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Age-dependent differences in the transcriptional profile of antigen presenting cells in response to immune… Wee, Kathleen 2015

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  AGE-DEPENDENT DIFFERENCES IN THE TRANSCRIPTIONAL PROFILE OF ANTIGEN PRESENTING CELLS IN RESPONSE TO IMMUNE STIMULATION AND INFECTION CONVERGES ON INTERFERON RESPONSE FACTORS by Kathleen Wee  B.Sc. (Honours), The University of British Columbia, 2009  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  DOCTOR OF PHILOSOPHY in The Faculty of Graduate and Postdoctoral Studies (Experimental Medicine)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  October 2015 © Kathleen Wee, 2015 ii  ABSTRACT  Newborns and older adults aged 65 and over have a heightened risk for severe infections, suggesting suboptimal immune responses. These two populations thus represent age-defined windows of vulnerability to infection. Antigen presenting cells (APC) are important in bridging the innate and adaptive immune systems as they express pathogen recognition receptors such as Toll-like receptors (TLR) that detect the presence of pathogens. They have been proposed to be partially responsible for the altered immunity observed at the extreme ends of the age-spectrum. However, the molecular mechanism/s that underlie this APC deficit have not been fully delineated. We chose to apply newly available cutting-edge tools to begin identifying such mechanisms.  With the availability of systems biological tools to interrogate APC function, our overarching hypothesis is that age-dependent differences in the transcriptional response of APC results in functional differences in response to immune stimulation and infection.  To address this hypothesis, we employed global transcriptional profiling to comprehensively investigate age-dependent differences in mRNA expression in the most important APC subsets from newborns, healthy young adults, and older adults following TLR stimulation or infection.   Following TLR7/8 stimulation, neonatal DC displayed altered expression of signaling pathways involved in the response to viral pathogens. Specifically, IRF-dependent MAPK pathway genes were expressed in an age-dependent manner at baseline, while age-dependent differences in the iii  expression of other IRF-dependent responses only occurred following stimulation. We also investigated the transcriptomic responses of monocytes to Listeria monocytogenes (Lm). Monocytes are one of the primary targets of Lm. Monocytes from newborns, young adults, and older adults differentially expressed guanylate binding proteins (GBP) in an age-dependent manner upon infection, along with significantly reduced IFN-β production in susceptible age groups, while signaling downstream of the IFN receptor complex was comparable. This suggests that age-related differences in IFN-β production in response to Lm infection led to reduced induction of GBPs in newborns and older adults compared to young adults. IFN-β production is also known to be IRF dependent. This convergence of age-dependent differences in immunity on IRF-regulated pathways begins to outline a possible molecular epicenter associated with suboptimal immune responses early and late in life.   iv  PREFACE  The studies and methods described in this thesis were approved by the Research Ethics Board of the University of British Columbia (Protocol H13-00347). Tobias R. Kollmann directed the initial conceptualization of this entire project, and was centrally involved the design, execution and interpretation of all steps from then on.   Chapter 2: Age-Related Differences in the MAPK and IRF Pathways Differentially Impact Respones of Human Dendritic Cells From Newborns and Healthy Adults  Chapter 2 is partly based on work that was begun with Dr. Edgardo S. Fortuno III in Dr. Christopher Wilson’s laboratory in Seattle, USA. I was responsible for analyzing all the raw microarray intensity data that was generated in the study, performing and supervising the qPCR validation for the arrays, and finally for designing the functional validation experiments using phospho-specific flow cytometry (phosphoflow) and Amnis imaging experiments. I also performed the data analysis for all the validation studies and wrote the manuscript being submitted to the Journal of Immunology. The relative contribution for each of the co-authors in the submitted manuscript are as follows:   I performed the bioinformatics analysis for all microarray samples. I validated the array results based on the bioinformatics analysis, including planning qPCR targets, designing and performing phosphoflow and Amnis experiments. I performed the phosphoflow experiments, and performed v  and analysis the Amnis nuclear translocation experiment. I was also responsible for submitting the microarray data to GEO and preparing the manuscript for publication.  Edgardo S Fortuno III designed the details of the microarray experiments from the initial concept proposed by Dr. Christopher Wilson and Dr. Tobias Kollmann. He then gathered and ran all the samples for the microarrays. He also performed preliminary analysis on the array results and checked the purity of the dendritic cell isolations for both cDC and pDC.   Bing Cai collected samples for qPCR validation of array results and also checked the purity of dendritic cell isolations for both cDC and pDC.  Sophia Lee collected samples for qPCR validation of array results and performed the qPCR validation experiments for the arrays. She also checked the purity of the dendritic cell isolations she performed for both cDC and pDC.  Hanul Park helped optimize the phosphoflow experiments and she fully analyzed the phosphoflow data that was generated by Kathleen Wee.   Chapter 3: Infection of Human Primary Monocytes with Listeria monocytogenes Induces Age-Dependent Differential Expression of Guanylate Binding Proteins.  vi  Chapter 3 is based on work initially conducted by Dr. Daniella Loeffler on Listeria monocytogenes infections in human primary dendritic cells, which I have adapted for human primary monocytes. Dr. Edgardo S. Fortuno III and I designed the microarray experiments and I collected all the samples necessary. I primarily designed all other experiments that are included in this chapter, along with some guidance from Dr. Ashley Sherrid. I also performed all the data analysis and wrote the manuscript to be submitted to Infection and Immunity based on a portion of this chapter.   Chapter 4: Age-Related Gene Expression Differences in Monocytes From Human Neonates, Young Adults, and Older Adults  Chapter 4 is an extension of the work done in Chapter 2 with the added LPS stimulation in our samples. I collected all of the monocyte samples and performed all the infections; Patricia Cho performed the LPS stimulation. This work has been published in PLOS ONE, with permission to use the final submitted manuscript for this thesis. The relative contribution for each of the authors are as follows:  Michelle M. Lissner, Brandon J. Thomas, myself, Tobias R. Kollmann, and Stephen T. Smale all conceived and designed the experiments. Michelle M. Lissner, Brandon J. Thomas, myself, and Ann-Jay Tong performed all the experiments necessary in publishing this manuscript. Michelle M. Lissner, Brandon J. Thomas, myself and Ann-Jay Tong all contributed to analyzing the data. vii  Michelle M. Lissner, Brandon J. Thomas, myself, Tobias R. Kollmann, and Stephen T. Smale all contributed to the writing of the manuscript.   viii  TABLE OF CONTENTS  ABSTRACT ................................................................................................................................... ii PREFACE ..................................................................................................................................... iv TABLE OF CONTENTS .......................................................................................................... viii LIST OF TABLES ..................................................................................................................... xiv LIST OF FIGURES .................................................................................................................... xv LIST OF ABBREVIATIONS ................................................................................................. xviii ACKNOWLEDGEMENTS ..................................................................................................... xxii DEDICATION.......................................................................................................................... xxiv CHAPTER 1: INTRODUCTION ................................................................................................ 1 1.1 An overview of the immune response in humans ........................................................ 1 1.1.1 The innate immune system ..................................................................................... 3 1.1.2 Antigen presenting cells of the innate immune system and antigen presentation ............................................................................................................................ 5 1.1.3 Overview of the adaptive immune system ............................................................ 7 1.2 Key molecular signaling pathways in the innate immune system ............................. 8 1.2.1 Toll-like receptor signaling pathway ..................................................................... 9 1.2.2 C-type lectin receptors .......................................................................................... 11 1.2.3 Cytosolic surveillance pathway ............................................................................ 12 ix  1.2.4 Pathogens activate multiple signaling pathways ................................................ 13 1.3 Development of the immune system ........................................................................... 14 1.3.1 An overview of the immune system in early life................................................. 14 1.3.2 The aging immune system .................................................................................... 15 1.4 Listeria monocytogenes: epidemiology, pathogenesis, and immune response ......... 16 1.4.1 Overview of Lm epidemiology ............................................................................. 16 1.4.2 Overview of the molecular pathogenesis of Lm infection ................................. 17 1.4.3 Immune response against Lm infection .............................................................. 19 1.5 Neonatal immune response: a system in flux during development ......................... 22 1.5.1 Cell extrinsic factors that affect the immune response in newborns................ 23 1.5.2 Cell intrinsic factors also have a large impact on the immune response of newborns............................................................................................................................... 23 1.6 Hypothesis and specific objectives .............................................................................. 24 1.6.1 Hypothesis .............................................................................................................. 25 1.6.2 Specific objectives ................................................................................................. 26 1.6.3 Importance and clinical implications .................................................................. 27 CHAPTER 2: AGE-RELATED DIFFERENCES IN THE MAPK AND IRF PATHWAYS DIFFERENTIALLY IMPACT RESPONSES OF HUMAN DENDRITIC CELLS FROM NEWBORNS AND HEALTHY ADULTS ............................................................................... 28 2.1 Introduction .................................................................................................................. 28 x  2.2 Methods ......................................................................................................................... 31 2.2.1 cDC and pDC purification ................................................................................... 31 2.2.2 TLR stimulation of peripheral and cord blood mononuclear cells, conventional and plasmacytoid dendritic cells ................................................................. 32 2.2.3 RNA extraction ...................................................................................................... 33 2.2.4 Microarray and real-time PCR ........................................................................... 33 2.2.5 Microarray processing and bioinformatics analysis .......................................... 34 2.2.6 Flow cytometry analysis on purified dendritic cell subsets and peripherial and cord blood mononuclear cells ............................................................................................. 35 2.2.7 Assessment of nuclear translocation of pIRF7 in conventional and plasmacytoid dendritic cells ................................................................................................ 36 2.3 Results ........................................................................................................................... 37 2.3.1 Key basal differences in gene expression between adult and newborn dendritic cells influence the transcriptional response to TLR7/8 stimulation. .............................. 37 2.3.2 MAP3K8 ranks high in the list of age-dependent, differentially expressed genes in both unstimulated and stimulated cDC. ............................................................. 39 2.3.3 Differential nuclear translocation of phosphorylated IRF7 occurs in adult cDC and pDC upon stimulation with 3M-003. .......................................................................... 42 2.3.4 ERK1/2, a downstream target of MAP3K8, was more highly phosphorylated in adult than neonatal dendritic cells, affecting the transcription of its downstream targets. ................................................................................................................................. 45 xi  2.4 Discussion ...................................................................................................................... 49 CHAPTER 3: INFECTION OF HUMAN PRIMARY MONOCYTES WITH Listeria monocytogenes INDUCES AGE-DEPENDENT DIFFERENTIAL EXPRESSION OF GUANYLATE BINDING PROTEINS ..................................................................................... 56 3.1 Introduction .................................................................................................................. 56 3.2 Methods ......................................................................................................................... 57 3.2.1 Bacterial strains, medium, and growth conditions ............................................ 57 3.2.2 Monocyte purification .......................................................................................... 58 3.2.3 Listeria monocytogenes infection of human whole blood and monocytes ........ 59 3.2.4 RNA extraction ...................................................................................................... 60 3.2.5 Microarray and real-time PCR ........................................................................... 60 3.2.6 Microarray processing and bioinformatics analysis .......................................... 61 3.2.7 Immunoblot analysis ............................................................................................. 62 3.2.8 Flow cytometry analysis on purified monocytes ................................................ 62 3.2.9 Statistical analysis ................................................................................................. 63 3.3 Results ........................................................................................................................... 63 3.3.1 Members of the guanylate binding proteins, a subfamily of interferon-inducible GTPases were differentially expressed in human primary monocytes upon infection with L. monocytogenes. ........................................................................................ 63 3.3.2 Type I and II interferons are differentially expressed in an age-dependent manner in human primary monocytes infected with wild-type Lm. .............................. 67 xii  3.3.3 Exogenous stimulation of human primary monocytes from newborns and young adults with interferons results in induction of GBP1 mRNA. ............................. 68 3.4 Discussion ...................................................................................................................... 71 CHAPTER 4: AGE-RELATED GENE EXPRESSION DIFFERENCES IN MONOCYTES FROM HUMAN NEONATES, YOUNG ADULTS, AND OLDER ADULTS...................... 75 4.1 Introduction .................................................................................................................. 75 4.2 Materials and methods................................................................................................. 78 4.2.1 Isolation of cells and stimulation conditions ....................................................... 78 4.2.2 RNA isolation, library preparation, and sequencing ......................................... 79 4.2.3 Bioinformatic analysis .......................................................................................... 79 4.3 Results ........................................................................................................................... 80 4.3.1 Gene expression cascades induced in monocytes by LPS and Lm ................... 80 4.3.2 K-means cluster analysis of LPS- and Lm-induced genes ................................ 85 4.3.3 Analysis of genes exhibiting statistically significant expression differences.... 89 4.3.4 A prominent role for IRF3 and type I IFN signaling in the neonate-adult differences............................................................................................................................. 92 4.3.5 Low-level inflammation in older adults .............................................................. 95 4.4 Discussion ...................................................................................................................... 97 CHAPTER 5: DISCUSSION ................................................................................................... 102 5.1 Introduction ................................................................................................................ 102 xiii  5.2 The interferons: a double-edged sword .................................................................... 102 5.3 Interferon-inducible GTPases involved in cell autonomous immunity are also differentially regulated in an age-dependent manner .................................................... 104 5.4 Study limitations ..................................................................................................... 107 5.5 Main conclusions .................................................................................................... 108 5.6 Future directions..................................................................................................... 110 BIBLIOGRAPHY ..................................................................................................................... 112 APPENDICES ........................................................................................................................... 136 Appendix A ............................................................................................................................ 136 Appendix B............................................................................................................................. 146 Appendix C ............................................................................................................................ 148    xiv  LIST OF TABLES  Table 1.1 Toll-like receptors and their ligands derived from pathogens………………………...10 Table 2.1 Top 10 differentially expressed genes in cDCs that were significantly different between adults and neonates in response to 3M-003 stimulation after 1 and 6 hrs……………...40 Table 2.2 Pathways overrepresented in an age-dependent manner in adult and neonatal conventional dendritic cells in response to 3M-003 after 1 and 6 hrs of stimulation……………41 Table 3.1 Differential mRNA expression of the guanylate binding protein (GBP) subfamily of interferon-inducible GTPases in human primary monocytes infected with wild-type L. monocytogenes after 2 and 6 hrs…………………………………………………………………64 Table A.1 Number of differentially expressed genes in response to 3M-003 stimulation was greater in cDCs than in pDCs in both age groups………………………………………………137 Table A.2 Number of differentially expressed genes that were age-dependently different in response to 3M-003 stimulation is greater in cDC than pDC…………………………………..137 Table A.3 Top 100 genes that were differentially expressed at baseline between adult and neonatal cDCs…………………………………………………………………………………..138 Table A.4 Antibodies and lasers used on the BD LSR II flow cytometer for cell purification checks and phosphoflow………………………………………………………………………..144 Table A.5 Antibodies and lasers used on the Amnis Imagestream for determining nuclear translocation of pIRF7 in 3M-003 stimulated PBMCs………………………………………....145 Table B.1 Fold change induction of other interferon-inducible GTPase subfamilies in Lm-infected monocytes from human newborns, young adults, and older adults…………………...147 xv  LIST OF FIGURES  Figure 1.1 Overview of myeloid cell differentiation in humans…………………………………4 Figure 1.2 Overview of antigen presentation using Listeria monocytogenes as an example…….5 Figure 1.3 Simplified overview of the MyD88-dependent and –independent signaling cascades downstream of TLR4…………………………………………………………………………….11 Figure 1.4 Age-dependent changes in the innate immune system……………………………….15 Figure 1.5 Schematic of the invasion and intracellular life cycle of Listeria monocytogenes…..18 Figure 1.6 Select, key immune cells involved in the response to Listeria monocytogenes infection………………………………………………………………………………………….20 Figure 1.7 Key innate immune pathways activated by Listeria monocytogenes in immune cells………………………………………………………………………………………………22 Figure 2.1 Adult and neonatal pDC responded more similarly with each other than do cDC upon stimulation with 3M-003…………………………………………………………………………38 Figure 2.2 Quantitative PCR of purified cDC and pDC from adults and neonates (cord) also showed that genes involved in the MAPK signaling pathway are differentially expressed in an age-dependent manner upon stimulation with 3M-003………………………………………….42 Figure 2.3 Neonatal (cord) cDC and pDC have significantly decreased levels of phosphorylated IRF7 protein than adults upon stimulation with 3M-003………………………………………...44 Figure 2.4 pERK1/2 levels were significantly lower in neonatal pDC than adult pDC upon stimulation with 3M-003…………………………………………………………………………47 Figure 2.5 Downstream targets of ERK1/2 MAPK signaling were differentially expressed in an age-dependent manner in cDC and pDC………………………………………………………...48 xvi  Figure 3.1 Quantitative PCR of purified human monocytes from newborns, young adults, and older adults also show that key interferon-inducible genes are differentially expressed in an age-dependent manner upon infection with L. monocytogenes………………………………………64 Figure 3.2 GBP1 mRNA is expressed in a mixed cell population in addition to being expressed at the protein level in purified monocytes………………………………………………………….66 Figure 3.3 Age-dependent, differential upregulation of Type I and Type II interferon in primary monocytes infected with L. monocytogenes……………………………………………………..68 Figure 3.4 Age-dependent differential expression of GBP1 occurs upon L. monocytogenes infection but not with exogenous stimulation with IFN-b and IFN-g+LPS……………………..70 Figure 4.1 Hierarchical clustering of LPS-stimulated monocyte transcriptomes from human neonates, adults and older adults………………………………………………………………...82 Figure 4.2 Hierarchical clustering of Lm-infected monocyte transcriptomes from human neonates, adults and older adults………………………………………………………………...84 Figure 4.3 Analysis of LPS-induced genes in monocytes by K-means cluster analysis………...86 Figure 4.4 Analysis of Lm-induced genes in monocytes by K-means cluster analysis………….88 Figure 4.5 Genes that exhibit the greatest expression deficit in LPS-stimulated cord blood monocytes in comparison to adult monocytes are regulated by IRF3 and/or Type I IFNs……...90 Figure 4.6 Genes that exhibit the greatest expression deficit in Lm-infected cord blood monocytes in comparison to adult monocytes are regulated by IRF3 and/or Type I IFNs……...91 Figure 4.7 Elevated expression of a broad range of inflammatory genes prior to stimulation of freshly isolated monocytes from older adults……………………………………………………97 Figure 5.1 IFN-inducible GTPase families in humans and mice……………………………….105 xvii  Figure A.1 Adult and neonatal cDCs and pDCs responded very similarly to stimulation with 3M-003 at all time points investigated……………………………………………………………..136 Figure A.2 Adult and neonatal cDCs and pDCs expressed similar amounts of IRF7 mRNA...143 Figure A.3 Median fluorescent intensities of phosphorylated IRF7 was significantly lower in newborn dendritic cells compared to adults at baseline…………………………………….....143 Figure B.1 Gating strategies used for flow cytometry experiments to determine the purity of human monocyte isolations performed…………………………………………………………146 Figure C.1 LPS-induced genes exhibiting statistically significant differences in transcript levels in cord blood and young adult monocytes……………………………………………………...149 Figure C.2 Lm-induced genes exhibiting statistically significant differences in transcript levels in cord blood and young adult monocytes………………………………………………………...151 Figure C.3 LPS-induced genes that exhibit statistically significant differences in basal transcript levels in monocytes from young and older adults……………………………………………...153    xviii  LIST OF ABBREVIATIONS  actA  Actin assembly-inducing protein AIM2  Absent in melanoma 2 AP1  Activator protein 1 APC  Antigen presenting cell  BSA  Bovine serum albumin  CAI  Cell autonomous immunity CASP1 Caspase 1 CBMC  Cord blood mononuclear cells CBP  CREB-binding protein CCR2  C-C chemokine receptor type 2 CD4  Cluster of differentiation 4 CD8  Cluster of differentiation 8 cDC  Conventional dendritic cell cDNA  Complementary DNA CDP  Common DC progenitor CLP  Common lymphoid progenitor CMLP  Common myelolymphoid progenitor CMP  Common myeloid progenitor cRNA  complementary RNA  DAMP  Danger-associated molecular pattern molecules DC  Dendritic cell DPBS  Dulbecco’s phosphate buffered saline DUSP  Dual specificity phosphatase dUTP  Deoxyuridine triphosphate  EDTA  Ethylenediaminetetraacetic acid EGR1  Early growth response protein 1 ER  Endoplasmic reticulum ERK1/2 Extracellular signal-regulated kinase 1 and 2  FBS  Fetal bovine serum  GBP  Guanylate binding protein GMP  Granulocyte and macrophage progenitor GTP  Guanosine triphosphate GTPase Guanosine triphosphatase GVIN/LVIG Very large inducible GTPase  Hpt  Hexose transporter xix  HSC  Hematopoietic stem cell  IFN  Interferon IFNAR Interferon-alpha/beta receptor IL  Interleukin InlA  Internalin A iNOS  Inducible nitric oxide synthase IRG  immunity related guanosine triphosphatase IRGM  Immunity-related GTPase family, M IRF  Interferon regulatory factor  JNK  c-Jun N terminal kinases  kDa  kilo-Dalton  LIMMA Linear models for microarray analysis LLO  Listeriolysin O Lm  Listeria monocytogenes LplA1  lipoate protein ligase LPS  Lipopolysaccharide  MAPK  Mitogen-activated protein kinase MAP3K4  Mitogen-activated protein kinase kinase kinase 4 MAP3K8 Mitogen-activated protein kinase kinase kinase 8 MC  Mononuclear cells MCP  Mast cell progenitor MDP  Macrophage and DC progenitor MDS  Multidimensional scaling MEP  Megakaryocyte and erythroid progenitor MFI  Median fluorescence intensity MHC  Major histocompatibility complex MHCI  MHC class I MHCII MHC class II miRNA micro-RNA mo-DC monocyte-derived DC MOI  Multiplicity of infection MPP  Multipotent progenitor cell mRNA  messenger RNA Mtb  Mycobacterium tuberculosis mTOR  Mechanistic target of Rapamycin MX  Myxovirus Mx1  MX dynamin-like GTPase 1 MyD88 Myeloid differentiation primary response gene 88  NF-kB  Nuclear factor kappa-light-chain-enhancer of activated B cells NK  Natural killer xx  NLR  NOD-like receptor NOD  Nucleotide-binding oligomerization domain receptor NO  Nitric oxide NOS  Nitric oxide synthase NRAMP-1 Natural resistance-associated macrophage protein 1  ORA  Overrepresentation analysis  PAMP  Pathogen associated molecular pattern PBMC  Peripheral blood mononuclear cells PBS  Phosphate buffered saline PBSAN PBS containing 0.5% BSA (Sigma) and 0.1% sodium azide PC-PLC Phosphatidylcholine-preferring PLC PCR  Polymerase chain reaction pDC  Plasmacytoid DC pERK1/2 Phosphorylated ERK1/2 PGN  Peptidoglycan PI-PLC Phosphoinositide phospholipase C pIRF7  Phosphorylated IRF7 PLC  Phospholipase C PMA  Phorbol 12-myristate 13-acetate PRR  Pathogen recognition receptor PTGS2 Prostaglandin-endoperoxide synthase 2  qPCR  Quantitative polymerase chain reaction  RIG-I  Retinoic acid – inducible gene 1 RLR  RIG-I-like receptor RNA-Seq RNA sequencing ROS  Reactive oxygen species RPKM  Reads per kilobase pair per million mapped reads  SEM  Standard error of the mean STAT  Signal transducers and activators of transcription   STING Stimulator of intereferon genes protein SWI/SNF Switch/sucrose nonfermentable  TBP  TATA-binding protein TCR  T cell receptor Th  T helper TipDC  TNF and iNOS producing DC TIR  Toll/interleukin-1 receptor TLR  Toll-like receptor TMEM173 Transmembrane protein 173 TNF-α  Tumor necrosis factor-α TRIF  TIR-domain-containing adapter-inducing interferon-β xxi   WT  Wild-type    xxii  ACKNOWLEDGEMENTS  First and foremost, I would like to express my utmost gratitude to Dr. Tobias Kollmann, for supporting my decision to transfer from the Experimental Medicine Master of Science program into the Doctoral program. His passion and dedication to his practice and his research have a driving force in my scientific research during the nearly six years I have spent in his lab. Without his guidance and trust, I would not have had the opportunity to strive and challenge myself to the limit with my knowledge and technical skills. I would not have been able to acquire as many skills as I did had it not been for his trust in giving me the opportunity to explore as many avenues as possible. Your encouragement and guidance have propelled me to the position where I am at today and have spurred a passion for translational research in me. I am also indebted to my committee members, namely Dr. Rachel Fernandez for your guidance and your insightful comments along this process, allowing me to think critically about my projects. I would also like to say thank you to Dr. Soren Gantt for his guiding suggestions and comments, reminding me of the importance of translating research on to the clinic.  I would also like to thank many past and present members of the Kollmann lab for all the help and laughter throughout the six years of my degree, making this one of the most impressionable life experiences to date. Special mentions go to Bing Cai, Dr. Kinga Smolen, Sophia Lee, Sheka Aloyouni, Erika Park, and Duncan MacGillivray, for being tremendous help in completing my experiments and providing a positive atmosphere in the lab. I am honoured to have shared my time there with you all. To Dr. Daniella Loeffler, Dr. Ashley Sherrid, and Dr. Edgardo S. Fortuno III, thank you for all the mentorship and guidance in shaping my thesis project.  xxiii   A special thank you goes to the nurses at the Labor and Delivery units at the University of Washington Medical Center Hospital and the BC Women’s Hospital for their help and their patience in collecting cord blood samples for this project.  Financial support from the Canadian Institute for Health Research (CIHR) Master’s award and the Natural Sciences and Engineering Research Council (NSERC) Industrial Postgraduate Scholarship award was greatly appreciated.  Lastly, I would like to extend my immense gratitude to my family and close friends for all their support throughout the years.      xxiv  DEDICATION  Firstly, this work is dedicated to my parents, for their unwavering love and support all these years. Thank you for giving me the opportunity to pursue my dreams in research. It was not what we all initially thought I would be doing but I am here now at the final stretch of my doctoral degree. Thank you for teaching me the value of hard work and education and to always aim high and reach for the stars; it has carried me through the toughest days and nights of this endeavour.  To my sister and best friend, Tracee, thank you for your love and patience throughout these six year. You have kept my spirits up with fun and healthy activities to help me keep my life balance. Thank you for brainstorming ideas with me late at night and helping me get through so many of my most challenging days as well.  To the rescue dog that brought me back to life, Miah, thank you for keeping me balanced and happy after long days of hard work. Thank you for giving me the gift of the here and now – it has taught me to focus on the present task at hand and greatly helped my focus and efficiency. You have brought me so much joy and ambition; it is one of the main reasons I am here today at the finish line. Don’t worry – I forgive you for trying to swat my laptop away while I try to write and work late at night.   To the love of my life, Wesley, thank you for your unwavering support in the last two years. Thank you for believing in me and always taking care of me at the toughest of times. Thank you for your patience and understanding, for spending your nights late in the lab with me to keep me xxv  company and make sure I get home safe. You have been my pillar of strength especially at the final push of this work and for that I am extremely grateful.  Finally but most importantly, this thesis is dedicated to the late Aaron Wyatt. Miss you everyday buddy. You have been my role model all throughout my years as a graduate student. You are never forgotten.   1  CHAPTER 1: INTRODUCTION  1.1 An overview of the immune response in humans  The immune system protects organisms from infections. All living organisms need the ability to protect against pathogenic infection, from single-celled organisms to humans (1,2).    Cell autonomous immunity (CAI) is the most ancient and ubiquitous form of host protection and guards individual cells against intracellular infection (reviewed in (3,4)). Since pathogens entering a host cell must cross cell membranes, the host cell expresses membrane localized pattern recognition receptors (PRRs) that detect pathogen associated molecular patterns (PAMPs) and danger receptors that monitor danger associated molecules (DAMPs) at each intracellular border (1,5). For example, galectins, a family of cytosolic lectins with specificity for β-galactosides detect membrane damage and upon activation induce autophagy of the damaged subcellular compartment (6). Cytosolic PRRs targeting foreign nucleic acids (DNA, RNA) induce a potent antiviral and antimicrobial state when activated (7–9). Moreover, infected cells increase expression of proton-dependent efflux pumps, such as natural resistance–associated macrophage protein–1 (NRAMP-1), that export iron from vacuoles to prevent access of captured microbes to this essential metal. While CAI is likely operative throughout life, it may play an especially important role in the earliest stages of embryonic development. However, changes of CAI as a function of age have not yet been investigated. With the evolution of multicellular organisms, immunity becomes increasingly important for 2  host protection. Evolutionarily, the earliest forms are referred to as innate immunity (1,2). An innate immune system is present in all multicellular organisms, including plants, insects, and animals (3). Similar to CAI, innate immune cells such as phagocytes are equipped with a wide range of PRRs recognizing PAMPs that upon ligation activate a complex cascade of cellular reactions, which in turn lead to production of a wide array of effector molecules (1,2,5). Innate immune cells also directly link to adaptive immunity, most importantly through antigen-presentation, expression of co-stimulatory molecules and cytokines (1). The activities of innate immunity are both rapid (preventing microbial spread) and nonspecific (protecting against multiple pathogens of diverse nature). The innate immune system exerts its function through both soluble as well as cell-mediated aspects. The evolution of the adaptive immune system occurred at around the same time as the appearance of vertebrates (1,2); immunological memory is a prominent characteristic of the adaptive immune system that allows the host to mount stronger protective immune responses faster upon subsequent exposure to the same pathogen (1).    All arms of the immune system, from CAI to innate to adaptive immunity are important for protection against pathogenic infection in all higher organisms; humans are no exception. It is believed that while CAI defends individual cells, it is the innate immune system that senses environmental changes for the entire organism, including exposure or infection with various pathogens and responds in a nonspecific manner, and then instructs adaptive immune functions to target specific threats (1,3,4). We thus chose to focus our analysis of age-dependent differences in immunity on innate immunity.    3  1.1.1 The innate immune system  Given that all pathogens crossing our physical barriers (skin, mucous membranes) encounter the innate immune system, innate immunity plays an essential role in immune surveillance and initiates an immune response upon recognition of an invader. However, it has only been in the last 20 years that investigations into the characterization and function of the components of the innate immune system have begun. There are three large components of the innate immune system: physical barriers (e.g. skin and mucosal surfaces), soluble components (e.g. the complement system), and cellular components (e.g. myeloid cells) (1). Physical barriers of the body protect the sterile organs of the host from pathogenic invasion (1). Proteins of the complement system are able to attach and lyse pathogens without requiring support from other cells (1). Cells of the myeloid lineage are the sentinel cells which upon encounter of pathogens initiate downstream innate effector responses as well as adaptive immune responses (1). Given the central role of myeloid cells, we chose to focus our attention on them to delineate possible age-dependent differences in function.  Figure 1.1 summarizes the development of the myeloid cells. A subset of these cells play an essential role in capturing and killing pathogens as well as presenting them to the adaptive immune system. These are the antigen presenting cells (APCs) of which monocytes, macrophages, and dendritic cells are the main cell types (1). In other words, the main function of these cells is to provide innate effector function as well as to instruct the adaptive immune system on what type of immune response is to be mounted in order to elicit immune protection and pathogenic clearance (1).  4    Figure 1.1 Overview of myeloid cell differentiation in humans. Myeloid cells originate from hematopoietic stem cells (HSC) and multipotent progenitor cells (MPP). This figure is derived from (10) and illustrates the progenitor cells that give rise to the different cell lineages. cDC, conventional DC; CDP, common DC progenitor; CLP, common lymphoid progenitor; CMLP, common myelolymphoid progenitor; CMP, common myeloid progenitor; DC, dendritic cell; GMP, granulocyte and macrophage progenitor; MCP, mast cell progenitor; MDP, macrophage and DC progenitor; MEP, megakaryocyte and erythroid progenitor; NK, natural killer; pDC, plasmacytoid DC. Permission to use original image obtained from RightsLink.   5  1.1.2 Antigen presenting cells of the innate immune system and antigen presentation  APCs capture pathogens and kill them within the phagosome. They then process antigens derived from killed pathogens and “load” them onto major histocompatibility complex (MHC) molecules to present to T cells in the process of antigen presentation (Figure 1.2). There are three major cell subsets that have antigen presenting capacity in the innate immune system: monocytes, macrophages, and dendritic cells.   Figure 1.2 Overview of antigen presentation using Listeria monocytogenes as an example.  The process of antigen presentation begins by the engulfment of pathogens by APCs such as monocytes, macrophages, dendritic cells, or B cells. Antigens which are released inside the phagolysosome are loaded onto MHCII molecules and transported to the cell surface, where they will go on to activate CD4+ T cells via interaction with the appropriate T cell receptor (TCR). Antigens which are found inside the cytosol are transported to the Golgi/endoplasmic reticulum (ER) complex, loaded onto MHCI molecules, and transferred to the secretory pathway to be transported to the cell surface. MHCI molecules loaded with antigen then interact with the TCR of CD8+ T cells in order to activate cytotoxic T cells.  attenuated Listeria with secreting  proteinsMHC Class IICD 8+CD4+nucleusERTAPOVAproteasomeMHC Class I6  Macrophages are a key myeloid cell type in the innate immune system that act as one of the first responders upon a pathogen breaching physical barriers (1). Their main role is to phagocytose pathogens and clear infection from the many different tissues they reside in (1). Macrophages are one of the mature forms of monocytes (1); however, there are other sources for tissue macrophages as well, such as those derived from the yolk sac during embryogenesis and the hematopoietic stem cells during fetal development (11,12). They are mostly found in tissues and not in circulating blood. Tissue resident macrophages take on specialized roles depending on the tissue they reside in, such as alveolar macrophages, which engulf pathogens as well as react to toxic gases and inhaled particles, and osteoclasts which are important cells for bone remodelling (13). They also play an important role in the clearance of infected cells that have been killed by cytotoxic CD8+ T cells by phagocytosing the remaining cellular debris (13,14).  Monocytes are located in blood and differentiate into dendritic cells (DC) or macrophages when they migrate into different tissue sites in the body (1). Monocytes are a relatively rare cell population, only making up 10% of the mononuclear cells in peripheral blood (15). They are one of the first cell types to respond to pathogenic attack, attracted by cytokines and chemokines that are released from the site of an infection. They are able to emigrate into the infected tissue and engulf pathogens, which initiate signals for the innate immune response, including producing chemokines and cytokines to attract yet other immune cell types. Upon differentiation into macrophages or dendritic cells and following phagocytosis of invading pathogens, monocytes can migrate out of the tissue and travel to lymph nodes in order to present antigens to the T cells, thus initiating the adaptive immune response.  7  DCs can be found in peripheral blood as well as tissue, where they have specific phenotypes depending on the specific location within the body (1): Kupffer cells in liver, Langerhans cells in the skin, etc.. However, in the blood, two circulating DC subsets can be found: the conventional dendritic cells (cDC) and plasmacytoid dendritic cells (pDC) (16). Cumulatively, they comprise a very small proportion of peripheral blood; cDC only make up 1.5% of mononuclear cells at maximum, and pDC are even rarer, only making up ~0.5% of mononuclear cells. Upon activation, cDC can rapidly upregulate MHCII, become highly phagocytic, and produce cytokines (16). When appropriately activated cDC can produce high quantities of interleukin (IL)-12, and IL-18 which in turn can activate Natural Killer (NK) cells and T cells to produce interferon gamma (IFN-γ) (17). On the other hand, pDC are known for their ability to produce large amounts type I IFN (IFN-α/IFN-β) upon stimulation (17–19).   1.1.3 Overview of the adaptive immune system  The adaptive immune system came later during evolution. Its main characteristic is the ability to provide immunological memory (1). This enables the host to mount protective immune responses faster upon reinfection with the same pathogen. It is made up of two main cell types: T cells and B cells.  B cells are also capable of antigen presentation just like the myeloid cells of the innate immune system. However, their main function is to produce antibodies that mediate protection against an array of extracellular pathogens (1). Once activated, B cells can produce several different isotypes of antibodies, of which IgG antibodies are most important for long lasting humoral 8  protection (1). B cells that produce large amounts of antibodies are effector B cells called plasma cells (1). A small subset of these activated B cells go on to become long-lived memory cells (1). These memory B cells can live up to 50-80 years old, thereby being capable of providing life-long immunity (1).   T cells are the major cell type that is responsible for cell-mediated immunity (1). They interface with APCs during the initiation of an adaptive immune response. There are two major subsets of T cells: cluster of differentiation (CD) 4 positive (CD4+) and CD8+ T cells (1). They integrate three different signals from the APC which dictate the type of adaptive immune response is required: the antigen loaded on the MHC molecule (which is recognized by the T cell receptor (TCR), the co-stimulatory molecule CD80/86 (which interacts with CD28 on T cells) and secreted cytokines (1). Once activated, T cells can perform an array of functions depending on the resulting phenotypes as T helper (Th) cells or effector T cells. For example, CD4+ Th2 cells provide help towards B cells in activation and production of antibodies (1) whereas CD4+ Th1 cells activate CD8+ cytotoxic cells to facilitate clearance of intracellular infections (1). Just like B cells, T cells also differentiate into both effector and long-lived memory T cells (1). It is these memory T cells that provide faster responses against reinfection of the same pathogen.  1.2 Key molecular signaling pathways in the innate immune system  The initiation of an innate immune response begins with the sensing of pathogens by the cellular component of the innate system. This is mediated by the ligation of pathogenic molecules with specific receptors on the host cell surface (1). Cytosolic receptors located within the cell also 9  detect intracellular pathogenic molecules (5). This signal is then transduced by several signaling cascades down to the nucleus to induce the transcription of genes which recruit other immune cells and initiate an adaptive immune response (1,2,5). There are many known molecular signaling cascades, which are shown to be active in the innate immune system. However, the specific cascades that are activated are dictated by the type of pathogen that is encountered. Some of the key innate signaling pathways are expanded upon below.    1.2.1 Toll-like receptor signaling pathway   Once innate immune cells, particularly APCs, encounter a pathogen, a signal is transduced to the nucleus in order to initiate responses that ultimately lead to the clearance of the pathogen from the organism (1). Signal transduction is facilitated by several important receptors on the cell surface that recognize pathogen associated molecular patterns (PAMPs); these are the host’s pathogen recognition receptors (PRRs) (1,2,5). Of these, arguable the most famous family to be discovered are the TLRs (1,2,5). TLRs recognize a wide array of extracellular and endosomal PAMPs; their ligands are summarized on Table 1.1 and show that TLR receptors are capable of recognizing both viral and bacterial PAMPs. The type of pathogen that is encountered will determine which TLRs will be activated. The signals derived from the combination of which TLRs are activated are integrated into an appropriate transcriptional profile that enables the infected cell to communicate with other cell types and initiate the immune response. Two main signaling cascades occur after the activation of TLRs, one requiring the adaptor protein myeloid differentiation primary response gene 88 (MyD88) (MyD88-dependent pathway) and one that does not (MyD88-independent pathway) called the TIR-domain-containing adapter-inducing 10  interferon-β (TRIF) pathway (2,5). Figure 1.3 shows an overview of the TLR4 pathway, illustrating both MyD88-dependent and –independent components of this pathway. The MyD88-dependent pathway ultimately leads to the activation of nuclear factor kappa-light-chain-enhancer of activated B cells (NF-kB) and the transcription of pro-inflammatory cytokines, such as IL-6 and tumor necrosis factor alpha (TNF-α) (2,5). The MyD88-independent pathway ultimately leads to the activation of interferon regulatory factor 3 (IRF3) and the transcription of type I IFNs (2,5).   Table 1.1 Toll-Like Receptors and their ligands derived from pathogens. Adapted from (2). Receptor Subcellular Localization Ligand Ligand Origin TLR1 Cell surface Triacyl lipopeptides  Soluble factors Bacteria and mycobacteria Neisseria meningitidis TLR2 Cell surface Lipoproteins/Lipopeptides Peptidoglycan Lipoteichoic acid Lipoarabinomannan Phenol-soluble modulin Glycoinositolphospholipids Glycolipids Porins Atypical lipopolysaccharide Zymosan Various pathogens Gram-positive bacteria Gram-positive bacteria Mycobacteria Staphylococcus epidermidis Trypanosoma cruzi Treponema maltophilum Neisseria Leptospirosa interrogans and Porphyronomas gingivalis Fungi TLR3 Endosomal Double-stranded RNA Viruses TLR4 Cell surface Lipopolysaccharide Taxol Fusion protein Gram-negative bacteria Plants Respiratory syncytial virus TLR5 Cell surface Flagellin Bacteria TLR6 Cell surface Diacyl lipopeptides Lipoteichoic acid Zymosan Mycoplasma Gram-positive bacteria Fungi TLR7 Endosomal Single-stranded RNA Viruses TLR8 Endosomal Single-stranded RNA Viruses TLR9 Endosomal CpG-containing DNA Bacteria and viruses  11   Figure 1.3 Simplified overview illustrating the MyD88-dependent and –independent signaling cascades downstream of Toll-like receptors. TLR stimulation results in the activation of two pathways. The MyD88-dependent leads to the activation of NF-kB and results in the expression of inflammatory cytokines. The other pathway is activated independently of MyD88 (TRIF-dependent) and results in the expression of IFNs and IFN-inducible gene products. Figure derived from (2). Permission to use original image obtained from RightsLink.  1.2.2 C-type lectin receptors  C-type lectin receptors (CLRs) are one of the first PRRs to be discovered. They comprise a large superfamily of proteins that recognize a diverse range of ligands which contain conserved carbohydrate motifs on both host and foreign proteins (20). CLRs are important in the innate immune response against fungal infections of which Dectin-1 and Dectin-2 have prominent roles 12  in innate signaling upon stimulation with fungal PAMPs (20,21). CLRs have also been shown to play an important role in antibacterial immunity. Studies have shown that CLRs play an important role in the recognition of mycobacterial surface components and induce the expression of cytokines as well (20,21). Signal transduction results in the production of Th17-inducing cytokines, pro-inflammatory cytokines as well as enhanced phagocytosis and respiratory burst (20).  1.2.3 Cytosolic surveillance pathway  The cytosolic surveillance pathway detects PAMPs within the cytosolic space (5). There are two main divisions of this pathway, nucleotide-binding oligomerization domain (NOD) - like receptors (NLRs) and retinoic acid – inducible gene 1 (RIG-I) - like receptors (RLRs). NLRs are a category of intracellular PRRs that cooperate with the TLR pathway. They recognize both proteins and RNA that are derived from pathogens, just like TLRs (5). Additionally, NLRs are capable of recognizing fungal PAMPs just like TLRs (5,6) and are also capable of activating NF-kB (5,6). More importantly, they activate key components of the inflammasome complex which leads to the activation of caspase 1 (CASP1) (5,9). This in turn leads to the enzymatic conversion of pro-IL-1β and pro-IL-18 into their mature forms (IL-1β and IL-18 respectively) (5,9). RLRs largely recognize foreign intracellular nucleic acids, including DNA and RNA (5), making them the main PRRs for detecting viral infections. Many of the RLRs converge onto transmembrane protein 173 (TMEM173), also known as stimulator of interferon genes (STING), which mediate the expression of type I IFNs IFN-α/β (7). TMEM173 itself is also an intracellular PRR for sensing foreign DNA (7).   13   1.2.4 Pathogens activate multiple signaling pathways  Pathogens are complex; they may express many different PAMPs at any given time. Therefore, pathogens can and do activate several PRRs in the context of infection. The host integrates all the signals derived from host-pathogen interaction, resulting in a response that is unique to the particular pathogen of interest. Host cells are able to integrate the signals coming from several different pathways, the end result being the activation of different combinations of cytokines, chemokines and effector molecules in response to infection. For example, we know that the TLR pathway can synergize with several other pathways, such as members of the NLR pathway, mitogen-activated protein kinase mitogen-activated protein kinase (MAPK) pathway, or the IRF pathways (8). Integration of signals also involves the negative regulators of the pathways, feedback and feed-forward loops, and antagonistic relationships between pathways. For example, the mammalian target of rapamycin (mTOR) pathway acts in concert with TLR pathways and regulates the expression of several important cytokines, such as IL-12 and IL-10 (22–25). Inhibition of the mTOR pathway using rapamycin results in the increased production of pro-inflammatory cytokines (24,25). Moreover, type I and type II IFNs are known to have an antagonistic relationship with each other (26). This antagonistic relationship is also found in the context of Listeria monocytogenes (Lm) infection, where an elevated type I IFN response is correlated with worse clinical outcome and a concomitant decreased in type II IFN production. On the other hand, elevated levels of IFN-γ  (i.e. type II IFN) and low levels of type I IFNs are correlated with robust production against Lm infection (14,26,27).  14  1.3 Development of the immune system  1.3.1 An overview of the immune system in early life  Clinical experience suggests that newborns mount suboptimal protective immune responses as compared to older children or adults (28–30); it takes nearly two years for human newborns to fully develop adult-like (what often is assumed to be “normal”) responses (28,31) (Figure 1.4.). Before birth, the human fetus (by extrapolation from data obtained with prematurely born infants) exhibit an “anti-inflammatory” profile, where they secrete high levels of IL-10 whereas they are unable to produce adult-like levels of many of the pro-inflammatory cytokines (28,31). Following birth, term infants increasingly assume a more adult like response. The last type of immune response that develops to adult-like levels at close to two years of age is the production of innate cytokines that support development of cell mediated immunity, best exemplified by the Th1 type response; robust CD8+ cytotoxic T cell responses are critical for fighting many different types of intracellular pathogens (1). It has been postulated that the development of the immune response in early life closely reflects their sequential exposure to different types of environments and encountered pathogens. For example, their anti-inflammatory state is necessary to prevent maternal-fetal rejection and miscarriage (32). During vaginal birth, newborns are exposed to the natural microbiota of the mother’s birth canal, which is their first exposure to potential pathogens (33). Therefore, Th17 responses, which are important for the recruitment of neutrophils to phagocytose extracellular bacteria (1), are the first to develop after birth. Newborns are subsequently exposed to many different viruses which facilitates their 15  antiviral responses to develop. Finally, once infants are more frequently exposed to larger numbers of intracellular pathogens, the Th1 responses increase up to adult levels.     Figure 1.4 Age-dependent changes in the innate immune system. Upon stimulation with TLR ligands in vitro, [1] cord blood from preterm infants produce high amounts of the anti-inflammatory cytokine IL-10 and low amounts of pro-inflammatory cytokines. [2] Cord blood from term infants produce large amounts of Th17 supporting cytokines upon TLR stimulation. [3] Expression of type I IFN in DCs is greatly reduced but reaches adult levels within weeks. [4] Pro-inflammatory cytokines reach adult-like levels of expression at around one year of age. [5] Finally, the production of Th1 supporting cytokines reach adult levels of expression at around two years of age. In older adults aged 65 and older, we observe increased levels of pro-inflammatory cytokines even in the absence of stimulation or infection, a phenomenon known as “inflammaging”. At the same time, they also express higher levels of IL-10 in response to TLR stimulation and lower levels of other innate cytokines. Figure obtained from (28), with permission from Dr. Tobias R. Kollmann.   1.3.2 The aging immune system  At the other end of the age spectrum, the aging immune system of individuals aged 65 and older also undergoes waning of protective responses. Some older individuals also have a higher 16  baseline expression of pro-inflammatory cytokines despite the absence of a proper stimulus. This phenomenon has been referred to as “inflammaging” (28,34) and is also illustrated in Figure 1.4. However, healthy aged adults typically express higher levels of IL-10 (35,36). Other innate immune functions are also altered in this age group, including the increased accumulation of oxidative radical species which relate back to increased levels of pro-inflammatory cytokines (37), also amplifying the inflammaging process. These innate changes lead to altered adaptive immunity, which together then result in an increased risk for particular infections with increasing age (34,37,38). The adaptive immune response of older adults also changes dramatically with age. Aging causes the number of memory B cells to decrease in the periphery while the numbers of naïve B cells remain the same (38). T cells are also impacted by the aging process. Aging CD4+ T cells have a reduced capacity to form functional immunological synapses with APCs, which leads to impaired T cell activation due to decreased IL-2. This in turn leads to poor expansion and generation of effector T cells (38). A similar scenario is also true for impaired activation of CD8+ T cells (38), leading to decreased protection against viral and intracellular bacterial pathogens. Together, these changes in T cells and B cells lead to poorer vaccination outcomes in the older adult population as well.   1.4 Listeria monocytogenes: epidemiology, pathogenesis, and immune response  1.4.1 Overview of Lm epidemiology  Clinical listeriosis is an infectious disease caused by Lm (39,40). Lm is a food-bourne pathogen, where infection largely occurs through the consumption of contaminated food (39,40). When 17  ingested in large enough quantities, Lm can cause a wide range of clinical symptoms – from mild gastroenteritis to sepsis and death (39,40). The clinical susceptibility to Lm infection is drastically different in different age groups and directly reflects the development and aging of the immune system. Most Lm infections in healthy young adults go unnoticed as subclinical infections, with some reported cases of mild gastroenteritis (39). However, newborns up to two months of age and older adults aged 65 years and old can suffer severe disseminated Lm infections. They often require hospitalization and many succumb to the infection (39,40). Lm is also responsible for a significant number of spontaneous miscarriages (39). With sporadic outbreaks and epidemics still occurring all throughout North America, Lm remains a dangerous pathogen in many immune compromised individuals.   1.4.2 Overview of the molecular pathogenesis of Lm infection  Lm is an intracellular pathogen that has a unique life cycle. Because Lm is usually contracted by ingestion of contaminated food, epithelial cells of the gut are one of the first cell types to be infected (14,27,41,42). Lm invades epithelial cells using internalin A (InlA), which binds to host E-cadherin (14,27,41). This interaction results in the uptake of Lm by the host cell using a zipper mechanism involving the clathrin-dependent endocytosis (14). Once inside the phagosome, the high acidity due to the fusion with lysosomal vesicles induces the expression of listeriolysin O (LLO), leading to disintegration of the phagolysosome, which liberates Lm to freely move within the cytosol of the host cell and replicate (14,41). Lm then expresses actin assembly-inducing protein (actA), which usurps the host’s filamentous actin fibres in order to propel its intracellular movements (14,41). Lm that propel themselves to the edge of cell then use their actin tails to 18  push against the cell membrane to infect neighbouring cells (14,41). To break free from the double membrane that is formed in this process, Lm expresses LLO and phospholipases phosphoinositide phospholipase C (PI-PLC) and phosphatidylcholine-preferring PLC  (PC-PLC) to dissolve the double membrane and enter the cytosol of the neighboring cells (14,41). It is this unique ability of Lm to move from cell to cell without contacting the extracellular space that allows it to evade the soluble immune response (mostly antibodies and complement) very well. The life cycle of Lm infection is summarized in Figure 1.5.   Figure 1.5 Schematic of the invasion and intracellular life cycle of Listeria monocytogenes. (A.) [1] Lm attaches to the host epithelial cell surface and induces uptake. [2] Internalized Lm 19  are trapped in the phagosome, but quickly escape into the cytosol by expression of LLO and phospholipases PC-PLC and PI-PLC. [3] In the cytosol, Lm adapt their metabolism by expression of genes such as the hexose transporter (Hpt) or lipoate protein ligase (LplA1), begin polymerizing actin, and replicate. [4a] Polarized expression of ActA allows Lm to hijack the host actin polymerization machinery in order to polymerize actin and move around the cytosol (B.). [4b] This prevents recognition by the host autophagic machinery (C.) and [4a] propels Lm across the cytosol until it encounters the cell membrane. [5] Protrusions from Lm spreads non-lytically to the neighbouring cell and ends up in a double membrane vacuole and then escapes using LLO and PLCs. [6] Cytosolic Lm then undergo novel rounds of replication and [7] spread. This figure is derived from (14). Permission to use original image obtained from RightsLink.  1.4.3 Immune response against Lm infection  Clearance of Lm infection relies on mounting a cell mediated adaptive immune response (14,27,43). However, early containment of Lm infection is dependent on innate immune cells – both granulocytes and APCs. In particular, neutrophils are highly phagocytic and are capable of capturing a large load of bacteria using neutrophil extracellular traps (14,27). More importantly, APCs are also highly phagocytic and will engulf bacteria to carry to the lymph nodes to activate T cells (14,44). Lm is also able to break into the cytoplasm of APCs by LLO expression; therefore, APCs present Lm antigens on both MHCI and MHCII, with activate both CD8+ cytotoxic T cells and CD4+ T helper cells, respectively (1,14,27,43). Because Lm is an intracellular pathogen, Lm-infected cells can only be identified and killed by cytotoxic CD8+ T cells (14,27,43). Once infected cells have been killed by CD8+ T cells, it is once again up to innate cells to clean up the free bacteria and cell debris. Inflammatory DCs and activated macrophages identify these and engulf them, fully clearing the infection (14). A summary of the key immune cells that are important for protection and clearance of Lm infection is illustrated in Figure 1.6.  20   Figure 1.6 Select, key immune cells involved in the response to Listeria monocytogenes infection. Neutrophils phagocytose Lm and generate nitric oxide synthase (NOS) and reactive oxygen species (ROS) to kill intracellular Lm. They amplify the inflammatory response against Lm infection by secreting IL-12. Macrophages also phagocytose Lm and release pro-inflammatory cytokines IL-1, TNF-α, and IL-12. TNF-α and IL-12 stimulate NK cells to produce IFN-γ, which in turn activates the antimicrobial potential of macrophages. Cytokines released by infected epithelial cells or macrophages induce IFN-γ production by DCs which subsequently activate macrophages and neutrophils to produce ROS and NOS to kill Lm. In the spleen, Lm infection induces the recruitment and differentiation of monocytes into inflammatory TNF and iNOS producing DCs (TipDCs) in a CCR2-dependent manner. TNF-α and NO produced by TipDCs participate in clearance of Lm. IFN-γ and IL-12 promote the differentiation of IFN-γ-producing Th1 CD4+ T cells. cDCs can both prime macrophages and induce the activation of CD8+ T cells to proliferate and to differentiate into cytotoxic CD8+ T cells. CD4+ regulatory T cells play a role in controlling CD8+ T-cell proliferation in a subsequent challenge with Lm. Figure is derived from (14). Permission to use original image obtained from RightsLink.  Several innate immune pathways are activated by Lm. Lm peptidoglycan (PGN) activates TLR2/6 heterodimers and signals through MyD88 to activate the NF-kB and MAPK signaling pathway, resulting in the expression of many chemokines and cytokines (14,27,41). Cytosolic 21  Lm is sensed by TMEM173, resulting in the expression of type I IFN (14,45–47). Intracellular Lm also activates the inflammasome via AIM2 and NLRP3 (9,14,48), resulting in the expression of pro-inflammatory IL-1β and IL-18. The induction of IFNs, in particular IFN-γ, is critical for mounting downstream protective T cell responses as this cytokine is responsible for the development of cell-mediated immunity (1,14,27,43). The induction of IFNs leads to the expression of IFN response genes, which are also an important component of CAI (3,49). For example, studies have shown that the knockout of GBP proteins results in the inability of the host to clear Lm infection in several murine models (3,49).   Much of what we know today about the innate immune response against Lm infection has been gleaned from mouse models. It is important to note that the signaling pathways that are activated in the context of Lm infection are different between human and mouse. For example, mouse studies have shown that IRF3 pathway is responsible for the expression of IFN-β upon in vivo Lm infections (50). This has not been reported in humans; instead, Reimer et al (2007) have shown that the p38 MAPK signaling is responsible for IFN-β expression in human macrophages. The molecular signaling cascades involved in the innate immune response are summarized in Figure 1.7. Finally, the majority of our molecular knowledge in the immune response to Lm infection has been investigated using adult cells, the age group that does not normally exhibit severe disease. Therefore, there is still a major gap in what we know regarding the molecular aberrations in the immune response of susceptible age groups (i.e. newborns and older adults) to Lm infection leading to severe disease.  22   Figure 1.7 Key innate immune pathways activated by Listeria monocytogenes in immune cells. Three major pathways are activated in Lm-infected antigen presenting cells: MyD88-dependent pathway via TLR signaling cascades, STING/IRF3-dependent pathway which detects intracellular Lm, and AIM-2-mediated inflammasome activation. Each pathway results in a distinct response to Lm infection. Dashed lines indicate infrequent events. Figure is derived from (51). Permission to use original image obtained from RightsLink.  1.5 Neonatal immune response: a system in flux during development  As previously stated, newborns exhibit a different immune response compared to healthy adults, often mounting impaired immune responses upon pathogenic challenge. Investigations into the cause of this impairment have shown that this difference in the immune response results from both cell extrinsic and cell intrinsic factors.     23  1.5.1 Cell extrinsic factors that affect the immune response in newborns  Cell extrinsic factors are those that are found in the extracellular space, such as soluble factors in plasma. A primary example of this is free adenosine (52). Levy et al (2006) have shown that newborns contain significantly higher concentrations of adenosine in their plasma, which in turn correlates with dampened expression of several pro-inflammatory cytokines, including TNF-α. Furthermore, neonatal cells are more sensitive to inhibitory effects of adenosine and dampens the production of Th1 polarizing cytokines in the newborn (52), resulting in vulnerability to many intracellular infections. Plasma swap experiments between cord blood and adult peripheral blood have indicated the existence of other soluble factors in neonatal plasma resulting in significantly increased expression of anti-inflammatory IL-10 and decreased expression of IFN-γ (53).  1.5.2 Cell intrinsic factors also have a large impact on the immune response of newborns  Cell intrinsic factors include molecular and cellular mechanisms within the host cell. Several groups have characterized cell intrinsic factors that lead to differences in the innate immune response compared to adults. Two main IRF pathways have been shown to be impaired in newborns. Danis et al (2008) have shown that IRF7 from newborn pDCs is not able to translocate to the nucleus; therefore, newborns are not capable of mounting a robust type I IFN response. Additionally, Aksoy et al (2007) have observed that IRF3 activity is also significantly lower in newborn monocyte-derived DCs. This decreased IRF3 activation is not due to the inability to translocate to the nucleus; rather, it is because they are unable to efficiently bind to CREB-binding protein (CBP) in order to remodel the chromatin in a Switch/sucrose 24  nonfermentable (SWI/SNF) dependent manner around their gene targets and initiate transcription (54,55). This in turn results in decreased expression of type I IFNs as well (28,55,56). Newborns have also been shown to have altered reactive oxygen species (ROS) activity (28), which may result in their inability to respond to and clear infection.   Furthermore, CAI is a cell intrinsic effector mechanism that protects cells from intracellular pathogens. For example, the expression of IFN-γ upon infection with Lm, Mycobacterium tuberculosis, or Toxoplasma gondii results in the expression of a number of GBP proteins in bystander splenocytes. Upon infection, they utilize GBP proteins to facilitate intracellular killing of the pathogens (4,49,57,58).   1.6 Hypothesis and specific objectives  To summarize, newborns and older adults are highly susceptible to severe infection. While we already know much about the molecular pathogenesis of many infectious diseases, we still have a poor understanding of the mechanistic basis for immunologic differences in newborns and older adults, and how altered/impaired innate immune responses at the extremes of age lead to increased susceptibility to infection. Several studies have begun investigating these differences at the molecular level; however, most have used mouse models of infection, cell lines, or expanded primary cell cultures to gain insight into molecular differences in newborns and older adults. Because most traditional molecular techniques require a large number of cells, it has previously been difficult to conduct studies using primary human cells.  25  The advent of more sensitive and accurate transcriptomic technologies has enabled us to capture all mRNA responses of the cell in its entirety. It is now possible to perform global, unbiased studies on primary cell populations in order to broadly interrogate the molecular differences in healthy versus susceptible populations. This increased sensitivity and accuracy enables us to investigate the responses of rare cell populations without the need for prior cell expansion in culture since in vitro culture, expansion, and differentiation changes the way cells respond to stimulation as compared to what occurs in vivo (59). We presume that this technology will more closely resemble what occurs in humans in response to stimulation or infection.   The expression of effector functions in response to stimulation by infection begins with the binding of transcription factors that are downstream of the receptor signaling cascades activated by a given pathogen. The binding of transcription factors to the promoters of their respective target genes leads to mRNA expression, the key second step in the response. Therefore, looking at mRNAs that are transcribed due to stimulation/infection allows us to investigate the global response to infection/stimulation during the first few minutes/hours, which most likely translates to differences in protein expression and therefore functional outcome.  1.6.1 Hypothesis  With this background, I hypothesize that the age-dependent differences in the transcriptomic signature of APCs from different age groups (i.e. newborns, healthy young adults, and older adults) correlates with impaired innate responses necessary to mount protective adaptive immune responses.  26   1.6.2 Specific objectives  Using transcriptomic technologies, the specific objectives of this thesis are as follows:  i. To determine the transcriptional profile of purified human primary circulating DCs from adults and newborns in response to stimulation with TLR7/8 ligand 3M-003. Both cDC and pDC are investigated, given their pivotal role in antigen presentation and bridging the innate and adaptive immune systems together. Because cDCs and pDCs are such rare cells in the blood, it was decided that a single, well-defined stimulus would provide us with cleaner signals in order to maximize the results of our transcriptomic approach on our rare cell populations of interest. This specific objective is investigated in Chapter 2 of this thesis.  ii. To determine the transcriptional profile of purified human primary monocytes from healthy young adults, newborns and older adults in response to Listeria monocytogenes infection. Because monocytes are relatively more abundant compared to DCs in the blood, it is now feasible to perform ex vivo infection in order to investigate the differences in the immune response to a model infection system (but still of real clinical pathogen concern) in an age-dependent manner, using two difference transcriptomic technologies – microarrays and RNASeq. These specific objectives are investigated in Chapter 3 and Chapter 4 of this thesis, respectively.  27  1.6.3 Importance and clinical implications  Morbidity and mortality from infections occur disproportionately among newborns and older adults. Thus, protecting these populations against infectious diseases is an important endeavour.   28  CHAPTER 2: AGE-RELATED DIFFERENCES IN THE MAPK AND IRF PATHWAYS DIFFERENTIALLY IMPACT RESPONSES OF HUMAN DENDRITIC CELLS FROM NEWBORNS AND HEALTHY ADULTS  2.1 Introduction  Neonates suffer more readily and more severely from infection than any other age group (18,29,30). This clinical phenotype likely has several reasons, amongst which suboptimal immune defense features prominently (60). For example, adaptive T and B cell responses in early life differ substantially from those in adult life (61). Nevertheless, adult-like adaptive immunity can already be induced in utero and around birth if activated effectively by antigen presenting cells (APC) (62). This suggests key functional differences between early life and adult life immunity must at least also reside in the APC compartment. There are a number of different APC, but for naïve T cells to become efficiently activated dendritic cells (DC) are essential (16). Given that the vast majority of T cells in the newborn are naïve, DC assume an even more prominent role in host defense against infection in early as compared to later life. DC are present at very low frequency (< 1% of white blood cells) in samples obtainable from humans. There are two main DC populations found in human peripheral blood: conventional DCs (cDC, also known as myeloid DC) and plasmacytoid DC (pDC) (16). cDC express modest amounts of Major Histocompatibility Complex 2 (MHCII) and costimulatory molecules at baseline but upon activation (e.g. via Toll-like receptors - TLRs) can rapidly upregulate MHCII; they are also highly phagocytic and produce a wide range of soluble mediators such as cytokines (16). For example, upon activation, cDC can produce high quantities of interleukin (IL)-12, and IL-18 29  which in turn can activate Natural Killer cells and T cells to produce interferon gamma (IFN-γ). In peripheral blood, pDC are even more rare than cDC, comprising only 0.3-0.5% of peripheral blood mononuclear cells (PBMC) (17). Just like cDC, pDC express modest amounts of MHCII (17). In addition, pDC express TLR7 and TLR9 and are known for their ability to produce large amounts of type I IFN upon stimulation that have profound impact on cell mediated immunity (17–19).  A number of age-dependent differences in the APC compartment have been described in the literature that have been proposed to underlie neonatal decreases in protective immune responses to infection, including differential chromatin remodelling and differential Interferon Regulatory Factor (IRF) activity in neonatal DC populations (19,28,54–56,63,64). However, given that both pDC and cDC are relatively rare cell populations in human blood, previous studies have largely used monocyte-derived DCs (mo-DC). Such mo-DC have been shown to differ substantially from naturally occurring cDC and pDC (16,59). Thus, clarity is required on the status of pDC and cDC in neonates in order to understand the innate APC-based immune responses that link to adaptive immunity, and with that possibly clinical outcome.   At the cellular level the immune response is a finely orchestrated sequence of events. It begins by sensing a pathogen, transducing that signal to the nucleus and finally eliciting a protective response, which ultimately clears infection and prevents reinfection. Given the complexity of the underlying biology of an immune response, assessing the global responses to stimulation in an unbiased manner via systems biology promises the most comprehensive insight (65). Specifically, transcriptomics collects data of all mRNA transcripts induced by stimulation and 30  has been shown to provide a useful starting point for a systems biological analysis of a complex biological system (65–68).  Technical innovations have reduced the quantity of input RNA (and thus cells) needed to conduct meaningful systems biological analysis (69–71). In parallel, improvement in standardized platforms that allow rapid purification of primary cells with only minimal perturbation have provided the vehicle to acquire samples of adequate purity (72,73). This enabled us to directly interrogate cell intrinsic differences at base line (ex vivo) and the cell response to stimulation (in vitro) of DC contrasting newborn to adult. To this end, we focused on determining the global transcriptional changes in purified human cDC and pDC derived from adult peripheral blood or neonatal cord blood in response to TLR stimulation. Specifically, we stimulated purified DCs with 3M-003, a TLR7/8 ligand, and investigated global transcriptional responses using microarrays.   Our data revealed significant age-dependent differences in IRF7-related regulatory events upstream of the mitogen-activated protein kinase (MAPK) signaling pathway with consequent alteration of the downstream extracellular signal-regulated kinase 1 and 2 (ERK1/2) activity resulting in altered effector cytokine expression. To our knowledge, this is the first report indicating such convergence of several age-dependent differences in molecular signaling cascades with functionally important changes in the host immune response.   31  2.2 Methods  2.2.1 cDC and pDC purification  Collection of blood samples was done in accordance to the protocol approved by the respective ethics boards of the University of Washington and the University of British Columbia.  Blood samples from umbilical cords were collected from healthy full-term babies delivered by Caesarian section, while peripheral blood samples were collected from healthy adult volunteers via venipuncture.  All blood samples were collected in heparinized tubes or syringes and were processed within two hours of collection.    To purify cDC for the Illumina microarrays and real-time PCR, we first isolated the mononuclear cells (MC) using the protocol outlined by Corbett et al. (2010).  Briefly, peripheral whole blood was diluted 1:1 with DPBS (Invitrogen). 30 mL of diluted blood was then layered onto 20 mL Ficoll-Paque (GE) for density centrifugation and separation at 900xg for 25min with the brakes off. The MCs were collected from the buffy coat layer, washed, and resuspended in ice-cold Ca2+- and Mg2+-free DPBS (Invitrogen) containing 0.5% human AB serum (Gemini-Bio) and 2 mM EDTA at a final density of 5 × 108 MC/ml.  Miltenyi’s protocol for using their CD1c (BDCA-1)+ Dendritic Cell Isolation Kit was followed. The eluted, purified cDC were then spun at 500×g, 5 min at room temperature, and the buffer aspirated.  The cells were resuspended in 475 µl of RPMI1640 with 10% human AB serum; 100 µl of this suspension was used to check the effectiveness of the purification via flow cytometry.  Between 0.15-1.6 × 106 cDC were 32  purified from each blood sample (60-100 ml starting blood volume) and at an average purity of 96.5% (range: 90.4-99.1%).    pDC were also isolated from MC using Miltenyi’s protocol using their Diamond Plasmacytoid Dendritic Cell Isolation Kit. The eluted, purified pDC were then spun at 500xg, 5 min at room temperature, and the buffer aspirated. The cells were resuspended in 475 µl RPMI 1640 with 10% human AB serum; 100 µl of this suspension was used to check the effectiveness of the purification by flow cytometry. Between 0.31-2.9 x 105 pDC were purified from each blood samples (with 60-100 ml starting blood volume) and at an average purity of 96.5% (90.4 to 99.1%). Purity was assessed using flow cytometry.  2.2.2 TLR stimulation of peripheral and cord blood mononuclear cells, conventional and plasmacytoid dendritic cells   Immediately after their purification, the cDC and pDC were plated on 96-well round-bottom plates containing either only culture media or media containing the TLR7/8 ligand, 3M-003 at a final concentration of 5 µM for various time periods. The cells were then harvested for subsequent RNA extraction.  MCs from adults and neonates were purified from whole blood and rested in culture medium for 1 hour. They were stimulated with 3M-003 at a final concentration of 5 µM for various time periods. 20 µM EDTA (Sigma) was added to the cells 15 min prior to harvest for subsequent analysis on flow cytometry and Amnis Imagestream analysis. 33    2.2.3 RNA extraction  After stimulation, cDC and pDC were lysed in Buffer RLT (Qiagen) and ran through a QiaShredder column (Qiagen) at 0 hr (unstimulated), 1 hr (in the presence of 3M-003), and 6 hrs (in the presence of 3M-003) for the microarray and qPCR experiments.  The Buffer RLT lysates were stored in -80°C prior to doing total RNA extraction using the protocol as outlined in Qiagen’s RNAeasy Mini Kit.  2.2.4 Microarray and real-time PCR  The mRNA in the purified total RNA was reversed transcribed to cDNA using T7-oligo(dT) primer and then T7 polymerase-amplified to biotin-labeled cRNA using the Illumina TotalPrep RNA Amplification Kit from Ambion.  The cRNA were quantified using a fluorescent RNA-binding dye and the fluorescence read by Invitrogen’s Qubit fluorometer.  This was followed with a quality check by running the cRNA in an Agilent 2100 Bioanalyzer.  cRNA was then used for hybridization on Illumina’s Sentrix Human-6 Whole-Genome Expression BeadChips (version 2).  The Bioanalyzer and the Illumina microarray hybridizations were performed by the core facility at the Center for Expression Assays at the University of Washington using the respective manufacturer’s standard protocol.  All microarray data have been submitted to GEO under the accession number GSE67057. Total RNA from a separate set of subjects was used to validate the gene expression gleaned from the microarray.  For these samples, cDNA were made 34  using random hexamers (Invitrogen) and the reverse transcriptase Superscript II (Invitrogen).  The cDNA were then used in a real-time PCR assay using SYBR Green PCR Master Mix (Bio-Rad) and an ABI 7300 Real-Time PCR System (Applied Biosystems).  2.2.5 Microarray processing and bioinformatics analysis  Non-normalized intensities values from the Illumina arrays were exported from GenomeStudio (Illumina) as a text file. All subsequent array analysis was performed using Bioconductor in R. All array probe intensities were normalized against the background. MDS and cluster analysis was performed to determine the similarity of the samples with respect to each other. Heatmaps were generated to further visualize the differential expression of genes in each of the different clusters and conditions. The LIMMA R package was used to determine genes that were differentially expressed in each indicated comparison. Genes that had 1.5 fold change difference in expression and a p value < 0.05 were considered differentially expressed after adjusting for multiple comparisons using the Benjamini-Hochberg correction. Lists of differentially expressed genes were then entered into the open source, web-based enrichment analysis tool WebGestalt (Zhang, Kirov, and Snoddy 2005). Pathway overrepresentation analysis was performed for comparisons of interest.    35  2.2.6 Flow cytometry analysis on purified dendritic cell subsets and peripherial and cord blood mononuclear cells  Samples used for checking the purity of cDC and pDC were spun down at 500xg, 5 min at room temperature then resuspended 100 µl of 1X BD FACS Lyse and frozen in -80C prior to performing flow cytometry. Samples were then thawed, spun, and resuspended in 200 ul PBS containing 0.5% BSA and 0.1% sodium azide (PBSAN). Cells were stained in a final volume of 100 µl in PBSAN for 45 min at room temperature containing the following cell surface markers: HLA-DR, CD14, CD11c, and CD123 (see Table A.4 for dilutions, clones, and fluorophores used). After two additional washes with PBSAN, cells were resuspended in PBS containing 1% paraformaldehyde and analyzed on LSRII Flow Cytometer (BD Biosciences) set up according to published guidelines (31). Compensation beads were used to standardize voltage settings and also used as single-stain positive and negative controls for each fluorophore used as described previously (31). Compensation was set in FlowJo (Tree Star) and samples were analyzed compensated.   PBMCs and CBMCs that were stimulated with 3M-003 were analyzed for expression of phosphorylated targets. Staining was performed according to Nolan et al.’s protocol except for a few changes (74). Briefly, harvested cells were fixed in formaldehyde (EM-grade, Fischer Scientific) at a final concentration of 1.5% for 10 min. Cells were washed and then permeabilized using ice cold methanol (HPLC-grade, Fischer Scientific) and stored in -80C prior to staining for flow cytometry. Cells were stained in a final volume of 100 µl in PBSAN for 30 min at room temperature containing the following antibodies: HLA-DR, CD14, CD11c, CD123, 36  MAP3K8, and pERK1/2 (see Table S4). Cells were washed and then stained with anti-rabbit goat secondary antibody conjugated to FITC for 15 min at room temperature. After two additional washes with PBSAN, cells were resuspended in PBSAN and analyzed on LSRII Flow Cytometer (BD Biosciences) set up according to published guidelines (31). Compensation was set in FlowJo (Tree Star) and samples were analyzed compensated.  Gating strategies used for all flow cytometry experiments are shown in Figure A.4.  2.2.7 Assessment of nuclear translocation of pIRF7 in conventional and plasmacytoid dendritic cells  MCs that were stimulated with 3M-003 were analyzed on the Amnis ImageStream to determine the level of nuclear translocation of pIRF7 upon stimulation. Cells were fixed with formaldehyde (EM-grade, Fischer Scientific) at a final concentration of 1.5% for 10 min at room temperature. Cells were washed and resuspended in 200 µl Amnis Wash Buffer and stored in 4C overnight. MCs were stained in a final volume of 100 µl in Amnis Wash Buffer for 10 min at room temperature containing the following surface markers: HLA-DR, CD11c, CD123 (see Table A.5 for dilutions, clones and fluorophores used). Cells were washed and resuspended in 100 µl Amnis PermWash Buffer containing pIRF7 antibody and stained for 20 min at room temperature. Afterwards, cells were washed and resuspended in 100 µl of Amnis Wash Buffer containing DAPI and analyzed on the ImageStream (Millipore). Compensation was set in IDEAS (Millipore) and samples were analyzed compensated. The number of cDC and pDC undergoing IRF7 nuclear translocation was determined using the Nuclear Translocation Wizard in IDEAS. 37  2.3 Results  2.3.1 Key basal differences in gene expression between adult and newborn dendritic cells influence the transcriptional response to TLR7/8 stimulation.  To assess mRNA production in cDC and pDC from newborns and adults in response 3M-003 stimulation, we performed hierarchical clustering and multidimensional scaling (MDS) plots to determine the global responses of each of the age groups at 1 and 6 hrs post-stimulation. As shown in Figure A.1, hierarchical clustering analysis on both cDC and pDC from adults and neonates revealed very similar responses at both time points, with or without stimulation. However, our MDS plots revealed that there were smaller secondary clusters segregating adult from newborn samples in each cluster, representing the different time points and conditions used in both cDC and pDC (Figure 2.1). Therefore, while the majority of transcriptomic responses to 3M-003 stimulation appeared similar across age groups, nuanced differences between adults and neonates were clearly detectable.  We next explored the age-dependent differences in gene expression at each of the time points. We first sought to determine which genes were differentially expressed in cDC and pDC upon stimulation with 3M-003 separately for each age group. As shown in Table A.1, 3M-003 induced the expression of more genes in cDC compared to pDC and in both adult and newborns. We then determined whether any of the differentially expressed genes exhibited age-dependent induction or expression, i.e. contrasted the age-groups. Table A.2 summarizes the number of differentially expressed genes that are also expressed in an age-dependent manner. Of note, the difference in 38  expression between the age groups remained statistically significant even after applying the Benjamini-Hochberg correction to correct for multiple comparisons.   Figure 2.1. Adult and neonatal pDC responded more similarly with each other than do cDC upon stimulation with 3M-003. Multi-dimensional scaling plots were generated from the probe intensities of the arrays of 3M-003-stimulated adult and neonatal cDC (A.) and pDC (B.). Purified DC subsets were isolated from six adult and six newborn samples and stimulated with 3M-003 at a final concentration of 5 µM for 1 and 6 hrs. RNA was collected for whole genome transcriptional profiling using microarrays. A0U=Adult 0h unstimulated control, A1S=Adult 1 hr 3M-003-stimulated, A6S=Adult 6 hrs 3M-003-stimulated, N0U=Neonate 0 hr unstimulated control, N1S=Neonate 1 hr 3M-003-stimulated, N6S=Neonate 6 hrs 3M-003-stimulated. Refer to A.B.39  Figure A.1 for heatmaps. See Tables A.1 and A.2 for the number of differentially expressed genes corresponding to the smaller age-dependent clusters.  2.3.2 MAP3K8 ranks high in the list of age-dependent, differentially expressed genes in both unstimulated and stimulated cDC.  MAP3K8 ranked within the top 100 genes that were differentially expressed at baseline (Table A.3) and was also within the top 10 genes that were differentially expressed between the two age groups upon stimulation with 3M-003. This was true both at 1 and 6 hrs (Table 2.1), where the log fold change difference between adults and newborns was 1.9 and 2.09, respectively. Other genes in the MAPK signaling pathway were also differentially expressed in both unstimulated controls and 3M-003 stimulated samples at both time points (data not shown). Pathway overrepresentation analysis (ORA) confirmed that the MAPK signaling pathway contained several age and stimulation-dependent differentially expressed genes (Table 2.2).    40  Table 2.1. Top 10 differentially expressed genes in cDCs that were significantly different between adults and neonates in response to 3M-003 stimulation after 1 and 6 hrs. n = 6 adults, 6 neonates. Time point (hr) Gene Name  Cord log(Fold Change) Adult log(Fold Change) Adjusted P value RefSeq ID 1 SERPINB2 5.900732619 3.154611234 1.30E-07 NM_002575.1 1 TRIB1 - 1.601530699 3.05E-06 NM_025195.2 1 DUSP4 3.9792323 7.11437711 4.61E-06 NM_057158.2 1 SYN2 1.907886316 - 6.25E-06 NM_133625.2 1 MAP3K8 1.700428824 3.687616301 6.25E-06 NM_005204.2 1 EMP1 3.559317435 1.404639856 4.57E-05 NM_001423.1 1 DUSP2 2.017858808 4.021470887 0.001102498 NM_004418.2 1 IL1B 1.890578031 3.422809311 0.001844597 NM_000576.2 1 C9ORF47 1.271514531 - 0.001844597 NM_001001938.1 1 G0S2 4.740562546 6.917242458 0.001844597 NM_015714.2 6 MAP3K4 2.195416039 -0.789029271 3.77E-09 NM_006724.2 6 MAP3K8 1.585182013 3.680155591 4.62E-06 NM_005204.2 6 DUSP4 3.952192024 7.062270914 5.55E-06 NM_057158.2 6 SBSN 2.847210677 0.849171591 6.21E-06 NM_198538.1 6 F2RL2 0.735015111 2.698762938 8.92E-06 NM_004101.2 6 TRIB1 -0.867952298 0.684669906 1.29E-05 NM_025195.2 6 GPR126 -1.040915791 -2.862914998 1.46E-05 NM_020455.4 6 GPR18 -1.422245072 -4.041827299 2.92E-05 NM_005292.2 6 SH3KBP1 0.336348827 1.775141368 3.30E-05 NM_001024666.1 6 DUSP2 1.94530831 4.248718015 4.98E-05 NM_004418.2  41  Table 2.2. Pathways overrepresented in an age-dependent manner in adult and neonatal conventional dendritic cells in response to 3M-003 after 1 and 6 hrs of stimulation. n = 6 adults, 6 neonates (cord) (*p < 0.05, **p < 0.01).  1 hr 6 hr Wikipathway Name Adjusted P value Ratio of Enrichment Adjusted P value Ratio of Enrichment Hypertrophy Model 0.0119* 16.3 0.0388* 4.69 Myometrial Relaxation and Contraction Pathways 0.0119* 4.79 0.0342* 2.24 MAPK Signaling Pathway 0.0119* 4.55 0.0342* 2.13 Adipogenesis 0.0477* 4.3 0.0054** 2.82 TGF beta Signaling Pathway 0.07 3.59 0.0342* 2.97  Confirmation by qPCR of the differential gene expression of several genes belonging to the MAPK signaling pathway in our array results was performed on six additional adult and newborn samples. We observed significantly higher induction of MAP3K8 mRNA in adult cDC at 1 and 6 hrs post-stimulation (Figure 2.2). MAP3K4 (another key MAP kinase) was also induced in both age groups in pDC but showed significantly higher levels of gene expression in adult cDC (Figure 2.2). In adult pDC, we observed significantly increased MAP3K8 mRNA expression 6 hrs post-stimulation and significant upregulation of IRF4 mRNA was observed in adults after 1 and 6 hrs of stimulation (Figure 2.2). Thus, targeted qPCR analysis confirmed and expanded our initial array-based findings of age-dependent differences in MAPK signaling pathway.  42   Figure 2.2. Quantitative PCR of purified cDC and pDC from adults and neonates (cord) also showed that genes involved in the MAPK signaling pathway are differentially expressed in an age-dependent manner upon stimulation with 3M-003.  cDC and pDC from each age group were stimulated with 3M-003 at a final concentration of 5 µM for 1 and 6 hrs. mRNA expression of MAP3K8, MAP3K4, and IRF4 were determined using qPCR. mRNA expression for each gene was normalized against β-actin transcripts. Data are presented as relative expression in comparison with the unstimulated controls at 0 hr for each age group. Shown are the average relative expression values for each independent individual ± SEM; unpaired Student’s t-test was performed, *p < 0.05, ***p < 0.001. Data is representative of 3 independent experiments with 4-6 individuals for each age group.  2.3.3 Differential nuclear translocation of phosphorylated IRF7 occurs in adult cDC and pDC upon stimulation with 3M-003.  We sought to determine the possible molecular cause(s) of differential MAP3K8 mRNA expression by interrogating relevant upstream regulatory proteins. Previous reports have linked IRF7 activity to MAP3K8 expression; however, studies regarding this interaction have been conducted as overexpression studies in human cell lines, not primary cells (75). We first assessed 0hr 1hr 6hr051015IRF43M-003**RelativemRNAExpression0hr 1hr 6hr0510152025MAP3K83M-003*CordAdult0hr 1hr 6hr012345MAP3K43M-0030hr 1hr 6hr0.00.51.01.52.02.5MAP3K4*3M-0030hr 1hr 6hr01234IRF43M-0030hr 1hr 6hr010203040MAP3K8****3M-003cDCpDC43  IRF7 mRNA expression in the two age groups but found that IRF7 mRNA levels in both adult and newborn cDC and pDC were similar (Figure A.2); this has previously been reported by Danis et al. (2008). IRF7 has to be phosphorylated (pIRF7) in order to translocate to the nucleus and exert regulatory functions (76–78). To determine total pIRF7 expression as well as nuclear translocation we made use of the Amnis Image Stream system that allows high throughput analysis of hundreds of cells/minute with single-cell resolution, yet permitting resolution regarding subcellular (nuclear vs. cytoplasmic) localization (19).   We stimulated adult and newborn mononuclear cells (PBMC and CBMC respectively) with 3M-003 and analyzed the ratio of translocated pIRF7 in cDC and pDC at 15 min, 2 hrs, 4 hrs and 6 hrs post-stimulation. Newborns had significantly less pIRF7 expressing cDC both at baseline and upon 3M-003 stimulation at 15 min, 2 hrs, and 4 hrs (Figure 2.3A).  Newborns also had fewer pIRF7 expressing pDC, with significant differences observed at 15 min and 4 hrs of 3M-003 stimulation (Figure 2.3A). Although the median fluorescence intensity (MFI) of pIRF7 was expected to decrease in both age groups over time of stimulation (79), the MFI of pIRF7 all neonatal DC always remained significantly lower compared to adults, both at baseline (Figure A.3) and upon 3M-003 stimulation (Fig. 2.3B). Functionally even more important, we determined that there were more nuclear translocation events in adult cDC and pDC compared to newborns upon stimulation with 3M-003 (Figure 2.3C). Specifically, only stimulated adult cDC showed fold increases in any nuclear translocation events based on unstimulated baseline. Finally, the mean fold change scores for pDC and cDC from neonatal cord samples did not surpass 1, suggesting a lack of response in nuclear translocation events upon stimulation with 3M-003 in neonatal samples (Figure 2.3C). 44   AdultCordcDCMFIpDC0.25 2 4 60200040006000800010000* **Time (hr)B.AdultCord602468pDC0.25 2 4*Time (hr)C.cDC0.25 2 4 60.00.51.01.52.0Time (hr)FoldChange%NuclearTranslocationCordAdultA.%pIRF7+cDC-- 3M-003 -- 3M-003 -- 3M-003 -- 3M-003020406080100 * * ** ******* *15 min 2 hr 4 hr 6 hrpDC%pIRF7+-- 3M-003 -- 3M-003 -- 3M-003 -- 3M-00302040608010015 min 2 hr 4 hr 6 hr**0.25 2 4 602000400060008000100001200014000*******Time (hr)45  Figure 2.3. Neonatal (cord) cDC and pDC have significantly decreased levels of phosphorylated IRF7 protein than adults upon stimulation with 3M-003. PBMCs or CBMCs from 5 individuals per age group were stimulated with 3M-003 at a final concentration of 5 µM for the length of time indicated. Cells were harvested and stained for the presence of phosphorylated IRF7 (pIRF7) in cDC and pDC subsets. (A.) Neonatal cDC have significantly less pIRF7 than adult cDC both at basal levels and upon stimulation with 3M-003 at all time points investigated. Similarly, neonatal pDC have less pIRF7 levels than adult pDC. Data are presented as relative protein expression of pIRF7 in comparison with the entire cDC/pDC of interest for each individual. Shown are the average percentage of pIRF7 positive cDC and pDC for each independent donor ± SEM. (B.) Neonatal cDC and pDC also had significantly lower pIRF7 median fluorescence intensity (MFI) than adult cells at most time points investigated in response to 3M-003 stimulation. Represented is the average of all MFI for each age group ± SEM. (C.) Neonatal cDC did not exhibit any pIRF7 nuclear translocation events compared to adults at any time points investigated. Additionally, adult pDC had significantly more nuclear translocation events than neonatal cells after 15 min of stimulation. Data are presented as the fold change ratio between the number of nuclear translocation events counted in stimulated vs unstimulated samples for each cell type and each time point. Shown are the mean % fold change of nuclear translocation events for all individuals in each age group ± SEM. All data were analyzed by two-way ANOVA followed by Bonferroni posttest; *p < 0.05, **p < 0.01, ***p < 0.001. See Figure A.2 for qPCR results confirming similar IRF7 mRNA levels in both age groups and Figure A.3 for data showing significantly lower pIRF7 protein levels in newborn DCs at baseline.  2.3.4 ERK1/2, a downstream target of MAP3K8, was more highly phosphorylated in adult than neonatal dendritic cells, affecting the transcription of its downstream targets.  MAP3K8 expression depends on pIRF7 activity (75). Moreover, MAP3K8  has been shown to activate ERK1/2 and consequently its downstream targets (80–85). We thus resorted to a flow cytometric approach in order to determine differential activation of ERK1/2 in 3M-003 stimulated samples.   At baseline, adults displayed relatively more phosphorylated ERK1/2 (pERK1/2) positive cDC than newborns (Figure 2.4A).  In response to 3M-003 stimulation, both adults and newborns had 46  decreased numbers of pERK1/2 positive cDC. PBMC and CBMC also had similar proportions of pERK1/2 positive pDC at baseline (Figure 2.4A). We next analyzed the fold change of pERK1/2 levels (by MFI) in each cell type per age group by determining the ratio of pERK1/2 in stimulated vs unstimulated samples. We found fold change upregulation in the case of adult pDC whereas there was a significant decrease in fold change of pERK1/2 in newborn pDC (Figure 2.4B). In fact, upon 3M-003 stimulation, the number of pERK1/2 positive pDC increased in adults but decreased to almost zero in neonatal pDC (Figure 2.4A). While this was significant when analyzed using Kruskal-Wallis test, this difference was no longer statistically significant following posttest analysis.    47   Figure 2.4. pERK1/2 levels were significantly lower in neonatal pDC than adult pDC upon stimulation with 3M-003. PBMCs and CBMCs from 5 individuals from each age group were stimulated with 3M-003 at a final concentration of 5 µM for 8 hrs. Cells were harvested and stained for the presence of phosphorylated ERK1/2 (pERK1/2) in cDC and pDC subsets. (A.) There were differences in the number of pERK1/2+ cDC between adults and newborns at baseline and upon stimulation. Adults and newborns have similar numbers of pERK1/2+ pDC at baseline. Upon stimulation, adults showed an increased proportion of pERK1/2+ pDC whereas newborns exhibited a lower proportion of pERK1/2+ pDC upon stimulation with 3M-003. The results of the Kruskal-Wallis test showed that there was a significant difference between each of the conditions; however, the results of the Dunn’s post-test showed that the differences were not significant. Data are presented as the average % positive of the parent cell subset population for each independent donor ± SEM. (B.) pDC but not cDC from neonates have lower induction of pERK1/2 upon stimulation with 3M-003 for 8 hrs. Data are presented as the average fold change in MFI between stimulated vs unstimulated samples for each independent donor used ± SEM. All data were analyzed by Kruskal-Wallis test followed by Dunn’s posttest; *p < 0.05.  Adult Neonate0.00.51.01.52.02.5 *FoldchangeMFIAdult Neonate0.50.60.70.80.91.01.1FoldchangeMFIcDC pDCUnstimulated 3M003 Unstimulated 3M003020406080Adult Neonate%PositiveofcDCUnstimulated 3M003 Unstimulated 3M00305101520Adult Neonate%PositiveofpDCA.B.48  Given these significant age-dependent differences in pERK1/2, we performed qPCR on several downstream targets of pERK1/2 to assess the functional impact of our findings. We found significantly higher IL1B, EGR1, PTGS2 and TNF mRNA expression in adult cDC compared to newborns at 1 and 6 hrs post-stimulation (Figure 2.5A). For pDC, only PTGS2 was significantly increased in adult than neonatal pDC (Figure 2.5B). This suggests that the upstream molecular signalling events we observed in IRF7, MAP3K8 and pERK1/2 activity indeed had impact on discrete downstream effector functions.   Figure 2.5. Downstream target genes of ERK1/2 MAPK signaling were differentially expressed in an age-dependent manner in cDC and pDC. Neonatal (cord) and adult cDC and pDC were isolated from CBMC and PBMC respectively and stimulated with 3M-003 at a final concentration of 5 µM for 1 and 6 hrs. (A.) mRNA expression of IL1B, EGR1, TNF, and PTGS2 were then determined using qPCR. mRNA expression for each gene was normalized to β-actin transcripts. (B.) The expression levels of the same gene targets was also determined in purified pDC isolated from adult and cord donors upon stimulation with 5 µM of 3M-003 for 1 and 6 hrs. (A. and B.) Data are presented as relative expression in comparison with the unstimulated controls at 0 hr for each age group. Shown are the average relative expression values for each independent individual ± SEM; unpaired Student’s t-test was performed, *p < 0.05, **p < 0.01, ***p < 0.001. Data is representative of 3 independent experiments with 4-6 individuals for each age group.  0hr 1hr 6hr0100200300400IL1B****3M-0030hr 1hr 6hr020406080PTGS2****3M-003RelativemRNAExpression0hr 1hr 6hr0204060EGR1**3M-0030hr 1hr 6hr0200040006000TNF**3M-003AdultCord0hr 1hr 6hr050001000015000IL1B3M-0030hr 1hr 6hr05001000150020002500EGR13M-0030hr 1hr 6hr0200400600800PTGS2**3M-003RelativemRNAExpressionAdultCord0hr 1hr 6hr010002000300040005000TNF3M-003A. cDC B. pDC49  2.4 Discussion  To delineate age-dependent differences in the response of dendritic cells, our comprehensive systems biological approach revealed that the main pathway overrepresented in an age-dependent manner was MAPK signaling pathway. Single-cell analysis provided evidence correlating this age-dependent differential activity of the MAPK signaling pathway with differential upstream IRF7 activity following stimulation of TLR7/8. Furthermore, activation and phosphorylation of ERK1/2, a downstream target of MAP3K8, led to differential mRNA expression of ERK1/2 targets upon TLR7/8 stimulation in adult but not neonatal samples. The data we present here begins to outline a convergence of several age-dependent differences in interconnected signalling cascades in newborn pDC and cDC.  Most literature that examined age-dependent differences in innate responses have used in vitro-derived mo-DC; given the many functional differences between mo-DC and primary DC, analysis of primary DC is needed (54–56,63,64,86). The recent advances in microarray technology and cell purification allowed us to interrogate these rare but important primary cell types with as few as 10,000 cells providing 50 ng of RNA for each experimental condition (69). While we initiated our age-contrasting analysis of DC responses to TLR7/8 ligand 3M-003 using global transcriptomic analysis, we verified key findings on the single cell level using state-of-the-art phospho-specific flow cytometry (phosphoflow) as well as subcellular imaging via the Amnis Image Stream.  50  In response to 3M-003 stimulation, adult and newborn purified DC displayed a largely similar induction of mRNA. However, upon closer inspection, key differences in cell-type and age-specific, stimulation-dependent responses emerged (Figure 2.1). MAP3K8 was the key gene differentially expressed in an age-dependent manner in both unstimulated as well as TLR7/8 stimulated DCs. MAP3K8 plays an important role in the immune response against many intracellular pathogens, including viruses, M. tuberculosis, and L. monocytogenes (81,82) and is thus possibly of significant biological relevance for the increased risk of severe infection in early life. MAP3K8 expression can be regulated by IRF7 (75), however, there are opposing views of whether it acts as a positive or negative regulator of type I IFN signaling (75,81,82,85). For example, MAP3K8 activity has been shown to play a role in promoting formation of IRF3/7 heterodimers (75), which is important in the robust production of IFN-α  (87). Yet other studies show that MAP3K8 expression downregulates the expression of IFN-β and other IRF3-dependent genes (75). Downstream, MAP3K8 phosporylates ERK1/2 via phosphorylation and activation of MEK1/2 (80–84,88), which is an important activator of immune responses to TLR stimulation (81,83,84,88,89). The ERK1/2 pathway has only been elucidated in adult mouse macrophages, where it has been shown to negatively regulate type I interferon responses to intracellular pathogenic infection (81,85). However, there are no such studies in human primary cell models in general, or in neonatal cells in particular.   We will first focus our discussion on the results with cDC. We found MAP3K8 expression to be significantly higher in adult than newborn cDC, both at baseline (unstimulated) as well as in response to stimulation of TLR7/8. This was further confirmed by pathway ORA indicating that multiple genes in the MAPK signaling pathway were differentially regulated in an age-dependent 51  manner. Upon examination of the known positive and negative regulatory components of this pathway we found several dual specificity phosphatase (DUSP) members (DUSP2, 4, and 5) to be more highly expressed in neonatal cDC array samples than in adult (data not shown). DUSP proteins negatively regulate the c-Jun N terminal kinases (JNK) MAPK pathway (and subsequently shut down ERK signaling) and ERK1/2 MAPK pathway (89). Thus we expected there to be less active pERK1/2 in neonatal cells at baseline; this was precisely what we found in our phosphoflow analysis (Figure 2.4B). Because adult cDC already displayed higher baseline levels of MAP3K8 and MAP3K4 (which activate pERK1/2 and JNK downstream pathways respectively), we also expected to find higher induction of downstream gene targets only in adults. This was confirmed (Figure 2.5) at the mRNA level for IL1B, TNF, EGR1, PTGS2.  In our effort to elucidate why adult cDC had higher basal levels of MAP3K8 we discovered differential nuclear translocation of pIRF7 in adult cDC than in neonatal samples. IRF7 nuclear translocation has been previously described in adult pDC (19), but this is the first report of pIRF7 nuclear translocation in cDC (Figure 2.3C). As expected, we observed lower numbers of pERK1/2 positive cDC in both adults and newborns upon 3M-003 stimulation even though our PMA stimulated controls showed increased levels of pERK1/2 (data not shown) (79). We also observed that MFI values remained similar between adult and newborn samples upon 3M-003 stimulation, indicating that pERK1/2 levels did not change dramatically per cell but the overall differences were instead due to the proportion of cells responding to stimuli (Figure 2.4). We speculate that this was due to the negative feedback loop of the MAPK signaling pathway that regulates gene expression after stimulation. Alternatively, because we investigated pERK1/2 levels at a later time point, we may not have used the optimal time point to observe the maximal difference in pERK1/2 activation between the two age groups in addition to a difference in 52  technique used by other groups, where western blots have been traditionally used to probe for pERK1/2 levels as opposed to phosphoflow that was used in this study.  Adult pDC also produced significantly more MAP3K8 transcripts than newborn pDC (Figure 2.2). We postulate that this age-dependent difference of MAP3K8 expression was not initially picked up by the expression array but only by qPCR due to the fact that the amount of RNA available from each of the pDC donors was extremely small, suggesting that although current platforms enable using smaller samples that this comes at a cost of sensitivity. This argues that platforms such as RNA-Seq may offer a more feasible approach (90). Once again, the DUSP negative regulators of the MAPK pathway were more highly expressed in newborn pDC than in adults in our array results. In particular, DUSP2 expression was higher at baseline and DUSP10 was higher in newborn than adult pDC after 1 hr of stimulation with 3M-003 (data not shown). Both of these DUSP genes inhibit JNK pathway and subsequently shut down the ERK1/2 pathway (89,91), which also manifests as lower ERK1/2 levels than we observed in our pDC phosphoflow data (Figure 2.4). Furthermore, the increased and unimpeded expression of MAP3K8 in adult pDC was also likely the result of the age-dependent differential nuclear translocation and activation of pIRF7 in adult pDC (Figure 2.3C). To further validate the age-dependent difference in this axis, we looked at the mRNA induction of the same downstream targets of ERK1/2 as we did for cDC, but found that only PTGS2 displayed significantly higher expression in adult pDC (Figure 2.5B). These four downstream targets of pERK1/2 signaling exhibit dose-dependent transcription based on the level of pERK1/2 activation (88). For example, the expression of IL1B and EGR1 genes require high levels of pERK1/2 activity to initiate transcription whereas PTGS2 and TNF do not require as high levels of pERK1/2 53  activation (88). Therefore, our results revealing differential expression of only PTGS2 in adult pDCs likely stems from the fact that we may not have induced sufficiently strong activation of ERK1/2 (88). Interestingly, IRF4 mRNA was also expressed significantly higher in 3M-003 stimulated adult pDC than newborn counterparts. IRF4 has been shown to lower type I IFN responses (92). This may also have major functional and regulatory implications; we thus plan to investigate the role of IRF4 in more detail.  While other groups have investigated the upstream regulators of MAP3K8 expression, these studies were conducted solely in cell lines; to date, we have not found any evidence of research into MAP3K8 regulation in primary cells. Given the central role of IRF7 in regulating MAP3K8, we chose to focus our analysis on this pathway first. Our data suggested similar levels of IRF7 mRNA levels in both of the two DC subsets at baseline and upon stimulation (Figure A.2); this confirms the previous finding of similar total IRF7 protein levels (19). However, our data suggest that adults have a higher fraction of pIRF7 positive cDC and pDC than neonates (Figure 2.3A and 2.3B), and that this relates functionally to higher nuclear translocation in both cDC and pDC (Figure 2.3C). This has previously been identified for pDC only (Danis et al. (2008)). Moreover, we observed translocation of pIRF7 only into the nucleus of adult, not neonatal pDC or cDC. This suggests not only a quantitative but possibly a qualitative (on/off) age-dependent difference in pIRF7 translocation in both DC subsets, with important implications for the upstream regulatory molecular events.   Our study has several limitations. We used 3M-003 as a synthetic ligand representing single-stranded RNA, which can represent a number of single stranded viruses that many newborns are 54  highly susceptible to, such as respiratory syncytial virus (93). Therefore, a logical future direction will be to determine whether we see similar responses in neonatal cDC and pDC upon viral infection in order to fully validate this age-dependent mechanism of TLR7/8 responses. Furthermore, the mechanisms proposed here may not necessarily be generalizable for other TLR ligands because each one will activate similar but not identical pathways in the same cell type (5,8). As well, the results of some of the flow cytometry based assays here only showed an age-dependent trend that didn’t reach significance; this is most likely due to the small sample sizes for each of the experiments. We will need to increase the number of donors for these assays in order to definitively determine whether the mechanism proposed here does indeed hold true despite the large biological variation. Additionally, we only examined the phosphorylated state of ERK1/2 at later time points, which likely has a large impact on the age-dependent differences that we observed. We plan on investigating the pERK1/2 levels of these two age groups at earlier time points (i.e. within the first hour of stimulation) in order to determine the full impact of the age-dependent nuclear translocation of pIRF7. Most importantly, our results do not provide evidence for causal relationships; rather, we present compelling correlational data. With the limited DC cell numbers that can be purified from human samples, it is not viable for us to perform knockdown or overexpression studies of these important modulators of immune responses. In order to provide causative evidence of the role for any of the key findings in this paper, we will have to await in vivo validation in humans by the discovery of disease-causing mutations in the genes of interest.  In summary, we provide evidence that implicates altered IRF activity in newborn cDC and pDC subsets as centrally involved in many of the known age-dependent differences of immunity. This 55  insight grew out of our detailed molecular dissection correlating age-dependent differences in MAPK and IRF signaling pathway activity. Specifically, we provide evidence that implicates differential pIRF7 nuclear translocation to be correlated with MAP3K8 levels both at baseline and upon stimulation, which then led to altered pERK1/2 MAPK activation and further downstream effector cytokine expression. To our knowledge, this is the first report to do so in human primary cells. Our data suggest that a more detailed dissection of the regulatory circuits surrounding the molecular mechanisms occurring upstream of IRF7 phosphorylation will provide the necessary insight to identify the source driving age-dependent differences in immunity.    56  CHAPTER 3: INFECTION OF HUMAN PRIMARY MONOCYTES WITH Listeria monocytogenes INDUCES AGE-DEPENDENT DIFFERENTIAL EXPRESSION OF GUANYLATE BINDING PROTEINS  3.1 Introduction  Newborns are known to mount poorly protective host immune responses to several pathogens (18,28–30,60). Adults aged 65 years and older also display waning protective immune responses, with increasing susceptibility to infectious diseases (28,34,38,40,94). The Gram positive bacterium Lm causes a largely subclinical infection for most healthy young adults that ingest contaminated food; however newborns up to two months of age and older adults aged 65 and over can suffer severely and may even succumb to the infection (39,95). This age-dependent susceptibility to listeriosis likely relates to suboptimal host-mediated protection.   Monocytes play a key role in the immune response to Lm infection. In particular, monocytes are important for the early containment of infection (27,96,97). They are also very important for the final clearance of infection after CD8+ cytotoxic T cells attack and kill Lm-infected cells, releasing viable bacteria that need to be phagocytosed (14). Some of the key cytokines that are produced upon host cell Lm infection are the interferons (IFNs) (14,96–99). In the mouse, induction of type I IFNs has been shown to be detrimental for the host, whereas the expression of type II IFN appears important in facilitating robust cytotoxic T cell responses that clear Lm infection (14,26). However, extrapolating from murine data to mechanisms clinically relevant in humans is potentially misleading, as the host responses to intracellular infection differ 57  substantially between these species (99–102).  Furthermore, there has been no study to date contrasting the response across the susceptible groups in an age-dependent manner, following the underlying rationale that the clinically observed increased susceptibility at the two extreme ends of the age spectrum may relate to the same molecular mechanism/s. To this end, we infected primary monocytes isolated from human newborns, healthy young adults, and older adults with wild-type Lm. We observed differential mRNA upregulation of a subfamily of effector proteins, the guanylate binding proteins (GBPs), which are IFN-inducible GTPases that are important in cell autonomous immunity and facilitate protection against intracellular pathogens such as Lm. Upon validation using qPCR, only young adults differentially upregulated GBPs upon infection with Lm; neither of the two susceptible age groups showed any differential expression of this subfamily upon infection. We further determined that IFNs, namely production of IFN-β, were likely to be responsible for this age-dependent difference in upregulation of GBPs, in that only young adults upregulated IFN-β mRNA upon Lm infection. Together, our data further highlight the importance of the IFN response axis in Lm infection, and for the first time identify key proteins involved in age-dependent differences in cell autonomous immunity.   3.2 Methods  3.2.1 Bacterial strains, medium, and growth conditions  Wild-type (WT) Lm strain 10403s was provided by Dr. D. Portnoy (University of California, Berkeley, CA) and cultured as previously described (103).  In brief, Lm were grown to mid-log 58  phase (OD600, 1.0) at 37°C, washed twice with endotoxin-free, isotonic saline (0.9% NaCl), resuspended in 20% (v/v) glycerol in 0.9% sterile saline, and stored at -80°C until use.  3.2.2 Monocyte purification  Collection of blood samples was done in accordance to the protocol approved by the respective ethics board at the University of British Columbia. Blood samples from umbilical cords were collected from healthy full-term babies delivered by Caesarian section, while peripheral blood samples were collected from healthy adult volunteers via venipuncture.  All blood samples were collected in heparinized tubes or syringes and were processed within two hours of collection.    To purify monocytes for the Illumina microarrays and real-time PCR, we first isolated the mononuclear cells (MC) using the protocol outlined in (31).  The MC were washed and resuspended in 800 µL ice-cold Ca2+- and Mg2+-free DPBS (Life Technologies) containing 0.5% human AB serum (Gemini-Bio) and 2 mM EDTA.  Miltenyi’s protocol for positive selection of monocytes using their CD14 Microbeads was used. The cells were resuspended in RPMI1640 (Life Technologies) with 10% FBS (Hyclone); 100 µl of this suspension was used to check the effectiveness of the purification via flow cytometry.  Between 5 - 50 × 106 monocytes were purified from each blood sample (60-100 ml starting blood volume) and at an average purity of 96.5%.   59  3.2.3 Listeria monocytogenes infection of human whole blood and monocytes   Immediately after their purification, 1x106 monocytes were plated into 96-well flat-bottom plates containing either only culture media, Lm at MOI=5, 10 ng/mL of IFN-γ (PBL Interferon Source) with 10 ng/mL lipopolysaccharide (LPS) (Invivogen) or 1000 units/mL of IFN-β (PBL Interferon Source). Cells infected with Lm were cultured for 30 min and then washed twice with DPBS (Life Technologies) and then resuspended in RPMI1640 with 10% FBS and 50 µg/mL gentamicin (Sigma). The cells were then harvested at various time points for subsequent RNA extraction.   500 µl of peripheral blood from adults or cord blood from newborns was infected with Lm at MOI=5 for 30 minutes. Gentamicin was then added to the blood at a final concentration of 50 µg/mL. Samples were harvested at 2 and 6 hrs post-infection for RNA extraction.  For protein quantification using Western blots, 2-4x106 monocytes were plated into 48-well plates containing either only culture media or Lm at MOI=5. Monocytes were incubated with Lm for 30 min and then washed twice with DPBS (Life Technologies) followed by resuspension in RPMI1640 supplemented with 10% FBS and 50 ug/mL gentamicin (Sigma). Cells were stimulated for a total of 8 and 24 hrs and then harvested for protein quantification.     60  3.2.4 RNA extraction  After stimulation, monocytes were lysed in Buffer RLT (Qiagen) and ran through a QiaShredder column (Qiagen) at 2 hrs and 6 hrs for the microarray experiments. Each time point had its respective unstimulated control for all stimulations and infections performed. The Buffer RLT lysates were stored in -80°C prior to performing total RNA extraction using the protocol as outlined in Qiagen’s RNAeasy Mini Kit.  Lm-infected whole blood samples were lysed and preserved using RNALater (Ambion, Life Technologies) and stored at -80°C prior to RNA extraction using the Ribopure RNA Purification Kit for Blood (Ambion, Life Technologies) according to the manufacturer’s protocol.  3.2.5 Microarray and real-time PCR  The mRNA in the purified total RNA isolated from Lm infected monocytes was reversed transcribed to cDNA using T7-oligo(dT) primer and then T7 polymerase-amplified to biotin-labeled cRNA using the TargetAmp-Nano Labeling Kit for Illumina from Epicentre. This was followed with a quality check by running the cRNA in an Agilent 2100 Bioanalyzer.  Good quality cRNA was then used for hybridization on Illumina’s HumanHT-12 Expression Beadchip (version 4.0).  The Bioanalyzer and the Illumina microarray hybridizations were performed by the core facility at the Centre for Molecular Medicine and Therapeutics at the Child and Family Research Institute (Vancouver, B.C.) using the manufacturer’s standard protocol.  All microarray data have been submitted to GEO under accession number GSE67983. Total RNA from a 61  separate set of subjects was used to validate the gene expression data gleaned from the microarray.  For these samples, cDNA were made using random hexamers (Invitrogen) and the reverse transcriptase Superscript II (Invitrogen).  The cDNA were then used in a real-time PCR assay using SYBR Green PCR Master Mix (Bio-Rad) to detect IFN-β and IFN-γ transcripts or using TaqMan Gene Expression Assays to detect GBP (Applied Biosystems) transcripts and an ABI 7300 Real-Time PCR System (Applied Biosystems).  3.2.6 Microarray processing and bioinformatics analysis  Non-normalized intensity values from the Illumina arrays were exported from GenomeStudio (Illumina) as a text file. All subsequent array analysis was performed using Bioconductor in R. All array probe intensities were normalized against the background. MDS and cluster analysis was performed to determine the similarity of the samples with respect to each other. Heatmaps were generated to further visualize the differential expression of genes in each of the different clusters and conditions. The LIMMA R package was used to determine genes that were differentially expressed in each indicated comparison. Genes that had 1.5 fold change difference in expression and a p value < 0.05 were considered differentially expressed after adjusting for multiple comparisons using the Benjamini-Hochberg correction. Lists of differentially expressed genes were then entered into the open source, web-based enrichment analysis tool InnateDB (104). Pathway overrepresentation analysis was performed for comparisons of interest.    62  3.2.7 Immunoblot analysis   Samples of Lm-infected monocytes used for validating array results were lysed with RIPA Lysis and Extraction buffer (Thermo Fisher Scientific) containing 1X HALT Protease & Phosphatase Inhibitor Cocktail (Thermo Fisher Scientific). The concentration of extracted protein was measured using Micro Pierce BCA Protein Assay Kit (Thermo Scientific). 25 µg of protein was loaded onto a polyacrylamide gel containing 10% sodium dodecyl sulfate for western blotting using monoclonal antibodies against GBP-1 (Novus Biologicals) and β-actin (Cell Signaling Technology). Fluorescently conjugated secondary antibodies were used to label both protein targets. The blots were scanned and analyzed using the Odyssey Classic (LI-COR Biosciences).  3.2.8 Flow cytometry analysis on purified monocytes  Samples used for checking the purity of monocytes were spun down at 500xg for 5 min at room temperature then resuspended in 100 µL of 1X BD FACS Lyse (BD Biosciences) and frozen in -80°C prior to performing flow cytometry. Samples were then thawed, spun, and resuspended in 200 µl PBS containing 0.5% BSA and 0.1% sodium azide (PBSAN). Cells were stained in a final volume of 100 µl in PBSAN for 45 min at room temperature, containing CD14-APC (BioLegend, clone M5E2) and HLA-DR-PerCP-Cy5.5 (BD Custom, clone TU36) at a final dilution of 1:100 in each sample. After two additional washes with PBSAN, cells were resuspended in PBS containing 1% paraformaldehyde and analyzed on LSRII Flow Cytometer (BD Biosciences), set up according to published guidelines (31). Compensation beads were used to standardize voltage settings and also used as single-stain positive and negative controls for 63  each fluorophore used as described previously (31). Compensation was set in FlowJo (Tree Star) and samples were analyzed compensated.   3.2.9 Statistical analysis   All quantitative data were presented as mean ± SEM. Statistical analyses were performed using GraphPad Prism 5 (version 5.01). The statistical difference between groups was determined by one-way ANOVA followed by Bonferroni posttest. p < 0.05 was considered statistically significant.  3.3 Results  3.3.1 Members of the guanylate binding proteins, a subfamily of interferon-inducible GTPases were differentially expressed in human primary monocytes upon infection with L. monocytogenes.  We performed microarrays on RNA extracted from Lm-infected human primary monocytes from newborns, healthy young adults, and older adult individuals after 2 and 6 hrs of Lm infection and observed that the GBPs were differentially upregulated (Table 3.1). Other IFN-inducible GTPase subfamilies were not differentially expressed upon Lm infection in human monocytes (Table B.1). We confirmed these array results for the GBP subfamily using qPCR on a separate set of individuals and confirmed that GBP1, GBP3, and GBP5 mRNA were differentially expressed in 64  an age-dependent manner upon Lm infection (Figure 3.1). Adults produced significantly more GBP1, GBP3, and GBP5 at 6 hrs; GBP4 was not induced in any age group (data not shown).   Table 3.1. Differential mRNA expression of the guanylate binding protein (GBP) subfamily of interferon-inducible GTPases in human primary monocytes infected with wild-type L. monocytogenes after 2 and 6 hrs. Shown are the mean fold change values compared to uninfected controls. NI = Not induced. n = 6 per age group.    Figure 3.1. Quantitative PCR of purified human monocytes from newborns, young adults, and older adults also show that key interferon-inducible genes are differentially expressed in an age dependent manner upon infection with L. monocytogenes. Monocytes from 6 individuals for each age group were infected with wild-type L. monocytogenes at MOI = 5 for 2 and 6 hrs. mRNA expression of GBP1, GBP3, and GBP5 were determined by qPCR. mRNA expression from each gene was normalized against TBP transcripts. Data are presented as relative expression in comparison to uninfected control samples at each corresponding time point. Shown are the average relative expression values for each individual ± SEM. All data were analyzed using one-way ANOVA followed by Bonferroni posttest; *p < 0.05, ***p < 0.001.  65  mRNA expression does not necessarily always correlate with protein expression. Further, the immune response is comprised of the integration of signals derived from different cell types in the blood and what is expressed in a cell type may or may not contribute to the overall response of the host to infection. Thus, we wanted to further validate the age-dependent differences in the GBP1 levels as an example of the subfamily in our cohorts of interest by looking at mRNA in Lm-infected whole blood as well as protein expression in purified monocytes from each age group. We infected peripheral whole blood from adults and cord blood from newborns for 2 and 6 hrs and determined the level of GBP1 mRNA expression by qPCR.  We observed significantly higher GBP1 mRNA in adult blood but not in newborn blood (Figure 3.2A). We also performed western blots on monocytes obtained from a separate set of subjects that were infected with wild-type Lm for 8 and 24 hrs. We did not observe any GBP1 protein expressed at 8 hrs (data not shown), but we found the GBP1 protein expressed in adults but not in neonatal monocytes 24 hrs post infection (Figure 3.2B-C). We were unable to isolate a sufficient number of monocytes from our older adult samples due to the limited available blood volume; therefore we could not perform the western blot to validate the array and qPCR results at the protein level for this age group.        66   Figure 3.2. GBP1 mRNA is expressed in a mixed cell population in addition to being expressed at the protein level in purified monocytes. (A.) Whole blood from healthy young adults and newborns were infected with wild-type L. monocytogenes at MOI = 5 for 2 and 6 hrs. mRNA expression of GBP was determined by qPCR. mRNA expression from each gene was normalized against TBP transcripts. Data are presented as relative expression in comparison to uninfected control samples at each corresponding time point. Shown are the average relative expression values for each individual ± SEM. (B. – C.)Primary monocytes from 3 young adults and 3 newborns were infected with wild-type L. monocytogenes at MOI = 5 for 24 hrs. Protein expression of GBP1 was determined using fluorescence Western Blot. (B.) A representative blot of a pair of L. monocytogenes-infected monocytes from young adult and newborn (neonate). Data are presented as relative expression values normalized against β-actin protein expression. (C.) Shown are the average relative expression values for each independent individual ± SEM. All data were analyzed using one-way ANOVA followed by Bonferroni posttest; *p < 0.05, ***p < 0.001.  67  3.3.2 Type I and II interferons are differentially expressed in an age-dependent manner in human primary monocytes infected with wild-type Lm.  Given that GBP proteins are important downstream effector molecules of IFN signaling, we sought to determine the upstream pathways leading to age-dependent differential expression of GBPs in monocytes infected with Lm. While type I (IFN-β and IFN-α) and II IFN (IFN-γ) are amongst the most potent inducers of GBPs, this relationship is more complex, as specific IFN-inducible GTPases are activated by specific IFNs (105–107). IFN-β was differentially expressed in an age-dependent manner (Figure 3.3), with significantly more transcripts in young adults compared to newborn and older adult samples. In response to Lm infection, both younger and older adult monocytes tended to produce more IFN-γ transcripts than neonatal monocytes (Figure 3.3). The absolute levels of IFN-γ transcripts however were highly variable between subjects, thus results were not statistically significant different when comparing the age groups.    68   Figure 3.3. Age-dependent, differential upregulation of type I and type II interferons in primary monocytes infected with L. monocytogenes. Primary monocytes from 6 newborns, 6 young adults, and 6 older adults were infected with wild-type L. monocytogenes at MOI=5 for 2 and 6 hrs. mRNA expression of IFN-β and IFN-γ were determined by qPCR. mRNA expression from each gene was normalized against β-actin transcripts. Data are presented as relative expression in comparison to uninfected control samples at each corresponding time point. Shown are the average relative expression values for each individual ± SEM. All data were analyzed using one-way ANOVA followed by Bonferroni posttest; ***p < 0.001.  3.3.3 Exogenous stimulation of human primary monocytes from newborns and young adults with interferons results in induction of GBP1 mRNA.  Two possible causes could lead to differential expression of GBP mRNA. The first possibility would be that there is a lack of type I and/or type II IFN production, which results in no transcription of GBP in neonates and older adults. The second possibility would be that the IFN response pathway in susceptible age groups (neonates and older adults) is functioning differently. Because neonates showed a larger deficit in IFN production compared to older adults, we focused on comparing neonatal to adult responses in this experiment. As shown in Figure 3.4A, the promoter region of GBP1 contains binding sites for several important transcription factors involved in innate immune signaling. Of these, signal transducers and 69  activators of transcription 1 (STAT1) is activated by both type I and II IFNs (14,26), reflecting the ability of GBPs to respond to both types of IFNs. IFN-γ+LPS has typically been employed as a powerful positive control for induction of GBP1 (108,58). We stimulated neonatal and young adult monocytes with IFN-β or IFN-γ+LPS for 6 hrs and measured induction of GBP1 using qPCR. Interestingly, we observed that neonatal samples stimulated with IFN-β or IFN-γ + LPS were able to induce the transcription of GBP1 mRNA to identical levels as those of young adults (Figure 3.4B).    70   Figure 3.4 Exogenous stimulation with IFN-β and IFN-γ + LPS results in robust GBP1 expression in newborn monocytes. Monocytes from 3 young adults and 3 neonatal samples were stimulated with IFN-β at 1000 units/mL or IFN-γ with LPS at 10 ng/mL each for 6 hrs. (A.) Graphical representation of the human GBP1 gene showing the promoter region containing 4 CpG methylation sites (cg1-4), transcription factor binding sites for IRF1/2, STAT1, and AP1, and the transcription start site (denoted by the black arrow). (B.) mRNA expression of GBP1 were determined by qPCR. mRNA expression from each gene was normalized against TBP transcripts. Data are presented as relative expression in comparison to uninfected control samples at each corresponding time point. Shown are the average relative expression values for each individual ± SEM. All data were analyzed using one-way ANOVA followed by Bonferroni posttest.    71  3.4 Discussion  Lm is a dangerous pathogen for the very young and very old (39,95). While the host response to Lm infection has been studied extensively in murine models, few have examined the human response to Lm. To our knowledge, our report is the first showing age-dependent differences in transcriptomic response to Lm infection in susceptible human age groups. Our work revealed that the GBP subfamily of IFN inducible GTPases was one of the most age-dependent differentially expressed gene sets upon infection with Lm compared to other IFN-inducible GTPase subfamilies, with significantly lower induction in the very young and very old. Our data also indicate that this age-dependent difference in GBP expression was likely due to a differential IFN-β response to Lm infection.  Together, this connects age-dependent differences in type I IFN signalling to cell autonomous immunity, and begins to delineate the underlying mechanisms related to age-defined windows of vulnerability to infections at the extreme ends of the age spectrum.  There are several subfamilies of IFN inducible GTPases (49,105–107); however, in our hands, only the GBP subfamily appeared differentially upregulated in an age-dependent manner upon Lm infection. GBPs have been shown to be key in protection from infection with a wide range of intracellular pathogens (108,57,106,107,109–115). They are involved in cell autonomous immunity and are responsible for clearing infections via autophagy and generation of reactive oxygen species (108,49,106,107), facilitating killing of intracellular pathogens. The function of the GBP subfamily is known to be cell type specific; to our knowledge there is currently no data regarding expression of GBPs in primary human monocytes, a cell type centrally important in 72  defense against Lm. Amongst the GBPs that we found were age-dependently differentially expressed (GBP1, GBP3, and GBP5), we focused on GBP1 because it has the richest body of literature regarding function thus far amongst the GBPs and has an established role in murine protective immunity in Lm, Mycobacterium tuberculosis, and Toxoplasma gondii infection (108,57,107,109–112,114). As shown in Figure 3.2A, we observed age-dependent differences in mRNA expression of GBP1 in Lm-infected whole blood as well as purified monocytes (Figure 3.1), where only adult samples upregulated GBP1 expression. Further, we detected discernable amount of GBP1 protein only in Lm-infected monocytes of young adults (Figure 3.2B-C). Thus, age-dependent differences in mRNA expression of GBPs translated to differences in protein expression, suggesting that GBP1 expression in response to Lm infection in human primary cells was indeed an age-dependent phenomenon.  Cell autonomous immunity (CAI) is one of the most ancient and ubiquitous forms of host protection and guards individual cells against intracellular infection (reviewed in (116)). While CAI is likely operative throughout life, changes of CAI as a function of age have to our knowledge not yet been investigated. Several key effector molecules of CAI have been identified, amongst which GBP feature prominently. There are several known inducers of GBP that could be functionally relevant to the age-dependent difference of GBP expression we identified (49,105–107). These include pro-inflammatory cytokines such as TNF-α and IL-1β in addition to type I and II IFNs (49,105–107). Our data suggest that amongst these, only IFN-β was differentially expressed in an age-dependent manner (Figure 3.3). Furthermore, upon inspection of the promoter region of GBP1, we found that there are interferon regulator factor (IRF) and STAT binding sites that are used to initiate transcription as shown in Figure 3.4A, 73  which suggested that type I IFN expression likely plays a key role in the expression of the GBPs in human monocytes. We thus attempted to identify the diversion point leading to age-dependent differences in the type 1 IFN signaling pathway related to differential expression of GBPs. After 6 hrs in culture, we only observed age-dependent differences in expression of GBP1 in Lm-infected cells and not in cells that were stimulated with exogenous IFNs; i.e. newborns were able to induce similar levels of GBP1 upon IFN stimulation, suggesting that the IFN signaling pathway in newborns appears to be intact. This indicates that lack of IFN-β production may be the upstream cause of the observed downstream differential expression of GBP proteins.   Given that IFN-β expression is dependent on IRF3 signaling, age-dependent differences in response to Lm infection are likely related to the known aberrant IRF3 signaling in newborns versus young adults (28,55,64). The lack of optimal IRF3 signaling as reported by other groups would likely result in less IFN-β expression and therefore produce less downstream expression of interferon-stimulated genes mRNA, such as the GBPs described here. There is currently no information regarding altered/deficient IFN-β and IRF signaling in older adults; based on our data we suspect that they, like newborns, may also have suboptimal IRF3 function.   Our study has several limitations that have to be taken into account. While we were able to validate the age-dependent, differential upregulation of GBP1 in adults at the mRNA and protein level, we suspect that there may be several other age-dependent differentially expressed genes that were undetectable due to our limited sample size. Furthermore, purified human primary cells are difficult to infect with Lm, with only 10% of the cell population being infected at MOI=5 (data not shown). Therefore, we are uncertain about whether the responses we have detected are 74  due to infected or bystander cells. Efforts to identify this using fluorescently tagged Lm are currently in progress.  In summary, we provide evidence that key IFN response genes are differentially expressed in an age-dependent manner upon infection of primary monocytes with Lm. This is the first report that identifies key effector functions of CAI to differ between age groups, and do so in a manner that correlates with age-dependent susceptibility to infection. Specifically, the GBP subfamily of IFN-inducible GTPases were differentially upregulated in adult monocytes but not in newborn or older adult individuals upon infection with Lm. This is most likely caused by a differential expression of IFNs, namely type I IFN (IFN-β). We further confirmed the differential expression of GBP1 at the protein level. Together, we identify the need to focus on cell autonomous immunity in addition to systemic adaptive and innate immunity in defining age-defined windows of vulnerability to infection at the extreme ends of the age spectrum.   75  CHAPTER 4: AGE-RELATED GENE EXPRESSION DIFFERENCES IN MONOCYTES FROM HUMAN NEONATES, YOUNG ADULTS, AND OLDER ADULTS  4.1 Introduction  Age-related differences in clinical susceptibility to infection have been extensively documented, with diminished protective responses and enhanced susceptibility observed in pre-term and term infants, as well as in older adults when compared to young adults (117–120). This clinical observation of an age-dependent risk for infectious morbidity and mortality has led to an interest in identifying the underlying mechanisms and deriving strategies to enhance protective immune responses at the extreme ends of life (28,117,120).  Differences in innate immune responses are thought to contribute to the overall susceptibility observed in neonates and older adults (28,61). Neonates have been reported to produce lower levels of effector molecules, such as oxygen radicals (28,121). A number of other proteins have also been reported at reduced levels in innate immune cells, including reduced expression of IFNα, CD40, CD80, CD83, and CD86 in neonatal plasmacytoid dendritic cells (118). Furthermore, newborns and older adults produce altered levels of cytokines that regulate the development of adaptive immunity (reviewed in (28)). For example, the heterodimeric, Th1-inducing innate cytokine, interleukin (IL)-12, is expressed at reduced levels in neonates, due to Chapter 5 is based on the recently accepted manuscript to PLoS One: Lissner MM, Thomas BJ, Wee K, Tong A-J, Kollmann TR, Smale ST. Age-related Gene Expression Differences in Monocytes from Human Newborns, Young Adults, and Older Adults. PLoS One employs the Creative Commons Attribution (CC BY) licence whereby authors maintain ownership of the copyright of their article.  76  the reduced expression of its p35 subunit (56,63,122). In contrast, the anti-inflammatory cytokine, IL-10, and the Th17-inducing cytokines, IL-6 and IL-23, have been observed at elevated levels in neonates (31,63). In older adults, a variety of innate effector responses appear to be reduced, including superoxide generation and the phagocytosis of microorganisms (37,123). Systemic low-level inflammation is another common characteristic of older adults that may alter their response to infection (reviewed in (28)).   The approaches used to identify age-dependent differences that lead to an increased risk to suffer from infection at the extreme ends of life have been largely balkanized and focused on a few particular components, the choice of which appears to depend on the expertise of a given group of investigators. What has been missing is an unbiased yet comprehensive interrogation of the events that occur in the very young and the very old following recognition of an infectious threat. In addition to our deficiency in knowledge of age-dependent differences in the immune system, little is known about the molecular mechanisms responsible for these differences. Reduced activation of transcription factors such as interferon response factor 3 (IRF3), defects in nucleosome remodeling, and differences in the expression of pattern recognition receptors and signaling molecules (e.g. MyD88) are among the mechanisms that have been proposed to be responsible for the diminished innate immune responses observed in neonates (28,54,55,124).  Age-dependent differences in hematopoietic stem cells and in the development of hematopoietic lineages have also been observed, providing one possible explanation for the immune response differences (125–127). According to this scenario, myeloid cell types may be fundamentally different in neonates, adults, and older adults, resulting in different gene expression responses 77  following stimulation or infection. As an alternative, the myeloid cell populations may be similar, but age-related differences in the blood or tissue microenvironment may lead to different responses (128). The response differences may be lost when cells from different age groups are cultured under the same conditions, or they may be retained via epigenetic mechanisms or other memory mechanisms (117).  DNA microarrays were previously used to obtain genome-scale insight into age-dependent differences in gene expression following infectious exposure (55). More recently, RNA sequencing (RNA-seq) has emerged as a more quantitative method for examining transcriptomes (129). The availability of the RNA-seq method provides an opportunity to unravel, with greater precision, the age-dependent differences in the immune system that increase risk for a serious outcome following infection. As a first step, the identification of age-related differences in gene expression following ex vivo infectious exposure of defined cell populations, along with the identification of differences in constitutive gene expression in these populations, would be of considerable value.  In this study, RNA-seq was used to compare the gene expression responses to LPS stimulation or Listeria monocytogenes (Lm) infection in cord blood monocytes and in peripheral blood monocytes from young and older adults. LPS provides an example of a well-defined innate immune stimulator; Lm causes suffering and dying in the very old and the very young, while most young adults rarely even display symptoms if infected (130). Our data reveal extensive similarities in constitutive gene expression and in the response to stimulation or infection in monocytes from the three age groups. Furthermore, most of the differences identified between 78  neonates and young adults could be connected to the previously reported reduction in IRF3 activity in neonates (55). In contrast, most differences between young adults and older adults appeared to result from a low-level inflammatory state (‘inflammaging’) that characterized monocytes from older adults. Interestingly, large differences in the expression of constitutively expressed genes, which would be expected if blood monocytes from neonates, adults, and older adults were fundamentally different, were not identified. This finding supports a hypothesis in which age-related environmental differences are responsible for the inability of neonatal monocytes to mount a robust IRF3-mediated response.   4.2 Materials and methods  4.2.1 Isolation of cells and stimulation conditions  All studies were approved by the Institutional Ethics Review Board at the University of British Columbia. Samples of cord blood from healthy, full-term elective Caesarean sections without labor and samples of healthy young adult and older adult peripheral blood were collected directly into sodium heparin-containing vacutainers (BD Biosciences). Within two hrs of the blood draw, mononuclear cells were isolated by density gradient centrifugation (31). Positive selection of monocytes from mononuclear cells was then carried out using Miltenyi microbeads according to the manufacturer’s protocol with some revisions. Briefly, mononuclear cells were incubated with 800 uL MACS buffer and 200 uL anti-human CD14 microbeads at 4oC. Cells were then washed with MACS buffer prior to positive selection of monocytes using Miltenyi selection columns. Purified monocytes from each donor were cultured in RPMI 1640 medium supplemented with 79  Glutamax (Gibco, Life Technologies) and 10% human AB serum (Gemini Bio Products). The monocytes were counted and plated onto 96 well plates at a density of 1x106 cells/well. Monocytes were stimulated with LPS (10 ng/ml) (InvivoGen) or infected with Lm at MOI=5. Wild-type (WT) Lm strain 10403s was provided by Dr. D. Portnoy (University of California, Berkeley, CA) and grown as described (103).  Mouse macrophages were prepared from the bone marrow of 6-week-old C57BL/6, IRF3-/-, or IFNAR-/- mice as described (131,132), and were stimulated with lipid A (100 ng/mL) (Sigma) after 6 days of differentiation.  4.2.2 RNA isolation, library preparation, and sequencing  Human monocyte RNA was purified using the RNeasy Mini Kit (Qiagen) according to the manufacturer’s protocol. Strand-specific libraries were prepared using 120 ng RNA input according to the “deoxyuridine triphosphate (dUTP)” method (133). Mouse macrophage experiments involved analyses of chromatin-associated RNAs, as previously described (131). A HiSeq 2000 (Illumina) was used for sequencing, with a single end sequencing length of 50 nucleotides. Sequencing data have been submitted to GEO under accession number GSE60216.  4.2.3 Bioinformatic analysis  All bioinformatic analyses were conducted using the Galaxy platform (134). Reads were aligned to the human GRCh37 or mouse mm9 reference genomes with Tophat (135) using most default 80  parameters. Alignments were restricted to uniquely mapping reads with two possible mismatches permitted. RPKM (reads per kilobase pair per million mapped reads) were calculated using Seqmonk (http://www.bioinformatics.babraham.ac.uk/projects/seqmonk/). Coexpressed gene classes were evaluated with Cluster3 by applying k-means clustering to mean-centered log2(RPKM) expression values (136). Statistically significant gene expression differences were evaluated using DESeq (137). Mouse orthologs of human genes were identified using BLAST (http://blast.ncbi.nlm.nih.gov/Blast.cgi). Pscan was used to detect DNA motifs overrepresented in each class between nucleotides -450 and +50 relative to the transcription start site (138).  4.3 Results  4.3.1 Gene expression cascades induced in monocytes by LPS and Lm  An attractive starting point toward a full understanding of age-related differences in immune responses is to employ RNA-seq to carefully examine mRNA transcript levels following stimulation or infection of defined cell types. Toward this goal, peripheral blood monocytes were obtained from healthy young adults between the ages of 19 and 45, and healthy older adults aged 65 years and older. In addition, neonatal monocytes were obtained from umbilical cord blood samples. The monocytes were stimulated with LPS or infected with Lm immediately after isolation to avoid alterations in cell properties caused by culturing. For both the LPS and Lm experiments, three individuals from each age group were analyzed. For the LPS experiments, samples for RNA-seq were secured at 0, 1, and 6 hrs post-stimulation. For the experiments involving live Lm infection, samples were collected for RNA-seq 2 and 6 hrs after infection; in 81  this experiment, uninfected cells (referred to as 0-hr time point) were collected after culturing without Lm for 2 hrs, whereas the unstimulated cells in the LPS experiment were collected immediately after isolation. After mRNA isolation and cDNA library preparation, RNA-seq was performed. The number of mapped reads ranged from 3.4 x 106 to 1.3 x 107 per sample.  An examination of the data sets from the LPS experiment identified 1147 annotated RefSeq genes that were induced by at least five-fold at the 1- or 6-hr time point (relative to the unstimulated sample) in at least one sample from any age group, and that exhibited a transcript level exceeding four RPKM following induction. To examine the relationship between the different time points and age groups in the response to LPS, hierarchical clustering was performed with these 1147 genes (Figure 4.1A). This analysis revealed that each of the nine samples from a given time point was more closely related to the other samples from the same time point than to any sample from the other two time-points. The most significant difference that showed a possible relationship to age was that the three unstimulated samples from older adults (OA1.0, OA2.0, and OA3.0) and one young adult unstimulated sample (A1.0) clustered separately from the remaining unstimulated samples from young adults and neonates.  Small age-related differences were also observed with the 6-hr time-point data, in that, with only one exception (neonatal sample N3.6), each age group clustered separately from the others. In contrast, the nine 1-hr time-point samples correlated closely, with no apparent age-related differences. The Pearson correlation values (R values) used for the hierarchical clustering are shown in Figure 4.1B. These results provide initial evidence that the vast majority of LPS-induced genes are induced similarly in the three age groups. 82    83  Figure 4.1. Hierarchical clustering of LPS-stimulated monocyte transcriptomes from human neonates, adults, and older adults. (A) RNA-seq experiments were performed with three independent human monocyte samples from cord blood (N), young adult peripheral blood (A), and older adult peripheral blood (OA) stimulated with LPS for 0, 1, and 6 hrs. Hierarchical clustering was performed with the 1147 genes found to be induced by at least 5-fold at the 1- or 6-hr time point in at least one sample and with an induced RPKM of at least 4 (genes smaller than 200 bp were also excluded from the analysis). Sample codes correspond to the age abbreviation followed by the sample number (1 through 3 for each age); the time point (0, 1, or 6 hr) is indicated after the period. Inducible transcriptomes exhibit strong time-dependent clustering, with much less age-dependent clustering. (B) Pearson correlation values (R) used for the hierarchical clustering in panel A are shown. Each time point from each sample was compared to every other sample and time point. R values are color-coded from low (green) to high (red). Samples on the X and Y axes are grouped first according to age group, then time point (0, 1, or 6), and then sample number (1-3).  Examination of the Lm data sets identified 865 annotated RefSeq genes that were induced by at least five-fold at the 2-hr or 6-hr time point in at least one sample, and that exhibited a transcript level exceeding four RPKM following induction. The hierarchical clustering results and the Pearson correlation values revealed even stronger correlations between age groups at each time point than were observed with the LPS data (Figure 4.2). That is, although strong time-dependent clustering was observed, no consistent age-related differences were observed at any of the time points.  84   Figure 4.2. Hierarchical clustering of Lm-infected monocyte transcriptomes from human neonates, adults, and older adults. (A) RNA-seq experiments were performed with three 85  independent human monocyte samples from cord blood (N), young adult peripheral blood (A), and older adult peripheral blood (OA) infected with Lm for 0, 2, and 6 hrs. Hierarchical clustering was performed with the 865 genes found to be induced by at least 5-fold at the 2- or 6-hr time point in at least one sample and with an induced RPKM of at least 4 (genes smaller than 200 bp were also excluded from the analysis). Sample codes correspond to the age abbreviation followed by the sample number (1 through 3 for each age); the time point (0, 2, or 6 hr) is indicated after the period. Inducible transcriptomes exhibit strong time-dependent clustering, with much less age-dependent clustering. (B) Pearson correlation values (R) used for the hierarchical clustering in panel A are shown. Each time point from each sample was compared to every other sample and time point. R values are color-coded from low (green) to high (red). Samples on the X and Y axes are grouped first according to age group, then time point (0, 2, or 6), and then sample number (1-3).  4.3.2 K-means cluster analysis of LPS- and Lm-induced genes  To extend the analysis of age-related differences in inducible gene expression, k-means clustering was used to define groups of genes that exhibited similar expression patterns among the three age groups and three time points. The k-means algorithm considers induction kinetics, induction magnitudes, and differences among age groups. Figure 4.3A shows the results obtained when the 1147 LPS-induced genes (using the average expression values from the three independent samples analyzed for each age group and each time point) were assigned to one of ten distinct clusters. As expected on the basis of the hierarchical clustering, extensive similarities were apparent in the three age groups in almost all of the clusters. The similarities are also apparent in line graphs showing the average relative expression levels for all genes in a given cluster (Figure 4.3B).  Only one cluster (Cluster I) was identified that showed substantial age-related differences (Figure 4.3A, B). Genes in this cluster were generally expressed at a lower level in both unstimulated and LPS-stimulated monocytes from neonates in comparison to the young adult and 86  older adult samples. Although the average induction magnitude for genes in this cluster was comparable among the age groups, the average expression level of these genes was significantly lower in neonates than in young adults at all three time points.   Figure 4.3. Analysis of LPS-induced genes in monocytes by K-means cluster analysis. (A) The 1147 genes that exceeded 200 bp in length, exhibited an RPKM of at least 4 in one sample, and were induced by LPS by at least 5-fold in the same sample were divided into 10 clusters by 87  k-means cluster analysis, which considers similarities in transcript levels for each gene across all 27 samples (3 age groups, 3 samples for each age group, and 3 time points for each sample). The three independent samples are shown in parallel for each age group. Colors indicate the percentile of the relative expression level (based on the log-transformed mean-centered RPKM for each gene), as indicated at the bottom. (B) The average relative transcript levels for genes within each cluster are shown for each age group (neonates, blue diamonds; young adults, red squares; older adults, green triangles).  K-means clustering of the Lm-induced genes also revealed extensive similarities among the three age groups (Figure 4.4). Only one cluster (Cluster G) showed slightly reduced average expression in the neonatal and older adult samples in comparison to the young adult samples.  88   Figure 4.4. Analysis of Lm-induced genes in monocytes by K-means cluster analysis.(A) The 865 genes that exceeded 200 bp in length, exhibited an RPKM of at least 4 in one sample, and were induced by Lm infection by at least 5-fold in the same sample were divided into 10 clusters by k-means cluster analysis, which considers similarities in transcript levels for each gene across all 27 samples (3 age groups, 3 samples for each age group, and 3 time points for each sample). The three independent samples are shown in parallel for each age group. Colors indicate the percentile of the relative expression level (based on the log-transformed mean-centered RPKM for each gene), as indicated at the bottom. (B) The average relative transcript levels for genes within each cluster and are shown for each age group (neonates, blue diamonds; young adults, red squares; older adults, green triangles).  89  4.3.3 Analysis of genes exhibiting statistically significant expression differences   Because the clustering results described above revealed extensive similarities with limited age-related differences, we envisioned that meaningful insights would require the use of defined parameters to identify genes that exhibited the greatest differential expression. Toward this end, we first focused our attention on genes induced to a statistically significant extent (p<0.01) that also exhibited differential expression between neonates and young adults at a high level of statistical significance (p<0.01). Only 118 of the 1147 LPS-induced genes met these criteria.  The 118 genes (gene identities listed in Figure C.1) were separated into groups according to the time point at which their maximum mRNA level was observed (Figure 4.5A: 1-hr peak expression for Groups I and II; 6-hr peak expression for Groups III-VI). The genes were then further grouped according to their expression level in neonates relative to their expression level in young adults (Figure 4.5A, column 7). (For this calculation, the baseline and maximum expression levels in young adults were defined as 0% and 100%, respectively; the maximum expression level in neonates was then determined as a percentage relative to that range.) This analysis revealed 35 genes that exhibited enhanced expression in the neonatal samples (Groups I and III, lightest shade of purple) and 83 genes that exhibited reduced expression (Groups II, IV, V, and VI, three darker shades of purple). Group VI contains the 34 genes that exhibited the greatest difference between neonates and young adults. For these genes, the maximum LPS-induced mRNA level in neonates was less than 20% of the maximum level observed in young adults.  90   Figure 4.5. Genes that exhibit the greatest expression deficit in LPS-stimulated cord blood monocytes in comparison to adult monocytes are regulated by IRF3 and/or type I IFNs. (A) LPS-induced genes exhibiting statistically significant differential expression in neonates and adults (n = 118) were grouped according to the time point at which their maximum transcript levels were observed (1 or 6 hrs). They were then grouped according to their relative maximum transcript levels in cord blood (neonates) versus young adults. Induced genes with a higher maximum transcript level in neonates than young adults (with statistically significant differential expression) are included in classes I (1-hr peak) and III (6-hr peak) (column 7). Genes exhibiting a maximum transcript level in neonates that was 50-100% of the young adult transcript level (but with statistically significant differential expression) are included in class IV (no genes with peak transcript levels at 1-hr fit this criterion). Genes exhibiting a maximum transcript level in neonates that was 20-50% of the young adult transcript level are in classes II (1-hr) and V (6-hr). Genes with a maximum transcript level in neonates below 20% of the young adult transcript level are in class VI. Columns 1-6 show the relative transcript levels (based on the log-transformed mean-centered RPKM) for these 118 classified genes in all samples and all time points from both neonates and young adults. Column 8 indicates genes that lack obvious mouse orthologs (lightest pink), genes that contain mouse orthologs that are either not expressed or not induced in mouse bone marrow-derived macrophages (dark pink), and genes containing mouse orthologs that are both expressed and induced by LPS (red). Columns 9 and 10 show relative expression of the mouse ortholog of the human gene in Lipid A-stimulated macrophages from IRF3-/- and IFNAR-/- mice, respectively (see blue scale at right). Note that these columns are only 91  relevant for genes shown in red in Column 8. Column 11 indicates genes with promoters that contain an IRF1 transcription factor binding motif between -450 and +50 bps relative to the transcription start site. (B) Enrichment of transcription factor binding sites determined using the Pscan program is shown for each gene class from panel A. Color intensity is proportional to the negative log(p-value).  A parallel analysis with the Lm samples identified 123 genes (listed in Figure C.2 that were inducible and differentially expressed between neonates and young adults with a high level of statistical significance (p<0.01 for both induction and differential expression). Grouping of these genes using the same strategy as above revealed 13 genes that were expressed more highly in neonates than young adults (Figure 4.6A, Groups I and V) and 110 genes that were expressed more highly in young adults than neonates Groups II-IV and VI-VIII). Forty-seven of these later genes exhibited mRNA levels in neonates that were less than 20% of the young adult levels (Groups IV and VIII).   92  Figure 4.6. Genes that exhibit the greatest expression deficit in Lm-stimulated cord blood monocytes in comparison to adult monocytes are regulated by IRF3 and/or Type I IFNs. (A) Lm-induced genes exhibiting statistically significant differential expression in neonates and young adults (n = 123) were grouped according to the time point at which their maximum transcript levels were observed (2 or 6 hrs). They were then grouped according to their relative maximum transcript levels in cord blood (neonates) versus young adults. Induced genes with a higher maximum transcript level in neonates than young adults (with statistically significant differential expression) are included in classes I (2-hr peak) and V (6-hr peak) (column 7). Genes exhibiting a maximum transcript level in neonates that was 50-100% of the young adult transcript level (but with statistically significant differential expression) are included in classes II (2-hr) and VI (6-hr). Genes exhibiting a maximum transcript level in neonates that was 20-50% of the young adult transcript level are in classes III (2-hr) and VII (6-hr). Genes with a maximum transcript level in neonates below 20% of the young adult transcript level are in classes IV (2-hr) and VIII (6-hr). Columns 1-6 show the relative transcript levels (based on the log-transformed mean-centered RPKM) for these 123 classified genes in all samples and all time points from both neonates and young adults. Column 8 indicates genes that lack obvious mouse orthologs (lightest pink), genes that contain mouse orthologs that are either not expressed or not induced in mouse bone marrow-derived macrophages (dark pink), and genes containing mouse orthologs that are both expressed and induced by LPS (red). Columns 9 and 10 show relative expression of the mouse ortholog of the human gene in Lipid A-stimulated macrophages from IRF3-/- and IFNAR-/- mice, respectively (see blue scale at right). Note that these columns are only relevant for genes shown in red in Column 8. Column 11 indicates genes with promoters that contain an IRF1 transcription factor binding motif between -450 and +50 bps relative to the transcription start site. (B) Enrichment of transcription factor binding sites determined using the Pscan program is shown for each gene class from panel A. Color intensity is proportional to the negative log(p-value).  4.3.4 A prominent role for IRF3 and type I IFN signaling in the neonate-adult differences   To gain insight into the mechanisms responsible for differential gene expression in neonatal and young adult monocytes, we first examined the requirements for expression of the mouse orthologs of the differentially expressed genes. This analysis took advantage of a large number of RNA-seq data sets that have been generated in our laboratory using mouse bone marrow-derived macrophages stimulated with the Lipid A component of LPS. This collection of data sets includes kinetic analyses of lipid A-induced gene expression in macrophages from a variety of 93  mutant mouse strains lacking key signaling molecules or transcription factors thought to be important for inducible transcription ((131) and unpublished results).  By examining the expression requirements for the mouse orthologs of the genes that were differentially expressed in human neonates and young adults, evidence was obtained that these genes frequently require the transcription factor IRF3 or Type I IFN receptor signaling. That is, many of the age-dependent differentially expressed genes were expressed at substantially reduced levels in Irf3-/- and/or Ifnar-/- macrophages stimulated with Lipid A.  To document the extent to which IRF3 and IFNAR signaling might contribute to the differential expression of LPS-induced genes in neonates and adults, human genes for which mouse orthologs could clearly be identified (114 of 118 genes; Figure 5.5A, column 8, dark pink and red) were first separated from the small number of genes lacking obvious mouse orthologs (Figure 4.5A, column 8, lightest pink). Then, the RNA-seq data sets were analyzed to identify genes that were both expressed (RPKM > 4 when maximally expressed) and induced (>5-fold) in both the human monocytes and wild-type mouse macrophages. The 38 genes that met these criteria (Figure 4.5A, column 8, red) were then evaluated for their dependence on IRF3 and IFNAR in mouse bone marrow-derived macrophages stimulated with Lipid A. The results revealed IRF3-dependence for 14 of the 16 genes in Group VI (Figure 4.5A, column 9, dark blue if <10% of the wild-type expression level in Irf3-/- macrophages and light blue if 10-33% of the wild-type level in Irf3-/- macrophages). 14 of the 16 genes also exhibited reduced expression in Ifnar-/- macrophages (column 10). IRF3- and/or IFNAR-dependence was also observed for most 94  Group V genes for which mouse orthologs were both expressed and induced in mouse macrophages (Figure 4.5A).  As an independent strategy, a transcription factor binding site motif analysis was performed using the Pscan program (138) with the promoter regions of all genes in Groups I through VI. The goal of this analysis was to identify transcription factors whose binding sites are over-represented in the promoters of specific clusters of genes. The small number of transcription factors for which significant enrichment was observed are shown in Figure 4.5B. Transcription factor binding motif enrichment generally was not observed for Groups I through V. However, highly significant enrichment of binding sites for IRF1, IRF2, STAT1, and a STAT2:STAT1 heterodimer was found at the promoters of Group VI genes (Figure 4.5B). The IRF1 and IRF2 binding sites used by the Pscan program are similar to the experimentally defined consensus IRF3 binding motif (139), which is not assessed by Pscan. Importantly, IRF and STAT motifs were identified in the promoters of the vast majority of Group VI genes, including most genes whose mouse orthologs could not be examined for IRF3 and IFNAR dependence due to lack of inducible expression in both mice and humans (Figure 4.5A, column 11).  Thus, both the functional analysis and motif analysis strongly support the hypothesis that reduced activation of IRF3- and IFNAR-dependent genes explains most gene expression differences between neonatal and adult monocytes. It is noteworthy that a previous study which documented reduced IRF3 activity in neonatal dendritic cells found that neonatal and adult cells were similarly responsive to IFNβ stimulation, suggesting that the reduced expression of 95  IFNAR-dependent genes is due to reduced IRF3 activity (resulting in reduced IFNβ expression) rather than a reduction in IFNAR signaling (55).  Consistent with the analysis of the LPS-induced genes, mouse orthologs of the human genes that exhibited differential expression upon Lm infection were generally found to exhibit IRF3- and/or IFNAR-dependence (Figure 4.6A). Furthermore, binding sites for IRF1, IRF2, STAT1, and the STAT2:STAT1 heterodimer were greatly enriched in the Group VIII genes and to a lesser extent in Group VII genes (Figures 4.6A and 4.6B). Thus, although IRF3 is thought to be activated by different pathways in LPS-stimulated and Lm-infected cells (51,140), a common reduction in IRF3 activity is likely to be responsible for the strongest gene expression differences between neonatal and adult monocytes.  4.3.5 Low-level inflammation in older adults  To evaluate gene expression differences between young adults and older adults, we first used the strategy described above to identify differentially induced genes. This analysis revealed minimal differences in transcriptional induction (data not shown), suggesting that the pathways involved in the responses to LPS and Lm in monocytes from the two age groups are highly similar. Instead, the largest differences were observed when examining transcript levels for inducible genes prior to stimulation. Specifically, 189 LPS-induced genes (>5-fold induction magnitude; induction significance p<0.01; maximum induced transcript level >4 RPKM) exhibited transcript levels that were significantly different (p<0.01) in unstimulated cells from young adults in comparison to older adults (Figure 4.7A; gene list in Figure C.3). For these 189 genes, Figure 96  4.7A, column 7 shows the ratio of the unstimulated transcript level in older adults to that in younger adults (OA0/A0). In this figure, the genes are grouped on the basis of their time point of maximum expression, and the genes were then rank-ordered by the ratio of the unstimulated transcript level. This analysis revealed that a large majority of the differentially expressed genes are expressed at an elevated level in older adults (shown as shades of red, see vertical color scale at right). In fact, 52% of the differentially expressed genes exhibited unstimulated transcript levels in older adults that were at least 3-fold higher than in young adults, whereas only 3% exhibited transcript levels that were at least 3-fold higher in young adults than in older adults. Similar results were observed in the Lm experiment (data not shown), but the number of genes showing differential expression was lower, probably because the unstimulated cells for the Lm experiment were cultured for 2 hrs prior to collection, whereas the unstimulated cells in the LPS experiment were collected without culturing.  Importantly, although relatively large differences in expression between young adults and older adults were observed in the unstimulated cells, the magnitudes of the differences were generally lower after stimulation. This is apparent in Figure 4.7A, column 8 (max OA/max A), which shows the ratio between the maximum induced transcript levels in older adults versus young adults. Because the same color scale is used for columns 7 and 8, it is readily apparent that the transcript level ratios move toward 1 after stimulation for most genes that are differentially expressed prior to stimulation. Figure 4.7B, which displays average transcript levels for all genes in Groups I and II, also shows that transcript levels in older adults were elevated to a greater extent prior to stimulation than after stimulation. Thus, an inflammatory state is readily apparent in unstimulated monocytes from older adults. This inflammatory state in unstimulated cells may 97  influence transcript levels observed after stimulation or infection, but to a limited extent relative to the differences observed in the basal state.   Figure 4.7. Elevated expression of a broad range of inflammatory genes prior to stimulation of freshly isolated monocytes from older adults. (A) LPS-induced genes exhibiting differential basal expression between adults and older adults (n = 189) are grouped according to maximum mRNA level. Columns 7 and 8 show the ratio of transcript levels between older adults and young adults before stimulation and at maximum transcript levels, respectively. (B) The average relative transcript levels within each cluster and for each age are shown.  4.4 Discussion  The diminished capacity of human neonates and older adults to mount an immune response to infectious agents has been well documented (28,120). However, because of the complexity of the 98  human immune system and limitations in the experimental approaches that are available for studying immune responses in humans, insights into the underlying mechanisms have been difficult to obtain. One starting point toward a mechanistic understanding can be characterized as reductionist, in which the goal is to first delineate age-related differences intrinsic to defined immune cell types in an ex vivo setting, with subsequent experiments focusing on how these intrinsic differences contribute to clinical observations in the far more complex in vivo setting.  In this study, RNA-seq was used to examine the intrinsic response of blood monocytes to LPS stimulation and Lm infection. The improved dynamic range of the RNA-seq method in comparison to microarray methods (129) led to the expectation that the results might reveal extensive differences among the age groups. Given this expectation, the most striking finding is perhaps the extensive similarity in both constitutive and inducible gene expression. The results suggest that a single mechanism – variable induction of IRF3 – may be responsible for most and perhaps all differences between neonatal and young adult monocytes. Another defined mechanism, variable low-level inflammation prior to induction, may explain most of the differences between young adults and older adults.  Our results strengthen previous evidence that reduced IRF3 activity makes a major contribution to the deficient innate responses of neonates to infectious stimuli (55). The previous study was performed with LPS-stimulated dendritic cells differentiated from cord blood or adult peripheral blood, whereas the current study was performed with freshly isolated monocytes stimulated with LPS or infected with Lm. In the previous study, a large number of IRF3- and Type 1 IFN-dependent genes were found to be expressed at reduced levels in neonates. The reduced 99  expression of these genes was attributed to reduced IRF3 activity because the neonatal and adult cells responded similarly to direct stimulation with IFNβ. Reduced IRF3 activity would lead to a broad reduction in the expression of IFN-dependent genes because IRF3 is critical for the initial induction of IFNB transcription in LPS-stimulated cells.  Interestingly, the previous study found that IRF3 translocated to the nucleus similarly in neonatal and adult cells, and its in vitro DNA-binding activity was similarly induced (55). However, its ability to bind endogenous target genes was reduced, suggesting that an additional event – possibly an additional post-translational modification – is needed for binding to target genes and may be reduced in neonatal cells. Of relevance, a separate study identified a major defect in IRF7 activation in neonatal plasmacytoid dendritic cells and, in this cell type, a defect in nuclear translocation of IRF7 was observed in neonates (19). An additional clue into the underlying mechanism is our finding of a similar deficiency in both LPS-stimulated and Lm-infected cells. LPS and Lm activate IRF3 via different signaling pathways – the TRIF pathway for LPS and the STING pathway for Lm (51,140) – suggesting that the reduced IRF3 activity in neonatal cells involves a mechanism that influences the activation of IRF3-dependent genes via both of these pathways.  In addition to elucidating the specific mechanism, it will also be important to understand why this difference exists between neonatal and adult monocytes. The simplest model is that neonatal monocytes are fundamentally different from adult monocytes and represent a developmentally distinct monocyte subtype. However, this model predicts that prominent gene expression differences would be observed prior to stimulation. The differentially expressed genes would be 100  expected to include cell surface markers that define different myeloid cell populations and genes that might help regulate IFN responses. Surprisingly, the expression profiles of the unstimulated monocytes from neonates and adults were remarkably similar (data not shown), with no large differences suggesting that they represent different myeloid subtypes, and no differences that would be predictive of the differential induction of IRF3-dependent genes.  One possible explanation for this apparent paradox is that the differences between neonatal and adult monocytes are due to the differential expression of micro- RNAs or long noncoding RNAs, which were not examined in this analysis. However, the differential expression or processing of non-coding RNAs would be expected to require the differential expression of transcription factors that regulate the non-coding RNA genes, or the differential expression of processing enzymes; these protein-coding genes would have been included in our analysis. Differences in alternative pre-mRNA splicing also were not examined in our analysis. Once again, differential splicing would be expected to require the differential expression of genes encoding splicing factors. A more likely possibility is that the pronounced difference in the induction of IRF3- dependent genes is regulated by genes whose expression levels vary by only a small and statistically insignificant amount.  Because the RNA-seq profiles failed to provide evidence that the neonatal and adult cells represent developmentally distinct monocyte subtypes, the neonatal-adult differences may instead be due to environmental differences that act on the fully differentiated cells to influence their capacity to induce IRF3 activity. Such a mechanism would need to influence IRF3’s capacity for induction for a prolonged time period, because the IRF3 difference has been 101  observed in dendritic cells differentiated for several days in vitro (55). This environmental difference may lead to small and stable differences in the expression of genes that regulate IRF3 activity. Alternatively, the neonatal microenvironment may alter the structure of chromatin at IRF3-dependent genes, resulting in a reduced capacity for IRF3 binding in response to a stimulus.  To summarize, the results of this study will help guide future efforts to understand the mechanisms responsible for the immune deficiencies observed in neonates and older adults. The results suggest that the intrinsic properties of blood monocytes are remarkably stable throughout life and vary to only a limited extent. The reduced capacity of neonatal monocytes to activate IRF3-dependent genes could play an important role in the deficient response of neonates to many microbial pathogens. Furthermore, the low-level inflammation that is readily apparent in monocytes from older adults could also influence anti-microbial responses. RNA-seq studies to quantitatively characterize intrinsic age-related differences in other innate and adaptive immune cell types should provide additional insights and should ultimately suggest strategies to enhance immune responses in deficient populations.    102  CHAPTER 5: DISCUSSION  5.1 Introduction  The innate immune response was once thought to be a simple system which just acts as a barrier to keep infections at bay while the adaptive immune system ramps up protective immune responses (1,2). Nowadays, there is an immense appreciation for what cells of the innate immune system are capable of doing – from sensing all types of pathogens to final clearance of infections (2). They are powerful complex key players of the human immune system. While the primary role of myeloid cells of the innate system are to deliver and present antigens to naïve T cells, they are also very critical for clearance of infection at the cellular level during different points in the infection cycle of many different pathogens. Their role in CAI is extremely important for the efficient clearance of intracellular infections most especially.  5.2 The interferons: a double-edged sword  Two main types of interferons are likely to be involved in the context of most infections with intracellular pathogens. Type I IFNs are comprised of IFN-β and IFN-α (1), of which IFN-α has 14 different subtypes (87). IFN-γ is the only type II IFN (1). Several studies have shown that newborns are capable of producing adult-like levels of IFN-γ when adequately stimulated (103,141,142); the same is true for older adults (35). Therefore, the main difference in inducing protective immune responses between age groups is likely to come from cytokines that are produced even earlier during the response. Our data support this hypothesis in that we see 103  expression of IFN-β in adults in response to stimulation with TLR ligands or Lm, but not in newborns or older adults (Figure 3.3).   Although these two families of IFNs rely on similar IRF and STAT signaling pathways in order to carry out their functions, they have a paradoxical, antagonistic relationship with each other. For example, the expression of type I IFN causes the downregulation of the IFN-γ receptor IFNGR1, thereby directly inhibiting type II IFN signaling (26). Despite this paradox, both type I and II IFNs are important in mounting protective immune responses (143). For example, IFN-γ has an established role in protective immune responses especially against intracellular infections (1,18,28–30). It also has an established role in the early protection against Lm infection via cytokine production by NK cells (14,27). At the same time, myeloid cells of the innate immune system respond to Lm infection by the release of type I IFNs (both IFN-β and IFN-α) (14,27,143), which have a detrimental role in protection of adult mice at least from Lm (26). However, there is usually no cell where these cytokines are co-expressed; NK cells are usually located in the periphery to activate macrophages (14,27) while activated myeloid cells travel to lymphoid tissues to interact with T cells (1,14,27,103) from which we get stronger, prolonged IFN-γ expression upon T cell activation.   In summary, the data presented in Chapter 2 implicates altered IRF activity in newborn cDC and pDC subsets as centrally involved in many of the known age-related differences of immunity. Through detailed molecular dissection, I was able to correlate age-dependent signalling activity in MAPK and IRF pathways, linking differential pIRF7 nuclear translocation with MAP3K8 levels both at baseline and upon stimulation, which then led to altered pERK1/2 MAPK 104  activation and further downstream effector cytokine expression. Moreover, data presented in Chapter 3 provides further evidence of the age-dependent IFN signaling using primary monocytes derived from human newborns and young adults. Our RNA-seq analysis showed that genes whose promoters harbour the IRF1/2 binding site are overrepresented only in adults upon infection and LPS stimulation. To our knowledge, this is the first report to do so in human primary cells without in vitro expansion.  5.3 Interferon-inducible GTPases involved in cell autonomous immunity are also differentially regulated in an age-dependent manner  Cell autonomous immunity is an ancient branch of the immune system. All living organisms require protection/response at the cellular level in order to fight off pathogens and survive (1–3). In other words, all living organisms have the ability to carry out CAI. Some well-known cellular mechanisms that are involved in cell autonomous immunity include autophagy, enhancing inflammasome assembly, membrane disruption, apoptosis control, and ROS production (3,49,105).   In mammals, the key effectors of cell autonomous immunity appear to be controlled by IFNs. They are called the IFN-inducible guanosine triphosphatases (GTPases) which contain four different subfamilies as illustrated in Figure 5.1 (3,49,105). There are both small (47 kDa) and large (~285 kDa) GTPases that belong to these four subfamilies (3,49,105,107). While there is some homology in mouse and human IFN-inducible GTPases, there are some that are species specific as well (3,49). Furthermore, these IFN-inducible GTPases can be induced by different 105  combinations of IFNs. For example, GBPs can be induced largely by IFN-γ but also by type I and type III IFNs (3). The IFN-induced GTP-binding protein MX dynamin-like GTPase 1 (Mx1) myxovirus (MX) subfamily of resistance proteins can be induced by type I and III IFNs (3). Some subfamilies can also be induced by certain pro-inflammatory cytokines; IL-1β and TNF can induce the expression of certain GBP1 (105).   Figure 5.1 IFN-inducible GTPase families in humans and mice. Phylogenetic tree of the four IFN-inducible GTPase subfamilies: 21-47 kDa IRGs, 65-73 kDa GBPs, 72-82 kDa MX proteins and 200-285 kDa GVINs. Names in parentheses indicate previous designations prior to the currently adopted MGI nomenclature with the exception of Lirg bidomain proteins. Two IRGM and Gbp5 isoforms are included, as well as a Gbp1 (Gbp2ts1) transpliced isoform. Selected H-Ras and dynamin GTPases are shown for G domain comparison; scale bar indicates substitutions/site. Figure adapted from the open access article by (49) on Elsevier.  Several groups have shown that IFN-inducible GTPases are necessary and essential in the clearance of many different intracellular pathogens (3,49,105). For example, GBPs have been shown to mediate the clearance of Mycobacterium tuberculosis (Mtb) and Lm in murine 106  splenocytes (57); members of the GBP subfamily act in synergy to clear pathogens from the spleen as deletion of the section encoding murine GBPs on chromosome 3 results in increased bacterial burden in the spleen after inoculation compared to single knock out models (108,57). This has also been shown in a few seminal studies in human ex vivo and in vitro experiments (113,115,144,145). The GBPs also act in concert with the immunity related guanosine triphosphatases (IRG) subfamily, which play a role in protein transport and autophagy, to traffic to Toxoplasma gondii vacuoles and facilitate membrane disruption (106).   Given the important role of IFNs in the context of intracellular infection, these cell autonomous effector mechanisms enable multiple cell types to facilitate clearance of many different types of pathogens. Therefore, it comes as no surprise that different IFN-inducible GTPases play different functional roles depending on the type of infection encountered (3,49,105). For example, GBP1 has been shown to inhibit apoptosis in human endothelial cells upon infection with Escherichia coli (115). GBP1 has also been shown to facilitate clearance of Lm infection in splenocytes by transporting proteins to foci of infection inside the cell (3,49,57,105). However, there are currently no studies showing this to be the same case in human primary cells. In fact, there are currently no studies showing what the function of GBP1 is in the context of the human immune system. Ours is the first account showing that GBP1 is differentially upregulated in the context of Lm infection and that this observation is age-dependent, indicating that it may be of significant clinical interest.  In summary, the data presented in Chapter 3-4 extend the observation of age-dependent difference in IRF responses to infection in newborns versus young adults. Specifically, we 107  provide evidence that key IFN response genes are differentially expressed in an age-dependent manner upon infection of primary monocytes with Lm. This is the first report that identifies key effector functions of CAI to differ between age groups, and do so in a manner that correlates with age-dependent susceptibility to infection. Specifically, the GBP subfamily of IFN-inducible GTPases were differentially upregulated in adult monocytes but not in newborn or older adult individuals upon infection with Lm. This is most likely caused by a differential expression of IFNs, namely type I IFN (IFN-β). We further confirmed the differential expression of GBP1 at the protein level.   5.4 Study limitations  These studies were performed primarily using purified human cell populations. This approach is important for determining the responses of each involved cell type and provide insight into the relative contributions of each component to an immune response. However, it is important to note that several cell types do in fact communicate with each other and integrate all the signals in their environment in order to mount an appropriate response. Therefore, it is important to validate these findings in a relevant mixed cell population, such as peripheral blood mononuclear cells to ensure the validity of the overall findings of this project. We have in fact performed this validation in our study in both Chapter 2 and Chapter 3 and indeed our findings in purified cDCs, pDCs, and monocytes do remain true.  Another major study limitation is that the newborn subjects in these studies were all delivered using the same method in order to standardize our procedures. All samples collected were 108  derived from placentas of mothers that underwent schedule Caesarean sections. It is well known that the mode of delivery greatly impacts the newborn’s microbiome and therefore also affects the development of the immune system (33,146). Therefore, we limit our conclusion statements regarding age-dependent differences in the response of newborns vs healthy adults just to newborns delivered by Caesarean section and not all newborns in general. This will need to be expanded upon in order to generalize our findings to the entire cohort of newborns.  Finally, we proposed a new, combined mechanism of susceptibility at the molecular level in two susceptible age groups – newborns and older adults. We have used TLR ligands as a simple model in very rare cell populations investigated (i.e. cDCs and pDCs) and we used a model intracellular pathogen Lm to infect monocytes. The conclusions of these studies show very similar results – that there is a deficit in the IFN response axis in newborns and older adults. However, different pathogens will activate slightly different combinations of signaling pathways to alert the immune system of the presence of a pathogen (1,5,8). Thus, we can only state that the IRF/IFN axis is important in APC functions in response to TLR stimulation and Lm infection until we have tested for other types of intracellular pathogens such as viruses and parasites.  5.5 Main conclusions  This is the first investigation of age-dependent responses in rare native innate immune cell populations using transcriptomic approaches. In particular, rare APC populations were successfully interrogated for age-related differences across three age groups in response to stimulation or infection. Taken together, my data identify a possible epicenter of age-dependent 109  host immunity. Specifically, I have found that IRF signaling, an important pathway that is activated upon infection with intracellular pathogens or following activation of TLR7/8, is altered in an age-dependent manner closely correlating with the age-dependent susceptibility to infection. The age-dependent IRF signaling described here is the first to be described using primary human cells in susceptible versus healthy age groups. The observations in this thesis thus greatly extend existing knowledge about the molecular basis of age-dependent IRF signaling in human monocyte derived-DCs, cell lines or mouse models. Additionally, I have found a link between two different prominent signaling pathways (i.e. MAPK and IRF signaling) in innate immunity that converge. Specifically, differential IRF7 activation and nuclear translocation correlated with increased levels of MAP3K8 expression in adults. Not only did newborns display a deficit in IRF signaling due to the inability of IRF7 to translocate to the nucleus, they also expressed less MAP3K8 to be able to activate the IRF7 (Figures 2.2 and 2.3). I have further extended this to a highly relevant infection model using Lm and showed that downstream effector molecules of IFN signaling were also differentially expressed in a similar fashion. In fact, GBPs as an entire subfamily of important effectors of IFN signaling were differentially expressed in an age-dependent manner in monocytes upon Lm infection.  In summary, my data for the first time identifies age-dependent IRF activity as a key molecular mechanism underlying the differences in immune response not just across different immune stimuli (TLR ligand and infection) but also across the human age span.    110  5.6 Future directions  It is now well-established that newborns born from Caesarean sections have an altered IRF signaling response to both TLR ligands and several pathogens (19,28,55,56,64). It is, however, not appropriate to extend this observation to all newborns because different modes of deliveries have an impact on the resulting development of the immune response as previously stated. In order to make this observation general enough to the broad population of newborns, these experiments need to be repeated in newborns that have undergone vaginal deliveries and those born prematurely.   While I have performed the validation of the age-dependent GBP1 expression in newborns versus young adults, this has not been done in our older adult cohort. Along with collecting whole blood samples for qPCR validation of our findings in a mixed cell population, sufficient samples also need to be collected samples to validate GBP1 at the protein level for older adults in order to complete the validation experiments of the Lm microarrays. Furthermore, studies investigating the function of the GBP proteins, namely GBP1, need to be performed in order to fully validate the array results and provide a correlation of expression with function.   Finally, our investigations into the transcriptomic responses of TLR7/8 stimulation in cDCs and pDCs have been validated at the protein level. However, 3M-003 is simply a synthetic ligand for TLR7/8. 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PLoS One. 2014;9(2).     136  APPENDICES  Appendix A Supplementary data for Chapter 2.   Figure A.1. Adult and neonatal cDCs and pDCs responded very similarly to stimulation with 3M-003 at all time points investigated. Hierarchical clustering was performed on the top 5,000 variant probes on the whole genome transcriptional profiles of purified cDC and pDC and heatmaps were generated. Purified dendritic cell subsets were isolated from six adult (A) and six neonatal (cord blood) (C) samples stimulated with or without 3M-003 at a final concentration of 5 uM for various time points as indicated. Shown are the average fold change intensity values for each dendritic cell subset.    420-2-4C A A C ACA6 hr 0 hr (unstimulated) 1 hrC A C A C A AC6 hr 0 hr (unstimulated) 1 hrcDC pDC137  Table A.1. Number of differentially expressed genes in response to 3M-003 stimulation was greater in cDCs than in pDCs in both age groups. n = 6 adults, 6 neonates. Cell Type Time (hrs) Age Number of Genes cDC 1 Adult 7,317   Newborn 4,196  6 Adult 13,754   Newborn 10,201 pDC 1 Adult 5,081   Newborn 5,076  6 Adult 7,628   Newborn 7,407  Table A.2. Number of differentially expressed genes that were age-dependently different in response to 3M-003 stimulation is greater in cDC than pDC. n = 6 adults, 6 neonates. Cell Type Time (hrs) Number of Genes cDC 0 2,273  1 205  6 1,215 pDC 0 756  1 98  6 154    138  Table A.3. Top 100 genes that were differentially expressed at baseline between adult and neonatal cDCs. Shown are the log(Fold Change) values using neonatal levels as the reference point. n = 6 adults, 6 neonates. Rank Gene Name Log(Fold Change) Adjusted P value 1 IGF2BP3 4.724336682 1.77E-28 2 PLAG1 2.280157149 2.29E-24 3 HBG1 6.001913541 1.69E-17 4 HBG2 5.937570576 6.51E-17 5 DUSP4 3.290382428 2.40E-15 6 MAF 2.609045181 4.21E-15 7 DSC2 3.576686911 6.96E-15 8 THBS1 4.964997465 1.42E-14 9 SASH1 2.679112512 3.02E-14 10 GPR126 -2.03194603 3.02E-14 11 CDKN2C -1.946412133 3.49E-14 12 GPR124 2.10516407 3.49E-14 13 TSRC1 1.399743669 1.03E-13 14 ADM 2.816779321 1.15E-13 15 NFIL3 1.832519759 1.41E-13 16 GNG11 2.638811551 1.47E-13 17 GALM -1.148337672 1.50E-13 18 ARID3A 0.825058755 1.72E-13 19 CDKN2C -1.338036232 5.53E-13 20 MGC18216 1.824207522 7.49E-13 139  Rank Gene Name Log(Fold Change) Adjusted P value 21 PIM1 1.985000895 1.01E-12 22 HBB 4.952939483 1.21E-12 23 EIF5A2 1.30587435 1.74E-12 24 HBA1 4.567098287 2.48E-12 25 TRIB1 1.341977908 4.10E-12 26 HBEGF 2.796493841 4.54E-12 27 PTP4A3 1.737790591 5.29E-12 28 HERPUD1 1.103366552 1.35E-11 29 S100P 2.925865301 2.57E-11 30 PRDM1 1.514065061 3.02E-11 31 PSMA1 1.434842182 3.02E-11 32 LAPTM4B 1.192829923 3.22E-11 33 SLC25A20 -0.937517156 4.06E-11 34 PELI1 1.416809833 4.38E-11 35 SLC29A1 1.573531007 5.68E-11 36 LOC641825 1.953702084 5.68E-11 37 GTF2IRD1 2.047433058 5.68E-11 38 HS.482814 2.001482158 5.87E-11 39 DUSP5 1.597812114 6.26E-11 40 MMP9 3.374739609 8.85E-11   140  Rank Gene Name Log(Fold Change) Adjusted P value 41 G0S2 2.817720299 9.41E-11 42 CDKN1C 2.671823447 1.13E-10 43 PLEKHG3 1.112482379 1.16E-10 44 SCHIP1 -1.271491102 1.37E-10 45 NPL 1.724468499 1.48E-10 46 EMP1 -1.655440402 1.60E-10 47 IRS2 2.18981798 1.60E-10 48 MSR1 1.708929483 2.65E-10 49 C16ORF69 0.939577444 3.19E-10 50 INPP5A 0.956001881 3.38E-10 51 CTLA4 2.004504425 3.38E-10 52 PDE4B 2.626978744 3.76E-10 53 FAM46C 2.052550183 3.78E-10 54 ADPRHL2 -0.738254974 5.16E-10 55 PLAUR 1.654353272 5.81E-10 56 GBP1 -1.912936743 8.26E-10 57 CPT1A -1.685386073 8.35E-10 58 NUP214 -0.645259768 1.01E-09 59 PLAU 1.59366561 1.15E-09 60 MIDN 1.173870787 1.36E-09   141  Rank Gene Name Log(Fold Change) Adjusted P value 61 MRFAP1L1 -0.976757033 1.63E-09 62 MSLN 1.923968352 1.84E-09 63 VEGFA 1.632359623 1.86E-09 64 PTP4A3 1.583927165 1.92E-09 65 ABCB4 -1.139721007 1.94E-09 66 RBMS1 1.203469304 1.94E-09 67 RAB37 1.285283523 2.32E-09 68 MAP3K8 1.338274971 2.32E-09 69 PSD3 1.324798245 2.60E-09 70 PDIK1L -0.732375818 3.34E-09 71 GFRA2 1.060571803 4.52E-09 72 HNRPLL -0.85376388 4.97E-09 73 HYLS1 -0.891994289 4.97E-09 74 SLC30A1 1.097896256 5.49E-09 75 LOC152485 1.167175013 5.64E-09 76 DDO -1.058565804 5.64E-09 77 PFKFB3 1.789836552 5.71E-09 78 ZBTB33 -0.915068392 5.98E-09 79 PFKFB4 0.803652875 8.36E-09 80 SLC2A3 1.572431474 9.58E-09   142  Rank Gene Name Log(Fold Change) Adjusted P value 81 LOC492311 -1.398026961 9.82E-09 82 DUSP2 1.737195392 1.00E-08 83 LOC440359 0.989256395 1.11E-08 84 INPP5A 1.011137419 1.35E-08 85 ORC5L -0.642391484 1.43E-08 86 STX2 0.788665964 1.51E-08 87 B3GNT5 1.627712288 1.56E-08 88 RRAS2 1.395499622 1.56E-08 89 ANKRD37 1.186514241 1.56E-08 90 ZNF703 1.342152168 1.75E-08 91 PBEF1 1.817313347 2.06E-08 92 TFRC 0.598429106 2.06E-08 93 C2ORF26 1.698838494 2.09E-08 94 LOC286334 -1.030112938 2.09E-08 95 MAFB 1.20022227 2.22E-08 96 HS.572444 1.722613137 2.81E-08 97 NFKBIZ 0.841852206 2.81E-08 98 DDIT4 1.148115804 2.93E-08 99 RIPK2 1.04500732 2.94E-08 100 ACAA2 -0.590517951 3.06E-08 143   Figure A.2. Adult and neonatal cDCs and pDCs expressed similar amounts of IRF7 mRNA. Adult and neonatal (cord) cDCs and pDCs were isolated from 3 individuals per age group and stimulated with 3M-003 at a final concentration of 5 uM for 1 and 6 hr. mRNA expression of IRF7 was then determined using qPCR and  normalized to β-actin mRNA. Data are presented as relative expression values in comparison with the unstimulated controls at 0 hr for each age group. Shown are the average relative expression values for each independent individual ± SEM; unpaired Student’s t-test was performed for statistical analysis.   Figure A.3. Median fluorescence intensities of phosphorylated IRF7 was significantly lower in newborn dendritic cells compared to adults at baseline. PBMCs or CBMCs from 5 individuals from each age group were cultured for the length of time indicated. Cells were harvested and stained for the presence of phosphorylated IRF7 (pIRF7) in cDC and pDC subsets. (A.) Neonatal cDC have decreased pIRF7 staining compared to adult cDC at all time points investigated. (B.) Neonatal pDC also show lower pIRF7 staining per cell compared to adult pDC samples at all time points tested. Represented is the average of all MFI for each age group ± SEM. All data were analyzed by two-way ANOVA followed by Bonferroni posttest; *p < 0.05, **p < 0.01.    0hr 1hr 6hr051015cDC3M-003RelativeIRF7mRNAExpression0hr 1hr 6hr0.00.51.01.52.02.5pDC3M-003AdultCord0.25 2 4 602000400060008000100001200014000****Time (hr)MFI0.25 2 4 602000400060008000AdultNeonate**Time (hr)A. B.144  Supplemental Procedures and Associated Tables and Figures  Table A.4. Antibodies and Lasers used on the BD LSR II Flow Cytometer for Cell Purification Checks and Phosphoflow. Supplemental methods information for flow cytometry experiments used in this study.  Characteristic Being Measured Antibody Name Clone Name Vendor Cat# Dilution Used Violet    V450 Cell Surface Protein CD11c B-Ly6 BD#560389 1:100 V500 Cell Surface Protein CD14 M5E2 BD#561391 1:100 efluor605 Cell Surface Protein HLA-DR LN3 eBio#93-9956 1:100 Red    APC Cell Surface Protein CD11c Bu15 eBio#17-0128-42 1:100 Blue    PE-Cy7 Cell Surface Protein CD123 6H6 eBio#25-1239-42 1:100 PerCP-efluor710 Intracellular Protein pERK1/2(T204/Y202) MILAN8R eBio#46-0910-41 1:100    145  Table A.5. Antibodies and Lasers used on the Amnis Imagestream for Determining Nuclear Translocation of pIRF7 in 3M-003 Stimulated PBMCs. Supplemental methods information pertaining to Amnis experiments performed in this study.  Characteristic Being Measured Antibody Name Clone Name Vendor Cat# Dilution Used 488 nm Laser    PerCP-efluor710 Cell Surface Protein CD11c 3.9 eBio#46-0116-41 1:50 PE-Cy7  Cell Surface Protein CD123 6H6 eBio#25-1239-42 1:100 PE-Cy7 Cell Surface Protein CD8 SK1 eBio#8025-0087-120 1:100 PerCP-efluor710 Cell Surface Protein HLA-DR L243 eBio#46-9952-41 1:50 AF488 Intracellular Protein  IRF-7 (pS477/pS479) K47-671 BD Phosphoflow 1:100 efluor605 Cell Surface Protein HLA-DR LN3 eBio#93-9956 1:50 405 nm Laser    DAPI Nucleus - Invitrogen Molecular Probes#D3571 1:10,000    146  Appendix B  Supplementary material for Chapter 3.  Figure B.1. Gating strategies used for flow cytometry experiments to determine the purity of human monocyte isolations performed. Mononuclear cells and purified monocytes were checked for purity after isolation using flow cytometry and gated according to their surface markers. After processing, purified monocytes have at least 90% purity.    147  Table B.1. Fold change induction of other interferon-inducible GTPase subfamilies in Lm-infected monocytes from human newborns, young adults, and older adults. Shown are the mean fold change values compared to uninfected controls. NI = Not induced. n = 6 per age group.    148  Appendix C  Supplementary material for Chapter 4. 149   Figure C.1. LPS-induced genes exhibiting statistically significant differences in transcript levels in cord blood and young adult monocytes. An expanded version of Fig. 5A is shown, 150  which includes the identities of the LPS-induced genes that are differentially expressed in cord blood and young adult monocytes. RefSeq IDs and gene names are shown for human genes and their mouse orthologs.  151   152  Figure C.2. Lm-induced genes exhibiting statistically significant differences in transcript levels in cord blood and young adult monocytes. An expanded version of Fig. 6A is shown, which includes the identities of the Lm-induced genes that are differentially expressed in cord blood and young adult monocytes. RefSeq IDs and gene names are shown for human genes and their mouse orthologs.  153   154  Figure C.3. LPS-induced genes that exhibit statistically significant differences in basal transcript levels in monocytes from young and older adults. An expanded version of Fig. 7A is shown, which includes the identities of LPS-induced genes that are differentially expressed in unstimulated young and older adult monocytes. Human RefSeq IDs and gene names are shown.  

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