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Exploring the development of endotoxin tolerance during sepsis and a possible immunomodulatory therapy Pena, Olga M. 2013

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 EXPLORING THE DEVELOPMENT OF ENDOTOXIN TOLERANCE DURING SEPSIS AND A POSSIBLE IMMUNOMODULATORY THERAPY  by OLGA M. PENA  Bachelor of Science Pontificia Universidad Javeriana, Bogota - Colombia 2001  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (MICROBIOLOGY AND IMMUNOLOGY)   THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  August, 2013   ? Olga M. Pena, 2013   ii Abstract The immune system responds to bacterial infections by inducing pro-inflammatory mediators, which recruit and activate immune cells to eliminate the invading microbe. However, a systemic and uncontrolled inflammatory reaction may lead to the development of sepsis, which is characterized by organ failure and eventually death. Classical (M1) and alternative (M2) macrophage polarization is known to occur in response to challenges within a microenvironment, like the encounter of a pathogen. Bacterial products like lipopolysaccharide (LPS), can be a potent inducer of inflammation and M1 polarization. LPS can also generate an effect in mononuclear cells known as endotoxin tolerance, defined as the reduced capacity of a cell to respond to LPS activation after an initial exposure to this stimulus. Using systems biology approaches in PBMCs, and macrophages, it was determined here that gene responses during endotoxin tolerance were similar to those found during M2 polarization, including reduced production of proinflammatory mediators, expression of genes involved in phagocytosis, control of oxidative stress, as well as tissue remodelling (Chapter 2). Moreover, an extensive bioinformatic meta-analysis was performed using these findings, characterizing unique LPS and endotoxin tolerance gene signatures. These signatures were compared with transcriptional changes observed in human sepsis cohorts based on our data or from the literature. Very interestingly, it was found that septic patients strongly presented an immunological profile associated with an endotoxin tolerance gene signature, rather than a dominant pro-inflammatory response as commonly believed to occur in early sepsis (Chapter 3).  Additionally, a potential immunomodulator for use in infections and sepsis was investigated at the mechanistic level. Here, the effect of synthetic innate defense regulator peptide (IDR1018) on macrophage differentiation was tested. The results obtained suggests that IDR-1018 drives macrophage differentiation towards an intermediate M1-M2 state, enhancing anti-inflammatory functions while maintaining certain pro-inflammatory activities important to the resolution of infection (Chapter 4). In conclusion, the unique endotoxin tolerance gene signature discovered here and found in septic patients, can be used as biomarkers, that allow characterization of the critical immunological status of the septic patient, enabling the application of appropriate immunological therapies that might improve the survival rate during this deadly syndrome.   iii Preface The majority of the research presented in this thesis was drawn from my own published papers or manuscripts in preparation. Below is a description of my own and my colleague's contributions to each chapter in this thesis.  Chapter 1: The section on the immunomodulatory properties of host defence peptides written here was partially taken from the review article published as Nicole Afacan, Amy Yeung, Olga M Pena, and Robert E. W. Hancock (2012). ?Therapeutic potential of host defense peptides in antibiotic-resistant infections? Current Pharmaceutical Design 18:807-19; and copyright permission was granted for use in this thesis. This section was entirely written by me in the original article.  Chapter 2:  A version of Chapter 2 has been published as Olga M. Pena, Jelena Pistolic, Disha Raj, Christopher D. Fjell, and Robert E. W. Hancock. (2011) ?Endotoxin tolerance represents a distinctive state of alternative polarization (M2) in human mononuclear cells?. Journal Immunology 186:7243-54; and copyright permission was granted for its use in this thesis. Jelena Pistolic performed the in vitro scrape assay and, together with me contributed to the design and performance of the MDM experiements. Disha Raj assisted with the endotoxin tolerance induction experiments as well as RT-qPCR and ELISA studies. Christopher Fjell collaborated on the bioinformatics analysis. I conceived the hypothesis, designed, performed and analyzed all other experiments, and wrote the manuscript. Robert Hancock was involved in designing and discussing the research plan and providing input regarding experimental results, as well as in editing the manuscript. Venous blood was collected from healthy adult donors in accordance with the ethical approval guidelines of the UBC Clinical Research Ethics Board Certificate # H04-70232, project name and number: Exploring Innate Immunity and the Novel Functions of Host Defence Peptides - C04-0232.  Chapter 3:  A version of Chapter 3 is currently being prepared for publication as Olga M. Pena, David G Hancock, Jeff Xia, Christopher Fjell, Robert E.W. Hancock. (2013) ?A unique  iv endotoxin tolerance profile strongly predominates during clinical sepsis?. David Hancock and I are joint first authors in this paper. I conceived the hypothesis that provided the foundation for the studies in this chapter. David and I discussed the research plan and designed the different computational approaches performed here. I was trained and assisted in different bioinformatic analyses by Jeff Xia, Chris Fjell and David Hancock. Robert Hancock was involved in suggesting different approaches, discussing experimental results, and editing the manuscript.   Chapter 4:  A version of Chapter 4 has been published as. Olga M. Pena, Nicole Afacan, Jelena Pistolic, Carol Chen, Laurence Madera, Reza Falsafi, Christopher D. Fjell, Robert E. W. Hancock. (2013) ?Synthetic cationic peptide IDR-1018 modulates human macrophage differentiation. PLoS One 8:e52449?, and copyright is retained by the authors for its use in this thesis under the Creative Commons Attribution Licence from PLoS One Journal. Nicole Afacan and Jelena Pistolic helped somewhat with the design and performance of the experiments. Laurence Madera collected and isolated PBMC and performed some ELISAs. Carol Chen and Chris Fjell contributed to the bioinformatics analyses. Reza Falsafi performed the RNA-Seq. I formulated the hypothesis, designed and performed the experiments and wrote the manuscript. Robert Hancock was involved in the concept and design of the research plan, the discussion of the results and establishment of the final conclusions. He and Nicole Afacan also edited the manuscript.               v Table of Contents Abstract .......................................................................................................................................... ii	 ?Preface ........................................................................................................................................... iii	 ?Table of Contents .......................................................................................................................... v	 ?List of Tables .............................................................................................................................. viii	 ?List of Figures ............................................................................................................................... ix	 ?List of Abbreviations ................................................................................................................... xi	 ?Acknowledgements .................................................................................................................... xiii	 ?Dedication ................................................................................................................................... xiv	 ?CHAPTER 1:	 ?	 ?INTRODUCTION ................................................................................................ 1	 ?1.1	 ? ENDOTOXIN TOLERANCE ............................................................................................. 1	 ?1.1.1	 ? Definition ............................................................................................................................ 1	 ?1.1.2	 ? Homo-tolerance vs. hetero-tolerance .................................................................................. 1	 ?1.1.3	 ? Molecular mechanisms ....................................................................................................... 2	 ?1.2	 ? MACROPHAGES ................................................................................................................ 3	 ?1.2.1	 ? Definition ............................................................................................................................ 3	 ?1.2.2	 ? Classification ....................................................................................................................... 3	 ?1.2.3	 ? Molecular mechanisms ....................................................................................................... 4	 ?1.2.4	 ? Biological functions ............................................................................................................ 6	 ?1.2.4.1	 ? Inflammation: ................................................................................................................... 6	 ?1.2.4.2	 ? Immunoregulation: ........................................................................................................... 6	 ?1.2.4.3	 ? Phagocytosis: ................................................................................................................... 6	 ?1.2.4.4	 ? Antigen presentation: ....................................................................................................... 6	 ?1.2.4.5	 ? Tissue remodelling: .......................................................................................................... 7	 ?1.2.4.6	 ? Metabolism: ..................................................................................................................... 7	 ?1.2.5	 ? Roles in health and disease ................................................................................................. 7	 ?1.2.5.1	 ? Cancer: ............................................................................................................................. 7	 ?1.2.5.2	 ? Metabolic syndromes: ...................................................................................................... 8	 ?1.3	 ? SEPSIS .................................................................................................................................. 8	 ?1.3.1	 ? Definition and epidemiology .............................................................................................. 8	 ?1.3.2	 ? General predispositions ....................................................................................................... 9	 ?1.3.3	 ? Pathophysiology .................................................................................................................. 9	 ?1.3.4	 ? Immunotherapies in sepsis ................................................................................................ 12	 ?1.4	 ? HOST DEFENSE PEPTIDES .......................................................................................... 12	 ?1.4.1	 ? Immunomodulatory activities ........................................................................................... 13	 ?1.4.1.1	 ? Chemotactic activity: ..................................................................................................... 13	 ?1.4.1.2	 ? Anti-endotoxic activity: ................................................................................................. 14	 ?1.4.1.3	 ? Wound healing and angiogenesis: ................................................................................. 15	 ?1.4.1.4	 ? Other functions: ............................................................................................................. 15	 ?1.5	 ? INNATE DEFENCE REGULATOR PEPTIDES ........................................................... 16	 ?1.6	 ? MAJOR OBJECTIVE OF THESIS ................................................................................. 17	 ?1.7	 ? HYPOTHESES .................................................................................................................. 17	 ?1.7.1	 ? Hypothesis I ...................................................................................................................... 17	 ?1.7.2	 ? Hypothesis II ..................................................................................................................... 17	 ?1.7.3	 ? Hypothesis III .................................................................................................................... 17	 ?CHAPTER 2:	 ? ENDOTOXIN TOLERANCE REPRESENTS A DISTINCTIVE STATE OF ALTERNATIVE POLARIZATION (M2) IN HUMAN MONONUCLEAR CELLS ... 18	 ? vi 2.1	 ? INTRODUCTION .............................................................................................................. 18	 ?2.2	 ? MATERIALS AND METHODS ...................................................................................... 19	 ?2.2.1	 ? Cells and Reagents ............................................................................................................ 19	 ?2.2.2	 ? MDM differentiation ......................................................................................................... 20	 ?2.2.3	 ? Endotoxin tolerance induction experiments ...................................................................... 20	 ?2.2.4	 ? RNA isolation ................................................................................................................... 21	 ?2.2.5	 ? Microarray experiment and analysis ................................................................................. 21	 ?2.2.6	 ? Quantitative real-time PCR (qRT-PCR) ........................................................................... 21	 ?2.2.7	 ? Enzyme-linked immunosorbent assay (ELISA) ............................................................... 22	 ?2.2.8	 ? In vitro scrape assay .......................................................................................................... 22	 ?2.2.9	 ? Flow cytometry analysis ................................................................................................... 22	 ?2.2.10	 ? Statistical analysis ........................................................................................................... 23	 ?2.3	 ? RESULTS ........................................................................................................................... 23	 ?2.3.1	 ? Kinetics of cytokine and chemokine production during endotoxin tolerance in PBMCs . 23	 ?2.3.2	 ? Microarray analysis revealed strong differences in gene expression during endotoxin tolerance 24	 ?2.3.3	 ? Pro-inflammatory mediators and chemokine gene expression profiles during endotoxin tolerance were similar to those found during alternative polarization .......................................... 29	 ?2.3.4	 ? Cell surface marker expression revealed a unique profile during endotoxin tolerance with similarities to an alternative polarization state .............................................................................. 31	 ?2.3.5	 ? Key genes related to phagocytosis and wound healing were strongly up-regulated during endotoxin tolerance ....................................................................................................................... 32	 ?2.3.6	 ? Endotoxin tolerance conditions enhanced wound healing properties of epithelial cells .. 36	 ?2.3.7	 ? Metallothioneins (MT) are strongly up-regulated during endotoxin tolerance ................. 38	 ?2.4	 ? DISCUSSION ..................................................................................................................... 39	 ?CHAPTER 3:	 ?	 ?A UNIQUE ENDOTOXIN TOLERANCE PROFILE PREDOMINATES DURING CLINICAL SEPSIS ................................................................................................... 44	 ?3.1	 ? INTRODUCTION .............................................................................................................. 44	 ?3.2	 ? MATERIALS AND METHODS ...................................................................................... 45	 ?3.2.1	 ? Dataset search and selection ............................................................................................. 45	 ?3.2.2	 ? Data Processing and analysis ............................................................................................ 46	 ?3.2.2.1	 ? Transcriptional analysis ................................................................................................. 46	 ?3.2.2.2	 ? Hypergeometric distribution analysis ............................................................................ 47	 ?3.2.2.3	 ? Gene clustering analysis ................................................................................................ 47	 ?3.2.2.4	 ? Pathway over-representation meta-analysis ................................................................... 47	 ?3.3	 ? RESULTS ........................................................................................................................... 47	 ?3.3.1	 ? Generation of LPS and endotoxin tolerance gene signatures ........................................... 47	 ?3.3.2	 ? Hypergeometric distribution analysis of LPS and endotoxin tolerance signatures consistently correlate with previously published in vitro and in vivo endotoxemia models ........ 52	 ?3.3.3	 ? Hypergeometric distribution analysis between LPS and endotoxin tolerance gene signatures and sepsis datasets demonstrated a strong endotoxin tolerance profile among septic patients????.. ........................................................................................................................ 54	 ?3.3.4	 ? Signaling pathway over-representation meta-analysis validated the presence of a strong immunosuppressive state in septic patients .................................................................................. 62	 ?3.3.5	 ? Identification of candidate endotoxin tolerance biomarkers ............................................. 64	 ?3.4	 ? DISCUSSION ..................................................................................................................... 65	 ?CHAPTER 4:	 ? SYNTHETIC CATIONIC PEPTIDE IDR-1018 MODULATES HUMAN MACROPHAGE DIFFERENTIATION .................................................................................. 69	 ? vii 4.1	 ? INTRODUCTION .............................................................................................................. 69	 ?4.2	 ? MATERIALS AND METHODS ...................................................................................... 71	 ?4.2.1	 ? Ethics statement, cells and reagents .................................................................................. 71	 ?4.2.2	 ? Human macrophage differentiation .................................................................................. 71	 ?4.2.3	 ? RNA isolation ................................................................................................................... 72	 ?4.2.4	 ? Quantitative real-time PCR (qRT-PCR) ........................................................................... 72	 ?4.2.5	 ? RNA-seq and analysis ....................................................................................................... 73	 ?4.2.6	 ? Enzyme-linked immunosorbent assay (ELISA) ............................................................... 73	 ?4.2.7	 ? Phagocytosis of apoptotic cells ......................................................................................... 74	 ?4.2.8	 ? Statistical analysis ............................................................................................................. 74	 ?4.3	 ? RESULTS ........................................................................................................................... 74	 ?4.3.1	 ? Macrophages differentiated in the presence of IDR-1018 showed an intermediate cytokine response profile when compared to M1 and M2 macrophages ...................................... 74	 ?4.3.2	 ? IDR-1018 differentiated macrophages responded to LPS stimulation in a complex manner???............................................................................................................................... 75	 ?4.3.3	 ? Macrophages differentiated in the presence of IDR-1018 exhibited a chemokine profile different from that of M2 macrophages: ....................................................................................... 78	 ?4.3.4	 ? Differentiation of macrophages in the presence of IDR-1018 induced the expression of wound healing associated genes ................................................................................................... 79	 ?4.3.5	 ? Macrophages differentiated in the presence of IDR-1018 displayed enhanced phagocytic properties towards apoptotic cells: ................................................................................................ 81	 ?4.3.6	 ? IDR-1018-differentiated macrophages maintained plasticity enabling a return to a pro-inflammatory state: ....................................................................................................................... 82	 ?4.3.7	 ? IDR-1018 treated monocytes and monocyte-derived macrophages expressed transcription factors important for the development of M2 macrophages: ........................................................ 83	 ?4.4	 ? DISCUSSION ..................................................................................................................... 86	 ?CHAPTER 5:	 ?CONCLUDING CHAPTER .............................................................................. 90	 ?5.1	 ? INTRODUCTION .............................................................................................................. 90	 ?5.2	 ? UNDERSTANDING THE DEVELOPMENT OF ENDOTOXIN TOLERANCE DURING SEPSIS ........................................................................................................................ 91	 ?5.3	 ? IDR-1018 AS A POSSIBLE IMMUNOMODULATORY THERAPY FOR TREATING SEPSIS ................................................................................................................... 95	 ?5.4	 ? CLOSING REMARKS AND FUTURE DIRECTIONS ................................................ 96	 ?BIBLIOGRAPHY ....................................................................................................................... 98	 ?APPENDIX ................................................................................................................................ 111	 ?  viii List of Tables Table 1.1: Examples of immunotherapies investigated for sepsis [64] ........................................ 12	 ?Table 2.1: Gene Ontology terms over-representation analysis. .................................................... 27	 ?Table 2.2: Transcription factor binding site over-representation analysis. ................................... 28	 ?Table 3.1: Dataset Descriptions .................................................................................................... 46	 ?Table 3.2: Selection of LPS and endotoxin tolerance signature genes. ........................................ 48	 ?Table 3.3: Signaling pathway over-representation analysis based on up-regulated genes. .......... 62	 ?Table 3.4: Signaling pathway over-representation analysis based on down-regulated genes. ..... 63	 ?Table 4.1: Primer List ................................................................................................................... 72	 ?Table 4.2: M2 subset of IDR-1018 transcriptional data integrated with IRF-4 binding sites. ..... 85	 ?             ix List of Figures Figure 1.1: Molecular basis of macrophages polarization .............................................................. 5	 ?Figure 1.2: Immunomodulatory activities of HDPs ...................................................................... 14	 ?Figure 2.1: Kinetics of cytokine and chemokine secretion in LPS-tolerant cells. ........................ 24	 ?Figure 2.2: Microarray analysis revealed strong differences in gene expression during endotoxin tolerance. ........................................................................................................................... 26	 ?Figure 2.3: Proinflammatory mediators and chemokine profile responses during endotoxin tolerance were similar to those observed during M2 polarization. ................................... 30	 ?Figure 2.4: Cell surface marker expression revealed a unique profile during endotoxin tolerance with similarities to an alternative polarization state. ........................................................ 31	 ?Figure 2.5: Key genes related to phagocytosis and wound healing were consistently upregulated during endotoxin tolerance. .............................................................................................. 33	 ?Figure 2.6: Gene expression profile in PBMC and Monocytes. ................................................... 34	 ?Figure 2.7: Upregulation of key genes related to phagocytosis and wound healing during endotoxin tolerance in human MDMs. ............................................................................. 35	 ?Figure 2.8: Endotoxin tolerance enhanced wound-healing properties in epithelial cells. ............ 37	 ?Figure 2.9: Metallothioneins were strongly up-regulated during endotoxin tolerance. ................ 38	 ?Figure 2.10: Differential expression of negative regulators during tolerance in PBMC. ............. 39	 ?Figure 3.1: Hypergeometric distribution analysis between LPS/Endotoxin Tolerance (ET) signature genes and endotoxemia model datasets. ............................................................ 53	 ?Figure 3.2: Hypergeometric distribution analysis correlating LPS and endotoxin tolerance gene signatures in sepsis datasets demonstrate a strong endotoxin tolerance profile among septic patients. ................................................................................................................... 56	 ?Figure 3.3: Gene Cluster Analysis. ............................................................................................... 61	 ?Figure 3.4: Candidate biomarkers to identify an endotoxin tolerance status during sepsis. ......... 64	 ?Figure 4.1: Chemokine expression in PBMC after treatment with IDR-1018 and negative control peptides1020 and 1015. .................................................................................................... 75	 ?Figure 4.2: Cytokine and chemokine responses of acrophages differentiated in the presence of IDR-1018 and LL-37. ....................................................................................................... 76	 ?Figure 4.3: Macrophages differentiated in the presence of IDR-1018 showed an intermediate cytokine response profile when compared to M1 and M2 macrophages. ......................... 78	 ? x Figure 4.4: Macrophages differentiated in the presence of IDR-1018 exhibited a chemokine profile different from that of M2 macrophages. ............................................................... 79	 ?Figure 4.5: Differentiation of macrophages in the presence of IDR-1018 induced the expression of wound healing associated genes. .................................................................................. 80	 ?Figure 4.6: Macrophages differentiated in the presence of IDR-1018 displayed enhanced phagocytic properties towards apoptotic cells. ................................................................. 81	 ?Figure 4.7: IDR-1018 differentiated macrophages maintained plasticity as they could return to a pro-inflammatory state. ..................................................................................................... 83	 ?Figure 4.8: IDR-1018 treated monocytes and IDR-1018 differentiated macrophages expressed transcription factors important for the development of alternative (M2) macrophages. .. 84	 ?                xi   List of Abbreviations  AP-1 Activator Protein 1 ARDS Acute Respiratory Distress Syndrome ATM Adipose Tissue Macrophages ARF1 Adp-Ribosylation Factor 1 ?-MSH Alpha-Melanocyte-Stimulating Hormone B2M Beta 2 Microglobulin CCL CC Chemokine Ligand CXCL CXC Chemokine Ligand CRAMP Cathelicidin-Related Antimicrobial Peptide COX-2 Cyclooxygenase-2 EGF Endothelial Growth Factor ELISA Enzyme-Linked Immunosorbent Assay ETV4 Ets Variant Gene 4 ETS E-Twenty Six ERK1/2 Extracellular Signal-Regulated Kinase-1/2 FBS Fetal Bovine Serum FPRL-1 Formyl-Peptide Receptor Ligand-1 GMCSF Granulocyte-macrophage colony-stimulating factor GO Gene Ontology JMJD3 Histone H3 Lys 27 (H3K27) Demethylase HDP Host Defense Peptide HK3 Hexokinase 3 MHC Histocompatibility Complex HIV Human Immunodeficiency Virus ICU Intensive Care Unit Ig Immoglobulin IKB Inhibitor Of Nuclear Factor-?b IDRs Innate Defence Regulators SHIP Inositol Polyphosphate-5-Phosphatase IDR Innate Defense Regulator IRF3 Interferon Regulatory Factor 3 IFN? Interferon-G IL Interleukin IRAK-1 Interleukin Receptor Associated Kinase 1 LIPA Lipase A LPS Lipopolysaccharide LTA Lipoteichoic Acid ALI Acute Lung Injury MARCO Macrophage Receptor with Collagenous Structure  xii  MCSF Macrophage Colony Stimulating Factor MEM Minimum Essential Media  MNC Mononuclear cells MR Mannose Receptor MTF1 Metal Regulatory Transcription Factor MMP Metalloproteinases MT Metallothioneins MAPK Mitogen-Activated-Protein Kinases MDM Monocyte Derived Macrophages MyD88 Myeloid Differentiation Primary Response 88 NO Nitric Oxide NF-?B Nuclear Factor Kappa-Light-Chain-Enhancer Of Activated B Cells PAMPs Pathogen-Associated Molecular Patterns PBMC Peripheral Blood Mononuclear Cells PPAR? Peroxisome Proliferator-Activated Receptor Gamma PI3K Phosphoinositide-3-Kinase PDGF Platelet Derived Growth Factor qRT-PCR Quantitative Real-Time Pcr RelB Reticuloendotheliosis Viral Oncogene Homolog-B STAT Signal Transducer And Activator Of Transcription  SIGIRR Single Immunoglobulin Domain-Containing Il1R-Related Protein SPI1 Spleen Focus Forming Virus Proviral Integration Oncogene Spi1 SOCS Suppressor-Of-Cytokine-Signaling SIRS Systemic Inflammatory Response Syndrome SHIP Inositol Polyphosphate-5-Phosphatase TBK-1 Tank-Binding Protein TRIF Tir-Domain-Containing- Adaptor-Inducing-Ifn-? TF Tissue Factor TRAF6 Tnf Receptor Associated Factor 6 Tollip Toll Interacting Protein TLR Toll Like Receptors TAK-1 Transforming-Growth-Factor-?-Associated-Kinase-1 TAM Tumor Associated Macrophages TNF? Tumor Necrosis Factor Alpha TNF?IP3 Tumor Necrosis Factor Alpha Induced Protein 3 UPP1 Uridine Phosphorylase FDA US Food And Drug Administration VEGF Vascular Endothelial Growth Factor VCAN Versican  xiii Acknowledgements This Doctoral Thesis was completed thanks to the funding obtained by the VANIER-Canadian Institute for Health Research (CIHR) Graduate Scholarship, The Kilam Graduate Scholarship and The UBC four-year fellowship. Additionally, research funding was also received thorough CIHR Grants awarded to Dr. Bob Hancock.  I would like to sincerely thank Dr. Hancock for his immense support through this journey. He has been an excellent Ph.D. supervisor, inspiring me, guiding me and encouraging me to become an independent and successful researcher. His advices not only in the academic arena but also in the development of extracurricular activities has allowed me to finish my Ph.D. with great experiences that have enriched my academic life and have strengthened my scientific curriculum.   Additionally, I would like to thank all the members in the Hancock laboratory especially former technician Jelena Pistolic, former postdoctoral fellow Donna Easton and Ph.D. students Nicole Afacan, Amy Yeung and Matthew Meyer, for their academic and personal support. The interesting scientific discussions, continual learning environment, and collaborative work performed, have been excellent to develop my scientific skills immensely. Likewise, the nice and friendly environment present in this lab has been great to enjoy this academic experience.  I would also like to acknowledge specially the help and support received by Donna Easton for proofreading this thesis and previous manuscripts. Her kind advice and comments have allowed me to improve my writing skills greatly.     xiv  Dedication  To my Dad, Agustin Pena, who always supported and encouraged me to obtain a better education. He, who died from sepsis 10 years ago, was my motivation to achieve the objective proposed in this thesis, contributing with important scientific findings to this field.  To my husband, Abid Assaf, who has always supported me through this academic journey with his unconditional love.  To my mom, Olga Serrato and my brothers John Jairo, Mauricio, Mario, Freddy and Hernando, who have always believed in me and encourage me to fulfill my dreams!  To my son, Jonas Assaf-Pena, who with his beautiful smiles, was my inspiration in the last stages of my Ph.D.    1 CHAPTER 1: INTRODUCTION  1.1 ENDOTOXIN TOLERANCE 1.1.1 Definition Also called deactivation, adaptation, desensitization, reprogramming, anergy and refractoriness; endotoxin tolerance is defined as the reduced capacity of the host (in vivo) or of cultured immune cells (in vitro) to respond to bacterial products, like lipopolysaccharide (LPS), following a first exposure to the same stimulus [1,2]. Beeson [3] first reported endotoxin tolerance in 1946 as the abolition of the fever response of rabbits undergoing repeated daily injections of the same dose of typhoid vaccine. It was later shown that the plasma of a tolerant rabbit could passively transfer, to another rabbit, tolerance to the pyrogenicity of bacterial endotoxin. In the 1960s, similar results were obtained in humans including reduced fever in response to endotoxin or killed bacteria in secondary infections [4] Later, it was found that neutrophils and monocytes isolated from septic patients exhibited a state of hypo-responsiveness, including the absence of pro-inflammatory cytokine production and low levels of human leukocyte antigen DR (HLA-DR) expression. Similarly, patients who survive acute septic shock have deficiencies in monocytic cell activation that persist for up to two weeks[5,6]. This hypo-inflammatory state after sepsis can be induced in vitro by pretreating and challenging macrophages with a bacterial signature molecule like LPS [7]. 1.1.2 Homo-tolerance vs. hetero-tolerance It has been claimed for years that endotoxin tolerance is a specific phenomenon. However more recent studies have suggested that this is not the case. For example Jacinto et al. [8,9] showed that lipoteichoic acid (LTA) could induce both homo- and hetero-tolerance. Homo-tolerance occurs when the same microbial product is used as both the primary and secondary stimulus and hetero- or cross-tolerance occurs when different microbial products are used for the primary and secondary stimuli. More interestingly, it has been demonstrated that hetero-tolerance always occurs if agonists of toll like receptors (TLR) that specifically stimulate only the myeloid differentiation primary response 88 (MyD88) dependent pathway are employed. However if TLR agonists specific to the MyD88 dependent and the MyD88 independent pathways are used as the first and second stimuli respectively (or vice versa), this results in augmented inflammatory responses rather than induction of tolerance. In terms of LPS cross-tolerance to other bacterial products there is still some controversy, possibly reflecting the unique ability of TLR4 to activate  2 both MyD88 dependent and independent pathways. In most instances, it has been shown that if LPS is used as a first stimulus, it will likely tolerize cells to a second (different) stimulus, but if this situation is reversed the cells will not become tolerant but instead will be primed [10]. Furthermore, some studies have shown that the induction of homo-tolerance affects a broader spectrum of signaling components than hetero-tolerance, including interleukin receptor associated kinase 1 (IRAK-1) and inhibitor of nuclear factor-?B-kinase (IKK) [11]. 1.1.3 Molecular mechanisms Even though many studies have addressed the possible molecular mechanisms that surround endotoxin tolerance, it is still poorly understood. The main problem lies with the variation in experimental set-ups used between publications including mouse versus human models, differing LPS concentrations and cell types, among others. Consistently, endotoxin tolerance has been linked to the deficient recruitment of the adaptor MyD88 to TLR4, decreased IRAK4-MyD88 association, deficient IRAK-1 activation, a variation in the composition of NF-?B subunits favoring p50, and decreased nuclear factor kappa-light-chain-enhancer of activated B cells (NF-?B) and activator protein 1 (AP-1) DNA binding to promoters [12]. Similarly, other molecules acting as negative regulators of the LPS signaling pathway have been implicated in the development of endotoxin tolerance. Examples of negative regulators that are up-regulated include: IRAK-M and suppressor-of-cytokine-signaling-1 (SOCS1), both of which affect IRAK-1 association with other signaling mediators; I?B?, which localizes in the nucleus and binds to the p50 subunit of NF?-B to prevent binding of the p50/p65 heterodimer to DNA; A20 also known as tumor necrosis factor alpha induced protein 3 (TNF?IP3), which removes ubiquitin moieties from TNF receptor associated factor 6 (TRAF6); peroxisome proliferator-activated receptor gamma (PPAR?), which decreases the production of pro-inflammatory cytokines; and the phosphoinositide-3-kinase (PI3K) pathway, which negatively regulates LPS signaling and is able to reverse endotoxin tolerance when inhibited in vitro [13,14]. Likewise, recent research has revealed that inositol polyphosphate-5-phosphatase (SHIP) and reticuloendotheliosis viral oncogene homolog-B (RelB) are implicated, since knockdown of these proteins causes the reduction or abrogation of endotoxin tolerance [15,16]. Certain other negative regulators have been linked with endotoxin tolerance based on their high mRNA expression in septic patients, including Myeloid Differentiation Primary Response 88 short (MyD88s) and single immunoglobulin domain-containing IL1R-related protein (SIGIRR) [13].   3 Moreover, it was recently shown that TLR-4 tyrosine phosphorylation is important for signaling and is impaired in endotoxin tolerant cells, while the involvement of Lyn tyrosine kinase may also be implicated in these processes [16,17]. Based on these observations, it is clear that tolerance phenomena have evolved as a complex, orchestrated, counter-regulatory response to inflammation, but it still remains to be determined if LPS tolerance involves a single critical signaling pathway for tolerance induction or sequential multiple changes in signaling events during induction and resolution. Understanding the development of a crucial immunological event such as endotoxin tolerance, and how it is mediated in major players of the innate immune response such as macrophages, is very important.   1.2 MACROPHAGES 1.2.1 Definition Macrophages are central players of inflammation and host defense. Elie Metchnikoff originally described them in the late 1800s, as phagocytic cells responsible for the elimination of pathogens and tissue homeostasis in a variety of organisms [18,19]. In fact, macrophages are responsible for regulating homeostasis in basal states and during infection through a variety of functions such as recruitment of immune cells, direct killing of microbes and antigen presentation, as well as orchestrating tissue remodeling during ontogenesis and the regulation of metabolic functions [20]. They arrive at the site of the infection as bone marrow hematopoietic progenitors and circulating monocytes where they subsequently differentiate into tissue macrophages 1.2.2 Classification Depending on changes in the microenvironment, macrophages can be programmed into different subsets commonly known as classical (M1) and alternative macrophages (M2), although the latter are often further subdivided. They are generally classified by this M1/M2 designation, analogous to the Th1/Th2 states of CD4 T lymphocytes, however unlike lymphocytes, which are locked in to their specific phenotypes due to chromatin modifications, macrophages are considered to have plasticity and flexibility as significant features of their properties and gene expression profiles [21]. In response to TLR ligands such as LPS, and Th1-cytokines like IFN? and TNF?, macrophages are skewed towards an M1 phenotype, producing high levels of pro-inflammatory cytokines and chemokines, for example tumor necrosis factor alpha (TNF?), interleukin (IL)6,  4 IL12, CCL3, CCL20, CXCL20), as well as possessing enhanced microbicidal activities that include but are not limited to increased production of reactive oxygen species [22]. In contrast, the development of M2 macrophages occurs in response to different signals like Th2-cytokines, such as IL4, IL13, IL10 and immune complexes accompanied by TLR agonists. Based on these signals, some researchers have attempted to expand this classification by naming these macrophages M2a, M2b and M2c, respectively. However, later discoveries of other M2-like stimulants such as macrophage colony stimulating factor (MCSF), other immune factors and the actual phenotype produced during endotoxin tolerance which was first described in my 2011 publication that comprises Chapter 2 of this thesis, have confirmed that this classification is over-simplified [22,23,24]. 1.2.3 Molecular mechanisms M1 macrophage activation is induced by TLR ligands such as LPS, Th1-type cytokines such as IFN?, and other immune stimulants such as granulocyte-monocyte colony stimulating factor (GM-CSF). In response, TLR4 and IFN signaling pathways are turned on, leading to the activation of the transcription factors NF-?B (subunits p65 and p50), interferon regulatory factor 3 (IRF3), AP-1, and signal transducer and activator of transcription 1 (STAT1), which leads to the transcription of M1-associated genes (Figure 1.1) [25]. In addition, it has been demonstrated that micro RNAs, which are short (?22 nucleotides) non-coding RNAs that can block the translation or promote the degradation of their mRNA targets can regulate the induction of the M1 program. For example, MicroRNA-155 was found to affect the IL13 dependent regulation of M2-associated genes, therefore sustaining the M1 phenotype [26]. In contrast, M2 macrophage activation is induced by Th2-type cytokines such as IL4 and IL13 which signal through the receptor IL4R? to activate signal transducer and activator of transcription 6 (STAT6), which in turn regulate the transcription of important M2 associated gene and transcription factors such as p50, IRF4 and PPAR?. Likewise, M2-inducer IL10 turns on the transcription factor STAT3 (Figure 1.1). Additionally, these transcription factors induce the activation of negative regulators such as IRAK-M, SOCS1, SOCS3, A20 and others, blocking the activation of other transcription factors associated with the M1 phenotype [27]. The function of these negative regulators is considered critical for the development and maintenance of the M2 phenotype. In fact, as discussed previously, it has been shown that these are critical in the development of an endotoxin tolerance state, which is now recognized as an M2-like phenotype [21,24].  5  Figure 1.1: Molecular basis of macrophages polarization The mechanisms leading to macrophages differentiation into M1 and M2 phenotypes are diverse. The figure shows the different signalling pathways activated under each program and the presence of important transcription factors and negative regulators. [Figure obtained with permission from Biswas SK, 2010 (25)]  Additionally, it has been recently discovered that epigenetic changes contribute greatly to silencing of specific pro-inflammatory genes and the overall development of the M2 program. For example, the histone demethylase JMJD3 regulates the transcription of M2-associated genes, by producing reciprocal changes in the methylation of histone H3K4 and H3K27 at the promoters of those M2-associated genes, inducing activation. It has also been proposed that JMJD3 regulates the expression of transcription factor IRF4, which in turn activates the M2 program [28,29].    are other characteristic features of M2 macrophages20. Furthermore, E-cadherin is a s lective marker of M2 macrophages and is linked to the mediation of homotypic cellular interactions such as macrophage fusion40. In general, M2 cells participate in polarized TH2 responses, parasite clearance, the dampening of inflammation, the promotion of tissue remodeling, angiogenesis, tumor progression and immuno-regulatory functions.Many other cytokines can govern M2 polarization. IL-33 is a cytokine of the IL-1 family associated with TH2 and M2 polarization41,42. IL-33 amplifies IL-13-induced polarization of alveolar macrophages to an M2 phenotype characterized by the upregulation of YM1, arginase 1, CCL24 and CCL17, which mediate lung eosinophilia and inflammation42. IL-21 is another TH2-associated cytokine shown to drive M2 activation of macrophages43.Tissue remodeling has long been associated with M2 polarization4,8. IL-4-activated macrophages, as well as cells exposed to IL-10, TGF-C and tumor supernatants, selectively express the fibronectin isoform MSF (migration-stimulating factor)44. MSF lacks a typical RGD (Arg-Gly-Asp) motif and is a potent motogen for monocytes; however, its role in ontogeny and immunopathology remains to be defined. M2 macrophages support angiogenesis and lymphangiogenesis by releas-ing proangiogenic growth factors such as IL-8, VEGFA, VEGFC and EGF4,45?47. Macrophages act as ?bridge cells? or ?cellular chaperones? that guide the fusion of endothelial tip cells (vascular anastomosis) and facilitate vascular sprouting45,48. These tissue-resident macrophages express the receptor tyrosine kinase Tie-2, similar to the proangio-genic Tie-2-expressing monocytes (TEMs). Interestingly, transcrip-tome profiling has shown that TEMs share several characteristics with M2-polarized cells49. Further studies should determine the exact relationship between TEMs and Tie-2-expressing tissue macrophages. Macrophages expressing the hyaluronan receptor LYVE-1 have also been reported to promote angiogenic as well as lymphaniogenic func-tions and show M2-like  characteristics31.The interaction of natural killer (NK) cells with mono uclear phagocytes goes beyond IFN-H production; indeed, NK c ll cytolytic activity is exerted preferentially on M2-polarized macrophages (C. Bottino et al., personal communication), which represents a poten-tial mechanism for further skewing and amplification of the TH1 response. Macrophages and NK cells are abundant in the placenta. Placental macrophages have an M2-like polarized phenotype25, as is the case for embryonal macrophages27. The interaction of placental macrophages with NK cells results in the induction of proangiogenic cytokines (VEGF and IL-8)36. Furthermore, crosstalk between NK cells and placental CD14+ myelomonocytic cells induces regulatory T cells (Treg cells) in an indoleamine dioxygenase? and TGF-C-dependent manner37. Thus, the interaction between NK cells and macrophages is probably involved in shaping key aspects of the placenta, such as its unique vascularization and the maintenance of immunosuppression in the placental microenvironment.TH2 cell?derived IL-4 and IL-13 direct M2 polarization of mac-rophages during helminth infection and allergy29,38. Indeed, some prototypical mouse M2 markers (such as YM1, FIZZ1 and MGL pro-teins) were first identified in parasite infection and allergic inflam-mation29,38,39. IL-4-treated macrophages have a phenotype of low expression of IL-12 and high expression of IL-10, the IL-1 decoy receptor and IL-1RA and share many of the features characteristic of M2-polarized macrophages1,8 (Fig. 1b). Importantly, IL-4-activated macrophages express a distinct set of chemokines, including CCL17, CCL22 and CCL24. The corresponding chemokine receptors CCR4 and CCR3 are present on Treg cells, TH2 cells, eosinophils and basophils23. Thus, the release of these chemokines results the recruitment of these cells and amplification of polarized TH2 responses. M2 macrophages also have distinct metabolic properties. Through the upregulation of ferroportin and the downregulation of H ferritin and hemeoxygenase, M2 macrophages favor enhanced release of iron, which supports cell proliferation21. The expression of folate receptor-C and uptake of folate NATURE IMMUNOLOGY  VOLUME 11   NUMBER 10   OCTOBER 2010 891TLR4IL-1RLPSMyD88 TRIFAP-1TBK1IKKiIRF3IL-12p40, TNF,IL-1B, IL-6Type I IFN, CXCL10NOS2STAT1IFNARIFNGRJakp65p65p50p50p50 p50IKBIKBPSOCS3SHIPM2SykPI(3)KSHP-1SHIPA20ABIN3SOCS3PGE2IL-10ITAMITIMM1YY YFcHRImmune complex (IgG)YM1FIZZ1CCL17CCL22ARG1CDH1JMJD3IRF4p50Jmjd3IL-33IL-4IL-13 IL-10ST2??SHIPSOCS3A20ABIN3H3K27Jak JakPPPPIL-4RA IL-10R?InductionInhibitionPPAR-GM2-likeP PPPPPPPPNegative regulatorsUpregulation Type 1 IFNIFN-GSTAT3STAT6PPIRF4Figure 2  Molecular pathways of macrophage polarization. M1 stimuli such as LPS and IFN-H signal through the TLR4, IFN-B, or IFN-C receptor (IFNAR) and IFN-H receptor (IFNGR) pathways, inducing activation of the transcription factors NF-LB (p65 and p50), AP-1, IRF3 and STAT1, which leads to the transcription of M1 genes (red lettering indicates molecules encoded). In contrast, M2 stimuli such as IL-4 and IL-13 signal through IL-4RB to activate STAT6, which regulates the expression of M2 genes (green lettering indicates molecules encoded). The regulation of these genes also involves JMJD3, IRF4, PPAR-H and p50. IL-10 and immune complexes, plus LPS and IL-1, trigger M2-like macrophage polarization. IL-10 signals through its receptor (IL-10R), activating STAT3. Immune complexes trigger FcHR signaling, leading to the expression of molecules such as A20, ABIN3, SOCS3, prostaglandin E2 and IL-10, which negatively regulate the TLR4 and IL-1R and interferon-signaling pathway. Activatory and inhibitory FcHR signaling is initiated by activation of Syk?phosphatidylinositol-3-OH kinase (PI(3)K)  and tyrosine phosphatase SHP-1?inositol phosphatase SHIP, respectively. Methylation of histone H3K27 is a post-translational modification linked to gene silencing. A20, deubiquitinating enzyme; ABIN3, A20-binding NF-LB inhibitor; IgG, immunoglobulin G; ILB, NF-LB inhibitor; IKKi, inducible ILB kinase; ITAM, intracellular tyrosine-based activatory motif; ITIM, intracellular tyrosine-based inhibitory motif; Jak, Janus kinase; TBK1, NF-LB activator; TRIF, adaptor protein.RE V IE W 6 1.2.4 Biological functions As discussed previously, changes in the microenvironment cause macrophages to be programmed into the M1 or M2 phenotypes. Each phenotype performs characteristic functions that will be briefly explained below: 1.2.4.1 Inflammation:  One of the best well-known functions of macrophages, inflammation, is a complex biological response to harmful stimuli such as microorganisms or damaged cells. Macrophages orchestrate inflammation by responding to these stimuli and producing copious amounts of pro-inflammatory mediators including cytokines and chemokines (e.g. TNF?, IL1?, IL12, CXCL10, CCL3). This cellular and humoral inflammatory network enhances the biological capabilities to destroy the harmful entity, by inducing the infiltration of other innate immune cells, such as monocytes and neutrophils, and activating their uptake and killing programs to perform a more efficient job. This function is in fact a hallmark of M1 macrophages [30]. 1.2.4.2 Immunoregulation:  Macrophages can also regulate the level of inflammation to prevent an excessive response and future self-damage. This is done by enhancing the expression of anti-inflammatory cytokines like IL10 and TGF?, which dampen or switch off the expression of pro-inflammatory genes. Additionally, the expression of chemokines such as CCL22 and CCL24 leads to the chemoattraction of Th2 and T-regulatory cells, which together promote the regression to basal homeostasis. This function is characteristic of M2 macrophages [22]. It is also known that under basal homeostatic conditions, in tissues like placenta and adipose tissues of lean subjects, M2 macrophages are found displaying immunoregulatory functions [25]. 1.2.4.3 Phagocytosis:  An important function of macrophages, phagocytosis, allows the system to process invading microbes, dead cells and other debris. Although, M1 macrophages possess enhanced microbial killing capabilities, they do not possess increased phagocytic rates. In contrast, M2 macrophages have increased expression of scavenger receptors like macrophage receptor with collagenous structure (MARCO) that mediate the phagocytosis of dead cells and other debris, thereby promoting the resolution of inflammation [22]. 1.2.4.4 Antigen presentation:  This function allows macrophages to communicate with the adaptive immunity arm, to allow a faster recognition of invading pathogens. M1 macrophages normally have enhanced  7 expression of major histocompatibility complex (MHC) type II, which allows the presentation of antigens from extracellular entities. Conversely, M2 macrophages have reduced antigen presentation capabilities [19,31].  1.2.4.5 Tissue remodelling:  Under physiological conditions, macrophages play a major role in events that requires tissue remodelling, such as organogenesis and vasculogenesis. Likewise, in pathological conditions such as tissue injury and infection, macrophages play an important role in orchestrating the expression of different growth factors such as vascular endothelial growth factor (VEGF), endothelial growth factor (EGF), and metalloproteinases such as metalloproteinase 9 (MMP9) that are crucial for the development of tissue remodelling, a function of M2 macrophages [32,33]. 1.2.4.6 Metabolism:  Macrophages are important regulators of metabolism. For example, macrophages from adipose tissues known as adipose tissue macrophages (ATM) are considered to have M2 characteristics only if they are from lean adipose tissue. It is believed that these macrophages maintain homeostasis by promoting lipolysis and therefore preventing inflammation in response to high fat concentrations [34]. 1.2.5 Roles in health and disease The aforementioned functions make clear that M1 and M2 macrophages play very important roles in health, providing two major influences on defense and homeostasis of the overall system. However, in cases where these two phenotypes become unbalanced and one or the other is relatively over-expressed, they may become associated with the enhancement of specific pathologies and this will now be discussed briefly. 1.2.5.1 Cancer:  Macrophages have been associated with the promotion of tumor progression through the infiltration of tumor associated macrophages (TAM), which are characterized by an extreme M2 phenotype [35]. In this context, macrophages are recruited by the tumor microenvironment and induced towards a TAM-M2 phenotype to carry out functions for cancer cells. Indeed, gene expression studies have shown that TAMs have enhanced production of tumor-promoting factors such as VEGF, EGFs and MMPs, promoting survival and metastasis mainly through angiogenesis [36,37].  8 1.2.5.2 Metabolic syndromes:  Macrophages also play an important role in the regulation of a balanced metabolic state, especially in lipid and glucose metabolism. For example, ATM from lean tissue are considered to have very similar features to M2 macrophages and promote lipolysis, which inhibits inflammation that is produced in response to high fat concentrations. Conversely, chronic inflammation appears to be a major problem in obesity-related conditions such as insulin resistance. During these conditions, ATM exacerbate the problem by switching to an M1-like phenotype that promotes inflammation by causing an increase of free fatty acids and glucose concentrations in circulation [38,39].  Having a clear understanding of the importance of M1 and M2 macrophages in health and disease, and the mechanisms by which they manage these events, is critical for designing pharmaceutical approaches that can tackle deadly syndromes such as sepsis that is described in the next section.  1.3 SEPSIS 1.3.1 Definition and epidemiology Recently, Angus et al. [40] described sepsis as a common and fatal condition, with an estimated incidence in the United States of more than 750,000 cases per year, resulting in around 210,000 deaths, and an estimated health care cost of $16.7 billion. These statistics indicate that sepsis is a common and significant condition. In the past, the definition of sepsis was imprecise and it was often described using alternative terms such as bacteremia, severe sepsis, sepsis syndrome and others. To solve this issue a conference was held to unify definitions of this syndrome [41] and the various syndromes associated with sepsis now have clinical definitions.  Systemic inflammatory response syndrome (SIRS) is defined as a clinical response arising from a nonspecific insult that includes two or more of the following symptoms: tachypnea or tachycardia; leukocytosis or leucopenia; and hyperthermia or hypothermia. Sepsis is designated as SIRS with a suspected or proven infection. Severe sepsis is defined as sepsis with organ dysfunction, while septic shock is severe sepsis with hypotension, despite adequate fluid resuscitation. Other clinical definitions related to sepsis are: bacteremia, which is the presence of bacteria in the blood, and acute lung injury (ALI) and acute respiratory distress syndrome (ARDS), which are often clinical complications associated with sepsis. Many  9 investigators have found that a variety of factors may contribute to the development of sepsis; some of the most common include gender, age and previous health conditions or genetic factors, as discussed in the next section. 1.3.2 General predispositions Several factors are known to predispose individuals to different levels of susceptibility to sepsis. In the case of gender for example, there are several well-described studies indicating that males are more susceptible than females, a difference that has been attributed to the immunomodulatory effects of sex hormones [42,43]. In terms of age, Martin et al. [44] mentioned that ?Sepsis appears to be a disease of the elderly,? reporting that the incidence of sepsis increases with age. In several studies the mean age of septic patients was 63 to 65 years old. The reason age is so strongly associated with both risk and outcome is multifactorial, but the main factor is likely to involve impairment of the immune system, including reduced antigen presentation by leukocytes and altered inflammatory cytokine expression. Predisposition to sepsis can also arise from issues such as previous health conditions, for example increased presentation of sepsis occurs in patients undergoing hemodialysis or peritoneal dialysis and/or those infected with human immunodeficiency virus (HIV) [45]. Genetic polymorphisms also influence susceptibility to sepsis, including host genetic variability in more than 30 genes encoding components of the innate immune system including TLR4, CD14 and cytokines like IL6 and IL10 [46,47,48,49]. One clear example is the TNF? gene, for which the presence of genetic polymorphisms has been associated with increased risk of mortality in septic shock [49]. 1.3.3 Pathophysiology In early sepsis, a strong inflammatory response is initiated by the innate recognition of microbial signature molecules (sometimes termed pathogen-associated molecular patterns or PAMPs) from microbes like bacteria, viruses or fungi. These signature molecules are recognized by pattern recognition receptors such as Toll like Receptors (TLRs). For example, LPS is a signature molecule of Gram-negative bacteria and is recognized by TLR4, which has the unique capacity amongst all TLRs to activate both MyD88 dependent and independent pathways. In the MyD88 dependent pathway, TIR domain containing adaptor protein (TIRAP), also known as Mal, serves as a bridge between MyD88 and TLR4, permitting the recruitment and activation of the IL1 associated kinase proteins IRAK4 and IRAK1. Phosphorylated IRAK1 associates with TNF Receptor Associated Factor 6 (TRAF6), leading to ubiquitination of TRAF6 by ubiquitin conjugating enzymes UBC13 and UEV1A. This induces the activation of  10 transforming-growth-factor-?-associated-kinase-1 (TAK-1), which in turn phosphorylates both mitogen-activated-protein kinases (MAPK) and the IKK complex. Inhibitor of nuclear factor-?B (IKB), which holds NF-?B in the cytosol, is phosphorylated by the IKK complex inducing its ubiquitination and degradation. Finally, this allows NF-?B to translocate to the nucleus and induce expression of many inflammatory target genes like TNF?, IL1? and IL6. Likewise, MAPK activation leads to the activation of other transcription factors such as AP-1 and Elk-1 that similarly induce the production of pro-inflammatory mediators. Furthermore, the MyD88 independent pathway, also know as TIR-domain-containing- adaptor-inducing-IFN-? (TRIF) pathway, involves signaling through TRIF, which can specifically interact with IKK and TANK-binding protein (TBK-1) leading to the phosphorylation and activation of IRF3. IRF3 can then induce the expression of IFN? and can also interact with TRAF6 and TBK-1 causing the activation of the IKK complex, thus feeding into the MyD88 dependent pathway. IFN? can activate cells through the interferon alpha/beta receptor (IFNAR), which can subsequently induce the expression of interferon related genes through the JAK-STAT pathway [50]. Consequently, the TLR-4 stimulation by LPS shows how a single microbial product can give rise to a potent induction of inflammatory genes using the activation and interaction of several signal transduction pathways. Therefore, if this stimulation is induced in an excessive manner, copious amounts of these inflammatory mediators will be produced reaching the circulation and inducing a systemic inflammatory reaction. It is worth mentioning however that this represents a dramatic oversimplification of the actual responses involved [51]. Dozens to hundreds of individual proteins interact with each of the proteins mentioned above and these interactors can influence signaling through a variety of means. This is described in more detail below. Sepsis has often been touted as a clear example of a systemic inflammatory response, however the results presented in this thesis are not consistent with such a simple depiction. Sepsis is often thought to be triggered and to progress when the host cannot contain an initial infection. Although it is not known why this occurs in some patients, it is most frequently explained as being due to the characteristics of the microorganism, including the microbial burden, the presence of super-antigens or other virulence factors, and resistance to antibiotics. As described in this thesis, and in light of the obvious genetic associations with mutations in inflammatory mediators, host factors must also play a major role. In cases where infections cannot be properly controlled immune cells are activated, releasing pro-inflammatory mediators  11 like TNF? and IL1?. These mediators are known to have direct effects on the endothelium, inducing the expression of tissue factors in endothelial cells and monocytes, which initiates the extrinsic pathway of coagulation leading to the production of thrombin and resulting in fibrin clots in the microvasculature. These pro-inflammatory mediators also activate the production of secondary molecules like phospholipase A2, increase concentrations of effectors like platelet activating factor, and promote nitric oxide synthase activity and leukotriene production, leading to vasodilatation and consequent hypotension [52,53,54]. The presence of increased levels of the pro-inflammatory cytokine TNF? in septic patients, combined with the excessive early immune responses observed in sepsis-mouse models, and the observation that inhibition of pro-inflammatory mediators leads to improved survival in animal models of endotoxic shock, suggested the popular belief amongst the research community and clinicians that sepsis is largely the result of an exuberant immune response [55]. Indeed, it is possible that this might occur in certain instances, especially where death occurs within the first 3 days and is associated with symptoms that occur during a "cytokine storm" (excessive pro-inflammatory cytokines) namely cardiovascular collapse, shock and multiple organ damage. However in many other cases, for those who survive the initial phases, the threat to organ function posed by the persistent systemic inflammatory response may explain the shift of the immune system towards an immunosuppressive state [56]. This state is characterized by reduction or loss of expression of pro-inflammatory mediators including TNF?, IL1? and IL6 [57,58] and up regulation or stabilization of levels of anti-inflammatory mediators [59,60,61]. Additionally, it has been shown that apoptosis of other immune cells, such as T lymphocytes and endothelial cells, is a hallmark of sepsis [62]. The major immunological changes occurring after the initial hyperinflammatory phase of sepsis appear to be an attempt to restore homeostasis, and are thought to lead to the loss of delayed-type hypersensitivity, an inability to clear infection and a predisposition to nosocomial infections; some of which are visibly evident and others are not as obvious. Indeed postmortem studies of septic patients have shown that many patients die with unresolved infectious foci [52,63]. Therefore, the development of this impaired immune response can increase the risk of death, in addition to that caused by other undelying health conditions such as cancer and/or cardiovascular disease. It has been suggested that this immunosuppressive state is the same as, or similar to, endotoxin tolerance, since the cellular responses found in septic patients align dramatically with what is observed in this phenomenon [4].  12  1.3.4 Immunotherapies in sepsis Due to the abnormal immune response observed in sepsis, numerous different attempts to develop immunotherapies have been made, largely aiming to knock down inflammatory responses. Over a course of close to 30 years of investigation, a wide variety of approaches have been used in clinical trials including the use of steroids, anti-endotoxin or anti-proinflammatory cytokines among others. Most immunotherapies tried have disappointingly failed [64]. Only one drug, activated protein C, has been approved by the US Food and Drug Administration (FDA) and was able to reach the market; however, just 10 years after the pharmaceutical company Eli Lily developed this drug, they withdrew it from the market due to a post-marketing clinical trial demonstrating a lack of efficacy [65]. Table 1.1 describes some of the different immunotherapies tried, the molecules targeted, and the consistent lack of therapeutic success.   Table 1.1: Examples of immunotherapies investigated for sepsis [64] Treatment  Target  Effects E5564  TLR4  No benefit TAK-242 TLR4  No benefit TNF Monoclonal Antibody TNF?  No benefit Etanercept TNF?  No benefit IL-1ra  IL1? and IL1? No benefit Heparin  Coagulation No benefit rh-antithrombin  Coagulation  No benefit rh-activated protein C Coagulation  No benefit rh-tissue factor pathway inhibitor Coagulation  No benefit Corticosteroids  Adrenal suppression  No benefit GM-CSF  Immunoparalysis No benefit  1.4 HOST DEFENSE PEPTIDES Produced by virtually all forms of life, host defence peptides (HDP) are an evolutionarily ancient and important component of the innate immune system involved in fighting infections and maintaining immunological balance. They are considered to have pleiotropic immune  13 functions that range from anti-inflammatory modulation of immune responses through endotoxin neutralization, to anti-infective mechanisms such as enhancing chemoattraction, regulation of chemokine and cytokine production, enhancement of adaptive immune responses, increasing cell survival, and promoting wound healing, as well as weak direct antimicrobial activity that has lent to the alternative name, antimicrobial peptides. Interestingly, HDP have retained their anti-infective activities over centuries with just a few pathogens developing resistance. This, plus the fact that their immunomodulatory activities are not sensitive to resistance, makes HDP excellent candidates for targeting multidrug resistant pathogens.  1.4.1 Immunomodulatory activities HDPs are produced by many cells, including epithelial and immune cells [66,67,68], acting at the site of infection in an autocrine and/or paracrine manner. HDPs can exhibit robust immunomodulatory activities and/or have direct antimicrobial activity. However under physiological conditions their antimicrobial activities are considerably dampened which is not the case for their selective immunomodulatory activities, suggesting that these are a more likely explanation for their protective effects against microbes in vivo [69,70]. These selective immunomodulatory capabilities make HDP the starting point for novel therapies against multi-drug resistant infections, acting on the host to boost protective immunity while modulating excessive inflammation [71]. Some of the most relevant immunomodulatory functions displayed by HDP are presented in figure 1.2 and described below. 1.4.1.1 Chemotactic activity: The presence of microorganisms induces the local production and/or release of chemo-attractant (chemotactic) agents, such as chemokines and HDPs, which are capable, with varying efficiency, of recruiting immune cells to the site of infection. HDPs can induce the expression of a broad range of chemokines, such as CXCL8/IL8, CCL2/MCP1, by neutrophils, monocytes and other immune cells [72,73]. These direct and indirect chemotactic activities represent a conserved ability among HDPs, making this a strategic tool for screening and selecting for improved synthetic immunomodulatory HDPs, also referred to as innate defence regulators (IDRs). The enhanced ability to induce chemokines was recently used to select three IDRs developed in our laboratory, IDR-1, IDR-1002 and IDR-1018 [74,75,76], which demonstrated an enhanced ability to induce chemokines in human peripheral blood mononuclear cells (PBMC). These activities also appear to partly underlie their protective effects in bacterial infections including those caused by multidrug resistant strains [77].  14    Figure 1.2: Immunomodulatory activities of HDPs  In addition to their direct antimicrobial activity (1), HDPs possess a variety of immunomodulatory functions, acting at different locations within the affected micro-environment. These include the direct or indirect recruitment of immune cells to the site of infection (2) and inhibition of pro-inflammatory mediators such as TNF-a (3). HDPs also induce dendritic cell differentiation and activation thus connecting the innate and adaptive arms of the immune system (4). Figure obtained with permission from Afacan N et al 2012 (184).  1.4.1.2 Anti-endotoxic activity: Lipopolysacharide (LPS), the outer membrane component of Gram-negative bacteria also known as endotoxin, is a classical inducer of systemic inflammation [78]. Many antibiotics stimulate the release of endotoxin, enhancing the occurrence of systemic inflammation and possibly inducing sepsis [79]. HDPs possess the capacity to dampen the production of endotoxin-induced pro-inflammatory mediators such as TNF-? by modulating TLR signaling pathways, and Therapeutic Potential of Host Defense Current Pharmaceutical Design, 2012, Vol. 18, No. 00    5 (TLR) signaling pathways, and in some cases partly by direct LPS binding [98-100]. These activities may underlie the profound anti-inflammatory activity evident in infection models treated with IDR peptides [43, 94], as well as in sterile inflammat on models. Immune Cell Differentiation  Immune cell differentiation is essential to the proper develop-ment of immune responses. HDPs appear to have a direct link withthis event as well. For example, LL-37 induces dendritic cell (DC) and bone forming-like cell differentiation [34, 101]. Similarly, hLF11, a lactoferrin-derived HDP well known for its in vivo protec-tive effects on MDR infections, promotes the differentiation of a macrophage subset with pro and anti-inflammatory capabilities that are highly effective against bacterial pathogens [102]. Peptides also have very distinct activities when interacting with classical (M1) and alternatively activated (M2) macrophages [103]. Wound Healing and Angiogenesis  Wound healing involves the re-growth of epithelial layers and the formation of new blood vessels (angiogenesis), which is neces-sary for the wounded site to return to homeostasis. HDPs play an important role in this process by acting directly on epithelial and endothelial cells, inducing promoting re-epithelialization and angi-ogenesis, effects that have been demonstrated both in vivo nd in vitro [104]. HDPs also induce wound healing indirectly through their chemotactic effects on epithelial cells and the induction of metalloproteinases [105, 106]. In fact, a lack of HDPs is linked to impaired re-epithelialization of chronic wounds [107, 108].  Other Functions  Enhancing cell survival and polarization of the adaptive im-mune system (adjuvant activity) are among other immunomodula-tory activities attributed to HDPs [109, 110]. The molecular mecha-nisms by which these and other HDPs functions occur are complex and slowly being discovered. Mounting evidence suggests that HDPs target multiple processes within a given cell, with the re-sponses depending on the nature of the peptide and the target cell type. In the case of LL-37, it appears to engage multiple receptors including the P2X7 receptor, formyl peptide receptor like-1 (FPRL-1) and other unknown Gi-protein coupled receptors, as well as GAPDH, an intracellular receptor [111-113].Some of the key sig-naling pathways that play a role in HDP immunomodulation in-clude the mitogen-activated protein kin ses (MAPK) p38, JNK, and extracellular signal-regulated kinase-1/2 (ERK1/2), as well as the Src-family kinases, NFB (transiently) and PI3 kinase pathways [114].   The immunomodulat ry activities displayed by HDPs have been demonstrated in animal models and appear to be important for pathogen clearance. Their role has been highlighted in models where the lack of HDPs leads to a reduced ability to clear infec-tions, such as in transgenic mice lacking -defensin 1 and catheli-cidin-related antimicrobial peptide (CRAMP) or in human patients                  Fig. (2). Immunomodulatory activities of HDPs: In addition to their direct antimicrobial activity (1), HDPs possess a variety of immunomodulatory functions, acting at different locations within the affected micro-environment. These include the direct or indirect recruitment of immune cells to the site of infection (2) and inhibition of pro-inflammatory mediators such as TNF-a (3). HDPs also induce dendritic cell differentiation and activation thus connecting the innate and adaptive arms of the immune system (4).  15 other mechanisms, and to some extent direct LPS binding [70,80]. These activities may underlie the evident anti-inflammatory activity observed in infection models treated with IDR peptides. 1.4.1.3 Wound healing and angiogenesis: Wound healing involves the re-growth of epithelial layers and the formation of new blood vessels (angiogenesis), which is necessary for the wounded site to be repaired. HDPs play an important role in this process by acting directly on epithelial and endothelial cells, and by inducing or promoting re-epithelialization and angiogenesis, effects that have been demonstrated both in vivo and in vitro [81]. HDPs also induce wound healing indirectly through their chemotactic effects on epithelial cells and the induction of metalloproteinases [81,82]. Conversely, lack of HDPs has been linked to impaired re-epithelialization of chronic wounds [83,84]. 1.4.1.4 Other functions: Enhancing cell survival and polarization of the adaptive immune system (adjuvant activity) are among the other immunomodulatory activities attributed to HDPs [85].   The molecular mechanisms by which these and other HDP-associated functions occur are complex and slowly being revealed. Mounting evidence suggests that HDPs target multiple processes within a given cell, with the responses depending on the nature of the peptide and the target cell type. LL-37, a HDP naturally produced by humans, appears to engage multiple receptors including the P2X7 receptor, formyl peptide receptor like-1 (FPRL-1) and other unknown G-protein coupled receptors, as well as GAPDH, an intracellular receptor [86,87,88]. Some of the key signaling pathways that play a role in HDP immunomodulation include the MAPK p38, JNK, and extracellular signal-regulated kinase-1/2 (ERK1/2), as well as Src-family kinases, NF-?B (transiently) and the PI3K pathways [80]. The immunomodulatory activities displayed by HDPs have been demonstrated in vivo in animal models and appear to be important for pathogen clearance. Their role has been highlighted by models in which the lack of HDPs leads to a reduced ability to clear infections, such as transgenic mice lacking ?-defensin 1 and cathelicidin-related antimicrobial peptide (CRAMP) or in human patients lacking LL-37 and ?-defensin 3. The reduced pathogen clearance is likely due to the dysregulation of the immune response but has also been described as involving decreased peptide-mediated killing leading to an increased susceptibility to infections [89,90,91]. The immunomodulatory and antimicrobial properties of HDPs make them excellent candidates for treating infections; especially those  16 caused by multidrug resistance pathogens, particularly in combination with other antimicrobial therapies. 1.5 INNATE DEFENCE REGULATOR PEPTIDES As mentioned above, innate defence regulator (IDR) peptides are synthetic peptides conceptually derived from natural HDPs. They are able to enhance protection against infections while suppressing potentially harmful inflammatory responses. For example, IDR-1, created in our laboratory, demonstrated therapeutic efficacy in mice infected with Staphylococcus aureus, Salmonella typhimurium, vancomycin-resistant Enterococci and methicillin-resistant S. aureus [75]. Increased monocyte recruitment to the site of the infection, modulation of chemokine production and suppression of pro-inflammatory mediators were the major immunomodulatory activities contributed by IDR-1 in these models.  IDR-1002, also designed in our laboratory, promoted leukocyte recruitment, induction of chemokines and protection against infection, at a much lower concentration than IDR-1 in S. aureus mouse models, indicating an increased potency of this peptide [76]. To date only limited clinical trials have been performed on these peptides. For example, Inimex Pharmaceuticals took their lead peptide, IMX942, through Phase Ia clinical trials, while the hormone peptide ?-melanocyte-stimulating hormone (?-MSH) has become a lead for therapeutic development at Action Pharma owing to its extensive modulation of pro- and anti-inflammatory responses [92]. A human lactoferrin derivative, hLF1-11 developed by AM-Pharma, prevented bacteraemia and fungal infections in Phase I trials in immunocompromised individuals by enhancing macrophage-mediated phagocytosis and killing of C. albicans and S. aureus [93]. Talactoferrin, developed by Agennix (www.agennix.com/) is a recombinant derivative of the cationic protein human lactoferrin. It possesses broad immunomodulatory activity including activation of dendritic cells, immune cell recruitment, and enhancement of adaptive immune responses. In 190 adult patients with severe sepsis enrolled at 24 leading centers across the U.S, a double-blind, placebo-controlled Phase II trial was performed to evaluate talactoferrin. The study showed a reduction in 28-day all-cause mortality. Exploratory analyses suggested that talactoferrin might be effective in reducing the levels of certain cytokines and chemokines that are important in the initiation and propagation of the inflammatory response in severe sepsis [94,95,96]  17 1.6 MAJOR OBJECTIVE OF THESIS The major objective of this thesis was to obtain a better understanding of the immunopathology of sepsis, exploring in depth the development of endotoxin tolerance as a major cause of immunosuppression that occurs during sepsis and as a cause of increased risk of death.  1.7 HYPOTHESES 1.7.1 Hypothesis I Endotoxin tolerance, or monocyte-reprogramming seen after repetitive doses of LPS, is a manifestation of an alternatively activated M2-phenotype. 1.7.2 Hypothesis II  During the course of sepsis, monocyte reprogramming rapidly develops, inducing a hypo-inflammatory state that if persistently maintained might increase the risk of death.  1.7.3 Hypothesis III IDR peptides can cause macrophage reprogramming, which is an important mechanism for their ability to protect against infections and inflammation.   18 CHAPTER 2: ENDOTOXIN TOLERANCE REPRESENTS A DISTINCTIVE STATE OF ALTERNATIVE POLARIZATION (M2) IN HUMAN MONONUCLEAR CELLS  2.1 INTRODUCTION Inflammation is a complex biological response to harmful stimuli such as microbial infection and tissue injury [97]. This rapid process involves a very substantial consumption of metabolic energy, and a parallel risk of tissue damage, multiple organ failure and death [98]. Therefore, extremely tight regulation is essential for preventing the deleterious consequences that an excessive response can have on body systems. Also called deactivation, adaptation, desensitization, and reprogramming; endotoxin tolerance is defined as the reduced capacity of the host (in vivo), or of cultured immune cells (in vitro), to respond to bacterial signatures, such as LPS, following a first exposure to such a stimulus [2,4]. Due to this characteristic desensitization response, endotoxin tolerance is considered an ancient regulatory mechanism to balance inflammation. Beeson [3] first reported endotoxin tolerance in 1946 as the abolition of the fever response in rabbits undergoing repeated daily injection of the same dose of typhoid vaccine. In the 1960s, similar results were obtained in humans including reduced fever in response to endotoxin or killed bacteria in secondary infections [1] and later, in 1988, it was demonstrated that macrophages play a central role during endotoxin tolerance [99]. Subsequently observations in monocytes isolated from septic patients, exhibited a state of cellular hypo-responsiveness, including the absence of pro-inflammatory cytokine production and low levels of HLA-DR expression [57,100]. Similarly, patients who survive acute septic shock have deficiencies in monocytic cell activation reflecting an endotoxin tolerance state that can persist for up to two weeks suggesting a stable expression of this phenotype [101]. Several studies have addressed the possible molecular mechanisms that surround endotoxin tolerance. In human systems, this phenomenon has consistently been linked with particular regulatory events including the deficient recruitment of the adaptor MyD88 to TLR4 [102], decreased IRAK4-MyD88 association [103], deficient IRAK1 activation [104], up-regulation of negative regulators such as IRAK-M [105], SOCS-1, Toll interacting protein (Tollip) and SHIP-1 [106]. Additionally, a variation in the composition of NF-?B subunits favoring p50 [107], and Rel-B [108], as well as the presence of PPAR? [109] have also been found to play an important  19 role in the development of endotoxin tolerance in humans. Interestingly, it has been also proposed that the responses seen during this process may not be controlled solely at the signaling level. Foster et al [110], showed that these responses could mainly be attributed to TLR-induced chromatin modifications and although this has only been demonstrated in mouse models, the presence of strongly conserved methylation patterns between mice and humans suggests that a similar mechanism may also take place in human systems [111]. Thus, it is clear that endotoxin tolerance is the consequence of a complex, orchestrated, regulatory response to battle inflammation. However, it is unlikely that this cellular reprogramming occurs merely to reduce inflammation, since the complement of genes differentially expressed during this state appear to direct the cell towards new cellular functions. Therefore, in our attempt to understand endotoxin tolerance from a holistic perspective, rather than from a single molecule standpoint, we decided to use a systems biology approach to study this cellular reprogramming and compare it with other known cellular programs. Our results led us to conclude that endotoxin tolerance is indeed a distinctive program of alternative polarization.  Classical and alternative polarization, also known as M1 and M2 respectively, is a very general term used to classify the responses observed in macrophages towards different stimuli in the microenvironment. Classical macrophage polarization is driven in response to microbial products or Th1 cytokines, such as IFN?, and is characterized by an enhanced capacity to kill intracellular microorganisms and produce generous amounts of pro-inflammatory mediators. Conversely, alternative macrophage polarization can be generated in response to a variety of stimuli such as Th2 cytokines like IL-4, IL-13 and IL-10; glucocorticoids or a mixture of Immoglobulin (Ig) complexes and TLR ligands, producing different forms of alternative polarization. The functions of alternatively activated macrophages involve the control of inflammatory responses, enhanced phagocytic activity and tissue repair [19,22,112]. This study illustrates the substantial similarities that exist between alternative polarization and endotoxin tolerance states, and proposes that this phenomenon can be considered as another form of alternative activation triggered by bacterial signatures such as LPS.   2.2 MATERIALS AND METHODS 2.2.1 Cells and Reagents For the isolation of blood mononuclear cells, venous blood was collected from healthy volunteers into heparin-containing Vacutainer tubes (BD Biosciences, San Jose CA) in  20 accordance with the ethical approval guidelines of the UBC Research Ethics Board. PBMC and monocyte derived macrophages (MDM) were isolated as described previously [80,113] and cultured in RPMI 1640 medium Figureemented with 10% (v/v) heat-inactivated fetal bovine serum (FBS), 2 mM L-glutamine, 25 mM HEPES and 1 mM sodium pyruvate (all from Invitrogen). Human bronchial epithelial cells (16HBE4o-) were obtained as a gift from Dr D. Gruenert (University of California, San Francisco, USA) and cultured in minimum essential media (MEM) with Earle?s Salts (Invitrogen, Carlsbad, CA) Figureemented with 10% (v/v) FBS and 2 mM L-glutamine. All cells were cultivated in a humidified 37 ?C incubator containing 5% CO2. Lipopolysacharide (LPS) was isolated from Pseudomonas aeruginosa (PA-H103), grown overnight in Luria-Bertani broth at 37?C, using the Darveau-Hancock method [114]. The isolated LPS pellets were extracted with a 2:1 chloroform:methanol solution to remove contaminating lipids. Purified LPS samples were quantitated using an assay for the specific sugar 2-keto-3-deoxyoctosonic acid (KDO assay) and resuspended in endotoxin-free water (Sigma-Aldrich, Saint Louis MO) and used at a concentration of 10 ng/ml.  2.2.2 MDM differentiation For MDM differentiation, PBMC isolation procedures were performed exclusively in PBS. PBMCs were then resuspended in serum-free RPMI media and plated at 5x106 cells/well in 6 well plates for 30 minutes. Subsequently, media was changed and fresh complete media containing M-CSF (10 ng/mL) (Research Diagnostic Inc, Concord, MA) was added. Cells were cultured for seven days, with media changes every second day. On day seven, cells were subjected to LPS and LPS/LPS treatments. 2.2.3 Endotoxin tolerance induction experiments Endotoxin tolerance was induced in cells using 10ng/mL LPS as first and second stimulus (Figure 2.1A). The experiment consisted of 3 treatments: control/untreated cells (No LPS), single dose LPS stimulated cells (LPS) and tolerized cells or cells that had been treated twice with LPS (LPS/LPS). For LPS/LPS tolerance treatments, cells were initially treated with LPS and incubated for 24 hrs before the second treatment. Supernatants were then collected from the plates, followed by washing and addition of fresh media. Cells were subsequently treated for second time with LPS. Four hours after the second treatment, supernatants and cell lysates were collected.  21 Supernatants were stored at -200C until cytokine and chemokine analysis was done, while cell lysates were stored at -800C in RLT lysis buffer (Qiagen, Valencia, CA) until the RNA isolation. 2.2.4 RNA isolation RNA isolation was carried out as described previously [80]. Briefly, RNA was isolated from cell lysates using Qiagen RNA Isolation Kit (RNeasy?Mini Kit (Qiagen, Valencia, CA), treated with RNase free DNase (Qiagen, Valencia, CA) and eluted in RNase-free water (Ambion, Austin, TX) as per the manufacturer?s instruction. The RNA concentration using a NanoDrop spectrophotometer, while RNA integrity and purity was determined was assessed by Agilent 2100 Bioanalyzer using RNA Nano kits (Agilent technologies). 2.2.5 Microarray experiment and analysis Microarrays were performed using RNA samples obtained 4 hours post treatment of PBMCs obtained from 4 different healthy donors, using the Illumina platform at the Genome BC Microarray Facility Platform, located at the Prostate Centre, Vancouver General Hospital, Vancouver, Canada. Complete microarray data has been deposited in the public database GEO accession number GSE22248 (http://www.ncbi.nlm.nih.gov/geo/), and selected differentially expressed genes with a fold change ? 1.5 and P-value <0.05 are also presented in the appendix section. Differential express genes were selected based on adjusted P-value <0.05. Transcriptional analysis of the microarray data was done using system biology tools developed in our lab including the InnateDB database (http://www.innatedb.ca) [115], Cerebral which was used for pathway network visualization, and MetaGEX that was used to obtain Gene Ontology (GO) Terms and transcription factor over-representation analysis (CD Fjell, unpublished data http://marray.cmdr.ubc.ca/metagex/). Genes with fold change of two or more were considered differentially expressed.  2.2.6 Quantitative real-time PCR (qRT-PCR) Differential gene expression identified through microarray analysis was validated via qRT-PCR, which was performed using the SuperScript III Platinum Two-Step qRT-PCR kit with SYBR Green (Invitrogen, Carlsbad, CA) as per the manufacturer?s instructions, and the ABI Prism 7000 sequence detection system (Applied Biosystems, Carlsbad, California). Briefly, 500 ?g of total RNA was reverse transcribed using qScriptTM cDNA Synthesis Kit (Quanta Biosciences, Gaithersburg, MD). PCR was conducted in a 12.5 ?l reaction volume containing 2.5 ?l of 1/5 diluted cDNA template. A melting curve was performed to ensure that any product detected was specific to the desired amplicon. Fold changes for LPS and LPS/LPS samples were  22 calculated compared to the ?No LPS? control, after normalizing the change in expression of the gene of interest to the housekeeping gene beta-2-microglobulin (B2M), using the comparative Ct method [116]. The primers (all from Invitrogen except MMP-9 which was from Alpha-DNA, Montreal, Canada) sequences used for qRT-PCR are available from the authors upon request.  2.2.7 Enzyme-linked immunosorbent assay (ELISA) ELISA was performed on supernatants collected 4 h post- treatment. These included TNF-?, MCP-1, CCL22 (R & D systems), IL-10 (eBioscience), and CCL-3 (Biosource). ELISA assays were performed according to the kit manufacturers? instruction. 2.2.8 In vitro scrape assay 16HBE4o- cells were grown to confluence in 6 well plates. Confluent cell monolayers were mechanically wounded using a rubber cell scraper (Sarstedt). Wounded 16HBE4o- cells were washed 3 times with 1X PBS to remove loose cells and debris, and were then incubated for 24 hours in a 37 ?C incubator containing 5% CO2, with supernatants collected at 4 hours from treated PBMCs. Re-epithelialization of the wounds was observed using an IX70 inverted microscope (Olympus, Center Valley, PA) with a camera using UltraView v4 software (Perkin Elmer Life Sciences, Wellesley, MA). Pictures taken were analyzed using Image-J software to measure the re-epithelialization area. The entire surface area of each well was photographed (3 fields of view per well) for each independent experiment, and analysis of re-epithelialization was done using Image-J software to measure the average surface area of wound closure. Calculations of mean % re-epithelialization represent mean values ? standard deviation (SD) of 6 independent experiments, normalized to the RPMI medium control. 2.2.9 Flow cytometry analysis  Whole blood samples were treated in the similar manner to that described above for PBMCs. Briefly, 2 ml of whole blood was treated with either a single LPS dose, or LPS/LPS at 10 ng/ml dose (to induce tolerance), or were left untreated. IFN-? at 20ng/ml, IL10 at 10ng/ml, or IL4 at 20 ng/ml were used as controls for classical and alternative cell polarization respectively. Cells were prepared for flow cytometry by staining 100 ?l of whole blood with fluorescently tagged monoclonal anti-CD163, anti-CD14 and CD206 antibodies (eBioscience) for 40 minutes, followed by a red blood cell lysis with RBC lysis buffer (BD Biosciences) for 10 minutes at room temperature in the dark. Cells were then washed three times in 1X PBS and resuspended in  23 0.5% formaldehyde in PBS. Analysis was performed using a FACSCalibur system and FlowJo Software, with a CD14+ gate used to select for monocytes.  2.2.10 Statistical analysis Statistical significance was determined using a two-tailed Student t-test for paired comparisons and a one-way ANOVA for multiple data sets using the Prism 4.0 software (*, P<0.05; **, P<0.01; ***, P<0.001).  2.3  RESULTS 2.3.1 Kinetics of cytokine and chemokine production during endotoxin tolerance in PBMCs To determine the kinetics of cytokine and chemokine production during endotoxin tolerance in human mononuclear cells, PBMCs were treated with either a single LPS (LPS) dose, or two LPS doses to induce tolerance (LPS/LPS) and were incubated for either 1, 2, 4 or 24 hours (as shown in Figure 2.1A). Cytokine/chemokine levels were determined by ELISA from harvested cell-free supernatants. Figure 2.1B shows that TNF-? was consistently down-regulated during endotoxin tolerance, with the strongest difference occurring at 4 hours after the second LPS stimulation (p<0.01). In contrast, the anti-inflammatory cytokine IL-10 was found to be strongly up-regulated at 24 hours after a single LPS stimulation and then stayed at lower concentrations during endotoxin tolerance (LPS/LPS). The chemokine MCP-1 was either up-regulated or present at similar levels of expression during endotoxin tolerance when compared with a single LPS treatment.          24  Figure 2.1: Kinetics of cytokine and chemokine secretion in LPS-tolerant cells. A, PBMCs were left untreated or challenged with LPS (10 ng/ml) in a single dose (LPS) or two doses at a 24-h interval (LPS/LPS) and incubated for 1, 2, 4, and 24 h as indicated. B, Tissue culture supernatants were collected, and TNF?, IL10, and MCP-1 production was determined by capture ELISA. Mean values 6 SD of four biological replicates are shown. *p , 0.05, **p , 0.01.  2.3.2 Microarray analysis revealed strong differences in gene expression during endotoxin tolerance  Global transcriptional profiling of PBMCs at 4 hours after the second LPS stimulus was done using the Illumina microarray platform. The microarray analysis performed on LPS and LPS/LPS treated samples compared to No LPS samples, consistently confirmed findings obtained previously by ELISA in terms of inflammatory mediators and revealed a large number of differentially expressed genes. An InnateDB analysis based on up-regulated genes was performed showing pro-inflammatory mediators, like TNF?, interferon-related genes and inflammasome-related genes unchanged during endotoxin tolerance (upper sub-networks, Figure 2.2), while a large variety of other genes such as chemokines and interestingly, other genes including the scavenger receptor MARCO, metalloproteinases and a whole family of metallothionein isoforms were shown to stay at a similar level of up-regulation or were increased decided to use a systems biology approach to study this cellularreprogramming and compare it with other known cellular pro-grams. Our results led us to conclude that endotoxin tolerance isindeed a distinctive program of alternative polarization.Classical (M1) and alternative (M2) polarization are very generalterms used to classify the responses observed in macrophagestoward different stimuli in the microenvironment. Classical mac-rophage polarization is driven in response to microbial products orTh1 cytokines, such as IFN-g, and is characterized by an enhancedcapacity to kill intracellular microorganisms and produce gener-ous amounts of proinflammatory mediators. Conversely, alterna-tive macrophage polarization can be generated in response toa variety of stimuli such as Th2 cytokines (IL-4, IL-13, and IL-10), glucocorticoids, or a mixture of Ig complexes and TLRligands, producing different forms of M2 polarization. The func-tions of alternatively activated macrophages involve the control ofinflammatory responses, enhanced phagocytic activity, and tissuerepair (21?23). This study illustrates the substantial similaritiesthat exist between M2 polarization and endotoxin tolerance statesand proposes that this phenomenon can be considered as anotherform of alternative activation triggered by bacterial signaturessuch as LPS.Materials and MethodsCells and reagentsFor the isolation of blood mononuclear cells (MNCs), venous blood wascollected from healthy volunteers into heparin-containing Vacutainer tubes(BD Biosciences, San Jose, CA) in accordance with the ethical approvalguidelines of the University of British Columbia Research Ethics Board.PBMCs and monocyte-derived macrophages (MDMs) were isolated asdescribed previously (24, 25) and cultured in RPMI 1640 medium sup-plemented with 10% (v/v) heat-inactivated FBS, 2 mM L-glutamine, 25mM HEPES, and 1 mM sodium pyruvate (all from Invitrogen, Carlsbad,CA). Human bronchial epithelial cells (16HBE4o2) were obtained as a giftfrom Dr. D. Gruenert (University of California, San Francisco, San Fran-cisco, CA) and cultured in MEM medium with Earle?s salts (Invitrogen)supplemented with 10% (v/v) FBS and 2 mM L-glutamine. All cells werecultivated in a humidified 37?C incubator containing 5% CO2.LPS was isolated from Pseudomonas aeruginosa (PA-H103), grownovernight in Luria?Bertani broth at 37?C using the Darveau?Hancockmethod (26). The isolated LPS pellets were extracted with a 2:1chloroform/methanol solution to remove contaminating lipids. PurifiedLPS samples were quantitated using an assay for the specific sugar 2-keto-3-deoxyoctosonic acid (KDO assay) and resuspended in endotoxin-freewater (Sigma-Aldrich, St. Louis, MO) and used at a concentration of 10ng/ml.MDM differentiationFor MDM differentiation, PBMC isolation procedures were performedexclusively in PBS. PBMCs were then resuspended in serum-free RPMI1640 media and plated at 5 3 106 cells/well in 6-well plates for 30 min.Subsequently, media was changed, and fresh complete media containingM-CSF (10 ng/ml) (Research Diagnostic, Concord, MA) was added. Cellswere cultured for 7 d, with media changes every second day. On day 7,cells were subjected to LPS and LPS/LPS treatments.Endotoxin tolerance induction experimentsEndotoxin tolerance was induced in cells using 10 ng/ml LPS as first andsecond stimulus (Fig. 1A). The experiment consisted of three treatments:control/untreated cells (no LPS), single-dose LPS stimulated cells (LPS),and tolerized cells or cells that had been treated twice with LPS (LPS/LPS).For LPS/LPS tolerance treatments, cells were initially treated with LPSand incubated for 24 h before the second treatment. Supernatants werethen collected from the plates, followed by washing and addition of freshmedia. Cells were subsequently treated a second time with LPS. Four hoursafter the second treatment, supernatants and cell lysates were collected.Supernatants were stored at 220?C until cytokine and chemokine analysiswas done, and cell lysates were stored at 280?C in RLT lysis buffer(Qiagen, Valencia, CA) until RNA isolation.RNA isolationRNA isolation was carried out as described previously (24). Briefly, RNAwas isolated from cell lysates using Qiagen RNA Isolation Kit (RNeasy-Mini Kit; Qiagen), treated with RNase-free DNase (Qiagen) and elutedin RNase-free water (Ambion, Austin, TX) as per the manufacturer?sinstructions. The RNA concentration was determined using a NanoDropspectrophotometer, and RNA integrity and purity were assessed with anAgilent 2100 Bioanalyzer using RNA Nano kits (Agilent Technologies).Microarray experiment and analysisMicroarrays using RNA samples obtained 4 h after treatment of PBMCsobtained from four different healthy donors were performed using theIllumina platform at the Genome BCMicroarray Facility Platform (ProstateCentre, Vancouver General Hospital, Vancouver, BC, Canada). CompleteFIGURE 1. Kinetics of cytokine and chemokine secretion in LPS-tolerant cells. A, PBMCs were left untreated or challenged with LPS (10 ng/ml) in a singledose (LPS) or two doses at a 24-h interval (LPS/LPS) and incubated for 1, 2, 4, and 24 h as indicated. B, Tissue culture supernatants were collected, and TNF-a,IL-10, and MCP-1 production was determined by capture ELISA. Mean values 6 SD of four biological replicates are shown. *p , 0.05, **p , 0.01.7244 ENDOTOXIN TOLERANCE A DISTINCTIVE STATE OF M2 POLARIZATION by guest on March 16, 2013http://jimmunol.org/Downloaded from  25 when compared to single LPS stimulations (lower sub-networks, Figure 2.2). To determine which biological processes were most strongly associated with the dysregulated genes, Gene Ontology (GO) terms over-representation analysis was filtered to display those biological processes with the highest P-values and odds ratios that were related to innate immunity. Highest odds ratios showed the main biological processes that were present under each condition, such as biosyntheis of IL12, IFN? and their respective activation of signaling pathways during single treatments with LPS. On the other hand, negative regulation of certain signaling pathways such as the NF?B cascade, cellular migration and proliferation were characteristic of endotoxin tolerance (Table 2.1). Similarly, we analyzed the upstream regions of dysregulated genes for over representation of transcription factor binding sites; drastic differences were found between the single LPS and endotoxin tolerance treatments. This analysis revealed in keeping with published data [117,118,119] that the binding sites for NF-?B, IRF-1 and STAT family members were the most significantly associated with inflammatory responses during single LPS treatments, while different members of the ETS family of transcription factors were most associated with endotoxin tolerance (Table 2.2). In addition, we performed a parallel bioinformatic analysis with another endotoxin tolerance microarray study reported in the literature recently [31]. Our findings showed high similarities in transcription factors including those from the E-Twenty Six (ETS) Family, (ETS1), ETS variant gene 4 (ETV4), spleen focus forming virus proviral integration oncogene spi1 (SPI1)] and metal regulatory transcription factor (MTF1), which modulate metallothionein responses (Data not shown). We also found matching signaling pathways that are mainly correlated with membrane trafficking and cell motility [ADP-ribosylation factor 1 (ARF1), Ras], endocytosis, wound healing [FGF, VEGF, EGF, Platelet derived growth factor (PDGF)], as well as insulin responses. The molecular basis for this differential expression in endotoxin tolerant cells compared to endotoxin stimulated cells was studied further and is discussed in more detail below.   26   Figure 2.2: Microarray analysis revealed strong differences in gene expression during endotoxin tolerance.  The demonstrated network/pathway diagrams are taken from an InnateDB analysis of the microarray data based on upregulated genes and visualized using the visualization tool Cerebral. In this visualization, genes/proteins (nodes) are shown as circles, and interactions between these nodes are shown as lines (edges). Two groups of subnetworks are shown with the upper subnetworks being the TLR4 to NF-kB pathway, characteristic of LPS and the lower subnetworks including wound-healing proteins such as chemokines, serpins, and metallothioneins, characteristic to LPS/LPS. Nodes are colored according to the degree of dysregulation from highly upregulated (deep red) through baseline (white). Endotoxin tolerance was induced in PBMCs (as shown in Figure 2.1A, with a second LPS stimulus for 4 h) and RNA isolated and used to perform microarrays using the Illumina platform. The full data set has been deposited in the Gene Expression Omnibus database (accession number GSE22248). Data analysis was performed using the InnateDB database (http://www.innatedb.ca), and pathway network visualization based on cellular interactions was performed using the linked Cytoscape plugin Cerebral.     microarray data have been deposited in the Gene Expression Omnibuspublic database (accession number GSE22248; http://www.ncbi.nlm.nih.gov/geo/). Differential express genes were selected based on an adjusted pvalue ,0.05 (a table is available from the authors upon request). Tran-scriptional analysis of the microarray data was performed using systembiology tools developed in our laboratory including the InnateDB database(http://www.innatedb.ca) (27), Cerebral, which was used for pathwaynetwork visualization, and MetaGEX, which was used to obtain GeneOntology (GO) terms and transcription factor overrepresentation analysis(C.D. Fjell, unpublished observations) (http://marray.cmdr.ubc.ca/meta-gex/). Genes with fold change of 2 or more were considered differentiallyexpressed.Quantitative real-time PCRDifferential gene expression identified through microarray analysis wasvalidated via quantitative real-time PCR (qRT-PCR), which was per-formed using the SuperScript III Platinum Two-Step qRT-PCR kit withSYBR Green (Invitrogen) as per the manufacturer?s instructions, and theABI Prism 7000 sequence detection system (Applied Biosystems,Carlsbad, CA). Briefly, 500 mg total RNAwas reverse transcribed usingqScript cDNA Synthesis Kit (Quanta Biosciences, Gaithersburg, MD).PCR was conducted in a 12.5-ml reaction volume containing 2.5 ml 1/5diluted cDNA template. A melting curve was performed to ensure thatany product detected was specific to the desired amplicon. Fold changesfor LPS and LPS/LPS samples were calculated compared with the ?noLPS? control, after normalizing the change in expression of the gene ofinterest to the housekeeping gene b2-microglobulin using the compar-ative threshold cycle method (28). Sequences of the primers [all fromInvitrogen except matrix metalloproteinase (MMP)-9, which was froma-DNA, Montreal, QC, Canada] used for qRT-PCR are available fromthe authors upon request.ELISAELISA was performed on supernatants collected 4 h posttreatment. Theseincluded TNF-a, MCP-1, CCL-22 (R&D Systems), IL-10 (eBioscience),and CCL-3 (Biosource). ELISA assays were performed according to thekit manufacturers? instructions.In vitro scrape assay16HBE4o2 cells were grown to confluence in 6-well plates. Confluent cellmonolayers were mechanically wounded using a rubber cell scraper(Sarstedt). Wounded 16HBE4o2 cells were washed three times with 13PBS to remove loose cells and debris and were then incubated for 24 h ina 37?C incubator containing 5% CO2, with supernatants collected at 4 hfrom treated PBMCs. Re-epithelialization of the unstained wounds wasobserved using an IX70 inverted microscope (Olympus, Center Valley, PA)with a camera using UltraView v4 software (PerkinElmer Life Sciences,Wellesley, MA). A magnification of310 was used to allow a major surfacearea to be covered. Photographs taken were analyzed using ImageJ soft-ware (National Institutes of Health, Bethesda, MD; http://imagej.nih.gov/ij/)to measure the re-epithelialization area. The entire surface area of eachwell was photographed (three fields of view per well) for each independentexperiment, and analysis of re-epithelialization was done using ImageJsoftware to measure the average surface area of wound closure. Calcu-lations of mean percentage re-epithelialization represent mean values 6SD of six independent experiments normalized to the RPMI 1640 mediumcontrol.FIGURE 2. Microarray analysis revealed strong differences in gene expression during endotoxin tolerance. The demonstrated network/pathway diagramsare taken from an InnateDB analysis of the microarray data based on upregulated genes and visualized using the visualization tool Cerebral. In this vi-sualization, genes/proteins (nodes) are shown as circles, and interactions between these nodes are shown as lines (edges). Two groups of subnetworks areshown with the upper subnetworks being the TLR4 to NF-kB pathway, characteristic of LPS and the lower subnetworks including wound-healing proteinssuch as chemokines, serpins, and metallothioneins, characteristic to LPS/LPS. Nodes are colored according to the degree of dysregulation from highlyupregulated (deep red) through baseline (white). Endotoxin tolerance was induced in PBMCs (as shown in Fig. 1A, with a second LPS stimulus for 4 h) andRNA isolated and used to perform microarrays using the Illumina platform. The full data set has been deposited in the Gene Expression Omnibus database(accession number GSE22248). Data analysis was performed using the InnateDB database (http://www.innatedb.ca), and pathway network visualizationbased on cellular interactions was performed using the linked Cytoscape plugin Cerebral.The Journal of Immunology 7245 by guest on March 16, 2013http://jimmunol.org/Downloaded from  27  Table 2.1: Gene Ontology terms over-representation analysis. GO terms with the highest P-values and odds ratios that were related to innate immunity are presented. (NS: Not significant).       GO Term LPS LPS/LPS P-value Odds Ratio P-value Odds Ratio Regulation of cytokine biosynthetic process 6.5E-08 4.94 0.035 2.74 Positive regulation of I-kb kinase/NF-kb cascade 0.00032 2.68 NS NS Leukocyte activation during immune response 0.00034 6.23 0.0066 NS Positive regulation of innate immune response 0.00041 7.27 NS NS Positive regulation of nitric oxide biosynthetic process 0.00042 7.27 0.036 3.77 Cytokine-mediated signaling pathway 0.00067 3.84 0.014 2.84 Regulation of apoptosis 0.0012 2.58 NS NS Positive regulation of NF-?-b transcr. Factor activity 0.0027 3.66 NS NS Response to interleukin-1 0.0045 9.32 NS NS Regulation of epidermal cell differentiation 0.0075 7.46 0.031 5.66 Positive regulation of IL-12 biosynthetic process 0.0079 13.98 NS NS JAK-STAT cascade 0.012 3.74 NS NS Tumor necrosis factor-mediated signaling pathway 0.017 5.33 NS NS Positive regulation of interferon-gamma production 0.024 6.99 NS NS Regulation of autophagy NS NS 0.0020 33.97 Positive regulation VEGF NS NS 0.0024 11.33 Positive regulation of pseudopodium assembly NS NS 0.0089 11.32 Proteoglycan metabolic process NS NS 0.015 8.50 Negative regulation of I-kb kinase/NF-kb cascade NS NS 0.015 8.49 Positive regulation of actin filament polymerization NS NS 0.022 6.79 Chemotaxis 0.0048 1.94 8.3E-13 4.77 Positive regulation of smooth muscle cell proliferation NS NS 0.0096 4.72 Regulation of adaptive immune response NS NS 0.00096 4.27 Positive regulation of mononuclear cell proliferation 0.0011 3.55 0.0011 3.79 Regulation of tyrosine phosphorylation of STAT protein NS NS 0.019 3.24 Locomotory behaviour 0.020 1.59 2.6E-9 3.23 Positive regulation of leukocyte activation 0.00069 2.75 0.00048 3.06 Ras protein signal transduction NS NS 0.020 2.44  28  Table 2.2: Transcription factor binding site over-representation analysis. Over-representation of the binding sites for transcription factors were determined based on evaluation of the promoter regions for dysregulated genes; those with highest significance (P-values) were selected. (NS: No significant)  TRANSCRIPTION FACTOR LPS LPS/LPS P-value P-value IRF1 [interferon regulatory factor 1 ]  3.4E-10 NS STAT1 [signal transducer and activator of transcription 1, 91kDa ]  1.5E-09 NS CEBPB [CCAAT/enhancer binding protein (C/EBP), beta ]  4.8E-08 NS IRF2 [interferon regulatory factor 2 ]  7.6E-06 NS RBPJ [recombination signal binding protein for immunoglobulin k J region ]  9.3E-06 NS STAT3 [signal transducer and activator of transcription 3]  1.1E-05 NS REL [v-rel reticuloendotheliosis viral oncogene homolog (avian) ]  1.9E-05 NS STAT5B [signal transducer and activator of transcription 5B ]  3.9E-05 NS FUS [fusion (involved in t(12;16) in malignant liposarcoma) ]  0.00034 NS STAT5A [signal transducer and activator of transcription 5A ]  0.00053 NS RELA [v-rel reticuloendotheliosis viral oncogene homolog A (avian) ]  0.00065 NS IRF7 [interferon regulatory factor 7 ]  0.00070 NS TP63 [tumor protein p63 ]  0.00070 NS HNRNPK [heterogeneous nuclear ribonucleoprotein K ]  0.0013 NS RUNX1 [runt-related transcription factor 1 ]  0.0013 NS MYC [v-myc myelocytomatosis viral oncogene homolog (avian) ]  0.0013 NS NRF1 [nuclear respiratory factor 1 ]  0.0019 NS ARNT [aryl hydrocarbon receptor nuclear translocator ]  0.0021 NS IRF3 [interferon regulatory factor 3 ]  0.0022 NS SPI1 [spleen focus forming virus (SFFV) proviral integration oncogene spi1 ]  0.016 2.8E-05 ETV4 [ets variant 4 ]  NS 4.7E-05 SREBF1 [sterol regulatory element binding transcription factor 1 ]  NS 0.00048 SP1 [Sp1 transcription factor ]  NS 0.00024 ETS1 [v-ets erythroblastosis virus E26 oncogene homolog 1 (avian) ]  0.033 0.0015 GABPA [GA binding protein transcription factor, alpha subunit 60kDa ]  NS 0.0022 ELF1 [E74-like factor 1 (ets domain transcription factor) ]  NS 0.0026 FOS [FBJ murine osteosarcoma viral oncogene homolog ]  0.015 0.0028 FOXO3 [forkhead box O3 ]  NS 0.0038 SRF [serum response factor]  NS 0.0039 CREM [cAMP responsive element modulator ]  NS 0.0047 MTF1 [metal-regulatory transcription factor 1 ]  NS 0.0047 CEBPE [CCAAT/enhancer binding protein (C/EBP), epsilon ]  NS 0.0089  29 TRANSCRIPTION FACTOR LPS LPS/LPS P-value P-value RELB [v-rel reticuloendotheliosis viral oncogene homolog B ]  NS 0.019 WT1 [Wilms tumor 1 ]  NS 0.019 ZNF76 [zinc finger protein 76 (expressed in testis) ]  NS 0.019 ZNF143 [zinc finger protein 143 ]  NS 0.019 AHR [aryl hydrocarbon receptor ]  NS 0.022  2.3.3 Pro-inflammatory mediators and chemokine gene expression profiles during endotoxin tolerance were similar to those found during alternative polarization To confirm the microarray analysis, real time quantitative PCR (qRT-PCR) analysis was performed. The pattern of dysregulation of several genes with prominent pro-inflammatory functions, as well as several chemokines, presented the first evidence that endotoxin tolerance might mediate an alternative polarization phenotype in PBMCs. Similarly to the observations for cytokine secretion shown in Figure 2.1B, and consistent with literature observations (37,38), TNF? gene expression was significantly reduced to basal levels (p<0.001) during endotoxin tolerance (4 h after the second treatment) (Figure 2.3A), as was Tissue Factor (TF) (p< 0.01). COX-2 was also significantly down-regulated during tolerance relative to the single LPS treatment. M1 polarization associated chemokines CCL-3 and CCL-20 (Figure 2.3B) were both significantly reduced in cells exhibiting endotoxin tolerance (p< 0.01). In contrast, during endotoxin tolerance, M2 associated chemokines CCL-22 and CCL-24 were enhanced by approximately 8- (p<0.001) and 73- fold (p<0.01) respectively, relative to the single LPS treatment (Figure 2.3C). Some of these findings were then followed up at the protein level by ELISA using PBMC (Figure 2.6A) and MDM (Figure 2.3D), where similar results were obtained, in confirmation of the previous results.   30 Cell surface marker expression revealed a unique profileduring endotoxin tolerance with similarities to an M2polarization stateCD163 and CD206 (mannose receptor) are cell surface markersthat are induced in M2-polarized cells (35). Based on the abovedata that was consistent with the hypothesis that endotoxin tol-erance skews cell polarization into an M2-like phenotype, a cellsurface marker analysis was performed in a CD14+ monocytepopulation to determine expression levels of CD163 and CD206by flow cytometry using different stimuli for M1 and M2 polari-zation controls. As shown in Fig. 4, CD163 was significantly in-duced in tolerized (LPS/LPS) (p , 0.05) compared with singleLPS treatment or untreated sample. These results were similar tothose obtained in the control samples treated with IL-10 (M2polarization-inducing control) compared with IFN-g (M1 polariza-tion-inducing control) treated cells (p , 0.05). In contrast, CD206did not present any significant expression among LPS treatments,although significant upregulation of this marker was observed onIL-4?treated cells compared with untreated cells (no LPS) (p ,0.05), M1-polarized cells (IFN-g) (p , 0.01), or LPS (singletreatment) treated cells (p , 0.01).Key genes related to phagocytosis and wound healing werestrongly upregulated during endotoxin toleranceIn addition to extensive differential gene regulation of cytokineand chemokine expression, endotoxin tolerance also induced genesassociated with phagocytosis and wound healing. Endotoxin tol-erance was induced in PBMCs using a 10 ng/ml LPS dose [asshown in Fig. 1A, with a 4-h second LPS (tolerizing) stimulus],and RNA/cDNA from these samples was used in gene expres-sion analysis by qRT-PCR. The scavenger receptors MARCO andCD23 have been previously linked with phagocytosis (36, 37).Both genes were strongly unregulated during endotoxin tolerancecompared with expression after a single LPS treatment, withFIGURE 3. Proinflammatory mediators and chemokine profile responses during endotoxin tolerance were similar to those observed during M2 polar-ization. Endotoxin tolerance was induced as shown in Fig. 1A, in PBMCs and MDMs, with a 4-h second LPS stimulus in the case of the LPS/LPS situation.Gene expression of proinflammatory mediators (A) and chemokines (B, C) was analyzed in PBMCs, selected on the basis of the output of the microarraystudies, and was assessed using qRT-PCR. Fold changes (y-axis) were normalized to b2-microglobulin (B2M). Protein expression was analyzed by ELISAon supernatants collected from MDMs 4 h poststimulation (D). Results are shown as the mean 6 SD of at least three independent experiments. *p , 0.05,**p , 0.01, ***p , 0.001.7248 ENDOTOXIN TOLERANCE A DISTINCTIVE STATE OF M2 POLARIZATION by guest on March 16, 2013http://jimmunol.org/Downloaded from  Figure 2.3: Proinflammatory mediators and chemokine profile responses during endotoxin tolerance were similar to t os  observed du ing M2 polarization.  Endotoxin tolerance was induced as shown in Figure 2.1A, in PBMCs and MDMs, with a 4-h second LPS stimulus in the case of the LPS/LPS situation. Gene expression of proinflammatory mediators (A) and chemokines (B, C) was analyzed in PBMCs, selected on the basis of the output of the microarray studies, and was assessed using qRT-PCR. Fold changes (y-axis) were normalized to b2-microglobulin (B2M). Protein expression was analyz d by ELISA on supernatants collected from MDMs 4 h post-stimulation (D). Results are shown as the mean 6 SD of at least three independent experiments. *p<0.05, **p<0.01, ***p<0.001.  31 2.3.4 Cell surface marker expression revealed a unique profile during endotoxin tolerance with similarities to an alternative polarization state CD163 and CD206 (mannose receptor) are cell surface markers that are induced in alternatively polarized cells [120]. Based on the above data that was consistent with the hypothesis that endotoxin tolerance skews cell polarization into an M2-like phenotype, a cell surface marker analysis was performed in the CD14+ monocyte population to determine expression levels of CD163 and CD206 by flow cytometry, using different stimuli for M1 and M2 polarization controls. As shown in Figure 2.4, CD163 was significantly induced in tolerized cells (LPS/LPS) (p<0.05) as compared to a single LPS treatment or untreated sample. These results were similar to those obtained in the control samples treated with IL10 (M2-inducing control), when compared to IFN? (M1-inducing control) treated cells (p<0.05). On the other hand, CD206 did not present any significant expression among LPS treatments, although significant up-regulation of this marker was observed on IL-4-treated cells when compared to untreated cells (No LPS) (P<0.05), M1-polarized cells (IFN?) (P<0.01) or LPS (single treatment) treated cells (P<0.01).  Figure 2.4: Cell surface marker expression revealed a unique profile during endotoxin tolerance with similarities to an alternative polarization state.  Whole-blood samples were treated with a single 4 h LPS treatment (LPS) or endotoxin tolerance with a 4 h second LPS stimulus (LPS/LPS) or with IFN? at 20 ng/ml, IL10 at 10 ng/ml, or IL4 at 20 ng/ml. Upon RBC lysis, the expression of the known alternative polarization markers CD163 and CD206 (mannose receptor) was analyzed by flow cytometry, gating for CD14+ monocytes. Results are shown as mean values 6 SD of three independent experiments. *p<0.05.     32 2.3.5 Key genes related to phagocytosis and wound healing were strongly up-regulated during endotoxin tolerance In addition to extensive differential gene regulation of cytokine and chemokine expression, endotoxin tolerance also induced genes associated with phagocytosis and wound healing. Endotoxin tolerance was induced in PBMCs using a 10 ng/ml LPS dose (as shown in Figure 2.1A, with a 4 h second LPS - tolerizing - stimulus), and RNA/cDNA from these samples was used in gene expression analysis by qRT-PCR. The scavenger receptors MARCO and CD23 have been previously linked with phagocytosis [121,122]. Both genes were strongly unregulated during endotoxin tolerance compared to expression after a single LPS treatment, with MARCO increasing by approximately 20 fold (p< 0.001), and CD23 increasing by 5 fold (p< 0.01) at 4 hours (Figure 2.5A). Gene expression analysis by qRT-PCR showed that growth factors VEGF and FGF-2 (Figure 2.5B), previously described to be produced by human monocytes [123,124], were significantly induced during tolerance conditions (p<0.01 and p< 0.05, respectively). Similar results were observed for the metalloproteases MMP7 and MMP9 (p<0.05). In addition, both the proteoglycan Versican (VCAN) and formyl-peptide receptor ligand-1 (FPRL-1) which have been shown to play significant roles in wound healing [125,126], were strongly up-regulated during endotoxin tolerance by ~20 (p<0.001) and 70 fold (p<0.05) respectively, relative to expression after a single LPS stimulation. These findings were confirmed using primary monocytes (Figure 2.6B) as well as MDMs (figure 2.7) with very similar results to the ones found in PBMCs. In addition, MMP-9 expression was also confirmed at protein level during tolerance (data not shown).  33  Figure 2.5: Key genes related to phagocytosis and wound healing were consistently upregulated during endotoxin tolerance.  Gene expression of phagocytosis-associated (A) and wound-healing?associated (B) genes was analyzed by qRT-PCR in LPS or LPS/LPS endotoxin tolerant PBMCs (as per Figure 2.1A, with a 4 h second LPS stimulus). Results shown are mean values 6 SD of four independent experiments. *p , 0.05, **p , 0.01, ***p , 0.001.        34  Figure 2.6: Gene expression profile in PBMC and Monocytes.  Cytokine and chemokine expression profile in PBMC measured by ELISA (A). Differential gene expression in primary human monocytes during tolerance showed a similar profile to that found in PBMC and MDM (B). Endotoxin tolerance (as shown in Figure 2.1A, with a 4 h second LPS stimulus) and compared to no treatment or a single 4 h LPS treatment. Protein expression was measured by ELISA and gene expression was assessed using qRT-PCR. Results are shown as mean values ? SD of three independent experiments. (*, p < 0.05; **, p < 0.01; ***, p < 0.001).  35  Figure 2.7: Upregulation of key genes related to phagocytosis and wound healing during endotoxin tolerance in human MDMs.  Expression of genes involved in phagocytosis and wound healing was analyzed by qRT-PCR in LPS or LPS/LPS endotoxin tolerant human MDMs (as per Figure 2.1A, with a 4-hsecond LPS stimulus). Results shown are mean values ? SD of four independent experiments. The responses in human monocytes are found in Figure 2.6B. *p , 0.05, **p , 0.01, ***p , 0.001.   36 2.3.6 Endotoxin tolerance conditions enhanced wound healing properties of epithelial cells Given that gene expression analysis revealed up-regulation of a variety of genes involved in wound healing, including growth factors and matrix metalloproteases (Figure 2.5B, 2.6B and 2.7), a functional wound healing assay was performed using bronchial epithelial cells in an in vitro scrape model. A monolayer of 16HBE4o- cells was ?wounded? by mechanical removal of cells and supernatants from PBMCs treated with LPS alone, LPS/LPS, or no LPS for 4 h post- stimulation (as per Figure 2.1A) were applied to. After 24 h of incubation, photographs were taken of the migration of 16HBE4o- cells into the wounded area, relative to the margins of the scrape zone. The surface areas of the re-epithelialization were determined relative to the original boundaries of the scrape zone using ImageJ software, and the % mean re-epithelialization (or wound closure) was determined relative to the media only treatment (fresh RPMI only). As shown in Figure 2.8, supernatants from tolerized PBMCs were able to induce significantly higher (p< 0.05) re-epithelialization relative to the LPS alone treatment in 6 independent experiments. Additionally, to confirm that the levels of re-epithelialization observed, were due to factors produced during the tolerance state and not to residual LPS, PBMC were treated as described previously but with a slight change in the protocol. In this, during the second LPS stimulation, cells were only treated for 1 hour, followed by two washes to remove residual LPS, and then incubated for the following 4 hours. This conditioned medium was then used during the scrape assays performed on epithelial cells. The data obtained showed that when using conditioned media that did not include trace amounts of LPS, a similar level of re-epithelialization, and significant up-regulation upon endotoxin tolerance was observed when compared to the regular method used (Figure 2.8).    37   Figure 2.8: Endotoxin tolerance enhanced wound-healing properties in epithelial cells. Supernatants from single LPS treated or endotoxin tolerized PBMCs (as per Figure 2.1A, with a 4-h second LPS stimulus) or RPMI1640 medium were applied to a mechanically wounded monolayer of 16HBE4o2 cells. In addition, during the second stimulus, cells were only treated for 1 h, followed by two washes to remove residual LPS, and then fresh medium re-added and incubated for the following 4 h (treatments are labeled with an asterisk [i.e., LPS* and LPS/LPS* in B]). After that time, this conditioned medium was removed and used during the scrape assays performed on 16HBE4o2 epithelial cells. A, After 24 h of incubation, photographs were taken of the advancing growth front of 16HBE4o2 cells, relative to the scrape point of origin marked on each well. The measurements of surface areas of re-epithelialization were determined using ImageJ software. Images shown are from a single experiment representative of six separate trials. Original magnification is 10X. B, The quantitation of re-epithelialization represents the mean values ? SD of at least three independent experiments. *p<0.05, **p<0.01   38 2.3.7 Metallothioneins (MT) are strongly up-regulated during endotoxin tolerance Metallothioneins are highly conserved metal-binding proteins. They are characterized by their multiple involvements in metal homeostasis, detoxification, modulation of inflammation and cell proliferation [127]. As shown in Figure 2.9, metallothioneins as a group were strongly up-regulated in endotoxin tolerance. In particular, MT-2A, MT-1E, and MT-1X showed an up-regulation during endotoxin tolerance by approximately 10, 16, and 18 fold, respectively. MT-1A and MT-1F furthermore showed an even more enhanced gene expression under tolerance conditions (25 and 35 fold increase over LPS alone treatment), while MT-1H showed the highest level of induction by >230 fold (p<0.01). Selected metallothioneins (MT1A, MT2A and MT-1E) were analyzed on tolerized MDMs revealing very similar findings to the ones obtained with PBMC (Figure 2.6). However, we were unable to confirm these changes at the protein level due to the lack of suitable reagents.  Figure 2.9: Metallothioneins were strongly up-regulated during endotoxin tolerance.  Endotoxin tolerance was induced in PBMCs (as shown in Figure 2.1A, with a 4 h second LPS stimulus) and compared to a single 4 h LPS treatment. Gene expression of various methalothioneins was assessed using qRT-PCR. Results are shown as mean values ? SD of four independent experiments. (*, p < 0.05; **, p < 0.01; ***, p < 0.001).  39  Figure 2.10: Differential expression of negative regulators during tolerance in PBMC.  Endotoxin Tolerance was discussed as described previously. RNA was isolated 4 hours post-stimulus and RT-qPCR was performed. Results are shown as mean values ? SD of three independent experiments.   2.4 DISCUSSION In our efforts to obtain a better understanding of this phenomenon from a broader perspective than many studies performed previously, we utilized a systems biology approach to observe the range of selective changes that occurred during endotoxin tolerance. These changes were similar to those that occur in alternatively polarized macrophages, leading us to propose that endotoxin tolerance represents a distinct state of alternative polarization. Our findings revealed the down-regulation of a broad variety of pro-inflammatory mediators. For example, the pro-inflammatory cytokine TNF?, an accepted marker for demonstrating endotoxin tolerance, was consistently reduced in our study, consistent with numerous other studies [e.g. [128,129]]. Similar findings were obtained for cyclooxygenase-2 (COX-2) and tissue factor (TF), mediators that play important roles in the activation of inflammation and coagulation, respectively. In addition, chemokines previously associated with classical activation or the M1 phenotype, such as CCL-3 and CCL-20 [130], were down-regulated, while chemokines that have been associated  40 with the alternative activation, M2 phenotype were up-regulated during endotoxin tolerance in human PBMC and MDM. We also observed that while CD206, a major IL4-induced alternative macrophage marker was unchanged, the expression of CD163, a key IL10-induced alternative macrophage marker was enhanced [19]. As there appears to be more than one type of alternative macrophage activation state (21), it is important to note that our data leads to the conclusion that endotoxin tolerance is a form of alternative activation but does not exclude the possibility that there will be distinct and possibly definitive differences from the M2 state induced by other agents. Furthermore, GO term over-representation analysis normally used to determine the operative biological processes, demonstrated that a single (inflammatory) LPS stimulation directs cells towards pro-inflammatory functions such as positive regulation of mediators like TNF?, IFN?, IL12 and nitric oxide (NO). Conversely, the induction of endotoxin tolerance re-directs biological processes turning off the aforementioned functions, while activating other immunological functions such as chemotaxis, cell proliferation and, interestingly, wound healing related processes such as the positive regulation of VEGF and proteoglycans. These findings are in agreement with our results from transcription factor binding site (TFBS) over-representation analysis, that predict the operative transcription factors by looking for common TFBS in the upstream regions of dysregulated genes. During single LPS stimulation, the analysis showed the presence of key transcription factors like the NF-?B subunit RelA (p65), as well as IRF and STAT family members, which are important for the production of pro-inflammatory mediators. In contrast, during endotoxin tolerance we found the presence of transcription factors such as the NF-?B family member RelB, involved in the modulation of inflammation [131] and which was previously described as an active participant of endotoxin tolerance [108]. Additionally, endotoxin tolerance lead to the over-representation of dysregulated genes with binding sites for the transcription factors important in wound healing processes. For example, the TFBS for Sp1 a transcription factor involved in angiogenesis through AKT-mediated induction of VEGF expression, was over-represented [124,132]. The TFBS for members of the ETS family like Ets1, Elf1 Spi1, which are involved in regulation of extracellular matrix [133], vascular development [134] and cellular migration [135], were also statistically significantly over-represented during endotoxin tolerance. These findings could be also connected to the substantial up-regulation of MCP-1 (CCL2) observed in our time course experiments during endotoxin tolerance, since  41 MCP-1 has been previously linked with the regulation of angiogenesis through activation of the Ets-1 transcription factor [136].  A characteristic function of an alternative polarization state is the enhancement of wound healing processes [22]. In alternatively activated murine macrophages, this function has been linked to the actions of IL4, leading to the expression of arginase-1 and the consequent expression of polyamines, which are important for wound repair [137]. In humans, this appears not to be the case [138], as arginase-1 is not expressed during alternative activation. Instead, it has been suggested that wound healing functions are linked to the presence of various matrix metalloproteinases (MMP), such as MMP-9 that has a role in attracting blood vessel associated stem cells [139], as well as MMP-12 that participates in remodeling of the extracellular matrix [140]. We have demonstrated for first time that, similar to alternative activation, cells undergoing endotoxin tolerance up-regulate not only MMP-9 but also a wide variety of genes that are important in wound repair, including MMP-7, also important in remodeling of the extracellular matrix, and FPRL-1 that has been linked to wound repair through its ligand LL-37 [126]. In addition, we observed up-regulation of growth factors such as VEGF and FGF-2, important for the growth of endothelial and epithelial layers, and proteoglycans such as Versican, which is an important component of the extracellular matrix in blood vessels. Using a scrape assay with HBE cells, we confirmed that enhanced wound healing properties were associated with the cellular reprogramming observed during endotoxin tolerance in vitro. This result is consistent with the enhanced expression of metalloproteinases such as MMP9, which plays a vital role in airway epithelial wound repair by regulating cellular functions, and matrix-bound growth factors [141,142]. Moreover, the presence of growth factors such as VEGF, which is associated with airway epithelial cell proliferation [143], could also be attributed to the improvement in re-epithelialization observed in our model. These results suggest that cellular reprogramming during endotoxin tolerance also acts to enhance repair of tissue damage after the response to an infectious insult. However, to verify the role of the complete wound-healing program during endotoxin tolerance, further in vivo analysis would be required.  Macrophages undergoing alternative polarization also tend to demonstrate enhanced phagocytic activity [144]. We have shown that during endotoxin tolerance, molecules involved in various aspects of phagocytosis, such as MARCO and CD23, are up-regulated [121,145]. These findings are supported by previous observations revealing the induction of these molecules in IL10- & IL4-induced alternative macrophages, respectively [140,146,147]. Our results  42 complement previous observations showing enhanced phagocytosis during endotoxin tolerance [31,148], suggesting an important role for this phenomenon in the clearance of microbes or apoptotic bodies after an initial inflammatory response.  Moreover, we identified for the first time the strong and consistent presence during endotoxin tolerance of six different MT isoforms. TFBS over-representation analysis also demonstrated the likely importance of the methallothionein-associated transcription factor, MTF-1. MTs are a group of small proteins with a variety of functions, including protection from oxidative damage, as well as influencing zinc homeostasis and angiogenesis, all of which may have important roles during endotoxin tolerance. MT expression has also been linked with protection from acute lung injury and cardiac dysfunction during endotoxemia mainly by modulating inflammation as well as enhancement of endothelial integrity [149,150]. However, MT expression has also been showed to be increased with age leading to a low bioavailability of zinc, a consequent thymic involution and a basic level of immunosuppresion in the elderly, phenomena that get stabilized at a very old age [151]. Only the expression of the MT-2A isoform has been described in the IL10-induced alternative polarization state [152], suggesting that the cellular reprogramming occurring during endotoxin tolerance might be a type of alternative polarization distinct from those described in the literature to date and MTs could be used as a powerful marker of this state. However, further analyses are needed to confirm this statement and to gain a better understanding of the biological role of these molecules in vivo. The variety of cellular modifications that occur during human endotoxin tolerance could be linked to the autocrine and paracrine responses mediated by soluble factors such as IL10, which, as shown here, is normally expressed in response to an initial LPS stimulus, and its expression is maintained at lower levels after a secondary stimulus. This would tend to lead to the generation of a transcriptional profile similar to that observed in alternative polarization by IL10. In fact, most of the genes, shown by William et al [153] to be up-regulated by a combination of LPS and IL10, were also up-regulated here during endotoxin tolerance. Furthermore, when we performed a parallel bioinformatic analysis with other endotoxin tolerance microarray data available in the literature [31], the results were consistent with the conclusions here, suggesting that important known alternative M2 functions such as phagocytosis [19], insulin responses [154] and wound healing [19] are associated with a tolerant/reprogrammed state. Additionally, different negative regulators have been linked to the transcriptional changes seen during the tolerance state; however some of these have been  43 identified only in animal models. To confirm the presence of these negative regulators in our model system, we performed RT-qPCR whereby fourteen different known negative regulators were screened (Figure 2.10). The results showed an enhanced expression of 8 regulators during the single LPS treatments (ST2L, SOCS-1, IRAK-M, SARM, RelB, A20, NFBIA and IKBZ). Most of these important regulators still demonstrated increased expression during the tolerance state (LPS/LPS). In two instances, SOCS-1, and RelB, expression was strongly up-regulated at a level similar or higher than that observed during a single LPS treatment. The increased presence of these two essential negative regulators coincided with our microarray findings and our transcription factor binding site over-representation analysis. These consistent findings may indicate that those negative regulators expressed during a single LPS treatment may be important for the initial termination of TLR responses, while those present during double LPS treatments may be responsible for the development and maintenance of a cellular tolerant/reprogrammed state. It is important to mention that some of the above-discussed data was also confirmed in MDM (Figure 2.7) and human primary monocytes (Figure 2.6B).  Overall these findings demonstrated that after an initial inflammatory stimulus, upon a second stimulus of the same type, human mononuclear cells undergo a polarization towards the M2 phenotype. Although it seems that these cellular transformations are meant to restore homeostasis by controlling hyperinflammation and healing the affected tissue, in a condition like sepsis the panorama may be different. During sepsis, the immune system is continuously exposed to bacterial products, and this may cause a prolonged endotoxin tolerance state. This state can become deleterious since secondary infections can take place, causing risk to the life of the patient. Since it is not clear when this occurs and if this can indeed increase the risk of death in septic patients, I studied this issue in the next chapter.        44 CHAPTER 3: A UNIQUE ENDOTOXIN TOLERANCE PROFILE PREDOMINATES DURING CLINICAL SEPSIS  3.1 INTRODUCTION Innate immunity, which represents the first line of host defense, is highly efficient against a world with tens of thousands of potentially pathogenic microbes. We are exposed to them in many ways, such as via the food we eat, the water we drink, the individuals and animals that we contact or the air we breathe, but it is very rare that we get an infection. We are protected by an innate immune response, in which a series of diverse mechanisms occur that have the basic aim of eliminating the invading pathogen. However if this response is too strongly stimulated it can result in inflammation-induced organ injury and deadly syndromes like sepsis. More recently, it has been acknowledged that sepsis is a more complicated condition. In the United States, sepsis is the leading cause of death in critically ill patients, where 750,000 patients per year are diagnosed with sepsis and more than 210,000 die. Sepsis is defined as a clinical response to a suspected or proven infection that includes two or more of the following symptoms: tachypnea or tachycardia; leukocytosis or leukopenia; and hyperthermia or hypothermia [41]. However, in recent years, the prevailing theory that sepsis represents an uncontrolled inflammatory response has been questioned, since numerous trials using blocking agents of the inflammatory cascade, such as corticosteroids, anti-endotoxin antibodies or TNF antagonists, have failed to effectively treat sepsis [78,155]. Recent findings regarding the establishment of secondary infections, such as the activation of dormant viruses like cytomegalovirus and herpes simplex virus [156,157], and the presence of unresolved septic foci at post-mortem [63] have indicated the possibility that septic patients undergo an immunosuppressive state. Additionally, ex vivo analysis of immune cells have shown features of immunosuppression, such as accelerated lymphocyte death, and reduced cytokine expression by monocytes and macrophages [6,158]. However, the dogma within the clinical sphere continues to recognise and treat sepsis as an excessive inflammatory response. As mentioned in the previous chapter, bacterial products like LPS can be potent inducers of inflammation, promoting a systemic production of pro-inflammatory mediators and recruiting/activating immune cells to eliminate the pathogen. However an excessive inflammatory reaction may lead to septic shock. In contrast, consecutive treatments with LPS can generate an opposite effect known as endotoxin tolerance or cell reprogramming, defined as the  45 reduced capacity of the cell to respond to LPS or other bacterial products following a first exposure to the stimulus. It is believed that this cellular reprogramming or endotoxin tolerance phenotype is associated with an immunosuppressive state that has been observed in late stage septic patients [4,6]. To address my aim to better understand the immunopathogenesis of sepsis and to define the actual immune status of septic patients from a systems biology perspective, an extensive bioinformatic meta-analysis, using our published data (Chapter 2 [24]) was conducted, unique LPS and endotoxin tolerance gene signatures in human blood mononuclear cells were characterized. These signatures were then compared with transcriptional changes observed in human sepsis cohorts as published in the literature and deposited in public databases, and also performed signaling pathways analysis on these datasets. Very interestingly, it was observed that septic patients, regardless of the timing of transcriptomic analysis relative to the onset of clinical sepsis, presented an immunological profile associated with an endotoxin tolerance gene signature, rather than a dominant pro-inflammatory response as the current dogma would predict. Thus, these findings challenge this dogma that sepsis is a hyper-inflammatory (cytokine storm) disease, suggesting instead that endotoxin tolerance (an immunosuppressive state) might occur much earlier in sepsis than previously suspected. This unique endotoxin tolerance gene signature could be used as a possible biomarker, helping us to characterize the critical immunological status of septic patients, enabling the application of appropriate immunological and supportive therapies that could improve the survival rate during this deadly syndrome.   3.2 MATERIALS AND METHODS 3.2.1 Dataset search and selection Searches of datasets were performed in public available repositories such as the National Centre for Biotechnology Information (Gene Expression Omnibus) and the European Bioinformatic Institute (Array express). The selection of datasets was based on the following inclusion criteria:  ? Cross-sectional or longitudinal cohort studies  ? Whole blood or purified leukocyte populations ? Paediatric or adult patients ? Healthy or SIRS subjects used as controls ? Only datasets published as part of a study in a scientific journal  46 The characteristics of each dataset are described in Table 3.1.  Table 3.1: Dataset Descriptions  N* GEO Number Pubmed # Study Design  Popul-ation Samples Cell Type Ad-mission Infectious source Disease Status Control Group 1 GSE28750 21682927 Cross-sectional Adults 41 Leukocytes ICU <24H> Any Sepsis Healthy 2 GSE13015 19903332 Cross-sectional Adults 106 Leukocytes Admission <24H> Any Sepsis Healthy 3 GSE9692 18460642 Cross-sectional Pediatric 45 Leukocytes ICU <24H> Any Septic Shock Healthy 4 GSE26440 21738952 Cross-sectional Pediatric 130 Leukocytes ICU <24H> Any Septic Shock Healthy 5 GSE5772 17575094 Cross-sectional Adults 94 Neutrophils ICU <24H> Bacterial infection Sepsis SIRS 6 GSE9960 19237892 Cross-sectional Adults 70 PBMC ICU <24H> Bacterial infection Sepsis SIRS 7 GSE6535 18379237 Cross-sectional Adults 72 Neutrophils ICU <24H> Bacterial infection Sepsis SIRS 8 GSE12624 18434886 Cross-sectional Adults 70 Leukocytes After Trauma <12H> Trauma/ Any Sepsis SIRS 9 GSE32707 22461369 Long-itudinal Adults 144 Leukocytes ICU <24H> Any Sepsis Subjects at time 0 10 GSE13904 19325468 Long-itudinal Pediatric 227 Leukocytes ICU <24H> Any Sepsis Healthy 11 GSE3284 16136080 In-vivo Adults 18 PBMC In vitro LPS Challenge Healthy Healthy 12 GSE15219 19414804 In-vitro Adults 16 PBMC In vitro LPS Challenge Healthy Healthy  3.2.2 Data Processing and analysis 3.2.2.1 Transcriptional analysis All data processing was performed in R using Bioconductor [159].Normalised data from each microarray dataset was downloaded from the Gene Expression Omnibus (GEO) using the GEOquery package [160]. An additional quantile normalization step was implemented to ensure consistent normalisation between datasets. Microarray samples that were visible outliers on boxplots and dendrograms were excluded from further analysis. Differential expression was calculated using the limma package. Significant differential expression between samples was defined as genes with an associated Benjamini-Hochberg [161] adjusted p-value ? 0.05.  47 3.2.2.2 Hypergeometric distribution analysis A hypergeometric distribution is normally used to calculate the probability for a random selection of an object without repetition. In this case, a hypergeometric distribution test was used to perform a pairwise comparison between the LPS or endotoxin tolerance conditions presented in the previous chapter and each of the published sepsis datasets. A fold change of 2 and an associated p-value ? 0.05 cut off were employed to determine the gene lists for each of the conditions used in the test. The sign of the gene expression was considered when doing hypergeometric test, meaning that the gene needed to be regulated in the same direction and significant in both comparisons to be part of the hypergeometric calculation. 3.2.2.3 Gene clustering analysis Gene clustering was performed based on the results obtained from the hypergeometric analysis. Data was uploaded to Inmex (http://www.inmex.ca/), a web-based publicly available bioinformatic tool created in our laboratory. Inmex generated a heat-map clustering diagram, which could be used to visualize the expression profiles for each dataset uploaded. 3.2.2.4 Pathway over-representation meta-analysis Raw data was downloaded from the publicly available repository GEO, and a meta-analysis of the differentially expressed (DE) genes from four different datasets (GSE28750, GSE13015, GSE9692 and GSE26440) was done using Inmex. Inmex was used to perform normalization and selection of the differentially expressed genes found in each individual dataset  using R and Bioconductor (p ? 0.05). This was followed by a meta-analysis using the Stouffer method [162], which is based on an inverse normalized transformation to combine p values, setting a significance level of p ? 0.05. The results obtained were then uploaded to InnateDB (http://www.innatedb.ca/), another web-based publicly available bioinformatics tool generated by our laboratory, using a threshold of significance in the over-representation analysis tests defined as a Benjamini-Hochberg adjusted p-value ? 0.05.  3.3 RESULTS 3.3.1 Generation of LPS and endotoxin tolerance gene signatures  In an effort to confirm the significance of the endotoxin tolerance data presented in chapter 2 and to find if septic patients are indeed undergoing an excessive immune response or an endotoxin tolerance state, a list of genes associated with each condition was generated. Using a Venn diagram analysis to find non-overlapping genes with a fold change cut-off of 2 and p- 48 value of 0.05, gene signatures for each condition were obtained. The genes presented on each list were unique or differentially expressed in opposite directions when compared to the other condition. The results of this analysis are presented in Table 3.2.  Table 3.2: Selection of LPS and endotoxin tolerance signature genes. LPS Signature Genes  Endotoxin Tolerance Signature Genes Gene Symbol Fold Change  Gene Symbol Fold Change CCL20 14.64  MT1G 61.12 CCL3L1 9.19  MT1H 51.06 G0S2 7.25  MT1M 23.79 CFB 6.07  CCL7 21.04 IFI44L 6.07  CCL24 19.76 TNFRSF4 5.53  MT1F 16.24 AK4 5.40  MT1X 14.76 IL19 5.14  LILRA3 14.04 IFIT3 5.12  C19orf59 12.65 ISG15 4.93  MMP7 12.04 HERC5 4.81  CA12 8.19 PDSS1 4.76  CCL1 7.07 BATF 4.74  CCL22 6.97 DNAAF1 4.72  PPBP 6.76 CCL4L1 4.56  FPR1 5.71 CKB 4.47  SERPINA1 5.65 XAF1 4.41  VCAN 5.27 RSAD2 4.38  FPR2 4.92 HEY1 4.23  CD93 4.58 PIM2 4.21  RETN 4.39 IFITM1 4.15  SERPINB7 4.25 CSF2 4.10  IL3RA 4.21 IFI44 3.69  KIAA1199 4.13 F3 3.59  HSD11B1 4.08 FAM129A 3.46  CCL19 4.08 IFIT2 3.45  ANKRD1 4.05 MX2 3.43  HIST2H2AA3 3.97 KCNJ2 3.42  S100A12 3.70 LY6E 3.33  MARCO 3.69 CCRN4L 3.26  HIST2H2AC 3.61 EIF2AK2 3.18  TREM1 3.47 MCOLN2 3.15  PTGES 3.33  49 LPS Signature Genes  Endotoxin Tolerance Signature Genes Gene Symbol Fold Change  Gene Symbol Fold Change IRF7 3.11  FBP1 3.18 CCL3L3 3.07  PDLIM7 3.10 S100A3 3.06  CYP27B1 3.04 RIPK2 3.00  ADAMDEC1 2.99 CXCL2 2.99  SLC16A10 2.95 TSPAN33 2.99  FCER2 2.94 FFAR2 2.97  RHBDD2 2.86 CLEC12A 2.96  PANX2 2.73 ADORA2A 2.94  PLAUR 2.70 SAMD9L 2.91  MGST1 2.66 KANK1 2.89  LILRA5 2.64 TNFRSF9 2.85  PTGR1 2.60 METTL1 2.82  DPYSL3 2.57 CYP26A1 2.82  OLIG2 2.54 SOD2 2.79  S100A9 2.50 NDP 2.76  HBEGF 2.49 GRAMD1A 2.75  CD14 2.47 SOCS1 2.72  EMR3 2.43 MSC 2.71  HK2 2.41 TRIM22 2.68  HPSE 2.37 CD40 2.66  SLC7A11 2.30 TNF 2.63  GK 2.30 CD80 2.60  HIST1H1C 2.28 DDX60 2.57  RAB13 2.25 PIM1 2.55  CDK5RAP2 2.22 CD83 2.53  NRIP3 2.20 TRIM25 2.52  GPR137B 2.19 CASP5 2.50  DDIT4 2.15 IFI35 2.49  UPP1 2.13 SLAMF7 2.48  S100A8 2.12 OAS2 2.47  CYP1B1 2.12 GBP4 2.44  NEFH 2.12 DDX21 2.44  MYADM 2.10 C1orf122 2.43  CD300LF 2.09 PIM3 2.41  ITGB8 2.09 PARP12 2.39  EMR1 2.07 TBC1D9 2.37  TGM2 2.07 C17orf49 2.35  HK3 2.06 GBP2 2.33  TMEM158 2.06 YRDC 2.33  PAPLN 2.05  50 LPS Signature Genes  Endotoxin Tolerance Signature Genes Gene Symbol Fold Change  Gene Symbol Fold Change DHX58 2.33  PROCR 2.04 NME1 2.33  EGR2 2.03 RNF144B 2.32  MXD1 2.02 TXN 2.27  FCER1G 2.00 PARP9 2.25  ALCAM -2.01 ISG20 2.25  PSTPIP2 -2.05 ALCAM 2.23  ADAM15 -2.09 PTX3 2.23  TLR7 -2.23 INSIG1 2.21  NQO1 -2.29 TNF?IP2 2.21  TSPAN4 -2.37 ANTXR2 2.20  CTSK -2.43 OASL 2.20  CST6 -2.51 MYC 2.20  LY86 -2.57 CXCL6 2.18  S100A4 -2.71 TNFSF10 2.16  PLD3 -3.11 DESI1 2.16  HTRA1 -3.30 NFKB2 2.15  IL18BP -3.46 GADD45G 2.15  CPVL -3.65 IRAK2 2.14  RARRES1 -3.82 FAM49A 2.13  ALDH1A1 -3.83 RTP4 2.12  CAMP -3.94 SAMD9 2.11  CST3 -4.17 UPB1 2.10  LIPA -4.52 B4GALT5 2.10  DHRS9 -5.66 EDN1 2.09  GPNMB -8.08 PAICS 2.09  CXCL10 -9.89 PMAIP1 2.08  RNASE1 -10.43 BCL3 2.06    ADA 2.06    JUNB 2.06    GADD45B 2.06    MTF1 2.06    GCH1 2.05    TNIP1 2.05    HSH2D 2.04    NOP16 2.03    NFKBIZ 2.02    HCAR2 2.02    SLCO4A1 2.01    LTA 2.01     51 LPS Signature Genes     Gene Symbol Fold Change    BOP1 2.01    ZC3H12A 2.00    IFFO1 -2.00    LXN -2.00    TSC22D3 -2.00    YWHAH -2.01    NUP214 -2.01    SDS -2.02    H2AFY -2.03    ZNF467 -2.04    SPIRE1 -2.04    CORO1B -2.04    KLHDC8B -2.06    ABCC5 -2.06    MYADM -2.08    SNCA -2.08    TM6SF1 -2.09    C5AR1 -2.10    PLIN2 -2.10    ZMIZ1 -2.13    CSF1R -2.15    ATP6V0A1 -2.15    CTSB -2.17    SLC36A1 -2.17    MS4A7 -2.21    SLAMF8 -2.21    LPAR6 -2.22    PLOD1 -2.24    IDH1 -2.26    PAQR4 -2.26    DAB2 -2.35    OLFML2B -2.36    C1orf85 -2.40    LTA4H -2.40    PLXDC2 -2.42    PFKFB4 -2.42    GRAMD4 -2.49    CORO1C -2.50    FGL2 -2.51    ALOX5 -2.52     52 LPS Signature Genes     Gene Symbol Fold Change    AVPI1 -2.52    CAMK1 -2.54    NCF4 -2.62    CD300LF -2.63    TMEM51 -2.67    CLEC5A -2.76    CUEDC1 -2.78    CLEC10A -2.85    C1orf162 -2.90    CD14 -2.91    CD86 -3.08    PDK4 -3.11    ACP5 -3.20    HAVCR2 -3.24    ASGR1 -3.42    TIMP2 -3.45    NCEH1 -3.58    NPL -3.66    RCBTB2 -3.88    RGS12 -4.03    GPR162 -4.41    ADAP2 -4.64    SLCO2B1 -5.07    FOS -5.27    HMOX1 -5.51    MERTK -7.65      3.3.2 Hypergeometric distribution analysis of LPS and endotoxin tolerance signatures consistently correlate with previously published in vitro and in vivo endotoxemia models  To corroborate the veracity of the gene signatures generated, a hypergeometric overlap analysis was performed between these and previously published endotoxemia models. The dataset published by Del Fresno et al [31] was selected because it used a similar in vitro endotoxin tolerance protocol to the one used by our group. The human in vivo endotoxemia model [163] was used to further observe the veracity of the gene signatures in a systemic environment. In this dataset (called here in vivo endotoxemia model), human subjects were  53 challenged with LPS in vivo and then whole blood was collected to observe transcriptional changes at specific time points. Both datasets were consistent with expected results, such as higher correlation with LPS signatures at early time points especially in the in vivo endotoxemia model (Figure 3.1A) and stronger correlation with endotoxin tolerance signatures at later time points as seen in the in vitro endotoxemia model (Figure 3.1B). For better visualization of the results, graphs were plotted based on inversed p-value (1/p-value)    Figure 3.1: Hypergeometric distribution analysis between LPS/Endotoxin Tolerance (ET) signature genes and endotoxemia model datasets.  Differentially expressed genes from a human endotoxemia model dataset performed in vivo (A) and a LPS/ET dataset performed in vitro (B) were each overlapped with the LPS and endotoxin tolerance (ET) signatures, using a hypergeometric analysis. The two datasets from LPS stimulation at early time points, show a strong bias towards the LPS signature (black bars), while the endotoxin tolerance stimulation observed in B, are unbiased at 3h but strongly biased towards the ET signature (gray bars) d.at 24h. A fold change of 2 and an associated p-value ? 0.05 cut off was used.  54 3.3.3 Hypergeometric distribution analysis between LPS and endotoxin tolerance gene signatures and sepsis datasets demonstrated a strong endotoxin tolerance profile among septic patients To confirm the immunological status of septic patients, a hypergeometric overlap analysis between selected datasets and the LPS and endotoxin tolerance gene signatures was performed. As shown in Figure 3.2A, differential gene expression profiles of adult and paediatric septic patients recruited within 24 hours after admission to the intensive care unit (ICU) in comparison to healthy controls, showed a strong correlation with the endotoxin tolerance gene signature. Only one dataset GSE13015 presented a high association with both LPS and endotoxin tolerance gene signatures, although the latter was two times higher. Similar results were obtained with datasets from patients with specific bacterial infections (Gram-negative and Gram-positive bacteria) in studies that utilised patients with Systemic Inflammatory Response Syndrome (SIRS) as a control group (Figure 3.2B). One sub-group (Gram Positive bacteria - Dataset GSE9960) presented a pattern similarly associated with both the LPS and endotoxin tolerance gene signatures, with a lower overall association with the endotoxin tolerance signature as the other datasets. However, this type of association was not observed for the Gram positive bacteria sub-group from dataset GSE6535. Only one dataset (GSE12624), which was from patients who had developed sepsis after trauma and were admitted to the hospital and recruited to the study within 12 hours of the incident, presented a low association towards the LPS signature. Likewise, an analysis was performed using longitudinal sepsis cohort studies (Figure 3.2C). All datasets except the GSE32707_SIRS and _sepsis subgroups, showed a strong association with the endotoxin tolerance signature and only a weak association with the LPS signature. The GSE32707_SIRS dataset showed no association with either signature. In all three datasets the association with the endotoxin tolerance signature strengthened as the patients progressed from SIRS to sepsis to shock, while the association with the LPS signature remained similarly low across all three states. Additionally, gene clustering analyses were performed and heat-maps were generated to visualize the expression profile of these datasets associated with the endotoxin tolerance gene signatures (Figure 3.3 A, B, C, D, and E). Two of these datasets had associated information regarding the survival of specific patients and thus it was possible to generate heat-maps to  55 compare the differentially expressed genes present among the survivors versus that in the non-survivors.      56  Figure 3.2: Hypergeometric distribution analysis correlating LPS and endotoxin tolerance gene signatures in sepsis datasets demonstrate a strong endotoxin tolerance profile among septic patients.  Adult and paediatric datasets from cross-sectional studies using healthy (A), and SIRS (B) subjects as controls were analyzed. Likewise, longitudinal sepsis cohort studies were analyzed (C). A cut-off of 2 for fold change and ? 0.05 for p-value was used to perform the analysis .   57                              GSE28750 A.   58                                GSE13015 Non-Survivors Survivors B.  59                              GSE9692 C.  60               D. GSE26240_Survivors  61                            Figure 3.3: Gene Cluster Analysis.  Heat-maps were generated based on the hypergeometric distribution analysis performed between each dataset and the endotoxin tolerance signature genes. Each heat-map shows the top 50 differentially expressed genes. Datasets presented are GSE28750 (A) GSE13015 (B), GSE9692 C), GSE26240_Survivors (D), GSE26240_Non-survivors (E).   GSE26240_Non-Survivors E.  62 3.3.4 Signaling pathway over-representation meta-analysis validated the presence of a strong immunosuppressive state in septic patients Transcriptome-based signaling pathway analysis can give a further insight into the biological changes occurring in the cell and in the overall system. Therefore, to provide further insights into the transformations occurring in the septic patients? immune system, a signaling pathway over-representation meta-analysis was performed using four different sepsis datasets. Analysis was done as described in materials and methods. As seen in Table 3.3, the results obtained revealed the up-regulation of gene silencing events by DNA methylation and transformation of histone tails, strong RNA metabolism and DNA repair. Likewise, Table 3.4 shows that there was a down-regulation of pro-inflammatory signaling pathways such as TLR endogenous signaling, TNF? and IL1 signaling pathways, as well as pathways associated with cytoskeletal organization and remodelling.  Table 3.3: Signaling pathway over-representation analysis based on up-regulated genes.  Gene Ratio is the percentage of genes present in the pathway relative to the total number of genes present in the pathway and annotated in InnateDB. Pathway Name Genes Ratio Adjusted p-value  Eukaryotic translation termination 82% 1.66E-25 Viral mRNA Translation 82% 1.66E-25 GTP hydrolysis and joining of the 60S ribosomal subunit 81% 3.04E-30 Nonsense mediated decay independent of the exon junction complex 80% 7.02E-25 SRP-dependent cotranslational protein targeting to membrane 76% 2.02E-25 Ribosomal scanning and start codon recognition 75% 2.2E-12 Translation initiation complex formation 75% 2.2E-12 The prc2 complex gene silencing through modification of histone tails 75% 0.011 Nonsense mediated decay enhanced by the exon junction complex 73% 1.98E-22 Mechanisms of transcriptional repression by dna methylation 73% 0.004 mRNA Decay by 3' to 5' exoribonuclease 73% 0.025 RNA polymerase III chain elongation 72% 0.0014 Pre-mRNA splicing 60% 1.94E-12 Spliceosome 59% 9.1E-14 Nucleotide excision repair 55% 0.0013 Base excision repair 55% 0.0084 Brca1 brca2 and atr pathways 55% 0.049 Validated targets of C-MYC transcriptional activation 47% 0.0014 Caspase cascade in apoptosis 46% 0.036   63 Table 3.4: Signaling pathway over-representation analysis based on down-regulated genes. Pathway Name Genes Ratio Adjusted p-value  Y branching of actin filaments 73% 0.0024 Smooth Muscle Contraction 64% 0.0026 Cytokine receptor degradation signaling  51% 0.0027 RAC1 signaling pathway 46% 0.0030 E2F mediated regulation of DNA replication 71% 0.0033 TNF? 31% 0.00510 NOD-like receptor signaling pathway 43% 0.0060 Endogenous TLR signalling 56% 0.0068 Interleukin-1 signaling 49% 0.0079 IL10 anti-inflammatory signaling pathway 69% 0.011 Alpha-synuclein signalling 55% 0.019 Growth hormone receptor signalling 55% 0.019 Kinesins 55% 0.019 Arf6 signaling events 48% 0.020 IL7 signaling pathway 31% 0.0299 Signal transduction through il1r 44% 0.0319 Osteopontin-mediated events 48% 0.0349 NF-?B activation by nontypeable Hemophilus influenza 46% 0.0429 GAB1 signalosome 64% 0.0499                  64 3.3.5 Identification of candidate endotoxin tolerance biomarkers  The frequency of appearance of each endotoxin tolerance signature gene was calculated among all datasets and its subgroups to obtain a group of biomarkers that could potentially identify endotoxin tolerance during sepsis. Figure 3.4 shows a list of the top up- and down-regulated genes found in our analysis.    Figure 3.4: Candidate biomarkers to identify an endotoxin tolerance status during sepsis.  Twenty-three genes were found to be associated with the ET signature in 5 or more datasets. Five genes were associated with the ET signature in 10 or more datasets and the maximum frequency was 15.      65 3.4 DISCUSSION Traditionally, sepsis syndrome is viewed as an excessive response from the immune system to destroy the invading pathogen and return to homeostasis. This idea was established based on the presentation of fevers, shock and respiratory failure by septic patients [52], however the persistent failure of many clinical trials trying to target this syndrome [155,164] has led to the declaration that a better understanding of the pathophysiology of sepsis is sorely needed. Therefore, to confirm the immunological status of patients with sepsis, an intensive bioinformatics analysis was performed here, by comparing publically available sepsis datasets to the data obtained in the previous chapter, enabling the characterization of whether the patients demonstrated the presence of either an excessive inflammatory response state or an endotoxin tolerance state that might be associated with sepsis.  Initially, a selection of signature genes was obtained from the previous LPS and endotoxin tolerance situations studied in vitro (Table 3.1). The LPS gene signatures found were well-recognized inflammatory markers, including components of the NFKB activation pathway (eg: NFKB2 and IRAK2), as well as markers of classical activation or the M1 phenotype, such as CD80 and CCL20. Likewise, the gene signatures obtained for endotoxin tolerance included genes such as VCAN, CCL22 and CCL24 that had been previously characterized by me as markers of the distinctive M2 phenotype (Chapter 2, [24]) To determine the significance of these gene signatures during sepsis, a hypergeometric distribution analysis was performed, to identify the overlap of these gene signatures with the transcriptional changes observed in septic patients. Interestingly, it was found that expression patterns of septic patients strongly correlated with the endotoxin tolerance gene signature, while the LPS signature was generally only weakly associated. As seen in Figure 3.2A, only one dataset (GSE13015) showed a high association with the LPS signature but even in this case the association with the endotoxin tolerance signature was more significant. This might have been due to the fact that patient recruitment for the other studies took place within 24 hours of admission to the ICU, which in many occasions occurs subsequent to patient arrival at the hospital. In contrast, the GSE13015 study was performed within 24 hours of direct admission to the hospital as descripbed in the manuscript, therefore possibly allowing the visualization of residual transcriptional changes associated with an acute infection and associated LPS signature. Likewise, a study performed with patients who developed sepsis, in which samples were collected within 12 hours of a trauma incident (GSE12624), presented a slightly greater  66 association with the LPS gene signature over the endotoxin tolerance one (Figure 3.2B). This is consistent with a very early rise in LPS responses that could not be strongly visualized due to the use of SIRS patients as controls in this study, instead of healthy subjects. Likewise, Figure 3.2C shows how the presence of an endotoxin tolerance state increases as the clinical status of the patient worsens from SIRS to septic shock and other sepsis-associated complications like ARDS. These findings suggest that an inflammatory response may indeed develop in the early hours of sepsis, but this rapidly shifts towards an endotoxin tolerance state, which dominates in the later hours/days after the establishment of the syndrome. The residual high levels of circulating cytokines observed in early and late stage sepsis patients might similarly reflect an earlier inflammatory event and/or stimulation of continuously renewed naive neutrophils. Additionally, to visualize the endotoxin tolerance-associated expression profiles of the septic patients, heatmap clustering diagrams were created as shown in Figure 3.3. Some of the datasets used (GSE13015 and GSE26240) had clinical information regarding the survival of the patients, allowing the visualization of the endotoxin tolerance gene signature that are more associated with the risk of death. Interestingly, these studies performed on adult and paediatric septic patients respectively, showed a strong down-regulation of genes like Toll like receptor 7 (TLR7), Lipase A (LIPA), Ly86 and CPVL. Down-regulation of TLR7, the toll like receptor that recognizes single stranded RNA, which is a common feature of viral genomes, may indicate the presence of secondary viral infections observed in septic patients such as those caused by Cytomegalovirus and Herpes simplex virus. Indeed it has been demonstrated in murine sepsis models that pre-stimulation of TLR7 improves the immune control of the inflammatory response [165] Disruption of lipogenic enzymes such as LIPA, which function in hydrolysing cholesterol esters and triglycerides leading to hyperlipidemia, has been observed during sepsis [166]. This finding may be associated with and/or a consequence of the insulin resistance observed in septic patients [167]. Ly86, also known as MD-1, appear to be an important player in the recognition of LPS, forming a complex with RP105 a TLR related molecule [168], and thus its down-regulation would re-enforce a reduced response to LPS in the microenvironment. CPVL, a recently-discovered carboxypeptidase [169] with an unknown function, may have an important role in the immune response, due to its strong down-regulation in septic patient. In contrast, genes like hexokinase 3 (HK3), uridine phosphorylase (UPP1), leukocyte immunogloblulin-like receptor 5 (LILRA5) and S100 calcium binding protein A12 (S10012) presented a distinctive pattern of up-regulation across most datasets. HK3, a hexokinase involved  67 in glucose phosphorylation [170], is possibly highly expressed as a response to the increased glucose levels seen during sepsis [171]. In addition, it has also been recently demonstrated that HK3 is involved in neutrophil differentiation [172], suggesting it may also have a direct effect on immune modulation. UPP1 regulates the presence of uridine, a pyrimidine nucleoside essential for the synthesis of RNA and bio-membranes [173], and its up-regulation may be a secondary response to TNF? expression, as has previously been shown [174]. LILRA5 has been associated with the induction, in monocytes, of calcium flux [175], which is very important for the engulfment of apoptotic cells and the production of anti-inflammatory mediators such as TGF? [176]. Therefore, up-regulation of LILRA5 could be induced in response to the increased appearance of apoptotic cells such as T-lymphocytes observed during sepsis. S100A12 and S100A8/9, which were also found to be up-regulated in sepsis datasets, both have pleiotropic immunological activities including strong chemotaxis of neutrophils and macrophages, putative antimicrobial activity and protection from oxidative stress [177,178]. Additionally, they have been involved in promoting increased vascular permeability by down-regulation of junction proteins in endothelial cells, leading to vascular leak and tissue edema, one of the major complications of sepsis [179,180]. Overall, it was interesting to observe a very strong consistency of the down-regulated genes in non-survivor patients, based on these results it is possible to hypothesize that the presence of these genes is highly important to increase the possibility of survival in septic patients.  Moreover, a signaling pathway over-representation analysis was also performed (Tables 2 & 3). These findings aligned well with the major endotoxin tolerance gene signatures found in all datasets and with previously reported pathological characteristics of sepsis. For example, down-regulation of classical pro-inflammatory pathways such as TNF?, IL1? and NF-?B were found. Additionally, that IL7 signaling pathway was on the list of down-regulated signaling pathways and down-regulation of this pathway might be associated with the enhanced apoptosis-mediated lymphocyte death that is seen in sepsis, since IL7 is a major inhibitor of this phenomenon [181]. This induction of apoptosis is seen not only in lymphocytes but also in other immune cell populations and could also be associated with the up-regulation of the caspase cascade and the C-MYC activation signaling pathway that are part of the apoptosis process and which were also found in this analysis [182]. Similarly, strict transcriptional regulation and silencing of selected genes observed during the endotoxin tolerance state could be a result of increased activation of  68 the spliceosome and DNA methylation signaling pathways as observed here consistent with previous publications [183]. As shown throughout this work, the endotoxin tolerance gene signatures found here have a strong clinical weight, as they are associated with many of the pathological complications that worsen the clinical condition of patients with sepsis. Therefore, to try to find candidate biomarkers that could be used to detect the immunosuppressive or endotoxin tolerance state in septic patients, a list of the most frequently identified genes corresponding to the endotoxin tolerance gene signature were selected (Figure 3.4). As discussed previously, some of these genes such as LY86, LIPA and CPVL could be strongly associated with the risk of death in patients, as they are consistently down-regulated in the septic patients who did not survive.  Overall, these findings demonstrate that patients with sepsis present a strong endotoxin tolerance profile, which appears to take place earlier than previously suspected. In fact, it appears that the majority of patients arriving to the hospital are mainly undergoing this immunosuppressive state and not an excessive inflammatory response as believed previously believed. We also confirmed that these endotoxin tolerance state tend to increase the risk of death. Therefore, the development and use of biomarkers like the ones presented in figure 3.4 could promptly recognize this immunosuppressive state of the septic patient, allowing physicians to give a more personalized supportive treatment. Future studies are needed to test our hypothesis that one or more of the biomarker genes identified in this study could be used to follow the progress of sepsis patients, predict clinical outcomes, and provide appropriate treatments. Likewise, the future clinical trials to test either new or previously tried and failed immunotherapies could be better designed by taking into account the immunosuppressive state of the patients, leading to improved clinical treatments and therefore greater patient survival, helping to win the battle against this deadly syndrome. The next chapter will consider the biological capabilities of a synthetic cationic peptide as a possible immunomodulator for treating sepsis.    69 CHAPTER 4: SYNTHETIC CATIONIC PEPTIDE IDR-1018 MODULATES HUMAN MACROPHAGE DIFFERENTIATION  4.1 INTRODUCTION The effectiveness of the innate immune response in eradicating pathogens is determined by numerous processeses that work simultaneously or consecutively. One important process in the innate immune response is the local production, or secretion from phagocytes, of host defence peptides (HDP) [184]. In addition to their modest antimicrobial activity, HDPs modulate the immune response to promote the clearance of pathogens, while preventing the deleterious effects of excessive inflammation. In addition, HDPs regulate the transition to adaptive immunity and promote wound healing [184]. Recent research has led to the generation of synthetic peptides that demonstrate enhanced key protective functions such as chemotaxis, wound healing, and immune cell survival, while suppressing pro-inflammatory responses to non-pathological levels [185]. These peptides, termed innate defence regulators (IDRs), enhance the efficiency of the immune response, making them an enticing new anti-infective strategy. They protect in mouse models against many different infections and inflammation and their activity in these models is compromised by treatment with liposomal clodronate indicating that protective activity is dependent on monocytes/macrophages [41,42]. Of the peptides designed to date, IDR-1018 is a promising candidate based on its minimal cytotoxic activity, ability to significantly reduce LPS-induced cytokine production, ability to promote chemokine production [186,187] and enhanced resolution of infection and inflammation in animal models [188].   Macrophages are vital components of the innate immune response during health and disease. They respond in a rapid and efficient manner to physiological changes and microbial challenges in the microenvironment, promoting the return to an appropriate homeostatic balance [189,190]. To accomplish this, macrophages can differentiate, where appropriate towards classically- (M1) or alternatively- (M2) activated phenotypes. M1 macrophages can be induced by Th1 cytokines such as interferon-? (IFN?) and TLR ligands like LPS. They are considered to be potent effector cells in inflammatory responses, able to effectively kill microorganisms and tumor cells and produce copious amounts of pro-inflammatory cytokines and specific chemokines. In contrast, M2 macrophages can be induced by Th2 cytokines such as IL4, IL13, IL10, other immune factors such as M-CSF, and consecutive, tolerizing exposures to LPS. They are considered to be primarily involved in tuning inflammatory responses, scavenging debris and  70 apoptotic cells and promoting angiogenesis, tissue remodeling and repair [19,22,24,191]. Although this simple classification of macrophages is practical, it does not account for the vast number of inducing factors and the complexity of the different phenotypes produced by them, leading to many variants of these two basic classes of macrophages. For example, various M2 subsets with different properties have been characterized [30,192] including M2a macrophages activated by IL4 or IL13, M2b macrophages activated by immune complexes, and M2c macrophages polarized with glucocorticoids or IL10 [192]. Therefore, sometimes choosing a stimulant to study M2 macrophage responses can be difficult. For example, Martinez et al found that stimulation of macrophages with M-CSF, a homeostatic growth factor, lead to the expression of an M2-like transcriptome very similar to that promoted by IL4, suggesting than under basal conditions macrophages default towards an M2 phenotype. Thus M-CSF has been one of the main stimulants chosen to study general M2 responses in many recent studies [29,193,194,195].  Plasticity, a hallmark feature of macrophages, allows them to differentiate into and switch between different phenotypes. However, in certain circumstances, this function is altered and macrophages can be locked into a specific phenotype, leading to pathological conditions such as chronic inflammatory diseases associated with an M1 phenotype, or immunosuppresive disorders associated with an M2 phenotype [31,196].  The molecular mechanisms that underlie the development of M1 and M2 macrophages, involve a network of molecules that activate specific transcription factors as well as inducing epigenetic and posttranscriptional changes. For instance, NF-?B and STAT1 are critical transcription factors involved in the induction of M1 macrophages by LPS and IFN? respectively. NF-?B and STAT1 subsequently induce the expression of signature M1 pro-inflammatory molecules including TNF?, COX-2, IL12, CCL3 [197,198]. On the other hand, M2a macrophages induced by IL4 and IL13 demonstrate activated STAT6, while M2c macrophages induced by IL10 have activated STAT3. These transcription factors interact and cooperate with other transcription factors such as PPAR? to inhibit M1 associated genes and up-regulate key M2 associated genes such as those encoding mannose receptor, IL10 and TGF? [199]. Epigenetic regulation is also critical for macrophage differentiation. For example, JMJD3, a H3K27-specific demethylase, is responsible for the differentiation of M2 macrophages in response to M-CSF exposures in vitro and host responses to helminth infection in vivo. The induced epigenetic changes lead to the activation of essential transcription factors such as IRF4, promoting the up-regulation of M2 signature genes [29,200]. Based on the role of  71 macrophages/monocytes in IDR peptide mediated protection against infection and initial studies performed on IDR-1018, we hypothesized that IDR-1018 promoted the differentiation of human macrophages towards an immunomodulatory phenotype similar to that of the M2 macrophages. By comparing macrophages differentiated in the presence of IDR-1018 alone, or in combination with an M2-inducing factor M-CSF (referred to here as IDR1018+M2), with those differentiated in the presence of IFN? or M-CSF, inducing an M1 or M2 phenotype, respectively, we were able to demonstrate that IDR-1018 stimulated a phenotype intermediate between these two extremes. 4.2 MATERIALS AND METHODS 4.2.1 Ethics statement, cells and reagents Venous blood was collected from healthy volunteers into heparin-containing Vacutainer tubes (BD Biosciences, San Jose CA) with previous written informed consent obtained from all the volunteers. This procedure and all research done using these samples was carried out in accordance with the guidelines of the UBC Clinical Research Ethics Board (UBC-CREB) and approved under the UBC-CREB# H04-70232. Peripheral blood mononuclear cells (PBMC) were isolated as described previously [80,201]. Jurkat cells were obtained from the ATCC (Lymphocytes Human Leukemia J45.01 - CRL-1990) and cultured as described by the ATCC. PBMC were cultured in complete media consisting of RPMI 1640 medium Figureemented with 10% (v/v) heat-inactivated FBS, 2 mM L-glutamine, and 25 mM HEPES (all from Invitrogen, Carlsbad CA). All cells were cultivated in a humidified 37 ?C incubator containing 5% CO2.  Lipopolysaccharide (LPS) was obtained from Pseudomonas P. aeruginosa PAO1, strain H103, grown overnight in Luria-Bertani broth at 37? and isolated using the Darveau-Hancock method which gives a highly purified LPS, free of proteins and lipids [114]. Purified LPS samples were quantified using the 2-keto-3-deoxyoctuloosonic acid assay and resuspended in endotoxin-free water (Sigma-Aldrich, Saint Louis MO). LPS was used at a concentration of 10 ng/ml. IDR-1018 (VRLIVAVRIWRR-CONH2) was synthesized by CPC Scientific (Sunnvale, CA) using solid phase Fmoc chemistry and purified (>95% purity) using reversed phase HPLC. The correct peptide mass was confirmed by mass spectrometry.  4.2.2 Human macrophage differentiation Human macrophage differentiation was performed as described previously [24,195], with some modifications. Briefly, after peripheral blood mononuclear cell isolation in PBS, cells were resuspended in serum-free RPMI medium and plated at 5x106 cells/well in 6 well plates for 30 minutes. Subsequently, media was changed and fresh complete media was added. Twenty four  72 hours later, adherent monocytes were gently washed and treated with the different stimuli as follows: IFN? at 20 ng/ml (Immunotools, Friesoythe, Germany) for M1 differentiation, M-CSF (Research Diagnostic Inc, Concord, MA) at 10ng/ml for M2 differentiation and IDR-1018 at 5 ug/ml. Cells were cultured for seven days, with gentle washes and media changes on the second and sixth day, during which treatments were re-added. Finally, on day seven, cells were gently washed and left untreated or challenged with LPS at 10 ng/ml. 4.2.3 RNA isolation  RNA was isolated from cell lysates 4 hours post-treatment using the Qiagen RNeasy Isolation Kit (Qiagen, Valencia, CA), as per the manufacturer?s instructions, treated with RNase free DNase (Qiagen, Valencia, CA), and eluted in RNase-free water (Ambion, Austin, TX). The RNA concentration was assessed using a NanoDrop spectrophotometer, while RNA integrity and purity was determined by Agilent 2100 Bioanalyzer using RNA Nano kits (Agilent technologies). 4.2.4 Quantitative real-time PCR (qRT-PCR) Gene expression was analyzed via qRT-PCR. It was performed using the SuperScript III Platinum Two-Step qRT-PCR kit with SYBR Green (Invitrogen, Carlsbad, CA) as per the manufacturer?s instructions, and the ABI Prism 7000 sequence detection system (Applied Biosystems, Carlsbad, California). Briefly, 500 ?g of total RNA was reverse transcribed using qScriptTM cDNA Synthesis Kit (Quanta Biosciences, Gaithersburg, MD). PCR was conducted in a 12.5 ?l reaction volume containing 2.5 ?l of 1/5 diluted cDNA template. A melting curve was performed to ensure that any product detected was specific to the desired amplicon. Fold changes were calculated after normalizing the change in expression of the gene of interest to the housekeeping gene encoding beta-2-microglobulin (B2M), using the comparative Ct method [116] The primers sequences (all from Invitrogen) used for qRT-PCR are presented in Table 4. Table 4.1: Primer List Gene Forward Reverse Cox-2 GTTCCACCCGCAGTACAG GGAGCGGGAAGAACTTGC  IL12-p40 CGGTCATCTGCCGCAAA TGCCCATTCGCTCCAAGA IL12-p35  GGTGAAGGCATGGGAACATT TGCCCATTCGCTCCAAGA VEGF GCACCATGGCAGAAGG CTCGATTGGATGGCAGTACT EGF ACGCCCTAAGTCGAGACCGGA TCGGGTGAGGAACAACCGCT Versican GTGACTATGGCTGGCACAAATTCC GGTTGGGTCTCCAATTCTCGTATTGC IRF-4 TCCCCACAGAGCCAAGCATAAGGT AGGGAGCGGCCGTGGTGAGCA STAT-3 CCTTGGCTGGCTAGCTCG TGAGTTGCCAAATCCGGC PPAR? AGTCCTCACAGCTGTTTGCCAAGC GAGCGGGTGAAGACTCATGTCTGTC  73  4.2.5 RNA-seq and analysis RNA-seq was performed by high-throughput next generation sequencing using the Illumina Genome Analyzer IIx platform. PBMC were initially obtained from 4 healthy donors, followed by monocyte isolation using the EasySep Monocyte Enrichment Without CD16 Depletion Kit. (Stem Cell Technologies, Vancouver, BC) as per the manufacturer?s instructions. Monocytes were stimulated for 4 hours with 20 ?g/ml IDR-1018 and compared to unstimulated monocytes. RNA was then extracted and its quality assessed as described above. For library preparation, 500 ng of total RNA was processed according to the Illumina TruSeq RNA sample preparation guide (Illumina catalogue number FC-122-1002). Briefly, mRNA was purified using poly-dT beads, followed by synthesis of the first and second cDNA strands, end repair addition of a single-A overhang, and ligation of adapters and unique barcodes, as per the manufacturer?s instructions. DNA enrichment was carried out via a 15-cycle PCR. Following quantification, 8 pM of dsDNA was used for cluster generation on a CBOT instrument (Illumina, San Diego, CA). RNA sequencing was done on a GAIIx instrument (Illumina), performed as a single read run with 51 amplification cycles. Data processing was carried out in house, using CASAVA to convert raw data and demultiplex to FASTQ sequence files Reads were aligned to the reference genome using Bowtie and Tophat, and then mapped to genes using the Bioconductor package GenomeRanges. Differential gene expression was determined using the edgeR Bioconductor package, and p-values were adjusted for multiple sampling using the Benjamini-Hochberg (false discovery rate) method. Differentially expressed genes are presented in table S2 and complete RNA sequencing data has been deposited in the Gene Expression Omnibus public database (GSE40131). Transcriptional and bioinformatic analysis of the RNA-Seq data was done using system biology tools developed in our laboratory including the InnateDB database (http://www.innatedb.ca) [115], and MetaGEX (http://marray.cmdr.ubc.ca/metagex/). Genes with fold changes of greater than 1.5 and p values <0.05 were considered differentially expressed.  4.2.6 Enzyme-linked immunosorbent assay (ELISA) ELISA was performed on supernatants collected at 4 and 24 h post-treatment. These included TNF?, IL10 (eBioscience), IP10, CCL22 (R & D systems), and CCL-3 (Biosource). ELISA assays were performed according to the kit manufacturers? instruction.    74 4.2.7 Phagocytosis of apoptotic cells  Phagocytosis of apopototic cells was investigated as described previously [202] with slight changes. Briefly, Jurkat cells were labeled with 0.25uM CFDA-SE (Invitrogen). Apoptosis was induced by 10 minutes of UV exposure followed by 5 hours of incubation. Apoptotic Jurkat cells were added to differentiated macrophages at a ratio of 10:1. Macrophages were then gently washed once and detached using trypsin-EDTA. Analysis of phagocytosis was performed using a FACSCalibur system and FlowJo Software, with a CD14+ gate used to select for macrophages. 4.2.8 Statistical analysis All treatments were compared to those for M1 macrophage responses, which were used as a control. Statistical significance was determined using a two-tailed Student t-test for paired comparisons using the Prism 4.0 software (*, P<0.05; **, P<0.01; ***, P<0.001).   4.3 RESULTS 4.3.1 Macrophages differentiated in the presence of IDR-1018 showed an intermediate cytokine response profile when compared to M1 and M2 macrophages The reduction of pro-inflammatory cytokines and the enhancement of anti-inflammatory mediators is a hallmark of alternatively activated M2 macrophages [112]; therefore we sought to examine this feature in macrophages differentiated in the presence of IDR-1018. To confirm the immunomodulatory effects of IDR-1018, several scrambled synthetic peptides were tested in PBMC to observe chemokine expression (e.g. Figure 4.1).  Additionally, to select an appropriate inducer of M2 differentiation, we carried out initial experiments using different differentiation protocols, including the one described by Martinez et al [191] using MCSF during the whole process of differentiation and then adding IL4 as M2 inducer (Figure 4.2). However, we elected to utilize a simpler protocol using only MCSF, which is a known inducer of the M2 phenotype and gave quite similar results (Figure 4.3). Moreover, natural host defense peptide LL-37 was also employed to observe its effects on macrophage differentiation (Figure 4.2). In both cases, IDR-1018 showed distinctive responses.      75 Figure 4.1: Chemokine expression in PBMC after treatment with IDR-1018 and negative control peptides1020 and 1015.  PBMC were treated with different peptide concentrations as shown in the graph. Twenty four hours post treatment, supernatants were collected and chemokine expression was analyzed by ELISA.  4.3.2 IDR-1018 differentiated macrophages responded to LPS stimulation in a complex manner.  Macrophages differentiated without specific stimulation (M0 cells) and those differentiated in the presence of IFN? (M1 cells) and M-CSF (M2 cells), were used as controls for comparison with IDR-1018 and IDR1018+M2 (i.e. with added M-CSF) differentiated macrophages. All differentiated macrophages were left untreated or challenged with LPS and cykine expression was analyzed by ELISA. Some responses were analogous to those of M2 macrophages, but distinct from that of M1 macrophages, in that the pro-inflammatory cytokine TNF? was strongly reduced while the anti-inflammatory cytokine IL10 was highly expressed at both 4 and 24 hours after stimulation (Figure 4.3A). Likewise, the transcription of another pro-inflammatory mediator, Cox-2, was diminished, as analyzed by RT-qPCR (Figure 4.3B). Conversely for other inflammatory mediators such as IL12 subunits, as well as IL-1RN and TGF-?, the responses of IDR-1018 differentiated macrophages were not significantly different when compared to M1 macrophage responses. In contrast, IDR-1018+M2 differentiated macrophages demonstrated similar or stronger responses to those observed for M2 macrophages in reducing all pro-inflammatory mediators while enhancing the anti-inflammatory ones (Figure 4.3). Overall it appeared that differentiation in the presence of IDR-1018 led to an intermediate phenotype that resembled specific aspects of M1 or M2 macrophages.  76   Figure 4.2: Cytokine and chemokine responses of macrophages differentiated in the presence of IDR-1018 and LL-37.  Adherent monocytic cells were differentiated into macrophages in the presence of MCSF for 7 days. IFN? (M1), IL-4 (M2), IDR-1018 or IL-37, were added or left untreated (M0). Macrophages were then challenge with/without LPS for 4 hours after which, the cytokine and chemokine responses were measured by ELISA. The data was analyzed for significant differences between the treatments and the M1 phenotype. Mean ? SD results are presented and are representative of 4 biological replicates. **, P<0.01; *, P<0.05. Note that the IDR-1018 treatment described here, is equivalent to IDR-1018+M2 treatment used in the whole manuscript.  TNF-?M0M0+LPS M1M1+LPS M2M2+LPS 10181018+LPSLL-37LL-37+LPS0100020003000****TNF? (pg/ml)IL-10M0M0+LPS M1M1+LPS M2M2+LPS 10181018+LPSLL-37LL-37+LPS02505007501000*ns*IL-10 (pg/ml)CCL-3M0M0+LPS M1M1+LPS M2M2+LPS 10181018+LPSLL-37LL-37+LPS05000100001500020000250003000035000***Fold Change CCL-3CCL-22M0M0+LPS M1M1+LPS M2M2+LPS 10181018+LPSLL-37LL-37+LPS0100200300400500***Fold Change CCL-22 77   A. TNF-?  4HM0M0+LPS M1M1+LPS M2M2+LPS 10181018+LPS1018+M21018+M2/LPS0100020003000******TNF? (pg/ml)TNF-?  24HM0M0+LPS M1M1+LPS M2M2+LPS 10181018+LPS1018+M21018+M2/LPS0500100015002000***TNF? (pg/ml)IL-10 4HM0M0+LPS M1M1+LPS M2M2+LPS 10181018+LPS1018+M21018+M2/LPS02505007501000***IL-10 (pg/ml)IL-10 24HM0M0+LPS M1M1+LPS M2M2+LPS 10181018+LPS1018+M21018+M2/LPS0250500750******IL-10 (pg/ml)B. Cox-2M0M0+LPS M1M1+LPS M2M2+LPS 10181018+LPS1018+M21018+M2/LPS050100150200*****Cox2 Expression/B2MIL12p40M0M0+LPS M1M1+LPS M2M2+LPS 10181018+LPS1018+M21018+M2/LPS0246**nsIL-12p40 expression/B2MIL12p35M0M0+LPS M1M1+LPS M2M2+LPS 10181018+LPS1018+M21018+M2/LPS02468***nsIL-12p35 expression/B2M 78 IL-1RNM0M0+LPS M1M1+LPS M2M2+LPS 10181018+LPS1018+M21018+M2/LPS01234***nsIL-1RN Expression/B2MTGF-?M0M0+LPS M1M1+LPS M2M2+LPS 10181018+LPS1018+M21018+M2/LPS0123***nsTGF-? Expression/B2M  Figure 4.3: Macrophages differentiated in the presence of IDR-1018 showed an intermediate cytokine response profile when compared to M1 and M2 macrophages.  Adherent monocytic cells were differentiated into macrophages in the presence of IFN? (M1), M-CSF (M2), IDR-1018 alone or in combination with M-CSF (1018+M2), or left untreated (M0). Macrophages were then left unstimulated or stimulated with LPS for 4 or 24 hours after which, the cytokine responses were measured by ELISA (A) and RT-qPCR (B). The data was analyzed for significant differences between the treatments and the M1 phenotype. Mean ? SD results are presented and are representative of 4 biological replicates. ***, P<0.0001; **, P<0.01; *, P<0.05.  4.3.3 Macrophages differentiated in the presence of IDR-1018 exhibited a chemokine profile different from that of M2 macrophages:   M1 and M2 macrophages have unique and very characteristic chemokine profiles [130]. For example, M2 macrophages exhibit reduced expression of chemokines such as CCL-3 and IP-10, and higher expression of CCL-22 compared to M1 macrophages. Therefore, the chemokine profile of IDR-1018 differentiated macrophages was examined for these chemokines. IDR-1018 differentiated macrophages presented a profile different from that of M2 macrophages. Although a similar reduction in IP-10 was observed compared to that of M1 macrophages, there was no reduction in CCL3 and a higher production of CCL22 (Figure 4.4). In contrast, IDR-1018+M2 differentiated macrophages presented a chemokine profile similar to that of M2 macrophages.   79  Figure 4.4: Macrophages differentiated in the presence of IDR-1018 exhibited a chemokine profile different from that of M2 macrophages.  Adherent monocytic cells were differentiated into macrophages in the presence of IFN? (M1), M-CSF (M2), IDR-1018 alone or in combination with M-CSF (1018+M2), or left untreated (M0). These macrophages were then stimulated for 6hr with LPS. Twenty four hours post stimulation; the chemokine responses were measured by ELISA and analyzed for significant differences when compared to the M1 phenotype. Raw values were normalized to M0 Macrophages. Mean ? SD results are presented and are representative of 4 biological replicates. **, P<0.01; *, P<0.05.  4.3.4 Differentiation of macrophages in the presence of IDR-1018 induced the expression of wound healing associated genes Wound healing is a characteristic function of M2 macrophages [203] and a number of wound-healing associated genes such as growth factors and components of the extracellular matrix are expressed in these cells. We recently demonstrated that IDR-1018 promotes wound healing in mice and pigs [204]. Therefore, the expression levels of these factors were investigated in IDR-1018 and IDR-1018+M2 differentiated macrophages (Figure 4.5). IDR-1018 and IDR-1018+M2 differentiated macrophages demonstrated differential effects on the expression of endothelial growth factor (EGF) and the proteoglycan Versican (VCAN) that were similar to or higher than those found on M2 macrophages. Although no significant differences were found in the basal levels of other wound healing genes such as VEGF and FPRL-1, when IDR-1018 and IDR-1018+M2 macrophages were stimulated with LPS, major differential changes were observed, leading to a profile that resembled that of the LPS stimulated M2 control. This response to LPS was observed with 3 of the 4 genes but not with EGF.  CCL-22M0M0+LPS M1M1+LPS M2M2+LPS 10181018+LPS1018+M21018+M2/LPS02000400060008000*NS*CCL-22 (pg/ml)IP-10M0M0+LPS M1M1+LPS M2M2+LPS 10181018+LPS1018+M21018+M2/LPS01500300045006000****IP-10 (pg/ml)CCL-3M0M0+LPS M1M1+LPS M2M2+LPS 10181018+LPS1018+M21018+M2/LPS01500300045006000****NSCCL-3 (pg/ml) 80 Figure 4.5: Differentiation of macrophages in the presence of IDR-1018 induced the expression of wound healing associated genes.  Adherent monocytic cells were differentiated into macrophages in the presence of IFN? (M1), M-CSF (M2), IDR-1018 alone or in combination with M-CSF (1018+M2), or left untreated (M0). These macrophages were then stimulated with LPS. Four hours post-stimulation, transcriptional changes in wound healing associated genes were measured by RT-qPCR, and analyzed for significant differences when compared to the M1 phenotype. Mean ? SD results are presented and are representative of 4 biological replicates. **, P<0.01; *, P<0.05.         VEGFM0M0+LPS M1M1+LPS M2M2+LPS 10181018+LPS1018+M21018+M2/LPS1234****VEGF Expression/B2MFPRL-1M0M0+LPS M1M1+LPS M2M2+LPS 10181018+LPS1018+M21018+M2/LPS369121518***FPRL-1 Expression/B2MEGFM0M0+LPS M1M1+LPS M2M2+LPS 10181018+LPS1018+M21018+M2/LPS1234*****EGF Expression/B2MVCANM0M0+LPS M1M1+LPS M2M2+LPS 10181018+LPS1018+M21018+M2/LPS1234***VCAN Expression/B2M 81 4.3.5 Macrophages differentiated in the presence of IDR-1018 displayed enhanced phagocytic properties towards apoptotic cells:    Figure 4.6: Macrophages differentiated in the presence of IDR-1018 displayed enhanced phagocytic properties towards apoptotic cells.  Macrophages were differentiated in the presence of IFN-? (M1), M-CSF (M2), IDR-1018 alone (1018) or in combination with M-CSF (M2+1018), or left untreated (M0). Then,macrophages were incubated for 4 hours with CFDA-SE labeled UV-induced apoptotic Jurkat cells. Macrophages were harvested and phogocytosis analyzed by flow cytometry, gating on the macrophage population. Representative zebra plots were created for each treatment, showing the percentage of macrophages with CFDA-SE positive apoptotic Jurkat cells (A). The geometric mean was measured for CFDA-SE positive gated macrophages (B). Mean ? SD results are presented and are representative of 3 biological replicates. ***, P<.0.0001; **, P<0.01; *, P<0.05.   Phagocytosis of apoptotic cells is another distinctive function of alternatively activated macrophages [144]. Using UV-induced apoptotic Jurkat cells labeled with CFDA-SE as targets for macrophages differentiated under different conditions, a phagocytosis assay was performed M0 M1 M2 1018 M2+1018010203040** *** ** GeoMeanB. A.  82 and analyzed by flow cytometry. Zebra plots were created for each treatment, showing the percentage of macrophages with CFDA-SE positive apoptotic Jurkat cells (Figure 4.6A). The geometric mean was measured for positive CFDA-SE gated macrophages (Figure 4.6B). The phagocytic activity, assessed by the geometric mean of control M1 macrophages was similar to that of M0 cells while M2 cells demonstrated approximately twice as much phagocytosis. IDR-1018-differentiated cells demonstrated a significant but slight increase in phagocytosis compared to the M1 control, but it was still far less than the M2 control. Interestingly, the phagocytic activity of IDR-1018+M2 differentiated macrophages was greater than that of the M2 macrophages.  4.3.6 IDR-1018-differentiated macrophages maintained plasticity enabling a return to a pro-inflammatory state:   Some of the pathologies associated with immunosuppression, e.g. endotoxin tolerance, are thought to result from macrophages that became locked into an M2 phenotype, indicating a loss of plasticity [31,205]. Given the proposed use of IDR peptides as therapeutics, it was important to determine if differentiation in the presence of IDR-1018 affected the normal plasticity of macrophages and whether they were able to enter an M1 (LPS responsive) state. Therefore macrophages were differentiated as described previously, in the presence of IDR-1018 alone or in combination with M-CSF, and subsequently treated with the M1-promoting cytokine IFN?. The pro-inflammatory cytokine TNF? and the anti-inflammatory cytokine IL10, as analyzed by ELISA, were used as phenotypic markers. IDR-1018 and IDR-1018+M2 differentiated macrophages stimulated with IFN?, exhibited increased production of TNF? and reduced IL10, similar to M1 macrophages, in contrast to the equivalent macrophages that had not been stimulated with IFN?, which demonstrated cytokine expression profiles partly resembling those of M2 macrophages (Figure 4.7). These results suggest that IDR-1018 differentiated macrophages maintain plasticity, allowing modulation of their responsiveness.    83  Figure 4.7: IDR-1018 differentiated macrophages maintained plasticity as they could return to a pro-inflammatory state.  Macrophages were differentiated as described previously and treated with +/- LPS. In addition, macrophages differentiated in the presence of IDR-1018 alone or in combination with M-CSF, were subsequently treated with M1-inducing IFN? for 24 hours then challenged with LPS. Four hours post-challenge, cytokine responses were analyzed by ELISA. Mean ? SD results are presented and are representative of 3 biological replicates. *, P<,0.05.  4.3.7 IDR-1018 treated monocytes and monocyte-derived macrophages expressed transcription factors important for the development of M2 macrophages:  Several transcription factors that promote the development of M2 macrophages have been previously identified [29,206,207,208]. Therefore we sought to analyze the role of these factors in IDR-1018 differentiated macrophages and IDR-1018 stimulated monocytes using RT-qPCR. Intermediate responses were observed for the 3 transcription factors analyzed: Interferon regulatory factor 4 (IRF4), signal transducer and activator of transcription 3 (STAT3) and peroxisome activated receptor gamma (PPAR?) (Figure 4.8A). IRF4 was not induced in M1 cells but was significantly up-regulated in M2 cells. IDR-1018 differentiated macrophages demonstrated a high IRF4 induction while IDR-1018+M2 differentiated cells more resembled M2 cells. With respect to STAT3, IDR-1018 differentiated macrophages demonstrated no induction, like M1 cells, while IDR-1018+M2 differentiated cells, demonstrated a slight induction of this transcription factor. PPAR? was down-regulated in M1 macrophages but not in M2 macrophages or macrophages differentiated in the presence of IDR-1018 or 1018+M2.   84 Figure 4.8: IDR-1018 treated monocytes and IDR-1018 differentiated macrophages expressed transcription factors important for the development of alternative (M2) macrophages.  (A) Macrophages were differentiated in the presence of IDR-1018 or the combination of IDR- 1018 and M2-inducing factor M-CSF, and RT-qPCR was performed to analyze the expression of different M2 specific transcription factors. (B) Additionally, monocytes were stimulated with IDR-1018 (5 ?g/ml) for 4 hours. In both cases, RNA was isolated and the expression of transcription factors was analyzed by RT-qPCR. Mean ? SD results are presented and are representative of 3 biological replicates. *, P<0.05.  Interestingly, the expression of all three M2-promoting transcription factors, PPAR?, IRF4 and STAT3, was up-regulated by 5- to 6-fold in monocytes stimulated with IDR-1018 compared to unstimulated monocytes (Figure 4.8B). Since IRF4 was hyper-expressed in IDR-1018 differentiated macrophages, we investigated the impact of its expression on downstream responses. High-throughput RNA-seq was performed on IDR-1018 differentiated monocytes, versus unstimulated monocytes, demonstrating 542 up-regulated and 334 down regulated genes. Intersection of the 876 differentially expressed (DE) genes in IDR-1018 treated monocytes with M2-polarized macrophage microarray data revealed that 165 (19%) DE genes were also associated with M2 macrophage phenotype [191]. To determine the extent of IRF4 influence on the M2-subset in the IDR-1018 response, we examine the presence of IRF4 binding sites [200], and revealed 71 genes (41%) had previously demonstrated IRF4 binding (Table 4.2). Some of the genes identified are known to be associated with the development of M2 macrophages and/or IRF4M0 M1 M2 1018 1018+M201234***IRF4 Expression/B2MSTAT3M0 M1 M2 1018 1018+M201234*nsnsSTAT3 Expression/B2MPPARgM0 M1 M2 1018 1018+M20.00.51.01.52.0***PPARg Expression/B2MIRF4Untreated 101802468 ***Gene Expression/B2MSTAT3Untreated 101802468 **Gene Expression/B2MPPARgUntreated 101802468 *Gene Expression/B2M 85 other activities associated with the M2 phenotype including the mannose receptor (MR) , PPAR? and matrix metalloprotease 9 (MMP9) [139,206,209].   Table 4.2: M2 subset of IDR-1018 transcriptional data integrated with IRF-4 binding sites.  M2-phenotype associated genes containing IRF-4 binding sites that were differentially expressed in human monocytes stimulated with 20 ?g/ml IDR-1018.  ENTREZ GENE ID Gene Description Fold change (1018) P value (1018) 11095 ADAMTS8  ADAM metallopeptidase with thrombospondin type 1 motif, 8  11.10 3.58E-04 10253 SPRY2  Sprouty homolog 2 (Drosophila)  8.83 8.70E-10 2119 ETV5  Ets variant 5  4.92 1.08E-04 8190 MIA  Melanoma inhibitory activity  4.39 3.96E-02 25907 TMEM158  Transmembrane protein 158 (gene/pseudogene)  3.95 7.66E-07 10693 CCT6B  Chaperonin containing TCP1, subunit 6B (zeta 2)  3.18 9.53E-03 3624 INHBA  Inhibin, beta A  3.08 6.83E-04 1959 EGR2  Early growth response 2  2.91 5.83E-05 3659 IRF1  Interferon regulatory factor 1  2.86 6.89E-05 5743 PTGS2  Prostaglandin-endoperoxide synthase 2  2.79 1.17E-04 3783 KCNN4  Potassium interm/small conduct. Calcium-active channel, n4  2.57 3.99E-04 6004 RGS16  Regulator of G-protein signaling 16  2.56 4.99E-02 22822 PHLDA1  Pleckstrin homology-like domain, family A, member 1  2.51 4.70E-04 7124 TNF  Tumor necrosis factor  2.35 1.46E-03 10630 PDPN  Podoplanin  2.33 1.22E-02 4318 MMP9  Matrix metallopeptidase 9  2.31 1.42E-03 5292 PIM1  Pim-1 oncogene  2.31 1.63E-03 284119 PTRF  Polymerase I and transcript release factor  2.29 2.68E-03 6772 STAT1  Signal transducer and activator of transcription 1, 91kda  2.26 1.91E-03 3939 LDHA  Lactate dehydrogenase A  2.21 2.44E-03 8651 SOCS1  Suppressor of cytokine signaling 1  2.17 7.25E-03 5328 PLAU  Plasminogen activator, urokinase  2.08 1.63E-02 7071 KLF10  Kruppel-like factor 10  2.05 6.45E-03 7035 TFPI  Tissue factor pathway inhibitor  2.03 3.63E-02 3640 INSL3  Insulin-like 3 (Leydig cell)  2.00 4.64E-02 9021 SOCS3  Suppressor of cytokine signaling 3  1.90 1.42E-02 5214 PFKP  Phosphofructokinase, platelet  1.85 1.87E-02 27074 LAMP3  Lysosomal-associated membrane protein 3  1.84 4.14E-02 4360 MRC1  Mannose receptor, C type 1  1.84 3.93E-02 8829 NRP1  Neuropilin 1  1.84 2.13E-02 80176 SPSB1  Spla/ryanodine receptor domain and SOCS box containing 1  1.82 3.57E-02 64386 MMP25  Matrix metallopeptidase 25  1.81 2.42E-02 10538 BATF  Basic leucine zipper transcription factor, ATF-like  1.79 4.23E-02 9308 CD83  CD83 molecule  1.79 2.49E-02 9672 SDC3  Syndecan 3  1.77 4.19E-02 10938 EHD1  EH-domain containing 1  1.74 3.29E-02 1130 LYST  Lysosomal trafficking regulator  1.74 3.25E-02 8767 RIPK2  Receptor-interacting serine-threonine kinase 2  1.74 3.81E-02 200734 SPRED2  Sprouty-related, EVH1 domain containing 2  1.74 3.89E-02 8460 TPST1  Tyrosylprotein sulfotransferase 1  1.74 4.18E-02 7052 TGM2  Transglutaminase 2  1.73 3.72E-02  86  4.4 DISCUSSION  The steady rise in antimicrobial resistance in recent years combined with a declining rate of antibiotic discovery has generated a major health challenge. Host-directed immunomodulatory therapies such as IDRs represent a promising new approach to combat this problem [185]. A characteristic of IDR peptides, and natural host defence peptides like LL-37, is their ability to suppress pro-inflammatory responses induced by bacterial molecules like lipopolysaccharide (LPS), which led us to an initial hypothesis that these peptides might bias macrophages towards an M2 phenotype. Here, we demonstrated the ability of IDR-1018 to modulate the differentiation of macrophages, a cell type that plays a major role during the immune response towards infection and is critical to IDR anti-infective activity [76,77]. However, contrary to our initial expectations, IDR-1018, when present during differentiation, led to a unique intermediate ENTREZ GENE ID Gene Description Fold change (1018) P value (1018) 56978 PRDM8  PR domain containing 8  1.72 4.54E-02 6890 TAP1 Transporter 1, ATP-binding cassette, sub-family B 1.72 3.63E-02 10221 TRIB1  Tribbles homolog 1 (Drosophila)  1.71 3.88E-02 1958 EGR1  Early growth response 1  1.70 4.01E-02 22809 ATF5  Activating transcription factor 5  1.68 4.45E-02 5468 PPARG  Peroxisome proliferator-activated receptor gamma  1.68 4.77E-02 6515 SLC2A3  Solute carrier family 2 (facilitated glucose transporter), member 3  1.67 4.84E-02 8877 SPHK1  Sphingosine kinase 1  1.67 5.00E-02 11237 RNF24  Ring finger protein 24  -1.68 4.88E-02 1955 MEGF9  Multiple EGF-like-domains 9  -1.72 3.85E-02 81537 SGPP1  Sphingosine-1-phosphate phosphatase 1  -1.75 4.26E-02 3613 IMPA2  Inositol(myo)-1(or 4)-monophosphatase 2  -1.77 3.95E-02 55 ACPP  Acid phosphatase, prostate  -1.79 4.27E-02 9936 CD302  CD302 molecule  -1.79 2.72E-02 23171 GPD1L  Glycerol-3-phosphate dehydrogenase 1-like  -1.84 4.01E-02 51449 PCYOX1  Prenylcysteine oxidase 1  -1.90 1.92E-02 79657 RPAP3  RNA polymerase II associated protein 3  -2.05 1.13E-02 27075 TSPAN13  Tetraspanin 13  -2.05 1.69E-02 84722 PSRC1  Proline/serine-rich coiled-coil 1  -2.07 4.31E-02 2628 GATM  Glycine amidinotransferase  -2.25 1.05E-02 4332 MNDA  Myeloid cell nuclear differentiation antigen  -2.26 1.90E-03 26471 NUPR1  Nuclear protein, transcriptional regulator, 1  -2.78 1.29E-03 970 CD70  CD70 molecule  -2.83 1.66E-02 3077 HFE  Hemochromatosis  -2.86 1.05E-03 6123 RPL3L  Ribosomal protein L3-like  -3.20 3.51E-03 11240 PADI2  Peptidyl arginine deiminase, type II  -3.61 3.76E-06 51704 GPRC5B  G protein-coupled receptor, family C, group 5, member B  -4.74 4.75E-03 65987 KCTD14  Potassium channel tetramerisation domain containing 14  -4.81 1.33E-04 27293 SMPDL3B  Sphingomyelin phosphodiesterase, acid-like 3B  -5.01 5.52E-04  87 macrophage phenotype with different characteristics reflecting both the classical M1 and the alternative M2 phenotype. In contrast, differentiation of macrophages in the presence of IDR-1018 together with an M2-inducing factor M-CSF resulted in an apparently enhanced M2 phenotype.  The distinctive intermediate state promoted by IDR-1018, was characterized by the selective down-regulation of certain pro-inflammatory mediators associated with M1 macrophages [130,210]. Thus TNF?, COX-2 and IP-10 were substantially diminished in the IDR-1018 differentiated macrophages, while IL12 and CCL3 were only slightly reduced. Interestingly, although IDR-1018 differentiated macrophages exhibited substantial up-regulation of two anti-inflammatory mediators strongly associated with M2 macrophages, IL-10 and CCL-22, and the expression of others such as TGF? remained unchanged  Wound healing and phagocytosis of apoptotic cells are hallmark functions of M2 macrophages [22,144,211]. IDR-1018 [204], like natural host defence peptides, has been shown to promote wound healing in mice and pigs [212,213]. Consistent with this, we observed here that IDR-1018 and IDR1018-M2 differentiated macrophages exhibited a basal increase in the expression of different wound healing genes (EGF, VCAN) that are critical for the process of wound healing and tissue repair. Interestingly, we found that certain wound healing genes such as VEGF and FPRL-1 were only differentially expressed after LPS stimulation, which may indicate that although these macrophages had developed an M2-like phenotype, they only expressed certain wound healing associated molecules in response to specific changes in the microenvironment. Phagocytosis of apoptotic cells was also affected by differentiation with IDR-1018 but only to a modest extent. In contrast, when IDR-1018 was used in combination with M-CSF, the phagocytic activity was even greater than that observed for M2 macrophages. These data suggest that IDR-1018 differentiated macrophages might play a major role during the resolution of an infection or after tissue injury by clearing the affected site of debris and apoptotic cells, a process required for the return to tissue homeostasis.   Macrophages display considerable plasticity, allowing them to change their responses depending on the challenges to which they are exposed. However, under certain circumstances, they may become locked in a specific state such as a classical M1 state, during chronic inflammatory and autoimmune diseases, or an alternative M2 state, during immunosuppressive disorders [205,214]. If IDR-1018 caused macrophages to become locked into a particular state, and particularly an M2-like immunosuppressive state, this would represent a substantial  88 limitation on its development as a therapeutic. Therefore we tested whether IDR-1018 influenced the plasticity of macrophages. Critically, both IDR-1018 differentiated and IDR-1018+M2 differentiated macrophages were able to alter their responses to a more M1-like phenotype after exposure to the M1 inducing cytokine IFN?, demonstrating that the phenotype induced by IDR-1018 could indeed be reversed.   The molecular determinants of macrophage differentiation vary depending on the inducing factor. Since IDR-1018 appeared to promote an intermediate phenotype, with certain features of the M2 phenotype, we examined transcription factors known to be involved in the early development of this state. RT-qPCR analysis of monocytes treated with IDR-1018 for a short period of time (representing the early stages of differentiation) demonstrated the strong transcriptional up-regulation of important transcription factors such as PPAR?, STAT3 and IRF4. In contrast, in later-stage IDR-1018 differentiated macrophages, only IRF4 remained up-regulated. This is partly consistent with studies done by Bohuel et al [206], who demonstrated that PPAR? is important to skewing mononuclear cells towards an M2 phenotype. Thus while several transcription factors, including PPAR?, are important for the initiation of differentiation in monocytes by IDR-1018, IRF4 might play an important role in sustaining the IDR-1018 phenotype once macrophages have matured and differentiated. Indeed IRF4 was found to be a central factor for the development of the M2 phenotype induced by M-CSF [29]. To further examine the role of IRF4 in macrophage differentiation induced by IDR-1018, we utilized system biology approaches by obtaining RNA-Seq data of IDR-1018 treated monocytes and integrated it with IRF4 binding site data from the literature [200]. Our analysis showed that 41% of the differentially expressed genes were common to M2 transcriptional data and contained an IRF4 binding sites, demonstrating the likelihood that these genes were controlled by the transcription factor IRF4. Importantly, many of the genes with IRF4 binding sites including PPAR?, mannose receptor (MR), and MMP9, have been associated with the development and key functions of the M2 phenotype  Based on the results presented here, we propose that the intermediate phenotype generated by IDR-1018, makes it a good candidate for modulating inflammatory disorders such as sepsis, where the immunological response towards an infection needs to be carefully manipulated, reaching a balance by reducing inflammation to a level that it will not be dangerous to the body but still able to fight secondary infections that may arrive.   89  Thus, IDR-1018 used alone or in combination with other molecules provides an interesting alternative to traditional therapies, modulating the activity of immune cells such as macrophages to generate an appropriate protective response.              90 CHAPTER 5: CONCLUDING CHAPTER  5.1 INTRODUCTION Sepsis remains the main cause of death from infections despite advances in modern medicine including the development of antibiotics, vaccines and efficient well equipped intensive care units [40]. Sepsis kills regardless of ethnicity, location or ability to access care. It is estimated that globally more than 18 million cases of sepsis occur each year, from which 570 people die every hour, causing more deaths than breast, lung and prostate cancer combined. Sepsis has been considered the primary cause of death associated with HIV/AIDS, malaria, pneumonia, and other community acquired infections, accounting for up to 60-80% of deaths in the developing world, affecting mainly children, the elderly, and pregnant women [215]. Despite these statistics, 6 out of 10 people have never previously heard of sepsis as reported by the Sepsis Alliance Organization [216]. For health care systems, sepsis is a major financial burden, causing an estimated $14.6 billion per year just in the United States [217]. These statistics have led international health organizations to declare sepsis as a global health medical emergency that needs to be rapidly and properly recognized by the medical community, so that supportive therapies, such as fluids and antibiotics, can be administered within the first few hours if the syndrome is suspected. However, even with early recognition of sepsis and the application of a resuscitation bundle therapy during the first six hours following its recognition, only a demonstrated 31% overall reduction of mortality was observed [218]. In addition, although several immunotherapies have been tested through clinical trials, all of them have failed with disappointing results. These findings demonstrate that a better understanding of the physiopathology of sepsis is essential in order to achieve better outcomes and reduce mortality with the help of appropriate supportive and immunological therapies.  Therefore, the main objective of this doctoral thesis was to obtain a better understanding of sepsis and the immunological chaos that takes place during this syndrome, as well as to study a possible immunotherapy that has the potential to become a future pharmaceutical approach for this deadly condition.     91 5.2 UNDERSTANDING THE DEVELOPMENT OF ENDOTOXIN TOLERANCE DURING SEPSIS Retrospective analysis of the cause of death of patients with sepsis, revealed that the syndrome begins with the establishment of an infection. This infection can be caused by any kind of microorganism, although bacterial infections have been strongly considered as the major cause of sepsis [41]. Underlying health conditions like HIV/AIDS and cancer, old or very young age, as well as genetic predispositions, such as those conferred by polymorphisms in the regulatory and coding regions of important genes related to the innate immune system, may place patients with sepsis at a disadvantageous starting point [44,45,48]. These risk factors leave the patients with immunocompromised systems, unable to effectively fight the initial infection with all of the required artillery of an effective immune system. Likewise, if a patient encounters microorganisms with potent virulence factors and antibiotic resistance, or acquires an excessive load of microbes, as may occur during abdominal trauma or after severe burns, this would also position them at a disadvantage against microbes that might lead to sepsis. Therefore a combination of these factors will be unquestionably detrimental for the patient, providing a maximum risk of septic death.  Although the combination of these risk factors would be the worst-case scenario, sepsis can also strike in otherwise healthy people and still lead to high mortality rates. How then does this occur? If we consider the case of bacterial infections, whole bacteria and bacterial products such as LPS are well-recognized promoters of inflammation, which is the expected response from the immune system in the face of bacterial invasion. After an infection is cleared, this inflammation subsides and the system returns to homeostasis. However, in situations like the ones mentioned above, such as the presence of an excessive load of bacteria with potent virulence factors, the microbes will resist the immune system leading to a more prolonged and difficult fight. During this battle, the immune system responds with a full complement of cellular and humoral components to destroy the pathogen. For example, immune cells like macrophages and neutrophils produce copious amounts of pro-inflammatory mediators like TNF? and IL1? through the activation of TLR by bacterial products, a rapid response that occurs within 30 to 90 minutes after exposure and in vitro peaks after 4-6 hours. They in turn activate a second cascade of inflammatory mediators such as cytokines, chemokines, lipid mediators and reactive oxygen species[219]. Some of these cytokines enhance the expression of cell adhesion molecules that allow the infiltration of new immune cells, including monocytes and neutrophils, which migrate  92 to the site of infection by following an increasing chemokine and host defense peptide gradient that is produced by local activated macrophages. Excessive and continuous production of mediators like TNF?, IL1?, NO, PAF and COX2 by immune cells also leads to an increase of vasodilatation, hypotension, coagulation, fever and pain that ultimately adversely affects blood circulation, thereby decreasing the oxygenation of tissues, leading to shock and organ damage [220,221]. While this is the accepted dogma in sepsis, it fails to account for the high death rate associated with this condition, since the majority of human infections lead to an initial strong inflammatory response and both are subsequently resolved through innate or adaptive immunity, with a resultant control of inflammation. This enormous gap in our understanding of why sepsis, and the associated potent pro-inflammatory response, fails to lead to the resolution of infections was addressed in this thesis. In the late stages of sepsis, patients often acquire recurrent secondary infections due to a suspected but poorly defined immunosuppressive state and this also increases their risk of death[101]. General knowledge of this state was established based on the observations that immune cells from septic patients did not respond to LPS stimulations ex vivo [4]. It was suggested that this immunosuppressive state is associated with a cellular immunological status known as endotoxin tolerance. Observed in immune cells in vitro, endotoxin tolerance is the lack or reduced response to stimulation by a TLR agonist, such as LPS, after a secondary exposure to the same stimulus. Although endotoxin tolerance has been widely studied, there are still many unknown aspects. In an effort to obtain a better understanding of this phenomenon from a broader perspective than many previous studies, I used here a systems biology approach to observe the range of selective changes that occurred during endotoxin tolerance. Through microarrays and advanced bioinformatic analysis of responses to LPS by PBMCs, monocytes, and monocyte-derived macrophages, I determined that gene responses during endotoxin tolerance were similar to those found during M2 polarization [24]. Cellular responses were observed that featured gene and protein expression events that are critical to the development of key M2 mononuclear functions, including reduced production of pro-inflammatory mediators and expression of genes involved in phagocytosis and tissue remodeling. Moreover, the expression of different metallothionein gene isoforms, known for their roles in the control of oxidative stress and in immunomodulation, was also found to be consistently up-regulated during endotoxin tolerance.   93 These findings are consistent with the conclusion that the endotoxin tolerance phenomenon can take place independently of adaptive immunity, as an ancient biological alternative to prevent excessive inflammation in situations where adaptive immunity is impaired or not present. In this context, endotoxin tolerance would appear to be an ideal method for the immune system to reach a balance; modifying existing responses through cellular reprogramming to focus on the phagocytosis of microbes and dead cells and wound/tissue healing at the site of infection. In the usual mammalian response to minor or chronic insults, where adaptive immunity is present, the crosstalk between monocytes/macrophages and T cells may be key to regulating classical and alternative activation, through the expression of Th1 and Th2 cytokines. However, during a rapid and potent inflammatory response, such as during bacterial infections that lead to sepsis, the role of adaptive immunity might be lost due to the speed of the response and the profound depletion of T and B cells [222], leaving endotoxin tolerance as the main mechanism to control inflammation. Nevertheless, this suppressed-inflammation/wound-healing state would likely be dangerous if sustained for a prolonged period as occurs in sepsis, since the strong immunosuppresion would lead to susceptibility to secondary infections, increasing the risk of death.  To confirm this concept, an extensive bioinformatic meta-analysis was undertaken, using the unique gene signatures associated with responses to LPS and endotoxin tolerance in human blood mononuclear cells that were identified through the studies in Chapter 2. These signatures were compared with transcriptional changes observed in human sepsis patient cohorts reported in the literature. Likewise signaling pathway over-representation meta-analysis was performed using different sepsis cohorts. Very interestingly, we found that septic patients, regardless of the conditions under which they were sampled, were more likely to present an immunological profile that was strongly associated with an endotoxin tolerance gene signature, rather than a dominant pro-inflammatory response, as previously believed in the field. This represents a major paradigm shift in a field that has seen very few advances for decades and may explain why suppression of inflammatory responses through anti-endotoxin, anti-cytokine and steroid approaches has been such a dismal failure in the clinic. Taken together these analyses demonstrate that an endotoxin tolerance profile strongly predominates during sepsis, indicating that most patients are in an immunosuppressive state, at least with respect to macrophage/monocyte responses. In retrospect, it is possible that the original (excessive) inflammatory cytokine response might occur very early within the first hours  94 of the microbial insult, leading to sustained high circulating levels of pro-inflammatory cytokines. The initial level of this inflammation would appears to depend on the age and underlying genetics of the patient, the grade of the insult, the virulence of the microorganism, and the sensitivity of the microbe to administered antibiotics. When excessive inflammation is observed at in vitro level, it appears to rapidly subside at a transcriptional level, and more slowly at the level of measured inflammatory cytokines, but meanwhile we suggest that the patient moves towards an immunosuppressive or basal state. Endotoxin tolerance is a phenomenon that can be observed in vitro in a single immune cell or a group of the same type of cells, such as monocytes or macrophages. However, in a systemic environment that includes all types of immune cells, rather than only monocytes and macrophages, the response may be orchestrated in a different manner. During sepsis the cells are fighting (responding to bacterial products and other cells? signals), adapting (becoming tolerant), dying, and being replaced by new cells that start the cycle all over again. This cycle takes a reasonably long time in monocytes and macrophages, which have half-lives ranging from 3 to 16 days respectively [223,224] , than it does in neutrophils that have a very short half-life of 6 to 8 hours [225]. Therefore, it is likely that in a challenging environment such as sepsis, monocytes and macrophages are the major players in promoting immunosuppression over the time frame associated with sepsis. Although neutrophils can also enter into an endotoxin tolerance state [226], their rapid turnover indicates that new circulating neutrophils continue to have their first encounters with bacterial products and thus are likely continue to generate the pro-inflammatory cytokines and chemokines, such as IL6 and IL8, that are observed in patients even in the later stages of sepsis [227]. Hence, at a systemic level, despite the rapid and excessive inflammatory response, we can conclude from the retrospective analyses in Chapter 3 that there is an almost continuous association with an endotoxin tolerance state. As the syndrome progresses, the immunosuppressive or endotoxin tolerance state shifts the balance towards an immune response that has a severely limited ability to defend against life threatening nosocomial infections, as more tolerized immune cells accumulate and the production of mature neutrophils is diminished [228] This chaotic immunological state not only opens the door to a whole new set of secondary infections, but also enhances physiological and immunopathological states such as tissue edema, lymphocyte death, and disturbed metabolic issues such as hyperglycaemia and hyperlipidemia, which rapidly increase the risk of death of the patient. Therefore, the development and use of biomarkers like the ones presented here (Chapter 3), might enable a  95 prompt recognition of the immunosuppressive state of the septic patient, allowing physicians to provide a more personalized supportive treatment to restore appropriate immune function.   5.3 IDR-1018 AS A POSSIBLE IMMUNOMODULATORY THERAPY FOR TREATING SEPSIS As mentioned previously, macrophages play a critical role in the innate immune response. To respond in a rapid and efficient manner to challenges in the micro-environment, macrophages are able to differentiate towards classically (M1) or alternatively (M2) activated phenotypes. Synthetic innate defense regulator (IDR) peptides, that were designed based on natural host defence peptides, have enhanced immunomodulatory activities and reduced toxicity leading to protection in infection and inflammation models that is dependent on innate immune cells like monocytes/macrophages [71]. Here the effect of IDR-1018 was tested on macrophage differentiation, a process essential to macrophage function and the immune response (Chapter 4, [229]). Using transcriptional, protein and systems biology analysis, it was observed that differentiation in the presence of IDR-1018 induced a unique signature of immune responses, including the production of specific pro- and anti-inflammatory mediators, expression of wound healing associated genes, and increased phagocytosis of apoptotic cells. Transcription factor IRF4 appeared to play an important role in promoting this IDR-1018-induced phenotype. The data suggests that IDR-1018 drives macrophage differentiation towards an intermediate M1-M2 state, enhancing anti-inflammatory functions while maintaining particular pro-inflammatory activities important to the resolution of infection.  Based on the results presented here, it is proposed that the intermediate phenotype generated by IDR-1018, makes it a good candidate for modulating inflammatory disorders such as sepsis. During mid to later stage sepsis, there is an imbalance towards an immunosuppressive state, also known as endotoxin tolerance, a phenomenon discovered here to be associated with the presence of M2-like mononuclear cells. Endotoxin tolerance needs to be very carefully and modestly adjusted, since the complete abolition of this tolerant state might result in uncontrolled inflammation, while its enhancement could result in uncontrollable secondary infections. The ability of IDR-1018 to subtly modulate macrophage differentiation by promoting anti-inflammatory activity as well as production of selected pro-inflammatory (and protective) mediators, particularly chemokines, makes it an attractive therapeutic option for this disorder. Additionally, IDR-1018, when used in combination with M-CSF, enhances the M2 regulatory  96 phenotype. This response might be beneficial in pathologies associated with excessive inflammation such as the very early stages of sepsis. Further, it was recently demonstrated that IDR-1018, administered in combination with an existing anti-malarial treatment, was able to alleviate cases of severe (cerebral) malaria through suppression of life-threatening neural inflammation. This peptide was also able to assist in the resolution of severe invasive S. aureus infections [188]. We propose that the observed IDR-1018 effects on macrophage differentiation represent one of the biological mechanisms underlying the success of these peptides observed in animal model infection studies, promoting a dampening of pro-inflammatory responses while maintaining protective responses. Thus IDR-1018, used alone or in combination with other molecules, provides an interesting alternative to traditional therapies of sepsis, modulating the activity of immune cells such as macrophages to generate an appropriate protective response. 5.4 CLOSING REMARKS AND FUTURE DIRECTIONS I finish this doctoral thesis having completed and confirmed the main objective and hypotheses proposed here. I have indeed obtained a better understanding of sepsis and the immunological pandemonium that takes place during this deadly syndrome. I have discovered that endotoxin tolerance is a distinctive state of M2 polarization that is strongly sustained leading to an immunosuppressive state during sepsis, increasing the risk of death. These findings challenge the dogma that sepsis is a hyper-inflammatory (cytokine storm) disease, suggesting instead that endotoxin tolerance might occur much earlier in sepsis than previously suspected. The unique endotoxin tolerance gene signature that I observed after repetitive doses of LPS, is a manifestation of an alternatively activated M2-phenotype and could be used to identify possible biomarkers that would help to characterize the critical immunological status of septic patients. This would, in turn, enable the administration of appropriate immunological and supportive therapies and improve the survival rate of this deadly syndrome. Synthetic peptides like IDR-1018, which act by modulating the immune system, could represent a powerful new class of therapeutics capable of treating sepsis and the rising number of multidrug resistant infections, as well as disorders associated with dysregulated immune responses. Future studies are necessary to confirm the use of endotoxin tolerance biomarkers, identified here, during the course of sepsis. Longitudinal sepsis studies are a necessary approach to exploring and understanding the real value of these molecules for identifying the immunological status of patients. Likewise, the development of diagnostic tests to detect these biomarkers at the protein level by qualitative and quantitative analysis would be an important  97 step. In addition, further studies of IDR-1018 in animal models are necessary to continue the path of development for this immunomodulator towards a future pharmaceutical approach to treating and preventing the progression of sepsis.                 98 BIBLIOGRAPHY 1. Cavaillon JM (1995) The nonspecific nature of endotoxin tolerance. Trends Microbiol 3: 320-324. 2. West MA, Heagy W (2002) Endotoxin tolerance: A review. Crit Care Med 30: S64-S73. 3. Beeson PB (1946) Development of tolerance to typhoid bacterial pyrogen and its abolition by reticulo-endothelial blockade. Proc Soc Exp Biol Med 61: 248-250. 4. 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(2013) Synthetic Cationic Peptide IDR-1018 Modulates Human Macrophage Differentiation. PLoS ONE 8: e52449.                       111 APPENDIX TABLE 1. LPS-Differentialy expressed genes (Fold change ? 1.5 with P-value <0.05)  Refseqs Symbols Fold_Change P.Value NM_000600.1 IL6 29.85452678 3.06E-11 NM_000963.1 PTGS2 24.25146474 7.03E-10 NM_000575.3 IL1A 21.85380032 1.08E-12 NM_004591.1 CCL20 14.6447732 9.21E-12 NM_000576.2 IL1B 13.06626262 1.00E-12 NM_005755.2 EBI3 10.87217583 6.04E-12 NM_024873.3 TNIP3 10.87031497 2.14E-15 NM_002575.1 SERPINB2 10.65388764 1.85E-07 NM_021006.4 CCL3L1 9.190350401 2.55E-08 NM_002164.3 INDO 8.384043842 1.31E-08 NM_015714.2 G0S2 7.245569957 1.87E-12 NM_001511.1 CXCL1 6.957558043 3.26E-12 NM_173843.1 IL1RN 6.403533978 3.66E-06 NM_001710.4 CFB 6.068512691 4.61E-09 NM_006820.1 IFI44L 6.066073434 0.000526155 NM_014398.2 LAMP3 6.022311864 1.17E-09 NM_004004.4 GJB2 5.590056981 1.92E-07 NM_003327.2 TNFRSF4 5.534201555 5.10E-06 NM_013410.2 AK3L1 5.403913504 4.04E-12 NM_005953.2 MT2A 5.281041152 1.63E-05 NM_181509.1 MAP1LC3A 5.279473439 3.58E-12 NM_173842.1 IL1RN 5.172725816 5.87E-06 NM_013371.2 IL19 5.140011348 2.71E-07 NM_001031683.1 IFIT3 5.118343433 0.000723086 NM_002983.1 CCL3 5.0560001 2.12E-08 NM_005101.1 ISG15 4.928824985 0.000322945 NM_033405.2 PRIC285 4.829372481 1.86E-05 NM_016323.2 HERC5 4.813771371 0.000169539 NM_020980.2 AQP9 4.795761484 2.59E-07 NM_014317.3 PDSS1 4.76365668 2.07E-11 NM_006399.2 BATF 4.742303365 2.81E-08 NM_178452.3 LRRC50 4.719656112 7.02E-07 NM_001001435.2 CCL4L1 4.561842318 2.69E-05 NM_001823.3 CKB 4.465155729 9.70E-11 NM_199139.1 XAF1 4.414809495 7.78E-05 NM_080657.4 RSAD2 4.381415055 0.000263649 NM_001549.2 IFIT3 4.235104968 0.000874653 NM_001040708.1 HEY1 4.232359582 8.11E-12 NM_006875.2 PIM2 4.211111673 3.34E-06  112 Refseqs Symbols Fold_Change P.Value NM_003641.3 IFITM1 4.146953534 0.000123943 NM_000758.2 CSF2 4.102857725 5.29E-06 NM_003088.2 FSCN1 3.997413 5.34E-06 NM_002462.2 MX1 3.68767646 0.001739519 NM_006417.3 IFI44 3.68589152 0.000348856 NM_198594.1 C1QTNF1 3.625152304 3.49E-08 NM_001993.2 F3 3.585507198 1.66E-06 NM_005558.3 LAD1 3.526747738 2.25E-07 NM_004419.3 DUSP5 3.518078902 5.29E-07 NM_052966.2 FAM129A 3.462393174 1.71E-09 NM_001547.4 IFIT2 3.447941566 0.000829531 NM_016817.2 OAS2 3.434044011 0.000332255 NM_002463.1 MX2 3.426856354 0.000958689 NM_000891.2 KCNJ2 3.420774106 1.52E-10 NM_001032409.1 OAS1 3.381319339 0.002139067 NM_017912.3 HERC6 3.380513216 0.00019602 NM_199139.1 XAF1 3.369675774 0.000794126 NM_145898.1 CCL23 3.341546055 2.84E-06 NM_002346.1 LY6E 3.325031939 0.000181414 NM_006273.2 CCL7 3.291260175 0.002090123 NM_005946.2 MT1A 3.267633702 0.000282606 NM_012118.2 CCRN4L 3.256809905 1.37E-09 NM_005746.2 NAMPT 3.211037194 2.63E-08 NM_002759.1 EIF2AK2 3.183057176 0.000173803 NM_153259.2 MCOLN2 3.154996714 2.99E-06 NM_004029.2 IRF7 3.12313236 3.79E-05 NM_004029.2 IRF7 3.107256813 7.82E-05 NM_001001437.3 CCL3L3 3.070558694 7.09E-07 NM_002960.1 S100A3 3.064612804 2.74E-12 NM_201623.2 CLEC12A 3.057488081 1.22E-05 NM_002994.3 CXCL5 3.049653104 1.30E-06 NM_003821.5 RIPK2 2.995296563 2.20E-08 NM_002089.3 CXCL2 2.994703009 3.88E-07 NM_178562.2 TSPAN33 2.988479197 3.92E-05 NM_005306.1 FFAR2 2.970689533 8.96E-05 NM_138337.4 CLEC12A 2.962419871 1.56E-06 NM_000675.3 ADORA2A 2.942065822 2.51E-07 NM_013371.2 IL19 2.940234584 5.43E-06 NM_152703.2 SAMD9L 2.9077828 4.59E-05 NM_015158.2 KANK1 2.891303139 2.32E-06 NM_001561.4 TNFRSF9 2.848425616 1.61E-07 NM_005371.4 METTL1 2.820122615 4.32E-10 NM_000783.2 CYP26A1 2.817072153 1.80E-06 NM_001024466.1 SOD2 2.794467811 5.38E-07 NM_004233.3 CD83 2.778919513 3.07E-06 NM_000266.1 NDP 2.761389858 8.57E-12  113 Refseqs Symbols Fold_Change P.Value NM_020895.2 GRAMD1A 2.751759672 1.36E-09 NM_032744.1 C6ORF105 2.73703217 0.000499893 NM_003745.1 SOCS1 2.723948079 6.13E-09 NM_005098.3 MSC 2.712657082 2.25E-07 NM_005746.2 NAMPT 2.712307061 8.67E-08 NM_006074.3 TRIM22 2.675463056 0.00013344 NM_001250.4 CD40 2.659105938 6.87E-09 NM_001032409.1 OAS1 2.650944503 0.00205419 NM_000594.2 TNF 2.633806437 0.000125712 NM_005191.3 CD80 2.596871332 2.66E-07 NM_017631.4 DDX60 2.573045184 9.68E-05 NM_002648.2 PIM1 2.54928532 3.83E-06 NM_175617.3 MT1E 2.530482745 1.67E-05 NM_001040280.1 CD83 2.530437222 2.26E-06 NM_005082.4 TRIM25 2.518533475 8.02E-06 NM_004347.1 CASP5 2.499368455 6.54E-08 NM_005533.2 IFI35 2.492877991 0.000314156 NM_021181.3 SLAMF7 2.476769386 1.91E-06 NM_002535.2 OAS2 2.467837521 0.000246881 NM_033255.2 EPSTI1 2.463394654 0.002267512 NM_052941.3 GBP4 2.443557836 0.000117102 NM_004728.2 DDX21 2.440670301 8.39E-10 NM_198213.1 OASL 2.437292043 0.000235367 NM_198446.1 C1ORF122 2.43377071 0.000233125 NM_016612.2 SLC25A37 2.4277032 3.09E-05 NM_024569.3 MPZL1 2.410184664 1.15E-06 XM_938171.2 PIM3 2.409947335 3.34E-08 NM_019618.2 IL1F9 2.394836864 0.002965417 NM_005107.2 ENDOGL1 2.38826377 2.28E-05 NM_022750.2 PARP12 2.385595751 6.18E-06 NM_015130.2 TBC1D9 2.368544688 3.26E-06 NM_032413.2 C15ORF48 2.363836474 1.08E-05 NM_174893.1 C17ORF49 2.354438722 5.01E-07 NM_004120.3 GBP2 2.334475725 2.22E-05 NM_024640.3 YRDC 2.332937558 1.90E-07 NM_024119.2 DHX58 2.330165024 5.36E-05 NM_000269.2 NME1 2.325874012 5.73E-07 NM_000389.2 CDKN1A 2.324329473 1.34E-05 NM_182757.2 RNF144B 2.323351007 3.41E-07 NM_152916.1 EMR2 2.286403806 6.33E-05 NM_003329.2 TXN 2.265836312 2.46E-06 NM_003897.3 IER3 2.26259843 6.41E-05 NM_031458.1 PARP9 2.247370864 0.000437932 NM_002201.4 ISG20 2.246019789 0.000143282 NM_001627.2 ALCAM 2.232964467 2.78E-07 NM_002852.2 PTX3 2.231400999 3.90E-09  114 Refseqs Symbols Fold_Change P.Value NM_001032731.1 OAS2 2.216867203 0.000873061 NM_198336.1 INSIG1 2.208630074 1.52E-05 NM_006291.2 TNFAIP2 2.208384901 5.15E-07 NM_058172.3 ANTXR2 2.201545887 1.24E-06 NM_003733.2 OASL 2.198452954 0.00011121 NM_002467.3 MYC 2.196787305 0.000497525 NM_004510.2 SP110 2.184378037 0.000675354 NM_006317.3 BASP1 2.178330449 2.80E-05 NM_002993.2 CXCL6 2.17804508 0.000520757 NM_003810.2 TNFSF10 2.159657483 0.000106412 NM_015704.1 FAM152B 2.157061756 5.12E-07 NM_001077493.1 NFKB2 2.148283311 6.89E-09 NM_006705.2 GADD45G 2.147885298 1.28E-06 NM_001570.3 IRAK2 2.14092156 0.000363864 NM_030797.2 FAM49A 2.13287185 2.10E-06 NM_005064.3 CCL23 2.127607076 1.87E-05 NM_022147.2 RTP4 2.124821467 0.000701654 NM_006452.3 PAICS 2.11261417 1.28E-06 NM_017654.2 SAMD9 2.109265757 8.39E-05 NM_016327.2 UPB1 2.095473547 2.93E-08 NM_004776.2 B4GALT5 2.095314948 6.99E-06 NM_001955.2 EDN1 2.094799354 2.22E-06 NM_001031683.1 IFIT3 2.08861077 0.001707205 NM_006452.3 PAICS 2.086895352 4.50E-07 NM_021127.1 PMAIP1 2.084513752 4.67E-07 NM_005178.2 BCL3 2.063833714 7.23E-07 NM_000022.2 ADA 2.063741184 2.17E-07 NM_002229.2 JUNB 2.062256843 3.37E-06 NM_015675.2 GADD45B 2.061068369 7.47E-08 NM_005955.2 MTF1 2.060150236 4.44E-07 NM_001024071.1 GCH1 2.047689393 2.06E-07 NM_006058.3 TNIP1 2.047355721 2.82E-05 NM_032855.2 HSH2D 2.040597028 0.000424884 NM_016391.3 HSPC111 2.033661172 8.65E-10 NM_001005474.1 NFKBIZ 2.023142548 1.35E-08 NM_177551.3 GPR109A 2.02085394 1.68E-06 NM_004510.2 SP110 2.016898909 0.000151001 NM_016354.3 SLCO4A1 2.010477973 1.06E-07 NM_000595.2 LTA 2.007300199 9.60E-07 NM_015201.3 BOP1 2.006729169 1.31E-10 NM_025079.1 ZC3H12A 2.003039235 2.91E-07 NM_017611.2 SLC43A3 1.999577414 2.13E-05 NM_173654.1 C3ORF64 1.999084046 0.001911076 NM_022767.2 ISG20L1 1.997090142 1.92E-10 NM_207315.2 CMPK2 1.989350966 0.001563563 NM_001066.2 TNFRSF1B 1.989101243 6.39E-05  115 Refseqs Symbols Fold_Change P.Value NM_001775.2 CD38 1.985629474 1.11E-05 NM_001042453.1 MST4 1.984436801 7.18E-08 NM_017554.1 PARP14 1.97807462 5.91E-05 NM_015840.2 ADAR 1.970195082 0.000101533 NM_001165.3 BIRC3 1.961656885 3.31E-07 NM_032148.2 SLC41A2 1.960559716 2.19E-06 NM_080424.1 SP110 1.9599598 0.000193325 NM_022168.2 IFIH1 1.957427368 0.000326629 NM_003272.1 GPR137B 1.956082436 1.67E-05 NM_002340.3 LSS 1.955993195 1.16E-07 NM_006018.1 GPR109B 1.953354905 3.83E-07 NM_005204.2 MAP3K8 1.953046147 0.002806559 NM_004223.3 UBE2L6 1.948712804 0.001050039 NM_016542.3 MST4 1.947014757 5.43E-06 NM_001001392.1 CD44 1.944242985 5.09E-08 NM_178232.2 HAPLN3 1.938529826 0.001503927 NM_002250.2 KCNN4 1.93782672 1.04E-05 NM_014330.2 PPP1R15A 1.936504487 0.000134855 NM_012420.1 IFIT5 1.930722733 4.37E-05 NM_002562.4 P2RX7 1.926426476 0.000847527 NM_138337.4 CLEC12A 1.918768007 2.29E-05 NM_138636.2 TLR8 1.918479877 1.05E-05 NM_005239.4 ETS2 1.917963861 2.12E-06 NM_145641.1 APOL3 1.917274075 3.91E-05 NM_014479.2 ADAMDEC1 1.916824979 9.28E-05 NM_024430.2 PSTPIP2 1.916814432 0.001131066 NM_032413.2 C15ORF48 1.915837085 1.30E-06 NM_016816.2 OAS1 1.910422002 0.000291417 NM_173475.1 DCUN1D3 1.909083414 6.85E-10 NM_002214.2 ITGB8 1.905047644 8.35E-06 NM_080655.1 C9ORF30 1.90340108 6.24E-08 NM_005531.1 IFI16 1.891680033 0.000410992 NM_001706.2 BCL6 1.888547409 7.62E-07 NM_014963.2 SBNO2 1.885551783 2.48E-06 NM_138287.2 DTX3L 1.88520497 0.00011539 NM_002189.2 IL15RA 1.878113849 9.48E-07 NM_014143.2 CD274 1.875293823 9.52E-09 NM_003890.1 FCGBP 1.870416292 0.002101548 NM_019058.2 DDIT4 1.870146616 0.001085118 NM_006509.2 RELB 1.868517063 1.33E-06 NM_007276.3 CBX3 1.865204924 9.01E-05 NM_016610.2 TLR8 1.848875314 1.91E-05 NM_003045.3 SLC7A1 1.84556091 1.97E-06 NM_017585.2 SLC2A6 1.838173979 0.000352038 NM_182679.1 GPATCH4 1.835224376 4.34E-07 NM_003953.4 MPZL1 1.834264603 1.08E-07  116 Refseqs Symbols Fold_Change P.Value NM_014096.2 SLC43A3 1.828938416 9.28E-05 NM_004053.3 BYSL 1.821770754 1.52E-07 NM_021960.3 MCL1 1.82135008 0.000511639 NM_001974.3 EMR1 1.81820025 0.000277332 NM_000585.2 IL15 1.817519376 3.20E-07 NM_006290.2 TNFAIP3 1.813855221 2.86E-05 NM_001781.1 CD69 1.812092858 1.36E-06 NM_018438.4 FBXO6 1.810005769 5.67E-05 NM_003956.3 CH25H 1.807454637 0.000443817 NM_001111.3 ADAR 1.803705349 2.30E-05 NM_024576.3 OGFRL1 1.803133363 9.99E-05 NM_001024844.1 CD82 1.797834568 0.001829381 NM_002231.3 CD82 1.796068933 0.000674096 NM_015871.3 ZNF593 1.795503333 1.92E-07 NM_002999.2 SDC4 1.79089146 4.06E-07 NM_015150.1 RFTN1 1.787462465 4.06E-08 NM_003364.2 UPP1 1.787274218 1.57E-06 NM_004741.1 NOLC1 1.786112575 4.73E-08 NM_000785.3 CYP27B1 1.784770051 1.72E-06 XM_927730.1 LOC644615 1.782769495 2.14E-05 NM_003955.3 SOCS3 1.777155653 6.12E-09 NM_001080535.1 LINCR 1.774295367 0.000432107 NM_014878.4 KIAA0020 1.772136233 1.63E-07 NM_001995.2 ACSL1 1.767415322 0.0004184 NM_016545.4 IER5 1.766882962 2.48E-08 NM_015278.3 SASH1 1.766002736 0.000249596 NM_031266.2 HNRNPAB 1.763373473 1.57E-09 NM_002502.3 NFKB2 1.761766076 1.62E-06 NM_015062.3 PPRC1 1.759295463 2.35E-08 NM_018993.2 RIN2 1.753159604 0.000645698 NM_001154.2 ANXA5 1.749524585 0.000410386 NM_198291.1 SRC 1.749130398 0.002448453 NM_002818.2 PSME2 1.748471337 0.000584521 NM_001077654.1 TNFAIP8 1.747839727 5.22E-06 NM_020179.1 C11ORF75 1.743515205 0.000232976 NM_152858.1 WTAP 1.743154978 5.71E-05 NM_001079821.1 NLRP3 1.738106541 0.00045761 NM_001017998.2 GNG10 1.7368902 0.000140193 NM_016817.2 OAS2 1.73459719 0.000887172 NM_014314.3 DDX58 1.729881361 0.000124837 NM_203379.1 ACSL5 1.728120525 2.50E-06 NM_013322.2 SNX10 1.725373546 0.0006997 NM_004906.3 WTAP 1.723782604 5.99E-07 NM_001040409.1 MTHFD2 1.720309714 1.96E-07 NM_001024688.1 NBN 1.719654647 8.06E-07 NM_001040443.1 PHF11 1.7175268 7.08E-05  117 Refseqs Symbols Fold_Change P.Value NM_006636.3 MTHFD2 1.716732021 1.49E-05 NM_000544.3 TAP2 1.714380844 0.002049273 NM_001024070.1 GCH1 1.713127336 2.62E-05 NM_032206.3 NLRC5 1.712910883 0.000621869 NM_000636.2 SOD2 1.710182861 0.001213115 NM_001024465.1 SOD2 1.709866145 2.52E-05 NM_032993.2 NOLA1 1.705857541 1.52E-07 NM_012341.2 GTPBP4 1.70569171 1.10E-07 NM_080422.1 PTPN2 1.697685886 9.84E-07 NM_005922.2 MAP3K4 1.69720148 1.29E-05 NM_006187.2 OAS3 1.695872573 0.002057703 NM_003292.2 TPR 1.695596407 0.000420423 NM_012474.3 UCK2 1.692824318 2.98E-06 NM_024930.1 ELOVL7 1.691707033 1.93E-07 NM_033294.2 CASP1 1.687782493 5.04E-05 NM_019042.3 PUS7 1.683826417 2.23E-06 NM_002341.1 LTB 1.682513423 0.000146631 NM_004418.2 DUSP2 1.680956541 8.23E-05 XM_001128702.1 SGPP2 1.680217009 0.000184792 NM_016234.3 ACSL5 1.678938764 1.48E-09 NM_003651.3 CSDA 1.675327822 0.001043267 NM_004499.3 HNRNPAB 1.674478919 1.24E-05 NM_016234.3 ACSL5 1.674442311 2.29E-05 NM_001040443.1 PHF11 1.674234238 2.22E-05 NM_004458.1 ACSL4 1.669950211 2.74E-05 NM_018092.3 NETO2 1.668687597 2.76E-05 NM_005494.2 DNAJB6 1.667204892 0.00016917 NM_001012633.1 IL32 1.66400772 0.001092891 NM_021035.2 ZNFX1 1.662627163 1.32E-05 NM_016183.3 MRTO4 1.660123676 1.55E-08 NM_022451.9 NOC3L 1.650935844 1.38E-05 NM_033625.2 RPL34 1.649420375 0.001080994 NM_018465.2 C9ORF46 1.648024824 1.11E-07 NM_206826.1 GNL3 1.647577426 0.000381606 NM_014038.1 BZW2 1.64712064 0.000157911 NM_001032998.1 KYNU 1.646875423 2.41E-07 NM_003174.3 SVIL 1.644935189 1.66E-06 NM_000593.5 TAP1 1.643839826 0.000279431 NM_001012636.1 IL32 1.640328382 0.00010683 NM_001031685.2 TP53BP2 1.638390869 2.30E-05 NM_014366.4 GNL3 1.632246068 1.45E-05 NM_004049.2 BCL2A1 1.629706662 4.53E-05 NM_003037.1 SLAMF1 1.62826446 5.40E-05 NM_006303.3 JTV1 1.625894236 7.16E-07 NM_006806.3 BTG3 1.621117049 0.001860462 NM_014290.1 TDRD7 1.619054212 0.000785417  118 Refseqs Symbols Fold_Change P.Value NM_001026.3 RPS24 1.616611647 0.000104347 NM_058246.3 DNAJB6 1.615601034 4.24E-07 NM_001905.1 CTPS 1.611744831 3.60E-06 NM_002756.3 MAP2K3 1.61151332 5.82E-06 NM_021958.2 HLX 1.610604684 9.24E-06 NM_002198.1 IRF1 1.606800204 0.000314085 NM_004240.2 TRIP10 1.605180868 2.44E-06 NM_002197.1 ACO1 1.598829144 0.000366658 NM_000527.2 LDLR 1.598250503 0.000693813 NM_018664.1 BATF3 1.59787554 0.000170827 NM_002664.1 PLEK 1.595817431 0.000116227 NM_001100422.1 LOC26010 1.593464006 0.001635767 NM_002166.4 ID2 1.592679046 0.000677378 NM_024662.1 NAT10 1.591678658 9.30E-10 NM_033294.2 CASP1 1.588080324 0.000336102 NM_174889.3 NDUFAF2 1.582981624 1.40E-05 NM_004099.4 STOM 1.581706468 0.000198379 NM_016052.3 RRP15 1.578856071 1.28E-05 NM_000958.2 PTGER4 1.578590133 0.000363919 NM_016032.2 ZDHHC9 1.577972117 1.38E-06 NM_002286.4 LAG3 1.57705768 0.001069519 NM_002791.1 PSMA6 1.574374468 2.24E-06 NM_001034833.1 NOLA2 1.574245684 0.000185464 NM_002657.2 PLAGL2 1.574227786 2.34E-08 NM_001212.3 C1QBP 1.573806949 0.000164453 NM_024420.1 PLA2G4A 1.573135342 0.000395172 NM_001914.2 CYB5A 1.571704503 0.000493886 NM_018381.2 FLJ11286 1.57036505 0.00057642 NM_000572.2 IL10 1.569118697 3.60E-05 NM_052889.2 COP1 1.568387523 3.26E-05 NM_006824.1 EBNA1BP2 1.567402334 5.10E-06 NM_032793.2 MFSD2 1.566750886 5.28E-06 NM_152857.1 WTAP 1.565043176 4.69E-05 NM_147686.1 TRAF3IP2 1.56399915 0.001983706 NM_032789.1 PARP10 1.563500031 0.000630759 NM_016069.8 MAGMAS 1.562795388 0.000122022 NM_021960.3 MCL1 1.562435804 0.000270483 NM_133646.2 ZAK 1.559615557 4.64E-06 NM_013248.2 NXT1 1.557220356 3.23E-05 NM_014331.3 SLC7A11 1.557197803 0.000475094 NM_006207.1 PDGFRL 1.554875188 3.08E-05 NM_002646.2 PIK3C2B 1.553770004 3.07E-05 NM_018482.2 DDEF1 1.551923244 1.63E-05 NM_012092.2 ICOS 1.550754197 0.001480519 NM_000228.2 LAMB3 1.550555557 9.15E-06 NM_021170.2 HES4 1.548511206 7.49E-06  119 Refseqs Symbols Fold_Change P.Value NM_182648.1 BAZ1A 1.547306222 3.76E-05 NM_000269.2 NME1 1.544032029 9.25E-06 NM_006813.1 PNRC1 1.543751701 3.60E-07 NM_001010859.1 LOC150297 1.543123 0.000525791 NM_002870.2 RAB13 1.54003866 5.35E-05 NM_002161.3 IARS 1.5388648 7.34E-05 NM_018072.4 HEATR1 1.53885945 4.09E-05 NM_015179.2 RRP12 1.533676668 0.000106318 NM_020117.9 LARS 1.533204549 9.60E-07 NM_148901.1 TNFRSF18 1.530372152 0.000171888 NM_003132.2 SRM 1.528340523 3.49E-05 NM_001225.3 CASP4 1.526985564 3.13E-05 NM_004688.1 NMI 1.525943106 0.001355498 NM_004595.2 SMS 1.524547211 0.001847644 NM_001959.3 EEF1B2 1.52191825 0.002041429 NM_005532.3 IFI27 1.521565841 0.000775733 NM_000877.2 IL1R1 1.519910598 0.000380955 NM_000610.3 CD44 1.518873628 0.001186194 NM_001024912.1 CEACAM1 1.518365444 0.002046423 NM_005008.2 NHP2L1 1.516896371 8.27E-05 NM_004895.3 NLRP3 1.516376551 0.003149909 NM_032194.1 BXDC1 1.516329744 3.28E-07 NM_015169.3 RRS1 1.516247529 5.14E-05 NM_004398.2 DDX10 1.515821213 2.33E-09 NM_006114.1 TOMM40 1.515366683 4.00E-06 NM_033027.2 AXUD1 1.515059206 0.002015592 NM_006993.1 NPM3 1.514666257 0.0006304 NM_175621.2 MTE 1.514355308 0.002100239 NM_018000.2 MREG 1.514132644 1.66E-06 NM_016077.3 PTRH2 1.513235615 1.23E-07 NM_020143.2 PNO1 1.512278403 1.82E-06 NM_032830.1 CIRH1A 1.511401719 2.54E-06 NR_000034.1 TMEM185B 1.510201483 0.000105045 NM_006341.2 MAD2L2 1.509929242 2.01E-05 NM_002662.2 PLD1 1.509337695 0.00054811 NM_020954.2 KIAA1618 1.509170283 0.003257463 NM_013943.1 CLIC4 1.507943475 9.45E-05 NM_005849.1 IGSF6 1.504647162 0.001964643 NM_033416.1 IMP4 1.501838052 1.08E-06 NM_033306.2 CASP4 1.501175653 1.73E-07    120   TABLE 2. LPS/LPS-Differentialy expressed genes (Fold change ? 1.5 with P-value <0.05)  Refseqs Symbols Fold_Change P.Value NM_005950.1 MT1G 61.12370572 1.39E-10 NM_002994.3 CXCL5 53.00462936 2.72E-14 NM_005951.2 MT1H 51.05641741 5.98E-12 NM_175617.3 MT1E 30.8685821 3.98E-13 NM_005953.2 MT2A 24.7527353 4.19E-09 NM_176870.2 MT1M 23.79458564 2.11E-12 NM_005946.2 MT1A 21.35187031 3.47E-09 NM_006273.2 CCL7 21.03823253 9.61E-08 NM_002991.2 CCL24 19.76296873 9.45E-09 NM_005949.2 MT1F 16.24260263 2.79E-11 NM_005952.2 MT1X 14.75973125 1.53E-09 NM_006865.2 LILRA3 14.04481823 3.39E-06 NM_174918.2 C19ORF59 12.64631649 3.90E-08 NM_002423.3 MMP7 12.03624817 9.32E-09 NM_005755.2 EBI3 11.49025367 4.33E-12 NM_006865.2 LILRA3 10.43091376 2.30E-06 NM_005107.2 ENDOGL1 9.995205569 7.70E-11 NM_175621.2 MTE 8.26832866 6.76E-12 NM_001218.3 CA12 8.19405442 2.04E-07 NM_000576.2 IL1B 7.73238933 2.76E-11 NM_000600.1 IL6 7.674191786 3.70E-08 NM_002164.3 INDO 7.254946322 3.36E-08 NM_002575.1 SERPINB2 7.097801672 1.93E-06 NM_002981.1 CCL1 7.068189936 5.08E-05 NM_002990.3 CCL22 6.974534061 4.63E-09 NM_002704.2 PPBP 6.757038907 2.30E-07 NM_002029.3 FPR1 5.712261602 2.27E-08 NM_001002236.1 SERPINA1 5.650833637 2.04E-08 NM_001002235.1 SERPINA1 5.641457553 4.19E-08 NM_181509.1 MAP1LC3A 5.613653922 2.11E-12 NM_004385.2 VCAN 5.272794689 8.43E-06 NM_003088.2 FSCN1 4.970996251 9.28E-07 NM_001005738.1 FPR2 4.923090409 3.61E-09 NM_014398.2 LAMP3 4.794882794 7.61E-09 NM_001462.3 FPR2 4.753844249 1.27E-08 NM_012072.3 CD93 4.578532416 8.12E-05 NM_020415.2 RETN 4.388313336 3.31E-06 NM_003784.2 SERPINB7 4.252166668 6.12E-07  121 Refseqs Symbols Fold_Change P.Value NM_002183.2 IL3RA 4.211524885 2.90E-10 NM_018689.1 KIAA1199 4.128996717 0.000250982 NM_001511.1 CXCL1 4.098255445 3.16E-10 NM_173843.1 IL1RN 4.087413248 7.72E-05 NM_181755.1 HSD11B1 4.0825652 3.69E-06 NM_006274.2 CCL19 4.079626412 3.38E-06 NM_006528.2 TFPI2 4.053747229 0.001117469 NM_014391.2 ANKRD1 4.052513278 6.50E-05 NM_003516.2 HIST2H2AA3 3.970933846 4.16E-05 NM_181755.1 HSD11B1 3.96011524 6.07E-06 NM_016612.2 SLC25A37 3.738214842 2.87E-07 NM_005621.1 S100A12 3.702117363 4.78E-06 NM_006770.3 MARCO 3.687053796 5.54E-05 NM_005746.2 NAMPT 3.685080878 5.82E-09 NM_003517.2 HIST2H2AC 3.614470782 1.56E-05 NM_173842.1 IL1RN 3.574578095 9.30E-05 NM_005746.2 NAMPT 3.563622614 3.37E-09 NM_133280.1 FCAR 3.539432523 4.01E-07 NM_004126.3 GNG11 3.536308151 0.001638044 NM_018643.2 TREM1 3.474721665 1.46E-08 NM_020980.2 AQP9 3.451529843 4.63E-06 NM_002659.2 PLAUR 3.425874193 0.000283849 NM_000575.3 IL1A 3.388899489 4.10E-07 NM_004419.3 DUSP5 3.373585433 8.07E-07 NM_005558.3 LAD1 3.358251211 3.73E-07 NM_004878.3 PTGES 3.327193155 7.71E-09 NM_000963.1 PTGS2 3.292462855 0.00013068 NM_058173.2 MUCL1 3.274849222 0.001474318 NM_145898.1 CCL23 3.195657692 4.43E-06 NM_001005376.1 PLAUR 3.188401008 0.000321203 NM_000507.2 FBP1 3.180222004 0.000590699 NM_005525.2 HSD11B1 3.134834828 2.41E-05 NM_213636.1 PDLIM7 3.097957319 1.19E-05 NM_000785.3 CYP27B1 3.039652681 3.00E-10 NM_014479.2 ADAMDEC1 2.993082453 2.24E-07 NM_198594.1 C1QTNF1 2.988015237 2.89E-07 NM_006317.3 BASP1 2.97484297 5.45E-07 NM_000578.3 SLC11A1 2.961152607 0.000497557 NM_018593.3 SLC16A10 2.947015658 0.00026479 XM_927730.1 LOC644615 2.940773969 9.25E-09 NM_002002.3 FCER2 2.938989162 2.53E-06 NM_019618.2 IL1F9 2.882455311 0.000640351 NM_001040456.1 RHBDD2 2.861785699 1.20E-10 NM_001040456.1 RHBDD2 2.854368397 8.39E-10 NM_032744.1 C6ORF105 2.801552394 0.000407485 NM_052839.2 PANX2 2.732725707 8.60E-08  122 Refseqs Symbols Fold_Change P.Value NM_000389.2 CDKN1A 2.714582332 1.88E-06 NM_001005376.1 PLAUR 2.703506231 0.000140446 NM_003784.2 SERPINB7 2.699492788 7.41E-07 NM_005408.2 CCL13 2.658450631 0.000897877 NM_145792.1 MGST1 2.656848472 7.30E-08 NM_003873.4 NRP1 2.652839692 3.71E-05 NM_021250.2 LILRA5 2.63910391 0.000158885 NM_006850.2 IL24 2.635671669 3.02E-06 NM_012212.2 LTB4DH 2.596020299 4.21E-10 NM_001912.3 CTSL1 2.581619031 0.001666664 NM_021006.4 CCL3L1 2.578731228 0.000437938 NM_001387.2 DPYSL3 2.57108203 5.33E-05 NM_005806.2 OLIG2 2.538883104 4.89E-05 NM_001218.3 CA12 2.518436598 2.49E-05 NM_002965.2 S100A9 2.501840388 2.21E-05 NM_001945.1 HBEGF 2.490295023 2.75E-07 NM_004004.4 GJB2 2.469310465 0.000269816 NM_000591.2 CD14 2.469293224 6.11E-07 NM_032571.2 EMR3 2.429075476 1.98E-07 NM_000189.4 HK2 2.413946205 7.76E-05 NM_006665.3 HPSE 2.370247152 7.99E-05 NM_014331.3 SLC7A11 2.303111765 4.83E-07 NM_203391.1 GK 2.299553268 7.72E-07 NM_005319.3 HIST1H1C 2.280281773 0.001203262 NM_002983.1 CCL3 2.266239344 7.73E-05 NM_002870.2 RAB13 2.253288756 2.62E-08 NM_201623.2 CLEC12A 2.242150621 0.000338741 NM_024873.3 TNIP3 2.226433965 1.44E-08 NM_001011649.1 CDK5RAP2 2.223076516 6.87E-08 NM_020645.1 NRIP3 2.19906321 0.001049778 NM_002638.2 PI3 2.196503235 0.000471446 NM_003272.1 GPR137B 2.193555542 2.72E-06 NM_002993.2 CXCL6 2.178379664 0.000519852 NM_019058.2 DDIT4 2.1501109 0.000180392 NM_003364.2 UPP1 2.130965694 5.47E-08 NM_002964.3 S100A8 2.124742807 0.000804337 NM_000104.2 CYP1B1 2.124484002 2.82E-07 NM_021076.2 NEFH 2.119253294 6.01E-05 NM_001001414.1 LOC342897 2.109049385 0.000899609 NM_001020820.1 MYADM 2.099354032 2.60E-05 NM_139018.2 CD300LF 2.089999322 8.06E-05 NM_002214.2 ITGB8 2.088397195 1.74E-06 NM_013439.2 PILRA 2.087131575 3.14E-05 NM_001974.3 EMR1 2.069927883 3.92E-05 NM_004613.2 TGM2 2.065156609 1.48E-06 NM_002115.1 HK3 2.061206193 3.89E-05  123 Refseqs Symbols Fold_Change P.Value NM_015444.2 TMEM158 2.057594862 2.29E-06 NM_001024844.1 CD82 2.052236133 0.000326933 NM_005064.3 CCL23 2.050667298 3.21E-05 NM_173462.3 PAPLN 2.049553429 2.17E-06 NM_006404.3 PROCR 2.038980991 1.63E-06 NM_000399.2 EGR2 2.032008415 7.55E-05 NM_198446.1 C1ORF122 2.025130733 0.001715453 NM_002357.2 MXD1 2.021939475 7.20E-05 NM_004106.1 FCER1G 2.002623689 0.000317116 NM_178272.1 PILRA 1.99542107 1.99E-05 NM_006705.2 GADD45G 1.993674092 4.36E-06 NM_178273.1 PILRA 1.989801832 0.000188936 NM_000433.2 NCF2 1.971501682 3.94E-07 NM_177925.2 H2AFJ 1.960301145 0.000109182 NM_024330.1 SLC27A3 1.948709878 3.84E-05 NM_138337.4 CLEC12A 1.945807452 0.00030488 NM_001066.2 TNFRSF1B 1.938600106 9.41E-05 NM_001629.2 ALOX5AP 1.934114389 7.86E-07 NM_003982.2 SLC7A7 1.928736387 0.000869858 NM_002231.3 CD82 1.924466281 0.000247098 NM_003254.2 TIMP1 1.918800219 7.66E-05 NM_021155.2 CD209 1.91196775 0.001254539 NM_005922.2 MAP3K4 1.904800733 1.27E-06 NM_001013251.1 SLC3A2 1.90434215 2.86E-05 NM_004995.2 MMP14 1.896914877 2.60E-06 NM_003329.2 TXN 1.894230949 4.02E-05 NM_024569.3 MPZL1 1.88835059 4.69E-05 NM_000245.2 MET 1.881931385 3.72E-05 NM_145256.2 LRRC25 1.881095623 5.25E-05 NM_013410.2 AK3L1 1.881052931 2.46E-06 NM_030797.2 FAM49A 1.86551147 1.98E-05 NM_138636.2 TLR8 1.861672562 1.78E-05 NM_017983.4 WIPI1 1.858997512 1.07E-05 NM_002668.1 PLP2 1.849178729 4.78E-05 NM_016610.2 TLR8 1.833428184 2.22E-05 NM_001012633.1 IL32 1.830124539 0.000241656 NM_080625.2 C20ORF160 1.829806429 3.16E-10 NM_015704.1 FAM152B 1.82569252 9.56E-06 NM_006931.1 SLC2A3 1.824755746 0.000793691 NM_003978.2 PSTPIP1 1.819707534 2.59E-07 NM_012447.2 STAG3 1.815002506 1.51E-05 NM_000783.2 CYP26A1 1.813031618 0.000607451 NM_006825.2 CKAP4 1.812861589 3.24E-07 NM_001815.2 CEACAM3 1.812237045 0.000535703 NM_003485.3 GPR68 1.809908496 6.31E-05 NM_001736.3 C5AR1 1.807192224 0.001026966  124 Refseqs Symbols Fold_Change P.Value NM_001024466.1 SOD2 1.794350248 0.000273407 NM_001764.2 CD1B 1.794133455 0.001105642 NM_003512.3 HIST1H2AC 1.784512941 3.18E-06 NM_002474.2 MYH11 1.78349816 2.20E-07 NM_002621.1 CFP 1.781162197 1.78E-06 NM_001001437.3 CCL3L3 1.776480467 0.000844405 NM_153321.1 PMP22 1.771463854 0.001712557 NM_000777.2 CYP3A5 1.769004264 1.75E-07 NM_015130.2 TBC1D9 1.766253747 0.000273058 NM_174893.1 C17ORF49 1.765832504 5.63E-05 NM_020300.3 MGST1 1.761133944 4.58E-05 NM_172245.1 CSF2RA 1.759169934 0.000584146 NM_025195.2 TRIB1 1.756969862 6.71E-05 NM_001860.2 SLC31A2 1.755253223 0.000135251 NM_201539.1 NDRG2 1.748753841 9.22E-07 NM_001024629.1 NRP1 1.738627773 8.45E-11 NM_152649.1 MLKL 1.733974625 0.001185994 NM_000169.2 GLA 1.71837737 1.13E-07 NM_006714.2 SMPDL3A 1.696955615 6.96E-06 NM_002421.2 MMP1 1.696313069 0.000388215 NM_018291.2 FLJ10986 1.693558241 2.84E-07 NM_003047.2 SLC9A1 1.68846561 1.03E-05 NM_198951.1 TGM2 1.686572658 3.14E-06 NM_003292.2 TPR 1.679969975 0.000492355 NM_032310.3 C9ORF89 1.676810703 0.000107392 NM_001013251.1 SLC3A2 1.668999535 1.28E-05 NM_004949.2 DSC2 1.667039888 0.000204587 NM_016545.4 IER5 1.662625781 1.10E-07 NM_079421.2 CDKN2D 1.657468005 6.55E-06 NM_003122.2 SPINK1 1.655091484 6.96E-06 NM_001014987.1 LAT 1.652472461 0.00019266 NM_147686.1 TRAF3IP2 1.639504005 0.000887927 NM_015577.1 RAI14 1.637815312 0.000185583 NM_000585.2 IL15 1.636819777 3.40E-06 NM_002197.1 ACO1 1.636110054 0.000235165 NM_000574.2 CD55 1.635686147 8.70E-06 NM_002068.1 GNA15 1.635142589 0.000858487 NM_024298.2 MBOAT7 1.634804891 4.37E-05 NM_002046.3 GAPDH 1.63475804 0.00041094 NM_170682.2 P2RX2 1.626077723 0.000590203 NM_000527.2 LDLR 1.62057473 0.000538524 NM_016354.3 SLCO4A1 1.608142462 1.16E-05 NM_002046.3 GAPDH 1.605959723 5.63E-05 NM_003020.1 SCG5 1.605441369 2.19E-05 NM_017905.3 TMCO3 1.602624805 3.19E-06 NM_018482.2 DDEF1 1.600599457 7.59E-06  125 Refseqs Symbols Fold_Change P.Value NM_001024460.1 VNN3 1.597414862 1.84E-05 NM_005098.3 MSC 1.596141908 0.000831817 NM_001014987.1 LAT 1.584133066 7.10E-05 NM_173701.1 WARS 1.583988381 0.000128888 NM_012223.2 MYO1B 1.583200635 0.000144688 NM_018690.2 APOB48R 1.577663929 4.17E-05 NM_003528.2 HIST2H2BE 1.577047341 1.51E-05 NM_000402.3 G6PD 1.575766162 0.000465486 NM_005191.3 CD80 1.575672983 0.000806638 NM_001040138.1 CKLF 1.571080182 0.00150502 NM_025009.3 CEP135 1.569591598 3.90E-07 NM_001012636.1 IL32 1.569491853 0.000263477 NM_016327.2 UPB1 1.566334541 1.41E-05 NM_003745.1 SOCS1 1.566006258 0.000103198 NM_005115.3 MVP 1.564218579 2.39E-05 NM_006270.3 RRAS 1.562371536 0.000276263 NM_005618.3 DLL1 1.559082087 0.000148195 NM_005738.3 ARL4A 1.558350785 0.000623024 NM_138373.3 MYADM 1.558158397 0.001319198 NM_002250.2 KCNN4 1.556920243 0.000591968 NM_018052.3 VAC14 1.551012822 5.95E-06 NM_000266.1 NDP 1.545414409 8.97E-07 NM_004843.2 IL27RA 1.543547254 0.000122833 NM_006702.3 PNPLA6 1.540656271 0.000853624 NM_144687.1 NLRP12 1.53515228 4.72E-05 NM_002110.2 HCK 1.530853489 0.001359489 NM_000270.1 NP 1.530832978 6.60E-05 NM_153449.2 SLC2A14 1.523944202 2.88E-05 NM_014045.3 LRP10 1.522914093 8.05E-06 NM_001628.2 AKR1B1 1.520831473 2.08E-05 NM_002662.2 PLD1 1.518390276 0.000482567 NM_138720.1 HIST1H2BD 1.509045976 0.001133591 NM_133271.1 FCAR 1.508940381 0.000547366 NM_001005267.1 C15ORF21 1.507713428 1.78E-07 NM_001543.3 NDST1 1.50706115 4.19E-06 NM_018686.3 CMAS 1.506463855 6.75E-05 NM_005485.3 PARP3 1.506056123 1.73E-06 NM_020155.2 GPR137 1.505704005 0.000628813 NM_002631.2 PGD 1.501740386 0.001036602   

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