T CELL PROLIFERATION OF SOUTHAFRICAN HIV-EXPOSEDUNINFECTED INFANTSbyDuncan Malcolm MacGillivrayB.A. (Honours), University of New Brunswick, 2011B.Sc. (Honours), University of New Brunswick, 2011A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCEinThe Faculty of Graduate and Postdoctoral Studies(Experimental Medicine)THE UNIVERSITY OF BRITISH COLUMBIA(VANCOUVER)August 2015© Duncan Malcolm MacGillivray, 2015ABSTRACTBackground: Prophylactic perinatal anti-retroviral drugs (ARVs) can effectively prevent mother to child transmission of HIV. Despite escaping HIV infection, HIV-exposed uninfected (HEU) infants suffer greater morbidity and mortality from other infections compared to unexposed (UE) peers. The reason for these worse outcomes is unknown, but could be adverse haematologic or immunologic effects of ARVs and/or exposure to maternal HIV infection. This study investigated a cohort of HEU and UE infants to determine whether perinatal exposure to maternal HIV infection and ARVs alter (1) the haematological compartment and/or (2) T cell responsiveness.Methods: In Cape Town, South Africa, clinical data as well as blood samples were collected from HEU (n=25) and UE (n=26) participants over the first 2 years of life. Samples from 2, 6, 12, 16, 52, 78, and 104 weeks of life were assessed by complete blood count (CBC). Peripheral blood mononuclear cells (PBMCs) were cryopreserved for in vitro assays. PBMCs from 6 and 12 weeks were stained with Oregon Green and stimulated with antibodies to CD3 (the T cell receptor; TCR) and CD28 for 7 days. CD4+ & CD8+ T cell subsets, proliferation and intra-cellular cytokine production were measured usingflow cytometry. Cell counts and flow cytometry data were compared between groups using Wilcoxon tests.Results: All but two HEU were exposed to ARV regimens that included azidothymidine (AZT). Haematological differences were observed in HEU subjects at 2 and 6 weeks of life. However, these differences were restricted to erythrocytes and HEU CBC values were within the normal range. No statistical differences were detected between HEU and UE participants with respect to T cell frequencies, proliferation, or intracellular cytokine staining following TCR stimulation.ii Conclusions: We observed transient haematological differences in erythrocytes of HEU participants during early infancy, which was likely the result of AZT toxicity. Response of PBMCs to direct TCR stimulation did not reveal any differences in T cell proliferation. This finding contrasts reports of increased HEU T cell proliferation to mitogens that involve cross-linking of antigen-presenting cells (APCs) with T cells, suggesting that HIV and/or ARV exposure may impact APCs more than intrinsic Tcell function.iiiPREFACE The literature review performed in preparation of this body of work, and some of the reviewed content has contributed to a publication: MacGillivray, D. M. & Kollmann, T. R. The Role of Environmental Factors in Modulating Immune Responses in Early Life. Frontiers in Immunology 5, (2014). Overlapping information is found both in the introduction and limitations section of this thesis. Ethics for the HEU/UE cohort were obtained both in the Republic of South Africa and Canada. The Health Research Ethics Committee of Stellenbosch University (protocol H09-02064) and the Institutional Review Board of the University of British Columbia both approved this study (protocol H11-01947). Informed consent was obtained from next of kin, caregivers, or guardians on behalf of infant participants by trained staff in the Republic of South Africa. Both Canadian (Brian Reikie, Candice Ruck, Kinga Smolen) and South African (Rozanne Adams, Santosh Pilai, Prof. Monika Esser) researchers were involved with participant enrollment, phlebotomy, sample processing shipping and receiving. Panel design for flow cytometry was designed by Bing Cai, Candice Ruck, and Kinga Smolen. Janami Steenkamp ran complete blood counts and maintained records for the haematological dataset. I validated and performed the flow cytometry assaysand analyzed all the data presented in this thesis.ivTABLE OF CONTENTSABSTRACT...............................................................................................................................................iiPREFACE.................................................................................................................................................ivTABLE OF CONTENTS...........................................................................................................................vLIST OF TABLES...................................................................................................................................viiLIST OF FIGURES................................................................................................................................viiiLIST OF ABBREVIATIONS....................................................................................................................ixACKNOWLEDGEMENTS......................................................................................................................xi1 INTRODUCTION..................................................................................................................................11.1 Mother to Child Transmission...............................................................................................21.2 Exposed but Not Infected.......................................................................................................31.3 Immune Abnormalities of HEU Infants.................................................................................41.4 ARV Usage and Toxicity........................................................................................................61.5 Cohort of South African HEU Infants...................................................................................81.6 Current Research Project.....................................................................................................102 METHODS............................................................................................................................................112.1 Ethical Considerations.........................................................................................................112.2 Enrollment & Exclusion Criterion.......................................................................................112.3 Cohort Demographics..........................................................................................................122.4 Haematological Methods.....................................................................................................122.5 Statistical Methods...............................................................................................................122.6 Technical Methods for Oregon Green Proliferation Assay..................................................132.6.1 Sample Thawing and Preparation for Oregon Green Assay:..............................132.6.2 Oregon Green Staining.......................................................................................132.6.3 Cell Stimulation:.................................................................................................142.6.4 PBMC Restimulation and Golgi Block:.............................................................152.6.5 Flow Cytometry:.................................................................................................152.6.6 Analysis (Compensation, Proliferation, ICS).....................................................173 RESULTS..............................................................................................................................................203.1 Demographics......................................................................................................................203.2 Haematology........................................................................................................................233.3 Flow Cytometry...................................................................................................................353.3.1 Gating and Cell Population Composition...........................................................353.3.2 Metrics of T Cell Proliferation............................................................................363.3.3 Intra-cellular Cytokine Staining.........................................................................404 DISCUSSION.......................................................................................................................................424.1 Cohort Demographics..........................................................................................................424.2 Haematological Findings.....................................................................................................424.3 Flow Cytometry...................................................................................................................444.3.1 Cell Subsets........................................................................................................454.3.2 Proliferation........................................................................................................474.3.2.1 Variability in HEU Proliferation across Studies.....................................484.3.2.2 APC Dependent and Independent Activation of T Cell Proliferation....504.3.3 Intra-cellular Cytokine Staining.........................................................................514.4 Two-hit Hypothesis for Infectious Disease Risk in HEU Infants........................................52v4.5 Study Limitations.................................................................................................................574.5.1 Potential Impact of Participant Race on Outcome..............................................574.5.2 Potential Impact of Breast Feeding on Study Outcomes....................................584.5.3 Potential Impact of Housing on Study Outcomes...............................................594.6 Conclusion...........................................................................................................................60BIBLIOGRAPHY....................................................................................................................................62APPENDIX..............................................................................................................................................71Supplemental Table of PHA Ligated Targets...........................................................................................71Recipes.....................................................................................................................................................72Reference Ranges.....................................................................................................................................73Flow Cytometry Supplemental Form.......................................................................................................75iLIST OF TABLESTable 1: Flow Cytometry Panel Information...........................................................................................16Table 2: Proliferation Definitions Adapted from Roederer (2011)..........................................................18Table 3: Household Demographics..........................................................................................................20Table 4: Maternal Demographics.............................................................................................................21Table 5: Infant Demographics..................................................................................................................22Table 6: Haematological Parameters of HEU and UE Subjects at 2 Weeks of Life................................24Table 7: Haematological Parameters of HEU and UE Subjects at 6 Weeks of Life................................25Table 8: Haematological Parameters of HEU and UE Subjects at 12 Weeks of Life..............................26Table 9: Haematological Parameters of HEU and UE Subjects at 26 Weeks of Life..............................27Table 10: Haematological Parameters of HEU and UE Subjects at 52 Weeks of Life............................28Table 11: Haematological Parameters of HEU and UE Subjects at 78 Weeks of Life............................29Table 12: Haematological Parameters of HEU and UE Subjects at 104 Weeks of Life..........................30Table 13: Flow Cytometry Gating for HEU and UE Subjects at 6 Weeks of Life...................................35Table 14: Flow Cytometry Gating for HEU and UE Subjects at 12 Weeks of Life. ...............................36Table 15: HEU and UE Subject CD4+ T cell Proliferation Metrics at 6 weeks of Life..........................36Table 16: HEU and UE Subject CD4+ T cell Proliferation Metrics at 12 weeks of Life........................37Table 17: HEU and UE Subject CD8+ T cell Proliferation Metrics at 6 weeks of Life..........................38Table 18: HEU and UE Subject CD8+ T cell Proliferation Metrics at 12 weeks of Life........................39Table 19: Intracellular Cytokine Production of Proliferating CD4+ T Cells at 6 weeks of Life.............40Table 20: Intracellular Cytokine Production of Proliferating CD4+ T Cells at 12 weeks of Life...........40Table 21: Intracellular Cytokine Production of Proliferating CD8+ T Cells at 6 weeks of Life. ...........41Table 22: Intracellular Cytokine Production of Proliferating CD8+ T Cells at 12 weeks of Life............41Table 23: Database Survey of Binding Targets of Both Phytohaemagglutinin (PHA) - L and PHA-E...71Table 24: Complete Blood Count Reference Ranges...............................................................................73Table 25: Normal Leukocyte/Lymphocyte Differential Ranges..............................................................84Table 26: Antibodies Used for Intracellular Flow Cytometry..................................................................79Table 27: Lasers Used for Intracellular Flow Cytometry.........................................................................80iLIST OF FIGURESFigure 1: Gating Strategy for Flow Cytometry........................................................................................20Figure 2: Mean Corpuscular Volume of HEU and UE Subjects over the First 2 Years of Life...............31Figure 3: Red Blood Cell Count for HEU and UE Participants over the First 2 Years of Life................32Figure 4: Red Blood Cell Distribution Width (RDW) of HEU and UE Subjects over the First 2 Years of Life...........................................................................................................................................................33Figure 5: Lymphocyte Count for HEU and UE Subjects over the First 2 Years of Life..........................34Figure 6: Two-hit Hypothesis for Altered Immunological Set-point of HEU Infants. ...........................55Figure 7: Balance in ARV Dose and Scheduling to Optimize Risk Reduction for Perinatal HIV Transmission Risk and for Early Life Environmental Infectious Disease Risk. .....................................56iiLIST OF ABBREVIATIONSAIDS Acquired immune deficiency syndromeARV Anti-retroviral drugsAZT AzidothymidineBD Becton, Dickenson and Company BiosciencesCBMC Cord blood mononuclear cellsCCR5 Cell to cell Chemokine receptor type 5CD Cluster of differentiationCFRI Child and Family Research InstituteCST Cytometer Setup and TrackingCXCR4 Chemokine receptor type 4DC Dendritic cellsDNA Deoxyribonucleic acidmtDNA Mitochondrial DNADPBS Dulbecco's Phosphate Buffered SalineEDTA Ethylenediaminetetraacetic acidFACsLyse BD FACSTM Lysing Solution FACsPerm Intracellular Fixation and Permeabilization Buffer SetFBS FisherbrandTM Research Grade Fetal Bovine Serum FBSHEU HIV-exposed but uninfectedHIV Human immunodeficiency virusICS Intra-cellular cytokine stainingIFN-y Interferon gammaIL InterleukinLSR BD LSR II is a flow cytometeriiiMCH Mean Corpuscular Haemoglobin MCHC Mean Corpuscular Haemoglobin ConcentrationMCV Mean Corpuscular VolumeMTCT Mother to Child Transmission (of HIV)NA Not assessedNVD Normal Vaginal DeliveryNVP NevirapinePBMC Peripheral blood mononuclear cellsPBSAN PBS and 0.1% sodium azidePCR Polymerase chain reactionPI Protease InhibitorPMTCT Prevention of Mother to Child Transmission (of HIV)qPCR quantitative Polymerase Chain ReactionRBC Red Blood CellRDW Red blood cell distribution widthRPMI Rosewell Park Memorial InstituteTCR T cell ReceptorTNF-a Tumour necrosis factor alphaUE Unexposed (to HIV) and uninfectedWBC White Blood CellivACKNOWLEDGEMENTSThis thesis would never have come to fruition without numerous individuals who have had substantial impact on my life, though it is impossible to fully list them. I thank my friends and family that have put up with my incessant rants while encouraging me to continually grow. I certainly wouldn'thave completed this task without the support of Ms. Dana Horrocks, who helped me recovery from my bike vs. car experiment. Next I would like to profoundly thank mentors I have had through this life, specifically Dr. Mike Duffy, Dr. Kenneth Kierstead, Mr. Jorge Hamilton, Mr. Mike Meade, and Dr. Adama Balde. I am forever indebted to these people, without whom I never would have dreamt to apply myself to the medical field. Yet for this body of work there is no contender, foremost I must thank Dr. Tobias Kollmann for the tremendous support I have received. The opportunities and ideas Tobi has provided me with has broadened my world-view and encouraged me to soldier on. With his passion and drive I am certain there is a bold future for this laboratory. I thank my committee members, who have generously provided me with their time, energy, and insight and have doubtlessly made me a better scientist. To Dr.Soren Gantt and Dr. Gregor Reid, I am grateful for your guidance and you have both taught me more than you realize. Finally, the people with whom I have worked alongside for the past two years. Dr. Bing Cai, without your kindness, skill, patience and knowledge none of this would have been possible. Dr. Kinga Smolen, I would have doubtlessly floundered without your high standards and planning, and would have been miserable without your friendship. To Nelly Amenyogbe, I see a bright future ahead for you and hope we can continue to drink all the coffee. And many more thanks are in order, but short on space and time I must simply thank you all. v1 INTRODUCTIONHumans are perpetually exposed to and infected by numerous viral, prokaryotic and eukaryotic agents; some colonize and live upon or within us, some perish due to physiological incompatibility with the niches provided by the human host, others still are destroyed by the immune system. The very young and very old are at greatest risk due to either inexperience or senescence of immune system, respectively (Kollmann et a., 2012). As a global community we have had many successes over the past century through increased prevention, diagnosis, and treatment of multiple infectious diseases. Some are more easily mitigated than others, yet on the spectrum of lethality and difficulty to treat, and humanimmunodeficiency virus type 1 (HIV) is among the most feared. This is in part because HIV is a retrovirus that integrates into the host genome, resulting in life-long infection (Craigie and Bushman, 2012). HIV preferentially targets cluster of differentiation 4 positive (CD4+) T cells, which have a central role in guiding the adaptive immune response (Dalgleish et al., 1984). When the immune system becomes sufficiently compromised by HIV-mediated depletion of CD4+ T cells, otherwise benign infections may become lethal. After initial infection (primarily through bodily fluids) there may be a long asymptomatic phase that if untreated progresses to acquired immune deficiency syndrome (AIDS). The HIV life cycle is outlined by Barré-Sinoussi et al. (2013). Briefly, HIV binds cell surface receptors CD4, CXCR4 (Feng et al., 1996), and/or CCR5 (Coakley et al., 2005) and fuses with the cell membrane. Once inside the cell, HIV RNA is transcribed into DNA, which can then translocate into thenucleus of the host cell whereupon the viral DNA can be integrated into the host genome. At this stage, the virus re-purposes host transcription and translation machinery to reproduce the viral RNA, reassemble virus, and infect other cells. Fortunately in the 1980's it was recognized that a drug developed as a cancer therapeutic, 1azidothymidine (AZT; also known as zidovudine or ZDV), decreased mortality and rates of opportunistic infections among AIDS patients (Fischl et al., 1987; Yarchoan et al., 1986). Multiple anti-retroviral drugs (ARVs) have since been developed to inhibit different stages of the viral life cycle by preventing fusion with host cells, inhibiting viral reverse transcriptase and integrase, and blocking of virus assembly maturation processes with protease inhibitors (PIs). Presently, in high resource settings such as Canada, HIV-infected individuals are predicted to live into their early 70s, though individual survival is dependent on sex, race, route of infection, and CD4+ T cell nadir (Sabin, 2013; Samji et al., 2013). In more resource limited settings, such as rural KwaZulu-Natal, South Africa, introduction of widespread use of ARVs have increased adult life expectancy from 49.2 to 60.5 years (Bor et al., 2013).Despite global efforts for improving treatment and prevention, to date cure remained elusive and no prophylactic vaccine has been brought to market.From a public health perspective, our ability to treat but not cure implies that prevention of transmission is our most viable option for management of HIV. Preventive measures including public education, screening in blood and organ donation, control of HIV infection with ARVs (treatment as prevention), and advocacy for safe sexual and intravenous drug use have reduced HIV transmission rates across the globe. However, beyond horizontal infection of adults, HIV can be transmitted vertically from HIV-infected mothers to their children either in utero, through fluids during birth, or viapostnatal exposure through breast-milk. This is referred to as mother-to-child transmission (MTCT). 1.1 Mother to Child Transmission MTCT of HIV can take place at any time during the perinatal period including in utero, at the time of birth, or through breastfeeding. MTCT rates without intervention vary from 14-48% depending on country the setting and frequency of breastfeeding (John and Kreiss, 1996). To address these high 2rates (and poor outcomes for afflicted children), strategies were developed in the 1990's to provide ARVs to women during pregnancy and to infants immediately after birth. While expansion of such programs for prevention of (P)MTCT remain sorely needed, their efficacy has been remarkable. Antenatal care, including education and planned elective Cesarean-section, combined with ARV regimes can reduce MTCT to under 3% even in resource-limited settings such as Malawi (Chasela et al., 2010) and Botswana (Shapiro et al., 2010). Concentrated efforts in low and middle income countries have lead to a reduction of MTCT from 26% in 2009 to 17% (World Health Organization, 2014). Current trends are promising but increased efforts are still needed.The most recent available global World Health Organization (WHO) report (July 2014) on 2013data estimated that 67% of ~1.5 million HIV-infected pregnant women received ARVs for prevention ofMTCT. Despite this commendable success approximately 240,000 children aged 0-14 were infected with HIV in 2013 (WHO, 2014). Prophylactic coverage with ARVs has been increasing to meet global goals of a 90% reduction in MTCT by this year (2015). Prevention of ~1 million infant HIV infections annually is a tremendous success and hopefully this number will only increase as MTCT programs expand and improve. However, the infants that have escaped perinatal HIV infection remain at increased risk from other infectious diseases when compared to their HIV-unexposed peers (Kuhn et al., 2007). This growing demographic with increased vulnerability to infection is yet another global challenge associated with HIV that requires further research and understanding. 1.2 Exposed but Not InfectedHIV-exposed but uninfected (HEU) infants reportedly have impaired growth (Filteau et al., 2011), increased rates of hospitalization (Koyanagi et al., 2011; Slogrove et al., 2012), increased severity of infection (Fawzy et al., 2011; Slogrove et al., 2012), and higher mortality (Brahmbhatt et al.,2006; Chatterjee et al., 2007; Fawzy et al., 2011; Landes et al., 2012; Marinda et al., 2007) when 3compared to HIV-unexposed (UE) controls. Primarily this evidence comes from studies throughout Africa including Botswana, Uganda, Gambia, Malawi, Zambia, Zaire and Zimbabwe (Filteau et al., 2009). The prevalence of perinatal HIV exposure among children can be shocking; in some parts of Sub-Saharan Africa 30% of all infants are HIV-exposed (Shapiro et al, 2010). However, this is not solely an African problem. There are consistent reports of increased infectious disease morbidity among HEU infants from India (Singh et al., 2011) and Belgium (Epalza et al., 2010). In other high income countries increased infectious disease morbidity may be obscured by fewer HEU infants and better overall health. Theories for why HEU infants have worse infectious disease outcomes include social factors (such as deficit of parental care, poverty, and education), increased exposure to infectious agents, altered feeding practices, immune abnormalities (derived from an altered maternal immune environment in the womb), and ARV toxicity (Afran et al., 2014). Though not perfectly controlled, social factors can be reasonably stratified in a given study. The following thesis is focused primarily on biological rather than social factors. Generally HEU infants have two unique exposures compared to their peers, ARVs and maternal HIV. Aside from noted worse infectious disease outcomes, HEU immune abnormalities have been primarily reported as altered immune cell counts and functions. 1.3 Immune Abnormalities of HEU InfantsThe primary immunological abnormality reported in the literature about HEU infants pertains to the frequency of immune cell subsets, and data comes from standard clinical surveillance or minimally invasive sampling of peripheral blood. CD4+ T cells have been relatively well studied in HEU infants, owing to both the vulnerability of CD4+ T cells to HIV infection and their important role as regulators of the immune system and acquired immunity. Lower CD4+ T cell counts (Bunders et al., 2005; Le Chenadec et al., 2003; Clerici et al., 2000; Kakkar et al., 2014; Miles et al., 2010; Miyamoto et al., 42010; Nielsen et al., 2001; Pacheco et al., 2006) and to a lesser extent lower CD8+ T cell counts (Bunders et al., 2005; Chenadec et al., 2003) have been reported in multiple studies of peripheral blood from HEU infants. If HEU infants have decreased circulating CD4+ T cells it is then tempting to speculate that having fewer cells limits antigenic coverage and response, eventually resulting in the observed increased severity of infectious agents. However, HEU T cell counts may be even more nuanced. It has been proposed that the difference in circulating cell numbers is in part due to missing subsets of CD4+ T cells. During analysis of HEU cord blood samples Nielsen et al. (2001) attributed reduction in the CD4+ T cell compartment (relative to control subjects) to fewer naïve and memory cellCD4+ T cell subsets. This was then correlated to thymic output through fetal thymic organ culture and assessment of T cell receptor (TCR) excision circle detection by quantitative polymerase chain reaction(qPCR). All but one of the participants in that were exposed to AZT therapy both pre and postpartum and it was suggested by the authors that reduced number of T cells was due to ARV toxicity on thymic progenitor cells (Nielsen et al., 2001). If the number of specific immune cells is altered this may or may not result in impaired immuneprotection (Afran et al., 2014). It is thus important to not only know if the number and type of T cells inHEU subjects is different from peers, but additionally to determine whether immune cell function is altered. While evaluation of immune cell function is difficult, in vitro assays can be useful for learning about hypo- or hyper-activity when results are compared to samples from appropriate controls. Multiple assays can be used as proxies for cell function, one of which is cellular proliferation. Measuring cellular proliferation enables the detection of antigen-specific responses as well as non-specific responses to mitogens. One high-resolution method for detection of proliferation is by Oregon Green (Oregon Green® 488 Carboxylic Acid Diacetate, Succinimidyl Ester) dye dilution that enables biological insight lacking in other methodologies (Hasenauer et al., 2012). For instance, if 64 cells havebeen detected as a result of proliferation, dye dilution allows for determining whether 1 cell (and all 5daughters) divided 7 times, or 16 cells went through 2 rounds of division. Assessment of proliferative capacity of HEU T cells has been studied in multiple age groups, generally with one of two foci: (1) detecting in utero sensitization and memory response to HIV antigens (Hygino et al., 2008; Miles et al., 2010) or (2) assessing the response to Bacillus Calmette-Guerin (BCG) vaccination (Mazzola et al., 2011; Kidzeru et al., 2014, Miles et al., 2010). These studieshave produced discordant results and used different methods and stimuli for assessing HEU T cell proliferation. Other groups that used different mitogenic stimuli have reported increased HEU proliferation responses to staphylococcal enterotoxin B (SEB; Kidzeru et al., 2014; at 6 and 14 weeks of life), and phytohaemagglutinin (PHA; Mazzola et al., 2011; at 6-8 months of life). Assessment of HEU infant T cell proliferative responses to BCG vaccination have provided discordant results, with nodifference (Miles et al, 2010 at 2 weeks; Jones at 24 hours and 16 weeks of life), weaker (Mazzola et al., 2011 at 6-8 months of life; Miles et al., 2010 at 10 weeks of life) and stronger (Kidzeru et al., 2014 at both 6 and 14 weeks of life) proliferative responses when compared to UE controls. With respect to HIV-specific responses some groups have reported intact proliferative responses to proteins derived from the virus (Miles et al., 2010) up to 10 weeks of age, while another group has noted no differences in cord blood (Hygino et al., 2008). With such variability in detection (or lack thereof) of HEU T cell counts, subsets, and proliferation, clarity is required to further our understanding of the HEU immune system. These studies may further be compounded by exposure to ARVs, which is explored in the following section. 1.4 ARV Usage and ToxicityAs new drugs come to market and schedules are improved, infants (as well as adults) are exposed to different ARVs. The most commonly used ARVs generally fall into three categories: (1) nucleoside analog reverse-transcriptase inhibitors (NRTIs); (2) non-nucleoside reverse-transcriptase 6inhibitors (NNRTIs), and (3) PIs. Multiple drugs are used in combination to prevent emergence of drug resistant strains (Chaix et al., 2007). Currently, the WHO recommendations (as of 2013) for MTCT prevention call for a minimum of lifelong 2 NRTIs and 1 NNRTI be provided for all pregnant women, independent of health status, in order to better control maternal disease, reduce transmission, and simplify access to care. Previous WHO recommendations were based on maternal T cell count (<350 cells/mm3) for eligibility for combination therapy and 6 weeks of daily nevirapine (NVP) administration for infants (Barron et al., 2013).Implementation of WHO guidelines is dependent on local policy, politics, and capacity. In 2008 the standard South African PMTCT regime was dual therapy (AZT and NVP) from 28 weeks gestation and NVP for women and infants within 72 of delivery and combination therapy (2 NRTIs and a NNRTIor PI) for pregnant women with CD4+ T cell counts below 200cells/mm3. In 2010 the MTCT guidelines were updated and aligned with WHO guidelines of a 350 cell/mm3 cutoff for maternal combination therapy and 6 weeks of daily NVP administration for infants (Barron et al., 2013). NVP is a NNRTI that has been associated with hepatotoxicity in some adults (Chu et al., 2010; Jamisse et al., 2007) but with more limited evidence of toxicity in infants. Rash (24%) and granulocytopenia (16%) were the most frequently reported side effects in infants during initial studies of drug tolerance (Pollard et al., 1998). In adults, toxicity is related to patient development of hypersensitivity responses to NVP, resulting in rash, eosinophilia, and hepatitis (Rivero et al., 2007). Due to the relatively short courses of infant treatment it is generally accepted that risk for hypersensitivity responses is minimal. NVP is used because it is potent, well tolerated, available in liquid form, and has been shown to reduce the risk of MTCT. AZT is a NRTI with relatively well described side effects. The primary concern with AZT usageis mitochondrial toxicity. This is likely due to inhibition of host cell gamma-polymerase and accumulation of somatic mitochondrial DNA mutations (Brinkman et al., 1998; Venhoff and Walker, 72006). Alternatively it has been hypothesized that mitochondrial damage is due to direct interference with mitochondrial bioenergetics cascades (Foster and Lyall, 2007; Scruggs and Dirks Naylor, 2008), and induction of reactive oxygen species (ROS) formation leading to cell damage (Yamaguchi et al., 2002). Ultimately AZT toxicity in HEU infants can result in lactic acidemia (Noguera, 2004), rare casesof severe neuropathy (Alimenti et al., 2003), cardiac developmental abnormalities (Lipshultz et al., 2011), and potential impairment of innate immune cell development (Dainiak et al., 1988). Perinatal ARV mono-therapy has been shown to transiently decrease neonatal haemoglobin concentrations, neutrophil and lymphocyte counts; whereas combination therapy (multiple ARVs) as been associated with larger effects that are longer lasting (Pacheco et al., 2006). In vitro studies have revealed that AZT exposure inhibits haematopoietic progenitor cells, which may explain altered cell counts (Dainiak et al., 1988). Regional variation in ARV agent, dose, and schedule for PMTCT are important considerations when assessing toxicity and outcomes of HEU infants (Mas, 2012). Regardless of potentially deleterious effects of ARV exposure, at this time prevention of perinatal HIV infection clearly outweighs the potential risks. It is not clear whether the immune system of HEU children is functionally different from their unexposed counterparts. If there are statistically detectable differences in immune function, it would then be important to determine the mechanisms(s) responsible. With this knowledge, we hope to equip the research community with insight needed to address downstream morbidity and mortality through more appropriate vaccine scheduling, monitoring, or intervention.1.5 Cohort of South African HEU InfantsHEU infants are a growing population that is at risk from infectious diseases. Immune abnormalities have been reported but discordant results from functional assays have proved difficult to 8reconcile. In an effort to more comprehensively investigate the immune system of HEU infants, a cohort was enrolled in the Republic of South Africa (Cape Town) to follow and assess the development of the immune system of HEU and UE infants over the first two years of life. This region has high endemic rates of HIV-infection and of general infectious mortality. The objectives of this study were to determine the effect of HIV- and ARV-exposure on: 1. Infectious disease outcomes.2. Haematological parameters using the complete blood count (CBC).3. Innate immune system function through response to stimulation with Toll-like receptor agonists.4. Functionality of CD4+ and CD8+ T cells by measuring proliferation assay.5. Antibody responses to early childhood vaccination. Several research papers have been published about this cohort as results have become available,with respect to increased infectious morbidity (Slogrove et al., 2010), vaccine responses (Reikie et al., 2012), and innate immune responses (Reikie et al., 2014). What remained to be investigated was the analysis of haematology datasets and in vitro assay for CD4+ and CD8+ T cell functionality. Specifically, previous findings in this cohort showed no differences between HEU and unexposed (UE) uninfected subjects for the number of reported infectious events; however, the risk ratio for HEU hospitalization was 2.74 (1.86-6.26) greater than for the UE group for 100 infant-months(Slogrove et al., 2012), suggesting greater severity of disease upon becoming infected. Whole blood response to Toll-like Receptor (TLR) ligands (Peptidoglycan, lipopolysaccharide, PAM3CSK4, polyinosinic:polycytidylic acid, resiquimod) revealed differences between HEU and UE monocyte, plasmacytoid and conventional dendritic cell (DC) cytokine production during only the first 6 months of life. Assessment of antibody responses to vaccination revealed HEU infants responded similar to UE9controls to tetanus, Haemophilus influenzae type b and hepatitis B vaccination, but stronger to pertussis(Reikie et al, 2014). Based on this data HEU participants appear to suffer from worse infections, to have modulated innate immunity in early life, but appear to develop normal B cell adaptive immune responses to vaccination. This would suggest that T cells were functional for adaptive immune responseto vaccination, but not inform us whether T cells are present in different numbers or have altered activity beyond support of B cell antibody production. In addition, up to this point we had no information on whether HEU infants experienced ARV-associated toxicity as the haematological dataset remained to be analyzed. 1.6 Current Research ProjectThis South African HEU cohort presents an opportunity to (1) examine the haematological compartment of infants over the first 2 years of life; (2) to assess the functionality of the T cell compartment in early life when risk is greatest; and (3) to provide pilot data for directing future studies.My role in this project has included analysis of haematological parameters over a 2-year period, and performing in vitro assays on a subset of samples to test the proliferative capability of cryopreserved T cells in PBMC cultures. This pilot study was intended to complete a comprehensive immunological description of HEU infants early in life when the risk of increased infectious morbidity is greatest; it allowed examination of a range of parameters, from haematology, innate immune PRR responses, T cell functionality, and antibody responses to vaccination.102 METHODS2.1 Ethical ConsiderationsEthics for this research was obtained both in the Republic of South Africa and Canada. The Health Research Ethics Committee of Stellenbosch University (Protocol H09-02064) and the Institutional Review Board of the University of British Columbia (Protocol H11-01947) both approved this study. Informed consent was obtained from next of kin, caregivers, or guardians on behalf of infantparticipants. 2.2 Enrollment & Exclusion CriterionIn order to test the hypothesis that HEU children have an underlying immunological deficit, a pilot cohort of HEU and UE children were recruited at birth at Tygerberg Academic Hospital, Cape Town, Republic of South Africa in 2009 and followed through 2010. Exclusion criteria for participationin the study were (1) inability of the parent or legal guardian to give informed consent, (2) diagnosis of significant medical conditions, and (3) any maternal illness within the last 24 hours (Reikie et al, 2014).Children of HIV-infected mothers were confirmed as HIV uninfected by HIV DNA PCR at 2, 6 and 12 weeks; and uninfected mothers’ HIV status was confirmed serologically (Slogrove et al 2012). Infants who were HIV seropositive at 18 months of age were excluded from all analysis. The cohort was followed up at 2, 6, 12, 26, 52, 78 and 104 weeks of age. Medical personnel conducting examination and health history were unblinded. Primary outcomes of the study were infection-associated hospitalizations, reported by Slogrove et al. (2012). At each follow up time point blood samples were drawn for haematological assessment and cryopreservation of peripheral blood mononuclear cells (PBMCs) for subsequent in depth analysis of immune capability early in life. These samples were transported to Vancouver on dry ice and stored in liquid nitrogen for up to 6 years prior to processing. 112.3 Cohort Demographics Cohort demographic data were collected by survey interview with the primary care-giver of the infants enrolled in the study at standard health centre visits. 2.4 Haematological MethodsParticipants from both HEU and UE cohort arms were sampled by a trained phlebotomist at 2, 6, 12, 26, 52, 78, and 104 weeks of age. Whole blood samples used for full blood count analysis were collected into pediatric EDTA tubes at all time-points. A Siemens ADVIA 2120 automated haematologyanalyzer was used for automated full blood and differential counts. Parameters analyzed included: haemoglobin, haematocrit, white blood cell (WBC) count, red blood cell (RBC) count, mean corpuscular volume (MCV), mean corpuscular haemoglobin (MCH), mean corpuscular haemoglobin concentration (MCHC), red blood cell distribution width (RDW), platelet count, neutrophil count, monocyte count, lymphocyte count, large unstained cell count, eosinophil count, and basophil count. 2.5 Statistical MethodsNon-parametric tests were employed as datasets failed skew and kurtosis tests for distance fromnormally distributed expectation, and data were visually assessed with observed deviations on distance from fitted normal lines on quantile-quantile (QQ) plots. Fisher tests were used on all categorical datasets (such as sex, ethnicity, and mode of delivery) whereas paired Wilcoxon Rank Sum and Signed Rank Tests (often referred to as Mann Whitney U Tests) were used on continuous datasets to provide test statistics and P-values. Alpha was set at a standard <0.05 threshold and was corrected for multiple comparisons using a Bonferroni adjustment as required. Analysis was performed in R version 3.0.3. HEU and UE arms of the cohort were compared on a number of parameters to determine whether or not the cohort was balanced, and to identify possible limitations of interpretation of subsequent data. In some cases data were not available for all subjects on a given parameter and these 12individuals were excluded from the assessment of that parameter, but the number of individuals not assessed was noted along with the statistical test of interest.2.6 Technical Methods for Oregon Green Proliferation Assay2.6.1 Sample Thawing and Preparation for Oregon Green AssayPreparation for the Oregon Green assay required cells to be thawed from cryopreservation and given 24 hours to rest and allow membrane reconstitution after thawing. Thawing procedure are criticalfor successful assay performance as cells have been stored in toxic DMSO. Cryovials were removed from liquid nitrogen storage and rapidly thawed in a 37°C water bath until only a few ice crystals remained. Then, cryovials were transferred to the biosafety cabinet (BSC) and contents were transferred into a 15mL falcon tube and were mixed with equal parts pre-warmed R10 media (see Appendix for details) and then topped up to 10ml with RPMI media. Samples were spun at 500 x g for 5 minutes at room temperature. Supernatant was aspirated and discarded, cells were then washed for another cycle and ultimately resuspended in 1mL of 60ug/mL Dnase/RPMI solution and incubated for 5 minutes. This was done to prevent cell clumping due to DNA debris from cells that did not survive freezing and thawing procedures. Samples were washed for another cycle, and were then resuspended at 1x106 cells/mL in R10 media in 6-well culture plates and transferred to a 37°C 5% CO2 incubator for24 hours before Oregon Green Staining and cell stimulation. 2.6.2 Oregon Green StainingAfter 24 hours of rest in the incubator, cells were mixed with 1:10 ratio of Ethylenediaminetetraacetic acid (EDTA) for ten minutes to minimize cell clumping. Cells were transferred to 15mL falcon tubes and washed once with RPMI. After centrifugation and aspiration of supernatant, cells were resuspended in 0.5mL of Staining Buffer (See Appendix for recipes), passed 13through a 70uM filter into a 50mL falcon tube, and enumerated with a haemocytometer using Trypan blue to check viability. Oregon Green solution was prepared immediately before use by combining Oregon Green dye (suspended in DMSO) with staining buffer for a final concentration of 5uM Oregon Green; this solution was added drop-wise to the resuspended cells while flicking the tube to ensure rapid and homogeneous mixing without unduly shocking cells with DMSO. After 5 minutes 1mL (equal parts) FBS was added to quench extracellular Oregon Green. The sample was then topped up to 40mL with Staining Buffer, and washed three times through centrifugation, aspiration of supernatant, and resuspension. The cell pellet was resuspended at 4x106 cells per ml and 125ul of cell suspension was plated in duplicate on a 96 well U bottomed polystyrene tissue culture plates (Becton Dickinson) for each condition. A similar protocol has been reported by Lyons et al. (2013). 2.6.3 Cell StimulationCell stimuli can be catered to the experimental question of interest. In order to elicit proliferation responses in T cell compartments mitogens are generally used to mimic Antigen Presenting Cells (APC) and antigen mediated cell stimulation. Stimulation of proliferation in this study was done by incubating soluble monoclonal anti-CD3 (HIT3a, 30ng/mL) and anti-CD28 (CD28.2, 1ug/mL) antibodies with samples. Soluble antibody was used instead of plate bound anti-CD3 to encourage homogeneous activation of all cells in culture compared to those on the periphery of the wells, this is important for consideration for the temporal kinetics of proliferation assessed by the various indexes used for analysis, outlined in section 2.6.6 An unstimulated control was prepared with both anti-CD49d (9F10, 1ug/mL) and anti-CD28 (1ug/mL) monoclonal antibodies (eBioscience, San Diego, CA) diluted in R10 media. CD49d binding does not result in activation of pathways leading to proliferation, and thus is used as a negative control for anti-CD3 antibody. Cell stimuli were prepared in R10 and pre-warmed to 37°C before being added to cells on a 96-well U bottomed plate. 125uL of 14stimuli were added to each well and mixed before the samples were placed in a 37°C 5%CO2 incubatorfor 7 days. 2.6.4 PBMC Restimulation and Golgi BlockAfter seven days in culture, cells were spun for 10 minutes at 300 x g at room temperature. 50ulof supernatant was removed and stored in a separate 96 well U bottomed plate for assessment of supernatant cytokines. A re-stimulation cocktail was prepared resulting in final concentrations of 10ng/mL phorbol 12-myristate 13-acetate (PMA), 1uM Ionomycin, and 2.5ug/mL Brefeldin diluted in R10 media. 50ul of restimulation cocktail was then added to induce protein synthesis but inhibit cellular secretion of protein into culture media. Samples incubated an additional six hours to give ample time for protein production for intracellular cytokine staining. After the six hours, samples were incubated an additional 10 minutes with 1:10 dilution of 20mM EDTA to prevent cell clumping. Plates were then spun at 500 x g for 5 minutes at room temperature, flicked, and resuspended in 100ul 1x FACSlyse. If samples were to be stained with viability dye they were washed first with 200ul DPBS, stained for 30 minuets with fixable Viability Dye, washed with 200ul DPBS twice and finally resuspended in 100ul 1x FACSlyse for storage in a -80°C freezer until staining with antibody and sample acquisition through flow cytometry, controls for the viability dye were washed but resuspended without the dye for the refrigerated incubation. 2.6.5 Flow CytometryCryo-preserved plates containing samples of interest were thawed in 37°C 5% CO2 incubator for 10 minutes and spun at 500 x g for 5 minutes. The supernatant was discarded, and samples were washed with PBSAN, and then resuspended in a 1:1 dilution of 1X BD FACsPERM solution and PBSAN for 10 minutes in the dark. Plates were then spun and samples resuspended in 50ul of PBSAN 15and master mix containing all antibodies. The antibodies, fluorochromes, clones, suppliers, and catalog numbers used can be found in Table 1. Antibody stock solution was diluted 1:100 on samples. Samples incubated for 40 minutes in the dark with antibody before being spun down and washed with PBSAN once and finally resuspended in 200ul PBSAN for acquisition on the flow cytometer. Table 1: Flow Cytometry Panel Information.Target Fluorochrome Clone Supplier Cat #CD3 APC-Alexa780 UCHT1 eBioscience 47-0038CD4 PE RPA-T4 eBioscience 12-0049CD8 PE-Cy7 SK1 eBioscience 25-0087Viability dye eFluor 450 NA eBioscience 65-0863-14 OG FITC NA Invitrogen C34555TNF alpha Alexa 700 MAb11 eBioscience 56-7349IFN gamma PE-CF594 B27 BD 562392IL-2* PerCP-eFluor710 MQ1-17H12 eBioscience 46-7029IL-13* APC JES10-5A2 BD 561162*Rat anti-human antibody; all other antibodies are mouse anti-human antibodiesSample acquisition was done using a LSRII Flow Cytometer and FACSDiva V.6 software (Becton Dickinson) in the CFRI Flow Cytometry Core. Samples were acquired uncompensated. Compensation controls included: unstained cells for setting forward and side scatter gates, mixes of unstained and cells stained with either Viability dye or Oregon Green dye, and antibody binding beads were used as single stain controls for all antibodies. Cytometer Set-Up and Tracking (CST) beads were used to standardize local cytometer setting of channel voltages. For all cell-based controls a minimum of 50,000 events were acquired, and for bead controls a minimum of 5,000 event beads were acquired. 162.6.6 Analysis (Compensation, Proliferation, ICS). Raw data files were saved, exported from Diva, and imported into Flowjo V 9.7.5 flow cytometry analysis software. Specificity of staining and cut-off between positive/negative cells was determined using a fluorescence-minus-one protocol. Briefly, doublet and debris events were excluded based of forward and side scatter. Then, cells negative for Viability dye (ie. Not dead cells) were gated upon. Next, CD3 positive, and then CD3+CD4+ and CD3+CD8+ events were identified. A graphical representation of the gating strategy can be found in Figure 1. Once CD4+ and CD8+ populations had been identified, proliferation modeling was performed on the density plots of Oregon Green fluorescence. Intracellular cytokine production was gated based on subsets of proliferating or non-proliferating cells. Data was exported in csv format for statistical analysis in R version 3.0.3 (R Development Core Team, 2008). Assessment of proliferation modeling included quality control resulting in subject exclusion due to background proliferation in the unstimulated control, and samples for which root mean square variance was larger than 0.5 (ie., Unsatisfactory model fitting). The model employed by Flowjo V.9.7.5 relies on fitting fluorescence log density plots with a Gaussian log-normal distribution with differing means to represent individual generations of cells that elicit similar fluorescence upon assay with a flow cytometer (Mario Roederer, 2011). The means of fitted model are then applied to the dataset and with associated standard deviations act as a probability based catchment for the cells with the most similar fluorescence pattern. Though there are more advanced models of proliferation, they generally require far more cells for each sampled individual in order to generate time-course data for additional calculations. The current analysis is the most comprehensive possible with available samples and cells per sample. Using the Flowjo (version 9.7.5) Proliferation modeler requires user input for appropriate fittingof models. Models can be fit in an unsupervised format; however, this method can be prone to over-17fitting a model in order to appease metrics for models of best fit. Thus, while there is inherent bias in modeling, if experienced users are blinded to origin of the sample, modeling should be relatively unbiased between groups. Model calculation of appropriate metrics (and naming of metrics) is softwaredependent and not standardized between software platforms. Definitions of critical importance for cross-platform analysis are shown in Table 2. Table 2: Proliferation Definitions Adapted from Roederer (2011).Parameter DescriptionProliferation Index The average number of divisions undergone by responding cells.Division Index The average number of divisions for the entire population (including non-proliferating cells). Expansion Index Fold expansion of the entire culture.Replication Index The fold expansion of responding cells in culture.% Divided The proportion of cells that initially responded to stimuli with at least 1 division. Peak ratio The ratio of fluorescence between peaks in the model fit. Values should be approximately 0.5 as dye should be dilutedby half with each subsequent generation. This is more of a quality control than a direct metric. Peak CV The variance of peak size, referring to the width of modeled generation peaks. This value should generally be between 4-7% and is less of a metric and more of quality control.SD (Div) The standard deviation of the Proliferation Index, this is a term that is used to assess the variance in population responses between subjects. It has less of an easily interpreted biological meaning other than the relative regulation in response to a given stimuli.18Figure 1. Gating Strategy for Flow Cytometry193 RESULTS3.1 DemographicsAssessment of the demographic data revealed important statistically significant differences between cohort arms aside from previously noted stark differences in ARV exposure, breastfeeding, and in utero HIV-exposure. Household demographics differed between the two arms, where HEU participants were more often found to live in informal housing (60% vs 19%) with less access to indoorrunning water (40% vs 81%), and fewer people living in both the bedroom and household with the infant when compared to UE controls (Table 3). Table 3: Household Demographics. HEU UE TestStatistic P-valueFormal Housing (%) 40% 81% 0.1653 0.0042*Access to Indoor Running Water (%) 40% 85% 0.1271 0.0014*No. people in bedroom with Infant 2.2 (1.14)a 3.12 (1.42)a 182.5 0.0159*No. of people in maternal household 4 (2.48)a 5.58 (2.23) 189.5 0.0159*Living conditions for HEU (N=25) and UE (N=26) cohort participants. Fisher tests and Wilcox tests were used for categorical and continuous data, respectively. Test Statistic denotes Odds ratio for Fisher tests or W value for Wilcoxon tests. Data either percentage of total or mean (standard deviation). aMissing data for 1 individual, excluded from the analysis. * P < 0.05. Assessment of mothers of HEU and UE participants revealed that there were no significant differences with respect to maternal age at delivery, number of pregnancies, marital status, level of education, or consumption of alcohol during pregnancy. However, a significant difference was observed between HIV-infected and uninfected mothers when smoking was assessed, with UE mothers more likely to have smoked during pregnancy (Table 4). The mean CD4+ T cell counts for mothers of HEU subjects was 326 (SD +\-155); these data was unavailable for UE mothers. No UE infants were 20exposed to ARVs. The majority of infants were exposed to maternal ARV treatment; 76% were exposedto dual therapy (AZT + NVP), fewer (16%) were exposed to combination therapy (either AZT + didanosine + lopinarvir/ritonavir or stavudine + lamivudine + efavirenz or NVP), and only two infants had no in utero exposure to ARVs. Table 4: Maternal Demographics. HEU (Mean & SD)UE (Mean & SD)TestStatistic P-valueAge at Delivery 25.96 (6.35) 27.58 (6.98) 290 0.51Number of Pregnancies 2.27 (1.15) 2.65 (1.6) 297.5 0.6HEU (%) UE (%) TestStatistic P-valueSmoking (Any %) 8% 42% 8.09 0.0087*Drinking (Any %) 0% 15% inf 0.1104Marital Status (% Married) 16% 23% NA 0.7265Education (% Attained)Primary School (Grades 1-7) 20% 12% NA 0.6227Some High School (Grades 8-11) 60% 58%All High School (Graduated) 16% 15%Tertiary Education 4% 15%Available data for mothers of HEU (N=25) and UE (N=26) cohort participants. Fisher tests were performed on categorical variables and Wilcoxon rank sum tests were used for continuous data. Test Statistic denotes Odds ratio for Fisher tests or W value for Wilcox tests. There was a statistically significant difference in the prevalence of smoking between cohort arms, which was much more common in UE (42%) mothers than HEU mothers (8%). Smoking and Drinking refer to any reported during pregnancy. * P < 0.05Statistically significant differences between cohort arms were apparent in the ethnicity of the infant participants (P < 0.001). Specifically, HEU subjects were more likely to be of African decent (84%) and UE controls were more likely to be of mixed decent with only 27% of direct African only decent (Table 5). For interpretation of any subsequent differences in our infant cohort arms it thus must 21be noted that any differences may be due to ethnicity rather than HIV-exposure. Additionally, it is noteworthy that the HEU arm of the cohort was primarily made up of female infants (76%) and was approaching the 0.05 threshold of significance for a difference from the UE arm (P=0.083). Table 5: Infant Demographics. HEU (%) UE (%) Test Statistic P-valueSex (Female) 76% 50% 3.09 0.0828Race (African decent only) 84% 27% 13.32 <0.001*Mode of Delivery (NVD) 95% 100% 0 0.4902HEU(Mean & SD)UE(Mean & SD) Test Statistic P-valueGestational Age at Birth (weeks) 37.9 (2.8) b 37.9 (2.3) 307 0.8787Birth Weight (g) 2973.1 (398.1) 3015.4 (31.7) 304.5 0.7061Birth Length (cm) 47.3 (4.8) a 49.9 (4.2) b 195 0.0558Head Circumference (cm) 33.63 (3.7) a 33.2 (2.2) c 273.5 0.9656Descriptive data for HEU (N=25) and UE (N=26) infants unless otherwise noted. Fisher tests were performed on categorical variables and Wilcox tests were used for continuous data. Test Statistic denotes Odds ratio for Fisher tests or W value for Wilcox tests. Ethnicity was statistically different between cohort arms, where HEU subjects were primarily of African decent (Xhosa, Malawian, or from Zimbabwe) UE participants were primarily coloured and in one case was white. NVD denotes normal vaginal birth.* P < 0.05223.2 HaematologyAssessment of the complete blood count data revealed statistically significant differences between HEU and UE cohort arms. Three of these differences were apparent after correction for multiple tests, namely the MCV (Figure 2) at 2 weeks of age (Table 6), and the RBC count (Figure 3) and RDW (Figure 4) at 6 weeks of age, were significantly higher in HEU subjects (Table 7). Prior to correction for multiple tests, several other parameters were noted to possibly differ between groups. At 2 weeks of age the haematocrit and red blood cell (RBC) count was lower in HEU subjects whereas themean corpuscular haemoglobin (MCH), and RBC distribution width were higher (Table 6). At six weeks of life the haemoglobin, haematocrit, and RBC count were lower in HEU subjects whereas the MCV, MCH and RBC distribution width were higher than UE controls (Table 7). Later in life basophils(52 weeks) and monocytes (104 weeks) were higher in HEU subjects (Tables 10 & 12). While there was no significant difference there appears to be a temporal lag in the lymphocyte count HEU subjects with respect to UE peers (Figure 5). 23Table 6: Haematological Parameters of HEU (25) and UE (26) Subjects over at 2 Weeks of Life. Time-point (2 weeks) HEU(median) IQRUE(median) IQR W-value P-valueHaemoglobin (gm/dL) 14.35 2.93 15.7 3.03 267.5 0.09644Haematocrit (%) 0.43 0.09 0.47 0.09 248 0.0452 ǂWBC count (103/uL) 11.82 4.45 11.54 2.47 438 0.20321RBC count (106/uL) 3.91 0.75 4.67 1.04 158 0.00112 ǂMCV (fL) 106.5 11.98 99.9 7.15 533.5 0.0003*MCH (pg/cell) 35.55 3.4 33.35 2.43 518 0.00086 ǂMCHC (gm/dL) 33.25 1.23 33.4 1.33 322 0.80412RBC Dist. Width (%) 17.55 2.25 16.6 1.65 481 0.00796 ǂPlatelets (103/uL) 521.5 193.25 474 198.5 461 0.09478Neutrophils (103/uL) 3.41 2.51 3.2 2.09 244 0.64872Monocytes (103/uL) 1.25 0.47 1.08 0.48 282 0.16410Lymphocytes (103/uL) 4.4 1.94 5.17 2.21 206 0.64877Large unstained cells (103/uL) 0.8 0.48 0.5 0.33 353.5 0.00151 ǂEosinophils (103/uL) 0.4 0.38 0.48 0.38 189.5 0.50859Basophils (103/uL) 0.08 0.06 0.09 0.05 215.5 0.99999Wilcoxon rank sum test with continuity correction and Bonferroni correction for multiple testing was used to determine whether any significant differences were detected between cohort arms. *Based on an alpha of 0.05 adjusted by a Bonferroni correction (k=112) for multiple tests values are considered to represent statistically significant differences between cohort arms. The adjusted P-value threshold becomes P < 0.00044 for statistically robust differences after appropriate correction. Denotes ǂparameters that after correction for multiple testing no longer meet the threshold for statistically significant differences but may be of biological interest if identified in other studies.24Table 7: Haematological Parameters of HEU (N=25) and UE (N=23) Subjects at 6 Weeks of Life. Time-point (6 weeks) HEU(median) IQRUE(median) IQR W-Value P-valueHaemoglobin (gm/dL) 10 1.3 10.7 2.18 188 0.00673 ǂHaematocrit (%) 0.3 0.05 0.33 0.05 190.5 0.00757 ǂWBC count (103/uL) 10.92 3.33 9.51 6.03 388 0.34450RBC count (106/uL) 3 0.34 3.52 0.81 115 0.0000831*MCV (fL) 97.3 6.5 90.85 8.15 495.5 0.00125 ǂMCH (pg/cell) 33.1 1.7 31.15 2 467 0.00714 ǂMCHC (gm/dL) 34 1.2 33.95 1.8 260 0.23040RBC Dist. Width (%) 17.7 1.8 15.55 1.73 516.5 0.000289*Platelets (103/uL) 495 196 514.5 163.75 325.5 0.85440Neutrophils (103/uL) 2.51 2.18 2.57 1.97 264 0.82450Monocytes (103/uL) 1.07 0.47 0.91 0.33 332 0.22820Lymphocytes (103/uL) 4.95 2.73 4.79 1.7 293 0.70900Large unstained cells (103/uL) 0.7 0.5 0.6 0.2 361 0.06522Eosinophils (103/uL) 0.28 0.18 0.35 0.28 215.5 0.20810Basophils (103/uL) 0.07 0.05 0.06 0.05 321 0.32960Wilcoxon rank sum test with continuity correction and Bonferroni correction for multiple testing was used to determine whether any significant differences were detected between cohort arms. *Based on an alpha of 0.05 adjusted by a Bonferroni correction (k=112) for multiple tests values are considered to represent statistically significant differences between cohort arms. The adjusted P-value threshold becomes P < 0.00044 for statistically robust differences after appropriate correction. Denotes ǂparameters that after correction for multiple testing no longer meet the threshold for statistically significant differences but may be of biological interest if identified in other studies.25Table 8: Haematological Parameters of HEU(N=24) and UE (N=21) Subjects at 12 Weeks of Life.Time-point (12 weeks) HEU(median) IQRUE(median) IQR W-value P-valueHaemoglobin (gm/dL) 10.3 1.4 10.5 0.5 252 0.48700Haematocrit (%) 0.3 0.04 0.31 0.02 269.5 0.73760WBC count (103/uL) 11.35 3.18 11.26 3.79 239 0.33600RBC count (106/uL) 3.65 0.36 3.7 0.25 241 0.35700MCV (fL) 83.2 5 83.6 4.9 213 0.59770MCH (pg/cell) 28.1 1.9 28.2 1.6 292 0.90930MCHC (gm/dL) 33.7 1.4 33.7 1 239.5 0.34040RBC Dist. Width (%) 15 1.9 14.4 1.1 340.5 0.26340Platelets (103/uL) 554 180 584 120 279 0.90720Neutrophils (103/uL) 3.17 1.69 3.23 1.73 244 0.90000Monocytes (103/uL) 0.82 0.41 0.7 0.25 271.5 0.63130Lymphocytes (103/uL) 5.67 1.99 5.58 1.58 226.5 0.59930Large unstained cells (103/uL) 0.5 0.4 0.5 0.2 238 0.51550Eosinophils (103/uL) 0.29 0.31 0.37 0.26 187 0.15320Basophils (103/uL) 0.07 0.04 0.06 0.03 238 0.99999Wilcoxon rank sum test with continuity correction and Bonferroni correction for multiple testing was used to determine whether any significant differences were detected between cohort arms. *Based on an alpha of 0.05 adjusted by a Bonferroni correction (k=112) for multiple tests values are considered to represent statistically significant differences between cohort arms. The adjusted P-value threshold becomes P < 0.00044 for statistically robust differences after appropriate correction. Denotes ǂparameters that after correction for multiple testing no longer meet the threshold for statistically significant differences but may be of biological interest if identified in other studies.26Table 9: Haematological Parameters of HEU (N=22) and UE Subjects at 26 (N=21) Weeks of Life. Time-point (26 weeks) HEU(median) IQRUE(median) IQR W-value P-valueHaemoglobin (gm/dL) 11.2 1 11 0.9 314 0.27530Haematocrit (%) 0.35 0.03 0.34 0.03 320 0.21780WBC count (103/uL) 11.93 5.08 10.65 4.05 311 0.30970RBC count (106/uL) 4.58 0.42 4.55 0.43 258 0.90370MCV (fL) 75.9 3.95 73.9 4.35 340.5 0.09455MCH (pg/cell) 25.2 1.05 24.5 2.1 311 0.30620MCHC (gm/dL) 32.5 2.1 33 1.7 230 0.46080RBC Dist. Width (%) 16 1.85 15.4 1.58 313 0.28590Platelets (103/uL) 485 122.5 502.5 161.25 247.5 0.72500Neutrophils (103/uL) 3.46 2.75 3.27 2.02 290 0.57490Monocytes (103/uL) 0.64 0.54 0.66 0.3 288 0.60520Lymphocytes (103/uL) 6.47 3.12 6.07 1.63 283 0.50290Large unstained cells (103/uL) 0.6 0.25 0.45 0.2 347 0.06608Eosinophils (103/uL) 0.24 0.22 0.35 0.27 199.5 0.15910Basophils (103/uL) 0.09 0.05 0.07 0.04 352 0.05306Wilcoxon rank sum test with continuity correction and Bonferroni correction for multiple testing was used to determine whether any significant differences were detected between cohort arms. *Based on an alpha of 0.05 adjusted by a Bonferroni correction (k=112) for multiple tests values are considered to represent statistically significant differences between cohort arms. The adjusted P-value threshold becomes P < 0.00044 for statistically robust differences after appropriate correction. Denotes ǂparameters that after correction for multiple testing no longer meet the threshold for statistically significant differences but may be of biological interest if identified in other studies.27Table 10: Haematological Parameters of HEU (N=21) and UE Subjects at 52 (N=20) Weeks of Life. Time-point (52 weeks) HEU(median) IQRUE(median) IQR W-value P-valueHaemoglobin (gm/dL) 11 0.8 10.8 1.2 268 0.54010Haematocrit (%) 0.35 0.02 0.34 0.04 295 0.20930WBC count (103/uL) 11.58 3.76 10.66 3.4 291 0.25240RBC count (106/uL) 4.48 0.48 4.58 0.38 224 0.68940MCV (fL) 78.6 4.65 75.1 7.5 319 0.07037MCH (pg/cell) 24.7 2.05 24.2 2.3 277.5 0.40390MCHC (gm/dL) 31.4 1.75 31.9 1.8 195.5 0.28470RBC Dist. Width (%) 15.1 1.55 14.1 1.3 320.5 0.06493Platelets (103/uL) 472 183.5 402 168 293.5 0.22620Neutrophils (103/uL) 4.67 3.23 3.04 3.71 268 0.54490Monocytes (103/uL) 0.8 0.42 0.59 0.29 297 0.19610Lymphocytes (103/uL) 5.61 2.51 5.09 2.07 316 0.08205Large unstained cells (103/uL) 0.7 0.3 0.5 0.2 322 0.05685Eosinophils (103/uL) 0.19 0.13 0.23 0.26 179 0.14480Basophils (103/uL) 0.08 0.05 0.05 0.03 254 0.00800 ǂWilcoxon rank sum test with continuity correction and Bonferroni correction for multiple testing was used to determine whether any significant differences were detected between cohort arms. *Based on an alpha of 0.05 adjusted by a Bonferroni correction (k=112) for multiple tests values are considered to represent statistically significant differences between cohort arms. The adjusted P-value threshold becomes P < 0.00044 for statistically robust differences after appropriate correction. Denotes ǂparameters that after correction for multiple testing no longer meet the threshold for statistically significant differences but may be of biological interest if identified in other studies.28Table 11: Haematological Parameters of HEU (N=17) and UE (N=20) Subjects at 78 Weeks of Life. Time-point (78 weeks) HEU(median) IQRUE(median) IQR W-value P-valueHaemoglobin (gm/dL) 10.85 1.25 10.9 1.9 177 0.74570Haematocrit (%) 0.36 0.03 0.35 0.05 191.5 0.95470WBC count (103/uL) 11.44 3.65 11.43 3.82 198 0.81070RBC count (106/uL) 4.7 0.44 4.76 0.46 174 0.68290MCV (fL) 75.25 6.93 73.6 9.9 212 0.52610MCH (pg/cell) 23.35 2.25 23.4 3.2 188.5 0.99999MCHC (gm/dL) 30.95 1.85 31.7 1.3 143 0.19930RBC Dist. Width (%) 14.9 2.25 15.4 1.9 174 0.68270Platelets (103/uL) 469.5 149 422 197 202 0.72470Neutrophils (103/uL) 4.07 1.84 3.78 2.82 193 0.92240Monocytes (103/uL) 0.67 0.39 0.57 0.29 210.5 0.55400Lymphocytes (103/uL) 5.42 1.69 4.81 3.24 199 0.78900Large unstained cells (103/uL) 0.5 0.18 0.5 0.3 215.5 0.47250Eosinophils (103/uL) 0.28 0.25 0.37 0.51 123 0.06482Basophils (103/uL) 0.07 0.03 0.07 0.04 164.5 0.49640Wilcoxon rank sum test with continuity correction and Bonferroni correction for multiple testing was used to determine whether any significant differences were detected between cohort arms. *Based on an alpha of 0.05 adjusted by a Bonferroni correction (k=112) for multiple tests values are considered to represent statistically significant differences between cohort arms. The adjusted P-value threshold becomes P < 0.00044 for statistically robust differences after appropriate correction. Denotes ǂparameters that after correction for multiple testing no longer meet the threshold for statistically significant differences but may be of biological interest if identified in other studies.29Table 12: Haematological Parameters of HEU (N=16) and UE (N=18) Subjects at 104 Weeks of Life. Time-point (104 weeks) HEU(median) IQRUE(median) IQR W-value P-valueHaemoglobin (gm/dL) 11.25 1.65 11.1 1.5 173 0.72710Haematocrit (%) 0.35 0.05 0.36 0.04 157.5 0.91100WBC count (103/uL) 11.61 3.64 8.96 3.58 204 0.18560RBC count (106/uL) 4.72 0.4 4.48 0.45 160.5 0.98740MCV (fL) 78.95 7.83 78 9.95 173 0.72740MCH (pg/cell) 23.7 3.95 23.1 3.8 182.5 0.51580MCHC (gm/dL) 31.3 2.08 29.7 3.8 169.5 0.81210RBC Dist. Width (%) 14.7 1.73 14.3 2.15 168.5 0.83660Platelets (103/uL) 512.5 155.25 440 208.5 199.5 0.23470Neutrophils (103/uL) 4.76 3.11 3.41 1.81 117 0.62090Monocytes (103/uL) 0.68 0.37 0.5 0.25 153 0.03805 ǂLymphocytes (103/uL) 4.71 1.73 4.45 1.21 129.5 0.29480Large unstained cells (103/uL) 0.5 0.18 0.4 0.2 138.5 0.14190Eosinophils (103/uL) 0.21 0.84 0.18 0.28 125 0.39410Basophils (103/uL) 0.06 0.05 0.04 0.02 146.5 0.07058Wilcoxon rank sum test with continuity correction and Bonferroni correction for multiple testing was used to determine whether any significant differences were detected between cohort arms. *Based on an alpha of 0.05 adjusted by a Bonferroni correction (k=112) for multiple tests values are considered to represent statistically significant differences between cohort arms. The adjusted P-value threshold becomes P < 0.00044 for statistically robust differences after appropriate correction. Denotes ǂparameters that after correction for multiple testing no longer meet the threshold for statistically significant differences but may be of biological interest if identified in other studies.30Figure 2. Mean Corpuscular Volume (MCV) of HEU and UE Subjects. Lines denote loess smoothed means with 95% CI, dots represent individual HEU (red) UE (blue) subjects. Statistically significant differences are apparent at approximately 14-50 days of life, and are undetectable at later time points. 31Figure 3. Red Blood Cell Count for HEU and UE Participants over the First 2 Years of Life. Lines denote loess smoothed means with 95% CI. Statistically significant differences are apparent at approximately 14-50 days of life, and are undetectable at later time points. 32Figure 4. Red Blood Cell Distribution Width (RDW) of HEU and UE Subjects over the First 2 Years ofLife. Lines denote loess smoothed means with 95% CI. Statistically significant differences are apparentat approximately 14-50 days of life, and are undetectable at later time points. 33Figure 5. Lymphocyte Count for HEU and UE Subjects over the First 2 Years of Life. Delay in lymphocyte count noted among HEU (red) compared to UE (blue) subjects. Lines denote loess smoothed means with 95% CI. There are no significant differences between HEU and UE groups over time, however there is an interesting delay in kinetics that appears to reflect a lag in HEU lymphocyte production. 343.3 Flow Cytometry3.3.1 Gating and Cell Population CompositionThere were no significant differences between cohort arms in the proportions of cells found in any of the flow cytometry gates at any of the time points (Table 13 & 14). The Wilcoxon rank sum test with an alpha threshold of 0.05 was used to determine whether statistically significant differences were present.Table 13: Flow Cytometry Gating for HEU and UE Subjects at 6 Weeks of Life. 6 Weeks of Life HEU UEGated population Mean SD Mean SD W-value P-valueDoublet Gate (%) 86.95 4.81 86.90 3.47 19 0.9372Debris Gate (%) 71 6.78 72.82 4.25 16 0.8089Viability Gate (%) 42.97 12.53 39.50 10.95 23 0.4848CD3+ Gate (%) 86.12 6.91 84.13 6.92 22 0.5887CD4+ Gate (%) 70.1 8.68 64.08 14.25 22 0.5887CD8+ Gate (%) 28 7.71 32.67 15.18 17 0.937235Table 14: Flow Cytometry Gating for HEU and UE Subjects at 12 Weeks of Life. 12 Weeks of Life HEU UEGated population Mean SD Mean SD W-Value P-valueDoublet Gate (%) 88.27 3.25 87.93 3.55 32.5 0.8281Debris Gate (%) 75.18 10.35 73.80 15.30 29.5 0.9999Viability Gate (%) 39.02 7.38 32.80 11.64 40 0.3014CD3+ Gate (%) 84.72 11.43 86.93 8.72 37 0.4805CD4+ Gate (%) 69.84 6.98 59.23 21.10 37 0.4805CD8+ Gate (%) 27 5.85 39.38 15.80 14 0.09343.3.2 Metrics of T Cell ProliferationThrough assessment of multiple proliferation parameters (definitions in Table 2) no statistically significant differences were noted between HEU and UE cohort arms at either time point for CD4+ (Table 15 & 16) or CD8+ T cells (Table 17 & 18). Table 15: Flow Cytometry Gating for HEU and UE Subjects in CD4+ T Cell Proliferation Metrics at 6 Weeks of Life. HEU (N=7) UE (N=8)CD4+ at 6 weeks Median IQR Median IQR W-Value P-ValueNumber of Peaks 6 2.5 8 1 17.5 0.2070Peak Ratio 0.42 0.03 0.44 0.03 19 0.3357Division Index 1.39 1.28 0.81 0.84 35 0.4634Proliferation Index 1.63 0.96 1.69 0.52 28 0.9999% Divided 65.1 41.85 46.7 38.38 35 0.4634Fluor Background 218 66 142 124.03 42.5 0.1049mRMS 4.9 5.79 3.58 2.74 31 0.7789mRMSV 0.2 0.07 0.27 0.1 21 0.4634Expansion Index 3.02 5.35 2.86 2.16 31 0.7789Sd Div 1.21 0.39 1.4 0.33 15 0.1520Replication Index 4.23 5.87 4.47 3.13 25 0.771936Table 16: Flow Cytometry Gating for HEU and UE Subjects in CD4+ T Cell Proliferation Metrics at 12Weeks of Life. HEU (N=11) UE (N=8)CD4+ at 12 weeks Median IQR Median IQR W-Value P-ValueNumber of Peaks 6 1 7 1 34.5 0.4334Peak Ratio 0.43 0.03 0.45 0.02 34.5 0.4564Proliferation Index 1.52 0.32 1.74 1.44 30.5 0.2827% Divided 44.4 23.75 53.4 28.13 39 0.7101Fluor Background 190 142 225 169 43 0.9671mRMS 5.72 3.7 5.16 2.86 49 0.7168mRMSV 0.16 0.06 0.21 0.11 25 0.1288Expansion Index 2.15 1.36 2.65 5.47 38 0.6574Sd Div 1.22 0.38 1.06 0.19 58.5 0.2473Replication Index 3.6 0.93 4.43 10.92 36 0.544837Table 17: Flow Cytometry Gating for HEU and UE Subjects in CD8+ T Cell Proliferation Metrics at 6 Weeks of Life. HEU (N=6) UE (N=6)CD8+ at 6 weeks Median IQR Median IQR W-Value P-ValueNumber of Peaks 6.5 1 8 0.75 6 0.0516Peak Ratio 0.45 0.02 0.46 0.03 14 0.5887Division Index 1.2 0.61 1.45 1.61 15 0.6991Proliferation Index 1.98 0.22 2.36 0.91 12 0.3939% Divided 60.55 26.48 66.7 39.15 14 0.5887Fluor Background 160 78.75 111.05 40.43 24 0.3939mRMS 3.3 2.63 2.68 2.14 22 0.5887mRMSV 0.27 0.1 0.26 0.1 25 0.3095Expansion Index 3.76 1.53 5.28 5.88 16 0.8182Sd Div 1.32 0.2 1.38 0.19 15 0.6991Replication Index 5.55 0.85 8.7 4.67 13 0.484838Table 18: Flow Cytometry Gating for HEU and UE Subjects in CD8+ T Cell Proliferation Metrics at 12Weeks of Life. HEU (N=10) UE (N=6) CD8+ at 12 weeks Median IQR Median IQR W-Value P-ValueNumber of Peaks 6.5 1 7 2 26.5 0.7333Peak Ratio 0.46 0.01 0.46 0.02 28 0.8705Division Index 0.91 0.43 1.18 0.51 20.5 0.3286Proliferation Index 1.71 0.36 1.88 0.69 21.5 0.3845% Divided 53.2 17.73 65 20.55 26 0.7128Fluor Background 200 132 208 96.25 33 0.7925mRMS 2.65 1.83 2.77 1.66 35 0.6354mRMSV 0.28 0.24 0.2 0.05 36.5 0.5149Expansion Index 2.79 1.34 3.74 2.25 23 0.4923Sd Div 1.21 0.24 1.12 0.34 32 0.8749Replication Index 4.02 1.56 4.58 4.7 22 0.4156393.3.3 Intra-cellular Cytokine StainingThere was no significant difference in intracellular production of TNF-alpha, IFN-gamma, IL-2 or IL-13 between HEU and UE subjects at either time point for CD4+ or CD8+ cell subjects (Table 19 & 20, and Table 21 & 22). A paucity of cytokine production was noted for TNF-alpha, IFN-gamma, and IL-13. Table 19: Intracellular Cytokine Production of Proliferating CD4+ T Cells at 6 Weeks of Life.CD4+ at 6 weeks HEU (N=7) UE (N=8) W-Value P-valueCytokine Median(%CD4) IQRMedian(%CD4) IQRTNF-alpha 1.67 1.66 1.72 3.99 32 0.69IFN-gamma 2.23 2.59 1.99 3.84 25 0.78IL-2 24.80 22.40 24.25 40.60 29 0.95IL-13 0.25 0.67 0.73 1.47 20 0.4Table 20: Intracellular Cytokine Production Analysis of Proliferating CD4+ T Cells Revealed No Differences between HEU and UE Subjects at 12 Weeks of Life. CD4+ at 12 weeks HEU (N=11) UE (N=8) W-Value P-valueCytokine Median(%CD4) IQRMedian(%CD4) IQRTNF-alpha 0.96 2.35 1.81 8.26 29 0.24IFN-gamma 0.65 1.04 1.59 3.99 24 0.21IL-2 29.70 17.85 41.35 39.58 35 0.49IL-13 0.33 1.07 1.23 2.72 37 0.640Table 21: Intracellular Cytokine Production Analysis of Proliferating CD8+ T Cells Revealed No Differences between HEU and UE Cohort Arms at 6 Weeks of Life. CD8+ at 6 weeks HEU (N=5) UE (N=6) W-value P-valueCytokine Median(%CD8) IQRMedian(%CD8) IQRTNF-alpha 0.34 0.25 1.25 2.39 8 0.2468IFN-gamma 6.00 10.85 8.78 1.81 14 0.9307IL-2 56.10 57.76 42.40 66.91 10 0.4286IL-13 0.17 0.09 0.31 1.06 7 0.1775Table 22: Intracellular Cytokine Production Analysis of Proliferating CD8+ T Cells Revealed No Differences between HEU and UE Cohort Arms at 12 Weeks of Life.CD8+ at 12 weeks HEU (N=11) UE (N=6) W-Value P-valueCytokine Median(%CD8) IQRMedian(%CD8) IQRTNF-alpha 0.35 0.73 0.34 0.70 27 0.5908IFN-gamma 6.30 5.04 11.19 9.76 16 0.0983IL-2 52.20 60.31 2.76 46.04 35 0.8837IL-13 0.38 1.08 0.68 4.53 24 0.4043414 DISCUSSION4.1 Cohort Demographics Beyond in utero exposure to HIV there are other potentially confounding variables between cohort arms. Participants in the HEU arm of the cohort were exposed to ARVs (AZT and NVP) and were exclusively formula fed in lieu of breastfeeding, the effects of which will be discussed below. Thisstudy was not powered to determine the effect size of potentially confounding factors that differed between cohort arms, including housing, breastfeeding and race. The implications of these potential confounding variables will however be explored within the acknowledged limitations. 4.2 Haematological FindingsThe most robust differences between HEU and UE subjects noted in this study were in the MCV (at 2 weeks), RBC count and RDW (at 6 weeks). Several other red blood cell parameters in early life (2-6 weeks) were considered before correction for multiple comparisons including the haematocrit, haemaglobin, RBC count, MCV, MCH, and RDW. The consistency of both statistically statistical differences and non-statistical trends in HEU RBC compartment together implicate an underlying biological process. HEU infants had comparably lower haemoglobin, haematocrit, and RBC count, but had increased MCV, MCH, RDW. Interpretation of these parameters suggest that HEU infants have a subtle macrocytic anemia early in life (2-6 weeks).All but 2 HEU infants in this cohort were exposed to AZT. Based on the literature AZT toxicity is a plausible explanation for the observed haematological differences based on animal models and clinical trials. Dose escalation experiments have revealed macrocytic anemia in rats and mice in response to AZT (Thompson et al., 1991) and prophylactic perinatal treatment of HEU infants with AZT has been associated with a mild, and reversible macrocytic anemia (Connor et al., 1994). In a 42retrospective analysis of over 4,000 French HEU infants Le Chenadec et al (2003) showed that infants exposed to AZT during the perinatal period had relative decreases in haemoglobin concentration, platelet, polynuclear neutrophils, lymphocytes, CD4+ and CD8+ T cell counts that persisted at least until 18 months of age. A connection between NVP and haematological abnormalities is less well characterized, yet it has been shown that HEU infants of mothers taking single-dose NVP have elevatedmonocyte and basophil counts at birth compared to HEU infants for up to 6 weeks of age (Schramm et al., 2010). There was a slight increase in HEU basophils and monocytes at 52 and 104 weeks of age respectively, but this was not statistically significant after correction for multiple tests. The relationship between HEU basophil and monocyte counts and NVP exposure may be of interest in further studies. Whether haematological observations are clinically relevant depends in part on regional normal values. Environmental and genetic composition of a given population can alter the relative normal ranges for the complete blood count (Adetifa at al., 2009). However, cohort medians for all complete blood count measurements fit within local reference values (Appendix Supplemental Table 2 & 3) that are a modified version of the 11th Edition of Dacie & Lewis Practical Haematology (Bain et al., 2011). Examining the plotted individual values in haematology Figures 2-4 reveals that some HEU individualswere outside of reference norms for HEU participants under 6 weeks of age. Thus the physiological implications of anemia must be considered. Anemia is defined as a decrease in RBCs of haemaglobin in the blood, and is due to three processes; (1) loss of blood cells through hemorrhage, (2) loss by destruction, or (3) reduced progenitoroutput of new RBCs into circulation (Aher, Malwatkar and Kadam, 2008). The third is most consistent with AZT toxicity on RBC progenitor function. Decreased oxygen carrying and delivery may have some physiological implications for some HEU infants. A recent study on mtDNA toxicity of ARVs and HIV exposure revealed decreased cord blood and increased infant peripheral blood production of mtDNA compared to controls, this was suggested to be a compensatory mechanism for reduction of 43ARV-associated mitochondrial toxicity in early life (Ross et al., 2014). The human body can adapt to decreased blood oxygen both chemically and mechanically. Low oxygen content in the blood can elicit production of 2,3 disphosphoglycerate which changes the hemoglobin-oxygen dissociation curve in favour of oxygen deliver (Sudha, 1981), and by increasing cardiac output the body can meet demand with increased rate of supply (Cropp, 1969). In severe anemia vasoconstriction may reduce blood flow to the gastrointestinal tract and skin, potentially weakening barrier defenses and increasing host susceptibility to some infections (Ludwig and Strasser, 2001). Hypoxia can promote activation of innate immune cells (Nizet and Johnson, 2009). It is important to note that early life normally involves transient anemia that has been largely described as non-pathogenic and is a development process that occurs because of hemolysis, hemodilution from expanded blood volume, and decreased erythropoiesis(O'Brien and Pearson, 1971). In summary, the altered haematological values observed in HEU infants in this cohort are likely due to AZT toxicity on progenitors limiting and altering the RBC compartment. The effects of which might contribute to immunological aberrance in HEU infants. However, the mild and transient nature of the haematological perturbations suggest that they are of little clinical significance. Further research into the dose, schedule, and side effects of ARVs may be indicated to optimize prophylaxis against HIVwith minimal potential for harm. 4.3 Flow CytometryComprehensive analysis of all data acquired by flow cytometry revealed no statistically significant differences between HEU and UE cohort arms at 6 and 12 weeks of life. Below each subsection of the flow cytometry data results will be explored in relation to results from the other subsections.444.3.1 Cell subsetsThere were no statistically significant differences between HEU and UE samples in the percentage of CD3+, CD4+ or CD8+ T cells at either 6 or 12 weeks of life. This is consistent with Hygino et al. (2008) who found no differences in the proportion CD8+ T cells in CBMCs of Brazilian HEU and UE subjects. This group found a non statistical trend of lower CD4+ counts only among ARVnaïve HEU CBMCs, implying maternal HIV status may alter neonatal T cells independent of ARVs. It is noteworthy that the methods employed by this study and Hygino et al. (2008) included PBMC isolation and plating a set numbers of cells; both studies also included infant ARV exposure. In contrast, reports of different proportion of CD4+ and CD8+ T cells in whole blood has been consistent finding from multiple groups using different techniques (Bunders et al., 2005; Le Chenadec et al., 2003; Kakkar et al., 2014; Mazzola et al., 2003;, Pachecho et al., 2006). It is important to note whether PBMC or whole blood assays reveal differences in cell subsets, as PBMC isolation and plating at a given cell count changes the concentration of cells per unit blood. In this study, the complete blood count in the haematology dataset lacked resolution on T or B cells but no statistically significant differences were noted in total lymphocyte counts. However, mean loss smoothed lines of lymphocyte in the CBC revealed what may be a trend towards a temporal delay in HEU lymphocyte count (per unit blood), the kinetics of which may be worthy of study in larger cohorts. Whole blood reports attribute decreased HEU CD4+ or CD8+ T cell counts to HIV and ARV exposure to cellular production. More detailed analysis of CD4+ sub-types has revealed fewer naïve and memory CD4+ T cell subsets (Clerici et al., 2000; Miles et al., 2008; Nielsen et al., 2001); and this was correlated to thymic output by one group through fetal thymic organ culture and assessment of T cell receptor (TCR) excision circle detection by qPCR (Nielsen et al., 2001). In the study by Nielsen et al. (2001) all but one of the participants was on AZT therapy pre and postpartum and it was proposed that reduced number of CD4+ cells may have been due to reduced thymic production of T cells caused 45by AZT toxicity on progenitor cells (Nielsen et al., 2001). Additional support for this idea comes from Le Chendadec et al. (2003) who found an inverse relationship between RBC toxicity with increased AZT (and other ARVs) exposure, this pattern was consistent for both CD4+ and CD8+ T cells for infants up to 18 months old; and in another study even up to 8 years of age in the CD8+ compartment (Bunders et al., 2005). In the United States and Puerto Rico HEU infants exposed to ARVs reportedly have lower CD4+ and CD8+ counts inversely correlated with ARV dose (Pachecho et al., 2006). AZT induced lymphoid progenitor toxicity is a reasonable theory that would be consistent with previously discussed haematological abnormalities. Contrary to this hypothesis there is some evidence suggesting that HEU infants may have altered T cell subsets independent of ARVs.Another subset analysis revealed fewer naïve and memory CD4+ T cells in HEU children (meanage 7.2 years) that had no perinatal ARV exposure (Clerici et al., 2000). The aforementioned data from Hygino et al. (2008) offers some support for the idea of HIV altering cell subsets, though without statistical evidence. Mothers in that study with detectable levels of circulating virus and a trend towardsdecreased T cell subsets were less likely to be on anti-retroviral regimens (only 13%); there is thus potential for alteration of the T cell compartment independent of exposure to ARVs. There was no data in these studies about breastfeeding, though given the time of publication it is likely infants were formula fed. The lack of breast feeding may provide another hypothesis explaining cell type discrepancies between HEU and UE cell counts. The immunological importance of breast milk is further discussed in study limitations. However, reduced breast feeding is associated with mortality in for HEU infants. Formula feeding leads to different microbial colonization of the infant after birth. The lack of breast feeding may lead to increased immune recruitment to the gut, resulting in a relative decrease in peripheral blood for standard clinical sampling. The observations of decreased naïve and memory CD4+ T cell subsets have relied on the cell marker CCR7, which functionally has a role as a tissue homing and extravasation 46control (Reinhold, 2008). The lack of naïve and memory cell may then be due to the lack of breast feeding rather than HIV exposure. HEU infants in our study were primarily formula fed, had AZT/NVPexposure, and in utero HIV exposure. Any or all of these factors may alter the HEU immune system, however, we noted no difference in percentages of CD4+ or CD8+ T cells at either 6 or 12 weeks of life. The current report of no differences in T cell subsets in PBMC samples from HEU and UE subjects in this study does little to validate or challenge findings and theory from other studies. Our dataset reveals a lack of differences that may indeed be true, or perhaps differences were not detected due to biological variability in this cohort, differences in PBMC subsets relative to more natural proportions in whole blood volumes, or due to lack of resolution on central memory or naïve cell subsets. It would have been ideal to increase our resolution to include additional cell subsets, such at T-regulatory cells (Foxp3+) or Th17 cells (associated with immune response at mucosal surfaces); however assessment of these subsets would have required modification of the flow cytometry panel, which had been fixed to provide consistency prior to the start of this project. Specifically, changes to the aforementioned flow cytometry panel was not possible due to previous assessment using this panel of later time-points of the same individuals, analysis of which also showed no significant differences between HEU and UE subjects (data not shown). Regardless of whether HEU infants have an altered CD4+ T cell compartment, functional readouts are required for detection of aberrant immune activity that are likely a more accurate proxy for infectious disease outcome. 4.3.2 ProliferationNo significant differences were detected between HEU and UE cohort arms with respect to any of the metrics of proliferation measured at either 6 or 12 weeks of life in CD4+ or CD8+ T cells. Initially this may seem to further complicate our understanding of HEU T cell proliferation, but taken 47within the necessary context of assay and stimuli this lack of significant differences represents an important contribution to proliferation studies. To understand and interpret the results presented in this body of work, and the relevant literature it is necessary to clarify differences between studies in methodology for detection of cellular proliferation. 4.3.2.1 Variability in HEU Proliferation Across StudiesWhile individual protocols may vary, the two most important aspects to understand for interpreting cellular proliferation studies are the assay used for detection and the stimuli for cellular activation. Assays for detection of proliferation are generally based on incorporation of detectable substrate during proliferation (such as [3H] thymidine incorporation), detection of proteins expressed during the cell cycle (for example Ki67 staining), or dye-incorporation and dilution. The last is generally the most sensitive method for picking up nuanced biological differences but also tends to be delicate, technically difficult, and require more advanced mathematical modeling for interpretation. 3H thymidine incorporation or Ki67 staining can provide a snapshot of proliferation, but can be misleadingwithout numerous samples for a time course. Consider stimulating cells for a given amount of time to allow for a response, then assay with either radioactive incorporation or protein detection. Only cells actively replicating during the detection phase will provide a signal, thus any cell that had proliferated (or would have given more time) will be undetected. If the kinetics of a biological response are important they will be missed unless sample intensive time courses are used. Dye dilution assay allow for tracking cellular proliferation on smaller samples for up to 7 daughter generations. The second important variable in assessing data pertaining to proliferation is the stimuli used. Ina natural setting the generally accepted mechanism responsible for T cell activation and proliferation is by cross-linking of antigen-bound receptor and a subsequent secondary signal delivered by co-stimulatory molecules expressed on the APC as well by secreted cytokines (Dixon et al., 1989; 48Lenschow et al., 1996). For antigen specific assays, the dose or processing of antigen (eg. lysate vs. peptide pools) can activate different subsets of T cells. For non-specific stimulation selection of mitogen can have important ramifications, because mitogens stimulate T cell proliferation via different mechanisms. SEB and PHA are commonly used mitogens for T cell proliferation that have been previously been used in assay of samples from HEU infants. SEB stimulates T cells by cross-linking the alpha chain of HLA-DR1 on APCs (Jardetzky et al., 1994) and the TCR on T cells (Hayball et al., 1994; Hsu and Huber, 1995). Binding of TCR and MHC II complex initiates intracellular T cell signaling, which is in turn supported by APC activation through cross-ligation of co-stimulatory accessory surface molecules such as LFA-1 and ICAM-1 and CD28 with CD80/86 (on T cells and APCrespectively) (Krakauer, 2013). In contrast, PHA is believed to be less discriminatory than SEB; however despite years of study and use as a T cell mitogen the exact mechanism of PHA mediated cellular activation is still unclear. PHA is thought to activate lymphocytes by binding and cross-linking surface receptors including debated binding of a 20kDa member of the CD3 complex (Kanellopoulos etal., 1985; Schneider et al., 2012; Valentine et al., 1985). PHA is a lectin that has N-acetylgalactosamine/galactose sugar specificity with numerous biological functions (Schneider et al., 2012) and binds least 16 described bindings of N-glycans (See Appendix Supplemental Table 1). Purified T cells do not proliferate in response to PHA, suggesting cross-linking of APCs with T cell is critical for mitogenic stimulation (Verwilghen et al., 1991). These differences make it clear that APCs are involved to some extent in mitogen based T cell stimulation. Contrasting APC and mitogen mediated T cell proliferation, T cells can be stimulated largely (though not entirely) independent of APCs by antibody binding to T cell CD3 and CD28 receptors. The TCR complex is made up of several subunits, including the conserved gamma, delta, zeta, and epsilon subunits of the CD3 complex and the alpha and beta (or gamma and delta) high variable domains (Malissen and Schmitt-Verhulst, 1993). The anti-CD3 antibody used in these experiments 49targets the epsilon subunit, resulting in conformation activation of either (or both) accessory heterodimers to the TCR variable alpha/beta (gamma/delta) chains. Binding and conformational changes that lead to downstream signal transduction. When paired with a secondary signal this results in cellular activation. The rational for using an anti-CD28 antibody is that the secondary signal for T cell activation is normally through ligation of B7 (on B cells, DCs, Macrophages) and CD28 (on T cells) (Linsley and Ledbetter, 1993). APCs in culture conditions can still potentially contribute to T cellproliferation though acting as both sources and sinks of cytokines and chemokines and through cell-cellinteractions. 4.3.2.2 APC Dependent and Independent Activation of T Cell Proliferation Given that SEB and PHA rely on APCs for T cell activation status it stands to reason that prior observation of HEU altered T cell proliferative responses may be due to differences in the standing APC population rather than intrinsic differences within the T cell compartment. This could explain whydifferent groups have reported divergent proliferation results with respect to BCG mediated proliferation responses in HEU subjects as there is global variability in innate immune response to pathogen associated molecular patterns (Smolen et al., 2014) as well as in population response to BCG vaccination (van den Biggelaar et al., 2009; Lalor et al., 2011; Mangtani et al., 2014). In addition, the innate immune study of infants in this cohort revealed that from 0-6 months of age monocytes and DCsfrom HEU infants were more polyfunctional and produced more cytokine than UE peers. SEB and PHA have reportedly shown either similar or increased induction of proliferation in HEU infants despite study variation of infant population, age, and exposure to ARVs. If results from other studies can be trusted to have truly detected (vs. assay artifact) increased proliferation, then this has been a consistent finding between multiple groups. 50Contrasting these APC mediated findings, the results from this study and those from Hygino et al. (2008) that used anti-CD3/28 antibody mediated T cell stimulation revealed no statistically significant differences at any time point. Though not definitive evidence, this suggests APC, not T cell function is altered in HEU infants. There are a few caveats with this interpretation, as none of the cohorts examining proliferation have published clear resolution on breastfeeding data and generally all include some form of exposure to ARVs. When ARV-naïve HEU CBMCs were stimulated with plate bound anti-CD3 they exhibited more proliferation than UE peers, but without statistical rigor (Hygino et al. 2008). The use of plate bound anti-CD3 by Hygino et al. (2008) may have different kinetics of cellular activation when compared to the soluble antibody used in this study (Li and Kurlander, 2010; Mueller et al., 1989). Given the differences in SEB and PHA activation compared to antibody activation of T cells it is still more appropriate to compare the results of this study with the more similar methods used by Hygino et al. (2008). It is possible then that there is some ARV independent immune priming taking place, but only in CBMCs as assay at a later time-point revealed no differences, and though ARV exposed results of our current study suggest that at early time-points (6 & 12 weeks) the T cell compartment is fully capable of response similar to that of controls. Based on the literature of APC dependent mitogenic activation with SEB and PHA in studies reporting increased HEU proliferation, and variable findings for antigen specific responses, and the current lack of differences in response to anti-CD3/28 activation, it becomes plausible that discordant findings about HEU T cell activity are mediated through indirect effects of APC modulation rather than direct T cell function. 4.3.3 Intra-cellular Cytokine StainingAn alternate and often reported immunological functional read-out is the production of cytokines or chemokines, proteins used by immune cells to communicate. In this study, detection 51antibodies for TNF-alpha, IFN-gamma, IL-2, and IL-13 revealed no differences in production between HEU and UE subjects. Previous work on this cohort by Reikie et al. (2014) noted differences in cytokine production by APCs in early life, thus we had suspected there would also be differences in intracellular cytokine production in T cells as well. However, within the context of the aforementioned TCR activation and lack of differences in proliferation, the lack of differences in T cell cytokine production is not entirely surprising. It was however unexpected that so few cells were primed to produce any cytokine in either UE or HEU following restimulation with PMA-ionomycin. Only IL-2 production was detected in a substantial proportion of proliferating CD4+ or CD8+ T cells, yet this wasvariable across subjects with a relatively broad IQR for the given medians. The paucity of cytokine production may have been due to the duration of time spent in the freezer as most of these samples were collected approximately 6 years prior to analysis. Given that adult control samples had detectable cytokine production, the lack of signal is not believed to be an artifact based on antibody staining or flow cytometry protocol. These findings are consistent with the lack of differences noted in proportion of cells in culture and proliferation responses between HEU and UE PBMCs in response to anti-CD3 stimulation. 4.4 Two-hit Hypothesis for Infectious Disease Risk in HEU InfantsHIV and other infectious agents can cross the placenta and infect the neonate. Even when the neonate escapes infection the HIV-infected womb may present an altered environmental niche for growth and development within. For instance, HIV-infected women are at increased risk chorioamnionitis and deciduitis (Ackerman and Kwiek, 2013). Increased infection or inflammation of the uterine environment due to decreased maternal host defenses exposes the developing immune system of the neonate with antigen and a potentially pro-inflammatory milieu. It is also noteworthy thatthe vaginal microbiota appears to be altered in HIV-infected women (Sewankambo et al., 1997), and it 52may be of import for early infancy colonization with microbes. Altered placental transfer of antibody has also been reported for HIV-infected women and their offspring which may represent weakened passive immunization of HIV-infected and HEU neonates alike (Cumberland et al., 2007; De Moraes-Pinto et al., 1998). The sum of these effects is conceptualized as an “active womb” of HIV-infected women that has potential to prime the neonatal immune system. The idea of immune priming is further supported by recent discussion on “trained” innate immunity (Netea, 2013), especially given the potential effects of in utero priming. If immune system priming by HIV infection related antigens primes immune cells to be more active this effect would be expected to be greatest early in life, and with turnover of most immune cells resolve gradually. This is what has been previously noted in the innate immune study of this cohort in response to TLR ligands, with the greatest differences in activation observed earliest in life and decreasing over time (Reikie et al., 2014). This prenatal immune priming is then met with the demands of HIV MTCT prophylaxis and associated drug toxicity. During the perinatal period both infant and mother are exposed to ARVs in order to reduce MTCT of HIV. Some of these drugs, such as AZT, have described toxicity that may impact the infant immune system. In this study we have shown what is most likely AZT toxicity affecting the RBC compartment transiently early in life. Toxicity impairs progenitor cell mitochondria. Should energy capacity be limited, it stands to reason that cells with high metabolic demand and turnover rates would be hardest hit. It follows that progenitor cells of the lineages that give rise to all red blood cells in the body were impaired by AZT exposure, giving rise to aberrant RBC morphology and counts. It is not a great leap to suggest that myeloid derived immune cells (such as granulocytes, monocytes, DCs, and macrophages) may also be influenced by a similar process. This would take placeconcurrently with potential decrease in oxygenation of tissue and associated physiological adaptations, such as vasoconstriction that may result in weakened barrier defense. AZT toxicity may then impact theneonatal immune system that is primed by the active womb to respond to stimuli, but with limited 53metabolic capacity for doing so. The amalgam of this two-hit insult to the developing HEU immune system is then challenged by additional postnatal exposures. These may include reduction of breast feeding and housing conditions that imply a lower socio-economic status. The primed immune system is then prepared to react but may be metabolically limited in doing so, all the while responding to an inordinate number of infectious threats. The net result is an abnormal immune set-point that appears to normalize as time from exposure to ARVs and the active womb increases. A visual representation of the two hit hypothesis and downstream environmental risks can be found in Figure 6 and 7. Though at this time wedo not fully understand how the systemic immune response to pathogen challenge takes place, this mayin part explain why HEU infants experience worse infectious disease outcomes. Current promising advancements include expanded (and improved) programs for PMTCT, better support for HIV-infected women, and promotion of breast feeding among HIV-infected and uninfected mother infant pairs. 54Figure 6. Two-hit Hypothesis for Altered Immunological Set-point of HEU Infants. (A) Mother infant pairs without anti-retroviral (ARV) prophylaxis results in HIV risk and active womb factors (chemokines, cytokines, antigens) capable of crossing the placenta, priming neonatal antigen-presenting cells (APCs). (B) Maternal ARV therapy helps control maternal infection, decreasing some active womb factors and risk of HIV transmission at the cost of minor ARV toxicity for developing neonatal progenitor cells. (C) Heavy schedule of maternal and neonatal ARV prophylaxis controls maternal HIV infection, dramatically reducing HIV risk and associated active-womb factors but at the cost of more deleterious metabolic toxicity for infant myeloid progenitor cells. 55Figure 7. Balance in ARV Dose and Scheduling to Optimize Risk Reduction for Perinatal HIV Transmission Risk and for Early Life Environmental Infectious Disease Risk.. HIV risk is still of greatest concern, but early infancy environmental infectious risk will ideally be addressed when selecting ARVs.564.5 Study LimitationsLimitations of this study include confounding variables of race, breast feeding practices, and housing situation (a proxy for SES) between HEU and UE participants. As with any pilot clinical study,an increased sample size and additional sites would have been desirable. Participant samples may have been impacted by cryopreservation, transportation, and storage time. This study was not powered to adjust for these confounding variables. 4.5.1 Potential Impact of Participant Race on OutcomeHEU participants were more likely to be of African rather than mixed racial ancestry when compared to UE peers. It is known that complete blood count values are context dependent, for instance Ugandan children aged 1-5 have relatively lower bottom thresholds for haemoglobin, haematocrit, MCV, and platelet counts and higher WBC count reference values than European standards (Kironde et al., 2013). Race related differences in haematological reference values have been observed to be consistent into adulthood (Lim et al., 2010). The observed differences in haematology during early time-points (2 and 6 weeks of age) disappear later in life (from 12 weeks to 2 years), thus it is assumed that race is less likely to be the underlying cause for these observations when compared toAZT exposure. Antigen-specific T cell proliferation responses have been shown to vary based on ethnicity, but these differences have not been recapitulated when considering T cell mitogens (Sugimoto, 2003). Individuals from different ethnicities may exhibit differences in proliferative capability to discrete antigens (this may be due to past timing and dose of exposures) but appear to have relatively robust responses to T cell mitogens. For this reason, it is less likely though not entirely implausible that racial composition dramatically affected proliferation results in this study. 574.5.2 Potential Impact of Breast Feeding on Study OutcomesAt the time of this study the World Health Organization promoted formula over breast feeding for HIV positive mothers in a justifiable fear of transmission of virus through breast-milk. However, in addition to highly nutritional contents, breast milk contains maternal immunological factors such as lysozyme that degrades Gram positive bacterial outer cell walls, and kappa-casein that blocks pathogenbinding to the gastric mucosa (Lönnerdal, 2003). Soluble and cell secreted cytokines including IL-1Beta, IL-6, IL-8, IL-10, tumor necrosis factor alpha, and granulocyte and macrophage-colony stimulating factor have been detected in human breast milk (Garofalo, 2010). The first meal of a breastfeeding neonate, colostrum, contains more immune factors that regular breast milk, including maternal immune cells such as neutrophils, macrophages, B and T cells (Peroni et al., 2013). The immunological factors transmitted by the mother may have a number of influences on the neonate, and it is tempting to speculate that these factors help guide the establishment and development of the infant gut microbiota (Field, 2005). In Uganda non-breastfeeding HEU infants (aged 6-11 months) had a higher relative risk for hospitalizations (10.1x), severe febrile illness (3.84x), severe diarrhea (6.37x), and severe nutritional deficit when compared to non-breastfed UE peers, but no differences were detected for breastfeeding HEU and UE children (Marquez et al., 2014). Except for malnutrition, observed morbidity in non-breastfed HEU subjects disappeared when assessing 12-24 months of age, suggesting that breastfeeding is most important during the first year of life. Additionally, in the Kesho Bora trial in Burkina Faso, Kenya, and South Africa, infant mortality at 18 months of age was 6% for non-breastfed and weaned HEU infants with an adjusted risk ratio 6.9 times (95% CI = 2.8-17.2) that of exclusively breastfed children (Cournil et al., 2013). With such an increased risk of early childhood mortality for HEU and HIV-infected children alike it is not surprising that in 2009 the World Health Organization have changed recommendations to promote exclusive breastfeeding for HIV-infected women, even with the associated risk of HIV transmission through breast milk. 58HEU infants in this cohort were not breast-fed. Breast milk is known to be immunomodulatory and be of great import for reducing the discrepancy in infectious disease morbidity and mortality of HEU infants, and it cannot be ruled out as an important confounding factor in this study. Remarkably, even with perinatal HIV exposure, AZT exposure, and no breastfeeding, we observed no statistical differences in T cell proportion, proliferation, or cytokine production at 6 or 12 weeks of life. This cohort is likely one of the few remaining examples of non-breastfed HEU immunology available for study and research similar to this study can now be carried out in a breast-fed cohort to understand (if any) the affect on the results. 4.5.3 Potential Impact of Housing on Study OutcomesHEU subjects were more likely to be from informal housing situations with more limited accessto indoor running water and had on average fewer members both in the household and in the bedroom with the infant. The increased prevalence of smoking during pregnancy among UE controls may furtherrepresent better access to resources when compared to HEU controls. Study of urban poverty in South Africa revealed that informal housing settlements can be accepted as a proxy for tenant deprivation to clean running water, adequate toilet facilities, refuse collection, and food security (Vearey et al., 2010). Based on that definition, demographics noted in this study imply that HEU infants disproportionately fall into the category of informal housing. The amalgamate of stressors associated with life in informal housing and potential increase in exposure to pathogens may be greater among HEU infants. However, it is difficult to assess whether these potential social determinants of health have a large impact on HEUinfectious disease outcomes. In Cape Town, South Africa, housing amelioration was not found to improve self-reported physical health but did decrease reported mental health issues (Shortt and Hammett, 2013). Better controlled assessment of socio-economic status and social determinants of health are currently being assessed in a larger follow up study. 594.6 ConclusionThis study revealed subtle but significant temporally restricted differences in RBCs. An altered erythroid compartment in HEU in early life has previously been reported, and likely relates to AZT toxicity. There were no statistically significant differences in CD4+ or CD8+ T cell proportion, proliferation or cytokine production between HEU and UE infants at 6 or 12 weeks of life. These results may be confounded by race, breast feeding practices, and socio-economic status between the HEU and UE subjects assessed in this study. However, our T cell functional analysis provides an important insight, namely that signaling directly through the TCR appears to be similar between HEU and UE infants in early life. If such lack of differences in T cell activation can be validated in another study, this would imply that functionally T cells of HEU subjects are intact. It is then suggested that theobserved increased infectious disease morbidity and mortality may then be due to differences in APCs that have been affected by the unique exposures of HEU infants, including the “active womb” and toxicity of AZT. This is consistent with what we have previously noted in our study focused on altered HEU innate immune responsiveness (Reikie et al., 2014) and intact humoral vaccine responses (Reikie et al., 2012). These two hits alter the immunological set point that is then challenged by post-natal life. Further research will be required to substantiate and test this double hit hypothesis. The purpose of this basic research is to inform clinical decisions. Effective PMTCT is of criticalimportance, and data must be collected to compare potential toxicity (often this may be sub-clinical) and outcomes from different drug choice, dose, duration, and combination. As the WHO recommendations have changed to promote breast feeding and provide three ARVs to all HIV-infected mothers for life, monitoring of ARV transmission through milk will be important to detect toxicity and optimize therapy. 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Database Survey of Binding Targets of Both Phytohaemagglutinin (PHA) – L and -E Lectin TargetsPHA P(E + L)Pulmonary-surfactant protein (SP)-A, SP-A1, SP-D, Galectin-12, Myeloid DAP12-associating lectin (MDL)-1, Oxidized LDL receptor, Macrophage mannose receptor, CD69, Eosinophil major basic protein, Macrophage mannose receptor, Mannose binding protein, Mannose binding protein A/C, Macrophage galactose Lectin 1, CD23, L-selectin, CD23.From: 71RecipesR10 Recipe500ml RPMI 1640 (25 mM Hepes with Glutamax-I) [Lot ID: 1292220]50ml FBS [Lot ID: FB004, Cat: 03-600-511]5ml Pen/Strep media (100ug/ml)1.92uL of Beta-mercaptoethanolCombine ingredients in RPMI container under sterile conditions. Filter through a 0.22uM filter. Keep sterile and store at 4°C.Freezing Media50 (FBS) : 30 (RPMI) : 20 (DMSO)Sterile preparation as needed in 10mL aliquots. Discard after use. Keep on ice 30 minutes before use for cryopreservation of PBMCs. Staining Buffer500ml dPBS (1x) [Lot ID: 1374899]25mlFBS [Lot ID: FB004, Cat: 03-600-511]Mix well, keep sterile. Aliquot as needed. Stored at 4 degree for no more than 1 day. 72Reference RangesLocal South African Standard Normal Values for the Complete Blood Count, provided by Janami Steenkamp from Cape Town, South Africa. Table 24: Normal Values, RBC, Hgb, Hct, Indices, RDW, Platelets (Conventional Units). Age (m) RBC(Mil/uL) Hgb (g/dl)Hct(%)MCV(fl)MCH(pg)MCHC(%) RDWPlatelets(Thou/uL)0-3 d 4.0-5.9 14.5-20.5 45-61 95-115 31-37 29-37 <18.0 250-4504-9 d 3.9-5.7 13.5-19.5 42-60 88-112 28-36 28-38 <18.0 200-40010-14 d 3.6-5.5 12.5-18.5 39-57 86-110 28-36 28-38 <17.0 250-45015-30 d 3.0-4.8 10.0-16.0 31-49 85-108 26-34 30-36 <17.0 250-4501-6 mo. 3.0-4.3 9.5-12.9 29-42 74-96 25-35 30-36 <16.5 300-7507-24 mo. 3.7-4.9 10.5-12.8 33-38 70-84 23-30 31-37 <16.0 250-60025-60 mo. 3.9-5.0 11.5-13.0 34-39 75-87 24-30 31-37 <15.0 250-5506-8yr 4.0-4.9 11.5-14.5 35-42 77-95 25-33 31-37 <15.0 250-550Modified from: Lubin BH, “Reference Values in Infancy and Childhood – Hematology of Infancy and Childhood” Nathan DG, and Oski FA 2nd ed, Philadelphia PA, WB Saunders Co 1981, 1552-74.Miller DR “Normal Value and Examination of Blood: Perinatal Period, Infancy, Childhood and Adolescence.” Blood Diseases of Infancy and Childhood, 5 th ed, St Louis, MO: CV Mosby co. 1984, 21-36.Novak RRW, “Red Cell Distribution Width in Pediactric Microcytic Anemias,” Pediatrics, In press.Laboratory Reference Range Study, BCH, Adult values, 1996 HE1A.69673Table 25: Normal Leukocyte/Leukocyte Differential Ranges. Numbers of leukocytes are in thousands/uL. Bands are reported with manual differentials only.Age TotalLeukocytes NeutrophilsBandNeutrophils Lymphocytes Monocytes Eosinophils Basophils0-24hr 9.0-30.0 6.0-26.0 0-1.3 2.0-11.0 0.5-1.5 0-0.7 0-0.11 wk 5.0-21.0 1.5-10.0 0-0.7 2.0-17.0 0.5-1.5 0.05-0.7 0-0.12 wk 5.0-20.0 1.0-9.5 0-0.7 2.0-17.0 0.5-1.5 0.05-0.7 0-0.11 mo. 5.0-19.5 1.0-9.0 0-0.7 2.5-16.5 0-0.8 0.05-0.7 0-0.16 mo. 6.0-17.5 1.0-8.5 0-0.7 4.0-13.5 0-0.8 0.05-0.7 0-0.11 y 6.0-17.5 1.5-8.5 0-0.7 4.0-10.5 0-0.8 0.05-0.7 0-0.12 y 6.0-17.0 1.5-8.5 0-0.7 3.0-9.5 0-0.8 0.05-0.7 0-0.14 y 5.5-15.5 1.5-8.5 0-0.7 2.0-8.0 0-0.8 0.05-0.7 0-0.16 y 5.0-14.5 1.5-8.0 0-0.7 1.5-7.0 0-0.8 0.05-0.7 0-0.18 y 4.5-13.5 1.5-8.0 0-0.7 1.5-6.8 0-0.8 0.05-0.7 0-0.110 y 4.5-13.5 1.8-8.0 0-0.7 1.5-6.5 0-0.8 0.05-0.7 0-0.1Adult 3.6-10.7 1.8-7.0 0-0.7 1.0-4.3 0-0.8 0-0.5 0-0.2Modified from: Lubin BH, “Reference Values in Infancy and Childhood – Hematology of Infancy and Childhood” Nathan DG, and Oski FA 2nd ed, Philadelphia PA, WB Saunders Co 1981, 1552-74.Miller DR “Normal Value and Examination of Blood: Perinatal Period, Infancy, Childhood and Adolescence.” Blood Diseases of Infancy and Childhood, 5 th ed, St Louis, MO: CV Mosby co. 1984, 21-36.Novak RRW, “Red Cell Distribution Width in Pediactric Microcytic Anemias,” Pediatrics, In press.Laboratory Reference Range Study, BCH, Adult values, 1996 HE1A.69674Flow Cytometry Supplemental Form1. Experiment Overview1.1. PurposeThe purpose of this experiment was to determine whether important differences in proliferation capability are present in HIV-exposed uninfected children early in life, and if present whether those differences change over time. 1.2. Keywords 1.3. Organization 1.3.1. Kollmann Lab, Child and Family Research Institute. 1.3.2. Dept of Experimental Medicine, University of British Columbia1.4. Primary Contact 1.4.1. P.I. Dr. Tobias Kollmann1.4.2. Graduate Student: D. MacGillivray. Duncan.m.macgillivray@gmail.com1.5. DateExperiments were set up from 01-10-2014 to 30-01-2015 and stained from 01-10-2014 to 30-01-2015. 1.6. ConclusionsNo difference between HEU and UE infants at 6 or 12 weeks of life with regard to cell subsets (CD4, CD8), Proliferation (in response to Anti-CD3/28 Stimulation), or intra-cellular cytokine production (IL2, IL13, TNFalpha, IFNgamma).1.7. Quality Control Measures 75Unstimulated controls were set up for each individual tested. Single bead controls were included in each experiment using 1ul of staining antibody except for the cases of Oregon Green and Viability dye staining. In these cases, control subject cells were either left unstained or stained and mixed together to determine non-specific fluorescence and appropriate machine settings. 2. Flow Sample/Specimen Description 2.1. Sample/Specimen Material 2.1.1. Biological Samples 2.1.1.1. Biological Sample NameAdult peripheral blood mononuclear cells were used for machine calibrating and preparation of Fluorescence Minus One (FMO) experiments. Infant peripheral blood mononuclear cells were used for the experimental conditions. 2.1.1.2.Biological Sample Source Healthy human adult and infant peripheral blood mononuclear cells, obtained by Ficoll-Hypaque separation of adult whole blood directly after blood collection.2.1.1.2.1. Biological Sample Source Organism 2.1.1.2.1.1. Taxonomy Homo sapiens sapiens.2.1.1.2.1.2. Age Adult and Infant.2.1.1.2.1.3. Gender Male and Female.2.1.1.2.1.4. Phenotype Healthy or offspring of HIV-infected mothers, resulting in exposure but not infection with HIV.762.1.1.2.1.5. Genotype Not Applicable. 2.1.1.2.1.6. Treatment PBMCs were isolated from peripheral blood using Ficoll gradient centrifugation. After purification, cells were washed twice with DPBS, enumerated, and resuspended at 1.0x106 cells/ml in RPMI supplemented with 10% DMSO, 50% RPMI and 40% Fetal Bovine Calf Serum for cryopreservation. PBMC were stored in liquid nitrogen until processing was possible. 2.1.2. Environmental Samples Not Applicable. 2.1.3. Control Sample Description Unstained samples were used as controls to gate live and dead cells. Single stain controls were set up by staining PBMC fractions with optimal concentrations of antibody for each fluorochrome, as titrated in pilot experiments. Isotype controls were used to correct for non-specific staining. 2.1.4 Sample Treatment Description Samples were rapidly thawed in a 37°C water bath and washed with R10 and RPMI media twice by centrifugation, aspiration of supernatant, and resuspension of the cell pellet before being incubated with 60ug/mL DNase for 5 minutes at 37°C. Cells were then washed once more before being resuspended at 2X106 cells/mL in R10 media and incubated overnight at 37°C and 5% C02. The following day samples are incubated in R10 for 10 minutes with 2mM EDTA. Cells are then resuspended in 0.5mL of staining buffer (5%FBS and 95% dPBS). An equal amount of Oregon Green suspension (5uM final volume, in staining buffer) is then added drop-wise and incubated at RT for 5 77minutes. A quenching equal amount of FBS is then added, samples diluted in an additional 30ml of staining buffer and washed 3x before being resuspended at 4x106 in R10 and plated with 500,000 cells per well. Prepared stimuli are added at equal volume (final concentrations: Unstimulated – 200ug/ml anti-CD28/49d, SEB – 0.25ug/ml, Stim – 10ug/ml anti-CD3 + 200 ug/ml anti CD28) and incubated for 7days at 37°C and 5% CO2. To detect intra-cellular cytokine production a standard golgi block and restimulation took place for 6 hours on the 7th day of proliferation. 50ul was removed from sample wells and stored in an additional 96 well plate, sealed, and frozen for future analysis. 50ul of PMA (final [10ng/ml]), Brefeldin A (final [2.5ugml]), Ionomycin (final [1uM]) suspended in R10 media was then added for 6 hours. Plates were then spun, flicked, and resuspended in BD FACsTM Lysis Buffer, washed in PBSAN (PBS with 1% sodium azide), resuspended in 100ul of eBio fixable Viability dye (1uL/1mL ), washed, and resuspended in 100ul FACsTM Lysis Buffer, sealed, and stored in -80°C until staining and acquisition on a BD LSRII. Sample plates were then thawed in a 37°C incubator for 15 minutes before being spun, and then washed in PBSAN before being resuspended in 120ul 1x BD FACsPerm for 10 minutes in the dark. After 10 minutes 100 ul of PBSAN was added to all wells to wash cells for an additional spin. Next samples were resuspended in 50ul of master mix staining buffer containing 1uL ofeach antibody used in the panel. Cells then incubated in the dark at RT for 40 minutes before being washed twice, resuspended in 200ul of PBSAN and acquired on the LSR. 783. Fluorescence Reagent DescriptionFluorescence-labeled antibodies used for flow cytometry are depicted in Table 26.Table 26: Antibodies Used for Intracellular Flow Cytometry.Target Fluorochrome Clone Supplier. Cat #CD3 APC-Alexa780 UCHT1 eBio 47-0038CD4 PE RPA-T4 eBio 12-0049CD8 PE-Cy7 SK1 eBio 25-0087Viability dye Eflour 450 NA eBio 65-0863-14 OG FITC Invitrogen C34555TNFa Alexa 700 MAb11 eBio 56-7349IFNg PE-CF594 B27 BD 562392IL-2* PerCP-eFluor710 MQ1-17H12 eBio 46-7029IL-13* APC JES10-5A2 BD 561162* Rat anti-human antibodies. All others are mouse anti-human antibodies. Instrument Details3.1. ManufacturerBD Biosciences3.2. ModelBD LSR II 4 Laser, Blue/Red/Violet/UV cat # 3475453.3. Instrument Configuration and SettingsAll lasers, filters and mirrors were manufactured by BD Biosciences. 3.3.1. Light SourcesLasers used include the Violet (405), Blue (488), Green (532), and Red (640) with associated 79photo multiplier tubes (PMT), long pass filters (LP), and band pass filters (BP) shown in below in FlowSupplemental Table 2. Table 27: Lasers Used for Intracellular Flow CytometryLASER PMT LP BPViolet(405) A 750 780/60B 685 710/50C 630 670/30D 595 610/20E 570 585/42F 545 560/40G 505 525/50H - 450/50Blue (488) A 685 695/40B 505 525/50C - 488/10Green (532) A 750 780/60B 685 710/50C 630 670/30D 600 610/20E - 582/15Red (640) A 750 780/60B 685 720/40C - 670/30Data Analysis4.1. FCS Data FileTo request raw data please contact MSc student Duncan MacGillivray at duncan.m.macgillivray@gmail.com or Dr. Tobias Kollmann at tkollmann@cw.bc.ca.4.1.1. Total Count of Events80Efforts were made to calculate as many cells as possible for each sample condition, with the exception of cell controls (50,000) and beads (5,000). 4.2. Compensation DescriptionCompensation matrices were calculated independently for each experiment, based on single stained beads and stained/unstained cell combinations. 4.3. Gating (Data Filtering) Description 4.3.1. Gating Information Initially doublets were excluded based on FSC-H and FSC-A. Proceeding singlets were gated toavoid debris. Next, viable cells (stained negative with Fixable Viability Dye) were gated and CD3+ cells were determined. CD3+ cells were further subdivided into CD4+ and CD8+. Oregon green dye dilution was examined through FITC density plots wherein proliferation modeling could take place. ICS was determined in both proliferated and non-proliferated cell types via quadrant gates. 81