyan N, Schuldt M, Volceanov L, Provoost S, Menchen T, Urrego D, Freytag B, Wallmeier J, Bals R, Beisswenger C, Maes T, van den Berge M, Brandsma CA, Heijink IH, Pardo L, Lizè M. miR449 protects multiciliated bronchial epithelial regeneration by repressing the ciliary disassembly pathway.

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