Phenotypic Pattern of Astrocyte Activation in Response to Aging

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PHENOTYPIC PATTERN OF ASTROCYTE ACTIVATION IN RESPONSE TO AGING

AND ALZHEIMER’S-LIKE PATHOLOGY IN CHIIMPANZEES

A thesis submitted

To Kent State University in partial

Fulfillment of the requirements for the

Degree of Master of Arts

Emily LaRee Munger

August, 2016

Except for previously published materials

Thesis written by

Emily LaRee Munger

B.S., Kent State University, 2014

M.A., Kent State University, 2016

Approved by

Dr. Mary Ann Raghanti , Advisor

Dr. Mary Ann Raghanti , Chair, Department of Anthropology

Dr. James L. Blank , Dean, College of Arts and Sciences

TABLE OF CONETENTS

LIST OF FIGURES...... v LIST OF TABLES...... v LIST OF ABBREVIATIONS...... vi ACKNOWLEDGEMENTS...... vii

CHAPTER 1 INTRODUCTION...... 1

1.1 Astrocytes’ Response to Pathology...... 3 Mild to Moderate Reactive Astrogliosis...... 5 Severe Diffuse Reactive Astrogliosis...... 5 Severe Reactive Astrogliosis with Compact Glial Scar Formation...... 6 1.2 Aging...... 7 1.3 Astrocytes and Alzheimer’s Disease...... 7 Reactive Astrogliosis…...... 8 Astrocytes and Amyloid-β...... 9 Astrocytes and Tau...... 11 Apolipoprotein E...... 12 Neuroinflammation and Oxidative Stress...... 13 Blood Brain Barrier………...... 14 Disruption of Interlaminar Astrocytes...... 15 1.4 The Great Apes and Alzheimer’s-like Pathology...... 16 1.5 Research Questions and Hypotheses...... 19

CHAPTER 2 METHODS...... 21 2.1 Specimens and Regions...... 21 2.2 Tissue Fixation...... 23 2.3 Sample Processing...... 23 2.4 Immunohistochemistry...... 24 2.5 Data Collection...... 25 2.6 Statistical Analysis...... 27 Sex and Subfields...... 28 Age and Pathology...... 29 Cortical Layers and Regions...... 30

CHAPTER 3 RESULTS...... 32

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3.1 Qualitative Examination of GFAP-ir Astrocytes...... 32 3.2 Sex Differences...... 35 3.3 Aging...... 35 3.4 Area Differences...... 39 3.5 Comparison with Pathological Markers...... 42

CHAPTER 4 DISCUSSION...... 45 4.1 Age...... 45 4.2 Pathology...... 46 4.3 Sex Differences...... 50 4.4 Conclusion...... 52 4.5 Future Directions...... 52

REFERENCES...... 54

APPENDIX A...... 81 APPENDIX B...... 84 APPENDIX C...... 86 APPENDIX D...... 87 APPENDIX E...... 90 APPENDIX F...... 92

LIST OF FIGURES

1. Astrocyte subtypes in primates...... 3 2. Mild, moderate, and severe astrogliosis...... 7 3. GFAP-ir staining surrounding the vasculature in chimpanzees...... 15 4. Progression of AD pathogenesis...... 23 5. Nissl-stained and GFAP immunostained sections of frontal cortex...... 26 6. Nissl-stained and GFAP immunostained sections of the MTG...... 27 7. Nissl-stained and GFAP immunostained sections of the HC...... 27 8. GFAP-ir staining...... 33 9. Severe inflammatory response of astrocytes in chimpanzees...... 34 10. Astrogliosis in response to amyloid and tau in the vasculature...... 34 11. Sex difference in GFAP-ir astrocyte soma volume in PFC layer III and V...... 35 12. GFAP-ir density and soma volume by age in the HC...... 36 13. GFAP-ir density and soma volume by age in the PFC...... 37 14. GFAP-ir density and soma volume by age in the MTG...... 38 15. GFAP-ir density by brain region...... 39 16. GFAP-ir density and soma volume compared by cortical layer and subfield...... 41 17. Astrocyte density compared against total brain age score...... 42 18. Astrocyte density compared against tau brain age score...... 43 19. Astrocyte density compared against Aβ brain age score...... 44

LIST OF TABLES

1. Specimens quantified in the PFC, MTG, and HC...... 21

LIST OF ABBREVIATIONS

Aβ – β-amyloid

AD – Alzheimer’s disease

ANOVA – analysis of variance

APP – amyloid precursor protein

ApoE – apolipoprotein E gene

BBB – blood brain barrier

β-secretase – amyloid precursor protein cleavage enzyme 1

CAA – cerebral amyloid angiopathy

CE – coefficient of error

CNS – central nervous system

GFAP – glial fibrillary acidic protein

GFAP-ir – glial fibrillary acidic protein immunoreactivity

GSH – glutathione

HC – hippocampus

LRP-1 – low-density lipoprotein receptor-related protein

MCI – mild cognitive impairment mRNA – messenger RNA

MTG – middle temple gyrus

NFT’s – neurofibrillary tangles

PFC – prefrontal cortex

ROS – reactive oxygen species

ACKNOWLEDGEMENTS

I would first like to give many thanks to my advisor, Dr. Mary Ann Raghanti, whose constant support and advice has allowed me to succeed in this program. Your excitement for this field and your research motivates me to do my best. You have helped me to grow both personally and professionally and I could never thank you enough. I would also like to thank Dr. Gemma

Casadesus-Smith and Dr. Richard Meindl for agreeing to be on my committee and for providing guidance and support throughout this process.

To my office mates, Cody Ruiz and Andrew Kramer, thank you for the many great conversations and for motivating me to keep going. A special thanks to Melissa Edler who trained me in the lab and provided constant support for my research. I am grateful for the guidance and advice you have given me over the past few years and I wish you all the luck as you begin the next chapter of your career.

I would like to thank my other half, whose constant support and love has allowed me to succeed and actually finish this thesis. Thank you for supporting me in my initial foray into

Anthropology and every moment since. Without you, I probably would have forgotten that there are things outside of school and research. So thank you for everything.

The project was funded by National Science Foundation (NSF BCS-1316829 to M.A.R.).

Brain materials for the study was obtained through the Great Ape Aging Project (NIH grant

AG014308, “A Comparative Neurobiology of Aging Resource”, J. Erwin, PI).

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CHAPTER 1

INTRODUCTION

Astrocytes played a unique role in human evolution (Colombo et al., 1995; Colombo et al., 2002; Oberheim et al., 2006; Oberheim et al., 2009). They are a type of glial cell, a non- neuronal brain cell, that was first described by Rudolf Virchow in 1846 as making up the glue of the brain (Virchow, 1846). Since that time, astrocytes have been regarded as passive cells that simply provide support to neurons within the central nervous system (CNS) (Nedergaard et al.,

2003; Moraga-Amoro et al., 2014). However, recent studies have demonstrated their importance in memory and information processing (Fellin et al., 2006; Henneberger et al., 2010; Panatier et al., 2006; Perea and Araque, 2005; Suzuki et al., 2011). The functions of astrocytes within the

CNS include synaptic modulation, neurotransmitter regulation, ion and water homeostasis, maintaining the integrity of the blood brain barrier (BBB), glial signaling, and are capable of calcium wave propagation (Avila-Muñoz and Arias, 2014; Cotrina and Nedergaard, 2005;

Parpura and Zorec, 2010; Rusakov et al., 2011; Sibille et al., 2014; Wallraff et al., 2006). In addition to their roles in the healthy CNS, astrocytes actively participate in the inflammatory responses associated with pathological conditions (Sofroniew and Vinters, 2010).

The number of astrocytes within the brain varies among species by both total number and ratio to neuron number (Nedergaard et al., 2003; Oberheim et al., 2006; Oberheim et al

2009). Astrocytes in the human brain are different from those of other species in many ways including an increased number of subtypes and extensions, larger size, an increased astrocyte to neuron ratio, domain integration, and ability to rapidly propagate calcium waves (Cajal, 1897;

Oberheim et al., 2006; Oberheim et al., 2009).

The different subtypes of astrocytes observed in the human cortex include the primate- specific interlaminar astrocytes located in layer I that extend long fibers deeper into the cortex to terminate in layers III and IV (Colombo et al., 1996; Colombo et al., 1997; Colombo et al., 1998;

Colombo et al., 2004; Colombo et al., 2001; Oberheim et al., 2009: Figure 1). Protoplasmic astrocytes are found in layers II through VI (see Figure 1). These astrocytes organize into specific domains and associate with blood vessels and neurons (Cajal, 1897; Oberheim et al.,

2006). Varicose projection astrocytes are specific to humans and chimpanzees. These astrocytes are found in layer V and VI and extend several long fibers towards the pia layer (see Figure 1).

This type of astrocyte exhibits many varicosities along their processes (Oberheim et al., 2006;

Oberheim et al., 2009). Lastly, fibrous astrocytes are found in the white matter and unlike protoplasmic astrocyte do not exist in organized domains (Oberheim et al., 2006: see Figure 1).

It was recently proposed that astrocyte morphology and function evolved in humans to support advanced cognitive functions and by doing so, rendered humans susceptible to many neuropathologies (Oberheim et al., 2006). Astrocyte dysfunction has been implicated in many pathologies of the CNS including amyotrophic lateral sclerosis, Down Syndrome, epilepsy,

Parkinson’s disease, and Alzheimer’s disease (AD) (Heneka et al., 2010; Markiewicz and

Lukomska, 2006; Sofroniew et al., 2010). Since AD is believed to be restricted to humans, examining the response of astrocytes to this pathology may help to further our understanding of this debilitating disease (Rapoport and Nelson, 2011).

Figure 1. Classes of human astrocytes (right): interlaminar astrocytes (light blue), protoplasmic astrocytes (dark blue), varicose projection astrocytes (pink), fibrous astrocytes (green) are located within specific layers of the cortex (right) (A). Scale bar, 100 μm (Adapted from Oberheim et al., 2006). Comparison of mouse, rhesus macaque, and human astrocytes (B). Scale bars, 20 μm (Adapted from Oberheim et al., 2012).

1.1 Astrocytes’ Response to Pathology

Verkhratsky and colleagues (2015) state that neurological disorders can conceptually be thought of as failures in the homeostatic mechanisms of the CNS. Therefore, since astrocytes are crucial to maintaining homeostasis within the brain, it can then be assumed that astrocyte dysfunction greatly contributes to the pathogenesis of these disorders (Verkhratsky et al., 2015).

A variety of brain insults, including disease and trauma, can trigger a condition generally referred to as reactive gliosis which includes astrogliosis (activated astrocytes) (Heneka et al.,

2010). Astrogliosis provides a mechanism to limit damage from spreading and helps to remodel and recover neural function after an insult (Li et al., 2008; Pekny and Nilsson, 2005; Rolls et al.,

2009). Reactive astrocytes protect the CNS in many ways including: uptake of potentially excitotoxic glutamate (Bush et al., 1999; Howell et al., 2007; Swanson and Ying, 2004),

3 protection form oxidative stress via glutathione (GSH) production (Chen et al., 2001; Kriegstein and Alvarez-Buylla, 2009; Swanson and Ying, 2004; Vargas et al., 2008), degradation of amyloid-β (Aβ) (Koistinaho et al., 2004), facilitation of blood brain barrier repair (Bush et al.,

1999), maintenance of extracellular fluid and ion homeostasis (Zador et al., 2009), and limiting the spread of inflammatory cells or infectious agents (Bush et al., 1999; Drogemuller et al., 2008;

Faulkner et al, 2004; Herrmann et al., 2008; Li et al., 2008; Myer et al., 2006; Okada et al., 2006;

Voskuhl et al., 2009). However, under extreme pathological conditions reactive astrocytes can become harmful and actually stimulate the progression of neuronal deterioration (Markiewicz and Lukomska, 2006). For example, if astrocytes are put under severe physiological stress several scenarios could occur that potentially act to promote neurotoxicity including: the release of a large quantity of glutamate, a substantial leak of potassium ions, or release of reactive oxygen species (ROS) including nitric oxide (Nedergaard and Dirnagl, 2005).

The most prominent hallmark of astrocyte activation is the upregulation of glial fibrillary acidic protein (GFAP) messenger RNA (mRNA) and protein expression, with the consequential hypertrophy of both soma and astrocytic processes (Pekny and Pekna, 2004; Wilhelmsson et al.,

2006; Wu et al., 2005). Activated astrocytes will also extend their processes towards the damaged tissue thus disturbing the domain organization of the protoplasmic astrocytes.

Astrogliosis occurs in a regional fashion with astrocytes adjacent to the site of neuronal injury immediately becoming activated (Sofroniew and Vinters, 2010).

Sofroniew and Vinters (2010) recently attempted to create a comprehensive definition of reactive astrogliosis. By their definition, reactive astrogliosis can be regarded as a spectrum of potential molecular, cellular and functional changes in astrocytes. These changes occur in response to any disruption to the CNS. The response of astrocytes varies with the severity of the

4 insult. Therefore, astrogliosis represents a continuum, from mild to severe, which follows the severity of the trauma or progression of the disease (Sofroniew and Vinters, 2010). Reactive astrogliosis can be triggered by molecules such as: large polypeptide growth factors and cytokines, mediators of innate immunity such as lipopolysaccharides and other toll-like receptor ligands, neurotransmitters, ROS, hypoxia, and products associated with neurodegeneration such as β-amyloid (Sofroniew and Vinters, 2010).

Mild to Moderate Reactive Astrogliosis

During this stage of reactive astrogliosis there is a slight but inconsistent upregulation of

GFAP (Sofroniew, 2009: Figure 2). Hypertrophy of the cell soma and processes can vary.

However, at this point the astrocytes remain organized within their domains (Sofroniew, 2009;

Wihelmsson et al., 2006). There is also little or no astrocyte proliferation. Mild to moderate astrogliosis is associated with mild non-penetrating injury, innate immune activation, and areas of distinct focal lesions (Sofroniew and Vinters, 2010). At this stage, functional changes exhibited by astrocytes can be reversed and astrocytes can return to the appearance found in healthy tissue (Sofroniew et al., 2009).

Severe Diffuse Reactive Astrogliosis

At this point, reactive astrogliosis is marked by severe upregulation of GFAP expression and hypertrophy of both the cell body and processes (see Figure 2). Astrocyte processes are extending beyond previous domain borders, causing a long lasting disruption to the astrocyte architecture (Sofroniew and Vinters, 2010). It has been postulated that the domain organization of astrocytes helps to increase the rate of information processing. Therefore, the loss of astrocyte

5 domains during pathological conditions can have severe consequences on cognitive function

(Oberheim et al., 2006). This loss of astrocyte domains occurs in regions where astrocytes are surrounding severe focal lesions, infections, or in regions that are responding to chronic neurodegeneration (Sofroniew and Vinters, 2010).

Severe Reactive Astrogliosis with Compact Glial Scar Formation

This stage is marked by a pronounced GFAP increase, hypertrophy of soma and processes, complete obliteration of astrocyte domains, astrocyte proliferation, and the formation of a narrow and compact glial scar (Sofroniew and Vinters, 2010: see Figure 2). This glial scar is believed to be a neuroprotective barrier as the scar forms along borders of severe tissue damage caused by necrosis, infection, or autoimmune-triggered inflammation (Bush et al., 1999;

Drogemuller et al., 2008; Faulkner and Herrmann, 2004; Herrmann et al., 2008; Sofroniew,

2009; Voskuhl and Meldolesi, 2005). The glial scar is formed by the interaction of newly proliferated astrocytes with elongated shapes and intertwining processes and other cell types such as fibromenigeal and microglia (Bush et al., 1999; Bundesen et al., 2003; Drogemuller et al., 2008; Faulkner et al., 2004; Herrmann et al., 2008; Voskuhl et al., 2009; Wanner et al.,

2013). The outcome is the formation of a long-lasting dense collagenous matrix. This matrix contains many molecular cues which act to inhibit axonal and cellular migration and as a consequence impairs neuronal remolding (Silver and Miller, 2004). Glial scar formation can be triggered by penetrating trauma, sever contusive trauma, invasive infections, chronic neurodegeneration, and inflammatory challenges (Sofroniew and Vinters, 2010).

Figure 2. Progression of reactive astrogliosis within the CNS of chimpanzees: mild (A), moderate (B), and severe (C). Scale bars, 25 μm.

1.2 Aging

Research has shown that some degree of reactive astrogliosis is associated with normal aging (Contrina and Nedergaard, 2002; Markiewicz and Lukomska, 2006). Astrogliosis is seen early in aged brains without the presence of pathological markers (Finch, 2003). Studies examining astrocytes response to age in humans and rodents have demonstrated an increased expression of GFAP mRNA and protein as well as an increased soma volume (Beach et al.,

1989; Finch et al., 2002; Goss et al., 1991; Morgan et al., 1997; Nicholas et al., 1993). It has been speculated that the up-regulation of GFAP in astrocytes may be a response to increased inflammation and oxidative stress loads of the aging brain, which could result in synaptic loss

(Contrina and Nedergaard, 2002; Morgan et al., 1997; Sohal and Weindruch, 1996). However, the effects of age on astrocyte morphology and function remain poorly understood.

1.3 Astrocytes and Alzheimer’s Disease

Alzheimer’s disease is a progressive neurodegenerative disease and is the most common cause of dementia (WHO, 2012). The progressive cell death seen in AD leads to memory loss and other cognitive impairments such as language difficulties, difficulty in planning and abstract

7 thought, aggression, and personality changes (Alzheimer’s Association, 2012; Crews and

Masliah, 2010; Evans et al., 1989; Markiewicz and Lukomska, 2006). A definitive diagnosis of

AD is only made postmortem and is based on two diagnostic markers: extracellular amyloid-β plaques and intracellular neurofibrillary tangles (NFT’s) (Alzheimer’s, 1910; Armstrong, 2009;

Montine et al., 2012). However, astrocyte activation has been proposed as a third marker

(Heneka et al., 2010). The involvement of glial cells, including astrocytes, in AD was first proposed by Alios Alzheimer himself who noted that glial cells surround neuritic plaques and associate with damaged neurons (Alzheimer, 1911).

Reactive Astrogliosis

In human AD brains, the main response by astrocytes is represented by prominent reactive astrogliosis (Cullen, 1997; Duffy and Rapport, 1980; Esiri et al., 1997; Mancardi et al.,

1983; Martin et al., 1994; Nagele et al., 2004; Schechter et al., 1981). It has been found that compared to aged-matched cognitively normal controls, the AD brain contains high amounts of

GFAP (Delacourte, 1990). It has been reported that the number of astrocytes does not differ between AD and physiologically normal aging nor does the number of astrocytes differ throughout the clinical course of the disease (Pelvig et al., 2003; Serrano-Pozo et al., 2013).

However, the number of GFAP immunoreactive (GFAP-ir) astrocytes, a marker for reactivity, increases with the progression of AD (Schecter et al., 1981; Serrano-Pozo et al., 2013). With

AD, GFAP increases in both the hippocampus (HC) and cortical areas. GFAP expression has been shown to progress with the advancement of the Braak and Braak stages of AD (Braak and

Braak, 1991; Duffy et al., 1980; Simpson et al., 2010).

The presence of reactive astrogliosis in human brains precedes the development of the characteristic lesions of AD (Wharton et al., 2009). The expression of GFAP has also been found to correlate inversely with cognitive function and this relationship exists independently of AD pathology (Kashon et al., 2004). However, by blocking the signaling cascade important for the induction of astrogliosis, cognitive function was shown to improve in a transgenic mouse model of AD (Furman et al., 2012). Therefore, reactive astrocytes may contribute to synaptic damage and neuronal loss which causes the cognitive impairment seen in AD (Wyss-Coray, 2006).

Plaques and tangles that are characteristic of AD typically present within neocortical layers II, III, and V (Pearson et al., 1985). A study by Beach and colleagues (1989) examined astrocyte reactivity in post mortem human AD tissue and tissue from aged matched and younger controls. The researchers found that cortical astrogliosis is concentrated in the upper 3 layers in the aged cohort compared to limited staining that was observed in the younger cohort. However, in the cerebral cortical tissue of the AD brain, they found heavy bands of GFAP immunostaining, corresponding to activated astrocytes, in cortical layers II, III, and V. These astrocytes were often co-reactive with Aβ plaques, forming a distinct ‘halo’ configuration around the deposits. The younger cohort had very little staining in the HC while astrogliosis was prominent in both aged and AD tissue, with the astrocyte response being more pronounced in the AD individuals (Beach et al., 1989).

Astrocytes and Amyloid-β

AD brains are characterized by prominent astrogliosis. This astrogliosis is mostly observed in astrocytes that surround amyloid plaques with the processes of activated astrocytes even participating in the formation of neuritic plaques (Beach and McGeer, 1988; Beach et al.,

1989; Duffy et al., 1980; Mancardi et al. 1983; Mandybur, 1989; Nagele et al., 2004; Schechter et al., 1981; Rodriguez et al., 2009). The activated astrocytes that surround the plaques often form dense layers of processes which form small glial scars (Jarvis et al., 2007; Nagele et al.,

2004; Pihlaja et al., 2008; Sofroniew, 2009; Thal et al., 2000; Wisniewski and Wegiel, 1991;

Wyss-Coray, 2006). However, it has been reported that reactive astrocytes do not associate with diffuse Aβ plaques in advanced AD (Joachim et al., 1989; Rozemuller et al., 1989; Suenaga et al., 1990). One study, using an AD mouse model where the two types of intermediate filaments in astrocytes (GFAP and vimetin) were nonfunctioning, found that astrocytes did not become hypertrophic or interact with plaques. However, the amyloid plaque load was increased (Kraft et al., 2013). This suggests that reactive astrocytes may play a role in inhibiting amyloid plaque formation and growth in AD (Pekny et al., 2014).

The exposure of cultured astrocytes to aggregated Aβ or plaques isolated from human

AD brains triggers reactive astrogliosis (DeWitt et al., 1998). Amyloid-β itself can directly induce astrocyte activation by binding to toll-like receptors and enhancing phosphorylation of extracellular signaling kinases as well as inducing an inflammatory signaling cascade (Garwood et al., 2011; Jana and Pahan, 2010; Webster et al., 2006). Additionally, although neurons are considered the main source of Aβ in the brain, astrocytes could directly contribute to the amyloid burden in AD. It has been shown that reactive astrocytes can produce significant amounts of Aβ under inflammatory conditions (Rossner et al., 2001, 2005). This may be linked to the amyloid precursor protein (APP) cleavage enzyme 1 (β-secretase) as it has been shown that reactive astrocytes which surround amyloid plaques display β-secretase immunoreactivity. β-secretase is an important enzyme in the amyloidgenic processing of APP (Avila-Muñoz and Arias, 2014;

Sastre et al., 2006; Zhao et al., 2011).

Interestingly, although the number of dense core plaques remains relatively stable throughout the clinical course of AD (Serrano-Pozo et al., 2011), both microglia and astrocytes increase linearly with the progression of the disease. For example, a positive correlation was found between astrogliosis in the temporal cortex and the progression of the disease, as measured by the onset of cognitive symptoms (Ingelsson et al., 2004). This association was found despite the finding that the plaque burden remained stable throughout the progression of the disease

(Ingelsson et al., 2004). Therefore, Serrano-Pozo and colleagues (2011) proposed that a threshold of amyloid burden is needed to trigger an astrocyte response, however, once triggered, the astrogliosis response increases independently of plaque load. Similarly, it has also been reported that astrocytes do not correlate with amyloid burden and senile plaque load (Simpson et al.,

2010).

Astrocytes are more efficient than microglia in taking up Aβ42, the pathogenic oligomer believed to be responsible for plaque formation, especially during the early stages of the disease

(Nielson et al., 2010). Astrocytes have been shown to be able to migrate towards and degrade plaques in an apolipoprotein E-dependent manner in brain slices from an AD mouse model

(Deane et al., 2008; Koistinaho et al., 2004; Nielson et al., 2010; Wyss-Coray, 2006; Zlokovic et al., 1996). Astrocytes can also clear Aβ by phagocytosis through secretions of Aβ-degrading proteases such as neprolysis, insulin degrading enzyme, and matrix metalloproteinase 2

(Dorfman et al., 2010; Koistinaho et al., 2004; Pihlaja et al., 2011; Yin et al., 2006).

Astrocytes and Tau

The density of NFT’s correlates best with the onset and progression of dementia than it does with the frequency of Aβ plaques (Braak et al., 1993; Jellinger, 1998). Interestingly, it also

11 has been shown that increased levels of GFAP correlate with increased tangle burden as well as disease duration (Ingelsson et al., 2004; Simpson et al., 2010). Therefore, the progression of reactive astrogliosis correlates with NFT burden and not plaques in the AD brain. Also, reactive astrocytes increase their proximity to NFT’s, similar to what is seen in the response to amyloid plaques (Serrano-Pozo et al., 2011).

Apolipoprotein E

A deficiency in Aβ clearance has been speculated as a likely contributor to Alzheimer’s pathogenesis (Mawuenyega et al., 2010; Selkoe, 2001; Zlokovic, 2008). This claim is substantiated by the apolipoprotein E gene (ApoE) being the greatest genetic risk factor for late onset AD (Bertram et al., 2007; Bu, 2009; Corder et al., 1993; Farrer et al., 1997; Huang and

Mucke, 2012). In the CNS, ApoE is mainly produced by astrocytes and regulates lipid homeostasis by mediating lipid transport across cells and may also likely modulate Aβ transport across the BBB from the brain to the systemic circulation (Boyles et al., 1985; Bu, 2009; Deane et al., 2008; Diedrich et al., 1991; Mahley and Rall, 2000; Mouchel et al., 1995; Pitas et al.,

1987a). Therefore, ApoE may affect Aβ clearance and if this system is disrupted, could result in greater amyloid deposition in the brain.

The ApoE gene has 3 different allele polymorphisms ε2, ε3, and ε4. Though ε3 is most common in the population with a frequency of 77.9%, the ε4 allele frequency increases from

13.7% to 40% in AD patients (Farrer et al., 1997). There is a 2-3 fold increase in risk for developing AD in individuals with 1 ε4 allele and this risk rises by 12 fold in those with 2 ε4 alleles (Roses et al., 1996). The different protein isoforms have varying abilities to bind lipids and Aβ (Chen et al., 2011; Frieden and Garai, 2012; Zhong and Weisgraber, 2009). ApoE

12 impedes amyloid clearance in an isoform specific fashion with least clearance being from the ε4 allele followed by ε3 with ε2 being the most efficient in clearing Aβ from the CNS (Deane et al.,

2008). Therefore, Aβ complexed with ApoE ε2 or ε3 is cleared from the brain at a faster rate than Aβ complexed to ApoE ε4 (Deane et al., 2008). ApoE ε4 may also have pro-inflammatory or less effective anti-inflammatory functions which likely lead to increased neuroinflammation

(Colton et al., 2004; Lynch et al., 2003). Therefore, ApoE ε4 increases the risk of AD by preventing amyloid clearance, increasing amyloid deposition and the formation of senile plaque within the brain, and by exacerbating an inflammatory response (Ellis et al., 1996; Fleisher et al.,

2012; Kok et al., 2009; Polvikoski et al., 1995; Schmechel et al., 1993).

Neuroinflammation and Oxidative Stress

Interest in the inflammatory responses associated with Alzheimer’s disease has increased.

Neuroinflammation is now commonly recognized as a hallmark of AD pathology and plays a significant role in modulation of the disease progression (McGeer and McGeer, 2010; Sastre et al., 2006). Age is the greatest risk factor for AD and there is accumulating evidence that chronic inflammation is associated with aging (Blasko et al., 2004). GFAP-ir astrocytes also increase with age. This glial response has been proposed to reflect an increased quantity of oxidatively- damaged proteins in the brain (Finch, 2003). The inflammatory profile of AD is marked by the activation of microglia, macrophages, and astrocytes with the subsequent release of cytokines, chemokines, and ROS (Akiyama et al., 2000). Specifically, reactive astrogliosis is associated with a series of cellular events including the release of nitric oxide, ROS, pro-inflammatory cytokines such as tumor necrosis factor α, interleukin-1β, interleukin-6, and prostaglandin

(Phillips et al., 2014).

In cell cultures, Aβ has been shown to induce mitochondrial depolarization and oxidative stress in astrocytes which leads to the release of ROS causing neuronal death (Abramov et al.,

2004). Aβ neurotoxicity has been associated with increased oxidative stress and the reduction of endogenous antioxidants (Behl et al., 1994; Casley et al., 2002a). Using ROS-sensitive dyes, researchers were able to show that Aβ increases the rate of ROS generation in astrocytes but not in neurons. This response in astrocytes was found to be calcium-dependent (Abramov et al.,

2004). Incubation with Aβ also caused the depletion of the antioxidant GSH in both astrocytes and neurons. However, the changes in calcium signaling and ROS was confined to astrocytes.

Astrocytes supply neurons with GSH and therefore disruption of astrocyte activity may disrupt their ability to provide GSH to surrounding neurons (Dringen, 2000).

Blood Brain Barrier

Dysregulation of cerebral vascular architecture is a prominent feature in AD pathology which is associated with ischemic injury and the breakdown of the BBB (Kalari, 1999; Kalaria,

2000). The integrity of the BBB is required for proper transport of Aβ into blood plasma (Birch,

2014). Receptors for advanced glycation end products mediate the transport of Aβ across the

BBB into the CNS and low-density lipoprotein receptor-related protein (LRP-1) mediates the efflux of Aβ from the CNS (Deane et al., 2003; Shibata et al., 2000). A decrease in LRP-1 has been seen in normal aging in rodents and nonhuman primates, and in AD patients which is associated with a decrease in Aβ clearance along with an increase in Aβ accumulation in the vasculature (Bading et al., 2002; Shibata et al., 2000). It has been proposed that astrocytes play an important role in the transport of amyloid protein out of the brain (Askarova et al., 2011).

Degeneration of the endothelial cell wall in AD can lead to accumulation of Aβ on the outer

14 basement membrane which causes local vascular neuroinflammation due to astrocytes, pericytes, and perivascular microglia (Zlokovic 2004; Zlokovic, 2005: Figure 3). Along with their role in

Aβ transport, astrocytes play an integral role in the induction and maintenance of the

BBB and therefore, astrocytes breakdown could lead to the breakdown of the BBB (Abbott,

2002; Bauer and Bauer, 2002; Reinhurt and Gloor, 1997; Zlokovic, 2005).

Figure 3. GFAP-ir astrocytes surrounding blood vessels in the CNS of chimpanzees. Scale bars, 250 μm (A) and 25 μm (B).

Disruption of Interlaminar Astrocytes

Evolution of the primate brain resulted in remarkable increases in both density and morphological complexity of astrocytes (Kettenmann and Verkhratsky, 2008). Colombo and

Reisin (2004) proposed that the computational properties of the primate cerebral cortex resulted from advantages provided by an effective neuronal organization, combined with changes to the distribution, subtype, and complexity of astrocytes within the cortex. The primate cortex is marked by a unique class of astrocytes, the interlaminar astrocyte, found in layer I which extend one or two long processes deep into the cortical layers (Colombo et al., 1996; Colombo et al.,

1997; Colombo et al., 1998; Colombo et al., 2004; Colombo et al., 2001; Oberheim et al., 2009).

The long processes take on the appearance of a palisade architecture and is characteristic of the

15 primate cortex (Colombo and Reisin, 2004). These long processes also violate the domain organization of the protoplasmic astrocytes and may serve as a non-synaptic pathway for long distance signaling among varying functional regions (Oberheim et al., 2006). It has been proposed that the long interlaminar processes may play a role in the columnar organization of the neocortex (Colombo 1996; Colombo et al., 2000; Colombo et al., 1998; Colombo et al., 1995).

AD results in severe changes to the cytoarchitecture of interlaminar processes (Colombo et al., 2002). The upper cortical layers where the cell somas of interlaminar astrocytes are found exhibit high densities of neuritic plaques in AD and the palisading structure of interlaminar astrocytes processes disappears (Colombo et al., 2002; Delaere et al., 1991; Pearson et al., 1985).

This loss of astrocyte processes is found in association with prominent astrogliosis and GFAP-ir in layers I-III in severe cases of AD, and could disrupt the glial-neuronal architecture and therefore signaling in the neocortex (Colombo et al., 2002). It has also been shown that under experimental conditions, such as mechanical lesion studies, there is a local disappearance of interlaminar processes in the cerebral cortex of Cebus apella monkeys (Colombo et al., 1998). In both cases it is unclear whether this loss occurs from retraction or absence of the astrocytes

(Colombo et al., 2002).

1.4 The Great Apes and Alzheimer’s-like Pathology

Nonhuman primates share a genetic closeness to humans, behavioral complexity, and relatively large brains. Nonhuman primates also naturally generate human sequence Aβ which is deposited within cerebral vasculature with age and also forms senile plaques (Heuer et al., 2012).

However, tau pathology is not widely found in the brains of nonhuman primates (Heuer et al.,

2012). Despite their similarities to humans, it is believed that nonhuman primates do not suffer

16 from dementias and cognitive neurodegenerative changes (Jucker, 2010; Levine and Walker,

2006). Therefore, it appears that AD may be a uniquely human disease. Comparable pathological markers of AD have not been found in the brains of any natural animal model (Perez et al.,

2013). Hence, the lack of AD-like changes in nonhuman primates may provide evidence to explain why human brains are uniquely susceptible to Alzheimer’s disease (Jucker, 2010; Levine and Walker, 2006; Rosen et al., 2011).

There is a high degree of homology in APP including the promotors and regulators of the gene between great apes (chimpanzees, gorillas, and orangutans) and humans (Adroer et al.,

1997). Studies in aged great apes have reported the presence of senile plaques that are immunoreactive for Aβ (Gearing et al., 1994; Gearing et al., 1996; Gearing et al., 1997; Kimura et al., 2001). A biochemical quantification for both isoforms of Aβ, Aβ40 and Aβ42, in a small sample of chimpanzees found that total Aβ is similar to what is seen in the human AD brains, however, the ratio of between the Aβ isoforms was variable (Rosen et al., 2011).

The deposition of Aβ in the vasculature has been commonly reported in aged chimpanzees and orangutans (Gearing et al., 1994; Gearing et al., 1996; Gearing et al., 1997;

Selkoe et al., 1987). Studies in aged chimpanzees have shown widespread Aβ vascular deposition, including in the neocortex and hippocampus (Gearing et al., 1997). One study observed mild cerebral amyloid angiopathy (CAA) in one aged female chimpanzee (Rosen et al.,

2008). Interestingly, CAA which is characterized by the extracellular deposition of Aβ in the walls of cerebral and leptomeningeal vessels, is a comorbidity of AD (Jellinger and Attems,

2005).

Genetic studies in great apes have revealed that humans and chimpanzees share a 100% sequence homology of tau and gorillas share a 99.5% sequence homology with humans (Holzer

17 et al., 2004). A study by Rosen and colleagues (2008) revealed the presence of tau immunoreactive neurons in a 41 year old female chimpanzee that possessed a region of ischemic necrosis. They observed tau positive neurons, neuropil threads, and plaques with the heaviest immunoreactivity in the prefrontal cortex followed by the temporal cortex. They reported that the subcortical regions showed less tauopathology compared to the cortical regions (Rosen et al.,

2008). Therefore, unlike in human AD cases, tauopathology in this chimpanzee was mainly observed in cortical neurons and was largely absent from the HC (Rosen et al., 2008).

Interestingly, in a study by Colombo and colleagues (2004) examining the interlaminar astrocytes in great apes discovered that a few chimpanzees lacked the palisade architecture thought to be a fixture of the primate brain. They found that orangutan and gorilla samples consistently showed a well defined GFAP-ir interlaminar palisade (Colombo et al., 2004).

However, it was observed that several chimpanzees showed cortical segments lacking the palisade and two chimpanzees (ages 5 and 37) had no interlaminar palisade (Colombo et al.,

2004). In the two chimpanzees lacking the interlaminar palisade, there was evidence of astrogliosis and astrocytosis. They suggested that the variability in individual chimpanzees may be due to a genetic polymorphism or an unknown biomedical reason (Colombo et al., 2004).

In terms of cognitive performance, aged chimpanzees have shown impairments on delayed response tasks which is a measure of frontal cortical function (Mishkin, 1957). However, they perform normally on associative memory tasks which are considered relatively independent from the frontal cortex (Riopelle and Rogers, 1965). Aged gorillas also perform normally in associative memory tasks (Martin et al., 1991; Martin et al., 1994).

1.5 Research Questions and Hypotheses

This project will examine astrocytes and their possible role in the immune response to

AD from a comparative neuroanatomical perspective. Brain tissue from chimpanzees, our closest living relatives, will be examined in this study (Goodman, 1999). All four major subclasses of astrocytes found in humans can also be found within chimpanzee brains (Oberheim et al., 2009).

Interestingly, the pattern of interlaminar astrocytes described in chimpanzees may reflect the presence of pathology which could have caused the disappearance of the palisade and reactive astrogliosis (Colombo et al., 2004). By examining Alzheimer’s-like pathology in chimpanzees, we hope to expand our current knowledge of AD and determine if this disease is truly human- specific, and to identify possible neuroanatomical substrate(s) that render the human brain uniquely susceptible to Alzheimer’s disease.

Research question 1: Do GFAP-ir astrocytes increase and become more hypertrophic with chronological age in chimpanzees.

We hypothesize that GFAP-ir astrocyte density and soma volume will increase with age in chimpanzees.

Research question 2: Do GFAP-ir astrocytes increase with the presence of Alzheimer’s-like pathology in chimpanzees?

The presence of reactive astrogliosis (GFAP-ir astrocytes) will be higher in samples where

Alzheimer’s-like pathology is present compared to those without pathology.

Research question 3: Are astrocytes hypertrophic in the presence of pathology in chimpanzees?

We hypothesize that astrocyte soma volume will be increased in samples with Alzheimer’s-like pathology.

Research question 4: Are interlaminar astrocytes affected by Alzheimer’s-like pathology in chimpanzees?

We hypothesize that in chimpanzees that possess by Alzheimer’s-like pathology, interlaminar astrocytes will become disrupted and the number of layer I GFAP-ir astrocytes will be increased in the samples with Alzheimer’s-like pathology.

CHAPTER 2

METHODS

2.1 Specimens and Regions

All postmortem chimpanzee (Pan troglodytes) brain samples were obtained from

AAALAC or AZA-accredited zoological facilities or research institutions. All individuals died of natural causes or from humane euthanization for quality of life. The study sample included 18 individuals (11 females and 7 males) ranging in age from 37 to 62 years old (Table 1).

Species Common Name Sex Age Pan troglodytes Chimpanzee F 37 Pan troglodytes Chimpanzee F 39 Pan troglodytes Chimpanzee M 39 Pan troglodytes Chimpanzee F 40 Pan troglodytes Chimpanzee M 40 Pan troglodytes Chimpanzee F 40 Pan troglodytes Chimpanzee M 40.5 Pan troglodytes Chimpanzee M 40.7 Pan troglodytes Chimpanzee F 41 Pan troglodytes Chimpanzee F 43 Pan troglodytes Chimpanzee F 45 Pan troglodytes Chimpanzee M 46.3 Pan troglodytes Chimpanzee F 49

Pan troglodytes Chimpanzee F 51 Pan troglodytes Chimpanzee M 57 Pan troglodytes Chimpanzee F 58

Pan troglodytes Chimpanzee F 58 Pan troglodytes Chimpanzee M 62

Table 1. Sex and age for all individuals included in this analysis, listed from youngest (37) to oldest (62).

The three areas that were analyzed in this study are the hippocampus, middle temporal gyrus (MTG), and prefrontal cortex (PFC) specifically Brodmann’s area 9. Brain regions were taken from either the right or left hemisphere depending on availability. These regions were chosen based on the guidelines set forth in the 2012 National Institute on Aging-Alzheimer’s

Association guidelines for the neuropathologic assessment of AD (Montine et al., 2012), the staging for NFT’s provided by Braak and Braak (1991), and staging of Aβ accumulation provided by Thal and colleagues (2002). These guidelines demonstrate the regional progression of AD pathology throughout the course of the disease. As depicted in Figure 4, tau pathology begins in the transentorhinal region (stages I-II) and progresses out through the HC (stages III-V) and eventually into the neocortex (stages V-VI). However, Aβ plaques are first seen in the neocortex and only in later stages of the disease are plaques observed in the HC (Braak and

Braak, 1991). Montine and colleagues (2012) provide a consensus guideline to assess neuropathologic evaluation of AD. These guidelines suggest a few initial areas of assessment for

Aβ pathology including: inferior parietal lobule, superior and middle temporal gyri, and the middle frontal gyri which includes Brodmann’s area 9. The middle temporal gyri and the HC are suggested areas for the evaluation for NFT’s (Montine et al., 2012).

Figure 4. Progression of AD pathogenesis (Adapted from Braak and Braak, 1991).

2.2 Tissue Fixation

The chimpanzee brains were collected upon death and immersion fixed in 10% buffered formalin for 7-10 days. The brains were then transferred to a 0.1 M phosphate buffered saline solution which contained 0.1% sodium azide. The tissue was stored at 4º C to prevent further shrinkage, blockage of antigens, and contamination by bacteria.

2.3 Sample Processing

Before sectioning, samples were cryoprotected in a graded series of sucrose solutions

(10%, 20%, and 30%) until fully saturated. Brain samples were quickly frozen using dry ice and sectioned using a freezing sliding microtome at a thickness of 40 μm (SM200R, Leica, Chicago,

IL). Sections were placed into individual centrifuge tubes containing freezer storage solution

(30% each distilled water, ethylene glycol, and glycerol and 10% 0.244 M PBS). Each tube was numbered sequentially and stored at -20º C until further histological or immunohistochemical

23 processing took place. A 1-in-10 series was taken from each sample and was stained for Nissl substance using a 0.5% cresyl violet. Cresyl violet allows for the visualization of cell somas and cytoarchitecture. Nissl-stained sections were used to delineate cortical layers I, III, and V in both the PFC and MTG and hippocampal subfields CA1 and CA3 (Figures 5-7).

2.4 Immunohistochemistry

Sections were immunostained for GFAP in order to visualize astrocytes within the tissue using the avidin-biotin-peroxidase method (e.g., Raghanti et al., 2013). GFAP expression can be regarded as a sensitive and reliable marker for labeling reactive astrocytes (Beach et al., 1989;

Hansen et al., 1986; Rodríguez et al., 2014; Sofroniew and Vinters, 2010). Tissue was pretreated for antigen retrieval for 30 minutes in a 0.05% solution of citraconic acid (pH 7.4) in a water bath (85 ͦ C). A solution of 75% methanol, 2.5% hydrogen peroxide (30%) and 22.5% distilled water was used to quench endogenous peroxidase for 20 minutes. Sections were then preblocked in a solution of 0.1 M phosphate buffered saline (PBS; pH 7.4), 0.6% Triton X-100, 4% normal serum, and 5% bovine serum albumin. Tissue was incubated with the primary antibody (anti-

GFAP rabbit-polyclonal antibody, AB5804, Millipore, Temecula, CA, USA) at a dilution of

1:12,500 in PBS for 48 hours. After incubation, sections were placed into a solution of biotinylated secondary antibody (1:200), PBS, and 2% normal serum. Sections were then placed in a solution of avidin-peroxidase complex (PK-6100, Vector Laboratories, Burlingame, CA,

USA) followed by NovaRED (SK-4800, Vector Laboratories, Burlingame, CA, USA) or 3, 3′- diaminobenzidine-peroxidase substrate with nickel enhancement (SK-4100, Vector Laboratories,

Burlingame, CA, USA) to allow for visualization.

2.5 Data Collection

Astrocyte density and soma volume were quantified using an Olympus BX-51 photomicroscope. This photomicroscope is equipped with a Ludl X,Y motorized stage,

Heidenhain z-axis encoder, StereoInvestigator software (MBF Bioscience, Williston, VT, USA, version 9), and digital camera that projects images onto a 24-inch LCD flat panel monitor. For both PFC and MTG, Nissl-stained sections were used to delineate cortical layers I, III, and V at low power (4X, 0.13 N.A.) (see Figures 5 and 6). Nissl-stained sections were also used to mark

CA1 and CA3 subfields of the HC also at low power (4X, 0.13 N.A.) (see Figure 7).

Astrocyte density was quantified using the optical fractionator program at high magnification (40X, 0.75 N.A.). A guard zone of 2 µm was used at the top and bottom of each section. Section thickness was also measured every 5th sampling site. The counting frame was set at 100 x 100 µm with an optical dissector height of 7 µm. All GFAP-ir astrocytes visualized in the counting frame were included in the study of astrocyte density. While collecting GFAP-ir cell density, soma volume was measured simultaneously using the StereoInvestigator nucleator program. Soma volume was collected on every 2nd marked astrocyte and 6 rays were used to define the soma. Astrocytes that did not have a clearly defined cell body were omitted from data collection of soma volume.

Figure 5. Nissl-stained (A) and GFAP immunostained (B) sections of chimpanzee frontal cortex with cortical layers labeled. Scale bars = 250 μm.

Figure 6. Nissl-stained (A) and GFAP immunostained (B) sections of chimpanzee MTG with cortical layers indicated. Scale bars = 250 μm.

Figure 7. Nissl-stained (A) and GFAP immunostained (B) sections of chimpanzee HC showing the delineation of the hippocampal subfields. Scale bars = 250 μm.

2.6 Statistical Analyses

Prior to statistical analyses, planimetric volumes calculated by StereoInvestigator were corrected using a factor that accounts for the variation and loss of tissue during histological processing. All statistical analyses were completed using IBM SPSS Statistics Data Editor 23.0

27 software. Alpha was set at 0.05 for all statistical tests with a more stringent Bonferroni correction applied for multiple comparisons to control against type I errors using the following formula:

훼 퐵퐶 = 푛 where:

α equals the pre-set alpha value of 0.05 and n equals the number of t-tests performed.

Sex and Subfields

Using an independent samples Student’s t-test the means for astrocyte density and soma volume were compared for sex differences in each region by cortical layer and subfield. For PFC and MTG the sample size for all tests was 18 (11 females and 7 males). For the HC, the sample size for both subfields when examining GFAP-ir density was also 18, however, due to the inability to collect soma volume because of the lack of immunoreactive astrocytes in some individuals there was a sample size of only 15 (9 females and 6 males) and 13 (8 females and 5 males) for CA1 and CA3 respectively. A Levene’s test for equality of variances was performed to test the assumption that the variances were equal between the two groups, male and female, for each t-test completed. This assumption was rejected for multiple tests. Therefore, for each t- test the reported test statistics assumes unequal variances. To correct for multiple comparisons

(8), alpha was adjusted to 0.0063.

An independent samples t-test was also used to compare differences in means between

GFAP-ir astrocyte density and soma volume between the two hippocampal subfields, CA1 and

CA3. Sample size for the two analyses was the same as the sample sizes in the t-tests comparing sex differences.

Age and Pathology

To determine if GFAP-ir astrocyte density or soma volume varied by age, a linear regression analysis was used. Density and soma volume, broken down by cortical layer and hippocampal subfield, were regressed upon age, the independent variable. A linear regression analysis was also used to compare density and soma volume by pathological markers of AD. For these analyses, the regressors were total brain age score, Aβ brain age score, and tau brain age score (Edler et al., 2016).

To evaluate the neuropathological changes that were present in each individual, Aβ brain age score, tau brain age score, and a total brain age score were calculated by compiling several established scoring methods for AD pathology and CAA (Edler et al., 2016). To assess Aβ load for each individual, the Consortium to Establish a Registry for AD (CERAD) protocol was used to score Aβ plaque frequency (Mirra et al., 1991). A modified version of the Thal phases was used to assess the regional progression of amyloid pathology (Montine et al., 2012; Thal et al.,

2002). Lastly, the results of two studies examining topographical distribution and frequency of

Aβ positive vasculature were used to establish the staging for CAA (Attems et al., 2007;

Vonsattel et al., 1991). These three scoring methods were used to calculate a score that represented the total Aβ pathology present (Edler et al., 2016). For each individual, all four brain regions (PFC, MTG, CA1, and CA3) were assessed and given a score that ranged from 0 to 3 for one scoring method. The scores for each region were then totaled to provide a number between 0 and 12 for each individual. This was repeated for each scoring method: Aβ plaque frequency, Aβ progression, and CAA. Therefore, each individual was given an Aβ brain age score ranging from

0 to 36.

To calculate tau brain age score, two scoring methods were used (Edler et al., 2016). The frequency of tau neuritic plaques was established using the protocol provided by CERAD (Mirra et al., 1991). NFT’s were assessed by an adapted four-stage version of the Braak staging (Braak and Braak, 1991; Braak et al., 2006, Montine et al., 2012). As before, each brain region (PFC,

MTG, CA1, and CA3) was given a score ranging from 0 to 3. These regions were summed for each individual giving a score ranging from 0 to 12. This was completed for both scoring methods. Therefore, each individual was given a tau brain age score that ranged from 0 to 24.

Total brain age score for each individual represents the sum of each of the five scoring methods and ranges from 0 to 60. Brain age score represents the overall disease state of the brain.

Cortical Layers and Regions

To examine differences in means among the three cortical layers (I, III, V) of the PFC and MTG, a one-way analysis of variance (ANOVA) was used. There were 18 individual responses for each group (layer) for each region in both density and volume analyses (n = 54 total). Post hoc testing was completed using independent samples t-test with a correction of alpha to 0.012.

To compare astrocyte density and volume by region, an overall average for each individual for each area was calculated using the following equations:

푋 +푋 +푋 푋 +푋 퐴푣 = 1 3 5 or 퐴푣 = 퐶퐴1 퐶퐴3 3 2 where:

X1, X3, and X5 represent astrocyte densities or soma volume for each cortical layer and XCA1 and

XCA3 represent densities or volumes for each of the two hippocampal subfields. Once the averages were calculated, a one-way ANOVA was performed. However, the test of homogeneity

30 of variances was rejected, meaning the variances among the three regions were not equal. To correct for this, a Kruskal Wallis test, the nonparametric equivalent to the ANOVA, was performed. As before, post hoc testing was completed using independent samples-t tests with an alpha of 0.012.

CHAPTER 3

RESULTS

3.1 Qualitative Examination of GFAP-ir Astrocytes

Immunostaining for GFAP was robust (figure 8) and GFAP-ir astrocytes could be visualized throughout the cortex (see figure 8A). Qualitatively, GFAP-ir astrocytes were present in higher quantities in the upper cortical layers with very few reactive astrocytes present in the deeper cortical layers (i.e., layers V and VI). GFAP-ir astrocyte were also more numerous in the neocortical regions compared to the HC. The HC displayed very limited immunostaining. At higher magnifications, GFAP-ir astrocytes were distinctive enough to distinguish soma shape, allowing measurement of soma volume (see figure 8 panel B and C).

Figure 8. Robust immunostaining of GFAP positive astrocytes. GFAP-ir astrocytes through the frontal cortex at low magnification (A) and individual GFAP positive astrocytes within the PFC and MTG (B and C respectively). Scale bar = 250 µm (A) and 25 µm (B and C).

The number and distribution of GFAP-ir astrocyte varied greatly among the individuals within this sample. There were individuals that had very few GFAP-ir astrocytes while other individuals had a high density of reactive astrocytes (figure 9). In a few of these individuals, the

GFAP-ir astrocytes appear to be lysed (see figure 9 panel A and C). Several of these individuals had severe reactive astrogliosis that was indicative of a systematic inflammatory response but did not appear to have AD-like pathology (see figure 9C). Qualitative examination also demonstrated the much of the GFAP-ir astrocytes could be found in association with blood vessels (figure 10). Many of these blood vessels, along with their association with reactive astrocytes, were positively stained for Aβ and tau pathology (see figure 10 panel A and C).

Figure 9. GFAP-ir astrocytes that appear to have lysed processes (A and B) and lysed astrocytes in the upper cortical layers in a chimpanzee frontal cortex (C). Chimpanzee frontal cortex displaying widespread reactive astrogliosis in an individual who does not display AD-like pathology (C). Scale bars = 25 µm (A and B) and 250 µm (C).

Figure 10. GFAP-ir astrocytes in association with blood vessels in frontal cortex. Blood vessels displaying co-localization of CP13-GFAP antibodies (A) and 6E10-GFAP (C). CP13 is a marker for phosphorylated tau and 6E10 stains for amyloid precursor protein, Aβ1-40 peptide, and Aβ1-42 peptide. Scale bars = 25 µm.

3.2 Sex Differences

There were no sex differences (all p’s > 0.0063) for GFAP-ir density or soma volume in any of the cortical layers or subfields examined with the exception of soma volume in the PFC layer III (t11.45 = -3.87, p < 0.0063; figure 11 panel A) and layer V (t15.56 = -3.27, p < 0.0063; figure 11 panel B; see appendix B for details). Female chimpanzees possessed a higher average soma volume in these two layers of the PFC compared to males (see figure 11).

Figure 11. GFAP-ir astrocyte soma volume between male and female chimpanzees in layer III (A) and layer V (B) of the PFC.

3.3 Aging

Linear regression analyses revealed that age did not correlate with either GFAP-ir astrocyte density or volume in any of the regions analyzed here (all p’s > 0.05; figures 12-14; see appendix C for details). Although not statistically significant, GFAP-ir density appeared to

increase in layer I with age in both PFC and MTG (see figures 13A and 14A). GFAP-ir astrocyte soma volume tended to decrease relative to chronological age in those same regions

(see figure 13B and 14B).

Figure 12. Linear regression results comparing astrocyte density and soma volume by age in the hippocampal subfields.

Figure 13. Results of the linear regression examining astrocyte density and soma volume by age in cortical layer I, III, V in the PFC.

Figure 14. Results of the linear regression analyses comparing age to astrocyte density and soma volume in cortical layers I, III, V in the MTG.

3.4 Area Differences

The ANOVA analysis comparing brain regions (PFC, MTG, and HC) revealed that

GFAP-ir soma volume was not different among regions (p > 0.05; see appendix D for details).

The Kruskal-Wallis analysis comparing GFAP-ir astrocyte density by brain region was significant (χ2 = 30.442, p < 0.05; see appendix D for details). Post hoc analyses via independent samples t-tests revealed that mean GFAP-ir astrocyte density was significantly lower in the HC compared to the other two regions, PFC (t28 = 8.049) and MTG (t16.752 = 4.322; both p’s < 0.017; see figure 15).

Figure 15. Mean GFAP-ir astrocyte density between the three brain regions.

The ANOVA analyses comparing astrocyte density in the PFC and MTG by cortical layers (I, III, V) found that a significant difference existed among the three layers in each region

(F2,51 = 25.075 for PFC and F2,51 = 8.058 for MTG, both p’s < 0.05; figure 16: see appendix D).

Further post hoc testing demonstrated that mean GFAP-ir density in layer I was higher relative to the other cortical layers for both regions (p’s < 0.017; see figure 16 panel A and C; see appendix

D for details). An ANOVA analysis revealed no difference in the mean soma volume by cortical layer in the PFC (p > 0.05; see figure 16B). However, there was a difference in mean soma volume by cortical layer in the MTG (F2,51 = 7.993, p < 0.05). Post hoc testing demonstrated that layer I astrocyte soma volume was greater than that of layer V (p < 0.017; see figure 16 panel D).

The independent samples t-test examining GFAP-ir density and soma volume was not significant between the hippocampal subfields (p’s > 0.05, see figure 16 E and F; see appendix D).

Figure 16. Average GFAP-ir astrocyte density and soma volume compared in each region by cortical layer or subfield.

3.5 Comparison with Pathological Markers

The analyses of brain age score found no statistical relationship between GFAP-ir astrocyte density and volume in any layer or subfield examined (all p’s > 0.05; see appendix E) except for GFAP-ir density in layer I of the PFC (F1,16 = 10.78, p < 0.05; figure 17). As displayed in Figure 17, GFAP-ir density increased with brain age, a marker for the overall disease state of the brain.

Figure 17. Astrocyte GFAP-ir increases in relation to total brain age score in layer I of the chimpanzee PFC.

Similarly, the analyses of tau score found no statistical relationship to astrocyte density or soma volume in any layer or subfield tested (all p’s > 0.05; see appendix E) except for layer I astrocyte density in the PFC (F1,16 = 5.089, p < 0.05). As with brain age, astrocyte GFAP- ir density increased as tau load increases in this specific layer and region (Figure 18).

Figure 18. GFAP-ir astrocyte density increased in relation to tau load in the chimpanzee PFC layer I.

The analysis of total Aβ brain age score showed no significant relationship to astrocyte density and soma volume in all layers and subfields (all p’s > 0.05) except for GFAP-ir density in layer I of the PFC (F1,16 = 10.743, p < 0.05). This follows a similar pattern to the other pathological indicators. GFAP positive astrocyte density increases with Aβ load in the PFC layer

I (Figure 19).

Figure 19. Astrocyte GFAP immunoreactivity in relation to Aβ load in the chimpanzee PFC layer I.

CHAPTER 4

DISCUSSION

The purpose of the present study was to examine the pattern of reactive astrogliosis in response to AD-like pathology in a large sample of aged chimpanzees. Alzheimer’s disease has been speculated to only afflict humans (Jucker, 2010; Levine and Walker, 2006; Rosen et al.,

2011). Chimpanzees are our closest living relatives and yet AD pathology has not been widely studied in this species. Similarly, astrocyte activation has not been examined within this species in relation to age. Since astrogliosis is considered a prominent feature of AD, examining the reactive astrocytes within chimpanzees is of great importance. Therefore, elucidating the astrocytic inflammatory pattern observed in chimpanzees is needed to further our understanding of whether or not AD is truly specific to humans.

4.1 Age

One of the greatest risk factors of AD is age (National Institute of Aging, 2012).

Therefore, we decided to examine astrogliosis, as measured by soma volume and GFAP-ir density, in relation to chronological age in this sample. We found that age is not correlated to

GFAP-ir density or soma volume in any region measured by cortical layer or subfield. This result counters previous studies which found an increased expression of GFAP mRNA, GFAP protein, and size in relation to age in both humans and rodents (Beach et al., 1989; Finch et al., 2002;

Goss et al., 1991; Landfield et al., 1977; Morgan et al., 1997; Nichols et al., 1993, O’Callaghan and Miller). It has been suggested that the increase of GFAP in astrocytes may be in response to increased inflammation and oxidative stress loads of the aging brain (Cotrina and Nedergaard,

2002; Morgan et al., 1997; Sohal and Weindruch et al., 1996).

The observed pattern of astrocyte morphology in the aging chimpanzee brain is distinct from humans. Unlike humans, GFAP expression and soma volume does not appear to increase with age in chimpanzees. This may potentially suggest either a lower oxidative stress load with age or the existence of a protective mechanism. Alternatively, the lack of activated astrocytes in aged chimpanzees may be due to the shorter lifespan of chimpanzees compared to humans. One study using human hippocampal tissue found that GFAP expression remained low until the age of 65 when it increased considerably (David et al., 1997). However, even in captivity, few chimpanzees survive beyond 50 years of age (Rowe et al., 2012). This suggests that widespread gliosis in humans may be a product of our increased longevity.

4.2 Pathology

We found that GFAP-ir density in layer I of the PFC increases in relation to both overall amyloid and tau load. Similarly, GFAP-ir cell density was higher in layer I of the PFC and MTG than either layer III or layer V. The specific vulnerability of these astrocytes is likely responsible for the trend toward an increased GFAP-ir density with age observed in layer I of the PFC and

MTG. Layer I of the primate cortex is marked by a unique class of astrocytes (Colombo et al.,

1996; Colombo et al., 1997; Colombo et al., 1998; Colombo et al., 2004; Colombo et al., 2001;

Oberheim et al., 2009). Primate specific interlaminar astrocytes found in layer I of the cortex extend long processes deep into the cortical layers and terminate in layers III and IV (Colombo et

46 al., 1995; Colombo et al., 2004; Colombo and Reisin, 200; Oberhiem et al., 2009). The upper cortical layers where these astrocytes are found exhibit a high density of neuritic plaques in AD

(Delaere et al., 1991; Geddes et al., 1997). Also, AD pathology may disrupt the columnar organization of these astrocytes. One study reported the loss of the interlaminar palisade in human AD cases. These cases also demonstrated prominent GFAP-ir expression (Colombo et al.,

2002). In the present study, these astrocytes often appear to be lysed, which may account for both the atrophy and the trend observed in the PFC with age showing decreased soma volume.

The PFC has been shown to be necessary for both episodic and working memory (Bauer and Fuster, 1976; Fuster and Alexander, 1971; Kane and Engle, 2002; Kubota and Niki, 1971;

Miller and Orbach, 1972). These memory domains are disrupted early in AD, as well as in mild cognitive impairment (MCI) which is defined in individuals by progressive memory loss that is distinct from normal aging (Baddeley et al., 1991; Belleville et al., 1996; Morris et al., 2001;

Saunders and Summers, 2011). In addition, MCI is thought to be a transitional stage between normal aging and dementia and is associated with an increased risk of progression into AD

(Almkvist et al., 1998; Bowen et al., 1997; Hanninen et al., 1995; Morris et al., 2001; Peterson et al., 1999). In humans, the group with the greatest risk of developing AD are those above 85 years of age (National Institute of Aging, 2012). Therefore, it is likely that the sample age range of the chimpanzees in the present study (37-62 years) is representative of this transitional stage between MCI and AD. Interestingly, in this study, the PFC, specifically layer I, was shown to be uniquely affected by pathology which corresponds to the early memory impairment seen in individuals with MCI. This study may suggest that primate specific astrocytes represent an early vulnerability in the pathogenesis of AD.

Colombo (2001) hypothesized that interlaminar astrocytes in the primate brain are the

47 glial counterpart of neuronal columnar organization. Interlaminar astrocytes have been found to respond to both purinergic and glutamatergic release from neurons by elevating calcium

(Oberheim et al., 2009). Though the function is unknown, these traits suggest that interlaminar astrocytes may provide a network for the long-distance coordination of intracellular communication, through calcium wave propagation, and thus, could work to integrate different domains (Oberheim et al., 2006). Interlaminar astrocytes may also work to regulate blood flow among functionally related regions (Oberheim et al., 2009). The response demonstrated by the interlaminar astrocytes in this study is similarly to the astroglial pattern expected in human AD cases. It appears that both in chimpanzees and humans, interlaminar astrocytes display astrogliosis that is marked by a disruption to the palisade. Therefore, the specific vulnerability of these astrocytes in the primate brain could account for some of the severe cognitive impairments observed in AD and other neurodegenerative diseases in humans.

We also found that the PFC and MTG had higher GFAP-ir density compared to the HC.

This is in accordance with Edler et al. (2016), who reported a higher total percent area occupied by amyloid plaques in the cortex compared to the HC. They also reported a greater vascular amyloid deposition in the cortex. This disruption to the vasculature could account for the strong astrogliosis response observed surrounding vessels in the cortex. Therefore, the greater astrocytic response observed in the cortex is likely due to an increase in AD pathology.

Qualitative examination of each individual chimpanzee indicated that those with the highest pathology load had the lowest GFAP-ir density (see Appendix A, example male chimpanzee age 57). In these chimpanzees, the inflammatory response mounted by the astrocytes may have reached a state of exhaustion. As previously studies have stated that astrocytes show intense gliosis and hypertrophy in AD brains (Delacourte, 1990; Halliday et al., 1996). However,

48 astrocytes can simultaneously undergo degenerative changes which exhibit apoptosis, increased expression of lysosomal activity, and decreased GFAP expression. Another study reported that apoptosis occurs in both astrocytes and neurons (Smale et al., 1995). This was supported by our earlier analysis of neuron and glia densities in the CA1 and CA3 subfields of the chimpanzee HC

(Munger et al., 2014). In that study, glia density was found to be significantly decreased with age in the CA3 subfield. The findings were not significant in the CA1, however, there was an overall trend towards decreased glial densities in older chimpanzees. All of the chimpanzees in this study were included in the Munger et al. (2014) study. This provides support that glia cells, including astrocytes, may also be degenerating in the aging chimpanzee brain.

A recent model of AD indicated that the response of astrocytes includes both astrogliosis and astrocyte degeneration (Verkhratsky et al., 2010). Astrodegeneration was first reported in preplaque stages of the HC, PFC, and entorhinal cortex in an AD mouse model (Kulijewicz-

Nawrot et al., 2012; Verkhratsky et al., 2010; Yeh et al., 2011). In the astroglial hypothesis, the initial impairments seen in AD, such as synaptic loss and malfunction, can be the result of a generalized atrophy of astrocytes (Kettenmann and Verkhratsky, 2008; Verkhratsky et al., 2010;

Verkhratsky and Parpura et al., 2016). Specifically, the loss of synaptic coverage by astrocytes and dysregulation of their homeostatic functions could be responsible for the early cognitive deficits observed in AD. According to this hypothesis, later stages of AD are then marked by activation of astrocytes triggered by the accumulation of AD pathology (Olabarria et al., 2010;

Rodriguez and Verkhratsky, 2011; Verkhratsky et al., 2010). Reactive astrocytes release inflammatory factors which can induce neuronal degeneration and cause severe dementia

(Verkhratsky et al., 2010). Ultimately, astrodegeneration could be responsible for the observed trend in this sample of decreased soma volume with both age and pathology load. The dual

49 astrocyte phenotype proposed by this model for AD could also account for some of the widespread variation observed in this sample.

4.3 Sex Differences

In layer III and layer V of the PFC, soma volume was significantly higher in female chimpanzees compared to males. This result indicates that astrocytes may be more prone to express an inflammatory phenotype in female chimpanzees in this area. In humans, AD is twice as prevalent in women as it is in men (Andersen et al., 1999; Bachman et al., 1992; Brookmeyer et al., 1998; Fratiglioni et al., 1997). In women, prolonged periods without ovarian hormones, such as estradiol, increases the risk of neurological disorders, cognitive impairment, and dementia (Rocca et al., 2009). The actions of estrogenic compounds on astrocytes may work to decrease neuronal damage by limiting astrocytes’ role in brain inflammation (Cerciat et al.,

2010; Garcia-Estrada et al., 1993). Therefore, the loss of estradiol as a consequence of menopause may be one of the driving factors behind the disproportionate occurrence of AD in women compared to men. However, studies examining menopause in chimpanzees are limited and often provide conflicting results.

In terms of reproduction, a decline in reproductive performance begins at similar ages in chimpanzees and humans (25-35 years) and approaches zero near the same age (50 years)

(Emery Thompson et al., 2007). One study using serum hormone levels in captive chimpanzees concluded that chimpanzees reach menopause at 40 years of age (Videan et al., 2006). This report was substantiated by other studies indicating that menopause in chimpanzees occurs near

40 years of age (Atsalis and Videan 2009; Videan et al. 2006, 2008). However, menstrual cycles in chimpanzees have been observed to persist well into the 5th and 6th decades of life (Graham

1979; Gould et al.1981; Lacreuse et al. 2008). Others have documented captive chimpanzees

50 giving birth to healthy infants after passing 40 and 50 years of age (Puschmann and Federer

2008; Ross, 2009). A recent report examining 9 female chimpanzees (ages 33.1 to 53.5 years) at

Yerkes National Primate Research Center found that 8 of the 9 chimpanzees continued to display ovarian cycles (Herdon et al. 2012). Combining the results from the Hardon et al. (2012) and

Lacreuse et al. (2008) studies, only 3 out of 23 chimpanzees older than 40 years of age, including

4 chimpanzees older than 50 years, were menopausal at the time that observations ceased.

Therefore, it is likely that menopause in chimpanzees does not occur until far beyond 40 years of age. We do not have records for cycling history for the female chimpanzees in this present study.

However, based on individual data presented in Appendix A, the majority of females with the highest soma volumes are younger than 45 years of age. Therefore, the increased inflammatory response is not likely due to a loss of estrogenic compounds.

As a confounding factor, two of the female chimpanzees suffered from strokes prior to death. After an ischemic event, astrocytic swelling is often observed as well as proliferation of astrocytes near the injured area (Anderson et al., 2003; Aschner, 1998; Landis et al., 1994; Ridet et al., 1997). Additionally, several of the female chimpanzees that display high soma volumes in this region (females age 37, 40, 49; see Appendix A) display a pattern of astrogliosis indicative of a CNS infection or widespread insult. Astrocytes respond vigorously to CNS infection and thus, demonstrate prominent astrogliosis (Eddleston and Mucke, 1993; Sofroniew and Vinters,

2010). Qualitative examination of these female chimpanzees revealed that prominent astrogliosis was found throughout the cortex with many of these astrocytes appearing to be hypertrophied.

Also, Elder and colleagues (2016) examined this sample of chimpanzees for Aβ and tau pathology. They found no sex differences in NFT’s and tau neuritic plaque densities, amyloid blood vessel volume, or APP/Aβ burden. There was only one sex difference in AD pathology

51 that was observed and it revealed that these male chimpanzees displayed a higher distribution of

Aβ42. These findings suggest that the increase in astrocyte soma volume in the PFC in female chimpanzees is not likely due to an increase in amyloid and/or tau burden. Therefore, the increased inflammatory response exhibited by female chimpanzees in layer III and V of the PFC is probably the result of an unknown CNS insult.

4.4 Conclusion

The phenotypic pattern displayed in this sample was highly variable among individuals.

It appears that neither GFAP-ir density nor soma volume correlate with age or pathology load in an expected manner. However, we have shown that layer I astrocytes in the PFC demonstrate an enhanced vulnerability to AD pathology in the chimpanzee brain. Since these astrocytes are specific to primates, it provides a unique clue into one of the potential mechanisms leading to humans’ higher susceptibility to neurodegenerative diseases.

4.5 Future Directions

In this study, no relationship was found between age and density of GFAP-ir astrocytes or soma volume. However, the sample age range of this present study is limited (all ages > 37 years). Therefore, to gain a better understanding of the changes to astrocyte phenotypes with age, the sample age range could be expanded to include younger chimpanzees. Also, studies have shown that increased GFAP expression with age is due to increased transcription (Morgan et al.,

1997; Morgan et al., 1999). Immunohistochemistry using anti-GFAP antibody is used to specifically mark GFAP protein within cells. If samples are available, using a second technique such as running a western blot to detect GFAP protein could be used to enhance the current

52 findings. Using a northern blot, GFAP mRNA expression in this sample could be used to determine if increased transcription of the GFAP gene is occurring. Additionally, in this study, we used soma volume as a marker of astrogliosis (hypertrophy) or degeneration (atrophy).

However, since GFAP is an intermediate filament found in astrocyte processes, another measure may be more appropriate. For example, using an imaging software to measure surface area of astrocytes or length of astrocyte processes. These measurements would provide information regarding the extension or retraction of the astrocyte processes, which in turn, can indicate the state of hypertrophy or atrophy of the cell. Lastly, the data presented in this study, GFAP-ir density and soma volume, will be analyzed more thoroughly with data collected by Elder and colleagues (2016). These future analyses will hopefully help to determine a more specific relationship between amyloid, tau, calbindin, and microglia within these chimpanzees. With a greater understanding of AD-like pathology in chimpanzees, we will be closer to answering the question of which, if any, characteristics of AD are truly specific to humans.

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APPENDIX A

Individual astrocyte density (population estimate/mm3) and soma volume (μm3) in PFC, MTG, and HC provided in age order, youngest to oldest. All individuals are of the species Pan troglodytes.

Density Soma Volume Region Individual Layer I Layer III Layer V Layer I Layer III Layer V Female age PFC 37 1770.17 2302.88 2253.29 628.34 607.11 530.69 Female age 39 3425.05 2589.15 812.69 1133.53 833.74 572.7 Male age 39 4482.52 2259.33 1576.64 336.54 351.75 280.57 Female age 40 3716.11 125.81 751.7 565.57 596.86 361.15 Male age 40 2832.25 1394.75 752.27 349.12 256.59 240.53 Female age 40 3793.6 2407.81 2637.85 501.2 536.33 386.27 Male age 40.5 2229.56 3218.23 1700.56 393.713 321.1 297.79 Male age 40.7 2775.5 861.47 331.88 319.917 252.19 175.75 Female age 41 4124.79 992.04 532.16 311.64 307.73 245.83 Female age 43 5208.19 752.5 1211 598.24 581.86 530.9 Female age 45 3548.16 1400.33 543.94 484.58 402.51 264.34 Male age 46.3 2394.47 1238.8 1497.03 451.09 345.54 356.73 Female age 49 2875.29 921.75 929.73 987.46 796.66 491.89 Female age 51 3014.39 879.65 723.95 515.39 373.81 435.37 Male age 57 7890.6 1535.68 383.53 397.23 296.15 191.39 Female age 58 3454.53 2175.5 3045.98 414.68 484.26 433.08 Female age 58 2735.82 1278.35 766.85 227.07 275.91 215.77 Male age 62 5239.41 3021.45 2616.99 355.58 321.73 287.13 Mean 3639.47 1630.86 1281.56 498.38 441.21 349.88 Std. Deviation ±1422.07 ±861.55 ±850.22 ±231.89 ±179.99 ±125.09

Female age MTG 37 1438.9 2936.99 3073.37 576.1 601.79 412.59 Female age 39 2895.98 435.04 176.66 707.46 378.07 342.06 Male age 39 3928.47 2201.96 2902.34 359.16 393.17 320.81 Female age 40 2249.79 182.49 376.23 551.66 391.34 303.65 Male age 40 2565.14 915.98 718.48 434.34 297.86 300.17 Female age 40 3604.43 3265.97 4444.24 682.98 510.75 440.5 Male age 40.5 2459.13 1227.26 1208.41 359.89 316.99 275.6 Male age 40.7 566.9 142.54 392.83 564.78 593.24 327.68 Female age 41 924.36 58.01 25.79 216.83 162.3 156.88 Female age 43 3675.96 781.87 2082.81 416.16 361.32 293.35 Female age 45 736.11 106.96 58.68 318.42 495.65 164.48 Male age 46.3 3046.48 1232.05 785.55 358.73 244.54 174.24 Female age 49 3260.19 1200.25 1995.61 387.15 300.36 261.96 Female age 51 2411.43 204.16 173.76 390.59 278.88 184.26 Male age 57 4113.18 38.14 19.1 380.27 177.51 95.72 Female age 58 2288.36 1452.53 2770.7 322.07 447.78 310.26 Female age 58 1735.89 1619.67 1972.37 330.9 253.24 241.14 Male age 62 4200 1930.59 1693.29 433.09 364.95 353.36 Mean 2561.15 1107.36 1381.68 432.81 364.98 275.49 Std. Deviation ±1144.38 ±992.47 ±1305.37 ±131.87 ±127.57 ±91.67

CA1 CA3 CA1 CA3 Female age HC 37 106.91 2140.99 507.59 437.56 Female age 39 138.08 310.44 382.87 284.15 Male age 39 22.30 0 144.85 N/A Female age 40 40.70 45.1 495.85 398.29 Male age 40 139.82 184.06 329.12 284.76 Female age 40 346.95 1324.72 319.32 285.12 Male age 40.5 97.66 187.4 192.07 216.46 Male age 40.7 93.812 288.35 325.33 232.78 Female age 41 21.31 7.84 319.26 N/A Female age 43 267.68 118.86 455.83 416.67 Female age 45 6.79 0 N/A N/A Male age 46.3 199.65 396.03 385.1 188.3 Female age 49 103.75 153.51 245.98 271.61 Female age 51 14.66 42.43 291.83 N/A Male age 57 0 0 N/A N/A Female age 58 0 202.49 N/A 222.766 Female age 58 195.01 1069.71 461.96 404.63 Male age 62 1023.61 2807.98 350.64 345.97 Mean 156.59 515.55 347.17 306.85 Std. Deviation ±237.43 ±807.01 ±105.63 ±84.7

APPENDIX B

Statistical results of the independent samples Student’s t-tests comparing astrocyte density and soma volume by sex in each region by cortical layer or subfield. The scores reported assume unequal variances for each test. Alpha was set at 0.0063. Averages along with standard deviation are provided for each group, male and female, by region broken down by layer and subfield for each.

Density Volume Region dƒ t-score significance dƒ t-score significance PFC Layer I 7.385 0.673 0.521 10.868 -2.506 0.029 Layer III 11.724 1.168 0.266 11.453 -3.865 0.002 Layer V 13.854 -0.063 0.951 15.556 -3.272 0.005 MTG Layer I 10.676 1.201 0.256 15.073 -0.582 0.569 Layer III 15.601 -0.032 0.975 12.606 -0.615 0.549 Layer V 15.983 -0.786 0.443 12.914 -0.416 0.685 HC CA1 6.802 0.803 0.449 10.927 -1.947 0.078 CA3 9.789 0.137 0.894 10.405 -2.143 0.057

Group Statistics Density Volume Region Sex Average St. Deviation Avegae St. Deviation PFC I Male 3977.76 ±2060.64 371.88 ±44.96 Female 3424.19 ±871.23 578.88 ±268.08 III Male 1932.82 ±914.76 306.43 ±39.93 Female 1438.71 ±809.53 526.98 ±182.51 V Male 1265.56 ±824.53 261.41 ±63.39 Female 1291.74 ±905.91 406.18 ±123.38 MTG I Male 2982.76 ±1287.3 412.89 ±28.3 Female 2292.85 ±1014.12 445.48 ±48.38 III Male 1098.36 ±818.12 341.18 ±50.09 Female 1113.08 ±1128.19 380.13 ±38.7 V Male 1102.86 ±959.6 263.94 ±35.55 Female 1559.11 ±1501.75 282.83 ±28.33 HC CA1 Male 225.26 ±358.42 287.85 ±96.05 Female 112.9 ±115.54 386.72 ±96.74 CA3 Male 551.97 ±1005.07 253.65 ±62.41 Female 492.37 ±706.77 340.1 ±82.39

APPENDIX C

Statistical results of the regression of GFAP-ir astrocyte density and soma volume on age in each region by cortical layer or subfield. Alpha was set at 0.05.

Density Volume Region R2 dƒ F significance R2 dƒ F significance PFC Layer I 0.15 1,16 2.83 0.112 0.064 1,16 1.097 0.31 Layer III 0.004 1,16 0.067 0.799 0.068 1,16 1.16 0.297 Layer V 0.033 1,16 0.541 0.473 0.044 1,16 0.74 0.402 MTG Layer I 0.078 1,16 1.346 0.263 0.175 1,16 3.391 0.084 Layer III 0.002 1,16 0.037 0.849 0.128 1,16 2.34 0.146 Layer V 0.001 1,16 0.009 0.925 0.115 1,16 2.072 0.169 HC CA1 0.144 1,16 2.688 0.121 0.005 1,16 0.061 0.809 CA3 0.066 1,16 1.127 0.304 0 1,16 0.001 0.972

APPENDIX D

Statistical results of the one-way ANOVA comparing astrocyte density and soma volume between regions and between cortical layers within the MTG and PFC for soma volume only.

The results of the nonparametric Kruskal-Wallis analysis comparing astrocyte density between cortical layers within the PFC is also presented. Alpha was set at 0.05 for the ANOVA and

Kruskal-Wallis tests. Post hoc testing, independent samples t-tests, are also displayed for each significant ANOVA/Kruskal-Wallis result. Alpha was set at 0.017. Statistical results of the independent samples t-test comparing astrocyte density and soma volume between hippocampal subfields is also displayed. Alpha was set at 0.05. Levene’s test of homogeneity of variance is presented for each ANOVA. Also, every t-test presented is paired with its associated Levene’s test for equality of variances. If Levene’s test was rejected the degree of freedom and t-score assumed unequal variances.

Region Differences Levene's Test of Homogeneity of Variances Kruskal-Wallis Levene Variable Statistic dƒ1 dƒ2 significance dƒ χ2 significance Density 3.709 2 51 0.031 2 30.442 0

Post Hoc Testing Levene's Test for Equality of Variances T-test t- Pair F significance dƒ score significance PFC- MTG 1.305 0.261 34 1.82 0.078 PFC-HC 4.038 0.054 28 8.049 0 MTG-HC 5.064 0.032 16.752 4.322 0

Levene's Test of Homogeneity of Variances ANOVA Levene Variable Statistic dƒ1 dƒ2 significance dƒ F significance Volume 3.082 2 45 0.056 2,45 2.085 0.136

PFC Cortical Layer Differences Levene's Test of Homogeneity of Variances ANOVA Levene Variable Statistic dƒ1 dƒ2 significance dƒ F significance Density 1.057 2 51 0.355 2,51 25.075 0

Post Hoc Testing Levene's Test for Equality of Variances T-test t- Pair F significance dƒ score significance I-III 1.134 0.294 34 5.125 0 I-V 1.313 0.26 34 6.038 0 III-V 0.024 0.878 34 1.224 0.229

Levene's Test of Homogeneity of Variances ANOVA Levene Variable Statistic dƒ1 dƒ2 significance dƒ F significance Volume 1.161 2 51 0.321 2,51 2.976 0.06

MTG Cortical Layer Differences Levene's Test of Homogeneity of Variances ANOVA Levene Variable Statistic dƒ1 dƒ2 significance dƒ F significance Density 1.108 2 51 0.338 2,51 8.058 0.01

Post Hoc Testing Levene's Test for Equality of Variances T-test t- Pair F significance dƒ score significance I-III 0.385 0.539 34 4.072 0 I-V 0.705 0.407 34 2.883 0.007 III-V 2.28 0.14 34 -0.71 0.483

Levene's Test of Homogeneity of Variances ANOVA Levene Variable Statistic dƒ1 dƒ2 significance dƒ F significance Volume 0.98 2 51 0.382 2,51 7.993 0.001

Post Hoc Testing Levene's Test for Equality of Variances T-test t- Pair F significance dƒ score significance I-III 0.015 0.903 34 1.568 0.1568 I-V 1.776 0.192 34 4.156 0 III-V 1.488 0.231 34 2.417 0.021

HC Subfield Differences Levene's Test for Equality of Variances T-test t- Variable F significance dƒ score significance Density CA1- CA3 11.194 0.002 19.921 -1.81 0.085

Volume CA1- CA3 0.201 0.658 26 1.102 0.28

APPENDIX E

Statistical results of the linear regression comparing brain age score, tau score, and Aβ with astrocyte density and soma volume in each region by cortical layer or subfield. Alpha was set at

0.05.

Brain Age Score Density Volume Region R2 dƒ F significance R2 dƒ F significance PFC Layer I 0.402 1,16 10.775 0.005 0.139 1,16 2.593 0.127 Layer III 0.018 1,16 0.287 0.599 0.117 1,16 2.115 0.165 Layer V 0.015 1,16 0.245 0.627 0.102 1,16 1.823 0.196 MTG Layer I 0.11 1,16 1.982 0.178 0.124 1,16 2.256 0.153 Layer III 0.001 1,16 0.012 0.925 0.016 1,16 0.253 0.622 Layer V 0.004 1,16 0.059 0.812 0.08 1,16 1.395 0.255 HC CA1 0.003 1,16 0.041 0.842 0.007 1,13 0.093 0.766 CA3 0.002 1,16 0.025 0.876 0.008 1,11 0.087 0.773

Tau Score Density Volume Region R2 dƒ F significance R2 dƒ F significance PFC Layer I 0.491 1,16 5.089 0.038 0.113 1,16 2.042 0.172 Layer III 0.009 1,16 0.145 0.709 0.122 1,16 2.232 0.155 Layer V 0.009 1,16 0.14 0.714 0.16 1,16 3.04 0.1 MTG Layer I 0.043 1,16 0.722 0.408 0.288 1,16 1.452 0.246 Layer III 0.001 1,16 0.014 0.907 0.009 1,16 0.146 0.707 Layer V 0.001 1,16 0.021 0.887 0.099 1,16 1.76 0.203 HC CA1 0.131 1,16 2.402 0.141 0.179 1,13 2.832 0.116 CA3 0.064 1,16 1.098 0.31 0.035 1,11 0.395 0.543

Aβ Score Density Volume Region R2 dƒ F significance R2 dƒ F significance PFC Layer I 0.402 1,16 10.743 0.005 0.075 1,16 1.292 0.272 Layer III 0.017 1,16 0.283 0.602 0.052 1,16 0.881 0.362 Layer V 0.05 1,16 0.84 0.373 0.025 1,16 0.409 0.531 MTG Layer I 0.169 1,16 3.265 0.09 0.106 1,16 1.896 0.187 Layer III 0 1,16 0.005 0.947 0.047 1,16 0.78 0.39 Layer V 0.001 1,16 0.019 0.891 0.06 1,16 1.03 0.325 HC CA1 0.161 1,16 3.074 0.099 0.054 1,13 0.746 0.403 CA3 0.086 1,16 1.506 0.237 0.084 1,11 1.015 0.335

APPENDIX F

Methodical details of the stereologic measures of GFAP-ir density and soma volume by region and cortical layer/subfield are provided. The Gunderson coefficient of error (CE) is provided for

GFAP-ir density and the average CE for volume is provided for soma volume. Table was adapted from Schmitz and Hof (2005).

Stereologic Estimates of GFAP Immunoreactivity in the PFC Density Soma Volume Layer Layer Layer Layer Layer Layer Variable I III V 1 III V Mean number of investigated sections 3 3 3 3 3 3 Mean section thickness 9.3 9.5 9.8 9.3 9.5 9.8 Mean number of investigated sampling fields 130 140 133 130 140 133 Mean number of counted cells 135 93 66 63 45 31 Coefficient of Error 0.09 0.14 0.16 0.009 0.026 0.034

Stereologic Estimates of GFAP Immunoreactivity in the MTG Density Soma Volume Layer Layer Layer Layer Layer Layer Variable I III V 1 III V Mean number of investigated sections 3 3 3 3 3 3 Mean section thickness 8.8 9.3 9.7 8.8 9.3 9.7 Mean number of investigated sampling fields 132 137 130 132 137 130 Mean number of counted cells 105 63 66 51 32 34 Coefficient of Error 0.11 0.21 0.2 0.012 0.047 0.027

Stereologic Estimates of GFAP Immunoreactivity in the HC Density Soma Volume Variable CA1 CA3 CA1 CA3 Mean number of investigated sections 3 3 3 3 Mean section thickness 10.5 11.1 10.5 11.1 Mean number of investigated sampling fields 153 147 153 147 Mean number of counted cells 9 24 5 16 Coefficient of Error 0.48 0.36 0.084 0.043