Modeling of neural differentiation by using embryonic stem cells to detect developmental toxicants
Dissertation
zur Erlangung des akademischen Grades eines Doktors der Naturwissenschaften (Dr. rer. nat.)
vorgelegt von
Bastian Zimmer
an der
Mathematisch-Naturwissenschaftliche Sektion Fachbereich Biologie
Konstanz 2011
Tag der mündlichen Prüfung: 02.12.2012 1. Referent: Prof. Dr. Marcel Leist 2. Referent: PD Dr. Gerrit Begemann 3. Referent: Prof. Dr. Marcus Groettrup 4. Referent: Prof. Dr. Gabsang Lee
List of publications
Publications, integrated in this thesis
Chapter C
Zimmer B, Kuegler PB, Baudis B, Genewsky A, Tanavde V, Koh W, Tan B, Waldmann T, Kadereit S, Leist M. Coordinated waves of gene expression during neuronal differentiation of embryonic stem cells as basis for novel approaches to developmental neurotoxicity testing. Cell Death Differ. 2011 Mar;18(3):383-95. Epub 2010 Sep 24.
Chapter D
Zimmer B, Schildknecht S, Kuegler PB, Tanavde V, Kadereit S, Leist M. Sensitivity of dopaminergic neuron differentiation from stem cells to chronic low-dose methylmercury exposure. Toxicol Sci. 2011 Jun;121(2):357-67. Epub 2011 Mar 7.
Chapter E
Zimmer B, Lee G, Meganathan K, Sacchinidis A, Studer L, Leist M. Genuine human neural crest cells as test system for developmental toxicity and potential rescue strategies. under review
Some text passages in the general introduction were taken from:
Kadereit S*, Zimmer B*, van Thriel C, Hengstler J.G., Leist M. Compound selection for modeling developmental neurotoxicity (DNT) and prenatal neurotoxicity with embryonic stem cells. accepted for publication in Frontiers in Bioscience *: both authors contributed equally
Publications, not integrated in this thesis
Kuegler PB, Zimmer B, Waldmann T, Baudis B, Ilmjärv S, Hescheler J, Gaughwin P, Brundin P, Mundy W, Bal-Price AK, Schrattenholz A, Krause KH, van Thriel C, Rao MS, Kadereit S, Leist M. Markers of murine embryonic and neural stem cells, neurons and astrocytes: reference points for developmental neurotoxicity testing. ALTEX. 2010;27(1):17- 42.
Kuegler PB, Baumann BA, Zimmer B, Kadereit S, Leist M, GFAP-independent inflammatory competence and trophic functions of astrocytes generated from murine embryonic stem cells, GLIA, in press
Waldmann, T., Weng, M., Zimmer, B., Pöltl, D., Scholz, D., Broeg, M., Kadereit, S.,Wuellner, U., Leist, M. Extensive transcriptional regulation of chromatin modifiers during human neurodevelopment. submitted Table of contents
A. Summary ...... 6 Zusammenfassung...... 7
B. General introduction ...... 9 1. (Neuro)development in general...... 9 1.1 Proliferation...... 11 1.2 Differentiation/Patterning...... 12 1.3 Migration...... 14 1.4 Neurite outgrowth...... 15 1.5 Synaptogenesis / Neurotransmitter household ...... 16 2. Environmental chemicals and (neuro)development...... 17 2.1 Barker Hypothesis...... 18 2.2 Time of insult vs. time of phenotype onset ...... 18 2.3 Susceptibility of the developing brain to chemicals...... 19 2.4 Environmental chemicals and developmental disabilities ...... 20 2.5 Phenotype vs. biological process ...... 22 3 Development of in vitro test systems in the 21st century ...... 23 4. Embryonic stem cells (ESC) as source for in-vitro testing ...... 24
Aims of the thesis...... 27 Chapter C...... Coordinated waves of gene expression during neuronal differentiation of embryonic stem cells as basis for novel approaches to developmental neurotoxicity testing...... 29 Abstract ...... 31 Introduction ...... 32 Results ...... 34 Discussion ...... 45 Materials and Methods...... 49 Supplements ...... 56 Chapter D...... Sensitivity of dopaminergic neuron differentiation from stem cells to chronic low-dose methylmercury exposure ...... 73 Abstract ...... 75 Introduction ...... 76 Materials and Methods...... 78 Results ...... 82 Discussion ...... 91 Supplements ...... 95 Chapter E...... Genuine human neural crest cells as test system for developmental toxicity and potential rescue strategies...... 101 Abstract ...... 102 Introduction ...... 103 Materials and Methods...... 105 Results ...... 107 Discussion ...... 114 Supplements ...... 118
F. Concluding discussion ...... 131 G. Bibliography ...... 142 Record of contribution ...... 169 Summary
A. Summary
Developmental disabilities and congenital malformations associated with neural development are increasing problems in western countries. More and more evidence emerges from human epidemiological studies that environmental chemicals as well as drug and food constituents contribute to such an increase. Unfortunately, developmental neurotoxicity is currently the least examined form of developmental toxicity. Less then 200 compounds worldwide, mostly pesticides, have been tested in vivo according to the OECD test guideline TG 426. This guideline requires expensive and labor intensive animal experiments which often lack human relevance. As embryonic stem cells are able to differentiate into every cell type of an organism and have been shown to recapitulate in vivo development in the culture dish, they are considered a powerful alternative to whole animal experiments. Since also human embryonic stem cells have been generated, it is possible to mimic effects of chemicals on human neural development in vitro today. In the framework of this doctoral thesis, we used murine and human embryonic stem cells to establish basic concepts of in vitro developmental toxicity testing and to develop new test systems based on these cells. In a first step, we characterized and modified a published 2-step neural differentiation protocol based on mouse ESC to fulfill the requirements of an in vitro toxicity test system. By using whole genome transciptome analysis we were able to identify different waves of gene expression. In a second step, we correlated these waves of gene expression with important steps of neural development. Proof-of-principle experiments showed that the waves identified could be the basis for endpoint selection and exposure windows. In the next step, we analyzed the effects of low-dose chronic methylmercury exposure on late neuronal differentiation. We identified dopaminergic neurons as relevant targets of mercury toxicity. We thereby were the first to correlate gene expression findings with functional readouts in an embryonic stem cell based in vitro developmental neurotoxicity test system. Additionally, we were able to correlate in-cell toxicant concentrations with relevant in vivo concentrations in such a setting. After having established the principles important for embryonic stem cell based developmental neurotoxicity testing, we developed a neural crest migration assay based on cells differentiated from human embryonic stem cells. We found that these cells detected adverse effects on cell migration, an important process during neural development, in a more sensitive way than non- neural cell lines. These findings contribute to the development of embryonic stem cell based in vitro assays and have set general principles on what needs to be assessed in such assays. 6 Zusammenfassung
Zusammenfassung
Entwicklungsstörungen und angeborene Fehlbildungen, die durch Fehler in der Nervensystementwicklung verursacht werden, sind ein steigendes Problem in westlichen Ländern. Immer mehr epidemiologischen Studien deuten darauf hin, dass Umweltchemikalien sowie Medikamente und Nahrungsmittelbestandteile zu diesen Problemen beitragen. Ent- wicklungsneurotoxiziät ist zurzeit die am wenigsten untersuchte Form von Entwicklungs- toxizität. Weltweit wurden bisher weniger als 200 chemische Verbindungen, meist Pestizide, gemäß der OECD Richtlinie TG 426 getestet. Diese Richtlinie erfordert teure und arbeitsin- tensive Tierexperimente, die nur selten die Situation im Menschen widerspiegeln. Da mit embryonalen Stammzellen die Embryonalentwicklung in der Kulturschale nachvollzogen werden kann, gelten sie als eine vielversprechende Alternative zu Tierexperimenten. Dadurch ist es heute möglich, die Auswirkungen von Chemikalien auf die Entwicklung des menschlichen Nervensystems in vitro zu untersuchen. In dieser Doktorarbeit verwendeten wir embryonale Stammzellen von Maus und Mensch um grundlegende Konzepte für die Untersuchung von entwicklungsneurotoxischen Substanzen in der Kulturschale sowie neue Testsysteme zu entwickeln. Zuerst passten wir ein bereits veröffentlichtes Protokoll, mit dem man embryonale Stamm- zellen der Maus in Nervenzellen differenzieren kann, an die Anforderungen eines in vitro Toxizitätstestsystems an. Dabei konnten wir unterschiedliche Wellen von Genexpression während der Entwicklung von Nervenzellen aus embryonalen Stammzellen identifizieren. In ersten Experimenten konnten wir zeigen, dass diese Wellen als eine Grundlage für spätere toxikologische Analysen dienen können. Anschließend analysierten wir die Auswirkungen von geringen, über einen längeren Zeitraum dosierten Quecksilberkonzentrationen auf die späte Nervensystementwicklung. Dabei fanden wir heraus, dass speziell dopaminerge Neuronen durch Quecksilber geschädigt werden. Außerdem konnten wir in einem auf embryonalen Stammzellen basierenden Entwicklungsneurotoxizitäts-Testsystem erstmals Genexpressionsdaten mit funktionellen Analysen kombinieren. Nachdem wir wichtige Grundlagen zur Untersuchung von Entwicklungsneurotoxizität gelegt hatten, entwickelten wir einen Zellwanderungstest basierend auf Neuralleistenzellen, die aus menschlichen embryo- nalen Stammzellen differenziert worden waren. Diese Zellen konnten nachteilige Effekte von Chemikalien auf die Zellwanderung besser als nicht neurale Zellen anzeigen. Mit dieser Arbeit konnten wir zur Entwicklung von Testsystemen, die auf embryonalen Stammzellen basieren, beitragen und grundlegende Prinzipien für diese Art von Tests etablieren. 7 Abbreviations
Abbreviations
ADHD: Attention deficit hyperactivity disorder ASD: autism spectrum disorder AVE: anterior visceral endoderm BBB: blood brain barrier BMP: bone morphogenic protein CNS: central nervous system DNT: developmental neurotoxicity ECM: extracellular matrix EMT: epithelial-to-mesenchymal transition ESC: embryonic stem cell ESNATS: Embryonic Stem cell-based Novel Alternative Testing Strategies FGF: fibroblast growth factor GABA: γ-Aminobutyric acid GD: gestational day GSK3: Glycogen synthase kinase 3 HTS: high-throughput screening IPS: induced pluripotent stem cells IQ: intelligence quotient LIF: Leukemia inhibitory factor LOAEL: lowest-observed-adverse-effect-level MeHg: methylmercury MLK: mixed lineage kinase NCC: Neural crest cell NSC: neural stem cell PCBs: Polychlorinated biphenyls PNS: peripheral nervous system PoT: pathways of toxicity RA: retinoic acid SHH: sonic hedgehog SVZ: sub ventricular zone US: United States
8 Chapter B – General introduction
B. General introduction
In order to establish in vitro based test systems to detect developmental toxicants, which are normally tested in whole animals, it is crucial to understand the key biological processes taking place during normal development. The following general introduction therefore describes key events during neurodevelopment and tries to bridge current knowledge about those processes with toxicological concepts.
1. (Neuro)development in general
The development of a complex vertebrate organism requires several tightly controlled and exactly timed mechanisms (Aylward 1997). The development of the sophisticated mammalian, and particularly the human, nervous system is one of the most complex processes in nature. During the development of the nervous system, several different cellular processes such as apoptosis, cell differentiation, patterning, neurite outgrowth and migration (which will be discussed in detail later) have to take place in a tightly regulated manner (Rao and Jacbson 2005). As illustrated in Figure 1, the development of the nervous system starts with a process called neurulation (Greene and Copp 2009). The notochord thereby induces the formation of the neural plate within the ectoderm (around gestational day (GD) 8 in rats and GD 14-19 in humans) (Rice and Barone 2000). Already at that Figure 1: Schematic illustration of stage, two different cellular identities can be neurulation Upon cell proliferation, the neural plate starts identified based on the expression of different to develop into a neural fold. Already at that markers and specifier genes (Betancur et al. stage NCC progenitors (grey) can be identified. The neural groove deepens and 2010; Betters et al. 2010; Sauka-Spengler and ultimately fuses to become the neural tube. NCCs (grey) now start migrating to their Bronner-Fraser 2008). On the one hand, the target sites. For more details see text neural plate border, giving rise to the neural
9 Chapter B – General introduction crest cells (NCC) (indicated as grey cells in Figure 1), which among many other structures develop into the peripheral nervous system (PNS) (Huang and Saint-Jeannet 2004), on the other hand the designated neural tube, which later gives rise to the central nervous system (CNS) (Gilbert and Singer 2006) (indicated as blue cells in the lower part of Figure 1). In humans, the neural plate develops into the neural groove due to massive cell proliferation at the boarders of the neural plate around GD 20. The two neural folds then fuse in the area later developing into the hindbrain to form the beginning neural tube in humans around GD 21 (Hood 2005). From now on, the neural groove closure proceeds in a zipper-like manner in both directions (anteriorly and posteriorly) (Wilson and Maden 2005). In humans, neural tube closure is completed between GD 26 and 28 (in rats GD 10.5 -11) with the anterior neuropore closing first (GD 24 – 26) and the posterior end closing later (between GD 25 – 28) (Hood 2005). Incomplete closure of the neural tube results in developmental congenital disorders such as spina bifida (Harris and Juriloff 2010; Mitchell et al. 2004) which is characterized among other symptoms by an open back. Approximately 1 in 1000 children born is affected by this birth defect. Chemicals like valproic acid have been described to increase the risk for spina bifida and craniofacial malformations. Especially the use of valproic acid during the first trimester of pregnancy increases the risk (Jentink et al. 2010; Ornoy 2009). The timeline of rat neural development is illustrated in Figure 2.
Figure 2: Timing of important processes of neural development in the rat. Modified from (Rice and Barone 2000)
After neurulation, early brain development is initiated within the different areas of the neural tube. The anterior part of the neural tube will develop into the brain (including fore-, mid- and hindbrain), while the posterior part will develop into the spinal cord (Altmann and Brivanlou 2001; Nishi et al. 2009). These dramatic morphological and cellular changes are 10 Chapter B – General introduction accomplished by different cellular processes such as proliferation, differentiation, migration and synaptogenesis (some of them are summarized in Figure 3). As these cellular processes need to be understood to be able to model them in vitro, some of them will be described in more detail.
1.1 Proliferation
After closure of the neural tube, the early brain consists of a single layer of cells. In order to develop into the sophisticated structure of an adult brain, massive cellular proliferation has to take place. Within the developing brain, 4 different main proliferative regions can be found, some of which persist into adulthood: The ventricular zone, the external granule layer of the cerebellum, the subventricular zone (SVZ) and the subhilar proliferative zone within the dentate gyrus, the two latter one persisting into adulthood (Rao and Jacbson 2005).
Figure 3: Key biological processes involved in differentiation of the nervous system. Modified from (Kadereit et al. 2011) and en.wikipedia.org/wiki/Central_nervous_system
The cerebral cortex may be the most remarkable structure of the mammalian, and especially the human, brain. It is made up by 10 – 20 billion interconnected neurons and 5 – 10 times more glial cells (Nowakowski and Hayes 1999). This enormous number of cells is created mainly by two different types of cell division. In the beginning, a limited number of 11 Chapter B – General introduction progenitor cells, is expanded by vertical cell division. Around 6 weeks after conception (in humans), the progenitor cells divide symmetrically (vertically), giving rise to two identical daughter cells, both of which re-enter the cell cycle (Choi 1988), expanding the progenitor pool. Next, the SVZ forms around 7 weeks after conception (Zecevic 1993). From week 9 to 10, the cortical plate compacts and the intermediate zone is expanded. During this process, cells start to exit the cell cycle due to asymmetric or horizontal cell division. During this process the two resulting daughter cells can be separated into an apical and a basal daughter cell. The cell attached to the ventricular surface remains within the progenitor pool, while the daughter cell which has lost contact to the lumen of the ventricle exits cell cycle and starts to differentiate. This process is considered a very early step characteristic for differentiation of precursor cells into neurons (Chan et al. 2002). During week 12 of development, the proliferative activity in the human cortex peaks but the ventricular zone is no longer increasing in width, due to a balance of cell division and young neurons leaving this zone (Simonati et al. 1999). In week 18 of gestation, neurogenesis in the human cortex ends and the generation of new neurons after week 18 becomes negligible for the development of the human brain (Chan et al. 2002). Although neurogenesis and cell proliferation mostly ends within this period, cell division and neurogenesis persist into adulthood within the SVZ (also a well known source of adult neural stem cells (NSC)) and the dentate gyrus (Lennington et al. 2003), as discovered by (Eriksson et al. 1998).
1.2 Differentiation/Patterning
Another very important process during development, and especially during development of the nervous system, is cell differentiation and the correct patterning of these cells. Differentiation of progenitor cells into more mature cell types begins as soon as the precursor cells have completed their last cell division (asymmetric) and are ready for cell migration. Cell differentiation and patterning are initiated by a complex interplay between intracellular and extracellular signals such as signaling through growth factors which lead to the expression of genes influencing either neuronal or glial cell differentiation (Kuegler et al. 2010). Such growth factors are present within the developing brain at different concentrations in different areas (Sansom and Livesey 2009). Those gradients are generated due to specific cell populations, such as the floor plate or the isthmic organizer secreting different types of 12 Chapter B – General introduction growth factors (Fasano et al. 2010). The different growth factors, secreted by different areas, and the resulting complex mixture of signals, which also differ in their intensity in different brain areas, modulate and refine the regional differentiation and patterning of differentiating cells (Takahashi and Liu 2006). For a long time, the early patterning of the nervous system was believed to be a default mechanism. As inhibitors of the bone morphogenic protein (BMP) signaling pathway such as noggin play an important role in promoting early neurodevelopment (Gaulden and Reiter 2008), it was thought that ectodermal cells differentiate into neural tissue as long as they are not exposed to BMPs (Munoz-Sanjuan and Brivanlou 2002). More recently, an additional important concept has emerged. Besides the important aspect of the absence of BMP signaling, the presence of fibroblast growth factor (FGF) signaling appears to play an important role (Levine and Brivanlou 2007; Stern 2006). FGFs seem to promote a “pre”- neural state (Stern 2005) priming the cells for neural differentiation. Besides BMP inhibition and FGF signaling, integrating patterning signals such as Wnt/GSK3 signaling which for instance regulates the duration of BMP/Smad1 signaling are important for correct neural development (Pera et al. 2003). After the initial specification of primitive neuroectodermal tissue, other factors in addition to BMPs and FGFs become more and more important. As it is believed that the early neural tissue has an innate anterior identity (Wilson and Houart 2004), structures prone to become anterior, such as the early telencephalon, need to be protected from caudalizing factors. This task is achieved again by specialized structures within the developing organism, such as the anterior visceral endoderm (AVE) in the mouse (Stern 2001). Not only structures like the AVE guarantee the identity of more anterior brain parts. Also the fact that signals, such as Wnts, FGFs, nodals and retinoic acid (just to name a few), required for directing neural tissue more caudally, are located in more caudal parts of the developing brain, plays an important role (Agathon et al. 2003). Since the mature mammalian brain has not only an anterior – posterior identity, but also a dorsal – ventral pattern, again different gradients and morphogens such as SHH (directing neural tissue to more ventral brain areas) are needed to guarantee a proper brain development (Gaspard et al. 2008; Gaspard and Vanderhaeghen 2010; Götz and Huttner 2005). During this complicated process of patterning, the signals not only have to be separated in space, they also have to be separate in the timing. For instance it is a well established concept in vivo and in vitro, that neurogenesis takes place before gliogenesis (Sugimori et al. 2007; Temple 2001). To make the whole process of differentiation and cell patterning even more 13 Chapter B – General introduction complicated, the timing of differentiation is not only important for different cell types such as astrocytes and neurons, also the differentiation of different sub-types of neurons depends on the correct timing of signals, which result in characteristic neurogenic waves (Gaspard et al. 2008). This concept of a wave-like process of neural development, in which different processes ranging from proliferation over differentiation to network formation overlap in a wave-like pattern, is well established in the literature. Meanwhile, these biological processes have been broken down to gene expression and specific gene signatures associated with different cell types and biological processes describing the initial morphological observations (Abranches et al. 2009; Aiba et al. 2006; Wei et al. 2002).
1.3 Migration
Besides cell proliferation and differentiation, correct cell migration is essential for the development of the nervous system (Ruhrberg and Schwarz 2010; Valiente and Marin 2010). Due to the complexity of cell migration during neural development and the scope of this PhD thesis, migration in the CNS will only be discussed to a minor extend, whereas neural crest cell (NCC) migration which plays an important role in the development of the PNS will be discussed in more detail. Nevertheless, general aspects such as migration along defined paths in a strictly timed manner are applicable and important for neural cell migration in general. CNS migration can be divided into two different modes of migration. The principle mode of migration within the cortex is the so-called radial migration. During radial migration, the neurons move along radially oriented glial fibers orthogonal to the surface of the brain. During tangential migration, the neurons (in rodents interneurons of the cerebral cortex) move parallel to the brain surface along axons or other neurons (Metin et al. 2008; Nadarajah and Parnavelas 2002). The migration of NCCs, in contrast to the migration of CNS progenitors, starts shortly after neurulation. At the end of neurulation, NCCs receive signals including BMPs, FGFs and Wnts secreted from the surrounding tissue, which lead to a process called epithelial-to- mesenchymal-transition (EMT) (Sauka-Spengler and Bronner-Fraser 2008). The cells then delaminate from the neural tube, sort into segregated migratory streams and migrate along distinct paths to their target sites in the periphery giving rise to a large variety of cells including bone and cartilage of the head, melanocytes, Schwann cells and sensory neurons (Harris and Erickson 2007; Krull 2001; Le Douarin and Kalcheim 2009).
14 Chapter B – General introduction
The migration of the 4 known different populations of NCCs, namely cranial, cardiac, vagal and trunk, is thereby tightly controlled as to the timing, the region along the neural tube the cells emerge from, and their route of migration (Ruhrberg and Schwarz 2010). These tightly regulated processes include dynamic events such as reorganization of the cytoskeleton and membrane compartments, rearrangements of the extracellular matrix (ECM) and cell junctions as well as detachment and reattachment via adhesion molecules (Kadereit et al. 2011). Thereby the whole process of migration is again guided by gradients of proteins which serve as chemoattractans such as CXCR4 (Kasemeier-Kulesa et al. 2010) or repellents such as Sema3A (Anderson et al. 2007; Schwarz et al. 2009a; Schwarz et al. 2009b). Ultimately, much of the cellular response to such chemokines is mediated via members of the Rho family of small GTPases, such as Rac, Rho or Cdc42. Signaling through those enzymes results in a reorganization of the actin cytoskeleton including actin polymerization, contraction via interaction with myosin and adhesion to the substrate, amongst others, via integrins (Becchetti and Arcangeli 2010; Kadereit et al. 2011; Kurosaka and Kashina 2008; Nobes and Hall 1995).
1.4 Neurite outgrowth
Once neuronal progenitor cells have reached their target site, they have to differentiate to fully mature neurons and generate the complex neurite network that is characteristic for the highly developed mammalian nervous system. Neurite outgrowth relies on intrinsic (e.g. expression of receptors) and extrinsic factors. An important extrinsic aspect is the interaction of the differentiating cells with components of the extracellular matrix (ECM) as well as with other cells, e.g. glial cells, via cell adhesion molecules (CAMs) and integrins (Kiryushko et al. 2004; Powell et al. 1997; Tarone et al. 2000). Similar to the processes already described for cell migration, gradients of attractants and repellents are sensed by the growth cone of growing neurites, leading to actin reorganization via GTPases and a directed growth of the neurite. It has been proposed that the signals from the ECM or extracellular guidance cues resulting in neuron polarization converge at the level of GSK3 (Kadereit et al. 2011; Yoshimura et al. 2006).
15 Chapter B – General introduction
1.5 Synaptogenesis / Neurotransmitter household
After the axonal growth cones have reached their targets, the functional units of the brain, the synapses, have to be established. The mammalian brain comprises 1011 neurons, which are connected to each other by up to 1015 synapses (Drachman 2005). The detailed wiring of the complex neuronal circuits in mammalian brains, which is to a large degree self-generated, depends on neurotransmitters and neuromodulators (Ruhrberg and Schwarz 2010). For example disturbances of the fetal dopamine transporter by cocaine resulted in a delayed postnatal synaptic maturation (Bellone et al. 2011).
Table 1: Summary of neurotransmitter effects during neural development neurotransmitter known function during neural development reference GABA Exerts a variety of trophic influences through the (Barker et al. 1998;
stimulation of the GABAAR; controls cell cycle Haydar et al. 2000; kinetics in neuronal progenitors Li and Xu 2008; Nakamichi et al. 2009) Glycine Increases the number of primary neurites and total (Tapia et al. 2000) neurite length Glutamate Promotes neuronal growth and differentiation; (Aruffo et al. 1987; activation of NMDA-R promotes neurite Pearce et al. 1987) outgrowth Acetylcholine Exerts chemoattractance and guidance for nerve (Zheng et al. 1994) growth cones
It has been shown that neurotransmitters, such as catecholamines can be detected in vertebrate as well as in invertebrate embryos before neurons are differentiated. As there is no synapse- based target yet, those early appearing neurotransmitters are thought to play an important role during the development of the nervous system. Therefore, a switch in the function of these neuroreactive molecules from the developmental to the maturation phase has been proposed (Pendleton et al. 1998). This theory is supported by the expression of different neurotransmitter receptors, such as the dopamine receptor D1A which regulates neuronal growth very early during development (Todd 1992). Furthermore, it has been shown that specific neurotransmitter receptors are expressed on progenitor cells of the CNS before
16 Chapter B – General introduction synapses are established, where they function as growth regulators during specific developmental periods (Nguyen et al. 2001). Table 1 gives a small summary of different types of neurotransmitters and their known functions during development. Besides, their function during early development described above, neurotransmitters play an essential role during the establishment of the neuronal connections via synapses within the brain. The process of synapse formation, called synaptogenesis can be broken down into two major stages. First, initial contact is made between the two cells and an immature synapse is formed. In the next step, synaptic connectivity is fine-tuned by the elimination (pruning) and strengthening of synapses. In order to achieve the goal of forming a functional synapse, the synapse-forming and the synapse-receiving cell exchange signals and initiate the second step of synaptogenesis. Dendrite as well as axon-specific protein complexes (e.g. active zone proteins, synaptic vesicle proteins) are recruited to the initial contact site and a functional synapse is formed (Bury and Sabo 2010; Colon-Ramos 2009; Garner et al. 2002; McAllister 2007; Munno and Syed 2003).
2. Environmental chemicals and (neuro)development
Due to this complexity and interplay of processes it is not surprising that already small mistakes in one of these processes have great impact on the whole development of the nervous system. It is estimated, that around 22% of the adults in the United States suffer from at least one mental illness and according to the World Health Organization, this percentage is going to increase in the near future (Andersen 2003; Holden 2000). Additionally, 3 – 12% (depending on the source) of the children under the age of 18 in the US bear at least one neurodevelopmental disorder (Boyle et al. 1994; Hass 2006; Schettler 2001). Many of these mental illnesses can be associated to genetic mutations (Rosenberg et al. 2007), but data from twin studies indicate that the influence of the environment on such disorders should not be regarded as minor (Fishbein 2000; McGuffin et al. 2001). Environmental chemicals such as mercury or lead are known to disturb neurodevelopment in humans and are therefore - among many other chemicals, which do not necessarily have the strong epidemiological supportive data of these two metals - suspected risk factors for neurodevelopmental disorders (Grandjean and Landrigan 2006).
17 Chapter B – General introduction
The following section describes why the developing brain is particularly vulnerable to toxic insults and why neurodevelopmental disorders might be associated to exposure to environmental chemicals.
2.1 Barker Hypothesis
Barker developed the concept that parameters of growth, such as birth weight or head circumference, can be used to predict the risk of adult diseases, such as coronary heart disease, stroke, insulin resistance and diabetes (Osmond and Barker 2000). Initially, the Barker Hypothesis was developed by David Barker in the 1990s (Barker 1997; Zadik 2003). By using large epidemiological studies as well as reconstruction of birth cohorts in the UK, Barker was able to correlate such adult disease with reduced fetal growth and impaired development during infancy (Barker et al. 1992; Barker et al. 1993). The influence of e.g. poor nutrition resulting in low birth weight as risk factor for disease later in life is nowadays firmly established in human epidemiology (Calkins and Devaskar 2011). Whether environmental chemicals could also lead to such effects still remains an unproven theory (Silbergeld and Patrick 2005). However, this theory is supported by studies correlating smoking during pregnancy with reduced birth weight and ultimately with cardiovascular disease (Bakker and Jaddoe 2011; Geelhoed et al. 2011). Additionally, it has been shown that effects of chemicals during early development can be transmitted through the germline up to 3 generations (Nilsson et al. 2008; Skinner et al. 2010). All these results suggest that negative events during early development lead to negative outcomes in the adult organism. As a result of the suspected relation between exposure to environmental chemicals during embryonic or fetal development and adult mental disease, the Barker Hypothesis was expanded to neurodevelopmental disorders during the Mount Sinai Conference on Early Environmental Origins of Neurological Degeneration in 2003 (Landrigan et al. 2005).
2.2 Time of insult vs. time of phenotype onset
Another concept in (neuro)developmental toxicology is in line with the Barker Hypothesis. This is the long latency period of many (developmental)neurotoxicants. It has been shown, that e.g. signs of toxicity of MeHg emerged several years after the cessation of a 7 year exposure period in nonhuman primates (Rice 1996). Furthermore, it has been shown that
18 Chapter B – General introduction effects of perinatal exposure to MeHg may emerge as late as 9 years after birth (Davidson et al. 2006). The long latency period of many suspected neurodevelopmental toxicants is often explained by using the two hit theory of neurodevelopmental toxicology (Wang and Slikker 2011). The theory explains the long latency period by the need for a second, not necessarily toxic, event for the symptoms to manifest. The theory also includes the possibility that a toxic event (second hit) in the adult or aging brain is more severe in a brain which was exposed to a toxic agent during development (first hit). This is supported by studies showing that. effects of developmental exposure to e.g. triethyltin only manifest in aged organisms (Barone et al. 1995). These effects are explained by a decrease in reserve/repair capacity of the brain caused by the first hit, due to which aging or a second hit have more severe outcomes later in life. Thereby the timing of disturbance of neurodevelopment by the first hit might also determine the type of defect or mental illness later in life (Watson et al. 1999).
2.3 Susceptibility of the developing brain to chemicals
Another well established concept is, that a developing organism/organ is much more susceptible to toxic insults than an adult organism/organ. It has been shown by many studies that low doses of chemicals not toxic for the mature CNS can cause defects in the developing nervous system (Claudio et al. 2000; Tilson 2000). Many studies support the fact, that the timing of a toxic insult is much more important than the type (e.g. type of chemical) (Fan and Chang 1996; Rice and Barone 2000). A particular sensitive time window seems to be the period of organogenesis (GD 20 – 40). It has been shown that radiation during this period often leads to malformations (De Santis et al. 2007). It is estimated, that 90% of all human embryos that experience a disturbance during early organogenesis are spontaneously aborted (Opitz et al. 1987). Additionally, protection and detoxification mechanisms are not fully developed in the early stages of development. An example would be PON1, the enzyme metabolizing chlorpyrifos and other organophosphate pesticides, which is not fully active in humans until the age of 9 (Huen et al. 2009). Other protection mechanisms, such the blood brain barrier (BBB) or DNA repair systems are either not present or not fully functional during development (Adinolfi 1985; Saunders 1986). Another important aspect is the lack of a liver detoxification system in the early fetus. During development, the fetus relies on the detoxification processes of the mother. Current existing exposure limits, aiming to protect the
19 Chapter B – General introduction nervous system, often do not take these aspects into consideration. They are designed to protect workers, not a developing fetus. Concentrations considered to be safe for adults might not necessarily be safe for a developing organism (Ginsberg et al. 2004; Tilson 2000). Additionally, many chemicals known to have an effect on the developing nervous system, such as e.g. MeHg, tend to accumulate in fetal blood or lipid rich compartments, such as the mother milk, resulting in much higher exposure concentrations for the fetus/child compared to the mother (Jensen 1983; Landrigan et al. 2002; Sonawane 1995).
2.4 Environmental chemicals and developmental disabilities
As already described earlier, neurodevelopmental disorders and mental illnesses are a real and increasing problem in western countries. It is well established that mental disorders like autism or schizophrenia are the result of the complex interplay between genetic and environmental factors. It is believed that the individual genetic background influences the response to environmental factors. This concept is often referred to as G x E interaction (Gene-Environment interaction) (Tsuang et al. 2004). A famous example would be the alcohol flush syndrome. Due to genetic mutations, the activity of the aldehyde dehydrogenase (ALDH) is decreased, resulting in flushing of the face after alcohol consumption. Such genetic mutations are mainly observed in the Asian population (Takeshita et al. 1996; Wermter et al. 2010). Whether such G x E interactions also play a role in environmental chemicals leading to mental disorders is still under debate and needs further investigation. The following paragraphs try to summarize the evidence for the correlation of environmental chemicals with some of the most well-known developmental disabilities.
• Mental retardation Mental retardation is a disorder appearing before adulthood, characterized by a low IQ (below 70), deficits in adaptive behaviors and impaired cognitive function (Fredericks and Williams 1998). Roughly 3% of the children in the US are affected by some form of mental retardation. Genetic disorders account for only 25-30% of the causes of mental retardation (Daily et al. 2000). Due to the fact that nervous system malformations ultimately lead to mental retardation, the disease should be regarded as a result of impairment to the CNS development in general, and not as an individual well defined disease.
20 Chapter B – General introduction
Although the genetic background accounts for almost a third of diagnosed mental retardation, it is nowadays well established that environmental factors significantly contribute to this disorder (Matilainen et al. 1995; Simonoff et al. 1996). Several chemicals, including ethanol, lead, MeHg and cadmium, have been associated with mental retardation and more subtle forms of reduced IQ (Beattie et al. 1975; Grandjean and Landrigan 2006; Marlowe et al. 1983; Mendola et al. 2002; Sokol et al. 2003).
• Schizophrenia Schizophrenia is characterized by delusions, sensory hallucinations and impairment of speech organization (Goldner et al. 2002). Schizophrenia and autism may be the only mental disorder for which a possible cause in neurodevelopment, namely a delay in neurodevelopment, is widely accepted and therefore resulted in the neurodevelopmental hypothesis of schizophrenia (Powell 2010). Substances such as lead, amphetamine, ketamine, phencyclidine or cigarette smoke have also been associated with schizophrenia (Keilhoff et al. 2004; Mouri et al. 2007; Opler et al. 2008; Zammit et al. 2003).
• Attention deficit hyperactivity disorder (ADHD) About 3-5% of the children worldwide suffer from the psychiatric disorder ADHD (Nair et al. 2006; Polanczyk et al. 2007). It usually starts before the age of 7 and often continues into adulthood (Azmitia and Whitaker-Azmitia 1991; Elia et al. 1999) and is more commonly diagnosed in boys than in girls (Dreyer 2006; Malhi and Singhi 2001). Environmental chemicals are suspected to account for the increase in children diagnosed with ADHD over the last years. Although many scientists believe that this increase is rather due to better diagnostic tools, awareness, or even faking of the disease (Cormier 2008; Sansone and Sansone 2011; Simpson et al. 2011), exposure to environmental factors and chemicals such as smoking, manganese, lead, ethanol and PCBs during pregnancy has been shown to be associated with ADHD (Aguiar et al. 2010; Bouchard et al. 2007; Braun et al. 2006; Eubig et al. 2010; Ha et al. 2009; Kukla et al. 2008).
21 Chapter B – General introduction
• Autism spectrum disorders (ASD) Autism spectrum disorders, including autism itself, asperger syndrome or pervasive developmental disorder, are neurodevelopmental disorders characterized by impairment of social interactions and communication, restricted and repetitive pattern of behavior and/or interest (Levy et al. 2009). These symptoms all reliably manifest before the age of 3 (Filipek et al. 1999; Nash and Coury 2003). As already discussed for ADHD boys bare a higher risk of developing ASD (Brugha et al. 2011; Newschaffer et al. 2007). It is estimated that 60 – 70/10 000 births are affected by this lifelong disorder (Fombonne 2009). What autism has in common with most neurodevelopmental disorders is that the causes are not really known or understood. Many possible causes have been proposed including genetics such as mutations in Mecp2 or Fmr1 (de Leon- Guerrero et al. 2011; Moy and Nadler 2008) and teratogenic agents (Arndt et al. 2005; Trottier et al. 1999). Teratogenic compounds suspected to be a possible cause for autism include, amongst others, thalidomide, heavy metals such as mercury, PCBs or pesticides (Bernard et al. 2001; Jolous-Jamshidi et al. 2010; McGovern 2007; Stromland et al. 1994).
2.5 Phenotype vs. biological process
All these mental disorders and their characteristic phenotypes are caused by a complex interplay of different biological processes. For psychiatric disorders, endophenotypes have been established to divide behavioral symptoms into stable phenotypes with a clear genetic background. Although the existing definition of endophenotypes is very strict and based on genetic criteria, requiring heritability, illness state independence and illness co-segregation within families (Berti et al. 2011; Gottesman and Gould 2003; Gould and Gottesman 2006; Hasler and Northoff 2011), the principle behind the concept could be very useful for developmental neurotoxicity testing in vitro (Kadereit et al. 2011). It will be very hard, most of the time impossible, to model a complex disorder such as e.g. schizophrenia with all its behavioral aspects 1:1 in vitro. Therefore, the concept of endophenotypes, adapted to DNT testing, could facilitate modeling such disease in vitro. Instead of correlating a phenotype to a genetic connection, phenotypes such as neuroanatomical or neurobehavioral changes could be associated to basic biological processes which can be modeled in an in vitro system. It is important to bear in mind that the biological 22 Chapter B – General introduction process might be disturbed long before the phenotype manifests (Collman 2011). The effects of many known DNT chemicals have already been linked to such basic biological processes. A prominent example would be schizophrenia, which has been linked to impaired neurogenesis and neuronal migration. Lead, an environmental chemical suspected to be a possible cause for schizophrenia (Opler et al. 2008) has been shown to impair neurogenesis and alter migration of neuronal progenitors (Dou and Zhang 2011; Jakob and Beckmann 1986). Therefore, modeling such processes in vitro, based on data how these processes are involved in impaired neural development or mental disorders, would facilitate the identification of chemicals causing such impairments.
3 Development of in vitro test systems in the 21st century
As already mentioned in the previous paragraph, modeling complex disease or adverse effects of chemicals in vitro is extremely challenging. Additionally, assessing outcomes of exposure to DNT chemicals, such as a reduced IQ level, is extremely complicated and labor intensive to achieve in animal models. Moreover, human beings are not 70 kg rats (Hartung 2009). Besides different metabolic features, the normal ontogeny of neural development in rodents is also different from humans. A striking difference is the considerably longer prenatal maturation of the nervous system in humans compared to rats. Such differences result in e.g. different exposure routes during critical periods of neural development. The exposure route in rats might therefore be via lactational transfer during the first postnatal week, whereas the same biological process would be targeted by a chemical via transplacental transfer during the 3rd trimester of pregnancy in humans (Clancy et al. 2007; Rice and Barone 2000). Although the general sequence of brain development is the same in humans and rodents, the total length of neural development is dramatically different, from days in rodents to weeks or months in humans. Besides those differences in timing of development, clear structural differences exist. The human neocortex and the visual system are larger compared to those structures in the rats, whereas in rodents the olfactory system, in relation to other brain regions, is much bigger than in humans. In addition to tragic events, such as the thalidomide catastrophe in the early 1960s or the recent failure of TGN1412 (Ances 2002; Annas and Elias 1999; Attarwala 2010; Stebbings et al. 2007; Stirling et al. 1997; Woollam 1978), data from pharmaceutical companies indicate that animal models are not always able to predict effects of chemicals or drugs on human development or disease situations. During the 1990s, only 11% of new possible drugs
23 Chapter B – General introduction entering clinical trials were registered. Out of these 11%, 23% even failed after registration. Both, late manifestation of low efficacy and safety issues (account for 30%) are major reasons for late failures of novel drugs (Kola and Landis 2004). Consequently, new and improved test systems, able to predict efficacy and especially toxicity in a human-specific manner are urgently needed (Wobus and Loser 2011). In order to solve some of these problems, the US National Research Council has developed a strategy and vision for toxicity testing in the 21st century (Gibb 2008; Krewski et al. 2010). This strategy includes the use of human-based cellular assays which are applicable to high- throughput screening (HTS). Such new test systems, in combination with e.g. systems biology and bioinformatics, would help to understand how chemicals affect normal cellular function, and how this altered function results in a disease/disorder phenotype (NRC 2007). Furthermore, pathways of toxicity (PoT) could be identified by using such an approach. Those PoTs, when they are established, well validated and - most important complete - would then allow risk assessment of chemicals based on disturbance of these PoT (Leist et al. 2008b).
4. Embryonic stem cells (ESC) as source for in-vitro testing
In order to achieve the goal of “Toxicology in the 21st century”, new assays, and as the vision proposes the use of cellular systems applicable to HTS, also new sources of unmodified, non cancerous, reliable cells are needed. Since the establishment of the first mouse (Evans and Kaufman 1981; Martin 1981) and human (Thomson et al. 1998) ESC lines and the generation of induced pluripotent stem cells (IPS) (Takahashi et al. 2007; Takahashi and Yamanaka 2006), a lot of effort has been undertaken to use these cells for regenerative medicine (Menendez et al. 2006; Nsair and MacLellan 2011), modeling of disease (Lee and Studer 2011) and development (Dvash and Benvenisty 2004) as well as for toxicity testing of chemicals (Anson et al. 2011; Wobus and Loser 2011) and drug screening (Laustriat et al. 2010; Pouton and Haynes 2007). Although mouse and human ESC, besides common characteristics, show several differences in e.g. culture requirements or marker expression (summarized in Table 2), ESC from both species have been shown to be useful tools to test the toxicity of different chemicals (Seiler and Spielmann 2011; Stummann and Bremer 2008; Stummann et al. 2008, 2009). An important feature of ESC for the use in screening of developmental toxicants is their ability to recapitulate in vivo development in vitro. It has been shown, that the expression of different markers in mouse ESC differentiating into the neural lineage closely resembles the onset of 24 Chapter B – General introduction expression of those markers during in vivo development (Barberi et al. 2003). Furthermore, it has been shown that in vitro differentiation of ESC responds to morphogens and growth factors such as sonic hedgehog (SHH) or retinoic acid (RA) in similar ways, as in vivo (Cazillis et al. 2006; Murry and Keller 2008; Okada et al. 2008). The potential of this new technology in toxicology has therefore also been taken up by large pharmaceutical companies like ROCHE, which use stem cells to screen drugs for cardiotoxicity and effects on neurogenesis (Baker 2010) as well as funding agencies like the European Union, which fund large consortia such as ESNATS to develop robust ESC-based assays to screen for toxic compounds (Wobus and Loser 2011).
Table 2: Comparison of mouse and human ESC modified from (Wobus and Boheler 2005) marker expression mouse ES cells human ES cells reference Oct3/4 + + (Pesce et al. 1999; Thomson et al. 1998) Nanog + + (Chambers et al. 2003; Mitsui et al. 2003) Sox2 + + (Avilion et al. 2003; Ginis et al. 2004) SSEA1 + - (Ginis et al. 2004; Solter and Knowles 1978) SSEA3/4 - + (Ginis et al. 2004) TRA-1-60/80 - + (Ginis et al. 2004) morphology high nucleo-cytoplasmatic ratio (Wobus and Boheler 2005) in vitro growth tight round clumps flat loose colonies (Wobus 2001) characteristics Teratoma + + (Thomson et al. formation in vivo 1998; Wobus et al. 1984) regulation of self LIF, BMPs FGF2, feeder cells or (Wobus and Boheler renewal matrigel 2005) differentiation pluripotent Pluripotent, able to (Draper and Fox potential differentiate into 2003; Odorico et al. trophoblast-like cells 2001)
As part of this large European consortium we, and especially myself during my PhD thesis, developed ESC-based assays which are able to detect developmental, particularly neurodevelopmental, toxicants. The results of my thesis are included in the following 3 chapters each representing an individual publication.
25 Chapter B – General introduction
As already mentioned at the beginning this general introduction aimed to bridge current knowledge about key events in neurodevelopment with toxicological concepts. Other important aspects for the work presented here such as neural crest markers and function as well as toxicity of MeHg or compounds like CEP-1347 are well introduced and discussed in the respective sections of the following chapters (including 2 accepted publications and 1 submitted manuscript).
26 Aims of the thesis
Aims of the thesis
Only very few chemical substances in our environment and in consumer products are fully characterized for their toxicity. Developmental neurotoxicity (DNT) is currently the least examined form of developmental toxicity. If at all, chemicals are tested for DNT in vivo according to the OECD guideline TG 426 (Makris et al. 2009). Subtle chemically-induced changes in e.g. cell positioning (migration) or cell patterning may result in a complex phenotype like reduced IQ. Such phenotypes are extremely difficult to assess in vivo and even harder in vitro. A recently published review reported testing of about 100 substances, mainly pesticides, and another study reported neurobehavioral risk assessment for 174 compounds (Makris et al. 2009; Middaugh et al. 2003). Apart from this small group, the chemicals in our environment have not been tested for DNT (Grandjean and Landrigan 2006). In addition, a clear association with human DNT has been shown in epidemiological studies only for a handful of chemicals such as some heavy metals (arsenic, lead, mercury), polychlorinated biphenyls (PCBs), solvents (alcohol, toluene), and pesticides (Grandjean and Landrigan 2006; Walkowiak et al. 2001). For about 100 additional chemicals, developmental toxicity can be inferred from animal studies (Crofton et al. 2011). To put it in a nutshell, our knowledge about the DNT potential of the chemical universe is extremely limited. To address this issue, the work described in this thesis was undertaken to develop new toxicological test systems based on the differentiation of embryonic stem cells into the neural lineage. The aims of this thesis were:
1. to characterize in vitro neural differentiation of embryonic stem cells according to the requirements of a toxicological test system
2. to develop differentiation protocols and test systems to model the different steps of neural development
3. to validate these test systems by using pharmacological tool compounds known to affect the processes modeled in the test systems and by providing a mechanistic rational for their action
4. to optimize test systems to detect functional effects of known developmental neurotoxicants
27
28 Chapter C – Coordinated waves of gene expression during neuronal differentiation of embryonic stem cells as basis for novel approaches to developmental neurotoxicity testing
Chapter C
Coordinated waves of gene expression during neuronal differentiation of embryonic stem cells as basis for novel approaches to developmental neurotoxicity testing
Bastian Zimmer1, Philipp B. Kuegler1, Birte Baudis1, Andreas Genewsky1, Vivek Tanavde2, Winston Koh2, Betty Tan2, Tanja Waldmann1, Suzanne Kadereit1, and Marcel Leist1
1Doerenkamp-Zbinden Chair for In Vitro Toxicology and Biomedicine, University of Konstanz, D-78457 Konstanz, Germany 2Bioinformatics Institute, 30 Biopolis Street, #07-01, 138671 Singapore, Singapore
Cell Death Differ. 2011 Mar;18(3):383-95. Epub 2010 Sep 24
29 Chapter C – Coordinated waves of gene expression during neuronal differentiation of embryonic stem cells as basis for novel approaches to developmental neurotoxicity testing
Abbreviations CNS: central nervous system DNT: developmental neurotoxicity DoD: day of differentiation EB: embryoid body ESC: embryonic stem cells GO: gene onthology mESC: murine embryonic stem cells N: gene onthology neuronal differentiation NPC: neural precursor cell RA: all-trans retinoic acid Shh: sonic hedgehog
30 Chapter C – Coordinated waves of gene expression during neuronal differentiation of embryonic stem cells as basis for novel approaches to developmental neurotoxicity testing
Abstract
As neuronal differentiation of embryonic stem cells recapitulates embryonic neurogenesis, disturbances of this process may model developmental neurotoxicity (DNT). To identify the relevant steps of in vitro neurodevelopment, we implemented a differentiation protocol yielding neurons with desired electrophysiological properties. Results from focused transcriptional profiling suggested that detection of non-cytotoxic developmental disturbances triggered by toxicants such as retinoic acid or cyclopamine was possible. Therefore, a broad transcriptional profile of the 20-day differentiation process was obtained. Cluster analysis of expression kinetics, and bioinformatic identification of overrepresented gene ontologies revealed waves of regulation relevant for DNT testing. We further explored the concept of superimposed waves as descriptor of ordered, but overlapping biological processes. The initial wave of transcripts indicated reorganization of chromatin and epigenetic changes. Then, a transient upregulation of genes involved in the formation and patterning of neuronal precursors followed. Simultaneously, a long wave of ongoing neuronal differentiation started. This was again superseded towards the end of the process by shorter waves of neuronal maturation that yielded information on specification, extracellular matrix formation, disease- associated genes, and the generation of glia. Short exposure to lead during the final differentiation phase, disturbed neuronal maturation. Thus, the wave kinetics and the patterns of neuronal specification define the time windows and endpoints for examination of DNT.
31 Chapter C – Coordinated waves of gene expression during neuronal differentiation of embryonic stem cells as basis for novel approaches to developmental neurotoxicity testing
Introduction
Ultimately, the entire complexity of the mammalian central nervous system (CNS) is generated during ontogenesis from a few single cells. Neuronal generation and differentiation, can be recapitulated by embryonic stem cells (ESC) under appropriate culture conditions (Abranches et al. 2009; Barberi et al. 2003; Conti and Cattaneo 2010; Gaspard et al. 2008; Götz and Huttner 2005; Kuegler et al. 2010)}. ESC-based studies of neurodevelopment allow investigations not easily possible in vivo (Leist et al. 2008a) .However, known differentiation protocols differ in their suitability for toxicological studies. For instance, older protocols involve a step of embryoid body (EB) formation (Strübing et al. 1995). Frequently, only a small number of the initially-present ESC form neurons and the observation of individual cells is hardly possible. Other protocols use co-cultures with stromal cell lines to differentiate ESC towards neurons, and would therefore introduce additional complexity into models for developmental neurotoxicity (DNT). A recently developed monolayer differentiation protocol allows monitoring of the differentiation procedure and of possible effects of different chemicals during the whole period of differentiation on a single cell level (Ying and Smith 2003). DNT is the form of toxicity least examined and hardest to trace, as it is not necessarily related to cell loss. Less than 0.1% of frequently used industrial chemicals have been examined, and for the few known toxicants the mechanism of action is still elusive (reviewed in (Bal-Price et al. 2009; Grandjean and Landrigan 2006; Makris et al. 2009)). Behavioral pathology in the absence of cell loss is also known from disease models, e.g. for Huntington’s disease (Hansson et al. 1999) or schizophrenia (Penschuck et al. 2006). Toxicants, such as mercury or lead may trigger behavioral or cognitive deficits without histophathological hallmarks (Grandjean and Landrigan 2006). Cellular physiology may be affected during the period of exposure (Rossi et al. 1993). This may eventually lead to changes in differentiation and patterning in the CNS, which is the basis for long term effects that are observed after the exposure to toxicants has ceased. CNS development is assumed to be orchestrated by waves of gene expression (Aiba et al. 2006; Wei et al. 2002) that determine different intermediate cell phenotypes. Some periods may be more sensitive to certain toxicants than others. Epidemiological proof for such “windows of sensitivity” in organ development with long term consequences for the organism
32 Chapter C – Coordinated waves of gene expression during neuronal differentiation of embryonic stem cells as basis for novel approaches to developmental neurotoxicity testing
comes from thalidomide exposure in man (Kuegler et al. 2010) and various animal models (Jongen-Relo et al. 2004). Current test systems based on the differentiation of stem cells to either cardiomyocytes (Marx-Stoelting et al. 2009) or neural cells (Bal-Price et al. 2009) neither yield mechanistic info, nor do they account for the complexity of CNS development, i.e. the establishment of a balance between multiple neuronal cell types (Kuegler et al. 2010; Rao and Jacbson 2005). The “toxicology for the 21st century” initiative (Collins et al. 2008; Leist et al. 2008b) suggests the identification of pathways as opposed to the current black-box test systems. In the case of ESC-based models of DNT, this requires a detailed understanding of the developmental process leading to multiple different cell types. Detailed knowledge on the waves of gene induction controlling different developmental steps would be an essential prerequisite. However, CNS development is proceeding at different paces. For instance, the anterior and posterior part of the neural tube follow different kinetics, and some regions of the CNS continue neurogenesis, while in other regions cells have already reached fully postmitotic stages (Rao and Jacbson 2005). Our study was undertaken to analyze the wave-like expression pattern of mESC neurodevelopment as a basis for the definition of test windows and markers. This knowledge should help to identify non-cytotoxic, but neuroteratogenic compounds able to shift neuronal composition or phenotypes. Finally, the markers should distinguish multiple cell types and differentiation stages, and be able to indicate subpopulations of cells.
33 Chapter C – Coordinated waves of gene expression during neuronal differentiation of embryonic stem cells as basis for novel approaches to developmental neurotoxicity testing
Results
Monolayer differentiation of mESC to neurons
On day-of-differentiation 20 (DoD20), the majority of cells was positive for the pan-neuronal markers Tuj1 and NeuN. Many cells also expressed the synapse associated markers SV2 and PSD95 (Fig. 1A). As a more quantitative overall measure for the robustness of the differentiation protocol, we chose mRNA expression, which we followed over time. The
Figure 1. Protein and mRNA-based markers of robust neuronal differentiation of mESC.
A. Cultures of mESC were fixed and stained on day 20 of differentiation. DNA, (blue) was stained with H-33342. Proteins are indicated as text on the micrograph in the same color as used for the display of their staining pattern. Tuj1: neuronal form of beta-III tubulin; NeuN: nuclear neuron-specific nuclear antigen, encoded by fox3) (Kim et al. 2009); GAD: glutamate decarboxylase; SV2: synaptic vesicle glycoprotein 2a; PSD95: post-synaptic density protein 95. Scale bars: 20 µm. B. mESC cultures (n = 5 biological experiments) were differentiated towards neurons, and RNA was prepared at the indicated days of differentiation. Gene expression of the stemness factor Oct4, ne neural stem cell marker Nestin, the mature neuronal marker Synaptophysin and the glial marker Gfap was quantified by quantitative RT-PCR. The means ± SD of the relative expression compared to day 0 (set to 1 on each diagram) was calculated and displayed (dotted lines). Relative gene expression data were also obtained by chip analysis and the means (n = 2) are displayed (solid line). Note the different scaling of the axes for chip or RT-PCR analysis, respectively, which was chosen for reasons of better comparability of the overall curve shapes. The figures in the diagram indicate the relative expression level on DoD20 (DoD7 for nestin) vs DoD0, and thus define the axis scaling. kinetics for different markers were highly reproducible across experiments (Fig. 1B). Differentiation to mature, electrophysiologically-active neurons was shown by the presence of voltage-dependent Na+ and K+ and Ca2+ channels in individual patch-clamped neurons (Fig. 2A-C, Fig. S1). Further experiments also identified spontaneous neuronal electrical activity (Fig. 2D) and action potentials (Fig. S1). Currents were also evoked by exposure to N-methyl- D-aspartate or kainic acid and blocked by the respective selective antagonists (Fig. 2E). Thus, our differentiation protocol yielded bona fide neurons. 34 Chapter C – Coordinated waves of gene expression during neuronal differentiation of embryonic stem cells as basis for novel approaches to developmental neurotoxicity testing
Transcription-based endpoints to identify disturbed neuronal differentiation
We next investigated whether subtle perturbations of the differentiation process below the cytotoxicity threshold would be detectable by mRNA-based readouts. Parallel mESC cultures
Figure 2. Electrophysiological evidence for successful neuronal development. Cells were differentiated on glass cover slips towards the neuronal lineage for 20-24 days and then placed into a temperature controlled recording chamber for whole cell patch-clamp studies. A. Representative example for the currents observed during the 20 ms voltage steps of the whole cell voltage clamp recording protocol displayed in B. Note that Na+ currents (downwards deflection) are observed at voltages ≥ -40 mV (solid line). Strong depolarizing and repolarizing (K+ currents; upwards deflection) are observed at depolarization to 0 mV (dashed line). C. For voltage clamp recording (voltage step from – 80 mV to 0 mV) of Ca2x channels Na+ and K+ channels were blocked by addition of tetrodotoxin, tetraethylammoniumchloride (5 mM), 4- aminopyridine (10 mM), and substitution of intracellular K+ ions by 120 mM Cs+. Moreover, the measurement of Ca-currents was favoured by a bath solution containing barium ions (10 mM) instead of calcium ions. Current traces were obtained without Ca2+-channel blocker, or with the blockers nimodipine (1 µM) or Cd2+ (1 mM) added. Current data at 15 ms after the voltage step were corrected for cell capacitance (indirect measure for cell size) and displayed. Data represent means ± SD. ** p < 0.01. D. Spontaneous action potentials were recorded in current clamp mode (0 pA). At the time indicated by an arrow, tetrodotoxin was added. The dashed line indicates 0 mV membrane potential. The scale bars indicate the dimensions of the membrane potential and the time domain. E. Recordings at individual neurons excited with specific glutamate receptor agonists in the presence or absence of blockers. Current traces were recorded after application of N-methyl-D- aspartate (NMDA) or kainic acid. All agonists were also tested in the presence of their respective specific antagonist (traces with 5-aminophosphovalerate (AP- 5), 6,7-dinitroquinoxalin-2,3-dione (DNQX)). The scale bars represent the current and time dimensions of the experiment. Data are representative for n ≥10 neurons (for agonists) and n = 3 for antagonists (on neurons with positive agonist response). were differentiated for 7, 15 and 20 days and mRNA was prepared for quantitative RT-PCR analysis. These cells were treated during two different time windows (DoD1-7, DoD8-15) with two neuro-teratogens (Fig. 3A). With the concentrations used here cell death was not detectable (data not shown) and cells looked viable and were morphologically
35 Chapter C – Coordinated waves of gene expression during neuronal differentiation of embryonic stem cells as basis for novel approaches to developmental neurotoxicity testing
indistinguishable from untreated cells (Fig. 3B). We used the morphogen retinoic acid (RA) as a known in vivo and in vitro reproductive toxicant and cyclopamine for its ability to alter
Figure 3. Detection of non-cytotoxic developmental disturbances by transcriptional analysis Cultures of mESC were neuronally differentiated for 7, 15 or 20 days as indicated in a-d. They were exposed to retinoic acid (RA) or cyclopamine (Cyclo) for the time periods indicated by the hatched boxes. A. RNA was isolated at the indicated days (diamond) and used for quantitative RT-PCR analysis of selected differentiation and patterning markers. Headings indicate the overall biological effect, such as accelerated neuronal differentiation (e.g. Neuronal diff. (+)) or altered patterning (e.g. Caudalization). Names are the official gene names, apart from the following: Vglut1 = Slc17a7, HB9 = Mnx1. The data indicate relative expression levels in % compared to untreated controls at the same time point, and are means ± SD from two to three independent experiments for each treatment and exposure schedule. Significance levels (by ANOVA within a given experimental condition) are indicated (*: p < 0.05, **: p < 0.01, ***: p > 0.001). The complete data set with standard deviations is given in Figure S2 B. Representative images of cultures on DoD15 in condition a. RA and Cyclopamine-treated cultures were viable indistinguishable from controls (ctrl.). sonic hedgehog (Shh) signaling resulting in the disruption of patterning gradients responsible for floor plate and ventral neurons (Gaspard et al. 2008; Rao and Jacbson 2005). As expected from the literature (Irioka et al. 2005), RA induced accelerated neuronal differentiation (increased synaptophysin expression) whereas cyclopamine reduced the expression of 36 Chapter C – Coordinated waves of gene expression during neuronal differentiation of embryonic stem cells as basis for novel approaches to developmental neurotoxicity testing
markers typical for more ventrally-located neurons like Shh, Nkx2.1 and Dlx1 but not overall neuronal differentiation (Fig. 3A). Thus, marker genes can indicate subtle shifts in differentiation patterns not visible morphologically. When cultures were exposed to cyclopamine from DoD1-7 and immediately analyzed thereafter, treatment did not affect the overall formation of NPC, but the reduced Shh expression suggested a reduced ventral development. In cells left to differentiate further without the compound, reduced Shh expression was still observable on DoD15. A shift of neurotransmitter phenotype from GABAergic (Gad2 as marker) to glutamatergic (vglut1 as marker) (Gaspard et al. 2008) was not observed after treatment for the first seven days, but a significant decrease of Gad2 (more ventrally prominent) was observable when the cells were treated between DoD8 and 15. In the case of RA, the acceleration of development (synaptophysin) was already significant at early stages and we found upregulation of markers usually expressed in caudal parts of the neural tube (HoxA6, Hb9), and associated with the development of motor neuron precursors (Isl1) (Fig. 3). We also examined whether inhibited differentiation was detectable by RNA markers. Early exposure to 3i, a kinase inhibitor mix known for inhibiting differentiation of mESC (Ying et al. 2008), resulted in cultures with retarded neural differentiation indicated by a decreased expression of Hes5, nestin and betaIIItubulin and an increased expression of Oct4 (Fig. S2). Treatment of cells with 3i after DoD7 (after neural differentiation had been initiated) did not return them to the stem cell state, but was cytotoxic. These examples demonstrated the usefulness of transcript profiling for detection of patterning disturbances. It may be necessary to measure the impact on differentiation at different DoD, and specific markers need to be selected for each stage.
Identification of clusters of genes regulated during neuronal differentiation of mESC
Using oligonucleotide microarrays, we analyzed changes in the transcriptome over time to identify toxicity markers. The differentiation kinetics of the cultures used for microarray analysis matched the ones observed during many other well-controlled experiments (Fig. 1B).
37 Chapter C – Coordinated waves of gene expression during neuronal differentiation of embryonic stem cells as basis for novel approaches to developmental neurotoxicity testing
The kinetics of expression of each gene represented on the chip was used as input for an unbiased clustering analysis, which yielded eight regulation profiles (Fig. 4A, S3), besides the genes not regulated at all. Cluster Ia was characterized by rapid, and cluster Ib by slow downregulation. These two clusters exemplify the principle of superimposed gene regulation
Figure 4. Cluster analysis of mRNA time course profiles, and their association with distinct phases of differentiation . A. Gene expression kinetics were determined for all genes represented on the chip. An unbiased clustering analysis of the kinetic profiles of all regulated genes was performed. For each cluster (named Ia, Ib, IIa, IIb, IIIa, IIIb, IV, V), the means of the absolute expression level of all genes in the respective cluster, for each analysis time point is displayed and plotted on a logarithmic scale; n: number of genes in the cluster B. Number of genes expressed in mESC (ESC = 40 analyzed, 33 found), neural precursor cells (NPC = 73 analyzed, 63 found) and developing neurons (N) were analyzed by extensive literature search (mESC, NPC) or GO-analysis (N). The relative distribution of these genes across the different clusters was calculated (in %) and displayed (e.g. 65% of all ESC markers were found in cluster Ia, 35% of all N markers in cluster III).
waves with different amplitudes. Clusters IIa and IIb contained genes that were transiently regulated at DoD7 (IIa: up, IIb: down). Cluster IIIa and IIIb were characterized by a rapid increase of transcripts between day 0 and DoD7 maintained then at high levels. Cluster IV contained genes, which remained low until DoD7 and then reached high levels on DoD15. The final cluster V comprised transcripts that were hardly upregulated until DoD15, and reached their maximum on DoD20 (Fig. 4A). The genes were subjected to a more detailed analysis. Of 40 genes that characterize the initial mESC stage (Kuegler et al. 2010), 33 were identified and all were downregulated. All mESC markers identified on the chip were found to be downregulated during differentiation (Fig. 4B). Most neural precursor cell (NPC) markers were found in clusters IIa and IIIa/b, containing genes upregulated early. In contrast to this, most neuronal markers were found in 38 Chapter C – Coordinated waves of gene expression during neuronal differentiation of embryonic stem cells as basis for novel approaches to developmental neurotoxicity testing
the clusters with increasing gene expression (III-V), while about 20% were found in cluster IIa (transient upregulation on DoD7) (Fig. 4B). The clusters identified by unbiased bioinformatics methods may therefore correspond to waves of real biological processes underlying the differentiation of mESC to neurons. To explore this working hypothesis, we continued with an analysis of the biological significance of genes in individual clusters.
Figure 5. Indication of a progressive change in chromatin organization and epigenetic factors in waves of fast and slow downregulation Gene lists of relevant processes were assembled both with the help of the GO data base and extensive literature search. The clusters were then queried for the presence of these genes. A. Processes linked to chromatin or DNA-repair and –replication are displayed, and for each of them the number of genes found to be regulated during neuronal differentiation of mESC is displayed in brackets. The individual genes are listed in Figure S4. Among the identified genes, four (Smarca1, Myst4, Jmjd3 and Hdac11) are known to be neurospecific, and five (Suz12, Ezh2, Bmi1, Cbx2 and Cbx8) are components of the polycomb repressor complexes (PRC), which play an important role in differentiation-related control of gene promoters. These genes could serve as sensitive markers to detect negative effects of compounds on early developmental processes. For each process, the percentage of genes present in the different clusters is indicated by colour-coded pie charts. All green shades represent clusters of genes downregulated from DoD0 to DoD20. B. Changes in chromatin structure during differentiation were visualized by DNA staining with DAPI (green) and confocal microscopic analysis. Left panel: undifferentiated mESC; right panels: neuronally differentiated cells on DoD20 that were stained with neuron-specific betaIIItubulin antibody (red). Scale bar: 10 µm.
39 Chapter C – Coordinated waves of gene expression during neuronal differentiation of embryonic stem cells as basis for novel approaches to developmental neurotoxicity testing
Loss of pluripotency is accompanied by progressive changes in transcripts responsible for chromatin organization and DNA/cell cycle functions
Genes in cluster I were analyzed for GO categories significantly overrepresented. Besides the cell cycle, we found chromatin structure and epigenetic processes to be affected (Fig 5A, S4). All genes known to be associated with chromosome structure, DNA replication, DNA repair and DNA methylation were downregulated. Also, most of the genes coding for histones, histone modifiers, chromatin remodeling and chromatin substructuring were found in clusters Ia/b. Confocal microscopy showed that chromatin distributed relatively homogeneously over the nucleus in mESC, but was organized entirely differently after 20 days of differentiation (Fig. 5B).
Correlation of neural precursor formation with a strong, transient change of gene expression levels
We examined whether genes of cluster II were specifically linked to the process of neural precursor cell (NPC) formation. Nestin was expected, and found, in cluster IIa. Nestin- positive cells were often arranged in ring-like structures, reminiscent of rosettes, or two-
Figure 6. Correlation of neural precursor formation with a transiently upregulated group of genes.
A. On DoD7, cultures were immunostained for the neural stem cell marker nestin (green) and DNA (red). Scale bar: 100 µm B. For quantification of nestin-positive NPC, cells were immunostained for nestin on DoD7, and analyzed by flow cytometry. Data are means ± SD of 7 independent differentiations. ***: p < 0.001. C. Relative expression profiles of genes from cluster IIa were calculated by normalization of expression of each gene to DoD0 expression, which was arbitrarily set to 1. The expression kinetics for each gene within that cluster are displayed. D. Genes upregulated during neuronal differentiation of mESC were analyzed for their role in regional specification of the brain and classified accordingly (colour-coding). The number of genes associated with each of the three chosen subregions of the brain, are displayed separately for each regulation cluster. A detailed list of genes with their regional assignment is given in figure S5.
40 Chapter C – Coordinated waves of gene expression during neuronal differentiation of embryonic stem cells as basis for novel approaches to developmental neurotoxicity testing
dimensional neural tubes (Elkabetz et al. 2008; Zhang et al. 2001) (Fig. 6A). Quantification by flow cytometry analysis confirmed the immunocytochemical finding that about 80% of all cells in the culture became nestin-positive (Fig. 6B). High synchronization of differentiation was suggested by the sharp expression profile of genes in cluster IIa (Fig. 6C). Besides nestin, many other genes typically associated with neuroepithelial precursors (NPC) and neurogenesis were found in cluster IIa (Fig. S3). Also some genes associated with early, but definitive neuronal development were identified (Dll1, Hes3). Cluster IIa also contained apparently unspecific genes (e.g. Jak2, Foxd4, Bcl-2, Kif21a, Agtr1a, Moxd1, Aacs, Arl2bp, and Scd2). We examined which gene ontology (GO) categories were statistically overrepresented by cluster IIa genes. The GO “nervous system development” emerged with a p-value < 10-13, and only neuronal/neurodevelopmental GOs were identified with the exception of ossification (eight weakly significant genes) (Table 1). Thus, genes of cluster IIa represent an important endpoint for testing of disturbed proliferation and differentiation during the neuroectodermal/neuronal development time window.
Table 1: GO categories significantly overrepresented in cluster IIa
* All categories identified by gProfiler bioinformatics analysis, with their p-values indicated after correction by removal of “nervous system development” genes from non-neuronal GOs.
Markers of regional fate decisions in the CNS
We examined the expression of regional markers in cluster IIa. Amongst the few markers expressed, those for forebrain (Foxg1) and hindbrain (Hoxa2/b2) were evenly distributed (Fig. 6D). Also in the clusters containing continuously upregulated genes (IIIa/b, IV+V), forebrain (Reln), midbrain (En1/2), and hindbrain (Lmx1a or Hoxa1) markers were evenly distributed (Fig. S5). Accordingly, our experimental model appears to reflect several parallel
41 Chapter C – Coordinated waves of gene expression during neuronal differentiation of embryonic stem cells as basis for novel approaches to developmental neurotoxicity testing
lines of in vivo neural specification, and the ratios of expression of different patterning markers may provide sensitive indicators of disturbed neurodevelopment.
Specificity for neuronal induction with respect to glial cells
The transcriptional profile allowed us a detailed analysis of potentially contaminating non- neuronal cells. Some small GFAP-positive cell areas were reproducibly (1-2 small
Figure 7. Analysis of glia-associated genes
A. DoD20 cultures were fixed and stained for GFAP (green; to identify astrocytes) and Tuj1 (red; to identify neurons). The left image shows a representative overview with large neuronal areas and one typical astrocytic island. The right image shows an astrocytic island in greater detail. Scale bars = 100 µm. B. The table in the bottom part indicates the glia-related genes identified in this study, sorted by the cluster of expression kinetics they fell into. Astrocyte-related genes searched for, but not identified here were glutamine synthetase (Glul), S100b, Slc1a2 (Glt-1, Eaat2), Connexin 30/43 (Gjb6/Gja1), NfiA (also found in oligodendrocytes). Oligodendrocyte-related genes not found here were ATP-binding cassette, sub- family A (Abca2), CNPase (Cnp1), a microtubule- associated protein (Mtap4), myelin-glycoproteins (Omg and Mog), Olig2/3 (Olig2, Olig3), myelin protein zero (Mpz), Ng2 (Cspg4), NfiA. C. Expression of selected astrocyte-related genes was monitored by qPCR. on day 0, 7, 15 and 20 of two differentiations. Data for each differentiation are given individually. The lines indicate the respective mean values.
islands/cm2) identified by immunocytochemistry (Fig. 7A). As an unbiased search for overrepresented GO categories did not result in any hits related to gliogenesis (Table 2), we used a list of 25 astrocyte-related genes (Kuegler et al. 2010) and found 11 of them to be upregulated on DoD20 compared to DoD20 (Fig. 7B). The early upregulation of 4 apparent astrocytic markers may be due to the generation of radial glia-like NPC at DoD7. This cell type, as exemplified by the upregulation of Fabp7 in cluster IIIb (Feng et al. 1994) or Ascl1 (=Mash1) (Battiste et al. 2007) shares many markers with astrocytes (Götz and Huttner 2005).
42 Chapter C – Coordinated waves of gene expression during neuronal differentiation of embryonic stem cells as basis for novel approaches to developmental neurotoxicity testing
The late upregulation of astroglial markers was corroborated by qPCR (Fig. 7C). Small increases of this astrocytic population may affect toxicity testing during the later differentiation phases. Microglia appeared to be absent. The contribution of oligodendrocytes appears to be negligible.
Specificity for neuronal induction with respect to other germ layer lineages
All GO categories significantly overrepresented by the genes of clusters III-V (upregulation on DoD20 vs. DoD0) were determined bioinformatically, and searched for evidence of non- neural cell type formation. Individual clusters did not indicate any non-neural cell types while representation of neuronal GOs was highly significant (Table 2). Upon pooled analysis of clusters IV and V, the GOs “blood vessel development” and “muscle organ development” emerged as significant. Thus a subpopulation of cells present on DoD20 may display smooth muscle features.
Table 2: GO categories that are overrepresented in the clusters comprising genes upregulated during differentiation
* All categories identified by gProfiler bioinformatics analysis, with their p-values indicated after correction by removal of “nervous system development” genes from non-neuronal GOs 43 Chapter C – Coordinated waves of gene expression during neuronal differentiation of embryonic stem cells as basis for novel approaches to developmental neurotoxicity testing
Waves of clustered genes related to neuronal induction
For characterization of the cultures, we used non-biased bioinformatics methods to identify overrepresented GOs (Table 2). In a complementary approach, based on literature and expert judgment we hand-picked interesting groups of genes (Fig. 8). The major result from this combination of strategies was our finding that the differentiation did not proceed as sequence of sequential steps, but rather involved strongly overlapping processes with one underlying large wave (cluster IIIa/b) superseded by shorter waves (cluster IV and V). For instance, generation of neurons and axogenesis/growth cone formation seemed to be ongoing in the entire period from DoD7 to DoD20 as indicated by groups of neuroreceptors and growth cone/axon guidance-related genes in cluster III (Fig. 8A, B). A larger group of genes associated with synaptic vesicles or the transmission of nerve impulse only appeared later (cluster IV/V). In the latest phase, genes associated with “responses to stress” and “hormonal stimuli”, “regulation of extracellular matrix components“ and genes known to be “associated with hereditary neurodegenerative diseases” were strongly up-regulated (Table 2, Fig 8C). Reanalysis by PCR confirmed the latter finding. The regulation factors for disease associated genes from DoD0 to DoD20 (n = 2 differentiations) were: 92-fold and 16-fold for the Alzheimer’s disease associated genes App and Mapt, 273-fold for the schizophrenia- associated gene Nrnx1, 91-fold for the prion protein Prnp, and 19-fold/56-fold for the Parkinson’s disease-related genes Pink1/ Snca. We wondered whether toxicants with a purported role in the developmental origins of neurodegenerative diseases (see Fig. S6), such as lead affect this very late phase of neuronal differentiation. The transcript levels of two neuronal markers and the set of disease associated genes were used to examine differences in differentiation. Lead exposure had a dampening effect on the expression of App, Mapt, Nrnx1 and Prnp (Fig. 8D). Thus, the knowledge on markers together with that of the expected timing of their expression provides an ideal toolkit for fine-mapping of subtle developmental disturbances.
44 Chapter C – Coordinated waves of gene expression during neuronal differentiation of embryonic stem cells as basis for novel approaches to developmental neurotoxicity testing
Discussion
We have here demonstrated a concept of overlapping waves of gene regulation and suggested its use to define protocols, test windows and endpoints for developmental neurotoxicity testing. Our findings should be helpful to close a gap between two highly developed, but isolated disciplines: experimental developmental neurobiology and toxicology. The former
Figure 8. Functional assignment of neuronal genes up-regulated in different waves
A combination of bioinformatics tools and literature information was used to search all upregulated clusters for conspicuous biological themes and for genes associated with them. Themes are displayed, and corresponding genes (with original NCBI gene names) are colour-coded according to the clusters they were found in (displayed graphically besides the legend, with dots on the lines representing DoD0, DoD7, DoD15 and DoD20). A. Core neurochemical themes. Note a relatively early induction of receptors and channels, compared to late emergence of genes coding for transporters and synaptic vesicles, and those related to neurodegenerative disease. B. Themes related to neurite growth indicate an early focus on growth cone formation and guidance. C. Genes related to extracellular matrix are displayed. D. The cells were treated with a non cytotoxic concentration (assessed by resazurin reduction and LDH release, data not shown) of lead (1 µM) only during the last phase of differentiation (DoD14-DoD20). RNA was isolated on DoD20 and used for quantitative RT-PCR analysis of genes associated with neurodevelopment and known to affect neuronal disease. Pink1 and Snca were not affected. Also their relative increase with respect to the pan-neuronal marker synaptophysin was not significant The data indicate relative expression levels in % compared to the untreated controls of the first differentiation on DoD20, and are means ± SD (n = 2). Significance levels (ANOVA) are indicated (*: p < 0.05, **: p < 0.01, ***: p > 0.001).
45 Chapter C – Coordinated waves of gene expression during neuronal differentiation of embryonic stem cells as basis for novel approaches to developmental neurotoxicity testing
has been highly successful in defining the functional importance, regional expression and cell type association of genes. The latter has an urgent need for robust and sensitive marker genes to identify disturbances of development. We showed that subtle changes in the speed of differentiation, or in dorso-ventral or anterior-posterior patterning due to toxicants can be detected by using the right choice of mRNA markers. Such changes may be considered in vitro correlates of known teratogenic effects of the chosen compounds. For instance, cyclopamine causes dramatic patterning disturbances (holoprosencephaly) in a defined period of brain development; and retinoic acid causes shifts in the anterior-posterior axis organization favoring the more posterior parts, as found here by transcript markers. Lead affects multiple neuronal types, which is in agreement with the broad pattern of disturbances found here (see Fig. S6 for references). The data also suggest some warning on the limitations of in vitro – in vivo correlations. Although our cyclopamine data suggest a disturbance in patterning, they would not indicate a problem in the separation of the forebrain hemispheres, as observed in animal studies. Thus, observations from stem cell systems will have a major value for raising alerts on certain compounds and pinpointing potential mechanisms, while complementary data from other systems may be required to predict specific effects on humans. Transcriptional profiling studies relying predominantly on bioinformatics analysis, suffers from the weakness and errors of data bases and algorithms. For example, assignment of genes to GO categories is not always perfect. For instance, the GO for gliogenesis contains ubiquitous signaling and metabolic molecules as well as highly specific transcription factors. On the other hand, typical astrocyte markers such as Gfap and glutamine synthetase are not members of this GO. Moreover the equal weight given to ubiquitous vs. specific genes in statistical analysis results in biological skewing. An additional problem is the visualization of the large amount of data in a form that generates meaningful knowledge. With these considerations in mind, we chose to combine bioinformatic analysis with classical knowledge-based approaches. During this procedure, the entire hit list of several thousand genes was manually screened, sorted and annotated. A consortium of experts was consulted, and results were compiled in an open access review format (Kuegler et al. 2010). We strongly advocate such combined approaches for toxicological systems biology, which is at present driven too strongly by computational methods (Hartung and Hoffmann 2009; Leist et al. 2008b).
46 Chapter C – Coordinated waves of gene expression during neuronal differentiation of embryonic stem cells as basis for novel approaches to developmental neurotoxicity testing
Electrophysiology studies have a rather qualitative character, as the cells that were patched may not represent the entire culture. However, our results fully corroborate earlier findings that functional neurons can be generated from mESC (Lee et al. 2000; Strübing et al. 1995). Immunostaining and quantitative RT-PCR were used as classical and established methods to link chip-based transcript profiling to other experiments that have been performed with much higher replicate number. In the future, extensive studies, involving RT-PCR controlled by internal standards, will be necessary for a quantitative definition of a final set of markers. Notably, we did not use differences in absolute numbers of regulations in the present study as basis for any of our conclusions, and all major conclusions are built on groups of co-regulated and biologically linked genes as opposed to speculations based on the presence or absence of a single gene. Even though mRNA correlated well with protein levels, as e.g. in brain inflammation studies (Lund et al. 2006), our approach should not be interpreted as phenotype definition on single cell resolution. The genes grouped within the clusters described here are not necessarily expressed in the same cell and therefore do not automatically describe a single biological entity. However, with these caveats, we feel that indicators of disturbances of the default development can be selected with confidence on the basis of our study. In the area of developmental toxicology and especially in DNT, cause-effects relationships are still mostly unknown, and human epidemiological data are only available for a handful of industrial chemicals (Grandjean and Landrigan 2006). Rodent data based on the OECD test guideline 426 are available for about 200 substances (Makris et al. 2009). With this lack of human-relevant information and the better animal data base, it appears reasonable to us to perform proof-of-principle experiments for the usefulness of a new approach in rodent cells first, and to validate human cells against these in case of a positive outcome. At present, DNT studies are based on e.g. behavioral, cognitive or neuropathological endpoints, and the next step towards mechanistic information would be an understanding of changes on the level of cells and gene expression. The overlapping waves defined here would provide a conceptual framework for this. Such waves (i.e. temporally and spatially shifting activation) of gene expression are known from many pioneering studies of mammalian in vivo CNS development (Rao and Jacbson 2005) and are for instance well-characterized in high density and resolution in the hippocampus (Mody et al. 2001). Waves have also been defined in vitro in mESC (Aiba et al. 2006; Schulz et al. 2009) or differentiating embryonic carcinoma cells (Przyborski et al. 2003; Wei et al. 2002). Here, we extended this concept, by relating regulation clusters to underlying biological processes important for toxicity testing.
47 Chapter C – Coordinated waves of gene expression during neuronal differentiation of embryonic stem cells as basis for novel approaches to developmental neurotoxicity testing
This translation from developmental biology to the toxicological perspective defines the windows of sensitivity relevant for test protocols. In the field of cardiac development, the mESC based embryonic stem cell test (EST) has been frequently applied (Marx-Stoelting et al. 2009). Exposure of cells during the entire test period is confounded by relatively unspecific toxicity. Therefore, separation of exposure into the proliferation and differentiation phase has been suggested (van Dartel et al. 2009). We want to expand this principle here by suggesting four relevant test periods. DoD1-7: testing of lineage commitment, efficiency of NPC formation and of epigenetic changes associated with the transition from pluripotent cells to more committed NPC. DoD8-15: major phase of neuronal patterning and vesicle development. DoD15-20: a more unexpected, but highly interesting and relevant phase, when most proliferation has ceased, and maturation becomes evident by expression of matrix components, important transporters and disease-associated genes. Our data on lead exposure during this phase show that it will be of high importance for future testing. DoD20+ has not been explored here. It requires further investigation to determine whether this period can be used as stable reference for neurotoxicity vs. DNT, or whether new processes such as synaptogenesis, gliogenesis, or myelination take a dominant role here. The major task for the future will be the validation of a larger set of such markers, first with known specific and mechanistically-defined disruptors of developmental pathways, then with known DNT compounds, in order to select the smallest group of final markers useful for a comprehensive description of toxicities triggered by the test compounds.
Conflict of interest The authors declare no conflict of interest
48 Chapter C – Coordinated waves of gene expression during neuronal differentiation of embryonic stem cells as basis for novel approaches to developmental neurotoxicity testing
Materials and Methods
Materials
Unless otherwise mentioned, cell culture media and reagents were from Invitrogen (Carlsbad, USA) and accessory reagents from Sigma. Antibodies: anti-Tuj1 (cat. # MMS-435P; Covance), anti-NeuN (cat. # MAB377; Chemicon), anti-GAD65 (GAD-6; DSHB), anti-SV2 (SV2; DSHB), anti-PSD95 (cat. # 51-6900; Zymed), anti-Nestin (cat. # MAB353; Chemicon), anti-GFAP (clone: G-A-5; Sigma) anti-Nestin-647 (clone: 25/NESTIN; BD Biosciences). mRNA Primer: Pou5f1-fw: CTCTTTGGAAAGGTGTTCAGCCAGAC, Pou5f1-re: CGGTTCTCAATGCTAGTTC- GCTTTCTC; Nestin-fw: CTGGAAGGTGGGCAGCAACT, Nestin-re: ATTAGGCAAGGGGGAAGAGAAGGATG; Synaptophysin-fw: GGGTCTTTGCCATCTTCGCCTTTG, Synaptophysin-re: CGAGGAGGAGTAGTCACCAACTAGGA; Gfap-fw: GCCCGGCTCGAGGTCGAG, Gfap-re: GTCTATACGCAGCCAGGTTGTTCTCT; Shh-fw: CAGCGGCAG- ATATGAAGGGAAGATCA, Shh-re: GTCTTTGCACCTCTGAGTCATCAGC; Hes5-fw: CCCAAGGAGAAAAACC- GACTGCG, Hes5-re: CAGCAAAGCCTTCGCCGC; Tubb3: GACAACTTTATCTTTGGTCAGAGTGGTGCTG, Tubb3- re: GATGCGGTCGGGGTACTCC; Nkx2.1-fw: TACCACATGACGGCGGCG, Nkx2.1-re: ATGAAGCGGGA- GATGGCGG; Dlx1-fw: TCACACAGACGCAGGTCAAGATATGG, Dlx1-re: AGATGAGGAGTTCGGATTCCAGCC; HoxA6-fw: CTGTGCGGGTGCCGTGTA, HoxA6-re: GCGTTAGCGATCTCGATGCGG ; Hb9-fw: CGAACCTCTTGGGGAAGTGCC, Hb9-re: GGAACCAAATCTTCACCTGAGTCTCGG; Vglut1-fw: GGTCACA- TACCCTGCTTGCCAT, Vglu1-re: GCTGCCATAGACATAGAAGACAGAACTCC; Gad2-fw: AAGGGGAC- TACTGGGTTTGAGGC, Gad2-re: AGGCGGCTCATTCTCTCTTCATTGT; Isl1-fw: ACCTTGCGGACCTGCTATGC, Isl1-re: CCTGGATATTAGTTTTGTCGTTGGGTTGC, Tubb3-fw: GACAACTTTATCTTTGGTCAGAGTGGTGCTG; Tubb3-re: GATGCGGTCGGGGTACTCC, Mapt-fw: ACACCCCGAACCAGGAGGA; Mapt-re: GCGTTGGAC GTGCCCTTCT ; App-fw: TCAGTGAGCCCAGAATCAGCTACG, App-re: GTCAGCCCAGAACCTGGTCG, Pink1-fw: GGGATCTCAAGTCCGACAACATCCT, Pink1-re: CTGTGGACACCTCAGGGGC; Snca-fw: ATGGAGTGACAACAGTGGCTGAGA, Snca-re: CACAGGCATGTCTTCCAGGATTCC; Prnp-fw: ACCATCAAGCAGCACACGGTC, Prnp-re: GACAGGAGGGGAGGAGAAAAGCA; Nrnx1-fw: GTGGGGAATGTGAGGCTGGTC, Nrnx1-re: TCTGTGGTCTGGCTGATGGGT Aqp4-fw: GCTCAGAAAACCCCTTACCTGTGG Aqp4-re: TTCCATGAACCGTGGTGACTCC Gjb6-fw: CGTACACCAGCAGCATTTTCTTCC Gjb6-re: AGTGAACACCGTTTTCTCAGTTGGC SparcL-fw: CCCAGTGACAAGGCTGAAAAACC SparcL-re: GTAGATCCAGTGTTAGTGTTCCTTCCG Slc1a3-fw: CTCTACGAGGCTTTGGCTGC Slc1a3-re: GAGGCGGTCCAGAAACCAGTC Pla2g7-fw: GGGCTCTCAGTGCGATTCTTG Pla2g7-re: CAACTCCACATCTGAATCTCTGGTCC Aldh1l1-fw: CTCGGTTTGCTGATGGGGACG Aldh1l1-re: GCTTGAATCCTCCAAAAGGTGCGG Pygb-fw: GGACTGTTATGATTGGGGGCAAGG Pygb-re: GCCGCTGGGATCACTTTCTCAG Vim-fw: GAGATGGCTCGTCACCTTCGTG Vim-re: CCAGGTTAGTTTCTCTCAGGTTCAGG Toxicants: Retinoic acid (cat. # R2625; Sigma), Cyclopamine: (cat. # 239803; Calbiochem), PD184352: (cat. # Axon 1368, axon medchem), SU5402 (cat. # 572631, Calbiochem), CHIR99021 (cat. # Axon 1386, axon medchem)
49 Chapter C – Coordinated waves of gene expression during neuronal differentiation of embryonic stem cells as basis for novel approaches to developmental neurotoxicity testing
Cell culture and differentiation
CGR8, a widely available murine ESC line suitable for feeder-free culture maintenance and with established potential to develop along the neuroectodermal and neuronal lineage (Conti et al. 2005; Suter et al. 2009) was kindly provided by K.-H. Krause (Geneva). Cells were cultured in complete Glasgow’s modified Eagles medium (GMEM), supplemented with 10% heat inactivated fetal bovine serum (FBS; PAA, Pasching, Austria), 2 mM Glutamax, 100 µM non-essential amino acids, 50 µM β-mercaptoethanol, 2 mM sodium pyruvate and 1000 U/ml leukemia inhibitory factor (Chemicon). Cells were kept at 37°C in 5% CO2 on tissue culture plates coated with 0.1% gelatin, and were routinely passaged every 48 h. The mESC were differentiated towards the neural lineage according to the protocol developed by Ying and colleagues (Ying and Smith 2003). At critical steps, we used the following parameters: cells were plated in the priming phase at 1.2 x 105 cells/cm2 in complete GMEM on 0.1% gelatin coated Nunclon culture dishes (Nunc, Langenselbold, Germany). Next day, for neural induction, cells were plated on gelatin-coated Nunclon dishes at 104 cells/cm2 in N2/B27 medium (composition as described in (Ying and Smith 2003), for a detailed description of B27 see (http://www.paa.com/cell_culture_products/reagents/growthsupplements/neuromix.html). On day 7 of differentiation (DoD7) for neuronal generation and maturation, cells were replated at 104 cells/cm2 on poly-L-ornithin (10 µg/ml) and laminin (10 µg/ml) coated Nunclon dishes in N2/B27 medium. Cells were fed every other day with complete medium change with N2/B27 medium.
Immunostaining and FACS analysis
For immunocytochemical analysis, cells were fixed with methanol (-20°C) or 4% paraformaldehyde (PFA) in phosphate-buffered saline (PBS) and permeabilized with 0.1% Triton X-100 in PBS. After blocking with 10% FBS, cells were incubated with primary antibodies (Tuj1 1:1000, NeuN 1:200, GAD65 1:200, SV2 1:200, PSD95 1:500, Nestin 1:500, Nestin-647 1:40, GFAP 1:800) over night. After incubation with appropriate secondary antibodies nuclei were counterstained with Hoechst H-33342 dye. Images were taken on the original cell culture dishes using an IX81 inverted microscope (Olympus, Hamburg, Germany) equipped with a 40x, NA 0.6 long range lens and processed using CellP imaging software (Olympus). For confocal microscopy cells were grown on 4-well chamber slides (Nunc), fixed with 4% PFA/2% sucrose in PBS and permeabilized with 0.6% Triton X-100 in 50 Chapter C – Coordinated waves of gene expression during neuronal differentiation of embryonic stem cells as basis for novel approaches to developmental neurotoxicity testing
PBS. After blocking with 5% BSA/0.1% Triton X100 in PBS cells were incubated with Tuj1 antibody in blocking buffer for one hour at room temperature. After incubation with appropriate secondary antibodies nuclei were counterstained with DAPI. Confocal images were taken using a Zeiss LSM 510Meta confocal microscope equipped with a Plan Apochromat 63x, NA 1.4 oil DIC lens. Images were analyzed and processed using ImageJ. For flow cytometry, cells were dissociated on DoD7 with accutase, fixed and permeabilized in Cytofix Buffer followed by Perm Buffer I (both BD Bioscience, Franklin Lakes, USA), and stained with anti-nestin antibody conjugated to Alexa-647, or isotype control. Cells were analyzed with an Accuri C6 flow cytometer (Accuri Cytometers, Ann Arbor, USA) and data processed with CFlow Plus (Accuri Cytometers).
Quantitative PCR and quality control of differentiation
Total RNA of five independent differentiation experiments, performed at different times, with different CGR8 cell batches, and by different operators was isolated at indicated time points for marker gene expression analyses using Trizol, the RNA was retro-transcribed with SuperScript II reverse transcriptase, and the resultant cDNAs were amplified in a Biorad Light Cycler (Biorad, München, Germany) with primers specific for the genes of interest and designed for a common melting temperature of 60°C. Real-time quantification for each gene was performed using SybrGreen and expressed relative to the amount of gapdh mRNA using the 2^(-Delta Delta C(T)) method (Livak and Schmittgen 2001). For each run, the consistency of conditions and constancy of gapdh amounts in the samples was controlled by assessment of its absolute cycle number (= 18 ± 0.5).
Gene expression analysis
Cells were used for RNA preparation as undifferentiated mESC before the priming phase (day 0), on DoD7 (before replating), on DoD15 and on DoD20. RNA was extracted from Trizol preparations and purified using RNeasy Mini prep columns (Qiagen). The total RNA harvested was quantified using a Nanodrop device (Thermo Scientific, USA) and its integrity was assessed using Agilent Bioanalyser (Agilent, USA). Illumina TotalPrep RNA Amplification Kit (Ambion, USA) and 500 ng total RNA of each sample was used according to the manufacturer’s protocol to produce biotin-labeled cRNAs. For hybridization onto Sentrix Mouse Ref.8 V2 mRNA microarray beadchips (Illumina), 750 ng labeled cRNA were
51 Chapter C – Coordinated waves of gene expression during neuronal differentiation of embryonic stem cells as basis for novel approaches to developmental neurotoxicity testing
incubated for 16 h at 58°C. After hybridization, chips were washed, blocked, and streptavadin-Cy3 stained. Fluorescence emission by Cy3 was quantitatively detected using BeadArray Reader Scan. Statistical analysis data is based on duplicate samples. Each of the samples contained pooled RNA from two differentiations to further increase robustness of results. Technical variation of the chip was minimal as tested by rerun of the same sample on two different arrays and by comparison of results from two beadchips within one array.
Data analysis
Original and processed data have been deposited for public access in the EBI Arrayexpress database (Accession Number to be supplied). For initial processing, data were uploaded to Beadstudio (Illumina) for background subtraction. Further processing (baseline transformation and normalization to 75 percentile) and analysis was performed with Genespring 9.0 (Agilent, Santa Clara, CA), and all normalized expression kinetics data sets were used as input for an unsupervised non-hierarchical clustering with relation to the average of expression of all genes on the chip, using the K-means algorithm. The eight major clusters were selected for further analysis. Within these, significant gene expression profiles were selected, based on a minimum regulation of 2.0-fold on any of the time points and on two- way ANOVA taking into account the regulation range and the variation between different arrays.
Patch-clamp recording
For functional characterization, neurons from at least three independent differentiations were tested for electrophysiological activity. Electrodes with a resistance of 2-5 MΩ were pulled of borosilicate glass (Clark, G150F, Warner Instruments, Hamden, CT, USA) on a Sutter Instruments (Novato, CA, USA) P-97 horizontal micropipette puller. All experiments were carried out using a custom built recording chamber (800 µl volume) made of Teflon within a temperature-controlled microscope stage (37°C). Whole cell voltage and current clamp recordings were obtained from cells at day of differentiation (DoD) 20-24. Cells were grown on coated glass cover slips (10 mm) from DoD7 on. Whole-cell currents were recorded using an L/M-EPC-7 amplifier (List Medical Electronic, Darmstadt, Germany), digitized at sampling frequencies between 10 kHz to 50 kHz using a DigiData 1320A AD/DA converter (Axon Instruments Inc.). The patch pipettes for spontaneous and evoked action potential measurements as well as for the neurotransmitter responses were filled with (in mM) 90 K+- 52 Chapter C – Coordinated waves of gene expression during neuronal differentiation of embryonic stem cells as basis for novel approaches to developmental neurotoxicity testing
gluconate, 40 KCl, 1 MgCl2, 10 NaCl, 10 EGTA, 4 Mg-ATP, 10 HEPES/KOH (pH 7.4 at
37°C), whereas the bath solution contained (in mM): 155 NaCl, 1 CaCl2, 3 KCl, 10 D-(+)- glucose, 10 HEPES/NaOH (pH 7.4 at 37°C). The protocol for recording of Na+ and K+ channels was as follows: cells were hyperpolarized to -90 mV, and subsequently stepped to a defined voltage as indicated and returned to -70 mV, before the next cycle with a different voltage step was run. Each cycle took 120 ms. For the neurotransmitter response measurements, the different substances were directly added as concentrated stock solutions to the recording chamber in amounts of 1-10 µL. Antagonists were added at least one min before the agonists. Recordings were initiated within 100 ms after addition of agonists. For the measurement of barium currents through calcium channels the pipette filling solution contained (in mM) 110 CsF, 10 NaCl, 20 TEA-Cl, 10 EGTA, 4 Na2-ATP, 10 HEPES/CsOH
(pH 7.4 at 37°C), whereas the bath solution contained (in mM) 130 NaCl, 10 BaCl2, 10 D-(+)- glucose, 5 tetraethylammonium chloride, 10 4-aminopyridine, 0.5 tetrodotoxin, 10 HEPES/NaOH (pH 7.4 at 37°C). All current signals were normalized against the individual cell capacitances (as a surrogate measure for cell size) and are expressed in current densities (current divided by cell capacitance). Liquid junction potentials (LJP) were measured and corrected, using the method described by Erwin Neher (1992) except for barium current measurements. Stimulation, acquisition and data analysis were carried out using pCLAMP 10.2 (Axon Instruments Inc.) and ORIGIN 8.0 (OriginLab Corp., MA, USA). Fast and slow capacitive transients were cancelled online by means of analogue circuitry. Residual capacitive and leakage currents were removed online by the P/4 method. Series Resistance Compensation was set to at least 50%. For analysis, traces were filtered offline at 5 kHz. Cells for measurements were chosen with respect to their morphological phenotype (small round highly elevated (phase-bright)) cell bodies with projections of at least five times cell body diameter, growing in network-like clusters containing at least 20-30 similar cells). The patch pipette was approached to these cells perpendicular to the plane formed by the cell membrane in the patch region.
53 Chapter C – Coordinated waves of gene expression during neuronal differentiation of embryonic stem cells as basis for novel approaches to developmental neurotoxicity testing
Statistics and data mining
The numbers of replicates of each experiment are indicated in figure legends. Data were presented, and statistical differences were tested by ANOVA with post-hoc tests as appropriate, using GraphPad Prism 4.0 (Graphpad Software, La Jolla, USA). Assignment of significantly overrepresented gene ontology (GO) categories to different clusters, and calculation of probabilities of a false-positive assignment was performed by G-profiler (http://biit.cs.ut.ee/gprofiler/ (Reimand et al. 2007)). For coverage of biological domains without appropriate and well-controlled GO category, relevant genes were assembled from the literature and cross-checked by 2-3 independent specialists. The number of genes within these groups identified in this study was indicated in relation to the overall number of possible hits or in relation to their distribution over different clusters. The genes defined in this study as embryonic stem cell markers or neural stem cell (NPC) markers were derived from recent literature(Kuegler et al. 2010). Neuronal (N) differentiation markers (n = 574) were defined as all members of gene ontology (GO) GO:0048699 (generation of neurons) corrected for those genes used as NPC markers. The graphical representation of identified genes (or groups) within their biological context is based on the major gene function as indicated on the NCBI- gene website and the literature. Importantly, members of each identified group were scored according to their suitability as markers for a PCR-based quality control of the differentiation pattern in toxicity experiments. Several selection rounds were run to identify the final set of markers displayed as example genes in the tables and some of the figures.
Toxicity experiments
Cells were exposed to chemicals during different phases of differentiation to test the suitability of the model system for neurotoxicity testing, and for testing of developmental neurotoxicity during defined time windows. Retinoic acid (1 µM), “3i” (a mixture of 0.8 µM PD184352, 2 µM SU5402, 3 µM CHIR99021) (Ying et al. 2008) or cyclopamine (1 µM) were added to cultures from DoD1-DoD7 or from DoD8-DoD15. Then the experiment was ended, or incubation continued in the absence of chemicals for additional 6 days. On the final day, RNA was prepared by the Trizol method for PCR analysis. For morphological observations, the monolayer regions within the culture wells were imaged. Genes were preselected before the analysis as endpoints for initial proof-of-concept experiments, and results from all genes chosen are presented.
54 Chapter C – Coordinated waves of gene expression during neuronal differentiation of embryonic stem cells as basis for novel approaches to developmental neurotoxicity testing
Acknowledgement
The Work was supported in part by the Doerenkamp-Zbinden Foundation, the DFG, the EU FP7 project ESNATS (ML, SK), an IRTG1331 fellowship (BZ) and a fellowship from the KoRS-CB (PBK). We are grateful to Giovanni Galizia and Sabine Kreissl for help with the electrophysiological recordings and indebted to Bettina Schimmelpfennig for invaluable experimental support. The monoclonal antibodies Gad-6 developed by D.I. Gottlieb and SV2 developed by K.M. Buckley were obtained from the Developmental Studies Hybridoma Bank developed under the auspices of the NICHD and maintained by The University of Iowa, Department of Biology, Iowa City, IA 52242. We thank K.H. Krause for the CGR8 mESC- line and J. Vilo and S. Ilmjärv for help with bioinformatics analysis.
55 Chapter C – Coordinated waves of gene expression during neuronal differentiation of embryonic stem cells as basis for novel approaches to developmental neurotoxicity testing
Supplements
Figure S1: Additional electrophysiological data
Cells were differentiated on glass cover slips towards the neuronal lineage for 20-24 days and then placed into a temperature controlled recording chamber for whole cell patch-clamp studies. A. Whole cell voltage clamp recording: Overall K+ currents were pharmacologically isolated from Na+ currents in the presence of 500 nM tetrodotoxin. The 120 ms cycle involved an initial hyperpolarization phase at -90 mV, a 20 ms ramping step (to -10 mV, 0 mV, 10 mV) and a -70mV repolarization step as indicated. B. A subgroup of K+ channels with slow activation kinetics and no spontaneous inactivation was triggered, when cells were held at -40 mV before the depolarizing voltage step. C. Mathematical subtraction of B from A indicates another group of K channels with fast activation, and spontaneous inactivation characteristics. The scale bars indicate time and current dimensions for figures A-C. D. Action potentials were evoked by triggering with a defined outward current of 20 pA. The trace indicates the voltage signal obtained in current clamp mode. Data are representative for 10 cells from 2 differentiations. Reversal of the current (-10 pA) showed only passive biophysical membrane properties on the voltage trace. Injection of a smaller current (5 pA) triggered action potentials with lower frequency (in 15 out of 19 cells; not shown). E. Current traces were recorded after stimulation of neurons with GABA in the presence or absence of the specific antagonist picrotoxin. The scale bars represent the current and time dimensions of the experiment. Data are representative for N ≥ 10 neurons (agonists) and n = 3 for antagonists.
56 Chapter C – Coordinated waves of gene expression during neuronal differentiation of embryonic stem cells as basis for novel approaches to developmental neurotoxicity testing
Figure S2: Additional data (standard deviations for figure 3)
Cultures of mESC were neuronally differentiated for 7, 15 or 20 days as indicated in a-d. They were exposed to retinoic acid (RA), cyclopamine (Cyclo) or 3i for the time periods indicated by the hatched boxes. RNA was isolated at the end of the incubations and used for quantitative RT-PCR analysis of selected differentiation and patterning markers. The data indicate relative expression levels (in %) compared to untreated cultures at the same time point and are means ± SD from two to three cultures for each treatment and exposure schedule. Non- significant expression differences with untreated differentiation cultures are indicated by grey shading; Up- and downregulations are color-coded: p < 0.05 (light green, down), p < 0.01(green, down), p < 0.001 (dark green, down), p < 0.05 (light red, up), p < 0.01 (red, up), p < 0.001 (dark red, up). Headings indicate the overall biological effect, such as accelerated neuronal differentiation (e.g. Neuronal diff. (+)), altered patterning (e.g. Caudalization) or evidence for altered cell composition (e.g. Neurotransmitters or Diff. block). Names are the official gene names, apart from the following: Vglut1 = Slc17a7, Oct4 = Pou5f1, HB9 = Mnx1. N.D.: not determined, as cells did morphologically not differentiate.
57 Chapter C – Coordinated waves of gene expression during neuronal differentiation of embryonic stem cells as basis for novel approaches to developmental neurotoxicity testing
Figure S3: Complete listing of the assignment of genes to the different clusters
The genes are named according to their official NCBI PubMed annotation and can be used without further modification for different bio-informatic analyses, such as testing for overrepresented gene ontologies with g:Profiler (http://biit.cs.ut.ee/gprofiler/). Note that the separator (comma, or blank) needs to be adjusted, depending on the software used.
Cluster Ia 0610005K03Rik, 0610039J04Rik, 1110002N22Rik, 1110006G06Rik, 1110030K22Rik, 1110033J19Rik, 1190003J15Rik, 1190005I06Rik, 1300013J15Rik, 1500001L15Rik, 1520401A03Rik, 1600021C16Rik, 1700007J06Rik, 1700012H05Rik, 1700013B16Rik, 1700013H19Rik, 1700019D03Rik, 1700019H03Rik, 1700028N11Rik, 1700029P11Rik, 1700071K01Rik, 1810008K03Rik, 1810015A11Rik, 2010001H14Rik, 2010111I01Rik, 2200001I15Rik, 2310003C23Rik, 2310008H09Rik, 2310042N02Rik, 2310045B01Rik, 2310073E15Rik, 2410004A20Rik, 2410004F06Rik, 2410008K03Rik, 2410012C07Rik, 2410018M08Rik, 2410081M15Rik, 2410116G06Rik, 2410127L17Rik, 2600005C20Rik, 2610103J23Rik, 2610305D13Rik, 2700038I16Rik, 2810410M20Rik, 4432409M07Rik, 4632409L19Rik, 4732467B22, 4922503E23Rik, 4930447C04Rik, 4930504E06Rik, 4930539E08Rik, 4930572J05Rik, 4931417G12Rik, 4933405K07Rik, 4933409I22, 4933437F05Rik, 5430425C04Rik, 5730436H21Rik, 5730593N15Rik, 5830436D01Rik, 5830482F20Rik, 5830482G23Rik, 6030457N17Rik, 6330406L22Rik, 6330579B17Rik, 8430410A17Rik, 9030407H20Rik, 9130210N20Rik, 9130422G05Rik, 9630015D15Rik, 9630033F20Rik, 9630048M01Rik, A230050P20Rik, A730016F12Rik, A830080D01Rik, A930010I20Rik, A930014K01Rik, Abi3, Acadm, Acate2, Acate3, Acoxl, Acrbp, Acsl1, Ada, Adam23, AI256711, AI427138, AI467606, AI747699, AI847670, Aim2, Aire, AK122209, Akap1, Aldh3a1, Aldh3b1, Alox12, Alox5ap, Amhr2, Ampd1, Angptl4, Ankrd25, Ankrd47, Aoah, Apobec3, Arc, Arhgap30, Arntl, Ase1, Ash2l, Asna1, Atic, Atp10a, Atp6v1c1, Atxn3, AU018091, AU023871, Aven, Avpi1, AW061290, AW491445, AY078069, B3gnt7, BC013481, BC038822, BC060631, BC067068, BC088983, BC099439, Bcas1, Bcas3, Bcat2, Bcl3, Bcl6b, Bid, Bnc2, Bnipl, Bpnt1, Bspry, Bteb1, C030039L03Rik, C030048B08Rik, C230052I12Rik, C330003B14Rik, C330016O10Rik, C330023M02Rik, C430004E15Rik, C730015A04Rik, C77032, C80913, C87860, Calca, Calml4, Camk1d, Cbr3, Cbx7, Ccrn4l, Cd6, Cdc37l1, Cdc5l, Cdh1, Cdh3, Cdkl2, Cds1, Cdsn, Cdyl, Cdyl2, Ceacam1, Centa1, Centb1, Chchd4, Chek2, Chrna9, Chtf18, Clca4, Clcnka, Clcnkb, Clpp, Clps, Clra, Clstn3, Cltb, Cobl, Coch, Cog8, Cox11, Crlf3, Cth, Cul4b, Cyb561, Cyc1, D030070L09Rik, D11Ertd636e, D14Ertd668e, D15Mgi27, D1Pas1, D230005D02Rik, D4Bwg1540e, D4Ertd22e, D5Bwg0834e, D630023F18Rik, D6Wsu176e, D8Ertd812e, D9Ertd280e, Dapp1, Ddc, Ddx27, Ddx4, Ddx49, Ddx58, Def6, Depdc6, Dhps, Dhrs10, Dhx16, Diap1, Dlc1, Dnaja3, Dnajc6, Dnd1, Dnmt3l, Dppa3, Dppa4, Dus3l, E130012A19Rik, E130014J05Rik, E130016E03Rik, E430034L04Rik, Ebaf, Eed, EG436240, Eif4ebp1, Ell3, Enah, Enpp4, Epb4.9, Epha2, Eras, Esco2, Esrrb, Etsrp71, Etv4, Etv5, Exosc5, Farsb, Fblim1, Fbxo15, Fbxo31, Fcgr2b, Fetub, Fgf1, Fgf17, Fgf4, Fiz1, Fkbp11, Fnbp1, Foxh1, Foxp1, Fpgs, Frrs1, G22p1, Gab1, Gabpa, Gclm, Gcnt2, Gdf3, Gfod1, Gfpt2, Gjb4, Gloxd1, Gls2, Glt28d1, Glud1, Gm1070, Gm128, Gm129, Gm1631, Gm1967, Gna14, Gnat1, Gngt2, Gnmt, Gnpda1, Got1, Gpr114, Gps1, Gpx2, Grhl3, Grtp1, Gsta4, Gtf2h1, Hars2, Hcph, Helb, Hirip5, Hist1h2ae, Hist1h2bf, Hist1h2bh, Hist1h2bk, Hist1h3a, Hist1h3d, Hist1h3e, Hist1h4i, Hist1h4j, Hist1h4k, Hist1h4m, Hk2, Hmgb2l1, Hmox1, Hook2, Hormad2, Hprt, Hr, Hsd17b1, Hspa9, Hspbap1, Hspca, Ide, Ifitm1, Ifitm2, Ifrd2, Igfals, Il17d, Il23a, Il27ra, Ildr1, Impa2, Inhbb, Irak3, Irf1, Isyna1, Itgae, Itgb7, Itpk1, Jak3, Jmjd1a, Jmjd2c, Kcnk5, Klf2, Klf5, Klf8, Krt1-17, Krt42, L1td1, L3mbtl2, Lad1, Laptm5, Lck, Leftb, Liph, LOC214531, LOC217066, LOC229665, LOC233038, LOC234374, LOC245128, LOC381411, LOC433801, LOC434197, LOC435970, LOC545471, LOC627585, LOC630579, LOC634428, Lrch4, Lrmp, Lrrc34, Ltbp4, Ly6g6c, Ly6g6d, Ly75, Manba, MAp19, Mapkapk3, Mbldc1, Mcl1, Mef2b, Mfap5, Mftc, MGC117846, Mia1, Mif4gd, Mlh3, Morc3, Mov10, Mras, Mrpl1, Mrpl40, Mrps18b, Mrps31, Mrps5, Ms4a6d, Msc, Msh6, Msrb2, Mtap7, Mtf2, Mthfd1, Mtss1, Mtus1, Mybl2, Myc, Mylpf, Myo1f, Myo1g, Myst4, Napsa, Nefh, Niban, Nlrp14, Noc4, Nol1, Nol6, Nos3, Nos3as, Notch4, Nqo3a2, Nr0b1, Nr1d2, Nr5a2, Nrp2, Nubp2, Oas2, Olfr985, Orc1l, Orc5l, ORF21, Osm, OTTMUSG00000010673, Ovol1, Parp14, Pcaf, Pcdh21, Pcolce2, Pcyt1b, Pdcd2, Pde4dip, Pdhb, Pdk1, Pdyn, Pecam1, Pfc, Pfkp, Pgc, Phc1, Phf17, Phlda2, Pigl, Pip3ap, Pitpnc1, Pla2g1b, Plcg2, Plekha4, Plekhf2, Plekhg5, Pml, Pnma2, Poli, Polr2e, Pou5f1, Ppan, Ppm1b, Prdm1, Prps1, Prx, Psmb10, Ptbp1, Ptk9l, Pwp2, Pycard, Rab25, Rab27a, Rabggta, Rad9b, Raet1b, Ralbp1, Rapsn, Rarg, Rasal1, Rasgrp2, Rasip1, Rbm13, Rbm35a, Rbp7, Rbpms, Rfx2, Rhebl1, Rmnd5b, Rnf125, Rnf138, Rnf17, Rnu3ip2, Rpl4, Rpp25, Rps6ka1, Rps6kl1, Sall1, Sall4, Sema4a, Sema4b, Sephs2, Sept1, Serpinb6c, Setd6, Sgk, Sgk2, Sgk3, Sh3gl2, Shmt1, Si, Sip1, Six1, Skap2, Skil, Slc11a1, Slc23a2, Slc25a12, Slc25a26, Slc27a2, Slc28a1, Slc35f2, Slc37a1, Slc7a3, Slc7a7, Smpdl3b, Snai3, Sntb2, Socs2, Socs3, Sod2, Sox2, Spats1, Spc24, Spnb4, Spp1, Spry4, Srm, St6galnac2, Stac2, Stat4, Steap, Stk31, Syt9, Tada2l, Taf5l, Taf7, Tbc1d10c, Tbc1d15, Tcea3, Tcf15, Tcfcp2l2, Tcfcp2l3, Tcfl5, Tdgf1, Tdh, Tek, Tekt1, Tex10, Tex11, Tex14, Tex19, Tfpi, Tgm1, Timp1, Tjp2, Tm4sf3, Tm4sf5, Tmc6, Tmem54, Tmem8, Tmprss13, Tmsb10, Tnip1, Tomm34, Tor3a, Tpd52, Trap1, Trib3, Trim28, Triml1, Trip12, Trp53, Tsga2, Tst, Ttc29, Tuba4, Tuba6, Ubash3a, Ubtf, Ubxd4, Ung, Upk1a, Upk2, Upp1, Usp28, Usp7, Utf1, Vangl1, Vwf, Was, Wdr20, Wdr31, Yrdc, ZBTB45, Zfp259, Zfp296, Zfp371, Zfp42, Zfp459, Zfp473, Zfp52, Zfp57, Zfp97, Zic3, Zp3, Zscan10, Zswim1
Cluster Ib 0610007P06Rik, 0610009E20Rik, 0610041E09Rik, 0910001A06Rik, 1110001A07Rik, 1110001A12Rik, 1110002E23Rik, 1110005A23Rik, 1110007A13Rik, 1110007C24Rik, 1110032N12Rik, 1110064P04Rik, 1110067D22Rik, 1200003I07Rik, 1200006O19Rik, 1200008O12Rik, 1200009B18Rik, 1200011O22Rik, 1200016B10Rik, 1300001I01Rik, 1300018L09Rik, 1500001M20Rik, 1500016H10Rik, 1700012G19Rik, 1700022C21Rik, 1700023O11Rik, 1700034H14Rik, 1700065O13Rik, 1810003N24Rik, 1810007M14Rik, 1810008O21Rik, 1810014F10Rik, 1810014L12Rik, 1810035L17Rik, 1810044O22Rik, 1810055E12Rik, 2010003J03Rik, 2010316F05Rik, 2310003F16Rik, 2310010B21Rik, 2310031L18Rik, 2310036O22Rik, 2310037I24Rik, 2310042G06Rik, 2310044G17Rik, 2310047C04Rik, 2310050B20Rik, 2310056P07Rik, 2310061F22Rik, 2310061I09Rik, 2310066N05Rik, 2310079N02Rik, 2410002O22Rik, 2410004L22Rik, 2410005K20Rik, 2410015N17Rik, 2410016F19Rik, 2410017P07Rik, 2410042D21Rik, 2410080P20Rik, 2410118I19Rik, 2510005D08Rik, 2510012J08Rik, 2600001B17Rik, 2600001J17Rik, 2600005O03Rik, 2600013N14Rik, 2610012O22Rik, 2610016F04Rik, 2610019N13Rik, 2610020C11Rik, 2610020N02Rik, 2610020O08Rik, 2610028L19Rik, 2610029G23Rik, 2610029K21Rik, 2610033H07Rik, 2610034N24Rik, 2610040E16Rik, 2610042L04Rik, 2610044O15Rik, 2610101N10Rik, 2610207I05Rik, 2610312B22Rik, 2610318I01Rik, 2610318N02Rik, 2610510J17Rik, 2610511O17Rik, 2610528A15Rik, 2610528E23Rik, 2610528H13Rik, 2610528M18Rik, 2700007P21Rik, 2700050L05Rik, 2700050P07Rik, 2700085M18Rik, 2700094F01Rik, 2700097O09Rik, 2810004A10Rik, 2810004N23Rik, 2810008M24Rik, 2810021B07Rik, 2810028N01Rik, 2810036L13Rik, 2810037C03Rik, 2810047L02Rik, 2810422B04Rik, 2810428I15Rik, 2810430M08Rik, 2810457M08Rik, 2810485I05Rik, 2900001O04Rik, 2900057D21Rik, 3110002L15Rik, 3110010F15Rik, 3110082I17Rik, 3200002M19Rik, 3300001M20Rik, 3300001P08Rik, 3732409C05Rik, 4122402O22Rik, 4632417K18Rik, 4732465J09Rik, 4732497O03Rik, 4833424P18Rik, 58 Chapter C – Coordinated waves of gene expression during neuronal differentiation of embryonic stem cells as basis for novel approaches to developmental neurotoxicity testing
4921505C17Rik, 4921524P20Rik, 4921537D05Rik, 4930427A07Rik, 4930432B04Rik, 4930453N24Rik, 4930485D02Rik, 4930519N13Rik, 4930542G03Rik, 4930547N16Rik, 4930562C03Rik, 4931400A14Rik, 4931407G18Rik, 4931431L11Rik, 4932441K18Rik, 4933403G14Rik, 4933411K20Rik, 4933424A10Rik, 4933424M23Rik, 4933424N09Rik, 4933435A13Rik, 5430400N05Rik, 5430438H03Rik, 5730466H23Rik, 5730555F13Rik, 5730599I05Rik, 5830411K18Rik, 5830416A07Rik, 5830426I05Rik, 5832424M12, 6230416J20Rik, 6330503K22Rik, 6330534C20Rik, 6430601A21Rik, 6530405K19, 6720458F09Rik, 6720485C15Rik, 6820449I09Rik, 7420416P09Rik, 8430423A01Rik, 9030612M13Rik, 9330134C04Rik, 9430010O03Rik, 9430034D17Rik, 9830124H08Rik, A030012M09Rik, A730024A03Rik, A730098D12Rik, A930009M04Rik, AA408296, AA408556, Aars, Aarsd1, Aatf, AB041544, Abce1, Abcf2, Abi2, Acac, Acly, Acot7, Acsl5, Actb, Actl6a, Actn3, Actr6, Adat1, Adk, Adprtl2, Adsl, Adss, AF233884, Aftph, Agps, Ahsa1, AI326906, AI449175, AI449441, AI838661, AK122525, Akap12, Akap9, Akr1b3, Amd2, Anapc5, Ankrd10, Anln, Anp32a, Anp32e, Aoc3, Ap3m1, Apitd1, Appbp1, Aprin, Aqr, Ard1, Arhgap12, Arl6ip2, Armc6, Armc8, Armcx1, Arpp19, Ars2, Asf1a, Asf1b, Aspscr1, Atad1, Atad2, Atad3a, Atf2, Atf4, Atic, Atm, Atp13a1, Atp5g1, Atrx, Attp, Atxn3, AU021838, AW011752, AW046396, AW060766, AW540478, AW549877, AW550801, AW555814, Axot, B020018G12Rik, B130055D15Rik, B230219D22Rik, Bag4, Banf1, Bbs5, BC002199, BC003885, BC003993, BC004701, BC006705, BC008103, BC016226, BC018399, BC018601, BC025462, BC027061, BC027231, BC030867, BC033596, BC048355, BC049806, BC050092, BC055324, BC062951, BC085271, Bcap37, Bcas2, Bccip, Bckdhb, Bclaf1, Bhlhb9, Bing4, Birc2, Birc5, Blm, Bms1l, Bok, Bop1, Bpnt1, Brca1, Brca2, Brip1, Brp16, Brwd1, Btbd14b, Bub1b, Bxdc1, Bxdc2, Bzw1, Bzw2, C1qbp, C230052I12Rik, C330005L02Rik, C330017I15Rik, C330018L13Rik, C6.1A, C77032, C78212, C79407, C86302, C920006C10Rik, Cacybp, Calmbp1, Catnal1, Cbfb, Cbr3, Cbx5, Ccar1, Ccdc111, Ccdc132, Ccdc58, Ccm1, Ccnb1, Ccnc, Ccne1, Ccne2, Ccnf, Cct3, Cct6a, Cct7, Cct8, Cdc14b, Cdc20, Cdc2l2, Cdc45l, Cdc6, Cdc7, Cdca2, Cdca3, Cdca5, Cdca7, Cdk2, Cdk2ap1, Cdkal1, Cenph, Cenpi, Cenpj, Cenpp, Cetn3, Cfdp1, Cggbp1, Chaf1a, Chaf1b, Chc1, Chd1, Chd1l, Chek1, Chek2, Cherp, Chic1, Chordc1, Chuk, Ciapin1, Clcn3, Clk1, Clk4, Clns1a, Clock, Clspn, Cnot10, Cnot2, Cnot4, Cnot7, Cops2, Cops3, Cox10, Cpsf6, Crbn, Crk, Crsp7, Crsp9, Csda, Csde1, Cse1l, Cspg6, Cstf2, Cstf3, Ctcf, Ctdp1, Ctdspl2, Ctps, Cugbp1, Cul1, Cul5, Cwf19l2, Cyb5r4, Cycs, Cyld, D0H8S2298E, D10627, D10Ertd322e, D130060C09Rik, D16Bwg1547e, D16Ertd472e, D19Bwg1357e, D19Ertd678e, D19Ertd703e, D1Wsu40e, D3Ertd194e, D4Wsu114e, D5Ertd689e, D7Rp2e, D8Ertd457e, Dars, Dbf4, Dbr1, Dbt, Dctd, Dcun1d4, Ddx1, Ddx18, Ddx20, Ddx21, Ddx3x, Ddx3y, Ddx46, Ddx48, Ddx50, Ddx51, Ddx52, Ddx54, Dek, Denr, Depdc1b, Dhodh, Dhx15, Dhx36, Dhx38, Dhx9, Diap3, Dicer1, Dido1, Dlg7, Dmtf1, Dnaja2, Dnajb1, Dnajc10, Dnajc2, Dnajc7, Dnajc8, Dnm1l, Dnmt1, Dnmt3b, Dok2, Dph2, Dpp3, Drbp1, Drg1, Dscr2, Dutp, E230015L20Rik, E2f2, E2f4, E430004F17Rik, E430028B21Rik, E430034L04Rik, Ebna1bp2, Ecd, Ecsit, Edd1, Eef1d, Eef1e1, Eftud2, EG433923, Ehmt1, Eif1a, Eif2b1, Eif2s2, Eif2s3y, Eif3s10, Eif3s4, Eif3s6ip, Eif3s7, Eif3s8, Eif3s9, Eif4g2, Eif5, Eif5a, Elac2, Elavl1, Elavl2, Elmo1, Elovl6, Elys, Eno1, Epc2, Eprs, Erh, Erich1, Espl1, Etf1, Evi5, Exo1, Exosc1, Exosc2, Exosc6, Exosc7, Ezh2, F630043A04Rik, F730047E07Rik, Fancd2, Fancl, Farsb, Fbl, Fbxo30, Fbxo33, Fbxo8, Fcmd, Fen1, Fgfr1op, Fignl1, Fkbp4, Fkbp5, Fmip, Fnbp3, Fnip1, Fnta, Fntb, Foxred1, Ftsj3, Fubp1, Fubp3, Fundc1, Fusip1, G22p1, G3bp, G3bp2, G430022H21Rik, G6pdx, Gabpa, Gars, Gart, Gbx2, Gca, Gcc2, Gcn5l2, Gemin4, Gemin5, Gemin6, Gga3, Gk5, Gldc, Gm83, Gnl3, Golga4, Gpbp1, Gpd2, Gpiap1, Gprk6, Gps1, Gpt2, Grcc2f, Grwd1, Gsg2, Gspt1, Gtf2e2, Gtf2h2, Gtf3c2, Gtf3c3, Gtpbp1, Gtpbp4, Gtse1, Guk1, H2afz, Hapln4, Hccs, Hcfc1, Hdac1, Hdgf, Hdlbp, Heatr1, Heatr3, Herc4, Hist1h2ab, Hist1h2ad, Hist1h2ag, Hist1h3c, Hist1h3h, Hist1h4f, Hmga1, Hmgb2, Hn1l, Hnrpa1, Hnrpa2b1, Hnrpdl, Hnrpf, Hnrph1, Hnrpk, Hnrpm, Hnrpu, Homer1, Hps3, Hrb2, Hsd3b2, Hsp105, Hspa4, Hspcb, Hspd1, Htatsf1, Hus1, Iars, Ibtk, Igf2bp1, Impact, Incenp, Ipmk, Iqgap2, Ireb2, Irgm, Isg20l2, Isy1, Itgb4bp, Ivns1abp, Jam2, Josd3, Jtv1, Kars, Kbtbd8, Kif11, Kif15, Kif18a, Kif22, Kif23, Kif4, Klhdc4, Kntc1, Kpna1, Kpna2, Kpna3, Kpnb1, Kpnb3, Kras2, Lama1, Lars, Lbr, Ldh1, Leo1, Lgtn, Lig1, Lin28, Lin54, Lin7c, Lmnb2, LOC211660, LOC216443, LOC237877, LOC329575, LOC380625, LOC381795, LOC382010, LOC432879, LOC433182, LOC434858, LOC639396, Lrpprc, Lsm4, Lsm6, Lyar, Lypla1, Lyrm5, Mad2l1, Map2k3, Map3k7ip3, Mars, Mat2a, Mat2b, Matr3, Mbd3, Mbip, Mbtd1, Mbtps2, Mcm10, Mcm2, Mcm3, Mcm4, Mcm5, Mcm6, Mcm7, Mcph1, Mdn1, Me2, Med18, Melk, Mettl2, Mettl3, Mfap1b, Mgea6, Mier1, Mina, Mki67ip, Mme, Mnd1, Mns1, Mphosph6, Mre11a, Mrpl10, Mrpl12, Mrpl15, Mrpl18, Mrpl20, Mrpl35, Mrpl36, Mrpl37, Mrpl45, Mrpl49, Mrpl50, Mrps25, Mrps26, Mrps31, Mrps7, Ms4a10, Msh2, Msh3, Mt1, Mtbp, MTERF, Mthfd2, Mto1, Mtrf1, Mtx1, Mum1, Mutyh, Mybbp1a, Myef2, Myg1, Mysm1, Nap1l1, Narg1, Narg1l, Narg2, Nars, Nasp, Ncaph2, Ncbp2, Ncl, Ncoa6ip, Ncor1, Ndc80, Nde1, Nedd1, Nfat5, Nfatc3, Nfyb, Nfyc, Nip7, Nipbl, Nipsnap1, Nle1, Nlk, Nme1, Nmt1, Nob1, Nol1, Nol10, Nol11, Nol5, Nol5a, Nola1, Nola2, Nola3, Npm3, Nqo1, Nsbp1, Nsd1, Nsf, Nsun2, Nsun5, Nubp1, Nudc, Nudcd3, Nudc-ps1, Nup107, Nup133, Nup155, Nup160, Nup43, Nup54, Nup62, Nup85, Nup88, Nup93, Nusap1, Nutf2, Nvl, Odc1, Ogt, Oprs1, Orc6l, Oxr1, Pa2g4, Paf53, Pafah1b1, Pank1, Papola, Papolg, Park7, Parl, Parp1, Pcf11, Pdcd10, Pdcl, Pdha1, Pdss1, Pes1, Pex13, Pfkl, Pfn1, Pgam1, Pgd, Phf5a, Phtf2, Piga, Pinx1, Pipox, Pir, Pitpnb, Pkm2, Plaa, Plk1, Plk4, Plp, Pmm2, Pnrc2, Pola2, Pold1, Pold2, Pold3, Poldip2, Pole, Polr3b, Polr3g, Polr3k, Pop1, Pou2f1, Ppa1, Pparbp, Ppat, Ppid, Ppm1g, Ppp1cb, Ppp1cc, Ppp1r9a, Ppp2r5c, Ppp4r1, Prkdc, Prkr, Prkwnk1, Prmt3, Prmt5, Prmt6, Prodh, Prosc, Prpf3, Prpf38b, Prpf39, Prpf8, Prps2, Psat1, Psip1, Psmb3, Psmc2, Psmc3ip, Psmc4, Psmc5, Psmd1, Psmd11, Psmd12, Psmd14, Psmd7, Psme3, Psme4, Psors1c2, Ptbp1, Ptpn12, Ptprk, Pum2, Pus3, Pvrl3, Pwp2, Pycr2, Qdpr, Qprt, Rab5a, Rad17, Rad23a, Rad50, Rad51, Rad51c, Rad54l, Rai14, Ranbp1, Ranbp2, Rangnrf, Rapgef6, Rars, Rarsl, Rasa1, Rbbp7, Rbm13, Rbm19, Rbm21, Rbm26, Rbm28, Rbm3, Rbm6, Rcl1, Recc1, Recql4, Rent1, Rev3l, Rfc2, Rfc3, Rfc5, Rfwd3, Rfx1, Rg9mtd1, Rg9mtd2, Ric3, Rif1, Ris2, Rnasen, Rnf134, Rnf138, Rnf139, Rnf44, Rnf6, Rngtt, Rnmt, Rpa1, Rpa2, Rpap1, Rpgr, Rpl23, Rpl29, Rpl30, Rpo1-4, Rpo2tc1, Rps27a, Rragb, Rrm1, Rrm2, Rrm2b, Rrn3, Rsf1, Rsrc2, Ruvbl1, Ruvbl2, Ryk, Sacm1l, Sall1, Sall4, Sap30, Sars2, Sart3, Sbno1, Scoc, Sdad1, Sdccag1, Seh1l, Senp8, Sept2, Serbp1, Sf3a1, Sf3a3, Sf3b3, Sf4, Sfpq, Sfrs1, Sfrs2, Sfrs3, Sfrs6, Sfrs7, Sgol1, Sh3d1B, Shcbp1, Shmt2, Shprh, Siah1b, Sirt1, Sirt6, Skb1, Skp1a, Skp2, Slc11a1, Slc12a2, Slc20a2, Slc25a15, Slc25a38, Slc25a5, Slc38a2, Slc6a15, Slc7a5, Slc7a6, Smarcad1, Smarcc1, Smc1a, Smc5l1, Smc6l1, Smek1, Smek2, Smfn, Smn1, Smyd5, Snap23, Snrpa, Snrpa1, Snrpd1, Snx10, Solt, Spag5, Spc24, Spcs3, Spg7, Srfbp1, Srl, Srp19, Srp54, Ss18, Sssca1, Stag1, Stag2, Stc2, Stip1, Stk2, Stk3, Stk6, Styx, Suhw3, Suhw4, Sult4a1, Supv3l1, Suz12, Syap1, Sybl1, Syncrip, Taf13, Taf15, Tarbp2, Tardbp, Tars, Tax1bp1, Tbc1d23, Tbrg4, Tcerg1, Tcof1, Tebp, Tex10, Tex292, Tex9, Tfam, Tfb2m, Tfdp1, Tfrc, Thg1l, Thoc1, Thoc4, Thop1, Thumpd3, Tiam1, Timeless, Timm8a1, Tipin, Tk1, Tle4, Tlk1, Tmem126a, Tmem20, Tmod3, Tmpo, Tnrc15, Tom1, Tomm70a, Top1, Top2a, Topors, Tpm3, Tpp2, Tpr, Tpx2, Tra1, Tra2a, Trh, Trim2, Trim23, Trim33, Trp53inp1, Trps1, Tssc1, Ttc35, Ttf2, Ttk, Ttll4, Ttpa, Tubg1, Tufm, Tug1, Twistnb, Tyms, Tyms-ps, U2af1-rs2, Ubap2, Ube1c, Ube1x, Ube2g1, Ube2q2, Ube3a, Ublcp1, Uble1b, Ubp1, Ubtf, Uchl3, Uchl5, Uhrf1, Umps, Upf1, Upf2, Usp1, Usp10, Usp15, Usp16, Utx, Uxt, Vac14, Vars, Vdac3, Vps35, Vps54, Vrk1, Wars, Wdhd1, Wdr18, Wdr3, Wdr4, Wdr75, Wdr77, Wdr9, Wrn, Wsb1, Wwp2, Xab1, Xlr3a, Xlr4a, Xpo4, Yme1l1, Yod1, Ythdf1, Ythdf3, Zc3hc1, Zc3hdc8, Zcchc3, Zcchc7, Zcchc8, Zcchc9, Zdhhc21, Zfml, Zfp1, Zfp101, Zfp106, Zfp131, Zfp143, Zfp148, Zfp160, Zfp2, Zfp281, Zfp292, Zfp322a, Zfp326, Zfp35, Zfp367, Zfp37, Zfp451, Zfp457, Zfp472, Zfp597, Zfp60, Zfp62, Zfp715, Zfp75, Zfp760, Zfx, Zranb2, Zrf2, Zw10
Cluster IIa 1110001C20Rik, 1110025F24Rik, 1300018P11Rik, 1500010M16Rik, 2010003O18Rik, 2210018M03Rik, 2210407G14Rik, 2310002B06Rik, 2310047D13Rik, 2310047I15Rik, 2310057H16Rik, 2410127E18Rik, 2600005N12Rik, 2600011E07Rik, 2610034M16Rik, 2700063G02Rik, 2810030E01Rik, 2810417H13Rik, 2810439F02Rik, 4121402D02Rik, 4921506I22Rik, 4930432O21Rik, 4930471O16Rik, 4932409F11Rik, 5033414D02Rik, 5530601I19Rik, 5730466C23Rik, 5930416I19Rik, 6030443O07Rik, 6330415F13Rik, 6330505F04Rik, 6430510M02Rik, 6720425G15Rik, 6720460F02Rik, 6720463E02Rik, 8430410K20Rik, 8430415E04Rik, 8430438D04Rik, 59 Chapter C – Coordinated waves of gene expression during neuronal differentiation of embryonic stem cells as basis for novel approaches to developmental neurotoxicity testing
A230072I16Rik, A430106J12Rik, Aacs, Acat2, Acsl4, Acsl6, Actg2, Adamts7, Adarb1, Agtr1a, Agtrl1, AI314180, AK129302, Aldh1a7, Ap2b1, Arf2, Arid3b, Arl2bp, Armcx2, Armcx3, Asb13, AU040950, AW742319, B130017I01Rik, B230217C12Rik, B3gnt5, B830028P19Rik, B930008K04Rik, B930093C12Rik, Bbs7, BC016198, BC025076, BC026590, BC029103, BC031748, BC043301, Bcl2, Bex2, Bmi1, Bmpr1a, Bmpr1b, C230071H18Rik, C230090D14, C630028C02Rik, Calca, Catnd2, Cbfb, Cblb, Cbx1, Cbx2, Ccnd1, Ccng1, Cdh2, Cdh6, Chrna3, Chst3, Cnksr2, Cp, Cpd, Csnk1g3, Cxadr, D030013I16Rik, D15Bwg0669e, D930028F11Rik, D9Wsu20e, Dcun1d4, Dgkk, Dhcr24, Dhcr7, Dll1, Dnajb4, Dpp8, Drctnnb1a, Dusp18, Dusp4, E130113K22Rik, E2f6, Efnb2, Egr2, Elavl4, Elovl2, Elovl4, Emilin2, Eml5, Epb4.1, Epb4.1l5, Epha7, Epm2aip1, Extl2, Fabp5, Fads1, Fbf1, Fbxo16, Fdps, Fgf13, Fgf5, Fgfr3, Fhl1, Fign, Foxd4, Foxg1, Fst, Fut10, Fzd2, Fzd3, Fzd7, Gja7, Gli3, Gm1008, Gm347, Gm644, Gm784, Gnaq, Golga1, Golph2, Golph4, Gpr23, Gpsm2, Greb1, Grip1, Gulp1, H19, H1fx, Has2, Hes3, Hip1, Hmgcs1, Hmgn2, Hn1, Hnrph3, Homer1, Hoxa2, Hoxb2, Hp1bp3, Hs2st1, Hsd17b7, Idh1, Ifit2, Ift81, Igf2bp3, Igsf4a, Insig1, Insl6, Isl1, Ivns1abp, Jak2, Jam3, Kcnh2, Khdrbs1, Kif1b, Kif21a, Kif3a, Klhl17, Klhl7, Krt10, Kynu, Lamp2, Lix1, Llgl1, LOC245522, LOC544988, LOC545007, Lor, Lrp12, Lrriq2, Lss, Ltbp1, Lyrm2, Manea, Map2k6, Mapk11, Mapk12, Mapk8, Mbnl1, Mecp2, Metrn, Mettl9, Mfng, Mkrn3, Mlf1, Mllt3, Mmp16, Mospd1, Moxd1, Mpdz, Mpp5, Msn, Msx3, Mtap, Mtap1b, Mvd, Myh10, Mylk, Myo5b, Nefm, Nes, Net1, Neurod4, Nin, Nkx2-9, Nr6a1, Nrarp, Nrg1, Nsdhl, Ntrk3, Nudt10, Nudt11, Nup210, Odz3, Ogfr, Onecut2, Opn3, P2ry5, Pank3, Panx1, Pbx3, Pcdh17, Pcdh7, Pcsk9, Peli2, Phf6, Phkb, Pik3r3, Pja2, Plagl2, Plcl2, Pls3, Polk, Ppm1e, Ppm1l, Ppnr, Ppp1r1b, Prdx2, Prkar1a, Prkcn, Prkra, Prkrir, Prox1, Prr7, Ptp4a2, Pxmp3, Rab28, Rab8b, Rabl4, Rala, Rassf2, Rb1, Rbl1, Rbx1, Rcbtb2, Rgs12, Rhobtb3, Rhoe, Rhpn1, Rln1, Rnf144, Rohn, Rsn, Satb1, Sc4mol, Sc5d, Scd2, scl000510.1_2, Scoc, Scube2, Sdc1, Sdcbp, Sema5b, Sfrp1, Sfrp2, Sgcb, Sh3bgrl, Sh3kbp1, Sh3md2, Siat8c, Slc25a24, Slc2a6, Slc35f1, Slc8a1, Smad5, Smarca1, Smarce1, Smo, Snapc3, Snn, Sox13, Sp8, Spag9, Sqle, Ssr3, St18, St8sia4, Stard4, Stxbp4, Tank, Tceal8, Tceb1, Tcf19, Tcf4, Tdrd3, Tdrkh, Tead2, Tgfbr1, Thbs1, Thsd7b, Tia1, Tirap, Tm4sf10, Tmem2, Tmem32, Tmem47, Tmod2, Tph1, Tpm4, Traf3, Traf4, Tro, Trove2, Ttc3, Ttc8, Tuba1a, Twsg1, Ube2e3, Ube2n, Ubtd2, Ulk2, Usp47, Vamp3, Vezf1, Waspip, Wdr51b, Wdr6, Wdr68, Wdt3-pending, Yaf2, Ypel1, Zbtb33, Zfhx1b, Zfp238, Zfp36l1, Zfp397, Zfp41, Zfp629, Zhx1, Zhx2, Zmynd11, Zswim5
Cluster IIb 0610009O03Rik, 0610039N19Rik, 1600023A02Rik, 1700011F14Rik, 1700029G01Rik, 1700051E09Rik, 1700083M11Rik, 1810014F10Rik, 2210409E12Rik, 2310004L02Rik, 2310009N05Rik, 2310046K01Rik, 2410080H04Rik, 2410146L05Rik, 2610019F03Rik, 2610033C09Rik, 2610528J11Rik, 2900011O08Rik, 3110043J09Rik, 3830422N12Rik, 4732472I07Rik, 4921530G04Rik, 4930432K21Rik, 4930517K11Rik, 4930569K13Rik, 4930583H14Rik, 4933428G20Rik, 6330530A05Rik, 9130404D14Rik, 9330186A19Rik, A130092J06Rik, A430089I19Rik, Aard, Abcb1b, Acas2l, Acp6, Adam19, Adora2b, Aes, Agpat2, AI428936, AI429613, Akp2, Akt1s1, Aldh2, Aldoa, Aldoc, Alpk3, Amdhd2, Amid, Ankrd38, Ankrd47, Anxa11, Anxa6, Anxa8, Ap1m2, Aplp1, Apoa2, Apoc1, Apoe, Aqp3, Arhgdib, Arhgef19, Arhgef3, As3mt, Ass1, Atad4, Atp1b1, Atp2a3, AU016977, AU022751, AW049765, Axud1, BC003277, BC004728, BC013481, BC021614, BC022765, BC023754, BC025833, BC051227, Bckdha, Bcl6, Blvrb, Bmp4, C130038G02Rik, C80638, Camk2b, Capg, Capn1, Capn5, Cblc, Cbs, Ccdc3, Cd68, Cd79b, Cd9, Cd97, Cdc42ep3, Chi3l1, Cklfsf4, Ckmt1, Cldn4, Clgn, Cnnm2, Cox7a1, Cox8c, Cpn1, Cpne8, Crtap, Crym, Csad, Csrp2, Cyp2j9, Cyp2s1, D130058I21Rik, D15Ertd366e, D2Ertd391e, Dact2, Dedd2, Defb42, Dgka, Dhrs6, Dhrs8, Dmrtc2, Dpp4, Dpp7, Dscr1l2, Dtx1, E030003N15Rik, Ech1, Echdc2, Efcab4a, Efhd1, Egfl7, Egln3, Egr1, Eif4a2, Ela2, Elf3, Elmo3, Emp1, Eng, Epas1, Epb4.9, Ephx1, Ephx2, Eppk1, Ercc2, Esam1, F11r, F2rl1, Fbxo2, Fbxo27, Fbxo6b, Fkbp6, Folr1, Foxa3, Fxyd5, Gadd45a, Galt, Gbp4, Gdf15, Gjb3, Glb1, Glrx, Gm397, Gm817, Gmfg, Gmpr, Gna15, Gnpda1, Gprc5a, Gpx4, Grasp, Grb7, Grn, Gsdmdc1, Gsta3, Gstm1, Gstm2, Gstp2, Gstt2, Gstt3, Gulo, Hagh, Hcn2, Hcph, Hexb, Hist1h2bj, Hsd3b7, Hspb1, Icam1, Idb1, Ier3, Ifi30, Ifitm3, Il28ra, Inpp5d, Irak2, Irf1, Irf6, Isgf3g, Itgb4, Itpka, Itpr3, Jak3, Jam2, Junb, Kcnk5, Kcnk6, Klf4, Klhl13, Klk1b27, Klk5, Klk6, Lat, Ldhc, Ldoc1, Lgals3, Lgals4, Llglh2, Lmna, Lmo6, LOC223262, LOC380705, LOC433722, LOC435337, LOC548597, LOC625360, LOC626391, LOC639910, Lrpap1, Lu, Ly6a, Ly6g6e, M6prbp1, Map3k6, Mapk13, Mgmt, Mical3, Mkrn1, Mmrn2, Mov10, Mov10l1, Mreg, Mta3, Mvp, Myd116, Myd88, Myl7, Nanog, Ndg2, Ndp52, Ndrg2, Nfatc2ip, Nfe2l2, Nfkbia, Nid2, Nodal, Nos1, Npepl1, Nptx2, Nr1h2, Nrgn, Nupr1, Oas1d, Oas1g, Ostf1, Pacsin1, Paox, Pcolce, Pcsk1n, Pde8a, Pdgfc, Pdk4, Pdlim1, Pecam1, Pem, Pim3, Pip5k2c, Piwil2, Pkp3, Pla2g10, Plac8, Plcb3, Plcd3, Plp2, Pltp, Plxdc1, Pmm1, Pnpla2, Ppfibp2, Ppgb, Ppm1k, Ppp1r13b, Ppp1r14a, Prss19, Prss35, Prune, Ptprv, Pvr, Rad52b, Rage, Rasgrp1, Rassf5, Rbpms, Rbpms2, Rec8L1, Relb, Renbp, Rlbp1, Rnf135, Robo4, Rpl3l, S100a10, S100a6, Sat1, Scarf1, Sdc4, Sdcbp2, Selenbp1, Serpinb6a, Sfn, Sgk3, Sh3tc1, Slc12a8, Slc1a1, Slc22a18, Slc24a6, Slc25a20, Slc29a1, Slc2a3, Slc38a4, Slc39a4, Smc1l2, Snta1, Snx3, Soat2, Sorl1, Sox15, Spic, Spint2, Ssbp4, St14, Stag3, Stard8, Stat3, Stat6, Stx3, Stxbp2, Sult2b1, Susd2, Syngr1, Syngr3, Tbx3, Tcfap2c, Tcl1, Tcstv1, Tcstv3, Tead4, Tgfb1, Tgif, Timm8a2, Tjp3, Tle6, Tmco4, Tmem23, Tmem38b, Tmie, Tnnt1, Tpd52, Trf, Trim25, Trim47, Tspan17, Tsrc1, Tuba3, Tulp2, Txnip, Ulk1, Vil2, Vkorc1, Wbscr21, Wdr21, Wdr34, Wdr45, Wt1, Zap70, Zbtb7, Zc3hdc1, Zfp36
Cluster IIIa 0610041G09Rik, 1110030H18Rik, 1200009O22Rik, 1500031H04Rik, 1700018O18Rik, 1810009M01Rik, 2310016C16Rik, 2510009E07Rik, 2600011E07Rik, 2610020H15Rik, 2810003C17Rik, 2900093B09Rik, 3110004L20Rik, 4632425D07Rik, 6330403K07Rik, A830059I20Rik, Abat, Acta2, Actc1, Anxa3, Anxa5, Apcdd1, App, Astn1, AW121567, AW146242, B230104P22Rik, B2m, BC034054, BC039093, BC046404, Bin1, Bmp1, C130076O07Rik, Capn6, Car4, Carhsp1, Cbln1, Ccnd2, Cdk5r1, Cdkn1a, Cdkn1c, Chrna4, Chst1, Cklfsf3, Clic6, Cmtm8, Cntnap2, Col2a1, Col4a1, Col4a2, Col5a1, Crabp1, Crabp2, Cxcr4, D0H4S114, Dab2, Dbx1, Defcr-rs2, Dlk1, Dmrta2, Dpysl2, Dpysl4, Dpysl5, Dtx4, Ednra, Ednrb, Efna5, Efnb1, Emid2, Eml1, Ephb1, Farp1, Fez1, Fgfbp3, Flrt3, Flt1, G431001E03Rik, Gadd45g, Gap43, Gm1673, Gpr177, Gprin1, Hes5, Hmgn3, Igf2, Igfbp4, Igfbp5, Insm1, Irf2, Irs2, Irx2, Irx3, Irx5, Krt1-18, Krt2-8, Lbh, Lhfp, Lhx1, LOC381633, Lrrn1, Ly6h, Maged2, Magee1, Mapkapk2, Mbp, Meis1, Mest, Mfap2, Mfap4, Mic2l1, Mmd, Mrg1, Ndn, Nedd9, Nelf, Nkx6-1, Nnat, Nr2f1, Olfm1, Otx1, Parva, Pdzrn3, Pea15, Pfn2, Phc2, Pkia, Pknox2, Pmp22, Podxl2, Prnp, Ptn, Ptprd, Punc, Rab6b, Rbmx, Reprimo, Rfx4, Rgma, Sall2, Scarf2, Sdc2, Sdc3, Sema3f, Sepn1, Sepw1, Serf1, Serpinh1, Shd, Shh, Slit2, Smarca2, Sox21, Sox9, Ssb4, Ssbp2, Sst, Stmn4, Syt11, T, Tcfap2b, Tdrd7, Timp2, Tmprss2, Tnfrsf19, Tnrc9, Trib2, Vim, Vtn, Zfp521, Zfp608, Zic1
Cluster IIIb 1110003A17Rik, 1110007C05Rik, 1110012D08Rik, 1110014L17Rik, 1110038D17Rik, 1110063G11Rik, 1190017O12Rik, 1200013B22Rik, 1300002F13Rik, 1500003O03Rik, 1500011H22Rik, 1700019E19Rik, 1700021K19Rik, 1700023M03Rik, 1700025G04Rik, 1700088E04Rik, 1810007P19Rik, 1810009H17Rik, 1810010N17Rik, 1810011O10Rik, 1810021J13Rik, 1810037C20Rik, 1810057P16Rik, 1810073P09Rik, 2010011I20Rik, 2210417J20Rik, 2310003P10Rik, 2310015N21Rik, 2310021P13Rik, 2310041H06Rik, 2310045A20Rik, 2310067E08Rik, 2610110G12Rik, 2610203E10Rik, 2610524A10Rik, 2610528K11Rik, 2700055K07Rik, 2700083E18Rik, 2810022L02Rik, 2810413I22Rik, 2810427I04Rik, 2900046G09Rik, 3110018K12Rik, 3632413B07Rik, 3632451O06Rik, 4631426J05Rik, 4632417K02, 4833421E05Rik, 4930438M06Rik, 4930488P06Rik, 4930506D23Rik, 4932442K08Rik, 5430432M24Rik, 5830404H04Rik, 5830461H18Rik, 5930434B04Rik, 6030410K14Rik, 6330442E10Rik, 6330505N24Rik, 6430559E15Rik, 6720430O15, 60 Chapter C – Coordinated waves of gene expression during neuronal differentiation of embryonic stem cells as basis for novel approaches to developmental neurotoxicity testing
8430427H17Rik, 9030205A07Rik, 9130005N14Rik, 9130213B05Rik, 9330132O05Rik, 9530068E07Rik, A230098A12Rik, A830020B06Rik, Aatk, Abca3, Abhd8, Acas2, Acta1, Actc1, Acvr1, Adcy2, Adcy6, AF322649, Ahi1, AI427515, AI591476, AI604832, AJ430384, Ak3, Akr1e1, Akt3, Alcam, Alg2, Als2cr2, Amfr, Angptl2, Ank2, Ank3, Ap3m2, Apbb1, Arg1, Arhgap24, Arid3a, Arl3, Arl4c, Arnt2, Ascl1, Asphd2, Atbf1, Atp6v0e2, Atp6v1g2, AU040320, AU040576, AW049604, AW456874, AW548124, AW822216, Axin2, Axl, B130050I23Rik, B230114P05Rik, B230312A22Rik, B230399E16Rik, B3gat1, B3gnt6, B430104H02Rik, B430218L07Rik, Bach2, Bag2, Bahd1, Bai2, BC006583, BC011468, BC019977, BC024955, BC025575, BC026432, BC026744, BC028975, BC030477, BC036718, BC046386, BC051244, BC063749, BC065120, BC067047, Bhlhb5, Bicc1, Bmp1, Bzrap1, C130086A10, C130099A20Rik, C230009H10Rik, C330023F11Rik, C630013N10Rik, Cacna1g, Cacna1h, Cacng4, Cadps, Cadps2, Cald1, Capn2, Car14, Cart, Casp6, Catnd2, Cbx8, Ccl27, Cd248, Cd40, Cd47, Cdh22, Cdkn1a, Ceecam1, Celsr1, Celsr2, Cerk, Chrd, Chst2, Cib2, Ciz1, Cldn10, Clic1, Clip1, Clk3, Cmtm8, Col23a1, Col4a5, Commd3, Copz2, Cotl1, Cplx2, Cpne2, Cri1, Crmp1, Csf1, Ctdspl, Ctgf, Ctxn, Cuedc1, Cugbp2, Cutl1, Cx3cl1, Cxx1c, Cxxc4, Cyfip2, Cyp51, Cyr61, D030028O16Rik, D10Bwg0940e, D10Ucla1, D11Bwg0517e, D14Ertd171e, D15Ertd405e, D2Bwg0891e, D2Bwg1356e, D330024H06Rik, D330037A14Rik, D430039N05Rik, D5Ertd593e, D630045E04Rik, D8Ertd82e, Dapk1, Dbn1, Dcx, Dcxr, Ddit4, Ddit4l, Ddr1, Defcr-rs7, Dp1, Dscr5, Dtx4, E130307J07Rik, E430021N18Rik, E430036I04Rik, Ebf2, Ebf3, Efha1, Efs, Ehbp1, Elavl3, Epb4.1l3, Epha3, Ephb2, Ets1, Evl, F2r, F8a, Fabp7, Fads2, Fbln2, Fbn1, Fbxo36, Fgf15, Fgf8, Fgfbp3, Fgfr2, Fjx1, Flrt3, Fndc3b, Foxa1, Foxa2, Foxb1, Foxc1, Frzb, Fstl1, Fzd1, Fzd10, Gabarap, Gadd45g, Gamt, Gdap1, Gdi1, Gdpd2, Ghr, Glt8d1, Gnai2, Gnas, Gnb4, Gnb5, Gng10, Gp38, Gpc1, Gpc2, Gpc3, Gpc4, Gpc6, Gpm6a, Gpr85, Gpsm1, Grb10, Grcc10, Gria3, Grik5, Grina, Grip1, Gsc, Gsh1, Gstm5, Gyg1, H1f0, H2-Ab1, H2-Ke6, Haghl, Hdac7a, Heph, Hes6, Hey1, Hpcal1, Hrmt1l1, Hs3st3a1, Hsd11b2, Hspa5bp1, Hyal1, Hyal2, Id2, Idb4, Igf1r, Igfbpl1, Il18, Ilk, Inpp5e, Inpp5f, Itga3, Itm2a, Jmjd3, Jun, Kcnab2, Kctd10, Kctd12, Kif1b, Kif3c, Kif5c, Kirrel3, Klhdc2, Kns2, L3mbtl3, Lbh, Lemd2, Leprel1, Letmd1, Lhfp, Lhfpl2, Litaf, Lman2l, Lmo4, Lmx1a, Lmyc1, LOC245297, LOC278097, LOC381813, Lrfn3, Lrig1, Lrp12, Lrp4, Lrrc49, Lrrc4b, Lrrk1, Lsamp, Lsp1, Lxn, Lztr1, Lzts2, Maged1, Mapk8ip1, Mapk8ip2, Mapre2, Marcks, Mark1, Mdk, Megf10, Mest, Mfap2, Mgst1, Mic2l1, Mllt11, Mmp15, Mmp2, Mtap2, Mtch1, Mtvr2, Mycbpap, Myt1, Naglu, Nat6, Nav1, Ncald, Ncan, Nck2, Ncoa6, Ndst1, Nedd9, Nek9, Nelf, Nfil3, Nicn1, Nkx2-2, Nkx2-3, Nlgn2, Nme5, Nnat, Nog, Nope, Notch3, Npr2, Nr2f2, Nrp, Nrxn2, Nsg1, Nsg2, Nt5m, Ntng1, Ntrk2, Ntrk3, Nuak1, Nudt7, Oat, Olfm1, Olfm2, Olfml2b, Olfml3, Osbpl6, Pacrg, Pacs1, Pak3, Palm, Pam, Papss1, Pard6g, Parp6, Pbx1, Pbx2, Pcdha6, Pcdhb22, Pcdhb3, Pcyox1, Peg3, Pftk1, Phf2, Phlda1, Pik3r1, Pitpnm2, Pitx2, Pja1, Plekhb2, Plekhg2, Plxna2, Pon2, Ppapdc1, Ppp1r3c, Ppp1r9b, Prickle1, Prkcm, Prkd2, Prmt2, Ptpns1, Ptpra, Ptprd, Ptprs, Ptx3, Purg, Rab11fip3, Rab13, Rab15, Rab38, Rab6ip1, Raet1b, Ramp2, Rasl10b, Rasl11b, Rassf4, Rbms1, Rbms3, Rbp1, Reep1, Reln, Rem2, Repin1, Rerg, Rgl1, Rgmb, Rgs16, Rgs17, Rgs4, Rhob, Rhod, Rhou, Rnf103, Rnf11, Ror2, Rshl2, Rsn, Rspo3, Rtn1, Rtn3, Rufy3, Rutbc2, Sat2, Sbk, Scand1, Scara3, Scd1, Scg3, Scrn1, Sdcbp, Sdk1, Selk, Sema3f, Sema6d, Sept3, sept5, Sept6, Sept8, Serinc2, Serpine2, Serpinf1, Sgne1, Sh3bp2, Shh, Siat10, Siat8b, Siat9, Six5, Slc22a17, Slc24a3, Slc27a3, Slc35c1, Slc39a6, Slc39a8, Slitl2, Slitrk5, Smad3, Smo, Smpd1, Smpd3, Snrk, Sort1, Sox17, Sox21, Sox5, Sparc, Spg20, Spin2, Spon1, Srebp2, Srr, Ssbp3, St5, St6gal1, St7, Stambp, Stk39, Stx7, Stxbp1, Sulf1, Suv39h1, Syt11, Tagln3, Tax1bp3, Tbc1d5, Tceal1, Tceal5, Tcf12, Tcf7l2, Tcfap2b, Tcte1l, Tesk1, Tex27, Tgfb1i1, Tgfb2, Thbs3, Thnsl2, Timp3, Tm4sf12, Tm4sf6, Tm4sf8, Tmc7, Tmem136, Tmem43, Tmem53, Tmem66, Tmem9, Tmem9b, Tnfaip2, Tpbg, Tpm1, Tpm2, Trafd1, Trim32, Trp53i11, Trp53inp2, Tsc22d3, Tspan7, Ttyh3, Tubb2b, Tulp4, Txndc13, Ube2e2, Ulk2, Unc5c, Uncx4.1, Usp11, Usp3, Vamp4, Vcam1, Vgll4, Vit, Vldlr, Wbp1, Wdr22, Wfdc1, Whrn, Wnt1, Wnt3a, Wnt5b, Wnt7a, Wnt7b, Wnt8b, Xpr1, Zadh2, Zbtb5, Zc3hdc6, Zfhx1a, Zfp202, Zfp251, Zfp282, Zfp30, Zfp307, Zfp316, Zfp334, Zfp46, Zfp537, Zfp637, Zyx
Cluster IV 0610007C21Rik, 0610031J06Rik, 1110003E01Rik, 1110007C02Rik, 1190002A17Rik, 1190007F08Rik, 1200006F02Rik, 1300010A20Rik, 1300010K09Rik, 1500004A08Rik, 1700027N10Rik, 1700047I17Rik, 1810009B06Rik, 1810020D17Rik, 1810054O13Rik, 1810057E01Rik, 2210403B10Rik, 2300002D11Rik, 2310058J06Rik, 2310061N23Rik, 2310066E14Rik, 2600003E23Rik, 2600010E01Rik, 2610027C15Rik, 2610204M08Rik, 2610318I18Rik, 2700050C12Rik, 2810046M22Rik, 2810417M05Rik, 2900026H06Rik, 3110032G18Rik, 3222401M22Rik, 3830422N12Rik, 4631423F02Rik, 4922503N01Rik, 4930402H24Rik, 5230400G24Rik, 5730410E15Rik, 5730469M10Rik, 5730537D05Rik, 6430598A04Rik, 8430420C20Rik, 9030409G11Rik, 9630019K15Rik, A2bp1, A730017C20Rik, A930021H16Rik, AB182283, Abcg4, Abhd4, Acadl, Adamts4, Adck4, Add1, Adfp, Adra2a, Adssl1, AI450948, AI481100, AI481750, AI842396, Aig1, AL024069, Amdhd2, Amotl2, Ankrd56, Anxa1, Anxa2, Ap3b2, Apob48r, Arf3, Arf5, Arfgap3, Arl10b, Arpc1b, Atf3, Atp6v0a1, Atp6v0b, Atp9a, B230380D07Rik, B3gat3, B430119L13Rik, Bag3, Bai2, BC003324, BC016235, BC018242, BC019731, BC023957, BC025872, BC026370, BC026585, BC029214, BC031353, BC038286, BC051083, BC058638, BC061259, Bcl11a, Bcl11b, Bcl9l, Bhlhb2, Blcap, Bmf, Bmp7, Bst2, Btbd14a, Btg1, C230075L19Rik, Cacnb3, Calu, Camk1, Car13, Car2, Caskin1, Cbara1, Cd151, Cd63, Cdc42ep5, Cdkn2a, Cdkn2b, Chgb, Chrnb1, Chst5, Chst8, Cipp, Cited1, Cited2, Cited4, Cldn3, Cldn6, Cln3, Cln6, Cntn1, Col16a1, Col1a2, Copz2, Cpxm1, Creb3, Creld1, Cryab, Csrp1, Cst3, Ctsz, Cul7, Cxx1a, Cyba, D7Bwg0611e, Dab2, Dap, Dapk2, Dbp, Dcamkl1, Des, Dfy, Dhrs7, Diras1, Dlx1, Dmrt3, Dnaic1, Dnajb10, Dnajc12, Dner, Dok5, Dpysl3, Dscr1l1, Dusp22, Dusp8, Dyrk1b, Dysf, E030006K04Rik, E130309F12Rik, Ecel1, Ecm1, Ednrb, Efemp2, Efna5, EG630499, Egfr, Elk3, Elovl1, Emp2, Emp3, En1, En2, Eno2, Eno3, Eps8l2, Fbln1, Fbxl12, Fbxo4, Fbxw4, Fezf2, Fgfbp1, Fhl2, Fkbp7, Foxc2, Foxd1, Foxq1, Frmd6, Furin, Fvt1, Gabarapl1, Gadd45b, Gal, Gata3, Gata6, Gats, Gba, Gbp2, Gdap1l1, Gdf10, Gdi1, Gipc1, Gipc2, Gja9, Gnao, Gnb2, Gng13, Gng8, Gns, Gpr124, Gpr137, Gpr146, Gprc5c, Gpx3, Grcc9, H13, H2-Bl, H2-Q7, H2-T17, H2-T23, Hcrtr1, Hdac11, Hebp1, Hexa, Hint3, Hnt, Hoxa1, Hoxc6, Hrc, Hs3st1, Hspb2, Hspb8, Idb3, Ifngr2, Igf2r, Igfbp2, Igfbp3, Igsf11, Igtp, Ihpk1, Il11ra1, Ilvbl, Immp2l, Islr2, Itga5, Itgb5, Itpr2, Kcna5, Kcnmb4, Kif1a, Kifc2, Klhl6, Kremen, Krt1-19, Krt7, Lamp2, Laptm4a, Ldb2, Leprel2, Lgals1, Lhx5, Lmcd1, Lmo2, LOC212390, LOC268935, LOC381297, LOC381629, Loh11cr2a, Loxl1, Lrp1, Lrp10, Lrrc8a, Lrrfip1, Lrrn2, Lrrn3, Mab21l2, Map3k11, Mbp, Meox1, Mfge8, Mgat4b, MGC106740, MGC18837, Micall2, Mmp14, Mmp23, Mogat2, Mpra, Msx1, Msx2, Mxd4, Mxra8, Myadm, Myl1, Myl3, Naga, Nagk, Nenf, Neu1, Neurog2, Nfatc4, Npc2, Npdc1, Oaz2, Optn, ORF18, P2rx4, Pbxip1, Pdgfrl, Pdlim2, Pdlim3, Pdlim4, Per2, Pga5, Pgm2, Phlda3, Pigt, Pla2g7, Plac1, Pld3, Plekha2, Plekha6, Plk2, Pltp, Podxl, Polg, Ppap2b, Ppic, Ppp3ca, Ppp3cc, Prkcz, Prnd, Prss8, Prtn3, Psap, Psd2, Pthr1, Ptpro, Pttg1ip, Pvrl2, Pxmp4, Pygb, Pygl, Pyy, Qscn6, Rab12, Rab3a, Rab7l1, Rabac1, Ralb, Rap2ip, Rassf3, Rbms2, Rcn3, Rftn2, Rgs3, Rhbdl4, Rhoc, Rhog, Rhoj, Rin3, Ripk4, Rit1, Rnf11, Rnf182, Rnf30, Rnpepl1, Rpel1, Rragd, Rras, Rtn1, Rtn2, Rtn4rl1, S100a11, Scn3b, Scotin, Scube3, Scx, Sdsl, Sepp1, Sertad4, Sesn1, Sez6, Sez6l, Sfxn5, Sgpl1, Sh3d4, Shc1, Sirt2, Slc13a4, Slc14a2, Slc17a6, Slc35a2, Slc38a5, Slc41a3, Slc4a2, Slc5a5, Slc7a8, Slc9a6, Soat1, Sp5, Spink3, Spint1, Spnb1, Sri, Stard10, Stmn3, Stmn4, Sv2a, Syn2, Syt1, Syt14l, Tacstd2, Tal2, Tapbp, Tbrg1, Tceal6, Tcfap2a, Tcirg1, Tcn2, Tes, Tex264, Tgfbr2, Thbs2, Thra, Tmbim1, Tmc4, Tmed3, Tmem108, Tmem116, Tmem119, Tmem150, Tmem35, Tmem66, Tnfrsf10b, Tnfrsf12a, Tnfrsf1a, Tnfrsf5, Tnnc1, Tpcn1, Tpm2, Tpra40, Trim3, Trpc4ap, Trpt1, Ttyh1, Tubb4, Tulp1, Twist1, Twist2, Uap1l1, Ubc, Unc5b, Unc84b, Usp2, Vamp5, Vamp8, Vat1, Viaat, Wbscr24, Wipi1, Wnt5a, Yipf3, Zcchc12, Zfp467, Zfpm1, Zic4
Cluster V 0710005M24Rik, 1110032E23Rik, 1190002H23Rik, 1200009I06Rik, 1300007L22Rik, 1300014I06Rik, 1500015O10Rik, 1700084C01Rik, 1810015C04Rik, 2300002D11Rik, 2310043K02Rik, 2310047C17Rik, 2410080H04Rik, 2510004L01Rik, 2610001E17Rik, 2610109H07Rik, 4921520P21Rik, 4930422J18Rik, 5330414D10Rik, 5430431G03Rik, 5730453H04Rik, 8430408G22Rik, 9130211I03Rik, 61 Chapter C – Coordinated waves of gene expression during neuronal differentiation of embryonic stem cells as basis for novel approaches to developmental neurotoxicity testing
9130213B05Rik, 9330140K16Rik, 9930017A07Rik, A630005A06Rik, AA467197, Acadl, Adcy8, Adra2b, Aebp1, AF067061, Afp, Ahnak, AI593442, Amn, Apoa1, Apod, Aqp1, Aqp4, Arhgap22, Arhgef6, Arid5b, Atp1a2, Atp1b2, Atp9a, Aurkc, B230342M21Rik, BC011209, BC040774, BC049816, BC055811, BC061212, BC080695, Bcan, Bet1l, Bgn, Bhmt2, Brdt, Bzrpl1, C130036G08, C1qtnf4, C2, Calb2, Camk2n1, Capzb, Car11, Cav1, Cbfa2t1h, Ccdc68, Cdh13, Cdo1, Cebpb, Chodl, Chpt1, Cidea, Cldn11, Cldn23, Clu, Col1a1, Col3a1, Col6a1, Col6a2, Col9a2, Corin, Cox6a2, Crhbp, Crim2, Crx, Crxos1, Csf1r, Csh2, Cspg5, Ctsb, Ctsc, Ctsf, Ctsh, Ctsj, Ctsr, Cxcl12, Cxcl16, Cygb, Cyp1b1, Cyp2d22, Cyp4f13, Cyp4v3, Cyp7b1, D12Ertd647e, D930010J01Rik, D9Ertd392e, Daf1, Dazl, Dcn, Ddit3, Diras2, Dkk3, Dkkl1, Dmkn, Dmrtc1a, Dmrtc1b, Dnaic1, Doxl2, Dpep3, E130203B14Rik, Eef1a2, Egfr, Emcn, Enpp1, Enpp2, Enpp5, Entpd2, Eps8l2, Fabp3, Fas, Fbxo32, Fcgrt, Fgd6, Fibcd1, Flrt2, Fn3k, Fos, Foxf2, Fthfd, G1p2, Gad1, Gas6, Gata2, Gbp1, Gch1, Gcnt1, Gfap, Gja9, Gjb2, Gm2a, Gm428, Gm648, Gm691, Gpm6a, Gria2, Guca1a, Gypc, H2-gs17, H2-T23, Hic1, Hist1h1c, Hist1h2bc, Hmgcs2, Hoxb4, Hoxd8, Hpgd, Hspa2, Htra1, Ifit3, Igfbp6, Igfbp7, Il10rb, Irs3, Itga11, Itm2b, Kai1, Kcnc4, Kdelr3, Kdt1, Kng1, Kng2, Lamp2, Lbp, Lcp1, Lgals3bp, Lgals6, Lgals9, Lgmn, Lims2, LOC240906, LOC270599, LOC333473, LOC433721, LOC434729, LOC547343, Lox, Ltbp3, Lum, Luzp2, Ly64, Mapt, Matn4, Mbnl2, Meox2, Mfge8, Mglap, Mgll, Mgst2, Mmd2, Mt3, Mx2, Myh8, Myl2, Myl4, Myo6, Nbl1, Ndrg1, Ndrl, Nfib, Nfix, Nid1, Nppb, Nrn1, Nrxn1, Nt5e, Nxf7, Oasl2, Ogn, Olfml1, Olig1, Osr2, OTTMUSG00000010537, P2ry14, Pdgfb, Pdgfra, Pdzk1, Pink1, Pla1a, Pnck, Pold4, Pparg, Prelp, Prg, Prkar1b, Pros1, Prrx1, Psca, Psx1, Psx2, Qpct, Rab11fip5, Rabac1, Rapgef3, Rarb, Rarres2, Rasgrf1, Rbp4, Rgs9, Rin2, Rit2, Rnase1, Rnase4, Rora, Rspo2, Rusc2, Rxra, S100a1, S100a13, Sbsn, Scg2, Scrg1, Sct, Serpina3n, Serpinb6b, Serpinb9e, Serpinb9f, Slc17a5, Slc17a8, Slc1a3, Slc23a3, Slc25a18, Slc2a13, Slc39a12, Slc40a1, Slc6a12, Slc6a13, Slco2a1, Slco4a1, Snca, Snx21, Sorbs1, Sostdc1, Sparcl1, Sphk1, Spon2, Sspn, Stra8, Syp, Syt4, Taf7l, Tgfbi, Tgm2, Thbd, Thy1, Tinagl, Tlr2, Tmem92, Tnc, Tpcn1, Trpm6, Ttr, Tuft1, Ube1l, Usp18, Usp29, Vdr, Wnt6, Ypel3, Zfp36l3
62 Chapter C – Coordinated waves of gene expression during neuronal differentiation of embryonic stem cells as basis for novel approaches to developmental neurotoxicity testing
Figure S4: Listing and assignment of the genes graphed in figure 5
All regulated genes were individually examined on the basis of the pertinent literature for identification of those involved in chromatin structure and epigenetic regulation. The genes were grouped into categories as indicated in the first column. Further functional information was added in the last column, where this was unambiguously available. Genes related to cell cycle, DNA replication and DNA repair were extracted from the relevant GO categories.
cluster accession symbol comment Ref. chromosome Ib NM_001077712.1 Stag2 (SA2) cohesin subunit 1 NM_021886.1 Cenph kinetochor organization 2 NM_009282.2 Stag1 (SA1) cohesin subunit 1 NM_025795.2 Ncaph2 (H2) condensin subunit 1 NM_012039.1 Zw10 kinetochor organization 4 NM_016692.1 Incenp centromere protein 2 Apitd1 NM_027263.1 Kinetochor organization 6 (Cenps) NM_019710.1 Smc1a cohesin subunit 1 NM_027263.1 Apitd1 centromere protein NM_145924.2 Cenpi centromere protein 3 NM_025495.1 Cenpp centromere protein 6 XM_127861.2 Cenpj centromere protein IIb NM_080470.1 Smc1b cohesin subunit (meiosis) 1 NM_016964.1 Stag3 (SA3) cohesin subunit (meiosis) 8 Cbx5 (HP1 heterochromatin Ib NM_001076789.1 heterochromatin structure 4 alpha) NM_172663.2 Epc2 enhancer of polycomb 10 NM_025900.1 Dek 11 Cbx1 (HP1 IIa NM_007622.2 heterochromatin structure 4 beta) x-inactivation NM_009122 Satb1 regulates ESC differentiation 12 and nanog expression heterochromatin protein 1, NM_010470.1 Hp1bp3 binding protein 3 Hmgb2l1 HMG box domain euchromatin Ia NM_178017.1 (Hmgxb4) containing 4 Highly expressed during Ib NM_016660.1 Hmga1 5 embryonic development NM_008252.2 Hmgb2 6 NM_016710.1 Nsbp1 7 IIa NM_016957.3 Hmgn2 8 IIIa NM_175074.1 Hmgn3 8 chromatin Smarcc1 Ia NM_009211.1 Component of esBAF 17 remodeling (BAF155) Smarcad1 XM_132597.3 9 (Etl1) SNF2-related helicase, NM_009530.1 Atrx 19, 20 Interneuronal survival Rbbp7 NM_009031.2 Histone chaperone 21 (Rbap46) NM_001081267.1 Rsf1 Spacing factor 21, 22 NM_024184.1 Asf1b nucleosome assembly 21 NM_015781.2 Nap1l1 nucleosome assembly 21, 23 NM_013733.2 Chaf1a nucleosome assembly 21 63 Chapter C – Coordinated waves of gene expression during neuronal differentiation of embryonic stem cells as basis for novel approaches to developmental neurotoxicity testing
NM_025541.2 Asf1a nucleosome assembly 21 Smarce1 IIa NM_020618.3 SWI/SNF-related 10 (BAF57) SWI/SNF-related, specific Smarca1 NM_053123.3 for postnatal neurons and in 25 (Snf2L) adult brain Smarca2 IIIa NM_011416.2 (brahma, SWI/SNF-related 10 Snf2a) IIIb NM_133741.1 Snrk SNF-1 related kinase 26 histone modification Ia NM_011791.2 Ash2l H3K4Me 11 Set-domain containing NM_001035123.1 Setd6 protein NM_021876.1 Eed H3K27Me 11 Myst4 histone acetyl transferase, NM_017479 28 (querkopf) cerebral cortex development H3K9Me1 and 2 NM_173001.1 Jmjd1a 11 demethylation H3K36Me1, 2 and 3 NM_144787 Jmjd2c 11 demethylation NM_029441.1 Cdyl2 chromodomain potein 29 NM_009881 Cdyl chromodomain potein 29 Ib NM_199196.1 Suz12 PRC1 associated protein 4 NM_007971.1 Ezh2 K27Me3 11 NM_008739 Nsd1 H3K36Me, H4K20Me 11 NM_145414.1 Nsun5 Putative methyltransferase 12 NM_172567.1 Mettl2 Putative methyltransferase 31 Putative methyltransferase, NM_144918.1 Smyd5 Set-domain containing NM_172545.1 Ehmt1 H3K9Me2 11 NM_133740.1 Prmt3 arginine methylation 32 BC006705 NM_145404.1 arginine methylation 13 (PRMT7) NM_178891.4 Prmt6 H3R2Me2 14 NM_007415.2 Parp1 poly-(ADP-ribosyl)ation 15 lysine demethylation, NM_009483.1 Utx 16 H3K27Me NM_008228.1 Hdac1 histone deacetylation 17 NM_019812.1 Sirt1 histone deacetylation 17 NM_021788.1 Sap30 histone deacetylation 38 NM_181586.2 Sirt6 histone deacetylation 17 NM_177239.2 Mysm1 histone de-ubiquitination 39 Wdr77 NM_027432.3 interaction with H2A 18 (MEP-50) NM_026539.1 Chd1l chromodomain protein 19 NM_007690.1 Chd1 chromodomain protein 19 NM_145125.1 Brwd1 bromodomain protein 20 IIa NM_007552.3 Bmi1 PRC complex 4 Ube2n NM_080560.2 histone ubiquitination 21 (Ubc13) Methyl transferase like NM_021554.2 Mettl9 protein chromodomain protein, NM_007623.2 Cbx2 (Pc) 9, 44 H3K27Me3 binding tudor domain protein, binds NM_172605.2 Tdrd3 45 RMe2 XM_131021.5 Tdrkh tudor domain protein 46 64 Chapter C – Coordinated waves of gene expression during neuronal differentiation of embryonic stem cells as basis for novel approaches to developmental neurotoxicity testing
IIIa NM_146142.1 Tdrd7 tudor domain protein 47 IIIb NM_011514.1 Suv39h1 H3K9Me3 11 NM_133182 Hrmt1l1 Prmt2 variant 1 48 NM_001077638.1 Prmt2 Prmt2 variant 2 48 H3K27Me demethylase, NM_001017426.1 Jmjd3 Required for neural 22 commitment NM_019572.2 Hdac7a histone deacetylation 17 chromo domain protein; NM_013926.1 Cbx8 9, 44 PRC1 complex NM_001045523.1 Bahd1 bromo domain protein 50 histone deacetylation, IV NM_144919.1 Hdac11 expressed during murine 51 brain development V NM_054054 Brdt bromo domain protein 52 histones Ia NM_178187.2 Hist1h2ae H2A consensus 23 NM_175665.1 Hist1h2bk histone H2B, S124A 23 NM_178197.1 Hist1h2bh H2B consensus 23 NM_178204 Hist1h3d H3.2 23 NM_178205.1 Hist1h3e H3.2 23 NM_013550.3 Hist1h3a H3.1 23 NM_178210.1 Hist1h4j H4 23 NM_175656 Hist1h4i H4 23 NM_175657.1 Hist1h4m H4 23 NM_178211.1 Hist1h4k H4 23 Ib NM_016750.1 H2afz H2A variant H2AZ 23 NM_175660.1 Hist1h2ab H2A consensus 23 NM_178186.2 Hist1h2ag H2A consensus 23 NM_178188.3 Hist1h2ad H2A consensus 23 NM_175653.1 Hist1h3c H3.1 23 NM_175655.1 Hist1h4f H4 23 IIa NM_198622.1 H1fx H1.X 24 IIb NM_178198.1 Hist1h2bj H2B consensus 23 V NM_015786.1 Hist1h1c H1.2 24 NM_023422.1 Hist1h2bc H2B; S75G 23 DNA methylation Ia NM_019448.2 Dnmt3l accessory Dnmt 4 Ib NM_010068.1 Dnmt3b de novo Dnmt 4 NM_010066.2 Dnmt1 maintenance Dnmt 4 NM_013595.1 Mbd3 binding to DNAMe 4 IIa NM_010788.1 Mecp2 binding to DNAMe 4 component of the Polycomb repressive complex (PRC), neuro-specific
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66 Chapter C – Coordinated waves of gene expression during neuronal differentiation of embryonic stem cells as basis for novel approaches to developmental neurotoxicity testing
Figure S5: Listing, assignment, and literature references of the genes graphed in figure 6D.
Gene Accession for- mid- hind- Cluster full name literature name number brain brain brain
App NM_007471.1 IIIa Amyloid beta (A4) precursor protein 52 Ascl1 NM_008553.2 IIIb Achaete-scute complex homolog 1 9, 12, 58, 59 Bcan NM_007529.1 V Brevian 76 Bcl11b NM_021399 V B-cell leukemia/lymphoma 11B 4, 5 ,6 Calb2 NM_007586.1 V Calbindin 2 4, 38 Chrd NM_009893.1 IIIb Chordin 65 Cxcl12 NM_001012477.1 V Chemokine (C-X-C motif) ligand 12 72, 73, 74 Dlx1 NM_010053.1 IV Distal-less homeobox 1 1, 2 ,3 Egr2 NM_010118.1 Iia Early growth response 2 31, 32 En1 NM_010133.1 IV Engrailed 1 25, 27, 28 En2 NM_010134.1 IV Engrailed 2 28, 29, 30 Fabp7 NM_021272.2 IIIb Fatty acid binding protein 7, brain 53, 54 Fez1 NM_007586.1 IIIa Fasciculation and elongation protein zeta 1 39 Fgf8 NM_010205.1 IIIb Fibroblast growth factor 8 27, 29, 90, 91 Fgfr2 NM_201601.1 IIIb Fibroblast growth factor receptor 2 49, 88 Fgfr3 NM_008010.2 Iia Fibroblast growth factor receptor 3 48, 49 Foxg1 NM_008241.1 Iia Forkhead box G1 9, 10 Gata2 NM_008090.3 V GATA binding protein 2 92, 93, 94 Gli3 NM_008130 Iia GLI-Kruppel family member GLI3 44, 45, 46, 47 Hes3 NM_008237.1 Iia Hairy and enhancer of split 3 18, 41, 43 Hoxa1 NM_010449.1 IV Homeo box A1 36 Hoxa2 NM_010451.1 Iia Homeo box A2 33 Hoxb2 NM_134032.1 Iia Homeobox B2 20 Irx2 NM_010574.2 IIIa Iroquois related homeobox 2 60, 62 Irx3 NM_008393 IIIa Iroquois related homeobox 3 60, 61 Irx5 NM_018826.2 IIIa Iroquois related homeobox 5 60, 61 Isl1 NM_021459.2 Iia ISL1 transcription factor, LIM/homeodomain 9, 11, 12 Lhx1 NM_008498 IIIa LIM homeobox protein 1 12, 80 Lhx5 NM_008499.2 IV LIM homeobox protein 5 80 Lmx1a NM_033652.2 IIIb LIM homeobox transcription factor 1 alpha 22, 23, 81, 82, 83 Mecp2 NM_010788.1 Iia Methyl CpG binding protein 2 68 Msx1 NM_010835.1 IV Homeobox, msh-like 1 18, 22, 23, 24 Ndst1 NM_008306.2 IIIb N-deacetylase/N-sulfotransferase 1 75 Neurog2 NM_009718.2 IV Neurogenin 2 12, 17, 18, 19 Nfib NM_008687.2 V Nuclear factor I/B 55, 56, 57 Nkx6-1 NM_144955.1 IIIa NK6 homeobox 1 22, 40, 41 Nog NM_008711.1 IIIb Noggin 65 Notch3 NM_008716.1 IIIb Notch gene homolog 3 50, 51 Nr2f1 NM_010151.1 IIIa Nuclear receptor subfamily 2, group F, member 1 13, 14, 15 Nr2f2 NM_183261.3 IIIb Nuclear receptor subfamily 2, group F, member 2 14, 63, 64 Nrg NM_178591.2 Iia Neuregulin1 6, 79 Otx1 NM_011023.2 IIIa Orthodenticle homolog 1 20, 21 Pitx2 NM_011098.2 IIIb paired-like homeodomain transcription factor 2 25, 89 Ptx3 NM_008987.2 IIIb Pentraxin related gene 25, 26 Reln NM_011261 IIIb Reelin 4, 16 Rfx4 NM_001024918.1 IIIa Regulatory factor X, 4 69, 70, 71 Rora NM_013646.1 V RAR-related orphan receptor alpha 34, 35 Shh NM_009170 IIIa Sonic hedgehog 4, 27 Smo NM_176996.3 Iia Smoothened homolog 37 Sox5 NM_011444.1 IIIb SRY-box containing gene 5 4, 7, 8 Tal2 NM_009317.2 IV T-cell acute lymphocytic leukemia 2 12, 66, 67 Tgfb2 NM_009367 IIIb Tgf-beta2 85, 86, 87 Wnt1 NM_021279.1 IIIb Wingless-related MMTV integration site 1 22, 27 Wnt3a NM_009522.1 IIIb 3a/wingless-related MMTV integration site 3A 77, 78 Wnt7a NM_009527.2 IIIb Wingless-related MMTV integration site 7A 84
The literature was searched for patterning-related genes (by function and/or expression). Those found to be regulated here were listed and assigned a role in forebrain, midbrain or hindbrain development (black boxes). The relevant literature sources are indicated. N.B.: 67 Chapter C – Coordinated waves of gene expression during neuronal differentiation of embryonic stem cells as basis for novel approaches to developmental neurotoxicity testing
sometimes functional importance (e.g. knockout phenotype) does not match the criteria for region markers and vice versa. Multiple regional assignments were allowed, where this was supported by the literature.
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68 Chapter C – Coordinated waves of gene expression during neuronal differentiation of embryonic stem cells as basis for novel approaches to developmental neurotoxicity testing
Figure S6: Neuroteratogenic effects of Retinoic acid, Cyclopamine and Lead compound species Neuro-teratogenic effect Ref. microcephalic, anterior brain decreased, Xenopus laevis hindbrain and spinal cord increased, 25,26 suppression of anterior CNS Spina bifida, microcephaly, exencephaly, Mesocicetus auratus 27 microcephaly all-trans crebellum malformations 28 Retinoic acid Rattus norvegicus exencephaly, anencephaly, spina bifida 29-31 (RA) Spina bifida, exophtalmos, exencephaly, Mus musculus 32,33 truncation of the anterior brain microcephaly, hydrocephalus, vermus Macaca mulatta 27 abnormality Hydrocephaly, decline in size of forebrain Homo sapiens* 28 regions cyclopia, fused telencephalon, defects in Gallus gallus midline patterning of the neural plate, 34 holoprosencephaly Sheep cyclopia Cyclopamine Rabbit cyclopia, cebocephalia 35 Rattus norvegicus cebocephalia 35 Mus musculus Exencephaly, holoprosencephaly 35,36 Mesocicetus auratus cebocephalia, exencephaly, encephalocele 35 Size increase in mossy fibres and the granule cell layer, inhibition of postnatal structuring, Rattus norvegicus 8,37-41 reduced VAChT and ChAT mRNA levels, increase in TH activity Changes in the cholinergic system, Mus musculus 37 hyperactive behaviour Change in glutamate synthesis, reduced Lead Cavia porcellus 37,42,43 glutamine synthetase activity Basal forebrain and primary visual cortex Monkeys damage, memory and learning defects, 37,44 impaired spatial tasks Increased distractability, attention deficit Homo sapiens disorder, decreased auditory sensitivity, 37,45,46 decreased visumotor performance Alzheimers disease47, Parkinsons disease48, Lead disease correlation Schizophrenia49 *: data for 13-cis-retinoic acid (Accutane®)
69 Chapter C – Coordinated waves of gene expression during neuronal differentiation of embryonic stem cells as basis for novel approaches to developmental neurotoxicity testing
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41. Bielarczyk, H, Tomsig, JL and Suszkiw, JB, (1994) Perinatal low-level lead exposure and the septo-hippocampal cholinergic system: selective reduction of muscarinic receptors and cholineacetyltransferase in the rat septum. Brain Res 643: 211-7. 42. Sierra, EM and Tiffany-Castiglioni, E, (1991) Reduction of glutamine synthetase activity in astroglia exposed in culture to low levels of inorganic lead. Toxicology 65: 295-304. 43. Sierra, EM, Rowles, TK, Martin, J, Bratton, GR, Womac, C and Tiffany-Castiglioni, E, (1989) Low level lead neurotoxicity in a pregnant guinea pigs model: neuroglial enzyme activities and brain trace metal concentrations. Toxicology 59: 81-96. 44. Reuhl, KR, Rice, DC, Gilbert, SG and Mallett, J, (1989) Effects of chronic developmental lead exposure on monkey neuroanatomy: visual system. Toxicol Appl Pharmacol 99: 501-9. 45. Otto, DA and Fox, DA, (1993) Auditory and visual dysfunction following lead exposure. Neurotoxicology 14: 191-207. 46. Schwartz, J and Otto, D, (1991) Lead and minor hearing impairment. Arch Environ Health 46: 300-5. 47. White, LD, Cory-Slechta, DA, Gilbert, ME, Tiffany-Castiglioni, E, Zawia, NH, Virgolini, M et al., (2007) New and evolving concepts in the neurotoxicology of lead. Toxicol Appl Pharmacol 225: 1-27. 48. Coon, S, Stark, A, Peterson, E, Gloi, A, Kortsha, G, Pounds, J et al., (2006) Whole- body lifetime occupational lead exposure and risk of Parkinson's disease. Environ Health Perspect 114: 1872-6. 49. Opler, MG, Buka, SL, Groeger, J, McKeague, I, Wei, C, Factor-Litvak, P et al., (2008) Prenatal exposure to lead, delta-aminolevulinic acid, and schizophrenia: further evidence. Environ Health Perspect 116: 1586-90.
72 Chapter D – Sensitivity of dopaminergic neuron differentiation from stem cells to chronic low-dose methylmercury exposure
Chapter D
Sensitivity of dopaminergic neuron differentiation from stem cells to chronic low-dose methylmercury exposure
Bastian Zimmer1, Stefan Schildknecht1, Philipp B. Kuegler1, Vivek Tanavde2, Suzanne Kadereit1 and Marcel Leist1
1Doerenkamp-Zbinden Chair of in-vitro Toxicology and Biomedicine, Department of Biology, Box 657, University of Konstanz, D-78457 Konstanz, Germany 2Bioinformatics Institute, Agency for Science Technology and Research (A*STAR), 30 Biopolis Street, #07-01, 138671 Singapore, Singapore
Toxicol Sci. 2011 Jun;121(2):357-67. Epub 2011 Mar 7
73 Chapter D – Sensitivity of dopaminergic neuron differentiation from stem cells to chronic low-dose methylmercury exposure
Abbreviations
CNS: central nervous system DAT: dopamine transporter DoD: day of differentiation LDH: lactate dehydrogenase MeHg: methylmercury mESC: murine embryonic stem cell MLK: mixed lineage kinase NSC: neural stem cell TH: Tyrosine Hydroxylase
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Abstract
Perinatal exposure to low doses of methylmercury (MeHg) can cause adult neurological symptoms. Rather than leading to a net cell loss, the toxicant is assumed to alter the differentiation and neuronal functions such as catecholaminergic transmission. We used neuronally-differentiating murine embryonic stem cells to explore such subtle toxicity. The mixed neuronal cultures that formed within 20 days contained a small subpopulation of tyrosine hydroxylase (TH)-positive neurons with specific dopaminergic functions such as dopamine transport (DAT) activity. The last 6 days of differentiation were associated with the functional maturation of already preformed neuronal precursors. Exposure to MeHg during this period downregulated several neuronal transcripts without affecting housekeeping genes or causing measurable cell loss. Profiling of mRNAs relevant for neurotransmitter systems showed that dopamine receptors were coordinately downregulated, while known counterregulatory systems such as Galr2 were upregulated. The chronic (6 days) exposure to MeHg, but not shorter incubation periods, attenuated the expression levels of endogenous neurotrophic factors required for the maturation of TH-cells. Accordingly, the size of this cell population was diminished, and DAT activity as its signature function was lost. When mixed lineage kinase activity was blocked during MeHg exposure, DAT activity was restored, and the reduction of TH levels was prevented. Thus, transcriptional profiling in differentiating mESC identified a subpopulation affected by MeHg and that responded specifically to protective measures.
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Introduction
Differentiating stem cells are emerging as test system for problems of developmental toxicity. Some differentiation protocols are well-suited to create mixed populations of different subtypes of neurons for the study of fate decision pathways (Gaspard and Vanderhaeghen 2010) or as assay systems to detect adverse effects of chemicals on the balance of neuron types during CNS development. Thy can reproduce aspects of the formation and patterning of tissues classically studied in vivo, while they take advantage from the ease of experimental manipulation offered by in vitro cultures (Kuegler et al. 2010; Zimmer et al. 2011a). The best-studied tool compound associated with developmental neurotoxicity is methylmercury (MeHg) (Grandjean and Landrigan 2006). Data derived from various animal models (Onishchenko et al. 2007) and in-vitro studies (Tamm et al. 2008) corroborate that MeHg can affect the developing brain at particularly low concentrations (Castoldi et al. 2008b; Clarkson 1997). The subtle effects of low dose mercury exposure are not associated with gross morphological changes within the developing brain (Slotkin et al. 1985), but they rather result in altered functions specific for certain neurotransmitters such as dopamine (Gimenez-Llort et al. 2001). Pathophysiological effects may manifest years or even decades after exposure to the toxicant (Newland and Rasmussen 2000). Altered neuronal wiring or differentiation decisions are one possible explanation for such a long term memory of toxicant exposure. The assumed molecular mechanisms of MeHg toxicity range from oxidative stress, over interference with calcium homeostasis to binding to protein sulfhydryl (SH) groups affecting e.g. microtubule function (for review see (Johansson et al. 2007)). Possibly, the relevant targets differ from cell type to cell type and may be specific for certain developmental stages and exposure conditions. Higher concentrations of MeHg affect various functions of different neural cell types (Morken et al. 2005). A short pulse of low nM concentrations slows the differentiation of neural stem cells (NSC) via activation of metalloproteinases involved in the notch (Tamm et al. 2008) signaling pathway. During later development and maturation of neurons, the role of notch signaling becomes minor and MeHg may then predominantly affect other targets, which may be related to migration (Moors et al. 2009) or neurite outgrowth (Radio et al. 2010). For instance, kinase signaling pathways can be modified, as exemplified by the activation of the c-jun N-terminal kinases (JNK) by methylmercury (Fujimura et al. 2009). This pathway, and especially the associated mixed lineage kinases (MLK) play an
76 Chapter D – Sensitivity of dopaminergic neuron differentiation from stem cells to chronic low-dose methylmercury exposure
important role in the degeneration of dopaminergic neurons, a subpopulation that is also particularly sensitive to long-term low dose exposure of methylmercury (Dare et al. 2003). As dopaminergic neurons are usually studied as minor fraction of mixed primary mesencephalic cultures, specific tools have been developed to selectively affect and assess these cells. For instance, the toxicant 1-methyl-4-phenyl-pyridinium (MPP+) is specifically taken up by dopaminergic neurons via the dopamine transporter (DAT), and may therefore be used to selectively kill these cells in a mixed culture, without adverse effects on other neurons. The structurally related compound 1-methyl-4-phenyl-tetrahydropyridine (MPTP) is not transported by DAT and therefore often used as negative control. The use of MPP+ as radioactive tracer also allows quantitative determinations of the relative number of DAT- positive neurons in a mixed culture, as it is accumulated only in these cells. In such short term uptake experiments only requiring minutes, the potential toxicity of the compound plays no role (Schildknecht et al. 2009). Rodents and humans have been shown to be susceptible to MeHg during the perinatal stage, including gestation and immediate post-natal periods (Castoldi et al. 2008b; Goulet et al. 2003), This time in development is associated with neuronal specification, maturation, and establishment of connectivity (Rao and Jacbson 2005). Such a distinct phase is well-defined and accessible to examination also in stem-cell based in vitro models (Zimmer et al. 2011a). In mESC, it has been demonstrated that subtle changes in the neuronal composition or maturation may be detected using mRNA expression as readout (Hogberg et al. 2010; Hogberg et al. 2009; Kuegler et al. 2010; Zimmer et al. 2011a). We examined here, whether the observation from animal models that low doses of MeHg lead to adverse effects related to the later function of neurotransmitter systems may be observed in a stem cell-based model. We provide proof-of-concept for the suitability of differentiating mESC to detect chronic low dose toxicity to maturing neurons. We further investigated whether a combination of RNA markers may give hints as to the type of target cells. Moreover, we studied how such transcriptional readouts correlated with functional measures used classically in neurodegeneration research to approach the mechanisms of adverse developmental effects.
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Materials and Methods
Unless otherwise mentioned, cell culture media and reagents were obtained from Invitrogen (Darmstadt, Germany, http://www.invitrogen.com) and accessory reagents from Sigma (Munich, Germany, http://www.sigmaaldrich.com). All chemicals not specified here in detail are listed in supplementary Table 1.
Cell culture and differentiation
The mouse ES cell line CGR8 (a kind gift from K. Krause, Geneva) was cultured at 37°C in
5% CO2 in a humidified atmosphere on plastic coated with 0.1% gelatin and routinely passaged 4 times a week. ES cells were cultured in Glasgow’s modified Eagles medium complemented with 10% heat inactivated fetal bovine serum (PAA (Coelbe, Germany, http://www.paa.com), Glutamax, non-essential amino acids, β-mercaptoethanol and sodium pyruvate. leukemia inhibitory factor (Millipore) was added at a final concentration of 1000u/ml. For neuronal differentiation the protocol established by Ying et. al (Ying and Smith 2003) was used with slight modifications. Briefly, one day before initiating differentiation cells were seeded under normal culture conditions to reach ~80% confluency after 24h. The next day, cells were transferred to gelatin coated plastic plates in N2/B27 medium at a density of 104 cells/cm2. Medium was changed every other day. After 7 days of differentiation cells were transferred to poly-L-ornithin (10 µg/ml) and Laminin (10 µg/ml) coated plates at a density of 104 cells/cm2 in N2/B27 medium. After a 3 day attachment phase, the medium was changed every other day until DoD20.
Mercury quantification
For determination of the mercury content of DoD20 cultures, they were incubated with 5 nM MeHg according to the standard incubation scheme from DoD14 until DoD20. Then, the medium was removed, cells were washed and then detached by scraping in 0.1 % Triton X- 100 in PBS + 5 % FCS. The cell suspension was homogenized by sonification, aliquoted and frozen until analysis by atomic absorption spectroscopy. All reagents and buffer components were carefully controlled for their mercury content (below detection limit). For calibration and positive controls, control cell lysates were spiked with different MeHg amounts. The detection limit of the method was 400 pM in cell lysate matrix. 78 Chapter D – Sensitivity of dopaminergic neuron differentiation from stem cells to chronic low-dose methylmercury exposure
MPP+ uptake
The assay was performed as described earlier (Schildknecht et al. 2009). Briefly, cells differentiated as described above were washed with Hank's Balanced Salt Solution (HBSS), containing Ca2+, pH 7.4, and then treated with the DAT blocker GBR 12909 (1 μM) or a solvent control, thirty min before 5 µM 1H-MPP+ + 4265 Bq/well 3H-MPP+ was added. After 60 min, the supernatants were collected, cells were washed 5× gently with warm HBSS and then lysed with PBS/0.1% Triton X-100. Radioactivity in cell lysates and the respective supernatants was measured using a Beckman LS-6500 scintillation counter (Beckman Coulter, Brea, CA, USA, http://www.beckmancoulter.com).
Quantitative Real-Time PCR
Quantifications were performed exactly as described earlier(Zimmer et al. 2011a). Briefly, total RNA was isolated using Trizol, and reverse transcribed (SuperScript II, Invitrogen). Quantitative RT-PCR was performed using a BioRad Light Cycler (Biorad, München, Germany, http://www.bio-rad.com). Real-time quantification for each gene was performed using SybrGreen. Data were normalized to gapdh mRNA, and expressed relative to the amount of untreated/undifferentiated samples, using the 2^(-Delta Delta C(T)) method (for a detailed primer list see supplementary table 1). SABioscience quantitative PCR Arrays (Neurotransmitter Receptors and Regulators, cat. # PAMM-060, SABiosciences, Frederick, MD, USA, http://www.sabiosciences.com) also used the SybrGreen method as above, and are based on validated and intron-spanning primers for target genes and several house-keeping controls. Data were normalized and analyzed using the web-based SABiosciences analyzing tool (http://www.sabiosciences.com/pcr/arrayanalysis.php). The group of downregulated genes not covered comprehensively in results comprised Chrna2, Chrna6, Chrnb3, Chrng, Gabra1, Gabra4, Gabrq, Galr3, Glra3, Prokr1, Nmur1, Ntsr1, Ppyr1, Prchr, Sstr3, Tacr2 and Tacr3.
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Immunostaining and Western blotting
For Western Blot analysis total protein was isolated using Trizol according to the manufactures manual. The protein concentration in the samples was determined using a BCA assay Kit (Pierce (Rockford, IL, USA, http://www.piercenet.com/)). Equal amounts of protein were subjected to SDS-polyacrylamide gel electrophoresis, transferred to nitrocellulose membranes (GE Healthcare (Munich, Germany, http://www.gehealthcare.com)), over night antibody incubation at 4°C, followed by horseradish peroxidase conjugated secondary antibody and developed via enhanced chemiluminescence. Immunocytochemistry was performed as follows: cells were fixed with ice-cold methanol or 4% paraformaldehyde (PFA) in PBS and permeabilized with 0.1% Triton X-100. After blocking with 10% FBS in PBS for 1h at room temperature cells were incubated with primary antibodies over night at 4°C. After incubation with the appropriate secondary antibodies images were taken on the original cell culture dishes using an IX81 inverted microscope (Olympus, Hamburg, Germany, http://www.olympus.de/microscopy) equipped with a 10x, NA 0.3, 20x, NA 0.45 and a 40x, NA 0.6 long range lens and processed using CellP imaging software (Olympus). For a detailed antibody list see supplementary table 1.
Cytotoxicity assays
Resazurin reduction and LDH release were used to exclude general cytotoxic effects after 6 days of exposure to CH3HgCl. During the normal neuronal differentiation (described above) indicated concentrations of CH3HgCl were added during normal medium changes (day 14, 16 and 18). On day 20 Resazurin (10 µg/ml final) was added 1h before fluorescence measurement (530 nmex: 590 nmem). LDH activity was determined using the same assay plates as for Resazurin reduction. Therefore LDH activity was detected separately in the supernatant and cell homogenate. Cells were lysed in 0.1% Triton X-100 in PBS at 4°C over night. 10 µl sample was added to 200 µl of reaction buffer containing NADH (100 µM) and sodium pyruvat (600 µM) in sodium phosphate buffer adjusted to pH 7.4 using 40.24 mM
K2HPO4 and 9.7 mM KH2PO4 buffer. Absorption at 340 nm was detected at 37°C in 1 min intervals over 20 min.
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Quantification of TH+ areas
DoD20 cultures stained with anti-TH antibody were analyzed by three different operators blinded to the treatment schedules. Four different wells of a 24 well plate were analyzed for each concentration in three different experiments. TH positive areas on the entire field (1.9 cm2) of a culture well were assigned different scores (0x, 1x or 2x, as indicated in Figure 5B). The untreated control was set as 100% and all other values were calculated relative to the respective untreated control.
Statistics and data mining
All data are summarized as means ± SD from at least 3 independent biological experiments, with at least 3 technical n per biological experiment unless otherwise mentioned. Data were presented, and statistical differences were tested by ANOVA with post-hoc tests as appropriate, using GraphPad Prism 4.0 (Graphpad Software, La Jolla, USA, http://www.graphpad.com). Published whole genome microarray expression data(Zimmer et al. 2011a) were used to extract all genes up-regulated at least 2-fold between DoD15 and DoD20. Then, this group was analyzed for statistically overrepresented gene ontologies with the web-based analysis tool g:Profiler (Reimand et al. 2007).
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Results
Marker expression during neuronal differentiation of mESC
The transcription factor Pou5f1 (Oct3/4), characteristic for pluripotent stem cells, was downregulated early during the neural induction phase. The neuroectodermal marker nestin
Figure 1. Generation of a mixed neuronal population from mESC
Pluripotent mESC were differentiated towards the neuronal lineage and characterized at different stages. (A) Quantitative RT-PCR analysis of relative transcript levels at 4 time points of differentiation. The expression on DoD0 (undifferentiated mESC) was arbitrarily set to 1. Data for the markers indicated in the micrographs are averages from three independent experiments. (B) Immunocytochemical characterization of neurons on DoD20. Arrows indicate synaptic vesicles within the neurite. Scale bars: 20 µm. (C) Genome wide analysis of genes upregulated on DoD20 vs. DoD15. The GO: “regulation of dopamine secretion” was highly overrepresented (p < 0.001) and the changes of 5 genes of this GO, identified on the chip, are displayed. (D) Dopamine transporter activity of DoD20 neurons after 72 h treatment with toxicants. ***: p-value ≤ 0.001
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(Nes) reached a peak on day of differentiation 7 (DoD7) and was again downregulated during the following 13 days of differentiation (Fig. 1A). Neuronal markers like Tubb3, Mtap2 and Ncam as well as synapse associated genes including Syp, Psd95 and Sv2a continuously increased during the neuronal maturation phase (Fig.1A). Neuronal subtype diversification was indicated by upregulation of different neurotransmitter metabolism associated genes like Th and Gad2 (Fig. 1A). Extensive immunocytochemical characterization on DoD20 confirmed that the differentiated cells expressed neuronal specific cytoskeleton proteins like MAP2 (Fig. 1B), Tuj1 (Fig. 1B) and NCAM (data not shown) as well as proteins associated with synaptic transmission like
Figure 2. Identification of CH3HgCl concentrations that do not affect overall viability of neurons differentiating from mESC
All toxicity experiments were performed according to a standardized scheme (top). Methylmercurychloride was added for all experiments for the last 6 days of differentiation (orange box). (A) Determination of the non- cytotoxic concentration range of CH3HgCl. (B) Detailed confirmation of non-cytoxic range of CH3HgCl concentrations. Note the different scaling of the x-axis. (C) Immunocytochemical display of the neurite network density and structure after treatment with 5 nM CH3HgCl. Scale bar: 50 µm. (D) quantitative RT-PCR analysis of the expression of three non-neuron specific housekeeping genes (Actb, B2m and Gapdh) after treatment with different non-cytotoxic concentrations of mercury. Displayed are the threshold cycle numbers for each mRNA (n = 2, with 3 technical replicates per n) as means ± SD.
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synaptophysin (Syp), SV2a (Fig. 1B), SNAP25 and PSD95 (data not shown). The post- mitotic neuronal protein NeuN was highly expressed in 70-80% of cells in the culture (Fig. 1B). The culture consisted of different sub-types of neurons confirmed by positive staining for GABA (data not shown) and GAD65 (Fig. 1B) for GABAergic neurons (48 ± 5%), TH for dopaminergic neurons (< 10%), Vglut for glutamatergic neurons (data not shown) and 5-HT for serotonergic neurons (data not shown). To obtain more information on changes taking place during the maturation phase of differentiation, a set of published (Zimmer et al. 2011a) whole genome microarray expression data for cultures on DoD15 and DoD20 were used to extract the genes upregulated at least 2- fold during the last 6 days of the 20 day differentiation. This list of genes was analyzed for statistically overrepresented gene ontologies in the area of specific biological processes or
Figure 3. Specific impairment of neuronal differentiation by non-cytotoxic concentrations of CH3HgCl
(A,B) Under conditions as in figure 2, non- cytoxic concentrations of CH3HgCl resulted in a concentration-dependent decrease of neuronal mRNAs. (C) Western blot analysis of the same cells indicated a decrease of b-III tubulin protein (Tuj1 antibody). Beta-actin was used as internal loading control. A representative blot is shown. (D) Cells were treated instead of CH3HgCl with 100 µM ascorbic acid or ASS, respectiveley, and mRNA expression of 4 neuronal mRNA species was analyzed. (Data are means ± SD from 2 experiments (each performed in triplicates)
pathways. The “regulation of dopamine secretion” emerged as only highly significant hit (Fig. 1C). This finding suggested a role of the maturation phase in our culture for dopaminergic
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neuron function. We tested whether the TH positive cells in the mixed cultures displayed dopamine-related functional properties as suggested by the expression analysis. Untreated DoD20 cells had the ability to accumulate radiolabelled MPP+ within 60 minutes. This indicates the presence of functional dopamine transporters (Schildknecht et al. 2009) (Fig. 1D). Going one step further, we tested whether this neuronal subpopulations was susceptible to selective cell death triggered via their dopaminergic machinery. DoD20 cultures were treated for 72 h with either 10 µM of the specific toxicant MPP+ or 20 µM of the innocuous precursor MPTP (Schildknecht et al. 2009). The toxicants did not affect the viability of the overall culture (not shown), and thus did not trigger unspecific cell death. However, specific toxicity to a dopaminergic subpopulation was indicated by the decreased DAT activity triggered by MPP+, and by the absence of such an effect after exposure to MPTP (Fig. 1D).
Specific neurodevelopmental disturbances by MeHg during the late maturation phase of neuronal differentiation
Our toxicity examinations focused on the period of DoD14 to DoD20, when most neuronal precursors were formed, and maturation of neuronal subtypes takes place. First the cytotoxicity of the model toxicant MeHg was tested and found to be in the range of 60 nM
(EC50). In a next step, multiple endpoints were used to confirm the initial finding (Fig. 2A) that concentrations of ≤ 5 nM MeHg were tolerated by the cells over 6 days without measurable signs of toxicity. Neither LDH-release, nor mitochondrial activity (resazurin), nor the amount and structure of the neuronal cytoskeleton were affected (Fig, 2 B, C). In addition the total amounts of various house-keeping mRNAs remained on a constant level (Fig. 2D). In the next set of experiments, we investigated whether neuronal-specific mRNAs were affected by MeHg concentrations that we had shown before to be non-cytotoxic within the same experimental system. Cultures were exposed to the toxicant from DoD14 on, and on DoD20 whole mRNA was isolated and expression levels of the synapse marker synaptophysin (Syp), and of neuronal cytoskeletal proteins (Mtap2, Tubb3) were analyzed. MeHg lead to a concentration-dependent decrease in expression by up to about 50% (Fig.3A) Markers related to neurotransmitter synthesis (Th, Gad2) were similarly affected (Fig.3B). This neuron-specific adverse effect was also observed on the protein level, as we found that beta-III tubulin protein, detected by Tuj1 antibody, which is specific for the neuronal isoform, was also decreased with increasing concentrations of mercury (Fig.3C). Negative control
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compounds, such as ascorbic acid or acetylsalicylic acid (ASS) had no effect on mRNA expression of neuron specific genes like Tubb3, Syp, Gad2 or Th (Fig.3D). To obtain information on the actual cellular concentration of MeHg that triggered the observed specific developmental neurotoxicity, cells were analyzed for their mercury content
Figure 4. Specific derangement of genes associated with neurotransmitter metabolism and signaling.
Differentiating mESC were exposed to 5 nM CH3HgCl for 6 days as in figure 2. RNA from three independent cultures was prepared from treated and control cells and each of them was analyzed twice by qPCR. (A) Scatter plot of data obtained with a RT-PCR array. The solid line indicates equal mRNA levels in treated and untreated cells. The area above contains genes upregulated, the area below genes downregulated by CH3HgCl. Dashed lines indicate 2-fold regulation. (B) Comparison of results obtained with commercially available focused RT-PCR arrays (grey bars) and conformation by independently designed primer pairs (black bars). mRNA levels were standardized to the housekeeping gene Gapdh and normalized to the mean expression in the corresponding untreated sample which was arbitrarily set to 1 (indicated by the dashed line). (C) Statistical analysis of the regulation of the expressed dopamine receptor subtypes (Drd1 – Drd4) after CH3HgCl treatment. *: p-value ≤ 0.05. All data are means of independent triplicates. Chrne: cholinergic receptor, nicotinic, epsilon polypeptide, Galr2: galanin receptor 2, Mc2r: melanocortin 2 receptor
by atomic absorption spectroscopy. The content after 6 day exposure to 5 nM was 2 ± 0.15 pmol/mg cellular protein or 10 ± 0.74 % of the total amount added. This suggests that MeHg had accumulated in the cells as expected from its hydrophobic properties. Based on average volume to protein ratios of brain cells, the actual cellular concentration was calculated to be in the range of 30 – 250 ppb, which still is in the lower end of concentrations known to cause in vivo effects after gestational exposure (Suppl. Table 2).
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Derangement of genes associated with neurotransmitter metabolism and signaling
To assess potential effects of sub-lethal concentrations of MeHg on neurotransmitter signaling and metabolism, we examined 84 transcripts of relevant genes assembled in a PCR array. When mRNA levels were compared between control cultures and cells that had been treated with MeHg from DoD14-DoD20, the majority of genes was observed to be unaffected (< 2- fold changes). This confirmed that the MeHg concentration was chosen in a way not to generally impair viability. However, the expression of two genes (Chrne and Galr2) was
Figure 5: Loss of dopaminergic neurons after chronic treatment with low concentrations of CH3HgCl.
(A) DAT activity (ability to take up MPP+) was measured in DoD20 cultures after 6 days of exposure to CH3HgCl or ascorbic acid (AA). *: p- value ≤ 0.05, **: p-value ≤ 0.01. (B) Representative images illustrating the quantification method for TH positive areas. Left: Score assignment for the different sizes of the TH+ areas ranging from 0x up to 2x. Areas were scored according to their size and density of TH positive neurons. Right: Higher magnification of a 0x area showing individual TH-positive neurons, but no clustering. Scale bar: 100 µm. (C) Quantification of TH+ areas, obtained in a double blinded approach as illustrated in B. The amount of TH positive cell areas in the respective untreated control was set to 100%. *: p-value ≤ 0.05
increased ≥ 2-fold by MeHg and the expression of 22 genes was decreased ≥ 2fold (Fig 4A). As a technical control for the validity of these data, we chose one gene from each regulation group (Gad1 (downregulated), Mc2r (no change) and Galr2 (upregulated), designed independent PCR primers, and reanalyzed the samples by conventional quantitative RT-PCR. The re-analysis fully confirmed the initial results, and suggests a high validity of the overall data set (Fig 4B). The biological heterogeneity of the downregulated genes reflects the broad in vivo actions of MeHg affecting different neuronal features. Interestingly, one conspicuous group of functionally-related genes that were co-coordinately downregulated, were the dopamine receptors Drd1a, Drd2, Drd3. Only Drd4 (low overall expression) was not affected by the treatment (Fig. 4C).
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Depletion of dopaminergic neurons after treatment with concentrations of MeHg, not generally toxic to neurons
As different transcriptional profiling analyses pointed to the dopaminergic signaling and response system as one of the targets of MeHg in differentiating mESC, we investigated whether the development of functional dopaminergic features was directly affected. We therefore measured dopamine transporter (DAT) activity of untreated cells (positive control) in comparison to cells treated from DoD14 till DoD20 with MeHg or ascorbic acid (negative control). Cultures treated with 500 pM or 5 nM, respectively, MeHg had a significantly decreased DAT activity compared to control cells, while 50 pM MeHg and ascorbic acid
Figure 6. Requirement of chronic exposure for developmental effects of CH3HgCl.
The treatment schedule to assess the windows of sensitivity for MeHg exposure is displayed on top. Cells were exposed to 5 nM toxicant from DoD14 for either 2 (a), 4 (b) or 6 days (c). The mRNA was isolated after the exposure period (indicated by diamonds) and analyzed by RT PCR for Th, sonic hedgehog (Shh), Wnt1, transforming growth factor beta (Tgfb), fibroblast growth fator-8 (Fgf-8) and glial-derived neurotrophic factor (Gdnf). The data were normalized to the levels in untreated controls (arbitrarily set to 1, indicated by the dashed line). *: p-value ≤ 0.05, **: p-value ≤ 0.01, ***: p-value ≤ 0.001
showed no effect (Fig.5A). In addition to this biochemical endpoint, we also investigated whether the number of tyrosine hydroxylase (TH)-positive neurons formed in the cultures was affected by MeHg. The distribution of these cells in the culture dish was inhomogeneous and characterized by clustering within small islands separated by areas devoid of TH (Fig. 5B). To obtain a quantitative measure, we manually scored the total number of islands per well by blinded operators. According to the island size, they were given scores 0x, 1x or 2x, with 0x designating areas with none or only randomly distributed TH cells, 1x referring to usual
88 Chapter D – Sensitivity of dopaminergic neuron differentiation from stem cells to chronic low-dose methylmercury exposure
clusters of up to 10 cells, and 2x designating larger clusters (Fig. 5B). The system resulted in robust and reproducible results across different observers, and showed that MeHg (5 nM) significantly decreased the amount of TH positive areas, while the negative control substance ascorbic acid did not show any effect (Fig. 5C).
Effects on TH expression and its regulators by chronic MeHg exposure
MeHg (≤ 5 nM) did not affect Th, Tubb3 or Syp after acute 48 h exposure of differentiated neurons, and even 72 h affected the viability only with an EC50 of 12 ± 0.3 µM (data not
Figure 7. Protection of dopaminergic neurons from MeHgCl induced toxicity using CEP-1347.
(A) Dopamine transporter (DAT) activity (evaluated by MPP+ uptake) after exposure of DoD14 cells for 6 days to 5 nM CH3HgCl alone or in combination with CEP-1347 as indicated. DAT activity in untreated samples was set to 100%. Data are means ± SD from 4 independent experiments. *: p-value ≤ 0.05, ***: p- value ≤ 0.001 (B) Quantitative RT-PCR analysis of neuronal genes in cells treated for 6 days with 5 nM CH3HgCl in the presence or absence of CEP-1347. The expression in untreated cells was set to 100%, indicated by the dashed line. n = 3 independent experiments ***: p-value ≤ 0.001
shown). We therefore hypothesized that dopaminergic neurons were not affected by the toxicant directly, but due to interference with their differentiation program and/or factors required for their development. As this may require chronic exposure to the toxicant, we examined how MeHg affected Th and its differentiation factors after incubation of DoD14 neurons for 2, 4 and 6 further days. Key drivers of dopaminergic differentiation, like Shh, Gdnf, Fgf8 or Wnt1 were strongly affected after 6 days (p < 0.01), but not after 2-4 days (Fig.6). This was paralleled by the kinetics of Th downregulation (Fig. 6). Various notch inhibitors (10 – 25 µM of the metalloprotease inhibitor GM6001; 1 µM of the potent gamma secretase inhibitor LY450139) had no rescuing effect at all in our model system (not shown).
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Thus, the mechanism of this chronic developmental toxicity appeared to be different from the reported MeHg effects on early neural stem cells(Tamm et al. 2008).
Pharmacological reversal of neurodevelopmental effects of MeHg
We next examined various mechanistic explanations for the effect of MeHg on TH neuron development. While, we failed to provide unambiguous evidence for the role of oxidative stress or the heat shock response (not shown), a clear and significant restoration of DAT activity was observed when MLKs were inhibited by CEP1347, an inhibitor known to reduce dopaminergic degeneration under stressful culture conditions (Boll et al. 2004). Inhibitor concentrations as low as 0.1 – 0.3 µM were sufficient to prevent the effects of 6 day exposure to MeHg (5 nM) (Fig. 7A), and the drug effect correlated with reduced c-jun phosphorylation, as detected by Western blot (not shown). Finally, we examined whether the observed functional rescue correlated with restoration of Th mRNA expression levels as a potential sign of a restored differentiation process even in the continued presence of MeHg. We analyzed Tubb3 as general neuronal marker, Gad1 as marker of the GABA subpopulation and Th. As expected from previous findings all 4 were downregulated by 6 day exposure to MeHg. Only the level of Th was significantly augmented by co-incubation with CEP-1347 (Fig.7B). Thus, while Th neurons are only one of the populations that may be affected by MeHg, the role of MLKs in the developmental toxicity of MeHg may be specific for this particular neuronal subpopulation.
90 Chapter D – Sensitivity of dopaminergic neuron differentiation from stem cells to chronic low-dose methylmercury exposure
Discussion
Here, we showed that neuronally-differentiating ESC can be used to obtain functional and mechanistic information on the toxicity of industrial chemicals like MeHg. We identified and characterized the particular adverse effects on the development of dopaminergic neurons. MeHg affected not only a large variety of mRNAs, but these phenotypic findings were corroborated by functional assays. Importantly, the adverse effects of the toxicant on developmental neuroplasticity were not related to growth inhibition or cytotoxicity. MeHg affected already formed neuronal precursors during their maturation phase, and required chronic exposure during this culture period to become manifest. We also identified the inhibition of mixed lineage kinases as a strategy to specifically reduce the developmental toxicity to differentiating dopaminergic neurons. Thus, this study represents a first step towards using stem cell-derived cells to solve mechanistic questions. As biological basis for our test method, we used a differentiation protocol by Ying et al. (Ying and Smith 2003), which yields a mixed neural culture on DoD20. The relative size of cellular populations in this system was very robust (Zimmer et al. 2011a), and therefore allowed the detection of toxicant effects on small subpopulations. We showed earlier that the late phase of this differentiation protocol (DoD15 - DoD20) is mainly characterized by the maturation of the neuronal system (Zimmer et al. 2011a), and we now provide additional evidence that dopaminergic neurons differentiate during this period. The functionality of this neuronal subtype was corroborated by their selective susceptibility to the toxicant MPP+, which is known to preferentially target dopaminergic neurons in vivo (Di Monte et al. 1996) and in vitro (Schildknecht et al. 2009), and by functional measurements of dopamine transporter (DAT) activity. Methylmercury is widely known from environmental disasters as in Minamata bay. However, human beings encounter the environmental toxicant usually over prolonged periods, e.g. during gestation, and at very low dose exposure. The actually measured final “cellular concentration” of MeHg was 0.3 ppm (Suppl. Table 2). This agrees well with the threshold of toxicant brain concentrations for observable clinical effects in humans, which is in the range of 0.3 ppm (Burbacher et al. 1990). In animals, effects have been reported at brain concentration as low as 0.1 ppm (Burbacher et al. 1990). Thus, the model system presented here, was able to identify and characterize toxicity at tissue levels corresponding to the low end of the reported effective concentration range in vivo. Furthermore our in vitro model
91 Chapter D – Sensitivity of dopaminergic neuron differentiation from stem cells to chronic low-dose methylmercury exposure
reflects a low dose, multiple exposure scenario, not unlike the gestational exposure in animal experiments. Only mercury exposure for the full period of 6 days resulted in a decrease of mRNAs important for the development of dopaminergic neurons like Shh or Wnt1. In a previous study it was found that such a disruption of patterning signals can lead to a shift in culture composition without any signs of cytotoxicity (Zimmer et al. 2011a). Our study adds to earlier knowledge on potential effects by MeHg by showing that differentiating mESC were sensitive to the toxicant during their maturation phase. The combination of expression profiling and biochemical analysis allowed the identification of a neuronal subtype and function affected. This marks a turning point in the use of ESC derived systems in toxicology, as the study not only corroborated earlier findings and showed a potential applicability of the model. It rather yielded new data on the pattern of transcripts affected, correlated the mRNA measurements with functional analyses and identified a potential pathway of toxicity and its counterregulation, that may explain the effects of very low level exposure during the important perinatal period. It is still unclear, which in vitro endpoints will correspond best to merely functional impairments in vivo. For instance rodents exposed to MeHg may display an altered locomotor activity without histopathological changes. A possible correlate may be the decrease of the general neuronal markers such as Tubb3, in the absence of any signs of cytotoxicity (resazurin reduction, LDH-release, expression of housekeeping genes, neurite network density). Our favored hypothesis is that the reduction of these general neuronal markers is rather due to a lower expression of these genes in most neuronal cells of the culture and rather not due to a loss of cells. This implies that the affected cells on DoD20 would still be defined as neurons, but with a lower expression of neuronal markers. In some cells this might lead to altered physiological performance or, in extreme cases, to a loss of function. The decreased amount of dopaminergic neurons in our model might therefore be explained either by an impaired differentiation or by selective toxicity. The exact mechanisms may be very complex and need further investigation in the future. Our findings that the total DAT activity was decreased after a 6-day MeHg exposure is in line with reports from in vivo experiments, reporting a decreased [3H]-dopamine uptake in mercury treated rat synaptosomes (Rajanna and Hobson 1985). Our observation of a decrease in TH+ areas after mercury treatment, in combination with non-detectable cytotoxicity, might be explained by a shift in neuronal culture composition, e.g. by an impaired differentiation of dopaminergic neurons which results in cells with strongly reduced expression of some of their marker
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genes. Such effects have been demonstrated elsewhere. For instance, human dopaminergic neuronal precursors can differentiate to neurons with strongly decreased TH expression in suboptimal culture environments (Paul et al. 2007), and a surviving dopaminergic neuronal subpopulation in mice treated with MPTP can transiently lose TH expression (Sager et al. 2010). A prominent group of receptors affected by MeHg treatment in our model were the dopamine receptors. This agrees well with animal studies (Dare et al. 2003; Gimenez-Llort et al. 2001; Rossi et al. 1997) and demonstrates that stem cell-based systems may detect changes in vitro, which are normally only detectable in an entire organism. A more detailed analysis of mRNAs associated with neurotransmitter synthesis and regulation revealed more changes in the relative expression of mRNAs that have been described earlier in in vivo studies (Johansson et al. 2007). For instance, the genes for Galr2 (galanin receptor 2) and Chrne (member of the nicotinic receptor family) were upregulated. We assume that this counterregulation may serve an endogenous protective function, as described earlier for MeHg-induced antioxidant systems (Ni et al. 2010; Woods and Ellis 1995). The protective function of galanin in experimental nerve injury is often promoted via the galanin receptor 2 (Elliott-Hunt et al. 2007; Lang et al. 2007). Chrne and related receptors have also been shown to play a role in neuroprotection in a large variety of circumstances, ranging from brain injury over MPTP toxicity to ethanol exposure (Bencherif 2009). Although MeHg is one of the best-known DNT compounds, the mechanisms underling its developmental neurotoxicity in the low-dose range are still unclear. In our system, notch signaling did not appear to be a relevant target, as neither the alpha secretase inhibitor GM6001, nor complete gamma-secretase inhibition resulted in any protection from MeHg (data not shown). Instead, we explored the MLK signaling pathway, as it has been demonstrated earlier, that CEP-1347 is able to increase the survival of immature TH-positive cells under stressful culture conditions (Boll et al. 2004). Our data show that overall DAT activity was brought back by the MLK inhibitor CEP-1347 to control levels, and that the amount of Th mRNA was significantly increased. In contrast to that, the MeHg-induced decreased Tubb3 and Gad1 mRNA expression levels were not altered by co-treatment with CEP-1347. Thus, the rescuing effect of the MLK inhibitor appeared specific for the dopaminergic subpopulation and suggests a specific effect of MeHg on this particular neuronal subtype.
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To our knowledge the present study is the first to investigate functional readouts to detect DNT in an ESC based in vitro system, and to identify an intervention that reduced the functional impairment by a toxicant. Such studies have been hampered in the past by the low robustness and high variability of stem cell systems, despite their great theoretical potential in developmental toxicology. Thanks to improved protocols developed by others we have been able to ask this new type of questions. Our data suggest that some toxicant effects may only be manifested later in life, when the normal number of dopaminergic neurons declines It has indeed been hypothesized that impaired development of TH neurons early in life is linked to neurodegenerative diseases such as Parkinson’s disease (PD) (Calne et al. 1986; Landrigan et al. 2005; Weiss et al. 2002). New, sensitive assay systems, like the one described here, may contribute to the elucidation of the role of early exposure to industrial chemicals like MeHg for neurodegenerative diseases.
Acknowledgement
The Work was supported by the Doerenkamp-Zbinden Foundation the FP7 ESNATS project the Konstanz-Zurich graduate school IRTG 1331 funded by the DFG (fellowship of BZ) and a fellowship from the KoRS-CB (PBK). The Agency for Science Technology and Research (A*STAR), Singapore supported the whole genome arrays & analysis. We are grateful to Sten Ilmjärv for bioinformatical support, and indebted to Bettina Schimmelpfennig, Betty Tan and Qianyi Lee for invaluable experimental support. The monoclonal antibody developed by Buckley, K.M. (SV2) was obtained from the Developmental Studies Hybridoma Bank developed under the auspices of the NICHD and maintained by The University of Iowa, Department of Biology, Iowa City, IA 52242.
Conflict of interest
The authors declare no conflict of interest.
94 Chapter D – Sensitivity of dopaminergic neuron differentiation from stem cells to chronic low-dose methylmercury exposure
Supplements
Supplementary Table 1
Primer list gene forward primer reverse primer Gapdh TGCACCACCAACTGCTTAG GGATGCAGGGATGATGTTC Actb CGTTGACATCCGTAAAGACCTCTATGCC CGTTGACATCCGTAAAGACCTCTATGCC B2m AGTTCCACCCGCCTCACATTG GTAGACGGTCTTGGGCTCGG Pou5f1 CTCTTTGGAAAGGTGTTCAGCCAGAC CGGTTCTCAATGCTAGTTC-GCTTTCTC Nestin CTGGAAGGTGGGCAGCAACT ATTAGGCAAGGGGGAAGAGAAGGATG Syp GGGTCTTTGCCATCTTCGCCTTTG CGAGGAGGAGTAGTCACCAACTAGGA Gad2 AAGGGGACTACTGGGTTTGAGGC AGGCGGCTCATTCTCTCTTCATTGT Tubb3 GACAACTTTATCTTTGGTCAGAGTGGTGCTG GATGCGGTCGGGGTACTCC Mapt2 GGTAATGTGAAGATTGACAGCCAAAAGTTGA GCAGTGACATCCTCAGCCAAAGT Ncam GTGAAGAAAAGACTCTGGATGGGCA TGGCACTTGGGTAGGCGAAG Psd95 GTAGCAGAGCAGGGGAAGCAC TACGATGGCTGAGAAGCACTCCG Sv2 CAGGCTCAGAATGCTTGCTGGTT CAAAGGCAGTAGTCCTCTTGTCGGAAG Th TACTGGTTCACTGTGGAGTTTGGGC CTGGATACGAGAGGCATAGTTCCTGAG Gad1 CATGTGGACATCTTCAAGTTCTGGCTG TGTGCTCAGGCTCACCATCG Mc2r CGTGGCAGTTTTGAAAGCACAGC TAGGGTGATGATGGTGCGGC Galr2 CCTATTCATCCTCAACCTGGGTGTGG GCCAGATACCTGTCCAGCGAG antibody list name dilution catalogue number provider actin 1:1000 A5441 Sigma GAD67 1:500 198003 SySy, Goettingen, Germany MAP2 1:100 4542 Cell Signaling, Danvers, MA, USA NeuN 1:200 MAB377 Millipore, Billerica, MA, USA SV2 1:200 SV2 DSHB, Iowa City, IA, USA Synaptophysin 1:200 MAB5258 Millipore TH 1:200 MAB318 Millipore Tuj1 1:1000 MMS-435P Covance, Munich, Germany
Chemical list: name Catalogue number Provider 3H-MPP+ NET9140 Perkin Elmer, Boston MA, USA Ascorbic acid A4403 Sigma Aspirin A6810 Sigma GBR D052 Sigma Methylmercurychloride 442534 Sigma MPP+ D048 Sigma MPTP M0896 Sigma
95 Chapter D – Sensitivity of dopaminergic neuron differentiation from stem cells to chronic low-dose methylmercury exposure
Supplementary Table 2
Comparison of concentration used in this study with literature data
Methylmercurychloride (MeHg): molecular weight Æ 251 g/mol Measurement 1 (M1): 1.88 pmol MeHg/mg protein (= 0.47 ng/mg protein) Measurement 2 (M2): 2.10 pmol MeHg/mg protein (= 0.52 ng/mg protein) Assumed cell volume: 1.7 µl/mg protein Cellular concentration (M1): 1.10 µM Æ 276 ng/ml = 276 ppb Cellular concentration (M2): 1.23 µM Æ 309 ng/ml = 309 ppb Conversion to ppb (on weight/weight) basis: assumption of 1 cm3 brain (1 ml) = 1 g Æ 1 ng MeHg/ml = 1 ppb.
Background: Several studies on human victims of large scale poisonings like in Iraq [1] and Minamata [2] or epidemiological data from fish-eating populations [3, 4] exposed to the toxicant by their diet, have shown that MeHg causes neurophysiological disturbances over a large range of concentrations. These studies indicated that the developing fetal brain is much more susceptible to such toxicants than the adult brain [5-8]. Rodents and humans have been shown to be susceptible to MeHg during the perinatal stage, including gestation and immediate post-natal periods [7, 9, 10]. Data derived from various animal models [11, 12] and in-vitro studies [13, 14] corroborate that MeHg can affect the developing brain at particularly low concentrations (< 1 ppm). The subtle effects of low dose mercury exposure are not associated with gross morphological changes within the developing brain [15, 16], but they rather result in altered functions specific for certain neurotransmitters such as dopamine [17- 19]. Pathophysiological effects may manifest years or even decades after exposure to the toxicant[20-22]. Dopaminergic neurons are a subpopulation that is particularly sensitive to long-term low dose exposure of methylmercury [18, 23-25]
Calculations: Rats exposed pre- or perinatally to MeHg doses that result in brain concentrations of about 500 ppb, show later robust behavioral changes [18, 26]. Our experiments were designed to model this situation. We used nominal MeHg medium concentrations of up to 5 nM. MeHg bioaccumulates not only in tissues, but also in cells in vitro. Therefore, we measured the actual amount of the toxicant in cell lysates and normalized it to the protein content. An average cell concentration of 500 pg/mg (= 2 pmol/mg) cellular protein was measured after 6 days. If we assume a cellular volume of 1.7 µl/mg protein, as measured for some brain cells [27], the final “cellular concentration” would be 1.2 ± 0.1 µM (corresponding to about 290 ng/ml, or about 0.3 ppm). This agrees well with the threshold of toxicant brain concentrations for observable clinical effects in humans, which is in the range of 0.3 ppm [28]. In animals, effects have been reported at brain concentration as low as 0.1 ppm [28]. Thus, the model system presented here, was able to identify and characterize toxicity at tissue levels corresponding to the low end of the reported effective concentration range in vivo.
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Supplementary Figure 3
Shh Tgfβ
10 Wnt1 Fgf8 Th Gdnf 5
2.0 1.5 1.0
rel. gene expression 0.5 0.0 16 18 20 day of differentiation
Figure S3. RNA expression time course of genes indicated in Figure 6 in untreated cultures.
Quantitative RT-PCR analysis of relative transcript levels at 3 time points of differentiation. The relative gene expression on the final day of differentiation (DoD20, differentiated neurons) was arbitrarily set to 1. Data for the genes indicated in the micrographs are averages from three independent experiments ± SD.
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Supplementary References
1. Amin-Zaki, L., et al., Prenatal methylmercury poisoning. Clinical observations over five years. Am J Dis Child, 1979. 133(2): p. 172-7. 2. Harada, M., Minamata disease: methylmercury poisoning in Japan caused by environmental pollution. Crit Rev Toxicol, 1995. 25(1): p. 1-24. 3. Debes, F., et al., Impact of prenatal methylmercury exposure on neurobehavioral function at age 14 years. Neurotoxicol Teratol, 2006. 28(5): p. 536-47. 4. Grandjean, P., et al., Cognitive deficit in 7-year-old children with prenatal exposure to methylmercury. Neurotoxicol Teratol, 1997. 19(6): p. 417-28. 5. Kondo, K., Congenital Minamata disease: warnings from Japan's experience. J Child Neurol, 2000. 15(7): p. 458-64. 6. Clarkson, T.W., The toxicology of mercury. Crit Rev Clin Lab Sci, 1997. 34(4): p. 369-403. 7. Amin-Zaki, L., et al., Perinatal methylmercury poisoning in Iraq. Am J Dis Child, 1976. 130(10): p. 1070-6. 8. Grandjean, P. and P.J. Landrigan, Developmental neurotoxicity of industrial chemicals. Lancet, 2006. 368(9553): p. 2167-78. 9. Falluel-Morel, A., et al., Developmental mercury exposure elicits acute hippocampal cell death, reductions in neurogenesis, and severe learning deficits during puberty. J Neurochem, 2007. 103(5): p. 1968-81. 10. Goulet, S., F.Y. Dore, and M.E. Mirault, Neurobehavioral changes in mice chronically exposed to methylmercury during fetal and early postnatal development. Neurotoxicol Teratol, 2003. 25(3): p. 335-47. 11. Onishchenko, N., et al., Developmental exposure to methylmercury alters learning and induces depression-like behavior in male mice. Toxicol Sci, 2007. 97(2): p. 428- 37. 12. Piedrafita, B., et al., Developmental exposure to polychlorinated biphenyls or methylmercury, but not to its combination, impairs the glutamate-nitric oxide-cyclic GMP pathway and learning in 3-month-old rats. Neuroscience, 2008. 154(4): p. 1408- 16. 13. Tamm, C., et al., High susceptibility of neural stem cells to methylmercury toxicity: effects on cell survival and neuronal differentiation. J Neurochem, 2006. 97(1): p. 69- 78. 14. Tamm, C., et al., Methylmercury inhibits differentiation of rat neural stem cells via Notch signalling. Neuroreport, 2008. 19(3): p. 339-43. 15. Bartolome, J.V., et al., Development of adrenergic receptor binding sites in brain regions of the neonatal rat: effects of prenatal or postnatal exposure to methylmercury. Neurotoxicology, 1987. 8(1): p. 1-13. 16. Slotkin, T.A., et al., Effects of neonatal methylmercury exposure on development of nucleic acids and proteins in rat brain: regional specificity. Brain Res Bull, 1985. 14(5): p. 397-400. 17. Dreiem, A., et al., Methylmercury inhibits dopaminergic function in rat pup synaptosomes in an age-dependent manner. Neurotoxicol Teratol, 2009. 31(5): p. 312- 7. 18. Gimenez-Llort, L., et al., Prenatal exposure to methylmercury changes dopamine- modulated motor activity during early ontogeny: age and gender-dependent effects. Environ Toxicol Pharmacol, 2001. 9(3): p. 61-70. 19. Johansson, C., et al., Neurobehavioural and molecular changes induced by methylmercury exposure during development. Neurotox Res, 2007. 11(3-4): p. 241-60. 98 Chapter D – Sensitivity of dopaminergic neuron differentiation from stem cells to chronic low-dose methylmercury exposure
20. Wagner, G.C., et al., Behavioral and neurochemical sensitization to amphetamine following early postnatal administration of methylmercury (MeHg). Neurotoxicology, 2007. 28(1): p. 59-66. 21. Newland, M.C. and E.B. Rasmussen, Aging unmasks adverse effects of gestational exposure to methylmercury in rats. Neurotoxicol Teratol, 2000. 22(6): p. 819-28. 22. Hughes, J.A. and S.B. Sparber, d-Amphetamine unmasks postnatal consequences of exposure to methylmercury in utero: methods for studying behavioral teratogenesis. Pharmacol Biochem Behav, 1978. 8(4): p. 365-75. 23. Lindstrom, H., et al., Effects of long-term treatment with methyl mercury on the developing rat brain. Environ Res, 1991. 56(2): p. 158-69. 24. Dare, E., et al., Effects of prenatal exposure to methylmercury on dopamine-mediated locomotor activity and dopamine D2 receptor binding. Naunyn Schmiedebergs Arch Pharmacol, 2003. 367(5): p. 500-8. 25. Vanduyn, N., et al., SKN-1/Nrf2 inhibits dopamine neuron degeneration in a Caenorhabditis elegans model of methylmercury toxicity. Toxicol Sci. 26. Pereira, M.E., et al., Methyl mercury exposure during post-natal brain growth alters behavioral response to SCH 23390 in young rats. Bull Environ Contam Toxicol, 1999. 63(2): p. 256-62. 27. Sarfaraz, D. and C.L. Fraser, Effects of arginine vasopressin on cell volume regulation in brain astrocyte in culture. Am J Physiol, 1999. 276(3 Pt 1): p. E596-601. 28. Burbacher, T.M., P.M. Rodier, and B. Weiss, Methylmercury developmental neurotoxicity: a comparison of effects in humans and animals. Neurotoxicol Teratol, 1990. 12(3): p. 191-202.
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100 Chapter E – Genuine human neural crest cells as test system for developmental toxicity and potential rescue strategies
Chapter E
Genuine human neural crest cells as test system for developmental toxicity and potential rescue strategies
Bastian Zimmer1, Gabsang Lee2, Kesavan Meganathan3, Agapios Sacchinidis3, Lorenz Studer2 and Marcel Leist1 1Doerenkamp-Zbinden Chair of in-vitro Toxicology and Biomedicine, Department of Biology, University of Konstanz, D-78457 Konstanz, Germany 2Developmental Biology Program, Sloan-Kettering Institute, 1275 York Ave, New York, New York 10065, USA. 3Institute of Neurophysiology, University of Cologne, D-50931 Cologne, Germany
Environ Health Perspect under review
101 Chapter E – Genuine human neural crest cells as test system for developmental toxicity and potential rescue strategies
Abstract
BACKGROUND: Information on the potential developmental toxicity (DT) of the majority of chemicals is scarce, and test capacities for further animal-based testing are limited. Therefore, new approaches with higher throughput are required. A screening strategy based on the use of relevant human cell types has been proposed by the EPA and others. As impaired neural crest (NC) function is one of the known causes for teratologic effects, NC assays are desirable for a DT test battery.
OBJECTIVE: Development of a robust and widely applicable human NC function assay, that allows sensitive screening of environmental toxicants, and definition of toxicity pathways.
METHODS: We generated human NC cells from pluripotent stem cells, and studied their migration and neural differentiation. We established an assay for migration of NC (MINC) and tested inhibitors of physiological pathways, environmental toxicants and their potential antidotes.
RESULTS: Methylmercury (50 nM) and lead (1 µM) specifically inhibited migration of NC, but not of other cell types. The MINC assay showed different sensitivities to various organic and inorganic mercury compounds, and two mercury antidotes showed a distinct bioactivity profile. To gain more information on response dynamics and biological relevance of the assay, we tested and identified several signaling pathways relevant for toxic disturbances of human NC.
CONCLUSIONS: Stem cell-derived NC cells faithfully model human NC functions, and reflect impairment of migration by developmental toxicants with good sensitivity and specificity. The MINC assay is amenable to high throughput testing of environmental chemicals.
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Introduction
Gestational and early-life exposure to chemicals can result in developmental toxicity (DT). Experimental and epidemiological studies have shown for example, that environmental agents may affected the developing peripheral and central nervous system in animals and man (Grandjean and Landrigan 2006; van den Hazel et al. 2006). At present, neurodevelopmental disorders affect 3-8% of the children born in Western countries, and the National Academy of Sciences has estimated that 12% of children in the US suffer at least from one mental disorder. Exposure to environmental chemicals has been identified as one of several risk factors facilitating or triggering such disorders (Hass 2006; van den Hazel et al. 2006). However, compelling epidemiologic evidence is only available for a small number of compounds, such as lead and methylmercury. Also, the number of different chemicals tested in animals is rather limited (Grandjean and Landrigan 2006). For instance, only a couple of hundred chemicals and pesticides have undergone testing according to national or international test guidelines for developmental neurotoxicity. However, the available comparative data indicate that mammals are often particularly sensitive to this form of hazard compared to other forms of toxicity (Raffaele et al. 2010). One third of all human congenital birth defects are associated with neural crest (NC) cells and their derivatives (Trainor 2010). The NC develops initially in parallel with the central nervous system precursors, and it is found on top (dorsally) and on both sides of the neural tube. A key event in vertebrate development is the delamination of NC cells from the neural tube and their migration to target sites in the periphery. There, they give rise to neurons and glial cells of the peripheral nervous system as well as non-neural cells such as melanocytes or bone and cartilage of the head (Le Douarin et al. 2008). Both, genetic factors (Lee et al. 2009) and environmental chemicals or drugs, such as mercury and anticonvulsants, have been identified as causes for NC developmental defects (Di Renzo et al. 2007; Fuller et al. 2002; Nishida et al. 1997). The current guidelines for toxicity testing of the organization for economic co-operation and development (OECD) are focused on in vivo data. Such animal-based testing of DT is expensive and requires highly qualified personnel. The enormous resource requirements preclude even the testing of the most abundant industrial chemicals already marketed (Hartung and Rovida 2009). Moreover, the field of developmental toxicology has experienced examples of strong species differences in the past (Hawkins 1983; Nau 1986). Therefore, the
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EPA and other key authorities suggested a new strategy for toxicity testing in the 21st century (NRC 2007). This suggests a shift towards the use of human cell-based systems and other assays that allow a high throughput of chemicals and the testing over large concentration ranges. A further element of the vision is the identification of pathways of toxicity, i.e. the accessibility of the chosen models to mechanistic studies (Andersen et al. 2011). Functional assays with primary human NC cells would fulfill such requirements, but they are not available for DT testing. As human pluripotent stem cells can give rise to any differentiated cell type, they are a powerful tool to mimic human development in vitro. Both embryonic stem cell (ESC) lines and induced pluripotent cells have recently been used to generate NC cells (Lee et al. 2010). If such cells could be used for toxicological testing, new improved assays for DT would become feasible. This would complement previous successful efforts using neuronal cells derived from different types of stem cells to model DT in the central nervous system (Moors et al. 2009; Stummann et al. 2009; Zimmer et al. 2011a; Zimmer et al. 2011b). We therefore carried out this study to develop a test system for DT based on human NC cells. The cells were generated from hESC and they were characterized in depth as to their genuine properties compared to other neural precursors. We were interested in identifying a functional endpoint that is (a) relevant to the in vivo situation and (b) that is susceptible to disturbance by chemicals. To evaluate the robustness of the test system and the feasibility of studies with reasonable throughput and precision, we probed concentration-response relationships of closely-related chemicals in different situations. Evaluation of NC migration appeared to yield useful toxicological information in an area of DT given only limited attention until now.
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Materials and Methods
Cell culture and neural differentiation protocols
The hESC H9 and the isogenic reporter (GFP under the endogenous Dll1 promoter) cell line H9-Dll1 (Placantonakis et al. 2009) were maintained on inactivated murine embryonic fibroblasts in medium supplemented with FGF2. Differentiation into neural crest (NC) cells was initiated on MS5 stromal cells and continued as described in supplementary methods and Fig. 1. Differentiation towards central nervous system neuroepithelial precursor cells (NEP) was performed as described earlier (Chambers et al. 2009). The HeLa 229, MCF-7, HEK 293 and 3T3 cell lines were cultured in DMEM supplemented with 10% fetal calf serum.
Immunocytochemistry
Cells were fixed directly on the cell culture plate. After incubation with the primary antibodies overnight, and staining with appropriate secondary antibodies and H-33342, cells were digitally imaged. For a detailed list of antibodies see supplementary table 1a. Cell proliferation was assessed using the Invitrogen Click-iT® EdU cell proliferation assay as described in the user manual.
Whole genome transcriptome analysis
RNA was isolated from the cell cultures and prepared for microarray hybridizations as described earlier (Wagh et al.). Then, gene expression analysis was performed as described in supplementary methods.
Cell migration analysis
Cell migration analysis was carried out using a scratch assay design as described in (Rodriguez et al. 2005). Briefly, a confluent layer of NC was scratched using a 20 µl pipette tip to create a cell free gap. For some control experiments, culture inserts (Ibidi Munich, Germany) were used to create a cell free gap. The width of the cell free gap was determined right after scratching the monolayer or removing the culture insert and used to define the region of interest (ROI). Then, the medium was removed and fresh medium containing desired test chemicals was added. After 48 h, a resazurin reduction assay was performed and then fresh medium containing the DNA dye H-33342 (1 µg/ml) was added. After 30 min, 105 Chapter E – Genuine human neural crest cells as test system for developmental toxicity and potential rescue strategies
random images along the scratch were taken at a 4x magnification. The number of cells (H- 33342 positive nuclei) within the ROI was assessed by manual counting.
Chemical exposure during migration
Cells were exposed to chemicals for 48 h in N2 medium containing epidermal growth factor (20 ng/ml) and FGF2 (20 ng/ml). After 48 h of exposure to chemicals, cells were incubated with resazurin (10 µg/ml) in their cell culture medium for 60 min to determine viability.
Resazurin reduction was analyzed in cell culture medium fluorimetrically (λex = 530 nm, λem = 590 nm). Readout values were normalized to untreated controls. For a detailed list of chemicals and their tested concentration range used in this study see supplementary Table 1b.
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Results
Characterization of hESC-derived NC cells
A prerequisite for the establishment of a robust toxicological in vitro test, which can also be used in different laboratories, is a protocol that allows production of large batches of identical cells. Moreover, only the ability to cyropreserve and ship such cells allows their broad applicability by laboratories not accustomed to the culture of hESC. To generate such a population of neural crest (NC) cells, we differentiated hESC, as described earlier (Lee et al. 2010), and cryopreserved large batches after an additional phase of NC amplification in medium containing EGF and FGF-2 (Fig. 1). The thawed cells were extensively phenotyped, and used for all further tests.
Figure 1. Characterization of hESC derived NC cells
The schematic representation (top) illustrates the differentiation protocol and experimental procedures. (A) Immunocytochemical characterization of hESC derived NC cells after thawing. Bars = 200 µm. (B) Flow cytometry analysis of NC cells for HNK1 and p75 expression. (C) Immunofluorescence analysis of peripheral neurons differentiated from NC cells. Bars = 50 µm Immunofluorescene analysis showed a homogeneous expression of the specific NC markers, AP2, Sox9 (data not shown), HNK1, and p75 and absence of the neuroepithelial marker Pax6 (Fig. 1A). The cells were positive for the general neuroectodermal marker nestin, but not the 107 Chapter E – Genuine human neural crest cells as test system for developmental toxicity and potential rescue strategies
Schwann cell marker GFAP or the neuronal marker Tuj1 (Fig. 1A). Flow cytometry analysis confirmed the high purity of the expanded and cryopreserved NC culture, with > 97% of all cells expressing at least one of the surface markers HNK1 or p75 (Fig. 1B). We also confirmed the NC properties, by differentiating the cells into peripheral neurons staining positive for beta-III tubulin (= Tuj1), peripherin, Brn3a and NeuN (Fig. 1C).
Distinction of hESC-derived NC cells from central neural precursors
We used broad transcriptome profiling to further investigate, the difference of NC and central nervous system neuroepithelial precursors (NEP). The mRNA expression profile of NEP, NCP and the corresponding hESC was compared. The 1802 transcripts upregulated in NCP compared to hESC included classical neural crest markers like Snail2 (154-fold), Sox9 (10-
Figure 2. Transcriptome analysis of NC cells
Whole genome-wide transcriptome data were obtained for hESC, NEP and NC. (A) The number of significantly upregulated or downregulated genes is shown for NC and NEP relative to hESC. (B) Two-dimensional principal component analysis of the chip data. Each circle indicates one experiment (n = 3 for each cell type). (C) Examples for gene onthologies with strongly differential expression in NC and NEP. The four chosen GOs contained on average 170 genes. The fraction of genes identified to be upregulated is indicated. n.p.: not present.
fold), and AP2 (Tfap2a, 8-fold), while the transcripts upregulated in NEP comprised expected genes like Pax6 (117-fold) and FoxG1 (16-fold). Only 470 upregulated transcripts were shared between NEP and NCP. Among the 2560 transcripts downregulated in NCP, characteristic pluripotency genes like Sox2 (165-fold), Nanog (110-fold) and Oct3/4 (Pou5f1, 108 Chapter E – Genuine human neural crest cells as test system for developmental toxicity and potential rescue strategies
27-fold) were identified. These genes, except for Sox2, were also identified amongst the downregulated genes of NEP (Fig. 2A). Principal component analysis of the complete transcriptome sets revealed that NEP and NCP were about as different from one another, as each of them was from hESC (Fig. 2B). For more detailed analysis of the types of genes up- regulated in the respective cell types, gene onthologies (GOs) that were statistically overrepresented were identified with bioinformatic tools. A striking feature of NC were 18 GOs associated with migration (supplementary table 1c), and several hundred genes involved in cell motility were upregulated. In contrast to that, GOs selectively associated with NEP were e.g. neural tube development and forebrain regionalization (Fig. 2C). Thus, NC were a genuine cell population, clearly distinguished from NEP or their common source, hESC.
Scratch assay characteristics using NC
NC cells need to migrate to fulfill their biological function, and disturbance of this process by developmental toxicants leads to malformations. Therefore, we established an assay to test such interferences. Migration of NC (MINC) cells into a cell free scratch area was followed for 48 h with established methods (Rodriguez et al. 2005). The variation of the scratch width was about 10% within and between experiments (supplementary Fig. 1A, B, C). As additional control for potentially confounding effects of scratching, we used a system (http://www.ibidi.de) in which a cell free gap was produced by a removable spacer without mechanical effects on cells or coating material. The gap width was 500 ± 50 µm, and cell migration was exactly the same as in the scratch assay (data not shown). To investigate the role of proliferation in our test system, we inhibited cell division by adding the cytostatic drug cytosine arabinoside (AraC). Under these conditions, DNA synthesis, as measured by incorporation of the thymidine analogue EdU, was completely inhibited, while the scratch was still repopulated to the same extent (supplementary Fig. 1 D, E). We conclude that the scratch assay in combination with our NC cells differentiated from hESC can serve as a specific migration test.
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Detection of pathway specific controls in the MINC
Human NC migration is guided by chemokines and intracellular signaling involving G- protein coupled receptors, small G-proteins and various kinases (Krull 2001; Kuriyama and Mayor 2008). Following the procedure suggested for developmental neurotoxicity assay
Figure 3. Response to pharmacological control compounds
Directly after scratching of NC cultures, different test compounds were added. Migration of untreated or semaphorin3A- exposed (sema3A; 100 ng/ml) NC cells was recorded by video microscopy (supplementary movie 1, 2). (A) Representative images are shown for four time points. White arrows indicate migrating cells. Bar = 50 µm. (B) Quantification of NC cell migration in the presence of sema3A. (C) Representative images of cell migration in the absence or presence of pertussis toxin (PTX). (D) Quantification of NC cell migration in the presence of PTX. (E) Positive and negative control compounds interfering with actin dynamics were tested in the MINC assay. Data are means ± SD of 3 independent experiments normalized to the untreated control. *: p < 0.05, **: p < 0.01, ***: p < 0.001. (F) Representative images of actin filaments in untreated migrating neural crest cells (left) and neural crest cells treated with 100 nM cytochalasin D (right). Actin filaments were visualized using phalloidin (red), H-33342 was used to counterstain nuclei (blue). Bars = 50 µm.
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development (Crofton et al. 2011), we used compounds interfering with these pathways, in order to gain information whether biologically-predictable disturbances of migration were detectable in our test system. As our chip analysis indicated that several semaphorin receptors (Nrp1: 22-fold; Plxnc1: 16-fold) were upregulated, we tested whether the NC repellent semaphorin 3A (Sema3A) is able to inhibit cell migration. Live cell time-lapse video microscopy showed the migration behavior of NC under normal conditions, and the strongly arresting effect of Sema3A on the cells (Fig. 3A, supplementary. video 1, 2). Quantification showed a concentration-dependent inhibition (Fig. 3B). Also blocking of receptor signaling via inhibitory G-proteins by pertussis toxin (PTX), inhibited NC cell migration (Fig. 3C, D). Furthermore, inhibition of Src family tyrosine kinases by PP2 or of c-jun N-terminal kinases (JNK) by SP600125, both of which are known to be involved in the control of cell migration, reduced NC cell migration (supplementary Fig. 2A, B). We also tested whether the MINC allowed the detection of compounds accelerating the process beyond the normal rate. We found that addition of the albumin-based product AlbuMax® doubled the number of migrating NC cells (supplementary Fig. 2C). Thus, proof-of-principle was provided that our toxicological test system detects compounds causing deviations of the assay endpoint to both directions.
Inhibition of NC cell migration by toxicants disturbing actin dynamics
A typical toxicity pathway related to cell motility is disturbance of cytoskeletal structures. We interfered with actin dynamics to further validate the MINC assay. All inhibitors were used at non-cytotoxic concentrations, and this was always tested directly within the MINC setup by a resazurin reduction assay performed at the end of the incubation period. Inhibition of actin polymerization by the mycotoxin cytochalasin D decreased NC migration (Fig. 3E). This effect correlated with dissolution of actin filaments in cells treated with the drug (Fig. 3F). The negative control compound D-mannitol did not show any effect up to millimolar concentrations. Indirect disturbance of actin polymerization by CK-666, an inhibitor of the Arp2/3 complex, decreased cell migration. The chemically-similar drug CK-689, which does not interact with Arp2/3, had no effect. Migration was also reduced by locostatin, an inhibitor of the Raf kinase inhibitor protein (RKIP), but not by the corresponding negative control compound (Fig. 3E). Moreover, inhibition of Rac1, an upstream controller of actin dynamics and cell motility, by NSC23766 reduced NC migration (supplementary Fig. 2D). These
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examples illustrate the mapping of a potential toxicity pathway with a larger set of positive and negative controls.
Heavy metals specifically inhibit migration of neural crest cells
After successful detection of adverse effects of pathway-specific compounds in the MINC, we examined known developmental toxicants. Lead reduced NC migration at concentrations ≥ 1
µM (Fig. 4A). The environmental toxicant methylmercury (CH3HgCl) inhibited NC migration at a concentration of 50 nM significantly in each of 13 independent assays. The average signal to noise ratio for this effect was 7.7 and significant inhibition by methylmercury was used as acceptance criteria for all further tests. The toxicity of methylmercury was also observed under altered assay conditions, e.g. in the presence of AlbuMax® (supplementary Fig. 2C). As
Figure 4. Inhibition of cell migration in NC cells by heavy metals
Effects of different metal compounds on NC cell migration. (A) Concentration response curves of lead acetate (Pb(CH3CO2)4) and CH3HgCl. (B) Concentration-response curves of the organomercury compound thimerosal and the inorganic mercury salt HgCl2. (C) Viability of cells (resazurin reduction) exposed to the 4 different heavy metal compounds. Data are means ± SD of 3 independent experiments normalized to untreated controls. *: p < 0.05, **: p < 0.01, ***: p < 0.001 112 Chapter E – Genuine human neural crest cells as test system for developmental toxicity and potential rescue strategies
the adverse effects of mercury compounds depend on their chemical structure, we tested a further organomercury compound, thimerosal, and an inorganic mercury salt, HgCl2.
Thimerosal reduced NC migration at least as potently and effectively as CH3HgCl, while inorganic mercury was about 10-fold less potent (Fig. 4B). All four heavy metal toxicants affected NC migration at concentrations that did not reduce cell viability (Fig. 4C). Moreover, NC cells appeared to be more susceptible to the inhibition of migration than other cells: the human cancer cell lines HeLa and MCF-7, the human embryonic kidney cell line HEK293 and the mouse fibroblast cell line 3T3 all migrated in the scratch assay, were all inhibited by cytochalasin D, but did not react to the low metal concentrations detected in the NC assay (supplementary Fig. 3). These results indicate that the MINC is highly sensitive to developmental toxicants and able to rank related compounds within a group on the basis of their potency.
Differential effects of chelating agents on migration inhibition by mercury compounds
Compound interactions are a particular challenge for test systems. We examined here how different clinically-used chelating agents affected the toxicity of various mercurials in the
MINC. We chose penicillamine and CH3HgCl for initial testing, and explored the ideal time point of chelator addition. Co-incubation and a 1 h offset both allowed the complete prevention of toxicity. A high penicillamine concentration (100 µM) still prevented methylmercury toxicity when added with a 5 h delay, while low concentrations (10 µM) lost their efficacy. After 12 h only small protective effects were observed (Fig. 5A).
Co-incubation with penicillamine (10 µM) also completely prevented toxicity of CH3HgCl, thimerosal and HgCl2, but not of HgBr2 and HgSO4. We obtained similar data also when 100 µM penicillamine was used, or when chelation was attempted with DMPS (Fig. 5B). Thus, two groups of mercury compounds behaved differently in the MINC when chelators were present.
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Figure 5. Effects of chelators on mercury toxicity
Migrating NC cells were treated for 48 h with different mercury compounds in the presence or absence of chelating agents. (A) Migrating NC cells were treated for 48 h with 50 nM CH3HgCl, and penicillamine was added at the indicated times AFTER the toxicant. Upper dashed line indicates the untreated control value, lower dashed line indicates the reduction in cell migration with 50 nM CH3HgCl. Data are displayed as means ± SD of 3 independent experiments normalized to the untreated control. (B) Co-incubation of migrating NC cells with one of 5 different mercury compounds and either penicillamine or DMPS. Dashed lines indicate the untreated control values. Chelators alone had no effect on cell migration.
Discussion
NC cells generate a large number of different cell types all over the body. Therefore, impairment of their migration can cause multiple developmental defects (Ferretti 2006). Vertebrate test systems have been developed using either explants from chicken and rodents (Garic et al. 2011; Li et al. 2001) or whole organisms such as zebrafish (Grimes et al. 2008). We described here a new approach, based directly on human NC cells. The impairment of migration was tested in this system for more than 20 compounds, and different signaling
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pathways were found to play practical role for the MINC. We found that environmental chemicals can be characterized alone or in their interaction with other compounds. In contrast to earlier studies with hESC-based systems in the area of neurotoxicity (Stummann et al. 2009), we used here a functional endpoint instead of phenotypic endpoints. Also the murine embryonic stem cell test (EST), validated by the European center for validation of alternative methods (ECVAM) uses a functional endpoint (beating of cardiomyocytes), and this allows a sensitive detection of a broad range of toxicants (Genschow et al. 2004). Impairment of NC development and function has been observed for a variety of chemicals, ranging from various fungicides and anticonvulsant drugs to ethanol and PCBs (Di Renzo et al. 2007; Fuller et al. 2002; Garic et al. 2011; Grimes et al. 2008). We have used in the first part of the study drug-like compounds with defined modes of action to interfere with pathways known to control NC migration. This yielded information on the performance parameters of the assay and the biological relevance of the test system. In the second part, we probed the toxicological relevance by using two well-known developmentally neurotoxic metals, lead and mercury. The concentration-response curves generated for all compounds allow estimates of their lowest observed effect levels (LOELs). For instance, a value of around 5 nM for methylmercury indicated high sensitivity in the human-relevant low concentration range (Zimmer et al. 2011b). The possibility to derive such LOELs has two interesting implications for the use of the MINC in hazard assessment. First, it may be used for ranking of the relative hazard within a group of related compounds. Such information would guide read-across procedures as used in REACH (Scialli 2008). Second, the LOELs may be used as point-of-departure for an in vitro – in vivo extrapolation of human adverse effects. In combination with exposure data, such information may contribute to a preliminary risk assessment of environmental chemicals to support prioritization of their further testing. Phenotypic characterization indicated a genuine NC profile of the cells also after expansion and freezing. The latter step was introduced following the guidelines of good cell culture praxis (Coecke et al. 2005) that suggest that assays are run with large batches of identical cells, also when performed in different labs. Video microscopy indicated a specific neural type of migration, characterized by searching movements of growth-cone like cellular processes, which differed strongly from the movement pattern of non-neural cells. The MINC also allowed the quantification of migration independent of differentiation and with negligible effects of proliferation on the readout. This situation differs from the one in central neural precursor cell assays, where migration is associated with differentiation (Moors et al. 2009).
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As various types of migration may be affected by different toxicity pathways, such detailed biological information seems important for the interpretation of assay results, and different assays are required to represent e.g. central and peripheral neuronal precursors. Concerning the robustness of the MINC under varying conditions, the serendipitous discovery of a specific protein factor (AlbuMax®) accelerating migration indicated that the MINC can detect not only inhibitory, but also accelerating compounds. Moreover, the relative inhibition by methylmercury with or without AlbuMax® was similar. Together with the chelator data, this suggests that compound mixtures may be investigated with the assay. This needs, however, further exploration, as the most relevant mixtures, e.g. in the field of endocrine disrupters (Silva et al. 2011) contain compounds with mostly similar types of action, while we presented examples here only on compounds with opposite modes of action. Methylmercury causes developmental disturbances, with potency and relative rank orders depending on the cell types studied. For instance, neuronal differentiation is affected at low nanomolar levels (Zimmer et al. 2011b), while attenuation of human neurite outgrowth (Stiegler et al. 2011), and inhibition of central neuronal migration (Moors et al. 2009) require 1-2 orders of magnitude higher concentrations. The MINC ranked amongst the more sensitive assays, and was affected differently by e.g. inorganic and organic mercury compounds. This has also been observed in several other experimental systems, such as human neurospheres, yeast, rodents, and man (Aschner and Ceccatelli 2010; Graeme and Pollack 1998; Moors et al. 2009). Also inorganic forms of mercury do not necessarily show similar biological behavior, as they differ in the extent of ionization (Kungolos et al. 1999). Such findings were corroborated and expanded here, by our observation of different effects of chelators on HgCl2 vs. HgBr2. It is well established that the capacity of chelators to prevent developmental toxicity of mercury compounds depends both on the chemistry of the metal and the neutralizing agent, and that biological systems react differently than predicted from pure chemical tests (Magos 1976; Rush et al. 2009). The initial data from the MINC suggest that chelator potency may be evaluated and ranked. In conclusion, the MINC assay may form part of a broader test battery for DT. Human variability may be modeled by using induced pluripotent stem cells as source material (Lee et al. 2010), and even higher throughput is allowed by new spacer technologies to create cell free gaps (Gough et al. 2011). A closer characterization of the biological foundations of the assay will assist the future mapping of toxicity pathways. The present data already suggest
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that the examination of a broad panel of environmentally-relevant compounds, alone or as mixtures, will yield human-relevant information, not easily obtained by other methods.
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Supplements
Table of contents
• Supplementary Table 1a (Table S1a) Detailed list of antibodies used in this study • Supplementary Table 1b (Table S1b) Detailed list of chemicals and growth factors used in this study • Supplementary Table 1c (Table S1c) Significantly overrepresented GOs associated with migration identified by whole genome expression analysis of neural crest cells • Supplementary Figure 1 (Fig. S1) Measurement of NC migration with a scratch repopulation assay • Supplementary Figure 2 (Fig. S2) Pharmacological modulation of NC migration
• Supplementary Figure 3 (Fig. S3) Migration characteristics of HEK293 cells assessed by LIVE cell video microscopy
• Supplementary movies 1, 2, 3 • Supplementary methods: 1. Differentiation of human embryonic stem cells 2. Cell culture of cell lines 3. Flow cytometry analysis 4. RNA isolation, Microarray labeling and hybridization 5. Statistical filtration of significantly expressed genes 6. Live cell video imaging of cell migration 7. Statistics and data mining
• Supplementary references
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Supplementary Table 1a:
Detailed list of antibodies used in this study target protein/antibody name dilution catalogue number provider Brn3a 1:500 AB5945 Millipore GFAP 1:800 G3893 Sigma HNK1 1:200 C6680 Sigma Nestin 1:500 MAB1259 R&D NeuN 1:200 MAB377 Millipore P75 1:100 AB-N07 ATS* Pax6 1:200 PRB-278P Covance Peripherin 1:200 SC-7604 Santa Cruz Phalloidin-568 (Actin) 1:100 A12380 Invitrogen Tubb3 1:1000 T2200 Sigma Tuj1 1:1000 MMS-435P Covance * ATS: Advanced Targeting Systems
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Supplementary Table 1b: Detailed list of chemicals and growth factors used in this study compound concentration catalogue number provider AlbuMax® II 5%() 11021 Invitrogen Ara-C hydrochloride 10 µM C6645 Sigma Ascorbic acid 200 µM A4034 Sigma BDNF 20 ng/ml 248-BD/CF R&D cAMP 1 mM A9501 Sigma CH3HgCl 0.5 – 50 nM (5 nM) 442534 Sigma CK-666 500 pM – 5 µM 182515 Calbiochem CK-689 500 pM – 5 µM 182517 Calbiochem cytochalasin D 1 – 100 nM C8273 Sigma D-Mannitol 1 µM – 1 mM M1902 Sigma DMPS 10 – 100 µM D8016 Sigma Dorsomorphin 600 nM 3093 Tocris D-Penicillamine 10 – 100 µM P4875 Sigma EGF 20 ng/ml 236-EG R&D FGF2 (differentiation) 20 ng/ml 233-FB/CF R&D FGF2 (hESC culture) 10 ng/ml 13256-029 Invitrogen FGF8 100 ng/ml 423-F8/CF R&D GDNF 20 ng/ml 212-GD/CF R&D HgBr2 50 nM 437859 Sigma HgCl2 0.5 – 50 nM (50 nM) 203777 Sigma HgSO4 50 nM 31013 Sigma Lead-acetate (Pb(CH3CO2)4) 0.1 – 5 µM (1 µM) 398845 Sigma Locostatin 500 pM – 5 µM 219469 Calbiochem Locostatin neg. ctrl. 500 pM – 5 µM 219470 Calbiochem NGF 10 ng/ml 256-GF/CF R&D Noggin 500 ng/ml 719-NG R&D NSC23766 10 nM – 5 µM 2161 Tocris NT3 10 ng/ml 267-N3/CF R&D Pertussis toxin 50 -100 ng/ml P2980 Sigma SB431542 10 µM 1614 Tocris Semaphorin3A 50 – 100 ng/ml 1250-S3 R&D Sonic Hedgehog (Shh) 20 ng/ml 1845-SH/CF R&D SP600125 0.5 – 10 µM S5567 Sigma Thimerosal 0.5 – 50 nM (1 nM) T4687 Sigma
The highest non-cytotoxic concentration, determined in a cell viability assay (resazurin reduction) after 48 h, was used as highest concentration for the NC cell migration assay. The high concentration indicated in the table corresponds to the highest non-cytotoxic concentration found in the pre-screening assay. Where concentration ranges are given for mercurial compounds or lead, the LOEL in the NC migration assay is indicated in brackets. Note the 10 - 50 fold differences between organic and inorganic mercury compounds.
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Supplementary Table 1c:
Significantly overrepresented GOs associated with migration identified by whole genome expression analysis
GO term term domain and name p-value GO:0040011 locomotion 2.02e-14 GO:0040012 regulation of locomotion 2.87e-12 GO:0016477 cell migration 8.72e-12 GO:0042330 taxis 8.21e-11 GO:0006935 chemotaxis 8.21e-11 GO:0030334 regulation of cell migration 1.08e-11 GO:2000145 regulation of cell motility 1.77e-11 GO:0048870 cell motility 3.50e-10 GO:0040017 positive regulation of locomotion 6.62e-08 GO:0040013 negative regulation of locomotion 3.53e-07 GO:2000147 positive regulation of cell motility 5.06e-07 GO:0030335 positive regulation of cell migration 5.06e-07 GO:0043542 endothelial cell migration 9.30e-07 GO:0009611 response to wounding 1.22e-06 GO:0042060 wound healing 1.35e-06 GO:0030336 negative regulation of cell migration 3.65e-06 GO:2000146 negative regulation of cell motility 3.65e-06 GO:0010594 regulation of endothelial cell migration 1.25e-05
Whole genome mRNA expression in neural crest cells was analyzed using the affymetrix microarray platform. Gene expression was compared to gene expression in undifferentiated human embryonic stem cells. Significantly upregulated genes in NC cells were then further analyzed using the web-based gene ontology (GO) analyzing tool g:Profiler (Reimand et al. 2007). Statistically overrepresented GOs dealing with cell migration are displayed.
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Supplementary Figure 1: Measurement of NC migration with a scratch repopulation assay
In a homogenous NC culture, cells were removed mechanically along an about 0.5 mm wide line using a pipette tip. Cells were visualized using the fluorescent nuclear stain H-33342. (A) Representative image of the scratch (0 h, left), used to define the region-of-interest (ROI) for quantification (red rectangle); a typical situation at the end of the assay is shown (48 h, right). (B) Measurement of the scratch width in 14 independent experiments (means of 3 technical replicates ± SD). (C) Mean scratch width ± SD of the experiments shown in B. (D) Cell proliferation was measured in NC cells during the migration assay in the presence or absence of 10 µM cytosine arabinoside (AraC). Proliferating cells incorporated EdU, and are stained in red. EdU (10 µM) was present throughout the assay (48 h). Bars = 100 µm. (E) Cell migration assay in the presence of 10 µM AraC. Data are normalized to untreated controls and displayed as means ± SD of 3 independent experiments.
122 Chapter E – Genuine human neural crest cells as test system for developmental toxicity and potential rescue strategies
Supplementary Figure 2 A, B:
Pharmacological modulation of NC migration
A 125
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Quantification of neural crest cell migration in the presence of 2 different kinase inhibitors. The NC cell migration assay was performed as described in material and methods. Cells were incubated with the inhibitors for the entire assay (48 h) (A) Neural crest cell migration was inhibited by 1 µM of the Src-family tyrosine kinase inhibitor PP2. (B) The selective inhibitor of c-Jun N-terminal kinase (JNK), SP600125 reduced cell migration in a concentration- dependent manner, with 5 and 10 µM of SP600125 reducing NC cell migration significantly. Note that no general cytotoxicity was observed in the resazurin assay (data not shown) Data are displayed as means ± SD of 2 independent biological experiments performed in quadruplicates. *: p < 0.05, ***: p < 0.001
123 Chapter E – Genuine human neural crest cells as test system for developmental toxicity and potential rescue strategies
Supplementary Figure 2 C, D:
C *** ** 200 ***
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NSC23766 concentration (C) The NC cell migration assay was performed in the presence of 5% AlbuMax® (the lipid rich version of bovine serum albumin). Incubation of migrating neural crest cells with AlbuMax® doubled cell migration (black bars). Methylmercury (grey bars) inhibited both the normal migration (without AlbuMax®) and the accelerated migration (with AlbuMax®) to the same extent (inhibition by 50 nM methylmercurychloride in the absence of AlbuMax® was about 57% compared to untreated cells. In the presence of AlbuMax®, NC cell migration was reduced by about 60%). Data are displayed as means ± SD of 2 independent biological experiments, each performed in triplicates. Data were normalized to untreated controls without AlbuMax®. **: p < 0.01, ***: p < 0.001. (D) NC cell migration was analyzed in the presence of the selective inhibitor of Rac1-GEF interaction, NSC23766. NC migration was significantly reduced in the presence of 1 or 5 µM NSC23766. All concentrations used here were non-cytotoxic, as assessed by resazurin reduction (not shown). Data are displayed as means ± SD of 3 independent biological experiments performed in quadruplicates. **: p < 0.01, ***: p < 0.001
124 Chapter E – Genuine human neural crest cells as test system for developmental toxicity and potential rescue strategies
Supplementary Figure 3:
Migration characteristics of non neural crest like cell lines
initial scratched space
initial scratched space
(A) Migration characteristics of the human embryonic kidney cell line HEK293 were analyzed using Live cell video microscopy (supplementary movie 3). Note the different migration style of HEK293 compared to NC cells (supplementary movie 1). HEK293 cells migrate as a closed front without any “leading” cells. In contrast to this, NC cells migrated individually and showed exploratory behavior by extending growth cone-like projections into different directions. Dashed lines indicate the initial scratch boarder. (B) Representative images of HEK293 cells treated with methylmercury, lead and cytochalasin D for 48 h during the cell migration assay. Nuclei were visualized using H-33342. Dashed lines indicate the initial cell free space. (C) Quantification of cell migration of 4 different cell lines in the presence of methylmercury, lead-acetate ((Pb(CH3CO2)4) or cytochalasin D. Data are 125 Chapter E – Genuine human neural crest cells as test system for developmental toxicity and potential rescue strategies
normalized to respective untreated controls (set to 100%) and displayed as means ± SD of 3 independent experiments. *: p < 0.05, **: p < 0.01, ***: p < 0.001
Supplementary movies 1, 2, 3 The movie files have been uploaded at the EHP website as “material not for review”. They may be considered as background information. Corresponding representative still images are found in figure 3 and in supplementary figure 3.
Supplementary movie 1 Migration analysis of untreated NC cells. Right after scratching, NC cells were imaged for 48 h as described in material and methods. The movie runtime of 1 min 22 sec corresponds to 48 h real time.
Supplementary movie 2 Migration analysis of NC cells treated with 100 ng/ml semaphorin3A. Right after scratching, NC cells were imaged for 48 h as described in material and methods. The movie runtime of 2 min 45 sec corresponds to 48 h real time.
Supplementary movie 3 Migration analysis of untreated HEK293 cells. Right after scratching HEK293 cells were imaged for 48 h as described in material and methods. The movie runtime of 1 min 16 sec corresponds to 48 h real time.
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Supplementary methods:
Differentiation of human embryonic stem cells
Differentiation of hESC to neural crest cells was performed exactly as described earlier in detail (Lee et al. 2010; Lee et al. 2007). Briefly, hESC were plated on a confluent layer of mitomycin C treated MS-5 stromal cells in KSR medium (DMEM supplemented with 15% serum replacement, 1x GlutaMax, non-essential amino acids (NEAA) and beta- mercaptoethanol, all ingredients from Invitrogen) (Lee et al. 2010). After 12 days of differentiation, medium was changed to DMEM/F12 supplemented with glucose, insulin, apo- transferrin, putrescine, selenite and progesterone as described (Lee et al. 2010) (from now on referred to as N2 medium), containing sonic hedgehog (SHH), fibroblast growth factor 8 (FGF8), brain-derived neurotrophic factor (BDNF) and ascorbic acid. Rosette structures were manually picked and harvested on day 21 of differentiation. The rosettes were then plated on previously poly-L-ornithine/laminin/fibronectin (PLO/L/FN) coated plates in N2 medium containing BDNF, SHH, FGF8 and ascorbic acid. After 7 days, cells were FACS sorted for positive expression of p75 (antibody obtained from Advanced targeting Systems) and HNK-1 (antibody obtained from Sigma). Appropriate secondary antibodies conjugated with PE and AlexaFluor647 were obtained from Invitrogen. Sorted cells were then expanded for 28 additional days in N2 medium supplemented with EGF (20 ng/ml) and FGF2 (20 ng/ml) (both R&D Systems Wiesbaden-Nordenstadt, Germany). Medium was changed every other day. After 28 days of expansion, including 4-5 passaging steps, cells were detached from the plates using accutase (PAA, Pasching, Austria) and cryopreserved in 90% FCS 10 % DMSO. Cells were stored in liquid nitrogen. Expanded and cyropreserved cells were used for all further experiments. Differentiation of NC cells into peripheral neurons was performed as described earlier (Lee et al. 2007). The cryopreserved cells were thawed and plated on PLO/L/FN coated plates at a density of 100 000 cells/cm2 in N2 medium containing EGF and FGF2. After a 1 day attachment phase, cells were cultured in N2 medium containing different cytokines (Lee et al. 2010) for additional 3 weeks. Medium was changed every other day. Differentiation of hESC to Pax6+ neuroepithelial cells was performed as described earlier (Chambers et al. 2009) with minor changes. The initial noggin concentration was decreased to 35 ng/ml. Instead 600 nM dorsomorphin was added to complement for noggin.
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Cell culture of cell lines The HeLa229 (ATCC number: CCL-2.1), MCF-7 (ATCC number: HTB-22), HEK293 (ATCC number: CRL-1573) and 3T3 (ATCC number: CCL-92) cell lines were cultured in DMEM supplemented with 10% FCS and 2 mM GlutaMax at 37°C in a humidified atmosphere containing 5% CO2. Cells were routinely passaged 3 times a week. The migration assay using these cell types was performed essentially as described for NC cells.
Flow cytometry analysis For flow cytometry analysis, cells were detached using Accutase (PAA) and stained with HNK1 and p75 specific antibodies for 30 min on ice. After incubation with the appropriate secondary antibodies for 30 min on ice, cells were analyzed using an Accuri C6 flow cytometer (Accuri Cytometers, Inc. Ann Arbor, MI USA). Data were processed and analyzed using the Accuri CFlow Plus software.
Microarray labeling and hybridization For global transcriptional profiling, the total RNA was isolated from neural progenitor cells using Trizol (Invitrogen, Damstadt, Germany), and purified with Qiagen RNeasy mini kits (Qiagen, Hilden, Germany). On column DNase digestion was performed as per the manufacturer’s protocol. Before microarray analysis, the RNA was quantified with a NanoDrop N-1000 spectrophotometer (NanoDrop, Wilmington, DE, USA), and the integrity of RNA was confirmed with a standard sense automated gel electrophoresis system (Experion, Bio-Rad, Hercules, CA, USA). The samples were used for microarray analysis when the RNA quality indicator (RQI) number was higher than 8. For RNA amplification and biotin labeling, 100 ng total RNA were amplified for 16 h with Genechip 3’ IVT Express Kit. After amplification, aRNA was purified with magnetic beads, and 15 μg of aRNA were fragmented with fragmentation buffer as per the manufacturer’s instructions. 12.5 μg fragmented aRNA were hybridized with Affymetrix Human Genome U133 plus 2.0 arrays as per the manufacturer’s instructions. The chips were placed in a GeneChip Hybridization Oven-645 for 16 h at 60 rpm and 45 ºC. For staining and washing, Affymetrix HWS kits were used on a Genechip Fluidics Station-450. For scanning, the Affymetrix Gene-Chip Scanner- 3000-7G was used, and the image and quality control assessments were performed with Affymetrix GCOS software. All reagents and instruments were acquired from Affymetrix (Affymetrix, Santa Clara, CA, USA).
128 Chapter E – Genuine human neural crest cells as test system for developmental toxicity and potential rescue strategies
Statistical filtration of significantly expressed genes Robust Multi-array Analysis was used for background correction and normalization. The raw dataset was transformed by Quantile normalization (Bolstad et al. 2003) with the R (Affy)- package (Gautier et al. 2004). MAS5 Expression Summary (Pepper et al. 2007) was used to detect present calls. Only 31567 probe sets out of 54613 received present calls as defined by the detection p-value of ≤ 0.05. Probe sets with “present” calls were selected and those with “absent” calls were eliminated. One way Anova calculation was performed considering ‘differentiation’ as a factor with hESC as the defined control group. Moderated t-test calculation was applied for pairwise comparisons of NEP vs. hESC and NC vs. hESC. Differentially expressed transcripts were filtered with an FDR - controlled P value of ≤ 0.05 (95% confidence interval). A second filter selected for fold-change values. The Benjamini-Hochberg method was used to adjust the raw p-values to multiple testing and to reduce the false discovery rate. Principal component (PC) analysis was performed using the Stats package in R. The first PC axis accounted for 37.4 % of the variance in the data set of variable transcripts and the second accounted for 21.1 %. All microarray raw data and results have been deposited in a public database (GEO). [reference number to be added after manuscript acceptance]
Live cell video imaging of cell migration Cells were seeded on 35 mm petri dishes (Ibidi GmbH, Munich, Germany) and treated as described above. Phase-contrast images from multiple predefined points (ROI) along the scratch were taken every 5 minutes for 48 h using a Nikon Biostation IM (Nikon GmbH, Duesseldorf, Germany) equipped with a 20x lens. Images were further processed and combined to video files using ImageJ. The width of an image frame is 240 µm.
Statistics and data mining For the migration assay, the number of migrated cells was manually counted in ≥ 4 different fields per experiment. The untreated control fields contained 150 ± 44 (mean ± SD) migrated cells per field. In 13 independent experiments 672 ± 118 (means ± SEM) cells were counted for the untreated controls. All data displayed are means from independent biological experiments. Each biological experiment consisted of at least 3 technical replicates. Statistical differences were tested with GraphPad Prism 5.0 (Graphpad Software, La Jolla, USA) by
129 Chapter E – Genuine human neural crest cells as test system for developmental toxicity and potential rescue strategies
applying ANOVA using Bonferroni's post-hoc test. Independent biological experiments (not technical replicates) were the basic unit used for statistical testing.
Supplementary References Bolstad BM, Irizarry RA, Astrand M, Speed TP. 2003. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19(2):185-193. Chambers SM, Fasano CA, Papapetrou EP, Tomishima M, Sadelain M, Studer L. 2009. Highly efficient neural conversion of human ES and iPS cells by dual inhibition of SMAD signaling. Nat Biotechnol 27(3):275-280. Gautier L, Cope L, Bolstad BM, Irizarry RA. 2004. affy - analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 20(3):307-315. Lee G, Chambers SM, Tomishima MJ, Studer L. 2010. Derivation of neural crest cells from human pluripotent stem cells. Nat Protoc 5(4):688-701. Lee G, Kim H, Elkabetz Y, Al Shamy G, Panagiotakos G, Barberi T, et al. 2007. Isolation and directed differentiation of neural crest stem cells derived from human embryonic stem cells. Nat Biotechnol 25(12):1468-1475. Pepper SD, Saunders EK, Edwards LE, Wilson CL, Miller CJ. 2007. The utility of MAS5 expression summary and detection call algorithms. BMC Bioinformatics 8:273. Reimand J, Kull M, Peterson H, Hansen J, Vilo J. 2007. g:Profiler--a web-based toolset for functional profiling of gene lists from large-scale experiments. Nucleic Acids Res 35(Web Server issue):W193-200.
130 Chapter F – Concluding discussion
F. Concluding discussion
The results chapter contains 2 independent publications and 1 submitted manuscript. Each publication includes an extensive discussion of the respective results. Therefore, the following concluding discussion only describes the important findings in the overall context of the development of in vitro based DNT test systems. The focus will be more on general aspects as well as an outlook on how developmental neurotoxicity can be tested using in vitro assays in the future.
Need for new test systems Due to changes in lifestyle and in our professional and domestic environment, we are exposed to more and more chemical substances in our daily life. This contrasts with the extremely limited knowledge about the developmental toxicity of such substances. A clear evidence for human developmental neurotoxicity exists only for a limited number of compounds (Grandjean and Landrigan 2006). In addition, it seems that scientific progress and integration of new knowledge has not yet reached the regulatory level, in particular with respect to developmental and neurodevelopmental toxicity. Regulatory testing uses a non-validated black box system, the animal experiment, to guarantee chemical safety, although the limitations of the system have been described extensively (Hartung 2009). In addition to that, expensive and labor intensive toxicity tests based on animal experiments are not applicable for testing large numbers of compounds (Hartung and Rovida 2009). The knowledge about biological processes and how compounds affect them adversely is only slowly integrated into the field of toxicology (Ankley et al. 2010). Therefore, new systems taking the biological aspect of toxicology into account are needed in order to (1) gain knowledge about the biological basis why a chemical is toxic and (2) to be able to test all the “daily life chemicals” for their toxicity potential, and thus guarantee consumer safety. One of the tools to approach this enormous task could be the use of embryonic and induced pluripotent stem cells (Anson et al. 2011; Winkler et al. 2009). Accordingly, the aim of this thesis was to develop new toxicological test systems based on the differentiation of embryonic stem cells into the neural lineage.
Embryonic stem cells as tools for toxicology In the first publication resulting from this thesis (Chapter C) we used murine embryonic stem cells which were differentiated to different sub-types of mature neurons in a 2-step protocol. 131 Chapter F – Concluding discussion
We initially used murine ESC instead of human ESC to establish general methods and concepts of toxicological test systems when using embryonic stem cells, because ESC from the mouse are easier to handle, grow faster and at that time, the culture protocols for differentiating ESC into the neural lineage were more sophisticated. Another important aspect for choosing mouse ESC as a starting point was the non-existence of an established correlation of in vivo developmental toxicological data with in vitro cellular assays. It has been shown that the correlation of toxicity between in vivo experiments carried out in different species or sometimes even within the same species is as low as 60% (Gottmann et al. 2001; Hartung 2009; Schardein et al. 1985). Therefore, one cannot automatically assume that results obtained from a human embryonic stem cell based in vitro assay are able to predict human in vivo toxicity. Due to the lack of strong human data on e.g. concentrations relevant for developmental toxicity of known DNT compounds which are needed for setting up such a correlation (Hayashi 2005), we decided to initially start with a murine ESC-based system.
Identification of relevant processes during in vitro neural differentiation In a first step, we characterized our 2-step mouse ESC-based in vitro neural differentiation protocol which was adapted from a published protocol (Ying and Smith 2003) and further improved to meet requirements for toxicity screening. By using whole genome transcriptome analysis, we confirmed earlier findings from the literature, showing that mouse ESC recapitulate in vivo developmental processes in vitro (Barberi et al. 2003). According to our experimental findings, we were able to assign different waves of gene expression to different biological processes, taking place during ESC differentiation into neurons. Thus, we extended published data focusing on early differentiation steps to full neural differentiation (Abranches et al. 2009; Aiba et al. 2006; Wei et al. 2002). The processes we identified were loss of pluripotency, differentiation into neural progenitor cells as well as establishment of the basic neurotransmitter metabolism and the extracellular matrix. The neurodevelopmental gene profiles we identified during this study were later confirmed and used by others for bioinformatic investigations (Theunissen et al. 2011). In the second step, we carried out proof-of-principle-experiments, showing that our differentiation system, and especially the different biological processes, can be used to detect adverse effects of chemicals on neural development. Besides delaying, accelerating or shifting neural differentiation by tool compounds in the absence of cytotoxicty, we were able to show effects on the gene expression profile of disease (e.g. schizophrenia) relevant genes such as Nrnx1 (Kirov et al. 2008). By treating the cells with non-cytotoxic concentrations of lead-acetate, a known human 132 Chapter F – Concluding discussion
DNT chemical, often associated with schizophrenia (Opler et al. 2008), the Nrnx1 expression was decreased. Hints from the genome wide analysis that our protocol generates a mixture of different subpopulations of neurons were confirmed by intensive immunocytochemical analysis. We were able to detect dopaminergic, GABAergic, serotonergic as well as glutamatergic neurons in our culture. We could show that the late phase of differentiation (DoD15 – DoD20) is mainly characterized by maturation of the cells, whereas neuronal cell differentiation seems to play a minor role. We therefore classified the late phase of our differentiation protocol as “maturation phase”. We used the knowledge obtained from the first publication for the second publication integrated in this manuscript (Chapter E). We asked (1) whether our system is able to yield mechanistic information about the toxicity of DNT compounds based on functional readouts and (2) how well our in vitro systems reflects in vivo mouse toxicity with regard to e.g. the toxic concentration range.
In vitro developmental toxicity based on functional readouts To answer these questions, we used methylmercury (MeHg) as tool compound. We chose methylmercury for several reasons. First, it is “THE developmental neurotoxicant”, both in humans and in animals (Castoldi et al. 2008a; Castoldi et al. 2008b). Second, although the exact mechanisms by which methylmercury exerts its neurotoxicity are not known, clear effects on biological processes are described in the literature (Atchison and Hare 1994; Farina et al. 2011). We chose to treat the differentiating cells with non-cytotoxic concentrations of MeHg during the late phase of differentiation. As already described earlier, our whole genome transciptome analysis pointed out that during this period mainly maturation of already differentiated neurons takes place and that processes like cell proliferation play a negligible role. As we detected a dose dependent decrease in neuronal-specific transcripts important for neurotransmitter synthesis, we took a closer look on genes involved in this process. Analysis by focused q-RT-PCR arrays pointed to a derangement in genes associated with neurotransmitter synthesis and metabolism. A whole group of genes which were negatively affected by low concentrations of MeHg were genes encoding for the different subtypes of dopamine receptors. By using uptake of radiolabeled MPP+ via the dopamine transporter and immunocytochemistry, we were able to correlate the findings obtained by q-RT-PCR with a loss of function and a decline in total numbers of TH+ dopaminergic neurons. To our knowledge, our system was the first to use functional readouts to detect DNT in an ESC- based system. Furthermore, we were able to correlate the adverse outcome of our functional 133 Chapter F – Concluding discussion endpoint with known effects of MeHg such as decreased dopamine uptake by synaptosomes after MeHg treatment (Rajanna and Hobson 1985). In addition, the group of dopamine receptors affected by MeHg in our in vitro system is a well known target of MeHg in vivo (Dare et al. 2003; Gimenez-Llort et al. 2001; Rossi et al. 1997). We therefore believe that, by using our in vitro system, it is possible to - in part - detect behavioral endpoints in vitro. Other in vitro based DNT test systems lack this important aspect of neurotoxicity and were therefore often regarded as weaker in their predictive capacity compared to in vivo studies (Tiffany- Castiglioni 2003).
Hazard classification using in vitro toxicology To our knowledge, we were also the first to quantify in-cell toxicant concentrations in an ESC-based in vitro DNT test system enabling us to define the hazard of the toxicant. A hazard is defined as the potential of a compound to cause harm, whereas risk is defined as the likelihood of a hazard to cause harm. Many current DNT in vitro test systems based on ESC, such as the one described in Chapter C and D, use long incubation periods, which often include medium changes with re-addition of the toxic compound, to model chronic exposure. For risk assessment and defining the sensitivity of an assay, the actual dose/concentration causing an adverse effect is very important. Most of the in vitro DNT test systems described in the literature do not consider potential accumulation, or binding of the compound to medium components or cell culture plastic when defining the concentration which causes an adverse effect. They simply rely on the concentration added to the medium. We took the next step and quantified the in-cell concentration of our DNT compound MeHg by using atomic absorption spectroscopy. We were able to show that MeHg accumulates in the cells during the incubation period of 6 days, resulting in a final cellular concentration of ~ 1 µM (cells were treated 3 times with 5 nM MeHg during the 6 day exposure period). We could also show that this concentration resembles the lower concentration end found to cause observable clinical effects in humans as well as in animals (Burbacher et al. 1990; Gimenez-Llort et al. 2001; Pereira et al. 1999). A useful tool to evaluate the mechanism, by which a compound exerts its toxicity, is to prevent this toxicity by pharmacological intervention. As described in more detail later, we were able to protect dopaminergic neurons from MeHg induced developmental toxicity by inhibiting MLKs (Figure 1). Although we have established the general principles of in vitro DNT testing in mouse embryonic stem cells as well as having shown that ESCs can successfully be used to model 134 Chapter F – Concluding discussion
DNT in vitro, still some questions need to be addressed by additional experiments. We identified different waves of gene expression associated with different biological processes. In additional experiments, we need to address the question whether such waves resemble different windows of susceptibility to different compounds. In preliminary experiments, we showed by using compounds such as cyclopamine that this might indeed be the case. However, additional experiments are needed to really map different compounds with different pathways of toxicity (PoTs) within the time course of neurodevelopment. We only showed effects of MeHg during the maturation phase. Whether this is the most susceptible phase or whether other phases of neuronal differentiation are more sensitive to MeHg-induced neurodevelopmental toxicity remains an open question and additional experiments are needed to give an answer.
Modeling human developmental toxicity Although some important aspects still need to be addressed in the mouse system we switched to human ESC as a more relevant system for human developmental toxicity. As proposed by the National Research Council of the USA (NRC) in its vision and strategy document for a toxicology of the 21st century (“Tox21 initiative”), new test systems developed should rely on human cells (NRC 2007). As human embryonic stem cells are able to give rise to every cell type of an organism, the first question to answer is, which developmental process should be modeled by the new test system. As we established general principles of toxicity to neuronal lineage differentiation in our mouse ESC-based system, we decided to concentrate on neurodevelopment. The next question was, which of the many processes involved in neural development - some are described in the general introduction - should be modeled by our system. A part of the nervous system which is often forgotten or neglected when talking about neural development is the PNS. Sensory neurons as well as Schwann cells arise from a common precursor, the neural crest cell (Bronner-Fraser et al. 1991). Additionally, this remarkable cell gives rise to many other cell types including bone and cartilage of the head, melanocytes and smooth muscle cells (Huang and Saint-Jeannet 2004). Some even claim that the NCC should be referred to as 4th germ layer (Noden and Schneider 2006; Vickaryous and Hall 2006). Therefore, adverse effects on neural crest cell differentiation have an impact on many different cell types and organs. It is estimated that one third of all congenital birth defects are associated with NCC and their derivatives (Trainor 2010). The most important feature of neural crest cells is their migratory potential. In order to be able to give rise to multiple cell 135 Chapter F – Concluding discussion types, NCCs have to migrate from the dorsal part of the neural tube along distinct paths into the periphery, were they then differentiate into different cell types. NCC migration defects have been associated with several diseases as well as with adverse effects of chemicals (Di Renzo et al. 2007; Fuller et al. 2002). We therefore decided to develop a human NCC-based test system which is able to model adverse effects on NCC migration in vitro.
Assessing neural crest cell migration in vitro In the third manuscript included in this thesis we could show that human ESC differentiated into a highly pure culture of NCCs can mimic NCC migration in vitro. We showed that not only choosing human cells is important to predict human toxicity, also the appropriate cell type for the question asked needs to be used. Only NCCs were able to identify the developmental neurotoxic effect of MeHg and lead at relevant in vivo concentrations in an in vitro migration assay. Human cancer cell lines as well as non-cancerous cell lines from human or mouse origin failed to identify MeHg and lead as developmentally toxic at relevant concentrations in the same assay. By using our new MINC (assay for migration in neural crest cells), we found that in vitro based assays are not only able to predict DNT per se. The MINC could distinguish between the toxicity of different mercurial compounds. We found that organomercurial compounds such as CH3HgCl inhibited NCC migration at 10 fold lower concentrations than inorganic mercury compounds such as HgCl2. Such differences in the developmental toxicity of mercury compounds are well known from the literature (Aschner and Ceccatelli 2010; Graeme and Pollack 1998; Moors et al. 2009). Comparing relative effects of related substances is often very useful. Using relative effects of different compounds in relation to each other would facilitate the prioritization progress for further in- depth analysis. Furthermore, we showed that the adverse effects of mercury on NCC migration can be prevented by chelating agents used to treat acute metal poisoning (Blanusa et al. 2005; Domingo 1995). As already described for our mouse system, we were able not only to detect the negative effects of a compound, we also showed possible protection mechanisms which could lead to a mechanistic description of toxicity. By using protecting/intervention strategies, also the specificity of the test system can be evaluated. Preventing toxicity by inhibiting/stimulating a given pathway defines the relevant targets of the toxic compound. An additional important aspect is that the human population is not exposed to single isolated compounds. The human “exposure” consists of a complex chemical mixture including metals, solvents, pesticides, drugs, and other chemicals (Feron et al. 1998; Jonker et al. 2004). We do not know whether adverse effects by a single compound are 136 Chapter F – Concluding discussion compensated by positive effects of another or whether two compounds act synergistically. Therefore, new test systems need to be able to detect effects of mixtures of compounds. By using chelating agents in combination with different mercury compounds, we showed that our MINC is able to detect different effects when different compound mixtures are applied. We did not investigate whether migration of NCC in our MINC assay is due to chemotaxis or chemokinesis. Chemotaxis is defined as active, directed movement of e.g. a cell towards/away from a certain chemical environment. Chemokinesis is - in contrast to chemotaxis - defined as random, non-vectorial movement of an e.g. cell (Becker 1977; Gee 1984; Wilkinson 1990). Our assay shows some mixed features. We do not know whether different toxic compounds affect the two biological processes in a similar way. We are currently trying to establish clear chemotaxis and chemokinesis assays (in collaboration with the Bioimaging Center at the University of Konstanz) based on NCCs to answer this question.
Defining pathways of toxicity The field of in vitro toxicology is growing rapidly and is regarded as the modern way of toxicity testing compared to in vivo testing. However, many systems described in the literature do not take important aspects of toxicology established in traditional in vivo systems into account. Additionally, in vitro-based systems are often also used as black box systems, thereby losing the great potential of these systems. Adding a compound and at the end measuring an adverse outcome without asking the question why or how this can be prevented does not yield any information about underlying toxicity mechanisms or PoTs. We could show that it is indeed possible to detect such underlying mechanisms in stem cell based toxicity test systems. Figure 1 tries to summarize how such mechanistic information can be obtained from test systems like those described in this thesis. We treated mouse embryonic stem cells differentiating to neurons during the maturation phase with non cytotoxic concentrations of MeHg. After 6 days of exposure, we detected a decrease in the overall number of dopaminergic neurons, as well as a decrease in genes important for dopaminergic differentiation such as Shh, Fgf8 or Wnt1. Only the full - 6 day - incubation period resulted in such adverse effects.
137 Chapter F – Concluding discussion
Figure 1: MLKs and neurodevelopmental toxicity of MeHg Based on experimental findings in our mouse ESC-based in vitro DNT test system, we identified MLKs as important players in MeHg induced toxicity for dopaminergic neurons. Whether MeHg leads to degeneration of dopaminergic neurons via direct activation of MLKs (1) or whether the differentiation of neural progenitors into dopaminergic neurons is inhibited via MeHg induced cellular stress (2) needs to be investigated further. For more details see text.
After the initial descriptive observation that a DNT compound - in this case MeHg - affects differentiating dopaminergic neurons, we identified mixed lineage kinases (MLKs) as a possible PoT through which MeHg exerts its toxicity. By using CEP-1347, an inhibitor of mixed lineage kinases (Maroney et al. 1999), we were able to protect dopaminergic neurons from MeHg induced toxicity. We found that this protective effect of CEP-1347 was restricted to dopaminergic neurons. CEP-1347 did not protect other neuronal subtypes such as GABAergic neurons. Whether MLKs are directly activated by MeHg or whether other stress pathways activated by MeHg lead to the activation of MLKs, still needs to be investigated. As we found that chronic low concentrations of MeHg decreased the expression of genes important for dopaminergic differentiation such as Shh or Fgf8 (left panel of Figure 1), additional experiments are needed to find out whether MLKs play an important role in this particular process. We were also not able to finally rule out whether chronic exposure to MeHg directly leads to dopaminergic cell death (right panel of Figure 1) or to reduced differentiation/maturation of dopaminergic neurons via inhibition of other signaling pathways such as SHH or FGF8, which are important for differentiation of dopaminergic neurons (left panel of Figure 1). Only the chronic exposure to MeHg resulted in a decrease in dopaminergic neurons, suggesting that direct acute cytotoxicity to those cells plays a minor role. As not only
138 Chapter F – Concluding discussion dopaminergic marker genes, but also other neuronal subtypes were affected by the treatment with mercury, we favor the theory that MeHg affects neuronal maturation and differentiation via acting on essential pathways (left panel of Figure 1). Many of the genes displayed in Figure 1 play important roles in the differentiation process of many neuronal subtypes (Liu and Zhang 2011). We therefore think that chronic exposure to MeHg leads to a reduction in signaling pathways important for neuronal maturation which results in a decreased neuronal phenotype of the cells in the end. As illustrated in Figure 1 and just described above, still a lot of work needs to be done to really dissect the mechanism how MeHg affects dopaminergic neurons in vitro. Nevertheless, this example shows how the knowledge about biological processes and mechanisms needs to be integrated in modern in vitro test systems to be able to map pathways of toxicity and move away from doing black box descriptive toxicology.
The future of developmental toxicity testing and risk assessment Although in vitro toxicology is rapidly evolving, it would be wishful thinking that in vitro DNT test systems are able to replace animal experiments already today. However, in vitro testing as part of an integrated testing strategy could accelerate toxicity testing by identifying potential toxicants which could be prioritized for in vivo testing. This would not only speed up the whole process, also the use of animals in toxicity testing could be reduced and refined (Coecke et al. 2007; Lein et al. 2007). Figure 2 of this general discussion tries to illustrate how knowledge about developmentally toxic chemicals should lead to the development of alternative testing strategies. Such a strategy should include different model systems ranging from lower organisms such as C. elegans (Helmcke et al. 2010) or zebrafish embryos (Scholz et al. 2008) over cell-based in vitro assays, such as the ones described in this thesis, over molecular assays, such as receptor activation or enzyme inhibition, to in silico methods (Matthews and Contrera 2007). On the basis of data on the toxicity potential of developmental toxicants, important pathways triggered/affected by such chemicals need to be identified and used to develop assays modeling those pathways. In a second step, relevant assays should then be combined to a test battery able to model different - optimal would be all - important aspects of the developmental process of interest (Figure 2).
139 Chapter F – Concluding discussion
Figure 2: Developing in vitro test batteries for risk assesment Based on data from e.g. the literature biological pathways and processes (e.g. neurite outgrowth) important for toxicity are identified and incorporated into alternative assays involving model organisms (C. elegans, zebrafish), cellular models and biochemical assays (e.g. estrogen receptor activation) (1). Combination of these individual assays into a test battery will lead to prioritization of testing and in the future ultimately directly to risk assessment and more consumer safety (2). For more details see text.
This has already been shown to yield good results in the field of reproductive toxicology (Schenk et al. 2010) and skin sensitization (Adler et al. 2011). Due to the 7th amendment to the EU Cosmetics Directive (Pauwels and Rogiers 2004) which bans on marketing of cosmetic products containing an ingredient which has been tested using animals, it is assumed that by 2013 in vivo testing is replaced by an in vitro test battery for that area. Part of such a test battery should be metabolic processes by e.g. liver enzymes. Compounds might be classified in an in vitro test battery lacking metabolic activity as negative, although they are known in vivo toxicants. A famous example would be n-hexane which needs to be metabolized to 2,5-hexanedione to become neurotoxic (Couri and Milks 1982). Our systems developed during this thesis also lack such a metabolic compartment. Therefore, substances applied to the systems need to be checked carefully on whether they require metabolic activation. Since we are part of the large European consortium ESNATS, systems modeling liver metabolism are developed by other consortium partners. Those systems will be used in a
140 Chapter F – Concluding discussion test battery as a kind of pre-incubation system for the compounds before they are tested in systems like the ones we developed. Unknown compounds which are flagged by such an alternative test battery as toxic would then be prioritized for further in vivo testing. The next step would then be to perform risk assessment to guarantee more consumer safety. We believe that in the future the knowledge about the toxicity of chemicals included in an in vitro test battery will be sufficient to fully replace many in vivo assays currently used for risk assessment, ultimately leading not only to a faster but also to a better consumer safety.
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168 Record of contribution
Record of contribution
Chapter C
I conceived, designed, performed and analyzed the major part of the experiments. The whole genome transcriptome analysis was performed by Vivek Tanavde, Winston Koh and Betty Tan in Singapore. Philipp B. Kuegler, Tanja Waldmann and Birte Baudis helped with the in depth analysis of the microarray data. Some RNA was provided by Bettina Schimmelpfennig. I wrote the manuscript in collaboration with Suzanne Kadereit and Marcel Leist.
The chapter is published in Cell Death and Differentiation.
Chapter D
I conceived, designed, performed and analyzed most of the experiments. Analysis of radiolabeled MPP+ uptake was performed by Stefan Schildknecht. Philipp B. Kuegler provided technical support and Vivek Tanavde provided some data. I wrote the manuscript in collaboration with Suzanne Kadereit and Marcel Leist. In addition, I prepared all the figures and tables of the manuscript.
The chapter is published in Toxicological Sciences.
Chapter E
I conceived, designed, performed and analyzed almost all of the experiments. The whole genome transcriptome analysis was performed by Kesavan Meganathan at the University of Cologne. I further analyzed the results provided. I prepared all the figures and wrote the manuscript in collaboration with Marcel Leist.
The chapter is submitted for publication and currently under review at Environmental Health Perspectives
169 Danksagung
Danksagung
Mein besonderer Dank gilt:
Meinem Doktorvater Prof. Dr. Marcel Leist für die freundliche Aufnahme in seine Arbeitsgruppe, die Überlassung des Themas, unermüdliche Hilfs- und Diskussionbereitschaft, sowie die Möglichkeit meine wissenschaftlichen Fertigkeiten in einem 2-monatigen Aufenthalt in New York zu verfeinern.
PD Dr. Gerrit Begemann für die bereitwillige Übernahme der Zweitgutachtertätigkeit.
Meinem Thesis Committee PD Dr. Gerrit Begemann und PD Dr. Mathias Schmidt, die immer ein offenes Ohr hatten.
Dr. Suzanne Kadereit für all ihren Einsatz und die Unterstützung.
All my new friends in New York for scientific help, advice material and of course for all the fun during the conferences.
Allen ehemaligen und derzeitigen Kollegen des LS Leist. Danke für eine tolle Zeit. Ich weiß, Ihr hattet es nicht immer leicht mit mir.
Allen ehemaligen und derzeitigen Mitgliedern des IRTG1331 für tolle Weihnachtsfeiern, Evaluationseminare und Hilfe bei Materialien.
All meinen guten Freunden zuhause, die, obwohl ich nicht häufig da war, mich immer behandelt haben als wäre ich nie weg gewesen.
Meiner ganzen Familie.
Meinen Eltern Marianne und Werner, die immer an mich geglaubt und mich immer voll unterstützt haben.
Meiner Freundin Victoria für einfach alles.
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