Morphology, Behavior, and the Sonic Hedgehog Pathway in Mouse Models of Down Syndrome

MORPHOLOGY, BEHAVIOR, AND THE SONIC HEDGEHOG PATHWAY IN MOUSE MODELS OF DOWN SYNDROME

by Tara Dutka

A dissertation submitted to Johns Hopkins University in conformity with the requirements for the degree of Doctor of Philosophy

Baltimore, Maryland July, 2014

Abstract

Down Syndrome (DS) is caused by a triplication of human chromosome 21 (Hsa21).

Ts65Dn, a mouse model of DS, contains a freely segregating extra chromosome consisting of the distal portion of mouse chromosome 16 (Mmu16), a region orthologous to part of Hsa21, and a non-Hsa21 orthologous region of mouse chromosome 17. All individuals with DS display some level of craniofacial dysmorphology, brain structural and functional changes, and cognitive impairment. Ts65Dn recapitulates these features of DS and aspects of each of these traits have been linked in Ts65Dn to a reduced response to Sonic Hedgehog (SHH) in trisomic cells.

Dp(16)1Yey is a new mouse model of DS which has a direct duplication of the entire

Hsa21 orthologous region of Mmu16. Dp(16)1Yey’s creators found similar behavioral deficits to those seen in Ts65Dn. We performed a quantitative investigation of the skull and brain of

Dp(16)1Yey as compared to Ts65Dn and found that DS-like changes to brain and craniofacial morphology were similar in both models. Our results validate examination of the genetic basis for these phenotypes in Dp(16)1Yey mice and the genetic links for these phenotypes previously found in Ts65Dn , i.e., reduced response to SHH.

Further, we hypothesized that if all trisomic cells show a reduced response to SHH, then up-regulation of the SHH pathway might ameliorate multiple phenotypes. We crossed Ts65Dn mice with Ptch1tm1Mps/+ mice, which up-regulate the canonical SHH pathway through the loss of function of one Ptch1 allele. The resulting four mouse genotypes were examined for craniofacial, behavioral and brain phenotypes. We found that Ptch1 heterozygotes displayed craniofacial and behavioral phenotypes that were mostly distinct from the effects of trisomy rather than complementary. However, as predicted some brain structural deficits were ameliorated in the

Ts65Dn Ptch1 heterozygotes.

Our studies comprise the first evaluation of Dp(16)1Yey morphology to confirm it as a

DS model and our additional DS model investigations indicate a more complex pathogenicity for

SHH related phenotypes in DS than constitutive uniform pathway repression.

Readers: Dr. Roger Reeves, Advisor

Dr. Mikhail Pletnikov, Thesis Committee Member

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Acknowledgements

I am extremely grateful to the many people who have provided their guidance and support over the last six years while I have toiled toward completing this doctoral research. First I would like to thank my advisor Dr. Roger Reeves. Though I have been a member of his lab for a relatively short period, just about three and a half years, I have learned so much in that time.

Roger taught me not only to execute experiments, but to think critically about what question I was asking. He also encouraged me to learn as much as I could and come at a problem from every angle, as least until the grants run out. I will also always be grateful to Roger for turning what could have been a huge upset in my academic career into a great opportunity.

I would like to thank the members of my thesis committee, Dr. Jonathan Pevsner, Dr.

William Pavan, and Dr. Mikhai Pletnikov for their tireless efforts to make my research the best it could be. Specifically I would like to thank: Dr. Pevsner for encouraging me to really learn and understand statistics to a far greater depth than I might have done on my own; Dr. Pavan for his insights into the SHH pathway and the genetics underlying my experiments; and Dr. Pletnikov for helping in the design and analysis of all of my behavioral tests.

Many others also contributed to the completion of this body of work. I am thankful for our wonderful collaborators at Penn State University, Dr. Joan Richtsmeier, her post-doc Dr.

Nandini Singh, and her former student Dr. John Starbuck who completed all of the craniofacial analyses described in this work. The microCT images utilized in these craniofacial studies were completed with the assistance of the Johns Hopkins Small Animal Imaging Resource Program.

Additionally, my behavioral test would not have been possible without the help of Joshua

Crawford and all the members of the Johns Hopkins School of Medicine Rodent Behavior Core who aided in designing my behavior tests, trained me to use the core, and kept all equipment running.

I want to thank all the members of the Reeves’ lab for making our lab such a great environment to work in. I always had someone willing to help with my behavioral tests, which made them significantly shorter and kept me from loosing mice and my sanity. Without Benjamin

Devenney, who taught me to handle all the mice without fear and handled them for me when they were too fast for me to catch I do not think I would have ever finished one behavior test, let alone six sets. I would also like to thank Dr. Donna Klinedinst who answered every question I had about previous lab experiments, helped run several weeks of rotarod, and started the Ptch mouse colony for me. My fellow students, Dr. Sarah Edie, Dr. Renita Polk, Jennifer Poitras, Dr. Annan

Yang, and Duane Currier, made the lab a great place to be, with lots of good times and good beer, and all helped run at least one test for me. I would also like to thank our post-docs Dr. Huiqing Li and Dr. Fabian Fernandez for their valuable scientific insight. I would also like to thank the former lab members, Dr. Randall Roper, Dr. Lisa Olson and Dr. Laura Baxter for aiding me in deciphering their experiments and notes from their days in the lab. Finally I would like to thank my undergrad intern Tabetha Ratliff who completed many days of tedious genotyping and imaging for this project and was a joy to work with.

I could not have completed this work without the Human Genetics graduate program which has been my home for the last 6 years. Dr. Dave Valle, Dr. Kirby Smith and Dr. Andy

McCallion have been wonderful mentors and worked very hard to make our program as rigorous and fulfilling as possible. Sandy Muscelli, our program administrator, kept the whole thing running and always had candy. My fellow students and all the friends I have made in the program made Hopkins a great place to be.

I would like to thank all my mentors in science without whom I could never have reached this goal. I had many wonderful instructors, first in the Montgomery County Public Schools, then at University of Maryland, and finally here at Hopkins. I also had the privilege of working with some truly excellent mentors during my three internships each of which taught me new exciting aspects of research: Dr. Minoru Ko, Dr. Mark Carter, Dr. Sarah Via, Dr. Roger Woodgate, and

Dr. John McDonald. Finally I would like to thank my advisors from my three rotations at

Hopkins, Dr. Steve Leach, Dr. Susan Michaelis, and Dr. Josh Mendel for their time and effort in training me to be a better geneticist.

I would not have made it to Hopkins and through this thesis without the love and support of my friends and my huge family. My dear friends outside of science, especially Carmel, have listened to me complain and forced me to relax. The close friends I have made at Hopkins, especially Sarah, Renita and Sam, have helped me trouble shoot my projects and commiserated when I couldn’t get something to work. My siblings, Caitie, Tim and Meg, have cheered me on and cheered me up. My paternal grandparents, Grandmommy and Granddaddy, Maxwell and Lee

Howard, were so proud to have me attend Granddaddy’s alma mater. I wish they could have seen this dream completed. My maternal grandparents, Grandma and Grandfather, Mary and James

Flaherty, have always listened to everything I had to say no matter if they understood me or not and I will be forever grateful for that safe space. My mom, Mary Howard, has been my inspiration my whole life. She showed me what smart, strong women could do and remains to this day one of the smartest people I have ever met. My dad, Regan Howard, helped inspire my love of science and showed me how to sustain joy in science and discovery. I hope to still love it as much as he does when I reach his age.

Last but not least I would like to dedicate this thesis to my husband Mike Dutka. He has been my anchor in the graduate school sea of insanity. He has stuck by me through this entire grueling process while he worked on his own thesis. He made sure I ate when I would have forgotten and cheered me up when everything exploded. He was the best and easiest decision I made in the last six years and I don’t know what I would have done without him.

Table of Contents

Page

Title…………………………………………………………………………………………i

Abstract…………………………………………………………………………………...... ii-iii

Acknowledgements………………………………………………………………………....iv-vi

Table of Contents…………………………………………………………………………...vii-x

List of Tables……………………………………………………………………………….xi

List of Figures………………………………………………………………………………xii-xiii

Chapter 1: Introduction……………………………………………………………………..1-15

Down Syndrome…………………………………………………………………...1-2

Mouse Models……………………………………………………………………..2-4

The Cerebellum in DS……………………………………………………………..5-9

The SHH Pathway…………………………………………………………………9-12

Connection of Central DS Phenotypes to SHH …………………………………...13-14

Treatment of the Cerebellum with SAG…………………………………………...14

Thesis ……………………………………………………………………...... 15

Chapter 2: Overlapping Trisomies for Human Chromosome 21 Orthologs Produce Similar

Effects on Skull and Brain Morphology of Dp(16)1Yey and Ts65Dn Mice……………...16-40

Introduction……………………………………………………………………...... 16-18

Materials and Methods…………………………………………………………….18-23

Gene Content Evaluation………………………………………………….18-19

Mice…………………………………………………………………….....19

Skull Morphometric Data Collection……………………………………..20

Analyses of Craniofacial Morphology……………………………………20-21

Collection of Brain Morphological Data………………………………….21-22

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Analysis of Dp(16)1Yey Brain Morphology ……………………………..22

Comparison of Relative Differences in the Brain Measurements between

Dp(16)1Yey, Ts65Dn, Ts1Cje, and Euploid Littermates…………………23

Results……………………………………………………………………………..23-37

Hsa21 homologous genes in Ts65Dn, Dp(16)1Yey, and Ts1Cje…..…….23-25

Craniofacial morphology and variance of Dp(16)1Yey mice

differs from euploid littermates………..………………………………….26-30

Craniofacial dysmorphology in Dp(16)1Yey is similar Ts65Dn…………31-32

Cerebellar dysmorphology in Dp(16)1Yey is similar to that in Ts65Dn

and Ts1Cje………………………………………………………………...33-37

Discussion………………………………………………………………………….38-40

Chapter 3: Does a Reduced Cellular Response to SHH Underlie Multiple Phenotypes of

Down Syndrome? ………………………………………………………………………….41-96

Introduction………………………………………………………………………..41-43

Materials and Methods…………………………………………………………….43-62

Mice……………………………………………………………………….43-44

Assessment of Birth Frequency and Weight……………………………...44-45

Behavior Data Collection…………………………………………………45-53

Handling………………………………………………………….46-47

Rotarod Data Collection.…………………………………………47

Y-maze Data Collection.…………………………………………48

Fear Conditioning Data Collection.……………………………...48-49

Morris Water Maze Data Collection.…………………………….49-52

Nesting Data Collection.…………………………………………52-53

Analysis of Survival………………………………………………………54

Analysis of Behavioral Tests……………………………………………...54-58

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Rotarod Analysis…………………………………………………55

Y-maze Analysis…………………………………………………56

Fear Conditioning Analysis………………………………………56

Morris Water Maze Analysis…………………………………….57

Nesting Analysis…………………………………………………58

Morphometrics Data Collection…………………………………………..58-59

Histological Preparation…………………………………………………..59

Collection of Brain Morphological Data…………………………………..59-60

Analysis of Brain Morphology……………………………………………61

Assessment of Tumor Burden…………………………………………….61-62

Results……………………………………………………………………………..62-94

Loss of Ptch1 affects birth frequency, survival, and weight……………...62-65

Trisomy does not alter tumor development in Ptch1+/- mice……………...66

Chronic up-regulation of the SHH pathway ameliorates some but not

most behavioral deficits in Ts65Dn…………………………..…………..67-83

Haploinsufficiency of Ptch1 differentially affects motor

learning depending on mouse ploidy……………………………..67-68

Haploinsufficiency of Ptch1 does not affect Y-maze performance

in Ts65Dn or euploid mice………………………………………69-70

Haploinsufficiency of Ptch1 reduces retention of contextual and

cued memory in trisomic mice…………………………………...71-74

Haploinsufficiency of Ptch1 reduces MWM performance in

Ts65Dn and euploid mice………………………………………...75-81

Haploinsufficiency of Ptch1 partially rescues nesting behavior

Ts65Dn mice……………………………………………………..82-83

Chronic up-regulation of the SHH pathway does not rescue the

craniofacial phenotype of the Ts65Dn mice……………………………....84-86

Haploinsufficiency of Ptch1 normalized cerebellar morphology in

Ts65Dn mice……………………………………………………………...87-92

Discussion………………………………………………………………………....95-98

Chapter 4: Conclusion……………………………………………………………………...99-104

Appendix 1: Hsa21 and Mmu16 Genes from the Homologene database………………….105-110

Appendix 2: Hsa21 and Mmu16 Genes from miRBase…………………………………....111

Appendix 3: Other genes with dosage changes in Ts65Dn and Ts1Cje…………………...112-113

Appendix 4: Ptch+/- Analyses……………………………………………………………....114-117

References……………………………………………………………………………….....118-134

Curriculum Vita………………………………………………………………………….....135-141

List of Tables

Page

Table I: Gene content of Ts65Dn , Ts1Cje and Dp(16)1Yey………………………………....25

Table II: Dp(16)1Yey mice and euploid littermate form difference results…………………..27

Table III: Dp(16)1Yey mice and euploid littermate variance difference results……………...29

Table IV: Cerebellar measurements in Ts65Dn, Ts1Cje, and Dp(16)1Yey…………………..35

Table V: Comparison of cerebellar measurements in Ts65Dn, Ts1Cje, and Dp(16)1Yey…...37

Table VI: Population statistics for all genotypes……………………………………………...65

Table VII: Average granule cell density as percent of Eu;Wt…………………...……………92

Table VIII: Overall comparisons between genotypes………………………………………...93-94

List of Figures

Page

Figure 1: Mouse models of Down Syndrome…………………………………………………..4

Figure 2: Mouse cerebellum……………………………………………………………………7

Figure 3: SHH pathway in the primary cilium………………………………………………..12

Figure 4: Linear distance comparison between Dp(16)1Yey and euploid……………………28

Figure 5: Comparison of variance of linear distances between Dp(16)1Yey and euploid……30

Figure 6: Comparison of magnitude of difference from euploid in craniofacial morphology between Dp(16)1Yey, Ts65Dn , and Ts1Cje………………………………………………….32

Figure 7: Comparison of cerebellar morphology between Dp(16)1Yey and euploid littermates……………………………………………………………………………………...34

Figure 8: Comparison of magnitude of difference from euploid in cerebellar morphology between Dp(16)1Yey, Ts65Dn , and Ts1Cje…………………………………………………36

Figure 9: Nesting category examples………………………………………………………….53

Figure 10: Performance of mice in accelerating rotarod………………………………………68

Figure 11: Performance of mice in Y-maze…………………………………………………...70

Figure 12: Performance of mice in FC training……………………………………………….72

Figure 13: Performance of mice in contextual and cued FC trial……………………………..74

Figure 14: Performance of mice in VP MWM………………………………………………..76

Figure 15: Performance of mice in HP MWM………………………………………………..78

Figure 16: Performance of mice in the probe test of the MWM………………………………81

Figure 17: Performance of mice in nesting assessment……………………………………….83

Figure 18: PCA of the all the cranial landmarks……………………………………………...85

Figure 19: PCA of facial landmarks…………………………………………………………..86

Figure 20: Cerebellar area and Purkinje cell linear density in all genotypes…………………88

xii

Figure 21: GC density for all genotypes…………………………………………………....91

Figure S1: Time spent in platform quadrant in MWM……………………………………..114

Figure S2: Average swim speed in probe test of MWM…………………………………...115

Figure S3: Craniofacial landmarks…………………………………………………………116-117

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Chapter 1: Introduction

Down Syndrome

Down syndrome (DS) is a complex developmental disorder cause by trisomy of human chromosome 21 (Hsa21). DS was first described clinically by Dr. John Langdon Down in 1866

[1]. Down’s original description of cognitive disability and characteristic facial features was expanded over the next century and a half to encompass more than 80 clinical traits [2]. The genetic cause of DS was identified in 1959, when Lejeune, Gautier and Turpin demonstrated the presence of an extra chromosome, subsequently identified a Hsa21, in the cells from individuals with DS [3,4]. DS occurs in approximately 1/700 to 1/1400 live births, making it the most common live-born human aneuploidy [5-9]. A far greater number of trisomy 21 conceptions (80-

95% by some estimates) do not survive to birth [10]. In most cases of DS the supernumerary chromosome is the result of a chromosomal segregation error occurring during meiosis I of maternal gamete generation, though a small percentage arises from meiosis II or paternal meiosis errors [11]. These errors increase with advanced maternal age and reduced meiotic recombination rates [11-13]. Polymorphisms in the folate pathway and the p53 pathway and lower socioeconomic status have also been associated with an increased maternal risk for DS [14-17].

Gene estimates for Hsa21 range between 300 and 600 genes [18,19]. Almost all people with DS have some memory and learning difficulties with characteristic changes in brain structure, e.g., small overall brain size, proportionally small cerebellum and hippocampus, as well as distinctive changes in craniofacial morphology, e.g., small overall skull size, proportionally smaller face and mandible [2,20-24]. The remaining clinical traits occur only in a subset of patients [18]. A wide range of variability in expressivity also exists even within these three penetrant traits [18]. The phenotypic variability occurs regardless of the proportion of Hsa21 triplicated and this has created problems with attempts to map traits to specific parts of Hsa21 in

1 humans using partial trisomies [25]. Additionally there are not a sufficient number of partially

Hsa21 trisomic people to power such a study [26].

Mouse Models

Studies on the genetic etiologies of DS phenotypes have relied heavily on mouse models.

Mouse models have many advantages over human studies including a short generation time, a controlled genetic background and environment, access to all tissues at all developmental time points, and the ability to make targeted genetic manipulations at multiple points in development.

Mouse models of DS are based on the three large regions of conserved synteny between Hsa21 and mouse chromosomes 16 (Mmu16), 17 (Mmu17) and 10 (Mmu10) [27]. Estimates vary as to the number of genes homologous between mouse and human in these regions, although 166 genes are considered highly conserved [19,28]. The use of mouse models in the study of complex developmental disorders such as DS is predicated upon the idea that homologous genes mis- expressed in a similar manner and combination will affect fundamental biological processes similarly across species. As such, the utility of the mouse model does not depend on an exact replication of the human phenotype, but rather on affecting the same developmental processes in a similar direction. The phenotypes of the principle mouse models have been extensively described in several recent reviews [18,27,29-32] and maps of the genetic content of these models is depicted in (Figure 1).

The most widely used mouse model of DS is the Ts65Dn model (Figure 1). In the

Ts65Dn mouse model, an additional chromosome contains the distal end of Mmu16 translocated close to the Mmu17 centromere [33,34]. This portion of Mmu16 contains a 15.6 Mb region of conserved synteny with Hsa21, from MRPL39 to ZFP295. Several genes whose orthologs are not found on Hsa21, beginning at the centromere of Mmu17 and ending between Synj2 and Pde10a, are also triplicated in Ts65Dn [35,36]. There is no direct information about possible contributions

2 of these non-Hsa21 homologues to Ts65Dn mouse phenotypes and they represent a caveat to all interpretations of DS-like phenotypes in these mice.

The Ts65Dn mouse models a number of DS manifestations. For example, deficiencies in bone formation [37,38]; brachycephaly, reduced facial and cranial vault dimensions [39-41]; reduced hippocampal volume [42]; and reduced cerebellar volume, linear Purkinje cell density, and granule cell (GC) density [43]; have been found in Ts65Dn mice which parallel similar deficits seen in humans with DS. Ts65Dn mice also display similar deficits to humans in hippocampal based behaviors, which in mice include performance in novel object and location recognition (NO/NL), fear conditioning (FC), Morris water maze (MWM), and nest building behavioral tests [44-46]. Additionally, occurrence of hematopoietic disease in the presence of

GATA1 mutations [47] and many similar effects on gene expression levels [48] have been discovered in humans with DS and Ts65Dn mice.

Other segmental trisomies, Ts1Cje and Ts1Rhr, were created to be trisomic for subsets of the genes trisomic in Ts65Dn (Figure 1). These mice have been utilized in comparative phenotypic mapping to determine regions of trisomy that are necessary or sufficient for phenotypes seen in Ts65Dn that parallel human DS. These studies showed that the phenotypic severity is tied to an extent to the total trisomic content [41,49-51]. For example, Ts1Cje mice are trisomic for about 67% of the genes that are trisomic in Ts65Dn mice (Table 2) [35,52] and demonstrate a generalized global reduction in craniofacial size [41] and a small cerebellum [49].

In contrast to Ts65Dn mice, Ts1Cje mice have normal linear Purkinje cell density and cerebellar

GC density relative to euploid littermates [49]. Ts1Cje also perform poorly on some hippocampal related behavior tasks [52,53], but not in all such tasks that are deficient inTs65Dn [54] and they have a less severe morphological phenotype in the hippocampus [53].

Figure 1: Mouse Models of DS. Figure 1 from [32] used under creative common license. Regions of Mmu 16, Mmu17, and Mmu10 orthologous to Hsa21 are shown above and below the Hsa21 chromosome. Each mouse model is depicted with the region triplicated as well as an approximate number of orthologous genes and the genes flanking the triplicated region. Tc1 is a transchromosomic mouse which carries a mostly intact copy of

Hsa21.

The Cerebellum in DS

One starkly evident and consistent phenotype in both humans with DS and Ts65Dn mice is a disproportionately small cerebellum with a coincident reduction in cerebellar GC density

[21,22,43]. Reductions in Purkinje cell linear density in the cerebellum have also sometimes been seen in Ts65Dn mice, though this phenotype has not been examined humans with DS [43].

Ts65Dn mice display normal cerebellar morphology at post-natal day 0 (P0) [55]. Deficits in cerebellar cross sectional area and GC density, however, begin appearing at P6 and are maintained into adulthood [55]. Clues to the etiology and impact of this deficit can be gained by observing normal cerebellar development and structure.

The cerebellar anlage arises in mice between embryonic day 9.5 (E9.5) and E11.5 and the final structure of the cerebellum is achieved by P15 [56]. In humans the cerebellum begins development during week six of embryogenesis and completes development in the second year of life [57,58]. The cortex of the adult cerebellum in both mice and humans consists of the internal granule cell layer (IGL), the molecular layer, and the Purkinje layer [59]. The molecular layer is mostly made up of the dendrites of the Purkinje cells and the axons of the GCs [59]. This cortex surrounds an inner core of white matter which contains three cerebellar nuclei, the lateral or dentate nucleus, the interposed nucleus, and the medial or fastigial nucleus [60]. The GCs make up the bulk of the cerebellar cortex volume and are the most numerous neurons in the brain [59].

The cells that will become the GCs, granule cell precursors (GCPs), undergo a first wave of expansion beginning at E13.5 in the mice and migrate to form the external granule cell layer

(EGL) of the nascent cerebellum [61]. The GCP proliferation phase in the EGL peaks between P5 and P8, declining after P8 to cease at P15 [61]. The post-natal phase of proliferation is driven by secretion of Sonic Hedgehog (SHH) from the Purkinje cell layer into the EGL beginning at E17.5

[61]. From P8 onward the GCP migrate inwards to form the IGL in the adult cerebellum [62].

The IGL develops in humans mostly in the first three years of life and is populated largely by the

EGL which develops at the end of the embryonic period and persists for up to two years post- natally [58,62]. It is this SHH induced post-natal phase of development in the cerebellum that is known to be most affected in Ts65Dn mice [55].

Structurally, when viewed in sagittal sections, the vermis of the adult cerebellum in mice and humans divides in to ten folia or lobules. These folia have been grouped into four distinct transverse zones: anterior zone (folia I-V), central zone (folia VI-VII), posterior zone (folium

VIII-anterior folium IX), and nodular zone (posterior folium IX- folium X) (Figure 2). The transverse zones are based on gene expression and zebrin II staining patterns. These zones further subdivide longitudinally into microzones that correspond to small patches of cerebellar area.

These structural patterns are maintained from the vermis into the hemispheres and each of these transverse zones contains a unique combination of functionally distinct afferent fibers. [59,63-66]

A B

Figure 2: Mouse Cerebellum. A) A whole mount adult cerebellum viewed dorsally. It is divided into three major regions: the vermis, paravermis and hemisphere. Within the vermis are the ten folia, those visible are indicated with roman numerals. The foliation pattern is conserved with some elaborations (lobulus simplex (LS), Crus I, Crus II) into the hemispheres. B) A sagittal section of the midline cerebellum close to the center of the vermis. The ten folia are labeled with roman numerals and the transverse zones are identified by color.

For many years the cerebellum was thought to contribute exclusively to motor learning and function. Humans with DS have been noted to display difficulties in motor learning and planning [67-69]. Behavioral testing has found that people with DS have alterations in cognitive control of movement [70], optokinetic nystagmus, and vestibular-ocular reflex (VOR) [71,72], as well as alterations in response to eye blink conditioning that develop with age [73,74].

Investigations of motor ability in the Ts65Dn mice have provided contradictory results. Ts65Dn mice have been found by some to perform as well or better than euploid mice in an accelerating rotarod test [43,75]. Others have found deficits in performance using static, constant speed, and progressive acceleration paradigms in the rotarod [76,77]. A recent study found that the Ts65Dn mice also display a deficit in VOR [78].

Recent research into functional topography has pointed to a division of function in the cerebellum in which the anterior cerebellum controls motor function and the posterior cerebellum plays a role in higher order cognition functions. The posterior cerebellum is vastly expanded in humans as compared to related mammals, similar to the expansion seen in the pre-frontal cortex, and current theories relate this expansion to an increased role of the cerebellum in cognitive tasks

[79]. The posterior cerebellum also demonstrates a more protracted development compared to the anterior portion [30]. Current evidence is for two or three representations of the cerebral cortex in the cerebellum [80]. The first map begins in the anterior zone with an inverted representation of the body and extends into the central zone with a representation of cognitive functions, the second map reverses the first map and begins in the central zone and extends through the posterior zone, and a third map repeating the order of the first may be present in the nodular zone [80]. Motor function has been mapped within these repeating representations to the anterior zone and folium

VIII while folia of the central zone and the nodular zone have been linked to attention and association functions [81-83]. VOR is associated with folia VI and VII and their hemispheric extensions, which combined form the oculomotor vermis [84]. Studies in humans have linked

8 alterations in language, working memory, timing, emotional processing, executive function, and attention deficits to folia VI and VII and their hemispheric portions, Crus I and Crus II [85].

Most of these higher cognitive functions are difficult to study in mice, but recent mouse studies have linked cerebellar function to working memory and development of spatial memory, as well as changes in social behavior and ability to focus [86-88]. Many behavioral tests once thought to test solely pre-frontal or hippocampal function, such as fear conditioning and MWM, are also now thought to have a cerebellar component as well [88-91]. Unfortunately, most of these tasks involve motor function, which confounds their use as a test for higher cognitive functions of the cerebellum. The hippocampus and prefrontal cortex display protracted development, similar to the cerebellum, and also display functional and structural deficits in people with DS and Ts65Dn mice [30,32]. New behavioral tests will be necessary to distinguish the effects of the dual roles of the cerebellum and to distinguish the effects of the cerebellum from those of the hippocampus and prefrontal cortex in higher cognition in humans with DS and in mouse models of DS.

The SHH Pathway

The SHH pathway is involved the patterning, growth and survival of many cells and tissues during embryogenesis and early development [92]. During embryogenesis SHH controls left-right and dorsal-ventral patterning through expression in midline tissues, e.g., node, notochord, and floor plate [93]. SHH expression in the zone of polarizing activity in the limb bud is critical for patterning of the distal joints [93]. The SHH pathway also plays a role in many epithelial-mesenchymal interactions during development and is critical in the development of most epithelial tissues and muscles [93]. SHH signaling is especially important in the developing central nervous system [94]. Mice lacking SHH fail to develop ventral midline structures and

SHH expression from the zona limitans intrathalamica is required later for the development of

9 thalamic neurons [94]. Following embryogenesis SHH is involved in the expansion of granule cells precursors in the cerebellum [61]. The Hh pathway is largely suppressed in adult tissue, but is known to be active in neurogenesis in the adult hippocampus and in synapse formation in the neocortex [95,96].

SHH is produced as a precursor protein containing a signal sequence which traffics SHH to the endoplasmic reticulum (ER). After translocation into the ER, SHH undergoes autocatalytic cleavage to produce an N-terminal signaling domain (HhN) and a C-terminal domain with no known function. During this autocatalytic reaction the C-terminal of HhN is modified with a cholesterol moiety. The cholesterol allows HhN to associate with membranes and facilitates the final processing step in which the acyltransferase Skinny hedgehog catalyzes the addition of a palmitic acid moiety to the N-terminus resulting in the mature signaling molecule (HhNp). The transmembrane protein Dispatched is required for secretion of the HhNp. It is unclear whether the hydrophobic HhNp is secreted as individual molecules or assembled into larger particles for transport at long distances.[93,97] A recent study found that mature SHH was transported in particles along specialized filapodia in the producing cell to act at long distances [98].

In the Hedgehog pathway, SHH operates by interaction with a transmembrane protein,

Patched (Ptch). There are two Ptch proteins in mammals, but the majority of signaling occurs through Ptch1. In the absence of SHH signaling Ptch1 inhibits downstream signaling by repressing another transmembrane protein Smoothened (Smo). When SHH interacts with Ptch1 this repression of Smo is removed and downstream Gli signaling is activated. Current data supports a mechanism in which Ptch1 facilitates the transport of small molecules, most likely sterols, which act on Smo to produce a conformational change to convert Smo between active and inactive states. [93,97] (Figure 3)

In mammals, Smo activation principally activates three transcription factors, Gli1-3 [99].

In the absence of SHH, Gli2/3 are phosphorylated and targeted to the proteosome to produce their transcriptional repressor forms (Figure 3a). Additionally, Suppressor of Fused (SuFu) binds to

10 unprocessed Gli2/3 and prevents their translocation to the nucleus [99] (Figure 3a). Smo activation suppresses this phosphorylation and causes SuFu to dissociate from Gli2/3, allowing these proteins to translocate to the nucleus in their activator forms [99] (Figure 3b). Gli dissociation from SuFu also increases cytoplasmic degradation of Gli2/3, which is important in regulating the balance of Gli proteins [99] (Figure3b). SuFu may also work in regulating the nuclear-cytoplasmic distribution of Gli2/3 [97]. Active SuFu also interacts with Gli proteins bound to DNA and with chromatin modulating factors to further suppress Gli transcriptional activation [100].

In mammals, the core components of this pathway are enriched in the primary cilium and the primary cilium is required for most SHH responses. In the absence of SHH, Ptch1 is principally located in the cilia (Figure 3a). Upon SHH activation, Ptch1 leaves the cilia to be replaced by Smo. Smo, Gli2/3, and SuFu then become enriched in the top of the cilia (Figure 3b).

This enrichment depends on Kif7 and results in the activation of Gli proteins. [99]

The full regulatory network activated by Gli2/3 for all the different developmental programs that utilize SHH is unknown. Gli1/2 is responsible for most activation functions while

Gli3 functions primarily as a repressor. Unlike Gli2/3 which are always present and processed into repressors in the absence of activation, Gli1 is induced by the canonical SHH pathway and serves mainly as positive feedback to prolong the response to SHH stimulation. SHH signaling strength and duration is also controlled by negative feedback in the system. Cell Adhesion

Molecule-Related/Downregulated by Oncogenes (CDO) and Brother of CDO (BOC) are transmembrane proteins that facilitate SHH binding to Ptch1 (Figure 3). Hedgehog-interacting protein (HIP), in contrast, binds to and reduces the range SHH. SHH stimulation up-regulates the transcription of Ptch1 and HIP and down-regulates the transcription of CDO and BOC creating a negative feedback loop that turns off SHH signaling over time. [93]

A B

Figure 3: SHH pathway in the Primary Cilium. A) In the absence of SHH: Ptch resides in the cilium and blocks Smo from entering the cilium. Active full length Gli (GliA) is sequestered with

SuFu and Kif7 or transits through the cilium acquiring an as yet unknown signal which causes the

GliA to be phosphorylated. Phosphorylated GliA is truncated to form the repressor form (GliR).

GliR translocates to the nucleus where it blocks transcription. B) In the presence of SHH: BOC and CDO facilitate SHH association with Ptch. Ptch leaves the cilium allowing Smo to enter and undergo a conformational change to its active form. Active Smo facilitates the transit of the

SuFu/Kif7/GliA complex to the tip of the cilium where GliA dissociates from SuFu. GliA is now free to translocate to the nucleus and activate transcription. GliA dissociated from SuFu also undergoes increased degradation.

Connection of Central DS Phenotypes to SHH

In our study of the cerebellum we noted that Ts65Dn mice have the same number of

GCPs as euploid mice at P0, but that there are fewer mitotic GCPs at this point in time [55]. By

P6, Ts65Dn mice display a deficit in GCP relative to euploid mice that results in a reduction of

GC density in adults [55]. The primary post-natal mitogen for GCPs is SHH [61]. We found that

GCPs harvested from P0 Ts65Dn mice had a lower mitogenic response to SHH in culture than cells harvested from their euploid litter mates [55]. These results tied a deficient response to SHH to the cerebellar hypoplasia and to the cognitive impairments resulting from these cellular deficits.

Following this discovery we theorized that a reduced response to SHH might underlie other DS related phenotypes. The midface and maxilla are disproportionately reduced in humans with DS and in Ts65Dn mice [40]. These structures derive from the first pharyngeal arch (PA1), which is formed beginning at E8.25 in the mouse [101]. PA1 is populated at by migrating cranial neural crest cells (NCCs) and develops into the mandibular and maxillary arches at E9.5 [101].

SHH is known to affect the migration and proliferation of NCC populations and loss of SHH results in hypoplasia of PA1 at E9.5 [101]. This made the NCC population of PA1 a good candidate for a SHH related phenotype to investigate in Ts65Dn mice. We examined the NCCs of the PA1 and found a deficit in mitotic NCCs in PA1 at E9.5 [102]. Cultured cells isolated from

PA1 also displayed a reduced proliferative response to SHH and that proliferation could be rescued to euploid levels with the addition of more SHH, as we had seen with the GCPs [102].

These results indicated that the craniofacial changes we see in Ts65Dn mice and in humans with

DS might relate to a global reduction in response to SHH in trisomic cells.

A reduced cellular response to Sonic Hedgehog (SHH) had now been implicated in the development of three DS related phenotypes in the mouse model Ts65Dn: cognitive impairment, craniofacial dysmorphology and brain dysmorphology [62]. As SHH signaling is important in a

13 large array of tissue development, especially in the CNS, we theorized that a reduced response to

SHH might underlie multiple DS related phenotypes in the Ts65Dn mouse [62]. If multiple phenotypes of DS are caused by a reduction in response of the SHH pathway during development it may be possible to develop treatments to target multiple aspects of DS through this pathway.

Treatment of the Cerebellum with SAG

Cerebellar hypoplasia in Ts65Dn mice provided an accessible target for treatment due to its development occurring largely after birth in these mice. We were able to target this deficit with an acutely timed pharmacological intervention. We injected P0 mice with SAG, a small molecule that activates the SHH pathway through direct interaction with Smo [45]. By timing this intervention to coincide with a point in development before a deficit in cell number is evident in trisomic mice [55] and when SHH is the most potent mitogen for the deficient cell population

[61] we were able to restore the cerebellar size and GC density to euploid levels [45,55]. The improvement in cerebellar structure coincided with an improvement in the performance of the trisomic mice in the MWM [45], a learning and memory task linked to hippocampal and cerebellar function [103]. These mice showed no improvement in the cellular deficits noted in the

Ts65Dn hippocampal dentate gyrus but there was a significant improvement in the duration of hippocampal long-term potentiation [45]. Performance in tasks measuring pre-frontal function was also unchanged [45]. We do not know if the changes in hippocampal based behavior and electrophysiology were a direct result of improved cerebellar function, a result of the influence of the rescued cerebellar structure on hippocampal connections, or a result of the influence of SAG on others SHH responsive cells at P0.

Thesis

The work of this thesis sought to utilize mouse models to further our understanding of the genetic etiologies of three phenotypes found consistently in DS: learning and memory deficits, cerebellar hypoplasia, and craniofacial malformations. In chapter two I re-examined whether

Ts65Dn mice are good models of DS or whether the non-Has 21 homologous genes on Ts65Dn affect the DS-like phenotypes we see in these mice. For this work I utilized the Dp(16)1Yey DS mouse model developed by Li et al. [104] which has a 22.9 Mb direct duplication of the entire

Mmu16 region that is in conserved synteny with Hsa21. Unlike Ts65Dn mice, Dp(16)1Yey mice only carry triplicated regions that are in conserved synteny with Hsa21 [35,104] and thus better represents the gene dosage in human DS. I first determined if Dp(16)1Yey displayed DS-like craniofacial and cerebellar morphology. Next I compared the morphology of these mice to that of

Ts65Dn and Ts1Cje in order to determine if the genetic differences between these three models translated to differences in these two consistently penetrant DS-like phenotypes.

In chapter three I sought to further our understanding of the impact of trisomy on cellular response to SHH stimulation by manipulating that pathway in the Ts65Dn model. I utilized a genetic model of constitutive up-regulation of the SHH pathway, the Ptch1tm1Mps/+ mouse, which has a single locus of Ptch1 knocked out and replaced with a LacZ cassette [105]. These mice evidence an increased response to SHH in all SHH responsive cells throughout development

[105,106]. I crossed the Ptch1+/- mouse to Ts65Dn and examined these mice for changes in behavioral, craniofacial, and cerebellar phenotypes to test if a uniform constitutive down- regulation of SHH responsiveness in all trisomic cells is the underlying etiology for multiple DS related phenotypes as proposed in [62]. These studies represent the first test of the utility of

Ts65Dn mice as compared to Dp(16)1Yey mice and the first test of the SHH pathway theory of

DS etiology during embryogenesis.

Chapter 2: Overlapping Trisomies for Human Chromosome 21 Orthologs Produce Similar

Effects on Skull and Brain Morphology of Dp(16)1Yey and Ts65Dn Mice

(Adapted from Starbuck JM, Dutka T, Ratliff TS, Reeves RH, Richtsmeier JT (2014) Am J Med

Genet A 21)

Craniofacial morphometric data collection and analysis performed by Dr. John M. Starbuck while a student in the lab of Dr. Joan Richtsmeier in the Department of Anthropology at Pennsylvania

State University.

Introduction

Down syndrome (DS) is caused by trisomy 21 and is the most common live-born human aneuploidy (1:319–1:1,000 live-births) [107]. Trisomy 21 results in dosage imbalance for hundreds of genes located on human chromosome 21 (Hsa21), with concomitant gene regulatory changes that affect numerous aspects of development and function. Individuals with DS always show some degree of intellectual disability, reduced cerebellar volume, and granule cell density, and dysmorphic facial features [2,21-23]. The processes by which gene dosage imbalance produces these features are not well understood, in part because many of the affected biological systems cannot be directly tested in humans. Genetically engineered mouse models are useful for elucidating the effects of gene-dosage imbalance on development and can contribute to therapies that ameliorate the effects of trisomy 21 [40,108,109].

The mouse orthologs of genes on Hsa21 are found on mouse chromosomes (Mmu)

Mmu10, Mmu16, and Mmu17 [27]. A number of DS mouse models have segmental trisomy for portions of these chromosomes that have conserved synteny with Hsa21 [32]. In the Ts65Dn

16 mouse model, an additional chromosome contains the distal end of Mmu16 translocated close to the Mmu17 centromere [33,34]. This portion of Mmu16 contains a 15.6Mb region of conserved synteny with Hsa21, from MRPL39 to ZFP295. Several genes whose orthologs are not found on

Hsa21, beginning at the centromere of Mmu17 and ending between Synj2 and Pde10a, are also triplicated in Ts65Dn [35,36]. There is no direct information about possible contributions of these genes to Ts65Dn mouse phenotypes.

The Ts65Dn mouse models a number of DS manifestations. For example, deficiencies in bone formation [37,38]; brachycephaly, reduced facial, and cranial vault dimensions [39-41], and reduced cerebellar volume and granule cell density [43] have been found in humans with DS and in Ts65Dn mice. Ts65Dn mice also have deficits on hippocampal-based behaviors and electrophysiology [34,44]. Additionally occurrence of hematopoietic disease in the presence of

GATA1 mutations [47], increased occurrence of heart defects with genetic modifiers [110], and many similar effects on gene expression levels [48] have been discovered in people with DS and in Ts65Dn mice.

Ts65Dn mice have been compared to other DS mouse models that are trisomic for subsets of the regions of conserved synteny with Hsa21to assess phenotypic differences and identify genes involved in the pathogenesis of DS. Ts1Cje mice are trisomic for about 67% of the genes that are trisomic in Ts65Dn mice (Table I) [35,52] and have a milder phenotype than

Ts65Dn mice. Specifically, Ts1Cje mice demonstrate a generalized global reduction in craniofacial size [41] and a small cerebellum [49]. In contrast to Ts65Dn mice, Ts1Cje mice have normal linear Purkinje cell density and cerebellar granule cell density relative to euploid littermates [49]. Ts1Cje mice also have improved performance on some hippocampal based behavioral tests compared to Ts65Dn mice [54].

The Dp(16)1Yey DS mouse model developed by Li et al. [104] has a 22.9Mb direct duplication of the entire Mmu16 region that is in conserved synteny with Hsa21. Unlike Ts65Dn mice, Dp(16)1Yey mice only carry triplicated regions that are in conserved synteny with Hsa21

[35,104] and thus better represent the gene dosage imbalance found in humans with DS. These mice have been noted to display the same hippocampal based electrophysiological and behavioral deficits seen in Ts65Dn mice [111]. These mice also demonstrate and increased incidence in heart defects, similar to what is seen in Ts65Dn mice and humans with DS [104].

When present in three copies, trisomic murine genes that are orthologous to Hsa21 genes are expected to similarly affect conserved genetic pathways and thereby provide a genetic model of human DS. The purpose of this investigation is to quantitatively evaluate Dp(16)1Yey mouse craniofacial and brain morphology as a model of DS and to compare homologous measures from two established mouse models for DS: the Ts65Dn mouse and the Ts1Cje mouse. This investigation compares the utility of the Ts65Dn and the Dp(16)1Yey mouse in future investigations of the genetic etiologies of these DS-like phenotypes.

Materials and Methods

Gene Content Evaluation

Human and mouse mammalian homologous protein coding gene content were obtained from the Homologene data report on the Mouse Genome Database: (Mouse Genome Informatics,

The Jackson Laboratory, Bar Harbor, Maine; accessed July 26th, 2013 ftp://ftp.informatics.jax.org/pub/reports/HOM_AllOrganism.rpt). MicroRNA content for human and mouse genomes were obtained using miRBase (accessed July 29th, 2013; www.mirbase.org).

Breakpoint locations for the chromosomal translocations in each model [35,36,104] were used to determine regions of conserved synteny with human chromosomes where gene dosage had been altered. The numbers of triplicated Hsa21 homologs in these regions were determined using the

Homologene IDs and miRBase nomenclature. The Ts65Dn mouse triplicated Mmu17 region that is not in conserved synteny with to Hsa21 was compared to the human genome to determine the

18 number of human homologs in that region. The same process was performed for the Ts1Cje mouse monosomic region of Mmu12. All lists of genes were checked for ORFs, predicted genes, and undefined RIKEN cDNA and these were included in the final gene totals. The reduced gene databases were created using R studio (Version 0.97.248) in R (Version 2.15.2; Date: 2012-10-

26).

Mice

All procedures were reviewed, approved, and carried out in compliance with animal welfare guidelines approved by the Johns Hopkins University and the Pennsylvania State

University Animal Care and Use Committees.

Ts65Dn mice were obtained from the Jackson laboratory and maintained in the Reeves’ laboratory colony as a C57Bl/6J x C3H/HeJ (B6 x C3H) advanced intercross. Dp(16)1Yey mice

[104] were the gift of Dr. Eugene Yu and were backcrossed for five generations onto a B6 background and bred to C3H mice to create the F1 generation used here. B6 Ts1Cje mice [52] were crossed to C3H mice and maintained by out-crossing to (B6 x C3H) F1 mice. Ts65Dn and

Ts1Cje mouse datasets were described previously [40,41,43,49].

Dp(16)1Yey mice and their euploid littermates were anesthetized with isoflourane and intracardially perfused with a solution of cold phosphate buffer saline (PBS) with heparin

(1unit/ml) followed by a solution of cold 4% paraformaldehyde (PFA). The heads were removed and drop-fixed in 4% PFA for at least 48hrs. The skulls were then washed and stored in PBS at

4°C.

Skull Morphometric Data Collection

Micro-computed tomography (µCT) images of Dp(16)1Yey (n = 12) and euploid (n = 12) skulls were acquired at the Johns Hopkins Medical Institution Research Building Imaging Center

(Gamma Medica X-SPECT/CT scanner, Northridge, CA, USA, 0.05mm resolution). Software package Avizo 6.3 (Visualization Sciences Group, VSG) was used to reconstruct µCT skull isosurfaces and acquire three-dimensional coordinates of landmark locations from each specimen.

Landmark measurement error was assessed using standard protocols. Following error assessment, three-dimensional coordinates of landmarks from the skull (n = 32) and mandible (n = 10) were collected following standard protocols and used in analyses. Full methods of craniofacial data collection can be found in the supplementary material in [28].

Analyses of Craniofacial Morphology

Differences in craniofacial form between Dp(16)1Yey and euploid littermates were estimated for subsets of landmarks from microCT images of the cranial vault, facial skeleton, cranial base, and mandible using Euclidean distance matrix analysis (EDMA) [112,113]. In addition to the statistical tests for difference in form, an EDMA-based ordination procedure

(PCOORD) [114] was used to determine how Dp(16)1Yey mice and euploid littermates vary in multivariate space. Moreover, differences in variances of craniofacial dimensions were estimated for Dp(16)1Yey mice and euploid littermates and statistically evaluated with a bootstrap approach using MIBoot, a Windows-based software for bootstrap based comparison of morphological integration patterns (Available at: http://getahead.psu.edu. Accessed on 20 December, 2013, T.

M. Cole III, 2002). Finally, we statistically compared the relative differences among DS mouse models (i.e., Dp(16)1Yey, Ts65Dn, and Ts1Cje) and their respective euploid littermates with

GDMA [113] using a subset of three dimensional coordinates of 21 landmarks available for all

20 mice (Ts65Dn and Ts1Cje datasets as described, see [40,41,43,49]). Full methods of craniofacial data analysis can be found in the supplementary material in [28].

Collection of Brain Morphological Data

The brains of Dp(16)1Yey mice (n = 9) and their euploid littermates (n = 9) were removed, sliced slightly off center along the sagittal axis, and embedded cut-side out in a paraffin block to obtain mid-line sagittal sections. For each mouse the left or right hemisphere was chosen at random for further processing and analysis. For embedding the tissue was progressively dehydrated by 30 minute washes of ethanol (30%, 50%, 70%, and 90%) finishing with two one- hour washes in 100% ethanol. The tissue was cleared with a wash of 1:1 ethanol and xylenes for

30 minutes followed by a one-hour room temperature wash in xylenes. The tissue was infused with paraffin overnight in a 1:1 mixture of paraffin and xylenes at 57°C. Finally, two paraffin washes were performed at 57°C over a 24hr period prior to embedding each brain in fresh paraffin. The tissue was sectioned by the Johns Hopkins Reference Histology Lab in 7 μm sections and stained with hematoxylin and eosin.

Three low magnification images of an entire midline sagittal section were obtained for each mouse using a Nikon SMZ 1500 attached to a Nikon Digital Sight DS fil camera. The proportional midline sagittal surface area of the cerebellum and the entire brain was measured using ImageJ (version 1.44p; Java 1.6.0_20 (32bit), Wayne Rasband, NIH) by placing a grid of

0.25mm2 blocks over each image and counting grid points that intersect with the cerebellum and brain [115]. Two higher magnification images of the midline sagittal cerebellum were obtained using the Nikon microscope and camera and merged with a Nikon software stitching function

Corresponding low magnification sections of the cerebellum were obtained using the Nikon

21 microscope and camera, and the lengths of folia were traced using the ObjectJ plugin

(Version:1.02o, Date: 2012-1-07; Norbert Vischner & Stelian Nastase, University of Amsterdam) for ImageJ (version 1.44p; Java 1.6.0_20 (32bit), Wayne Rasband, NIH). To determine the granule cell counts, high magnification images of the internal granule cell layer of folia V, VI and

IX were taken using a Zeiss Axioskop two plus attached to a Panasonic GP-KR222 with a 63x oil immersion lens using the capture video input function in the program Stereologer (version 1.3,

Hopkins, ©1998). Three independent, non-overlapping, images were taken for each folium and two sections were imaged for each mouse for a total of 18 sections per-mouse. Cells were counted in a 5000μm2 area using ImageJ (version 1.44p; Java 1.6.0_20 (32bit), Wayne Rasband,

NIH).

Analysis of Dp(16)1Yey Brain Morphology

Proportional midline sagittal cross-sectional area of the cerebellum was calculated as a proxy for the ratio of cerebellar volume to overall brain volume [43]. The individual measurements for each cerebellum section were divided by the total brain measurements for that section and ratios were averaged over the three sections for each mouse. Purkinje cell counts were divided by length of the folia to calculate the linear density of each section and then averaged for each mouse. Granule cell counts were divided by the area counted and averaged over the 18 images for each mouse as a proxy for the granule cell density [43]. All calculations were preformed in R studio (Version 0.97.248) in R (Version 2.15.2; Date: 2012-10-26) A repeated measures ANOVA was conducted using ezANOVA() ez package (Version: 4.2-0; Date: 2013-06-

20) to assess the mean trisomic and euploid values for each cerebellar measurement while incorporating multiple measurements of each animal. Normality was checked by visual inspection of plots of residuals against fitted values.

Comparison of Relative Differences in the Brain measurements between Dp(16)1Yey,

Ts65Dn, Ts1Cje, and Euploid Littermates

Three measures of cerebellar morphology were assessed across Dp(16)1Yey, Ts65Dn, and Ts1Cje mice and respective euploid littermates: 1) the ratio of midline cerebellar area to total brain area, 2) linear Purkinje cell density, and 3) cerebellar granule cell density. An index for each measurement was created by dividing individual trisomic mouse average values by the average of their respective euploid littermates and multiplying the resulting value by 100 (Figure

6) [43,49]. All calculations were preformed in R studio (Version 0.97.248) in R (Version 2.15.2;

Date: 2012-10-26). Indices for each model were compared using a Kruskal-Wallis test followed by individual contrasts with Bonferroni adjusted p-values ( = 0.10) using kruskal() from the package agricolae (Version: 1.1-4; Date: 2013-4-8) .

Results

Hsa21 homologous genes in Ts65Dn, Dp(16)1Yey, and Ts1Cje

Based on the examination of the Homologene database report obtained from JAX and the miRBase database, the number of Hsa21 gene homologues that are trisomic in Ts65Dn and

Dp(16)1Yey mice is 100 and 115 genes, respectively (Table I; Appendix I and Appendix II). A total of 41 trisomic Ts65Dn mouse genes are located on the Mmu17 region of the extra chromosome, with 38 genes having Hsa6 homologs and one gene having a Hsa15 homolog

(Table I; Appendix III).

The triplicated Mmu16 region of the Ts1Cje mouse includes 67 of the Hsa21 homologs found in the other models. The corresponding segment of Hsa21 includes 77 genes (Table I;

Appendix I and Appendix II). Ts1Cje mice are monosomic for seven genes from the telomeric

23 region of Mmu12 that are homologous to Hsa7 genes (Table I; Appendix III). Thus, Dp(16)1Yey mice provide a better genetic representation, with more Hsa21 homologues than Ts65Dn or

Ts1Cje mice and no additional triplicated or monosomic genes

Table I: Gene content of Ts65Dn , Ts1Cje and Dp(16)1Yey Homologene Genes miRBase Total All KRTAP ORF RIKEN Predicted miRNA Ts65Dn Mmu16 Region of Conserved Synteny Mouse 104 13 - 6 0 4 108 Human 116 26 2 - - 9 125 Homologous in Mouse 98 11 - 4 - 2 100 Mmu17 Triplicated Region Mouse 41 0 - 1 2 1 42 Homologous in Mouse 39 0 - 1 1 0 39 Homologous in Humans (Hsa6 & Hsa15) 33 * 0 1 - - 0 33 Dp(16)1Yey Mmu16 Region of Conserved Synteny Mouse 116 13 - 6 0 7 123 Human 128 26 3 - - 14 142 Homologous in Mouse 110 11 - 4 - 5 115 Ts1Cje Mmu16 Region of Conserved Synteny Mouse 68 0 - 3 0 3 71 Human 71 0 2 - - 6 77 Homologous in Mouse 66 0 - 2 0 1 67 Mmu12 Monosomic Region Mouse 7 0 - 0 0 0 7 Homologous in Mouse 7 0 - 0 0 0 7 Homologous in Humans (Hsa7) 7 0 0 - - 0 7

Craniofacial morphology and variance of Dp(16)1Yey Mice differs from euploid littermates

Dp(16)1Yey mice and euploid littermates were compared using a global EDMA form analysis and found to exhibit significant form differences for the cranial vault, face, base, and mandible (Table II). An average of 50% of linear difference significantly differed between

Dp(16)1Yey and euploid samples and of those that differed significantly 88% were smaller in

Dp(16)1Yey as compared to euploid mice. Dp(16)1Yey as compared to euploid mice exhibit a small face with a shortened and broadened jaw and broadened skull (Figure 4). Dp(16)1Yey mice and euploid littermates also show significantly different variances between approximately 20% of all unique linear distances estimated among cranial landmarks (Table III), with 97% of these having higher variance in Dp(16)1Yey mice than in euploid mice (Figure 5).

Table II: Dp(16)1Yey mice and euploid littermate form difference results

Confidence interval results for local linear distance (LD) differences Number of significantly Number of significantly different LDs larger Global form Percentage of significantly different LDs smaller in in Dp(16)1Yey difference results different LDs Dp(16)1Yey mice mice 22/55 7/55 Cranial Vault* 29/55 (avg. magnitude 4.54% (avg. magnitude (p-value < 0.001) (52.73%) smaller) 6.4% larger) 27/45 Face* 28/45 (avg. magnitude 5.5% 1/45 (p-value < 0.001) (62.22%) smaller) (6.2% larger) 22/55 Cranial Base* 23/55 (avg. magnitude 3.87% 1/55 (p-value < 0.001) (41.82%) smaller) (3.1% larger) 17/45 3/45 Mandible* 20/45 (avg. magnitude 4.75% ( avg. magnitude (p-value < 0.001) (44.44%) smaller) 7.3% larger) * indicates that testing for global differences in form for each craniofacial region was statistically significant.

Figure 4: Linear distance comparison between Dp(16)1Yey and euploid. Linear distances that are significantly different by confidence interval testing (α = 0.10) and differ by ≥ 3% between

Dp(16)1Yey mice and euploid littermates. A) Lateral, B) superior, and C) inferior views of the mouse cranium and D) superior and E) lateral views of the mandible are provided. Linear distances that are significantly smaller (solid) and larger (dashed) in Dp(16)1Yey trisomic mice relative to euploid are depicted.

Table III: Dp(16)1Yey mice and euploid littermate variance difference results Variance results Percentage of LDs with Number of LDs with significantly higher significant differences in variance in Dp(16)1Yey trisomic mice variance based on based on confidence interval tests (range confidence interval tests of variance increase) Cranial Vault 14/55 14/55 (25%) (122-380% increase)

Face 8/45 8/45 (18%) (164-263% increase) Cranial Base 7/55 6/55 (13%) (106-207% increase)

Mandible 10/45 10/45 (22%) (115-236% increase)

Figure 5: Comparison of variance of linear distances between Dp(16)1Yey and euploid.

Linear distances with significantly different variances between Dp(16)1Yey mice and euploid littermates. Lateral (A), superior (B), and inferior (C) views of the mouse cranium and superior

(D) and lateral (E) views of the mandible are provided. Significant differences showing increased

(solid) and decreased variances (dashed) in Dp(16)1Yey trisomic mice relative to euploid are depicted.

Craniofacial dysmorphology in Dp(16)1Yey is similar Ts65Dn

GDMA was used to statistically compare differences between Dp(16)1Yey mice and euploid littermates to differences between Ts65Dn mice and euploid littermates. The pattern of differences between these trisomic mouse models and their respective euploid littermates is similar, only 7% of all unique linear distances showed differences, though the magnitude of the differences is relatively greater between Dp(16)1Yey mice and euploid littermates (Fig 6a and

6b). In contrast a GDMA comparing Dp(16)1Yey mice and euploid littermates to differences between Ts1Cje mice and euploid littermates determined that 26% linear distances exhibited statistically significant differences (Figure 6c and 6d). Ts1Cje were previously reported to have a relatively large reductions craniofacial size [49], which could account for the greater effect of

Ts1Cje relative to euploid littermates on measures of the facial skeleton as compared to

Dp(16)1Yey mice relative to their respective euploid littermates.

Figure 6: Comparison of magnitude of difference from euploid in craniofacial morphology between Dp(16)1Yey, Ts65Dn , and Ts1Cje. Relative differences between crania of

Dp(16)1Yey mice and euploid littermates are more similar to differences betweenTs65Dn and euploid littermates than those defined between Ts1Cje mice and euploid littermates. Dashed lines represent linear distances that exhibit differences of significantly larger magnitude between

Dp(16)1Yey mice and euploid littermates relative to Ts65Dn mice and euploid littermates (A and

B) or relative to Ts1Cje mice and euploid littermates (C and D). Solid lines represent linear distances that exhibit differences that are significantly larger in the comparison of Ts1Cje mice and euploid littermates relative to Dp(16)1Yey mice and euploid littermates (C and D). Color figure can be viewed online at http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1552-4833.

Cerebellar dysmorphology in Dp(16)1Yey is similar to that in Ts65Dn and Ts1Cje

The cross sectional area of the cerebellum normalized to total brain area in Dp(16)1Yey mice was 91.1% of the area of euploid littermates (p-value = 0.02). Within the cerebellum the

Purkinje cell linear density of Dp(16)1Yey mice was 86.5% of euploid littermates (p-value =

0.002), and the granule cell density was 87.2% of euploid littermates (p-value = 0.03) (Figure 7;

Table IV).

We reported previously [49] that Ts65Dn was significantly smaller than euploid on these three measurements while the Ts1Cje mouse was not different from euploid in Purkinje cell linear density, showed a trend (but not formal significance) for a reduction in cerebellar granule cell density, and was significantly different in cross-sectional area (Table IV). Despite the differences in gene dosage between the three models considered here, the midline cerebellar area was reduced to the same extent in all three models. In contrast, linear Purkinje cell density was reduced similarly in Ts65Dn and Dp(16)1Yey models, but not affected in Ts1Cje mice. The reduction in granule cell density in the Dp(16)1Yey mice exhibited an intermediate value between the other two models, while the Ts65Dn and Ts1Cje models were significantly different from each other (Figure 8; Table V).

Figure 7: Comparison of cerebellar morphology between Dp(16)1Yey and euploid littermates. Dp(16)1Yey mice are significantly reduced in cerebellar midline area measurements,

Purkinje cell density, and cerebellar granule cell density. Graphical representation of A) the average midline cerebellar area as a fraction of the total brain area for each mouse as well as the overall average ratio for each ploidy; B) the average Purkinje cell density in cells per mm for each mouse as well as the overall average for each ploidy; C) the average cerebellar GC density in cells per μm2 for each mouse as well as the overall average for each ploidy.

Table IV: Cerebellar measurements in Ts65Dn, Ts1Cje, and Dp(16)1Yey Dp(16)1Yey Ts65Dna Ts1Cjeb Normalized Cerebellar Midline Histology 91.1% of euploid 81.7% of euploid 86.3% of euploid n= 9 Dp(16)1Yey n= 7 Ts65Dn n= 6 Ts1Cje n= 9 euploid n= 8 euploid n= 6 euploid p=.02 p=.002 p=.001 Purkinje Cell Density 86.5% of euploid 89.5% of euploid 98.3% of euploid n= 9 Dp(16)1Yey n= 6 Ts65Dn n= 6 Ts1Cje n= 9 euploid n= 6 euploid n= 6 euploid p=.002 p=.03 p=.27 Cerebellar Granule Cell Density 87.2% of euploid 76.0% of euploid 91.2% of euploid n= 9 Dp(16)1Yey n= 8 Ts65Dn n= 5 Ts1Cje n= 9 euploid n= 8 euploid n= 5 euploid p=.03 p=.0001 p=.09 a Data for the Ts65Dn mice from [43] b Data for the Ts1Cje mice from [49].

Figure 8: Comparison of magnitude of difference from euploid in cerebellar morphology between Dp(16)1Yey, Ts65Dn , and Ts1Cje. Dp(16)1Yey mice are not significantly different from Ts65Dn in cerebellar midline area measurements, Purkinje cell density, or cerebellar granule cell density relative to euploid, but are significantly different from Ts1Cje in linear

Purkinje cell density relative to euploid. Graphical representation of the indices of A) midline cerebellar area as a fraction of the total brain area as compared to euploid; B) linear Purkinje cell density as compared to euploid; C) cerebellar GC density as compared to euploid in each model.

Table V: Comparison of cerebellar measurements in Ts65Dn, Ts1Cje, and Dp(16)1Yey Different Different Different from from from Dp(16)1Yey Ts65Dna Ts1Cjeb Normalized Cerebellar Midline Histology Ts65Dna no - No Ts1Cjeb no no - Dp(16)1Yey - no No Purkinje Cell Density Ts65Dna no - yes Ts1Cjeb yes yes - Dp(16)1Yey - no yes Cerebellar Granule Cell Density Ts65Dna no - yes Ts1Cjeb no yes - Dp(16)1Yey - no No a Data for the Ts65Dn mice from [43]. b Data for the Ts1Cje mice from [49].

Discussion

The craniofacial complexes of people with DS typically exhibit dysmorphology, brachycephaly, and reduced facial dimensions (e.g., midfacial retrusion, micrognathia)

[23,40,116-122]. We observed a similar pattern of morphological effects in Dp(16)1Yey mice, including reduced dimensions of the maxillae and palate (Figure 4), brachycephaly, and reduced mandibular size. Additionally, Dp(16)1Yey mouse skulls exhibited increased variance relative to euploid littermates for specific linear distances (Figure 5), not unlike increased variance in human

DS suggested by other studies [123,124]. Dp(16)1Yey mice separated from euploid littermates when evaluated in high dimensional form space (Fig. 5), which supports our results that morphology and variance patterns differ for trisomic and euploid mice. Additionally the cerebellum is small and hypocellular in humans with DS [21] and deficits in cerebellar midline area and cerebellar granule cell density seen in the Dp(16)1Yey parallel these differences (Figure

7).

Trisomy affects skull (Figure 4; Table II) and brain (Figure 7; Table IV) features of

Dp(16)1Yey mice, and also affects the same structures in Ts65Dn and Ts1Cje mice

[40,41,43,49]. Craniofacial and brain morphology were affected similarly in Ts65Dn and

Dp(16)1Yey mice (Figure 8; Table V), although the magnitude of morphological differences between Dp(16)1Yey mice and euploid littermates was differed somewhat from the magnitude of differences found between Ts65Dn mice and euploid littermates. The brain and skull were also affected in Ts1Cje mice, but to a lesser degree (Figure 7; Table IV), suggesting that some genes that are trisomic in Ts65Dn and Dp(16) 1Yey mice but not in Ts1Cje mice contributed to effects on craniofacial and brain development (Figure 7; Tables I and IV). The similarity among the three models suggests that neither monosomy for seven genes from Mmu12 (Ts1Cje mice) nor trisomy for 42 genes that are not homologous to genes on Hsa21 (Ts65Dn mice) (Table I) have a large impact on these diverse manifestations. The few highly conserved protein coding and microRNA

38 genes that differ between Ts65Dn and Dp(16)1Yey (Table 1) also do not appear to greatly influence these phenotypes as these mice were indistinguishable statistically (Table V).

It has been suggested that the regions at dosage imbalance in the Ts65Dn mouse that are not conserved with Hsa21 preclude the inclusion of this mouse as a model for DS. If this were the case, then the phenotypic similarities between the Ts65Dn mice and the Dp(16)1Yey mice observed in this and other studies [104,111] would logically not be due to specific gene dosage effects of their shared triplicated Hsa21 homologs. I reject this flawed reasoning and assert the usefulness of the many studies of the Ts65Dn mouse over the last 20 years. The collected knowledge of gene dosage imbalance effects gathered in Ts65Dn is reinforced by this study showing quantitative effects on brain and skull morphology that are nearly identical in the

Dp(16)1Yey and Ts65Dn mouse models and that closely parallel alterations seen in humans with

DS.

None of these three model mice create a perfect model of human DS. The 42 triplicated

Ts65Dn mouse genes that are orthologous to genes on Hsa6 and Hsa15 (Table I), along with the seven monosomic Ts1Cje mouse genes that are orthologous to genes on Hsa7, confound the interpretation of gene dosage effects. Also, in contrast to 95% of people with DS, Dp(16)1Yey mice do not carry an extra chromosome that disrupts centromere number. In human DS, the extra chromosome must be independently replicated and segregated at mitosis, and must find its appropriate place within the three-dimensional structure of the interphase nucleus. No murine model with trisomic mouse chromosome segments will exactly duplicate all the genes that are found on Hsa21, because Hsa21 contains genes without direct orthologs in the mouse and vice versa. A given murine model must be evaluated for phenotypes of interest with the assumption that trisomy for corresponding genes affects corresponding developmental and regulatory pathways in both species in similar, but not identical, ways. It follows that trisomy will frequently affect the same structures or functions, but not that the effects will be quantitatively equivalent in

39 two different species. The criteria for evaluating the usefulness of mouse models for human conditions must include more than the presence or absence of particular genes.

The Dp(16)1Yey mouse model will likely supplant the Ts65Dn mouse model, primarily because of simpler husbandry rather than gene content or phenotypic differences. Ts65Dn mice are difficult to breed compared to Dp(16)1Yey mice, in part, because male Ts65Dn mice are typically sterile [33,125]. Of note, human males with DS are usually sterile, but male

Dp(16)1Yey mice are fertile. The simpler husbandry of Dp(16)1Yey mice should make this model more broadly useful. It will be important to control for the parent contributing the duplicated chromosome segment, as the maternal contribution to the placenta reflects the mother’s genotype and maternal effects may be expected with trisomic dams.

The multiple segmental trisomies in Ts1Cje, Ts65Dn, and Dp(16)1Yey mouse models allow correlation of phenotypic outcomes to the effects of genes from the overlapping trisomic regions. The choice of which mouse model to use in an investigation should depend on the scientific question under investigation, the relevant phenotypes, and the presence of genes or other features (e.g., an additional freely segregating chromosome, susceptibility to environmental effects, possible placental contributions) that influence the biological trait(s) of interest.

Chapter 3: Does a Reduced Cellular Response to SHH Underlie Multiple Phenotypes of

Down Syndrome?

(Adapted from Dutka T and Reeves RH and from Singh N, Dutka T, Reeves RH, and

Richtsmeier JT, two manuscripts in preparation)

Craniofacial morphometric data collection and analysis performed by Dr. Nandini Singh while a post-doc in the lab of Dr. Joan Richtsmeier in the Department of Anthropology at Pennsylvania

State University.

Introduction

Trisomy for human chromosome 21 (Hsa21) is the most common live born human aneuploidy, occurring in approximately 1/700 live births [126]. Trisomy of Hsa21 results in

Down Syndrome (DS) which is among the most complex survivable genetic disorders [18].

Individuals with DS always display some degree of intellectual disability, craniofacial dysmorphology, and brain dysmorphology [32]. Phenotypes of DS are believed to result from combinations of genetic perturbations caused by excess expression of dosage sensitive genes from Hsa21 during development and adulthood [18]. Genetic studies have focused on discovering alterations in the function of specific pathways and in the expression of individual genes which may be utilized as targets for drug intervention [18].

Mouse models with segmental trisomy for regions orthologous to Hsa21 have been utilized to study the effects of excess gene dosage of Hsa21 homologs on developmental processes, structure, and cognition [32]. Ts65Dn, a mouse model of DS, displays cognitive deficits [32], craniofacial dysmorphology [40], and brain dysmorphology [43] that parallel deficits seen in people with DS. Defects in the response of trisomic cells to Sonic Hedgehog

(SHH) in the mouse model Ts65Dn have been implicated in the development of each of these three DS related phenotypes in Ts65Dn mice [62]. We theorized that a reduced response to SHH might underlie multiple DS related phenotypes in the Ts65Dn mouse [62].

The SHH pathway is involved in the patterning, growth and survival of many cells and tissues during embryogenesis and early development [92]. SHH signaling is especially important in the developing central nervous system [94]. The SHH pathway is largely suppressed in adult tissues, but is known to be active in neurogenesis in the adult hippocampus [95]. SHH operates by interaction with a transmembrane protein Patched1 (Ptch1) [62]. In the absence of SHH signaling

Ptch1 inhibits downstream signaling by another transmembrane protein Smoothened (Smo) [62].

When SHH interacts with Ptch1 this repression of Smo is removed and downstream Gli signaling is activated [62] (Figure 1). Ptch1 can also act in non-canonical pathways in response to SHH to control cyclin B1 regulated proliferation and can be cleaved to a conformation that regulates apoptosis [127].

With an acutely timed pharmacological intervention, we were able to target a single DS related phenotype, the hypoplasia of the cerebellum, which is tied to reduced response to SHH in the granule cell precursors (GCPs) of the Ts65Dn mouse [43,55]. We injected pups on the day of birth (P0) with SAG, a small molecule that binds to Smo to activate the SHH pathway [45]. By timing this single treatment to coincide with a point in development before a deficit in cell number is evident in trisomic mice [55] and when SHH is the most potent mitogen for the deficient cell population [61] we were able to restore the cerebellar size and granule cell (GC) density to euploid levels [45,55]. The improvement in cerebellar structure coincided with an improvement in the performance of the trisomic mice in the MWM [45], a learning and memory task linked to hippocampal and cerebellar function [103].

If the deficit in response to SHH seen in trisomic GCPs is uniformly present in all SHH responsive trisomic cells, then this deficit may be responsible for many of the phenotypes we see in DS. In that case a uniform up-regulation of the SHH pathway in trisomic cells throughout

42 development may ameliorate phenotypes in multiple cell types. Hypoplasia of the cerebellum provided an accessible target due to its development occurring largely after birth in these mice

[61]. Several other phenotypes in Ts65Dn that are thought to be related to a SHH response deficit occur in cell populations that rely on SHH signaling during embryogenesis [62]. Pharmacological interventions in these cell populations are difficult due to the precise timing and spatial localization of SHH signaling during development and the fact that many developmental processes rely on a gradient of SHH signaling [92]. To test our hypothesis of a uniform SHH response deficit we instead utilized a genetic model of constitutive up-regulation of the SHH pathway, the Ptch1tm1Mps/+ mouse, which has a single locus of Ptch1 knocked out and replaced with a LacZ cassette [105]. These mice evidence an increased response to SHH in all SHH responsive cells throughout development and have symptoms consistent with Gorlin syndrome.

Gorlin Syndrome is a human genetic disorder associated with a loss of function of a single Ptch1 locus which is characterized by tumor formation in the skin (basal cell carcinoma, BCC), cerebellum (medulloblastoma, MB) and soft tissue (rhabdomyosarcoma, RMS) and by cognitive deficits [89,105,106]. Here we crossed the Ptch1+/- mouse to Ts65Dn and examined these mice for changes in behavioral, craniofacial, and cerebellar phenotypes seen in Ts65Dn mice that parallel phenotypes seen in humans with DS.

Materials and Methods

Mice

All procedures were reviewed, approved, and carried out in compliance with animal welfare guidelines approved by the Johns Hopkins University and the Pennsylvania State

University Animal Care and Use Committees.

Ts65Dn mice were obtained from the Jackson laboratory and maintained in the Reeves’ laboratory colony as a C57Bl/6J x C3H/HeJ (B6 x C3H) advanced intercross. B6;129-

Ptch1tm1Mps/J mice were obtained from the Jackson laboratory, backcrossed for five generations onto a B6 background and bred to C3H mice to create the F1 generation. The Ptch1tm1Mps/+ B6 x

C3H mice were maintained in the Reeves’ laboratory colony as a B6 x C3H advanced intercross.

Ts65Dn females were crossed to Ptch1tm1Mps/+ males. All breeders were screened for the Pde6brd1 retinal degeneration locus and breeding pairs for behavioral experiments were created such that no offspring would be homozygous for the degeneration locus. Mice were housed one to five to a cage. Mice were weighed and genotyped by PCR for the break point in Ts65Dn [35,36] or the neomycin cassette in Ptch1tm1Mps/+ [105] in the first week of life. PCR primers for genotyping were as follows: Neo – Left: AGA CAA TCG GCT GCT CTG AT; Right: ATA CTT TCT CGG

CAG GAG CA; Ts65Dn primers obtained from [36]. The four genotypes resulting from this cross, Euploid;Wildtype , Euploid;Ptch1tm1Mps/+, Ts65Dn;Wildtype, Ts65Dn;Ptch1tm1Mps/+ will be referred to hereafter in this text as Eu;Wt, Eu;Ptch+/-, Ts;Wt, and Ts;Ptch+/-.

One to fourteen days after the final behavior test or one day after observing symptoms of

MB (symptoms outlined [128,129]) mice were anesthetized with isoflourane and intracardially perfused with a solution of cold phosphate buffer saline (PBS) with heparin (1unit/ml) followed by a solution of cold 4% paraformaldehyde (PFA). The heads were removed and drop-fixed in

4% PFA for at least 48hrs. The skulls were then washed and stored in PBS at 4 °C.

Assessment of Birth Frequency and Weight

All calculations were preformed in R studio (Version 0.97.248) in R (Version 2.15.2;

Date: 2012-10-26). Frequency of each genotype at birth was compared to expected Mendelian ratios using a Pearson’s Chi-squared test (2) (CrossTables(); Package: gmodels; Version:

2.15.4.1; Date: 2012-06-27). Post-hoc assessment of the 2 test was performed using the

44 standardized residuals (SR) that were output from the CrossTables() function. Weights in the first week were transformed to improve normality of the model, as assessed by visual inspection of

QQ plots and residuals, with a Box-Cox transformation (BC) using boxcox () from the MASS package (Version: 7.3-22; Date: 2012-10-08) and analyzed by a one-way ANOVA based on genotype overall and a two-way ANOVA based on mutation and ploidy statuses using ezANOVA() in the ez package (Version: 4.2-0; Date: 2013-06-20). Planned contrasts (PLNs) of the all genotypes compared to Ts;Ptch1+/- mice were performed by means of a summary of the model created with total genotype as a single factor using lm() from the base stats package

(Version: 2.15.2; Date: 2012-10-27). As the groups were unbalanced all ANOVAs were calculated with Type 3 sums of squares. The following numbers of mice were used for the birth frequency and week one weights: Eu;Wt n = 162, Eu;Ptch1+/- n = 85, Ts;Wt n = 88, and

Ts;Ptch1+/- n = 51.

Behavior Data Collection

Behavioral tests were run on two sets of mice beginning after 2.5 months of age and completing testing by 5.5 months of age. Both sets included both male and female mice in approximately equal proportions. The first group of mice began testing with novel object/novel location (NONL), followed by rotarod and then by fear conditioning (FC). The second group of mice began testing with Y-maze, followed by FC, then Morris Water Maze (MWM), and finally nesting. Within the second group a small subset mice did not participate in MWM due to constraints on testing time. Additionally, a small group of Eu;Wt and Eu;Ptch+/- mice, littermates to group two mice, participated only in the nesting test. All nesting results were combined for analysis. Some mice did not participate in all tests due to non-performance of tasks, death, or removal from the trials after developing symptoms consistent with the presence of a MB

[128,129]. Littermates of mice from the behavioral sets that did not participate in any trials due to

45 time constraints were maintained for survival analysis. Procedural problems created a lack of distinction between the Eu;Wt and Ts;Wt controls in the NONL and FC results in the first set of mice (data not shown). The FC protocol was subsequently altered to address these issues in the second set of mice. Researchers were blind to genotype throughout behavioral testing.

Handling

Mice were handled for a week prior to beginning testing for each of the two sets and for 5 days prior to each subsequent test with the exception of nesting. Handling consisted of a gradual acclimation of the mouse to being held by the tail while resting on the palm of the researcher’s hand as the researcher walked in the behavioral facility.

In detail: Mice were taken each day to the behavioral facility from the high risk housing area. On day one mice were allowed to explore the researcher’s hands in their home cage for 20 seconds. Each mouse was then picked up briefly by the tail to a height of 30cm above the cage and quickly returned to the cage floor. This process was performed twice for each cage.

On day two mice were allowed to explore the hands in their home cage for 20 seconds.

Each mouse was then picked up briefly by the tail to a height of 30cm above the cage and quickly returned to the cage floor. Mice were next allowed to explore the hands in their home cage for an additional 20 seconds and then each mouse was then placed on the palm of hand while holding the tail with the other hand and allowed to walk on the hand for 10 seconds before being returned to the home cage.

On day three mice were allowed to explore the hands in their home cage for 20 seconds.

Each mouse was then placed on the hand while holding the tail and allowed to walk on the hand for 10 seconds before being returned to the home cage. Next mice were allowed to explore the hands in their home cage for 10 seconds and then placed on the hand while holding the tail and allowed to walk on the hand for 20 seconds before being returned to the home cage.

On days four through seven mice were allowed to explore the hands in their home cage for 10 seconds. Each mouse was then placed on the hand while holding the tail and allowed to walk on the hand for 20 seconds before being returned to the home cage. This process was repeated for each mouse, proceeding 1 cage at a time, and each mouse in the cage was taken through the process once before repeating. Gloved hands were disinfected with Vimoba between each cage and between each set of acclimation procedures within a cage. Starting on day five the researcher walked slowly around the area while holding the mouse. For subsequent tests the process was begun as on day three and proceeded as described, taking a total of five days.

Rotarod Data Collection

Mice were aged 3 to 4.2 months. All tests used a Columbus instruments Rotamex-5 controlled by the Rotamex-5 Interface Software (Version 1.3.0 ©1999-2000). The mouse rod surface was constructed from grey PVC with a knurled finished, lane width of 9.3cm, and diameter of 3cm. The fall distance was 46.3cm.

Nine days following completion of the NONL paradigm mice were pre-trained on the rod on day zero at 4rpm for 30 seconds. On days one through three mice participated in four trials per day with a one hour inter trial interval. In each trial mice were evaluated over five minutes as the speed increased by 0.1rpm per second from 4rpm to 34 rpm. The time and speed at which mice fell from the wheel or failed to stay on top was recorded as well as non–performing behaviors

(giving–up, running wrong direction, trying to change directions, and spinning completely around wheel). Apparatus surfaces were cleaned with Vimoba (plastic surfaces) or 70% isopropanol

(metal surfaces) between each set of mice in each trial.

Y-maze Data Collection

Mice were aged 2.5 to 4 months. Y-maze was performed in a clear plexiglass apparatus with 38cm L, 7.5cm W, 13cm H arms oriented at equal angles to each other. The clear plexiglass was covered on the outside with opaque white paper. One arm was blank white, a second arm had

1mm thick black dots spaced 2cm apart vertically and 2.5cm apart horizontally, and a third arm had 1mm thick black X’s drawn to a height and width of 1.5cm2 and spaced 2.5cm from each other’s center. The floor of each arm was blank white.

Mice were placed at the end of one arm facing the center to begin the test. The starting arm for each mouse was chosen using a random number generator. Mice were allowed to explore the apparatus for 5 minutes. The test was recorded from a position directly above the apparatus using a Sony HD camcorder. Mice were tracked from the video using ANY-maze (Stoelting co

©1999-2010, Version 4.72, release 2010-1-14, CMU1394 digital camera drive © 2000-2006). An arm entry was recorded if 85% of the animal’s body crossed the threshold of the arm and an exit was recorded if less than 15% of the body remained in the arm. Each instance of arm entry was recorded manually from observing the tracking by ANY-maze. Triplet patterns were elaborated manually from the recorded arm entries and categorized as broken or complete alternations, where a complete alternation consisted of a triplet including entry into all 3 arms. Percent alternations were calculated as the completed alternations divided by the total triplets times 100.

Arm entry numbers were recorded as a measure of animal activity. The apparatus was cleaned with Vimoba between each mouse.

Fear Conditioning Data Collection

Mice were aged 3 to 4.5 months. FC began 14 to 17 days following the Y-maze with training in the conditioned context (CC) (Coulbourn Instruments Habitat Operant Cage, Model

H10-11M-TCSF, 18cm L x 17.5cm W x 28.5cm H) in a sound proof container. Training consisted of 2 minutes of acclimation to the CC followed by a 5-trial session of delay FC using a white noise conditioned stimulus (CS) (30 sec, 73-75 dB) paired in the final 2 seconds with a foot shock unconditioned stimulus (US) (2 sec, .7 mA, Coulbourn Precision Programmable Animal

Shocker Model H13-15) and a 30 second inter-trial interval.

Twenty-four hours after training mice were returned to the CC for a contextual trial lasting 5 minutes. Forty-eight hours after training mice were returned to the chambers which were disguised with opaque white boxes (14cm D x 14cm W x 18cm H) to create a novel context

(NC). Mice were allowed to acclimate to the NC for 2 minutes followed by a cued trial consisting of 3 minutes of the CS. Apparatus surfaces were cleaned with 70% isopropanol (metal surfaces) or Vimoba (plastic surfaces) between each mouse. Observations were made by video recordings from the center ceiling of the box. The trials were controlled and the recordings were analyzed by

FreezeScan (CleverSys, ©2003, version 2, build 2011-2-2) with the following settings (calculate inter-frame motion and light-zone every 5 seconds; inter-frame motion noise filtering radius is 1 second; freezing action condition inter-frame motion is less than 15 seconds; automata sequence parameters are for freeze (N=24; M=22) for move (N=10; M= 8); freeze detection threshold was static set to 12 with the (Animal Model Low= 0, High= 255)). Following each day of training or testing mice were placed in holding cages until all mice in a cage had completed the test for that day. Then all mice were returned to the home cage.

Morris Water Maze Data Collection

MWM was conducted in a white circular plastic tank (75 cm H x 120 cm D at the top

(116.5-117cm D at water level)). The tank was divided evenly into quadrants. A clear platform

(11cm in D X 37.5cm H) was placed in the center of a quadrant 15-17cm from the side of the tank (Med Associates Inc, ENV-595M On-Demand Water Maze (Atlantis) Platform for Mouse).

The tank was filled with water to a height of 0.5 cm above the platform. Water temperature ranged between 20 and 23 degrees Celsius. White non-toxic tempura paint was used to make the water opaque. Four visual cues were placed one to a quadrant, equidistant along the rim of the tank. The cues consisted of:

1. A black 5 pointed star with each point 4.5cm H x 4.5cm W at the base (11.5cm from

indent between points to opposite point) on a white field 17cm x 17cm. One point was

1.3cm from the bottom, one point was 1.6cm from the top, one point was 1.5cm from the

left side looking down, one point was 1.5cm from the right side looking down, and one

point was 2.5cm from the top.

2. A black square 12.5cm x12.5cm containing four white circles (4.3cm D) on a white field

15.5cm x 16cm. The circles were 1.1cm from the right of the square side, 1.5cm from the

left, 1.8cm from the top, and 1.3cm from the bottom. The circles were arranged in a

square and were 0.8cm apart vertically and 1.3cm apart horizontally. The black square

was located 2cm from the top and 1.3cm from the left in the white field looking down.

3. A black circle 13.8cm D on a white field 16cm H x 17.5cm W located 1.1cm from the top

and 1.3cm from the left.

4. A black rectangle 15.4cm W x 11.7cm H with a line width of 0.8cm on a white field

18.2cm W x 15cm H. The rectangle was 2.2cm from the top and 1.4cm from the left side

looking down. Inside the rectangle 2.3cm from the top of the base line of the rectangle

was a black equilateral triangle pointing toward the top of the rectangle which was 5.5cm

tall with a line width of 1cm.

The star was positioned opposite rectangle with the triangle. The circle was opposite the square with the circles. The trials were recorded from a position directly above the apparatus using a

Sony HD camcorder. Mice were tracked by their centers from the video using ANY-maze

2000-2006).

In the visible platform (VP) test a flag was placed on the center of the platform (post

11cm H x 2cm D with three different colored stripes of opaque tape (hot pink (top); hot orange

(middle) hot green (bottom)); clear base 1cm H x 4cm L x 4cm W). Mice were aged 3.5 to 5 months. Fourteen to sixteen days following the last day of FC mice were trained for the VP test.

Training consisted of a single day with three blocks of four trials for each mouse. In each trial the platform was place in one of the four quadrants and the mouse was release into the center of the tank.

Twelve days following the VP test mice began training for the HP test. For the hidden platform (HP) test mice were aged 4 to 5.5 months. Training consisted of three days of three blocks of four trials for each mouse. In each trial the platform was left in the center of a single quadrant, hidden under the water, and the mouse was released from the side of the tank in one of the four quadrants.

For both VP and HP the time until reaching the platform was measured for each mouse by stopwatch and rounded down to the nearest second. If mice did not find the platform within one minute they were placed on the platform. After finding or being placed on the platform the mice were allowed to remain there for 30 seconds. For each block of trials the start quadrant and order of quadrants for the platform (VP) or mouse (HP) was determined by a random number generator. The start quadrant was different for each block of trials within a day and all four quadrants were included in each block of trials. After each block of trials the mice were allowed to rest in a holding cage with a heat lamp until the next mouse finished their trial and then the mice were returned to the home cage. In the HP the ANY-maze software was used to measure the path efficiency to the platform for each trial.

The probe trial was conducted 24 hours after the start time of the last day of HP. The platform was lowered in the tank and held below the surface by a magnet. Mice were placed in

51 the center of the tank and allowed to swim for 3 minutes. The ANY-maze software was used to measure the time spent in each quadrant as well as time spent in the area where the platform had been located, number of entries into the platform zone, path efficiency to first platform entry, and average swimming speed while mobile for each mouse.

Nesting Data Collection

Mice who participated in nesting had previously performed: 1) Y-maze, FC and MWM;

2) just Y-maze and FC; or 3) no behavior tests prior to the nesting trial. All mice in the nesting trial took part at the same age as their littermates. Nesting trials took place one to ten days following the probe trial. For the nesting trial mice age 4 to 5.5 months were singly housed in a new cage with an intact nesting square for 24 hours. At the end of the trial the mice were removed and three photos of the cage were taken from several angles. These photos were de-identified and then placed into four categories based on the amount of destruction of the nesting square and the organization of the nest. The categories consisted of nesting square barely touched, nesting square in large pieces, nesting square destroyed with organized nest, and nesting square destroyed with disorganized nest (Figure 9). The pictures were identified and category for each mouse recorded.

If the categories did not agree between several pictures for one mouse, which occurred twice, the most frequent category chosen was used for analysis.

A B

C D

Figure 9: Nesting category examples. Representative examples of each of the nesting categories that were used during the visual assessment in the nesting analysis. A) Nesting square barely touched; B) nesting square torn into large pieces; C) nesting squared destroyed and nest material scattered; D) nesting squared destroyed and nest material organized.

Analysis of Survival

Survival was recorded until the end of behavioral testing (4 to 5.5 months of age).

Analysis of frequency of survival to end of behavioral tests (4 to 5.5 months of age) was conducted using a 2 test (CrossTables(); Package: gmodels; Version: 2.15.4.1; Date: 2012-06-

27) confirmed with a FET (fisher.test(); base stats package; Version: 2.15.2; Date: 2012-10-27) owing to the low frequency in some categories. Post-hoc assessment of the chi-squared test was performed using the SR that were output from the CrossTables() function and post-hoc assessment of the FET was performed by pair-wise FET’s between all genotypes with a

Bonferroni correction (=0.05). The following numbers of mice were used for the survival analysis: Eu;Wt n = 97, Eu;Ptch1+/- n = 57, Ts;Wt n = 46, and Ts;Ptch1+/- n =32.

Analysis of Behavioral Tests

All calculations were preformed in R studio (Version 0.97.248) in R (Version 2.15.2;

Date: 2012-10-26). All one-way analyses were performed based on genotype overall. To determine if the effects of the mutation differed depending on the ploidy of the mouse most analyses of the four genotypes were also, where possible, factored by presence or absence of trisomy and presence or absence of the Ptch1 mutation and examined for the simple effects of these two factors as well as for possible the interaction effects between those two factors.

ANOVAs were conducted using ezANOVA() in the ez package (Version: 4.2-0; Date: 2013-06-

20) and the linear model (LM) from these ANOVAs retrieved using lm() from the base stats package (Version: 2.15.2; Date: 2012-10-27). Repeated measures ANOVAs (RM ANOVA) were also conducted with the ezANOVA() function and confirmed with linear mixed effects (LME) models using lme() from the nlme package (Version: 3.1-105; Date: 2012-09-24). When linear

54 models (LM, LME) were appropriate, PLNs of the all genotypes compared to Ts;Ptch1+/- mice were performed by means of a summary of the model created with total genotype as a single factor using lme() from the nlme package or lm() from the base stats package. Where appropriate post-hoc comparisons were conducted on these same linear models using testInteractions() from the Phia package(Version 0.1-3; Date: 2013-07-04) with a Holm correction (=0.05) unless otherwise specified. All models (ANOVA, RM ANOVA, LME, LM) were assessed for conformance to model assumptions by visual inspection of plots of residuals against fitted values and QQ plots. Where necessary data was transformed prior to analysis with a Box-Cox transformation (BC) using boxcox () from the MASS package (Version: 7.3-22; Date: 2012-10-

08) to improve conformance of the model to the assumptions of the statistical tests. In the RM

ANOVA the Greenhouse-Giesser sphericity corrected p-values (p[GG]) were reported in cases of violation of sphericity as assessed by the Mauchly's test for sphericity in the ezANOVA() function. As the groups were unbalanced all ANOVAs and RM ANOVAs were calculated with

Type 3 sums of squares.

Rotarod Analysis

Giving up or running in the wrong direction was counted as failure to perform and those scores were not included in the day trial averages. Mice who failed to perform on three or more trials in a day were not included in the analysis. The trials were averaged over each day for each mouse and these averages were used for subsequent analysis. To improve the fit of the model one mouse was removed as an outlier as determined using outTest(), the Bonferroni outlier test in the car package (Version: 2.0-18; Date: 2013-06-11). BC transformed data was analyzed by ploidy and mutation and by genotype overall using RM ANOVAs confirmed with LMEs. PLNs to the

Ts;Ptch+/- were obtained from the LME using the genotypes as a single factor.

Y-maze Analysis

Percent alternation and number of arm entries required a Robust MANOVA (R-

MANOVA) due to lack of homogeneity of covariance (cov(); base stats package; Version: 2.15.2;

Date: 2012-10-27) and multivariate normality (Shapiro test; mshapiro.test(); Package: mvnormtest; Version: 0.1-9; Date: 2012-04-04). The test chosen was a R-MANOVA performed on ranked data using Munzel and Brunner’s method, implemented in R using the mulrank() function [130] in the WRS package (Version: 0.24; Date: 2014-01-20). Individually, percent alternation was found to be normal and analyzed by a two-way ANOVA factored by ploidy and mutation. A one-way ANOVA factored by genotype was also performed and the resulting LM assessed for the PLNs. Arm entries were found not to be normal and were unable to be adequately transformed. Arm entries were analyzed by a Kruskal-Wallis (KW) test factored by genotype overall followed by Bonferroni corrected post-hoc comparisons at an  of 0.05 using kruskal() from the package agricolae (Version: 1.1-4; Date: 2013-4-8).

Fear Conditioning Analysis

On the training day percent of time spent freezing for each mouse was calculated in 30 second bins for a total of 14 bins total. On the context and cued trial days percent of time spent freezing for each mouse was calculated in 60 second bins for 5 bins total for each of the two trial days. BC transformed data was analyzed by ploidy and mutation and by genotype overall using

RM ANOVAs confirmed with LMEs on each day. For the training day post-hoc comparisons using the one-way LME based on genotype were performed to examine the data for differences in freezing levels in each bin for all genotypes. On the test days PLNs to the Ts;Ptch+/- were obtained from the one-way LME based on genotype.

Morris Water Maze Analysis

For the VP and HP tests all data, latency and path efficiency (HP only), was averaged by block of trials for each mouse. All data was BC transformed to improve normality and analyzed by ploidy and mutation using a RM ANOVA confirmed with a LME for each test. A RM

ANOVA and a LME using the genotypes as a single factor was also performed for all data in each test. PLNs to Ts;Ptch+/- were obtained from the one-way LMEs based on genotype overall.

For the VP test post-hoc comparisons using the one-way LME were performed to examine the data for differences in visual recognition and swimming ability between all genotypes. A

Pearson’s correlation test (PCC) comparing the latency and path efficiency data in the HP test was calculated using cor.test() from the base stats package to confirm latency as a measure of learning and memory.

For the probe test percent of time spent in the quadrant containing the platform and platform entries per second spent in the quadrant containing the platform were found to be normal and analyzed by a one-way ANOVA factored by genotype and a two-way ANOVA factored by ploidy and mutation. Percent of time spent in the platform zone while in the quadrant containing the platform and average mouse swimming speed were found to not be normal and were BC transformed before analysis with a one-way ANOVA factored by genotype and a two-way

ANOVA factored by ploidy and mutation. PLNs to the Ts;Ptch+/- were obtained from the one- way LMs based on overall genotype. PCC tests comparing platform entries per second spent in the quadrant containing the platform and percent of time spent in the platform zone while in the quadrant containing the platform to path efficiency to first platform entry were calculated using cor.test() from the base stats package to confirm these measures as indicators of learning and memory.

Nesting Analysis

Frequency of categorization for nesting was analyzed using 2 test (CrossTables();

Package: gmodels; Version: 2.15.4.1; Date: 2012-06-27) confirmed with a FET (fisher.test(); base stats package; Version: 2.15.2; Date: 2012-10-27) owing to the low frequency in some categories.

Post-hoc assessment of the 2 test was performed using the SR that were output from the

CrossTables() function and post-hoc assessment of the FET was performed by pair-wise FETs between all genotypes with a Bonferroni correction (=0.05).

Morphometrics Data Collection

Micro-computed tomography (µCT) images of skulls from the first behavioral set were acquired at the Johns Hopkins Medical Institution Research Building Imaging Center (Gamma

Medica X-SPECT/CT scanner, Northridge, CA, USA, 0.05mm resolution). Eu;Wt n = 13,

Eu;Ptch1+/- n = 10, Ts;Wt n = 9, and Ts;Ptch1+/- n = 9.

We used Principal coordinate analysis after Generalized Procrustes superimposition of the landmark data to quantify and visualize craniofacial form. The 3D coordinates of 40 landmarks were collected on the entire cranium (Appendix 4, Figure S3). The landmark configuration of all specimens were superimposed using generalized Procrustes analysis (GPA) that extracts shape coordinates from the original landmarks by rotating, translating, and scaling the data, and subsequently yielding a measure for size called centroid size [131]. All subsequent analyses were done using the Procrustes shape coordinates. We used principal component analysis to examine the overall cranial shape variation in the sample. We also conducted a separate PCA with just the facial landmarks to better examine the localized effects on the face.

The shape variation along the respective PC axes was visualized via wireframe diagrams. All the analyses were conducted in MorphoJ and programming software R version 3.1.0 [132,133].

Histological Preparation

The brains of a subset of mice from both behavioral data sets were removed from the skulls, sliced slightly off center along the sagittal axis, and embedded cut-side out in a paraffin block to obtain mid-line sagittal sections. Brains from Ptch1+/- mice, both euploid and Ts65Dn, were screened for the presence of large MBs. Mice with tumors disrupting the cerebellar architecture were eliminated from the histological assessment of cerebellar structure. For each mouse the left or right hemisphere was chosen at random for further processing and analysis. For embedding the tissue was progressively dehydrated by 30 minute washes of ethanol (30%, 50%,

70%, and 90%) finishing with two one-hour washes in 100% ethanol. The tissue was cleared with a wash of 1:1 ethanol and xylenes for 30 minutes followed by a one-hour room temperature wash in xylenes. The tissue was infused with paraffin overnight in a 1:1 mixture of paraffin and xylenes at 57°C. Finally two paraffin washes were performed at 57°C over a 24hr period prior to embedding each brain in fresh paraffin. Ninety sections, each seven µm thick, were obtained from the Johns Hopkins Reference Histology Lab that spanned the midline of the brain. Sections were stained with hematoxylin and eosin.

Collection of Brain Morphological Data

Investigators were blind to genotype throughout the histological assessment. Sectioned brains were quality controlled for correspondence to midline sagittal morphology. In some cases sectioning errors allowed mice to be used in some analyses that lack sufficient integrity for other analyses.

To determine proportional midline sagittal surface area of the cerebellum three low magnification images of the entire midline sagittal section were obtained for each mouse using a

Nikon SMZ 1500 attached to a Nikon Digital Sight DS fil camera (NIS-Elements BR 3.00, SP7,

Wayne Rasband, NIH) by placing a grid of 0.25mm2 blocks over each image and counting grid points that intersect with the cerebellum and brain [115].

To determine linear Purkinje cell density two higher magnification images of the midline sagittal cerebellum were obtained by taking several (12-35) higher magnification images using the Nikon microscope and camera and stitching these images together using Microsoft Image

Rasband, NIH) and the lengths of folia were traced using the ObjectJ plugin (Version:1.02o,

Date: 2012-1-07; Norbert Vischner & Stelian Nastase, University of Amsterdam).

To determine the GC counts high magnification images of the internal granule cell layer of folia V, VI and IX were taken using a Zeiss Axioskop two plus attached to a Panasonic GP-

KR222 with a 63x oil immersion lens using the capture video input function in the program

Stereologer (version 1.3, Hopkins, ©1998). Three independent, non-overlapping, images were taken for each folium and two sections were imaged for each mouse for a total of 18 sections per- mouse. Cells were counted in a 5000μm2 area using ImageJ (version 1.44p; Java 1.6.0_20 (32bit),

Wayne Rasband, NIH). A correction factor (1.28x) was calculated and applied to sections that were stained faintly using lab protocols to account for the lower numbers of GCs observed in these vs. those stained darkly by the Reference Histology Lab.

Analysis of Brain Morphology

All calculations were performed in R studio (Version 0.97.248) in R (Version 2.15.2;

Date: 2012-10-26). All data was BC transformed using boxcox () from the MASS package

(Version: 7.3-22; Date: 2012-10-08) to improve normality of the model. Normality was checked by visual inspection of plots of residuals against fitted values and QQ plots. For the cerebellar area and Purkinje cell assessments BC transformed data was analyzed by ploidy and mutation using a RM ANOVA confirmed with a LME as previously described for the behavioral tests.

Transformed data was also analyzed by genotype overall using a RM ANOVA and LME model as described and PLNs of all genotypes compared to Ts;Ptch1+/- were conducted using this LME when the genotype test was significant.

To examine the GC density, first the average GC density for each mouse was calculated as in [43,45,55]. This average data was BC transformed and analyzed by a two-way ANOVA factored by ploidy and mutation. A one-way ANOVA factored by genotype was also performed and the resulting LM assessed for the PLNs. Next all data points were used to look for different effects based on developmental zone. BC transformed data was analyzed by genotype and by ploidy and mutation using a RM ANOVA on all data points. Finally data was separated by folium, BC transformed for each folium, and analyzed by ploidy and mutation using a RM

ANOVA confirmed with a LME. BC transformed data for each folium was also analyzed by genotype using a RM ANOVA confirmed with an LME and PLNs of all genotypes compared to

Ts;Ptch1+/- were conducted using this LME.

Assessment of Tumor Burden

Mice were sacrificed as soon MB symptoms developed or up to 2 weeks following the final test. Brains from all Ptch+/- mice from both ploidies were dissected from the stored heads

61 and examined grossly for the presence of MB. Three brains containing grossly visible MBs were prepared and sectioned as described for the cerebellar assessment. Brains without visible MBs were used in the cerebellar assessment to make up a cohort of 9 to 14 mice for each group. The whole and half brains from Ptch+/- mice remaining after sectioning for cerebellar histology were stained with Lac-z to visualize any MBs. The whole brains were sliced sagittally into 3 to 4 thick sections with a razor blade prior to staining. The tissue was washed for 1.5 hrs in 0.1M phosphate buffer with 2mM MgC12, 0.01% deoxycholate and 0.02% NP-40, and stained at 37 °C for 24 hours in X-gal mixer containing 2mM MgCl2, 5 mM potassium ferrocyanide, and 5 mM potassium ferricyanide supplemented with 1 mg/ml X-gal (Thermo Fisher Scientific Fermentas, Pittsburgh,

PA, USA). The tissue was transferred to 1X PBS and the stain darkened by storage at 4°C prior to imaging. Stained brains were examined for MBs disrupting cerebellar morphology, typically with some LacZ staining. The total frequency of MBs in Ptch1+/- mice within each ploidy was assessed by a 2 test (CrossTables(); Package: gmodels; Version: 2.15.4.1; Date: 2012-06-27). The frequency of tumors in Ptch1tm1Mps/+ mice with in each ploidy that had no symptoms of illness prior to death was also assessed by this method.

Results

We crossed Ptch+/- onto trisomic, Ts65Dn to generate four genotypes: Euploid mice with two normal copies of the Ptch1 gene (Eu;Wt), Euploid mice heterozygous for a lacZ knock-in that produced Ptch1tm1Mps/+ (Eu;Ptch+/-) [105], Ts65Dn;Wildtype (Ts;Wt), and

Ts65Dn;Ptch1tm1Mps/+ (Ts;Ptch+/-). These mice were examined for a number of behavioral and structural phenotypes that are affected in Ts65Dn mice that do or may arise from perturbations in

SHH signaling as a result of trisomy. The results of these comparisons are summarized in table

VIII.

Loss of Ptch1 affects birth frequency, survival, and weight

We found that the frequencies of the four genotypes at birth were significantly altered from Mendelian ratios (p= 6.1e-07, 2). There were significantly more Eu;Wt mice and significantly fewer Ts;Ptch+/- mice than expected (p< 0.05, SR; Table VI; Table VIII). At the end of the behavioral testing, 4 to 5.5 months of age, there was also a significant difference in survival across genotypes (p= 3.8e-04, 2; p= 1e-04, FET; Table VI). Based on overall survival of all mice surveyed, a larger proportion than expected of Eu;Wt mice survived to the completion of the behavior tests (p< 0.05, SR; Table VI). Additionally, a smaller proportion than expected of

Eu;Ptch+/- mice survived to the completion of the tests (p< 0.05, SR; Table VI). Pair-wise comparisons found that Eu;Wt had had a significantly larger proportion survive than Ts;Ptch+/-

(p= 0.005, FET) and Eu;Ptch+/- (p= 3e-04, FET) and a somewhat larger proportion than Ts;Wt

(p= 0.08, FET) (Table VIII).

Both haploinsufficiency of Ptch1 and Ts65Dn are additionally known to effect body size

[105,134]. We found that, in accordance to their previously reported effects, trisomy significantly reduced body size (p= 7.6e-23, ANOVA) and haploinsufficiency of Ptch1 significantly increased body size (p= 0.015, ANOVA) in the first week of life (Table VI). Genotype overall also significantly affected body size (p= 8.0e-23, ANOVA) and Ts;Ptch+/-mice weighed significantly less than Eu;Wt mice (p= 5e-08, PLN) and Eu;Ptch+/-mice (p= 6e-10, PLN) and trended toward a higher weight than Ts;Wt mice (p=0.08, PLN) (Table VI; Table VIII).

The original investigation of Ptch+/- mice observed a small fraction of hindlimb defects

(polydactyly or syndactyly) and soft tissue tumors (RMS) in the mice [105]. We did not observe any hindlimb changes or soft tissue tumors in our dissected behavioral cohort (Eu;Ptch+/- n=35;

Ts;Ptch+/- n= 27). Three mice among those examined for initial weight (Eu;Ptch+/- n = 85;

Ts;Ptch+/- n = 51) were born with curly tails (2 euploid, 1 trisomic), an indication of a neural tube

63 defect [103]. These mice survived and the tail phenotype was not evident in the adults. They were included in subsequent analyses.

Table VI: Population statistics for all genotypes

Genotype Percent Frequency Percent Frequency Average Weight at 1 At Birth Survival Week (g) ± SEM Eu;Wt 42% 98% 4.2 ± 0.1

Eu;Ptch1+/- 22% 77% 4.4 ± 0.1

Ts;Wt 23% 86% 3.0 ± 0.1

Ts;Ptch1+/- 13% 78% 3.3 ± 0.1

Trisomy does not alter tumor development in Ptch1+/- mice

Mice lacking a copy of Ptch1 were created originally as a model for human MB and

Gorlin syndrome [105]. Between 14% and 30% of all Ptch1+/- spontaneously develop these cerebellar tumors, depending in part on genetic background [105,129]. At 3 weeks, more than

50% of mice lacking a copy of Ptch1 show ectopic areas of pre-neoplastic cells [135]. Tumors are observed as early as 5 weeks of age [105] and the peak incidence of occurrence was found in one study to be between 16 and 24 weeks [129]. A single report indicates that MB is rare among those with DS [136].

We found no impact of trisomy on overall frequency of MB in the Ptch1+/- mice that took part in the behavioral study (euploid= 29%; trisomic= 30%). The study occurred between 10 and

24 weeks of age. While this assessment did not include mice that died prior to the behavior testing, there was also no significant difference in survival between both groups of Ptch1+/- mice

(Table VI; Table VIII). The frequency of tumors in mice that remained asymptomatic until sacrificed also did not significantly differ between groups (euploid= 19%; trisomic= 26%).

We lacked sufficient affected mice to determine whether those that eventually developed

MB behaved substantially differently from their peers in the behavioral tests while otherwise asymptomatic. However, none of the mice with tumors at the end of their life was found to perform consistently outside the range of others of their genotype in any behavioral test. Thus we found no reason to remove these animals from our behavior analyses.

Chronic up-regulation of the SHH pathway ameliorates some but not most behavioral deficits in Ts65Dn

Haploinsufficiency of Ptch1 differentially affects motor learning depending on mouse ploidy

We first examined the mice using an accelerating rotarod test, which has been used frequently as a measure of motor learning [137]. Humans with DS have difficulties with fine motor skills and motor planning [67-69], but these deficits have proven difficult to consistently replicate in mouse models of DS. Ts65Dn mice have been found by some to perform as well as or better than euploid mice in this test [43,75]. In contrast, Tc1 mice, which have a transchromosomic Hsa21, perform consistently worse than euploid littermates in an accelerating rotarod trial [138,139]. We performed this test to determine whether haploinsufficiency of Ptch1 affected motor learning and whether Ts65Dn would show an affect using the acceleration paradigm that found a deficit in Tc1 mice [138].

All mice showed significant improvement in ability and motor learning over the course of the test (p[GG]= 1.6e-23, RM ANOVA; p<1e-04, LME) and there were no significant interactions between training day and genetic factors (Figure 10). As we observed previously,

Ts;Wt performed as well as Eu;Wt mice in accelerating rotarod experiments [43] (Figure 10).

Ts;Ptch1+/- demonstrated improved performance compared to Ts;Wt, while Eu;Ptch1+/- demonstrated reduced performance compared to Eu;Wt. This differential effect was reflected in a significant interaction between ploidy and mutation statuses as model factors (p= 0.022, RM

ANOVA; p= 0.02, LME; Figure 10). We also found that genotype overall also had a significant impact on performance (p= 0.0065, RM ANOVA; p= 0.005, LME) and that Ts;Ptch1+/- mice performed significantly better than Eu;Wt (p= 0.009, PLN), Ts;Wt (p= 0.03, PLN), and

Eu;Ptch1+/- mice (p= 8e-04, PLN) (Figure 10; Table VIII).

Figure 10: Performance of mice in accelerating rotarod. Haploinsufficiency of Ptch1 improves motor learning ability in Ts65Dn mice and reduces motor learning ability in euploid mice (p= 0.022, RM ANOVA; p= 0.02, LME). Trace of the latency to fall for each genotype on each day the accelerating rotarod protocol averaged over the four trials conducted per day. Error bars are Morey corrected SEM. Eu;Wt n= 21, Eu;Ptch+/- n= 15, Ts;Wt n= 17, Ts;Ptch+/- n= 16.

Haploinsufficiency of Ptch1 does not affect Y-maze performance in Ts65Dn or euploid mice

We next assessed the impact of reduced Ptch1 expression (SHH pathway up-regulation) on performance in the Y-maze. This is a non-aversive task that is considered a measure of active retrograde working spatial memory and does not require strong training, relying instead on the innate curiosity of the mouse to explore new territory [137]. Ts65Dn mice display a consistent deficit in this task (reviewed [32]) and this outcome was unaffected by an injection of SAG at P0

[45]. There was a significant effect of overall genotype on the outcomes of the percent alternation and arm entries in the Y-maze (p=9e-04, R-MANOVA). We found that trisomic mice had fewer complete alternations with more overall exploratory activity than did euploid mice (Figure 11).

Haploinsufficiency of Ptch1 did not significantly alter this phenotype (Figure 11; Table VIII).

All Ts65Dn mice had significantly fewer complete alternations than all euploid mice

(55% vs 64%, p= 0.0045, ANOVA) and there was a trend toward lower performance comparing

Ptch1+/- to wildtype mice (58% vs 63%, p= 0.080, ANOVA), but no significant interaction between ploidy status and mutation status (Figure 11a). We observed a significant impact of genotype overall on percent alternation as well (p= 0.015, ANOVA) and found that Ts;Ptch1+/- mice had significantly fewer alternations than Eu;Wt (52% vs. 66%, p= 0.002, PLN) and somewhat fewer than Eu;Ptch1+/- mice (52% vs. 61%, p=0.054, PLN) (Figure 11a; Table VIII).

There was a significant impact of genotype on overall activity as measured by total arm entries (p= 6e-05, KW). Post-hoc comparisons determined that trisomic mice had significantly more arm entries than euploid mice regardless of genotype at Ptch1 (32 entries each vs. 23 entries each; p< 0.05; Figure 11b). These results indicate that the lower percent alternations observed in the Ts65Dn mice was not a result of lower levels of exploration. Haploinsufficiency of Ptch1 had no impact on the characteristic hyperactivity of Ts65Dn mice (Figure 11b; Table VIII).

Figure 11: Performance of mice in Y-maze. Haploinsufficiency of Ptch1 does not impact Y- maze performance in Ts65Dn and Euploid mice. Genotype significantly affected the percent alternation and arm entries in the Y-maze (p= 9e-04, robust MANOVA). A) Average percent alternations and B) average arm entries separated by genotype. Trisomy significantly decreased percent alternations (p= 0.0045, ANOVA) and increased arm entries (p= 6e-05, KW, post-hoc

=0.05) while haploinsufficiency of Ptch1 had no significant effects. Error bars are SEM.

Eu;Wt n= 33, Eu;Ptch+/- n= 21, Ts;Wt n= 20, Ts;Ptch+/- n= 11.

Haploinsufficiency of Ptch1 reduces retention of contextual and cued memory in trisomic mice

We investigated the effect of the Ptch1 mutation on performance in a fear conditioning

(FC) paradigm. This is an aversive task that pairs a context (conditioned context; CC) and a noise

(conditioned stimulus; CS) to an electrical shock (unconditioned stimulus; US) [140]. The US generates a natural fearful response manifested as freezing behavior. Following training both the

CC and the CS can elicit the same freezing response in the absence of the US [140]. Memory of the CS and CC rely on somewhat different brain regions; memory of the CC is thought to rely more heavily on hippocampal and cerebellar function [90,141]. Ts65Dn mice generally display lower levels of freezing in the CC (contextual fear) than do euploid mice (reviewed [32]); response to the CS (cued fear) varies in different studies [46,142-144]. We found a significant interaction between ploidy, mutation, and training time on the training day of FC (p[GG]=

0.0023, RM ANOVA; p= 1e-04, LME) and an interaction between genotype and training time

(p[GG]= 1.9e-07, RM ANOVA; p< 1e-04, LME) (Figure 12). Post-hoc-comparisons within each

30 second time bin during the seven minute training found no significant differences in percent freezing between any genotypes in the final two time bins, indicating that while learning progressed differently for each genotype, the final level of learning or freezing was similar for all four genotypes (Figure 12).

Figure 12: Performance of mice in FC training. Trace of average percent freezing in 30 second bins of time for each genotype over the course of the training day. Bins 30 to 120 track the acclimation to the CC. Thirty seconds of CS (white noise, ~75dB) paired in the final 2 seconds with a US (shock, 0.7ma) occurred during bins 150, 210, 270, 330 and 390 with and ITI occurring in the intervening bins. Each bin refers to the 30 seconds preceding the stated time in seconds.

Ploidy status, mutation status, and training time significantly interacted with each other to effect the trace of freezing time on the training day of FC (p[GG]= 0.0023, RM ANOVA; p =1e-04,

LME). Genotype overall also interacted with training time to effect the trace of freezing time on the training day of FC (p[GG]=1.9e-07, RM ANOVA; p <1e-04, LME). Post-hoc-comparisons within each time bin found no significant differences in percent freezing between any genotypes the final two bins, i.e., at completion of training. Error bars are Morey corrected SEM.

Eu;Wt n= 23, Eu;Ptch+/- n= 20, Ts;Wt n= 20, Ts;Ptch+/- n= 11.

In the contextual fear test, trisomy significantly decreased the freezing response relative to both euploid groups over time (p[GG]= 0.0051, RM ANOVA; p= 0.008, LME; Figure 13a).

Haploinsufficiency of Ptch1 trended toward a differential effect, decreasing memory retention in the trisomic animals but showed no effect in euploid animals (p= 0.067, RM ANOVA; p= 0.06,

LME; Figure 13a). Genotype overall also had a significant impact on retention of fear memory over time in the contextual fear trial (p[GG]= 0.014 RM ANOVA; p= 0.006, LME; Figure 13a).

The linear trend over time reflected significantly lower memory retention in Ts;Ptch+/- compared to all other genotypes (both euploids, p= 3e-04; Ts;Wt 0.04; PLNs) and the quadratic trend in

Ts;Ptch+/- reflected significantly lower memory retention as compared to both wildtype groups

(Eu;Wt, p= 0.02; Ts;Wt, p= 0.03; PLNs) (Figure 13a).

In cued fear the loss of a single Ptch1 locus was correlated with a significant further decrease in performance of trisomic animals while having no impact on behavior in euploid animals (p= 0.048, RM ANOVA; p= 0.04, LME; Figure 13b). As in the context trial, genotype overall had a significant impact on retention of cued fear memory over time (p[GG]= 3.1e-06,

RM ANOVA; p< 1e-04, LME) and Ts; Ptch1+/- had a significantly lower memory over time compared to all other genotypes (Eu;Wt p=0.001 - quadratic, p=0.02 - cubic; Eu;Ptch+/- p=0.01 - linear, p= 4e-04 – quadratic; Ts;Wt mice p=0.009 - quadratic; PLNs; Figure 13b).

Haploinsufficiency of Ptch1 thus increased the deficits in contextual and cued fear memory in

Ts65Dn mice rather than ameliorating the effects of trisomy on these tasks (Figure 13; Table

VIII).

Figure 13: Performance of mice in

contextual and cued FC trial.

Haploinsufficiency of Ptch1 reduces

retention of contextual and cued memory

in Ts65Dn mice. Trace of average percent

freezing in one minute bins of time for

each genotype over the course of the A) contextual trial day and B) cued trial day. Each bin refers to the minute preceding the stated time in minutes. Haploinsufficiency of Ptch1 trended toward further decreasing freezing levels beyond the deficits of trisomy in the contextual trial (p= 0.067, RM ANOVA; p= 0.06, LME) and caused a significant further reduction in freezing in trisomic animals in the cued test (p= 0.048, RM

ANOVA; p= 0.04, LME) while having no impact on the euploid animals. Error bars are Morey corrected SEM. Eu;Wt n=23, Eu;Ptch+/- n=20, Ts;Wt n=20, Ts;Ptch+/- n= 11.

Haploinsufficiency of Ptch1 reduces MWM performance in Ts65Dn and euploid mice

We examined the mice further using the visible platform (VP), hidden platform (HP) and probe tests of the Morris water maze (MWM) [103]. The VP test in the MWM examines praxic and taxic learning strategies [145] which rely on several brain areas, but do not involve hippocampal function [103]. Ts65Dn mice are usually found to perform comparably to euploid mice in this task (reviewed [32]). We found that all genotypes showed significant improvement in performance, i.e., reduced latency to platform, over the course of the training in VP MWM (p=

2.2e-32, RM ANOVA; p <1e-04, LME; Figure 14). Trisomy significantly increased latency relative to both euploid groups (p= 0.017, RM ANOVA; p= 0.02, LME; Figure 14).

Haploinsufficiency at the Ptch1 locus also led to significantly increased latency (p= 0.029, RM

ANOVA; p= 0.02, LME; Figure 14). There was no interaction between ploidy and mutation statuses, suggesting that the reduction in performance caused by these two factors was additive. A significant difference in learning based on genotype was observed (p= 0.014, RM ANOVA; p=

0.01, LME) and Ts;Ptch1+/- mice performed significantly worse than Eu;Wt (p= 0.003, PLN;

Figure 14). Post-hoc comparisons found no significant differences between genotypes in any single block of trials, indicating that neither trisomy nor the mutation caused difficulties in seeing visual cues or in swimming ability that would result in large differences in latency and preclude use of the HP and probe tests (Figure 14; Table VIII).

Figure 14: Performance of mice in VP MWM. Haploinsufficiency of Ptch1 reduces VP MWM performance in Ts65Dn and Euploid mice. Trace of average latency to platform in each block of trials in the VP test. Trisomy reduced performance in the VP (p= 0.017, RM ANOVA; p= 0.02,

LME). Loss of a single Ptch1 locus also reduced performance in the VP (p= 0.029, RM ANOVA; p= 0.02, LME). There were no interaction effects in and no significant differences in latency in the VP for the final block of trials. Error bars are Morey corrected SEM. Eu;Wt n= 20,

Eu;Ptch+/- n= 17, Ts;Wt n= 14, Ts;Ptch+/- n= 9.

The HP and probe trials of the MWM examine allocentric and egocentric learning strategies, which are thought to have strong input from the cerebellum (egocentric) and the hippocampus (allocentric) [146,147]. Multiple studies have found a deficit in Ts65Dn performance in the HP and probe tests in the MWM (reviewed [32]); these deficits were corrected in adults with an acute stimulation of the SHH pathway at P0 [45]. In HP MWM task, all mice again showed improvement in performance over the course of the training, i.e., reduced latency to the platform; however, these improvements differed based on genotype. The presence of trisomy significantly increased latency relative to all euploids over time (p= 0.0055, RM ANOVA; p=

0.005, LME; Figure 15a). Haploinsufficiency of the Ptch1 locus also significantly increased latency relative to all wildtype mice over time (p= 0.037, RM ANOVA; p= 0.02, LME; Figure

15a). As in VP, there was no interaction between ploidy and mutation, indicating an additive impact of these two factors in reduced ability to locate the hidden platform. In HP, the genotype overall also had a significant impact on the trend of learning (p= 0.010, RM ANOVA; p= 0.006,

LME) and Ts;Ptch1+/-mice performed significantly worse in trends over time compared to all other genotypes (Eu;Wt p=1e-04- linear; Eu;Ptch1+/- p=0.005 - linear; Ts;Wt p=0.02 – quintic;

PLNs; Figure 15a). To confirm the accuracy of average latency to platform as a measure of search strategy we examined the average ratio of path efficiency for each block of trials in the HP and found similar overall results to those seen with latencies (Figure 15b). The path efficiencies were also highly correlated to the latencies (p< 2.2e-16, PCC; Table VIII).

A Figure 15: Performance of mice in HP

MWM. Haploinsufficiency of Ptch1

reduces HP MWM performance in Ts65Dn

and Euploid mice. A) Trace of latency and

B) path efficiency to finding the platform

averaged by genotype and block of trials

over 9 blocks of trials in HP MWM with

Morey corrected SEM for error bars.

B Trisomy increased latency (p= 0.055,

RM ANOVA; p= 0.005, LME) and

decreased path efficiency relative to all

euploids over time (p= 0.053, RM ANOVA;

p= 0.03, LME). Loss of a single Ptch1 locus

also increased latency (p= 0.037, RM

ANOVA; p= 0.02, LME) and decreased

path efficiency over time (p= 0.020,

RM ANOVA; p= 0.004, LME). There were no interaction effects. In both measures the genotype overall also had a significant impact on the trend of learning (latency: p= 0.010, RM ANOVA; p=

0.006, LME) (path efficiency: p= 0.012, RM ANOVA; p =0.008, LME) and Ts;Ptch+/- mice performed significantly worse overtime as compared to all mice as measured by latency and

Eu;Wt mice as measure by path efficiencies (p<0.05, PLNs). The path efficiencies were also highly correlated to the latencies (p< 2.2e-16, PCC). Eu;Wt n= 23, Eu;Ptch+/- n= 17,

Ts;Wt n= 16, Ts;Ptch+/- n= 10.

In the MWM probe test, there was no significant effect of genotype or ploidy or mutation status on percent of time spent in the quadrant containing the platform (Appendix 4, Figure S1).

All genotypes evidenced some memory of the former platform location by spending the majority of the trial in the quadrant containing the platform (Appendix 4, Figure S1). We next examined time spent in the platform zone while in the correct quadrant. Mice would be expected to encounter a subset of the quadrant space by chance during the time spent in that quadrant. The platform zone occupies 3.6% of quadrant space. A larger percentage of the time spent in the former location of the platform would indicate a higher specificity of spatial learning and reference to spatial cues as well as a more optimal search strategy in these mice [146,148,149].

Additionally mice that quickly realize the absence of a platform and adopt a wider search strategy are not penalized by this measure as they would be with the more traditional normalization to total time of trial [146,148,149].

We found that trisomy significantly reduced the normalized time spent in the platform zone (p= 1.4e-05, ANOVA; Figure 16a). Haploinsufficiency of Ptch1 also significantly reduced the percentage of time spent in the platform zone while in the correct quadrant (p= 0.014,

ANOVA; Figure 16a). As in HP and VP, there was no interaction between ploidy and mutation status indicating an additive impact of these two factors on performance. The overall genotype also had a significant impact on the time spent in the area of the platform while in the quadrant containing the platform (p= 4.7e-05, ANOVA) and Ts;Ptch1+/- mice performed significantly worse than Eu;Wt (p= 7e-06, PLN), Eu;Ptch1+/- (p= 7e-05, PLN), and Ts;Wt mice (p= 0.006,

PLN) (Figure 16a). Similar results were found when examining platform site crossovers normalized to time spent in the quadrant containing the platform (Figure 16b) and both of these measures were correlated to path efficiency to first platform zone entry in the probe trial (p=

0.001, PCC; Figure 16c; Table VIII).

Average speed while mobile was assessed during the probe trial to ensure that differences in latencies or times in the tests were not due to slower swimming speeds. There was no

79 significant difference in average speed when comparing genotype overall, however, when genotype was broken into ploidy and mutation status the speed was found to be significantly reduced by a loss of a single Ptch1 locus (18 cm/s vs. 16 cm/s; p= 0.029, ANOVA; Appendix 4,

Figure S2). In the HP, the accuracy of the latencies as a measure of strategy was confirmed by assessment of path efficiency, indicating that this small difference in speed is likely irrelevant to the HP and VP tests where all times were rounded down to the nearest second and averaged over

4 trials (Figure 14 and 15). In the probe test, percent time and entries measured were correlated with path efficiency to first platform entry, indicating they were a good measure of memory and search strategy (Figure 16; Table VIII).

Figure 16: Performance of mice in the probe test of the MWM. Haploinsufficiency of Ptch1 reduces MWM performance in

Ts65Dn and Euploid mice in the MWM probe test. Average A) percent of time spent in the platform zone while in the quadrant that contained the platform; B) platform entries normalized to the time spent in the quadrant containing the platform; C) path efficiency to first entry into the platform zone during the probe test with SEM for error bars. Trisomy reduced time in the platform zone (p= 1.4e-05, ANOVA), platform entries (p= 1.7e-4, ANOVA) and path efficiency (p= 0.019, ANOVA). Loss of a single Ptch1 locus also reduced time in the platform zone (p= 0.014, ANOVA) and platform entries (p=0.045, ANOVA). There were no interaction effects in any tests. Eu;Wt n= 23, Eu;Ptch+/- n= 17, Ts;Wt n= 16, Ts;Ptch+/- n= 10.

Haploinsufficiency of Ptch1 partially rescues nesting behavior Ts65Dn mice

The final behavioral test examined the ability of singly housed mice to form a nest.

Singly housed mice typically form a single organized nest in the corner of a cage [150]. Nesting strategy has been linked to hippocampal function and may require input from the cerebellum

[86,87,150-152]. It is considered to reflect context discrimination and spatial learning [86,87,150-

152]. Ts65Dn mice have been shown to have a decreased ability to form a complete intact nest

[46,153].

We found that there was a significant difference by genotype in the distribution of nesting behavior (p= 2e-04, 2 & FET; Figure 17). Ts;Wt mice exhibited poor nesting strategy, with a larger proportion of nesting squares barely touched or torn into large pieces (p<0.05, SR; Figure

17). While the nest building ability of Ts;Wt mice was significantly worse than both euploid groups (p=0.002, both, FET), Ts;Ptch1+/- displayed an intermediate ability between Ts;Wt and the two euploid groups and was not statistically distinguishable from either (p<0.1, FET; Figure 17).

Eu;Ptch1+/- and Eu;Wt did not differ in nesting ability. Overall in nesting behavior the animals grouped by ploidy, though the addition of haploinsufficiency for Ptch1 slightly improved behavior in Ts;Ptch1+/- mice (Table VIII).

Figure 17: Performance of mice in nesting assessment. Haploinsufficiency of Ptch1 partially rescues nesting behavior Ts65Dn mice. Percent frequency of nesting behavior by genotype (see

Figure 9 for categories of behavior). There was a significant difference by genotype in the distribution of nesting behavior (p= 2e-04 2 & FET). Ts;Wt mice were significantly more likely to have barely touched the nesting square or to have torn it into large pieces (p<0.05, SR).

Eu;Wt n= 42, Eu;Ptch+/- n= 19, Ts;Wt n= 18, Ts;Ptch+/- n= 10.

Chronic up-regulation of the SHH pathway does not rescue the craniofacial phenotype of the Ts65Dn mice

Principal component analysis (PCA) of the Procrustes residuals of 3D landmark coordinate data was used to analyze overall shape variation in the sample. This method transforms the set of variables entered into the analysis into principal components (PCs). The PCs are not correlated to each other and account for the maximum amount of variation in the sample.

In most cases, the first few PCs explain most of the variance in the dataset. Results of the PCA show clear separation between the euploid and the Ts65Dn groups along PC1, which accounts for

31.7% of the total shape variance in the dataset. A slight separation between the Eu;Wt and

Eu;Ptch1+/- is also evidenced along PC 1. The within-group variation among the Ts65Dn mice is larger than in the Euploids, but there is little difference between the Ts;Wt and Ts;Ptch1+/- in aspects of craniofacial shape. The shape variation along PC 1 is associated with changes in the superior aspect of the cranial vault, which is supero-inferiorly flatter in the euploid mice than in the Ts65Dn, which have a more globular and ‘raised’ cranial vault. The overall shape of the euploid cranium is also more elongated relative toTs65Dn skulls, revealing the typical brachycephaly skull shape previously defined for Ts65Dn [40]. PC 2 (16.5% of shape variation) is related to the within-group variation in the euploid and Ts65Dn groups. Shape changes on PC 2 are related to the position of the snout and posterior cranial vault, and these features vary similarly among all four groups, regardless of ploidy. (Figure 18; Table VIII)

We also analyzed the facial landmarks separately to examine possible localized differences between and within the Ts65Dn and euploid groups. PCA results showed clear differences between the Ts65Dn and euploid mice, but not between the wildtype and the Ptch1+/- mice (Figure 19; Table VIII).

Eu;Wt +/- Eu;Ptch Ts;Wt Ts;Ptch+/-

Figure 18: PCA of the all the cranial landmarks. Plot of PC 1 (31.7%) vs. PC 2 (16.57%) and associated shape variation from the negative to the positive end of the respective axes. Shape changes are depicted via wireframe diagrams. The solid gray wireframes represent the overall mean shape of the sample and the dashed black wireframes illustrate the shape changes along the scores of PC 1 and PC 2 relative to the mean shape (gray wireframe) of the sample. PC 1 primarily separates the Eu;Wt and Eu;Ptch1+/- from the Ts;Wt and Ts;Ptch1+/-, but also captures slight differences between the Eu;Wt and Eu;Ptch1+/- (occupied by the negative end of PC1).

Shapes variation on PC 1 mainly relates to aspects of the cranial vault, which is relatively ‘flatter’ superior-inferiorly in the Eu;Wt and Eu;Ptch1+/- compared to the raised and globular shape of the

Ts;Wt and Ts;Ptch1+/-. PC 2 captures changes in the position of the snout relative to the cranial base (being slightly upwardly deflected on the positive end; downwardly deflected on the negative end), as well as the overall profile of the neurocranium.

Eu;Wt Eu;Ptch+/- Ts;Wt Ts;Ptch+/-

Figure 19: PCA of facial landmarks. Plot of PC 1 (33%) vs. PC 2 (18.2%) and associated shape variation from the negative to the positive end of the respective PC axes. Shape changes are depicted via wireframe diagrams. The solid gray wireframes represent the overall mean shape of the sample and the dashed black wireframes illustrate the shape changes along the scores of PC 1 and PC 2 relative to the mean shape (gray wireframes) of the sample. PC 1 separates the Eu;Wt and Eu;Ptch1+/- from the Ts;Wt and Ts;Ptch1+/-. PC 1 mainly relates to changes in the position of the nasal bones and anterior aspect of the snout. PC 2 captures changes in the anterior-posterior dimension (elongation vs. shortening).

Haploinsufficiency of Ptch1 normalized cerebellar morphology in Ts65Dn mice

The cerebellum is much smaller and hypocellular in people with DS and in the Ts65Dn mouse [43]. The Ts65Dn cerebellar structure was normalized in adult mice by stimulation of the

SHH pathway on the day of birth [45]. We determined the Purkinje cell linear density, the proportional midline sagittal cerebellar area, and cerebellar GC density in adult mice from all four genotypes. For the Purkinje cell density we saw no significant differences in density between any of the genotypes (Figure 20b). Haploinsufficiency for Ptch1 eliminated the deficit in cerebellar cross sectional area in trisomic mice while having no significant effect on the euploid mice, a result reflected in a significant interaction effect between ploidy and mutation status (p= 0.012,

RM ANOVA; p= 0.007, LME; Figure 20a). We found that the normalized cerebellar cross- sectional area was reduced to 85% of the Eu;Wt values in Ts;Wt mice. In contrast the normalized cerebellar cross-sectional area was 107% of Eu;Wt values in Ts;Ptch1+/- mice. In Eu;Ptch1+/- mice the normalized cerebellar cross-sectional area was 105% of Eu;Wt values. Mice also demonstrated a significant difference in the proportional midline sagittal surface area of the cerebellum based on genotype overall (p= 0.044, RM ANOVA; p= 1e-04, LME) and Ts;Ptch1+/- mice were significantly larger than Ts;Wt mice (p= 1e-04, PLN; Figure 20a; Table VIII).

Figure 20: Cerebellar area and Purkinje cell linear density in all genotypes. Genotype, ploidy and mutation had no effect on linear Purkinje cell density. Graphical representation of A) the average midline cerebellar area as a fraction of the total brain area for each mouse as well as the overall average ratio for each ploidy; B) the average linear Purkinje cell density in cells/cm for each mouse. Cerebella in Ts;Ptch+/- mice were larger than Ts;Wt mice but did not differ from

Eu;Wt mice (p= 0.012, RM ANOVA; p= 0.007, LME). Genotype overall had a significant effect on the cerebellar size (p= 0.044, RM ANOVA; p= 1e-04, LME) and we found a significant difference between Ts;Ptch1+/-mice and Ts;Wt mice (p= 1e-04, PLN). For cerebellar area: Eu;Wt n= 10, Eu;Ptch+/- n= 10, Ts;Wt n= 8, Ts;Ptch+/- n= 9. For Purkinje cell linear density:

Eu;Wt n = 11, Eu;Ptch1+/- n = 10, Ts;Wt n = 10 and Ts;Ptch1+/- n = 10.

Reduced cerebellar cross sectional area in Ts65Dn has been correlated with changes in

GC density [43]. We showed previously that this hypoplasia could be rescued by administration of a SHH pathway agonist at birth [45]. We found that haploinsufficiency of Ptch1 also rescued

GC density deficits in Ts65Dn mice. Looking at the average GC density in each mouse there was a significant difference in GC density based on genotype overall (p= 0.015, ANOVA) and

Ts;Ptch1+/- had a significantly higher GC density than Ts;Wt mice (p= 0.03, PLN). We found that trisomy significantly decreased GC density (p= 0.042, ANOVA) and the presence of the Ptch1 mutation significantly increased GC density (p= 0.010, ANOVA) (Figure 6b). We found no interaction effect between ploidy and mutation status, indicating that GC density was increased in

Ptch1+/- mice regardless of ploidy.

To find the average GC density we examined the GC density in folia from three different developmental zones (folia V, VI, IX) [66]. When analyzing all measurements without averaging by mouse we found that genotype had a significant interaction effect with the folium examined

(p[GG]= 0.029, RM ANOVA; Figure 21b). Ploidy also had a significant interaction effect with the folium examined (p[GG]= 0.012, RM ANOVA; Figure 21b) indicating that the magnitude of effect of trisomy may slightly vary by developmental zone. In contrast Ptch1 mutation significantly increased GC density in all folium without an interaction effect (p= 0.010, RM

ANOVA; Figure 21b; Table VIII).

When we broke down the GC density by the folia representing the three developmental zones trisomy significantly decreased density in folia V (p= 0.032, RM ANOVA; p= 0.05, LME),

VI and IX (both: p= 0.0087, RM ANOVA; p= 0.01, LME) and the presence of the Ptch mutation significantly increased density in folia V (p= 0.0042, RM ANOVA; p= 0.003, LME), VI and IX

(both: p= 0.014, RM ANOVA; p= 0.009, LME) (Figure 21b; Table VII). Genotype overall also had a significant impact on GC density in folia V (p= 0.0073, RM ANOVA; p= 0.005 LME), VI and IX (p= 0.0070, RM ANOVA; p= 0.004 LME, both) (Figure 21b; Table VII). Ts;Ptch1+/- mice

89 had significantly higher GC density as compared to Ts;Wt mice in folium V (p= 0.04, PLN) and a somewhat higher GC density in folia VI and IX (both: p= 0.06, PLNs). Ts65Dn; Ptch1+/-mice also had a somewhat lower density than Eu;Ptch1+/- in folia V (p= 0.1, PLN), VI and IX (both: p=0.07, PLNs)(Figure 21b; Table VIII).

Figure 21: GC density for all genotypes. Haploinsufficiency of Ptch1 normalized cerebellar GC density in Ts65Dn mice. Graphical representation of the average A) cerebellar GC density in cells per μm2 for each mouse as well as the overall average for each ploidy and B) cerebellar GC density in cells per μm2 within folia V, VI, and IX for each mouse as well as the overall average for each ploidy. Trisomy significantly decreased GC density depending on the folium examined

(p[GG]= 0.012, RM ANOVA). The presence of the Ptch1 mutation significantly increased GC density in all folia (p= 0.010, RM ANOVA). Eu;Wt n= 11, Eu;Ptch+/- n= 10, Ts;Wt n= 12,

Ts;Ptch+/- n= 12.

Table VII: Average granule cell density as percent of Eu;Wt

Eu;Ptch1+/- Ts;Wt Ts;Ptch1+/- All Folia 105% 94% 102% Folium V 111% 94% 103% Folium VI 109% 92% 100% Folium IX 103% 97% 103%

Table VIII: Overall comparisons between genotypes Performance of genotype B relative to performance of genotype A* Genotype A Eu;Wt Eu;Wt Ts;Wt Eu;Ptch+/- Eu;Wt Ts;Wt Genotype B Ts;Wt Ts;Ptch+/- Ts;Ptch+/- Ts;Ptch+/- Eu;Ptch+/- Eu;Ptch+/- Population Measures Weight worse (lower) worse (lower) better (higher) worse (lower) worse (higher) ? (too much higher) Frequency at Birth worse (lower) worse (lower) worse (lower) worse (lower) worse (lower) same Survival worse (lower) worse (lower) worse (lower) worse (lower) worse (lower) same Behavior Rotarod (latency to fall) same better (longer time) better (longer time) better (longer time) worse (shorter time) worse (shorter time) Y-maze Percent Alternation worse (less worse (less worse (less worse (less worse (less better (more alternations) alternations) alternations) alternations) alternations) alternations) Total Entries worse (more entries) worse (more entries) same worse (more entries) same better (less entries) Fear Conditioning Contextual (Percent Freezing) worse (less freezing) worse (less freezing) worse (less freezing) worse (less freezing) same better (more freezing) Cued (Percent Freezing) worse (less freezing) worse (less freezing) worse (less freezing) worse (less freezing) same better (more freezing) Morris Water Maze Visible Platform (latency) worse (longer worse (longer worse (longer worse (longer worse (longer same latency) latency) latency) latency) latency) Hidden Platform (latency) worse (longer worse (longer worse (longer worse (longer worse (longer better (shorter latency) latency) latency) latency) latency) latency) Hidden Platform (path worse (longer path) worse (longer path) worse (longer path) worse (longer path) worse (longer path) better (shorter path) efficiency) Probe (percent time in worse (less time) worse (less time) worse (less time) worse (less time) worse (less time) better (more time) platform) Probe (platform crossings) worse (less worse (less worse (less worse (less worse (less better (more crossings) crossings) crossings) crossings) crossings) crossings) Probe (path efficiency) worse (longer path) worse (longer path) worse (longer path) worse (longer path) worse (longer path) better (shorter path) Nesting worse (less organized worse (less organized better (more worse (less organized same better (more nests) nests) organized nests) nests) organized nests) *value judgment of better or worse based on relative performance of Eu;Wt as normal standard; ? = unknown if better or worse

Table VIII Continued: Overall comparisons between genotypes Performance of genotype B relative to performance of genotype A* Genotype A Eu;Wt Eu;Wt Ts;Wt Eu;Ptch+/- Eu;Wt Ts;Wt Genotype B Ts;Wt Ts;Ptch+/- Ts;Ptch+/- Ts;Ptch+/- Eu;Ptch+/- Eu;Ptch+/- Craniofacial Overall Shape worse (smaller skull) worse (smaller skull) same worse (smaller skull) worse (smaller skull) better (larger skull) Face Shape worse (smaller face) worse (smaller face) same worse (smaller face) worse (shorter face) better (larger face) Cerebellar Morphology Cross Sectional Area worse (area smaller) same better (area same same better (area same as normalized ) euploid) Granule Cell Density (overall) worse (lower same better (density better (lower worse (higher ? (too much higher density) normalized ) density) density) density) Granule Cell Density (Folium V) worse (lower same better (density better (lower worse (higher ? (too much higher density) normalized ) density) density) density) Granule Cell Density (Folium worse (lower same better (density better (lower worse (higher ? (too much higher VI) density) normalized ) density) density) density) Granule Cell Density (Folium worse (lower same better (density better (lower worse (higher ? (too much higher IX) density) normalized ) density) density) density) Tumor Burden N/A N/A N/A same N/A N/A Tumor Burden (Asymptomatic) N/A N/A N/A same N/A N/A *value judgment of better or worse based on relative performance of Eu;Wt as normal standard; ? = unknown if better or worse

Discussion

Mutations in PTCH1, PTCH2 or SUFU result in Basal Cell Nevus Syndrome (also known as Gorlin syndrome). As suggested by the name, a major feature of the syndrome is basal cell carcinoma, but it is also associated with MB and a range of other features, including cognitive impairment [154]. We demonstrate that Ptch1+/- mice have deficits in hippocampal and cerebellar related learning and memory in the MWM and rotarod tasks. The poor rotarod performance correlated with supernumerary GCs in the cerebellum, suggesting that there may be an acceptable range of GC density for optimal cerebellar function. These results point to specific areas of the brain for that are sensitive to SHH pathway disruption and that may contribute to cognitive deficits in people with Gorlin’s Syndrome or DS.

A prior report measured reduced MB incidence in people with DS [136], however, we did not see a reduced incidence of MB in our Ts;Ptch1+/- mice despite that fact that these trisomic mice are protected against some cancers in mice [155,156]. The Ptch1+/- mice develop a specific subtype of MB (~30% of all MB) that frequently has mutations in the SHH pathway [106].

Trisomy 21 may be protective against the other subtypes which would account for the reduced incidence. Additionally, trisomy may not be protective against MB caused by constitutive loss of

Ptch1. We could find no reported cases of haploinsufficiency of Ptch1 in an individual with DS.

The GCPs in newborn Ts65Dn mice have a lag in SHH stimulated proliferation resulting in a smaller cerebellar size and lower GC density in adult mice [43,55]. This deficit can be rescued by acute stimulation of the SHH pathway at birth [45]. Here we demonstrated that chronic and constitutive up-regulation of the SHH pathway can also restore the cerebellar size and GC density in Ts65Dn mice to the levels of euploid mice. The rescue of GCP generation here is consistent with the earlier conclusion that an acute up-regulation of the canonical pathway by

SAG is responsible for rescue of the cerebellar hypoplasia phenotype in trisomic mice [45].

Increased GC density was seen with haploinsufficiency of Ptch1 in both euploid and trisomic

95 animals in contrast to our observations with the SAG injections [55], but in keeping with previous findings in Ptch1+/- mice [106]. Our results here support the supposition that the deficit in response to SHH in the GCPs is the result of a reduction in the response levels of the canonical

SHH pathway acting through the Ptch1 receptor to de-repress Smo. The results of the SAG study indicate that rescue of the cerebellum structure may enhance contextual learning and memory ability in these mice without substantially altering cellular deficits found in the hippocampus in

Ts65Dn [45]. Localized SHH stimulation of the cerebellum thus presents a promising target for pharmacological intervention in DS.

With chronic up-regulation of the SHH pathway response we only received a complete rescue of the cerebellar hypoplasia phenotype. This rescue correlated with enhanced motor learning ability in the accelerating rotarod, where Ts;Wt mice already performed as well as or better than euploid [43,75]. In the SAG mice this rescue of cerebellar structure correlated with an increased ability in the MWM [45]. However, the vestibulo-occular reflex (VOR), which is known to depend on cerebellar function, was not ameliorated by SAG treatment [78]. Nest building, which is deficient in Ts65Dn, was also partially restored in the Ts;Ptch1+/- mice. The nest building task relies on context discrimination as mediated by the hippocampus with some cerebellar input [86,87,150-152]. In the nesting task, the cerebellum is thought to contribute to the mouse’s ability to focus on the nesting task and the exploration of its environment [87]. Deficits in nesting ability in mouse models of other disorders have been linked with hypoplasia of folia VI and VII which are part of the oculomotor vermis [87]. Examination of the VOR in the Ts;Ptch1+/- mice may give us an indication of whether the observed partial rescue of nesting ability occurred due to the success of chronic, uniform up-regulation of the SHH pathway in rescuing VOR where acute SAG treatment failed or due to rescue of another higher cognitive function controlled by the cerebellum.

Chronic up-regulation of the SHH pathway did not ameliorate most of the DS related phenotypes in Ts65Dn mice. Ptch1+/- had a marginal impact on body size in the Ts65Dn mice and

96 there was no rescue of survival or of the craniofacial morphology in these mice. As with SAG treatment [45], there was no effect on measures of working memory or hyperactivity in the Y- maze. In contrast to the acute up-regulation of SHH pathway with SAG, chronic stimulation with a loss of a single Ptch1 locus created deficits as well as benefits. In contextual memory tasks, such as FC and MWM, Ptch1+/- either increased the deficit present only in Ts65Dn mice or created an independent deficit in both euploid and trisomic mice rather than ameliorating the deficits as seen with SAG treatment. In either case, chronic up-regulation of the pathway did not ameliorate the deficits as was seen with SAG treatment. Investigation of the combined impact of trisomy and Ptch1+/- on hippocampal function may provide insight into the multiple roles and timing of SHH in CNS development and the roles of this pathway in both DS and Gorlin

Syndrome.

Cumulatively, our results are not consistent with a uniform deficit in response to SHH in all trisomic cell populations (Table VIII). Our results indicate that only some cell types evidence an amelioration of trisomic phenotypes where others likely did not benefit or were further impaired. Our results confirmed that a deficit in the mitogenic SHH pathway acts on GCPs via canonical SHH signaling [55]. SHH, however, can also act as a morphogen and act through non- canonical pathways. How SHH is able to act in these multiple roles is as yet incompletely understood. Different growth factors are co-expressed with SHH in different tissues.

Additionally, a gradient of SHH levels is frequently required to specify development of one linage over another lineage or to indicate specific levels of proliferation. The exact stoichiometries of SHH relative to Ptch1, other receptors, and other growth factors that are necessary to signal a specific lineage differentiation or level of cellular proliferation are unknown.

[92,93]

Our results indicate that the response to SHH is not uniformly impaired in trisomic cells

SHH response deficits may depend on the specific stoichiometries of Ptch1, SHH, and other

97 factors so that the simple reduction in Ptch1+/- mice was not uniformly effective. While our results suggest that the SHH pathway does not present a “silver bullet” to restore many aspects of the developmental perturbations due to trisomy, this pathway still presents a promising target for treatment of some specific DS phenotypes. For each cell type in DS where a deficit in SHH response is identified, there is potential to create a timed and targeted corrective stimulation that may serve to improve quality of life in people with DS.

Chapter 4: Conclusion

Since the discovery of trisomy of Hsa21 as the genetic cause of DS in 1959 the challenge of DS research has been to understand how this excess gene dosage affects developmental programs. Many of the phenotypes seen in children and adults with DS are the result of perturbations during embryogenesis. These alterations are difficult to study in humans; we do not have access to fetuses with DS at every stage of embryonic development. While some aspects of the impact of trisomy 21 on human cell function can be captured with newly developed induced pluripotent stem cell technology [157-159], cellular models are limited by their inability to replicate cell-cell contacts and full developmental programs. Animal models and mouse models in particular, however, represent a versatile tool in DS research. Many of the more fundamental aspects of human embryogenesis are also present in mice. In contrast to humans, mice have a short generation time and a controlled genetic background and environment. Additionally we have access to all tissues at all developmental time points in mice and the ability to make targeted genetic manipulations at multiple points in development.

Twenty years ago, the development of the Ts65Dn mouse represented a huge leap forward in our tools for understanding DS and how gene dosage effects might translate to the phenotypes we see in humans. Using the Ts65Dn model we have linked the cerebellar hypoplasia phenotype to a deficit in GC density and linked this deficit to a lower response to SHH [43,55].

We have also utilized Ts65Dn to validate genetic modifiers of congenital heart disease [110].

Perhaps most importantly, research conducted on Ts65Dn mice has been successful in identifying possible treatments for cognitive deficits in DS [45,143,160,161]. However, the 42 genes triplicated in the Ts65Dn mouse that are orthologous to genes on Hsa6 and Hsa15 complicate interpretations of gene dosage effects and represent a caveat to research conducted with this model. The question remained whether the phenotypes and gene expression changes observed in

Ts65Dn actually represented what occurred in humans with DS or whether they were an artifact

99 of the additional genes triplicated in this model. The development of the Dp(16)1Yey mouse model allowed us to address the lingering doubts created by the non-Hsa21 orthologous triplicated genes in Ts65Dn.

Dp(16)1Yey was developed to contain a complete triplication of the entire Mmu16 region that is in conserved synteny with Hsa21, slightly longer than the region of Mmu16 triplicated in

Ts65Dn. Unlike Ts65Dn mice, Dp(16)1Yey mice only carry triplicated regions that are in conserved synteny with Hsa21 [35,104]. These mice display deficits in learning and memory, increases in congenital heart disease, and gastrointestinal changes that parallel phenotypes seen in humans with DS [104,111]. The work of this thesis shows for the first time that Dp(16)1Yey mice also replicate changes in craniofacial and cerebellar structure seen in humans with DS.

Additionally, this work represents the first head to head comparison of phenotypes between

Ts65Dn mice and Dp(16)1Yey mice. We showed that the craniofacial features and cerebellar structural deficits observed in Dp(16)1Yey are indistinguishable from those seen in Ts65Dn.

Additionally both models performed similarly in comparison to the smaller segmental trisomy,

Ts1Cje. These results not only validate the use of Dp(16)1Yey and Ts65Dn in further study of these aspects of DS, but also confirm the value of the insights into these aspects of DS gained from the last 20 years of research conducted using the Ts65Dn model.

Moving forward researchers will be able to decide which model, Dp(16)1Yey or Ts65Dn, best suits the needs of their project and the aspect of DS under investigation. Dp(16)1Yey is not a perfect model of DS. In contrast to 95% of people with DS and all Ts65Dn mice, Dp(16)1Yey mice do not carry an extra chromosome that disrupts centromere number. In humans with DS and

Ts65Dn mice, the extra chromosome must be independently replicated and segregated at mitosis, and must find its appropriate place within the 3D structure of the interphase nucleus. However,

Ts65Dn mice are difficult to breed compared to Dp(16)1Yey mice, in part, because male Ts65Dn mice, like human males with DS, are typically sterile [33,125], while male Dp(16)1Yey mice are fertile.

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No murine model with trisomic mouse chromosome segments will exactly duplicate all the genes that are found on Hsa21, because Hsa21 contains genes without direct orthologs in the mouse and vice versa. A given murine model must be evaluated for phenotypes of interest with the assumption that trisomy for corresponding genes affects corresponding developmental and regulatory pathways in both species in similar, but not identical, ways. It follows that trisomy will frequently affect the same structures or functions, but not that the effects will be quantitatively equivalent in two different species. The criteria for evaluating the usefulness of mouse models for human conditions must include more than the presence or absence of particular genes. Instead the choice of which mouse model to use must depend on the scientific question under investigation, the relevant phenotypes, and the presence of genes or other features that influence the phenotype of interest.

In this thesis I also utilized the Ts65Dn mouse to further probe the connection between multiple DS phenotypes and the SHH pathway. In Ts65Dn the hypoplasia in the cerebellum and in PA1 had been specifically tied to a reduction in mitogenic response to SHH in trisomic cells

[55,102]. These studies pointed to the possibility that trisomy causes a uniform deficit in response to SHH in all SHH responsive cells throughout development [62]. We previous demonstrated that an acute stimulation of SHH pathway could rescue the hypoplasia of the cerebellum and some learning and memory deficits in Ts65Dn mice [45]. With SHH playing a role in multiple tissues and time points during embryogenesis a pharmacological intervention would have difficulty treating all responsive cell types with the appropriate dose. Using a gene knock-out mouse model

I was able to specifically target this pathway and create a trisomic mouse in which every SHH responsive cell would contain approximately 50% of the normal level of the main repressor for the SHH pathway, Ptch1. This model allowed us to test the theory of a uniform SHH response deficit as the etiology of multiple DS phenotypes.

Examining the Ts;Ptch1+/- mice for behavioral, craniofacial, and cerebellar phenotypes I found that a chronic, uniform up-regulation of the SHH pathway did not ameliorate most DS-like

101 phenotypes in the Ts65Dn mice. I was able to confirm our previous finding of a SHH response deficit in the canonical mitogenic SHH pathway as responsible for the cerebellar hypoplasia.

Cerebellar morphology in Ts;Ptch1+/- mice was restored to the level of Eu;Wt mice. I also found a partial rescue in nesting behavior and an improvement over Eu;Wt and Ts;Wt performance in the rotarod. The increased ability in the accelerating rotarod is an interesting indication that the improvement seen in cerebellar structure may not result in an exact correspondence to the Eu;Wt structure. Also, as in the MWM behavioral rescue in the SAG experiments, it is unclear whether the improvement seen in nesting behavior is a result of the improvement in cerebellar structure and its effects on higher cognition as mediated by the cerebellum, or a result of an effect on brain structures left unsurveyed in this investigation. The lack of rescue of the craniofacial phenotype and increased behavioral deficits in Ts;Ptch1+/- mice as compared to Ts;Wt in several other cognitive tasks, however, indicate that a response deficit to SHH in all trisomic SHH responsive cells may not exist or at least that the degree of deficit is not uniform across cell types.

Previous experiments detected a deficit in the mitogenic SHH pathway and our results confirmed a deficit in the mitogenic SHH pathway acting through canonical SHH signaling in

GCPs [55]. SHH, however, can also act as a morphogen and act through non-canonical pathways.

How SHH is able to act in these multiple and multifaceted roles is as yet incompletely understood. Different growth factors are co-expressed with SHH in different tissues.

Additionally, a gradient of SHH levels is frequently required to specify development of one linage over another lineage and to indicate specific levels of proliferation. The exact stoichiometries of SHH relative to Ptch1, other receptors, and other growth factors that are necessary to signal a specific lineage differentiation or level of cellular proliferation are unknown.

[92,93]

If the deficit in response to SHH in trisomic cells is not uniform across all aspects of

SHH signaling several possibilities exist. First, the deficit in response to SHH may exist in every responsive cell but not be uniform across all cell types due to differences in gene expression.

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Second, the deficit in response to SHH may exist in every responsive cell only in the canonical pathway; the knock-out of a Ptch1 allele would be expected to disrupt non-canonical pathways utilizing Ptch1 independent of effects of trisomy. Third, the deficit in response to SHH may exist in every responsive cell only at specific stoichiometries of Ptch1, SHH, and other factors necessary to complete specific functions (e.g., mitogenic vs. morphogenic); the knock-out of

Ptch1 would disrupt activities at ratios unaffected by trisomy. For all of these possibilities, the reduction of Ptch1 levels would be sufficient for rescue in only in some cell types or functions. In other instances it would either be insufficient or create an increased response to SHH over normal levels. Any of these three possibilities, or a combination of them, explain the results we observed in the Ts;Ptch1+/- mice. We cannot know which possibility is correct without specific knowledge of the exact SHH and Ptch1 levels in each cell types. Additionally, we do not know whether the level of SHH expression is also affected in some trisomic cell populations. Further exploration of the changes in craniofacial morphology and investigations of brain electrophysiology in the

Ts;Ptch1+/- and Eu;Ptch1+/- mice may provide valuable insight into the role of the SHH pathway in the development of these structures.

While our study indicates that the SHH pathway does not present a target for which there might be a “silver bullet” to cure DS, the SHH pathway still presents a promising target for treatment of some specific DS phenotypes. Correction of the cerebellar hypoplasia through SAG treatment ameliorated some cognitive deficits in Ts65Dn mice [45]. This treatment option could also be utilized in humans where cerebellar growth occurs mostly in the first three years of life.

As with any treatment, we must be wary of off target effects. If there is not a uniform deficit we risk damaging other developing structures if we stimulate the SHH pathway in a way that is not precisely targeted in timing, dose and location. The poor behavioral performance of the

Eu;Ptch1+/- mice in some tasks indicates that too high a response to SHH can be as problematic as too low a response. For each cell type in DS where a deficit in SHH response is found, however,

103 there is potential to create a timed corrective stimulation that may by degrees serve to improve quality of life in people with DS.

I believe that this work demonstrates the utility and power of the available mouse models in investigating the genetic perturbations that underlie the phenotypes we see in DS. We confirmed that two currently available mouse models, Dp(16)1Yey and Ts65Dn, recapitulate craniofacial and cerebellar phenotypes seen in DS to a similar degree. Additionally, in the

Ts65Dn model, we demonstrated that while SHH stimulation can ameliorate some DS-like phenotypes, a uniform up-regulation of the pathway is not beneficial overall. Future studies may look at the few differences between Dp(16)1Yey and Ts65Dn to gain a greater understanding of how DS-like phenotypes arise during development. Additionally, either model could be utilized to understand the cognitive benefits to correcting the hypoplasia of the cerebellum by using conditional knock-out or transgenic mice to stimulate the SHH pathway at specific times in specific cell types. Both of these possibilities will lead to a greater understanding of the genotype- phenotype connection in DS and in normal development and may lead to new treatments to improve quality of life in people with DS.

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Appendix 1: Hsa21 and Mmu16 Genes from the Homologene database.

Genes triplicated in Dp(16)1Yey are represented in yellow, gray, and blue; Ts65Dn is represented by gray and blue; and Ts1Cje is represented by only blue.

Human Human Human Genomic Genetic Gene HomoloGene Mouse Gene Mouse Genetic Mouse Genomic Coordinates Location Symbol ID Symbol Location Coordinates 1 Chr21:15481134-15579254(-) Chr21 q11.2 LIPI 77864 Lipi Chr16 43.22 cM Chr16:75540514-75586056(-) 2 Chr21:15588466-15600693(+) Chr21 q11 RBM11 16988 Rbm11 Chr16 43.22 cM Chr16:75592844-75602829(+) 3 Chr21:15743436-15755509(-) Chr21 q11 HSPA13 5062 Hspa13 Chr16 43.36 cM Chr16:75755190-75766821(-) 4 Chr21:15857549-15955723(-) Chr21 q11 SAMSN1 11148 Samsn1 Chr16 43.36 cM Chr16:75858793-76022270(-) 5 Chr21:16333556-16437126(-) Chr21 q11.2 NRIP1 2606 Nrip1 Chr16 43.65 cM Chr16:76287400-76373827(-) 6 Chr21:17102496-17252377(+) Chr21 q11.2 USP25 8374 Usp25 Chr16 44.40 cM Chr16:77014069-77116779(+) 7 Chr21:18885224-18965897(+) Chr21 q21.1 CXADR 1024 Cxadr Chr16 45.28 cM Chr16:78301496-78359785(+) 8 Chr21:18965968-18985268(-) Chr21 q21.1 BTG3 4953 Btg3 Chr16 45.36 cM Chr16:78359860-78377192(-) 9 Chr21:19161284-19191703(-) Chr21 q21.1 C21orf91 9696 D16Ertd472e Chr16 45.36 cM Chr16:78544012-78576657(-) 10 Chr21:19400190-19639687(+) Chr21 q11.2 CHODL 11795 Chodl Chr16 45.39 cM Chr16:78930948-78951733(+) 11 Chr21:19641433-19775970(-) Chr21 q21.1 TMPRSS15 2075 Tmprss15 Chr16 45.41 cM Chr16:78953008-79091097(-) 12 Chr21:22370633-22912517(+) Chr21 q21.1 NCAM2 3336 Ncam2 Chr16 45.89 cM Chr16:81200697-81624285(+) 13 Chr21:26957968-26979801(-) Chr21 q21.3 MRPL39 9679 Mrpl39 Chr16 46.92 cM Chr16:84717576-84735742(-) 14 Chr21:27011594-27089874(+) Chr21 q21.2 JAM2 10929 Jam2 Chr16 46.92 cM Chr16:84774123-84825928(+) 15 Chr21:27096791-27107965(-) Chr21 q21.1 ATP5J 1272 Atp5j Chr16 46.92 cM Chr16:84827866-84835625(-) 16 Chr21:27107258-27144771(+) Chr21 q21.3 GABPA 1543 Gabpa Chr16 46.92 cM Chr16:84834925-84863779(+) 17 Chr21:27252861-27543446(-) Chr21 q21.3 APP 131156 App Chr16 46.92 cM Chr16:84954440-85173707(-) 18 Chr21:27838528-27945581(-) Chr21 q21.2 CYYR1 14191 Cyyr1 Chr16 47.45 cM Chr16:85421533-85553397(-)

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19 Chr21:28208606-28217728(-) Chr21 q21.2 ADAMTS1 21381 Adamts1 Chr16 48.20 cM Chr16:85793827-85803113(-) 20 Chr21:28290231-28339439(-) Chr21 q21.3 ADAMTS5 5109 Adamts5 Chr16 48.34 cM Chr16:85858170-85901125(-) 21 Chr21:30244513-30257695(-) Chr21 q21.3 N6AMT1 5637 N6amt1 Chr16 49.52 cM Chr16:87354185-87368742(+) 22 Chr21:30300466-30365277(-) Chr21 q22.11 LTN1 32272 Ltn1 Chr16 49.52 cM Chr16:87376651-87432606(-) 23 Chr21:30378080-30391685(-) Chr21 q22.11 RWDD2B 9654 Rwdd2b Chr16 49.53 cM Chr16:87433407-87440573(-) 24 Chr21:30396938-30426809(+) Chr21 q22.11 USP16 38183 Usp16 Chr16 49.54 cM Chr16:87454703-87483517(+) 25 Chr21:30428647-30446010(-) Chr21 q22.11 CCT8 4802 Cct8 Chr16 49.57 cM Chr16:87483326-87495873(-) 26 Chr21:30452873-30548204(+) Chr21 q22.3 MAP3K7CL 23198 Map3k7cl Chr16 49.58 cM Chr16:87553330-87595336(+) 27 Chr21:30671220-30734217(+) Chr21 q22.11 BACH1 916 Bach1 Chr16 49.84 cM Chr16:87698945-87733346(+) 28 Chr21:30909254-31312282(-) Chr21 q22.11 GRIK1 68992 Grik1 Chr16 50.23 cM Chr16:87896196-88056176(-) 29 Chr21:31538241-31538971(-) Chr21 q22.11 CLDN17 8116 Cldn17 Chr16 51.12 cM Chr16:88505807-88506978(-) 30 Chr21:31586324-31588469(-) Chr21 q22.11 CLDN8 8117 Cldn8 Chr16 51.22 cM Chr16:88560828-88563183(-) 31 Chr21:31653627-31655276(-) Chr21 q22.11 KRTAP24-1 88056 Krtap24-1 Chr16 51.32 cM Chr16:88610709-88612279(-) 32 Chr21:31661463-31661832(-) Chr21 q22.11 KRTAP25-1 129901 33 Chr21:31691449-31692607(-) Chr21 q22.11 KRTAP26-1 88911 Krtap26-1 Chr16 51.39 cM Chr16:88646824-88647796(-) 34 Chr21:31709331-31710012(-) Chr21 q22.11 KRTAP27-1 90787 Krtap27-1 Chr16 51.39 cM Chr16:88671046-88671654(-) 130759 2310061N02Rik Chr16 51.40 cM Chr16:88707171-88707962(-) 35 Chr21:31743709-31744557(-) Chr21 q22.1 KRTAP13-2 131252 Krtap13-1 Chr16 51.40 cM Chr16:88728862-88729609(+) 36 Chr21:31768392-31769140(+) Chr21 q22.1 KRTAP13-1 114409 Krtap13 Chr16 51.40 cM Chr16:88750747-88751628(-) 114409 2310034C09Rik Chr16 51.40 cM Chr16:88758900-88759529(+) 114409 2310057N15Rik Chr16 51.40 cM Chr16:88773181-88774206(-) 37 Chr21:31797711-31798230(-) Chr21 q22.1 KRTAP13-3 105701 38 Chr21:31802594-31803076(+) Chr21 q22.1 KRTAP13-4 131246 56600 Krtap14 Chr16 51.41 cM Chr16:88825291-88826145(-) 39 Chr21:31812646-31813098(+) Chr21 q22.1 KRTAP15-1 8439 Krtap15 Chr16 51.41 cM Chr16:88829009-88829844(+) 129813 Krtap19-3 Chr16 51.42 cM Chr16:88877513-88878038(-) 64327 Krtap19-4 Chr16 51.42 cM Chr16:88884786-88885089(-)

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130038 Krtap20-2 Chr16 51.42 cM Chr16:89205861-89206388(+) 83481 Krtap8-1 Chr16 51.42 cM Chr16:89487374-89487952(-) 104469 Krtap7-1 Chr16 51.42 cM Chr16:89507704-89508323(-) 45517 Krtap11-1 Chr16 51.42 cM Chr16:89570176-89571183(-) 40 Chr21:31863782-31864275(-) Chr21 q22.1 KRTAP19-3 64327 41 Chr21:31869174-31869428(-) Chr21 q22.1 KRTAP19-4 89274 42 Chr21:31933417-31933608(-) Chr21 q22.1 KRTAP19-7 131249 43 Chr21:31962424-31962716(-) Chr21 KRTAP22-2 130048 44 Chr21:31964759-31965374(+) Chr21 q22.1 KRTAP6-3 131248 45 Chr21:31973440-31973586(+) Chr21 q22.1 KRTAP22-1 131251 46 Chr21:31986005-31986223(-) Chr21 q22.1 KRTAP6-1 131247 47 Chr21:31988774-31988944(+) Chr21 q22.1 KRTAP20-1 89275 48 Chr21:32007583-32007780(+) Chr21 q22.1 KRTAP20-2 130038 49 Chr21:32015183-32015455(+) Chr21 q22.11 KRTAP20-3 129893 50 Chr21:32090843-32091095(-) Chr21 KRTAP21-3 130049 51 Chr21:32119269-32119520(-) Chr21 q22.1 KRTAP21-2 133360 52 Chr21:32127457-32127696(-) Chr21 q22.1 KRTAP21-1 120256 53 Chr21:32185015-32185570(-) Chr21 q22.1 KRTAP8-1 83481 54 Chr21:32201357-32202051(-) Chr21 q22.1 KRTAP7-1 104469 55 Chr21:32252963-32253874(-) Chr21 q22.1 KRTAP11-1 45517 56 Chr21:32410478-32410795(-) Chr21 q22.11 KRTAP19-8 89529 57 Chr21:32490736-32931290(-) Chr21 q22.11 TIAM1 2443 Tiam1 Chr16 51.50 cM Chr16:89787111-89980080(-) 58 Chr21:33031935-33041244(+) Chr21 q22.11 SOD1 392 Sod1 Chr16 51.56 cM Chr16:90220742-90226322(+) 59 Chr21:33043313-33104431(-) Chr21 q22.1 SCAF4 16227 Scaf4 Chr16 51.57 cM Chr16:90229139-90284425(-) 60 Chr21:33245628-33376377(+) Chr21 q22.1 HUNK 8742 Hunk Chr16 51.71 cM Chr16:90386397-90499553(+) 61 Chr21:33640530-33651376(-) Chr21 q22.11 MIS18A 41294 Mis18a Chr16 51.87 cM Chr16:90719312-90727404(-) 62 Chr21:33664124-33687095(+) Chr21 q22.1 MRAP 12669 Mrap Chr16 51.90 cM Chr16:90738324-90749785(+)

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63 Chr21:33683330-33765312(-) Chr21 q22.11 URB1 45941 Urb1 Chr16 51.92 cM Chr16:90751527-90810413(-) 64 Chr21:33784752-33887697(+) Chr21 q22.11 EVA1C 14383 Eva1c Chr16 52.02 cM Chr16:90826719-90905109(+) 65 Chr21:33947151-33957845(-) Chr21 q22.11 TCP10L 77329 66 Chr21:33973984-33984918(-) Chr21 q22.1 C21orf59 10941 1110004E09Rik Chr16 52.17 cM Chr16:90925809-90934927(-) 67 Chr21:34001069-34100351(-) Chr21 q22.2 SYNJ1 48252 Synj1 Chr16 52.18 cM Chr16:90936092-91011308(-) 68 Chr21:34106210-34144169(-) Chr21 q21.3 PAXBP1 9604 Paxbp1 Chr16 52.30 cM Chr16:91014037-91044543(-) 69 Chr21:34162984-34186053(-) Chr21 q22.11 C21orf62 49594 4932438H23Rik Chr16 52.35 cM Chr16:91053935-91095122(-) 70 Chr21:34398216-34401504(+) Chr21 q22.11 OLIG2 4241 Olig2 Chr16 52.60 cM Chr16:91225550-91228677(+) 71 Chr21:34442450-34444728(+) Chr21 q22.11 OLIG1 9667 Olig1 Chr16 52.67 cM Chr16:91269772-91271933(+) 72 Chr21:34602231-34636827(+) Chr21 q22.11 IFNAR2 49242 Ifnar2 Chr16 52.82 cM Chr16:91372783-91405589(+) 73 Chr21:34638665-34669539(+) Chr21 q22.11 IL10RB 523 Il10rb Chr16 52.87 cM Chr16:91406164-91425834(+) 74 Chr21:34697214-34732129(+) Chr21 q22.11 IFNAR1 524 Ifnar1 Chr16 52.98 cM Chr16:91485238-91507441(+) 75 Chr21:34775202-34809828(+) Chr21 q22.11 IFNGR2 4041 Ifngr2 Chr16 53.07 cM Chr16:91547072-91565169(+) 76 Chr21:34804793-34852316(-) Chr21 q22.11 TMEM50B 21234 Tmem50b Chr16 53.11 cM Chr16:91574503-91597800(-) 77 Chr21:34860238-34864023(-) Chr21 q22.11 DNAJC28 9869 Dnajc28 Chr16 53.17 cM Chr16:91615726-91619026(-) 78 Chr21:34876238-34915198(-) Chr21 q22.11 GART 637 Gart Chr16 53.18 cM Chr16:91621186-91646952(-) 79 Chr21:34915350-34949812(+) Chr21 q22.11 SON 10551 Son Chr16 53.22 cM Chr16:91647506-91679221(+) 80 Chr21:34949857-34961014(-) Chr21 q22.1 DONSON 32350 Donson Chr16 53.25 cM Chr16:91677268-91688765(-) 81 Chr21:34961647-35014160(-) Chr21 q21.3 CRYZL1 3749 Cryzl1 Chr16 53.26 cM Chr16:91689322-91728975(-) Chr21 q22.1- 82 Chr21:35014784-35261609(+) q22.2 ITSN1 2277 Itsn1 Chr16 53.28 cM Chr16:91729281-91920597(+) 83 Chr21:35275757-35288158(-) Chr21 q22.11 ATP5O 1283 Atp5o Chr16 53.26 cM Chr16:91684398-91931687(-) 84 Chr21:35445823-35515334(+) Chr21 q22.11 MRPS6 41871 Mrps6 Chr16 53.44 cM Chr16:92058270-92112227(+) 85 Chr21:35445870-35478561(+) Chr21 q22.12 SLC5A3 31412 Slc5a3 Chr16 53.44 cM Chr16:92058322-92087473(+) 86 Chr21:35736323-35743440(+) Chr21 q22.12 KCNE2 71688 Kcne2 Chr16 53.55 cM Chr16:92292389-92298129(+) 87 Chr21:35747749-35761452(+) Chr21 q22.12 SMIM11 129924 Smim11 Chr16 53.55 cM Chr16:92301286-92313041(+) 57100 4930563D23Rik Chr16 53.56 cM Chr16:92318763-92321441(-) 88 Chr21:35818986-35884573(-) Chr21 q22.12 KCNE1 185 Kcne1 Chr16 53.57 cM Chr16:92346001-92359468(-)

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89 Chr21:35888782-35987382(-) Chr21 q22.12 RCAN1 3251 Rcan1 Chr16 53.60 cM Chr16:92391953-92466146(-) 90 Chr21:36041688-36090519(+) Chr21 q22.12 CLIC6 43154 Clic6 Chr16 53.64 cM Chr16:92485736-92541243(+) 91 Chr21:36160098-36421595(-) Chr21 q22.3 RUNX1 1331 Runx1 Chr16 53.70 cM Chr16:92601467-92826066(-) 92 Chr21:37406839-37432819(-) Chr21 q22.13 SETD4 41173 Setd4 Chr16 54.52 cM Chr16:93583457-93604063(-) 93 Chr21:37442285-37445462(+) Chr21 q22.13 CBR1 37524 Cbr1 Chr16 54.53 cM Chr16:93607837-93610505(+) 94 Chr21:37507263-37518860(+) Chr21 q22.2 CBR3 20332 Cbr3 Chr16 54.58 cM Chr16:93683215-93690990(+) 95 Chr21:37536839-37666572(+) Chr21 q22.2 DOPEY2 21068 Dopey2 Chr16 54.63 cM Chr16:93711907-93810585(+) 96 Chr21:37692487-37748944(+) Chr21 q22.13 MORC3 32257 Morc3 Chr16 54.88 cM Chr16:93832121-93876072(+) 97 Chr21:37757689-37789125(+) Chr21 q22.13 CHAF1B 48346 Chaf1b Chr16 54.96 cM Chr16:93883901-93906115(+) 98 Chr21:37832919-37948867(-) Chr21 q22.3 CLDN14 8115 Cldn14 Chr16 54.99 cM Chr16:93919032-94008837(-) 99 Chr21:38071991-38122510(+) Chr21 q22.13 SIM2 3716 Sim2 Chr16 55.05 cM Chr16:94085260-94127032(+) 100 Chr21:38123189-38362545(-) Chr21 q22.13 HLCS 37302 Hlcs Chr16 55.12 cM Chr16:94129306-94287856(-) 101 Chr21:38378863-38391959(+) Chr21 q22.2 RIPPLY3 10350 Ripply3 Chr16 55.17 cM Chr16:94328420-94336935(+) 102 Chr21:38437664-38445458(-) Chr21 q22.2 PIGP 32444 Pigp Chr16 55.18 cM Chr16:94358763-94371015(-) 103 Chr21:38445571-38575408(+) Chr21 q22.2 TTC3 2487 Ttc3 Chr16 55.18 cM Chr16:94370618-94469222(+) 104 Chr21:38595726-38639833(-) Chr21 q22.2 DSCR3 4415 Dscr3 Chr16 55.23 cM Chr16:94497783-94526830(-) 105 Chr21:38739859-38887679(+) Chr21 q22.13 DYRK1A 55576 Dyrk1a Chr16 55.30 cM Chr16:94570010-94695067(+) 106 Chr21:38996778-39288741(-) Chr21 q22.1 KCNJ6 1688 Kcnj6 Chr16 55.44 cM Chr16:94749266-94997696(-) 107 Chr21:39628664-39673748(+) Chr21 q22.2 KCNJ15 1690 Kcnj15 Chr16 55.86 cM Chr16:95257558-95300211(+) 108 Chr21:39739183-40033704(-) Chr21 q22.3 ERG 15848 Erg Chr16 56.04 cM Chr16:95359169-95586593(-) 109 Chr21:40177231-40196879(+) Chr21 q22.2 ETS2 3838 Ets2 Chr16 56.64 cM Chr16:95702075-95721051(+) 110 Chr21:40547372-40555440(-) Chr21 q22.3 PSMG1 2759 Psmg1 Chr16 56.76 cM Chr16:95979933-95990960(-) 111 Chr21:40557404-40685712(-) Chr21 q22.2 BRWD1 23130 Brwd1 Chr16 56.77 cM Chr16:95992449-96082526(-) 112 Chr21:40714241-40721047(-) Chr21 q22.2 HMGN1 108186 3643 Hmgn1 Chr16 56.83 cM Chr16:96120618-96127729(-) 113 Chr21:40752213-40769815(+) Chr21 q22.3 WRB 37945 Wrb Chr16 56.84 cM Chr16:96145407-96157852(+) 114 Chr21:40777770-40816128(-) Chr21 q22.2 LCA5L 47978 Lca5l Chr16 56.84 cM Chr16:96158407-96192257(-)

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115 Chr21:40817797-40887433(+) Chr21 q22.3 SH3BGR 56376 Sh3bgr Chr16 56.86 cM Chr16:96200470-96228933(+) 116 Chr21:41029254-41034815(+) Chr21 q22.3 B3GALT5 13230 B3galt5 Chr16 56.88 cM Chr16:96235801-96319859(+) 117 Chr21:41117334-41174023(+) Chr21 q22.2 IGSF5 12437 Igsf5 Chr16 56.93 cM Chr16:96361668-96525580(+) 118 Chr21:41239347-41301322(+) Chr21 q22.2 PCP4 4519 Pcp4 Chr16 56.97 cM Chr16:96467606-96525793(+) 119 Chr21:41384343-42219039(-) Chr21 q22.2 DSCAM 74393 Dscam Chr16 57.02 cM Chr16:96592079-97170752(-) 120 Chr21:42539728-42648524(+) Chr21 q22.3 BACE2 22696 Bace2 Chr16 57.40 cM Chr16:97356728-97439012(+) 121 Chr21:42688661-42729654(+) Chr21 q22.3 FAM3B 10766 Fam3b Chr16 57.47 cM Chr16:97471050-97504936(-) 122 Chr21:42733950-42780869(+) Chr21 q22.3 MX2 74299 123 Chr21:42792520-42831141(+) Chr21 q22.3 MX1 1844 124 Chr21:42836478-42880085(-) Chr21 q22.3 TMPRSS2 4136 Tmprss2 Chr16 57.53 cM Chr16:97564684-97611195(-) 125 Chr21:43159529-43187249(-) Chr21 q22.3 RIPK4 10772 Ripk4 Chr16 57.63 cM Chr16:97741933-97763737(-) 126 Chr21:43218385-43299591(-) Chr21 q22.3 PRDM15 56941 Prdm15 Chr16 57.66 cM Chr16:97791520-97851850(-) 127 Chr21:43305219-43373999(-) Chr21 q22.3 C2CD2 18368 C2cd2 Chr16 57.70 cM Chr16:97855210-97922633(-) 128 Chr21:43406940-43430496(-) Chr21 q22.3 ZBTB21 10799 Zbtb21 Chr16 57.75 cM Chr16:97947435-97962621(-)

110

Appendix 2: Hsa21 and Mmu16 Genes from miRBase

Dp(16)1Yey is represented by yellow, gray, and blue; Ts65Dn is represented by gray and blue; and Ts1Cje is represented by blue only. All of the mouse miRNAs have a human ortholog.

Human microRNA: Accession ID Chromosome Start End Strand Homology MI0000101 hsa-mir-99a chr21 17911409 17911489 + * MI0000064 hsa-let-7c chr21 17912148 17912231 + * MI0000470 hsa-mir-125b-2 chr21 17962557 17962645 + * MI0014244 hsa-mir-548x chr21 20058408 20058482 - MI0021275 hsa-mir-6130 chr21 24451606 24451714 + MI0000681 hsa-mir-155 chr21 26946292 26946356 + * MI0017400 hsa-mir-4759 chr21 28326280 28326362 + MI0015867 hsa-mir-4327 chr21 31747612 31747696 - MI0022213 hsa-mir-6501 chr21 34922968 34923034 + MI0003906 hsa-mir-802 chr21 37093013 37093106 + * MI0022220 hsa-mir-6508 chr21 40818936 40818995 + MI0017401 hsa-mir-4760 chr21 41584279 41584358 - MI0014245 hsa-mir-3197 chr21 42539484 42539556 + MI0022659 hsa-mir-6814 chr21 43166932 43167001 -

Mouse microRNA Accession ID Chromosome Start End Strand Homology MI0000146 mmu-mir-99a chr16 77598936 77599000 + * MI0000559 mmu-let-7c-1 chr16 77599657 77599750 + * MI0000152 mmu-mir-125b-2 chr16 77646273 77646343 + * MI0000177 mmu-mir-155 chr16 84714140 84714204 + * MI0021896 mmu-mir-6367 chr16 91527443 91527544 - MI0004249 mmu-mir-802 chr16 93369720 93369816 + * MI0022811 mmu-mir-6964 chr16 97877233 97877291 -

111

Appendix 3: Other genes with dosage changes in Ts65Dn and Ts1Cje.

A single Hsa15 gene is highlighted in yellow.

Ts65Dn Chromosome: Mmu 17 portion from Homologene Homologous Homologous Homologous Human Genomic Human Genetic Human Gene HomoloGene Mouse Gene Mouse Genetic Mouse Genomic Coordinates Location Symbol ID Symbol Location Coordinates 1 Chr6:166571144-166582157(-) Chr6 q27 T 2393 T Chr17 4.92 cM Chr17:8434423-8442496(+) 2 Chr6:167525295-167552629(+) Chr6 q27 CCR6 3214 Ccr6 Chr17 4.90 cM Chr17:8236043-8257127(+) 3 Chr6:159057506-159065804(-) Chr6 q25.2-q25.3 DYNLT1 4754 Dynlt1a Chr17 3.88 cM Chr17:6310547-6317474(-) 4754 Dynlt1b Chr17 3.97 cM Chr17:6430112-6436293(+) 4754 Dynlt1f Chr17 4.11 cM Chr17:6648941-6655939(-) 4754 Dynlt1c Chr17 4.15 cM Chr17:6601671-6609774(+) 4 Chr6:165740776-166075588(-) Chr6 q26 PDE10A 4852 Pde10a Chr17 4.96 cM Chr17:8525372-8986648(+) 5 Chr6:167412816-167454066(+) Chr6 q27 FGFR1OP 5116 Fgfr1op Chr17 4.88 cM Chr17:8165501-8196804(+) 6 Chr6:155054512-155155194(+) Chr6 q25.1-q25.3 SCAF8 8928 Scaf8 Chr17 1.96 cM Chr17:3114972-3198855(+) 7 Chr6:155577264-155635617(-) Chr6 q25.1-q25.3 TFB1M 9343 Tfb1m Chr17 2.01 cM Chr17:3519263-3557713(-) 8 Chr6:166778407-166796501(-) Chr6 q27 MPC1 9384 Mpc1 Chr17 4.92 cM Chr17:8282904-8297661(+) 9 Chr6:167704803-167729502(+) Chr6 q27 UNC93A 10356 Gm9992 Chr17 4.73 cM Chr17:7363712-7385305(-) 10 Chr6:159398266-159421198(-) Chr6 q25.3 RSPH3 12043 Rsph3b Chr17 4.51 cM Chr17:6904716-6948356(-) 12043 Rsph3a Chr17 4.82 cM Chr17:7945614-7979556(+) 11 Chr6:159071046-159185908(+) Chr6 q25.3 SYTL3 12855 Sytl3 Chr17 4.33 cM Chr17:6673458-6738042(+) 12 Chr6:167738574-167756177(+) Chr6 q27 TTLL2 12917 Ttll2 Chr17 4.72 cM Chr17:7350904-7352696(-) 13 Chr6:159590429-159693140(+) Chr6 q25 FNDC1 19648 Fndc1 Chr17 4.77 cM Chr17:7738569-7827302(-) 14 Chr6:167343004-167370077(-) Chr6 q27 RNASET2 31190 Rnaset2b Chr17 4.57 cM Chr17:6978860-6998193(+)

112

31190 Rnaset2a Chr17 4.87 cM Chr17:8128598-8147788(-) 15 Chr6:157099064-157531913(+) Chr6 q25.1 ARID1B 32344 Arid1b Chr17 2.83 cM Chr17:4994332-5347656(+) 16 Chr6:158733692-158932860(+) Chr6 q25-q26 TULP4 32467 Tulp4 Chr17 3.72 cM Chr17:6106437-6251128(+) 17 Chr6:166733516-166755991(-) Chr6 q27 SFT2D1 34525 Sft2d1 Chr17 4.92 cM Chr17:8311102-8396852(+) 18 Chr6:155411423-155578857(+) Chr6 q25.2 TIAM2 40796 Tiam2 Chr17 1.99 cM Chr17:3326573-3519397(+) 19 Chr6:158530536-158589312(-) Chr6 q25.3 SERAC1 41900 Serac1 Chr17 3.67 cM Chr17:6042196-6079741(-) 20 Chr6:157710054-157745253(-) Chr6 q25.3 TMEM242 44020 Tmem242 Chr17 3.16 cM Chr17:5410864-5440260(-) 21 Chr6:158957468-159056467(+) Chr6 q25.3 TMEM181 44787 Tmem181a Chr17 3.85 cM Chr17:6270475-6305783(+) 22 Chr6:159456024-159466184(-) Chr6 q25.3 TAGAP 44943 Tagap Chr17 4.82 cM Chr17:7926000-7934897(+) 23 Chr6:158589379-158620376(+) Chr6 q25.3 GTF2H5 45635 Gtf2h5 Chr17 3.70 cM Chr17:6079786-6086517(+) 24 Chr6:155585147-155597682(+) Chr6 q25 CLDN20 47972 Cldn20 Chr17 2.01 cM Chr17:3532554-3533213(+) 25 Chr6:155716502-155777037(-) Chr6 q25.3 NOX3 49435 Nox3 Chr17 2.05 cM Chr17:3635240-3696261(-) 26 Chr6:158244294-158366109(+) Chr6 q25.1-q26 SNX9 49454 Snx9 Chr17 3.51 cM Chr17:5841380-5930711(+) 27 Chr6:165693153-165723111(-) Chr6 q27 C6orf118 49841 1700010I14Rik Chr17 5.53 cM Chr17:8988333-9008319(+) 28 Chr6:166719168-166721871(-) Chr6 q27 PRR18 52195 Prr18 Chr17 4.92 cM Chr17:8340406-8344112(+) 29 Chr6:159186773-159240456(-) Chr6 q25.3 EZR 55740 Ezr Chr17 4.38 cM Chr17:6738041-6782784(-) 30 Chr6:157802557-158094977(+) Chr6 q25.3 ZDHHC14 62301 Zdhhc14 Chr17 3.23 cM Chr17:5492600-5753891(+) 31 Chr15:59499015-59500787(+) Chr15 q22.2 LDHAL6B 69533 Ldhal6b Chr17 3.17 cM Chr17:5417323-5418767(-) 83254 Tcp10a Chr17 4.72 cM Chr17:7324646-7345974(+) 86977 T2 Chr17 4.92 cM Chr17:8396629-8422728(+) 32 Chr6:166822854-167275771(-) Chr6 q27 RPS6KA2 100680 Rps6ka2 Chr17 4.70 cM Chr17:7170115-7303313(+) 33 Chr6:158402888-158520208(+) Chr6 q25.3 SYNJ2 117703 Synj2 Chr17 3.59 cM Chr17:5941280-6044290(+) 128617 Gm1604b Chr17 4.61 cM Chr17:7025837-7087467(+)

Ts65Dn Chromosome: Mmu 17 portion from miRBase Accession ID Chromosome Start End Strand

MI0004660 mmu-mir-692-1 chr17 6895229 6895337 -

113

Appendix 4: Ptch+/- Analyses

Figure S1: Time spent in platform quadrant in MWM. All genotypes increase the time spent in the platform quadrant during the MWM probe test. The average percent of time spent in the quadrant that previously contained the platform. Error bars are SEM. Eu;Wt n=23, Eu;Ptch+/- n=17, Ts;Wt n=16, Ts;Ptch+/- n= 10.

114

Figure S2: Average swim speed in probe test of MWM. Average swim speed in the probe test of the MWM was un-affected by genotype. The average swim speed by genotype during the probe trial of the MWM. Average swim speed among the mice did not differ among the four genotypes overall, but there was a slight reduction in speed in the Ptch1 mutants (16 ± 1 cm/s vs

18 ± 1 cm/s, p= 0.029, ANOVA). Error bars are SEM. Eu;Wt n=23, Eu;Ptch+/- n=17, Ts;Wt n=16, Ts;Ptch+/- n= 10.

115

Figure S3: Craniofacial landmarks

Forty landmarks used in craniofacial the study

1. Anterior nasal spine is the most anterior point of interpremaxiallary suture at base of nasal aperture, midline; 2. Nasale is the intersection of nasal bones at rostral point; 3. Nasion is the intersection of nasal bones at caudal point; 4. Bregma is the intersection of frontal bones and parietal bones at midline; 5. Intersection of parietal bones with anterior aspect of interparietal bone; 6. Intersection of interparietal bone with squamous portion of occipital bone; 7. Midsagittal point on the posterior margin of the foramen magnum; 8. Midsagittal point on the anterior margin of the foramen magnum; 9 & 25. Anterior-most point at intersection of premaxilla and nasal bones; 10 & 26. Anterior notch on frontal process lateral to infraorbital fissure; 11 & 27.

Intersection of frontal process of maxilla with frontal and lacrimal bones; 12 & 28. Fronto-

116 squamosal intersection at temporal crest; 13 & 29. Intersection of zygomatic process of maxilla with zygoma (jugal), superior surface; 14 & 30. Intersection of zygoma with zygomatic process of temporal, superior aspect; 15 & 31. Intersection of parietal temporal and occipital bones; 16 &

32. Medial most tip of the tympanic bulla; 17 & 33. Postero-lateral most point on the tympanic bulla; 18 & 34. Antero-superior most tip on the nasal bone; 19 & 35. Point left of nasion intersection of nasal bone and premaxilla; 20 & 36. Most antero-lateral point on corner of the basioccipital; 21 & 37. Most anterior point of the anterior palatine foramen; 22 & 38. Most posterior point of the anterior palatine foramen; 23 & 39. Intersection of the maxilla and sphenoid on the inferior alveolar ridge; 24 & 40. Most anterior medial point on the left carotid canal.

Twenty-three facial landmarks used in the separate PCA of the face

29. Intersection of zygomatic process of maxilla with zygoma (jugal), superior surface; 14 & 30.

Intersection of zygoma with zygomatic process of temporal, superior aspect; 18 & 34. Antero- superior most tip on the nasal bone; 19 & 35. Point left of nasion intersection of nasal bone and premaxilla; 21 & 37. Most anterior point of the anterior palatine foramen; 22 & 38. Most posterior point of the anterior palatine foramen; 23 & 39. Intersection of the maxilla and sphenoid on the inferior alveolar ridge

117

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Curriculum Vita

Tara Dutka

(née Howard)

Born: May 13th, 1986, Baltimore, MD.

Education:

Johns Hopkins University School of Medicine, Baltimore, MD,

Ph.D. – Candidate in the Pre-doctoral Training Program in Human Genetics, McKusick –

Nathans Institute of Genetic Medicine

Expected Completion: July, 2014

Advisor: Dr. Roger Reeves

Committee: Dr. Mikhail Pletnikov, Dr. Jonathan Pevsner, Dr. William Pavan

Dissertation: Morphology, Behavior and the Sonic Hedgehog Pathway in Mouse Models

of Down Syndrome

University of Maryland, College Park, MD

B.S., Biochemistry, cum laude, May, 2008

B.A., Anthropology, magna cum laude, May, 2008

Department of Anthropology, University of Maryland, College Park - Independent

study investigating the relationship of mtDNA, geographic location, language spoken,

and ethnic group classifications in Africa, September, 2007 - December, 2007

Department of Classics, University of Maryland, College Park in Italy - Study abroad

of classical Greek and Roman culture, January, 2006

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University College, Cork, Ireland, - Study abroad of Archaeology, January, 2007 -

May, 2007

Research Experience:

McKusick –Nathans Institute of Genetic Medicine, Johns Hopkins University School of

Medicine, Baltimore, MD,

Doctoral Candidate

Dr. Roger Reeves - October, 2010 - Present

Validated Dp(16)1Yey as a new mouse model of Down syndrome (DS) and confirmed the

validity of the previous model used, Ts65Dn, on the basis of morphological as well as

genetic correspondence to human disease. Determined that the reduced response to Sonic

Hedgehog (SHH) seen in the canonical pathway of some trisomic cells in a DS mouse model

is not present uniformly across all cell types and developmental time points by crossing

Ts65Dn to a model of constitutive up-regulation of the canonical SHH pathway.

Doctoral Candidate

Dr. Josh Mendell - March, 2009 - October, 2010 (Lab moved to UT Southwestern)

Created multiple reporter resources and utilized qRT-PCR to investigate reports of changes

in miRNA biogenesis efficiency related to a loss of pluripotency in development and a gain

of pluripotency in cancer. Reporters will be used to screen for molecules that alter Drosha

activity to expand the understanding of miRNA biogenesis regulation.

Graduate Rotation Student

Dr. Susan Michaelis - December, 2008 - February, 2009

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Developed a western blot protocol to look for the presence of the small protein tail cleaved

in Lamin-A processing. This cleavage is prevented in progeria and the possible function of

the cleaved tail is unknown.

Graduate Rotation Student

Dr. Steve Leach - September, 2008 - December, 2008

Created transgenic zebrafish to investigate possible stem cells present in pancreas

development.

Laboratory of Genomic Integrity, National Institute of Child Health and Human

Development, National Institutes of Health, Bethesda & Rockville, MD

Undergraduate Intern

Dr. Roger Woodgate - June, 2007 - June, 2008

Contributed to ongoing projects for determining the exact role of various proteins in the

repair and maintenance of DNA.

Department of Entomology, University of Maryland, College Park, MD

Summer Research Associate

Dr. Sarah Via - June, 2006 - August, 2006

Completed initial assays to investigate a possible speciation event and determine the

migration patterns of pea aphids.

Laboratory of Genetics, National Institute on Aging, National Institutes of Health,

Baltimore, MD

Summer Intern

Dr. Minoru Ko - June, 2005 - August, 2005

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Completed a preliminary assay in mouse embryonic stem cells to determine the cellular

location of the mRNA of RNA binding proteins in order to look for genes necessary for

pluripotency.

Publications:

Articles:

Starbuck, J.M.*, T. Dutka*, T. S. Ratliff, R. Reeves, J.T. Richtsmeier. Overlapping Trisomies for

Human Chromosome 21 Orthologs Produce Similar Effects on Skull and Brain Morphology of

Dp(16)1Yey and Ts65Dn Mice. American Journal of Medical Genetics Part A 9999:1–10.

*These authors contributed equally.

Abstracts:

Dutka, T., N. Singh, J.T. Richtsmeier, R. Reeves. Effects of Up-regulation of the SHH Pathway in Ts65Dn, a Mouse Model of Down Syndrome; (2648F). Presented at the 63rd Annual Meeting of The American Society of Human Genetics, October 25, 2013 in Boston, MA.

Singh, N., T. Dutka, B. Devenney, R. Reeves, J.T. Richtsmeier. Effects of sonic hedgehog on craniofacial morphology: implications for patients with Down syndrome. Presented at the 36th

Annual Meeting of The Society of Craniofacial Genetics and Developmental Biology, October

22, 2013 in Boston, MA.

Howard, T.C.+, J. McDonald, K. Karata, A Vaisman, and R. Woodgate. Microbiological

Processes of Translesion Synthesis. Poster presented at NIH Summer Research Program Poster

Day, August, 2007. Bethesda, MD.

138

Howard, T.C. +, M.G. Carter, C.A. Stagg, K. Aiba, and M.S.H. Ko. Characterization of the

Expression Patterns of RNA Binding Proteins in Mouse ES Cells. Poster presented at NIA and

NIDA Summer Student Poster Day and NIH Summer Research Program Poster Day, August,

2005 in Baltimore and Bethesda, MD.

+Last name changed from Howard to Dutka in 2012.

Mentoring Experience:

McKusick –Nathans Institute of Genetic Medicine, Johns Hopkins University School of

Medicine, Baltimore, MD,

Reeves Lab

Graduate rotation student - Xuan Pham - September, 2011 - December, 2011

Undergraduate intern - Tabetha Ratliff - February, 2011 - June, 2013

Trained in scientific background, lab techniques, analysis, presentation skills

Assigned projects and checked progress

Scientific Outreach:

American Association of University Women STEM Conference. October, 2012.

Explained my research activities and job and advocated for careers in genetics and other STEM fields to middle school aged girls.

Teaching Experience:

Biochemistry, Cellular and Molecular Biology, Johns Hopkins University School of

Medicine, Baltimore MD

Teaching Assistant - Graduate Course in Genetics

Yeast Genetics Section for Dr. Susan Michaelis - October, 2010 - January, 2011

139

Mouse Genetics Section for Dr. Roger Reeves - October, 2012 - January, 2013

Created and graded a problem set and a section of the final exam

Held a review session and office hours

Department of Biology, University of Maryland, College Park, MD

Tutor - Introduction to Biology - September, 2008 - December, 2008

Explained topics from class and assisted in test preparation

Volunteer Activities:

Incentive Mentoring Program - September, 2008 - March, 2012

Tutored and organized activities for multiple at risk high school students and assisted one student in applying to college and maintaining her financial aid in her first years at school.

Professional Societies:

Society for Neuroscience (2011)

American Society of Human Genetics (2010 - 2013)

Society for Craniofacial Genetics and Developmental Biology (2013 - Present)

American Chemical Society (2013 - Present)

Honors Societies:

Phi Lambda Upsilon

Phi Beta Kappa

Phi Kappa Phi Honor Society

Golden Key Honor Society

140

Award and Honors:

Dean’s List College Anthropology (2006, 2007)

Outstanding Student Award, College of Life Science (2005, 2006, 2007)

Young Study Abroad Scholarship (2007)

University of Maryland Presidential Award Scholar (2004 - 2007)

Maryland Distinguished Scholar Award (2004 - 2007)

Maryland Honors Certificate (2008)

Orbital Sciences Corporation Scholarship Recipient (2004, 2005)

141