The Application of Mouse and Human Embryonic Stem Cells with Transcriptomics in Alternative Developmental Toxicity Tests

The application of mouse and human embryonic stem cells with transcriptomics in alternative developmental toxicity tests

A bridge from model species to man

ISBN: 978-94-6203-861-5 Cover and layout: Maud van Deursen, Utrecht the Netherlands Production: CPI/ Wöhrmann Print Service, Zutphen the Netherlands The application of mouse and human embryonic stem cells with transcriptomics in alternative developmental toxicity tests

A bridge from model species to man

De toepassing van embryonale stamcellen van muis en mens met transcriptomics in alternatieve testen voor ontwikkelingstoxiciteit

Een brug van modelorganisme naar de mens (met een samenvatting in het Nederlands) Proefschrift

ter verkrijging van de graad van doctor aan de Universiteit Utrecht op gezag van de rector magnificus, prof.dr. G.J. van der Zwaan, ingevolge het besluit van het college voor promoties in het openbaar te verdedigen op dinsdag 7 juli 2015 des middags te 2.30 uur

door

Sjors Hubertus Wilhelmina Schulpen geboren op 5 januari 1983 te Sittard Promotoren: Prof. dr. A.H. Piersma Prof. dr. M. van den Berg

Dit proefschrift werd mogelijk gemaakt door financiële steun van:

Institute for Risk Assessment Sciences van de Universiteit Utrecht, Laboratorium voor gezondheidsbeschermingsonderzoek van het Rijksinstituut voor Volksgezondheid en Mileu, Greiner Bio-One en Stichting Proefdiervrij. Contents

Abbreviations 6 Chapter 1 9 Introduction Chapter 2 23 Dose response analysis of monophthalates in the murine embryonic stem cell test assessed by cardiomyocyte differentiation and gene expression Chapter 3 39 A statistical approach towards the derivation of predictive gene sets for potency ranking of chemicals in the mouse embryonic stem cell test Chapter 4 55 An abbreviated protocol for multi-lineage neural differentiation of murine embryonic stem cells and its perturbation by methylmercury Chapter 5 77 Distinct gene expression responses of two anticonvulsant drugs in a novel human embryonic stem cell based neural differentiation assay protocol Chapter 6 93 Gene expression regulation and pathway analysis after valproic acid and carbamazepine exposure in a human embryonic stem cell based neurodevelopmental toxicity assay Chapter 7 109 Comparison of gene expression regulation in mouse- and human embryonic stem cell assays during neural differentiation and in response to valproic acid exposure Chapter 8 127 Summary and general discussion Nederlandse samenvatting 139 Appendices 147 References 171 Dankwoord 195 List of publications 201 Curriculum Vitae 207 Abbreviations

BSA Bovine serum Albumin CBZ Carbamazepine CM Culture medium DMEM Dulbecco’s Modified Eagle’s Medium DNT Developmental neuro toxicity testing EB Embryoid bodies ECVAM European Center for the Validation of Alternative Methods ESC embryonic stem cells ES-D3 Embryonic stem cell line D3 EST Embryonic stem cell test FBS Fetal bovine serum GO Gene Ontology hESC Human embryonic stem cells hESTn Human neural embryonic stem cell test IC50 Inhibitory concentration of cell viability 50% ID50 Inhibitory concentration of differentiation 50% iPS Induced pluripotent stem cells ITS Insulin transferrin selenium KOSR Knockout serum replacement LIF Leukemia Inhibitory Factor LOOCV Leave one out cross validation MBuP monobutyl phthalate MBzP monobenzyl phthalate MEFs Mouse embryonic fibroblasts MeHg Methylmercury MEHP mono-(2-ethylhexyl) phthalate mESC Mouse embryonic stem cells mEST Mouse embryonic stem cell test mESTc Mouse cardiac embryonic stem cell test mESTn Mouse neural embryonic stem cell test MMC Mitomycin C

6 Abbreviations

NEAA Non Essential Amino Acids; NOAEL No observed adverse effect level NPC Neural precursor cells OECD Organization for economic cooperation and development PBS Phosphate-buffered saline PCA Principal component analysis PDL Poly-D-Lysine RA Retinoic acid RIN RNA Integrity number RMA Robust multichip average RT-PCR Real-time polymerase chain reaction US EPA US environmental protection agency VPA Valproic Acid WEC Whole embryo culture

7 8 CHAPTER1

Introduction

9 10 Introduction

Toxicology During life, organisms including man are exposed to diverse hazardous compounds. Toxico- logical research focuses on the adverse effects of chemicals on organisms. The term ‘toxicology’ is derived from the Greek word toxidos, meaning poisonous. The route of exposure together with the dosing, which depends on the amount and exposure time, are two major factors 1 contributing to the level of effect. Additional factors which are of importance are i.e. age, genetic background, gender and diet. In toxicological test protocols, like acute, sub chronic and chronic toxicity tests, multiple toxicological endpoints are studied. In these studies, a single brief exposure (i.e. 24h), repeated exposures over a longer time (weeks or months), or life time are studied, respectively. In acute toxicity testing, classically the dose that is lethal for half of the animals (LD50) is determined [1, 2]. In sub-chronic tests, the species tested are exposed for a longer period of time, in which several end points of toxicity are studied, to determine a no observed adverse effect level (NOAEL). This is the highest dose level at which no adverse effects are observed, which is used for further risk assessment calculations [3]. Sub-chronic studies address aspects such as food consumption, body weight, behavior, ECG, EEG, hema- tology, blood chemistry, urinalysis and fecal analysis. Pathologic examination is performed on organs after sacrifice. Chronic studies are performed over a significant part of the life span or the entire lifetime of a test species. In additional mutagenicity studies, performed in cultured bacteria- and mammalian cells in vitro, the potency of a compound to induce genetic damage, in terms of genetic mutations in germ- and somatic cells is examined. In carcinogenicity studies, often performed if mutagenicity studies are positive, indicating that a compound might cause cancer, the tumor incidence is determined by histological examination of organs of the test species [3, 4]. For some compounds also skin sensitization and irritancy are tested. In addition, ecotoxicity studies are performed to study effects of environmental exposure.

Reproductive and developmental toxicology Reproductive toxicology is the science that studies the potential of a compound to affect male and female fertility. Parental endpoints include development of the female egg- and male sperm cell, fertilization, implantation, delivery of the offspring and lactation, and postnatal growth including attainment of full reproductive function [3]. Developmental toxicology focuses on the development of the embryo and fetus during pregnancy, including early embryogenesis, organ formation, fetal development and growth. Teratogenicity, referring to the Greek word teratos, meaning monstrosity, relates to the study of malformations, and can be considered as an aspect of developmental toxicity [4]. Back in time, teratological sciences started as a descriptive science of mystical and scientific theories which explained congenital malformations, first introduced by I.G. de Saint-Hillaire (1805–1861) [5]. Until the 1940s it was believed that all birth defects were hereditary. However, in the 1930s and 1940s it was shown by Josef Warkany that birth defects could be induced by exogenous factors [6]. First, appearance of skeletal abnormalities in the offspring of rats were linked to a deficient diet [6]. Thereafter, congenital malformations of the eyes induced in rats was demonstrated to be caused by maternal vitamin A deficiency [7].

11 A human counterpart was reported in 1950s when aminopterin, an analog of folic acid, ex- plored to be used for abortion, caused congenital malformations [8, 9]. Subsequently, in the early 1960s there was a catastrophe with thalidomide. Thalidomide was used against nausea and vomiting in pregnant women and effective against influenza [5]. This drug was presumed safe, but although minimal toxicity occurred in adults, a high degree of embryotoxicity occurred, causing severe congenital malformations in more than 10,000 children [10]. Maternal exposure during pregnancy caused reduction deformities of the limbs up to total absence of all limbs. Nonetheless, the thalidomide catastrophe increased the understanding of developmental toxicology and demonstrated the importance of hazard identification of chemicals for development. In 1959 and in a monograph of 1973 Wilson defined teratology as the science dealing with adverse effects of the environment on the developing system, including the germ cells, embryo, fetus and immature individuals [11, 12]. He proposed 6 main principles of teratology. They included that susceptibility depends on genotype, the developmental stage at the time of exposure, the compound specific mode of action or mechanism and the nature of the compound. Furthermore, the four manifestations of deviant development are death, malformation, growth retardation, and impaired function, their frequency and severity being dose dependent [11]. In the 1980ies, the organization for Economic Cooperation and Deve- lopment (OECD) established protocols worldwide, describing test guidelines for reproductive and developmental toxicity testing of chemicals. Among these tests, are the prenatal developmental toxicity (OECD TG414) and the two-generation reproductive toxicity study (OECD TG416), and the reproductive toxicity screening tests (OECD TG421 and 422) [13]. In 2009 the European commission implemented the legislation for Registration, Evaluation, Autho- rization and restriction of CHemicals (REACH). This legislation implies that all compounds produced and used in the European Union need to be tested for toxicity, from a certain tonnage production level including reproductive and developmental toxicity testing [14, 15]. These tests are all based on animal studies.

Developmental neurotoxicology An important developmental process is the development of the neural system. The nervous system is both structurally and functionally complex. The developing brain is more suscep- tible to toxic interference and can be affected at doses much lower than those affecting adult brain function[16]. During brain development essential cellular processes occur, including proliferation, migration, differentiation, synaptogenesis, apoptosis, and myelination. Altera- tions in these processes can result in severe congenital abnormalities of the nervous system of humans, including anencephaly, hydrocephaly, and herniation of the spinal cord [17]. Furthermore, neurodevelopmental disorders associated with toxic exposure include various clinical disorders in humans, e.g., schizophrenia, dyslexia, epilepsy, and autism. During fetal development the placenta offers some protection, however there are many chemicals, i.e. metals, for which the placenta is not an effective barrier and therefore they can enter the fetus [18, 19]. Even after birth, it takes 6 months before the blood-brain barrier, which protects the brain against toxic chemicals, is completely formed [20]. The development of the brain lasts at least until adolescence [21]. During neurodevelopment there are critical windows of vulnerability. There are several examples in which compound exposure specifically resulted

12 Introduction in developmental neuro toxicity testing (DNT) in de developing nervous system, whereas the same exposure caused little neurotoxicity in adults [22]. The difference of effect is mainly observed after exposure to neuro-toxicants that act reversibly on synaptic function, like nico- tinic agonists or acetylcholine esterase inhibitors [23]. During neural development, disruption of signals, essential for the formation of correct networks during critical time windows, can 1 result in permanently malformed neural networks [23]. Furthermore, after birth, children can have a relatively higher exposure compared to adults, because of higher intake in parallel with higher intake of drinking water, food or air breathed per unit body weight [24]. Mo- reover, metabolic detoxification and blood-brain barrier function are less complete than in adults [23]. Testing for DNT is therefore an important toxicological area in the hazard and risk assessment of chemicals. DNT includes any adverse effect caused by compound exposure on the normal development of nervous system structures and functions [25]. Currently, there are two regulatory guidelines used to test chemicals for DNT: One established by the Orga- nization for Economic Co-operation and Development (OECD 2007, OECD TG 426) and the other by the US Environmental Protection Agency (US EPA) (US EPA, 1998a;) OPPTS 870.6300 (U.S. EPA 1998j) [26]. In general, for a DNT test an estimated one year and one million dollars is required per chemical [26].

In vitro toxicology Regulatory toxicity testing requires an enormous number of test animals. Only for the RE- ACH program these tests together have been estimated to use an average of 4,710 animals per chemical [27]. At least 30.000 chemical compounds need to be tested, with an expecta- tion of up to 10 million experimental animals required [28]. About 65% of the test animals used for the REACH program are used for reproductive toxicity testing [15]. These in vivo tests are time consuming, expensive and give rise to many ethical concerns. To reduce the number of test animals used for reproductive toxicity testing, there is a need for high throughput alternative test methods. Much research has been performed concerning development of in vitro methods, in order to contribute to the reduction of animal use for toxicological testing. For developmental toxicity testing, during the last decades several in vitro systems have been developed. These methods vary from alternatives representing pattern formation during embryogenesis based on (dissociated) mammalian cells, like cells from the limb buds and nervous system including neural crest, neuronal cells and brain in the micromass (MM) test [29-31]. Furthermore, alternative tests comprising complete embryos in vitro, include whole frog embryos in the frog embryo teratogenesis assay (FETAX) [29, 31] and rat post implantation whole embryo culture (WEC) [32] and more recently the Zebrafish embryotoxicity Test (ZET) [33-35]. Both the limb bud MM and WEC have been validated alternatives for developmental toxicity testing. However, these tests require animal material and the sacrifice of pregnant animals.

13 Embryonic stem cells The use of embryonic stem cells (ESC) has gained increasing interest the last decade. Re- generative abilities of organisms, like in flatworms, which regenerate tissues with speed and precision, have always been of great interest for man. Mammals have limited regenerative ability. However, regeneration of organ tissues is still possible in mammals, including man, like liver regeneration, e.g. after removal of a segment of the liver [36], which is based on proliferation and differentiation of pre-existing stem cells or progenitor cells. The history of stem cell knowledge goes far back in time. In 1932 and 1936, Sabin et al. published the first paper with the phrase ‘stem cells’ in the abstract [37, 38]. However, further back in time it was in work by Ernst Haeckel in 1868 that the word “Stammzelle”, in other words “stem cell”, first appeared [39, 40]. In 1885 it was August Weismann who discussed stem cells using the term ‘germ plasm’ [41]. In 1908 the Russian histologist Alexander Maximow came up with the phrase ‘stem cell’ in his model of hematopoiesis, which are now defined as hematopoietic stem cells [42]. In 1964 the definitive evidence of stem cell existence was delivered by the work of James Till and Ernest McCulloch [43] in which self-renewing cells in the mouse bone-marrow where demonstrated to give rise to both red and white blood cells [39, 43]. Now it is known that stem cells are the origin for all types of cells. Now what is a stem cell? In general three types of cells can be distinguished which make-up the human body. First, the somatic cells, being the cells in their differentiated state who make-up the human adult. These cells have their own copies of the genome (except for red blood cells, which don’t have a nucleus) [36]. Secondly, the germ cells, which give rise to the gametes, including the egg- and sperm cells. Thirdly, the stem cells. The definition of a stem cell is a cell with the potential to give rise to mature specialized cell types [44]. When a stem cell divides, the daughter cells can either form a (differentiated) specialized cell, or a new stem cell [36].

There are different types of stem cells, classified depending on their cellular stage. After fertilization a zygote is formed. Subsequently, the first 2, 4, 8, and 16 blastomeres result from cleavage in the early embryo. These cells are totipotent cells [36], which can give rise to the entire organism. However, they do not have the capability to self-renew. After about 4 days, these totipotent cells begin to specialize in which the zygote transforms into a blastocyst containing approximately 200-300 cells, a hallow ball of cells [44]. The blastocyst consists of an outer layer (trophoblast) and an inner cell mass (embryoblast), from which the embryo deve- lops. The cells in the inner cell mass are pluripotent stem cells, which are able to differentiate into (almost) all cell types that arise from the three germ layers [44], called endoderm, mesoderm and ectoderm. These three germ layers are the offset for differentiation towards all cell types. Besides ESC, in literature embryonic germ cells, embryonic carcinoma cells and the multipotent adult progenitor cells from bone marrow are described as pluripotent stem cells [45]. The final class of stem cells retained in the adult organism are multipotent stem cells, able to give rise to a limited range of cells and tissues, which are appropriate to their location. Thus, blood stem cells can give rise to platelets, red- and white blood cells, whereas skin stem cells give rise to various types of skin cells.

14 Introduction

Stem cells and developmental toxicology The first attempts to test for cytotoxicity, embryotoxicity or teratogenicityin vitro, date back to the early eighties and nineties [46, 47]. However, these in vitro assays were based on somatic cells, which didn’t properly reflect the reaction of embryonic cells to toxic compounds [48, 49]. Stem cells offer a relevant characteristic in cellular assays for testing developmental toxi- 1 city. The most important aspect of pluripotent stem cells that can be employed in embryotoxicity testing is their ability to differentiate into all cell types originating from the three germ layers [50]. Therefore, ESC offer the ability to mimic early in vivo embryonic development at the cellular level [48, 49], covering cellular developmental processes and their gene expression patterns of early embryogenesis during in vitro differentiation [51].

The embryonic stem cell test The use of ESC for teratogenicity testing was first described by Laschinsky et al. in 1991 [52] in which cytotoxicity was measured and compared in both ESC and mouse fibroblasts to assess the embryotoxic potential of teratogenic agents. In 1994, Newall and Beedles in addition to cytotoxicity, also measured the effects of teratogenic compounds on the colony-forming potential of ESC [53], which they described as ‘the stem-cell test’ . However, in this test only a few embryotoxic agents were detected correctly [48]. This was possibly due to the fact that there were only two end points selected for bio statistical assessment, cytotoxicity and colony- forming potential, which delivered insufficient results [48]. To overcome these limitations a third endpoint was added, in which changes in cell differentiation were studied [48]. The embryonic stem cell test (EST) was first described by Spielmann et al.[54]. The EST was designed to test for potential in vitro embryotoxicity of chemicals, pharmaceuticals and other drugs. The test is based on the ability of ESC to spontaneously differentiate into myocardial foci and the influence on differentiation of the exposure to compounds. Together with the effect on differentiating D3 ESC (ES-D3), both cytotoxic effects on these differentiating cells together with the cytotoxic effect in adult mouse 3T3 fibroblast were studied [ Estevan]. ES-D3 are able to develop into contracting cardiomyocytes, when cultured in cell aggregates, called embryoid bodies(EB). These EB resemble the anterior prestreak stage embryo with the ability to generate derivatives of all three primary germ layers [55]. These contracting cardiomyocytes have an interplay between several related cell types present in culture, like sinusoidal, atrial and ventricular cells [56]. The three endpoints selected, were used to classify the embryo toxicants in three classes of in vivo embryotoxicity categories: strong-, weak-, or non-embryo toxic [48, 54]. These endpoints included inhibition of cell proliferation (cytotoxicity), both of 3T3 cells and differentiating ESC, after 10 days of culturing in presence of a compound, and the inhibition of differentiation of ESC into cardiomyocyte foci after 10 days of treatment [54]. Differentiation evaluation is based on morphological analysis of beating cell clusters. The effect of compounds is described as the concentration causing a 50 % of inhibition of differentiation (ID50) after 10 days of exposure. The cytotoxic effect is estimated by determining 50% of proliferation inhibition (IC50) after 10 days of exposure of ES-D3 and 3T3 cells [48]. To evaluate and validate the EST, experimental validation was performed in an ECVAM (European Center for the Validation of Alternative Methods) study [54], including a pre-validation phase in which 20 compounds were tested and a formal validation

15 phase [57]. 78% of the chemicals were classified correctly. Furthermore the class of strong embryo-toxicants were predicted 100% correctly [57]. The predictability of the non- and weakly embryotoxic compounds was 70% and 72% respectively.

Because of the large number of chemicals which need to be tested, the time and costs, there is a high need for alternative methods for DNT [58-61]. Several new alternative DNT testing methods have been developed, i.e. using zebrafish [33, 34, 62, 63] and ESC. In 1983 an in vitro neural differentiation protocol was established. However, this protocol was based on neural differentiation of embryonal carcinoma cells, induced with retinoic acid (RA) [64]. The first protocols in which mouse ESC (mESC) cells were induced to differentiated in vitro into neural cells was published in 1996 by Okabes et al. [65]. After then, various protocols have been developed in which mESC cells successfully differentiated into a variety of neuronal and glial cells [65-69].

Mouse neural embryonic stem cell test For DNT an in vitro neural differentiation assay with a relatively short duration and high throughput would be advantageous. In Theunissen, et al. [70] we developed a mESC based neural differentiation assay suitable for DNT , which was based on methods described by Okabe et al. [65] and Bibel et al. [71]. Within 11 days mESC were differentiated into neural cells, first by EB formation, after which neural differentiation was stimulated by serum deprivation and in parallel de addition of RA. After 11 days neural outgrowth was present around the EB. The effect of compound exposure on the formation of neural outgrowth was taken as a measure for DNT. Although the toxicological read out based on neural outgrowth was successful, there were some limitations. A morphological read-out does not elucidate functional or underlying molecular mechanisms of action. Furthermore, the morphological scoring was performed after 11 days. The implementation of transcriptomics as a read out for DNT gave additional information on compound mechanism of action and reduced the culture period to 4 days, including 24h of exposure, initiated at day 3 of the protocol [72].

Human embryonic stem cell based developmental toxicity assays Mouse based test systems have a limited ability to correlate toxicity outcomes to human response, due to differences in biological mechanisms, pathways and pharmacokinetic properties [73, 74]. Differences in developmental toxicity response of animals to teratogens have been demonstrated in the past[73, 74]. The most remarkable example of this problem is the thalidomide catastrophe, which had been tested in rodent, demonstrated to have no effect in mice and rats [75, 76] and was considered safe for pregnant women [77]. Thalidomide was marketed to treat morning sickness in pregnant women. However it resulted in limb defects in newborns and was subsequently removed from the market [78]. Other examples are corticosteroids and acetylsalicylic acid, which are embryotoxic in mice, but not in man [73, 79, 80]. Therefore there is a high need to develop methods which are accurate for human developmental toxicity screening. Given the origin, human embryonic stem cells (hESC) offer a great potential in this area.

16 Introduction

The popularity of the use of human cell line based alternative test systems, including the application of hESC, has increased during last couple of years. It is an important aspect in the 21st-century toxicity testing strategy paradigm published by the US National Academy of Sciences [81, 82] . The use of hESC involves strict legislations, especially in view of ethical aspects. In Europe, the use of hESC is subject to national laws and regulations, which vary 1 from country to country. The Netherlands applies the Embryo Act of September 2002 (de embryowet), describing the use of embryos for research including the isolation of ESC from such embryos. One of the requirements is that the research must have the aim to lead to new insights in medical science and the informed consent of the donor is required. The use of hESC in the United States has always been allowed. However, there were some restrictions on funding and use. Federal funding was limited to non-embryonic stem cell research and the number of ESC lines used for research was limited. In 2009 these restrictions were removed. The most common source for hESC are cleavage stage human embryo’s produced by in vitro fertilization (IVF). This is also the source of the hESC line used for the research described in this thesis. The H9 (WA-09-DL-11) cells, commercially available, were donated after informed consent and institutional review board approval [83]. The embryos were cultured to the blastocyst stage and subsequently the cell mass was isolated. The H9 cell line does have a normal XX karyotype. Teratogenicity testing using hESC may give a more accurate prediction of effects in man than animal tests or in vitro assays. Furthermore, the predictability will improve and additionally reduce reliance on animal studies, cost, and time, and interspecies variations can be avoided [51, 74, 84]. For DNT screening, pluripotent hESC could overcome limitations of the use of mESC and may allow reliable assessment of a test compound’s developmental effect. A variety of studies establishing assays in which hESC differentiate into neuronal tissue have been described [85-90].

The use of hESC for neural differentiation has been published before (2009). Many approaches focus on regeneration and replacement therapies [87, 91] and have been based on various cell types, such as stem cells from umbilical cord blood [92], induced pluripotent stem cells (iPS) [93], neural progenitor cells [94, 95] and hESC [91]. The neural differentiation assays published, using hESC, differed in terms of culture method, varying from single- to multiple step approaches, used to obtain different endpoints, including rosette formation [91, 92], neural progenitors [96] neural sphere- [97] and EB formation [87]. Differentiation time varies between a few days, until a few weeks, depending on the end-points [88, 98-100]. Wit- hin the methods, different tissue culture well coatings, additives and culture conditions are being used. Based on these differentiation assays we have developed an assay in which hESC are differentiated in vitro towards neural cells to be used to study developmental neurotoxicity.

Toxicogenomics The classical end-point and readout in the EST is based on morphological examinations. However, in general these endpoints do not give any mechanistic information. The rise of genomic applications has provided new tools to study these mechanisms and furthermore, to characterize, classify and potentially predict teratogens [101]. It is assumed that the expression of genes is changed by a direct or indirect result of toxicant exposure, leading to toxicity [102].

17 The term toxicogenomics represents the interface of multiple functional genomics approaches, applied to understand mechanisms of toxicity [103]. The use of genomics to study toxicity was first described in 1999 by Nuwaysir et al. [102], where it was mentioned that toxicogenomics illustrates the integration of toxicological research with the emerging new technologies designed to broadly interrogate the functional genome, i.e. RNA, protein, meta- bolite profiling, and polymorphisms/functional DNA mutations [103]. The introduction of the microarray chip in the mid 1990’s, gave rise to a technology for such assessments. There were two main types of platforms introduced, based on complementary DNA (cDNA) or oligonucleotides which are immobilized to a solid support [103]. The first platform was produced with ‘on-chip synthesis’, containing sets of short oligonucleotide sequences, which are short pieces of single stranded DNA (or RNA) molecules, for each gene transcript, with com- pilation of the individual gene probe sets to cover the entire genome [104]. The other platform included coated glass slides with spotted longer length cDNA [105, 106]. Both platforms allow probing of the whole genomic transcript profile or to monitor the expression of genes of any biological sample RNA that is hybridized to it. To (quantitatively) detect expression of genes, firstly fluorescent cDNA is generated from both the control and test samples via a reverse transcriptase reaction. Both the control and test samples can be labeled with different fluorescent nucleotides. Subsequently, the cDNA’s generated (probes) are mixed and hybridized to the array. Next, the fluorescent signal is detected by scanning confocal microscopy. Using image analysis software, the ratio of both fluorochromes is determined from each DNA and corrected for the background signal. Using this technology the response to compound exposure at the gene expression level can be studied. The application of toxicogenomics is discussed in many studies [103, 107-109].

In 2001 the first studies were published using microarray analysis to study embryonic development [110, 111] and developmental toxicity [112] in vivo. Other studies followed soon [69, 113, 114]. The application of in vitro systems, including zebrafish embryos [35, 115] and the WEC [116] together with transcriptomics, have provided easier tools to search for molecular targets and markers of developmental toxicity. In the study of Robinson et al., it was demonstrated that gene expression changes could be linked to morphological changes, occurring at a later time point [114], underlining the applicability of transcriptomics to characterize, classify, and potentially predict teratogens. More recently, the identification of embryotoxic compounds on the basis of alterations in gene expression of differentiating stem cells, either to osteoblast [117], neural [72, 118] or myocardial cells, has proven possible [119-123]. In a series of studies, gene expression studies in stem cells have been demonstrated to be able to give compound- and concentration specific responses [119, 124, 125]. With these studies marker genes and gene sets predictive for developmental toxicity were obtained, able to discriminate classes of developmental toxicants.

18 Introduction

This thesis In this thesis the application of toxicogenomics in both mouse- and human neural differentiation ESC assays as an alternative model to study neurodevelopmental toxicity is investigated.

The application of mouse embryonic stem cells 1 The application of mESC in the mEST as an in vitro method to study the developmental toxic effects of compounds has been studied extensively. In the original EST mESC differentiate into contracting cardio myocytes within 10 days. The effect of compound exposure on differentiation is used as a measurement for developmental toxicity. However for screening purposes, a 10 day test takes a long time. Even more importantly, with a read-out based on inhibition of differentiation scoring ‘yes or no’ the outcome does not give any information on mode of action of a compound. Gene expression changes as a consequence of compound exposure have been proven to function as a sensitive, rapid and mechanistically informative read out to study the effect of compound exposure. It has been demonstrated that compounds cause specific gene expression changes in a concentration related response, already after a short time of exposure [114, 118, 119].

In chapter 2 we studied whole genome expression in response to compounds within one compound class. The goal was to study whether genomics, as a tool for developmental toxicity evaluation, is able to detect compound specific responses, even within one class. Further- more, potency ranking performed on basis of the effect of compound specific gene expression changes was compared to potency ranking of teratogenic effects occurring in in vivo. For this study we selected the compound class of phthalates which are present in many consumer products and cause developmental and reproductive toxicity [126-128]. They are used as plasticizers, to soften and maintain flexibly of polyvinyl chloride (PVC) plastics, as solvents, they are present in cosmetics and latex products [126, 129]. The effect of equimolar concentrations of 3 embryotoxic phthalate monoesters, MEHP, MBuP and MBzP, and 1 non- embryotoxic monoester, MMP was studied on whole genome gene expression, to obtain a concentration-response on transcriptomics.

The use of whole genome array analysis to study developmental toxicity results in a lot of data and is currently still too expensive to ultimately be used for high throughput screening. Predefined gene sets, containing a minimal number of genes able to detect and classify developmental toxicity would be preferable. This requires a limited group of mode of actions allowing detection of all developmental toxicants, that can be covered by studying a limited number of genes regulated in the relevant toxicity pathways. However, individual compound classes probably have their own mode of action, both involved in pharmaceutical- as toxicity mode of action, which makes it difficult to obtain one set of genes, able to detect developmental toxicity. In chapter 3 we evaluate a proposed statistical method to obtain gene sets functional for toxicity evaluation of one class of compounds, which is based on the phthalate compound class. An assembly of additional predefined gene sets ultimately enables classification of new (unknown) test compounds on the basis of the regulation of the predefined gene set.

19 During embryonic development, neural development is one of the earliest differentiation lineages, which continues until adulthood, resulting in a very complex organ system. Com- pound exposure during any of the neurodevelopmental stages can cause severe malformations. It is important to study the DNT potential of compounds. In regulatory tests for DNT a lot of test animals are involved. Therefore, development of alternative tests concerning DNT is desired. In chapter 4 a mESC based neural differentiation assay is described, in which mESC differentiate into neural cells within 11 days. It is demonstrated that MeHg, as a model compound known to cause DNT, affected neural outgrowth and genes involved in differentiation.

The application of human embryonic stem cells Human cell based assays are preferred to avoid interspecies differences and the need for interspecies extrapolation. Based in existing hESC based neural differentiations assays, we have developed a novel assay applicable for DNT, described in chapter 5. This assay involves different developmental processes (proliferation, migration and differentiation), including embryoid formation, which was established by aggregate formation. The formation of neuronal-like structures is obtained without the use of numerous growth factors and a relatively shortened differentiation time of 11 days. After 11 days a network of βIII-tubulin positive cells, containing a minimal cell number with SSEA4 positive cells was formed. Subsequently the differentiation was studied in more detail monitoring gene expression changes. Therefore genes were selected which are involved in stem cell pluripotency and self-renewal; pou5f1, Nanog, and neural cells and development, βIII-tubulin, Map2, Neurog1, Mapt and REElIN. To test the applicability for DNT the effects of two anticonvulsant drugs, valproic acid (VPA) and carbamazepine (CBZ), which are used to treat epilepsy and are known to cause neuro developmental toxicity, were studied after 7 days of exposure. Initially the differentiation and effect of compound exposure was studied on 7 genes. The effect on cell viability was studied after 5 days of exposure.

Since gene expression during early differentiation is useful for revealing compound mechanism of action [130] as regards embryotoxicity, the transcriptomic effects either after 1 or 7 days exposure to corresponding pharmaceutical concentrations VPA or CBZ were evaluated in chapter 6. We used whole genome array analysis to study the mode of action triggered by compound exposure.

As a final study, gene expression changes during differentiation and as a response to VPA exposure both in the mouse neural embryonic stem cell test (mESTn) and human neural embryonic stem cell test (hESTn) are compared in chapter 7, to evaluate the differences and commonalities between these two assays. Regulation of genes involved both in neural developmental processes and in pharmacological mode of action and processes are studied.

20 Introduction

21 22 CHAPTER2

Dose response analysis of monophthalates in the murine embryonic stem cell test assessed by cardiomyocyte differentiation and gene expression

Sjors H.W. Schulpen, Joshua F. Robinson, Jeroen L.A. Pennings, Dorien A.M. van Dartel, Aldert H. Piersma

Reproductive toxicology (2013) 35:81-8

23 Abstract The Embryonic Stem cell Test (EST) is based on compound-induced inhibition of cardiomyocyte differentiation of pluripotent stem cells. We examined the use of transcriptomics to assess concentration-effect relationships and performed potency ranking within a chemical class. Three embryotoxic phthalate monoesters, monobutyl phthalate (MBuP), monobenzyl phthalate (MBzP) and mono-(2-ethylhexyl) phthalate (MEHP) and the non-embryotoxic monomethyl phthalate (MMP) were studied for their effects on gene expression. Effects on gene expression were observed at concentrations that did not inhibit cardiomyocyte differentiation or induce cytotoxicity. The embryotoxic phthalate monoesters altered the expression of 668 commonly expressed genes in a concentration-dependent fashion. The same potency ranking was observed for morphology and gene expression (MEHP> MBzP> MBuP> MMP). These results indicate that integrating transcriptomics provides a sensitive method to measure the dose-dependent effects of phthalate monoester exposure and enables potency ranking based on a common mode of action within a class of compounds. Transcriptomic approaches may improve the applicability of the EST, in terms of sensitivity and specificity.

24 Dose response analysis of monophthalates 1. Introduction Current regulations for chemical risk assessment require large numbers of animals for developmental toxicity testing [15]. A variety of alternative animal-free methods have been designed, but to date, none of them have been implemented in regulatory toxicology. This is partly due to the complexity of embryonic development, which precludes the simple one in one replacement of in vivo animal studies by alternative methods. The Embryonic Stem cell Test (EST) is one of the most widely studied alternative assays in the area of developmental toxicity [54, 131]. In the EST, the effects of compound exposure are scored based on the cardiac muscle cell differentiation of pluripotent embryonic stem cells. The endpoint is assessed by counting beating muscle cell foci in culture under the microscope. Test compound effects 2 are compared with their in vivo embryotoxicity, usually defined as including effects on growth, survival, and the induction of malformations. Although derivatives of all three embryonic germ layers have been observed in EST, the in vitro end point of cardiac muscle differentiation is a reductionistic representation of embryogenesis, which requires thorough evaluation of predictability [132]. Various validation studies have addressed the predictive capacity of the test, with varying success rates [133, 134]. The ECVAM validation study showed a prediction accuracy of EST of 78 % for non- and weak embryotoxic compounds and a 100% predictivity for the strong embryotoxicants [4]. A subsequent study with a different series of compounds was less successful [5]. In a series of studies by van Dartel et al [119-124, 135], the use of transcriptomics was assessed to improve the readout in parallel with classical morphological evaluations. We have demonstrated transcriptomic approaches to provide a more detailed assessment of compound effects (i.e. mode-of action) and to enable classification of compounds based on similar gene expression profiles. Furthermore, differential gene expression facilitates extrapolation of findings to relevant effectsin vivo, and may potentially increase the predictive ability of developmentally-toxic compounds in the EST. In addition, these studies indicated the importance of dose in predictivity and specificity of gene expression changes. In our first concentration effect study, using Flusilazole as the model compound, we observed concentration-dependent effects on gene expression in association with increased inhibition of cardiomyocyte differentiation and cytotoxicity [119]. Higher concentrations resulted in greater impacts on gene expression pathways related to Flusilazole’s mode of action. Furthermore, alterations in gene expression were apparent at concentrations well below those affecting EST differentiation or cytotoxicity, suggesting changes in gene expression may provide a more sensitive endpoint than classical morphological endpoints and raising the question of how these endpoints were related and how adversity should be operationally defined within the EST. In a subsequent study [123], it was shown that compounds within a particular class similarly impacted gene expression related to their mode-of-action, which enabled the discrimination between compound classes based on signature specificity. The combination of class-specificity and concentration-response of differential gene expression indicates that transcriptomics may be useful for potency ranking of compounds within a chemical class.

25 To determine if transcriptomic based assessments could be used for potency ranking within a chemical class, we performed concentration-response studies using four phthalate monoesters as model compounds. Phthalates are plasticizers that are widely applied in consumer products. They are not covalently bound and can therefore leach out, which can result in human exposure. Phthalate exposure have been shown to cause developmental toxicity in animal models, including neural tube defects, cardiovascular effects, and reduced anogenital distance [136-142]. The monophthalate metabolites generated in vivo by hydrolysis of diphthalates have been identified as the proximal toxic derivatives with their potency being dependent on the carbon chain length. In the present study we tested three embryotoxic phthalate monoesters: monobutyl phthalate (MBuP), monobenzyl phthalate (MBzP) and mono-(2-ethylhexyl) phthalate (MEHP), and one relatively non-embryotoxic phthalate monoester, monomethyl phthalate (MMP), of which the parent compounds have been shown to display different embryotoxic potencies in vivo and in vitro [124, 143-147]. We investigated the potencies of these four phthalate monoesters in the EST by concentration-effect assessment, relating changes on the transcriptomic level to classical morphological readouts (differentiation inhibition and cytotoxicity). Using a battery of bioinformatic strategies, we showed that transcriptomic-based assessments can be used for potency ranking for this class of compounds similarly to potency ranking in vivo, providing a more sensitive and objective endpoint. Furthermore, our results feed into the discussion about the relevance of different readouts in in vitro assays in view of embryotoxicity in vivo and the relevance of transcriptomic changes in relation to adverse morphological effects.

26 Dose response analysis of monophthalates 2. Methods 2.1 Embryonic stem cell culture ES-D3 (ATCC) pluripotent embryonic stem cells (ESC) were cultured in complete medium (CM), consisting of Dulbecco’s Modified Eagle’s Medium (DMEM) (Gibco, Gaithers- burg, MD, Cat#11960-044) supplemented with 20% Fetal bovine serum (FBS), Hyclone, Logan, UT, Cat#SH30070.03), 1% non-essential amino acids, (Gibco, Gaithersburg, MD, Cat#11140-035), 2 mM L-Glutamine (Gibco, Gaithersburg, MD, Cat#25030-024), 1% 5000 IU/ ml Penicillin/ 5000 µg/ml Streptomycin (Gibco, Gaithersburg, MD, Cat#773139), and 0.1mM β-Mercaptoethanol (Sigma-Aldrich, Zwijndrecht, Cat#31350- 2 010). ESC pluripotency was maintained by adding Leukemia Inhibitory Factor (LIF), 1000 units/ml to the culture medium. ESC were kept for a maximum of 25 passages and are passaged every other day.

2.2 Morphological differentiation in EST. Differentiation was initiated by culturing the cells via the ‘hanging drop method’ in which approximately 750 cells/drop were cultured in 56 droplets of 20µl of CM, on the inner side of a lid of a Petri dish (94x16mm, Greiner Cat#633171), containing 5 ml Phosphate Buf- fered Saline (PBS), Ca2+ and Mg2+ free (Gibco, Gaithersburg, MD, Cat#14190-094) (Figure 1). ESC were cultured in a humidified atmosphere (37ºC 5%CO2) for three days during which the cells proliferated and formed cell aggregates (i.e. Embryoid Bodies (EB)). EB were transferred to a bacterial Petri dish (60x15mm, Greiner Cat#628103) containing 5 ml CM and cultured in a humidified atmosphere (37ºC, 5% CO2) for two days. Subsequently each individual EB was transferred to a single well of a 24-wells plate (TPP, Trasadingen, Swit- zerland, Cat#92024), containing 1ml CM. Within 5 days aggregates further differentiated and displayed spontaneously contracting cell foci. At day 10, all wells were microscopically examined and scored based on the presence or absence of contracting cells.

2.3 Phthalate Exposure ESC were exposed to phthalate monoesters (MEHP CAS# 4376-20-9 (Wako-chemicals), MBuP CAS#131-70-4 (TCI Europe), MBzP CAS# 2528-16-7 (TCI Europe) or MMP CAS#4376-18-5 (Sigma Aldrich) in 0.3% DMSO during the differentiation protocol from day 3 onwards. Phthalates were dissolved in DMSO and tested in a concentration-response fashion (0.00441, 0.0143, 0.441, 0.143, 0.441, 1.43 and 4.41 mM). Concurrent solvent control cultures (0.3% DMSO) were exposed from day 3 onward, and collected after twenty-four hours (day 4, time 24h). Earlier studies have shown that concentrations up to 1% DMSO did not affect the outcome of the assay. Phthalate-exposed cultures were taken after 24 hours of exposure. All groups contained 7-8 replicates, individually cultured. EB were collected in RNA protect in a 15ml tube (Qiagen #76526) and stored at -20ºC prior to RNA extraction.

27 2.4 RNA Processing RNA was extracted by using the RNA Mini-extraction kit (Qiagen). EB in a 15 ml centrifuge tube were spun down and the RNA protect was discarded. RLT buffer (Qiagen) was added and the EB were broken down into smaller parts by gently pipetting up and down. The cell suspension was subsequently transferred to a RNeasy spin column (Qiagen). The RNA was further extracted as described in the manufacturer’s protocol. Finally, RNA was eluted in RNase free water and stored at -80ºC. The concentration of the RNA was measured by Nanodrop (Thermo Scientific). The quality of the RNA was checked using the 2100 Bio- Analyzer (Agilent) and Shimadzu MultiNA. Samples with a RNA Integrity Number (RIN) ≥ 8 were used for microarray analysis.

2.5 Microarray Analysis Samples were randomized, blinded and further processed for array analysis at ServiceXS (Leiden, The Netherlands) using Affymetrix HT MG 430 PM array plates. Array analysis methods were according to manufacturer’s protocols. Briefly, an automated process of washing, attaining and scanning of the array plates was carried out by the Affymetrix GeneChip console. For each individual sample 100ng RNA was used for the labelling reaction. The Af- fymetrix 3’IVT-Express labelling kit (#901229) was used to synthesize biotin-labelled cRNA. Subsequently, 7.5ug cRNA of each sample was fragmented using a hybridization cocktail. Next, 0.0375ng/μl of cRNA was hybridised on the Affymetrix HT MG-430 PM array. After hybridisation, washing and staining was performed with the Affymetrix HWS Kit (#901530). The Array plates were scanned via the Affymetrix GeneTitan scanner. Data quality was checked by Affymetrix Expression Console software.

2.6 Data Processing Data was normalized using the Robust Multichip Average (RMA) algorithm [148], using an MBNI custom CDF version (http://brainarray.mbni.med.umich.edu). Statistical analyses were performed in R (http://www.R-project.org) and Microsoft Excel, using Ln-transformed normalized intensity values. For each individual gene the fold change ratio for the different phthalate exposures was calculated as the geometric average value compared to the geometric average value of the control samples taken at day 4.

2.7 Determining Phthalate Dose-Dependent Gene Expression Alterations Genes differentially expressed across the non-cytotoxic concentration range of each of the phthalate monoesters were determined via a one way ANOVA with significance thresholds of P≤0.001 and FDR≤5%. Cytotoxic concentrations were not included in this step due to the large magnitude of gene expression changes induced at highly cytotoxic concentrations, which would obscure the ability to detect low concentration effects. Hierarchical clustering was conducted using Euclidean distance and Ward linkage and combined with heatmap plots using R, based on all tested concentrations. The overlapping genes and Venn diagrams were constructed using Venny [149] (http://bioinfogp.cnb.csic.es/tools/venny/index.html). Wit- hin genes identified to be differentially expressed via ANOVA, a secondary post-hoc t-test

28 Dose response analysis of monophthalates between each dose group and solvent control was conducted to further investigate dose-dependent response effects on the significance of gene expression alterations.

2.8 Quantitative Functional Analysis of Differentially Expressed Genes Enrichment of Gene Ontology (GO) biological processes was performed using GenMAPP (MAPPFinder) [150] and the DAVID [151]. Within genes identified to be commonly significantly altered by all three of the embryotoxic phthalates, we evaluated for enrichment of 4394 GO biological processes. In total, we identified 112 GO biological processes (≥6 genes per GO biological process, z>2.0 and P<0.05) to be significantly enriched. For further exami- 2 nation of dose-dependent effects of genes within enriched GO biological processes, we selected 4 GO biological processes for our analysis (embryonic development, pattern specification process, regulation of cell cycle, and regulation of apoptosis).

2.9 Differentiation Inhibition ESC cultures parallel to those used for gene expression assessment were examined on day 10 by light microscopy for contracting muscle cell foci formation [152]. Concentration-effect curves were fitted with a log-logistic model using PROAST software [153]. We calculated the concentration which resulted in the absence of differentiation in 50% of the wells of a 24-well-plate (ID50). Concurrent solvent control cultures (0.3% DMSO) displayed at least 21/24 wells with beating muscle foci.

2.10 Cell Viability To determine the cell viability of ESC after compound exposure, cells were cultured (500 cells/well, containing 5µl/ml LIF) in a 96-well-plate for 5 days in presence of the compound at multiple concentrations, together with the solvent control, a positive control (0.1µg/ml 5-FU) and negative control (500µg/ml Pen/ Strep). After 5 days, cell viability was determined by fluorescent measurement [152]. The cells were incubated with resazurin for 4h at 37°C. Secondly, dye reduction, a measure for the number of viable cells per well, was measured at 530nm and 590nm (excitation and emission, respectively) using FLUOstar Galaxy microplate reader (BMG Lab. Technologies, Offenburg, Germany). A concentration-effect curve of each compound was fitted using a log-logistic model by using PROAST software [153]. Via this software, the concentration was calculated that resulted in an inhibition of proliferation by 50% (IC50).

29 Day 1-3 Day 3-5 Day 5-10

Hanging Drops Exp.t=0h Exp.t=24h Exp.t=7days

EB formation Scoring

Solvent control (0.3%DMSO) ‘Classical’ read-out Phthalate monoester exposed

Solvent control (0.3% DMSO) RNA Extraction Phthalate monoester exposed

Figure 1: Study design of the EST 10 day differentiation protocol. Cells were cultured form day 1-3 in hanging drops of culture medium to form embryoid bodies (EB). EB were then cultured in bacterial dishes from day 3-5 and subsequently transferred to a 24-wells plate, with each well containing 1 EB. After 5 more days, at day 10 of the protocol, wells were scored for the presence of contracting myocardial cell foci. Compound exposure (Exp.t = exposure time) started at day 3, and lasted for 24 hours for gene expression analysis, and for 7 days for the classical test with the endpoint of contracting cell foci. Solvent control samples for gene expression analysis were cultured in presence of 0.3%DMSO and were taken at t=0h and t=24h. Solvent controls for the classical test were cultured in the presence of 0.3% DMSO for 7 more days, until day 10.

30 Dose response analysis of monophthalates 3. Results 3.1 Classical endpoints: cytotoxicity and inhibition of myocardial differentiation The embryotoxic phthalate monoesters (MEHP, MBzP and MBuP) all showed concentration-dependent effects on cell viability and myocardial differentiation (Figure 2A). Based on the concentration-response curves the IC50 for MEHP was calculated at 0.64 mM, for MBzP at 2.73 mM and for MBuP at 3.39 mM. The ID50 concentrations were calculated based on the concentration-response curves at 0.41 mM for MEHP, at 0.76 mM for MBzP and at 1.44 mM for MBuP. MMP affected neither cell viability nor differentiation at the highest concentration tested (4.41 mM). 2

A MEHP MBzP MBuP MMP Concentration(mM) 1.2 1.0 1.0 1.0

1.0 0.8 0.8 0.8 0.8 0.6 0.6 0.6 0.6 0.4 0.4 0.4 IC50: 0.643 mM 0.4 IC50: 2.73 mM IC50: 3.39 mM IC50: - 0.2 0.2 ID50: 0.4051 mM ID50: 0.7604 mM 0.2 ID50: 1.44 mM ID50: - 0.2

-4 -3 -2 -1 0 -4 -3 -2 -1 0 -4 -3 -2 -1 0 -4 -3 -2 -1 0 log10-conc mM log10-conc mM log10-conc mM log10-conc mM 0.004 0.014 0.044 0.143 0.441 1.432 4.407 0.004 0.004 0.014 0.044 0.143 0.441 1.432 4.407 0.014 0.044 0.143 0.441 1.432 4.407 0.004 0.014 0.044 0.143 0.441 1.432 4.407 B

Differentially Differentially Differentially Differentially Expressed Expressed Genes: Expressed Genes: Expressed Genes: 2396 1940 1433 Genes: 239 - 8 0 8

Figure 2: Concentration-effect analysis of inhibition of cell viability, cardiomyocyte differentiation and effects on differential gene expression. A: Concentration dependent effects of cytotoxicity ( ) and inhibition of differentiation ( ) (from left to right MEHP, MBuP, MBzP and MMP). B: Hierarchical cluster plots of Concentration dependent effects on gene expression. Exposure to MMP did not affect cell viability and myocardial differentiation.

31 3.2 Concentration-dependent gene expression responses For MEHP, 2396, MBzP, 1940, MBuP, 1433 and MMP, 239 genes were differentially expressed across all non cytotoxic concentrations, calculated by one-way ANOVA (P≤0.001 and FDR≤5%). Gene expression effect for all tested concentrations were visualized, showing concentration dependent effects already evident at the lowest doses (Figure 2B). For each of the four phthalate monoesters, we applied a secondary post-hoc t-test (p≤0.001 and FDR≤5%) to the genes identified to be significantly regulated (identified by using ANO- VA) to examine the concentration-dependent effects on the number of genes altered per compound. For all four phthalate monoesters, in general, we observed a concentration-dependent increase in the number of genes altered with increasing concentration (Figure 3A). The largest number of genes affected at any non-cytotoxic concentration was observed after exposure to MEHP, whereas the lowest number was seen after exposure to MMP.

1750 A 600 B

1250 500

1500 400

1000 300 750 MEHP MEHP MBzP 200 MBzP

(Post-hoc t-test p<0.001) t-test (Post-hoc 500 MBuP MBuP MMP MMP

out of the overlapping genes (668) overlapping the out of 100 Number of genes differentially expressed differentially genes Number of

250 expressed differentially genes Number of

0 0 0.004 0.014 0.044 0.143 0.441 1.432 4.407 0.004 0.014 0.044 0.143 0.441 1.432 4.407 Concentration (mM) Concentration (mM)

Figure 3: A: Concentration-response of the number of genes regulated per compound calculated with post-hoc t-test (p≤0.001). B: Concentration-response of the number of genes differentially expressed per compound out of the 668 overlapping genes (■ MEHP, ♦ MBzP, ▲ MBuP, ● MMP ) per concentration.

3.3 Commonly regulated genes between compounds As shown in Figure 4, we identified 668 genes that were differentially regulated by all three embryotoxic phthalates, under non-cytotoxic concentrations. Between embryotoxic and non- embryotoxic phthalates there was an overlap of 66 genes. Within the 668 overlapping genes between the embryotoxic phthalates we calculated the number of genes which were differentially expressed per compound and concentration (Figure 3B). MEHP was most effective in terms of the numbers of genes significantly regulated. After exposure to 0.441 mM MEHP, 531 out of the 668 genes were significantly differentially expressed. At the highest concentration (4.407 mM) of MBzP, MBuP and MMP, 552, 558 and 89 genes were significantly differentially expressed, respectively.

32 Dose response analysis of monophthalates

Overlapping genes after exclusion of the highest dose

MBuP MBeP MEHP MBuP

MEHP MMP 332 612

200 194 1083 211 343 15 1061 73 602 17 668 2 434 211 66 410 11 24 11 627 22

MBzP

Figure 4: A. Venn diagram showing 668 overlapping genes between embryotoxic phthalates (MEHP, MBzP and MBuP) calculated with ANOVA (p≤0.001 and FDR≤5%). B. 602 genes were commonly differentially expressed between the embryotoxic phthalates but not MMP, whereas 66 genes were overlapping between all four phthalates.

3.4 Response of genes grouped within biological processes. Within the 668 overlapping genes, in total, we identified 112 enriched GO biological processes (supplementary Table 1). Numerous biological processes involved in e.g. cell proliferation, differentiation, transcription, cell death and embryonic development were significantly enriched. On average, concentration-dependent effects were observed for the genes involved in each of these enriched biological processes for each of four compounds. As an example, in Figure 5, the absolute average fold change of regulated genes within four selected enriched biological processes (embryonic development, pattern specification, regulation of apoptosis and regulation of cell cycle) is shown for each of the four compounds. These selected categories were part of over represented themes within the 112 total enriched GO biological processes, and also, were of great interest due to their known link with mechanisms underlying developmental toxicity. For each of these four biological processes examined, the order of potency was MEHP > MBzP> MBuP > MMP. In general, for each of four phthalate monoesters, we observed similar effects (in terms of the magnitude of fold change) across genes related to embryonic development, pattern specification and cell cycle across concentration. For the most potent compound, MEHP, the concentration-dependent relative gene expression changes within the four selected biological processes are depicted in Figure 6 (A, E-G). In general, genes within enriched pathways i.e. cell cycle, embryonic development, regulation of apoptosis and pattern specification, were either up- or down regulated. Genes associated with developmental processes were largely down regulated.

33 1 1 Embryonic development Pattern speci cation 0.9 0.9

0.8 0.8

0.7 0.7

0.6 0.6

0.5 0.5

0.4 0.4

0.3 0.3

0.2 0.2

0.1 0.1 MEHP Abs. avg. fold change (ln-scale) fold avg. Abs. Abs. avg. fold change (ln-scale) fold avg. Abs. 0 0 MBzP 0 0.004 0.014 0.044 0.143 0.441 1.432 4.407 0 0.004 0.014 0.044 0.143 0.441 1.432 4.407 MBuP Concentration (mM) MMP Concentration (mM)

1 Regulation of cell cycle 1 0.9 Regulation of apoptosis 0.9 0.8 0.8 0.7 0.7 0.6 0.6

0.5 0.5

0.4 0.4

0.3 0.3

0.2 0.2

0.1 0.1 Abs. avg. fold change (ln-scale) fold avg. Abs. change (ln-scale) fold avg. Abs. 0 0 0 0.004 0.014 0.044 0.143 0.441 1.432 4.407 0 0.004 0.014 0.044 0.143 0.441 1.432 4.407 Concentration (mM) Concentration (mM)

Figure 5: Absolute average fold change of regulated genes (n) within biological processes. A: Embryonic development (n=26 out of 362 annotated). B: pattern specification process (n=14 out of 212 annotated). C: regulation of cell cycle (n=25 out of 461 annotated). D: regulation of apoptosis (n=25 out of 469 annotated). (■ MEHP, ♦MBzP, ▲ MBuP, ● MMP).

34 Dose response analysis of monophthalates

Embryonic Development

10 10 10 10 MEHP N(up)=8 N(down= 18 MBzP N(up)= 10 N(down)=16 MBuP N(up)=11 N(down)=15 MMP N(up)=11 N(down)=15

0 0 0 0

0 0.004 0.014 0.04 0.14 0.441 1.43 4.41 0 0.004 0.014 0.04 0.14 0.441 1.43 4.41 0 0.004 0.014 0.04 0.14 0.441 1.43 4.41 0 0.004 0.014 0.04 0.14 0.441 1.43 4.41

Average fold change fold Average A B C D 0.1 0.1 0.1 0.1 2 Regulation of cell cycle Regulation of Apoptosis Pattern Speci cation

10 10 10 MEHP N(up)=14 N(down)=11 MEHP N(up)=11 N(down)=14 MEHP N(up)=4 N(down)=10

0 0 0

0 0.004 0.014 0.04 0.14 0.441 1.43 4.41 0 0.004 0.014 0.04 0.14 0.441 1.43 4.41 0 0.004 0.014 0.04 0.14 0.441 1.43 4.41

Average fold change fold Average E F G 0.1 0.1 0.1

Figure 6: A-D: Up (■)- and down (▲) regulation of genes involved in embryonic development after exposure to MEHP(A), MBzP (B), MBuP (c) and MMP (D). E-F Up (■)- and down (▲) - regulation of genes involved in regulation of cell cycle (E), regulation of apoptosis (F) and pattern specification (G) after exposure to MEHP.

4. Discussion Category approaches can be employed to estimate possible toxic properties of chemicals in the absence of animal studies [142]. This can be done using in silico methods, e.g. by interpolation of chemical structure and/or physicochemical properties within a chemical series. Considering existing toxicity profiles of other chemicals within the class, the toxicity potential of a chemical can be characterized and estimated. Emerging data indicate that in vitro methods studying biological effects of chemicals can also be employed in a category approach. We and others have shown that e.g. within EST [154-156] and in rodent whole embryo culture [127] potency ranking of chemicals was shown be consistent with potency ranking in developmental toxicity studies in rodents. This approach has also been applied to the chemical category of phthalates, which are developmentally toxic with their potency being dependent on carbon chain characteristics such as length and branching [157]. In the present study, we hypothesized that a phthalate gene expression signature in EST could be defined that would allow potency ranking of phthalates. On the basis of earlier work [119, 121, 123, 124] we assume that gene expression is likely to be a more objective measure, more sensitive, and may provide additional information on mode of action as compared to the classical morphological readout in EST.

35 The four phthalate monoesters tested in this study displayed different potencies as determined by cytotoxicity (IC50) and cardiomyocyte differentiation inhibition (ID50). Their ranking on the basis of this classical readout (MEHP > MBzP > MBuP > MMP) is similar to the ranking observed in other in vitro as well as in vivo studies [127, 142]. In rodent whole embryo culture, total morphological scores showed the same ranking, whereas in animal studies, the same was observed for overall developmental NOAELs. Extrapolation between in vitro and in vivo potency ranking can be compromised by differences in compound kinetics, affecting compound concentration at the toxicological target [158]. The current comparison of cardiomyocyte differentiation with differential gene expression within the same cell system does not suffer from this level of complexity. However, exposure duration was different, as we compared gene expression in EST after 24 hours exposure with cardiomyocyte differentiation monitored after an additional 6 days exposure, assuming that early gene expression may be a precursor effect for morphological differentiation observed later. Earlier studies have shown that in EST the 24 hour time point shows both the largest gene expression changes as well as the lowest variability between replicates as compared to later time points in EST [122], which suggests this time point is optimal for detecting statistically significant gene expression changes.

Studying concentration-dependent differential gene expression in EST considering the total number of genes significantly regulated with concentration, we observed a very similar potency ranking as with morphological differentiation inhibition. Moreover, the developmentally toxic phthalate monoesters tested showed a significant overlap of 668 genes significantly regulated. These were taken as the phthalate gene expression signature and used for further analysis of relative potencies of the phthalate monoesters tested. Among the 668 genes, 66 were also significantly regulated after MMP exposure, although the potency of MMP for affecting these genes was much lower than of the embryotoxic phthalates. Likewise, among the 602 genes not significantly regulated by MMP in the present study, many showed a dose- related expression trend in MMP, which however did not reach statistical significance given the limits applied. These findings illustrate that any absolute cutoff for gene selection is ar- bitrary in view of the dose-response characteristics of gene expression. Therefore, for the present analyses, we have pragmatically included all 668 genes, whether or not they also gave a significant response in the non-embryotoxic phthalate, in order not to a priori exclude from the analysis any genes statistically significantly responsive to embryotoxic phthalates as they could give important information on the nature of phthalate effects. Apart from these commonalities, qualitative differences in genes expressed between these phthalate monoesters were potentially interesting for further analysis, although, this is not the subject of the present manuscript. Commonly regulated genes were significantly overrepresented by 112 GO biological processes, showing a broad spectrum of phthalate gene expression effects concomitant with differentiation inhibition. Interestingly, developmental and cell cycle gene sets behaved similarly in terms of fold change response (Figure 5), whereas IC50 and ID50 values differed within compounds. Therefore, we could not identify direct correlations between these two gene set functions and the corresponding classical effect types. This finding is principally different to observations in a Flusilazole concentration-response analysis [119]. In that study, the concentration-response curve of the cell cycle gene set occurred at higher concentrations

36 Dose response analysis of monophthalates than the curve for the developmental gene set, which was reminiscent of differences in ID50 and IC50 observed in the cardiac muscle differentiation and cytotoxicity end points. Howe- ver, it should be noted that in the present study, IC50 and ID50 values were less than a factor 4 apart for each of the compounds tested, which makes it difficult to discriminate underlying concentration-response effects at the level of gene expression.

For each of the phthalate monoesters tested, gene expression proved more sensitive than the ID50, as the lowest concentrations tested invariably showed significant gene expression effects (e.g. Figure 6) without any observable inhibition of differentiation. Moreover, the effect size found at the lowest concentration tested remained at the same level at various higher concen- 2 trations tested, until beyond a certain compound-specific concentration threshold gene expression increased greatly. One crucial question related to the interpretation of in vitro assays in general is the definition of the threshold of adversity. Any level of compound exposure will always lead to a physiological response of the exposed cells, but is not necessarily an adverse or toxic response. With the continuous introduction of more sophisticated and sensitive methods of effect assessment (both in animal studies as well as in in vitro models) the question of the definition of the threshold of adversity gains importance [159-161]. In EST, the ID50 is usually taken as the endpoint to consider as a corollary for developmental toxicity [132, 152]. If for the sake of the argument we assume that this ID50 is the relevant measure for adversity, how then should the observed gene expression changes be interpreted? In the present concentration-response curves, the dramatic increase in differential gene expression at higher doses correlates relatively well with ID50 values (Figures 4, 5 and 6). This observation suggests that the limited and stable gene expression effects at lower doses represent non-toxic physiological effects, whereas the additional high dose effect represents the toxic response. Indeed, the responses for MMP never exceed those for the non-toxic doses of the other phthalates. These findings therefore may provide clues for defining adversity on the level of gene expression. If such thresholds can be reliably defined and extrapolated to in vivo effective doses, they may provide important alternative tools for hazard and risk assessment.

The current study suggests that it is possible to perform adequate potency ranking, which mimics the in vivo situation, within a chemical class of developmental toxicants on the basis of differential gene expression by concentration-response analysis using the EST. These findings add to the globally growing database of gene expression response-data aimed at enhancing our understanding of physiological and toxic responses of biological systems and to improve mode of action informed methods for chemical hazard identification. More studies are needed to define the meaning of gene expression signatures in the context of toxicity in EST as well as in more general terms. In addition, the differences in the types of responding genes observed between the phthalate monoesters tested await more detailed analysis.

37 38 CHAPTER3

A statistical approach towards the derivation of predictive gene sets for potency ranking of chemicals in the mouse embryonic stem cell test

Sjors H.W. Schulpen, Jeroen L.A. Pennings, Elisa C.M.Tonk, Aldert H. Piersma

Toxicology Letters (2014) 225(3):342-9

39 Abstract The embryonic stem cell test (EST) is applied as a model system for detection of embryotoxicants. The application of transcriptomics allows a more detailed effect assessment compared to the morphological endpoint. Genes involved in cell differentiation, modulated by chemical exposures, may be useful as biomarkers of developmental toxicity. We describe a statistical approach to obtain a predictive gene set for toxicity potency ranking of compounds within one class. This resulted in a gene set based on differential gene expression across concentration-response series of phthalatic monoesters. We determined the concentration at which gene expression was changed at least 1.5-fold. Genes responding with the same potency ranking in vitro and in vivo embryotoxicity were selected. A leave-one-out cross-validation showed that the relative potency of each phthalate was always predicted correctly. The classical morphological 50% effect level (ID50) in EST was similar to the predicted concentration using gene set expression responses. A general down-regulation of development-related genes and up-regulation of cell-cycle related genes was observed, reminiscent of the differentiation inhibition in EST. This study illustrates the feasibility of applying dedicated gene set selections as biomarkers for developmental toxicity potency ranking on the basis of in vitro testing in the EST.

40 Derivation of predictive gene sets Introduction In the process of development of novel compounds, it is important to compare toxic potencies, in order to prioritize for development those compounds which are effective in their pros- pective application (e.g. as a pharmaceutical or as a plasticizer) in the presence of the lowest toxic potency. Classically, toxic potency is assessed in animal studies, which are costly, time consuming, and ethically less favorable. These aspects apply especially to animal-consuming developmental toxicity studies. It has been shown that toxic potency of a compound may also be inferred from concentration-responses of its gene expression changes in alternative test systems [119, 162-167]. Based on these findings we hypothesized that a suitable gene set can be used in order to predict the potency of un-ranked compounds within the same class. Here we applied a statistical approach to obtain a set of genes which can function to rank compounds within a chemical class, using monophthalates as an example, within the embryonic stem cell test (EST) as a model system. The EST is a widely studied alternative test, proposed for the assessment of developmental toxicity of chemicals and pharmaceuticals [54, 131, 168]. This test is based on the potential of mouse embryonic stem cells (mESC) to differentiate into con- 3 tracting myocardial cells within a period of 10 days. The inhibitory effect of compounds on the formation of contracting cardiac myocyte foci is the morphological endpoint of the assay. Concentration response analysis within the EST allows for calculation of the concentration at which there is 50% inhibition of differentiation (ID50). The inclusion of gene expression as a readout parameter in EST has resulted in a reduced test period of four days [121, 122]. Previous studies have shown that in the first twenty-four hours of exposure major gene expression changes occur, specifically in proliferation- and differentiation-related genes, and that gene expression is highly sensitive to compound exposure [122]. In a previous analysis, we showed that 668 genes responded in a concentration-response like fashion to each of three embryotoxic phthalates, (Schulpen et al., 2012). Overall gene expression patterns of this set were mostly consistent with the relative potency ranking of the phthalates. To explore this latter finding, we used the corresponding data in a new analysis, to derive a suitable predictive gene set that could be used as a manageable biomarker to predict relative potency within the chemical class of monophthalates. Toxicity data of phthalates obtained in existing animal studies [127, 142] were used to determine a priori relative in vivo potency of phthalates for this investigation. Genes were selected based on the concentrations at which each phthalate gave a 1.5 fold expression change in an individual gene. A set of 97 genes was obtained for which the ranking of these concentrations was consistent with in vitro embryotoxicity ranking. Potency ranking could be predicted by the relative order in which the majority of the genes in the set were changed by at least 50% to the control level. The robustness of this approach was demonstrated with a leave-one-out cross-validation experiment, in which each of the four monophthalates was left out one by one, a gene set was derived based on the three remaining phthalates, and subsequently, data for the left out phthalate were used to predict its relative potency on the basis of the derived gene set. We characterized the gene set as to biological processes involved. Furthermore, we derived effective concentrations on the basis of gene expression and compared them with classical ID50 concentrations.

41 Methods Embryonic stem cell culture. Mouse embryonic stem cells were continuously cultured on gelatin coated dishes in Dul- becco’s modified Eagle’s medium (DMEM,Gibco, Gaithersburg, MD, Cat#11960-044), supplemented with 20% foetal bovine serum (FBS, Hyclone, Logan, UT, Cat#SH30070.03), 1% non-essential amino acids, (Gibco, Gaithersburg, MD, Cat#11140-035), 2 mM L-glutamine (Gibco, Gaithersburg, MD, Cat#25030-024), 1% 5000 IU/ml penicillin/5000g/ml streptomycin (Gibco, Gaithersburg, MD, Cat#773139), and 0.1 mM β-mercaptoethanol (Sigma– Aldrich, Zwijndrecht, Cat#31350-010), in the presence of leukaemia inhibitory factor (LIF, Millipore, Temecula, California), 1000 units/ml, to maintain pluripotency. ESC were kept for a maximum of 25 passages and were passaged to new coated dishes every other day.

Differentiation To initiate cell differentiation, pluripotent stem cells were cultured in hanging drops of culture medium on the inside of a lid of a Petri-dish, containing 5 ml phosphate buffered saline (PBS), Ca2+ and Mg2+ free (Gibco, Gaithersburg, MD, Cat#14190-094) as shown in Figure 1. Within three days, the cells proliferated and clustered to form cell aggregates, so-called Embryoid Bodies (EB). The EB were transferred to bacterial dishes containing 5ml of culture medium in which they were cultured for 2 days. The EB were then plated individually in wells of a 24-well plate and cultured for 5 additional days in which the EB further differentiated and formed foci of contracting cardiomyocytes [152].

Exposure Exposure was started three days after initiation of differentiation, at the point at which the EB were transferred to dishes [152] . The cells were exposed to one of the four phthalate monoesters: mono-(2-ethylhexyl) phthalate (MEHP, CAS# 4376-20-9, Wakochemicals, Neuss, Germany), monobutyl phthalate (MBuP, CAS#131-70-4, TCI, Zwijndrecht, The Netherlands), monobenzyl phthalate (MBzP, CAS# 2528-16-7, TCI, Zwijndrecht, The Ne- therlands) or monomethyl phthalate (MMP, CAS#4376-18-5, Sigma–Aldrich, Zwijndrecht, The Netherlands) in 0.3% DMSO for 7 days, in equimolar concentrations (0.00441, 0.0143, 0.0441, 0.143,0.441, 1.43 and 4.41 mM). At day 10 cardiomyocyte differentiation was microscopically examined by monitoring the wells of an individual 24-wells plate containing contracting cell foci. Via a concentration-response curve the ID50 concentration was calculated using Proast [169].

42 Derivation of predictive gene sets

Day 1-3 Day 3-5 Day 5-10

Hanging Drops Exp.t=0h Exp.t=24h Exp.t=7days

EB formation Scoring

Solvent control (0.3%DMSO) ‘Classical’ read-out Phthalate monoester exposed

Solvent control (0.3% DMSO) RNA Extraction Phthalate monoester exposed

Figure 1: Study design Mouse embryonic stem cells (mESC) were cultured in hanging drops on the inside of a lid of a Petri- dish for three days. At day 3, embryoid bodies (EB) had formed and were transferred to a bacterial Petri dish and cultured in culture medium containing the compound. For microarray analysis the EB were stored in RNA-protect at -20ºC after 24 hours of exposure (day 4) and subsequently RNA was extracted. For morphological read-out and calculation of differentiation inhibition EB were individually 3 transferred to a well of a 24-wells plate at day 5 and cultured for five more days. At day 10 the cells were microscopically examined and wells containing contracting cell foci were scored.

Transcriptomics The following concentrations of phthalate monoesters were tested using microarray analysis of whole cells: for the effect on gene expression of MEHP the effect of concentrations 0.00441, 0.0143, 0.0441, 0.143 and 0.441 mM was tested. For MBzP and MBuP concentrations 0.00441, 0.0143, 0.0441, 0.143, 0.441, 1.43 and 4.41 mM were tested. For MMP concentrations 0.143, 0.441, 1.43 and 4.41mM were tested. Each concentration of each compound was tested in 8 replicates. One dish contained one concentration of a single monoesther phthalate. Exposure was initiated at day 3. After 24 hours exposure the cells were collected in RNA-protect and stored at -20°C, up to RNA extraction. RNA was extracted with the QIACube by using the RNA Mini-extraction kit (Qiagen). After extraction the RNA concentration was measured with a Nanodrop spectrophotometer (Thermo Scientific). The quality of the RNA was assessed by using a Bioanalyzer (Agilent). RNA samples with sufficient quality and purity, based on RIN values were selected for microarray analysis. Samples were randomized, blinded and further processed for array analysis at ServiceXS (Leiden, The Ne- therlands) using Affymetrix HT MG-430 PM array plates. Array analysis methods were according to manufacturer’s protocols. Briefly, an automated process of washing, attaining and scanning of the array plates was carried out by the Affymetrix GeneChip console. For each individual sample 100 ng RNA was used for the labelling reaction. The Affymetrix 3’IVT- Express labeling kit (#901229) was used to synthesize biotin-labeled cRNA. Subsequently, 7.5 µg cRNA of each sample was fragmented using a hybridization cocktail. Next, 0.0375 ng/ µl of cRNA was hybridized on the Affymetrix HT MG-430 PM array. After hybridization, washing and staining was performed with the Affymetrix HWS Kit (#901530). The array plates were scanned via the Affymetrix GeneTitan scanner. Data quality was checked by Affymetrix Expression Console software.

43 Data analysis Raw microarray data used in this study were taken from [Schulpen 2012]. Raw and normalized data have been deposited in NCBI’s Gene Expression Omnibus and are accessible through GEO Series accession number GSE53969. First, to ensure compound-class specificity, the genes significantly regulated by by all three embryotoxic phthalates (MBuP, MBeP and MEHP) were selected across the non-cytotoxic concentrations (OWA P≤ 0.001, FDR≤ 5%), resulting in the 668 genes previously described by [Schulpen 2012].

Subsequently, this set of genes was refined using a regulation threshold (i.e. a minimal fold change to vehicle control by which a gene has to respond) and potency ranking criteria (i.e. the concentrations at which a gene exceeds the regulation threshold follow a suitable ranking for the respective phthalates). The obtained set was used to compare relative potencies based on the concentration at which at least 50% of the genes in a set met the regulation threshold. The robustness of the method was verified using a leave-one-out-cross-validation (LOOCV) [170]. The biological functionality of the gene set obtained by the previous steps was further characterized by GO term overrepresentation analysis using the DAVID website [171], using the EASE algorithm, the mouse genome background, all levels in the GO hierarchy and otherwise default settings; and CoPub textmining analysis for visualization of gene-function associations in literature (http://services.nbic.nl/copub5) [172]. For CoPub, Entrez GeneIDs for the genes were used as input, after which the set of terms was expanded by associated GO Biological Processes, based on literature co-occurrence using default settings (correlation R scaled score threshold 35, Co-publication threshold 100 abstracts). This expanded terms set (containing genes and GO biological processes) was subsequently visualized as a network, using an R scaled score filter value of 40. The description file for this association network was downloaded and manually curated to remove GO terms that are redundant (e.g. apoptosis – cell death) or insufficiently specific (e.g. metabolism) to make the network less dense while maintaining the functional information. The network was subsequently visualized in Cytoscape [173] with additional colors to indicate GO processes and up- or down regulated genes, respectively.

Results Background information on concentration-response, ID50 derivation and potency ranking of the present data are given in Schulpen et al., 2012. Figure 2 shows the number of genes regulated, out of the 668 genes regulated by all embryotoxic phthalates, at 1.1-, 1.25-, 1.5- and 2 fold above or below background expression level, in a concentration-response for each compound tested. Logically, the lower the threshold of regulation, the more genes were found to exceed the threshold. At the lowest threshold (1.1 fold), the positive phthalates could not be distinguished as the number of genes regulated is very similar among compounds. Conver- sely, at the 100% (2-fold) regulation level, discrimination failed due to very few genes reaching the threshold at each compound. The 50% (1.5-fold) regulation level appeared to discriminate similarly to the ID50 and was taken for further study. Table 1 shows the number of genes regulated for each of these designs. For derivation of a potency-ranking gene set from the responsive genes, we subsequently selected those genes for which the individual relative expres-

44 Derivation of predictive gene sets sion between compounds followed the potency ranking as determined by ID50, i.e. they must reach their expression threshold according to conc. MEHP ≤ conc. MBzP ≤ conc. MBuP ≤ conc. MMP. This further limited the gene selections, e.g. to 97 genes at the 50% level (Table 1).

600

400

200 3 # genes exceeding the threshold # genes exceeding 0 0.001 0.01 0.1 1 10 Concentration mM (log scale)

Figure 2: Compound- and concentration-dependent regulation of gene expression For each compound (■ MEHP, ♦MBzP, ▲ MBuP, ● MMP) and tested concentration, the figure displays the number of genes exceeding a pre-set threshold (1.10 , 1.25 , 1.50 and 2.00 time change in individual gene expression).

# genes exceeding the threshold Threshold 1,1 1,25 1,5 2 Compound MEHP 653 403 132 38 MBzP 667 562 351 169 MBuP 665 508 211 58 MMP 355 71 26 6 Correct order 258 240 97 35

Table 1 The number of genes which exceeded the pre-set threshold of regulation change after they were exposed to the highest concentration which was tested per compound. Subsequently the number of genes fulfil- ling the correct order of relative expression, i.e. as MEHP ≤ MBzP ≤ MBuP ≤ MMP was calculated.

Figure 3 shows the number of up-and down-regulated genes within the 97 gene set for each compound in concentration-response. The embryotoxic phthalates showed an almost even

45 share of up- and down-regulated genes within this gene set. The 8 genes responsive to the non-embryotoxic phthalate MMP (at the highest two concentrations only) were all down-regulated. The genes in the 97 gene set are shown in supplementary Table 2. Using Gene Ontology enrichment, this set was found enriched for terms related to cell proliferation and differentiation (Figure 4 and Table 2), biologically relevant processes in the EST assay. Also within these processes, both up- and down-regulated genes occurred in response to phthalate exposure. These processes were all clearly enriched in the 97 gene set as compared to the 668 gene set regulated by all three embryotoxic phthalates, as also indicated in Figure 4. In order to illustrate the specificity of enrichment in the 97 gene set as compared to the 668 gene set, Figure 4 also shows the GO term ‘cellular metabolic process’. This GO term, containing >40% of the genes in both cases, was enriched in the 97 gene set as compared to the 668 gene set to a much lesser extent than the proliferation and differentiation related GO terms.

1 00 A 50 B

8 0 30

6 0 10

C 4 0 -10

-30 2 0

# genes exceeding the threshold the exceeding genes # -50 0 0.001 0.01 0.1 1 10 0.001 0.01 0.1 1 10 Concent ra tion mM (log scale)

Figure 3 (A) The number of genes exceeding the ≥ 1.5 threshold per compound (■ MEHP, ♦MBzP, ▲ MBuP, ● MMP) of the 97 gene set selected for their ranking-specific expression at the level of ≥ 1.5 regulation. (B,C) Concentration-dependent up- and down-regulated genes exceeding the threshold for each compound.

46 Derivation of predictive gene sets Via text mining and network analysis of the 97 genes, associations between genes and related GO terms were visualized (Figure 5). Out of the 97 genes, 51 genes, (of which 16 genes were down-regulated and 35 genes were up-regulated) were found to be linked to 26 GO terms on basis of co-occurrence in literature abstracts, when using the stringency criteria applied. The network could superficially be divided into two parts, with down-regulated genes prefe- rentially appearing in the lower part of the network, linked to developmental processes such as gastrulation, transcription and cell adhesion and differentiation. The upper part of the network showed a relative abundance of up-regulated genes, connected to terms involved in (inhibiting) cell proliferation, e.g. cell cycle, mitosis and apoptosis.

0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%

cellular metabolic process

developmental process cell differentiation 3 organ development

embryonic development pattern specification process

transcription factor activity

regulation of apoptosis

regulation of cell cycle

Figure 4 Percentage of genes up- and down-regulated for a number of biological processes/ GO terms within the 97- ranking gene set (light grey [■]- and dark grey bar [■], respectively) and within the 668 genes commonly regulated by all three embryo toxic phthalates (black bar [■]).

47 Ccrn4l cell activation immune response translation Nab2 necrosis Nfil3 Sertad1 inflammatory response to heat Pim3 protein folding responseGadd45b Fgf15Relb Ltb Baz1a Zfp36 Ccno transcriptionNfkb2 Dusp2 cell cycle Dnajb2 Hspa1b apoptosis Nr4a1 m phase Tnfrsf12a Hhex Txnip Gbx2 Btg2 Cdkn1a Foxd3 Junb Irf2 mitosis Jun senescence Chrd gastrulation Plin2 signal Foxa2 cell differentiation Rhob transduction dna synthesis Dppa3 cell proliferation Timp4 Tagln Cpt1a Irs2 angiogenesis fatty acid oxidation Ephb1 Cyr61 Enpp2 Fgfbp1 Flnc Slc25a20 Ndrg1 S1pr2 cytoskeleton Igfbp3 growth vasodilation Fzd5 cell adhesion Calca Cnn2 hormone metabolism Pvr Lrig1 bone remodeling Epb4.1l3

Figure 5 Network visualization of 97 genes based on co-occurrence of genes and/or biological processes in abstracts from the PubMed literature database. Dark grey dots(●) represents up – and light grey dots (●)down-regulated genes, respectively. Gradient dots (●)represent associated GO biological processes.

Biological process Included in GO set97 % up down P-value set668 % P-value developmental process 2743 36 37,1% 19 17 2,23E-07 120 18,0% 9,52E-04 cell differentiation 1569 27 27,8% 14 13 1,21E-07 77 11,5% 4,86E-04 organ development 1663 26 26,8% 12 14 1,47E-06 68 10,2% 5,54E-02 embryonic development 670 12 12,4% 4 8 1,02E-03 35 5,2% 9,67E-03 pattern specification process 284 5 5,2% 1 4 7,07E-02 16 2,4% 5,33E-02 regulation of cell cycle 214 6 6,2% 6 0 6,39E-03 16 2,4% 5,78E-03 regulation of apoptosis 553 8 8,2% 6 2 3,18E-02 35 5,2% 4,44E-04 transcription factor activity 776 12 12,4% 7 5 1,60E-03 39 5,8% 6,54E-03 cellular metabolic process 5857 42 43,3% 14 18 2,75E-02 280 41,9% 2,57E-15 Table 2

Table 2 Enrichment of GO biological processes. For the 97 and 668 gene sets the number of genes included in the GO process is listed, with the accompanying percentage in the gene set, the number of up- and down- regulated genes and p-value.

48 Derivation of predictive gene sets In order to establish whether this approach, leading to 97 genes sufficient for potency ranking of four phthalate concentration-response curves, would also hold for other selections of phthalate monoesters and would be predictive for the potency ranking of an unknown compound within the class, a leave-one-out cross-validation prediction experiment was carried out. Figure 6 shows the ranking of the left-out compound (dotted line) on the basis of a gene set derived from the three remaining phthalates (full lines) using the same strategy as applied for the entire dataset. Exclusion of MEHP for gene set derivation resulted in a 319 gene set, whilst after exclusion of MBzP and MBuP, 111 and 126 gene sets were derived. Exclusion of MMP resulted in a 99 gene set. From Figure 6, it appears that in each case, the left-out compound was estimated correctly as to its relative potency.

3 50 2 50

1 50 1 40 1 40 A C 1 20 1 20 3

1 00 1 00

8 0 8 0

6 0 6 0

4 0 4 0

2 0 2 0

0 0 0.001 0.01 0.1 1 10 0.001 0.01 0.1 1 10

1 40 1 40 B D 1 20 1 20

1 00 1 00 # genes exceeding the threshold the exceeding genes # 8 0 8 0

6 0 6 0

4 0 4 0

2 0 2 0

0 0 0.001 0.01 0.1 1 10 0.001 0.01 0.1 1 10

Concentration mM (log scale)

Figure 6 Cross validation of potency ranking based on gene expression by leave-one-out approach. Gene sets (of genes showing potency-dependent ≥ 1.5 times regulation) were derived from data of all but one compound, and the left out compound ([A] ■ MEHP , [B] ♦MBzP , [C] ▲ MBuP , [D] ● MMP ) was scored on the basis of that gene set. In each case, the tested compound (dotted line) ended up at its predicted relative potency.

49 For comparison of gene expression-based potencies to classical ID50 values, gene expression concentration-response curves were enhanced by interpolation. For each gene, expression changes intermediary between two concentrations were calculated via linear Splines interpolation. Using these imputed data, we determined for each compound the concentration at which 50% of the genes in a gene set met the 1.5-fold threshold of regulation. For example, for the 97 gene set, a number of additional expression levels were imputed for MEHP between the 4th and 5th concentration (0.143 and 0.441 mM, respectively) and at 0.297 mM more than half of the 97 genes appeared to exceed the threshold. Using such imputed data, the curves as shown in Figure 3 were redrawn to show a more fluent and realistic curve shape (Figure 7). Based on the latter curves, effective concentrations were determined that corresponded to regulation of 50% of the genes within each of the gene sets used for potency ranking; i.e. the 97-gene set as well as the various cross-validation sets (Table 3). These effective concentrations not only consistently showed the correct ranking, but in addition were in the same order of magnitude as the classical ID50.

100

20 # genes exceeding the threshold the exceeding genes # 0 0.001 0.01 0.1 1 10 Concentration mM (log scale)

Figure 7 Initial concentration response curves of affected genes out of the 97 genes per concentration were based on 7 concentrations. To improve the curve, the effect of additional concentrations were calculated based on interpolation. For each compound (■ MEHP , ♦MBzP , ▲ MBuP , ● MMP ) the concentration at which 50% of the gene set exceeded the threshold was calculated and depicted within the graph (dotted line).

50 Derivation of predictive gene sets

All comp no MEHP no MBzP no MBuP no MMP ID50 # Genes 97 319 111 126 99 50% 49 160 56 63 50 Conc. mM mM mM mM mM mM MEHP 0,297 0,767 0,288 0,267 0,295 0,405 MBzP 1,053 1,877 0,972 0,758 1,053 0,760 MBuP 2,056 4,320 1,748 1,291 2,056 1,440

Table 3 3 Comparison between the morphological ID50 of the EST versus the concentration which resulted in a 1.5 fold up- or down regulation of 50% of the assembled gene set for all compounds and after one of the four compounds was left out of the calculations via LOOCV. 3 Discussion The application of animal-free alternative models in developmental toxicology is compli- cated by the complexity of the developmental process. The predictability of an alternative test is limited by its biological domain, which is intrinsically reductionist as compared to the intact pregnant animal. Whereas a test may show good predictability for one group of chemicals, results may not be that favorable for another compound group [57, 174]. Conversely, the optimal in vitro test for detecting developmental toxicity of a given chemical class will depend on the presence of the critical biological domain in the test. As an example, the limb bud micromass test appeared very useful for predicting retinoid embryotoxic potency [175]. Therefore, testing a chemical group using a relevant in vitro test will potentially provide in vitro information on which reliable prediction of in vivo toxicity can be based. In addition to the biological domain of in vitro assays, the end point monitored in vitro provides another crucial aspect of interpretation. In the EST, morphological assessment of beating cardiac muscle foci is the classical end point [54]. We and others have shown that monitoring gene expression regulation provides an additional level of specificity as to compound effects in EST, enabling more detailed effect assessment on the molecular level, and improving predictability [124, 176-178]. The magnitude of gene expression changes appeared dependent on exposure level [119, 162-167]. In addition, exposure to different compounds resulted in different and specific gene expression patterns, both qualitatively (i.e. different genes being regulated) and quantitatively (i.e. different magnitudes of regulation for the regulated genes) [120, 124, 179]. These response characteristics may be used to test and classify unknown compounds within classes responsive in EST. In an earlier study [162] we showed that phthalate developmental toxicity can reliably be detected in the EST. We also identified 668 genes that showed a mono- tonic concentration-response upon exposure to each of three embryotoxic phthalates across all tested concentrations. Interestingly, trends in the magnitude of gene expression responses were comparable to the in vivo and in vitro ranking in terms of potency of these phthalates [127, 142]. This data set was used here to explore the possibilities of potency ranking using transcriptomics data.

51 The rationale behind the statistical approach is that the potency of compounds within a class is based on the concentration at which an adverse effect occurs, or effects that fall outside normal variation. In our in vitro gene expression model system, this would correspond to the concentration at which genes pass a regulation threshold that exceeds typical assay variation. In the EST, this suitable threshold could vary from 25 to 100% (Figure 2), we selected 50% as a practical compromise for a threshold value that can reliably be detected and a resulting gene set size that allows compound potency discrimination.

The present study shows that potency ranking of chemicals within a structural similarity group can be achieved based on the regulation of a gene set of 97 genes. Although the threshold and gene set size might need adjustment for other compound classes, or other model systems, the method behind our approach can easily be applied for potency prediction of other compound classes for which a suitable biological testing system exists. The LOOCV experiment underscored the robustness of this approach and was essential to show that the derived gene set was not only descriptive of relative potency but also predictive. This leave- one-out-approach has been successfully used before in other studies [121, 135, 180]. Ad- ditional phthalates tested with this gene set will have to provide further confirmation of this approach. Furthermore, using the cutoff of 50% of the genes within the set to be regulated at least 50%, effective concentrations were found that were close to the classical EST ID50 values. Functional analysis of the 97 genes revealed an overrepresentation of cell cycle and developmental related genes, confirming the biological relevance of the gene set, given that cell proliferation and differentiation are key processes in the EST [152]. Other developmental toxicants have also been shown to interfere with these processes [119, 121].

Highly connected genes within the phthalate response gene network (Figure 5), although in- herently biased by the state of scientific knowledge, may represent key genes regulating embryotoxicity of phthalates. Down regulated Fgf1 (Fibroblast growth factor), involved in transcription, the highest linked process in the network, plays an important role in early embryonic development [181]. Cell cycle, the second most linked GO process within the network, was linked to 8 up regulated genes and 1 down regulated gene, namely Ccno, playing a role in DNA repair [182, 183]. The up-regulated Cdkn1a gene had the highest number of interactions of 13. Together with Btg2 with 10 interactions they are both involved in regulation of cell cycle progression in response to a variety of stress stimuli [184, 185]. Furthermore, the involvement of Cdkn1a in cell proliferation, differentiation and cell migration has been described [186]. Relb, a member of the NF –κB family [187], had 9 interactions, together with Jun, containing 8 interactions. They were both up-regulated and play a role in regulation of transcription of many biological processed such as inflammation, immunity, differentiation, cell growth and apoptosis [187-189]. In line with earlier studies we observed down regulation of genes involved in differentiation caused by compound exposure [120, 179]. This included the Gbx2 gene, involved in neural development [190, 191], which was previously found to be part of a gene set predictive for developmental toxicity [135]. Furthermore Foxd3 and Foxa2, members of the Forkhead transcription factors, coding for transcription factors which play a critical role in the formation of many organs and tissues during development, were linked to transcription, gastrulation and cell differentiation [192, 193]. The Ndrg1 gene, which was

52 Derivation of predictive gene sets down-regulated, is significantly linked to cell differentiation. The Ndrg1 gene is coding for a protein involved in stress responses, hormone responses, cell growth and differentiation [194]. Overall, the observed inhibition of differentiation-related genes as opposed to the up- regulation of cell cycle-related genes, observed here in response to phthalate exposure, confirms that the morphologically observed reduction of cardiac muscle cell differentiation is not a nonspecific artifact, but is mediated by specific differentiation inhibition, which represents the biological domain of the EST.

Gene expression monitoring in the EST has proven instrumental in determining specific differentiation-inhibiting responses to chemical exposures, shedding further light on the biological applicability domain of the assay. Here, this approach confirmed the appropriateness of the assay for detecting developmental toxicity of the compound class employed. We have shown a proof of principle, which could be used to further develop a high throughput test system, based on a more limited gene set, which may be readily applied for phthalate potency ranking, or for other dedicated classes of compounds. Also, by applying gene expression analysis, the culture duration has been reduced from 10 to 4 days relative to the classical EST. In 3 the present study, functional gene annotation was applied as a crucial step needed to provide biological relevance to an otherwise purely statistically derived gene set. Although prediction should be statistically robust, it is the biological relevance that provides the principle confirmation of the approach. Prediction models that do not address biological relevance may provide false certainty and may not hold when new compounds are tested [174]. Based on pathway analysis the predictive gene set may perhaps be further reduced in size if genes in each pathway could be identified that are representative of overall pathway response. The US National Academy of Sciences report on Toxicity Testing in the 21st Century has formulated a principle for such an approach [81]. Identifying such regulatory nodes within pathways may even allow reduction and refinement within predictive gene sets [116]. The question remains whether gene expression concentration-response analysis can function to calculate a threshold concentration above which adverse effects occur. The gene expression cutoffs chosen by us in the present study appeared representative of the classical ID50 in EST. How such values generally compare to adverse health effects can currently only be deduced by comparison to in vivo data. More in depth mechanistic analysis of gene expression, in conjunction with additional levels of physiological regulation such as represented by e.g. metabolomics and proteomics, is both warranted and necessary before in vitro based prediction of adverse health effects can reliably be made.

53 54 CHAPTER4

An abbreviated protocol for multi-lineage neural differentiation of murine embryonic stem cells and its perturbation by methylmercury

Peter T. Theunissen, Sjors H.W. Schulpen, Dorien A.M. van Dartel, Sanne A.B. Hermsen, Frederik J. van Schooten, Aldert H. Piersma

Reproductive Toxicology (2010) 29(4):383-92.

55 Abstract Alternative assays are highly desirable to reduce the extensive experimental animal use in developmental toxicity testing. In the present study, we developed an improved test system for assessing neurodevelopmental toxicity using differentiating mouse embryonic stem cells. We advanced previously established methods by merging, modifying and abbreviating the original 20-day protocol into a more efficient 13-day neural differentiation protocol. Using morphological observation, immunocytochemistry, gene expression and flow cytometry, it was shown predominantly multiple lineages of neuro-ectodermal cells were formed in our protocol, and to a lower extent endodermal and mesodermal differentiated cell types. This abbreviated protocol should lead to an advanced screening method using morphology in combination with selected differentiation markers aimed at predicting neurodevelopmental toxicity. Finally, the assay was shown to express differential sensitivity to a model developmental neurotoxicant, methylmercury.

56 mESC neural differentiation assay

Introduction Chemical risk assessment is currently still highly dependent on globally harmonized experimental animal studies. Reproductive toxicity testing proposed within the European chemical safety legislation (REACH) has been estimated to require approximately 60% of the total animals needed for toxicity testing [15]. Therefore, this area of toxicity testing is a high priority area for designing alternative assays to reduce experimental animal use. An established in vitro test system for developmental toxicity is the embryonic stem cell test (EST) [133], in which the developmental toxic effect of compounds on stem cell differentiation towards beating cardiomyocytes is assessed. Despite the promise of this in vitro developmental methodology, a recent validation study suggests a number of in vivo developmental toxic compounds are misclassified as negative in the EST, one of which being methylmercury chloride [133]. Misclassifications may be partly due to the prediction model used, in which both proliferation and differentiation parameters play a role. However, these compounds may have been misclassified, because they may not affect mesodermal-derived cardiomyocyte differentiation in vivo, but primarily affect alternative differentiation routes, such as the ectodermal or the endodermal routes [133]. Additional alternative in vitro embryonic stem cell differentiation assays complementary to the EST may improve the prediction for such compounds. Several neural differentiation protocols for murine embryonic stem cells (ESC) have been described since the mid 1990s, most of which were developed for mechanistic studies of embryonic cell differentiation [65, 68]. These methods use a variety of factors to reach a similar 4 extent of neural differentiation. They include retinoic acid (RA) [68, 71], serum deprivation [65], hormones and growth factors and supporting matrices for a range of different neural end points [196]. Most methods make use of three-dimensional embryoid body (EB) formation [65, 68], while some methods use a two-dimensional monolayer culture [197, 198], or a combination of these two [71]. For the rapid prediction of neurodevelopmental toxicity, it is urgently needed to develop a short duration, high-throughput model, which sufficiently mimics the in vivo differentiation of ESC towards neuronal-type cells. In the present study we have designed a testing model based on methods described by Okabe et al. (1996)[65] and Bibel et al. (2004)[71] using EB formation and stimulation of neural differentiation using RA and serum deprivation. These models appear to approach the in vivo situation in terms of their neural differentiation pattern. However, we have reduced the length of a combination of these two protocols to enable increased throughput of compound testing. Furthermore, multi-lineage (ectodermal, mesodermal and endodermal) differentiation of the combined protocols was studied.

In vivo studies have shown that the development of the brain is a process sensitive towards developmental toxic challenges [199-201]. During neural system development of the embryo, stem cells differentiate into many types of neurons, glial cells and neuronal epithelial cells [202]. In this highly tuned process, interaction between specific cell types is essential for proper differentiation and the establishment of optimal ratios of cell types in the brain in time and space. It is known from in vivo studies that neurodevelopmental toxicants, such as methylmercury (MeHg) [203] and ethanol [204], can influence these ratios and disrupt the developing brain. Therefore, improvement of assessing the putative effects of neurodevelop

57 mental toxicants is expected by monitoring over time the varying quantities of specific neural cell types using a battery of differentiation markers. In this study, we used MeHg to evaluate the responsiveness of our assay. Two well known incidents of MeHg poisoning occurred in the early 1950s at the Minamata Bay in Japan and 1970s in Iraq. In Japan, due to environmental poisoning more than 21.000 individuals claimed to be affected by MeHg poisoning [203]. Pregnant women who manifested mild or no symptoms were reported to give birth to infants with severe developmental disabilities, including cerebral palsy, mental retardation, and seizures [203]. In Iraq, pregnant women were exposed to MeHg by dietary intake and a total of 83 women participated in a developmental assessment of their offspring. Results suggested a dose–response relationship associated with delayed neurodevelopmental milestones [205]. Today, MeHg is widely used as a model neurodevelopmental toxic compound. In the present study we have successfully designed a 13-day differentiation protocol, in which multiple lineages of neural and other brain-associated cells are formed. In addition, we developed a screening method using a group of differentiation markers, which may be used to predict neurodevelopmental toxicity. Finally, the model was shown to have differential sensitivity to a developmental neurotoxicant, MeHg. This work represents the first steps towards an assay for assessing developmental neurotoxicity in vitro.

Materials and methods Culture Media Complete medium (CM) contained Dulbecco’s modified eagle’s medium (DMEM) medium (Gibco BRL, Gaithersburg, MD, USA) supplemented with 20% fetal bovine serum (Hyclone, Logan, UT, USA), 1% nonessential amino acids (Gibco BRL, Gaithersburg, MD, USA), 1% penicillin/streptomycin (Gibco BRL, Gaithersburg, MD, USA), 2 mM L-glutamine (Gibco BRL, Gaithersburg, MD, USA) and 0.1 mM β-mercapto-ethanol (Sigma–Aldrich, Zwijnd- recht, The Netherlands). Low serum medium (LS), had the same composition as CM except that the serum percentage is 10%. Insulin-transferrin-selenite-fibronectin medium (ITS) contained DMEM/Ham’s nutrient mixture F12 (DMEM/F12) medium (Gibco, BRL, Gaithers- burg, MD, USA) supplemented with 0.2 µg/ml bovine insulin (Sigma-Aldrich, Zwijndrecht, The Netherlands), 1% Penicillin/streptomycin (Gibco BRL, Gaithersburg, MD, USA), 2 mM L-glutamine (Gibco BRL, Gaithersburg, MD, USA), 30 nM sodium selenite (Sigma-Aldrich, Zwijndrecht, The Netherlands), 50 µg/ml apo-transferrin (Sigma-Aldrich, Zwijndrecht, The Netherlands) and 2.5 µg/ml fibronectin (Invitrogen, Carlsbad, CA, USA). N2 medium contained DMEM/F12 medium (Gibco, BRL, Gaithersburg, MD, USA) supplemented with 0.2 µg/ml bovine insulin (Sigma-Aldrich, Zwijndrecht, The Netherlands), 1% Penicillin/ streptomycin (Gibco BRL, Gaithersburg, MD, USA), 30 nM sodium selenite (Sigma-Aldrich, Zwijndrecht, The Netherlands), 50 µg/ml apo-transferrin (Sigma-Aldrich, Zwijndrecht, The Netherlands), 20 nM progesteron (Sigma-Aldrich, Zwijndrecht, The Netherlands) and 100 µM putrescine (Sigma-Aldrich, Zwijndrecht, The Netherlands).

58 mESC neural differentiation assay

Embryonic Stem Cell Culture Murine embryonic stem cells (ESC) (ES-D3, ATCC, Rockville, MD, USA) were routinely sub-cultured every 2–3 days and grown as a monolayer in CM supplemented with leukemia inhibiting factor (LIF) (Chemicon, Temecula, CA, USA) at a final concentration of 1000 units/ml. The cells were maintained in a humidified atmosphere at 37°C and 5% CO2.

Neural Differentiation Protocol Differentiation of ESC into the neural lineage was carried out in hanging drop culture, based on methods described by Okabe et al. [65] and Bibel et al. [71]. In brief, stem cell suspensions (3.75×104 cells/ml) were placed on ice before the initiation of the culture. Drops (20µl) containing 750 cells in CM were placed onto the inner side of the lid of a 90 cm Petri dish filled with phosphate-buffered saline (PBS) (Gibco BRL, Gaithersburg, MD) and incubated at 37 °C, 90% relative humidity and 5% CO2. After 3 days of hanging drop culture embryoid bodies (EB) had formed and were subsequently transferred to bacterial Petri dishes (Greiner Bio-one, Frickenhausen, Germany) containing CM supplemented with 0.5 µM retinoic acid (RA) (Sigma-Aldrich, Zwijndrecht, The Netherlands). On day 5, EB were plated on laminin (Roche, Basel, Switzerland) coated dishes (Corning Incorporated, Corning, NY, USA) in LS medium supplemented with 2.5 µg/ml fibronectin (Invitrogen, Carlsbad, CA, USA). One day later, on day 6, the LS medium was replaced by ITS medium. In the short protocol, on day 7, EB were washed with phosphate buffered saline (PBS) (Gibco BRL, Gaithersburg, 4 MD, USA) and incubated in cell dissociation buffer (Gibco BRL, Gaithersburg, MD, USA) for 3 minutes. Then EB were carefully detached from the Petri dish and replated on poly-L- ornithine (Sigma-Aldrich, Zwijndrecht, The Netherlands) and laminin coated dishes in N2 medium. After 6 hours incubation, basic fibroblast growth factor (bFGF) (Strathmann-Biotec AG, Englewood, CO, USA) was added to the medium at a concentration of 10 ng/ml. Con- cerning the medium and long protocols, ITS medium was replaced every other day until replating of the EB was performed on day 10 or day 13 for the medium or long protocol, respectively. After replating the EB, the N2 medium supplemented with bFGF was replaced every other day for 7 days in all three protocols (Figure 1).

Antibodies The following primary antibodies were used: rabbit anti βIII-tubulin (1:2000) (Sigma-Al- drich, Zwijndrecht, The Netherlands), mouse anti nestin (1:50) (Millipore, Billerica, MA, USA), rabbit anti Glial Fibrilary Acidic Protein (GFAP) (Dako, Glostrup, Denmark), mouse anti Stage specific protein antigen-1 (1:400) (SSEA1) (1:400) (Millipore, Billerica, MA, USA), myelin-associated oligodendrocyte basic protein (1:50) (MOBP) (Santa Cruz Biotechnology Inc, Santa Cruz, CA, USA), Oligodendrocyte marker O4 (1:50) (Sigma-Aldrich, Zwijnd- recht, The Netherlands). Secondary antibodies were polyclonal swine anti rabbit FITC (Dako, Glostrup, Denmark), rat anti mouse IgG1-PE (BD Biosciences, Franklin Lakes, NJ, USA), rat anti mouse IgG1-FITC (BD Biosciences, Franklin Lakes, NJ, USA), APC anti mouse IgM1 (Biolegend, San Diego, CA, USA), donkey anti rabbit IgG Cy3 (Jackson ImmunoResearch Europe Ltd, Suffolk, UK). In addition, TO-PRO-3 (1:1500) (Invitrogen, Carlsbad, CA, USA) was used as nucleus staining. 59 Day 0 CM ; 20% serum

Day 3 CM + RA ; 20% serum

Day 5 LS ; 10% serum Laminin Coang Exposure

Day 6 ITS ; 0% serum

Replate Day 7 N2 ; 0% serum PLO + Laminin Coating

Day 11 Morphological Scoring

Day 13 FACS analysis

Figure 1 Neural differentiation short protocol. After a 3 day formation of embryoid bodies (EB) in hanging drops in 20% serum containing medium (CM), EB were cultured in bacteriological dishes in CM with retinoic acid (RA) for 2 days. At day 5 EB were plated on laminin coated dishes in 10% serum containing medium (LS). At day 6 the medium was refreshed with serum free medium (ITS). At day 7 EB were replated on poly-L-ornithin/laminin coated dishes and serum free N2 medium supplemented with bFGF. Morphological scoring was performed on day 11 and flow cytometric analysis was performed on day 13, the end stage of the protocol. In the medium and long protocol, the ITS phase had a duration of 4 and 7 days, respectively.

60 mESC neural differentiation assay

Immunocytochemistry Cells were washed with ice-cold PBS, fixed with cold 2% paraformaldehyde (Sigma-Aldrich, Zwijndrecht, The Netherlands) in PBS for 5 minutes and washed once with PBS. Then the cells were incubated for 5 minutes in HEPES buffered saline solution (HBSS) (Lonza, Wal- kersville, MD, USA) supplemented with 0.1% saponin (Sigma-Aldrich, Zwijndrecht, The Netherlands) and incubated for 10 minutes in glycine (Sigma-Aldrich, Zwijndrecht, The Ne- therlands) in PBS. Cells were washed three times 5 minutes in PBS with 1% albumin from bovine serum (BSA) (Sigma-Aldrich, Zwijndrecht, The Netherlands) and 0.1% saponin after which the sample was exposed to the primary antibody diluted in HBSS with 0.1% saponin for 30 minutes at room temperature (RT). Then the cells were washed three times 5 minutes in PBS with 1% BSA and 0.1% saponin, after which the cells were exposed to the secondary antibody diluted in 1% BSA and 0.1% saponin for 30 minutes at RT. The cells were washed two times 5 minutes with HBSS with 0.1% saponin in PBS and were treated with TO-PRO3 in PBS for 15 minutes at RT in the dark and afterwards washed once with PBS. Cells were closed in Vectashield (Vector Laboratories Inc, Burlingame, CA, USA) and the glass slide was closed using nailpolish. Samples were examined under a BX51 fluorescence microscope (Olympus, Zoeterwoude, The Netherlands) with CellF software for analysis (Olympus, Zoe- terwoude, The Netherlands). Observations were performed under the fluorescence microscope. With negative ( - ) scoring, there were no positive cells present. With (++) scoring, the highest number of cells positive for the marker was observed in the cultures. With (+) scoring, 4 only smaller groups of the cell type were present. At least 3 samples per experiment were examined. For each differentiation protocol studied, at least 3 experiments were performed.

Flow Cytometry Cell types present at the end stage of the three protocols with varying culture times were counted by using fluorescent activated cell sorting (FACS). EB and cells in culture were dissociated for 30 minutes using cell dissociation buffer and resuspended into a single cell suspension. Cells were then washed with PBS and permeabilised with 4% paraformaldehyde for 30 minutes at RT. Cells were subsequently washed with FACS buffer (0.5% BSA, 5mM ethylenediaminetetraacetic acid (EDTA) (Sigma-Aldrich, Zwijndrecht, The Netherlands) in PBS), permeabilised with permeabilization buffer (0.15% saponin in PBS) for 30 minutes at RT and again washed three times with wash buffer (1% BSA, 0.15% saponin in PBS) and incubated with the primary antibodies (anti-βIII-tubulin, anti-nestin and anti-SSEA1) diluted in wash buffer for 30 minutes at RT (see antibody section). After a triple wash with wash buffer, cells were incubated with the secondary fluorescent labeled (FITC, PE and APC) antibodies diluted in wash buffer for 30 minutes at RT in the dark. Cells were washed three times with wash buffer and resuspended in FACS buffer. Cells were analyzed using a FACSCalibur flowcytometer (BD Biosciences, Franklin Lakes, NJ, USA) and CellQuest Pro software (BD Biosciences, Franklin Lakes, NJ, USA).

61 RNA isolation and Real Time Polymerase Chain Reaction (RT-PCR) Total RNA was isolated from differentiated ESC at days 5, 6 and 7 and at the end stage of the short (day 13), medium (day 17) and long (day 20) protocols using the Qiagen RNEasy Mini Kit (Qiagen Benelux, Venlo, The Netherlands) following manufacturer’s instructions. RNA concentration and integrity was determined using the Nanodrop (Isogen Lifescience, de Meern, The Netherlands) and the 2100 Bioanalyzer (Agilent, Santa Clara, CA, USA), respectively. For conversion to cDNA 50 ng total RNA was reversed transcribed using reverse transcriptase, primers and probes, obtained from Applied Biosystems (High capacity cDNA reverse transcriptase kit) (Applied Biosystems Inc, Foster City, CA, USA). RT-PCR was performed using Taqman® fast universal PCR (Applied Biosystems Inc, Foster City, CA, USA) and specific TaqMan® Gene Expression Assays (Applied Biosystems Inc, Foster City, CA, USA) for each gene studied: Ectodermal genes studied were Sox1, an early marker for neural cell fate [206], paired box gene 6 (Pax6) a marker for early neural differentiation and radial glia cells [207] and nestin a neural precursor marker [208] [209]. Endodermal genes studied were Gata4, a transcription factor which activates endoderm specific gene products [210], alpha fetal protein (AFP), a serum protein secreted by visceral endoderm [211, 212] and hepatocyte nuclear factor 4 alpha (HNF4α), a transcription factor which regulates expression of several hepatic genes [213]. Mesodermal genes studied were T, involved in formation of posterior mesoderm, an early peaking mesodermal marker [214], Nkx2-5, an early mesodermal marker [215] and myosin heavy chain 6 (Myh6), a marker for myosin [216]. For mature neural cells the following markers were used: markers for glutamatergic neurons, vesicular glutamate transporter 1 and 2 (VGluT1 and VGluT2) [217], for GABA-ergic neurons, vesicular GABA transporter (VGAT) [218] and glutamate decarboxylase 65 (GAD65) [219], tyrosine hydroxylase (TH) for dopaminergic neurons [220], Brn3 for sensory neurons [221], HB9 for motor neurons [222] and GFAP as a marker for astrocytes [223]. RT-PCR was performed using the 7500 Fast Real-Time PCR System (Applied Biosystems Inc, Foster City, CA, USA). All gene expression assays were validated in the test system using the HPRT and GUSb housekeeping genes and gene expression for all genes in the experiments was corrected for these stable housekeeping genes.

Cytotoxicity Assay To determine cytotoxicity of methylmercury chloride (MeHg) (Sigma-Adrich, Zwijndrecht, The Netherlands), ESC were plated in 96-well plates at 500 cells per well in CM supplemented with LIF and allowed to attach for 2 hours before MeHg exposure at concentrations of 3.3 nM, 33 nM, 100 nM, 200 nM, 550 nM and 1 μM. Cells were subsequently cultured for 5 days at 37°C and 5% CO2, with a medium renewal containing MeHg for exposure on day 3. On day 5 (approximately 80% cell confluence in non-exposed controls), CellTiter-blue (Pro- mega, Leiden, The Netherlands) was added to each well and incubated for 2 hours. After the incubation period, fluorescence was read using a FLUOstar spectrofluorometer (FLUOstar Optima, BMG Labtech, de Meeren, The Netherlands) at 544 (excitation) and 590 nm (emission). Three independent experiments were performed. Concentration-response analysis was performed using PROAST (Possible Risk Obtained from Animal Studies) software (RIVM, Bilthoven, The Netherlands).

62 mESC neural differentiation assay

Morphological scoring and flow cytometry for effects on neural outgrowth The short 13 day neural differentiation protocol was used to test the effects of MeHg on neural differentiation (Figure 1). MeHg was tested during different phases of the protocol: the serum-containing phase (day 3-6), the serum-free phase (day 6-13), or the combined exposure protocol (day 3-13). MeHg was diluted in DMSO and added to the medium to final concentrations in culture of 2.5 nM, 25 nM and 250 nM and control EB were treated with 0.01% DMSO. Effects were determined using morphological assessment on the extent of neural outgrowth at day 11 using an IX51 inverted microscope (Olympus, Zoeterwoude, The Netherlands) with CellD software (Olympus, Zoeterwoude, The Netherlands). Morphologi- cal neural outgrowth was scored as <75% or ≥75% of the neural corona surrounding each EB, irrespective of the distance of outgrowth from the EB. Flow cytometric analysis was performed on day 13 to study (neural)-differentiation key proteins (βIII-tubulin, nestin and SSEA1). Morphological scoring (day 11) and flow cytometric analysis (day 13) were performed on different days for logistic reasons. Three individual experiments (each with n=3) were performed.

Results Abbreviation of the neural differentiation protocol In order to enhance throughput of the neural differentiation assay, shorter versions of the original combined 20 days neural differentiation protocol (long protocol) were designed and 4 tested. Initial morphological observations showed that neural differentiation was present from day 7 onwards. Therefore the ITS phase was abbreviated to study its effect on the course of neural differentiation. With this in mind, we developed two new protocols, in which the ITS phase was reduced from the original 7 days to 4 (medium protocol) and 1 (short protocol) days, respectively. These three protocols (long, medium and short) were characterized over time both morphologically as well as using immunocytochemistry, RT-PCR and flow cytometric analysis.

Morphology Comparing across all three protocols using light microscopy, we identified similar differentiation patterns morphologically, including similarities in appearance and size of neurite outgrowth (Figure 2).

Immunocytochemistry Fluorescent staining of key proteins for neural differentiation was performed using immunocytochemistry and studied under a fluorescence microscope (Table 1 and Figure 3). Expression of SSEA1 was found abundantly from the start of the protocol at day 0 to day 6 and was reduced over time to low expression levels. Expression of nestin positive cells was observed from day 5 onwards through the whole EB and decreased after replating of the EB over time and was still present at the end stages of all three models. The first βIII-tubulin positive cells were observed from day 5 onwards and the number of βIII-tubulin positive cells increased over time in all three models. The first signs of GFAP positive cells were observed in the EB

63 at day 13 in all three models. From day 13 onwards, GFAP positive cells migrated out of the EB, resulting in GFAP positive cells mainly found in the EB in the short protocol, and GFAP positive cells both in- and outside the EB at the end of the medium and long protocols. During the whole culture period, no positive staining for oligodendrocytes was observed using the markers O4 and MOBP.

Figure 2: Typical final morphological appearance of the A) short (Day 13) (10x), B) medium (Day 17) (10x) and C) long (Day 20) protocols (10x).

64 mESC neural differentiation assay

End Short End Medium End Long Antibody Function Day 0 Day 3 Day 4 Day 5 Day 13 Day 17 Day 20 SSEA1 Pluripotency ++ ++ ++ ++ + + + Nestin Neural Precursor - - - + ++ ++ ++ βIII-Tubulin Neurofilament - - - + ++ ++ ++ GFAP Astrocytes - - - - + ++ ++ O4 Immature Oligodendrocytes - nt nt nt - - - MOBP Mature Oligodendrocytes - nt nt nt - - -

++ = abundantly present; + = low presence; - = not present; nt = not tested

Table 1 Presence of neural differentiation proteins over time observed by fluorescence microscopy during the shared first phase and at the end phase of each of the three protocols.

Figure 3 Fluorescence labeling of fixed cells. Protein staining in green, nucleus staining in red. A) SSEA1 staining at day 3 on frozen section of EB (20x); B) Astrocyte staining at day 13 of short protocol (20x); C) nestin staining at day 6 (10x) and D) at day 13 of the short protocol (10x); E) βIII-tubulin staining on day 6 (10x) and F) on day 13 of the short protocol (10x).

65 Flow Cytometry For all three protein differentiation markers (βIII-tubulin, nestin and SSEA1) we did not ob- serve a significant (p<0.05) difference between separate experiments within any of the three protocols (Figure 4A-C). Quantities of total protein at the end of culture were highly consistent and reproducible within each of the protocols (data not shown). We observed a tendency of increasing numbers of positive cells with protocol duration for each differentiation marker studied (Figure 4D). This was reflected in significant difference of the percentage of βIII-tubulin positive cells when comparing the short and long protocol (p<0.01). No significant differences (p<0.05) were identified for the SSEA1 and nestin protein when comparing across all three protocols.

A B

100% 100% Exp. 1 Exp. 1

Exp. 2 Exp. 2 s 80% 80% pe Exp. 3 pe s Exp. 3 ty ty Exp. 4 Exp. 4 cell 60% cell 60%

40% 40% of positive of positive % % 20% 20%

0% 0% βIII-Tubulin Nestin SSEA1 βIII-Tubulin Nestin SSEA1 C D 100% 100% Exp. 1 Short

Exp. 2 Medium 80% 80% pe s pe s Exp. 3 * Long ty ty Exp. 4 cell cell 60% 60%

40% 40% of positive of positive % % 20% 20%

0% 0% βIII-Tubulin Nestin SSEA1 βIII-Tubulin Nestin SSEA1

Figure 4 Reproducibility of the A) short, B) medium and C) long protocols by flow cytometric analysis for SSEA1, nestin and βIII-tubulin (n=4 per experiment, error bars in S.E.M.). D) Compari- son of percentage of SSEA1, nestin and βIII-tubulin positive cells for the short, medium and long protocols by flow cytometric analysis. Each experiment was performed 4 times with n=4 per experiment (error bars in S.E.M.).

66 mESC neural differentiation assay

Early neural differentiation upon serum deprivation To further characterize the protocol, a series of differentiation markers was studied from day 5 to day 7 using RT-PCR during serum deprivation. This stage of the assay is shared by the three protocols studied. Two independent experiments were performed (Figure 5). Concerning the ectodermal markers, whilst Sox1 and Pax6 did not show significant changes over time. The neural precursor marker nestin increased with time (p=0.009). No significant changes were observed for the early mesoderm marker T and Nkx2-5 and the cardiac muscle marker Myh6. Also no significant differences (p<0.05) were observed for the endodermal markers Gata4, AFP, and HNF4α. The neuron-type specific genes studied (VGluT2 p=0.007, GAD65 p=0.02, VGAT p=0.02) showed a particularly remarkable significant increased expression from day 5 to 7 in the protocol. The two independent experiments essentially showed the same trends over time. Thus, between day 5 and 7 of the protocol the cultures were shown to contain cells of each of the three germ layers, as well as various neural cell types with neural markers increasing with culture time.

2 *

0 * * 4 -2 *

-4 Ct

-∆ -6

-8

-10

-12 5 6 7 5 6 7 5 6 7 5 6 7 5 6 7 5 6 7 5 6 7 5 6 7 5 6 7 5 6 7 5 6 7 5 6 7 y y y y y y y y y y y y y y y y y y y y y y y y y y y y y y y y y y y y Da Da Da Da Da Da Da Da Da Da Da Da Da Da Da Da Da Da Da Da Da Da Da Da Da Da Da Da Da Da Da Da Da Da Da Da Sox1 Pax6 Nestin TNkx2.5 Myh6 GATA4AFP HNF4αVGluT2GAD65 VGAT

Ectoderm Mesoderm Endoderm Neural genes

Figure 5 Gene expression for specific germ layers and neuron type genes. RT-PCR of ectodermal (Sox1, Pax6 and nestin), mesodermal (T, Nkx2-5 and Myh6), endodermal genes (Gata4, AFP and HNF4α) and specific neuron types (VGluT2, GAD65 and VGAT) at day 5, 6 and 7 of the protocols was performed in two separate experiments. The following markers are significantly different over time according to one way Anova; nestin p=0.009; VGluT2 p=0.007; GAD65 p=0.02; VGAT p=0.02. –ΔCT is the relative difference of the cycle of threshold (Ct) of an investigated gene corrected for a housekeeping gene.

67 Neural differentiation at endpoint of three models RT-PCR assessment of nine different neural markers at the end stage of each of the three protocols generally revealed no significantly different expression levels between protocols (p<0.05). The only exception was GFAP expression, which was significantly (p=0.04 short versus medium; p=0.003 short vs. long) more intense in the medium and long protocol as compared to the short protocol (Figure 6). Nevertheless, the short protocol still showed significant levels of this marker. 6 ** * 4

Ct -2 -∆

-4

-6

-8

-10 t t t t t t t t t t d. d. d. d. d. d. d. d. d. d. or or or or or or or or or or ng ng ng ng ng ng ng ng ng ng lo lo lo lo lo lo lo lo lo lo sh me sh me sh me sh me sh me sh me sh me sh me sh me sh me Nestin VGluT1 VGluT2 GAD65VGATTph1THBrn3Hb9 GFAP

Figure 6 RT-PCR on neural precursors (nestin), specific neuron types (VGluT1, VGluT2, GAD65, VGAT, TH, Brn3 and Hb9) and astrocytes (GFAP) at the end stages of the short (day 13), medium (day 17) and long (day 20) protocols. *p=0.04; **p=0.003.

Effects of the neurotoxicant MeHg MeHg was used as a model compound to test effects in the neural differentiation protocol. In undifferentiated embryonic stem cells, using the Alamar Blue cytotoxicity assay, the half maximal inhibitory concentration for cytotoxicity (IC50) was 222 nM (188.4 nM - 264.4 nM 95% confidence interval) (Figure 7) (test performed in triplicate). This data was used as a reference level for MeHg IC50 in following experiments.

68 mESC neural differentiation assay

Figure 7 Alamar Blue cytotoxicity concentration-response of MeHg. IC50: 222 nM (188 nM – 264 nM).

Developmental toxicity: Morphological scoring Effects of MeHg (2.5, 25 and 250 nM) on cell morphology in the assay was tested after exposure during different phases of the short term protocol: the serum-containing phase (day 3-6), the serum-free phase (day 6-13), or these time windows combined (day 3-13) (Figure 8). Three independent experiments were performed in triplicate. EB outgrowth was morphologically scored at day 11. All three treatment periods showed a significant concentration-effect on neural outgrowth. At a concentration of 250 nM, an absence of neural outgrowth in addition to cell death was observed after exposure during the day 6-13 and the day 3-13 protocol. This in contrast to the observed outgrowth after day 3-6 exposure, which indicates a higher sensitivity to MeHg in serum free medium compared to medium with serum. In addition, exposure to MeHg during the serum free only period showed a stronger effect at 2.5 and 25 nM when compared to treatment during the combined exposure protocol.

69 100% d3 - d6 d6 - d13 d3 - d13 80% ** *

al outgrowth # 60% *** *** #

40% #

20% % of EB with >75% neur ## ## 0% DMSO 2.5 nM 25 nM 250 nM

Figure 8 Morphological scoring. Percentage of EB with >75% neural outgrowth after MeHg treatment (2.5, 25 and 250 nM) during the serum containing phase of the protocol (day 3-5), the serum free phase of the protocol (day 6-13) or the combined exposure protocol (d3-13). * p<0.05, ** p<0.005, *** p<0.005, # p<0.00005, ## p<10-13. All differences are compared to DMSO control (Error bars in S.E.M.).

Developmental toxicity: Flow cytometric analysis Effects of MeHg on the percentages of cells positive for the markers SSEA1, nestin and βIII-tubulin were assessed after exposure during different phases of the protocol: the serum- containing phase (day 3-6) (Figure 9A), the serum-free phase (day 6-13) (Figure 9B), or the combined exposure protocol (day 3-13) (Figure 9C). Samples were analyzed using flow cytometry at the end stage of the short protocol on day 13. No significant differences were observed for βIII-tubulin for all exposure windows and all concentrations tested (p<0.05). A significant increase of nestin positive cells (p<0.02) was observed at 250 nM in all exposure protocols when compared to control. Also, a significant increase (p<0.002) of SSEA1 positive cells was observed at 250 nM after exposure from day 6-13.

70 mESC neural differentiation assay

A 100%

s DMSO 0.01%

80% 2.5 nM MeHg 25 nM MeHg

60% 250 nM MeHg

40% * % of positive cell type 20%

0% βIII-Tubulin Nestin SSEA1

100% B DMSO 0.01% s 2.5 nM MeHg 80% 25 nM MeHg

60% 250 nM MeHg

40% * 4 % of positive cell type 20% **

0% βIII-Tubulin Nestin SSEA1

C 100% DMSO 0.01% s

80% 2.5 nM MeHg 25 nM MeHg

60% 250 nM MeHg

40% * % of positive cell type 20%

0% βIII-Tubulin Nestin SSEA1

Figure 9 Flow cytometric analysis of the effect of MeHg on the percentage cells positive for the markers SSEA1, nestin and βIII-tubulin after treatment with 2.5, 25 or 250 nM MeHg during A) day 3-5, B) day 6-13 or C) day 3-13. * p<0.02, ** p<0.002, all differences in comparison to vehicle control (Error bars in S.E.M.).

71 Discussion Abbreviation of the protocol The first aim of this study was to design an efficient ESC neural differentiation assay. To abbreviate the published 20 day protocols, the ITS phase was reduced from 7 days in the long protocol to 4 days and 1 day in the medium and short protocols, respectively. Morpho- logical observations (Figure 2) and immunocytochemical staining of key markers for pluripotency, neural precursor cells and mature neural cells (Figure 3) showed similar profiles when comparing all three protocols. Flow cytometry using these markers showed that each of the protocols gave reproducible results, indicating the robustness of the model (Figure 4). Expres- sion levels of neural precursor genes and genes for a range of specific neuron types (Figure 6) showed a very similar expression profile at the end stage of all three protocols. These findings confirmed the similarity of neural differentiation in the three protocols and showed at the same time the feasibility of the abbreviated protocol. Concerning glial cell differentiation, immunocytochemistry showed that the amount of GFAP positive cells at the end stage of the short protocol was visibly lower compared to the medium and long protocol. In the short protocol, GFAP positive cells were observed mainly inside or very near to the EB, whereas in the other protocols, GFAP positive cells were abundant both in and outside the EB (data not shown). Similarly a lower expression of the GFAP gene (Figure 6) also showed a lower expression in the short protocol compared to the other protocols, confirming the immunocytochemical observations. Although the amount of GFAP positive cells appeared lower in the short protocol, still a considerable number of cells expressing GFAP were observed, retaining the possibility that effects on differentiation towards astrocytes can be monitored. Together, these data show that abbreviation of the ITS phase from 7 days to 1 day provides similar neural differentiation profiles. Therefore, the short protocol was used for further experiments.

Advantages of the protocol for developmental toxicity testing Our abbreviated neural differentiation assay has a number of advantages over previously described systems for early neurodevelopmental toxicity screening. An often used method for in vitro neurodevelopmental toxicity makes use of neural progenitor cells (NPC) [224-226]. These cells are already in an advanced stage of neural differentiation as compared to ESC. Therefore, early effects on the formation and balance between ectoderm, mesoderm and endoderm germ layers and their consequences for neural differentiation cannot be detected using NPC. In our ESC neural differentiation culture, RT-PCR showed that especially ectodermal and endodermal, but also mesodermal gene expression was present, which offers that the assay captures a broader spectrum of developmental mechanisms, suggesting the possibility for improved prediction for compounds indirectly affecting neural cell differentiation. Our system uses standardized EB formation with the hanging drop method and maintains EB structure throughout the entire protocol, which supports stable culture conditions. In some developmental toxicity studies, ESC monolayer culture was used for neural differentiation [198], or EB were dissociated to single cells during the protocol [227], creating a less

72 mESC neural differentiation assay stable protocol. In our model EB are replated, instead of dissociated, to keep the multi-cellular spatial structure of the EB intact. EB formation furthermore allowed differentiation to mimic the in vivo developmental process in a more complete manner compared to monolayer differentiation, due to the presence of all three germ layers [228].

Characterization of the short protocol RT-PCR showed that early neural differentiation genes, such as Sox1 and Pax6, were stably expressed between days 5 and 7 (Figure 5). Nestin expression was increasing from days 5 to 7, indicating increasing neural differentiation, with a comparable expression level at day 13 of the culture compared to day 7 (Figure 6). Expression of several endodermal and ectodermal genes was detected from day 5-7. Mesodermal gene expression was also detected in our assay, although the genes studied were expressed at lower levels compared to the ectodermal and endodermal differentiation. These results showed that all three major embryonic differentiation routes were affected in the culture from day 5 through day 7. Furthermore, we showed that in our model GABA-ergic and glutamatergic neurons are formed early on, at least from day 5 onwards. These neural cell types belong to the first classes of neurons to developin vivo and have an important role in controlling cell division, neuronal migration and maturation during neural development [229-231]. It has been shown that the neurotransmitters of these two neuronal classes have an effect on further neural differentiation pathways in vivo and in vitro. At the end of the culture (day 13), a higher expression for VGluT2 is observed compared 4 to low expression levels for VGluT1, which are both markers for different types of glutamatergic neurons. In vivo, it is shown that during embryonic development, VGluT2 expressing glutamatergic neurons are more abundant compared to VGluT1 expressing glutamatergic neurons [231]. After birth VGluT2 positive neurons decline in number and VGluT1 positive neurons increase in number [232]. The higher expression of VGluT2 expression compared to VGluT1 expression at the end stage of our culture reflects the in vivo situation during development. Taken together, the abbreviated protocol appeared to display an array of characteristics reminiscent of germ layer development and neuronal differentiation, which may be useful in the detection of chemicals affecting these processes.

Neurodevelopmental toxicity testing Previous studies performed in our laboratory showed that proliferation and differentiation of ESC in the cardiac EST (ESTc) are highly intertwined processes [152]. It was recommended that, to largely limit exposure to the differentiation phase of the assay, it would be advantageous that EB be exposed to compounds from day 3 in the assay onwards [152]. Up to day 3, the ESTc and our neural differentiation assay protocols are identical. Therefore, in the present study MeHg exposure was performed from day 3 onward. EB were treated either during the serum-containing phase (day 3-6), the serum free phase (day 6-13), or the combined exposure protocol (day 3-13), in order to determine whether the presence of serum would influence the sensitivity of the cells. Morphological scoring for neural outgrowth was performed on day 11 (Figure 8) and flow cytometric analysis was performed on day 13 (Figure 9). For morphological scoring, all three treatments showed clear dose-responses in the 2.5 – 25 nM range, indicating that the assay is highly sensitive to MeHg. In an earlier study,

73 effects of MeHg on MAP2 gene expression in human ESC were shown at 320 nM [233] and a study using human NSC [226] reported an effect on neurite outgrowth at concentrations between 500 and 700 nM MeHg. Furthermore, a study using mouse ES cell neural differentiation [227] using a variation of the long protocol based on the protocol described by Okabe et al.[65] found an effect on Mtap2 gene expression after 14 days of neural differentiation at a concentration of 100 nM MeHg. In our assay, a significant effect is observed at 2.5 nM MeHg, indicating that our model appeared relatively sensitive in detecting effects of MeHg on neural differentiation. The MeHg concentration-responses showed a more severe effect after day 6-13 exposure as compared to day 3-6 and day 3-13 exposure protocols (Figure 8). The fact that the latter two are similar indicates that early effects largely determine the overall outcome of the assay. The culture composition at the start of day 6-13 treatment in terms of cell populations is likely different with and without previous day 3-6 exposure, which may explain the more severe effect observed after d6-13 treatment only. The complete absence of neural outgrowth observed exclusively after late (day 3-13 and day 6-13) exposures to the highest concentration tested may be related to the cytotoxicity of the compound observed at this concentration (Figure 7), which is likely more pronounced under serum free conditions. The suggestion that high exposure may modulate cell population ratios was supported by flow cytometric analysis performed for the pluripotency marker SSEA1, the neural precursor marker nestin and the neuron specific cytoskeletal proteinβ III-tubulin (Figure 9). Significantly more nestin positive cells were identified in a concentration-dependent fashion in culture using all three exposure protocols. Also, after high concentration day 6-13 treatment, the number of SSEA1 positive cells was significantly higher compared to control. These findings suggest that MeHg exposure resulted in increased undifferentiated and neural precursor cells in the assay relative to βIII-tubulin positive neural cells. In addition, the observed SSEA1 increase implies that MeHg may be more toxic to neural cells than to undifferentiated cells. The morphological effect of reduced neural outgrowth in our assay is not reflected in the percentage of βIII-tubulin positive cells after MeHg treatment. However, it has been shown that MeHg has an effect on dendrite and axon growth in PC12 cells [234] and in primary mouse dopaminergic mesencephalic cells [235]. In another study, the effect of MeHg on a range of neural differentiation related genes was studied on mouse ESC neural differentiation [227]. Only the marker Mtap2 showed a significant decrease at non-cytotoxic MeHg levels. In addition to its role in the maturation of neurons, Mtap2 is also associated with dendrite and axon growth [236]. Our combined morphological and flow cytometric data suggest that MeHg did not affect the number of βIII-tubulin positive cells, but did reduce the growth and migration of neurites.

In summary, we have successfully designed an abbreviated 13-day mouse ESC neural differentiation protocol. We have shown that each of the three embryonic germ layers as well as multiple lineages of neural and other brain-associated cells were represented in our protocol. In addition, using a variety of differentiation markers we showed differential effects of MeHg exposure on different cell types as well as on neuronal outgrowth. We consider these findings promising as we anticipate that the multi-lineage character of the assay may allow improved detection of neurodevelopmental toxicants in view of the large spectrum of possible target

74 mESC neural differentiation assay cell types present in the assay. The model is not yet ready to be used as a standardized assay for assessing developmental toxicity in vitro. The applicability of the assay for the detection of neurodevelopment toxicants awaits further assessment of assay performance with a series of neurodevelopment toxicants.

75 76 CHAPTER5

Distinct gene expression responses of two anticonvulsant drugs in a novel human embryonic stem cell based neural differentiation assay protocol

Sjors H.W. Schulpen, Esther de Jong, Liset J.J. de la Fonteyne, Arja de Klerk, Aldert H. Piersma

Toxicology In vitro. (2014) 15;29(3)

77 Abstract Hazard assessment of chemicals and pharmaceuticals is increasingly gaining from knowledge about molecular mechanisms of toxic action acquired in dedicated in vitro assays. We have developed an efficient human embryonic stem cell neural differentiation test (hESTn) that allows the study of the molecular interaction of compounds with the neural differentiation process. Within the 11-day differentiation protocol of the assay, embryonic stem cells lost their pluripotency, evidenced by the reduced expression of stem cell markers Pou5F1 and Na- nog. Moreover, stem cells differentiated into neural cells, with morphologically visible neural structures together with increased expression of neural differentiation-related genes such as βIII-tubulin, Map2, Neurogin1, Mapt and Reelin. Valproic acid (VPA) and carbamazepine (CBZ) exposure during hESTn differentiation led to concentration-dependent reduced expression of βIII-tubulin, Neurogin1 and Reelin. In parallel VPA caused an increased gene expression of Map2 and Mapt which is possibly related to the neural protective effect of VPA. These findings illustrate the added value of gene expression analysis for detecting compound specific effects in hESTn. Our findings were in line with and could explain effects observed in animal studies. This study demonstrates the potential of this assay protocol for mechanistic analysis of specific compound-induced inhibition of human neural cell differentiation.

78 hESC neural differentiation assay

Introduction Humans are exposed daily to chemicals that lack information concerning toxicity and their potential health hazards. For instance, under the REACH legislation for chemical safety in Europe, reproductive and developmental toxicity testing is only mandatory at high tonnage production levels. These tests are costly and require an estimated 65% of all animal use under REACH [15]. There is a high need for efficient predictive alternative test methods, in order to inform about reproductive and developmental toxicity at lower production levels, and to reduce animal use in toxicological hazard assessment. During the last decades, much research has been performed towards the development of in vitro methods, which can contribute to the reduction of animal use in toxicological testing. For developmental toxicity testing several in vitro systems have been developed, varying from whole embryo cultures to assays based on cell lines [31].

The use of embryonic stem cells as a corollary of cell differentiation in the embryo is practical since they are relatively easy to culture, have a self-renewal capacity and can be cultured in undifferentiated state. Furthermore, they can differentiate into cell lineages originating from all three germ layers [83], which make them suitable to study early developmental processes at the cellular level. In the embryonic stem cell test (EST), developed in 1993 by Heuer et al. [54, 237], ES-D3 mouse embryonic stem cells differentiate into contracting cardiomyocytes. Embryotoxicants exert an inhibitory effect on the differentiation process, resulting in a concentration dependent inhibition of contracting cardiomyocyte foci formation. More recent studies have enhanced the mechanistic readout of EST by incorporation of transcriptomics [123, 178] and by the establishment of differentiation culture protocols for several other differentiation routes, e.g. neural and osteoblast differentiation [70, 117]. Compound exposure affected gene expression already after 24 hours of exposure and resulted in compound- and concentration dependent responses [122]. Gene expression analysis enhanced mechanistic 5 insight into differentiation inhibition caused by compound exposure [120, 122, 124].

The classical EST assays are based on murine embryonic stem cells, which are not completely representative for human cells. Current trends in toxicological hazard assessment move towards human based test systems [92, 238-240] to facilitate human hazard and risk assessment. The application of established human embryonic stem cell lines can be instrumental in avoiding interspecies extrapolation and would result in reduced animal use in hazard assessment. Several human stem cell based differentiation assays have been developed, either for regeneration and replacement therapies [87, 91], to study mechanisms involved in neurogenesis [90, 91, 93, 241, 242], or for neurodevelopmental toxicity testing [87, 92, 243-245]. The assays differed widely in terms of culture method, with single- and multiple step replating approaches, and including rosette, neurosphere or embryoid body formation. Differentiation time differed between tests from a few days to several weeks, depending on the end-points assessed. In addition, different culture well coatings, additives and culture conditions have been employed. In the present study we developed a straightforward and relatively fast differentiation method in which pluripotent WA09 (H9) human embryonic stem cells differentiate into neural cells. In this method hESC differentiated through a minimal number of culture steps

79 and few culture medium additives. Thus, a more spontaneous differentiation was achieved. Differentiation was studied using immunostaining, in which stem cells were stained with anti- SSEA4 and neural differentiation was evidenced by neuron specific antiβ III-tubulin staining. RT-PCR analysis was used to study the expression of mRNA transcripts associated with stem cell renewal and maintenance of pluripotency using Pou5F1 and Nanog. Gene expression involved in neurogenesis was studied with Neurogin1 and Reelin and neurons were evidenced by the expression of βIII-tubulin, MAP2 and MAPt.

To study the effectiveness of this model as an in vitro method to evaluate neurodevelopmental toxicity, the effects of valproic acid (VPA) and carbamazepine (CBZ) on neural differentiation were studied. Both VPA, an anticonvulsant and therapeutic drug for bipolar disorder (BPD) [246] and CBZ, an anticonvulsant drug [247], are known to cause neurodevelopmental toxicity in vivo [248, 249], VPA exposure during pregnancy can cause neural tube defects, spina bifida aperta, cleft palate and limb defects [246, 250]. CBZ can cause spina bifida, cardiovascular anomalies, cleft palate, skeletal- and brain anomalies [251-253]. Ear- lier whole genome array gene expression studies in mouse EST have shown abundant gene expression responses of VPA with many thousands of genes responding, whereas CBZ, given at equipotent concentrations as to morphological cell differentiation inhibition, showed a relatively limited gene expression response [118, 179]. These results already indicated that differential gene expression analysis may reveal compound-specific effects at concentrations showing similar morphological differentiation inhibition. In this study first the effects on cell viability were determined with a resazurin cytotoxicity assay. Subsequently, differential gene expression responses of selected genes were studied in non cytotoxic concentration ranges of VPA and CBZ.

Methods Human embryonic stem cell culture Human embryonic stem cells (hESC) (WA09-DL11, WiCell, Madison, Wisconsin) were cultured in 6-well plates (Corning, NY, Cat#3516) in hESC culture medium (CM), containing: DMEM-F12 (Gibco, Gaithersburg, MD, Cat#31330-038) supplemented with 20% Knock Out Serum Replacement (KOSR) (Gibco, Gaithersburg, MD, Cat#10828), 1mM L-Glu- tamine (Gibco, Gaithersburg, Cat#25030-024), 0.5% 5000 IU/ ml Penicillin/ 5000 μg/ ml Streptomycin (Gibco, Gaithersburg, MD, Cat#15070), 1% non-essential amino acids, (Gibco, Gaithersburg, MD, Cat#11140-035), 0.1mM β-Mercaptoethanol (Sigma-Aldrich, Zwijndrecht, Cat#31350-01) and 0.2µg/ml fibroblast growth factor-basic(bFGF) (Gibco, Gaithersburg, MD, Cat#13256-029). To maintain pluripotency, the hESCs were cultured on inactivated mouse embryonic fibroblasts. hESC culture medium was refreshed every day.

Mouse embryonic fibroblasts Mouse embryonic fibroblasts (MEFs)( ATCC, Wesel, Germany CF-1 SCRC-1040) were cultured in T175 culture flasks in MEF culture medium (MM), containing: DMEM (Gibco, Gai- thersburg, MD, Cat#11960-044) supplemented with 15% Fetal bovine serum (FBS) Hyclone,

80 hESC neural differentiation assay

Logan, UT, Cat#SH30070.03), 1% 5000IU/ ml Penicillin/ 5000 μg/ml Streptomycin (Gib- co, Gaithersburg, MD, Cat#15070), 1% 100mM Sodium Pyruvate (Gibco, Gaithersburg, MD, Cat#11360-039) and 2 mM L-Glutamine (Gibco, Gaithersburg, MD, Cat#25030-024). When 90% confluent, the cells were incubated with 10µg/ml Mitomycin C (MMC) (M0503, Sigma-Aldrich,Zwijndrecht, The Netherlands) for 3 hours at 37°C to mitotically inactivate the cells. Subsequently, the MMC solution was removed and the cells were washed with MM followed by Dulbecco’s Phosphate-Buffered Salin D-PBS with Ca2+ and Mg2+ (PBS +/+) (Gibco, Gaithersburg, MD, Cat#14040-174). To detach the cells they were incubated with Trypsin-EDTA (Gibco, Gaithersburg, MD, Cat#25200-056) for 1-2 minutes. Trypsin was inactivated by adding MM at twice the volume of trypsin, and a single cell suspension was produced by gently pipetting the suspension up and down. After the cells were transferred to 15ml tubes, they were centrifuged 6min at 300rpm at 37°C. The cells were either stored in liquid nitrogen at 1.2∙106 cells/ml per vial or directly used as feeder cells at 2∙105 cells/ well. Inactivated MEFs were seeded into wells and incubated for 24 hours in a humidified atmosphere (37ºC 5%CO2) to attach and were used at maximum up to two weeks after seeding. hESC culture hESC were routinely cultured on a layer of inactivated MEFs and passaged between 1-3 times per week, depending on growth speed and morphological quality. Since the cell number cannot be controlled, and hESC cells were passaged in fragments, the cell clusters transferred to new inactivated MEF coated dishes differed in size (Figure 2A). After most of the clusters achieved sufficient size, differentiated clusters and areas were manually removed during the culture period, the optimal clusters were passaged again. Culture medium was refreshed 5 every day. Based on the morphological quality of the full-grown hESCs clusters, they were passaged either mechanically, by using a dissection needle, or enzymatically with 1mg/ml Collagenase IV (Gibco, Gaithersburg, MD, Cat#17104-019). For enzymatic passaging, the cells were incubated at 37°C with 1mg/ml Collagenase IV for 5-10 minutes. After the edges of cell clusters curled up microscopically observable, the Collagenase IV solution was discarded and the cells were washed with DMEM-F12 (Gibco, Gaithersburg, MD, Cat#31330- 038). Additionally, 2ml of CM was added per well. The clusters were manually removed by gently scraping with a 5ml pipet, while simultaneously dispensing the medium. The cell clusters were transferred to a 15ml tube. The clusters were dispersed into smaller clusters by gently pipetting up and down with a 15ml pipet. Finally, the cell suspension was subdivided across new wells, containing inactivated MEFs, in a 1:2 – 1:3 ratio, depending on the amount of cell clusters. The H9 cell batches received from WiCell had passage number 27/28. We could culture them in undifferentiated state up to at least passage 60 without microscopically observed morphological changes. We did not do karyotyping during subculture. The experiments in this manuscript were carried out with passage numbers 36/37, which is ten passages beyond the initial cell batch.

81 hESC neural differentiation Four or five days after the hESC clusters were passaged and cultured on inactivated MEFs, they were enzymatically dissociated by incubating with 1mg/ml Collagenase IV (Gibco, Gai- thersburg, MD, Cat#17104-019) for 10-15 minutes, until the edges of the hESC cultures curled up and mostly detached from the MEFs. After removal of the Collagenase the cells were washed with DMEM-F12 (Gibco, Gaithersburg, MD, Cat#31330-038) and 2ml of CM was added to the wells. The cells were collected with a 5ml pipet from the bottom of the well, while simultaneously dispensing medium. For gene expression analysis the hESC clusters were pooled and subsequently transferred to bacterial dishes (60x15mm, Greiner Cat#628103) containing 3ml of CM (final volume 5ml). The cells were incubated for four days in a humidified atmosphere (37ºC 5%CO2). At day 4 the cell aggregates were transferred to a 15ml tube. After the aggregates sank to the bottom of the tube, the CM was removed and replaced by 2ml ITS medium; DMEM-F12 (Gibco, Gaithersburg, MD, Cat#31330-038), supplemented with 1% 5000 IU/ ml Penicillin/ 5000 μg/ml Streptomycin (Gibco, Gaithersburg, MD, Cat#15070), 1.5 mM L-Glutamine (Gibco, Gaithersburg, MD, Cat#25030-024) and 10% ITS premix (BD Bioscience, Bedford, MA, Cat#354350). Subsequently, the aggregates were cultured for 3 days on Poly-D-Lysine (PDL) (0.1mg/ml, Sigma-Aldrich, Zwijndrecht, Cat# P6407) and Laminin (0.01mg/ml Sigma Sigma-Aldrich, Zwijndrecht, Cat# L2020) coated tissue culture dishes 35x10mm (Corning Incorporated, Corning, NY, USA Cat#430165). At day 7 the ITS medium was replaced by Neurobasal medium (Gibco, Gaithersburg, MD, Cat#21103-049), supplemented with N-2 premix (Gibco, Gaithersburg, MD, Cat#17502- 048), B27 premix (Gibco, Gaithersburg, MD, Cat#17504-044) and 1% 5000 IU/ ml Penicil- lin/ 5000 μg/ml Streptomycin (Gibco, Gaithersburg, MD, Cat#15070). After two days the N2/B27 medium was refreshed and cells were cultured for two additional days.

Immuno staining Cells attached in culture dishes were washed with cold PBS Ca2+ and Mg2+ free (PBS -/-)(Gibco, Gaithersburg, MD, Cat#14190-094) and fixed with cold 4% paraformaldehyde (Sigma–Aldrich, Zwijndrecht, The Netherlands, Cat#604380) for 10min at room temperature. The cells were washed twice with cold PBS (-/-), permeabilized in 0.2% Triton X-100 (Sigma–Aldrich, Zwijndrecht, The Netherlands, Cat#93443) for 5 minutes at 4ºC. After the cells were rinsed twice with PBS (-/-), they were incubated 1 hour at 37ºC with blocking buffer : 1% Bovine serum albumin (BSA) (Sigma–Aldrich, Zwijndrecht, The Netherlands, Cat#3808), 0.5% Tween-20 (Sigma–Aldrich, Zwijndrecht, The Netherlands, Cat#93773) in PBS (-/-). The cells were washed with PBS (-/-) and incubated with the primary antibody (AB) overnight at 4ºC. Stem cells were characterized using mouse anti-SSEA-4 (Millipore, Billerica, MA, USA, Cat# FCMAB4304) which binds to the stage-specific embryonic antigen-4, [83], at a 1:3000 dilution within dilution buffer containing 0.5% BSA and 0.5% Tween-20. Neural differentiation was characterized using rabbit anti-βIII-tubulin (Sigma–Al- drich, Zwijndrecht, The Netherlands, Cat#T2200) [254], at a 1:3000 dilution within dilution buffer. After incubation the cells were rinsed with PBS (-/-) and incubated with secondary AB for 1 hour at 37ºC. Anti-SSEA4 was incubated with 1:200 diluted Goat anti-mouse IgG γ Chain specific TRITC conjugated polyclonal antibody (Millipore, Billerica, MA, USA,

82 hESC neural differentiation assay

Cat#AP503R). Anti-βIII-tubulin was incubated with 1:200 diluted swine anti-rabbit FITC Dako, Glostrup, Denmark, Cat#F0205). After incubation the cells were washed with PBS(-/-) and nuclei were stained with ProLong Gold antifade reagent with DAPI (Lifetechnologies, Carlsbad, CA, USA Cat#P36934)

Cytotoxicity hESC were allowed to adhere for 2 hours on a Matrigel coated 96-well plate at 2000 cells/ well at humidified atmosphere (37ºC 5%CO2) with addition of 10 μM ROCK inhibitor Millipore, Billerica, MA, USA, Cat# 688000) to the hESC medium. After attachment of the cells to the wells, the cells were exposed to either 0.0033 – 2.0mM VPA (Sigma–Aldrich, Zwijndrecht, The Netherlands, CAS#1069-66-5) or 0.0033-2.0mM CBZ Sigma–Aldrich, Zwijndrecht, The Netherlands, CAS#298-46-4) (Solved in 0.25% DMSO) in hESC medium for 5 days, at 6 technical replicates per concentration. After 3 days the medium was refreshed, containing the same test compound and concentration. At day 5 the cell viability was determined using Cell-titer blue viability assay (Promega, Leiden, The Netherlands, Cat#G8081), in which the cells were incubated with resazurin for 4 h at 37 ºC.Viable cells were able to convert resazurin (a redox dye) into a fluorescent product (resofurin) which was measured with FLUOstar spectrofluorometer (FLUOstar Optima, BMG Labtech, de Meeren, The Ne- therlands) at 544nm (excitation) and 590nm (emission). Via concentration response curves using a log-logistic model, generated with Proast software [153], the compound concentration inducing 50% inhibition of cell viability (IC50), was calculated.

Gene expression analysis. To study the gene expression over time during the differentiation period, samples were collected at day 0 (n=6), 1 (n=6), 4 (n=2), 7(n=6), 9 (n=2) and 11 (n=2). All cultures originated 5 from the same pool of cells, collected from a large group of undifferentiated hESC cultures. Per sample 500 ng RNA was reversed transcribed into complementary DNA (cDNA) by using the High capacity cDNA reverse transcriptase kit (Applied Biosystems Inc., Foster City, CA, USA, Cat# 4387406). Gene expression was measured by Real-time PCR (RT-PCR) using TaqMan fast universal PCR Master mix (Applied Biosystems Inc., Foster City, CA, USA, Cat#4366073) and specific TaqMan Gene Expression Assays (Applied Biosystems Inc., Foster City, CA, USA). Genes both associated with self-renewal and maintenance of pluripotency of stem cells POU domain class 5 transcription factor 1 (POU5F1) (Applied Biosystems Cat#Hs00999632_g1) and (Nanog) (Applied Biosystems Cat#Hs02387400_g1) as well as genes involved in neural differentiation and associated with neural cells βIII-tubulin (TUBB) (Applied Biosystems Cat#Hs00964962_g1) Microtubule-associated protein 2 (MAP2) (Ap- plied Biosystems Cat#Hs00258900_m1) Neurogin1 (Applied Biosystems Cat#Hs01029249_ m1) Microtubule-associated protein tau (MAPT) (Applied Biosystems Cat#Hs00902194_m1) and Reelin (Reln) (Applied Biosystems Cat#Hs01022646_m1) were validated to be used with Housekeeping genes hypoxanthine guanine phosphoribosyltransferase (HPRT) (Applied Biosystems Cat#Hs02800695_m1) and Glucuronidase beta (GUSb) (Applied Biosystems Cat#Hs00939627_m1) to calculate ΔCt values. Gene expression levels were corrected for the average Ct values of the 2 house keeping genes and the control (ΔΔCt) [255].

83 RNA extraction Cells ready for RNA extraction were stored at -20ºC in RNA protect (Qiagen Benelux, Ven- lo, The Netherlands, Cat# 76526). RNA was extracted using the Qiacube (Qiagen) and a Qiagen RNA Mini-extraction kit (Qiagen, Cat#74104) including a DNAse treatment step (Qiagen Cat# 79254) following the manufacturer’s protocol. The extracted RNA was eluted in RNase free water and stored at -80 ºC. RNA concentrations were measured using the Nanodrop (Thermo Scientific).

Compound mediated effect on differentiation Cells were exposed to 0.033 (n=2), 0.1 (n=2), 0.33mM (n=4) and 1.0mM (n=4) VPA (Sig- ma–Aldrich, Zwijndrecht, The Netherlands, CAS#1069-66-5) or 0.01mM (n=2), 0.033 (n=2), 0.1mM (n=4) and 0.33(n=3) CBZ (Sigma–Aldrich, Zwijndrecht, The Netherlands, CAS#298-46-4) starting at initiation of differentiation (day 0) and continued until day 7.

Since all genes studied were significantly regulated in control cultures after 7 days, the cells were exposed from day 0 until day 7 of the differentiation period and RNA of exposed and non-exposed samples was extracted at culture day 7.

Data analysis Gene expression was studied using Graphpad Prism (version 6). Statistics were calculated by using analysis of Graphpad Prism in which the mean of each group was compared with the mean of the solvent control with post-hoc t-test (Dunnett’s multiple comparison test, with a 95% confidence interval). Significance of concentration response was calculated with a Post- hoc t-test for linear trend. Indication of a P-value < 0.0001: ****, 0.0001 to 0.001: ***, 0.001 to 0.01: ** and 0.01 to 0.05: *

84 hESC neural differentiation assay

Results hESC cell culture hESC were routinely cultured and passaged 1-3 times per week. As the hESC cell cultures were passaged in fragments, the resulting cell clusters adhering to new inactive MEF coated dishes differed in size (Figure 1A-B). The cultures were observed daily and examined morphologically both by light microscopy and by fluorescent immuno-staining with anti-SSEA4. Within the cultures of undifferentiated hESC, usually some spontaneously differentiated cells were present, expressing βIII-tubulin observed after immuno-staining (Figure 1C).The extent of βIII-tubulin expressing cells was very low and stable with increasing passage number. Quality of the cells was monitored using immuno staining for SSEA-4 and βIII-tubulin and by RT-PCR analysis for all genes employed in this study. A B C

Figure 1: Culture of pluripotent human embryonic stem cells (A)H9 hESC were cultured on mitotically inactivated MEFs (magnification: 40x). (B) SSEA4 immunostaining of an undifferentiated hESC cluster (red). Cell nuclei were stained with DAPI (blue) (magnification: 40x). (C) βIII-tubulin immunostaining of some spontaneously differentiated neural cells in hESC 5 clusters (green). Undifferentiated hESC clusters were stained with anti-SSEA4 (red). Cell nuclei were stained with DAPI (blue) (Magnification 100x). hESC differentiation Cells were cultured in CM for 4 days in which cell aggregates were formed (Figure 2AB). At day 4 the cells were transferred to PDL and Laminin coated dished in which the aggregates attached to the bottom of the dishes (Figure 2C). The cells were cultured in ITS containing medium until day 7 during which cells migrated out of the aggregates and dendritic structures became visible (Figure 2C). At day 7 the ITS culture medium was replaced by Neurobasal medium containing N2 en B27. After 11 days the cultures consisted of cells representing neural cell morphology with relatively few cells expressing stem cell marker SSEA4, as demonstrated with immunological staining with anti-SSEA4 and anti-βIII-tubulin (Figure 2D-G).

85 A B C D

PDL/ Laminin PDL/ Laminin

E F

Figure 2: Neural differentiation method (A) After enzymatic dissociation, hESC clusters were transferred to culture dishes containing CM and cultured 4 days to form cell aggregates. (B)At day 4 the aggregates were transferred to PDL/laminin coated dished and cultured in ITS medium for 3 days. The aggregates attached to the dishes and cells started to migrate out of the aggregates. (C) At day 7 the ITS medium was replaced by N2 medium and cultures were continued for 4 days. Neural structures became visible. (D-G) At day 11 there were very few SSEA4 positive cells present (red), and a network of cells, expressing βIII-tubulin (green) had formed. Cell nuclei were stained with Dapi (blue). (Magnification E:40x, F:100x, G:400x).

RT-PCR analysis of hESC renewal and differentiation The expression of genes involved in stem cell renewal and maintenance of pluripotency, as well as marker genes for neural differentiation were studied using RT-PCR analysis (Figure 3). After 4 days the gene expression of both stem cell markers Pou5F1 and Nanog had significantly decreased, with a continuing decrease in expression until day 11. The genes involved in neurogenesis showed a significant up-regulation over time. Already 24h after initiation of neural differentiation βIII-tubulin, Map2 and Neurog1 were significantly up-regulated. After 7 days, all genes studied involved in neurogenesis were significantly upregulated.

86 hESC neural differentiation assay

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Pou5f1 Nanog Beta III Map2 Neurog1 Mapt REELIN

Figure 3: Relative gene expression of stem cell- and neural development related genes during differentiation. The expression of stem cell related genes, Pou5F1 and Nanog, together with genes expressed in neural development, βIII-tubulin, Map2, Neurogin1, Mapt and Reelin, were measured at day 0 (■), 1 (■), 4 (■), 7 (■), 9 (■) and 11(■).

Effects of compounds on hESC viability Concentration responses in the hESC cytotoxicity assay resulted in IC50 values of 1.71mM of VPA (Figure 4A) and 0.436mM of CBZ (Figure 4B). 5

A B 2 2 1. 1. .0 .0 81 81 0. 0. .6 .6 Rel. value Rel. value Rel. 40 40 0. 0. .2 .2 00 00 0. 0. 0.0033 0.010 0.033 0.10 0.33 1.0 2.0 0.0033 0.010 0.033 0.10 0.33 1.0 2.0 Conc (mM) Conc (mM)

Figure 4: Cytotoxicity Cytotoxicity concentration-response curves in undifferentiated hESC culture of VPA (IC50 1.71mM) (A) and CBZ (IC50 0.436mM) (B).

87 Effect of compounds on gene expression in differentiating hESC The effects of VPA and CBZ on differentiation were tested by gene expression at concentrations below the IC50. Both for VPA and CBZ a concentration related effect on the expression of most of the genes studied was observed at day 7 of differentiation. (Figure 5 and 6).Exposure to VPA caused a significant concentration dependent regulation of gene expression. There was a decrease in expression of the stem cell related genes Pou5F1 and Nanog. The neural development related genes βIII-tubulin, Neurog1 and Reelin, were all significantly down regulated in a concentration dependent response, whereas both Map2 and Mapt showed a significant concentration dependent increased expression compared to the control. CBZ did not cause a significant concentration response of the stem cell related genes Pou5F1 and Nanog. Of the neural developmental involved genes, Map2 gene expression did not respond significantly to CBZ exposure. However, βIII-tubulin, Neurog1, Mapt and Reelin all had a significant concentration-response and showed a down regulation in response to an increasing concentration of CBZ.

2 ****

**** ** **** ** *** ** *

0 -ΔΔCt **** * *

-2 ***

****

-4

Pou5f1 Nanog Beta III Map2 Neurog1 Mapt REELIN

Figure 5: The effect of VPA exposure on gene expression in hESTn. Differentiating cells were exposed from day 0 to 7 to increasing concentrations of VPA; Solvent control (0.25% DMSO) (...), 0.033mM (■), 0.1mM (■), 0.33mM (■) and 1.0m (■). The effect on stem cell related genes, Pou5F1 and Nanog, and on genes involved in neural development, βIII-tubulin, Map2, Neuro- gin1, Mapt and Reelin, was measured at day 7 and compared to the average ΔΔCt of gene expression in unexposed differentiating cells at day 7.

88 hESC neural differentiation assay

** -ΔΔCt

* ** ** *

-2

****

-4

Pou5f1 Nanog Beta III Map2 Neurog1 Mapt REELIN

Figure 6: The effect of CBZ exposure on gene expression in hESTn. Differentiating cells were exposed from day 0 to 7 to increasing concentrations of CBZ; Solvent control (medium) (...), 0.01mM (■), 0.033mM (■), 0.1mM (■) and 0.33mM (■). The effect on stem cell related genes, Pou5F1 and Nanog, together with genes involved in neural development, βIII-tubulin, Map2, Neurogin1, Mapt and Reelin, was measured at day 7 and compared to the average ΔΔCt of gene expression in unexposed differentiating cells at day 7.

Discussion 5 Defects of the neural tube are among the most common malformations in mammalian species [256]. Mouse ESC have proven a relatively easy starting point for neuro developmental toxicity assay design. [70]. However, in view of interspecies differences hESC could provide improved predictability of the assay for developmental toxicity in man. During the last decade several in vitro developmental toxicity assays based on hESC have been developed [51, 92, 240, 243, 245, 257, 258]. They have been based on various cell types, such as stem cells from umbilical cord blood [92], induced pluripotent stem cells (IPS) [93], neural progenitor cells [94, 95] and hESC [91]. Some methods made use of primary cultures of neural cell rosettes [244] or neurospheres [245]. These tests are usually complex in terms of culture conditions, and have a long duration, up to several weeks, which provide practical disadvantages as compared to mESTn. Additionally, they often focus on a single developmental process, such as cell proliferation, embryoid body formation or neurite outgrowth [240, 244]. We developed a relatively short duration- and simple culture procedure encompassing different developmental processes, i.e. cell proliferation, aggregate formation, neural cell differentiation, and neurite outgrowth. Our method presented here is based on a commercially available human embryonic stem cell line H9, employed extensively in embryonic cell differentiation research [49]. Furthermore, this hESC differentiation assay is comparable in duration to the mouse embryonic stem cell neural differentiation assay described by Theunissen et al. [70].

89 This facilitates comparative studies of compound effects among EST assays based human versus murine stem cells.

Loss of stemness and gain of neural cell characteristics in our assay was studied with a set of 7 marker genes. Both Nanog and Pou5F1 are transcription factors involved in pathways controlling pluripotency and self-renewal by molecular reprogramming [259, 260]. Neural differentiation was studied with βIII-tubulin, a microtubule element exclusively expressed within neurons [261]. Microtubule associated proteins (MAPs) mediate interaction between neurons and microtubule elements. MAP2 is a high molecular weight MAP, and MAPtau (MAPt) has a low molecular weight [262]. Neurogin1, a basic-helix-loop-helix (bHLH) transcription factor, plays a role in neural tube patterning and folding after neuro-ectoderm has formed [263]. Furthermore, Neurogin1 slows proliferation and promotes neural differentiation and neuronal subtype specification of glutaminergic and GABAergic linages [263]. Reelin plays a role in neural GABAergic Purkinje cell migration from the proliferative areas towards the pial surface of the cerebral cortex during corticogenesis. [264-266].

In our hESTn, whereas both stem cell markers Nanog and Pou5F1 showed a decrease over time, all neural differentiation related genes studied showed a significant increase in expression, demonstrating neural differentiation. Already 24 hours after initiation of differentiation, significant increases were observed in the expression of neural markers βIII-tubulin, Map2 and Neurogin1, in contrast to no significant changes in expression of the stem cell related genes. This suggests continued stem cell proliferation at these early stages in the differentiation protocol, in the presence of increasing differentiation into neural cells. Virtual absence of stem cells at day 11 in the assay was indicated by very low numbers of cells expressing SSEA-4. In the mouse ESTn [70], which is comparable in terms of the time course of differentiation, there was little to no increase in the expression of neural genes occurring during the first three days of the mESC neural differentiation method. This dissimilarity may partly be due to the differences in initial cell aggregate formation between methods. Moreover, in contrast to mouse ES cell cultures, ’undifferentiated’ human ES cell cultures were shown to systematically contain some cells expressing βIII-tubulin, albeit very few, which may explain the faster progression of neural differentiation progression in the human versus the mouse based ESTn assays.

VPA and CBZ are anticonvulsant pharmaceuticals both of which exert their pharmacological effect via increasing GABA- and Glutamatergic neurotransmitter levels [267, 268]. Furthermore, VPA has neuro-protective effects by stimulating neural differentiation and neurite outgrowth, caused by the inhibition of the release of pro-inflammatory factors, inhibition of the GSK3/ Pl3-kinase/Akt/ NFκβ pathway, the release of neurotrophic factors, like GDNF and BDNF and the inhibition of histone deacetylase enzymes (HDAC) [269, 270] with a beneficial effect in neurodegenerative conditions [269, 271]. However, HDAC inhibition and consequent chromatin remodeling during early development is suggested to cause (neuro) developmental abnormalities [268, 269]. Therefore, these anticonvulsant drugs have been associated both with neurotoxic as well as neuroprotective characteristics. In our hESTn assay, in therapeutic concentration ranges both compounds affected the expression

90 hESC neural differentiation assay of genes involved in pluripotency and/or neuro-development, in a compound and concentration specific response. VPA clearly showed a differentiation stimulus, evidenced by a decrease of stem cell related genes and an increase in the expression of βIII-tubulin and Neurogin1, Map2 and MAPt. This is reminiscent of the neuro-protective and remodeling effect and the induction of neural outgrowth caused by VPA exposure [272, 273]. However, with increasing concentration the expression of βIII-tubulin and Neurogin1 was inhibited, together with an inhibition of Reelin expression across all concentrations. This supports the assumption that the nature of the VPA effect is concentration dependent, with a neuroprotective effect at lower exposures and a neurotoxic effect at higher exposures [274]. Therefore, therapeutic concentrations which are beneficial for the pregnant woman may in parallel cause developmental toxicity, since VPA is known to accumulate within the embryo [268, 274, 275]. These effects of VPA in hESTn were qualitatively similar to earlier findings in mESTn in a whole genome array study after 24 hours exposure in the same concentration range [118, 179]. A VPA-induced decreased expression of βIII-tubulin, Pou5f1 and Neurogin1 was seen in both the mESTn and hESTn. In parallel, an increased expression of Mapt and Map2 was observed in both models upon VPA exposure.

CBZ affected gene expression to a lesser extent compared to VPA. This difference in the nature of the responses is in agreement with the effects found in vivo, in which CBZ also has been demonstrated to be less teratogenic [248, 249]. CBZ exposure did not show effects on the expression of the stemness related genes. However, βIII-tubulin, Neurogin1, MAPt and Reelin were all down regulated, with a significant concentration dependent trend, suggesting an inhibiting effect on neural cell differentiation. The mechanism of action of CBZ has not been revealed as extensively as for VPA. However it is known that CBZ also is an inhibitor of HDAC, which has been linked to its teratogenic effect [276, 277]. No significant effects on the selected genes of this study were observed in the mESTn, measured after 24 hours exposure 5 to CBZ [179]. However, a direct comparison of quantitative responses is precluded by necessary differences in culture conditions and assay design.

Taken together, we accomplished a rapid, standardized hESC neural differentiation assay for neurodevelopmental toxicity. Unexposed differentiated cell cultures had lost their pluripotency markers and consisted largely of neural cells expressing βIII-tubulin, as confirmed with immuno-staining. Given that VPA and CBZ caused unique compound specific changes in gene expression in the assay, we demonstrated the responsiveness of the model to neurodevelopmental toxicants. This neurodevelopmental toxicity assay will be further studied for application as a rapid first tier screening for the detection of potential neurodevelopmental toxicants.

91 92 CHAPTER6

Gene expression regulation and pathway analysis after valproic acid and carbamazepine exposure in a human embryonic stem cell based neuro-developmental toxicity assay

Sjors H.W. Schulpen, Jeroen L.A. Pennings, Aldert H. Piersma

Toxicological sciences (2015) accepted for publication

93 Abstract Differentiating pluripotent stem cells in vitro have proven useful for the study of developmental toxicity. Here we studied the effects of anticonvulsant drug exposure in a human embryonic stem cell (hESC) based neurodevelopmental toxicity test (hESTn). During neural differentiation the cells were exposed, for either 1 or 7 days, to non-cytotoxic concentration ranges of valproic acid (VPA) or carbamazepine (CBZ), anti-epileptic drugs known to cause neurodevelopmental toxicity. The effects observed on gene expression and correlated processes and pathways were in line with processes associated with neural development and pharmaceutical mode of action. In general, VPA showed a higher number of genes and molecular pathways affected than CBZ. The response kinetics differed between both compounds, with CBZ showing higher response magnitudes at day 1, versus VPA at day 7. With this study we demonstrated the potential and biological relevance of the application of this hESC based differentiation assay in combination with transcriptomics, as a tool to study neuro-developmental toxicity.

94 hESTn gene expression response after VPA or CBZ exposure

Introduction Pluripotent stem cells are employed in in vitro assays to study developmental toxicity in vitro [48, 174, 239]. Several protocols have been developed in which stem cells differentiate towards different cell linages in vitro, in which in vivo embryonic cell differentiation is mimicked [70, 92, 131, 152, 168, 240, 244, 278, 279]. The mouse embryonic stem cell test (mEST) has proven a valid method to study the effects of compounds during cardiomyocyte differentiation, functioning as a parameter for developmental toxicity [54, 131, 133, 152, 280]. Furthermore, protocols in which mouse embryonic stem cells differentiate into other cell lineages, like neural and osteoblast differentiation, have been developed [70, 117]. The original morphological endpoints take beyond a week to be expressed in these assays, and are difficult to quantify objectively. The inclusion of transcriptomics has led to more detailed read-out parameters, giving improved biological insight and reduced duration of the cell culture protocols [72, 123, 240, 257, 281].

In the last decade, the application of embryonic stem cells from human origin has become of great interest and is widely studied resulting in various culture and differentiation protocols [51, 84, 91, 94, 240, 241, 244, 278, 282]. The use of hESC as a starting point to study human developmental toxicity in vitro, in contrast to mESC, excludes the need for interspecies extrapolation. In an earlier study, we have developed a neural differentiation protocol using H9 hESC in vitro (hESTn) to study neuro developmental toxicity [283].

Here we study the gene expression and pathway response of the hESTn in terms of compound- and concentration specific effects on cell differentiation. The effects on gene expression are studied also on basis of gene ontology (GO) and pathway analysis and compared to in vivo effects, in order to evaluate biological relevance of gene expression responses. We exposed the hESTn for either 1 or 7 days to increasing concentrations of valproic acid (VPA) and carbamazepine (CBZ) as model compounds. Both compounds are pharmaceuticals used as anti-epileptic drugs and are known to cause neurodevelopmental toxicity [269, 274, 276, 284, 285]. Furthermore, in earlier in vitro studies it was demonstrated that these compounds affected myocardial and neural mESC differentiation [118, 179, 286] and affected genes involved in hESC pluripotency and neural differentiation. However, in the latter study the effects on 6 hESC differentiation were assessed for 7 genes only, using Q-PCR. Here we expanded the study of VPA and CBZ effects in hESTn by whole genome transcriptomics array analysis.

Material and Methods Human embryonic stem cell culture H9 hESC (WA09-DL11, WiCell, Madison, Wisconsin) were cultured as described in Schul- pen et al. 2014 [283]. Briefly, the hESC were routinely cultured on mytomycin mitotically inactivated mouse embryonic fibroblast at 37˚C in hESC medium, containing DMEM-F12, supplemented with 20% Knock Out Serum Replacement (KOSR), 1mM L-Glutamine, 0.5% 5000 IU/ ml Penicillin/5000 μg/ml Streptomycin, 1% non-essential amino acids, 0.1mM

95 β-Mercaptoethanol and 0.2µg/ml fibroblast growth factor-basic(bFGF). hESC were passaged 2-3 per week.

Neural differentiation hESC cells were differentiated as described in Schulpen et. al 2014 (Figure 1) [287]. Briefly, hESC were enzymatically dissociated after incubation with Collagenase IV and transferred to bacterial culture dishes containing hESC culture medium (Figure 1A-B). Within 4 days the cells formed cell aggregates, which were transferred to Poly-D-Lysine (PDL)/ Laminin coated cell culture dishes containing DMEM-F12 supplemented 1% 5000 IU/ ml Penicillin/ 5000 μg/ml Streptomycin, 1.5 mM L-Glutamine and 10% insulin, transferrin and Selenium (ITS) premix (Figure 1C-D). The cell aggregates attached to the bottom of the dishes and were cultured for 3 days. At day 7 the ITS medium was replaced by neurobasal medium supplemented with 1% 5000 IU/ ml Penicillin/ 5000 μg/ml Streptomycin, N-2- and B27 premix (Figure 1E). For the morphological end-point (not used in this study) the cells were cultured until day 11, with a medium refreshment at day 9 (Figure 1F).

Exposure Differentiating cell cultures were exposed for either 1 or 7 days (indicated with a green bar in Figure 1) to either 0.1 (n=2), 0.33 (n=6) or 1mM (n=6) VPA (Sigma–Aldrich, CAS#1069- 66-5), or 0.033 (n=2) , 0.1 (n=6) or 0.33 mM (n=6) CBZ (Sigma–Aldrich, CAS#298-46-4), starting after the hESC were dissociated (Day 0). These concentrations had been shown to produce less than 30% viability loss in a 5 day cell viability assay [287]. VPA and CBZ were dissolved in medium and 0.25% DMSO respectively. Unexposed control samples were collected at day 0 (n=6) , 1 (n=5) , 4 (n=4) ,7 (n=6) , and 9 (n=2). The control samples taken at day 1 and 7 served as time matched controls for VPA exposure after 1 and 7 day respectively. At day 1 and 7 also samples cultured with 0.25% DMSO (n=6) were collected serving as a time-matched controls for 1 and 7 day CBZ exposed groups, respectively Together with medium replacement at day 0 and 4 VPA or CBZ were added back to the culture medium at the appropriate concentrations.

RNA extraction and processing Cells ready for RNA extraction (indicated in Figure 1 as ‘RNA extraction Cont. + exp’), controls and exposed respectively, were directly collected in RNA protect (Qiagen) and subsequently stored at -20˚C. Before RNA extraction, the cells in RNA protect were thawed, centrifuged and the RNA protect was discarded. The RNA was extracted using the QiaCube and RNeasy mini kit (Qiagen) including an DNase incubation step, following the manufactures manual. The extracted RNA was stored at -80˚C. The concentration of the RNA samples was measured using the Nanodrop (Thermo Scien- tific).

96 hESTn gene expression response after VPA or CBZ exposure

Microarray analysis RNA samples were randomized and further processed for hybridization to Affymetrix HT HG-U133 + PM plates at the Microarray department (MAD) of the University of Amster- dam, The Netherlands. Amplification, labeling and hybridization was performed according to Affymetrix protocols, using an automated Affymetrix Genechip console. For each individual sample 100 ng RNA was used for the biotin-labelling reaction. The labeled cRNA was fragmented and hybridized to the Affymetrix HT HG-U133 + PM plates. After staining, the plates were scanned with Genechip HT array plate scanner and analysed with Affymetrix HT software suite. The microarray data is accessible at NCBIs Gene Expression Omnibus, under GEO accession number GSE 64123.

Data analysis and processing Quality control and normalization of Affymetrix CEL files were performed using the Arra- yAnalysis website (http://www.arrayanalysis.org/) (Maastricht University, The Netherlands) [288], using the Robust Multichip Average (RMA) algorithm [148] and MBNI custom CDF version 14 [289]. Two samples were discarded after quality check: CBZ 0.33mM day 1 and day 7. Normalized data was Log2 transformed. Of each sample the average fold change (FC) was calculated using R. This resulted in expression ratio values of the cultures exposed to either VPA or CBZ compared to the time-matched control samples. Differentially expressed genes were identified using a one-way Anova (OWA) analysis with a significance threshold of P ≤ 0.001 and FDR ≤ 5% using R. Overlapping genes were calculated and displayed as Venn diagrams using Venny [149].

Principal component analysis (PCA) was used to visualize samples in relation to time (differentiation) and compound exposure (concentration) dependent effect using R. Input for the PCA analysis consisted of expression data for the union of genes differentially expressed over time (between three control time points) and those differentially expressed across all concentrations either after 1 or 7 days of exposure to either VPA or CBZ.

Significantly affected pathways and Gene Ontology (GO) terms were identified with Tox- 6 profiler [290], on the basis of whole genome expression ratios after 7 days. Significance was calculated based on T- and E-values. The T-value was obtained by a T-test between the expression changes for a defined gene set compared to all other genes. The E-value was the associated two-tailed P-value with Bonferroni correction for the number of gene sets tested [290]. To study and compare the effect on a particular GO-term caused by each individual concentration both after 1 and 7 days of exposure both after VPA and CBZ exposure, the genes involved in individual GO-terms were selected and the average FC for each sample was calculated. Pathway analysis of significantly differentially expressed genes after 1 day of either VPA or CBZ exposure was performed using Metacore (Thomson Reuters, Philadelphia, USA) (https://portal.genego.com). For each significant gene the average FC was calculated, both after 1 or 7 days of exposure.

97 Exposure 7 days VPA: 0.1, 0.33 and 1.0mM CBZ: 0.033, 0.1 and 0.33mM Exposure 1 day A C E F

Day 0 1 4 7 9 11

Medium hESC medium ITS medium Neurobasal medium + N2/B27 RNA extraction Cont. Cont. + exp. Cont. Cont. + exp. Cont. Cont.

PDL/ Laminin PDL/ Laminin PDL/ Laminin

B D G

Figure 1: Method and study design Schematic representation of study design. For details, see materials and methods.

Results Differentially expressed genes After 1 and 7 days of exposure to VPA, 3696 and 5233 genes were significantly regulated (p ≤ 0.001 and FDR ≤ 5%) across all concentrations, respectively, with 2234 of these genes regulated at both time points (Figure 2). After 1 and 7 days of exposure to CBZ, 682 and 1090 genes were significantly regulated across all concentrations respectively, with 112 of these genes regulated at both time points. Moreover, the vast majority of VPA-regulated genes were not regulated by CBZ, and time- and compound-dependent differences in responsive genes overlapping among experimental groups were apparent (Figure 2).

VPA day 7 CBZ day 1 5233 682 VPA day 1 CBZ day 7 3696 1090 2519 230

1840 151 34 1268 328 118 36

29 71 293 13 247 110

Figure 2: Venn-diagram of statistically significant gene expression changes (P ≤ 0.001 and FDR ≤ 5%) after either 1 or 7 days of exposure to either VPA or CBZ.

98 hESTn gene expression response after VPA or CBZ exposure

PCA Analysis Time- and compound concentration dependent global gene expression changes were visualized in PCA plots (Figure 3A-D). We used day 0, day 1 and day 4 control gene expression data to compare the effects of 1 day exposure (Figure 3A-B), and day 4, day 7 and day 9 control gene expression data for comparing the effects of 7 day exposure (Figure 3C-D). These figures show that exposure caused compound- and concentration-dependent deviation from the time-dependent differentiation track, both after 1 and 7 days of exposure. Moreover, the inserted venn diagrams in Figure 3 show that in each case both anticonvulsant drugs affected genes that were regulated time-dependently as well as genes that were not regulated time-dependently. As an example, valproate after 1 day of exposure regulated 698 time- regulated genes plus 2998 time-independent genes, whereas 1152 time-regulated genes were not affected by valproate exposure (Figure 3A). Similar patterns of common and unique gene expression changes between time and exposure were observed for the other exposures applied (Figure 3 B-D).

A PCA VPA day 1 C PCA VPA day 7 0 2 Cont.d0 Cont.d4 Cont.d1 Cont.d7 0

Cont.d4 1 Cont.d9

5 0.01mM 0.01mM 1 0.33mM 0.33mM 1.0mM 1.0mM 0 ) ) 1 % % 8 2 0 5 1 . . 7 4 1 1 ( (

2 2 C C 5 P P 0 1 - 0 5 - 0 2 - 1152 698 2998 982 1037 4196 0 1 - Time Exp. Time Exp.

-10 -5 0 5 10 15 20 -10 0 10 20

PC1 (51.72%) PC1 (59.23%) PCA CBZ day 7 B PCA CBZ day 1 D 5 1 Cont.d0 Cont.d4 5 Cont.d7 Cont.d1 1 Cont.d4 Cont.d9 0.033mM 0.033mM 0.1mM

0 0.1mM 1 0.33mM 0.33mM 0 1 ) ) % % 6 1 3 . 8

. 6 0 5 3 2 3 (

(

5 2 2 C C P P 0 0 5 - 5 -

1654 196 486 1783 236 854 Time Exp. 0

Time Exp. 0 1 1 - -

-10 -5 0 5 10 -15 -10 -5 0 5 10

PC1 (34.81%) PC1 (36.02%)

Figure 3 PCA plots of global gene expression response after 1 day of either VPA- (A) or CBZ(B) exposure compared to control samples at day 0,1 and 4 and response after 7 days of either VPA- (C) or CBZ(D) exposure compared to control samples at day 4,7 and 9.

99 Tox profiler Gene ontology analysis Using Tox-profiler, analysis of the complete dataset of compound-induced gene expression changes revealed 126 gene ontology (GO) gene groups significantly regulated in at least one of the experimental groups in this study (T-value ≥ 3.5 and E ≤ 0.05)(Figure 4). Both anticonvulsant drugs showed common and specific GO terms regulated, revealing compound- specific characteristics of the gene expression response. We further analyzed the extent of gene expression response within three GO terms regulated by VPA but not CBZ, ‘ion transport’, ‘synapse’ and ‘axon’, three GO terms regulated by CBZ but not VPA, ‘angiogenesis’, ‘cholesterol homeostasis’ and ‘anterior/ posterior pattern formation’, and two GO terms regulated both by VPA and CBZ, ‘calcium ion binding’ and ‘chromatin modification’ (Figure 5). This analysis shows that the percentage of genes responding significantly (p<0.001) within GO-terms was in all cases higher after VPA- as compared to CBZ exposure, even for those GO-terms which responded significantly to CBZ exposure only. The absence of significance of the VPA response in these CBZ-regulated GO terms is most likely due to the abundance of gene expression changes induced by VPA in general, increasing the threshold for statistical significance of VPA-mediated regulation for individual GO-terms. Also when comparing equimolar concentrations (0.33 mM) only, the maximal observed response to VPA was higher than for CBZ in these GO terms. Furthermore, whereas the VPA-response within GO terms increased between day 1 and 7 of exposure, CBZ generally tended to show a similar or even higher response level at day 1 as compared to day 7.

100 hESTn gene expression response after VPA or CBZ exposure

VPA CBZ Day 1 7 1 7 Conc. (mM) 0.1 0.33 1.0 0.1 0.33 1.0 0.03 0.1 0.33 0.03 0.1 0.33

camera-type eye development structural constituent of eye lens anatomical structure formation involved in morphogenesis 10 -5 0 5 10 melanosome membrane chromosome cell cycle cell division mitosis DNA replication response to DNA damage stimulus DNA repair chromatin modification spliceosomal complex nuclear speck mRNA processing chromatin binding nucleoplasm RNA splicing RNA binding regulation of transcription nucleolus transcription coactivator activity MHC class I protein complex antigen processing and presentation of peptide antigen via MHC class I MHC class I receptor activity antigen processing and presentation ion transport positive regulation of synaptic transmission synapse synaptic transmission synaptic vesicle membrane neuropeptide receptor activity neuropeptide signaling pathway axon endocrine pancreas development positive regulation of neuron differentiation growth factor activity platelet alpha granule lumen multicellular organismal development anterior/posterior pattern formation embryonic skeletal system morphogenesis neuron migration nervous system development sequence-specific DNA binding transcription factor activity cholesterol homeostasis platelet activation fibrinogen complex lipid transporter activity cholesterol transporter activity lipoprotein metabolic process eukaryotic cell surface binding chylomicron very-low-density lipoprotein particle phospholipid efflux positive regulation of cholesterol esterification cholesterol efflux reverse cholesterol transport high-density lipoprotein particle remodeling collagen binding platelet-derived growth factor binding extracellular matrix organization extracellular matrix structural constituent extracellular matrix binding cell adhesion collagen collagen fibril organization calcium ion binding extracellular space proteinaceous extracellular matrix response to steroid hormone stimulus blood vessel development transforming growth factor beta receptor signaling pathway aldo-keto reductase activity response to organic substance response to cytokine stimulus negative regulation of signal transduction positive regulation of anti-apoptosis response to hydrogen peroxide basolateral plasma membrane positive regulation of cell migration response to hormone stimulus platelet-derived growth factor receptor signaling pathway caveola insulin-like growth factor binding calcium-dependent phospholipid binding regulation of cell growth positive regulation of cell-substrate adhesion response to nutrient response to calcium ion blood coagulation protein binding, bridging integrin-mediated signaling pathway response to drug patterning of blood vessels positive regulation of peptidyl-serine phosphorylation fibrinolysis response to progesterone stimulus 6 response to mechanical stimulus protein homodimerization activity response to hypoxia organ regeneration apical plasma membrane focal adhesion angiogenesis basement membrane skin development cell-matrix adhesion cellular response to hormone stimulus heparin binding glycosaminoglycan binding skeletal system development chemokine activity positive regulation of lipoprotein lipase activity troponin complex spherical high-density lipoprotein particle sugar binding membrane fraction integral to plasma membrane proteolysis regulation of blood pressure peptidase activity endoplasmic reticulum Golgi membrane Golgi apparatus endoplasmic reticulum lumen

Figure 4 Comparative Heat map of relative expression of GO terms significantly regulated after at least one exposure scenario.

101 Enriched after VPA exposure Enriched after CBZ exposure

Ion transport Angiogenesis 60 Day 1 40 #genes= 479 35 #genes= 98 50 Day 7 30 40 25 30 20 15 20 10 10 5 0 0 0.1 0.33 1 0.033 0.1 0.33 0.1 0.33 1 0.033 0.1 0.33 VPA CBZ VPA CBZ Synapse Cholesterol homeostasis 60 40 #genes= 232 #genes= 37 50 35 30 40 25 30 20

20 15 10 10 5 0 0 0.1 0.33 1 0.033 0.1 0.33 0.1 0.33 1 0.033 0.1 0.33 VPA CBZ VPA CBZ

Axon Anterior/ Posterior pattern formation 40 40 35 #genes= 107 35 #genes= 77 30 30 25 25 20 20 15 15 10 10 5 5 0 0 0.1 0.33 1 0.033 0.1 0.33 0.1 0.33 1 0.033 0.1 0.33

% Genes sign. enrich. within GO-term % Genes sign. enrich. VPA CBZ VPA CBZ

Enriched both after VPA and CBZ exposure

Calcium ion binding Chromatin modification 40 40 35 #genes= 853 35 #genes= 183 30 30 25 25 20 20 15 15 10 10 5 5 0 0 0.1 0.33 1 0.033 0.1 0.33 0.1 0.33 1 0.033 0.1 0.33 VPA CBZ VPA CBZ

Concentration (mM)

Figure 5 Selected responses of GO-term significantly enriched in T-profiler after either VPA exposure (A), CBZ exposure (B) or in both experiments (C), expressed as. Percentage of genes within terms significantly differentially regulated.

102 hESTn gene expression response after VPA or CBZ exposure

Metacore pathway analysis Using Metacore pathway analysis, significantly differentially expressed genes after 1 day of VPA and CBZ exposure were linked to 61 and 31 significantly regulated pathways (P≤0.001) (data shown in supplementary Table 3). Based on literature, 8 pathways (shown in Table 1 and supplementary Figure 1) involved in pharmacologic mechanism of action, (neuro) development, and toxicity, were selected for further analysis. VPA affected a higher number of genes in each of these pathways and showed a related higher level of significance, compared to CBZ exposure.

VPA CBZ # of genes overrepre- # of genes overrepre- # of genes regulated sentation regulated sentation Pathway in pathway after exposure p-value FDR after exposure p-value FDR Development_WNT signaling pathway. Part 2 53 17 5.70E-05 3.73E-03 9 1.80E-05 1.11E-03 Development_TGF-beta receptor signaling 50 16 9.67E-05 5.49E-03 9 1.10E-05 9.28E-04 Development_Oligodendrocyte differentiation from adult stem cells 51 15 4.47E-04 9.28E-03 4 5.93E-02 2.43E-01 Neurophysiological process_ACM regulation of nerve impulse 46 14 4.68E-04 9.49E-03 1 7.42E-01 8.23E-01 Development_Role of CDK5 in neuronal development 34 11 1.07E-03 1.66E-02 1 6.32E-01 7.78E-01 Regulation of GSK3 beta in bipolar disorder 45 13 1.27E-03 1.89E-02 5 9.27E-03 9.35E-02 Neurophysiological process_Constitutive and regulated NMDA receptor trafficking 63 16 1.69E-03 2.12E-02 1 8.44E-01 8.67E-01 Signal transduction_Erk Interactions: Inhibition of Erk 34 10 3.94E-03 3.44E-02 1 6.32E-01 7.78E-01

Table 1 Pathways significantly enriched after VPA exposure and/or after CBZ exposure, involved in neurogenesis or pharmaceutical mechanism of action, showing the total number of genes described within the pathway and the number statistically significantly regulated after exposure.

103 Discussion We studied the global gene expression response of a human embryonic stem cell based neural differentiation assay to two anti-epileptic pharmaceuticals, VPA and CBZ. These anti-convulsant drugs are known to cause neurodevelopmental toxicity in experimental animals and man. In this study, differentiating hESC cells were exposed for 1 or 7 days to VPA or CBZ. The exposure duration was based on earlier studies. In Schulpen et al. [287] we demonstrated significant differentially expression of 2 genes involved in stem cell renewal and maintenance of pluripotency (Pou5F1 and Nanog) with in parallel 5 genes involved in neurogenesis (βIII- tubulin, Neurogenin1, Reelin, MAPt and MAP2) after 7 days of differentiation. However, in line with earlier studies indicating that early gene expression responses are most specific for revealing compound mechanism of action [130] an early time point during differentiation may be preferable. In addition, a fast read-out system is preferable for an in vitro screening test. Selected exposures were close to the pharmaceutical systemic concentration range, which is between 0.3-0.8 mM and 0.02-0.05 mM for VPA and CBZ, respectively [291-294]. The tested concentrations did not affect cell viability by more than 30%, as determined in [287].

Although the mechanisms involved in its therapeutic mode of action are not fully understood [295, 296], several mechanisms of VPA have been described. The major mechanisms of pharmacological action involve the enhancement of the g-aminobutyric acid-mediated (GABA) neurotransmission and inhibition of voltage-gated sodium channels by which high-frequency firing of neurons is inhibited [267, 269, 295, 296]. Furthermore, VPA inhibits histone deacetylase (HDAC) enzyme activity, stimulates mitogen-activated protein kinase (MAPK) [296] and affects Wnt/b-catenin and the ERK Pathway [295]. The precise involvement of the latter two processes in therapeutic effects is unknown [295]. Additionally, VPA is known to be neuroprotective through an HDAC inhibitory effect and ERK pathway involvement [271]. However, the dosimetry underlying neuroprotective versus neurotoxic effects is unclear [269]. VPA exposure during pregnancy has been associated with a variety of developmental anomalies, neural tube defects, spina bifida, cleft lip and palate, cardio vascular abnormalities, developmental delay, limb defects, autism and bipolar [274] disorder. The mechanisms involved in teratogenicity are not clear. However, the inhibitory effect of VPA on HDAC may disturb normal gene transcription during fetal cell proliferation and differentiation, which is initiated by hyperacetylation of the DNA histones by histone acetyltransferase enzymes (HATs) which results in unraveling chromatins, facilitating transcription [268].

CBZ is a commonly prescribed anti-convulsant drug, which acts via multiple mechanisms like blocking and potentiation of cation channels [277, 297]. Furthermore, it modulates the release, uptake and receptor binding of neurotransmitters [277]. Many animal studies have shown CBZ effects on the developing embryo. However, the results of studies in pregnant women are conflicting, because some describe an increased rate of anomalies, while others did not find and increase. Malformations associated with maternal use of CBZ include neural tube defects, cleft palate, cardiac- and urinary tract anomalies [249, 284, 285]. The mechanism underlying CBZ toxicity is not known. However, CBZ is an effective HDAC inhibitor [277], which may explain the developmental anomalies.

104 hESTn gene expression response after VPA or CBZ exposure

The Venn diagram analysis revealed that both compounds showed time dependent changes in gene expression, including common genes as well as unique genes regulated by either compound. The PCA plot confirmed these findings, with additional concentration dependent effects, at the level of regulation of the entire genome. (Neuro-) developmental GO terms, like ‘ Embryonic and Tissue Morphogenesis’ were observed in the overlapping field in the Venn- diagram between VPA- and CBZ exposure after 7 days and the overlapping field between VPA day 1 and 7, like ‘Axonogenesis’ and ‘Neuron Projection Morphogenesis’. Furthermore, the overlapping field between VPA exposure after day 1 and 7 included pharmacological mode of action associated GO terms like, ‘Chromatin- and Histone Modification’. Overlap- ping fields between CBZ after 1 and 7 days of exposure resulted in no significant GO-term enrichment. The heat map of GO terms significantly regulated by at least one compound (Figure 4) extends the observation that the VPA response was more pronounced in terms of both the number of genes regulated and the magnitude of the responses. Although this is in part related to differences in molar concentration tested, the concentration-dependent gene regulation within selected GO terms (Figure 5) confirms that VPA induces a stronger response than CBZ at equimolar concentrations. Moreover, similar findings have occurred in other systems in which the gene expression responses of VPA and CBZ showed comparable differences [115, 118, 179]. This is in line with congenital malformations found in vivo after exposure, in which CBZ seems to be less teratogenic compared to VPA [248]. Interestingly, Figure 4 also revealed differences in magnitude of responses with time of exposure between both drugs. VPA tended to show a higher response at day 7, whereas CBZ was more effective at day 1. Given the variety of GO terms regulated overall, the specificity of the response did not seem to differ between time points. Literature data indicate that early gene expression responses are more specific than later ones, the latter primarily revealing nonspecific downstream effects [130]. Time-dependent effects may also be developmental stage dependent as exposures occur over several important developmental windows. These findings indicate that the study design, including the exposure timing and duration, may affect the outcome of in vitro test systems.

Beyond the GO term library, global molecular pathways such as collected in Metacore offer a more specific tool for the analysis of gene expression responses. Although these pathways 6 have not been fully validated, they can give insight into the interpretation of individual genes related to pharmacological action and developmental neurotoxicity of the tested compounds. Metacore clearly detected specific pharmacological and neurodevelopmental pathways affected by both compounds, and again the response to VPA was more pronounced than that to CBZ. These findings are generally in line with the abundant knowledge on VPA modes of action versus limited knowledge base on CBZ mechanisms of action. It is of interest to note the regulation of WNT and TGFB signaling by both compounds, two processes that are of crucial importance in neurodevelopment. The WNT signaling pathway plays an important role in embryonic development involved in signaling during mesoderm, neuroectoderm and body axis formation [298]. Furthermore, the Wnt/β-catenin signaling plays a major role in maintaining self-renewal as well as in regulating ESCs differentiation.

105 In our analysis, we observed a significant down regulation of Tcf/ Lef genes in the Wnt/β- catenin signaling pathway, which enhances self-renewal and results in differentiation resistan- ce in mouse ESC [299]. The TGFβ signaling pathway controls cellular processes including cell proliferation, differentiation and migration [300, 301]. The ERK-pathway, involved in cell growth, proliferation and cell survival [302], and an important target in neuronal signal transduction and involved in neuronal maturation and survival, known to be activated by VPA exposure [296], involved in cell growth, proliferation and cell survival [302], was found to be significantly enriched after VPA exposure. CBZ only regulated 1 gene involved within the ERK pathway. Within neurons, activation of NMDA receptors leads to stimulation of Calmodulin regulated Calcineurin A activation which subsequently activates STEP, which is expressed in several neuronal cell types [303]. Activated STEP limits the duration of ERK activity [303], regulating the duration of ERK signaling in neurons. After exposure to VPA we found an upregulation of the Calmodulin/Calcineurin A/ STEP genes. Further study into these signaling pathways and their response to anticonvulsant drugs in developmental systems may reveal common mechanisms of neurodevelopmental toxicity for this class of drugs. Both processes ‘Neurophysiological process_ACM regulation of nerve impulse’ and ‘Neurophysiological process_Constitutive and regulated NMDA receptor trafficking’, involved in neurotransmission, were both affected after VPA exposure, resulting in 14 and 16 genes significantly regulated, respectively. After CBZ exposure only 1 gene (IP3 receptor) was regulated in both processes. Interestingly, although it is known that these anti-epileptics will act on glutamatergic neurotransmission, reducing fast excitatory neurotransmission [304] after VPA exposure, all genes regulated in theses pathways, except one (PKA-reg) were up regulated, which is in contrast to what was expected. The biological significance of regulation of genes in this pathway is limited by the fact that full glutamatergic cell maturation is not likely in this short term cell differentiation model. VPA and CBZ affected 13 and 5 genes within the ‘regulation of GSK3 beta in bipolar disorder’ pathway, respectively. It has been suggested that in bipolar disorder, mood stabilizers including VPA, CBZ and Lithium also act by the down regulation of glutametergic signaling [305], in which GSK3 beta plays and important role and is a molecular target [246].

Taken together, this study shows that the hESTn assay allows the investigation of common and unique genome wide gene expression changes and their functional coupling to specific pharmacological and neurodevelopmental regulatory pathways. The human origin of the cells circumvents the need for interspecies extrapolation. Although a stronger effect on gene expression was observed after 7 day of exposure, the brief exposure of 1 day already reveals neurodevelopment-specific gene expression responses. Thisallows for a brief culture duration and increased throughput. Human stem cell based in vitro tests such as the hESTn can assist in a screening situation to prioritize compounds for further development as potential drugs.

106 hESTn gene expression response after VPA or CBZ exposure

107 108 CHAPTER7

Comparison of gene expression regulation in mouse- and human embryonic stem cell assays during neural differentiation and in response to valproic acid exposure

Sjors H.W. Schulpen, Peter T. Theunissen , Jeroen L.A. Pennings , Aldert H. Piersma

Reproductive toxicology (2015) accepted for publication

109 Abstract Embryonic stem cell tests (EST) are considered promising alternative assays for developmental toxicity testing. Classical mouse derived assays (mEST) are being replaced by human derived assays (hEST), in view of their relevance for human hazard assessment. We have compared mouse and human neural ESTn assays for neurodevelopmental toxicity as to regulation of gene expression during cell differentiation in both assays. Commonalities were observed in a range of neurodevelopmental genes and gene ontology (GO) terms. The mESTn showed a higher specificity in neurodevelopment than the hESTn, which may in part be caused by necessary differences in test protocols. Moreover, gene expression responses to the anticonvulsant and human teratogen valproic acid were compared. Both assays detected pharmacological and neurodevelopmental gene sets regulated by valproic acid. Common significant expression changes were observed in a subset of homologous neurodevelopmental genes. We suggest that these genes and related GO terms may provide good candidates for robust biomarkers of neurodevelopmental toxicity in hESTn.

110 Comparing mESTn and hESTn

Introduction A variety of in vitro methods have been developed for the study of mechanisms involved in embryonic development. These methods can be employed to elucidate effects of compound exposure correlated to toxicity. Ultimately, these test methods could be used for developmental toxicity screening of compounds and may contribute to the reduction of experimental animal use. Embryonic stem cells (ESC) can differentiate in vitro into different cell types, enabling the study of mechanisms of differentiation and developmental toxicity [48, 306, 307]. Several assays have been developed in which mouse embryonic stem cells (mESC) differentiate in to various cell types like cardiomyocytes [132, 237], neural cells [70, 87] and osteoblasts [117]. Processes involved in development and regulated as a response to compound exposure may differ between species on the molecular level [308]. Therefore, test systems based on cells of human origin are preferred for human risk evaluation. During the last decade, the application of human embryonic stem cells (hESC) in toxicity testing has been extensively studied and differentiation assays have been developed [51].

We have previously developed ESC based neural differentiation assays, with cells from either mouse (mESTn) [70] or human (hESTn) origin [309]. Both methods were based on the same principles. First, differentiation was initiated by changing the culture conditions that maintain pluripotency to differentiation stimulating conditions. In both methods, ESC aggregates were used to facilitate the differentiation process, and the morphological endpoint was reached after 11 days, at which clear neurological structures are abundantly present. Gene expression changes have been demonstrated to provide a sensitive and informative readout in these assays to study the developmental toxicity of substances [122, 240, 310]. Compound exposure during differentiation causes concentration specific gene expression changes. Having extensive gene expression data available on both mouse and human EST cell differentiation, as well as of the effects of substances on that process, we have the unique opportunity to directly compare both methods. It can be hypothesized that homologous genes and gene pathways, regulated in both mouse and human EST, may be among the most robust and predictive candidate biomarkers of neurodevelopmental toxicity. Therefore, we compared mESTn and hESTn as to gene expression changes during neural differentiation. Furthermore, we compared the effects on gene expression in mESTn and hESTn of valproic acid (VPA), an anticonvulsant and neurodevelopmental toxicant in vivo [267, 274, 275].

Method Stem cell culture and neural differentiation 7 mESC culture and neural differentiation was performed according to the protocol published by Theunissen et al. [70]. Briefly, mESC (ES-D3) were routinely cultured on gelatin coated culture dishes, in presence of leukemia inhibiting factor (LIF) and were sub-cultured every 2-3 days. The mESC culture medium (mCM) contained DMEM, supplemented with 20% fetal bovine serum, 1% nonessential amino acids, 1% penicillin/streptomycin, 2mM L-glutamine and 0.1mM β-mercapto ethanol. Neural differentiation was initiated by culturing the ESC in hanging drop culture, in which the cells were cultured in droplets of stem cell medium

111 where they formed embryoid bodies (EB). After 3 days the cells were transferred to bacterial dishes and cultured in suspension of mCM, supplemented with 0.5 μM retinoic acid (RA). On day 5 EB were plated on laminin coated dishes and cultured in mCM containing 10% FBS and supplemented with 2.5 μg/ml fibronectin. On day 6, the mCM was replaced by ITS medium, containing DMEM/F12, supplemented with 0.2μg/ml insulin, 50μg/ml apo-transferrin, 30nM sodium selenite, 1% penicillin/streptomycin, 2mM L-glutamine and 2.5μg/ml fibronectin. On day 7 the EB were replated on poly-l-ornithine and laminin coated dished and cultured in DMEM/F12 medium, supplemented with 0.2μg/ml insulin, 1% penicillin/ streptomycin, 30nM sodium selenite, 50μg/ml apo-transferrin, 20 nM progesteron, 100 μM putrescine and basic fibroblast growth factor (bFGF). The medium was replaced every other day for 7 days, until day 11. hESC culture and neural differentiation were performed according to the protocol published by Schulpen et al. [283]. Briefly, hESC were cultured on a feeder layer of mitotically inactivated mouse embryonic fibroblasts in hESC culture medium hCM, containing DMEM-F12 supplemented with 20% Knock Out Serum Replacement (KOSR), 1mM L-Glutamine, 0.5% 5000 IU/ ml Penicillin/ 5000 μg/ml Streptomycin, 1% non-essential amino acids, 0.1mM β-Mercaptoethanol and 0.2µg/ml bFGF. The hESC cells were sub-cultured 1-3 times per week and hCM was refreshed every day. To initiate neural differentiation, the hESC clusters were enzymatically dissociated, transferred to bacterial dishes, and cultured in suspension in hCM. At day 4 the cell aggregates were transferred to Poly-D-Lysine and laminin coated dishes containing DMEM-F12 supplemented with 1% 5000 IU/ ml Penicillin/ 5000 μg/ml Streptomycin, 1.5 mM L-Glutamine and 10% ITS premix. After 2 days the medium was refreshed. At day 7 the medium was replaced by Neurobasal medium, supplemented with N-2 premix, B27 premix and 1% 5000 IU/ ml Penicillin/ 5000 μg/ml Streptomycin. After 2 days the medium was refreshed.

VPA exposure VPA exposure data was obtained in earlier studies of the mESTn [118] and hESTn [311]. Exposure in both assay systems was started at the onset of differentiation initiation in cell aggregates. The VPA concentrations tested were based on human pharmacological relevant concentrations and below 80% cytotoxicity in vitro. Optimal exposure concentrations were determined in earlier individual studies, performed independently, resulting in comparable concentrations of VPA exposure [118, 283]. mESC had been exposed for 24 hours, from day 3 in the protocol, to either 0.015 mM, 0.06mM, 0.25mM or 1.0mM VPA. Each concentration contained 8 replicates (n=8). hESC had been exposed for 24 hours, from day 0 of the protocol, to either 0.1mM (n=2), 0.33mM (n=6) or 1.0mM (n=6) VPA. Existing data from these studies were used in the present comparative investigation.

RNA extraction Cells ready for RNA extraction were harvested and stored at -20˚C in RNA protect (Qiagen Benelux, Venlo, The Netherlands). Differentiating mESC were collected at days 0, 3, 4, 5, 6 and 7. Each control group contained 8 replicates, except for day 0, which contained 4 repli-

112 Comparing mESTn and hESTn cates. Differentiating hESC were collected at day 0 (n=6) ,1 (n=5), 4 (n=4), 7 (n=6), 9 (n=2) and 11 (n=4). Mouse and human ESC exposed to VPA were collected at day 1 and 4, respectively. RNA was extracted using the manufacturer’s protocol. The extracted RNA was eluted in RNase free water and stored at -80˚C, until analysis.

Microarray analysis Mouse- and human RNA samples were randomized and processed for hybridization to whole Mouse Genome 430 2.0- or human HT HG-U133 + PM Affymetrix genechips, respectively and further processed as described in Theunissen et al. [70] and Schulpen et al. [283].

Data analysis and statistics Quality control and normalization of Affymetrix CEL files was performed using either RMAexpress [312]for mouse Affymetrix genechips, or ArrayAnalysis website (http://www. arrayanalysis.org/) (Maastricht University, The Netherlands) [288] for human Affymetrix gene chips, using the Robust Multichip Average (RMA) algorithm [148] and MBNI custom CDF version 15 [289]. Subsequently, normalized data was Log2 transformed. For further analysis, mouse gene ID were transformed in human gene homologues, using R-software (version 2.15.0) and data downloaded from NCBI homoloGene (http://www.ncbi.nlm.nih. gov/homologene). Additionally, the genes which were present both on mouse- and human Affymetrix gene chips were selected. This resulted in a gene set with a total number of 14939 genes, which were used in this study for all further analyses.

Significant differentially expressed gene expression Differentiation was studied by calculating the significantly regulated genes for each sample using a one-way Anova (OWA) analysis with a significance threshold of P ≤ 0.001 and a maximum absolute fold change (FC) across time points ≥ 2, using R. For each significant gene the FC was calculated compared to the average fold change across all time points per species, using R. VPA significantly regulated genes were calculated using R with a significance threshold of P ≤ 0.001 and FDR ≥ 5%. Heat-map visualization and hierarchical clustering was performed using Genemath XT (applied Maths, Sint-Martens-Latem, Belgium), using Euclidean distance and Ward linkage. Commonalities among genes significantly regulated during differentiation in both assays and after VPA exposure were calculated and displayed in Venn-diagrams, using Venny [149].

Functional analysis 7 Significantly enriched GO terms of specifically selected gene sets (p<0.01) during either mESTn- or hESTn differentiation were analyzed using David[151]. Human tissue enrichment of the 14939 genes was analyzed using Tox-profiler[290]. For each sample the enrichment was calculated with a significance of T-value ≥ 3.5 and E ≤ 0.05. The T-test is a value obtained by a T-test between the expression changes for a defined set of genes versus all other genes [290]. The E-value is the associated two-tailed P-value with Bonferroni correction for

113 the number of gene sets tested. If a GO-term or human tissue specific gene set was significantly regulated by at least one experimental group, it was selected for further analysis and other experimental groups were included in the comparison. The effects on GO-term- and human tissue enrichment after VPA exposure of the 14939 genes, were analyzed using Tox- profiler.

114 Comparing mESTn and hESTn

Results A B mESTn hESTn -2 -1 0 1 2 mESTn hESTn Day 0 3 4 5 6 7 0 1 4 7 9 11 Day 0 3 4 5 6 7 0 1 4 7 9 11 1 1 1.5 1 1 1.5 0.5 2 0.5 2 -0.5 0 5 10 -0.5 0 5 10 -1.5 3 -1.5 2 1.5 2 1.5 0.5 0.5

3 -0.5 0 5 10 -0.5 0 5 10 -1.5 -1.5 3 1.5 4 3 1.5 0.5 0.5

-0.5 0 5 10 -0.5 0 5 10

-1.5 -1.5 4 4 1.5 4 1.5 0.5 5 0.5

-0.5 0 5 10 -0.5 0 5 10

-1.5 -1.5 1.5 1.5 5 5 0.5 0.5 -0.5 0 5 10 -0.5 0 5 10 6 -1.5 -1.5 5 1.5 1.5 6 0.5 6 0.5 -0.5 0 5 10 -0.5 0 5 10 -1.5 -1.5 1.5

7 0.5 7 -0.5 0 5 10 -1.5 8 1.5 6 0.5 -0.5 0 5 10 8 -1.5 mESTn hESTn C 2824 950 1025

mESTn hESTn mESTn hESTn DDay 0 3 4 5 6 7 0 1 4 7 9 11 E Day 0 3 4 5 6 7 0 1 4 7 9 11 TFAP2B 1.5 NR2F2 PLAT NRP1 1 1 SLIT2 TSHZ3 0.5 DACH1 KCTD12 LRRN1 FGFBP3 -0.5 GPC3 2 0 5 10 FOXC1 MLLT3 ZADH2 SERPINF1 -1.5 HOXB4 TGFB2 CAMK2N1 SOX9 3 1.5 TMEM132C PAX6 PMP22 2 HAPLN1 0.5 MYL3 HOXA1 HOXA5 PTN RFX4 -0.5 0 5 10 HOXB3 HOXC4 TSHZ1 HOXB5 -1.5 HOXB6 HOXB2 MEIS1 A COL3A1 A F NR2F1 ZIC1 FABP7 1.5 CDH11 ASCL1 mESTn hESTn DCC 3 EPHA3 0.5 PHF21B CTNNA2 PCDHB16 Day 0 3 4 5 6 7 0 1 4 7 9 11 GDF10 CADPS FOXA2 -0.5 0 5 10 LHFP SNAP25 FST CHGB ST18 SP5 -1.5 TMEM163 MYB ELAVL3 GREM2 ZIC2 NEFL 1.5 TUBB3 CYP26A1 FSTL5 APLNR 4 NEFM 4 DNER DKK1 0.5 EBF1 4 POU4F1 PRICKLE1 ISL1 LHX1 STMN4 CARTPT SP8 -0.5 0 5 10 PPP1R17 RTN1 FGF8 NHLH1 T B DCX -1.5 VTN OTX2 TAGLN3 PIM2 EBF2 ELAVL4 VRTN 1.5 ONECUT2 HES5 CER1 SST EOMES POSTN 5 INSM1 MIXL1 0.5 EBF3 NHLH2 GDF3 SPARCL1 PRDM14 HMP19 -0.5 LUM LEFTY2 0 5 10 RELN LHX2 LEFTY1 SLITRK1 PLS1 ROBO2 -1.5 CNRIP1 KRTCAP3 DDIT4 KCNK1 MFAP4 HEY1 TUNAR 1.5 PARM1 TMEM255A CDA NPR3 EPHA1 6 RNASE4 0.5 SMOC2 GALNT3 RGS4 PRDM6 HAS3 RSPO2 POLR3G 6 PCLO -0.5 0 5 10 ATF5 FAM46B DDIT3 SLC1A4 PLA2G16 CRYAB B3GNT7 5 -1.5 AGTR1 KLB IFITM1 COPZ2 CD93 GRHL3 TIMP3 FOXH1 1.5 CD248 P4HA2 SALL4 SLC24A3 PMAIP1 SCG2 NCAN PSMB8 0.5 NAP1L5 NODAL 6 MAP6 RAB33A DNMT3B ELAVL2 UBE2QL1 ZSCAN10 -0.5 0 5 10 AFAP1L2 CNTN2 AKR1B10 THSD7A TUBA4A DKK2 -1.5 MAPT APOBEC3B ITIH2 SNAP91 SGK1 SIX1 EPHA2 THBD PRSS35 SOCS2 NRN1 SNHG3 CRISPLD2 7 PPBP ZIC3 SPINK1 TXNIP RBPMS2 PRPH L1TD1 EGFL6 TTR COCH SLC17A6 NPNT PCOLCE2 DCN PYCARD RBP4 HBE1 DPPA4 AFP BNC1 ZFP42 REEP1 APELA 7 Figure 1 Heatmap comparing gene expression changes with time in mESTn and hESTn. A: heatmap of 3774 mouse genes significantly regulated in mESTn compared with the expression changes of their human homologs in hESTn; B: heatmap of 1975 human genes significantly regulated in hESTn compared with the expression changes of their mouse homologs in mESTn; C: Venn diagram showing 950 genes of which homologs were regulated over time in both mESTn and hESTn; D: Heatmap of 950 homologous genes commonly regulated in both mESTn and hESTn; E and F: enlarged representation with gene name details of clusters 4A and 6 in D.

115 Comparison of time-related gene expression changes in mESTn and hESTn During mESTn differentiation 3774 genes were significantly regulated across time. For these mouse genes and their human homologs measured in the hESTn, FC at each time point for each gene was calculated as compared to the species average FC of each particular gene over time, and illustrated in a heat map using hierarchical clustering (Figure 1A). Six clusters could be identified based on their gene expression pattern and are graphically represented (Figure 1A). The enrichment for GO biological processes of genes present in each cluster was studied using DAVID. Clusters 1-4 showed a down regulation of genes over time in mESTn, which was less clear in hESTn. Clusters 2 and 3 contained significantly enriched GO-terms. Cluster 2 involved developmental processes like gastrulation, pattern specification and embryonic morphogenesis. Gene expression in this cluster peaked at day three in mESTn. Cluster 3 contained significantly enriched GO-terms involved in DNA and RNA processing. In mESTn, both clusters 5 and 6 showed an up regulated gene expression with time in which (neuro) developmental processes, such as neuron differentiation, pattern specification, skeletal system development and embryonic morphogenesis, were highly enriched. A similar gene expression change with time was observed in hESTn in cluster 6.

In hEST, 1975 genes were significantly differentially regulated over time. The FC per gene was calculated relative to the average expression per gene and compared to expression changes of homologous genes in mESTn (Figure 1B). Visualization in a heat map and hierarchical cluster analyses resulted in eight clusters identified based on gene expression patterns (Figure 1B). Out of the down regulated gene clusters in hESTn (1-4), clusters 3 and 4 contained significantly enriched GO-terms associated with cell cycle and proliferation, like cell cycle, mitosis and nuclear division. The mESTn displayed a similar trend in cluster 3, whereas in cluster 4 hardly any expression changes occurred in mESTn. Clusters 5-8 included genes which were up-regulated over time. Significantly enriched GO-terms in cluster 5 were involved in development and differentiation related processes, including neuron differentiation, pattern specification, embryogenesis and cartilage- and skeletal development. A similar expression pattern was observed for mESTn in cluster 5. The GO-terms observed in cluster 6 were linked to (macro) molecular factors, such as extracellular matrix organization, macromolecular complex- and plasma lipoprotein particle remodeling. Cluster 7 contained GO-terms including blood coagulation, hemostasis and response to wounding. In mESTn, cluster 7 barely showed regulation of gene expression.

Between mESTn and hESTn, 950 homologous genes were significantly regulated in both assays (Figure 1C). These genes were not confined to specific locations inFigure 1A and 1B. After heat map visualization and hierarchical clustering analysis we identified 7 clusters for further analysis (Figure 1D). Clusters 1-3 and 5 showed an opposite pattern of gene expression regulation in both assays. However, no GO-terms were found significantly enriched in either of these clusters. Cluster 4 showed genes that were up-regulated across time in both assays. GO- terms associated with (neural) development, like neuron differentiation and development, axonogenesis, and skeletal system development were highly enriched. In contrast, genes in cluster 6 showed an overall down regulation with time, some after an initial up-regulation

116 Comparing mESTn and hESTn during the first days into the assay. Early developmental processes associated GO-terms, like pattern specification, embryonic development, gastrulation, endodermal development, where significantly enriched in this cluster. Individual genes regulated in the most responsive gene clusters with time in Figure 1C, up-regulated genes in cluster 4A and down-regulated genes in cluster 6, are shown in figures 1EF.

Mouse Human -15 -10 -5 0 5 10 15 0 3 4 5 6 7 0 1 4 7 9 11 * * * * * * * * * * * * Fetalbrain * * * * * * * * * Caudatenucleus * * * * * * * * * Thalamus * * * * * * * * * CingulateCortex A * * * * * * * * * MedullaOblongata * * * * * * * * * Subthalamicnucleus * * * * * * * * * Globuspallidus * * * * * * * * * ParietalLobe * * * * * * * * * Pons * * * * * * * * * TemporalLobe * * * * * * * * * * * CerebellumPeduncles * * * * * * * * * * * * Cerebellum Amygdala * * * * * * * * * WholeBrain * * * * * * * * * Hypothalamus * * * * * * * * * OccipitalLobe * * * * * * * * * PrefrontalCortex * * * * * * * * * Placenta 1 * * * * * * * * * * * Adipocyte * * * * * * * * * * * Uterus * * * * * * * * * * CardiacMyocytes B * * * * * * * * * * * SmoothMuscle 2 * * * * * * * * * * * * * * * Liver * * * * * * * Fetalliver * * * * * * * * * * * Fetallung * * * * * * * Ovary * * * * * * * * Pituitary C * * * * * * PancreaticIslets * * * * * * TrigeminalGanglion * * * * * * Spinalcord * * * * * * * OlfactoryBulb * * * * * * * * Ciliaryganglion * * * * * * * DRG * * * * * Appendix * * Thyroid * * * * * * FetalThyroid 1 * * * * * * * UterusCorpus * * ColorectalAdenocarcinoma * * * Bronchialepithelialcells * Adrenalgland * * AdrenalCortex D * * * * Skin * * * * TestisGermCell 2 * Prostate * Heart * Bonemarrow * * * * * Whole Blood * * * * Tongue * SkeletalMuscle * * * * Kidney 3 * * * * * Trachea * * * * * * Atrioventricularnode 7 * * * * * Lung * * * * Pancreas

Figure 2 Hierarchical clustering of tissues significantly enriched according to Tox-profiler analysis (*: T-value ≥ 3.5 and E ≤ 0.05) during differentiation in the mESTn (green header) and in the hESTn (purple header). Tissue names on the right are color coded for their primary germ layer origin: ectodermal (yel- low), mesodermal (blue) and endodermal (pink) tissues.

117 Tissue-correlated gene expression was analyzed using Tox-profiler. Per experimental group the significantly regulated tissue-related gene expression (Figure 2) was determined, based on whole genome gene expression changes. Using hierarchical clustering, 4 clusters (A-D) were identified based on regulation patterns over time. Cluster A exclusively contains tissues of ectodermal origin, including specific brain tissues, of which characteristic gene sets were significantly up-regulated in both assays and all tissues. Cluster B contains several mesodermal originated tissues (B1) showing steep up-regulation of characteristic gene sets in hESTn, and three endodermal tissues (B2) of which characteristic gene sets were al significantly regulated in the hESTn with time, with a lower relative magnitude of regulation in mESTn. Cluster C contains 8 tissue gene sets of which 6 related to ectodermal tissues, which were significantly enriched in the hESTn throughout the differentiation period. Cluster D is a mixture of meso- endo- and ectodermal originated tissues. The responses for cluster C and D tissues were less prominent in mESTn than in hESTn.

Comparison of VPA-mediated gene expression changes in mESTn and hESTn After VPA exposure, in the mESTn 3281 genes were significantly differentially expressed (Figure 3). Matching these mouse genes with available human homologues and selecting those genes present on both mouse and human gene expression arrays resulted in 2941 genes available for comparison. Exposure to VPA in the hESTn resulted in 3354 differentially expressed genes. There were 988 homologous genes regulated in both assays by VPA exposure. Between assays, 143 genes were commonly regulated both over time and after VPA exposure (Figure 3, field G). GO-term analysis of this selection of genes revealed processes involved in cell proliferation and regeneration. In fields A, B, C, E and F of the Venn diagram in Figure 3, developmental and neural GO-terms were significantly enriched. Genes exclusively regulated in hESTn over time (I), were involved in GO-terms describing blood (coagulation), wound healing and hemostasis. VPA-regulated genes in both mESTn and hESTn which were not regulated over time in both assays (N) contained GO-terms associated with the pharmaceutical mode of action of VPA, including histone acetylation and modification, and chromatin modification. The latter was also significantly enriched in the gene group not regulated over time and exclusively regulated in hESTn after VPA exposure (O). The converse gene group not regulated by time but exclusively in mESTn after VPA exposure (M), was characterized by the GO-terms protein localization and cell cycle/ proliferation processes. An overview of regulated GO-terms is given in supplementary Table 4.

118 Comparing mESTn and hESTn

Time VPA mESTn hESTn mESTn hESTn

2824 950 1025 1953 988 2366

hESTn mESTn Time VPA mESTn hESTn I 582 936 M Time VPA J E N A 385 110 452 O F K 1425 1512 254 80 G B L 143 653 253 C H 313 168 D 433

Figure 3 Venn diagram: numbers of commonly and uniquely regulated genes (and their homologs) over time during differentiation in the mESTn and hESTn (top left); numbers of commonly and uniquely regulated genes (and their homologs) after VPA exposure in the mESTn and hESTn (top right); combined distribution of time and VPA regulated genes (and their homologs) in mESTn and hESTn (bottom).

Heat map and cluster analysis of concentration-dependent VPA effects resulted in two main groups of regulated genes, characterized by either down- or up-regulation with increasing concentrations, respectively, as observed in the hESTn (Figure 4, clusters 1-3 down versus clusters 4-6 up). However, these trends in some regions conflicted with findings in mESTn. Clus- ter 2, containing genes associated with chromatin modification and remodeling and (positive) regulation of gene expression, showed a down regulation in the hESTn, whereas mESTn showed partly down- and partly up-regulation of homologous genes. Cluster 3 showed concentration dependent down regulation in both assays. Go-terms significantly enriched in this cluster were associated with sterol biosynthetic process and cell death [307, 308]. Cluster 4 included genes related to neuron developmental processes, including neuron development 7 and differentiation, together with catabolic and metabolic processes, characterized by up- regulation, which showed a stronger effect on FC observed in hESTn than in mESTn. Clus- ter 5 showed an up-regulation in both mESTn and hESTn. The GO terms associated with this cluster where involved in metabolic process, oxidation and chemical homeostasis. Cluster 6, with GO terms related to neurogenesis, such as axonogenesis, neuron projection development and cell morphogenesis involved in differentiation, was strongly up regulated in the hESTn, and to a lesser extent in the mESTn.

119 mESTn hESTn Conc (mM) 0.015 0.06 0.250 1.00 0.10 0.33 1.00 1 -2 -1 0 1 2

Figure 4 Heat map representation after hierarchical clustering of homologous genes regulated by increasing concentrations of VPA exposure in both the mESTn (left) and hESTn (right).

120 Comparing mESTn and hESTn

The effect on gene expression after VPA exposure was studied based on enrichment of GO- terms and of human tissues. In the heat map, illustrating GO-term T-scores, four clusters (1-4) could be distinguished (Figure 5, left). Cluster 1 includes neural processes, which showed concentration dependent up-regulation in both assays. The second cluster (2) contains cellular processes involved in cell division and isoprenoid-/cholesterol biosynthetic process. The latter two are involved in the regulation of transcription events affecting lipid synthesis, meiosis and developmental patterning [315]. VPA exposure caused a significant down regulation of these processes in the mESTn, which was not observed in homologous genes in the hESTn. The third cluster (3), included GO-processes involved in chromatin modification, showing down- regulation. The fourth cluster (4) contained GO-terms involved in nuclear processes and replication. The GO-terms in cluster 4 showed a concentration dependent down-regulation.

mESTn hESTn mESTn hESTn Conc (mM) 0.015 0.06 0.250 1.00 0.10 0.33 1.00 Conc (mM) 0.015 0.06 0.250 1.00 0.10 0.33 1.00 Golgi apparatus WholeBrain OccipitalLobe calcium ion binding PrefrontalCortex synapse Amygdala Fetalbrain axon 1 CerebellumPeduncles neuropeptide receptor activity Cerebellum Pons sugar binding 1 Caudatenucleus ion transport TemporalLobe neuropeptide signaling pathway Hypothalamus Subthalamicnucleus growth factor activity Globuspallidus structural constituent of ribosome ParietalLobe MedullaOblongata anaphase-promoting complex... Thalamus negative regulation of ubiquitin-protein ligase activity... CingulateCortex Thymus positive regulation of ubiquitin-protein ligase activity... Lymphnode translation OlfactoryBulb Kidney 2 ribosome TestisLeydigCell cholesterol biosynthetic process 2 TestisInterstitial isoprenoid biosynthetic process TestisSeminiferousTubule Testis cytokine activity DRG rRNA processing Pituitary Spinalcord tRNA processing WHOLEBLOOD regulation of transcription Ovary Heart oxicoreductase activity 3 Leukemialymphoblastic(molt4) chromatin modification Fetallung Bronchialepithelialcells chromatin binding 3 UterusCorpus nucleolus ADIPOCYTE sequence-specific DNA binding CardiacMyocytes SmoothMuscle transcription factor activity Skin histone H3 acetylation Uterus PLACENTA mRNA processing Ciliaryganglion 4 nuclear speck transcription coactivator activity 7 RNA splicing -15 -10 -5 0 5 10 15 RNA binding spliceosomal complex nucleoplasm

Figure 5 Enriched GO-terms (left) and tissues (right) after exposure to increasing concentrations of VPA in the mESTn and hESTn based on 14939 genes using Tox-profiler

121 The effects observed on the enrichment on the level of tissues, was similar between both assays (Figure 5, right). Three clusters could be distinguished in the heat map (1-3), representing T-scores of the human tissue gene sets. The strongest effect in both assays was observed on neural systems (cluster 1). Here gene groups representing specific brain areas were strongly up-regulated in both assays with increasing concentrations of VPA. Cluster 2 also included neural tissues like thymus, dorsal root ganglion (DRG) and spinal cord, in which thymus-related genes were only regulated in the hESTn. Other tissues, like sex-organ associated tissues, heart, blood and kidney were up-regulated in hESTn. Segment 3 was mainly characterized by the strong down-regulation after 0.1mM VPA in the hESTn. Regulated tissues included adipocyte, cardiomyocytes, smooth muscle and fetal lung. An overview of regulated GO- terms and tissues is given in supplementary Table 5.

Discussion

Embryonic stem cell tests Human embryonic stem cell based assays for developmental toxicity testing of chemicals have gained interest in the past decade. This interest stems from the hypothesis that human hazard is probably best assessed in assays with human biological systems, which is one of the key messages from the NAS landmark report on toxicity testing in the 21st century [316]. In the past, primarily mESC were used for the stem cell test [50] which provided ground work for development of the EST [54]. The EST was designated more recently as one of the most promising validated in vitro tests for developmental toxicity prediction [51]. However, it has been shown that different species may respond differently as to developmental toxicity [317]. Perhaps the most striking example is the difference in response to thalidomide which caused severe malformations during human development, whereas it caused fetal death in rodents [74, 317] The use of human embryonic stem cells would avoid interspecies extrapolation in alternative assays.

Human embryonic stem cells can be manipulated in vitro to differentiate into several cell types in vitro like, neuronal, skin, adrenal and keratinocyte [85, 250, 318], blood, endothelial, kidney, bone, muscle, heart [319-323], pancreas and liver cells [324-326], cardiomyocytes [4, 322, 327], and fetal ventricular myocytes [326], as summarized by Pellizer 2005 [329]. Hu- man embryonic stem cells have been suggested to be “toxicology’s new best friends” [330]. In 2008 the use of hESC for developmental toxicity testing was published [51, 243]. Since then several protocols have been published on the use of hESC for developmental toxicity testing [84, 317, 331, 332]. In vitro hESC neural differentiation was used to study neurodevelopmental toxicity of ethanol and retinoic acid [90, 244]. Several other studies using hESC for neuro developmental toxicity testing have been published [92, 240, 333, 334].

122 Comparing mESTn and hESTn

Comparing cell differentiation in mESTn and hESTn We have developed experience with both mouse and human ESTn, specifically with regard to gene expression regulation during neural differentiation as well as to the effects of compounds thereupon [70, 283]. These data allowed us to compare mouse and human EST, in order to identify commonalities and differences, possibly indicating conserved genes responsive in both assays that could be used as prioritized predictive markers for developmental neuro toxicity (DNT). We are aware that differences observed between both assays are caused by an intimate interplay between cell characteristics and culture conditions, which are partly interdependent. Whereas the mESC employed can be cultured relatively easily on gelatin coated surfaces in standard media containing LIF [152, 335], hESC employed in these studies required feeder cells and sub culturing in cell clusters for retaining stem cell characteristics [283]. This causes necessary differences in their differentiation induction protocols as well, with an initial aggregation step needed for mouse stem cells but not for human stem cell clusters before differentiation is initiated. In order to standardize between assays as well as possible, we have adapted the assay protocols such that both share an 11 day differentiation protocol, with a 24 hour period of exposure to test compounds, initiated at the start of cell differentiation, followed by analysis of gene expression. We hypothesize that commonalities in the expression regulation of homologous genes in both assays, that persist in the presence of differences in species, cell lines and assay conditions, may indicate genes and functionally related gene groups that might provide robust biomarkers for neurodevelopmental toxicity.

Common gene expression regulation in mouse and human ESTn were related to early development, involving genes which were down regulated over time (Figure 1D and F, cluster 6). Among them was PRICKLE, involved in neural outgrowth and associated with neural tube defects [336, 337]. EPHA1 is a receptor tyrosine kinase implicated in guidance of the migration of axons and cells in the nervous system [335, 339]. EOMES is required for specification and proliferation of intermediate progenitor cells and their offspring in the cerebral cortex [339]. CER1 plays a role in anterior neural induction and somite formation during embryogenesis [340]. FGF8 is required for normal brain, eye, ear and limb development during embryogenesis [341, 342]. LHX1 plays a role in neurogenesis [343]. Additional genes, like Brachyury (T) and MIXL1, are both involved in regulation of mesoderm formation and differentiation [152, 214, 344, 345]. MIXL1 is also involved in endodermal cell differentiation [346]. LEFTY1 and -2,both play a role in left-right asymmetry of organ systems during development [347].

Common gene expression changeswere observed in neural differentiation related genes up- 7 regulated over time (Figure 1 D and E, cluster 4). DNER is an activator of the NOTCH1 pathway, which plays a role in neural progenitors and neuronal differentiation. Furthermore, DNER acts as and mediator of neural-glia interactions during astrocytogenesis and developing cerebellum [348]. DCX is a micro-tubule-binding protein regulates cytoskeletal dynamics and neuronal morphogenesis [349]. RELN plays a role in layering neurons in the cerebral cortex and cerebellum [264, 265]. Pou4F1 is involved in the regulation of specific gene expression within a subset of neuronal lineages and development of the sensory nervous

123 system [350]. Furthermore, Tubb3, the major constituent of microtubules [351] and MAPt, which promotes microtubule assembly and stability [352], were regulated in both assays with differentiation. Thus, significant functional commonalities were observed between homologous genes related to neural differentiation in both assays.

Examination of tissue-specificity of gene regulation during differentiation demonstrated numerous neural- and brain tissue related gene sets commonly significantly regulated between both assays (Figure 2, cluster A1). In addition, especially in hESTn an additional number of gene sets related to ectodermal tissues weres significantly enriched, whereas gene sets related to tissues from other germ layers were significantly regulated as well. This wider array of differentiation in hESTn as compared to mESTn may in part be due to methodological differences between assays. In the mESTn, but not in hESTn, differentiation was induced by retinoic acid (RA) addition, a major physiologic neural differentiation inducer [68, 71]. Ho- wever, the presence of cell types outside the primary differentiation route studied in a given EST, whilst not informative in terms of the specific end point measured, may enhance the sensitivity of the system through cell-cell interactions among different cell types that may modulate differentiation. For instance, in the original mESTc, monitoring murine cardiac muscle cell differentiation, at day 7, 17% MHC, 6% actinin positive cardiac myocytes were found [335]. At day 10, 76.7% Myh6 positive cells have been reported by others [152]. Cardiomyo- cytes are mesoderm-derived, a germ layer that is formed by induction between ectoderm and endoderm. Therefore, stimuli representing both these germ layers are likely represented in the assay to allow mesoderm differentiation. Likewise, in hESTn, effects on differentiation of non-neuronal cell types may affect neural differentiation patterns and thereby influence sensitivity of the assay.

Comparing VPA responses in mESTn and hESTn The data for this comparative study originates from different experiments with different designs. However, in both cases the concentration response was based on 96 individual samples spread over the concentration response, covering the therapeutic range. The 988 genes commonly regulated after VPA exposure in the hESTn and mESTn were enriched in processes associated with its pharmacological action, such as histone acetylation, chromatin modification, lipid biosynthetic process and cholesterol biosynthetic process. These gene sets were regulated by VPA but not by time in the assays, and were indeed enriched in section N of the Venn-diagram (Figure 3). Neurodevelopmental processes were mainly enriched among genes appearing in section C and 3F (Figure 3), indicating statistically significant regulation of neural related GO terms by VPA being observed in mESTn but not hESTn. The wider array of differentiation occurring in the hESTn, discussed above, may have caused less statistically significant enrichment of the neural differentiation associated GO terms in the hESTn. Differences and commonalities between assays were observed in the directionality of gene expression regulation after VPA exposure. Genes up-regulated in the hESTn showed either a common or opposite expression change with time in the mESTn (Figure 4, cluster 4 and 6, respectively). A different expression directionality was observed e.g. in genes significantly regulated in chromatin and histone modification, which play a role in pharmacological mode of action of VPA (Figure 4, cluster 2)[269, 273].

124 Comparing mESTn and hESTn

The 143 genes regulated both by time and VPA exposure in both assays, although selected based on a single tested compound only, may represent promising differentiation-related biomarkers for species-independent compound-mediated DNT. Significantly enriched GO- terms represented by genes among the 143 genes were regulation of cell proliferation and regeneration. In addition, the 143 genes regulated in both assays and after VPA exposure contained important genes involved in neural associated GO-terms like (regulation of) neuron differentiation and nervous system, neural plate development and neurofilament cytoskeleton organization, including TUBB3, HOXA1, ADM, LHX1, EPHA2, GAP43, STMN2, MAP2, NEFL, CHRNA3, METRN, DNMT3B, T, NODAL, INA, ZIC3 and CTGF. Furthermore, important genes involved in general development were enriched in processes like embryonic- and cell morphogenesis, ectodermal development, (anterior and posterior) axis- and pattern formation, such as FGF8, MYC, TXNIP, ANXA1, CTGF, COL1A2, MLLT3 and RHOU. These 25 genes are potential candidate marker genes for DNT. GAP43, INA and LHX, have been published before as candidate markers for DNT in vitro [317-319] which supports their universal responsiveness in such assays.

Conclusion Both systems nicely illustrated successful neural differentiation of ESC in vitro, with both commonalities and unique gene expression changes occurring in response to time and VPA exposure. The mESTn assay clearly shows a more specific neurodevelopmental differentiation pattern, whereas the hESTn also showed differentiation of cell types originating from other germ layers as well. Both these assays have their advantages, for instance as to specificity and species of origin. With further optimization of the human assay, e.g. removal of the need for a feeder layer for undifferentiated hES cells and opening the possibility for subculture from controlled cell suspensions, the assay may become technically easier. This analysis identifies a number of genes and their homologues in the other species that respond similarly in both assays. This provides support for the application of these genes and their representative GO terms as possible biomarkers in neurodevelopmental toxicity testing in ESTn.

125 126 CHAPTER8

Summary and general discussion

127 128 Summary and general discussion

Overview of findings of this thesis The need for alternative test models for developmental toxicity testing for human risk evaluation is described in chapter 1. Stem cells offer a great opportunity to study developmental toxicity in vitro. They are relatively easy to culture and able to differentiate into all cell types, containing molecular processes and gene expression similar to those occurring during in vivo development.

In the first part of this thesis we used mESC, and the principle of the EST, to study the effects on gene expression changes after exposure to phthalates using a category approach. Phtha- lates, used as plasticizers in many consumer products, are known to cause developmental toxicity and are therefore used as model compounds.

In chapter 2 the effect of 4 phthalates on whole genome gene expression was studied to assess concentration effect relationships and potency ranking based on gene expression. Com- monly regulated genes by all three embryotoxic phthalates were derived, resulting in a set of 668 genes. 112 GO biological processes were significantly enriched, including cell proliferation, differentiation , transcription, cell death and embryonic development. Compounds were ranked from highest to lowest potency: MEHP, MBuP, MBeP and MMP. This was in line with the potency ranking in other systems in vivo and in vitro. However, gene expression showed to be more sensitive, remarkably already after 24 hours of exposure, in contrast to 7 days of exposure in the classical evaluation. Lower concentrations already affected gene expression, in the absence of observed effects on differentiation or proliferation.

The use of whole genome gene expression as a read out for developmental toxicity provides promising opportunities for developmental toxicant detection. But the complexity of gene expression changes makes interpretation difficult. For screening and evaluation purposes, selection of biomarker genes would be advantageous. In chapter 3 a statistical method was proposed, based on a threshold setting, to obtain a gene set predictive for potency ranking of compounds, within one class of compounds. We used the set of monophthalates as an example. Herewith a set of 97 genes was obtained including genes involved in developmental processes, showing mainly down regulation, and genes involved in cell proliferation, containing mainly up regulated genes. Through a validation, in which one phthalate was left out of the selection and treated as an unknown, the robustness of this approach was demonstrated. In the EST the threshold of adversity is classically based on the ID50 concentration. Still the question is how gene expression can be used to obtain such a threshold of adversity. Based on the concentration response curves, representing the number of genes out of 97 to be regulated > 1.5 fold, a 50% effect level was calculated. This BMC was compared to the classical ID50 and demonstrated to be representative of the classical ID50.

For DNT a mESC neural differentiation assay was assessed, described in chapter 4. mESC differentiated into neural cells within 11 days, producing cells that both expressed neural 8 genetic markers and showed neural outgrowth surrounding the EBs. A concentration dependent effect of MeHg was observed on neural outgrowth, assessed at day 11 and flow cytometric analysis of neural markers at day 13.

129 In the second part of this thesis we have focused on development of an hESC based neural differentiation assay, applicable for neurodevelopmental toxicity testing. In chapter 5 neural differentiation of hESC was studied based on morphological examination, using immune fluorescent staining of a SSEA-1, an embryonic stem cell marker and βIII-tubulin, a neural marker. A clear decrease of the stem cell related genes, with in parallel elevating expression of the neural involved genes was observed. Thereafter, the effects of two anti-convulsant drugs, VPA and CBZ, were studied on cell viability and differentiation. Both compounds are developmental toxicants, especially affecting neural differentiation. The effects of VPA and CBZ exposure for 7 days on neural differentiation were studied using gene expression analysis. Stem cell related genes showed a concentration dependent decrease in expression after exposure to VPA, whereas no significant changes were observed after exposure to CBZ. Neural associated genes were all significantly affected after VPA exposure. CBZ caused a significant concentration dependent down regulation of 2 out of 5 neural associated genes. Stem cell- and neural cell differentiation markers can be used for developmental toxicity detection. This study provides proof of principle for the use of gene expression in detecting effects of chemicals in differentiating hESC.

Since gene expression during early differentiation is specific for revealing compound mechanism of action [130], we directly compared effects at day 7 (taken as an end-point in chapter 5) with the effects after 24 hours of exposure within the same study. Gene expression as a response to exposure to VPA or CBZ was studied on whole human genome arrays in chapter 6. VPA induced a stronger effect on gene expression and significantly enriched GO-processes compared to CBZ, which was in line with effects observed in vivo in which less teratogenic effects in terms of congenital malformations are found [248]. After VPA exposure, GO terms involved in (neural) development, like embryonic and tissue morphogenesis, axonogenesis, and neuron projection morphogenesis, were significantly enriched. On molecular pathway analysis also a more pronounced response was observed after VPA exposure, compared to CBZ exposure. Both compounds affected the WNT and TGFB regulation and signaling, two processes important for neurodevelopment.

In chapter 7 gene expression regulation during neural differentiating of mouse- and human ESC and after VPA exposure were compared to identify commonalities and differences between the mESTn and hESTn. Common genes, regulated in both systems, included genes related to early development and neural differentiation. The neural related genes showed an up regulation in both assays. Numerous neural and brain tissues were demonstrated to be significantly regulated in both assays. However, the hESTn demonstrated additional significantly regulated ectodermal and furthermore meso- and endodermal tissues significantly enriched. Exposure to VPA caused in both the mESTn and hESTn a significantly enrichment of processes associated with its pharmacological action and neuro developmental processes. Genes both regulated over time and differentiation in both assays may be promising differentiation related biomarkers for compound mediated DNT. These genes included important genes involved in neural processes. In general, as compared to mESTn, the hESTn showed a wider variety in terms of differentiation and response to VPA. However, the effects observed after VPA exposure, correlated to pharmacological mode of action, in the hESTn seemed to be more pronounced. 130 Summary and general discussion

Discussion Animal testing for human risk assessment Many compounds we use, like pharmaceuticals or chemical substances, need essential information concerning their potential toxicological risk. To protect human health, toxicological research is necessary. Currently, most toxicological testing strategies rely on animal studies, which have long been considered as a regulatory gold standard [84]. These studies, which often involve a high number of test animals with inconvenient circumstances for animals, take a lot of time, are expensive and have been shown to not always have outcome predictive for human risk. Different species have differences in pharmacokinetics, metabolism and toxicokinetics, resulting in different toxicological response to compound exposure and possible misinterpretations [295]. Species differences in toxicological outcome have occurred, for instance in the case of 13-cis retinoic acid and thalidomide [76, 353]. If risk assessment of chemicals, to establish the potential risk or safe human use of these compounds, e.g. in pharmaceutical evaluation or development of consumer products, is based on non-human species, compounds can be misclassified. This may result in unnecessary ban from the market or entering the market while they are not safe for human use. Current animal based test models for detecting developmental toxicity, demonstrated a 60-80% concordance to known human teratogenicity [354, 355]. Taken together, this demonstrates the need for alternatives for animal experimental methods, which should be reliable for human toxicity prediction, and preferably faster and more cost effective.

Alternatives for animal testing In vivo vs in vitro testing In vitro methods offer useful alternatives to in vivo tests, contributing to the reduction of the use of animals. However, in vivo toxicological responses involve many different processes, including interaction between several organ systems. In vitro systems are often limited to relatively simple cell populations. Establishment of in vitro systems containing several cellular systems is challenging. Nevertheless, the development of relatively simple in vitro systems may pave the way for developing better predictive test systems, which can be implemented in testing strategies or ultimately can be combined to result into improved and more sophisticated in vitro test systems, or batteries of test systems.

Embryonic stem cells ESC offer a great in vitro alternative for studying developmental toxicity, as applied in the EST in various differentiation protocols. The potential of a compound to affect cell differentiation of ESC has been validated as a measure for developmental toxicity. Using the inhibitory effect characteristics within the mESTc, compounds were classified as strong, weak or non embryotoxic [54]. Although the validity was demonstrated in the ECVAM validation study [54], in follow up validation studies a lower predictive capacity of the EST was demonstrated, 8 as many substances were misclassified [174]. The classical endpoint and read out of the EST is based on the inhibition of cardiomyocyte differentiation by compound exposure.

131 This effect is taken as a measurement for general developmental toxicity. Addition of other differentiation routes may enhance the predictivity of the EST.

Neurodevelopmental toxicity Rationale An additional important aspect of developmental toxicity concerns developmental neurotoxicity testing. The development of the nervous system is very complex, which involves several specific developmental processes including proliferation, migration, differentiation, synaptogenesis, apoptosis, and myelination. Interference with one of these processes can result in severe developmental neurotoxicity [356]. During human brain development there are critical and specific vulnerable windows of sensitivity with unique susceptibility, because they are dependent on temporal and regional appearance of the critical developmental processes [356]. Exposure to toxicants may result in developmental effects that cannot be observed after exposure of the mature brain [16].

One in every six children show developmental disabilities, of which the majority are associated to neurodevelopmental disorders, like learning disabilities, sensory deficits, developmental delays, and cerebral palsy [357]. Furthermore, 1 in 1000 new-borns suffer from severe congenital abnormalities of the nervous system, including anencephaly, hydrocephaly, and herniation of the spinal cord [17]. There are several clinical disorders associated with disturbed neuro-developmental processes, like schizophrenia, dyslexia, epilepsy, and autism [356]. This illustrates the importance of neuro developmental toxicity testing of xenobiotic compounds. However, validation of these human based test systems still needs to be performed, to prove both the additional value and higher predictivity of these test systems. If valid, ultimately these systems will be more effective and still reduce the cost compared to in vivo testing and contribute to a higher throughput for chemical testing.

Human neural EST for neurodevelopmental toxicity testing Using human cells may improve the predictivity of the in vitro assays. We have developed a hESC-based neural differentiation assay in which neural cells were obtained within 11 days, comprising developmental processes including proliferation, differentiation and migration with coherence between these processes and different (neural) cell types. In parallel, other in vitro systems have been developed in which hESC where applied to be used to study developmental neuro toxicity. These assays differed, compared to our assay, mainly in the differentiation endpoints. Several protocols were based on neural rosette formation, suggested to mimic neural tube formation [93, 244]. In Krug 2013 et al. [240], five different hESC neural differentiation assays are described, all recapitulating different phases of early neural development, including germ layer formation [358], neural ectodermal induction [359], neural tube formation using neural rosette formation [233], transition from neural precursor cells to mature neurons and maturation of neuronal precursor cells towards post-mitotic neurons with neurites, derived from a human foetal brain [360, 361].

132 Summary and general discussion

Talens-Visconti et al. also developed an assay in which all these developmental processed were mimicked, however, the aim was to obtain early forebrain neural development, within 35 days [90].

Limitations of embryonic stem cell assays Interaction with organ systems and barriers Although our hESTn has been demonstrated contain multiple cell types, still a disadvantage in this in vitro assays is the lack of integration of other important cellular systems or organ systems.

Metabolic activity An important aspect is the lack of metabolic capacity. However, this is of concern in many other in vitro test system. The effects of compounds in the EST are based on substances which do not require metabolization, before eliciting their embryonic potential. Although stem cells can be used to test embryotoxicty of the metabolites, the differences in metabolization between species plays an important role in the toxicological impact of a compound and therefore play a role in the misclassifications in vivo. The incorporation of metabolic activity into in vitro systems is of interest and would ideally be added to the assay [362, 363].

Effects of culture conditions Differentiation in vitro differs in many aspects from the in vivo development. Culture conditions play an important role and need to be consistent. Foetal calf serum can vary in composition. Chemical defined serum replacement and culture medium give a more precise culture condition and controlled environment. In our hESTn we cultured the cells with a chemically defined serum, knock out serum replacement (KOSR). Furthermore culture conditions can have an effect on free available concentrations of the test substance. Misclassifications of compounds can be caused by composition of the culture medium. For instance, it is known that MeHg binds to serum proteins, present in the culture medium [364, 365]. In the Re- protect study, it was later demonstrated that even 45% of the misclassifications were caused by the composition of the medium, often caused by binding of substances to serum albumin [366].

During in vitro culturing, compounds can be oxidized which result in a varying concentration during lab experiments. It is suggested that, next to frequent refreshment of the culture medium and test compound, culturing under reduced oxygen levels will improve hESC self- renewal and pluripotency [367, 368]. Furthermore, insufficient or insoluble compounds can also give false outcomes, which is a general limitation in both in vitro and in vitro systems. On the other hand, during in vivo development concentrations to which the embryo is exposed can differ by accumulation in the placenta. Sometimes it is difficult to assess exactly the active concentration of the toxicant. Nevertheless, for human risk evaluation, it is of importance 8 to know the exact concentrations the in vitro and in vivo differentiating cells are exposed to, to have a proper comparison and ultimately to facilitate extrapolation.

133 Further optimization of culture conditions The hESC cell culture and neural differentiation can be further optimized, making it more suitable for DNT. For instance, mESC easily maintain an undifferentiated state when cultured in vitro, by addition of leukaemia inhibitory factor (LIF) [51]. Currently, the hESC are cultured on inactivated MEFs to maintain pluripotency. Feeder replacement is preferred to reduce variables in culture, which possibly affect the cell culture and (molecular mode of action) outcome. Several protocols have now been published in which the hESC were cultured on alternative coatings, like Vitronectin, replacing the use of inactivated MEFs. Furthermore, the use of these alternative coatings will save time and makes the hESC culture less laborious. Additionally, as we are now using medium, with manual addition of the essential growth factors and components, culture media has been developed, like essential 8 medium, by which the composition of the medium is defined and standardized by the manufacturer. We have performed pilot studies (not published), using a combination of Vitronectin and essential 8 medium to culture hESC, with good results. The neural differentiation protocol can be optimized especially in the initiation phase. hESC cells need to have cell-cell contact to survive and generally only form new colonies out of cell colony fragments [369-371]. To initiate neural differentiation, we enzymatically dissociated the hESC clusters from the inactivated MEFs and subsequently have let them spontaneously form cell aggregates. For differentiation for toxicity screening, uniform cell aggregates are preferred, to minimize differences in factors influencing differentiation and gene expression. Development of a protocol in which hESC can be cultured as single cells, for instance by addition of ROCK inhibitor [372], will contribute to uniform cell aggregate formation. Nevertheless, although human based test systems will provide enhanced mechanistic mode of action information and probably a more accurate outcome concerning toxic concentrations, still in vitro to in vitro extrapolation is required for human risk assessment.

Furthermore, the question remains whether hESC are necessary for human risk evaluation based on in vitro assays, or whether a cell types will suffice? However, the main goal of this research is to study and evaluate developmental toxicity, which makes ESC the most obvious alternative source to be used. We have demonstrated that in both the mESTn and hESTn common genes were differentially expressed both during differentiation and after exposure to VPA. It can be thinkable that there are conserved genes, which are regulated during differentiation or respond to compound exposure in both species, making mESC also suitable for human risk evaluation. To investigate this, the effects of additional compounds in both systems need to be compared. Additionally, we have not demonstrated that the genes found to be significantly differentially regulated in our systems were specifically expressed during ESC differentiation and not in other cell types as well. The effect on gene expression of differentiated cell types can be studied, to demonstrate the necessity of using human and/ or mouse ESC. Nevertheless, before establishment of the current EST, in assays in which stem cells were used to establish in vitro embryotoxicity, based on the cytotoxic effect on both embryonic (colony forming potential) and adult cells [52, 53], many compounds were classified incorrectly. This was overcome by the addition of the endpoint of alterations in cell differentiation of ESC [48] demonstrating the usefulness of using differentiating cells. Moreover, many genes

134 Summary and general discussion regulated in the mouse and human ESTc and ESTn have been demonstrated to be involved in development and neural differentiation, supporting the use of ESC.

However, alternatives to these hESC can be considered. Recently several publications, using iPS, as an alternative to ESC have appeared. iPS are derived from mature differentiated cells, and reprogrammed to express particular pluripotency genes [373-375]. Although the resulting cells share many of the same properties of ESC, using these cells it is not sure whether the gene expression differs from ESC, resulting in differed responses to toxic events. Further- more, the reprogramming efficiency and differentiation reproducibility have low yields, resulting in inefficient differentiation [376]. However, continuously new and improved methods for culturing and to increase the yield of differentiation are published. This may result in an alternative to the use of hESC in the future. Several other stem cells which are considered as pluripotent stem cells, like embryonic carcinoma cells and the multipotent adult progenitor cells from bone marrow can offer an alternative source. However many molecular events and co-factors are involved in genetic changes during differentiation and as a response to compound exposure, which therefore first need to be compared to the use of hESC line. It should be considered whether this is worthwhile, because a lot of time is required to study the differences between these cell types. Furthermore, also progenitor cells, including neuro progenitor cells are commercially available, offering a great alternative to study neuro toxicity. However, especially the early genetic changes occurring in the firsts differentiation processes of embryonic stem cells are essential to be studied [130], which probably will not be detected if the first differentiation and cell fate is already determined.

Biomarkers Transcriptomics as a read out for developmental toxicity Both in vitro and in vivo systems are regularly based on morphological scoring. The examination of the effects of compounds on differentiating stem cells in the EST is based on the inhibitory effect on contracting cell foci formation. The addition of molecular endpoints, including gene expression, has been shown to enhance the predictivity and to provide additional mechanical insight [121, 134, 377]. Transcriptomics had already been used before to identify the mode of toxic action of industrial and environmental chemicals [378-381]. Early gene expression responses involved in differentiation processes are most specific for revealing compound mechanism of action [130]. Therefore, to study effects of compound exposure, initiation of exposure was assessed at day 3 in the mESTc and mESTn, after which the EB were formed, since EB formation is primarily characterized by cell proliferation [152]. In pilot biomarker studies (data not published) hESC neural differentiation already was initiated the first day, after the cells were detached from their feeder layer. This can probably be explained by the methodological differences between these assays, in which hESC were cultured in cell clusters, compared to single cells in the mESTc and mESTn, which first need to form cell aggregates/ EB structures. Secondly, it is known that hESC will lose their stem cell properties after they are removed from their feeder layer of inactivated MEFs [83]. The 8 differentiation onset was confirmed by the findings described in chapter 6, demonstrating essential genes involved in early development and germ layer destination, showing temporary

135 up-regulation starting between day 0 and 1, whereas this was observed in mESTn at day 3.

Concentration response A major challenge in studying the effect of a compound on gene expression is the establishment of the proper concentrations or doses to be tested. Human embryotoxic concentrations often originate from epidemiological- and case studies, which don’t provide any dose-response relationships [355]. Furthermore, both therapeutic window and consequently embryotoxic concentration differ between species and even between individuals. For human risk assessment, the concentrations tested in vitro must be comparable- and relevant to human pharmacological concentrations or external exposure levels. The differences between therapeutic dose, reversible-, adaptive- and adverse doses may be small. For example, vitamin A (retinol) does not have an adverse developmental toxic effect on the fetus when taken at or below the recommended daily allowance. However, if exceeding this dose, it can cause malformations both in animals and humans (Teratology Society, 1987)[84]. Therefore it is preferred to study the effects both on cell viability and differentiation in concentration series, to assess concentration-response relationships.

We and others have demonstrated that gene expression occurs both in a compound- and concentration specific response. In earlier studies, embryonic stem cells showed specific gene expression changes caused by compound exposure [118, 119, 124, 125, 162]. Hrach et al 2011 used an in vitro hepatocyte culture in which transcriptomics was used to generate a predictive in vitro model for classification, demonstrating a model for improved mechanistic understanding [382]. Furthermore, it was demonstrated that the gene expression could be linked to morphological changes [114].There is possibly a general gene expression mediated mode of action involved in toxicity [121, 135]. However, it seems that each compound (class) affects specific and essential genes. We have demonstrated in chapter 3 that compounds within one class of compounds affect commonly regulated genes in a compound specific concentration response. We were able to rank these compounds based on potency, comparable to the in vivo ranking, emphasizing the importance and sensitivity of using concentration response analysis based on gene expression. The use of potency ranking based on concentration response has been described before [119, 154-156, 383]. This demonstrated the applicability of transcriptomics to be used as a tool to study the effects, and even to classify and predict potency of compounds, if the expression of the right and optimal gene sets are monitored. Obtaining these gene sets, enormous studies comprising many experimental groups and generating a lot of data, need to be performed. These profiles can subsequently result in achievement of gene sets usable for (developmental) toxicity screening. However, the gene expression response has been demonstrated to be more sensitive compared to traditional toxicological endpoints, occurring at concentrations below the no observed adverse effect level (NOAEL) [161]. There- fore, the use of transcriptomics as a read out for developmental toxicity is still under discussion. Nevertheless, the use of concentration response analysis based on gene expression, may improve traditional toxicological studies, in which a NOAEL or benchmark dose is a measure for a threshold of an adverse effect [161]. For now, traditional toxicological parameters determine whether a cellular response reflects an adaptive or adverse response [384].

136 Summary and general discussion

Risk assessment Threshold of adversity Another aspect important for risk evaluation is to obtain a threshold of adversity based on gene expression changes, informative for in vivo risk evaluation. Within the classical EST, the ID50 is taken as a threshold of adversity. We studied the use of a benchmark approach using gene expression, calculating the concentration at which 50% of the number of genes within the predefined set of genes after phthalates exposure, exceeded a threshold after which the expression was changed by more than 1.5 fold by compound exposure. These calculated benchmark concentrations, correlated to the classical ID50 values, supported the hypothesis that gene expression can be used for toxicity screening and assessment of threshold of adversity. To prove the validity of this statistical method in combination with the use of transcriptomics, first other phthalates need to be tested of which the potency is known, and subsequently the approach should be repeated for other compound classes. Both for the ID50 and the effect on gene expression, phenotypic anchoring is of importance. Using epidemiological studies and animal studies, the thresholds of adversity need to be compared, and for many compounds the difference of overlap need to be established. However, gene expression seems to be useful to determine a concentration causing adverse effects in vivo, gene expression by itself should not be considered to be adverse, since the gene expression changes occurring in the cells are essential to drive a response at tissue or organ level [161]. Furthermore, if gene expression is not detectable anymore, it can be caused by the resolving power of the method.

Testing strategy ESC provide a useful alternative, which in combination with transcriptomics offers a relatively quick method to study effects of chemicals on embryonic cell differentiation and giving insight in underlying biological mechanisms. This ultimately contributes to- and can be used for screening purposes. Because of the complexity of a human organism, it will be challenging to obtain an in vitro testing method, which will be 100% predictable for human risk. It will be more reasonable, for now, to foresee that these tests will more and more prove their validity and also their limitations, and thereafter are integrated in testing strategies. For in vitro testing, testing strategies will be mandatory, in terms of combinations of different testing methods. A battery of several short, well designed and validated in vitro methods will contribute to accurate alternative testing strategies. Together with the rapid improving -omic technologies and lab tools for culture of these cells, the future offers great prospects using combination of these techniques for human developmental toxicity testing.

137 138 SAMENVATTING

Nederlands

139 140 Nederlandse samenvatting

Inleiding Voor veilig gebruik van stoffen die kunnen voorkomen in veel gebruikte producten, zoals bijvoorbeeld chemicaliën, is er informatie nodig over of deze stoffen schadelijk zijn voor de gezondheid. Binnen de toxicologie wordt de eventuele schadelijkheid van stoffen bestudeerd. Stoffen kunnen verschillende schadelijke effecten hebben. Veel factoren spelen een rol in de mate van schadelijkheid. De twee meeste belangrijkste factoren zijn de hoeveelheid en moment van inname. Zo is er bijvoorbeeld een verschil tussen effecten op kinderen en volwas- senen. Daarnaast spelen verschillen tussen individuen, zoals erfelijkheidsfactoren en de route van innamen een rol. Een stof kan worden ingenomen via inademing, oraal, of opname via de huid. In het ergste geval kan een stof dodelijk zijn. Daarnaast kan er schade aangebracht worden aan organen, huid irritaties geven, of zorgen voor een allergie. Op een langere ter- mijn kan een stof leiden tot de vorming van kanker indien een stof schade aanbrengt aan het genetische materiaal in onze cellen. Zoals gezegd, speelt het moment van inname een be- langrijke rol in de effecten die een stof kunnen hebben. Zo is tijdens de ontwikkeling, tijdens de zwangerschap en tot volwassenheid, een hoge gevoeligheid voor blootstelling aan stoffen. Binnen de reproductie- en ontwikkelingstoxicologie wordt de mogelijke schadelijkheid van stoffen op de voortplanting en ontwikkeling bestudeerd. Reproductietoxicologie richt zich op de effecten op de vruchtbaarheid, zoals de effecten op de ontwikkeling van de ei- en zaad- cel. De effecten op de ontwikkeling van het embryo en de foetus wordt bestudeerd binnen de ontwikkelingstoxicologie. Een onderdeel van ontwikkelingstoxicologie is de zogenaamde teratologie, waarbij de afwijkingen, oftewel misformaties bestudeerd worden.

Tot de veertiger jaren werd aangenomen dat alle geboorteafwijkingen veroorzaakt worden door erfelijkheid. Vanaf toen werd het steeds meer duidelijk dat er externe factoren, zoals blootstelling aan toxische stoffen, kunnen resulteren in geboorteafwijkingen. Een bekend en tragisch voorbeeld, was in de jaren zestig, toen er baby’s werden geboren met verkorte of to- tale afwezigheid van ledenmaten. Dit werd veroorzaakt door een Thalimodide, een medicijn gebruikt tegen ochtendmisselijkheid en braken. Deze gebeurtenis benadrukt het bestuderen van effecten op voortplanting en ontwikkeling.

De ontwikkeling van het neurale systeem, zoals het brein en het zenuwstelsel, is een belangrijk proces dat al vroeg tijdens de ontwikkeling start en doorgaat tot aan volwassenheid. Tijdens deze ontwikkeling kan blootstelling aan stoffen ernstige nadelige effecten hebben. Voorbeel- den van deze neurale-ontwikkelingstoxiciteit zijn anencefalie (neuraalbuisdefect), hydrocefa- lie (waterhoofd) en herniatie van het ruggenmerg. Daarnaast zijn er verschillende klinische afwijkingen geassocieerd met neuro-ontwikkelingstoxiciteit, zoals schizofrenie, dyslexie, epi- lepsie en autisme. Vandaar dat het bestuderen neuro-ontwikkelingstoxiciteit een belangrijk deel is van ontwikkelingstoxicteit.

141 Er is een Europese wetgeving, genaamd REACH, wat staat voor registratie evaluatie, au- torisatie en beperking van chemicaliën, die voorschrijft dat alle stoffen geproduceerd in de Europese unie getest moeten zijn voor toxiciteit. Daardoor zijn er veel proefdieren nodig. Er is een schatting dat voor de REACH wetgeving er per stof gemiddeld 4710 proefdieren nodig zijn. Hiervan is ongeveer een 65% van de proefdieren nodig voor het bestuderen van ontwikkelingstoxiciteit. Deze testen dragen veel ethische aspecten met zich mee, daarnaast zijn ze duur en kosten veel tijd. Daarom is het erg wenselijk om alternatieve testmethodes te gebruiken. De laatste jaren zijn er verschillende alternatieve test methodes opgezet, waarbij het effect wordt getest op bijvoorbeeld cellen gekweekt in een laboratorium schaal , ook wel in vitro methodes genoemd. Voor het bestuderen van ontwikkelingstoxiciteit bieden embryonale stamcellen een goed alternatief. Het is mogelijk deze stamcellen in vitro te laten differentiëren tot verschillende celtypes, op een dusdanige manier dat de in vivo ontwikkeling nagebootst wordt. Om de toepasbaarheid van stamcellen te bestuderen, hebben we in dit proefschrift muizen- en humane embryonale stamcellen (ESC) gebruikt. We hebben muizen stamcellen laten differentiëren tot kloppende hartspiercellen en neurale cellen. Hier hebben we het effect van stoffen op bestudeerd, waarvan bekend is dat ze ontwikkelingstoxiciteit veroorzaken op basis van effecten op genexpressie.

Resultaten In Hoofdstuk 2 zijn muizen embryonale stamcellen (mESC) gedifferentieerd naar hartspiercellen. Het effect van stoffen op de formatie van kloppende hartspiercellen vanuit mESC wordt gebruikt en is een gevalideerde alternatieve methode, voor het bepalen ontwikkelingstoxicteit, in de zogenaamde embryonale stamceltest (EST). Ontwikkelingstoxische stoffen hebben een remmend effect op de vorming van de kloppende hartspiercellen. We hebben het effect van 4 verschillende ftalaten bestudeerd. Ftalaten zijn weekmakers die gebruikt worden in onder andere plastics. Van 3 van deze ftalaten MEHP, MBzP, MBuP is bekend dat ze een effect hebben op de ontwikkeling van het embryo. 1 ftalaat, MMP, heeft geen effect op de ontwikkeling. Het effect op genexpressie van toenemende concentraties van de individuele ftalaten is na 24 blootstellen bestudeerd en is vergeleken met het ‘klassieke’ effect na 7 dagen blootstellen. De vraag was of de effecten die gevonden werden, met elkaar overeenkomen waarbij de embryo- toxische potentie van de stoffen, zoals bekend is uit dierstudies en andere alternatieve testen, te vergelijken is met de resultaten gebaseerd op uitlezing op basis van genexpressie en de klassieke uitlezing. Deze studie heeft aangetoond dat de effecten op genexpressie overeenkomen, wat de bruikbaarheid van genexpressie voor het bestuderen van ontwikkelingstoxiciteit en on- derscheidend vermogen op basis van potentie binnen een stofklasse aantoont. De effecten op gen expressie zijn verder detail bestudeerd waarbij er naar biologische relevantie is gekeken.

Na blootstelling aan de 3 emryo-toxische ftalaten, werden 668 genen geïdentificeerd die significant gereguleerd waren over alle concentraties en ftalaten. Deze 668 genen zijn geassocieerd met biologische processen, betrokken bij celdeling, differentiatie, transcriptie van genen, cel- dood en embryonale ontwikkeling. De mate waarin de gen-transcriptie gereguleerd was over de toenemende concentraties, is vergelijkbaar met de mate van embryo-toxiciteit bekend van dierstudies en andere in vitro studies. Echter is dit effect al aanwezig na 24uur blootstelling,

142 Nederlandse samenvatting ten opzichten van 7 dagen blootstellen in de klassieke EST. Dit bewijst de gevoeligheid en bruikbaarheid van genexpressie voor het bestuderen van ontwikkelingstoxiciteit.

De studie beschreven in hoofdstuk 2 is gebaseerd op complete genoom micro array analyses. Buiten de kosten, gaat hier veel data mee gepaard, wat de uitlezing complex maakt. Voor een alternatieve methode, die gebruikt moet worden voor de uitlezing van ontwikkelingstoxicteit, is het wenselijk een makkelijkere methode te hebben, waarbij het effect op een laag aantal genen gebruikt kan worden als uitlezing. Daarvoor hebben we in hoofdstuk 3 een statistische methode beschreven die toegepast kan worden voor het verkrijgen van gecomprimeerde genensets, die bruikbaar zijn voor de voorspelling- en het bestuderen van ontwikkelingstoxiciteit, binnen 1 klasse van stoffen. Op basis van de verkregen set genen van hoofdstuk 1, is met een statistische methode, waarbij gebruik gemaakt is van een zogenaamde drempelwaarde benadering, een gecomprimeerde set van 97 genen verkregen. Met deze set genen is het mogelijk de ftalaten te ordenen op basis van potentie.

In hoofdstuk 4 is een methode beschreven voor mESC differentiatie tot neurale cellen. Bin- nen 11 dagen ontwikkelde mESC zich tot neurale cellen, met neurale uitgroei en toename in expressie van neurale genen. Het effect van methylkwik op de neurale uitgroei en genexpressie genen is bestudeerd. Hierbij is een concentratieafhankelijk effect aangetoond op de neurale uitgroei.

In het tweede deel van het proefschrift is gericht op de toepassing van humane embryonale stamcellen (hESC). Verschillende soorten organisme reageren anders op toxische blootstel- lingen. Dit komt onder andere door variatie in genetische achtergrond, verschil in farma- cokinetiek en metabolisme. Het gebruik van humane cellen voor humane risicobeoordeling kan een betere voorspelbaarheid geven. Daarnaast kunnen overlappende genen tussen de muis en humaan gebaseerde test systemen goede kandidaat genen zijn voor het vaststellen- en bestuderen van ontwikkelingstoxiciteit. In hoofdstuk 5 is een neurale differentiatie methode beschreven, waarin hESC binnen 11 dagen differentieerde tot neurale cellen. Deze gedifferentieerde cellen vormde een neuraal netwerk, aangetoond met immunofluorescentie kleuringen en expressie van genen betrokken bij neurale differentiatie. Vervolgens is het effect op de neurale differentiatie na 7 dagen blootstelling aan twee anti-epileptica, valporaat zuur (VPA) of carbamazepine (CBZ), bestudeerd. Hiervan is bekend dat zij een effect hebben op neurale ontwikkeling. Na bootstelling aan VPA is een afname in expressie van 5 vooraf gese- lecteerde neurale differentiatiegenen aangetoond. Blootstelling aan CBZ resulteerde in een effect op 2 van de 5 neurale differentiatiegenen. Met deze studie is het principe aangetoond dat hESC in combinatie met genexpressie gebruikt kunnen worden voor het detecteren van de effecten van chemicaliën in differentiërende hESC voor het besturen en vaststellen van (neurale) ontwikkelingstoxiciteit.

Om verder in te gaan op de gevoeligheid van dit testsysteem en mechanismen gereguleerd door VPA en CBZ is in hoofdstuk 6 het effect van 7 dagen blootstelling vergeleken met 24uur blootstelling op basis van een compleet genoom genexpressie analyse. In dit hoofdstuk is

143 aangetoond dat VPA een sterker effect heeft op genexpressie in vergelijking met CBZ. Dit is in overeenstemming met in vivo studies, waaruit bekend is dat CBZ een minder teratogeen effecten heeft, als er gekeken wordt naar aangeboren afwijkingen. De veranderde gen expressie na blootstelling is significant te koppelen aan processen betrokken bij (neurale) ontwikkeling en mechanisme betrokken bij de farmacologische werking.

In hoofdstuk 7 zijn de muis- en humane neurale differentiatie methodes met elkaar vergeleken. Binnen deze vergelijking is gekeken naar de normale differentiatie, waarbij de gen expressie veranderingen over de tijd tussen beide methodes is vergeleken. Hierbij is tussen beide methodes een overlap te vinden van genen die betrokken zijn bij vroege ontwikkeling en neurale differentiatie. De humane neurale differentiatie methode laat daarnaast ook een significante verandering zien van andere weefseltypes. Vervolgens is het effect van 24 uur blootstelling aan VPA tussen beide methodes met elkaar vergeleken. Hierbij is te zien dat in beide methodes VPA blootstelling leidt tot een significante verandering in processen die in de literatuur gekoppeld zijn aan farmaceutische gecorreleerde mechanismen van VPA en neurale ontwikkeling. Er lijken sterkere effecten op te treden in de humane neurale differentiatie methode.

Samengevat, met dit proefschrift bewijzen we de bruikbaarheid van ESC in combinatie met genexpressie, waarbij in vitro differentiatie kan dienen als een waardevolle alternatieve methode voor het bestuderen, en hopelijk uiteindelijk ook kan functioneren als test methode, voor ontwikkelingstoxiciteit. Gezien de complexiteit van een humaan organisme, is het een uitda- ging een in vitro methode te verkrijgen die 100% voorspelbaar zal zijn voor humane risico’s. Dit soort testen zullen, en moeten steeds verder onderzocht en ontwikkeld worden, waardoor tegelijkertijd zowel hun waarde steeds meer duidelijk wordt, als ook de beperkingen. Deze testen zijn veelbelovend en zullen bijdragen aan validiteit vooral met een toepassing binnen teststrategieën. Zoals een samenvoeging van verschillende testen in een zogenaamde testbat- terij benadering. Samen met de snel toenemende technologie binnen de genexpressie analyse en verbeterde laboratoriumvoorzieningen en technieken is er een goed vooruitzicht voor het gebruik van stamcellen voor humane toxiciteitstesten.

144 Nederlandse samenvatting

145 146 APPENDICES

147 148 Appendices

Supplementary Table 1: Chapter 2 Enriched GO biological processes within the 668 commonly regulated genes after exposure to MEHP, MBzP and MBuP

'GOID 'GO Name 'Number Changed 'Number Measured 'Number in GO 8150 biological_process 443 10164 14509 9987 cellular process 387 8519 12349 8152 metabolic process 304 6209 8113 44237 cellular metabolic process 283 5523 7311 44238 primary metabolic process 279 5535 7338 43170 macromolecule metabolic process 247 4803 6411 65007 biological regulation 205 3464 4497 43283 biopolymer metabolic process 203 3596 4641 50789 regulation of biological process 198 3207 4133 50794 regulation of cellular process 190 2918 3752 6139 nucleobase\, nucleoside\, nucleotide and nucleic acid metabolic process 163 2654 3483 32502 developmental process 155 2561 3292 19222 regulation of metabolic process 131 1999 2559 31323 regulation of cellular metabolic process 126 1922 2466 16070 RNA metabolic process 123 2033 2562 6350 transcription 121 1804 2319 19219 regulation of nucleobase\, nucleoside\, nucleotide and nucleic acid metabolic process 119 1776 2273 45449 regulation of transcription 118 1747 2228 6351 transcription\, DNA-dependent 110 1671 2133 32774 RNA biosynthetic process 110 1673 2137 6355 regulation of transcription\, DNA-dependent 109 1645 2088 30154 cell differentiation 106 1692 2098 48869 cellular developmental process 106 1692 2098 7275 multicellular organismal development 96 1735 2140 48856 anatomical structure development 90 1566 2002 48468 cell development 80 1220 1610 48731 system development 73 1340 1704 48513 organ development 63 1077 1345 43687 post-translational protein modification 58 1090 1375 48518 positive regulation of biological process 56 802 1009 48523 negative regulation of cellular process 51 751 967 48519 negative regulation of biological process 51 805 1018 48522 positive regulation of cellular process 51 704 901 9653 anatomical structure morphogenesis 50 916 1089 6996 organelle organization and biogenesis 49 774 1201 12501 programmed cell death 45 554 683 8219 cell death 45 573 709 16265 death 45 573 709 6915 apoptosis 44 549 674 7049 cell cycle 39 571 762 6259 DNA metabolic process 37 492 733 22402 cell cycle process 34 453 646 902 cell morphogenesis 28 390 485 32989 cellular structure morphogenesis 28 390 485 9790 embryonic development 26 354 362 43067 regulation of programmed cell death 26 366 475 74 regulation of progression through cell cycle 25 284 459 51726 regulation of cell cycle 25 287 461 65008 regulation of biological quality 25 400 548 42981 regulation of apoptosis 25 362 469 9893 positive regulation of metabolic process 24 353 423 9719 response to endogenous stimulus 23 234 279 31325 positive regulation of cellular metabolic process 22 335 399 7001 chromosome organization and biogenesis (sensu Eukaryota) 22 241 390 51276 chromosome organization and biogenesis 22 252 399 45935 positive regulation of nucleobase\, nucleoside\, nucleotide and nucleic acid metabolic 20 274 321 6974 response to DNA damage stimulus 20 210 253 'GOID 'GO Name 'Number Changed 'Number Measured 'Number in GO 6325 establishment and/or maintenance of chromatin architecture 20 190 313 6323 DNA packaging 20 192 324 149 45941 positive regulation of transcription 19 270 314 2520 immune system development 19 220 257 42127 regulation of cell proliferation 19 299 425 40007 growth 19 193 252 48534 hemopoietic or lymphoid organ development 18 205 243 16477 cell migration 17 245 285 50793 regulation of developmental process 17 226 256 7417 central nervous system development 16 181 224 16568 chromatin modification 16 140 165 30097 hemopoiesis 15 182 220 6281 DNA repair 15 170 202 30030 cell projection organization and biogenesis 15 214 249 32990 cell part morphogenesis 15 214 249 48858 cell projection morphogenesis 15 214 249 1501 skeletal development 15 154 205 43068 positive regulation of programmed cell death 15 158 226 7389 pattern specification process 14 180 212 8361 regulation of cell size 14 102 142 40008 regulation of growth 14 127 169 43065 positive regulation of apoptosis 14 157 224 16049 cell growth 13 97 139 45786 negative regulation of progression through cell cycle 13 114 147 48666 neuron development 13 181 213 30029 actin filament-based process 12 161 207 7420 brain development 12 141 152 9792 embryonic development ending in birth or egg hatching 12 160 149 3002 regionalization 11 131 133 43066 negative regulation of apoptosis 11 148 187 43069 negative regulation of programmed cell death 11 150 190 1558 regulation of cell growth 11 78 116 9628 response to abiotic stimulus 10 122 155 6917 induction of apoptosis 10 119 183 12502 induction of programmed cell death 10 119 183 7409 axonogenesis 10 133 158 9607 response to biotic stimulus 10 119 274 6260 DNA replication 10 126 161 9952 anterior/posterior pattern formation 9 84 83 6333 chromatin assembly or disassembly 9 83 180 51094 positive regulation of developmental process 9 66 70 43086 negative regulation of enzyme activity 8 52 71 30099 myeloid cell differentiation 8 86 95 30900 forebrain development 8 67 65 6469 negative regulation of protein kinase activity 7 42 52 51348 negative regulation of transferase activity 7 43 54 6730 one-carbon compound metabolic process 7 77 102 1503 ossification 7 80 89 31214 biomineral formation 7 81 90 45596 negative regulation of cell differentiation 7 72 75 7411 axon guidance 7 69 89 7369 gastrulation 6 53 51 32259 methylation 6 42 68 7050 cell cycle arrest 6 45 68 7281 germ cell development 6 25 34 'GOID 'GO Name 'Number Changed 'Number Measured 'Number in GO 8150 biological_process 443 10164 14509 9987 cellular process 387 8519 12349 8152 metabolic process 304 6209 8113 44237 cellular metabolic process 283 5523 7311 44238 primary metabolic process 279 5535 7338 43170 macromolecule metabolic process 247 4803 6411 65007 biological regulation 205 3464 4497 43283 biopolymer metabolic process 203 3596 4641 50789 regulation of biological process 198 3207 4133 50794 regulation of cellular process 190 2918 3752 6139 nucleobase\, nucleoside\, nucleotide and nucleic acid metabolic process 163 2654 3483 32502 developmental process 155 2561 3292 19222 regulation of metabolic process 131 1999 2559 31323 regulation of cellular metabolic process 126 1922 2466 16070 RNA metabolic process 123 2033 2562 6350 transcription 121 1804 2319 19219 regulation of nucleobase\, nucleoside\, nucleotide and nucleic acid metabolic process 119 1776 2273 45449 regulation of transcription 118 1747 2228 6351 transcription\, DNA-dependent 110 1671 2133 32774 RNA biosynthetic process 110 1673 2137 6355 regulation of transcription\, DNA-dependent 109 1645 2088 30154 cell differentiation 106 1692 2098 48869 cellular developmental process 106 1692 2098 7275 multicellular organismal development 96 1735 2140 48856 anatomical structure development 90 1566 2002 48468 cell development 80 1220 1610 48731 system development 73 1340 1704 48513 organ development 63 1077 1345 43687 post-translational protein modification 58 1090 1375 48518 positive regulation of biological process 56 802 1009 48523 negative regulation of cellular process 51 751 967 48519 negative regulation of biological process 51 805 1018 48522 positive regulation of cellular process 51 704 901 9653 anatomical structure morphogenesis 50 916 1089 6996 organelle organization and biogenesis 49 774 1201 12501 programmed cell death 45 554 683 8219 cell death 45 573 709 16265 death 45 573 709 6915 apoptosis 44 549 674 7049 cell cycle 39 571 762 6259 DNA metabolic process 37 492 733 22402 cell cycle process 34 453 646 902 cell morphogenesis 28 390 485 32989 cellular structure morphogenesis 28 390 485 9790 embryonic development 26 354 362 43067 regulation of programmed cell death 26 366 475 74 regulation of progression through cell cycle 25 284 459 51726 regulation of cell cycle 25 287 461 65008 regulation of biological quality 25 400 548 42981 regulation of apoptosis 25 362 469 9893 positive regulation of metabolic process 24 353 423 9719 response to endogenous stimulus 23 234 279 Supplementary31325 positive regulation Table 1 of: cellularChapter metabolic 2 continued process 22 335 399 7001 chromosome organization and biogenesis (sensu Eukaryota) 22 241 390 Enriched51276 chromosome GO biological organization and processes biogenesis within the 668 commonly22 regulated252 genes after399 exposure to MEHP, MBzP45935 positiveand regulationMBuP of nucleobase\, nucleoside\, nucleotide and nucleic acid metabolic 20 274 321 6974 response to DNA damage stimulus 20 210 253 'GOID 'GO Name 'Number Changed 'Number Measured 'Number in GO 6325 establishment and/or maintenance of chromatin architecture 20 190 313 6323 DNA packaging 20 192 324 45941 positive regulation of transcription 19 270 314 2520 immune system development 19 220 257 42127 regulation of cell proliferation 19 299 425 40007 growth 19 193 252 48534 hemopoietic or lymphoid organ development 18 205 243 16477 cell migration 17 245 285 50793 regulation of developmental process 17 226 256 7417 central nervous system development 16 181 224 16568 chromatin modification 16 140 165 30097 hemopoiesis 15 182 220 6281 DNA repair 15 170 202 30030 cell projection organization and biogenesis 15 214 249 32990 cell part morphogenesis 15 214 249 48858 cell projection morphogenesis 15 214 249 1501 skeletal development 15 154 205 43068 positive regulation of programmed cell death 15 158 226 7389 pattern specification process 14 180 212 8361 regulation of cell size 14 102 142 40008 regulation of growth 14 127 169 43065 positive regulation of apoptosis 14 157 224 16049 cell growth 13 97 139 45786 negative regulation of progression through cell cycle 13 114 147 48666 neuron development 13 181 213 30029 actin filament-based process 12 161 207 7420 brain development 12 141 152 9792 embryonic development ending in birth or egg hatching 12 160 149 3002 regionalization 11 131 133 43066 negative regulation of apoptosis 11 148 187 43069 negative regulation of programmed cell death 11 150 190 1558 regulation of cell growth 11 78 116 9628 response to abiotic stimulus 10 122 155 6917 induction of apoptosis 10 119 183 12502 induction of programmed cell death 10 119 183 7409 axonogenesis 10 133 158 9607 response to biotic stimulus 10 119 274 6260 DNA replication 10 126 161 9952 anterior/posterior pattern formation 9 84 83 6333 chromatin assembly or disassembly 9 83 180 51094 positive regulation of developmental process 9 66 70 43086 negative regulation of enzyme activity 8 52 71 30099 myeloid cell differentiation 8 86 95 30900 forebrain development 8 67 65 6469 negative regulation of protein kinase activity 7 42 52 51348 negative regulation of transferase activity 7 43 54 6730 one-carbon compound metabolic process 7 77 102 1503 ossification 7 80 89 31214 biomineral formation 7 81 90 45596 negative regulation of cell differentiation 7 72 75 7411 axon guidance 7 69 89 7369 gastrulation 6 53 51 32259 methylation 6 42 68 7050 cell cycle arrest 6 45 68 7281 germ cell development 6 25 34

150 Appendices

Supplementary Table 2: Chapter 3 Geneset of 97 genes

Number Gene ID symbol Number Gene ID symbol 1 11745 Anxa3 50 381066 Zfp948 2 217578 Baz1a 51 70564 5730469M10Rik 3 233016 Blvrb 52 11980 Atp8a1 4 12227 Btg2 53 217827 BC002230 5 104479 Ccdc117 54 12310 Calca 6 239083 Ccnb1ip1 55 227541 Camk1d 7 12457 Ccrn4l 56 23831 Car14 8 12575 Cdkn1a 57 12411 Cbs 9 12798 Cnn2 58 229905 Ccbl2 10 12894 Cpt1a 59 66717 Ccdc96 11 16007 Cyr61 60 218630 Ccno 12 27981 D4Wsu53e 61 12667 Chrd 13 56812 Dnajb2 62 12696 Cirbp 14 13537 Dusp2 63 12724 Clcn2 15 14181 Fgfbp1 64 73708 Dppa3 16 68794 Flnc 65 18606 Enpp2 17 17873 Gadd45b 66 13823 Epb4.1l3 18 15511 Hspa1b 67 270190 Ephb1 19 72500 Ier5l 68 353283 Eras 20 16009 Igfbp3 69 14170 Fgf15 21 16363 Irf2 70 14226 Fkbp1b 22 384783 Irs2 71 15376 Foxa2 23 16476 Jun 72 15221 Foxd3 24 16477 Junb 73 14367 Fzd5 25 16994 Ltb 74 14472 Gbx2 26 76781 Mettl4 75 14584 Gfpt2 27 69028 Mitd1 76 15242 Hhex 28 98932 Myl9 77 16590 Kit 29 17937 Nab2 78 237010 Klhl4 30 18030 Nfil3 79 16206 Lrig1 31 18034 Nfkb2 80 71897 Lypd6b 32 15370 Nr4a1 81 67636 Lyrm5 33 67399 Pdlim7 82 109241 Mbd5 34 223775 Pim3 83 17349 Mlf1 35 11520 Plin2 84 17988 Ndrg1 36 52118 Pvr 85 75533 Nme5 37 19698 Relb 86 55983 Pdzrn3 38 243923 Rgs9bp 87 106522 Pkdcc 39 11852 Rhob 88 67916 Ppap2b 40 14739 S1pr2 89 244058 Rgma 41 55942 Sertad1 90 212892 Rsph4a 42 57279 Slc25a20 91 110595 Timp4 43 21345 Tagln 92 244579 Tox3 44 27279 Tnfrsf12a 93 67971 Tppp3 45 30933 Tor2a 94 22044 Trh 46 22003 Tpm1 95 71860 Wdr16 47 56338 Txnip 96 66995 Zcchc18 48 22695 Zfp36 97 80892 Zfhx4 49 627049 Zfp800

151 Supplementary Table 3: Chapter 6 Pathways enriched after 1 day VPA (left) or CBZ (right) exposure

VPA P-value CBZ P-value Cytoskeleton remodeling_TGF, WNT and cytoskeletal remodeling 1,3197E-08 Cytoskeleton remodeling_TGF, WNT and cytoskeletal remodeling 3,4963E-19 Apoptosis and survival_TNF-alpha-induced Caspase-8 signaling 2,2875E-06 Cytoskeleton remodeling_Cytoskeleton remodeling 8,54623E-15 Proteolysis_Role of Parkin in the Ubiquitin-Proteasomal Pathway 3,80005E-06 Cell adhesion_Chemokines and adhesion 5,79221E-14 Cell adhesion_Chemokines and adhesion 5,21678E-06 Cell adhesion_ECM remodeling 1,87673E-06 Cytoskeleton remodeling_Cytoskeleton remodeling 7,91334E-06 Cell adhesion_Plasmin signaling 5,27265E-06 Cytoskeleton remodeling_CDC42 in cellular processes 9,76098E-06 Oxidative stress_Role of Sirtuin1 and PGC1-alpha in activation of antioxidant defense system 7,29345E-06 Signal transduction_Activation of PKC via G-Protein coupled receptor 1,05947E-05 Transcription_Role of AP-1 in regulation of cellular metabolism 1,01608E-05 Neuroprotective action of lithium 1,53074E-05 Development_TGF-beta receptor signaling 1,0982E-05 Action of GSK3 beta in bipolar disorder 1,6763E-05 Development_Regulation of epithelial-to-mesenchymal transition (EMT) 1,32336E-05 Cell adhesion_Histamine H1 receptor signaling in the interruption of cell barrier integrity 2,22365E-05 Role of Tissue factor-induced Thrombin signaling in cancerogenesis 1,75173E-05 Neurophysiological process_Synaptic vesicle fusion and recycling in nerve terminals 4,32967E-05 Development_WNT signaling pathway. Part 2 1,80052E-05 Signal transduction_cAMP signaling 4,58301E-05 Expression targets of Tissue factor signaling in cancer 2,79772E-05 Development_WNT signaling pathway. Part 2 5,69742E-05 Development_TGF-beta-dependent induction of EMT via MAPK 5,21417E-05 Transcription_Sirtuin6 regulation and functions 7,03474E-05 Cell adhesion_Integrin-mediated cell adhesion and migration 6,10235E-05 Development_TGF-beta receptor signaling 9,67446E-05 Cell cycle_Initiation of mitosis 6,17256E-05 Transport_Clathrin-coated vesicle cycle 0,00010354 Development_Growth hormone signaling via PI3K/AKT and MAPK cascades 0,000177526 Development_WNT signaling pathway. Part 1. Degradation of beta-catenin in the absence WNT signaling 0,000110992 Cytoskeleton remodeling_FAK signaling 0,000213398 Immune response_CD28 signaling 0,000123446 Cell cycle_Chromosome condensation in prometaphase 0,000271389 Ligand-independent activation of Androgen receptor in Prostate Cancer 0,000138973 Normal and pathological TGF-beta-mediated regulation of cell proliferation 0,00031783 Development_VEGF signaling via VEGFR2 - generic cascades 0,000155396 Signal transduction_PTEN pathway 0,000319127 Development_Regulation of cytoskeleton proteins in oligodendrocyte differentiation and myelination 0,000198914 Cytoskeleton remodeling_Integrin outside-in signaling 0,000475687 Regulation of lipid metabolism_Regulation of lipid metabolism via LXR, NF-Y and SREBP 0,000204212 HBV signaling via protein kinases leading to HCC 0,000519752 Signal transduction_Additional pathways of NF-kB activation (in the cytoplasm) 0,000208636 Muscle contraction_Regulation of eNOS activity in endothelial cells 0,000533993 Development_Differentiation of white adipocytes 0,000208636 Immune response_IL-13 signaling via PI3K-ERK 0,00053968 Cell cycle_Influence of Ras and Rho proteins on G1/S Transition 0,000208636 Development_Beta-adrenergic receptors transactivation of EGFR 0,000605513 Apoptosis and survival_TNFR1 signaling pathway 0,000213265 Transcription_Transcription regulation of aminoacid metabolism 0,000644533 Apoptosis and survival_Caspase cascade 0,000256013 DNA damage_ATM / ATR regulation of G2 / M checkpoint 0,000779348 Cytoskeleton remodeling_Neurofilaments 0,000258527 Cell adhesion_PLAU signaling 0,000809653 Transcription_Transcription regulation of aminoacid metabolism 0,000258527 Cytoskeleton remodeling_Substance P mediated membrane blebbing 0,0009467 Development_FGFR signaling pathway 0,000265187 Apoptosis and survival_Endoplasmic reticulum stress response pathway 0,00096965 Cell cycle_Chromosome condensation in prometaphase 0,000284847 Development_VEGF-family signaling 0,001063185 Muscle contraction_Regulation of eNOS activity in endothelial cells 0,000286603 Development_Role of proteases in hematopoietic stem cell mobilization 0,001520744 Signal transduction_Additional pathways of NF-kB activation (in the nucleus) 0,000314942 Cell adhesion_Histamine H1 receptor signaling in the interruption of cell barrier integrity 0,001748771 Apoptosis and survival_Endoplasmic reticulum stress response pathway 0,000334529 Development_Thrombopoietin-regulated cell processes 0,001748771 Immune response_Fc epsilon RI pathway 0,000334529 Cytoskeleton remodeling_Fibronectin-binding integrins in cell motility 0,001789061 Development_GM-CSF signaling 0,000352845 Development_Gastrin in cell growth and proliferation 0,001979406 Development_Thrombopoietin-regulated cell processes 0,000363861 DNA damage_ATM/ATR regulation of G1/S checkpoint 0,00207103 Airway smooth muscle contraction in asthma 0,000418962 Cell cycle_Start of DNA replication in early S phase 0,00207103 Immune response_ETV3 affect on CSF1-promoted macrophage differentiation 0,000437777 Development_PIP3 signaling in cardiac myocytes 0,002196978 Signal transduction_PKA signaling 0,000447337 Stimulation of TGF-beta signaling in lung cancer 0,002451133 Development_Oligodendrocyte differentiation from adult stem cells 0,000447337 Tissue Factor signaling in cancer via PAR1 and PAR2 0,002726777 Neurophysiological process_ACM regulation of nerve impulse 0,00046829 Development_VEGF signaling via VEGFR2 - generic cascades 0,002878384 Cell cycle_Role of Nek in cell cycle regulation 0,000598308 Development_Growth hormone signaling via STATs and PLC/IP3 0,003111283 Effect of H. pylori infection on gastric epithelial cell proliferation 0,000643818 Role of growth factor receptors transactivation by Hyaluronic acid / CD44 signaling in tumor progression 0,003111283 Development_Insulin, IGF-1 and TNF-alpha in brown adipocyte differentiation 0,000702419 Influence of low doses of Arsenite on Glucose stimulated Insulin secretion in pancreatic cells 0,003529272 Signal transduction_PTMs in IL-12 signaling pathway 0,000754947 Development_IGF-1 receptor signaling 0,003693777 LRRK2 in neurons in Parkinson's disease 0,000805025 Action of GSK3 beta in bipolar disorder 0,003928055 Immune response _IFN gamma signaling pathway 0,000870585 Immune response_Oncostatin M signaling via MAPK in human cells 0,003986109 Development_EGFR signaling pathway 0,000913439 Immune response_Generation of memory CD4+ T cells 0,003986109 Huntingtin-depended transcription deregulation in Huntington's Disease 0,000918903 Immune response_HMGB1/RAGE signaling pathway 0,004066562 PDE4 regulation of cyto/chemokine expression in arthritis 0,00094652 Cell cycle_Influence of Ras and Rho proteins on G1/S Transition 0,004066562 Apoptosis and survival_FAS signaling cascades 0,001011606 Immune response _IFN gamma signaling pathway 0,004466473 Immune response_IL-1 signaling pathway 0,001011606 Signal transduction_Soluble CXCL16 signaling 0,004483723 Development_Role of CDK5 in neuronal development 0,001067597 Cell adhesion_Endothelial cell contacts by non-junctional mechanisms 0,004608072 Immune response_Platelet activating factor/ PTAFR pathway signaling 0,001071606 Translation_Non-genomic (rapid) action of Androgen Receptor 0,005608919 Immune response_Antigen presentation by MHC class II 0,00116696 Development_Cytokine-mediated regulation of megakaryopoiesis 0,005840428 Regulation of GSK3 beta in bipolar disorder 0,001274752 Apoptosis and survival_BAD phosphorylation 0,00691993 VPA P-value CBZ P-value Cell cycle_Initiation of mitosis 0,001290365 IGF family signaling in colorectal cancer 0,007499788 Regulation of lipid metabolism_Insulin regulation of glycogen metabolism 0,001310378 Development_Role of CNTF and LIF in regulation of oligodendrocyte development 0,008111765 Cytoskeleton remodeling_Role of PKA in cytoskeleton reorganisation 0,001353046 Cell cycle_Transition and termination of DNA replication 0,008111765 153 Development_Angiopoietin - Tie2 signaling 0,001396897 Development_c-Kit ligand signaling pathway during hemopoiesis 0,008121644 Immune response_NFAT in immune response 0,00145363 K-RAS signaling in pancreatic cancer 0,008431395 Cell cycle_Cell cycle (generic schema) 0,001574563 Immune response_IL-1 signaling pathway 0,008431395 Transcription_Epigenetic regulation of gene expression 0,001592268 Development_Alpha-2 adrenergic receptor activation of ERK 0,008779741 Development_GDNF family signaling 0,00159245 Transcription_Androgen Receptor nuclear signaling 0,009266724 Immune response_MIF - the neuroendocrine-macrophage connector 0,00159245 Regulation of GSK3 beta in bipolar disorder 0,009266724 Signal transduction_PTEN pathway 0,00159245 Neuroprotective action of lithium 0,009475187 Neurophysiological process_Constitutive and regulated NMDA receptor trafficking 0,001693175 Immune response_MIF-induced cell adhesion, migration and angiogenesis 0,010157395 Signal transduction_PTMs (phosphorylation and acetylation) in TNF-alpha-induced NF-kB signaling 0,001715364 Development_TGF-beta-dependent induction of EMT via RhoA, PI3K and ILK. 0,010157395 Transcription_Transcription factor Tubby signaling pathways 0,001833538 FGF signaling in pancreatic cancer 0,010157395 Apoptosis and survival_Role of IAP-proteins in apoptosis 0,001836168 Signal transduction_PTMs in IL-17-induced CIKS-independent signaling pathways 0,010157395 Ovarian cancer (main signaling cascades) 0,002020758 Ovarian cancer (main signaling cascades) 0,010209079 Signal transduction_JNK pathway 0,002153734 DNA damage_Brca1 as a transcription regulator 0,010378947 Development_Beta adrenergic receptors in brown adipocyte differentiation 0,002305273 Development_Osteopontin signaling in osteoclasts 0,010378947 DNA damage_ATM/ATR regulation of G1/S checkpoint 0,002398832 Cell adhesion_Gap junctions 0,010378947 Regulation of metabolism_Triiodothyronine and Thyroxine signaling 0,00242541 Development_PDGF signaling via MAPK cascades 0,011105064 Muscle contraction_Relaxin signaling pathway 0,00242541 Immune response_IL-6 signaling pathway 0,011651523 Development_Role of Thyroid hormone in regulation of oligodendrocyte differentiation 0,00242541 Immune response_ETV3 affect on CSF1-promoted macrophage differentiation 0,011651523 Immune response_HSP60 and HSP70/ TLR signaling pathway 0,002623956 Cytoskeleton remodeling_Reverse signaling by ephrin B 0,011651523 Neurophysiological process_Dynein-dynactin motor complex in axonal transport in neurons 0,002623956 Signal transduction_PTMs in IL-12 signaling pathway 0,012111335 Immune response_CCL2 signaling 0,002623956 Development_Growth factors in regulation of oligodendrocyte precursor cell proliferation 0,012652152 Regulation of metabolism_Role of Adiponectin in regulation of metabolism 0,002679521 Development_PDGF signaling via STATs and NF-kB 0,013020165 Signal transduction_AKT signaling 0,002679521 Cell cycle_Role of Nek in cell cycle regulation 0,013020165 Development_A2A receptor signaling 0,002679521 Cell cycle_Role of APC in cell cycle regulation 0,013020165 Development_Gastrin in differentiation of the gastric mucosa 0,002912128 Transport_The role of AVP in regulation of Aquaporin 2 and renal water reabsorption 0,014305845 Development_Thromboxane A2 signaling pathway 0,002959454 Some pathways of EMT in cancer cells 0,015497026 Transcription_N-CoR/ SMRT complex-mediated epigenetic gene silencing 0,002959454 Development_HGF-dependent inhibition of TGF-beta-induced EMT 0,016054808 Normal and pathological TGF-beta-mediated regulation of cell proliferation 0,003091792 Development_CNTF receptor signaling 0,016054808 Cell cycle_Nucleocytoplasmic transport of CDK/Cyclins 0,003092393 Development_Role of cell-cell and ECM-cell interactions in oligodendrocyte differentiation and myelination 0,016054808 Immune response_CRTH2 signaling in Th2 cells 0,00330499 Development_EGFR signaling pathway 0,016511363 Immune response_C5a signaling 0,003585583 Reproduction_GnRH signaling 0,017590187 Translation _Regulation of EIF2 activity 0,003641189 Immune response_Oncostatin M signaling via MAPK in mouse cells 0,01772494 Development_Gastrin in cell growth and proliferation 0,003870086 Role of Tissue factor in cancer independent of coagulation protease signaling 0,01772494 Signal transduction_Erk Interactions: Inhibition of Erk 0,003935206 Apoptosis and survival_Role of PKR in stress-induced apoptosis 0,018074163 Apoptosis and survival_Role of CDK5 in neuronal death and survival 0,003935206 Development_TGF-beta-induction of EMT via ROS 0,018958251 Apoptosis and survival_Cytoplasmic/mitochondrial transport of proapoptotic proteins Bid, Bmf and Bim 0,003935206 wtCFTR and deltaF508-CFTR traffic / Clathrin coated vesicles formation (normal and CF) 0,018958251 Regulation of lipid metabolism_Regulation of lipid metabolism by niacin and isoprenaline 0,004043258 Cytoskeleton remodeling_Role of Activin A in cytoskeleton remodeling 0,018958251 Neurophysiological process_Glutamate regulation of Dopamine D1A receptor signaling 0,004043258 Immune response_Oncostatin M signaling via JAK-Stat in human cells 0,018958251 Immune response_TNF-R2 signaling pathways 0,004043258 Development_FGFR signaling pathway 0,019462702 Development_EPO-induced MAPK pathway 0,004043258 Immune response_IL-9 signaling pathway 0,019499399 VPA P-value CBZ P-value Cytoskeleton remodeling_TGF, WNT and cytoskeletal remodeling 1,3197E-08 Cytoskeleton remodeling_TGF, WNT and cytoskeletal remodeling 3,4963E-19 Apoptosis and survival_TNF-alpha-induced Caspase-8 signaling 2,2875E-06 Cytoskeleton remodeling_Cytoskeleton remodeling 8,54623E-15 Proteolysis_Role of Parkin in the Ubiquitin-Proteasomal Pathway 3,80005E-06 Cell adhesion_Chemokines and adhesion 5,79221E-14 Cell adhesion_Chemokines and adhesion 5,21678E-06 Cell adhesion_ECM remodeling 1,87673E-06 Cytoskeleton remodeling_Cytoskeleton remodeling 7,91334E-06 Cell adhesion_Plasmin signaling 5,27265E-06 Cytoskeleton remodeling_CDC42 in cellular processes 9,76098E-06 Oxidative stress_Role of Sirtuin1 and PGC1-alpha in activation of antioxidant defense system 7,29345E-06 Signal transduction_Activation of PKC via G-Protein coupled receptor 1,05947E-05 Transcription_Role of AP-1 in regulation of cellular metabolism 1,01608E-05 Neuroprotective action of lithium 1,53074E-05 Development_TGF-beta receptor signaling 1,0982E-05 Action of GSK3 beta in bipolar disorder 1,6763E-05 Development_Regulation of epithelial-to-mesenchymal transition (EMT) 1,32336E-05 Cell adhesion_Histamine H1 receptor signaling in the interruption of cell barrier integrity 2,22365E-05 Role of Tissue factor-induced Thrombin signaling in cancerogenesis 1,75173E-05 Neurophysiological process_Synaptic vesicle fusion and recycling in nerve terminals 4,32967E-05 Development_WNT signaling pathway. Part 2 1,80052E-05 Signal transduction_cAMP signaling 4,58301E-05 Expression targets of Tissue factor signaling in cancer 2,79772E-05 Development_WNT signaling pathway. Part 2 5,69742E-05 Development_TGF-beta-dependent induction of EMT via MAPK 5,21417E-05 Transcription_Sirtuin6 regulation and functions 7,03474E-05 Cell adhesion_Integrin-mediated cell adhesion and migration 6,10235E-05 Development_TGF-beta receptor signaling 9,67446E-05 Cell cycle_Initiation of mitosis 6,17256E-05 Transport_Clathrin-coated vesicle cycle 0,00010354 Development_Growth hormone signaling via PI3K/AKT and MAPK cascades 0,000177526 Development_WNT signaling pathway. Part 1. Degradation of beta-catenin in the absence WNT signaling 0,000110992 Cytoskeleton remodeling_FAK signaling 0,000213398 Immune response_CD28 signaling 0,000123446 Cell cycle_Chromosome condensation in prometaphase 0,000271389 Ligand-independent activation of Androgen receptor in Prostate Cancer 0,000138973 Normal and pathological TGF-beta-mediated regulation of cell proliferation 0,00031783 Development_VEGF signaling via VEGFR2 - generic cascades 0,000155396 Signal transduction_PTEN pathway 0,000319127 Development_Regulation of cytoskeleton proteins in oligodendrocyte differentiation and myelination 0,000198914 Cytoskeleton remodeling_Integrin outside-in signaling 0,000475687 Regulation of lipid metabolism_Regulation of lipid metabolism via LXR, NF-Y and SREBP 0,000204212 HBV signaling via protein kinases leading to HCC 0,000519752 Signal transduction_Additional pathways of NF-kB activation (in the cytoplasm) 0,000208636 Muscle contraction_Regulation of eNOS activity in endothelial cells 0,000533993 Development_Differentiation of white adipocytes 0,000208636 Immune response_IL-13 signaling via PI3K-ERK 0,00053968 Cell cycle_Influence of Ras and Rho proteins on G1/S Transition 0,000208636 Development_Beta-adrenergic receptors transactivation of EGFR 0,000605513 Apoptosis and survival_TNFR1 signaling pathway 0,000213265 Transcription_Transcription regulation of aminoacid metabolism 0,000644533 Apoptosis and survival_Caspase cascade 0,000256013 DNA damage_ATM / ATR regulation of G2 / M checkpoint 0,000779348 Cytoskeleton remodeling_Neurofilaments 0,000258527 Cell adhesion_PLAU signaling 0,000809653 Transcription_Transcription regulation of aminoacid metabolism 0,000258527 Cytoskeleton remodeling_Substance P mediated membrane blebbing 0,0009467 Development_FGFR signaling pathway 0,000265187 Apoptosis and survival_Endoplasmic reticulum stress response pathway 0,00096965 Cell cycle_Chromosome condensation in prometaphase 0,000284847 Development_VEGF-family signaling 0,001063185 Muscle contraction_Regulation of eNOS activity in endothelial cells 0,000286603 Development_Role of proteases in hematopoietic stem cell mobilization 0,001520744 Signal transduction_Additional pathways of NF-kB activation (in the nucleus) 0,000314942 Cell adhesion_Histamine H1 receptor signaling in the interruption of cell barrier integrity 0,001748771 Apoptosis and survival_Endoplasmic reticulum stress response pathway 0,000334529 Development_Thrombopoietin-regulated cell processes 0,001748771 Immune response_Fc epsilon RI pathway 0,000334529 Cytoskeleton remodeling_Fibronectin-binding integrins in cell motility 0,001789061 Development_GM-CSF signaling 0,000352845 Development_Gastrin in cell growth and proliferation 0,001979406 Development_Thrombopoietin-regulated cell processes 0,000363861 DNA damage_ATM/ATR regulation of G1/S checkpoint 0,00207103 Airway smooth muscle contraction in asthma 0,000418962 Cell cycle_Start of DNA replication in early S phase 0,00207103 Immune response_ETV3 affect on CSF1-promoted macrophage differentiation 0,000437777 Development_PIP3 signaling in cardiac myocytes 0,002196978 Signal transduction_PKA signaling 0,000447337 Stimulation of TGF-beta signaling in lung cancer 0,002451133 Development_Oligodendrocyte differentiation from adult stem cells 0,000447337 Tissue Factor signaling in cancer via PAR1 and PAR2 0,002726777 Neurophysiological process_ACM regulation of nerve impulse 0,00046829 Development_VEGF signaling via VEGFR2 - generic cascades 0,002878384 Cell cycle_Role of Nek in cell cycle regulation 0,000598308 Development_Growth hormone signaling via STATs and PLC/IP3 0,003111283 Effect of H. pylori infection on gastric epithelial cell proliferation 0,000643818 Role of growth factor receptors transactivation by Hyaluronic acid / CD44 signaling in tumor progression 0,003111283 Development_Insulin, IGF-1 and TNF-alpha in brown adipocyte differentiation 0,000702419 Influence of low doses of Arsenite on Glucose stimulated Insulin secretion in pancreatic cells 0,003529272 Signal transduction_PTMs in IL-12 signaling pathway 0,000754947 Development_IGF-1 receptor signaling 0,003693777 LRRK2 in neurons in Parkinson's disease 0,000805025 Action of GSK3 beta in bipolar disorder 0,003928055 Immune response _IFN gamma signaling pathway 0,000870585 Immune response_Oncostatin M signaling via MAPK in human cells 0,003986109 Development_EGFR signaling pathway 0,000913439 Immune response_Generation of memory CD4+ T cells 0,003986109 Huntingtin-depended transcription deregulation in Huntington's Disease 0,000918903 Immune response_HMGB1/RAGE signaling pathway 0,004066562 PDE4 regulation of cyto/chemokine expression in arthritis 0,00094652 Cell cycle_Influence of Ras and Rho proteins on G1/S Transition 0,004066562 Apoptosis and survival_FAS signaling cascades 0,001011606 Immune response _IFN gamma signaling pathway 0,004466473 Immune response_IL-1 signaling pathway 0,001011606 Signal transduction_Soluble CXCL16 signaling 0,004483723 SupplementaryDevelopment_Role ofTable CDK5 3in: neuronal Chapter development 6 continued 0,001067597 Cell adhesion_Endothelial cell contacts by non-junctional mechanisms 0,004608072 PathwaysImmune response_Platelet enriched activating after 1factor/ day PTAFR VPA pathway (left) signaling or CBZ (right) exposure 0,001071606 Translation_Non-genomic (rapid) action of Androgen Receptor 0,005608919 Immune response_Antigen presentation by MHC class II 0,00116696 Development_Cytokine-mediated regulation of megakaryopoiesis 0,005840428 Regulation of GSK3 beta in bipolar disorder 0,001274752 Apoptosis and survival_BAD phosphorylation 0,00691993 VPA P-value CBZ P-value Cell cycle_Initiation of mitosis 0,001290365 IGF family signaling in colorectal cancer 0,007499788 Regulation of lipid metabolism_Insulin regulation of glycogen metabolism 0,001310378 Development_Role of CNTF and LIF in regulation of oligodendrocyte development 0,008111765 Cytoskeleton remodeling_Role of PKA in cytoskeleton reorganisation 0,001353046 Cell cycle_Transition and termination of DNA replication 0,008111765 Development_Angiopoietin - Tie2 signaling 0,001396897 Development_c-Kit ligand signaling pathway during hemopoiesis 0,008121644 Immune response_NFAT in immune response 0,00145363 K-RAS signaling in pancreatic cancer 0,008431395 Cell cycle_Cell cycle (generic schema) 0,001574563 Immune response_IL-1 signaling pathway 0,008431395 Transcription_Epigenetic regulation of gene expression 0,001592268 Development_Alpha-2 adrenergic receptor activation of ERK 0,008779741 Development_GDNF family signaling 0,00159245 Transcription_Androgen Receptor nuclear signaling 0,009266724 Immune response_MIF - the neuroendocrine-macrophage connector 0,00159245 Regulation of GSK3 beta in bipolar disorder 0,009266724 Signal transduction_PTEN pathway 0,00159245 Neuroprotective action of lithium 0,009475187 Neurophysiological process_Constitutive and regulated NMDA receptor trafficking 0,001693175 Immune response_MIF-induced cell adhesion, migration and angiogenesis 0,010157395 Signal transduction_PTMs (phosphorylation and acetylation) in TNF-alpha-induced NF-kB signaling 0,001715364 Development_TGF-beta-dependent induction of EMT via RhoA, PI3K and ILK. 0,010157395 Transcription_Transcription factor Tubby signaling pathways 0,001833538 FGF signaling in pancreatic cancer 0,010157395 Apoptosis and survival_Role of IAP-proteins in apoptosis 0,001836168 Signal transduction_PTMs in IL-17-induced CIKS-independent signaling pathways 0,010157395 Ovarian cancer (main signaling cascades) 0,002020758 Ovarian cancer (main signaling cascades) 0,010209079 Signal transduction_JNK pathway 0,002153734 DNA damage_Brca1 as a transcription regulator 0,010378947 Development_Beta adrenergic receptors in brown adipocyte differentiation 0,002305273 Development_Osteopontin signaling in osteoclasts 0,010378947 DNA damage_ATM/ATR regulation of G1/S checkpoint 0,002398832 Cell adhesion_Gap junctions 0,010378947 Regulation of metabolism_Triiodothyronine and Thyroxine signaling 0,00242541 Development_PDGF signaling via MAPK cascades 0,011105064 Muscle contraction_Relaxin signaling pathway 0,00242541 Immune response_IL-6 signaling pathway 0,011651523 Development_Role of Thyroid hormone in regulation of oligodendrocyte differentiation 0,00242541 Immune response_ETV3 affect on CSF1-promoted macrophage differentiation 0,011651523 Immune response_HSP60 and HSP70/ TLR signaling pathway 0,002623956 Cytoskeleton remodeling_Reverse signaling by ephrin B 0,011651523 Neurophysiological process_Dynein-dynactin motor complex in axonal transport in neurons 0,002623956 Signal transduction_PTMs in IL-12 signaling pathway 0,012111335 Immune response_CCL2 signaling 0,002623956 Development_Growth factors in regulation of oligodendrocyte precursor cell proliferation 0,012652152 Regulation of metabolism_Role of Adiponectin in regulation of metabolism 0,002679521 Development_PDGF signaling via STATs and NF-kB 0,013020165 Signal transduction_AKT signaling 0,002679521 Cell cycle_Role of Nek in cell cycle regulation 0,013020165 Development_A2A receptor signaling 0,002679521 Cell cycle_Role of APC in cell cycle regulation 0,013020165 Development_Gastrin in differentiation of the gastric mucosa 0,002912128 Transport_The role of AVP in regulation of Aquaporin 2 and renal water reabsorption 0,014305845 Development_Thromboxane A2 signaling pathway 0,002959454 Some pathways of EMT in cancer cells 0,015497026 Transcription_N-CoR/ SMRT complex-mediated epigenetic gene silencing 0,002959454 Development_HGF-dependent inhibition of TGF-beta-induced EMT 0,016054808 Normal and pathological TGF-beta-mediated regulation of cell proliferation 0,003091792 Development_CNTF receptor signaling 0,016054808 Cell cycle_Nucleocytoplasmic transport of CDK/Cyclins 0,003092393 Development_Role of cell-cell and ECM-cell interactions in oligodendrocyte differentiation and myelination 0,016054808 Immune response_CRTH2 signaling in Th2 cells 0,00330499 Development_EGFR signaling pathway 0,016511363 Immune response_C5a signaling 0,003585583 Reproduction_GnRH signaling 0,017590187 Translation _Regulation of EIF2 activity 0,003641189 Immune response_Oncostatin M signaling via MAPK in mouse cells 0,01772494 Development_Gastrin in cell growth and proliferation 0,003870086 Role of Tissue factor in cancer independent of coagulation protease signaling 0,01772494 Signal transduction_Erk Interactions: Inhibition of Erk 0,003935206 Apoptosis and survival_Role of PKR in stress-induced apoptosis 0,018074163 Apoptosis and survival_Role of CDK5 in neuronal death and survival 0,003935206 Development_TGF-beta-induction of EMT via ROS 0,018958251 Apoptosis and survival_Cytoplasmic/mitochondrial transport of proapoptotic proteins Bid, Bmf and Bim 0,003935206 wtCFTR and deltaF508-CFTR traffic / Clathrin coated vesicles formation (normal and CF) 0,018958251 Regulation of lipid metabolism_Regulation of lipid metabolism by niacin and isoprenaline 0,004043258 Cytoskeleton remodeling_Role of Activin A in cytoskeleton remodeling 0,018958251 Neurophysiological process_Glutamate regulation of Dopamine D1A receptor signaling 0,004043258 Immune response_Oncostatin M signaling via JAK-Stat in human cells 0,018958251 Immune response_TNF-R2 signaling pathways 0,004043258 Development_FGFR signaling pathway 0,019462702 Development_EPO-induced MAPK pathway 0,004043258 Immune response_IL-9 signaling pathway 0,019499399

154 VPA P-value CBZ P-value Cytoskeleton remodeling_TGF, WNT and cytoskeletal remodeling 1,3197E-08 Cytoskeleton remodeling_TGF, WNT and cytoskeletal remodeling 3,4963E-19 Apoptosis and survival_TNF-alpha-induced Caspase-8 signaling 2,2875E-06 Cytoskeleton remodeling_Cytoskeleton remodeling 8,54623E-15 Proteolysis_Role of Parkin in the Ubiquitin-Proteasomal Pathway 3,80005E-06 Cell adhesion_Chemokines and adhesion 5,79221E-14 Cell adhesion_Chemokines and adhesion 5,21678E-06 Cell adhesion_ECM remodeling 1,87673E-06 Cytoskeleton remodeling_Cytoskeleton remodeling 7,91334E-06 Cell adhesion_Plasmin signaling 5,27265E-06 Cytoskeleton remodeling_CDC42 in cellular processes 9,76098E-06 Oxidative stress_Role of Sirtuin1 and PGC1-alpha in activation of antioxidant defense system 7,29345E-06 Signal transduction_Activation of PKC via G-Protein coupled receptor 1,05947E-05 Transcription_Role of AP-1 in regulation of cellular metabolism 1,01608E-05 Neuroprotective action of lithium 1,53074E-05 Development_TGF-beta receptor signaling 1,0982E-05 Action of GSK3 beta in bipolar disorder 1,6763E-05 Development_Regulation of epithelial-to-mesenchymal transition (EMT) 1,32336E-05 Cell adhesion_Histamine H1 receptor signaling in the interruption of cell barrier integrity 2,22365E-05 Role of Tissue factor-induced Thrombin signaling in cancerogenesis 1,75173E-05 Neurophysiological process_Synaptic vesicle fusion and recycling in nerve terminals 4,32967E-05 Development_WNT signaling pathway. Part 2 1,80052E-05 Signal transduction_cAMP signaling 4,58301E-05 Expression targets of Tissue factor signaling in cancer 2,79772E-05 Development_WNT signaling pathway. Part 2 5,69742E-05 Development_TGF-beta-dependent induction of EMT via MAPK 5,21417E-05 Transcription_Sirtuin6 regulation and functions 7,03474E-05 Cell adhesion_Integrin-mediated cell adhesion and migration 6,10235E-05 Development_TGF-beta receptor signaling 9,67446E-05 Cell cycle_Initiation of mitosis 6,17256E-05 Transport_Clathrin-coated vesicle cycle 0,00010354 Development_Growth hormone signaling via PI3K/AKT and MAPK cascades 0,000177526 Development_WNT signaling pathway. Part 1. Degradation of beta-catenin in the absence WNT signaling 0,000110992 Cytoskeleton remodeling_FAK signaling 0,000213398 Immune response_CD28 signaling 0,000123446 Cell cycle_Chromosome condensation in prometaphase 0,000271389 Ligand-independent activation of Androgen receptor in Prostate Cancer 0,000138973 Normal and pathological TGF-beta-mediated regulation of cell proliferation 0,00031783 Development_VEGF signaling via VEGFR2 - generic cascades 0,000155396 Signal transduction_PTEN pathway 0,000319127 Development_Regulation of cytoskeleton proteins in oligodendrocyte differentiation and myelination 0,000198914 Cytoskeleton remodeling_Integrin outside-in signaling 0,000475687 Regulation of lipid metabolism_Regulation of lipid metabolism via LXR, NF-Y and SREBP 0,000204212 HBV signaling via protein kinases leading to HCC 0,000519752 Signal transduction_Additional pathways of NF-kB activation (in the cytoplasm) 0,000208636 Muscle contraction_Regulation of eNOS activity in endothelial cells 0,000533993 Development_Differentiation of white adipocytes 0,000208636 Immune response_IL-13 signaling via PI3K-ERK 0,00053968 Cell cycle_Influence of Ras and Rho proteins on G1/S Transition 0,000208636 Development_Beta-adrenergic receptors transactivation of EGFR 0,000605513 Apoptosis and survival_TNFR1 signaling pathway 0,000213265 Transcription_Transcription regulation of aminoacid metabolism 0,000644533 Apoptosis and survival_Caspase cascade 0,000256013 DNA damage_ATM / ATR regulation of G2 / M checkpoint 0,000779348 Cytoskeleton remodeling_Neurofilaments 0,000258527 Cell adhesion_PLAU signaling 0,000809653 Transcription_Transcription regulation of aminoacid metabolism 0,000258527 Cytoskeleton remodeling_Substance P mediated membrane blebbing 0,0009467 Development_FGFR signaling pathway 0,000265187 Apoptosis and survival_Endoplasmic reticulum stress response pathway 0,00096965 Cell cycle_Chromosome condensation in prometaphase 0,000284847 Development_VEGF-family signaling 0,001063185 Muscle contraction_Regulation of eNOS activity in endothelial cells 0,000286603 Development_Role of proteases in hematopoietic stem cell mobilization 0,001520744 Signal transduction_Additional pathways of NF-kB activation (in the nucleus) 0,000314942 Cell adhesion_Histamine H1 receptor signaling in the interruption of cell barrier integrity 0,001748771 Apoptosis and survival_Endoplasmic reticulum stress response pathway 0,000334529 Development_Thrombopoietin-regulated cell processes 0,001748771 Immune response_Fc epsilon RI pathway 0,000334529 Cytoskeleton remodeling_Fibronectin-binding integrins in cell motility 0,001789061 Development_GM-CSF signaling 0,000352845 Development_Gastrin in cell growth and proliferation 0,001979406 Development_Thrombopoietin-regulated cell processes 0,000363861 DNA damage_ATM/ATR regulation of G1/S checkpoint 0,00207103 Airway smooth muscle contraction in asthma 0,000418962 Cell cycle_Start of DNA replication in early S phase 0,00207103 Immune response_ETV3 affect on CSF1-promoted macrophage differentiation 0,000437777 Development_PIP3 signaling in cardiac myocytes 0,002196978 Signal transduction_PKA signaling 0,000447337 Stimulation of TGF-beta signaling in lung cancer 0,002451133 Development_Oligodendrocyte differentiation from adult stem cells 0,000447337 Tissue Factor signaling in cancer via PAR1 and PAR2 0,002726777 Neurophysiological process_ACM regulation of nerve impulse 0,00046829 Development_VEGF signaling via VEGFR2 - generic cascades 0,002878384 Cell cycle_Role of Nek in cell cycle regulation 0,000598308 Development_Growth hormone signaling via STATs and PLC/IP3 0,003111283 Effect of H. pylori infection on gastric epithelial cell proliferation 0,000643818 Role of growth factor receptors transactivation by Hyaluronic acid / CD44 signaling in tumor progression 0,003111283 Development_Insulin, IGF-1 and TNF-alpha in brown adipocyte differentiation 0,000702419 Influence of low doses of Arsenite on Glucose stimulated Insulin secretion in pancreatic cells 0,003529272 Signal transduction_PTMs in IL-12 signaling pathway 0,000754947 Development_IGF-1 receptor signaling 0,003693777 LRRK2 in neurons in Parkinson's disease 0,000805025 Action of GSK3 beta in bipolar disorder 0,003928055 Immune response _IFN gamma signaling pathway 0,000870585 Immune response_Oncostatin M signaling via MAPK in human cells 0,003986109 Development_EGFR signaling pathway 0,000913439 Immune response_Generation of memory CD4+ T cells 0,003986109 Huntingtin-depended transcription deregulation in Huntington's Disease 0,000918903 Immune response_HMGB1/RAGE signaling pathway 0,004066562 PDE4 regulation of cyto/chemokine expression in arthritis 0,00094652 Cell cycle_Influence of Ras and Rho proteins on G1/S Transition 0,004066562 Appendices Apoptosis and survival_FAS signaling cascades 0,001011606 Immune response _IFN gamma signaling pathway 0,004466473 Immune response_IL-1 signaling pathway 0,001011606 Signal transduction_Soluble CXCL16 signaling 0,004483723 Development_Role of CDK5 in neuronal development 0,001067597 Cell adhesion_Endothelial cell contacts by non-junctional mechanisms 0,004608072 Immune response_Platelet activating factor/ PTAFR pathway signaling 0,001071606 Translation_Non-genomic (rapid) action of Androgen Receptor 0,005608919 Immune response_Antigen presentation by MHC class II 0,00116696 Development_Cytokine-mediated regulation of megakaryopoiesis 0,005840428 Regulation of GSK3 beta in bipolar disorder 0,001274752 Apoptosis and survival_BAD phosphorylation 0,00691993 VPA P-value CBZ P-value Cell cycle_Initiation of mitosis 0,001290365 IGF family signaling in colorectal cancer 0,007499788 Regulation of lipid metabolism_Insulin regulation of glycogen metabolism 0,001310378 Development_Role of CNTF and LIF in regulation of oligodendrocyte development 0,008111765 Cytoskeleton remodeling_Role of PKA in cytoskeleton reorganisation 0,001353046 Cell cycle_Transition and termination of DNA replication 0,008111765 Development_Angiopoietin - Tie2 signaling 0,001396897 Development_c-Kit ligand signaling pathway during hemopoiesis 0,008121644 Immune response_NFAT in immune response 0,00145363 K-RAS signaling in pancreatic cancer 0,008431395 Cell cycle_Cell cycle (generic schema) 0,001574563 Immune response_IL-1 signaling pathway 0,008431395 Transcription_Epigenetic regulation of gene expression 0,001592268 Development_Alpha-2 adrenergic receptor activation of ERK 0,008779741 Development_GDNF family signaling 0,00159245 Transcription_Androgen Receptor nuclear signaling 0,009266724 Immune response_MIF - the neuroendocrine-macrophage connector 0,00159245 Regulation of GSK3 beta in bipolar disorder 0,009266724 Signal transduction_PTEN pathway 0,00159245 Neuroprotective action of lithium 0,009475187 Neurophysiological process_Constitutive and regulated NMDA receptor trafficking 0,001693175 Immune response_MIF-induced cell adhesion, migration and angiogenesis 0,010157395 Signal transduction_PTMs (phosphorylation and acetylation) in TNF-alpha-induced NF-kB signaling 0,001715364 Development_TGF-beta-dependent induction of EMT via RhoA, PI3K and ILK. 0,010157395 Transcription_Transcription factor Tubby signaling pathways 0,001833538 FGF signaling in pancreatic cancer 0,010157395 Apoptosis and survival_Role of IAP-proteins in apoptosis 0,001836168 Signal transduction_PTMs in IL-17-induced CIKS-independent signaling pathways 0,010157395 Ovarian cancer (main signaling cascades) 0,002020758 Ovarian cancer (main signaling cascades) 0,010209079 Signal transduction_JNK pathway 0,002153734 DNA damage_Brca1 as a transcription regulator 0,010378947 Development_Beta adrenergic receptors in brown adipocyte differentiation 0,002305273 Development_Osteopontin signaling in osteoclasts 0,010378947 DNA damage_ATM/ATR regulation of G1/S checkpoint 0,002398832 Cell adhesion_Gap junctions 0,010378947 Regulation of metabolism_Triiodothyronine and Thyroxine signaling 0,00242541 Development_PDGF signaling via MAPK cascades 0,011105064 Muscle contraction_Relaxin signaling pathway 0,00242541 Immune response_IL-6 signaling pathway 0,011651523 Development_Role of Thyroid hormone in regulation of oligodendrocyte differentiation 0,00242541 Immune response_ETV3 affect on CSF1-promoted macrophage differentiation 0,011651523 Immune response_HSP60 and HSP70/ TLR signaling pathway 0,002623956 Cytoskeleton remodeling_Reverse signaling by ephrin B 0,011651523 Neurophysiological process_Dynein-dynactin motor complex in axonal transport in neurons 0,002623956 Signal transduction_PTMs in IL-12 signaling pathway 0,012111335 Immune response_CCL2 signaling 0,002623956 Development_Growth factors in regulation of oligodendrocyte precursor cell proliferation 0,012652152 Regulation of metabolism_Role of Adiponectin in regulation of metabolism 0,002679521 Development_PDGF signaling via STATs and NF-kB 0,013020165 Signal transduction_AKT signaling 0,002679521 Cell cycle_Role of Nek in cell cycle regulation 0,013020165 Development_A2A receptor signaling 0,002679521 Cell cycle_Role of APC in cell cycle regulation 0,013020165 Development_Gastrin in differentiation of the gastric mucosa 0,002912128 Transport_The role of AVP in regulation of Aquaporin 2 and renal water reabsorption 0,014305845 Development_Thromboxane A2 signaling pathway 0,002959454 Some pathways of EMT in cancer cells 0,015497026 Transcription_N-CoR/ SMRT complex-mediated epigenetic gene silencing 0,002959454 Development_HGF-dependent inhibition of TGF-beta-induced EMT 0,016054808 Normal and pathological TGF-beta-mediated regulation of cell proliferation 0,003091792 Development_CNTF receptor signaling 0,016054808 Cell cycle_Nucleocytoplasmic transport of CDK/Cyclins 0,003092393 Development_Role of cell-cell and ECM-cell interactions in oligodendrocyte differentiation and myelination 0,016054808 Immune response_CRTH2 signaling in Th2 cells 0,00330499 Development_EGFR signaling pathway 0,016511363 Immune response_C5a signaling 0,003585583 Reproduction_GnRH signaling 0,017590187 Translation _Regulation of EIF2 activity 0,003641189 Immune response_Oncostatin M signaling via MAPK in mouse cells 0,01772494 Development_Gastrin in cell growth and proliferation 0,003870086 Role of Tissue factor in cancer independent of coagulation protease signaling 0,01772494 Signal transduction_Erk Interactions: Inhibition of Erk 0,003935206 Apoptosis and survival_Role of PKR in stress-induced apoptosis 0,018074163 Apoptosis and survival_Role of CDK5 in neuronal death and survival 0,003935206 Development_TGF-beta-induction of EMT via ROS 0,018958251 Apoptosis and survival_Cytoplasmic/mitochondrial transport of proapoptotic proteins Bid, Bmf and Bim 0,003935206 wtCFTR and deltaF508-CFTR traffic / Clathrin coated vesicles formation (normal and CF) 0,018958251 Regulation of lipid metabolism_Regulation of lipid metabolism by niacin and isoprenaline 0,004043258 Cytoskeleton remodeling_Role of Activin A in cytoskeleton remodeling 0,018958251 Neurophysiological process_Glutamate regulation of Dopamine D1A receptor signaling 0,004043258 Immune response_Oncostatin M signaling via JAK-Stat in human cells 0,018958251 Immune response_TNF-R2 signaling pathways 0,004043258 Development_FGFR signaling pathway 0,019462702 Development_EPO-induced MAPK pathway 0,004043258 Immune response_IL-9 signaling pathway 0,019499399

155 Supplementary figure 1: Chapter 6 Regulated genesAft erin Metacore VPA epathways.xposu Averagere FC of regulated genes is illustrated in a bar (up regulation )(down After CBZ exposure regulation A ).f Averageter V FCPA after e xposu1day exposurere to VPA(left) 0.1mM( ), 0.33mM( ), 1.0mM ( ), or 7 days, 0.1mM( After CBZ exposure ), 0.33mM(1-3:), 1.0mM( day 1,). Average 4-6: FC d afteray 1 7 day exposure to CBZ (right) 0.03mM( ), 0.1mM( ), 0.33mM ( 1-3: day 1, 4-6: day 7 ), or 7 days1-3: 0.03mM( da),y 0.1mM( 1, 4-6:), 0.33mM( day 7). Development_WNT signaling pathway. Part 2 VPA 1-3: day 1, 4-6: day 7 Development_WNT signaling pathway. Part 2 VPA Development_WNT signaling pathway. Part 2 VPA

Development_TGF-beta receptor signaling VPA Development_TGF-beta receptor signaling VPA After VPA exposure 1-3: day 1, 4-6: day 7 1, 4-6: day 1-3: day exposure After VPA

156

Neurophysiological process_ACM regulation of nerve impulse VPA Neurophysiological process_ACM regulation of nerve impulse VPA Appendices

After VPA exposure After CBZ exposure 1-3: day 1, 4-6: day 7 1-3: day 1, 4-6: day 7 Development_WNT signaling pDevelopment_WNTathway. Part signaling2 VPA pathway. Part 2 VPA

Development_TGF-betaDevelopment_TGF-beta receptor signaling receptor V signalingPA VPA After CBZ exposure 1-3: day 1, 4-6: day 7 1, 4-6: day 1-3: day After CBZ exposure

157

Neurophysiological process_ACM regulation of nerve impulse VPA After VPA exposure After CBZ exposure 1-3: day 1, 4-6: day 7 1-3: day 1, 4-6: day 7 Development_WNT signaling pathway.. Part 2 VPA

Development_TGF-beta receptor signaling VPA

Development_Oligodendrocyte differentiation from adult stem cells VPA

Neurophysiological process_ACM regulation of nerveNeu impulseroph VPAysiologicalical pprocess_ACM regulation of nerve impulse VPA After VPA exposure 1-3: day 1, 4-6: day 7 1, 4-6: day 1-3: day exposure After VPA

158 After VPA exposure After CBZ exposure 1-3: day 1, 4-6: day 7 1-3: day 1, 4-6: day 7 Development_WNT signaling pathway. Part 2 VPA

Development_TGF-beta receptor signaling VPA

Appendices

Development_Oligodendrocyte differentiation from adult stem cells VPA

Neurophysiological process_ACM regulNeurophysiologicalation of process_ACM nerve impulse regulation ofV PnerveA impulse VPA After CBZ exposure 1-3: day 1, 4-6: day 7 1, 4-6: day 1-3: day After CBZ exposure

159 After VPA exposure After CBZ exposure 1-3: day 1, 4-6: day 7 1-3: day 1, 4-6: day 7 Development_Role of CDK5 in neuronal developmentDevelopment_Role of CDK5 in neuronal development

Action of GSK3 beta in bipolar disorder VPA Action of GSK3 beta in bipolar disorder VPA After VPA exposure 1-3: day 1, 4-6: day 7 1, 4-6: day 1-3: day exposure After VPA

160

Signal transduction_Erk Interactions Inhibition of Erk VPA Appendices

After VPA exposure After CBZ exposure 1-3: day 1, 4-6: day 7 1-3: day 1, 4-6: day 7 Development_Role of CDK5 in neuDevelopment_Roleronal developme of CDK5n int neuronal development

Action of GSK3 beta in bipolarAction diso ofr derGSK3 V betaPA in bipolar disorder VPA After CBZ exposure 1-3: day 1, 4-6: day 7 1, 4-6: day 1-3: day After CBZ exposure

161

Signal transduction_Erk IInteractions IInhibition of Erk VPA After VPA exposure After CBZ exposure 1-3: day 1, 4-6: day 7 1-3: day 1, 4-6: day 7 Development_Role of CDK5 in neuronal development

Action of GSK3 beta in bipolar disorder VPA

Neurophysiological process_Constitutive and regulated NMDA receptor trafficking VPA

Signal transduction_Erk Interactions Inhibition of Erk VPASignal transduction_Erk Interactions Inhibition of Erk VPA After VPA exposure 1-3: day 1, 4-6: day 7 1, 4-6: day 1-3: day exposure After VPA

162 After VPA exposure After CBZ exposure 1-3: day 1, 4-6: day 7 1-3: day 1, 4-6: day 7 Development_Role of CDK5 in neuronal development

Action of GSK3 beta in bipolar disorder VPA

Appendices

Neurophysiological process_Constitutive and regulated NMDA receptor trafficking VPA

Signal transduction_Erk IInteracSignaltions transduction_Erk IInhibition Interactions of Erk V InhibitionPA of Erk VPA After CBZ exposure 1-3: day 1, 4-6: day 7 1, 4-6: day 1-3: day After CBZ exposure

163 Supplementary Table 4: Chapter 7 Regulated Go-terms sections Venn diagram

Venn diagram section Term Count % PValue A neuron differentiation 76 5,340829234 4,10E-11 pattern specification process 55 3,865073788 4,49E-11 regionalization 45 3,162333099 1,02E-10 neuron fate commitment 17 1,194659171 3,69E-08 cell fate commitment 32 2,248770204 6,13E-08 embryonic morphogenesis 52 3,654251581 1,56E-07 cell projection organization 58 4,075895994 3,38E-07 spinal cord development 15 1,054111033 6,24E-07 embryonic development ending in birth or egg hatching 53 3,72452565 9,50E-07 regulation of transcription from RNA polymerase II promoter 93 6,535488405 1,54E-06 chordate embryonic development 52 3,654251581 1,64E-06 positive regulation of developmental process 46 3,232607168 1,74E-06 positive regulation of cell differentiation 39 2,740688686 6,05E-06 anterior/posterior pattern formation 28 1,967673928 8,28E-06 cell differentiation in spinal cord 11 0,773014758 8,37E-06

B embryonic organ development 23 3,522205207 1,44223E-07 pattern specification process 29 4,441041348 2,15779E-07 embryonic skeletal system development 14 2,143950995 2,35633E-06 regulation of transcription from RNA polymerase II promoter 51 7,810107198 3,40186E-06 embryonic morphogenesis 29 4,441041348 3,55264E-06 regionalization 22 3,36906585 5,54464E-06 tube morphogenesis 17 2,603369066 8,88042E-06 tube development 23 3,522205207 9,37257E-06 chordate embryonic development 29 4,441041348 1,48193E-05 embryonic development ending in birth or egg hatching 29 4,441041348 1,74984E-05 gland development 17 2,603369066 1,94038E-05 negative regulation of transcription from RNA polymerase II promoter 25 3,82848392 2,11822E-05 skeletal system morphogenesis 15 2,297090352 3,51216E-05 morphogenesis of a branching structure 12 1,837672282 4,85244E-05 skeletal system development 27 4,134762634 5,60746E-05

C cell morphogenesis involved in neuron differentiation 14 4,47284345 8,08053E-05 neuron projection morphogenesis 14 4,47284345 9,79154E-05 axonogenesis 13 4,153354633 0,00015231 neuron projection development 15 4,792332268 0,000170358 cell projection organization 18 5,750798722 0,000267407 neuron development 17 5,431309904 0,000319664 cell morphogenesis involved in differentiation 14 4,47284345 0,000369902 cell projection morphogenesis 14 4,47284345 0,000386967 cell part morphogenesis 14 4,47284345 0,000588324 homeostatic process 27 8,626198083 0,000701631 oxidation reduction 24 7,667731629 0,000856328 neuron differentiation 18 5,750798722 0,001857437 axon guidance 8 2,555910543 0,00274069 cellular homeostasis 18 5,750798722 0,003518015 chemical homeostasis 19 6,07028754 0,003965831

D ribonucleoside monophosphate biosynthetic process 5 1,154734411 0,001928364 regulation of neurogenesis 12 2,771362587 0,002039208 ribonucleoside monophosphate metabolic process 5 1,154734411 0,002653977 regulation of cell development 13 3,002309469 0,003628734 cortical cytoskeleton organization 4 0,923787529 0,003966829 regulation of cell projection organization 8 1,847575058 0,00518911 regulation of nervous system development 12 2,771362587 0,006154336 regulation of neuron projection development 7 1,616628176 0,006249495 regulation of growth 17 3,926096998 0,007511045 muscle organ morphogenesis 3 0,692840647 0,007904272 regulation of transcription from RNA polymerase II promoter 29 6,697459584 0,007909904 regulation of cell size 12 2,771362587 0,01027966 cell morphogenesis 17 3,926096998 0,011096516 regulation of axonogenesis 6 1,385681293 0,011146654 regulation of neuron differentiation 9 2,07852194 0,013970691

Venn diagram 164section Term Count % PValue E skeletal system development 28 7,291666667 4,93343E-09 endocrine system development 12 3,125 4,28031E-07 extracellular matrix organization 14 3,645833333 7,04501E-07 regulation of system process 24 6,25 7,15613E-07 enzyme linked receptor protein signaling pathway 24 6,25 4,04154E-06 extracellular structure organization 16 4,166666667 5,09196E-06 embryonic skeletal system development 11 2,864583333 9,73828E-06 response to hypoxia 14 3,645833333 1,23123E-05 tube development 18 4,6875 1,27154E-05 positive regulation of nitrogen compound metabolic process 34 8,854166667 1,2819E-05 blood vessel development 19 4,947916667 1,41441E-05 cellular component morphogenesis 25 6,510416667 1,51402E-05 positive regulation of nucleobase, nucleoside process 33 8,59375 1,71552E-05 regulation of blood pressure 12 3,125 1,76915E-05 positive regulation of cellular biosynthetic process 35 9,114583333 1,82713E-05

F pattern specification process 23 9,05511811 1,05E-10 embryonic morphogenesis 22 8,661417323 8,31E-09 skeletal system development 22 8,661417323 1,62E-08 neuron development 22 8,661417323 4,67E-08 neuron differentiation 25 9,842519685 5,05E-08 cell morphogenesis involved in differentiation 18 7,086614173 1,79E-07 axonogenesis 16 6,299212598 2,40E-07 cell morphogenesis involved in neuron differentiation 16 6,299212598 6,70E-07 neuron projection morphogenesis 16 6,299212598 8,53E-07 cell projection morphogenesis 17 6,692913386 9,96E-07 negative regulation of cell differentiation 16 6,299212598 1,02E-06 anterior/posterior pattern formation 13 5,118110236 1,42E-06 neuron projection development 17 6,692913386 1,79E-06 cell part morphogenesis 17 6,692913386 1,79E-06 sensory organ development 16 6,299212598 2,12E-06

G regulation of cell proliferation 18 12,67605634 1,40E-04 regeneration 6 4,225352113 2,12E-04 release of cytochrome c from mitochondria 4 2,816901408 5,77E-04 cell death 15 10,56338028 0,0015607 response to organic substance 15 10,56338028 0,001601961 death 15 10,56338028 0,001665563 apoptotic mitochondrial changes 4 2,816901408 0,001840725 apoptosis 13 9,154929577 0,002823155 programmed cell death 13 9,154929577 0,003185044 anterior/posterior pattern formation 6 4,225352113 0,005053099 enzyme linked receptor protein signaling pathway 9 6,338028169 0,005726602 anterior/posterior axis specification 3 2,112676056 0,006918487 positive regulation of apoptosis 10 7,042253521 0,007007349 positive regulation of programmed cell death 10 7,042253521 0,0073203 response to drug 7 4,929577465 0,00734193

H negative regulation of catalytic activity 9 5,357142857 0,004378214 negative regulation of MAP kinase activity 4 2,380952381 0,004501427 retinal ganglion cell axon guidance 3 1,785714286 0,008347815 negative regulation of protein kinase activity 5 2,976190476 0,00881143 negative regulation of kinase activity 5 2,976190476 0,009902373 regulation of MAP kinase activity 6 3,571428571 0,010231023 mesenchymal cell development 4 2,380952381 0,011868473 mesenchymal cell differentiation 4 2,380952381 0,011868473 negative regulation of transferase activity 5 2,976190476 0,012339116 mesenchyme development 4 2,380952381 0,012510931 negative regulation of molecular function 9 5,357142857 0,012869295 regulation of synapse organization 3 1,785714286 0,016099321 negative regulation of signal transduction 7 4,166666667 0,017300536 regulation of synapse structural plasticity 2 1,19047619 0,018541962 urogenital system development 5 2,976190476 0,019428591

Venn diagram section Term Count % PValue I coagulation 16 2,749140893 5,52245E-07 blood coagulation 16 2,749140893 5,52245E-07 hemostasis 16 2,749140893 1,1696E-06 regulation of body fluid levels 17 2,920962199 7,73174E-06 wound healing 20 3,436426117 7,81673E-06 response to hormone stimulus 28 4,810996564 3,05498E-05 response to endogenous stimulus 29 4,982817869 6,67212E-05 vasculature development 21 3,608247423 0,000112361 response to wounding 34 5,841924399 0,000113185 mitosis 19 3,264604811 0,000176523 nuclear division 19 3,264604811 0,000176523 negative regulation of multicellular organismal process 16 2,749140893 0,00018118 M phase of mitotic cell cycle 19 3,264604811 0,000218672 positive regulation of multicellular organismal process 20 3,436426117 0,000223058 acute inflammatory response 12 2,06185567 0,000224099

J regulation of calcium-dependent cell-cell adhesion 2 1,818181818 0,011645806 UDP-glucuronate metabolic process 2 1,818181818 0,023157685 response to protein stimulus 4 3,636363636 0,024654368 DNA replication 5 4,545454545 0,025045483 DNA metabolic process 8 7,272727273 0,028121784 glucuronate metabolic process 2 1,818181818 0,028863877 uronic acid metabolic process 2 1,818181818 0,028863877 lipid homeostasis 3 2,727272727 0,035703754 mitochondrial DNA replication 2 1,818181818 0,040177709 cell recognition 3 2,727272727 0,040970317 mitochondrial DNA metabolic process 2 1,818181818 0,045785724 regulation of DNA replication 3 2,727272727 0,050834239 regulation of cGMP biosynthetic process 2 1,818181818 0,051361384 phosphorus metabolic process 11 10 0,056121273 phosphate metabolic process 11 10 0,056121273

K response to organic substance 8 10 0,023766102 response to lipopolysaccharide 3 3,75 0,03782352 parturition 2 2,5 0,04305858 response to molecule of bacterial origin 3 3,75 0,046191576 steroid metabolic process 4 5 0,046682919 cholesterol metabolic process 3 3,75 0,052113167 negative regulation of cell proliferation 5 6,25 0,055595403 endocytosis 4 5 0,057470414 membrane invagination 4 5 0,057470414 sterol metabolic process 3 3,75 0,061471036 membrane organization 5 6,25 0,065186154 response to interleukin-1 2 2,5 0,065772577 regulation of protein transport 3 3,75 0,075904538 response to endogenous stimulus 5 6,25 0,077773989 G2/M transition of mitotic cell cycle 2 2,5 0,080620384

L sprouting angiogenesis 3 1,185770751 0,005272438 regulation of vesicle-mediated transport 6 2,371541502 0,007030127 hormone metabolic process 6 2,371541502 0,010556844 mitotic cell cycle 11 4,347826087 0,017870782 cytokine production 4 1,581027668 0,019633345 nuclear division 8 3,162055336 0,020623087 mitosis 8 3,162055336 0,020623087 M phase 10 3,95256917 0,022483938 M phase of mitotic cell cycle 8 3,162055336 0,022505786 organelle fission 8 3,162055336 0,024984902 wound healing 7 2,766798419 0,032701095 cell cycle phase 11 4,347826087 0,035104257 angiogenesis 6 2,371541502 0,038139426 blood coagulation 5 1,976284585 0,038899308 coagulation 5 1,976284585 0,038899308

Venn diagram section Term Count % Pvalue M protein localization 71 7,593582888 6,76518E-05 establishment of protein localization 61 6,524064171 0,000358835 cellular protein localization 38 4,064171123 0,000370125 cellular response to stress 48 5,13368984 0,000405183 cellular macromolecule localization 38 4,064171123 0,000425615 mitotic cell cycle 35 3,743315508 0,000437278 DNA metabolic process 44 4,705882353 0,000438913 cell cycle 61 6,524064171 0,000445008 protein transport 60 6,417112299 0,000479839 cell cycle process 47 5,026737968 0,000704397 intracellular protein transport 34 3,636363636 0,001069938 regulation of ubiquitin-protein ligase activity 12 1,28342246 0,001619616 negative regulation of ubiquitin-protein ligase activity 11 1,176470588 0,00168709 negative regulation of ligase activity 11 1,176470588 0,00168709 modification-dependent macromolecule catabolic process 46 4,919786096 0,001710448

N coenzyme metabolic process 15 3,311258278 2,50041E-05 cofactor metabolic process 17 3,752759382 2,62761E-05 histone acetylation 8 1,766004415 0,000158937 sterol biosynthetic process 7 1,545253863 0,000188843 protein amino acid acetylation 8 1,766004415 0,000265478 protein amino acid acylation 8 1,766004415 0,000647874 generation of precursor metabolites and energy 19 4,194260486 0,000766764 lipid biosynthetic process 19 4,194260486 0,001098892 chromatin modification 17 3,752759382 0,001262183 vesicle-mediated transport 27 5,960264901 0,002174395 regulation of monooxygenase activity 5 1,103752759 0,003030109 histone modification 10 2,207505519 0,003178713 intracellular transport 29 6,401766004 0,003338714 histone H3 acetylation 5 1,103752759 0,003512521 cholesterol biosynthetic process 5 1,103752759 0,003512521

O mRNA metabolic process 63 4,169424222 6,96529E-09 RNA processing 79 5,228325612 1,25028E-07 mRNA processing 53 3,507610854 3,48691E-07 RNA splicing 46 3,044341496 3,9158E-06 macromolecule catabolic process 96 6,353408339 7,945E-06 cellular macromolecule catabolic process 90 5,956320318 1,11218E-05 chromatin modification 42 2,779616148 4,15205E-05 modification-dependent macromolecule catabolic process 71 4,698874917 0,000121703 modification-dependent protein catabolic process 71 4,698874917 0,000121703 cell cycle phase 55 3,639973527 0,000131709 proteolysis involved in cellular protein catabolic process 73 4,831237591 0,000158336 protein catabolic process 75 4,963600265 0,000168428 cellular protein catabolic process 73 4,831237591 0,000184307 GPI anchor metabolic process 11 0,727994705 0,000246534 nuclear-transcribed mRNA catabolic process 11 0,727994705 0,000246534 Venn diagram section Term Count % PValue A neuron differentiation 76 5,340829234 4,10E-11 pattern specification process 55 3,865073788 4,49E-11 regionalization 45 3,162333099 1,02E-10 neuron fate commitment 17 1,194659171 3,69E-08 cell fate commitment 32 2,248770204 6,13E-08 embryonic morphogenesis 52 3,654251581 1,56E-07 cell projection organization 58 4,075895994 3,38E-07 spinal cord development 15 1,054111033 6,24E-07 embryonic development ending in birth or egg hatching 53 3,72452565 9,50E-07 regulation of transcription from RNA polymerase II promoter 93 6,535488405 1,54E-06 chordate embryonic development 52 3,654251581 1,64E-06 positive regulation of developmental process 46 3,232607168 1,74E-06 positive regulation of cell differentiation 39 2,740688686 6,05E-06 anterior/posterior pattern formation 28 1,967673928 8,28E-06 cell differentiation in spinal cord 11 0,773014758 8,37E-06

D ribonucleoside monophosphate biosynthetic process 5 1,154734411 0,001928364 regulation of neurogenesis 12 2,771362587 0,002039208 ribonucleoside monophosphate metabolic process 5 1,154734411 0,002653977 regulation of cell development 13 3,002309469 0,003628734 cortical cytoskeleton organization 4 0,923787529 0,003966829 regulation of cell projection organization 8 1,847575058 0,00518911 regulation of nervous system development 12 2,771362587 0,006154336 regulation of neuron projection development 7 1,616628176 Appendices0,006249495 regulation of growth 17 3,926096998 0,007511045 muscle organ morphogenesis 3 0,692840647 0,007904272 regulation of transcription from RNA polymerase II promoter 29 6,697459584 0,007909904 regulation of cell size 12 2,771362587 0,01027966 cell morphogenesis 17 3,926096998 0,011096516 regulation of axonogenesis 6 1,385681293 0,011146654 regulation of neuron differentiation 9 2,07852194 0,013970691

Venn diagram section Term Count % PValue E skeletal system development 28 7,291666667 4,93343E-09 endocrine system development 12 3,125 4,28031E-07 extracellular matrix organization 14 3,645833333 7,04501E-07 regulation of system process 24 6,25 7,15613E-07 enzyme linked receptor protein signaling pathway 24 6,25 4,04154E-06 extracellular structure organization 16 4,166666667 5,09196E-06 embryonic skeletal system development 11 2,864583333 9,73828E-06 response to hypoxia 14 3,645833333 1,23123E-05 tube development 18 4,6875 1,27154E-05 positive regulation of nitrogen compound metabolic process 34 8,854166667 1,2819E-05 blood vessel development 19 4,947916667 1,41441E-05 cellular component morphogenesis 25 6,510416667 1,51402E-05 positive regulation of nucleobase, nucleoside process 33 8,59375 1,71552E-05 regulation of blood pressure 12 3,125 1,76915E-05 positive regulation of cellular biosynthetic process 35 9,114583333 1,82713E-05

Venn diagram section Term Count % PValue 165 I coagulation 16 2,749140893 5,52245E-07 blood coagulation 16 2,749140893 5,52245E-07 hemostasis 16 2,749140893 1,1696E-06 regulation of body fluid levels 17 2,920962199 7,73174E-06 wound healing 20 3,436426117 7,81673E-06 response to hormone stimulus 28 4,810996564 3,05498E-05 response to endogenous stimulus 29 4,982817869 6,67212E-05 vasculature development 21 3,608247423 0,000112361 response to wounding 34 5,841924399 0,000113185 mitosis 19 3,264604811 0,000176523 nuclear division 19 3,264604811 0,000176523 negative regulation of multicellular organismal process 16 2,749140893 0,00018118 M phase of mitotic cell cycle 19 3,264604811 0,000218672 positive regulation of multicellular organismal process 20 3,436426117 0,000223058 acute inflammatory response 12 2,06185567 0,000224099

H negative regulation of catalytic activity 9 5,357142857 0,004378214 negative regulation of MAP kinase activity 4 2,380952381 0,004501427 retinal ganglion cell axon guidance 3 1,785714286 0,008347815 negative regulation of protein kinase activity 5 2,976190476 0,00881143 negative regulation of kinase activity 5 2,976190476 0,009902373 regulation of MAP kinase activity 6 3,571428571 0,010231023 mesenchymal cell development 4 2,380952381 0,011868473 mesenchymal cell differentiation 4 2,380952381 0,011868473 negative regulation of transferase activity 5 2,976190476 0,012339116 mesenchyme development 4 2,380952381 0,012510931 Supplementarynegative Table regulation 4: Chapter of molecular 7 function continued 9 5,357142857 0,012869295 regulation of synapse organization 3 1,785714286 0,016099321 Regulated Go-termsnegative regulation sections of signal transduction Venn diagram 7 4,166666667 0,017300536 regulation of synapse structural plasticity 2 1,19047619 0,018541962 urogenital system development 5 2,976190476 0,019428591

Venn diagram 166section Term Count % Pvalue M protein localization 71 7,593582888 6,76518E-05 establishment of protein localization 61 6,524064171 0,000358835 cellular protein localization 38 4,064171123 0,000370125 cellular response to stress 48 5,13368984 0,000405183 cellular macromolecule localization 38 4,064171123 0,000425615 mitotic cell cycle 35 3,743315508 0,000437278 DNA metabolic process 44 4,705882353 0,000438913 cell cycle 61 6,524064171 0,000445008 protein transport 60 6,417112299 0,000479839 cell cycle process 47 5,026737968 0,000704397 intracellular protein transport 34 3,636363636 0,001069938 regulation of ubiquitin-protein ligase activity 12 1,28342246 0,001619616 negative regulation of ubiquitin-protein ligase activity 11 1,176470588 0,00168709 negative regulation of ligase activity 11 1,176470588 0,00168709 modification-dependent macromolecule catabolic process 46 4,919786096 0,001710448

L sprouting angiogenesis 3 1,185770751 0,005272438 regulation of vesicle-mediated transport 6 2,371541502 0,007030127 hormone metabolic process 6 2,371541502 0,010556844 mitotic cell cycle 11 4,347826087 0,017870782 cytokine production 4 1,581027668 0,019633345 nuclear division 8 3,162055336 0,020623087 mitosis 8 3,162055336 0,020623087 M phase 10 3,95256917 Appendices0,022483938 M phase of mitotic cell cycle 8 3,162055336 0,022505786 organelle fission 8 3,162055336 0,024984902 wound healing 7 2,766798419 0,032701095 cell cycle phase 11 4,347826087 0,035104257 angiogenesis 6 2,371541502 0,038139426 blood coagulation 5 1,976284585 0,038899308 coagulation 5 1,976284585 0,038899308

167 Supplementary Table 5: Chapter 7 Regulated Go-terms and tissues after VPA-exposure, analysed with Tox-profiler

Regulated GO-terms VPA15 VPA60 VPA250 VPA1000 VPA.0.1.Day1 VPA.0.33.Day1 VPA.1.0.Day1 anaphase-promoting complex -5,24 -4,23 -4,03 -3,19 0,42 0,36 -0,23 positive regulation of ubiquitin-protein ligase activity -5,3 -4,67 -4,41 -3,51 0,76 0,39 -0,42 nuclear speck 1,16 0,18 -0,67 -1,88 0,96 -2,79 -4,4 chromatin modification 4,38 2,66 0,13 -0,09 1,94 -3,73 -5,06 spliceosomal complex -1,64 -2,57 -3,63 -4 1,34 -3,78 -5,22 rRNA processing -4,32 -2,99 -3,95 -3,87 0,54 -2,87 -3,46 ion transport 1,78 1,73 1,94 2,87 0,46 4,29 5,57 neuropeptide signaling pathway 1,91 1,23 2,35 2,56 -0,15 3,57 4,76 sequence-specific DNA binding -0,24 -2,45 -3 -3,01 -2,01 -5,51 -5,09 translation -5,35 -4,11 -4,9 -3,56 0,51 -0,71 -1,46 Golgi apparatus 5,77 4,96 5,89 4,69 0,1 1,67 1,99 tRNA processing -4,72 -3,8 -4,42 -3,93 -0,91 -2,29 -2,37 structural constituent of ribosome -5,5 -3,95 -4,65 -3,52 0,64 0,6 0,04 nucleolus -2,05 -3,37 -4,35 -5,02 1,53 -6,47 -8,08 sugar binding 0,98 1,42 1,12 1,74 0,64 4,03 5,53 growth factor activity -3,54 -3,95 -3,91 -4,81 -0,14 0,42 -0,23 ribosome -6,21 -4,57 -5,57 -4,41 0,43 -0,11 -0,47 histone H3 acetylation -0,38 -0,34 -1,27 -1,25 -0,16 -3,23 -4,3 transcription factor activity 0,16 -2,54 -3,65 -4,77 -2,43 -7,21 -7,73 transcription coactivator activity 1,13 -0,13 -0,81 -1,47 -0,11 -3,64 -4,8 cholesterol biosynthetic process -3,44 -2,26 -4,35 -5,96 -0,89 -1,52 -1,78 calcium ion binding 3,61 4,02 4,08 5,57 1,34 7,34 8,96 oxidoreductase activity, 4,64 3,63 1,58 -0,34 1,96 0,69 -0,81 negative regulation of ubiquitin-protein ligase activity -5,26 -4,44 -4,11 -3,16 0,41 0,26 -0,34 synapse 2,94 2,96 3,11 3,63 1,4 3,85 4,76 isoprenoid biosynthetic process -3,19 -2,23 -3,71 -4,94 -0,94 -1,73 -2,55 cytokine activity -3,13 -3,14 -3,22 -4,35 -0,67 -1,22 -2,03 nucleoplasm -1,85 -2,67 -3,17 -4,56 2,73 -2,82 -5,21 mRNA processing 0,48 -0,88 -0,7 -2,29 1,28 -3,07 -5,01 axon 2,01 2,26 3,48 5,89 0,34 3,55 5,26 neuropeptide receptor activity 1,21 0,45 0,66 0,81 -0,27 5,76 4,81 chromatin binding 4,1 1,34 0,72 0,18 1,39 -3,38 -4,51 RNA splicing -0,73 -1,55 -2,44 -3,69 2,1 -3,8 -5,94 RNA binding 0,64 -0,87 -1,63 -3,15 2,06 -3,41 -6,11 regulation of transcription 5,4 2,57 0,87 -1,33 1,28 -7,55 -9,69

Regulated Tissues VPA15 VPA60 VPA250 VPA1000 VPA.0.1.Day1 VPA.0.33.Day1 VPA.1.0.Day1 PLACENTA 0,09 -0,43 0,98 1,46 -7,06 -1,22 0,71 Subthalamicnucleus 0,54 1,33 3,3 6,04 -1,93 7,87 10,5 LymphomaburkittsRaji -1,95 -1,39 -0,87 -0,61 1,15 3,46 2,77 Leukemialymphoblastic(molt4) 0,12 0,05 -0,34 -2,16 2,95 4,45 2,37 Pons -0,9 -0,23 1,98 6,03 -0,98 8,3 10,27 Caudatenucleus 0,26 0,55 2,5 6,05 -1,3 7,91 9,85 BM-CD105+Endothelial -2,53 -1,89 -1 -1,65 1,32 4,94 2,85 PB-CD8+Tcells -0,48 0,04 0,28 0,38 -1,56 4,26 2,78 Thalamus 0,37 1,1 2,97 5,41 -1,06 9,16 12,06 PB-CD14+Monocytes -3,85 -2,75 -1,75 -0,61 -1,28 5,85 6,19 PB-CD56+NKCells -1,45 -1,34 -0,24 1,35 -1,49 3,98 4,3 LymphomaburkittsDaudi -1,23 -0,39 0,1 0,83 1,7 3,5 2,56 CerebellumPeduncles 2,51 2,86 3,6 4,75 -1,79 6,9 9,36 TemporalLobe 0,01 0,69 2,36 5,49 -1,26 7,43 9,9 Kidney -1,61 -1,02 0,13 1,26 -1,34 2,29 4,14 TestisLeydigCell -0,39 -0,69 0,33 2,22 1,81 3,66 3,33 Globuspallidus 0,08 1,26 3,12 5,53 -2,17 7,98 11 ParietalLobe 0,66 1,61 3,47 6,74 -1,7 8,28 12,04 Skin -0,35 -0,05 0,6 0,63 -5,24 -0,57 -2,98 OlfactoryBulb -0,29 -0,75 -0,43 -0,09 -3,6 2,43 2,82 721_B_lymphoblasts -1,26 -0,69 0,14 0,81 3 6,04 2,5 Ovary -1,36 -1,59 -0,78 -0,08 -0,11 3,68 1,45 TestisSeminiferousTubule -0,03 -0,57 0,45 2,57 2,18 4,15 4,95 168Ciliaryganglion 0,63 0,58 1,76 2,67 -4,99 0,78 -0,71 CingulateCortex 0,25 1,05 2,82 6,02 -1,89 8,84 12,02 OccipitalLobe 1,94 2,92 5,12 8,19 -0,93 9,5 12,33 CardiacMyocytes 0,58 -0,03 1,5 1,57 -4,96 3,86 0,35 Heart -1,43 -0,96 -1,24 -0,27 2,06 3,03 3,71 Amygdala 2,18 3,7 6,25 9,53 -1,68 10,92 13,9 BM-CD34+ -1,31 -0,83 -0,53 -1,79 2,05 6,22 3,83 PB-BDCA4+Dentritic_Cells -1,42 -1,05 -0,34 0,04 -0,21 3,8 2,75 Lymphnode -0,07 -0,43 0,16 0,42 -1,17 3,96 2,32 PB-CD4+Tcells -0,08 0,4 0,77 1,43 -1,4 4,64 3,91 Bronchialepithelialcells -2,45 -1,72 -0,58 -1,21 -5,4 -0,18 -1,3 MedullaOblongata 0,69 1,6 3,31 6,56 -2,24 8,87 11,73 Cerebellum 2,69 3,02 3,62 4,32 -2,41 6,81 8,93 UterusCorpus -2,22 -2,54 -0,98 -1,57 -5,05 1,31 -1,57 Fetalbrain 3,33 4,6 6,74 8,48 -1,54 9,9 11,37 ADIPOCYTE 1,4 0,63 2,15 2,67 -4,66 4,2 2,41 Uterus 0,76 -0,48 0,69 1,13 -6,91 -0,86 -2,88 Pituitary 0,59 0,76 2,04 4,12 0,51 4,97 5,23 SmoothMuscle 1,31 -0,14 2,42 2,11 -4,73 4,23 0,69 Spinalcord -0,59 0,11 1,19 2,86 -0,74 5,22 6,82 PrefrontalCortex 1,87 2,5 4,08 6,54 -1,73 9,3 12,57 TestisGermCell -0,55 -0,71 0,57 2,95 0,01 4,77 4,85 BM-CD33+Myeloid -3,27 -2,23 -1,84 -1 -1,31 4,34 4,57 Fetallung -3,43 -4,21 -2,7 -1,55 -5,18 -0,94 -2,2 DRG 0,52 0,09 1,46 3,5 -0,97 2,94 4,15 Hypothalamus 1,43 2,42 3,95 5,66 -3,48 8,51 10,93 Testis -0,83 -1,15 -0,47 1,68 1,72 4,35 4,76 Thymus -0,37 -0,17 0,13 0,1 -0,02 3,54 3,1 WholeBrain 1,1 2,68 4,87 8,16 -1,95 10,04 14,15 PB-CD19+Bcells -1,87 -0,83 0,56 1,95 0,61 5,06 4,06 WHOLEBLOOD -2,64 -2,5 -1,59 -0,93 -2,21 5,75 5,96 TestisInterstitial -0,64 -0,47 0,81 1,83 1,17 4,14 4,39 Regulated GO-terms VPA15 VPA60 VPA250 VPA1000 VPA.0.1.Day1 VPA.0.33.Day1 VPA.1.0.Day1 anaphase-promoting complex -5,24 -4,23 -4,03 -3,19 0,42 0,36 -0,23 positive regulation of ubiquitin-protein ligase activity -5,3 -4,67 -4,41 -3,51 0,76 0,39 -0,42 nuclear speck 1,16 0,18 -0,67 -1,88 0,96 -2,79 -4,4 chromatin modification 4,38 2,66 0,13 -0,09 1,94 -3,73 -5,06 spliceosomal complex -1,64 -2,57 -3,63 -4 1,34 -3,78 -5,22 rRNA processing -4,32 -2,99 -3,95 -3,87 0,54 -2,87 -3,46 ion transport 1,78 1,73 1,94 2,87 0,46 4,29 5,57 neuropeptide signaling pathway 1,91 1,23 2,35 2,56 -0,15 3,57 4,76 sequence-specific DNA binding -0,24 -2,45 -3 -3,01 -2,01 -5,51 -5,09 translation -5,35 -4,11 -4,9 -3,56 0,51 -0,71 -1,46 Golgi apparatus 5,77 4,96 5,89 4,69 0,1 1,67 1,99 tRNA processing -4,72 -3,8 -4,42 -3,93 -0,91 -2,29 -2,37 structural constituent of ribosome -5,5 -3,95 -4,65 -3,52 0,64 0,6 0,04 nucleolus -2,05 -3,37 -4,35 -5,02 1,53 -6,47 -8,08 sugar binding 0,98 1,42 1,12 1,74 0,64 4,03 5,53 growth factor activity -3,54 -3,95 -3,91 -4,81 -0,14 0,42 -0,23 ribosome -6,21 -4,57 -5,57 -4,41 0,43 -0,11 -0,47 histone H3 acetylation -0,38 -0,34 -1,27 -1,25 -0,16 -3,23 -4,3 transcription factor activity 0,16 -2,54 -3,65 -4,77 -2,43 -7,21 -7,73 transcription coactivator activity 1,13 -0,13 -0,81 -1,47 -0,11 -3,64 -4,8 cholesterol biosynthetic process -3,44 -2,26 -4,35 -5,96 -0,89 -1,52 -1,78 calcium ion binding 3,61 4,02 4,08 5,57 1,34 7,34 8,96 oxidoreductase activity, 4,64 3,63 1,58 -0,34 1,96 0,69 -0,81 negative regulation of ubiquitin-protein ligase activity -5,26 -4,44 -4,11 -3,16 0,41 0,26 -0,34 synapse 2,94 2,96 3,11 3,63 1,4 3,85 4,76 isoprenoid biosynthetic process -3,19 -2,23 -3,71 -4,94 -0,94 -1,73 -2,55 cytokine activity -3,13 -3,14 -3,22 -4,35 -0,67 -1,22 -2,03 nucleoplasm -1,85 -2,67 -3,17 -4,56 2,73 -2,82 -5,21 mRNA processing 0,48 -0,88 -0,7 -2,29 1,28 Appendices-3,07 -5,01 axon 2,01 2,26 3,48 5,89 0,34 3,55 5,26 neuropeptide receptor activity 1,21 0,45 0,66 0,81 -0,27 5,76 4,81 chromatin binding 4,1 1,34 0,72 0,18 1,39 -3,38 -4,51 RNA splicing -0,73 -1,55 -2,44 -3,69 2,1 -3,8 -5,94 RNA binding 0,64 -0,87 -1,63 -3,15 2,06 -3,41 -6,11 regulation of transcription 5,4 2,57 0,87 -1,33 1,28 -7,55 -9,69

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193 194 DANKWOORD

195 196 Dankwoord

Dankwoord De totstandkoming van dit proefschrift was niet gelukt zonder de hulp die ik van velen heb gekregen de afgelopen jaren. Graag wil ik alle mensen die hierbij betrokken zijn geweest bedanken!

Allereerst Aldert, na mijn afstudeerstage heb jij me de mogelijkheid gegeven dit onderzoek te doen. Hier ben ik je erg dankbaar voor. Ik vind je een prettig en inspirerend persoon om mee samen te werken. Daarnaast hebben we veel leuke en interessante gesprekken gehad over uiteenlopende onderwerpen, van wetenschap tot reizen, kamperen en klussen. Je hebt me enorm veel geleerd en vaak uitgebreid de tijd genomen.

Martin, ik wil je graag bedanken dat je me de mogelijkheid hebt gegeven dit onderzoek uit te voeren. Het onderzoek is mogelijk gemaakt door financiering vanuit Chemscreen, het Netherlands Toxico-Genomics Center (NTC) en het RIVM. Joost, jij was mijn stagebegeleider tijdens mijn HBO-stage. Hier heb ik veel van jou geleerd. Bedankt voor de vele gezellige momenten tijdens het werk, na en buiten het werk. Ik vind het super leuk dat jij mijn paranimf bent. Peter, wij hebben elkaar leren kennen toen we samen het RIVM terrein opliepen voor een stage gesprek. Daarna zijn we elkaar weer bij TNO tegengekomen. Vervolgens ben jij mijn stagebegeleider bij het RIVM geworden tijdens mijn master afstudeerstage. Je hebt me veel geleerd en hebben daarnaast veel gezellige en leuke momenten in het lab en daarbuiten gehad. Het kon niet mooier dan dat jij mijn paranimf bent. De mensen van de leuke en gezellige RTX afdeling: Dorien, bedankt voor alle uitleg en prettige samenwerking. Esther, met jou ben ik samen van start gegaan met de humane embryonale stamcellen. Bedankt voor de fijne samenwerking. Sanne, wij waren in het begin kamergenoten, met veel gezelligheid. Ilse, wij zijn aantal jaren kamergenoten geweest, ik heb veel met je gelachen, dankbaar gebruik gemaakt van je taxirit en assistentie naar de eerste hulp en daarnaast ook vele prettige en serieuze gesprekken gehad. Josh, you have been a great roommate, you taught me a lot about genomics and we had much fun outside the lab. Aart, de wijze en ervaren man van de RTX-groep.

De CMR groep met eerst als afdelingshoofd Harry, met jou heb ik vele interessante gesprekken gevoerd en gezellige momenten gehad, o.a. bij de borrels en barbecues met de afdeling bij jou thuis. Later als afdelingshoofd Jan, bedankt voor onder andere alle gesprekken en hulp die je mij geboden hebt. Annemieke, bedankt voor de mogelijkheid om binnen jouw centrum mijn promotie te mogen uitvoeren en nog excuses voor het gazon en geluidsoverlast bij jou thuis.

Er zijn veel mensen op het lab die ik graag wil bedanken. Als eerste Arja en Liset, met jullie heb ik samen voor de klus gestaan de humane embryonale cellen letterlijk leven in te blazen en differentiatieprotocollen op te zetten. Ik wil jullie graag bedanken voor de prettige samenwerking. We hebben voor vele uitdagingen gestaan die we samen tot een succes hebben gebracht. Henny, Hennie, Conny, Eric en Sandra, veel dank voor alle uitleg en hulp op het

197 lab. Edwin, jij ook veel dank voor je lab-wijsheid, goede (stof)afwegingen en gezelligheid bij de koffie, borrels en in de kantine. Jeroen, jij hebt mij heel veel geleerd over genomics. Vaak als ik met mijn handen in het haar zat, was jij de redder in nood.

Alle collega’s van GzB, ik heb al die jaren met heel veel plezier op de afdeling gewerkt. Ik ben altijd met veel plezier gaan werken en daar hebben jullie allemaal voor gezorgd. Met een zeer goede sfeer en alle gezelligheid naast het werk, van lab-uitjes, spellenavonden tot (kerst) borrels. Hans het was altijd erg gezellig om o.a. samen met Peter en jou de filmavonden en borrels te organiseren. Mijn kamergenoten, Linda en mede AiO’s Myrtho en Kirsten bedankt voor alle gezelligheid en dat ik altijd terecht kon met frustraties en om dingen te bespreken. Hedwig, vaak goede gesprekken gehad, onder andere op onze weg terug naar huis op de fiets. Anne, samen als stagiaire begonnen bij het RIVM en later weer beide als AiO. De gezelligheid buiten het werk en alle potjes tennis waarbij je mij hebt verslagen. Jochem, bedankt voor gezelligheid als kamergenoot en daarna als buurman. Alle andere mede AiO’s, Mirjam, Joantinne, Marja en Sander, we hebben veel van elkaar geleerd en een gezellige tijd samen gehad. De mensen van het secretariaat, Willy, Janet en later Helena, Jane, en Corrie, met een warm ontvangst werd ik altijd onthaald bij jullie. Bedankt voor al jullie hulp en leuke momenten. Laura, bedankt voor je hulp bij mijn onderzoek. Je was een prettige stagiaire om mee samen te werken. Nynke, wij zijn elkaar tegen gekomen bij onze eerste POT-cursus en hebben er een leuke vriendschap aan overgehouden.

Lieve Maud, bedankt voor al jou steun, rust en geduld de afgelopen jaren. Jij hebt vaak al mijn verhalen aangehoord, met de daarbij behorende frustraties en vreugdemomenten. Het is fantastisch en ben erg blij met dat jij dit proefschrift hebt opgemaakt en de kaft hebt ont- worpen.

Tot slot pap, mam en Joyce, zonder jullie was dit nooit mogelijk geweest. Door jullie ben ik altijd gesteund, ik kan altijd terecht voor een luisterend oor en hebben vaak met veel interesse naar al mijn verhalen geluisterd. Bedankt voor alle mogelijkheden die jullie mij in mijn leven tot nu toe hebben geboden.

198 Dankwoord

199 200 LIST OF PUBLICATIONS

201 202 List of publications

Articles Schulpen SHW, Theunissen PT, Pennings JLA, Piersma AH. Comparison of gene expression regulation in mouse- and human embryonic stem cell assays during neural differentiation and in response to valproic acid exposure. Reproductive toxicology (2015), accepted for publication

Schulpen SHW, Pennings JLA, Piersma AH. Gene Expression Regulation and Pathway Analysis After Valproic Acid and Carbamazepine Exposure in a Human Embryonic Stem Cell-Based Neurodevelopmental Toxicity Assay. Toxicological sciences (2015), accepted for publication; doi: 10.1093/toxsci/kfv094

Schulpen SHW, de Jong E, de la Fonteyne LJ, de Klerk A, Piersma AH. Distinct gene expression responses of two anticonvulsant drugs in a novel human embryonic stem cell based neural differentiation assay protocol. Toxicology In vitro (2015) 29(3):449-57. van Dartel DAM, Schulpen SHW, Theunissen PT, Bunschoten A, Piersma AH, Keijer J. Dynamic changes in energy metabolism upon embryonic stem cell differentiation support developmental toxicant identification. Toxicology (2014) 324:76-87.

Schulpen SHW, Pennings JLA, Tonk EC, Piersma AH. A statistical approach towards the derivation of predictive gene sets for potency ranking of chemicals in the mouse embryonic stem cell test. Toxicology Letters (2014) 225(3):342-9.

Kroese ED, Bosgra S, Buist HE, Lewin G, van der Linden SC, Man HY, Piersma AH, Rorije E, Schulpen SHW, Schwarz M, Uibel F, Vugt-Lussenburg BM, Wolterbeek AP, van der Burg B. Evaluation of an alternative in vitro test battery for detecting reproductive toxicants in a grouping context. Reproductive Toxicology (2014) S0890-6238(14)00256-1.

Schulpen SHW, Robinson JF, Pennings JLA, van Dartel DAM, Piersma AH. Dose response analysis of monophthalates in the murine embryonic stem cell test assessed by cardiomyocyte differentiation and gene expression. Reproductive toxicology (2013) 35:81-8

Piersma AH, Bosgra S, van Duursen MB, Hermsen SA, Jonker LR, Kroese ED, van der Lin- den SC, Man H, Roelofs MJ, Schulpen SHW, Schwarz M, Uibel F, van Vugt-Lussenburg BM, Westerhout J, Wolterbeek AP, van der Burg B. Evaluation of an alternative in vitro test battery for detecting reproductive toxicants. Reproductive Toxicology (2013)38:53-64.

Al-Salmani K, Abbas HH, Schulpen SHW, Karbaschi M, Abdalla I, Bowman KJ, So KK, Evans MD, Jones GD, Godschalk RW, Cooke MS. Simplified method for the collection, storage, and comet assay analysis of DNA damage in whole blood. Free Radical Biology and Medicine (2011) 51(3):719-25.

203 Theunissen PT, Schulpen SHW, van Dartel DAM, Hermsen SAB, van Schooten FJ, Piers- ma AH. An abbreviated protocol for multilineage neural differentiation of murine embryonic stem cells and its perturbation by methyl mercury. Reproductive Toxicology (2010) 29(4):383- 92.

Book Chapter Schulpen SHW, Piersma AH. The embryonic stem cell test. Methods in Molecular Biology (2013) 947:375-82.

204 List of publications

205 206 CURRICULUM VITAE

207 208 Curriculum Vitae

Curriculum Vitae Sjors Hubertus Wilhelmina Schulpen was born on the 5th of January 1983 in Sittard, the Netherlands. After he graduated from secondary education at Mavo land van Gulick in 1999, he studied Biotechnology, at Leeuwenborgh opleidingen in Sittard. During this study he performed several internships. The first internship was carried out at the department of Health Risk Analysis and Toxicology (GRAT), University of Maastricht, under supervision of Jan van Maanen and Jacco Briedé. The second internship was performed at the department of Medical Micro-Biology, University of Maastricht, under supervision of Sita Nys. The third internship was performed at the department of Environment, Health and Safety, DSM, under supervision of Fons van der linden and Eric Wester. His final internship was carried out at the department Genetics and Cell Biology, section population genetics, genomics and bioinformatics, University of Maastricht, under supervision of Aimée Paulussen and Rob Janssen.

After graduating in 2004, he started the bachelor program Life Sciences at the Hogeschool van Utrecht with molecular biology as a specialism. During this study he carried out two internships. The minor internship was carried out at the department of Toxicology, National Institute for Public Health and the Environment (RIVM), under supervision of Joost Melis and Suzan Wijnhoven. His bachelor internship was carried out at the department of Toxico- logy and Applied Toxicology, TNO, under supervision of Didima de Groot.

After graduating in 2007, he continued with the Master Program Molecular Health Scien- ces at the University of Maastricht. He conducted his minor internship at the department of Toxicology, University of Maastricht, The Netherlands and The University of Leicester, United Kingdom, under supervision of Roger Godschalk and Marcus Cooke. Furthermore, he performed a major internship at the Laboratory for Health Protection Research of the RIVM, under Supervision of Peter Theunissen and Aldert Piersma. Sjors obtained his master’s degree in 2009, after which he started as a PhD student at the Institute for Risk Assess- ment Sciences (IRAS) with a cooperation with the laboratory for Health Protection Research at the RIVM. During his PhD-project he completed the Postgraduate Education in Toxico- logy (PET), which will result in a European registration as toxicologist. Since March 2015 Sjors works as a scientist at the department of Environmental Safety and Security.

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