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Regulation of neuronal plasticity by the factor NFIL3 van der Kallen, L.R.

2016

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Regulation of neuronal plasticity by the transcription factor NFIL3

Loek van der Kallen

The research described in this thesis was performed at the department of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research, Neuroscience Campus Amsterdam, VU University, Amsterdam, The Netherlands.

Cover by Loek van der Kallen

VRIJE UNIVERSITEIT

Regulation of neuronal plasticity by the transcription factor NFIL3

ACADEMISCH PROEFSCHRIFT

ter verkrijging van de graad Doctor aan de Vrije Universiteit Amsterdam, op gezag van de rector magnificus prof.dr. V. Subramaniam, in het openbaar te verdedigen ten overstaan van de promotiecommissie van de Faculteit der Aard- en Levenswetenschappen op dinsdag 2 februari om 15.45 uur in de aula van de universiteit, De Boelelaan 1105

door Loek van der Kallen Geboren te Utrecht promotoren: prof.dr. A.B. Smit prof.dr. J Verhaagen copromotor: dr. R.E. van Kesteren

The knack of flying is learning how to throw yourself at the ground and miss

Douglas Adams Leescommissie: prof.dr. M.P. Smidt prof.dr. S. Spijker dr. H.D. MacGillavry dr. K. Bossers dr. A.J.A. Groffen

Contents

Chapter 1 ......

General introduction ...... 9

Chapter 2 ......

Genetic deletion of the transcriptional repressor NFIL3 enhances axon growth in vitro but not axonal repair in vivo ...... 29

Chapter 3 ......

Behavioral characterization of Nfil3 knockout mice ...... 57

Chapter 4 ......

Behavioral inflexibility in mice lacking the transcriptional repressor NFIL3 .. 71

Chapter 5 ......

CAGE sequencing reveals aberrant immediate-early expression during memory reconsolidation in Nfil3 knockout mice ...... 87

Chapter 6 ......

General discussion ...... 107

English summary ...... 119

References ...... 125

Acknowledgements ...... 143

About the author ...... 151

Chapter 1

General introduction

Chapter 1

Neurons retain a level of plasticity in order to adapt to external needs and stimuli throughout adult life. This plasticity occurs at the level of neuronal structure and function. In particular, plasticity of neurons can be observed at their numerous points of contact, the synapses. These synapses are built of axonal pre- and dendritic postsynaptic elements (spines). At the structural level, for instance, a continuous turnover of dendritic spines is observed in many brain areas, which affects synapse density and can be modified by neuronal activity [1], aging [2], in diseases, such as schizophrenia [3] and fragile-x syndrome [4], and during and after drug use [5]. Dendrite morphology can also be affected, for instance, by stress [6,7], in Alzheimer’s [8] and Huntington’s disease [9], fragile-x syndrome [4] and after drug use [5]. Axons also show variation in length and arborisation throughout adulthood, both under normal conditions [10,11] and, for instance, after diverse forms of brain injury [12]. Thus, dendritic and axonal plasticity enable synaptogenesis, i.e. the creation of new synapses, whenever this is required [13,14]. More specifically, an increase in synapse densities results from differences in the rates of spine generation, stabilization and elimination [15], processes that persist into adulthood [14]. In addition to apparent structural plasticity, in the CNS other forms of plasticity are found. Modulation of the firing strength of existing synapses occurs, both pre- [16,17] and postsynaptically [18,19], and either directly, or indirectly through signals from glia [20]. Collectively, all these different forms of neuronal plasticity enable changes at a circuitry level that allow individuals to optimally cope with a continuously changing environment. Forms of plasticity that occur over longer timescales, e.g. 2 hours and more, require de novo gene transcription and synthesis [21,22,23]. Transcription factors that regulate the transcriptional response to diverse forms of stimulation are key elements in the signaling pathways leading to long-term neuronal plasticity. In this chapter, I will discuss the role of transcription factors in various forms of neuronal plasticity. Specifically, I will introduce two forms of plasticity; regenerative plasticity following peripheral nerve injury and synaptic plasticity underlying cognitive processing, and the role of in

10 General introduction each. Subsequently, I will discuss transcriptional regulation and gene regulatory networks in more detail, and introduce the two transcription factors that are the main focus of this thesis. Finally, I will briefly introduce the techniques that were used in this thesis to study neuronal regeneration, cognition and transcriptional regulation.

Neural plasticity and the regeneration of injured axons Regenerating neurons show a large degree of plasticity. Damaged axons need to grow new arbors in appropriate directions, identify and connect with appropriate targets, and fine-tune their firing strength. When lesions occur in the central nervous system (CNS), for instance the spinal cord, during adult life, axons will not regenerate, likely due to non-permissive signals expressed by oligodendrocytes and glial scar tissue, and the debilitating effects are enormous [24,25]. However, when a nerve lesion occurs in the peripheral nervous system (PNS), axons are better capable of entering a state of plasticity and growth. One of the main differences between the CNS and the PNS environment is that PNS axons are not myelinated by oligodendrocytes but by Schwann cells, which provide a more permissive environment for regenerative axon growth. The growth-promoting properties of Schwann cells have been exploited for instance to generate permissive nerve grafts for spinal cord injury sites [26,27]. In human patients, autologous Schwann cells have been transplanted into spinal cord injury sites during clinical trials with results ranging from no adverse effects [28] to positive effects on motor and sensory functions [29]. Another cell type that is efficient at inducing a regenerative growth state in neurons is olfactory ensheathing cells, which are currently also used in clinical trials with some cautiously promising results [30]. The regenerative plasticity of a neuron is in part determined intrinsically, as was demonstrated by the so-called conditioning lesion paradigm, where a CNS injury shows better regeneration when preceded by a PNS lesion [31]. Three types of injury signals together prepare and induce the cell body to respond to the specific needs of axonal regeneration [32]. Firstly, a

11 Chapter 1 rapid influx of calcium and sodium induces depolarization and a train of action potentials, resulting in swelling of the neuronal cell body and a strong increase in cellular metabolism and protein synthesis (Fig. 1.1) [33]. Secondly, disruption of molecular transport from the originally innervated target prevents retrograde flow of negative regulators of plasticity (Fig. 1.2) [34]. Thirdly, positive injury signals, including derived from neighboring axons and glia, but also locally translated proteins and activated kinases, induce plasticity in the cell body (Fig. 1.3) [35,36]. As was also demonstrated with the conditioning lesion paradigm, the strength of the regenerative response that is initiated depends on the type of injury signals the cell body receives [31].

Figure 1 – Three types of positive and negative axonal injury signals travel to the nucleus. (1) Axon potentials due to influx of sodium and calcium reach the soma first. (2) Retrograde transport of plasticity inhibiting factors is disrupted. (3) Retrograde transport of plasticity promoting factors, derived from the extracellular environment or newly synthesized and/or activated in the axon, is enhanced. Reproduced with permission from [37].

12 General introduction

Axon regeneration is controlled by regeneration-associated When injury signals reach the cell body a response is induced that includes the induction of regeneration-associated genes (RAGs) that are necessary for the neuron to enter a state of growth [38]. RAGs are genes specifically associated with successful axon growth and regeneration, and for an increasingly number of RAGs their mechanisms of action are elucidated. A classic RAG is GAP43, which is strongly upregulated upon axonal injury [39]. GAP43 protein is then transported to the growth cone where together with another growth cone protein, CAP-23, it is capable of promoting elongation of injured axons into a peripheral nerve graft in the spinal cord [39,40]. Other RAGs appear to be more involved with overcoming the growth-inhibiting environment surrounding the growth cone. Arginase-1 (Arg-1) for instance, which is strongly induced upon axonal injury, helps injured axons to overcome inhibition by MAG and by myelin in general [41]. The induction of RAGs requires activation by transcription factors such as cAMP responsive element binding protein (CREB). The initial calcium influx in injured neurons (Fig 1.1), for instance, activates cAMP signaling which then activates CREB [42] and induces expression of many RAGs, including Arg-1 and GAP43 [41,43]. Other transcription factors such as C/EBPβ trigger a second wave of gene expression that is required for axon growth [44,45]. Several regeneration-associated transcription factors have been identified, including c-Jun, ATF3, , Smad1, and STAT3 [46,47,48,49,50], but it remains unclear which transcription factors are pivotal in activating the complete regenerative response. Most likely this will require the action of multiple transcription factors that are simultaneously and/or sequentially activated in order to elicit a coordinated shift in the gene network underlying axon growth [51]. At least in part the expressional response will be induced through changes in the acetylation state of histones in promoter regions of RAGs [52,53].

13 Chapter 1

Neuronal plasticity and cognition Cognitive processing also requires neurons to be plastic in order to adapt to a changing and challenging environment. Our brains continuously update the connectivity strength of existing synapses, and create new or loose ones. In rodents, these types of plasticity are induced, for instance, by housing in an enriched environment. Differential housing causes increased cortical thickness [54], dendrite length, arborization and spine density in rats [55,56]. These morphological changes are accompanied by changes in the expression of key plasticity genes such as brain-derived neurotrophic factor (BDNF), nerve growth factor (NGF), neurotrophin-3 (NT-3) and postsynaptic density protein 95 (PSD- 95) [57,58]. Furthermore, housing in enriched environments also increased the levels of NMDA- and AMPA-type glutamate subunits, indicating the molecular and physiological mechanisms underlying increased plasticity as synaptic long-term potentiation (LTP) [59,60,61]. With paired-pulse facilitation, presynaptic activity coincides with postsynaptic activity, resulting in facilitated signal transduction [62]. Long-term potentiation (LTP) and long-term depression (LTD) occur when pre- and postsynaptic activity occur in multiple bursts at high and low (respectively, ~100, ~1 Hz) frequencies [63]. These synaptic plasticity mechanisms are assumed to be the most likely neural substrate for memory, and are actually believed to underlie any type of learning [63]. Neuronal plasticity supports healthy brain functioning, and environmental enrichment has protective effects in a large number of animal models of brain disorders, including Huntington’s disease [64,65], Alzheimer’s disease [66,67], Parkinson’s disease [68,69], epilepsy [70], fragile X syndrome [71] and Down syndrome [72,73]. LTP and LTD occur everywhere in the nervous system, but they are best studied in the hippocampus, which is commonly known for its essential role in memory formation. Indeed hippocampal LTP and LTD are necessary for memory formation [74,75], and were recently also

14 General introduction demonstrated important for higher executive functions and behavioral flexibility [76,77,78]. Executive functions entail the modulation of lower-level processes such as recognition of visual and auditory information by higher level tasks such as dialing a telephone number in reverse order, making plans for the future or updating a learning rule [79]. The ability to respond to our environment in a non-standard manner allows us to behave flexibly and not be slaves to our environment. Brain regions crucial for executive functions are located in the prefrontal cortex [80], but these also rely on their interconnectedness with subcortical structures. When the hippocampus is lesioned for instance, mice lose their ability for flexibility in relearning a location in a water-cross maze [78]. Also, when an NMDA receptor inhibitor is injected systemically in rats this induces potentiation in the hippocampus resulting in impaired performance in attentional-set-shifting and delayed- spatial-alternation tasks [76]. Synaptic plasticity in the hippocampus is thus responsible for various types of learning, both in simple learning tasks and in complex cognitive tasks.

Synaptic plasticity is controlled by plasticity genes Whereas short-term memory is expressed independent of protein expression, long-term memory requires the synthesis of new genes and proteins [81]. Similarly, LTP and LTD can be separated in a rapid early phase and a transcription- and translation-dependent late phase [82,83]. The expression of late LTP thus requires specific transcription factors to initiate transcription of plasticity genes. Some transcription factor genes, like c-Fos, are induced very rapidly and independent of protein expression [84], and were therefore named immediate early genes (IEGs). Currently many genes, including Arc, c-Fos, JunB, Egr1 and Dusp1, are classified as IEGs involved in memory formation [85,86,87], and their expression can be induced within minutes after stimulation [88]. Since IEG expression can occur independent of protein synthesis, it follows that the cellular machinery for transcription is continuously present, awaiting activating signals to initiate their function. We now know that a complex of general

15 Chapter 1 transcription factors (TFIIA, TFIIB, TFIID, TFIIE, TFIIF and TFIIH) and RNA polymerase II bind to the promoter of genes like c-Fos in a stepwise manner forming the pre-initiation complex [89,90] and waits there poised for activation (fig. 2) [88,91]. At this point the complex typically undergoes cycles of short sequences of transcriptional initiation, releasing small RNAs (abortive initiation). Only when other gene-specific activators are sufficiently in place, which for c-Fos includes CREB and SRF [92], the polymerase can clear the promoter region. Interactions with the promoter are broken, and the complex is replaced with elongation factors. During and after elongation the mRNA is

Figure 2 – Immediate early gene expression and synaptic plasticity. Concerted activation of synapses targeting the neuron’s dendrite result in an influx of calcium and triggers action potentials. For ‘rapid’ IEGs the transcription initiation complex sits poised and waiting for activation by specific transcription factors, in this case pTEFb. Slower genes require the assembly of general transcription factors, RNA polymerase II and histone modifications, and take longer to produce. Reproduced with permission from [91].

16 General introduction capped, spliced and 3’ polyadenylated. Finally, during transcription termination, the polymerase dissociates from the DNA and the completed transcript. Cells only express a relatively small portion of the genome. Depending on their function, only a specific subset of genes is required, and both the type of genes expressed and the quantity expressed per gene can change depending on cellular demands. Energy-wise, the most efficient way to regulate gene expression is as early as possible, around initiation. During development, specific genes are methylated to alter the availability of DNA in a near- permanent way, for instance to prevent a cell from changing its type of differentiation [93]. For transient regulation of gene expression, mammalian cells often change the way DNA is wrapped up around histones. DNA-histone interactions are necessary to efficiently store the enormous length of DNA in the nucleus of the cell. The basic unit of DNA packaging is a nucleosome, comprised of two copies of histones H2A, H2B, H3 and H4 arranged into a small wheel with 147 basepairs of DNA wrapped tightly around it in 1.67 superhelical turns [94]. The resulting complex of DNA and proteins is known as chromatin. Nucleosome assembly and disassembly is regulated by chromatin remodeling enzymes, and promoters of genes that are transcribed are typically less dense packed than silent genes, which makes the DNA more accessible for binding of general and specific transcription factors [95]. Many transcriptional activators and repressors affect DNA-histone interactions through posttranslational modifications of histone proteins that change the affinity between histones and DNA [96]. Other activators act more directly by recruiting co-activators involved in promoter clearance [88,91]. Thus, the combined action of basal transcription factors, chromatin remodelers and transcriptional activators forms the basis for the transient regulation of gene expression enabling long-term changes in synaptic plasticity in neurons.

Gene regulatory networks In addition to activating transcription factors, there are also transcriptional repressors that act by inhibition of RNA polymerase II binding, promoter

17 Chapter 1

Figure 3 – Transcriptional regulatory network motifs. Examples of how transcription factors can directly or indirectly affect single or multiple target genes, alone or together with other transcription factors. Note that transcription factors can also repress target gene expression, and that multiple types of simple motifs can combine into more complex ones. Reproduced with permission from [97]. melting (opening of the DNA helix), or promoter clearance. They can do so by competing with other factors for DNA binding (steric hindrance), by changing the 3D-structure of bound molecules, or by altering their chemical nature [98]. Not only does the balance of activating and inhibiting transcription factors precisely control the rate of transcription, the genes encoding these transcription factors often contain regulatory binding sites for their own protein products and/or other transcription factors, allowing positive and negative feedback and feed-forward mechanisms to precisely control the timing of gene expression. Together these gene regulatory connections result in cell type- and condition-specific gene networks that determine both permanent

18 General introduction and transient cell fates and states [99]. Gene regulatory networks are governed by regulatory motifs that can be classified in a few simple categories (fig. 3). A clear example of a gene network is the circadian , where among others the transcriptional repressor NFIL3 and activator DBP, both basic (bZIP) transcription factors, cycle in a daily rhythm and antagonistically regulate the circadian expression of other clock genes [100,101]. Other transcriptional regulatory mechanisms in biology are meant to respond to a transient stimulus rather than cycle spontaneously. Loops or chains in networks offer ways to

Figure 4 – Feedforward loops. The eight theoretical types of feedforward loops are shown. In a coherent loop, the sign of the direct path from X to Z is the same as the indirect path through Y. Incoherent loops have opposite signs for the two paths. Reproduced with permission from [102].

19 Chapter 1

refine the timing of target gene expression and other options for differential regulation. One common feed-forward loop that is often found in gene regulatory networks consists of a transcription factor ‘X’ that is activated in response to a stimulus – often through phosphorylation – and then targets a second transcription factor ‘Y’ and a set of target genes ‘Z’. The loop is formed when Y also targets Z. When X is activating and Y is repressing, the loop is called incoherent for its opposing effects on Z. An incoherent feed-forward loop (IFFL) acts as a pulse generator and accelerates the response time of target genes [102,103]. In the next section I will discuss a type I incoherent feed-forward loop that may be involved in regulating neuronal plasticity.

CREB and NFIL3: a feed-forward loop controlling neuronal plasticity? CREB and NFIL3 belong to the family of basic leucine zipper (bZIP)-family transcription factors. The leucine zipper domain of bZIP transcription factors consists of a series of leucines separated 7 amino acids, thus forming an alpha helix with one hydrophobic side where dimerization occurs. The basic regions adjacent to the zipper domains are then ideally positioned to bind DNA [104]. We previously demonstrated that the bZIP transcription factors CREB and NFIL3 form a type 1 IFFL [43]. Specifically, NFIL3 acts as a feed-forward repressor of CREB target genes that regulate axonal regeneration after peripheral nerve injury (Figure 5) [43]. Knockdown or inhibition of NFIL3 in vitro resulted in enhanced axon growth, confirming the idea that regeneration-associated genes whose expression is controlled by CREB are higher expressed in the absence of feed-forward repression by NFIL3.

20 General introduction

Figure 5 –CREB and NFIL3 form a type 1 incoherent feed-forward loop. Neuronal injury activates CREB through cAMP/PKA signaling. Target genes include several known RAGs, but other plasticity genes may also be affected. CREB also induces NFIL3 expression, which then acts as a feed-forward repressor of CREB target genes. Note that NFIL3 also represses its own expression, and may regulate expression of non- CREB target genes. Reproduced with permission from [43].

Elevation of intracellular Ca2+ can activate CREB through different signaling pathways. CREB can be phosphorylated in a Ca2+-dependent manner by protein kinase A (PKA) [105] and by calcium/calmodulin-dependent (CaM) kinases [106,107,108]. Its sensitivity to intracellular Ca2+ concentrations makes CREB an important transcription factor for many types of neuronal plasticity, including LTP and memory formation [44,109]. CREB targets therefore also include, in addition to aforementioned RAGs, many memory-associated IEGs

21 Chapter 1 and other plasticity genes, including c-Fos, FosB, Nr4a3, c-Jun, Egr1 and C/EBPβ [110,111,112]. Since NFIL3 is also highly expressed in brain structures that are required for learning and memory, including the hippocampus [100,113], this leaves open the hitherto unexplored possibility that not only axon regeneration but also memory formation and cognitive processing require feed-forward control of CREB target genes by NFIL3. In particular, NFIL3 might be involved in the exact timing of plasticity gene expression, which may be crucial to couple input signals to a correct plasticity response. In my thesis I addressed the transcriptional regulatory functions of NFIL3 in axonal repair as well as in learning and memory. As such I used animal models for regeneration and cognition, combined with methods for high- throughput gene expression analysis. In the following sections I will briefly introduce these models and methods.

Regeneration studies in mouse models I first wanted to test whether NFIL3 is as important for axon repair in vivo as the above-mentioned in vitro experiments suggested. Depending on the location of a nervous system injury, in the central or peripheral nervous system, and the level of injury, various tests can be used to examine axon repair and functional recovery. Tests exist to assess sensory function, motor function or both. The toe pinch reflex test is a typical sensory test that consist of pinching the outer tips of each digit on the hind limbs and pricking the plantar surface of the foot with sharp forceps [114,115]. Another way to test sensation of (harmful) stimuli is by placing the animal with the affected limb on a hot plate and measure the time until they withdraw the paw [116]. A task that is more focused on motor skills, but also measures sensory function, is the narrow beam or balance beam task. The narrow beam task assesses the ability of a mouse to maintain balance while traversing a narrow beam to reach a safe platform. The task was originally designed to assess motor deficits in aging rats [117], but is also useful for assessing motor coordination in lesioned mice [118,119]. A task that depends slightly more on motor skills is the rotarod task,

22 General introduction where mice have to balance on a round beam rotating at accelerating speed, and latency to fall is recorded [120]. Normal gait and walking patterns can also be analyzed by using ink on the limbs [121], or by video tracking the animals from below [122]. Other tasks that simply test general locomotion, such as the open field task [123], are less suited to study recovery of sensory or motor function because there is not enough incentive for the injured animal to move at its maximum capacity. Here, we used a combination of narrow beam and rotarod testing to evaluate the effects of Nfil3 gene deletion or NFIL3 dominant-negative inhibition on functional recovery following sciatic nerve lesion.

Cognitive testing in mouse models I have also studied NFIL3 function in relation to neuronal plasticity and cognition using an Nfil3 knockout mouse model. When conducting cognitive tests in mouse models it is important to specify the type of memory that is tested. Working memory, a term that is sometimes interchangeably used with short-term memory although this is still a matter of debate [124,125], provides temporary storage of information [126]. Working memory also allows manipulation of information for complex tasks, albeit with a limited capacity [126]. Short-term memory entails the information storage component of working memory and information is stored for a similar duration, while long- term memory has an enormous capacity and endures over time [127]. When setting up memory tests in rodents one must take care to precisely prove which form of memory is affected and which forms are intact, whether they are confounded, and avoid confusion of interpretation between these. When investigating contextual memory processing in mouse models, one often uses spatial components in the task, as is for instance very prominent in the Morris water maze [128], the Barnes maze [129], and the T-maze [130]. Control experiments on locomotor behavior are necessary to interpret results from these tasks. When a reinforcing stimulus is used to change motivational load, it is important to evaluate whether this is aversive or appetitive in nature

23 Chapter 1

[131]. Reinforcing stimuli may involve stress. How much stress an animal experiences during the task is crucial to its memory performance [132]. Performing control experiments for how sensitive experimental groups are to anxiety and stress will help to disprove alternative explanations for results obtained in different types of memory tasks. An additional complicating factor to take into account when working with laboratory animals is the effect of the experimenter. We circumvented interference by an experimenter by using a novel experimental setup in which mice are observed in their home cage and perform various automated cognitive tasks over the time span of one week [133]. Although experiments performed by different experimenters and in different laboratories can in principle be compared as long as proper control groups are included [134], continuous automated observation results in monitoring more naturalistic behavior of animals and provides richer sets of data than conventional experiments, both quantitatively and qualitatively. In my thesis, I have used a combination automated home cage phenotyping and conventional behavioral tests to study the role of NFIL3 in cognition.

Genome wide expression analysis Because NFIL3 is a transcription factor that potentially controls the expression of many genes, transcriptome-wide strategies are preferred when studying the effects of Nfil3 deletion at the level of gene expression. The use of microarrays to measure gene expression has become very common. Glass slides that contain one or several probes for every gene in the genome, and that through hybridization with fluorescently tagged cDNA, are used to quantify expression levels from the intensities of the fluorescent signal. Relative changes in expression levels, for instance in mouse knockout versus wildtype tissue, are easily detected using dual color labeling of the cDNA. Whereas relatively cheap and efficient, a downside of this approach is the probe bias introduced by the fact that only known genes are present on the microarray. Another downside is

24 General introduction that microarray technology provides little information about the use of alternative transcription start sites. A current alternative is to measure gene expression using next generation Cap Analysis of Gene Expression (CAGE) sequencing [135,136]. With CAGE, the first most 5’ part (~ 27 base pairs) of al mRNA molecules in a sample are isolated and reverse transcribed into cDNA, followed by amplification and massive parallel sequencing. CAGE thus provides unbiased insight into absolute mRNA quantities as well as alternative transcription start site usage. However, due to the large amount of CAGE fragments detected in an experiment, complex data-processing is required to group fragments into higher order promoter tags that can be used for quantification. Also, depending on experimental design, complex statistical methods and designs are necessary for which no generally accepted standards and software are available yet. In my thesis I used CAGE analysis to determine NFIL3 target genes that are relevant for learning and memory.

Aim and outline of the thesis The aim of my thesis is to demonstrate the relevance of the CREB-NFIL3 feed- forward transcriptional loop in regulating neuronal plasticity in vivo, both regenerative plasticity of injured neurons and synaptic plasticity underlying learning and memory. I hypothesized that genetic removal or inactivation of NFIL3 increases the expression of regeneration- and memory-associated genes that are controlled by CREB, and thus may enhance axon repair and cognitive processing. This was tested experimentally. In Chapter 2, I asked whether the regenerative outcome after peripheral nerve injury improves by functionally blocking NFIL3. I used genetic deletion in mice and viral induction of a dominant-negative NFIL3 construct in rats to show that, in contrast to our prediction/expectation, both regenerative axon growth and functional recovery are impaired rather than improved when NFIL3 is not present. Microarray analysis subsequently showed that the expression of regeneration-associated genes is not altered, but that other gene

25 Chapter 1 programs that are not relevant for regeneration, and that may even negatively affect regeneration, are activated. In Chapter 3, I studied the role of NFIL3 during cognitive processes in the central nervous system. I performed a broad behavioral screen of Nfil3 knockout mice using a wide variety of both supervised and unsupervised behavioral tests. My results indicate that absence of NFIL3 results in a number of behavioral deficits that are best characterized as impaired cognitive flexibility. I hypothesize that lack of feed-forward repression of CREB target genes causes animals to respond inappropriately to novel stimuli and causes them to persist in displaying previously learned behaviors instead of adapting to a new situation. In Chapter 4, I tested whether the behavioral inflexibility phenotype observed in Nfil3 knockout mice is indeed due to lack of feed-forward repression of CREB target genes? I used next generation CAGE sequencing to identify memory genes whose expression is altered during in Nfil3 knockout mice during initial learning or during reconsolidation in a contextual fear memory task. I found that several memory-associated IEGs that are normally induced in knockout mice during initial learning, fail to become repressed during reconsolidation, whereas in wildtype mice they are. These findings thus provide a gene regulatory basis for behavioral inflexibility observed Nfil3 knockout mice. In Chapter 5, I will discuss the implications of my findings in general. What lessons can be learned from the combined results obtained in the two plasticity paradigms that I studied, and how these results might be used for future studies aimed at modulating axon regeneration or cognitive performance.

26 General introduction

27

Chapter 2

Genetic deletion of the transcriptional repressor NFIL3 enhances axon growth in vitro but not axonal repair in vivo

Loek R. van der Kallen1, Ruben Eggers2, Erich M. Ehlert2, Joost Verhaagen2, August B. Smit1, Ronald E. van Kesteren1 1 Center for Neurogenomics and Cognitive Research, Neuroscience Campus Amsterdam, VU University, Amsterdam, The Netherlands 2 Department of Neuroregeneration, Netherlands Institute for Neuroscience, Amsterdam, The Netherlands

Chapter 2

Abstract Axonal regeneration after injury requires the coordinated expression of genes in injured neurons. We previously showed that either reducing expression or blocking function of the transcriptional repressor NFIL3 activates transcription of regeneration-associated genes Arg1 and Gap43 and strongly promotes axon outgrowth in vitro. Here we tested whether genetic deletion or dominant- negative inhibition of NFIL3 could promote axon regeneration and functional recovery after peripheral nerve lesion in vivo. Contrary to our expectations, we observed no changes in the expression of regeneration-associated genes and a significant delay in functional recovery following genetic deletion of Nfil3. When NFIL3 function was inhibited specifically in dorsal root ganglia prior to sciatic nerve injury, we observed a decrease in regenerative axon growth into the distal nerve segment rather than an increase. Finally, we show that deletion of Nfil3 changes sciatic nerve lesion-induced expression in dorsal root ganglia of genes that are not typically involved in regeneration, including several olfactory receptors and developmental transcription factors. Together our findings show that removal of NFIL3 in vivo does not recapitulate the regeneration-promoting effects that were previously observed in vitro, indicating that in vivo transcriptional control of regeneration is probably more complex and more robust against perturbation than in vitro data may suggest.

Introduction Successful regeneration of injured axons depends on sufficient extrinsic growth permissiveness and the capacity to activate a neuron-intrinsic gene program that supports axon extension [137,138,139]. The injured adult CNS is rich in inhibitory factors and injured CNS neurons have a low intrinsic capacity to initiate the regrowth of injured axons [25], in contrast to the PNS [140,141]. Understanding the interaction between extrinsic growth permissiveness and intrinsic growth potential in the CNS and PNS may help to design new regeneration-promoting therapies that can be used to treat spinal cord injuries, stroke or neurodegenerative diseases [142].

30 No improvement in regeneration in NFIL3 knockout in vivo

Transcription factors have been put forward as interesting targets for promoting neuron-intrinsic growth properties because they are in principle able to alter the expression of multiple regeneration-associated genes simultaneously and in a coordinated fashion [51,143]. Previous research for instance showed that neuronal expression of a constitutively active form of the transcription factor CREB can overcome inhibitory environmental constraints and promotes regeneration of lesioned dorsal column axons [144]. Recently we showed that either knockdown or functional inactivation of NFIL3, a transcription factor that is structurally related to CREB, significantly increases axon growth of cultured dorsal root ganglion (DRG) neurons in vitro [43]. NFIL3 and CREB are both basic leucine zipper (bZIP) transcription factors that share similar DNA binding domains. Using a combination of transcription factor knockdown or overexpression, luciferase reporter assays, gene expression analysis and chromatin immunoprecipitation we were able to show that CREB and NFIL3 form an incoherent transcriptional feed-forward loop (IFFL) that controls the expression of several regeneration-associated genes [43,45]. IFFLs are a common transcription regulatory motif used to control both the timing and pulse-like expression of target genes [102,145]. In DRG neurons, CREB activates regeneration- associated genes, whereas NFIL3, which itself is activated by CREB, acts as a feed-forward repressor of these same genes, thus providing precise temporal control over gene expression. Our findings suggested that removal of NFIL3 might also increase CREB-mediated transcription and regenerative axon growth potential in vivo. To test this hypothesis we generated Nfil3 knockout mice and quantified functional recovery and injury-induced gene expression following a lesion of the sciatic nerve. Contrary to our expectations we observed that deletion of Nfil3 in vivo did not alter the expression of regeneration-associated genes and caused a significant delay in functional recovery following sciatic nerve injury. In addition, dominant-negative inactivation of NFIL3 specifically in DRGs impaired regenerative.

31 Chapter 2

Materials and Methods Animals Adult male Wistar rats or C57BL/6J mice were used in this study. All animals were individually housed at a 12 h light/dark cycle with ad libitum access to food and water. All animal experiments were approved by the animal ethics committees of the VU University Amsterdam and the Royal Academy of Arts and Science. All surgery was performed under Hypnorm/Dormicum anesthesia, and all efforts were made to minimize animal suffering. Nfil3 knockout mice Nfil3lox/lox mice were generated using homologous recombination in mouse embryonic stem cells and subsequent blastocyst injection of the appropriately targeted ES cells. The mouse 13 sequence (nucleotides 53,060,000–53,080,000) was retrieved from the Ensembl database and used as reference. The mouse RP23-12B19 BAC DNA was used for generating the homology arms and conditional region for the gene targeting vector, and for generating Southern blotting probes for confirming correct targeting. The 5’ homology arm (~5.5 kb), 3’ homology arm (~3.5 kb), and conditional region (~3.0 kb) were generated by PCR, cloned sequentially in the FtLoxNwCD vector and confirmed by restriction digestion and end-sequencing. The final vector was obtained by standard molecular cloning. In addition to the homology arms, the final vector also contained loxP sequences flanking the conditional KO region (~3.0 kb), a Neo expression cassette flanked by FRT sequences, and a DTA expression cassette. The final vector was confirmed by both restriction endonuclease digestion and by end sequencing. NotI was used to linearize the final vector prior to electroporation, and 30 μg of linearized vector DNA was electroporated into C57BL/6 ES cells and selected with 200 μg/ml G418. We selected 192 ES clones for PCR based screening and multiple potentially targeted ES clones were identified, expanded, and confirmed by Southern blot analysis to be correctly targeted and harboring a single Neo cassette insertion. Blastocyst injections were performed to create male chimeras for breeding with C57BL/6 wildtype females. To generate Nfil3 null mice, heterozygous Nfil3lox/+

32 No improvement in regeneration in NFIL3 knockout in vivo mice were crossed with 129Cre mice expressing Cre-recombinase under the control of a human CMV promoter [146]. The 129Cre mice had been backcrossed to C57Bl/6J mice for at least 10 generations, and the colony of heterozygous Nfil3+/- mice was maintained by backcrossing to inbred C57Bl/6J mice (Charles River Laboratories, L’Arbresle, France) for more than 3 generations before experiments were performed. Male Nfil3-/- knockout mice (further referred to as Nfil3 KO mice) and Nfil3+/+ wildtype littermates (further referred to as WT mice) were used in all experiments. DRG neuron cultures and quantification of neurite growth DRGs were dissected from E13-14 wildtype and Nfil3 KO mouse embryos, transferred to a 3.5 cm dish (Greiner) containing isolation buffer (Hanks buffered saline solution containing 7 mMHEPES; both from Gibco) and kept on ice. After removal of the nerve roots and other associated tissues, DRGs were transferred to a 15 ml tube containing 4.5 ml isolation buffer 0.5 ml 2.5% trypsin (Gibco) was added and DRGs were incubated at 37°C for 15 min. After 15 min 125 μl DNaseI (4 mg/ml; Roche) was added and DRGs were incubated at 37°C for another 15 min. After 15 min 5 ml DMEM containing 10% ES-FBS and 1% penicillin/streptavidin (all from Gibco) was added to inactivate the trypsin. The cell suspension was then centrifuged for 5 min at 1,000 rpm, the medium removed and cells resuspended in 1 ml culture medium [450 ml MEM (Gibco), 4.4 ml 45% D-(+)-glucose (Sigma), 5 ml GlutaMax (Gibco), 50 ml FBS (HyClone), 50 ng/ml nerve growth factor (Sigma; freshly added from 50 μg/ml stock solution)]. Cells were plated in poly-L-lysine (Sigma)-coated 96-well plates at a seeding density of ~15,000 cells/well and incubated at 37°C/ 5% CO2. The following day the medium was replaced by Neurobasal medium [480 ml Neurobasal (Gibco), 4.4 ml 45% D-(+)-glucose (Sigma), 5 ml Gluta- Max (Gibco), 10 ml B27 supplement (Gibco), 50 ng/ml nerve growth factor (Sigma; freshly added from 50 μg/ml stock solution)]. Neurobasal medium was replaced every 3 days. Cells were fixed after 1, 5 or 8 days in culture, stained with anti- neurofilament (1:2000; Novus Biologicals, Littleton, CO) and neurite lengths

33 Chapter 2 were measured using the Simple Neurite Tracer plug-in in ImageJ (v1.48). Per time point per genotype 5 wells were analyzed. Expression constructs and virus production DN-NFIL3 [43] was subcloned into the AAV-IRES-EGFP construct kindly provided by Dr. Dietmar Fischer (University of Ulm, Germany). As a control, an empty AAV-IRES-EGFP construct was used. AAV serotype 5 (AAV5; plasmid generously provided by Dr. Jurgen Kleinschmidt, University of Heidelberg, Germany) particles were produced and purified using an iodixanol cushion as previously described [147,148,149]. Viral vector stocks were stored at -80°C in Dulbecco’s phosphate buffered saline with MgCl2 and CaCl2 (Invitrogen) supplemented with 5% sucrose. Virus titers were determined by quantitative PCR and were typically 1-3x1012 genomic copies/ml. Rat surgical procedures Adult male Wistar rats (180–220 g) were anesthetized with Hypnorm/Dormicum (0.08/0.02 ml/100 g I.M.). L4 and L5 DRGs were exposed and injected with 1 μl (1-3x109 genomic copies) virus per DRG using a glass capillary pulled to a fine point and attached to a Hamilton syringe, at a speed of 0.2 μl/min [147]. After the injection the muscle layers were sealed with dissolvable sutures and the skin was closed with Michell-clips. Two weeks later the animals received a sciatic nerve crush. Animals were sedated with 1.8% isoflurane in 0.9 L/min medical compressed air. The sciatic nerve was exposed in the left hind leg and crushed by closing locking forceps with ribbed jaws for 30 sec. The crush site was marked by 10/0 ethilon surgical sutures in the epineurium and afterwards the skin was closed with Michell-clips. One week later animals were sedated with 1.8% isoflurane and the sciatic nerve was exposed again. At 1 cm distal to the crush site the sciatic nerve was transsected and the proximal end was submerged for 30 min in a small cup containing the retrograde tracer FastBlue. Also, part of the distal end of the sciatic nerve was removed for immunohistochemical analysis. Afterwards the nerve was washed to remove excess tracer and the muscle and skin were closed as before. One week later animals were anesthetized with Avertin (250 mg/kg I.P.) and

34 No improvement in regeneration in NFIL3 knockout in vivo perfused with saline followed by 4% paraformaldehyde (PFA) in saline. Injected DRGs were dissected, post-fixed overnight and transferred to a 25% sucrose solution overnight at 4°C. Tissue was embedded in Tissue-Tek, snap frozen in dry ice-cooled isopentane and stored at -80°C. DRG immunohistochemistry and analysis DRGs were sectioned at 20 μm, post-fixed in 4% PFA, blocked in blocking buffer (PBS containing 1% BSA, 5% FCS and 0.3% Triton X-100) and stained with anti- βIII-tubulin (1:500; clone Tuj1; Covance, Berkeley, CA) and anti-GFP (1:4000; Abcam, Cambridge, UK), followed by donkey anti-mouse Alexa594 (1:400), biotinylated goat anti-rabbit (1:300) and streptavidin- Alexa488 (1:400). All sections were captured at fixed exposure settings using an Axioplan 2 fluorescence microscope (Zeiss, Sliedrecht, The Netherlands) and a 10x objective. Images were analyzed using a custom algorithm in Image-Pro Plus software (MediaCybernetics, Rockville, MD) as previously described [147]. The algorithm automatically identifies all DRG nuclei based on the Tuj1 staining, which specifically stains neuronal cytoplasm and leaves the nucleus unstained and visible as a dark round object. GFP and FastBlue intensities were then measured in a 4-pixel ring around the nucleus. Background intensity levels were measured in non-injected DRGs processed in the same way. Neurons showing >2-fold higher intensities than the mean background level of GFP or FastBlue were classified as transduced and traced neurons, respectively. Sciatic nerve immunohistochemistry and analysis Sciatic nerves distal from the site of tracer treatment were removed, fixed in 4% PFA and sectioned at 20 μm. Sections were blocked in blocking buffer (PBS containing 1% BSA, 5% FCS and 0.3% Triton X-100) and stained with anti- neurofilament (2H3; 1:1000; Hybridoma Bank, University of Iowa, IA). Images were captured at fixed exposure settings using an Axioplan 2 fluorescence microscope and a 20x objective. Surface areas of the different nerve fascicles were calculated using Image-Pro Plus software. The area was then automatically segmented, and segments were randomly selected to sample a minimum of 40% of the total area. In each selected segment, all neurofilament

35 Chapter 2 positive axons were counted manually using a light cursor. The total number of regenerating axons was then calculated by multiplying the number of counted axons with the total fascicle area divided by the measured area. The observer was blind for the experimental condition both during image acquisition and image analysis. A two-tailed Student’s t-test was performed to test for significance and a p-value <0.05 was considered statistically significant. Mouse surgical procedures Adult male mice were anesthetized with and I.P. injection of Hypnorm (0.1 mg/kg fentanyl citrate/ 3.3 mg/kg fluanisone HCl) and Dormicum (8.3 mg/kg Midazolam). The sciatic nerve was exposed in the left hind leg, transsected, and the nerve endings were subsequently coaptated using two 10/0 ethilon sutures and the skin was closed with Michell-clips. Throughout and after the operations mice were kept warm until they regained consciousness, and Temgesic (0.01 ml/100 g S.C.) was administered 1x per day until 3 days after the operations.

After the experiment mice were sacrificed by CO2/O2 suffocation. Microarrays L4 and L5 DRGs were dissected at day 2 and day 5 after sciatic nerve lesion and immediately frozen on dry ice. Control tissue was taken from the non-injured side. RNA was isolated using TRI Reagent (Molecular Research Center, Inc., Cincinnati, OH) according to the manufacturers guidelines. RNA quality control was performed using an RNA 6000 Nano chip (Agilent, Santa Clara, CA) on the Bioanalyzer (Agilent, Santa Clara, CA), and concentrations were determined with the Nanodrop ND1000 spectrophotometer (Thermo Fischer Scientific, Waltham, MA). RNA samples were amplified, labeled and hybridized to Agilent 4x44K whole mouse genome microarrays using standard Agilent protocols (Agilent). For each condition we used four independent replicates, two labeled with Cy3 and two with Cy5, and single-color intensities were used for further analyses. Arrays were scanned using an Agilent scanner and data were extracted using Agilent Feature Extraction software. Array data were further processed using the R-packages Bioconductor [150] and limma (Linear Models for Microarray Data) [151] for background correction (Edwards) and quantile

36 No improvement in regeneration in NFIL3 knockout in vivo normalization. For statistical analyses we used the two class unpaired time course approach implemented in SAMR (statistical analysis of microarrays) [152] and created a linear model with the factors time, genotype and time_genotype in limma [151]. Heat maps and hierarchical clusters were generated using gplots [153]. Transcription factor binding site overrepresentation analysis was performed using oPOSSUM 3.0 [154,155] with the following settings: background genes: all genes present on the microarray, transcription factor profiles used: all vertebrate JASPAR CORE profiles with a minimum specificity of 8 bits, conservation cutoff: 0.4, matrix score threshold: 85%, sequence searched: 2000/0 bp upstream/downstream. Functional annotation clustering was performed using DAVID (Database for Annotation, Visualization and Integrated Discovery) 6.7 [156,157] using the following settings: Kappa similarity overlap: 3, similarity threshold: 0.50, classification group membership: initial: 3, final: 3, multiple linkage threshold: 0.50. Enrichment thresholds: 1.0. For analysis of differential expression of groups of genes sharing the same ontologies, we first averaged expression values for genes that were measured by multiple probes using reshape2 [158] and plyr [159]. Data was then converted into ‘ExpressionSet’ format using Biobase [150], genes were annotated using the org.Mm.eg.db database [160], grouped into ontologies of similar biological process, molecular function, or cellular component (group-size between 10 and 500 genes), and tested for grouped differential expression using globaltest [161]. Open field test Animals were introduced into a corner of the white square open field box (50x50 cm, walls 35 cm high) illuminated with a single white fluorescent light bulb from above (200 lx), and exploration was tracked for 10 min (Viewer 2, GmbH, Bonn, Germany). Total distance moved was calculated using Viewer 2 (Biobserve GmbH, Bonn, Germany).

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Accelerating rotarod test Motor function was evaluated using an accelerating rotarod (Roto-rod series 8, IITC Life Science, Woodland Hills, CA, USA). On day one, mice received two habituation trials of 120 s (acceleration of from 0 to 20 rpm in 120 s) followed by 3 training trials (acceleration of from 0 to 40 rpm in 180 s). On day 2, mice received 5 additional training trials. The maximum Rotation Per Minute (RPM) reached before falling off was the dependent measure. Narrow beam test The narrow beam setup consisted of two platforms (30x30 cm), one exposed and one sheltered from light and containing bedding material, connected by a narrow beam (26 mm x 1 m) at 60 cm above the floor. Mice were trained 3x once per day to traverse the beam. After reaching the sheltered platform mice were allowed to stay there for 1 min before being placed back into the home cage. During crossing mice were filmed with a video camera and afterwards crossing time, paw slips, and total duration of hanging from the front paws (side-hanging) were scored. The observer was blinded for genotype and post- operation time. An error score was computed as follows: 1 point per paw slip, 5 points for 1–5 sec of side-hanging, 10 points for 5–15 sec of side-hanging, and 15 points for more than 15 sec of side-hanging. These values were chosen such that on average the maximum penalty for side-hanging was equal to the maximum penalty for paw slips. After the lesion, a narrower beam (12.5 mm) was mounted on top of the original beam to increase the difficulty of the task. Crossing performance was tested every 2–3 days for 1 month. Statistical analysis Open field, rotarod and narrow beam data and were analyzed with repeated measures ANOVA We considered p-values <0.05 as significant. Data were log- transformed in situations where Mauchly’s sphericity test was not met, and when this was not sufficient we applied Huynh- Feldt correction. All graphs show mean values ± SEM.

38 No improvement in regeneration in NFIL3 knockout in vivo

Fig 1. Generation and validation of Nfil3 KO mice. (a) A schematic representation of the knockout strategy is indicated. (b) Southern blot analysis confirming correct homologous recombination at the 5’ probe side using AvrII digestion yielding fragments of 11.4 kb (wildtype) and 7.6 kb (mutant), at the 3’ probe side using EcoRV digestion yielding fragments of 12.2 kb (wildtype) and 8.9 kb (mutant), and at the Neo cassette using NheI digestion yielding a band of 12 kb (mutant only). (c) Nfil3 mRNA levels in Nfil3 KO and wildtype brains as measured by quantitative real-time PCR. Gene expression was normalized against Gapdh expression.

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Results Generation of Nfil3 knockout mice To investigate the role of NFIL3 in axon regeneration we generated conditional Nfil3 KO mice using homologous recombination in embryonic stem cells (Fig 1a). We confirmed correct insertion of LoxP sites and the Neo cassette by Southern blot analysis (Fig 1b). To generate full KO mice, conditional KO mice were crossed with 129Cre mice expressing Cre-recombinase in germ cells [146]. Absence of Nfil3 mRNA was confirmed by quantitative real-time PCR (Fig 1c).

DRG neurons lacking Nfil3 show enhanced axon growth in vitro We previously showed that reducing Nfil3 expression in either DRG neurons or DRG-derived F11 cell using RNA interference significantly increases axon growth [43]. To demonstrate unequivocally that enhanced axon growth is due to lack of Nfil3 we investigated the growth characteristics of cultured DRG neurons from Nfil3 KO mice and wildtype littermate controls in vitro. Dissociated embryonic DRG neurons were plated in 96-well plates and cultured for 1, 5 or 8 days. Cells were then fixed and stained with anti-neurofilament. Quantification of axon lengths revealed a significant increase in neurite outgrowth in KO cultures compared with cultures prepared from wildtype littermates at all three time points (Fig 2a). Specifically, the average axon length of Nfil3 KO neurons was 25% higher at DIV1 (222±10 μm vs. 178±9 μm; n = 69/71), 56% higher at DIV5 (931±101 μm vs. 595±48 μm; n = 26/31) and 41% higher at DIV8 (1134±77 μm vs. 806±57 μm; n = 32/34) (Fig 2b). This increase in axon lengths is comparable to what we previously observed when we knocked down the expression of Nfil3 in either dissociated DRG neurons or in F11 cells [43]. We conclude that genetic deletion of Nfil3 in DRG neurons enhances axon growth.

Nfil3 knockout mice show impaired functional recovery after sciatic nerve lesion We next wanted to test whether Nfil3 KO mice also show improved functional recovery from nerve injury in vivo. First we measured general locomotor

40 No improvement in regeneration in NFIL3 knockout in vivo

Fig 2. Nfil3 deletion enhances axon growth of DRG neurons in culture. (a) Example images of cultured embryonic DRG neurons from wildtype mice (top panels) and Nfil3 KO mice (bottom panels) at 1, 5 and 8 days in vitro (DIV; scale bar: 500 μm). (b) Quantification of axon lengths showed that the average axon length of Nfil3 KO neurons was significantly higher compared to wildtype neurons at DIV1 (222±10 μm vs. 178±9 μm; n = 69/71), at DIV5 (931±101 μm vs. 595±48 μm; n = 26/31) and at DIV8 (1134±77 μm vs. 806±57 μm; n = 32/34) (Student’s t test; mean ± SEM; **p < 0.01). activity in uninjured mice using open field and rotarod tests. The total distance moved in the open field task was unchanged in Nfil3 KO mice (n = 12, t(22) = -

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0.27, p = 0.98, Fig 3a), nor did we observe a difference in rotarod performance

(n = 12, F(1,22) = 1.02, p = 0.32, Fig 3b), indicating that general movement and motor skills are unaltered in Nfil3 KO mice. Next, we applied sciatic nerve transsection lesions to Nfil3 KO mice and wildtype littermates and assessed functional recovery by testing performance in the narrow beam task every other day. As indicators of functional recovery we measured crossing latencies

(Fig 3c) and we calculated a weighted error score based on paw slips and time spent side-hanging on the bar (Fig 3d; see materials and methods section for detail). Importantly, we observed no differences in crossing latency and error score during pre-training (Fig 3c and 3d), confirming that basal locomotion and motor skills are unaffected in Nfil3 KO mice. After the training sessions, beam width was reduced from 26 mm to 12.5 mm to increase the difficulty of the task. Following sciatic nerve injury, Nfil3 KO mice showed significantly decreased performance and slower recovery compared with wildtype littermates, both in crossing latency (genotype, n = 11/10, F(1,19) = 8.893, p =

0.008, Fig 3c) and in error score (genotype, n = 11/10, F(1,19) = 7.145, p = 0.015; genotype_time, F(12,228) = 2.131, p = 0.016, Fig 3d). These data show that Nfil3 deletion in vivo causes a delayed functional recovery from sciatic nerve lesion, despite the increased axon growth of DRG neurons observed in cultured DRG neurons (Fig 2) [43].

Inactivation of NFIL3 impairs DRG axon growth following sciatic nerve crush in vivo Because the positive effect of Nfil3 deletion on DRG axon growth in vitro apparently does not translate into improved functional recovery following sciatic nerve crush in vivo, we next wanted to know how absence of NFIL3 affects axon regeneration in vivo. To allow unequivocal quantification of regenerating fibers in the sciatic nerve, and to exclude possible confounding effects of Nfil3 deletion in other cell types or systems, we used a retrograde tracing technique combined with dominant-negative inhibition of NFIL3 levels

42 No improvement in regeneration in NFIL3 knockout in vivo

Fig 3. Nfil3 deletion impairs functional recovery from peripheral nerve injury in vivo. (a) Nfil3 KO mice show no differences in the total distance moved in the open

field task (n = 12, t(22) = - 0.27, p = 0.98). (b) The latency to fall off an accelerating rotarod was not affected in Nfil3 KO

mice (n = 12, F(1,22) = 1.02, p = 0.32). (c) Nfil3 KO mice have a significantly longer beam crossing latency than wildtype mice (main effect

genotype, n = 11/10, F(1,19) = 8.893, #p = 0.008). Post-hoc t-tests indicated indicate significant differences in performance at post-lesion days 5, 13, 15 and 17 (**p < 0.01, *p < 0.05). (d) Nfil3 KO mice also make significantly more errors when crossing the beam (main effect

genotype, n = 11/10, F(1,19) = 7.145, #p = 0.015; interaction genotype*time,

F(12,228) = 2.131, $p = 0.016). Post-hoc t-tests indicate significant differences in performance at post-lesion days 9, 12, 13, 15 and 19 (**p < 0.01, *p < 0.05).

specifically in L4 and L5 DRG neurons in rats. We previously showed that expression of a dominant-negative mutant of NFIL3 (DN-NFIL3) enhances axon growth of rat DRG neurons and DRG-like F11 cells in vitro [43]. We injected L4

43 Chapter 2 and L5 DRGs unilaterally with AAV virus expressing DN-NFIL3 and GFP together, or GFP only as control. Two weeks after viral transduction a crush lesion was aplied at the level of the sciatic nerve. One week thereafter we transected the sciatic nerve at 1 cm distal of the lesion. The nerve segment distal from this lesion was removed for quantification of regenerating axons, and the proximal nerve stump was loaded with the retrograde tracer FastBlue. One week later, animals were sacrificed and the DRGs were taken out for histological analysis. This experimental procedure is schematized in Fig 4a. DRG sections were stained for βIII-tubulin and for GFP (Fig 4b), allowing quantification of all neurons (βIIItubulin- positive), transduced neurons (βIII-tubulin- and GFP- positive), and neurons that regenerated an axon beyond 1 cm distal from the crush site (βIII-tubulin- and FastBlue-positive). The total fraction of FastBlue- positive neurons was slightly lower in DN-NFIL3 treated animals compared with controls (Fig 4c), but this difference was not significant (n = 8, t(14) = 1.180, p = 0.25). However, when we calculated the number of neurons that was both GFP- and FastBlue- positive, i.e., the fraction of neurons that was actually transduced

Fig 4. Dominant-negative inhibition of NFIL3 impairs regenerative axon growth in vivo. (a) Overview of the experimental design. At day 0 L4/L5 DRGs were injected with AAV5 virus expressing either DN-NFIL3 and GFP, or GFP only. At day 14 the animals received a unilateral crush of the sciatic nerve. At day 21 we transsected the sciatic nerve 1 cm distal from the crush and treated the proximal stump with the retrograde tracer FastBlue. The distal stump was removed for histological analysis. At day 27 animals were sacrificed, and the DRGs were removed for histological analysis. (b) Examples of control and DN-NFIL3 treated DRG sections stained with anti-βIII-tubulin in red, anti- GFP in green, and showing FastBlue labeling in blue (scale bar: 100 μm). (c) The total fraction of FastBlue-positive βIII-tubulin expressing neurons was slightly lower in DN-

NFIL3-treated animals compared with controls (n = 8, t(14) = 1.180, p = 0.25). (d) When the quantification of FastBlue-positive cells was limited to GFP-positive (i.e. irally transduced) neurons, a significant reduction was observed in DN-NFIL3-treated animals compared with controls (n = 8, t(9.214) = 2.390, p = 0.040). (e) Examples of control and DN-NFIL3 treated sciatic nerve sections stained with anti-βIII-tubulin (scale bar: 100 μm). (f) No significant difference was observed in fiber densities in the sciatic nerve at 1 cm distal of the crush (n = 8, t(14) = 0.095, p = 0.925).

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and had extended axon up until 1 cm distal of the crush site, we found a significant reduction in DN-NFIL3 treated animals compared with controls (n =

8, t(9.214) = 2.390, p = 0.040, Fig 4d), indicating that DN-NFIL3 expression reduces axon regeneration in vivo. Importantly, no difference was observed in the total number of GFP-positive neurons between treatment conditions (n = 8, t(14) = 1.690, p = 0.113). In the sciatic nerve, where fibers from transduced and untransduced cells were indistinguishable, fiber density did not differ between treatments (n = 8, t(14) = 0.095, p = 0.925, Fig 4e and 4f). Together these data indicate that, in line with the reduced functional recovery observed in Nfil3 KO mice, regenerative axon growth is impaired in neurons in which NFIL3 function is inhibited. This reduction in regenerative axon growth is specifically observed in neurons that express DN-NFIL3, but the overall effect (i.e., total fraction of FastBlue-positive cells and total number of fibers in the sciatic nerve) is probably masked by the fact that many neurons were not transduced by the virus.

Established NFIL3 regeneration-associated target genes are not dysregulated in Nfil3 knockout mice To understand why Nfil3 deletion does not promote axon regeneration and functional recovery in vivo, we next tested the transcriptional role of NFIL3 in injured DRG neurons. We performed mRNA expression microarray analysis on DRGs following sciatic nerve lesion in Nfil3 KO mice and wildtype controls, using contralateral DRGs as control tissue (n = 4 per genotype per condition; GEO accession number GSE66259). We focused on expression differences that occur relatively early after injury, i.e., at 2 days and 5 days post-lesion, since this is the period when the highest expression levels of Nfil3 are observed [43]. Using linear modeling we identified 5489 unique genes significantly regulated due to the lesion at either 2 or 5 days post-lesion, independent of genotype (S1 Table). To allow comparison of our findings with previously published regeneration- associated gene expression profiling studies we downloaded data from Kim et al. [162] describing gene expression data in mouse DRGs at 5 days post-lesion

46 No improvement in regeneration in NFIL3 knockout in vivo

Fig 5. NFIL3 deletion does not alter the expression of regeneration-associated genes. (a) Gene expression was profiled in Nfil3 KO mice and wildtype controls at 2 days and at 5 days post-lesion, relative to non-injured control DRGs. Gene regulation values in wildtype animals at post-lesion day 5 showa highly significant correlation (r = 0.48, p < 2.2x10-16) with previously published data [162] describing injury-induced gene expression changes in mouse DRGs at the same time point (GEO datasets GSM827127/8). (b) The expression of well-established regeneration-associated genes and/or NFIL3 target genes is not affected in Nfil3 KO animals compared with wildtype controls. Of the 20 genes indicated here, 16 are in the core set of regeneration- associated genes identified in three or more independent microarray studies (bold print) [163], and 8 are experimentally validated NFIL3 target genes (underlined) [43,45]. No

significant differences were observed between expression profiles of Nfil3 KO animals and wildtype controls.

47 Chapter 2 compared with uninjured control tissue (GEO sets GSM827127 and GSM827128). We filtered for genes that passed the reported detection test (p < 0.05), calculated gene regulation values relative to the uninjured control levels and compared these to our own regulation values in wildtype mice at the same time point (i.e., 5 days post-lesion). We found that the two datasets are significantly correlated (r = 0.48, df = 5236, p < 2.2x10-16, Fig 5a). These findings indicate that we profiled valid injury-induced and regeneration- associated genes. We next asked whether Nfil3 deletion causes a dysregulation of known regeneration-associated genes. We compared expression profiles in knockout and wildtype mice of 20 genes that are consistently found regulated in multiple gene expression studies [163] and/or contain previously identified and experimentally validated NFIL3 binding sites [43,45]. All these genes showed strong injury-induced regulation over time, but for none we could observe a difference in expression between knockout and wildtype DRGs (Fig 5b). Even Gap43 and Arg1, which we previously showed to bind NFIL3 in vivo, show no increased expression in Nfil3 KO mice compared to WT. From this we conclude that removal of NFIL3 does not de-repress established NFIL3 target genes.

Genes differentially expressed in Nfil3 knockout mice To determine which other genes may be regulated by NFIL3 following sciatic nerve lesion, we used time course analysis in SAM and time_genotype interaction analysis in limma to detect expression profiles that significantly differ between genotypes. We identified 59 genes that were differentially regulated due to the lesion in Nfil3 KO mice compared with wildtype controls (adjusted p < 0.05; Fig 6a). When we tested these genes for overrepresented transcription factor binding sites in 0–2000 bp region upstream of the transcription start site, we found NFIL3 binding sites to be most significantly overrepresented based on the combined Fisher score and Z-score reported in oPOSSUM [154,155] (Fig 6b), indicating that these genes most likely represent true NFIL3 target genes. Functional enrichment analysis identified two gene

48 No improvement in regeneration in NFIL3 knockout in vivo

Fig 6. NFIL3 deletion alters the expression of a small set of NFIL3 target genes. (a) The injury- induced regulation of 59 genes

was found significantly different in Nfil3 KO animals compared with controls. Color scale indicates log2-fold expression difference between genotypes.

(b) Within the promoter regions of these 59 genes, NFIL3 binding sites are the most overrepresented binding sites

judged on the combined Fisher score and Z-score computed by oPOSSUM.

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ontology terms that are significantly overrepresented within these genes: ‘olfactory signal transduction’ and ‘transcriptional regulation’ (Table 1). Global testing subsequently revealed that at 5 days post-lesion there is a significant dysregulation in Nfil3 KO mice of multiple genes that are involved in basal transcriptional regulation (Table 2), while no significant overrepresentation of terms was detected at day 2 post-lesion. Taken together these data suggest that the Nfil3 deletion affects the expression of a small set of target genes in vivo. These genes include regulators of basal gene transcription, in particular at day 5 post-lesion, as well as several transcriptional regulators that are not known to be involved in axon regeneration.

Table 1. Gene ontology terms overrepresented within genes differentially regulated in Nfil3 KO mice.

Functional class Gene names Enrichment score

Olfr551, OlfrR65, Olfr1270, Olfr914, Olfr1330, LOC100046375, Olfr64, Gpr141, Olfactory signal transduction 1.21 Olfr125, Vmn1r63, Pax6, Trpv3, Clec2e, Dpy19l3, Uxs1, Acvr1c

Mtf1, Foxa2, Pax6, Myog, LOC100046232, Transcription regulation 1.17 Cenpa, Rfx7, Rag1, Nfil3

Discussion In this study we characterized the role of the transcriptional repressor NFIL3 in axon regeneration following nerve injury using genetic deletion and dominant- negative inhibition. In line with previous findings [43] we show that genetic deletion of Nfil3 promotes axon growth in cultured DRG neurons in vitro. In strong contrast however, genetic deletion or dominant-negative inhibition of NFIL3 in vivo did not enhance regenerative axon growth, did not improve functional recovery, and did not alter the expression of known regeneration- associated genes following sciatic nerve lesion. Instead, we observed a

50 No improvement in regeneration in NFIL3 knockout in vivo

reduction in regenerative axon growth, a delay in functional recovery, and a dysregulation of genes not previously linked to axon regeneration.

Table 2. Gene ontology terms overrepresented within genes differentially regulated at 5 days post- lesion.

GO term GO description

GO: 0002862 Negative regulation of inflammatory response to antigenic stimulus GO: 0001012 RNA polymerase II regulatory region DNA binding GO: 0000977 RNA polymerase II regulatory region sequence-specific binding GO: 0007623 Circadian rhythm GO: 0000979 RNA polymerase II core promoter sequence-specific DNA binding GO: 0001078 RNA polymerase II core promoter proximal region sequence-specific DNA binding transcription factor activity involved in negative regulation of transcription GO: 0001227 RNA polymerase II core promoter regulatory region sequence-specific DNA binding transcription factor activity involved in negative regulation of transcription GO: 0001046 Core promoter sequence-specific DNA binding GO: 0001047 Core promoter binding GO: 0000982 RNA polymerase II core promoter proximal region sequence-specific DNA binding transcription factor activity

Many transcription factors are regulated by nerve injury [143]. These transcription factors are interesting targets for promoting regenerative axon growth because they are potentially able to coordinate the expression of multiple regeneration-associated genes simultaneously [51]. Several recent studies indeed showed that inducing the expression or activity of transcription factors provides an efficient way to enhance the regenerative potential of axotomized neurons in vivo. Expression of constitutively active CREB, for

51 Chapter 2 instance, promotes regeneration of lesioned dorsal column axons [144], and activation of SMAD1, by increasing the levels of BMP2 or -4 in vivo, enhances axon growth of adult DRG neurons in vitro [50]. Overexpression or activation of a transcription factor is however difficult to control and may result in unwanted side effects, and inactivation of inhibitors of regeneration may provide an even better approach to promote axon regeneration. In this respect, Moore et al. for instance showed that genetic deletion of KLF4, a KLF family transcription factor that inhibits axon growth, enhances regeneration of damaged retinal ganglion cell axons into the optic nerve [47]. We recently showed that NFIL3 is a potent neuron-intrinsic inhibitor of axon growth in injured cultured adult DRG neurons [43]. From a panel of 62 transcription factors that were up or downregulated during regeneration of DRG neurons after sciatic nerve injury in vivo, knockdown of NFIL3 had the strongest outgrowth promoting effect in vitro. Moreover, NFIL3 was shown to repress known regeneration-associated genes, including Gap43 and Arg1. Here we could replicate these findings by demonstrating that genetic deletion of Nfil3 in mice results in a strong increase in neurite outgrowth from cultured DRG neurons compared with wildtype neurons. However, Nfil3 KO mice did not show enhanced functional recovery from sciatic nerve injury. In fact we observed a significant reduction in crossing latency and error score in the narrow beam task, and our data indicate that maximal recovery in Nfil3 KO animals is delayed by approximately one week compared with wildtype controls. These data suggest that regenerative axon growth in Nfil3 KO mice is reduced rather than enhanced. Although basal locomotor activity and motor skills were not affected we cannot exclude the possibility that adverse side effects of the global gene deletion are responsible for the impairment in functional recovery. Specifically, NFIL3 is known to play an important role in development [113,164,165], and an altered immune response could have contributed to the delay in functional recovery. Therefore we used a DRG neuron-specific dominant-negative approach to test whether inhibition of NFIL3 function affects regenerative growth directly in a cell-autonomous

52 No improvement in regeneration in NFIL3 knockout in vivo manner. The DN-NFIL3 construct used here was shown to bind to NFIL3 and to increase axon growth in cultured DRG neurons [43]. We transduced neurons in L4 and L5 DRGs and lesioned the sciatic nerve two weeks later. We observed that within the populations of neurons that expressed DN-NFIL3, less neurons were able to extend an axon up to 1 cm beyond the lesion side compared with GFP transduced controls. Due to the relatively low transduction rates (10– 15%) this effect was not detected in the total number of traced neurons or in the number of fibers in the distal nerve stump. Taken together however, these observations convincingly show that inhibition of NFIL3 function reduces regenerative axon growth only in neurons that actually express DN-NFIL3. Given the fact that NFIL3 inactivation reduces axon regeneration in vivo in a cell-autonomous manner, whereas previously we showed that either knockdown or dominant-negative inhibition of NFIL3 increases the expression of regeneration-associated genes and enhances axon growth in vitro, we next wanted to know how Nfil3 deletion affects injury-induced gene expression in the DRG. Microarray expression profiling at 2 and 5 days after sciatic nerve lesion revealed the induction of a large set of regeneration-associated genes. However, focusing on the most common regeneration-associated genes, including seven experimentally validated NFIL3 target genes [45], no significant differences were observed in expression profiles between Nfil3 KO mice and wildtype controls. These findings might explain why we did not find a positive effect of Nfil3 deletion on axon regeneration and functional recovery, but they do not explain why regeneration in Nfil3 KO mice is impaired in comparison with wildtype controls, suggesting that two distinct mechanisms are involved. In this respect it was interesting to find that in addition to regeneration- associated genes not being differentially regulated, a small set of other genes did show changes in expression in Nfil3 KO mice. In particular we found components of olfactory signal transduction pathways and several transcription factors dysregulated. Interestingly, one of the upregulated transcription factors, PAX6, is a regulator of olfactory system development [166], suggesting that the dysregulation of olfactory signaling pathways may be due to a de-repression of

53 Chapter 2

Pax6 in the absence of NFIL3. Induction of this, and possibly also other, gene expression programs that have no role in axon regeneration or may even actively repress axon regeneration might explain the adverse effects of Nfil3 deletion on regeneration. There may be several reasons why Nfil3 deletion promotes axon growth in vitro, but not axon repair in vivo. Firstly, the timescale on which these processes take place are very different. In vitro, adult DRG neurons extend axons over large distances within the first 48 hours in culture, whereas in vivo it takes two weeks before functional recovery become apparent. We previously showed that NFIL3 suppresses regenerative axon growth by repressing CREB target genes [43]. CREB activity may thus be necessary to reveal the growth- promoting effects of Nfil3 deletion, and possibly injured neurons in vivo are not able to sufficiently activate CREB target genes long enough to benefit from the absence of feed-forward repression by NFIL3. In that case, Nfil3 deletion together with constitutive activation of CREB might help to improve regenerative axon growth. Secondly, the cellular and extracellular environment in vivo is very different from the in vitro situation. There may be many factors that interact with regenerating axons and change the gene expression response in injured DRG neurons such that the gene network state changes and the beneficial effects of Nfil3 deletion cannot be expressed. Taken together our data show that removal of the transcriptional repressor NFIL3 in vivo does not recapitulate the regeneration promoting effects that were previously observed in vitro. We know that NFIL3 acts together with other transcription factors, including CREB and C/ EBPs, in a gene regulatory network that controls the expression of several regeneration- associated genes [45]. Apparently removal of NFIL3 in vitro is sufficient to alter the state of this network, whereas in vivo network control is more complex and more robust against external manipulation. On a more general note our data also indicate that in vitro data on the transcriptional regulation of axon growth may be difficult to translate to therapeutic intervention strategies for promoting neuronal regeneration in vivo.

54 No improvement in regeneration in NFIL3 knockout in vivo

Supporting Information S1 Table. List of 5489 genes that are significantly regulated due to sciatic nerve lesion at either 2 or 5 days post-lesion. Available from: http://journals.plos.org DOI: 10.1371/journal.pone.0127163

Acknowledgments We would like to thank Lody R. de Groot, Rolinka J. van der Loo, François Rustenburg, Pim van Nierop, Ruud Wijnands and A. Mariette Lenselink for technical assistance.

55

Chapter 3

Behavioral characterization of Nfil3 knockout mice

L.R. van der Kallen1, M. Loos1,2, R. van der Loo1, M.J.M. Sassen1, J. Verhaagen3, A.B. Smit1 and R.E. van Kesteren1 1Center for Neurogenomics and Cognitive Research, Neuroscience Campus Amsterdam, VU University, De Boelelaan 1085, 1081 HV Amsterdam, The Netherlands 2Sylics (Synaptologics BV), PO box 71033, 1008 BA Amsterdam, The Netherlands 3Department of Neuroregeneration, Netherlands Institute for Neuroscience, Meibergdreef 47, 1105 BA Amsterdam, The Netherlands

Chapter 3

Abstract The transcriptional repressor NFIL3 is widely expressed in many different tissues including the brain, however, a role for NFIL3 in controlling behavior has thus far not been identified. In this study we therefore performed an extensive behavioral characterization of Nfil3 knockout mice. Knockout mice did not differ from wildtype mice in basal locomotor and anxiety tests, but they were specifically impaired in an avoidance learning task that was performed in an automated home cage environment. Specifically, knockout mice persistently kept using the preferred entrance of their shelter after an aversive light stimulus was coupled to it. These data show for the first time that Nfil3 knockout mice have cognitive impairments, and suggest a role for NFIL3 in behavioral flexibility.

Introduction NFIL3 is a transcriptional repressor that belongs to the basic leucine zipper (bZIP) family of transcription factors [167]. Many tissues express NFIL3 and different functions have been identified accordingly, including the regulation of spinal cord motor neuron growth and survival [168], regeneration-associated gene expression in dorsal-root ganglia [43], circadian clock gene expression in the supraoptic nucleus [169], periovulatory gene expression in ovaries [170], FOXO-induced gene expression in cancer [171], von Willebrand factor gene expression in endothelial cells [172] and the development of natural killer cells [173] and IgE class switching [164] in the immune system. NFIL3 expression levels are particularly high in the brain as well [100,113], but a specific role for NFIL3 in regulating behavior has not yet been described. Several groups recently reported independently that Nfil3 knockout mice have no gross phenotypic abnormalities, no reduced life expectancy, and no altered breeding ratios, and primarily show a selective loss of natural killer cells of the immune system [113,164,173,174], suggesting that global Nfil3 knockout mice may be used to investigate behavioral deficits due to loss of NFIL3 expression in the brain. We generated a global Nfil3 knockout mouse (see

58 Behavioral characterization of Nfil3 knockout mice

Chaptr 2), and here we performed an in-depth behavioral characterization of these mice using a low-throughput battery of standard behavioral tests that assess general activity, locomotion and anxiety as well as high-throughput automated home cage phenotyping in order to obtain a rich set of behavioral parameters ranging from basal activity to simple cognitive performance [175,176,177]. The obvious advantage of automated phenotyping is that mice are observed in their home cage and perform various tasks over the time span of one or two weeks without interference by an experimenter [133]. Although experiments performed by different experimenters and in different laboratories are in principle comparable as long as proper control groups are included [134], continuous automated observation results in more naturally behaving animals and provides much more data than conventional experiments, both quantitatively and qualitatively. Our findings show that Nfil3 knockout mice are normal in general activity, locomotion and anxiety, but show a specific deficit in avoidance learning as measured in the automated home cage setup. These data are the first indication that NFIL3 is specifically involved in learning and memory.

Materials and Methods Animal cohorts For behavioral experiments, male 8 to 10 week old mice were singly housed on sawdust in standard Makrolon type II cages enriched with cardboard nesting material for at least one week prior to experiments, with water and food ad libitum (7:00/19:00 lights on/off). Experiments were carried out in accordance with the European Communities Council Directive of 24 November 1986 (86/609/EEC), and with approval of the local animal care and use committee of the VU University. To reduce the number of animals for testing, the following behavioral tests were performed in a single mouse cohort in the following order: instrumented home cage, grip strength test, novel home cage induced hypophagia, elevated plus maze, open field test, dark/light box, rotarod. The order of testing was designed to be increasing in presumed invasiveness and to

Chapter 3 minimize carry-over effects. Mice were ~10 weeks of age at the start of the tests, and were tested for 5 weeks in a row. For the instrumented home cage an extra cohort was used at 11 weeks of age. Instrumented home cage Animal activity was automatically monitored in PhenoTyper cages (model 3000, Noldus Information Technology, Wageningen, The Netherlands) by overhead video tracking (EthoVision HTP 2.1.2.0, based on EthoVision XT 4.1, Noldus Information Technology, Wageningen, The Netherlands) and processed to generate behavioral parameters using AHCODATM analysis software (Synaptologics BV, Amsterdam, The Netherlands) as described in detail previously [133]. The home cage was equipped with a shelter with two entrances, fixed in one of the corners. Two bright white LED were mounted on two cage walls, shining inward through the Perspex wall into the shelter to provide an aversive light stimulus during the aversive learning task starting on day 4. During the first three days general behavior was monitored, using a core set of 20 parameters of spontaneous behavior (see Supplementary Methods;[178]). On day 4, in preparation of the aversive learning task, the initial preference was calculated as follows: [(number of entries through the preferred entrance) – (number of entries through non-preferred entrance)]/(total number of entries) [133]. On days 5 and 6, animals were conditioned to switch the use of their developed preferred shelter entrance to the non-preferred entrance by switching on an aversive light stimulus if they entered through their initially preferred entrance. On day 7 conditioning was stopped, and the stability of the change in preference was analyzed. Open field Animals were introduced into a corner of the white square open field box (50 × 50 cm, walls 35 cm high) illuminated with a single white fluorescent light bulb from above (200 lx), and exploration was tracked for 10 min (Viewer 2, GmbH, Bonn, Germany). Time spent in, number of entries into the center square area (20 × 20 cm), and total distance moved were calculated using Viewer 2 (Biobserve GmbH, Bonn, Germany).

60 Behavioral characterization of Nfil3 knockout mice

Grip strength Animals were placed on a grid connected to a force gauge (1027DSM Grip Strength Meter, Columbus instruments, Columbus, OH, USA) and pulled backwards until they let go of the grid, measuring strength in term of the peak force (N). This procedure was performed 5 times with only the front paws and 5 times with all 4 paws, and the average of each 5 repetitions was taken as grip strength. Accelerating rotarod Motor function was evaluated using an accelerating rotarod (Roto-rod series 8, IITC Life Science, Woodland Hills, CA, USA). On day one, mice received two habituation trials of 120 s (acceleration of from 0 to 20 rpm in 120 s) followed by 3 training trials (acceleration of from 0 to 40 rpm in 180 s). On day 2, mice received 5 additional training trials. The maximum Rotation Per Minute (RPM) reached before falling off was the dependent measure. Elevated plus maze Mice were tested on the elevated plus maze (EPM) as described previously [179] with minor modifications. Briefly, mice were introduced into the center of an EPM (arms 30 × 6 cm, walls 35 cm high, elevated 50 cm above the ground), facing one of the closed arms. The EPM was illuminated with a single white fluorescent light bulb from above (open arms 70 lx, closed arm 30 lx) and exploratory behavior was video tracked for 5 min (Viewer 2, Biobserve). The borders between the center and the arms of the maze were defined at 3 cm into each arm, and the time spent in the center or in the arms was calculated accordingly. Dark/light box Mice were introduced into the dark compartment (<10lux, length x width x height: 25 x 25 x 30 cm) of a dark/light box. After 60 seconds, the motorized black door to an identically sized brightly lit compartment (320 lx) was opened and exploration was tracked for 5 min (Viewer 2, Biobserve). Latency to enter the light compartment, the number of entries into the light compartment and time spent in the light compartment were calculated.

Chapter 3

Novel home cage-induced hypophagia During 3 days preceding the test, a highly palatable snack (crumbles of cream cracker) was introduced into the familiar metal food cup in the home cage once a day. During the test, mice were placed into a new home cage and the latency to start eating was scored. If mice did not start eating within 600 s, a maximum time of 600 s was assigned.

Table 1. Results for 20 core behavioral parameters tested in the automated home cage. T-test p-values are indicated (** p < 0.01, * p < 0.05).

Parameter Group Parameter p-value long movement . velocity 0.03* long arrest threshold 0.52 kinematics mean long arrest duration - light 0.11 long movement threshold 0.003** long shelter visit duration - dark 0.30 short shelter visit threshold 0.17 sheltering long shelter visit fraction of total visits 0.90 long shelter visit threshold 0.73 activity duration - habituation ratio dark 0.55 habituation activity duration - habituation ratio light 0.37 dark/light index activity duration - dark/light index 0.16 activity change in anticipation of dark 0.61 activity change in anticipation of light 0.55 activity pattern activity change in response to dark 0.42 activity change in response to light 0.98 activity duration - dark 0.18 mean activity duration - dark 0.64 activity OnShelter zone number - dark 0.37 activity duration - light 0.13 mean activity duration - light 0.06

62 Behavioral characterization of Nfil3 knockout mice

Statistics For single parameter measurements, means were compared using a student’s t-test; for multiple parameters measurements over time a repeated measures ANOVA was used. Avoidance learning in the automated home cage was compared using generalized estimating equations as described previously [133]. All graphs display means ± SEM.

Results Nfil3 KO mice showed normal locomotion We first assessed general activity and locomotion in Nfil3 KO mice. We performed an extensive unsupervised behavioral phenotyping of KO mice in an automated home cage. Of the 20 core parameters that were extracted, Nfil3 KO mice differed in only two from WT littermates; the threshold between short and long movement segments was 16% lower in KO mice compared to WT mice

(n = 19/21, T37,56 = 3.11, P = 0.003, Table 1), and during long movement segments, Nfil3 KO mice had a 7% lower maximum velocity (n = 19/21 , T35,85 = 2.25, P = 0.03, Table 1). Despite NFIL3’s known role in regulating molecular

Figure 1. Nfil3 KO mice do not differ from WT mice in activity and motor function. (a) Nfil3 KO mice show no differences in the total distance moved in the open field task. (b) WT and KO mice have similar grip strength as measured in front paws or in all four paws. (c) The latency to fall off a rotarod was not affected in Nfil3 KO mice.

Chapter 3 clock mechanism [100,180], no alterations were observed in parameters that measure activity in anticipation of or response to light/dark phase shifts (Table 1). We also found that the total distance moved in the open field task (n = 12, t(22) = -0.27, p = 0.98, Fig. 1a), grip strength in the front paws (n = 12, t(22) = -

0.43, p = 0.67, Fig. 1b) or all 4 paws (n = 12, t(22) = 1.22, p = 0.24, Fig. 1b), and rotarod performance (n = 12, F(1,22) = 1.02, p = 0.32, Fig. 1c) were unchanged in KO mice. Taken together, these data show that Nfil3 KO mice do not differ from WT mice in general locomotion and muscle strength, and that Nfil3 KO mice only have slightly altered movement patterns. Apparently these alterations are only detectable when animals are observed over longer periods of time in a home cage, as they were not observed in the open field or elevated plus maze tests.

Figure 2. Nfil3 KO mice show normal anxiety. (a) Nfil3 KO mice show no differences in the time spent in center in the open field task. (b) WT and KO mice spent similar amounts of time in the open and closed arms and in the center of the elevated plus maze. (c) There was no difference in the latency to start eating in the novel home cage- induced hypophagia task. (d) The ratio of time spent in the dark and the light compartment of the dark/light box was similar in WT and KO mice.

Nfil3 KO mice showed normal anxiety We next tested general anxiety in Nfil3 KO mice. We first measured the reluctance of mice to enter the center area during the open field test, and observed no difference in the time spent in the center (n = 12, t(22) = -0.05, p =

64

Table 2. Results form low-throughput behavioral tasks. Post-hoc p-values based on estimated marginal means are indicated. p- Task parameter mean SEM value wild wild knockout knockout type type Front paws (N) 0.90 0.92 0.02 0.04 0.69 Grip strength All paws (N) 1.95 1.86 0.05 0.06 0.23 Distance travelled (cm) 2280 2286 126 185 0.98 # visits to center 9.58 9.38 0.93 1.47 0.91 Open Field Time spent in center (s) 6.91 6.98 0.86 1.15 0.96 Latency to visit center (s) 44.14 48.02 13.02 14.90 0.85 Time spent in open vs 0.42 0.41 0.15 0.18 0.97 closed arms Elevated plus maze # visits to open vs closed 1.25 0.62 0.40 0.16 0.16 arms Novel home cage induced Latency to eat (log2(s)) 2.13 1.92 0.11 0.14 0.26 hypophagia Fall latency day 1 (s) 77.33 71.72 5.60 2.75 0.75 Fall latency day 2 (s) 86.03 88.48 3.95 4.25 0.46 Rotarod Fall latency day 3 (s) 87.18 89.78 3.86 5.55 0.13 Fall latency day 4 (s) 92.53 98.92 4.04 6.25 0.26 Fall latency day 5 (s) 93.83 96.97 5.68 5.75 0.38 Time spent in light vs. dark 0.40 0.38 0.09 0.05 0.89 Dark/light box # visits to light vs. dark 3.73 3.93 0.43 0.36 0.73

Chapter 3

0.96, Fig. 2a), the number of center visits (n = 12, t(18,528) = 0.12, p = 0.91), and the latency to visit the center (t(22) = -0.20, p = 0.85, Table 2). In the elevated plus maze, we observed no difference in the reluctance to visit the open arms, as measured by the time spent in the open arm (n = 12, t(22) = -0.43, p = 0.67,

Fig. 2b) and the number of open arm visits (n = 12, t(22) = 0.03, p = 0.98, Table 2). Also, no difference was observed in the latency to start eating a familiar snack when introduced into a novel home cage (novel home cage-induced hypophagia) (n = 12, t(22) = 1.16, p = 0.26, Fig. 2c). Finally, Nfil3 KO mice did not differ in their response to bright light as measured in the dark/light box by the time spent in the light compartment (n = 12, t(22) = 0.12, p = 0.91, Fig. 2d) and the number of visits to the light compartment (n = 12, t(22) = -0.34, p = 0.74, Table 2). We conclude that the response of Nfil3 KO mice to anxiogenic stimuli does not differ from WT mice.

Nfil3 KO mice showed impaired avoidance learning in the automated home cage In the automated home cage, animals were tested in an avoidance learning task [133]. Mice were provided with a shelter with two entrances, and allowed to develop a preferred entrance during days 1-4. On day 5 and 6, usage of the preferred entrance was sanctioned by a mild aversive stimulus (shelter illumination, Fig. 3a) and the change in entrance preference was recorded. Although Nfil3 KO mice did show a decrease in preference index, during the second and last day of conditioning (day 5-6) and the day thereafter (day 7), this change was significantly smaller compared to WT mice (day 6 vs. day 4, n = 2 8/11; genotype * time, GEE, χ (1) = 20.50, p < 0.001; day 7 vs. day 4, n = 8/11; 2 genotype * time, GEE, χ (1) = 6.854, p = 0.009, Fig. 3b), indicating an impairment in avoidance learning.

Discussion In this study we performed a broad phenotypic screen to characterize behavioral abnormalities in NFIL3 deficient mice. Our data show that Nfil3 KO

66 Behavioral characterization of Nfil3 knockout mice

Figure 3. Nfil3 KO mice show impaired avoidance learning. (a) Animals were observed for 7 days in an automated home cage. The cage contained a shelter with two entrances and mice automatically developed a preferred entrance during the first 4 days. On days 5 and 6, usage of the preferred entrance was coupled to a mild aversive stimulus, i.e., shelter illumination, to test the ability of mice to change their preference. (b) WT mice switched their entrance preference, as indicated by a shift in preference index, whereas Nfil3 KO showed a much smaller shift (n = 8/11, *** p < 0.001, ** p < 0.01). mice are normal in most aspects of behavior, including overall activity, locomotion, grip strength and anxiety. In an automated home cage environment however Nfil3 KO mice showed subtle differences in the segmentation of short and long movement bouts, and a lower maximum velocity during long movement bouts. Since these differences required a whole week of continuous observation to be detected in the automated home cage setup, and they were not observed in the open field test or the light/dark box, we assume that they do not interfere with other test results. One possible explanation for the observed locomotor phenotype is loss of Nfil3 expression in neurons that control locomotion. It was previously reported that NFIL3 is expressed in cerebellar Purkinje-cells [113], spinal cord motor neurons [168] and dorsal root ganglion sensory neurons [43]. Conditional deletion of Nfil3 in each of these cell types could be used to determine the specific contribution of NFIL3 to locomotor behavior. Interestingly, automated home cage testing also revealed a significant cognitive impairment in Nfil3 KO mice. Specifically, KO mice did not switch their

67 Chapter 3 preferred shelter entrance when it was coupled to an aversive light stimulus. Since KO mice showed normal behavior in the dark/light box, and had no abnormal daily activity pattern, this difference in avoidance learning was unlikely due to differences in light detection or light aversion per se. Avoidance learning depends at least in part on hippocampal function [181,182]. In that respect it is interesting to note that NFL3 is highly expressed in the hippocampus [100,113]. Also, we previously showed that NFIL3 represses genes that are activated by the bZIP transcription factor CREB [43], which is has been demonstrated in many studies to be critically required for hippocampal learning and memory [183,184,185]. Our findings thus suggest that cognitive deficits in Nfil3 KO mice may be due to a dysregulation of plasticity-associated genes that are controlled by CREB. Dedicated hippocampal learning tests and parallel gene expression analyses will be required to further test this hypothesis.

68 Behavioral characterization of Nfil3 knockout mice

69

Chapter 4

Behavioral inflexibility in mice lacking the transcriptional repressor NFIL3

L.R. van der Kallen1, M. Loos1,2, R. van der Loo1, M.J.M. Sassen1, J. Verhaagen3, A.B. Smit1 and R.E. van Kesteren1 1Center for Neurogenomics and Cognitive Research, Neuroscience Campus Amsterdam, VU University, De Boelelaan 1085, 1081 HV Amsterdam, The Netherlands 2Sylics (Synaptologics BV), PO box 71033, 1008 BA Amsterdam, The Netherlands 3Department of Neuroregeneration, Netherlands Institute for Neuroscience, Meibergdreef 47, 1105 BA Amsterdam, The Netherlands

Chapter 4

Abstract The basic leucine zipper transcription factors CREB and NFIL3 coordinately regulate the expression of genes required for axonal regeneration. CREB activates specific target genes, whereas NFIL3 acts as a feed-forward repressor of these same genes. Together they determine the timing and dynamic properties of regeneration-associated gene expression, and removing NFIL3 from neurons substantially enhanced axon growth in vitro. Here we hypothesized that CREB and NFIL3 may also coordinately regulate hippocampal learning and memory. Activation of hippocampal neurons, either in vitro using forskolin or in vivo using a contextual fear condition stimulus, induced Nfil3 expression. In hippocampus-dependent memory tasks we did not observe differences in learning. However, in the Barnes maze, Nfil3 knockout mice persistently kept exploring the escape location during the probe trial, and in a contextual fear conditioning task, mice failed to alter their freezing behavior when the context was changed from conditioned to neutral or vice versa. Taken together, these data show that Nfil3 knockout mice have difficulties in adapting their behavior to changing environments. We conclude that NFIL3 has a critical role in regulating behavioral flexibility.

Introduction The basic leucine zipper (bZIP) transcription factor CREB is required for learning and memory [186]. CREB protein is constitutively expressed in neurons, and is activated through phosphorylation by kinases such as calcium/calmodulin- dependent protein kinase II (CaMKII) or protein kinase A (PKA), which in turn are activated by upstream plasticity signals, Ca2+ and cAMP respectively. Activated CREB subsequently binds to cAMP response element (CRE) binding sites present in the promoters of plasticity genes [187], thus rapidly translating experience into a first wave of plasticity gene expression. Genes activated by CREB include many downstream activating and repressing transcription factors that together with CREB regulate the timing, level, and specificity of the plasticity response [44]. When a constitutively active CREB protein is expressed

72 Behavioral inflexibility in NFIL3 knockout mice in mouse forebrain, late-phase LTP and long-term memory (LTM) are enhanced [188], confirming a central role for CREB in plasticity signaling in neurons, and linking CREB directly to cognitive function. NFIL3, like CREB, belongs to the family of bZIP transcription factors, but is known to repress rather than activate gene expression [167]. In primary cultured DRG cells, we showed that CREB and NFIL3 compete for the same binding sites in the DNA, and that the same plasticity genes that are initially activated by CREB are subsequently repressed by NFIL3 [43]. Such a regulatory motif is known as a type 1 incoherent feed-forward loop (IFFL), named incoherent because of the opposite effects of the two transcription factors on a shared group of target genes. IFFLs are known to control the level and timing of target gene expression. More specifically, experimental and mathematical modeling studies showed that target genes that are under IFFL control have faster expression changes in to stimuli and pulse-like expression [102,189]. NFIL3 is highly expressed in the hippocampus [100,113], suggesting that CREB target genes required for hippocampus-dependent memory formation are also under feed-forward control by NFIL3. Here, we show that Nfil3 expression in hippocampal neurons is upregulated by neuronal stimulation in vitro and in vivo. To assess cognitive performance in Nfil3 knockout mice we tested working memory, spatial memory and contextual memory using T-maze, Barnes maze and contextual fear conditioning tasks. Our data show that Nfil3 knockout mice have no working memory deficit, but are specifically impaired under conditions where they have to change their response towards a previously learned stimulus. We thus conclude that Nfil3 knockout mice show impaired behavioral flexibility.

Materials and Methods Primary cultures Primary hippocampal cultures were prepared as described previously [190]. Briefly, hippocampi were dissected from E18 embryos and collected in Hanks balanced salts solution (HBBS; Sigma-Aldrich, St Louis, MO), buffered with 7

73 Chapter 4 mM HEPES (Invitrogen, Carlsbad, CA). Hippocampi were incubated for 30 min in HBBS containing 0.25% trypsin (Invitrogen) at 37 °C. After washing, neurons were triturated using fire-polished Pasteur pipettes, counted and plated in Neurobasal medium supplemented with 2% B-27, 1.8% HEPES, 1% glutamax, 1% Pen Strep (all from Invitrogen) and 0.2% 14.3 mM β-mercapto-ethanol. Cells were plated in 96-wells plates (Cellstar, Greiner Bio-One, Frickenhausen, Germany) that were previously coated with poly-D-lysine (Sigma-Aldrich) and 5% heat-inactivated horse serum (Invitrogen). Neurons were then plated at a 2 seeding density of 830 cells/mm and cultured at 37 °C/5% CO2 for up to 15 days. Half of the medium was replaced with fresh medium every week. Quantitative PCR Q-PCR was performed as described previously [45]. Briefly, we used the ABI 7900HT detection system in combination with the SYBR Green detection kit (Applied Biosystems). Site-specific primers were used for Nfil3 (5’- GAGGGTGTAGTGGGCAAGTC-3’ and 5’-ATCCGAAGCTTGTGCGGTAA-3’), Cebpd (5’-AACCCGCGGCCTTCTACGAG-3’ and 5’-TGGCGCTCTCGTCGTCGTAC-3’), Arc (5’- CCAGCTCGCTGCATGGCCTT-3’ and 5’-CTTGACCTGGCAGGCAGGGC-3’), c-Fos (5’- GGGACAGCCTTTCCTACTACCATT-3’ and 5’-TTGGCACTAGAGACGGACAGATC-3’) and Ef1a (5’-ACCCTCCACTTGGTCGTTTTG-3’ and 5’-AGCTCCTGCAGCCTTCTTGTC- 3’). Normalized expression values were calculated by subtracting the Ct value of the housekeeping gene Eif1a from the Ct values of the genes of interest. Specificity of the primers was controlled by inspection of dissociation curves. Animal cohorts For behavioral experiments, male 8 to 12 week old mice were singly housed on sawdust in standard Makrolon type II cages enriched with cardboard nesting material for at least one week prior to experiments, with water and food ad libitum (7:00/19:00 lights on/off). Experiments were carried out in accordance with the European Communities Council Directive of 24 November 1986 (86/609/EEC), and with approval of the local animal care and use committee of the VU University. Working memory was assessed in a T-maze, spatial memory in a Barnes maze, and contextual memory in a contextual feat conditioning test.

74 Behavioral inflexibility in NFIL3 knockout mice

The T-maze and Barnes maze tests were performed in the same cohort of mice described in Chapter 3, after the dark/light box test. Fear conditioning with context A tested first was performed in two separate cohorts of naive animals, one group was 11 weeks old, the other 19 weeks. A separate cohort was tested at 13 weeks of age with context B tested first. Spontaneous alternation The task was executed in a T-Maze consisting of a main shaft (30 x 10 cm; start location) and two identical side arms (30 x 10 cm) with standard bedding material on the floor. Two drop-down doors were used to allow separation of each of the side arms from the rest of the maze. A third drop-down door was placed 23 cm up the main shaft to be able to confine mice in the start arm. A central divider panel was placed between the third door and the upper side of the T to separate the left and right side of the maze. At the start of the so-called sample trial, mice were placed at the base of the T facing the bottom. A trial began when the door in the start arm was opened, and mice were allowed to freely explore the maze. After completely entering a side arm, this arm was closed, the middle divider panel was removed, and after an interval trial interval (ITI) of 30 s the mouse was returned to the start arm. During the subsequent choice trial, the door in the start arm was opened again and the mouse was allowed to freely explore the maze again. Entrance into the previously unexplored arm was scored as an alternation. The animals were tested two times within one day, consisting of 1 sample and 1 choice trial, with at least 2 hours in between sessions. Barnes maze The Barnes maze consisted of a circular light grey platform (diameter 122 cm) with a ring of 24 holes spaced equidistantly from each other and from the edge of the platform. One hole was connected to an escape box, the others to a short tube angled straight down to the floor 60 cm below. Four distinct, distal spatial cues were mounted on the walls, ~70 cm from the edge of the maze. The Barnes maze was surrounded by three air fans (60 cm away from the maze spaced ~120° apart) producing a variable airflow across the entire maze by a

75 Chapter 4 slow 90° horizontal movement, proving both an aversive environment as well as dispersion of any odor cues. To prevent odor cues, the maze was thoroughly cleaned with 70% ethanol solution and rotated between trials. Animals received two trials per day during 5 days. During the first two trials bedding material was placed in the escape box. Animals that failed to locate the escape box within 5 min were gently guided there by the experimenter and left there for 1 min. The second trial on the 5th day was a probe test, during which the escape box was replaced with a tube similar to the other holes. The location of mice (head position) was monitored (Viewer 2, Biobserve), and hole visits and latency to reach the target location were recorded. A hole visit was defined in Viewer 2 software when the head of the mouse was inside the zone drawn immediately around a hole; consecutive visits to the same hole were counted as single hole visit. For data analysis, the maze was divided up into 8 peripheral zones and a center zone, and localization data were averaged over zones according to their relative distance from the target region. Fear conditioning All experiments were carried out in a fear conditioning setup (TSE Systems, Chesterfield, MO). Training and testing was performed in a plexiglas chamber with a stainless steel grid floor (context A) with constant illumination (100–500 lx) and background sound (white noise, 68 dB sound pressure level). The chamber was cleaned with 70% ethanol before each session. Training consisted of placing mice in the chamber for a period of 180 s, after which a 2 s foot shock (0.7 mA) was delivered through the grid floor. Mice were returned to their home cage 30 s after the shock ended. A retrieval session was performed 24 h later by placing the mice in the conditioned context (context A) again for a period of 180 s. Baseline activity, exploration and freezing were assessed automatically. Freezing was defined as the absence of any detectable activity during 2 s. Alternatively, mice were tested in in a neutral plexiglas chamber with a smooth plastic floor (context B), no white noise background sound, and a different scent (cage was wiped with 1% acetic acid before each session). After

76 Behavioral inflexibility in NFIL3 knockout mice

2 h, mice that were initially tested in context A were tested in contest B, and vice versa. Statistics For single parameter measurements, means were compared using a student’s t-test; for multiple parameters measurements over time a repeated measures ANOVA was used. Spontaneous alternation data were compared between genotypes using the chi-squared test. Barnes maze data were compared using a repeated measures ANOVA, and post-hoc testing was performed using estimated marginal means. Gene expression was compared using a two way ANOVA (timepoint*genotype/treatment) with a student’s t-test as post-hoc test. All graphs display means ± SEM.

Results Forskolin stimulation induces Nfil3 mRNA expression in hippocampal neurons in vitro Previous studies showed that Nfil3 is expressed in all regions of the hippocampus [113,191]. Here, we tested whether raising the levels of cAMP, which is essential for long-term memory formation, induces Nfil3 expression. Primary hippocampal neurons were stimulated after 14 days in culture with 10µM forskolin (FSK) or control solvent (ethanol), and RNA was extracted at 1, 2 and 4 h after stimulation. Nfil3 expression showed a 3-fold induction after 2 h and returned to normal levels after 4 h (n = 4, treatment: F(1,21) = 43.437, p <

0.001; post-hoc: 1 h: t(5) = -2.051, p = 0.096; 2 h: t(6) = -7.352, p < 0.001; 4 h: t(6) = -1.775, p < 0.126, Fig. 1a). As a control, we also measured the expression of two other plasticity genes, Cebpd and Arc [85,192]. Cebpd expression was significantly induced at 2 and 4 h after stimulation (n = 4, treatment: F(1,21) =

41.349, p < 0.001; post-hoc: 1 hrs: t(5) = -2.038, p = 0.097; 2 hrs: t(2.031) = -4.965, p = 0.004; 4 hrs: t(6) = -4.578, p = 0.004, Fig.1b), whereas Arc expression showed a faster response, and was significantly increased only at 1 h after FSK stimulation (n = 4, treatment: F(1,21) = 7.816, p = 0.012; post-hoc: 1 h: t(5) = -

4.060, p = 0.010; 2 h: t(6) = -1.878, p = 0.110; 4 h: t(6) = -0.192, p = 0.854, Fig. 1c).

77 Chapter 4

Figure 1. Forskolin induces Nfil3 mRNA expression in cultured hippocampal neurons. (a) Stimulation with forskolin (FSK) of hippocampal neurons after 14 days in culture resulted in a robust increase in Nfil3 expression is at 2 h after stimulation. (b) In the same cells, Cebpd expression is upregulated at 2 h and 4 h after FSK stimulation, and (c) Arc at 1 h (n = 4, *** p < 0.001, ** p < 0.01).

These data show that Nfil3 expression is induced upon activation of CREB signaling in hippocampal neurons, and that the timescale of induction falls in the range of that of classical plasticity genes that are known to be activated by CREB.

Nfil3 KO mice show no deficits in worming memory Working memory in Nfil3 KO mice was assessed in a T-maze. Mice were allowed to explore in a T-maze and choose an arm, and after a 30 s interval allowed to explore the maze again (Fig. 2a). When experience from the sample trial is memorized correctly, natural curiosity should drive mice to enter the previously unexplored arm first during the test trial. Such spontaneous alternations were scored as correct responses and taken as a measure for working memory. Nfil3

KO mice did not differ from WT mice (n = 12, χ(1) = 0.223, p = 0.637, Fig. 2b), indicating that there is no deficit in working memory in Nfil3 KO mice.

Nfil3 KO mice show altered performance in the Barnes maze We then tested spatial memory on a Barnes maze. Mice were trained during 5 consecutive days for two sessions per day to locate the one out of 24 holes that

78 Behavioral inflexibility in NFIL3 knockout mice

Figure 2. Working memory is not affected in Nfil3 KO mice. (a) Mice were allowed to explore a T-maze and choose either one of the two side arms. After a 30 s inter-trial interval mice were allowed to explore the maze again. When the previously unexplored arm was entered first, this was scored as an alternation. (b) Both WT and KO mice show alternations above chance level (50%), and no difference was detected in alternation frequency between genotypes. was coupled to an invisible escape box located below the maze platform (Fig. 3a). Escape latency was recorded, and while a significant interaction effect was found between genotypes (n = 12, F(3.748,82.457) = 2.825, p = 0.033, Fig. 3b), post- hoc testing revealed no significant difference at any time point (for means and SEM, see Table 1). The number of errors (wrong hole visits) made during training was also recorded, and showed no interaction between genotypes (n =

12, F(1.776,38.850) = 0.616, p = 0.526, Fig. 3c). On day 5, a probe trial was performed, during which the escape box was removed and locomotion was

79 Chapter 4

Figure 3. Spatial memory is altered in Nfil3 KO mice. (a) Animals were trained to locate an escape box placed under one of 24 holes (indicated in grey) in a Barnes maze. For probe trial analysis, the maze was divided into a central zone, and eight peripheral zones of similar size. Peripheral zones were numbered based on their distance to the zone containing the escape box (zone 0). (b-c) During training, no differences were observed between genotypes in the escape latency (b) or the number of errors made (c), indicating that KO and WT mice were equally capable of learning the task. (d) During the probe trial, when the escape box was removed, a significant interaction effect of genotype and zone visits was detected. Post-hoc testing showed that Nfil3 KO mice paid significantly more visits to the target zone (zone 0) and significantly less to the zones 3 and 4 (n = 12, * p < 0.05). tracked. For analysis, the maze was divided in 8 peripheral zones that were classified as zones 0-4 according to their distance from the escape box, and one central zone (Fig. 3a). Both WT and KO mice paid significantly more visits to

80 Behavioral inflexibility in NFIL3 knockout mice zones close to the escape box location, and less to the opposite zones (n= 12,

F(2.537,64.234) = 115.093, p < 0.001, Fig. 3d). However, Nfil3 KO mice showed a significantly stronger bias towards these target zones than WT mice

(genotype*distance: n = 12, F(2.537,64.234) = 19.540, p = 0.004, Fig. 3d). Post-hoc testing showed that Nfil3 KO mice made more visits to the target zone (p = 0.012), and less to zones 3 ( p = 0.044) and 4 (p = 0.011) compared with WT controls (Fig. 3d, see Table 1 for means and SEM).

Contextual fear-conditioning induces Nfil3 expression in the hippocampus Contextual memory performance was tested in a fear conditioning task. This type of learning involves the hippocampus [183], and occurs on a short timescale with single-trial training, which allowed us to first test whether Nfil3 mRNA levels in the hippocampus are induced upon learning. Hippocampi were removed at 2, 4 and 8 h after animals had received a mild foot shock in a novel environment. We found that Nfil3 was upregulated at all time points (n = 6,

F(3,22) = 11.667, p < 0.001; post-hoc: 2 h: t(9) = -2.965, p = 0.016; 4 h: t(10) = -4.606, p < 0.001; 8 h: t(10) = -5.549, p < 0.001, Fig. 4a). We also measured the expression levels of two genes known to be regulated by fear conditioning, i.e.,

Arc and Fos [193]. Arc was upregulated at all time points (Arc: n = 6, F(3,22) =

Figure 4. Induction of Nfil3 and immediate early gene expression in the hippocampus after contextual fear conditioning. (a) Nfil3 and (b) Arc mRNA expression are upregulated at 2 h, 4 h and 8 h after fear conditioning. (c) c-Fos mRNA expression is upregulated at 2 h and 8 h after conditioning (n = 6, *** p < 0.001, ** p < 0.01, * p < 0.05).

81 Chapter 4

67.573, p < 0.001; post-hoc: 2 h: t(4.559) = -7.693, p < 0.001; 4 h: t(9) = -26.978, p <

0.001; 8 h: t(10) = -13.339, p < 0.001, Fig. 4b), whereas c-Fos was upregulated most prominently at 2 h, but also at 8 h (n = 6, F(3,22) = 7.938, p = 0.001; post- hoc: 2 h: t(9) = -3.597, p = 0.006; 4 h: t(6.335) = -1.900, p = 0.104; 8 h: t(10) = -5.943, p < 0.001, Fig. 4c). Thus, Nfil3 expression in the hippocampus is induced by fear conditioning in the same time window as the classical immediate early genes c- Fos and Arc.

Figure 5. Altered memory performance after contextual fear conditioning in Nfil3 KO mice. (a) Memory retrieval after fear conditioning was measured in two groups of mice. All mice were shocked in context A. After 24 h later, one group of mice was tested in context A (conditioned context), followed 2 h later by context B (neutral context). The other group was first tested in context B, and then in context A. (b) Compared with WT controls, Nfil3 KO mice showed the same amount of freezing in context A, but they froze significantly more when subsequently tested in context B. (c) When the testing order was reversed, KO and WT mice showed baseline freezing levels in context B, but KO mice froze significantly less than WT controls when in subsequently tested in context A (n = 12, ** p < 0.01).

Nfil3 KO mice showed impaired memory adaptation in a contextual fear- conditioning task We subsequently measured freezing behavior at 24 hours after conditioning. Animals were tested either in the same context where they received the shock (context A) or in a novel neutral context (context B; Fig. 5a), and we controlled for the order of testing. As expected, mice froze more in context A than in

82 Behavioral inflexibility in NFIL3 knockout mice

context B, irrespective of the order of testing (context A before B, n = 22, F(1,42)

= 51.681, p < 0.001, Fig. 5b; context B before A, n = 12, F(1,22) = 80.077, p < 0.001, Fig. 5c). We did however detect a significant interaction effect of test order and genotype (context A before B, n = 22, F(1,42) = 4.403, p = 0.042, Fig. 5b; context B before A, n = 12, F(1,22) = 4.551, p = 0.044, Fig. 5c). Post-hoc testing showed that Nfil3 KO and WT mice showed similar freezing levels in the context that was tested first (context A before B, n = 22, p = 0.588, Fig. 5b; context B before A, n = 12, p = 0.12, Fig. 5c), but significantly differed in the context that was tested thereafter (context A before B, n = 22, t(42) = -2.706, p < 0.01, Fig. 5b; context B before A, n = 12, p = 0.002, Fig. 5c). Specifically, Nfil3 KO mice froze more than WT mice in context B when preceded by context A, but less in context A when preceded by context B, indicating that when presented with a changing environment, they are less capable of adapting their behavior accordingly.

Discussion In this study we hypothesized that as a feed-forward repressor of CREB target genes [43], NFIL3 might play a crucial role in regulating temporal aspects of hippocampal learning and memory processes that depend on CREB. We indeed observed specific alterations in spatial navigation and contextual fear conditioning, which together suggest a deficit in the ability to adapt behavior in response to environmental changes. Automated home cage testing already revealed significant cognitive impairments in Nfil3 KO mice (see Chapter 3). Specifically, KO mice did not switch their preferred shelter entrance when it was coupled to an aversive light stimulus. Here, we observed normal working- and short-term memory in the T- maze, but altered performance in the Barnes maze and fear conditioning tasks. In the Barnes maze, KO mice paid significantly more visits to the target region during the probe trial. This could indicate that KO mice have better memory for the escape hole than WT mice. However, KO and WT mice displayed similar learning curves during test trials. Since during the probe trial the escape hole is

83 Chapter 4 not connected anymore to an escape box, the persistence of KO mice to keep returning to this location could also be interpreted as an inability to update their memory with this new information. This type of behavioral inflexibility would be in line with the avoidance learning impairments observed in the automated home cage setup. To further discriminate between initial learning and behavioral flexibility, we also performed contextual fear conditioning. By alternately exposing mice to either the conditioned (aversive) context or a novel (neutral) context, we observed that KO mice learn to associate the aversive context with the shock, but that they fail to reduce their freezing behavior when they are subsequently tested in a neutral environment. Conversely, when tested first in the neutral environment they do not freeze, but their freezing response does not increase when subsequently tested in the aversive environment. These findings seem to confirm our initial assumption that Nfil3 KO mice have no initial learning deficit, but that they are impaired under conditions where they are presented with are presented with a novel situation and need to adjust their behavior accordingly. Based on our results we hypothesize that NFIL3 acts as transcriptional repressor of neuronal plasticity. Lack of NFIL3 may lower the threshold for neurons to become activated and recruited into memory traces, resulting in strong memories that are difficult to replace. In addition, CREB-dependent induction of NFIL3 [43] in neurons that are activated by experience may prevent these same neurons from being recruited into new memory traces upon subsequent stimulation. This would allow new experiences to be encoded separately from old ones and enable flexible behavioral responses. Behavioral flexibility, or more specifically cognitive flexibility, has been described as the ability to adapt behavior in response to changes in the environment, and is part of the set of executive functions [194]. Executive function entails the modulation of lower-level processes by those at a higher level. Depending on the current goal, lower-level perceptual analysis needs to be modulated in order to produce appropriate behavior [79]. Lesion studies have pointed out

84 Behavioral inflexibility in NFIL3 knockout mice the orbitofrontal cortex [195], the medial prefrontal cortex [196] and the striatum [197] as possible neural substrates of behavioral flexibility. Recently however it was also reported that injection of MK-801, an N-methyl-d- aspartate receptor antagonist, into the hippocampus disrupts behavioral flexibility [76], thus linking hippocampal plasticity directly to behavioral flexibility. Our data suggest that hippocampal expression of the transcriptional repressor NFIL3 is required for flexible adjustments of memories in order to adequately respond to a changing environment. NFIL3 target gene expression analysis, preferably at the single cell level, will be required to further test these hypotheses. In conclusion, we show that Nfil3 KO mice are affected in specific higher cognitive functions. The observed deficits are best explained as an impairment of behavioral flexibility, potentially due to loss of feed-forward repression of neuronal plasticity genes. This loss of feed-forward inhibition could initially lead to stronger memory traces, but might also result in behavioral impairments when multiple memory traces have to compete with one another.

85

Chapter 5

CAGE sequencing reveals aberrant immediate-early gene expression during memory reconsolidation in Nfil3 knockout mice

Loek R. van der Kallen1, A. Mariette Lenselink1, Ingrid M.C. Bakker2, Luba Pardo2, Lisanne Vijfhuizen3, Rolinka J. van der Loo1, Arn M.J.M. van den Maagdenberg3, Sabine Spijker1, Joost Verhaagen4, August B. Smit1, Ronald E. van Kesteren1 1Center for Neurogenomics and Cognitive Research, Neuroscience Campus Amsterdam, VU University, Amsterdam, The Netherlands 2 Department of Human Genetics, Section of Medical Genomics, VU Medical Center, Amsterdam, The Netherlands 3Department of Human Genetics and Neurology, Leiden University Medical Center, Leiden, The Netherlands 4Department of Neuroregeneration, Netherlands Institute for Neuroscience, Amsterdam, The Netherlands

Chapter 5

Abstract Long-term memory formation relies on synaptic activity, and on de novo gene expression involving the coordinate action of multiple transcription factors. We previously showed that the basic leucine zipper transcription factors CREB and NFIL3 coordinately regulate a gene expression program in peripheral neurons after injury. We also showed that animals deficient in NFIL3 expression show deficits in memory processing resulting in decreased behavioral flexibility. Based on the latter findings, we hypothesized that the expression of memory genes is also controlled by NFIL3. To test this, we compared hippocampal gene expression between wildtype and Nfil3 knockout mice during memory consolidation and reconsolidation induced by contextual fear conditioning. We used CAGE sequencing to measure gene expression at 0 h (control), 0.5 h and 6 h after the first exposure (memory consolidation) or re-exposure (memory reconsolidation) to a shock-conditioned context. We show that immediate- early gene expression in wildtype mice is increased during memory consolidation, but less so during memory reconsolidation. In contrast, Nfil3 knockout mice express various immediate-early genes during reconsolidation, likely resulting from the loss of repression by NFIL3. Therefore, we suggest that feed-forward repression by NFIL3 provides a mechanism by which neurons that were previously active and are part of an existing memory trace might reduce the chance of becoming reactivated by novel experience and are recruited into multiple memory traces at the same time. Overlapping memory traces due to an insufficient repression of memory gene expression in already activated neurons might explain the inability of Nfil3 knockout mice to show appropriate behavioral adaptation towards a changing environment.

Introduction Gene transcription is essential for memory formation. Transcription inhibitors were shown to impair the formation of a long term memory [198,199], as well as its underlying cell physiological correlates, i.e., long term potentiation (LTP) [200] and long term depression (LTD [201]. Transcriptional regulation in general

88 CAGE sequencing on hippocampi after fear conditioning is guided by transcription factors (TFs) that specifically activate or repress the genes that are required to achieve cell type- or condition-specific patterns of gene expression. Cyclic AMP response element binding protein 1 (CREB) in particular was shown in many studies to be an essential TF in the control of long term memory formation [44,202]. Memory-inducing stimuli increase second messenger levels, among which cAMP, in subsets of neurons that together form a memory trace. cAMP activates PKA, which can phosphorylate and activate CREB [108]. Inhibition of PKA is sufficient to block L-LTP as induced by three trains of high frequency stimulation [200]. Hippocampal presynaptic terminals show increased turnover after high frequency stimulation, and this effect is completely blocked by specific PKA inhibitors [203]. Taken together, a large body of evidence indicates that CREB-induced changes in synaptic strength and stability represent a neural substrate of memory formation, recording experiences from the outside world in the brain. Downstream of CREB is a wave of TF expression that allows for further diversification, specification and modulation of the gene expression response underlying synaptic plasticity. Especially the need for a transcriptional repressor has been postulated as important to allow a neuron to decide whether and how to participate in a memory trace [204]. When such a repressor would act on the same genes that are activated by CREB, this type of regulatory module is named a type 1 incoherent feed-forward loop (I1-FFL). In general, I1-FFLs generate a pulse-like expression and enable response acceleration of target genes [102,103]. Such a mechanism has been described in dorsal root ganglion neurons during axon regeneration, with CREB as the activating transcription factor and nuclear factor regulated (NFIL3) as the feed-forward repressor together regulating the expression of regeneration-associated genes [43,45]. NFIL3 is also expressed in the hippocampus [113], which led us to hypothesize that CREB and NFIL3 can form an I1-FFL that regulates gene expression underlying memory formation. Subsequent research revealed that when NFIL3 is genetically deleted and mice are sequentially presented with conditioned and novel contexts after fear conditioning their behavioral

89 Chapter 5 responses to the aversive and the neutral stimuli start to merge, and they keep behaving as if presented with the first context they were initially presented with [Chapter 4]. Together with other experiments these results suggest that Nfil3 knockout mice do not specifically allocate neurons to a memory trace, resulting in impaired cognitive flexibility. Although cognitive flexibility is generally grouped under executive functioning that is controlled by the prefrontal cortex [79], recent evidence suggests that lower brain regions such as the basal ganglia and the hippocampus are also involved [76,77,205]. Here we hypothesize that NFIL3 is essential for repressing gene expression in hippocampal neurons that were previously activated and are already part of an existing memory trace. This might prevent these same neurons from becoming reactivated upon novel experience, and thus allow new memory traces to be formed and to compete with older ones, resulting in flexible behavioral adaptations. As an initial step towards testing this hypothesis we used cap analysis of gene expression (CAGE) to profile transcriptome changes during fear conditioning (memory consolidation) and during re-exposure to the conditioned context 24 hours later (memory reconsolidation). We show that in wildtype mice immediate-early gene expression is induced during consolidation, but less so during reconsolidation. In NFIL3 deficient mice however, immediate-early gene expression is not repressed during reconsolidation.

Materials and methods Animal conditions. All experiments were carried out in accordance to the Animal User Care Committee of the Vrije Universiteit. Adult male mice were individually housed at a 12 h light/dark cycle with ad libitum access to food and water. Experiments were performed during the light phase. The animals were sacrificed by means of cervical dislocation at time points indicated below. Fear conditioning. All experiments were carried out in a fear conditioning system (TSE Systems). Training and testing were performed in a Plexiglas

90 CAGE sequencing on hippocampi after fear conditioning chamber with a stainless steel grid floor with constant illumination (100–500 lx) and background sound (white noise, 68 dB sound pressure level), situated in a gray box to shield it from the outside. The chamber was cleaned with 70% ethanol before each session. Training consisted of placing mice in the chamber for a period of 180 s, after which a 2 s foot shock (0.7 mA) was delivered through the grid floor. Mice were returned to their home cage 30 s after the shock ended. Control mice received a 0.7 mA 2 s foot shock immediately upon placement in the conditioning chamber, after which they were allowed to explore the context for 210 s. An immediate shock does not induce contextual fear conditioning, even though the animals receive the same aversive stimulus [206,207,208]. Baseline activity, exploration and freezing were assessed automatically. Freezing was defined as lack of any movement besides respiration and heart beat during 3 s intervals and is presented as a percentage of the total test time. Animals undergoing retrieval to induce reconsolidation were placed back in the fear conditioning system 24 h after conditioning. Tissue preparation and RNA isolation Dorsal hippocampi were dissected at 0 h (naïve), 0.5 h or 6 h after fear conditioning, or 0 h (no retrieval), 0.5 h or 6 h after retrieval, and tissue was stored at -80 °C until further use. RNA was isolated using a Nucleospin RNA II kit (Macherey-Nagel) and RNA integrity was assessed using an RNA 6000 Pico total RNA Bioanalyzer chip (Agilent). CAGE library preparation and sequencing Sample libraries were prepared using the previously described method of Takahashi et al. [209]. In brief, first-strand cDNA was synthesized from total RNA using random primers. Subsequently the 5’ ends of mRNA were selected with cap-trapping and ligated to a linker containing an EcoP15I restriction site and a sequence for sample identification (barcode) at the end of the cDNA strand. Samples were then pooled and the linker was used to prime second- strand cDNA synthesis followed by digestion with EcoP15I to release 27- CAGE tags. After ligation of a second linker the CAGE tags were amplified

91 Chapter 5 using PCR, purified, and the pools (7-8 samples per flow cell lane) were sequenced on the Illumina Genome Analyzer GLXII platform. Data processing Initial processing of read data consisted of removal of reads marked by the Illumina software as not passing the quality filters. For each lane, reads were assigned to sample files using the linker barcodes and subsequently the linkers, barcodes and sequencing artifacts were removed to yield 25-27bp tags. Next, CAGE tags were mapped to the mouse genome (mm10) obtained using Bowtie [210] and allowing 2 nucleotide mismatches or indels. Tags mapping to multiple (2-10) genomic locations were assigned positions using a rescue strategy described previously [211] and combined with unique tags. The tags were grouped into CAGE clusters using a clustering pipeline from Omics Science Center bioinformatics at the RIKEN [212,213]. In short, a leveled approach was performed, using tags or clusters on the same genome strand. In level 1 (L1), tags mapping to the same genomic location were considered CAGE Transcription Start Sites (CTSSs) if they were present in at least two samples with a minimum of 1 tag per million (tpm). If L1 CAGE tags mapped within a vicinity of 21 bp, with a total count of 5 tpm, they were assigned into level 2 (L2) clusters. These were further grouped into level 3 (L3) clusters if they overlapped within a region of 40 base pairs, to encompass regions shared between proximal TSSs [214]. For downstream analysis 35,182 L3 tag clusters (TCs) were used. We then analyzed the correlation across samples (MDS/hierarchical clustering) and tested for number of zero counts and promoter tags found per sample. Samples not meeting the quality criteria (<5M reads, abnormal clustering) were excluded from clustering and further analysis. To assign CAGE tags to genes, we annotated the TCs to mouse genes by mapping the coordinates of TCs to all available transcripts from Ensembl gene models [215]. To do this, we downloaded all Ensembl transcripts from the UCSC genome browser (Dec 2011/mm10; downloaded on January 2013) (http://genome.ucsc.edu/cgi-bin/hgTables) [216]. Perl custom scripts and BEDTools [217] were used to map the coordinates of the TCs to genomic

92 CAGE sequencing on hippocampi after fear conditioning regions corresponding to a locus. TCs that did not map within the gene region (± 300 base pairs) were considered intergenic. Furthermore, we divided the TCs into mutually exclusive classes according to the gene region they mapped to. TCs that mapped to 5’-UTR region or -300/+100 bps of a known TSS (core promoter region) were labeled as canonical. The remaining non-canonical ones were labeled as 3’-UTRs, 5’-UTRs AS (antisense), 3’-UTRs AS, intronic, exonic, intronic AS and exonic AS depending on the gene region they mapped to. Then, we used Ensembl Biomart [218] to assign genes to different gene classes (e.g: protein-coding genes, lncRNA and others), and selected 17,700 TCs that are located in promoter regions only. Statistical analysis. We performed the analyses in Bioconductor [150], using software packages Limma [219] and EdgeR [220]. A factor grouping was created of all conditions separately and a factor to correct for the effect of the lane in which the sample was tested. Normalization factors were calculated to scale the raw library sizes. We then transformed the data to log2-counts per million, estimated the mean variance relationship, and used this to compute appropriate observational-level weights. We then fitted each TC to multiple linear models by weighted least squares. Every combination of our 4 main factors (genotype, shock type, memory phase and time point) was transformed into separate factors and specific contrasts were created to enable statistical testing of specific conditions/groups. Heatmaps were generated using gplots [221]. Transcription factor binding site overrepresentation analysis was performed using oPOSSUM 3.0 [154,155] with the following settings: background genes: all genes present in the promoter-linked subset of TCs, TF profiles used: all vertebrate JASPAR CORE profiles with a minimum specificity of 8 bits, conservation cutoff: 0.4, matrix score threshold: 85%, sequence searched: 2000/0 bp upstream/downstream. Functional annotation clustering was performed using DAVID (Database for Annotation, Visualization and Integrated Discovery) 6.7 [156,157] using default settings, and an enrichment score threshold of 1.

93 Chapter 5

Figure 1. Experimental design. Wildtype (white cages) and knockout mice (gray cages) were subjected to fear conditioning using a delayed shock (upper half) or immediate shock (lower half). At the indicated time points after the shock (left half, i.e. during consolidation) or re-exposure (right half, i.e. during reconsolidation), mice were sacrificed and dorsal hippocampi were dissected for RNA extraction (n = 3). For the 0 hrs consolidation time point, naïve animals were used (n = 4), and no distinction was made between shock conditions.

Results Experimental setup Hippocampal gene expression profiling was performed at different time points after contextual fear conditioning in Nfil3 KO and wildtype animals (Figure 1). Experimental animals received a delayed shock and underwent fear

94 CAGE sequencing on hippocampi after fear conditioning conditioning; control animals received an immediate shock, which does not induce fear conditioning [206,207,208]. In each group, animals were sacrificed at 0 h (naïve), 0.5 h or 6 h after receiving the shock, allowing temporal profiling of memory consolidation. Another group of animals was sacrificed either at 24 h (0 h of reconsolidation), or at 0.5 h or 6 h after re-exposure. This setup allowed us to analyze gene expression changes in time due to differences in memory induction (delayed vs. immediate shock), memory phase (consolidation vs. reconsolidation), or genotype (wildtype vs. Nfil3 KO).

CAGE analysis For gene expression profiling we used cap analysis of gene expression (CAGE). CAGE sequencing allows quantitative measurements of all mRNA variants present in a sample in an unbiased manner through sequencing the first 27 base pairs of each transcript. In addition to gene expression analysis, it is particularly useful to detect alternative transcription start site usage and to

Figure 2. Multidimensional scaling plots of distances between gene expression profiles. (a) Samples are coded for the lane in which they were run on the Illumina Genome Analyzer, which had a maximum of 8 samples per lane. Abnormal clustering is observed in lanes H, J, and partially also in lanes G and Q, and all samples outside of the boxed region were removed. (b) In the remaining samples no abnormal clustering patterns were observed.

95 Chapter 5

Figure 3. Global analysis of CAGE data. (a) Average TC counts per sample group ranged from 9.8 to 13.3 million. (b) Histograms of binned TC sizes (log-10 scale) show that most TCs are covered by 10-100 reads. (c) TCs could be annotated to different gene regions in amount, with 50% (17,700 out of 35,328) corresponding with known transcription start sites. analyze promoter regions for TF binding sites [135,136]. After clustering individual reads into TCs, expression profiles between samples were plotted based on hierarchical clustering and 13 samples were excluded because of abnormal clustering (Figure 2). The remaining samples contained 11.9 million reads per sample on average (Figure 3a), divided over 35,328 TCs. Most TCs contained between 10 and 100 reads, but TCs with over one million reads were

96 CAGE sequencing on hippocampi after fear conditioning also found (Figure 3b). About half of all TCs (17,700) corresponded with known promoter regions (Figure 3c). These were used for analysis in this study.

Differentially expressed TCs between genotypes Statistical analysis was performed to detect differences in TC expression within or between any of the four main factors in our experimental design (Supplementary Table S1). We observed seven differentially expressed TCs for the main effect of genotype in the delayed shock group (Figure 4). Among these, Nfil3 itself was consistently upregulated in knockout animals. Since in our knockout construct we only removed the coding exon 2, and not exon 1 and the associated transcription start site, these findings confirm that in the absence of NFIL3-mediated negative auto-feedback Nfil3 transcription is increased [43]. Interestingly, among the other six genes there are two, Mier1 and Gcfc2, which also encode transcriptional repressors, suggesting dysregulation of transcription regulation in Nfil3 KO mice. In the immediate shock group only Nfil3 was differentially regulated, indicating that gene expression differences due to genotype are only revealed upon memory induction.

Figure 4. Heatmap showing expression patterns of genes differentially regulated for the main factor genotype in the delayed shock group. Seven genes are differentially expressed due to Nfil3 deletion, including Nfil3 itself. Colors indicate expression differences on a log-2 scale (see color key), compared to the wildtype 0 h consolidation time point (i.e., naïve animals, reference condition).

97 Chapter 5

Table 1. Gene ontologies enriched in TCs differentially regulated at 0.5 h.

Functional class Gene names Enrichment score

Transcription regulation Egr1, Nr4a1, Hes5, Nfia, Npas4 2.15

NFIL3 represses immediate-early gene expression during reconsolidation We hypothesized that the expression of specific memory genes might be dysregulated in Nfil3 KO mice during memory reconsolidation, resulting in inflexibility of behavioral adaption as described previously [Chapter 4]. We observed 65 differentially expressed TCs for the main effect of time point 0.5 h vs 0 h in the delayed shock groups (Figure 5a), representing potential immediate-early genes in relation to memory formation. Importantly, 18 of these genes were also differentially expressed after an immediate shock (genes indicated in boldface in Figure 5a), suggesting that they may be involved in memory induction by the novel context in general, and not specifically in the formation of a shock-context association. We annotated these TCs to genes and gene ontology clusters, and the ontology cluster ‘transcriptional regulation’ showed enrichment scores higher than 1 in this group of TCs (Table 1). Also, many of the genes in this list are known immediate-early genes, such as Nr4a1, Egr2, Arc and Fos [85,86,222,223]. Notably, for the most regulated genes (top 12 of the heatmap in Figure 5a) the increase in expression from 0 h to 0.5 h during reconsolidation was higher in Nfil3 KO animals compared with wildtypes. We tested whether there was a difference in the increase in expression from 0 h to 0.5 h in reconsolidation versus consolidation between wildtype and knockout animals specifically within this gene subset. Differences between wildtype and knockout expression were calculated and found to significantly

deviate from zero (one sample t-test, t(64) = 4.800, p < 0.001; Figure 5b), indicating that indeed these genes are on average higher expressed in Nfil3 KO mice at the 0.5 h reconsolidation time point. Furthermore, when selecting the 18 most differentially regulated TCs (log2 difference >0.75, indicated with a red box in Figure 4b), transcription factor binding site overrepresentation analysis

98 CAGE sequencing on hippocampi after fear conditioning

Figure 5. Differential gene expression response at 0.5 h vs. 0 h and independent of memory phase. (a) Heatmap showing expression patterns of genes differentially regulated at 0.5 h vs. 0 h in the delayed shock group. Genes (regular script) are only found in the delayed shock subset of samples, genes (boldface) were also differentially expressed in the immediate shock subset. Colors indicate expression differences on a log-2 scale (color key), compared to the wildtype 0 h consolidation time point (i.e. naïve animals, reference condition). (b) Histogram of overall difference in expression regulation between wildtype and knockout animals at the 0.5 h reconsolidation time point. The dashed line represents the expected mean under the hypothesis that there is no difference in regulation between genotypes. The boxed area includes the 18 most regulated TCs with an expression difference > 0.75 on a log-2 scale. (c) Transcription factor binding site overrepresentation analysis using these 18 most regulated TCs. High Fisher scores and the Z-scores together indicate a high overrepresentation, making NFIL3 binding sites the third highest scoring motif, behind SRF and TBP.

99 Chapter 5 showed that NFIL3 binding sites are highly enriched in the promoter regions of the corresponding genes (Figure 4c).

Table 2. Gene ontologies enriched in TCs differentially regulated between consolidation and reconsolidation at 0 h. Enrichment Functional class Gene names score Tmem63a, Tmem98, Tmem88b, B3galt5, Transmembrane protein Enpp4, Nipal4, Tmbim1, Opalin, Sft2d1, 3.12 Tmem125 Myelin related proteins Mbp, Cldn11, Ugt8a, Gjc3 2.71

Genotype difference within TCs differentially expressed between consolidation and reconsolidation at 0 hrs An additional large group of differentially expressed genes was observed between consolidation and reconsolidation at time point 0 h (104 genes; Figure 6a). Since the 0 h reconsolidation animals were not re-exposed to the conditioned context, this group not only served as the control for the reconsolidation groups, but it also represents a 24 h consolidation time point, and could reveal gene expression underlying long-term memory formation. We annotated the TCs to genes and gene ontology clusters, and trans-membrane proteins and myelin related proteins showed enrichment scores higher than 1 in these TCs (Table 2). Again, there was a difference in expression between genotypes during reconsolidation. We tested this hypothesis within this subset of TCs by calculating differences between wildtype and knockout animals and found that this significantly differed from zero (one sample t-test, t(103) = -7.573, p < 0.001), indicating overall lower expression in Nfil3 KO mice (Figure 6b). No overrepresentation of Nfil3 binding sites was found in the promoters of genes coupled to these TCs, but instead we observed overrepresentation of Zfp423 and Plag1 sites (Figure 6c).

100 CAGE sequencing on hippocampi after fear conditioning

Figure 6. Heatmap showing expression patterns of genes differentially regulated between consolidation and reconsolidation for time point 0 h in the delayed shock group. Colors indicate expression differences on a log-2 scale (color key), compared to the wildtype 0 h consolidation time point (i.e. naïve animals, reference condition).

Discussion Our aim was to measure hippocampal gene expression during memory consolidation and reconsolidation in a fear conditioning paradigm, and in particular to identify changes herein due to deletion of the transcriptional repressor NFIL3. Previous research showed that NFIL3 represses CREB target

101 Chapter 5 genes that are important for axon growth [43]. Fear conditioning induces Nfil3 expression in the hippocampus [Chapter 4], which led us to hypothesize that NFIL3 might also repress CREB target genes that are important for memory processing. Dysregulation of memory genes might explain the behavioral flexibility deficit that we previously observed in Nfil3 KO mice. We used next generation sequencing, in particular cap analysis of gene expression (CAGE), for detecting changes in gene expression. The ‘golden standard’ for analyzing high-throughput next generation sequencing data has not been determined yet. We chose to use log-transformation followed by linear modeling using Limma [219] because the distribution of TC sizes shows a clear positive skew. Since the setup of our experiment consisted of four main factors (genotype, shock type, memory phase and time point), each consisting of 2 to 3 levels, data analysis required models that can handle multiple levels of complexity. We chose to model every combination of factors into a new factor, yielding 24 factors in total. After fitting to the linear models we selected specific contrasts, enabling us to test for effects not just in the complete dataset, but also specifically for subsets of conditions/contrasts. Other packages used were EdgeR [220] and DESeq [224], which gave similar results. The use of CAGE analysis in a complex multifactorial experimental design like ours has not been reported previously, and while analyzing our data we noticed that combining multiple factors in a single statistical model yielded very few differentially expressed genes after appropriate correction for multiple testing. We therefore used our data to first identify changes in gene expression in all possible contrasts, and subsequently performed rigid hypothesis testing within these sets of genes. When we focused on genotype as the main factor underlying differences in gene expression, we found Nfil3 to be consistently upregulated in knockout animals. This is quite possible since the deletion only involved exon 2, which contains the complete coding sequence, leaving exon 1 intact. Apparently neurons regulate Nfil3 mRNA expression through negative feedback, and lack of NFIL3 protein increases expression of the remaining exon

102 CAGE sequencing on hippocampi after fear conditioning

1, which is selectively captured by CAGE analysis. Indeed, NFIL3 was shown to bind to and repress its own promoter [43]. Two other transcriptional repressors, Mier1 and Gcfc2, are also downregulated in Nfil3 knockout mice. No link between these genes and Nfil3 has been previously reported, but both are known to be expressed in the adult hippocampus [225]. Interestingly, their downregulation was only observed in the delayed shock group, suggesting that together with NFIL3 they form a transcriptional regulatory unit underlying hippocampal memory formation. In line with previous observations [226], the total number of differentially expressed genes due to genotype was relatively small, indicating that behavioral deficits due to Nfil3 deletion are likely caused by subtle changes in gene expression. When we next compared gene expression at 0.5 h to the 0 h time point, independent of the memory phase (i.e., consolidation and reconsolidation combined), we found 65 differentially regulated genes. As expected at this early time point after stimulation, many of these genes were immediate-early genes. Statistical testing within this subset of genes revealed that in wildtype mice immediate-early gene expression was lower during reconsolidation compared to consolidation, whereas in Nfil3 KO mice these same immediate-early genes showed similar expression levels during both consolidation and reconsolidation of memory. Notably, in the promoter regions of these genes, NFIL3 binding sites are frequently represented, supporting the idea that NFIL3 is directly responsible for the observed expression differences. These findings may offer an explanation for the behavioral inflexibility observed in Nfil3 knockout mice [Chapter 4]. In particular, NFIL3 may be required to suppress immediate-early gene expression in neurons that already were recruited in a memory trace (consolidation) and are subsequently presented with a novel experience (reconsolidation). Without such a feed-forward repression of memory genes, neurons have an increased chance of becoming simultaneously activated by multiple types of experience in a short time window, causing animals to persistently display inappropriate behaviors and to fail to adapt to new situations.

103 Chapter 5

Of the 65 genes that were differentially regulated at 0.5 h, 18 were also regulated in the immediate shock groups. While this condition does not elicit freezing [206,207,208], this only proves that the mice fail to associate the context with the shock. Thus, although no association is formed between shock and context, novel context alone is sufficient to induce immediate-early gene expression and form a memory at the molecular level. Therefore, the immediate shock condition is not an ideal control when studying the molecular basis of memory formation in general. For the main effect of memory phase (consolidation vs. reconsolidation) we did not find any differentially regulated genes. However, when we examined 104 genes that are specifically regulated at time point 0 h we did observe differential expression patterns between genotypes during reconsolidation, with Nfil3 knockout animals showing lower expression of most of these genes. Note that at this time point the reconsolidation group was not re-exposed to the conditioned context, and that it could thus also be interpreted as a 24 h consolidation time point. In that respect, the observed overrepresentation of genes important in myelinization and axon potential conductance could reflect a difference in long-term memory consolidation in Nfil3 knockout mice. Specific long-term memory tasks should be conducted to determine if such a difference also exists at the behavioral level. In conclusion, we found changes in memory-related gene expression that might explain the behavioral flexibility deficits observed in Nfil3 knockout mice. These findings need to be validated in independent samples, for instance by quantitative PCR measurements. Furthermore, we have not yet analyzed the CAGE data with respect to alternative transcription start site usage or the expression of novel transcripts. An extended reanalysis of this data might thus reveal additional mechanisms underlying hippocampal memory processing that have not been studied so far.

104 CAGE sequencing on hippocampi after fear conditioning

105

Chapter 6

General discussion

Transcriptional regulation of neuronal plasticity: are axon regeneration and memory processing regulated by the same molecular mechanisms?

Chapter 6

Background Neuronal plasticity or neuroplasticity generally refers to any alteration in neuronal structure, connectivity or function that results from experience, environmental change or injury. Neuronal plasticity has evolved as an intrinsic property of the nervous system to allow individuals to respond rapidly to environmental change without the need for genetic or epigenetic adaptations, which occur at a much longer time scale [227]. In addition to processes that promote plasticity, cellular and molecular mechanisms evolved to constrain neuronal plasticity within physiological boundaries [228,229]. This is best exemplified by looking at the plastic properties of the adult brain. Plasticity is extremely high during brain development, allowing neurons to extend processes over long distances, form many connections with target neurons in various remote brain areas, and experience-dependently remove many of these connections to fine-tune neural network function. In the adult brain, this type of plasticity is strongly reduced in those brain regions that are involved in storing experience-dependent modifications, such as memories [230,231,232]. If adult plasticity would be as high as it is during development, simple and patterned brain functions such as motor control would become seriously impaired. In contrast, if plasticity would be completely absent in the adult brain, we would not be able to adapt our behavior in response to environmental change or remember anything that happened more than a few minutes earlier. Normal adult brain function thus requires a plasticity optimum, and both hyper- and hypoplasticity may cause neurological and neuropsychiatric disorders [227]. Interestingly, the same constraint on plasticity does apply to injured neurons in the central nervous system (CNS), preventing these from re-growing damaged axons over long distances and re-establishing new functional connections, as is evident from the devastating and often permanently disabling effects of for instance spinal cord injuries and strokes [233,234]. Apparently nature did not provide mechanisms to increase plasticity in such conditions in order to repair injured neurons, and the potential evolutionary

108 General discussion benefits of repairing damaged axons did not overrule the disadvantages of hyperplasticity under healthy conditions. In contrast to the poor regeneration in the CNS, regeneration of injured axons does occur in the peripheral nervous system (PNS). The PNS and CNS differ from each other in cellular composition, i.e., Schwann cells are only present in the PNS and oligodendrocytes and astrocytes are uniquely present in the CNS. This has a significant impact on differential regenerative capacity. It is not clear to what extent neuron-intrinsic regeneration-promoting properties in the PNS are also unique and evolved independently from the CNS, or whether they result from an ineffective suppression of developmental plasticity allowing injured neurons to recapitulate developmental mechanisms of axon growth and synapse formation [235,236]. It is clear however that many common cellular and molecular mechanisms exist that regulate adult plasticity and memory processing in the CNS as well as functional axonal repair in the PNS [237,238]. Studying these common regulatory mechanisms in more detail may help us to improve our understanding on what determines different forms of neuronal plasticity, and how we can develop new strategies for promoting neuroregeneration and enhancing cognition.

CREB and NFIL3: a common transcriptional module regulating neuronal plasticity? Axon regeneration and memory formation both require the expression of new genes [21,23], which is regulated by transcription factors (TFs). One of the key TFs in both processes is the basic leucine zipper (bZIP) TF cAMP response element binding protein (CREB). Upon peripheral nerve injury, CREB is activated in the soma of injured neurons and induces the expression of regeneration- associated genes, such as growth-associated protein 43 (Gap43) and arginase 1 (Arg1) [239,240]. Activation of CREB [144] or induction of Arg1 expression [41] both enhance regenerative axon growth. CREB is also essential for memory formation and an early mediator of the gene expression response that is necessary for the structural and functional synaptic alterations underlying

109 Chapter 6 learning. CREB induces the expression of multiple immediate-early genes that are considered to be required for memory formation, including the proto- oncogene cFos, early growth response protein 1 (Egr1), dual specific phosphatase 1 (Dusp1) and activity-regulated cytoskeleton-associated protein (Arc) [241,242,243,244]. CREB loss-of-function and gain-of-function studies in many animal models have confirmed its crucial role in memory formation [109,245]. The research described in this thesis started with the observation that another bZIP TF, nuclear factor interleukin 3 regulated (NFIL3), acts together with CREB to regulate the expression of regeneration-associated genes [43]. Specifically, RNAi-mediated knockdown of Nfil3 expression and dominant- negative inhibition of NFIL3 both enhanced axon growth of cultured adult dorsal root ganglion (DRG) neurons that underwent axotomy during isolation and thus are a good model for axon regeneration. It was then demonstrated that NFIL3 represses some of the same regeneration-associated genes that are initially induced by CREB. Because NFIL3 expression itself was also induced by CREB, NFIL3 acts as a feed-forward repressor of CREB target genes, and CREB and NFIL3 function together in a so-called type 1 incoherent feed-forward loop (IFFL). The function of IFFLs is two-fold: they enhance the onset of target gene expression, and they facilitate a more sharp, pulse-like expression of target genes [103]. NFIL3 is also expressed in the brain, in particular in the hippocampus [chapter 4], and represses CREB target genes that are involved in memory formation [chapter 5]. Based on these data we hypothesized that NFIL3 is one of the evolutionary constraints that evolved to restrict adult neuroplasticity and to maintain plasticity within physiological boundaries, while at the same time limiting the capacity of adult neurons to regenerate damaged axons. In particular, we were interested to investigate how genetic deletion of NFIL3 would affect axon regeneration and memory formation. We expected deletion of NFIL3 to improve axon regeneration by removing feed-forward repression of regeneration-associated genes and extending their expression over a longer period of time, whereas memory formation would be disrupted

110 General discussion because memory-associated gene expression would be no longer restricted to the temporal window that is required for input-specific learning. In addition, we wanted to know if CREB and NFIL3 target a similar set of genes in axon regeneration paradigms compared to memory paradigms, or whether regeneration and memory formation depend on different gene expression programs regulated by these TFs.

NFIL3 and axon regeneration In chapter 2 we described the effects of NFIL3 deletion on axon regeneration in vivo. Contrary to what we expected, functional recovery after peripheral nerve injury was significantly impaired in Nfil3 knockout mice. Moreover, dominant negative inhibition of NFIL3 specifically in DRG neurons reduced axon regeneration in vivo, indicating that the impairment in functional recovery is probably not due to lack of Nfil3 expression in other cells, e.g. the Schwann cells in the nerve, than the injured neurons themselves. Although there may be experimental or technical limitations that might explain these observations, it is tempting to speculate here as to what the biological mechanism might be for these unexpected findings. First, the role of NFIL3 as a feed forward repressor is specifically linked to CREB. However, NFIL3 may have other target genes than the CREB targets that are being repressed. Indeed, previous chromatin immunoprecipitation experiments showed that in F11 cells, which are DRG-like neuroblastoma cells, there is not much overlap between CREB targets and NFIL3 targets [45], suggesting that NFIL3 can regulate gene expression independent of CREB. Moreover, some studies suggest that NFIL3 can also activate genes [246], and deleting Nfil3 could thus result in reduced expression of important regeneration-associated genes, although this could not be confirmed by our own gene expression analysis of injured DRGs in Nfil3 knockout mice (see also below) [226]. It is also possible that the potentially beneficial effects of Nfil3 deletion on axon regeneration that we observed in vitro [43] are simply not revealed under the experimental conditions that we used in vivo. Previous studies on the dynamics of feed-forward regulation of

111 Chapter 6 gene expression were all performed in simple model organisms and on very short time scales. For instance, mathematical modeling of the galactose system in E. coli, in which the transcriptional activator cAMP receptor protein (CRP) and the transcriptional repressor GalS form a type 1 IFFL, predicted that feed- forward repression by GalS generates a pulse of target gene expression within four hours, which was subsequently confirmed in GalS mutant bacteria [102]. How target gene levels develop over longer time periods remains unknown. Deletion of Nfil3 may thus have resulted in the induction of regeneration- associated gene expression for a short period following injury, but may have been insufficient in maintaining high expression levels over the entire period of axon regeneration. Finally, the peripheral nerve regeneration paradigm that we used in chapter 2 may not be the best paradigm to test the effects of Nfil3 deletion. Peripheral axon regeneration occurs spontaneously, and regeneration-associated genes are strongly induced already by the injury itself and may not benefit from removing NFIL3-mediated repression. To further address these issues in the future, the experimental design can be modified in two ways. First, the effects of Nfil3 deletion could be studied in a central injury paradigm where regeneration-associated genes are expressed at much lower levels and spontaneous recovery does not occur, i.e., in lesioned corticospinal or rubrospinal neurons. Thus, if NFIL3 does play a role in the suppression of RAGs in CNS neurons it would be easier to detect an effect of NFIL3 deletion in CNS versus PNS neurons. Second, CREB activity could be constitutively increased in order to maintain high target gene expression over the entire regeneration period. Under these altered experimental conditions it might be possible to reveal regeneration-promoting effects of NFIL3 deletion.

NFIL3 and memory In chapters 3 and 4 we addressed the question whether NFIL3 might act together with CREB in regulating learning and memory. Nfil3 mRNA levels are high in the hippocampus [113] and are induced by raising cAMP levels and by inducing contextual fear memory [chapter 4]. These findings are consistent

112 General discussion with the idea that NFIL3 might also act as a feed-forward repressor of CREB- induced memory gene expression. To test this hypothesis, we subjected Nfil3 knockout mice to different learning and memory tasks and showed specific memory deficits in all of them. To interpret these data we need to make a distinction between tasks with many repeated stimuli or learning instances, and tasks with only few. Fear conditioning depends on an aversive stimulus that is intense and simple enough that it only needs one training session. Interestingly, we did not observe a difference between Nfil3 knockout mice and wildtype controls upon the first memory retrieval test, indicating that NFIL3 is not involved in the formation of an initial fear memory. However, when we subsequently exposed animals to a neutral context, Nfil3 knockout mice froze as if they were exposed to the conditioned context, whereas wildtype animals froze significantly less. These observations are consistent with the idea that NFIL3 and CREB act as a IFFL: the initial memory is formed in the absence of NFIL3 and depends only on activation of CREB and subsequent induction of CREB-dependent memory genes, whereas a second, in this case dissimilar, retrieval test also depends on the feed-forward inhibition of these same CREB target genes by NFIL3. Which genes these are, and how they may explain the memory phenotype, will be discussed in the next section. In contrast to fear conditioning, in the avoidance-learning task animals are exposed to a certain stimulus many times before being exposed to a conflicting one. This makes the learning environment different. In the avoidance learning task in the automated home cage, mice are allowed to develop a preferred shelter entrance out of two options over a period of four days before that same preferred entrance is punished with a bright light and animals need to shift towards the other entrance in order to avoid the light. The Barnes maze also differs from fear conditioning, since mice are subjected to nine training sessions spread out over five days, where each time the same hole is rewarded with escape from the platform, before an altered situation is presented during the probe test, when the possibility to escape is no longer present. Repeated exposure to a consistent stimulus will result in multiple

113 Chapter 6 memory consolidation events. According to our hypothesis, feed-forward repression by NFIL3 becomes increasingly important with each session to modify the gene expression response and determine both the strength and the broadness of the memory trace. Absence of NFIL3 might create a too strong and too widely distributed memory trace, which might cause impairment, when in a similar context suddenly a different response is required. Indeed, in the avoidance learning task, we observed that Nfil3 knockout mice are not able to switch their shelter entrance preference when the use of their preferred entrance is coupled to an aversive light stimulus, and in the Barnes maze, exploration of holes other than the original escape box position is reduced, even when the escape box itself is absent during the probe trial. We conclude that Nfil3 knockout mice are more rigid in their behavior, and less flexible to change their response relative to what was previously appropriate in the same context. It is worth noting that the Barnes maze phenotype, i.e., increased exploration of the target hole during the probe trial, is normally interpreted as enhanced memory, but that the combined results of all behavioral tests lead us to conclude that it should rather be interpreted as an expression of behavioral inflexibility.

NFIL3 and gene expression We also investigated the changes in gene expression in Nfil3 knockout mice, in particular those underlying the lack of regeneration [chapter 2] and the observed fear conditioning phenotype [chapter 5]. Firstly, we wondered whether there are similarities in target gene expression underlying the two processes, independent of genotype. Surprisingly, there was absolutely no overlap between the top 250 genes that showed a change in expression following nerve injury and the genes that showed a change in expression following fear conditioning, whether it was during consolidation or reconsolidation of the memory. At first glance it thus seems that the two forms of neuronal plasticity have very different underlying molecular and cellular mechanisms. It should be noted however that the time points that were chosen

114 General discussion for gene expression analysis differ between the two paradigms, i.e. 0.5-30 hours for fear conditioning and 2-5 days for regeneration. The time points were chosen to cover the dynamics of the biological processes that we are interested in, but we may have captured different phases of the plasticity response. This might explain why we did not observe any effects of NFIL3 deletion on regeneration-associated gene expression, even that of genes that are known to be activated by CREB in vivo, and that are repressed by NFIL3 in vitro. In the fear conditioning paradigm, on the other hand, we did identify a substantial set of target genes that could be classified as memory genes and that were differentially expressed in Nfil3 knockout mice compared with wildtype controls, specifically when comparing the 0.5 hour memory reconsolidation time point. In particular, a number of immediate-early genes that are known to be involved in hippocampal memory processing were higher expressed at this time point in Nfil3 knockout mice, indicating that in wildtype mice these genes are normally repressed by NFIL3 during memory reconsolidation only, and not during memory consolidation. These findings support the theory that NFIL3 acts as a feed-forward repressor of memory genes, and that its effects are only observed upon re-exposure of animals to a similar stimulus within a certain time window. Sustained elevation of immediate-early gene expression in Nfil3 knockout mice implies complete remodeling of the memory trace, causing more neurons to pass the threshold to be recruited to encode any possible aspect of the event and participate in the memory trace. When being exposed to events that differ from each other but share some characteristics, a memory trace that is more strongly or more broadly distributed across these characteristics would have a higher chance to be activated when a slightly similar stimulus is presented. In wildtype mice on the other hand, suppression of immediate-early gene expression by NFIL3 specifically in neurons that were previously activated by a learning stimulus would thus reduce the chance of these same neurons becoming reactivated when a similar stimulus presents, allowing animals to adapt their behavior accordingly instead of inappropriately repeating the originally learned behavior.

115 Chapter 6

The fact that memory gene expression differences due to NFIL3 deletion are only revealed upon repeated exposure to a stimulus might provide a new theory for not observing regeneration-associated gene expressional changes in Nfil3 knockout mice after nerve injury. It would be interesting to see whether repeated stimulation of injured neurons, for instance with pharmacological enhancers of cAMP signaling, would result in enhanced target gene expression and improve regenerative outcome.

Conclusions Taken together, our data seem to support the hypothesis that NFIL3 is a feed- forward repressor of memory genes that are activated by CREB, but that it fails to perform a similar role in regeneration-associated expression under the in vivo peripheral nerve regeneration conditions that we studied. It will be interesting to see whether similar gene regulation mechanisms occur in other genetic models of learning and memory disorders. If NFIL3 turns out as a major contributor to memory trace formation, its role in memory dysfunction, or more specifically, cognitive flexibility disorders of the human brain might be studied [247,248].

116 General discussion

117

English summary

English summary

Neurons, connectivity and plasticity Nerve cells have many connections with each other in order to communicate. With the signals they send they organize all behavior, from simple locomotor functions to complex cognitive functions. In order for individuals to respond to a changing environment, connections between nerve cells need to be flexible – commonly referred to as plasticity. Connections can be strengthened or weakened, and new connections can be made. During development these processes occur continuously all over the brain, while in the adult brain flexibility or plasticity is strongly decreased, but still present. I investigated the molecular mechanisms that determine when and how neurons become more or less flexible in their connectivity, in particular during regenerative axon growth and higher cognitive functioning.

Gene regulation and neuronal plasticity Genetic information is stored in DNA in the nucleus of each cell. Genes contain the information to produce specific proteins, which are the molecules responsible for performing most cellular functions. In order to make proteins, genes are copied or transcribed into messenger RNA, which is transported out of the nucleus and translated into proteins in the cytoplasm. Depending on the cell type and circumstances, different proteins are needed in different quantities. Transcription factors are proteins that bind to the DNA and stimulate or inhibit the process of transcription, thus regulating the amounts and types of proteins in the cell. Regulation of gene expression by transcription factors is one of the main mechanisms for cells to determine what kind of program they want to execute, including the gene expression program required for neuronal plasticity. An important transcription factor regulating plasticity in neurons is CREB, which is continuously present in the cell and is functionally activated by phosphorylation. Among the genes that are activated by CREB are other transcription factors, including NFIL3. Previous research showed that

119 English summary

CREB and NFIL3 regulate the same plasticity genes, CREB activating their expression, and NFIL3 inhibiting it. Such a regulatory network is known as an incoherent feed-forward loop, which can speed up the process of activating gene expression and causes pulsed expression of target genes. Based on these findings we hypothesized that NFIL3 is an important regulator of plasticity gene programs during axon regeneration and during learning and memory.

NFIL3 and neuronal plasticity during regeneration from axonal injury Neurons are connected to their targets via synapses, in which an electrical signal from the firing cell is translated into a chemical signal between cells, and back into an electrical signal again by the receiving cell. These synapses are connected to the cell body of the firing cell by long fibers called axons, which enable them to reach other cells over distances of more than one meter in humans. When axons are damaged in the adult central nervous system, i.e., in the brain or in the spinal cord, they cannot be repaired, with devastating consequences. However, after injury to the peripheral nervous system, i.e., all neurons and axons outside the brain and spinal cord, damaged axons do regenerate and functional recovery does occur. Previous research showed that CREB activation stimulates axon growth after injury, and that NFIL3 expression is induced during successful regeneration in the peripheral nervous system, but not when regeneration is absent, like in the injured central nervous system, suggesting that NFIL3 might contribute to axon regeneration. Further experiments in cultured cells, however, unexpectedly showed that inhibiting NFIL3 expression or reducing NFIL3 activity caused a large increase in the growth of axon-like structures, in line with NFIL3 being a feed-forward repressor of CREB target genes. In chapter 2 we set out to test whether inhibiting NFIL3 in live animals would increase axon regeneration and functional recovery after peripheral nerve injury. To do this we damaged the largest nerve in the body, the sciatic nerve which is located in the hind limb, in mice in which the Nfil3 gene was deleted, or in rats by injecting the dorsal root ganglia, where

120 English summary the cell bodies of the injured are located, with a virus designed to introduce a dominant negative variant of the NFIL3 protein, which binds and inactivates the normal NFIL3 protein. Surprisingly, mice deficient in NFIL3 signaling performed worse during recovery than normal littermates when crossing a narrow beam, making more paw slips and taking more time. In line with these observations, a fiber tracing experiment in the rat showed that cells with blocked NFIL3 signaling showed less axon growth. Analysis of gene expression patterns in regenerating neurons in mice showed that classical regeneration-associated genes, which we expected to be differentially regulated in Nfil3 knockout mice, did not show any difference compared with control animals. We concluded that inhibiting NFIL3 expression or signaling does not increase the rate of success of regeneration. Possibly, the observed decrease in regeneration is caused by altered expression patterns in developmental genes and in a group of genes known to be involved in olfactory signaling.

NFIL3 and neuronal plasticity during learning and memory formation Neurons in the adult brain are also able to make new connections, for instance during learning processes. CREB is known to be important in strengthening connections and making new ones in brain regions like the hippocampus, which is important for learning and memory. Recently it was reported that also NFIL3 is expressed in the hippocampus, which led us to hypothesize that the incoherent feed-forward loop of CREB and NFIL3 also regulates neuronal plasticity in the brain, affecting cognitive processes. In chapter 3, we compared Nfil3 knockout mice with normal littermates for a wide range of basic functions such as movement and activity (locomotion), muscle strength, anxiety, and day/night cycle, and found no differences. We then placed the mice in a high- throughput phenotyping cage with continuous camera observation during one week, which is uniquely suited to observe behavior without any interference by the experimenter. Basal behavior was unaffected again, but Nfil3 knockout mice behaved differently in a simple cognitive task. The animals had a shelter in their cage with two separate entrances and were allowed to develop a

121 English summary preferred entrance during the first 4 days of the experiment. During days 5 and 6, usage of the preferred entrance was coupled to illumination of the shelter, which is a mildly aversive stimulus for mice. Nfil3 knockout mice showed only a mild decrease in their original entrance preference, while normal littermates completely switch preference. In chapter 4 we further investigated cognitive performance in Nfil3 knockout mice using several low-throughput tasks. During fear conditioning, mice were allowed to explore a novel environment for three minutes and then exposed to a mild foot shock. After a 30 s recovery period the animal was placed back into the home cage. Memory of the event was then tested by placing the animal into the cage where it was shocked and observing the amount of freezing behavior that is exhibited. In this test, Nfil3 knockout mice initially froze at normal levels compared with wildtype mice, but they showed higher freezing levels when subsequently tested in a slightly altered environment, which should be perceived as neutral and elicit no freezing behavior. In another task, called the Barnes maze, mice were trained to find an escape hole out of 24 holes on a large round table. Nfil3 knockout mice learned to perform this task at the same rate as wildtype littermates, but when after a week of training the escape hole was removed, the mice were unable to adapt to the novel situation and kept exploring the region that used to house the escape hole. Taken together, we concluded that Nfil3 knockout mice have impaired cognitive flexibility and are less capable of adjusting their behavior in new situations.

NFIL3 and the expression of plasticity genes A strongpoint of the fear conditioning task is that the learning event is clearly focused in time, and underlying changes in gene expression can be examined easily. In chapter 4 we showed that NFIL3 expression is induced in hippocampal neurons exposed to CREB stimulation in vitro or fear conditioning in vivo. In chapter 5 we studied the effects of NFIL3 on the expression of other genes. In particular, we examined the global hippocampal gene expression response in

122 English summary

Nfil3 knockout mice after exposure to fear conditioning or re-exposure to the conditioned context using state-of-the-art next generation CAGE sequencing. With CAGE sequencing, the first 27 base pairs of all messenger RNA molecules are read and counted, which is coupled back to known genes but also potentially to novel transcribed regions of the genome. We found that normal mice show a marked peak in plasticity gene expression at 30 minutes after fear conditioning, and that these same genes are activated to a lesser extent at 30 minutes after re-exposure, possibly due to feed-forward repression by NFIL3. Indeed, in Nfil3 knockout mice these same plasticity genes were significantly higher expressed at 30 minutes after re-exposure than in wildtype mice. We concluded that elevated levels of plasticity gene expression during this phase results in impaired cognitive flexibility observed in the behavioral tasks outlined above. In particular, neurons that were previously activated and are involved in a particular memory may be easily re-activated in the absence of NFIL3 when a similar but different stimulus presents, making it difficult for animals to adapt their behavior in response to environmental changes.

Conclusion We conclude that CREB and NFIL3 together regulate the expression of plasticity genes. In the brain, NFIL3 deficiency leads to a dysregulation of plasticity genes and an impairment of cognitive flexibility. Removal of NFIL3 does however not boost regeneration-associated gene expression to enhance regenerative axon growth and functional recovery in the peripheral nervous system in live animals, while it does in culture experiments. Further research will be needed to determine the spatial and temporal requirements for NFIL3 expression to affect plasticity in different neuronal systems and under different experimental conditions.

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Acknowledgements

Acknowledgements

So this is it, it finally happened. After a lot of experiments, with help from a lot of people, followed by a lot of writing, also with help from a lot of people, I managed to produce this manuscript you are currently perusing. Obviously I never would have managed this on my own, and I want to take this opportunity to thank everyone who has contributed in any way to this achievement. And since this is my book, I’m going to do it my way. I’m going to take ‘contribution’ in the broadest sense of the word possible and try to express my gratitude to everyone who has supported me and my happiness in any way up unto this point. While ambitious, rest assured that it is also completely sincere. The fact that I want to thank so many people does not diminish the gratefulness per capita in any way, rest assured! I guess I’m just a very grateful person, and if I don’t express this enough, I will try to make amends here. Since the list is very long, let me emphasize that it is not ordered by importance – making a ranking of friends would disregard the uniqueness of every individual and their interaction with me. Some friends I see a lot, some not so much, but it often strikes me how seeing old friends quickly transforms into an experience like there was never any break in our contact. As I said, I’m a very grateful person. So my acknowledgements are ordered by clusters of people. While writing this, extremely important people keep popping into my head that I almost forgot, so allow me to also apologize if I somehow forgot to mention you, it was an accident, I promise. Please also note that because 1) such a large part of my friends are non-Dutch speakers, 2) the acknowledgements are without a doubt the section of a thesis that is read the most, and 3) everyone I know speaks English, I have chosen to write the entire section in English, just like I do with my Facebook posts for similar reasons. I have to start by thanking Ronald, my copromotor. Your sharp mind, your ability to see the bigger picture, your supervision style which was at times very loose and trusting, and at other times strict and motivating, your assistance

143 Acknowledgements with the writing process.. I don’t see how I could have completed this project without your help, thank you so much. Guus, thank you for your confidence in me to start this project, your insightful feedback, your investments in the social aspects of the team and allowing me to play a role in that. Joost, always knowledgeable, adamant in your critique and trusting my work from afar. Thank you of course also for making the funds available for this project. Harold, thank you for your extensive supervision of me during those first 6 months, for the inheritance of a broad, versatile and challenging project, for the co-authorship on the MCN paper, and especially for the silly jokes in the lab and at the lunch table. Frank, thank you for supervising me in Utrecht, for always pushing me to new levels, for teaching me a not just how to gather data using a ridiculous amount of lab-techniques, but also how to present it well at lab meetings, how to think of new venues of research to try, and how to booby-trap someone’s ice-bucket in a sneaky way. Marten, thank you for always giving us a hard time at meetings, the intense discussions, the late night drinks with the group, for offering me a position in your lab and gracefully accepting that I declined and still agreeing to speak at my Master’s graduation and doing an amazing job at that. Jesse, thank you for the competition that felt like coöperation (or was it the other way around?). For also pushing me to new limits and helping me pay Frank back at his booby-traps. And thanks to all the other Smidt-lab members tolerating my stumbling presence and aiding me in my journey from nitwit to halfwit. Wietske, Thijs and Sophie, my very own internship students, thank you for asking me all those pointed questions that helped me think about the project in ever new ways. Thanks for making me realize how much time my own supervisors have invested in me and for allowing me to pay it forward, and help me develop my own didactic skills at the same time.

144 Acknowledgements

Ruben, Erich, Matt, Nitish, thank you for the invaluable help with the regeneration experiments, for teaching me how to get one µl into a DRG and for the amusing banter in between. Rolinka, Marion, Yvonne, Roel, Iryna, Maarten and Pim, to me it felt you guys were rulers of various domains of the lab, so knowledgeable and experienced. Thank you for keeping the lab up and running and showing my way around it. Sabine, Mark, Ka-Wan, Michel, Riejanne, thank you for all the pointed questions at presentations, keeping me sharp. Bart, Pieter, Daniëlle, Harold, Marlene, Andrea, Jochem, Nutabi, Patrizia and Priyanka, my senior MCN PhD students, thank you for setting an excellent example of a great combination between good science, good friendships and good parties. Danai, Karen, Esther, Céline, Anne-Lieke, Leanne, Mariëtte, Ning, Sigrid, Nikhil, Natasha, Joerg, Clara, Mariana, and Sarah, thanks for helping me set that example on to the next generation of PhD students. No wonder I found it hard to leave the VU. René, Lody, Ruud, Remco, the way you guys also keep/kept the lab running should never be underestimated, thank you guys. Frank, Joost, Robbie, Desiree, Bastijn, Erik, Ingrid and Chris, you crazy bunch of FGA techs, thanks for having me over in your room so often I almost got my own chair. Tony, Cillian, Tatiana, Javier, Alessandro, Vincent, Marieke, Anna, Marherita, Linda, Sonia, Ana, Marinka and Vera, thanks for also allowing me to infiltrate all those FGA borrels, movie- and board game-nights. Emiel, Emmeke, Liane, Mohit, ‘the other’ Margherita, Sasja and Mendelt, way to go to help bridge these inter-departmental gaps, it’s all one big VU anyway. Margot and Madeleine, thank you for hiring me, for your faith in me as a teacher, and creating such an open atmosphere, it really is a pleasure to work here. To the internship students at MCN and FGA, thank you Jolien, Nina, Uyen, Jytte and the rest for the good times in- and outside of the lab.

145 Acknowledgements

To my fellow G&L teachers, Jelske, Sjirk, Eva, Brechje, Eric, Lisanne, Esther, Jeroen, Marijke and Gerda, making all that education happen is a real team- effort, and because of your energy and all your qualities I think we do a great job. Contributing to that makes me to go work with pleasure. Jelske Wiesje, I have to thank you especially for being an awesome roomie, tolerating my talking, my chaotic desk, and my horribly bad jokes. Ilona, Maaike, Alison, Jocelyn and Laura, of course you are also part of the atmosphere at the department I appreciate so much. Kas, Nienke en Rolinka, it’s so nice to have a connection between my old and my new job at the VU, and having a group of friends that enjoy cheesy 90’s music as much I do is a hilarious bonus. Benjamin, Rachel, Vera, Maria, Babette, Tobias, Ilse, Irma, Inge, Tanja and of course Thea, thanks for the wonderful intervision sessions, I still learn a lot there and always come back from them with new ideas and renewed enthousiasm. To all the other G&L coördinators and education affiliates, thank you for tolerating this cheeky and revolutionary judo experiment and allowing it to reform and I hope improve higher education. Have faith, we mean you no harm. Martin, Marten, Anton, Yaobi, Antoni, Liam, Danielle, Nata, Simone, Vincent, Debbie, Robert and Martijn, I got to know all of you by your partnerships with colleagues, so thank you so much for tolerating all that bio- and neuro-talk and still hanging out with us, we really appreciate it. To the people at Gyrinus Natans, thank you for creating such a great place to blow off steam after work. Ruben, Robin, Amar, Malu, Ilse, Kelly, Sam, and all the others, cheers to many more embarrassing Stelling nights. I would also like to thank all the G&L students for keeping me sharp with your questions and allowing me to transfer my passion for the life sciences to you. My roommates at Casa Assendelft! From the original Sarah, Joep and Rebekka, to Heleen, Yasemin, Lilian and Melanie, and ending with Gina, Robin and Milou, I’m ridiculously lucky to have ended up in this house (coin toss included), such a

146 Acknowledgements fun and relaxed environment. I must be crazy to be leaving, luckily the new house has yet to be built, but when it’s done, don’t be strangers! I will miss you guys dearly. Char. What can I say. In the whole world by far still the person who understands me the best, thanks everything we’ve shared, for pushing me to do things I never would have done otherwise, for the smoothest break-up in the world and the unique continued friendship thereafer. Ita, it’s up to you to keep her happy now, good to see she’s in good hands. Ernestine, Aart, Francine, but certainly also Opa Klaas en Oma Jos, thank you for tolerating a cheeky teenage boy, for the warm and instant acceptance in your midst, for putting a roof over my head when I needed it. Caro, Andre, Björn, Jigeesha, Jonathan, Yakmee, Sabrina, Raphaël, Leonie, Sujan, thank you for the crazy weekends in whichever city, filled with games, movies, improv, dancing and bizarrely nice food. For the friendship that endures distances with ease. Hope to see you soon again. Dalton buddies, Soi, Kickert, Jo, Nick, Nel , Vi, Tom, Samy en Maaike, thanks for the good times in school, the slumber parties, the holidays, the new year dips and the yearly reunions. We should have more of those! To my martial arts instructors, Thank you Kancho Guus, Sensei Graham and all the Sempais and sparring partners at the Iga Warrior for teaching me to keep a clear mind under duress, for showing me what my own body is capable of, and for the awesome stealth challenges in the woods at night. Arigato gozaimashita. Thank you Sifu Benno and Sifu Pele and all the sparring partners at Team UCT for rolling around with me, improving my game, supporting me at tournaments, and having so much respect for each other that I hardly ever have injuries, which is special for a martial art that can leave you utterly exhausted in a matter of minutes. Ossu! Thank you Zeilschool ‘t Vossenhol for teaching me so much more than sailing. How to make something out of nothing, how to entertain a group of 48+16 children, how to open a bottle in countless different ways and how to sleep like a ninja. From Stichting to Ouwe Bal to ‘05 to the fresh new staff, from Pupil to

147 Acknowledgements

Senior, “alles wat ik had heb ik gegeven, zolang het duurde was je heel m’n leven”. Sincerely, Loek van der Knallen. Thanks for all the groups that have accepted me in their midst at festivals and parties (and extra cheers if you’re in multiple categories)! The people at Lowlands, Sziget, Fusion, Milkshake, We are Electric, Het Ardennenweekend, Buiten Westen, Hitzone Party, Morning Gloryville, Pleinvrees, Missie Rubberboot, ADE, St Paddy’s, Georgie’s, Queensday. You know who you are. Thanks for the dancing and all the crazy adventures. Thank you Michel and Desiree for pulling me into the Fisherman’s Friends, Daniël, Liane, Kim, Mark, Yoka, Guido, Tycho, Rianne, Koen, Nicolette, Marcel, Sabine, Dick and Marie, you’re a wonderfully warm, hospitable and slightly crazy group of people, keep that up. Jasper and Carolien, thanks for the workouts, the crazy paintball adventures and for forgiving me for the ice-cube incident. Gentlemen from the Z-channel, Frank, Rico, Ferry, Auke and of course Julz and Laura, thank you for the bad jokes, for the adventures that seem to be in a different city every time, and the wingman failures and triumphs. Lotje rukje ijs, you wacky running fanatics, Rico, Jorden, Rianne, Dieuwke, Jaap, Sven, Erik, Yaobi, José, Annemarie, Tim, Dewi, Quirijn, Ruben, Stijn, Irina, Sander, Ineke, Almer, Lenneke, Terry, Marleen, Nenah, Vera, Esther, Mark, Tessy and all the others I might be forgetting, thanks for the Lowlands breakfasts, the Halloween parties and the snowboard adventures. Joppe, Guido, Daan, Bas, Robbert, and of course Reineke, Anne, Fab en Thirza, Thanks for everything we’ve shared, from playing games outside to computer games, board games, martial arts, high school parties and other nightly expeditions, holidays, karting, snowboarding, fierce discussions, weddings, baby showers, moving houses, etc. It’s good to know that I can always count on you, and please know that you can always count on me as well. To my own family, Mom, Dad, Moniek, Willeke, growing up I have known good times and bad times, and they have shaped who I have become. Thanks for supporting me and putting up with me, I think in the end I turned out okay.

148 Acknowledgements

Opa, Oma, Dick, Mieke, Arjen, Eveline, Els, Bob, and all the van der Kallen relatives that are too numerous to mention, thanks for all the good times at birthday parties, weddings and family weekends. I’d like to make a special mention to Michel and Desiree, Priyanka and Martin, Danai and Marten, Bart and Tatiana, Nienke and Martijn, and the entire families Maat, Görts, Koldewijn, van der Kooij and Boere for welcoming me into their homes so many times, some even up until the point of having a second home. The value of this to me cannot be overestimated. My paranymphs, Guido, Frank, thank you guys so much for your willingness to wear this silly penguin outfit on that big stage with me. I’ve been through enough with the both of you to be able to say that you’re both absolutely crazy, don’t ever change that.

149

There are three things all wise men fear:

the sea in storm, a night with no moon, and the anger of a gentle man.

Patrick Rothfuss

About the author

About the author

Loek was born in Utrecht in 1986, and after finishing high school at Scholengemeenschap Dalton Voorburg in 2004, he started with bachelor Psychobiology at the Universiteit van Amsterdam. During the first internship of the research master Neuroscience & Cognition which he started in 2007 at Universiteit Utrecht he investigated the role of transcription factor Pitx3 on the development of the mesencephalic dopamine system. During the second internship at the VU University Amsterdam, he investigated another transcription factor, Nfil3, and its molecular binding partners. In 2009, he continued working on Nfil3 in this research project, and expanded the subject to include not only neuronal plasticity in peripheral nerve regeneration, but also during cognitive tasks in the central nervous system. Loek is currently still employed at the VU University, but now holds a teaching position at the department of Health and Life, an interdisciplinary bachelor were biomedical science and health science are combined.

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