Modeling Neurodevelopment and Cortical Dysfunction in SPG11-Linked Hereditary Spastic Paraplegia Using Human Induced Pluripotent Stem Cells

Modeling neurodevelopment and cortical dysfunction in SPG11-linked hereditary spastic paraplegia using human induced pluripotent stem cells

Untersuchung der Neuronalentwicklung und kortikalen Dysfunktion der SPG11 assoziierten Heriditären Spastische Paraplegie unter Zuhilfenahme von humanen induzierten pluripotenten Stammzellen

Der Naturwissenschaftlichen Fakultät der Friedrich-Alexander-Universität Erlangen-Nürnberg zur Erlangung des Doktorgrades Dr. rer. nat.

Vorgelegt von:

Himanshu Kumar Mishra

aus Bhagalpur, Indien

Als Dissertation genehmigt von der Naturwissenschaftlichen Fakultät

der Friedrich-Alexander-Universität Erlangen-Nürnberg

Tag der mündlichen Prüfung: 26.02.2016

Vorsitzender des

Promotionsorgans: Prof. Dr. Jörn Wilms

Gutachter/in: Prof. Dr. Jürgen Winkler

PD. Dr. Andreas Gießl

“I have no special talents. I am only passionately curious.”

― Albert Einstein

Table of Contents

1 List of figures ...... 01 2 List of tables ...... 04 3. Declaration ...... 05 4. Summary/Zusammenfassung ...... 06 4.1 Summary ...... 06 4.2 Zusammenfassung ...... 08 5. Introduction ...... 10 5.1 Motor neuron diseases ...... 10 5.2 Hereditary spastic paraplegias (HSP) ...... 13 5.2.1 Genetic heterogeneity and clinical symptoms ...... 15 5.2.2 Neuropathology and emerging molecular mechanisms ...... 17 5.2.3 Treatment of HSP ...... 19 5.3 SPG11/Spatacsin ...... 20 5.3.1 Clinical symptoms and disease etiology of SPG11 ...... 20 5.3.2 Mutation spectrum of SPG11 ...... 21 5.3.3 Cellular localization and and functions of spatacsin ...... 23 5.4 Human induced pluripotent stem cell (iPSC) technology ...... 26 5.4.1 Reprogramming strategies to generate iPSCs ...... 26 5.4.2 Modeling neurodegenerative diseases using iPSCs ...... 27 5.5 Aims and hypotheses of the thesis ...... 28 6. Materials and Methods ...... 31 6.1 SPG11 patients and Control subjects ...... 31 6.2 Cell culture ...... 31 6.2.1 Fibroblasts derivation ...... 31 6.2.2 Generation of iPSCs ...... 33 6.2.3 Neural differentiation paradigm ...... 33 6.2.4 Karyotyping ...... 35 6.2.5 Neurite length and arborization analysis ...... 35 6.2.6 Electrophysiology ...... 36 6.3 Animals...... 36 6.3.1 Synaptosomes preparation ...... 37 6.4 Molecular Biology ...... 38 6.4.1 Real Time PCR analysis ...... 38 6.4.2 Western Blot ...... 39 Table of Contents

6.4.3 Transfections of siRNA and plasmid DNA ...... 40 6.4.4 TOP-flash/FOP-flash Luciferase reporter assay ...... 40 6.5 Immunofluorescence staining and analysis ...... 41 6.5.1 Proliferation analysis ...... 41 6.5.2 Cell death analysis ...... 43 6.5.3 Acetyl tubulin staining ...... 43 6.6 Flow cytometry-PI analysis ...... 44 6.7 Pharmacological rescue of NPCs proliferation ...... 44 6.8 Synaptic vesicle transport experiments ...... 45 6.9 Microscopy ...... 45 6.9.1 Fluorescence and confocal microscopy ...... 45 6.9.2 Electron microscopy ...... 46 6.10 Statistics ...... 46 7. Results ...... 47 7.1 Generation of human models for SPG11 and expression analysis of SPG11 ...... 47 7.1.1 Generation of human iPSCs from SPG11 patients and controls ...... 47 7.1.2 Differentiation and characterisation of neuronal cultures derived from iPSCs ..... 51 7.1.3 Spatacsin is present in human and mouse cortical projection neurons ...... 52 7.1.4 Spatacsin is expressed in human embryonic and adult mouse neurons...... 54 7.1.5 Spatacsin is expressed throughout mouse brain development ...... 55 7.1.6 Spatacsin is ubiquitously distributed in axons and dendrites of cortical neurons . 57 7.1.7 Spatacsin is localised in neurites, growth-cones and synapses of neurons ...... 59 7.2 Modeling neurodevelopmental phenotypes of SPG11 using iPSC derived NPCs .... 61 7.2.1 Impaired generation of cortical neural rosettes in SPG11-iPSCs ...... 61 7.2.2 Transcriptome analysis of control and SPG11-NPCs ...... 63 7.2.3 Reduced proliferation and neurogenesis in SPG11-NPCs ...... 71 7.2.4 Altered cell cycle distribution and stage-specific anomalies in SPG11-NPCs ..... 72 7.2.5 Checkpoint genes are downregulated in SPG11-NPCs ...... 75 7.2.6 SPG11-NPCs are less prevalent in cytokinesis and undergo cell death ...... 75 7.2.7 Impaired GSK3ß/ß-Catenin signaling in SPG11-NPCs ...... 78 7.2.8 Loss of spatacsin compromises proliferation of neuronal cell line ...... 80 7.2.9 GSK3 inhibitors rescue proliferation and neurogenesis defects in SPG11-NPCs . 82 7.2.10 Premature differentiation of SPG11-NPCs is ameliorated by GSK3 inhibitor ..... 84 7.2.11 Impairment of autophagy related pathways in SPG11-NPCs ...... 86 7.3 Modeling neurodegenerative phenotypes of SPG11 using iPSC derived neurons .... 88 7.3.1 Dysfunction of spatacsin in SPG11-dNeurons leads to aberrant gene expression 88 7.3.2 Neurite outgrowth and complexity are compromised in SPG11-dNeurons ...... 90 7.3.3 Disruption of spatacsin destabilizes microtubules in SPG11-dNeurons ...... 93

Table of Contents

7.3.4 Spatacsin dysfunction impairs axonal transport in SPG11-dNeurons ...... 95 7.3.5 Spatacsin dysfunction disturbs Na+/K+ current density in SPG11-dNeurons ...... 97 8. Discussion ...... 99 8.1 Spatacsin expression in human and mouse cortical neurons ...... 100 8.2 Neural rosettes underdevelopment recapitulates corticogenesis defects in SPG11- iPSCs ...... 102 8.3 Reduced capacity of SPG11-iPSCs to generate NPCs and Neurons ...... 103 8.4 GSK3ß inhibition rescues proliferation and neurogenesis defects in SPG11-NPCs ...... 106 8.5 Neurodevelopmental defect is linked with impaired autophagy related pathways in SPG11-NPCs ...... 107 8.6 Proposed mechanism for the neurodevelopmental defects in SPG11-NPCs ...... 109 8.7 Disruption of spatacsin leads to axonal pathologies in SPG11-dNeurons ...... 111 8.8 Dysfunction of spatacsin impairs axonal transport resulting in axonal degeneration of SPG11-dNeurons ...... 113 8.9 Proposed temporal model of neurodevelopmental and neurodegenerative phenotypes in SPG11 patients ...... 115 9. Conclusion ...... 117 10. References ...... 118 11. Abbreviations ...... 130 12. Acknowledgements ...... 134 13. Supplementary Information ...... 135

List of figures

1. List of figures

Figure 1: Schematic representation of the pyramidal tract of the motor pathway ...... 12

Figure 2: Schematic representation of CSMN and its modulation by local neuronal circuitries and long-range projection neurons ...... 13

Figure 3: Timeline of most common HSP gene discoveries and the frequencies of the most frequent familial HSP genes ...... 14

Figure 4: Clinico-genetic entities associated with HSP according to the motor neuron phenotypic presentation in HSP patients ...... 16

Figure 5: HSP proteins involved in distinct cellular functions of neurons divided in major functional modules...... 18

Figure 6: Schematic model for the functional role of spatacsin ...... 24

Figure 7: Generation of iPSCs from SPG11 patients and Controls...... 48

Figure 8: Characteristics of generated iPSCs ...... 49

Figure 9: Schematic representation of the neuronal differentiation paradigm...... 51

Figure 10: Characterization of spatacsin expression in human-derived neurons ...... 53

Figure 11: Spatacsin antibody specifically recognizes spatacsin full length ...... 53

Figure 12: SPG11 is expressed in murine cortical neurons ...... 55

Figure 13: Spatial characterization of spatacsin expression in mouse brain ...... 56

Figure 14: Spatacsin was present in the most distal tips of neurites of iPSC-dNeurons and mouse cortical neurons ...... 58

List of figures

Figure 15: Characterization of spatacsin expression in synapses ...... 60

Figure 16: Corticogenesis defects in SPG11-iPSCs ...... 62

Figure 17: Global gene expression analysis of day three neural progenitor cells (NPCs) generated from control and SPG11-iPSCs ...... 64

Figure 18: Transcriptional dysregulation of important neurodevelopment related pathways in SPG11-NPCs ...... 66

Figure 19: KEGG pathway representation of Wnt/GSK3ß and Cell cycle related differentially regulated genes in SPG11-NPCs ...... 68

Figure 20: Reduced proliferation and neurogenesis in SPG11-NPCs ...... 72

Figure 21: SPG11-NPCs show altered cell cycle distribution and stage-specific downregulation of important cellular markers ...... 73

Figure 22: qPCR analysis of checkpoint genes in control and SPG11-NPCs ...... 76

Figure 23: Decreased number of NPCs at the abscission stage of the cytokinesis and increased cell death in SPG11-NPCs ...... 77

Figure 24: Increased GSK3ß activity leads to reduced ß-Catenin levels in SPG11-NPCs ...... 79

Figure 25: Knockdown of spatacsin impairs proliferation of SH-SY5Y cells ...... 81

Figure 26: GSK3 antagonists (CHIR99021 and Tideglusib) rescue proliferation and neurogenesis defects of SPG11-NPCs ...... 83

Figure 27: Premature differentiation of SPG11-NPCs is ameliorated by GSK3 inhibitor, Tideglusib...... 85

Figure 28: Neurodevelopmental defect is associated with the impaired autophagy related pathways in SPG11-NPCs ...... 87

List of figures

Figure 29: Expression analysis of transport-related genes in iPSCs-derived neurons ...... 89

Figure 30: Significant reduction in axonal complexity of iPSC-dNeurons from SPG11 patients ...... 91

Figure 31: Reduction of acetyl-tubulin and accumulation of membrane-like deposits in iPSC- dNeurons from SPG11 patients ...... 94

Figure 32: Illustration of time-lapse monitoring for SV transport in synaptophysin-mCherry+ iPSC-dNeurons grown in microfluidic chambers ...... 96

Figure 33: Electrophysiological abnormalities in SPG11-dNeurons ...... 98

Figure 34: Schematic model of GSK3ß mediated neural development in control and SPG11- NPCs ...... 110

Figure 35: Proposed two distinct stages of SPG11 disease pathology ...... 116

List of tables

2. List of tables

Table 1: Classification of motor neuron diseases ...... 11

Table 2: Important SPG11genetic mutations identified in HSP patients...... 22

Table 3: Clinic of SPG11 patients and control subjects ...... 32

Table 4: iPSC and NPC lines used in the study ...... 34

Table 5: Gene ontology (GO) analysis showing overrepresented pathways enriched in SPG11- NPCs ...... 69

Table 6: Primers used for qRT-PCR analysis ...... 135

Table 7: List of antibodies ...... 136

Table 8: List of chemicals, media, and reagents ...... 138

Table 9: List of cell culture media ...... 141

Table 10: List of kits and master mixes ...... 142

Table 11: List of protein standards ...... 143

Table 12: List of buffers and solutions ...... 143

Table 13: List of disposables ...... 145

Table 14: List of instruments ...... 146

Declaration

3. Declaration

The present PhD thesis is based on the results which are in part already published in a peer- reviewed research article and partly belong to a submitted manuscript that is currently under revision.

Most of the findings in sec. 7.1 and 7.3 are published in: Perez-Branguli F*, Mishra HK*, Prots I, Havlicek S, Kohl Z, Saul D, Rummel C,Dorca-Arevalo J, Graef D, Sock E, Blasi J, Groemer TW, Schlötzer-Schrehardt U, Winkler J, Winner B. Dysfunction of spatacsin leads to axonal pathology in SPG11-linked hereditary spastic paraplegia. Human Molecular Genetics 2014. (* Equal contribution as first author).

The article was published in Open Access by Oxford University Press under the terms of the Creative Commons Attribution License, which permits unrestricted reuse, distribution, and reproduction in any medium. This research article has been referenced in the text and figures as follows: (Perez-Branguli, Mishra et al. 2014).

The results in sec. 7.2 are mostly included in the submitted manuscript currently under revision: Mishra HK, Prots I, Havlicek S, Kohl Z, Perez-Branguli F, Boerstler T, Anneser L, Minakaki G, Wend H, Hampl M, Leone M, Brückner M, Klucken J, Reis A, Boyer L, Schuierer G, Behrens J, Lampert A, Engel FB, Gage FH, Winkler J, Winner B. GSK3ß- dependent dysregulation of neurodevelopment in SPG11-patient iPSC model. Annals of Neurology (under revision). The manuscript has been referenced accordingly in the text and figures as follows: (Mishra et al., under revision).

The material of the present thesis has not been previously published or written by another person, except where due reference is made in the text of the thesis.

Summary / Zusammenfassung

3. Summary/Zusammenfassung

3.1 Summary

Hereditary spastic paraplegias (HSPs) are a heterogeneous group of inherited motor neuron

diseases characterized by progressive spasticity and weakness of the lower limbs. Mutations in

the Spastic Paraplegia Gene11 (SPG11), encoding spatacsin, cause the most frequent form of

autosomal recessive HSP. SPG11 patients are clinically distinguishable from most other HSPs,

by severe cortical atrophy and presence of a thin corpus callosum (TCC), associated with

cognitive deficits.

Partly due to lack of a relevant disease model, the distinct cellular and molecular mechanisms

modulating these symptoms have not been deciphered so far. We generated induced pluripotent

stem cells (iPSCs) from three SPG11 patients, having heterozygous nonsense and/or splice site

mutations, and two age matched controls. We differentiated these iPSCs into forebrain neuronal

cells and investigated the neuronal pathology associated with the disease. The overall aim of our

study was to (i) investigate the spatio-temporal localization and expression analysis of spatacsin in different cell types available (ii) to recapitulate early neurodevelopmental deficits at the cortical neural progenitor cells (NPCs) stage, and (iii) to delineate the neurodegenerative phenotype and slowly progressive cortical degeneration in terminally differentiated neurons.

We show here, preferential expression of spatacsin in human neurons, particularly in cortical projection neurons. Importantly, spatacsin is temporally expressed all throughout neuronal differentiation and maturation. Our NPC model evidenced, widespread transcriptional alterations

Summary / Zusammenfassung

in neurodevelopmental pathways, associated with proliferation deficit and impaired cortical neurogenesis. Interestingly, these early developmental phenotypes were rescued by GSK3 modulation. Examination of terminally differentiated neurons from SPG11 patients revealed axonal degeneration, impaired vesicular transport and reduced neuritic complexity.

In conclusion, our human iPSC model reveals a novel temporal scenario for SPG11: early onset proliferation and neurogenesis anomalies during cortical development (in first two decades), mimicking a TCC and cortical atrophy. Progressive axonal degeneration, in the ensuing decades, results in impaired axonal transport, with the clinical correlates of spastic paraparesis and peripheral neuropathy. Furthermore, this in-vitro model offers an ideal platform to screen novel therapeutic compounds for an intervention during early disease stages, thereby paving the road to discover new treatment strategies for SPG11 related HSPs.

Summary / Zusammenfassung

3.2 Zusammenfassung

Hereditäre Spastische Paraplegien (HSPs) stellen eine heterogene Gruppe erblicher

Motoneuronenerkrankungen dar, die durch fortschreitende spastische Paraparese bestimmt sind.

Mutationen im Spastic Paraplegia Gene11 (SPG11), das für Spatacsin kodiert, verursachen die

häufigste Form der autosomal-rezessiven HSP. SPG11-Patienten können klinisch von den

meisten anderen HSP-Varianten anhand einer schweren kortikalen Atrophie und einem dünnen

Corpus Callosum (DCC) unterschieden werden, was mit kognitiven Defiziten einhergeht.

Teilweise wegen des Mangels an relevanten Krankheitsmodellen, sind die unterschiedlichen

zellulären und molekularen Mechanismen welche die Symptome verursachen, bisher nicht

bekannt. Wir haben daher humane induzierte pluripotente Stammzellen (iPSCs) von drei SPG11-

Patienten erzeugt, die heterozygote nonsense und/oder splice site Mutationen tragen. Zudem haben wir zwei mit dem Patientenalter übereinstimmende hiPSC-Kontrollen generiert.

Anschliessend haben wir die hiPSCs zu Neuronen des Telencephalons differenziert und die neuronale Pathologie untersucht, die mit der Erkrankung zusammenhängt.

Das Ziel der Arbeit war es (i) die räumlich-zeitliche Lokalisation und Expression von

SPG11/Spatacsin in den unterschiedlichen Zelltypen zu untersuchen, (ii) die Defizite der frühen

Neuralentwicklung an den kortikalen neuralen Vorläuferzellen (NPCs) zu analysieren (iii) und den neurodegenerativen Phänotyp und die allmählich fortschreitende koritkale Degeneration in den ausdifferenzierten Neuronen zu untersuchen.

Wir zeigen hier, dass Spatacsin bevorzugt in humanen Nervenzellen exprimiert wird, insbesondere in kortikalen Projektionsneuronen. Wichtig ist, dass Spatacsin während der kompletten neuronalen Differenzierung und Reifung exprimiert wird. Unser NPC-Modell hat

weit verbreitete Transkriptions Veränderungen in den Signalwegen der Neuronalentwicklung,

Summary / Zusammenfassung

die mit Proliferationsdefiziten und der Beeinträchtigung der kortikalen Neurogenese verbunden

sind. Interessanterweise konnten diese frühen Entwicklungsphänotypen durch GSK3 Modulation

revertiert werden. Darueberhinaus zeigte die Untersuchung der terminal differenzierten

Neuronen aus SPG11 Patienten einen eingeschränkten axonalen Transport und eine reduzierte

Komplexität der Nervenzellfortsaetze im Vergleich zu den Kontrollen.

Zusammenfassend postuliert unser humanen iPSC Modell zum ersten Mal eine neues zeitliches

Szenario für die SPG11 Pathologie: früh einsetzende Proliferations- und Neurogenese Defizite in der kortikalen Entwicklung (in den ersten beiden Lebensjahrzehnten), welche die Pathologie des

DCC und der kortikale Atrophie repraesentiert. In den darauf folgenden Jahrzehnten kommt es zur fortschreitenden Axondegeneration, mit gestörtem axonalem Transport, was mit dem klinischem Auftreten der spastischen Paraparese und einer Neuropathie korreliert. Darüberhinaus bietet dieses in-vitro Modell eine geeignete Plattform, um nach neuen Wirkstoffen zu screenen, die schon im frühen Stadium der Erkrankung eingreifen, was die Möglichkeit neuartiger

Therapieansätze für die SPG11-assoziierte HSP eröffnet.

Introduction

5. Introduction

5.1 Motor Neuron Diseases (MNDs)

MNDs are a heterogeneous group of progressive neurodegenerative disorders with a prevalence

of 4–6/100,000 (Leigh, Abrahams et al. 2003). They are either sporadic or familial and are more prevalent in males compared to female (1.4:1). The incidence increases with age, with the mean age of onset of 63 years (Ringel, Murphy et al. 1993). It ranks as the third most common

neurodegenerative disorder after Alzheimer’s and Parkinson’s disease (Talbot 2002).

Degeneration involves upper motor neurons located in the cerebral cortex and lower motor neurons located in ventral horns of the spinal cord and brainstem motor nuclei. The MNDs are classified into three groups based on the type of motor neurons affected, primarily upper motor neurons, primarily lower motor neurons or those that affect both (Table 1). Upper motor neuron involvement is primarily accompanied by muscle weakness and spasticity of the limbs due to elevated muscle tone. Lower motor neuron involvement is associated with hyporeflexia of the limbs, poor muscle tone, fasciculation of the tongue, peripheral neuropathy and subsequent muscle atrophy (Donaghy 1999).

The upper and lower motor neurons together, constitute the pyramidal motor tract of central nervous system and coordinate the voluntary movements of the body (Lemon 2008; Winner,

Marchetto et al. 2014) (Figure 1). The upper motor neurons also known as cortico spinal motor neurons (CSMN), are characterized by large pyramidal cell bodies called Betz cells, located in layer V of the cerebral cortex (Ozdinler and Macklis 2006), a single apical dendrite that extends toward layer I, displaying major branching and arborization, especially within layer II/III, and a

Introduction

very long axon that projects toward spinal cord targets (Lopez-Bendito and Molnar 2003;

Molyneaux, Arlotta et al. 2007). The neural activities of the CSMNs are constantly regulated by local neuron circuitry and long distance projection neurons (Figure 2), such as the thalamocortical neurons and callosal projection neurons (Ikrar, Olivas et al.). In the brainstem and spinal cord, the CSMNs synapse directly or indirectly via interneurons with bulbar or lower motor neurons.

Table 1: Classification of motor neuron diseases

Disorder Age at onset

Primary upper motor neuron involvement

Hereditary spastic paraplegia Childhood - elderly

Primary lateral sclerosis Adult - elderly

Pseudobulbar palsy Adult

Primary lower motor neuron involvement

Spinal muscular atrophy Infantile - adult

Spinal and bulbar muscular atrophy Elderly

Proximal hereditary motor neuronopathy Infantile - adult

Hereditary bulbar palsy Childhood - elderly

Combined upper and lower motor neuron involvement

Amyotrophic lateral sclerosis Childhood - elderly

Adult onset 15–50 years, elderly onset over 50 years. Adapted from: (Donaghy 1999)

The lower motor neurons (also known as α- motor neurons) are located in the ventral horn of the spinal cord and control effector muscles in the periphery by forming specialized synapses called neuromuscular junctions (Hollyday, Hamburger et al. 1977; Landmesser 2001). Lower motor neurons are cholinergic and receive inputs from upper motor neurons, sensory neurons as well as

Introduction

from interneurons. The synchronized output of these two neuronal populations is manifested by

muscle contraction leading to movement of legs, arms, and hands.

Figure 1: Schematic representation of the pyramidal tract of the motor pathway

Adapted from: (Winner, Marchetto et al. 2014)

The motor neurons are the longest known cell types in the body and their axons extend up to 1 metre in length (Kandel, Schwartz et al. 2000). This exceptional and unique anatomical feature mediates a multitude of voluntary movements, but at the same time requires complex transport mechanisms for the proper intracellular sorting and distribution of newly synthesized proteins, lipids, mRNAs, organelles, synaptic components, neurptrophins and injury response signaling over long distances (Goldstein, Wang et al. 2008; Hirokawa, Niwa et al. 2010).

Introduction

Figure 2: Schematic representation of corticospinal motor neuron (CSMN in green) and its modulation by local neuronal circuitries (red/brown) and long-range projection neurons (blue/pink).

Adapted from: (Jara, Genc et al. 2014)

The enormous length of these motor neurons along with highly selective, tightly regulated nature

of transport mechanism makes these neurons greatly suspectible to anomalies in axonal

development and homeostasis. This subsequently leads to progressive defects in axonal maintenance resulting in neurological conditions broadly classified as MNDs and primarily affecting the group of diseases referred to as hereditary spastic paraplegia (HSP) (Blackstone,

O'Kane et al. 2011; Fink 2013).

5.2 Hereditary Spastic Paraplegias (HSP)

Hereditary spastic paraplegias (HSP) are rare and heterogeneous groups of motor neuron diseases, presenting with progressive spasticity and weakness of the lower limbs (Schule and

Introduction

Schols 2011; Novarino, Fenstermaker et al. 2014). HSP was first described in 1880 by a German

neurologist, Adolph Strümpell (Tallaksen, Durr et al. 2001). He described “a pure spastic

movement disorder of the legs” in two brothers who developed a spastic gait at the ages of 37

and 56 years. He further demonstrated the pathological changes of the spinal cord, especially the

degeneration of the pyramidal tracts. In 1888, a French physicist, Maurice Lorrain, described the

pathology and clinical features of HSP. The HSPs are therefore also referred to as Strümpell-

Lorrain syndrome (Fink 2003). The first detailed classification of the disease into pure and

complicated forms of spastic paraparesis was done by Anita Harding in 1983 (Harding 1983).

Figure 3: Timeline of most common HSP gene discoveries (left panel) and the frequencies of the most frequent familial HSP genes (right panel).

Adapted from: (Lo Giudice, Lombardi et al. 2014)

HSPs are hereditary disorders and can be transmitted to the offsprings as autosomal dominant, autosomal recessive, or X-linked recessive trait (Harding 1993; Fink 2013). The major

neuropathologic feature of the disease is the progressive degeneration of the longest corticospinal

tract axons at the distal ends (Salinas, Proukakis et al. 2008; Blackstone, O'Kane et al. 2011).

Introduction

5.2.1 Genetic heterogeneity and clinical symptoms

To date, genetic testing has identified 84 different loci and 67 known causative spastic paraplegia

genes (SPGs) that are numbered in order of their discovery (Schlipf, Schule et al. 2014; Tesson,

Koht et al. 2015). As with other large groups of genetically heterogeneous disorders a wide

spectrum of pathological mutations including frameshift, nonsense, truncating, missense

mutations, large exon deletions and splice site mutations have been described (Fink 2003; Fink

2013; Novarino, Fenstermaker et al. 2014). Figure 3 shows the most frequent familial HSP causative genes and their timeline of discoveries. The variation in severity of the lower limb spasticity and weakness, coupled with the presence of additional neurological symptoms and occasionally systemic abnormalities lead to wide spread clinical variation between and within genetic types of HSP.

Out of more than 80 genetic types of HSP, the indepth clinical traits and neuropathology of only a few of the HSP genes, such as SPG4, SPG11, and SPG3A have been principally acknowledged due to their higher frequency of occurrences in more than a dozen families. The range of clinical data for the remaining HSP subtypes is still missing with the limited insights from the indexed cases on the basis of particular mutation(s) identified. Clinically the heterogeneous nature of

HSPs have been broadly classified into two subgroups: “pure” HSPs where progressive spasticity in the lower limbs due to pyramidal tract degeneration is the only neurological manifestation and

“complicated” HSPs, where lower limb spasticity is associated with a variety of other neurological, non-neurological signs and clinical features including ataxia, seizures, cognitive impairment, amyotrophy, extrapyramidal disturbance or peripheral neuropathy (Fink 1993). Very few epidemiological studies of HSP have been performed, but prevalence is estimated at 3–10

Introduction

cases per 100, 000 populations in Europe and a global average prevalence of 1.8/100, 000 for

both ADHSP and ARHSP (Braschinsky, Luus et al. 2009; Ruano, Melo et al. 2014). A recent study, involving a cohort of more than 600 HSP patients, made a comprehensive and systematic

Figure 4: Clinico-genetic entities associated with HSP according to the motor neuron phenotypic presentation in HSP patients.

Adapted from: (Tesson, Koht et al. 2015)

analysis of disease expression, progression and modifying factors contributing to HSP etiology.

The study highlighted a high frequency of autosomal-dominant and familial inheritance patterns in HSP, combined 54% (Schüle et al., personal communication). In another study, pure forms of

HSP were shown to be more prevalent in Northern Europe, Japan, and North America

(Braschinsky, Luus et al. 2009; Ishiura, Takahashi et al. 2014). Most cases of pure HSP are

autosomal dominant, whereas complicated forms tend to be autosomal recessive (Harding 1993;

Introduction

Coutinho, Barros et al. 1999; Fink 2003). The age of onset varies widely, even within a given

genetic type of HSP, from early infancy to late adulthood (70 years of age). Autosomal recessive

HSPs are often early onset disorders and the disease is usually more severe compared to the

dominant forms. The rate of progression and the extent of spastic severity are quite variable in

HSP patients. Some individuals have a static, non-aggravating form of gait disability, while in others the disease is relentlessly worsening and after a period of progressive decline; reach a

functional threshold beyond which there is no further severe deterioration (Coutinho, Barros et

al. 1999; Erichsen, Koht et al. 2009; Fink 2013). The wide spectrum variability of disease onset

and severity, depending on the types of motor neurons affected, weakens the genotype-

phenotype predictions in individual cases (Figure 4). This might be the result of patient specific

mutations and the synergistic effects of genetic and non-genetic modifiers on the resultant

phenotypic expression (Tesson, Koht et al. 2015).

5.2.2 Neuropathology and emerging molecular pathogenesis of HSP

Neuropathological data of post mortem studies from HSP patients consistently reported

retrograde degeneration of the longest nerve fibres in the corticospinal tracts and posterior

columns. Other reports also included distal degeneration of the corticospinal tract extending up

to pons, cerebral peduncles, medulla, and internal capsule (Schwarz and Liu 1956; Sack, Huether

et al. 1978; Deluca, Ebers et al. 2004). Pathological findings from AR-HSP patients revealed

severe atrophy of brain, loss of anterior horn cells and nucleus gracilis neurons and degeneration

of dorsal spinocerebellar and corticospinal tracts (Nomura, Koike et al. 2001; Wakabayashi,

Kobayashi et al. 2001). Similar pathological findings were also reported from other HSP types

(Schwarz and Liu 1956; White, Ince et al. 2000). Although neuronal cell loss, in particular the

Introduction

loss of Betz cells in layer V of the motor cortex, has been reported in some cases of HSP, it does

not seem to be the primary cause for disease manifestation (Bruyn, van Dijk et al. 1994;

Wharton, McDermott et al. 2003; Blackstone 2012; Fink 2013). Therefore studying HSP, with more than 67 reported causative genes described, leading to the predominant axon degeneration

Figure 5: HSP proteins involved in distinct cellular functions of neurons divided in major functional modules

Adapted from: (Lo Giudice, Lombardi et al. 2014)

at the distal ends of cortical spinal tracts, provides an important means to understand the specific

molecular mechanisms underlying axonal maintenance and degeneration in neurons. Increasing

knowledge about the diversity of genes affected in HSP and the analysis of their functional

Introduction

proteins suggests a wide variety of primary molecular abnormalities which underlie the distinct

genetic types of HSP (summarized in Figure 5). These include disruption of axonal transport of

macromolecules, organelles, and other cargoes (e.g. SPG30/KIF1A, SPG10/KIF5A), membrane

trafficking, disturbance in ER morphology (e.g. SPG12/Reticulon 2, SPG3A/Atlastin,

SPG4/Spastin, and SPG31/REEP1), cytoskeletal organization, mitochondrial abnormality

(SPG13/chaperonin 60/heat shock protein 60) and lipid metabolism which predominantly affects

the axonal homeostasis of the long projecting CSMN neurons (Crosby and Proukakis 2002;

Soderblom and Blackstone 2006; Kasher, De Vos et al. 2009; Fink 2013).

5.2.3 Treatment of HSP

Till date, there is no effective therapy to prevent or even halt the progression of the disease. The only medications available help to reduce the spasticity of the muscles. Moreover, due to the wide variability in the severity and rate of progression of disease in HSP patients, the efficacy of

medication is quite inconsistent. Physical therapy accompanied with regular exercises help to

keep the muscles relaxed, and maintain the strength of the lower limbs in many HSP patients

(Fink 2003). However, patients already in severe state of disease do not realize any much improvement. Sometimes assisting patients to use a prosthetic device in the ankle or toes help them to recover from toe-dragging. Medications based on GABAB receptor agonist Baclofen, α2

adrenergic agonist Tizanidine, sodium and calcium channel blocker Tolperisone, are clinically

used to reduce the muscle stiffness (Fink 2003; Soderblom and Blackstone 2006).

The intrathecal administration of Lioresal is a great boon for patients who experience less weakness in their muscles (Fink 2013). Similarly another drug, Dantrolene, which

Introduction

mechanistically blocks all sarcoplasmic reticulum mediated calcium release have also been

shown to be effective in many patients. Oxybutynin is usually prescribed to attenuate the

constant urinary urgency (Soderblom and Blackstone 2006; Fink 2013). Administration of

botulinum toxin (also known as Botox) is beneficial to relax the muscles focally by blocking the

release of acetylcholine at the neuromuscular junctions and this treatment is preferable at the

hamstrings and adductors (Fink 2013). However all these medications do not, in any way,

provide a long-lasting relief to the patients and hence molecular insights gained at the cellular

level using recent advancements in reprogramming technology could pave the way for

delineating the disease mechanisms and designing novel therapeutic strategies for HSPs in future

(Takahashi, Tanabe et al. 2007; Havlicek, Kohl et al. 2014; Perez-Branguli, Mishra et al. 2014).

5.3 SPG11/Spatacsin

5.3.1 Clinical symptoms and disease etiology of SPG11

Mutations in SPG11, also known as KIAA1840, are the most frequent cause of autosomal recessive HSP (AR-HSP) (Stevanin, Santorelli et al. 2007). In addition to spasticity, SPG11 patients are characterized by the presence of thin corpus callosum (TCC) and cognitive impairment (Winner, Uyanik et al. 2004; Hehr, Bauer et al. 2007). Mutations in SPG11 gene

account for more than 41–77% of reported AR-HSP-TCC families (Ruano, Melo et al. 2014;

Tesson, Koht et al. 2015). Disease onset mainly occurs during infancy/adolescence and may be accompanied by pseudobulbar symptoms such as dysarthria and dysphagia, amyotrophy, bladder dysfunction, and sensorimotor peripheral neuropathy (Hehr, Bauer et al. 2007; Rajakulendran,

Paisan-Ruiz et al. 2011; Wakil, Murad et al. 2012).

Introduction

The AR forms of HSPs develop disease symptoms quite early, mostly starting in the second decade of life with very severe forms and complex phenotypes, which vary among different families. So far, more than forty-eight SPG loci and forty-one genes have already been associated with the AR-HSP (Vazza, Zortea et al. 2000; Hodgkinson, Bohlega et al. 2002;

Blumen, Bevan et al. 2003). Mutations resulting in loss of protein or mutant forms of protein can lead to combined degeneration of central and peripheral axonal processes, loss of pyramidal neurons in the cortex and alpha-motor neurons in the spinal cord. Mutations in SPG11 have also been reported to cause juvenile amyotrophic lateral sclerosis type 5 (ALS5) and Kjellin syndrome (Orlen, Melberg et al. 2009), which in addition to TCC, primarily involves progressive amyotrophy of thenar and hypothenar muscles, cognitive impairment, pigmentary retinopathy and cerebellar signs such as dysarthria, nystagmus, and ataxia (Hanein, Martin et al. 2008).

5.3.2 Mutation spectrum of SPG11

Till date, more than 120 SPG11 mutations equally distributed within the gene have been described (available at “The Human Gene Mutation Database – HGMD“). Known mutations include missense, nonsense, splice mutations, small deletions, or insertions which are distributed all throughout the gene without any obvious clustering in mutational hotspots (Hehr, Bauer et al.

2007; Stevanin, Santorelli et al. 2007; Rajakulendran, Paisan-Ruiz et al. 2011; Wakil, Murad et al. 2012; Pensato, Castellotti et al. 2014). Table 4 illustrates important mutations reported in a

SPG11-linked HSP cohort. A large number of mutations of SPG11/KIAA1840 genetic variants predict the truncation of the corresponding proteins, thus confirming a predominant loss-of- function mechanism. The full-length 8-kb SPG11 transcript encompasses 40 exons on

Introduction

chromosome 15q21.1, encoding for the 2443 amino-acid protein known as spatacsin, a name

attributed to the characteristic nature of ‘spasticity with thin or atrophied corpus callosum

Table 2: Important SPG11 genetic mutations identified in HSP patients

Adapted from: (Pensato, Castellotti et al. 2014)

syndrome protein’ (Stevanin, Santorelli et al. 2007). Human spatacsin shares 85% identity with the homologous protein in dog, 76% with mouse and 73% with rat. Very little is known about the structure and important functional domains of spatacsin but recent studies employing zebrafish SPG11 orthologs suggests that spatacsin is very crucial for the development and maintenance of the central nervous system (Southgate, Dafou et al. 2010).

Introduction

5.3.3 Cellular localization and functions of spatacsin

Although a comprehensive analysis of SPG11 distribution in different cell types and sub-cellular

localization of spatacsin is still missing; recent studies have revealed that spatacsin is expressed

ubiquitously in the nervous system and is predominantly expressed in cerebral cortex,

cerebellum, hippocampus and pineal gland (Stevanin, Santorelli et al. 2007; Hanein, Martin et al.

2008). At sub-cellular level, spatacsin showed partial colocalisation with microtubules,

endoplasmic reticulum and vesicles involved in protein trafficking (Murmu, Martin et al. 2011).

Although the precise function of the protein remains unknown, it is believed to be essential for

the survival of neurons (Crimella, Arnoldi et al. 2009; Southgate, Dafou et al. 2010). In another

study with zebrafish models, morpholinos enabled knockdown of zspg11 showed compromised

outgrowth of spinal motor axons, suggesting spatacsin may be important for the formation of

neuromuscular junctions during development (Martin, Yanicostas et al. 2012). Along with

spatacsin, another HSP protein, spastizin, encoded by SPG15 also shows similar distribution

pattern and together they form an integral part of adaptor protein 5, a multi-protein complex

involved in sorting and transport of cargoes (Slabicki, Theis et al. 2010). Spastizin has been

shown to be localized at the midbody of dividing cells and facilitate the abscission stage of

cytokinesis, to generate two daughter cells at the end of the cell cycle (Sagona, Nezis et al.

2010). Spatacsin has been proposed to have an N-terminal β-propeller–like domain and shows sequence homology with clathrin heavy chain and coat protein complex β’-COP (component of the COPI coat) which, like clathrin, is thought to drive vesicle formation by assembling into a cage-like structure. Both spatacsin and spastizin functionally interact with the heterotetrameric

AP-5 adaptor protein complex, subunits of which are mutated in another form of HSP, SPG48

(Hirst, Barlow et al. 2011; Hirst, Borner et al. 2013). SPG11 is localized at the outer part of the

Introduction

Figure 6: Schematic model for the functional role of spatacsin

(A) Organization of the adaptor protein complex, AP5 (left panel) and overview of the endosomal sorting and trafficking complex (Right panel). (B) Schematic model of Autophagy Lysosome Reformation (ALR) in starvation and feeding conditions. The spastizin-spatacsin complex localizes to the lysosome/autolysosome by interaction of the spastizin FYVE domain and PI(3)P, and this complex is also an essential component for lysosome vesiculation. Adapted from: (Hirst, Barlow et al. 2011; Chang, Lee et al. 2014)

AP-5-containing coat, possibly acting as a membrane deforming scaffold and SPG15 acts as docker of the sorting complex onto the membrane (Hirst, Borner et al. 2013) (Figure 6A).

Spatacsin has also recently been associated with the autopahgy lysosomal reformation (ALR), a

Introduction

pathway that recycles lysosomes from autolysosomes by initiating lysosomal tubule formation

(Figure 6B). Loss of spatacsin resulted in depletion of lysosomes and accumulation of Lamp1

positive intracellular autofluorescent material in SPG11 patient derived fibroblasts and murine

cortical neurons (Renvoise, Chang et al. 2014; Varga, Khundadze et al. 2015). Fibroblasts from

SPG11 and SPG15 patients also demonstrated accumulation of immature autophagosomes

suggesting endolysosomal trafficking defects as converging pathogenic mechanism in AR-HSP

(Vantaggiato, Crimella et al. 2013; Renvoise, Chang et al. 2014). Besides aberrations in lyososomal biogenesis, could result in dysregulation of the intracellular autophagy pathway and reduced delivery of two targeted autophagocytic proteins (SQSTM1 and MAP1LC3B) to form autolysosomes (Khundadze, Kollmann et al. 2013; Renvoise, Chang et al. 2014; Varga,

Khundadze et al. 2015). Both spatacsin and spastizin have also been shown to act in similar pathway regulating the development of motor neurons in zebrafish (Martin, Yanicostas et al.

2012).

However, these models do not mimic the complex signaling pathways in humans. In particular,

the complexity and temporal changes associated with cortical development, the subtype specific

and spatio-temporal cortical alignment of distinct neuronal populations implicated in progressive

neurodegenerative diseases. Defects in the formation of the corpus callosum (CC) are an

indicator of defect in neurodevelopment (Edwards, Sherr et al. 2014; Paul, Corsello et al. 2014)

and of the early disease onset accompanied by cognitive impairment present in SPG11 patients

(Hehr, Bauer et al. 2007). SPG11, unlike other HSPs, has also recently been grouped into the broad category of developmental disorders representing agenesis (hypoplasia) of the corpus callosum (Paul, Brown et al. 2007). This clinical phenotype points to an early developmental aberration of the disease caused by SPG11 mutations, the mechanisms underlining which have

Introduction

not been elucidated so far. These mixed findings necessitate the quest for a reliable human model

to delineate the subtle intricacies of human brain development and recapitulating the complex

cellular phenotypes, arising out of early anomalies in signaling cues of SPG11-linked HSPs.

5.4 Human induced pluripotent stem cell (iPSC) technology

5.4.1 Reprogramming strategies to generate iPSC

During embryonic development, cells start from an early undifferentiated stage of zygote and

gradually progress through a more specialized state as it divides further down the lineages. The

cells gradually take up specialized functions of the somatic tissues. Cells of the early zygote can

give rise to all embryonic and extraembryonic tissues (Kelly 1977), and are therefore called

“totipotent,” while cells of the inner cell mass (ICM) of the blastocyst can give rise to all

embryonic but not all extraembryonic tissues, and are hence called “pluripotent.” Cells residing

in adult tissues, such as adult stem cells, can only generate its cells for few passages to give rise

to cell types within their lineage and are called either “multipotent” or “unipotent,” During the

1950s, Briggs and King (Briggs and King 1952) established the technique of SCNT, or

“cloning,” to probe the developmental potential of nuclei isolated from late-stage embryos and tadpoles by transplanting them into enucleated oocytes. This work, together with seminal experiments by Gurdon (Gurdon 1962; Gurdon, Lane et al. 1971), showed that differentiated

amphibian cells indeed retain the genetic information necessary to support the generation of

cloned frogs. The major conclusion from these and subsequent findings was that development

imposes reversible epigenetic, rather than irreversible genetic changes on the genome during

cellular differentiation. A major breakthrough was reached in 2006, when Yamanaka and

Introduction

Takahashi discovered initially a pool of 24 pluripotency-associated candidate genes that could

activate a dormant cell into the characteristic ESC like morphology which they termed as

induced pluripotent stem cell (iPSC) (Takahashi and Yamanaka 2006).

Successive rounds of elimination of individual factors then led to the identification of the

minimally required core set of four genes, comprising Klf4, Sox2, c-Myc, and Oct4. Soon after

this study, several laboratories, including Yamanaka's group (Okita, Ichisaka et al. 2007), were

able to reproduce and improve upon these findings. iPSCs have been derived from a number of different species including humans (Takahashi, Tanabe et al. 2007; Yu, Vodyanik et al. 2007;

Park, Zhao et al. 2008) rats (Li, Tong et al. 2008), and rhesus monkeys (Liu, Zhu et al. 2008) by

expression of the four Yamanaka factors. Similarly, iPSCs have been derived from different

somatic cell populations, such as keratinocytes neural cells (Aasen, Raya et al. 2008; Eminli,

Utikal et al. 2008; Kim, Zaehres et al. 2008; Maherali, Ahfeldt et al. 2008), stomach and liver

cells (Aoi, Yae et al. 2008), and melanocytes (Utikal, Polo et al. 2009), exhibiting the

universality of induced pluripotency.

5.4.2 Modeling neurodegenerative diseases using iPSCs

The study and treatment of many degenerative diseases such as type I diabetes, Alzheimer's

disease, and Parkinson's disease is limited by the accessibility of the affected tissues, as well as

the inability to grow the relevant cell types in culture for extended periods of time. The idea

behind so-called “disease modeling” is to derive iPSCs from patients' skin cells and then

differentiate them in-vitro into the affected cell types, thereby recapitulating the disease in a Petri

dish. The advantage of this approach over currently used strategies is that the very cell type that

Introduction

is compromised in the disease can be recreated in culture to be studied, even when the cell type

has degenerated in the patient. Moreover, since iPSCs can be propagated for several passages in

culture, they provide an unlimited source of desired specialized cells. Ultimately, the goal of this

approach is to use these in-vitro models of disease to identify novel drugs to treat the disease.

iPSC disease models have been successfully generated for a wide range of diseases, including

Parkinsons disease, Huntington’s Disease, Down syndrome, Amyotrophic Lateral Sclerosis

(ALS) and spinal muscular atrophy (SMA) etc (Bellin, Marchetto et al. 2012).

5.5 Aims and hypotheses of the thesis

Although SPG11 is the most frequent form of AR-HSP, the lack of appropriate disease model

has greatly hampered our knowledge about the localization and function of spatacsin in neurons,

the role of spatacsin in human brain development and the pathogenic mechanisms underlying the

severeity of neurological symptoms in HSP patients. As such, no curative therapies or medications are currently available for SPG11. With the advent of iPSC technology, new and improved methods of generating disease models have been instigated (Robinton and Daley

2012). The overall aim of this study was to to generate for the first time, a human iPSC model of

SPG11 and use SPG11-iPSC derived cortical neural progenitor cells (SPG11-NPCs) and

differentiated neurons (SPG11-dNeurons) to recaptitulate the neurodevelopmental and

neurodegenerative phenotypes of SPG11. The overall hypothesis is that mutations in SPG11

have a detrimental effect on the development and function of human cortical neurons. The

specific aims for investigation were: (i) to comprehensively investigate the spatio-temporal

localization, distribution and expression analysis of SPG11 in different cell types available, (ii)

to recapitulate early neurodevelopmental anomalies at the cortical neural progenitor cell (NPCs)

Introduction

stage, and (iii) to delineate the neurodegenerative phenotype, mimicking slowly progressive cortical degeneration in terminally differentiated neurons.

Hypothesis 1: Spatacsin is expressed in multiple cell types and throughout brain development

Previous studies using non-neuronal cellular model, showed spatacsin to be partially colocalised

with intracellular organelles such as mitochondria and endoplasmic reticulum. However the data

pertaining to human cell types is still not available. So the primary hypothesis of this study, was

that spatacsin is expressed in different cell types in the CNS, predominantly in the neuronal cells.

We examined the expression pattern of spatacsin, in discrete brain regions at different developmental stages of mice brain. In addition, we also analyzed for the first time, the

distribution of spatacsin in developmental stages of human neurons, including cortical projection

neurons.

Hypothesis 2: SPG11-NPCs recapitulate neurodevelopmental phenotype of SPG11

The presence of a TCC and cortical atrophy in SPG11 patients suggests an early developmental

phenotype. However, unavailability of early stage CNS cell types, have hampered the research

into the role of spatacsin in neural development and its association with important signaling

mechanisms. Furthermore, the importance of spatacsin in callosal developmental guidance cues

shaping the neural circuitry, cytoarchitecture of the corpus callosum and generation of the distinct laminar pattern of the human cortex have not been investigated. SPG11, unlike other

HSPs, has recently been grouped into the broad category of neurodevelopmental disorder resembling the agenesis of the corpus callosum (Paul, Brown et al. 2007). We therefore,

Introduction

hypothesized that SPG11-NPCs, show cell cycle anomalies and impaired proliferation, thereby altering the temporal and cellular fate of the cortical progenitors. We examined this by performing a global transcriptome analysis on day three NPCs from control and SPG11 patients.

We further, investigated the proliferation using a BrdU assay and endogenous marker expression.

Hypothesis 3: SPG11-iPSC derived neurons (SPG11-dNeurons) exhibit HSP patients’ specific temporal and progressive neurodegenerative phenotype

The characteristic hallmark of HSPs is the progressive degeneration of the long-projecting cortico-spinal tracts (CSMN), which controls the voluntary motor activities including the movement of our limbs. The precise mechanism regulating this degeneration is still unclear. The presence of the membranous bodies in the sural nerves of SPG11 patients, led us to hypothesize that mutations in SPG11 have a detrimental effect on the neuritic complexity and trafficking of cargoes in the terminally differentiated neurons. We started to examine this by analyzing the cellular morphology of differentiated neurons and subsequently investigated the intracellular traffic by measuring the anterograde and retrograde traffic of synaptic vesicles in control and

SPG11-dNeurons.

Materials and Methods

6. Materials and Methods

6.1 SPG11 patients and Control subjects

The patients (n = 3; hereafter referred to as SPG11-1, SPG11-2 and SPG11-3) were Caucasians

with clinically confirmed symptoms of AR-HSP, having previously described heterozygous mutations in SPG11 (Hehr, Bauer et al. 2007; Bauer, Winner et al. 2009). The cases of SPG11-1

and SPG11-3 have been reported in Perez-Branguli et al. (Perez-Branguli, Mishra et al. 2014).

SPG11-1 and SPG11-2 are sisters (Figure 7) and have a heterozygous nonsense mutation at

c.3036C>A/ p.Tyr1012X in exon 16 and a c.5798 delC/ p.Ala1933ValfsX18 mutation in exon 30

(Hehr, Bauer et al. 2007; Bauer, Winner et al. 2009). SPG11-3 has a heterozygous nonsense

mutation at c.267G>A / p. Trp89X in exon 2 and a splice site mutation 1457-2A > G in intron 6

[corresponding to the previously reported mutation c.1757-2A > G] (Hehr, Bauer et al. 2007).

The Controls (n=2; hereafter referred to as CTRL-1 and CTRL-2) were healthy Caucasian

individuals with no history of movement disorder or neurologic disease, as reported in previous

studies (Havlicek, Kohl et al. 2014; Perez-Branguli, Mishra et al. 2014). The detailed clinical and

genetic characteristics are summarized in Table 3.

6.2 Cell culture

6.2.1 Fibroblast derivation

The human fibroblasts were obtained from dermal punch biopsies from one of the upper arm as

previously described (Havlicek, Kohl et al. 2014; Perez-Branguli, Mishra et al. 2014), following

Materials and Methods

Table 3: Clinic of SPG11 patients and control subjects

Mutation Sex HSPRS 3Tesla Age at Barthel- Cogniti Landmark Muscle Motor- MRI onset Index ve of wasting sensory (max.52) impairm disability (upper/lo neuropath /Examinati ent (1-4) wer y on limbs) (Y)

Exon 16: F 39 TCC, 25/40 30 % + 4 + + c.3036C>A WML, SPG11-1 heterozygote cortical p.Tyr1012X atrophy

Exon 30: c.5798 delC heterozygot p.Ala1933Valfs X18 SPG11-2 Identical to F 33 TCC, 25/35 60 % + 3 + + SPG11-1 WML, cortical atrophy

Exon 2: F 35 TCC, 31 / 44 55 % + 4 + + c.267G > A WML, SPG11-3 p. Trp89X cortical

atrophy Intron 6: 1457-2 A > G splice mutation - M 0 - - / 52 100 % - - - -

CTRL-1

- F 0 - - / 45 100 % - - - -

CTRL-2

Patients: SPG11-1, SPG11-2, SPG11-3,controls: CTRL-1, CTRL-2, F: Female, HSPRS: hereditary spastic paraplegia rating scale, MRI: magnetic resonance imaging, TCC: thin corpus callosum, WML: white matter lesion, Y: years, Barthel index of activity of daily living (max. 100%). (Mishra et al., under revision)

Materials and Methods

Institutional Review Board approval (Nr. 4120: Generierung von humanen neuronalen Modellen

bei neurodegenerativen Erkrankungen) and informed consent at the movement disorder clinic at

the Department of Molecular Neurology, Universitätsklinikum Erlangen (Erlangen, Germany).

Fibroblasts were cultured in IMDM/Glutamax containing 15% fetal bovine serum (Invitrogen)

and penicillin/streptomycin (Pen/Strep; Invitrogen).

6.2.2 Generation of iPSCs

Fibroblasts from SPG11 patients and CTRLs were reprogrammed using retroviral transduction of

the transcription factors Klf4, c-Myc, Oct4, and Sox2 as previously described (Takahashi,

Tanabe et al. 2007; Havlicek, Kohl et al. 2014; Perez-Branguli, Mishra et al. 2014). We generated two iPSC lines from each patient [SPG11-11, SPG11-12 (from SPG11-1); SPG11-21,

SPG11-22 (from SPG11-2) and SPG11-31, SPG11-32 (from SPG11-3)] and CTRL [CTRL-12,

CTRL-13 (from CTRL-1) and CTRL-21, and CTRL-22 (from CTRL-2)], as described in, Table

4. The iPSC lines were characterized for pluripotency markers at mRNA transcript (RT-PCR) and protein levels (immunostainings) and screened for karyotypic alterations using G-banding chromosomal analysis as described earlier (Havlicek, Kohl et al. 2014; Perez-Branguli, Mishra et al. 2014). The above-mentioned SPG11 mutations were reconfirmed in the SPG11-iPSC lines.

6.2.3 Neural differentiation paradigm

NPCs were generated from the iPSC lines as described earlier (Havlicek, Kohl et al. 2014; Perez-

Branguli, Mishra et al. 2014). Briefly, embryoid bodies (EBs) were generated by transferring the

iPSCs to ultra-low attachment plates and maintained in suspension in neural induction medium

Materials and Methods

for one week [NIM: DMEM F/12, supplemented with N2/B27)] before being plated onto

polyornithine (PORN)/laminin coated plates to generate neural rosettes. The rosettes were

enzymatically dispersed into single cells and maintained as proliferative NPCs in neural

proliferation medium [NPM: NIM supplemented with FGF2 (20ng/ml)]. We established two

NPC lines from each SPG11 patient [SPG11-111, SPG11-121 (from SPG11-1); SPG11-211,

SPG11-221 (from SPG11-2); SPG11-311, SPG11-321 (from SPG11-3)] and CTRL [CTRL-122,

CTRL-131, (from CTRL-1); CTRL-213, CTRL-221 (from CTRL-2)], as described in Table 4.

NPCs were maintained at high density (1.5 – 2.0 x106 cells/well), grown on PORN/laminin-

coated six-well plates in NPM and split once every week.

Table 4: iPSC and NPC lines used in the study

SPG11/CTRL Fibroblast iPSC lines NPC lines lines

SPG11-1 SPG11-1 SPG11-11 SPG11-111

SPG11-12 SPG11-121

SPG11-2 SPG11-2 SPG11-21 SPG11-211

SPG11-22 SPG11-221

SPG11-3 SPG11-3 SPG11-31 SPG11-311

SPG11-32 SPG11-321

CTRL-1 CTRL-1 CTRL-12 CTRL-122

CTRL-13 CTRL-131

CTRL-2 CTRL-2 CTRL-21 CTRL-213

CTRL-22 CTRL-221

Materials and Methods

Terminal differentiation of NPCs was initiated in neural differentiation medium [NDM: NIM

supplemented with 20 ng/ml brain-derived neurotrophic factor (Preprotech), 20 ng/ml glial cell

line-derived neurotrophic factor (Peprotech), 1 mM dibutyryl-cyclic AMP (Sigma–Aldrich) and

200 nM ascorbic acid (Sigma–Aldrich)] at a density of 40,000 cells/cm2 on PORN/laminin-

coated plates, or glass coverslips as described earlier (Havlicek, Kohl et al. 2014; Perez-

Branguli, Mishra et al. 2014). Neural cells were cultured under these conditions for 4-8 weeks

with a half medium change every week.

6.2.4 Karyotyping

Standard G-banding chromosome analysis was performed at the Centre for Human Genetics,

Regensburg, Germany.

6.2.5 Neurite length and arborization analysis in iPSC-dNeurons

HPSC-dNeurons from SPG11 patients and controls were grown in microfluidic chambers

(SND450, Xona Microfluidics, Temecula; Figure 30C) as described before (Taylor, Rhee et al.

2006). The grooves in the microfluidic chambers enabled the axons to grow parallel and in one direction (Figure 30C). A total of 60,000 NPCs were plated on the soma side and cells were cultured for 15 days in NDM. Axonal like processes passing through the grooves were visualized using a Zeiss inverted fluorescent microscope (Zeiss). At least 20 cells per NPC line were imaged for analysis. Neurites of individual cells, starting from the end of the groove, were traced using NeuronJ (ImageJ; sbweb.nih.gov/ij/) to calculate the total neurite length and the branching points.

Materials and Methods

6.2.6 Electrophysiology

Whole-cell patch clamp recordings were performed on six-week differentiated neural cells as

previously described (Havlicek, Kohl et al. 2014). Briefly, patch clamp recordings were

performed at RT using an EPC 10 amplifier (HEKA electronics) in artificial cerebrospinal fluid

bubbled with 95% O2 5% CO2 (in mM: 125 NaCl, 3 KCl, 1 CaCl2, 1 MgCl2, 1.25 NaH2PO4,

25 NaHCO3, and 10 d-glucose, pH 7.4). The internal solution contained (in mM): 4 NaCl, 135

K-gluconate, 3 MgCl2, 5 EGTA, 5 HEPES, 2 Na2-ATP, and 0.3 Na3-GTP (pH 7.25).

Electrophysiological experiments were carried out in collaboration with the lab of Prof. Angelika

Lampert, Institute for Physiology and Pathophysiology, University of Erlangen-

Nuernberg/RWTH Aachen.

6.3 Animals

Wild type (WT) C57BL/6 mice at embryonic (E15 and E18), postnatal (P10) and adult ages

(P150) were used. All experiments were carried out in accordance with the European

Communities Council Directive of 24 November 1986 (86 / 609 / EEC). Adult mice were

transcardiallyperfused with PBS and 4% PFA; and the brains were dissected, and coronally

sliced at 35 µm thickness using a Leica SM-2010R cryostat (Leica). Brain sections thus were

submitted to epitope retrieval in citrate buffer (DAKO) for 30 min at 80 oC, rinsed several times

in TBS+ (Tris-buffered saline and 0.05 % Triton x-100) at RT, blocked with blocking solution

(TBS+ supplemented with 3 % normal donkey serum and 3 % Triton x-100), and incubated with

primary antibodies ON at 4 oC. After several rinses, sections were incubated with suitable

fluorescent secondary for 1 at RT, rinsed again in Tris-buffered saline (TBS), mounted for

Materials and Methods

further examination in a LSM-780 microscopesetup (Carl Zeiss).

Cultures of mouse cortical neurons and hPSC-dNeurons were fixed in 4 % PFA for 15 min at

RT. After several rinses with PBS, cultures were pre-incubated for 60 min at RT with PBS supplemented with 0.1 % Triton x-100 (PBST) and incubated ON at 4 °C with primary antibodies diluted in immunofluorescence buffer 2 (IFB2: PBST supplemented with 5 % normal donkey serum). Next, samples were rinsed several times with PBST, and incubated with suitable fluorescent secondary antibodies diluted in IFB2 for 60 min at RT. Moreover, F-actin was detected by incubating fluorescent phalloidin diluted in PBS for 30 min at RT. After several washes with PBS, cells were mounted for further microscopic analysis. All IFs were visualized using a Zeiss inverted fluorescent Apotome.2 and LSM-780 microscope setups (Carl Zeiss).

6.3.1 Synaptosome preparations

Synaptosomes were prepared as previously published (Huttner, Schiebler et al. 1983). Briefly, forebrains from three to four adult mice were dissected and kept in a cold glass-Teflon homogenizer together with 30 ml of pre-chilled 4 mM Hepes-NaOH; pH = 7.3 supplemented with 0.32 M sucrose. Tissue homogenization was performed by 10 up-down strokes at 900 rpm, and centrifuged at 800 x g for 10 min at 4ºC. The resulting Supernatant (S1) was centrifuged at

10,000 x g for 15 min at 4ºC, and the newly obtained pellet (P2) at 10,000 x g for 20 min with the aim to isolate the synaptosome fraction (P3). Synaptosomes were broken by osmotic shock, and the synaptosomal plasmatic membranes (P4) separated by centrifugation at 25,000 x g for 20 min at 4ºC. The resulting supernatant (S4) was further centrifuged at 30,000 × g overnight (ON) at 4ºC in order to separate cytosolic fraction (S5) and synaptic vesicles fraction (P5).

Materials and Methods

Synaptosomal fractions were prepared for further IB assays. To perform IF on whole

synaptosomes, the synaptosome pellet P2 was re-suspended in 4 ml of 0.32 M sucrose, loaded on a discontinuous Ficoll gradient (4 ml of Ficoll at 12 %, 1 ml at 9 % and 4 ml at 5 %) and centrifuged at 40,000 x g for 35 min at 4 °C. The synaptosomal enriched fractions were collected at the two interfaces, between 5 % - 9 %, and 9 % - 12 % of Ficoll concentration. After protein quantification the synaptosomal fraction was distributed in aliquots of 0.5 mg and centrifuged at

20,800 × g for 12 min at 4 °C and re-suspended in 50 μl of sodium buffer (20 mM Hepes-NaOH pH = 7.3; 10 mM glucose, 5 mM KCl, 140 mM NaCl, 5 mM NaHCO3, 1 mM MgCl2, 1.2 mM

Na2HP04). Then, synaptosomes were mounted on PDL coated slides (Superfrost Plus, Thermo

scientific). After fixation in 4% PFA and three washes with PBS, mounted synaptosomes were

incubated with blocking buffer (PBS containing 0.2 % gelatin and 20 % of normal goat serum)

for 60 min at room temperature (RT). Next, synaptosomes were incubated ON at 4 °C with

antibodies against spatacsin, the presynaptic marker SNAP25 (Synaptosomal-associated protein

25) or the synaptic vesicle marker VAMP2 (Vesicle-associated membrane protein 2) in

immunofluorescence buffer 1 (IFB1: PBS containing 0.2% gelatin and 1% normal goat serum).

After three washes with PBS, synaptosomes were incubated for 60 min at RT with the

fluorescent secondary antibodies diluted in IFB1, washed in PBS and finally mounted.

Synaptosomes were examined using a Leica TCS-SL confocal microscope (CCiTUB, Biology

Unit of Campus of Bellvitge, University of Barcelona, Spain).

6.4 Molecular Biology

6.4.1 Real time PCR analysis

Materials and Methods

Real time PCR (qRT-PCR) analysis was performed using the protocol described earlier

(Havlicek, Kohl et al. 2014; Perez-Branguli, Mishra et al. 2014), on RNA isolated from SPG11

and CTRL NPC lines. Briefly, total RNA from NPCs (grown for 3 days) was isolated using

RNeasy Mini kit (Qiagen), including on-column DNA digestion. Five hundred ng of RNA were

reversely transcribed to cDNA using QuantiTect RT-PCR kit (Qiagen) according to the

manufacturer’s instructions. RT-PCR was then performed using 1 µl of cDNA, transcript-

specific primers (200 nM each) and SybrGreen Master Mix (Applied Biosystems) in a total

volume of 20 µl using 7300 Real Time PCR System (Applied Biosystems). The primer pairs

used are listed in Supplementary information Table 6.

6.4.2 Western blot

NPCs were washed with PBS and lysed in either modified RIPA lysis buffer [150 mM NaCl, 50

mM Tris/HCl, pH 7.4, 1% NP40, 0.25% dexoycholic acid, 0.1% SDS, 1mM EDTA, 10 mM Na-

pyrophosphate, 2 mM Na-orthovanadate, 1 mM NaF and protease inhibitor cocktail (Roche)] or

hypotonic buffer [25 mM Tris/HCl, pH 8.0, 1 mM EDTA and protease inhibitor cocktail

(Roche)] at 4°C for 10 min. Lysates were cleared by centrifugation at 11,000 ×g for 10 min at

4ºC. Post centrifugation, supernatants were collected and used for quantification and protein

samples preparation. Twenty-µg protein samples were loaded on 12% gel for SDS-

polyacrylamide gel electrophoresis (SDS-PAGE) followed by blotting onto PVDF membrane.

After incubating the membrane in 5% non-fatty milk powder (blocking solution), the membranes were incubated with primary antibodies (see Supplementary Material, Table S1) overnight at

4°C. The next day, membranes were incubated with horseradish peroxidase-conjugated

secondary antibodies diluted in blocking solution for 1h at RT. Blots were developed on

Materials and Methods

Hyperfilms (GE Healthcare) using Luminol-based chemiluminescent solutions (ECL blotting solution, Amersham) and quantified using ImageJ software (NIH, USA).

6.4.3 Transfections of siRNA and plasmid DNA

The human neuroblastoma cell line (SH-SY5Y, Leibnitz Institute DSMZ – German Collection of

Microorganisms and Cell Cultures) was transfected using Lipofectamine 2000 (Life

Technologies) according to the manufacturer’s instructions. Briefly, 1.8 x105 cells/well in a 24- well plate were plated in triplicate for the CTRL and knockdown conditions. The next day, SH-

SY5Y cells were co-transfected with 30 pmol spatacsin siRNA (siSPG11, Santa Cruz) or

siLuciferase (siLuc, Shanghai GenePharma), both previously described (Perez-Branguli, Mishra et al. 2014) and 400 ng of pAc-mGFP plamid. Medium was replaced after 24 hours and the cells

were kept in culture for an additional two days to ensure the proper visualization of GFP in

transfected cells. Cells were permeabilized using CSK buffer followed by fixation in methanol for 5 min at −20°C and immunostaining analysis was performed as described above using PCNA antibody (1:200) as a proliferation marker. DAPI (1:2000) was used to label the cell nuclei.

6.4.4 TOP-flash/FOP-flash luciferase reporter assay

To assess the transcriptional activity of β-catenin, HEK293T cells (2 x 105 cells/well in a 12 well

plate) were co-transfected with β-catenin responsive firefly luciferase reporter plasmids

(Veeman, Slusarski et al. 2003) containing either multimeric LEF/TCF cognate sequences

(pTOP-flash) or mutated binding sites (pFOP-flash) and pUHD16‐1 (encoding the β-

galactosidase) as previously described (Dehner, Hadjihannas et al. 2008). Briefly, 24 hrs post-

transfection, cells were harvested and cell extracts were prepared using a lysis buffer containing

Materials and Methods

100 µl of 25 mM Tris‐phosphate (pH 7.8), 2 mM EDTA (pH 8.0), 5% glycerol, 1% Triton

X‐100, 20 mM DTT. Luciferase activity was assessed using 10 μL cell lysate and 100 μL

luciferase assay reagent containing 100 mM potassium phosphate (pH 7.8), 15 mM MgSO4, 5

mM ATP and 0.2 mM d‐luciferin (PJK GmbH, Germany). The luciferase activity measured was

normalized against the β-galactosidase activity to correct for different transfection efficiencies as previously described (Dehner, Hadjihannas et al. 2008). Promoter activity was calculated by

dividing relative light units of specific TOP-flash and relative light units of non-specific FOP- flash, obtained from three separate experiments, each of which were performed in triplicates.

6.5 Immunofluorescence stainings and analysis

NPCs (80,000 cells/cm2) were grown on PORN/laminin-coated coverslips, fixed in ice-cold

methanol −20°C for 5 min (for H3P detection) or in 4% paraformaldehyde in phosphate-buffered

saline (PBS) at room temperature (RT) for 15-20 min and permeabilized with 0.3% Triton X-100

in PBS (PBST) for 10 min at RT. Nonspecific binding sites were blocked by incubating the cells

with 3% donkey serum in PBST for 30 min at RT (PBST+). Primary antibodies were diluted in

PBST+ and incubated with samples overnight at 4°C. The following day, the coverslips were

washed 3 times in PBST and incubated with secondary antibodies for 1-2 hours at RT. After 3

washing steps with PBS and two dips in water, coverslips were mounted on glass microscope

slides (Thermo Scientific) in Aqua Polymount (Polysciences). The primary and secondary

antibodies used are listed in Supplementary Information, Table 7. Nuclei were visualized using

DAPI (4', 6'-diamidino-2-phenylindole, 0.5 μg/ml).

Materials and Methods

6.5.1 Proliferation analysis

PCNA staining: Immunofluorescence staining was performed as previously described (Engel,

Hauck et al. 1999), with slight modifications. Briefly, NPCs (80,000 cells/cm2) were grown on

PORN/laminin-coated coverslips, washed with PBS and permeabilized with 0.5% Triton X-100

in CSK buffer [100 mM NaCl, 300 mM sucrose, 3 mM MgCl2, and 10 mM PIPES (pH 6.8)]

twice at 4°C for 30 seconds and then fixed in ice cold methanol for 5 min at −20°C. Non-specific

binding sites were blocked by incubating the cells with 3% donkey serum in PBS containing

0.3% Triton X-100 (PBST+) for 30 min at RT. Immunostaining analysis was then performed as described above using mouse monoclonal antibody to PCNA (PC10 diluted 1:50, Santa Cruz).

Nestin and Sox2 antibodies (both1:300) were used to identify NPCs, while DAPI (0.5 μg/ml)

was used to label cell nuclei. Proliferating cells were estimated by counting the number of

PCNA-positive cells out of Nestin/Sox2 double-labeled cells.

BrdU assay: NPCs were plated at a cell density of 80,000 cells/cm2 on glass coverslips pre-

coated with PORN/laminin solution in NPM. On day 3 of culture, the cells were treated with 30

µM BrdU (Sigma) for one hour and fixed with 4% paraformaldehyde in PBS at RT for 15-20

min. The cells were then treated with 2N HCL/0.3% Triton X-100 for 30 min at 37oC, followed

by 0.1 mM sodium tetraborate (pH 8.5) for 5 min and three washes in PBS. Immunostaining

analysis was performed as described above using rat monoclonal antibody against BrdU (1:100;

Abcam). Nestin, Sox2 antibodies and DAPI were used as described above to identify the NPCs

and to label the cell nuclei, respectively. Proliferating cells were estimated by counting the

number of BrdU-positive cells out ofNestin/Sox2 double-labeled cells.

Quantification of cell numbers: Images were acquired using a Zeiss inverted fluorescence

microscope (Observer Z1, Zeiss). Three random images per coverslip (triplicate coverslips per

Materials and Methods

cell line) were acquired for analysis. N >400 cells were counted per NPC line using ImageJ

software (NIH, USA). For quantification of data, unless otherwise stated, Nestin/Sox2 double-

positive cells were used as reference representing the total number of NPCs and the respective

proliferation markers (PCNA, H3P, AuroraB, BrdU) or dead cells (Image-iT® DEAD™) are

expressed as percentage of the Nestin/Sox2 double-positive cells. The data are represented as

means ± SEM of each NPC line from at least three independent experiments performed in

triplicate.

6.5.2 Cell death analysis

CTRL- and SPG11-NPCs (80,000 cells/cm2) were cultured on PORN/laminin pre-coated glass

coverslips in NPM. On day 3 of culture, the NPCs were incubated with 100 nM Image-iT®

DEAD™ viability stain (Life Technologies Inc, USA) for 30 min at 37°C as recommended by

the manufacturer. The cells were then fixed with 4% paraformaldehyde in PBS at RT for 15-20

min and immunostaining analysis was performed as described above. Nestin, Sox2 antibodies

and DAPI were used as described above to identify the NPCs and to label the cell nuclei,

respectively. Cell death was estimated by counting Image-iT® DEAD™-positive cells out of

Nestin/Sox2 double-labeled cells.

6.5.3 Acetyl-tubulin staining

HPSC-dNeurons and GFP+ murine cortical neurons were fixed first using ice cold methanol at -

200C for 10 min, followed by washing and second fixation in 4% PFA at RT for 15 min. After

three washes in PBS, neuronal cultures were probed with α-acetyl-tubulin together with α –

Materials and Methods

ßIIItubulin in hPSC-dNeurons and with α -GFP in murine cortical neurons for further IF

examinations employing Zeiss inverted fluorescent Apotome.2 and LSM-780 microscope setups

(Carl Zeiss).

6.6 Flow cytometry-PI analysis

Flow cytometry analysis was performed as described earlier (Ahmed, Smoot et al. 2000) on

CTRL- and SPG11-NPCs. Briefly, 80,000 cells/cm2 (plated in triplicates per NPC line) were

cultured on PORN/laminin-coated 12 well plates in NPM. On day 3 of culture, the NPCs were

harvested, washed in PBS and resuspended in 500µl lysis buffer (0.1% sodium citrate, 0.1%

Triton-X-100 and 0.1mM EDTA, 50µg/ml PI and 10µg/ml RNaseA) for 30 min in the dark at

RT. Flow cytometry analysis was performed within an hour after labeling with PI using Gallios

Flow cytometer (Beckman Coulter GmbH). Data of a minimum 30,000 cells per sample were

collected. Flow cytometry data were processed on viable cells by excluding the cell aggregates

followed by estimation of the population of each cell cycle phase using FlowJo software (version

8.5.3, FlowJo Inc).

6.7 Pharmacological rescue of NPCs proliferation

SPG11 NPCs were plated at a cell density of 80,000 cells/cm2 on PORN/laminin-coated glass

coverslips in NPM. The next day, cells were treated with (in µM) 1, 2, 3, 5 and 10 of the GSK3

inhibitors, CHIR99021 (R&D Systems) and Tideglusib (Selleckchem), to determine the optimum

concentration of the drug needed for rescue. After 24 hours of exposure, the cells were kept in

culture for one additional day. Cells were permeabilized using CSK buffer followed by fixation

Materials and Methods

in methanol for 5 minutes at -20°C and immunostained using PCNA antibody as described above.

6.8 Synaptic vesicle transport experiments

Neurons derived from healthy subjects (CTRL-1 and CTRL-2) and SPG11 (SPG11-1 and

SPG11-2) patients’ iPSC were grown on microfluidic chambers, where 2x104 human astrocytes

(Science Cell Research Laboratories) were plated on the axonal side (Havlicek, Kohl et al.

2014). After two weeks in culture, iPSC-dNeurons were then infected with lentivirus (LV) encapsulating a mCherry-synaptophysin construct (Havlicek, Kohl et al. 2014). Six days after

LV infection, live-cell imaging was performed in mCherry-synaptophysin+ axonal processes passing through the grooves (Figure 29A) in recording buffer 2 (10 mM HEPES pH = 7.5; 144 mM NaCl, 2.5 mM KCl, 2.5 mM CaCl2, 2.5 mM MgCl2, 10 mM glucose; Ω = 309 mmol / Kg), at stable temperature and balance CO2 conditions, and employing an Nikon Eclipse Ti inverted fluorescent microscope (Nikon) equipped with a EMCCD camera. Recording conditions were setup as 1 frame per second with a total duration time of 10 min. At least 20 neurites per experimental condition were recorded and analyzed. To obtain SV transport data, time-lapse videos were converted into two-dimensional kymographs by mathematical algorithms (ImageJ).

For each evaluated hPSC-dNeuron line, axonal processes were classified as they had SVs in motion; and their net transport direction (anterograde and retrograde) was calculated by subtracting the average distance per SV moving anterogradelly and retrogradelly [(anterograde

Σdx / anterograde SVs) - (retrograde Σdx / retrograde SVs)].

6.9 Microscopy

Materials and Methods

6.9.1 Fluorescence microscopy

Fluorescence images were acquiredwith the Observer.Z1 fluorescence microscope equipped with

a monochrome AxioCam MRm camera or with the LSM 780 confocal microscope (Institute for

Physiology and Pathophysiology, University of Erlangen/ Nürnberg). Exposure times were set

accordingly to receive brightest signals without saturating pixel values.

6.9.2 Electron microscopy

Transmission electron microscopy was performed by Prof. Ursula Schlötzer-Schrehardt,

Department for Ophthalmology, University Hospital Erlangen. Neuronal cultures grown on

plastic coverslips were fixed in 2.5% glutaraldehyde in 0.1 M phosphate buffer, postfixed in 2%

buffered osmium tetroxide, dehydrated in graded alcohol concentrations, and embedded in epoxy

resin according to standard protocols. Ultrathin horizontal sections were stained with uranyl

acetate and lead citrate and were examined with a transmission electron microscope (EM 906E;

Carl Zeiss NTS).

6.10 Statistics

Statistical analysis was performed using Prism software (version 5.0, GraphPad). Student's t-test

was applied when comparing the means between two groups representing means of each cell

line. To compare three or more groups, one-way or two-way ANOVA followed by

Dunnett's/Bonferroni post hoc test for multiple comparisons were used. Probability values (p values) ≤ 0.05 were considered statistically significant (* p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001).

All data are shown as mean ± SEM from ≥ 3 independent experiments performed in triplicates.

Results

7. Results

7.1 Generation of human models for SPG11 and expression analysis of SPG11

A major roadblock in the study of SPG11 has been the availability of appropriate model systems to elucidate the cellular and molecular mechanisms related to the disease pathophysiology. To overcome this problem, we generated a human induced pluripotent stem cell (iPSC) model of

SPG11 using patients’ derived skin fibroblasts (Takahashi, Tanabe et al. 2007). We differentiated these iPSCs and human embryonic stem cells (h-ESC) into forebrain neuronal cells and analysed the expression of spatacsin (Perez-Branguli, Mishra et al. 2014).

7.1.1 Generation of human iPSCs from SPG11 patients and Controls

The fibroblasts lines were established from three Caucasian SPG11 patients, described here as

SPG11-1, SPG11-2 and SPG11-3 and two healthy age matched controls, CTRL-1 and CTRL-2.

The comprehensive neurological analysis, mutational spectrum and long term follow up of the

SPG11 patients were performed at the movement disorder clinic of the Department of Molecular

Neurology, University Hospital Erlangen, and are summarized in Table 3. Brain MRI analysis

revealed a thin corpus callosum in each of the three SPG11 patients compared to control (Figure

7A). The flat-tire representation (Figure 7A below) of the cortex revealed severe cortical atrophy

and widespread deformation of gyri and sulci in the motor region of the cortex. Pedigree analysis

showed the typical autosomal recessive mode of inheritance in the patients (Figure 7B). The

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Figure 7: Generation of iPSCs from SPG11 patients and Controls

(A) MRI analysis of control and SPG11 patients included in the study. (B) Pedigrees of SPG11 families. Female index patients are represented in black circles. (C) Mutations analysis in SPG11 patient fibroblasts. Patients 1 and 2 (SPG11-1, SPG11-2) have heterozygous nonsense mutations at c.3036C> A/p.Tyr1012X in exon 16 and c.5798 delC/p.Ala1933ValfsX18 in exon 30. Patient 3 (SPG11-3) has a heterozygous nonsense mutation at c.267G > A/p. Trp89X in exon 2 and a splice site mutation 1457-2A > G in intron 6. (Mishra et al., under revision)

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spatacsin heterozygous mutations (c.3036C>A, c.5798 delC, c.267G > A and c.1757-2A > G),

reported earlier (Hehr, Bauer et al. 2007; Bauer, Winner et al. 2009), were confirmed in the

fibroblast lines from SPG11 patients (Figure 7C).

Figure 8: Characteristics of generated iPSCs

(A) SPG11 and CTRL-iPSCs express the endogenous pluripotency markers Nanog and Tra-1-60. Representative images from CTRL-12 and SPG11-12. Scale bar = 100 µm. (B) SPG11 and CTRL-iPSCs, but not fibroblasts, express Oct4 and Nanog transcripts, as shown by RT-PCR analysis. (C) Undirected differentiation of iPSCs. Representative images for SMA (mesoderm), GATA4 (endoderm), and Nestin/Sox2 (ectoderm). Nuclei were visualized with DAPI. Scale bar = 50 µm. (D) All iPSC lines maintained a stable karyotype as shown by G-banding. Representative image for SPG11-12. (Mishra et al., under revision)

The fibroblast lines from each control and SPG11 patients were expanded for two to three

passages in-vitro in FBS containing medium to have sufficient amount of proliferating cells

before initiating the reprogramming paradigm. To generate induced pluripotent stem cells

(iPSCs), fibroblast lines were transfected with retroviral reprogramming vectors encoding Oct-4,

Sox2, Klf4 and c-Myc, as previously described (Takahashi, Tanabe et al. 2007; Havlicek, Kohl

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et al. 2014; Perez-Branguli, Mishra et al. 2014). After 2-3 weeks, round, compact iPSC colonies

emerged from the pool of fibroblasts and were morphologically distinguishable from the feeder

cells by the presence of tight boundary of cells with a very high nuclear to cytoplasmic ratio.

These round colonies were manually picked under a stereomicroscope and clonally expanded to

produce the respective iPSC lines. We generated two iPSC lines, from each SPG11 patient and

CTRL (Table 4). To assess the pluripotency of the generated clones, we analysed the expression of endogenous pluripotency markers such as Nanog and TRA-1-60 (Figure 8A). Furthermore, pluripotency was confirmed at mRNA level by reverse transcription–polymerase chain reaction

(RT–PCR) analysis for endogenous expression of Oct-4 and Nanog transcripts, which were predominantly expressed by iPSCs, but not by the respective fibroblasts (Figure 8B). One of the

major hallmarks of pluripotent cells is their ability to generate cell types of all three embryonic

germ layers. To ascertain this phenotype in our iPSC lines, we performed undirected in-vitro

differentiation assays in which the CTRL and SPG11-iPSCs were allowed to differentiate

spontaneously in the presence of FBS containing medium. Immunofluorescence stainings of the

2-weeks differentiated cells stained positive for Smooth muscle actin (SMA, mesoderm), GATA-

binding protein 4 (GATA4, endoderm), and Nestin/ Sox2 double-positive cells (ectoderm),

thereby confirming the pluripotency of the iPSC lines (Figure 8C). All iPSC lines used in this study maintained a normal karyotype as revelaed by G-banding karyotypic analysis (Figure 8D).

In addition DNA fingerprinting confirmed that the iPSCs were derived from their respective fibroblasts (University Hospital Regensburg, data not shown).

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7.1.2 Differentiation and characterisation of neuronal cultures derived from iPSCs

Neuronal cells (NPCs and neurons) were generated using the embryoid aggregate-based protocol as previously described (Havlicek, Kohl et al. 2014; Perez-Branguli, Mishra et al. 2014), and outlined in Figure 9A. We generated two NPC lines from each CTRL and SPG11 patients. The

NPCs maintained a dorsal telencephalic progenitor fate and expressed early neural precursor markers Nestin and Sox2 (Figure 19A).

Figure 9: (A) Schematic representation of the neuronal differentiation paradigm

(B) Electrophysiological analysis of the differentiated neurons. (i) Representative image of a patched neuron. (ii) Both CTRL and SPG11 differentiated neurons display voltage-gated sodium and potassium channels (voltage-clamp recordings). The outward currents are indicative of voltage-gated potassium channels and the inward currents suggest the presence of voltage-gated sodium channels. The trace evoked by a depolarizing pulse to 0 mV is highlighted in red. (iii) Representative current-clamp recording. Step-current injections (15 pA) evoked tonic firing (action potentials) in differentiated neurons. Scale bar = 50 µm. (Mishra et al., under revision)

Both CTRL and SPG11-NPCs had a very high proportion of Nestin/Sox2 double positive cells

(CTRL: 83% vs. SPG11: 81%; Fig. 20A-C). The cellular and molecular phenotypes of CTRL

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and SPG11-NPCs are discussed in detail in the later part of the thesis. To generate terminally

differentiated neurons, proliferating NPCs were plated at low density cultures (40,000 cells/cm2)

on PORN/laminin-coated plates, or glass coverslips. The cells were cultured in neural

differentiation medium for 6-weeks with a half-medium change once a week. The differentiated

neuronal cells were subsequently analysed for TuJ-1 (ß-III-Tubulin, neuronal marker) or Glial fibrillary acidic protein (GFAP, astroglial marker) expression (Figure 20D). Neurons co-stained for the cortical marker Ctip2 (COUP-TF-interacting protein-2), thereby revealing that our directed

neural differentiation paradigm generate a rather dorsal telencephalic excitatory neurons of the

human cortex, which are the main susceptible degenerating neurons in HSPs (Arlotta,

Molyneaux et al. 2005). We further examined the functionality of the 6-weeks differentiated

neurons by patch-clamp technique. Electrophysiological analysis showed that both patient and

control neurons fire regular burst of action potentials and generate sodium and potassium

currents, currents, characteristic of neurons, suggesting the presence of functional neuron-like cells (Figure 9B).

7.1.3 Spatacsin is present in human and mouse cortical projection neurons

SPG11 encodes a 2443 amino acid protein, spatacsin (Stevanin, Santorelli et al. 2007), but the

structure and biological function, besides its spatio-temporal expression profiles in human brain

is not specifically delineated. We sought out to investigate the expression of spatacsin in human

neurons derived either from human embryonic stem cells (HUES6) or from human induced

pluripotent stem cells (hereafter referred to as hPSC-dNeurons, Figure 10A). We observed

spatacsin to be expressed prominently during early neural morphogenesis stage (at day 15 of

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Figure 10: Characterization of spatacsin expression in human-derived neurons

(A) Blots of HUES6 (hPSC), HUES6-dNeurons cultured in neuronal differentiation media for 15 (hPSC-dNeurons 15D) or 40 days (hPSC-dNeurons 40D). Blots were probed with α-spatacsin. Oct4 and MAP2 were employed as stem cell and neuronal markers, respectively. β-Actin served as loading control. (B) Blots of homogenates from human astrocytes (HA-c), HUES6-dNeurons (hPSC- dNeurons) and SH-SY5Y cells were probed with α- spatacsin, α-GFAP and α-GADPH as loading control. (C) hPSC-dNeurons cultures transfected with SPG11::GFP (SPG11-GFP, green) were labeled with α-βIII-tubulin (red) and DAPI (blue). Scale bar = 20 µm. (D) hPSC-dNeurons cultures transfected with SPG11::GFP (green) were stained with α-Citp2 (red). Scale bar = 5 µm. (E) hPSC- dNeurons transfected with SPG11::GFP (SPG11- GFP; green) colabeled with antibodies against α- vGlut2 or α-calbindin (red; arrows). Scale bar = 20 µm. (Perez-Branguli, Mishra et al. 2014)

Figure 11: Spatacsin antibody specifically recognizes spatacsin full length protein

(A) IB of embryonic brain (Brain E18) and cortical neurons (C. Neurons) probed with either spatacsin antibody (α-spatacsin blot) or Ig kappa isotype control (Isotype Blot). ßIIItubulin was used as loading control. (Perez-Branguli, Mishra et al. 2014) differentiation) and also at the late maturation stage (at day 40 of neuronal differentiation).

Additionally, spatacsin expression levels were higher in hPSC-dNeuron cultures compared to human astrocytes (HA-c) and were comparable to the blots using the human neuroblastoma cell line SH-SY5Y (Figure 10B and Murmu et al, 2012). The specificity of the employed antibody

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was confirmed by the absence of a band in the isotype control (Figure 11). Moreover, GFP-

tagged-spatacsin (GFP-Spat) overexpressed in HEK293 cells was detectable with both GFP and

spatacsin antibodies (data not shown). Since mutations in SPG11 promote progressive cortical

degeneration (Hehr, Bauer et al. 2007), we further investigated the expression profile of SPG11

in Ctip2 positive cortical neurons which are predominantly expressed by cortical layer 5/6

neurons in-vivo, and include among them corticospinal motorneurons (Arlotta, Molyneaux et al.

2005), the main cell type affected in HSP. For this, we expressed GFP under the regulation of the

SPG11 promoter (SPG11::GFP) in hPSC-dNeuron cultures. Interestingly, GFP+ cells co-

expressed with neuronal markers β-III-tubulin, the cortical marker Citp2 and the projection

neuron marker vGlut2, but not with the interneuronal marker calbindin (Figure 10C–E).

7.1.4 Spatacsin is expressed in human embryonic and adult mouse neurons

We next characterized spatacsin expression and function within human pluripotent stem cell

derived forebrain neurons and mouse cortical neurons. This dual model system approach led us to investigate regulation of its expression throughout the development of the human and murine forebrain neuronal cultures. The expression of spatacsin was analysed by comparing mouse cortical neurons transfected with constructs containing a SPG11 promoter driving GFP expression (SPG11::GFP) or a CMV promoter (CMV::GFP; Figure 12A and B). We observed a preference of spatacsin for neurons, as more than 90% of SPG11::GFP positive cells colabeled for the neuronal marker MAP2 compared to only 40% MAP2+ cells of the CMV::GFP population (Figure 12B and C). A detailed phenotypic analysis of the SPG11::GFP transfected murine cultures revealed that the GFP expression was detected in vGlut2+ projection neurons and

in vGAT+ interneurons, but never overlapped with the glial marker GFAP (Figure 12D). Using a

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quantitative approach, we detected higher expression levels of spatacsin in projection neurons

(134.8 ± 6 fluorescent units per cell) compared to interneurons (79 ± 4.4 fluorescent units per cell).

Figure 12: SPG11 is expressed in murine cortical neurons

(A) Scheme illustrating mouse cortical cultures transfected with either CMV::GFP or SPG11::GFP vectors. Cultures transfected with CMV::GFP (CMV-GFP) showed GFP expression in all types of cells, whereas after transfection with SPG11::GFP (SPG11-GFP), the GFP signal was almost exclusively detected in neurons. (B) Mixed mouse cortical cultures were transfected with either CMV::GFP (CMV-GFP) or SPG11::GFP (SPG11-GFP) vectors. Neurons were labeled with α-MAP2. Yellow arrows indicated GFP+ neurons whereas cyan arrowheads GFP+ non- neuronal cells; scale bar = 50 µm. (C) Graph representing the percentage of different types of cells expressing GFP in mixed cultures transfected with CMV::GFP (CMV-GFP) and SPG11::GFP (SPG11-GFP) respectively. Note that more than 97 % of the SPG11::GFP transfected cells are neurons. (D) Mouse cortical cultures were transfected with the SPG11::GFP (SPG11-GFP) vector and probed with α-vGlut2, α-vGAT and α-GFAP to label projecting neurons, interneurons, and astrocytes. Cortical neurons, but not astrocytes were able to express GFP (arrows). Scale bar = 50 µm. (Perez-Branguli, Mishra et al. 2014)

7.1.5 Spatacsin is expressed throughout mouse brain development

We then scrutinized the spatial expression of spatacsin during mouse brain development and in adulthood. For this, we performed western blot analysis on mouse brain tissues at the embryonic age (E18) which showed a single band at the expected size of 268 KDa in all CNS regions analysed (Figure 13A). This stage is highly important for cortical neurogenesis and migration of

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Figure 13: Spatial characterization of spatacsin expression in mouse brain

(A) Blots of different mouse brain regions showing expression of spatacsin at embryonic (E18) and adult ages (P150). GADPH was used as loading control. Cortex (Ctx), cerebellum (CB), hippocampus (HC), thalamus (TH), spinal cord (SC), and SH-SY5Y cells were examined. (B) Panoramic overview of the cortical column probed with α-spatacsin, α-NeuN as neuronal marker and α-GFAP as glial marker. Spatacsin+ cells were also posive for the neuronal marker NeuN (arrowheads). Cortical layers (from I to VI) and white matter (WM) were indicated accordingly. Scale bar = 100 µm. (C) Detailed micrograph of the cortical layer V confirmed that NeuN+ neurons (red) express spatacsin (green) at low (arrowheads), and at high levels (arrows and inset). Scale bar = 20 µm. (D) Micrographs revealed that NeuN+ (red and arrowheads) but no GFAP+ cells (magenta and arrows) express spatacsin (insets). Scale bar = 20 µm. (Perez- Branguli, Mishra et al. 2014)

newly generated neurons to their precise laminar fate leading to the proper development of cortical layers. Noteworthy, a transient up-regulation of spatacsin signal during postnatal development (P10) was noticed in all the brain regions (data not shown). In adult CNS tissue

(P150) spatacsin expression was higher in the cortex, hippocampus and cerebellum than in the thalamus and the spinal cord (Figure 13A). We next performed immunohistochemistry (IHF) on the cortex of the adult mouse employing the spatacsin antibody. Spatacsin was expressed at low

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level in NeuN+ neurons from cortical layers II to VI (Figure 13B-C); and at high level in few

neurons which were found in cortical layers III and V (Figure 13B-C). Although spatacsin was

strongly expressed in majority of the neurons, we could not detect any GFAP+ cells showing

spatacsin expression in mouse brains (Figure 13D). Furthermore, these data are in accordance

with findings previously reported by Murmu and collaborators (Murmu, Martin et al. 2011).

7.1.6 Spatacsin is ubiquitously distributed in axons and dendrites in cortical neurons

Next, we used our hPSC-dNeuronal cultures and mice cortical cultures to examine the specific

subcellular localisation of spatacsin within neurons. We observed a high expression of spatacsin

in axonal (TAU) and dendritic (MAP2) processes, suggesting that spatacsin re-arranged together

with the cytoskeleton (Figure 14A). We then quantified the spatacsin expression level in the

axonal and dendritic processes of differentiated neurons and found no significant difference in

their localisation pattern, suggesting it is ubiquitously expressed in neurons (Figure 14B).

Interestingly, spatacsin spots overlapped with phalloidin (F-Actin) even in the most distal parts of branches, growth cone filopodias and spines (Figure 14C). Consistent with the findings in

murine cortical neurons, hPSC-dNeuron cultures also showed a cytosolic punctuated pattern of

spatacsin expression, thereby suggesting that spatacsin could be associated with the synaptic

vesicles in the neuronal compartments (Figure 14D).

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Figure 14: Spatacsin was present in the most distal tips of neurites of human pluripotent stem cell-derived neurons (hPSC-dNeuron) and mouse cortical neurons

(A) Mouse cortical neurons showed spatacsin expression (gray) together with the axonal marker α-TAU (green) and the dendritic marker α-MAP2 (red). Scale bar = 20 µm. (B) Graph for spatacsin expression as arbitrary fluorescent units (AFU) in axons (TAU+ neurites) and dendrites (MAP2+ neurites) of mouse cortical neurons; data represented as mean ± SD (P > 0.05); n ≥ 50 neurons per experimental condition were evaluated. (C) Spatacsin was observed in filopodia and membrane protusions (arrows) of growth cones of mouse cortical neurons. Scale bar = 5 µm. (D)

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Immunofluorescence analysis of hPSC-dNeuron cultures showed α-spatacsin (gray) overlapped with α-MAP2 (red) and α-TAU (green) markers. Scale bar = 50 µm. (Perez-Branguli, Mishra et al. 2014)

7.1.7 Spatacsin is localised in neurites, growth-cones and synapses of differentiated neurons

The punctuated pattern of spatacsin localization in the neuronal compartment suggested its

interaction with the synaptic components of the neuronal machinery. Interestingly, spatacsin

from hPSC-dNeurons and mouse cortical neuron cultures partially overlapped with presynaptic

marker (VAMP2) and postsynaptic marker (PSD95) markers (Figure 15A and B respectively),

suggesting a potential role of spatacsin in synaptic modulation.

To further corroborate the potential role of spatacsin in synaptic remodeling, development and

subcellular localization within synaptic boutons of neuritic processes, we employed a

biochemical approach to study the synaptic compositions in the adult mouse brain. For this we

isolated synaptosome fractions from mouse forebrains, which resemble the morphological

features and most of the chemical properties of the original neuronal terminals. The synaptosome

preparations (SS) were further fractionated into synaptosomal plasmatic membrane (PM),

cytosolic (Cyt) and SV fractions. In unison with our immunostaining results, spatacsin was

predominantly detected in the Cyt fraction (Figure 15C). Interestingly, the distribution of

spatacsin in the different fractions was very similar to the ones obtained for the cytoskeletal

markers TAU, βIIItubulin, and βactin (Figure 15C). We further scrutinized whole synaptosome

preparations using confocal microscopy and observed that the spatacsin signal overlapped with

the vesicle marker VAMP2 and the presynaptic marker SNAP25 (Figure 15D), indicting distinct

roles of spatacsin within synapses.

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Figure 15: Characterization of spatacsin expression in synapses

(A and B) Spatacsin (blue) partially overlapped (indicated in white dotted lines) with the presynaptic marker VAMP2 (green) and the postsynaptic marker PSD95 (red) in HUES6-dNeurons (A) and in mouse cortical cultures (B). Scale bars = 1 µm. (C) Blots of samples from mouse synaptosome (SS) and the following synaptosomal factions: synaptosomal plasmatic membrane (PM), synaptosomal cytosol (Cyt) and synaptic vesicles (SV). Blots were probed with α-spatacsin, the postsynaptic markers α-PSD95 and α-MARCKS, the presynaptic markers α- SNAP25 and α-syntaxin1, the vesicle markers α-vGlut2, α-synaptophysin, and α-VAMP2; and the cytoskeleton markers α-MAP2, α-TAU, α-β-actin and a-βIII-tubulin. Spatacsin was predominantly in the Cyt fraction. (D) Purified mouse synaptosomes probed with α-spatacsin (green) together with either α-VAMP2 or α-SNAP25 (red). Scale bars = 40 and 1 µm in overviews and insets, respectively. (Perez-Branguli, Mishra et al. 2014)

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7.2 Modeling neurodevelopmental phenotypes of SPG11 using iPSC derived NPCs

SPG11 patients are characterized by additional presence of a thin corpus callosum (TCC),

cortical atrophy and cognitive impairment, with disease onset in the second decade of life

(Winner, Uyanik et al. 2004; Stevanin, Azzedine et al. 2008; Orlacchio, Babalini et al. 2010).

SPG11, unlike other HSPs, has also recently been grouped into the broad category of

developmental disorders representing agenesis (hypoplasia) of the corpus callosum (Paul, Brown

et al. 2007). This clinical phenotype points to an early developmental aberration caused by

SPG11 mutations. Besides, our expression analysis results for SPG11 showed spatacsin to be

spatio-temporally expressed all throughout the embryonic brain development and different stages

of cortical neuronal differentiation and maturation. We therefore hypothesized an early defect in

the development of cortical neural progenitor cells (NPCs) in SPG11. We started to investigate

this developmental phenotype by first examining the generation of cortical neural rosettes (NRs),

reminiscent of characteristic in-vivo human cortical neuroepithelium, generating diverse

populations of telencephalic progenitors.

7.2.1 Impaired generation of cortical neural rosettes in SPG11-iPSCs

We developed a human iPSC based cortical differentiation paradigm as depicted in Figure 9A.

After one week of neural induction in suspension culture, free floating EBs were plated on polyornithine (PORN)/laminin coated plates as previously described (Havlicek, Kohl et al. 2014;

Perez-Branguli, Mishra et al. 2014). Within three to four days, small elongated cells emerged in

the center of the differentiating EBs which gradually formed radially organized columnar

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Figure 16: Corticogenesis defects in SPG11-iPSCs

(A) Differentiating EBs grown in presence of neural induction medium showing generation of neural rosettes in control and SPG11-iPSC lines. Scale bar = 100 µm. (B) SPG11-iPSCs exhibit a marked reduction in the generation of neural rosettes compared to control, reflecting anomalies in corticogenesis stage in SPG11 patients. Data are represented as mean ± SEM. * p < 0.05, ** p < 0.01 by Student’s t-test (B). (Mishra et al., unpublished data)

epithelial cells called cortical neural rosettes (NRs), resembling neural tube of developing human

brain (Fig. 16 A). The NRs mostly localized in centre of the colonies, providing architectural support for the generation and proliferation of diverse cortical progenitor pool from the neuroepithelium sheet. Whereas EBs derived from controls had abundant, large sized and

densely packed generation of cortical rosettes, emerging from majority of differentiating EBs,

those derived from SPG11-iPSCs had only few well-organized rosettes and were mostly

dispersed all throughout the colonies (Figure 16 A). Furthermore, SPG11-iPSC derived NRs

(SPG11-NRs) were smaller in size and appeared sparsely packed with progenitors. By the end of

week-2 of differentiation paradigm (Figure 9A), columnar rosette cells in controls evidenced

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robust expansion and formed multiple layers of NRs forming central neuroepithelial islands, while in SPG11-iPSCs; they mostly remained flattened with occasional scattered NRs during the entire in-vitro cortical development stage (Mishra et al., unpublished data). We next counted the distinct neural tube-like structures emanating out of each differentiating EBs and found almost two fold reduced generation of SPG11-NRs (CTRL: 18.67 ± 2.45 vs. SPG11-1: 7.875 ± 0.85;

SPG11-2: 11.63 ± 1.16; SPG11-3: 10.63 ± 1.10, p ≤ 0.0 269; Figure 16B), suggesting early corticogenesis defects in SPG11-iPSCs. Furthermore, SPG11-NRs showed enhanced frequency of flat neuronal like cells emanating out from developing neuroepithelial sheets highlighting premature non-proliferative neurogenic divisions of progenitor cells. Since the generation of

SPG11-NRs was significantly reduced compared to controls (Figure 16B), we had to plate twice the number of iPSCs and EBs in SPG11 lines to get similar number of NRs required for the subsequent generation of proliferative NPC lines.

7.2.2 Transcriptome analysis of control and SPG11-NPCs

We next investigated the transcriptional program of SPG11-NPCs. For this RNA was analyzed from the control and SPG11-NPCs at day three in culture by microarray analysis using the

Affymetrix GeneChip HG_U133_plus_2 (Figure 17A-E). We restricted the analysis to those transcripts whose expression was significantly altered (fold change ≥ 1.5, p ≤ 0.05) in SPG11 compared to control. Hierarchical clustering visualized several gene clusters distinguishing

SPG11 patient groups from that of the control group (Figure 17A). Of these, a total of 1,537 transcripts were differentially regulated (ANOVA; p ≤ 0.05) in SPG11 compared to control. In particular 959 transcripts were significantly upregulated and 578 were significantly downregulated. To gain insights into the functionality of the differentially regulated transcripts, we performed a Gene Ontology term (GO-term) biological process analysis by use of the Gene

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Figure 17: Global gene expression analysis of day three neural progenitor cells (NPCs) generated from CTRL and SPG11-iPSCs

(A) Heat map showing hierarchical clustering of differentially expressed genes in SPG11-NPCs compared to CTRL-NPCs. (B) Histographs of total number of differentially expressed genes (upregulated genes in red, downregulated genes in green, ≤p 0.05). (C) Ge ne Ontology term analysis of important biological processes enriched within differentially expressed genes (p-values: Benjamini-Hochberg corrected). n = 2 samples from each individual. (Mishra et al., under revision)

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Figure 18: Transcriptional dysregulation of important neurodevelopment related pathways in SPG11-NPCs

(A) Wnt/GSK3ß pathway-related genes differentially regulated in SPG11-NPCs. (B) Cell cycle-related genes differentially regulated in SPG11-NPCs. (C) Callosal developmental related genes differentially regulated in SPG11-NPCs. (D) List of differentially regulated transcripts related to autophagy, endolysosomal and ER stress pathways. Upregulated transcripts shown in red, downregulated transcripts shown in green, ≤p 0.05, analyzed by ANOVA (A-D). (Mishra et al., under revision)

Ontology enRIchment anaLysis and visuaLizAtion tool (GOrilla) database (http://cbl-

gorilla.cs.technion.ac.il/) (Figure 17C and Table 5). Interestingly, the over-represented biological processes in SPG11 patients included mostly distinct stages of neurodevelopmental pathways including regulation of neurogenesis (p ≤ 6.07E-04, Fold Enrichment (FE): 2.5), regulation of nervous system development (p ≤ 6.15E-04, FE: 2.44), regulation of neuron differentiation

(2.01E-03, FE: 2.58), axon guidance (1.99E-03, FE: 2.64), synapse organization (4.35E-02, FE:

3.75), extracellular matrix organization (4.95E-02, FE: 2.33). Kyoto Encyclopedia of Genes and

Genomes (KEGG) pathway representation (http://www.genome.jp/kegg/pathway.html) of the

individual set of genes, outlined important components of the Wnt / GSK3ß signaling to be

dysregulated in SPG11-NPCs (Figure 19A). Surprisingly, components of Wnt / ß-Catenin

destruction complex (Axin2, APC and PKA) and negative regulator of Wnt signaling (FRZB,

SMAD4, CBP, TJP2 and Slit1) are differentially upregulated in SPG11-NPCs (Figure 18A).

Conversely, the positive modulators of Wnt / GSK3ß pathway (TCF7L2, FZD3, KIF3A, PLCD1,

JAM2, FOSL2) and components of Calcium signaling regulation (NFAT, CaN, PKC, CaMK1D)

are significantly downregulated, thereby implicating defects in the downstream cell cycle and

proliferation pathways. Indeed several positive regulators of cell cycle (CCNA1, PERP, DAB2,

JAM2, USP53, MAPKAPK3, CDC42EP3) are significantly downregulated (Figure 18B) while

negative modulators (CDH1, PPCDC, CDK6, POGZ, ATMIN, MAPK8IP1, TP53RK,

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Figure 19: KEGG pathway representation of Wnt/GSK3ß and Cell cycle related differentially regulated genes in SPG11-NPCs

(A) Wnt/GSK3ß pathway genes differentially regulated in SPG11-NPCs. (B) Cell cycle pathway related genes differentially regulated in SPG11-NPCs. (Mishra et al., under revision)

RBBP6) are strongly upregulated in SPG11-NPCs indicative of early developmental abberation

of cortical progenitors in SPG11.

The proper development of corpus callosum in humans is regulated by host of guidance cues and

receptors which when bound to their ligands, guide them along the midline to form layer specific neurons, modulating their axonal projections, neuritic branching and dendritic arborization, besides regulating the temporal and spatial orientation of the callosal fibres (Luders, Thompson et al. 2010; Paul 2011). Positive regulators of neuronal morphogenesis including the attractive guidance cues (ITSN1, CAMK1D, CDC42EP3, PIP4K2A, PRKCZ, WDR54, PRKCI and

EIF2AK4) were significantly downregulated (Figure 18C) while various repulsive guidance cues

(SEMA3A, EPHB1, ASPM, LPXN, PLXNB1, NOG, NFIA, NR2F1, NCAM1 and PIK3R1) which tend to repel the callosal fibres away from the midline during formation of commissural fibres in the brain, are upregulated in SPG11 patients. Thus the global transcriptome profiling suggested an early incongruity of the neurodevelopmental pathways and detrimental impact on the proliferation and neurogenesis in SPG11-NPCs (Mishra et al., under revision).

Components of the lysosomal biogenesis pathway such as LAMTOR3, LYST are significantly downregulated while LAPTM4B, regulating the endo-lysosomal pathway for autophagosome maturation is upregulated in patients (Figure 18D). Several members of the HOX gene families

(HOXB6, HOXB7, HOXB8, and HOXB9), which have been shown to be strong repressors of

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Table 5: Gene ontology (GO) analysis showing overrepresented pathways enriched in SPG11-NPCs

GO Term Description (Biological Processes) P-value Enrichment # (corrected) Count GO:0050767 regulation of neurogenesis 6.07E-04 2.51 38

GO:0051960 regulation of nervous system development 6.15E-04 2.44 41

GO:0045664 regulation of neuron differentiation 2.01E-03 2.58 31

GO:0050768 negative regulation of neurogenesis 2.76E-02 3.07 17

GO:0045595 regulation of cell differentiation 7.18E-04 1.85 71

GO:0007411 axon guidance 1.99E-03 2.64 30

GO:0051961 negative regulation of nervous system 1.05E-02 3.14 19 development GO:0097485 neuron projection guidance 2.24E-03 2.64 30

GO:0050808 synapse organization 4.35E-02 3.75 12

GO:0030198 extracellular matrix organization 4.95E-02 2.33 24

GO:0010975 regulation of neuron projection development 4.96E-02 2.5 21

GO:0007155 cell adhesion 1.61E-02 1.85 50

GO:0016477 cell migration 3.99E-02 1.94 38

GO:0010721 negative regulation of cell development 4.14E-02 2.74 19

GO:0001505 regulation of neurotransmitter levels 4.18E-02 4.02 11

GO:0045665 negative regulation of neuron differentiation 4.25E-02 3.32 14

GO:0050793 regulation of developmental process 3.93E-04 1.73 94

GO:2000026 regulation of multicellular organismal 4.04E-04 1.91 75 development GO:0022610 biological adhesion 1.61E-02 1.84 50

GO:0043062 extracellular structure organization 4.86E-02 2.32 24

GO:0060284 regulation of cell development 1.49E-03 2.24 43

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GO:0051239 regulation of multicellular organismal process 1.66E-03 1.61 99

GO:0022603 regulation of anatomical structure 2.71E-03 2.08 46 morphogenesis GO:0006928 movement of cell or subcellular component 5.91E-03 1.76 65

GO:0051093 negative regulation of developmental process 9.39E-03 2 44

GO Term Description (Cellular Compartment) P-value Enrichment # (corrected) Count GO:0097458 neuron part 4.78E-06 2.23 61

GO:0043005 neuron projection 1.70E-04 2.36 42

GO:0043197 synapse 1.24E-02 2.84 18

GO:0044309 dendritic spine 2.72E-02 4.04 10

GO:0005886 neuron spine 3.00E-02 3.95 10

GO:0042995 cell-cell junction 4.95E-03 2.55 25

GO:0030054 cell projection 8.21E-03 1.72 58

GO:0045202 cell junction 1.19E-02 1.73 53

GO:0044420 extracellular matrix component 1.20E-03 4.26 15

GO:0005604 basement membrane 1.50E-03 5.55 11

(p value corrected ≤ 0.05, ANOVA); Biological pathways (upper panel), Cellular Component (lower panel). (Mishra et al., under revision)

developmental autophagy (Banreti, Hudry et al. 2014; Campello and Cecconi 2014), are massively upregulated (10 to 30 folds) in SPG11, thereby suggesting a deleterious impact of defective autophagy on the developmental potential of SPG11-NPCs during cortical neurogenesis and callosal morphogenesis. Transcripts associated with ER stress and unfolded protein response (UPR) family genes that sense any abnormality in physiological metabolic activities and activates the stress response (Fernandes, Harder et al. 2013; Yoshikawa, Kamide et

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al. 2014; Sanderson, Gallaway et al. 2015), during cellular damages, such as ATF3, ATF6,

ATF7IP2 are two to five fold downregulated, whereas CREBZF, a basic region-leucine zipper

transcription factor known as a potent suppressor of cellular growth (Zhang, Thamm et al. 2015)

is significantly upregulated, suggesting a failure of the maintenance of the cellular homeostasis

in SPG11-NPCs (Figure 18D). This anomaly is highly sensitive for neurons, in which

deregulation of this degradation process can induce cell dysfunction. A member of the Rab small

GTPase protein family, ARHGAP18, important regulator of membrane trafficking and fusion

events (Stenmark 2009), showed two fold decreased expression in SPG11-NPCs. In addition,

members of the endosomal pathway including RAB23, RAB33B, RAB40B and MAP1LC3C

which play an active role in the cycling and formation of autophagosomes (Ao, Zou et al. 2014),

to engulf the damaged organelles showed more than two fold decreased expression in the

patients.

7.2.3 Reduced proliferation and neurogenesis in SPG11-NPCs

The SPG11-NPCs compared to the CTRL, grew more slowly under standard culture conditions, resulting in a 36-50% decrease in the density of Nestin/Sox2 double-positive cells in SPG11 lines (cells/mm2 CTRL: 285 ± 14 vs. SPG11: 159 ± 9, p ≤ 0.0003; Figure 20A-B). However, there was no significant difference in the proportion of the Nestin/Sox2 double-positive cells

(over DAPI) between CTRL and SPG11-NPCs (CTRL: 83% vs. SPG11: 81%; Figure 20C),

suggesting no apparent predilection in the neural induction from iPSCs. We differentiated the

SPG11- and CTRL-NPCs into functionally active forebrain glutamatergic neural cells over a

period of 6 weeks (Figure 20D-F and Figure 9B). SPG11-NPCs observed a significant reduction in total neural cell density (cells/mm2 CTRL: 82.90 ± 12.19 vs. SPG11: 29.08 ± 5.052, p=

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0.0025) (Figure 20D-E). The percentage of TuJ1-positive neurons in SPG11 patients was also significantly diminished (CTRL: 72.82 % vs. SPG11: 59.11 %, p<0.0001) suggesting an impairment of cortical neurogenesis (Figure 20F).

Figure 20: Reduced proliferation and neurogenesis in SPG11-NPCs

(A) Representative images of Nestin/Sox2 double-positive NPCs generated from CTRL and SPG11-iPSCs. Nuclei were visualized with DAPI. Scale bar = 50 µm. (B) SPG11-NPCs exhibit a decreased Nestin/Sox2 cell density compared to CTRL. (C) No difference in Nestin/Sox2 double-positive cells (% over DAPI) between CTRL and SPG11-NPC lines. (D) Differentiated neuronal cells expressing neuron-specific (Tuj1) and glia-specific (GFAP) markers. Nuclei were visualized with DAPI. Scale bar = 50 µm. (E) SPG11-NPCs exhibit a marked reduction in the neuronal cell density compared to CTRL. (F) SPG11-NPCs show reduced generation of Tuj1-positive neurons compared to CTRLs, reflecting anomalies in neurogenesis in SPG11 patients. Data are represented as mean ± SEM. ** p < 0.01, *** p < 0.001 by one way ANOVA followed by Dunnett's post hoc multiple comparison test (B, C, E, F). (Mishra et al., under revision)

7.2.4 Altered cell cycle distribution and stage-specific anomalies in SPG11- NPCs

We analyzed the rate of proliferation of NPCs between SPG11 patients and CTRLs following pulse labeling of 5-bromo-2'-deoxyuridine (BrdU) for one hour (Figure 21A). A significantly reduced number of BrdU-labeled Nestin/Sox2 double-positive cells (% CTRL: 33.86 ± 1.5 vs.

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Figure 21: SPG11-NPCs show altered cell cycle distribution and stage-specific downregulation of important cell cycle markers

(A) Representative (A) Representative images of Nestin/Sox2-positive cells co-labeled with BrdU-positive nuclei for CTRL and SPG11-NPCs. Nuclei were visualized with DAPI. Scale bar = 50 µm. (B) SPG11-derived

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Nestin/Sox2-positive cells have significantly reduced numbers of BrdU-labeled cells compared to CTRLs, highlighting a profound diminution in the NPC pool. (C) CTRL and SPG11-NPCs (Nestin/Sox2+) co-labeled with the endogenous proliferation marker PCNA. Nuclei were visualized with DAPI. Scale bar = 50 µm. (D) SPG11- derived Nestin/Sox2 double-positive NPCs have significantly reduced numbers of PCNA co-labeled cells. (E) Diminished number of Nestin/Sox2-positive NPCs co-labeled with mitotic marker phospho-Histone H3 (H3P) of SPG11-NPCs, suggesting a compromised mitosis. (F) Schematic representation of important checkpoint and senescence genes of the cell cycle. CDK= cyclin-dependent kinase, p21Cip1= cyclin-dependent kinase inhibitor 1, p27Kip1= cyclin-dependent kinase inhibitor 1B, p57Kip2= cyclin-dependent kinase inhibitor 1C, GADD45α= growth arrest and DNA damage-inducible protein alpha. (G-K) Flow cytometry-PI staining analysis shows the respective distribution of cells in G1, S and G2/M phases of the cell cycle for CTRL and SPG11-NPC lines. (I) No significant difference in the percentage of viable cells in the G1 phase of cell cycle between CTRL and SPG11- NPCs. However, SPG11-NPCs have a highly diminished percentage of viable cells in the S phase (J) and G2/M phase (K). Data represented as mean ± SEM; ** p < 0.01, *** p < 0.001 by one way ANOVA followed by Dunnett's post hoc multiple comparison test (B, D, E, H-J). (Mishra et al., under revision)

SPG11: 23.21 ± 1.1, p<0.0001; Figure 21B) was detected in SPG11-NPCs reflecting a compromised proliferation of NPCs in SPG11 patients. Subsequently, using the endogenous proliferation marker, proliferating cell nuclear antigen (PCNA; Figure 21C), we detected a ~50% reduction of the PCNA-labeled Nestin/Sox2 double-positive cells in SPG11-NPCs (% CTRL:

40.84 ± 2.1 vs. SPG11: 21.59 ± 1.0, p < 0.0001; Figure 21D). We wondered whether the stages of the cell cycle were affected, so we analyzed the mitotically active dividing cells for expression of phospho-Histone H3 (Ser10) protein (H3P). In agreement with our previous observations, we found a two-fold decrease in H3P-labeled Nestin/Sox2 double-positive cells in SPG11-derived cells (% CTRL: 10.05 ± 0.8 vs. SPG11: 4.87 ± 0.35, p<0.0001; Figure 21E).

We then analyzed the proportional distribution of cells in different stages of the cell cycle

(Figure 21F) using flow cytometry. Intercalation of propidium iodide (PI) inside the cellular

DNA of NPCs enabled the quantification of the frequency of cells in distinct phases of the cell cycle (Figure 21G-K). While the percentage of cells in the G1 phase of cell cycle was unchanged in SPG11 (Figure 21I), a significantly reduced number of cells was present in the S phase

(CTRL: 5% vs. SPG11: 2.6%; Figure 21J) and G2/M phase (CTRL: 10% vs. SPG11: 5.4%;

Figure 21K), suggesting that mutant SPG11 led to specific S and G2/M changes in the NPCs that were not present in the matching fibroblasts and iPSCs of SPG11 (data not shown).

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7.2.5 Checkpoint genes are downregulated in SPG11-NPCs

Considering that the cell cycle distribution analysis revealed highly reduced numbers of SPG11-

NPCs in S phase and G2/M phase (Figure 21F-K), we subsequently analyzed the mRNA

expression profiles of checkpoint genes regulating the progression of NPCs from G0/G1, S and

G2/M stages of the cell cycle (Figure 21F and 22). We did not find any significant difference in

the expression of G0/G1 to S phase regulating cyclin dependent kinase genes (CDK4 and CDK6,

Figure 22A-B). Thereafter, we checked for the genes regulating the progression of the S phase of

the cell cycle and detected a two-fold downregulation of the CDK2 expression in SPG11-NPCs

(% of CTRL: SPG11: 0.51 ± 0.09%, p = 0.0172; Figure 22C).

In addition, a more than two-fold decrease in the expression of growth arrest and DNA damage- inducible protein alpha (GADD45α), a marker of DNA repair enzymes important for replication of the DNA strand, was present in SPG11-NPCs (% of CTRL: SPG11: 0.24 ± 0.04%, p =

0.0358; Figure 22D). We further analyzed the expression of the CDK1 gene regulating the transition to the G2/M phase and found an almost two-fold downregulation of CDK1 compared to CTRL (% of CTRL: SPG11: 0.65 ± 0.05%, p = 0.0013; Figure 22E), suggesting that altered expression of cell cycle genes might modulate the impaired proliferation of SPG11-NPCs

(Mishra et al., under revision).

7.2.6 SPG11-NPCs are less prevalent in cytokinesis and undergo cell death

To test whether the impaired proliferation of the SPG11-NPCs was associated with a defect in

cytokinesis, we examined the NPCs using the cytokinesis marker, Aurora B (Figure 23A).

Interestingly, in SPG11-NPCs, we found a more than two-fold decrease in the number of Aurora

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Figure 22: qPCR analysis of checkpoint markers in CTRL and SPG11-NPCs

(A) mRNA analysis revealed no significant difference for G1 phase cell cycle checkpoint markers CDK4 (A) and CDK6 (B). ns = not significant. (C-D) Expression of cell cycle genes at the S phase CDK2 (C) and DNA repair enzyme gene GADD45α (D) is significantly downregulated in SPG11-NPCs. (E) A significant downregulation of G2/M phase cell cycle gene CDK1 highlighted a perturbed cell cycle activity in SPG11-NPCs. mRNA levels were normalized against GAPDH. Data represented as mean ± SEM. * p < 0.05, ** p < 0.01 by two-tailed Student’s t-test (A-D). (Mishra et al., under revision)

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B-labeled Nestin/Sox2-positive cells (CTRL: 3.0 % vs. SPG11: 1.2%) (Figure 23B), highlighting significantly reduced numbers of newly formed daughter cells due to the slow proliferation of

SPG11-NPCs.

Figure 23: Decreased number of NPCs at the abscission stage of the cytokinesis and increased cell death in SPG11-NPCs

(A) Representative images of NPCs co-labeled with cytokinesis marker Aurora B. Nuclei were visualized with DAPI. Scale bar = 20 µm. (B) Decreased numbers of Nestin/Sox2-positive cells co-labeled with Aurora B of SPG11 NPCs compared to CTRL, reflecting a diminished number of newly formed daughter cells due to slow proliferation of SPG11-NPCs. (C) Immunofluorescent images of CTRL and SPG11-NPCs stained with cell viability marker Image-iT® DEAD™. Nuclei were visualized with DAPI. Scale bar = 50 µm. (D) SPG11-NPCs have two- to three- fold increase in number of dead cells compared to CTRL. Data represented as mean ± SEM. ** p < 0.01, *** p < 0.001 by one way ANOVA followed by Dunnett's post hoc multiple comparison test (B and D). (Mishra et al., under revision)

Thereupon, using the Image-iT® DEAD™ viability stain, we asked if the reduced proliferation of SPG11-NPCs was accompanied by reduced viability of the precursor cells (Figure 23C).

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Compared to the CTRLs, a two- to three-fold increase in the number of Image-iT DEAD cells

was present in SPG11-NPCs (% CTRL: 5.2 ± 0.37 vs. SPG11: 13.37 ± 0.60, p<0.0001) (Figure

23D), suggesting that, in addition to the proliferation defect, the mutations in SPG11 led to a

reduced viability of neural cells (Mishra et al., under revision).

7.2.7 Impaired GSK3ß/ß-Catenin signaling in SPG11-NPCs

The GSK3ß/ ß-Catenin signaling pathway is a well-known regulator of progenitor proliferation

and differentiation during brain development (Kim, Wang et al. 2009; Mao, Ge et al. 2009). We investigated the protein expression of GSK3ß and ß-Catenin in CTRL- and SPG11-NPCs and detected a two-fold decrease in the expression of the Ser9-phosphorylated form of GSK3ß (p-

GSK3ß) in SPG11-NPCs (% of CTRL: SPG11: 0.54 ± 0.03%, p<0.0001; Figure 24A-B). Since phosphorylation at Ser9 residue reduces the regulatory activity of GSK3ß (Cohen and Frame

2001; Engelman, Luo et al. 2006), a significant decrease in the expression of inactivated p-

GSK3ß suggested an increased GSK3ß activity in SPG11-NPCs. We next assessed the expression of ß-Catenin, the major downstream target of GSK3 signaling. Interestingly, ß-

Catenin exhibited a two-fold decrease in expression in SPG11-NPCs compared to the CTRL (% of CTRL: SPG11: 0.56 ± 0.03%, p<0.0001; Figure 24C-D). To confirm the role of loss of spatacsin in regulating the decreased ß-Catenin levels in SPG11 patients, we made use of ß-

Catenin reporter construct using TOP/FOP flash luciferase activity in HEK293T cells (Veeman,

Slusarski et al. 2003). Knockdown of SPG11 using siRNA, reduced the TOP flash activity, while overexpression of SPG11 rescued the TOP flash activity (Figure 24E-F), thereby providing additional confirmation that loss of spatacsin in SPG11-NPCs modulates the ß-Catenin levels and hence the proliferation of these progenitor cells.

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Figure 24: Increased GSK3ß activity leads to reduced ß-Catenin levels in SPG11-NPCs

(A) Comparison of the p-GSK3ß (Ser9) protein expression in CTRL and SPG11-NPC lines. (B) Protein expression of p-GSK3ß (Ser9) using Western blot was significantly reduced in SPG11-NPCs compared to CTRL. p-GSK3ß expression was normalized against total-GSK3ß. (C) ß-Catenin protein levels in CTRL and SPG11-NPC lines. (D) Quantification of signals revealed a significant decrease in the ß-Catenin protein levels in SPG11-NPCs compared to

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the CTRL. ß-Catenin expression was normalized against GAPDH. (E) Schematic representation of ß-Catenin (TCF/LEF) reporter activity assayed using TOP/FOP flash luciferase assay. (F) TOP flash activity is reduced in siRNA-mediated SPG11 knocked down HEK293T cells. TOP flash activity is increased by overexpression of SPG11 in siRNA-transfected HEK293T cells, (n = 3). Data represented as mean ± SEM. * p < 0.05, by two-tailed Student’s t-test. (G) Representative western blot for expression of senescence marker p27Kip1 in CTRL and SPG11- NPC lines. (H) Quantification of signals revealed a two- to three-fold increase in the p27 Kip1protein levels in SPG11-NPCs compared to CTRLs. Data represented as mean ± SEM. n = 3, * p < 0.05, ** p < 0.01 by one way ANOVA followed by Dunnett's post hoc multiple comparison test (B, D, H). (Mishra et al., under revision)

We next asked if the impaired GSK3 signaling led to a subsequent increase in the senescence

activity of the precursor cells and analyzed the senescence marker p27Kip1 (Figure 24G).

Surprisingly, we found a two- to three-fold increase in expression of p27Kip1 protein levels in

SPG11-NPCs compared to CTRL (% CTRL: 0.87 ± 0.22 vs. SPG11: 2.5 ± 0.2, p= 0.0007;

Figure 24H), thereby highlighting a pronounced senescence activity of SPG11-NPCs. Taken

together, these findings suggested a partial dysregulation of GSK3ß/ß-Catenin signaling

modulating the proliferation of SPG11-NPCs (Mishra et al., under revision).

7.2.8 Loss of spatacsin compromises proliferation of neuronal cell line

To assess the impact of the loss of function of spatacsin on the proliferation of actively dividing

neural cells, we utilized undifferentiated SH-SY5Y cells, a human neuroblastoma cell line capable of differentiating from a neural precursor stage into neuron-like cells (Lopes, Schroder et

al. 2010; Yan, Zhao et al. 2014). We knocked down spatacsin in SH-SY5Y cells using siRNA for

SPG11 (siSPG11), previously described in a loss of function study (Perez-Branguli, Mishra et al.

2014), along with a pAc-mGFP construct to visualize the transfected cells, and analyzed the

proliferation based on PCNA expression (Figure 25A). As expected, we found a significant

reduction in the number of GFP-positive cells co-labeled with PCNA in siSPG11-transfected

SH-SY5Y cells compared to control siRNA (% CTRL: 37.43 ± 1.6 vs. siSPG11: 28.30 ± 2.4, p =

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0.0055), thereby confirming that loss of function of spatacsin is able to compromise proliferation of neural cells (Figure 25B).

Figure 25: Knockdown of spatacsin impairs proliferation of SH-SY5Y cells

(A) Representative images of CTRL and siSPG11-transfected SY5Y cells. Nuclei were visualized with DAPI. Scale bar = 20 µm. (B) Reduced number of GFP-positive cells co-labeled with proliferation marker PCNA in siSPG11- transfected SY5Y cells, reflecting diminution in the proliferation of cells upon loss of spatacsin. Data represented as mean ± SEM. ** p < 0.01 by two-tailed Student’s t-test between CTRL and siSPG11-transfected SY5Y cells. Arrowheads in (A) show GFP-positive cells co-labeled with proliferation marker PCNA. (Mishra et al., under revision)

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7.2.9 GSK3 inhibitors (CHIR99021 and Tideglusib) rescue proliferation and neurogenesis defects of SPG11-NPCs

The global transcriptome analysis outlined several important transcripts of the canonical Wnt /

GSK3 signaling (Axin2, APC, FRZB, FRZD3, TCF7L2) to be differentially regulated in the modulation of the neurodevelopmental phenotypes in SPG11 (Figure 18A and 19A). Inhibition of the GSK3 pathway was recently shown to enhance the proliferation of NPCs in rodent brain

(Nedachi, Kawai et al. 2011). Encouraged by these observations we hypothesized that the inhibition of the central Wnt modulator, GSK3ß, should activate the downstream signaling cascade mediating the proliferation and neurogenesis in SPG11-NPCs. We therefore treated

SPG11-NPCs with 3 uM of the GSK3 inhibitor CHIR99021 and with 3 uM of clinical GSK3 blocker (Tideglusib) and analyzed the proliferation using PCNA antibody (Figure 26A). The

GSK3 inhibitor significantly increased the number of PCNA-labeled cells in the treated group

(SPG11-CHIR) compared to the untreated group (% SPG11-NT: 22.55 ± 1.6 versus % SPG11-

CHIR: 31.51 ± 1.1, p = 0.0003; Figure 26B). While lower doses (1 µM and 2 µM) did not significantly restore the proliferation of SPG11-NPCs, higher concentrations (5 µM and 10 µM) of CHIR99021, in addition to increasing the proliferation of NPCs, led to excessive cell death in a dose-dependent manner (data not shown). Consistent with restored proliferation in SPG11-1-

NPCs after GSK3 inhibitor treatment, we noted a similar restoration of proliferation in

Tideglusib treated SPG11-NPCs (Figure 26C) compared to untreated group (% SPG11-NT:

25.56 ± 1.382 versus SPG11-Tide: 35.06 ± 0.9807, p < 0.0001). More importantly, treatment of

Tideglusib during neuronal differentiation stage of the progenitors substantially increased the number of Tuj1 and GFAP positive neuronal cells in SPG11 patients (% SPG11-NT: 30.33 ±

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Figure 26: GSK3 antagonists (CHIR99021 and Tideglusib) rescue proliferation defects of SPG11-NPCs

(A) NPCs were treated with 3µM of the GSK3 inhibitor (Tideglusib) for 24 hours. Representative images of untreated SPG11-NPCs (SPG11-NT) and Tideglusib-treated SPG11-NPCs (SPG11-Tide) on Day 3. Cell

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proliferation was analyzed using co-labeling of PCNA in Nestin/Sox2-positive NPCs. Nuclei were visualized with DAPI. Scale bar = 50 µm. (B) Increased numbers of Nestin/Sox2-positive cells co-labeled with PCNA in CHIR99021-treated SPG11-NPCs. (C) Tideglusib-treated SPG11-NPCs, compared to untreated NPCs, revealed restoration of cell proliferation similar to the CTRL-NPCs. (D) Neural differentiation in presence of 3µM of the GSK3 inhibitor (Tideglusib) increased the numbers of Tuj1-GFAP-positive cells in Tideglusib-treated SPG11- NPCs, compared to untreated NPCs, thereby rescuing the neurogenesis defect of SPG11. Data represented as mean ± SEM. * p ≤ 0.05, ** p ≤ 0.01, *** p < 0.001 by Student’s t-test (B-D). (Mishra et al., under revision)

2.219 versus SPG11-Tide: 61.71 ± 5.854, p < 0.0001; Figure 26D), thereby rescuing the

neurogenesis defects in the SPG11 patients (Mishra et al., under revision).

7.2.10 Premature differentiation of SPG11-NPCs is ameliorated by GSK3 inhibitor

Anomalies in cell cycle progression have been shown to influence the fate and generation of

neuronal subtypes in the developing nervous sytem (Sherr and Roberts 1999; Georgopoulou,

Hurel et al. 2006). In addition, increased expression of senescence markers alters the intricate

balance of cell cycle inducing factors towards premature exit, implicating an impaired generation

of neural cells during the crucial stage of cortical development (Zindy, Cunningham et al. 1999;

Goto, Mitsuhashi et al. 2004; Nguyen, Besson et al. 2006). Cell cycle expression analysis,

revealed enhanced expression of p27Kip1 senenscence marker in SPG11-NPCs (Mishra et al., under revision). So we hypothesized an early cell cycle exit, resulting in premature differentiation of SPG11-NPCs and therefore stained the progenitors with Doublecortin (DCX), a marker for newly generated neurons. DCX positive neuronal cells, with axon-like processes were

around two to three fold increased in SPG11-NPCs compared to CTRLs (% CTRL: 11.40 ± 1.3

versus % SPG11-1: 21.32 ± 1.6; SPG11-2: 22.82 ± 2.47; SPG11-3: 31.32 ± 3.01; p < 0.05;

Figure 27A-B), suggesting an enhanced pre-mature differentiation of cortical progenitors

(Mishra et al., unpublished data). We then treated our NPCs with 3 µM of clinical GSK3

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Figure 27: Premature differentiation of SPG11-NPCs is ameliorated by GSK3 inhibitor, Tideglusib

(A-B) SPG11-NPCs show increased rate of premature differentiation compared to CTRL-NPCs. (A) Representative images of progenitor cells from CTRL and SPG11-NPCs co-labelled with DCX marker. Nuclei were visualized with DAPI. Scale bar = 50 µm. (B) SPG11-NPCs have significantly increased numbers of DCX positive neuronal cells compared to CTRLs, highlighting a profound diminution in the NPC pool. (C-D) GSK3 inhibitor, Tideglusib ameliorates premature differentiation of SPG11-NPCs. (C) NPCs were treated with 3µM of the Tideglusib for 24 hours. Representative images of untreated SPG11-NPCs (SPG11-NT) and Tideglusib-treated SPG11-NPCs (SPG11- Tide) on Day 3, co-labelled with DCX positive neuronal cells. Nuclei were visualized with DAPI. Scale bar = 50 µm. (D) Tideglusib-treated SPG11-NPCs, compared to untreated NPCs, revealed decreased numbers of DCX- positive cells, restoring cell proliferation similar to the CTRL-NPCs. Data represented as mean ± SEM. * p≤ 0.05, ** p ≤ 0.01, *** p < 0.001 by one way ANOVA followed by Dunnett's post hoc multiple comparison test (C) and Student’s t-test (D) respectively. (Mishra et al., unpublished data)

blocker, Tideglusib and analysed the progenitor cells. Interestingly, consistent with our previous results, Tideglusib treatment significantly ameliorated the spontaneous differentiation of SPG11-

NPCs, in the treated group (SPG11-1-Tide) compared to the untreated group (% SPG11-1-NT:

20.82 ± 1.9 versus % SPG11-1-Tide: 11.84 ± 1.4, p = 0.5018; Figure 27C). Similar decrement were found in SPG11-2-Tide and SPG11-3-Tide treated groups (% SPG11-2-NT: 22.49 ± 2.4

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versus % SPG11-2-Tide: 13.84 ± 1.9, p = 0.5844) and (% SPG11-3-NT: 31.32 ± 3.01 versus %

SPG11-3-Tide: 19.17 ± 2.11, p = 0.4547; Figure 27C).

7.2.11 Impairment of autophagy related pathways in SPG11-NPCs

Cortical progenitors undergoing proliferation and differentiation in developing brain generate

vast diversity of neuronal cells, undergo synaptogenesis and migration, all of which require

constant supply of cellular components, guidance cues, neurotrophins, and transcription factors

in a highly dynamic energy driven process (Cecconi, Di Bartolomeo et al. 2007; Moreau, Luo et

al. 2010; Vazquez, Arroba et al. 2012; Lee, Hwang et al. 2013). Autophagy in association with

their endolysosomal components have recently been shown to play an important role in different

stages of neural development (Hara, Nakamura et al. 2006; Komatsu, Waguri et al. 2006; Zhao,

Huang et al. 2009; Mizushima and Levine 2010; Lee, Hwang et al. 2013). We therefore

hypothesized that the neurodevelopmental defects of diminished proliferation and compromised

neurogenesis could be due to the failure of the cellular homeostasis (Cecconi, Di Bartolomeo et

al. 2007; Lv, Jiang et al. 2014), modulated by the defects in the autophagy-endolysosomal

pathway in SPG11-NPCs. Microarray analysis of the SPG11-NPCs revealed several transcripts

associated with autophagy, ER stress and the endo-lysosomal pathway to be differentially regulated in SPG11-NPCs (Mishra et al., under revision). Consistent with a defect in autophagy- lysosomal regulation, revealed by the global transcriptome profiling, we found an enhanced accumulation of the lipidated form of autophagosome, LC3-II protein levels, by western blot analysis in SPG11- NPCs compared to the control (Figure 28A-B).

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Figure 28: Neurodevelopment defect is associated with the impaired autophagy related pathways in SPG11- NPCs (A) Western blot analysis of autophagosome marker LC3-I and II in control and SPG11-NPCs. (B) SPG11-NPCs show increased accumulation of LC3-II, lipidated form of autophagy marker, under basal condition. (C) Western blot analysis of p62, a marker for cargo destined to be degraded by autophagy in control and SPG11-NPCs. (D) p62 protein levels are significantly increased in SPG11-NPCs, under basal condition. Data represented as mean ± SEM. * p ≤ 0.05, by Student’s t-test (B, D). (Mishra et al, unpublished data)

We then examined the level of p62 proten, the main cargo protein for LC3-II. Surprisingly, western blot analysis revealed a sharp increase in the p62 levels in SPG11-NPCs compared to control (Figure 28C-D), thereby suggesting a defect in its turnover due to a failure of the clearance process.

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7.3 Modeling neurodegenerative phenotypes of SPG11 using iPSC derived neurons

Clinically, HSPs are characterized by the degeneration of axonal projections of corticospinal

tracts and dorsal columns, leading to the spasticity, muscular weakness and paraparesis of limbs

(Harding 1983; McDermott and Shaw 2002). SPG11, being a complicated form of HSP, is

plagued further by the presence of additional neurological symptoms, including sensorimotor

peripheral neuropathy, cerebellar atrophy, epilepsy, extrapyramidal retinal signs (Winner,

Uyanik et al. 2004; Abdel Aleem, Abu-Shahba et al. 2011; Rajakulendran, Paisan-Ruiz et al.

2011). A long-term follow-up of SPG11 patients revealed the presence of membranous bodies in the sural nerves suggesting a progressive disbalance of cargo-transport along the microtubular

network of axonal projections (Hehr, Bauer et al. 2007). We examined the progressive degenerative changes in SPG11-iPSC derived neurons (SPG11-dNeurons), by first analyzing the gene expression paradigm in the 6-weeks differentiated neuronal cultures of control and SPG11 patients.

7.3.1 Dysfunction of spatacsin in SPG11-dNeurons leads to aberrant gene expression

We investigated the transcriptional signature of the genes enc oding important proteins involved

in the transport machinery using real-time PCR (RT-PCR) assays. In SPG11-dNeurons, we were

able to detect a significant reduction of the expression of kinesin motor proteins, including

KIF3A, KIF5A and kinesin light chain 1 (KLC1) which are involved in anterograde transport

processes (Figure 29A–C).

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Figure 29: Expression analysis of transport-related genes in iPSCs-derived neurons

RT-PCR analysis of genes in SPG11 patient-derived neurons revealed a significant reduction in the mRNA expression of kinesin-related genes, KIF3A (A), KIF5A (B) and KLC1 (C), but no difference in the expression level of DYNC1LI2 (D) compared with control. Similarly, expression of synaptic genes, VAMP2 (E), SYN1 (F), but not postsynaptic density protein 95 (PSD95) (G) and synaptotagmin 12 (SYT12) (H) are strongly reduced in patient neurons. A strong downregulation of the cytoskeletal tubulin-associated genes, MAPTAU (I) and TAU tubulin kinase 1 (TTBK1) (J) further suggested dysregulation of transport activity in the patient neurons. Plotted are means of each line performed in triplicates, from two independent experiments. Data shown as mean ± SD. mRNA levels were normalized against two housekeeping genes (HKGs = GAPDH and β2M). (Perez-Branguli, Mishra et al. 2014)

In contrast, we did not observe significant expression differences in the retrograde motor protein, dynein cytoplasmic1 light intermediate chain 2 (DYNC1LI2). Next, we checked for presynaptic

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markers including vesicle-associated membrane protein 2 (VAMP2) and synapsin 1 (SYN1)

(Figure 29E–F) and cytoskeleton-associated genes including, microtubule associated protein tau

(MAPTAU) and TAU tubulin kinase 1 (TTBK1) (Figure 29I-J). Indeed, we could detect a

significant downregulation of these associated genes. More striking was the fact that neuronal

markers like postsynaptic density protein 95 (PSD95) and synaptotagmin 12 (SYT12) (Figure

29G-H) were not altered thereby implying that the dysfunction of SPG11 only disrupts the expression of specific subsets of neuron-related genes. These results implicated that loss of spatacsin might have a detrimental effect on the neuronal morphology and transport activity of human neurons (Perez-Branguli, Mishra et al. 2014).

7.3.2 Neurite outgrowth and complexity are compromised in SPG11-dNeurons

Our SPG11 expression analysis results showed spatacsin to be preferentially localized in neurons

and distributed ubiquitously in cytosol, axonal as well as dendritic compartments (Perez-

Branguli, Mishra et al. 2014). The presence of spatacsin in temporal stages of neuronal differentiation revealed that SPG11 orchestrates neuronal development from early stages and also in maintaining neuronal architecture. We therefore asked if spatacsin regulates the

complexity and dendritic arborization of the differentiated neurons. For this we transfected our 6-

weeks old dense neuronal cultures with the pEF-1-dTomato construct as previously reported

(Janssen, Robinson et al. 2010; Havlicek, Kohl et al. 2014). This allowed us to visualize in depth,

the morphology of very few neurons in dense neuronal cultures, and precisely outline the

neuronal contours and all the neuritic processes emanating out from individual neurons. SPG11-

dNeurons revealed extensive reductions in the neuritic arborizations and complexity compared

the controls (Figure 30A-B). However multidirectional growth of neurites (Figure 30B),

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Figure 30: Significant reduction in axonal complexity of iPSC-dNeurons from SPG11 patients (A) Representative figure of hiPSC-dNeuron cultures transfected with pEF1-dTomato. The cells analyzed were coexpressing dTomato and the neuronal marker βIII-tubulin. Scale bar = 20 µm. (B) Neuronal cells from control and SPG11 patient, transfected with pEF1-dTomato, showing a marked decrease in neurite complexity. Scale bars = 50 µm. (C) Schematic representation of the microfluidic chamber showing the cell soma side and axonal side; inset shows the grooves in the chamber along which axons unidirectionally pass through. (D) Tracing of control and

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SPG11 neurites reaching the axonal side. Neurites in SPG11-dNeurons are shorter and have less branches compared with controls. (E–G) Graphs representing (E) the reduced number of neurites crossing the grooves, (F) the reduced total neurite length (µm) and (G) the reduced number of branching points in SPG11 compared with controls. Data are represented as mean ± SD (*P < 0.05 and **P ≤ 0.005); n ≥ 20 axonal processes per each cell line. (Perez- Branguli, Mishra et al. 2014)

hampered the identification and precise quantification of the axonal branches, from the

neighbouring cells. To overcome this, we plated the neuronal cultures in microfluidics chambers, which enabled neuronal processes on the cell soma side, to pass through the 450 µm long

microgrooves, to the axonal chamber on the opposite side (Taylor, Rhee et al. 2006). This not only directed neuronal processes to grow parallel and unidirectional along the narrow grooves

(Figure 30C) but also prevented the glial cells to pass through the grooves, enabling us to preferentially evaluate the morphology of neurons. The microfluidics system thus, allowed us to measure subtle differences in neuronal architecture between control and SPG11-dNeurons

(Figure 30D). We next used this system to visualize and compare the temporal extension and guidance of neuronal processes along the grooves. Surprisingly, only 50% of the SPG11 neurites reached the axonal side of the microfluidics chamber compared to controls (Figure 30E), manifesting abnormalities in not only axonal extension but also temporal axon guidance defects in SPG11-dNeurons (Perez-Branguli, Mishra et al. 2014). Furthermore, we measured the total length of neurites of Tuj1 positive and pEF-1-dTomato co-labelled cells from control and SPG11 patients. Intriguingly, SPG11-dNeurons revealed two to three-fold reductions in neurite length compared to controls (Figure 30F), suggesting loss of spatacsin inhibits neurite elongation.

Finally, we compared the complexity of neurons by quantifying the total branching points per neurite in control and SPG11-dNeurons. In unison with our previous results, SPG11-dNeurons demonstrated a significant diminution in neuritic branching compared to controls (Figure 30G).

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This analysis revealed for the first time, that both neurite length and complexity are severely compromised in human neurons carrying SPG11 mutations.

7.3.3 Disruption of spatacsin destabilizes microtubules in SPG11-dNeurons

Sural nerve biopsies of SPG11 patients revealed, presence of the membranous bodies, suggesting disbalance of cargo movement along the microtubular network (Hehr, Bauer et al. 2007).

Similarly, electrons dense, double membraneous structures along with remnants of cytosol and membrane organelles, were reported in ultrastructure micrographs of SPG15 patient fibroblasts

(Vantaggiato, Crimella et al. 2013). Neuronal morphological analysis from our own results, showed a highly compromised neuritic cytoarchitecture and axonal outgrowth (Perez-Branguli,

Mishra et al. 2014). We, therefore, hypothesized that this decreased complexity is due to reduced stability of microtubules in SPG11-dNeurons. Acetylation of Tubulin, a posttranslational modification, in the differentiated neurons leads to stability of tubulin and hence the microtubular network, regulating the structure and cargo traffic in neurons. For this we performed immunostainings on our control and SPG11-dNeurons and measured the fluorescence intensity of acetyl-Tubulin normalized to the ß-III Tubulin levels (Figure 31A). Interestingly, compared to controls, we observed around two-fold reduced levels of acetylated tubulin signal in

SPG11-dNeurons (Figure 31B), indicating that dysfunction of spatacsin may interfere with the stabilization of microtubules.

To further confirm our hypothesis, we comprehensively analyzed the neuritic processes by investigating the electron microscopic data of our control and SPG11 patients’ neuronal cultures.

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Figure 31: Reduction of acetyl-tubulin and accumulation of membrane-like deposits in iPSC-dNeurons from SPG11 patients

(A) Cultures of hiPSC-dNeurons of SPG11 patients (SPG11) and healthy subjects (CONTROL) were colabeled with acetyl-tubulin (α-Ac-tubulin, red) and βIII-tubulin (α-βIII-tubulin, green). Scale bar = 10 µm. (B) Graph for acetylated tubulin signal (Ac-tubulin) in cultures of iPSC-dNeurons of two controls (CTRL-1 and CTRL-2) and two SPG11 patients (SPG11-1 and SPG11-2). Levels of acetylated tubulin signal were significantly decreased in iPSC- dNeurons of SPG11 patients compared with controls. Data were expressed as arbitrary fluorescent units (AFU), and represented as mean ± SD (*P < 0.05);≥ 100n neurons per experimental condition were evaluated. (C) Ultrastructural analysis of the axonal processes of iPSC-dNeurons from control (CTRL-1) and SPG11 patient (SPG11-1). Arrows indicate the presence of membranous inclusions in SPG11 neurons. Scale bars = 2 µm. (Perez- Branguli, Mishra et al. 2014)

Intriguingly, while ultramicrographs of SPG11-dNeurons, revealed a large number of inclusions, membrane encircled structures and vacuoles of diverse electron density and size within neurites, those within controls had only rare signs of inclusion bodies (Figure 31C). Moreover, neurites in

SPG11-dNeurons looked more randomly arranged with interrupted microtubules, while neurites

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from the controls were mostly parallel with linearly arranged microtubules. These distinctive

ultrastructural phenotypes in SPG11 neurites strikingly revealed that the dysfunction of spatacsin

has a deleterious impact on the stability and maintenance of SPG11-dNeurons.

7.3.4 Spatacsin dysfunction impairs axonal transport of synaptic vesicles in SPG11-dNeurons

The morphological impairment, coupled with ultrastructural discrepancies and mRNA

expression analysis data of differentiated neurons led us to investigate if disruption of spatacsin has a functional impact on the transport of synaptic vesicles (SV) in SPG11-dNeurons. We examined this by performing time-lapse microscopy imaging on 3-weeks differentiated neuronal cultures from control and SPG11patients (Figure 32A-C). We plated the cells in microfluidic chambers (as described previously) allowing unidirectional and parallel growth of axonal projections, to distinguish between antero- and retrograde transport events in axons (Figure

32A). Lentivirus mediated overexpression of Synaptophysin-mCherry construct visualized the synaptic vesicles in the analyzed neurons. Interestingly, our assays revealed that the transport activity in neurons derived from SPG11 patients were significantly different from controls

(Figure 32B and C). We found a two to three fold increase in the neurites of SPG11 patients, showing no SV transport, suggesting a severe defect in the initiation of cargo movement in

SPG11-dNeurons (Figure 32C). Surprisingly, we found a significant alteration in transport directions of synaptic vesicles in SPG11 patients. However, we did not observe any significant difference in the mean velocities of SV for both anterograde and retrograde transport events (data not shown). We observed 20% to 45% reduction in the anterograde transport events in SPG11- dNeurons compared to controls (Figure 32C).

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Figure 32: Illustration of time-lapse monitoring for SV transport in synaptophysin-mCherry+ iPSC- dNeurons grown in microfluidic chambers

(A) SVs mowing towards either the axon or the cell side were considered as anterograde and retrograde transports, respectively. (B) Kymographs representing SV transport in synaptophysin-mCherry+ axonal processes of neurons derived from controls (CONTROL) and SPG11 (SPG11) iPSCs . The x-axis represents the distance (x = 250 µm) between cell side (p) and axon (d) sides. The y-axis indicates time-lapse duration in min (y = 10 min). Vertical lines exemplified stationary SVs (x < 5 µm), trajectories with x ≥ 5 µm were considered moving SVs. Movements toward ‘p’ or ‘d’ revealed retrograde or anterograde transport, respectively. (C) Graphs indicated a significant difference in the transport fate of SPG11 neurons (SPG11-1 and SPG11-2) in comparison to controls (CTRL-1 and CTRL-2). All data were represented as mean ± SD; ***P < 0.005; ≥n 20 axons per experimental condition. (Perez-Branguli, Mishra et al. 2014)

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Conversely, retrograde transport events were 10% to 15% increased in SPG11-dNeurons compared to controls (Figure 32C). Altogether, these findings showed that dysfunction of

spatacsin compromised the initiation of the SV movement and disturbed the critical balance of

transport activity in SPG11 patients' neurons (Perez-Branguli, Mishra et al. 2014).

7.3.5 Spatacsin dysfunction disturbs Na+/K+ current density in SPG11- dNeurons

We further investigated the electrophysiological properties in CTRL- and SPG11-dNeurons.

Surprisingly, we observed an elevated Na+/K+ current density in SPG11-dNeurons (Figure 33).

In phasically firing neurons, SPG11-dNeurons displayed more than three fold enhanced Na+

currents than controls (CTRL: 777.64 ± 102.92 pA versus SPG11: 2283.88 ± 547.88 pA, p =

0.00581; Figure 33A and C), whereas K+ currents remained the same. Overall, the ratio between

Na+ and K+ currents is three fold increased in SPG11-dNeurons (CTRL: 0.78 ± 0.9 versus

SPG11: 2.84 ± 0.51, p = 0.000241; Figure 33D) compared to controls. In tonically firing

neurons, SPG11-dNeurons showed more than two fold reduced K+ currents (CTRL: 3719.80 ±

466.39 pA versus SPG11: 1277.67 ± 636.86 pA, p = 0.019943; Figure 33B and E), whereas Na+

currents remained the same. In summary, SPG11-dNeurons exhibited more than three fold higher

ratio of Na+/ K+ current density (CTRL: 0.82 ± 0.2 versus SPG11: 2.65 ± 0.69, p = 0.01839;

Figure 33F), compared to controls, suggesting axonal degeneration in SPG11 is accompanied with electrophysiological disturbances in differentiated neurons (Mishra et al, unpublished data).

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Figure 33: Electrophysiological abnormalities in SPG11-dNeurons

(A-B) Representative images of action potential graphs in phasic firing neurons (A) and tonic firing neurons (B). (C- D) Na+/K+ current density of differentiated neurons during phasing firing of actional potential. (C-D) Na+/K+ current density of differentiated neurons during tonic firing of actional potential. Data represented as mean ± SEM. * p < 0.05, ** p < 0.01, *** p < 0.001 by Student’s t-test (C-F). (Mishra et al., unpublished data)

Discussion

8. Discussion

While selective degeneration of the neurites of long-range projection neurons is characteristic for most HSPs, severe cortical atrophy and a TCC are frequently present in the second decade of life of SPG11 patients (Winner, Uyanik et al. 2004; Hehr, Bauer et al. 2007; Stevanin, Santorelli et al. 2007). This phenotype raises the question whether the severity of the cortical manifestations and early onset of the disease is of neurodegenerative or neurodevelopmental origin. Defects in the formation of the corpus callosum (CC) are an indicator of anomalies in neurodevelopment

(Paul, Corsello et al. 2014). SPG11, unlike other HSPs, has also recently been grouped into the broad category of developmental disorders representing agenesis (hypoplasia) of the corpus callosum (Paul, Brown et al. 2007), suggesting an early developmental aberration of the disease caused by SPG11 mutations. However, due to unavailability of brain tissues from young SPG11 patients, this has not been extensively studied.

To overcome this problem, we generated a human iPSC model of SPG11 using patient derived skin fibroblasts and generated neuronal cells for deciphering the disease related phenotypes.

Based on our new insights from SPG11 patient derived cortical neural progenitors (NPC) model and the terminally differentiated neurons, we propose a new temporal scenario for SPG11 disease progression. We report that SPG11-NPCs show widespread alterations in the transcripts related to neurodevelopmental pathways such as cell cycle, neurogenesis, axonal morphogenesis including callosal developmental guidance cues, and neuronal projection pathways, leading to proliferation deficit and impaired cortical neurogenesis. In patients, this would translate to a disease start with an early onset neurodevelopmental phenotype (in the first two decades).

Furthermore, in our neural model, terminal differentiation of SPG11-NPCs, over an extended period of six weeks in culture, evidenced prominent morphological and functional abnormalities

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including compromised neuritic complexity, reduced axonal stability, altered electro-

physiological activity and pathological accumulation of membranous bodies within axonal

processes, suggesting neurodegenerative changes in SPG11-dNeurons. This was further

substantiated by a reduction in the anterograde vesicle trafficking, indicative of impaired axonal

transport in the long-projecting cortical neurons. These findings might be related to the neurodegenerative phenotype in adulthood of SPG11 patients. Our human iPSC model, thus repitulates the distinct temporal stages of disease manifestations in SPG11 patients and also provides an ideal platform for screening potential therapeutic compounds for an early intervention of the disease.

8.1 Spatacsin expression in human and mouse cortical neurons

The majority of our knowledge on SPG11 is based on clinical observations in HSP patients. Our

report is the first detailed characterization of spatacsin in hPSC-dNeurons. Our data show the preferential expression of spatacsin in human neurons compared with glia, in particular in human

Ctip2+ cortical neurons. Such results were corroborated by our mouse cortical cultures and immunohistochemical analysis of mouse brain and were in line with previously published expression of spatacsin in non-diseased postmortem human cortical layer V (Murmu, Martin et al. 2011). In hPSC-dNeurons and mouse cortical neurons, spatacsin colocalized with synaptic vesicles, microtubules and actin in axons and dendrites (Perez-Branguli, Mishra et al. 2014). In this regard, in synaptosomes, spatacsin signal overlapped with presynaptic and vesicle compartments. Further, biochemical analysis confirmed that spatacsin prominently colocalized with cytoskeletal markers in the cytosolic synaptosome fraction, suggesting a dynamic localization of spatacsin in synapses, possibly due to on and off cycling to SVs. Hirst and

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collaborators suggested that spatacsin, AP5 and spastizin form a coat-like complex where spatacsin localizes on the surface of the complex, functioning as protein scaffold, whereas spastizin would be the docker of the coat onto membranes and AP5 the protein sorter (Hirst,

Barlow et al. 2011; Hirst, Borner et al. 2013). Altogether, these results provide a comprehensive analysis of spatacsin expression in human and murine neural system, since previous reports in the spatacsin field were based on non-neuronal cell lines (Murmu, Martin et al. 2011; Hirst,

Borner et al. 2013). More importantly, RT-PCR analysis of SPG11-dNeurons showing-specific downregulation of synaptic, motor and tubulin-associated markers, are consistent with our previous human expression data of spatacsin, performed in neuronal cultures and synaptosomes

(Perez-Branguli, Mishra et al. 2014).

8.2 Neural rosettes underdevelopment recapitulates corticogenesis

defects in SPG11-iPSCs

In this study, we report an early developmental defect in the the generation and maintenance of

cortical neural rosettes (NRs) in SPG11-iPSCs. The NRs appearing as columnar epithelial cells

are the initital source of complex progenitor pool which, in sequential progressive commitments

to symmetric and asymmetric divisions, generates secondary populations of entire cortical

progenitors, found in-vivo within the inner and outer subventricular zones (Rakic 2009; Shepherd

2011). Any discrepancy at this juncture or delayed neural induction has a detrimental effect on

neuronal cell fate, morphogenesis, neuronal migration and proper establishment of synaptic

connections (Pilz, Stoodley et al. 2002; Abdel Razek, Kandell et al. 2009). SPG11-iPSCs

exhibited abnormally small and sparsely developed rosettes (Mishra et al., unpublished data).

This anomaly suggests dysregulation of molecular processes that control patterning, neocortical

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organization and projectional identity during generation of organized radial scaffold to guide the

proliferation, differentiation and migration of newly generated neuronal populations (Gaitanis and Walsh 2004).

Interestingly, the vast expansion and diversity of human cortex arises from the robust expansion of columnar progenitor cells, modeled in-vitro by the rapid proliferation and sequential generation of layer-specific neural cell fate from the developing neuroepithelium-like sheet (Shi,

Kirwan et al. 2012). Subtle temporal changes in the relative proliferation and differentiation of these neural progenitors result in detrimental effects on the region specific cortical surface area and associated neuronal circuitries including disturbed cortical gyrification (Vogeley, Schneider-

Axmann et al. 2000), generation of thin corpus callosum (Foong, Maier et al. 2000; Keshavan,

Diwadkar et al. 2002) and cerebellar abnormalities (Katsetos, Hyde et al. 1997; Rasser, Schall et al. 2010). Our findings that the neuroepithelial progenitors in SPG11-NRs fail to undergo robust

cellular proliferation and generation of fully developed cortical rosettes compared to the controls

during neural induction stages suggest that dysfunction of spatacsin disturbs the early

neurodevelopmental stages of these cortical progenitors (Mishra et al., unpublished data). The frequent occurance of flat neuronal cells in the differentiating EBs highlights premature exhausation of progenitors at the expense of initial proliferation cycles, suggesting cytoarchitectural anomalies, mis-positioning of projection neurons and altered neuronal cell density during the development of six layered neocortex in SPG11.

It would be interesting in future to investigate the regional identity and expression profile of these neuroepithelial progenitors. Additionaly, live-cell imaging of the developing cortical rosettes in SPG11-iPSCs would shed vital information pertaining to the lineage commitment, cell cycle rhythmic perturbation and temporal identity of the prematurely exiting cortical progenitors

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during the laminar patterning of the cortical plate. Currently we do not know much about the

specific mechanisms and the role of spatacsin in orchestrating these developmental abberations

in SPG11-NRs and therefore we sought out to investigate for the first time, the role of SPG11

mutations on the proliferation and developmental neurogenesis of cortical progenitors.

8.3 Reduced capacity of SPG11-iPSCs to generate NPCs and neurons

We observed a substantial decrease of SPG11-NPCs due to a decrease in the S phase and G2/M

phase of the cell cycle and an increase in cell death. We hypothesized that a loss of function

mechanism of spatacsin is responsible for this proliferation deficit and confirmed this by knockdown of spatacsin in SH-SY5Y cells. While some HSP-related genes, like spastin (SPG4), spartin (SPG20) and spastizin (SPG15) are localized in the midbody and appear to facilitate

cytokinesis during cell division (Connell, Lindon et al. 2009; Sagona, Nezis et al. 2010;

Renvoise, Stadler et al. 2012), to our knowledge, proliferation of human cortical progenitors has

only been studied in SPG4-NPCs, where no proliferation deficit, matching a pure spastic

paraplegia phenotype was present (Havlicek, Kohl et al. 2014). TCC is a characteristic hallmark

of SPG11 patients. However, the molecular mechanisms underlying this phenotype are not

known. Dysregulation in the formation of the corpus callosum can arise due to a multitude of

factors originating during the cortical developmental steps such as defects in proliferation,

anomalies of guidance cues and receptors, axonal outgrowth, neuritic branching, midline

telencephalic patterning, neuronal fate specification and guidance of commissural axons (Schell-

Apacik, Wagner et al. 2008; Tang, Bartha et al. 2009). Indeed, highly up-regulated genes

including those that negatively function in callosal guidance, neuronal migration, and previously

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implicated in developmental defects associated with Autism Spectrum Disorders (Edwards,

Sherr et al. 2014), were overrepresented in SPG11-NPCs (Mishra et al., under revision).

Surprisingly, we also noticed a two-fold increased expression of SATB2 gene (Special AT-rich sequence-binding protein 2) in SPG11-NPCs, an important regulator of callosal neurons, which might lead to premature differentiation of cortical progenitors. SATB2 positive upper layer neurons (Layer II/III) are temporally generated in the later stages of corticogenesis, after the deeper layer neurons (Layer V/VI) are already formed (Tuoc, Radyushkin et al. 2009). This anomaly suggests a temporal depletion of cortical progenitors, destined for generating the superficial callosal projection neurons (Layer II/III). Anomalies in cell cycle progression, and enhanced expression of senescence markers, have been shown to influence the sequential fate and generation of neuronal subtypes in the developing nervous sytem (Sherr and Roberts 1999;

Goto, Mitsuhashi et al. 2004; Georgopoulou, Hurel et al. 2006; Nguyen, Besson et al. 2006).

Consistent with these findings, we found two to three fold enhanced generation of doublecortin

(DCX) positive neuronal-like cells in SPG11-NPCs, evidencing premature neuronal differentiation of cortical progenitors (Mishra, et al, unpublished data). This complements our additional findings; suggesting that defects in the proliferation of SPG11-NPCs might lead to enhanced depletion and perturbation of temporal neurogenic fates and hence impaired cortical neural circuitry and laminar patterning of neocortex in SPG11.

Our data point towards distinct temporal functions of spatacsin causing axonal transport deficits in corticospinal projections in matured neurons in-vitro and most likely during adulthood in patients (Perez-Branguli, Mishra et al. 2014). It is imperative to speculate that the cortical changes (TCC and cortical atrophy) in SPG11 patients, when they first present with spastic paraplegia, are rather the results of defects in proliferation during cortical development (Winner,

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Uyanik et al. 2004; Hehr, Bauer et al. 2007; Stevanin, Azzedine et al. 2008), thereby highlighting

a novel role for spatacsin in early neural development. Our current model of SPG11 disease

pathology starts with an early onset neurodevelopmental phenotype (first two decades),

consisting of a proliferation deficit and cortical neurogenesis abberations. After transition around

the second and third decade of life, additional neurodegenerative phenotypes are observed. The major adult-onset phenotype is reflected by axonal degeneration, resulting in impaired axonal transport, with the clinical correlates of spastic paraparesis and peripheral neuropathy.

A neurodevelopmental phenotype consisting of defects in proliferation combined with increased cell death has been reported in other neurodegenerative diseases, including Down’s syndrome and myotonic dystrophy type1, partly accompanied with dysregulation of GSK3/mTOR signaling (Denis, Gauthier et al. 2013; Hibaoui, Grad et al. 2014). Similarly, reduced neuronal connectivity and altered neuronal gene expression profiles regulating neural migration, axonal outgrowth, guidance and cell adhesion network were reported in a cohort of schizophrenia patient hiPSC-derived neurons (Brennand, Simone et al. 2011). An autism spectrum disease model of fragile-X syndrome also revealed an aberrant neuronal differentiation, defective neurite extension and abnormal calcium activity (Liu, Koscielska et al. 2012), reflecting early developmental abnormalities at the cellular level and likely contributing to disease phenotypes at

the systems level in early-onset disease of the central nervous system. Collectively, our data

suggest that the discrepancy in the generation of the neural cells from SPG11-NPCs is due to the

developmental defects in the NPCs rather than the viability of the generated neurons. However,

we do not rule out the possibility of motor neuron degeneration at the later stages in adulthood,

due to the increased rate of auotphagy linked lysosomal abnormalities and such other

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neurodegenerative changes in SPG11 patients’ neurons (Varga, Khundadze et al. 2015) (Chang,

Lee et al. 2014; Renvoise, Chang et al. 2014).

8.4 GSK3ß inhibition rescues proliferation and neurogenesis defects of SPG11-NPCs

The Wnt/β-Catenin signaling pathway is an important regulator of the cell cycle machinery and proliferation of NPCs and regulates distinct stages of neural development, including cell division, specification, differentiation, and migration (Clevers 2006). Dysregulation of GSK3 signaling has been associated with DISC1 and fragile X syndrome previously (Mao, Ge et al.

2009; Portis, Giunta et al.). Increased GSK3ß activity results in proteolytic degradation of its downstream substrate, ß-Catenin. Impaired Wnt signaling, as seen in SPG11-derived iPSCs, suggested an adverse impact on the cortical development in SPG11. Our model suggests that an increase in GSK3 activity leads to increased ß-Catenin degradation. Reduced ß-Catenin levels in

SPG11-NPCs result in a decrease in NPC proliferation in SPG11 patients, resulting in impaired cortical development. The senescence marker p27Kip1 has been shown to disrupt the cell cycle progression to S phase (Kaproth-Joslin, Li et al. 2008); thus the elevated senescence activity of

SPG11-NPCs (p27Kip1 expression levels) might cause an early cell cycle exit and hence temporal dysregulation of neurogenesis during cortical development. Pharmacological treatment with the

GSK3 inhibitors Tideglusib and CHIR99021 activates the canonical Wnt pathway by inhibiting

GSK3 signaling and thereby restores the proliferation and neurogenesis of SPG11-NPCs (Figure

24B-D). Additionally, inhibition of GSK3 pathway ameliorated the premature differentiation of

SPG11-NPCs, restoring the cell cycle anomalies and proliferative neurogenic divisions during cortical laminar formation.These findings provide additional confirmation that

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neurodevelopmental defects in the SPG11-NPCs are linked to dysregulation of GSK3ß signaling.

More importantly, it highlights the rescue of the impaired neurogenesis in SPG11 by inhibition of the GSK3 pathway. This pharmacological intervention presents a new direction for translating

the molecular insights gained at the cellular level using human reprogramming technology to

develop novel therapeutics for SPG11.

8.5 Neurodevelopmental defect is linked with the impaired autophagy related pathways in SPG11-NPCs

Neurodevelopment including cortical neurogenesis is a highly dynamic and complex process that

involves strictly regulated steps of proliferation and differentiation of progenitors over an

extended period of time to generate functionally distinct neuronal circuitries. Besides, it involves

the temporally regulated generation and migration of layer specific neurons, which undergo

axonal pathfinding, synapse formation and myelination (Boland and Nixon 2006; Komatsu,

Waguri et al. 2006; Cecconi, Di Bartolomeo et al. 2007). All these processes require exquisite

supply of nutrients, energy, neurotrophins, ligands, receptors, guidance cues all in a spatially and

sequentially regulated manner (Boland and Nixon 2006; Moreau, Luo et al. 2010). Any

discrepancy in the adequate supply of these molecules leads to anomalies in gene expression

dynamics, morphological and functional specialization aberrations affecting the structural and

functional integrity of the neural networks (Hara, Nakamura et al. 2006; Komatsu, Waguri et al.

2006; Mizushima and Levine 2010; Lv, Jiang et al. 2014). Interestingly, SPG11-NPCs showed a

cohort of genes associated with ER stress and endolysosomal membrane trafficking to be

differentially regulated during cortical differentiation (Mishra et al., under revision), suggesting

primarily defects in the maintenance of cellular homeostasis (Hollenbeck 1993; Pacheco, Kunkel

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et al. 2007; Ramocki and Zoghbi 2008; Sanderson, Gallaway et al. 2015). Our results are consistent with previously reported findings, suggesting a deleterious impact of defective autophagy on the developmental potential of neural progenitors during cortical neurogenesis

(Vazquez, Arroba et al. 2012; Lv, Jiang et al. 2014). Furthermore, enhanced accumulation of

lipidated form of LC3-II positive autophagosome in SPG11-NPCs evidenced a primary defect in

its turnover due to the failure of the clearance process. Lipidated autophagosome, LC3-II are

rapidly sorted and targeted to lysosomes for fusion and form the autolysosomes (Eskelinen and

Saftig 2009). The main cargo protein smoothly executing this critical role is p62. p62 is constantly degraded through active autophagy such that it’s amount is at a low level in a normal

healthy cells (Komatsu and Ichimura 2010). A defect in either the autophagosome maturation or

its subsequent degradation would lead to its accumulation inside the cell. This anomaly is highly

sensitive for neurons, in which deregulation of the degradation process can induce cellular

dysfunction (Settembre, Fraldi et al. 2008). Chang et al. recently showed a primary role of

spatacsin in the cycling and biogenesis of lysosomes, required for the formation of autolysosomes (Chang, Lee et al. 2014). Consistent with this finding, Varga et al, reported in- vivo autophagic-lysosomal dysfunction in a knock out (KO) mouse model of SPG11.

Surprisingly, autopahgic defects were visible at the very early stage of two months in Purkinje cells of spatacsin knock out (KO) mice, even before they could detect any neurodegeneration or motor defects in these mice (Varga, Khundadze et al. 2015). This substantiates our hypothesis that the failure of the cellular homeostasis is present at the early neurodevelopment stage, which tends to accumulate over the years before the onset of cortical neurodegeneration at later stages.

More importantly, we provide the first evidence in human cortical progenitors, a crucial link

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between the neurodevelopmental defects of SPG11 patients and the cellular dysfunction due to

defects in autophagy-lysosomal pathway.

Furthermore, these findings suggest that defects in proliferation and neurogenesis, accompanied

by endolysosomal dysfunctions, are the first consequences of spatacsin-mediated GSK3

signaling defect while the motor neuron degeneration, synaptic dysfunction, movement and gait

disorder develop in later stages, when the degradative pathways override the compensatory phase

of the disease. The cellular adaptations to compensate this dysregulation in early phase is not clear and thus our work provides an impetus to further investigate the Wnt signaling pathway in corticogenesis, to elucidate the other components of this pathway mediating the distinct disease phenotypes in SPG11.

8.6 Proposed mechanism for the neurodevelopmental defects in SPG11-NPCs

SPG11 patients’ iPSC derived cortical progenitors showed reduced proliferation, impaired cell

cycle and dysregulation of GSK3ß/ ß-Catenin signaling, resulting in compromised cortical neurogenesis (Figure 34). Based on our in-vitro results, we thus propose a novel role of spatacsin

in neural development regulating the distinct temporal stages of cortical formation and

morphogenesis, thereby modulating the overall neural circuitry and cytoarchitecture of human

brain. Cortical progenitors in presence of spatacsin undergo normal proliferation and

neurogenesis. Due to mutations in SPG11, this function is disturbed leading to cell cycle

aberrations and ensuing proliferation defect and anomalies in neuronal differentiation of the

cortical progenitors. Modulation of the GSK3 signaling pathway partially restores this

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neurodevelopmental defects, thereby providing a much-needed platform to screen novel therapeutic compounds for an early intervention of the disease in SPG11 patients.

Figure 34: Schematic model of GSK3ß mediated neural development in control and SPG11-NPCs

Increased GSK3 activity leads to reduced ß-Catenin level in SPG11-NPCs, thereby compromising the proliferation and neurogenesis in SPG11 patients. Impairment in autophagy-lysosomal pathway under basal condition disturbs the homeostatic balance of the cortical progenitors leading to increased cell death. Pharmacological treatment with GSK3 inhibitor, Tideglusib and CHIR99021, activates the canonical Wnt pathway by inhibiting GSK3 signaling and thereby restores the proliferation and neurogeneis to the control level (shown by dashed green arrows). (Mishra et al., under revision)

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8.7 Disruption of spatacsin leads to axonal pathologies in SPG11- dNeurons

SPG11-dNeurons showed a significant reduction of the neurite complexity suggesting detrimental effects on the maintenance of cortical cytoarchitecture and synaptic integrity in HSP patients. A decreased neurite complexity is also present in neurons derived from SPG4 patients

(Havlicek, Kohl et al. 2014), which suggests that neurite complexity defects may be a unifying

pathology in different HSP subtypes. Interestingly, spatacsin knockdown in mouse cortical

neurons transfected before and after axon/dendrite specification indicated that the loss of

spatacsin not only blocks axonal outgrowth but may also induce axonal retraction (Perez-

Branguli, Mishra et al. 2014). The retraction of cortical axons induced by spatacsin dysfunction

might be in accordance with the presumed retrograde degeneration of long cortical projections

leading to progressive spasticity and paraperesis in SPG11 patients (Winner, Uyanik et al. 2004;

Hehr, Bauer et al. 2007; Salinas, Proukakis et al. 2008; Schule, Schlipf et al. 2009). Interestingly,

most of the neurons expressing spatacsin at high levels were located in the cortical Layers III and

V, the topographical site of transcallosal and cortico-spinal projections, respectively (Paul,

Brown et al. 2007; Fame, MacDonald et al. 2010). A recent study involving SPG11-KO mouse model, revealed progressive loss of large diameter axons of the corticospinal tract in motor cortex of 16-month old murine brain, similar to most other mouse models for HSP (Deutch,

Hedera et al. 2013; Khundadze, Kollmann et al. 2013; Varga, Khundadze et al. 2015). In aggrement with these findings, we found increased cell death and dystrophic neurites in cortical

projection neurons, including Ctip2 positive layer V neurons at later stages of neuronal

differentiation (Mishra et al., unpublished data). Specifically this degeneration was accompanied

with prominent accumulation of membranous bodies within the long projecting neurites of the

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cortical neurons, therby highlighting that SPG11-patients’ derived neural model exhibit overt

neuronal loss and recapitulate the progressive degeneration of dorsal telencephalic projection

neurons; the hallmark pathology underlying the clinical motor symptoms of HSPs.

It is well known that the acetylation of tubulin contributes to axonal connectivity by facilitating

the stabilization of the microtubules and the recruitment of motor proteins to the tubulin rails

(Reed, Cai et al. 2006; Millecamps and Julien 2013). Stability of the microtubules is not only

important for regulating neuronal morphology but also establishing and maintaining neuronal

polarity, scaffolding signaling molecules to form signaling hubs and trafficking of cargoes from

cell soma to axonal terminals (Conde and Caceres 2009; Etienne-Manneville 2009; Poulain and

Sobel 2009). Reduced stability of microtubular networks modulating neuronal dysfunction has

been widely reported in several neurodegenerative diseases such as Amyotrophic Lateral

Sclerosis (ALS), Parkinson's disease (PD), and Alzheimer's disease (AD) (Garcia and Cleveland

2001; Baird and Bennett 2014). Intriguingly, we observed disruption of spatacsin in human

neurons induces a significant reduction of acetylated tubulin, which suggests that spatacsin may

play an important role in the motor machinery by influencing tubulin-microtubule turnover. A

previous report showed similar findings in a zebrafish model, wherein blockade of zspg11

expression presented with a marked reduction of acetylated tubulin along with outgrowth defects

(Southgate, Dafou et al. 2010).

Coincident with axonal pathologies, we observed an abnormal increase in the Na+/ K+ current

density in SPG11-dNeurons compared to controls, evidencing electrophysiological disturbances in terminally differentiated neurons. Our data are consistent with the recent findings of electrical hyperexcitability in another motor neuron disease, ALS; suggesting abnormal ion channel

activities result in downstream degenerative pathways and subsequently lead to progressive loss

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of motor neurons (Vucic, Nicholson et al. 2008; Wainger, Kiskinis et al. 2014; Devlin, Burr et al.

2015).

8.8 Dysfunction of spatacsin impairs axonal transport causing axonal degeneration in SPG11-dNeurons

The abundance of pleomorphic membranous material in suralis nerve biopsies of SPG11 patients

is characteristic for a severe axonal neuropathy (Hehr, Bauer et al. 2007). The accumulation of vesicle-like bodies, the downregulation of motor genes and synaptic markers in SPG11- dNeurons as well as the impairment of synaptic vesicle (SV) transport in SPG11-dNeurons,

highly resemble this clinical observation and are possibly implying an alteration in the axonal

transport and SV pathway. The SV cycle is mainly based on its initial excision from trans-Golgi

network and its subsequent recycling in the early endosome (Sudhof 2004). It was recently

speculated that in other HSPs (e.g. SPG47, SPG50, SPG51 and SPG52) the neuronal

degeneration may be due to defects in the AP4 complex, which is associated with the transport

between the trans-Golgi network and endosomes (Hirst, Irving et al. 2013). This is interesting,

since we observed a preferential impairment of the axonal traffic in the anterograde direction within axonal processes of SPG11-dNeurons, implicating disturbance of cargo trafficking caused by reduced levels of spatacsin as a potential contributor to the pathogenesis in HSP (Perez-

Branguli, Mishra et al. 2014).

Anterograde transport provides newly synthesized components essential for neuronal migration,

axonal pathfinding, membrane function and maintenance (Smith 1980; Goldstein and Yang

2000). The important cargoes for anterograde transport include the numerous synaptic vesicles,

cytoplasmic organelles, tubulovesicular structures, mRNAs, proteins and mitochondria which are

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involved in diverse cellular activities involving neuronal survival, synapse formation, signaling as well as apoptotic clearance (Hirokawa, Noda et al. 2009; Hirokawa, Niwa et al. 2010). Any

alteration in the crucial trafficking events could have a detrimental effect on neuronal

metabolism, intracellular neural transmission and effective response to trophic signals or stress

insults (Morfini, Szebenyi et al. 2002; Millecamps and Julien 2013). Intriguingly, the mean velocity as well as the frequency distribution of the cargo speeds was unaltered but the number of axonal processes showing no SV transport and more retrograde transport was increased in

SPG11-dNeurons (Perez-Branguli, Mishra et al. 2014). Altogether, these findings evidenced that dysfunction of spatacsin caused a severe defect in the initiation of the transport processes and the direction of SV movement in SPG11 patients' neurons.

The mechanistic basis for the imbalance of axonal transport directions is not known. Given the complexity of axonal transport machinery, several molecules and signaling pathways could influence the trafficking events. Anomalies in GSK3 signaling have been shown to disturb anterograde axonal transport and targeting of cargoes to specific subcellular domains in neurons

(Morfini, Szebenyi et al. 2002). Microtubule associated protein Tau can influence intracellular transport by blocking the initial attachment of kinesin motor proteins to microtubules and inhibiting kinesin motility along the microtubules (Ebneth, Godemann et al. 1998; Dixit, Ross et al. 2008). Similarly, dysfunction of microtubular network, anomalies in cargo complex assembly or cargo attachment to motors, as well as complications in regulatory elements could aggravate the stringent intracellular spatial and temporal movement of cargoes and directly trigger motor neuron degeneration (Chevalier-Larsen and Holzbaur 2006; Goldstein 2012; Millecamps and

Julien 2013). Since we observed a preferential dysregulation of GSK3ß signaling in SPG11-

NPCs during cortical neurogenesis, it would be interesting to investigate in future, whether

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pharmacological modulation of GSK3 signaling could rescue the anomalies in axonal transport

and thereby halt the progressive neurodegeneration in SPG11-dNeurons.

8.9 Proposed temporal model of neurodevelopmental and neurodegenerative phenotypes in SPG11 patients

By employing a complementary approach using human cortical progenitors and terminally

differentiated neural model, we provide the first evidence that spatacsin has a major impact on

neurite plasticity through maintaining neural development, cortical neural diversity,

cytoarchitecture, neuronal morphogenesis, cytoskeleton stability and intracellular transport.

Based on this comprehensive analysis of expression pattern of spatacsin and distinct cellular and

molecular phenotypes orchestrated by spatacsin dysfunction during early development (in

SPG11-NPCs) and late neuronal maturation stage (in SPG11-dNeurons), we propose a novel

temporal scenario for SPG11 (Figure 35) in which the disease starts with an early onset

neurodevelopmental phenotype (in the first two decades), consisting of a proliferation deficit and

impaired cortical neurogenesis, with clinical correlates of TCC and cortical atrophy. After

transition around the second and third decade of life, an additional neurodegenerative phenotype

is present. Loss of spatacsin, coupled with impaired cortical neurogenesis, reduced axonal

complexity, and loss of endolysosomal homeostasis, over the decades, results in accumulation of

degradative phenotypes in the patients’ neurons (Mishra et al., under revision).

The molecular dysregulation in early stages leads to gradual detoriation in the trafficking of

signaling cues, contributes to the profound impairment of cortico-cortical and cortico-spinal

projections eventually resulting in failure of neural circuitries at the systems level.

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Figure 35: Proposed two distinct stages of SPG11 disease pathology

Neurodevelopmental phenotype, which predominates in the early onset within the first two decades, is characterized by a proliferation deficit and impaired cortical development. The neurodegenerative phase, marked by progressive spasticity and paraparesis, leads to functional neuronal deficits, motor neuron degeneration, cognitive impairment and peripheral motor and sensory neuropathy. (Mishra et al., under revision)

The major cellular phenotype is axonal degeneration, resulting in perterbed axonal transport,

with the clinical correlates of spastic paraparesis and peripheral neuropathy. Our human iPSC

model thus reveals for the first time, the significance of loss of function of spatacsin in diseased

human neurons, provides cellular and molecular basis for recapitulating some of the complex

clinical attributes manifested in SPG11 patients and most importantly provides an opportunity to

utilize these functional and signaling cues for an early intervention of the disease.

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Abbreviations

9. Conclusion

We established for the first time a human iPSC derived neuronal model of SPG11-linked HSP,

using skin fibroblasts of three SPG11 patients and two age matched controls. Using multistep

differentiation paradigm, we generated cortical neural progenitors (NPCs) and terminally differentiated neurons from hPSCs. Using these neuronal cells, we delineated the functional role of spatacsin in distinct stages of neuronal development and maintenance.

We provide compelling evidence that spatacsin is preferentially expressed at all stages of neuronal differentiation and maturation in human and mice cortical neurons, thereby evidencing

its deleterious impact on the development and function of long-projecting cortical SPG11-

dNeurons. The neural progenitor model further reveleaed an aberrant transcriptional signature for

neurodevelopmental pathways in SPG11-NPCs, compared to the CTRL-NPCs. This was

substantiated by cell cycle anomalies, proliferation deficit and impaired corticogenesis in

SPG11-NPCs. Interestingly, the developmental phenotypes were rescued by GSK3 modulation.

The neurodegenerative phenotype in the terminally differentiated SPG11-dNeurons, highlighted profound axonal degeneration with reduced anterograde vesicular cargo-trafficking.

In conclusion, our human iPSC model reveals a novel temporal function for spatacsin: regulating an early onset, proliferation and neurogenesis anomalies in cortical development (in first two decades), mimicking a TCC and cortical atrophy. This is followed by progressive axonal degeneration, in the ensuing decades, resulting in impaired axonal transport, with the clinical correlates of spastic paraparesis and peripheral neuropathy. Furthermore, this human in-vitro model offers an ideal platform to screen novel therapeutic compounds, for an early intervention, thereby paving the road to discover new treatment strategies for SPG11 related HSPs.

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Abbreviations

10. References

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Vucic, S., G. A. Nicholson, et al. (2008). "Cortical hyperexcitability may precede the onset of familial amyotrophic lateral sclerosis." Brain 131(Pt 6): 1540-50. Wainger, B. J., E. Kiskinis, et al. (2014). "Intrinsic membrane hyperexcitability of amyotrophic lateral sclerosis patient-derived motor neurons." Cell Rep 7(1): 1-11. Wakabayashi, K., H. Kobayashi, et al. (2001). "Autosomal recessive spastic paraplegia with hypoplastic corpus callosum, multisystem degeneration and ubiquitinated eosinophilic granules." Acta Neuropathol 101(1): 69-73. Wakil, S. M., H. N. Murad, et al. (2012). "Autosomal recessive hereditary spastic paraplegia with thin corpus callosum among Saudis." Neurosciences (Riyadh) 17(1): 48-52. Wharton, S. B., C. J. McDermott, et al. (2003). "The cellular and molecular pathology of the motor system in hereditary spastic paraparesis due to mutation of the spastin gene." J Neuropathol Exp Neurol 62(11): 1166-77. White, K. D., P. G. Ince, et al. (2000). "Clinical and pathologic findings in hereditary spastic paraparesis with spastin mutation." Neurology 55(1): 89-94. Winner, B., M. C. Marchetto, et al. (2014). "Human-induced pluripotent stem cells pave the road for a better understanding of motor neuron disease." Hum Mol Genet 23(R1): R27-34. Winner, B., G. Uyanik, et al. (2004). "Clinical progression and genetic analysis in hereditary spastic paraplegia with thin corpus callosum in spastic gait gene 11 (SPG11)." Arch Neurol 61(1): 117- 21. Yan, Y., J. Zhao, et al. (2014). "Tetramethylpyrazine promotes SH-SY5Y cell differentiation into neurons through epigenetic regulation of Topoisomerase IIbeta." Neuroscience 278: 179-93. Yoshikawa, A., T. Kamide, et al. (2014). "Deletion of Atf6alpha impairs astroglial activation and enhances neuronal death following brain ischemia in mice." J Neurochem 132(3): 342-53. Yu, J., M. A. Vodyanik, et al. (2007). "Induced pluripotent stem cell lines derived from human somatic cells." Science 318(5858): 1917-20. Zhang, R., D. H. Thamm, et al. (2015). "The effect of Zhangfei/CREBZF on cell growth, differentiation, apoptosis, migration, and the unfolded protein response in several canine osteosarcoma cell lines." BMC Vet Res 11: 22. Zhao, Y., Q. Huang, et al. (2009). "Autophagy impairment inhibits differentiation of glioma stem/progenitor cells." Brain Res 1313: 250-8. Zindy, F., J. J. Cunningham, et al. (1999). "Postnatal neuronal proliferation in mice lacking Ink4d and Kip1 inhibitors of cyclin-dependent kinases." Proc Natl Acad Sci U S A 96(23): 13462-7.

129

Abbreviations

11. Abbreviations

BCA = bicinchoninic acid

BDNF = brain derived neurotrophic factor

BMBF = Bayerisches Ministerium für Bildung und Forschung cm2 = square centimeter

CO2 = carbon dioxide

Ctip2 = COUP TF-interacting protein 2

CTRL = control

DAPI = 49,6-diamidino-2-phenylindole

DNA = deoxyribonucleic acid

dNTP = deoxynucleotidetriphosphate

EB = embryoid body

EDTA = ethylenediaminetetraacetic acid

EGTA = ethyleneglycoltetraacetic acid

FBS = fetal bovine serum

FGF2 = fibroblast growth factor 2

g = gravitational force (9.80665 newtons of force per kilogram of mass)

GAPDH = Gene or protein name Glyceraldehyde-3-phosphate dehydrogenase

130

Abbreviations

Gata4 = GATA-binding protein 4

GDNF = glial derived neurotrophic factor

GFAP = glial fibrillary acidic protein

GFP = green fluorescent protein

iPSC = human induced pluripotent stem cells

HUES6 = human embryonic stem cells

hPSC = HUES6/human induced pluripotent stem cells

HSP = hereditary spastic paraplegia

IFB2=immunofluorescence buffer 2: PBST supplemented with 5 % normal donkey serum

KCl = potassium chloride

Klf4 = Gene name Krueppel-like factor 4 mg = milligram

MgCl2 = magnesium chloride

min = minute

ml = milliliter

mM = millimolar

MOI = multiplicity of infection

NaCl = sodium chloride

NaHCO3 = sodium hydrogen carbonate

131

Abbreviations

NaH2PO4 = sodium dihydrogen phosphate

NEAA = non-essential amino acids

NPC = neural precursor cell

NR = neural rosettes

Oct3/4 = Gene name Octamer binding transcription factor 3/4

O2 = oxygen

PBS = phosphate buffered solution

PFA =paraformaldehyde

PORN = polyornithine

PSD-95 = Postsynaptic Density 95

PVDF = Polyvinylidene fluoride

RNA = ribonucleic acid

RT = room temperature

RT-PCR = reverse transcription polymerase chain reaction

s = second

SMA = Smooth Muscle Actin

Sox2 = Gene name SRY (sex determining region Y)-box 2

SPG11 = Spastic paraplegia gene 11

SPG11-EBs = SPG11 patient iPSC derived EBs

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Abbreviations

SPG11-NPCs = SPG11 patient iPSC derived NPCs

SPG11-NRs = SPG11 patient iPSC derived NRs

SPG11-dNeurons = SPG11 patient iPSC derived neurons

TAE = buffer containing Tris base, acetic acid and EDTA

U = unit

V = Volt wt = wild type

µl = microliter

µm = micrometer

µM = micro molar

% = per cent

°C = degree Celsius

133

Acknowledgements

12. Acknowledgements

I would like to express my sincere gratitude to my supervisor Prof. Beate Winner, for her constant guidance, academic support and unfailing encouragement throughout my PhD work. Her strong motivation, scientific expertise, generous care and patience encouraged my research work, instilled faith in my scientific endeavours and allowed me to excel as a research scientist. Her personal attention and advice on both, research as well as on my career have been invaluable and I feel fortunate, having got an opportunity to work under her able supervision.

I am also greatly indebted to my mentor and guide, Prof. Juergen Winkler, for his valuable advice, constructive criticism and his extensive discussions around my work. His intellectual feedbacks, constant revision, meticulous analysis and interpretation of scientific data, have been a source of inspiration during the happy and hard moments to push me and motivate me, to seek new challenges in my scientific persuits. I would also like to thank Prof. Winkler and Prof. Robert Slany for the critical evaluation of my thesis.

I am indebted to my labmates Holger Wend, Daniela Gräf, Naime Denguir, Lukas Anneser, Tom Boerstler, Diana Schmidt, Annika Sommer, Martin Regensburger, Tania Rizo and Katrin Simmnacher for providing a stimulating and fun filled atmosphere all throught my stay in Erlangen. My thanks go in particular to Steven Havlicek, Iryna Prots and Francesc Perez- Branguli with whom I started this work and many rounds of discussions, coupled with their critical insights and technical advice, helped me in timely completion of the experiments. Most of the results described in this thesis would not have been obtained without a close collaboration with different laboratories and therefore, I would like to thank Zacharias Kohl, Leah Boyer, Martin Hampl, Sonja Plötz, Marina Leonne, Martina Bruekner, Prof. Angelika Lampert, Prof. Ursula Schlötzer-Schrehardt, Prof. Fred Gage, Prof. Teja Groemer, Prof. Felix Engel and Prof. Juergen Behrens for all their significant contributions to this study.

Last but not the least, I would like to thank my parents, Vivekanand Mishra and Puspa Mishra; my brother Sudhanshu Mishra and sister Archana Rajhans, for all their sincere encouragement and inspiration throughout my research work, and lifting me uphill during this phase of inevitable ups and downs in life. I dedicate this work to them.

134

Supplementary Information

13. Supplementary Information

Table 6: Primers used for qRT-PCR analysis

Genes Forward Primer Reverse Primer KIF5A TTACCTGGACAAAATTCGTGACC GGTGACAGCCACATGACGAT

KIF3A CTGCCTTGGTTGATGGAAAAAG CGATTGGCATACCGTAATGTACT

KLC1 CAGCTAACCTACTGAATGATGCC GCTCTTTTACACAACGGCTCT

DYNC1LI2 GGCTAGTGTTTTACGTGAGCA TGGGGAACCTTGACAACCTTC

PSD95 GTGGAGGAGATTCGAGGCTTC ACGAACTTTGTTTGCTGTCTTCT

VAMP2 CTCAAGCGCAAATACTGGTGG TGATGGCGCAAATCACTCCC

TTBK1 GCTGTGGCAGGAACGAGAA AGTTTGAAGGCTTGATGTCACG

MAPT GTGGCCAGGTGGAAGTAAAATCT GGTCAGCTTGTGGGTTTCAATCT

SYN1 AGCTCAACAAATCCCAGTCTCT CGGATGGTCTCAGCTTTCAC

SYT12 CAAAGGCAGTCTCAGCATTGA CCAAAGGTGTTGCTCACGG

CDK4 ATGGCTACCTCTCGATATGAGC CATTGGGGACTCTCACACTCT

CDK6 TCTTCATTCACACCGAGTAGTGC TGAGGTTAGAGCCATCTGGAAA

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Supplementary Information

Genes Forward Primer Reverse Primer CDK2 CATTCCTCTTCCCCTCATCA CAGGGACTCCAAAAGCTCTG

CDK1 TTTTCAGAGCTTTGGGCACT CCATTTTGCCAGAAATTCGT

GADD45α GAGAGCAGAAGACCGAAAGGA CAGTGATCGTGCGCTGACT

GAPDH TGTTGCCATCAATGACCCCTT CTCCACGACGTACTCAGCG

Tab le 7: List of antibodies

Target Species Producer Application Dilution

Actin Ms Sigma WB 1:1000

Alexa 488 Dk Jackson IR IF 1:800

Alexa DyeLight 488 Dk Jackson IR IF 1:800

Alexa 555 Dk Jackson IR IF 1:800

Alexa DyeLight 549 Dk Jackson IR IF 1:800

Alexa 647 Dk Jackson IR IF 1:800

Alexa DyeLight 649 Dk Jackson IR IF 1:800

Aurora B Ms BD Transduction IF 1:200

BrdU Rt Abcam IF 1:100

ß-Catenin Rb Santa Cruz WB 1:1000

Cleaved Caspase3 Rb Cell Signaling IF 1:1600

Ctip2 Rat Abcam IF 1:300

Cyclin D1 Ms Santa Cruz WB 1:500

DCX Gt Santa Cruz IF 1:200

GAPDH Ms Calbiochem WB 1:15000

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Supplementary Information

Target Species Producer Application Dilution

GATA4 Rb Santa Cruz IF 1:200

GFAP Gt Abcam IF 1:1000

Calbindin Rb Cell Signaling IF 1:250

GFP Chk Invitrogen IF 1:1000

GSK3ß Rb Abcam WB 1:1000

γ- Adaptin Ms BD Transduction WB 1:5000

H3P (ser10) Rb Millipore IF 1:200

Map2a/b Ms Sigma IF 1:400

Nanog Gt R&D Systems IF 1:200

Nestin Ms Millipore IF 1:300

PSD-95 Gt Abcam IF 1:400 p-GSK3ß (ser9) Rb Cell Signaling WB 1:1000 p27 Kip1 Ms BD Transduction WB 1:2000

PCNA Ms Santa Cruz IF 1:50

SMA Ms Sigma IF 1:400

Sox2 Rb Cell Signaling IF 1:300

Spatacsin Rb Prteogenix IF 1:250

Spatacsin Ms Abcam WB 1:500

Synaptophysin Ms Sigma IF 1:500

Tau Gt Santa Cruz IF 1:50

Tubulin, alpha isoform Rb Cell Signaling WB 1:15000

Tubulin, β3 isoform Ms Covance IF 1:350

Tubulin, β3 isoform Rb Covance IF 1:350

Tra1-60 Ms Millipore IF 1:200

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Supplementary Information

Target Species Producer Application Dilution

Tubulin, acetylated Ms Sigma IF 1:1000 v-Glut2 Ms Synaptic System IF 1:1000 v-Gat Ms Synaptic System IF 1:250

VAMP2 Ms Synaptic System WB 1:2000

SNAP25 Ms Synaptic System WB 1:2000

NeuN Ms Millipore IF 1:250

MARCKS Rb Abcam WB 1:250

Synaptophysin Ms Sigma WB 1:1000

Syntaxin1 Ms Sigma WB 1:2000

Oct4 Ms Santa Cruz IF 1:200

Abreviations: Chk, chicken; Dk, donkey; Gt, goat; IF, immunofluorescence; Ms, mouse; Rb, rabbit; WB, Western blotting

Tab le 8: List of chemicals, media, and reagents

Name Producer 2-Propanol Carl Roth, Karlsruhe Acetic acid Carl Roth, Karlsruhe Acrylamide/bisacrylamide solution (29:1, 30%) Carl Roth, Karlsruhe Adenosine 3′,5′-cyclic monophosphate (cAMP) Sigma Aldrich, Steinheim Agar Life Technologies, Karlsruhe Agarose Bioline, Luckenwalde Ammoniumchlorid Carl Roth, Karlsruhe Ammoniumperoxodisulfate (APS) Carl Roth, Karlsruhe Ampicillin Bioline, Luckenwalde Aquapolymount Polysciences, Eppelheim

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Supplementary Information

Name Producer Ascorbic acid Sigma Aldrich, Steinheim Beta mercaptoethanol Sigma Aldrich, Steinheim Blotting Grade Blocker BioRad, München Brain-derived neurotrophic factor, human (BDNF) Preprotech, Rocky Hill, USA Bromophenol blue Carl Roth, Karlsruhe Complete mini EDTA- free protease inhibitor tablets Roche Diagnostics, Mannheim DAPI (4’,6-diamidino-2-phenylindol) Merck, Darmstadt Deoxynucleotide triphosphates (dNTPs) NEB, Frankfurt

Dimethylsulfoxide (DMSO) Carl Roth, Karlsruhe DMEM/F12/Glutamax Life Technologies, Karlsruhe

Ethanol Carl Roth, Karlsruhe Ethidiumbromide Carl Roth, Karlsruhe Ethylenediaminetetraacetic acid (EDTA) Carl Roth, Karlsruhe Fetal bovine serum (FBS) Life Technologies, Karlsruhe Fibroblast growth factor, basic, human (FGF2) Preprotech, Rocky Hill, USA Glial-derived neurotrophic factor (GDNF) Preprotech, Rocky Hill, USA Glycerol Life Technologies, Karlsruhe Glycin Carl Roth, Karlsruhe Goat serum Carl Roth, Karlsruhe Hank’s buffered salt solution (HBSS) Sigma Aldrich, Steinheim HEPES Carl Roth, Karlsruhe Horse serum Carl Roth, Karlsruhe Hydrochloric acid (1N, 2N, 5N) Carl Roth, Karlsruhe IMDM/Glutamax Life Technologies, Karlsruhe Isopropanol (2-propanol) Carl Roth, Karlsruhe Knock out serum replacement (KOSR) Life Technologies, Karlsruhe Laminin, mouse natural protein Life Technologies, Karlsruhe

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Supplementary Information

Name Producer Loading dye (6x) NEB, Frankfurt Lipofectamin 2000 Reagent Life Technologies, Karlsruhe Magnesium chloride NEB, Frankfurt Methanol Carl Roth, Karlsruhe mTeSR1 medium Stemcell Technologies, Grenoble, F Nonident P40 Sigma Aldrich, Steinheim Paraformaldehyde Carl Roth, Karlsruhe Penicillin/Streptomycin Life Technologies, Karlsruhe Phosphate buffered saline (PBS) Life Technologies, Karlsruhe Poly-D,L-ornithin (PORN) Sigma Aldrich, Steinheim SB431542 Sigma Aldrich, Steinheim SOC medium Bioline, Luckenwalde Sodium chloride Carl Roth, Karlsruhe Sodium dodecyl Sulphat (SDS) Sodium dodecyl Sulphat (SDS) Sodium fluoride Sigma Aldrich, Steinheim Sodium orthovanadate Sigma Aldrich, Steinheim Sodium pyrophosphate Sigma Aldrich, Steinheim Tetraethylmethylendiamine (TEMED) Carl Roth, Karlsruhe Trishydroxychloride (Tris HCl) Carl Roth, Karlsruhe Trishydroxymethylaminomethane (Tris Base) Carl Roth, Karlsruhe Triton X100 Sigma Aldrich, Steinheim TrypLE Life Technologies, Karlsruhe Tween 20 Sigma Aldrich, Steinheim

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Supplementary Information

Tab le 9: List of cell culture media

Name Components

Differentiation medium Ascorbic acid, 200 nM

BDNF, 20 ng/ml

cAMP, 1 mM

GDNF, 20 ng/ml

N2B27 medium

Fibroblast medium IMDM/Glutamax

FBS, 15% (v/v)

HEK medium IMDM/Glutamax

FBS, 10% (v/v)

Human ES medium 2-mercaptoethanol, 55 µM

DMEM/F12/Glutamax

FGF2, 10 ng/ml

Knock out serum replacement, 20% (v/v)

NEAA (1x)

SB431542, 10 µM iPSC freezing medium DMSO, 10% (v/v)

in knock out serum replacement

Live-cell imaging buffer CaCl2, 2.5 mM

Glucose, 10 mM

HEPES, 10 mM

KCl, 2.5 mM

MgCl2, 2.5 mM

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Supplementary Information

Name Components

NaCl, 144 mM

in H2O, sterile filtered

N2B27 medium B27 supplement (1x), without VitA

DMEM/F12/Glutamax

N2 supplement (1x)

PenStrep (1x)

NPC freezing medium DMSO, 10% (v/v)

N2B27 medium

NPC medium FGF2, 20 ng/ml

N2B27 medium

Tab le 10: List of kits and master mixes

Name Producer BCA Assay Thermo Scientific, Waltham, USA ECL Western blotting detection reagent GE Healthcare, Freiburg ECL Select Western blotting detection reagent GE Healthcare, Freiburg EndoFree Plasmid Maxi Kit (10) Qiagen, Hilden Image-iT DEAD Green viability stain Life Technologies, Karlsruhe QIAshredder Qiagen, Hilden Quantitect Reverse Transcription Kit Qiagen, Hilden RNeasy Mini Kit Qiagen, Hilden SYBR Green PCR Master Mix Life Technologies, Karlsruhe TaqMan Universal Master Mix 2 Life Technologies, Karlsruhe

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Supplementary Information

Tab le 11: List of protein standards

Name Producer

Precision Plus Protein Dual Color Standards BioRad, München

Novex Sharp Pre-stained Protein Standard Life Technologies, Kalsruhe

Tab le 12: List of buffers and solutions

Name Components

APS solution Ammonium peroxydisulfate, 10% (w/v)

in H2O

Borate buffer Boric acid, 150 mM

in H2O, pH 8.35

Blocking solution (Western blotting) Blotting Grade Blocker, 5% (w/v)

in TBS-T

Laemmli buffer, (5x) 2-mercaptoethanol, 25% (v/v)

Bromopehnol blue, 0.05% (w/v)

Glycerol, 50% (v/v)

SDS, 10% (w/v)

Tris-HCl, 300 mM

in H2O, pH 6.8

Laminin solution Laminin, 5 µg/ml

in HBSS

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Supplementary Information

Name Components

Lysis buffer Glycerol, 10% (v/v)

HEPES, 50 mM

NaCl2, 150 mM

NaF2, 1 mM

Na-orthovanadate, 2 mM

NP40, 2% (v/v)

Na-pyrophosphate, 10 mM

Complete mini protease inhibitor cocktail, 1 tablet/ 10 ml lysis buffer

Matrigel solution DMEM/F12/Glutamax

Matrigel, 0.5 mg/ml

PBS ++ (antibody permeabilisation/ blocking Goat serum, 3% (v/v) buffer) PBS, (1x)

TritonX-100, 0.3% (v/v)

PFA solution Paraform aldehyde, 4% (w/v)

in H2O

PORN solution Polyornithine, 10-50 µg/ml

in borate buffer

Running buffer (10x), (for SDS-PAGE) Glycin, 2M

SDS, 10% (w/v)

Tris-base, 250 mM

in H2O

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Supplementary Information

Name Components

SB431542 (1000x) SB431542, 10 mM

in DMSO

Stripping buffer (1x) NaOH, 0.3 M

in H2O

TAE buffer (10x) Tri-acetate, 400 mM

EDTA, 10 mM

in H2O, pH 8.3

TBS-T buffer (wash buffer, Western blotting) Tris-base, 20 mM

Tween-20, 0.05% (v/v)

NaCl2, 150 mM

in H2O, pH 7.6

Transfer buffer (wet blotting) Methanol, 20% (v/v)

in running buffer

Y-27632 (ROCK inhibitor) Y-27632, 10 mM

in DMSO

Tab le 13: List of disposables

Name Producer

Cell lifter Corning, New York, USA

Cell scraper TPP, Trasadingen, Switzerland

Coverslips, glass Thermo Scientific, Waltham, USA

Coverslips, plastic Thermo Scientific, Waltham, USA

Falcon tubes (15/ 50 ml) Sarstedt, Nümbrecht

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Supplementary Information

Name Producer

Pipets (2/ 5/ 10/ 20 ml) GreinervBio-One, Frickenhausen

Pipet tips (10/ 200/ 1000 µl) Sarstedt, Nümbrecht

Pipet filter tips (20/ 200/ 1000 µl) Nerbe-Plus, Winsen

PeqLab, Erlangen

Plates (24-/12-/6-Well culture plates) Corning, New York, USA

Polyvinylidene fluoride membrane (PVDF) BioRad, München

Reaction tubes (0.2/ 0.5/ 1.5/ 2.0 ml) Eppendorf, Hamburg

T-25, T-75, or T-150 cell culture flasks TPP, Trasadingen, Switzerland

Ultra low attachment plates (6 Well) Corning, New York, USA

Whatman filter paper Hartenstein, Würzburg

Whatman filter paper, extra thick BioRad, München

Tab le 14: List of instruments Name Producer

Apotome.2, for fluorescence microscopy Zeiss, Jena

Camera, color, AxioCam ERc 5s Zeiss, Jena

Camera, monochrome, AxioCam MRm Zeiss, Jena

Camera, EMCCD, DU-885 Andor, Belfast, UK

Centrifuge, table top, 5417 R Eppendorf, Hamburg

Centrifuge, Heraeus Megafuge 1.0R Thermo Scientific, Waltham, USA

Centrifuge, Heraeus Megafuge 16R Thermo Scientific, Waltham, USA

Cytometer, FACSCalibur Becton Dickinson, Heidelberg

Developing machine, Curix60 AGFA, Mortsel, Belgium

Flow cabinet, HeraSafe KS Thermo Scientific, Waltham, USA

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Supplementary Information

Name Producer

Flow Cytometer Beckman Coulter GmbH, Germany

Fluorescence lamp, HXP 120C Pulch & Lorenz, March

Freezer, -80°C, HeraFreeze Thermo Scientific, Waltham, USA

Gel electrophoresis chamber, Mini-Protean Tetra Cell BioRad, München

Gel electrophoresis chamber (DNA/ RNA) PeqLab, Erlangen

Incubator, 37°C, Heraeus BBD 6220 Thermo Scientific, Waltham, USA

Incubator, 37°C, HeraCell 240i Thermo Scientific, Waltham, USA

Microfluidic chambers, SND450 Xona Microfluidics, Temecula, USA

Microscope, confocal, LSM 780 Zeiss, Jena

Microscope, fluorescence, Axio Observer.Z1 Zeiss, Jena

Microscope, live cell imaging, Nikon Eclipse Ti Nikon, Düsseldorf

Nanodrop ND-1000 PeqLab, Erlangen

Pipet Boy, automatic

Pipet Pipetus, automatic Hirschmann, Eberstadt

Pipet, manual (2/ 20/ 200/ 1000) Gilson, Middleton, USA

Power supply, PowerPac HC BioRad, München

Power supply, PowerPac P25 Biometra, Göttingen

Roller, RM 5 CAT, Staufen

Rotor, ultracentrifuge, SW41 Beckman Coulter, Krefeld

Shaker, orbital, Unimax 1010 Heidolph, Schwabach

Shaker, wave, Polymax 1040 Heidolph, Schwabach

SteriCup (125/ 250/ 500 ml) Millipore, Schwalbach

Thermal Cycler, C1000 BioRad, München

Thermomixer comfort Eppendorf, Hamburg

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Supplementary Information

Name Producer

Ultracentrifuge, Optima LE-80KK Beckman Coulter, Krefeld

Vortexer, Reax Top Heidolph, Schwabach

Water bath, 37°C, TW20 Julabo, Seelbach

Wet blotting module, XCell II Life Technologies, Karlsruhe

7300 Real Time PCR System Applied Biosystems, Karlsruhe

148