Derived Neural Progenitor Cells Reveal Early Neurodevelopmental Phenotype for Koolen-De Vries Syndrome

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Modeling Neurodevelopmental Disorders Using Human Pluripotent Stem Cells:

From Epigenetics Towards New Cellular Mechanisms

Katrin Linda

Financial Support for printing this thesis was kindly provided by:

Radboudumc Nijmegen Donders Institute for Brain, Cognition and Behaviour

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Modeling Neurodevelopmental Disorders Using Human Pluripotent Stem Cells:

From Epigenetics Towards New Cellular Mechanisms

PROEFSCHRIFT ter verkrijging van graad van doctor aan de Radboud Universiteit Nijmegen op gezag van de rector magnificus prof. dr. J.H.J.M. van Krieken, volgens besluit van het college van decanen in het openbaar te verdedigen op woensdag 16 december 2020 om 14:30 uur precies door

Katrin Linda geboren op 24 mei 1989 te Goch (Duitsland)

Promotoren Prof. dr. ir. J.H.L.M. van Bokhoven Dr. N. Nadif Kasri

Copromotor Dr. B.B.A. de Vries

Manuscriptcommissie Prof. dr. E.J.M. Storkebaum Dr. V.M. Heine (Amsterdam UMC) Prof. dr. Y. Elgersma (Erasmus MC)

Table of Contents

Chapter 1 General Introduction 7

Chapter 2 Rapid neuronal differentiation of induced pluripotent 55 stem cells for measuring network activity on micro- electrode arrays

Chapter 3 Neuronal network dysfunction in a model for Kleefstra 85 syndrome mediated by enhanced NMDAR signaling

Chapter 4 KANSL1 deficiency causes neuronal dysfunction by 141 oxidative stress-induced autophagy

Chapter 5 Proliferation deficit in patient- derived neural progenitor 195 cells reveal early neurodevelopmental phenotype for Koolen-de Vries Syndrome

Chapter 6 Discussion 229

Appendix

Summary 254

Samenvatting 256

Acknowledgements 258

About the Author 261

Portfolio 262

Research Data Management 266

Introduction

Katrin Linda

Part of this chapter has been published as a review:

Linda, Katrin & Fiuza, Carol & Nadif Kasri, Nael. (2017). The promise of induced pluripotent stem cells for neurodevelopmental disorders. Progress in Neuro-Psychopharmacology and Biological Psychiatry.

Neurodevelopmental disorders (NDDs), including intellectual disability (ID) and autism spectrum disorders (ASD) are genetically and phenotypically highly heterogeneous and present a major challenge in clinical genetics and medicine. The identification of underlying genetic defects and risk factors is becoming increasingly efficient due to genome-wide interrogation methodologies1–3, yet the mechanisms underlying the pathophysiology for most of these disorders remain elusive. An emerging concept is that the genetic deficits converge on certain key ‘hub’ signaling pathways, whose deregulation is common to many syndromes and underlies prominent synaptic dysfunction and protein misregulation. Genetic information obtained from disease causing mutations has been used as a molecular stepping- stone to understand the underlying pathophysiology by manipulation of disease genes in cellular or animal models such as rodents, flies and zebrafish. Further mechanistic insights into cognitive disorders have been inferred from post-mortem studies. These disease models have, however, obvious limitations in the sense that they either do not recapitulate the exact mutation and/or genetic background of the patients, in case of animal models, or they do not allow discerning causality versus compensation mechanisms, in case of post-mortem analysis. In 2006 Yamanaka and his colleagues revolutionized disease modeling by successfully reprogramming murine fibroblasts to a pluripotent cell type, so-called induced pluripotent stem cells (iPSCs)4. One year later they succeeded to reproduce the protocol for human fibroblasts5. In the past ten years, technologies to generate iPSCs have vastly improved6. IPSCs can be generated from all sources of somatic cells, including blood cells, keratinocytes, and buccal cells, eliminating the need to perform skin biopsies7. Next to this, there have been many technical advances enabling the differentiation from pluripotent cells towards relatively pure populations of specific neuronal subtypes, including excitatory neurons, specific subtypes of inhibitory neurons, astrocytes, oligodendrocytes and microglia8. The differentiation process appears to follow physiological developmental stages of progression, lineage restriction and neuronal/ synaptic maturation that can be directly observed using appropriate reporter lines, live-cell imaging modalities, and single-cell as well as neural network electrophysiological recordings. This allows recapitulating disease progression step- by-step in 2D and 3D. Brain organoids, for example, have recently emerged as an

8 exciting new human brain model, recapitulating the early steps of brain development in health and disease9,10. Furthermore, the use of patient and control-derived iPSCs provide an unlimited supply of material for molecular, proteomic and electrophysiological studies as well as enabling the observation of normally inaccessible neurodevelopmental processes.

More than 1000 monogenic causes of ID have currently been identified (sysid.cmbi.umcn.nl)11. Analyses of the molecular and cellular pathways have identified signaling cascades linked to synaptic function. Therefore part of the ID and ASD related disorders may be considered as synaptic disorders or “synaptopathies”12. Indeed, frequently observed neuronal features in patients with ID (postmortem material) are alterations in the size, shape, stability and/or number of dendritic spines. In accordance, dendritic spine phenotypes and associated changes in synaptic transmission and plasticity in mouse or cellular knock-down studies for ID/ASD genes have been identified13.

Although many ID/ASD gene products are expressed at the synapse, a large part of ID/ASD genes also code for epigenetic modifiers. Epigenetic mechanisms showed to regulate the expression of genes that affect brain development at multiple levels, like neurodevelopment, synapse formation, function and regulation (reviewed by Kleefstra et al.)14. However, to enable targeted, effective therapies, a better understanding of how changes in epigenetic regulation affect common cellular and molecular mechanisms, especially during human brain development and within neuronal cells, is required. Research summarized in this thesis focusses on two examples of NDDs caused by mutations in genes encoding for proteins of histone modifying complexes: Kleefstra syndrome (KS) and Koolen-de Vries syndrome (KdVS). Through these examples we illustrate how patient-derived iPSCs can efficiently be used to examine disease material of human origin, understand disease mechanisms and allow for therapeutic intervention, in cells that carry the genetic background of individual patients.

1. Neurodevelopment and Synaptic Communication

Brain development is a timely tightly regulated program that requires proper coordination of gene expression and protein interactions to control survival, proliferation, differentiation and migration signals. Among the different brain regions, the cerebral cortex belongs to the most complex biological structures, and is known to be essential for human-specific higher cognitive functions. In line with its’ complex functions, cerebral cortex organization displays various levels of complexity. It contains lots of different types of neurons in different cortical areas and layers. Cortical neurons can be divided in two main cell classes: Pyramidal excitatory neurons and inhibitory interneurons. A majority (70-80%) of cortical neurons are pyramidal neurons that send long-range projections to other cortical or subcortical target regions15,16. The remaining proportion of cortical neurons are interneurons that display only local connectivity16. Pyramidal neurons and interneurons can be further divided into subclasses of specialized neuronal cells and within the cortex these neuronal cells are organized in 6 layers17.

Formation of the mammalian cortex starts during embryonic development with the establishment of the neural plate. During further developmental steps the neural plate acquires regional identity. During this regionalization the dorsal telencephalic progenitor territory is formed, which will develop into the cerebral cortex. Symmetric cell divisions of neural stem cells (NSCs) generate a layer of cells called the ventricular zone (VZ) at the rostral end of the neural tube. Downregulation of epithelial markers initiates the transition into asymmetrically dividing radial glia (RG) cells18–20. One daughter cell remains in the VZ to maintain the RG pool serving as a scaffold for migrating, differentiating RGs. Those differentiating cells generate neuronal and glial cortical progenitors that will give rise to distinct types of postmitotic neurons, forming the six cortical layers in an inside-out manner17. While excitatory neurons migrate from the dorsal telencephalon, GABAergic interneurons differentiate from progenitors of the ventral telencephalon.

The evolution of the neocortex in mammals is thought to be a crucial advance, which allowed higher cognitive function. Although previously described processes during cortical development are evolutionary highly conserved, shape, size, and neuron 10 number of neo-cortices of different mammalian species display huge variation21,22. Throughout evolution the human cortex has greatly expanded, which is associated with increased cognitive abilities. Expanded cortical volume and surface area are mainly resulting from increased proliferative capacity of the NPCs at early developmental stages23–26. Compared to mice, the developing cortices of humans and other gyrencephalic mammals harbor a larger VZ and SVZ, reflecting the increased number of progenitor cells. Human specific genes that are active during human corticogenesis, like SRGAP2, ARHGAP11B, NOTCH2NL, and FZD8 could be associated with improved maintenance and clonal expansion of human cortical progenitors27–32. An elongated amplification period allows an increased number of divisions within the progenitor cell population24,33. Elevated numbers of NPCs eventually not only rise the capacity of generating a much larger number of neurons, but also provide structural support for neuronal migration through their fibers34. Next to the elongated progenitor amplification period another hallmark of gyrencephalic species is an increased RG cell diversity, which positively affects the complexity and the huge variety of neuronal cell types in the developing cortex of primates35,36. Special emphasis lies on the highly abundant, human-specific, so-called outer RG cells25,26.

1.1 Synapse formation and maintenance Neurons communicate with each other through electrical signaling at specialized sites called synapses. Synapses serve as the basic functional units in neuronal communication to process, transmit and store information37–39. They are composed of a presynaptic terminal and a postsynaptic target site, separated by the synaptic cleft40,41. Communication between neurons works through the release of chemical messengers (neurotransmitters) from the presynaptic neuron, that diffuse across the synaptic cleft and bind to appropriate receptors at the postsynaptic cell. Synapse formation and maintenance are highly regulated processes, which depend strongly on tightly controlled protein-protein interactions to support neurotransmitter release and reception. Simplified, neurotransmitter containing synaptic vesicles are docked and primed for release in a specialized region on the presynaptic membrane, the active zone42. When a signal is transmitted the vesicles fuse with the presynaptic

11 membrane and neurotransmitters are released. After crossing the cleft (~20 nm) they bind to their corresponding receptors on the postsynaptic side. Depending on the neurotransmitter two major categories of synapses have been classified: excitatory and inhibitory synapses. The main neurotransmitter of excitatory synapses is glutamate. When glutamate binds to its’ postsynaptic receptors, namely α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPAR) and N-methyl-D-aspartate receptors (NMDAR), these receptors open and positively charged ions stream into the cell. The inflow of those ions depolarizes the membrane potential and, when the threshold is reached, an action potential is evoked and the signal will be propagated43. Inhibitory neurons release the neurotransmitter γ-Aminobutyric acid (GABA), which causes opening of GABARs at the postsynaptic site, resulting in the inflow of negatively charged Cl- ions. Negatively charged ions hyperpolarize the membrane potential. Thereby inhibitory synapses suppress action potential formation and refine the amount of excitatory synapses44.

In human prefrontal cortex, excitatory synaptogenesis begins approximately after 18 weeks of fetal gestation45. In an immature state, excitatory synapses form between the presynaptic axon and the dendritic shaft or filopodia-like spine precursors. During development these spine precursors mature into polarized mushroom-shaped structures with a distinct head and neck46. The spine head encompasses the most prominent postsynaptic component of excitatory synapses, the postsynaptic density (PSD). The PSD is an electron-dense structure that contains a variety of scaffolding proteins like PSD95, Shank, and Homer47,48. Scaffold proteins are required to cluster glutamate receptors, recruit signaling proteins, and to anchor those components to the microfilament cytoskeleton of the spine

Synaptic development in mammalian cerebral cortex is a dynamic process involving simultaneous formation and elimination 49,50. During early neuronal development new neurons and synapses are formed at an amazing rate. An excessive number of excitatory synapses is essential for the assembly of neural circuits. The density of dendritic spines peaks in early childhood around the age of two years. However, by then, the number of neurons and synapses far exceeds the number of synapses that is functionally needed or even preferred. A process through which these extra

12 synapses are eliminated is synaptic pruning. A steep decline of synapses during late childhood and adolescence provides selection and maturation of synapses and neural circuits and increases the efficiency of the neurological system51.

1.2 Synaptic plasticity The remarkable computational power and cognitive capacity of the brain originates not only from the enormous molecular and functional diversity of neuronal cells and the huge number of synapses, but it also relies on the capacity of synapses to strengthen or weaken in response to activity. So-called synaptic plasticity is thought to contribute to learning and memory and other cognitive functions. The idea that changes in the synaptic strength mediate the storage of information acquired during learning has a long history. At the beginning of the 20th century Ramón y Cajal was the first one hypothesizing that learning results from changes in synaptic strength. Later Donald Hebb refined this hypothesis52. Until today huge experimental evidence from different brain regions53–55 lead to the well-accepted theory of long-lasting increases and decreases in synaptic strength, known as long-term potentiation (LTP) and long-term depression (LTD), as the cellular mechanisms of learning and memory56,57.

While the number of NMDARs at each synapse remains relatively stable, the number of AMPARs varies markedly58,59. By regulating exo- and endocytosis of AMPARs synaptic strength can be changed60–63. At the synapse NMDAR activation is followed by increased Ca2+ levels. This increase then activates Ca2+-calmodulin-dependent kinase (CaMKII). CaMKII triggers AMPAR insertion into the postsynaptic membrane through phosphorylation of downstream targets, including Ras, and thereby elevates synaptic efficacy64.

Exo- and endocytosis of AMPARs play a critical role not only in synapse-specific, so-called Hebbian plasticity, but also in homeostatic plasticity65. Synaptic scaling, a well characterized form of homeostatic plasticity, monitors and scales synapses globally to stabilize neuronal network activity66,67. Negative feedback processes help to scale neuronal postsynaptic strength within the entire synapse population to compensate for increased or decreased overall input. Chronically increased network

13 activity triggers AMPAR removal from synapses, while chronic suppression increases synaptic AMPAR abundance68. Hyper- or hypoactivity would otherwise cause neuronal network dysfunction, which might then, for example, manifest in epileptic seizures.

AMPARs mediate the overwhelming majority of fast excitatory neurotransmission in the CNS and are essential for regulating synaptic strength by means of synaptic plasticity. Therefore, they are especially important for nearly all aspects of brain function, including learning, memory, and cognition. AMPARs are heteromeric ligand-gated ion channels composed of four separate subunits (GluA1-4). The composition of AMPAR heteromeric subunits varies upon developmental stage. GluA4, for example, is expressed mainly during early development and present at low levels in adult brain, whereas adult (rat) hippocampal neurons mainly comprise combinations of GluA1/2 or GluA2/3 AMPARs69. While the extracellular and transmembrane regions of AMPAR subunits are very similar, the intracellular regions, that contain the regulatory domains for various signaling pathways, are variable. GluA1, and GluA4 have a long cytoplasmic carboxy-terminal tail (C-tail) and are rapidly mobilized from the receptor pool in the Endoplasmic Reticulum (ER) to the cell surface. GluA2 and GluA3 contain short C-tails and traffic slowly from the ER. During LTP this means that GluA1 containing AMPARs are activity-dependently delivered to synapses and later replaced by GluA2/370,71. The GluA2 subunit contains an RNA editing site that substitutes the glutamine residue Q607 to an arginine residue (Q/R editing)72. In adult neurons almost all GluA2 subunits are edited, what additionally slows down ER trafficking72. RNA editing of the GluA2 subunit also determines Ca2+ permeability of AMPARs. Only AMPARs containing an unedited GluA2 subunit or GluA2-lacking AMPARs are calcium-permeable. LTP induction triggers the insertion of GluA2-lacking AMPARs to the synapse. The Ca2+ influx then is thought to drive the insertion of GluA2-containing receptors to stabilize LTP73.

1.3 Autophagy Autophagy is a process that was only recently discovered to play a role during synapse formation, function, and maintenance74–76. Autophagy is a catabolic process 14 important for the clearance of cytosolic contents, protein aggregates and damaged organelles, which is essential for cell homeostasis and survival. Upon autophagy activation double-membrane vesicles, so called autophagosomes, sequester cytosolic contents for degradation. Autophagosomes then fuse with lysosomes to form autolysosomes. In the acidic environment of autolysosomes proteins and organelles are subsequently degraded by lysosomal enzymes77. There is emerging evidence for an important physiological function of autophagy in neuronal health (reviewed by Liang et al., Vijayan et al.)78,79.

Besides its’ role in maintaining cellular homeostasis and facilitating a stress response in neurons, autophagy is also an important mechanism during neurodevelopmental processes. Results of various studies point towards a role for autophagy in negative regulation of axonal outgrowth80–82. Furthermore, impaired autophagy through knockdown of several autophagy related (ATG) genes in Drosophila melanogaster resulted in a reduced number and size of presynaptic terminals at the neuromuscular junction83. Furthermore, autophagy showed to play a role in synaptic pruning, the loss of synaptic spines. Over-activation of the mechanistic target of rapamycin (mTOR), known to inhibit autophagy, causes reduced synaptic pruning and increased spine density in autism spectrum disorders84. Additionally, autophagy impairment in neuron surrounding glial cells and microglia also led to pruning deficits85.

To enable the dynamics of synaptic plasticity (e.g. activity-dependent modifications in synaptic strength and efficacy), high protein turnover is required in which autophagy possibly plays a role pre- and postsynaptically by controlling the number of synaptic vesicles, as well as the number of receptors, respectively. Reactive oxygen species (ROS) are thought to be essential signaling molecules during induction of synaptic plasticity86 and local light-activated increase ROS induces autophagy at the synapse87, pointing towards a function for autophagy in ROS induced synaptic plasticity. In hippocampal neurons chemical LTD induction caused increased autophagosome formation that was thought to play a role in AMPAR degradation88. In line with these findings Nikoletopoulou and colleagues showed that autophagy is involved in BDNF-mediated synaptic plasticity and changes of synaptic

15 transmission by facilitating degradation of postsynaptic proteins89. Furthermore, autophagy plays a role in increased glutamatergic synaptic transmission in hippocampal neurons after melamine exposure, an industrial chemical 90. Taken together these findings suggest a critical role for autophagy regulation in neuronal development and synaptic function.

2. Neurodevelopmental Disorders

Neurodevelopmental disorders (NDDs) summarize a highly heterogenous group of disorders in which the development of the central nervous system is impaired. With a frequency of roughly 1 in 10 children in the U.S.91 and an onset in early childhood with often lifelong courses, care and the costs represent major challenges for healthcare systems. Intellectual disability (ID), attention-deficit hyperactivity disorder (ADHD), and autism spectrum disorders (ASD) represent common NDDs, but also neuropsychiatric disorders, such as schizophrenia and bipolar disorder, as well as conditions like epilepsy belong to this group of disorders. The wide range of symptoms includes impaired motor function, cognition, learning, verbal and non- verbal communication, and neuropsychiatric problems, all indicating abnormal brain function.

Tight regulation and the temporal and spatial complexity of genetic and environmental interactions make neurodevelopment highly vulnerable to changes. Deviation from the normal developmental trajectory affect cell proliferation, differentiation, migration, neuronal architecture, and function and ultimately lead to abnormal neuronal connectivity and a neurodevelopmental phenotype45,92. NDDs showed to be highly heritable, but generally have a multifactorial etiology. Next to a major genetic predisposition various environmental stressors, including chemical pollutants, nutritional factors, and (maternal) infection or stress, might interfere with “normal” brain development and increase the risk for neurological alterations and manifestation of clinical conditions like ID, ASD, and ADHD93. Even for NDDs with a strong genetic component the dynamic interplay between genes and environment

16 affects neuronal development and maturation and therefore determines type, onset, and severity of symptoms.

NDDs are phenotypically and genetically highly heterogenous94. The phenotypic heterogeneity, meaning that different mutations in the same gene can give rise to strikingly different phenotypes, highlights the vulnerability of neurodevelopmental trajectories and the importance of protein-protein interactions, genetic background and environmental factors. While the phenotypic heterogeneity nicely illustrates the complexity of the etiology of neurodevelopmental disorders, the genetic heterogeneity might offer a path to a better understanding of pathological disease mechanisms. Mutations in different genes can cause highly comparable phenotypes. Often these genes are known to encode proteins that function within the same cellular pathway95–98. Even stronger, the high comorbidity of NDDs indicates shared, underlying biological mechanism and identifying these mechanisms can help to develop more targeted, effective therapies. Technological advances in sequencing lead to the identification of hundreds of NDD associated genes in the past. Several of these genes encode proteins involved in neuronal function, like ion channels, or proteins essential for synaptic function, i.e. neurotransmitters and their receptors99. Mutations in those genes contribute to manifestation of NDDs by direct alteration of neural circuit functions. Maybe less intuitive is the over-representation of mutations in genes encoding proteins that play a role in regulating chromatin structure14. By changing chromatin structure these epigenetic regulatory proteins and complexes modulate gene expression. This suggests disturbed epigenetic regulation of neuronal development, and probably also function, as a central contributor to nervous system impairment in NDDs. Mutations within the group of genes that encode for proteins of the epigenetic machinery highlight the genetic and phenotypic heterogeneity of NDDs14,100.

2.1 Epigenetics in NDDs The word “epigenetic” is derived from the ancient Greek prefix “ἐπί” (epi), which means “on top of”, and “γένος” (genos), the Greek word for “gene” and literally translates to “in addition to changes in genetic sequence.” The term describes cellular mechanisms that alter gene activity at multiple levels, without changing the 17

DNA sequence. Epigenetic changes can switch genes on or off and thereby determine which proteins are transcribed. While several modifications are quite stable and can be transmitted to daughter cells others are dynamically regulated depending on environmental changes and the specific needs of a cell at a given time point. At the biochemical level, epigenetic mechanisms include DNA methylation of cytosine residues and post-translational modifications (PTM) of histones. DNA methylation of CpG islands directly interferes with transcription factors binding to promoter sites and thereby facilitates gene repression (reviewed by Li 2002; Jin, Li and Robertson 2011)101,102. Post-translational modifications of the N-terminal tails of several histones can alter chromatin structure and control gene expression positively or negatively. While acetylation opens up chromatin and is therefore associated with active gene transcription101, methylation can either facilitate repression by trimethylation of histone 3 lysine residue 9 (H3K9me3) or activation by H3K4me3101. The epigenetic machinery is composed of several classes of proteins. Categories of epigenetic players include those that introduce various chemical modifications on DNA and histones (“writers”) and enzymes that remove those tags (“erasers”). Proteins that identify and interpret these modifications are also known as “readers”. Mutations associated with NDDs are found in all three classes of those epigenetic players103,104. Chromatin regulators are ubiquitously expressed; however, cognitive deficits indicate that deficiency of various epigenetic proteins have huge effects in brain related gene expression. The most simple explanation for the over- representation of mutations in genes of the epigenetic machinery would be that these genes regulate the expression of those neuronal and synaptic genes that encode for ion channels, neurotransmitter and their receptors, that are often mutated in other NDDs105. However, the etiology of NDDs caused by mutations in epigenetic genes is a lot more complex. Epigenetic mechanisms control gene expression and therefore can affect neuronal function at many different levels ranging from differentiation and migration to circuit formation and synaptic function.

Structural brain abnormalities are characteristic for many NDDs, pointing towards abnormal early brain development106. During prenatal human brain development profound dynamic epigenomic remodeling takes place to orchestrate brain growth, differentiation and cell type specificity107,108. Alterations in the epigenetic machinery 18 can have adverse effects on the time windows of NSC expansion and neuronal differentiation resulting in disturbed cortical expansion. Recent studies indicate reversible, dynamically regulated DNA methylation throughout embryogenesis and probably the postnatal CNS, pointing towards an essential role for DNA methylation during development109. Evidence for the importance of DNA methylation in neural development is provided by a comparative and integrative analyses of the DNA methylome in 34 postmortem SVZ tissue specimens from ASD cases and age- matched, normally developing individuals. Findings suggest a delayed DNA methylation during the course of brain development in ASD patients110. Furthermore, mutations in the DNA methyltransferase gene DNMT3A are associated with ASD and ID, manifesting in the Tatton-Brown-Rahman Syndrome111–113.

Epigenetic mechanisms are not only essential to control cell identity, but dynamic regulation of epigenetic marks is especially important to adjust gene expression during developmental stages and environmental changes in order to maintain cellular homeostasis and safeguard optimal function. Impaired learning and memory is one of the most common features of various NDDs. The process of memory formation is complex and requires dynamic alterations in protein synthesis, gene expression and structural properties of neurons and synapses. Memory is first acquired and then converted to a more persistent state in a process known as consolidation, before it can be retrieved upon re-exposure to the initial environmental stimulus114. Within neurons this means that synaptic depolarization activates complex molecular signaling cascades that ultimately control transcription of different proteins. These proteins regulate lasting structural and physiological changes of the synapse115. A growing body of evidence suggests a role for epigenetic mechanisms in synaptic function and plasticity implicated in learning and memory116.

Most of the evidence for a role of epigenetic regulation in neurodevelopment and neuronal/ synaptic function originates from studies that made use of various NDD mouse models deficient for different proteins of the epigenetic machinery. More detailed examples of how aberrant epigenetic regulation affects brain development

19 and circuit formation will therefore be discussed based on concrete examples of NDDs: Rett, Kleefstra, and Koolen- de Vries syndrome.

2.2 Rett syndrome Rett syndrome (RTT; OMIM #312750) is a postnatal, progressive, neurodevelopmental disorder. It affects almost exclusively females, with a prevalence of 1 in 10.000 live births117,118. Patients with RTT develop apparently normal until 6–18 months of age, but then quickly regress, losing language and acquired motor skills, and develop ataxia, respiratory dysrhythmias, seizures, cognitive deficits, and stereotyped hand movements. Postmortem tissue analysis of RTT patients showed a 12–34% reduction in brain weight and volume, associated with abnormally small, densely packed neurons with reduced dendritic complexity and spine density119,120. MRI data showed selective reductions in the volume of the parietal and temporal lobes121.

In 90–95% RTT is caused by a loss of function mutation in the gene encoding for methyl CpG-binding protein 2 (MECP2)117, a nuclear, methyl-DNA binding protein122 that showed to suppress gene transcription123. MECP2 is ubiquitously expressed, but enriched in the brain. Protein levels are low during prenatal period and increase progressively during neuronal maturation in early childhood. Studies using mice with deficient Mecp2 expression (Mecp2−/y) indicated that Mecp2 contributes to neuronal activity and plasticity. Mecp2−/y mice show lower spontaneous cortical activity due to a reduction in the total excitatory synaptic drive and, at the same time, a rise in the total inhibitory drive124–126. Mice with a brain specific deletion of Mecp2 (Nestin- Mecp2-/y) captured the key phenotypes, like reduced movement, respiratory abnormalities, and early lethality, observed in Mecp2-null mice. This demonstrates the crucial role of Mecp2 specifically in the brain127,128. Furthermore, brain-region and cell-type specific Mecp2 loss reveals diverse effects in different cell types and brain regions. Mecp2 deletion in the spinal cord and brain stem results in abnormal breathing and heart rate129,130, while the knock-out of Mecp2 in noradrenergic and dopaminergic neurons causes motor deficits. Mecp2 knock-out in all inhibitory neurons (Viaat-Mec2p-/y) strikingly recapitulates the full knock-out phenotype131. Restoration of Mecp2 expression in GABAergic neurons showed to be sufficient to 20 rescue several symptoms in a RTT mouse model, highlighting the importance for Mecp2 in regulating function of GABAergic neurons132.

Next to region and cell type-specific effects, MECP2-loss also showed to affect multiple developmental stages. The onset of RTT symptoms at early childhood suggests that MECP2 plays a role in later brain developmental stages affecting neuronal maturation and circuit formation. Indeed, several studies observed reduced soma size, and dendritic arborization, as well as reduced spine density and abnormal spine morphology in Mecp2-deficient mice suggesting delayed neuronal maturation. However, recent evidence suggest that Mecp2 also plays a role in even earlier developmental stages affecting proliferation, differentiation, and migration. First of all, clinical studies report subtle symptoms in the classical RTT patients before the age of 6 months133–135. And, although Mecp2 expression in mouse NPCs was found to be low, NPCs extracted from Mecp2-null mice showed increased proliferative capacity and altered morphology136. These findings suggest a role for Mecp2 not only in neuronal maturation, but probably also during earlier brain developmental processes, like NPC maintenance and subsequent differentiation.

Multiple lines of evidence implicate imbalanced excitatory and inhibitory activity (E/I) as a shared pathophysiological mechanism of several NDDs like ASD, Schizophrenia, Fragile X syndrome, and epilepsy137–139. The E/I balance controls synaptic inputs to prevent inappropriate responses of neurons to input strength, and thereby helps to maintain patterns of network activity140,141. Mecp2 knockout during adulthood leads to rapid progression of RTT-like symptoms in mice, suggesting that Mecp2 is essential for the maintenance of mature neuronal networks 142,143. Neurons from Mecp2−/y mice exhibit smaller miniature excitatory postsynaptic currents (mEPSCs), with no changes in miniature inhibitory postsynaptic currents (mIPSCs), resulting in an overall reduced excitation144,145. In line with the essential function in GABAergic neurons, E/I balance in RTT mouse models showed to be shifted towards increased excitation131,146–148. Results of Banerjee and colleagues reveal a combined reduction of excitatory and inhibitory conductance in pyramidal neurons of Mecp2−/y mice, with a net increase in the E/I ratio149.

The observed reduction in dendritic complexity and spine density point towards decreased synaptic connectivity in Mecp2-deficient networks. Indeed, the number of excitatory synapses onto pyramidal neurons was found to be decreased in RTT mice. Reduced connectivity could also be proven in several electrophysiological experiments, where decreased amplitude and frequency of spontaneous excitatory neurotransmission was observed144,150–152. However, reduced excitability is probably not only caused by changes in morphology. During development a gradual shift in composition of NMDARs from contribution of the NR2B subunit to NR2A is believed to facilitate experience- or activity-dependent synaptic plasticity of neural circuits153,154. Mecp2-deficient neurons show to have decreased NR2A subunits and increased NR2B subunits compared to wildtype mice155, suggesting developmental stagnation in the replacement of NMDAR subunits that may contribute to decreased excitability of excitatory neurons. In addition, deficits in synaptic function are associated with changes in long-term potentiation (LTP), a phenomenon that is a well-characterized cellular correlate of long-term memory. Data on LTP measurements in Mecp2-null mice suggest that deficits in LTP may occur secondary to reduced synaptic connectivity between excitatory neurons150.

Until today, almost 900 different mutations in MECP2 have been identified in RTT patients. Tanaka et al. examined the global gene expression regulated by MECP2 in different patient derived iPSC lines, control human iPSCs and human embryonic stem cells (hESCs). RTT patient iPSCs are distinguishable from normal iPSCs and ESCs156. Differentially expressed genes are involved in neuronal development and mitochondrial function. Detailed comparison between patient lines revealed genotype-dependent gene expression changes156, suggesting that the mutation itself and the genetic background contribute to the variability in observed phenotypes. Results in mice studies provide additional evidence for cell type- and mutation- specific changes in gene expression157. Nuclear transcriptomes in excitatory and inhibitory cortical neurons of mice carrying frequent RTT-associated mutations (encoding T158M and R106W) reveal gene-expression changes that are largely specific to the RTT-associated mutation and cell type157. These findings highlight that genetic and phenotypic diversity need to be considered to fully understand the exact function of the protein. Future studies need to delineate effects of numerous 22

MECP2 mutations on transcriptional regulation in various cell-types during different developmental stages. However, the mutation diversity among human patients can hardly be recapitulated in mouse models.

2.3 Kleefstra syndrome Kleefstra Syndrome (KS; OMIM #610253) is a rare genetic disorder, characterized by moderate to severe ID, childhood hypotonia, and distinctive facial features including arched eyebrows with synophrys, a short nose and a tented shaped mouth with a pouting lower lip (Kleefstra et al. 2009). KS patients have a high prevalence to develop psychiatric disorders as for example autistic-like behavior, obsessive- compulsive disorder, and depression. Other frequently observed symptoms are congenital and urogenital defects, epilepsy/ febrile seizures, and microcephaly158. KS is caused by heterozygous loss of EHMT1, either by a submicroscopic 9q34.3 deletion incorporating EHMT1 or by pathogenic variants in EHMT1159. Almost all cases reported to date occurred from de novo mutations. A few rare cases with a parent having a somatic mosaicism for an interstitial 9q34.3 deletion or balanced translocation involving the 9q34.3 region have been reported. Recently mutations in EHMT1 have been associated with isolated idiopathic ASD160 and schizophrenia161,162.

EHMT1 encodes for the euchromatin histone methyltransferase 1 (EHMT1) protein, a histone methyltransferase. Together with EHMT2 it is part of a chromatin remodeling complex that catalyzes histone 3 lysine 9 mono- and dimethylation (H3K9me1 and H3K9me2). These post-translational histone modifications promote heterochromatin formation, associated with transcriptional repression163. Knockout of Ehmt1 (Ehmt1-/-) in mice leads to early embryonic lethality, indicating that EHMT1- regulated gene expression plays an essential role in early developmental processes, like cell differentiation163,164. Heterozygous loss of Ehmt1 in Drosophila could be associated with deficits in learning and memory165–167. In mice heterozygous loss of Ehmt1 recapitulates the core phenotype of KS. Impaired learning and memory was observed in fear extinction and novel and spatial recognition tasks168. Behavioral changes, like reduced exploration, increased anxiety, and altered social behavior were associated with autistic-like behavior158,169. 23

Cognitive impairments in patients as well as the animal models suggest an essential function for EHMT1 regulated gene expression in brain development and neuronal function. Increased cell proliferation in the subgranular zone of the dentate gyrus (DG), a brain region associated with adult neurogenesis, was observed in Ehmt1-/+ mice170. Common KS symptoms, including epileptic seizures and learning and memory impairments, suggest altered synaptic function and network formation. Essential for learning and memory is synaptic plasticity. While Hebbian plasticity was not affected in our KS mouse model168, homeostatic synaptic scaling showed to be deregulated171. In control conditions chronic activity deprivation increased the amount of neuronal H3K9me2 and thereby decreased Bdnf expression, a neurotrophin that is essential for homeostatic plasticity172 and thereby promoted synaptic scaling up. EHMT1-deficiency prevented synaptic scaling up in vitro and in vivo171. That los-off-function of EHMT1 causes only a few and modest changes in gene expression under basal conditions, but affects the ability to respond to changes in activity through the repression of genes involved in synaptic scaling nicely illustrates that mutations in epigenetic modifiers do not necessarily affect neuronal function by directly controlling the expression of synaptic proteins, but especially affect the ability of cells to adjust to changing environment. Higher order cognitive functions like language, learning and memory formation require postmitotic neurons to be especially adaptive. The inability of neuronal cells with an impaired epigenetic machinery to adjust might, at least partially, explain common NDD symptoms, like impaired learning and memory and communication.

2.4 Koolen-de Vries syndrome Koolen- de Vries syndrome (KdVS; OMIM #610443), also known as 17q21.31 microdeletion syndrome, is a multisystemic disorder. The main clinical features encompass a variety of symptoms like mild to severe ID, developmental delay, hypotonia, epilepsy, heart- and kidney defects, and distinct facial features, as for example a long face, tubular shaped nose with bulbous nasal tip, and low-set, prominent ears. Patients often have a character that is described as sociable, cheerful, and cooperative. The syndrome is either caused by a microdeletion in chromosomal region 17q21.31, or by a truncating variant in the KAT8 regulatory NSL

24 complex unit 1 (KANSL1) located in the 17q21.31 region173,174. Almost all known cases are caused by de novo mutations, whereas the exceptions might be due to somatic mosaicism in one parent.

The 17q21.31 deletion is typically 500 to 650 kb in size (hg19: chr17:43,700,000- 44,250,000) and incorporates several genes including CRHR1, STH, MAPT and KANSL1175–177. The region is flanked by extensive low copy repeats (LCRs) or segmental duplications that mediate nonallelic homologous recombination178. Genetic testing of the 17q21.31 genomic region is challenging due to the genomically complex variation of the 17q21.31 locus179. The main variation is caused by two distinct haplotypes: the H1 haplotype, which is most common, and the inverted H2 haplotype, that has a frequency of about 20% in European individuals180. The frequency of de novo 17q21.31 microdeletions is low, but carriers of the H2 haplotype have a predisposition for the deletion175–177,181,182. The H2 haplotype is flanked by homologous segmental duplications that increase the risk for a deletion by mediating intrachromosomal non-allelic homologous recombination or an interchromosomal unequal crossover between the H1 and H2 haplotypes178. Next to patients with the “classic” deletions several atypical 17q21.31 deletions have been described in children with clinical features typically associated with KdVS173,183,184. These atypical deletions encompass at least KANSL1. Furthermore, de novo pathogenic KANSL1 variants were identified in individuals with core clinical features of the syndrome. So far, comparison of the phenotype of patients with the microdeletion and patients with KANSL1 mutations have not revealed significant differences, further showing that heterozygous loss of KANSL1 is actually causing KdVS173,174,179.

KANSL1 encodes for a protein, KANSL1, that is known to be a scaffold of the nonspecific lethal (NSL) complex, essential for acetylation of Histone 4 on Lysine 16 (H4K16ac). H4K16ac activates gene transcription by influencing chromatin structure and the interplay with non-histone proteins185. Gene expression regulated by the NSL complex is essential for several cellular processes, and includes house-keeping genes, and genes associated with DNA repair186,187, cell cycle progression188, autophagy and cell survival189, and maintaining pluripotency in embryonic stem

25 cells190. The importance of the NSL complex during embryonic development is highlighted by the finding that Kat8 -/- mouse embryos lack the ability to acetylate H4K16 and cannot develop beyond blastocyst stage191. In non-proliferating cells H4K16ac mediated gene expression showed to be required to induce cellular stress responses192. Furthermore, a study examining the effects of Methamphetamine on glutamate receptor expression revealed decreased H4K16ac on GluA1, GluA2, and GluN1 promoters, pointing towards synapse-specific gene expression mediated by the NSL complex193.

Additional to its’ role in NSL complex formation and chromatin modulation, KANSL1 re-locates to the microtubules during mitosis. There it functions as microtubules- associated protein by promoting microtubule nucleation and stabilization of the surrounded chromosomes. This is important for the assembly of mitotic spindles. RNAi mediated knockdown of KANSL1 showed to decrease mitotic activity194,195. Furthermore KANSL1 is associated with regulation of mitochondrial transcription and respiration196.

Effects of KANSL1 haploinsufficiency have already been studied in Drosophila and mouse models, revealing deficits in short-term memory and remarkable differences in social behavior. Furthermore, results within electrophysiological studies in mice indicated impaired synaptic transmission. Gene expression showed to be changed significantly within pathways affecting synapse function, development, and neurogenesis173,197. However, the underlying cellular mechanisms how KANSL1 affects neuronal development and function remain elusive.

3. The Use of iPSCs to Model Neurodevelopmental Disorders

Recent models of NDDs have largely been based on cellular and molecular studies of mouse and rat. Although many of the key features of cortical structure are general to all mammals, including a six-layered organization and regionalization into sensory, motor, and association areas these models lack human specific features of cortical development. Rodent neocortex is relatively small and non-folded (lissencephalic),

26 meaning its’ ability to model the developmental mechanisms of larger and highly folded (gyrencephalic) neocortex, such as that of humans, is limited. Furthermore, animal models lack variability in mutations and genetic background, which could be important to evaluate how mutations in genes encoding for epigenetic modifiers can affect neuronal development and synaptic function.

The theoretical potential of using iPSCs as disease model is obvious, but it is important to understand the advantages and limitations to be able to use the full potential of this model198. To evaluate iPSCs as model for NDDs one needs to consider the different protocols available for 2D and 3D differentiation. They all offer great possibilities, but certainly come along with several individual drawbacks as well, that need to be taken into account.

3.1 Unravel human-specific mechanisms of neurodevelopment The model system that probably offers the greatest potential for modeling human brain development are brain organoids derived from human iPSCs. These self- assembled, 3D cultures of in vitro generated brain material can be used to model cytoarchitecture and developmental trajectories found in vivo10,199–202. The field of brain organoids made huge achievements in the past years. Spontaneous differentiation protocols, like the one developed by Lancaster and colleagues, provide iPSCs great freedom for self-organization. IPSCs aggregate to form embryoid bodies (EBs), that are first embedded into an extracellular matrix, like Matrigel, and, upon expansion, transferred to a spinning bioreactor providing enhanced nutrient and oxygen diffusion to promote tissue expansion and neuronal maturation10. Although omitting the inductive differentiation signaling, cell proportions and spatial organization showed to be highly unpredictable, transcriptomic analysis revealed a large variety of cell types of various lineages like forebrain, midbrain, and hindbrain, that follow the sequential stages of early embryonic development10,203. Alternatively, protocols that use patterning factors to guide differentiation to region-specific brain-organoids have a remarkably higher reproducibility. Small molecules and growth factors are applied throughout differentiation to instruct iPSCs to form cells and tissues representative of certain brain regions, such as the cerebral cortex, hippocampus and midbrain199,201,204,205. 27

While patterned protocols exhibit less variation across batches and cell lines the interference of iPSC self-organization and cell-cell interactions through the application of external factors results in organoids that are composed of relatively small, less well defined neuroepithelial structures206,207. An alternative approach for brain region-specific organoids is to remove external factors after successful patterning during the initial stages of differentiation, so that subsequent differentiation follows more an intrinsic program. This approach successfully generated large ventricle-like structures with elaborate laminar organization and architecture202,207. While unpatterned organoids might be very useful to explore cell- type diversity during whole-brain development, brain region-specific organoids are more consistent with respect to cell-type proportions. Furthermore, fusing two organoids of different brain regions, to generate so-called assembloids, allow the investigation of the interaction between specific brain regions with more consistent molecular and functional characterization208.

For successful human disease modeling similarities of organoids to human embryonic development should be higher than in rodent models. Recently, comparison of chromatin accessibility in organoids and early human fetal tissue revealed high similarities around 100 days after induction, indicating similar gene expression programs to be active209. Furthermore, the temporal order of neurogenesis in cortical organoids showed to follow the ‘inside-out’ rules that have been demonstrated in vivo. However, extensive mixing and colocalization of upper and deep layer neurons are often observed, pointing towards incomplete specification of cortical lamination202,207. Comparing human organoids to other primate iPSC-derived organoids revealed differences in the proliferative dynamics of NPCs that lead to different neurogenesis outputs, which may explain the neocortical size differences among primates20. In line with this finding, upregulation of genes involved in proliferation of RG cells in human organoids compared with chimpanzee organoids was observed in single cell RNA sequencing studies210,211. These results indicate that human organoids might enable us to unravel human specific mechanisms in NDDs.

Modeling disease in brain organoids is currently mostly limited to deficits that are related to early embryonic stages, such as cell-cycle disruption, proliferation, and differentiation10,212,213. Mitotic deficits in outer radial glia cells for example were found in organoids derived from iPSCs of patients with Miller-Dieker syndrome, a syndrome characterized by lissencephaly212. Brain organoids also offer the possibility to study the effect of environmental factors on early brain development. Well known examples for this are studies investigating how Zika virus causes microcephaly213. As already mentioned, especially those neuronal manifestations that cannot be linked to a single gene, like ASDs, are difficult to model in mice. In brain organoids generated from ASD patient cell material the balance between excitatory and inhibitory neurons was disturbed 214,215. Furthermore assembloids revealed a defect in inhibitory cell migration for Timothy syndrome, a syndrome characterized by multiorgan dysfunction including autism208.

Although all these examples illustrate the great potential that brain organoids offer in modeling early- and mid- stages of embryonic development, organoids also present several drawbacks when used to model NDDs. Neuronal cells are functional, but not mature enough to reliably investigate possible alterations during synaptogenesis and neuronal network formation within NDDs. Spontaneous neuronal activity could be detected in brain organoids, however, this required more than hundred days culturing216. Time, but especially the reproducibility regarding cell- type proportions, make large scale functional investigations and drug treatment interventions difficult.

3.2 Examine function in iPSC-derived neurons Organoids offer great potential to investigate changes in early neurodevelopment, but are less suitable when synaptic function and network formation should be examined. However, many 2D protocols have been developed and improved in the past years to differentiate iPSCs into different disease specific cell types, like astrocytes, excitatory, inhibitory, or dopaminergic neurons. Those protocols can be very useful to study cellular function and network development also in a patient- specific background. Initially the differentiation into neurons lagged somewhat behind research of other organs, given a large variability in the differentiation 29 protocols and the immaturity of the generated cells8. Recent technical advances have however enabled the differentiation from pluripotent cells towards relatively pure populations of specific neuronal subtypes with bona fide neurophysiological properties. These protocols are often highly reproducible. The possibility for high- throughput drug screenings, taking patient-specific genetic background into account, and excluding the possibility to find false positive effects, due to mechanistic differences between species, is one of the greatest potentials that development of highly reproducible differentiation protocols offers.

Unlike organoids, 2D in vitro models have been used extensively to examine functional changes in disease relevant cell types of different NDDs, including Fragile X syndrome (FXS), Williams-Beuren syndrome (WBS) and Phelan-McDermid syndrome (PMDS). Most iPSC-based disease models already proved to be valid to unravel new disease mechanisms. One of the first NDDs for which iPSCs were used to examine molecular mechanisms was RTT. Additionally, RTT patient derived iPSCs were also already used for first drug tests, making RTT a nice example to illustrate the potential of iPS cells to examine neuronal function and gain new insights in underlying molecular mechanisms, that will eventually help to develop and test new, more targeted therapies for NDDs.

In 2009 the first RTT patient-derived iPSCs were generated217 and since then these cells were used to examine neuronal phenotypes. Morphologically, neurons derived from RTT-iPSCs have smaller nuclear and soma size and reduced spine density218– 221, phenotypes reminiscent of those seen in RTT patients and mouse models222,223. At the functional level RTT-derived neurons showed decreased spontaneous Ca2+ oscillations, a reduction in frequency and amplitude of mEPSCs221, impaired action potential generation, changes in voltage-gated Na+ currents224 and an impaired GABA functional switch from excitation to inhibition due to a decrease in KCC2 expression in developing human RTT neurons225. Another study investigating RNA sequencing results in an RTT patient iPSC model of adult differentiated neurons discovered GABAergic circuit disruption. Furthermore, the same study found decreased α-tubulin acetylation resulting in disturbed cytoskeleton dynamics, which could be reverted by HDAC6 inhibition, the main α-tubulin deacetylase226. Mellios et

30 al. recently identified a role for MECP2 in controlling early human neurogenesis. In MECP2 deficient cells miR-199 and miR-214 expression levels were increased affecting ERK- and AKT-signaling pathways227. They made use of 2D iPSC-derived neuronal differentiation, as well as cerebral organoids to identify disturbed miRNA- mediated signaling downstream of MECP2 influencing neurogenesis via interactions with central molecular hubs that are already linked to ASDs. These findings provide evidence for a role of MECP2 not only in neuronal function, but also during differentiation227.

MECP2 is alternatively spliced into MECP2e1 and MECP2e2 isoforms that encode distinct proteins228,229. Most RTT mutations in MECP2 affect both, but the identification of an RTT patient in which only the MeCP2e1 isoform was affected, suggested that MECP2e1 is sufficient to cause RTT. Djruic et al. generated cortical neurons derived from patient iPSCs with isoform specific mutations in MeCP2e1230. Similar to previous studies they found a reduction in soma size, which could be rescued by MECP2e1 gene transfer, but not by increased MeCP2e2 expression, indicating that decreased soma size is due to a reduction in the MeCP2e1 transcript. Also as in agreement with earlier studies they found changes in action potential generation, voltage-gated Na+ currents224, and mEPSC frequency and amplitude221. Impairments in glutamatergic synapses were also found in neurons derived from iPSCs of two females diagnosed with an atypical RTT caused by CDKL5 pathogenic mutations231. An imbalanced expression of excitatory/inhibitory synaptic proteins could be observed in neurons derived from patient iPSCs with MECP2 and CDKL5 mutations232, but also in neurons derived from iPSCs harboring FOXG1 mutations, which is a third rare RTT causing variant233.

Initially MECP2's function was identified as a transcription suppressor, but more recent work suggests that MECP2 can also function as transcription activator by recruiting the cAMP response element binding protein (CREB) to gene promoters234. Bu and colleagues used several engineered hESC lines, carrying different MECP2 mutations and RTT-iPSC lines to study the molecular mechanisms underlying RTT. MECP2 missense mutations significantly reduced neurite outgrowth, dendritic arborization, and mitochondrial function in a genotype-dependent manner.

Furthermore, MECP2-deficient neurons consistently showed lower levels of CREB and phospho- CREB, indicating that CREB signaling indeed plays an important role in RTT pathology235.

Since RTT symptoms are so broad, several studies have tried to dissect the contribution of various brain regions and neuronal subtypes to the pathogenesis of the disorder. For example, MECP2 is expressed in several classes on interneurons, in which they contribute to distinct aspects of the RTT phenotype. In addition, MECP2 is also expressed in non-neuronal cells like microglia and astrocytes236–238. A first example of how iPSC technology might help unveil cell-type specific contribution to RTT came from a study of Williams et al. in 2014, in which they examined the effect of astrocytes differentiated from several RTT patients with different mutations on the morphology of wild-type mouse neurons. Co-culturing wild-type rodent neurons with patient-derived astrocytes resulted in significantly decreased soma size, shorter total neurite length, and a reduction in terminal ends. Similar growth deficits were observed when hESC derived wild-type neurons were used. However, there was no change in the number of excitatory synapses observed, indicating that changes in excitatory synaptic function are due to specific MECP2 function in neuronal cells239. Such findings illustrate the importance of elucidating the role of MECP2 in different cell types in order to fully understand the pathogenic mechanisms causing RTT. The possibility to generate disease specific cell-types from iPSC lines (astrocytes, microglia, interneurons)8 will enable us to examine the contribution of each cell type separately.

The re-activation of Mecp2 in Mecp2-null mice can reverse certain neurological symptoms in vivo suggesting that RTT can be treated to a certain extend240,241. A very interesting possibility for drug intervention is the application of Insulin like Growth Factor 1 (IGF1). IGF1, a modulator of plasticity and brain maturation, activates pathways that control synaptic function and is reduced in Mecp2-null mice brains242. Administration of IGF1 to Mecp2 KO mice ameliorated several RTT-like symptoms242,243. Hence, when IGF1 was applied to RTT-patient iPSC-derived neurons, it promoted an increase in the number of glutamatergic synapses221 and rescued the impaired GABA functional switch from excitation to inhibition in RTT-

32 iPSC-derived neurons225. This suggests that the RTT neuronal phenotype can be reversed in human cells too. Interestingly, IGF1 has also been shown to be able to rescue synaptic phenotypes in iPSC-derived neurons from idiopathic cases of ASD244 and Phelan-McDermid syndrome245, suggesting that IGF1 might have wide- spread beneficial effects246. In a pilot study with six young girls with classic RTT Pini et al. examined major risks associated with IGF1 administration to assess the safety and tolerability. Results showed that the treatment did not cause any validated side effects and was well tolerated by the patients. Certain improvements in cognitive, social and autonomic abilities of the girls were observed, however additional studies are needed to assess the real benefit of the treatment247,248.

Another possible target for therapeutic intervention was presented by Bu et al.235. As reported earlier they showed that CREB signaling was downregulated in MECP2- deficient cells. Hence, direct manipulation of CREB in RTT forebrain neurons, either by overexpression or pharmacological activation of CREB signaling, rescued the phenotypes in neurite growth, dendritic complexity, and mitochondrial function. Similar results were observed with rolipram, which is known to be a pharmacological activator of CREB signaling. Chronic administration of rolipram in female RTT mice rescued several behavioral defects, illustrating the potential therapeutic effect of this drug235. Since 60% of MECP2 mutations in RTT are nonsense mutations leading to decreased MECP2 expression, Marchetto et al. sought to rescue RTT phenotype through re-expression of MECP2 by the application of the antibiotic Gentamicin. Gentamicin binds 16S ribosomal RNA and thereby impairs proof reading. When applied to RTT cells with nonsense mutations a random amino acid will be incorporated at the stop codon position, so that a full-length protein is produced. One week after Gentamicin treatment iPSC-derived neurons showed increased MECP2 protein levels and higher numbers of glutamatergic synapses221.

Similar neurobiological phenotypes in previously described, independent studies, as well as the fact that neurons derived from patient cell material recapitulate the phenotypes observed in post-mortem human brain and mouse studies, support the idea that RTT iPSC-derived neurons are a valid model to examine the molecular mechanisms of RTT syndrome. Next to that it will help to understand the high

33 variability within the patient group, as certain phenotypes might be assigned to certain mutations in different regions of the MECP2 gene. The mentioned examples illustrate the potential for patient specific iNeurons to model NDDs. Next to RTT other NDD models have been investigated using iPSC-derived models over the past years. These include PMDS, FXS, and WBS. Encouraging is that most studies have been able replicate the functional findings that have been observed previously in animal models, in some cases with the added advantage of discovering new mechanistic insight. The implementation of genome editing methodologies to insert or correct point mutations to study the degree to which the observed phenotype is a result of polygenic interactions or a direct result of the mutation of interest has been extremely important for the validity of the iPSC models. Results are summarized in Figure 1, illustrating what a powerful tool iPSC are to study genetic disorders in order to dissect molecular and cellular pathways implicated in disease pathology during early stages of human neurodevelopment.

Figure 1. Summary of neuronal phenotypes observed in rodent and human models of NDDs. (Top) Figure shows the neuronal deficits at the level of neuronal progenitor cells (NPCs) and differentiated neurons for Fragile X syndrome, Rett syndrome, Williams-Beuren syndrome and Phelan-McDermid syndrome. (Bottom) Figure shows the neuronal deficits in the corresponding murine models for above- described syndromes.

4. Scope of the Thesis

To improve the quality and efficiency of NDD treatments, research to identify disease-causing mechanisms of these disorders is required. Increased understanding of molecular mechanisms underlying observed phenotypes will provide promising starting points for the development of more targeted, and hopefully efficient therapies. The above described, recent advances in the field of iPSC technology encouraged us to develop human in vitro models for Kleefstra syndrome (KS) and Koolen-de Vries syndrome (KdVS) to gain insights into neuronal abnormalities and underlying molecular mechanisms within a patient specific genomic background. Both syndromes are caused by mutations in genes encoding for proteins that are part of the epigenetic machinery. Therefore, results will help to increase our understanding of the complexity of epigenetic regulation in neural development and function and potentially identify new molecular and cellular mechanisms.

After elucidating the potential of iPSCs and iNeurons as model for investigating underlying molecular mechanisms of neuronal dysfunction in NDDs in chapter 1, chapter 2 will illustrate how a protocol for rapid neuronal differentiation driven by the transgene Neurogenin 2 (Ngn2) was optimized for measuring network activities on micro-electrode arrays (MEAs). In chapter 3 this protocol was applied for KS patient- derived iPSCs. It describes how we identified enhanced NMDAR signaling mediating neuronal network dysfunction in EHMT1-deficient cortical excitatory neurons. Chapter 4 describes the development of a human in vitro model for KdVS to examine a role for KANSL1 in autophagy regulation. Furthermore, it links deregulated autophagy to a neuronal phenotype. Chapter 5 will focus more on an early neurodevelopmental phenotype in KdVS. Results point towards a proliferation deficit in KdVS iPSCs and NPCs derived thereof. After gaining new insights into the pathogenicity of mutations in genes of the epigenetic machinery in KS and KdVS, chapter 6 will evaluate previously described results in a broader context with special emphasis on autophagy. Based on the concept that dysregulation of common key ‘hub’ signaling pathways similarly affect protein-protein interactions and synaptic function, to explain the comorbidities observed within the group NDDs, we can

36 hypothesize that autophagy is one of those ‘hubs’. Proving this hypothesis might help to develop more targeted therapies for KS and KdVS, but also for NDDs in general.

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Rapid neuronal differentiation of induced pluripotent stem cells for measuring network activity on micro-electrode arrays

Monica Frega*, Sebastianus H. C. van Gestel*, Katrin Linda, Jori van der Raadt, Jason Keller, Jon-Ruben Van Rhijn, Dirk Schubert, Cornelis A. Albers, Nael Nadif Kasri

*these authors contributed equally

This chapter has been published in:

J. Vis. Exp. (119), e54900, doi:10.3791/54900 (2017).

Abstract

Neurons derived from human induced pluripotent stem cells (hiPSC) provide a promising new tool for studying neurological disorders. In the past decade, many protocols for differentiating hiPSCs into neurons have been developed. However, these protocols are often slow with high variability, low reproducibility, and low efficiency. In addition, the neurons obtained with these protocols are often immature and lack adequate functional activity both at the single-cell and network levels unless the neurons are cultured for several months. Partially due to these limitations, the functional properties of hiPSC-derived neuronal networks are still not well characterized. Here, we adapt a recently published protocol that describes production of human neurons from hiPSCs by forced expression of the transcription factor neurogenin-212. This protocol is rapid (yielding mature neurons within 3 weeks) and efficient, with nearly 100% conversion efficiency of transduced cells (>95% of DAPI-positive cells are MAP2 positive). Furthermore, the protocol yields a homogeneous population of excitatory neurons that would allow the investigation of cell-type-specific contributions to neurological disorders. We modified the original protocol by generating stably transduced hiPSC cells, giving us explicit control over the total number of neurons. These cells are then used to generate hiPSC-derived neuronal networks on micro-electrode arrays. In this way, the spontaneous electrophysiological activity of hiPSC-derived neuronal networks can be measured and characterized, while retaining interexperimental consistency in terms of cell density. The presented protocol is broadly applicable, especially for mechanistic and pharmacological studies on human neuronal networks.

Introduction

The development of human induced pluripotent stem cell (hiPSC) differentiation protocols to generate human neurons in vitro has provided a powerful new tool for studying neurological disorders. Until recently, the study of these disorders was severely hampered by the lack of model systems using human neurons. Although rodents can be used to study neurological disorders, the results of such studies cannot be translated easily to humans1. Given these limitations, hiPSC-derived neurons are a promising alternative model that can be used to elucidate molecular mechanisms underlying neurological disorders and for in vitro drug screening.

In the past decade, several protocols to convert hiPSCs into neurons have been developed2-8. However, these protocols are still limited in many ways. First, the protocols are often time-consuming: generating neurons with adequate maturation (i.e., synapse formation) and functional activity requires months of culturing procedures, which renders large-scale studies difficult9. In addition, hiPSC-to-neuron conversion efficiency is low. Protocols often yield a heterogeneous population of neurons, and thus do not allow studies of specific subsets of neuronal cells. Moreover, the protocols are not reproducible, yielding different results for different iPSC lines10,11. Lastly, the maturation stage and functional properties of the resulting neurons are also variable10.

To address these problems, Zhang et al. (2013)12 developed a protocol that rapidly and reproducibly generates human neurons from hiPSCs by overexpressing the transcription factor neurogenin-2. As reported by the authors, differentiation occurs relatively quickly (only two to three weeks after inducing expression of neurogenin- 2), the protocol is reproducible (neuronal properties are independent of the starting hiPSC line), and the hiPSC-to-neuron conversion is highly efficient (nearly 100 %). The population of neurons generated with their protocol is homogeneous (resembling upper-layer cortical neurons), allowing the investigation of cell-type specific contributions to neuronal disorders. Furthermore, their hiPSC-derived neurons exhibited mature properties (e.g., the capability to form synapses and robust functional activity) after only 20 days.

Characterizing the electrophysiological properties of hiPSC-derived neurons at the network level is an important prerequisite before hiPSC technology can be exploited for the study of human diseases. For this reason, many research groups have recently begun to investigate stem-cell-derived neurons at the network level using micro-electrode array (MEA) devices (Multichannel Systems, Reutlingen, Germany)13-16. The electrodes of a MEA are embedded in a substrate on which neuronal cells can be cultured. MEAs can be used to explore the electrophysiological properties of neuronal networks and the in vitro development of their activity. Currently, MEAs are used only in combination with differentiation protocols that take several months to yield mature networks. Hence, combining MEAs with a rapid differentiation protocol should facilitate the use of this technology in large-scale studies of neurological disorders.

Here, we present a modification of the Zhang et al. (2013)12 protocol and adapt it for use on MEAs. In particular, rather than relying on an acute lentiviral transduction, we instead created an hiPSC line stably expressing rtTA/Ngn2 before inducing differentiation. We did this primarily to have reproducible control over the neuronal cell density, since the neuronal cell density is critical for neuronal network formation, and for good contact between the neurons and the electrodes of the MEA17,18. Although the Zhang et al. protocol is very efficient with respect to conversion of transduced hiPSCs, it is inherently variable with respect to the final yield of neurons from the number of hiPSCs plated initially (see Figure 2E in Zhang et al.)12. With a stable line, we eliminate many issues causing variability, such as lentiviral toxicity and infection efficiency. We then optimized the parameters that reliably produce hiPSC-derived neuronal networks on MEAs, obtaining network maturation (e.g., synchronous events involving a majority of the channels) by the third week. This rapid and reliable protocol should enable direct comparisons between neurons derived from different (i.e., patient-specific) hiPSC lines as well as provide robust consistency for pharmacological studies.

Protocol

All experiments on animals were carried out in accordance with the approved animal care and use guidelines of the Animal Care Committee, Radboud University Medical Centre, the Netherlands, (RU-DEC-2011-021, protocol number: 77073).

1. Glia cell isolation and culture NOTE: The protocol presented here is based on the work of McCarthy and de Vellis19, and a very similar detailed protocol for mouse astrocytes is available20. To generate primary cultures of cortical astrocytes from embryonic (E18) rat brains, a pregnant rat needs to be sacrificed, the embryos need to be harvested from the uterus, and the brains need to be isolated from the embryos. To fill a T75 flask, the cortices from 2 embryonic brains need to be combined. As an alternative, commercially-available purified and frozen astrocytes can be purchased.

1.1 Prepare the T75 culture flask 1) Dilute poly-D-lysine (PDL) in sterile, ultrapure water to a final concentration of 10 µg/ml. 2) Add 5 ml of the diluted PDL to the T75 culture flask. Swish around gently to wet the entire growth surface. 3) Place the flask in a humidified 37 °C incubator for 3 hr. 4) Aspirate the PDL from the flask. Rinse the flask 3 times with 5 ml sterile water to remove unbound PDL. Aspirate the water completely.

NOTE: The flask can be left to dry in a laminar flow hood or used immediately.

1.2 Dissection of the cortices 1) Prepare 50 ml dissection medium: Lebovitz’s L-15 medium with 2 % (v/v) B-27 supplement. Keep on ice. 2) Anesthetize the rat deeply with isoflurane in an induction chamber (small Plexiglas box) until respiration ceases (~5-8 min). Remove rat from the induction chamber and immediately euthanized by cervical dislocation. 3) Spray the abdomen of the rat with 70 % EtOH and wipe away the excess. Expose 59 and remove the uterus from the dam via Caesarean section using a pair of scissors21. 4) Cut individual embryos from their amniotic sacs with scissors, transfer to a sterile petri dish filled with cold dissection medium, and keep on ice. 5) Transfer embryos again to a new, sterile 6-cm petri dish filled with cold dissection medium. Extract brains from the embryos under a stereo microscope. To expose the brain, gently peel away the skin and skull using forceps. Gently scoop out the entire brain and transfer to a 35-mm petri dish with fresh, cold dissection medium.

NOTE: Whole brains dissected from embryos can be stored in dissection medium on ice for many hours without losing cellular viability.

6) Separate the two hemispheres of each brain by cutting through the midline with fine-tipped spring scissors or a scalpel. Carefully strip off the meninges with straight fine-tipped forceps.

NOTE: It is very important to remove the meninges completely. This prevents fibroblast contamination of the astrocyte culture. Fibroblasts are rapidly dividing cells and will eventually displace the other cells.

7) Remove the midbrain/striatum and the olfactory bulb with spring scissors or a scalpel. Also make sure to remove the hippocampus (C-shaped structure that is perimedian and caudal with respect to the cortex) with spring scissors or a scalpel. 8) Collect the cortical hemispheres in a 15 ml centrifuge tube filled with 5 ml dissection medium. Keep on ice.

1.3 Dissociation of the cortices 1) Prepare 2 ml Ca2+/Mg2+-free Hank’s Balanced Salt Solution (HBSS) with 0.25 % trypsin (dissociation medium). Prepare 50 ml high-glucose Dulbecco’s Modified Eagle’s Medium (DMEM) with 15 % (v/v) Fetal Bovine Serum (FBS) and 1 % (v/v) penicillin/streptomycin (culture medium) and filter sterilize. 2) Let the tissue settle to the bottom of the centrifuge tube. Carefully aspirate as much of the dissection medium as possible from above the tissue. 3) Wash the tissue with 5 ml Ca2+/Mg2+-free HBSS (without trypsin) and allow the 60 tissue to settle at the bottom of the tube. 4) Carefully aspirate the HBSS. Add 2 ml dissociation medium and flick the tube gently to mix the enzyme around the tissue. Incubate in a 37 °C water bath for 5–10 minutes. Flick the tube a few times during incubation to agitate the tissue. 5) Immediately triturate the tissue using a 1000 µl pipette tip. Set the pipetter volume to about 800 µl. Aspirate the pieces and eject forcefully onto the side of the tube, directly above the fluid line. However, try to minimize bubbles or foaming. Repeat until the tissue is sufficiently dissociated, about 15–20 times. 6) Add 8 ml culture medium to inactivate the trypsin. Gently mix by inverting the tube several times. 7) Pass the cell suspension through a 70 µm cell strainer placed on top of a 50 ml centrifuge tube. Rinse the 15 ml tube with culture medium and filter the medium through the cell strainer to collect the medium in the 50 ml tube with the cell suspension. Rinse the cell strainer a few times with culture medium. After rinsing, the final volume should be about 20–25 ml. 8) Pellet the cells at 200 x g for 10 min. 9) Carefully aspirate as much medium as possible, without touching the cell pellet. 10) Resuspend the cells in 1 ml culture medium using a 1000 µl pipette. Add 11 ml pre-warmed culture medium and mix gently (to prevent bubbles) using a 10 ml pipette. 11) Rinse the PDL-coated T75 flask once with 5 ml culture medium. Aspirate the medium and transfer the cell suspension to the flask. All cells are in the suspension are plated, and we find it generally unnecessary to count them, since astrocytes cannot be differentiated from other cells in the suspension.

12) Place the flask into a humidified 37 °C incubator with an atmosphere of 5 % CO2 for two days.

1.4 Expansion and maintenance of the astrocytes 1) Replace the entire medium for the first time 2 days after initial plating. Replace the entire medium afterwards every 3 days. Always pre-warm the fresh medium to 37 °C before adding to the cells.

NOTE: The astrocytes require 7–10 days to reach approximately 90 % confluency 61

(the astrocytes appear as a densely packed tessellated monolayer, with microglia and oligodendrocytes lying on top and intermixed).

2) When the astrocytes reach approximately 90 % confluency, shake the flask to remove the contaminating glial cells: - Remove the flask from the incubator and tighten the cap (phenolic) or cover the port (filtered) - To remove microglia, shake the flask on an orbital platform at 180 rpm for 1 h. - Aspirate the medium. Rinse once with 5 ml pre-warmed culture medium, aspirate and replace with 12 ml culture medium. - To remove the oligodendrocytes, return the flask to the orbital platform and shake at 250 rpm, 37 C for a minimum of 7 hr, but preferably overnight. - Aspirate the medium. Rinse once with 5 ml pre-warmed culture medium, aspirate and replace with 12 ml culture medium. Return the flask to the incubator. - When 100 % confluent, split the astrocytes using standard procedures at a ratio of 1:3 to 1:2 with 0.05 % trypsin-ethylenediaminetetraacetic acid (EDTA). A T75 flask at 100 % confluency will typically yield about 4.0 x 106 cells in total. Under this schedule, the cultures can typically be split once per week.

NOTE: When the astrocytes reach confluency, they can be harvested and used for hiPSC differentiation as described below in protocol step 3.4. The astrocytes can be split at least once without a noticeable loss of viability. They can be maintained for up to 2 months in culture. From experience, primary embryonic day 18 rat astrocytes progressively become terminally differentiated and/or lose viability after repeated splitting. Although it is possible to freeze the astrocytes for future use, we prefer to isolate the astrocytes from fresh embryonic brains when required.

2. Generation of rtTA/Ngn2-positive hiPSCs NOTE: The hiPSCs used for our experiments were generated in-house by lentiviral transduction of human fibroblasts with the reprogramming factors cMYC, SOX2, OCT4 and KLF4.

NOTE: For the generation of rtTA/Ngn2-positive hiPSCs, lentiviral vectors are used to stably integrate the transgenes into the genome of the hiPSCs. The protocol for the production of the lentivirus has been published previously22. The details of the lentiviral packaging vectors that are used to produce the rtTA and Ngn2 lentivirus particles are provided in the Table of Specific Materials/Equipment. The transfer vector used for the rtTA lentivirus is pLVX-EF1α-(Tet-On-Advanced)-IRES-G418(R); i.e., this vector encodes a Tet-On Advanced transactivator under control of a constitutive EF1α promoter and confers resistance to the antibiotic G418. The transfer vector used for the Ngn2 lentivirus is pLVX-(TRE-thight) -(MOUSE) Ngn2- PGK-Puromycin(R); i.e., this vector encodes the gene for murine neurogenin-2 under control of a Tet-controlled promoter and the puromycin resistance gene under control of a constitutive PGK promoter. Hence, by using these two transfer vectors, an hiPSC line can be created for which the expression of murine neurogenin-2 can be induced by supplementing the medium with doxycyclin. For the transduction of the hiPSCs, the supernatant with the lentivirus particles is used (referred to as ‘lentivirus suspension’ in the remainder of the text), i.e., without concentrating the particles using ultracentrifugation.

2.1 Plate the hiPSCs (day 1) NOTE: The volumes that are mentioned in this protocol assume that the hiPSCs are cultured in a 6 well plate and that the cells of one well are harvested. In addition, it is assumed that the cells are plated subsequently in 12 wells of a 12 well plate.

1) Prepare 10 ml cold DMEM/F12 with 1 % (v/v) basement membrane matrix (BMM) to obtain diluted BMM. Add 800 µl diluted BMM per well of a 12 well plate. Incubate for at least 1 hr in a humidified 37 °C incubator with an atmosphere of 5 % CO2. Before usage, incubate the plate for 1 hr at room temperature. 2) Warm 15 ml Essential 8 (E8) medium with 1 % (v/v) penicillin/streptomycin, 9 ml 63

DMEM/F12 and 1 ml cell detachment solution (CDS) to room temperature. Supplement the E8 medium with Rho-associated protein kinase (ROCK) inhibitor. 3) Aspirate the spent medium of the hiPSCs and add 1 ml CDS to the hiPSCs.

Incubate 3-5 min in a humidified 37 °C incubator with an atmosphere of 5 % CO2. Check under the microscope whether the cells are detaching from one another. 4) Add 2 ml DMEM/F12 in the well, gently suspend the cells with a 1000 µl pipette and transfer the cells to a 15 ml tube. Add 7 ml DMEM/F12 to the cell suspension. Spin the cells at 200 x g for 5 min. 5) Aspirate the supernatant and add 2 ml of the prepared E8 medium. Obtain a cell suspension in which the hiPSCs are dissociated (do not form cell clumps) by putting the tip of a 1000 µl pipette against the side of the 15 ml tube and resuspending the cells gently. Check under the microscope whether the cells are dissociated. 6) Determine the number of cells (cells/ml) using a hemocytometer chamber.

NOTE: A 6 well plate well at 80-90 % confluency will typically yield 3.0-4.0 x 106 cells in total.

7) Aspirate the diluted BMM from the wells of the 12 well plate. Dilute the cells to obtain a cell suspension of 3.0 x 104 cells/ml. Plate 1 ml of the cell suspension per well of the 12 well plate. 8) Place the 12 well plate overnight in a humidified 37 °C incubator with an atmosphere of 5 % CO2.

2.2 Transduce the hiPSCs with rtTA and Ngn2 lentivirus (day 2) 1) Warm 12 ml E8 medium with 1 % (v/v) penicillin/streptomycin to room temperature. Supplement the E8 medium with ROCK inhibitor and polybrene to a final concentration of 8 µg/ml to the E8 medium. 2) Thaw the aliquots with lentivirus suspension. Add polybrene to a final concentration of 8 µg/ml to the lentivirus suspension. 3) Aspirate the spent medium and add 1 ml of the prepared E8 medium to each well. 4) Perform the transduction with different amounts of the rtTA- and Ngn2-lentivirus suspensions. For example, transduce the hiPSCs by adding 100 µl of both the rtTA- lentivirus and Ngn2-lentivirus suspension to one well of the 12 well plate. For the other wells, use 200 µl, 300 µl, 400 µl and 500 µl lentivirus suspension instead of

100 µl. The hiPSCs of two wells of the 12 well plate should not be transduced; they will serve as controls during the selection.

NOTE: The transductions are preferably performed in duplicate, so that the transduction efficiency can be estimated more accurately after the start of the selection (see protocol step 2.4.2). The amount of lentivirus suspension that is required to efficiently transduce the majority of the hiPSCs depends on the titer of the lentivirus suspension and the hiPSC line that is used. In this study, we usually use 100-500 µl of lentivirus suspension to transduce the hiPSCs.

5) Place the 12 well plate in a humidified 37 °C incubator with an atmosphere of 5 %

CO2 for 6 hr. 6) Before the end of the 6 hr incubation period, warm 12 ml E8 medium with 1 % (v/v) penicillin/streptomycin and 12 ml Dulbecco’s Phosphate-Buffered Saline (DPBS) to room temperature. Supplement the E8 medium with ROCK inhibitor. 7) Aspirate the spent E8 medium. Wash each well with 1 ml DPBS. Add 1 ml of the prepared E8 medium to each well. 8) Place the 12 well plate overnight in a humidified 37 °C incubator with an atmosphere of 5 % CO2.

2.3 Refresh the E8 medium (day 3) 1) Warm 12 ml E8 medium with 1 % (v/v) penicillin/streptomycin to room temperature. 2) Aspirate the spent medium from the wells of the 12 well plate and add 1 ml of the prepared E8 medium to each well. 3) Place the 12 well plate overnight in a humidified 37 °C incubator with an atmosphere of 5 % CO2.

2.4 Perform selection with puromycin and G418 (day 4-8) NOTE: Depending on the cell division rate of the hiPSC line and the efficiency of the lentiviral transduction, the cells may reach 70-80 % confluency during the selection period, at which point the cultures need to be split. Because the timing of the splitting cannot be predicted in advance, it will not be mentioned in the protocol. However, 65 instead of refreshing the E8 medium supplemented with the mentioned concentrations of puromycin and G418, one can split the hiPSC culture as a normal hiPSC culture (including plating the cells on vitronectin-coated plates). The only exception is that the E8 medium should be supplemented with the mentioned concentrations of the antibiotics to continue the selection.

1) Warm 12 ml E8 medium with 1 % (v/v) penicillin/streptomycin to room temperature. Add puromycin and G418 for selection; different amounts of the antibiotics are added during the selection period (Table 1).

Table 1: Concentrations of antibiotics during the selection period. Concentrations of the puromycin and G418 during the 5 days of the selection period. Final concentration of G418 Final concentration of puromycin

Day 4 250 µg/ml 2 µg/ml

Day 5 250 µg/ml 2 µg/ml

Day 6 250 µg/ml 1 µg/ml

Day 7 250 µg/ml 1 µg/ml

Day 8 250 µg/ml 1 µg/ml

2) Estimate the efficiency of the transduction by estimating the percentage of G418- and puromycin-resistant cells. To estimate the percentage of resistant cells, estimate the percentage of dead cells (non-resistant cells) for the different conditions (the cultures transduced with the different amounts of lentivirus suspension) and for the non-transduced cells (the cells that serve as a selection control). Calculate the percentage of resistant cells as [100 % - (percentage of dead cells)].

NOTE: If the transductions with the different amounts of lentivirus suspension were performed in duplicate, the transduction efficiency can be estimated more accurately. The condition with the non-transduced cells serves as a selection control; the percentages of dead cells for the cultures transduced with the different amounts of lentivirus suspension should be lower. The estimated percentage of resistant cells 66 is used to choose the hiPSCs that are likely positive for both transgenes. In general, we choose the hiPSCs from the transduction condition were > 90 % of the cells survive the 5-day selection period.

3) Aspirate the spent medium of the hiPSCs and add 1 ml of the prepared E8 medium to the wells. 4) Place the 12 well plate overnight in a humidified 37 °C incubator with an atmosphere of 5 % CO2.

2.5 Stop the selection and start regular culturing (day 9 and later) 1) After the 5-day-selection period, culture the rtTA/Ngn2-positive hiPSCs as normal hiPSCs, with the exception that the E8 medium of the cells is supplemented with G418 to a final concentration of 50 µg/ml and with puromycin to a final concentration of 0.5 µg/ml.

NOTE: The cells can now be frozen (according to standard protocols for cryopreservation of cells) to serve as a backup. This is an important step for the reproducibility of the differentiation protocol, because it allows the use of the same batch of rtTA/Ngn2-positive hiPSCs for many future differentiation experiments.

3. Differentiation of rtTA/Ngn2-positive hiPSCs to neurons on 6-well MEAs and glass coverslips NOTE: In this protocol, the details are provided for differentiating rtTA/Ngn2-positive hiPSCs on two different substrates, i.e., 6 well MEAs (devices composed of 6 independent wells with 9 recording and 1 reference embedded microelectrodes per well) and glass coverslips in the wells of a 24 well plate. The protocols, however, can easily be adapted for larger substrates (e.g., for the wells of 12 well or 6 well plates), by scaling up the mentioned values according to the surface area.

3.1 Prepare the MEAs or glass coverslips (day 0 and day 1) 1) The day before the start of the differentiation, sterilize the 6 well MEAs at 120 °C for 1 hr and subsequently expose them to ultraviolet (UV) light for 2 hr. Sterilize the

67 glass coverslips at 180 °C for 5 hr. 2) Dilute the adhesion protein poly-L-ornithine in sterile ultrapure water to a final concentration 50 µg/ml. Coat the active electrode area of 6 well MEAs by placing a 100 µl drop of the diluted poly-L-ornithine in each well. Place the coverslips in the wells of the 24 well plate using sterile tweezers. Add 800 µl of the diluted poly-L- ornithine in each well. Prevent the coverslips from floating by pushing them down with the 1000 µl pipette tip. 3)Incubate the 6 well MEAs and 24 well plate overnight in a humidified 37 °C incubator with an atmosphere of 5 % CO2. 4)The next day, aspirate the diluted poly-L-ornithine. Wash the glass surfaces of the 6 well MEAs and the coverslips twice with sterile ultrapure water. 5) Dilute laminin in cold DMEM/F12 to a final concentration of 20 µg/ml (for the 6 well MEAs) and 10 µg/ml (for the glass coverslips). Immediately coat the active electrode area of the 6 well MEAs by placing a 100 µl drop in each well. Similarly, add 400 µl of the diluted laminin in each well of the 24 well plate to coat the coverslips. Prevent the coverslips from floating by pushing them down with the 1000 µl pipette tip. 6) Incubate the 6 well MEAs and 24 well plate in a humidified 37 °C incubator with an atmosphere of 5 % CO2 for at least 2 hr.

3.2 Plate the hiPSCs (day 1) NOTE: The volumes that are mentioned in steps 3.2.1-3.2.4 assume that the rtTA/Ngn2-positive hiPSCs are cultured in a 6 well plate and that the cells of one well are harvested. The volumes that are required for plating the cells on the 6 well MEAs and/or the coverslips depends on the number of 6 well MEAs and/or the number of coverslips that are used in the experiment; the numbers specified in steps 3.2.6- 3.2.8 allow scaling to different experiment sizes.

1) Warm DMEM/F12, CDS and E8 medium with 1 % (v/v) penicillin/streptomycin to room temperature. Add doxycycline to a final concentration of 4 µg/ml and ROCK inhibitor to the E8 medium. 2) Aspirate the spent medium of the rtTA/Ngn2-positive hiPSCs and add 1 ml CDS to the hiPSCs. Incubate 3-5 min in a humidified 37 °C incubator with an atmosphere

68 of 5 % CO2. Check under the microscope whether the cells are detaching from one another. 3) Add 2 ml DMEM/F12 in the well, gently suspend the cells with a 1000 µl pipette and transfer the cells to a 15 ml tube. Add 7 ml DMEM/F12 to the cell suspension. Spin the cells at 200 x g for 5 min. 4) Aspirate the supernatant and add 2 ml of the prepared E8 medium. Dissociate the hiPSCs by putting the tip of a 1000 µl pipette against the side of the 15 ml tube and resuspending the cells gently. Check under the microscope whether the cells are dissociated. 5) Determine the number of cells (cells/ml) using a hemocytometer chamber.

NOTE: A 6 well plate well at 80-90 % confluency will typically yield 3.0-4.0 x 106 cells in total.

6) Aspirate the diluted laminin. For the 6 well MEAs, dilute the cells to obtain a cell suspension of 7.5 x 105 cells/ml. Plate the cells by adding a drop of 100 µl of the cell suspension on the active electrode area in each well of the 6 well MEAs. For the coverslips, dilute the cells to obtain a cell suspension of 4.0 x 104 cells/ml. Plate the cells by adding 500 µl of the cell suspension to the wells of the 24 well plate.

NOTE: The final cell density on the MEAs is higher than on the coverslips (cf. Figure 1A and B). We found that this high cell density was required for proper recording of the network activity. In the protocol, the numbers are provided that turned out to be optimal for the assays.

7) Place the 6 well MEAs and 24 well plate in a humidified 37 °C incubator with an atmosphere of 5 % CO2 for 2 hr (MEAs) or overnight (24 well plate). 8) After 2 hr, carefully add 500 µl of the prepared E8 medium to each well of the 6 well MEAs. Place the 6 well MEAs overnight in a humidified 37 °C incubator with an atmosphere of 5 % CO2.

3.3 Change the medium (day 2) 1) The next day, prepare DMEM/F12 with 1 % (v/v) N-2 supplement, 1 % (v/v) non-

69 essential amino acids and 1 % (v/v) penicillin/streptomycin. Add human recombinant neurotrophin-3 (NT-3) to a final concentration of 10 ng/ml, human recombinant brain- derived neurotrophic factor (BDNF) to a final concentration of 10 ng/ml, and doxycycline to a final concentration of 4 µg/ml. Warm the medium to 37 °C. 2) Add laminin to a final concentration of 0.2 µg/ml to the medium. Filter the resulting medium. 3) Aspirate the spent medium from the wells of the 6 well MEAs and the 24 well plate and replace it with the prepared medium. 4) Incubate the 6 well MEAs and 24 well plate overnight in a humidified 37 °C incubator with an atmosphere of 5 % CO2.

3.4 Add rat astrocytes (day 3) NOTE: The volumes that are mentioned in this protocol assume that the rat astrocytes are cultured in T75 culture flasks.

NOTE: It is critical that the rat astrocytes that are added to the cultures are of good quality. We use two criteria to check if the rat astrocytes are of good quality. First, the rat astrocyte culture should be able to grow confluent within ten days after the isolation from the rat embryonic brains. Second, after splitting the rat astrocyte culture, the rat astrocytes should be able to form a confluent, tessellated monolayer (Figure 1C). If the rat astrocyte culture does not fulfill these two criteria, we advise not to use this culture for differentiation experiments.

1) Warm 0.05 % trypsin-EDTA to room temperature. Warm the DPBS and DMEM/F12 with 1 % (v/v) penicillin/streptomycin to 37 °C. 2) Aspirate the spent medium of the rat astrocyte culture. Wash the culture by adding 5 ml DPBS and swish it around gently. 3) Aspirate the DPBS and add 5 ml 0.05 % trypsin-EDTA. Swish the trypsin-EDTA around gently. Incubate in a humidified 37 °C incubator with an atmosphere of 5 %

CO2 for 5-10 min. 4) Check under the microscope whether the cells are detached. Detach the last cells by hitting the flask a few times.

5) Add 5 ml of DMEM/F12 to the flask. Triturate the cells gently inside the flask with a 10 ml pipette. Collect the cell suspension in a 15 ml tube. 6) Spin the tube at 200 x g for 8 min. 7) Aspirate the supernatant and resuspend the cells in 1 ml of DMEM/F12. Determine the number of cells (cells/ml) using a hemocytometer chamber. 8) Add 7.5 x 104 astrocytes per well of the 6 well MEAs. Add 2.0 x 104 astrocytes per well of the 24 well plate. 9) Incubate the MEAs overnight in a humidified 37 °C incubator with an atmosphere of 5 % CO2.

3.5 Change the medium (day 4) 1) Prepare neurobasal medium with 2 % (v/v) B-27 supplement, 1 % (v/v) L-alanyl- L-glutamine and 1 % (v/v) penicillin/streptomycin. Add NT-3 to a final concentration of 10 ng/ml, BDNF to a final concentration of 10 ng/ml, and doxycycline to a final concentration of 4 µg/ml. In addition, add cytosine β-D-arabinofuranoside to a concentration of 2 µM.

NOTE: Cytosine β-D-arabinofuranoside is added to the medium to inhibit astrocyte proliferation and to kill the remaining hiPSCs that are not differentiating into neurons.

2) Filter the medium and warm to 37 °C. 3) Aspirate the spent medium from the wells of the 6 well MEAs and the 24 well plate and replace it with the prepared medium. 4) Maintain the 6 well MEAs and the 24 well plate in a humidified 37 °C incubator with an atmosphere of 5 % CO2.

3.6 Refresh the medium (day 6-28) NOTE: Starting from day 6, refresh half of the medium every two days. From day 10 onwards, the medium is supplemented with FBS to support the astrocyte viability.

1) Prepare neurobasal medium with 2 % (v/v) B-27 supplement, 1 % (v/v) L-alanyl- L-glutamine and 1 % (v/v) penicillin/streptomycin. Add NT-3 to a final concentration of 10 ng/ml, BDNF to a final concentration of 10 ng/ml, and doxycycline to a final 71 concentration of 4 µg/ml. From day 10 onwards, also supplement the medium with 2.5 % (v/v) FBS. 2) Filter the resulting medium and warm to 37 °C.

2) Remove half of the spent medium from the wells of the 6 well MEAs and the 24 well plate using a 1000 µl pipette and replace it with the prepared medium. 3) Maintain the 6 well MEAs and the 24 well plate in a humidified 37 °C incubator with an atmosphere of 5 % CO2.

4. Establish the neurophysiological profile of hiPSC-derived neurons NOTE: Two to three weeks after the induction of differentiation, the hiPSC-derived neurons can be used for different downstream analyses. In this section, examples of some downstream analyses are given that can be performed to establish the neurophysiological profile of the hiPSC-derived neurons.

4.1) Characterize the neuronal network activity using MEAs

1) Record 20 min of electrophysiological activity of hiPSC-derived neurons cultured on MEAs. During the recording, maintain the temperature at 37°C, and prevent evaporation and pH changes of the medium by inflating a constant, slow flow of humidified gas (5 % CO2, 20 % O2, 75 % N2) onto the MEA.

2) After 1200 x amplification (MEA 1060, MCS), sample the signal at 10 kHz using the MCS data acquisition card. Analyze the data (spike and burst detection) using a custom software package named SpyCode23.

4.2) Characterize the single-cell electrophysiological activity

1) Transfer the cover slips containing the hiPSC-derived neuronal cultures to a submerged fixed-stage recording chamber in an upright microscope. Record 20 min of spontaneous action potential-evoked postsynaptic currents (sEPSC) (details can be found in reference 24).

2) Detect the synaptic event using MiniAnalysis (Synaptosoft Inc, Decatur, GA, USA).

4.3) Characterize the neuronal morphology and synapsin expression

1) Fix and stain the hiPSC-derived neurons for MAP2 and synapsin-1/2 (details can be found in references 22, 24 and 25).

2) Quantify the number of synapsin-1/2 puncta using image analysis software.

Representative results

Here we have successfully modified a protocol in which hiPSCs are differentiated directly into cortical neurons by over-expressing the transcription factor neurogenin- 212 and we have adapted it for the use of MEAs. This approach is fast and efficient allowing us to obtain functional neurons and network activity already during the third week after the induction of differentiation.

During the course of the differentiation protocol, the cells morphologically started to resemble neurons: small processes were formed and neurons started connecting to each other (Figure 1A). We established a neurophysiological profile of the neurons derived from a healthy-control hiPSC line, by measuring their neuronal morphology and synaptic properties during development. hiPSC-derived neurons were stained for MAP2 and synapsin-1/2 at different days after the start of differentiation (Figure 2A). The derived neurons show mature neuronal morphology already three weeks after the induction of differentiation. The number of synapsin-1/2 puncta (a measure for the number of synapses) was quantified based on synapsin-1/2 immunocytochemistry stainings. The number of synapsin-1/2 puncta is increasing over time, suggesting that the level of neuronal connectivity is also increasing (Figure 2B). The number of synapsin-1/2 puncta 23 days after the induction of differentiation is 1.14 puncta/10 µm dendrite.

To characterize the electrophysiological activity of the hiPSC-derived neurons, we used whole-cell patch-clamp recordings, i.e., intrinsic properties and excitatory input onto these neurons were measured during development. The neurons were able to generate events already one week after the of differentiation and the percentage of spiking cells was increasing over time (Figure 2C). Furthermore, the neurons received excitatory synaptic input already one week after the induction of differentiation: both frequency and amplitude of the excitatory synaptic input increased during development reaching a high electrophysiological level three to four weeks after the induction of differentiation (e.g., 14.6 ± 2.3 spike/s 23 days after the induction of differentiation) (Figure 2D-F).

To better understand how single-cell activity combines to form network-level functions, it is essential to study how neurons work in concert. In vitro neuronal networks cultured on MEAs constitute a valuable experimental model for studying the neuronal dynamics. We recorded 20 minutes of electrophysiological network activity of neurons derived from a healthy-control hiPSC line cultured on 6 well MEAs (Figure 2G). Few weeks after the induction of differentiation, the neurons derived from healthy-control hiPSCs formed functionally active neuronal networks, showing spontaneous events (0.62 ± 0.05 spike/s; Figure 2H). At this stage of development (i.e., 16 days after the start of the differentiation) no synchronous events involving all the channels of the MEAs are detected (Figure 2I). The level of network activity is increasing during the development: during the fourth week after the induction of differentiation, the neuronal networks showed high level of spontaneous activity (2.5 ± 0.1 spike/s; Figure 2H) in all the wells of the device. The networks also exhibited synchronous network bursts (4.1 ± 0.1 burst/min, Figure 2I) with long duration (2100 ± 500 ms).

Given the results, the quality of the resulting hiPSC-derived neurons can be assessed by making a neurophysiological profile of the cells. That is, three to four weeks after the start of the differentiation, the morphology, synapsin-1/2 expression and electrophysiology of the neurons can be assessed. At that time point, the hiPSC- derived neurons are expected to show a neuronal-like morphology, to be synapsin-

74 positive when performing immunocytochemistry, and to exhibit spontaneous electrophysiological activity (both at the single-cell and network level).

Figure 1. hiPSC differentiation into neurons. A) Three time points of hiPSCs differentiation into neurons on coverslips. B) Plating of hiPSCs on MEAs. C) Astrocytes at 100 % confluency in T75 flask (note that the cells form a tessellated monolayer). Scale bars: 150 µm.

Figure 2. hiPSC-derived neurons characterization. A) hiPSC-derived neurons were stained for MAP2 (green) and synapsin-1/2 (red) at different days after the start 76 of differentiation. Scale bar: 10 µm. B) Quantification of synapsin expression in two independent experiments. C) percentage of spiking cells at different days after the induction of differentiation. D) Representative traces of excitatory synaptic input received by hiPSC-derived neurons at different days after differentiation. E) Frequency of excitatory synaptic input during development. F) Amplitude of excitatory synaptic input during development. G) Neurons derived from one hiPSC line were cultured on a 6 well MEA and network activity is shown for hiPSC-derived neuronal networks 16 and 23 days after the induction of differentiation. The activity recorded from each well (sampling rate of 10 kHz) is indicated with a different color (5 minutes of the 20 minutes of recording are shown). H) Firing rate 16 and 23 days after the induction of differentiation. I. Bursting rate 16 and 23 days after the induction of differentiation.

Discussion

Here we have implemented an efficient hiPSC-differentiation protocol published by Zhang et al. (2013)12 for measuring the network activity of hiPSC-derived neuronal networks on MEAs. We adapted the original protocol by creating an rtTA/Ngn2- positive hiPSC line before inducing neuronal differentiation. This additional step allows us to control the neuronal cell density on the MEA. Control over the neuronal density was an important pre-requisite for adapting the protocol to MEAs and for ensuring consistency. To measure the activity of neuronal networks using MEAs, the neurons must form dense networks directly on top of the MEA electrodes17,18. This necessarily requires tight control over the plating density of the neurons. The rtTA/Ngn2-positive hiPSC line allows for control of neuron density because this tactic does not rely on acute lentiviral transductions of hiPSCs prior to differentiation; the rtTA/Ngn2-positive hiPSC line therefore nearly eliminates any variation in the final yield due to, for example, lentiviral toxicity and variable infection efficiency.

Another critical step of the experimental procedure is the number of the rat astrocytes that are co-cultured with the differentiating hiPSCs. Astrocytes actively contribute to the refinement of developing neural circuits by controlling synapse formation, maintenance, and elimination, all of which are important processes for neuronal functioning. The protocol presented in this paper is highly astrocyte-dependent: to fully mature and form functional synapses, the neurons require support from the astrocytes. The number of astrocytes should be roughly equal to the number of 77 hiPSC-derived neurons to support the maturation of the neurons and the formation of neuronal networks exhibiting spontaneous activity. Since our astrocyte protocol yields primary cell cultures with a limited life span, the isolation of rat astrocytes has to be performed regularly.

Our adaptation of the protocol published by Zhang et al. (2013)12 for use with MEA technology will likely significantly improve our ability to study the network activity of hiPSC-derived networks. Previously, protocols used for studying hiPSC-derived neuronal networks with MEAs relied on time-consuming differentiation procedures13- 16. The protocol from Zhang et al. (2013) provides a rapid alternative, and our modification removes a source of variability, which makes it now more feasible to use hiPSC-derived neurons in combination with MEA technology, especially in high- throughput or pharmacological studies. In addition, because the method published by Zhang et al. (2013)12 yields a homogeneous population of upper-layer cortical neurons, our adapted protocol makes possible focused studies into the network activity of this particular neuronal subset.

Nonetheless, this approach has several limitations. First, the homogeneity of the cultures can also be considered a disadvantage, because the cultures are less likely to resemble in vivo networks, where different classes of neurons (i.e., inhibitory and excitatory neurons) constitute a heterogeneous network. To further enhance the use of the hiPSC-derived neurons with MEA technology, it will be important to develop rapid (transgene-based) differentiation protocols for other neuronal cell populations. If protocols become available, the in vitro networks would mimic in vivo networks more closely. Second, at present rat astrocytes must be added to the hiPSC-derived neurons for growth support, and therefore the resulting neuronal network is not a human neuronal network sensu stricto. Reliable protocols for differentiating hiPSCs into astrocytes may in the future solve this problem26. Third, two-dimensional neuronal networks, as described here, are a limited model for studying complex three-dimensional in vivo neuronal networks. Fortunately, protocols describing three-dimensional cultures of rat primary neurons in combination with MEA technology are already available27,28. Prospectively, the combination of rapid differentiation protocols for obtaining hiPSC-derived neurons and astrocytes with 78 three-dimensional culture techniques and MEA technology should provide novel insight into the biological mechanisms underlying neurological disorders.

Acknowledgments

The authors thank Jessica Classen for performing the whole-cell patch-clamp experiments. The hiPSCs used in our experiments were kindly provided by Huiqing Zhou and Willem van den Akker from the Radboud University Nijmegen. The transfer vectors used in this protocol were kindly provided by Oliver Brüstle, Philipp Koch and Julia Ladewig from the University of Bonn Medical Centre.

References

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Neuronal network dysfunction in a model for Kleefstra syndrome mediated by enhanced NMDAR signaling

Monica Frega*, Katrin Linda*, Jason M. Keller, Güvem Gümüş-Akay, Britt Mossink, Jon-Ruben van Rhijn, Moritz Negwer, Teun Klein Gunnewiek, Katharina Foreman, Nine Kompier, Chantal Schoenmaker, Willem van den Akker, Ilse van der Werf, Astrid Oudakker, Huiqing Zhou, Tjitske Kleefstra, Dirk Schubert, Hans van Bokhoven, Nael Nadif Kasri

*these authors contributed equally

This chapter has been published in:

Nat Commun 10, 4928 (2019). https://doi.org/10.1038/s41467-019-12947-3.

Abstract

Kleefstra syndrome (KS) is a neurodevelopmental disorder caused by mutations in the histone methyltransferase EHMT1. To study the impact of decreased EHMT1 function in human cells, we generated excitatory cortical neurons from induced pluripotent stem (iPS) cells derived from KS patients. Neuronal networks of patient- derived cells exhibit network bursting with a reduced rate, longer duration, and increased temporal irregularity compared to control networks. We show that these changes are mediated by upregulation of NMDA receptor (NMDAR) subunit 1 correlating with reduced deposition of the repressive H3K9me2 mark, the catalytic product of EHMT1, at the GRIN1 promoter. In mice EHMT1 deficiency leads to similar neuronal network impairments with increased NMDAR function. Finally, we rescue the KS patient-derived neuronal network phenotypes by pharmacological inhibition of NMDARs. Summarized, we demonstrate a direct link between EHMT1 deficiency and NMDAR hyperfunction in human neurons, providing a potential basis for more targeted therapeutic approaches for KS.

Introduction

Advances in human genetics over the past decade have resulted in the identification of hundreds of genes associated with intellectual disability (ID) and autism spectrum disorder (ASD)1. Within this group of genes the number of chromatin regulators is remarkably high2-4. These ASD/ID-linked chromatin regulators are engaged in genome-wide covalent DNA modifications, post-translational modifications of histones, and control of genomic architecture and accessibility5 to control the expression of genes important for neurodevelopment and/or neuroplasticity3. Nevertheless, there is still a large gap between elucidating the genetic architecture of neurodevelopmental disorders (NDDs) and deciphering the cellular or molecular pathobiology6. In particular, we require better understanding of the relevance of genetic changes with respect to downstream functional consequences and whether there is overlap between patients within the clinical spectrum6.

Kleefstra syndrome (KS) (OMIM#610253) is an example of a rare monogenic NDD with ID, ASD, hypotonia and dysmorphic features7-9. KS is caused by heterozygous de novo loss-of-function mutations in the EHMT1 gene (Euchromatin Histone Lysine Methyltransferase 1) or by small 9q34 deletions harboring the gene7. In a complex with EHMT2, EHMT1 methylates histone 3 at lysine 9 (H3K9me1 and H3K9me2), promoting heterochromatin formation leading to gene repression10. Constitutive and conditional loss of EHMT1 function in mice and Drosophila lead to learning and memory impairments11-13. Additionally, Ehmt1+/- mice recapitulate the developmental delay and autistic-like behaviors that are observed in KS patients14,15. At the cellular level, these mice show a significant reduction in dendritic arborization and number of mature spines in CA1 pyramidal neurons11. The dynamic regulation of H3K9me2 by EHMT1/2 is also involved in synaptic plasticity and learning and memory16-18. EHMT1 and 2 are required for synaptic scaling, a specific form of homeostatic plasticity, by regulating the expression of brain-derived neurotrophic factor (BDNF)16. Yet it remains unknown how deficits caused by loss of EHMT1 mechanistically affect the development of human neuronal networks.

Human induced pluripotent stem (iPS) cell technology19 enables us to the study the specific role of individual cell types in developing neural circuits. Patient-derived neurons allow us to examine the early pathophysiology of NDDs using single-cell and neuronal network electrophysiological recordings to recapitulate disease progression20,21. Here, we generated iPS cell lines from three patients with different EHMT1 loss-of- function mutations to differentiate them into excitatory cortical neurons. Through in- depth characterization at single-cell and neuronal network level, we uncovered a robust and defined phenotype that was consistent across all patient lines and was also observed in neurons with CRISPR-engineered disruption of EHMT1. At the molecular and cellular level, we show that the electrophysiological phenotype is mediated by upregulation of NMDA receptor (NMDAR) subunit 1, which we also find in Ehmt1+/- mice. We conclude by showing that pharmacological inhibition of NMDARs rescues the KS-associated network phenotypes. Therefore, our findings establish a direct link between EHMT1 deficiency in human neurons and NMDAR hyperfunction, providing new insights into the pathophysiology of KS.

Results

Single-cell level characterization of KS neurons We generated iPS cell lines from two patients with KS and two healthy subjects

(respectively KS1, KS2 and C1, C2) (Fig. 1A and Supplementary Fig. 1, Material and

Methods). One patient (KS1) had a frameshift mutation in EHMT1 leading to a premature stop codon (p.Tyr1061fs, patient 25 in22), while the other patient had a missense mutation in the Pre-SET domain (p.Cys1042Tyr, patient 20 in8), predicted to disrupt the conformation of this domain. As expected, Western blot and real-time quantitative PCR (RT-qPCR) analyses revealed a 50% reduction of EHMT1 expression in KS1, while KS2 showed normal EHMT1 expression levels (Fig. 1B and Supplementary Fig. 2A). In addition to these lines, iPS cells were generated from an individual who has a mosaic microdeletion on chromosome 9q34 (233 kb) including

23 EHMT1 . We selected an iPS clone harboring the KS-causing mutation (KSMOS) as

88 well as a control clone not carrying the EHMT1 deletion (CMOS) (Fig. 1A and Supplementary Fig. 1, 2). This isogenic pair shares the same genetic background except for the KS-causing mutation, thereby reducing variability and enabling us to directly link phenotypes to heterozygous loss of EHMT1. Western blot analysis and

RT-qPCR analysis showed a 40% reduction of EHMT1 expression in KSMOS compared to CMOS (Fig. 1B and Supplementary Fig. 2A). All selected clones showed positive expression of pluripotency markers (OCT4, TRA-1-81, NANOG and SSEA4) and single nucleotide polymorphism (SNP) arrays were performed to confirm genetic integrity (Supplementary Fig. 1, Material and Methods).

We differentiated iPS cells into a homogeneous population of excitatory upper layer cortical neurons (iNeurons) by forced expression of the transcription factor transgene Ngn224. For all experiments, iNeurons were co-cultured with freshly isolated rodent astrocytes25 to facilitate neuronal and network maturation (Fig. 1C). All iPS lines were able to differentiate into MAP2-positive neurons at similar efficiency (Supplementary

Fig. 2C, D). EHMT1 expression was reduced in neurons derived from KS1 and

KSMOS, but not KS2 (Supplementary Fig. 2B) compared to controls. However, all KS iNeurons showed reduced H3K9me2 immunoreactivity, indicative of EHMT1 haploinsufficiency (Supplementary Fig. 2E). Twenty-one days after the start of differentiation (days in vitro, DIV), both, control and KS iNeurons showed mature neuronal morphology. We measured this by reconstruction and quantitative morphometry of DsRed-labeled iNeurons. We observed no significant differences between control and KS iNeurons in any aspect of neuronal somatodendritic morphology, including the number of primary dendrites, dendritic length and overall complexity (Fig. 1D and Supplementary Fig. 3A-F).

ID and ASD have been associated with synaptic deficits in rodents and humans21,26. We therefore investigated whether EHMT1-deficiency leads to impairments in synapse formation. To this end, we stained control and KS iNeurons for pre- and post-synaptic markers (i.e. synapsin1/2 and PSD95, respectively) at DIV 21. We observed that putative functional synapses were formed on control and KS iNeurons (Supplementary Fig. 3G), without any indications for differences in the number of synaptic puncta between the different iPS cell lines (Fig. 1E and Supplementary Fig. 89

3G). Furthermore, whole-cell patch-clamp recordings at DIV 21 of iNeurons grown in the presence of tetrodotoxin (TTX) also revealed no differences in the frequency or amplitude of AMPA receptor (AMPAR)-mediated miniature excitatory postsynaptic currents (mEPSCs, Supplementary Fig. 3H). This indicates that KS iNeurons are generally not impaired in the AMPAR component of the excitatory input they receive (Fig. 1F and Supplementary Fig. 3H). At DIV 21 nearly 90% of control and KS patient iNeurons fired multiple action potentials, indicative of a mature state of their electrophysiological properties (Supplementary Fig. 3I). When recording intrinsic passive and active properties from control and KS patient iNeurons at DIV 21, we found no differences when comparing controls with KS lines. However, we did observe minor differences in action potential decay time and Rheobase for the specific lines KS2 and KS1, respectively. These differences were not observed between the isogenic lines (Supplementary Fig. 3J-N, Supplementary Data 1). Collectively, our data indicate that there were no significant differences between control and KS patient iNeurons with regard to neuronal morphology, excitatory synapses and intrinsic properties.

Figure 1. Generation and characterization of iPS cell-derived neurons from KS patients. A) KS and control iPS lines used in this study. B) Western blot and quantification of EHMT1 protein levels in iPS cells, n=6-7 for each condition. C) Schematic presentation of the differentiation protocol, including representative images of control iNeurons immunostained for MAP2 during development (scale bar 10 µm). D) Representative somatodendritic reconstructions of control and KS iNeurons (scale bar 50 µm). E) Representative images of control and KS iNeurons stained for MAP2 (red) and synapsin 1/2 (green) at DIV 21 (scale bar 5 µm) and quantification of synapsin puncta, n=25 for C1; n=13 for C2; n=15 for CMOS; n=17 for KS1; n=11 for KS2; n=15 for KSMOS. F) Representative example traces of mEPSCs from control and KS iNeurons at DIV 21. Quantification of the frequency and amplitude of mEPSCs in control (C1, CMOS and CCRISPR) and KS (KS1, KSMOS and KSCRISPR) iNeurons (i.e. pooled value for control and KS iNeurons), n=30 for C, n=29 for KS. Data represent means ± SEM. ** P<0.005, *** P<0.0005, **** P<0.0001, one- way ANOVA test and post hoc Bonferroni correction was performed between controls and KS iNeurons and Mann-Whitney test was performed between two groups.

KS Neuronal networks show an aberrant pattern of activity Dysfunction in neuronal network dynamics has been observed in the brain of patients with psychiatric and neurological conditions27. In addition, neuronal network dysfunction has been identified in model systems for several ID/ASD syndromes20,28. Therefore, despite the normal properties of KS iNeurons on single cell level, we hypothesized that impairments during brain development caused by loss of EHMT1 would be reflected at the network level. To test this hypothesis, we examined and compared the spontaneous electrophysiological population activity of neuronal networks derived from controls and KS patients growing on micro-electrode arrays (MEAs) (Fig. 2A). MEAs allow us to non-invasively and repeatedly monitor neuronal network activity through extracellular electrodes located at spatially-separated points across the cultures.

First, we monitored the in vitro development of control neuronal networks on MEAs. We found that the activity pattern of control iNeuron networks changed progressively over several weeks (Fig. 2B-F), similarly to what was observed in rodent neuronal cultures29. In particular, during the first three weeks of differentiation, control iNeurons mainly showed sporadic action potentials, i.e. spikes (Fig. 2B-F, DIV 17), indicating they were immature and not yet integrated in a network. By week four electrical activity was organized into rhythmic, synchronous events (network burst), composed of many spikes occurring close in time and across all electrodes (Fig. 2B- F, DIV 24). This indicates that the iNeurons had self-organized into a synaptically- connected, spontaneously active network. Both the firing rate, which is a general measure of total activity across the entire network, and network burst activity increased during development but plateaued by DIV 28 (Fig. 2C, D). After this time point neuronal network activity remained stable (Fig. 2C-F). We observed no difference in the overall level or pattern of activity between the C1, C2 and CMOS at DIV 28 (Fig. 2G, I-L). The highly reproducible network characteristics observed across all controls provided us with a consistent, robust standard to which we could directly compare KS-patient derived neuronal networks.

Next, we characterized the neuronal networks derived from KS patients. Similar to controls, network burst activity appeared by the fourth week in vitro (Fig. 2H). The 92 global level of activity of KS networks was similar to controls (i.e. the firing rate, Fig. 2I). However, we found that network bursts occurred at lower frequency and with longer duration (Fig. 2J, K, M and Supplementary Fig. 4A, B). As a consequence of the lower network burst frequency, the inter-burst interval was longer (Fig. 2L, N and Supplementary Fig. 4C, D). We also observed that spike organization differed from controls, which was indicated by the smaller percentage of spikes occurring outside the network bursts (Fig. 2O). A final aspect is that KS networks also exhibited an irregular network-bursting pattern, illustrated by the statistically larger coefficient of variation (CV) of the inter-burst interval (Fig. 2P). Interestingly, the increased burst duration phenotype observed at the network level was also present at single-cell level (Supplementary Fig. S3O). Indeed, whole-cell voltage-clamp recordings of spontaneous excitatory postsynaptic currents (sEPSCs), showed that the activity received by KS-derived neurons was composed by longer burst durations than in control. Taken together, our data show that KS neuronal networks consist of fewer and irregular network bursts, and the bursts themselves were longer in duration than in control networks.

Figure 2. Spontaneous electrophysiological activity of control- and KS patient- derived neuronal networks. A) Representative image of a control-derived neuronal network on MEAs stained for MAP2 (red) and PAN Axon (green). B) Representative raster plot of electrophysiological activity exhibited by control-derived neuronal 94 network at different time points during development. C-F) Quantification of network properties as indicated. G-H) Raster plot of spontaneous electrophysiological activity exhibited by G) control and H) KS networks at DIV 28. 6 s of raw data showing a burst recorded from a representative channel are shown for each iPS line. I-L) Quantification of network parameters as indicated. M-N) Histogram showing the distribution of M) the network burst duration (bin size=100 ms) and N) the network inter-burst interval (bin size=1 s). O) Quantification of percentage of spike outside network burst and P) quantification of coefficient of variability of the inter-burst interval. n=23 for C1; n=10 for C2; n=10 for CMOS; n=15 for KS1; n=15 for KS2; n=12 for KSMOS. Data represent means ± SEM. * P<0.05, ** P<0.005, **** P<0.0001, one- way ANOVA test and post hoc Bonferroni correction was performed between controls and KS patient-derived cultures and Mann-Whitney test was performed between two groups.

CRISPR/Cas9 deletion of EHMT1 recapitulates KS phenotypes To further address whether heterozygous loss of EHMT1 is causing the observed KS patient-derived network phenotypes, we expanded our analysis and generated a second set of isogenic human iPS cells. We made use of CRISPR/Cas9 gene editing technology to generate an isogenic control and EHMT1 mutant iPS cell line with a premature stop codon in exon 2 (CCRISPR and KSCRISPR, Fig. 3A and Supplementary Fig. 5F, G). Western blot and RT-qPCR analysis revealed that EHMT1 expression was significantly reduced in KSCRISPR iPS and iNeurons compared to CCRISPR (Fig.

3B, E and Supplementary Fig. 5F, G). Both, CCRISPR and KSCRISPR iPS cells differentiated equally well to iNeurons (Supplementary Fig. 5H). Furthermore,

KSCRISPR iNeurons showed reduced H3K9me2 immunoreactivity compared to

CCRISPR iNeurons (Supplementary Fig. 5I). We observed no differences in the formation of functional synapses, based on immunocytochemistry and mEPSC recordings between CCRISPR and KSCRISPR, corroborating our results with the other

KS cell lines (Fig. 3C, F, Supplementary Fig. 3H). At the network level, CCRISPR showed a control-like network phenotype (Fig. 3D, G-K). KSCRISPR networks exhibited a phenotype similar to the other KS patient networks with less frequent network bursts, longer duration and in an irregular pattern. This establishes a causal role for EHMT1 in the observed neuronal network phenotypes.

Our results demonstrated that EHMT1 deficiency causes a reproducible neuronal network phenotype. We observed only non-significant iPS cell line-specific variability in the functional network properties of, both, the control and KS groups, so that the multiple descriptive parameters extracted from the raw MEA recordings clearly delineated control from KS networks. This was confirmed in an unbiased discriminant analysis of network parameters, where control and KS networks clearly clustered away from each other. This separation was not observed when the analysis was performed on single-cell parameters (i.e. morphology and intrinsic properties, Supplementary Fig. 6A-F). Our direct comparison of iNeurons derived from iPS cells with a frameshift, missense or deletion in EHMT1 showed that the phenotype is due to aberrant EHMT1 enzymatic activity rather than the disrupted protein.

Figure 3. Spontaneous electrophysiological activity of neuronal network derived from control- and CRISPR/Cas9-edited iPS cells. A) Isogenic line: CCRISPR and KSCRISPR. B) Western blot showing the EHMT1 protein levels in iPS cells. C) Representative images of iNeurons stained for MAP2 (red) and synapsin 1/2 (green) at DIV 21 (scale bar 5 µm). D) Representative raster plots showing spontaneous activity exhibited by CCRISPR and KSCRISPR at DIV 28 on MEAs. 6 s of raw data showing a burst recorded from a representative channel are shown. E) Quantification of relative EHMT1 protein level, n=5. F) Quantification of synapsin puncta in CCRISPR and KSCRISPR iNeurons, n=9 for CCRISPR; n=13 for KSCRISPR. G-K) Quantification of network parameters as indicated, n=7 for CCRISPR; n=12 for CCRISPR. 96

Data represent means ± SEM. * P<0.05, ** P<0.005, *** P<0.0005, **** P<0.0001, one-way ANOVA test and post hoc Bonferroni correction was performed between controls and KS networks and Mann-Whitney test was performed between two groups.

KS iNeurons show increased sensitivity to NMDAR antagonists KS patient-derived neuronal networks showed an aberrant pattern of activity, mainly characterized by network bursts with longer durations than controls. Previous studies on rodent-derived neuronal networks have shown that burst duration is directly influenced by AMPARs and NMDARs. Specifically, previous reports used receptor- type specific antagonists to show that AMPAR-driven bursts have short durations while NMDAR-driven bursts have comparatively longer durations30. We therefore hypothesized that increased NMDAR activity contributed to the lengthened bursts in KS networks. To test this, we pharmacologically blocked either AMPARs or NMDARs and compared the effect on control and KS neuronal network activity at DIV 28. In accordance with previous work30,31, we found that acute treatment with an AMPAR antagonist (NBQX, 50 µM) abolished all network burst activity, whereas inhibiting NMDARs (D-AP5, 60 µM) only slightly decreased the network burst activity (Fig. 4A, C) for control networks. This indicated that network burst activity is mainly mediated by AMPARs. In particular, we found it to be mediated by GluA2-containing AMPARs, since the network bursts were not blocked with Naspm (10 µM), an antagonist that selectively blocks GluA2-lacking AMPARs (Supplementary Fig. 7B, pre-D-AP5). Similar to controls, in KS networks NBQX completely abolished network burst activity (Fig. 4B, D). However, in stark contrast to controls, D-AP5 robustly suppressed the bursting activity in EHMT1 deficient lines (Fig. 4B, D and Supplementary Fig. 7A). Interestingly, the suppression of network burst activity by D-AP5 in KS networks was transient. Network burst activity showed ~50% recovery after 30 min and had returned to baseline (i.e. pre-D-AP5 levels) after 24 hours (Fig. 4E and Supplementary Fig. 7C). The early stages of homeostatic plasticity, specifically synaptic upscaling, are initiated by global neuronal inactivity and characterized by insertion of GluA2-lacking AMPARs (i.e. Ca2+-permeable AMPARs, CP-AMPARs) into the synapse to restore activity32. To determine the nature of the recovery, we first blocked KS networks with D-AP5, and added Naspm after 1 hour. 97

Naspm completely blocked the reinstated network bursting activity, indicating the recovery in KS networks was due to synaptic insertion of GluA2-lacking AMPARs (Fig. 4E and Supplementary Fig. 7B). Of note, although in control networks burst activity was not suppressed by D-AP5, we did observe that Naspm had a small but significant effect on the burst rate. This indicates that NMDAR blockage in controls also induced less pronounced synaptic insertion of GluA2-lacking AMPARs. After 24 hours, the network activity in KS networks was again completely suppressed with NBQX but only partially with Naspm, suggesting that GluA2-lacking AMPARs were actively exchanged with GluA2-containing AMPARs (Supplementary Fig. 7C). Collectively, our results demonstrate that NMDAR inhibition with D-AP5 induces synaptic plasticity in KS networks, allowing reinstating network burst activity through the incorporation of GluA2-lacking AMPARs, which later on are replaced by GluA2- containing AMPARs.

Intrigued by these findings, we sought an alternative way to block network bursting. The classic method for inducing synaptic upscaling, with the sodium channel blocker TTX33, would necessarily prevent observation of early stages of the dynamic recovery on MEAs. To circumvent this issue, we used the anti-epileptic drug

+ Retigabine, which is a voltage-gated K -channel (Kv7) activator that effectively hyperpolarizes the resting membrane potential in neurons34. We reasoned that Retigabine would have the combined effect of acutely hampering neurons in reaching action potential threshold while leaving the voltage-gated Na+ channels unaffected. Thus, while simultaneously strengthening the Mg2+ block on NMDARs, we could still observe any (re)occurring network activity on the MEA. Indeed, when we applied Retigabine (10 µM) to the networks, they were temporarily silenced, with no discernible spiking or bursting activity. Within 100 min, there was again a Naspm- sensitive recovery mediated by GluA2-lacking AMPARs occurring in control and KS networks, with an identical pattern (Supplementary Fig. 7D and 8A). This reinforced the notion that the electrophysiological differences we observed earlier between control and KS networks are a direct consequence of NMDAR activity in the KS iNeurons. Furthermore, we found that Retigabine treatment in KS networks decreased the burst length (Supplementary Fig. 7D, 8A), suggesting that Retigabine-

98 induced plasticity allowed KS networks to switch from an NMDAR-dependent to AMPAR-driven bursting, similar to controls.

Figure 4. Effect of AMPA- versus NMDA-receptor blockade on control and KS patient-derived neuronal network activity. A-B) Representative raster plots showing 3 min of spontaneous activity from A) control (CMOS) and B) KS (KSMOS) neuronal networks grown on MEAs at DIV 28. Where indicated, the cells were either non-treated (NT) or treated with 50 µM NBQX to block AMPA receptors or 60 µM D- AP5 to block NMDA receptors. C-D) Bar graphs showing the effect of NBQX and D- AP5 on the network burst rate for C) control and D) KS neuronal networks (CMOS and KSMOS respectively). The values are normalized to the NT data (n=6 for CMOS and n=6 for KSMOS in all conditions). E) The effect of a 1-hour D-AP5 treatment on CMOS and KSMOS neuronal network activity. After 1 hour, the calcium-permeable AMPA receptor blocker Naspm (10 µM) or NBQX were added. Data represent means ± SEM. ** P<0.005, *** P<0.0005, n=8 for CMOS and n=9 for KSMOS, one-way ANOVA test followed by a post hoc Bonferroni correction was performed between conditions.

NMDARs are upregulated in KS iNeurons The results from our pharmacological experiments suggested that NMDAR expression might be increased in KS iNeurons relative to controls. To test this hypothesis, we measured the transcripts of the most common NMDAR and AMPAR subunits by RT-qPCR for CMOS and KSMOS iNeurons (Supplementary Fig. 7E). We

99 were intrigued to find a 4-fold upregulation of GRIN1 mRNA, which encodes NMDAR subunit 1 (NR1), the mandatory subunit present in functional NMDARs. We found no significant changes in any other NMDAR (GRIN2A, GRIN2B, GRIN3A) or AMPAR (GRIA1, GRIA2, GRIA3, GRIA4) subunit that we analyzed. We further corroborated these results with Western blot analysis, which revealed significantly increased NR1 expression for KSMOS and KSCRISPR iNeurons compared to CMOS and CCRISPR (Fig. 5A, Supplementary Data 1). Our previous functional data indicated that the reduction in methyltransferase activity of EHMT1 was directly responsible for the phenotypes we observed at the network level. Therefore, we used chromatin immunoprecipitation qPCR (ChIP-qPCR) to investigate whether the increased GRIN1 expression correlated with reduced H3K9me2 at the GRIN1 promoter. Our results showed that for KSMOS and KSCRISPR iNeurons H3K9me2 occupancy was reduced at the GRIN1 promotor (Fig. 5B). In accordance with our previous study in Ehmt1+/- mice16, we also found that the occupancy at the BDNF promoter was reduced in KSMOS and KSCRISPR iNeurons (Fig. 5B). Next, using immunocytochemistry, we found that NR1 was significantly increased in KSMOS and KSCRISPR compared to CMOS and CCRISPR iNeurons (Fig. 5C). Finally, we investigated whether the increased NR1 level also resulted in increased synaptic NMDAR activity. To this end we infected control or KS iNeurons at 7 DIV with an adeno-associated virus (AAV2) expressing mCherry- tagged channelrhodopsin (± 80 % infection efficiency). We recorded from uninfected cells in voltage clamp at a holding potential of -70 mV (AMPAR) or +40 mV (NMDAR) and measured (blue)light-evoked synaptic responses by exciting nearby channelrhodopsin-expressing cells (mCherry positive) (Fig. 5D). The

NMDAR/AMPAR ratio showed to be significantly increased in KSMOS and KSCRISPR iNeurons (Fig. 5E). This increase in NMDAR/AMPAR ratio is likely due to an increased NMDAR activity since frequency and amplitude of AMPAR-mediated mEPSCs remained unchanged (Supplementary Fig. 3H).

Taken together, our data show that the loss of EHMT1 activity in KS lines results in a reduction of the repressive H3K9me2 mark, causing an upregulation of NR1 and increased synaptic NMDAR activity.

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Figure 5. NMDA receptor subunit 1 is upregulated in KS patient-derived neurons. A) Representative western blots and graph showing the NR1 protein levels in control and KS iNeurons at DIV 28. Values of KSMOS are normalized to CMOS and values of KSCRISPR are normalized to CCRISPR (n=3). B) ChIP assay of H3K9me2 followed by promoter-specific (BDNF_pr4, GRIN1_pr2, and PPIA_pr2) qPCR performed in iNeurons from CMOS, KSMOS, CCRISPR and KSCRISPR (n=4 for mosaic lines and n=3 for CRISPR lines). C) Representative images showing NR1 expression and quantification for CMOS, KSMOS, CCRISPR and KSCRISPR (n=12). D) Schematic representation of methodology to obtain the AMPA and NMDA ratio. E) Representative traces and quantification of NMDA/AMPA ratio for CMOS, KSMOS, CCRISPR and KSCRISPR (n=8). Blue bars indicate light stimulus. Scale bar 100 ms, 10 pA. Data represent means ± SEM. * P<0.05, ** P<0.005, *** P<0.0005, one-way ANOVA test and post hoc Bonferroni correction was performed between conditions.

Altered neuronal network activity in Ehmt1+/- mice Having established that EHMT1-deficiency alters neuronal network activity due to NR1 upregulation in KS iNeurons, we next set out to measure neuronal network activity in Ehmt1+/- mice, a validated mouse model that recapitulates the core features of KS14,15. Similar to what we found in iNeurons, primary cultures of cortical

101 neuronal networks derived from Ehmt1+/- mice showed network bursts with lower frequency and longer duration compared to cultures from littermate controls. The mean firing rate was unaltered (Fig. 6A). Using whole-cell voltage clamp recordings in acute brain slices, we measured the ratio between AMPAR- and NMDAR- mediated currents from cortical Layer 4 to Layers 2/3 synapses. We found that the NMDAR/AMPAR ratio was significantly increased in cortical networks of Ehmt1+/- mice compared to WT littermates (Fig. 6B). We found no changes in the kinetics of NMDAR-mediated currents, suggesting that there is no difference in the expression of NMDAR subunits 2A or 2B between WT and Ehmt1+/- mice (Supplementary Fig. 6B)35. Finally, we found no changes in the frequency or amplitude of AMPAR- mediated mEPSCs suggesting that the increased NMDAR/AMPAR ratio in Ehmt1+/- mice is due to increased NMDAR activity (Fig. 6C).

Figure 6. Neuronal network activity and NMDAR/AMPAR ratio in Ehmt1+/- mice. A) Representative raster plot showing 2 min of recording of the activity exhibited by cultured primary neurons derived from Ehmt1+/+ and Ehmt1+/- mice grown on MEAs at DIV 20. Three network bursts are highlighted for each raster. Graph showing the firing rate, network bursting rate and network burst duration of cultures from Ehmt1+/+ and Ehmt1+/- mice (grey and orange bar respectively) on MEA at DIV 28. n=8 for Ehmt1+/+ and n=6 for Ehmt1+/-. B) NMDAR/AMPAR ratio and decay constant (i.e. Tau) in cortical slices (layer 2/3 auditory cortex) from Ehmt1+/+ and Ehmt1+/- mice at P21. n=6 for Ehmt1+/+ and n=8 for Ehmt1+/-. C) Representative example traces of 102 miniature excitatory postsynaptic currents (mEPSCs) excitatory events recorded from layer 2/3 from Ehmt1+/+ and Ehmt1+/- mice. Graph showing quantification of the frequency and amplitude of mEPSCs of Ehmt1+/+ and Ehmt1+/- mice. n=8 for Ehmt1+/+ and n=8 for Ehmt1+/-. Data represent means ± SEM. * P<0.05, Mann- Whitney test was performed between two groups.

NMDAR inhibition rescues KS neuronal network phenotypes Our previous experiments showed that by inhibiting NMDARs in KS networks for 24 hours, we could shift the balance so that neuronal networks were progressively driven by GluA2-containing AMPARs, similar to controls (Supplementary Fig. 7C). Based on these results, we reasoned that the phenotypes in KS networks could be rescued by chronically inhibiting NMDARs in mature neuronal networks. We chose to potently block the channel pore of NMDARs, and thereby primarily inhibiting postsynaptic calcium flux, with the selective, non-competitive open-channel blocker MK-80136. The immediate effects of MK-801 (1 µM) on KS network activity were similar to D-AP5, where network bursting was transiently suppressed and then recovered by insertion of GluA2-lacking AMPARs, which were again completely inhibited by Naspm (Fig. 7A and Supplementary Fig. 8B). Next, we treated CMOS and

KSMOS networks beginning at DIV 28 for 7 days with MK-801. We found that chronically blocking NMDARs in KSMOS networks reversed the major network parameters that we measured in KSMOS iNeurons, bringing them closer to controls (Fig. 7B-G). More specifically, we observed an increased burst frequency (Fig. 7D), with a parallel reduction in the inter-burst interval (Fig. 7F). Furthermore, the network burst duration was reduced (Fig. 7E) and the network bursting became more regular (Fig. 7G) showing values similar to control, indicative of a shift towards AMPAR- driven networks. A discriminant analysis based on the aforementioned parameters showed that the KSMOS networks treated with MK-801 for 7 days became segregated from untreated KSMOS networks and closer to CMOS networks (Fig. 7H). Similar effects of MK-801 treatment were also observed in KSCRISPR (Supplementary Fig. 8C-G). These results indicate that the aberrant activity pattern of KS patient-derived neuronal networks can be rescued by specifically inhibiting NMDAR activity.

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Figure 7. NMDAR antagonist MK-801 rescues KS network phenotypes. A) MK- 801 (1 µM) effect on KSMOS neuronal network activity (DIV 28). After 90 min, Naspm was added. Graph showing the effect of MK-801 (1 µM) treatment on the neuronal network burst frequency for CMOS and KSMOS derived neuronal network 20 min after application. The values are normalized to the non-treated (NT) condition. B) Representative raster plot showing the activity exhibited by KS patient-derived neuronal network (KSMOS) grown on MEAs either non-treated or after one week of treatment with MK-801. 6 s of raw data showing a burst recorded from a representative channel are shown for the NT and the treated conditions. C-G) Quantification of network properties as indicated, n=8 for CMOS; n=6 for KSMOS and n=6 for KSMOS treated with MK-801. H) Canonical scores plots based on discriminant analyses for control (i.e. C1, C2, CMOS, CCRISPR), KSMOS and KSMOS treated with MK- 801 (84% correct classification). Discriminant functions are based on using the following network activity parameters: firing rate, network burst rate, network burst duration, percentage of spike outside network burst and coefficient of variability of the inter-burst interval. Group envelopes (ellipses) are centered on the group centroids, n=50 for controls; n=6 for KSMOS and KSMOS treated with MK-801. Pie charts visualize accuracy of discriminant analyses functions by showing the relative distribution of lines per a-priory group after reverse testing for group identity. Data represent means ± SEM. * P<0.05, *** P<0.0005, **** P<0.0001, one-way ANOVA test and post hoc Bonferroni correction was performed between conditions.

104

Discussion

In this study, we developed a human model of KS that enabled us to identify specific functional aberrations, from gene expression to neuronal network behavior, due to EHMT1 haploinsufficiency. We found that excitatory networks derived from KS patients showed a distinct and robust neuronal network phenotype with striking similarity across different types of mutations in EHMT1. The phenotype was characterized by network bursts with a longer duration, lower frequency and more irregular pattern compared to controls. At the cellular level, we demonstrated that the network phenotype was mediated by NR1 upregulation.

Interestingly, the neuronal phenotype was consistent across species and model systems. Indeed, we found that network bursts also occurred with a longer duration and irregular pattern in dissociated neuronal networks from either embryonic rats (i.e. where Ehmt1 was downregulated through RNA interference29,37) or Ehmt1+/- mice. This indicates that some network parameters are consistently and similarly altered in divergent KS models. The appearance of a consistent phenotype both in a system where excitatory and inhibitory transmission were present in a system where inhibition was absent (human iNeurons) indicates a major contribution from aberrant excitatory neurotransmission to KS pathobiology. One major characteristic of KS networks was the longer duration of the network bursts. A change in burst length can be indicative of synaptic changes in GABARs, NMDARs and/or AMPARs31. Given that inhibition was absent in our human model, we focused our analysis on NMDARs and AMPARs. We present several lines of evidence that suggest the long bursts exhibited by KS networks are mediated by upregulation of NR1. First, by acutely blocking AMPARs and NMDARs with chemical inhibitors, we show that network burst activity in KS networks is strongly dependent on NMDAR-mediated transmission, in contrast to control networks, where network bursts are mainly dependent on AMPAR-mediated transmission. Second, we were able to reverse KS network phenotypes, including the long network burst duration, by blocking NMDAR activity. Third, we found that NR1 is upregulated in KS iNeurons at both, mRNA and protein level. Fourth, NR1 upregulation is paralleled by H3K9me2 hypomethylation at the GRIN1 promoter. Fifth, NMDAR/AMPAR ratio was increased

105 in KS iNeurons. Finally, we found increased NMDAR-mediated currents in the cortex of Ehmt1+/- mice. This cross-species comparison further validates that observed effects are due to decreased EHMT1 enzymatic function and show that development of network activity in human and mouse cortical networks may follow evolutionarily conserved and stable epigenetic programming.

Genetic evidence has directly implicated NMDARs in NDDs. For example, multiple heterozygous mutations in NMDAR subunit genes have been identified to be causal for ID, ASD or epilepsy38. NMDAR dysfunction is mainly attributed to hypofunction, but there are observations associating NMDAR hyperfunction to ID/ASD39: Upregulated NR1 protein levels were found in the cerebellum of ASD patients40 and the NR2A and NR2B subunits of the NMDAR were found to be upregulated in the valproic acid (VPA) animal model of autism41. In the Rett syndrome mouse model42, loss of MECP2 function resulted in developmental dysregulation of NMDAR expression35,43. Of note, work in mouse models of NDDs show that the changes in NMDARs expression are temporally and spatially restricted. This illustrates the importance of evaluating at what developmental age and in which brain regions changes in NMDAR expression occur, which is especially relevant for the design of rescue strategies44,45,46,47,48. Our data shows that NR1 is upregulated in KS iNeurons of cortical identity and support the idea that dysfunctional glutamatergic neurotransmission plays a role in NDDs. By blocking NMDARs in KS networks, we were able to induce the early phase of synaptic upscaling enabling the incorporation of GluA2-lacking receptors, followed by the insertion of GluA2-containing receptors. This plasticity mediated by AMPARs was more easily initiated in KS than in control networks and allowed KS networks to switch, at least temporarily, from a mainly NMDAR-driven network to an AMPAR- driven network. Interestingly, we also observed that Naspm had an effect on control cells that were treated with NMDAR antagonists, suggesting that GluA2-lacking receptor were inserted and/or possibly exchanged for GluA2-containing receptors after NMDAR antagonist treatment. Together, this suggests that loss of EHMT1 could facilitate NMDAR-mediated plasticity after a comparatively milder stimulus, in this case NMDAR blockade with D-AP5. This is in agreement with a recent study showing that inhibition of EHMT1/EHMT2 activity reinforces early-LTP in an 106

NMDAR-dependent manner18. The authors showed that pharmacologically blocking EHMT1/2 before a mild LTP stimulus increased the LTP response, highlighting a role for EHMT1 and associated H3K9me2 in metaplasticity18. A link between NMDARs and EHMT1 has also been shown in vivo where NMDAR activity regulates the recruitment of EHMT1/2 and subsequent H3K9me2 levels at target gene promoters in the lateral amygdala, in the context of fear learning49. This, together with our data, suggests that there is a reciprocal interaction, between NMDAR activity and EHMT1 function, and a positive feedback mechanism where EHMT1 methylates the NR1 promoter upon activation by NMDARs. If and under which circumstances such a feedback mechanism is active during development and/or in adulthood deserves further investigation. At the molecular level we and others have shown that EHMT1 haploinsufficiency can cause modest transcriptional changes12,16,37,50. We found that the upregulation of NR1 correlates with reduced H3K9me2 occupancy at the GRIN1 promoter. This suggests that during normal development EHMT1 is directly involved in the regulation of NR1 expression. However, we cannot exclude that EHMT1 also regulates NR1 expression via other, less direct mechanisms. For example, EHMT1 is a member of a large complex that includes the Neuron-Restrictive Silencer Factor (NRSF/REST) repressive unit, which is important for repressing neuronal genes in progenitors, including NR151-53. In addition, growth factors such as BDNF, which is increased upon EHMT1 deletion16, have been shown to increase NR1 mRNA levels in cultured embryonic cortical neurons54. Finally, we know from previous studies that EHMT1 regulates genome-wide H3K9me2 deposition, altering the expression of multiple genes12,16,37,50.

An important aspect of our study is the identification of a robust and consistent network phenotype linked to KS on MEAs. We show that KS networks differed from controls based on a set of parameters describing the neuronal network activity and using discriminant analysis. Our analysis showed that individual unrelated controls clustered with little variation, which was exemplified by the fact our predictive group membership analysis was able to accurately assign control networks to the control group (Fig. S6). In contrast, we found that KS patient lines significantly differed from controls, including their respective isogenic controls (CMOS vs KMOS and CCRISPR vs 107

KSCRISPR). It should be noticed here that the 233 kB deletion in KSMOS incorporates another gene (CACNA1B) that might affect neuronal function, which might explain minor changes compared to the other KS lines. When we performed predictive group membership analysis, we found that individual KS networks were mostly assigned to the corresponding patient line (Fig. S6), indicating that this level of analysis on neuronal networks can potentially detect patient-specific phenotypic variance that arises early in development. It is foreseeable that, in future studies, an in-depth interrogation of the network activity for networks consisting of different human derived cell types or networks from brain organoids would allow measuring more complex neuronal signals on MEAs. This would be especially relevant for the stratification of genetically complex disorders (e.g., idiopathic forms of ASD) as these in vitro network phenotypes could then be used as endophenotype in pharmacological studies.

We show that it is possible to rescue the neuronal phenotype by blocking NMDARs in mature networks, a finding that has important clinical relevance. For example, NMDAR antagonists like ketamine and memantine have been used successfully in mouse models to treat RTT 46,47 and other NDDs45,48 and led in some cases to improvements in small open-label trials for autism55-57. These studies, as well as ours, provide preclinical proof of concept that NMDAR antagonists could ameliorate neurological dysfunction and reverse at least some circuit-level defects. Furthermore, our observation that neuronal phenotypes can be rescued in mature networks agrees with previous data showing that reinstating EHMT1 function in adult flies is sufficient to rescue memory deficits13. This adds to a growing list of genetically defined ID syndromes that might be amenable to postnatal therapeutic intervention58.

Summarized, our study shows that combining iPS cell-derived human neuronal models with neuronal network dynamics is a promising tool to identify novel targets for possible treatment strategies for NDDs such as ID and ASD.

108

Methods

Patient information and iPS cell generation In this study we used in total four control and four iPS cells with reduced EHMT1 function. In contrast to a previous study59 we included patients in this study that present the full spectrum of KS associated symptoms, including ID and ASD. KS1 and KS2 originate from two individuals: a female KS patient with a frameshift and a missense mutation in the EHMT1 gene, respectively (patient 25 in 22 and patient 20

8 in ). An isogenic pair of control (CMOS) and KS line (KSMOS) originated from an individual diagnosed with a mosaic heterozygous 233 kbp deletion of chromosome 9 which includes the EHMT1 gene (deletion starts after exon 4) and CACNA1B gene23. Clones were selected after performing RT-qPCR on genomic DNA with primers spanning Exon 3: F-GAAGCAAAACCACGTCACTG; R- GTAGTCCTCAAGGGCTGTGC. Exon 4: F-CCCAGAGAAGTTCGAGAAGC, R- GGGTAAAAGCTGCTGTCGTC. Exon 5 F- CAGCTGCAGTATCTCGGAAG, R- AACATCTCAATCACCGTCCTC. Exon 6 F- GACTCGGATGAGGACGACTC, R- GGAAGTCCTGCTGTCCTCTG. All iPS cells used in this study were obtained from reprogrammed fibroblasts. For most of the lines reprogramming to iPS cells was induced by retroviral vectors expressing four transcription factors: Oct4, Sox2, Klf4,

60 and cMyc. For control line (C2), which was obtained from Mandegar et al., episomal reprogramming was performed, whereas the CCRISPR line was generated by non- integrating Sendai virus. Generated clones (at least two per patient line) were selected and tested for pluripotency and genomic integrity based on single nucleotide polymorphism (SNP) arrays. IPS cells were always cultured on Matrigel (Corning, #356237) in E8 flex (Thermo Fisher Scientific) supplemented with primocin (0.1 µg/ml, Invivogen) and low puromycin (0.5 µg/ml) and G418 concentrations (50

µg/ml) at 37˚C/5% CO2. Medium was refreshed every 2-3 days and cells were passaged twice per week using an enzyme-free reagent (ReLeSR, Stem Cell Technologies). Collecting patient material and establishing hiPSCs have all been performed according to locally (Radboudumc) IRB protocols.

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CRISPR/Cas9 editing of EHMT1 We made use of the CRISPR/Cas9 technology in order to create a heterozygous EHMT1 mutation in Exon 2 in an iPS cell line derived from a healthy 51-year-old male to mimic KS in isogenic cell lines, generated by KULSTEM (Leuven, Belgium). In brief, 1x106 iPSCs in single-cell suspension were nucleofected with Cas9 ribonucleoprotein complexes (10 µg/reaction S.p. Cas9 Nuclease 3NLS (IDT 1074181), CRIPSPR crRNAs (IDT) and CRISPR tracrRNA (IDT 1072532) 0.4 nmoles each/reaction) and a donor vector (5 µg/ reaction). The two crRNAs were designed to specifically target EHMT1 (TCTAACAGGCAGTTCCGGCGAGG and TAACAGGCAGTTCCGGCGAGGGG). The donor vector was a piggyback construct containing a hygromycin selection cassette as well as sequences that enable homology-directed repair ensuring the insertion of premature stop codons in Exon 2 of the EHMT1 gene. Cells were nucleofected using the Human Stem Cell Nucleofector® Kit 2 (Lonza, VPH-5022) in combination with the AMAXA-2b nucleofector, program F16. After nucleofection cells were resuspended in E8 flex (Thermo Fisher Scientific) supplemented with Revitacell (Thermo Fisher Scientific). When the iPS cells reached a confluency of about 40% selection was started using 5 µg/ml hygromycin. The antibiotics concentration was increased to up to 200 µg/ml over 2 weeks. Hygromycin resistant colonies were picked and PCR validation was used to ensure heterozygous editing of Exon2. To prove reduced EHMT1 expression in the CRISPR/Cas9-edited clone RT-qPCR and Western Blot were performed to measure EHMT1 protein levels in CCrispr and KSCrispr. DNA fingerprinting profile was performed on both lines by qPCR detection of a reference set of SNP panel using the TaqMan assays from Life Technologies. The genome-edited iPS cell line shows identical SNP profile with the corresponding iPS cell line used for gene targeting. Off-target of genome editing was verified by sequencing the top four off-target sites of each gRNA have been sequenced and no INDELs were detected (Supplementary Fig. 5). The generated iPS cells were validated for pluripotency markers and quantitative analysis of tri-lineage differentiation potential was performed. All generated iPS cell lines have the capacity to differentiate towards all three germ layers (endoderm, mesoderm, ectoderm). To this end embryoid bodies were generated in 24-well Corning low attachment plates. For spontaneous differentiation,

110 the culture was kept for 7 days in E6 medium (Thermo Fisher Scientific). The medium was changed every 2 days. Cells were harvested after 7 days for RNA extraction with the GenElute Mammalian Total RNA kit (Sigma). cDNA synthesis was performed with Superscript III and used for qPCR according to manufacturer’s protocol with TaqMan human iPS cell Scorecard assay (Life Technologies). Data analysis was performed with Scorecard software (online tool Life technologies), comparing with a reference set of pluripotent stem cell lines.

Western Blot For Western Blot cell lysates were made from confluent wells in 6-well plates of either iPS cells or iNeurons. Medium was always refreshed 4 hours beforehand. Protein samples were loaded, separated by SDS-PAGE, and transferred to nitrocellulose membrane (BIO-RAD). The membrane was then probed with an EHMT1 antibody (1:1000; Abcam ab41969) or NMDAR1 (1:100; Biolegend 818601). To control for loading differences, we probed with anti- gamma tubulin (1:1000; Sigma T5326) or GAPDH (1:1000; Cell Signaling #2118). For visualization horseradish peroxidase- conjugated secondary antibodies were used (1:20000 for both; goat anti-mouse: Jackson ImmunoResearch Laboratries, 115-035-062. Goat-anti rabbit: Invitrogen, A21245).

Neuronal differentiation iPS cells were directly derived into, excitatory cortical Layer 2/3 neurons by overexpressing the neuronal determinant Neurogenin 2 (Ngn2) upon doxycycline treatment based on Zhang et al. 24 and as we described previously 25. To support neuronal maturation, freshly prepared rat astrocytes 25 were added to the culture in a 1:1 ratio two days after plating. At days in vitro (DIV) 3 the medium was changed to Neurobasal medium (Thermo Fisher Scientific) supplemented with B-27 (Thermo Fisher Scientific), glutaMAX (Thermo Fisher Scientific), primocin (0.1 µg/ml), NT3 (10 ng/ml), BDNF (10 ng/ml), and doxycycline (4µg/ml). Cytosine b- D- arabinofuranoside (Ara-C) (2µM; Sigma) was added once to remove any proliferating cell from the culture. From DIV 6 onwards half of the medium was refreshed three times a week. The medium was additionally supplemented with 2,5% FBS (Sigma)

111 to support astrocyte viability from DIV 10 onwards. Neuronal cultures were kept through the whole differentiation process at 37°C/5%CO2.

Cortical cultures from mice Primary cortical neurons were prepared from Ehmt1+/- and Ehmt1+/+ mice from individual E16.5 embryos as previously described 16. Since the genotype was unknown at the time of harvest, each embryo was collected and the brains were processed separately. Each whole brain was kept on ice in 1 mL L-15 medium, organized separately in a 24-well plate, and tail clips were collected for genotyping.

Neuronal morphology reconstruction To examine morphology of neurons cells on coverslips were transfected with plasmids expressing Discosoma species red (dsRED) fluorescent protein one week after plating. DNAin (MTI-GlobalStem) was used according to manual instructions. Medium was refreshed completely the day after DNAin application. After the treatment cells were cultured as previously described.

After three weeks of differentiation cells were fixed in 4% paraformaldehyde/ 4% sucrose (v/v) in PBS and mounted with DAKO. Transfected neurons were imaged using a Zeiss Axio Imager Z1 and digitally reconstructed using Neurolucida 360 software (MBF–Bioscience, Williston, ND, USA). For large cells multiple 2- dimensional images of these neurons were taken and subsequently stitched together using the stitching plugin of FIJI 2017 software. The 3-dimensional reconstructions and quantitative morphometrical analyses focused on the somatodendritic organization of the neurons. We defined origins for individual primary dendrites by identifying emerging neurites with diameters that were less than 50% of the diameter of the soma. Axons, which were excluded from reconstructions and further analysis, we visually identified by their long, thin properties, far reaching projections and numerous directional changes Neurons that had at least two primary dendrites and reached at least the second branching order were considered for analysis. For morphometrical analysis we determined soma size, number of primary dendrites, length and branching points per primary dendrite and total dendritic length. To measure the total span of the dendritic field (receptive area) of a neuron we 112 performed convex hull 3D analysis. Note, that due to the 2-dimensional nature of the imaging data, we collapsed the convex hull 3D data to 2-dimensions, resulting in a measurement of the receptive area and not the volume of the span of the dendritic field. Furthermore, Sholl analysis was performed to investigate dendritic complexity in dependence form distance to soma. For each distance interval (10 µm each) the number of intersections (the number of dendrites that cross each concentric circle), number of nodes and total dendritic length was measured. Discriminant function analysis with canonical discriminant functions and reclassification of group membership based on parameters describing neuronal morphology were performed in SPSS.

Immunocytochemistry Cells were fixed with 4% paraformaldehyde/ 4% sucrose (v/v) for 15 minutes and permeabilized with 0.2% triton in PBS for 10 minutes at RT. Non-specific binding sites were blocked by incubation in blocking buffer (PBS, 5% normal goat serum, 1% bovine serum albumin, 0.2% Triton) for 1 hour at RT. Primary antibodies were diluted in blocking buffer incubated overnight at 4°C. Secondary antibodies, conjugated to Alexa-fluorochromes, were also diluted in blocking buffer and applied for one hour at RT. Hoechst 33342 (Molecular Probes) was used to stain the nucleus before cells were mounted using DAKO fluorescent mounting medium (DAKO). Neurons were fixed at DIV 21 for synaptic stainings. Used primary antibodies were: mouse anti-MAP2 (1:1000; Sigma M4403); guinea pig anti-MAP2 (1:1000; Synaptic Systems 188004); guinea pig anti-synapsin 1/2 (1:1000; Synaptic Systems 106004); mouse anti-PSD95 (1:50; Thermo Fisher Scientific MA1-045); rabbit anti-GFAP (1:500; Abcam ab7260), mouse anti-pan axon (1:1000; Covance SMI-312R), mouse anti-NMDAR1 (1:1000; ThermoFisher Scientific 54.1). Secondary antibodies that were used are: goat anti-guinea pig Alexa Fluor 647 (1:2000, Invitrogen A-21450); goat anti-rabbit Alexa Fluor 488 (1:2000, Invitrogen A-11034); goat anti-rabbit Alexa Fluor 568 (1:2000, Invitrogen A-11036); goat anti-mouse Alexa Fluor 488 (1:2000, Invitrogen A-11029); goat anti-mouse Alexa Fluor 568 (1:2000, Invitrogen A-11031). We imaged at a 63x magnification using the Zeiss Axio Imager Z1 equipped with apotome. All conditions within a batch were acquired with the same settings in order

113 to compare signal intensities between different experimental conditions. Signals were quantified using FIJI software. The number of synaptic puncta was determined per individual cell via manual counting and divided by the dendritic length of the neuron.

To investigate H3K9me2 iNeurons were fixed at DIV3. Used primary antibodies are: rabbit anti-H3K9me2 antibody (1:500, Millipore 07-441), and guinea pig anti-MAP2 antibody (1:1000, Synaptic Systems 188004). Secondary antibodies used are: goat anti-rabbit Alexa Fluor 488 (1:2000, Invitrogen A-11034) and goat anti-guinea pig Alexa Fluor 647 (1:2000, Invitrogen A-21450). Epifluorescent images were taken with the same exposure time using Zeiss Axio Imager Z1 with apotome. H3K9me2 was assessed based on color intensity measurements by ImageJ analysis tools.

Quantification of mRNA by RT-qPCR RNA samples were isolated from both, iPS cells or iNeurons (DIV28) using Nucleo Spin RNA isolation kit (Machery Nagel, 740955.250) according to the manufacturer’s instructions. RNA samples (200 ng) were converted into cDNA by iScript cDNA synthesis kit (BIO-RAD, 1708891). cDNA products were cleaned up using the Nucleospin Gel and PCR clean-up kit (Machery Nagel, 740609.250). Human-specific primers were designed with Primer3plus and IDT PrimerQuest (https://eu.idtdna.com) tools, respectively. Primer sequences are given in supplementary Table 1. qPCRs were performed in the 7500 Fast Real Time PCR System apparatus (Applied Biosystems) with GoTaq qPCR master mix 2X with SYBR Green (Promega, A600A) according to the manufacturer's protocol. The PCR program was designed as following: After an initial denaturation step at 95ᵒC for 10 min, PCR amplifications proceeded for 40 cycles of 95ᵒC for 15 s and 60ᵒC for 30 s and followed by a melting curve. All samples were analyzed in duplicate in the same run, placed in adjacent wells. Reverse transcriptase-negative controls and no template-controls were included in all runs. The arithmetic mean of the Ct values of the technical replicas was used for calculations. Relative mRNA expression levels were calculated using the 2^-ΔΔCt method61 with standardization to housekeeping genes.

114

Chromatin immunoprecipitation (ChIP) -qPCR ChIP was performed according to the Blueprint-IHEC (international human epigenome consortium) protocol with slight modifications. Briefly, 500.000 cells were used for one immunoprecipitation reaction. Cells were first crosslinked with disuccinimidyl glutarate (DSG) at a final concentration of 2 mM for 30 min at RT followed by second crosslinking treatment with 1% formaldehyde for 8 min at RT. Crosslinking was stopped by adding glycine to a final concentration of 0.125 M for 10 min. Samples were washed and homogenized in cold lysis buffer (10 mM Tris (pH 8.0), 0.2%NP40, 0.2% TritonX-100, 1 mM EDTA, 0.5 mM EGTA, 5X protease inhibitors EDTA-free, and 10 mM NaCl) for 15 minutes on ice. Nuclei were lysed with Nuclei Lysis Buffer (1mM EDTA, 0.5 mM EGTA, 50 mM HEPES) for 20 min rotating at 4ᵒC. After centrifugation the collected extracts were resuspended in sonication buffer (20 mM HEPES pH=7.6, 1% SDS, 2.5X protease inhibitors EDTA-free) and sonicated with Bioruptor Power-up (Diagenode) to shear DNA into 150- 700 bp fragments. Cleaned-up chromatin was diluted with ChIP dilution buffer (0.15% SDS, 1% TritonX-100, 1.2 mM EDTA, 16.7 mM Tris pH=8, 167 mM NaCl. Immunoprecipitation was performed with anti-H3K9me2 (5 µg/reaction, Abcam ab1220) and anti-EHMT1 (15µg/reaction, Abcam ab41969) antibodies overnight rotating at 4˚C. As a negative control, anti-human IgG (10 µg/reaction, Abcam ab2410) was used in place of specific antibodies. Chromatin was enriched by 10 μL of each Protein A (Invitrogen, 10001D) and Protein G Dynabeads (Invitrogen, 10003D) for 2 h rotating at 4˚C. Beads were washed once with Wash Buffer 1 (2mM EDTA, 20 mM Tris pH=8, 1% TritonX-100, 0.1% SDS, 150 mM NaCl), twice with Wash Buffer 2 (2mM EDTA, 20 mM Tris pH=8, 1% TritonX-100, 0.1% SDS, 500 mM NaCl), twice with Wash Buffer 3 (1mM EDTA, 10 mM Tris pH=8) and eluted in elution buffer (1% SDS; 100 mM NaHCO3) by rotating for 20 minutes at RT. Eluate was de- crosslinked in elution buffer with 40 µg proteinase-K and 200 mM NaCl overnight at 65˚C. DNA material was cleaned with QIAquick MinElute PCR Purification Kit (Qiagen, 28006) according to manufacturer’s instructions. 5 μL of eluted DNA sample was used for qPCR analysis. qPCRs were performed as described earlier. For primer sequences see Supplementary Table 2. All samples were analyzed in duplicate in the same run and placed in adjacent wells. ChIP-qPCR results are

115 expressed as % input, which was calculated by the following formula: 100*2-ΔCt, where ΔCt= Ct [ChIP] - (Ct[input]-Log2(input dilution factor)).

Micro-electrode array recordings and data analysis All recordings were performed using the 24-well MEA system (Multichannel Systems, MCS GmbH, Reutlingen, Germany). MEA devices are composed by 24 independent wells with embedded micro-electrodes (i.e. 12 electrodes/well, 80 µm in diameter and spaced 300 µm apart). Spontaneous electrophysiological activity of iPS cell-derived neuronal network grown on MEAs was recorded for 20 min. During the recording, the temperature was maintained constant at 37°C, and the evaporation and pH changes of the medium was prevented by inflating a constant, slow flow of humidified gas (5% CO2 and 95% O2) onto the MEA plate (with lid on). The signal was sampled at 10 kHz, filtered with a high-pass filter (i.e. Butterworth, 100 Hz cutoff frequency) and the noise threshold was set at ± 4.5 standard deviations.

Data analysis was performed off-line by using Multiwell Analyzer (i.e. software from the 24-well MEA system that allows the extraction of the spike trains) and a custom software package named SPYCODE developed in MATLAB (The Mathworks, Natick, MA, USA) that allows the extraction of parameters describing the network activity 62.

The mean firing rate (MFR) of the network was obtained by computing the firing rate of each channel averaged among all the active electrodes of the MEA. Burst detection. Bursts were detected using a Burst Detection algorithm. The algorithm is based on the computation of the logarithmic inter-spike interval histogram in which inter-burst activity (i.e., between bursts and/or outside bursts) and intra-burst activity (i.e., within burst) for each recording channel can be easily identified, and then a threshold for detecting spikes belonging to the same burst is automatically defined. From the burst detection, the number of bursting channels (above threshold 0.4 burst/sec and at least 5 spikes in burst with a minimal inter-spike-interval of 100 milliseconds) was determined. Network burst detection. Synchronous events were detected by looking for sequences of closely-spaced single-channels bursts. A 116 network burst was defined as burst that occurs in >80% of the network active channels. The distributions of Network Burst Duration (NBD, s) and Network Inter- Burst Interval (NIBI, interval between two consecutive network bursts, s) were computed using bins of 100 ms and 1 s respectively. Network burst irregularity. Irregularity was estimated by computing the coefficient of variation (CV) of the network inter-burst intervals (NIBI), which is the standard deviation divided by the mean of the NIBI.

Discriminant function analysis with canonical discriminant functions and reclassification of group membership based on parameters describing neuronal network activity were performed in SPSS.

Chemicals All reagents were prepared fresh into concentrated stocks as indicated below, and stored frozen at −20°C. The following compounds were used in pharmacological experiments: 1-Naphthyl acetyl spermine trihydrochloride (“Naspm”, 100 mM in PBS, Tocris Cat No 2766); (+)-MK 801 maleate (“MK-801”, 100 mM in DMSO, Tocris Cat No 0924); 2,3-Dioxo-6-nitro-1,2,3,4-tetrahydrobenzo[f]quinoxaline-7-sulfonamide (“NBQX”, 100 mM in DMSO, Tocris Cat No 0373); D-2-amino-5-phosphonovalerate (“D-AP5”, 50 mM in PBS, Tocris Cat No 0106); Retigabine (100 mM in DMSO, Tocris Cat No 6233). For all experiments on MEAs, and immediately before adding a compound to the cells, an aliquot of the concentrated stock was first diluted 1:10 in room temperature DPBS and vortexed briefly. Then, the appropriate amount of working dilution was added directly to wells on the MEA and mixing was primarily through diffusion into the (500 µl) cell culture medium. Of note, where relevant the DMSO concentration in the medium was always ≤0.05% v/v.

Pharmacological Experiments Control and KS patient neuronal networks were treated acutely with D-AP5 (60 µM), NBQX (50 µM), MK-801 (1 µM), Naspm (10 µM) or Retigabine (10 µM) at DIV 28 after a 20-min recording of spontaneous activity. Then, the recording was paused, the compounds were added to the MEA, and the recording was re-started after 5 min. We recorded neuronal network activity for 60 min with D-AP5 or NBQX, 90 min 117 with MK-801, 100 min with Retigabine, and for 20 min after the addition of Naspm. In experiments where we examined the effect of chronic NMDAR blockade, control and KS patient neuronal networks were treated with 1 µM MK-801 starting at DIV 28 and lasting 7 days total. MK-801 was replenished every two days, where it was freshly diluted to 1 µM in complete Neurobasal medium for the routine half-medium change. All experiments were performed at 37°C.

Single-cell electrophysiology

For single cell recordings we used neurons derived from C1, C2, CMOS, CCRISPR and

KS1, KS2, KSMOS, KSCRIPSR after three weeks of differentiation. Experiments were performed in a recording chamber on the stage of an Olympus BX51WI upright microscope (Olympus Life Science, PA, USA) equipped with infrared differential interference contrast optics, an Olympus LUMPlanFL N 40x water-immersion objective (Olympus Life Science, PA, USA) and kappa MXC 200 camera system (Kappa optronics GmbH, Gleichen, Germany) for visualization. Recordings were made using a SEC-05X amplifier (NPI Electronic GmbH, Tamm, Germany), low-pass filtered at 3 kHz and digitized at 10kHz using a 1401 acquisition board and recorded with Signal (CED, Cambridge, UK). Recordings were not corrected for liquid junction potential (± 10 mV).

We performed the recordings of neurons cultured on cover slips under continuous perfusion with oxygenated (95% O2/5% CO2) artificial cerebrospinal fluid (ACSF) at

30°C containing (in mM) 124 NaCl, 1.25 NaH2PO4, 3 KCl, 26 NaHCO3, 11 Glucose,

2 CaCl2, 1 MgCl2 (adjusted to pH 7.4). Patch pipettes (6-8 MΩ) were pulled from borosilicate glass with filament and fire-polished ends (Science Products GmbH, Hofheim, Germany) using the PMP-102 micropipette puller (MicroData Instrument, NJ, USA). For current clamp recordings of the intrinsic electrophysiological properties, we filled pipettes with a potassium-based solution containing 130 mM K-

Gluconate, 5 mM KCl, 10 mM HEPES, 2.5 mM MgCl2, 4 mM Na2-ATP, 0.4 mM Na3- ATP, 10 mM Na-phosphocreatine, 0.6 mM EGTA (adjusted to pH 7.25 and osmolarity 290 mosmol). We determined the resting membrane potential (Vrmp) after achieving whole-cell configuration and only considered neurons with Vrmp of -55 mV and lower for further testing and analyses. Recordings were not corrected for liquid 118 junction potentials of ca. -10 mV. All further electrophysiological characterizations we performed at a holding potential of -60 mV. We determined the passive membrane properties via a 0.5 s hyperpolarizing current of -25 pA. Active intrinsic properties, i.e. action potential (AP) firing characteristics we derived from the first AP elicited by a 0.5 s depolarizing current injection just sufficient to reach AP threshold. For voltage clamp recordings we filled pipettes with a cesium-based solution containing 115 mM cesium methanesulfonate, 20 mM CsCl, 10 mM HEPES, 2.5 mM MgCl2, 4 mM Na2ATP, 0.4 mM Na3GTP, 10 mM sodium phosphocreatine, and 0.6 mM EGTA adjusted to a pH of 7.4. Spontaneous action potential-evoked postsynaptic currents (sEPSC) were recorded in drug-free ACSF. Bursts were detected when at least 5 events with a minimal inter-event-interval of 100 ms were exhibited by a cell. Burst detection, burst frequency and duration were computed with software developed in MATLAB (The Mathworks, Natick, MA, USA). Miniature excitatory postsynaptic currents (mEPSC) were measured in voltage clamp at a holding potential of −60 mV in the presence of 1 μM tetrodotoxin (Tocris, Bristol, UK) and 100 μM picrotoxin (Tocris, Bristol, UK). Evoked responses (AMPAR and NMDAR) were measured by infecting iNeurons at DIV 7 with 0.5 ul AAV-mCherry- Chr2 (UNC Vector Core, USA). Uninfected cells were recorded in voltage clamp at a holding potential of -70 mV (AMPAR) or +40 mV (NMDAR). Light-evoked synaptic responses were induced by exciting nearby infected cells (mCherry positive) with blue light (COOLLED pE-200; 10 ms, 470 nm). Stimulus strength, location of stimulus was adjusted to 30-50 pA postsynaptic AMPAR-mediated responses at -70 mV holding voltage, and 30 sweeps were averaged for the final trace. AMPA amplitudes were quantified as the peak amplitude at -70 mV holding voltage, and the NMDA amplitudes were quantified as the average of 5 ms of the trace 65 ms after the stimulus to prevent contamination of AMPAR-mediated response at +40 mV. NMDA/AMPA ratios were calculated by averaging responses in Clampfit (vs 10.7, Molecular Devices, CA, USA). Intrinsic electrophysiological properties were analysed using Signal and MatLab (MathWorks, MA, USA), while mEPSCs were analysed using MiniAnalysis 6.0.2 (Synaptosoft Inc, Decatur, GA, USA) as described earlier16. Discriminant function analysis with canonical discriminant functions and

119 reclassification of group membership based on neuronal intrinsic properties were performed in SPSS.

Acute slice electrophysiology We used litter-matched Ehmt1+/- and Ehmt1+/+ mice of adolescent age (postnatal day 21-24). Acute slices were prepared as described earlier 16. In brief, animals were deeply anesthetized with isoflurane, then quickly decapitated. 350-µm-thick coronal slices were cut using a microtome (HM650V, Thermo Scientific) in ice-cold "cutting and storage" ACSF containing 87 mM NaCl, 11 mM glucose, 75 mM sucrose, 2.5 mM KCl, 1.25mM NaH2PO4, 0.5 mM CaCl2, 7 mM MgCl2, and 26 mM NaHCO3, continuously oxygenated with 95% O2/5% CO2. Slices were incubated for 1 h at 32°C, after which they were allowed to cool down to room temperature. The slices were then transferred to a recording setup as described above, and incubated in recording ACSF (124 mM NaCl, 10 mM glucose, 3 mM KCl, 1.25 mM NaH2PO4, 2 mM CaCl2, 1 mM MgCl2, and 26mM NaHCO3) at 30°C with added 100 mM Picrotoxin to block GABAergic transmission. A bipolar electrode (CE2C55, FHC) coupled to an SD9 stimulator (Grass Instruments, RI, USA) was inserted into layer 4 of the auditory cortex, and pyramidal cells were patched in layer 2/3 above the bipolar electrode (<200 µm lateral distance) using 3-6 MΩ borosilicate pipettes filled with a Cesium- based intracellular solution containing 115 mM CsMeSO3, 20 mM CsCl2, 10 mM

HEPES, 2.5 mM MgCl2, 4 mM Na2ATP, 0.4 mM NaGTP, 10 mM Na- Phosphocreatine, 0.6 mM EGTA and 5 mM QX-314. The cells were held in voltage- Clamp mode controlled by an SEC 05-LX amplifier (NPI, Tamm, Germany), low-pass filtered at 3 kHz and sampled at 20 kHz with a Power-1401 acquisition board and Signal software (CED, Cambridge, UK). Data was analyzed in Clampfit 10.7 (Molecular Devices). The AMPA trace was measured at -70 mV holding potential; stimulus strength was adjusted to 50-100 pA postsynaptic response, and 60 sweeps were averaged for the final trace. NMDA was measured at +40 mV holding potential. Cells were discarded if the measured AMPA response contained multiple peaks, indicating multisynaptic input, or if the averaged amplitude was below 25 pA. AMPA amplitudes were quantified as the peak amplitude at -70 mV holding voltage, and

120 the NMDA amplitudes were quantified as the average of 5 ms of the trace 65 ms after the stimulus artefact.

Animals For the animal experiments presented in this study, mice heterozygous for a targeted loss-of-function mutation in the Ehmt1 gene (Ehmt1+/− mice) and their WT littermates on C57BL/6 background were used, as previously described 11. Animal experiments were conducted in conformity with the Animal Care Committee of the Radboud University Nijmegen Medical Centre, The Netherlands, and conform to the guidelines of the Dutch Council for Animal Care and the European Communities Council Directive 2010/63/EU.

Statistics The statistical analysis for all experiments was performed using GraphPad Prism 5 (GraphPad Software, Inc., CA, USA). We ensured normal distribution using a Kolmogorov-Smirnov normality test. To determine statistical significance for the different experimental conditions p-values <0.05 were considered to be significant. Statistical analysis between lines were performed with two-ways ANOVA and Post- hoc Bonferroni correction. We analyzed significance between Control and Kleefstra groups by means of the Mann-Whitney-U-Test. Data are presented as Mean ± standard error of the mean (SE). Details about statistics are reported in Supplementary Data 1 (Excel file Statistics).

Data availability statement The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. All data generated or analysed during this study are included in this published article (and its supplementary information files) .

121

Acknowledgements

We thank dr. J. Ladewig for providing the Ngn2 lentiviral construct. Funding: This work was supported by grants from: the Netherlands Organization for Scientific Research, open ALW ALW2PJ/13082 (to H.v.B., N.N.K, H.Z. T.K. D.S); grant 012.200.001 (to N.N.K); the Netherlands Organization for Health Research and Development ZonMw grant 91718310 (to T. K.), 91217055 (to. H.v.B and N.N.K), SFARI grant 610264 (to N.N.K) and the Jerome Lejeune Foundation (to H.v.B). GGA is supported by a postdoctoral research grant from TÜBITAK (1059B191600426).

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Supplementary Information

Supplementary Figure 1. Characterization of control and KS patients iPS cells. A) Representative images of iPS cell colonies from 3 control and 3 KS lines (C1, C2, CMOS; KS1, KS2, KSMOS) stained for different pluripotency marker (scale bar 50 µM). All lines used in this study were examined for the expression of the nuclear marker OCT4 and NANOG (green) and the surface marker TRA-1-81 and SSEA4 (red) by means of immunocytochemistry. B) QPCR data for control (left panel, C1, C2 and CMOS) and KS patient (right panel, KS1, KS2 and KSMOS) derived iPS cells showing an upregulation of pluripotency markers in iPS cells relative to their expression in corresponding parent fibroblasts (black).

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Supplementary Figure 2. Characterization of control and KS iPS cells and iNeurons. A-B) Graphs respectively showing relative EHMT1 mRNA level in A) iPS cells and in B) iNeurons. Each dot represents one experiment: n=3 for all lines (i.e. controls: C2, CMOS; KS: KS1, KS2, KSMOS). Values from KS1 and KS2 are normalized to C2; values from KSMOS are normalized to CMOS. C) Representative figure of control and KS iNeurons stained for MAP2 at DIV 21 (scale bar 50 µM). D) Quantification of the density of neurons derived from 3 controls and 3 KS iNeurons at 21 DIV. E) Representative figure of control and KS iNeurons stained for H3K9me2 (green) and MAP2 (pink) at DIV 3 (scale bar 5 µm) and quantification of H3K9me2 mean fluorescence intensity (MFI). n=38 for C2; n=15 for CMOS; n=130 for KS1; n=52 for KS2; n=24 for KSMOS. Values from KS1 and KS2 are normalized to C2; values from KSMOS are normalized to CMOS. Data represent means ± SEM. ** P<0.005, ***

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P<0.0005, one-way ANOVA test and post hoc Bonferroni correction was performed between controls and KS-derived cultures.

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Supplementary Figure 3. Single-cell characterization of control and KS iNeurons. A-E) Graphs respectively showing the A) neuronal size, B) number of primary dendrites, C) covered surface, D) number of nodes and E) mean dendritic length for neurons derived from 3 control and 3 KS iNeurons. F) Graph showing the Sholl analysis on mean dendritic length, number of intersections and number of nodes. Dendrite intersections were counted at 10 μm intervals. Each dot represents one cell: n=22 for C1; n=7 for C2; n=7 for CMOS; n=7 for KS1; n=8 for KS2; n=16 for KSMOS. G) Representative figure of neurons derived from CMOS, KSMOS, CCRISPR and KSCRISPR stained for synapsin 1/2 (red) and PSD95 (green) at DIV 21 (scale bar 10 µm) to indicate the formation of functional synapses with a pre- and post-synaptic site and quantification of the co-localization (n=4 for CMOS, n=4 for KSMOS, n=6 for CCRISPR and n=5 for KSCRISPR). H) Graph showing the frequency and amplitude of mEPSCs received by control (C1, CMOS, CCRISPR) and KS patient (KS1, KSMOS, KSCRISPR) derived neurons. n=10 for C1, n=11 for CMOS, n=9 for KS1, n=12 for KSMOS, n=8 for CCRISPR, n=8 for KSCRISPR. I) Representative example traces of action potentials generated by control and KS patient iNeurons (grey and orange respectively) and graph showing the percentage of cells generating a train of 10 action potentials (n=60 for C; n=85 for KS). J-M) Graphs respectively showing J) the amplitude of the action potentials (AP), K) the decay time, L) the rheobase (i.e. current needed to generate an AP), M) the AP threshold and N) the resting membrane potential for neurons derived from 3 control and 3 KS iNeurons. Each dot represents one cell: n=24 for C1; n=12 for C2; n=24 for CMOS; n=33 for KS1; n=22 for KS2; n=30 for KSMOS. O) Example traces and quantification of whole cell voltage- clamp recordings of spontaneous excitatory postsynaptic currents (sEPSCs) in control (C1, CMOS, CCRISPR) and KS iNeurons (KS1, KSMOS, KSCRISPR). Each dot represents one cell: n=21 for control; n=21 for KS. Data represent means ± SEM. * P<0.05, **** P<0.0001. One-way ANOVA test and post hoc Bonferroni correction was performed between controls and KS patient-derived cultures.

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Supplementary Figure 4. Distribution of network burst duration and interval for control and KS neuronal networks. A-B) Graphs showing the network burst duration distribution for A) control- and B) Kleefstra patient-derived neuronal networks (bin size=100 ms) C-D) Graphs showing the network burst interval distribution for C) control- and D) Kleefstra patient-derived neuronal networks (bin size=1 s).

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Supplementary Figure 5. Characterization of CRIPR/cas9-edited iPS cell and iNeurons. A) Representative images of iPS cell colonies (CCRISPR and KSCRISPR) stained for different pluripotency marker (scale bar 50 µM). CCRISPR and KSCRISPR iPS cells lines were examined for the expression of the nuclear marker OCT4 and NANOG (green) and the surface marker TRA-1-81 and SSEA4 (red) by means of immunocytochemistry. B) QPCR data for control (CCRISPR) and CRISPR-edited (KSCRISPR) line showing an upregulation of pluripotency markers in iPS cells relative to their expression in the fibroblasts. C) Quantitative analysis of tri-lineage differentiation potential; both CCRISPR and KSCRISPR lines have the capacity to differentiate towards all three germ layers D) DNA fingerprinting: the provided genome-edited iPS cell line shows identical SNP profile with the corresponding parent iPS cell line used for gene targeting. E) Top four off-target sites of each gRNA have been sequenced and no mutations were detected. F-G) Graphs respectively showing relative EHMT1 mRNA level in F) iPS cells and G) iNeurons. Each dot represents one experiment: n=3 for CCRISPR and KSCRISPR. Values from KSCRISPR are normalized to CCRISPR. H) Representative figure of CCRISPR- and KSCRISPR-derived neuronal networks stained for MAP2 at DIV 21 (scale bar 50 µm) and quantification of the density CCRISPR and KSCRISPR iNeuons at DIV 21. I) Representative figure of control and KS iNeurons stained for H3K9me2 (green) and MAP2 (pink) at DIV 3 (scale bar 5 µm) and quantification of H3K9me2 mean fluorescence intensity (MFI) in CCRISPR and KSCRISPR iNeurons. n=36 for CCRISPR; n=140 for KSCRISPR. Values from KSCRISPR are normalized to CCRISPR. J) Quantification of the nuclei size. n=61 for CCRISPR and n=63 for KSCRISPR. Data represent means ± SEM. ** P<0.005, *** P<0.0005, one-way ANOVA test and post hoc Bonferroni correction was performed between controls and KS-derived cultures.

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Supplementary Figure 6. Discriminant analysis classification of control and KS iNeurons. A-C) Discriminant function analysis with canonical discriminant functions classifies C1, C2, CMOS, KS1, KS2 and KSMOS lines based on A) the neuronal size, the number of primary dendrites, the covered surface, the mean dendritic length and the number of nodes (i.e. morphology) (43% correct classification), B) the amplitude of the AP, the decay time, the rheobase and the AP threshold (i.e. intrinsic properties) (32% correct classification) and C) discriminant function analysis with canonical discriminant functions classifies C1, C2, CMOS, CCRISPR, KS1, KS2, KSMOS and KSCRISPR lines based on the firing rate, network bursting rate, network burst duration, percentage of spike outside network burst and coefficient of variability of the inter- burst interval (i.e. neuronal network activity) (70% correct classification). Group envelopes (ellipses) are centered on the group centroids. The experiments for each line and the ellipses are shown in different colors (i.e. black, dark grey, light grey and white for C1, C2, CMOS, CCRISPR and purple, yellow, light red and dark red for KS1, KS2, KSMOS, KSCRISPR respectively). Morphology: each dot represents one cell: n=22 for C1; n=7 for C2; n=7 for CMOS; n=7 for KS1; n=8 for KS2; n=16 for KSMOS. Intrinsic properties: each dot represents one cell: n=24 for C1; n=12 for C2; n=24 for CMOS; n=33 for KS1; n=22 for KS2; n=30 for KSMOS. Neuronal network activity: Each dot represents one experiment: n=23 for C1; n=10 for C2; n=10 for CMOS; n=7 for CCRISPR; n=15 for KS1; n=15 for KS2; n=12 for KSMOS and n=12 for KSCRISPR. D-F) Pie diagrams indicating the predicted group membership for each line based on D) neuronal morphology, E) intrinsic properties and F) neuronal network activity.

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Supplementary Figure 7. Effect of AMPA- and NMDA-blockage on control and KS network. A) Graph showing the effect of D-AP5 (60 µM) treatment on the neuronal network burst frequency for control- and KS networks at DIV 28. The D- AP5 response (10 min after application) is shown for C1, C2, CMOS, CCRISPR, KS1, KSMOS and KSCRISPR. The values are normalized by the non-treated (NT) condition. The values are shown in different colors (i.e. black, dark grey, light grey for C1, C2, CMOS and purple, light red and dark red for KS1, KSMOS, KSCRISPR respectively). n=6 for C1, n=6 for C2, n=3 for CMOS, n=10 for KS1, n=14 for KSMOS, n=14 for KSCRISPR. B) Graph showing the effect of Naspm (10 µM) treatment on the neuronal network burst frequency for CMOS and KSMOS in two conditions: non-treated cultures; after 1 hour of treatment with D-AP5. The values are normalized to the non-treated (NT) condition. The values are shown in different colors (i.e. light grey for CMOS and light red for KSMOS respectively). n=6 for CMOS and KSMOS not treated; n=10 for CMOS and KSMOS D-APV treated. C) Graph showing the effect of Naspm (10 µM) on KSMOS neuronal network bursting activity 24 hours after D-AP5 treatment (NT indicates network burst activity before D-AP5 treatment). Naspm alone is not completely blocking the network bursting activity anymore. The network bursting activity is blocked when NBQX (50 µM) is added. n=3. The values are normalized to the bursting frequency 24 hours after D-AP5 treatment. D) Retigabine (10 µM) effect on CMOS and KSMOS neuronal network activity during time. After 100 min, Naspm (10 µM) was added in the medium and the network burst disappeared in both CMOS and KSMOS. The values are normalized to the non-treated (NT) condition. Graph showing the duration of the network burst of KSMOS before and after 100 min of Retigabine (10 µM) treatment. n=3. E) Bar graph showing relative GRIA1, GRIA2, GRIA3, GRIA4, GRIN1, GRIN2A, GRIN2B and GRIN3A mRNA expression in control and KS iNeurons at DIV 28. KSMOS mRNA expression is normalized to CMOS (n=3). Data represent means ± SEM. * P<0.05, *** P<0.0005, **** P<0.0001, one-way ANOVA test and post hoc Bonferroni correction was performed between controls and KS networks and Mann-Whitney test was performed between two groups. 136

Supplementary Figure 8. NMDAR antagonist MK-801 rescues KSCRISPR network phenotypes. A) Retigabine (10 µM) effect on KSCRISPR neuronal network activity during time. After 70 min, Naspm (10 µM) was added in the medium and the network burst disappeared. The values are normalized to the non-treated (NT) condition. Graph showing the duration of the network burst of KSCRISPR before and after 100 min of Retigabine (10 µM) treatment. n=4. B) MK-801 (1 µM) effect on KSCRISPR neuronal network activity (DIV 28). After 60 min, Naspm was added. Graph showing the effect of MK-801 (1 µM) treatment on the neuronal network burst frequency for CCRISPR and KSCRISPR derived neuronal network 20 min after application. The values are normalized to the non-treated (NT) condition. C-G) Quantification of network properties as indicated, n=8 for CCRISPR; n=6 for KSCRISPR and n=6 for KSCRISPR treated with MK-801. Data represent means ± SEM. * P<0.05, ** P<0.005, *** P<0.0005, **** P<0.0001, one-way ANOVA test and post hoc Bonferroni correction was performed between conditions.

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Supplementary Table 1. Primers designed for RT-qPCR experiments targeting human transcripts.

Human Gene Symbol Primer sequence 5’ → 3’ EHMT1 Forward GCTGGGAGAAGAGACACCTA Reverse TGCTGGCATCGCTGTTT GRIA1 Forward GCAGCAGTGGAAGAATAGTGATG Reverse ATCACCTTCACCCCATCGTA GRIA2 Forward GCTTGGTGCTAAATTGCTGT Reverse TCCAAGAAAAGTAGAGCATCCA GRIA3 Forward TTCCCACTGGAGGCATGTG Reverse CATCAGCAATATTCGTGTCATGC GRIA4 Forward TGCTGCAACTAAGACCTTCGTTAC Reverse TCGAGTATCCCCTGTCTGTGTC GRIN1 Forward CGCCGCTAACCATAAACAAC Reverse GGGGAATCTCCTTCTTGACC GRIN2A Forward AGCTGCTACGGGCAGATG Reverse CCTGGTAGCCTTCCTCAGTG GRIN2B Forward GGAGTTCTGGTTCCTACTGGG Reverse TCTCATGGGAACAGGAATGG GRIN3A Forward CATTCCAGTGATCAGCATCG Reverse GGGTAAGGAGGAGGAAGTCG PPIA (housekeeping) Forward CATGTTTTCCTTGTTCCCTCC Reverse CAACACTCTTAACTCAAACGAGGA

138

Supplementary Table 2. Primers designed for ChIP-qPCR experiments targeting human promoter regions. Human Promoter Primer sequence 5’ → 3’ BDNF_pr4 Forward TGT TTG GTC GGC TAG AAA GC

Reverse CCCAGATAACGTTACACCAAG

GRIN1_pr2 Forward ACCCCAAGATCGTCAACATT

Reverse GGCATTGAGCTGAATCTTCC

PPIA_pr1 Forward TTGGCCTGACCTAATGTTGG

Reverse TCCTATGGCTTCCCTTTCAG

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140

KANSL1 Deficiency Causes Neuronal Dysfunction by Oxidative Stress-Induced

Autophagy

Katrin Linda, EIly I. Lewerissa, Anouk H. A. Verboven, Michele Gabriele, Monica Frega, Teun M. Klein Gunnewiek, Lynn Devilee, Edda Ulferts, Astrid Oudakker, Chantal Schoenmaker, Hans van Bokhoven, Dirk Schubert, Giuseppe Testa, David A. Koolen, Bert B.A. de Vries, Nael Nadif Kasri

This chapter is prepared for publication: bioRxiv (2020). doi: https://doi.org/10.1101/2020.08.07.241257.

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Graphical Abstract

142

Abstract

Autophagy is a catabolic process of programmed degradation and recycling of proteins and cellular components. Mounting evidence indicates a crucial role of autophagy in neuronal health and function and tight transcriptional regulation via histone modifications. However, the associated transcriptional program in neurons remains poorly understood. Here, we investigate the role in autophagy of KANSL1, a member of the nonspecific lethal (NSL) complex, which acetylates histone H4 on lysine 16 (H4K16ac) to facilitate transcriptional activation. Loss-of-function of KANSL1 is strongly associated with the neurodevelopmental disorder Koolen-de Vries Syndrome (KdVS).

Starting from KANSL1-deficient human induced-pluripotent stem (iPS) cells, both from KdVS patients and genome-edited lines, we identified superoxide dismutase 1 (SOD1), an antioxidant enzyme, to be significantly decreased, leading to a subsequent increase in oxidative stress and autophagosome accumulation. In KANSL1-deficient neurons, autophagosome accumulation at excitatory synapses resulted in reduced synaptic density, reduced AMPA receptor-mediated transmission and impaired neuronal network activity. Furthermore, we found that increased oxidative stress-mediated autophagosome accumulation leads to increased mTOR activation and decreased lysosome function, further preventing the clearing of autophagosomes. Finally, by pharmacologically reducing oxidative stress, we could restore the aberrant autophagosome formation as well as synaptic and neuronal network activity in KANSL1-deficient neurons. Our findings thus point towards an important relation between oxidative stress-induced autophagy and synapse function and demonstrate the importance of H4K16ac-mediated changes in chromatin structure to balance reactive oxygen species- and mTOR-dependent autophagy in neurons.

143

Introduction

Autophagy is a well-conserved, cellular process controlling the degradation and recycling of proteins, which is essential for cells to maintain protein homeostasis. Three major types of autophagy have been identified: chaperone-mediated autophagy, micro-autophagy, and, most extensively studied, macro-autophagy (hereafter referred to as autophagy). There is emerging evidence for an important physiological role of autophagy in neuronal health and function1,2. In neurons, autophagy plays a pivotal role in synaptic plasticity and memory formation3. Within the most prominent neurodegenerative disorders, like Alzheimer’s disease (AD), Parkinson’s (PD), and Huntington’s disease, autophagy is protective against neurodegeneration4,5. Disrupted autophagy showed to promote neurodegeneration in those disorders6–8. During development autophagy is required for synaptic pruning. Impaired autophagy due to over-activation of the mammalian target of rapamycin (mTOR) leads to reduced synaptic pruning and subsequent increased spine density, which has been observed in patients with autism spectrum disorder9. Further, to enable activity-dependent modifications in synaptic strength and efficacy, high protein turnover is required in which autophagy has been proposed to play a role, at the pre- and postsynapse, by controlling the number of synaptic vesicles and ionotropic GABA and glutamate receptors10–12.

In recent years, compelling evidence has revealed a crucial role for tight transcriptional regulation of autophagy related (ATG) genes. Several epigenetic mechanisms, including histone modifications, are essential for the transcriptional control of these ATG genes and thus autophagy 3. Under nutrient-rich conditions the dimethylation of histone H3 at lysine residue 9 (H3K9me2) represses ATG gene expression in HeLa and primary human T-cells13. Similarly, H3K27me3 represses transcription of several negative regulators of mTOR, ensuring high mTOR activity in order to suppress autophagy14. Furthermore, ATG gene expression is regulated by the acetylation of histone H4 at lysine residue 16 (H4K16ac), which is mediated by the acetyltransferase hMOF (also KAT8/ MYST1)15. While H4K16ac generally ensures active ATG gene expression, autophagy induction is followed by a reduction in H4K16ac and decreased ATG gene expression15. This negative feedback-loop 144 prevents prolonged autophagy and subsequent cell death, indicating how essential balanced autophagy is for cell survival, but also function.

The histone acetyltransferase hMOF works within the non-specific lethal (NSL) complex where KANSL1 functions as a scaffold protein16. Heterozygous loss of KANSL1 has been identified as the genetic cause for Koolen-de Vries syndrome (KdVS), formerly known as 17q21.31 micro-deletion syndrome17–19. This multisystemic disorder encompasses mild to moderate ID, developmental delay, epilepsy, distinct facial features, congenital malformations, and friendly behavior. In HeLa cells, KANSL1 has been implicated in microtubule nucleation and stabilization during mitosis20 and, more recently, also in mitochondrial respiration21. In mouse and Drosophila genetic manipulations of KANSL1 have implicated KANSL1/ hMOF signaling in short-term memory formation and social behavior18,22. In the KANSL1- deficient mouse model, the behavioral deficits in mice were accompanied with impaired synaptic transmission22. Accordingly, changes in gene expression in these mice have been linked to synapse function, development, as well as neurogenesis22. However, the molecular and cellular processes through which KANSL1 affects neuronal function, in particular in human neuronal networks, remains unaddressed. The function of KANSL1 as scaffold of the NSL complex and the essential role of H4K16ac in autophagy regulation suggest that deregulated autophagy might underlie the neuronal deficits in KdVS.

Starting from KANSL1-deficient human iPS cells (iPSCs), both from KdVS patients and genome-edited lines, we identified a previously unrecognized mechanism in which loss of KANSL1 resulted in autophagosome accumulation due to increased oxidative stress. In KdVS patient-derived neurons, a disbalance between mTOR- and oxidative stress-mediated autophagy resulted in reduced synaptic function and network activity, which could be rescued by lowering the amount of reactive oxygen species (ROS). Our findings establish ROS-mediated autophagy as a contributing factor to KdVS presenting a new avenue for autophagy modulation in neurons and possible therapeutic intervention.

145

Results

Heterozygous loss of KANSL1 leads to accumulation of autophagosomes in iPSCs To examine the role of KANSL1 in autophagy regulation, we generated an isogenic heterozygous KANSL1-deficient iPSC line (CRISPR1, Fig. 1A). We used

CRISPR/Cas9 genome editing, in a healthy control iPSC line (C1, mother from KdVS individual, Supplementary Figure 1B-D), to induce a heterozygous frameshift mutation in exon 2, leading to a premature stop-codon. Additionally, we reprogrammed fibroblasts from a KdVS patient with small deletions in exon 2 of

KANSL1 (KdVS1), leading to a frameshift and premature stop-codon. KdVS1 was generated from the daughter of C1. The shared genetic background of C1, CRISPR1 and KdVS1 allowed us to examine phenotypes caused by heterozygous loss of KANSL1. To ensure that our findings are common in KdVS patient-derived cells and not specific to the shared genetic background, we additionally compared lines with an independent genetic background. A second KdVS patient iPSC line (KdVS2) was generated from a female patient with a mutation in Exon 2 that is similar to the mutation in KdVS1. The third KdVS iPSC line (KdVS3) was generated from a patient with a 580-kb heterozygous deletion at chromosome 17q21.31 encompassing five known protein-coding genes (CRHR1, SPPL2C, MAPT, STH, and KANSL1). Notably, there is no distinction between KdVS patients with KANSL1 mutations versus 17q21.31 deletion at the clinical level19. Finally, we obtained a gender- matched (female) external control iPSC line (C2), which we used for comparison with the KdVS patient lines. All selected clones were positive for pluripotency markers (OCT4, TRA-1-81, and SSEA4) (characterization of iPSCs is illustrated in Supplementary Figure 1A). As expected, Western blot, immunocytochemistry and quantitative real-time PCR (qPCR) analysis showed reduced KANSL1 expression in all patient lines, as well as the CRISPR-edited line (Figure 1B, Supplementary Figure 1E, 2A). Loss of KANSL1, as scaffold protein of the NSL complex, however, did not lead to a general reduction in H4K16ac (Supplementary Figure 1F), which is in alignment with recent observations of Nsl1 knockdown in mouse embryonic stem cells23.

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To investigate the role of KANSL1 in autophagy we measured autophagosomal marker proteins p62 and LC3I/II in control and KANSL1-deficient iPSCs under basal conditions. Western blot analysis showed increased p62 as well as LC3II levels (Figure 1 C-D) in all KANSL1-deficient iPSCs. We corroborated these results by immunocytochemistry, in which we observed increased numbers of p62 and LC3 particles in all KANSL1-deficient iPSCs, when compared to their respective controls (Figure 1 E-F). LC3II is a well-characterized marker for autophagosomes24,25. During acute activation LC3I is lipidated to LC3II and then locates to the double membrane of autophagosomes26. Next to elevated LC3II levels, we found increased expression of WIPI2, encoding a protein that binds to the early forming autophagosomes, where it is involved in LC3 lipidation27 (Supplementary Figure 2B), strongly suggesting increased autophagosome formation in KANSL1-deficient iPSCs. P62 is an “adaptor” protein that interacts with LC3II when bound to targets like protein aggregates or mitochondria, to promote their selective uptake and degradation28. Upon fusion with lysosomes, lysosomal enzymes degrade autophagosomal content, including p62, while LC3II at the outer membrane remains stable (reviewed by Glick et al.)29. The fact that we observed increased levels of LC3II and p62 under basal conditions, even in the absence of inhibitors of lysosomal proteolysis, indicates that autophagosomes accumulate in KANSL1-deficient iPSCs.

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Figure 1. Autophagosome accumulation in KdVS patient derived iPSCs. A) Schematic overview of control and KdVS iPSC lines used in this study. B) Quantification of KANSL1 protein levels relative to respective control line and example Western blots for all iPSC lines. N = 8 for C1; n = 6 for KdVS1; n = 8 for CRISPR1; n = 5 for C2; n = 5 for KdVS2; n = 4 for KdVS3. C) Example Western blots and quantification for LC3II protein levels normalized to control. n = 9 for C1; n = 9 for KdVS1; n = 6 for CRISPR1; n = 4 for C2; KdVS2; and KdVS3. D) Example Western 148 blots and quantification for p62 protein levels relative to the respective control line. n = 12 for C1; n = 9 for KdVS1; n = 9 for CRISPR1; n = 13 for C2; n = 13 for KdVS2; n = 10 for KdVS3. E) Representative images and particle quantification for iPSC colonies stained for LC3. Scale bar = 20 µm. n = 10 for C1; n = 10 for KdVS1; n = 11 for CRISPR1; n = 16 for C2; n = 17 for KdVS2; n = 17 for KdVS3. Particle quantification was normalized to respective control line. Ordinary one-way ANOVA and Sidak’s multiple comparison test was used to determine statistically significant differences. F) Representative images and particle quantification for iPSC colonies stained for p62. Scale bar = 20 µm. n = 39 for C1; n = 45 for KdVS1; n = 35 for CRISPR1; n = 16 for C2; n = 17 for KdVS2; n = 19 for KdVS3. Data presented in this figure was collected in at least 3 independent experiments. Statistically significant differences were tested through Kruskal-Wallis and Dunn’s multiple comparison test, if not mentioned differently. *P < 0.05, **P < 0.01, ***P < 0.005, ****P < 0.0001.

Autophagosome accumulation is caused by increased oxidative stress The mTOR pathway is a central regulator of autophagy, controlling multiple aspects including initiation, course, and termination of the autophagy process (Supplementary Figure 2D). Upon activation, the mTOR complex 1 (mTORC1) represses autophagy by phosphorylating ULK1 and thereby repressing its’ kinase function within the early steps of autophagosome biogenesis30–32. To test whether autophagosome accumulation in KANSL1-deficient iPSCs is caused by reduced activity of mTORC1 we measured p-mTOR, which is the phosphorylated, activated form of mTOR. Surprisingly, we found an increased p-mTOR/ mTOR ratio in KANSL1-deficient iPSCs, indicating increased mTOR activity and thus reduced mTOR mediated autophagy (Figure 2A-B). Increased phosphorylation of ULK1 at serine residue-75733 in KANSL1-deficient iPSCs further confirmed a reduction in mTOR mediated autophagy (Supplementary Figure 2C). To test whether the mTOR- associated autophagy pathway is intact in KANSL1-deficient iPSCs we treated the cells with rapamycin, a compound that activates autophagy through mTOR inhibition. Rapamycin reduced p-mTOR levels, as well as p62 protein levels in KANSL1- deficient iPSCs, normalizing the levels between genotypes. This indicates that autolysosome formation and subsequent protein break-down are functional upon rapamycin-induced autophagy (Figure 2A, C). Accumulation of autophagosomes was, therefore, neither mediated by a decrease in mTOR signaling, which even

149 showed to be increased, nor due to impaired lysosomal function in KANSL1-deficient iPSCs.

To identify mTOR-independent molecular mechanisms that can lead to increased autophagosome formation/ accumulation, we considered chromatin- immunoprecipitation sequencing data on differentially activated promoters (H3K4me3) in the KdVS mouse model 22. Gene ontology (GO) analysis identified changed promoter activation of genes within oxidoreductase and mitochondria pathways22, suggesting increased oxidative stress in KdVS mice. We performed qPCRs for a set of H4K16ac regulated ATG genes15, that showed differentially activated promoters in the KdVS mouse model. We identified superoxide dismutase 1 (SOD1), an antioxidant enzyme that reduces superoxide, to be about one third lower expressed in KdVS patient derived iPSCs (Supplementary Figure 2A). Western blot analysis further confirmed lowered SOD1 protein levels (Figure 2D). Notably, the expression of the closely related paralog SOD2, which functions in the mitochondria, was unaffected (Supplementary Figure 2A). Reduced expression of antioxidant enzymes, like SOD1, leads to less efficient ROS neutralization and increased oxidative stress. Since increased ROS can activate autophagy in an mTOR-independent manner34, we hypothesized that oxidative stress could be causal to the elevated autophagosome formation in KANSL1-deficient cells. To test this hypothesis, we directly measured ROS in control and KANSL1-deficient iPSCs, using a fluorogenic, cell-permeable dye, that exhibits green fluorescence upon oxidation by ROS. We found higher fluorescence levels in KANSL1-deficient cells (Figure 2E), suggesting increased oxidative stress. Because loss-of-function of different NSL complex proteins, including KANSL1, has previously been associated with reduced mitochondrial function in non-neuronal cells21, we assessed mitochondrial respiration utilizing Seahorse assay. However, our results indicate that the oxygen consumption rate was not affected in KANSL1-deficient iPSCs (Supplementary Figure 3), suggesting that increased oxidative stress is not occurring due to reduced mitochondrial function.

Next, we sought to counteract the increased ROS levels by applying apocynin, a compound that prevents the formation of superoxide by suppressing NADPH 150 oxidase. We were able to reduce ROS levels significantly in KdVS patient-derived iPSCs (Figure 2E). Importantly, this was accompanied with significantly lower p62 accumulation (Figure 2F), further suggesting that higher oxidative stress levels cause autophagosome accumulation in KANSL1-deficient iPSCs.

n.s. 5 ** 4 n.s. 1.5 *** ** 4 ** * C1 KdVS1 CRISPR1 3 ** kDa RAP 3 1.0 289 p mTOR 2 n.s. mTOR 289 2 0.5 ** 1 p62 62 1 SOD1 18

GAPDH 36 GAPDH 36 0 0 0.0 RAP RAP C KdVS CRISPR C KdVS 1 1 1 1 1 1 1CRISPR1 n.s. n.s. *** 10 10 n.s. *** **** 8 8 n.s. 6 *** 6

4 4 n.s. n.s. 2 2

0 0 CellROX Hoechst APO p62 APO C1 KdVS CRISPR C KdVS CRISPR 1 1 1 1 1 Figure 2. Increased oxidative stress causes autophagosome accumulation. A) Representative Western blots for p-mTOR, mTOR and p62 with and without Rapamycin (RAP) treatment. RAP samples were incubated with 200 µM Rapamycin for 10 minutes, followed by 2h without Rapamycin before cell lysis. B) Quantification of p-mTOR/ mTOR ratio. n = 9 for C1; n = 5 for C1 +RAP; n = 7 for KdVS1; n = 5 for KdVS1 +RAP; n = 9 for CRISPR1; n = 5 for CRISPR1 +RAP. Protein levels were normalized to untreated control. Treatment efficiency was assessed by means of unpaired t test between untreated and RAP treated samples of C1. C) Quantification of p62. n = 8 for C1; n = 5 for C1 +RAP; n = 12 for KdVS1; n = 4 for KdVS1 +RAP; n = 10 for CRISPR1; n = 4 for CRISPR1 + RAP. Protein levels were normalized to untreated control. D) Representative Western blots for SOD1 and SOD1 protein quantification. Protein levels were normalized to control. n = 6 for C1; n = 5 for KdVS1; n = 6 for CRISPR1. E) Representative images of iPSC colonies stained with CellROX and fluorescence quantification. iPSCs were either untreated or treated overnight (o/n) with 100 µM apocynin (APO). n = 26 for C1; n = 23 for C1+APO; n = 12 for KdVS1; n = 14 for KdVS1+APO; n = 26 for CRISPR1; n = 28 for CRISPR1+APO. 151

Fluorescence levels were normalized to untreated control. Scale bar = 20 µm. F) Representative images of iPSC colonies stained for p62 and particle analysis. iPSCs were either untreated or treated o/n with 100 µM APO. n = 27 for C1; n = 24 for C1+APO; n = 33 for KdVS1; n = 22 for KdVS1+APO; n = 23 for CRISPR1; n = 23 for CRISPR1+APO. Fluorescence levels were normalized to untreated control. Scale bar = 20 µm. Data presented in this figure was collected in at least 3 independent experiments. Statistically significant differences were determined by means of Kruskal Wallis and Dunn’s multiple comparison test. *P < 0.05, **P < 0.01, ***P < 0.005, ****P < 0.0001.

ROS -induced increase in autophagosomes in KANSL1-deficient neurons Dysregulated autophagy has been associated with neurodevelopmental disorders, including autism spectrum disorders9,35–37, and neurodegenerative diseases such as PD38–41 and amyotrophic lateral sclerosis (ALS)42,43, where familial cases are associated with mutations in SOD1 44. Autophagy is crucial to proper axon guidance, vesicular release, dendritic spine architecture, spine pruning, and synaptic plasticity3,9,37,45,46. Hence, we next examined whether the increase in autophagosomes observed in iPSCs is also present in KANSL1-deficient neurons, and whether a defective autophagy pathway affects specific aspects of neuronal function. To this end we differentiated iPSCs into a homogeneous population of excitatory cortical layer 2/3-like neurons (iNeurons) by forced expression of the transcription factor transgene Neurogenin-2 (Ngn2)47,48. For all experiments, iNeurons were co-cultured with freshly isolated rodent astrocytes to facilitate maturation (Figure 3A). All iPSC lines were able to differentiate into MAP2-positive neurons with comparable cell densities (Figure 3B, Supplementary Figure 5A). KANSL1 expression was lower in neurons derived from KANSL1-deficient iPSCs (Figure 3C) compared to controls. Similar to iPSCs, KANSL1-deficiency did not lead to a general reduction in H4K16ac in iNeurons (Figure 3D). Twenty-one days after the start of differentiation (days in vitro, DIV), both, control and KdVS iNeurons showed a fully developed neuronal morphology, measured by reconstruction and quantitative morphometry of MAP2-labeled iNeurons (Figure 3B, Supplementary Figure 4). KANSL1-deficiency did not result in any significant alteration in the neuronal somatodendritic morphology, including soma size, the number of primary

152 dendrites, dendritic length and overall complexity (Supplementary Figure 4). Next, we measured ROS levels in DIV 21 iNeurons and found elevated ROS levels in KANSL1-deficient iNeurons (Figure 3E-F). To further corroborate our results, we immunostained iNeurons for 8-oxo-dG, an antibody targeting oxidized DNA, to assess levels of oxidative stress. KANSL1-deficient iNeurons all showed 50-100% increased 8-oxo-dG immunoreactivity (Figure 3F). This increase in ROS was also accompanied by increased levels of p62, LC3 and WIPI2 in KANSL1-deficient iNeurons at DIV21 (Figure 3G-H and Supplementary Figure 5B). Summarized, these results indicate that in KANSL1-deficient iNeurons ROS levels and the number of autophagosomes are increased, similar to our observations in KdVS patient-derived iPSCs.

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Figure 3. Autophagy phenotype in KdVS patient derived iNeurons. A) Schematic illustration of the protocol that was used for iPSC differentiation into neurons by means of doxycycline inducible Ngn2 expression. B) Representative images of iNeuron cultures at DIV21 stained for pan-axon and MAP2. Scale bar = 50 µm. C) Representative images of iNeurons at DIV21 stained for KANSL1 and MAP2. n = 33 for C1; n = 36 for KdVS1; n = 29 for CRISPR1; n = 26 for C2; n = 32 for KdVS2; n = 29 for KdVS3. Fluorescence quantification of nuclear KANSL1 signal was normalized to the respective control lines. Scale bar = 10 µm. D) Representative images of iNeurons at DIV21 stained for H4K16ac and MAP2. n = 7 for C1; n = 8 for

154

KdVS1; n = 8 for CRISPR1; n = 8 for C2; n = 8 for KdVS2; n = 11 for KdVS3). Fluorescence quantification for H4K16ac immunostainings was normalized to the respective control lines. Scale bar = 10 µm. Significance was determined by means of ordinary one-way ANOVA and Sidak’s multiple comparison test. E) Representative images for CellROX staining for iNeurons derived of C1 and KdVS1 and fluorescence quantification for CellROX measurements at DIV21, normalized to the respective control lines. n = 125 for C1; n = 142 for KdVS1; n = 92 for CRISPR1; n = 78 for C2; n = 105 for KdVS2; n = 109 for KdVS3. Scale bar = 50 µm. F) Representative images for 8 oxo-dG stainings and fluorescence quantification for 8 oxo-dG stainings of iNeurons at DIV21 for all lines normalized to the respective control lines. n = 107 for C1; n = 102 for KdVS1; n = 80 for CRISPR1; n = 57 for C2; n = 105 for KdVS2; n = 86 for KdVS3. Scale bar = 10 µm. G) Representative images of LC3 immunostainings and fluorescence quantification relative to the respective control line lines. n = 90 for C1; n = 91 for KdVS1; n = 85 for CRISPR1; n = 39 for C2; n = 44 for KdVS2; n = 51 for KdVS3. Scale bar = 10 µm. H) Representative images of p62 stainings and fluorescence quantification relative to the respective control line lines. n=69 for C1; n = 60 for KdVS1; n = 85 for CRISPR1; n = 38 for C2; n = 42 for KdVS2; n = 47 for KdVS3. Scale bar = 10 µm. All data was obtained in at least 3 independent experiments. Statistically significant differences were tested through Kruskal-Wallis and Dunn’s multiple comparison test, if not mentioned differently. *P < 0.05, **P < 0.01, ***P < 0.005, ****P < 0.0001

Decreased synaptic activity in KdVS patient derived iNeurons In human and rodents neurodevelopmental disorders have been associated with synaptic deficits. Also in the KdVS mouse model, KANSL1-deficiency has already been shown to result in impaired synaptic transmission22. Autophagosomes have been localized at the synapse, where mTOR-induced autophagy plays a role in synaptic pruning9. In addition, SOD1 and ROS have an essential role in synaptic function and memory49–51, but the link between ROS, autophagy and synaptic function remains unclear. Given our abovementioned observations we first investigated whether increased oxidative stress causes autophagosome formation at the synapse in iNeurons. To do so, we infected control iNeurons with a lentivirus to express LC3-GFP. At DIV21 we removed B27 from the medium in order to increase oxidative stress and treated the cells for 10 minutes with bafilomycin to prevent fusion of autophagosomes with lysosomes. Then we co-stained for synapsin

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1/2. LC3 puncta co-localized with synapsin (Figure 4A), showing autophagosome formation at the synapse.

Next, we stained iNeurons for synapsin 1/2 at DIV21 and found significantly fewer synapsin puncta in KANSL1-deficient iNeurons compared to controls (Figure 4B), which indicates a reduced amount of putative functional synapses. To test whether there are indeed less functional synapses we performed whole-cell voltage-clamp recordings of spontaneous excitatory postsynaptic currents (sEPSCs) for three-week old C1-, KdVS1- and CRISPR1-derived iNeurons. KANSL1-deficient neurons (KdVS1 and CRISPR1) showed a clear reduction in amplitude and frequency of sEPSCs, compared to control C1 iNeurons (Figure 4C-D). Changes in frequency and amplitude suggest both, a pre- and postsynaptic deficit. Furthermore, we found that the KANSL1-deficient neurons received significantly less bursts of sEPSCs than controls (p<0.0001; Figure 4E), overall suggesting that KANSL1-deficient iNeurons received less excitatory input.

Dysfunction in neuronal network dynamics has been observed in the brain of patients with neurodevelopmental disorders52–54. In addition, neuronal network dysfunctions have been identified in model systems for several ID/ASD syndromes55–57. Our single-cell electrophysiological data prompted us to examine and compare the spontaneous electrophysiological population activity of control and KANSL1- deficient neuronal networks growing on micro-electrode arrays (MEAs). Through extracellular electrodes located at spatially-separated points across the cultures MEAs allowed us to monitor neuronal network activity non-invasively and repeatedly. As shown previously47, electrical activity of control neuronal networks grown on MEAs organized into rhythmic, synchronous events (network burst), composed of numerous spikes occurring close in time and throughout all electrodes, four weeks after differentiation was induced (Figure 4F). This indicates that the iNeurons had self-organized into a synaptically-connected, spontaneously active network that matures over time. As expected from the reduced number of functional synapses, we observed significantly less frequent network bursts in networks composed of

KdVS1-, as well as CRISPR1-derived iNeurons (Fig. 4F, H). The global level of spiking activity was significantly lower in networks of the KdVS patient-derived 156 iNeurons (KdVS1), but not the CRISPR1 iNeurons (Figure 4G). However, we found that in both, the KdVS1 and CRISPR1 iNeurons, the percentage of random spikes was significantly increased (Figure 4I). Together with a decrease in network burst rate, this indicates that KANSL1-lacking networks organized differently compared to controls, in line with a more immature state in which activity mainly occurs outside network bursts47,55 (Figure 4I). This was further illustrated by a more irregular network-bursting pattern, quantified as a larger coefficient of variation (CV) (Figure

4J) of the inter-network burst interval for KdVS1- and CRISPR1-derived networks.

2.0 **** **** synapsin1/2 LC3 GFP merge synapsin1/2 MAP2 **** ****

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Figure 4. Synaptic phenotype in KdVS patient derived iNeurons. A) Schematic presentation of protocol to co-localize LC3 and synapsin. Image of a dendrite of control iNeuron at DIV21 after o/n incubation w/o B27 and treated with 200 nM Bafilomycin for 10 minutes before fixation. Scale bar = 10 µm. B) Representative images showing dendrites stained for MAP2 and synapsin 1/2 and synapsin puncta quantification at DIV21 for all lines. n = 60 for C1; n = 57 for KdVS1; n = 24 for CRISPR1; n = 15 for C2; n = 20 KdVS2; n = 34 for KdVS3. Scale bar = 20 µm. Ordinary one-way ANOVA and Sidak’s multiple comparison test were used to test for statistically significant differences. C) Representative voltage clamp recordings at Vh = -60mV showing sEPSCs at DIV21. D) sEPSC amplitude and frequency quantification (n = 9 for C1; n = 11 for KdVS1; n = 10 for CRISPR1, obtained in two independent experiments). E) Percentages of cells for which 0 EPSC bursts, less than 5 EPSC bursts, 5 or more EPSC bursts, and 10 or more bursts were detected. F) Schematic representation for neuronal network measurements on MEAs (3 minutes of recording). Representative raster plots for C1, KdVS1 and CRISPR1 derived networks that were plated at similar high densities, measured at DIV 30. G) Quantification of the mean firing rate and (H) network burst rate, (I) percentage of random spikes, and (J) coefficient of variation (CV) calculated on the inter network burst interval. n = 15 for C1; n = 18 for KdVS1; n = 16 for CRISPR1. If not stated differently, data presented in this figure was obtained in at least three independent experiments and statistically significant differences were tested through Kruskal- Wallis and Dunn’s multiple comparison test. *P < 0.05, **P < 0.01, ***P < 0.005, ****P < 0.0001.

Apocynin treatment rescues synaptic phenotype in KdVS derived neurons To investigate whether increased oxidative stress is causal to the autophagosome formation and subsequent reduced synaptic activity in KdVS patient-derived iNeurons we treated iNeurons with 100 µM apocynin. First, we confirmed that prolonged apocynin treatment reduced ROS levels and autophagosome accumulation in KANSL1-deficient iNeurons of KdVS1 and CRISPR1, through 8 oxo- dG and p62 immunostainings, respectively (Fig. 5 A-B). In control line C1 apocynin treatment did not change 8 oxo-dG and p62 levels significantly. Having reduced ROS in KdVS iNeurons with apocynin we next examined the effect on synapse density. Prolonged apocynin treatment (for 14 days) increased synapsin puncta significantly in KdVS iNeurons, to a level that was comparable to control iNeurons (Figure 5C). Of note, apocynin treatment had little effect on control iNeurons, suggesting that ROS levels in normal circumstances are low in these cells. In order to show that the increase in synapsin puncta correlates with an increase in synaptic activity we measured neuronal network activity for apocynin-treated iNeurons. As described

158 previously, neuronal network bursts occurred significantly less often in KANSL1- deficient networks. Reducing oxidative stress by means of apocynin treatment significantly increased the number of network bursts for CRISPR1 to control level (Fig. 5 D, F). This was further accompanied with unaltered mean firing rate, a reduction in the percentage of random spikes and a reduction in the CV of the inter- network burst interval, indicating that the network pattern became more regular and mature, similar to control networks (Figure 5 E-H). This is also illustrated by canonical scores plots based on discriminant analyses for C1, CRISPR1 and

CRISPR1 treated with apocynin (Fig. 5I) and reclassification of group membership (Supplementary Figure 6).

n.s. n.s. APO 4 *** APO 5 * n.s. n.s. n.s*.* **** 3 4 APO n.s. APO 3 2 2 APO APO 1 1

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0 0 3 APO APO 0 APO 0.0 APO 3 0 3 C CRISPR C CRISPR Function1 1 1 C1 CRISPR1 C1 CRISPR1

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Figure 5. Apocynin treatment rescues synaptic phenotype. A) Representative images of 8 oxo-dG stainings and 8 oxo-dG quantification for untreated and APO treated iNeurons relative to untreated control cells at DIV21. n = 39 for C1; n = 46 for C1 +APO; n = 42 for KdVS1; n = 51 for KdVS1 +APO; n = 26 for CRISPR1; n = 25 for CRISPR1 +APO. Scale bar = 20 µm. B) Representative images of p62 stainings of iNeurons at DIV21 and fluorescence quantification in untreated and APO treated iNeurons relative to untreated control cells at DIV21. n = 43 for C1; n = 34 for C1+APO; n = 34 for KdVS1; n = 38 for KdVS1+APO; n = 40 for CRISPR1; n= 39 for CRISPR1+APO. Scale bar= 20µm. C) Representative images of dendrites stained for MAP2 and synapsin 1/2 for C1, KdVS1 and CRISPR1 at DIV21 either untreated or APO treated and synapsin quantification. n = 11 for C1; n = 9 for C1+APO; n = 12 for KdVS1; n = 12 for KdVS1+APO; n = 10 for CRISPR1; n = 10 for CRISPR1+APO (in two independent batches). Scale bar = 20µm. Two-way ANOVA was used to determine statistically significant changes. D) Representative raster plots for untreated C1 network and untreated and Apocynin treated CRISPR1 network at DIV30 (3 min. of recording). Quantification of (E) mean firing rate, (F) NB frequency, (G) percentage of random spikes, and (H) CV of inter-network burst interval. n = 15 for C1; n = 17 for C1 +APO; n = 16 for CRISPR1; n = 15 for CRISPR1 +APO. I) Canonical scores plot based on discriminant analyses for C1, CRISPR1 and CRISPR1 treated with APO. Discriminant functions are based on using the following network activity parameters: firing rate, network burst rate, network burst duration, percentage of spike outside network burst and coefficient of variability of the inter- network burst interval. Group envelopes (ellipses) are centered on the group centroids. All data presented in this figure were generated in at least 3 independent experiments and statistically significant differences were tested through Kruskal- Wallis and Dunn’s multiple comparison test, if not mentioned differently. *P < 0.05, **P < 0.01, ***P < 0.005, ****P < 0.0001.

Impaired feedback-loop and decreased lysosomal activity in KdVS cells Having established that KANSL1-deficiency results in increased oxidative stress causing elevated autophagosome formation and reduced synaptic activity, we wondered what causes the autophagosome accumulation in KANSL1-deficient cells. Increased autophagic flux is known to induce a regulatory feedback-loop in which induction of autophagy is coupled to a reduction in H4K16ac through downregulation of hMOF in MEF cells15. However, we did not find a reduction in H4K16ac in KANSL1-deficient cells at the basal level. This led to the question, whether an impaired feedback-loop activation via the NSL complex is underlying the observed autophagosome accumulation.

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To answer this question, we first examined at which step during autophagy signaling the negative feedback-loop is activated. After activation there are three main stages of autophagy: (i) the formation of initiation complexes, (ii) autophagosome formation and (iii) fusion with lysosomes. We pharmacologically inhibited the different stages within the autophagy pathway to identify which stage triggers the feedback-loop activation in control line C2. Blocking autophagosome formation with wortmannin, a PI3-kinase inhibitor, did not result in reduced H4K16ac after rapamycin treatment (Figure 6A). However, blocking lysosome fusion with bafilomycin resulted in activation of the negative feedback-loop (Figure 6A), indicated by reduced levels of H4K16ac. This suggests that the formation of autophagosomes is the signal for the feedback-loop activation. Notably, the reduction in H4K16ac was independent of the autophagy induction protocol, since a similar decrease in H4K16ac was observed when iNeurons were stimulated with buthionine sulfoximine (BSO) in order to activate ROS-mediated autophagy58 (Supplementary Figure 7A). In contrast to wild type cells, autophagosome accumulation in KANSL1-deficient cells did not result in reduced H4K16ac, pointing towards changes in NSL complex-mediated feedback- loop activation (Figure 6B, Supplementary Figure 7A). Although we did not observe reduced H4K16ac, we found increased mTOR phosphorylation (Figure 2A, F) in KANSL1-deficient iPSCs, which might be part of a negative feedback-loop that is activated independently from KANSL1 and the NSL complex to prevent prolonged autophagy activation. These findings point towards a tightly controlled, balanced interaction between the different autophagy regulating mechanisms. Autophagosome formation induces independent signaling cascades to negatively regulate autophagy through, on the one hand, increased mTOR activity, and, on the other hand, reduced H4K16ac levels to repress ATG gene expression. In KANSL1- deficient cells autophagy regulation is out of balance; H4K16ac is not reduced, enabling prolonged ROS activated autophagy.

Since active mTOR signaling is known to decrease lysosomal activity59–61, we tested whether lysosomal activity might be affected by the imbalanced autophagy regulation in KANSL1-deficient cells. First, we quantified LAMP1, a lysosomal marker, in rapamycin treated iPSCs by means of immunocytochemistry and Western blot. We confirmed that mTOR-dependent autophagy activation increases 161 autophagosome formation (Figure 6C, E) as well as lysosomal activity (Figure 6C- D, F) in control cells. BSO treatment, to activate ROS-induced autophagy, increased LC3II levels comparable to rapamycin treatment (Figure 6C, E), whereas LAMP1 levels remained low (Figure 6C-D, F). We found increased autophagosome formation in KANSL1-deficient cells to be initiated by increased ROS, which points towards less efficient lysosome activation. Indeed, quantifying LAMP1 particles in KANSL1-deficient iNeurons indicated significantly lower lysosomal activity in these cells when compared to control iNeurons (Figure 6H). Additionally, decreased ROS levels through apocynin treatment showed to reduce the p-mTOR/ mTOR ratio and also increased LAMP1 levels in KANSL1 deficient iPSCs (Supplementary Figure 7B- D). Together, our data thus suggest that increased ROS levels activate autophagy. Subsequently, autophagosome formation induces mTOR phosphorylation leading to reduced lysosomal activity which reinforces autophagosome accumulation in KANSL1-deficient iNeurons (Figure 6G, 7).

162

UNTR RAP n.s. ***** RAP 2.0 n.s**. 2.0 *** 1.5 *** n.s. WRT RAP WRT 1.5 1.5 1.0 C1 C1 1.0 1.0 RAP BAF RAP BAF 0.5 0.5 0.5

CRISPR CRISPR 0.0 1 1 0.0 WRT 0.0 BAF RAP H4K16ac H4K16ac C RAP RAP 1 CRISPR1

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Figure 6. Feedback loop activation and reduced lysosomal activity. A) Representative images of iPSCs treated with Rapamycin (RAP) and/or Wortmannin (WRT) or Bafilomycin (BAF) stained for H4K16ac. Fluorescence was always normalized to untreated control samples. n = 29 for RAP/WRT treatments; n = 25 for RAP/BAF treatments. Two-way ANOVA and Tukey’s multiple comparison test were used to test for statistically significant differences. Scale bar = 50 µm. B) Representative images of iPSC colonies of C1 and CRISPR1 untreated or treated with RAP (200 µM) for 10 minutes. Two hours after the treatment cells were fixed and stained for H4K16ac. n = 12 for C1; n = 15 for C1 +RAP; n = 10 for CRISPR1; n = 14 for CRISPR1 +RAP. Two-way ANOVA and Sidak’s multiple comparison test were used to determine statistically significant reduction. Scale bar = 50 µm. C) Representative Western blot for LAMP1 and LC3 after autophagy induction for 10 minutes by either RAP (200 µM) or BSO (200 µM) in control iPSCs. D) LAMP1 protein level quantification and (E) LC3II quantification relative to untreated control. 163 n = 7 for all conditions. Kruskal Wallis and Dunn’s multiple comparison test were used to test for statistically significant differences. F) Representative images of LAMP1 stainings in control iPSCs treated with RAP (200 µM) or BSO (200 µM) for 10 minutes before fixation and particle analysis for LAMP1. n = 11 for all conditions. Scale bar = 50 µm. Ordinary one-way ANOVA and Dunnett’s multiple comparison test were used to determine statistically significant differences. G) Schematic representation of autophagy showing the 2 different autophagy inducing pathways discussed (mTOR and ROS). NSL complex mediated feedback-loop is induced by autophagosome formation. At the same time mTOR phosphorylation is increased, which subsequently reduces lysosomal activity.LC3II protein level quantification. H) Representative images of three-week old neurons from C1, KdVS1 and CRISPR1 stained for MAP2 and LAMP1 and LAMP1 particle quantification. n = 43 for C1; n = 29 for KdVS1; n = 36 for CRISPR1. Results were normalized to control. Scale bar = 10 µm. Ordinary one-way ANOVA and Dunnett’s multiple comparison test were used to determine significant differences for the number of particles. Differences in particle size were tested through Kruskal Wallis and Dunn’s multiple comparison test. Data presented in this figure was obtained in at least two independent experiments. *P < 0.05, **P < 0.01, ***P < 0.005, ****P < 0.0001.

Discussion

In this study we used a human in vitro model for KdVS to examine the role for KANSL1 in autophagy regulation and to gain insight into how deregulated autophagy affects neuronal function. We found that KANSL1-deficiency leads to increased oxidative stress and autophagosome formation in iPSCs and iNeurons (Figure 7). In neurons increased ROS activated autophagy showed to reduce neuronal synaptic connectivity and activity, revealed on single cell, as well as on network level. The observed neuronal phenotype could be rescued by treatment with apocynin, an antioxidant that reduced oxidative stress and autophagosome accumulation.

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Figure 7. Autophagy Regulation in control and KdVS cells. Schematic representation of autophagy regulating mechanisms. In control cells both, dephosphorylation of mTOR and increased ROS levels, induce autophagosome formation. Upon completion, autophagosomes induce negative feedback-loops. ATG gene expression is reduced through decreased H4K16ac levels, on the one hand. On the other hand, mTOR phosphorylation increases upon autophagosome accumulation to inhibit mTOR regulated autophagy, and subsequently reduces lysosomes. In KdVS cells prolonged oxidative stress induces autophagy, primarily mTOR independent. While H4K16ac is not reduced, enabling expression of ATG genes and continuous autophagosome formation, the accumulation of autophagosomes induces a reduction in mTOR activity, reducing lysosomal activity, and preventing autophagosomal clean-up. Aberrant feedback-loop activation in KdVS cells therefore causes an imbalance in oxidative stress versus mTOR activated autophagy reinforcing elevated autophagosome accumulation.

Imbalanced mTOR- and ROS-mediated autophagy in KdVS cells Heterozygous loss of KANSL1 lead to imbalanced autophagy regulation in KANSL1- deficient iPSCs and neurons. On the one hand, we found increased ROS-mediated autophagy, and, on the other hand, we found reduced mTOR-mediated autophagy. Previous studies in neurons have mostly associated deregulated autophagy to increased or decreased activity of mTOR-regulated autophagy9,36,62. Here, we show that the interplay between both autophagy pathways is essential for balanced autophagy regulation. Little is known about the interaction between the different autophagy pathways and how exactly the feedback-loop is controlled. It has been 165 reported that upon autophagy induction, H4K16ac is downregulated in order to reduce ATG gene expression and prevent prolonged autophagy15. Here, we provide more insight in this feedback-loop activation by showing that in control iPSCs acute autophagosome formation induces H4K16ac reduction within a couple of hours. In contrast, acute activation of autophagy in KANSL1-deficient cells did not result in a significant H4K16ac reduction. This is in line with our observation that ROS- mediated accumulation of autophagosomes in KANSL1-deficient cells (iPSCs and iNeurons) did not affect H4K16ac levels when compared to control cells. According to what is known about the role of H4K16ac in autophagy regulation, and especially in negative feedback-loop activation, this should eventually lead to cell death. However, we cultured KANSL1-deficient iNeurons for three weeks and longer without observing increased cell death. This indicates that there might be an additional feedback mechanism active that is inhibiting aspects of prolonged autophagy that would otherwise lead to cell death, independent from the NSL complex mediated feedback63. We observed increased mTOR activity in KANSL1- deficient iPSCs, which we interpreted as possible feedback reaction to counteract hyperactive ROS-induced autophagy. Upon starvation induced autophagy, mTOR is first deactivated, but reactivated shortly after autophagic degradation, even during prolonged starvation. mTOR activation is known to inhibit the expression of lysosomal genes by phosphorylating the transcription factor TFEB and preventing its’ nuclear translocation 60,64,65. In a model for Gaucher's disease, mTOR hyperactivity was shown to cause lysosomal dysfunction in neurons66. Similarly, we found increased levels of active mTOR (p-mTOR) and reduced levels of the lysosomal marker LAMP1 in KANSL1-deficient iNeurons. This indicates that hyperactive ROS-mediated autophagosome formation initiates a feedback-loop that increases mTOR activity which subsequently blocks lysosomal gene expression in KANSL1-deficient cells. Interestingly, valproic acid treatment, a histone deacetylase inhibitor, not only prevents H4K16ac downregulation after autophagy induction, but also increases autolysosome formation and causes cell death15. Combining these findings with our observation that we did not observe increased cell death; we suggest that activation of mTOR in KANSL1-deficient cells is required to avoid prolonged lysosomal degradation and subsequent cell death. While our results show

166 that rapamycin treatment can efficiently induce autophagosome clearance within a few hours, the interplay between oxidative stress- and mTOR-mediated autophagy indicate that long-term rapamycin treatment as a therapeutic strategy is questionable. Continuous over-activation of even two autophagy activating pathways in combination with lacking negative feedback, would probably lead to cell death in KANSL1 deficient cells. In contrast, we find that preventing ROS production with apocynin was sufficient to rescue the autophagy phenotypes. More importantly, apocynin was sufficient to restore normal neuronal functioning, suggesting that increased ROS is causal to the autophagy phenotypes and at the core of KdVS pathophysiology.

Loss of function mutations in KANSL1 have unequivocally been associated with KdVS. More recently, two single nucleotide polymorphism (SNP) in KANSL1 (Ile1085Thr and Ser718Pro) have been identified as risk factors for PD in a genome-wide association study (GWAS)67. This is of interest since it is well established that both, oxidative stress and autophagy deficits, are strongly associated with PD and other neurodegenerative disorders68–70. Furthermore, it is widely accepted that ROS activate autophagy to restore cellular homeostasis as a cytoprotective feedback mechanism. Evidence from ageing and neurodegenerative models indicate impaired neuronal function and ultimate neuronal cell death when this regulatory link is disturbed by either reduced autophagic flux, or elevated ROS formation exceeding capacity for cytoprotection71. Our results provide additional evidence for the crucial link between ROS and autophagy for regulation of neuronal cell homeostasis and function, not only during neurodegeneration but also in the context of a neurodevelopmental disorder. Since the SNPs in KANSL1 identified in PD GWAS are unlikely to lead to loss-of-function it will be of particular interest to investigate the functional consequences of these SNPs in autophagy.

ROS-induced autophagy regulates synaptic function Whereas autophagy has been extensively studied in the context of neurodegenerative disorders, its’ role during neuronal development has remained understudied. Especially how mTOR-independent autophagy affects synaptic development and function is largely unknown. This is of relevance since it is known 167 that levels of ROS, or more specifically superoxide, at the synapse plays a crucial role in synaptic plasticity72. On the one hand, local increase in superoxide levels upon neuronal stimulation showed to be essential to induce hippocampal long-term potentiation (LTP)73–75. On the other hand, increased levels of superoxide contribute to age-related impairments in hippocampal LTP and memory76. Our single-cell electrophysiology data showed reduced synaptic input, i.e. reduced sEPSC frequency and less frequent sEPSC bursts. In addition, we observed a significantly reduced sEPSC amplitude pointing towards a reduction in functional AMPA receptors at the synapse. This indicates that chronic, ROS-induced autophagy might affect receptor distribution at the post-synaptic site. It is already known that chemical induction of long-term depression (LTD) induces NMDAR-dependent autophagy through mTOR inhibition, resulting in AMPAR break down11. These results further support the evidence that ROS-mediated autophagy is contributing to the regulation of synaptic plasticity. ROS-mediated changes during synaptic plasticity might occur through the activation of autophagy to induce synapse-specific break-down of synaptic proteins. More detailed investigation is, however, needed to understand how synaptic input- and target-specificity (e.g. AMPAR subunits) is achieved during autophagy at synapses in light of synaptic plasticity.

Taken together our findings establish a link between ROS-mediated autophagy, synaptic dysfunction and KdVS. Future research should further examine the role of autophagy in neurodevelopment, focusing on the interplay between the different autophagy induction pathways and negative feedback-loops and how these affect synaptic function.

Methods

Patient information and iPSC line generation In this study we used two control and three KdVS patient derived iPSC lines (Figure 1). All patients that were included in this study present the full spectrum of KdVS

19 17,77 associated symptoms. KdVS1 and KdVS2 originate from two individual, female

KdVS patients with mutations in KANSL1, while the iPSC line KdVs3 originates from

168 a female patient with a 17q microdeletion19 78. We used one independent female control lines (C2). Next to this control line we also used a parent control line for patient line KdVS1 (C1). C1 was also used for CRIPSR/Cas9 genomic engineering in order to create a KANSL1 mutated line with the same genetic background (CRISPR1). A 1 bp insertion in Exon 2 resulted in a premature stop codon and heterozygous loss of KANSL1, highly similar to the patient lines KdVS1 and KdVS2.

All iPSCs used in this study were obtained from reprogrammed fibroblasts. KdVs1

TM and C1 were generated by making use of the Simplicon reprogramming kit (Millipore). Overexpression of the four factors Oct4, Klf4, Sox2, and Glis1 was introduced by a non-integrative, non-viral one step transfection. KdVS2 was reprogrammed by lentiviral mediated overexpression of Oct4, Sox2, Klf4, and cMyc.

For KdVS3 episomal reprogramming was performed with the same reprogramming factors. C2 was generated from fibroblasts of a 36-year-old female control obtained from the Riken BRC - Cell engineering division (HPS0076:409B2). iPSC clones used in this study were validated through a battery of quality control tests including morphological assessment. All clones expressed the stem cell markers OCT4, SOX2 NANOG, SSEA-4, and TRA1-81 (see supplementary Figure 1 A).

Genome editing by CRISPR/Cas9

We performed CRISPR/Cas9 genome editing on C1 following the protocol from Ran et al. (2013). First sgRNA targeting exon 2 of KANSL1 was cloned into the targeting vector (pX459v2). Successful cloning was validated by PCR.

Single iPSCs were then nucleofected with the pX459v2 plasmid coding for the Cas9 protein and the sgRNA using P3 primary Cell 4D nucleofector kit (Lonza, V4XP-302). After nucleofection cells were plated on a 6-well plate in E8 flex medium supplemented with RevitaCell. 24 Hours after nucleofection medium was refreshed and supplemented with puromycin (1 µg/ml), RevitaCell was removed. After o/n incubation medium was refreshed with E8 flex (Thermo Fisher Scientific) to remove dead cells and stop the selection. The culture was maintained until colonies were large enough to be picked. By means of Sanger sequencing we checked for

169 heterozygous loss-of-function mutations. Positive colonies were re-plated as single cells to ensure clonal expansion of iPSCs positive for the selected mutation.

KANSL1 sgRNA oligos:

5’- CACCGGAGCCCGTTTTCCCCCATTG-3’

3’- CCTCGGGCAAAAGGGGGTAACCAAA-5’

Generation of rtTA/Ngn2-positive iPSCs For the generation of rtTA/ Ngn2-positive iPSCs, lentiviral vectors were used to stably integrate the transgenes into the genome of the iPSCs. The vector used for the rtTA lentivirus is pLVX-EF1α-(Tet-On-Advanced)-IRES-G418(R); it encodes a Tet-On Advanced transactivator under control of a constitutive EF1α promoter and confers resistance to the antibiotic G418. The lentiviral vector for murine neurogenin- 2 (Ngn2) was pLVX-(TRE-thight)-(MOUSE)Ngn2- PGK-Puromycin(R), encoding for Ngn2 under control of a Tet-controlled promoter and the puromycin resistance gene under control of a constitutive PGK promoter. Both vectors were packaged into lentiviral particles using the packaging vectors psPAX2 lentiviral packaging vector (Addgene #12260) and pMD2.G lentiviral packaging vector (Addgene #12259). We infected our iPSC lines with both lentiviruses in order to ensure Ngn2 expression when the medium was supplemented with doxycycline. To select for iPSCs that were transduced with both vectors we started G418 (0.5 µg/ml) and puromycin (0.1 ug/ml, Invivogen) selection 48 hours after infection. The antibiotics concentration was doubled at day two and three of the selection process. iPSCs surviving the selection process were cultured at general iPSC culture conditions (see below). iPSC Culture and drug treatment iPSCs were always cultured on Matrigel (Corning, #356237) in E8 flex (Thermo Fisher Scientific) supplemented with primocin (0.1 µg/ml, Invivogen) and low puromycin and G418 concentrations (0.5 µg/ml) at 37˚C/5% CO2. Medium was refreshed every 2-3 days and cells were passaged 1-2 times per week using an enzyme-free reagent (ReLeSR, Stem Cell Technologies). For autophagy induction

170 cells were treated with 200 µM Rapamycin (ChemCruz) for 10 minutes, before medium was refreshed. To block autophagosome formation or lysosome fusion cells were treated with 200 nM Wortmannin (Invivogen) or Bafilomycin (Millipore), respectively prior Rapamycin treatment. If not mentioned differently, cells were lysed/ fixated after 2 hours of incubation. For Apocynin (Santa Cruz Biotechnology) rescue experiments iPSCs were plated as single cells. The day after plating the cells were treated for 24 hours with 100 µM Apocynin before cells were fixed. To stimulate ROS production cells were treated for 10 minutes with 100 µM BSO (Sigma). After medium was refreshed, cells were incubated for 2 hours before lysate preparation or fixation.

Neuronal differentiation iPSCs were directly derived into upper-layer, excitatory cortical neurons by doxycycline induced overexpression of Ngn2 according to an already published protocol47. To start neuronal differentiation Accutase (Sigma) was applied to generate single cells. iPSCs were then plated onto MEAs (600 cells/mm2) or glass, nitric-acid treated coverslips (100 cells/mm2) in E8 basal medium (Thermo Fisher Scientific) supplemented with primocin (0.1 µg/ml, Invivogen), RevitaCell (Thermo Fisher Scientific), and doxycycline (4µg/ml). MEA plates, as well as coverslips, were pre-coated with Poly-L-Ornithine (50 µg/mL, Sigma) for 3 hours at 37 °C and 20 µg/mL human laminin (L521, Biolamina) overnight (o/n) at 4 °C. The day after plating medium was changed to DMEM-F12 (Thermo Fisher Scientific) supplemented with N2 (Thermo Fisher Scientific), NT3 (Promokine), BDNF (Promokine), NEAAS (Sigma), doxycycline (4µg/ml), and primocin (0.1 µg/ml). To support neuronal maturation, freshly prepared rat astrocytes were added to the culture in a 1:1 ratio two days after plating. At DIV 3 the medium was changed to Neurobasal medium (Thermo Fisher Scientific) supplemented with B-27 (Thermo Fisher), glutaMAX (Thermo Fisher), primocin (0.1 µg/ml), NT3, BDNF, and doxycycline (4 µg/ml). Cytosine β-D-arabinofuranoside (Ara-C) (2 µM; Sigma) was added once to remove proliferating cells from the culture. From DIV 6 onwards half of the medium was refreshed three times a week. Addition of doxycycline was stopped after two weeks to reduce stress. The medium was additionally supplemented with 2,5% FBS

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(Sigma) to support astrocyte viability from DIV10 onwards. Neuronal cultures were kept through the whole differentiation process at 37°C/ 5%CO2. For rescue experiments cells were treated with 100 µM Apocynin every other day when medium was not refreshed from DIV6 onwards.

Immunocytochemistry Cells were fixed with 4% paraformaldehyde/ 4% sucrose (v/v) and permeabilized with 0.2% triton in PBS for 10 minutes. Aspecific binding sites were blocked by incubation in blocking buffer (PBS, 5% normal goat serum, 5% normal horse serum, 5% normal donkey serum, 1% bovine serum albumin, 1% Glycine, 0.2% Triton) for 1 hour at room temperature (RT). Primary antibodies were diluted in blocking buffer for o/n incubation at 4°C. Secondary antibodies, conjugated to Alexa-fluorochromes, were also diluted in blocking buffer and applied for 1 hour at RT. Hoechst was used to stain the nucleus before cells were mounted using DAKO (DAKO) fluorescent mounting medium and stored at 4°C.

Used primary antibodies were: mouse anti-MAP2 (1:1000; Sigma M4403); guinea pig anti-MAP2 (1:1000; Synaptic Systems 188004); guinea pig anti-synapsin ½ (1:1000; Synaptic Systems 106004); rabbit anti-PSD95 (1:50; Cell Signaling D27E11); mouse anti-pan axon (1:1000; Covance SMI-312R); rabbit anti-p62 (1:500; Sigma p0067); mouse anti-LC3 (1:500; NanoTools 0231-100/LC3-5F10); rabbit anti- LAMP1 (1:200; L1418-200ul); mouse anti 8-oxo-dG (1:100; R&D Systems 4354-MC- 050).

Secondary antibodies that were used are: goat anti-guinea pig Alexa Fluor 647 (1:2000, Invitrogen A-21450); goat anti-rabbit Alexa Fluor 488 (1:2000, Invitrogen A- 11034); goat anti-rabbit Alexa Fluor 568 (1:2000, Invitrogen A-11036); goat anti- mouse Alexa Fluor 488 (1:2000, Invitrogen A-11029); goat anti-mouse Alexa Fluor 568 (1:2000, Invitrogen A-11031).

Cells were imaged with the Zeiss Axio Imager 2 equipped with apotome. All conditions within a batch were acquired with the same settings in order to compare signal intensities between different experimental conditions.

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Signals were quantified using FIJI software. The number of synaptic puncta was quantified per individual cell via manual counting and divided by the dendritic length of the neuron. For p62 and LC3 puncta quantification the Particle Analyzer tool was used.

Oxidative stress quantification To quantify oxidative stress/ ROS we used either 8 oxo-dG stainings or CellROX® assays (Thermo Fisher; Green Reagent). 8 oxo-dG (8-oxo-2'-deoxyguanosine) is an oxidized derivative of deoxyguanosine, one of the major products of DNA oxidation. Quantifying 8 oxo-dG can therefore be used to measure oxidative stress levels. CellROX® Green Reagent is fluorogenic, cell-permeant dye which is weakly fluorescent in a reduced state and exhibits bright green photostable fluorescence upon oxidation by ROS. Quantifying fluorescence therefore is an indication for oxidative stress. For both assays, cells were prepared according to previously mentioned protocols. For 8 oxo-dG we performed ICC on PFA fixed cells with a specific antibody (R&D System) to quantify DNA oxidation. For CellROX measurements we added the probe (5µM) to living cells and incubated for 30 minutes before fixation. After fixation the cells were mounted with DAKO and imaged after o/n incubation within 24 hours with the Zeiss Axio Imager 2 w/o apotome. Fluorescence was quantified using FIJI image software. Seahorse Mito Stress Test Oxygen consumption rates (OCR) were measured using the Seahorse XFe96 Extracellular Flux analyzer (Seahorse Bioscience). iPSCs were seeded at a concentration of 10,000 per well in E8 basal medium supplemented with primocin, 10 µg/mL RevitaCell, and allowed to adhere at 37°C and 5% CO2. The day after plating RevitaCell was removed from the medium. One hour before measurement, culture medium was removed and replaced by Agilent Seahorse XF Base Medium (Agilent) supplemented with 10 mM glucose (Sigma), 1 mM sodium pyruvate (Gibco), and 200 mM L-glutamine (Life Sciences) and incubated at 37°C without

CO2. Basal oxygen consumption was measured six times followed by three measurements after each addition of 1 µM of oligomycin A (Sigma), 2 µM carbonyl cyanide 4-(trifluoromethoxy) phenylhydrazone FCCP (Sigma), and 0.5 µM of

173 rotenone (Sigma) and 0.5 µM of antimycin A (Sigma), respectively. One measuring cycle consisted of 3 minutes of mixing, 3 minutes of waiting and 3 minutes of measuring. The OCR was normalized to citrate synthase activity, to correct for the mitochondrial content of the samples79. The citrate synthase activity was measured according to the protocol described by Srere et al.80, modified for Seahorse 96 wells plates, as previously reported81. In short, after completion of OCR measurements the Seahorse medium was replaced by 0.33% Triton X-100, 10 mM Tris-HCl (pH 7.6), after which the plates were stored at -80°C. Before measurements, the plates were thawed and 3 mM acetyl-CoA, 1 mM DTNB, and 10% Triton X-100 was added. The background conversion of DTNB was measured spectrophotometrically at 412 nm and 37 °C for 10 minutes at 1-minute intervals, using a Tecan Spark spectrophotometer. Subsequently, the reaction was started by adding 10 mM of the citrate synthase substrate oxaloacetate, after which the ΔA412 nm was measured again for 10 minutes at 1-minute intervals. The citrate synthase activity was calculated from the rate of DTNB conversion in the presence of oxaloacetate, subtracted by the background DTNB conversion rate, using an extinction coefficient of 0.0136 µmol-1. cm-1.

Morphology To examine dendritic morphology of neurons, cells on coverslips were stained for MAP2 after three weeks of differentiation. Neurons were imaged using an Axio Imager Z1 with 568 nm laser light and an Axiocam 506 mono and digitally reconstructed using Neurolucida 360 software (MBF–Bioscience, Williston, ND, USA). When cells were too large for one image, multiple images of these neurons were taken and subsequently stitched together using the stitching plugin of FIJI 2017 software.

Only cells that had at least two dendritic branches had been selected for analysis. If the diameter of an extension of the soma was less than 50% of the diameter of the soma, it was an individual primary dendrite. If larger, the extension was evaluated to be made of two different primary dendrites. Axons were not considered in this analysis. For detailed morphological analysis different parameters were chosen to be considered: Branched structure analysis, soma size, number of nodes (dendritic 174 branching points), number of dendrites, dendritic length and the mean length of all cells was conducted. Additionally, convex hull 3D analysis was performed, measuring the area of the dendritic field of a neuron. A net connecting the most distant parts of the neurons dendrites is formed, measuring the volume of the area. Since the pictures obtained were 2D, the convex hull parameter was divided by 2 for all tracings, resulting in a measurement of the surface area and not the full volume. Furthermore, Sholl analysis was performed to investigate dendritic complexity82. Concentric circles are placed in certain, coherently large radii centered at the soma. The distance between two concentric circles forms a shell. The interval chosen for these shells was 10 µm. Within each shell, the number of intersections (the number of dendrites that cross each concentric circle), number of nodes and total dendritic length was calculated.

Western blot For Western blot cell lysates were made from iPSC cultures that were 80-90% confluent on a 6-well plate. Medium was always refreshed the day before. Drug treatments were applied as described earlier. To lyse the cells medium was removed and the well was washed with 2 ml ice cold PBS before 100 µl lysis buffer was applied (RIPA buffer supplemented with PhosSTOP (Roche) and protease inhibitors (cOmplete Mini, EDTA free, Roche). Before blotting, the protein concentration was determined by means of a PierceTM BCA protein assay (Thermo Fisher Scientific). For each sample the same amount of protein between 5 - 7.5 µg was loaded and separated by SDS-PAGE. Depending on the primary antibody separated proteins were transferred to nitrocellulose (BioRad) or, for LC3 probing (1:200; NanoTools 0231-100/LC3-5F10), PVDF membrane (BioRad). Other primary antibodies that were used are: KANSL1 (1:500; Sigma HPA006874), P62 (1:200; Sigma p0067), SOD1 (1:1000; Abcam ab13498), mTOR (1:1000; Cell Signaling 2972), p-mTOR (1:1000; Cell Signaling 2971), GAPDH (1:1000; Cell Signaling 2118), ULK1 (1:1000; Cell Signaling 8054), p-ULK1 (1:1000; Ser757) (1:1000; Cell Signaling 14202), and LAMP1 (1:200; Sigma L1418-200ul). For visualization horseradish peroxidase- conjugated secondary antibodies were used: Goat anti-mouse (1:20000; Jackson

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ImmunoResearch Laboratories 115-035-062), and Goat anti-rabbit (1:20000; Invitrogen G21234).

Quantitative polymerase chain reaction RNA samples were isolated from iPSCs using the NucleoSpin RNA isolation kit (Machery Nagel) according to the manufactures’ instructions. cDNAs were synthesized by iScript cDNA synthesis kit (BioRad) and cleaned up using the Nucleospin Gel and PCR clean-up kit (Machery Nagel). Human specific primers were designed with help of the Primer3plus tool (http://www.bioinformatics.nl/cgi- bin/primer3plus/primer3plus.cgi) (see Supplementary Table 1). PCR reactions were performed in the 7500 Fast Real Time PCR System apparatus (Applied Biosystems) by using GoTaq qPCR master mix 2x with SYBR Green (Promega) according to the manufacturer's protocol. All samples were analyzed in triplo in the same run and placed in adjacent wells. Reverse transcriptase-negative controls and no template- controls were included in all runs. The arithmetic mean of the Ct values of the technical replicates was used for calculations. Relative mRNA expression levels for all genes of interest were calculated using the 2^-ΔΔCt method with standardization to PPIA (Peptidylprolyl Isomerase A) expression level. All expression analyses were done for three different biological replicates in three independent experiments.

Micro-electrode array recordings and analysis Recordings of the spontaneous activity of iPSCs-derived neuronal networks were performed at DIV 30 using the 24-well MEA system (Multichannel Systems, MCS GmbH, Reutlingen, Germany). Each well embedded with 12 electrodes, 80 µm in diameter and spaced 300 µm apart). Spontaneous electrophysiological activity of iPSC-derived neuronal network was recorded for 10 minutes. During the recording, the temperature was maintained constant at 37°C, and the evaporation and pH changes of the medium was prevented by inflating a constant, slow flow of humidified gas (5% CO2, 20% O2, 75% N2) onto the MEA. The signal was sampled at 10 kHz, filtered with a high-pass filter (i.e. butterworth, 100 Hz cutoff frequency). The noise threshold was set at ±4.5 standard deviations.

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Data analysis was performed off-line by using Multiwell Analyzer, software from the Multiwell MEA system that allows the extraction of the spike trains and parameters describing the network activity.

The mean firing rate (MFR) of the network was calculated by computing the firing rate of each channel averaged among all the active electrodes of the MEA (MFR>0.1 Hz). Burst detection. The algorithm defines bursts with a maximum of 50 ms inter spike interval (ISI) to start a burst, and a maximum of 50 ms ISI to end a burst, with a minimum of 100 ms inter burst interval (IBI). The percentage of random spikes (PRS) was defined by calculating the percentage of spikes not belonging to a burst for each channel and averaging among all the active electrodes of the MEA. For Network burst detection we were looking for sequences of closely-spaced single- channels bursts. A network burst was identified if it involved at least the 80% of the network active channels. Irregularity was estimated by computing the CV of the network burst inter burst interval (NIBI), which is the standard deviation divided by the mean of the NIBI. Discriminant function analysis with canonical discriminant functions based on parameters describing neuronal network activity were performed in SPSS.

Single cell electrophysiology recordings and analysis For recording spontaneous action potential-evoked postsynaptic currents (sEPSC) we used neurons derived from C1, KdVS1 and CRISPR1 after three weeks of differentiation. Experiments were performed in a recording chamber on the stage of an Olympus BX51WI upright microscope (Olympus Life Science, PA, USA) equipped with infrared differential interference contrast optics, an Olympus LUMPlanFL N 60x water-immersion objective (Olympus Life Science, PA, USA) and kappa MXC 200 camera system (Kappa optronics GmbH, Gleichen, Germany) for visualization.

We performed the recordings of neurons cultured on cover slips under continuous perfusion with oxygenated (95% O2/5% CO2) artificial cerebrospinal fluid (ACSF) (pH 7.4) at 30°C containing 124 mM NaCl, 1.25 mM NaH2PO4, 3 mM KCl, 26 mM NaHCO3, 11 mM Glucose, 2 mM CaCl2, 1 mM MgCl2. Patch pipettes (6-8 MΩ) were pulled from borosilicate glass with filament and fire-polished ends (Science Products 177

GmbH, Hofheim, Germany) using the PMP-102 micropipette puller (MicroData Instrument, NJ, USA). SEPSCs recordings were performed in voltage clamp mode, pipettes were filled with a cesium-based solution containing (in mM) 115 CsMeSO3, 20 CsCl, 10 HEPES, 2.5 MgCl2, 4 Na2-ATP, 0.4 Na3-ATP, 10 Na-phosphocreatine, 0.6 EGTA (adjusted to pH 7.2 and osmolarity 304 mOsmol). Spontaneous action potential-evoked postsynaptic currents (sEPSC) were recorded in ACSF without additional drugs at a holding potential of -60 mV. All recordings were acquired using a Digidata 1140A digitizer and a Multiclamp 700B amplifier (Molecular Devices, Wokingham, United Kingdom), with a sampling rate set at 20 kHz and a lowpass 1kHz filter during recording. Recordings were not analyzed if series resistance was above 20 MΩ or when the recording reached below a 10:0 ratio of membrane resistance to series resistance. SEPSCs were analyzed using MiniAnalysis (Synaptosoft Inc). For the traces that contained strongly accumulated sEPSCs mediated by presynaptic synchronized bursts of action potentials, these bursts of sEPSCs were counted by visual inspection.

Statistics The statistical analysis of the data was performed using GraphPad Prism 8 (GraphPad Software, Inc., CA, USA). We first determined whether data was normally distributed. We tested statistical significance for different experimental conditions by ordinary one-way ANOVA or two-way ANOVA when different cell-lines and drug- treated samples were included. Individual samples were compared using Sidak’s or Dunnett’s multiple comparisons test. When only two conditions were compared, we used unpaired t test. When data was not normally distributed, we applied Kruskal- Wallis test combined with Dunn’s or Sidak’s multiple comparison test. Results with P values lower than 0.05 were considered as significantly different (*), P <0.01 (**), P <0.001 (***), P <0.0001 (****). Data is shown in violin plots or as mean and standard error of the mean (SE) in bar diagrams.

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Acknowledgements

Funding: This work was supported by grants from the Netherlands Organization for Health Research and Development (ZonMW grants 91217055 to N.N.K and H.v.B. and, 91786319 and 91212109 to B.B.A.d.V.), SFARI grant 610264 (to N.N.K.). ERA- NET NEURON DECODE! grant (NWO) 013.18.001 (to N.N.K.) and Koolen-de Vries Syndrome Foundation (to D.A.K. and B.B.A.d.V).

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Disease: A Systematic Review and Meta-Analysis. Frontiers in Molecular Neuroscience (2018). 71. Sedlackova, L., Kelly, G. & Korolchuk, V. I. The pROS of Autophagy in Neuronal Health. Journal of Molecular Biology (2020). 72. Beckhauser, T. F., Francis-Oliveira, J. & De Pasquale, R. Reactive oxygen species: Physiological and physiopathological effects on synaptic plasticity. Journal of Experimental Neuroscience (2016). 73. Klann, E. Cell-permeable scavengers of superoxide prevent long-term potentiation in hippocampal area CA1. J. Neurophysiol. (1998). 74. Thiels, E. et al. Impairment of long-term potentiation and associative memory in mice that overexpress extracellular superoxide dismutase. J. Neurosci. (2000). 75. Knapp, L. T. & Klann, E. Potentiation of hippocampal synaptic transmission by superoxide requires the oxidative activation of protein kinase C. J. Neurosci. (2002). 76. Hu, D., Serrano, F., Oury, T. D. & Klann, E. Aging-dependent alterations in synaptic plasticity and memory in mice that overexpress extracellular superoxide dismutase. J. Neurosci. (2006). 77. Keen, C., Samango-Sprouse, C., Dubbs, H. & Zackai, E. H. 10-year-old female with intragenic KANSL1 mutation, no KANSL1-related intellectual disability, and preserved verbal intelligence. Am. J. Med. Genet. Part A (2017). doi:10.1002/ajmg.a.38080 78. Maley, A. M., Spraker, M. K., De Vries, B. B. A. & Koolen, D. A. Vitiligo in the Koolen-de Vries or 17q21.31 microdeletion syndrome. Clinical Dysmorphology (2015). 79. Rodenburg, R. J. T. Biochemical diagnosis of mitochondrial disorders. Journal of Inherited Metabolic Disease (2011). 80. Srere, P. A. B. T.-M. in E. [1] Citrate synthase: [EC 4.1.3.7. Citrate oxaloacetate-lyase (CoA-acetylating)]. in Citric Acid Cycle 13, 3–11 (Academic Press, 1969). 81. Klein Gunnewiek, T. M. et al. m.3243A > G-Induced Mitochondrial Dysfunction Impairs Human Neuronal Development and Reduces Neuronal Network Activity and Synchronicity. Cell Rep. 31, 107538 (2020). 82. SHOLL, D. A. Dendritic organization in the neurons of the visual and motor cortices of the cat. J. Anat. 87, 387–406 (1953).

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Supplementary Information

185

Supplementary Figure 1. iPSC characterization. A) Representative images for pluripotency marker detection in iPSC lines. Scale bar = 20 µm. B) Schematic presentation of the KANSL1 gene and protein. Mutations in KANSL1 deficient lines are marked. C) Nucleotide sequence of KANSL1 gene. Exon 2 that was used for sgRNA design and CRISPR/ Cas9 editing. D) Sanger sequencing result for CRISPR1 indicating a one- nucleotide insertion in one allele at the targeted restriction site. E) q-PCR result for KANSL1 to prove lower expression in KdVS1 patient derived iPSC line and CRISPR1 when compared to the respective control line C1. n=6 for all samples. F) Representative images for iPSC colonies stained for H4K16ac and H4K16ac quantification. n = 13 for C1, KdVS1 and CRISPR1; n = 7 for C2, KdVS2 and KdVS3. Fluorescence for KANSL1 deficient lines was normalized to the respective control lines. Scale bar = 50µm.Ordinary one-way ANOVA and Sidak’s multiple comparison test were used to test for statistically significant difference. *P < 0.05, **P < 0.01, ***P < 0.005, ****P < 0.0001.

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Supplementary Figure 2. Autophagy Phenotype in iPSCs. A) qPCR results for several H4K16ac regulated ATGs and KANSL1. cDNA levels in KdVS patient derived lines were always compared to levels in control iPSCs. n = 3. B) Representative images and signal quantification of iPSC colonies stained for autophagosome marker WIPI2. KdVS patient derived cells were compared to control iPSCs. n= 5 for control; n=12 for KdVS. Fluorescence was normalized to mean fluorescence of the respective control per experiment. Scale bar= 20 µm C) Representative Western Blots and quantification for ULK1 phosphorylation (Ser757). P-ULK1/ ULK1 ratio was normalized to control per experiment. n = 4 for control; n = 8 for KdVS. D) Schematic representation of mTOR dependent autophagy activation and autophagosome formation. Decreased mTOR phosphorylation will enable ULK complex activation which then induces phagophore formation. LC3I is lipidated to LC3II with help of WIPI2. P62 serves as adaptor protein between LC3II and autophagosomal cargos. Upon completion autophagosomes fuse with lysosomes to form autolysosomes and subsequently degrade cargos including p62. Statistically significant differences were determined by means of unpaired t test. *P < 0.05, **P < 0.01, ***P < 0.005, ****P < 0.0001.

187

Supplementary Figure 3. Mitochondrial function. A) Seahorse assay results for KdVS compared to control iPSCs showing basal, maximal, and ATP-linked respiration. B) We used oxygen consumption rate (OCR) as a measure of mitochondrial respiration and normalized to oxaloacetate-induced citrate synthase activity. n = 15 per condition for each line. Statistically significant differences were determined by means of t test. *P < 0.05, **P < 0.01, ***P < 0.005, ****P < 0.0001.

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Supplementary Figure 4. Neuronal Morphology. A) Representative images of reconstructed neurons derived of KdVS1 and C1 iPSCs, respectively. Scale bar = 50µm. B)-F) Detailed morphological analysis revealed no significant differences in morphology between the different lines. n = 33 for control and n = 29 for KdVS. Statistical differences were determined by t test. *P < 0.05, **P < 0.01, ***P < 0.005, ****P < 0.0001.

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Supplementary Figure 5. Neuronal Cell Density and Autophagy phenotype. A) Cell counts of fixed and stained iNeurons on c/s at DIV21 to ensure comparable cell densities between the lines and experiments. Ordinary one-way ANOVA was used for statistical analysis. B) Representative images and quantification for WIPI2 at DIV21. n = 25 for control; n = 31 for KdVS. Fluorescence was normalized to control. Scale bar= 20 µm. Unpaired t test was used to test for statistically significant differences. *P < 0.05, **P < 0.01, ***P < 0.005, ****P < 0.0001.

Supplementary Figure 6. Reclassification of group membership. A) Pie charts visualize accuracy of discriminant analyses functions by showing the relative distribution of lines per a-priory group after reverse testing for group identity.

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Supplementary Figure 7. Oxidative stress causes KdVS phenotype. A) Representative images of iNeuron nuclei stained for H4K16ac with and w/o BSO driven autophagy induction for C1 and KdVS1 and fluorescence quantification. All fluorescence values were normalized to the untreated control. Scale bar = 20 µm. n = 21 for C1; n = 26 for C1+BSO; n = 17 for KdVS1; n = 18 for KdVS1+BSO. Ordinary one-way ANOVA was used for statistical analysis. B-D) Representative Western blots for p-mTOR, mTOR, and LAMP1. iPSCs were either untreated or treated o/n with 100 µM APO. p-mTOR/mTOR an LAMP1 levels were quantified relative to untreated control. n = 5 for C1; n = 5 for C1+APO; n = 7 for KdVS; n = 7 for KdVS+APO. Statistically significant differences were determined by means of Kruskal Wallis and Dunn’s multiple comparison test. *P < 0.05, **P < 0.01, ***P < 0.005, ****P < 0.0001.

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Supplementary Table 1. List of qPCR primers.

GENE DIRECTION SEQUENCE KANSL1 forward ATCCTCCACACAGTCCCTTG reverse CCCCTTCTCCTCCTTACTGG MAP1LC3B forward GAGAAGCAGCTTCCTGTTCTGG reverse GTGTCCGTTCACCAACAGGAAG ULK1 forward GTTCCAAACACCTCGGTCCT reverse GCTCAGGGATGGTTCCAACT SOD1 forward TGAAGAGAGGCATGTTGGAGAC reverse CAAGCCAAACGACTTCCAGC SOD2 forward CTGCTCCCCGCGCTTT reverse GCTGGTGCCGCACACT FOS forward GCGTTGTGAAGACCATGACAG reverse TCTAGTTGGTCTGTCTCCGCT MYC forward CAGCGACTCTGAGGAGGAAC reverse GCTGCGTAGTTGTGCTGATG

Supplementary Table 2. List of vectors.

PLASMID SOURCE EXPERIMENT

PLVX-EF1Α-(TET-ON-ADVANCED)- J. Ladewig Generating Ngn2/rTTA IRES-G418(R) positive iPSC lines PLVX-(TRE-THIGHT)-(MOUSE)NGN2- J. Ladewig Generating Ngn2/rTTA PGK-PUROMYCIN(R) positive iPSC lines PSPAX2 Addgene Lentiviral preparations LENTIVIRAL PACKAGING VECTOR #12260 PMD2.G Addgene Lentiviral preparations LENTIVIRAL PACKAGING VECTOR #12259 FUW MCHERRY-GFP-LC3 Addgene Autophagosome/ #110060 synapsin co-localization

192

193

194

Proliferation deficit in patient- derived neural progenitor cells reveal early neurodevelopmental phenotype for Koolen-de Vries Syndrome

Katrin Linda, Anouk H. A. Verboven, Vanessa Budny, Brooke L. Latour, David A. Koolen, Bert B. A. de Vries, Nael Nadif Kasri

This chapter is in preparation for publication

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Abstract

Koolen-de Vries syndrome (KdVS) is a rare genetic neurodevelopmental disorder with multisystemic phenotypes. Patients suffer from developmental delay, intellectual disability, facial dysmorphisms, epilepsy, and congenital malformations in multiple organ systems, including the brain. Heterozygous loss-of-function mutations in KANSL1 are causing KdVS. KANSL1 is widely expressed early during development but it’s role during early brain development is not well-established. Here, using transcriptomic analysis of KANSL1-deficient human induced-pluripotent stem cells (iPSCs) from KdVS patients and genome-edited lines, we identified aberrant cell-cycle regulation. Analysis of cell-cycle pointed towards reduced mitotic activity in KdVS patient-derived iPSCs as well as neuronal progenitor cells (NPC), which was further accompanied with a decreased proliferative capacity of NPCs. In analogy to the results obtained in human cells we observed decreased proliferation and increased immature neurons in the subgranular zone of the dentate gyrus of KANSL1-deficient mice. Taken together our results point towards a critical role of KANSL1 in regulating NPC proliferation, which potentially affects early brain development.

196

Introduction

Koolen- de Vries syndrome (KdVS), also known as 17q21.31 micro-deletion syndrome, is a rare multisystemic disorder. The main clinical features encompass mild to moderate intellectual disability (ID), developmental delay, epilepsy, distinct facial features, congenital malformations such as heart and kidney defects and friendly behavior. Furthermore, KdVS patients may display structural abnormalities of the nervous system in ~50% of cases including corpus callosum hypoplasia, enlarged ventricles, hydrocephalus and heterotopia1–3. Initially a microdeletion within the 17th chromosome incorporating the genes MAPT, KANSL1, CRHR1, STH, and SPPL2C was identified in KdVS patients4–6. In 2012, loss-of-function mutations restricted to the KANSL1 gene were found in patients presenting the full spectrum of KdVS symptoms, which identified heterozygous loss of KANSL1 as genetic cause for KdVS2,7.

KANSL1 is a widely expressed and evolutionarily highly conserved gene2,8, which encodes for the scaffold protein of the non-specific lethal (NSL) complex. This complex contains the histone acetyltransferase KAT8 (hMOF) and thereby has an essential function in the acetylation of different histone H4 lysine residues, especially in histone 4 lysine 16 acetylation (H4K16ac)8, associated with active gene expression. Gene expression regulated by the NSL complex is critical for several cellular processes, such as apoptosis and differentiation9–12. Next to its’ role in chromatin modulation, KANSL1 re-locates to the microtubules during mitosis. As a microtubule-associated protein it promotes microtubule nucleation and stabilization of the surrounded chromosomes. This is important for the assembly of mitotic spindles. Notably, RNAi mediated knockdown of KANSL1 showed to decrease mitotic activity and proliferation rate13,14.

Effects of KANSL1 haploinsufficiency have already been studied in Drosophila and mouse models, revealing deficits in short-term memory and remarkable differences in social behavior2,15. A mouse model mimicking the human 17q21.31 microdeletion also revealed brain abnormalities in, like microcephaly15. Furthermore, electrophysiological studies in these mice indicated impaired synaptic transmission15. Gene expression showed to be changed significantly within 197 pathways affecting synapse function and development as well as neurogenesis15. Although animal models recapitulate core features of KdVS, they have obvious limitations as they fail to mimic the exact mutation and/or genetic background of the patients. Additionally, physiological differences make it difficult to fully understand human disease pathology. A way to overcome the inaccessibility of human neural disease material is offered by induced pluripotent stem cells (iPSCs). This technology vastly improved in the past decade16 increasing the efficiency and reproducibility to generate iPSCs as well as to differentiate them towards the desired neuronal cell type. This enables us to examine human neuronal development and function within control and patient specific genetic background in an in vitro experimental set-up. We already made use of KdVS patient derived iPSCs to identify autophagy as cellular process affecting synaptic function in post-mitotic excitatory neurons (chapter 4 this thesis). However, the structural brain abnormalities in patients and KdVS mouse model, as well our gene expression results, which identified neurogenesis as one of the main affected pathways in the KdVS mouse model15, point towards an early developmental phenotype. Such early neurodevelopmental changes which lead to structural brain abnormalities are often associated with disrupted neural progenitor cell (NPC) regulation by premature differentiation, reduced proliferation, or cell-cycle disruption17. While changes in neuronal function of KdVS cells can clearly be linked to the function of KANSL1 as scaffold of the NSL complex, changes in NPC proliferation might at least be partially due to its’ function as microtubule stabilizing protein. Different studies already reported that altered cell-cycle properties can induce apoptosis or preterm differentiation which eventually results in abnormal neurogenesis18,19. To examine disease causing mechanisms like these in a human model is of special interest, due to the well-characterized unique aspects of human brain development20–23.

We here used a cohort of KdVS patient derived iPSCs to examine how heterozygous loss of KANSL1 affects early brain developmental processes in a patient-specific genetic background. We identified reduced proliferative capacity and mitotic activity in KANSL1-deficient NPCs.

198

Results

Transcriptomic analysis points towards mitotic deficits in KdVS patient-derived iPSCs In this study we used a set of seven different iPSC lines, including four KANSL1- deficient and three control lines (Figure 1A). All iPSC lines were reprogramed from fibroblasts. All cells originated from female individuals, except for one male control line. We reprogrammed two lines from KdVS patients with amino acid deletions in exon 2 of the KANSL1 gene (KdVS1 and KdVS2), leading to frameshift and premature stop-codons. The third KdVS line was reprogrammed from fibroblasts of a KdVS patient, where heterozygous loss of KANSL1 was due to a 17q21.31 microdeletion

(KdVS3). Patients with a mutation in KANSL1 are phenotypically indistinguishable

3 from patients with the microdeletion . The first control line (C1), originated from the mother of patient KdVS1, meaning that KdVS1 and C1 have an overlapping genetic background. Two other, independent control lines (C2 and C3) were used to take normal variation into account. Next to the KdVS patient lines a CRISPR-edited line of C1 with a KANSL1 mutation in Exon 2 (CRISPR1) was used. All iPSCs were positive for the pluripotency markers OCT4, NANOG, SSEA-4, and TRA1-81 (Supplementary Figure 1A). This cohort enabled us to examine how KANSL1- deficiency affects cellular functions in a human in vitro model, focusing on neuronal development and function.

RNA sequencing (RNA-seq) was performed to compare gene expression profiles of iPSCs obtained from KdVS patients and controls. In total, RNA samples from all seven iPSC lines were included. RNA was isolated from four replicates per line and RNA-seq was performed. Because we included one male control line, whereas all other lines are female, all reads belonging to genes positioned on the Y chromosome were excluded after data pre-processing. Subsequently, raw counts were normalized and corrected for batch effect.

Principal component analysis (PCA) shows the largest variation between samples on gene expression level was explained by gender differences (Suppl. Figure 1A). Interestingly, PC1 also shows samples from KdVS patients with a mutation in

199

KANSL1 cluster together, including the CRISPR line in which a similar mutation was introduced, whereas the KdVS patient with the 17q21.31 microdeletion (KdVS3) clusters away from the other KdVS patients (Suppl. Figure 1B). This finding indicates that changes in gene expression in iPSCs linked to the KdVS phenotype depend on the type of mutation. On PC4 and PC5, samples from the 17q21.31del patient cluster together with samples from the other KdVS patients and the CRISPR line (Suppl. Figure 1C), and these cluster away from controls, indicating that there are also shared changes in gene expression between the 17q21.31del patient line and other KdVS lines.

To identify mechanisms underlying Koolen-de Vries Syndrome, independent of the type of mutation causing the syndrome, we performed differential expression (DE) analysis by comparing samples from KdVS lines and the CRISPR line (four iPSC lines) to samples from all control lines (three iPSC lines). In total 19 genes were significantly differentially expressed (adj. P-value < 0.05) between KdVS lines and controls (Figure 1B). Gene set enrichment analysis revealed significant enrichment of genes in 441 gene sets (adj. P-value < 0.05). Interestingly, the strongest enrichment for upregulated genes was observed for GO-terms and pathways linked to cell cycle regulation (Figure 1C). We also determined enrichment of genes in transcription factor target gene sets. These gene sets contain genes that are regulated by specific transcription factors. Interestingly, the majority of transcription factor gene sets represented targets of E2F-related genes. E2F-related genes are known to regulate expression of genes involved in cell cycle, providing more evidence that cell cycle processes might be dysregulated in KdVS24.

Many of the GO terms and pathways affected represented genes part of specific cell cycle phases or cell cycle checkpoints. Interestingly, also regulation of transcription of cell cycle genes by TP53 is affected. TP53 mainly regulates expression of genes involved in G1 and G2 cell cycle arrest, thereby inhibiting cell cycle transition25. One of the most important genes involved in G1 arrest of which expression is regulated by TP53 is CDKN1A26. CDKN1A functions in a complex with cyclin A (CCNA2), or cyclin E (CCNE1 and CCNE2) to inactivate CDK2, thereby preventing G1 to S-phase transition27. These genes are all upregulated in KdVS, indicating cells might remain 200 in the G1 phase (Figure 1D). Also expression of E2F7 is upregulated, which is known to be directly induced by TP53 and known to repress E2F1, a transcription factor that promotes expression of genes involved in transition from G1 to S-phase28. TP53 also regulates genes needed for G2 arrest such as GADD45A29, which is upregulated in KdVS as well. GADD45A binds to AURKA, which inhibits its catalytic activity thereby preventing AURKA-mediated G2 to M-phase transition30,31. These findings indicate that iPSCs from KdVS lines stay in the G1 and G2 phase of the cell cycle for a prolonged time, thereby decreasing the rate of cell division. The fact that genes of which their expression are directly regulated by TP53 are affected can be explained by significant enrichment of upregulated genes involved in the stabilization of p53, leading to phosphorylation of p53 thereby making it less vulnerable to degradation by MDM232.

AJAP1 DRAM1 GPR143 SYAP1 EIF1AX FTL K L K L PPDPF 17q21del. NAXE c.531 540del c.540delA ZNF662 RAB13 MIR222HG NPTX2 LINC00707 CNTN1 K L MTRNR2L1 AP000902.1 c.602insT MEG3 CCDC152

ZNF717

C C C C C C C C C KdVS KdVS KdVS KdVS KdVS KdVS CRISPR CRISPR KdVS KdVS KdVS C C C KdVS KdVS CRISPR KdVS

1 1 1 3 3 3 2 2 2 1 3 2

2 2 2 3 3 3 1 1 1 2 3

CRISPR1 C1 KdVS1 KdVS2 KdVS3 C2 C3 1

1 1 1

The role of GTSE1 in G2/M progression after G2 checkpoint Mitotic metaphase and anaphase ATP sy nthesis coupled electron transport Regulation of mitotic cell cy cle Anaphase promoting complex dependent catabolic process Mitotic spindle organization Size Mitotic prometaphase 100 CCNE2 Stabilization of p53 200 RBL1 Signal transduction involved in cell cycle checkpoint 300 CENPJ G1 DNA damage checkpoint 400 CCNE1 PLK3 M phase CDK1 G1/S DNA damage checkpoints Gene ratio 0.7 CCNA2 resolution of sister chromatid cohesion AURKA 0.6 Activation of APC/C and APC/C:Cdc20 CCNB1 0.5 mediated degradation of mitotic proteins BAX 0.4 PLK2 respiratory chain complex 0.3 CNOT8 metabolism of polyamines CDC25C 0.2 cell cycle checkpoints ZNF385A TP53 regulates transcription of cell cycle genes CDKN1A E2F7 Mitotic sister chromatid segregation GADD45A

Oxidative phosphorylation0 BTG2

CRISPR CRISPR KdVS KdVS KdVS KdVS KdVS KdVS KdVS KdVS C C C KdVS C C C C C CRISPR KdVS KdVS CRISPR KdVS C C C C

2 2 1 3 2 1 3 1 1 3 2 3

2.2 2.3 2.4 2.5

3 3 3 2 2 2 1 1 1 3 2 1

1 1 1 Normalized enrichment score 1

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Figure 1. RNA seq. indicates abnormal cell-cycle regulation in KANSL1 deficient iPSCs. A) Schematic representation for the different iPSC lines used. B) Differentially expressed (DE) genes between samples from KdVS patients and controls (P <0.05). Voom-transformed counts corrected for batch effect were scaled per gene. C) Top 20 gene sets are shown enriched for upregulated genes. Normalized enrichment score is shown on the x-axis. Gene set size is shown by the circle size. Gene ratio represents the number of leading-edge genes in the gene set versus gene set size. D) Heatmap showing leading edge genes identified for the Reactome pathway: TP53 regulates transcription of cell cycle genes. Voom (log2-) transformed counts corrected for batch effect were scaled per gene.

Reduced mitotic activity in KdVS patient-derived iPSCs RNA-seq results indicate that KANSL1 deficiency affects cell-cycle regulation. Therefore, we decided to examine possible cell-cycle deficits in more detail by means of immunocytochemistry (ICC), detecting Ki67 and phospho-histone H3 (pHH3). Ki67 is a nuclear protein that is expressed in all cells that are in active phases of the cell cycle, but not in resting, non-cycling cells (G0 phase) and can therefore be used as a marker for cellular proliferation (Figure 2A). When cells enter mitosis, this is accompanied by phosphorylation of histone H3 on serine 10. Upon exit from mitosis histone H3 is dephosphorylated again33. That phosphorylation and dephosphorylation events are well characterized and therefore pHH3 can serve as reliable marker for mitotic cells (cells in late G2- or M-phase) (Figure 2A). The number of proliferating (Ki67+) and mitotic (pHH3+) cells in the iPSC colonies was quantified. By definition iPSCs are self-renewal, meaning that they have limitless proliferation capacity. Therefore, iPSCs cannot be Ki67 negative. Indeed, Ki67 stainings showed to be 100% positive for all colonies of all iPSC lines, confirming their stem-cell identity. However, the mitotic marker pHH3 revealed differences in the number of mitotic cells. While the control lines (C1 and C3) revealed 3-4% mitotic cells, KdVS patient-derived iPSCs (KdVS1 and KdVS3) showed only 2-3% mitotic cells. This means that, although there is no difference in the number of proliferating cells, control iPSCs divide more often or faster. During culturing we observed that control iPSCs reached confluency faster than KdVS patient derived iPSCs (data not shown), corroborating our ICC experiments and indicating changed cell- cycle regulation and reduced proliferation in KANSL1-deficient iPSCs.

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Figure 2. Cell-cycle abnormalities in KdVS iPSCs. A) Schematic representation of the cell-cycle, indicating that Ki67 as a marker for proliferation and pHH3 as marker for actively dividing cells. B) Representative images of iPSC colonies stained for Ki67 (green) and pHH3 (red). Scale bar = 50 µm. C) Quantification of Ki67+ pHH3+ iPSCs. Ordinary one-way ANOVA was used to test for statistically significant differences. * P < 0.05, *** P < 0.0005, **** P < 0.0001.

Reduced proliferation observed in KdVS-derived NPCs Our results in iPSCs suggest a general proliferation deficit in KdVS patient cells. Multiple studies identified altered proliferation and differentiation of NPCs as a major convergence point for NDDs (reviewed by Ernst)34. NPCs are the progenitors of the central nervous system giving rise to many glial and neuronal cell types. Contrary to iPSCs, they have only a limited proliferative capacity and this capacity determines the size of the NPC pool and eventually the number of neurons and other neuronal cell types35,36. Alterations could already be linked to structural brain abnormalities like micro- and macrocephaly. We therefore sought to confirm the changes in proliferation in NPCs. To differentiate iPSCs towards NPCs we applied a well- established protocol for dual SMAD inhibition (dSMADi), that recapitulates key hallmarks of early cortical development (Figure 3A). The synergistic inhibition of two

203

SMAD pathways by applying Dorsomorphin/ Noggin and SB431542 rapidly differentiates iPSCs towards the neuroectodermal fate to generate NPCs37. To confirm NPC identity, we checked the cells for marker expression of NESTIN, PAX6 and SOX2 by means of ICC (Figure 3B-C). The dendritic marker MAP2 was included as negative control. The identity check was repeated regularly for different passages (P). KdVS patient-derived iPSCs were not notable compromised in their ability to generate NPCs.

Cells of the control line C1 and the KdVS patient-derived lines KdVS1 and KdVS3 that were positive for NPC markers and negative for the neuronal marker MAP2 were used for cell-cycle assays at P 0/1, 2, and 3. Ki67 staining revealed that within the population of control NPCs (C1) almost 80% were proliferating cells (Figure 3D). Over time the number of proliferating cells decreased slowly. While we found 85% of the cells to be positive for Ki67 at P0, at P3 less than 70% of C1 NPCs were proliferating (supplementary Figure 3A). For KdVS1 and KdVS3 about 60% of all cells were positive for the proliferation marker Ki67, which is significantly less when compared to control NPCs (Figure 3D). This significant difference is mainly due to the quick decrease in proliferating cells. Already at P1 the fraction of Ki67 positive cells decreased to 60% for both lines (KdVS1 and KdVS3) and remained low for P2 and 3 (Supplementary Figure 3A). We also found significantly less mitotic NPCs for

KdVS1 and KdVS3 when compared to C1 (Figure 3D). While 3% of control NPCs were pHH3 positive, only 1,4% of KdVS1 derived NPCs and 1% of KdVS3 NPCs were positive for this mitotic marker (Figure 3D). The fraction of mitotic cells remained stable during passaging for all lines (supplementary Figure 3B). The reduction in Ki67 positive NPCs, already during the first passages, indicates that the proliferative capacity of KANSL1 deficient cells is limited compared to control NPCs. Furthermore, results for the mitotic marker pHH3 suggest reduced mitotic activity for the remaining proliferating KdVS-derived NPCs.

The observed changes in proliferation and cell-cycle might not only negatively affect NPC pool size, but also differentiation. More precisely, reduced proliferation and increased cell-cycle length point towards premature differentiation in KANSL1 deficient NPCs36. To investigate a possible differentiation phenotype, we induced 204 neuronal differentiation right after passaging the cells by removing the growth factors EGF and FGF from the maintenance medium. Six days after induction, cells were fixed and stained for Ki67 and the dendritic marker MAP2. For the control line C1, as well as for the KdVS-derived cells (KdVS1), the fraction of Ki67 cells decreased while the number of MAP2 cells increased, indicating enhanced neuronal differentiation for both lines (Supplementary Figure 3C). We did not detect any significant differences between control and KdVS cultures, suggesting that KdVS and control cells can differentiate equally well to MAP2-postive neurons. However, examining the Ki67/MAP2 ratio already 1-2 days after induction might reveal whether KANSL1- deficient NPCs tend to differentiate earlier/ faster.

induction replate rosettes

confluent iPSCs in 5 i Hoechst NEST. PAX6 Hoechst MAP2 SOX2

Ki67 pHH3

Proliferation Mitosis

100 10 *** **** 80 8

60 6 ** 40 4 *** 20 2

0 0

Figure 3. Reduced proliferation and mitosis in KdVS patient derived NPCs. A) Schematic representation of NPC differentiation protocol by dSMADi. B) Representative images of NPC rosettes at P3 stained for NESTIN, PAX6, SOX2, and MAP2. Scale bar = 100 µm. C) Representative images for NPCs of C1, KdVS1, and KdVS3 stained for the NPC markers NESTIN (grey) and PAX6 (magenta) at P0. Scale bar = 50 µm. D) Representative images of NPCs stained for Ki67 (green) and pHH3 (red) at P3 and quantification of Ki67+ pHH3+ NPCs. Scale bar = 50 µm. Ordinary one-way ANOVA was used to test for statistically significant differences.

205

Ordinary one-way ANOVA was used to test for statistically significant differences. * P < 0.05, *** P < 0.0005, **** P < 0.0001.

Adult neurogenesis deficit in KdVS mouse model Previously presented results indicate general cellular functions for KANSL1 in mitosis, and proliferation, that are essential to maintain NPC identity. We reasoned that if reduced proliferative capacity due to heterozygous loss of KANSL1 leads to premature differentiation, this should not be restricted to embryonic neurogenesis, but also be observed during adult neurogenesis. Adult neurogenesis describes the formation of functional, mature neurons from neural stem cells in adults. This process is restricted to the subgranular zone (SGZ) of the dentate gyrus (DG) and the subventricular zone of the lateral ventricles38–40. In these brain regions new neurons are generated and integrated into already established neuronal circuits throughout adulthood. Adult neurogenesis is known to be essential during, for example, learning and memory, a process that showed to be impaired in KdVS animal models2,15. Interestingly, our results for differential gene expression in the KdVS mouse models identified genes that are essential for regulation of adult neurogenesis as differentially expressed. This encouraged us to use the mouse model to investigate whether heterozygous loss of Kansl1 leads to proliferation deficits and premature differentiation during adult neurogenesis.

We prepared free floating sections at postnatal day (P) 21 for Kansl1+/+ (WT) and Kansl1+/- (HET) mice and examined proliferation and neurogenesis by means of immunohistochemistry (IHC). We focused our analysis on the DG of the hippocampus, a well-characterized brain region in terms of adult neurogenesis38. Again, we made use of the marker Ki67 to quantify proliferating cells. Similar to what we found for KdVS patient-derived NPCs, the number of proliferating cells in the DG of Kansl1+/- mice was significantly reduced (Figure 3B).

After confirming reduced proliferation in the DG of Kansl+/- mice we examined whether KANSL1-deficiency leads to aberrant neuronal differentiation by comparing Doublecortin (Dcx) expression in WT and Kansl+/- mice. Dcx is a microtubule-

206 associated protein, which is expressed in neuronal precursor cells and immature neurons. In the adult brain Dcx expression is preserved only in areas of adult neurogenesis and this restricted expression in developing neurons makes it a suitable marker to quantify neurogenesis41. We imaged and quantified Dcx-positive cells in the subgranular layer of the DG of WT and Kansl+/- mice. Whereas the total number of Dcx-positive cells was not changed between WT and Kansl+/- (Figure 3c), we observed significant differences in the morphology of Dcx-positive cells. While about 40% of the Dcx-positive cells of WT slices showed complex dendritic arborization only 20% of the Dcx positive cells in slices of Kansl+/- mice had a similar morphology (Figure 3c). Increased dendritic complexity of these early, developing neurons is an indication for a later developmental stage41. We find an increased percentage of less complex, so less mature Dcx positive cells in Kansl+/- mice. Taken together our observations revealed a decreased number of proliferating cells and an elevated proportion of highly immature differentiating neurons in the subgranular zone of the DG, suggesting aberrant adult neurogenesis in Kansl+/- mice.

Changes in adult neurogenesis might not only be caused by reduced proliferation. Next to its’ function as microtubule associated protein during mitosis, KANSL1 also functions as scaffold protein of the NSL complex, mediating H4K16ac and thereby regulating gene expression. This is why we also examined H4K16ac signatures in control mouse brains by means of IHC. H4K16ac was increased in the SGZ of the DG compared to other hippocampal regions. This suggests enhanced H4K16ac- mediated gene expression in a region of adult neurogenesis, pointing towards a synergistic role of KANSL1 in NPC proliferation and differentiation, affecting the regulation of (adult) neurogenesis.

207

100 Kansl1 H4K16ac WT HET

0 WT HET

Dcx 200 100 WT

n.s. 80 150 60 Dcx not branching cells 100 40 HET Dcx branching cells ** 50 20

0 0 WT HET WT HET Figure 4. Abnormal adult neurogenesis in KdVS mouse model. A) Kansl1 expression in WT mouse hippocampus (p56), adapted from Allen Brain Atlas. B) Representative image of H4K16ac in WT mouse hippocampus (p21). C) Representative images of Ki67 detection in DG of WT and HET mice and quantification of Ki67 positive cells. We used three individuals per genotype. Scale bar = 100 µm. D) Representative images of Dcx stainings in WT and HET mice (P21) and Dcx quantification and morphological analysis. T test was used to analyze statistically significant differences. * P < 0.05, *** P < 0.0005, **** P < 0.0001.

Discussion

In this study we wanted to get insight into brain developmental abnormalities caused by heterozygous loss of KANSL1. To this end we developed a human in vitro model and generated KdVS patient derived NPCs by applying a protocol of dSMADi. We examined expression of cell-cycle and proliferation markers and found that NPCs derived from KdVS patient lines showed to be less proliferating and less mitotic active compared to control NPCs.

As a microtubule-associated protein KANSL1 plays an important role in cell division. Meunier and his colleagues already found that RNAi-mediated knockdown of KANSL1 leads to several mitotic defects, including prolonged arrest in a prometaphase-like state, misaligned chromosomes and various spindle organization defects. Furthermore, about 61% of the cells exhibiting a KANSL1 knockdown were 208 not able to finish mitosis within the 24h measuring time13. Here, we used KdVS patient-derived iPSCs to examine functions of KANSL1. Similarly, to previously reported findings, we observed a decreased fraction of mitotic cells for KdVS patient- derived iPSCs and NPCs when compared to control cells. Those findings in cells with KdVS patient genetic background demonstrate a disease relevant role for KANSL1 in cell-cycle regulation. Our findings in iPSCs point towards a general proliferation deficit in KANSL1-deficient stem cells. This will affect progenitor pool size and differentiation during organogenesis and might, at least partially, explain the various congenital abnormalities, including brain, heart, and kidney structural abnormalities often seen in several KdVS patients2,3.

In addition, KdVS patient derived NPCs were used to get insights into deregulated processes in early brain development, that might help to explain the various structural brain abnormalities observed in the majority of KdVS patients. Indeed, our results suggest a decreased proliferative capacity and mitotic activity of KdVS patient-derived NPCs, which will lead to a reduced size of the NPC pool, and, subsequently, a reduced number of neuronal cells, which at least might partially explain structural brain abnormalities, such as corpus callosum hypoplasia/aplasia and heterotopia observed for KdVS patients.

However, reduced number of cell divisions and a potentially increased cell-cycle length might not only affect brain morphology by directly affecting NPC pool size, it might also affect cell composition and network formation by changing neuronal differentiation processes. Results of several studies showed that altered cell cycle properties result in abnormal neurogenesis. These studies indicate that altered cell- cycle regulation either leads to apoptosis or to preterm differentiation18,19. Kohwin and Doe observed that the length of the cell-cycle influences cell fate by regulating the expression of Notch targets and pro-neural genes42. We did not perform apoptosis assays to examine whether cell death is increased in cell cultures of KdVS patient-derived cells. However, we carefully monitored all cells during culturing and did not note increased levels of dead cells in KANSL1-deficient cultures. Furthermore, Dcx stainings of brain slices from KdVS mice revealed an increased proportion of early differentiating neurons that lack dendritic arborisation. Taken 209 together, our findings suggest that KANSL1-mediated changes in proliferation and cell-cycle length rather lead to an increased tendency to differentiate than to induce apoptosis, but additional research is required to support this.

We focused here mainly on how KANSL1 as microtubule- associated protein affects proliferative capacity and mitosis in KdVS patient derived NPCs and concluded that KANSL1 deficiency results in a decreased NPC pool and potentially increased premature neuronal differentiation. However, this is probably a limited view that does not explain the full phenotype as the role of KANSL1 as scaffold protein of the NSL complex should be taken into account. As already mentioned, H4K16ac mediated gene expression is essential for the maintenance of stem cell identity by controlling/ activating signaling pathways that negatively regulate differentiation. We observed a decrease in proliferating NPCs after several passages (Supplementary Figure 3A). In KANSL1 deficient cells this was observed already within the first passages (P0- P1) (Supplementary Figure 3A), which points towards decreased proliferative capacity, but might also indicate loss of NPC identity and premature differentiation. Evidence for a role of H4K16ac mediated gene expression in NPC maintenance is provided by results obtained in a model with cerebrum-specific knockout of Kat8, the histone acetyltransferase within the NSL complex, in mice43. Kat8 deletion caused reduced NPC proliferation, premature neurogenesis, and massive apoptosis43. Except for massive apoptosis these observations are highly similar to what we find in KANSL1 deficient NPCs, indicating that KANSL1 deficiency also affects NSL complex mediated gene expression affecting NPC maintenance. A candidate pathway that is controlled by H4K16ac is the Notch signaling pathway44, which plays an important role in orchestrating the balance between neural stem cells and neuronal differentiation 20,45–47. Notch transcripts are mainly found in the subventricular zone, a brain region where also NPCs reside48,49. This indicates importance of Notch signaling for maintaining and expansion of the neural progenitor pool50–52. Furthermore, it was found that NPCs express Notch signals especially during proliferation45–47,51. Active Notch signaling reduces expression of pro-neural factors and thereby maintains neural stem cell identity53. Interestingly, low Notch activity coincides with increased neuronal differentiation49. Based on these preliminary studies, we can hypothesize that KANSL1 deficiency affecting H4K16ac 210 might lead to impaired Notch signaling which results in premature neuronal differentiation. This is corroborated by our findings that gene expression related to adult neurogenesis, which includes genes of the Notch signaling pathway, is changed in the KdVS mouse model when compared to WT mice15.

We observed increased H4K16ac in a brain region of adult neurogenesis in mice (Figure 4B). Furthermore, our gene expression results for the KdVS mouse model provide evidence for reduced Notch signaling activity15. These observations indicate that we indeed need to examine the dual role of KANSL1 as microtubule associated protein during mitosis and as scaffold of the NSL complex to fully understand pathological disease mechanisms affecting NPC maintenance and differentiation in KdVS. The mouse model might not be ideal for this as there are species specific differences in brain structure and neurological development54. Especially the increased cell type diversity regarding NPCs and neurons in humans compared to mice22,55 highlight the importance for a human model. The model system that probably offers the greatest potential for modeling human brain development are brain organoids derived from human iPSCs. These self-assembled, 3D cultures of in vitro generated brain material can be used to model cytoarchitecture and developmental trajectories found in vivo. Single-cell RNA-sequencing results of several studies illustrate the fundamental processes of cellular diversification and specification in brain organoids (reviewed by Arlotta and Pasca, Qian, Song and Ming)23,56. Additionally, transcriptomic analysis indicate a large variety of cell types that follow the sequential stages of early embryonic development23. This makes human iPSC derived organoids a more suitable model for examining how changed NPC proliferation affects neurogenesis and cell fate of differentiating cells for NDDs like KdVS.

Taken together, this study identified abnormalities in stem cell proliferation in KdVS patient derived cells (iPSCs and NPCs), which might explain the brain abnormalities often observed for KdVS patients. However, more research is needed to fully understand the early neural developmental phenotype. The dual role of KANSL1 as microtubule- associated protein and as scaffold of the NSL complex should be of special interest to find links between the different cellular and molecular 211 mechanisms. Making use of our patient derived iPSCs to generate NPCs and ideally also 3D cortical organoids will be useful in vitro model systems to elucidate synergistic effects of KANSL1 on NPC maintenance and differentiation. Results will not only help to elucidate changes in embryonic neurogenesis in KdVS, but also increase our general understanding of cellular and molecular pathways regulating early brain developmental processes.

Methods

Patient information and iPSC line generation In this study we used a set of three control and three KdVS patient derived iPSC lines (Figure 1). All patients that were included in this study present the full spectrum of KdVS associated symptoms, including structural brain abnormalities. KdVS1 and

KdVS2 originate from two individual, female KdVS patients with frameshift mutations in KANSL1, while the iPS line KdVS3 originates from a female patient with a 17q microdeletion. We used two independent female control lines (C2 and C3). Next to this control line we also used a relative control line for patient line KdVS1 (C1).

All iPSCs used in this study were obtained from reprogrammed fibroblasts. KdVS1 and C1 were generated by making use of the SimpliconTM reprogramming kit (Millipore). Overexpression of the four factors Oct4, Klf4, Sox2, and Glis1 was introduced by a non-integrative, non-viral one step transfection. KdVS2 was reprogrammed by lentiviral mediated overexpression of Oct4, Sox2, Klf4, and cMyc.

For KdVS3 episomal reprogramming was performed with the same reprogramming factors. C2 was generated from fibroblasts of a 36 old female control obtained from the Riken BRC - Cell engineering division (#HPS0076:409B2). The third control line was generated from fibroblasts of a 30-year old healthy, male individual by episomal reprogramming. We obtained this line from Coriell Institute (#GM25256). iPSC clones used in this study were validated through a battery of quality control tests including morphological assessment. All clones expressed the stem cell markers Oct4, Sox2 Nanog, SSEA-4, and Tra1-81 (see supplement). Additionally, SNP- arrays were performed to ensure genomic stability. 212

Culturing induced pluripotent stem cells (iPSCs) iPSCs were always cultured on Matrigel (Corning, #356237) in E8 flex (Thermo Fisher Scientific) supplemented with primocin (0.1 µg/ml, Invivogen) at 37⁰C with 5%

CO2. Medium was refreshed every 2-3 days and cells were passaged 1-2 times per week using an enzyme-free reagent (ReLeSR, Stem Cell Technologies).

RNA sequencing RNA-seq was performed on hiPSCs from 3 control lines, 3 KdVS patient lines and one CRISPR line that mimics the mutation in KANSL1 in KdVS patients. RNA was isolated from four biological replicates of each iPSC line at ~80% confluency, from two different batches representing different days of isolation (two replicates per batch), resulting in a total of 28 samples. RNA was isolated with the Quick-RNA Microprep kit (Zymo Research, R1051) according to manufacturer’s instructions. RNA quality was checked using Agilent’s Tapestation system (RNA High Sensitivity ScreenTape and Reagents, 5067-5579/80). RIN values ranged between 7.9 – 9.8. RNA-sequencing (RNA-seq) library preparation was performed using a published single-cell RNA-seq protocol from Cao et al. 201757 which was adapted for bulk RNA- seq experiments. The library preparation was performed in two separate batches.

Batch 1 included lines C1, CRISPR1, and KdVS1. Batch 2 included lines C2, C3,

KdVS2, and KdVS3.

For each sample, 10 ng total RNA (in 0.65 µL) was mixed with 0.1 µL dNTP mix (10 mM each) (Invitrogen, 10297018), 0.15 µL ERCC RNA Spike-In Mix (100.000x diluted) (Thermo Scientific, 4456740), 0.15 µL nuclease-free water (NF H2O) and 0.4 µL anchored oligo-dT (2.5 µM) primer (5ʹ- ACGACGCTCTTCCGATCTNNNNNNNN[10bpindex]TTTTTTTTTTTTTTTTTTTTTT TTTTTTTTVN-3ʹ, where “N” is any base and “V” is either “A”, “C” or “G”; IDT) in a tube containing 7 µL Vapor-Lock (Qiagen, 981611) to prevent evaporation. Each sample was incubated for 5 min at 65°C and directly placed on ice. First strand reaction mix was added, consisting of 0.4 µL Maxima RT buffer (5X) (Thermo Scientific, EP0751), 0.05 µL RNasin Plus (Promega, N2611) and 0.1 µL Maxima H Minus Reverse Transcriptase (Thermo Scientific, EP0751). Reverse transcription was performed by incubating the samples at 50°C for 30 min and terminated by

213 heating at 85°C for 5 min. For second strand synthesis, 2 µL RT product was mixed with 7.7 µL NF H2O, 2.5 µL Second Strand Buffer (Invitrogen, 10812014), 0.25 µL dNTP mix (10 mM each), 0.35 µL DNA polymerase I (E. coli) (NEB, M0209), 0.09 µL DNA ligase (E. coli) (NEB, M0205) and 0.09 µL Ambion RNase H (E. coli) (Invitrogen, AM2293). Second strand synthesis was performed by incubating samples at 16°C for 150 min, followed by 75°C for 20 min. Next, 0.5 µL Exonuclease I (NEB, M0293) was added per sample and incubated at 37°C for 60 min. cDNA samples were pooled per sets of 7-9 samples, including one sample without RNA input per pool. Vapor-Lock was removed and samples were added up with NF H2O to a total volume of 107.6 µL (to correct for loss of sample volume due to evaporation). Each pool of samples was then purified using 79 µL beads buffer (20% PEG-8000 in 2.5 M NaCL, final concentrations) and 50 µL Ampure XP Beads (Beckman Coulter, A63881), and eluted in 7 µL NF H2O.

Tagmentation was performed per pool by adding 3 µL double-stranded cDNA sample to 5.5 µL Nextera TD buffer (Illumina, 15027866), 2.5 µL NF H2O and 1.0 µL TDE1 Enzyme (Illumina, 15027865), which was incubated at 55°C for 5 min. Samples were directly placed on ice for at least 3 min. The reaction was terminated by adding 12 µL Buffer PB (QiaQuick, 19066) and incubating for 5 min at room temperature. Samples were purified using 48 µL Ampure XP beads (Beckman Coulter, A63881) and eluted in 10 µL NF H2O. Next, each sample was mixed with 2 µL P5 primer (10 µM), (5ʹ-AATGATACGGCGACCACCGAGATCTACAC [i5]ACACTCTTTCCCTACACGACGCTCTTCCGATCT-3ʹ; IDT), 2 µL P7 primer (10 µM): (5ʹ- CAAGCAGAAGACGGCATACGAGAT[i7]GTCTCGTGGGCTCGG-3ʹ; IDT) and 20 µL NEBNext High-Fidelity 2X PCR Master Mix (NEB, M0541). Amplification was performed using the following program: 72°C for 5 min, 98°C for 30 sec, 15 cycles of (98°C for 10 sec, 66°C for 30 sec, 72°C for 1 min) and a final step at 72°C for 5 min. Samples were purified using 32 µL Ampure XP beads (Beckman Coulter, A63881) and eluted in 12 µL NF H2O. Libraries were visualized by electrophoresis on a 1% agarose and 1X TAE gel containing 0.3 µg/mL ethidium bromide (Invitrogen, 15585011). Gel extraction was performed to select for products between 200 – 1000 bp using the Nucleospin Gel and PCR Clean-up kit (Macherey-Nagel, 740609). Samples were eluted in 11 µL NF H2O. cDNA concentrations were measured by 214

Qubit dsDNA HS Assay kit (Invitrogen, Q32854). Product size distributions were visualized using Agilent’s Tapestation system (D5000 ScreenTape and Reagents, 5067-5588/9). Libraries were sequenced on the NextSeq 500 platform (Illumina) using a V2 75 cycle kit (Read 1: 18 cycles, Read 2: 52 cycles, Index 1: 10 cycles).

Pre-processing of RNA-seq data Base calls were converted to fastq format and demultiplexed using Illumina’s bcl2fastq conversion software (v2.16.0.10) tolerating one mismatch per library barcode. Reads were filtered for valid unique molecular identifier (UMI) and sample barcode, tolerating one mismatch per barcode. Trimming of adapter sequences and over-represented sequences was performed using Trimmomatic (version 0.33)58. Trimmed reads were mapped to the human reference genome (GRCh38.p12). Mapping was performed using STAR69 (version 2.5.1b) with default settings (-- runThreadN 1, --outReadsUnmapped None, --outFilterType Normal, -- outFilterScoreMin 0, --outFilterMultimapNmax 10, --outFilterMismatchNmax 10, -- alignIntronMin 21, --alignIntronMax 0, --alignMatesGapMax 0, --alignSJoverhangMin 5, --alignSJDBoverhangMin 3, --sjdbOverhang 100). Uniquely mapped reads (mapping quality of 255) were extracted and read duplicates were removed using the UMI-tools software package59. Raw reads from BAM files were further processed to generate count matrices with HTSeq60 (version 0.9.1) using reference transcriptome Gencode GRCh38.p12 (release 29, Ensembl 94).

RNA-seq data analysis Raw counts from count tables were transformed to counts per million (cpm) using edgeR (R package)61. Transcripts with a cpm > 2 in at least four samples were included for further analysis. Transcripts located on the Y chromosome were excluded. Counts were voom transformed (log2-transformation on cpm values) and corrected for library preparation batch effect using limma (R package)62. Principal component analysis (PCA) was performed on the voom-transformed and batch corrected counts using the prcomp function from stats (R package). Differential expression (DE) analysis was performed using limma, by comparing samples from the three KdVS lines and the CRISPR line (16 samples) to samples from the three

215 control lines (12 samples). Variation between replicates from the same cell line is expected to be smaller compared to the variation between replicates from different lines belonging to the same group of comparison (KdVS or controls), which is taken into account in the DE analysis by setting blocks per individual line. Genes with a Benjamini-Hochberg (BH)-corrected p-value < 0.05 were considered to be significantly differentially expressed between the two conditions.

Gene set enrichment analysis (GSEA) was performed using fgsea (R package)63. Genes were ranked based on the t-statistic from the DE analysis. Enrichment of genes was tested in Gene Ontology (GO) terms (C5 collection), Reactome pathways (C2 canonical pathways sub-collection), transcription factor targets (TFT) gene sets (C3 TFT subcollection) from the Molecular Signatures Database (MSigDB) using msigdbr version 7.0.1 (R package)64. Gene symbols corresponding to transcripts that were not part of the final gene list were removed from the selected gene sets. Subsequently, gene sets with remaining gene set size > 5 and < 500 were used for GSEA. Human gene symbols (Ensembl version 94) from the final gene list were converted to gene symbols from Ensembl version 97, corresponding to the version of gene symbols used in MSigDB. Gene sets with a Benjamini-Hochberg (BH)- corrected p-value < 0.05 were considered to be significantly enriched for up- or down-regulated genes. Heatmaps were generated using voom-transformed and batch corrected counts scaled per gene.

Dual SMAD inhibition To start differentiation to NPCs, iPSCs were singularized by allying Accutase (Sigma; A6946). 250000 single cells were then plated on 1 Matrigel- coated well of a 6-well plate in E8 basal medium (Thermo Fisher) supplemented with primocin (0.1 µg/ml, Invivogen), RevitaCell (Thermo Fisher). When the wells were confluent after approximately three days the medium was changed to Neural Induction Medium (NIM). Half of the NIM was refreshed every day. Between day 8 and 12 after induction rosettes became visible. ReLeSR was used to transfer the rosettes to a 6- well plate, pre-coated with Poly-L-Ornithine (50 µg/mL, Sigma) for 3 hours at 37 °C and 20 µg/mL mouse laminin (#L2020, Sigma) overnight (o/n) at 4 °C. The next day, the medium was changed to Neural Maintenance Medium (NMM) with 20ng ml-1 216 fibroblast growth factor (FGF) (#100-18B, Peprotech) and 20ng ml-1 epidermal growth factor (EGF) (#AF-100-15, Peprotech). Maintenance medium was changed every other day. NPCs were either kept in culture until passage 5 at 37⁰C with 5%

CO2 or frozen down in NMM with 10% DMSO and stored at -80⁰C.

- Neural Induction Medium (NIM)

NIM was prepared by adding the dual SMAD inhibitors Noggin (1:1000) (#719NG050, Bio-Techne) or Dorsomorphin (1:10.000) (#S7306, Sigma) and SB431542 (1:1000) (#1614, Tocris,) to the neural maintenance medium. The medium was stored at 4⁰C and used within one week.

- Neural Maintenance Medium (NMM)

NMM was prepared by making a 1:1 mixture of N-2 medium and B-27 medium. N- 2 medium contained DMEM/F12, Glutamax (1:100) (#35050-061, Gibco), N-2 (1:100) (#17502-048, Gibco), Insulin (1:1000) (#A11382IJ, Gibco), Nonessential amino acids (1:100) (#M7145, Sigma life science), 2-mercaptoethanol (1:1000) (#21985-023, Gibco) and Penicillin-Streptomycin (1:100) (#P4333, Sigma). B-27 medium contained Neurobasal (#21103-049, Gibco), B-27 (1:50) (#17504-044, Gibco), Glutamax (1:100) and Penicillin-Streptomycin (1:100). The medium was stored at 4⁰C and used within three weeks. Immediately before use, EGF (1:10.000) and FGF (1:10.000) were added to the medium.

Differentiation of NPCs towards neurons NPCs were plated on coverslips in a 24-well plate coated with PLO/Laminin. Cells were either plated in small colonies by using ReleSR or plated as single cells by using Accutase solution (see 2.3.4). To induce differentiation EGF and FGF were removed from NMM. Medium was refreshed every other day. After six days the cells were fixated and stained for different markers.

Immunostaining After washing the cells with ice cold PBS, the cells were fixated for 15 minutes at room temperature (RT) with 4% Paraformaldehyde (#441244, Aldrich) and 4% 217 sucrose (#s7903, Sigma) in PBS (#P5493, Sigma). After washing with PBS, permeabilization was performed by adding 0.2% Triton X-100 (#T8787, Sigma) in PBS for 10 minutes at RT. After another washing step, cells were blocked with blocking buffer, containing 5% normal goat serum (#10000C, Invitrogen), 5% normal donkey serum (#017000121, Jackson ImmunoResearch), 5% normal horse serum (#26050-088, Invitrogen), 1% BSA (#A7906, Sigma-Aldrich), 1% Glycine (#G8898, Sigma) and 0.4% Triton X-100 in PBS for 1 hour at RT. The primary antibodies, diluted in blocking buffer, were added to the cells and incubated over night at 4⁰C. The secondary antibodies, diluted in blocking buffer, were added and incubated for one hour at RT in the dark. After staining the nucleus using Hoechst (1:10.000) (#H3570, Invitrogen) the coverslips were mounted with DAKO (#S3023, Dako) on microscope objectives and stored at 4⁰C protected from light.

218

- Primary antibodies used:

NAME USE HOST DILUTION MANUFACTURER POD. NO.

KANSL1 KANSL1 Rabbit 1:200 Sigma #HPA006874 marker

PHH3 Mitotic marker Mouse 1:100 Invitrogen #MA5-15220

MAP2 Neuronal Mouse 1:1000 Sigma #ab16667 marker

MAP2 Neuronal Guinea 1:200 Synaptic systems #188004 marker pig

SOX2 NPC marker Rabbit 1:500 Abcam #ab97959

NESTIN NPC marker Mouse 1:100 Thermo Scientific #MA1-110

PAX6 NPC marker Rabbit 1:300 Covance #PRB-278P

KI-67 Proliferation Rabbit 1:50 Abcam #ab16667 marker

- Secondary antibodies used:

NAME DILUTION MANUFACTURER PROD. NO.

GOAT ANTI-RABBIT ALEXA FLOUR 1:2000 Invitrogen #A11034 488

GOAT ANTI-MOUSE ALEXA FLOUR 1:2000 Invitrogen #A11031 586

GOAT ANTI-MOUSE ALEXA FLOUR 1:2000 Invitrogen #A21245 647

GOAT ANTI-GUIN.PIG ALEXA 1:2000 Invitrogen #A21450 FLOUR 647

219

Data analysis The ratio of the cells positive for mitotic/ proliferation marker was calculated relative to Hoechst stained cells. The statistical analysis of the data was performed using GraphPad Prism 8 (GraphPad Software, Inc., CA, USA). By using the Shapiro-Wilk normality test and the Kolmogorov-Smirnov test, it was analysed whether the data was normally distributed. To tested for significant differences between two different cell lines or conditions either a parametric unpaired t-test or a nonparametric Mann- Whitney test was used. For all comparison analyses with more than two groups, either a one-way ANOVA test for normally distributed data was used or a Kruskal- Wallis test for not normally distributed data. One-way ANOVA was followed by a Tukey test for comparisons of all pairs of columns. A Kruskal-Wallis test was always followed by a Dunn’s multiple comparisons test to analyze possible differences between single groups. Results with p values lower than 0.05 were considered as significantly different (*), p <0.01 (**), p <0.001 (***), p<0.0001 (****). Data are shown as mean and standard error of the mean (SE).

Kansl1+/- mouse model The Kansl1+/- mutant mice were derived in a C57BL/6N genetic background from the unique IKMP ES cell clone HEPD0766_8_G02. Kansl1tm1b(EUCOMM)Hmgu [Jongmans et al. 2006] animals were obtained by breeding Kansl1tm1a(EUCOMM)Hmgu/+ mice with animals expressing the Cre recombinase to generate the Kansl1tm1b(EUCOMM)Hmgu/+ (noted here Kansl1+/-). The local ethics committee, Com’Eth (n°17), approved all mouse experimental procedures, under the accreditation number 2012–069.

Mouse brains were obtained from the group of Y. Herault (IGBMC Strasbourg). Brains from Kansl1+/+ and Kansl1+/- mice were dissected at postnatal day 21. They were fixed in 4% PFA and 4% sucrose over night at 4°C. After embedding the brain in 2% agar, 60 micrometer sections were made.

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Staining free floating slices After washing with PBS and permeabilization in 1% Triton slices were incubated o/n at 4 degrees in blocking buffer. Primary antibodies were diluted in blocking buffer and incubated with the slices for 30 minutes at RT followed by 24 h at 4 degrees. After several washing steps secondary antibodies were diluted in blocking buffer and incubated with the slices for 3 hours at RT. To stain nuclei Hoechst was diluted 1:5000 in PBS and slices were incubated for 15 minutes before mounting with Dako and stored at 4 degrees. Stained slices were imaged by means of conventional fluorescence microscopy (Zeiss).

- Primary antibodies:

NAME USE HOST DILUTION MANUFACTURER PRO. NO.

H4K16AC H4K16ac Rabbit 1:200 Abcam #ab109463 marker

DCX neurogenesis Rabbit 1:200 Abcam #ab18723 marker

- Secondary antibodies:

NAME DILUTION MANUFACTURER PROD. NO.

GOAT ANTI-RABBIT ALEXA FLOUR 1:2000 Invitrogen #A11034 488

GOAT ANTI-RABBIT ALEXA FLOUR 1:2000 Invitrogen # A11036 586

221

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Supplementary Information

Supplementary Fig. 1. Characterization of control and KdVS patient iPSCs. A) Representative images of iPSC colonies from control and KdVS lines (C1, C2, KdVS1, KdVS2, KdVS3) stained for different pluripotency marker (scale bar = 50 µm).

Supplementary Figure 2. Principal component analysis (PCA). PCA was performed on gene expression profiles of RNA-seq samples from KdVS and control iPSCs. Genes on the Y chromosome were excluded. Voom (log2)-transformed counts were used, corrected for batch effect. A) PC1 and PC2, samples marked by gender (pink = female, blue = male). B) PC1 and PC2, samples marked by type of mutation (pink = 17q21.31 microdeletion, green = control, blue = insertion or deletion in exon 2 of KANSL1). C) PC4 and PC5, samples marked by status (pink = control, blue = KdVS patient).

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Supplementary Figure 3. Cell-cycle characteristics during individual passages. A) Quantification of proliferating (Ki67+) cells during individual passages for control (C1) and KdVS (KdVS1 and KdVS3) lines. B) Quantification of mitotic + + (Ki67 pHH3 ) proportion of NPCs derived of control (C1) and KdVS (KdVS1 and KdVS3) iPSCs. * P < 0.05, *** P < 0.0005, **** P < 0.0001,

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Discussion

Katrin Linda

Part of this chapter has been published as a review:

229

Neurodevelopmental disorders (NDDs), including intellectual disability (ID) and autism spectrum disorders (ASD), are a group of disorders in which the development of the central nervous system (CNS) is impaired. Early onset and the often life-long course present major challenges in clinical genetics and medicine. The identification of underlying genetic defects and risk factors is becoming increasingly efficient due to genome-wide interrogation methodologies that led to the identification of hundreds of NDD associated genes. New genetic information obtained from disease causing mutations has been used as starting-point for the development of cellular- and animal models. Research unravelling underlying pathological mechanisms of NDDs mainly focused on neural and behavioral phenotypes in model systems for each disorder/ gene individually. Although this research significantly impacted our knowledge regarding neurodevelopmental and synaptic mechanisms underlying neuronal circuit formation, our broader understanding of NDDs and the effectivity to uncover critical cellular mechanisms required for the development of targeted therapies is still limited. NDDs share a high degree of clinical comorbidity, suggesting common etiologies across different disorders. An emerging concept is that the genetic deficits converge on certain key ‘hub’ signaling pathways whose dysregulation underlies prominent synaptic dysfunction and aberrant neurodevelopment within several NDDs. Evidence for this concept is provided by results of the different gene specific NDD models that often identify disturbed neuronal progenitor cells (NPC) proliferation, excitatory/ inhibitory (E/I) balance, and/or synaptic plasticity in animal and cellular NDD models.

Chapter 2 of this thesis describes how we optimized a protocol for rapid neuronal differentiation of human iPSCs driven by the transgene Neurogenin 2 (Ngn2) to measure network activities on micro-electrode arrays (MEAs). In the following to chapters this protocol was applied to examine underlying molecular mechanisms of different NDDs. In chapter 3 we identified enhanced NMDAR signaling mediated neuronal network dysfunction in EHMT1-deficient, Kleefstra syndrome (KS) patient derived cortical excitatory neurons. These findings illustrate how mutations in genes that encode proteins of the epigenetic machinery can affect synaptic function. In chapter 4 a human in vitro model for Koolen- de Vries syndrome (KdVS) was used to identify a role for KANSL1 in autophagy regulation. Furthermore, deregulated 230 autophagy could be linked to a synaptic and neuronal network phenotype. While chapter 3 and 4 focus more on molecular mechanisms that regulate neuronal function in mature neurons, chapter 5 focused on an earlier neurodevelopmental phenotype of NPCs in KdVS. These results indicate that heterozygous loss of KANSL1 not only changes neuronal function, but potentially also affects early neuronal differentiation.

1. Role of Autophagy in Neuronal Function and Development

In chapter 4 of this thesis I describe how imbalanced autophagy affects neuronal function and network formation in a model for Koolen- de Vries syndrome (KdVS). Autophagy is a catabolic process important for the clearance of cytosolic contents, protein aggregates and damaged organelles, which is essential for cellular homeostasis and survival. Neurons are highly sensitive to aberrant autophagy regulation. First, the complex and polarized neuronal architecture requires specialized intracellular vesicle trafficking for efficient cargo recycling. The endolysosomal network in neurons is highly specialized. While the biogenesis of autophagosomes mainly occurs in the distal portion of the axon, endocytic vesicles are often found in somatodendritic domain1. Efficient retrograde transport is required to fuse with lysosomes, that predominantly localize in the perinuclear region2. Furthermore, due to their postmitotic nature, neurons are highly sensitive to the accumulation of toxic proteins and damaged organelles and therefore appear to be particularly dependent on autophagy. Autophagy is constitutively active in healthy neurons. Consequently, decreased autophagy and subsequent accumulation of toxic proteins and damaged organelles rapidly reduces cell viability and potentially affects neuronal function3–5. Neuron-specific knockout of the autophagy genes atg5 or atg7 in mice caused abnormal protein aggregation and eventual neurodegeneration corroborating that autophagy is essential for neuronal homeostasis and quality control6,7. Similarly to these observations, reduced survival and early-onset, progressive neurodegeneration have been observed in subsequent models, targeting several genes of the core autophagic pathway8–10. Furthermore, dysfunctional autophagy at pre- and post-synaptic sites leads to aging and 231 neurodegeneration11–14. In line with this, defective autophagy is associated with accumulation of protein aggregates and neurodegeneration in several disorders like Parkinson’s and Alzheimer’s disease15,16, emphasizing the importance of autophagy for cellular homeostasis and protection against neurodegeneration. Although autophagy has been studied extensively in the context of neuronal homeostasis and linked to neurodegeneration, there is now mounting evidence for a role of autophagy in neuronal development.

In neurons we observed increased LC3 puncta at synapses upon autophagy induction, indicating that balanced autophagy regulation is not only essential to maintain neuronal homeostasis, but also plays a synapse specific role. Indeed, autophagy is induced by synaptic plasticity paradigms17 and neuronal autophagy and formation of autophagosomes at synapses showed to be activity dependent18.These findings point towards regulation of synaptic autophagy through long-term plasticity required for learning and memory formation. At the postsynaptic site plasticity is associated with degradation of neurotransmitter receptors, which involves trafficking in autophagosomal structures18–21. At dopaminergic neurons autophagy modulates synapse ultrastructure and controls neurotransmitter release at the presynaptic site 22. In line with this, the presynaptic proteins endophilin and its partner synaptojanin, known to regulate synaptic vesicle endocytosis and recycling, turned out to positively regulate synaptic autophagy23–26. Furthermore, the active zone protein Bassoon has been proposed to inhibit autophagy27 and the small GTPase Rab26 was reported to cluster synaptic vesicles and direct them to autophagosomes for degradation28,29. Taken together these results provide evidence for a functional link between synaptic vesicle cycling and autophagy. Interestingly, in chapter 4 of this thesis we describe how increased oxidative stress induces autophagosome accumulation in KdVS patient-derived iPSCs and how this imbalanced autophagy causes reduced neuronal function and network formation in iNeurons derived of these iPSCs. Which synaptic proteins are affected by increased autophagosome formation at synapses of KANSL1-deficient iNeurons, remains unknown. However, autophagy contributes to the degradation of AMPA receptors in cultured neurons20, suggesting that increased autophagosome formation in KdVS iNeurons reduces surface AMPA receptors at synapses and thereby destabilizes synapses. This is in 232 line with our observation that the spontaneous excitatory postsynaptic current (sEPSC) amplitude in KANSL1-deficient iNeurons was significantly reduced compared to control iNeurons. Besides, it is known that low levels of reactive oxygen species (ROS), more specifically superoxide, at the synapse, indeed play a crucial role in synaptic plasticity30,31. Thus, a possible explanation for reduced synaptic strength and connectivity might be that increased ROS levels activate autophagy at the synapse leading to continuous breakdown of pre- and/or postsynaptic proteins in KdVS-derived iNeurons. Furthermore, there is also evidence for a role of autophagy during synaptogenesis as mouse models reveal an essential function for autophagy in synaptic pruning32,33. Those results indicate that reduced autophagy leads to an overall increase in synaptic spines32,33. Extrapolated to our finding of increased autophagy activation in KANSL1-deficient iNeurons, this could mean that reduced number of synapses might be caused by increased synaptic pruning. A better understanding through which mechanisms increased autophagy activation reduces synaptic connectivity of KdVS neurons requires further examination.

In chapter 5 of this thesis results reveal a proliferation deficit in patient- derived NPCs pointing towards an early neurodevelopmental phenotype for KdVS. In how far deregulated autophagy potentially affects neuronal differentiation in KANSL1- deficient cells remains unknown. However, we find similar autophagy phenotypes in iPSCs and iNeurons, suggesting that increased oxidative stress triggers autophagosome formation in KANSL1-deficient cells is independent from the cell type and probably also occurring in NPCs. Autophagy has already been implicated in early neurodevelopmental processes, like modulating progenitor cells and their differentiation into neurons (reviewed by Casares-Crespo et al.)34. There is an established link between autophagy triggering Notch degradation35. Notch is a well- characterized master regulator of neurogenesis and neuronal development 36–39 and its’ transcripts are mainly found in the subventricular zone, the brain region where NPCs reside 40,41. Furthermore, Notch signaling reduces expression of pro-neural factors and thereby maintains neural stem cell identity42. The Notch signaling pathway is especially active during proliferation37–39,43 and low Notch activity coincides with increased neuronal differentiation41. In hematopoietic stem cells Cao and his colleagues even identified increased ROS levels activating autophagy which 233 than degrades Notch proteins44, which is essential for adequate differentiation. Furthermore, recent findings indicate that the autophagy-lysosomal pathway regulates NPC maintenance, activation, and the survival and maturation of newly born neurons34. In neural stem cells (NSCs) ROS are normally maintained at a low level, while increased ROS levels inhibit self-renewal and promote differentiation45. Those findings suggest that not only reduced proliferation rates affect the maintenance of KANSL1-deficient NPCs, but increased ROS levels potentially reinforce decreased NPC pool size through prolonged autophagy activation which subsequently triggers Notch degradation and induces premature differentiation.

Taken together these findings indicate that next to its’ importance for general maintenance of cell homeostasis, autophagy also plays an essential role for different neuron-specific mechanisms related to differentiation and synaptic function, which guarantees the formation of appropriate neuronal connections. Unbalanced autophagy regulation in KdVS can be linked to different neurodevelopmental phenotypes. Consequently, autophagy might be a promising candidate for one of the key hubs signaling pathways that are affected in NDDs.

Figure 1. Autophagosome formation at the synapse. Schematic presentation of autophagosome formation at the pre- and postsynaptic site. Autophagy activation through reduced mTOR signaling, and probably also increased ROS, at the presynapse promotes neurotransmitter uptake inhibiting vesicular release. At the postsynaptic site NMDAR activation is associated with autophagosome formation to promote AMPAR uptake and breakdown. This process is involved in synaptic plasticity. ROS regulating synaptic plasticity might have a similar role.

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2. Autophagy in NDDs

After summarizing evidence for the various roles of autophagy in neurodevelopment and neurotransmission, it is not surprising that defects in autophagy signaling can also be linked to NDDs. The biochemical steps, through which autophagy is activated in neurons and other cells are well-delineated. mTOR and AMP-activated kinase (AMPK) metabolic signaling cascades regulate its induction through Unc-51-like autophagy-activating kinase 1 (ULK1/Atg1). Activated mTOR phosphorylates ULK1 at Serine residue 757 and thereby inhibits autophagy46. In response to cellular stress, like for example amino acid starvation, loss of neurotrophic factors, protein aggregates and/or sensory deprivation, AMPK activates ULK1 by phosphorylation of Serine residue 317. Upon activation, ULK1 promotes phosphorylation and activation of Beclin1 (Atg6), to induce autophagosome formation (nucleation phase)47. Beclin1 promotes lipidation of LC3I (Atg8) to generate its’ lipidated form LC3II, which in turn localizes to the autophagosomal membrane and enables elongation and maturation of autophagosomes48. Autophagosomes activate cargo adaptor proteins such as p62 to deliver ubiquitinated proteins, protein aggregates and compromised organelles for degradation49. Ultimately, mature autophagosomes fuse with lysosomes to form autolysosomes, where cargo is actively degraded via the autosomal/lysosomal pathway.

Several monogenic disorders of the autophagy pathway, defined as “congenital disorders of autophagy”, have been identified through next-generation sequencing50,51. Mutations affect various proteins of the autophagy pathway from early induction phases up to autolysosome formation. Mutations in autophagy related genes and subsequent aberrant autophagy regulation often cause multisystemic disorders, but mainly affect the central and peripheral nervous systems50,51. Common features of this class of disorders are structural brain abnormalities, intellectual disability, and developmental delay, as well as epileptic seizures, and neurodegeneration resulting in progressive impairment52. Homozygous loss-of- function mutations in EPG5 cause Vici syndrome, a severe progressive neurodevelopmental, multisystemic disorder incorporating developmental delay, agenesis of the corpus callosum, bilateral cataracts, and progressive

235 cardiomyopathy and variable immunodeficiency. More than 60% of the patients develop seizures, that often evolve as epileptic encephalopathy53,54. Profound developmental delay, progressive microcephaly, and failure to thrive are common features that suggest a neurodegenerative component following the prominent neurodevelopmental defect54. The EPG5 protein plays an essential role in fusion of autophagosomes with late endosomes/lysosomes and thereby affects the late stages of autophagy55. Loss of EPG5 in vivo blocks autophagy, causing progressive motor deficit and neurodegeneration56. Similarly, also mutations in SNX14 potentially affect lysosome fusion, as the gene encodes for a protein of the sorting nexin family that binds lysosomal membrane phosphatidylinositol residues. Mutations cause autosomal-recessive spinocerebellar ataxia57,58. Patients show progressive cerebellar neurodegeneration, developmental delay, intellectual disability, and epileptic seizures58. Mutations in TBCK cause a neurodevelopmental syndrome with ID, congenital hypotonia, leukoencephalopathy, progressive motor neuronopathy, and epileptic seizures 59–61. Although its exact protein function remains elusive loss- of-function mutations in TBCK lead to mTOR inhibition and consequently to uncontrolled autophagy induction. Furthermore an augmented autophagic flux that is insensitive to pro-autophagic stimuli leads to autophagosome accumulation59,61.

Within the group of autophagy disorders those that are associated with mutations in genes encoding for proteins of the mTOR pathway represent the so-called ‘mTORopathies’62. Tuberous sclerosis complex (TSC) characterized by the growth of benign tumors in multiple organs and often comorbid with ID, ASD and epilepsy63. TSC is caused by heterozygous loss of either TSC1 or TSC2. Together the two proteins form the TSC1/TSC2 complex, a well-characterized inhibitor of mTOR. Autophagy is suppressed in TSC patients by constitutively active mTOR signaling. In a TSC mouse model with heterozygous loss-of-function mutation of the Tsc2 gene mTOR hyperactivity was associated with increased dendritic spine density in the cerebral cortex33. The mTOR inhibitor rapamycin was sufficient to rescue the synaptic pruning deficits. However, rapamycin treatment was not efficient in Tsc2 mutant mice that carried additional homozygous loss of function mutations in Atg7, that encodes for an essential autophagy enzyme, linking the synaptic pruning deficits directly to mTOR function in autophagy suppression33. Elevated synaptic 236 connectivity is associated with the behavioral symptoms characterizing autism spectrum disorder (ASD). Conclusively, these results suggest that genetic variants that impair autophagy mediated-synaptic pruning might increase susceptibility to develop ASD symptoms64. This hypothesis is supported by the finding that copy number variations (CNVs) in autophagy genes are enriched in ASD patients65.

The above listed examples illustrate the importance of autophagy at different levels of neuronal function. Also, they provide clear evidence for deregulated autophagy causing neurodevelopmental phenotypes, like developmental delay, ID, ASD, and epilepsy. However, most of the studies that link autophagy deficits to neurodevelopmental phenotypes focus on mTOR dependent autophagy regulation. Contrary to this, our results identified primary mTOR-independent autophagy induction through increased ROS levels to be causative for reduced synaptic activity in KdVS. Furthermore, our results verified that autophagy is a tightly regulated process, where activation is always coupled to various negative feedback signals. More precisely, our findings indicate that elevated oxidative stress induced autophagy has a secondary, negative effect on mTOR activity. Thereby, we show that the interplay between different activation and inhibition pathways is essential for balanced autophagy regulation in order to maintain cellular functions. There is little known about the interaction between the different autophagy pathways and how exactly the feedback-loop is controlled, but our findings in KANSL1-deficient cells in chapter 4 illustrate that not only aberrant autophagy activation, but especially the combination with impaired feedback signaling and consequently imbalanced autophagy can cause neurodevelopmental phenotypes.

3. Epigenetic Regulation of Autophagy

The role of autophagy in neurons specifically during development and at the synapse is well characterized enabling an easy link between mutations in autophagy related (ATG) genes and NDDs. However, the group of NDDs caused by ATG mutations is relatively small compared to the number of NDDs that are associated with mutations in genes encoding for epigenetic regulatory proteins and complexes modulating

237 gene expression. In order to understand how frequent nervous system impairments in NDDs with disturbed epigenetic regulation underlies aberrant autophagy regulation we first need to examine epigenetic control of autophagy induction, elongation and inhibition.

Autophagy is generally seen as cytoplasmic event, therefore nuclear events were for a long time neglected, however, recent findings identified an epigenetic and transcriptional network that regulates autophagy66,67. Generally posttranslational modifications like methylation, and acetylation modulate chromatin state and control accessibility for the transcription machinery, thereby either facilitating or repressing gene expression68,69. Histone modifications play a role in controlling different aspects of autophagy, like autophagic flux, sustained autophagy, and cell fate decision70–73. While acute and rapid induction of autophagy occurs mainly in the cytoplasm, prolonged activation, for example during starvation, requires increased transcription of ATGs. A specific example is presented by coactivator-associated arginine methyltransferase 1 (CARM1) mediated demethylation of Histone H3 at arginine residue R17 (H3R17me2), associated with transcriptional activation74. During glucose starvation CARM1 functions as a co-activator of transcription factor EB (TFEB) to activate transcription of various autophagy and lysosomal genes70. Interestingly, under glucose-rich conditions CARM1 levels rapidly decrease through the ubiquitin-dependent degradation system67. The dynamic stabilization and destabilization of histone modifiers, like CRAM1 in the nucleus nicely illustrates the importance of tight control of autophagy-related gene expression depending on the nutritional status of the cell.

Autophagy is known to be cytoprotective, but prolonged autophagy eventually leads to cell death75, which shows how critical a balanced control of autophagy activation and inhibition is for cell survival. The histone mark that is associated with cell fate decisions during autophagy is the acetylation of histone H4 at lysine residue 16 (H4K16ac). While H4K16ac is generally associated with active transcription, autophagy induction is associated with a rapid decrease in H4K16ac and decreased expression of ATGs. Rapamycin mediated autophagy activation coincides with downregulation of the H4K16 acetylating enzyme hMOF (also known as Kat8). 238

Preventing the H4K16ac downregulation after autophagy activation by either overexpressing hMOF or inhibiting the respective deacetylase SIRT1 increases autophagic flux and eventually leads to cell death72.

Next to those histone marks that are associated with active autophagy related gene expression, histone marks that counteract active transcription were identified. Under nutrient-rich conditions Euchromatic histone-lysine N-methyltransferase 2 (EHMT2/ G9a) catalyzes methylation of histone H3 at Lysine 9 (H3K9me2) to repress autophagy related gene expression71. Nutrient deprivation in turn leads to dissociation of EHMT2 from promoter sites and activates gene transcription of ATGs. Enhancer of zeste homolog 2 (EZH2) mediates H3K27me3 under nutrient rich conditions. H3K27me3 is associated with repressed transcription of several negative mTOR regulators, like TSC2 and thereby inhibits autophagy activation73. These insights in autophagy-related histone modifications open op new possibilities in investigating how altered epigenetic regulation affects neuronal autophagy in NDDs.

Dynamic histone modifications have a key function during cell-regulatory events and synaptic plasticity is an especially complex cellular mechanism that requires dynamic alterations in gene expression, protein synthesis, and structural properties of neurons and synapses to facilitate learning and memory. Neurons with impaired epigenetic machinery are thought to be less responsive to extracellular- environmental changes. The gene expression and transcription machinery cannot adjust as rapid as it would be required within processes of synaptic plasticity, which might potentially explain why impaired learning and memory is one of the most common features of various NDDs, including KdVS. Under basal conditions H4K16ac levels are unaltered in KdVS patient derived iPSCs and iNeurons. However, the induction of autophagy through different signaling pathways causes a rapid decrease in H4K16ac levels as essential step of a negative feedback-loop in control cells72 which was not observed in KANSL1-deficient cells (chapter 4). This observation indicates that KANSL1-deficient cells are indeed not able to adjust histone modifications regarding H4K16ac as rapid as control cells. Furthermore, deficiency in feedback-loop induction after autophagy activation might affect synaptic plasticity and provide an explanation for impaired learning and memory in KdVS. 239

Chemical long-term depression (LTD) in cultured neurons induces autophagy to facilitate the degradation of AMPA receptorss20. More recent finding of Shehata and colleagues indicate that autophagy enhances synaptic destabilization and subsequent memory loss18. Within the setting of LTD rapid activation of the feedback-loop therefore might be a critical step to control AMPAR degradation and prevent massive loss of synapses and memory. Taken together these findings corroborate the idea that imbalanced autophagy regulation through impaired H4K16ac in KdVS affects synaptic function. How exactly these changes affect LTD as well as long-term potentiation (LTP) and learning and memory requires further investigation.

H3K9me2 is repressing ATG gene expression, but data reporting deregulated autophagy in KS is missing. In chapter 3 I show that H3K9me2 levels are globally reduced in EHMT1-deficient KS patient derived iPSCs and iNeurons76. This would suggest increased ATG expression in these cells. Indeed, preliminary data showed increased LC3I and LC3II levels in KS patient derived iNeurons, when compared to their isogenic control (Figure 2B). A possible link to impaired synaptic function might be provided by BDNF. Nikoletopoulou et al. showed that BDNF signaling suppresses autophagy by transcriptional downregulation of key autophagy components and BDNF deficiency causes an uncontrolled rise in autophagic degradation affecting also synaptic function. Synaptic defects caused by BDNF deficiency could be rescued by blocking autophagy77. These results indicate that BDNF signaling functions as additional autophagy regulating mechanism. However H3K9me2 is not only regulating ATG expression, it has also been shown to repress BDNF expression78. In healthy neurons chronic activity deprivation increased the amount of neuronal H3K9me2, thereby decreased BDNF expression, and subsequently promotes synaptic scaling up. In EHMT1 deficient neurons synaptic scaling up was prevented in vitro and in vivo78. While under basal conditions increased BDNF signaling in EHMT1-deficient cells might inhibit autophagy and thereby stabilize AMPAR levels at synapses, conditions that initiate synaptic plasticity might result in deregulated autophagy and subsequently impaired plasticity. Previous research, including results presented in chapter 3, presented direct targets of H3K9me2

240 regulated gene expression and results nicely illustrated how aberrant H3K9me2 at promoter sites of Bdnf and NMDAR cause impaired synaptic function in KS76,78. However, the well-established link between H3K9me and autophagy related gene expression, as well as mounting evidence for the importance of balanced autophagy for neuronal and synaptic development provide an encouraging research approach for the future, as impaired autophagy regulation might have additional effects on neuronal function in KS. Taken together recent findings, including those presented in this thesis, provide remarkable evidence for the importance of epigenetic mechanisms to regulate autophagy at multiple levels during brain development, network formation and synaptic function. Interestingly, maintenance of neuronal homeostasis through autophagy has already been linked to several to neurodegenerative disorders, like Parkinson’s and Alzheimer’s disease (PD and AD)15,79. Drugs that were tested for beneficial autophagy induction in AD and PD could directly be tested for treatment of NDDs that are associated with deficient autophagy regulation. Hereby, our patient derived iPSC model offers the possibility for drug screenings within a patient specific genetic background. That modulating autophagy can rescue neurodevelopmental phenotypes is nicely illustrated by a recent study in fragile X (Fmr1-KO) mice80. Fragile X syndrome (FXS) is known to be one of the most frequent forms of heritable ID and autism and Fmr1-KO mice display changes in dendritic spine structure, synaptic plasticity, and cognition. Yan and colleagues observed reduced autophagy through enhanced mTOR activity in hippocampal neurons of fragile X mice. Through shRNA mediated activation of autophagy they were able to at least partially rescue aberrant spine structure, synaptic plasticity, and cognition in these mice80. As mentioned earlier, finding key ‘hub’ signaling pathways in NDDs is essential for development of targeted and effective therapies for different reasons. First because, although cognitive impairments are prominent within the group of NDDs that are caused by mutations in epigenetic genes, epigenetic modifiers are usually ubiquitously expressed and therefore affect also other organs and tissues. Notably, in KdVS both heart- and renal defects can be observed in up to one third to half of the patients81. When general cellular mechanisms like autophagy can be identified as underlying disease pathology, the efficiency of possible treatments might be

241 highly advantageous, not only for cognitive symptoms. Furthermore, developing therapeutics to treat a whole group of rare syndromes is likely feasible, while the development of therapeutics for a single disorder might raise too high costs.

Figure 2. Histone modifications control ATG gene expression. A) Schematic presentation of different histone modifications associated with ATG gene expression. B) Representative western blots for autophagosomal marker p62 and LC3 of control (C) and EHMT1- deficient (KS) neurons after 3 weeks of differentiation. Quantification indicates increased LC3II protein levels in EHMT1-deficient iNeurons (P = 0,018), while p62 levels do not change significantly (P = 0,208). Statistical significance was tested by means of t test.

4. Future disease modeling

In the last decade, the use of stem cells in biomedical research has taken an enormous flight, especially following the discovery of the broad differentiation potential of human iPSCs. The findings described in this thesis present examples that illustrate what a powerful tool iPSCs are to study genetic disorders in order to dissect molecular and cellular pathways implicated in disease pathology during early stages of human neurodevelopment. We focused on well-defined, monogenic syndromes for which the disease causal genes have been identified and well- characterized mouse models have already been developed. Encouraging is that findings in iPSCs and iPSC-derived neurons could be corroborated in the respective mouse model and vice versa (chapter 3 and 5). The combined use of mouse and human iPSC models provides an efficient strategy to validate results. Further, the implementation of genome editing methodologies to insert or correct point mutations 242 to study the degree to which the observed phenotype is a result of polygenic interactions or a direct result of the mutation of interest, has been extremely important for the validity of the iPSC models and a huge advantage over mouse models.

Still, there are several challenges remaining in order to reach the full potential of iPSCs as a human disease model. For example, how to assess reliably identified molecular and cellular pathways in a single patient line beyond the sources of variability inherent in the iPSC-based approach82. Typically, multiple patient lines are assessed to confirm molecular and functional changes observed in patient lines versus healthy controls. However, this has the inherent drawback that information from individual patients might get lost. The ability to investigate a disease in a patient- specific background should be exactly the strength of using iPSCs as human model. The use of clinical carefully selected stratified cohorts will be essential to deal with phenotypic heterogeneity observed within different disease groups. Furthermore, development and standardization of differentiation protocols coupled to efficient and reliable readouts will certainly help to be able to draw more firm conclusions from the analysis of a single patient line. This will be especially relevant when iPSC-derived models will be used for personalized medicine. A nice illustration comes from a recent study in which they interrogated the neural network of a stratified group of patients with idiopathic autism spectrum disorders (ASDs), using micro-electrode arrays (MEAs)83. Whereas all patient-iPSC derived neural networks showed similar impairments, IGF1 was able to revert the phenotypes in some but not all patients. These findings show the importance to assess phenotypes and drug responses in single patient lines. It also shows that neural network interrogation using MEA technology is sensible enough to pick up individual differences and can be a helpful tool to prescreen patients for future clinical trials. The observation that IGF1 administration is able to reverse Rett, Phelan-McDermid syndrome and some ASD phenotypes in iPSC-derived neurons is certainly encouraging84.

At the mechanistic level iPSC-derived models of NDDs mostly recapitulate the synaptic phenotypes observed in animal models, further suggesting that ID and ASD are diseases of the synapse. Interestingly, although deficits in Hebbian and non-

243

Hebbian synaptic plasticity have been observed in most NDD animal models, to our knowledge, studies have yet to report on deficits in synaptic plasticity in patient iPSC- derived neurons. For example whereas much of the work on FXS has focused on the mGluR5 pathway85 it is surprising that till date no studies have examined whether the mGluR theory of FXS86 holds true in FXS-iNeurons. It will be interesting to investigate whether mGluR-dependent LTD can be induced in the current iPSC- derived neuronal cultures and whether it is enhanced in FXS and ASD iPSC models. Possibly these studies are complicated by the fact that currently most studies have been performed in 2D–cultures, in which it is notoriously more difficult to reliably measure synaptic plasticity, such as LTP and LTD. The recent developments in brain organoids87–91 might circumvent these problems and allow for interrogation of synaptic plasticity and neural network connectivity. Finally, one can ask whether the full potential of iPSC technology for NDDs already has been reached. One of the advantages over postmortem tissue is the possibility to monitor neuronal development and disease progression over time. This would help to understand when in development genetic insults would manifest themselves and when therapeutic targeting would be most efficient.

Most studies, including results described in chapter 3 and 4, have mainly focused on the characterization of fully differentiated neurons or stem cells. However, our findings presented in chapter 5 suggest that examining the process of differentiation might reveal new insights into epigenetic regulation of neurodevelopment and neurodevelopmental phenotypes. Here, the use of human iPSCs should be of special interest, because several studies indicate that the regulation of NPC proliferation and differentiation are unique processes in human brain development. A rare example of monitoring the course of differentiation is provided by Lu et al. where changes in transcription caused by loss of FMRP expression were monitored during neuronal differentiation revealing a window of opportunity92. A possibility to study neuronal maturation, connectivity and synaptic activity in a more natural environment is provided by transplanting iPSC-derived neurons or NPCs into mouse brains. Several studies on Alzheimer's and Parkinson’s disease have already shown that these neurons properly integrate into mouse neuronal networks in vivo93–95. This strategy will provide more insight into specific connections and synapse formation of 244 patient derived iPSCs in an in vivo system. In summary, iPSCs offer a great potential for research, especially for NDDs. The possibility to recapitulate the early stages of the disease, generate limitless patient material, disease specific cell types and the option for genome editing give researchers a large degree of flexibility that murine models cannot provide. Furthermore, iPSC models represent a promising cellular tool for drug screening, diagnosis and personalized treatment.

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Appendix

Summary

Samenvatting

Acknowledgements

About the Author

Portfolio

Data Management Statement

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Summary

Research presented in this thesis aimed to identify new molecular and cellular mechanisms that increase our understanding of the complexity of epigenetic regulation in neural development and function. This was done by using human in vitro cellular models of induced pluripotent stem cells (iPSCs) derived from Kleefstra syndrome (KS) and Koolen-de Vries syndrome (KdVS) patients.

Chapter 2 describes a protocol that we adapted for rapid neuronal differentiation driven by the transgene Neurogenin 2 (Ngn2). This protocol yields a homogeneous population of excitatory neurons within three weeks. We modified the original protocol by generating iPSC lines with a stably integrated, doxycycline inducible Ngn2 transgene. Thereby we reached nearly 100% conversion efficiency of transduced cells and could optimize the procedure for monitoring neuronal network formation on micro-electrode arrays (MEAs). The consistency and reliability of the protocol allow direct comparison of neurons differentiated from control- and patient- derived iPSC. Furthermore, it is broadly applicable, especially for mechanistic and pharmacological studies on human neuronal networks.

In chapter 3 the previously illustrated protocol was applied for KS patient-derived iPSCs. We show that neuronal networks of KS iNeurons exhibit network bursting with a reduced rate, longer duration, and increased temporal irregularity. We show reduced deposition of the repressive mark H3K9me2 at the GRIN1 promoter. This finding correlated with upregulated expression of NMDA receptor (NMDAR) subunit 1. By pharmacological inhibition of NMDARs, we rescued the KS patient-derived neuronal network and demonstrated a direct link between EHMT1 deficiency and NMDAR hyperfunction in KS patient-derived neurons.

In chapter 4 we developed a similar human in vitro model for KdVS to investigate the molecular mechanisms underlying synaptic dysfunction in this neurodevelopmental disorder. More specifically we examined the role of KANSL1 in autophagy and linked deregulated autophagy to functional phenotype in excitatory cortical neurons. Our results showed that the number of autophagosomes is increased in both, KANSL1 deficient iPSCs and iNeurons. Elevated oxidative stress 254 and subsequent deregulated negative feedback resulted in autophagosome accumulation. In maturing KdVS iNeurons, this imbalanced autophagy regulation reduced the synaptic density and network activity. By reducing oxidative stress through the application of an antioxidant drug we were able to rescue the functional deficits in KANSL1-deficient neurons.

Chapter 5 focuses on changes in early brain developmental processes caused by heterozygous loss of KANSL1 in KdVS. Our RNA sequencing results revealed aberrant cell-cycle regulation in KANSL1-deficient iPSCs. Cell-cycle analysis in KdVS patient-derived iPSCs and neural progenitor cells (NPCs) pointed towards reduced mitotic activity. In NPCs this was accompanied with a decrease in proliferative capacity. Furthermore, we observed decreased proliferation and increased immature neurons in the subgranular zone of the dentate gyrus of KANSL1-deficient mice, resembling the results obtained in human cells.

Summarized, the findings presented in this thesis provide new mechanistic insights into neuronal abnormalities and underlying mechanisms for KS and KdVS that might serve as promising starting points for the development of more targeted and efficient therapies.

255

Samenvatting

Onderzoek gepresenteerd in dit proefschrift was gericht op het identificeren van nieuwe moleculaire en cellulaire mechanismen die ons begrip van de complexiteit van epigenetische regulatie in neurale ontwikkeling en functie vergroten. Dit werd gedaan door gebruik te maken van humane in vitro cellulaire modellen van geïnduceerde pluripotente (iPS) stamcellen afkomstig van Kleefstra-syndroom (KS) en Koolen-de Vries-syndroom (KdVS) -patiënten.

Hoofdstuk 2 beschrijft een protocol, dat we hebben aangepast voor snelle neuronale differentiatie aangedreven door het transgen Neurogenin 2 (Ngn2). Dit protocol levert binnen drie weken een homogene populatie van exciterende neuronen op. We hebben het oorspronkelijke protocol aangepast door iPS celllijnen te genereren met een stabiel geïntegreerd, doxycycline-induceerbaar Ngn2- transgen. Daardoor bereikten we een conversie-efficiëntie van bijna 100% van getransduceerde cellen en konden we de procedure voor het monitoren van neuronale netwerkvorming op micro-elektrode-arrays (MEA's) optimaliseren. De consistentie en betrouwbaarheid van het protocol maken directe vergelijking mogelijk van neuronen van controle en patiënten iPSC. Bovendien is het breed toepasbaar, vooral voor mechanistische en farmacologische studies op menselijke neuronale netwerken.

In hoofdstuk 3 werd het eerder geïllustreerde protocol toegepast voor iPS cellen van KS patiënten. We laten zien dat neuronale netwerken van KS iNeuronen een netwerk vertonen dat barst met een verminderde snelheid, langere duur en verhoogde temporele onregelmatigheid. We tonen een vermindering van het repressieve merk H3K9me2 bij de GRIN1-promotor aan. Deze bevinding correleerde met opwaarts gereguleerde expressie van NMDA-receptor (NMDAR) -subeenheid 1. Door farmacologische remming van NMDAR's hebben we het KS- patiëntgerelateerde neuronale netwerk hersteld en een direct verband aangetoond tussen EHMT1-deficiëntie en NMDAR-hyperfunctie in KS-patiëntgerelateerde neuronen.

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In hoofdstuk 4 ontwikkelden we een vergelijkbaar human in vitro model voor KdVS om de moleculaire mechanismen te onderzoeken die ten grondslag liggen aan synaptische disfunctie bij deze neurologische ontwikkelingsstoornis. We hebben de rol van KANSL1 in autofagie onderzocht en afwijkende autofagie regulaite gekoppeld aan een functioneel fenotype in excitatoire corticale neuronen. Onze resultaten lieten zien dat het aantal autofagosomen is verhoogd in zowel KANSL1- deficiënte iPS cellen als iNeuronen. Verhoogde oxidatieve stress en daaropvolgende gedereguleerde negatieve feedback resulteerde in autofagosoomaccumulatie. In KdVS iNeuronen verminderde deze onevenwichtige autofagieregulatie de synaptische dichtheid en netwerkactiviteit. Door oxidatieve stress te verminderen met een antioxidant waren we in staat de functionele tekorten in KANSL1-deficiënte neuronen te herstellen.

Hoofdstuk 5 richt zich op veranderingen in vroege ontwikkelingsprocessen van de hersenen in KdVS. Onze resultaten voor RNAseq onthulden afwijkende celcyclusregulatie in KANSL1-deficiënte iPS cellen. Celcyclusanalyse in iPS cellen en neurale voorlopercellen (NPC's) afkomstig van KdVS patiënten wezen op verminderde mitotische activiteit. Bij NPC's ging dit gepaard met een afname van de proliferatieve capaciteit. Verder hebben we verminderde proliferatie en verhoogde onrijpe neuronen in de subgranulaire zone van de dentate gyrus van KANSL1- deficiënte muizen gevonden, vergelijkbaar met de resultaten in humane cellen.

Samengevat laten de bevindingen in dit proefschrift nieuwe mechanistische inzichten in neuronale afwijkingen en onderliggende mechanismen voor KS en KdVS toe. Deze kunnen als startpunten dienen voor de ontwikkeling van meer doelgerichte en efficiënte therapieën.

257

Acknowledgements

During the past years there were many moments of joy and happiness, that I will never forget, but also a lot of hard work and frustration. At the end of this thesis I want to thank all the people that shared these moments with me and without whom it would not have been possible to finish this.

First of all, special thanks to all patients and their families that believed in this project, donated their fibroblasts and even raised money to give me some extra time to finish this. Obviously, without you the content of this book would be completely different. Meeting some of the Kool kids and their families at KdVS patient gatherings belong to my most enjoyable memories of the past years. Your enthusiasm and trust in our work that I experienced during those days always helped to motivate myself to continue during times where things were not running smooth.

I also want to thank my supervisors for giving me the possibility to start this journey, believing in me, and supporting me during the past years. Hans, thank you for being my promotor and all your feedback and suggestions during our group meetings. Bert and David, your different view on things was always inspiring. Bert, I always appreciated your ability to find something positive in any situation, especially when I thought that the results were not a step forward at all. Nael, thank you, not only for your daily supervision, but especially for giving me the freedom to do the things I like to do an letting me find my way, while at the same time providing the guidance that was required to bring this to a good end.

To all my colleagues and especially all members of the molecular neurophysiology group: Brooke, Eline, Britt, Ilse, Anouk, Elly, Jon-Ruben, Moritz, Marina, Shan, Xiuming, Teun, Naoki, Rachel, Iris, Dirk, Astrid, Ka Man, and Chantal. I have to thank you for a lot of helpful feedback, new ideas and inspiration, and for changing medium or adding drugs during weekends, when I had no time :D. But more important are all those moments, next to the work-related stuff. It is because of our lunch breaks, coffee breaks and spontaneous chats at the hallway that I always enjoyed to come to work. As a group, we had great times during the FENS meetings in Copenhagen and (especially) in Berlin, at the yearly Christmas market visits, and many other 258 occasions. I am looking forward to hopefully many more of these moments with you in the upcoming years! Anouk, we not only shared our cell lines during the past years: I think there is no one else I have travelled more with during the past 3,5 years. Thank you for all the nice memories about Berlin, Sicilly (2x), L.A., and Park City. Teun, we have experienced the best and worst Sushi ever together in Japan – this was an unforgettable trip. Dirk, Moritz, and Jon-Ruben, when I started as a student in this group I hardly knew anything about electrophysiology, and although I am still far from being an expert in this field, thanks to you, I have learned a lot about channels, ions, and receptors. Jon-Ruben, also thank you for helping with laptop emergencies ;). Astrid, Chantal, and Ka Man thanks you so much for all those daily little things you do to make our life at the labs easier! Eline and Britt, thank you for your highly reliable company during lunch breaks and great times in the cell culture ;). Ilse, we have been sharing hotel rooms and since a few months also the office. It is so good to have someone next to me that is always ready to lend an ear!

During the past years I had many master and bachelor students that helped me culturing, staining, blotting and analyzing: Didi, Tessa, Carolina, Aya, Elly, Heleen, Lynn, Vanessa, Carlijn, and Edda, thank you so much! Each one of you did a great job and I enjoyed being your supervisor and helping you to become self-confident, independent students during the months of your internships.

And then there are these few very special people: Elly, I can mention you here in almost every paragraph. During my time as a PhD student you have been a colleague, a great master student, and most important, a friend. With your smile you always made the most difficult moments a bit easier! I am looking forward to many more cappuccinos, body workout classes, koopavonden, and Ikea visits :). Monica and Bas, no way that I would have finished this without you! We have started the iPSC adventure together as colleagues and quickly became really good friends. We have enjoyed so many great dinner evenings (and here I also want to thank Mattia and Zeinab, not only for cooking ;)). You were always there for me and I will never forget that. Dyah, you also cannot be placed in a single category. We have met the first time more than 12 years ago, when we started at the HAN and who could have

259 imagined that we would end up as PhD students in the same department with the desks next to each other?! I am grateful that we could go this way together!

Neben der harten Arbeit war das Abschalten in der Freizeit immer wichtig: Mädels, hier kommt ihr ins Spiel. Anne, Annika, Carolin, Catrin, Lara und Larissa, ein riesengroßes Dankeschön für all die schönen, unvergesslichen Momente in den letzten Jahren. Mit euch zu lachen lässt mich immer wieder den Alltagsstress vergessen und ich hoffe, dass wir noch viele, viele Jahre gemeinsam lachen und füreinander da sind.

Sabine, Farideh und Katharina, wir sehen uns nicht mehr so oft wie damals in unserer Schulzeit und trotzdem oder vielleicht auch gerade deshalb ist unsere Freundschaft ganz besonders wichtig für mich. Wenn wir uns dann sehen sind die paar Stunden mit euch beim Schlemmen und über das quatschen, was in den letzten Monaten alles passiert ist fast wie Urlaub :).

Anne, du bist schon immer diese eine ganz besondere Freundin für mich, mit der ich alles teilen kann und ein Danke dafür, dass wir wahrscheinlich auch noch die nächsten 20 Jahre die schönen und nicht so schönen Momente teilen werden reicht eigentlich überhaupt nicht aus: Hunderttausend Mal Danke!

Meine Familie, Mama und Papa, Helen, Jan-Hendrik und Mathis, Andrea, Lena, und Florian und Oma und Opa: Bei euch muss ich mich nicht nur für die letzten Jahre bedanken, sondern für viel mehr! Danke, dass ihr immer an mich geglaubt, und mich immer unterstützt habt. Ich liebe die Momente, wenn wir heute zusammensitzen und herzhaft über früher lachen können.

Jan-Peter, ich weiß nicht ob ich ohne dich heute fertig wäre. Das alles hier aufzuschreiben war für mich wohl die schwierigste Aufgabe, und Dank Corona- Lockdown weiß das vermutlich niemand besser als du. Aber du hast mich immer zum Weiterschreiben motiviert, wenn es nötig war. Danke für dein Verständnis, das Erinnern an Pause und Feierabend und jede kleine Ablenkung!

260

About the Author

Katrin Linda was born on May 24th, 1989 in Goch, Germany. In 2008 she started to follow the Life Science program at the HAN University of Applied Science in Nijmegen. After obtaining her Bachelor of Applied Science degree in 2012 she decided to join the Master program on Medical Biology at the Radboud University. As part of this program Katrin examined the added effects of dexamethasone and mesenchymal stem cells on early natural killer cell activation during a first internship at the Laboratory of Medical Immunology of the Radboudumc under supervision of Prof. Dr. Irma Joosten. During this period Katrin´s interest in cell signalling mechanisms in health and disease grew. For her final Master internship, she joined the group of Dr. Nael Nadif Kasri at the department of Cognitive Neuroscience within the Donders Centre for Neuroscience, Nijmegen, in November 2013. There, she worked on a project that aimed to identify the role of the ADHD associated gene Cdh13 in GABAergic interneurons. This internship aroused Katrin´s interest in neuroscience, therefore she decided to accept the challenge to work as a Ph.D. student at the department of Human Genetics at the Radboudumc under supervision of Dr. Nael Nadif Kasri and Dr. Bert de Vries in 2015. Results of this work are summarized in this dissertation. Since her time as Ph.D. student Katrin got fascinated by the possibilities that patient derived iPSCs offer to examine molecular and cellular mechanisms of neurodevelopment in the context of neurodevelopmental disorders, so she continued her work as postdoctoral fellow in the group of Dr. Nael Nadif Kasri.

261

Portfolio

Name: Katrin Linda Promotor: Prof. dr. ir. J.H.L.M. van Bokhoven, Dr. N. Nadif Kasri Co-Promotor: Dr. B.B.A. de Vries Graduate School: Donders Graduate School for Cognitive Neuroscience

COURSES/ WORKSHOPS

November 2019: Design and Illustration (Radboud University Nijmegen)

February 2019: Scientific Writing (Radboud University Nijmegen)

January 2019: Science Journalism and Communication (Radboud University Nijmegen)

November 2018: Analytical Storytelling (Radboud University Nijmegen)

November 2018: Career Guidance (Radboud University Nijmegen)

September 2018: Scientific Integrity Course (Donders Institute, Radboud University Nijmegen)

February 2018: The Art of presenting Science (Radboud University Nijmegen)

June 2015: Neurogenetics (Cognitive Neuroscience Master program, Radboud University Nijmegen)

February 2015: Laboratory Animal Sciences (Animal Research Facility, Radboud University Nijmegen)

CONFERENCES/ MEETINGS

March 2020: Poster Presentation at 2nd Neuroepigenetics & Neuroepitranscriptomics Conference. Nassau, Bahamas

July 2019: Oral presentation at Koolen-de Vries Syndrome Patient Advocacy Summit. Park City, USA

July 2019: Poster presentation at ISSCR Annual Meeting. Los Angeles, USA 262

June 2019: Oral presentation at Dutch Neuroscience Meeting. Lunteren, The Netherlands

April 2019: Oral presentation at 14th Troina meeting on Genetics of Neurodevelopmental Disorders. Troina, Italy

July 2018: Poster presentation at the FENS. Berlin, Germany

May 2018: Poster presentation at ISDN. Nara, Japan

April 2018: Oral presentation at 13th Troina meeting on Genetics of Neurodevelopmental Disorders. Troina, Italy

March 2018: Oral presentation at 4th Dutch Neurodevelopment Disorders Day. Rotterdam, The Netherlands

July 2017: Oral presentation at Koolen-de Vries Syndrome Patient Advocacy Summit. Memphis, TN, USA

June 2017: Poster presentation at Dutch Neuroscience Meeting. Lunteren, The Netherlands

April 2017: Poster presentation at 12th Göttingen Meeting of the German Neuroscience Society. Göttingen, Germany

April 2016: Oral presentation at 11th Troina meeting on Genetics of Neurodevelopmental Disorders. Troina, Italy

October 2015: Oral presentation at Nijmegen Maastricht Science Day. Nijmegen, The Netherlands

PRIZES

March 2020: Poster Award winner 2nd Neuroepigenetics & Neuroepitranscriptomics Conference

June 2019: Travel Award winner ISSCR Annual Meeting 2019

June 2018: Radboud Internationalisation Grant for “Outgoing PhD candidates”

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May 2018: Bursary winner ISDN 2018

June 2017: Poster prize at Dutch Neuroscience Meeting

PUBLICATIONS

KANSL1 Deficiency Causes Neuronal Dysfunction by Oxidative Stress- Induced Autophagy. Linda K, Lewerissa EI , Verboven AHA , Gabriele M, Frega M, Klein Gunnewiek TM, Devilee L , Ulferts E, Oudakker A, Schoenmaker C, van Bokhoven H, Schubert D, Testa G, Koolen DA, de Vries BBA, Nadif Kasri N. bioRxiv (2020). m.3243A > G-Induced Mitochondrial Dysfunction Impairs Human Neuronal Development and Reduces Neuronal Network Activity and Synchronicity. Klein Gunnewiek TM, Van Hugte EJH, Frega M, Guardia GS, Foreman K, Panneman D, Mossink B, Linda K, Keller JM, Schubert D, Cassiman D, Rodenburg R, Vidal Folch N, Oglesbee D, Perales-Clemente E, Nelson TJ, Morava E, Nadif Kasri N, Kozicz T. Cell Reports (2020) 31(3): 107538.

Distinct Pathogenic Genes Causing Intellectual Disability and Autism Exhibit a Common Neuronal Network Hyperactivity Phenotype. Frega M, Selten M, Mossink B, Keller JM, Linda K, Moerschen R, Qu J, Koerner P, Jansen S, Oudakker A, Kleefstra T, van Bokhoven H, Zhou H, Schubert D, Nadif Kasri N. Cell Reports (2020) 30(1): 173-186.e6.

Neuronal network dysfunction in a model for Kleefstra syndrome mediated by enhanced NMDAR signaling. Frega M*, Linda K*, Keller JM, Gümüş-Akay G, Mossink B, van Rhijn JR, Negwer M, Klein Gunnewiek T, Foreman K, Kompier N, Schoenmaker C, van den Akker W, van der Werf I, Oudakker A, Zhou H, Kleefstra T, Schubert D, van Bokhoven H, Nadif Kasri N. (* contributed equally) Nature Communications (2019) 10(1): 4928.

The promise of induced pluripotent stem cells for neurodevelopmental disorders. Linda K, Fiuza C, Nadif Kasri N. Progress in neuro-psychopharmacology & biological psychiatry (2018) 84: 382–391.

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Mouse models of 17q21.31 microdeletion and microduplication syndromes highlight the importance of Kansl1 for cognition. Arbogast T, Iacono G, Chevalier C, Afinowi NO, Houbaert X, van Eede MC, Laliberte C, Birling MC, Linda K, Meziane H, Selloum M, Sorg T, Nadif Kasri N, Koolen DA, Stunnenberg HG, Henkelman RM, Kopanitsa M, Humeau Y, De Vries BBA, Herault Y. PLOS Genetics (2017) 13(7): 1–25.

Rapid Neuronal Differentiation of Induced Pluripotent Stem Cells for Measuring Network Activity on Micro-electrode Arrays. Frega M, van Gestel SH, Linda K, van der Raadt J, Keller J, Van Rhijn JR, Schubert D, Albers CA, Nadif Kasri N. Journal of Visualized Experiments (2017) 119.

Added effects of dexamethasone and mesenchymal stem cells on early Natural Killer cell activation. Michelo CM, Fasse E, van Cranenbroek B, Linda K, van der Meer A, Abdelrazik H, Joosten I. Transplant Immunology (2016) 37: 1–9.

The Koolen-de Vries syndrome: A phenotypic comparison of patients with a 17q21.31 microdeletion versus a KANSL1 sequence variant. Koolen DA, Pfundt R, Linda K, Beunders G, Veenstra-Knol HE, Conta JH, Fortuna AM, Gillessen-Kaesbach G, Dugan S, Halbach S, Abdul-Rahman OA, Winesett HM, Chung WK, Dalton M, Dimova PS, Mattina T, Prescott K, Zhang HZ, Saal HM, Hehir- Kwa JY, Willemsen MH, Ockeloen CW, Jongmans MC, Van der Aa N, Failla P, Barone C, Avola E, Brooks AS, Kant SG, Gerkes EH, Firth HV, Õunap K, Bird LM, Masser-Frye D, Friedman JR, Sokunbi MA, Dixit A, Splitt M; DDD Study, Kukolich MK, McGaughran J, Coe BP, Flórez J, Nadif Kasri N, Brunner HG, Thompson EM, Gecz J, Romano C, Eichler EE, de Vries BB. European Journal of Human Genetics (2016) 24(5):652-9.

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Research Data Management

Type of data Subject Way of Storage to anonymiza- privacy tion

Antibodies No n.a. Antibodies were registered in the antibody database from the research group of molecular neurophysiology: H:\GR Theme groups\17 PI Group Nael NadifKasri\Goods\MolNeuroFys_A ntibodies

Antibodies were stored with a traceable number in the assigned freezer/fridge at the Department of Human Genetics, according to the manufacturer’s instructions.

Fluorescence No n.a. Fluorescence imaging data were images stored on the private network of the molecular neurophysiology group at the Department of Human Genetics: T:\PIgroup-Nael- NadifKasri\Katrin\imaging.

Plasmids No n.a. Plasmids were registered in the molecular neurophysiology group database: H:\GR Theme groups\17 PI Group Nael NadifKasri\Goods\MolNeuroFys_C lones. Plasmids were stored in the assigned freezer at -80°C at the Department of Human Genetics.

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Patient- Yes All patients Cell lines received a traceable derived cell received an number and were registered at the lines untraceable cell culture facility of the number, the Department of Human Genetics. identity of the Cell lines were frozen in liquid patient is only nitrogen and stored in the known by the assigned freezer at -80°C at the research PI Department of Human Genetics. and treating The number of the cell lines used physician. can be found in the labjournal of Katrin Linda, stored at the Department of Human Genetics or Labguru in the respective project folder: https://radboudumc.labguru.com/k nowledge/project. Contact person to find the cell lines in the assigned freezer: Saskia van der Velde-Visser.

Other cell lines No n.a. Cell lines were stored with a traceable number at the cell culture facility at the Department of Human Genetics, according to the manufacturer’s instructions. The number of the cell lines used can be found in the labjournal of Katrin Linda, stored at the Department of Human Genetics or Labguru in the respective project folder: https://radboudumc.labguru.com/k nowledge/project. Contact person to find the cell lines in the assigned freezer: Saskia van der Velde-Visser.

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MEA No n.a. Raw data of microelectrode array recordings (MEA) recordings were stored on the private network of the molecular neurophysiology group at the Department of Human Genetics: T:\PIgroup-Nael- NadifKasri\Katrin\MEA results

Documen- No n.a. Documentation is stored in lab tation and files books that are stored at the containing Department of Human Genetics exp. data and in Labguru: presented in https://radboudumc.labguru.com/k this thesis nowledge/project

Files can be found on the T drive of the Human Genetics Department:

T:\PIgroup-Nael-NadifKasri\Katrin n.a.: not applicable.

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269

Donders Graduate School for Cognitive Neuroscience

For a successful research Institute, it is vital to train the next generation of young scientists. To achieve this goal, the Donders Institute for Brain, Cognition and Behaviour established the Donders Graduate School for Cognitive Neuroscience (DGCN), which was officially recognized as a national graduate school in 2009. The Graduate School covers training at both Master’s and PhD level and provides an excellent educational context fully aligned with the research program of the Donders Institute.

The school successfully attracts highly talented national and international students in biology, physics, psycholinguistics, psychology, behavioral science, medicine and related disciplines. Selective admission and assessment centers guarantee the enrolment of the best and most motivated students.

The DGCN tracks the career of PhD graduates carefully. More than 50% of PhD alumni show a continuation in academia with postdoc positions at top institutes worldwide, e.g. Stanford University, University of Oxford, University of Cambridge, UCL London, MPI Leipzig, Hanyang University in South Korea, NTNU Norway, University of Illinois, North Western University, Northeastern University in Boston, ETH Zürich, University of Vienna etc.. Positions outside academia spread among the following sectors: specialists in a medical environment, mainly in genetics, geriatrics, psychiatry and neurology. Specialists in a psychological environment, e.g. as specialist in neuropsychology, psychological diagnostics or therapy. Positions in higher education as coordinators or lecturers. A smaller percentage enters business as research consultants, analysts or head of research and development. Fewer graduates stay in a research environment as lab coordinators, technical support or policy advisors. Upcoming possibilities are positions in the IT sector and management position in pharmaceutical industry. In general, the PhDs graduates almost invariably continue with high-quality positions that play an important role in our knowledge economy.

For more information on the DGCN as well as past and upcoming defenses please visit: http://www.ru.nl/donders/graduate-school/phd/ 270