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Profiling the early molecular response of the human bronchial epithelium to Aspergillus fumigatus using… Toor, Amreen 2018

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PROFILING THE EARLY MOLECULAR RESPONSE OF THE HUMAN BRONCHIAL EPITHELIUM TO ASPERGILLUS FUMIGATUS USING A MULTI-OMICS APPROACH  by  AMREEN TOOR  B.Sc., Western Washington University, December 2014    A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF  THE REQUIREMENTS FOR THE DEGREE OF  MASTER OF SCIENCE   in   THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES   (Experimental Medicine)    THE UNIVERSITY OF BRITISH COLUMBIA  (Vancouver)    August 2018   ã Amreen Toor, 2018      ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, a thesis/dissertation entitled:  Profiling the early molecular response of the human bronchial epithelium to Aspergillus fumigatus using a multi-omics approach  submitted by Amreen Toor  in partial fulfillment of the requirements for the degree of Master of Science in Experimental Medicine  Examining Committee: Dr. Scott Tebbutt Supervisor  Dr. Margo Moore Supervisory Committee Member  Dr. Delbert Dorscheid Supervisory Committee Member Dr. Pascal Lavoie Additional Examiner     Additional Supervisory Committee Members:  Supervisory Committee Member  Supervisory Committee Member     iii Abstract   Aspergillus fumigatus (A. fumigatus) is an opportunistic fungal pathogen that is widely distributed in nature through the release of conidiospores (conidia). Upon inhalation, fungal conidia (2-3 µm) are capable of reaching the bronchial and alveolar epithelia. This interaction between conidia and airway epithelial cells may result in the development of allergic, chronic or invasive aspergillosis in susceptible hosts. Characterization of the early molecular response of host using a multi-OMICs molecular approach is an important first step for better understanding the host-pathogen interaction.  The aim of my research was to investigate the early molecular response of host upon interaction with A. fumigatus using an in-vitro model that closely recapitulates the in-vivo bronchial epithelium, and assess the applicability of this model to study host-pathogen interactions. A multi-OMICs approach utilizing NanoString and shotgun proteomics was applied to primary human bronchial epithelial cells (HBECs) grown for 21-28 days as differentiated air-liquid interface (ALI) cultures. Comparative analyses were conducted to compare the gene expression profiles of ALI cultures to submerged monolayer cultures of human airway epithelial cell line (1HAEo-) upon conidial exposure. In addition, transcriptional profiles of ALI cultures upon exposure to wild-type (WT) conidia of A. fumigatus were compared to Kdnase mutant strain (Δkdnase) of A. fumigatus and to Respiratory Syncytial Virus (RSV).  Unlike submerged monolayer cultures, ALI cultures of primary HBECs internalized less than 1% of bound conidia 6 hours post-exposure. Transcriptomic and proteomic analyses of primary HBECs in ALI revealed that exposure to the fungus enriched the expression of genes related to cell cycle regulation, apoptosis/autophagy, iron homeostasis, calcium metabolism,  iv complement and coagulation cascades, endoplasmic stress and the unfolded protein response. Comparative analyses to submerged monolayer cultures of 1HAEs indicated that the host molecular response in each model is different. The immune response in differentiated ALI cultures upon exposure to Δkdnase A. fumigatus conidia and RSV was pathogen-specific. Hence, ALI cultures of primary HBECs can provide novel insights into the mechanisms involved in the early molecular response associated with this opportunistic fungal pathogen.    v Lay Summary Humans inhale more than one hundred conidiospores (spores) of the fungus Aspergillus fumigatus (A. fumigatus) every day. In healthy individuals, these are effectively cleared from the lung; however, in some individuals with immune defects, exposure of airway cells to these spores can result in a variety of diseases. The aim of this study was to better understand the early molecular response of airway cells when they are exposed to A. fumigatus spores. We used a cell culture model that closely mimics the structure of the cells in the airway and assessed the molecular response of these cells by measuring changes in RNA and protein expression after adding fungal spores. To evaluate the model, the molecular response was compared to a different cell-culture model, and RNA expression in the presence of A. fumigatus spores was compared to that of other pathogens. Genes and pathways were identified using this cell culture model.     vi Preface Chapter 3 and 4 contain material that is currently under preparation for submission. Luka Culibrk assisted in the ALIs experiment #1 (Chapter 3) and 1HAEs experiment (Chapter 4). Fresh conidia for these experiments was provided by Alison Hadwin in Dr. Margo Moore’s laboratory. Dimethyl labeling for shotgun proteomics experiments was performed by Dr. Leonard Foster’s group. Cells cultures of ALIs and 1HAEs for all experiments were provided by Dr. Gurpreet Singhera in Dr. Del Dorscheid’s laboratory. I conducted all the other experiments and did the corresponding data analyses.      vii Table of Contents Abstract ..................................................................................................................................... iii Lay Summary .............................................................................................................................. v Preface ...................................................................................................................................... vi Table of Contents ..................................................................................................................... vii List of Tables............................................................................................................................... x List of Figures ........................................................................................................................... xii Acknowledgments ................................................................................................................... xiv Chapter 1 Introduction ............................................................................................................... 1 1.1 Aspergillus fumigatus.................................................................................................................... 1 1.1.2 Virulence factors of A. fumigatus .......................................................................................................... 4 1.1.3 Cell wall of A. fumigatus ....................................................................................................................... 8 1.1.4 Human diseases caused by A. fumigatus ............................................................................................... 9 1.2 Airway Epithelium ....................................................................................................................... 13 1.2.1 Function of airway epithelium in aspergillosis ..................................................................................... 13 1.2.2 Pattern recognition receptors ............................................................................................................. 15 1.2.3 Host innate immune response to A. fumigatus .................................................................................... 19 1.2.3 Host adaptive response to A. fumigatus .............................................................................................. 22 1.3 Overview of experimental goals of the present research ............................................................ 25 1.4 Strengths and limitations of the chosen cell culture models ....................................................... 27 Chapter 2 Methods ................................................................................................................... 28 2.1 A. fumigatus strain and growth conditions ................................................................................. 28 2.2 Dkdnase A. fumigatus strain and growth conditions................................................................... 28 2.3 Overview of experiments ............................................................................................................ 29 2.4 ALI cultures of primary HBECs ..................................................................................................... 30 2.5 Submerged monolayer cultures of 1HAEs ................................................................................... 31 2.6 Visualizing interaction of A. fumigatus conidia with primary HBECs grown in ALI at 2, 6, 12 or 24 hours by confocal microscopy ........................................................................................................... 31 2.7 Visualizing interaction of A. fumigatus conidia with submerged monolayer cultures of 1HAEo- cells at 6 hours by confocal microscopy ............................................................................................ 33 2.8 DNA, RNA and protein preparation from ALI cultures ................................................................. 34 2.9 DNA, RNA and protein preparation from 1HAEs cultures............................................................ 35 2.10 NanoString nCounter RNA transcript expression analysis ......................................................... 36 2.10.1 nCounter Immune Profiling Panel ..................................................................................................... 36 2.10.2 nCounter Asthma Elements Panel ..................................................................................................... 37 2.11 Shotgun proteomics analysis using Liquid chromatography-tandem mass spectrometry (LC-MS/MS) of ALI and 1HAE cultures ..................................................................................................... 37  viii 2.12 Statistical analyses of RNA transcript abundance in ALIs and 1HAEs cultures ........................... 40 2.12.1 Pre-processing .................................................................................................................................. 40 2.12.2 Differential abundance analysis ........................................................................................................ 41 2.12.2.1 Differential abundance analyses results in Chapter 3 ...................................................................... 41 2.12.2.1 Differential abundance analysis results in Chapter 4 ....................................................................... 42 2.13 Statistical analysis of protein expression .................................................................................. 43 2.13.1 Pre-processing of ALI samples ........................................................................................................... 43 2.13.2 Pre-processing of 1HAEs cultures ...................................................................................................... 43 2.13.3 Differential abundance analyses ....................................................................................................... 44 2.14 Bioinformatics analysis ............................................................................................................. 44 Chapter 3 Host response to Aspergillus fumigatus conidia in an air-liquid interface model of human bronchial epithelium .................................................................................................... 45 3.1 Introduction ................................................................................................................................ 45 3.2 Overview of experimental design for transcriptomic and proteomic studies .............................. 48 3.3 Results ........................................................................................................................................ 50 3.3.1 Visualizing interaction of A. fumigatus conidia in well-differentiated ALI cultures of primary HBECs using confocal microscopy.................................................................................................................................... 50 3.3.2 Quantification and quality assessment of RNA samples ....................................................................... 53 3.3.3 Analysis of RNA transcript response to A. fumigatus ........................................................................... 53 3.3.4 Analysis of the proteomic response to A. fumigatus ............................................................................ 61 3.4 Discussion ................................................................................................................................... 68 3.4.1 Visualizing interaction of A. fumigatus conidia in well-differentiated ALI cultures of primary HBECs .... 69 3.4.2 Analysis of primary HBECs ALI cultures transcriptomics to A. fumigatus conidia .................................. 71 3.4.3 Analysis of primary HBECs ALI cultures proteomics to A. fumigatus conidia ......................................... 75 3.5 Summary ..................................................................................................................................... 79 Chapter 4 General applicability of ALI cultures for studying host-pathogen interactions at the molecular level ......................................................................................................................... 80 4.1 Introduction ................................................................................................................................ 80 4.2 Overview of experiment design for comparative transcriptomic and proteomic studies ............ 82 4.3 Results ........................................................................................................................................ 85 4.3.1 Visualizing interaction of A. fumigatus conidia in submerged monolayer cultures of 1HAEo- cells using confocal microscopy.................................................................................................................................... 85 4.3.2 Quantification and quality assessment of RNA samples ....................................................................... 87 4.3.3 Analysis of transcriptomic and proteomic response to A. fumigatus in submerged monolayer cultures of 1HAEs ......................................................................................................................................................... 88 4.3.4 Analysis of RNA transcripts in ALI cultures of primary HBECs upon exposure to Δkdnase A. fumigatus conidia ...................................................................................................................................................... 101 4.3.5 Analysis of RNA transcript abundance in ALI cultures of primary HBECs upon exposure to Δkdnase A. fumigatus conidia and WT A. fumigatus conidia for 6 hours ....................................................................... 106 4.3.6 Analysis of RNA transcript abundance in ALI cultures of primary HBECs upon exposure to RSV .......... 108 4.3.7 Analysis of RNA transcript abundance in high TEER and low TEER HBECs-ALI cultures ........................ 114 4.4 Discussion ................................................................................................................................. 114 4.4.1 Interaction of A. fumigatus conidia in submerged monolayer cultures of 1HAEs ................................ 115  ix 4.4.2 Analysis of submerged monolayer culture of 1HAEs upon exposure to A. fumigatus conidia after 6 hours ........................................................................................................................................................ 116 4.4.3 Analysis of pathogen-specific response in ALI cultures ...................................................................... 121 4.4.4 Analysis of high TEER and low TEER ALI cultures ................................................................................ 130 4.5 Summary ................................................................................................................................... 132 Chapter 5 General conclusions and future directions ............................................................. 134 References .............................................................................................................................. 138 Appendix 1: List of differentially abundant mRNA transcripts identified using Asthma Elements Panel in ALI cultures of primary HBECs upon exposure to A. fumigatus conidia ..... 164 Appendix 2: List of differentially abundant mRNA transcripts identified using Immune Profiling Panel in ALI cultures of primary HBECs upon exposure to A. fumigatus conidia ...... 165 Appendix 3: List of differentially abundant proteins identified using Immune Profiling Panel in ALI cultures of primary HBECs upon exposure to A. fumigatus conidia .................................. 167 Appendix 4: List of differentially abundant RNA transcripts identified using Asthma Elements Panels in submerged monolayer cultures of 1HAEs upon exposure to A. fumigatus conidia . 172 Appendix 5: List of differentially abundant RNA transcripts identified using Immune Profiling Panel in submerged monolayer cultures of 1HAEs upon exposure to A. fumigatus conidia ... 174 Appendix 6: List of differentially abundant proteins identified using LC-MS/MS in submerged monolayer cultures of 1HAEs upon exposure to A. fumigatus conidia ................................... 176 Appendix 7: List of differentially abundant RNA transcripts identified using Immune Profiling Panel in ALI cultures of primary HBECs upon exposure to Δkdnase A. fumigatus conidia ...... 178 Appendix 8: List of differentially abundant RNA transcripts identified using Immune Profiling Panel in ALI cultures of primary HBECs upon exposure to Δkdnase A. fumigatus conidia and WT A. fumigatus conidia ........................................................................................................ 180 Appendix 9: List of differentially abundant RNA transcripts identified using Immune Profiling Panel in ALI cultures of primary HBECs upon exposure to RSV ............................................... 181 Appendix 10: List of differentially abundant RNA transcripts identified using Immune Profiling Panel in high TEER and low TEER ALI cultures of primary HBECs ............................................ 184     x List of Tables Table 2.1 Confocal microscope settings for acquired images after 6 and 24 hours. ................... 33 Table 3.1: Trans-Epithelial Electrical Resistance (TEER) Values of 12 ALI cultures exposed to A. fumigatus conidia. .................................................................................................................... 53 Table 3.2: Top 5 networks identified using Ingenuity pathway analysis (IPA)............................. 64 Table 3.3: Enriched Reactome pathways for differentially abundant proteins identified using Enrichr. ..................................................................................................................................... 65 Table 3.4: Enriched Gene Ontology (GO) terms for 73 up-regulated differentially abundant proteins identified using Gene Ontology Consortium. (MF=Molecular Function; CC=Cellular Component; BP=Biological Processes) ....................................................................................... 67 Table 3.5: Enriched Gene Ontology (GO) terms for 80 down-regulated differentially abundant proteins identified using Gene Ontology Consortium. (MF=Molecular Function; CC=Cellular Component; BP=Biological Processes) ....................................................................................... 68 Table 4.1: RNA concentrations and RIN for control and infected 1HAE cultures. ....................... 87 Table 4.2: Trans-Epithelial Electrical Resistance (TEER) values, RNA concentrations and RIN for 12 ALI cultures in Experiment 2. ................................................................................................ 88 Table 4.3: Enriched Reactome pathways for 63 differentially abundant RNA transcripts identified in Asthma Elements Panel in 1HAEs upon exposure to A. fumigatus conidia, as identified by Enrichr. ................................................................................................................. 92 Table 4.4: RNA transcripts (6) that were differentially expressed in both HBECs-ALI cultures and 1HAE submerged monolayer cultures upon exposure to A. fumigatus conidia using the Immune Profiling Panel. .......................................................................................................................... 97 Table 4.5: Enriched Pathways for up-regulated proteins in 1HAEs upon exposure to A. fumigatus. ................................................................................................................................ 99 Table 4.6: Enriched Pathways for down-regulated proteins in 1HAEs upon exposure to A. fumigatus. .............................................................................................................................. 100 Table 4.7: 8 proteins overlapped between ALI cultures and 1HAEs submerged monolayer cultures upon exposure to A. fumigatus conidia. .................................................................... 101  xi Table 4.8: Overlapping RNA transcripts between 52 differentially abundant RNA transcripts of ∆kdnase A. fumigatus conidia infected ALI cultures and 41 differentially abundant RNA transcripts of WT A. fumigatus conidia infected ALI cultures................................................... 105 Table 4.9: 12 RNA transcripts were up-regulated upon exposure to ∆kdnase A. fumigatus conidia compared to WT A. fumigatus conidia in ALI cultures of primary HBECs. ................................ 108 Table 4.10: 5 RNA transcripts were down-regulated upon exposure to ∆kdnase A. fumigatus conidia compared to WT A. fumigatus conidia in ALI cultures of primary HBECs. .................... 108 Table 4.11: Enriched Reactome pathways for up-regulated RNA transcripts upon exposure to RSV in ALI cultures of primary HBECs. ..................................................................................... 112 Table 4.12: Enriched Reactome pathways for down-regulated RNA transcripts upon exposure to RSV in ALI cultures of primary HBECs. ..................................................................................... 113     xii List of Figures Figure 1.1 Sporulating structure of A. fumigatus in asexual reproduction. .................................. 2 Figure 1.2 Three processes of reproduction are recognized in A. fumigatus. ............................... 3 Figure 1.3 Chest computed tomography and brain magnetic resonance image showing invasive pulmonary aspergillosis. ........................................................................................................... 12 Figure 1.4 Inhalation of A. fumigatus conidia leads to initiation of immune response by lung epithelial cells and tissue-resident innate cells. ......................................................................... 20 Figure 1.5 Innate activation of T-helper responses to A. fumigatus. .......................................... 25 Figure 2.1 Overview of experiments. ......................................................................................... 30 Figure 3.1: Experimental design for transcriptomics and proteomics analyses. ......................... 49 Figure 3.2 Differential staining of extracellular and internalized conidia by anti-A. fumigatus antibody using confocal microscopy at 6 hours post-infection. ................................................. 51 Figure 3.3: Differential staining of extracellular and internalized conidia by anti-A. fumigatus antibody using confocal microscopy after 24 hours of co-incubation. ....................................... 52 Figure 3.4: Principal Component Analysis of 6 ALI samples from Experiment #1. ...................... 54 Figure 3.5: MA plot of RNA transcript analysis using NanoString’s Element’s Asthma Panel. ..... 55 Figure 3.6: PCA plot before and after batch correction of all samples. ...................................... 57 Figure 3.7: MA plot and pathway enrichment analysis of RNA transcripts differentially abundant in Immune Profiling Panel ......................................................................................................... 59 Figure 3.8: Gene ontology enrichment analysis of differentially abundant RNA transcripts in Immune Profiling Panel. ............................................................................................................ 60 Figure 3.9: Volcano plot and network analysis of differentially abundant proteins identified using shotgun proteomics. ........................................................................................................ 62 Figure 4.1: Experimental design for comparative analyses. ....................................................... 84 Figure 4.2: Differential staining of extracellular and internalized conidia by anti-A. fumigatus antibody using confocal microscopy at 6 hours post-exposure in submerged monolayer cultures of 1HAEs. .................................................................................................................................. 86  xiii Figure 4.3: Principal Component Analysis (PCA) and MA Plot of 1HAEs exposed to A. fumigatus conidia for 6 hours (Asthma Elements Panel). ........................................................................... 90 Figure 4.4: Principal Component Analysis (PCA) and MA Plot of 1HAEs exposed to A. fumigatus conidia for 6 hours (Immune Profiling Panel). ........................................................................... 94 Figure 4.5: Gene ontology enrichment analysis of differentially abundant RNA transcripts identified using Immune Profiling Panel in submerged monolayer cultures of 1HAEs upon exposure to A. fumigatus. ......................................................................................................... 96 Figure 4.6: Volcano plot of 558 quantified proteins identified using shotgun proteomics in submerged monolayer cultures of 1HAEs upon exposure to A. fumigatus conidia. ................... 98 Figure 4.7 PCA plot and MA Plot of ALI cultures exposed to Δkdnase A. fumigatus conidia for 6 hours using Immune Profiling Panel. ....................................................................................... 102 Figure 4.8: Gene ontology enrichment analysis of differentially abundant RNA transcripts identified using Immune Profiling Panel in ALI cultures upon exposure to Δkdnase A. fumigatus. ............................................................................................................................................... 104 Figure 4.9: PCA plot and MA Plot analyses of ALIs exposed to Δkdnase A. fumigatus conidia for 6 hours using Immune Profiling Panel. ....................................................................................... 107 Figure 4.10: PCA plot and MA Plot analyses of ALIs exposed to RSV for 6 hours using Immune Profiling Panel. ........................................................................................................................ 110 Figure 4.11 Volcano plot of differentially abundant RNA transcripts in high TEER samples compared to low TEER samples. ............................................................................................. 114     xiv Acknowledgments I would like to thank my supervisor and mentor Dr. Scott Tebbutt for supporting and guiding me throughout my graduate studies. He has provided me with enormous opportunities to learn and grow, and his valuable feedback has been very essential in all aspects of my project. I have enjoyed working on this project under his supervision. Thanks for always being so generous of your time and knowledge. I would like to thank my committee members, Dr. Margo Moore and Dr. Del Dorscheid for the support, guidance and feedback. They have gone far beyond the role of committee members and been available for feedback at all times. Thanks for supervising me on experiment protocols, conference abstracts and manuscripts; you were always only an email away. Thanks to all the Tebbutt lab graduate and Co-op students who I have had the pleasure of working with over the last two years. Thanks to Dr. Amrit Singh, Dr. YoungWoong Kim, Yolanda Yang and Daniel He for all the valuable feedback and advice. I am very grateful for all the support, guidance and memories. Thanks to Luka Culibrk and Karen Tam for assisting me on this project.  Thanks to all the Moore and Dorscheid lab members. Specially, thanks to Alison Hadwin and Dr. Gurpreet Singhera for their valuable support and guidance in the lab at all times. Thanks for being there for everything, Gurpreet. I want to acknowledge everyone at the Centre for Heart Lung Innovation. Thanks to Anna Siedlecki, Daniela Castillo-Saldana, Basak Sahin, Jasmine Yang, Beth Whalen, Amrit Samra, Dean English, Aaron Barlow and Ivan Leversage for their continued support and guidance.   xv          TO MY PARENTS for raising me to believe that with hard work and perseverance anything is possible. Thanks, Mom and Dad.  TO MY BROTHERS for inspiring me to become stronger, more resilient and chase those gains every day. Thanks, Daman and Kanwar.   &  TO ALL THE AMBITIOUS WOMEN  For always supporting, inspiring and feeding me. Thanks, Nani, Bibi, Pua, Kamal Massi, Raji Massi, Deepa Massi, Ginder Massi and Devin.     1 Chapter 1 Introduction  1.1 Aspergillus fumigatus   Aspergillus fumigatus (A. fumigatus) is a filamentous, saprotrophic, ubiquitous fungal pathogen. It plays an important role in recycling environmental carbon and nitrogen, and is widely distributed in nature through the release of asexually produced conidia, also known as conidiospores (Latgé 1999). These fungal conidia are 2-3 µm in size and are typically grey-green in color and echinulate (Figure 1.1). They can disseminate in high concentrations in the atmosphere by air currents, and exist in the air both indoors and outdoors (1-100 conidia m-3) (Latgé 1999, 2001).   The genus Aspergillus consists of >250 different species. The defining characteristic of these species is a spore-bearing structure in the asexual reproductive cycle that resembles an aspergillum, a device used in the Roman Catholic Church to sprinkle holy water (Figure 1.1). These saprotrophic species belong to the phylum Ascomycota and some species are used for industrial processes to produce enzymes, food products and commodity chemicals (Bennett 2010). The sexual reproduction cycle was recently characterized in A. fumigatus and results in the production of cleistothecia containing ascospores (Figure 1.2) (O’Gorman, Fuller, & Dyer, 2009) . Parasexual reproduction is also recognized in A. fumigatus, and involves nuclear fusion of genetically different, but compatible hyphae (Figure 1.2) (Verweij et al. 2016; Pontecorvo 1956).   2 Figure 1.1 Sporulating structure of A. fumigatus in asexual reproduction. Light microscopy of A. fumigatus spore-bearing structure, called a conidiophore. (Figure adapted from Latgé, 1999)  3  Figure 1.2 Three processes of reproduction are recognized in A. fumigatus. A. fumigatus reproduces by asexual (orange), sexual (blue) and parasexual (purple) reproduction. (Figure adapted from Verweij et al. 2016) Most humans inhale several hundred conidia every day. Inhalation of these airborne conidia in immunocompromised individuals or those with certain risk factors can result in a range of illnesses, collectively known as aspergillosis. A. fumigatus is also a major respiratory pathogen in birds (Arné et al. 2011). In addition to A. fumigatus, there are other Aspergillus species that are opportunistic pathogens as well, such as A. nidulans, A. niger, A. terrus and A. flavus (Latgé 1999).   4 1.1.2 Virulence factors of A. fumigatus  The virulence of A. fumigatus is multifactorial and has been challenging to elucidate. Upon reaching the host environment, the fungus is presented with different conditions than its normal niche in decaying organic matter. The structural features of conidia that allow it to survive inside the host as well as fungal proteins that promote conidial growth in the lung comprise the virulence of A. fumigatus. In addition, both the immune status of the host and the biological characteristics of the fungus contribute to the virulence of A. fumigatus (Latgé 2001; Sales-Campos et al. 2013).  Despite being technically challenging, mostly single-gene deletion studies have been conducted to study the virulence of A. fumigatus so far. For example, loss-of-function mutants have been difficult to generate. This is because genes contributing to virulence of the fungus are also important for its growth and in some cases, A. fumigatus can compensate for a loss of specific gene through a different pathway due to redundancy in number of its pathways (Croft et al. 2016). Some features that contribute to growth under specific conditions have been identified and include nutrient uptake, thermotolerance, secreted toxins and proteases, and features of the conidial surface. These features have been the focus of several studies conducted over the past decades (Sales-Campos et al. 2013; Latgé 2001; Rhodes 2006).  Within the host environment, A. fumigatus is presented with various stress conditions during the course of infection. In particular, it must be able to adapt to the host-derived sources of nutrients; e.g., A. fumigatus has shown to acquire amino acids from the host by secreting proteases (Sales-Campos et al. 2013). Ferric iron is another essential nutrient for cellular processes and virulence in Aspergillus species as it plays an important role in the redox  5 reactions of both the host cell and the fungus (Canessa and Larrondo 2013). However, excess iron can cause harm via the formation of reactive oxygen species (ROS) and result in oxidative stress. Since the host environment restricts availability of free iron by producing iron-binding proteins, such as transferrin and lactoferrin, the ability to obtain iron from host is important for the survival of A. fumigatus (Parrow, Fleming, and Minnick 2013). Specifically, A. fumigatus acquires iron through high affinity uptake, either by reductive iron assimilation or siderophore biosynthesis (Schrettl et al. 2004; Moore 2013). In reductive iron assimilation, a plasma membrane bound reductase reduces ferric iron into a more soluble ferrous form. However, genetic disruption of reductive iron assimilation did not affect A. fumigatus virulence (Schrettl et al. 2004). A. fumigatus also secretes iron-chelating siderophores, such as fusarinine C, triacetylfusarinine C, and produces intracellular siderophores such as ferricrocin and hydroxyferricrocin (Brown and Goldman 2016). A. fumigatus was avirulent in a mouse model of invasive aspergillosis when siderophore biosynthesis was abolished by gene deletion (Schrettl et al. 2004; Hissen et al. 2005). Hence, adaptation to iron limitation by siderophore production is essential for A. fumigatus virulence and can be considered as a true virulence factor of the fungus.   A. fumigatus is also more thermotolerant than any other Aspergillus species. It can efficiently grow between 37 °C and 50 °C, allowing it to survive within mammalian lung at temperatures 37 °C or above (Rhodes 2006; Bhabhra and Askew 2005). Stresses such as elevated temperatures within the host have been shown to increase the production of Heat shock protein 90 (Hsp90) by A. fumigatus. Hsp90 is an essential ATP-dependent molecular chaperone involved in protein folding, transport, maturation and degradation (Cowen and  6 Lindquist 2005). Repression of Hsp90 resulted in decreased spore viability and hyphal growth as well as major defects in germination and conidiation in-vitro; it plays an important role in cell wall integrity as inhibition of Hsp90 increased susceptibility to heat stress and caspofungin (Lamoth et al. 2012; Lamoth, Juvvadi, and Steinbach 2016). Hence, the ability of A. fumigatus to survive in elevated temperatures is important for its growth in-vitro. A. fumigatus produces a number of toxic secondary metabolites, proteases and other fungal products that contribute to its virulence. Toxic secondary metabolites include gliotoxin, trypacidin and verruculogen (Frisvad et al. 2009). Gliotoxin has been shown to slow ciliary beat frequency and damage human respiratory epithelium in-vitro; damage to the mechanical barrier respiratory epithelium can allow A. fumigatus to establish in the airways (Amitani et al. 1995). In addition, gliotoxin has immunosuppressive activity in-vivo, indicating that these metabolites may result in immunosuppression in the host (Sutton et al. 1994; Tomee and Kauffman 2000). A. fumigatus also produces proteases which have been shown to cause cell detachment and induce expression of pro-inflammatory cytokines in airway epithelial cells in-vitro. Production of pro-inflammatory cytokines may cause local inflammation, which combined with desquamation of cells, can allow fungal attachment and penetration (Tomee et al. 1997). For example, fungal serine proteases (Alp) induced expression of Interleukin-8, resulted in neutrophil recruitment and inflammation (Chotirmall et al. 2014) . The conidial surface also possesses features that may contribute to the virulence of A. fumigatus. Conidia contain a gray-green pigment, 1, 8-Dihydroxynaphthalene (DHN)-melanin, which functions to protect the conidia from ultraviolet light, enzymatic lysis and oxidation. It also protects A. fumigatus from phagocytosis as phagocytes have been shown to internalize  7 non-melanized conidia in greater numbers than melanized conidia (Thywißen et al. 2011; Volling et al. 2011). The outer layer of the conidial cell wall is called the rodlet layer that is composed of hydrophobin proteins such as RodA. The rodlet layer can prevent conidial detection by masking pathogen-associated molecular patterns (PAMPs) such as b-1,3-glucan and a-mannose on the cell wall (Lee and Sheppard 2016). RodA is covalently bound to the conidial cell wall through glycosylphosphatidylinositol-remnants. The presence of this hydrophobin layer prevents recognition of dormant fungal conidia by the host immune system and consequently, prevents an inflammatory response. Upon conidial swelling and germination, the rodlet layer is rapidly shed, resulting in exposure of the underlying cell-wall PAMPs (Aimanianda et al. 2009). Sialic acids have been detected on the surface of conidia (Julie A. Wasylnka and Moore 2000). They are a family of more than 50 substituted derivatives of a nine-carbon monosaccharide (neuraminic acid) that have been shown to play important roles in bacterial and viral pathogenesis (Varki and Schauer 2009). In A. fumigatus, adhesion to the extracellular matrix components in the host such as fibronectin, a component of basal lamina, is mediated by negatively-charged carbohydrates (Julie A. Wasylnka and Moore 2002). A. fumigatus conidia have sialic acids on their surface, and pathogenic Aspergillus species had greater amounts of sialic acids than non-pathogenic species (J. A. Wasylnka, Simmer, and Moore 2001). In cultured macrophages and type II pneumocytes, removal of sialic acid residues decreased binding of conidia to fibronectin as well as phagocytosis of conidia by cultured murine macrophages (Julie A. Wasylnka and Moore 2003; Warwas et al. 2007). There are two naturally occurring sialic acids, N-acetylneuraminic acid (Neu5Ac) and 2-keto-3-deoxy-D-glycero-D-galacto-nononic acid  8 (Kdn), with substitution at carbon 5 with an N-acetyl or an -OH group, respectively (Varki and Schauer 2009). These sialic acids can be released from the glycans on the cell wall by glycoside hydrolase enzymes, called sialidases. An exo-sialidase that prefers Kdn as a substrate (hence, it is a Kdnase) was recently identified in A. fumigatus (Telford et al. 2011) .The A. fumigatus Kdnase was shown to be important for fungal cell wall integrity and to contribute to virulence in the mouse model of invasive aspergillosis (Nesbitt et al. 2018).  1.1.3 Cell wall of A. fumigatus  The cell wall of A. fumigatus mediates contact with the host and acts as a protective barrier important for survival (Chotirmall et al. 2014). In addition, the dynamic and versatile structure of the cell wall plays an important role in fungal growth and protects the fungus against environmental stresses. The composition of the wall varies during different stages of fungal life cycle and in response to different growth conditions (Latgé, Beauvais, and Chamilos 2017).  In conidia, the wall consists of two layers composed of various polysaccharides: the inner cell wall (alkali-insoluble) contains 38% b-1,3-glucan, 26% galactomannan and 5.6% chitin/chitosan. These polymers provide structure and rigidity to the cell wall. The outer cell wall (alkali-soluble) consists of 14% a-1,3-glucans, 13% galactomannan, 5% b-1,3-glucan, and 0.5% chitin/chitosan. These polysaccharides are non-covalently attached and form a looser network of macromolecules. In contrast, the alkali-insoluble cell wall of hyphae (the fungal filaments) contains 30% b-1,3-glucans, 17% chitin/chitosan, 5% galactomannan, 4% galactosaminogalactan (GAG) and b-1,3;1,4-glucans. The alkali-soluble outer cell wall contains 42% a-1,3-glucans, 2.3% GAG, 1.4% galactomannan. Unlike conidia, hyphae produce an  9 extracellular matrix (ECM), composed of GAG (Mouyna and Fontaine 2009). Secretion of GAG has immunosuppressive properties as it can induce apoptosis in neutrophils (Robinet et al. 2014). The absence of GAG from the host makes them potential targets for novel anti-fungal treatments (Lee and Sheppard 2016). 1.1.4 Human diseases caused by A. fumigatus  A. fumigatus can cause a range of diseases, referred to as aspergillosis. The symptoms and clinical outcomes depend on the immune status of the host, and three forms of the disease are recognized: allergic, chronic and invasive aspergillosis.  The most common form of allergic aspergillosis is known as allergic bronchopulmonary aspergillosis (ABPA). ABPA is characterized by a severe allergic reaction which is triggered due to the secretion of toxins and allergens from prolonged fungal exposure (Margalit and Kavanagh 2015; Dewi, van de Veerdonk, and Gresnigt 2017). ABPA individuals usually have defects in their airway mucosal defenses; specifically, impaired mucociliary clearance and epithelial cell function. It is estimated that ABPA occurs in 1-2% of asthmatic subjects and 7-9% cystic fibrosis patients (Knutsen and Slavin 2011). Genetic predisposition to developing ABPA also exists (Tracy et al. 2016). The global burden of patients with ABPA is estimated to exceed 4.8 million patients (Soubani and Chandrasekar 2002). ABPA is likely to be under-diagnosed; e.g., in developing countries, it has been reported that ~ 1/3 of patients with ABPA are misdiagnosed as having pulmonary tuberculosis (Agarwal et al. 2013).   In cystic fibrosis patients, impaired mucociliary clearance along with immune dysfunction promotes fungal establishment and hinders clearance (Balloy and Chignard 2009). Bronchoalveolar lavage (BAL) of ABPA patients have shown the presence of eosinophils,  10 neutrophils, lymphocytes and often fungal hyphae as well (Agarwal et al. 2013; Wark and Gibson 2001). In addition, A. fumigatus antigens can elicit a polyclonal antibody response, resulting in elevated total IgE and A. fumigatus -specific IgE and IgG antibodies. The immune response of patients with ABPA is generally associated with immune deviation towards a hyperactive TH2 response and is characterized by the production of IL-4, IL-5, IL-13 and IL-10 (Moss 2005; Agarwal et al. 2013). These cytokines also drive differentiation of B cells to secrete IgE antibodies specific to A. fumigatus and activation of eosinophils (by IL-5). The TH2 inflammatory response from continuous airway sensitization activates mast cell degranulation and results in airway mucus production, hyper-responsiveness, inflammation and bronchiectasis, symptoms that characterize ABPA. Increased mucus production in the airways can result in biofilm formation, which can assist in fungal growth (Dewi, van de Veerdonk, and Gresnigt 2017). The symptoms of ABPA patients are a consequence of the immune response to A. fumigatus and include wheezing, productive cough, low-grade fever, hemoptysis, malaise, weight loss as well as worsening of asthma or CF. Diagnostic tests include Aspergillus skin test to check for the presence of IgE antibodies specific to A. fumigatus, total serum IgE levels, serum IgE and IgG antibodies specific to A. fumigatus, peripheral eosinophilia, sputum cultures, pulmonary function tests and radiological investigations (Agarwal et al. 2013). Following diagnosis, corticosteroids are used to suppress the inflammatory pathways to further prevent lung damage with corticosteroids, and antifungal agents are given to eradicate fungi from the airways (Soubani and Chandrasekar 2002).  In patients with pre-existing lung cavities formed due to tuberculosis, sarcoidosis, chronic obstructive pulmonary disease or other cavitary lung diseases, exposure to A. fumigatus  11 can cause chronic pulmonary aspergillosis (CPA) (Kawamura et al. 2000; Soubani and Chandrasekar 2002). CPA may be associated with an aspergilloma (fungal ball) that forms as a non-invasive fungal growth within the lung cavity. The majority of CPA patients are asymptomatic or experience mild hemoptysis. Severe hemoptysis may occur due to local invasion or mechanical damage of blood vessels lining the cavity from toxins released by the fungus (Soubani and Chandrasekar 2002). More severe symptoms associated with the underlying chronic lung disease include weight loss, profound fatigue, chronic productive cough and shortness of breath (Denning et al. 2003). After diagnosis, disease progression is observed in most patients; however, the fungal mass may also be surgically removed in patients who have more significant hemoptysis (Schweer et al. 2014). Various antifungal agents, such as azoles, have also been used for treating CPA patients; however, there is no consistent evidence that aspergilloma responds to antifungal drugs (Schweer et al. 2014).  The most severe form of aspergillosis is called invasive aspergillosis (IA) which affects individuals who have impaired immune defenses (Dagenais and Keller 2009). In IA, inhaled conidia penetrate the epithelial and endothelial barriers, resulting in germination and proliferation of the fungus within lung tissue. The infection can also spread to other organs, particularly to the brain via the bloodstream (Espinosa and Rivera 2016) (Figure 1.3).  12  Figure 1.3 Chest computed tomography and brain magnetic resonance image showing invasive pulmonary aspergillosis. A) Chest computed tomography image showing cavitary lesion in the left upper lobe in an allogenic hematopoietic stem-cell transplantation recipient. B) Brain magnetic resonance image from the same patient showing a lesion due to disseminated IPA. (Figure adapted from Kousha, Tadi, and Soubani, 2011)  Symptoms of IA include cough, fever, chest pain, dyspnea and hemoptysis (Cadena, Thompson, and Patterson 2016). The major risk factors for developing IA include neutropenia secondary to allogeneic hematopoietic stem cell transplant or solid organ transplant, and hematological malignancy. IA is estimated to occur in 5-25% of acute leukemia patients, 5-10% of patients with allogenic bone marrow transplantation, and in 0.5-5% of individuals after cytotoxic treatment of blood diseases or solid-organ transplantation (Latgé 1999). Patients infected with human immunodeficiency virus, those with undergoing high-dose corticosteroid therapy, or those with a genetic immunodeficiency such as chronic granulomatous disease (CGD) are also at risk (Ben-Ami, Lewis, and Kontoyiannis 2010). Mortality rates for IA range from 40 to 90% in high risk populations, even with drug treatment, and depend on multiple factors such as host immune status, the site of infection and the treatment (Dagenais and Keller 2009).  A B  13 The diagnosis of IA remains challenging, and the gold standard is histopathological examination of lung tissue obtained by thoracoscopic or open-lung biopsy. Other diagnostic methods include the detection of Aspergillus antigens in body fluids. PCR detection of fungal DNA has not yet proven to be clinically useful (R. A. Barnes and White 2016) but detection of galactomannan in serum using ELISA has been approved by US Food and Drug Administration (FDA) for the diagnosis of IA (Verdaguer et al. 2007). Other antifungals can be used as alternatives or as salvage or in adjunct, including liposomal amphotericin B, and echinocandin derivatives such as caspofungin, micafungin and anidulafungin (Panackal, Bennett, and Williamson 2014). The treatment duration depends on patient’s response and often lasts from several months to >1 year (Kousha, Tadi, and Soubani 2011).  1.2 Airway Epithelium  The primary point of contact with fungal conidia is the airway epithelium which initiates the immune response (Paris et al. 1997). The airway epithelium has numerous functions: it regulates lung fluid balance, attracts and activates inflammatory cells in response to injury, and regulates airway smooth muscle function by secreting mediators (Knight and Holgate 2003). Since they are an initial point of contact between the host and the fungus, and because the host immune response determines the outcome, a better understanding of how airway epithelial cells respond to conidia will shed light on the unique pathogenesis of the various Aspergillus-related disease. 1.2.1 Function of airway epithelium in aspergillosis  The bronchial epithelium of the conducting airways consists of a pseudostratified epithelium consisting of columnar epithelial cells; these include mucus-secreting goblet cells,  14 ciliated cells and basal cells (Knight and Holgate 2003). Ciliated cells are the predominant cell type within the airways, accounting for 50% of all epithelial cells. Ciliated cells arise from either basal or secretory cells (Spina 1998; Ayers and Jeffery 1988) and each cell is estimated to possess 300 cilia/cell with multiple mitochondria under the apical surface (Knight and Holgate 2003). Goblet cells protect the airway epithelial surface by producing mucus that traps inhaled particles; ciliated cells aid in removal of these trapped particles by moving mucus toward the throat where it can be swallowed or expectorated. Producing correct amounts of mucus is important for efficient mucociliary clearance, and goblet cell hyperplasia and metaplasia is commonly observed in chronic airway inflammatory diseases such as chronic bronchitis and asthma (Jeffery 1991). In immunocompetent hosts, most of A. fumigatus conidia are eliminated by mucociliary clearance (Rogers 1994; Balloy and Chignard 2009). As noted above, in ABPA patients, inhaled conidia cause a TH2 mediated inflammatory response resulting in enhanced mucus production as well as production of cytokines, growth factors and chemokines. Airway damage and hyper-responsiveness is accompanied by production of T-cells, eosinophils, basophils and other immune cells (Osherov 2012). Due to their small size (2.5 µm diameter), A. fumigatus conidia that escape the physical barrier can reach the lower bronchial airways to the terminal bronchioles and alveoli (P. D. Barnes and Marr 2006). Alveoli consist of type I and type II alveolar epithelial cells, and alveolar macrophages. Type I are thin, non-dividing squamous cells that enable rapid gas exchange. They cover 95% of the alveolar surface but are the least common of all major cell classes present in the lungs. Type II alveolar cells are cuboid and differentiate into type I cells (Crapo et al. 1982). They also secrete surfactant proteins, Surfactant Protein A (SP-A) and Surfactant  15 Protein D (SP-D). SP-A and SP-D are collectins, calcium-dependent lectins (with collagenous region and carbohydrate recognition domain) that opsonize conidia and enhance killing by neutrophils and macrophages (Madan et al. 1997). Type-II epithelial cells also secrete cytokines, chemokines and antimicrobial peptides. Airway epithelial cells can phagocytose conidia that are then trafficked to acidic organelles. However, it has been demonstrated that conidia can survive and germinate in the acidic organelles of epithelial cells in-vitro (Paris et al. 1997; Julie A. Wasylnka and Moore 2002, Wasynka and Moore, 2003).  In an immunocompetent host, alveolar macrophages are able to phagocytose dormant or swollen conidia, and kill them within 30 hours (Schaffner et al. 1983). Macrophages secrete chemokines to recruit neutrophils for elimination of conidia and germinating hyphae (Balloy and Chignard 2009); however, in immunocompromised patients, inhaled A. fumigatus conidia can enter the alveoli, germinate and penetrate the epithelial barrier to cause IA. This is due to the dysfunction of immune defenses that are necessary for recruitment of alveolar macrophages and neutrophils (Osherov 2012).  1.2.2 Pattern recognition receptors Professional and non-professional phagocytes in the airways recognize pathogen-associated molecular patterns (PAMPs) on the cell wall of A. fumigatus by pattern recognition receptors (PRRs) (Sales-Campos et al. 2013). These PAMPs on conidial cell wall include β-glucan, chitin, mannan or galactomannan. PRRs recognizing these components can be separated into two categories, soluble and cell surface PRRs. More details are presented below.  16 1.2.2.1 Soluble PRRs  The soluble PRRs act as opsonins, and include the acute-phase proteins, complement proteins, anti-microbial peptides and cytokines (Wong and Aimanianda 2017). Acute-phase proteins are proteins whose plasma concentrations increase or decrease by at least 25% during infection or inflammation. These include cytokines, C-reactive proteins and several components of complement proteins such as C3, C4 and mannose-binding lectin (Gabay and Kushner 1999). The complement system consists of the classical, lectin pathway and alternative pathways, and upon activation, C3 convertase is formed by binding of pathogen-associated molecular patterns (PAMPs) (Walport 2001). C3 convertase cleaves C3 into opsonins, C3b and iC3b to result in 1) opsonization by C3b and iC3b; 2) recruitment of immune cells by production of anaphalytoxins C3a and C5a; and 3) direct lytic killing of the pathogen by formation of membrane attack complex (MAC). Due to the thick fungal cell wall, MAC is unlikely to kill the fungus by membrane lysis. Hence, complement system enhances immune recognition by conidia opsonization (Wong and Aimanianda 2017). All three forms of the complement system are known to be activated by different forms of fungus (Kozel et al. 1989).  The alternative pathway is activated by the resting conidia; however, classical pathway is known to be activated as conidia mature, exposing conidia to the innate immune system and resulting in C1q interacting with surface bound IgG and IgM (Kozel 1996; Aimanianda et al. 2009). In contrast, the lectin pathway is known to be activated by the binding of mannose-binding lectin (MBL) or ficolin with serine proteases on the pathogen surface, resulting in formation of a complex that cleaves C4 to form C3 convertase, and activates C3. (Fujita 2002). MBL opsonizes dormant conidia by binding to mannose in a calcium-dependent manner, and  17 directly activates C3 without the formation of C3 convertase. Similarly, ficolin-2 opsonizes conidia by recognizing N-acetylglucosamine (GlcNAc) and activates C3. Hence, the lectin pathway is not activated by dormant conidia; instead, C3 is activated directly to opsonize the conidia (Dumestre-Pérard et al. 2008; Bidula et al. 2013; Wong and Aimanianda 2017).  Ficolin-3 (FCN3) is another soluble PRR secreted by type II alveolar epithelial cells that binds to A. fumigatus in calcium-dependent manner (Bidula et al. 2013). Pentraxin-related protein 3 (PTX3) is also a soluble PRR and an acute-phase protein that binds to A. fumigatus conidia through the N-terminal domain and recognizes galactomannan. Similarly, C-reactive protein enhances conidial recognition in neutrophils through the Fcg Receptor II in-vitro (Moalli et al. 2010). Phagocytic cells also recognize conidia through complement receptors (CR1, CR3, and CR4) and Fcg receptors (Wong and Aimanianda 2017). Opsonized conidia are recognized through calreticulin-CD91 complex in macrophages as well (Ogden et al. 2001; Vandivier et al. 2002).  1.2.2.2 Cell surface PRRs  Cell surface PRRs allow binding to the pathogen before phagocytosis. These include dectin-1, a transmembrane C-type lectin receptor (CLR) that recognizes b-1,3-glucans, found on swollen and germinating conidia. In macrophages, dectin-1 recognizes swollen conidia both at the cell surface as well as in phagolysosomes (Faro-Trindade et al. 2012; Bercusson, de Boer, and Armstrong-James 2017). Increased expression of dectin-1 was observed in human bronchial epithelial cells upon exposure to A. fumigatus as well (W.-K. Sun et al. 2012). Dectin-1 can interact with signaling receptors to activate downstream signaling pathways via both spleen tyrosine kinase (Syk)-dependent and Syk-independent signaling cascades. Upon ligation of  18 extracellular domain in Syk-dependent signaling, cytoplasmic immunoreceptor tyrosine-based activation motif (ITAM)-like is phosphorylated, resulting in recruitment of Syk and caspase recruitment domain-containing protein 9 (CARD9). This results in the activation of transcription factors, including NF-κB, production of reactive oxygen species (ROS) and pro-inflammatory cytokines such as TNF-α and IL-12. These cytokines promote TH1/TH17 differentiation to recruit neutrophils and macrophages in response to the fungus (Drummond et al. 2011). Dectin-2 is another CLR expressed on dendritic cells and macrophages that recognizes a-mannans on the outer layer of conidia (H. Sun et al. 2013). Detection of swollen conidia by Dectin-2 results in the production of IL-1β, IL-10, IL-23p19 and TNFα via NF-κB mediated by Syk. In macrophages differentiated from human monocytic cell line, blocking of Dectin-2 results in reduced conidial killing (Sun et al. 2013, 2014).  Other signaling PRRs include the Toll-Like Receptors (TLRs). TLRs are membrane receptors consisting of a leucine-rich extracellular domain that recognizes PAMPs and an intracellular Toll/Interleukin-1 Receptor (TIR) domain for downstream signaling (Kawai and Akira 2006). Upon recognition of pathogen, signaling cascade results in activation of transcription factors such as NFκB, which leads to the production of cytokines and chemokines (Kawai and Akira 2006; Kawasaki and Kawai 2014). TLRs 1, 2, 4, 5 and 6 are found on the cell membrane, and TLRs 3, 7, 8 and 9 are found on the intracellular compartments. All TLRs are expressed by epithelial cells, alveolar macrophages and neutrophils (except for TLR3 which is not expressed in neutrophils) (Balloy and Chignard 2009). Both TLR2 and TLR4 play an important role in recognizing A. fumigatus conidia; however, the PAMPs with which they interact remain to be identified. TLR2 recognizes ligands on conidia and hyphae whereas TLR4  19 only recognizes ligands on conidia (Netea et al. 2003). TLR3 has been shown to be localized to the endosomal compartments in dendritic cells and epithelial cells, and detects double-stranded RNA released from the conidia as it enters the endosomal pathway (Beisswenger, Hess, and Bals 2012). TLR9 has also been shown to recognize unmethylated CpG DNA on A. fumigatus and is primarily found on dendritic cells and B cells (Ramirez-Ortiz et al. 2008). 1.2.3 Host innate immune response to A. fumigatus  Upon recognition, the innate immune system removes A. fumigatus conidia using the mechanical and anatomical barriers of the respiratory tract, professional and non-professional phagocytes and antimicrobial peptides. As previously mentioned, inhaled conidia are trapped in the mucus and removed by ciliated cells. However, when conidia bypass this anatomical barrier and reach the alveoli, cells encounter phagocytic cells such as neutrophils and alveolar macrophages (AM).  The AM are the first line of defense against conidia within the alveoli (Morton et al. 2012). Internalization of conidia by AM involves actin polymerization and endocytosis into endosomes that then fuse with lysosomes to form phagolysosomes (Wasynka and Moore, 2003). In the phagolysosome, fungal killing is achieved by both oxygen-dependent and oxygen-independent processes. Upon swelling of conidia, oxidative killing involves activation of Nicotinamide Adenine Dinucleotide Phosphate (NADPH) oxidase system that produces reactive oxygen species (ROS) that kill fungal conidia. In non-oxidative killing, acidification of the phagolysosome results in conidial degradation by hydrolytic enzymes, such as cathepsin D and chitinase (Brakhage et al. 2010). Upon phagocytosing conidia, AM release cytokines and chemokines, such as TNFα, MIP-α, IL-1β, IL-1α, IL-6, G-CSF and GM-CSF, which recruit innate  20 immune cells, including neutrophils, dendritic cells, monocytes, mast cells, eosinophils and natural killer (NK) cells (Espinosa and Rivera 2016) (Figure 1.4).  Figure 1.4 Inhalation of A. fumigatus conidia leads to initiation of immune response by lung epithelial cells and tissue-resident innate cells. Upon recognition of conidia by lung epithelial cells, chemokines and cytokines are produced, resulting in recruitment of neutrophils and subsequent recruitment of monocytes, dendritic cells, mast cells, eosinophils and NK cells. (Figure adapted from Espinosa and Rivera, 2016) Along with AM, neutrophils can also engulf and kill conidia via NADPH oxidase-mediated oxidative killing (Figure 1.4). Other mechanisms employed by neutrophils in elimination of A. fumigatus include production of lactoferrin and release of antimicrobial proteases by degranulation (Feldmesser 2006; Espinosa and Rivera 2016). The two predominant types of granules released during degranulation include azurophil granules and specific granules.  21 Azurophil granules are the primary granules and consist of fungicidal hydrolytic enzymes such as myeloperoxidase, cathepsin G, elastase and proteinase 3. Specific granules are the secondary granules and consist of lactoferrin, transcobalamin II etc. Hence, NADPH oxidase promotes activation of hydrolytic enzyme as well as degranulation (Segal 2005; Espinosa and Rivera 2016). Neutrophils also produce mesh-like extracellular traps (NETs), which are extracellular structures made of chromatin with proteins from neutrophilic granules attached (Brinkmann et al. 2004). These may inhibit fungi, however the role of NETs in killing A. fumigatus hyphae is controversial (P et al. 2016).  Dendritic cells (DCs) have been shown to play an important role in host defense against A. fumigatus as well (Figure 1.4). Three major subtypes of DCs in the lung include conventional DCs (cDCs), plasmacytoid DCs (pDCs) and monocyte-derived DCs (moDCs) (Kushwah and Hu 2011). They can phagocytose opsonized or unopsonized conidia and hyphae upon recognition by PRRs, such as Dectin-1, Dendritic Cell-Specific Intercellular adhesion molecular-3-Grabbing Non-integrin (DC-SIGN), complement receptor 3 (CR3) and FcγRIII (Bozza et al. 2002; Mezger et al. 2008; Charles O. Morton et al. 2011). Neutrophils are known to express CCL3/MIP-1α and CCL4/MIP-1β, which can recruit DCs to the site of infection (Scapini et al. 2000). In response to A. fumigatus, DCs produce proinflammatory cytokines such as TNFα, IL-6, IL-12, IL-1α and IL-1β (Bozza et al. 2002; Mezger et al. 2008; Charles O. Morton et al. 2011). In addition, infection of human DCs by A. fumigatus conidia in-vitro results in secretion of chemokines for recruitment of neutrophils and AM (CXCL8/IL8, CCL3, CCL4 and CCL5) as well as effector memory T cells and naïve T cells (CCR6 and CCR7 ) (Gafa et al. 2007; Charles O. Morton et al. 2011). This indicates  22 that DCs play an important role in both innate and adaptive immune responses against A. fumigatus (Espinosa and Rivera 2016; Margalit and Kavanagh 2015). Other immune cells involved in host response against A. fumigatus include eosinophils, mast cells and NK cells (Figure 1.4). Eosinophils consist of granules that have been shown to consist antimicrobial proteins with fungicidal activity (Patterson and Strek 2014). There is evidence that they play a role in defense against A. fumigatus as mice deficient in eosinophils have been shown to have increased fungal burden and impaired production of pro-inflammatory cytokines and chemokines, compared to wild-type mice (Lilly et al. 2014). In contrast, in ABPA patients, recruitment of eosinophils can contribute to epithelial damage (Espinosa and Rivera 2016). Exposure to A. fumigatus can also result in degranulation of mast cells; however they cannot inhibit fungal growth (Urb et al. 2009; Bradding, Walls, and Holgate 2006). Antimicrobial peptides also play an important role in the innate immune response to fungal infection. These include defensins and cathelicidins, which permeabilize fungal membranes and result in nonoxidative killing of fungi. Human β-defensin 2 is the most commonly expressed defensin in the lung (Smet and Contreras 2005; Alekseeva et al. 2009). In addition, LL-37 (or CAMP (Cathelicidin Antimicrobial Peptide) is the only human cathelicidin antimicrobial peptide expressed by neutrophils and airway epithelial cells, and is found to be highly expressed during inflammation (Bals et al. 1998; Chotirmall et al. 2013). 1.2.3 Host adaptive response to A. fumigatus Along with innate immune response, adaptive immune response is essential in host defense against A. fumigatus as well. Specifically, T-helper responses are activated during  23 interaction of host with A. fumigatus (Dewi, van de Veerdonk, and Gresnigt 2017). Innate effector cells, such as dendritic cells, have been shown to be involved in activating and differentiating naïve CD4+ T-helper cells into different effector cells (Ramirez-Ortiz and Means 2012). In addition, binding of T-cell receptor to major histocompatibility complex (MHC) class II, interaction of co-stimulatory molecules present on the surface of T-cells and antigen presenting cells, and autocrine production of IL-2 and other cytokines from different innate immune cells allows T-helper cell proliferation and differentiation into TH1, TH17, TH22, TH2, TH9, Treg and Tr1 cells (Barrios et al. 2005; Dewi, van de Veerdonk, and Gresnigt 2017; Bozza et al. 2002) (Figure 1.5).  Upon infection with A. fumigatus, human DCs produce significant amounts of IL-12, a TH1 cells inducing cytokine (Gafa et al. 2007). These TH1 cells are characterized by transcription factor TBET and production of IFN- γ, and promote clearance of fungus from the lungs. An IFN- γ deficient mice has been shown to have impaired protective antifungal immunity and robust TH2 responses (Cenci et al. 1999).  TH2 cells are characterized by the transcription factor GATA3 and the production of IL-4, IL-5, IL-10 and IL-13, which mediate anti-inflammatory responses, allergy and fungal persistence in the lungs (Moss 2005; Dewi, van de Veerdonk, and Gresnigt 2017). Production of IL-10 by TH2 cells suppresses production of pro-inflammatory cytokines and chemokines as well as inhibits T-cell activation and IFN- γ production (Del Sero et al. 1999). A TH9 subset of cells has been shown to responses closely associated to TH2 responses, and are characterized by production of IL-9 (Kaplan, Hufford, and Olson 2015). These TH9 subset of cells play a role in the allergic responses to A. fumigatus in cystic fibrosis as well (Moretti et al. 2017).  24 Unlike TH2 responses, TH17 responses are important for fungal clearance in the host. They are characterized by the expression of transcription factor, RAR-related orphan receptor C (RORC), and the production of IL-17A, IL-17F and IL-22 cytokines (Dewi, van de Veerdonk, and Gresnigt 2017). IL-17A and IL-17F cytokines can trigger the recruitment and activation of neutrophils as well as production of pro-inflammatory cytokines IL-6, IL-1β, TNF-α and G-CSF (van de Veerdonk et al. 2009; Way, Chen, and Kolls 2013; Werner et al. 2009; Dewi, van de Veerdonk, and Gresnigt 2017). As mentioned previously, neutrophils can produce ROS, proteolytic enzymes and anti-microbial peptides to eliminate the fungus. In addition, TH17 response can also activated upon interaction of dectin-1 by β-glucans (Rivera et al. 2011; Dewi, van de Veerdonk, and Gresnigt 2017). Regulatory T cells (Treg) play an important role in host defense against the fungus by regulating the inflammatory response and controlling inflammation by releasing anti-inflammatory cytokines such as IL-10 and TGF-β (Vignali, Collison, and Workman 2008). Treg cells include the natural Treg (nTreg) and induced Treg (iTreg) cells. nTreg cells regulate the early infection by limiting neutrophil activity, and iTreg cells limit inflammation in the later stages of infection (Montagnoli et al. 2006). In addition, Aspergillus-specific Type (1) regulatory T-cells (Tr1) have been found in the peripheral blood of human and mice, showing Aspergillus-specific response by the host (Bedke et al. 2014).  25  Figure 1.5 Innate activation of T-helper responses to A. fumigatus. Production of distinct cytokines by innate immune cells upon recognition of PPRs, antigen presentation by via MHC-II, and binding of co-stimulatory molecules results in activation and differentiation of naïve CD4+ T-helper cells to distinct lineages: TH1, TH17, TH22, TH9, TH2, Treg and Tr1. Figure adapted from van de Veerdonk, and Gresnigt 2017 1.3 Overview of experimental goals of the present research  A. fumigatus can cause a spectrum of lung diseases; the particular manifestation of disease symptoms depends on the immune status of the host. Hence, the initial interaction between conidia and the environment of the lung is important. Upon inhalation, the first cell encountered by conidia is most likely to be a type of airway epithelial cell, either bronchial or alveolar.  The overall aim of my project was to investigate the early molecular response of human bronchial epithelial cells upon interaction with A. fumigatus conidia. We hypothesize that novel  26 insights into the host response to A. fumigatus conidia can be obtained by using a multi-OMIC molecular approach. The specific objectives of my study were: 1. to develop a co-culture interaction model of A. fumigatus conidia with primary human bronchial epithelial cells (HBECs) that better recapitulates the in-vivo airway epithelium,  2.  to use a multi-OMIC molecular approach to measure gene expression changes in host upon exposure to A. fumigatus conidia using this model,  3.  to evaluate the applicability of this model for studying other host-pathogen interactions. This work is presented in the following chapters. In Chapter 3, a co-culture model was developed using primary human bronchial epithelial cells (HBECs) grown for 21-28 days as air-liquid interface (ALI) cultures that contained basal, mucus-secreting goblet and ciliated cells, to better recapitulate the in-vivo bronchial epithelium. Using this model, the early molecular response was analyzed using transcriptomics and proteomics to measure gene expression changes in host upon exposure to A. fumigatus conidia. In Chapter 4, I describe comparative analyses that I used to evaluate the applicability of this model to other host-pathogen systems. Specifically, gene expression profiles of ALI cultures were compared to an in-vitro model that uses submerged monolayer cultures of the human airway epithelial cell line (1HAEo-), and the specificity of response in ALI cultures was assessed by comparing the response to the immune response of primary HBECs upon exposure to a mutant strain of A. fumigatus conidia as well as to Respiratory Syncytial Virus (RSV). In Chapter 5, I present the overall conclusions of my studies and provide some ideas for future directions.  27 1.4 Strengths and limitations of the chosen cell culture models In general, in-vitro cell culture models of human lung can involve submerged monolayer cultures from immortalized respiratory cell lines, such as 1HAEo- used in Chapter 4, or primary cells isolated from lung tissue, used in Chapter 3. Even though cell lines are transformed and cancerous, and less representative of the airway epithelium (Bhowmick and Gappa-Fahlenkamp 2016), they are easily accessible, cost-effective and can be used at high passages compared to primary cells (Kaur and Dufour 2012). However, when grown as submerged monolayer cultures, both lack the physiological features of the in-vivo airway epithelium, such as the mucocililary barrier of the pseudostratified in-vivo epithelium (Kaur and Dufour 2012; O’Boyle et al. 2017). Formation of robust tight junctions and trans-epithelial electrical resistance (TEER) values varies within both cell types and is dependent on culture conditions as well (Bhowmick and Gappa-Fahlenkamp 2016). In Chapter 3, a more complex primary cell based ALI model was used to generate polarized cells. These ALI cultures can differentiate into mucus-producing goblet cells, ciliated cells as well as comprise the ciliary activity, form tight junctions and produce mucus (Karp et al. 2002; Lopez-Souza, Avila, and Widdicombe 2003). However, unlike the intact host epithelium, these ALI cultures used in our study lack both continuous clearance of mucus and other innate immune effector cells, such as dendritic cells, macrophages and neutrophils. Nevertheless, we have used combination of cell culture models to study the molecular response of the host in bronchial epithelium upon exposure to A. fumigatus in our study. Further details on both cell culture models are provided in Chapter 3 and Chapter 4.   28 Chapter 2 Methods 2.1 A. fumigatus strain and growth conditions All experiments were performed using a green fluorescent protein (GFP) expressing strain of A. fumigatus derived from ATCC 13073 (American Type Culture Collection, Manassas, VA) (Julie A. Wasylnka and Moore 2002), unless otherwise stated. The conidia from this strain are referred to as wild-type (WT) A. fumigatus conidia in Chapter 4. Briefly, the strain was transformed by electroporation with a plasmid containing the codon-optimized sgfp gene and the construct yielded stable, high expression of GFP in both conidia and hyphae (Julie A. Wasylnka and Moore 2002). To obtain fresh conidia for each experiment, the GFP-transformed A. fumigatus strain was grown on yeast-agar-glucose (YAG) media at 30 °C until sporulation. Mature conidia were harvested by gently scrubbing the plates using sterile cotton swabs with phosphate-buffered saline plus 0.05% Tween-20 (PBS-T). The conidial suspension was filtered through sterile glass wool, vortexed, pelleted and re-suspended in 1 ml PBS. The suspension was washed twice with PBS to remove any trace of PBS-T prior to quantification using a hemocytometer. 2.2 Dkdnase A. fumigatus strain and growth conditions   Kdnase is an exo-sialidase identified in A. fumigatus (Telford et al., 2011). The A. fumigatus kdnase knockout strain was prepared as described in Nesbitt et al. (2018), and is referred to as the Dkdnase A. fumigatus strain . Briefly, the strain was transformed by a disruption construct, containing 1000 bp sequences of DNA encoding the upstream and  29 downstream regions of the kdnase gene, which was introduced into the WT strain using Agrobacterium-mediated transformation (Nesbitt et al. 2018). To obtain fresh conidia for an experiment, the Dkdnase A. fumigatus strain was grown on yeast-agar-glucose (YAG) media supplemented with 100 μg/mL hygromycin at 30 °C until sporulation. Mature conidia were harvested by gently scrubbing the plates using sterile cotton swabs with phosphate-buffered saline plus 0.01 % Tween-20 (PBS-T). The conidial suspension was filtered through sterile glass wool, vortexed, pelleted and re-suspended in 1 ml PBS. The suspension was washed twice with PBS to remove any trace of PBS-T prior to quantification using a hemocytometer. 2.3 Overview of experiments The research presented here is primarily from three different experiments (Figure 2.1). Air-Liquid Interface cultures (ALIs) of primary human bronchial epithelial cells (HBECs) (details below) in experiment #1 consisted of 6 samples (control (n=3) and infected (i.e., exposed to A. fumigatus conidia) (n=3)) and experiment #2 consisted of 12 samples (control (n=3), infected (n=3), Δkdnase-infected (n=3) and Respiratory Syncytial Virus (RSV)-infected (n=3)). The control samples were incubated with PBS alone, infected samples with A. fumigatus conidia suspended in PBS, Δkdnase-infected samples with Δkdnase A. fumigatus conidia suspended in PBS, and RSV-infected with RSV suspended in PBS for 6 hours at 37 °C. Experiment #3 was conducted with 1HAEs (details below) incubated with DMEM + 10% FBS (control, n=3) and A. fumigatus conidia suspended in DMEM + 10% FBS (infected, n=3) for 6 hours at 37 °C.  30  Figure 2.1 Overview of experiments.  Two separate experiments (experiment #1 and #2) were conducted using ALI cultures of human bronchial epithelial cells (HBECs) and corresponding analyses are presented in Chapters 3 and 4. Analyses from the 1HAEs experiment (experiment 3) are also presented in Chapter 4. The number of replicates per treatment in each experiment were 3 (n=3). 2.4 ALI cultures of primary HBECs Human lungs of de-identified healthy donors, deemed unsuitable for transplantation and donated to medical research, were obtained from the International Institute for the  Experiment #1 (ALIs) Infected  (n=3)  MOI=10 conidia/cell  Control (n=3)  Experiment #2 (ALIs) Infected  (n=3)  MOI=10 conidia/cell  Control (n=3)  Δkdnase-infected  (n=3)  MOI=10 conidia/cell  RSV-infected (n=3)  MOI=1 virus/cell  Experiment #3 (1HAEs) Control (n=3)  Infected  (n=3)  MOI=10 conidia/cell   31 Advancement of Medicine (Edison, NJ) for primary cell isolation as per approval by the Research Ethic Board (REB) of University of British Columbia / Providence Healthcare (REB# H00-50100). Bronchial epithelial cells were isolated by protease digestion as described by Gray and colleagues, and cultured in bronchial epithelial growth medium (Lonza, Mississauga, ON, CC-3170) at 37 °C in 5% CO2 (Gray et al. 1996). ALI cultures of primary HBECs were generated using cells at passage one or two in ALI PneumaCult medium (Stemcell Technologies, Vancouver, BC, Catalog# 05001). The cultures were grown on inserts for 21-28 days on 12-(Griener Bio-One, Catalog# 665180) or 24-well (Greiner Bio-One, Catalog# 662160) plates to generate a pseudostratified epithelium. The differentiated ALI cultures contained basal, mucus-producing goblet and ciliated cells. Barrier function of epithelial cells was assessed by measuring Trans-Epithelial Electrical Resistance (TEER) values. 2.5 Submerged monolayer cultures of 1HAEs SV40-transformed normal human airway epithelial cell line, 1HAEo-, was used for experiments (Cozens et al. 1992). Cells were grown in a 5% CO2 atmosphere at 37 °C as submerged monolayer cultures in Dulbecco's Modified Eagle Medium (DMEM) supplemented with 10% Fetal Bovine Serum (FBS). Subcultures were routinely performed before cells reached 80% confluency. Cells grown on 12-well plates (Griener Bio-One, Catalog# 665180) were used for experiments. 2.6 Visualizing interaction of A. fumigatus conidia with primary HBECs grown in ALI at 2, 6, 12 or 24 hours by confocal microscopy The apical side of ALI cultures of primary HBECs (21-28 days old) grown in a 24-well plate (Greiner Bio-One, Catalog# 662160) were co-incubated with A. fumigatus conidia  32 suspended in PBS at a multiplicity of infection (MOI) of 10 conidia/cell. ALI PneumaCult medium (Stemcell Technologies, Vancouver, BC, Catalog # 05001) was added to the basal side for 2, 6, 12 or 24 hours at 37 °C. For each time point, culture medium from the apical and basal side were removed. Both apical and basal sides of the inserts were then washed three times with sterile PBS and fixed in 4% paraformaldehyde (PFA) in PBS for 20 minutes at room temperature (RT). Cells were washed and re-hydrated for 10 mins in PBS at RT.  The apical side was incubated with monoclonal mouse anti-A. fumigatus antibody (Thermo Fisher Scientific, Catalog# MA174434), diluted 1:300 in PBS, overnight at 4 °C. Following two washes with PBS, the apical side of each insert was incubated with goat anti-mouse IgG highly cross-adsorbed secondary antibody conjugated with Alexa Fluor 594 (Thermo Fisher Scientific, Catalog# A-11032), diluted 1:200 in PBS, for 1hr at RT. The inserts were washed with PBS twice. ALI membranes were removed from the inserts and transferred to a chamber slide prior to mounting with ProLongTM Gold Antifade Mountant with DAPI (Thermo Fisher Scientific, Catalog# P36935).  Cover-slipped slides were visualized with Zeiss LSM-800 inverted confocal microscope system using a 63x/1.4NA oil immersion lens (Carl Zeiss Inc, plan-apochromat 63x/1.4 NA Oil DIC M227), collected with the Zen Black software. The laser lines used were 405 nm (DAPI), 488 nm (GFP) and 594 nm (Alexa 594), fired sequentially to avoid cross-talk, and detected at 410-508 nm, 490-606 nm and 605-734 nm, respectively (Table 2.1).    33 Table 2.1 Confocal microscope settings for acquired images after 6 and 24 hours. Zeiss LSM-800 inverted confocal microscope settings ALI cultures of primary HBECs exposed to A. fumigatus conidia for 6 hours (Figure 3.2) Figure 2.3: ALI cultures of primary HBECs exposed to A. fumigatus conidia for 24 hours (Figure 3.3) Lens 63x/1.4NA Oil 63x/1.4NA oil Dimension size 512 x 512 pixels, 16-bit 1912 x 1912 pixels, 12-bit Image size 67.48 x 67.48 μm 134.95 x 134.95 x 104.25 μm Lasers Track 1 594 nm : 70% Track 2 488 nm : 6.0% Track 3 405 nm : 2.0% Track 1 594 nm : 60% Track 2 488 nm : 8.0% Track 3 405 nm : 3.0% Filters Track 1 Ch2 : 599-734 Track 2 ChS1 : 490-606 Track 3 Ch1 : 410-508 Track 1 Ch2 : 599-734 Track 2 ChS1 : 490-606 Track 3 Ch1 : 410-508 Master gain Track 1 Ch2: 666 Track 2 ChS1: 716 Track 3 Ch1: 652 Track 1 Ch2: 708 Track 2 ChS1: 750 Track 3 Ch1: 670 Pinhole Track 1 Ch2: 68 μm Track 2 ChS1: 90 μm Track 3 Ch1: 41 μm Track 1 Ch2: 58 μm Track 2 ChS1: 48 μm Track 3 Ch1: 41 μm Digital gain Track 1 Ch2: 1.00 Track 2 ChS1: 1.00 Track 3 Ch1: 1.00 Track 1 Ch2: 1.00 Track 2 ChS1: 1.00 Track 3 Ch1: 1.00 2.7 Visualizing interaction of A. fumigatus conidia with submerged monolayer cultures of 1HAEo- cells at 6 hours by confocal microscopy 1HAEo- cells grown in a 24-well plate were co-incubated with A. fumigatus conidia (MOI=10 conidia/cell) suspended in DMEM + 10% FBS for 6 hours at 37 °C. After 6 hours, cells were washed once with sterile PBS prior to the addition of 0.25% Trypsin. The plate was incubated for 5 minutes at 37 °C  after which DMEM + 10% FBS was added to neutralize the trypsin. The cells were collected in a microcentrifuge tube and centrifuged for 5 mins at 1000 g to generate a cell pellet. The supernatant was removed and the pellet washed twice with RT PBS . The pellet was fixed with 4% PFA for 20 minutes at RT. The fixative was removed and cells  34 were re-hydrated for 10 mins in PBS at RT. Another PBS wash (5 minutes) was conducted and the cell pellet was incubated with monoclonal mouse anti-A. fumigatus antibody (Thermo Fisher Scientific, Catalog# MA174434), diluted 1:300 in PBS, overnight at 4 °C. Following washing twice with PBS, cells were incubated with goat anti-mouse IgG highly cross-adsorbed secondary antibody conjugated with Alexa Fluor 594 (Thermo Fisher Scientific, Catalog# A-11032), diluted 1:200 in PBS for 1hr at RT. The cell pellet was washed twice with PBS and 1 μg/μl of DAPI in PBS (1:1000 dilution) was added and incubated for 5 minutes at RT. After two washes with PBS, 10 μl of cell pellet was transferred to a chamber slide before cover-slipping. Cover-slipped slides were visualized with Zeiss LSM-800 inverted confocal microscope system using a 63x/1.4NA oil immersion lens (Carl Zeiss Inc, plan-apochromat 63x/1.4 NA Oil DIC M227), collected with the Zen Black software. The laser lines used were 405 nm (DAPI), 488 nm (GFP) and 594 nm (Alexa 594), fired sequentially to avoid cross-talk, and detected at 410-508 nm, 490-606 nm and 605-734 nm, respectively. 2.8 DNA, RNA and protein preparation from ALI cultures  DNA, RNA and proteins were extracted from ALI cells from experiment #1 and experiment #2. As shown in Figure 2.1, experiment #1 consisted of 6 ALI samples: ALI cultures of primary HBECs were incubated with PBS alone (n=3) and A. fumigatus conidia suspended in PBS, MOI= 10 conidia/cell (n=3), on the apical side, for 6 hours at 37 °C. Experiment #2 consisted of 12 ALI samples: ALI cultures of primary HBECs were incubated with PBS alone (n=3), A. fumigatus conidia in PBS (MOI= 10 conidia/cell (n=3)), Dkdnase A. fumigatus conidia suspended in PBS (MOI= 10 conidia/cell (n=3)), and RSV suspended in PBS (MOI=1 RSV/cell (n=3)), on the apical side for 6 hours at 37 °C.  35  The basal sides of ALI cultures in both experiments were incubated with ALI PneumaCult medium (Stemcell Technologies, Vancouver, BC, Catalog #05001). For each experiment, 6 hours post-exposure to PBS, conidia in PBS, or RSV in PBS, the culture supernatants from both apical and basal sides were removed. Both sides of the membrane were washed three times with sterile PBS to remove any unbound conidia. The membrane of each insert was detached with a sterile pipette tip, by gently pushing on the edge, and collected in a microcentrifuge tube. Lysis Buffer Q (300 μl) was added to each tube according to the standard operating protocol of the RNA/DNA/Protein Purification Plus Micro Kit (Norgen Biotek Corp, Item# 51600). Membranes were stored at -80 °C until DNA, RNA and proteins extractions were performed (according to the protocol provided by RNA/DNA/Protein Purification Plus Micro Kit (Norgen Biotek Corp, Item# 51600).  2.9 DNA, RNA and protein preparation from 1HAEs cultures DNA, RNA and Protein were also extracted from experiment #3 conducted using 1HAEs cultures (Figure 2.1). Prior to exposure to A. fumigatus conidia, DMEM+10% FBS was aspirated using a sterile glass pipette from 1HAE cells grown in a 12-well plate. 1HAE cell cultures were incubated with DMEM+10% FSB alone (n=3) or A. fumigatus conidia suspended in DMEM+10% FBS (MOI= 10 conidia/cell (n=3)) for 6 hours at 37 °C. The culture supernatants were removed after 6 hours. Each well was washed with sterile PBS three times to remove any unbound conidia. To each well, 0.25% trypsin was added and the plate was incubated at 37 °C for 5 mins. DMEM+ 10% FBS was added to neutralize the trypsin. The cell suspension from each well was collected in 2 ml microcentrifuge tubes and centrifuged at 1000 g for 5 mins. Cell pellet was washed twice with PBS and Lysis Buffer Q was added to each tube according to the standard  36 operating protocol of the RNA/DNA/Protein Purification Kit (Norgen Biotek Corp, Item # 51600). The cell pellets suspended in Lysis Buffer Q were stored in -80 °C until extractions were performed (according to the protocol provided by RNA/DNA/Protein Purification Plus Micro Kit (Norgen Biotek Corp, Item# 51600).  2.10 NanoString nCounter RNA transcript expression analysis RNA yield from each sample in all three experiments were determined using a NanoDrop ND-100 spectrophotometer (Thermo Scientific, Wilmington, DE) and RNA integrity was determined using a 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA). 2.10.1 nCounter Immune Profiling Panel The RNA transcript abundance was analyzed using the NanoString nCounter Immune Profiling panel (NanoString Technologies, Seattle, WA) from 100 ng of extracted RNA from ALI cultures (n=6 for experiment #1 and n=12 for experiment #2) and 1HAEs cultures (n=6 for experiment #3) (Figure 2.1). Twelve samples were analyzed using the nCounter Immune Profiling Panel each time: 6 ALI samples from experiment #1 plus 6 1HAE cultures in experiment #3, and 12 ALI samples from experiment #2 were assessed, respectively.  The Immune Profiling panel consisted of 770 genes (730 well-annotated immune genes and 40 housekeeping genes). Briefly, 70 µl of hybridization buffer was added to Reporter CodeSet (XT Formulation, Lot# RC4887X1 and Lot# RC5148X1) to prepare the master mix. To set up the hybridization reactions, each sample tube contained 8 μl of master mix and 5 μl of extracted RNA sample. Capture ProbeSet (2 μl) (XT Formulation, Lot# CP4887X1 and Lot# CP5148X1) was added to each tube. Samples were hybridized at 65 °C for 19 hours. The hybridized samples were analyzed using FLEX system’s nCounter Prep Station using the high  37 sensitivity protocol, and the cartridge was scanned using Maximum resolution (Max FOV) in the nCounter Digital Analyzer to generate RCC files.  2.10.2 nCounter Asthma Elements Panel The RNA transcript abundance was analyzed from 100 ng of extracted RNA using the nCounter Asthma Elements Panel (NanoString Technologies, Seattle, WA) using ALI samples from experiment #1 (n=6) and 1HAEs samples from experiment #3 (n=6) (Figure 2.1). The Asthma Elements panel consisted of 180 genes (Singh et al. 2018).  To set up the hybridization reactions, each sample tube consisted of 10 μl of hybridization buffer, 5 μl of TagSet Master mix (TagSet-168, Lot #TS4004), 5 μl of Extension TagSet (TagSet-ex24, Lot #TS4004), 1 μl of 30x working probe A pool (inactive probe was added), 1 μl of 30x working probe B pool, 3 μl of DNAase/RNAase free water and 5 μl of RNA Sample (20 ng/μl), for a total volume of 30 μl. The samples were hybridized at 67 °C for 16 hours. The hybridized samples were analyzed FLEX system’s nCounter Prep Station using the high sensitivity protocol, and the cartridge was scanned using Maximum resolution (Max FOV) in the nCounter Digital Analyzer to generate RCC files.  2.11 Shotgun proteomics analysis using Liquid chromatography-tandem mass spectrometry (LC-MS/MS) of ALI and 1HAE cultures Extracted proteins from ALI samples in experiment #1 (n=6) and 1HAEs samples in experiment#3 (n=6) were assessed using liquid chromatography-tandem mass spectrometry (LC-MS/MS). The 6 samples of both ALIs and 1HAEs experiment consisted of control (PBS alone, n=3) and infected samples (A. fumigatus conidia suspended in PBS, n=3), which were solubilized in a small volume of 6 M urea/2 M thiourea (in 10mM Hepes, pH 8.0). Proteins were then  38 precipitated using ethanol-acetate method (Foster, de Hoog, and Mann 2003). The protein concentrations of the samples were measured by the Bradford assay, followed by digestion in solution using Trypsin and Lys-C according to reference (Foster, de Hoog, and Mann 2003). Digested peptides were purified and concentrated on C18 STAGE-tips, eluted in 80% acetonitrile, 0.5% acetic acid, and dried in a vacuum concentrator (Eppendorf).  Dried peptides were re-suspended in 100 mM triethylammonium bicarbonate and chemical di-methylation labeling was performed using light (12CH2O) or heavy (13CD2O) isotopologues of formaldehyde. The light label was used for control samples and the heavy label for the infected samples. Light sodium cyanoborohydride solution (1M) was added to the light labeled samples and 1 M heavy sodium cyanoborodeuteride to the heavy labeled samples. The samples were vortexed and incubated at ambient temperature in the dark for 90 minutes. NH4Cl (3 M) was added to the samples after which they were incubated at ambient temperature in the dark for 10 minutes. Samples were acidifed to pH < 2.5 by adding 1% trifluoroacetic acid (TFA). After full sodium cyanoborohydride degradation, each heavy labeled sample was combined with the light labeled sample (n=3 for each experiment) and STAGE-tip purified. Eluted samples were dried and re-suspended in 20% acetonitrile and 0.1% formic acid for subsequent fractionation. Peptides were separated offline using basic reverse phase fractionation as described previously (Udeshi et al. 2013).  Peptides fractions were analyzed by a quadrupole–time of flight mass spectrometer (Impact II; Bruker Daltonics) coupled to an Easy nano LC 1000 HPLC (ThermoFisher Scientific) using an analytical column that was 40–50 cm long, with a 75-μm inner diameter fused silica with an integrated spray tip pulled with P-2000 laser puller (Sutter Instruments) and packed  39 with 1.9 μm diameter Reprosil-Pur C-18-AQ beads (Maisch, http://www.Dr-Maisch.com). The columns were operated at 50 °C using an in-house built column heater. Buffer A consisted of 0.1% aqueous formic acid, and buffer B consisted of 0.1% formic acid and 80% (vol/vol) acetonitrile in water. A standard 90-min peptide separation was performed, and the column was washed with 100% buffer B before re-equilibration with buffer A.  The Impact II was set to acquire in a data-dependent auto-MS/MS mode with inactive focus fragmenting the 20 most abundant ions (one at the time at rate of 18-Hz) after each full-range scan from m/z 200 to m/z 2,000 at 5 Hz rate. The isolation window for MS/MS was 2–3 depending on the parent ion mass to charge ratio, and the collision energy ranged from 23 to 65 eV depending on ion mass and charge. Parent ions were then excluded from MS/MS for the next 0.4 min and reconsidered if their intensity increased more than five times. Singly charged ions were excluded from fragmentation.  Raw mass spectrometry data was analyzed using MaxQuant 1.5.1.0. The search was performed against a database comprised of the protein sequences from Uniprot’s human and A. fumigatus entries plus common contaminants with cysteine carbamidomethylation and methionine oxidation, protein N-acetylation as fixed and variable modifications, respectively. Light and heavy dimethylation at lysine side chains and peptide N-termini were used for quantitation. Peptides and proteins identified with false discovery rate (FDR) ≤1% were retained for further analyses.   40 2.12 Statistical analyses of RNA transcript abundance in ALIs and 1HAEs cultures 2.12.1 Pre-processing  The following pre-processing guidelines were applied to 6 ALI samples from experiment #1, 12 ALI samples from experiment #2, and 6 1HAEs samples from experiment #3, respectively.  RCC files generated by NanoString were imported to R Studio to assess quality of all samples using the following quality control (QC) parameters: a) Imaging QC- Each lane is imaged in discrete units called Fields of View (FOVs). All samples had FOV registration (FOV Count (number of FOVs for which imaging was attempted)/ FOV Counted (number of FOVs successfully imaged)) more than 75%. b) Binding Density QC- Binding density is the measure of the number of optical features per square micron. If too many codes overlap, binding density is high. All samples had the binding density between 0.005-2.25. c) Positive Control Linearity QC- There are six positive control corresponding to six different concentration in 30 μl hybridization- 128 fM, 32 fM, 8 fM, 2 fM, 0.5 fM and 0.125 fM. All samples had correlations greater than r=0.9 for the positive controls. d) Positive Control Limit of Detection QC- This is an estimate of systemic background controls within any single hybridization reaction. All samples had counts for 0.5 fM positive control above the mean of negative controls.  41 e) Positive Control Scaling Factor QC- All lanes had positive control scaling factor within a range of 0.3-3 (If outside the range, it may indicate under-performance of the lane). All samples were normalized using the positive controls to normalize for all platform associated sources of variation in each experiment. To do this, geometric mean of positive controls for all samples was calculated, which was then divided by geometric mean of each sample, to generate a positive control normalizing factor. The raw counts were multiplied by the positive control normalization factor. Genes with positive control normalized counts for at least 2 samples less than the maximum value of negative controls were excluded in each experiment. All samples from each experiment were then normalized separately using total sum normalization. To do this, counts of all genes were summed for each sample. Total sum normalizing factor for each sample was then calculated by dividing the mean sum of all samples by the sum of each sample. All genes in each sample were multiplied by total sum normalization factor. 2.12.2 Differential abundance analysis 2.12.2.1 Differential abundance analyses results in Chapter 3 Chapter 3 reports differential RNA transcript abundance analyses of ALI cultures using Immune Profiling Panel and Asthma Elements Panel. ALI cultures incubated with PBS alone and A. fumigatus conidia suspended in PBS from both experiment #1 and experiment #2 were analyzed using the Immune Profiling Panel. These samples were combined together after total sum normalization was performed for differential abundance analysis (total n=12, n=6 for control (PBS alone) and n=6 for infected (PBS+ A. fumigatus conidia). All 12 samples were  42 adjusted for batch effects using ComBat function in the Surrogate Variable Analysis (sva) package [version 3.22.0] in R statistical computing program.  ALI cultures incubated with PBS alone and A. fumigatus conidia suspended in PBS from experiment #1 were analyzed using Asthma Elements Panel. Differential abundance of RNA transcripts in ALI cultures of primary HBECs upon exposure to A. fumigatus was determined using least squares regression in the Linear Models for MicroArrays (LIMMA) [version 3.30.13] package. TEER values were added as a co-variate to the linear model. A p-value < 0.05 was considered statistically significant and the Benjamini-Hochberg False Discovery Rate (BH-FDR) of 30% was applied as well. All software packages used to perform differential expression analyses were accessed through The Comprehensive R Archive Network (CRAN) (https://cran.rproject.org/).  2.12.2.1 Differential abundance analysis results in Chapter 4 Chapter 4 reports differential RNA transcript abundance analyses of 1HAEs cultures incubated with PBS alone (control, n=3) and A. fumigatus conidia suspended in PBS (infected, n=3), analyzed using Immune Profiling Panel and Asthma Elements Panel. Differential abundance analysis of RNA transcripts in submerged monolayer cultures of 1HAEs upon exposure to A. fumigatus conidia for 6 hours was determined using least squares regression in the Linear Models for MicroArrays (LIMMA) [version 3.30.13] package in R statistical computing program. A p-value < 0.05 was considered statistically significant and the BH-FDR of 30% was also applied. In addition, 12 ALI samples from experiment #2 were also assessed using Immune Profiling Panel. Differential abundance analysis was conducted by comparing ALI cultures  43 incubated with PBS alone (control, n=3) to ALI cultures incubated with Δkdnase A. fumigatus (Δkdnase infected, n=3) or RSV (RSV infected, n=3). Similarly, least squares regression in the Linear Models for MicroArrays (LIMMA) [version 3.30.13] package in R statistical computing program was used. A p-value < 0.05 was considered statistically significant and the BH-FDR of 30% was also applied. Lastly, differential abundance analysis was also conducted between 4 High TEER samples and 2 low TEER samples in experiment #1, without accounting for the addition of conidia. 2.13 Statistical analysis of protein expression  2.13.1 Pre-processing of ALI samples In all three samples from experiment #1, 2875 proteins were quantified. Protein ratios were log2 transformed. To confirm for MaxQuant normalized ratios, median had to be zero for the log2 transformed ratios in each sample. Proteins with at least 2 out of 3 samples with quantification events were included in the analysis. Of the 2875 proteins, 1793 proteins remained after filtering.  2.13.2 Pre-processing of 1HAEs cultures In all three samples, 1247 proteins were quantified. Protein ratios were log2 transformed. Since MaxQuant normalized ratios did not have a median of zero, log2 transformed data distributions of all samples were shifted to the median of zero. Proteins with at least 2 out of 3 samples with quantification events were included in the analysis. Of the 1247 proteins, 553 proteins remained after filtering.   44 2.13.3 Differential abundance analyses  Differential abundant analysis was conducted in (LIMMA) [version 3.30.13] in R for both experiment #1 of ALIs and experiment #3 of 1HAEs. Model matrix was created to test if ratios were different from 1 using moderated t-test in LIMMA. A p-value < 0.05 was considered statistically significant. A BH-FDR of 30% was also applied. 2.14 Bioinformatics analysis Pathway enrichment analyses of differentially abundant RNA transcripts and proteins were conducted in Enrichr (http://amp.pharm.mssm.edu/Enrichr/) (E. Y. Chen et al. 2013; Kuleshov et al. 2016). Data were also analyzed using Ingenuity Pathway Analysis (IPA) (QIAGEN Inc., https://www.qiagenbioinformatics.com/products/ingenuity-pathway-analysis) to assess the top networks associated with differentially abundant proteins (Krämer et al. 2014). The Cytoscape plug-in, ClueGo [Version 2.5.0] + CluePedia [Version 1.5.0] (Bindea et al. 2009; Bindea, Galon, and Mlecnik 2013) was used to generate functionally grouped networks of enriched gene ontology (GO) terms associated with biological processes, molecular function and cellular components for differentially abundant RNA transcripts (p-value < 0.01).  Gene Ontology Consortium’s PANTHER Overrepresentation Test (http://www.geneontology.org/page/go-enrichment-analysis) was also used to generate enriched GO terms for biological processes, molecular functions and cellular components of differentially abundant proteins (The Gene Ontology Consortium 2017; Ashburner et al. 2000). Homo sapiens was used as a reference list and Fisher’s Exact with FDR multiple test correction was used (BH-FDR < 0.05).  45 Chapter 3 Host response to Aspergillus fumigatus conidia in an air-liquid interface model of human bronchial epithelium 3.1 Introduction Cell culture model systems have been important for studying basic cell biology, replicating disease mechanisms and for testing novel drug compounds over the past decades (Segeritz and Vallier 2017). Substantial work has been conducted using cell culture models to understand the interaction between the airway epithelium and the airborne fungal pathogen, Aspergillus fumigatus (A. fumigatus). However, most of these studies have utilized submerged monolayer cultures to model the airway epithelium; in particular, using bronchial epithelial cells and type II alveolar epithelial cells. Studies have yet to be conducted using type I alveolar cells. (Croft et al. 2016). The human bronchial epithelial cell lines such as BEAS-2B (Albright et al. 1990; Balloy et al. 2008; Fekkar et al. 2012) and 16HBE14o- (Forbes et al. 2003; Gomez et al. 2010), have been primarily used to model bronchial epithelial infections, such as those occurring in individuals with allergic bronchopulmonary aspergillosis (ABPA), a disease that affects asthmatics and patients with cystic fibrosis (Knutsen and Slavin 2011; Tracy et al. 2016). 16HBE14o- cells have been shown to internalize 30-50% of bound conidia within 6 hours of co-incubation (Gomez et al. 2010). To investigate A. fumigatus infections of the lower airways, e.g., invasive aspergillosis (Dagenais and Keller 2009; Espinosa and Rivera 2016),submerged monolayer cultures of A549o-, a type II pneumocyte cell line derived from human lung carcinoma, have been extensively used (Julie A. Wasylnka and Moore 2002; Alekseeva et al. 2009). A549 cells internalize 30% of bound conidia (Julie A. Wasylnka and Moore 2002). In  46 addition, one study employed a co-culture model of the human alveolus with primary human pulmonary artery endothelial cells and A549 epithelial cells to model IA (Gregson, Hope, and Howard 2012). The advantages of these cell line are their cost-effectiveness, ease to use and indefinite growth; however, as these cells are transformed, their responses to pathogens may differ from the intact host (Kaur and Dufour 2012). Some investigators have used submerged monolayer cultures of primary human airway epithelial cells to gain insights into the host-pathogen interaction (Oguma et al. 2011; Oosthuizen et al. 2011). In contrast to cell lines, primary cells are not transformed and, at least during early passages, are a better model of the physiological state of the cells in-vivo. Key experiments conducted using cell lines are usually repeated with primary cells (Kaur and Dufour 2012). However, conventional submerged monolayer cultures of primary cells still have limitations such as they allow only one cell type to be cultured in a monolayer; consequently, they do not possess the varied cell types and complex functionality of the in-vivo epithelium (O’Boyle et al. 2017). A few studies have analyzed the interaction of lung epithelial cells and A. fumigatus in-vivo using animal models (Kurup and Grunig 2002); however, the molecular response is complex, and it is difficult to collect data from a single epithelium. Furthermore, there are also quantitative and qualitative differences between the anatomy, physiology and molecular responses of humans and other animals (Kheradmand et al. 2002; Porter et al. 2009). As an alternative to submerged cultures, others have successfully grown bronchial epithelial cells at an air-liquid interface (ALI) for 21-28 days to generate polarized epithelial cells that possess tight junctions and form a pseudo-stratified epithelium that contains basal cells, mucus- 47 secreting goblet cells and ciliated cells. For example, phagocytosis of A. fumigatus conidia by epithelial cells has been shown using 14-day old ALI cultures of human primary nasal epithelial cells (Botterel et al. 2008) and porcine tracheal epithelial cells (Khoufache et al. 2010). However, to our knowledge, there have been no studies of the early molecular response of the host airway cells to A. fumigatus conidia that use well-differentiated ALI cultures (21-28 days old) of human bronchial epithelial cells. Submerged cell culture models have been used to assess conidial internalization by cells upon exposure to A. fumigatus (Paris et al. 1997; Julie A. Wasylnka and Moore 2003, 2002; Botterel et al. 2008; Han et al. 2011; Rammaert et al. 2015). Other studies quantified the A. fumigatus-induced release of cytokines (Zhang et al. 2005; W.-K. Sun et al. 2012; Tomee et al. 1997; Borger et al. 1999; Kauffman et al. 2000; Bellanger et al. 2009) or activation of signaling proteins and pathways (Han et al. 2011; Sharon et al. 2011; Balloy et al. 2008). To date, there have been few studies that utilize high-throughput “omics” techniques to study the early molecular response of human bronchial epithelial cells to A. fumigatus. Over the past decades, “omics” techniques have emerged as effective tools in basic, translational and clinical research that provide a better understanding of the complex host response and reveal novel molecular mechanisms in host-pathogen interactions (Culibrk, Croft, and Tebbutt 2016; Jean Beltran et al. 2017). The transcriptomic response of cultured lung epithelial cells, 16HBE14o- and A549, to A. fumigatus has been studied using genome-wide microarray analysis (Gomez et al. 2010; Sharon et al. 2011). Oosthuizen et al. (2011) used a dual-organism transcriptomic approach to profile both host and A. fumigatus responses in parallel. More recently, the transcriptome of the  48 transformed human lung epithelial cell line (A549) interacting with A. fumigatus was assessed using RNA-sequencing (Chen et al. 2015). Proteomic analyses include a secretome analysis of cultured human bronchial epithelial cells (BEAS-2B) in response to A. fumigatus using differential in-gel electrophoresis (Fekkar et al. 2012). Since previous studies have primarily utilized submerged monolayer cultures, more work needs to be conducted using differentiated cultures of primary cells. The aims of the research outlined in this chapter were to investigate the uptake of A. fumigatus conidia by primary human bronchial epithelial cells (HBECs) grown as ALI cultures that contained basal, mucus-secreting goblet and ciliated cells, and analyze the early molecular response of HBECs upon exposure to A. fumigatus conidia using transcriptomic and proteomic analyses. The immune response of the host upon interaction with A. fumigatus was assessed by analyzing the transcriptomics, and an unbiased approach was used to study the molecular response using proteomics. The hypothesis for this chapter is that a multi-OMIC molecular approach in a co-culture model that closely mimics the in-vivo airway epithelium will provide novel insights into the early molecular response by the host upon interaction with A. fumigatus conidia. 3.2 Overview of experimental design for transcriptomic and proteomic studies To determine the early molecular response of ALI cultures of primary HBECs to exposure to A. fumigatus conidia, two separate experiments were conducted (each using cells from a different donor) (Figure 3.1). For each experiment, ALI cultures of primary HBECs were incubated with PBS alone (control , n=3) or A. fumigatus conidia suspended in PBS, MOI=10 conidia/cell (infected, n=3). The basal sides of ALI cultures were incubated with ALI PneumaCult  49 medium (Stemcell Technologies, Vancouver, BC, Catalog #05001). The transcriptome was analyzed using nCounter Immune Profiling Panel (n=12, control (n=6) and infected (n=6)) and nCounter Asthma Elements Panel (n=6, control (n=3) and infected (n=3)). Shotgun proteomics was conducted using Liquid Chromatography Tandem Mass Spectrometry (LC-MS/MS). Differentially abundant RNA transcripts and proteins upon exposure to A. fumigatus were identified.   Figure 3.1: Experimental design for transcriptomics and proteomics analyses.  Two separate experiment were performed, each with 3 control and 3 infected samples. The control samples were incubated with PBS alone and infected samples with A. fumigatus conidia suspended in PBS for 6 hours at 37 °C. The 6 samples from experiment #1 were analyzed using nCounter Asthma Panel and LC-MS/MS. For nCounter Immune Profiling Panel, 6 samples from experiment #1 and 6 samples from experiment #2 were analyzed. Experiment #1 Infected  (n=3)  MOI=10 conidia/cell  Control (n=3)  Experiment #2 Infected  (n=3)  MOI=10 conidia/cell  Control (n=3)  DNA/RNA/Protein extracted  (6-hour incubation at 37°C) DNA/RNA/Protein extracted (6-hour incubation at 37°C) nCounter Asthma Elements Panel LC-MS/MS nCounter Immune Profiling Panel  50 3.3 Results 3.3.1 Visualizing interaction of A. fumigatus conidia in well-differentiated ALI cultures of primary HBECs using confocal microscopy Well-differentiated ALI cultures were exposed to GFP-expressing A. fumigatus conidia for 2, 6, 12 or 24 hours to investigate how primary HBECs interact with A. fumigatus conidia. The extent of conidial internalization was assessed by visualizing differentially stained conidia using confocal microscopy. After 2 hours, ALI cultures of primary HBECs had a small number of conidia bound but no internalization was observed. After 6 hours, only few conidia were bound, and less than 1% of bound conidia were estimated to be internalized as shown in Figure 3.2.  After 12 hours, the number of conidia internalized appeared to be similar to that after 6 hours. However, after 24 hours, hyphae formation was observed from the bound conidia (Figure 3.3). Increased mucus production was observed when supernatants were removed from the apical side of ALI cultures after each time-point; this was only observed in the samples containing conidia. It is likely that the mucus production reduced conidial access to the epithelial surface. Therefore, gene expression and proteomic studies were conducted using cultures at 6 hours post-exposure to elucidate the early molecular response of the airway epithelium, prior to significant fungal growth. This time-point is also consistent with the previously published studies using submerged monolayer cultures (Gomez et al. 2010; Oosthuizen et al. 2011).    51        Figure 3.2 Differential staining of extracellular and internalized conidia by anti-A. fumigatus antibody using confocal microscopy at 6 hours post-infection. GFP-expressing A. fumigatus conidia and primary HBECs grown in ALI were co-incubated for 6 hours, fixed and stained with DAPI to label cell nuclei, and a monoclonal anti-A. fumigatus antibody was used to label extracellular conidia, before visualization using confocal microscopy. One representative field is shown in the following channels: A) wavelength 594nm for anti-A. fumigatus antibody (red); B) wavelength 495nm for GFP (green); C) wavelength 405nm for DAPI (blue); D) merged GFP, anti-A. fumigatus antibody and DAPI image. Conidia not labeled by the anti-A. fumigatus antibody and only visible in the green but not the red channel were considered to be internalized by ALI cultures of primary HBECs (shown with arrows). Field of view is 1912x1912 pixels, and scale bar is 10 μm.  A B C D  52         Figure 3.3: Differential staining of extracellular and internalized conidia by anti-A. fumigatus antibody using confocal microscopy after 24 hours of co-incubation. GFP-expressing A. fumigatus conidia and primary HBECs grown in ALI were co-incubated for 24 hours and processed as described in Figure 3.2 legend. A) wavelength 594nm for anti-A. fumigatus antibody (red); B) wavelength 495nm for GFP (green); C) wavelength 405nm for DAPI (blue); D) merged GFP, anti-A. fumigatus antibody and DAPI image. Hyphae (white arrows) germinated from the bound conidia are shown; all hyphae were extracellular as evidenced by the green and red fluorescence. Field of view is 512x512 pixels with a zoom of 2x, and the scale bar is 10 μm.    A B C D  53 3.3.2 Quantification and quality assessment of RNA samples   The concentrations of RNA extracted from experiment #1 and experiment #2 are shown in Table 3.1. RNA integrity was also assessed to ensure that the RNA was not degraded and of good quality. The RIN scores of all samples were acceptable (typically, values >8.0) (Table 3.1), except that sample infected-2 from experiment #1 was not quantified. However, further inspection of the specific chromatogram peaks for 18S and 28S indicated that total RNA for this sample was intact (data not shown). Table 3.1: Trans-Epithelial Electrical Resistance (TEER) Values of 12 ALI cultures exposed to A. fumigatus conidia.  Experiment #1 12-well plate format Experiment #2 24-well plate format Sample TEER Value (ohms) RNA concentration (ng/µl) RIN Sample TEER Value (ohms) RNA concentration (ng/µl) RIN Control-1 360 251.08 7.60 Control-1 1030 74.54  8.40 Control-2 440 150.00 10 Control-2 1029 75.9 8.50 Control-3 130 92.91 10 Control-3 1022 66.25 8.10 Infected-1 405 245.22 9.60 Infected-4 1268 71.69 9.30 Infected-2 380 195.86 N/A Infected-5 1056 106.1 9.40 Infected-3 115 64.93 8.20 Infected-6 1197 80.32 8.10 3.3.3 Analysis of RNA transcript response to A. fumigatus Preliminary analysis of samples from experiment #1 using principal component analysis (PCA) plot showed that majority of variation between control and infected samples was due to the differences between the TEER values of the samples, rather than due to the presence of the A. fumigatus conidia (Figure 3.4). Hence, in experiment #2, ALI cultures with approximately similar TEER values, as shown in Table 2.1, were selected to reduce variation associated with differences between TEER values. In addition, TEER values were included as a co-variate in the  54 linear models, as previously mentioned. The two samples with low TEER values (control-3 and infected-3) were also grouped together, separately from the other ten samples of well-differentiated ALIs.  Figure 3.4: Principal Component Analysis of 6 ALI samples from experiment #1. As shown, principal component 1 (PC1) is describing 63% of variation between 4 samples (control-1-Exp1=360, infected-1-Exp1=405, control-2-Exp1=440, infected-2-Exp1=380 ) with high TEER values and 2 samples (control-3-Exp1=130, infected-3-Exp1=115)  with low TEER values. Hence, majority of variation between control and infected samples is due to the differences in TEER value. 3.3.3.1 nCounter Asthma Elements Panel Transcriptomics of immune related genes associated with Asthma in ALI cultures of primary HBECs grown were analyzed upon interaction with A. fumigatus conidia. Differential abundance analysis of control (n=3) and infected (n=3) samples showed 7 RNA transcripts to be differentially abundant (P-value < 0.5) (Appendix 1). We used a MA plot, a Bland-Altman plot  55 where data was transformed onto M (log2 ratio) and A (mean average) scales, to compare control ALI cultures with infected cultures. Figure 3.5 shows that 3 RNA transcripts were up-regulated and 4 were down-regulated (Figure 3.5). None of the RNA transcripts were significant under BH-FDR < 0.30.   Figure 3.5: MA plot of RNA transcript analysis using NanoString’s Element’s Asthma Panel.  MA plot of RNA transcripts differentially abundant in primary HBECs grown in ALI upon exposure to A. fumigatus  for 6 hours. 7 genes were differentially abundant (P-value < 0.05). Of these, 3 genes were up-regulated (labeled in green) and 4 genes were down-regulated (labeled in blue) upon exposure to conidia.   The 3 up-regulated RNA transcripts were Metallophosphoesterase Domain Containing 1 (MPPED1), Lymphocyte Cytosolic Protein 1 (LCP1) and General Transcription Factor IIH Subunit 2 (GTF2H2). The 4 down-regulated RNA transcripts were Cytokine Inducible SH2 Containing Protein (CISH), Complement C5a Receptor 1 (C5AR1), MAF BZIP Transcription Factor (MAF) and Huntingtin Interacting Protein 1 (HIP1).  56 3.3.3.2 nCounter Immune Profiling Panel Transcriptomics of immune related genes of primary HBECs grown in ALI were analyzed. ALI cultures of both control and infected samples were treated identically, except for the addition of PBS to control and A. fumigatus conidia suspended in PBS to the infected samples. Principal Component Analysis (PCA) plot showed separation between control and infected samples upon batch correction (Figure 3.6B), compared to samples without batch correction (Figure 3.6A).    57 Figure 3.6: Principal Component Analysis (PCA) plot before and after batch correction of all samples.  A) PCA plot of all 12 samples before batch correction. B) PCA plot of all 12 samples after batch correction was performed using ComBat function in SVA package.  A B  58 Of the 359 RNA transcripts assessed for differential abundance analysis, 41 RNA transcripts were differentially abundant (P-value < 0.05) at 6 hours post-exposure to A. fumigatus conidia (Figure 3.7) (Appendix 2). Of these 41, 11 RNA transcripts were significant under BH-FDR < 0.30, labeled in Figure 3.7A. Compared to control ALI cultures, 28 RNA transcripts were up-regulated and 13 RNA transcripts were down-regulated. The up-regulated RNA transcript with maximum Log2 fold change (Log2 fold change=0.9027, p-value= 0.0092, BH-FDR= 0.2998) was CCL15 (C-C Motif Chemokine Ligand 15). For the down-regulated differentially abundant RNA transcripts, CXCL5 (Chemokine (C-X-C motif) Ligand 5) had the maximum Log2 fold change (Log2 fold change=-1.1465, p-value= 0.0320, BH-FDR=0.3315). KEGG pathway enrichment analysis was conducted in Enrichr (http://amp.pharm.mssm.edu/Enrichr/) to determine major biological themes associated with differentially abundant RNA transcripts (E. Y. Chen et al. 2013)(Kuleshov et al. 2016). KEGG pathways related to apoptosis, natural killer cell mediated cytotoxicity, JAK-STAT signaling pathway and NF-kappa B signaling pathway were associated with the 28 up-regulated RNA transcripts in the infected ALI cultures (Figure 3.7B). The 13 down-regulated RNA transcripts were mainly enriched for cytokine-cytokine receptor interaction and genes involved in the complement and coagulation cascades (Figure 3.7B).   59   Figure 3.7: MA plot and pathway enrichment analysis of RNA transcripts differentially abundant in Immune Profiling Panel A) MA plot of RNA transcripts abundant by primary HBECs grown in ALI upon exposure to A. fumigatus 6 hours post-exposure. 41 genes were differentially abundant (P-value < 0.05). Of these, 28 genes were up-regulated (green) and 13 genes were down-regulated (blue) upon exposure to conidia. 11 genes were significant under BH-FDR < 0.30 (labeled genes). B) Enrichr identified enriched KEGG pathways for upregulated (green) and downregulated (blue) RNA transcripts upon exposure to A. fumigatus conidia (Adjusted P-value > 0.05).    A CXCL5, CXCL6 C3, CFB CXCL5, CXCL6, TNFRS11A CXCL5, CXCL6, TNFRS11A, IL2RG SYK, LCK, BCL2L1 IFNL1, IL5RA, IL6R, BCL2L1 SYK, LCK, CASP3, NFATC2 CASP8, CASP3, FADD, CTSS, BCL2L1 B  60 Gene Ontology (GO) enrichment analysis using the Cytoscape plug-in, CLUEGO, showed that differentially abundant RNA transcripts were enriched for death-inducing signaling cellular component, and icosanoid secretion, monocyte chemotaxis, myeloid leukocyte migration were the major biological processes (Figure 3.8).   Figure 3.8: Gene ontology enrichment analysis of differentially abundant RNA transcripts in Immune Profiling Panel.  A) Functionally Grouped network of Gene Ontology enrichment analysis of differentially abundant RNA transcripts in ClueGO App (P-value < 0.1). The differentially abundant RNA transcripts were enriched in death-inducing signaling complex, myeloid leukocyte migration, monocyte chemotaxis, icosanoid secretion, regulation of phagocytosis, cytokine receptor activity, and protein phosphorylated amino acid binding.    61 3.3.4 Analysis of the proteomic response to A. fumigatus LC-MS/MS was used to assess the proteomic response of primary HBECs in ALI to conidial exposure. Untargeted protein differential abundance analysis 6 hours post-exposure to A. fumigatus conidia was assessed in control (n=3) and infected (n=3) samples.  Differential abundance analysis of 1793 proteins in LIMMA using normalized ratios of Heavy (infected) to Light (control) protein samples showed that 153 proteins were differentially abundant 6 hours post-exposure to A. fumigatus conidia in infected samples (Figure 3.9A) (Appendix 3). Of these 153, 22 proteins were significant under BH-FDR < 0.30. Compared to control samples, 73 proteins were up-regulated and 80 proteins were down-regulated. Three proteins, CALR (Calreticulin), NUCB2 (Nucleobindin 2) and SET (SET nuclear proto-oncogene), had fold-changes greater than 2 (Figure 3.9). Of these 3 proteins, CALR had the highest fold change of 5.723. Ingenuity pathway analysis was conducted to analyze the top networks associated with differentially abundant proteins (Krämer et al. 2014); cell cycle, gene expression and tissue morphology was the top network with a score of 53 (Figure 3.10). IPA score is based on the fit of a network to the input of genes. It is generated from the p-value and represents the likelihood of finding the input of genes together in the network. For example, a score of 53 indicates that there is 1E-53 chance that the input genes are together in a particular network due to random chance. Overall, the top 5 networks were associated with cell death, protein synthesis and post-translational modification (Table 3.2).    62   Figure 3.9: Volcano plot and network analysis of differentially abundant proteins identified using shotgun proteomics. Volcano plot of 1793 quantified proteins. Differential abundance analysis showed that 153 proteins were differentially abundant upon 6 hours post-exposure to A. fumigatus (P-Value < 0.05). Of these 153, 73 were upregulated (pink) and 80 were down-regulated (green). Three proteins, SET, NUCB2 and CALR, had a fold-change greater than 2.  63  Figure 3.10 Network analysis of 153 proteins using Ingenuity pathway analysis (IPA). Genes in the top network was related to cell cycle, gene expression and tissue morphology (Score =53). The up-regulated proteins upon exposure to A. fumigatus are shown in green and down-regulated genes are shown in red.  64 Table 3.2: Top 5 networks identified using Ingenuity pathway analysis (IPA).   I also conducted a pathway enrichment analysis for differentially abundant proteins in Enrichr using the Reactome database (http://amp.pharm.mssm.edu/Enrichr/) (E. Y. Chen et al. 2013)(Kuleshov et al. 2016). The enriched pathways for differentially abundant proteins were: Translation, Metabolism of proteins, 3`-UTR mediated translational regulation, Nonsense mediated decay, Major pathway of rRNA processing in the nucleolus and Metabolism of amino acids and derivatives (Table 3.3).    Network Score Cell Cycle, Gene Expression, Tissue Morphology 53 Cancer, Cell Death and Survival, Organismal Injury and Abnormalities 44 Cellular Development, Protein Synthesis, Gene Expression 34 Post-Translational Modification, Protein Degradation, Endocrine System Disorders 27 Drug Metabolism, Protein Synthesis, Renal Damage 23  65 Table 3.3: Enriched Reactome pathways for differentially abundant proteins identified using Enrichr.  ID Term Overlap P-value Adjusted P-value R-HSA-72766 Translation 22/151 3.90703E-22 2.53566E-19 R-HSA-392499 Metabolism of proteins 43/1074 7.60833E-20 2.4365E-17 R-HSA-157279 3' -UTR-mediated translational regulation 18/106 1.57142E-19 2.4365E-17 R-HSA-168255 Influenza Life Cycle 17/136 3.60394E-16 1.7992E-14 R-HSA-1799339 SRP-dependent co-translational protein targeting to membrane 16/107 1.47713E-16 8.71507E-15 R-HSA-927802 Nonsense-Mediated Decay (NMD) 16/106 1.26443E-16 8.20614E-15 R-HSA-6791226 Major pathway of rRNA processing in the nucleolus 14/166 3.73975E-11 8.66821E-10 R-HSA-71291 Metabolism of amino acids and derivatives 17/335 8.42701E-10 1.82304E-08 R-HSA-72163 RNA Splicing - Major Pathway 10/134 8.2771E-08 1.6787E-06 R-HSA-597592 Post-translational protein modification 14/521 4.86567E-05 0.0007702 R-HSA-199977 ER to Golgi Anterograde Transport 7/131 6.75186E-05 0.001043323 R-HSA-381038 XBP1(S) activates chaperone genes 4/53 0.000720512 0.009543107  66 To determine functional processes associated with 73 up-regulated proteins and 80 down-regulated proteins, gene ontology (GO) enrichment analysis was conducted using Enrichment analysis tool in the Gene Ontology Consortium (http://geneontology.org) (Ashburner et al. 2000; The Gene Ontology Consortium 2017). Some of the enriched terms for up-regulated proteins were cadherin binding, spliceosomal complex, endoplasmic reticulum-Golgi intermediate compartment and translation initiation factor activity (BH-FDR < 0.05) (Table 3.4). The down-regulated proteins were associated with extracellular exosome, nonsense-mediated decay, rRNA processing, structural constituent of ribosome, extracellular matrix, oxidation-reduction process (Table 3.5). Some terms related to translation and splicing were enriched in both up- and down-regulated proteins.    67 Table 3.4: Enriched Gene Ontology (GO) terms for 73 up-regulated differentially abundant proteins identified using Gene Ontology Consortium. (MF=Molecular Function; CC=Cellular Component; BP=Biological Processes) GO Term Overlap P-Value FDR MF cadherin binding (GO:0045296) 11/295 9.19E-09 2.12E-05 CC cell-cell adherens junction (GO:0005913) 5/92 2.31E-05 2.11E-03 CC ruffle (GO:0001726) 6/165 3.03E-05 2.24E-03 MF protein complex binding (GO:0032403) 13/796 4.19E-06 2.42E-03 CC spliceosomal complex (GO:0005681) 6/188 6.14E-05 3.46E-03 CC endoplasmic reticulum-Golgi intermediate compartment (GO:0005793) 5/115 6.42E-05 3.51E-03 CC U12-type spliceosomal complex (GO:0005689) 3/27 1.57E-04 7.70E-03 CC precatalytic spliceosome (GO:0071011) 3/29 1.90E-04 8.90E-03 CC peptidase complex (GO:1905368) 4/93 3.76E-04 1.53E-02 MF translation initiation factor activity (GO:0003743) 4/52 4.45E-05 1.86E-02 MF cytoskeletal protein binding (GO:0008092) 12/884 6.14E-05 2.18E-02 CC centriolar subdistal appendage (GO:0120103) 2/9 6.53E-04 2.45E-02 CC centriole (GO:0005814) 4/127 1.16E-03 3.78E-02 MF protein homodimerization activity (GO:0042803) 11/811 1.29E-04 4.27E-02 CC spliceosomal snRNP complex (GO:0097525) 3/63 1.61E-03 4.55E-02 CC cell cortex part (GO:0044448) 4/139 1.61E-03 4.60E-02     68 Table 3.5: Enriched Gene Ontology (GO) terms for 80 down-regulated differentially abundant proteins identified using Gene Ontology Consortium. (MF=Molecular Function; CC=Cellular Component; BP=Biological Processes) GO Term Overlap P-Value FDR CC extracellular exosome (GO:0070062) 43/2757 4.67E-17 8.94E-14 BP nuclear-transcribed RNA catabolic process, nonsense-mediated decay (GO:0000184) 14/119 1.40E-16 2.17E-12 BP SRP-dependent cotranslational protein targeting to membrane (GO:0006614) 12/92 8.61E-15 4.46E-11 BP translational initiation (GO:0006413) 13/143 3.96E-14 7.70E-11 BP rRNA processing (GO:0006364) 14/261 3.42E-12 3.12E-09 MP structural constituent of ribosome (GO:0003735) 12/169 6.19E-12 2.85E-08 CC cytosolic large ribosomal subunit (GO:0022625) 7/65 1.34E-08 1.22E-06 CC cytosolic small ribosomal subunit (GO:0022627) 5/47 1.86E-06 1.15E-04 MP protein binding (GO:0005515) 67/11523 1.16E-06 1.34E-03 MP rRNA binding (GO:0019843) 5/59 5.27E-06 4.05E-03 CC spliceosomal complex (GO:0005681) 6/188 1.17E-04 6.03E-03 CC focal adhesion (GO:0005925) 8/394 1.82E-04 8.95E-03 CC extracellular matrix (GO:0031012) 9/549 3.40E-04 1.42E-02 BP oxidation-reduction process (GO:0055114) 13/949 9.16E-05 1.85E-02 CC integral component of membrane (GO:0016021) 9/68 4.55E-04 1.86E-02 BP carbohydrate metabolic process (GO:0005975) 9/472 1.13E-04 2.25E-02 CC nuclear lumen (GO:0031981) 28/3938 1.06E-03 3.97E-02 3.4 Discussion  The research outlined in this chapter investigated the early molecular response of differentiated primary human bronchial epithelial cells to A. fumigatus conidia using both transcriptomic and proteomic approaches. Confocal microscopy was used to visualize the  69 interaction of the bronchial epithelium with A. fumigatus. Two separate panels were used to analyze the transcriptional response of primary HBECs grown in ALI to A. fumigatus: the Asthma Elements Panel was used to test the applicability of NanoString in detecting the expression of 180 asthma-related genes in bronchial epithelial cells; the Immune Profiling Panel, (770 immune-related genes) was used to assess the immune response of primary HBECs grown in ALI to A. fumigatus. Finally, we used an untargeted approach using Liquid Chromatography-Tandem Mass Spectrometry to assess the proteomic response.  3.4.1 Visualizing interaction of A. fumigatus conidia in well-differentiated ALI cultures of primary HBECs  We demonstrated that although primary HBECs grown in ALI cultures are capable of phagocytosing conidia, even after 6 hours post-exposure, the proportion of bound conidia internalized was estimated to be less than 1%. Multiple studies have reported internalization of conidia by immortalized and primary airway epithelial cells in-vitro and ex-vivo using variety of infection systems and cultures models (Julie A. Wasylnka and Moore 2003, 2002; Paris et al. 1997; Zhang et al. 2005). Cultured bronchial epithelial cells and type II alveolar cells have been shown to internalize approximately 30-50% of bound conidia in both a concentration and time-dependent manner. However, no studies have reported in-vivo internalization of conidia by airway epithelial cells. Furthermore, no data has been published on phagocytosis of conidia by fully-differentiated ALI cultures of primary HBECs (21-28 days of growth). Two previous studies have quantified uptake of A. fumigatus conidia by 14-day old ALI cultures: Botterel et al. (2008) showed that human nasal epithelial cells internalized 21.8 ± 4.5% of bound conidia after 4 hours, and a study by Khoufache et al., (2010) found that 21.9 ± 1.4% of conidia were  70 internalized by porcine tracheal epithelial cells after 8 hours (Botterel et al. 2008)(Khoufache et al. 2010). Another study by Beisswenger et al. (2012) reported that primary HBECs grown in ALI are activated by resting conidia resulting in activation of IFN-b signaling pathway, but no phagocytosis results were reported using live A. fumigatus conidia (Beisswenger, Hess, and Bals 2012). In differentiated ALI cultures of primary HBECs, negligible phagocytosis of conidia was observed up to 12 hours post-exposure, and after 24 hours, bound conidia had germinated and formed hyphae. The lower rate of conidial internalization in our study may have been due to the more differentiated state of ALI cultures; in particular, the presence of goblet cells that secreted mucus. Infected cells contained significantly more mucus than ALI cultures exposed to PBS alone; this was observed during the staining process for confocal microscopic analysis. We speculate that mucus secretion along with ciliated cells may have prevented the level of phagocytosis, reported in un-differentiated, submerged monolayer cultures. Hence, further research needs to be conducted to better understand the role of ciliated and mucus-goblet cells present in differentiated cultures of primary HBECs upon exposure to A. fumigatus in healthy and diseased individuals. These results are supported by those of Rammaert et al, who measured internalization of fungal conidia by the bronchial epithelium of mice in-vivo using transmission electron microscopy. These authors found no phagocytosis of either A. fumigatus or Lichtheimia corymbifera spores in the first 18 hours post-exposure (Rammaert et al. 2015). Despite the low rate of phagocytosis, we observed significant changes in the transcriptome and proteome of ALI cultures exposed to conidia. Thus, the bronchial epithelium of healthy individuals does not  71 require phagocytosis of conidia in large numbers to initiate an immune response, and the effect of fungal conidia on the early host response is mediated either by A. fumigatus conidia binding to host bronchial epithelia, or alternatively, by interaction with molecules secreted by germinating conidia.  We used a relatively high MOI of 10 to assess conidial phagocytosis to ensure that a maximum number of cells interacted with conidia. Although this MOI likely overestimates normal levels of exposure of the in-vivo bronchial epithelium, it is consistent with previous in-vitro studies conducted using cell lines (Julie A. Wasylnka and Moore 2003, 2002; Gomez et al. 2010; Oosthuizen et al. 2011).  For transcriptomic and proteomic studies, we exposed ALI cultures to conidia for 6 hours because our confocal microscopy analysis revealed that phagocytosis did not increase with increasing incubation times, and germination of A. fumigatus conidia occurred at 24 hours. Previous studies from our laboratory have also used 6 hours of conidial exposure (Gomez et al. 2010; Oosthuizen et al. 2011) so we were able to compare the results between the previous submerged culture systems and primary HBECs grown in ALI cultures.  3.4.2 Analysis of primary HBECs ALI cultures transcriptomics to A. fumigatus conidia The NanoString nCounter platform was used to assess host gene expression in differentiated cultures of primary HBECs upon exposure to A. fumigatus conidia. Initial analysis of gene expression changes from experiment #1 indicated that majority of the variation between samples was due to differences between TEER values instead of due to the presence of fungal conidia. Therefore, a strict filtering was used to exclude genes with low expression and to avoid false positives.   72 Of the 770 genes on the Immune Profiling Panel (n=12) and 180 genes on Asthma Elements Panel (n=6), 73 genes overlapped. Hence, Asthma Elements Panel allowed profiling of additional 107 genes as well. The MAF bZIP Transcription Factor (MAF), was among the differentially abundant RNA transcripts in both panels; compared to control samples, MAF was down-regulated in both analyses upon exposure to A. fumigatus, and it was the most significant differentially abundant RNA transcript in the Immune Profiling Panel analysis. MAF is a TH2 associated proto-oncogene, involved in the production of IL-4, a TH2 associated cytokine that promotes differentiation of naïve CD4+ T cells into IL-4 producing TH2 cells (i.e., positive feedback) (J. I. Kim et al. 1999). In cultures of HBECs, IL-4 has been shown to up-regulate the production of monocyte chemoattractant protein-1 (MCP-1) (Ip, Wong, and Lam 2006), which mediates the activation and recruitment of monocytes, mast cells, basophils, eosinophils and TH2 cells from the vascular compartment to bronchoalveolar space (Romagnani 2002). Increased expression of MCP-1 has also been observed in bronchial tissues of asthmatic patients (Sousa et al. 1994). In addition, it has been shown that A. fumigatus elicits a strong TH2 response in ABPA patients (Becker et al. 2015) who have skewed pulmonary immune responses. Hence, downregulation of MAF upon exposure to A. fumigatus in primary HBECs from healthy individuals, may indicate a protective response to the fungus.  RNA transcripts related to complement and coagulation cascades were also differentially abundant in both panels. For example, C3 (Complement C3) and CFB (Complement Factor B) in the Immune Profiling Panel, and C5AR1 (Complement C5a Receptor 1) in the Asthma Elements Panel were down-regulated upon exposure to A. fumigatus. This is  73 consistent with previous findings that showed that A. fumigatus binds to complement regulators to evade host attack mediated by the complement system (Behnsen et al. 2008). Along with MAF, the Immune Profiling Panel revealed differential abundance of RNA transcripts regulating T cell proliferation and the TH1/ TH2 responses as well. These included Interferon-Lamba 1 (IFN-λ1 or IL-29), Indoleamine 2,3-dioxygenase (IDO-1) and Interleukin 5 Receptor Subunit Alpha (IL-5RA). IFN-λ was up-regulated upon exposure to A. fumigatus conidia. Along with having anti-viral properties, IFN-λ is an inhibitor of TH2 responses, limiting the secretion of IL-4, IL-5 and IL-13 cytokines (Koltsida et al. 2011). Reduced expression of IFN-λ was also reported in bronchial epithelial cells of asthmatic individuals upon infection with rhinovirus, indicating that it could play an immune-protective role in lower airways. (Bullens et al. 2008). IDO-1, down-regulated upon exposure to A. fumigatus, is known to catalyze the first step in the degradation of tryptophan (Schmidt et al. 2009). High expression of IDO-1 is thought to be associated with down-regulation of immune response as degradation of tryptophan results in inhibition of T cell proliferation and apoptosis. IL-5RA was up-regulated in ALI cultures of primary HBECs upon exposure to A. fumigatus. IL-5RA is a receptor for Interleukin-5 (IL-5), a TH2 cytokine that promotes eosinophil differentiation, and results in mucus metaplasia and airway eosinophilia upon induction of allergic airway disease in bronchial epithelia of mice (C. A. Wu et al. 2010). Hence, transcriptomics of primary HBECs grown in ALI exposed to A. fumigatus for 6 hours show differential abundance of RNA transcripts regulating both TH1 and TH2 responses. The genes encoding the neutrophil chemoattractants, such as CXCL5 and CXCL6, were down-regulated upon exposure to A. fumigatus whereas Annexin-1 (ANXA1) was up-regulated  74 upon exposure to A. fumigatus. ANXA1 is known to limit neutrophil recruitment and production of pro-inflammatory mediators (Sugimoto et al. 2016). Hence, this may be a protective response in the immunocompetent host to avoid tissue damage from prolonged inflammatory responses. These genes are likely to play an important role in IA, since neutrophils have been shown to control germination of A. fumigatus in-vivo (Feldmesser 2006). The differentially abundant RNA transcripts in the Immune Profiling Panel were also enriched in pathways and gene ontologies related to the innate immune response. These findings are consistent with the previous results from our lab of cell-spore submerged co-cultures using primary airway epithelial cells but not transformed cell lines (Gomez et al. 2010; Oosthuizen et al. 2011). RNA transcripts associated with apoptosis, such as CASP3 (Caspase 3), CASP8 (Caspase 8), FADD (Fas-associated protein with death domain), LCN2 (Lipocalin 2), and BCL2L1 (BCL2 Like 1), were up-regulated in the Immune Profiling Panel suggesting that the interaction of conidia with epithelial cells may promote apoptosis. Apoptosis is non-inflammatory programmed cell death that occurs during cellular homeostasis and morphogenesis, as well as in response to intracellular infections (Thompson 1995). Other forms of cell death include necrosis and pyroptosis. Necrosis is a consequence of physical damage, ROS production or danger signals that results in the release of intracellular contents into the extracellular environment upon cytoplasmic swelling and osmotic lysis. In contrast, pyroptosis results in inflammasome mediated caspase-1 activation and results in release of pro-inflammatory cytokines (Thompson 1995). It has been shown that pathogenic Candida sp. can induce apoptosis in epithelial cells, and pro- and anti-apoptotic genes are activated as early as 6 hours post-infection, followed by necrosis (Villar and Zhao 2010; Moyes et al. 2014). Little is  75 known about the fungal components of A. fumigatus that may induce apoptosis or other forms of cell death. Differential abundance of up-regulated RNA transcripts associated with apoptosis may be due to a secondary metabolite produced by A. fumigatus called gliotoxin. It is known to induce pathways associated with apoptosis in human bronchial epithelial cells by the Bcl-2 pathway (extrinsic pathway) as well as via a caspase-dependent mechanism (intrinsic pathway) (Geissler et al. 2013)(Zheng et al. 2017). Genes upregulated in our study such as CASP8, BCL2L1 are involved in such pathways, and these have been shown to have a role in promoting autophagy as well as apoptosis (Gurung and Kanneganti 2015). Autophagy is a pro-survival mechanism that allows cell to survive prolonged stress caused by infectious agents or nutrient deprivation by clearing damaged proteins, organelles or by providing the cell with energy and anabolic building blocks (Gump and Thorburn 2011). Hence, increased expression of genes associated with apoptosis and autophagy in the presence of A. fumigatus indicates that HBECs may be undergoing cell stress and/or nutrient deprivation, resulting in apoptosis or/and autophagy to prevent fungal invasion of the host. More studies need to be conducted to elucidate the role of defense systems such as apoptosis and autophagy in order to advance our knowledge regarding interaction of A. fumigatus and epithelial cells.  3.4.3 Analysis of primary HBECs ALI cultures proteomics to A. fumigatus conidia  We also quantified changes to the proteome of primary HBECs upon exposure to A. fumigatus conidia. The data revealed proteins regulating the secretory pathway to be significantly up-regulated in the infected samples after 6 hours. Recent studies have also implicated the role of autophagy in the secretory pathways; specifically, autophagy deficiency has been associated with decreased mucus secretion by decreasing generation of ROS, which  76 reduces calcium release from ER. Therefore, up-regulation of RNA transcripts associated with autophagy may be associated with excessive mucus production observed during confocal analysis in primary HBECs upon exposure to A. fumigatus compared to control samples. The top three proteins, CALR, NUCB2 and SET had the highest fold change of all the proteins; these proteins are involved in protein folding and quality control as well as calcium metabolism. CALR, an intracellular chaperone, has been shown to mediate phagocytosis of A. fumigatus conidia by forming a calreticulin-CD91 complex (Wong and Aimanianda 2017). Up-regulation of CALR has also been associated with increased Ca2+ storage capacity in the endoplasmic reticulum (ER) as well as increased sensitivity to apoptosis (Mery et al. 1996; Johnson et al. 2001). NUCB2 is a calcium binding protein (Kalnina et al. 2009), whereas, SET is a multi-tasking protein involved in processes such as apoptosis, transcription, nucleosome assembly and histone chaperoning (Beresford et al. 2001). Up-regulation of proteins related to calcium metabolism and binding, such as CALR and NUCB2, indicates ER stress, which can promote cell-death (apoptosis) or cell-survival (autophagy), and release of Ca2+ from the ER (Sano and Reed 2013). Features of ER stress include high protein demand, infection, inflammatory cytokines and mutant protein expression in the ER. As a response, the unfolded protein response (UPR) is activated (Hetz 2012). Enrichment of pathway and gene ontologies associated with UPR confirms the existence of ER stress in the infected samples.  Activation of UPR can result in the up-regulation of molecular chaperones to assist in protein folding, a halt to protein translation and degradation of misfolded proteins (Hetz 2012). Cell death results when UPR fails to restore ER homeostasis (Oslowski and Urano 2011). In our study, other than CALC, heat shock protein 90α (HSP90AA1 or HSP90α) was also up-regulated  77 upon exposure to A. fumigatus. HSP90α is an isoform of molecular chaperone Hsp90, and has been shown to be up-regulated in the presence of stress (Zuehlke et al. 2015). Furthermore, elevated levels of HSP90α were reported in the serum of individuals with chronic obstructive pulmonary disease (COPD) (Hacker et al. 2009). HSP90AA1 was one of the 8 differentially abundant genes from the current study that was also differently abundant in our previous study (Gomez et al. 2010); however, using the submerged cultures, HSP90AA1 was down-regulated upon exposure to A. fumigatus conidia. Nevertheless, this gene appears to play an important role in the host-pathogen interaction. Along with UPR, ER stress can result in release of Ca2+ from the lumen into the cytosol. High levels of cytosolic Ca2+ have been shown to attenuate nonsense mediated decay (NMD) (Nickless, Bailis, and You 2017). The results from our gene ontology analysis are in agreement with this report: the majority of proteins associated with NMD were down-regulated upon exposure to conidia. Inhibition of NMD can also result from phosphorylation of EIF Factor 2 Subunit Beta (EIF2S2) (Nickless, Bailis, and You 2017). Interestingly, EIF2S2 protein was up-regulated upon exposure to conidia in our study.  We also found that proteins that regulate other translational processes were both up-regulated and down-regulated in primary HBECs upon exposure to A. fumigatus, as indicated by enriched pathways and gene ontologies. For example, proteins associated with eukaryotic translation initiation (EIF) such as EIF factor-3 Subunit J (EIF3J), EIF Factor 2 Subunit Beta (EIF2S2) (as noted above) were up-regulated upon exposure to A. fumigatus conidia. The majority of ribosomal proteins including those from the 60S large subunit (RPL) and 40S small subunit (RPS), such as RPL3, RPS8 , RPS5, were down-regulated upon exposure to A. fumigatus  78 in our study. Ribosomal proteins can play a role in regulating apoptosis, cell cycle and cell proliferation (X. Xu, Xiong, and Sun 2016). Moreover, down-regulation of ribosomal protein synthesis could serve to lower the overall protein traffic into the ER during ER stress.  Proteins regulating cellular processes such as cell cycle progression were also up-regulated upon exposure to A. fumigatus in our study. These included SET and HSP90AA1, as previously mentioned. However, other proteins that we found to be up-regulated upon exposure to A. fumigatus and that were essential for cell cycle progression and formation of cilia included Cenexin (ODF2), Dynactin (DCTN1), FGFR1 Oncogene Partner (FGFR1OP or FOP), Histone deacetylase 1 (HDAC1) and Tumor Protein P53 Binding Protein 1 (TP53B1). HDAC1 is known to be important for cell cycle progression and is associated with chronic lung disease in human. Inhibition of HDACs results in cellular growth arrest, differentiation and apoptosis (Shakespear et al. 2011). TP53B1 binds to tumor suppressor protein p53 and is involved in DNA-damage signaling pathways (Rappold et al. 2001). ODF2 is known to be important component of centrosome and basal body and is necessary for the formation of primary cilia. It is also reported to be up-regulated in quiescent cells (Pletz et al. 2013). DCNT1, is also involved in mitotic spindle assembly and primary cilia formation (Ayloo et al. 2014; T.-Y. Chen et al. 2015). FGFR1OP is known to be essential for ciliogenesis as well (Mojarad et al. 2017). Hence, upregulation of genes regulating cell cycle and formation of cilia in primary HBECs upon exposure to A. fumigatus may be important to prevent fungal invasion of host tissue. Another key process that was enriched in the down-regulated proteins in our study was cellular iron homeostasis (Ferritin Light Chain (FTL), Superoxide Dismutase (SOD1), ATPase H+ Transporting V0 Subunit D1 (ATP6V0D1)). In a previous study using submerged cultures of 1HAE  79 cells, we found that genes associated with iron-uptake were up-regulated in A. fumigatus conidia (Oosthuizen et al. 2011). Moreover, a number of studies have shown that iron acquisition is important for A. fumigatus virulence (Hissen et al. 2005; Schrettl et al. 2004). Specifically, FTL was up-regulated in 16HBE14o- cells upon exposure to A. fumigatus conidia (Gomez et al. 2010). However, down-regulation of proteins such as FTL, an iron-binding protein, increases the amount of free iron available to the fungus (Theil 2004). Since free iron is a catalytic agent for the Fenton reaction that generates free radicals, it can result in oxidative damage in epithelial cells (Antosiewicz et al. 2007). 3.5 Summary The research presented in this chapter demonstrated that, unlike submerged monolayers, primary HBECs grown in ALI internalize less than 1% of bound A. fumigatus conidia. ALI models mimicking the bronchial epithelial barrier in the conductive zone of the respiratory tract can be used to study transcriptomics and proteomics of bronchial epithelial cells upon exposure to A. fumigatus conidia. The major pathways that were enriched in the up-regulated genes upon exposure to A. fumigatus conidia included apoptosis/autophagy, translation, unfolded protein response, and cell cycle. In contrast, complement and coagulation pathways, iron homeostasis, non-sense mediated decay and rRNA binding pathways were down-regulated upon conidial exposure. Stress responses such as autophagy, unfolded response and non-sense mediated decay may protect the host against infection and promote cell-survival.     80 Chapter 4 General applicability of ALI cultures for studying host-pathogen interactions at the molecular level  4.1 Introduction  Due to the extensive costs and time involved in using in-vivo models, there is a need to identify and validate appropriate in-vitro models for studying host-pathogen interactions. Over the past decades, submerged monolayer cultures of continuous cell lines and primary cells have been primarily used to understand immune and molecular responses of the host upon exposure to the fungus, Aspergillus fumigatus (A. fumigatus). However, submerged cultures do not fully represent the complexity of cell types and morphology of human lung epithelia. Furthermore, many investigators use immortalized cell lines that may possess significant genetic and biochemical changes from normal tissue (Kaur and Dufour 2012). In Chapter 3, we used well-differentiated air-liquid interface (ALI) cultures of primary human bronchial epithelial cells (HBECs) to determine the early molecular response of the host to A. fumigatus. To determine whether there is a wide applicability of this system for studying host-pathogen interactions, we compared the molecular response of ALI cultures of HBECs (ALI-HBECs) to submerged monolayer cultures of the human airway epithelial cell line (1HAEo-). Host response was evaluated by changes to the transcriptome and the proteome. We also assessed the specificity of response to pathogens by exposing ALI-HBECs to two different strains of A. fumigatus conidia as well as respiratory syncytial virus (RSV). Finally, to examine the changes in RNA transcripts associated with differentiation and development of ALI cultures, we compared the transcriptomes of low-TEER versus high-TEER samples from experiment #1.  81 Most studies of conidial internalization have been conducted using submerged monolayer cultures of immortalized cell lines (Paris et al. 1997; Julie A. Wasylnka and Moore 2003, 2002; Botterel et al. 2008; Han et al. 2011). Interestingly, unlike ALI cultures of primary HBECs (see Chapter 3), it has been reported that submerged monolayer cultures of airway epithelial cells internalize 30-50% of bound conidia after 6 hours (Gomez et al. 2010). The low rate of internalization by ALI-HBECs is consistent with observations of negligible internalization of A. fumigatus conidia in airway epithelial cells in-vivo (Rammaert et al. 2015). Thus, submerged monolayer cultures may not be representative of the host response to fungal conidia. The significant differences in phagocytosis as well as physiological differences between differentiated ALI cultures of HBECs and un-differentiated submerged monolayer cultures led us to hypothesize that the host response to pathogen exposure in these two model systems would be different. For comparative gene expression profiling, we used submerged cultures of 1HAEo-, a Simian Virus (SV)-40 T antigen-transformed epithelial cell line. To evaluate how well the HBECs-ALI model mimics the in-vivo epithelium and whether it provides a pathogen-specific molecular response, we measured the specificity of response to different pathogens. To do this, we compared the immune responses to wild type strain (WT) of A. fumigatus conidia (reported in Chapter 3), to a mutant strain of A. fumigatus conidia and to the well-studied pathogenic virus, respiratory syncytial virus (RSV). The mutant stain of A. fumigatus has a deletion of the sialidase gene and is referred as the Dkdnase strain. Kdnase is an exo-sialidase that prefers 2-keto-3-deoxy-D-glycero-D-galacto-nononic acid (Kdn) over N-acetylneuraminic acid (Neu5Ac) as a  82 substrate. Kdnase has been shown to contribute to A. fumigatus cell wall integrity and virulence (Nesbitt et al. 2018).  We used the viral pathogen, respiratory syncytial virus (RSV) to assess the specificity of response in HBECs-ALI cultures. RSV belongs to the genus Pneumovirus and is an enveloped virus with negative sense single-strand RNA (ssRNA). The genome is reported to be 15kb nucleotides in length, and encodes eleven proteins, nine structural and two non-structural proteins. Of the nine, five are involved in nucleocapsid structure and/or RNA synthesis, and the remaining four form the viral envelope (T. H. Kim and Lee 2014; Collins and Graham 2008). It can inhibit type 1 interferon host response by interrupting the JAK-STAT signaling pathway. Even though it is a major cause of respiratory infection in young children, there is no licensed vaccine against RSV yet (T. H. Kim and Lee 2014).  4.2 Overview of experiment design for comparative transcriptomic and proteomic studies To investigate the early molecular response elicited by submerged monolayer cultures of 1HAEo- cells upon exposure to A. fumigatus, 1HAE cells were incubated without (control, n=3) or with (infected, n=3) A. fumigatus for 6 hours at 37 °C (Figure 4.1). nCounter Asthma Elements Panel and nCounter Immune Profiling Panel were used to profile transcriptomics, and LC-MS/MS was used to profile proteomics of 1HAE cells upon exposure to A. fumigatus conidia. Differentially abundant RNA transcripts and proteins were identified and compared to differentially abundant RNA transcripts and proteins identified in ALI cultures upon exposure to A. fumigatus conidia.   83 To assess the specificity of response, ALI cultures of primary HBECs were exposed to Dkdnase A. fumigatus conidia (infected, n=3) or RSV (infected, n=3) (Figure 4.1). Differential abundance analysis was conducted by comparing the control samples from experiment #2 (n=3) to the infected (Dkdnase- or RSV-infected, n=3 each) samples.  To assess A. fumigatus specific response in ALI cultures, differential abundance analysis was also conducted between ALI cultures exposed to WT A. fumigatus conidia (experiment #2, infected n=3) and Dkdnase A. fumigatus (Dkdnase-infected n=3). The PCA plot in Chapter 3 (Figure 3.4) showed that majority of the variation between ALI samples in experiment #1 was due to the differences between differentiation of cultures. Hence, to further investigate these differences, RNA transcripts of genes assessed using nCounter Immune Profiling Panel were used to conduct differential abundance analysis between 4 High TEER samples and 2 low TEER samples.     84    Figure 4.1: Experimental design for comparative analyses. Submerged monolayer cultures of 1HAE cells were used to compare transcriptomics and proteomics to that of ALIs from Chapter 3. Control (n=3) and infected (n=3) samples were analyzed using nCounter Asthma Elements Panel and nCounter Immune Profiling Panel, previously used to assess transcriptomics of ALIs in Chapter 3 as well. Proteomics were analyzed using LC-MS/MS. To assess the specificity of response in these differentiated cultures, ALI cultures were exposed to Dkdnase A. fumigatus conidia (Dkdnase-infected, n=3), and RSV (RSV-infected, n=3), along with ALI cultures incubated with PBS (control, n=3).  nCounter Immune Profiling Panel Control (n=3)  Primary HBECs grown in ALI exposed to Dkdnase A. fumigatus conidia (6 hours at 37 °C) Infected (n=3) MOI=10 conidia/cell  Infected (n=3) MOI=1 virus/cell  Control (n=3)  Primary HBECs grown in ALI exposed to RSV (6 hours at 37 °C) LC-MS/MS &  nCounter Asthma Elements Panel Control (n=3)  Infected (n=3)  MOI=10 conidia/cell  1HAEo- submerged monolayer cultures exposed to A. fumigatus conidia (6 hours at 37 °C)  85 4.3 Results 4.3.1 Visualizing interaction of A. fumigatus conidia in submerged monolayer cultures of 1HAEo- cells using confocal microscopy Submerged monolayer cultures of 1HAE cells were exposed to GFP-expressing A. fumigatus conidia for 6 hours to investigate how submerged monolayer cultures interact with A. fumigatus conidia, compared to ALI cultures. The extent of conidial internalization was assessed by visualizing differentially stained conidia using confocal microscopy. As shown in the representative image in Figure 4.2, 6 hours post-exposure, more conidia were bound to submerged monolayer cultures of 1HAEs than differentiated ALI cultures of primary HBECs (Chapter 3, Figure 3.1). In addition, more bound conidia were internalized by submerged monolayer cultures of 1HAEs than differentiated ALI cultures of primary HBECs as well.   86   Figure 4.2: Differential staining of extracellular and internalized conidia by anti-A. fumigatus antibody using confocal microscopy at 6 hours post-exposure in submerged monolayer cultures of 1HAEs.  GFP-expressing A. fumigatus conidia and 1HAEo- cells grown in submerged monolayer cultures were co-incubated for 6 hours, fixed and stained with DAPI to label cell nuclei, and a monoclonal anti-A. fumigatus antibody was used to label extracellular conidia, before visualization using confocal microscopy. One representative field is shown in the following channels: A) wavelength 495nm for GFP (green); B) wavelength 594nm for anti-A. fumigatus antibody (red); C) wavelength 405nm for DAPI (blue); D) merged GFP, anti-A. fumigatus antibody and DAPI image. Conidia not labeled by the anti-A. fumigatus antibody and only visible in the green but not the red channel were considered to be internalized by 1HAEo- cells (as indicated by the white arrows).   D A C B B  87 4.3.2 Quantification and quality assessment of RNA samples The concentrations of RNA extracted from all 1HAE samples, along with RNA integrity number (RIN) is reported in Table 4.1. TEER values, RNA concentration and RIN are reported for ALI samples in Experiment #2 are reported in Table 4.2. RNA integrity was assessed to ensure that the RNA was of good quality (RIN>8). RIN was not reported for Sample infected-3-RSV; however, further inspection of the specific chromatogram peaks for 18S and 28S indicated that total RNA for this sample was intact (data not shown). Table 4.1: RNA concentrations and RIN for control and infected 1HAE cultures. Submerged monolayer cultures of 1HAEs 24-well plate format Sample RNA concentration (ng/μl) RIN Control-1 92.27 9.50 Control-2 89.34 9.60 Control-3 165.09 9.10 Infected-1-Wildtype A. fumigatus conidia 60.41 8.30 Infected-2-Wildtype A. fumigatus conidia 35.35 7.80 Infected-3-Wildtype A. fumigatus conidia 61.17 8.90     88 Table 4.2: Trans-Epithelial Electrical Resistance (TEER) values, RNA concentrations and RIN for 12 ALI cultures in Experiment 2. Experiment #2 (ALI cultures of primary HBECs) 24-well plate format Sample TEER (ohms) RNA concentration (ng/μl) RIN Control-1 1030 74.54 8.40 Control-2 1029 75.9 8.50 Control-3 1022 66.25 8.10 Infected-1-Wildtype A. fumigatus conidia 1268 71.69 9.30 Infected-2-Wildtype A. fumigatus conidia 1056 106.1 9.40 Infected-3-Wildtype A. fumigatus conidia 1197 80.32 8.10 Infected-1-Dkdnase A. fumigatus conidia 1018 135.3 9.60 Infected-2-Dkdnase A. fumigatus conidia 947 79.94 9.10 Infected-3-Dkdnase A. fumigatus conidia 940 83.93 9.60 Infected-1-RSV 1754 71.12 8.40 Infected-2-RSV 1611 90.77 9.20 Infected-3-RSV 1990 69.01 N/A 4.3.3 Analysis of transcriptomic and proteomic response to A. fumigatus in submerged monolayer cultures of 1HAEs 4.3.3.1 Analysis of RNA transcripts in submerged monolayer cultures of 1HAEs upon exposure to A. fumigatus conidia The transcriptome of 1HAEs grown in submerged monolayer cultures were analyzed using the nCounter Immune Profiling Panel and the nCounter Asthma Elements Panel to assess  89 the similarities and differences in the early molecular response to that of ALI cultures upon exposure to A. fumigatus. Samples of both control (n=3) and infected (n=3) were treated identically, except for the addition of A. fumigatus conidia suspended in PBS to the infected samples and PBS alone to the control samples.  4.3.3.1 nCounter Asthma Elements Panel The PCA plot showed that most of the variation between samples was due to exposure to A. fumigatus conidia (Figure 4.3.A). Of the 123 genes assessed for differential RNA transcript abundance analysis, 63 RNA transcripts were differentially abundant upon exposure to A. fumigatus conidia (P-value < 0.05) (Figure 4.3.B) (Appendix 4). In addition, 100 RNA transcripts were significant under BH-FDR < 0.3. Compared to control samples, 4 RNA transcripts were up-regulated and 59 were down-regulated 6 hours post-exposure to A. fumigatus conidia in submerged monolayer cultures of 1HAEs. The 4 RNA transcripts that were up-regulated in infected samples compared to control samples included Complement C5a Receptor 1 (C5AR1), Mitochondrially Encoded Cytochrome B (MT-CYB), Contactin Associated Protein Like 3 (CNTNAP3) and Cytochrome B isoform (CYTB_comp5).  90  Figure 4.3: Principal Component Analysis (PCA) and MA Plot of 1HAEs exposed to A. fumigatus conidia for 6 hours (Asthma Elements Panel).  A) PCA plot showing that PC1 is explaining 65.5% of variation between control (Blue) and infected (Red) samples. B) MA plot of 63 RNA transcripts differentially abundant in 1HAEs upon exposure to A. fumigatus for 6 hours. The top 10 genes are labeled, these included GTF2H2 and C5AR1 that were also differentially abundant in ALI cultures upon exposure to A. fumigatus. A B  91 Pathway enrichment analysis for differentially abundant RNA transcripts was conducted in Enrichr using the Reactome database (http://amp.pharm.mssm.edu/Enrichr/) (E. Y. Chen et al. 2013)(Kuleshov et al. 2016) (Table 4.3). Innate Immune system, TLR3/TLR4 signaling, Dectin-1 signaling, Endosomal/Vacuolar pathway and C-type lectin receptors were some of the enriched pathways (Table 4.3).    92 Table 4.3: Enriched Reactome pathways for 63 differentially abundant RNA transcripts identified in Asthma Elements Panel in 1HAEs upon exposure to A. fumigatus conidia, as identified by Enrichr. Term P-value Adjusted P-value Combined Score Genes Innate Immune System_Homo sapiens_R-HSA-168249 1.702E-08 7.900E-06 43.493 CFD;MAP2K2;C5AR1;HLA-B; UBE2D1; ARPC4;DUSP6;NFKB1;CTSS;NFKBIA;PPP3R1;CD59;PSMF1;FADD;CD46 Immune System_Homo sapiens_R-HSA-168256 1.160E-07 2.692E-05 35.512 CFD;MAP2K2;C5AR1;HLA-B; UBE2D1; ARPC4; HLA-A; TNFRSF1B; DUSP6; NFKB1; CTSS; IL17RA;NFKBIA;PPP3R1;CD59;PSMF1;FADD;CD46;ATG7 Toll-Like Receptors Cascades_Homo sapiens_R-HSA-168898 4.706E-06 7.097E-04 26.281 NFKBIA;UBE2D1;FADD;NFKB1;DUSP6;CTSS MyD88-independent TLR3/TLR4 cascade_Homo sapiens_R-HSA-166166 1.257E-05 8.056E-04 23.805 NFKBIA;UBE2D1;FADD;NFKB1;DUSP6 TRIF-mediated TLR3/TLR4 signaling_Homo sapiens_R-HSA-937061 1.257E-05 8.056E-04 23.709 NFKBIA;UBE2D1;FADD;NFKB1;DUSP6 Toll Like Receptor 3 (TLR3) Cascade_Homo sapiens_R-HSA-168164 1.257E-05 8.056E-04 23.602 NFKBIA;UBE2D1;FADD;NFKB1;DUSP6 CLEC7A (Dectin-1) signaling_Homo sapiens_R-HSA-5607764 1.389E-05 8.056E-04 23.162 NFKBIA;PPP3R1;UBE2D1;PSMF1;NFKB1 Endosomal/Vacuolar pathway_Homo sapiens_R-HSA-1236977 6.118E-06 7.097E-04 22.335 HLA-B;HLA-A;CTSS Activated TLR4 signaling_Homo sapiens_R-HSA-166054 2.526E-05 1.302E-03 21.074 NFKBIA;UBE2D1;FADD;NFKB1;DUSP6 C-type lectin receptors (CLRs)_Homo sapiens_R-HSA-5621481 3.964E-05 1.672E-03 21.065 NFKBIA;PPP3R1;UBE2D1;PSMF1;NFKB1   93  Of the 63 differentially abundant RNA transcripts for 1HAEs and 7 differentially abundant for ALIs upon exposure to A. fumigatus conidia obtained using the nCounter Asthma Elements Panel, 2 RNA transcripts overlapped; however, these showed opposite direction of change: General Transcription Factor IIH Subunit 2 (GTF2H2) and C5AR1 (Complement C5a Receptor 1). GTF2H2 was up-regulated in ALIs and down-regulated in 1HAEs, and C5AR1 is down-regulated in ALIs and up-regulated in 1HAEs, upon exposure to A. fumigatus conidia. 4.3.3.1.2 nCounter Immune Profiling Panel Principal component analysis (PCA) plot of control and infected samples analyzed using nCounter Immune Profiling Panel is shown in Figure 4.4A. Control and infected samples were separated with a small overlap between samples, indicating that majority of variation between samples was due to the presence of A. fumigatus conidia. Differential abundance analysis of 353 RNA transcripts showed 41 RNA transcripts to be differentially abundant in submerged monolayer cultures of 1HAEs upon exposure to A. fumigatus conidia for 6 hours (Figure 4.4B) (Appendix 5). Of these 41, 2 RNA transcripts were significant under BH-FDR < 0.30 (labeled in Figure 4.4B).   94  Figure 4.4: Principal Component Analysis (PCA) and MA Plot of 1HAEs exposed to A. fumigatus conidia for 6 hours (Immune Profiling Panel).  A) PCA plot showing separation between control (Blue) and infected (Red) samples. B) MA plot of 41 RNA transcripts differentially abundant in 1HAEs upon exposure to A. fumigatus for 6 hours. 2 genes were significant under BH-FDR < 0.3 (labeled).   A B  95 GO enrichment analysis using the Cytoscape plug-in, CLUEGO, showed that up-regulated differentially abundant RNA transcripts were enriched for postive regulation of tumor necrosis factor production, positive regulation of tyrosine phosphorylation of STAT protein, positive regulation of leukocyte migration, regulation of regulatory T-cell differentiation, negative regulation of reproductive process and megakaryocyte differentiation (Figure 4.5). The down-regulated RNA transcripts were mainly enriched for negative regulation of T-cell proliferation and dendritic cell differentiation (Figure 4.5).  96  Figure 4.5: Gene ontology enrichment analysis of differentially abundant RNA transcripts identified using Immune Profiling Panel in submerged monolayer cultures of 1HAEs upon exposure to A. fumigatus.  Functionally-grouped network of Gene Ontology enrichment analysis for differentially abundant RNA transcripts in ClueGO App is shown (P-value < 0.1). The up-regulated RNA transcripts, compared to control samples, were enriched in positive regulation of leukocyte migration, positive regulation of tumor necrosis factor production, regulation of regulatory T cell differentiation. Boxed terms: The down-regulated RNA transcripts were enriched for negative regulation of T-cell proliferation and dendritic cell differentiation.   GO Terms for down-regulated RNA transcripts  97 Of the 41 RNA transcripts differentially abundant for ALIs (Chapter 3, Appendix 2) and 41 RNA transcripts differentially abundant for 1HAEs upon exposure to A. fumigatus for 6 hours (P-value < 0.05), 6 RNA transcripts overlapped, reported in Table 4.4; however, only 4 of the 6 changed in the same direction. Table 4.4: RNA transcripts (6) that were differentially expressed in both HBECs-ALI cultures and 1HAE submerged monolayer cultures upon exposure to A. fumigatus conidia using the Immune Profiling Panel. ALI cultures of HBECs  Submerged monolayer cultures of 1HAEs Average Expression (Log2) Log2 FC Overlapping RNA transcripts Log2 FC Average Expression (Log2) 11.649 -0.672 CXCL6- C-X-C Motif Chemokine Ligand 6 0.272 4.999 5.339 0.646 IFNL1-Interferon Lamba 1 0.430 5.776 5.575 0.428 NFATC2-Nuclear Factor Of Activated T-Cells 2 0.299 9.198 9.929 0.265 CASP3- Caspase 3 -0.316 10.185 11.116 0.715 SPA17-Sperm Autoantigenic Protein 17 0.178 8.635 5.394 0.368 FADD- Fas Associated via Death domain 0.230 6.454 4.3.3.2 Analysis of the proteome of submerged monolayer cultures of 1HAEs in response to A. fumigatus conidia In total, 1247 proteins were identified of which 558 proteins were quantified (at least 2 out of 3 quantification events). Differential abundance analysis using normalized ratios of Heavy (infected, n=3) to Light (control, n=3) labeled protein samples showed that 54 proteins were differentially abundant 6 hours post-exposure to A. fumigatus conidia (Figure 4.6) (Appendix 6). Of these 54, 4 proteins were significant under BH-FDR < 0.30, labeled in Figure 4.6. These 4  98 proteins included, Albumin (ALB), Ral GTPase Activating Protein Catalytic Alpha Subunit 1 (RALGAPA1), Keratin 10 (KRT10) and Splicing Factor 3a Subunit 2 (SF3A2).    Figure 4.6: Volcano plot of 558 quantified proteins identified using shotgun proteomics in submerged monolayer cultures of 1HAEs upon exposure to A. fumigatus conidia.  Differential abundance analysis showed that 54 proteins were differentially abundant 6 hours post-exposure to A. fumigatus (P-Value < 0.05). 4 proteins were differentially abundant at BH-FDR < 0.3 (labeled). Of the 54, 34 were up-regulated (green) and 20 were down-regulated (blue) upon exposure to A. fumigatus conidia. Compared to control samples, 34 were up-regulated and 20 were down-regulated upon exposure to A. fumigatus. The up-regulated proteins were associated with nonsense mediated decay, gene expression, eukaryotic translation initiation, influenza infection, RNA splicing and rRNA processing (Table 4.5). The down-regulated proteins were associated with platelet  99 degranulation, response to elevated platelet cytosolic Ca2+, disease and vesicle mediated transport (Table 4.6). Table 4.5: Enriched Pathways for up-regulated proteins in 1HAEs upon exposure to A. fumigatus. Term P-value Adjusted P-value Combined Score Genes Nonsense Mediated Decay (NMD) independent of the Exon Junction Complex (EJC)_Homo sapiens_R-HSA-975956 3.915E-07 3.149E-05 28.761 RPL4;RPS4X;RPS16;RPL10A;EIF4G1 Gene Expression_Homo sapiens_R-HSA-74160 1.222E-06 3.149E-05 28.720 YWHAE;RPL4;SF3A2;RPS27L;HNRNPR;RPL10A;YWHAZ;RPS4X;HNRNPK;RPS16;HNRNPC;HNRNPA0;EIF4G1 Eukaryotic Translation Initiation_Homo sapiens_R-HSA-72613 1.343E-06 3.149E-05 26.000 RPL4;RPS4X;RPS16;RPL10A;EIF4G1 Influenza Infection_Homo sapiens_R-HSA-168254 4.695E-06 8.255E-05 24.453 RPL4;RPS4X;RPS16;KPNA2;RPL10A RNA Splicing - Major Pathway_Homo sapiens_R-HSA-72163 2.982E-06 6.291E-05 24.388 HNRNPK;SF3A2;HNRNPR;HNRNPC;HNRNPA0 Major pathway of rRNA processing in the nucleolus_Homo sapiens_R-HSA-6791226 8.493E-06 1.280E-04 22.694 RPL4;RPS4X;RPS16;RPS27L;RPL10A     100 Table 4.6: Enriched Pathways for down-regulated proteins in 1HAEs upon exposure to A. fumigatus. Term P-value Adjusted P-value Combined Score Genes Platelet degranulation_Homo sapiens_R-HSA-114608 1.502E-04 1.551E-02 16.939 TF;HSPA5;ALB Response to elevated platelet cytosolic Ca2+_Homo sapiens_R-HSA-76005 1.724E-04 1.551E-02 16.435 TF;HSPA5;ALB Disease_Homo sapiens_R-HSA-1643685 5.219E-03 1.518E-01 12.440 RBP4;NPM1;RPL18A;ALB Vesicle-mediated transport_Homo sapiens_R-HSA-5653656 1.235E-02 1.518E-01 9.090 LMAN1;YWHAB;ALB Of the 2875 proteins identified in the HBECs-ALIs experiment and 1247 proteins identified in 1HAEs experiment, 1008 proteins overlapped. For the 153 proteins differentially abundant in the HBECs-ALIs experiment and 54 proteins differentially abundant in the 1HAEs experiment, 8 proteins overlapped with 7 of 8 showing changes in the same direction (Table 4.7).    101 Table 4.7: 8 proteins overlapped between ALI cultures and 1HAEs submerged monolayer cultures upon exposure to A. fumigatus conidia. ALI cultures of HBECs  Submerged monolayer cultures of 1HAEs Log2 FC Overlapping Proteins Log2 FC -3.002 ALB-Albumin -1.611 1.323 SF1- Splicing Factor1 -0.757 -1.426 KRT1-Keratin 1 -0.791 1.098 MATR3-Matrin 3 1.739 0.866 CAST-Calpastatin 1.035 0.878 LMNA-Lamin A/C 0.370 0.934 RRBP1-Ribsome Binding Protein 1 1.029 -1.388 KRT2-Keratin 2 -1.232  4.3.4 Analysis of RNA transcripts in ALI cultures of primary HBECs upon exposure to Δkdnase A. fumigatus conidia  The PCA plot showed overlap between control and infected samples, indicating that the majority of the variation between samples may not be due to whether or not ALI cultures were exposed to Δkdnase A. fumigatus conidia (Figure 4.7A). Differential RNA transcript analysis showed 52 RNA transcripts to be differentially abundant upon exposure to Δkdnase A. fumigatus conidia in ALI cultures of primary HBECs for 6 hours (P-value < 0.05) (Figure 4.7B) (Appendix 7). Of the 52 RNA transcripts, 40 were up-regulated and 12 were down-regulated in Δkdnase A. fumigatus conidia exposed ALI cultures compared to control ALI cultures. 31 RNA transcripts were significant under BH-FDR < 0.30.   102 Figure 4.7 PCA plot and MA Plot of ALI cultures exposed to Δkdnase A. fumigatus conidia for 6 hours using Immune Profiling Panel.  A) PCA plot did not show separation between control (Blue) and infected (Red) samples. B) MA plot showed 52 RNA transcripts to be differentially abundant upon exposure to Δkdnase A. fumigatus conidia in ALI cultures of primary HBECs for 6 hours (P-value < 0.05). Top 10 RNA transcripts are labeled. B A  103 Gene ontology enrichment analysis showed enrichment of negative regulation of cytokine production, heterophilic cell-cell adhesion via plasma membrane cell adhesion molecules, regulation of osteoclast differentiation, type 1 interferon signaling pathway to be enriched for up-regulated RNA transcripts (Figure 4.8A), and positive regulation of granulocyte chemotaxis to be primarily enriched in down-regulated RNA transcripts in ALI cultures upon exposure to Δkdnase A. fumigatus conidia for 6 hours (Figure 4.8B).         104                             Figure 4.8: Gene ontology enrichment analysis of differentially abundant RNA transcripts identified using Immune Profiling Panel in ALI cultures upon exposure to Δkdnase A. fumigatus. A. Functionally grouped network of gene ontology enrichment analysis of differentially abundant RNA transcripts in ClueGO App is shown (P-value < 0.1). The up-regulated RNA transcripts upon exposure to Δkdnase A. fumigatus conidia were enriched in type I interferon signaling pathway, negative regulation of cytokine production, regulation of osteoclast differentiation etc. B. The down-regulated RNA transcripts were enriched for positive regulation of granulocyte chemotaxis.  A B  105 Of the 52 RNA transcripts differentially abundant in response to Δkdnase A. fumigatus conidia and 41 RNA transcripts differentially abundant in response to WT A. fumigatus conidia, 11 RNA transcripts overlapped (Table 4.8). These 11 included Bone Marrow Stromal Cell Antigen 2/Tetherin (BST2), Interferon Lambda 1/IL-29 (IFNL1), C-X-C Motif Chemokine Ligand 6 (CXCL6), C-X-C Motif Chemokine Ligand 5 (CXCL5), Serum Amyloid A1 (SAA1), Complement Factor B (CFB), Indoleamine 2,3-Dioxygenase 1 (IDO1), Transcription Factor Binding to IGHM Enhancer 3 (TFE3), Complement C3 (C3), LCK Proto-Oncogene, Src Family Tyrosine Kinase (LCK), and Fas Associated Via Death Domain (FADD), shown in Table 4.8. All RNA transcripts changed in the same direction upon exposure to either Δkdnase A. fumigatus conidia or WT A. fumigatus conidia (same Log2 Fold Change). Table 4.8: Overlapping RNA transcripts between 52 differentially abundant RNA transcripts of ∆kdnase A. fumigatus conidia infected ALI cultures and 41 differentially abundant RNA transcripts of WT A. fumigatus conidia infected ALI cultures    Control vs. ∆kdnase A. fumigatus conidia infected ALI cultures  Control vs. Wild type A. fumigatus conidia infected ALI cultures Average Expression Log2 FC Overlapping Genes Log2 FC Average Expression 7.136 1.113 BST2 0.503 6.960 5.696 1.167 IFNL1 0.646 5.339 11.798 -1.540 CXCL6 -0.672 11.649 9.647 -1.956 CXCL5 -1.147 9.029 15.583 -0.862 SAA1 -0.613 14.601 12.949 -0.632 CFB -0.447 12.147 10.972 -0.871 IDO1 -1.018 9.724 5.733 0.605 TFE3 0.494 5.387 13.608 -0.535 C3 -0.467 12.909 4.655 0.651 LCK 0.662 5.216 5.380 0.484 FADD 0.368 5.394  106 4.3.5 Analysis of RNA transcript abundance in ALI cultures of primary HBECs upon exposure to Δkdnase A. fumigatus conidia and WT A. fumigatus conidia for 6 hours To further assess differences between the immune response associated with mutant strain of A. fumigatus conidia compared to WT strain, differential abundance analysis was conducted between ALI cultures exposed to WT A. fumigatus conidia and Δkdnase A. fumigatus conidia. PCA plot showed overlap between samples upon exposure to WT A. fumigatus conidia and Δkdnase A. fumigatus conidia. Of the 446 genes assessed for differential RNA transcript abundance analysis, 17 RNA transcripts were differentially abundant upon exposure to Δkdnase A. fumigatus conidia compared to WT A. fumigatus conidia (P-value < 0.05) (Figure 4.9) (Appendix 8). None were significant under BH-FDR < 0.3.    107                                      Figure 4.9: PCA plot and MA Plot analyses of ALIs exposed to Δkdnase A. fumigatus conidia for 6 hours using Immune Profiling Panel.  A) PCA plot did not show separation between WT-infected (Blue) and Δkdnase-infected (Red) samples. B) MA plot showed 17 RNA transcripts to be differentially abundant upon exposure to Δkdnase A. fumigatus conidia compared to WT A. fumigatus conidia in HBECs for 6 hours (P-value < 0.05). Top 5 RNA transcripts are labeled.  B A  108 Compared to Wild-type A. fumigatus conidia exposed ALI cultures, 12 RNA transcripts were up-regulated (Table 4.9) and 5 were downregulated (Table 4.10).  Table 4.9: 12 RNA transcripts were up-regulated upon exposure to ∆kdnase A. fumigatus conidia compared to WT A. fumigatus conidia in ALI cultures of primary HBECs.   Table 4.10: 5 RNA transcripts were down-regulated upon exposure to ∆kdnase A. fumigatus conidia compared to WT A. fumigatus conidia in ALI cultures of primary HBECs. Gene Gene Name Log2 FC P.Value TFRC Transferrin Receptor -0.889 2.498E-02 IL6R Interleukin 6 Receptor -0.750 2.909E-02 CCL28 Mucosae-Associated Epithelial Chemokine -0.846 3.099E-02 HLA-DMA Major Histocompatibility Complex, Class II, DM Alpha -0.660 4.208E-02 KIT KIT Proto-Oncogene Receptor Tyrosine Kinase -0.706 4.817E-02  4.3.6 Analysis of RNA transcript abundance in ALI cultures of primary HBECs upon exposure to RSV The PCA plot showed that the majority of the variation between control and infected samples was due to exposure to RSV (Figure 4.10A). Differential RNA transcript analysis showed Gene Gene Name Log2 FC P.Value BLK B Lymphoid Tyrosine Kinase 1.280 0.003 IL19 Interleukin 19 1.692 0.007 TLR6 Toll like receptor 6 0.960 0.016 AKT3 AKT Serine/Threonine Kinase 3 1.686 0.019 IRAK2 Interleukin 1 Receptor Associated Kinase 2 0.767 0.019 NT5E 5'-Nucleotidase Ecto 0.784 0.024 CD14 CD 14 Molecule 1.035 0.026 STAT4 Signal Transducer And Activator Of Transcription 4 0.832 0.037 MICA MHC Class I Polypeptide-Related Sequence A 0.788 0.040 DMBT1 Surfactant Pulmonary-Associated D-Binding Protein 1.534 0.041 IL32 Tumor Necrosis Factor Alpha-Inducing Factor 0.668 0.044 CDH5 Cadherin 5 0.955 0.046  109 82 RNA transcripts to be differentially abundant upon exposure to RSV in ALI cultures of primary HBECs (Figure 4.10B) (Appendix 9). Of the 82, 30 were up-regulated and 52 were down-regulated upon exposure to RSV, compared to control ALI cultures. 132 RNA transcripts were significant under BH-FDR <0.30.    110  Figure 4.10: PCA plot and MA Plot analyses of ALIs exposed to RSV for 6 hours using Immune Profiling Panel.  A) PCA plot showed separation between control (Blue) and infected (Red) samples. Hence, most of the variation between samples could be explained due to the presence of RSV.  B) MA plot showed 82 RNA transcripts to be differentially abundant upon exposure to RSV. Compared to control samples, 30 RNA transcripts were up-regulated (green) and 52 RNA transcripts were down-regulated (blue) (P-value < 0.05). RNA transcripts significant under BH-FDR < 0.1 are labeled. A B  111  Enrichment analysis in Enrichr identified Reactome pathways associated with chemokine receptors, immune system, class A/1 (Rhodopsin-like-receptors), cytokine signaling, peptide ligand-binding receptors and signaling by interleukins to be enriched among up-regulated RNA transcripts in ALI cultures upon exposure to RSV (Table 4.11). For the down-regulated RNA transcripts, immune system, cytokine signaling, innate immune system, signaling by interleukins and anti-viral mechanism by IFN-stimulated genes were among some of the enriched Reactome pathways (Table 4.12).    112 Table 4.11: Enriched Reactome pathways for up-regulated RNA transcripts upon exposure to RSV in ALI cultures of primary HBECs. Term Overlap P-value Adjusted P-value Combined Score Genes Chemokine receptors bind chemokines_Homo sapiens_R-HSA-380108 6/56 2.060E-10 2.330E-08 43.833 CXCL8;CXCR1;CCL20;CXCL1;CXCL2;CCR3 Immune System_Homo sapiens_R-HSA-168256 14/1547 1.150E-08 6.520E-07 40.643 CSF3;IL1RN;RIPK2;TNFRSF18;SH2D1B;TICAM1;ISG20;CLEC4A;NFKBIA;IL6;IL23A;IL12A;TLR4;HLA-DOB Class A/1 (Rhodopsin-like receptors)_Homo sapiens_R-HSA-373076 7/323 3.970E-07 1.120E-05 31.195 CXCL8;CXCR1;ADORA2A;CCL20;CXCL1;CXCL2;CCR3 Cytokine Signaling in Immune system_Homo sapiens_R-HSA-1280215 8/620 2.610E-06 4.910E-05 30.344 ISG20;CSF3;IL1RN;IL6;RIPK2;IL23A;TNFRSF18;IL12A Peptide ligand-binding receptors_Homo sapiens_R-HSA-375276 6/193 3.660E-07 1.120E-05 28.947 CXCL8;CXCR1;CCL20;CXCL1;CXCL2;CCR3 Signaling by Interleukins_Homo sapiens_R-HSA-449147 6/392 2.180E-05 1.842E-04 25.581 CSF3;IL1RN;IL6;RIPK2;IL23A;IL12A Diseases of Immune System_Homo sapiens_R-HSA-5260271 3/24 6.030E-06 7.580E-05 23.692 NFKBIA;TICAM1;TLR4 TRIF-mediated TLR3/TLR4 signaling_Homo sapiens_R-HSA-937061 4/97 1.290E-05 1.218E-04 23.262 NFKBIA;RIPK2;TICAM1;TLR4 Toll Like Receptor 2 (TLR2) Cascade_Homo sapiens_R-HSA-181438 3/92 3.495E-04 1.580E-03 14.426 NFKBIA;RIPK2;TLR4    113  Table 4.12: Enriched Reactome pathways for down-regulated RNA transcripts upon exposure to RSV in ALI cultures of primary HBECs. Term Overlap P-value Adjusted P-value Combined Score Genes Immune System_Homo sapiens_R-HSA-168256 24/1547 9.074E-14 4.020E-11 67.036 DUSP4;MAP3K1;SYK;TNFRSF12A;STAT1;DDX58;IL34;NOD1;IFIT1;TIRAP;HLA-DMA;MAVS;HLA-DMB;INPP5D;SAA1;TXNIP;MAPK1;KLRD1;IKBKG;IL6R;HRAS;TLR3;JAK1;MAPK3 Cytokine Signaling in Immune system_Homo sapiens_R-HSA-1280215 15/620 3.076E-11 3.406E-09 57.559 DUSP4;TNFRSF12A;SYK;STAT1;DDX58;IL34;NOD1;IFIT1;INPP5D;MAPK1;IKBKG;HRAS;IL6R;JAK1;MAPK3 Innate Immune System_Homo sapiens_R-HSA-168249 16/807 1.133E-10 5.017E-09 53.814 DUSP4;MAP3K1;SYK;DDX58;NOD1;TIRAP;MAVS;SAA1;TXNIP;MAPK1;KLRD1;IKBKG;HRAS;TLR3;JAK1;MAPK3 Signaling by Interleukins_Homo sapiens_R-HSA-449147 12/392 2.768E-10 1.115E-08 52.833 DUSP4;SYK;STAT1;INPP5D;IL34;MAPK1;NOD1;IKBKG;HRAS;IL6R;JAK1;MAPK3 Activated TLR4 signalling_Homo sapiens_R-HSA-166054 9/112 1.175E-11 2.603E-09 51.564 DUSP4;MAP3K1;SAA1;MAPK1;NOD1;IKBKG;TIRAP;TLR3;MAPK3 TRAF6 mediated NF-kB activation_Homo sapiens_R-HSA-933542 5/24 3.993E-09 8.845E-08 35.791 MAVS;MAP3K1;DDX58;SAA1;IKBKG Interleukin-3, 5 and GM-CSF signaling_Homo sapiens_R-HSA-512988 7/261 4.821E-06 5.209E-05 26.651 DUSP4;SYK;INPP5D;MAPK1;HRAS;JAK1;MAPK3 MAP kinase activation in TLR cascade_Homo sapiens_R-HSA-450294 5/60 4.781E-07 8.472E-06 26.452 DUSP4;MAPK1;NOD1;IKBKG;MAPK3 Antiviral mechanism by IFN-stimulated genes_Homo sapiens_R-HSA-1169410 5/72 1.196E-06 1.963E-05 25.282 STAT1;DDX58;IFIT1;MAPK3;JAK1  114 4.3.7 Analysis of RNA transcript abundance in high TEER and low TEER HBECs-ALI cultures  Differential RNA transcript analysis was performed on 447 RNA transcripts. Of the 447, 286 RNA transcripts were differentially abundant in high TEER samples compared to low TEER samples (P-Value < 0.05). 250 RNA transcripts were significant under BH-FDR < 0.30. Of the 286, 143 RNA transcripts were up-regulated and 143 were down-regulated in high TEER samples, compared to low TEER samples (Figure 4.11) (Appendix 10). 8 differentially abundant RNA transcripts had log2 fold change greater than 5, labeled in Figure 4.11. Figure 4.11 Volcano plot of differentially abundant RNA transcripts in high TEER samples compared to low TEER samples. Volcano plot showing 286 out of 440 RNA transcripts to be differentially abundant in high TEER samples compared to low TEER samples, represented by blue and green dots. Of these 286, 143 were up-regulated (green) and 143 were down-regulated (blue) in high TEER samples compared to low TEER samples. 8 RNA transcripts had a log2 fold change greater than 5 (labeled). 4.4 Discussion  The research outlined in this chapter assessed the applicability of air-liquid interface (ALI) cultures of primary human bronchial epithelial cells (HBECs) for studying the early molecular response of human bronchial epithelium to A. fumigatus conidia. We monitored the  115 extent of conidia internalization and gene expression profiles of primary HBECs-ALI cultures and compared the results to submerged monolayer cultures of 1HAEs after exposure to A. fumigatus conidia. The specificity of response in HBECs-ALI cultures upon exposure to different pathogens was also investigated. To assess A. fumigatus specific response in HBECs-ALI, response to WT conidia was compared to a mutant fungal strain and to a respiratory syncytial virus (RSV). Differential abundance analysis was also conducted between high TEER and low TEER samples.  4.4.1 Interaction of A. fumigatus conidia in submerged monolayer cultures of 1HAEs   We confirmed that submerged monolayer cultures of 1HAEs are capable of phagocytosing conidia. After 6 hours, the proportion of bound conidia internalized was more than 1% (previously estimated for ALI cultures of primary HBECs upon exposure to A. fumigatus in Chapter 3). Previous studies from our laboratory and others has shown that submerged monolayer cultures of bronchial epithelial cells or type II alveolar cells, internalize up to 50% of bound conidia (Zhang et al. 2005; Gomez et al. 2010; Julie A. Wasylnka and Moore 2003). Amitani and Kawanami (2009) used an organ culture model of the human bronchial epithelium, which had an air-mucosal interface, to study the interaction of respiratory mucosa with A. fumigatus. They showed that 6 hours after infection, the majority of conidia were adhered to damaged epithelial cells, and that conidia also bound to the indentations on the surface of non-ciliated epithelial cells (Amitani and Kawanami 2009). These data indicate that in the presence of mucus and cilia from functional goblet cells and ciliated cells, respectively, only a small number of conidia can bind and be internalized by bronchial epithelium. This was well- 116 represented by ALI cultures of HBECs since less than 1% of bound conidia were internalized after 6 hours.  4.4.2 Analysis of submerged monolayer culture of 1HAEs upon exposure to A. fumigatus conidia after 6 hours nCounter Asthma Elements Panel and nCounter Immune Profiling Panel were used to assess the gene expression response in submerged monolayer cultures of 1HAEs upon exposure to A. fumigatus conidia for 6 hours. Of the 38 overlapping genes between both panels, Fas Associated Via Death Domain (FADD) replicated in the differentially abundant RNA transcripts in both panels. FADD is an adaptor protein for caspase-8 and is involved in inflammasome activation (Gurung et al. 2014). However, it was up-regulated in nCounter immune profiling panel and down-regulated in nCounter Asthma Elements Panel, indicating a need for replicating these experiments using a larger sample size.  nCounter Asthma Elements Panel showed RNA transcripts related to Dectin-1 signaling pathway to be differentially abundant in 1HAEs upon exposure to A. fumigatus. These RNA transcripts included NFKB Inhibitor Alpha (NFKBIA), Protein Phosphatase 3 Regulatory Subunit B, Alpha (PPP3Ri), Ubiquitin Conjugating Enzyme E2 D1 (UBE2D1), Proteasome Inhibitor Subunit 1 (PSMF1), and Nuclear Factor Kappa B Subunit 1 (NFKB1). These findings are consistent with the results of Sun et al., who reported that human bronchial epithelial cells recognize A. fumigatus through dectin-1 receptors and respond by producing reactive oxygen species, antimicrobial peptides and cytokines (W.-K. Sun et al. 2012). Hence, internalization of large numbers of conidia in 1HAEs may be associated with recognition of bound conidia by dectin-1  117 signaling pathway, which, in contrast to the HBECs-ALI model, may only be possible in the absence of mucociliary clearance.  Two RNA transcripts, GTF2H2 and C5AR1, overlapped between differentially abundant RNA transcripts identified using nCounter Asthma Elements Panel in 1HAEs and ALIs upon exposure to A. fumigatus conidia. Both genes changed in opposite directions in both in-vitro models: GTF2H2 was up-regulated in ALIs and down-regulated in 1HAEs, and C5AR1 was down-regulated in ALIs and up-regulated in 1HAEs, compared to control samples. GTF2H2 is a component of TFIIH complex, which is required for transcription, DNA repair and cell cycle control (Gibbons et al. 2012). Previously, it has been shown that cell cycle was down-regulated in submerged monolayer cultures of 16HBE14o- upon exposure to A. fumigatus after 6 hours (Gomez et al. 2010). Down-regulation of GTF2H2 in infected samples of 1HAEs compared to control samples may be due to a general stress response resulting from high number of conidia interacting with cells in this in-vitro model than the ALI model. Likewise, up-regulation of C5AR1, a receptor of complement system, in infected samples of 1HAEs may also be associated with more conidia binding to the surface of submerged monolayer in-vitro model, resulting in the activation of PRRs associated with the complement system in response to fungal conidia. Of the 41 RNA transcripts differentially abundant in submerged monolayer cultures of 1HAEs, C-X-C Motif Chemokine Ligand 11 (CXCL11) and Toll like receptor 6 (TLR6) passed the BH-FDR < 0.30. Both were up-regulated in 1HAEs upon exposure to A. fumigatus. TLR6, along with TLR2 and TLR4, recognizes A. fumigatus and has been shown to be up-regulated in mice upon allergenic sensitization to A. fumigatus (Kurup, Raju, and Manickam 2005). Activation of TLR6 is known to be important for IL-23 production and the TH17 response; both cytokines  118 regulate allergic-inflammatory responses in chronic-fungal induced asthma. Therefore, along with dectin-1 signaling, TLR6 recognizes A. fumigatus in 1HAEs and both PRRs may be important for initiating an immune response upon binding and internalization of A. fumigatus conidia in HBECs. Genes associated with complement and coagulation pathways were also up-regulated in 1HAEs upon exposure to A. fumigatus. These included Complement C1s subcomponent (C1S) in the Immune Profiling Panel and Complement C5a Receptor 1 (C5AR1) in Asthma Element Panel. The expression of complement proteins, along with other PRRs, such as TLRs and Dectin-1, may be associated with conidial binding and internalization in the human bronchial epithelium.  CXCL11, which passed the BH-FDR < 0.30 along with TLR6, is a ligand for CXCR3, and is known to be strongly induced by IFN-γ (Hirota et al. 2006). It can recruit T-cells, natural killer cells and macrophages to the site of infection by binding to CXCR3 (Torraca et al. 2015). IFN-γ is known to play a role in host defense against A. fumigatus and promotes fungal clearance in invasive pulmonary aspergillosis (Shao et al. 2005). Other RNA transcripts associated with interferon signaling included Interferon Induced Protein with Tetratricopeptide Repeats 2 (IFIT2) and Interferon Lambda 1 (IFNL1). Both were up-regulated in 1HAEs upon exposure to A. fumigatus conidia in our study. IFNL1 was also up-regulated in ALIs upon exposure to A. fumigatus conidia, indicating that interferon signaling may play an important role in eliciting appropriate immune response to the fungus. The majority of GO terms were associated with innate and adaptive immune response for differentially abundant RNA transcripts. For example, regulation of regulatory T cell differentiation was enriched in the up-regulated RNA transcripts in 1HAEs upon exposure to A. fumigatus. The RNA transcripts associated with this GO term included B-cell lymphoma 6  119 (BCL6), Nuclear Factor of Activated T-cells 2 (NFATC2) and Runt Related Transcription Factor 1 (RUNX1). Specifically, NFATC2, along with other NFAT proteins, plays an important role in T cell activation and differentiation by producing cytokines such IL-2, IL-4 and IFN-γ (Gabriel et al. 2016). It was also upregulated in ALIs upon exposure to A. fumigatus conidia. RNA transcripts regulating T cell activation and the TH1/ TH2 responses were differentially abundant in ALIs upon exposure to A. fumigatus conidia as well, indicating another mechanism which may be important in preventing fungal invasion. Genes associated with GO term positive regulation of tumor necrosis factor production (Fas Associated Via Death Domain (FADD), Janus kinase 2 (JAK2), PYD and CATD Domain Containing (PYCARD), Thrombospondin 1 (THBS1)) were up-regulated upon exposure to A. fumigatus conidia in our study. Along with IFN-γ, Tumor necrosis factor (TNF)-α has shown to play protective roles in invasive aspergillosis in mice, and TNF-α deficient mice are more susceptible to infection with A. fumigatus (Nagai et al. 1995). The down-regulated genes were enriched for negative regulation of T-cell proliferation and dendritic cell differentiation. RNA transcripts with negative regulation of T-cell proliferation included Caspase 3 (CASP3), CD274 Molecule (CD274), and CCAAT/Enhancer Binding Protein Beta (CEBPB). Interestingly, α-(1,3)-glucan on the cell wall of A. fumigatus has shown to promote TH1 responses in dendritic cells upon down-regulation of CD274/PD-L1 pathway (Stephen-Victor et al. 2017). The genes associated with dendritic cell differentiation included AXL Receptor Tyrosine Kinase (AXL), Colony Stimulating Factor 2 (CSF2), and RELB Proto-Oncogene, NF-KB Subunit (RELB). Dendritic cells also play an important role in the initiating TH2 responses and cytokines, such as CSF2, also known as Granulocyte-macrophage colony- 120 stimulating factor (GM-CSF), plays an important role in maturation of dendritic cells to promote TH2 biased differentiation of CD4+ T cells. Elevated levels of GM-CSF in epithelial cells have been demonstrated to increase eosinophil activation and survival in asthmatics. It can recruit circulating neutrophils, macrophages and lymphocytes to the site of infection, and is used to treat neutropenia caused by cytotoxic chemotherapy in cancer patients, AIDs patients and patients after bone marrow transplantation. Even though GM-CSF is an essential pro-inflammatory cytokine that plays an important role in various inflammatory diseases, overexpression of GM-CSF may lead to severe inflammation and tissue damage due to macrophage accumulation and eosinophilia as well (Shi et al. 2006).  In addition to differences in internalization of conidia at 6 hours post-exposure, the differentially abundant RNA transcripts and proteins were also different in submerged monolayer culture of 1HAEs and differentiated ALI cultures of primary HBECs. Of the 41 differentially abundant RNA transcripts identified in both models using the immune profiling panel, 6 RNA transcripts overlapped. CXCL6, neutrophil chemoattractant, was down-regulated in ALIs but was up-regulated in ALIs upon exposure to A. fumigatus conidia. Proteins related to nonsense mediated decay, and rRNA processing were up-regulated in 1HAEs upon exposure to A. fumigatus. In contrast, these pathways were enriched in the down-regulated proteins in ALIs. These results suggest that the molecular response may be dependent on conidial binding and internalization in each model. Proteins regulating RNA splicing were among the up-regulated proteins in 1HAEs upon exposure to conidia; these included several proteins from the heterogeneous nuclear ribonucleoproteins (hnRNPs) family: hnRNPR, hnRNPK, hnRNPA0, hnRNPC. hnRNPs are  121 multifunctional proteins that play an important role in RNA export, localization, translation and stability (Chaudhury, Chander, and Howe 2010). Despite having a high overlap between identified proteins, only 8 differentially abundant proteins overlapped between both models. Thus, differentially abundant genes in both models also varied at the protein-level. Enrichment of genes associated with interferon signaling pathways, complement and coagulation pathway, nonsense mediated decay and rRNA processing in both models indicates that these pathways may play an important role in early molecular response to A. fumigatus. However, genes associated with these pathways were regulated differently in each model, as shown by differentially abundant RNA transcripts in each model. This indicates that the molecular response may be dependent on activation of different PRRs in each model, which in turn is related to the extent of binding of A. fumigatus conidia to host epithelia and/or by secretion of molecules from the surface of the conidia. Therefore, ALI cultures, with mucus-producing goblet cells and ciliated cells, may be a better model for studying host response in intact bronchial epithelium to fungal conidia and may more closely mimic the in vivo interaction between host and pathogen. 4.4.3 Analysis of pathogen-specific response in ALI cultures NanoString nCounter platform was used to assess the specificity of response in primary HBECs upon exposure to different pathogens. TEER values were included as a covariate in the linear models during differential abundance analysis to account for variability in differentiation between samples.  Of the 52 RNA transcripts that were differentially abundant upon exposure to Δkdnase A. fumigatus conidia and 41 RNA transcripts that were differentially abundant upon exposure  122 to wild type (WT) A. fumigatus conidia in ALI cultures of HBECs, 11 RNA transcripts overlapped. Interestingly, the overlapping genes changed in the same direction (same log2 FC) in both experiments. These included RNA transcripts involved in producing soluble mediators of the innate immune system such as cytokines, chemokines and complement system. Specifically, RNA transcripts involved in cytokine production (FADD, SAA1, BST2, IFNL1, IDO1, C3), regulation of complement and coagulation pathways (CFB, C3) and neutrophil chemotaxis (CXCL5, CXCL6) were differentially abundant. Some of the overlapping genes are involved in T-cell proliferation, such as FADD, LCK, IFNL1, and IDO1. Specifically, FADD and IDO1 are involved in TH2 type immune response. This indicates that these genes and pathways may play an important role in mediating early immune response against fungal pathogens in human bronchial epithelium. Gene ontology enrichment analysis of differentially abundant RNA transcripts upon exposure to Δkdnase A. fumigatus showed enrichment of type 1 interferon signaling pathway in the up-regulated RNA transcripts. Specifically, interferon-stimulated genes (ISGs) were differentially abundant. These included Interferon alpha-inducible protein 27 (IFI27), Bone Marrow stromal antigen 2 (BST2), Interferon-induced transmembrane protein 2 (IFITM2), Interferon-induced 35kDA protein (IFI35) (Schoggins and Rice 2011). Furthermore, an Interferon Regulatory Factor (IRF), Interferon regulatory factor 5 (IRF5), was differentially abundant upon exposure to Δkdnase A. fumigatus conidia (Schoggins and Rice 2011). IRFs are produced upon detection of microbial products by PRRs and cytokines. These IRFs result in activation of interferons (IFNs) through JAK/STAT pathway to induce production of interferon-stimulated genes (ISGs). However, these ISGs can also be induced directly by IRFs in an IFN-independent pathway as well (Schoggins and Rice 2011). Type I IFNs, such as IFN-α and IFN-β, are  123 polypeptides secreted by infected cells and have been primarily known for anti-viral immune response, but can be expressed upon exposure to non-viral pathogens as well (Malireddi and Kanneganti 2013). The three major functions of type I IFNs include induction of cell-intrinsic antimicrobial states upon infection, modulating innate immune response to promote antigen presentation and natural killer cell functions, and activating adaptive immune system to result in high-affinity antigen-specific T and B cell responses and immunological memory (Ivashkiv and Donlin 2014). However, their role in fungal infections is not well-understood; two studies have indicated that Candida spp. stimulate the expression of IFN-β in mouse bone marrow-derived DCs and macrophages (Malireddi and Kanneganti 2013). Interestingly, GO ontologies associated with IFN-α and IFN-β were enriched for up-regulated RNA transcripts upon exposure to Δkdnase A. fumigatus conidia as well. None of these RNA transcripts, associated with Type I IFNs, were differentially abundant in ALI cultures upon exposure to WT A. fumigatus conidia. However, type III IFN, IFNL1, was differentially abundant upon exposure to both Δkdnase A. fumigatus conidia and Wild-type A. fumigatus conidia. We speculate that the Δkdnase, which has an altered cell wall composition (Nesbitt et al. 2018) may activate both type I and type III interferons by binding PRRs distinct from the ones bound by WT conidia. Further research is required to confirm this hypothesis. Previously, it has been reported that higher number of macrophages are recruited in mouse lung upon exposure to Δkdnase A. fumigatus conidia than WT A. fumigatus conidia (Nesbitt et al. 2018). This was supported by enrichment of macrophage activation in GO terms. The RNA transcripts enriched included Hepatitis A virus cellular receptor 2 (HAVCR2/TIM3), Toll like Receptor Adaptor Molecular 1 (TICAM1/TIM1/TRIF), Interleukin-1 receptor like 1 (IL1RL1),  124 Toll-like receptor 4 (TLR4) and Toll like receptor 6 (TLR6). None of these RNA transcripts were differentially abundant in ALI cultures upon exposure to WT A. fumigatus conidia. TLR4 has been previously shown to be involved in recognition of A. fumigatus conidia (Netea et al. 2003). It is activated upon binding of two adaptor molecules, myeloid differentiation marker 88 (My D88) and TRIF. Specifically, TRIF- mediated signaling results in less toxic inflammatory response than MyD88-mediated signaling (Kolb et al. 2014). TRIF dependent signaling is also essential for the production of type 1 IFNs (Kolb et al. 2014). TLR signaling can result in production of pro-inflammatory cytokines and antimicrobial small molecules, such as nitric oxide, which can stimulate macrophage activation as well (Kaisho and Akira 2002). Hence, Δkdnase A. fumigatus may activate TLR4 signaling via TRIF adaptor molecule to produce type 1 IFNs. GO terms related to TH1 and TH2 type immune response were also enriched in up-regulated RNA transcripts with the Δkdnase strain. Specifically, RNA transcripts associated with negative regulation of TH1 type immune response were among the differentially abundant: Interleukin-1 receptor like 1 (IL1RL1), Hepatitis A virus cellular receptor 2 (HAVCR2), and Tumor necrosis factor ligand superfamily member 4 (TNFSF4). None of these genes were differentially abundant in ALI cultures upon exposure to WT A. fumigatus. IL1RL1, receptor for IL-33, can result in induction of TH2 type immune response by producing pro-inflammatory cytokines (Akhabir and Sandford 2010). Therefore, exposure to Δkdnase A. fumigatus conidia in ALI cultures may elicit a TH2 response  RNA transcripts associated with granulocyte chemotaxis were down-regulated in ALI cultures upon exposure to Δkdnase A. fumigatus. These included C-X-C Motif Chemokine Ligand 5 (CXCL5), C-X-C Motif Chemokine Ligand 6 (CXCL6) and S100 Calcium binding protein (S100A7).  125 CXCL5 and CXCL 6 were also previously down-regulated in ALI cultures upon exposure to WT A. fumigatus conidia, indicating that these chemokines may be important for general immune response towards fungal pathogens. Interestingly, RNA transcripts regulating complement and coagulation cascades were both up-regulated and down-regulated in ALI cultures upon exposure to Δkdnase A. fumigatus. These included Complement Factor Properdin (CFP) and Complement C6 (C6) in the up-regulated RNA transcripts as well as Complement Factor B (CFB), Complement C3 (C3), Complement Factor I (CFI) and Complement Component 4B (C4B) in the down-regulated RNA transcripts. A. fumigatus has shown to evade complement system by secreting extracellular proteases (Behnsen et al. 2008). However, how Δkdnase A. fumigatus regulates different components of complement system is yet to be studied. Enrichment of genes regulating cytokine production, neutrophil chemotaxis,  complement and coagulation cascades and T-cell differentiation in ALI cultures upon exposure to both Δkdnase and WT A. fumigatus conidia indicates the importance of these pathways in early immune response against fungal pathogens. Up-regulation of PRRs such as complement proteins, TLR6 and TLR4 in ALI cultures upon exposure to Δkdnase A. fumigatus but not in WT A. fumigatus conidia, indicates pathogen specific response. Hence, in the presence Δkdnase in A. fumigatus, human bronchial epithelium may activate different PRRs to activate immune response to the mutant strain of A. fumigatus. Differential abundance analysis was conducted between ALI cultures exposed to Δkdnase A. fumigatus conidia and WT A. fumigatus conidia to assess differentially abundant RNA transcripts between the WT and mutant strain of A. fumigatus. The up-regulated RNA  126 transcripts in the Δkdnase A. fumigatus conidia infected ALI cultures compared to WT A. fumigatus conidia infected ALI cultures included Toll-like Receptor 6 (TLR6), Cluster of Differentiation 14 (CD 14), Surfactant Pulmonary-Associated D-Binding Protein (DMBT1), Interleukin 1 Receptor Associated Kinase 2 (IRAK2), 5`-Nucleotide Ecto (NT5E) and Interleukin 32 (IL32). TLR-6 recognizes lipoproteins, peptidoglycans, lipotechoic acids, zymosan, and mannan, and CD 14 is also co-receptor with TRL4 for LPS recognition (Kawasaki and Kawai 2014). DMBT1 plays important role in mucosal protection, and has been shown to be up-regulated in inflamed mucosa of Crohn’s disease patients (End et al. 2009). IRAK-2 is known to induce NF-kB activation through TLR signaling, and may play an important role in regulating expression of various inflammatory genes (Jain, Kaczanowska, and Davila 2014). IL-32 is also known to be involved in production of various chemokines and inflammatory cytokines such as TNF-α (Khawar, Abbasi, and Sheikh 2016).  CCL28, Mucosae-Associated Epithelial Chemokine, was down-regulated in ALI cultures upon exposure to Δkdnase A. fumigatus conidia, compared to WT A. fumigatus conidia. Previously, down-regulation of CCL28 has been associated with less inflammatory condition in the intestines (Rashidiani et al. 2017). TFRC, receptor involved in cellular uptake of iron from transferrin, was down-regulated in ALI cultures upon exposure to mutant conidia compared to WT conidia. It is highly expressed in rapidly dividing cells (Ponka and Lok 1999), and down-regulation of TFRC may indicate a quiescent state of cells and down-regulation of cell-cycle progression. Hence, RNA transcripts involved in pathogen recognition and production of inflammatory cytokines were differentially abundant in ALI cultures upon exposure to Δkdnase A. fumigatus conidia, compared to WT A. fumigatus conidia.  127 To further assess the specificity of response to different pathogens, differentially abundant RNA transcripts in ALI cultures of primary HBECs upon exposure to RSV for 6 hours were analyzed using Nanostring’s nCounter Immune profiling panel. Of the 82 differentially abundant RNA transcripts in RSV exposed ALI cultures and 41 differentially abundant RNA transcripts in WT A. fumigatus conidia exposed ALI cultures, only 4 genes overlapped. Of these 4, SAA1 and LCN2 were down-regulated in both experiments. SYK and IL6R were down-regulated in RSV exposed ALI cultures compared to controls, and up-regulated in WT A. fumigatus exposed ALI cultures compared to controls. Differentially abundant RNA transcripts and enriched pathways indicated virus recognition by TLR4, TLR6, and TLR3. Both extracellular receptors, TLR4 and TLR6, were up-regulated, whereas TLR3, intracellular TLR was down-regulated upon exposure to RSV compared to controls. It was previously reported that F glycoprotein, found on the RSV surface, can activate TLR4, which plays an important role in the activation of innate immune response to RSV infection (Haynes et al. 2001). TICAM1/TRIF was also up-regulated upon exposure to RSV, indicating that activation of Toll-like signaling may be independent of MyD88. TLR4 activation can result in the expression of pro-inflammatory cytokines, such as Interkeukin-6 (IL-6) (Kurt-Jones et al. 2000). IL-6, along with Interleukin 23 Subunit Alpha (IL23A), were among the top two differentially abundant RNA transcripts in ALI cultures upon exposure to RSV. Previously, higher levels of IL-6 during RSV infection have been reported in serum levels and secretions of RSV-infected individuals (Sheeran et al. 1999). IL-6 plays an important role in both innate and adaptive immunity. It can recruit neutrophils to the site of inflammation and can affect macrophage differentiation as well (Liu et al. 1997; Chomarat et al. 2000). IL23A encodes  128 subunit of Interleukin 23 (IL-23), a cytokine that drives differentiation of human TH17 cells. Excess secretion of IL-6 is also associated with TH17 differentiation (Feng et al. 2015). TH17 cells play an important role in host defense against extracellular pathogens, and recruit neutrophils and macrophages to the site of infection. IL-6 and IL-23 expression has been reported in BEAS-2B cells upon RSV infection by Feng et al. (2015). This shows that the human bronchial epithelium can release inflammatory cytokines upon RSV infection to initiate an immune response resulting in the migration of TH subsets. In the RSV-exposed HBECs-ALI cells, up-regulated RNA transcripts included nitric oxide synthase 2 (NOS2A). Nitric oxide has been shown to inhibit replication of other viruses, including rhinovirus (Vareille et al. 2011). Susceptibility to RSV has been associated with single-nucleotide polymorphisms in NOS2A (Janssen et al. 2007). NF-kB Inhibitor Alpha (NFKBIA) was up-regulated upon exposure to RSV; this is known as a major negative regulator of NF-kB activity (Hayden and Ghosh 2008). However, not much is known about its role in RSV infection.    Reactome pathways enriched for up-regulated RNA transcripts upon RSV infection included chemokine receptors bind chemokines. These included RNA transcripts associated with chemokine receptors, CXCR1 and CCR3, and chemokines, CXCL2, CXCL8, CXCL1, and CCL20. CXCR1 is a receptor for CXCL8 and primary RSV infections have been characterized by intense neutrophil recruitment due to high levels of CXCL8, resulting in mucosal inflammation and increased airway secretions, coughing and sneezing (Ugonna et al. 2016). Along with CXCL8, CXCL1 and CXCL2 are involved in neutrophil chemotaxis and activation (Sawant et al. 2016) (W. B. Xu et al. 1995). The receptor CCR3 has been also previously reported to be up-regulated in bronchial biopsies of inflamed asthmatic airways compared to non-diseased (Beck et al. 2006).  129 The differential abundance of RNA transcripts regulating neutrophil recruitment is supported by studies that showed rapid neutrophil infiltration in severe primary RSV infection: 80% of cells in bronchoalveolar lavage in RSV patients were neutrophils (Stoppelenburg et al. 2013). Other chemokines such as CCL3, CXCL11 and CXCL10 were down-regulated upon exposure to RSV in ALI cultures compared to control samples. Down-regulation of TLR3 has been previously associated with down-regulation of CXCL10 but not CXCL8 (Rudd et al. 2005). This was supported in the differential abundance results as both TLR3 and CXCL10 were down-regulated, and CXCL8 was up-regulated in ALI cultures upon exposure to RSV. CXCL10 was among the top most highly down-regulated RNA transcripts with Log2 Fold change of -3.06, along with IL-34 (Log2 Fold change -3.11). CXCL10 is known for regulating interferon response and activating TH1 cells to site of infection. It is also a chemoattractant for monocytes, T cells and NK cells, and increased expression has been associated with advanced human cancers (LIU, GUO, and STILES 2011). Hence, down-regulation of CXCL10 may be associated with host defense against RSV. The two top most significant down-regulated RNA transcripts upon exposure to RSV in ALI cultures were Nuclear Factor of Activated T-Cells 4 (NFATC4) and Interleukin 34 (IL-34). NFAT genes are known to be involved in T-cell activation and differentiation (Gabriel et al. 2016), whereas IL-34 can induce activation of macrophages (Masteller and Wong 2014).  Therefore, activation of PRRs such as TLR4, TLR6, and TLR3 in the presence of RSV but not WT fungal conidia indicates specific detection of viral components by differentiated ALI cultures. The high expression of cytokines genes such as IL-6, IL-23A, along with chemokines and chemokine receptors are critical to prevent viral infection. Overall, our data shows that RSV  130 infection may result in a more severe early immune response than fungal infection in cultured bronchial epithelial cells. 4.4.4 Analysis of high TEER and low TEER ALI cultures Previously, TEER values have been used to assess the integrity of tight junctions and cellular barriers in cell culture models of epithelial/endothelial monolayers and ALI cultures. A wide range of TEER values has been reported for pulmonary models in previous studies, ranging from 150 Ω.cm2 for the transformed human Type 2 epithelial cell line (A549) to 3133 Ω.cm2 for primary human nasal epithelial cells (Srinivasan et al. 2015). Factors that may affect TEER measurements have been identified in previous studies, and include temperature, cell passage number and cell culture medium composition. Primary cells can have a high degree of variability in TEER values due to differences between donor, cell passage and experiments (Stewart et al. 2012). Therefore, by using TEER value as an assessment of culture differentiation and development in experiment #1, RNA transcripts that were differentially abundant in 4 well-differentiated ALI cultures compared to 2 non-well-differentiated ALI cultures were assessed. The up-regulated differentially abundant RNA transcripts in high TEER ALI cultures consisted of 5 RNA transcripts with high log2 fold change (greater than 5). These included Forkhead Box J1 (FOXJ1), Cyclin Dependent Kinase 1 (CDK1), Complement Component 6 (C6), Sperm-autoantigenic protein 17 (SPA17), and Interleukin-5 receptor alpha (IL5RA). The FOXJ1 RNA transcript had the highest log2 fold change of 8.81 with the greatest statistical significance (P-value < 0.05). FOXJI is a transcription factor, known to be involved in the formation of motile cilia, and has been shown to be conserved across vertebrates (Choksi et al. 2014). It was also identified to be highly expressed in 28-day old ALI cultures of primary HBECs in a study where  131 microarray analysis was conducted to assess genes involved in mucociliary differentiation (Ross et al. 2007). FOXJ1 regulates expression of SPA17 in both Xenopus and mouse (Thomas et al. 2010). SPA17 was also among the 5 highly expressed RNA transcripts. It was previously shown to be highly expressed in tissues with high number of ciliated cells, such as olfactory sensory neurons, and is important for cilia formation in mouse (McClintock et al. 2008). IL-5RA, a receptor for IL-5, was also among the 5 highly expressed RNA transcripts. IL-5 is known to be involved in eosinophil differentiation and survival. The regulatory factor X (RFX) family DNA-binding proteins can also bind to the cis element of IL-5RA (Kouro and Takatsu 2009). Interestingly, RFX transcription factors have been shown to regulate ciliary genes as well (Piasecki, Burghoorn, and Swoboda 2010). RNA transcripts regulating cell cycle and important for cellular proliferation, such as CDK1, were also highly expressed in high TEER samples. CDK1 is known to be a central regulator in cell division and controls cells undergoing G2 phase and mitosis (Diril et al. 2012). C6 was also among the highly expressed RNA transcripts in high TEER ALI cultures compared to low TEER ALI cultures. Overall, RNA transcripts of genes regulating ciliogenesis were highly expressed in high TEER samples compared to low TEER samples, indicating that along with having a more intact cellular barrier, high TEER cultures may have more cilia than low TEER samples. The down-regulated differentially abundant RNA transcripts with log2 fold change greater than 5 included Fibronectin-1 (FN1), Thrombospondin-1 (THBS1) and Chemokine Ligand 5 (CXCL5). FN1 is a glycoprotein that plays an important role in cell adhesion, migration, growth and differentiation (Pankov and Yamada 2002). It is also a ligand of THBS1, an extracellular molecule that functions by binding to multiple ligands (Resovi et al. 2014). CXCL5, also known  132 as epithelial cell derived neutrophil attractant 78, is primarily expressed in epithelial cells, and is involved in recruitment and activation of neutrophils by binding to CXCR2. Overexpression of CXCL5 is associated with tumor proliferation, growth and migration (Xia et al. 2015). Other down-regulated differentially abundant RNA transcripts included Transforming growth factor beta-2 (TGFB2), an extracellular glycoprotein involved in cell proliferation and differentiation (M. Wu, Chen, and Li 2016). Therefore, extracellular proteins regulating cell proliferation and differentiation had a lower expression level in high TEER samples compared to low TEER samples. 4.5 Summary The research presented in this chapter demonstrated that, unlike differentiated ALI cultures of primary HBECs, submerged monolayer cultures of 1HAEs bind and internalize more conidia. This could be due to the absence of ciliated and mucus-producing goblet cells in submerged monolayer cultures, which were present in differentiated ALI cultures of primary HBECs. Even though similar pathways were enriched for differentially abundant RNA transcripts in both in-vitro models, the genes regulating these pathways were different in each model. This may be due to the differences in binding of A. fumigatus conidia to host epithelia or from secretion of molecules from the surface of the conidia, which can activate different PRRs. Therefore, ALI cultures, with mucus-producing goblet cells and ciliated cells, may be a better model for studying host response in intact bronchial epithelium to fungal conidia. Nevertheless, in damaged epithelia, as may occur in patients with underlying disease (Davies 2009), newly-migrated epithelial cells may behave more like the submerged monolayers. Even ALI cultures may not have consistent levels of differentiation which can affect their response to pathogens.  133 Therefore, prior to conducting experiments, it is also important to assess the cellular integrity and differentiation of ALI cultures. TEER values are a useful surrogate for differentiation through which the presence of mucus and cilia may also be monitored. Finally, using different fungal strains and a respiratory virus, we showed that the transcriptomic and proteomic responses in ALI cultures of primary HBECs is pathogen specific.    134 Chapter 5 General conclusions and future directions The aims of the research presented here were to employ an in-vitro model that closely mimics the bronchial epithelial barrier in the conductive zone of the respiratory tract to study the early molecular response of the host to A. fumigatus. Hence, an ALI model of primary HBECs with basal cells, ciliated cells and mucus-producing goblet cells, was utilized to study the transcriptomics and proteomics of bronchial epithelial cells upon exposure to A. fumigatus conidia. Using this model, we showed that unlike submerged monolayer cultures, differentiated ALI cultures of primary HBECs internalized less than 1% of bound conidia. This could be due to the mucociliary barrier produced by ALI cultures, which is not generated in submerged monolayer cultures. Chapter 4 showed that submerged monolayer cultures of 1HAEs internalized more conidia than ALI cultures. This also shows that intact ALI cultures well-mimic the in-vivo host-pathogen interaction where pathogenic fungal spores are efficiently removed from the respiratory tract of healthy individuals without causing an allergic response or infection. Hence, well-differentiated ALI cultures of primary HBECs can be used for studying the early molecular response of intact bronchial epithelium to conidia for future studies. It was shown using a multi-OMICs approach that interaction of A. fumigatus with ALI cultures of primary HBECs elicits molecular response by up-regulating pathways associated with apoptosis/autophagy, translation, unfolded protein response and cell cycle. In contrast, complement and coagulation pathways, iron homeostasis, non-sense mediated decay and rRNA binding pathways were down-regulated upon exposure to A. fumigatus. Hence, ALI cultures showed that the effect of fungal conidia on early response could either be mediated by binding  135 of A. fumigatus conidia or the interaction of molecules from the surface of conidia could also initiate the host response.  For the first time, differentiated primary cell cultures along with a multi-OMICs approach was used to study proteomics and transcriptomics in the host-pathogen interaction. Therefore, it was important to further evaluate the applicability of ALI cultures to study host-pathogen interactions for future studies. This was performed by two separate approaches, first by conducting comparative transcriptomics and proteomics studies in submerged monolayer cultures of 1HAEs upon exposure to A. fumigatus, and then by assessing pathogen-specific response in ALI cultures.  Even though submerged monolayer cultures of 1HAEs are morphologically different, the pathways enriched were similar to that of ALI cultures upon conidial exposure. These included interferon signaling pathways, complement and coagulation pathway, non-sense mediated decay, and rRNA processing pathways. However, only a small number of differentially abundant RNA transcripts and proteins overlapped between both models. Genes associated with PRR signaling pathways, such as Dectin-1 signaling and TLR signaling, were differentially abundant in 1HAEs upon exposure to A. fumigatus conidia. This further supports that in the absence of ciliated and mucus-producing goblet cells, more conidia can bind with bronchial epithelial cells, and may result in the activation of PRRs to elicit an immune response. The research presented here also showed that it may be important to use ALI cultures with approximately similar TEER values in order to ensure that these cultures closely mimic the in-vivo epithelium as well as to decrease variability within ALI cultures.  136 Transcriptomics of Δkdnase A. fumigatus conidia identified expression of PRRs, which were not expressed upon exposure to WT A. fumigatus conidia. However, further work needs to be conducted to elucidate the importance of Kdnase in fungal virulence upon interaction with host bronchial epithelium. RSV resulted in expression of neutrophil chemoattractants as well as pro-inflammatory cytokines in HBECs. These specific responses upon exposure to different pathogens indicated that response in ALI cultures is pathogen-specific. One limitation of this study was the small sample size which reflects the exploratory nature of the conducted experiments; however, it resulted in difficulty in obtaining robust significant results. To overcome this and to prioritize biologically significant findings, statistical significance was tested using both nominal (P-value < 0.05) and adjusted P-values (BH-FDR). The small sample size may have yielded a high signal-to-noise ratio, generating high number of false positives in our study. Hence, further analysis is necessary of these exploratory findings.  The results of the present study motivates research in several different avenues. Nevertheless, to increase confidence in these results, it is important to validate them using a larger sample size. NanoString only allowed profiling of a limited number genes associated with the immune response. Therefore, high throughput techniques, such as RNA-sequencing and single-cell sequencing, would be ideal to gain a better understanding of early molecular response of host to A. fumigatus in this in-vitro model for future studies. Genes of interest from these studies can also be validated using single-cell western platforms to confirm expression levels. Shotgun proteomics was also utilized for the first time to profile host proteomics upon interaction with A. fumigatus. Novel proteins and pathways were identified in both in-vitro models, which can be also be further investigated in future studies.   137 ALI cultures of primary HBECs can also be used to understand how host pathology in asthmatic and cystic fibrosis patients may influence the early immune response to A. fumigatus conidia as well. The identification of different genes and pathways associated with differentiated ALI cultures of asthmatic cells may help characterize the molecular response involved in A. fumigatus induced asthma.  Overall, the research presented here demonstrates that ALI models of primary HBECs can provide novel insights into the mechanisms associated with this opportunistic fungal pathogen. The main significance of this work is three-fold. First, it has shown that differentiated cultures of primary HBECs internalize less conidia than submerged monolayer cultures. This may be better representative of the nature of interaction between inhaled A. fumigatus conidia and the bronchial epithelium in-vivo. It is likely that in the presence of dysfunctional mucociliary barrier, this interaction can result in a range of diseases associated with A. fumigatus. 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Zuehlke, Abbey D., Kristin Beebe, Len Neckers, and Thomas Prince. 2015. “Regulation and Function of the Human HSP90AA1 Gene.” Gene 570 (1): 8–16. https://doi.org/10.1016/j.gene.2015.06.018.     164 Appendix 1: List of differentially abundant mRNA transcripts identified using Asthma Elements Panel in ALI cultures of primary HBECs upon exposure to A. fumigatus conidia    Gene logFC AveExpr t P.Value adj.P.Val 1 LCP1 0.67090443 4.55039823 4.2859063 0.00615763 0.34052965 2 HIP1 -0.7292261 3.79930367 -4.1782702 0.006888 0.34052965 3 MPPED1 0.69755766 3.67932166 4.15019285 0.00709437 0.34052965 4 CISH -0.5202025 4.53258812 -2.9861611 0.02683136 0.83501083 5 C5AR1 -0.575807 6.31062395 -2.7714702 0.03507604 0.83501083 6 GTF2H2 0.67184439 4.38087244 2.76384101 0.03541588 0.83501083 7 MAF -0.6945296 7.07663281 -2.6565935 0.0405908 0.83501083     165 Appendix 2: List of differentially abundant mRNA transcripts identified using Immune Profiling Panel in ALI cultures of primary HBECs upon exposure to A. fumigatus conidia  Gene logFC AveExpr t P.Value adj.P.Val 1 MAF -0.4436 6.8257 -4.0697 0.0017 0.2998 2 LCN2 -0.4520 14.5953 -3.6039 0.0039 0.2998 3 TFE3 0.4945 5.3870 3.3656 0.0061 0.2998 4 SELPLG 0.8303 5.1662 3.3349 0.0064 0.2998 5 BST2 0.5030 6.9595 3.2716 0.0072 0.2998 6 IDO1 -1.0180 9.7237 -3.2703 0.0072 0.2998 7 CFB -0.4470 12.1470 -3.2635 0.0072 0.2998 8 ERCC3 0.3835 7.3092 3.2473 0.0074 0.2998 9 IL6R 0.6520 6.2706 3.2187 0.0079 0.2998 10 COG7 0.4324 8.6739 3.1759 0.0085 0.2998 11 CCL15 0.9027 4.9134 3.1349 0.0092 0.2998 12 IFNL1 0.6464 5.3388 3.0762 0.0102 0.3056 13 IL5RA 0.8170 7.1506 2.9933 0.0119 0.3105 14 CASP8 0.2756 7.2907 2.9055 0.0138 0.3105 15 FADD 0.3682 5.3941 2.8880 0.0143 0.3105 16 SAA1 -0.6128 14.6011 -2.8665 0.0149 0.3105 17 PIN1 0.4260 8.2660 2.8513 0.0154 0.3105 18 FCF1 0.3481 9.8494 2.8109 0.0164 0.3105 19 MFGE8 -0.5326 9.8809 -2.8137 0.0164 0.3105 20 RRAD 0.5903 8.9349 2.7718 0.0177 0.3148 21 NFATC2 0.4277 5.5747 2.7392 0.0188 0.3148 22 CTSS 0.5095 11.0279 2.7252 0.0193 0.3148 23 FEZ1 -0.3836 8.0604 -2.6415 0.0224 0.3166 24 CD164 0.2501 11.3788 2.6347 0.0226 0.3166 25 CD44 -0.4266 10.8492 -2.6106 0.0237 0.3166 26 CXCL6 -0.6718 11.6494 -2.5913 0.0246 0.3166 27 IL2RG -0.4563 5.2380 -2.5626 0.0259 0.3166 28 SPA17 0.7151 11.1160 2.5533 0.0263 0.3166 29 ALAS1 0.2781 8.5584 2.5398 0.0268 0.3166 30 LCK 0.6621 5.2162 2.5347 0.0272 0.3166 31 C3 -0.4674 12.9094 -2.5320 0.0273 0.3166  166 32 ANXA1 0.4099 14.6046 2.5143 0.0282 0.3166 33 CXCL5 -1.1465 9.0287 -2.4441 0.0320 0.3315 34 CD24 0.2928 12.7260 2.4390 0.0321 0.3315 35 ATF1 0.3061 7.8794 2.4359 0.0323 0.3315 36 PLA2G6 0.3900 5.7471 2.3602 0.0372 0.3669 37 SYK 0.2668 7.3223 2.3484 0.0378 0.3669 38 BCL2L1 0.2614 11.1098 2.3045 0.0409 0.3776 39 TNFRSF11A -0.4019 6.5494 -2.3052 0.0410 0.3776 40 CASP3 0.2649 9.9287 2.2454 0.0454 0.3808 41 MERTK 0.2628 5.6880 2.2055 0.0488 0.3808     167 Appendix 3: List of differentially abundant proteins identified using Immune Profiling Panel in ALI cultures of primary HBECs upon exposure to A. fumigatus conidia   Gene logFC AveExpr t P.Value adj.P.Val 1 CALR 5.72296 5.72296 29.47107 0.0000005 0.00089 2 NUCB2 2.76004 2.76004 10.35694 0.00011 0.06737 3 SET 2.69194 2.69194 10.28771 0.00011 0.06737 4 MATR3 1.73914 1.73914 9.19187 0.0002 0.07522 5 TPM3;DKFZp686J1372 1.74663 1.74663 9.08705 0.00021 0.07522 6 CBX5 1.31134 1.31134 7.05672 0.00072 0.19998 7 EEA1 1.2188 1.2188 6.59847 0.001 0.19998 8 EIF4B 1.3481 1.3481 6.59087 0.001 0.19998 9 TMEM205 -0.87489 -0.87489 -5.81094 0.00099 0.19998 10 RDX 1.17865 1.17865 6.42933 0.00113 0.20248 11 ST13;ST13P5;ST13P4 1.32255 1.32255 6.20186 0.00134 0.20541 12 NME1 1.27855 1.27855 6.16663 0.00137 0.20541 13 HMGB3 1.35247 1.35247 5.75821 0.00189 0.26094 14 EIF3J 1.0537 1.0537 5.62692 0.0021 0.26108 15 LSM8 1.37488 1.37488 5.47232 0.00239 0.26108 16 INS;INS-IGF2 1.06686 1.06686 4.92386 0.00236 0.26108 17 ALB -1.61075 -1.61075 -4.87969 0.00248 0.26108 18 CFAP58 0.94581 0.94581 5.16217 0.00311 0.27517 19 RSPH4A 0.94347 0.94347 5.15343 0.00314 0.27517 20 HDGF 0.94134 0.94134 5.13432 0.00319 0.27517 21 UBXN1 0.93814 0.93814 5.07921 0.00335 0.27517 22 ALYREF 1.04981 1.04981 5.0702 0.00338 0.27517 23 LRRFIP1 0.9023 0.9023 4.71811 0.00464 0.35025 24 KTN1 1.29127 1.29127 4.70766 0.00469 0.35025 25 HDAC1 0.85978 0.85978 4.61618 0.00511 0.35219 26 RRBP1 1.02929 1.02929 4.51269 0.00563 0.37412 27 KRT1 -0.79086 -0.79086 -4.23219 0.00502 0.35219 28 SBDS -0.90827 -0.90827 -4.41308 0.0062 0.38953 29 SNRPE 0.81285 0.81285 4.39667 0.0063 0.38953 30 ATP6V0D1 -0.79455 -0.79455 -4.23908 0.00736 0.41216 31 AP2M1 -0.56892 -0.56892 -3.97634 0.00674 0.40306  168 32 CHMP2A -0.81106 -0.81106 -4.08437 0.00859 0.4293 33 EIF2S2 0.84864 0.84864 4.05977 0.00881 0.4293 34 API5 -0.60699 -0.60699 -3.92988 0.00712 0.41205 35 ODF2 0.74095 0.74095 3.95798 0.00978 0.43992 36 ETF1 -0.92092 -0.92092 -3.82712 0.00805 0.42489 37 HSPE1 -0.65222 -0.65222 -3.82659 0.00806 0.42489 38 YBX1;YBX3;YBX2 0.75167 0.75167 3.88876 0.01052 0.44549 39 PPP1CA -0.55032 -0.55032 -3.74789 0.00886 0.4293 40 RPL13 -0.80619 -0.80619 -3.71773 0.00919 0.43359 41 PLGRKT 0.8548 0.8548 3.78172 0.01177 0.46411 42 SERPINB4;SERPINB3 -0.54233 -0.54233 -3.6638 0.00981 0.43992 43 SEC11A 0.82671 0.82671 3.73292 0.0124 0.46411 44 NASP 0.74792 0.74792 3.72871 0.01246 0.46411 45 KRT2 -1.23232 -1.23232 -3.60867 0.0105 0.44549 46 EIF4E -0.74466 -0.74466 -3.65987 0.01342 0.46411 47 NDUFAF2 0.83641 0.83641 3.65533 0.01348 0.46411 48 DHX15 -0.50393 -0.50393 -3.58093 0.01087 0.44549 49 ISYNA1 -0.66913 -0.66913 -3.64376 0.01365 0.46411 50 ESYT2 0.78023 0.78023 3.57616 0.01093 0.44549 51 PRKCSH 1.06681 1.06681 3.63507 0.01378 0.46411 52 CRIP2 0.59493 0.59493 3.48924 0.01218 0.46411 53 IPO4 -0.76666 -0.76666 -3.49703 0.01603 0.46834 54 S100P 0.64847 0.64847 3.49635 0.01605 0.46834 55 C12orf10 0.65603 0.65603 3.4914 0.01614 0.46834 56 STX12 -0.78664 -0.78664 -3.43421 0.0172 0.46834 57 CKMT1A;CKMT1B -0.46602 -0.46602 -3.39159 0.01377 0.46411 58 CPT1A -0.85461 -0.85461 -3.4245 0.01738 0.46834 59 RBM47 -0.65321 -0.65321 -3.41848 0.0175 0.46834 60 HEXA -0.53532 -0.53532 -3.38001 0.01398 0.46411 61 SNRPA -0.49907 -0.49907 -3.35343 0.01446 0.46547 62 HNRNPC;HNRNPCL1 1.58017 1.58017 3.34906 0.01454 0.46547 63 TP53BP1 0.69088 0.69088 3.36815 0.01852 0.47436 64 ARFGAP2 -0.6967 -0.6967 -3.35522 0.01879 0.47455 65 RELA 0.64402 0.64402 3.27974 0.01588 0.46834 66 BUB3 -0.44757 -0.44757 -3.25582 0.01638 0.46834 67 TMEM14C 0.59921 0.59921 3.26378 0.02085 0.49983 68 POLR2C -0.6317 -0.6317 -3.26139 0.02091 0.49983 69 RPL7A -0.57434 -0.57434 -3.24605 0.01659 0.46834 70 RPL13A -0.60359 -0.60359 -3.23669 0.01679 0.46834  169 71 RPL28 -0.65822 -0.65822 -3.21726 0.01721 0.46834 72 ACADSB -0.6338 -0.6338 -3.19468 0.02257 0.51236 73 PGRMC1 0.53587 0.53587 3.17861 0.0181 0.473 74 RPS20 0.51895 0.51895 3.17413 0.0182 0.473 75 COG5 -0.57998 -0.57998 -3.1589 0.02353 0.52736 76 STMND1 0.71323 0.71323 3.13267 0.02426 0.52948 77 RAB10 0.66297 0.66297 3.12472 0.01941 0.4834 78 DNAJC10 -0.58361 -0.58361 -3.11837 0.02467 0.52948 79 ANK3 -1.33082 -1.33082 -3.10577 0.02503 0.52948 80 TXNDC12 0.54822 0.54822 3.10075 0.02003 0.49194 81 CFAP36 0.5854 0.5854 3.04006 0.02704 0.53112 82 GBE1 -0.62675 -0.62675 -3.0482 0.02146 0.50626 83 KIF5B 0.56744 0.56744 3.01499 0.02785 0.53112 84 LMO7 0.58382 0.58382 3.01145 0.02797 0.53112 85 RPL3 -0.51794 -0.51794 -3.02467 0.02213 0.51192 86 GSTT1 -0.43706 -0.43706 -3.02004 0.02227 0.51192 87 ENKUR 0.62207 0.62207 2.95959 0.02975 0.53112 88 H2AFY2 -0.59469 -0.59469 -2.94411 0.0303 0.53112 89 UGGT1 -0.67482 -0.67482 -2.94379 0.03031 0.53112 90 MANF -0.75803 -0.75803 -2.94025 0.03044 0.53112 91 SOD1 -0.57894 -0.57894 -2.96076 0.02409 0.52948 92 APOO 0.5377 0.5377 2.92769 0.0309 0.53112 93 RPLP0;RPLP0P6 -0.40564 -0.40564 -2.92979 0.0251 0.52948 94 TBCB -0.75197 -0.75197 -2.9001 0.03194 0.53112 95 PSMA4 0.70297 0.70297 2.8911 0.03229 0.53112 96 HSP90AA1 1.33988 1.33988 2.917 0.02553 0.53112 97 RSPH3 0.57347 0.57347 2.86304 0.0334 0.53351 98 SNX12 -0.68226 -0.68226 -2.85744 0.03362 0.53351 99 UBQLN1 0.41236 0.41236 2.88319 0.02671 0.53112 100 ATP5L -0.50211 -0.50211 -2.88314 0.02671 0.53112 101 MCU -0.52275 -0.52275 -2.84919 0.03396 0.53413 102 FTL -0.67758 -0.67758 -2.85177 0.02786 0.53112 103 ASPH -0.40332 -0.40332 -2.84071 0.02828 0.53112 104 RPL27 -0.53245 -0.53245 -2.80189 0.03596 0.54158 105 PREP -0.42289 -0.42289 -2.83877 0.02835 0.53112 106 RPS8 -0.53285 -0.53285 -2.83489 0.0285 0.53112 107 BCCIP 0.5117 0.5117 2.7761 0.03711 0.54158 108 EIF4A3 -0.42784 -0.42784 -2.79937 0.0299 0.53112 109 ABRACL 0.51896 0.51896 2.75658 0.03801 0.54158  170 110 LDHA -0.5104 -0.5104 -2.796 0.03003 0.53112 111 DCTN1;DKFZp686E0752 0.50775 0.50775 2.75009 0.03831 0.54158 112 CYFIP1;CYFIP2 -0.44521 -0.44521 -2.79154 0.03021 0.53112 113 MYO6 0.52172 0.52172 2.73328 0.0391 0.54352 114 GLG1 -0.37932 -0.37932 -2.76502 0.03131 0.53112 115 CDK5 -0.57636 -0.57636 -2.75814 0.03161 0.53112 116 PFKP -0.42249 -0.42249 -2.75309 0.03182 0.53112 117 SF1 -0.75727 -0.75727 -2.74767 0.03206 0.53112 118 EIF1;EIF1B 0.49427 0.49427 2.69085 0.0412 0.55333 119 S100A11 0.78714 0.78714 2.71967 0.0333 0.53351 120 RPL17 -0.77599 -0.77599 -2.71671 0.03343 0.53351 121 TEKT2 0.83154 0.83154 2.66355 0.04261 0.55358 122 LMNA 0.36964 0.36964 2.69458 0.03446 0.53721 123 FGFR1OP 0.48869 0.48869 2.62366 0.04476 0.56923 124 APEH -0.38335 -0.38335 -2.66966 0.03565 0.54158 125 SF3B5 0.72859 0.72859 2.61077 0.04549 0.57002 126 KRAS 0.39786 0.39786 2.66225 0.03601 0.54158 127 TMED7;TICAM2 -0.41772 -0.41772 -2.66214 0.03601 0.54158 128 HDHD3 -0.65187 -0.65187 -2.60003 0.0461 0.57002 129 SNRNP200 -0.53801 -0.53801 -2.59338 0.04648 0.57019 130 PIR 0.47667 0.47667 2.58879 0.04675 0.57019 131 MYH9 0.78867 0.78867 2.64148 0.03704 0.54158 132 RPS18 -0.39185 -0.39185 -2.64125 0.03706 0.54158 133 CAST 1.0354 1.0354 2.62633 0.03782 0.54158 134 IGFBP3 -0.40502 -0.40502 -2.62031 0.03813 0.54158 135 MAP2K3 -0.67652 -0.67652 -2.55684 0.04865 0.57765 136 UQCRB -0.49097 -0.49097 -2.61596 0.03836 0.54158 137 SRP14 0.54208 0.54208 2.54755 0.04922 0.58055 138 ALDH1A1 -0.5166 -0.5166 -2.60703 0.03883 0.54352 139 HIST1H4A -0.41764 -0.41764 -2.57641 0.0405 0.55333 140 RPS6 -0.49713 -0.49713 -2.55926 0.04147 0.55333 141 TMED2 0.44778 0.44778 2.55511 0.0417 0.55333 142 F11R -0.5095 -0.5095 -2.55407 0.04176 0.55333 143 G6PD -0.42977 -0.42977 -2.55081 0.04195 0.55333 144 DHRS7 -0.39571 -0.39571 -2.55048 0.04197 0.55333 145 RPS11 -0.36969 -0.36969 -2.54488 0.04229 0.55354 146 RECQL -0.36164 -0.36164 -2.52953 0.0432 0.55723 147 VWA5A -0.4533 -0.4533 -2.51851 0.04386 0.56172 148 RPS5 -0.43004 -0.43004 -2.49502 0.04531 0.57002  171 149 LRPAP1 0.44064 0.44064 2.48498 0.04594 0.57002 150 AARS -0.35524 -0.35524 -2.46276 0.04737 0.57393 151 GMPPA -0.34083 -0.34083 -2.45492 0.04789 0.57631 152 CHMP1A 0.64099 0.64099 2.44598 0.04849 0.57765 153 CMAS -0.33708 -0.33708 -2.42971 0.0496 0.5812     172 Appendix 4: List of differentially abundant RNA transcripts identified using Asthma Elements Panels in submerged monolayer cultures of 1HAEs upon exposure to A. fumigatus conidia  Gene logFC AveExpr t P.Value adj.P.Val 1 GTF2H2 -0.6173758 6.05859516 -5.4298887 0.00084566 0.0571225 2 CFD -0.4660308 6.4212631 -4.8021953 0.00173971 0.0571225 3 C5AR1 0.49861492 5.33708415 4.47257256 0.00259853 0.0571225 4 GLIPR1 -0.5977106 8.75258603 -4.4555241 0.00265417 0.0571225 5 CMC1 -0.3000108 9.25490306 -4.4030697 0.00283371 0.0571225 6 FAM8A1 -0.3451338 6.81395208 -4.3690468 0.00295725 0.0571225 7 ARPC4 -0.3036772 12.2231276 -3.9573893 0.00502429 0.0571225 8 PPP3R1 -0.3005676 11.7084588 -3.9222804 0.00526279 0.0571225 9 TNFRSF1B -0.6494562 5.66567677 -3.916591 0.00530258 0.0571225 10 DESI1 -0.2594803 8.68786154 -3.8929128 0.00547173 0.0571225 11 ABHD5 -0.4125353 8.10080624 -3.817691 0.00604907 0.0571225 12 ZNF185 -0.4095381 8.46050578 -3.7277127 0.00682772 0.0571225 13 RPS6 -0.266151 14.4475456 -3.6708817 0.00737489 0.0571225 14 HLA-B -0.2796081 9.27218399 -3.6367336 0.00772629 0.0571225 15 NFKBIA -0.3827931 11.8984636 -3.434903 0.01020784 0.0571225 16 GLIPR1_isoform -0.4901081 8.47856847 -3.4173976 0.01046026 0.0571225 17 CTSS -0.352995 5.33282352 -3.3936646 0.01081321 0.0571225 18 CARM1 -0.2483937 10.1503974 -3.3843308 0.0109555 0.0571225 19 SF3B1 -0.2654039 10.8905609 -3.3715327 0.01115385 0.0571225 20 RHOA -0.2494785 12.6161572 -3.3472575 0.01154072 0.0571225 21 HLA-A -0.2943595 10.377421 -3.346282 0.01155656 0.0571225 22 C9orf78 -0.293774 9.9763331 -3.3351739 0.01173861 0.0571225 23 EWSR1 -0.2380538 11.7419259 -3.3078263 0.01219999 0.0571225 24 NAPA -0.2152195 10.5358534 -3.2871365 0.01256189 0.0571225 25 NFKB1 -0.3593485 9.2177274 -3.2759501 0.01276231 0.0571225 26 MRPS5 -0.2350307 10.6077023 -3.2757057 0.01276673 0.0571225 27 C1orf27 -0.2720099 8.47091179 -3.268846 0.01289136 0.0571225 28 TMBIM6 -0.2624047 12.7152208 -3.2403343 0.01342344 0.0571225 29 SCARNA5 -0.2877923 13.4904315 -3.1838529 0.01454771 0.0571225 30 FADD -0.2790219 9.51615975 -3.1793564 0.01464142 0.0571225  173 31 SERPINA1 -0.5340733 9.5664963 -3.176207 0.01470743 0.0571225 32 CD59 -0.2935295 12.3783736 -3.1653311 0.01493784 0.0571225 33 MT-CYB 0.23935838 16.3294482 3.13380583 0.01562763 0.0571225 34 MAP2K2 -0.2339406 10.7609856 -3.1266014 0.01578996 0.0571225 35 PSMF1 -0.228888 9.843818 -3.0927812 0.01657622 0.05806868 36 UBE2D1 -0.2536149 9.59763965 -3.0641184 0.01727503 0.05806868 37 ACOT9 -0.2707873 10.3939095 -3.0385406 0.01792494 0.05806868 38 TPP1 -0.2103372 9.74482455 -3.0340451 0.01804181 0.05806868 39 MSN -0.2422125 12.3174396 -3.0142099 0.01856716 0.05806868 40 CD46 -0.2116994 10.8430624 -2.9781255 0.01956468 0.05806868 41 PTPN18 -0.2268256 8.30474756 -2.9579914 0.02014567 0.05806868 42 SH3BGRL3 -0.2440266 13.1083182 -2.9556659 0.02021393 0.05806868 43 FAM133B -0.2835377 6.76672227 -2.952731 0.02030043 0.05806868 44 IL17RA -0.3817831 7.03742849 -2.8980806 0.02198426 0.06145601 45 ATG7 -0.2565495 8.88047108 -2.8253868 0.02445425 0.06526207 46 GBE1 -0.214744 8.496586 -2.8200137 0.02464799 0.06526207 47 PLAUR -0.4596932 11.7895899 -2.8120652 0.02493754 0.06526207 48 TEX261 -0.2529273 10.9415303 -2.7158296 0.02873864 0.07268716 49 EIF2B4 -0.2144785 9.20711883 -2.7107184 0.02895667 0.07268716 50 CNTNAP3 0.29898477 6.78696669 2.69238728 0.02975286 0.07319204 51 CDK5RAP3 -0.1866358 9.05635819 -2.6665715 0.03091286 0.07418068 52 DAP -0.1986902 11.2048555 -2.6499087 0.0316864 0.07418068 53 IKBIP_isoform -0.2228666 9.80034687 -2.6440306 0.03196403 0.07418068 54 PABPC1 -0.2030409 11.5680946 -2.608476 0.03369788 0.07675627 55 CHP1 -0.2174015 9.45089435 -2.5920462 0.03453164 0.07722529 56 VPS13A_isoform -0.3030373 5.80737077 -2.5289801 0.0379346 0.08254448 57 B3GNT5 -0.4040308 10.2208089 -2.5233918 0.03825232 0.08254448 58 CYTB_comp57541_c0_seq1 0.21516976 15.4579184 2.43829437 0.04344209 0.09160571 59 RRAD -0.3848514 5.39934156 -2.4306694 0.04394095 0.09160571 60 ZNF281 -0.20343 10.057551 -2.4031147 0.0457929 0.09244841 61 DUSP6 -0.4786688 7.54848784 -2.4016333 0.04589468 0.09244841 62 SEMA4D -0.1907052 6.13127368 -2.3914592 0.04660001 0.09244841 63 COPB1_isoform -0.1947803 8.07254338 -2.3762801 0.04767287 0.0930756    174 Appendix 5: List of differentially abundant RNA transcripts identified using Immune Profiling Panel in submerged monolayer cultures of 1HAEs upon exposure to A. fumigatus conidia  Gene logFC AveExpr t P.Value adj.P.Val 1 CXCL11 0.9191757 4.77082399 8.76075048 0.00011904 0.0420219 2 TLR6 0.77125351 5.30366308 6.64954605 0.00054691 0.09652877 3 JAK2 0.38990425 6.87353335 4.72035483 0.00321228 0.30924503 4 IFIT2 0.2341136 8.58937646 4.51229898 0.00399876 0.30924503 5 ZNF205 -0.2838895 5.43362231 -4.2371369 0.0053936 0.30924503 6 CXCR1 0.55752541 4.44295954 3.86841863 0.00819806 0.30924503 7 BCL6 0.43916062 5.18307412 3.79740643 0.00890767 0.30924503 8 CXCL6 0.27267483 4.99928735 3.78104568 0.00908068 0.30924503 9 PRKCE 0.17189381 7.95571562 3.65857378 0.01050085 0.30924503 10 RELB -0.1836117 8.56685631 -3.5722104 0.01165007 0.30924503 11 CD83 -0.269433 8.09121013 -3.5009849 0.01270289 0.30924503 12 STAT2 0.22776945 7.88929967 3.4764449 0.0130896 0.30924503 13 CSF2 -0.3357516 5.4483015 -3.4709246 0.01317837 0.30924503 14 AXL -0.1948483 11.095775 -3.4644154 0.0132839 0.30924503 15 IFNL1 0.43071411 5.77598355 3.46385418 0.01329304 0.30924503 16 NFATC2 0.29919582 9.19833285 3.40314461 0.01432411 0.30924503 17 TCF7 0.39153791 7.33852886 3.32701628 0.01574329 0.30924503 18 THBS1 0.22307793 14.5052609 3.29808026 0.01632262 0.30924503 19 CASP3 -0.3151583 10.1849158 -3.282466 0.01664492 0.30924503 20 AMMECR1L 0.16467555 9.12788856 3.17125351 0.01915234 0.32037056 21 CYFIP2 0.2608157 6.25863401 3.153515 0.01958914 0.32037056 22 C1S 0.67415996 4.41108307 3.13853702 0.01996644 0.32037056 23 SPA17 0.17818136 8.63483173 2.96015035 0.0251221 0.37752238 24 SMAD3 0.1548152 10.8463914 2.94365447 0.02566724 0.37752238 25 MAP3K1 0.2467808 7.15498836 2.8859566 0.02767685 0.39079712 26 MCAM 0.19112259 8.30661361 2.84359287 0.02926023 0.39726387 27 PYCARD 0.19781912 7.52831629 2.75772178 0.03277669 0.39791924 28 TNFRSF1A -0.1344485 10.708049 -2.731376 0.03394443 0.39791924 29 MAPK11 0.35303245 4.97911097 2.67798961 0.03644938 0.39791924 30 FADD 0.23056071 6.45451139 2.66866436 0.03690682 0.39791924 31 IL32 -0.2029644 11.1289339 -2.6358316 0.03856674 0.39791924  175 32 ADA 0.28191558 9.21443942 2.60878105 0.03999401 0.39791924 33 RUNX1 0.1643913 9.74012428 2.57401754 0.04191115 0.39791924 34 CD274 -0.2565487 5.6501759 -2.5732399 0.04195513 0.39791924 35 ITGA2B 0.41552039 5.44403806 2.57306677 0.04196493 0.39791924 36 OSM 0.66243683 4.62430529 2.56257714 0.04256319 0.39791924 37 CCL20 -0.5307588 5.15543735 -2.5223068 0.04494517 0.39791924 38 PDGFRB 0.59764095 6.59857102 2.52153193 0.04499236 0.39791924 39 IFNB1 -0.3588966 5.0186175 -2.5129707 0.04551728 0.39791924 40 CEBPB -0.3062697 11.8851524 -2.5120692 0.04557292 0.39791924 41 TTK 0.13745984 9.64108202 2.501715 0.04621725 0.39791924     176 Appendix 6: List of differentially abundant proteins identified using LC-MS/MS in submerged monolayer cultures of 1HAEs upon exposure to A. fumigatus conidia  Gene logFC AveExpr t P.Value adj.P.Val 1 ALB -3.0023602 -3.0023602 -13.315007 3.00E-05 0.01674497 2 RALGAPA1 -1.8647323 -1.8647323 -7.6670734 0.00120714 0.27901937 3 SF3A2 1.94379343 1.94379343 7.08894272 0.00165106 0.27901937 4 KRT10 -1.463663 -1.463663 -5.6812946 0.00200014 0.27901937 5 RPL18A -0.9788533 -0.9788533 -5.1899689 0.00302282 0.33734714 6 HNRNPK 1.22198134 1.22198134 4.68618962 0.00475885 0.404422 7 KRT9 -1.313281 -1.313281 -4.5357962 0.0054845 0.404422 8 CNBP 1.29234664 1.29234664 4.86025022 0.00707074 0.404422 9 HNRNPA0 1.16643339 1.16643339 4.79433294 0.00743711 0.404422 10 SF1 1.32274783 1.32274783 4.36389071 0.00647513 0.404422 11 RPF2 1.22409529 1.22409529 4.70471235 0.00797248 0.404422 12 KRT1 -1.4264988 -1.4264988 -3.8146008 0.01132782 0.45724709 13 CAPRIN1 1.00421073 1.00421073 3.97831666 0.01454675 0.45724709 14 HNRNPC 0.93325733 0.93325733 3.77948645 0.01175831 0.45724709 15 MATR3 1.09821374 1.09821374 3.82539284 0.01666376 0.45724709 16 NAP1L1 0.73076146 0.73076146 3.61789214 0.01399456 0.45724709 17 RPS27L;RPS27 0.89296627 0.89296627 3.72115607 0.01831693 0.45724709 18 CAST 0.86638623 0.86638623 3.64518246 0.01964455 0.45724709 19 TF -1.0419985 -1.0419985 -3.6375618 0.01978387 0.45724709 20 LMNA 0.87819679 0.87819679 3.50490435 0.01584464 0.45724709 21 RPL4 0.63505369 0.63505369 3.41825788 0.0174512 0.45724709 22 KPNA2 0.74414419 0.74414419 3.362452 0.01858282 0.45724709 23 HSPA5 -0.5904296 -0.5904296 -3.3616182 0.01860035 0.45724709 24 RBM3 0.71031373 0.71031373 3.28764744 0.02023124 0.45724709 25 HNRNPR 0.83971944 0.83971944 3.29218824 0.02751352 0.45724709 26 BZW1 1.0535723 1.0535723 3.28228405 0.02778273 0.45724709 27 SPTBN1 0.78111988 0.78111988 3.24377522 0.02885907 0.45724709 28 RPS4X 0.58114616 0.58114616 3.15372218 0.02360813 0.45724709 29 YWHAB -0.649399 -0.649399 -3.1018239 0.02508239 0.45724709 30 EIF4G1 1.39269782 1.39269782 3.13512135 0.03216781 0.45724709 31 RBP4 -1.1029861 -1.1029861 -3.0897753 0.02543914 0.45724709  177 32 RRBP1 0.93418151 0.93418151 3.08644129 0.03379187 0.45724709 33 YWHAZ 0.72093397 0.72093397 3.04590391 0.02678666 0.45724709 34 PFN2 -1.0821497 -1.0821497 -3.0317114 0.03573243 0.45724709 35 KRT2 -1.3884239 -1.3884239 -3.0067839 0.03665881 0.45724709 36 RPS16 0.80449506 0.80449506 2.96555443 0.0294651 0.45724709 37 YWHAE 0.5339016 0.5339016 2.9508395 0.02998709 0.45724709 38 HGFAC -1.0060502 -1.0060502 -2.9586144 0.03852867 0.45724709 39 PGD -0.5832711 -0.5832711 -2.9451758 0.03019073 0.45724709 40 SH3BGRL3 0.99583032 0.99583032 2.907924 0.04061619 0.45724709 41 NONO 0.62139047 0.62139047 2.90297375 0.03175711 0.45724709 42 CHORDC1 -0.7754687 -0.7754687 -2.9050675 0.04073764 0.45724709 43 PHGDH 0.51124895 0.51124895 2.88701481 0.03237267 0.45724709 44 FDFT1 0.68441513 0.68441513 2.88068842 0.0417914 0.45724709 45 NPM1 -0.5276766 -0.5276766 -2.7987124 0.03602543 0.45724709 46 LRRC59 0.64156907 0.64156907 2.79102219 0.0363644 0.45724709 47 2-Sep -0.6657523 -0.6657523 -2.7704271 0.03728958 0.45724709 48 LMAN1 -1.0350467 -1.0350467 -2.7403729 0.03868634 0.45724709 49 RPL10A 0.72483474 0.72483474 2.73656254 0.03886749 0.45724709 50 ETFB -0.4950135 -0.4950135 -2.6912792 0.04109283 0.45724709 51 ARHGDIA 0.54328957 0.54328957 2.67807586 0.04176763 0.45724709 52 PPP1CA;PPP1CC 0.63537855 0.63537855 2.61955657 0.04490748 0.48189178 53 HNRNPDL -0.576268 -0.576268 -2.5645093 0.04809665 0.49398739 54 VIM 0.59229976 0.59229976 2.53379672 0.04998215 0.49398739     178  Appendix 7: List of differentially abundant RNA transcripts identified using Immune Profiling Panel in ALI cultures of primary HBECs upon exposure to Δkdnase A. fumigatus conidia  Gene logFC AveExpr t P.Value adj.P.Val 1 TPTE 1.70687711 4.56904922 6.92829332 0.00025722 0.0729208 2 CD79B 1.16604173 5.08418981 6.21691315 0.00049139 0.0729208 3 BST2 1.11320421 7.13571335 6.14994696 0.00052374 0.0729208 4 CFP 1.11245365 5.32794505 5.72117641 0.00079759 0.0729208 5 HAVCR2 1.21502545 4.50174475 5.71434577 0.00080309 0.0729208 6 IFNL1 1.16722054 5.6962652 5.38478905 0.001127 0.07777562 7 CXCL6 -1.5400571 11.7979496 -5.2089998 0.00135804 0.07777562 8 SMPD3 1.10414054 5.47066744 5.20048872 0.0013705 0.07777562 9 TLR6 1.0123559 5.54920547 4.7514782 0.00225031 0.11351547 10 FOS 1.1460866 8.68855531 4.4375264 0.0032375 0.1469823 11 CXCL5 -1.9555697 9.64655601 -4.3052585 0.00379003 0.15642507 12 MAGEC2 0.74933257 4.4252311 4.06736786 0.00506508 0.17758159 13 SAA1 -0.8621851 15.5833191 -4.0315297 0.00529518 0.17758159 14 S100A7 -1.0809435 7.71638138 -4.0045343 0.00547608 0.17758159 15 C4B -0.9299218 6.50386333 -3.8361492 0.00676944 0.18716844 16 CFB -0.6317316 12.949439 -3.828638 0.00683445 0.18716844 17 TNFSF4 1.15853871 4.93218254 3.75598589 0.00749972 0.18716844 18 PLAUR 0.6920706 8.70786915 3.72268626 0.00782799 0.18716844 19 IDO1 -0.8712363 10.9717861 -3.7221862 0.00783304 0.18716844 20 CDH5 0.73404913 5.1250934 3.66547672 0.00842915 0.19134178 21 IFI27 1.00886001 9.72991902 3.5819324 0.0093992 0.20320181 22 TFE3 0.6049885 5.73341369 3.47188531 0.01086653 0.22424556 23 RUNX3 0.80644817 5.44129834 3.42241525 0.01160546 0.22908165 24 ITGAL 1.04368379 4.65698675 3.3739938 0.01238161 0.23208724 25 IL1RN 0.69159911 9.35569966 3.35038974 0.01278013 0.23208724 26 CREB5 0.65007458 5.66172851 3.31187465 0.01346043 0.23503976 27 HLA-DMB -0.5463503 9.60823682 -3.2567236 0.01450337 0.23575019 28 IL22RA1 0.67245799 4.68236411 3.25488224 0.01453966 0.23575019 29 ITGA5 0.59034459 9.52652113 3.19663403 0.01573947 0.24640412  179 30 IFI35 0.59683678 6.16526773 3.15999425 0.01654834 0.25043154 31 CXCL11 0.62139683 6.26770858 3.10694831 0.0177993 0.26067358 32 CD40LG 0.54152918 4.77602767 2.95070439 0.02210764 0.30905104 33 ARG2 0.94661546 6.67209186 2.92729867 0.02284329 0.30905104 34 NFATC1 0.58146423 6.6364877 2.91240473 0.0233249 0.30905104 35 IL18R1 -0.6174011 5.06155455 -2.8972649 0.02382552 0.30905104 36 C6 0.76687908 9.00945627 2.81586714 0.02671924 0.33695933 37 TIGIT 0.89521377 4.6735492 2.78419051 0.02794373 0.34287713 38 C3 -0.535071 13.6080562 -2.7521307 0.0292434 0.34938168 39 TFRC -0.4802386 9.62755042 -2.7179932 0.03069744 0.35734967 40 LCK 0.65130851 4.65533902 2.64110415 0.03425731 0.38866099 41 TRIM39 0.55071549 6.13622918 2.61334604 0.03564641 0.38866099 42 TFEB 0.51391433 6.42742474 2.60732459 0.03595542 0.38866099 43 IL1RL1 0.53239774 4.97401543 2.58892549 0.03691708 0.38916158 44 IRF5 0.60255404 6.95788021 2.57401407 0.0377161 0.38916158 45 IL12A 0.43650649 4.82204056 2.4813743 0.04309993 0.4137631 46 FADD 0.48426287 5.38048704 2.47269255 0.04364373 0.4137631 47 CFI -0.6524025 9.86701246 -2.4609077 0.04439327 0.4137631 48 TLR4 0.61556428 5.78503702 2.45776111 0.04459564 0.4137631 49 LAG3 0.73393842 5.43376417 2.45656118 0.04467307 0.4137631 50 TICAM1 0.5388668 7.70599657 2.42016786 0.04708838 0.4137631 51 ETS1 0.49063155 9.82631131 2.41697362 0.0473067 0.4137631 52 IFITM2 0.44034986 8.73450578 2.41573894 0.04739137 0.4137631     180 Appendix 8: List of differentially abundant RNA transcripts identified using Immune Profiling Panel in ALI cultures of primary HBECs upon exposure to Δkdnase A. fumigatus conidia and WT A. fumigatus conidia  Gene logFC AveExpr t P.Value adj.P.Val 1 BLK 1.2802672 5.07710528 4.47824206 0.00290477 0.95589574 2 IL19 1.69180815 5.75515448 3.79037864 0.00685714 0.95589574 3 TLR6 0.96041687 5.63651953 3.14839539 0.01628435 0.95589574 4 AKT3 1.68630523 4.7508057 3.05460798 0.01856606 0.95589574 5 IRAK2 0.76740658 8.1261155 3.04563997 0.01880142 0.95589574 6 NT5E 0.783637 6.73871381 2.87039714 0.02409596 0.95589574 7 TFRC -0.8890544 9.69668034 -2.8451814 0.02497903 0.95589574 8 CD14 1.03536161 5.09028543 2.82262024 0.02579804 0.95589574 9 IL6R -0.7504457 7.01864429 -2.7390025 0.02908832 0.95589574 10 CCL28 -0.8458976 6.69485544 -2.6951612 0.03098653 0.95589574 11 STAT4 0.83199114 6.48377889 2.57269512 0.03700537 0.95589574 12 MICA 0.78757028 6.28322755 2.5155143 0.04021996 0.95589574 13 DMBT1 1.53431968 7.13376405 2.49480416 0.04145433 0.95589574 14 HLA-DMA -0.6599028 8.92532037 -2.4845518 0.0420798 0.95589574 15 IL32 0.66836048 8.98867756 2.45927349 0.04366387 0.95589574 16 CDH5 0.95529569 5.04705897 2.42832261 0.04568731 0.95589574 17 KIT -0.7055288 4.82724284 -2.3921767 0.04817276 0.95589574     181  Appendix 9: List of differentially abundant RNA transcripts identified using Immune Profiling Panel in ALI cultures of primary HBECs upon exposure to RSV  Gene logFC AveExpr t P.Value adj.P.Val 1 IL23A 4.08035799 8.07660769 7.00468529 0.00031314 0.06745488 2 IL6 4.35395494 5.41255498 6.96305517 0.00032408 0.06745488 3 NFATC4 -2.3342263 5.03117799 -6.4272781 0.00051172 0.06745488 4 IL34 -3.1093466 4.61519788 -6.2588908 0.00059432 0.06745488 5 CCR3 1.89265985 5.63609683 5.35429375 0.00140299 0.09995833 6 MR1 -1.8992834 7.1126572 -5.1804617 0.0016739 0.09995833 7 IFIT1 -2.38775 5.93243794 -5.1545222 0.00171917 0.09995833 8 TXNIP -2.2460912 9.77029717 -5.13102 0.00176138 0.09995833 9 TGFB2 -1.992002 8.62003867 -4.9219618 0.00219265 0.11060706 10 TLR3 -1.7630758 8.33383794 -4.599718 0.00310974 0.1261876 11 CXCL2 1.77669865 12.0141898 4.5802955 0.00317742 0.1261876 12 CXCL8 1.8552697 15.2647988 4.5367178 0.00333535 0.1261876 13 SF3A3 -1.564113 7.7113195 -4.3753437 0.00400133 0.1352695 14 TNFSF10 -1.5485324 10.4716267 -4.2988221 0.00436794 0.1352695 15 INPP5D -1.4381088 6.93519602 -4.2760218 0.00448429 0.1352695 16 SAA1 -2.050136 14.9423127 -4.2164399 0.00480495 0.1352695 17 IL1RN 2.0121762 9.25248512 4.12340349 0.00535782 0.1352695 18 NFKBIA 1.72339002 12.0894775 4.11644968 0.00540189 0.1352695 19 S100A8 1.62704237 8.56470665 4.03570093 0.00594423 0.1352695 20 ITGB4 -1.3308707 9.61945726 -4.0336156 0.00595901 0.1352695 21 TLR4 2.04167771 5.87539423 3.85291476 0.00740864 0.15630108 22 STAT1 -1.3180121 9.45255463 -3.834816 0.00757406 0.15630108 23 IL12A 1.53679533 4.59567383 3.74972607 0.00840832 0.163275 24 CCL3 -1.5209514 4.24399631 -3.6607187 0.00939059 0.163275 25 PTGS2 2.3561344 10.4728712 3.61117455 0.00999146 0.163275 26 THBS1 -2.1986506 9.07976775 -3.5794437 0.01039837 0.163275 27 EDC3 -1.1917353 8.04900171 -3.5783829 0.01041228 0.163275 28 ATG16L1 -1.3547264 7.52892213 -3.5693267 0.0105319 0.163275 29 ISG20 1.60963361 9.33378939 3.56050279 0.01064989 0.163275  182 30 CCL20 1.8669129 14.4764331 3.55022954 0.0107891 0.163275 31 HLA-DOB 1.88271175 4.7199322 3.48506227 0.01171985 0.16748967 32 CXCL1 1.28244834 14.6387136 3.47935568 0.01180544 0.16748967 33 NRP1 -1.246277 6.59135888 -3.4371654 0.01245982 0.1682505 34 ADORA2A 2.47524631 5.14147336 3.42818728 0.01260412 0.1682505 35 MAP3K1 -1.214158 7.88718862 -3.4058649 0.01297085 0.1682505 36 DUSP4 -1.1817042 8.38410574 -3.3712747 0.01356231 0.17103578 37 DDX58 -1.3817852 6.49639451 -3.3164252 0.01456123 0.17867023 38 TNFRSF12A -1.153588 6.66220949 -3.2366198 0.0161602 0.18666237 39 POLR2A -1.7649297 8.52369831 -3.2260731 0.01638538 0.18666237 40 SH2D1B 2.28999279 5.3932286 3.22325937 0.01644602 0.18666237 41 TNFRSF10C -1.4007361 6.58238428 -3.1454481 0.01822327 0.19782718 42 DOCK9 -1.0974068 8.94675917 -3.1211379 0.01882027 0.19782718 43 FAS -1.544692 4.63465532 -3.1137856 0.01900496 0.19782718 44 BATF 1.44631406 6.50637834 3.10717509 0.01917268 0.19782718 45 TIRAP -1.5039064 4.70597948 -3.0873083 0.01968642 0.19861408 46 TCF7 -1.1960109 6.37631171 -3.0292393 0.02127492 0.20785088 47 CLEC4A 1.95718929 4.72275195 3.02078045 0.0215176 0.20785088 48 SYK -1.2863558 7.29092671 -2.9430455 0.02389212 0.2259796 49 TNFRSF18 1.00605837 5.02075554 2.91424366 0.02484194 0.23016818 50 TICAM1 1.22842519 7.70913341 2.88330319 0.02590746 0.23523976 51 RIPK2 1.40628197 8.25803445 2.85030442 0.02709787 0.23577001 52 IKBKG -1.252725 6.47405303 -2.8500522 0.02710719 0.23577001 53 TP53 -1.2432714 8.71609622 -2.8163653 0.02838326 0.23577001 54 BAX -1.1913696 9.27321823 -2.810971 0.02859347 0.23577001 55 ZC3H14 -0.9607168 7.92257701 -2.8052482 0.02881829 0.23577001 56 MAPK1 -0.9898236 9.46890794 -2.7986018 0.02908176 0.23577001 57 NOS2A 3.04119649 4.54506058 2.77455489 0.03005661 0.23606695 58 HLA-DMB -1.0071394 9.23823616 -2.7658247 0.03041907 0.23606695 59 HLA-DMA -1.2249067 8.70633595 -2.7482295 0.03116376 0.23606695 60 LCN2 -1.1236789 15.1595479 -2.7422823 0.03141982 0.23606695 61 IL6R -1.3260035 6.54048624 -2.7275015 0.03206593 0.23606695 62 KLRD1 -1.1905517 4.52562638 -2.7180685 0.03248561 0.23606695 63 IL10 1.94072598 4.32858938 2.70755014 0.03296046 0.23606695 64 CSF3 2.96634773 6.46944505 2.69207877 0.0336723 0.23606695 65 LAMP1 -0.9640301 11.0606453 -2.6875707 0.03388276 0.23606695 66 ARG2 1.9161097 6.95488449 2.67833894 0.0343181 0.23606695 67 JAK1 -0.8823472 9.54048692 -2.6578029 0.03530785 0.23925021 68 CXCL10 -3.0594129 4.9928707 -2.6268089 0.03685906 0.24608845  183 69 HRAS -1.3902739 6.87216773 -2.6055607 0.03796388 0.24979135 70 MAPK3 -0.8995922 9.22507352 -2.5486473 0.04109811 0.26333588 71 NOD1 -1.2910843 5.39889746 -2.547179 0.04118248 0.26333588 72 ETS1 0.921144 9.77507521 2.53443669 0.04192229 0.26434335 73 DNAJC14 1.09239862 5.97069263 2.5190878 0.04283196 0.2644039 74 CXCR1 1.46175083 4.54667945 2.50794458 0.0435053 0.2644039 75 ABCF1 -0.8440083 7.89227663 -2.5008678 0.04393866 0.2644039 76 EPCAM -0.8753913 9.85450428 -2.4788693 0.0453148 0.2644039 77 CXCL11 -1.0107434 5.45236651 -2.4727882 0.0457031 0.2644039 78 ZKSCAN5 -1.578365 4.51614075 -2.4690069 0.04594629 0.2644039 79 DMBT1 2.54418627 7.21006904 2.46804132 0.04600861 0.2644039 80 GTF3C1 -0.9955002 8.57740042 -2.4527836 0.0470051 0.26571671 81 DPP4 -1.8991842 4.91421389 -2.4467159 0.04740761 0.26571671 82 MAVS -1.1400472 8.0201358 -2.4216281 0.0491103 0.26995484     184 Appendix 10: List of differentially abundant RNA transcripts identified using Immune Profiling Panel in high TEER and low TEER ALI cultures of primary HBECs   Gene logFC AveExpr t P.Value adj.P.Val 1 THBS1 -5.0555458 9.32388124 -23.438415 3.89E-08 8.94E-06 2 FOXJ1 8.8057566 9.8618738 22.1881573 5.77E-08 8.94E-06 3 SPA17 5.616669 10.3470116 21.9390433 6.26E-08 8.94E-06 4 TGFB2 -3.7071148 8.3703389 -21.205358 8.00E-08 8.94E-06 5 NT5E -3.2999999 7.00923775 -19.857492 1.28E-07 1.15E-05 6 CXCL5 -5.8709887 8.195778 -19.334282 1.55E-07 1.16E-05 7 CD4 4.10436651 5.71777256 18.1756403 2.42E-07 1.37E-05 8 RORC 4.23856163 6.04197861 18.1449987 2.45E-07 1.37E-05 9 LCK 3.54041102 5.54135021 17.6370993 3.00E-07 1.49E-05 10 C6 5.19077224 6.17678569 17.019193 3.87E-07 1.73E-05 11 RRAD 3.62078752 8.37471869 16.425964 4.98E-07 1.89E-05 12 NRP1 -3.2931151 6.51651204 -16.387494 5.06E-07 1.89E-05 13 IL32 -3.7488172 8.61415218 -16.008974 5.98E-07 2.06E-05 14 MFGE8 -2.5425339 9.23470292 -15.765488 6.67E-07 2.08E-05 15 CTSS 3.19331161 10.3570114 15.5702991 7.29E-07 2.08E-05 16 LGALS3 2.58512271 12.8988791 15.5202161 7.46E-07 2.08E-05 17 IDO1 -3.9031976 8.5636913 -14.997 9.51E-07 2.50E-05 18 BST1 3.85533971 5.25130267 14.6077033 1.15E-06 2.84E-05 19 CD44 -2.543779 10.4389604 -14.499733 1.21E-06 2.84E-05 20 CDK1 6.1282991 6.55909018 14.2004904 1.40E-06 2.99E-05 21 ISG20 2.31291393 8.32786734 14.1907009 1.41E-06 2.99E-05 22 IL5RA 5.3199854 6.2629278 14.0793296 1.49E-06 3.02E-05 23 CX3CL1 -2.3387463 8.76040066 -13.92286 1.61E-06 3.13E-05 24 CFP 3.46446755 4.23343464 13.6199012 1.88E-06 3.50E-05 25 CCL15 4.06729699 4.26109032 13.518973 1.98E-06 3.54E-05 26 CD9 2.03002337 11.6805046 13.1658459 2.39E-06 4.10E-05 27 SIGIRR 1.96902155 7.50313551 13.0914715 2.48E-06 4.11E-05 28 DUSP6 -2.6082522 8.78095668 -12.817805 2.88E-06 4.60E-05 29 IRF2 2.02159282 9.38167493 12.2005854 4.07E-06 6.28E-05 30 MCAM -2.7065691 5.20811996 -11.898834 4.85E-06 7.23E-05 31 CD68 3.21744691 6.93594774 11.516782 6.09E-06 8.78E-05  185 32 FEZ1 -2.4982167 7.86805362 -11.404724 6.52E-06 8.85E-05 33 CSF1 -3.2993755 5.5826769 -11.360478 6.69E-06 8.85E-05 34 VEGFC -2.413583 6.61316195 -11.351072 6.73E-06 8.85E-05 35 ITGB4 -1.946274 10.627268 -11.292507 6.98E-06 8.91E-05 36 SELPLG 3.5667035 5.05284211 11.241981 7.20E-06 8.94E-05 37 SPINK5 3.21681089 6.62656408 11.1006802 7.86E-06 9.50E-05 38 HLA-DPA1 1.7661062 7.91391771 10.8629695 9.13E-06 0.00010732 39 CD19 3.51047589 4.31387471 10.8236891 9.36E-06 0.00010732 40 BCL6 2.32781805 8.84290216 10.760435 9.75E-06 0.00010811 41 LTK 3.45501238 4.33832741 10.7343494 9.92E-06 0.00010811 42 CD276 -1.8014133 8.51843044 -10.686991 1.02E-05 0.0001088 43 CD74 1.61717298 10.5433834 10.6025774 1.08E-05 0.00011064 44 RORA 2.68857759 6.57427819 10.5894235 1.09E-05 0.00011064 45 AKT3 -1.8351457 5.36816065 -10.471115 1.18E-05 0.0001169 46 CXCL6 -2.1794567 11.4805136 -10.342919 1.28E-05 0.00012448 47 MERTK 1.69021408 5.45916697 10.1806539 1.43E-05 0.00013582 48 LAG3 1.80771343 5.07662938 10.0383072 1.57E-05 0.00014634 49 HLA-DRA 2.28341387 10.6077337 10.0095786 1.60E-05 0.00014634 50 ITGA6 -2.0265007 8.5285077 -9.9198911 1.71E-05 0.00015254 51 TLR1 1.6838806 6.95661813 9.85959508 1.78E-05 0.00015592 52 CCL2 -4.313811 5.23238318 -9.8298442 1.82E-05 0.00015611 53 ARG2 2.23098984 6.46088401 9.7533203 1.92E-05 0.00016042 54 HLA-DRB3 2.09824935 8.50899028 9.73686205 1.94E-05 0.00016042 55 VCAM1 -4.4389026 4.04308937 -9.6781105 2.02E-05 0.00016415 56 COG7 1.67399614 8.07561399 9.64457602 2.07E-05 0.00016508 57 ADA -1.7305761 6.55440659 -9.516717 2.27E-05 0.00017252 58 C3 -1.5306565 12.3006306 -9.5061126 2.28E-05 0.00017252 59 SAA1 -2.5205066 13.6918879 -9.4904535 2.31E-05 0.00017252 60 MUC1 1.82702459 11.3222977 9.48590669 2.32E-05 0.00017252 61 POU2F2 -2.0881457 4.45949755 -9.2807247 2.69E-05 0.00019689 62 DDX58 -1.6927158 6.43273029 -9.2047396 2.84E-05 0.00020482 63 HLA-DQB1 1.91405058 5.78906684 9.13324655 3.00E-05 0.0002125 64 PNMA1 1.66406774 8.07043 9.0443154 3.20E-05 0.00022063 65 PVR -1.8760068 7.35883499 -9.0408862 3.21E-05 0.00022063 66 CCL28 -2.3663594 8.14283439 -8.9809395 3.36E-05 0.00022407 67 RUNX3 -1.9868343 4.09869652 -8.9659859 3.39E-05 0.00022407 68 CXCL8 1.8643379 14.5977771 8.96022408 3.41E-05 0.00022407 69 PDGFC -2.3625977 6.64387599 -8.8888099 3.60E-05 0.00023307 70 AXL -1.9101428 6.73565895 -8.8067956 3.83E-05 0.00024456  186 71 FOS 1.83533203 8.87558263 8.7664283 3.95E-05 0.00024652 72 ULBP2 -2.5916177 6.51286376 -8.7596234 3.97E-05 0.00024652 73 IFITM2 2.04334113 8.56299164 8.72260487 4.09E-05 0.00025017 74 BLNK 2.17405465 4.80096442 8.65081656 4.32E-05 0.00025566 75 MAPK3 1.48380997 8.75774569 8.64805827 4.33E-05 0.00025566 76 CNOT10 1.39470723 8.15549591 8.64252129 4.35E-05 0.00025566 77 SLAMF7 -1.6852245 4.38526909 -8.6181187 4.43E-05 0.00025718 78 IL1B -4.0118905 8.0319027 -8.4969677 4.87E-05 0.00027916 79 TTK 3.13861439 4.77348579 8.44440942 5.08E-05 0.00028732 80 CASP3 1.28334601 9.72688765 8.42226925 5.17E-05 0.00028876 81 ANXA1 1.68428788 14.2935214 8.38093884 5.34E-05 0.00029473 82 IRF5 1.80897059 5.69930203 8.3463906 5.49E-05 0.00029928 83 HLA-DRB4 1.74370826 6.7163015 8.30136486 5.69E-05 0.00030654 84 IGF2R -1.5208454 7.24472017 -8.2460645 5.95E-05 0.0003167 85 CFD 3.20265692 4.79791788 8.08044936 6.81E-05 0.00035713 86 HLA-DMB 2.29863994 6.8157884 8.06986335 6.87E-05 0.00035713 87 HLA-DMA 2.48619806 6.70521874 7.98852986 7.35E-05 0.00037756 88 IFIT2 -1.9596443 5.89016414 -7.929745 7.72E-05 0.00039199 89 CCL17 2.15361854 4.56705405 7.85862773 8.19E-05 0.00041137 90 CXCL11 -2.4634386 4.92708259 -7.8381921 8.33E-05 0.00041386 91 HLA-DPB1 1.6583895 6.65605312 7.79262581 8.66E-05 0.00042275 92 NFATC2 2.17066457 4.75467629 7.78701374 8.70E-05 0.00042275 93 IL6R 1.90285252 5.63542398 7.68016915 9.53E-05 0.00045805 94 UBC 1.17526361 14.8972411 7.58904637 0.00010307 0.00049015 95 VEGFA 2.35816085 10.3951424 7.54322941 0.00010725 0.00050464 96 RPS6 1.25843178 14.0353664 7.52137198 0.00010931 0.00050543 97 CD274 -2.9155298 4.83337163 -7.5174962 0.00010968 0.00050543 98 TNFRSF10C -1.7163964 6.60686466 -7.4853369 0.0001128 0.00051451 99 IL1R2 2.98956013 3.80563349 7.47242254 0.00011408 0.0005151 100 F2RL1 -1.3692354 8.22535331 -7.4266086 0.00011876 0.00053087 101 TAP2 -1.239874 7.64389216 -7.4024598 0.00012132 0.00053692 102 ST6GAL1 1.82788582 9.5766261 7.36827735 0.00012504 0.00054796 103 TNFSF14 -3.2835545 3.71723311 -7.322025 0.00013028 0.00056538 104 FYN -1.427281 5.48747114 -7.2400955 0.00014017 0.00060248 105 STAT6 1.13438398 8.7486708 7.22554507 0.00014202 0.00060459 106 CCR3 1.46667292 4.81982219 7.20109777 0.00014518 0.00060891 107 INPP5D -1.6294801 7.18735907 -7.1966885 0.00014576 0.00060891 108 FUT7 1.65694639 5.50218683 7.11470285 0.000157 0.00064979 109 HLA-DQA1 1.66620291 6.4982689 7.09450845 0.00015991 0.00065578  187 110 CD97 1.33041394 9.54137451 7.03086609 0.0001695 0.00068879 111 LYN 1.74185632 7.94325318 7.02057803 0.00017111 0.00068907 112 ITGB1 -1.1421213 12.2415914 -6.9726415 0.00017884 0.00071377 113 PLA2G6 1.36283605 5.39008558 6.87394848 0.00019602 0.00077541 114 REL 1.40263075 6.76148 6.73833121 0.00022272 0.0008718 115 SMPD3 3.13278598 3.97711893 6.73093237 0.00022429 0.0008718 116 CFB -1.2726754 11.3772408 -6.7103157 0.00022873 0.00088139 117 CXCL10 -2.9707743 4.37107843 -6.6839906 0.00023454 0.00089606 118 PPBP -3.9701948 3.92646076 -6.6188919 0.00024963 0.00094564 119 ISG15 -1.4727324 7.31279934 -6.5862989 0.00025759 0.0009676 120 IKBKG -1.0166741 6.07793415 -6.5469498 0.00026759 0.00098993 121 EPCAM 1.18270228 9.44528389 6.5391848 0.00026961 0.00098993 122 C1S -1.8793452 5.63612068 -6.5370019 0.00027018 0.00098993 123 CD38 1.40871919 5.78148687 6.39916733 0.00030918 0.00110782 124 TUBB -1.0754493 9.83625931 -6.3892792 0.00031221 0.00110782 125 FN1 -7.2144928 10.1146807 -6.3867094 0.000313 0.00110782 126 CSF2 -3.8458134 4.0165175 -6.3865966 0.00031304 0.00110782 127 LIF -2.7913833 7.68740515 -6.3810691 0.00031475 0.00110782 128 ITGA1 -3.8260776 5.30175162 -6.3450829 0.00032617 0.00113903 129 HPRT1 1.23625852 8.03180421 6.29593278 0.00034251 0.00118684 130 ICAM1 -1.4250311 9.75013312 -6.2710583 0.00035113 0.00120734 131 ITGB3 -2.1622321 5.7912867 -6.2469882 0.0003597 0.00122737 132 CCL14 2.34104393 3.98448557 6.18195134 0.00038405 0.00130053 133 ALAS1 1.19662434 8.01384149 6.08853107 0.0004223 0.00141932 134 CD3EAP -1.6258942 4.8707868 -6.0411907 0.00044329 0.00147875 135 ALCAM 0.9966213 10.9863527 5.99693427 0.00046397 0.00153068 136 MYD88 -0.9408786 8.0947153 -5.9920905 0.0004663 0.00153068 137 LAMP3 1.40674069 5.81506203 5.98575189 0.00046937 0.00153068 138 SDHA 1.09399492 7.95626708 5.97920597 0.00047256 0.00153068 139 BIRC5 1.72064448 4.85761937 5.96275215 0.00048068 0.00154579 140 CCL20 1.79972679 11.9137909 5.94652158 0.00048885 0.00156083 141 PRKCD 1.08104153 7.77320595 5.92071912 0.00050215 0.00159194 142 TAP1 -0.9818088 6.92713521 -5.8623783 0.00053375 0.00166739 143 PIN1 1.3694009 8.10171766 5.8593479 0.00053545 0.00166739 144 NOTCH1 -1.5932193 6.60052253 -5.8563294 0.00053715 0.00166739 145 IL1RL1 -1.7701812 5.953072 -5.8340407 0.00054989 0.00169516 146 TNFRSF12A -1.242782 8.43498934 -5.8134922 0.00056193 0.00171296 147 IRF1 0.96875744 7.69933141 5.81114526 0.00056332 0.00171296 148 IL18 -0.865874 8.56699651 -5.7949469 0.00057305 0.00173062  188 149 SYK 0.93645485 7.0950374 5.78249224 0.00058065 0.00173062 150 CYFIP2 1.15324693 5.2775834 5.78234152 0.00058075 0.00173062 151 C4B 1.94782542 5.3918407 5.76830151 0.00058946 0.00174494 152 STAT2 -0.8280825 8.40027923 -5.729137 0.00061453 0.0018072 153 CKLF -0.9739711 8.63673628 -5.6501813 0.00066876 0.00195383 154 TP53 -0.89534 8.2475802 -5.6348991 0.00067986 0.00196492 155 ITGA4 -2.4947084 4.2578274 -5.6328735 0.00068135 0.00196492 156 PDCD1LG2 -4.4704421 3.09625433 -5.6028745 0.00070379 0.00201663 157 TREM1 0.9320258 4.84827665 5.55850812 0.0007385 0.00210261 158 ECSIT 0.85495687 6.98512186 5.55115914 0.00074443 0.00210608 159 FCF1 1.1136413 9.44676023 5.52821604 0.00076329 0.00214426 160 IL6 -2.4944955 5.76028926 -5.5231549 0.00076752 0.00214426 161 IFI27 -1.7361384 9.79016437 -5.4456922 0.00083564 0.00232006 162 TNFRSF10B -1.0401442 7.94302429 -5.4250381 0.00085491 0.00235893 163 TXNIP -1.1339157 9.55722049 -5.2440016 0.00104663 0.00284622 164 IRF7 0.90574104 6.36231261 5.24382978 0.00104684 0.00284622 165 IL6ST -0.9972808 9.466299 -5.2406388 0.00105062 0.00284622 166 LCN2 -1.0011042 13.5200177 -5.1923711 0.00110971 0.00298818 167 IL17RA -0.8916225 6.08043536 -5.1585762 0.00115326 0.00308688 168 C4BPA 2.52284868 4.15545657 5.06610869 0.00128241 0.00341213 169 LAMP1 -0.7917591 10.3628209 -4.9554216 0.00145845 0.0038432 170 TFRC 0.8460526 9.43067287 4.9535671 0.00146162 0.0038432 171 ZNF143 0.90172342 7.73573306 4.93465742 0.00149436 0.00389669 172 PSMB10 1.43566682 8.40143703 4.93178779 0.0014994 0.00389669 173 ATG5 0.81309586 8.48278623 4.84630859 0.00165843 0.00428509 174 IL2RG 1.10581389 4.75919748 4.82510306 0.00170071 0.00436906 175 ETS1 -1.0201285 8.67984097 -4.8090179 0.00173356 0.00442802 176 IL1A -1.3949356 9.57381982 -4.7980751 0.00175632 0.00446064 177 BAX -0.7002691 9.14614906 -4.7876143 0.00177837 0.00449115 178 PRPF38A 0.75808924 8.14179039 4.76299676 0.0018315 0.00459932 179 TLR2 -1.1329579 7.85830249 -4.6595615 0.00207459 0.00518068 180 IFNAR2 0.7283278 8.47667687 4.62724969 0.00215764 0.00535814 181 PRKCE 0.93903948 4.78264772 4.58474958 0.00227249 0.00561217 182 BCL2L1 0.77566384 10.7528001 4.57587477 0.00229731 0.0056423 183 C2 1.04174822 5.70134007 4.56400091 0.00233099 0.00569372 184 CD55 1.31422652 4.53432537 4.55704681 0.00235096 0.0057113 185 TOLLIP -0.7804515 8.29652981 -4.5490354 0.0023742 0.00573659 186 TNFRSF11A 1.57527245 5.55318593 4.52836951 0.00243533 0.00585266 187 SOCS1 -1.1392336 4.24950155 -4.5187923 0.00246425 0.00589048  189 188 GPI 0.93454305 9.23543972 4.47658595 0.00259623 0.00617296 189 LAMP2 -0.6895552 8.38748194 -4.412946 0.00281013 0.00664352 190 CFI 0.76073174 8.68887614 4.40904255 0.00282387 0.00664352 191 HLA-DOB -1.0281661 4.59202514 -4.4006687 0.00285358 0.00667827 192 IFITM1 0.97731126 10.5567994 4.39588865 0.00287069 0.00668333 193 APP -0.7566278 11.9828304 -4.340335 0.00307805 0.00712896 194 EBI3 -1.7632224 3.03431973 -4.2997618 0.00323984 0.00746499 195 OAS3 -1.3498643 6.18710821 -4.2779644 0.00333058 0.00763471 196 RIPK2 -0.6748318 7.14714542 -4.2535875 0.00343536 0.00783472 197 CD8A -1.1253141 4.94315959 -4.2254883 0.00356062 0.00803078 198 TNFRSF1A -0.7110377 9.31379308 -4.2223936 0.00357472 0.00803078 199 PPIA -0.6667796 7.48189837 -4.2222839 0.00357522 0.00803078 200 CD164 0.78680592 11.0266367 4.20829095 0.00363975 0.00813483 201 CEBPB 0.76585934 10.7136522 4.19988962 0.00367909 0.00818187 202 PTGS2 -1.4521404 9.36071459 -4.1821548 0.00376369 0.00832857 203 ATF1 0.8230818 7.6000251 4.16701396 0.0038376 0.00842526 204 TREM2 -1.5357023 3.93072187 -4.1639456 0.00385276 0.00842526 205 TNF -2.6502994 4.25512197 -4.1616949 0.00386393 0.00842526 206 IL23A 1.41633013 5.72119604 4.12153889 0.00406924 0.00882986 207 AMMECR1L -0.8755408 7.36337761 -4.1126872 0.00411608 0.00888835 208 IL18R1 -1.0189276 4.73518627 -4.1074888 0.00414386 0.00890532 209 IFNGR1 -0.6951754 10.5441184 -4.0085652 0.00471338 0.01008076 210 LRP1 -1.5255878 6.66539005 -3.979887 0.004894 0.01041724 211 IL34 -0.9484004 3.91638984 -3.9681681 0.00496997 0.01052879 212 HLA-E 0.57223438 10.0031562 3.93456614 0.00519497 0.01095355 213 IL1R1 -0.8810458 7.50229743 -3.8857431 0.00554184 0.01162047 214 REPS1 0.83227934 9.02599948 3.88283677 0.00556327 0.01162047 215 NOD2 0.69368255 6.03377378 3.87285459 0.00563754 0.01172085 216 TNFRSF14 0.68489287 7.79530284 3.86693038 0.00568213 0.01175886 217 LTBR -0.799034 8.96750679 -3.8604471 0.00573137 0.01180609 218 IKBKE -0.7189894 7.43381435 -3.8176722 0.00606803 0.01244225 219 ITGA5 -1.2403207 8.92493303 -3.8086741 0.00614155 0.012479 220 POLR2A -0.6883828 8.53320886 -3.8086442 0.00614179 0.012479 221 BST2 0.71299782 6.71379981 3.75404993 0.00660901 0.01336756 222 S100A7 -1.5863293 6.66612675 -3.6915793 0.00719127 0.01447972 223 CXCL14 2.2374467 4.40242314 3.68591793 0.00724671 0.01452592 224 MX1 -0.6120037 8.54871961 -3.6619511 0.00748658 0.01493973 225 DMBT1 2.01220274 4.49497391 3.6502311 0.00760698 0.01511253 226 CREB5 -1.2771861 4.85318076 -3.6202637 0.00792444 0.01567356  190 227 IL11 -1.140949 4.47313589 -3.499625 0.0093544 0.01842034 228 ERCC3 0.84726456 7.15040133 3.45780121 0.00991295 0.0194346 229 CXCL1 0.74385108 13.8183495 3.45174823 0.00999671 0.01945743 230 TCF7 0.88893613 6.32490688 3.45067392 0.01001165 0.01945743 231 JAK3 -0.7007398 4.03391824 -3.4057033 0.01065922 0.02062629 232 MICA -0.7529527 5.97036691 -3.3502934 0.01151941 0.02219472 233 CD36 -1.1464355 4.96193031 -3.341813 0.01165748 0.02236435 234 CYLD -0.7730032 8.44031219 -3.3040495 0.01229413 0.02348493 235 PSMB9 0.67269941 8.50633889 3.29527905 0.01244723 0.02367622 236 CXCL3 -0.7844273 9.68164049 -3.2625553 0.01303669 0.02469237 237 SF3A3 -0.5366615 7.48623569 -3.2338137 0.01357897 0.02561096 238 CXCR1 1.20181821 4.88682404 3.22613845 0.01372781 0.02569772 239 TMUB2 -0.5724277 6.99659977 -3.2255166 0.01373994 0.02569772 240 NFATC1 1.00507069 5.52725541 3.21999931 0.01384811 0.0257921 241 ICAM4 0.64007597 5.50174479 3.20667132 0.01411316 0.02616055 242 CARD11 -0.9370703 5.93397337 -3.2038487 0.01416998 0.02616055 243 ITGB2 0.79222897 5.53307798 3.20129934 0.01422151 0.02616055 244 LCP1 -0.9085976 4.36178929 -3.1945008 0.0143599 0.02630687 245 IFNL1 1.14858969 4.94454492 3.13030325 0.01573952 0.02865227 246 MAF -0.7725887 6.91710978 -3.1290255 0.01576836 0.02865227 247 EGR1 -1.2136418 5.57561555 -3.1222831 0.01592151 0.02874373 248 TAPBP -0.5935387 9.62238652 -3.1211542 0.0159473 0.02874373 249 PLAU -0.9876726 12.05267 -3.1172635 0.01603655 0.0287885 250 EP300 -0.7809329 7.88416379 -3.1094934 0.01621636 0.02899486 251 CDH1 -0.5391207 11.5865634 -3.0721561 0.01711069 0.03044639 252 HLA-B -0.4726275 11.8346921 -3.0699797 0.01716441 0.03044639 253 TLR4 -1.5847796 4.14529479 -3.066092 0.01726081 0.03049637 254 TICAM2 -0.9040445 4.89062528 -3.0434285 0.01783426 0.03138549 255 DUSP4 -0.6310664 8.78464841 -3.0382008 0.01796937 0.03149925 256 TRIM39 0.54968884 5.71134917 3.02140475 0.01841084 0.03214705 257 RAG1 -1.6316789 3.7359914 -3.0149347 0.01858394 0.03231061 258 PYCARD 0.72266476 6.4972952 3.01251652 0.01864908 0.03231061 259 NUP107 0.51831685 7.81233908 2.93762034 0.0207904 0.03588151 260 MAP2K1 0.58902787 9.54482319 2.88241312 0.02253356 0.03874039 261 MST1R 0.44962871 7.35071898 2.86606483 0.02307869 0.03952557 262 FLT3LG 0.77212078 4.14926868 2.86192785 0.02321882 0.03961378 263 CD24 0.52411925 12.6289585 2.83885658 0.02401679 0.04081941 264 ATG7 0.43099845 7.11679014 2.82197573 0.0246188 0.04168411 265 IL1RN 1.1194677 8.76007077 2.8188944 0.02473038 0.04171503  191 266 PSMB8 0.54714264 9.98667536 2.81489709 0.02487592 0.04174423 267 MAP2K2 -0.4227603 9.94290872 -2.8132959 0.02493447 0.04174423 268 ENG -2.2750366 3.38916584 -2.8071066 0.02516215 0.04196821 269 THBD -1.3987143 7.06868854 -2.803815 0.02528411 0.04201486 270 APOE -3.095447 3.89089208 -2.7900315 0.02580158 0.04271596 271 CDKN1A -0.5020514 10.3696341 -2.7750301 0.02637736 0.04340923 272 DHX16 0.49966029 7.14161863 2.77407256 0.02641457 0.04340923 273 SERPING1 -0.6980886 6.43839817 -2.7606725 0.02694099 0.04411217 274 ABL1 -0.6476722 7.10092933 -2.6463659 0.03190043 0.05204194 275 HMGB1 0.54616466 9.97699036 2.636933 0.03234992 0.05258332 276 TGFB1 -0.6755 8.10213444 -2.6213117 0.03310875 0.05362177 277 SMAD2 0.42150521 9.19539833 2.60700796 0.03381972 0.0545755 278 USP39 0.43389843 7.43607994 2.54451868 0.03711544 0.05967842 279 MIF 0.68695216 10.974079 2.52342113 0.03830138 0.06136458 280 GPATCH3 0.65532247 6.13796569 2.51063335 0.0390391 0.06232313 281 SBNO2 0.59098071 7.98100487 2.47370991 0.04125207 0.06562161 282 STAT1 -0.4852358 8.82360229 -2.4633889 0.04189334 0.0664054 283 TNFSF10 -0.3825809 11.2162004 -2.4539603 0.04248807 0.06711013 284 JAK1 -0.587534 8.87283453 -2.402573 0.04588445 0.07221954 285 MICB -0.5038537 4.9129001 -2.3687882 0.04826697 0.07570293 286 IRF3 0.49740046 5.96321051 2.35776821 0.04907099 0.07669487  

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