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Endocrine-immune networks : modulatory role of early-life influences Bodnar, Tamara 2016

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 i ENDOCRINE-IMMUNE NETWORKS: MODULATORY ROLE OF EARLY-LIFE INFLUENCES  by  Tamara Bodnar  BSc, BA, The University of British Columbia, 2009  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Neuroscience)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  November 2016  © Tamara Bodnar, 2016   ii Abstract The central nervous system, endocrine system and immune system are developmentally and functionally intertwined, with shared receptors and regulatory feedback. Importantly, these systems are highly susceptible to environmental modulation, particularly the early life environment, with impacts on physiological function and communication among systems. Prenatal alcohol exposure (PAE) has been shown to alter neuroendocrine and immune systems, with marked downstream effects. With the overarching aim of elucidating networks of endocrine and immune parameters responsible for long-lasting immune system deficits that occur following PAE, networks were first evaluated under control conditions. Using Sprague Dawley rats from Charles River and Harlan vendor colonies with known endocrine/immune differences, we found a higher incidence and severity of adjuvant-induced arthritis (AA) in Harlan than Charles River rats, which was tied to differential activation of endocrine/immune networks. The multisystem approach utilized in this colony model was applied as a basic framework for investigating the impact of PAE in the AA model. In parallel to heightened sensitivity of Harlan rats, adult PAE rats showed an increased incidence and severity of AA, which were related to alterations in key endocrine/immune networks. Furthermore, examination of a second ‘hit’ of chronic mild stress exposure during adolescence showed heightened levels of inflammation-related damage with the two hits of PAE and stress, further suggestive of the immunomomodulatory impact of early life conditions. Finally, to determine whether the altered course of AA detected following PAE had its origins in the early postnatal period, a developmental time course of immune parameters was investigated. A specific immune signature of PAE was identified on postnatal day 8, characterized by alterations in the cytokine balance within the periphery and brain, with likely consequences for nervous, endocrine and immune system development. Due to the importance of the early postnatal period in shaping development of physiological systems, we propose that endocrine and immune alterations induced by PAE set the stage for increased sensitivity to AA and likely other auto-immune disorders in adulthood. Moving forward,   iii this points to the potential utility of administering immune-based interventions in early life, with the aim of mitigating deficits associated with Fetal Alcohol Spectrum Disorders (FASD).     iv Preface Portions of the Chapter 1 (introduction) have been modified from a previously published book chapter: Bodnar T., Weinberg, J. Prenatal Alcohol Exposure: Impact on Neuroendocrine-Neuroimmune Networks. In: Cui C. G, L., Noronha, A., ed. Neural-Immune Interactions in Brain Function and Alcohol Related Disorders. New York, NY: Springer; 2013:312-62.  A version of the material presented in Chapter 2 has been published as:  Bodnar, T.S., Hill, L.A., Taves, M.D., Yu, W., Soma, K.K., Hammond, G.L., Weinberg, J., 2015. Colony-Specific Differences in Endocrine and Immune Responses to an Inflammatory Challenge in Female Sprague Dawley Rats. Endocrinology 156, 4604-4617. J. Weinberg and I designed the experiments, and executed all animal experiments, with the assistance of W. Yu. L. Hill performed the CBG measurements, Scatchard analysis, and Serpina6 sequencing. I conducted all additional assays/analyses, analyzed the data statistically, and wrote the manuscript. L. Hill and G. Hammond wrote the methods and results for the CBG measurements, Scatchard analysis, and Serpina6 sequencing, and contributed to this section of the discussion. L. Hill, G. Hammond, and J. Weinberg provided critical feedback and suggested edits prior to submission of the final manuscript. A version of the material presented in Chapter 3 has been submitted for publication as: Bodnar, T.S., Taves, M.D., Lavigne, K.M., Woodward, T.S., Soma, K.K., Weinberg, J., 2016. Differential activation of endocrine-immune networks by arthritis challenge: Insights from colony-specific responses. M. Taves assisted with tissue collected and measured local corticosterone levels. K. Lavigne and T. Woodward provided background on the CPCA technique and assistance with the analysis. I wrote the manuscript and all authors provided critical feedback and suggested edits prior to submission of the final manuscript. Chapter 4 is original and unpublished. I performed all animal experiments with the assistance of W. Yu and a number of undergraduate students (Andrew Choe, Divya Patel, Nikki Kitay, Katie Anderson). Wax It Histology Services Inc. sliced the hind paw samples, performed the hematoxylin and   v eosin (H&E) staining and L. Ellis designed the immunohistochemistry for the macrophages (CD 163). E. Morgan co-scored the H&E and macrophage slides using ImageJ. David Mak assisted with hind paw tissue homogenizations and contributed to the cytokine analyses, and supplementary material compilation. L. Hill performed the CBG measurements wrote the CBG methods. I was responsible for all additional measurements, statistical analyses and manuscript preparation. J. Weinberg provided critical feedback on the content. A version of the material presented in Chapter 5 has been published as: Bodnar, T.S., Hill, L.A., Weinberg, J., 2016. Evidence for an immune signature of prenatal alcohol exposure in female rats. Brain Behav Immun. In press. I was responsible for the design of the experiment and tissue and data collection. L. Hill performed the CBG measurements, wrote the CBG methods, and provided feedback on the CBG results and discussion section. I was responsible for all additional measurements, statistical analyses and manuscript preparation. J. Weinberg provided critical feedback and suggested edits prior to submission of the final manuscript. Chapter 6 (discussion) is original and unpublished. The animal studies presented in this thesis were performed according to the National Institutes of Health Guide for the Care and Use of Laboratory Animals, and approved by the University of British Columbia Animal Care Committee (certificates: A06-0017, A07-0381, A10-0136, A10-0016, A12-0032).   vi Table of Contents  Abstract .......................................................................................................................................... ii!Preface ........................................................................................................................................... iv!Table of Contents ......................................................................................................................... vi!List of Tables .............................................................................................................................. xiii!List of Figures ............................................................................................................................. xiv!List of Abbreviations ................................................................................................................. xvi!Acknowledgements .................................................................................................................... xix!Dedication ................................................................................................................................... xxi!Chapter 1: Introduction ................................................................................................................1!1.1! General Overview and Hypotheses .................................................................................... 1!1.2! Deficits in Immune Competence in Children with Fetal Alcohol Spectrum Disorders (FASD) ........................................................................................................................................ 2!1.3! Animal Models of Prenatal Alcohol Exposure .................................................................. 4!1.4! Sexual Dimorphism in the Immune Response ................................................................... 6!1.5! Prenatal Alcohol Exposure, Cytokines, and the HPA Axis ............................................... 8!1.6! Mechanisms Underlying Alcohol Effects on the Immune Function ............................... 10!1.6.1! Altered Endocrine/Immune Networks ...................................................................... 10!1.6.2! Early-Life Exposure to Maternally-Derived Cytokines ............................................ 11!1.7! Chronic Immune Challenge: The Adjuvant-Induced Arthritis (AA) Model ................... 12!1.8! Strain-Based Differences in HPA Parameters and Response to Immune Challenge ....... 13!1.9! Vendor Colony Differences in Physiological Responses ................................................ 14!  vii 1.10! Thesis Overview ............................................................................................................ 15!Chapter 2: Colony Specific Differences in Endocrine and Immune Responses to an Inflammatory Challenge in Female Sprague Dawley Rats. .....................................................16!2.1! Introduction ...................................................................................................................... 16!2.2! Materials and Methods ..................................................................................................... 18!2.2.1! Animals ..................................................................................................................... 18!2.2.2! Induction and Clinical Assessment of AA ................................................................ 18!2.2.3! Termination and Tissue Collection ........................................................................... 20!2.2.4! Plasma ACTH, Corticosterone, and CBG Measurements ........................................ 20!2.2.5! Serpina6 Sequencing ................................................................................................ 21!2.2.6! Hind Paw Homogenization ....................................................................................... 22!2.2.7! Multiplex Cytokine Immunoassays and Protein Quantification ............................... 22!2.2.8! Statistical Analyses ................................................................................................... 22!2.3! Results .............................................................................................................................. 23!2.3.1! Arthritis Severity: Date of Onset and Clinical Scores .............................................. 23!2.3.2! Arthritis Severity: Dose Comparison ........................................................................ 23!2.3.3! Body Weight and Paw Volume ................................................................................. 26!2.3.4! Plasma Corticosterone, ACTH, and CBG ................................................................. 27!2.3.5! Serpina6 Coding Sequences for CBG ....................................................................... 30!2.3.6! Plasma CBG-Corticosterone Binding Affinities ....................................................... 30!2.3.7! Hind Paw Cytokine Levels ....................................................................................... 31!2.3.8! Plasma Cytokine Levels ............................................................................................ 33!2.4! Discussion ........................................................................................................................ 36!  viii Chapter 3: Differential Activation of Endocrine-Immune Networks by Arthritis Challenge: Insights from Colony-Specific Responses ..................................................................................44!3.1! Introduction ...................................................................................................................... 44!3.2! Materials and Methods ..................................................................................................... 46!3.2.1! Animals ..................................................................................................................... 46!3.2.2! Adjuvant-Induced Arthritis (AA) Induction and Clinical Assessment ..................... 47!3.2.3! Termination and Tissue Collection ........................................................................... 48!3.3! Tissue Homogenization ................................................................................................... 48!3.3.1! Multiplex Cytokine Immunoassays and Protein Quantification ............................... 49!3.3.2! Steroid Extraction ..................................................................................................... 49!3.3.3! Endocrine Measures .................................................................................................. 50!3.3.4! Statistical Analyses ................................................................................................... 50!3.4! Results .............................................................................................................................. 52!3.4.1! Analysis of Estrous Cycle ......................................................................................... 52!3.4.2! Local Corticosterone Levels Increased in the Joints and Immune Tissues with Severe Arthritis ..................................................................................................................... 52!3.4.3! Hypothalamic Cytokine Levels Increased to a Greater Extent in Charles River Compared to Harlan Rats with AA ....................................................................................... 54!3.4.4! Hippocampal Cytokine Levels Were Mildly Affected by AA, with Colony Differences in Overall Levels of IFN-ɣ and KC/GRO ......................................................... 56!3.4.5! Splenic Levels of Proinflammatory Cytokines Increased with AA in Both Charles River and Harlan Rats ........................................................................................................... 58!  ix 3.4.6! The Global Cytokine Profile Following CFA Injection Differed Between Charles River and Harlan Rats ........................................................................................................... 60!3.4.7! CPCA Analysis Indicates That Charles River and Harlan Rats Rely on Different Endocrine/Immune Networks Throughout the Course of AA .............................................. 62!3.5! Discussion ........................................................................................................................ 66!Chapter 4: Modulatory Role of Prenatal Alcohol Exposure and Adolescent Stress on the Response to Chronic Inflammatory Challenge in Female Rats ...............................................73!4.1! Introduction ...................................................................................................................... 73!4.2! Methods............................................................................................................................ 75!4.2.1! Breeding .................................................................................................................... 75!4.2.2! Prenatal Diets and Feeding ....................................................................................... 76!4.2.3! Adolescent Exposure to Chronic Mild Stress (CMS) ............................................... 76!4.2.4! Induction and Clinical Evaluation of AA ................................................................. 77!4.2.5! Termination and Tissue Collection ........................................................................... 78!4.2.6! Plasma ACTH, Corticosterone, Estradiol, and Corticosteroid Binding Globulin (CBG) Measurements ........................................................................................................... 79!4.2.7! Tissue Homogenization for Protein and Cytokine Measurements ........................... 79!4.2.8! Multiplex Cytokine Measurements ........................................................................... 80!4.2.9! Protein Quantification ............................................................................................... 80!4.2.10! Tibiotarsal Joint Sectioning and Staining (H&E, Toluidine Blue) ......................... 80!4.2.11! CD163 Immunohistochemistry (IHC) for Synovial Macrophage Density and CD163+ Chondrocyte Analysis ............................................................................................. 81!4.2.12! Histopathological Evaluation of Tibiotarsal Joints by H&E .................................. 82!  x 4.2.13! Cartilage Thickness Measurements ........................................................................ 83!4.2.14! Statistical Analyses ................................................................................................. 83!4.3! Results .............................................................................................................................. 84!4.3.1! Body Weight and Corticosterone Levels During CMS (P31 – 41) .......................... 84!4.3.2! AA Incidence and Severity ....................................................................................... 85!4.3.3! ACTH and Corticosterone Levels ............................................................................. 88!4.3.4! CBG Levels ............................................................................................................... 88!4.3.5! Estradiol Levels ........................................................................................................ 89!4.3.6! Plasma Cytokine Levels ............................................................................................ 91!4.3.7! Cytokine Levels in the Hind Paw ............................................................................. 94!4.3.8! Histopathological Evaluation of Tibiotarsal Joints by H&E Staining ...................... 94!4.3.9! Cartilage Thickness ................................................................................................... 96!4.3.10! Macrophage Density in the Synovium and CD163+ Chrondrocyte Staining in the Cartilage ................................................................................................................................ 99!4.3.11! Effects of Pair-Feeding ......................................................................................... 102!4.3.11.1! AA Incidence and Severity During the Resolution Phase ............................. 102!4.3.11.2! Corticosterone Levels on Day 16 ................................................................... 103!4.4! Discussion ...................................................................................................................... 105!Chapter 5: Evidence for an Immune Signature of Prenatal Alcohol Exposure in Female Rats. .............................................................................................................................................114!5.1! Introduction .................................................................................................................... 114!5.2! Materials and Methods ................................................................................................... 116!5.2.1! Breeding .................................................................................................................. 116!  xi 5.2.2! Prenatal Diets and Feeding ..................................................................................... 116!5.2.3! Tissue Collection .................................................................................................... 117!5.2.4! Corticosterone Radioimmunoassay ........................................................................ 118!5.2.5! CBG Measurement .................................................................................................. 118!5.2.6! Tissue Homogenization .......................................................................................... 119!5.2.7! Multiplex Cytokine and CRP Measurements ......................................................... 119!5.2.8! Protein Quantification ............................................................................................. 120!5.2.9! Statistical Analyses ................................................................................................. 120!5.3! Results ............................................................................................................................ 122!5.3.1! Pregnancy Outcome ................................................................................................ 122!5.3.2! Offspring Body, Brain, and Spleen Weight ............................................................ 123!5.3.3! Serum Measures of Corticosterone, CBG, and CRP .............................................. 124!5.3.4! Cytokine Levels in Serum, Whole Brain, and Spleen at Birth (P1) ....................... 126!5.3.5! Cytokine Levels in Serum, Brain, and Spleen on P8 .............................................. 128!5.3.6! Cytokine Levels in Serum, Brain, and Spleen at Weaning (P22) ........................... 131!5.4! Discussion ...................................................................................................................... 134!Chapter 6: Conclusion ...............................................................................................................140!6.1! Summary and Cross-cutting Features ............................................................................ 140!6.2! Importance of the Developmental Origins of Health and Disease Framework ............. 143!6.3! Limitations ..................................................................................................................... 145!6.4! Future Directions ........................................................................................................... 146!References ...................................................................................................................................148!  xii Appendix A Summary of Endocrine and Joint Measures in Rats that Fail to Recover from AA................................................................................................................................................. 166!Appendix B Summary of Cytokine Measures in Rats that Fail to Recover from AA ............ 167!Appendix C Cytokine Levels in the Hind Paw at the Peak of AA ......................................... 168!Appendix D Histopathological Analysis of the Tibiotarsal Joint: Scoring Criteria ............... 170!Appendix E Ethanol Diet Consumption on GD 7, 14, and 21 ................................................ 172!Appendix F Summary of P-Values for Age Effects by Tissue Compartment ........................ 173!   xiii List of Tables  Table 2.1 Oligonucleotide primers used to sequence. ................................................................................ 21!Table 3.1 Component loadings for the endocrine/immune variance constrained to that predictable from colony and AA severity. .............................................................................................................................. 65!Table 4.1 Body weight and corticosterone levels during the CMS period (P31 – 41). .............................. 85!Table 5.1 Pregnancy outcomes and maternal body weights during gestation and lactation. .................... 123!   xiv List of Figures  Figure 2.1 Experimental timeline. ............................................................................................................... 19!Figure 2.2 AA severity. ............................................................................................................................... 25!Figure 2.3 Body weight and paw volume following CFA injection. .......................................................... 27!Figure 2.4 Basal ACTH, corticosterone (total and free) and corticosterone binding globulin (CBG), and characterization of CBG protein by scatchard plot. .................................................................................... 29!Figure 2.5 Cytokine levels in the hind paw at the peak of inflammation. .................................................. 32!Figure 2.6 Plasma cytokine levels at the peak of inflammation. ................................................................ 35!Figure 3.1 Local corticosterone levels in the paws and immune tissues. ................................................... 53!Figure 3.2 Cytokine levels in the hypothalamus. ........................................................................................ 55!Figure 3.3 Cytokine levels in the hippocampus. ......................................................................................... 57!Figure 3.4 Cytokine levels in the spleen. .................................................................................................... 59!Figure 3.5 Heatmap of the overall cytokine response to CFA-injection in the hypothalamus, hippocampus, and spleen. ................................................................................................................................................... 61!Figure 3.6 Endocrine/immune networks activated in response to CFA injection. ..................................... 66!Figure 4.1 Clinical course of AA. ............................................................................................................... 87!Figure 4.2 Corticosterone, CBG, and estradiol levels at the peak and following recovery from AA. ....... 90!Figure 4.3 Plasma cytokine levels at the peak of AA. ................................................................................ 92!Figure 4.4 Plasma cytokine levels following recovery from AA. .............................................................. 93!Figure 4.5 Histopathology by H&E staining of the tibiotarsal joint. .......................................................... 95!Figure 4.6 Cartilage integrity and thickness at the tibiotarsal joint. ........................................................... 98!Figure 4.7 CD163+ macrophage density in the synovium and CD163+ chondrocyte staining in the cartilage. .................................................................................................................................................... 101!Figure 4.8 Effects of pair-feeding in the AA model. ................................................................................ 104!  xv Figure 5.1 Body, brain, and spleen weight on P1, 8, and 22. ................................................................... 124!Figure 5.2 Corticosterone, corticosterone binding globulin, and C-reactive protein levels. .................... 125!Figure 5.3 Cytokine levels in blood, whole brain, and spleen at birth (P1). ............................................. 127!Figure 5.4 Cytokine levels in blood, hypothalamus, prefrontal cortex, hippocampus, and spleen on P8. 131!Figure 5.5 Cytokine levels in blood, hypothalamus, prefrontal cortex, hippocampus, and spleen on P22. ................................................................................................................................................................... 134!   xvi List of Abbreviations AA    Adjuvant-Induced Arthritis ACTH    Adrenocorticotropic Hormone Adj/AA   CFA injected, clinical signs of arthritis (clinical score ≥ 1) Adj/M-M   Mild-Moderate Arthritis (clinical score ≥ 8)  Adj/NA   CFA injected, no clinical signs of arthritis Adj/Rec   CFA injected, recovered from arthritis (clinical score = 0) Adj/S    Severe Arthritis (clinical score ≥1 but < 8)  ASD    Autism Spectrum Disorders  ANOVA   Analysis of Variance AUC    Area Under the Curve AVP    Arginine Vasopressin BAC    Blood Alcohol Content  C    Control CBG    Corticosteroid Binding Globulin CD    Cluster of Differentiation CFA    Complete Freund’s Adjuvant ConA    Concanavalin A CPCA    Constrained Principal Component Analysis CRH    Corticotropin-Releasing Hormone CRP    C-Reactive Protein CS    Clinical Score CMS    Chronic Mild Stress CNS    Central Nervous System CTCF    CCCTC-Binding Factor   xvii DOHaD   Developmental Origins of Health and Disease  DCC    Dextran-Coated Charcoal  DHC    Dehydrocorticosterone DMARD   Disease-Modifying Antirheumatic Drugs EDTA    Ethylenediaminetetraacetic Acid F344    Fisher Rats FAS    Fetal Alcohol Syndrome FASD    Fetal Alcohol Spectrum Disorders GD    Gestation Day H&E    Hematoxylin & Eosin Staining HPA     Hypothalamic-Pituitary-Adrenal HPLC    High Performance Liquid Chromatography HPX    Hippocampus HSP    Heat-Shock Protein HYPO    Hypothalamus IFN-ɣ    Interferon gamma IgM    Immunoglobulin M IHC    Immunohistochemistry IL    Interleukin KC/GRO (CXCL1)  Keratinocyte Chemoattractant/Growth-Regulated Oncogene LD    Lactation Day LEW    Lewis Rats LPS    Lipopolysaccharide MCP-1 (CCL2)   Monocyte Chemotactic Protein-1 MMP    Matrix Metalloproteinase    xviii Non-CMS   Non-Stress P    Postnatal Day PAE    Prenatal Alcohol Exposure PF    Pair-Fed PFC    Prefrontal Cortex PCA    Principal Component Analysis RA    Rheumatoid Arthritis RIA    Radioimmunoassay SD    Sprague Dawley  SNP    Single Nucleotide Polymorphism   SPE    Solid Phase Extraction Th    T Helper Cells TLR    Toll-Like Receptor TNF-α    Tumor Necrosis Factor alpha    xix Acknowledgements First and foremost, I would like to express my sincerest gratitude to my supervisor, Dr. Joanne Weinberg, for providing unlimited support and guidance and endless opportunities throughout my years as a graduate student. Thank you for passing on your love of research and teaching, for encouraging me in all pursuits, and for giving me every opportunity to develop into an independent scientist. It has been my privilege to learn from your example. I would also like to thank my current committee members, Dr. Kiran Soma, Dr. Geoff Hammond, and Dr. Christopher Overall for their valuable feedback, insights on experimental design, and for pushing me to explore new areas of research. Thank you also to past committee members Drs. Waterfield, Miller, and Kobor for their support and guidance during the initial stages of these projects.   I would like to express my sincerest gratitude to members of the Weinberg lab family (past and present), without whom none of this work would have been possible. Lab managers Wayne Yu and Linda Ellis, thank you for sharing your technical expertise, hard work and commitment, and friendship over the years. Thank you to Dr. Charlis Raineki for reading many a rough draft and for providing invaluable feedback and insight. Parker Holman, thank you for always entertaining scientific discussions and for expanding my interests beyond the lab bench and into the classroom. To the team of enthusiastic undergraduates who contributed to this work, I cannot thank you enough for all of your dedication, support, and hard work over the years. Special thanks to Andrew Choe, Diyva Patel, and Nikki Kitay for their extensive contributions to the animal work. Also, thank you to David Mak and Erin Morgan for their years of hard work and significant contributions to many aspects of these projects.  This work would not have been possible without collaborations with fellow graduate students – Dr. Matthew Taves (Soma lab), Lesley Hill (Hammond lab), and Katie Lavigne (Woodward lab), thank you for all for your insights, hard work, and friendship.    xx  I would like to acknowledge the funding support that I have received over the years including the UBC Aboriginal Fellowship, and the Natural Sciences and Engineering Research Council of Canada (NSERC) Alexander Graham Bell Canada Graduate Scholarships (CGS-M, CGS-D). I would also like to acknowledge the funding agencies that provided the grant support that made this work possible: NIH/NIAAA RO1 AA022460 and R37 AA007789, NeuroDevNet (Canadian Networks of Centres of Excellence), and the Canadian Foundation on Fetal Alcohol Research to JW.    Thank you to other members of the scientific community for contributions and support of this work. Thank you to Dr. Eric Sandberg and others at Meso Scale Discovery (MSD) for their exceptional technical support that made the cytokine assays possible. Dr. Antoine Dufour (Overall lab) and Dr. Tony Ng (VGH pathology) provided insight and expertise in joint analyses, with a special thanks to Dr. Waterfield for encouraging me to pursue this aspect of the project. Thank you also to Dr. Woodward for providing valuable CPCA training and instruction.   Finally, thank you to family, friends, and fellow scientists for the sharing of ideas, equipment, and enthusiasm that not only made this work possible but also a wonderful experience.        xxi Dedication  This thesis is dedicated to my parents for their continued encouragement and for providing me with every opportunity to follow my dreams.  1 Chapter 1: Introduction 1.1 General Overview and Hypotheses  The nervous system, endocrine system, and immune system exist within a complex regulatory network of connections through nerve pathways, hormonal cascades, and cellular interactions (Besedovsky and del Rey, 1996; Besedovsky and Rey, 2007; Blalock and Smith, 2007), with shared ligands, receptors, and regulatory feedback (Besedovsky and del Rey, 1996; Besedovsky and Rey, 2007; Blalock and Smith, 2007). As a result, subtle changes in one system can have concurrent effects in the other systems. Within the context of disease, cross-discipline evaluation of networks and mechanisms underlying symptoms has produced compelling evidence for multidisciplinary approaches.  Here, with the overarching aim of elucidating networks of endocrine and immune parameters that may be responsible for long-lasting immune system deficits that have been shown to occur following prenatal alcohol exposure (PAE) in both animal models (Drew et al., 2015; Topper et al., 2015b), and the clinical setting (Gauthier et al., 2004; Johnson et al., 1981), networks underlying immune alterations/inflammation were first evaluated under control conditions. Specifically, we exploited previously reported vendor colony-based differences in behavioural (Fitzpatrick et al., 2013), pain (Yoon et al., 1999), endocrine, and immune responses (Pecoraro et al., 2006; Turnbull and Rivier, 1999) to gain insight into physiological changes and inflammatory modulators in arthritis. Using Sprague Dawley (SD) rats from two different vendor colonies (Harlan vs. Charles River), we examined key endocrine and immune parameters following immune challenge with adjuvant-induced arthritis (AA), a model of human rheumatoid arthritis (RA). Based on previously characterized colony differences, we hypothesized that Charles River and Harlan rats would differ in the incidence and severity of inflammation in the AA model and that differential networks of endocrine and immune parameters activated in response to AA could be identified and provide insight into possible mechanisms underlying these colony differences. The colony model allowed for differences in the response to AA to be examined using a multisystem and network approach and this was applied as a basic framework for guiding the   2 investigation of the impact of PAE in the AA model. A previous study from our lab (Zhang et al., 2012) demonstrated that PAE females show a more severe and prolonged course of AA. Building on this work, and with the approach developed in the colony model, we investigated endocrine and immune parameters that might be predictive of both the heightened sensitivity to AA and the impaired recovery in PAE compared to control (Charles River) rats. We hypothesized that we would replicate the altered disease course seen in our previous study and that networks of endocrine and immune variables mediating the differential course of AA detected in PAE rats could be identified. Following these studies, to determine whether the altered inflammatory response detected following PAE has its origins in the prenatal or early postnatal period, we investigated a developmental time course of immune function in PAE and control offspring. Our aim was to uncover an immune signature of alcohol exposure that may be predictive of long-lasting alterations in immune competence. This was evaluated through complementary measurements of endocrine and immune function. Here, we hypothesized that PAE would alter the developmental immune profile, resulting in a pro-inflammatory bias in early life, with altered levels of endocrine parameters and key modulators of immune function. Taken together, the identification of an early-life immune signature of prenatal alcohol exposure, thought to underlie the alterations in the response to inflammatory challenge later in life, may be a further step in the identification of potential intervention targets.  1.2 Deficits in Immune Competence in Children with Fetal Alcohol Spectrum Disorders (FASD) Alcohol exposure in utero can have numerous adverse effects on a developing fetus. The term fetal alcohol spectrum disorder (FASD) refers to the broad spectrum of structural, neurocognitive and behavioral abnormalities or deficits that can occur following prenatal alcohol (ethanol) exposure (PAE) (Astley and Clarren, 2000). At the most severe end of the spectrum is Fetal Alcohol Syndrome (FAS),   3 which involves the complete phenotype of characteristic facial anomalies, growth retardation and central nervous system (CNS) abnormalities (Stratton, 1996). Following the description of FAS by Jones and Smith in 1973, there has been extensive interest in investigating the effects of PAE on the function of the immune system. However, clinical data on deficits in immune competence in children with FASD remain somewhat limited. An early review of thirteen documented cases of FAS in infants and children by Johnson and colleagues (Johnson et al., 1981) showed a higher incidence of minor infections, such as recurrent otitis media and respiratory infections, as well as major life-threatening bacterial infections, in alcohol-exposed compared to non-exposed children.  Subsequent studies confirmed an increased vulnerability to recurrent serious otitis media and recurrent upper respiratory infections, both of which have significant implications for hearing loss and consequently, speech and language problems, as well as learning disabilities in children with FAS (Church and Kaltenbach, 1997). In utero alcohol exposure has also been shown to have an adverse impact on numerous parameters of immune function, including decreased eosinophil and neutrophil cell counts and leukocyte responses to mitogens, resulting in an increased incidence of hypogammaglobulinemia (Johnson et al., 1981).  A more recent study by Gauthier and colleagues (Gauthier et al., 2004) showing that very low birth weight newborns exposed to alcohol in utero have a 15-fold higher incidence of early-onset sepsis as compared to the matched control group, lends additional support to the early findings of alterations in immune competence following in utero alcohol exposure. Gauthier and colleagues (2005) have also examined the risk of infection in newborns of alcohol consuming and control mothers in one of the largest clinical studies of this type (n=872) (Gauthier et al., 2005).  Due to the large sample size, timing and degree of alcohol consumption before and during pregnancy could be assessed and linked to neonatal infection risk. The level of alcohol intake was found to be an important factor in predicting neonatal infection risk, with increasing levels of alcohol consumption by the mother 3 months prior to conception   4 or during the first, second or third trimesters of pregnancy resulting in a significantly increased risk. In addition, binge drinking was found to increase the risk of neonatal infection by approximately 4-fold. When controlling for many possible confounds including maternal smoking, low maternal income and small gestational age, high levels of maternal drinking (binge drinking), specifically during the second trimester, was found to increase the risk of infection by approximately 4-fold, compared to unexposed newborns. Interestingly, the second trimester appears to be particularly sensitive to the immunoteratogenic effects of alcohol, perhaps as during this period many immunological milestones are achieved in the fetus. As such, alcohol exposure during the second trimester may permanently impact the fetal immune system, resulting in an increased risk of infection at birth, and potentially extending to an increased infection risk in later life.  While studies to date on children with FASD have provided clear evidence of alterations in immune function, detailed information regarding the effects of alcohol on the developmental time course of the naïve immune system, including effects on T and B cell development, as well as susceptibility to infections and autoimmune disorders can be difficult to obtain in human studies. Human studies are also limited by the lack of information regarding the level, timing and duration of alcohol consumption, as well as confounding factors such as concomitant exposure to maternal smoking, drugs of abuse, and stress, all of which have been shown to impact development of the immune system (Abel and Hannigan, 1995; Dietert and Piepenbrink, 2008). Animal models, which allow for the precise control of genetic and environmental factors, are as important for research on the immune system as they are for research on other aspects of alcohol’s adverse effects.   1.3 Animal Models of Prenatal Alcohol Exposure Research using rodent models has confirmed the clinical findings of alterations in immune function following in utero alcohol exposure [reviewed in (Weinberg, 1994; Zhang et al., 2005)], and has increased our understanding of both the spectrum of effects in different organ systems and the   5 mechanisms mediating the immunoteratogenic effects of alcohol. Interestingly, deficits in innate immunity have typically not been observed in animal studies. For example, Grossmann et al. (1993) (Grossmann et al., 1993), utilizing a primate model, reported no significant effects of in utero alcohol exposure on total numbers of white blood cells, leukocyte subsets or monocyte phagocytic activity compared to that in control subjects. In contrast, marked deficits in adaptive immunity have been reported consistently in PAE offspring. Prenatal alcohol exposure alters development of the thymus in both rats and mice. In a mouse model, maternal consumption of an ethanol-containing diet delayed development of the thymus (Ewald and Walden, 1988), decreased thymus cell numbers, and diminished mitogen-induced cell proliferation in late term fetuses (Ewald and Frost, 1987). Decreased thymus weight, size, and cell counts have also been observed at birth in a rat model (Redei et al., 1989). These changes have been shown to persist throughout the preweaning period, and into adolescence (Ewald, 1989; Ewald and Frost, 1987; Giberson and Blakley, 1994; Taylor et al., 1999a), although data on mice suggested that recovery or catch up in total thymocyte numbers may occur (Ewald, 1989).  Decreased mitogen-induced proliferation of thymic cells was reported to persist until weaning in PAE male rats (Redei et al., 1989) but paradoxically, proliferation was shown to be greater than control levels in adolescence (Chiappelli et al., 1992; Wong et al., 1992). The mechanisms underlying this increased thymocyte proliferation remains to be elucidated. The adverse effects of prenatal alcohol exposure last well into adulthood, and appear to involve primarily alterations in cell-mediated rather than humoral immunity. PAE animals, typically exposed via maternal consumption of ethanol-containing liquid diets, have decreased numbers of Thy1.2+, CD4+, CD8+, and IgG+ splenocytes (Ewald and Huang, 1990; Giberson and Blakley, 1994; Giberson et al., 1997). In addition, data from both rodent and primate studies indicate that splenic lympocytes taken from PAE males from adolescence through young adulthood show decreased proliferative responses to mitogens (Gottesfeld and Abel, 1991; Gottesfeld and Ullrich, 1995; Grossman et al., 1993; Jerrells and Weinberg, 1998; Monjan and Mandell, 1980; Norman et al., 1989; Redei et al., 1989; Weinberg and   6 Jerrells, 1991), although in some studies, it was shown that the proliferative response may normalize by eight months of age (Gottesfeld and Ullrich, 1995; Norman et al., 1991). As noted, humoral immunity appears to be less affected by fetal alcohol exposure than cellular immunity. For example, in both rats (Gottesfeld and Ullrich, 1995) and non-human primates (Grossmann et al., 1993) serum immunoglobulin response to immunization was shown to be unaffected by prenatal exposure to alcohol. On the other hand, abnormal development of B cell lineages in mouse bone marrow, spleen, and liver has been reported. Decreased numbers of splenic B cells and decreased B cell proliferative response to LPS (Wolcott et al., 1995), as well as delayed B cell maturation (Biber et al., 1998) have been observed. In addition, numbers of both immature and mature B cells in spleen and bone marrow were found to be decreased at birth, although most recovered to normal levels by 3 to 5 weeks after birth (Moscatello et al., 1999; Robinson and Seelig, 2002). Increased susceptibility to infections following prenatal alcohol exposure has also been reported in rodent models, in parallel with the human data, and provides further evidence of alterations in immune competence. For example, work by McGill and colleagues (McGill et al., 2009) found that mice exposed to alcohol during gestation and lactation showed enhanced disease severity, as well as increased and sustained pulmonary viral titers following influenza virus infection. Similarly, in a study of Macaca nemestrina, 4 of 18 (22%) animals exposed to once weekly oral doses of alcohol died or were euthanized as a result of infectious disease or failure to thrive during the first year of life, whereas the controls remained healthy (Grossmann et al., 1993).   1.4 Sexual Dimorphism in the Immune Response  Sex differences in responsiveness to immune challenges provide an additional level of complexity in understanding the data regarding the adverse effects of prenatal alcohol exposure. Sexual dimorphism of the immune system is well known (Da Silva, 1999; Grossman, 1990). Both humoral and cell-mediated immune responses are more active in adult females than in males (Grossman, 1990; Martin, 2000). For   7 example, the thymus is larger in female than in male mice, and castration of young males leads to feminization (i.e., increased weights) of the immune organs. In response to immunization, women develop higher antibody titers than males and they show a higher rate of transplant rejection. Females also represent the majority of patients affected with autoimmune disorders, ranging from 65-75% of patients with RA to 85% of patients with Hashimoto’s thyroiditis and Grave’s disease (Da Silva, 1999). In humans, circulating levels of IgM are significantly elevated in girls compared to boys as early as 6 years of age, and juvenile RA can appear before 5 years of age. Gonadal hormones clearly play an important role in immune sexual dimorphism both in adulthood and during development. It appears that both the type and concentration of sex hormones within the microenvironment play a key role in lymphocyte maturation (Grossman, 1990). Both the immune organs and lymphocytes have receptors for the sex steroid hormones, linking the endocrine and immune systems (Da Silva, 1999; Grossman, 1990). In addition, data suggest that other hormone systems and complex hormonal interactions can affect developing lymphocytes and regulate adult effector cells. Estrogen appears to be particularly important in the development of sexual dimorphism in the immune system, both through direct effects on immune cells and through modulation of other hormone systems, including adrenal glucocorticoids, thyroid hormones, and growth hormone. These hormonal systems interact with each other and with the immune system to influence sexually dimorphic immune responses. Thymic hormones, and cytokines generated by activated lymphocytes may also play a role in sexually dimorphic immune responses (Grossman, 1990). Sexual dimorphism related to fetal alcohol-associated alterations in behaviour (McGivern et al., 1984; Weinberg et al., 2008), HPA function (Fonzo et al., 1967; Weinberg et al., 2008; Williams et al., 1985), neuroimmune interactions (Spinedi et al., 2002; Spinedi et al., 1992) and susceptibility to inflammatory disorders (Van Eden et al., 2002) have been characterized. Despite recognition that these sex differences exist, a large proportion of studies using rodent and non-human primate models of FASD are conducted only in males or with both sexes but too small a sample size to achieve the statistical power required to   8 detect sexual dimorphism. Lending further support to the importance of considering sex differences are findings of sexual dimorphisms in the immune responses following prenatal alcohol exposure (Daneva et al., 1993; Lee and Rivier, 1996; Spinedi et al., 2002; Spinedi et al., 1992; Weinberg and Jerrells, 1991), many of which may also be impacted by fetal alcohol-induced, sex-specific HPA differences. One of the first demonstrations of fetal alcohol effects on immune function of females is the data from Halasz and colleagues (Halasz et al., 1993) who found that the challenge of chronic alcohol exposure in adulthood selectively increased Con A-induced lymphocyte proliferation in PAE females, but not males. Consistent with these data, work from our laboratory (Giberson et al., 1997) reported an interaction between prenatal alcohol and chronic cold stress. After one day of cold stress, PAE females but not males exhibited increased lymphocyte proliferation in response to pokeweed mitogen (a T cell-dependent B cell mitogen) and Con A challenge. In contrast, PAE males exposed to one or three days of cold stress had increased basal corticosterone concentrations compared to PAE males not exposed to cold. Altered interactions between T and B cells may underlie the alcohol-induced alterations in immune responsiveness observed in response to stressors.  1.5 Prenatal Alcohol Exposure, Cytokines, and the HPA Axis Cytokines secreted by immune cells, including IL-1, IL-2, IL-4, and IL-6, influence the function of hypothalamic neurosecretory and thermoregulatory neurons, and pituitary cells (Cunningham and De Souza, 1993; Dunn and Wang, 1995; Rivier, 1993; Zalcman et al., 1994), resulting in activation of the HPA axis and inducing “sickness behavior” (Dantzer, 2001a; Watkins and Maier, 2000). IL-1, IL-6, and TNF-α are also produced in the hypothalamus by microglia and macrophages (Hetier et al., 1988; Sebire et al., 1993), and thus, can directly influence neuroendocrine function. For example, IL-1 stimulates the release of corticotropin-releasing hormone (CRH) and arginine vasopressin (AVP) from the hypothalamus (Chover-Gonzalez et al., 1994; Suda et al., 1990) and IL-1, IL-6, and TNF-α stimulate ACTH secretion from the anterior pituitary (Kehrer et al., 1988; Lyson and McCann, 1991; Sharp et al.,   9 1989). During prolonged stress, cytokines exert effects at the level of the pituitary and adrenal glands (Besedovsky and Rey, 2007). In turn, the glucocorticoids play a major role in the stress-induced suppression of immune/inflammatory reactions.  Prenatal alcohol exposure has been shown to alter neuroendocrine and behavioral responses to cytokines. For example, the LPS-induced febrile response was shown to be blunted in PAE males (Taylor et al., 2002; Taylor et al., 1999b). It was suggested that a decreased hypothalamic IL-1β response to LPS administration, possibly due to an impaired release of endogenous pyrogens, could underlie the differential responsiveness observed (Yirmiya et al., 1993; Yirmiya et al., 1996; Ylikorkala et al., 1988). PAE has also been shown to result in a dampened TNF-α response to the immune challenge of IL-1β and LPS during the preweaning period (Zhang et al., 2005). Of note, this reduction was found to persist into adolescence in PAE males, but not in females (Chiappelli et al., 1997; Lee and Rivier, 1993). Following weaning and into adulthood, hormonal responses to immune signals are also increased in PAE animals. Alcohol exposure in utero appears to induce a pro-inflammatory profile bias, suggested by the finding of greater ACTH and/or corticosterone responses to IL-1β and LPS in PAE compared to control offspring (Kim et al., 1999; Lee and Rivier, 1996; Yirmiya et al., 1998). As well, embryos exposed in vitro to alcohol had greater tissue levels of TNF-α and IL-6 than control embryos (Vink et al., 2005). PAE males also exhibited increased plasma concentrations of pro-inflammatory cytokines (IL-1β, TNF-α, IL-6) to LPS challenge following repeated restraint stress (Zhang et al., 2005). Importantly, however, corticosterone responses to LPS were comparable among groups. These data support and extend previous studies suggesting that although PAE animals may not differ in cytokine responses under basal or non-stressed conditions, they may be more vulnerable to the adverse effects of stress on immune function (Zhang et al., 2005). Furthermore, these data have important implications for understanding differential vulnerability to inflammatory disorders such as arthritis in PAE offspring.      10 1.6 Mechanisms Underlying Alcohol Effects on the Immune Function  1.6.1 Altered Endocrine/Immune Networks Prenatal exposure to alcohol has been shown to have adverse effects on neuroendocrine function, which could in turn alter the bidirectional communication between neuroendocrine and neuroimmune networks. Importantly, the nervous system, endocrine system and immune system exist within a complex regulatory network of connections (Besedovsky and del Rey, 1996; Besedovsky and Rey, 2007; Blalock and Smith, 2007). The central nervous system can regulate both the endocrine and immune systems directly, through autonomic innervation of lymphoid organs and tissues and endocrine glands, and indirectly, through release of neurotransmitters. In turn, the nervous system can “read” information from both the endocrine and immune systems. Cytokines can also influence or stimulate hormone release from the hypothalamus, pituitary and endocrine glands, and in themselves may have neuroendocrine effects. Similarly, hormones produced by the endocrine gland and pituitary not only feedback to the CNS to influence neural and endocrine function, but also have immunoregulatory functions. Lymphocytes express receptors for hormones and neurotransmitters, including CRH, ACTH, glucocorticoids, norepinephrine and epinephrine. The glucocorticoid hormones can exert profound influences on T cell function through their interaction with glucocorticoid receptors on T cells, which modulate trafficking, homing, proliferation, activation, and apoptosis (Dhabhar et al., 1996). In addition, the autonomic nervous system can exert regulatory actions on the immune system. Noradrenergic neurotransmitters can target most immune cells including thymocytes, T lymphocytes, macrophages and plasma cells, resulting in selective suppression of Th1-mediated inflammation and cellular immunity, which, in turn, will favor humoral immunity as well as protecting the organism from the adverse effects of proinflammatory cytokines.  Reciprocal expression of receptors for hormones, neurotransmitters and neuropeptides, and shared hormonal/peptide ligands or products by immune, endocrine and neural cells underlie the bidirectional   11 communication that allows these systems to “speak a common biochemical language” (Blalock and Smith, 2007), and thus influence each other to maintain homeostasis. It has been suggested that the immune system acts as a “peripheral receptor organ” able to transmit information to the brain about responses to external or internal antigenic stimuli (Besedovsky and Rey, 2007). Similarly, Blalock & Smith (Blalock and Smith, 2007) suggest that through the sharing of ligands and receptors, the immune system can serve as a “sixth sense” to detect things the body cannot otherwise hear, see, smell, taste or touch, and to signal and mobilize the body to respond to pathogens, tumors, allergens and other immune challenges.  Prenatal alcohol exposure can link into this neuro-immune-endocrine circuit to exert its teratogenic effects in numerous ways. Alcohol may: 1) have direct effects on the ontogeny of the immune and endocrine systems, and on CNS development and function; 2) act indirectly through effects on neuroendocrine function; and 3) disrupt the intimate bidirectional link between the neuroendocrine and immune systems.  1.6.2 Early-Life Exposure to Maternally-Derived Cytokines It is well established that chronic alcohol consumption increases proinflammatory cytokine levels (Crews et al., 2006; He and Crews, 2008), and if alcohol is consumed during pregnancy, the developing fetus is exposed to heightened cytokine signals. While the extent to which these cytokines have access to the developing fetus remains controversial (Aaltonen et al., 2005; Dahlgren et al., 2006; Zaretsky et al., 2004), many lines of evidence link early life increases in cytokine levels with increased vulnerability to later-life infections and cytokine overproduction (Bauman et al., 1997; Meyer et al., 2009; Pang et al., 2003). It is hypothesized that immune stimulation during sensitive periods of development may affect cytokine production, glial morphology, brain development and neuroimmune responsiveness into adulthood [reviewed in (Bilbo and Schwarz, 2009)]. In the case of in utero alcohol exposure, maternal increases in cytokines may be transmitted or elaborated on by the fetus and alcohol may directly activate   12 the fetal immune system resulting in increased pro-inflammatory cytokines during development. It is important to note that cytokines, in addition to their role in immune system activation, are also involved in normal brain development, including a role in neuronal plasticity, morphogenesis, growth and differentiation (Ader, 1998; Schwarz, 2012b). Cytokines play a role in mediating neuronal migration, synaptogenesis, synaptic pruning and stem cell fate during development (Bajetto et al., 2001; Nakashima and Taga, 2002). Abnormally high cytokine levels, however, may alter many of the important neuronal processes listed above, resulting in abnormal brain connectivity and ultimately increased immune system impairments and vulnerability to neurodevelopmental disorders (Bauman et al., 1997; Bilbo and Schwarz, 2009; Meyer et al., 2009).  1.7 Chronic Immune Challenge: The Adjuvant-Induced Arthritis (AA) Model Chronic inflammatory challenge provides a mechanism for testing immune function in adulthood. Data suggest that exposure to a second-hit in adulthood may help to unmask specific deficits engrained in the system during early life (Choy et al., 2009; Maynard et al., 2001; Pantelis et al., 2003), such as occurs with prenatal alcohol exposure. Specifically, the AA model is suitable as a chronic inflammatory challenge as it has been well characterized due to its extensive use in immunological research over the past half century (Cai et al., 2006b; Freund, 1956; Pearson, 1956, 1963; Ratkay et al., 1993; Stolina et al., 2009; Szekanecz et al., 2000). Furthermore, the AA model is well established as a model of human RA (Bendele, 2001), a condition that recent reports suggest is more prevalent in young adults with FASD (Himmelreich et al., 2016; Weinberg, 2016).  AA is stimulated through an intradermal injection of Complete Freund’s Adjuvant (CFA). Approximately 9–13 days post-injection, signs of inflammatory onset, including redness and swelling of the front and hind paws, begin to appear (Cai et al., 2006b). AA develops as a result of an immune response to components of the cartilage matrix, initiated by a T cell response to an exogenous antigen, likely a component of a mycobacterial heat-shock protein [HSP; (Billiau and Matthys, 2001)]. It is   13 suspected that the arthritis-like condition resulting from CFA injection is due to the presence of a cartilage mimicking epitope found in the mycobacterial 65 kD heat-shock protein [hsp65; (Ratkay, 1994b)]. HSPs are found in both prokaryotes and eukaryotes and function as chaperones for other proteins during folding, assembly, transport, etc., (Billiau and Matthys, 2001). Conditions of cellular stress can increase endogenous HSPs, many of which bind to toll-like receptors (TLRs), resulting in the production of a “danger signal” within the cell (Billiau and Matthys, 2001). Notably mycobacterial HSPs also activate TLRs, thus activating this inflammatory signal cascade.  Importantly, the AA model is particularly useful as it allows for examination of a number of different parameters, beyond what can be tested with many other immune challenge models, such as lipopolysaccharide (LPS) stimulation. CFA injection causes AA in variable percentage of animals, dependent on numerous factors including CFA dose, organism [the rat being the most common (Hegen et al., 2008)], strain, and other variables (Cai et al., 2006a). Severity of arthritis is also variable using this model. In mice lacking matrix metalloproteinase 8 (Mmp8-/-), arthritis severity is greatly increased, with animals displaying heightened joint inflammation and exacerbated neutrophil accumulation in the synovial tissue (Cox et al., 2010). The rate of resolution following AA can also be measured and compared, as this is generally a self-limiting condition (Asquith et al., 2009). Finally, rat AA models, being the preferred model of the pathology occurring with RA, have been and continue to be widely used in the evaluation of anti-arthritic agents (Bolon et al., 2011).  1.8 Strain-Based Differences in HPA Parameters and Response to Immune Challenge  Strain based differences in the activation of the hypothalamic-pituitary-adrenal (HPA) response to stress and immune challenge have been well documented. At one end of the spectrum are Lewis (LEW) rats, with a hyporeactive HPA axis (Oitzl et al., 1995), which leaves them highly susceptible to inflammatory insults (Sternberg et al., 1989), including AA (Banik et al., 2002). At the other extreme, Fisher (F344) rats generally fail to develop arthritis (Banik et al., 2002), a finding linked to proper   14 stimulation of the HPA axis by inflammatory mediators and subsequent feedback between HPA and immune systems [reviewed in (Joe et al., 1999)]. Further examination of the HPA response to challenge has revealed significantly higher levels of corticosterone, as well as corticosteroid-binding globulin (CBG) in F344, compared to LEW rats (Dhabhar et al., 1993). Notably, F344 and LEW rats are identical at the major histocompatibility complex (Dhabhar et al., 1993). Comparatively, outbred SD rats, the maternal strain for the inbred F344 and LEW rats, are one of the most widely used rat strain the endocrine/neuroendocrine field. SD rats are generally more comparable to LEW rats in terms of HPA axis activity and response to AA (Banik et al., 2002; Dhabhar et al., 1993). Parallels exist with regards to the increased susceptibility to inflammatory challenge and HPA dysfunction in LEW rats and what is observed in a subset of patients with RA. With chronic RA, there is evidence for alterations in the HPA axis. Under basal conditions, the HPA axis has generally been shown to be normal, with the exception of evidence for lower basal cortisol levels in a subset of patients [reviewed in (Jessop and Harbuz, 2005)]. By contrast, HPA alterations in RA patients become more apparent with challenge. For example, a subset of patients with RA failed to show an increase in plasma cortisol levels following joint replacement surgery (Chikanza et al., 1992). In addition, RA patients also show early escape of cortisol from dexamethasone suppression indicative of impaired glucocorticoid feedback (Harbuz et al., 2003).  1.9 Vendor Colony Differences in Physiological Responses  Rats of the same strain, obtained from different vendor colonies, have generally been considered comparable; however, a growing body of work suggests that subtle genetic drift between suppliers and/or facility based differences in environment may change the animals and alter comparability (Pecoraro et al., 2006). Significant colony-based differences in numerous parameters have been identified including measurable differences in the disruptive effects of dopamine agonists of sensorimotor gating [between Harlan-derived and Bantin-Kingman SD and Wister rats; (Swerdlow et al., 2000)], differences in   15 metabolism and HPA function [between Harlan-, Charles River- and Simonsen-derived SD rats; (Pecoraro et al., 2006)], and differences in the Pavlovian conditioned approach behavior (between Harlan- and Charles-derived SD rats; (Fitzpatrick et al., 2013)]. In addition, Turnbull and Rivier (1999) showed that colony also affects parameters of the immune and neuroendocrine systems [comparing Harlan- and Charles River-derived SD rats (Turnbull and Rivier, 1999)]. These colony differences echo the strain-based differences in endocrine and immune responses listed above. However, with regards to colony, differences are generally more modest and the underlying mechanism(s) generally unknown. Here, colony differences are utilized as a tool for exploring networks of endocrine immune parameters underlying subtle differences in responses in the AA model.  1.10 Thesis Overview The experimental data in this thesis will be presented in four chapters, addressing the specific hypotheses listed in section 1.1 above. Together, Chapter 2 entitled “Colony specific differences in endocrine and immune responses to an inflammatory challenge in Sprague Dawley rats” and Chapter 3 entitled “Differential activation of endocrine-immune networks by arthritis challenge: Insights from colony-specific responses” exploited previously reported colony-differences to probe for endocrine and immune parameters underlying differential responses to AA challenge. The results from these two chapters were then applied as a basic framework for guiding the investigation into the impact of PAE in the AA model, which is then reported in Chapter 4 entitled “Modulatory role of prenatal alcohol exposure and adolescent stress on the response to chronic inflammatory challenge in female rats”. Next, to determine whether the altered inflammatory response detected following PAE has its origins in immune disturbances during the early postnatal period, we investigated a developmental time course of immune function in PAE and control offspring in Chapter 5 entitled “Evidence for an immune signature of prenatal alcohol exposure in female rats”. Finally, in the conclusion, major findings from the data chapters will be integrated and limitations as well as future directions discussed.   16 Chapter 2: Colony Specific Differences in Endocrine and Immune Responses to an Inflammatory Challenge in Female Sprague Dawley Rats.  2.1  Introduction Rats of the same strain are generally considered comparable, but genetic drift within colonies of a single strain may occur. Significant differences in various measures have been reported among Sprague Dawley (SD) rats from different vendors, including effects of dopamine agonists on sensorimotor gating (Swerdlow et al., 2000), Pavlovian conditioned behaviors (Fitzpatrick et al., 2013), hypoxic responses (Fuller et al., 2001), noradrenergic innervation of the spinal cord (Clark et al., 1991), neuropathic pain behaviors (Yoon et al., 1999), and metabolic, endocrine, and immune function (Turnbull and Rivier, 1999).  Utilizing the adjuvant-induced arthritis (AA) model of rheumatoid arthritis used extensively in immunological research (Freund, 1956), the current study investigated how the endocrine and immune systems of SD rats from two different vendors (Harlan versus Charles River) respond to an inflammatory challenge. Briefly, AA is induced through an intradermal injection of complete Freund’s adjuvant (CFA). Inflammation then develops as a result of an immune response against components of the cartilage matrix, initiated by a T cell response to an exogenous antigen, likely a component of a mycobacterial 65 kD heat-shock protein (Billiau and Matthys, 2001), thought to contain a cartilage mimicking epitope (Ratkay, 1994b). This AA model allows examination of a number of parameters, including the percentage of rats that develop inflammation [not all rats will develop AA following adjuvant injection (Cai et al., 2006a)] and the rate of resolution from inflammation, with these factors depending on dose and site of injection (Cai et al., 2006a), sex (Schuurs and Verheul, 1990) and strain (Banik et al., 2002; Swingle et al., 1969; Woodward et al., 2006), as well as environmental factors (Zhang et al., 2012).  Cytokines and hormones of the hypothalamic-pituitary-adrenal (HPA) axis are among the most consistently studied mediators of inflammation. Furthermore, there is significant interplay between the   17 immune and endocrine systems, with shared receptors, ligands, and regulatory feedback mechanisms (Haddad et al., 2002). Cytokines stimulate the HPA axis following an immune challenge, affecting release of hormones at the level of the hypothalamus and pituitary gland, which, in turn, has downstream effects on immune function and resolution of inflammation (Bumiller et al., 1999). As a result, changes in communication between cytokines and the HPA axis can impair recovery and lead to and/or exacerbate underlying pathological conditions (Rivier, 1994). In the AA model, plasma corticosterone levels increase with disease onset, whereas adrenocorticotropic hormone (ACTH) levels decrease and there is a loss of the corticosterone diurnal rhythm (Sarlis et al., 1992). Conversely, in the absence of a functional HPA axis (adrenaletomized animals), AA onset is earlier, more severe, and a fatal course of inflammation often results (Harbuz et al., 1993). In addition to HPA axis activation, cytokines increase in the circulation and synovial tissue during active AA, and strong positive correlations have been described between plasma cytokine levels, joint cytokine levels and joint circumference (Szekanecz et al., 2000). As a result, agents targeting inflammatory cytokines are used clinically for treatment of rheumatoid arthritis (Hueber et al., 2010), especially in combination with glucocorticoid-based treatments (Genovese et al., 2008; Smolen et al., 2010).   Charles River (Hollister, CA) and Harlan (Indianapolis, IN) SD rats exhibit different endocrine and immune responses to inflammatory challenge with LPS, turpentine, and IL-1β, showing unique ACTH and cytokine (IL-6) responses but no differences in the corticosterone response (Turnbull and Rivier, 1999). Moreover, Charles River (Raleigh, NC) SD rats have been shown to have a more pronounced ACTH response to restraint stress when compared to SD rats from Simonsen (Gilroy, CA), with Harlan (Kent, WA) SD rats demonstrating a lack of an ACTH response (Pecoraro et al., 2006). Differences in corticosteroid binding globulin (CBG), the major transport protein for glucocorticoids, have not been previously reported in specific rat strains, but the effects of differential plasma CBG levels on inflammatory outcomes are suspected based on differences between strains.  For example, Fisher rats exhibit higher plasma CBG and corticosterone levels than Lewis rats (Dhabhar et al., 1993), and generally   18 fail to develop arthritis, a finding linked to appropriate stimulation of the HPA axis by inflammatory mediators and subsequent feedback between HPA and immune systems (Joe et al., 1999). Based on these findings, the current study tested the hypothesis that differential endocrine and immune responses may underlie the different responses to inflammation of Harlan versus Charles River SD rats.   2.2 Materials and Methods 2.2.1 Animals  Adult female Sprague Dawley rats were obtained from Charles River Laboratories International, Inc. (St. Constant, QC, Canada) and Harlan Laboratories, Inc. (Hsd: Sprague Dawley SD, Frederick, MD) (n = 29/vendor). Rats were raised in barrier rooms in their respective vendor facilities, under controlled temperatures (21-23°C), with access to autoclaved diets (Charles River Rodent Diet 5075, Woodstock, Ontario, Canada; Harlan Teklad 2018S, Madison WI), and maintained on a 12:12 hour light/dark cycle. Rats were shipped by air under climate-controlled conditions (transport time – Charles River ~24h, Harlan ~24 – 48h), and were pair housed in a colony room on a 12:12 hour light/dark cycle, with controlled temperature (21-22°C) and ad libitum access to standard laboratory chow (Purina Laboratory Rodent Diet #5001, Delta, BC, Canada) and water. All procedures were in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals, and approved by the University of British Columbia Animal Care Committee.     2.2.2 Induction and Clinical Assessment of AA Only female rats were used because they exhibit heightened immune responses (Grossman, 1989) and increased prevalence of autoimmune disorders (Da Silva, 1999), including increased susceptibility to experimentally induced arthritis (Holmdahl, 1995; Remmers et al., 2002). On postnatal day 55-60, rats were anesthetized with isoflurane, and each received two 0.05 mL intradermal injections at the base of the tail. Rats were injected with either complete Freund’s adjuvant (CFA; n=7-8/vendor/dose) or   19 physiological saline (control; n=6-7/vendor). CFA was prepared by grinding Mycobacterium tuberculosis H37 RA (Difco laboratory, Detroit, MI) and dissolving the powder in incomplete Freund’s adjuvant (Zhang et al., 2012). Two doses of CFA were prepared initially: 12 mg/mL and 3 mg/mL, resulting in a dose of 1.2 or 0.3 mg/rat. These doses were chosen with the aim of identifying a dose that results in mild-moderate arthritis in ~50% of rats from each vendor. At 0.3 mg, none of the Charles River rats showed signs of inflammation on day 15 post-injection; however, ~40% of Harlan rats displayed signs of severe inflammation. As a result, two CFA doses were added, 0.6 mg and 0.2 mg/rat, designed to induce mild-moderate inflammation in Charles River and Harlan rats, respectively. Following injections, rats were single housed, weighed and paw volume was measured using a plethysmometer (IITC Life Science Inc., Woodlan Hills, CA). On days 6, 9, 11, 13, and 15 post-injection, rats from both the CFA- and saline-injected conditions were anesthetized, weighed, paw volume was measured, and clinical signs of arthritis assessed (Fig. 2.1). To calculate the clinical score, each of the four paws was scored on a 0-4 point scale (Zhang et al., 2012). Across doses, rats achieving a clinical score ≥ 8 at any point during the study were classified as developing severe arthritis (Adj/S) whereas rats with a clinical score ≥1 but < 8 were classified as developing mild-moderate arthritis (Adj/M-M).    Figure 2.1 Experimental timeline. Day$post)injec/on:$$ 0$ 6$ 9$ 11$ 13$ 15$ 16$Figure 1 $  20 Rats received intradermal injections of Complete Freund’s Adjuvant (CFA) on postnatal day 55-60 (hereafter referred to as day 0). Baseline measurements of body weight, hind paw volume, and clinical score were collected on day 0, and post-injection measurements were collected on days 6, 9, 11, 13, and 15. Tissue and plasma were collected on day 16 post-injection (peak of inflammation).  2.2.3 Termination and Tissue Collection  On day 16 post-injection, rats were decapitated between 08:00 and 10:30 hr and trunk blood was collected in polystyrene tubes containing EDTA. Blood was centrifuged and plasma was collected and stored at -80°C. Hind paws were removed at the level of the tibiotarsal joint, flash frozen in liquid nitrogen and stored at -80°C. Vaginal lavage samples were collected and assessed cytologically to determine estrous cycle stage.  2.2.4 Plasma ACTH, Corticosterone, and CBG Measurements Plasma ACTH and corticosterone (total) were measured using the ImmuChem Double Antibody hACTH 125I radioimmunoassay kit and the ImmuChem Double Antibody Corticosterone 125I radioimmunoassay kit (MP Biomedicals, LLC, Orangeburg, NY, USA), respectively. The minimum detectable concentrations were 5.7 pg/mL for ACTH , and 7.7 ng/mL for corticosterone, with intra- and inter-assay coefficients of variation of <10 % for both assays.  The steroid-binding capacity of CBG was measured using a ligand-saturation assay that uses dextran-coated charcoal (DCC) to separate CBG-bound from free [3H]-corticosterone (PerkinElmer Lifer Sciences, Waltham, MA) (Hammond and Lahteenmaki, 1983; Smith and Hammond, 1991). Plasma samples were diluted (1:1,500) in phosphate buffered saline (PBS) and stripped of endogenous steroids by incubation with DCC. Samples were then incubated with 1nM [3H]-corticosterone in the absence or presence of excess corticosterone, to monitor non-specific binding, followed by the measurement of bound [3H]-corticosterone after adsorption of free steroid with DCC for 10 minutes at 0°C. All samples   21 were measured in the same assay and the intra-assay variability was <10% (Hammond and Lahteenmaki, 1983). The dissociation-rate constant (Kd), was determined by Scatchard analysis at 4°C, in which Charles River and Harlan plasma samples were incubated with increasing amounts of [3H]-corticosterone, as described previously (Smith and Hammond, 1991). Levels of CBG-bound corticosterone were calculated using the mass action equation, according to Boksa (Boksa, 1997), using a Kd of 45 nM, as determined for rat CBG at 37°C (Perrin and Forest, 1975). The CBG-bound corticosterone levels were then used to calculate the free corticosterone levels using the following equation: free corticosterone = [total corticosterone] – [CBG-bound corticosterone].  2.2.5 Serpina6 Sequencing Genomic DNA was isolated from blood lymphocytes using the QIAamp DNA minikit (QIAGEN Sciences, Valencia, CA). PCR amplification was performed on the rat Serpina6 exons and proximal promoter with specific oligonucleotide primer pairs (Table 1) and Platinum Pfx polymerase (Invitrogen, Burlington, ON, Canada). The PCR products were sequenced and results were analyzed using 4Peaks software (version 1.7.2, mekentosi.com).   Sequence PCR and Sequencing Primers 5’!  3’ PCR Product (bp) Promoter  (+550 to -41) CCAGCATGCATAAGGTTCAGC GGTTGTGCTTTGCTGCCAGG 591 Exon 1 Forward: CCAGCAAACAAGATTTAGTAGG Reverse: GCTGTGTTCTGGAGTGCAGC 199 Exon 2 Forward: GCCAATGTGAAGGAAGGATAGG    Reverse: CCAACCTGGTAGAGATTGGC 862 Exon 3 Forward: GCAGGTGGCTGCATAGCTGG Reverse: GGCTAGAGAACCTCACAGCC 556 Exon 4 Forward: CCTATCCCCAAGTTTAACCAGG Reverse: GGGTTTGTCATTTGGGACC 460 Exon 5 Forward: GGCTATTTCACCTTCCATGG Reverse: CCTCTTTCTCAGTGCTCCCTTC 462    Table 2.1 Oligonucleotide primers used to sequence.   22 2.2.6 Hind Paw Homogenization  Hind paw samples were manually crushed on dry ice, added to tubes containing 1.3 g garnet and 2 mL cold lysis buffer, and homogenized using an Omni Bead Ruptor 24 (Omni International, Kennesaw, GA). Homogenates were sonicated three times (5 sec/cycle) on ice, centrifuged and the supernatants were stored at -20°C for protein and cytokine measurements.  2.2.7 Multiplex Cytokine Immunoassays and Protein Quantification Cytokine assays were performed using a custom Meso Scale Discovery rat cytokine 8-plex panel capable of simultaneously measuring IL-1β, IL-4, IL-6, IL-10, IFN-ɣ, KC/GRO, MCP-1, and TNF-α (catalog #: N05IA-1, MSD, Rockville, MD). Plates were read using a Sector Imager 2400 and data analyzed using the MSD Discovery Workbench software v. 4.0 (MSD, Rockville, MD).  Total protein levels were quantified in hind paw tissue homogenates using the Pierce Microplate BCA Protein Assay Kit (Pierce Biotechnology, Rockford, IL). Tissue cytokine levels were adjusted and reported as pg cytokine/mg of protein.  2.2.8 Statistical Analyses Data were analyzed using analyses of variance (ANOVAs) for the factors of colony and AA severity, or colony and dose, as appropriate, with repeated measures as required, followed by Fisher post hoc tests to examine significant main effects and interactions (significant ANOVA and post hoc p values reported in text; F statistic reported in figure legends). The assumption of sphericity was examined using Mauchly’s test and when violated, F-values were corrected using Greenhouse-Geisser estimates of sphericity. In line with our hypotheses that Charles River and Harlan rats would differ in severity of inflammation and in hormone and cytokine responses, planned pairwise comparisons were carried out as indicated. Differences were considered significant at p≤0.05, and trends (p>0.05 and <0.085) were examined, as appropriate.   23  2.3 Results 2.3.1 Arthritis Severity: Date of Onset and Clinical Scores The mean day of arthritis onset (first day of clinical score > 0) did not differ between Harlan and Charles River rats, ranging from a mean of day 11.1 (± 1.20) to 13.0 (± 0.63) post-injection for all CFA doses examined. For comparisons of average clinical scores, only days 11, 13, and 15 post-injection are shown (Fig. 2.2A, B), as clinical scores were ≤ 2 prior to day 11.  At the 0.3 mg CFA dose, Harlan rats had increasing clinical scores over time, which is indicative of increasing arthritis severity, whereas Charles River rats had little to no inflammatory response at this dose (Fig. 2.2A; day x vendor interaction, p<0.01). Based on our a priori hypothesis, as well as a trend for a colony difference in the average clinical scores (between-subjects effect, p=0.072), planned pairwise comparisons on day 15, at the peak of inflammation, indicated that Harlan rats displayed higher clinical scores than Charles River rats (p<0.5), supporting the suggestion of greater arthritis severity in Harlan compared to Charles River rats at the 0.3 mg dose. Comparatively, at the high (1.2 mg) CFA dose, colony differences in inflammation were not observed, with both Charles River and Harlan rats showing increasing clinical scores over time (Fig. 2.2B; main effect of day, p<0.01). There were no colony differences in the proportion of rats within each stage of the estrous cycle in controls (saline-injected) or CFA injected rats.  2.3.2 Arthritis Severity: Dose Comparison To further investigate dose-response differences, an additional low (0.2 mg) CFA dose was tested in Harlan rats and an additional intermediate (0.6 mg) CFA dose was tested in Charles River rats. As noted in Methods, separate doses were selected for Charles River and Harlan rats due to the more severe arthritic profile observed in Harlan rats at the 0.3 mg dose. Stacked bar graphs (Fig. 2.2C, D) represent the proportion of rats from all CFA doses at each arthritis severity level – no clinical signs of arthritis   24 (Adj/NA), mild-moderate arthritis (Adj/M-M) or severe arthritis (Adj/S). It should be noted that clinical score groupings are based on the highest clinical score reached by a rat during the course of the experiment: day 0-16.  A comparison of severity profiles across the three selected CFA doses demonstrates that at the lowest doses tested in rats from each vendor (Charles River: 0.3 mg; Harlan: 0.2 mg), there were no cases of severe arthritis. However, at the other two doses, fewer Harlan (0.3 and 1.2 mg: 13%) than Charles River (0.6 mg: 33%; 1.2 mg: 38%) rats failed to develop clinical signs of arthritis, and there were more cases of mild-moderate arthritis in Harlan (0.3 mg: 50%; 1.2 mg: 63%) compared to Charles River (0.6 mg: 33%; 1.2 mg: 38%) rats. A direct comparison of Charles River and Harlan rats at the 0.3 mg and 1.2 mg doses (Fig. 2.2C, D) also indicates increased AA severity in Harlan rats. At the 0.3 mg dose, 38% of Harlan rats developed severe AA compared to 0% of Charles River rats, and 13% of Harlan rats failed to develop AA compared to 43% of Charles River rats. At the 1.2 mg/mL dose, while similar arthritis profiles were observed for Charles River and Harlan rats at the most severe level of inflammation (Adj/S; 25% for each vendor), colony differences similar to those at the lower CFA dose were again observed for rats failing to develop arthritis (Charles River: 38%, Harlan: 13%), as well as for those developing mild-moderate inflammation (Charles River: 38%, Harlan: 63%).    25  Figure 2.2 AA severity. (A) At 0.3 mg CFA, Harlan showed increasing clinical scores over time with no change in clinical scores over time for Charles River rats [main effect of day, F(2, 26)=5.62, p<0.01;  day × colony interaction, F(2, 26)=6.35, p<0.01]. On d15, average clinical score was greater in Harlan than Charles River rats [§, p=0.016; trend for effect of colony: F(1, 13)=3.85, p=0.072]. (B) At 1.2 mg CFA, Charles River and Harlan rats both showed increasing clinical scores over time [main effect of day, F(1.16, 16.27)=10.10, p<0.01]. No colony specific differences were detected at this dose. Data presented as mean ± SEM; n = 7 – 8 per colony/dose; dotted line = clinical score of 8. Post hoc: *p<0.05; **p< 0.01; Severity: Charles River020406080100% animals0.3 mg 0.6 mg 1.2 mgCharles River C Severity: Harlan020406080100% animals0.2 mg 0.3 mg 1.2 mgD Harlan 1.2 mg11 13 150246810Day post-injectionClinical Score* ***11 13 150246810Day post-injectionClinical Score#****§0.3 mgCharles River 1.2 mgHarlan 1.2 mgCharles River 0.3 mgHarlan 0.3 mgCharles River 1.2 mgHarlan 1.2 mgCharles River 0.3 mgHarlan 0.3 mgCharles River 1.2 mgHarlan 1.2 mgCharles River 0.3 mgHarlan 0.3 mgrl s iv r .  rl  .  rl s iv r .  rl  .  A B Adj/NAAdj/M-MAdj/SAdj/NAAdj/M-MAdj/SAdj/NAAdj/M-MAdj/SFigure 2   26 ***p<0.001 comparison to d11, unless indicated otherwise). §: p<0.05, pairwise comparison between Charles River and Harlan rats.  (C, D) Arthritis severity profiles (% of rats in each category) compared by stacked bar graphs across the three CFA doses for Charles River (C; 0.3, 0.6, 1.2 mg) and Harlan (D; 0.2, 0.3, 1.2 mg) rats.   2.3.3 Body Weight and Paw Volume  Weight and paw volume changes over the course of the experiment (measured on days 0 – 15 post-injection) collapsed across dose are shown in Figure 2.3. Overall, both body weight and paw volume were significantly higher in Charles River compared to Harlan rats both on the day of injection and at the peak of inflammation (main effects of colony, ps<0.001), and as expected, both body weight and paw volume increased as rats grew throughout the experiment (day × colony interaction, ps<0.001). Not surprisingly, rats developing severe arthritis (Adj/S) exhibited reduced weight gain, and increased paw volume, compared to those with no clinical signs of arthritis (control, Adj/NA) or with mild-moderate arthritis (Adj/M-M) (main effects of AA severity for weight and paw volume, ps<0.001).             27  Figure 2.3 Body weight and paw volume following CFA injection.  (A) Body weight increased in all rats from d0 – d15 [main effect of day, F(1, 49)=537.49, p<0.001] and weights were higher in Charles River versus Harlan rats on both days [day × colony interaction, F(1, 49)=61.14, p<0.001; §§§]. Body weight was impacted by severity of arthritis [main effect of AA severity, F(3, 49)=2.71, p=0.05], with lower body weights in rats developing severe arthritis (Adj/S), compared to all other conditions. (B) Paw volume increased from d0 – d15 [main effect of day, F(1, 49)=42.24, p<0.001], and was also impacted by severity of arthritis [main effect AA severity, F(3, 11.51)=49.0, p<0.001] with higher paw volumes in Adj/S compared to all other conditions on d15. Charles River rats also had higher paw volumes than Harlan rats on d0 and d15 [main effect of colony, F(1, 49)=36.04,  p<0.001; §§§].   2.3.4 Plasma Corticosterone, ACTH, and CBG  On day 16, basal plasma ACTH levels were not different between colonies and did not differ by AA severity (Fig. 4A). Plasma corticosterone levels, however, increased with CFA injection (main effect Weight175200225250275Day Post-InjectionWeight (g)CR - SalCR - Adj/NACR - Adj/SH - SalH - Adj/NAH - M-MH - Adj/SCR - Adj/M-MD0 D15§§§§§§*/***/**Charles River - Adj/NACharles River - ControlHarlan - ControlHarlan - Adj/NACharles River - Adj/M-MHarlan - Adj/M-MCharles River - Adj/SHarlan - Adj/SA Paw Volume234567Day Post-InjectionPaw Volume (mL)D0 D15CR - SalCR - Adj/NACR - Adj/M-MCR - Adj/SH - SalH - Adj/NAH - Adj/M-MH - Adj/S***§§§§§§B Charles River - Adj/NACharles River - ControlHarlan - ControlHarlan - Adj/NACharles River - Adj/M-MHarlan - Adj/M-MCharles River - Adj/SHarlan - Adj/SFigure 3   28 of AA severity, p<0.001; Fig. 2.4B), with the highest corticosterone levels in Adj/S rats (Adj/S > control, Adj/NA, ps<0.05). Free plasma corticosterone also increased with CFA injection (main effect of AA severity, p<0.001; Fig. 2.4D) in both Charles River and Harlan SD rats, with patterns of free corticosterone closely paralleling those of total corticosterone.  Plasma CBG levels decreased with AA severity (main effect of AA severity, p<0.001), with lowest levels detected in Adj/S rats (Adj/S < control, p<0.001 for Charles River, p<0.05 for Harlan; Fig. 2.4C), and were significantly different between colonies, with lower levels overall in Harlan compared to Charles River (main effect of colony, p<0.01). Planned pair-wise comparisons also revealed that even in the absence of AA (control, Adj/NA), CBG levels were significantly lower in Harlan compared to Charles River rats (ps<0.05; Fig. 2.4B). In addition, the overall plasma CBG profile differed between the colonies, with Charles River showing a more marked drop in CBG levels than Harlan rats. With severe arthritis, however, CBG levels did not differ between Charles River and Harlan rats.     29  Figure 2.4 Basal ACTH, corticosterone (total and free) and corticosterone binding globulin (CBG), and characterization of CBG protein by scatchard plot. (A) Plasma ACTH levels did not change with CFA injection and were not different between colonies. (B) Plasma corticosterone levels increased with CFA injection in rats from both colonies [main effect of AA severity, F(3, 49)=7.00, p<0.001]. Colony specific differences were not detected. Note: Corticosterone levels log-transformed for A Plasma CORT Levels02004006008001000CORT (nM)SalineAdj/NAAdj/M-MAdj/SCharles River Harlan****************Corticosterone (nM) B Plasma CBG Levels0100020003000CBG (nM)Charles River Harlan*********§§ §CBG (nM) C Figure 4 050100150200250[ACTH] pg/ml Charles River HarlanACTH (pg/ml) ControlAdj/NAAdj/M-MAdj/SControlAdj/NAAdj/M-MAdj/SControlAdj/NAAdj/M-MAdj/SControlAdj/NAAdj/M-MAdj/SBound/Free 0.0 0.1 0.2 0.3 0.4 0.50.00.10.20.3Bound (nM)Bound/FreeHarlanCharles River020406080100Charles River Harlan* ************/***/**Free Corticosterone (nM) DE  30 statistical analysis; untransformed corticosterone levels presented in the Figure. (C) Plasma CBG levels decreased with AA [main effect of AA severity, F(3, 49)=10.99, p<0.001], with differential effects by colony [main effect of colony, F(1, 49) = 9.72, p<0.01]; Harlan rats show decreased CBG in control and Adj/NA conditions (§§/§) compared to Charles River rats. (D) Free corticosterone levels increased with CFA injection in rats from both colonies [main effect of AA severity, F(3, 48)=8.40, p<0.001]. Colony specific differences in free corticosterone levels were not detected. Note: Free corticosterone levels were log-transformed for statistical analysis; untransformed corticosterone levels presented in the Figure. Data presented as mean ± SEM (A – D). Adj/NA: Charles River: n=14, Harlan: n=6; Adj/M-M: Charles River: n=3, Harlan: n=12; Adj/S: Charles River: n=4, Harlan: n=5; control, saline-injected: Charles River: n=7, Harlan: n=6. Post hoc: *p<0.05; **p<0.01; ***p<0.001 (comparison to control, unless indicated otherwise). §/§§: p<0.05, 0.01, respectively, indicating pairwise comparisons between colonies. (E) Scatchard plots indicate no difference in the rate dissociation constant (Kd) between Charles River (Kd = 1.15 nM) and Harlan (Kd = 1.13 nM) control rats, indicating no difference in CBG-corticosterone binding affinities between vendor colonies.   2.3.5 Serpina6 Coding Sequences for CBG  When the Serpina6 coding sequences for CBG were compared in the two SD rat colonies, we found a synonymous single nucleotide transition (C>T) in exon 2 within the codon for Phe152, and two non-synonymous single nucleotide transitions (A>G) in exon 4 that cause amino acid substitutions (Ile298Met and Met307Val) in Charles River SD rats. However, there were no sequence differences within the 439bp proximal promoter region we have defined previously (Underhill and Hammond, 1995).  2.3.6 Plasma CBG-Corticosterone Binding Affinities  To determine if the difference in CBG coding sequences between Charles River and Harlan SD rats alters CBG-corticosterone binding affinity, plasma samples from control rats from each colony were subjected to Scatchard analysis, using 3H-corticosterone as the radiolabeled ligand. The results indicated that CBG from Charles River and Harlan rats have similar rate dissociation constants (Kd) for   31 corticosterone, 1.15 nM and 1.13 nM, respectively (Fig. 2.4E), and therefore, similar affinities (affinity constant, Ka = 1/Kd).   2.3.7 Hind Paw Cytokine Levels In the hind paw, the levels of five pro-inflammatory cytokines and chemokines (Fig. 2.5A-E) increased with the development of AA (main effects of AA severity ps<0.002). By contrast, the anti-inflammatory cytokines IL-4 and IL-10 were uniformly undetectable (not shown).  Cytokine profiles differed between colonies (Fig. 2.5A-C). Interestingly, both TNF-α and MCP-1 levels (vendor × AA severity interactions, p<0.01 and trend p=0.064, respectively) were lower in Harlan than Charles River rats, but only in the absence of AA (for both control and Adj/NA, Harlan < Charles River, ps<0.01, with the exception that for MCP-1, for control, trend p=0.08). In addition, both TNF-α and MCP-1 levels were higher in the Adj/NA compared to the control condition in Charles River rats (ps<0.05), but were higher in the Adj/M-M compared to the control condition in Harlan rats (ps<0.001) (Fig. 2.5A, B), indicating colony differences in the overall cytokine patterns, in a disease state-dependent manner. On the other hand, for KC/GRO, levels were lower overall in Harlan compared to Charles River, independent of AA severity (main effect of colony, p<0.01). For IL-1β and IL-6 (Fig. 2.5D, E), levels increased in all rats receiving CFA injection (main effects of AA severity, IL-1β: p<0.05, IL-6: p<0.01), regardless of whether clinical signs of arthritis developed (Adj/NA, Adj/M-M, Adj/S > controls). However, while interactions between AA severity and colony were not detected for either IL-1β or IL-6, inspection of Figure 2.5 suggests that the overall increase of IL-1β in the Adj/NA condition appears to be driven by Charles River rats, whereas the overall increase in IL-6 in the Adj/M-M condition appears to be driven by Harlan rats. This is consistent with the colony specific changes in TNF-a and MCP-1. IFN-ɣ levels, while appearing to respond to AA, were not detectable in all animals (percentage of rats with detectable levels indicated in graph), and thus not normally distributed, and were not analyzed statistically.   32  Figure 2.5 Cytokine levels in the hind paw at the peak of inflammation. (A) TNF-α and (B) MCP-1 levels were differentially impacted by arthritis severity in Charles River versus Harlan rats [for TNF-α, main effect of AA severity, F(3, 49)=20.26, p<0.001; colony × AA severity interaction, F(3, 49)=5.29, p<0.01; for MCP-1, main effect of AA severity, F(3, 49)=20.68, p<0.001; trend for colony × AA severity interaction, 0.00.51.01.52.02.5[TNF-α] pg/mgCharles River Harlan********§§ §§**********TNF-α ATNF-α (pg/mg) 0200400600[MCP-1] pg/mgCharles River Harlan******* §§×***********MCP-1 MCP-1 (pg/mg) B0510152025[KC/GRO] pg/mgCharles River Harlan**************§§KC/GRO KC/GRO (pg/mg) C0102030[IL-1β] pg/mgCharles River Harlan******* ***********IL-1β DIL-1β (pg/mg) 0100200300[IL-6] pg/mgCharles River Harlan**********************EIL-6 IL-6 (pg/mg) 012345[IFN-?] pg/mgCharles River Harlan0%29% 80%83%33%75%33%36%IFN-ɣ FIFN-ɣ (pg/mg) ControlAdj/NAAdj/M-MAdj/SControlAdj/NAAdj/M-MAdj/SControlAdj/NAAdj/M-MAdj/SControlAdj/NAAdj/M-MAdj/SFigure 5 TNF-α (pg/mL) KC/GRO (pg/mL) IL-6 (pg/mL) MCP-1 (pg/mL) IL-1β (pg/mL) IFN-ɣ (pg/mL)   33 F(3, 49)=2.59, p=0.064]. TNF-α levels were lower in Harlan compared to Charles River control rats (§§), and TNF-α and MCP-1 were lower in Harlan compared to Charles River rats in the absence of AA (§§). (C) KC/GRO levels increased in both Charles River and Harlan with CFA-injection [main effect of AA severity, F(3, 49)=13.28, p<0.001]. Overall, KC/GRO levels were lower in Harlan compared to Charles River rats, irrespective of AA severity [main effect of colony, F(1, 49)=10.23, p<0.01 §§]. (D) IL-1β levels increased with CFA-injection, with no differences between colonies [main effect of AA severity, F(3, 49)=18.54, p<0.001]. (E) IL-6 levels increased with arthritis in all Charles River and Harlan rats that developed clinical signs of arthritis, compared to controls [main effect of AA severity, F(3, 48)=12.42, p<0.001]. (F) For IFN-ɣ, data were non-normally distributed following transformation and were not analyzed statistically (percent of rats with detectable levels within each severity group indicated on graph). Note: Lower limits of detection (LLOD) for cytokine assays - KC/GRO: 2.62 pg/mL; IFN-ɣ: 15.8 pg/mL; IL-10: 38.6 pg/mL; IL-1β: 6.7 pg/mL; IL-4: 7.8 pg/mL; IL-6: 35.4 pg/mL; MCP-1: 9.3 pg/mL; TNF-α: 1.7 pg/mL. Cytokine levels were Blom transformed for statistical analysis; untransformed data (pg cytokine/mg total protein) is presented in the Figure. Data presented as mean ± SEM. Adj/NA: Charles River: n=14, Harlan: n=6; Adj/M-M: Charles River: n=3, Harlan: n=12; Adj/S: Charles River: n=4, Harlan: n=5; control, saline-injected: Charles River: n=7, Harlan: n=6. Post hoc: *p<0.05; **p<0.01; ***p<0.001 (comparison to control, unless indicated otherwise). §/§§: p<0.05, 0.01, respectively; post hoc comparisons between colonies.  2.3.8 Plasma Cytokine Levels In the plasma, the levels of TNF-α, MCP-1, KC/GRO, IL-6, and IFN-ɣ all generally increased with the development of AA (Fig. 2.6A – E). However, the anti-inflammatory cytokines measured (IL-4, IL-10), as well as IL-1β, were uniformly undetectable (not shown). Circulating levels of TNF-α (A), IL-6 (D), and IFN-ɣ (E) increased with severe AA (Adj/S) but in the control, Adj/NA and/or Adj/M-M groups, cytokine levels were low or undetectable. As a result of the low cytokine levels, the percentage of rats with detectable levels within each severity condition are indicated on the graphs and the data were not analyzed statistically.   34 By contrast, MCP-1 levels increased with CFA injection (Fig 2.6B; main effect of AA severity, p<0.001) and KC/GRO levels increased with severe AA (Fig 2.6C; main effect of AA severity, p<0.01). In addition, KC/GRO levels were lower overall in Harlan compared to Charles River rats (main effect of colony, p<0.01).    35  Figure 2.6 Plasma cytokine levels at the peak of inflammation. (A) TNF- α, (D) IL-6, and (E) IFN-ɣ generally increased in rats with arthritis but were low or undetectable in most disease states other than severe AA (Adj/S), were not normally distributed, and were not analyzed statistically (percent of rats with detectable levels within each severity group is indicated on the graph). (B) MCP-1 increased 01020304050Charles River Harlan0% 71% 0% 100% 0% 33% 92% 100%TNF-α ATNF-α (pg/mg) 0200040006000Charles River Harlan******** **********MCP-1 MCP-1 (pg/mg) B0100200300400Charles River Harlan************§§KC/GRO KC/GRO (pg/mg) C0200400600Charles River Harlan29% 71% 100% 100% 50% 33% 75% 100%DIL-6 IL-6 (pg/mg) 0200400600Charles River Harlan36% 0% 75%17%17% 33% 80%29%IFN-ɣ EIFN-ɣ (pg/mg) ControlAdj/NAAdj/M-MAdj/SControlAdj/NAAdj/M-MAdj/SControlAdj/NAAdj/M-MAdj/SControlAdj/NAAdj/M-MAdj/SFigure 6   36 with CFA injection in both Charles River and Harlan rats [main effect of AA severity, F(3, 48)=9.68, p<0.001]. (C) KC/GRO levels only increased with severe AA [main effect of AA severity, F(3, 48)=6.00, p<0.01] and overall were lower in Harlan than Charles River rats [main effect of colony, F(1, 48)=9.21, p<0.01; §§]. Note: Lower limits of detection (LLOD) for cytokine assays – KC/GRO: 3.3 pg/mL; IFN-ɣ: 104.0 pg/mL; IL-10: 32.2 pg/mL; IL-1β: 23.5 pg/mL; IL-4: 8.26 pg/mL; IL-6: 74.4 pg/mL; MCP-1: 5.27 pg/mL; TNF-α: 12.6 pg/mL. Cytokine levels were Blom transformed for statistical analysis; untransformed data (pg cytokine/ml) is presented in the Figure. Data are presented as mean ± SEM. Adj/NA: Charles River: n=14, Harlan: n=6; Adj/M-M: Charles River: n=3, Harlan: n=12; Adj/S: Charles River: n=4, Harlan: n=5; control, saline-injected: Charles River: n=7, Harlan: n=6. Post hoc: *p<0.05; **p<0.01; ***p<0.001 (comparison to control, unless indicated otherwise). §§: p<0.01, post hoc comparisons between colonies.  2.4 Discussion Our data demonstrate differences in both the incidence and severity of inflammation, and the endocrine and immune response following CFA injection in SD rats obtained from Charles River and Harlan Laboratories. Overall, we found that Harlan rats were more susceptible to inflammation, developing a more severe course of arthritis at lower doses of CFA, compared to Charles River rats. In addition, while rats from both vendors showed increasing corticosterone levels with the development of AA, Harlan had lower CBG than Charles River rats in the absence of arthritis (control and Adj/NA groups). As a result, the decrease in plasma CBG that occurs with the development of arthritis was less pronounced in Harlan rats. Moreover, Harlan and Charles River rats exhibited differential cytokine patterns with inflammation in the hind paw. While SD rats from both colonies showed the highest cytokine/chemokine levels with severe inflammation, Charles River rats responded at lower levels of AA severity. That is, Charles River rats generally showed an increase in cytokine levels to the challenge of CFA injection, even in the absence of clinical signs of arthritis (Adj/NA), whereas Harlan rats generally showed an initial increase only with mild-moderate arthritis. These data suggest that colony-based differences in endocrine and immune measures not only serve as a sensitive index of inflammation but   37 that these differences could be exploited to understand factors responsible for differential responses to inflammatory challenges in general.  Both Charles River and Harlan rats showed increasing basal total and free corticosterone with increasing AA severity, likely due to increased levels of proinflammatory cytokines driving increased HPA axis activity and a loss of the corticosterone diurnal rhythm (Sarlis et al., 1992). With the development of severe arthritis there was also increased variability in corticosterone levels, a finding common to the AA model (Harbuz et al., 1997; Harbuz et al., 1999; Webster et al., 2002), and likely occurs as a result of individual differences in the severity of arthritis, with subsequent differential effects on changes to the diurnal corticosterone rhythm. Of note, differential corticosterone profiles were detected between Charles River and Harlan SD rats, with Charles River rats displaying increased mean corticosterone levels (total and free) in the absence of clinical signs of AA (Adj/NA), whereas Harlan rats had a more stepwise increase in corticosterone levels with increasing AA severity, although these differences failed to reach statistical significance. Interestingly, however, the corticosterone profiles mimic the colony-specific cytokine responses discussed below, with Charles River rats showing increased cytokine levels in the Adj/N-A group, compared to a stepwise cytokine increase with increasing AA severity for Harlan rats.  Importantly, differences in CBG levels were also detected, with lower CBG in Harlan than Charles River rats under control conditions and in rats with no clinical signs of arthritis (Adj/NA group). This difference may in part explain previous findings of differences in ACTH levels seen after administration of LPS and turpentine in rats from the same SD colonies studied here (Turnbull and Rivier, 1999). Plasma CBG levels were not measured in the latter study, but large increases in plasma corticosterone were observed in all rats treated with LPS or turpentine, irrespective of the SD colony. If CBG levels were different between colonies, as we have now observed, it would be expected that the lower levels of CBG in Harlan SD rats would lead to higher free corticosterone levels, which would be available to feedback onto ACTH, thus explaining the lower ACTH levels in Harlan compared to Charles   38 River SD rats (Turnbull and Rivier, 1999). In addition, higher CBG levels in Charles River SD rats could explain why they have increased levels of basal corticosterone (Turnbull and Rivier, 1999), as more of the steroid would be bound to CBG. In the present study, while ACTH levels were not statistically different between colonies, mean ACTH levels were lower in Harlan compared to Charles River rats in the Adj/S condition, which may also be suggestive of colony differences in overall HPA activity. Differences in baseline CBG levels between Harlan and Charles River SD rats were not due to differences in CBG steroid-binding affinity despite the presence of two amino acid differences in their CBG sequences. There were also no differences between the two colonies of SD rats in their Serpina6 proximal promoter sequences that contain a C/EBPβ regulatory site known to be important for the acute phase response (Poli, 1998; Verhoog et al., 2014), Therefore, while we cannot exclude the possibility that the differences in the CBG coding sequences alter the secretion rates of CBG from hepatocytes in vivo, it is possible that differences in transcription factor levels or differences in regulatory sequences outside of the proximal promoter sequence might account for this.  Lower plasma CBG levels in Harlan rats suggest a lower corticosterone reservoir, which could play a key role in the observed increased susceptibility to the immune challenge. It is known that human CBG is targeted by proteases such as neutrophil elastase (Hammond et al., 1990), significantly decreasing its binding capacity for glucocorticoids and allowing for their targeted delivery at sites of inflammation. Immunoreactive CBG degradation products have also been observed in wound fluids of rats subjected to thermal injury (Garrel et al., 1993). Thus, the larger corticosteroid reservoir in Charles River SD rats may function to more effectively deliver corticosterone to sites of inflammation and result in reduced inflammation in these rats. Of note, with the development of arthritis, CBG levels decreased in SD rats from both colonies, with significant differences in CBG levels detected between controls and rats with severe arthritis. In the severe arthritis state, when CBG levels are significantly lower and total corticosterone levels are significantly higher, there was a substantial increase in free corticosterone, which would also be available at target tissues. The decrease in plasma CBG levels observed with increasing   39 arthritis severity in both Charles River and Harlan SD rats may reflect either an increase in the plasma clearance of CBG (Mast et al., 1991), and/or a decrease in hepatic CBG production in response to increases in the plasma levels of corticosterone (Smith and Hammond, 1992) and proinflammatory cytokines (Bernier et al., 1998). In support of the latter, it has been shown that IL-6 decreases CBG levels, likely through decreased stability of CBG mRNA (Bartalena et al., 1993), with TNF-α also causing a decrease in plasma CBG levels (Fleshner et al., 1997).   To investigate the possible role of differential activation of cytokine networks in mediating the responses to AA in Charles River and Harlan rats, a subset of key pro- and anti-inflammatory cytokines were quantified in the hind paw and plasma. Activated immune cells produce a wide array of cytokines, which in turn activate other immune cells, resulting in the production of a complex cytokine network, the regulation and dynamics of which are still not yet fully understood (Feldmann and Maini, 2001). With AA and rheumatoid arthritis, cytokine production leads to matrix metalloproteinase production and osteoclast activation, resulting in the characteristic symptoms of bone destruction and extracellular matrix breakdown (Gravallese and Goldring, 2000). Examination of cytokine levels in the hind paw revealed, as expected, increased levels of pro-inflammatory cytokines with AA in SD rats from both colonies, with undetectable levels of anti-inflammatory cytokines (IL-4, IL-10) in all rats. However, although Charles River and Harlan SD rats showed similar cytokine/chemokine levels under conditions of severe inflammation, subtle but important differences in hind paw cytokine response profiles were detected for TNF-α, KC/GRO (CXCL1), and MCP-1 (CCL2). In general, TNF-α is involved in systemic inflammation, specifically the acute phase reaction, and is mainly produced by macrophages, whereas KC/GRO and MCP-1 are inflammatory chemokines, KC/GRO being a chemo-attractant for neutrophils, and MCP-1 being a chemo-attractant for monocytes. Increases in TNF-α, KC/GRO, and MCP-1 in the AA paradigm are well established (Billiau and Matthys, 2001; Stolina et al., 2009; Szekanecz et al., 2000), and all three cytokines are critically involved in rheumatoid arthritis pathogenesis. TNF-α, KC/GRO, and MCP-1 are elevated in synovial   40 fluid of patients with rheumatoid arthritis (Feldmann et al., 1996; Harigai et al., 1993; Iwamoto et al., 2008; Koch et al., 1992), and anti-TNF agents are currently widely used in rheumatoid arthritis therapeutics, with agents targeting the KC/GRO pathway also generating interest (Feldmann and Maini, 2001; Iwamoto et al., 2008; Maini and Taylor, 2000; Szekanecz et al., 1998). Interestingly, differences in TNF-α and MCP-1 levels in Charles River versus Harlan SD rats were detected both under control conditions and in adjuvant-injected rats that did not develop arthritis (Adj/NA), but not under conditions of active inflammation (Adj/M-M and Adj/S), whereas KC/GRO levels were lower overall in Harlan compared to Charles River rats.  In the plasma, cytokine levels generally increased with AA; however, levels were lower than in the hind paw, with low/undetectable levels for a number of plasma cytokines (TNF-α, IL-6, IFN-ɣ) in the control, Adj/NA and/or Adj/M-M groups. Similar to what was observed in the hind paw, KC/GRO levels were lower in the plasma of Harlan compared to Charles River rats. As well, Charles River rats in the Adj/NA condition had stronger TNF-α and IFN-ɣ responses than those in the Adj/M-M condition, again paralleling results in the hind paw. In addition, the overall plasma cytokine response for KC/GRO and MCP-1 in Harlan animals appears to be lower than that of Charles River animals, with the increase in cytokine levels seen with increasing AA severity likely driven by the response in Charles River rats. While not analyzed statistically, Harlan rats also appear to produce less IFN- ɣ and IL-6 than Charles River rats with severe arthritis. Few studies have probed for colony based differences in the cytokine response to challenge in a single rat strain (Turnbull and Rivier, 1999), but our findings are consistent with more extensive evidence of differential cytokine activation in response to AA in rats from different strains. For example, Banik and colleagues (2002) found that while administration of a CFA dose of 0.6 mg/rat to Lewis rats resulted in AA in 100% of rats, while there was little to no effect in Wistar and SD rats (Banik et al., 2002). Even increasing the doses to 1.0 – 1.2 mg/rat resulted in AA in only 58% and 45% of Wistar and SD rats, respectively (Banik et al., 2002). Similarly, while Lewis rats generally develop more severe AA than SD   41 rats (Rosenthale, 1970), time course analysis of serum levels of TNF-α has revealed enhanced TNF-α levels in SD (Laboratory Animal Unit, the University of Hong Kong, China) over Lewis rats throughout the disease course (Woodward et al., 2006), suggesting that TNF-α is a critical component of the cytokine network in response to AA in SD rats. Moreover, we found increased levels of a number of cytokines in SD rats that did not develop clinical signs of arthritis, including TNF-α, MCP-1, KC/GRO, and IL-1β. Specifically, we found that TNF-α and MCP-1 levels were higher in Charles River than Harlan rats, but only in absence of inflammation (Adj/NA, control). In addition, there were differential TNF-α and MCP-1 profiles between colonies: Charles River rats had increased TNF-α and MCP-1 levels in the Adj/NA compared to control condition, whereas Harlan rats had increased levels TNF-α and MCP-1 levels in the Adj/M-M compared to control condition. Furthermore, although statistical interactions between colony and AA severity were not detected for IL-1β and IL-6, the data suggest that the overall increase in IL-1β in rats with no clinical signs of arthritis (Adj/NA) is driven primarily by the response in Charles River rats, while the overall elevation in IL-6 in rats with mild-moderate arthritis (Adj/M-M) is driven primarily by the response in Harlan rats, a finding that parallels the differences detected in TNF-α and MCP-1 profiles. These divergent cytokine profiles between Charles River and Harlan rats suggest that they may rely on different cytokine networks, and do so in a disease state-dependent manner.  The finding that even in the absence of clinical signs of arthritis, important physiological differences were observed in a single strain of rat obtained from different vendors is novel and important. Rarely have cytokine levels been assessed in the absence of clinical signs of AA, with measurements more commonly made only in rats developing overt signs of AA and/or by collapsing across groups to simply include a CFA-injected versus a control group. However, our findings of differences in the TNF-α and MCP-1 responses in Charles River versus Harlan SD rats to pre-clinical AA highlight the importance of separating out these disparate states of preclinical versus clinical inflammation, and suggest that the same cytokines may differentially mediate severity of inflammation in rats from the two colonies. Furthermore, pre-clinical levels of cytokines and other proteins are used as biomarkers and predictors of   42 disease status in rheumatoid arthritis (Deane et al., 2010), and our findings on preclinical inflammation, unmasked by utilizing colony specific differences, may provide important insight into the human condition.  While we have shown genetic differences between Charles River and Harlan rats, supporting the hypothesis that genetic drift has occurred between these two SD rat populations, the role of environmental factors, such as diet, early-life rearing conditions, and shipping conditions, cannot be discounted. These environmental factors are all known to impact physiological parameters and naturally differ between vendors. While early-life environmental conditions may be affecting epigenetic regulation of endocrine and immune genes (Champagne and Curley, 2009; Perera and Herbstman, 2011), the finding of genetic differences between Charles River and Harlan rats supports genetic drift, with further genetic investigation required to determine the extent of the genetic differences. Although our studies only provide correlative evidence of colony differences in endocrine and immune function, as well as genetic differences between colonies, the greater severity of inflammation observed in Harlan versus Charles River rats, together with differential plasma CBG levels and patterns of cytokine activation, is novel and provides a foundation for future mechanistic studies. Furthermore, our findings indicate that in addition to strain, the breeding colony of rats must be taken into account in both the experimental design process, and the interpretation and generalization of published literature, as it may explain conflicting physiological/neurobiological findings reported by different laboratories using SD rats. SD rats are considered a standard and reliable model for rheumatoid arthritis, being an outbred strain with a heterogenic background, which allows for evaluation of possible genetic factors involved in inflammation and response to treatment (Woodward et al., 2006). Our data demonstrate that the colony of origin should also be considered in the evaluation of anti-arthritic agents. Moreover, rather than viewing colony differences as a limitation, the present data suggest that differences between SD rats from different colonies could be used as a powerful tool for revealing differential endocrine and immune factors   43 underlying inflammation, and to provide insight into the pathophysiology of and treatment strategies for rheumatoid arthritis.    44 Chapter 3: Differential Activation of Endocrine-Immune Networks by Arthritis Challenge: Insights from Colony-Specific Responses  3.1 Introduction Rheumatoid arthritis (RA) is a chronic inflammatory condition with an etiology that is not yet well understood. While considered an autoimmune disease, there is no clear consensus as to the exact triggers of RA, and a growing list of possible autoantigens and infectious agents, as well as environmental conditions has been associated with RA (Blass et al., 1999; Imboden, 2009; Straub and Cutolo, 2001). There is also variability in prevalence and incidence (Mijiyawa, 1995) as well as the clinical presentation of RA (Rasker and Cosh, 1992). Even in patients with significant overlap in RA symptoms, dissimilar physiological profiles, with differential cytokine expression patterns, leukocyte involvement and infiltration, and synovial damage is common (Thurlings et al., 2008; Ulfgren et al., 2000). Given this inherent variability, it is not surprising that there is also a variable response to treatment (Anderson et al., 2000).  It is, however, known that RA is exacerbated by chronic stress (Straub et al., 2005), although the mechanisms by which this occurs remain unclear. Although glucocorticoids are known to be involved in RA, both immunosuppressive and immunostimulating effects of glucocorticoids have been reported (Dhabhar, 2009). Alterations in hypothalamic-pituitary-adrenal (HPA) reactivity have, however, been shown to be a critical factor in experimentally-induced arthritis (Sternberg et al., 1992; Sternberg et al., 1989), as well as in a subset of cases of RA. Relative adrenal insufficiency, inappropriate activation of the HPA axis resulting in an impaired ability to inhibit ongoing inflammation (Imrich et al., 2010), and other forms of HPA axis dysregulation have been observed to varying degrees in patients with RA (Harbuz et al., 2003). Yet a clear HPA deficit has yet to be identified (Jessop and Harbuz, 2005), and thus it has been suggested that investigations into the HPA response, in concert with immune markers (cytokines), will be more informative than continued probing of the HPA axis alone (Choy, 2012; Eijsbouts et al., 2005).   45 Peripheral cytokines are integral in RA pathology (Feldmann et al., 1996) and there is also evidence of increased cytokine levels in the brain (Fuggle et al., 2014). Importantly, RA and other conditions associated with chronic inflammation are known risk factors for mental illness (Benros et al., 2011), likely mediated, at least in part, by cytokine disturbances. While the mechanisms remain unclear, there is evidence that the blood brain barrier is compromised in RA (Jacobs et al., 2012) and in animal models of experimentally-induced arthritis (Nishioku et al., 2011). TNF-α, a key cytokine in RA pathogenesis, likely plays a role in neuroinflammation with RA (Fuggle et al., 2014), as some of the neuropsychiatric features associated with the disorder, such as fatigue and central sensitization to pain may be dampened with anti-TNF agents (Maini and Taylor, 2000; Rech et al., 2013). However, further investigation into the role of the numerous other cytokines involved in RA-mediated neuroinflammation, and how cytokine levels vary by disease state, is needed. We have exploited known vendor colony-based differences in endocrine/immune responses to elucidate endocrine and immune mechanisms underlying RA, utilizing the adjuvant-induced arthritis (AA) model, a well-established model of human RA. The response to AA was investigated in female subjects due to the increased rates of autoimmune disorders in women compared to men (Lawrence et al., 1998), and previously reported heightened sensitivity to experimentally induced arthritis in female rodents (Holmdahl, 1995). Furthermore, Sprague Dawley (SD) rats from two different vendors – Harlan and Charles River – were utilized as a tool to probe the possible basis for the variability in RA course. We showed previously (Bodnar et al., 2015) that colony of origin impacts the AA disease course. Compared to Charles River rats, Harlan rats have increased incidence and severity of AA, lower corticosteroid binding globulin (CBG) despite similar circulating corticosterone levels, and different patterns of cytokine activation in the hind paw.   The current study builds on these findings to extend our investigation of endocrine (corticosterone) and immune (cytokines) parameters in key immune compartments, including the paws, the main site of inflammation in the AA model, spleen, thymus, popliteal lymph nodes, and brain.   46 Following investigation of these individual tissue compartments (univariate analyses), and to probe for networks of endocrine/immune variables underlying these differential responses, we combined datasets from this and the previous study, which resulted in a large dataset suitable for network analysis, and performed a constrained principal component analysis (CPCA). CPCA is a multivariate technique combining multiple regression and principal component analysis (PCA) into a unified framework (Hunter and Takane, 2002), which has the advantage of being able to relate the networks back to the independent variables. While CPCA has been used in a wide variety of fields (Hyman et al., 2013; Metzak et al., 2011; Raineki et al., 2014; Woodward et al., 2015) to the best of our knowledge, this is the first time CPCA has been used to examine endocrine and immune parameters. This technique is particularly well suited to the examination of endocrine and cytokine activity and interactions, as the complex balance of variables, rather than changes in a single variable, is most relevant to disease pathophysiology. Importantly, our multi-systems approach is critical for better understanding of the underlying pathophysiology in complex diseases such as RA (Tak et al., 1997). We propose that utilizing colony-based differences together with a network approach to the assessment of endocrine and immune responses to an inflammatory challenge will not only provide novel information on key modulators or mediators of inflammation, but also a more complete and nuanced clinically-relevant representation of factors influencing disease incidence and course.  3.2 Materials and Methods 3.2.1 Animals  Adult female Sprague Dawley rats (postnatal day [P] 40 ± 2) were obtained from Charles River Laboratories International, Inc. (St. Constant, QC, Canada) and Harlan Laboratories, Inc. (Frederick, MD) (n = 29/vendor). Conditions in these colonies prior to arrival at the University of British Columbia (UBC) were previously reported (Bodnar et al., 2015). At UBC, rats were pair-housed in a single colony room, and maintained under controlled temperatures (21-22°C), on a 12:12 hour light/dark cycle. Ad libitum   47 access to standard laboratory chow (Purina Laboratory Rodent Diet #5001, Delta, BC, Canada) and water was provided throughout the experiment. All procedures were in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals, and approved by the University of British Columbia Animal Care Committee.     3.2.2 Adjuvant-Induced Arthritis (AA) Induction and Clinical Assessment  AA was induced as previously reported (Bodnar et al., 2015). Briefly, on P55-60, rats received intradermal injections of either complete Freund’s Adjuvant (CFA; n=7-8/vendor/dose) or physiological saline (control; n=6-7/vendor), at the base of the tail. CFA was prepared using Mycobacterium tuberculosis H37 RA (Difco laboratory, Detroit, MI) dissolved in incomplete Freund’s adjuvant (Zhang et al., 2012). Two initial doses of CFA were prepared – high, 1.2mg/rat and low, 0.3 mg/rat were selected with the aim of identifying a dose that would result in arthritis onset in approximately 50% of rats. However, due to the previously observed heightened response of Harlan-derived rats to the low (0.3 mg) dose (40% of Harlan vs 0% of Charles River rats developed severe inflammation) (Bodnar et al., 2015), two additional doses of CFA were added: 0.6 mg for Charles River and 0.2 mg for Harlan. Overall, the CFA doses selected resulted in low (Charles River: 0.3 mg; Harlan: 0.2 mg), moderate (Charles River: 0.6 mg; Harlan: 0.3 mg), and high (Charles River & Harlan: 1.2 mg) levels of AA, as previously reported (Bodnar et al., 2015). Following injection, rats were weighed and clinical scores, a baseline measure of AA severity, measured on days 6, 9, 11, 13, and 15 post-injection. Each of the four paws was scored on a 0-4 point scale, (0=no inflammation, 1=single focus of redness or swelling, 2=two or more foci of redness or swelling, 3=confluent but not global swelling, 4 = severe global swelling; total possible clinical score=16). A clinical score ≥8 at any point during the study was classified as severe arthritis (Adj/S) whereas a clinical score ≥1 but <8 was classified as mild-moderate arthritis (Adj/M-M). Rats were then categorized as: control (saline-injected), CFA-injected but no clinical signs of arthritis (Adj/NA), mild-  48 moderate (Adj/M-M), or severe (Adj/S) AA. Levels of physiological parameters were then analyzed by colony and arthritis severity.  3.2.3 Termination and Tissue Collection  At the peak of inflammation, day 16 post-injection, rats were quickly decapitated (<2 min; between 08:00 and 10:30 hr), and trunk blood collected. Draining popliteal lymph nodes, thymus, spleen, hypothalamus, hippocampus, front paws (at the level of the radiocarpal joint), and hind paws (at the level of the tibiotarsal joint) were collected and flash frozen in liquid nitrogen. All tissue samples were stored at −80°C until assayed for protein and/or steroid levels. Vaginal lavage samples were collected and assessed cytologically for estrous cycle stage.  3.3 Tissue Homogenization Tissue samples were homogenized in cold lysis buffer. Brain (hypothalamus, hippocampus) and spleen (0.15-0.20g) were homogenized using the Omni Bead Ruptor 24 (Omni International, Kennesaw, GA). Hind paw were homogenized as reported previously (Bodnar et al., 2015), with a separate aliquot removed for steroid extraction. Following homogenization, all tissue samples were centrifuged at 1,400 g for 10 min at 4°C with supernatant collected for total protein quantification and cytokine analysis. Prior to steroid measurements, organs (front paw, spleen, popliteal lymph nodes, thymus) were weighed to the nearest 0.1mg, and plasma and whole blood measured to the nearest µl. Samples (with the exception of hind paws) were then homogenized in 3 volumes of water with a tissue homogenizer and diluted in 16 volumes of methanol. Front paws were weighed, pulverized in powdered dry ice with a mortar and pestle, and diluted in methanol. Hind paws were homogenized for cytokine analysis as above and an aliquot subsequently diluted in methanol. Similar to previous studies using brain tissue (Bailey et al., 2013; Taves et al., 2011), we used protein levels in the hind paw to determine tissue weight, using a hind paw conversion factor calculated from samples from both Charles River and Harlan rats, at various   49 severity states [y=6.258x; where y=tissue mass (g), x=protein concentration (mg)]. After addition of methanol, all samples were thoroughly mixed and incubated overnight at 4ºC.   3.3.1 Multiplex Cytokine Immunoassays and Protein Quantification Cytokine levels were analyzed using a custom Meso Scale Discovery rat cytokine 8-plex panel, allowing for the measurement of IL-1β, IL-4, IL-6, IL-10, IFN-ɣ, KC/GRO (CXCL1), MCP-1 (CCL2), and TNF-α (catalog #: N05IA-1, MSD, Rockville, MD), plates read using a Sector Imager 2400, and data analyzed using the MSD Discovery Workbench software v. 4.0 (MSD, Rockville, MD). The lower limit of detection (LLOD) for the assays varied by plate and by analyte. The following LLOD ranges were observed (pg/mL): KC/GRO: 0.74–3.31; IFN-ɣ: 15.8–104; IL-10: 4.12–44.1; IL-1β: 6.03–23.5; IL-4: 1.94–8.26; IL-6: 35.4–144; MCP-1: 4.76–10.9; TNF-α: 1.73–12.6. Values falling below the LLOD were replaced with 0 pg/mL in all analyses and figures. Note: cytokines are presented in the following consistent order in figure 3.2–4 : interleukins (IL-1β, IL-4, IL-6, IL-10), TNF-α, IFN-ɣ, and finally chemokines (KC/GRO, MCP-1), with undetectable cytokines omitted, when necessary. Total protein levels were quantified in tissue homogenates using the Pierce Microplate BCA Protein Assay Kit (Pierce Biotechnology, Rockford, IL). Tissue cytokine levels were adjusted and reported as pg cytokine/mg of protein.  3.3.2 Steroid Extraction  Steroids were extracted from samples (plasma, thymus, spleen, popliteal lymph nodes, front paw, and hind paw) using solid phase extraction (SPE) with C18 columns, as previously described (Newman et al., 2008). Homogenates were centrifuged at 3,000g for 10 min at 2ºC, and supernatant aliquots (≤1.0mL) were diluted with 10 mL deionized water before loading onto 500mg C18 columns primed with 3mL HPLC-grade ethanol and equilibrated with 10mL deionized water. Columns were washed with 10mL of   50 40% methanol (Brummelte et al., 2010) and steroids were eluted with 5mL of 90% methanol. Eluates were dried at 40ºC in a vacuum centrifuge (Thermo Scientific SPD111V). Dried steroid residues were resuspended in steroid diluent with 5% ethanol to aid in resuspension (Newman et al., 2008). Corticosterone recovery was determined by spiking tissue pools with known amounts of corticosterone, and comparing spiked and unspiked samples (Taves et al., 2016a; Taves et al., 2011).  3.3.3 Endocrine Measures Plasma ACTH levels and the steroid binding capacity of CBG were measured as previously reported (Bodnar et al., 2015). Total (bound plus free) corticosterone levels were measured in plasma, thymus, spleen, popliteal lymph nodes, front paw, and hind paw using the ImmuChem Double Antibody Corticosterone 125I radioimmunoassay (RIA) kit (MP Biomedicals, LLC, Orangeburg, NY, USA), as previously described (Taves et al., 2015). Cross-reactivity was 100% for corticosterone and less than 1% for all other tested steroids. The minimum detectable concentration of corticosterone was 1.56 pg/RIA tube, and the intra- and inter-assay coefficients of variation were <10.3% and 7.2%, respectively. All samples were measured in duplicate.  3.3.4 Statistical Analyses Rats from the two colonies (Harlan, total n=28; Charles River: total n=29) were classified by injection condition (control, saline-injected: n=6-7/colony; CFA-injected: n=22-23/colony). Rats in the CFA condition were then further stratified by their AA severity in order to better compare levels of endocrine and immune markers in the two colonies under comparable arthritis conditions. Thus rats were categorized as: 1) failure to develop clinical signs of inflammation (Adj/NA [adjuvant-injected, no arthritis]; Charles River n=14, Harlan n=6); 2) mild-moderate AA (Adj/M-M; Charles River n=3, Harlan n=12), or severe AA (Adj/S; Charles River n=4, Harlan n=5). Note: the low n in the Adj/M-M and Adj/S   51 conditions for Charles River rats was not by design but rather reflects the low incidence of mild-moderate and severe arthritis for Charles River rats, a small but clinically important group. Data were first analyzed by analysis of variance (ANOVA) for the factors of colony and AA severity, followed by Fisher post hoc tests, as appropriate (IBM SPSS Statistics). Differences were considered significant at p≤0.05. Significant ANOVA F statistic and p values are reported in the text; post hoc p values are reported in figure legends. Outliers (±2.5 SD>mean) were removed from the cytokine analyses, when appropriate. Corticosterone and cytokine data were not normally distributed and were transformed using the Blom rank-based normalization method (Blom, 1958), prior to statistical analysis. Untransformed data are presented in the figures for clarity. Heatmaps were built on z-scored data, averaging cytokine levels by severity state and colony to demonstrate overall cytokine patterns. Heatmaps were generated using R statistical software.  CPCA (performed using Matlab) was utilized to identify networks of analytes collectively altered by AA across the various compartments analyzed. CPCA combines multivariate multiple regression and principal component analysis into a unified framework, and allows for the identification of networks (components) that are specifically predictable from the independent variables of interest (Takane, 2001, 1991). Briefly, CPCA involves first regressing the matrix of dependent variables (i.e., the z-score transformed endocrine and inflammatory measures) on the independent variables (i.e. colony and AA severity), resulting in a matrix of predicted scores reflecting the variance in endocrine and inflammatory measures that is predictable from colony and AA severity, referred to as the predictable variance. The second step in CPCA consists of a principal component analysis (PCA) on the predictable variance, which reveals multiple networks of endocrine and inflammatory measures that are directly predictable from the experimental manipulations. PCA is a data reduction technique that uses information about the dominant patterns of intercorrelation among a set of variables to reduce these variables into a smaller number of components (or networks) that best explain the variance in the dataset. A PCA on the predictable variance in the current study resulted in a number of components representing networks of   52 endocrine and immune parameters that were stimulated (or not) in response to AA. The component loadings, listed in Table 3.1, indicate the degree to which each of the 44 variables examined (analytes in various tissue compartments) load onto/fit within each component. PCA solutions were separately rotated using Varimax with Kaiser normalization, and the number of components extracted was determined using scree plots (Cattell and Vogelmann, 1977). In order to determine the degree to which the experimental conditions (vendor and inflammatory status) differed in terms of the networks activated in response to the immune challenge, correlations were computed between the experimental groups and the component scores from each of the extracted components (Fig. 3.6).   3.4 Results 3.4.1 Analysis of Estrous Cycle There were no differences in the proportion of rats within each stage of the estrous cycle (proestrus, estrous, diestrus) by colony or arthritis severity state (Bodnar et al., 2015), and >90% of rats were in diestrous at the time of termination. Thus, data were not further stratified by estrous stage for the subsequent analyses.  3.4.2 Local Corticosterone Levels Increased in the Joints and Immune Tissues with Severe Arthritis  Corticosterone levels in the front paw differed by AA severity [main effect of AA severity: F(3,49)=5.38, p=0.003], with levels increasing in both Charles River and Harlan rats with severe AA(Adj/S), as expected (Fig 3.1A). Overall, the pattern of corticosterone in the hind paw was similar to that in the front paw, although the ANOVA failed to reach significance [AA severity: F(3,48)=2.28, p=0.091] (Fig 3.1B); of note, however, inspection of Fig 3.1B reveals that the trend for increased corticosterone may be driven by the Adj/S group of Charles River rats. In the spleen and popliteal lymph nodes, corticosterone levels increased with development of AA (Adj/M-M, Adj/S) in rats from both   53 colonies [main effects of AA severity – spleen: F(3,48)=6.95, p=0.001; popliteal lymph nodes: F(3,43)=4.60, p=0.007; Fig 3.1C, D). Corticosterone levels did not differ by AA severity or colony in the thymus (Fig 3.1E).   Figure 3.1 Local corticosterone levels in the paws and immune tissues.   54 Bars represent mean corticosterone level ± SEM. Data are presented as ng corticosterone/g tissue with statistical analysis conducted on Blom transformed (normalized) data. The presence of an asterisk (*) indicates a main effect of AA severity, with the comparison to the control group, unless otherwise indicated. Post hoc: * p < 0.05; ** p < 0.01; *** p < 0.001; Control: saline-injected; Adj/NA: no clinical signs of AA following CFA injection; Adj/M-M: mild-moderate AA, clinical score ≥1, <8; Adj/S: severe AA, clinical score ≥ 8.   3.4.3 Hypothalamic Cytokine Levels Increased to a Greater Extent in Charles River Compared to Harlan Rats with AA  In the hypothalamus, colony differences in control (saline-injected) cytokine levels were detected: Harlan had higher levels of IL-6 and IFN-ɣ than Charles River rats (Fig. 3.2C, F). Hypothalamic TNF-α, IFN-ɣ, and IL-6 responses to CFA-injection also differed between colonies, as reflected by interactions between colony and AA severity [TNF-α: F(3,48)=2.75, p=0.05; IFN-ɣ: F(3, 49)= 4.81, p=0.005; IL-6: F(3,49)=3.82, p=0.016]. Levels of TNF-α and IFN-ɣ increased with mild-moderate AA (Adj/M-M) in Charles River but not Harlan rats (Fig. 3.2E, F), and thus were lower in Harlan compared to Charles River in the Adj/M-M condition. Furthermore, IL-6 levels increased with severe AA (Adj/S) in Charles River rats, whereas in Harlan rats, mean hypothalamic IL-6 levels were highest under control conditions (Fig. 3.2C).   For the chemokines examined, in both Charles River and Harlan rats, MCP-1 levels increased in all CFA-injected compared to control rats [main effect of AA severity: F(1,47)=6.03, p=0.018] (Fig. 3.2H). As a group, however, Harlan lower had overall MCP-1 levels than Charles River rats [main effect of colony: F(3,47)=4.32, p=0.009]. Comparatively, KC/GRO increased with severe AA in rats from both colonies [main effect of AA severity: F(3,49)=3.51, p=0.022] (Fig. 3.2G).  Levels of IL-1β, IL-4, and IL-10 were undetectable in at least one condition and thus not analyzed statistically (Fig. 3.2A, B, D; percent of detectable samples indicated). Of note, for Charles River, the highest mean cytokine levels were detected in rats that developed AA (Adj/M-M or Adj/S   55 conditions), whereas, for Harlan, the highest mean levels of IL-1β, IL-4, and IL-10 were in those that failed to develop clinical signs of inflammation (Adj/NA). Surprisingly, no Harlan rats with severe AA (Adj/S) had detectable levels of IL-1β or IL-10.  Figure 3.2 Cytokine levels in the hypothalamus.   56 Bars represent mean cytokine level ± SEM. Data are presented as pg cytokine/mg protein with statistical analysis conducted on Blom transformed (normalized) data. The presence of an asterisk (*) indicates a main effect of AA severity, with the comparison to the control group, unless otherwise indicated. The “×” symbol indicates a significant main effect of colony. The “§” symbol indicates a significant colony × AA severity interaction, with the symbol denoting a comparison between colonies, within rats of the same AA severity state. Percentages on bars indicate the percent of rats with detectable cytokine levels, (within each AA severity state) and these data (A, B, D) were not analyzed statically due to non-normal distribution. Post hoc: */§/× p < 0.05; **/§§ p < 0.01; *** p < 0.001; Control: saline-injected; Adj/NA: no clinical signs of AA following CFA injection; Adj/M-M: mild-moderate AA, clinical score ≥1, <8; Adj/S: severe AA, clinical score ≥ 8.   3.4.4 Hippocampal Cytokine Levels Were Mildly Affected by AA, with Colony Differences in Overall Levels of IFN-ɣ and KC/GRO  In the hippocampus, IFN-ɣ levels were higher, and KC/GRO levels were lower, in Harlan compared to Charles River rats in the control condition [main effects of colony – IFN-ɣ: F(1,48)=6.58, p=0.013; KC/GRO: F(1,48)=4.19, p=0.046] (Fig. 3.3D, E). In addition, only two cytokines responded to CFA injection: for both Charles River and Harlan rats, KC/GRO levels [F(3,48)=3.01, p=0.039] were increased in the Adj/NA and Adj/S conditions, while IL-10 levels [F(3,47)=4.72, p=0.006] were decreased in the Adj/NA compared to control and Adj/S conditions (Fig. 3.3E, C). Levels of IL-1β, IL-6, and MCP-1 did not differ by AA status or vendor. Notably, however, similar to what was observed in the hypothalamus, in Charles River rats, mean levels of IL-1β, IL-6, and MCP-1 were highest in rats that developed severe arthritis (Adj/S) whereas in Harlan rats, mean cytokine levels were highest in controls (Fig. 3.3A, B, F). Hippocampal levels of IL-4 and TNF-α were low or below the limit of detection for the majority (>50%) of rats (data shown in cytokine heatmaps only – Fig. 3.5).   57  Figure 3.3 Cytokine levels in the hippocampus. Bars represent mean cytokine level ± SEM. Data are presented as pg cytokine/mg protein with statistical analysis conducted on Blom transformed (normalized) data. The presence of an asterisk (*) indicates a main effect of AA severity, with the comparison to the control group, unless otherwise indicated. The “×” symbol indicates a significant main effect of colony. Post hoc: */× p < 0.05; ** p < 0.01; Control: saline-injected; Adj/NA: no clinical signs of AA following CFA injection; Adj/M-M: mild-moderate AA, clinical score ≥1, <8; Adj/S: severe AA, clinical score ≥ 8.      58 3.4.5 Splenic Levels of Proinflammatory Cytokines Increased with AA in Both Charles River and Harlan Rats In the spleen, levels of IL-1β, IL-6, TNF-α, KC/GRO, and MCP-1 increased with AA in rats from both Charles River and Harlan colonies [main effects of AA severity: IL-1β: F(3,46)=14.53, p<0.001; IL-6: F(3,48)=9.90, p<0.001; TNF-α: F(3,49)=3.88, p=0.014; KC/GRO: F(3,48)=12.69, p<0.001; MCP-1: F(3,49)=17.28, p<0.001;]. IL-1β, KC/GRO, and MCP-1 levels increased in all CFA-injected rats, whether or not they showed clinical signs of inflammation, with the highest cytokine levels in rats that developed severe AA (Adj/S) (Fig. 3.4A, E, F). Comparatively, levels of IL-6 and TNF-α only increased above control levels with the development of mild-moderate and/or severe AA (Fig. 3.4B, C). Furthermore, levels of KC/GRO were higher overall in Charles River compared to Harlan rats [main effect of colony – F(1,48)=10.40, p=0.002]. Levels of IFN-ɣ were undetectable in at least one severity state and thus were not analyzed statistically (Fig. 3.4D; percent of detectable samples indicated). Of note, 100% of Charles River rats that developed arthritis (Adj/M-M, Adj/S) had detectable levels of IFN-ɣ in the spleen, compared to 0% of Adj/M-M and 40% of Adj/S in Harlan rats. Finally, levels of the anti-inflammatory cytokines IL-4 and IL-10 were low or below the limit of detection for the majority (>50%) of rats from both colonies.   59  Figure 3.4 Cytokine levels in the spleen. Bars represent mean cytokine level ± SEM. Data are presented as pg cytokine/mg protein with statistical analysis conducted on Blom transformed (normalized) data. The presence of an asterisk (*) indicates a main effect of AA severity, with the comparison to the control group, unless otherwise indicated. The “×” symbol indicates a significant main effect of colony. Percentages on bars indicate the percent of rats with detectable cytokine levels, (within each AA severity state) and these data (D) were not analyzed statically due to non-normal distribution. Post hoc: * p < 0.05; **/×× p < 0.01; *** p < 0.001; Control: saline-injected; Adj/NA: no clinical signs of AA following CFA injection; Adj/M-M: mild-moderate AA, clinical score ≥1, <8; Adj/S: severe AA, clinical score ≥ 8.       60 3.4.6 The Global Cytokine Profile Following CFA Injection Differed Between Charles River and Harlan Rats  The global cytokine response to CFA injection in the hypothalamus, hippocampus, and spleen, is depicted in the heatmap in Figure 3.5. In the spleen, the profile between colonies was comparable, with severely arthritic rats demonstrating the greatest cytokine increases. In the brain, however, cytokine activation patterns in response to CFA injection differed between colonies. For Charles River rats in general, hypothalamic cytokine levels were highest in rats that showed clinical signs of AA (Adj/M-M, Adj/S) and hippocampal cytokine levels were highest in rats that developed severe AA (Adj/S). An opposing profile was detected in Harlan rats: hypothalamic cytokine levels were generally highest in controls and rats that failed to develop clinical signs of AA (Adj/NA) and hippocampal cytokine levels were highest in controls. Of note, similar profiles were detected for both pro- and anti-inflammatory cytokines – the two “opposing” groups of cytokines generally increased or decreased together, as can be seen in Figure 3.5.    61                   Figure 3.5 Heatmap of the overall cytokine response to CFA-injection in the hypothalamus, hippocampus, and spleen. Columns represent severity states (control, Adj/NA, Adj/M-M, Adj/S), split by colony, as indicated. Rows represent mean cytokine levels (z-scored data) by severity state, within each tissue. Colors indicate deviations from the mean of zero, as indicated in the colour key. Control: saline-injected; Adj/NA: no clinical signs of AA following CFA injection; Adj/M-M: mild-moderate  AA, clinical score ≥1, <8; Adj/S: severe AA, clinical score ≥ 8.     62 3.4.7 CPCA Analysis Indicates That Charles River and Harlan Rats Rely on Different Endocrine/Immune Networks Throughout the Course of AA  The experimental model (i.e. colony and AA severity) accounted for 28.06% of the total variance in the parameters examined. A principal component analysis (PCA) on the endocrine/immune variance constrained to that predictable from colony and AA severity revealed a three-component solution. The component loadings for all of the parameters, for each of the three components, are listed in Table 3.1 (note: previously reported variables27 are indicated in blue in the table). The first component explained 34.87% of the predictable variance (9.79% of the total variance), the second component explained 26.69% of the predictable variance (9.17% of the total variance), and the third component explained 15.09% of the predictable variance (4.23% of the total variance). The three components were defined based on the endocrine/immune parameters that showed the highest loadings on each component (Table 1): Component 1 was defined as Endocrine/Immune Response in Peripheral Tissue, as it included mainly cytokines within the hind paw and corticosterone levels in plasma, paws, lymph nodes, and spleen. Component 2 was defined as Proinflammatory Chemokine Response in Peripheral Tissue & Brain, as it included mainly KC/GRO and MCP-1 in plasma, spleen, and brain. Component 3 was defined as Central Balance of Pro-/Anti-Inflammatory Cytokines, as it included both pro-/anti-inflammatory cytokines (IL-6, IL-4, IL-10) exclusively within the brain.   In order to examine whether rats from the two colonies differentially relied on different networks (components) at different stages of disease, correlations between subjects’ component scores and their AA condition were performed (Fig. 3.6). The Endocrine/Immune Response in Peripheral Tissue network (Component 1), was negatively correlated with the Adj/NA condition for both Charles River (r=−0.29, p=0.027) and Harlan (r=−0.36, p=0.006) rats, and with the Adj/M-M condition for Charles River rats (r=−0.31, p=0.018), but positively correlated with the Adj/M-M condition (r=0.31, p=0.018) and the Adj/S condition (r=0.79, p<0.001) for Harlan rats (Fig 3.6A). The Proinflammatory Chemokine Response in Peripheral Tissue & Brain network (Component 2) was positively correlated with Harlan control rats   63 (r=0.66, p<0.001) and Charles River rats in the Adj/S condition (r=0.55, p<0.001), as well as negatively correlated with Charles River rats that failed to develop AA (Adj/NA; r=−0.59, p<0.001) (Fig 3.6B). Finally, the Central Balance of Pro-/Anti-Inflammatory Cytokines network (Component 3), showed negative correlations with both Charles River (r=−0.36, p=0.006) and Harlan (r=−0.44, p=0.001) control rats, as well as Harlan Adj/M-M rats (r=−0.30, p=0.025), and positive correlations with Charles River but not Harlan rats in the Adj/NA (r=0.45, p<0.001) and Adj/S (r=0.72, p<0.001) conditions (Fig 3.6C).     64 Variables Comp1 Comp2 Comp3 Hind paw IL-1β 0.65 0.36 0.13 Hind paw MCP-1 0.64 0.33 0.09 Hind paw TNF-α 0.57 0.31 -0.10 Hind paw KC/GRO 0.53 0.43 0.15 Hind paw IL-6 0.53 0.18 0.09 CBG -0.49 -0.18 -0.37 Plasma TNF-α 0.46 0.41 -0.11 Hind paw IFN-ɣ 0.45 0.05 0.12 Spleen CORT 0.43 0.08 0.00 Plasma CORT 0.43 0.16 -0.14 Front paw CORT 0.41 0.27 0.13 Popliteal lymph node CORT 0.37 0.07 0.02 Hypothalamus TNF-α -0.34 -0.02 0.11 Plasma KC/GRO 0.15 0.65 0.17 Spleen MCP-1 0.40 0.59 0.19 Plasma IL-6 0.39 0.55 0.19 Plasma IL-1β 0.03 0.44 0.22 Hypothalamus KC/GRO 0.13 0.43 0.03 Plasma MCP-1 0.38 0.42 0.18 Spleen KC/GRO 0.29 0.41 -0.15 Hypothalamus MCP-1 -0.07 0.40 -0.05 Spleen IL-6 0.31 0.40 0.13 Hippocampus KC/GRO 0.06 0.40 -0.12 Spleen IL-1β 0.31 0.39 -0.09 Spleen TNF-α 0.35 0.37 0.22 Spleen IFN-ɣ 0.03 0.36 -0.12 Plasma IFN-ɣ 0.19 0.36 0.12 Plasma ACTH 0.17 0.35 0.16 Hypothalamus IL-6 0.02 0.20 0.49 Hippocampus IL-4 0.02 -0.02 0.42 Hippocampus IL-10 0.16 -0.09 0.37 Hypothalamus IL-10 -0.26 0.04 0.36 Hind paw IL-4 0.00 0.03 0.36 Hippocampus IL-6 0.18 -0.22 0.34 Hippocampus IL-1β 0.03 0.00 0.29 Hippocampus IFN- ɣ 0.15 -0.33 0.33 Hippocampus MCP-1 -0.07 0.14 0.01 Hypothalamus IL-4 -0.21 0.05 -0.02 Hind paw CORT 0.29 0.29 0.02 Spleen IL-10 0.17 0.25 -0.05 Spleen IL-4 0.13 0.25 -0.06 Thymus CORT 0.18 -0.13 -0.07 Whole blood CORT 0.12 0.06 -0.18 Plasma IL-4 -0.02 -0.03 -0.05 Note. Values ≥ 0.34 are set in bold. Previously reported measures21 are indicated in blue    65 Table 3.1 Component loadings for the endocrine/immune variance constrained to that predictable from colony and AA severity.  Networks are interpreted using loadings set in bold font.            66  Figure 3.6 Endocrine/immune networks activated in response to CFA injection. Graphical representation of the correlations between the experimental condition (colony, AA severity) and the subjects’ component scores for component 1 (Endocrine/Immune Response in Peripheral Tissue), 2 (Proinflammatory Chemokine Response in Peripheral Tissue & Brain), and 3 (Central Balance of Pro-/Anti-Inflammatory Cytokines) from the CPCA. * p < 0.05; ** p < 0.01; *** p < 0.001. Control: saline-injected; Adj/NA: no clinical signs of AA following CFA injection; Adj/M-M: mild-moderate AA, clinical score ≥1, <8; Adj/S: severe AA, clinical score ≥ 8.         3.5 Discussion  The present data provide insight into important differences in the endocrine and immune responses of SD rats from Charles River versus Harlan colonies to an inflammatory challenge. Building on our previous data showing that colony of origin impacts the AA disease course, HPA mediators, and cytokine profile in the plasma and hind paws (Bodnar et al., 2015), we now show that Harlan rats generally have a blunted corticosterone response in the hind paws and immune tissues and dampened   67 cytokine responses in the spleen, compared to Charles River rats. Moreover, Harlan rats have an unexpected pattern of cytokine activation in the brain, particularly the hypothalamus, generally showing highest cytokine levels under control conditions, while Charles River rats showed highest cytokine levels with arthritis. The CPCA analysis revealed that with active AA, Harlan rats showed particular reliance on network 1, which included peripheral endocrine/immune and HPA activation, whereas Charles River rats relied primarily on activation of networks 2 and 3, showing enhanced involvement of chemokines and central cytokines. These findings demonstrate that, in contrast to the evaluation of single, independent parameters, a network approach leads to a better understanding of possible differential underlying pathophysiology in subjects with clinically similar levels of inflammation. For example, while 22-23 rats per colony were injected with CFA, only a small proportion of Charles River rats developed AA (Adj/M-M, n=3, Adj/S, n=4), compared to a larger number of Harlan rats (Adj/M-M, n=12, Adj/S, n=5). This highlights the generally low incidence of AA in Charles River rats, in line with previous reports using the CFA model (Banik et al., 2002; Cai et al., 2006a), and the power of probing for differential endocrine/immune networks activated within the same AA severity conditions. Our data suggest that colony differences could be exploited further to model and better understand the variable course and response to treatment that is characteristic of RA.  As noted, females were used in this study due to the increased rates of autoimmune disorders in women compared to men (Lawrence et al., 1998), and previously reported heightened sensitivity to experimentally induced arthritis in female rodents (Holmdahl, 1995). Importantly, while estrous cycle stage can impact immune measures (Gaillard and Spinedi, 1998), chronic immune system activation has been shown to result in estrous cycle disturbance and sustained diestrus to prevent ovulation (Avitsur and Yirmiya, 1999; Rivest et al., 1993). This is in line with the findings of the current study, where, as noted, 90% of rats were in dietrus, and indicates that subdivision of outcomes by estrous stage would not be informative.    68 We have previously reported that AA increases plasma corticosterone levels in female rats from both Charles River and Harlan colonies (Bodnar et al., 2015). Here, we found that corticosterone changes in plasma do not necessarily parallel corticosterone changes in other tissues. For example, while arthritis resulted in increased corticosterone across multiple tissues, these increases were most dramatic in the front paws of both Charles River and Harlan, and in the spleen, and popliteal lymph nodes of Charles River rats. This suggests differential local regulation of corticosterone levels between colonies, potentially via inflammation-induced cleavage of CBG and increased corticosterone bioavailability (Bodnar et al., 2015), local regeneration of corticosterone from dehydrocorticosterone (DHC) by 11β-hydroxysteroid dehydrogenase 1(Gomez-Sanchez et al., 2008; Taves et al., 2016b), or local corticosterone metabolism (Buttgereit et al., 2009). Each of these processes can be driven by pro-inflammatory cytokines, which generally increased to a greater extent in Charles River than Harlan rats. The relative lack of corticosterone increase in the hind paws of Harlan rats, the primary location of inflammation in the AA model, is of interest, as corticosterone has significant anti-inflammatory properties [reviewed in (Dinarello, 2010)]. Decreased corticosterone availability at sites of inflammation such as the paws may be due in part to lower circulating CBG in Harlan, and may help explain the increased incidence of AA in Harlan compared to Charles River rats (Bodnar et al., 2015). In the hypothalamus, colony differences in the cytokine response to CFA injection indicated that, in general, Charles River rats responded as expected, with increased TNF-α, IFN-ɣ, and IL-6 production with the development of AA (Adj/M-M and/or Adj/S), while Harlan rats, remarkably, had high hypothalamic levels of these three cytokines under control conditions. Furthermore, 0% of Harlan rats had detectable hypothalamic levels of IL-1β or IL-10 with severe AA, as compared to 100% for IL-1β and 50% for IL-10 in Charles River rats. Importantly, chronic inflammation models, such as the AA model, induce inflammation in the central nervous system (CNS) via glial cell activation (Sweitzer et al., 1999). For example, intraplantar administration of CFA not only induced peripheral inflammation but also robust microglial cell activation and concomitant increased expression of proinflammatory cytokines   69 (IL-1β, TNF-α, IL-6) (Raghavendra et al., 2004). Cytokines have a wide range of effects on homeostatic functions within the CNS and play a key role in sickness behaviors including anhedonia, anorexia, reduced quality of sleep, decreased motor activity, and social withdrawal (Dantzer, 2004). In addition, cytokines likely modulate nociceptive responses including hyperalgesia and allodynia (DeLeo et al., 1996). Finally, chronic neuroinflammation/increased cytokine levels in the brain is thought to occur in people with autoimmune or inflammatory disorders such as RA (Fuggle et al., 2014). For example, treatment with anti-TNF-α agents including infliximab have been shown to effectively and quickly reduce fatigue in RA (Maini and Taylor, 2000). As well, neuroinflammation is thought to underlie the depression that may accompany this disorder (Najjar et al., 2013). Thus, the attenuated cytokine response in the brain of Harlan rats may reflect an inappropriate response to immune challenge, and might affect expression of sickness behaviors, which are critical for reorganizing the organism’s priorities and allowing it to cope with disease (Dantzer, 2001b). Furthermore, in light of the observed colony differences in the neuroimmune response to chronic inflammatory challenge, our results suggest that Charles River-derived rats would be the better subjects in models of neuroinflammation or inflammatory mediated depression.  In the spleen, proinflammatory cytokines were found to increase, as expected, with CFA injection and/or arthritis onset. Colony differences were detected in KC/GRO levels only, with lower cytokine levels in Harlan rats as a group, compared to Charles River rats. However, it appears as if Harlan rats had a somewhat attenuated overall cytokine response, particularly in those that developed severe AA, as the overall statistical increase in MCP-1, IL-6, and TNF-α with increasing AA severity appears to be driven by the response in Charles River rats. Further, while all Charles River rats with mild-moderate and severe arthritis had detectable levels of IFN-ɣ, in Harlan rats 0% mild-moderate AA cases and 40% of severe AA cases had detectable IFN-ɣ levels, and overall IFN-ɣ levels, while not analyzed statistically, appear to be attenuated in Harlan rats. While the joints are generally the inflammatory sites of most interest in arthritis models, the spleen, a critical secondary lymphoid organ, is also an important inflammatory site in this model. For example, arthritis can be passively transferred through inoculation of a healthy rat with spleen   70 cells of an arthritic donor (Pearson and Wood, 1964). Rats with severe AA, as in the present study, develop enlarged spleens with grey/white spots, which has been identified as reactive granulomas that surround adjuvant material (Pearson and Wood, 1964). Thus an attenuated splenic cytokine response in Harlan rats is unexpected, particularly as these rats were shown to develop a more severe arthritic profile than Charles River rats (Bodnar et al., 2015).  To begin to explore patterns of cytokine activation by AA severity that may be indicative of mechanistic differences between colonies, heatmaps were constructed to summarize and provide an overview of cytokine levels within the hypothalamus, hippocampus, and spleen. As a whole, in the hypothalamus an opposing cytokine pattern between colonies was revealed – highest cytokine levels in the Adj/M-M and Adj/S disease states for Charles River rats, compared to highest cytokine levels in the control and Adj/NA states for Harlan rats. Similarly, in the hippocampus, Charles River rats overall had the highest cytokine levels with severe AA (Adj/S), compared to Harlan rats that showed the highest overall cytokine levels in controls. By contrast, rats from the two colonies had more similar patterns of response in the spleen, with the highest cytokine levels with severe AA (Adj/S) (with the exception of increased IL-4 and KC/GRO in the Adj/NA condition for Charles River rats). Taken together, this heatmap data summary reinforces the notion that Charles River and Harlan rats may activate different networks of cytokines in a unique and disease state-dependent manner.  To further investigate differential underlying mechanism of AA, and in order to provide a broader and more physiologically relevant examination of endocrine/immune networks in Charles River and Harlan rats, we combined data sets from this study and our previous study (Bodnar et al., 2015) [plasma corticosterone, CBG, and adrenocorticotropic hormone (ACTH), and plasma and hind paw cytokine levels] and performed a CPCA that included a total of 44 endocrine and immune parameters. CPCA is the ideal statistical technique as it allowed for networks of endocrine/immune parameters to be identified and related back to the independent variables being investigated. In addition, as the networks were based on the endocrine/immune variance constrained to that predictable by the independent variables, we were able   71 to identify how these constrained variance-specific networks related to colony and AA status. As with the heatmap graphical representation of the data, commonalities between the univariate results and the CPCA analysis are evident; however, CPCA facilitated a more global interpretation of this large dataset. CPCA revealed three components, i.e., three endocrine/immune networks, that together represented 76.65% of the predictable variance (or 28.06% of the total variance) in the parameters examined.  Overall, with AA (Adj/M-M, Adj/S), Harlan rats activated only network 1 (Endocrine/Immune Response in Peripheral Tissue). Comparatively, Charles River rats activated networks 2 and 3 (Proinflammatory Chemokine Response in Peripheral Tissue & Brain and Central Balance of Pro/Anti-Inflammatory Cytokines) with severe AA. Taken together, this indicates that with AA, Harlan rats showed increased reliance on peripheral endocrine/immune and HPA axis activation (network 1), as compared to the enhanced involvement of chemokines and central cytokines (networks 2 and 3) for Charles River rats. Thus, these data suggest that under similar AA severity conditions, Harlan and Charles River rats rely/activate differential endocrine/immune pathways, suggesting that the underlying mechanisms and physiological regulation of disease differ between the colonies. Of note, Harlan rats also show an unexpected pattern of activation of network 2 (Proinflammatory Chemokine Response in Peripheral Tissue & Brain) in the control, saline-injected condition. This heightened activation of the proinflammatory chemokine-dominated network in the basal state in Harlan rats in both peripheral and central compartments may underlie, at least in part, the increased incidence and severity of AA observed in Harlan compared to Charles River rats (Bodnar et al., 2015). Taken together, our findings of activation of different endocrine/immune networks in Charles River and Harlan rats in response to inflammatory challenge highlights the potential utility of exploiting colony differences in the exploration of diseases with variably clinical presentation, such as RA, not only for understanding underlying disease mechanisms but also for the more targeted evaluation of therapeutic agents.  Moving forward, in the push to understand disease mechanisms and resiliency factors, and design precise intervention strategies, an understanding of networks of physiological and neurobiological   72 variables is paramount. By exploring how seemingly similar disease states, such as specific severity levels of arthritis in the current study, rely on different endocrine and immune networks, we can further the understanding of variability in disease. Gone is the idea of elucidating singular molecular targets, which would then form the basis of a universal treatment. Rather, personalized medicine, the optimization of treatment to the individual, taking into account genetics, environment, and lifestyle factors, is emerging as an important new approach in disease prevention, diagnosis, and treatment. While personalized medicine is becoming common in the cancer field, treatment efficacy for rheumatoid arthritis also varies widely and may benefit from increased investment in individualized care (Tak, 2012).     73 Chapter 4: Modulatory Role of Prenatal Alcohol Exposure and Adolescent Stress on the Response to Chronic Inflammatory Challenge in Female Rats  4.1 Introduction The global prevalence of rheumatoid arthritis (RA) is estimated at 0.24% (Cross et al., 2014). However, in certain subgroups, the prevalence of RA is much higher – as high as 4–8% in certain North American Indigenous populations (Ferucci et al., 2005; Peschken and Esdaile, 1999) and approximately two times higher in women compared to men (Cross et al., 2014). In addition, while there is a growing list of environmental risk factors and triggers of RA, such as smoking, obesity, and recent infection (Alamanos and Drosos, 2005; Symmons, 2002), it is less clear whether the prenatal environment can impact later life risk of autoimmune disorders such as RA. In this regard, one of the challenges relates to the difficulty in disentangling genetic from environmental influences in prenatal models. Thus, investigation of the prenatal environment will necessitate disease models in which genetics do not play a predominant role.  One such well-suited example is Fetal Alcohol Spectrum Disorder (FASD), which refers to the constellation of deficits associated with alcohol-exposure during pregnancy. FASD prevalence is estimated at 1% in some population areas (Chudley et al., 2005), to as high as 10.9-25.2 per 1000 live births or 1.1-2.5% in other areas (May et al., 2015). While there is evidence of a role for genetics in addiction in general (Kreek et al., 2005), FASD occurs across most regional, cultural, and societal environments. Moreover, while there is extensive evidence for immune disturbances following prenatal alcohol exposure [reviewed in (Bodnar and Weinberg, 2013)] there has been very little investigation into the prevalence of autoimmune disorders in individuals with FASD. This likely stems from the fact that Fetal Alcohol Syndrome (FAS), at the severe end of the FASD spectrum, was only identified in 1973 (Jones and Smith, 1973) and as a result, most individuals who received an early FAS diagnosis are only now approaching the age of typical RA onset. Recently, however, results from an informal health survey   74 initiated by young adults with FASD suggest, for the first time, that prenatal alcohol exposure (PAE) may result in a heightened vulnerability to autoimmune disorders, including RA specifically, the prevalence of RA may be as high as 7.2% (N=356) in individuals with FASD (Himmelreich et al., 2016; Weinberg, 2016).  A key contributing factor in the RA disease course may involve communication between the hypothalamic-pituitary-adrenal (HPA) axis and inflammatory cytokines produced by the immune system. Specifically, while the HPA response to stressors may be relatively normal, inappropriate activation of the HPA axis in response to inflammation (resulting in impaired ability to inhibit ongoing inflammation) is suspected in at least a subset of patients with RA (Eijsbouts et al., 2005; Imrich et al., 2010). Of relevance, in addition to altered immune function (Bodnar et al., 2015; Drew et al., 2015), alterations in the HPA system have been well described in individuals with FASD (Keiver et al., 2015) and in PAE animal models (Schneider et al., 2004; Weinberg et al., 2008). As well, children with FASD are at a heightened risk of experiencing increased early-life stress due to foster care placements, childhood abuse and neglect, and other adverse environments (Streissguth et al., 2004; Werner, 1986), which has been shown to impact both HPA (Tarullo and Gunnar, 2006) and immune systems (Fagundes et al., 2013). As a result, investigations into altered HPA-immune interactions may be particularly relevant for elucidating mechanisms underlying the increase in autoimmune disorders following prenatal alcohol exposure.   Here, we used a rat model of PAE in order to investigate whether in utero alcohol exposure has an impact on incidence, severity, and course of arthritis. This was accomplished using the adjuvant-induced arthritis (AA) model, a well-established model of human RA. Furthermore, in order to probe for a modulatory role of stress and to examine interactive effects of PAE and stress exposure on arthritis outcomes, in adolescence half the rats were exposed to chronic mild stress (CMS) and half remained undisturbed. Female offspring were utilized, due to the increased incidence of RA in women compared to men (Lawrence et al., 1998) and previously reported heightened sensitivity to experimentally induced arthritis in female rodents (Holmdahl, 1995). Building on our previous finding that PAE results in a   75 prolonged course of disease and increased AA severity (Zhang et al., 2012), here, we examined two critical time points, at the peak of inflammation (16 days post-adjuvant injection), and during recovery from inflammation (a maximum of 60 days post-adjuvant injection), allowing for the complete disease course to be examined. Using clinically relevant measures such as cytokine and HPA parameters, estradiol levels and histological examination of the tibiotarsal joint (joint integrity and inflammation by hematoxylin and eosin staining, cartilage thickness, and macrophage density), we were able to elucidate endocrine and immune signatures predictive of the heightened sensitivity to AA in PAE rats, and to investigate the parameters underlying alterations in recovery and possible long-term effects of increased AA severity in PAE rats. We hypothesized that PAE would result in a more severe and prolonged course of AA, which would be further exacerbated by stress exposure during adolescence. Specifically, we expected to detect a mismatch among arthritis severity, HPA activity and cytokine levels in PAE rats. Importantly, insight into the physiological underpinnings of arthritis in a PAE model may eventually lead not only to improved treatment in FASD populations, but also to a broader understanding of environmental risk factors and genetic-independent mechanisms of RA.  4.2 Methods 4.2.1 Breeding Male and female Sprague-Dawley rats (Charles River Laboratories, St. Constant, Québec, Canada) were pair-housed by sex, given ad libitum access to water and standard laboratory chow, and handled daily for a 7–10 day habituation period. Colony rooms were maintained on a 12:12 hr light/dark cycle, and at 20–23°C. Nulliparous females (n=57) were then pair-housed with a male and vaginal lavage samples collected daily to check for sperm, indicating gestation day 1 (GD1). All procedures were in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals, and approved by the University of British Columbia Animal Care Committee.        76 4.2.2 Prenatal Diets and Feeding On GD1, pregnant females were single housed on ventilated racks and assigned to one of three treatment groups: (1) Prenatal alcohol exposure (PAE; n=17) – ad libitum access to an alcohol-containing liquid diet with 36% of total calories derived from ethanol, 6.37% v/v; (2) Pair-fed (PF; n=17) – liquid control diet with maltose dextrin isocalorically substituted for ethanol, in the amount consumed by a PAE partner (g/kg/body wt/day of gestation; (3) Control (C; n=23) – pelleted version of the liquid control diet, ad libitum. All diets were formulated to provide optimal nutrition during pregnancy (Weinberg/Keiver High Protein Ethanol [#710324] and Control [#710109] Diets; Weinberg/Keiver High Protein Pelleted Control Diet [#710109]; Dyets Inc. Bethlehem, PA) and were presented fresh daily, one hour prior to lights off (1800–1900 hr). Pregnant dams were weighed weekly throughout gestation. On GD17 a blood sample was collected from the tail vein from a subset of PAE, PF, and C dams (n=3–6), 2 hr after lights off. Blood alcohol content (BAC) was measured as previously reported (Hellemans et al., 2010; Uban et al., 2010) and ranged from ~90–150 mg/dl in PAE rats. On GD21, experimental diets were replaced with standard laboratory chow (19% Protein Extruded Rodent Diet, #2019, Teklad Global), ad libitum, and rats were continued on this diet throughout the lactation period. On postnatal day 1 (P1), litters were culled to 6 males and 6 females, when possible, and weighed weekly. Litters were weaned on P22 and group-housed on standard non-ventilated racks by litter and sex. One female per litter, per testing condition, was used in the study to control for litter effects. On P30, female siblings were separated and grouped into experimental pairs with a non-littermate of the same prenatal treatment group.  4.2.3 Adolescent Exposure to Chronic Mild Stress (CMS) On P31, all rats were weighed and a basal blood sample was collected from the tail vein (0800 – 1000 hr). Rats were then assigned to either the non-CMS (no stress) or CMS group. CMS consisted of exposure to stressors, administered twice daily, in a randomized and unpredictable order, for ten days (P31–41), at variable times of day, once in the AM (between 0700 and 1100 hr) and once in the PM   77 (between 1300 and 18:30 hr), with a minimum of 2 hours between stressors. Stressors included: 1) Platform: 10 min exposure on a 90 cm high Plexiglass platform (20x20cm); 2) Cage tilt: home cage tilted 30o for 2h; 3) Novel cage: exposure to a new cage with CareFRESH (Healthy Pet, Ferndale, WA) bedding for 1h without food or water; 4) Soiled cage: exposure to a soiled cage from a sex-matched rat for 1h; 5) Restraint: restraint in PVC tubes for 30 min (15 x 6 cm); 6) Social isolation: isolation without food and water for 12h overnight in a smaller cage (Allentown mouse cage: 32 x 17 x 14 cm); 7) Water deprivation: following social isolation, rats were returned to their home cage with an empty water bottle for 1 hr. While stressors occurred in a variable order, each rat was exposed to the same number of each stressor over the ten day period. On P41, all rats (non-CMS, CMS) were weighed and a basal blood sample collected, as above. Rats in the non-CMS group were undisturbed from P31–41, with the exception of blood sampling and regular cage changes.  4.2.4 Induction and Clinical Evaluation of AA On P55-60, rats were briefly anesthetized with isoflurane, and received two 0.05 mL intradermal injections at the base of the tail, of either 0.6 mg complete Freund’s adjuvant (CFA) or saline. CFA was prepared by grinding Mycobacterium tuberculosis H37 RA (Difco laboratory, Detroit, MI) into a powder that was dissolved in incomplete Freund’s adjuvant (Zhang et al., 2012). Immediately following the initial injections, rats were housed in soft CareFRESH bedding on ventilated racks. Two overlapping cohorts were run: one cohort was terminated on day 16 post-injection (previously shown to be the peak of inflammation (Zhang et al., 2012) – induction-to-peak phase) (n=12-14/prenatal treatment/stress condition), and the other cohort was terminated following recovery from AA (after maintaining a clinical score of zero for ten days or at a maximum of 60 days post-injection – resolution phase) (n=14-18/prenatal treatment/ stress condition). Saline-injected rats were terminated on day 16 post-injection or for the resolution phase, in parallel with their adjuvant-injected counterparts. To assess AA severity, rats were briefly anesthetized, weighed, paw volume measured using a plethysmometer (IITC Life Science   78 Inc., Woodlan Hills, CA), and clinical signs of arthritis (clinical score) assessed on days 6, 9, 11, 13, 15 post-injection, and every three days thereafter, up to 57 days post-injection (saline-injected rats were anesthetized in parallel with their adjuvant-injected counterparts). To calculate clinical score, each of the four paws was scored on a 0-4 point scale, where 0 = no signs of arthritis, 1 = single focus of redness or swelling, 2 = two or more foci of redness or swelling, 3 = confluent but not global swelling, 4 = severe global swelling. All measurements were performed blind to prenatal and adolescent treatment, and injection status by two independent researchers on each measurement day. In addition, health status was carefully monitored twice daily – rats examined in the home cage for signs of pain, discomfort or infection, as well as for general signs of health including alertness, activity, coat quality, color of the ears, and ability to rear. The injection site at the base of the tail was also monitored daily for signs of redness, swelling, and infection and treated appropriately, when necessary. At the onset of arthritis, pain levels were assessed daily, using an established pain scoring system from 0 (pain free) – 4 (severe pain). As the goal of this experiment was to examine the course of AA, and not to induce significant pain, rats achieving a pain score of ≥ 3 were administered buprenorphine (0.03 mg/kg), an opioid analgesic agent, and subcutaneous fluids twice daily. Rats that maintained a pain score of ≥ 3 for 2 days (humane endpoint) were terminated the next morning under basal conditions.   4.2.5 Termination and Tissue Collection On each termination day (day 16 for onset-to-peak phase, recovery time point for resolution phase), rats were quickly decapitated (between 0800 and 1030 hr) and trunk blood was collected in polystyrene tubes containing 3.75% EDTA and 0.05% aprotinin. Blood was centrifuged and plasma collected and stored at -80°C. Hind paws were removed at the mid-tibia level and skin removed. One paw was flash frozen in liquid nitrogen and stored at -80°C until measurement of cytokine and protein levels. The other paw was stored in a 15 mL tube with 10% buffered formalin. Vaginal lavage samples were collected for cytological assessment of estrous cycle stage.    79  4.2.6 Plasma ACTH, Corticosterone, Estradiol, and Corticosteroid Binding Globulin (CBG) Measurements Plasma ACTH and total corticosterone levels were measured using radioimmunoassay (RIA) kits and standard protocols (ImmuChem Double Antibody hACTH 125I RIA kit and ImmuChem Double Antibody Corticosterone 125I RIA kit (MP Biomedicals, LLC, Orangeburg, NY, USA). The minimum detectable concentrations were 5.7 pg/mL for ACTH and 7.7 ng/mL for corticosterone. Plasma estradiol was measured using the Ultra-Sensitive Estradiol RIA (Beckman Coulter, Mississauga, Ontario, Canada), with a minimum detectable concentration of 2.2 pg/ml. Intra- and inter-assay coefficients of variation were <10% for all RIA’s. The steroid-binding capacity of CBG was measured using a ligand-saturation assay that uses dextran-coated charcoal (DCC) to separate CBG-bound from free [3H]-corticosterone (PerkinElmer Lifer Sciences, Waltham, MA) (Hammond and Lahteenmaki, 1983; Smith and Hammond, 1991). Plasma samples were diluted (1:1500 or 1:3000) in phosphate buffered saline (PBS) and stripped of endogenous steroids by incubation with DCC. Samples were then incubated with 1 nM [3H]-corticosterone in the absence or presence of excess corticosterone, to monitor non-specific binding, followed by the measurement of bound [3H]-corticosterone after adsorption of free steroid with DCC for 10 minutes at 0°C. The intra-assay variability was <10% (Smith and Hammond, 1991)  4.2.7 Tissue Homogenization for Protein and Cytokine Measurements Hind paws were crushed on dry ice, added to tubes containing 1.3 g garnet and 2 mL cold lysis buffer, and homogenized using an Omni Bead Ruptor 24 (Omni International, Kennesaw, GA). Homogenates were then sonicated three times (5 sec/cycle) on ice, centrifuged and supernatant collected and stored at -20°C, with separate aliquots for protein and cytokine measurements.     80 4.2.8 Multiplex Cytokine Measurements Multiplex cytokine assays were performed using the Meso Scale Discovery (MSD) proinflammatory panel 1 rat V-PLEX kit [IL-1β, IL-2, IL-4, IL-5, IL-6, IL-10, IL-13, IFN-ɣ, KC/GRO (CXCL1), TNF-α] (catalog #: K15044D-1, MSD, Rockville, MD). Samples were diluted (1:4 for serum, 1:2 for paw homogenate) in diluent 42 and cytokine assays performed using the standard MSD protocol. The plate was read using a Sector Imager 2400 (MSD, Rockville, MD) and data analyzed using the MSD Discovery Workbench software v. 4.0 (MSD, Rockville, MD). The lower limit of detection (LLOD) range for each analyte was as follows (pg/mL): IL-1β: 14.3–19.5; IL-2: 44.3–101.0; IL-4: 0.26–0.48; IL-5: 20.6–22.0; IL-6: 24.1–39.9; IL-10: 3.11–3.76; IL-13: 1.2–2.49; IFN-ɣ: 1.39–2.15; TNF-α: 0.38–1.48; KC/GRO: 2.11–7.36.   4.2.9 Protein Quantification Total protein levels were measured in paw homogenates using the Pierce Microplate BCA Protein Assay Kit (reduction agent compatible; Pierce Biotechnology, Rockford, IL), as previously reported (Bodnar et al., 2015). Paw cytokine levels were then adjusted and values reported as pg cytokine/mg of protein.   4.2.10 Tibiotarsal Joint Sectioning and Staining (H&E, Toluidine Blue) Following storage in formalin for 48 hr, paws were transferred to tissue cassettes and placed in a decalcification solution containing 10% formic acid, 10% formalin, 80% H2O. The decalcification solution was replaced daily, for a total of three days, after which cassettes were transferred to 70% ethanol. Following decalcification, tissue samples were dehydrated in graded ethanols, followed by xylene, and paraffin wax. Tissue was then embedded and trimmed. Midaxial sections (5 µm) were collected using a microtome (Leica Microsystems, Concord, Ontario, Canada) and mounted on microscope slides. Sections were collected when the tibia, talus, navicular tarsal, and calcaneus were   81 visible, such that the articulating cartilage and synovium could be examined. Slides (n = 3/rat) were then incubated at 37°C overnight, fixed for 2 hours at 60°C, and stained with hematoxylin and eosin (H&E). Dehydration, embedding, slicing and H&E staining were performed by Wax-It Histology Services Inc. (Vancouver, BC, Canada). For toluidine blue staining, slides (n = 1/rat) were incubated at 37°C for 2 hours, dehydrated through a series of ethanol and xylene steps, followed by a 0.04% toluidine blue stain as described previously (Schmitz et al., 2010).   4.2.11 CD163 Immunohistochemistry (IHC) for Synovial Macrophage Density and CD163+ Chondrocyte Analysis Slides (n = 1/rat) were incubated at 50°C for 30 min and deparaffinized through a series of xylene and graded ethanol steps. Next, sections were outlined with a hydrophobic pen, washed in tris-buffered saline (TBS), then incubated in proteinase K in TBS for 10 minutes in a 37°C water bath. Slides were then washed in TBS, and a blocking buffer consisting of TBS-T (TBS + 0.025% Triton-X) + 4% normal horse serum was added to each slide for 3 hr. Mouse anti-rat CD163 primary antibody (clone ED2, Serotec, Raleigh, NC) was then applied to slides (1:100) and incubated overnight at 4°C. Next, slides were washed in TBS-T, and biotinylated horse anti-mouse IgG (rat adsorbed, Vector Laboratories, Burlingame, CA) in blocking buffer was applied for 1 hr at room temperature. Slides were then washed in TBS-T and alkaline phosphatase streptavidin (APS; 1:500) was added in blocking buffer and incubated at room temperature for 30 min. Slides were then washed in TBS-T and a BCIP/NBT working solution (Sigma-Aldrich, Oakville, Ontario, Canada) was added to each slide for 30 min at room temperature, protected from light. Finally, slides were washed in TBS, dehydrated in graded ethanols, followed by xylene, and coverslipped with permount.  Images of cartilage and CD163+ staining (Fig 4.7 E–M) were captured using a QIClick CCD Camera (QImaging, Surrey, BC) and processed using Northern Eclipse version 8 (Empix, Mississauga, ON). Using ImageJ, a threshold was applied to the CD163 slides, such that the background was   82 eliminated and macrophage staining was detectable. Image Overlay Utility (G. Keller) was used to superimpose the H&E and CD163 sections; the synovium (at the inferior edge of the tibia) was traced and the signal measured using ImageJ. Scans of whole hind paw sections (maximum resolution 20x) were acquired using an Aperio ScanScope CS slide scanner (Leica Biosystems, Concord, ON). Close-up images (C – F) were generated by zooming in on the whole paw scans using Aperio ImageScope v12.2.2.5015 (Leica Biosystems, Buffalo Grove, IL).  4.2.12 Histopathological Evaluation of Tibiotarsal Joints by H&E Hind paws were given scores of 0-5 for: soft tissue inflammation (1 = minimal infiltration of immune cells in periarticular tissue, no edema; 5 = severe edema, infiltration of immune cells); and bone erosion (1= small areas of bone resorption, rare osteoclasts; 5 = severe full-thickness defects in compact bone, marked loss of medullar bone of distal tibia, numerous osteoclasts). Scores of 0-3 were given for: bone marrow activity (1 = slight increase in number of cells; 3 = larger increase in cell numbers); skeletal muscle integrity (1 = muscle infiltrated by a small number of immune cells, consistent striated shape maintained; 3 = significant infiltration of immune cells, distortion in shape and striated appearance); and synovial tissue status (1 = slight hyperplasia of synovial tissue; 3 = pannus formation and synovial hyperplasia). Scoring criteria was based on previously published histopathological assessment criteria (Bendele et al., 1999; Helyes et al., 2004; Ratkay, 1994a) (complete criteria for the current study in Appendix D). Sections were assessed by two independent experimenters in a blinded fashion, using an Axioskope mot plus microscope (Zeiss, Toronto ON). Representative H&E images showing a healthy tibiotarsal joint, and the effects of active AA are shown in Figure 4.5A–F. Scans of whole hind paw sections were captures as above.     83 4.2.13 Cartilage Thickness Measurements Using joint sections stained with toluidine blue, three images (5x; microscope and camera as described above) of the cartilage of the distal tibia were captured (superior, inferior, mid distal tibia). Using ImageJ (National Institutes of Heath, Bethesda, MD), cartilage thickness was assessed by measuring the distance between the joint space and the edge of the cartilage (dark purple staining). Three cartilage thickness measurements were collected at each location and averaged. When the cartilage at any one of these locations was completely invaded by pannus, thickness was set to zero. Representative images showing intact cartilage of the tibia and talus in a healthy joint, and cartilage loss with active AA are shown in Figure 4.6A–F. Scans of whole hind paw sections were captured as above.  4.2.14 Statistical Analyses  Data were analyzed using analysis of variance (ANOVA) for the factors of prenatal treatment, CMS condition, and AA severity, as appropriate, followed by Fisher post hoc tests to examine significant main effects and interactions (significant ANOVA p values and F statistics reported in text, post hoc p values reported in figures) (IBM SPSS Statistics). Estrus stage was included as a covariate in the estradiol analyses but was not significant, likely as a result of estrus cycle disturbances with chronic immune system activation (Avitsur and Yirmiya, 1999; Rivest et al., 1993). AA incidence (development of AA after CFA injection at any point during the experimental period), recovery from AA, and achievement of humane endpoint were coded as yes/no variables and were analyzed by logistic regression for the factors of prenatal treatment and CMS condition. Body weight and corticosterone levels during the CMS period (P31–41) and clinical score data were analyzed using repeated measures ANOVA for the factors of prenatal treatment and CMS condition. Differences were considered significant at p≤0.05. Outliers (±2.5 SD>mean) were removed from cytokine and hormone analyses, when appropriate. Corticosterone, estradiol, CBG, and cytokine levels were non-normally distributed and were transformed using the Blom   84 rank-based normalization method (Blom, 1958), prior to statistical analysis. Untransformed data are presented in the figures for clarity.  During the resolution phase, a smaller percentage of rats reached humane endpoint or did not resolve from AA by day 60 post-injection (non-CMS: C, n=1, PAE, n=2; CMS: C, n=3, PAE, n=5). While these cases are included in the measures of AA incidence, severity, and recovery, in order to focus on factors modulating AA resolution, and due to the low N in the non-CMS condition, which limited statistical analyses, levels of endocrine and immune parameters, and histological assessment data for these cases are included in Appendix A & B. Overall, in this subgroup, endocrine and immune measures paralleled the responses observed on day 16 in the Adj/AA group, as all of these rats continue to show clinical signs of AA (Appendix A & B).  Finally, while a PF group was included in the initial study design, as described above, in most cases the response in the PF group was not statistically different from that of the C group. In the results, in order to focus specifically on the effects of prenatal alcohol exposure in the AA model, specific effects of pair-feeding are discussed separately (section 4.3.10). Limitations and issues associated with this complex treatment group are further addressed in the discussion.   4.3 Results  4.3.1 Body Weight and Corticosterone Levels During CMS (P31 – 41) Analysis of body weight revealed an increase from P31–P41 in all females, as expected [F(1, 160)=7700.47, p<0.001]; however, at the end of the CMS period, rats in the CMS condition weighed significantly less than their non-CMS counterparts [F(1, 160)=16.91, p<0.001] (Table 4.1). Comparatively, analysis of basal corticosterone levels showed an increase in corticosterone levels from P31 to P41 in rats in the CMS condition only [F(1, 135)=5.57, p<0.05] (Table 4.1). There were no prenatal treatment effects on either body weight or basal corticosterone levels during the CMS period.   85  Table 4.1 Body weight and corticosterone levels during the CMS period (P31 – 41).  Body weight increased from P31 – P41 across prenatal treatment and CMS conditions (***); however, on P41, rats in the CMS condition weighed significantly less than their non-CMS counterparts (✢✢✢). Basal corticosterone levels increased from P31 – 41 in rats in the CMS condition only (❖). Symbols represent significant post-hoc p-values; ❖: p<0.05; ***/✢✢✢: p<0.001; P: postnatal day; C: control; PAE: prenatal alcohol exposure; Non-CMS: non-stress group; CMS: stress group.  4.3.2 AA Incidence and Severity Arthritis incidence is depicted in Figure 4.1, which shows the percent of rats with clinical signs of AA from day 0–15 (onset-to-peak phase; Fig 4.1A) and day 18–57 (resolution phase; Fig 4.1B) post-injection. In the regression model, AA incidence was found not to be associated with prenatal treatment or CMS condition in the onset-to-peak phase. In the resolution phase, however, an association between AA incidence and prenatal treatment was detected. The odds of developing AA with CFA injection increased by 1.95 for PAE compared to C rats (p=0.016; 95% confidence interval for odds ratio: 1.13, 3.38). In other words, the probability that a PAE rat would develop AA with CFA injection was 66.1%. Logistic regression to examine the probability of recovery from AA or achievement of humane endpoint during the resolution phase did not identify an association with prenatal treatment or CMS condition. CMS Condition Prenatal Treatment Pre-CMS (P31) Post-CMS (P41) Weight (g) Corticosterone (nM) Weight (g) Corticosterone (nM) Non-CMS C 103.9 ± 1.7 33.2 ± 4.0 173.5±2.3*** 25.6 ± 4.3 PAE 104.3 ± 1.7 36.1 ± 6.1 177.5±2.2*** 27.7 ± 4.3 CMS C 103.7 ± 1.8 45.4  ± 14.2 158.9±2.3***/ 164.2 ± 36.4 PAE 103.1 ± 1.5 38.4 ± 2.9 160.3±1.9***/ 121.7 ± 25.7   86 Analysis of clinical scores on day 16 revealed, as expected, an overall increase in clinical scores over time (day 11–15 following adjuvant injection) [F(1, 104)=25.93, p<0.001], with no differences among groups (Fig. 4.1C). During the resolution phase, however, while clinical scores changed over time across groups [F(16, 752)=4.81, p<0.001], there was a differential pattern by prenatal treatment [interaction between measurement day and prenatal treatment; F(16, 752)=2.71, p<0.05]; PAE rats had higher clinical scores than C rats from day 18 post-injection through to day 57, with the exception of days 42 and 45 (Fig. 4.1D). Finally, a between-subjects effect of prenatal treatment was also detected during the resolution phase [F(1, 47)=9.21, p<0.01], which indicated increased clinical scores in PAE compared to C rats, overall. Consistent with the findings above, analysis of the clinical score data by area under the curve (AUC) did not reveal any differences during days 1-16 (onset-to-peak phase) (Fig. 4.1E). During the resolution phase, however, AUC analysis showed an increased AUC in PAE compared to C rats, regardless of CMS condition (Fig. 4.1F) [main effect of prenatal treatment: F(1, 56)=7.42,  p<0.01].   87  Figure 4.1 Clinical course of AA. A – B: Incidence of AA as the percentage of CFA-injected rats showing clinical signs of AA during the induction-to-peak phase (A) and the resolution phase (B). C – D: Average clinical scores (± SEM) for rats that developed AA during the induction-to-peak phase (C) and the resolution phase (D). E – F: Area under the curve (AUC; mean ± SEM) of the average clinical scores during the induction-to-peak phase (E) and the resolution phase (F). a/× symbols indicate a significant post-hoc, with the symbol denoting a comparison with the control group, unless otherwise   88 indicated. The  “××” symbol indicates a main effect of prenatal treatment, with the comparison to the control rats, across CMS conditions. a: p<0.05; aa/××: p<0.01; C: control; PAE: prenatal alcohol exposure; Non-CMS: non-stress group; CMS: stress group.  4.3.3 ACTH and Corticosterone Levels  ACTH levels were generally unaffected in the AA model (data not shown). On day 16, basal corticosterone levels generally increased with CFA injection, with higher corticosterone levels in the Adj/NA and Adj/AA compared to the saline condition [main effect of AA severity: F(2, 68)=5.95, p<0.01], but no differential effects of prenatal treatment or CMS (Fig. 4.2A). At the recovery time point, however, prenatal treatment was found to modulate the effect of AA severity on basal corticosterone levels [interaction: prenatal treatment × AA severity: F(2, 56)=3.74, p<0.05]. In C rats, corticosterone levels were highest in rats that received an adjuvant injection but failed to develop clinical signs of AA (Adj/NA), whereas in PAE rats, corticosterone levels were highest in those that recovered from AA (Adj/Rec) (Fig. 4.2B). In addition, in the Adj/Rec condition, corticosterone levels were higher in PAE than control rats (Fig. 4.2B).  4.3.4 CBG Levels On day 16, CBG levels differed by CMS condition, in an AA severity state-dependent manner [interaction: CMS condition ⅹ AA severity: F(2, 67)=3.80, p<0.05]. CBG levels were decreased with active AA (Adj/AA), compared to the saline condition regardless of CMS exposure, and in the non-CMS condition, were lower in the Adj/NA compared to the saline condition, an effect driven by the response in PAE rats (Fig 4.2C). In addition, an interaction between prenatal treatment and CMS condition [F(1, 67)=5.73, p<0.05] indicated that in the non-CMS condition, CBG levels were lower in PAE compared to control rats. At the recovery time point, CBG levels were found to be modulated by prenatal treatment, CMS condition and AA severity [interaction: prenatal treatment x CMS condition x AA severity: F(2,   89 59)=5.00, p<0.01]. In the non-CMS condition, PAE rats that recovered from AA (Adj/Rec) had lower CBG levels compared to saline-injected PAE rats, and compared to their C counterparts. In the CMS condition, by contrast, there was a drop in CBG in all CFA injected (Adj/NA, Adj/Rec) control rats.  4.3.5 Estradiol Levels  On day 16, estradiol levels were found to decrease with CFA injection (Adj/NA, Adj/AA), across prenatal treatments and CMS conditions [main effect of AA severity: F(2, 63)=9.24, p<0.001] (Fig. 4.2E). At the recovery time point, estradiol levels were again found to be affect by AA; however, a different profile was detected, with the highest estradiol levels in rats that failed to develop clinical signs of AA (Adj/NA) [main effect of AA severity: F(2, 58)=4.19, p<0.05] (Fig. 4.2F). In addition, rats in the CMS condition had higher estradiol levels overall, compared to rats in the non-CMS condition (Fig. 4.2F).   90  Figure 4.2 Corticosterone, CBG, and estradiol levels at the peak and following recovery from AA. Bars represent mean hormone level ± SEM. Data are presented in raw form, with statistical analysis conducted on Blom transformed (normalized) data. The presence of an asterisk (*) indicates a significant post-hoc comparison to the saline-injected group, within prenatal treatment and CMS condition, unless otherwise indicated. The “×” symbol indicates a significant post-hoc comparison to the control group, within CMS condition and AA severity state,   91 unless otherwise indicated. The “§” indicates a significant post-hoc comparison to the non-CMS condition. */×/§: p<0.05; **/××/§§: p<0.01; ***: p<0.001; D16: day 16 post-CFA injection; REC: following recovery from AA; C: control; PAE: prenatal alcohol exposure; Non-CMS: non-stress group; CMS: stress group; Saline: saline-injected; Adj/NA: no clinical signs of AA following CFA injection; Adj/AA: clinical score ≥1; Adj/Rec: following recovery from AA, clinical score = 0 for 10 days.  4.3.6 Plasma Cytokine Levels  On day 16, at the peak of arthritis, plasma cytokine levels generally increased with CFA-injection and/or AA onset. Overall, compared to the saline condition, TNF-α levels increased with CFA-injection (Adj/NA, Adj/AA) and KC/GRO levels increased in rats that developed AA (Adj/AA) (Fig. 4.3D, E) [main effects of AA severity: KC/GRO – F(2, 66) =13.28, p<0.001; TNF-α: F(2, 67)=48.88, p<0.001]. Moreover, both prenatal treatment and CMS condition influenced KC/GRO levels; in the non-CMS condition, KC/GRO levels were higher in PAE compared to C rats, and overall, PAE rats in non-CMS condition had higher KC/GRO levels than those in the CMS condition [interaction: prenatal treatment × CMS condition interaction: F(2, 66)=4.73, p=0.03]. One exception to the general pattern of increasing plasma cytokine levels was IL-2 – levels were lower in the Adj/AA compared to the saline condition (Fig. 4.3A) [main effect of AA severity: F(2, 67)=11.82, p<0.001]. IL-6 levels were below the lower limit of detection in all disease state except with active AA (Adj/AA), with one noteable exception – levels of IL-6 were detectable in non-CMS PAE rats that failed to develop clinical signs of AA (Adj/NA) (statistical analyses not performed) (Fig. 4.3B). Similarly, IL-10 levels could not be analyzed (group mean = 0 for saline-injected non-CMS PAE rats); of note, however, for rats that developed AA (Adj/AA), mean IL-10 levels were highest in the CMS PAE group (Fig. 4.3C). Plasma levels of IL-1β, IL-4, IL-5, IL-13, and IFN-ɣ were below the lower limit of detection for >50% of subjects (data not shown).   92  Figure 4.3 Plasma cytokine levels at the peak of AA. Bars represent mean cytokine level ± SEM. Data are presented as raw cytokine levels (pg/ml) with statistical analysis conducted on Blom transformed (normalized) data. The presence of an asterisk (*) indicates a significant post-hoc comparison to the saline-injected group, within prenatal treatment and CMS condition, unless otherwise indicated. The “×” symbol indicates a significant post-hoc comparison to the control group, within CMS condition. The “§” indicates a significant post-hoc comparison to the non-CMS condition, as indicated. **/××: p<0.01; ***/§§§: p<0.001; D16: day 16 post-CFA injection; C: control; PAE: prenatal alcohol exposure; Non-CMS: non-stress group; CMS: stress group; Saline: saline-injected; Adj/NA: no clinical signs of AA following CFA injection; Adj/AA: clinical score ≥1.   93  At the recovery time point, similar to what was observed on day 16, IL-2 levels decreased in rats that failed to develop clinical signs of inflammation (Adj/NA) compared to saline-injected rats (Fig. 4.4A) [main effect of AA severity: F(2, 58)=5.16, p<0.01]. Comparatively, TNF-α levels were increased with CFA-injection (Adj/NA) and following recovery from AA (Adj/Rec) (Fig. 4.4B) [main effect of AA severity: F(2, 58)=5.75, p<0.01]. IL-2 and TNF-α levels were also affected by prenatal treatment, with decreased IL-2 levels in PAE rats, and increased TNF-α levels in PAE rats, compared to C [main effect of prenatal treatment – IL-2: F(1, 58)=5.81, p=0.019; TNF-α: F(1, 58)=10.88, p<0.01]. Levels of KC/GRO and IL-10 were detectable at the recovery time point but were unaltered by prenatal treatment, CMS condition or time point measured in this model. Levels of IL-1β, IL-4, IL-5, IL-6, IL-13, and IFN-ɣ were below the lower limit of detection for >50% of subjects (data not shown). Figure 4.4 Plasma cytokine levels following recovery from AA. Bars represent mean cytokine level ± SEM. Data are presented as raw cytokine levels (pg/ml) with statistical analysis conducted on Blom transformed (normalized) data. The presence of an asterisk (*) indicates a significant post-hoc comparison to the saline-injected group, within prenatal treatment and CMS condition. The “×” symbol indicates a significant post-hoc comparison to the control group, within CMS condition. ×: p<0.05; **/××: p<0.01; REC: following recovery from AA; C: control; PAE: prenatal alcohol exposure; Non-CMS: non-stress group; CMS:   94 stress group; Saline: saline-injected; Adj/NA: no clinical signs of AA following CFA injection; Adj/AA: clinical score ≥1; Adj/Rec: following recovery from AA, clinical score = 0 for 10 days.  4.3.7 Cytokine Levels in the Hind Paw On day 16, hind paw levels of IL-1β, IL-4, IL-6, IL-10, IL-13, and TNF-α were increased in the Adj/AA condition, compared to the saline and Adj/NA conditions (data shown in Appendix C). IL-2, IL-5, and IFN-ɣ levels were unaltered in this model. At the recovery time point, levels of IL-1β, IL-2, IL-4, IL-5, IL-6, IL-10, IL-13, TNF-α, IFN-ɣ, KC/GRO were detectable but were unaltered in this model (data not shown).  4.3.8 Histopathological Evaluation of Tibiotarsal Joints by H&E Staining On day 16, as expected, the total H&E score was highest in rats that developed active AA (Adj/AA), across prenatal treatment groups and CMS conditions [main effect of AA severity: F(2, 67)=34.30, p<0.001] (Fig 4.5G). Comparatively, at the recovery time point, the total H&E score was affected by prenatal treatment, CMS condition, and AA severity [interaction: prenatal treatment × CMS condition × AA severity – F(2, 55)=3.14, p<0.05]. In the non-CMS C rats, total H&E score did not differ by AA severity, whereas PAE rats that recovered from AA (Adj/Rec) had a higher total H&E score than both saline-injected and Adj/NA rats, as well as C rats that recovered from AA (Fig. 4.5G). By contrast, in the CMS condition, for both C and PAE rats, total H&E score was increased in rats that recovered from AA (Adj/Rec), compared to saline-injected rats (Fig. 4.5H). Finally, despite no external signs of inflammation, PAE rats in the Adj/NA group had an increased total H&E score, compared to their saline-injected counterparts (Fig. 4.5H). Figure 4.5 shows representative whole paw scans (A, B) and images (5x magnification) of the inferior edge of the tibia, including the synovium (C, D) and mid-tibial cartilage (E, F), for saline-injected rats (A, C, E) and rats that developed AA (B, D, F).    95  Figure 4.5 Histopathology by H&E staining of the tibiotarsal joint.   96 A – B: Scans of whole hind paws showing H&E staining in a healthy, saline-injected rat (A) and the inflammation associated with AA (B). C – F: Images (5x) showing the inferior edge of the tibia (C, D) and mid articulating cartilage between the tibia (Ti) and talus (Ta) (E, F). In the healthy, saline-injected condition (C, E), the articulating cartilage is seen in light pink, at the ends of tibia and talus, and the joint space is clear, with the healthy edge of the synovium (Syn) visible. With active AA (D, F), the inferior edge of the tibia is eroded by pannus (P) formation, as is the articulating cartilage at the inferior edge of the tibia and talus, and the joint space is invaded by inflammatory cells of the pannus. G – H: Total H&E score (mean ± SEM). The presence of an asterisk (*) indicates a significant post-hoc comparison to the saline-injected group, within prenatal treatment and CMS condition, unless otherwise indicated. The “×” symbol indicates a significant post-hoc comparison to the control group, within CMS condition and AA severity state. The “§” indicates a significant post-hoc comparison to the non-CMS condition, within AA severity state. */§: p<0.05; **/××: p<0.01; ***: p<0.001; H&E: hematoxylin and eosin; D16: day 16 post-CFA injection; REC: following recovery from AA; C: control; PAE: prenatal alcohol exposure; Non-CMS: non-stress group; CMS: stress group; Saline: saline-injected; Adj/NA: no clinical signs of AA following CFA injection; Adj/AA: clinical score ≥1; Adj/Rec: following recovery from AA, clinical score = 0 for 10 days.  4.3.9 Cartilage Thickness As noted, cartilage thickness was measured in three locations: the inferior and superior edge of the tibia, and mid-tibial cartilage. The same overall pattern was observed in these three locations and only inferior tibia measurements are reported in figure 4.6. On day 16, cartilage thickness decreased in rats that developed AA (Adj/AA) compared to saline-injected rats, and increased in CFA injected rats that failed to develop clinical signs of AA (Adj/NA), compared to the Adj/AA group, across prenatal treatments and CMS conditions [main effect of AA severity: F(2, 66)=6.06, p<0.01] (Fig 4.6G). At the recovery time point, cartilage thickness was not altered in an AA-dependent manner; however, PAE rats were found to have increased cartilage thickness overall compared to controls, regardless of CMS condition [main effect of prenatal treatment: F(1, 57)=8.08, p<0.01] (Fig. 4.6H). Figure 4.6 shows representative whole paw scans (A,   97 B) and images (5x magnification) of the cartilage of the tibia and talus in saline-injected rats (C, E) and rats that developed AA (D, F).   98  Figure 4.6 Cartilage integrity and thickness at the tibiotarsal joint.   99 A – B: Scans of whole hind paws showing cartilage (toluidine blue) staining in a healthy, saline-injected rat (A) and the cartilage loss associated with AA (B). C – F: Images (5x) showing the inferior edge of the tibia (C, D) and mid articulating cartilage between the tibia (Ti) and talus (Ta) (E, F). In the healthy, saline-injected condition (C, E), the articulating cartilage is visible in dark purple, extending continuously at the distal end of the tibia and proximal end of the talus. With active AA (D, F), cartilage at the inferior edge of the tibia is eroded (E). Mid articulating cartilage shows thinning, decreased staining, and loss of the defined architecture seen in the saline condition. G – H: Average cartilage thickness (mean ± SEM) at the inferior edge of the tibia on day 16 (G) and following recovery from AA (H). The presence of an asterisk (*) indicates a significant post-hoc comparison to the saline-injected group, within prenatal treatment and CMS condition, unless otherwise indicated. The “×” symbol indicates a significant post-hoc comparison to the control group, within CMS condition. *: p<0.05; **/××: p<0.01; D16: day 16 post-CFA injection; REC: following recovery from AA; C: control; PAE: prenatal alcohol exposure; Non-CMS: non-stress group; CMS: stress group; Saline: saline-injected; Adj/NA: no clinical signs of AA following CFA injection; Adj/AA: clinical score ≥1; Adj/Rec: following recovery from AA, clinical score = 0 for 10 days.  4.3.10 Macrophage Density in the Synovium and CD163+ Chrondrocyte Staining in the Cartilage On day 16, as expected, macrophage (CD163+) density was highest in rats that developed AA (Adj/AA), across prenatal treatments and CMS conditions [main effect of AA severity: F(2, 63)=5.32, p<0.01] (Fig. 4.7C). Importantly, at the recovery time point, macrophage density was affected by prenatal treatment and CMS condition, in an AA severity-dependent manner [interaction: prenatal treatment × CMS condition × AA severity – F(2, 58)=5.28, p<0.01]. Specifically, in the CMS condition, PAE rats that recovered from AA (Adj/Rec), had higher macrophage density than the PAE saline-injected and Adj/NA conditions, as well as higher density than their control CMS and PAE non-CMS Adj/Rec counterparts. Whole paw scans showing representative levels of macrophage staining in saline-injected rats and rats that developed AA are shown in Figure 4.7A–B. Figure 4.7E–M shows CD163+ chondrocyte staining (E,   100 H, K), cartilage staining (F, I, L), and the overlay between the two, with areas of cartilage thickening characterized by increased CD163+ chrondrocyte staining.    101  Figure 4.7 CD163+ macrophage density in the synovium and CD163+ chondrocyte staining in the cartilage.   102 A – B: Scans of whole hind paws showing CD163+ staining in a healthy, saline-injected rat (A) and the increase in staining associated with AA (B). C – D: Average macrophage density (mean ± SEM) within the synovium on day 16 (C) and following recovery from AA (D). The presence of an asterisk (*) indicates a significant post-hoc comparison to the saline-injected group, within prenatal treatment and CMS condition, unless otherwise indicated. The “×” symbol indicates a significant post-hoc comparison to the control group, within CMS condition and AA severity state. The “§” indicates a significant post-hoc comparison to the non-CMS condition, within prenatal treatment and AA severity state. E – M: Mid tibia/talus CD163 staining (E, H, L, K), cartilage (toluidine blue) staining (F, I, L), and the overlay of CD163 and cartilage staining (G, J, M). Images in panels E – G are representative of staining in a healthy, saline-injected rat, and panels H – J and K – L show areas of increased cartilage thickness, populated by increased numbers CD163+ chondrocytes (arrows) that occurs with active AA. *: p<0.05; **/§§: p<0.01; ***/×××: p<0.001; D16: day 16 post-CFA injection; REC: following recovery from AA; C: control; PAE: prenatal alcohol exposure; Non-CMS: non-stress group; CMS: stress group; Saline: saline-injected; Adj/NA: no clinical signs of AA following CFA injection; Adj/AA: clinical score ≥1; Adj/Rec: following recovery from AA, clinical score = 0 for 10 days. Scale bars: 0.1 mm.  4.3.11 Effects of Pair-Feeding 4.3.11.1 AA Incidence and Severity During the Resolution Phase Arthritis incidence for the C vs. PF comparison is depicted in Figure 4.8A, which shows the percent of rats with clinical signs of AA from day 18–57 (resolution phase) post-injection. In the regression model, an association between AA incidence and prenatal treatment was detected. The odds of developing AA with CFA injection increased by 5.0 for PF compared to C rats (p<0.01; 95% confidence interval for odds ratio: 1.75, 14.63). In other words, the probability that a PF rat would develop AA with CFA injection was 83%.  Analysis of clinical scores between C and PF rats during the resolution phase showed, as expected, a change in clinical scores over time across groups [F(16, 816)=12.10, p<0.001], a between-subjects effect of prenatal treatment during the resolution phase [F(1, 51)=10.42, p<0.01], and a differential   103 pattern over days by prenatal treatment [interaction between measurement day and prenatal treatment; F(16, 816)=13.55, p<0.001]. Overall, PF rats had higher clinical scores than C rats from day 15 – 27 post-injection (Fig. 4.18B).  Finally, analysis of the clinical score data by area under the curve (AUC) during the resolution phase, showed an increased AUC in PF compared to C rats, regardless of CMS condition (Fig. 4.8C) [main effect of prenatal treatment: F(1, 62)=9.46,  p<0.01].  4.3.11.2 Corticosterone Levels on Day 16 At the peak of inflammation both CMS and AA severity were found to have a modulatory role on corticosterone levels [interaction: CMS x AA severity – F(2, 62)=6.75, p<0.01] (Fig 4.8D). In both the non-CMS and CMS conditions, corticosterone levels were lower in in the absence of AA (Adj/NA group), than with active AA (Adj/AA). Higher corticosterone levels in the non-CMS compared to the CMS condition following saline-injection were driven by high basal corticosterone levels in non-CMS saline-injected PF rats.     104  Figure 4.8 Effects of pair-feeding in the AA model. A: Incidence of AA as the percentage of CFA-injected rats showing clinical signs of AA during the resolution phase. B: Average clinical scores (± SEM) for rats that developed AA during the resolution phase. C: Area under the curve   105 (AUC; mean ± SEM) of the average clinical scores during the resolution phase. The ‘a ’symbol indicates a significant post-hoc in the repeated measures ANOVA, for the comparison of PF and C groups. D: Corticosterone levels (mean ± SEM) on day 16 post-injection. Corticosterone data are presented in raw form, with statistical analysis conducted on Blom transformed (normalized) data. The presence of an asterisk (*) indicates a significant post-hoc comparison to the saline-injected group, within prenatal treatment and CMS condition, unless otherwise indicated. The “×” symbol indicates a significant post-hoc comparison to the control group, within CMS condition. The “§” indicates a significant post-hoc comparison to the non-CMS condition, within prenatal group and AA severity. *: p<0.05; **/aa/xx: p<0.01; §§§: p<0.001; PF: pair-fed; Non-CMS: non-stress group; CMS: stress group; Saline: saline-injected; Adj/NA: no clinical signs of AA following CFA injection; Adj/AA: clinical score ≥1.  4.4 Discussion  The current study supports and extends the previous work demonstrating alterations in the course and severity of AA following PAE (Zhang et al., 2012) and has important implications for understanding the clinical evidence of increased RA prevalence in adults with FASD (Weinberg, 2016). Here, we confirm our finding that in utero alcohol exposure results in an increased incidence and severity of, and impaired recovery from, AA. To probe for endocrine and immune parameters underlying this altered AA course, we examined rats at: 1) the induction-to-peak phase of AA, which helped to elucidate endocrine and immune signatures predictive of the heightened sensitivity to AA in PAE rats; and 2) the resolution phase, to investigate the parameters underlying impaired recovery and the longer-term effects of increased AA severity in PAE rats. Overall, our endocrine results show alterations in the corticosterone/CBG balance in non-CMS PAE rats that recovered from external signs of inflammation, suggestive of impaired resolution from inflammation. In addition, our results point to a potentially protective role of increased estradiol levels in counteracting AA onset. Histopathological assessment of the tibiotarsal joint points to increased AA severity at recovery in the CMS condition overall, but greater severity in PAE rats at recovery under both non-CMS and CMS conditions. Plasma cytokine levels were shown to increase with active AA, as expected, with prenatal treatment differences (decreased IL-2 and increased TNF-α in PAE compared to   106 C rats) detected again at the recovery time point. We also see increased synovial macrophage density with the combination of PAE and CMS exposure. Finally, increases in CD163+ chondrocytes are detected in the tibial cartilage with active AA, which may underlie the increased cartilage thickness that occurs with CFA injection (Adj/NA). Taken together, we show that in utero alcohol exposure differentially impacts AA severity and increases vulnerability to the second hit of CMS exposure during adolescence. Moreover, there appear to be differential cumulative effects of this double hit on key endocrine, immune, and histopathological parameters, which is suggestive of a significant immunomodulatory role of early environmental conditions. Our finding of increased AA incidence in PAE rats is in line with the clinical findings of increased rates of autoimmune disorders, including RA, in adults with FASD (Himmelreich et al., 2016; Weinberg, 2016). Due to the scarcity of clinical data in adults with FASD (Moore and Riley, 2015), it remains to be determined whether other factors, such as RA severity, are also greater in FASD as in our PAE animal model. However, data point toward an earlier age of onset of RA in individuals with FASD (Himmelreich et al., 2016; Weinberg, 2016), and in other populations, this is associated with faster and more severe progression of disease. Moreover, the clinical score profile during the resolution phase suggests impaired recovery in PAE rats. Unexpectedly, we show impaired resolution for PAE rats in the non-CMS condition, while exposure to adolescent CMS accelerated recovery. This is, however, in line with previous findings of accelerated recovery if stress exposure occurred prior to CFA administration, although the mechanism is currently unknown (Amkraut et al., 1971). Impaired resolution is also apparent from the incidence data where 40% of PAE rats in the non-CMS condition failed to recover from AA by 60 days post-injection, which amounts to approximately double the total incidence in C rats. This alteration in the overall trajectory of AA in this PAE animal model suggests significant effects on the pathophysiology of disease with in utero alcohol exposure. Of clinical relevance, differences in disease progression may suggest differential responses to RA intervention in individuals with FASD.    107 Inappropriate activation of the HPA axis in the context of ongoing inflammation appears to be implicated in the pathophysiology of at least a subset of RA cases (Imrich et al., 2010). We have shown previously that lower basal levels of CBG, the major transport protein for glucocorticoids, likely plays a critical role in the risk of AA onset due to a reduction in the corticosterone reservoir available to target inflammation (Bodnar et al., 2015). This motivated our investigation of HPA function in the present study, and indeed, the data support a role for CBG in the differential AA course in PAE compared to control rats. On day 16, in the CMS condition, we found that CBG levels decreased with the onset of AA, as expected, which replicates our previous work (Bodnar et al., 2015; Hill et al., 2016). Comparatively, in the non-CMS condition, we saw an unexpected drop in CBG levels with CFA injection, prior to the onset of AA (Adj/NA), which was more marked in PAE rats, and thus resulted in lower CBG levels overall in PAE compared to control rats. Accelerated CBG proteolysis prior to the onset of AA may represent a maladaptive response such that PAE rats cannot further cleave CBG to release corticosterone to dampen inflammation following AA onset. Our finding of decreased CBG in conjunction with increased corticosterone levels in non-CMS PAE rats that recovered from AA (Adj/Rec) also likely represents CBG proteolysis. While this is an expected response with inflammation, it is noteworthy that this shift in the corticosterone/CBG balance is occurring in rats that recovered (no clinical signs of AA). As such, it is likely that despite resolution as measured by external signs of inflammation, their HPA profile suggests that PAE rats may continue to experience inflammation at the level of the joints. Support for this comes from our finding of increased total H&E scores in this group. Taken together, this combination of a drop in CBG in the face of increased corticosterone levels and increased tibiotarsal inflammation/damage is more characteristic of the profile reported on day 16, at the peak of inflammation, and suggests that from a physiological perspective, resolution from AA is impaired by prenatal alcohol exposure. CMS exposure during adolescence allowed for investigation into the modulatory role of stress during a sensitive developmental period on the response to chronic immune challenge. Amkraut and colleagues (1971) were the first to show an impact of stress pre-exposure on the course of AA, demonstrating that   108 group housing stress, initiated one week prior to CFA injection, accelerates both AA onset and recovery (Amkraut et al., 1971). Our data, to the best of our knowledge, are the first to demonstrate that stress exposure during adolescence, ending 20 days prior to immune challenge, significantly impacts the pathophysiology of AA. Specifically, CMS exposure resulted in impaired recovery from AA. Despite no external signs of inflammation (clinical score = 0), the elevated total H&E score in rats in the Adj/Rec condition represents prolonged inflammation and/or destruction at the level of the tibiotarsal joint, and thus ongoing inflammation. While increased H&E scores occurred in both C and PAE rats in the Adj/Rec condition, we also showed that adjuvant-injected PAE rats that never developed clinical signs of inflammation (Adj/NA) displayed increased H&E scores indicative of ongoing inflammation and/or persistent joint damage. In other words, for PAE rats, the second hit of CMS during adolescence alters AA pathophysiology such that subclinical AA results in visible damage/inflammation in the joint.  Utilization of female rats in this study enabled us to investigate the modulatory role of estrogen in our PAE model. While clinical data are inconclusive as to the mechanism underlying the increased risk of RA in females, hormonal factors have been shown to influence disease (Alamanos and Drosos, 2005). Estrogens have consistently been shown to have a protective role in terms of RA risk and/or severity in conditions such as pregnancy (Ostensen, 1999) and oral contraceptive administration (Brennan et al., 1997; Spector et al., 1990) where levels are increased (reviewed in (Islander et al., 2011)). Similarly, peak RA incidence occurs with the drop in sex hormone production at menopause (Goemaere et al., 1990). The role of sex hormones has also been shown in animal models, with ovariectomy resulting in increased incidence and severity of experimentally-induced arthritis (Holmdahl et al., 1986; Holmdahl et al., 1987). In the current model at the peak of inflammation, estradiol levels were shown to drop with CFA injection, which is in line with chronic immune system activation disrupting the estrous cycle and resulting in sustained diestrus to prevent ovulation (Avitsur and Yirmiya, 1999; Rivest et al., 1993). Not surprisingly, we did not find an association between estrus stage and estradiol levels, likely due to the high proportion of rats in diestrus at termination (64%). At the recovery time point, however, our finding of highest   109 estradiol levels in CFA-injected rats that failed to develop AA (Adj/NA), across prenatal treatments and stress conditions, is unexpected. We propose that the increased estradiol levels in the Adj/NA group may have exerted a protective effect against AA development in this model. In support of this, estradiol administration has been shown to dampen arthritis symptoms in animal models (Ratkay et al., 1994). However, further investigation will be required to probe for a protective role for estradiol in the AA model.   There is extensive evidence that prenatal and early postnatal (third trimester equivalent) alcohol exposure affect the cytokine balance, generally resulting in increased cytokine levels in both periphery and brain (Bodnar et al., 2016; Drew et al., 2015; Topper et al., 2015a). However, the extent to which cytokine disturbances following PAE play a role in the altered course of AA or RA is relatively unknown. Two lines of evidence, however, point to the potential for cytokine disruption with arthritis. First, our previous work shows that PAE rats fail to activate key inflammation-related genes with AA challenge (Lussier et al., 2015). Second, decreased methylation at CCCTC-binding factor (CTCF) binding regions has been shown in a paternal preconception alcohol exposure model (Knezovich and Ramsay, 2012). Of note, CTCF binding occurs at the IL-6 promoter and demethylation of the IL-6 promoter region has been associated with increased IL-6 production and an increased risk of RA (Nile et al., 2008). Complementing these results, we now show increased IL-6 levels on day 16 in non-CMS PAE rats in the Adj/NA condition, a cytokine response that is typically seen only with active AA. This heightened level of IL-6 is, however, in line with the aforementioned drop in CBG in this group, as IL-6 has been shown to decrease CBG levels produced by liver cells (Emptoz-Bonneton et al., 1997) as part of the mechanism to amplify free corticosteroid levels (Bernier et al., 1998; Emptoz-Bonneton et al., 1997). This IL-6/CBG profile provides further support for our conclusion that despite no visible external signs of inflammation, these PAE rats are experiencing a subclinical inflammatory response to CFA. A differential cytokine response was also detected at the recovery time point where IL-2 levels were lower and TNF-α levels were higher in PAE compared to C rats. Taken together, these prenatal treatment differences in cytokine profiles may   110 underlie, at least partly, differences in AA disease course. Finally, as disease-modifying antirheumatic drugs (DMARD) targeting cytokines, such as anti-TNF (Taylor and Feldmann, 2009) and IL-6 (Emery et al., 2008; Wendling et al., 1993) agents, are commonly used, factors affecting the cytokine balance and trajectory through the course of arthritis, may significantly impact response to treatment. Infiltration of immune cells into the synovium, subsequent inflammation, and bone and cartilage destruction are defining features of RA and animal models of arthritis (Firestein, 2003). Of the immune cells involved, macrophages likely play a central pathogenic role, with other cell types such as neutrophils and T and B lymphocytes occupying more secondary roles (Li et al., 2012). Specialized macrophages populations can be found in most tissues and organs (Gordon and Taylor, 2005), where they play key roles including removal of dead/dying cells and regulation of tissue homeostasis through sampling of the external environment (Neutra et al., 1996). Importantly, macrophage populations have been identified within the field of developmental immunotoxicology as being particularly vulnerable to early-life insults, due to their wide-ranging distribution and their role in sampling the external environment, which overlaps with many portals of potential exposure to immunotoxic agents (Dietert, 2014). Here we show that the double insults of in utero alcohol and adolescent CMS exposure result in increased macrophage density within the synovium following recovery from AA (Adj/Rec). This finding, unique to the CMS PAE group only, and not found in the other groups that showed increased joint inflammation/damage (total H&E score) suggests the possibility of differential mechanisms of AA. Specifically, joint damage in the CMS PAE group may be more macrophage-dependent, potentially as a result of macrophage dysfunction resulting in misdirected/misregulated inflammation and tissue damage (Dietert, 2014). Importantly, macrophage dysfunction in the lung (Ping et al., 2007) and spleen (Tonk et al., 2013) has been shown previously following in utero or early postnatal alcohol exposure; however, to the best of our knowledge, this is the first report of potential macrophage dysfunction in a 2-hit scenario – PAE, which sensitizes/reprograms the organism, and later life CMS exposure.   111 Macrophage levels were quantified using the cell surface glycoprotein CD163, a highly specific macrophage surface markers, which is expressed at high levels by most mature tissue macrophage populations (Polfliet et al., 2006), and not expressed by synovial intimal fibroblasts in disease states such as RA (Fonseca et al., 2002). This marker also has the added benefit of being able to detect the recently reported CD163+ chondrocytes, which are shown to phagocytose cellular debris within arthritic cartilage such as the knee and temporomandibular joint (Jiao et al., 2013). Importantly, with arthritis progression, the percentage of CD163+ chondrocytes increases, resulting in enhanced migration and phagocytosis of cellular debris within the cartilage (Jiao et al., 2013). This is in line with our findings of tibial cartilage hyperplasia, which when overlayed with CD163+ staining, appears to be highly populated by CD163+ chondrocytes. As a result, we propose that our unexpected findings of increased cartilage thickness in the Adj/NA group on day 16 may be a result of increased phagocytotic activity of CD163+ chondrocytes, which is able to keep AA associated inflammation in check. Comparatively, the decrease in cartilage thickness with active AA (Adj/AA) and associated increase in total H&E score, are likely a result of pannus invasion and destruction of the articular cartilage. Of note, at the recovery time point, we also showed overall increased cartilage thickness in PAE compared to C rats, under both non-CMS and CMS conditions, which may point toward heightened or long-lasting CD163+ activity within the articulating cartilage. Overall, pair feeding increased the incidence and severity of AA as indicated by the increased odds of developing AA and the AUC for clinical scores, in a manner that paralleled the response in PAE rats. Examination of the clinical score curves through the resolution phase, however, revealed a differential pattern compared to that of alcohol-exposed rats. PF rats showed increased clinical scores from days 15 – 27, compared to controls, indicative of a higher “peak” AA response. Notably, and unlike PAE rats, which showed a prolonged elevation in clinical scores from peak through resolution (days 18 through 57 post-injection, with the exception of days 42 and 45), the overall resolution from AA was not affected in PF rats. Thus, it appears that while some of the overarching features, such as incidence and   112 severity, may be similar in PF and PAE rats, the AA disease mechanism are different from those in PF rats. Further support for differential AA disease mechanisms comes from the assessment of AA parameters in PF rats. Other than the high corticosterone levels in the non-CMS saline-injected PF rats, as noted above, the increased incidence and severity was not reflected in the other parameters we examined. In other words, unlike with alcohol-exposure, we did not detect specific pair-feeding effects (differences between C and PF rats) at the level of the additional hormonal, cytokine, and histopathological measures examined, again suggestive of differential disease trajectory/mechanism. Pair-feeding, while accounting for the decreased food intake associated with alcohol consumption, is a confounded and thus limited “control” group. Pair-feeding can never account for the significant nutritional effects of alcohol, including alterations in absorption and utilization of nutrients (Weinberg, 1984). In addition, PF rats are not fed ad libitum, like their PAE counterparts, and while they receive the same amount of food as PAE rats (controlled for body weight), they are likely not experiencing the decrease in appetite associated with alcohol consumption. This may be related to the finding that while pair-feeding protocols match calorie consumption between PF and PAE rats, alcohol consumption increases leptin production in adipose tissue, which is associated with increases in satiety (Lin et al., 1998). Importantly, PF rats generally consume their entire day’s ratio of food almost immediately, and as a result, they are essentially food deprived until the next feeding (Gallo and Weinberg, 1981). This is not only an abnormal feeding pattern, but also introduces a mild prenatal stress component to the pair-feeding paradigm which in itself may have a long-term impact on the neuroendocrine axis in the offspring (Vieau et al., 2007), and represents a clear confound in the pair feeding model. Currently, there is controversy as to the utility of the PF group. While a unilateral decision as to the importance of a PF group may never be reached, their use may depend on the experimental question. Here, we were probing for the impact of in utero alcohol consumption on immune outcomes. Importantly research examining human alcoholics has shown that decreased food consumption is intimately linked to chronic alcohol-intake [reviewed in (Yeomans et al., 2003)] As a result, in order to examine the full range of effects associated with alcohol consumption, it   113 may not always be necessary or important to disentangle the impact of alcohol from decreased food intake. From a mechanistic perspective, alcohol consumption and decreased food intake are linked and the effects of one may potentiate or dampen the effects of the other. Thus, clear separation of the impact of each may lead to the description of arbitrary conditions that do not best model human alcohol consumption. In other experimental designs, however, where the goal is to examine the specific teratogenic effects of the alcohol molecule itself, and not chronic alcohol consumption, the use of a PF group may be warranted.  In summary, our findings of increased incidence and severity of AA, and impaired recovery from AA following prenatal alcohol exposure, highlight the immunomodulatory impact of early-life events in this chronic inflammatory model. Pair-feeding was also shown to result in an increased incidence and severity of AA, with no effect on recovery, with overall differential mechanisms of diseases suspected in the PF compared to PAE condition. Furthermore, distinct effects of both alcohol exposure and adolescent stress exposure as well as the combination of these two hits are described at the level of hormonal and cytokine responses, and histopathology of the tibiotarsal joint, which together suggest differential pathophysiology of disease. These data point to the necessity for further clinical investigation into incidence and prevalence of RA, and extending to other autoimmune disorders in adults with FASD, and ultimately, to the importance of developing targeted treatment strategies.    114 Chapter 5: Evidence for an Immune Signature of Prenatal Alcohol Exposure in Female Rats.  5.1 Introduction There is increasing evidence for immune and neuroimmune abnormalities in the etiology and pathophysiology of numerous neurodevelopmental disorders including schizophrenia (Noto et al., 2015) and autism spectrum disorder (ASD) (Krakowiak et al., 2015). Evidence for underlying immune/neuroimmune abnormalities in Fetal Alcohol Spectrum Disorder (FASD), which includes the broad range of deficits/disorders that arise following in utero alcohol exposure, is also emerging  [reviewed in (Drew and Kane, 2014)]. Children with FASD have a higher incidence of both minor (e.g., recurrent otitis media, respiratory infections), and major, (e.g., sepsis) infections compared to non-exposed children (Gauthier et al., 2004; Johnson et al., 1981) as well as an increased incidence of malignancies, including cancers of embryonic origin (neuroblastoma, ganglioneuroblastoma, medulloblastoma) [reviewed in (Gottesfeld and Abel, 1991)], and leukemias (Latino-Martel et al., 2010). Animal models of prenatal alcohol exposure (PAE) support and extend the clinical findings. Increased susceptibility to infections (Grossmann et al., 1993; McGill et al., 2009) and malignancies (Gottesfeld and Abel, 1991), deficits in immune organ development (Bray et al., 1993; Ewald and Frost, 1987; Ewald and Walden, 1988; Redei et al., 1989), decreased splenic lymphocyte, T lymphoblast, and B cell proliferative responses to stimulation (Gottesfeld et al., 1990; Jerrells and Weinberg, 1998; Weinberg and Jerrells, 1991; Wolcott et al., 1995), blunted LPS-induced febrile responses (Taylor et al., 1999b), dampened cytokine responses to immune challenge (Chiappelli et al., 1997; Kim et al., 1999; Lee and Rivier, 1993), and a more severe and prolonged course of inflammation in an adjuvant-induced arthritis model (Zhang et al., 2012) have been reported in models of in utero alcohol exposure [reviewed in Bodnar and Weinberg 2013].    115 It is well established that chronic alcohol consumption increases proinflammatory cytokine levels (Crews et al., 2006; He and Crews, 2008). In this context, if alcohol is consumed during pregnancy, the developing fetus is likely exposed not only to alcohol but also to heightened cytokine signals. In addition to their function in the immune response, cytokines are essential to brain development. Cytokine receptors are expressed on neuronal cells in the fetal brain (Gilmore et al., 2004) and play important roles in key neuronal processes, such as neurogenesis (Smith et al., 2007), myelination (Jakovcevski et al., 2009), synaptogenesis, synaptic pruning, and modulation of synapse strength (Stephan et al., 2012) [reviewed in (Deverman and Patterson, 2009)]. As such, deviations in the normal cytokine balance have been shown to alter the course of normal brain development (Cai et al., 2000). Further support of this notion comes from the “prenatal cytokine hypothesis”, put forth in the field of schizophrenia, which links increased cytokine exposure during the prenatal period and alterations in the trajectory of brain development (Howard, 2013). Only a few studies to date have focused on neuroimmune function/inflammation following prenatal alcohol exposure, and of these, most have focused primarily on males or have pooled the data from males and females (Drew et al., 2015; Topper et al., 2015b). Of note, in relation to neuroimmune function in general, females often show an increased incidence of autoimmune/inflammatory diseases or disorders compared to males (Whitacre, 2001), and in many cases, increased rates of inflammatory mediated depressive-like behaviors (Tonelli et al., 2008).  In the present study, we utilized our well-established animal model of prenatal alcohol exposure to investigate whether PAE affects development of neuroimmune function during early life. In the context of the discussion above, and as a first step in examining neuroimmune/ inflammatory changes in our model, we conducted the present study in females. Specifically, we examined levels of key pro- and anti-inflammatory cytokines in peripheral and central compartments, at a number of key developmental ages [birth (postnatal day 1 [P1], P8 and weaning (P22)] in order to probe for a unique, immune-based signature of alcohol exposure. Furthermore, due to the extensive interplay between the immune and   116 endocrine systems, with shared ligands, receptors, and regulatory feedback (Haddad et al., 2002), we also examined hypothalamic-pituitary-adrenal (HPA) parameters (plasma corticosterone and corticosterone binding globulin [CBG]) to determine whether they might have a modulatory role on immune function.  We hypothesized that in utero alcohol exposure would alter the developmental immune profile, as indexed by alterations in pro- and anti-inflammatory cytokine levels. Based on recent findings of increased cytokine levels in the hippocampus, cortex, and cerebellum following alcohol administration during the early postnatal period (third trimester equivalent model) (Drew et al., 2015; Topper et al., 2015b), we predicted that PAE would result in overall increased cytokine levels within the brain and blood.   5.2 Materials and Methods 5.2.1 Breeding Male and female Sprague-Dawley (SD) rats (Charles River Laboratories, St. Constant, Quebec, Canada) were pair-housed by sex in clear polycarbonate cages with corn-cob bedding, and handled daily for a one week habituation period. Colony rooms were maintained on a 12:12 h light/dark cycle (lights on at 0700 hr), at 20 – 23°C, and rats were given ad libitum access to water and standard laboratory chow (18% Protein Extruded Rodent Diet, #2018, Teklad Global). Nulliparous females (250 – 325 g; n=43) were pair-housed with a male and vaginal lavage samples were collected daily for estrous cycle staging and to check for the presence of sperm, indicating gestation day 1 (GD1). All animal procedures were in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals, and approved by the University of British Columbia Animal Care Committee.     5.2.2 Prenatal Diets and Feeding On GD1, females were single housed and assigned to one of three treatment groups: (1) Prenatal alcohol exposure (PAE) – ad libitum access to an alcohol-containing liquid diet with 36% of total calories   117 derived from ethanol, 6.37% v/v, n=13; (2) Pair-fed (PF) – liquid control diet with maltose dextrin isocalorically substituted for ethanol, in the amount consumed by a PAE partner (g/kg/body wt/day of gestation, n=15); (3) Control (C) – pelleted version of the liquid control diet, ad libitum, n=15. All diets were formulated to provide optimal nutrition (Weinberg/Keiver High Protein Experimental Diet #710324, Control (PF) Diet #710109, and Pelleted Control Diet #102698, Dyets Inc. Bethlehem, PA, USA) and were presented daily, one hour prior to lights off (1800-1900 hr) in order to maintain the normal corticosterone circadian rhythm in PF dams, which are fed a restricted ration (Gallo and Weinberg, 1981; Krieger, 1974). Rats in all groups had ad libitum access to water and were weighed weekly throughout gestation. On GD17 a blood sample was collected from the tail vein from a subset of PAE, PF, and C dams (n=3 – 6) at lights on (0700 hr). Blood alcohol levels were measured as previously reported (Hellemans et al., 2010; Uban et al., 2010) and ranged from ~80 – 150 mg/dl in PAE rats. On GD21, diets were replaced with standard laboratory chow (19% Protein Extruded Rodent Diet, #2019, Teklad Global), ad libitum, and rats were continued on this diet throughout lactation.   5.2.3 Tissue Collection On the day of birth (postnatal day 1; P1), litters were culled to 6 males and 6 females, when possible. Female pups from each prenatal group were randomly assigned to one of three termination ages, with tissue collected on P1, P8, or P22 (n=7-11/group). Litters were maintained at a minimum of 8 pups (4 males, 4 females), with a balanced sex ratio, through the end of the experiment (P22), in order to control for changes in maternal behaviour, which can occur with small litters or litters with an unbalanced sex ratio (Alleva et al., 1989; Moore and Morelli, 1979). The dam and remaining pups were weighed weekly during the lactation period and any pup deaths or abnormalities were noted. On the day of tissue collection (1400-1700 hr), female rats were quickly removed from the home cage and decapitated. Trunk blood was collected, serum allowed to separate for 2 hours, and samples centrifuged at 2,190 g for 10 min at 4°C. Serum samples were stored at -80°C until assayed for hormone and cytokine levels. Due to the   118 small brain size on P1, the whole brain was removed from the skull, the cerebellum and olfactory bulbs removed, and the brain weighed and quickly frozen on dry ice. On P8 and 22, the whole brain was again removed and weighed following removal of the cerebellum and olfactory bulbs, and the hypothalamus, PFC, and hippocampus were dissected on ice and frozen on dry ice. At all ages, the spleen was removed, weighed, and frozen on dry ice. To control for litter effects, only one rat from each litter was used per age.   5.2.4 Corticosterone Radioimmunoassay Total serum corticosterone levels were measured using the ImmuChem Double Antibody Corticosterone 125I radioimmunoassay kit (MP Biomedicals, LLC, Orangeburg, NY, USA), according the manufacturer’s instructions with one modification – the lowest standard was further diluted to detect lower corticosterone concentrations (Taves et al., 2015). The minimum detectable corticosterone concentration was approximately 7.7 ng/mL, and the intra- and inter-assay coefficients of variation were <10 % and <7 %, respectively. All samples were measured in duplicate.   5.2.5 CBG Measurement The plasma corticosterone-binding capacity of CBG was measured using an established ligand-saturation assay (Smith and Hammond, 1991). Briefly, serum samples were diluted (1:1000 for P1 and P22, 1:200 for P8) in phosphate buffered saline (PBS) and stripped of endogenous steroids by incubation with dextran coated charcoal (DCC) for 30 minutes at room temperature. Samples were then incubated with ~10 nM of [3H]-corticosterone (PerkinElmer Lifer Sciences, Waltham, MA) in the absence or presence of excess corticosterone, to monitor non-specific binding. After the adsorption of free steroid with DCC for 10 minutes and centrifugation at 0°C, CBG-bound [3H]-corticosterone was measured in the supernatants using a scintillation photospectrometer.     119 5.2.6 Tissue Homogenization  Lysis buffer was prepared containing 150 mM NaCl, 20 mM Tris pH 7.5, 1 mM EDTA, 1 mM EGTA, and 1% Triton X-100, and immediately prior to homogenization the following were added (per 10 mL lysis buffer): 1 cOmplete mini protease inhibitor cocktail tablet (Roche Diagnostics, Indianapolis, IN), 100 µl phosphate inhibitor 2 & 3 (Sigma-Aldrich, St. Louis, MO), 100 µl 1 M NaF, and 40 µl PMSF (from 500 mM stock in DMSO). Brain samples (whole brain on P1; hypothalamus, PFC, and hippocampus on P8, 22) were added to 1.6 ml tubes containing 8 zirconium oxide beads and lysis buffer (whole brain – 550 µl; hypothalamus – 100 µl; PFC & hippocampus – 250 µl). Brain samples were homogenized using the Omni Bead Ruptor 24 (Omni Internation, Kennesaw, GA) in 4 – 5 cycles (speed: 2.10 – 3.10, time: 5 sec), with 1 min on ice in between cycles. Spleen samples were homogenized as above, with modifications – 1.3 g garnet was added to the tubes prior to homogenization, 250 – 1,000 µl lysis buffer was used based on spleen size (P1: 250 µl; P8: 500 µl; P22: 1,000 µl), and samples were homogenized in 3 – 4 cycles (speed: 6.0, time: 10 sec). In addition, spleen samples were sonicated (sonicator model 4C15; Fisher Scientific, Pittsburgh, PA) three times (5 sec/cycles, amplitude 50%), on ice. Following homogenization/sonication, tissue samples were centrifuged at 1,400 g for 10 min at 4°C. Separate aliquots of supernatant were removed for protein quantification and cytokine analysis, and stored at -20°C until assayed.  5.2.7 Multiplex Cytokine and CRP Measurements Multiplex cytokine assays were performed using the Meso Scale Discovery (MSD) proinflammatory panel 1 rat V-PLEX kit. This cytokine panel allows for the simultaneous measurement of IL-1β, IL-2, IL-4, IL-5, IL-6, IL-10, IL-13, IFN-ɣ, KC/GRO (CXCL1), and TNF-α (catalog #: K15044D-1, MSD, Rockville, MD). Samples were diluted (1:4 for serum, 1:2 for tissue) in diluent 42 and assays were performed using the standard MSD protocol. The plate was read using a Sector Imager 2400 (MSD, Rockville, MD) and data were analyzed using the MSD Discovery Workbench software v. 4.0   120 (MSD, Rockville, MD). The lower limit of detection (LLOD) for the assays varied by plate and analyte. The following LLOD ranges were observed (pg/mL) – IL-1β: 7.76 – 17.8; IL-2: 21.50 – 55.30; IL-4: 0.15 – 0.36; IL-5: 9.23 – 15.90; IL-6: 5.66 – 11.40; IL-10: 2.10 – 5.79; IL-13: 0.85 – 1.43; IFN-ɣ: 0.31 – 0.82; TNF-α: 0.31 – 0.748; KC/GRO: 0.55 – 6.45.  CRP levels were detected using antibody pairs (catalog #: DY1744, R&D Systems, Minneapolis, MN), with capture antibodies printed in the wells of standard-bind multiplex assay plates (MSD proprietary printing service). Serum samples were diluted in PBS + 1% BSA (1:50,000 dilution), followed by a 1:2 dilution in diluent 42 (MSD, Rockville, MD). Detection antibodies were derivatized with electrochemiluminescent SULFO-Tag NHS-ester by MSD and 2.88 µl SULFO-TAG labeled Streptavidin was added, with the detection antibody, according to standard MSD protocol. The plate was read and analyzed as above. The LLOD for CRP was: 3.92 – 4.62 pg/ml.   5.2.8 Protein Quantification Total protein levels were quantified in tissue homogenates using the Pierce Microplate BCA Protein Assay Kit (reduction agent compatible; Pierce Biotechnology, Rockford, IL). Tissue homogenates were diluted (1:41) and the standard BCA protocol was followed with one modification: 5 µl of compatibility reagent solution was added to each sample. Tissue homogenate samples were run in quadruplicate to determine the average protein concentration. Tissue cytokine levels were then adjusted and values reported as pg cytokine/mg of protein.   5.2.9 Statistical Analyses Maternal data during gestation and lactation were analyzed using repeated measures analyses of variance (ANOVA) (IBM SPSS Statistics), with prenatal treatment as the between-subjects factor, and day of gestation or lactation as the within-subjects factor. As separate cohorts of offspring from each prenatal group were terminated on P1, P8, or P22 (n=7-11/group), body, brain, and spleen weight, and   121 hormone data were analyzed by ANOVAs for the factor of prenatal treatment at each age. Significant main effects were further analyzed by Fisher post hoc tests. Cytokine data were initially analyzed using ANOVAs for the factors of prenatal treatment and age. Consistent age effects were detected for the majority of cytokines (see Appendix F), as expected (Giulian et al., 1988; Zhao and Schwartz, 1998), and there were no differential patterns of change in PAE compared to control offspring across development. Separate ANOVAs were then run at each age to examine prenatal treatment effects. Tissue (brain, spleen, serum) cytokine levels were also examined by independent ANOVAs as cytokine levels have previously been established to vary by tissue type (Deverman and Patterson, 2009). In line with our hypotheses, planned pairwise comparisons were carried out, as indicated. Outliers (±2.5 SD > mean) were removed from cytokine analyses, when appropriate. Corticosterone, CRP, and cytokine levels were not normally distributed and were Blom transformed (Blom, 1958) for statistical analysis. Untransformed data are presented in Fig. 5.2–5.5, for clarity. Note: Significant effects of KC/GRO were not detected at any age and data were omitted from all graphs. Pearson’s correlations between ethanol consumption during the second half of gestation (GD12 – 21) and both spleen weight and cytokine levels were also performed. The relationship between the variables was first examined by scatterplot and regression in order to confirm a linear relationship. Data met the assumptions of the Pearson’s correlation.   Differences were considered significant at p ≤ 0.05, and trends (p > 0.05 and < 0.085) were examined, by planned pairwise comparisons, according to our a prior hypotheses. Non-significant effects were not reported. Significant ANOVA F statistics and p values were reported in the text; post hoc p values were reported in figure legends. Significant p-values for main effects and post-hoc comparisons were reported according to the following range: p<0.05 (*); 0.05 > p>0.01 (**); p<0.001 (***).     122 5.3 Results 5.3.1 Pregnancy Outcome Analysis of maternal weight throughout gestation revealed, as expected, a significant interaction between prenatal group and gestation day [F(6, 120)=12.80, p<0.001], with PAE and PF dams weighing less than C dams from GD 7 through GD 21 (Table 3.1). Total intake of liquid ethanol diet is shown in the Appendix E. There were no prenatal group effects on maternal weight during lactation, and no prenatal treatment effects on gestation length, number of pups per litter, or number of pup deaths (Table 5.1).   Pregnancy outcome variable Prenatal treatment group C PF PAE Number of pregnant dams 15 15 13 Length of gestation (d) 22.9 ± 0.1 22.7 ± 0.1 22.7 ± 0.2 Number of pups 13.1  ±  0.08 14.5  ±  0.06 12.5 ±  0.08 Number of pups deaths (P1-22) 5 3 4 Dam weight (g) GD1 292.9 ± 6.7 289.1 ± 6.7 293.0 ± 7.2 GD7 334.5 ± 6.8 309.7 ± 6.8 312.5 ± 7.3✢ GD14 388.7 ± 6.6 350.4 ± 6.6 350.5 ± 7.1✢✢✢ GD21 486.5 ± 9.3 441.4 ± 9.3 423.4 ± 10.0✢✢ LD1 402.5 ± 11.9 380.0 ± 9.7 359.5 ± 9.7 LD8 372.8 ± 10.2 366.0 ± 8.3 360.5 ± 8.3 LD15 352.8 ± 7.5 365.2 ± 6.1 362.8 ± 6.1 LD22 355.8 ± 5.1 347.8 ± 4.2 341.7 ± 4.2   123  Table 5.1 Pregnancy outcomes and maternal body weights during gestation and lactation. Data are presented as mean ± SEM; Post hoc : ✢PAE = PF < C; ✢ p<0.05; ✢✢ p<0.01; ✢✢✢ p<0.001; GD: gestational day; LD: lactation day; d: day; C: control; PF: pair-fed; PAE: prenatal alcohol exposure.  5.3.2 Offspring Body, Brain, and Spleen Weight On P1, both PAE and PF pups had lower body weight than C pups [F(2, 26)=4.92, p<0.05] (Fig 5.1A), with catch up growth such that there were no differences in weight by P8 (Fig. 5.1B). Similarly, PAE and PF pups had lower brain weight than C pups on P1 [F(2, 26)=8.76, p<0.01] (Fig 5.1D), with catch up growth by P8 (Fig 5.1E). However, on P22, despite no significant differences in body weight, brain weight was increased in PAE and PF compared to C pups [Fig. 5.1F; F(2, 22)=5.08, p<0.05] (Fig 5.1F). Of note, when corrected for body weight, brain weight was not different among prenatal groups on P22 (data not shown). Spleen weight was significantly increased in PAE compared to both C and PF pups on P22 [F(2, 23)=13.06, p<0.001] (Fig 5.1I) but not on P1 or P8 (Fig 5.1G, H). There was, however, a strong positive correlation between spleen weight on P8 and maternal alcohol consumption during the second half of pregnancy (GD12 – 21; r = 0.73, R2 = 0.533; p<0.05).     124 Figure 5.1 Body, brain, and spleen weight on P1, 8, and 22. Bars represent mean body (A – C), brain (D – F) or spleen (G – I) weight ± SEM. In the case of a main effect of prenatal treatment, the asterisk (*) indicates a significant post hoc, with the comparison being made to the control group, unless otherwise indicated. Post hoc: * p<0.05; ** p<0.01; *** p<0.001; P1: n = 8 – 10/prenatal treatment group; P8, 22: n = 7 – 8/prenatal treatment group; P1, 8, 22: postnatal day 1, 8, 22; C: control; PF: pair-fed; PAE: prenatal alcohol exposure.  5.3.3 Serum Measures of Corticosterone, CBG, and CRP There were no differences in corticosterone levels among prenatal treatment groups at any age (Fig. 5.2A – C). CBG levels, however, were lower in PAE compared to C and PF pups at birth and P8 [P1: F(2, 24)=5.13, p<0.05; P8: F(2, 23)=6.11, p<0.01] (Fig 5.2D, E].    125 Examination of CRP levels revealed a trend for a main effect of prenatal treatment on P1 [F(2, 22)=3.18, p=0.06], with planned pairwise comparisons showing lower CRP levels in PAE compared to C pups (Fig 5.2G). CRP levels did not differ among groups on P8 or P22 (Fig. 5.2H – I).  Figure 5.2 Corticosterone, corticosterone binding globulin, and C-reactive protein levels. Bar represent mean levels of corticosterone (A – C), corticosteroid binding globulin (D – F), or C-reactive protein (G – I) ± SEM. Levels of corticosterone and C-reactive protein are presented as nM and µg/ml, respectively, with statistical analysis of Blom transformed (normalized) data. CBG levels (nM) were not transformed for statistical analysis. In the case of a main effect of prenatal treatment, the asterisk (*) indicates a significant post hoc. The “×” indicates a significant planned pairwise comparison following a trend for a main effect of prenatal treatment.   126 For post hocs/planned pairwise comparisons, the symbol denotes a comparison with the control group, unless otherwise indicated. Post hoc: * p<0.05; ** p<0.01; *** p<0.001; Planned pairwise comparison: × p<0.05; P1: n = 8 – 11/prenatal treatment group; P8, 22: n = 7 – 8/prenatal treatment group; CBG: corticosteroid binding globulin; CRP: C-reactive protein; P1, 8, 22: postnatal day 1, 8, 22; C: control; PF: pair-fed; PAE: prenatal alcohol exposure.  5.3.4 Cytokine Levels in Serum, Whole Brain, and Spleen at Birth (P1) On P1, serum cytokine levels were low overall, with detectable levels of TNF-α, IFN-ɣ, IL-4, IL-10, and IL-13 only (Fig 5.3), and no significant effects of prenatal treatment. In whole brain on P1, however, lower levels of IFN-ɣ were found in PAE compared to C pups [F(2, 21)=4.13, p<0.05; Fig. 5.3D). By contrast, in the spleen we found specific effects of pair-feeding: IL-2 and IL-5 levels were higher in the spleen of PF compared to C and PAE pups [IL-2: F(2, 20)=3.96, p<0.05; IL-5: F(2, 19)=4.55, p<0.05; Fig 5.3B, G].   127  Figure 5.3 Cytokine levels in blood, whole brain, and spleen at birth (P1). Bars represent mean cytokine level ± SEM. Data are presented as pg cytokine/mg protein for brain and spleen or pg cytokine/ml for serum, with the difference in unit denoted by the double line intersecting the x-axis. Cytokine levels within each tissue were analyzed by separate analyses of variance (ANOVAs) using Blom transformed (normalized)   128 data. Serum levels of IL-1β, IL-2, IL-6, and IL-5 and whole brain levels of IL-4 and IL-13 were below the lower limit of detection of the assay. In the case of a main effect of prenatal treatment, the asterisk (*) indicates a significant post hoc. For post hocs, the symbol denotes a comparison with the control group, unless otherwise indicated. Post hoc: * p<0.05; ** p<0.01; *** p<0.001; n = 7 – 11/prenatal treatment group; P1: postnatal day 1; C: control; PF: pair-fed; PAE: prenatal alcohol exposure.  5.3.5 Cytokine Levels in Serum, Brain, and Spleen on P8 On P8 we detected generally elevated serum cytokine levels in PAE pups. TNF-α levels were higher in PAE compared to both PF and C pups [F(2, 23)=5.13, p<0.05], and IL-13 and IFN-ɣ levels were higher in PAE compared to PF pups [IL-13: F(2, 23)=4.49, p<0.05; IFN-ɣ: F(2, 23)=3.64, p<0.05] (Fig 5.4C, I, D).  Increased levels of cytokines were also observed in the hippocampus and PFC of PAE pups. In the hippocampus, levels of IFN-ɣ and IL-1β were higher in PAE compared to both PF and C pups [IFN-ɣ: F(2, 21)=4.36, p<0.05; IL-1β: F(2, 21)=5.28, p<0.05], and IL-4 was increased in PAE compared to C pups [F(2, 21)=3.76, p<0.05] (Fig 5.4D, A, F). As well, while main effects failed to reach significance for IL-2, IL-5, and TNF-α [IL-2: F(2, 21)=3.28, p=0.058; IL-5: F(2, 21)=3.20, p=0.061; TNF-α: F(2, 21)=3.23, p=0.060], planned pairwise comparisons revealed increased cytokine expression in PAE compared to C pups (p<0.05) (Fig 5.4B, G, C). In the PFC, levels of IL-5 were increased in PAE compared to C pups [F(2, 22)=3.85, p<0.05], and IL-6 levels were higher in both PAE and PF compared to C pups [F(2, 21)=4.90, p<0.05] (Fig 5.4G, E).  By contrast, in the hypothalamus we detected lower levels of cytokines in PAE pups. Specifically, IL-2 levels were lower in PAE compared to C pups [F(2, 22)=4.56, p<0.05] (Fig 5.4B). Furthermore, while main effects for IL-1β and TNF-α failed to reach significance [IL-1β: F(2, 22)=3.09, p=0.068; TNF-α: F(2, 22)=2.51, p=0.107], planned pairwise comparisons revealed lower IL-1β and TNF-α levels (p<0.05) in PAE compared to C pups.    129 Similarly, cytokines levels were lower in the spleen of PAE pups, with decreased levels of IL-13, IL-5, IL-10, and IL-6 in PAE compared to C and PF pups and decreased IL-4 in PAE compared to C pups [IL-13: F(2, 23)=11.41, p<0.001; IL-5: F(2, 23)=8.66, p<0.01; IL-10: F(2, 23)=4.56, p<0.05; IL-6: F(2, 23)=6.53, p<0.01; IL-4: F(2, 23)=3.67, p< 0.05] (Fig 5.4E – I). In addition, while a main effect or prenatal treatment was not detected for IL-2 [F(2, 23)=3.09, p=0.067], planned pairwise comparisons revealed lower IL-2 levels in PAE compared to C pups (Fig 5.4B).                     130                   131 Figure 5.4 Cytokine levels in blood, hypothalamus, prefrontal cortex, hippocampus, and spleen on P8. Bars represent mean cytokine level ± SEM. Data are presented as pg cytokine/mg protein for brain (hypothalamus, PFC, hippocampus) and spleen or pg cytokine/ml for serum, with the difference in unit denoted by the double line intersecting the x-axis. Cytokine levels within each tissue were analyzed by separate analyses of variance (ANOVAs) using Blom transformed (normalized) data. Serum levels of IL-1β and IL-5 were below the lower limit of detection of the assay. In the case of a main effect of prenatal treatment, the asterisk (*) indicates a significant post hoc. The “×” indicates a significant planned pairwise comparison. For post hocs/planned pairwise comparisons, the symbol denotes a comparison with the control group, unless otherwise indicated. Post hoc: * p<0.05; ** p<0.01; *** p<0.001; Planned pairwise comparisons: × p<0.05; n = 7 – 8/prenatal treatment group; P8: postnatal day 8; HYPO: hypothalamus; PFC: prefrontal cortex; HPX: hippocampus; C: control; PF: pair-fed; PAE: prenatal alcohol exposure.  5.3.6 Cytokine Levels in Serum, Brain, and Spleen at Weaning (P22) Serum cytokines returned to control levels in PAE pups by weaning. In PF pups, however, circulating cytokine levels were low on P22, with lower levels of IL-13 in PF compared to both C and PAE, and lower levels of IFN-ɣ, IL-10, and IL-4 in PF compared to C pups [IL-13: F(2, 23)=6.97, p<0.01; IFN-ɣ: F(2, 23)=4.90, p<0.05; IL-10: F(2, 23)=4.47, p<0.05; IL-4: F(2, 23)=6.00, p<0.01] (Fig 5.5I, D, H, F). Serum levels of IL-2 and IL-6 were undetectable in PF pups on P22 (Fig. 5.5B, E).  In the hypothalamus and hippocampus, cytokine levels normalized, with no prenatal treatment effects detected. Comparatively, in the PFC, PF had higher levels of L-4, compared to PAE pups, as well as lower levels of IL-6 and IL-1β compared to both C and PAE pups [IL-4: F(2, 23)=3.53, p<0.05; IL-6: F(2, 23)=3.52, p<0.05; IL-1β: F(2, 23)=4.94, p<0.05] (Fig 5.5F, E, A).  In the spleen, despite significant increases in spleen weight in PAE offspring on P22, levels of pro- and anti-inflammatory cytokines did not differ among prenatal treatment groups. However, significant correlations between maternal alcohol consumption during the second half of pregnancy (GD 12 – 21) and levels of three cytokine were identified: levels of IL-6, IL-4, and IL-2 (Fig 5.5E, F, B),   132 which were lower in PAE compared to C/PF pups on P8, were all negatively correlated with maternal alcohol consumption on P22 (IL-6: r = -0.838, R2 = 0.702, p=0.009; IL-4: r = -0.707, R2 = 0.500, p=0.050; trend for IL-2: r = -0.648, R2 = 0.420, p=0.082).                    133    134 Figure 5.5 Cytokine levels in blood, hypothalamus, prefrontal cortex, hippocampus, and spleen on P22. Bars represent mean cytokine level ± SEM. Data are presented as pg cytokine/mg protein for brain (hypothalamus, PFC, hippocampus) and spleen or pg cytokine/ml for serum, with the difference in unit denoted by the double line intersecting the x-axis. Cytokine levels within each tissue were analyzed by separate analyses of variance (ANOVAs) using Blom transformed (normalized) data. Serum levels of IL-1β and IL-5 and hippocampal levels of IL-13 were below the lower limit of detection of the assay. In the case of a main effect of prenatal treatment, the asterisk (*) indicates a significant post hoc and the symbol denotes a comparison with the control group, unless otherwise indicated. Post hoc: * p<0.05; ** p<0.01; *** p<0.001; n = 7 – 8/prenatal treatment group; P22: postnatal day 22; HYPO: hypothalamus; PFC: prefrontal cortex; HPX: hippocampus; C: control; PF: pair-fed; PAE: prenatal alcohol exposure.  5.4 Discussion  Our data demonstrate significant effects of PAE on key developmental parameters, HPA axis mediators, and both central and peripheral immune measures, in female offspring. Importantly, despite some overlap between PAE and PF offspring in body and brain weights, we detected unique endocrine and immune signatures for each of these prenatal treatments. While corticosterone levels did not differ among prenatal treatment groups, CBG levels were lower in PAE compared to C and PF pups at birth and P8. CRP levels were also lower in PAE compared to C pups at birth. As well, spleen weight was significantly increased in PAE compared to both C and PF pups on P22 and there was a strong positive correlation between spleen weight on P8 and maternal alcohol consumption during the second half of pregnancy. On P8, PAE offspring also had higher cytokine levels in the PFC and hippocampus, and lower levels in the hypothalamus and spleen, compared to C pups, whereas PF offspring had increased splenic cytokine levels on P1, and lower circulating and PFC cytokine levels on P22. Altered cytokine levels together with altered endocrine-immune interplay during a sensitive early-life period can shape the developmental trajectory. Thus, these findings lend support to the hypothesis that pervasive alterations in immune function associated with PAE may have their roots in aberrant early-life cytokine activity.   135 Finally, it will be important in future studies to determine whether similar changes in endocrine and immune function also occur in PAE male offspring.  The finding that PAE and PF dams both weighed less than controls is not surprising, as we reduce the food ration of PF dams to match that of PAE dams, and supports previous data from our laboratory (Hellemans et al., 2008; Lan et al., 2009; Uban et al., 2010) and others (Abel, 1978; Thomas et al., 2000). Consistent with these findings were lower body and brain weights in PAE and PF compared to control pups at birth, with catch-up growth by P8. Interestingly, PAE and PF offspring had increased brain weights compared to controls on P22. Rapid compensatory brain growth has been linked to impairments in cognitive performance (Fisher et al., 2006). Similarly, accelerated growth of immune organs has been associated with impaired system development (Alonso-Alvarez et al., 2007; Pihlaja et al., 2006), with a likely impact on health (Metcalfe and Monaghan, 2001). This is in line with our finding of increased spleen weight in PAE pups (P22) and the positive correlation between spleen weight on P8 and maternal alcohol consumption during the second half of pregnancy, when spleen organogenesis occurs. While spleen size may be altered by infection (Roberts and Weidanz, 1978), autoimmune disorders (Garchow et al., 2011), cancers (Moloney et al., 1970), and prenatal cocaine exposure (Sobrian et al., 1990), to our knowledge, this is the first report of increased spleen weight following PAE. Furthermore, splenic levels of IL-13, IL-5, IL-6, IL-4 and IL-2 were markedly lower in PAE compared to PF and/or C offspring on P8. Interestingly, this dampening of splenic cytokine levels predated the increase in spleen weight on P22, which suggests that cytokine alterations may be an important factor in the mechanism underlying the increased immune organ weight. Additional support for this comes from the correlation between splenic cytokine (IL-6, IL-4, IL-2) levels on P22 and maternal alcohol consumption. Thus it appears that higher levels of gestational alcohol consumption are directly linked to changes in spleen weight and a dampening of splenic cytokine levels during the pre-weaning period. Early-life changes in the developmental trajectory of the spleen could have long-lasting effects on adult immune cell populations and responses.    136 While there were no significant differences among groups in corticosterone levels, CBG, the major transport protein for glucocorticoids, was lower in PAE offspring during the first week of life, consistent with our previous findings (Weinberg, 1989). Lower CBG levels in the context of similar corticosterone levels implies higher free corticosterone levels in PAE offspring. This may be particularly detrimental during the stress hyporesponsive period when low corticosterone levels support normal brain development (Lupien et al., 2009) and likely normal immune system development (Buckingham et al., 1996). As well, CBG serves as a reservoir for glucocorticoids, allowing for targeted delivery of corticosterone in the context of an immune challenge (Hammond, 1990; Perogamvros et al., 2012). Thus, decreased CBG levels during early-life could be one of the underlying factors resulting in the previously reported increase in early-life major and minor infections (Gauthier et al., 2004; Johnson et al., 1981). Overall, serum cytokine levels were low at birth and there were only modest increases in pro-inflammatory serum cytokine levels of PAE pups on P8. In the brain, by contrast, PAE pups exhibited increased hippocampal levels of TNF-α, IFN-ɣ, IL-1β, IL-2, IL-4 and IL-5, as well as increased levels of IL-5, and IL-6 in the PFC. Alterations in the fine cytokine balance during early-life may have significant consequences for brain development, including both structure and function [reviewed in (Deverman and Patterson, 2009). While it is not yet possible to elucidate the molecular and developmental effects of disturbances in this specific combination of cytokines, there is value in contextualizing our findings within the growing body of research on the effects of overexpression of individual cytokines within the brain. For example, elevated levels of TNF-α have been shown to be neurotoxic to the developing brain, disrupting the blood-brain barrier in vivo (Megyeri et al., 1992), and inducing apoptosis of oligodendrocytes and impairments in myelination in vitro (Cammer and Zhang, 1999; Pang et al., 2005; Selmaj and Raine, 1988). Similarly, in vitro studies have linked both IL-1β and IL-2 to oligodendrocyte toxicity (Curatolo et al., 1997; Takahashi et al., 2003) and in vivo, IL-1β injection has been shown to impair myelination and induce apoptosis in the brain (Pang et al., 2005). On the other hand, in vitro work has linked IL-4 with neurogenesis and oligodendrogenesis (Butovsky et al., 2006) and enhanced survival   137 of neurons (Araujo and Cotman, 1993). Moreover, studies have shown a link between elevated maternal and child IL-4 levels and an increased risk of an autism diagnosis (Abdallah et al., 2013; Goines et al., 2011; Krakowiak et al., 2015). In our model, however, it is currently unclear whether increased IL-4 is having positive compensatory effects, or potentially interfering with normal apoptosis that is critical for brain development. On the other hand, IL-6, which has both pro- and anti-inflammatory properties, is shown to affect neuronal survival and differentiation and modulate production of neurotrophins (Frei et al., 1989; Hama et al., 1989; Satoh et al., 1988). However, enhanced IL-6 may have negative impacts on brain development; Harding et al., (2004) showed that a single nucleotide polymorphism (SNP) in the IL-6 gene promoter leads to enhanced IL-6 production and is associated with hemorrhagic brain injuries and white mater damage in preterm infants (Harding et al., 2004). Finally, our findings of increased cytokine levels in the brain of PAE females are in line those from studies using other exposure models (third trimester equivalent model of alcohol vapor inhalation or exposure by gavage) and in different species (rats and mice) and sexes (Drew et al., 2015; Topper et al., 2015b), which speaks to the robustness of these data and supports the suggestion that neuroinflammation may be a cross-cutting feature of FASD.  Importantly, the altered cytokine profile in PAE pups was only detected on P8, a time when microglia are more active and cytokine-producing (Schwarz, 2012a), which would suggest an altered microglial activational state. This is in line with findings by Drew et al., (2015), showing increased numbers of primed/active microglia in alcohol-exposed offspring on P10 (Drew et al., 2015). While cytokine changes resolved by P22, it remains to be determined if this represents achievement of a normal cytokine balance or the transition of microglia to a quiescent state, which could mask underlying neuroimmune disturbances. Nevertheless, as cytokines play an important role in neurodevelopment, alterations in cytokine levels during sensitive developmental windows, may have long-lasting impacts on brain structure and function [reviewed in (Deverman and Patterson, 2009)].  Unlike the PFC and hippocampus, in the hypothalamus, cytokine levels (IL-2, IL-1β, TNF-α) were generally lower in PAE offspring compared to controls. As infection induces release of cytokines   138 and other factors from immune cells, which act at the level of the hypothalamus to alert the nervous system to the infection, and initiates the febrile response, and sickness behaviors, it is possible that early-life alterations in hypothalamic cytokine production could underlie, at least in part, the decreased resistance to both major and minor infections in children with FASD. Whether there is a link between alterations cytokine levels and dysregulation at the level of the hypothalamus of PAE offspring, as suggested by previous work of Taylor and colleagues (Taylor et al., 1999b; Yirmiya et al., 1993), will require further investigation.  Effects of pair-feeding were also detected in this study. Importantly, the cytokine signature of PF offspring was temporally and directionally different from that of PAE pups. Overall, pair-feeding was associated with a general pattern of increased splenic cytokines at birth and decreased circulating and PFC cytokine levels at weaning compared to C and/or PAE offspring, whereas PAE was generally associated with higher cytokine levels in the PFC and hippocampus, and lower cytokine levels in the hypothalamus and spleen compared to C and/or PF pups at P8. As one of the confounds associated with pair-feeding is the mild prenatal stress associated with reduced food intake of the dam, the prenatal stress literature may provide some insight into our findings, as there is substantial evidence that prenatal stress has an inhibitory effect on immune function of offspring [reviewed in (Merlot et al., 2008)]. For example, in a rhesus monkey model, prenatal stress impaired the in vitro cytokine (TNF-α and IL-6) release with LPS challenge (Coe et al., 2002), which is generally in line with our finding of decreased circulating cytokines in PF rats at weaning. Similarly, maternal food restriction also typically results in an impaired cytokine response to challenge (Desai et al., 2009). Taken as a whole, pair-feeding, while serving as a control for the reduced food intake of alcohol-consuming dams, is a treatment in itself, consisting of both food restriction and the mild prenatal stress of maternal hunger. The marked differences in the effects of PAE and pair-feeding in the present study, however, is suggestive of differential programming of offspring neuroendocrine/neuroimmune function by these early-life insults. Support for this comes from   139 our previous findings (Glavas et al., 2007) showing that even when there is some overlap between PAE and pair-feeding effects, the mechanisms underlying these effects are different in PAE and PF offspring.   In summary, our findings of a unique cytokine profile in PAE rats, during a period when the brain is maturing and being remodeled, provide insight into factors that may underlie some of the long-term negative health consequence observed in children with FASD. As cytokines are key contributors to early brain development, changes to the delicate cytokine balance for even a short period during early development may have significant lifelong consequences for neuroimmune processes. Of particular clinical relevance, given the high rate of mental health disorders in children with FASD, and the growing evidence for immune disturbances as an underlying factor in depression and anxiety disorders (Lyall et al., 2014; Meyer, 2013), we suggest that the altered cytokine levels detected in the brain of PAE offspring could play a role, at least in part, in the increased vulnerability to later-life mental health disorders observed in FASD. Furthermore, it is possible that lower cytokine levels in the spleen, a critical immune organ, and the hypothalamus, a critical integrator between immune and endocrine responses, may underlie the increased susceptibility to infections following in utero alcohol exposure. Together, our findings highlight the future possibility that immune-based intervention strategies, particularly targeting early-life, could be considered as an adjunctive novel therapeutic approach for individuals with FASD.           140 Chapter 6: Conclusion 6.1 Summary and Cross-cutting Features  The work presented in this dissertation highlights the impact of environmental influences on the response to chronic inflammatory challenge. Beginning with a colony model, probing for differences in SD rats from Charles River and Harlan Laboratories, differential sensitivity to immune challenge was identified. Outbred SD rats were originally derived by Robert Dawley in 1925, with acquisition of SD rats in 1980 and 1996 by Harlan and Charles River, respectively (Chow, 2007). Thus differences in physiology likely arose as a result of differences in breeding/selection practices as well as environmental differences between the two facilities over time. Environment, and particularly early environment, has been shown to impact a wide range of physiological systems including metabolic, endocrine, and immune systems (Entringer et al., 2010; Gluckman et al., 2008). Here, we show colony differences in the response to AA, with Harlan rats displaying increased incidence and severity of AA compared to Charles River rats. Probing for underlying physiological parameters that might explain the increased sensitivity of Harlan rats to inflammation revealed lower basal CBG levels, suggestive of a lower corticosterone reservoir available to combat inflammation in Harlan rats, which may underlie their increased vulnerability in the AA model.   Building on these findings, we probed for differential reliance/activation of endocrine and immune pathways under similar AA severity conditions. In other words, Charles River and Harlan rats were compared using established categories of AA severity (Adj/NA: CFA injected, no clinical signs of AA; Adj/M-M: mild-moderate AA, clinical scores ≥ 1, < 8; Adj/S: severe AA, clinical scores ≥ 8), which allowed us to move beyond differences in AA incidence to compare the trajectory of AA. This was accomplished through extensive investigation into corticosterone and cytokine responses in a range of key compartments including immune tissues, brain, and the hind paws, the tissue most affected in the AA model. Taken together, these data revealed reliance on differential endocrine and immune networks between Charles River and Harlan rats. With active AA, Charles River show enhanced involvement of   141 chemokine and central cytokine networks, whereas Harlan rats activate peripheral immune and hypothalamic-pituitary-adrenal networks. Importantly, these findings demonstrated that comparable levels of AA can be achieved through differential endocrine and immune pathways and that early environment may have a critical impact on physiological trajectory of disease. Insights gained from the colony model were invaluable in guiding our investigation into factors underlying the modulatory role of prenatal alcohol exposure in the response to AA challenge. First of all, based on the unexpectedly high cytokine levels detected in the brain of Harlan rats under control (saline-injected) conditions, Charles River rats, displaying the expected rise in brain cytokine levels with AA, were selected as the colony of choice for the prenatal alcohol exposure studies. Importantly, neuroinflammation/increased cytokine levels in the brain generally occur in individuals with inflammatory disorders such as RA (Fuggle et al., 2014), and thus Charles River rats represented a more relevant model of human disease. Secondly, the colony model provided a preview into key parameters that could be useful in highlighting differential disease courses following PAE.  In parallel to the heightened sensitivity of Harlan rats in the AA model, PAE rats also showed an increased incidence and severity of AA. We investigated endocrine and immune parameters that might be predictive of both the heightened sensitivity to AA and the impaired recovery in PAE compared to control rats. Based on the importance of CBG in the colony model, CBG levels were examined for their involvement following prenatal alcohol exposure. PAE rats were shown to display a more rapid drop in CBG levels (levels decreasing in the absence of clinical signs of AA; Adj/NA group) on day 16 post-injection. We proposed that accelerated CBG cleavage, prior to the onset of AA, may represent an inappropriate response such that PAE rats cannot further cleave CBG to release corticosterone to dampen inflammation with AA onset. While different from the CBG profile in the colony model in which basal CBG appeared to be a critical factor, nevertheless CBG was also important in the PAE model, with early-onset of CBG cleavage following CFA-injection likely impacting disease course.   142 In order to better relate our findings in the PAE model to the clinical condition of RA, in addition to examining responses at the peak of inflammation (day 16 post-injection), the resolution phase extending to 60 days post-injection was examined. This extends well beyond the time of typical recovery in the AA model (Asquith et al., 2009) and allowed for the investigation of endocrine and immune parameters contributing to, or resulting in, impaired recovery from AA. Importantly, and in support of our previous findings (Zhang et al., 2012) we showed impaired recovery from AA in PAE rats. Again, CBG levels support a differential disease course in PAE rats at the recovery time point with decreased CBG levels combined with increased corticosterone levels detected in PAE rats that recovered from AA (resolution from clinical signs of AA; Adj/Rec). Here, the CBG/corticosterone balance is suggestive of continued inflammation and possibly joint damage despite resolution from external signs of inflammation. Based on our findings of colony differences in the AA model, likely attributable to environmental differences between colonies over time, we aimed to further test the modulatory role of the early environment in the PAE model. Specifically, rats were exposed to CMS during adolescence to examine both the unique effects of stress exposure as well as the addition of a second “hit” following PAE. Of relevance to the human condition, children with FASD are at a heightened risk of experiencing increased early-life stress due to foster care placements, childhood abuse and neglect, and other adverse environments (Streissguth et al., 2004; Werner, 1986). As a whole, CMS increased AA severity, evidenced by increased damage within the tibiotarsal joint in control rats, and increased macrophage density with the combination of PAE and CMS exposure. Having established a differential course of disease in the PAE model, we investigated key endocrine and immune parameters during early life with the hypothesis that early-life changes might set the stage for adult responses. In support of this hypothesis, it has been shown that alterations in the cytokine balance during the early postnatal period can have a profound impact on later-life disease (Meyer et al., 2009). In line with our previous findings in the colony and adult PAE models, we found   143 alterations in CBG levels during the early postnatal period. Specifically, CBG levels were lower in PAE compared to C rats from birth through to P8, with no differences in corticosterone levels. As CBG serves as a reservoir, allowing for targeted delivery of corticosterone in the context of an immune challenge, a lower reservoir may leave the organism more vulnerable to infections. In support of this finding, children with FASD have been shown to showed increased susceptibility to both major and minor infections (Johnson et al., 1981), including a 15-fold higher incidence of early-onset sepsis in very low birth weight newborns (Gauthier et al., 2004) and we propose that decreased CBG levels may be a critical factor underlying this increased susceptibility. However, clinical cohorts will be required to substantiate this hypothesis. Our investigation of the impact of PAE on immune parameters during early life revealed a unique cytokine profile in PAE compared to control offspring on P8, with higher cytokine levels in the plasma, PFC and hippocampus. These findings are in line with other reports of increased cytokine levels following prenatal or early postnatal alcohol exposure, using a range of exposure models, and species (Drew et al., 2015; Topper et al., 2015b). Taken together, due to the importance of the early postnatal period in shaping development of key physiological systems, we propose that our reported changes in the early-life cytokine balance following PAE impacts development of the immune and associated systems, and may be underlying our reported heightened sensitivity to AA in adulthood.   6.2 Importance of the Developmental Origins of Health and Disease Framework Our data point to the enduring nature of alterations in the early environment, with changes in the prenatal and/or early postnatal period having long-term consequences for endocrine and immune responses. This supports mounting evidence showing that exposures/events that occur early in life can have a long-lasting impact on adult health outcomes (Wadhwa et al., 2009). Collectively, the impact of early life on adult health outcomes is known as the Developmental Origins of Health and Disease (DOHaD), an approach originally formulated by Barker and colleagues based on epidemiological   144 research showing a relationship between low birth weight and cardiovascular disease in adulthood (Barker et al., 1993; Barker and Osmond, 1986; Barker et al., 1989). Currently, the DoHaD approach extends beyond cardiovascular outcomes, recognizing a link between a range of early life perturbations and later life health outcomes including but not limited to depression and anxiety, diabetes, obesity, and stress responses (Wadhwa et al., 2009). The early environment is thought exert its influence through the mechanism of fetal programming of developing physiological and behavioral systems (Godfrey and Robinson, 1998).  Specifically, signals received by the developing organism are thought to induce predictive adaptive responses, which preemptively adapt the organism’s phenotype to best suit the anticipated external environment (Hanson et al., 2011). Problems arise, however, when there is a developmental mismatch between the in utero and early postnatal conditions, which shape the predictive adaptive responses, and the sustained postnatal environment (Godfrey et al., 2007). As a result, the adaptive responses made by the organism during development may no longer confer a fitness advantage but may be disadvantageous, resulting in an increased risk of disease in adulthood (Hanson et al., 2011).  This is an important framework for interpretation of the effects of prenatal alcohol exposure, and specifically for the role of the immune system on long-term health outcomes. The immune system is critically susceptible to environmental influences (Dietert, 2014) and due to the role of the immune system, extending beyond protection from infections to include a key role in brain development, alterations in this system as a result of prenatal alcohol or other exposures may confer an increased risk for a range of disorders in adulthood. In other words, we proposed that many of the long-term health consequences observed in children with FASD, including the increased risk of auto-immune disorders (Himmelreich et al., 2016; Weinberg, 2016) and mental health disorders (O'Connor et al., 2002) may be occurring through the mechanism of fetal programming of the immune system. While this link has yet to be firmly established, intervention strategies targeting immune function in response to adverse early life conditions may help to reveal the broader role of the immune system in health across the lifespan.    145 6.3 Limitations   Data presented in this thesis strengthen the body of literature supporting the immunomodulatory impact of prenatal alcohol exposure; however, these studies were not designed to elucidate the exact mechanism(s) of alcohol’s effects. For example, in order to make a direct link between cytokine increases in the blood and brain and increased sensitivity to inflammation in adulthood, mechanistic probing involving cytokine knock down and subsequent response to inflammatory challenge will be required. Similarly, to firmly establish the importance of alterations in CBG levels and/or cleavage mechanisms will require manipulation of CBG levels via exogenous increases in CBG or a CBG knock-out model.  In addition, a time course analysis of CBG levels and cleavage status following a CFA injection, from inflammation onset through to recovery, would aid in understanding the impact of alterations in CBG levels following prenatal alcohol exposure. Indeed, such a time course study under control conditions, done in collaboration with Lesley Hill and the Hammond laboratory (Hill et al., 2016), supports the suggestions that CBG levels and timing of proteolysis may play a role in the differential inflammatory response of PAE rats in the AA model. Future mechanistic studies in PAE animals are warranted.   In addition to need for further mechanistic approaches, the question as to the source/cause of the increase in cytokine levels in PAE pups on P8 was not identified. It has been shown previously that chronic alcohol consumptions increases circulating cytokine levels (Crews et al., 2006; He and Crews, 2008) and using maternal immune activation models, that cytokines can pass through the placenta to reach the fetus (Dahlgren et al., 2006; Gayle et al., 2004). In addition, cytokines have also been shown to pass from mother to offspring through the milk (Bocci et al., 1993). However, in our model, the appearance of increased circulating cytokines, as well as increased levels within the brain (PFC, hippocampus) does not support the direct transfer of cytokines from mother to fetus/pup. By contrast, we hypothesize that fetal programming of endocrine/immune systems, as a result of exposure to ethanol as well as to increased cytokine levels as a fetus, altered the developmental trajectory of the immune system. Based on the work of others (Drew et al., 2015), we expect that microglia, key immune cells within the   146 brain, show an altered profile, remaining in an activated state, producing cytokines, and that this could be the source of the increased cytokine signal within the brain. Furthermore, it should be noted that cytokine levels were not unilaterally increased in the brain on P8 but that within the hypothalamus, cytokines were decreased with PAE. We expect that alterations in feedback between the endocrine and immune systems at the level of the hypothalamus may be responsible for the dampening of cytokine levels within this region; however, this was not tested directly.  6.4 Future Directions  Moving forward, the data presented in this thesis support the importance of the evaluation of a range of parameters through a multisystem approach in the push to understanding the pathophysiology of disease. To that end, probing of other systems, beyond and together with the nervous, endocrine, and immune systems will be required to gain an all-encompassing understanding of the effects of in utero alcohol exposure. One important system that has yet to be examined in this model is the gut, and specifically the gut-microbiota-brain axis. It is known that chronic alcohol consumption results in compromised gut-barrier function and increased rates of dysbiosis (Bull-Otterson et al., 2013; Keshavarzian et al., 2001) and as a result, in utero alcohol exposure would be expected to have an impact of the immature, developing gut. Moreover, dysbiosis during early life is linked to a proinflammatory state and increased incidence of inflammatory-related diseases in adulthood (Hooper et al., 2012; Tamburini et al., 2016; Yan et al., 2011). Of relevance, alterations in the microbiome may confer increased risk of disease through the mechanism of altered development of the immune system (Tamburini et al., 2016). Thus, in the context of the findings presented in this thesis, future work to investigate the impact of in utero alcohol exposure on the gut microbiome will complement the growing body of work on immune alterations in PAE models. While mechanistic approaches and a clear understanding of the origin/cause of cytokine disturbances in PAE models are important, investigations into intervention strategies are equally   147 important. Unlike other neurodevelopmental disorders where the underlying cause(s) are still under investigation, such as autism (Won et al., 2013) or schizophrenia (Fatemi and Folsom, 2009), alcohol is a known teratogen and while further description of alcohol-related disturbances in physiological function is important, intervention is the key to better health outcomes. Investigations into immune disturbances following in utero alcohol exposure have not received the same attention as other areas, such as changes in brain structure and function [reviewed in (Moore et al., 2014)] and epigenetic alterations [reviewed in (Ungerer et al., 2013)]. However, due to the pervasiveness of immune disturbances across PAE models (Drew et al., 2015; Topper et al., 2015b), and the link between immune function and overall physical as well as mental health (Raison et al., 2006), the immune system may be an ideal pharmacological target in FASD. Moreover, immune activation/cytokines play a key role in brain development, and increasing evidence demonstrates that altered immune activation may underlie altered cognition, attention, behavior, self-regulation, and adaptive functioning, and thus research on immune-based interventions will have broad implications for improving overall function of individuals with FASD [reviewed in (Drew and Kane, 2014)]. As such, future investigations examining the safety and utility of anti-inflammatory agents applied during early postnatal life will be important. This is particularly urgent in that currently, with the exception of ongoing work to evaluate the therapeutic potential of choline supplementation (Wozniak et al., 2015), there are no available drugs specifically shown to cause significant improvements in FASD.   Finally, FASD is often viewed as a childhood disorder, with the majority of support and research efforts targeted on the early years of life, yet the effects of in utero alcohol exposure do not dissipate in adulthood. Despite this, to date, very little is known regarding outcomes extending into adulthood (Moore and Riley, 2015). 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Mean (± SEM) levels of endocrine and joint parameters in rats that failed to recovery from AA (clinical score ≥ 1), or that reached humane endpoint during the resolution phase.      167 Appendix B  Summary of Cytokine Measures in Rats that Fail to Recover from AA CMS Condition Prenatal Treatment % Unresolved IL-2 TNF-α Non-CMS C 5.5% 161.4 12.9 PAE 40.0 % 790.7 ± 290.6 7.7 ± 1.5 CMS C 21.4% 38.1 ± 38.0 9.5 ± 2.9 PAE 27.8% 0.0 ± 0.0 8.7 ± 1.1  Summary of cytokine measures.  Mean (± SEM) cytokine levels in rats that failed to recovery from AA (clinical score ≥ 1), or that reached humane endpoint during the resolution phase.       168 Appendix C  Cytokine Levels in the Hind Paw at the Peak of AA  Hind paw cytokine levels. Bars represent mean cytokine level ± SEM. Data are presented as raw cytokine levels (pg/ml) with statistical analysis conducted on Blom transformed (normalized) data. The presence of an asterisk (*) indicates a significant post-hoc comparison to the saline-injected group, unless otherwise indicated, and within prenatal treatment and CMS condition. The “§” indicates a significant post-hoc comparison between non-CMS and CMS conditions. §§: p<0.01; ***: p<0.001. D16: day 16 post-CFA injection; C: control; PAE: prenatal alcohol exposure; Non-CMS:   169 non-stress group; CMS: stress group; Saline: saline-injected; Adj/NA: no clinical signs of AA following CFA injection; Adj/AA: clinical score ≥1;      170 Appendix D  Histopathological Analysis of the Tibiotarsal Joint: Scoring Criteria Score Inflammation Bone erosion Bone marrow  Skeletal muscle Synovium - synovial hyperplasia & pannus 0 Normal Normal Normal - fat found in marrow space, small numbers of hematopoeitic cells, megakaryocytes Normal - muscle displays consistent, striated appearance (stains dark pink in H&E). Does not contain any other cell types Normal 1 Minimal infiltration of inflammatory cells  (e.g. neutrophils, lymphocytes, macrophages) in periarticular tissue, soft tissue Small areas of resorption, not readily apparent on low magnification, in the distal tibial trabecular or compact bone Slight increased in bone marrow activity - increased number of cells. Muscle infiltrated by a small number of immune cells; consistent striated shape and uniform diameter is maintained. Slight hyperplasia of the synovial tissue Rare osteoclasts (osteoclasts in areas of active bone resorption in the distal tibia counted; confirmed under 40x) 2 Mild infiltration of immune cells More numerous areas of resorption, not readily apparent on low magnification, in the distal trabecular or compact bone Increased bone marrow activity - significant increase in cells, decrease in fat visible. Increased infiltration by immune cells into the muscle, which affect overall shape (disorganization). Hyperplasia of the synovial tissue, contributing to pannus formation with mononuclear cell infiltration. Synovial tissue may begin to invade the joint space. More numerous osteoclasts 3 Moderate infiltration of immune cells with mild edema (swelling) Obvious resorption of the medullary trabecular (marrow cavity trabecular bone) and compact bone, without full thickness defects; loss of some medullary trabeculae; lesions apparent on low magnification Significant increase in bone marrow activity. Mostly contains cells (dark staining), very little fat visible Significant infiltration by immune cells - muscle shape is altered/compromised. Significant synovial hyperplasia and pannus formation with extensive mononuclear cell infiltration. Synovial tissue significantly invades the joint space (and/or neutrophil nets). More numerous osteoclasts    171 Score Inflammation Bone erosion  4 Marked infiltration of immune cells with marked edema Marked full-thickness defects in the compact bone, often with distortion of the profile of the remaining compact bone surface; marked loss of the medullary bone of the distal tibia; no resorption in the smaller tarsal bones Numerous osteoclasts 5 Severe infiltration of immune cells with severe edema Severe full-thickness defects in the compact bone, often with distortion of the profile of the remianing compact surface; marked loss of the medullary bone of the distal tibia; resorption also present in the smaller tarsal bones Numerous osteoclasts  Histopathological analysis of the tibiotarsal joint: Scoring criteria. Hind paw sections stained with hematoxylin and eosin were given a score from 0 – 5 for inflammation and bone erosion, and from 0 – 3 for bone marrow activity, and skeletal muscle and synovium integrity, according to the guidelines provided above. These criteria were established with advice from Dr. Tony Ng (Pathologist at Vancouver General Hospital), Dr. Antoine Dufour (Overall laboratory), and Dr. Waterfield (UBC emeritus professor).  172  Appendix E  Ethanol Diet Consumption on GD 7, 14, and 21  Ethanol diet consumption on GD 7, 14, and 21. Amount of liquid ethanol diet consumed (ml) adjusted by body mass (g) on gestation days 7, 14, 21. PF dams received liquid diet in the amount consumed by a PAE partner and as a result, PF consumption is equivalent to the graph presented above. Data are presented as mean ± SEM with points representing individual dams. n = 13; GD: gestational day.     173 Appendix F  Summary of P-Values for Age Effects by Tissue Compartment  IL-1β IL-2 TNF-α IFN-ɣ IL-6 IL-4 IL-5 IL-10 IL-13 Blood N/A N/A *** *** N/A *** N/A * *** HYPO *** - *** *** *** *** *** *** *** PFC * * * *** *** * ** - - HPX ** - *** *** *** * *** ** N/A Spleen *** *** *** *** *** *** *** *** ***  Summary of p-values for age effects by tissue compartment. Analyses of variance (ANOVAs) for the factors of prenatal treatment and age revealed significant age effects for the majority of cytokines examined. P-values for the significant effects of age are summarized in the table: * p < 0.05; ** p < 0.01; *** p < 0.001. Non-significant p-values are indicated by a dash (“-”). In the case of undetectable cytokine levels for a prenatal group or age, data were not analyzed statistically, as indicated by “N/A” in the table. For hypothalamus (HYPO), prefrontal cortex (PFC) and hippocampus (HPX), cytokine levels on postnatal day (P) 8 and P22 were included in the analyses. For blood and spleen, cytokine levels on P1.  

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