Astrocyte Development and Function Is Fgf8 Signaling

ASTROCYTE DEVELOPMENT AND FUNCTION IS

FGF8 SIGNALING DEPENDENT

A dissertation submitted to

Kent State University in partial fulfillment

of the requirements for the degree of

Doctor of Philosophy

Courtney E. Stewart

May 2019

Except for previously published materials

Dissertation written by

Courtney E. Stewart

B.S., Kent State University 2013

Ph.D., Kent State University 2019

Approved by

______, Chair, Doctoral Dissertation Committee Wilson C.J. Chung Ph.D. ______, Members, Doctoral Dissertation Committee Jennifer A. McDonough Ph.D. ______, Samuel D. Crish Ph.D. ______, Kristy Welshhans Ph.D. ______, Mary Beth Spitznagel Ph.D.

Accepted by

______, Chair, Department of Biomedical Sciences Ernest J. Freeman Ph.D. ______, Dean, College of Arts and Sciences James L. Blank Ph.D. Table of Contents

Table of Contents .....………………………………………...………………………………….. iii

List of Figures …...... ………………………………………………………………………….. vi

List of Tables …………...... …………………………………………….....…………………. viii

List of Abbreviations ………...... ………………………………………….………………... x

Acknowledgments ....………………...... …………………………………………………... xii

Chapter 1: General Introduction …...... ………………………...... ………………...... 1

1.1. Fibroblast growth factor signaling in brain development ...... 1

1.2. FGF8 signaling in astrocyte development ...... 4

1.3. FGF8 signaling in astrocyte function and astrocyte specific diseases ...... 7

1.4. FGF8 signaling in astrocyte function ...... 10

1.5. Models of astrocyte activation within MS ...... 12

1.6. Specific aims ...... 16

1.7. References ...... 19

Chapter 2: Perinatal Midline Astrocyte Development is Impaired in Fibroblast Growth Factor 8

Hypomorphic Mice ...... 30

2.1. Introduction ...... 30

2.2. Materials and Methods ...... 33

2.3. Results ...... 40

2.4. Discussion ...... 55

2.5. References ...... 61

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2.6 Supplementary information ...... 65

Chapter 3: Perinatal FGF8 Hypomorphic Mouse Anterior Midline Glial Cell Development is

Independent of Genetic Sex ...... 66

3.1. Introduction ...... 66

3.2. Materials and Methods ...... 69

3.3. Results ...... 72

3.4. Discussion ...... 79

3.5. References ...... 82

Chapter 4: Cuprizone Induced Mouse Astrocyte Activation is Fibroblast Growth Factor 8

Signaling-dependent ...... 86

4.1. Introduction ...... 86

4.2. Material and Methods ...... 89

4.3. Results ...... 93

4.4. Discussion ...... 106

4.5. References ...... 110

Chapter 5: Analyzing Cuprizone Effects on Corpus Callosum Thickness with Magnetic

Resonance Imaging in the Adult Mouse Brain ...... 113

5.1. Introduction ...... 113

5.2. Material and Methods ...... 115

5.3. Results ...... 118

5.4. Discussion ...... 122

5.5. References ...... 125

Chapter 6: General Discussion ...... 128

6.1. FGF8 regulates astrocyte development during CC formation ...... 129

6.2. FGF8 is of importance during astrocyte activation when challenged with the neurotoxin cuprizone ...... 131

6.3. The cellular signaling mechanisms underlying FGF8-dependent astrocyte development and function ...... 135

6.4. Future directions ...... 136

6.5. Conclusions ...... 138

6.6. References ...... 139

List of Figures

Figure 2.1. GAP43 immunoreactivity in anterior-dorsal midline region in PN0 WT, Fgf8+/neo , and Fgf8neo/neo hypomorphic mice ...... 41

Figure 2.2. NP-1 immunoreactivity in anterior-dorsal midline region in E16.5 and PN0 WT,

Fgf8+/neo , and Fgf8neo/neo hypomorphic mice ...... 43

Figure 2.3. Incidence of apoptosis within the anterior-dorsal midline region in E16.5 and E17.5

WT, Fgf8+/neo , and Fgf8neo/neo hypomorphic mice ...... 45

Figure 2.4. S100b immunoreactivity within the anterior-dorsal midline region in E17.5 and PN0

WT, Fgf8+/neo , and Fgf8neo/neo hypomorphic mice ...... 47

Figure 2.5. GFAP immunoreactivity within the anterior-dorsal midline region in E17.5 and PN0

WT, Fgf8+/neo , and Fgf8neo/neo hypomorphic mice ...... 51

Figure 2.6. GFAP immunoreactivity within the anterior-dorsal midline region in adult WT and

Fgf8+/neo hypomorphic mice ...... 54

Figure 2.7. GLAST-1 immunoreactivity within the anterior-dorsal midline region in PN0 WT,

Fgf8+/neo , and Fgf8neo/neo hypomorphic mice ...... 65

Figure 3.1. GFAP immunoreactivity within the anterior-dorsal midline in male and female E17.5

WT, Fgf8+/neo , and Fgf8neo/neo hypomorphic mice ...... 73

Figure 3.2. GFAP immunoreactivity within the anterior-dorsal midline in male and female PN0

WT, Fgf8+/neo , and Fgf8neo/neo hypomorphic mice ...... 75

Figure 3.3. GFAP immunoreactivity within the anterior-dorsal midline in male and female PN5

WT, Fgf8+/neo , and Fgf8neo/neo hypomorphic mice ...... 77

Figure 3.4. GFAP immunoreactivity within the PVN in male and female PN5 WT, Fgf8+/neo , and

Fgf8neo/neo hypomorphic mice ...... 78

Figure 4.1. GFAP immunoreactivity within the genu in 2 week and 3 week CPZ-treated adult

WT and Fgf8+/neo hypomorphic mice ...... 95

Figure 4.2. GFAP immunoreactivity within the cingulum in 2 week and 3 week CPZ-treated adult WT and Fgf8+/neo hypomorphic mice ...... 96

Figure 4.3. GFAP immunoreactivity within the cingulate cortex in 2 week and 3 week CPZ- treated adult WT and Fgf8+/neo hypomorphic mice ...... 98

Figure 4.4. Manual cingulum astrocyte traces of 2 week and 3 week CPZ-treated adult WT and

Fgf8+/neo hypomorphic mice ...... 100

Figure 4.5. Bar graphs representative of relative CC Gfap expression in 2 week, 3 week and 6 week adult WT and Fgf8+/neo hypomorphic mice ...... 102

Figure 4.6. Bar graphs representative of relative CC Fgfr1 expression in 2 week, 3 week and 6 week adult WT and Fgf8+/neo hypomorphic mice ...... 104

Figure 4.7. Bar graphs representative of relative CC Stat3 expression in 2 week, 3 week and 6 week adult WT and Fgf8+/neo hypomorphic mice ...... 106

Figure 5.1. Bar graphs representative of CC volumes from 0, 2, 3, 6, and 8 week MRI images of adult standard chow and 0.2% CPZ chow fed WT and Fgf8+/neo hypomorphic mice ...... 120

Figure 6.1. Fgf8 signaling effects during CPZ-induced astrocyte activation ...... 135

vii

List of Tables

Table 2.1. Forward and reverse primers used for RT-qPCR ...... 60 Table 4.1. Forward and reverse primers used for RT-qPCR ...... 109 Table 5.1. Week to week comparisons of the temporal effects of standard chow treatment on CC volumes p-values...... 120 Table 5.2. Week to week comparisons of the temporal effects of 0.2% CPZ chow treatment on CC volumes p-values...... 121

viii

List of Abbreviations ACC Agenesis of the corpus callosum AD Alzheimer’s Disease ALS Amyotrophic Lateral Sclerosis ANOVA Analysis of variance AR Androgen receptor BMP Bone morphogenic protein BPM Beats per minute CC Corpus callosum CPZ Cuprizone CRH Corticotropin releasing hormone DCC Deleted in colorectal cancer DNMT DNA methyltransferase DW Diffusion weighted E0.5 Embryonic day 0.5 E9 Embryonic day 9 E10.5 Embryonic day 10.5 E11.5 Embryonic day 11.5 E14.5 Embryonic day 14.5 E15.5 Embryonic day 15.5 E16.5 Embryonic day 16.5 E17.5 Embryonic day 17.5 E18.5 Embryonic day 18.5 ERα Estrogen receptor alpha ERβ Estrogen receptor beta ERE Estrogen response element FGF Fibroblast growth factor FGFR Fibroblast growth factor receptor

GAP43 Growth associated protein 43 GFAP Glial fibrillary acidic protein GLAST Glutamate aspartate transporter GnRH Gonadotropin releasing hormone GW Glial wedge HD Huntington’s Disease Hprt Hypoxanthine-guanine phosphoribosyltransferase IG Indusium griseum IHC Immunohistochemistry IL-10 Interleukin 10 IL-27 Interleukin 27 IL-6 Interleukin 6 IR Immunoreactivity KS Kallman Syndrome LIF Leukimia inhibiting factor mDA Midbrain dopaminergic neurons MMCm Medial motor column neurons MRI Magnetic resonance imaging MS Multiple Sclerosis MZ Midline zipper NFIA Nuclear factor type 1 A NFIB Nuclear factor type 1 B NP-1 Neuropilin-1 NSC Neural stem cell OP Olfactory placode PB Probst bundle PD Parkinson’s Disease

PN0 Postnatal day 0 PN5 Postnatal day 5 PN21 Postnatal day 21 POA Preoptic nucleus PVN Paraventricular nucleus PRE Progestin response element RGC Radial glia cell RT-qPCR Real-time quantitative polymerizing chain reaction S100b Calcium binding protein SCN Suprachiasmatic nucleus SEM Standard error of the mean STAT3 Signal transducer and activator of transcription 3 SVZ Subventricular zone T1 T1 Radiofrequency pulse T2 T2 Radiofrequency pulse TF Transcription factor TNF-α Tumor necrosis factor alpha VP Vasopressin WT Wildtype

Acknowledgments

There are many to which I am truly grateful and without their knowledge, guidance, friendship, love and support, reaching this point in my life and academic career may not have been possible.

Thank you all for being my teachers, confidants, coaches, cheering squad, and my second family.

To my committee: first and foremost, my mentor, Dr. Wilson Chung. You have made me a better scientist, teacher, and a stronger person. You’ve seen me through low points and high points, always believing in me when I couldn’t believe in myself. Metaphorically and quite literally, you’ve given me the branch I now use and will continue to use to stand steadily when I don’t believe I can. I can only hope to one day mentor my own student within my own sandbox.

Dr. Kristy Welshhans, thank you for teaching me primary cell culture and always being there when I had questions. To Dr. Samuel Crish and Dr. Jennifer McDonough, and Dr. Mary Beth

Spitznagel thank you all for your input and comments during my graduate studies.

To my labmates: Megan, thank you for everything you do in the lab and as a friend. I started as your mentor, but you’ve become my equal. Lab life, and certainly conferences, would not have been the same. You are and will be a fantastic researcher! To Brittany, Paula, Bri,

Andrew, and Marija, your contributions to this work are sincerely appreciated. You were wonderful students and I wish you the best.

To family and friends: My parents, James and Lorrie, you may not understand every detail of what I’ve done these last few years but know that I’ve gotten this far because, leading by example, you’ve raised me to be determined, diligent and to never stop trying. In moments of self-doubt you were there to listen, encourage, and redirect me towards the main goal. My sister,

Lindsey, you have and always will be my best friend, greatest listener, and most honest person.

You tell me the hard truths and give me a new perspective continuously. Although, I should be

xii simply thanking you, I want to tell you that I am extremely proud of you! You’ve accomplished so much for yourself and are a fantastic teacher. Matt, you are my voice of reason. You’ve reminded me of the joys in life be it music, food, or genuine laughter. You’ve reignited the creative spark I thought had long burned out. Thank you for everything! John, Spencer, Morgan,

Annie, and Brian, thank you all for continuously supporting me and being my sounding board during both the fun and difficult times.

xiii

Chapter 1

General Overview

1.1. Fibroblast growth factor signaling in brain development

The fibroblast growth factor (FGF) family consists of 22 proteins, 18 of which are secreted and act through four membrane tyrosine kinase FGF receptors (FGFR) (D. M. Ornitz, 2000; David

M. Ornitz & Itoh, 2015). FGF/FGFR binding, a heparin sulfate mediated process, induces FGFR dimerization activating a multitude of intracellular signaling cascades, such as, Pi3 kinase

(K)/Akt, Ras-MAPK, PLCɣ/Ca2+, and JAK/STAT (Goetz & Mohammadi, 2013; David M.

Ornitz & Itoh, 2015). Secreted FGFs use these signaling mechanisms to regulate cell proliferation, specification, differentiation, migration and survival (Dono et al., 2002; Ford-

Perriss, Abud, & Murphy, 2001; Goldfarb, 2001; D. M. Ornitz, 2000; David M. Ornitz & Itoh,

2015). Many of the secreted FGFs also have roles in brain development. For example, FGF2 regulates cortical neurogenesis (Raballo et al., 2000), FGF13 regulates neuronal migration (Q. F.

Wu et al., 2012), FGF17 regulates cerebellum development (Xu, Liu, & Ornitz, 2000) and together, FGF18 and FGF8 regulate brain asymmetry (Ohuchi, Kimura, Watamoto, & Itoh,

2000; Xu et al., 2000).

We are particularly interested in the FGF8 subfamily, comprised of FGF8, FGF17 and

FGF18, which is critical for the developing CNS (Ohuchi et al., 2000; Q. F. Wu et al., 2012; Xu et al., 2000). Of the three, FGF8 has the broadest role in brain development (Ford-Perriss et al.,

2001; D. M. Ornitz, 2000; David M. Ornitz & Itoh, 2015). For instance, Fgf8 knockouts do not survive past gastrulation (Meyers, Lewandoski, & Martin, 1998; Sun, Meyers, Lewandoski, &

Martin, 1999). To circumvent this developmental restriction, we and others turned to Fgf8 conditional knockouts or hypomorphs to investigate its effects during embryonic and postnatal brain development (Meyers et al., 1998). These Fgf8 deficient mice lack olfactory bulbs, a cerebellum and have impaired hypothalamic and corpus callosum (CC) development supporting the premise that FGF8 function is required for brain development (Hebert, Lin, Partanen,

Rossant, & McConnell, 2003; Kawauchi et al., 2005; Meyers et al., 1998; Theil, Dominguez-

Frutos, & Schimmang, 2008).

1.1.2 FGF8 is a pro-survival factor

These observed developmental brain defects are likely, in part, the result of FGF8’s role as a pro- survival trophic factor (Kawauchi et al., 2005; Storm, Rubenstein, & Martin, 2003; Tsai, Brooks,

Rochester, Kavanaugh, & Chung, 2011). For example, we previously showed that gonadotropin- releasing hormone (GnRH) neurons are absent in homozygous (Fgf8neo/neo) Fgf8 hypomorphic mice and reduced in heterozygous (Fgf8+/neo) littermates (Chung, Moyle, & Tsai, 2008).

Currently, the precise cause of the absence of the GnRH neuronal system is unknown, although some earlier studies investigating the effects of FGF8 hypomorphy or knockout during telencephalon development found a dramatic rise in apoptosis, further indicating that FGF8 is a pro-survival factor (Chung et al., 2008). Our studies measuring apoptosis in the olfactory placode

(OP), the presumed birthplace of GnRH neurons, support this idea. Indeed, reduced FGF8 signaling led to a 10-fold increase in OP apoptosis when compared to normal FGF8 signaling; future studies will verify this possibility.

1.1.3 FGF8 regulates neuronal fate-specification

Alternatively, the observed brain defects in Fgf8 hypomorphic mice could be the result of

FGF8’s role in regulating neuronal fate-specification. For example, FGF8 has been shown to drive cell-fate specification of many hypothalamic neurons, including oxytocin, kisspeptin, and corticotropin-releasing hormone (CRH) (Brooks, Chung, & Tsai, 2010; Rodriguez, Stevenson,

Stewart, Linscott, & Chung, 2015; Tata, Chung, Brooks, Kavanaugh, & Tsai, 2012). FGF8s actions during cell-fate specification are not restricted to the hypothalamic regions as cortical neuron cell fate is also Fgf8-dependent (Peterson et al., 2000). Specifically, in vitro and in vivo studies have shown that FGF8 signaling is required for Cajal-Retzius neuron development which begins between E10.5 and E13.5 and is involved in cortical neuron radial migration (Peterson et al., 2000). Furthermore, FGF8 was shown to regulate in vitro dopaminergic neuronal differentiation (Dode et al., 2003).

1.1.4 FGF8 regulates neurocircuitry

FGF8s actions are also involved in axon targeting. For instance, peripherin-IR vomeral nasal nerve fibers, which guide GnRH neurons from the OP and into the forebrain, failed to cross into the brain in Fgf8neo/neo mice (Chung et al., 2008). Similarly, FGF8 controls axon targeting within midbrain dopaminergic (mDA) neurons and medial motor column (MMCm) neurons (Edwards,

Richards, Sherr, & Barkovich, 2014; Leighton B.N. Hinkley et al., 2016). Specifically, FGF8

3 increased MMCm neurite outgrowth via FGFR1 in vitro (Edwards et al., 2014). Moreover, in vitro rat mDA neurons exhibited impaired axon outgrowth when exposed to FGF8 and axon fibers appeared to be repulsed by FGF8 (Leighton B.N. Hinkley et al., 2016). Together these studies showed that FGF8 plays a myriad of roles during brain development, including in axon targeting and may directly act as a guidance molecule for some cell types. This led us to hypothesize that disrupted axon targeting or cellular migration may also be, in part, the cause of the aforementioned disrupted CC development observed in Fgf8 hypomorphic mice.

1.2. Fibroblast growth factor 8 FGF8 signaling plays a role in astrocyte development

Fibroblast growth factors, including FGF8, regulate neural stem cell (NSC) expansion, a self- renewal phase, which occurs early in development (Guillemot & Zimmer, 2011; Murao,

Noguchi, & Nakashima, 2016). NSC self-renewal is a form of proliferation as a means to expand and maintain a progenitor pool prior to the onset of early cell-fate specification. This NSC expansion is mediated by multiple transcription factors (TF), such as, HES1, SOX2, GLI-1, and

POU3F2, which modulate self-renewal, proliferation, or inhibit differentiation depending on the activated signaling pathway (Leighton B. N. Hinkley et al., 2012). These TFs can act through

Wnt/B-catenin and Notch pathways to mediate self-renewal, and/or Shh-Gli and RTK pathways to mediate proliferation.

FGFs bind to their cognate receptors, FGFR1-4, activating RTK pathways likely regulating proliferation. Indeed, FGF2 plays a role in cortical and SVZ progenitor cell proliferation (Brown,

Anderson, Symington, & Paul, 2012; Raballo et al., 2000). In addition to aiding in NSC expansion, FGFs also regulate the ability of NSCs to differentiate into different neural cell types.

For example, at low concentrations (0.1 ng/mL) FGF2 added to cortical precursor cells induced

4 neuronal differentiation while higher concentrations (1.0 to 10 ng/mL) induced glial differentiation (Conrad, Kriebel, & Hetzel, 1978). Here, we focus on astrocyte development and function, and therefore, astrocytic development will be described in more detail.

In mice, before astrocyte development begins, gliogenesis, a nuclear factor type 1 A (NFIA)- dependent process begins around embryonic day (E) 11.5. Nuclear factor type 1 A (NFIA), a notch-target gene found in the nucleus of NSCs, inhibits neurogenesis while inducing glutamate aspartate transporter (GLAST) expression in NSCs (Chaboub & Deneen, 2012; Deneen et al.,

2006; Gallo & Deneen, 2014). The acquisition of GLAST by NSCs begins around E11.5/12.5 and denotes a conversion to radial glial (RGC) cell fate and the onset of the “gliogenic switch”.

RGCs, although the primary astrocytic precursor, are not restricted to an astrocytic fate at this point which allows RGCs to further accumulate before differentiating into neurons, oligodendrocytes or astrocytes.

Which cell type RGCs become, depends on the TF environment present during embryonic brain development (McCarthy, Amateau, & Mong, 2002). In mice once RGCs are fated to become astrocytes, expression profile studies showed that they will express β-catenin, Slit1 and vimentin during early stages, while GLAST, FGFR3, NFIA, and Sox9 are expressed during the middle stages, and lastly, GFAP, ApoE, S100b, and Aquaporin 4 are expressed during the final stage astrocyte development (Molofsky et al., 2013). In addition, to age-dependent protein expressions, astrocyte precursors within each developmental stage differ functionally. For instance, early stage astrocyte precursors play a role in axon guidance, mid-stage precursors play a role in fatty acid biosynthesis, and late-stage or mature astrocytes play a role in postnatal synaptic plasticity (Molofsky et al., 2013).

Recent data showed that FGFs similarly play a role at each stage either by: inducing the gliogenic switch (i.e., astrocytes or oligodendrocytes), maintaining astrocyte precursor cell survival, and/or regulating terminal astrocyte differentiation (Bartlett et al., 1998; Hajihosseini &

Dickson, 1999; Lin & Goldman, 2009). Interestingly, the actions of specific FGFs are dose- dependent and therefore, a single FGF itself could have multiple roles. For example, FGF2 is pro-survival factor at low doses (e.g., 1-10 ng/ml) but is pro-astrogliogenic at high doses (e.g.,

10-100 ng/ml) (Kimura, Nakajima, & Yoshino, 1990). Taken together, these data indicate that

FGFs inhibit neurogenesis from NSCs, in order to induce astrogenesis.

Currently, very little is known how or when FGF8 specifically acts during astrocyte development. Based on previous studies, FGF8 may increase NFIA and NFIB in the anterior- midline. For example, FGF8 overexpression in NFIA knockouts increased GLAST expression but failed to increase GFAP (Pringle et al., 2003). DNA methyltransferases (DNMT) 1 mutant mouse studies led to one possible explanation that NFIA may initiate the dissociation of

DNMTs on the GFAP promoter likely promoting astrocyte maturation, but the exact role or roles of FGF8 in this mechanism is not known (Jin, Kang, & Kim, 2009). Alternatively, or coincidently, FGF8 may act as pro-gliogenic factor. Normally, NSCs will develop towards glial cell fate under the direction of bone morphogenetic protein (BMP) and/or leukemia inhibiting factor (LIF) (Bond, Bhalala, & Kessler, 2012; Chuang, Tung, & Lin, 2015; Jiwang Zhang & Li,

2005). Both BMPs and LIF restrict RGCs to an astrocytic fate by altering the STAT3 binding site within the GFAP promoter region; however, the signaling pathways activated by each TF differ (Bonaguidi et al., 2005; Jiwang Zhang & Li, 2005). In mice studies, BMPs drive astrogliogenesis by activating SMAD pathways to form a coactivating complex comprised of

STAT3, p300 and CBP. This complex then binds to STAT3 binding sites on the GFAP promoter

6 to drive transcription (Adachi, Takanaga, Kunimoto, & Asou, 2005; Bonaguidi et al., 2005). LIF drives astrogliogenesis by binding with gp130 and activating JAK/STAT pathways (Adachi et al., 2005; Bonaguidi et al., 2005). FGF8 ability to activate STATs within mouse glioma endothelial cells suggests that FGF8 could use this pathway to induce astrogliogenesis (Yang,

Qiao, Meyer, & Friedl, 2009).

Fibroblast growth factor 8 may also induce terminal astroglial differentiation from cortical precursor cells suggesting a role in astrocyte maturation, which may be facilitated by FGFR3, because cortical white matter astrocytes primarily express FGFR3 (Hajihosseini & Dickson,

1999; MacArthur et al., 1995; D. M. Ornitz et al., 1996; Pringle et al., 2003). These studies led us to hypothesize that Fgf8 signaling is required for proper astrocyte development and/or maturation.

1.3. FGF8 signaling in astrocyte-dependent corpus callosum formation

As previously described, Fgf8 mutant mice exhibit agenesis of the corpus callosum (ACC)

(Huffman, Garel, & Rubenstein, 2004; Storm et al., 2006; Tole, Gutin, Bhatnagar, Remedios, &

Hebert, 2006) but the exact role of FGF8 in CC formation is unknown. We know that FGF8 acts as a pro-survival factor, determines cell-fate, and plays a role in axon targeting. However, it is unknown which FGF8-specific action or actions caused ACC in Fgf8 hypomorphic mice.

Interestingly, FGF8 may play a multi-faceted role, as all the actions listed above occur in a very organized manner during normal CC development, which will be outlined in the following sections.

Corpus callosum development begins around E15.5, where key midline glial structures known as the indusium griseum (IG), midline zipper (MZ) and glial wedge (GW), act as sources

7 of chemoattractants and chemorepellents to guide pioneer pathfinding axons from cingulate cortical neurons towards the anterior-dorsal midline to form an interhemispheric guidance tract

(Koester & O'Leary, 1994; Nishikimi, Oishi, & Nakajima, 2013; Piper et al., 2009; Zhou et al.,

2013). Callosal axons then follow this tract and form the CC a process that is completed around

E17.5. Disruption of this process causes ACC. The anatomical hallmark of ACC is that the interhemispheric callosal axons fail to cross into the contralateral hemisphere, but the callosal axons project ventrally and posteriorly to form an abnormal anterior-dorsal ipsilateral midline structure called Probst bundle (Pb) (Ren, Zhang, Plachez, Mori, & Richards, 2007; Tovar-Moll et al., 2007). Corpus callosum agenesis may be caused by abnormal or failed glial cell development in any of the midline glial structures, which will disrupt the necessary guidance cue gradients used by pioneer axons and/or callosal axons. Indeed, studies in conditional Fgf8 null mice show that the lack of FGF8 eliminates glial fibrillary acidic protein (GFAP) expressing astrocytes in the IG and MZ, which in part could explain the abnormal CC formation in these hypomorphic mice (Moldrich et al., 2010; Nishikimi et al., 2013; Brian G. Rash & Linda J. Richards, 2001;

Tianzhi Shu, Puche, & Richards, 2003; Smith et al., 2006). These data suggest that FGF8 acts as a pro-survival factor for astrocytes. Therefore, we hypothesize that FGF8, in addition to acting as an astrocytic maturation factor, also provides trophic support during astrocyte development.

The incidence of ACC is potentially influenced by genetic sex as more than 50 congenital

X-linked syndromes, such as Aicardi and Opitz syndromes, exhibit ACC (Cox et al., 2000;

Elgamal et al., 2014; King, Bowen, Goulding, & Doran, 1998; Richards, Plachez, & Ren, 2004).

Moreover, ACC occurrence is much higher in female mice than in male mice. Overall CC volume is also sexually dimorphic with male rodents having more (Cerghet, Skoff, Swamydas, &

Bessert, 2009). Furthermore, castration studies or other hormonal manipulations altered the

8 volume of rodent CCs, strengthening the hypothesis that genetic sex may play a role in ACC. At the molecular level, the GFAP promoter in rats was shown to harbor estrogen response elements

(ERE) and progestin response elements (PRE) implying that GFAP transcription may be, in part, modulated by gonadal steroids (Gomes, Paulin, & Moura Neto, 1999). Based on the current data, it is unclear if there exists a developmental sex difference in midline astrocytes in Fgf8 hypomorphic mice which may exacerbate the observed ACC. Therefore, genetic sex will be included as an experimental variable. Interestingly, human studies have contradictory results. For example, postmortem studies have showed that overall human male CC area is larger, yet this finding is contradicted when human female CC area is found to be larger when compared to relative brain size. This controversy is ongoing as studies using advanced magnetic resonance imaging (MRI) fail to replicate the earlier postmortem studies or find significant sex differences at all. (Bishop & Wahlsten, 1997; Constant & Ruther, 1996; DeLacoste-Utamsing & Holloway,

1982; Figarsky, Sidtis, & Ardekani, 2012; Kim & Juraska, 1997; Mack, Boehm, Berrebi, &

Denenberg, 1995; Nunez & Juraska, 1998; Pilgrim & Reisert, 1992). Lastly, it is possible that there exists a species-dependent difference in CC size. For example, comparative studies investigating CC size within chimpanzees and capuchin monkeys showed that chimpanzees had larger overall size (Phillips, Kapfenberger, & Hopkins, 2009). Moreover, comparative studies investigating CC size within four species of monkey, showed that one species was dimorphic and the remaining three were not (Holloway & Heilbroner, 1992).

1.3.2. Clinical effects of ACC

Many disorders have underlying callosal malformations. For example, partial agenesis of the CC or overall reduced volume is observed in Turner syndrome (TS), Fetal alcohol syndrome (FAS),

Kallmann syndrome (KS), and premature births (Dode et al., 2003; Kimura et al., 1990; Peterson et al., 2000; Riley et al., 1995). Clinically, humans with ACC exhibit symptoms similar to those observed in autism spectrum disorder patients, such as, impaired language/speech development and processing, social skills, decision making, problem solving, and occasionally intelligence

(Brown et al., 2012; D’Antonio et al., 2016; Elgamal et al., 2014; Goodyear, Bannister, Russell,

& Rimmer, 2001; Leighton B. N. Hinkley et al., 2012; Leighton B.N. Hinkley et al., 2016).

Unlike most patients on the autism spectrum disorder, ACC patients will also exhibit impaired fine and gross motor control and coordination (D’Antonio et al., 2016; Elgamal et al., 2014;

Goodyear et al., 2001). Interestingly, KS has been linked to genetic mutations in both FGF8 and

FGFR1 genes (Dode et al., 2003; Hardelin & Dode, 2008). Furthermore, some KS patients exhibit ACC and suffer from involuntary mirrored-movements (Conrad et al., 1978; Krams et al.,

1999; Manara et al., 2014). Together, these data imply that FGF signaling dysfunction may be the underlying cause of ACC in KS.

1.4. FGF8 signaling in astrocyte function

One key astrocytic function is to become reactive or activate in times of CNS stress during chemical or physical trauma (Liddelow et al., 2017; Onoda, Takeda, & Umezawa, 2017; Pekny

& Nilsson, 2005). Astrocyte activation increases GFAP expression and morphological branching complexity. Given that FGF8 signaling may play a role in maintaining GFAP expression, it may also aid in increasing GFAP during astrocyte activation. In support, previous studies showed that

FGF2 induced astrocyte activation and to modify astrocyte morphology (Reilly, Maher, &

Kumari, 1998; Weibel et al., 1985; Wolburg, Neuhaus, Pettmann, Labourdette, &

Sensenbrenner, 1986). FGF2 likely signals through FGFR1, as previous studies showed FGF2

10 and FGFR1 co-localize in the nuclei of reactive astrocytes after injury (Clarke, Berry, Smith,

Kent, & Logan, 2001). FGF8 modulates FGFR1 and 3 expression levels in a dose-dependent manner (Kang, Lee, Han, Choi, & Song, 2014; Mott, Chung, Tsai, & Pak, 2010). In addition,

FGF8 plays a role in perinatal astrocyte maturation, acquisition of GFAP expression, and astrocyte morphology in vitro (Gobius et al., 2016; Kang et al., 2014; Stewart et al., 2016).

Therefore, these data indicate that FGF8 may regulate astrocyte activation.

1.4.2 Astrocyte activation in astrocytopathies.

Understanding how FGF8 may regulate astrocyte activation is of importance given that many neurodegenerative diseases exhibit aberrant astrocyte activation, such as Huntington’s Disease

(HD), Parkinson’s Disease (PD), Alzheimer’s Disease (AD), amyotrophic lateral sclerosis (ALS) and multiple sclerosis (MS) which are now considered, in part, “astrocytopathies” based on recent studies examining astrocyte-dependent pathological mechanisms (Chun & Lee, 2018;

Gray, 2014; Joe et al., 2018; Yamanaka & Komine, 2018). For example, reactive astrocytes play a role in the MS pathological environment by involving the immune system or directly targeting myelination mechanisms. Activated astrocytes secrete pro-inflammatory cytokines, such as IL-6 and TNFα to heighten the immune response, possibly leading to further tissue damage.

Conversely, astrocytes also secrete anti-inflammatory substances IL-10 and IL-27 slowing disease progression. These cellular responses are examples of the dual role of activated astrocytes, and although the underlying mechanisms are poorly understood.

1.5 Models of astrocyte activation within MS

Glial fibrillary acidic protein was discovered in 1969 by Dr. Laurence Eng within the fibrous astrocytes present in sclerotic plaques of human patients suffering from MS (Eng, 1985). It is this disease characteristic that makes MS a useful model for studying astrocyte activation since is reactive astrocytes are readily detected at demyelination sites. Currently, there are three widely used MS mouse models: Theiler’s Murine Encephalitis Virus-induced Demyelinating Disease

(TMEV-IDD), experimental autoimmune (EAE), and CPZ induced MS. All three models exhibit astrocyte activation and have similar demyelination patterns however the mechanisms by which this is achieved differ (Jin et al., 2009; D. P. McCarthy, Richards, & Miller, 2012; Tsunoda et al.,

2016).

TMEV-IDD models most closely mimic human MS because they are virally induced similar to recent MS pathological studies, which are linking the disease to Epstein-Barr viral infections.

The TMEV-IDD model works by intracranially exposing macrophages, microglia, and astrocytes to low virulent strains of the TMEV pathogen (Jin et al., 2009; D. P. McCarthy et al., 2012;

Tsunoda et al., 2016). This pathogen triggers demyelination via CD4+ Th1 T cell which express auto antigens to myelin proteins MOG and MBP (Jin et al., 2009). Eventually this leads to an autoimmune reaction characterized by a heightened inflammatory response, macrophage, microglial and astroglial activation, axon loss, and oligodendrocyte death after 1-2 months post- infection, and subsequent demyelination after 3 months post-infection however there is a strain- dependent difference in virus vulnerability. For example, the common C57BL/6 mouse strain is resistant to the TMEV virus entirely and fail to develop a demyelinating disease (Prajeeth et al.,

2014; Tsunoda et al., 2016).

The second, and most common MS model, is EAE, which can be induced actively or passively. Active EAE models begin with a peripheral immunization containing myelin antigens in a bacterial adjuvant, followed by a peripheral injection of pertussis toxin and when given together leads to CD4+ and CD8+ T cell activation within the CNS ultimately targeting myelin peptides, MOG, MBP, and PLP (Constantinescu, Farooqi, O'Brien, & Gran, 2011; Croxford,

Kurschus, & Waisman, 2011; Lassmann & Bradl, 2017; D. P. McCarthy et al., 2012). In contrast, passive EAE can be induced by transferring activated T cells from an EAE animal to a non-EAE animal bypassing the bacterially mediated induction phase. This reduces the peripheral immune response allowing for a better understanding of the role of activated T cells in MS pathology (D. P. McCarthy et al., 2012; Stromnes & Goverman, 2006). Disease onset in EAE occurs quicker than TMEV, around 9-12 days post-infection, and begins with a microglial and macrophage mediated response where myelin sheaths and oligodendrocytes producing myelin are damaged during a heightened immune response (D. P. McCarthy et al., 2012). Disease course varies with strain and induction antigen used. For example, the most common strain/antigen combination uses C57BL/6 mice and MOG immunization causing mice to exhibit chronic demyelination pattern; whereas a second combination using SJL/J mice and PLP immunization causes mice to exhibit a relapse-remitting demyelination pattern (deLuca et al., 2010; D. P.

McCarthy et al., 2012).

Both the TMEV-IDD and EAE models rely on an immune response to induce MS disease characteristics and to our knowledge the susceptibility or lack thereof to TMEV viruses or EAE models has not been examined in the 129-parent strain of our Fgf8 hypomorphic mouse.

Moreover, it is possible that our mice may exhibit exacerbated humoral immunity which may

13 make it difficult to determine if the Fgf8 deficit impaired astrocyte activation (Riley et al., 1995).

Therefore, we used the cuprizone (CPZ) model system.

Cuprizone is widely used to study demyelination and remyelination processes as it targets mature oligodendrocytes, all the while not involving the immune system leading to oligodendrocyte cell death and an MS phenotype in rodents. CPZ is also potent astrocytic activator targeting glial cell populations within CC. However, the mechanism this toxin uses to cause rapid gliosis, oligodendrocyte cell death, and demyelination, is largely unknown (Carlton,

1967; Viktoria Gudi, Gingele, Skripuletz, & Stangel, 2014; Hibbits, Pannu, Wu, & Armstrong,

2009; T. Skripuletz, Gudi, Hackstette, & Stangel, 2011; Tagge et al., 2016; Tezuka et al., 2013).

However, recent studies have shown that the oligodendrocyte apoptosis may be mediated through mitochondrial dysfunction ultimately affecting ATP synthesis within these cells (Praet,

Guglielmetti, Berneman, Van der Linden, & Ponsaerts, 2014; Taraboletti et al., 2017).

Although details of the molecular mechanism are sparse, the timing and spatial pattern of demyelination is predictable and readily reproducible. For example, CPZ will cause rapid astrogliosis and microgliosis prior to the well-known oligodendrocyte cell death which occurs after 3 weeks of CPZ exposure. Demyelination for most mouse strains begins at 3 weeks and is complete by 5-6 weeks. At this point, if CPZ is removed from the diet remyelination will begin and recovery is complete within 6 weeks. However, if CPZ exposure extends to 12 weeks, GFAP accumulation is extensive and remyelination fails to occur (V. Gudi et al., 2009; Viktoria Gudi et al., 2014; Hibbits et al., 2009; Hibbits, Yoshino, Le, & Armstrong, 2012; Tagge et al., 2016).

Much of the research gathered from this model system aims to understand why after chronic exposure oligodendrocyte precursor cells (OPCs) fail to mature and remyelinate the

14 demyelinated lesions (Carlton, 1967; V. Gudi et al., 2009; Viktoria Gudi et al., 2014; Hibbits et al., 2009; Hibbits et al., 2012; T. Skripuletz et al., 2011).

Given that FGF8 is needed for anterior-midline astrocyte development and may play a role in astrocyte activation, Fgf8 hypomorphic astrocytes may be impaired in their ability to assist oligodendrocytes with myelination. In support, previous studies have shown that oligodendrocytes fail to myelinate in astrocyte-free environments in vivo and in vitro (Mohan et al., 2014; Nash, Ioannidou, & Barnett, 2011; Talbott et al., 2005). Interestingly, FGF/FGFR signaling also plays a role in this process further supporting the idea that Fgf8 hypomorphic mice would be more vulnerable to a CPZ-challenge (Cohen & Chandross, 2000; Furusho, Dupree,

Nave, & Bansal, 2012; Pukos, Yoseph, & M. McTigue, 2018).

1.5.2. Detection of myelin disruption

Although the mechanisms underlying each model vary, they all disrupt myelin production and lead to demyelination. Detecting myelin disruption is valuable when assessing vulnerability toward a particular model system, however, most available and accepted assays used to assess myelin disruption are invasive histological techniques including lipid soluble dyes which actually detect the phospholipids or lipoproteins within myelin such as luxol fast blue (LFB) and

Sudan black staining techniques, gold-based salts such as Black-Gold and Black-Gold II.

LFB dye component reacts with the basic groups on myelin lipoproteins staining healthy myelin blue and demyelinated axons then appear white (Eng, 1985). Sudan black dyes, including Sudan

1-4 and Oil O Red, react with the acidic groups on myelin lipoproteins and stain myelin black to reddish in color, respectively (D. P. McCarthy et al., 2012; Tsunoda et al., 2016). Black-Gold stains react with myelin specific proteins and protein-lipid complexes, staining myelin sheath

15 bundles dark red and individual myelinated axons black (Hardelin & Dode, 2008).

Immunohistochemistry against myelin proteins including MOG, MBP, and PLP protein are also commonly used. However, these assays are limited because the only allow for a snapshot view of the demyelination process, an inherent dynamic process.

A possible tool available to evaluate CPZ effects on Fgf8 hypomorphic mice, long term, is magnetic resonance imaging (MRI), which can follow white matter development and abnormalities by using radio frequency (RF) pulses to disturb or excite protons within the water and lipids of myelin. The protons then emit signals as they begin to reach equilibrium or “relax” which are processed into images by using magnetic field gradients (Auer, Vagionitis, & Czopka,

2018; Welker & Patton, 2012; Jiangyang Zhang, 2010). Depending on water vs. lipid content in the myelin the RF pulse and magnetic field gradient used is adjusted to provide optimal contrast and resolution in the final image which depicts myelination as an intense white color.

By using MRI, we would be able to temporally follow how CPZ is affecting the Fgf8 hypomorphic mice myelin. If Fgf8 hypomorphic astrocytes are indeed more vulnerable to CPZ, it is possible that, in addition to altered astrocyte function, the rate of demyelination will be altered in these mice. Specifically, we expect that the rate and/or extent of demyelination will be worsened and will be reflected by a decrease in CC volume within Fgf8 hypomorphic mice when compared to their WT littermates.

1.6. Specific aims of dissertation

Based on the aforementioned data, we conclude that FGF8 signaling is crucial for astrocyte development and function. Therefore, the following aims will test the hypothesis that FGF8 acts

16 as pro-astrogliogenic factor during perinatal astrocyte development, and consequently determine adult astrocyte function.

1.5.1 Aim 1

Determine the role of FGF8 during perinatal mouse anterior-midline glia cell maturation, branching and survival.

Currently, it is thought that FGF8-signaling contributes to midline development by regulating cell death. For instance, reduced FGF8 function caused developing neural cells to undergo premature apoptosis (Kawauchi et al., 2005; Stevenson, Corella, & Chung, 2013; Storm et al.,

2003; Tsai et al., 2011). Furthermore, similar midline defects have been observed in Fgfr1 mutant mice suggesting FGF8/FGFR1 signaling is crucial for proper midline development

(Smith et al., 2006). This led us to question whether FGF8 signaling regulates GFAP astrocyte cell death. Alternatively, FGF8 may regulate midline GFAP astrocyte development. FGF8 was identified as a critical maturating factor during MZ astroglial differentiation and likely regulates perinatal midline astroglial GFAP expression by acting upstream of NFIA ultimately inducing the gliogenic switch. This led us to question whether FGF8 signaling regulated perinatal and adult astrocyte development. These data support the premise that FGF8 is required for proper developmental patterning of the anterior-posterior axis and brain midline (T. Okada, Y.

Okumura, J. Motoyama, & M. Ogawa, 2008). Therefore, we hypothesized that FGF8 function is critical for the development and survival of the GFAP midline astrocytes and is sex- dependent.

1.5.2. Aim 2

Determine whether reduced Fgf8 signaling disrupts adult astrocyte function and responsiveness to cuprizone induced neurotoxic damage

Astrocyte activation is a process characterized by an upregulation of GFAP and an increase in branching complexity following brain injury or stress. Previous studies showed that FGF2 is needed to induce astrocyte activation and that both FGF2 and FGFR1 co-localize in the nuclei of reactive astrocytes after injury (Clarke et al., 2001). Moreover, FGF2 co-localized with reactive

GFAP astrocytes following spinal cord injury in mice (Fahmy & MOFTAH, 2010). These studies showed that FGF signaling plays a role in GFAP astrocyte activation and led us to ask if

FGF8 signaling plays a role in astrocyte activation. In support, FGF8 and FGF2 have been shown to modify morphology and maturation of astrocytes by rearranging intermediate filaments and extending processes (Wolburg, 1986). Interestingly, FGF8 has also been shown to modify astrocyte morphology via FGFR3 and can modulate the FGF receptor levels (Clarke, 2001;

Kang, 2014). Therefore, we hypothesized that FGF8 is needed for proper astrocyte activation.

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CHAPTER 2

Perinatal Midline Astrocyte Development is Impaired in Fibroblast Growth Factor 8 Hypomorphic Mice This work has been published previously as “ Perinatal midline astrocyte development is impaired in fibroblast growth factor 8 hypomorphic mice.” Stewart et. al, Brain Research, 2016, 1646, 287-296.

2.1. Introduction

During the course of our investigations using Fgf8 hypomorphic mice for elucidating the role of fibroblast growth factor (FGF) 8 function during pathogenesis of Kallmann syndrome (KS), a form of congenital hypogonadotropic hypogonadism that results in the absence of pubertal onset and infertility (Brooks et al., 2010; Chung et al., 2008; Chung & Tsai, 2010; Falardeau et al.,

2008; Stevenson et al., 2013; Tata et al., 2012; Tsai et al., 2011), we confirmed other studies that the corpus callosum commissure depends on FGF8 function (Huffman et al., 2004; Meyers et al.,

1998; Moldrich et al., 2010; Storm et al., 2003). Similarly, we found that the development of the vomeronasal nerve fiber tract, which connects olfactory sensory neurons to the olfactory bulb, was severely compromised. Specifically, the peripherin-immunoreactive (IR) vomeronasal nerves failed to cross from the nasal compartment into the neural compartment (Chung et al.,

2008). Together these earlier studies indicate the importance of FGF8 function during the formation of major axonal pathways and commissures in the mouse brain.

In the current studies, we focused on the perinatal development of the corpus callosum, which is disrupted in KS patients harboring mutations in Fgf genes (Dode et al., 2003). This

30 anatomical defect may explain the clinical presentation of involuntary mirror movements described for KS patients. The results from these clinical studies and basic animal research point to the vulnerability of the corpus callosum during its formation to deficits in FGF signaling

(Fenlon & Richards, 2015; Huffman et al., 2004; Smith et al., 2006; Storm et al., 2003; Tole et al., 2006). The sequence of events during corpus callosum formation is well- described. Initially, pioneer pathfinding axons from cingulate cortical neurons extend towards the anterior-dorsal midline to form an interhemispheric guidance tract around embryonic day (E) 15.5 (Koester &

O'Leary, 1994; Nishikimi et al., 2013; Piper et al., 2009; Zhou et al., 2013). The high Netrin-1 levels found in the anterior-dorsal telencephalon midline regions, such as the septum, are thought to attract extending Disrupted in colon cancer (DCC) callosal pioneering axons towards the brain midline (Fothergill et al., 2014).

These attractive guidance cues act in concert with repulsive cues, such as semaphorins and

Slit2, originating from the three anterior-dorsal telencephalon midline guidepost glial cell populations: glial wedge (GW), indusium griseum (IG), and midline zipper (MZ) (Piper et al.,

2009). Anatomically, these three midline glial cell populations form the impermissible outer- borders of a Netrin-1 expressing permissive conduit in the anterior-dorsal telencephalon that bridges the hemispheres, thereby enabling interhemispheric crossing of callosal pioneer axons

(Bagri et al., 2002; B. G. Rash & L. J. Richards, 2001; Smith et al., 2006). The central anatomical hallmark of agenesis of the corpus callosum (ACC) is that the interhemispheric callosal axons fail to cross into the contralateral hemisphere, a process that is completed around

E17.5. Instead, the callosal axons project ventrally and posteriorly to form an abnormal anterior- dorsal ipsilateral midline structure called Probst bundle (Pb) (Ren et al., 2007; Tovar-Moll et al.,

2007).

The importance of FGF signaling during corpus callosum formation is further reiterated in mice with defects in Fgf8 or Fgf receptor (Fgfr) 1. Indeed, ACC has been shown to occur in mice harboring defects in FGF8 or FGFR1 function (Huffman et al., 2004; Moldrich et al., 2010;

Smith et al., 2006), which is not surprising given that Fgf8 and Fgfr1 mRNA levels are highly expressed within the anterior-dorsal midline around E14.5 - E16.5 (i.e., during corpus callosum formation) (Moldrich et al., 2010; Smith et al., 2006). Moreover, evidence from many studies showed FGF function controls the normal development of key midline glial structures, such as the IG and MZ (Moldrich et al., 2010; Nishikimi et al., 2013; B. G. Rash & L. J. Richards, 2001;

T. Shu, Butz, Plachez, Gronostajski, & Richards, 2003; Smith et al., 2006). Specifically, studies in conditional Fgf8 null mice indicate that the lack of midline FGF8 function virtually eliminates all glial fibrillary acidic protein (GFAP)-IR glial cells in the anterior-dorsal midline IG and MZ

(Moldrich et al., 2010). These results indicate that FGF8 function regulates the formation of the corpus callosum by providing trophic support, which in turn prevents the abnormal elimination of IG and MZ GFAP-IR glial cells.

The presumed elimination of anterior-dorsal midline GFAP-IR glial cells in conditional

Fgf8 null mice supports the general assumption that FGF8 function is critical for the survival of the GFAP-IR midline glial cells. The role of FGF8 as a pro-survival trophic factor is well- documented during embryonic brain development (Kawauchi et al., 2005; Storm et al., 2003;

Tsai et al., 2011). However, results from our laboratory indicate that FGF8 function may not be limited to the role of a pro-survival trophic factor, but instead may also be important for the timing of cellular maturation. For instance, when compared to newborn wildtype (WT) mice, the

/number of vasopressin-secreting neurons in the paraventricular nucleus (PVN) of the hypothalamus is about 40 - 50% reduced in heterozygous (+/neo) and homozygous (neo/neo) Fgf8

32 hypomorphic littermates, a genotype-dependent difference that was absent in adulthood

(McCabe et al., 2011; Rodriguez et al., 2015). These results suggest that the deficiency in Fgf8 expression did not eliminate the vasopressin-secreting PVN neurons, but rather delayed the onset of vasopressin expression. These alternative FGF8 functions led us to ask whether the deficit in

Fgf8 expression found in Fgf8 hypomorphic mice similarly affected the maturation of GFAP-IR glial cells. Together, our data presented in this paper, indicate that the primary function of FGF8 signaling during perinatal development may not be limited to promoting anterior-dorsal midline

GFAP-IR glial cell survival, but may also contribute to the onset of GFAP expression.

2.2. Materials and Methods

2.2.1. Animals

Adult 129P2/OlaHsd*CD-1 male Fgf8 hypomorphic heterozygous (+/neo) x female Fgf8+/neo mice were timed-bred in the late afternoon in our animal facility (12L:12D cycle) with access to food and water ad libitum. All procedures were approved by the Institutional Animal Care and Use

Committee at Kent State University. Fgf8 hypomorphic mice exhibit a ~25% and ~55% reduction in Fgf8 mRNA expression in Fgf8+/neo and Fgf8neo/neo mice, respectively (Meyers et al.,

1998). In the morning, females with a sperm plug were denoted as embryonic day (E) 0.5.

2.2.2. Brain Tissue

E16.5 and E17.5 WT, Fgf8+/neo and Fgf8neo/neo fetal brain tissue was obtained by euthanizing timed-bred pregnant females via cervical dislocation on E16.5 or E17.5. Fetal pups were then removed from the uterine horn and euthanized. PN0 WT, Fgf8+/neo and Fgf8neo/neo brain tissue was obtained by euthanizing newborn pups within hours after birth. Adult WT and Fgf8+/neo

33 brain tissue was obtained from 2-4 month old male mice. The brains were immersion-fixed in

4% paraformaldehyde (PFA) or 5% acrolein/0.1 M phosphate buffer, stored in 30% sucrose, and genotyped using PCR for Fgf8, neomycin and SRY. Serial coronal WT, Fgf8+/neo, and Fgf8neo/neo sections (25 µm for perinatal PFA-fixed, 45 µm for perinatal acrolein-fixed, and 40 µm for adult

PFA-fixed) were obtained using a cryostat (Leica CM 1950, Buffalo Grove, IL), and thaw- mounted on slides coated with gelatin (Sigma-Aldrich, St. Louis, MO).

2.2.3. Thionine Apoptosis Detection

Two serial coronal sections from E16.5, and E17.5 WT, Fgf8+/neo and Fgf8neo/neo hypomorphic mice were washed in distilled H2O, 2 x 2 min, incubated in thionine solution for 2 min, washed in distilled H2O, 2 x 2 min, then washed in 70% ethanol/glacial acetic acid, 1 x 1.5 min, dehydrated with ethanol, cleared with xylene and coverslipped with DPX (Merck, Billerica,

MA).

2.2.4. Immunohistochemistry (IHC)

Sections from WT, Fgf8+/neo and Fgf8neo/neo hypomorphic mice were processed simultaneously to minimize the variability in immunoreactivity. Serial coronal sections were incubated in 1% hydrogen peroxide/TBS solution for 15 min at room temperature, washed in TBS, 3 x 5 min, and incubated in primary rabbit polyclonal anti-GAP43 (1:1000) (Abcam, Cambridge, England), rabbit polyclonal anti-GLAST-1 (1:6000) (Abcam, Cambridge, England), rabbit polyclonal anti-

GFAP (1:3500 prenatal, 1:8000 adult) (Thermo Scientific, Waltham, MA), rabbit polyclonal anti-S100b (1:500 prenatal, 1:20000 adult) (Abcam, Cambridge, England) or goat polyclonal anti-Neuropilin-1 (NP-1) (1:500) (R&D Systems, Minneapolis, MN) made in TBS/0.3% Triton-

X (Fisher Scientific, Pittsburgh, PA) and 2% normal goat serum for 2 days at 4C. Sections were washed and incubated with biotinylated-goat anti-rabbit (1:600) or biotinylated-rabbit anti-goat

(1:500) for 2 hrs at room temperature followed by ABC (1:800) (Vector Laboratories,

Burlingame, CA) in TBS for 2 hrs at room temperature, and reacted with 0.05% diaminobenzidine (i.e., GAP43, GLAST-1 and GFAP) or 0.05% diaminobenzidine/0.1% nickel ammonium sulfate (i.e., S100b and NP-1) (Sigma-Aldrich, St. Louis, MO) and 0.01% H2O2

/TBS for 20 min. Sections were dehydrated with ethanol, cleared with xylene and coverslipped with DPX (Merck, Billerica, MA). These standardized conditions were used for each of the primary antibodies, which minimized the variability between immunohistochemical stainings.

2.2.5. Image Analysis

For our quantification studies, we defined the anterior-dorsal midline as the anterior-posterior region that is immediately rostral and caudal of the coronal section where the corpus callosum axons first begin to cross into the contralateral hemisphere. Furthermore, anterior-dorsal midline was dorsally and ventrally bordered by the IG and MZ, respectively (Fig. 1A).

2.2.6. Prenatal Apoptosis Quantification

The incidence of apoptotic cells in anterior-dorsal midline was estimated from 2 representative and atlas-matched thionin-stained sections across prenatal WT, Fgf8+/neo and Fgf8neo/neo hypomorphic mice and expressed as the number of apoptotic cells per µm2 (i.e., atlas plate 21 to plate 22). Apoptotic cell nuclei were identified as very intense thionin-stained, condensed, and often had fragmented nuclear material using a 40X objective (Fig. 3 inset). This method of detecting apoptotic cells has been used previously, and shown to yield the same results as

35 alternative methods, such as TUNEL or activated caspase-3 immunoreactivity (Ahern et al.,

2013; Chung, Swaab, & De Vries, 2000; Park, Tobet, & Baum, 1998). Apoptotic cells were counted manually within a fixed region of interest rectangle (1449.1 µm2) by an observer using a

40X objective, who had no knowledge of the genotypes of the fetal mice.

2.2.7. Adult S100b

Three serial coronal sections representative of the anterior-dorsal midline (i.e., plate 21 to plate

23) were atlas-matched across adult WT and Fgf8+/neo hypomorphic mice using anatomical landmarks. The distance between each immunostained section was 80 μm. Grayscale digital images were captured using a 4X objective mounted on an Olympus microscope fitted with a color camera (SC30, Olympus, Corporation of the Americas, Center Valley, PA) connected to a

PC computer. Anterior-dorsal midline S100b-IR cells were analyzed using Olympus CellSens software (Olympus Corporation of the Americas, Center Valley, PA). The number of S100b-IR cells per µm2 in the anterior-dorsal midline was counted manually within a fixed region of interest rectangle (401,136 µm2) by an observer who had no knowledge of the genotypes of the adult mice.

2.2.8. Prenatal GFAP

Three serial coronal sections through the anterior-dorsal midline (i.e., plate 21 to plate 23) were atlas-matched across prenatal WT, Fgf8+/neo and Fgf8neo/neo hypomorphic mice using anatomical landmarks. The distance between each immunostained section was 50 μm. Grayscale digital images were captured using a 10X objective mounted on an Olympus microscope fitted with a color camera (SC30, Olympus, Corporation of the Americas, Center Valley, PA) connected to a

PC computer. GFAP-IR midline glial cells were analyzed using Olympus CellSens software

(Olympus Corporation of the Americas, Center Valley, PA). A threshold mask was generated which reliably and accurately covered the GFAP immunoreactivity in prenatal WT mice.

Anterior-dorsal midline GFAP density was measured as immunoreactivity covered by pixels in a fixed region of interest rectangle (86,100.6 μm2).

2.2.9. Adult GFAP

Three serial coronal sections representative of the anterior-dorsal midline (i.e., plate 21 to plate

PC computer. GFAP-IR midline glial cells were analyzed using Olympus CellSens software

(Olympus Corporation of the Americas, Center Valley, PA). A threshold mask was generated which reliably and accurately covered the GFAP immunoreactivity in adult WT mice. Anterior- dorsal midline GFAP density was measured as immunoreactivity covered by pixels in a fixed rectangle (419,955.2 μm2).

2.2.10. Real-time quantitative PCR (RT-qPCR)

Total RNA was isolated using TriPure reagent (Roche, Basel, Switzerland) from micro-punched

(0.75 mm diameter) brain midline tissue (i.e., atlas plate 22) in WT, Fgf8+/neo and Fgf8neo/neo hypomorphic mice. Total RNA (100 ng) was used to synthesize cDNA using the First Strand cDNA Synthesis Kit (New England Biosystems, Ipswich, MA) according to manufacturer’s

37 instructions. RT-qPCR was performed in triplicate using a Mastercycler EP Realplex2

(Eppendorf, Hauppauge, NY) with SYBR Green PCR Master Mix gene expression assay

(Roche, Basel, Switzerland). Relative Gfap and S100b mRNA expression was calculated using the ΔΔ-2CT method. Hypoxanthine phosphoribosyltransferase 1 (Hprt-1) was used as a housekeeping gene. Primer pairs used are listed in table 1.

2.2.11. Sholl and Process Length Analysis

Grayscale digital images were captured using a 20X objective mounted on an Olympus microscope fitted with a color camera (SC30, Olympus, Corporation of the Americas, Center

Valley, PA) connected to a PC computer. Randomly selected GFAP-IR midline glial cells were converted to 16-bit format and traced using the Fiji Sholl Analysis plugin for ImageJ (NIH,

Bethesda, MD) as shown in previous studies (Kellner et al., 2014; Meijering et al., 2004; Sholl,

1953). Following, the cells were overlaid with concentric circles spaced at 0.5 µm intervals in order to measure the branching complexity of the GFAP-IR glial cells. These trace data were expressed as the number of intercrossing/distance from soma. Randomly selected GFAP-IR midline glial cells were converted to 8-bit format and skeletonized. The process length was manually traced using the NeuronJ Analysis plugin for ImageJ (NIH, Bethesda, MD), and expressed as length in pixels.

2.2.12. Statistical Analysis

38 randomized and coded by an independent investigator. All measurements were conducted by an observer without knowledge of sex and genotype. The number of animals analyzed for each study is indicated in the figure legends.

2.3 Results

2.3.1. Fgf8 hypomorphy causes agenesis of the corpus callosum

Immunohistochemistry (IHC) for the membrane receptor, GAP43, in postnatal day (PN) 0 mice showed that callosal axons failed to cross the midline (i.e., ACC) in Fgf8neo/neo hypomorphic mice. In contrast, the corpus callosum developed normally in WT and Fgf8+/neo hypomorphic littermates (Fig. 1A, B). In addition, it was clear that the callosal axons in Fgf8neo/neo mice were extending caudally and forming the characteristic ipsilateral Pb tracts (Fenlon & Richards, 2015;

T. Shu & Richards, 2001), instead of following their normal coronal interhemispheric path (Fig.

2.1 C).

Figure 2.1. Photomicrographs of GAP43 immunoreactivity in anterior-dorsal midline region in

PN0 WT (n = 5), Fgf8+/neo (n = 5) and Fgf8neo/neo (n = 5) hypomorphic mice (A-C). Note:

Fgf8neo/neo mice lack of interhemispheric connectivity, and the presence of Probst bundles (Pb).

Region of interest (ROI) for all figures hereafter is denoted by the dashed rectangle and contains the IG = indusium griseum, CC = corpus callosum, and MZ = midline zipper. Asterisks indicate the lateral ventricles. Scale bar is 500 µm.

2.3.2. Fgf8 hypomorphy did not affect NP-1 immunoreactivity in embryonic or newborn mice

We used NP-1 IHC to visualize the pioneer callosal axons in E16.5 WT, Fgf8+/neo and Fgf8neo/neo hypomorphic mice (Fig. 2.2. A-C). These studies showed that NP-1-IR callosal axons are already exhibiting the ability to cross from the ipsilateral to the contralateral hemisphere in WT and Fgf8+/neo hypomorphic mice. In Fgf8neo/neo mice, NP-1-IR pioneer callosal axons extended towards the midline but did not cross into the contralateral hemisphere (Fig. 2.2. C). Moreover, our studies in PN0 Fgf8 hypomorphic mice showed that NP-1-IR callosal axons were concentrated in the dorsal aspect of the corpus callosum of WT and Fgf8+/neo hypomorphic mice

(Fig. 2.2. D-E). Remarkably, a similar dorsal localization of NP-1 immunoreactivity was also detected in PN0 Fgf8neo/neo hypomorphic mice; however, unlike in PN0 WT and Fgf8+/neo littermates, the NP-1-IR callosal axons did not cross the anterior-dorsal midline (Fig. 2.2. F).

Figure 2.2. Photomicrographs of NP-1 immunoreactivity in anterior-dorsal midline in E16.5 WT

(n = 4), Fgf8+/neo (n = 4) and Fgf8neo/neo (n = 4) hypomorphic mice (A-C) and PN0 WT (n = 4),

Fgf8+/neo (n = 4) and Fgf8neo/neo (n = 4) hypomorphic mice (D-F). Asterisks indicate the lateral ventricles. Arrow indicates NP-1 expressing axonal fibers. Scale bar is 500 µm.

2.3 Fgf8 hypomorphy did not affect anterior-dorsal midline apoptosis in embryonic mice

Previous studies showed that reduced FGF8 function causes developing neural cells to undergo premature apoptosis (Kawauchi et al., 2005; Stevenson et al., 2013; Storm et al., 2003; Tsai et al., 2011). Therefore, we measured the incidence of apoptosis in the anterior-dorsal midline region. The term “incidence of apoptosis” was used to indicate the total number of apoptotic cells per µm2. Apoptotic cell nuclei were identified as very intense thionin-stained, condensed, and often had fragmented nuclear material at 40 x magnification (Fig. 2.3. inset). The number of apoptotic cell nuclei in anterior-dorsal midline was estimated from 2 representative and atlas- matched sections. The incidence of apoptosis was approximately 2-fold higher in E17.5 than in

E16.5 hypomorphic mice. However, one-way ANOVAs showed that the incidence of apoptosis did not differ between genotypes on E16.5 (F = 0.29, p = 0.75) (Fig. 2.3. A) or E17.5 (F = 2.2, p

= 0.14) (Fig. 2.3. B). Raw apoptotic cell counts were the following: E16.5 WT (26.7 ± 8.41

SEM), Fgf8+/neo (22.5 ± 1.50 SEM), and Fgf8neo/neo (26.7 ± 2.73 SEM). E17.5 WT (57.3 ± 4.03

SEM), Fgf8+/neo (61.6 ± 17.3 SEM), and Fgf8neo/neo (44.3 ± 12.0 SEM).

Figure 2.3. Bar graphs of the incidence of apoptosis within the anterior-dorsal midline region in

E16.5 WT (n = 3), Fgf8+/neo (n = 4) and Fgf8neo/neo (n = 3) hypomorphic mice (A) and E17.5 WT

(n = 7), Fgf8+/neo (n = 7) and Fgf8neo/neo (n = 4) hypomorphic mice (B). The incidence of apoptosis did not differ between the genotypes on E16.5 or E17.5. Photomicrograph of apoptotic cells (arrows) with intense thionine staining, condensed, and fragmented nuclear material (inset).

Scale bar is 20 µm.

2.3.4. Fgf8 hypomorphy did not affect early glial cell markers S100b or GLAST-1 immunoreactivity in the anterior-dorsal midline

We used the glial precursor marker, S100b (a calcium-binding protein), to examine whether Fgf8 hypomorphy affected the presence of immature and mature glial cells. Our results showed that

S100b immunoreactivity in the anterior-dorsal midline region of E17.5 (Fig. 2.4. A-C) and PN0

(Fig. 2.4. D-F), mice was very low in contrast to adult mice (Fig. 2.4. I, J). However, it was clear that Fgf8 hypomorphy did not markedly disrupt the level of S100b immunoreactivity in the anterior-dorsal midline in E17.5, PN0 or adult hypomorphic mice. Our RT-qPCR assays confirmed these immunohistochemical results. One-way ANOVA showed that S100b mRNA expression did not differ between genotypes at PN0 (F = 0.07, p = 0.93) (Fig. 4G). In addition, we used a radial glial marker, GLAST-1, to examine whether Fgf8 hypomorphy affected the presence of radial glial cells. Similar to S100b, we report no marked genotype-dependent differences in GLAST-1 immunoreactivity in the anterior-dorsal midline of PN0 Fgf8 hypomorphic mice (Supplementary Fig. 1).

Figure 2.4. Photomicrographs of S100b-IR anterior-dorsal midline glial cells in E17.5 WT (n =

4), Fgf8+/neo (n = 4) and Fgf8neo/neo (n = 4) hypomorphic mice (A-C) and PN0 WT (n = 4),

Fgf8+/neo (n = 4) and Fgf8neo/neo (n = 4) hypomorphic mice (D-F). IG (arrow) and MZ (arrowhead)

47 are indicated. Asterisks indicate the lateral ventricles. Box indicates midline region examined.

Scale bar is 200 µm. Bar graph showing that the relative S100b mRNA expression within the dorsal-ventral axis of the anterior-dorsal midline region did not differ between PN0 WT (n = 4),

Fgf8+/neo (n = 4) and Fgf8neo/neo (n = 4) hypomorphic mice (G). Bar graph showing that the number of S100b-IR glial cells did not differ in the anterior-dorsal midline region between adult

WT and Fgf8+/neo hypomorphic mice (H). Photomicrographs of S100b-IR anterior-dorsal midline glial cells in adult WT (n = 4) and Fgf8+/neo (n = 4) hypomorphic mice (I, J). Scale bar is 20 µm.

Photomicrographs of S100b immunoreactivity in E17.5 WT, Fgf8+/neo and Fgf8neo/neo hypomorphic mice anterior-dorsal midline region including IG and cortical subventricular zone

(SVZ) (insets). Arrows indicate S100b immunoreactivity. Scale bar is 20 µm.

2.3.5. Fgf8 hypomorphy reduced anterior-dorsal midline GFAP expression in embryonic and newborn mice

The inability of callosal fibers to cross may be due to defects in FGF8-dependent development of

IG and MZ glial cell populations. Earlier studies showed these anterior-dorsal midline glial cell populations are important for midline fusion, and interhemispheric callosal axon guidance

(Fenlon & Richards, 2015; Richards et al., 2004; T. Shu & Richards, 2001; Smith et al., 2006).

We limited our studies to the IG and MZ glial cell populations using the mature astrocyte IHC marker GFAP in E14.5, E17.5 and PN0 Fgf8 hypomorphic mice.

2.3.6 GFAP immunoreactivity GFAP immunoreactivity was not detected at E14.5 in the IG or MZ in WT, Fgf8+/neo, or

Fgf8neo/neo mice. Visually, GFAP immunoreactivity levels in the IG and MZ of WT mice were much higher than in the Fgf8+/neo or Fgf8neo/neo hypomorphic mice on E17.5. Image analysis confirmed this conclusion. One-way ANOVA showed that the combined anterior-dorsal midline

GFAP immunoreactivity level was genotype-dependent (F = 8.9, p < 0.05) (Fig. 2.5. A-C). Post- hoc analysis showed that E17.5 GFAP immunoreactivity was significantly higher in WT than in

Fgf8+/neo (p < 0.01) or Fgf8neo/neo mice (p < 0.005). GFAP immunoreactivity did not differ between Fgf8+/neo and Fgf8neo/neo mice (p = 0.28) (Fig. 2.5. G). Subsequent analyses of PN0 Fgf8 hypomorphic mice found that GFAP immunoreactivity remained genotype-dependent (one-way

ANOVA, F = 5.3, p < 0.05). Post-hoc analysis showed that PN0 GFAP immunoreactivity was significantly higher in WT than in Fgf8neo/neo (p < 0.05). GFAP immunoreactivity did not differ between WT and Fgf8+/neo (p = 0.28) or Fgf8+/neo and Fgf8neo/neo mice (p = 0.09) (Fig. 2.5. H).

2.3.7. Gfap RT-qPCR

One-way ANOVA showed a significant genotype effect (F = 7.12, p < 0.05) in midline Gfap mRNA expression at PN0. Post-hoc analysis showed that relative Gfap mRNA expression was approximately 5-fold lower in Fgf8neo/neo than WT mice (p < 0.05). In contrast, Gfap mRNA expression did not differ between WT and Fgf8+/neo (p = 0.16) or Fgf8+/neo and Fgf8neo/neo mice (p

= 0.05) (Fig. 2.5. I).

Figure 2.5. Photomicrographs of GFAP-IR anterior-dorsal midline glial cells in E17.5 WT (n =

6), Fgf8+/neo (n = 7) and Fgf8neo/neo (n = 7) (A-C) and PN0 WT (n = 6), Fgf8+/neo (n = 4) and

Fgf8neo/neo (n = 5) Fgf8 hypomorphic mice (D-F). IG (arrow) and MZ (arrowhead) are indicated.

Asterisks indicate the lateral ventricles. Box indicates midline region examined. Scale bar is 200

µm. Bar graph showing the anterior-dorsal midline GFAP density in E17.5 hypomorphic mice

(G). Differences in letters denote significance (p < 0.05) between genotypes. Bar graph showing the anterior-dorsal midline GFAP density in PN0 hypomorphic mice (H). Asterisk denotes

51 significance (p < 0.05) between genotypes. Note: Although not significant (p = 0.09) there is an apparent trend in GFAP density between Fgf8+/neo and Fgf8neo/neo hypomorphic mice. Bar graph showing the relative Gfap mRNA expression within the dorsal-ventral axis of the anterior-dorsal midline region of WT (n = 4), Fgf8+/neo (n = 4) and Fgf8neo/neo (n = 4) PN0 hypomorphic mice (I).

Differences in letters denote significance (p < 0.05) between genotypes. Note: Although not significant (p = 0.05) there is an apparent trend in relative Gfap expression between Fgf8+/neo and

Fgf8neo/neo hypomorphic mice.

2.3.8. Adult anterior-dorsal midline GFAP immunoreactivity did not differ between WT and Fgf8+/neo hypomorphic mice To study whether the effects of Fgf8 hypomorphy of GFAP immunoreactivity were permanent or transient, we analyzed GFAP immunoreactivity in adult WT and Fgf8+/neo hypomorphic mice

(Fig. 2.6. A, B). Student t-test showed no significant difference between adult WT and Fgf8+/neo hypomorphic mice (Df = 8.0, p = 0.72) (Fig. 6C). No adult comparisons with Fgf8neo/neo hypomorphic mice were made, because these mice die within 24 hours after birth.

2.3.9. Fgf8 hypomorphy did not affect adult anterior-dorsal midline glial cell branching complexity or process length

Previous in vitro studies showed that FGF8 increased the branching complexity of astrocytes

(Kang et al., 2014). Therefore, we analyzed whether Fgf8 hypomorphy reduced the branching complexity of anterior-dorsal midline GFAP-IR glial cells. Our results showed that branching complexity of GFAP-IR glial cells did not differ between adult WT and Fgf8+/neo hypomorphic mice. Two-way ANOVA showed that GFAP glial cell branching complexity changed with distance (F = 9.9, p < 0.001). However, there was no main genotype effect on GFAP-IR glial cell branching (F = 0.05, p = 0.83) nor was there an interaction between distance and genotype (F =

0.26, p = 1.0) (Fig. 2.6. D). We also analyzed whether Fgf8 hypomorphy reduced the overall process length of anterior-dorsal midline GFAP-IR glial cells and found that total process length of anterior-dorsal midline GFAP-IR glial cells did not differ between adult WT (8,548.52 pixels) and Fgf8+/neo (8,203.78 pixels) hypomorphic mice.

Figure 2.6. Photomicrographs of GFAP-IR anterior-dorsal midline glial cells in adult WT (n =

4) and Fgf8+/neo (n = 4) hypomorphic mice (A, B). IG (arrow) and MZ (arrowhead) are indicated.

Asterisks indicate the lateral ventricles. Box indicates midline region examined. Scale bar is 200

µm. Bar graph showing that the anterior-dorsal midline GFAP-IR glial cell density in adult mice did not differ between WT and Fgf8+/neo hypomorphic mice (C). Sholl analysis showed that the branching complexity of adult midline GFAP-IR glial cells did not differ between WT and

Fgf8+/neo hypomorphic mice (D). Photomicrograph of GFAP-IR anterior-dorsal midline glial cell processed for Sholl analysis (inset). Scale bar is 20 µm.

2.4. Discussion

The main goal of these studies was to characterize the effects of FGF8 function on the prenatal, newborn and adult anterior-dorsal midline GFAP-IR glial cells as it pertains to corpus callosum formation. Our results confirmed earlier findings showing that the lack of FGF8 function reduced or eliminated prenatal GFAP immunoreactivity in the prenatal anterior-dorsal midline glial cell populations (i.e., IG and MZ). These results seem to support the hypothesis that a deficiency in

FGF8 function eliminates midline glial cells that are destined to become GFAP-expressing astrocytes, which consequently disrupted corpus callosum formation. However, results from our

GFAP studies in newborn and adult mice warrant a more nuanced interpretation. Indeed, the incidence of apoptosis in the anterior-dorsal midline region did not differ between E16.5 or

E17.5 WT, Fgf8+/neo or Fgf8neo/neo hypomorphic mice. In support, S100b and GLAST-1 expression did not differ between genotypes. Interestingly, while anterior-dorsal midline GFAP immunoreactivity differed between E17.5 WT and Fgf8+/neo hypomorphic mice, this difference was no longer present between newborn and adult WT and Fgf8+/neo hypomorphic mice. Taken together, the most parsimonious interpretation of these prenatal and postnatal data is that Fgf8 hypomorphy did not eliminate GFAP glial cells, but rather delayed the perinatal onset of Gfap mRNA expression in these glial cells.

Our results indicate that the incidence of apoptosis in the anterior-dorsal midline did not differ between genotypes on E16.5 or E17.5. However, it is possible that reduced FGF8 signaling eliminated GW glial precursor cells prior to CC formation at E16.5 and E17.5. Moreover, the primordial E9.0 telencephalon in mouse embryos with reduced Fgf8 expression exhibited more apoptosis than WT embryos (Storm et al., 2006). Similar results have been found in the E10.5 olfactory placodal region in Fgf8 hypomorphic mice (Kawauchi et al., 2004; Tsai et al., 2011).

Indeed, defects in GW development underlie ACC in Nfia and Fgfr1 mutant mice (T. Shu et al.,

2003; Smith et al., 2006). In addition to the GW defect, Nfia mice exhibit reduced Slit2 expression in the GW and a complete loss of Slit2 expression in the IG, which in part, may have caused ACC in those mice (T. Shu et al., 2003). Therefore, it is possible that early apoptosis disrupted GW formation potentially leading to a decrease in midline Slit2 expression, which could account for the observed ACC in Fgf8 hypomorphic mice. Further detailed studies focused on GW development would address this possibility.

Although early apoptosis in the GW may potentially explain the Fgf8neo/neo phenotype, it does not explain the postnatal restoration of GFAP expression in the anterior-dorsal midline observed in Fgf8+/neo mice. Postnatal restoration of GFAP expression within the anterior-dorsal midline suggests that a deficit in FGF8 function may have: 1) delayed the migration and/or 2) delayed the development of glial precursor cells destined to form the IG and MZ. For instance, exogenous FGF8 induced glial cell migration, whereas loss of FGFR1 function prevented radial glial cell translocation from the GW to the IG (Smith et al., 2006). Therefore, a deficit in FGF8 function may similarly disrupt GW migration and explain the absence/reduction of GFAP glial cells in the anterior-dorsal midline of our Fgf8 hypomorphic mice.

Alternatively, FGF8 function plays a role in the perinatal maturation of neural cells. We and others showed Fgf8 hypomorphy delayed the maturation of hypothalamic vasopressin (VP) neurons (McCabe et al., 2011; Rodriguez et al., 2015). Although, newborn Fgf8 hypomorphic mice have less VP neurons in the hypothalamus than their WT littermates, this genotype- dependent difference was no longer present in PN21 or adult WT and Fgf8+/neo hypomorphic mice (Rodriguez et al., 2015). Similarly, we found that anterior-dorsal midline GFAP immunoreactivity no longer differed between newborn and adult WT and Fgf8+/neo hypomorphic

56 mice, whereas the anterior-dorsal midline GFAP immunoreactivity was drastically reduced in

E17.5 Fgf8+/neo compared to WT mice. Together, these observations indicate that FGF8 function is potentially required for the perinatal maturation of anterior-dorsal midline GFAP-IR glial cells.

Although the adult anterior-dorsal midline GFAP immunoreactivity was not different between WT and Fgf8+/neo hypomorphic mice, this does not rule out the possibility that the developmental deficit in FGF8 function may have disrupted the function of GFAP glial cells. For instance, primary GFAP expressing cortical astrocytes treated with FGF8 exhibited an increase in branching complexity. These earlier observations led us to examine the question of whether the branching complexity of GFAP midline glial cells is compromised in adult Fgf8 hypomorphic mice. However, we found that Fgf8 hypomorphy did not affect midline glial cell branching complexity or process length suggesting that the GFAP glial cells may have fully recovered from the FGF8-dependent delay in cellular development.

FGF8 function may affect GFAP expression, possibly through FGFR1. For example, glial- specific Fgfr1 disruption reduced GFAP expression (Smith et al., 2006) suggesting that the lack of FGF8/FGFR1 signaling may be the underlying cellular signaling mechanism that caused the prenatal and newborn reduction/absence of GFAP expression in Fgf8 hypomorphic mice.

Moreover, in vitro studies showed that FGF8 autoregulates Fgfr1 and Fgfr3 mRNA levels in immortalized neuronal and fibroblast cell lines in a reciprocal fashion which was prevented with the FGFR antagonist PD173074 (Mott et al., 2010). These results indicate that the embryonic reduction/absence of GFAP glial cells within the IG and MZ may have been exacerbated by the loss of FGF8-dependent induction of FGFR1 levels in GFAP precursor glial cells.

Previous studies showed that mouse ACC is the result of disrupted callosal pioneer axon guidance (Bagnard, Lohrum, Uziel, Puschel, & Bolz, 1998; Bagri et al., 2002; Piper et al., 2009;

Ren et al., 2007). Our studies indicate that NP-1-IR axons were still present in the anterior-dorsal midline region of E16.5 and PN0 Fgf8+/neo and Fgf8neo/neo hypomorphic mice. This result indicates that the callosal pioneer axons can reach the anterior-dorsal midline region but were unable to cross into the contralateral hemisphere. Although we do not know the cause of this finding, previous studies found that the FGF-dependent MZ glial population is required for fusing the telencephalonic hemispheres (Silver, Edwards, & Levitt, 1993; Silver & Ogawa,

1983). Therefore, we hypothesize that disruption of perinatal IG and MZ GFAP glial cell development may interrupt hemispheric midline fusion, thereby eliminating the anatomical substrate for pioneer callosal axons to cross the anterior-dorsal midline.

Although, our results concur with previous studies indicating that reduction of FGF8 signaling contributes to ACC (Huffman et al., 2004; Moldrich et al., 2010), other studies found that ACC could be a result of FGF8 upregulation. For example, an overexpression of FGF8 in

Rfx3 and Gli3 null mice resulted in impaired midline glial guidepost cell positioning, altered radial glial translocation, and prevented callosal axons from crossing the anterior-dorsal midline

(Amaniti et al., 2013; Benadiba et al., 2012; Magnani et al., 2012). Together these studies show that FGF8 expression must be tightly regulated for anterior-dorsal midline and corpus callosum development.

It is well-known that KS patients with Fgf gene mutations may harbor ACC (Dode et al.,

2003). This anatomical defect is believed to be partially responsible for the involuntary mirror movements described in KS patients. Moreover, ACC has been reported in CHARGE syndrome patients (Tellier et al., 1998) indicating that Fgf gene mutations are not solely responsible for

ACC in KS patients. Based on our Fgf8 hypomorphic mouse studies, we hypothesize that Fgf8 and/or Fgfr1 mutations in humans may disrupt or delay the developmental process of anterior- dorsal midline GFAP glial cells, and that this delay contributes to ACC in KS patients.

Gfap F: 5'-GGCGCTCAATGCTGGCTTCA-3

R: 5'-TCTGCCTCCAGCCTCAGGTT- 3'

S100b F: 5'-CGGACACTGAAGCCAGAGAG-3'

R: 5'-CCGGAGTACTGGTGGAAGAC-3'

Hprt-1 F: 5'-CTCATGGACTGATTATGGACAGGAC-3'

R: 5'-GCAGGTCAGCAAAGAACTTATAGCC-3'

Table 2.1. Real-time qPCR forward (F) and reverse (R) primers used.

2.5. References

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2.6 Supplementary information

Supplementary Figure 1. Photomicrographs of GLAST-1 immunoreactivity in anterior-dorsal midline in PN0 WT (n = 4), Fgf8+/neo (n = 4) and Fgf8neo/neo (n = 4) hypomorphic mice (A-C). IG

(arrow) and MZ (arrowhead) are indicated. Asterisks indicate the lateral ventricles. Note:

Fgf8neo/neo mice lack of interhemispheric connectivity, and the presence of Probst bundles (Pb).

Scale bar is 500 µm.

CHAPTER 3

Perinatal FGF8 Hypomorphic Mouse Anterior Midline Glial Cell Development is

Independent of Genetic Sex

3.1. Introduction

The corpus callosum (CC) is the largest interhemispheric commissure within the brain, which is critical for the proper transfer and integration of sensory and cognitive information between hemispheres. Partial or complete agenesis of the CC (ACC) is a common birth defect occurring in 2-3 per 1000 births and associated with more than 50 congenital syndromes in humans (Elgamal et al., 2014; Richards et al., 2004). Interestingly, many of the congenital syndromes associated with ACC are also X-linked, such as Aicardi and Opitz syndromes (Cox et al., 2000; King et al., 1998). This suggests that ACC and potentially the mechanisms involved in normal CC development may be genetic sex-dependent. In further support, the incidence of ACC is much higher in adult female mice when compared to adult male mice. However, in contrast prenatal human ACC is more prevalent in males (Goodyear et al., 2001). Moreover, CC volume, and total axonal fiber number within the genu and splenium are greater in male rats; whereas volumetric CC studies in humans yield contradictory results (Bishop & Wahlsten, 1997;

Constant & Ruther, 1996; DeLacoste-Utamsing & Holloway, 1982; Kim & Juraska, 1997; Mack et al., 1995; Nunez & Juraska, 1998; Pilgrim & Reisert, 1992)

Mechanisms that regulate CC development are well known. At the start of CC development, pioneer pathfinding axons from cingulate cortical neurons extend towards the anterior-dorsal

66 midline to form an interhemispheric guidance tract around embryonic day (E) 15.5 (Koester &

O'Leary, 1994; Nishikimi et al., 2013; Piper et al., 2009; Zhou et al., 2013). These pathfinding axons use anterior-dorsal midline chemoattractant (i.e. Netrin-1) and chemorepellent (i.e. Slit2) as guidance cues. This anterior-dorsal midline chemotaxis originates from three anterior-dorsal telencephalon midline guidepost glial cell populations: glial wedge (GW), indusium griseum

(IG), and midline zipper (MZ) (Piper et al., 2009). Anatomically, these three midline glial cell populations form the impermissible outer-borders of a Netrin-1 to form permissive conduit in the anterior-dorsal telencephalon that bridges the hemispheres, thereby enabling interhemispheric crossing of callosal pioneer axons (Bagri et al., 2002; B. G. Rash & L. J. Richards, 2001; Smith et al., 2006). The central anatomical hallmark of ACC is that the interhemispheric callosal axons fail to cross to the contralateral hemisphere, a process that normally is completed around E17.5 during mouse brain development. Instead, the callosal axons project ventrally and posteriorly to form an abnormal anterior-dorsal ipsilateral midline structure called Probst bundle (Pb) (Ren et al., 2007; Tovar-Moll et al., 2007).

Recently, we and others showed that the development of the three midline glial cell populations is FGF/FGFR signaling-dependent. For instance, studies using conditional Fgf8 null mice found that the lack of midline FGF8 function virtually eliminates all glial fibrillary acidic protein (GFAP)-IR astrocytes in the anterior-dorsal midline IG and MZ (Moldrich et al., 2010), indicating FGF8 provides trophic support. Moreover, studies in Fgf receptor (Fgfr) 1 deficient mice showed that a glial-specific Fgfr1 disruption reduced anterior-dorsal midline GFAP expression, and perturbed radial glial cell migration from the GW to the IG (Smith et al., 2006).

Furthermore, we showed that FGF8 also plays a role in the perinatal maturation of anterior- dorsal GFAP-IR midline glial cells. Indeed, E17.5 and postnatal day (PN) 0 Fgf8 hypomorphic

67 mice exhibited a delay in the onset of anterior-dorsal midline GFAP expression; a transient effect which was absent in adult mice (Stewart, CE. et al., 2016).

Remarkably, previous studies showed that adult GFAP expression is influenced by genetic sex and hormones. Indeed, GFAP expression varied during the rodent estrus cycle in areas, such as the hippocampus and interpeduncular nucleus (Arias et al., 2009; Hajos et al., 2000).

Moreover, Gfap mRNA expression was induced by numerous hormones, such as thyroid hormones, estradiol, and androgens the overall GFAP expression varied in response to circulating androgens in the medial amygdala (Gomes et al., 1999; Ryan T. Johnson, Breedlove,

& Jordan, 2008; Morrison, De Vellis, Lee, Bradshaw, & Eng, 1985; Stone et al., 1998).

During the late perinatal (i.e., E17.5) and early postnatal period (i.e., PN0) male testes secrete testosterone which can be aromatized into estrogens that mediate the so-called

“masculinization” of the male brain (Arnold & Gorski, 1984; MacLusky & Naftolin, 1981).

Conversely, a lack of such exposure results in a “feminized” brain. Perinatal estrogens promote genetic sex-dependent brain development via estrogen receptors (ERs), which belong to the superfamily of nuclear steroid receptors. Furthermore, the differentiation of astrocytes within the perinatal hypothalamus is induced by the steroid hormone, estradiol (M. M. McCarthy, Amateau,

& Mong, 2002). Based on the current data, we hypothesized that developmental sex differences may be may present during the perinatal development of GFAP-IR midline astrocytes. Given that these key GFAP-IR glial guidepost cell populations are required for proper axonal targeting and interhemispheric crossing, genetic sex-dependent development of these glial cell populations may contribute to the genetic sex-dependent development of the brain circuitry.

3.2. Material and Methods

3.2.1. Animals

Committee at Kent State University. Fgf8 hypomorphic mice exhibit a ~25% and ~55% reduction in Fgf8 mRNA expression in Fgf8+/neo and Fgf8neo/neo mice, respectively. In the morning, females with a sperm plug were denoted as embryonic day (E) 0.5.

3.2.2. Brain Tissue

E17.5, and PN0 WT, Fgf8+/neo and Fgf8neo/neo fetal brain tissue was obtained by euthanizing timed-bred pregnant females via cervical dislocation on E17.5 and PN0. Fetal pups were then removed from the uterine horn and euthanized. PN0 WT, Fgf8+/neo and Fgf8neo/neo brain tissue was obtained by euthanizing newborn pups within hours after birth. PN5 WT and Fgf8+/neo brain tissue was obtained by euthanasia. All brains were immersion-fixed in 4% paraformaldehyde

(PFA), stored in 30% sucrose, and genotyped using PCR for Fgf8, neomycin and SRY. Serial coronal WT, Fgf8+/neo, and Fgf8neo/neo sections (25 µm) were obtained using a cryostat (Leica CM

1950, Buffalo Grove, IL), and thaw-mounted on slides coated with gelatin (Sigma-Aldrich, St.

Louis, MO).

3.2.3. Immunohistochemistry (IHC)

Sections from WT, Fgf8+/neo and Fgf8neo/neo hypomorphic mice were processed simultaneously to minimize the variability in immunoreactivity. Serial coronal sections were incubated in 1%

69 hydrogen peroxide/TBS solution for 15 min at room temperature, washed in TBS, 3 x 5 min, and incubated in primary, rabbit polyclonal anti-GFAP (1:3500) (Thermo Scientific, Waltham, MA) made in TBS/0.3% Triton-X (Fisher Scientific, Pittsburgh, PA) and 2% normal goat serum for 2 days at 4C. Sections were washed and incubated with biotinylated-goat anti-rabbit (1:500) for 2 hrs at room temperature followed by ABC (1:500) (Vector Laboratories, Burlingame, CA) in

TBS for 2 hrs at room temperature, and reacted with 0.05% diaminobenzidine (Sigma-Aldrich,

St. Louis, MO) and 0.01% H2O2 /TBS for 20 min. Sections were dehydrated with ethanol, cleared with xylene and coverslipped with DPX (Merck, Billerica, MA). These standardized conditions were used for each of the primary antibodies, which minimized the variability between immunohistochemical stainings.

3.2.4. Image Analysis

3.2.5. GFAP

PC computer. GFAP-IR midline glial cells were analyzed using Olympus CellSens software

(Olympus Corporation of the Americas, Center Valley, PA). A threshold mask was generated which reliably and accurately covered the GFAP immunoreactivity in perinatal WT mice.

Anterior-dorsal midline GFAP density was measured as immunoreactivity covered by pixels per fixed region of interest rectangle (419955.2 μm2). Similarly, two serial coronal sections representative of the PVN (i.e., plate to plate) were atlas-matched across prenatal WT and

Fgf8+/neo hypomorphic mice using anatomical landmarks. The distance between each immunostained section was 50 μm. Grayscale digital images were captured using a 10X objective mounted on an Olympus microscope fitted with a color camera (SC30, Olympus,

Corporation of the Americas, Center Valley, PA) connected to a PC computer. GFAP-IR midline glial cells were analyzed using Olympus CellSens software (Olympus Corporation of the

Americas, Center Valley, PA). A threshold mask was generated which reliably and accurately covered the GFAP immunoreactivity in WT mice. Anterior-dorsal midline GFAP density was measured as immunoreactivity covered by pixels per fixed region of interest rectangle (591252.4

μm2).

3.2.6. Statistical Analysis

Data were analyzed using student t-tests, one-way and two-way analysis of variance (ANOVA) with genotype as between subject variables. Student Newman-Keuls tests were used for post hoc analysis. Differences were considered significant if p < 0.05. Animals and treatments were randomized and coded by an independent investigator. All measurements were conducted by an observer without knowledge of sex and genotype. The number of animals analyzed for each study is indicated in the figure legends.

3.3. Results

3.3.1. Fgf8-dependent reduction in anterior-dorsal midline GFAP expression in embryonic and newborn mice is not sex-dependent

FGF8-dependent development of IG and MZ GFAP-IR glial cell populations has been shown to cause ACC. The incidence of ACC is potentially influenced by genetic sex. Earlier studies showed ACC is a result of absent or immature anterior-dorsal midline GFAP-IR glial cells.

Moreover, the synthesis of GFAP is potentially induced by sex steroids. Therefore, we explored whether there existed a sex difference in the perinatal development of anterior-dorsal midline

GFAP-IR cells during 3 distinct time points of astrocyte development.

3.3.2. E17.5 GFAP immunoreactivity

GFAP immunoreactivity within the anterior-dorsal midline, including the IG and MZ, in male and female WT, Fgf8+/neo, or Fgf8neo/neo mice was compared. Visually, GFAP immunoreactivity levels in the IG and MZ of both male and female WT mice were much higher than in the

Fgf8+/neo or Fgf8neo/neo hypomorphic mice on E17.5. Two-way ANOVA showed that the combined anterior-dorsal midline GFAP immunoreactivity level was genotype-dependent in males but not female mice (F = 9.26, p < 0.05) (Fig. 3.1.). Post-hoc analysis showed that E17.5

GFAP immunoreactivity was significantly higher in WT males than in Fgf8+/neo (p < 0.005) or

Fgf8neo/neo male mice (p < 0.001). GFAP immunoreactivity did not differ between Fgf8+/neo and

Fgf8neo/neo male mice (p = 0.36). Secondly, two-way ANOVA analysis showed that there was no main sex effect on E17.5 GFAP immunoreactivity (F = 1.1, p = 0.31). However, GFAP immunoreactivity did change with genotype (F = 9.3, p < 0.005). There was no interaction between genotype and sex (F = 0.62, p = 0.54).

Figure 3.1. Bar graph showing the anterior-dorsal midline GFAP density in E17.5 hypomorphic male (WT, n = 6, Fgf8+/neo, n = 7, Fgf8neo/neo, n = 5) and female mice (WT, n = 6, Fgf8+/neo, n = 5,

Fgf8neo/neo, n = 8). Asteriks denote significance (p < 0.05) between genotypes.

3.3.3. PN0 GFAP immunoreactivity

Fgf8+/neo or Fgf8neo/neo hypomorphic mice on PN0. Two-way ANOVA showed that the combined anterior-dorsal midline GFAP immunoreactivity level was genotype-dependent in males and females (F = 8.1, p < 0.05) (Fig. 3.2.). Post-hoc analysis showed that PN0 GFAP immunoreactivity was significantly higher in WT than in Fgf8+/neo or Fgf8neo/neo mice (p < 0.05 in both sexes.) GFAP immunoreactivity did not differ between Fgf8+/neo and Fgf8neo/neo mice (p =

0.28 males; p=0.18 females). Subsequent, two-way ANOVA analysis showed that there was no main sex effect on PN0 GFAP immunoreactity (F = 0.16, p = 0.7). However, GFAP immunoreactivity did change with genotype (F = 8.5, p < 0.005). There was no interaction between genotype and sex (F = 0.5, p = 0.62).

Figure 3.2. Bar graph showing the anterior-dorsal midline GFAP density in PN0 hypomorphic male (WT, n = 5, Fgf8+/neo, n = 4, Fgf8neo/neo, n = 4) and female mice (WT, n = 3, Fgf8+/neo, n = 3,

Fgf8neo/neo, n = 3). Asterisks denote significance (p < 0.05) between genotypes.

3.3.4. PN5 GFAP immunoreactivity

GFAP immunoreactivity within the anterior-dorsal midline, including the IG and MZ, in male and female WT, or Fgf8+/neo mice was compared. Visually, GFAP immunoreactivity levels in the

IG and MZ of both male and female WT mice were similar to the Fgf8+/neo hypomorphic mice at

PN5. Two-way ANOVA showed that the combined anterior-dorsal midline GFAP immunoreactivity level was not genotype-dependent in males or females (F = 0.26, p = 0.62)

(Fig. 3.3.). Given that male testes begin secreting testosterone during this time point, GFAP-IR within the paraventricular nucleus (PVN), or a midline hypothalamic region which is potentially sex steroid responsive, was also analyzed. Visually, GFAP-IR levels within the PVN of both male and female WT mice were similar to the Fgf8+/neo hypomorphic mice at PN 5. Two-way

ANOVA showed that the PVN GFAP-IR level was not genotype-dependent in males or females

F = 0.49, p = 0.49) (Fig. 3.4.).

Figure 3.3. Bar graph showing the anterior-dorsal midline GFAP density in PN5 hypomorphic male (WT, n = 5, Fgf8+/neo, n = 4) and female mice (WT, n = 3, Fgf8+/neo, n = 4). Note: although note significant, there is a trend for a genotype effect (p = 0.053).

Figure 3.4. Bar graph showing the PVN GFAP density in PN5 hypomorphic male and female mice (WT, n = 5, Fgf8+/neo, n = 4) and female mice (WT, n = 4, Fgf8+/neo, n = 5).

3.4. Discussion

The main goal of these studies was to characterize the effects of FGF8 signaling deficits and genetic sex on the prenatal, newborn and early postnatal anterior-dorsal midline GFAP-IR glial cells as it pertains to corpus callosum formation. Our results showed that GFAP expression within the anterior-dorsal midline of E17.5 and PN0 mice is FGF8-dependent. Interestingly, PN5 anterior-dorsal midline GFAP expression is not FGF8-dependent. These results confirmed earlier findings showing that the lack of FGF8 function delays the onset of GFAP expression in the prenatal anterior-dorsal midline glial cell populations (i.e., IG and MZ), which disrupts corpus callosum formation (Stewart, 2016, Gobius 2016). Although FGF8 function clearly plays a role in GFAP expression, genetic sex had no effect on anterior-dorsal midline GFAP expression at these early ages. We further conclude that reported sex-dependent differences in GFAP expression or astrocyte function are region specific, and likely due to secondary post-pubertal modifications in sex steroid/hormone production.

Glial fibrillary acidic protein expression varies during the rodent estrus cycle in areas, such as, the hippocampus and interpeduncular nucleus (Arias et al., 2009; Hajos et al., 2000).

Furthermore, GFAP synthesis in rats was shown to be induced by many hormones, such as, thyroid hormone T3, estradiol, and testosterone (Gomes et al., 1999). Interestingly, astrocytes express ERα and ERα receptors on the cytoplasmic and intracellularly (Fuente-Martin et al.,

2013; R. T. Johnson, Schneider, DonCarlos, Breedlove, & Jordan, 2012; Ryan T. Johnson et al.,

2008). Moreover, there are sex differences in the way ERα is trafficked from the cell nucleus to the cytoplasm. Specifically, hypothalamic astrocytes from female rats and mice had more ERα trafficked to the cytoplasm when exposed to estradiol than male mice (Kuo et al., 2010). These data, together with the fact that the GFAP promoter has ERE sites, could explain why GFAP

79 varies during rodent estrus (Arias et al., 2009; Gomes et al., 1999; Hajos et al., 2000). In addition to estrogens, astrocytes also express androgen receptors (AR) and respond to circulating androgens. The effect of androgen on astrocytes largely depends on region. For example, overall

GFAP expression and astrocyte branching morphology was shown to increase in response to circulating androgens in the medial amygdala and suprachiasmatic nucleus (SCN) (Ryan T.

Johnson et al., 2008; Karatsoreos, Butler, Lesauter, & Silver, 2011; Morrison et al., 1985; Stone et al., 1998).

These adult data support the idea that GFAP expression and astrocyte function (i.e., morphology) are under peri- and postpubertal hormonal control. However, male rat astrocytes within the preoptic area respond to testosterone and estradiol as early as PN0, with exogenous testosterone or estradiol resulting in increased astrocytic branching. Moreover, neonatal female rats given exogenous testosterone or estradiol exhibit male-like preoptic area astrocytes (M. M.

McCarthy, 2008; M. M. McCarthy et al., 2002). These data show that neonate astrocytes can directly respond to hormonal changes, albeit it likely depending on brain region.

Alternatively, the neonatal astrocytic response observed in the POA after the introduction of estradiol may be indirect. In support, ER expression was not found on POA rat astrocytes in male or females but rather on neurons (Amateau & McCarthy, 2002; Lenz, Nugent, & McCarthy,

2012; M. M. McCarthy, 2008; M. M. McCarthy et al., 2002; Mong & McCarthy, 2002; Schwarz

& McCarthy, 2008). Interestingly, estradiol is thought to increase neuronal GABA production which then modifies the astrocyte morphology. The neonatal male has more GABA in the POA and arcuate which may explain why neonatal astrocytes in males rather than females are more responsive to hormones fluctuation.

Although neonatal male astrocytes respond more to hormones, adult female astrocytes rapidly respond to hormones throughout the estrus cycle. This, in part, is because astrocytes have both ERα and β, respond to estradiol, and there are EREs within the GFAP promotor which play a role in synthesis. Interestingly, the amount and type of ERs changes with age in females and is brain region-specific. For example, perimenopausal cortical astrocytes exhibit a shift in the ratio of ERα: ERβ, which alters astrocytic and neuronal responsiveness to estradiol. Specifically, aged acyclic female rats exhibit an increase in astrocytic ERα, a decrease in ERβ, and increased GFAP production. These data indicate that female astrocytes are hormonally responsive well-into adulthood. Moreover, hormones such as estradiol normally repress GFAP synthesis that is until

ERα levels increase with age or fluctuate throughout the estrus cycle (Arias et al., 2009; Hajos et al., 2000; Hou, Yang, & Voogt, 2003; Liu et al., 2016; Rahn, Iannitti, Donahue, & Taylor, 2014).

Taken together, these data indicate that perinatal astrocyte development in Fgf8 hypomorphic mice is not genetic sex dependent. It is possible that the regions which show sexually dimorphic astrocytes, such as the amygdala, SCN, and POA, simply do so in preparation for the role these regions have in reproduction and reproductive behaviors in adulthood (Been & Petrulis, 2010; Boden & Kennaway, 2006; Newman, 1999). For example circadian rhythm plays a role in reproductive function and is maintained by an astrocyte-neuron interaction within the SCN (Brancaccio et al., 2019). The anterior midline region, or more specifically the CC, does not play a role in reproduction and would not need to be sexually dimorphic. GFAP synthesis, however, is hormonally responsive specifically through an interaction with ERα. Given that FGF8 is involved in GFAP acquisition, it is possible that early

Fgf8 deficits could alter transcriptional machinery used during hormonally induced increases in

GFAP expression. However, more studies are needed to rule out or confirm this possibility.

3.5. References

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Chapter 4

Cuprizone Induced Astrocyte Activation is Fibroblast Growth Factor 8 Signaling

Dependent

4.1. Introduction

Previously, we showed that Fgf8 hypomorphic mice harbor agenesis of the corpus callosum (CC) as a result of impaired brain midline astrocyte development (Moldrich et al.,

2010; Stewart et al., 2016). Specifically, perinatal Fgf8 hypomorphic mice lacked glial fibrillary acidic protein (GFAP) expressing brain midline astrocytes. We showed that at embryonic (E) day

17.5 and postnatal (PN) day 0 homozygous (Fgf8neo/neo) hypomorphic mice had no brain midline

GFAP expression, while heterozygous (Fgf8+/neo) mice exhibited a 2-fold decrease in brain midline GFAP expression that reached wildtype (WT) levels at PN0. This transient developmental deficit led us to infer that a deficit in FGF8 function caused a delay in midline astrocyte development rather than their elimination. Indeed, the expression of early astrocyte lineage markers, such as glutamate aspartate transporter (GLAST)-1 and calcium binding protein

S100b were unaffected by Fgf8 hypomorphy (Stewart et al., 2016).

However, while FGF8 deficiency did not eliminate midline astrocytes, the delay in their perinatal maturation may have impaired crucial astrocytic functions, such as astrocyte activation, which, in part, rely on the level of GFAP expression (Kramann et al., 2019). Therefore, our following studies investigated whether brain midline callosal astrocytes in adult Fgf8+/neo hypomorphic mice were functionally abnormal compared to those in WT mice. These studies were based on results from previous investigations indicating that FGF signaling may

86 significantly contribute to the cellular function of astrocytes. For instance, FGF2 induced astrocyte activation and modified astrocyte morphology (Chadi & Gomide, 2004; Clarke et al.,

2001). In support, FGF2 and FGFR1 co-localize in the nuclei of reactive astrocytes surrounding cerebral lesions (Clarke et al., 2001). More recently, however, FGF8 was shown to also modify primary cortical astrocyte morphology and to a greater degree than FGF2 (Kang et al., 2014). For example, unlike FGF8 exogenous FGF2 failed to increase the number of branches, increase branch length and did not increase astrocytic branch complexity. A similar detriment may be present in callosal astrocytes.

To study this possibility, we turned to an experimental model system in which we used cuprizone, a copper chelator commonly used to model the cellular myelin changes during multiple sclerosis (MS), to stimulate astrocyte activation and investigate whether reduced FGF8 signaling impaired astrocytic response in the postnatal mouse. Indeed, mouse astrocytes undergoing cellular activation exhibit GFAP upregulation, and increased branching morphology

(Pekny & Nilsson, 2005; Pekny & Pekna, 2014; Sovrea & Bosca, 2013). Although much is still unknown, studies indicate that damaged neurons secrete multiple cytokines and growth factors, such as tumor necrosis factor (TNF) -α, interleukin (IL)-1β and FGF2 to induce astrocyte activation (Balasingam, Tejada-Berges, Wright, Bouckova, & Yong, 1994; Chadi & Gomide,

2004; Ito et al., 2006; Pekny & Pekna, 2014; Sticozzi et al., 2013; L. Zhang et al., 2000). These factors, elicit the astrocyte activation through the signal transducer STAT3 (Akira, 1999). Loss- of-function studies showed the central role that STAT3 function plays is in astrocyte differentiation, GFAP expression acquisition, and astrocyte hypertrophy (LeComte, Shimada,

Sherwin, & Spees, 2015; Levine et al., 2016; Pekny & Pekna, 2014; Sriram, Benkovic, Hebert,

Miller, & O'Callaghan, 2004). Given that our studies and others showed FGF8 controls perinatal

87 brain midline astrocyte differentiation and maturation, increased astrocyte branching complexity, and that FGFR signaling phosphorylates STAT3, we hypothesize that FGF8 signaling is STAT3- dependent.

Here, we induced astrocyte activation in WT and Fgf8+/neo hypomorphic adult mice with

CPZ, which causes pronounced white matter astrocyte activation, as denoted by an increase in

GFAP-IR after approximately 3 weeks of exposure (Viktoria Gudi et al., 2014; Hibbits et al.,

2012; Mierzwa, Zhou, Hibbits, Vana, & Armstrong, 2013). This model system allows us to target the astrocyte population within the CC, which our previous studies showed to be dependent upon perinatal FGF8 signaling (Gobius et al., 2016; Stewart et al., 2016). First, we examined GFAP expression within the genu and cingulum of the CC and cingulate cortex of 2 and 3 week standard or CPZ supplemented chow fed adult WT and Fgf8+/neo mice. Second, we examined the cingulum astrocytic branching morphology in 2 and 3 week standard or CPZ supplemented chow fed WT and Fgf8+/neo mice. Lastly, we examined Gfap, Fgfr1, and Stat3 expression in 2, 3, and 6 week standard or CPZ supplemented chow fed WT and Fgf8+/neo mice.

In general, our studies led us to conclude that Fgf8 deficits impaired adult CPZ-dependent astrocytic activation in a regionally specific manner. Furthermore, we showed that prolonged

CPZ exposure altered Fgfr1 and Stat3 mRNA expression within the CC. Taken together; these results indicate that FGF8/FGFR1 signaling may be crucial for STAT3 mediated white matter astrocyte activation.

4.2. Material and Methods

4.2.1. Animals

Committee at Kent State University. Fgf8 hypomorphic mice exhibit a ~25% and ~55% reduction in Fgf8 mRNA expression in Fgf8+/neo and Fgf8neo/neo mice, respectively (Meyers et al.,

1998).

4.2.2. Cuprizone treatment

CPZ is classically used in MS research to examine demyelination and remyelination mechanisms. However, CPZ reliably induces astrocyte activation, specifically within the CC, as marked by GFAP upregulation (Hibbits et al., 2012; Pekny & Pekna, 2014; Sovrea & Bosca,

2013). Therefore, we used a 0.2% (w/w) CPZ supplemented chow design to target and activate callosal astrocytes. To study FGF8’s role in astrocyte activation adult (2 to 4 month old) male

Fgf8 hypomorphic mice were fed standard chow (Prolab, LabDiet, St. Louis, MO) or 0.2%

(w/w) cuprizone (CPZ) (Sigma, IL) supplemented chow ab libitum. Animal weight was recorded and food was replaced every two days until the mice were sacrificed. All mice were single- housed.

4.2.3. Brain tissue collection and processing

At the end of the designated treatment length, mice were transcardially perfused with cold saline

(0.9% NaCl) followed by 4% paraformaldehyde (PFA) /1% glutaraldehyde. Following, brains

89 were extracted, and immersion-fixed in 4% PFA/ 1% glutaraldehyde overnight and stored in

30% sucrose/0.1M PBS. Serial coronal WT, Fgf8+/neo, and Fgf8neo/neo sections (50 µm) was obtained using a cryostat (Leica CM 1950, Buffalo Grove, IL).

4.2.4. Immunohistochemistry

Sections from WT, Fgf8+/neo hypomorphic mice were processed simultaneously to minimize the variability in immunoreactivity. Serial coronal sections were incubated in 1% hydrogen peroxide/TBS solution for 15 min at room temperature, incubated with 0.3M glycine/0.1M PBS for 15 minutes, washed in TBS, 3 x 5 min, washed in 0.1M sodium citrate/0.1M PBS (pH 6.0) with agitation at 60C, 4 x 15 min, washed in TBS, 3 x 5 min, and incubated in rabbit polyclonal anti-GFAP (1:6000 adult) (Thermo Scientific, Waltham, MA) made in TBS/0.3% Triton-X

(Fisher Scientific, Pittsburgh, PA) and 2% normal goat serum for 2 days at 4C. Sections were washed and incubated with biotinylated-goat anti-rabbit (1:500) for 2 hrs at room temperature followed by ABC (1:500) (Vector Laboratories, Burlingame, CA) in TBS for 2 hrs at room temperature, and reacted with 0.05% diaminobenzidine and 0.01% H2O2/TBS for 20 min.

Sections were dehydrated with ethanol, cleared with xylene and coverslipped with permount

(Merck, Billerica, MA).

4.2.5. GFAP immunoreactivity quantification

Three serial coronal sections representative of the anterior-dorsal midline (i.e., plate 21 to plate

23) were atlas-matched across adult WT and Fgf8+/neo hypomorphic mice using anatomical landmarks. The distance between each immunostained section was 100 μm. Grayscale digital images were captured using a 4X objective mounted on an Olympus microscope fitted with a

90 color camera (SC30, Olympus, Corporation of the Americas, Center Valley, PA) connected to a

PC computer. GFAP-IR midline glial cells were analyzed using Olympus CellSens software 92

(Olympus Corporation of the Americas, Center Valley, PA). A threshold mask was generated which reliably and accurately covered the GFAP immunoreactivity in adult WT mice. Genu

(36,456.3 μm2), cingulum (103,584.0 μm2), and cingulate (73,064.3 μm2) GFAP density was measured as immunoreactivity covered by pixels in a fixed rectangle (Respective ROI areas for each region is listed within parentheses).

4.2.6. Corpus callosum dissection

The corpus callosum was dissected from fresh non-fixed brains. A razor blade was used to excise a single coronal section exposing the CC. To standardize this, each coronal section was bordered by set rostral and caudal anatomical landmarks. Using the ventral region of the brain, the rostral cut was made when the lateral olfactory tract narrows causing a slight indentation which can be observed from the sagittal profile of the brain (i.e., atlas plate 22). The caudal cut was made directly in front of the optic chiasm (i.e., atlas plate 30). All surrounding tissue was removed and discarded. The CC tissue was place in a sterile Eppendorf tube with 500 µL of TriPure on ice.

All tools and surfaces were treated with an RNAse away solution to prevent RNA degradation.

4.2.7. RT-qPCR

Total RNA from the corpus callosum was isolated using TriPure (Roche, Basel, Switzerland) or

TriSure (i.e., atlas plate 22). Total RNA (250 ng) was used to synthesize cDNA using the First

Strand cDNA Synthesis Kit (New England Biosystems, Ipswich, MA) according to manufacturer’s instructions. RT-qPCR was performed in triplicate using a Mastercycler EP

Realplex2 (Eppendorf, Hauppauge, NY) with SYBR Green PCR Master Mix gene expression assay (Roche, Basel, Switzerland). Relative Gfap, Fgfr1, and Stat3, mRNA expression was calculated using the ΔΔ-2CT method. Hypoxanthine phosphoribosyltransferase 1 (Hprt-1) was used as a housekeeping gene. Primer pairs used are listed in table 4.1.

4.2.8. Astrocytic branching analysis

Gray scale digital z-stacked images were captured using a 40X objective mounted on an

Olympus microscope (BX-UCB) fitted with a color camera (SC30, Olympus, Corporation of the

Americas, Center Valley, PA) connected to a PC computer. Randomly selected GFAP glial cells were manually traced in ImageJ (NIH, Bethesda, MD). Colored overlays were used during tracing to denote primary (i.e., blue) secondary (i.e., red), tertiary (i.e., yellow) and quaternary

(i.e., green) branches. Approximately 9 to 10 cells were traced within the cingulum region of each mouse by two blind observers.

4.2.9. Statistical analysis

Data were analyzed using two-way analysis of variance (ANOVA) with genotype as between subject variables. Student Newman-Keuls tests were used for post hoc analysis. Differences were considered significant if p < 0.05. Animals and treatments were randomized and coded by an independent investigator. All measurements were conducted by an observer without knowledge of genotype or treatment. The number of animals analyzed for each study is five unless otherwise indicated in the figure legends.

4.3. Results

4.3.1. Fgf8 hypomorphy affects CPZ-induced corpus callosum astrocyte activation in a regionally specific manner

First, we analyzed GFAP expression within the genu after 2 weeks of CPZ treatment. Two-way

ANOVA showed that GFAP-IR within the genu did not change after 2 weeks of CPZ treatment when compared to control animals. There were no genotype effects nor interactions between genotype and treatment groups. Second, we analyzed GFAP expression within the genu after 3 weeks of CPZ treatment. Two-way ANOVA showed that GFAP-IR within the genu increased after 3 weeks of CPZ-fed when compared to control-fed animals (F= 14.2, p < 0.05). There were no genotype effects nor interactions between genotype and treatment groups (Fig. 4.1.). Next, we analyzed GFAP expression within the cingulum after 2 weeks of CPZ treatment. Two-way

ANOVA showed that GFAP-IR within the cingulum of WT mice did not increased after 2 weeks of CPZ-fed when compared to control-fed animals. However, post-hoc analysis showed that CPZ treated WT mice did have more GFAP-IR within the cingulum when compared to CPZ treated

Fgf8+/neo mice (F= 8.27, p < 0.05), indicating that there was a genotype effect within the treatment group. Second, we analyzed GFAP-IR within the cingulum after 3 weeks of CPZ treatment. Two-way ANOVA showed that GFAP-IR within the cingulum increased after 3 weeks of CPZ-fed when compared to control-fed mice (F= 10.4 p < 0.05). There was a significant genotype effect (F= 5.06, p < 0.05) and a significant interaction between treatment and genotype (F=5.77, p < 0.05). Post-hoc analysis showed that the cingulum of CPZ-fed WT mice had a significant increase in GFAP-IR (F=10.4, p < 0.006) when compared to Fgf8+/neo mice. Furthermore, post-hoc analysis showed that cingulum of CPZ fed Fgf8+/neo mice did not differ from control WT and/or Fgf8+/neo mice (Fig. 4.2.).

Figure 4.1. GFAP-IR in 2 week (A-D) and 3 week (G-I) treated WT and Fgf8 hypomorphic mice. Dashed boxes hereafter indicate the ROI. Scale bar is 200 µm. Bar graph showing genu

GFAP density in 2 week treated mice (Fgf8+/neo 0.2% CPZ chow group, n = 4) (E). Bar graph showing genu GFAP density in 3 week treated mice (J). Asterisk denotes significance (p < 0.05).

Figure 4.2. GFAP-IR in 2 week (A-D) and 3 week (G-I) treated WT and Fgf8 hypomorphic mice. Scale bar is 200 µm. Bar graph showing cingulum GFAP density in 2 week treated mice

(Fgf8+/neo 0.2% CPZ chow group, n = 4) (E). Bar graph showing cingulum GFAP density in 3 week treated mice (J). Asterisk denotes significance (p < 0.05).

4.3.2. CPZ-induced cingulate cortical astrocyte activation is Fgf8-independent

Cingulate cortex GFAP-IR levels in CPZ animals were higher than in control animals. Two-way

ANOVA showed that cingulate cortical GFAP-IR was higher in CPZ animals than in control animals after 2 (F = 12.2, p < 0.05) and 3 (F = 12.6, p < 0.05) weeks. However, there was no genotype-dependent effect nor interaction between genotype and treatment groups (Fig. 4.3.).

Figure 4.3. GFAP-IR in 2 week (A-D) and 3 week (G-I) treated Fgf8 hypomorphic mice. Scale bar is 200 µm. Bar graph showing cingulate cortex GFAP density in 2 week treated hypomorphic mice (WT and Fgf8+/neo 0.2% CPZ chow group, n = 4) (E). Bar graph showing cingulate cortex

GFAP density in 3 week treated hypomorphic mice (J). Asterisk denotes significance (p < 0.05).

Note: Although not significant (p = 0.051), there is a trend in GFAP density between CPZ treated WT and Fgf8+/neo hypomorphic mice.

4.3.3. CPZ-induced changes in astrocytic branching within the cingulum are Fgf8-dependent

Our results showed that branching complexity of GFAP-IR astrocytes differed between adult

CPZ fed animals 2 weeks. Specifically, Two-way ANOVA showed that 2 weeks of CPZ increased the number of secondary branches (F=7.53, p < 0.05). Post-hoc analysis showed that

CPZ-fed WT mice had more secondary branches when compared to WT controls (F=7.53 p <

0.05). Interestingly, the number of secondary branches in CPZ fed Fgf8+/neo mice did not differ from WT and/or Fgf8+/neo control mice. Furthermore, two-way ANOVA showed that 2 weeks of

CPZ also increased the number of tertiary branches (F=8.68, p < 0.05). Post-hoc analysis showed that CPZ-fed Fgf8+/neo mice had more tertiary branches when compared to Fgf8+/neo controls (F=8.68, p < 0.05). Interestingly, the number of tertiary branches in CPZ fed WT mice did not differ from WT and/or Fgf8+/neo control mice. No primary or quaternary branching differences were observed. Moreover, these secondary and tertiary branching differences were no longer present at 3 weeks of CPZ treatment (Fig. 4.4.).

Figure 4.4. Manual cingulum astrocyte traces of 2 week (A-D) and 3 week (G-J) treated Fgf8 hypomorphic mice. Scale bar = 20 µm. Bar graphs showing secondary (E), tertiary (F) branch quantification in 2 week and secondary (K), tertiary (L) branch quantification in 3 week treated

Fgf8 hypomorphic mice. Asterisk denotes significance (p < 0.05).

4.3.4. CPZ-induced Gfap mRNA expression

CPZ-treatment for 2, 3, and 6 weeks increased Gfap mRNA expression when compared to control animals (F=64.9 p < 0.001; F=26.5 p < 0.001; F=76.6 p < 0.001). Furthermore, this increase was more profound with time. Two-way ANOVA showed that Gfap expression increased approximately 5-fold at 2 weeks (F = 64.9, p < 0.05), 7-fold at 3 weeks (F = 26.5, p <

0.05) and 10-fold at 6 weeks (F = 76.6, p < 0.05) (Fig. 4.5.). However, this increase was not dependent on genotype and there was no interaction between genotype and treatment.

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Figure 4.5. Bar graphs representative of relative CC Gfap expression in 2 week, 3 week, and 6 week (A-C) treated Fgf8 hypomorphic mice. Asterisk denotes significance (p < 0.05).

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4.3.5. CPZ-induced Fgfr1 mRNA expression CPZ-treatment for 2, 3, and 6 weeks increased Fgfr1 mRNA expression when compared to control animals (F=21.3, p < 0.001; F=7.8, p < 0.05; F=13.1, p < 0.05). Two-way ANOVA showed that Fgfr1 expression increased approximately 1.5-fold at 2 weeks and 1.5-fold at 3 weeks and that it was not genotype dependent. At 6 weeks, Fgfr1 expression increased in a genotype-dependent fashion. Two-way ANOVA showed that CPZ fed mice had more Fgfr1 expression at 6 weeks (F = 13.1, p < 0.05). Post-hoc analysis showed that Fgf8+/neo mice increased approximately 3-fold when compared to Fgf8+/neo control mice (p < 0.05) (Fig. 4.6.).

Interestingly, Fgfr1 mRNA expression within 6 week treated CPZ WT mice did not differ when compared to WT and/or Fgf8+/neo control mice.

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Figure 4.6. Bar graphs representative of relative CC Fgfr1 expression in 2 week, 3 week

(Fgf8+/neo 0.2% CPZ chow group, n = 4), and 6 week (A-C) treated Fgf8 hypomorphic mice.

Asterisk denotes significance (p < 0.05).

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4.3.6. CPZ-induced Stat3 mRNA expression

Stat3 expression did not differ with CPZ treatment at 2 weeks when compared to control animals. In contrast, Stat3 expression increased with CPZ treatment at 3 and 6 weeks when compared to control animals (F=8.01 p < 0.05; F=9.31 p < 0.05) and was not genotype- dependent. Post-hoc analysis showed that Stat3 expression increased 1.7-fold with 3 weeks of

CPZ treatment (F=8.01, p < 0.05) in Fgf8+/neo mice when compared to Fgf8+/neo control mice.

Moreover, post-hoc analysis showed that Stat3 expression increased 1.7-fold with 6 weeks of

CPZ treatment (F=9.31, p < 0.05) in Fgf8+/neo mice when compared to Fgf8+/neo control mice

(Fig. 4.7.).

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Figure 4.7. Bar graphs representative of relative CC Stat3 expression in 2 week, 3 week

(Fgf8+/neo 0.2% CPZ chow group, n = 4), and 6 week (A-C) treated Fgf8 hypomorphic mice.

Asterisk denotes significance (p < 0.05).

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4.4. Discussion

Here we found that CPZ-induced astrocyte activation is impaired in Fgf8 hypomorphic mice in a region-specific manner. In response to 3 weeks of CPZ treatment, GFAP-IR within the genu increased in WT and Fgf8+/neo hypomorphic mice. In contrast, after 3 weeks of CPZ treatment the induction of GFAP-IR in the cingulum was detected in WT, but not in Fgf8+/neo hypomorphic mice. These data indicate that reduced FGF8 signaling not only delays the maturation of brain midline astrocytes, but also their ability to respond to toxic insults, such as CPZ. Interestingly, while we found that CPZ induced Gfap mRNA expression, and that this effect became more profound with prolonged CPZ treatment, this effect was not genotype-dependent. Taken together, these data suggest that FGF8 may affect the post-translation processing of the GFAP transcript rather than Gfap mRNA expression.

We observed that after 2 weeks of CPZ treatment GFAP-IR within the cingulate cortex increased in both WT and Fgf8+/neo mice. However, after 3 weeks of CPZ treatment, GFAP-IR within the cingulate cortex of Fgf8+/neo mice did not increase when compared standard chow fed

WT and Fgf8+/neo mice. This region-specific genotypic effect may be a result of an Fgf8 medial to lateral expression gradient (Dubrulle & Pourquie, 2004; Toshiaki Okada, Yuki Okumura, Jun

Motoyama, & Masaharu Ogawa, 2008; Tatsuya, Takako, Tetsuichiro, Keiichi, & Noriko, 2017).

Indeed, in situ hybridization studies showed that Fgf8 expression is much higher in the anterior medial telencephalon when compared to the anterior lateral telencephalon. This suggests that region-specific Fgf8 deficits may cause region-specific astrocytic activation.

Given that we observed a region-specific effect in GFAP-IR, we also measured the complexity of astrocyte branching in the cingulum which was the area most affected by the Fgf8 deficit in adult mice. Our results showed that 2 weeks of CPZ treatment increased secondary

106 branching in WT mice when compared to standard chow fed WT mice. Moreover, 2 weeks of

CPZ treatment increased tertiary branching in Fgf8+/neo mice when compared to standard chow fed Fgf8+/neo mice. However, there was not a genotypic dependent effect within the 2 week CPZ treated mice. Surprisingly, the branching morphology did not differ between WT and Fgf8+/neo after 3 weeks of CPZ, suggesting that FGF8’s role in CPZ-induced morphological changes is more important during the onset of astrocyte activation but not during states of prolonged activation. In support, previous studies indicated that FGF8 increases branching morphology in primary cortical astrocyte cultures (Kang et al., 2014). However, how FGF8 and CPZ interact to regulate astrocyte activation is unknown.

Fgf8 deficiency and CPZ differentially affected GFAP expression indicating that Fgf8 is required for normal GFAP expression, and that CPZ likely acts through as an additional transcriptional activator. Indeed, CPZ increased Gfap mRNA in both WT and Fgf8+/neo mice at 2,

3, and 6 weeks in a non-genotype dependent fashion. These results suggest that CPZ may induced and initiate GFAP transcription separate from the pathways that facilitate FGF8’s effects on GFAP expression. For example, CPZ may alter other transcriptional regulators, such as

STAT3 which is required for astrocyte differentiation, Gfap transcription, reactive astrocyte proliferation and survival (Akira, 1999; Herrera, Chen, & Schubert, 2010; LeComte et al., 2015;

Lee, Han, Lee, Park, & Kim, 2010; Levine et al., 2016; Ochiai, Yanagisawa, Takizawa,

Nakashima, & Taga, 2001; Sriram et al., 2004). In support, Stat3 mRNA levels increased more in 3 and 6 week CPZ fed Fgf8+/neo hypomorphic mice when compared to Fgf8+/neo hypomorphic mice fed standard chow. Furthermore, CPZ fed WT mice did not exhibit this increase when compared to WT and Fgf8+/neo mice fed standard chow. Together, this suggests either Fgf8+/neo hypomorphic mice are more vulnerable to CPZ and/or that the excess Stat3 mRNA is a

107 compensatory mechanism which acts to resolve the lack of GFAP in the CPZ fed Fgf8+/neo hypomorphic mice.

Alternatively, CPZ may be targeting the molecular components within the FGF signaling cascade, such as FGFRs, which may also alter Stat3 mRNA expression given that STAT3 activation is dependent on the expression of phosphorylated FGFR1 and we know that Fgf8 deficits lead to a reduction in FGFR1 (Mott et al., 2010). Therefore, we examined Fgfr1 mRNA expression following CPZ exposure. We found that Fgf8+/neo mice exposed to CPZ for 6 weeks exhibited an increase in Fgfr1 expression when compared to Fgf8+/neo control mice. Interestingly, there was a general increase in Fgfr1 expression following 2 and 3 weeks of CPZ exposure but it was not genotype-dependent. Together, these data imply that the delay in CPZ-induced astrocyte activation observed in Fgf8 hypomorphic mice was likely due to CPZ-dependent attenuation of

Fgfr1 expression, which subsequently impaired Stat3 activation ultimately leading to the observed reduction in GFAP-IR.

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Gfap F: 5'-GGCGCTCAATGCTGGCTTCA-3

R: 5'-TCTGCCTCCAGCCTCAGGTT- 3' Fgfr1 F: 5'-ATGGTTGACCGTTCTGGAAG -3'

R: 5'-TGGCTATGGAAGTCGCTCTT -3'

Stat3 F: 5'-ACGACCTGCAGCAATACCAT -3'

R: 5'-AACGTGAGCGACTCAAACTG -3' Hprt-1 F: 5'-CTCATGGACTGATTATGGACAGGAC-3'

R: 5'-GCAGGTCAGCAAAGAACTTATAGCC-3'

Table 4.1. Real-time qPCR forward (F) and reverse (R) primers used.

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4.5. References

Akira, S. (1999). Functional Roles of STAT Family Proteins: Lessons from Knockout Mice. STEM CELLS, 17(3), 138-146. doi: doi:10.1002/stem.170138 Balasingam, V., Tejada-Berges, T., Wright, E., Bouckova, R., & Yong, V. W. (1994). Reactive astrogliosis in the neonatal mouse brain and its modulation by cytokines. J Neurosci, 14. Chadi, G., & Gomide, V. C. (2004). FGF-2 and S100beta immunoreactivities increase in reactive astrocytes, but not in microglia, in ascending dopamine pathways following a striatal 6- OHDA-induced partial lesion of the nigrostriatal system. Cell Biol Int, 28(12), 849-861. doi: 10.1016/j.cellbi.2004.08.005 Clarke, W. E., Berry, M., Smith, C., Kent, A., & Logan, A. (2001). Coordination of fibroblast growth factor receptor 1 (FGFR1) and fibroblast growth factor-2 (FGF-2) trafficking to nuclei of reactive astrocytes around cerebral lesions in adult rats. Mol Cell Neurosci, 17(1), 17-30. doi: 10.1006/mcne.2000.0920 Dubrulle, J., & Pourquie, O. (2004). fgf8 mRNA decay establishes a gradient that couples axial elongation to patterning in the vertebrate embryo. Nature, 427(6973), 419-422. doi: 10.1038/nature02216 Dudka, A. A., Sweet, S. M. M., & Heath, J. K. (2010). STAT3 BINDING TO THE FGF RECEPTOR IS ACTIVATED BY RECEPTOR AMPLIFICATION. Cancer research, 70(8), 3391-3401. doi: 10.1158/0008-5472.CAN-09-3033 Franklin, K. B. J., & Paxinos, G. (2008). The Mouse Brain in Stereotaxic Coordinates: Academic Press. Gobius, I., Morcom, L., Suarez, R., Bunt, J., Bukshpun, P., Reardon, W., . . . Richards, L. J. (2016). Astroglial-Mediated Remodeling of the Interhemispheric Midline Is Required for the Formation of the Corpus Callosum. Cell Rep, 17(3), 735-747. doi: 10.1016/j.celrep.2016.09.033 Gudi, V., Gingele, S., Skripuletz, T., & Stangel, M. (2014). Glial response during cuprizone- induced de- and remyelination in the CNS: lessons learned. Frontiers in Cellular Neuroscience, 8, 73. doi: 10.3389/fncel.2014.00073 Herrera, F., Chen, Q., & Schubert, D. (2010). Synergistic Effect of Retinoic Acid and Cytokines on the Regulation of Glial Fibrillary Acidic Protein Expression. Journal of Biological Chemistry, 285(50), 38915-38922. doi: 10.1074/jbc.M110.170274 Hibbits, N., Yoshino, J., Le, T. Q., & Armstrong, R. C. (2012). Astrogliosis During Acute and Chronic Cuprizone Demyelination and Implications for Remyelination. ASN Neuro, 4(6), AN20120062. doi: 10.1042/AN20120062 Ito, H., Yamamoto, N., Arima, H., Hirate, H., Morishima, T., & Umenishi, F. (2006). Interleukin-1beta induces the expression of aquaporin-4 through a nuclear factor-kappaB pathway in rat astrocytes. J Neurochem, 99. doi: 10.1111/j.1471-4159.2006.04036.x

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Kang, K., Lee, S. W., Han, J. E., Choi, J. W., & Song, M. R. (2014). The complex morphology of reactive astrocytes controlled by fibroblast growth factor signaling. Glia, 62(8), 1328- 1344. doi: 10.1002/glia.22684 LeComte, M. D., Shimada, I. S., Sherwin, C., & Spees, J. L. (2015). Notch1-STAT3-ETBR signaling axis controls reactive astrocyte proliferation after brain injury. Proc Natl Acad Sci U S A, 112(28), 8726-8731. doi: 10.1073/pnas.1501029112 Lee, H. S., Han, J., Lee, S. H., Park, J. A., & Kim, K. W. (2010). Meteorin promotes the formation of GFAP-positive glia via activation of the Jak-STAT3 pathway. J Cell Sci, 123(Pt 11), 1959-1968. doi: 10.1242/jcs.063784 Levine, J., Kwon, E., Paez, P., Yan, W., Czerwieniec, G., Loo, J. A., . . . Wanner, I.-B. (2016). Traumatically injured astrocytes release a proteomic signature modulated by STAT3- dependent cell survival. Glia, 64(5), 668-694. doi: 10.1002/glia.22953 Livak, K. J., & Schmittgen, T. D. (2001). Analysis of relative gene expression data using real- time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods, 25(4), 402-408. doi: 10.1006/meth.2001.1262 Meyers, E. N., Lewandoski, M., & Martin, G. R. (1998). An Fgf8 mutant allelic series generated by Cre- and Flp-mediated recombination. Nat Genet, 18(2), 136-141. doi: 10.1038/ng0298-136 Mierzwa, A. J., Zhou, Y.-X., Hibbits, N., Vana, A. C., & Armstrong, R. C. (2013). FGF2 and FGFR1 signaling regulate functional recovery following cuprizone demyelination. Neurosci Lett, 548, 280-285. doi: http://dx.doi.org/10.1016/j.neulet.2013.05.010 Moldrich, R. X., Gobius, I., Pollak, T., Zhang, J., Ren, T., Brown, L., . . . Richards, L. J. (2010). Molecular regulation of the developing commissural plate. J Comp Neurol, 518(18), 3645-3661. doi: 10.1002/cne.22445 Mott, N. N., Chung, W. C., Tsai, P. S., & Pak, T. R. (2010). Differential fibroblast growth factor 8 (FGF8)-mediated autoregulation of its cognate receptors, Fgfr1 and Fgfr3, in neuronal cell lines. PLoS One, 5(4), e10143. doi: 10.1371/journal.pone.0010143 Ochiai, W., Yanagisawa, M., Takizawa, T., Nakashima, K., & Taga, T. (2001). ASTROCYTE DIFFERENTIATION OF FETAL NEUROEPITHELIAL CELLS INVOLVING CARDIOTROPHIN-1-INDUCED ACTIVATION OF STAT3. Cytokine, 14(5), 264-271. doi: http://dx.doi.org/10.1006/cyto.2001.0883 Okada, T., Okumura, Y., Motoyama, J., & Ogawa, M. (2008). FGF8 signaling patterns the telencephalic midline by regulating putative key factors of midline development. Dev Biol, 320(1), 92-101. doi: https://doi.org/10.1016/j.ydbio.2008.04.034 Pekny, M., & Nilsson, M. (2005). Astrocyte activation and reactive gliosis. Glia, 50(4), 427-434. doi: 10.1002/glia.20207 Pekny, M., & Pekna, M. (2014). Astrocyte reactivity and reactive astrogliosis: costs and benefits. Physiol Rev, 94(4), 1077-1098. doi: 10.1152/physrev.00041.2013

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Sovrea, A. S., & Bosca, A. B. (2013). Astrocytes reassessment - an evolving concept part one: embryology, biology, morphology and reactivity. J Mol Psychiatry, 1, 18. doi: 10.1186/2049-9256-1-18 Sriram, K., Benkovic, S. A., Hebert, M. A., Miller, D. B., & O'Callaghan, J. P. (2004). Induction of gp130-related cytokines and activation of JAK2/STAT3 pathway in astrocytes precedes up-regulation of glial fibrillary acidic protein in the 1-methyl-4-phenyl-1,2,3,6- tetrahydropyridine model of neurodegeneration: key signaling pathway for astrogliosis in vivo? J Biol Chem, 279(19), 19936-19947. doi: 10.1074/jbc.M309304200 Stewart, C. E., Corella, K. M., Samberg, B. D., Jones, P. T., Linscott, M. L., & Chung, W. C. J. (2016). Perinatal midline astrocyte development is impaired in fibroblast growth factor 8 hypomorphic mice. Brain Res, 1646, 287-296. doi: http://dx.doi.org/10.1016/j.brainres.2016.06.015 Sticozzi, C., Belmonte, G., Meini, A., Carbotti, P., Grasso, G., & Palmi, M. (2013). IL-1beta induces GFAP expression in vitro and in vivo and protects neurons from traumatic injury-associated apoptosis in rat brain striatum via NFkappaB/Ca(2)(+)-calmodulin/ERK mitogen-activated protein kinase signaling pathway. Neuroscience, 252, 367-383. doi: 10.1016/j.neuroscience.2013.07.061 Tatsuya, S., Takako, K., Tetsuichiro, S., Keiichi, I., & Noriko, O. (2017). Organizing activity of Fgf8 on the anterior telencephalon. Development, Growth & Differentiation, 59(9), 701- 712. doi: doi:10.1111/dgd.12411 Zhang, L., Zhao, W., Li, B., Alkon, D. L., Barker, J. L., Chang, Y. H., . . . Rubinow, D. R. (2000). TNF-alpha induced over-expression of GFAP is associated with MAPKs. Neuroreport, 11(2), 409-412.

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Chapter 5

Analyzing Cuprizone Effects on Corpus Callosum Thickness with Magnetic Resonance

Imaging in the Adult Mouse Brain

5.1. Introduction

Fgf8 hypomorphic mice exhibit disrupted perinatal CC formation, which we showed was the result of impaired anterior midline astrocyte development (Gobius et al., 2016; Stewart et al.,

2016). Furthermore, due to this developmental Fgf8 signaling deficit, these astrocyte populations are functionally impaired well into adulthood. For example, we showed that these astrocytes experience a delay in GFAP acquisition and have an abnormal astrocytic branching morphology during cuprizone-induced astrocyte activation. These observations led us to hypothesize that

Fgf8 hypomorphic mice are more vulnerable to the cytotoxic challenge caused during CPZ ingestion.

In our prior studies, we utilized CPZ strictly as an astrocytic activator and only assessed the effects of CPZ by comparing GFAP-IR and astrocyte morphology, an invasive experimental design. Moreover, this approach allowed us to only observe temporally limited snapshots of CPZ effects. However, CPZ can cause varied effects to multiple cell types over time. For example,

CPZ is classically used in MS research to examine demyelination and remyelination mechanisms with most of the research focused on oligodendrocytes, which die following 3 weeks of CPZ feeding (Benardais et al., 2013; Praet et al., 2014; Taraboletti et al., 2017; Tezuka et al., 2013).

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Moreover, studies comparing acute (i.e., 6 weeks) vs. chronic (i.e., 12 weeks) CPZ exposure show differences in astrocyte activation (Hibbits et al., 2009; Thomas Skripuletz et al., 2008).

Specifically, an astrocyte’s ability to deactivate or appear quiescent after removing CPZ from rodent chow is inhibited following chronic exposure. Furthermore, the level of astrocyte activation has been shown to be related to myelination recovery and/or scar formation post- relapse in MS (Viktoria Gudi et al., 2014; Hibbits et al., 2009; Hibbits et al., 2012). Therefore, we decided to assess the temporal effects of CPZ exposure on myelination and CC volume in

Fgf8 hypomorphic mice using a non-invasive imaging approach that is made possible by using magnetic resonance imaging (MRI) (Oakden, Bock, Al-Ebraheem, Farquharson, & Stanisz,

2017; Thiessen et al., 2013).

The use of MRI in myelin detection in living subjects, either in normal or diseased human and rodent brains, has been used for decades and works by using radiofrequency (RF) pulses to disturb or excite protons within the water and lipids of myelin. The protons then emit signals as they begin to reach equilibrium or “relax” which are processed into images by using magnetic field gradients (Auer, Vagionitis, & Czopka, 2018; Welker & Patton, 2012; Jiangyang Zhang,

2010). Depending on water vs. lipid content in the myelin the RF pulse and magnetic field gradient used is adjusted to provide optimal contrast and resolution in the final image. Two of the most common pulse sequences used for myelin detection are T1, spin-lattice relaxation, or the rate at which excited protons reach equilibrium and T2, spin-spin relaxation, or the rate at which MR signal decays while spins interact. In human studies, T1 is best used during early myelination formation when more water is present, while T2 is best used for maturing myelin

114 when more lipids and cholesterol are present. Myelination occurs at varying rates depending on the brain region and therefore a combination of T1 and T2 images are used to accurately assess human myelin development and abnormalities (MacKay & Laule, 2016; Welker & Patton,

2012). Next magnet strength is optimized based on the signal to noise ratio, tissue susceptibility, chemical shift or shimming, and RF effects. Human studies regularly use 1.5T to 3T magnets as they provide the best signal to noise ratio for most tissues while causing few artifacts. Recently,

7T up to 10.1T magnets have been used which does increase image resolution but can result in shimming or other artifacts depending on the tissue being examined (Laader et al., 2017).

Here, we used an Icon 1 Tesla pre-clinical MRI (Bruker, MA) to collect images to examine the effect of CPZ and the length of CPZ exposure on overall CC thickness in adult WT and Fgf8+/neo hypomorphic mice. This approach is non-invasive and will allow us to see the temporal effects of CPZ in the Fgf8 hypomorhic mouse. We hypothesize that Fgf8 hypomorphic mice will be more vulnerable to CPZ which will result in more rapid demyelination and subsequently be observed as an overall decrease in CC volume.

5.2. Materials and methods

5.2.1. Animals

Committee at Kent State University. Fgf8 hypomorphic mice exhibit a ~25% and ~55% reduction in Fgf8 mRNA expression in Fgf8+/neo and Fgf8neo/neo mice, respectively (Meyers et al.,

1998).

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5.2.2. Cuprizone treatment

To study FGF8’s role in astrocyte activation, adult 2 to 4-month-old male Fgf8 hypomorphic mice were fed standard chow (Prolab, LabDiet, St. Louis, MO) or 0.2% (w/w) cuprizone (CPZ)

(Sigma, IL) supplemented chow ab libitum. Animal weight was recorded and food was replaced every two days until the mice were sacrificed. All mice were single-housed.

5.2.3. MRI image collection

MRI scans were taken bi-weekly for 8 weeks. An Icon 1 Tesla pre-clinical MRI (Bruker, MA) was used to collect diffusion weighted (DW), T1 and T2 weighted image stacks from each cohort. 4 cohorts were examined, WT and Fgf8+/neo fed standard chow and WT and Fgf8+/neo fed

0.2% CPZ chow. Each mouse was weighed, then anesthetized using 1.5% continuous flow isoflurane in conjunction with a mixture of 5 mg/kg butorphanol (Zoetis, Kalamazoo, MI) and

0.04 mg/kg atropine (Vedco, St. Joseph, MO) that was injected subcutaneously. Once the mouse was no longer responsive to a toe pinch, it was placed on the tooth bar of the animal housing unit, respiratory and heart monitors were then placed on the mouse and loosely taped down.

Once the respiration curve and BPM could be observed on the PC-sam software (SA instruments, INC. Stony Brook, NY), the animal housing unit was placed into the MRI RF coil and into the MRI scanner. While in the scanner, the mouse continuously received 1.5% isoflurane and 98.5% O2 and heart and respiration rate were monitored throughout the duration of the scan for any signs of distress (i.e., increased heart rate, shallow or rapid breath curves). If any sign of distress occurred, the scan was immediately stopped. The angle of the RF coil was calibrated to 0 degrees prior to conducting a localizer scan which orients the mouse and sets the starting point (i.e., olfactory bulb) and end point (i.e., at the end of the cerebellum) of each scan

116 prior to DW, T1 FLASH, and T2 RARE image collection. Scan parameters were as follows: repetition time (TR) = 2800 ms, Echo time (TE) = 85 ms, scan averages = 20, acquisition time,

7:28, and flip-angle = 70. After the completion of each scan, the mouse was removed from the

MRI and placed in a warming chamber to recover. No more than 8 scans were conducted in a single day to prevent overheating of the magnet and conserve image quality.

5.2.4. Corpus callosum volume estimates

To estimate CC volume in each mouse over time, two T2 weighted images containing the CC were stacked and converted to TIFF stacks using Fiji Image J (NIH, Bethesda, MD). Using the freehand tool in ImageJ, manual CC traces were drawn on each section by an observer who had no knowledge of genotype or treatment. Collected CC areas were converted to volumetric data by multiply the raw area values by the thickness of the cross-sectional slices (0.75 mm) collected during the MRI scan and summed.

5.2.5. Statistical analysis

Data were analyzed using three-way analysis of variance (ANOVA) with time, genotype and chow as between subject variables. Student Newman-Keuls tests were used for post hoc analysis.

Differences were considered significant if p < 0.05. Animals and treatments were randomized and coded by an independent investigator. All measurements were conducted by an observer without knowledge of genotype and treatment. The number of animals analyzed for each study is five unless otherwise indicated in the figure legends.

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5.3. Results

5.3.1. Corpus callosum volume

Our results showed that corpus callosum volume differed with time and treatment but not genotype. Specifically, we observed a decrease in volume in adult CPZ fed animals after 4 weeks and 8 weeks. Three-way ANOVA showed that time was significant (F = 20.5, p < 0.001) and chow was significant (F = 16.8, p < 0.001). There was also an interaction between time and chow

(F = 4.94, p < 0.05) (Fig. 5.1). Post-hoc analysis showed that 4 weeks CPZ-fed mice had less volume when compared to week 0 (p < 0.001), 2 (p < 0.001), 6 (p = 0.002), and 8 (p < 0.001).

Week to week comparisons in between CPZ-fed mice CC volumes can be found in table 5.1. post-hoc analysis also showed that standard chow 4 week fed mice also exhibited time- dependent decreases in CC volume when compared to week 0 (p = 0.017), 2 (p = 0.005) and 8 (p

=0.018). Week to week comparisons in between standard chow fed mice CC volumes can be found in table 5.2. Given that the same cohorts of mice were analyzed through the course of this study, significant changes in CC volume under normal conditions are unlikely but rather this significance could be the result of poor image acquisition and/or MRI artifacts.

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Figure 5.1. Bar graphs representing manually traced CC volumes between WT and Fgf8+/neo hypomorphic mice recorded from MRI images collected over time. Aterisks denote significance.

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Factor compared: Time within Standard Chow Week p value 0 vs. 2 0.714 0 vs. 4 0.017* 0 vs. 6 0.074 0 vs. 8 0.842 2 vs. 4 0.005* 2 vs. 6 0.054 2 vs. 8 0.561 4 vs. 6 0.321 4 vs. 8 0.018* 6 vs. 8 0.117

Table 5.1. Week to week comparisons of the temporal effects of standard chow treatment on CC volumes p-values. Asterisks denotes significance.

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Factor compared: Time within 0.2% CPZ Chow Week p value 0 vs. 2 0.963 0 vs. 4 <0.001* 0 vs. 6 <0.001* 0 vs. 8 <0.001* 2 vs. 4 <0.001* 2 vs. 6 <0.001* 2 vs. 8 <0.001* 4 vs. 6 0.002* 4 vs. 8 0.003* 6 vs. 8 0.814

Table 5.2. Week to week comparisons of the temporal effects of 0.2% CPZ chow treatment on CC volumes p-values. Asterisks denotes significance.

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5.4. Discussion

Here, we showed that Fgf8 hypomorphic mice were not more vulnerable to CPZ than their WT littermates based on CC volumes recorded from MRI images collected using 1T pre-clinical

MRI. Although, these results do not rule out the possibility that Fgf8 hypomorphic mice are indeed more vulnerable which we showed using other research approaches (Refer to chapter 4), this non-invasive approach may be viable in the future. Indeed, we found that a 0.2% CPZ diet decreased CC myelination volume after 4 weeks, a decrease that was not observed at 6 weeks.

Interesting, when the animals were examined at 8 weeks, we found a reoccurrence of decreased

CC volume.

Although, spontaneous remyelination can begin around week 5, it is unlikely that enough myelin was generated within 2 weeks to account for the volume lost in week 4. Indeed, remyelination readily begins after CPZ is removed from the diet, but demyelinated axons are not completely repaired for 3 to 5 weeks and even then, the myelin generated is thinner than the original sheath and should be detected on MRI (Carlton, 1967; V. Gudi et al., 2009; Viktoria

Gudi et al., 2014; Hibbits et al., 2009; Hibbits et al., 2012; Mierzwa et al., 2013; Oakden et al.,

2017; T. Skripuletz et al., 2011; Tezuka et al., 2013; Thiessen et al., 2013). Furthermore, in our paradigm a diet of 0.2% was persistent for 8 weeks until the day the mice were euthanized further decreasing the likelihood of spontaneous remyelination. Alternatively, we may also be detecting residual CNS debris from axonal damage (Egawa, Lok, Washida, & Arai, 2017).

Therefore, we will further discuss optimal MRI image collection standards and if better non- invasive assays are available.

The use of MRI in myelin detection, either in normal or diseased brains, has been used for decades and works by using radiofrequency pulses to disturb or excite protons within the water

122 and lipids of myelin. The protons then emit signals as they begin to reach equilibrium or “relax” which are processed into images by using magnetic field gradients (Auer, Vagionitis, & Czopka,

2018; Welker & Patton, 2012; Jiangyang Zhang, 2010). Depending on water vs. lipid content in the myelin the RF pulse and magnetic field gradient used can be changed to provide optimal contrast and resolution in the final image that shows intensely white myelinated structures and greyish-white brain tissue. This contrast will lessen over time as the myelin becomes damaged in

TMEV-IDD, EAE, or in our case CPZ models. Therefore, intensity and/or volumetric studies of white matter tracts can be used to monitor demyelination.

Two of the most common pulse sequences used for myelin detection are T1, spin-lattice relaxation, or the rate at which excited protons reach equilibrium and T2, spin-spin relaxation, or the rate at which MR signal decays while spins interact (MacKay & Laule, 2016; Welker &

Patton, 2012; Q.-Z. Wu et al., 2008). In human studies, T1 is best used during early myelination formation when more water is present, while T2 is best used for maturing myelin when more lipids and cholesterol are present. Myelination occurs at varying levels depending on the brain region, and therefore a combination of T1 and T2 images are used to accurately assess human myelin development and abnormalities (Ganzetti, Wenderoth, & Mantini, 2014; Welker &

Patton, 2012). Moreover, magnet strength optimization is based on the signal to noise ratio, tissue susceptibility, chemical shift or shimming, and RF effects (Krupa & Bekiesińska-

Figatowska, 2015). The biggest limiting factor in our studies was that we used an Icon 1 Tesla pre-clinical MRI (Bruker, MA) and subsequently the magnet gradient did not provide adequate image resolution. Indeed, we were only able to use T2 images to quantify CC myelination, because the T1 images had high noise signals and poor resolution. Moreover, DW images were susceptible to ring-like or shading artifacts, which further reduced the reliability of our data.

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Moreover, we encountered several other technical difficulties that hampered our progress and the reliability of our measures, such as, abnormal anesthetic response in some of our Fgf8 hypomorphic mice. Specifically, our mice had a tendency to gasp while under anesthetic, even though respiratory rate and BPM were stable. In some cases, the mice were generating enough movement to cause artifacts in the image at which point based on the stability of the mouse would decide to attempt another scan.

Although there were mechanical and technical issues, using MRI as a non-invasive tool to assess myelin damage is promising. Ideally, to achieve better results in the future, we would use a minimum 3T MRI scanner with the current standard for rodent studies being 4.7T to 11.7T.

This would significantly enhance the resolution of collected images (Denic et al., 2011; Duyn,

2013; Heath, Hurley, Johansen-Berg, & Sampaio-Baptista, 2018; Laader et al., 2017; Q.-Z. Wu et al., 2008). Alternatively, we could add a contrast agent, such as gadolinium (Gd) to increase the signal intensity of T1 images which can then be used together with T2 images to analyze myelin (Dibb, Li, Cofer, & Liu, 2014; Frullano, Zhu, Miller, & Wang, 2013). Furthermore, MRI may also be correlated with a minimally invasive biomarker assays to allow for a more in-depth quantification of CPZ effects on CC myelination. To date, the only known biomarker is myelin oligodendrocyte glycoprotein (MOG). Both CPZ fed mice and humans with relapse remitting

MS have excess circulating-free methylated MOG gene DNA in blood serum. The peak of expression for this biomarker in CPZ fed mice is around 2 weeks providing the earliest time point of reliable non-invasive examination to date (Olsen et al., 2016). This method may be useful but would not prove that the hypothesized vulnerability to CPZ in Fgf8 hypomorphic mice is due to astrocyte developmental deficits. Future studies will test the viability and efficacy of this approach in rodents.

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5.5. References Auer, Franziska, Vagionitis, Stavros, & Czopka, Tim. (2018). Evidence for Myelin Sheath Remodeling in the CNS Revealed by In Vivo Imaging. Current Biology, 28(4), 549- 559.e543. doi:https://doi.org/10.1016/j.cub.2018.01.017 Benardais, K., Kotsiari, A., Skuljec, J., Koutsoudaki, P. N., Gudi, V., Singh, V., . . . Stangel, M. (2013). Cuprizone [bis(cyclohexylidenehydrazide)] is selectively toxic for mature oligodendrocytes. Neurotox Res, 24(2), 244-250. doi:10.1007/s12640-013-9380-9 Carlton, W. W. (1967). Studies on the induction of hydrocephalus and spongy degeneration by cuprizone feeding and attempts to antidote the toxicity. Life Sci, 6(1), 11-19. Denic, Aleksandar, Macura, Slobodan I., Mishra, Prasanna, Gamez, Jeffrey D., Rodriguez, Moses, & Pirko, Istvan. (2011). MRI in rodent models of brain disorders. Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics, 8(1), 3-18. doi:10.1007/s13311-010-0002-4 Dibb, Russell, Li, Wei, Cofer, Gary, & Liu, Chunlei. (2014). Microstructural origins of gadolinium-enhanced susceptibility contrast and anisotropy. Magnetic resonance in medicine, 72(6), 1702-1711. doi:10.1002/mrm.25082 Duyn, Jeff. (2013). MR susceptibility imaging. Journal of magnetic resonance (San Diego, Calif. : 1997), 229, 198-207. doi:10.1016/j.jmr.2012.11.013 Egawa, N., Lok, J., Washida, K., & Arai, K. (2016). Mechanisms of Axonal Damage and Repair after Central Nervous System Injury. Translational stroke research, 8(1), 14–21. doi:10.1007/s12975-016-0495-1 Frullano, Luca, Zhu, Junqing, Miller, Robert H., & Wang, Yanming. (2013). Synthesis and characterization of a novel gadolinium-based contrast agent for magnetic resonance imaging of myelination. Journal of medicinal chemistry, 56(4), 1629-1640. doi:10.1021/jm301435z Ganzetti, Marco, Wenderoth, Nicole, & Mantini, Dante. (2014). Whole brain myelin mapping using T1- and T2-weighted MR imaging data. Frontiers in Human Neuroscience, 8(671). doi:10.3389/fnhum.2014.00671 Gobius, I., Morcom, L., Suarez, R., Bunt, J., Bukshpun, P., Reardon, W., . . . Richards, L. J. (2016). Astroglial-Mediated Remodeling of the Interhemispheric Midline Is Required for the Formation of the Corpus Callosum. Cell Rep, 17(3), 735-747. doi:10.1016/j.celrep.2016.09.033 Gudi, V., Moharregh-Khiabani, D., Skripuletz, T., Koutsoudaki, P. N., Kotsiari, A., Skuljec, J., . . . Stangel, M. (2009). Regional differences between grey and white matter in cuprizone induced demyelination. Brain Res, 1283, 127-138. doi:10.1016/j.brainres.2009.06.005 Gudi, Viktoria, Gingele, Stefan, Skripuletz, Thomas, & Stangel, Martin. (2014). Glial response during cuprizone-induced de- and remyelination in the CNS: lessons learned. Frontiers in Cellular Neuroscience, 8, 73. doi:10.3389/fncel.2014.00073

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Heath, Florence, Hurley, Samuel A., Johansen-Berg, Heidi, & Sampaio-Baptista, Cassandra. (2018). Advances in noninvasive myelin imaging. Dev Neurobiol, 78(2), 136-151. doi:10.1002/dneu.22552 Hibbits, Norah, Pannu, Ravinder, Wu, T. John, & Armstrong, Regina C. (2009). Cuprizone demyelination of the corpus callosum in mice correlates with altered social interaction and impaired bilateral sensorimotor coordination. ASN Neuro, 1(3), e00013. doi:10.1042/AN20090032 Hibbits, Norah, Yoshino, Jun, Le, Tuan Q., & Armstrong, Regina C. (2012). Astrogliosis During Acute and Chronic Cuprizone Demyelination and Implications for Remyelination. ASN Neuro, 4(6), AN20120062. doi:10.1042/AN20120062 Krupa, Katarzyna, & Bekiesińska-Figatowska, Monika. (2015). Artifacts in magnetic resonance imaging. Polish journal of radiology, 80, 93-106. doi:10.12659/PJR.892628 Laader, Anja, Beiderwellen, Karsten, Kraff, Oliver, Maderwald, Stefan, Wrede, Karsten, Ladd, Mark E., . . . Umutlu, Lale. (2017). 1.5 versus 3 versus 7 Tesla in abdominal MRI: A comparative study. PLoS One, 12(11), e0187528. doi:10.1371/journal.pone.0187528 MacKay, Alex L., & Laule, Cornelia. (2016). Magnetic Resonance of Myelin Water: An in vivo Marker for Myelin. Brain plasticity (Amsterdam, Netherlands), 2(1), 71-91. doi:10.3233/BPL-160033 Meyers, E. N., Lewandoski, M., & Martin, G. R. (1998). An Fgf8 mutant allelic series generated by Cre- and Flp-mediated recombination. Nat Genet, 18(2), 136-141. doi:10.1038/ng0298-136 Mierzwa, Amanda J., Zhou, Yong-Xing, Hibbits, Norah, Vana, Adam C., & Armstrong, Regina C. (2013). FGF2 and FGFR1 signaling regulate functional recovery following cuprizone demyelination. Neuroscience Letters, 548, 280-285. doi:http://dx.doi.org/10.1016/j.neulet.2013.05.010 Oakden, W., Bock, N. A., Al-Ebraheem, A., Farquharson, M. J., & Stanisz, G. J. (2017). Early regional cuprizone-induced demyelination in a rat model revealed with MRI. NMR Biomed, 30(9). doi:10.1002/nbm.3743 Olsen, John A., Kenna, Lauren A., Tipon, Regine C., Spelios, Michael G., Stecker, Mark M., & Akirav, Eitan M. (2016). A Minimally-invasive Blood-derived Biomarker of Oligodendrocyte Cell-loss in Multiple Sclerosis. EBioMedicine, 10, 227-235. doi:10.1016/j.ebiom.2016.06.031 Praet, J., Guglielmetti, C., Berneman, Z., Van der Linden, A., & Ponsaerts, P. (2014). Cellular and molecular neuropathology of the cuprizone mouse model: clinical relevance for multiple sclerosis. Neurosci Biobehav Rev, 47, 485-505. doi:10.1016/j.neubiorev.2014.10.004 Skripuletz, T., Gudi, V., Hackstette, D., & Stangel, M. (2011). De- and remyelination in the CNS white and grey matter induced by cuprizone: the old, the new, and the unexpected. Histol Histopathol, 26(12), 1585-1597. doi:10.14670/hh-26.1585

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Skripuletz, Thomas, Lindner, Maren, Kotsiari, Alexandra, Garde, Niklas, Fokuhl, Jantje, Linsmeier, Franziska, . . . Stangel, Martin. (2008). Cortical demyelination is prominent in the murine cuprizone model and is strain-dependent. The American journal of pathology, 172(4), 1053-1061. doi:10.2353/ajpath.2008.070850 Stewart, Courtney E., Corella, Kristina M., Samberg, Brittany D., Jones, Paula T., Linscott, Megan L., & Chung, Wilson C. J. (2016). Perinatal midline astrocyte development is impaired in fibroblast growth factor 8 hypomorphic mice. Brain Research, 1646, 287- 296. doi:http://dx.doi.org/10.1016/j.brainres.2016.06.015 Taraboletti, Alexandra, Walker, Tia, Avila, Robin, Huang, He, Caporoso, Joel, Manandhar, Erendra, . . . Shriver, Leah P. (2017). Cuprizone Intoxication Induces Cell Intrinsic Alterations in Oligodendrocyte Metabolism Independent of Copper Chelation. Biochemistry, 56(10), 1518-1528. doi:10.1021/acs.biochem.6b01072 Tezuka, Tomoaki, Tamura, Makoto, Kondo, Mari A., Sakaue, Masaki, Okada, Kinya, Takemoto, Kana, . . . Kajii, Yasushi. (2013). Cuprizone short-term exposure: astrocytic IL-6 activation and behavioral changes relevant to psychosis. Neurobiology of disease, 59, 63- 68. doi:10.1016/j.nbd.2013.07.003 Thiessen, Jonathan D., Zhang, Yanbo, Zhang, Handi, Wang, Lingyan, Buist, Richard, Del Bigio, Marc R., . . . Martin, Melanie. (2013). Quantitative MRI and ultrastructural examination of the cuprizone mouse model of demyelination. 26(11), 1562-1581. doi:doi:10.1002/nbm.2992 Welker, K. M., & Patton, A. (2012). Assessment of normal myelination with magnetic resonance imaging. Semin Neurol, 32(1), 15-28. doi:10.1055/s-0032-1306382 Wu, Qi-Zhu, Yang, Qing, Cate, Holly S., Kemper, Dennis, Binder, Michele, Wang, Hong- Xin, . . . Egan, Gary F. (2008). MRI identification of the rostral-caudal pattern of pathology within the corpus callosum in the cuprizone mouse model. 27(3), 446-453. doi:doi:10.1002/jmri.21111 Zhang, Jiangyang. (2010). Diffusion tensor imaging of white matter pathology in the mouse brain. Imaging in medicine, 2(6), 623-632. doi:10.2217/iim.10.60

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Chapter 6 General Discussion

Brain development is dependent upon multiple ligand/receptor combinations within

FGF/FGFR signaling. FGFs are crucial for brain development as they regulate cell proliferation, specification, differentiation, migration and survival (Dono et al., 2002; Ford-Perriss et al., 2001;

Goldfarb, 2001; D. M. Ornitz, 2000; David M. Ornitz & Itoh, 2015). Most FGF ligand actions during brain development are region-specific and many knockout studies have shown FGFs are involved in neural induction, brain symmetry, cortical, forebrain, midbrain and cerebellar development (Ford-Perriss et al., 2001). For example, FGF2 knockouts fail to develop a cortex due to impaired cortical neurogenesis (Raballo et al., 2000). Interestingly, some FGFs have multiple roles in brain development. For example, FGF8 mutants fail to develop olfactory bulbs, a hypothalamus, or a corpus callosum primarily due to FGF8s role as a neuronal and astrocytic pro-survival factor (Ohuchi et al., 2000; Toshiaki Okada et al., 2008; Tatsuya et al., 2017). In addition, research from our laboratory indicates that FGF8 can also affect neuronal and astrocyte differentiation (Linscott & Chung, 2016; Rodriguez et al., 2015; Stewart et al., 2016; Tata et al.,

2012).

Here, we examined the role of FGF8 signaling in astrocyte development and function. To the best of our knowledge, we are the first to report that FGF8 is required for proper midline astrocyte development. Specifically, we showed that a Fgf8 deficit delayed the acquisition of

GFAP ultimately impairing the maturation of midline astrocytes, and subsequently resulting in

ACC. To our surprise, this delay in astrocyte maturation was transient, as adult Fgf8

128 hypomorphic mice expressed midline GFAP astrocyte levels identical to their WT littermates indicating that astrocyte development in mice with FGF8 deficits normalized in adulthood.

However, this did not rule out the possibility that perinatal Fgf8 deficits may have permanent effects on astrocyte function, such as astrocyte activation. Therefore, we stressed Fgf8 hypomorphic astrocytes with CPZ, a toxin which targets and activates corpus callosum specific astrocytes, and compared the astrocyte activation response between WT and Fgf8+/neo hypomorphic mice (V. Gudi et al., 2009; Viktoria Gudi et al., 2014). Interestingly, we found that a perinatal Fgf8 deficit did permanently impair midline astrocytic function. Specifically, CC astrocytes within adult Fgf8+/neo CPZ-fed hypomorphic mice exhibited a delay in GFAP acquisition, an altered astrocytic branching morphology, and an abnormal second messenger system profile when compared to WT CPZ-fed mice. These studies demonstrate that, in addition to FGF8’s role as a source of astrocytic trophic support, FGF8 also plays an important role in

GFAP acquisition, astrocyte maturation and astrocyte activation. Together, these studies have expanded our knowledge on, not only FGF8’s role in brain development, but also how FGF8 regulates astrocyte development and function.

6.1. Fgf8 signaling regulates perinatal anterior midline astrocyte development during CC formation

FGF8 signaling has many roles in brain development, including its role as a source of trophic support for midline astrocytes. Prior studies showed that FGF8 conditional null mice exhibited an absence of prenatal midline astrocytes which correlated with ACC. These studies led to the conclusion that FGF8 functioned as a survival factor during prenatal astrocyte development

(Moldrich et al., 2010). However, these studies did not test this conclusion with cell death assays.

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In our studies, we examined apoptotic cell death in the developing anterior brain midline by quantifying the number of pyknotic cells. Our results showed that the incidence of apoptosis did not differ between E16.5 or E17.5 WT and Fgf8 hypomorphic mice, leading us to infer that the exact role of FGF8 it is not simply to act as a trophic factor.

Here, we found that FGF8 also plays a role in midline astrocyte development, which is likely the causative factor behind ACC observed in Fgf8 hypomorphic mice. The development of key midline astrocyte populations, IG and MZ, was impaired by the Fgf8 hypomorphy and as a result nearly eliminated GFAP expression. These results agreed with previous studies (Moldrich et al., 2010; Richards et al., 2004; Smith et al., 2006), however, a key question remained: At what point during astrocyte development is Fgf8 most crucial? Therefore, we analyzed early and mid-stage astrocyte marker expression, GLAST and S100b, respectively at PN0, and found that a perinatal Fgf8 hypomorphy did not impair GLAST or S100b expression indicating that radial glial astrocyte precursors develop normally and followed the expected astrocyte differentiation pathway but failed to fully mature in time into midline GFAP expressing astrocytes.

Although, our results agree with previous studies indicating that a reduction in FGF8 signaling contributes to ACC (Huffman et al., 2004; Moldrich et al., 2010), other studies found that ACC could be a result of FGF8 upregulation. For example, an overexpression of FGF8 in

Rfx3 and Gli3 null mice resulted in impaired midline glial guidepost cell positioning, altered radial glial translocation, and prevented callosal axons from crossing the anterior-dorsal midline

(Amaniti et al., 2013; Benadiba et al., 2012; Magnani et al., 2012). Together these studies show that FGF8has dose-dependent effects on astrocytes, and therefore must be tightly regulated for normal anterior-dorsal midline and corpus callosum development.

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This lack of midline GFAP expression resulted ACC which we determined by analyzing

NP-1-IR was not due to disrupted callosal axon targeting implying that mature astrocytes did not affect callosal targeting of the anterior midline regions, but rather the FGF8-dependent MZ astrocyte population may be required for fusing the telencephalonic hemispheres to provide an anatomical substrate for callosal fibers to cross interhemispherically (Silver et al., 1993; Silver &

Ogawa, 1983). In support, recent studies have shown that this mature GFAP expressing astrocyte population specifically is needed to remodel the interhemispheric midline and aid in the formation of a midline substrate that callosal axons cross upon during CC formation (Gobius et al., 2016).

6.2. FGF8 is of importance during astrocyte activation when challenged with the neurotoxin cuprizone

We and others showed that midline astrocyte GFAP acquisition and morphology is dependent upon FGF8 (Kang et al., 2014; Moldrich et al., 2010; Smith et al., 2006; Stewart et al.,

2016). Astrocyte activation occurs in many neurological disorders and is usually characterized with GFAP upregulation and increased astrocyte branching morphology. Depending on the degree and length of insult, astrocyte activation it can be protective or cause further neurological damage. Therefore, understanding the underlying molecular mechanisms is of importance. Here, we used the CPZ model system, which is known to activate astrocytes when incorporated into rodent chow, to study whether delayed astrocytes maturation due to Fgf8 hypomorphy was detrimental for astrocyte function

Based on our perinatal data, we predicted that Fgf8 hypomorphic mice would be particularly vulnerable to CPZ, and subsequent astrocyte activation would be impaired. Indeed,

131 we found that CPZ-induced astrocyte activation was impaired in Fgf8 hypomorphic mice in a region-specific manner. Specifically, cortical and genu-localized CC astrocyte activation was not

FGF8-dependent. In contrast, astrocyte activation within the cingulum of the CC as measured with GFAP and astrocyte branching was FGF8-dependent. These data indicate that reduced

FGF8 signaling not only delayed the maturation of brain midline astrocytes, but also disrupted their ability to respond to toxic insults, such as CPZ.

The observed region-specific CC vulnerability could be a result of a Fgf8 medial to lateral expression gradient (Dubrulle & Pourquie, 2004; Toshiaki Okada et al., 2008; Tatsuya et al., 2017). FGF8 signaling is known to act as an anterior-posterior (AP) patterning morphogen during development but recent studies showed that it may also control medial-lateral patterning.

Specifically, overexpressing FGF8 expression rescued the expression of Lhx2, a gene essential for lateral cortex development, in Lhx2 mutants. This patterning TF is normally expressed in a medial-lateral fashion suggesting FGF8 also has medial-lateral patterning properties (Toshiaki

Okada et al., 2008). Furthermore, FGF8 overexpression expanded the dorsal frontal cortex why simultaneously shrinking the parietal cortex supporting the idea that FGF8 regulates medial- lateral patterning in the brain (Tatsuya et al., 2017). However, more studies will need to be done to confirm if FGF8 acts similarly in the perinatal and adult anterior-midline.

Fgf8 hypomorphy and CPZ differentially affected GFAP expression indicating that Fgf8 is required for normal GFAP expression, and that CPZ likely acts as an additional transcriptional activator. We excised the complete CC and found that surprisingly CPZ increased Gfap mRNA in a non-genotype dependent fashion. Furthermore, we found that a key GFAP transcriptional activator, Stat3, mRNA levels increased in 3 and 6 week CPZ fed Fgf8+/neo hypomorphic mice when compared to Fgf8+/neo hypomorphic mice fed standard chow. CPZ fed WT mice did not

132 exhibit this increase when compared to WT and Fgf8+/neo mice fed standard chow. Together, this suggests either Fgf8+/neo hypomorphic mice are more vulnerable to CPZ and/or that the excess

Stat3 mRNA is a compensatory mechanism, which acts to resolve the lack of GFAP in the CPZ fed Fgf8+/neo hypomorphic mice.

STAT3 activation is dependent on the expression of phosphorylated FGFR1 and we know that Fgf8 deficits lead to a reduction in FGFR1 (Mott et al., 2010). Surprisingly, we found that Fgf8+/neo mice exposed to CPZ for 6 weeks exhibited an increase in Fgfr1 expression when compared to Fgf8+/neo control mice. Interestingly, there was a general increase in Fgfr1 expression following 2 and 3 weeks of CPZ exposure, but it was not genotype dependent.

Together, these data imply that the delay in CPZ-induced astrocyte activation observed in Fgf8 hypomorphic mice was likely due to CPZ-dependent attenuation of Fgfr1 expression, which subsequently impaired Stat3 activation ultimately leading to the observed reduction in GFAP-IR

(summarized in figure 6.1). These data also explain a possible molecular mechanism by which

CPZ leads to microgliosis and astrogliosis which potentially may exacerbate and/or cause oligodendrocyte cell death in this model. For example, STAT3 activity has been shown to regulate microglia responsiveness to beta amyloid, and regulates GFAP transcription (Eufemi et al., 2015; Herrmann et al., 2008; Hong & Song, 2014; Steelman et al., 2016). Furthermore, disrupted STAT3 activation may impair remyelination given that oligodendrocyte-specific

STAT3 activation is required for proper remyelination.

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Figure 6.1. Fgf8 signaling effects during CPZ-induced astrocyte activation.

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6.3. The cellular signaling mechanisms underlying FGF8-dependent astrocyte development and function

Our data showed that FGF8 did indeed play a role in anterior-midline astrocyte development and that perinatal Fgf8 deficits had permanent effects on CPZ-induced astrocyte activation

(Stewart et al., 2016). However, more work is needed to narrow down which signaling pathway(s) FGF8 activates to induce astrogliogenesis and/or terminal astrocyte differentiation, and astrocyte activation. Based on previous studies, FGF8 likely is involved in both steps of astrocyte development. For example, NFIA and NFIB expression increased in response to FGF8 overexpression in E12 mice (Gobius et al., 2016). This FGF8-NFIA/B interaction could have implications during early stages of astrogliogenesis, specifically during the gliogenic switch when NFIA is required for GLAST expression onset. Furthermore, this FGF8-NFIA/B may drive astrocyte differentiation as NFIA aids in demethylation of the GFAP promotor during development. Specifically, DMNT1 mutant mice studies showed NFIA may initiate the dissociation of DNA methyltransferases (DNMTs) and without this demethylation GFAP transcription would likely be impaired (Jin et al., 2009). In support, FGF8 overexpression in

NFIA knockout mice increased GLAST expression but failed to increase GFAP (Pringle et al.,

2003).

Alternatively, FGF8 may act as pro-gliogenic factor driving astrocyte cell fate. Normally,

NSCs develop towards glial cell fate under the direction BMP/SMAD signaling and/or

LIF/JAK/STAT signaling (Bond et al., 2012; Chuang et al., 2015; Jiwang Zhang & Li, 2005).

Activation of these cascade ultimately alters the STAT3 binding site on the GFAP promoter and

STAT3 is critical for astrocyte differentiation (Bonaguidi et al., 2005; Hong & Song, 2014;

Jiwang Zhang & Li, 2005). Interestingly, we did find that relative Stat3 expression was reduced

135 in a genotype-dependent fashion in PN0 Fgf8 hypomorphic mice (unpublished data). Moreover,

STAT3 activation is dependent on the expression of phosphorylated FGFR1 and we know that

Fgf8 deficits lead to a reduction in FGFR1 (Mott et al., 2010). Furthermore, FGF8 has been shown to activate STATs within mouse glioma endothelial cells which suggests that FGF8 could directly affect STAT3 to induce astrogliogenesis, or drive GFAP transcription during astrocyte differentiation and/or astrocyte activation (Yang et al., 2009).

6.4. Future directions

Collectively, these studies demonstrate that FGF8 signaling is more deeply involved in midline astrocyte development, maturation, and function than previously thought. Specifically, we show that FGF8’s role is not simply to provide trophic support, as it is required for midline astrocytes to acquire GFAP and/or fully mature in a timely manner. Moreover, we show that Fgf8 deficient midline astrocytes exhibit a delay in astrocyte activation. However, the exact molecular mechanism by which FGF8 uses to elicit these astrocyte-specific functions remains to be elucidated. For example, which receptor(s) does FGF8 act through to induce the onset of GFAP expression in the midline both during development and astrocyte activation? Which second messenger systems are involved?

Currently, expanding FGF8’s role in this temporal component of GFAP acquisition is difficult as FGF8 knockout mutations are embryonically lethal and newborn Fgf8neo/neo hypomorphic mice die within 6 hours after birth. Therefore, our postnatal studies were limited to only analyzing Fgf8+/neo mice. However, we did show that E17.5 Fgf8+/neo mice exhibited a genotypic-dependent decrease in midline GFAP-IR, which normalized to WT levels merely a day and a half later at PN0. Interestingly, PN0 Fgf8neo/neo mice did not show any sign GFAP-IR recovery. Although, impossible to test in vivo in our current model system, we hypothesize that

136 aged Fgf8neo/neo mice would eventually show an increase in GFAP-IR. In support, PN5 Fgf8+/neo mice nearly had more midline GFAP-IR than their WT littermates (p = 0.053). Alternatively, aged Fgf8neo/neo mice may never show an increase in GFAP as there could exist a critical time window and/or a Fgf8 threshold needed to initiate GFAP acquisition. In order to best test these possibilities, we could use genotype-specific midline astrocyte cultures treated with exogenous

FGF8. Alternatively, we could isolate radial glial neural progenitors and attempt to differentiate them into GFAP expressing astrocytes (Varga & Nagy, 2017). We could then analyze the effects of FGF8 on GFAP protein and Gfap expression within the cultures using immunocytochemistry and RT-qPCR, respectively. Furthermore, these in vitro systems could be used to identify which receptor or receptors FGF8 acts through to induce midline astrocyte GFAP acquisition and how astrocyte-specific FGFR levels or activity change with varying doses of FGF8.

Although the studies were conducted in adult mice and were not astrocyte specific, the molecular data collected from the CC of CPZ-fed Fgf8 hypomorphic mice did reveal that Stat3 and Fgfr1 expression is altered during astrocyte activation and could be, in part, involved in proper developmental GFAP acquisition in these mice. In support, we did find that relative Stat3 expression was reduced in a genotype-dependent fashion in PN0 Fgf8 hypomorphic mice

(unpublished data). However, these data were also not astrocyte specific. As described above, we could use an in vitro astrocytic culture model treated with CPZ. We could also use the same CPZ paradigm as we did here, but in mice where astrocytic STAT3 and/or FGFR1 has been conditionally removed (Herrmann et al., 2008; Smith et al., 2006). These mice could further be crossed with Fgf8 hypomorphic mice to examine whether Fgf8 deficits combined with astrocyte- specific STAT3 and FGFR1 deficits exacerbate the impairments we observed in astrocyte activation.

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6.5. Conclusions

The current study showed that FGF8 signaling is required for midline astrocyte development.

Specifically, perinatal Fgf8 deficits delayed GFAP acquisition and subsequently delayed midline astrocyte maturation, ultimately resulting in ACC. Furthermore, these perinatal Fgf8 deficits permanently impaired midline astrocyte activation. For example, astrocyte activation was disrupted in the CC of Fgf8 hypomorphic mice when presented with a CPZ challenge. This study is the first to report that FGF8 signaling is required for proper astrocyte development and astrocyte activation. Here, we have elucidated possible underlying mechanisms through which

CPZ induces astrogliosis, microgliosis, and oligodendrocyte apoptosis. This study is of importance for this knowledge could be used to improve current treatments for various neurological disorders involving astrocyte activation, such as, Alzheimer’s Disease,

Huntington’s Disease, and multiple sclerosis.

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