The Role of Perineuronal Nets in Learning and Memory

by

Moushumi Nath

A thesis submitted in conformity with the requirements for the degree of Master of Science

Department of Physiology University of Toronto

© Copyright by Moushumi Nath 2018

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The Role of Perineuronal Nets in Learning and Memory

Moushumi Nath

Master of Science

Department of Physiology

University of Toronto

2018

Abstract

The balance between stability and plasticity in the brain determines our ability to learn. A key

molecular regulator of this balance includes perineuronal nets. Perineuronal nets are specialized

extracellular matrix structures surrounding neurons, and have a presumed function in promoting

stability within neural circuits. We investigated the effects of disrupting perineuronal nets,

thereby enhancing plasticity, on learning and memory in mice. We found that perineuronal net

disruption in the hippocampal CA1 region is associated with faster water maze reversal learning,

and increased contextual fear generalization. Moreover, the population of active neurons is larger

at retrieval of a contextual fear memory. These results demonstrate that perineuronal nets are

important for learning and memory, and that shifts in perineuronal nets is associated with altered

circuit level activity.

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Acknowledgments

I would like to thank Dr. Paul Frankland for providing me the opportunity to conduct this

project, Dr. Evelyn Lambe and Dr. Maithe Arruda-Carvalho for their guidance throughout the

project, Adam Ramsaran for his initial assistance in developing the project, and the rest of the

JFlab.

To Monsieur Mysterious Person Whose Name Shall not be Disclosed, thank you for entertaining

me. Running errands with you, watching you consume food, and being miserable together – ah,

what fun times. I shall forever cherish these moments. Time well spent.

To Julia Yu, thank you so much for supporting me throughout this project and beyond. More

food adventures please – I wish you all the best.

To Xinwen Zhu – i.e. my intellectual soul mate – here is your promised haiku:

From start to finish

You’ve always been there for me (“you’ve” counts as 1 syllable)

Forever grateful

Emotionally and intellectually – you have been a wonderful friend. You were there when I was

“synthesizing” a project, facilitated my thought processes, helped me work through things, and

more importantly – listened to what I had to say – rants, epiphanies, and cries. I cannot thank you

enough. But I cannot leave you with only good notes, so I’ll just end with…Mark is still cooler.

I wish you all the best!

Special shout-out to my incognito group. =)

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Table of Contents

Acknowledgments ..................................................................................................................... iii

Table of Contents ...................................................................................................................... iv

List of Figures .......................................................................................................................... vii

Chapter 1 Introduction ................................................................................................................ 1

1.1 Behavioural Framework .................................................................................................. 1

1.1.1 The Stability-Plasticity Dilemma ......................................................................... 1

1.1.2 Biological Substrates of Stability-Plasticity: The Brain........................................ 1

1.1.3 Forgetting, Behavioural Flexibility, and Generalization ....................................... 2

1.2 Perineuronal Nets ........................................................................................................... 3

1.2.1 Perineuronal Net Composition ............................................................................. 3

1.2.2 Tools to Manipulate Perineuronal Nets ................................................................ 4

1.2.3 The Function of Perineuronal Nets ...................................................................... 4

1.2.4 Perineuronal Nets and the Excitation: Inhibition Balance .................................... 9

1.2.5 Perineuronal Nets and Disorders.........................................................................11

1.3 Hippocampus .................................................................................................................11

1.3.1 Memory Function ...............................................................................................12

1.3.2 The Trisynaptic Loop .........................................................................................13

1.4 Engram ..........................................................................................................................14

1.4.1 Observational Studies .........................................................................................14

1.4.2 Loss-of-Function and Gain-of-Function Studies .................................................15

1.5 Summary and Research Questions .................................................................................15

1.5.1 Summary Points .................................................................................................15

1.5.2 Research Questions ............................................................................................16

Chapter 2 Methods ....................................................................................................................17

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Methods ................................................................................................................................17

2.1 Animals .........................................................................................................................17

2.2 Stereotaxic Surgeries .....................................................................................................17

2.3 Behavioural Assays........................................................................................................17

2.3.1 Water Maze ........................................................................................................17

2.3.2 Contextual Fear Conditioning .............................................................................18

2.4 Histology .......................................................................................................................19

2.4.1 Tissue Preparation ..............................................................................................19

2.4.2 Immunohistochemistry .......................................................................................19

2.4.3 Imaging and Analysis .........................................................................................20

2.5 Engram Experiment .......................................................................................................20

2.5.1 Mice ...................................................................................................................20

2.5.2 Contextual Fear Conditioning .............................................................................20

2.5.3 Immunohistochemistry .......................................................................................21

2.5.4 Imaging and Analysis .........................................................................................21

2.6 Statistical Analysis.........................................................................................................21

Chapter 3 Results ......................................................................................................................22

Results ..................................................................................................................................22

3.1 Histology .......................................................................................................................22

3.2 Behavioural Flexibility ..................................................................................................24

3.3 Generalization................................................................................................................26

3.4 ArcTRAP.......................................................................................................................28

Chapter 4 Discussion .................................................................................................................30

Discussion ............................................................................................................................30

4.1 Summary of Results .......................................................................................................30

4.1.1 Behavioural Effects ............................................................................................30

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4.1.2 Neural Representation Effects ............................................................................30

4.2 Model ............................................................................................................................31

4.2.1 Facilitated Extinction .........................................................................................31

4.2.2 Increased Behavioural Flexibility .......................................................................31

4.2.3 Increased Generalization ....................................................................................32

4.2.4 Overlap between Arc and cFos ...........................................................................32

4.3 Results Summary ...........................................................................................................32

4.4 Future Directions ...........................................................................................................33

4.5 Conclusion: Perineuronal Nets and the Stability-Plasticity Dilemma ..............................34

References .................................................................................................................................36

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List of Figures

Figure 1 Histological examination of perineuronal net density in the hippocampal CA1 region.

Figure 2 Perineuronal net disruption in CA1 is associated with faster reversal water maze

learning.

Figure 3 Perineuronal net disruption in CA1 is associated with enhanced contextual fear

generalization.

Figure 4 Histological examination of ArcTRAP and cFos cell densities in CA1 at the encoding

and retrieval of a contextual fear memory.

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Chapter 1 Introduction

1.1 Behavioural Framework

1.1.1 The Stability-Plasticity Dilemma

An information processing system requires plasticity, in order to integrate incoming information,

and stability, in order to store the acquired information. However, too much plasticity is

predicted to result in catastrophic forgetting, or forgetting of previously acquired information,

and too much stability is expected to result in the entrenchment effect, or saturation in the ability

to acquire new information. These constraints on the system’s ability to process information (or

learn) describe the stability-plasticity dilemma (Mermillod, Bugaiska, & Bonin, 2013). An

information processing system requires a balance between stability and plasticity in order to

optimize learning.

1.1.2 Biological Substrates of Stability-Plasticity: The Brain

The brain is a biological information processing system that is constrained by stability and

plasticity. This stability-plasticity balance is represented by neural circuit dynamics. Plasticity is

the change in neural connection strengths, through which information is thought to be acquired.

Stability is the maintenance of these neural connections, through which information is

presumably stored (Abraham & Robins, 2005). For example, auditory fear conditioning is

associated with the strengthening of synapses in the amygdala (Rogan, Staubli, & LeDoux,

1997), and preventing this strengthening prior to training blocks fear memory acquisition

(Fanselow & Kim, 1994). Interference with stably formed synapses, either through a weakening

of synapse strength resulting from long-term depression (Nabavi et al., 2014) or through a

reduction in spine size (Hayashi-Takagi et al., 2015), is associated with deficits in fear and motor

memory expression, respectively. These data support the idea that neural circuit plasticity is

required for memory acquisition and neural circuit stability is necessary for memory

maintenance.

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1.1.3 Forgetting, Behavioural Flexibility, and Generalization

The interaction between stability and plasticity can allow for forgetting, behavioural flexibility,

and generalization (Richards & Frankland, 2017). Specifically, stability of a specific network of

neurons is associated with memory persistence. Plasticity within this network can result in (1)

erasure of the stored memory, (2) updating of that memory, or (3) generalization of the memory.

1.1.3.1 Memory Erasure

Circuit remodeling, or plasticity, can facilitate forgetting of previously acquired memories. For

example, increasing neurogenesis in the hippocampus through exercise after contextual fear

conditioning, but not before, is associated with impaired contextual fear memory expression,

indicative of forgetting (Akers et al., 2014). Blocking exercise-mediated increases in

neurogenesis abolishes this effect (Akers et al., 2014). Plasticity may therefore facilitate

forgetting through retrieval errors or changes in the original memory expression.

1.1.3.2 Memory Updating

Plasticity is also associated with increased behavioural flexibility. This flexibility is

demonstrated by the ability to form novel associations between cues. For example, exposure to

an enriching environment results in faster acquisition of a new platform location in the Morris

Water maze task (Garthe, Roeder, & Kempermann, 2016). In other words, the original platform

location was readily updated to the new platform location, exhibiting flexible behaviour instead

of persistent behaviour, which in this context would have been maladaptive. Blocking

neurogenesis in these animals, however, abolished the increased flexible learning of the new

platform location (Garthe et al., 2016). These results suggest that increased plasticity may be

associated with increased behavioural flexibility.

1.1.3.3 Memory Generalization

Behavioural outputs acquired and attributed to a specific situation can be generalized to a novel

situation. The more similar two cues are, the more likely an animal is to demonstrate

generalization of a behaviour that was acquired in response to only one of the two cues (Resnik

& Paz, 2015). Plasticity may facilitate generalization. For example, increasing variability in

connection weights in artificial neural networks facilitates generalization (Hinton & van Camp,

1993; Richards & Frankland, 2017). This variability may represent plasticity. In contrast,

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plasticity in the form of neurogenesis has been associated with increased behavioural

discrimination (Pistikova, Brozka, & Stuchlik, 2017). The differences in effect of plasticity on

generalization may depend on the timing of plasticity induction, where this plasticity occurs, the

nature of the behavioural task, and the basic circuit organization in which plasticity is being

induced. Nonetheless, whereas stability would predict a persistent specific expression of a

memory, plasticity may predict a variable expression of a memory under novel contexts.

1.2 Perineuronal Nets

Perineuronal nets appear to be key molecular regulators of this stability-plasticity balance in the

brain. This section will review what is known about perineuronal nets, their presumed function,

and how they can be used as a tool to manipulate the stability-plasticity balance in the brain.

1.2.1 Perineuronal Net Composition

Perineuronal nets are specialized extracellular matrix structures. They form net-like structures

around the soma and proximal dendrites of neurons and appear to delineate synapses onto the

neuronal surface (Celio & Blumcke, 1994; Celio, Spreafico, De Biasi, & Vitellaro-Zuccarello,

1998; Dzyubenko, Gottschling, & Faissner, 2016). They are composed of a mixture of

carbohydrates and proteins, synthesized by both glial cells and neurons (Volterra & Meldolesi,

2005). The components of perineuronal nets include:

(1) Hyaluronan: Hyaluronan is a linear polymer of disaccharides that forms the backbone of

perineuronal nets. Its presumed function is to anchor the rest of the perineuronal net

structure to the neuron through interactions with the hyaluronan synthase enzyme

expressed on the neuronal surface (Carulli, Rhodes, & Fawcett, 2007; Wang & Fawcett,

2012).

(2) Chondroitin-sulfate proteoglycans: Chondroitin-sulfate proteoglycans consist of a protein

core bound to chondroitin sulfates (carbohydrate groups). These proteoglycans interact

with other components of perineuronal nets, including hyaluronan, link proteins, and

tenascin-R. They have a presumed role as a scaffolding structure for other molecules.

Examples of chondroitin-sulfate proteoglycans include aggrecan, neurocan, versican, and

brevican (Wang & Fawcett, 2012).

(3) Link proteins: Link proteins are small proteins that include the cartilage link protein 1

and brain link protein 2. They function in linking the hyaluronan backbone to the

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chondroitin-sulfate proteoglycans and contribute to the stability of perineuronal nets

(Carulli et al., 2007; Dzyubenko et al., 2016; Wang & Fawcett, 2012).

(4) Tenascin-R: Tenascin-R is a trimeric glycoprotein that is present in perineuronal nets and

can be found bound to chondroitin-sulfate proteoglycans (Wang & Fawcett, 2012).

1.2.2 Tools to Manipulate Perineuronal Nets

The function of perineuronal nets has been primarily examined through animal models in which

the aforementioned components were disrupted. These disruptions were made through the

production of genetic knockouts or through enzymatic degradation. For example, link protein

knockouts, such as that of cartilage link protein 1 (Romberg et al., 2013), chondroitin-sulfate

proteoglycan knockouts, such as that of aggrecan (Giamanco, Morawski, & Matthews, 2010),

and tenascin-R knockouts (Morellini et al., 2010) have been previously generated. Enzymatic

degradation of perineuronal nets has been achieved through infusions of chondroitinase ABC, a

bacterial enzyme that primarily degrades chondroitin-sulfate proteoglycans (but that may not be

highly specific) (Hylin, Orsi, Moore, & Dash, 2013; Pizzorusso et al., 2002; Romberg et al.,

2013), or hyaluronidase, which degrades the hyaluronan backbone of perineuronal nets (Hylin et

al., 2013). In addition to directly targeting perineuronal net components, one can manipulate

proteins involved in the regulation of perineuronal nets, such as hyaluronan synthases (which

anchor the nets to the cell surface) (Arranz et al., 2014) and matrix metalloproteases (which

degrade perineuronal nets) (Brew, Dinakarpandian, & Nagase, 2000).

1.2.3 The Function of Perineuronal Nets

Perineuronal nets have a hypothesized role in promoting stability within neural circuits. This

section will examine the different fields of evidence supporting this hypothesis.

1.2.3.1 Perineuronal Nets Over Time

The development of perineuronal nets appears to coincide with the closure of early

developmental critical periods. For example, in the central auditory system, perineuronal nets

develop gradually from the brainstem to the cortex, up until the sixth postnatal week in the

auditory cortex of rats. This development occurs after the onset of acoustically-evoked signal

processing within the cortex at postnatal day 12 (Sonntag, Blosa, Schmidt, Rubsamen, &

Morawski, 2015). In the visual system, perineuronal net development has been shown to

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coincide with the closure of the visual critical period, in the dorsal lateral geniculate nucleus of

the thalamus in cats (Hockfield, Kalb, Zaremba, & Fryer, 1990), and in the visual cortex of rats

and mice (Pizzorusso et al., 2002; Ye & Miao, 2013). Moreover, sensory deprivation can delay

the development of perineuronal nets. This has been shown in dark-reared cats and mice

(Hockfield et al., 1990; Ye & Miao, 2013) and in deaf rats (Myers, Ray, & Kulesza, 2012).

These results suggest that experience-dependent activity may contribute to the development of

perineuronal nets and that this development coincides with the closure of critical periods in

different systems.

1.2.3.2 Perineuronal Nets Over Space

An examination of the distribution of perineuronal nets across brain regions suggests that these

structures are denser in primary sensory and motor cortices than in higher order associative

cortices (Hylin et al., 2013). Histological analyses reveal increased perineuronal net staining in

layers IV and VI of the somatosensory, auditory, and visual cortices of gerbils and rats (in

addition to the retrosplenial cortex, prepiriform cortex, and hippocampal CA3 region) (Bruckner

et al., 1994). In macaque monkeys, the distribution of perineuronal nets is greater in layer III and

V of the somatosensory and motor regions, but diffuse in the prefrontal regions and cingulate

cortex across the cortical layers (McGuire, Hockfield, & Goldman-Rakic, 1989). Therefore,

perineuronal net distribution varies across cortical layers and between brain regions. This

distribution appears to roughly correlate with regions associated with higher circuit stability

(Hylin et al., 2013).

1.2.3.3 Perineuronal Nets and Manipulative Experiments

1.2.3.3.1 Perineuronal Nets Promote Stability in the Brain

To further investigate the function of perineuronal nets, experimental studies have been

performed that support a role for perineuronal nets in mediating the balance between stability

and plasticity in the brain.

Pizzorusso et al., (2002) have shown that the enzymatic degradation of perineuronal nets in the

visual cortex of adult rats reopens the critical period for visual cortical plasticity. In these rats,

normal ocular dominance column patterning within the visual cortex can be restored following

monocular deprivation. In contrast, rats with intact perineuronal net development exhibited

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altered ocular dominance column patterning following monocular deprivation. This pioneering

work demonstrated that perineuronal nets promote stability within circuits. Degradation of

perineuronal nets reinstates plasticity in circuits to levels measurable to that during early

developmental critical periods.

Perineuronal nets have additionally been examined under the context of axonal regeneration. In

this study, Alilain et al., (2011) observed an upregulation of perineuronal nets surrounding

phrenic motor neurons following cervical spinal cord injury. Degradation of these nets facilitated

extensive axonal regeneration. These results emphasize the inhibitory influence of perineuronal

nets on plasticity, under the context of injured tissue.

1.2.3.3.2 Perineuronal Nets in Forgetting, Behavioural Flexibility, and Generalization

This section will examine the behavioural effects of perineuronal net manipulations under the

context of forgetting, flexibility, and generalization.

Perineuronal Nets in Forgetting

1.2.3.3.2.1.1 Acquisition versus Forgetting Based Impairments

Several experiments have been performed in which perineuronal net disruption was associated

with impaired behavioural task performance. Tenascin-C knockout mice are impaired in the

one-trial learning step-down avoidance task (Strekalova et al., 2002). In this task, mice were

trained to not step down from a platform in order to avoid receiving an electric footshock.

Tenascin-C mice would more quickly step down than control mice during the recall session.

Perineuronal net disruption in the hippocampus has also been shown to be associated with

impaired contextual fear conditioning. This has been shown through enzymatic degradation of

perineuronal nets using hyaluronidase, or chondroitinase ABC and hyaluronidase in the

hippocampi of mice and rats, respectively (Hylin et al., 2013; Kochlamazashvili et al., 2010).

Hippocampal perineuronal net disruption has also been associated with impaired auditory fear

memory expression acquired under the trace but not delayed paradigm; whereas medial

prefrontal cortical perineuronal net disruption has been associated with impaired auditory fear

memory expression acquired under both trace and delayed paradigms (Hylin et al., 2013). In

contrast to these studies, one study showed that tenascin-R knockout mice exhibit no deficits in

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contextual fear conditioning (Morellini et al., 2010). Differences in contextual fear conditioning

effects may arise from the different mechanisms by which perineuronal nets were disrupted

(enzymatic versus genetic manipulations of specific components), the regions in which they were

disrupted (global versus specific), and the training conditions and experimental timeline.

These results suggest that increasing plasticity by perineuronal net disruption during task

acquisition impairs task performance. However, it remains to be seen whether disrupting

perineuronal nets post-task acquisition might also lead to behavioural defects. Such experiments

would allow us to distinguish whether perineuronal net mediated alterations in circuit

stability/plasticity occur through impairments in learning (i.e. perineuronal nets are disrupted

during task acquisition) and/or through impairments in forgetting (i.e. perineuronal nets are

disrupted post-task acquisition).

1.2.3.3.2.1.2 Erasure of a Memory Achieved through Extinction Learning

Perineuronal net disruption has been associated with erasure of memories following extinction

learning. For example, Gogolla et al., (2009) demonstrated that removal of perineuronal nets in

the amygdala of mice using chondroitinase ABC prior to auditory fear conditioning was

associated with facilitated extinction learning and reduced reinstatement of the fear memory,

suggestive of memory erasure. However, these effects were not observed when perineuronal nets

were disrupted after contextual fear training.

Similarly, perineuronal net disruptions using chondroitinase ABC in the amygdala of rats is

associated with reduced reinstatement of morphine-induced conditioned place preference

following extinction training, or of heroin and cocaine seeking behaviour (Xue et al., 2014).

Therefore, extinction learning following perineuronal net disruption promotes memory erasure,

observed under auditory fear conditioning and drug-based conditioned place preference

paradigms.

Perineuronal Nets in Behavioural Flexibility

The degradation of perineuronal nets has been associated with increased behavioural flexibility,

as measured by facilitated reversal learning of different cue associations. For example, tenascin-

R knockout mice are faster at acquiring the new location of a hidden platform in a spatial water-

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maze task, suggesting flexibility in updating of the original platform location (Morellini et al.,

2010). Tenascin-R knockout mice also demonstrate rapid learning of an odor reward association

task, in which odor-reward associations were reversed (Morellini et al., 2010). Therefore,

perineuronal net disruption promotes behavioural flexibility.

Perineuronal Nets in Generalization

Conflicting results have been observed regarding the effects of perineuronal net disruption on

context discrimination. For example, in a novel object recognition task, brevican knockout mice

show similar preference levels in a spatial Y maze task between the familiar and novel arms of

the maze. In contrast, control mice exhibit a greater preference for the novel arm compared to the

familiar arm (Favuzzi et al., 2017). These results suggest that perineuronal net disruption is

associated with decreased context discrimination, or memory for the familiar arm (suggestive of

forgetting). In a different study, cartilage link protein 1 knockout mice and mice with

chondroitinase ABC mediated disruption of perineuronal nets in the perirhinal cortex show

enhanced object recognition memory (Romberg et al., 2013). That is, they spent a greater amount

of time with a novel object presented at test session than control mice. This result suggests that

perineuronal net disruption may in fact be associated with facilitated cue separation or better

retention of the familiar objects.

Differences between these two studies may arise from the method of perineuronal net

manipulation (the specific components that were knocked out and whether they were disrupted

enzymatically or genetically), the region of manipulation, and the type of task performed. For

example, whereas Favuzzi et al., (2017) used a spatial Y maze task, Romberg et al., (2013) used

objects to test for novelty preference. Presumably, the arms of the Y maze task are more similar

and constitute the only two options compared to the object task, in which very different objects

and multiple objects were presented. The more similar two cues, such as the arms of the maze

compared to the objects, the more likely one is to observe diminished discrimination between

cues (Resnik & Paz, 2015).

Decreased novelty preference, however, would support the predicted effects of enhanced

generalization following perineuronal net disruption (given increased plasticity). This is based on

the fact that generalization and novelty preference rely on discrimination between cues.

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1.2.3.4 Perineuronal Nets and Behavioural Summary

To summarize, observational studies demonstrate that perineuronal net development coincides

with the closure of critical periods and that the distribution of perineuronal nets appears to be

denser in regions associated with greater stability. Experimental studies demonstrate that

perineuronal net disruption is able to reinstate plasticity, and is associated with enhanced

forgetting, behavioural flexibility, and possibly generalization.

1.2.4 Perineuronal Nets and the Excitation: Inhibition Balance

This section will review the possible mechanisms by which perineuronal nets might modulate the

stability-plasticity balance in the brain, including through influences on the excitation-inhibition

balance within circuits and additional molecular mechanisms.

1.2.4.1 Perineuronal Nets and Parvalbumin Cells

Perineuronal nets have been found to generally surround GABAergic parvalbumin-expressing

cells (Bruckner et al., 1994; Hartig, Brauer, & Bruckner, 1992). These are fast-spiking cells that

receive convergent inputs primarily from excitatory principal cells, producing broad tuning

curves to stimuli (Hu, Gan, & Jonas, 2014). In turn, parvalbumin-positive cells have highly

divergent inhibitory projections. These projections can participate in feedforward or feedback

inhibition. The latter form enables inhibition of surrounding cell activity, employing a “winner-

take-all” mechanism (Hu et al., 2014). This function may facilitate, for example, pattern

separation (Hu et al., 2014).

Perineuronal nets have been shown to enhance the excitability of parvalbumin-expressing cells.

Degradation of perineuronal nets is associated with reduced firing rates of parvalbumin-positive

cortical neurons (Balmer, 2016).

Interestingly, monocular deprivation has been shown to inhibit parvalbumin-positive cell activity

(Hu et al., 2014) and transplantation of embryonic parvalbumin-positive cells is able to reinstate

cortical plasticity to allow for proper ocular dominance column patterning (Tang, Stryker,

Alvarez-Buylla, & Espinosa, 2014). These effects parallel those effects observed with

perineuronal net manipulations: monocular deprivation delays perineuronal net development

(Hockfield et al., 1990) and perineuronal net disruption reinstates visual cortical plasticity for

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ocular dominance column patterning even after the closure of the endogenous critical period

(Pizzorusso et al., 2002). (Presumably, the firing activity patterns between embryonic

parvalbumin-positive cells and mature parvalbumin-positive cells without perineuronal nets are

similar, yielding the same effects on cortical plasticity.) In addition, the distribution of

parvalbumin positive cells with perineuronal nets changes with learning. For example, in the

HVC nucleus of zebrafinches, the percentage of parvalbumin positive cells surrounded by

perineuronal nets was correlated to song maturity (Balmer, Carels, Frisch, & Nick, 2009).

Together, these results suggest that perineuronal nets and parvalbumin expressing interneurons

are tightly coupled in expression and functional effects.

Perineuronal nets have also been observed surrounding other cell types, although to a much

lesser extent. These cell types include: glutamatergic pyramidal cells of the neocortex (Alpar,

Gartner, Hartig, & Bruckner, 2006), cholinergic principal cells of the oculomotor nucleus

(Morawski, Bruckner, Jager, Seeger, & Arendt, 2010), aspartergic neurons of the deep cerebellar

nuclei (Kumoi, Saito, Kuno, & Tanaka, 1988), and glycinergic neurons of the medial nucleus of

the trapezoid body (Balmer, 2016). Perineuronal net associations with these cell types have also

been implicated in learning and memory processes. For example, excitatory neurons in the

amygdala surrounded by perineuronal nets were preferentially recruited to an auditory fear

memory, as suggested by an increased overlap in labeling with the cFos activity marker

(Morikawa, Ikegaya, Narita, & Tamura, 2017).

1.2.4.2 The Molecular Mechanisms of Action of Perineuronal Nets

There are several mechanisms by which perineuronal nets may interact with the neurons they

surround. These include:

(1) Regulation of the distribution of glutamate receptors. Perineuronal nets limit the lateral

diffusion of AMPA receptors. Removal of perineuronal nets is associated with increased

extrasynaptic receptor diffusion, AMPA receptor exchange, and an increased paired-

pulse ratio (Frischknecht et al., 2009; McRae & Porter, 2012).

(2) Influence on long-term synaptic plasticity. For example, synapses from CA3 in the CA2

stratum radiatum of the hippocampus are resistant to long-term potentiation induction.

The removal of perineuronal nets around these excitatory pyramidal neurons facilitates

long-term potentiation induction (Carstens, Phillips, Pozzo-Miller, Weinberg, & Dudek,

11

2016). In contrast, perineuronal net removal in the hippocampal CA1 region was

associated with a decrease in long-term potentiation (Bukalo, Schachner, & Dityatev,

2001).

(3) A presumed function in ion buffering, based on its polyanionic structure (Brückner

(Bruckner et al., 1994; Morawski et al., 2010; Morawski et al., 2015).

(4) Protection from oxidative stress (Morawski, Bruckner, Riederer, Bruckner, & Arendt,

2004).

(5) Possible interactions with the cytoskeleton (Celio et al., 1998).

1.2.5 Perineuronal Nets and Disorders

Perineuronal nets have been implicated in epilepsy, Alzheimer’s disease, and schizophrenia.

Interestingly, altered parvalbumin activities have also been associated with epilepsy, Alzheimer’s

disease, and schizophrenia, in addition to depression and autism (Hu et al., 2014). In epilepsy,

mutations in certain extracellular matrix components and changes in the expression of certain

components have been associated with epilepsy. For example, a decrease in the expression of

proteoglycan link protein 1, hyaluronan synthase 3, and aggrecan has been observed in epileptic

prone tissue (Soleman, Filippov, Dityatev, & Fawcett, 2013). Alzheimer’s disease has in turn

been associated with an increase in perineuronal net expression (Vegh et al., 2014). Degradation

of these structures reversed the early memory deficits observed in an Alzheimer’s mouse model

(Vegh et al., 2014). In schizophrenic patients, decreases in perineuronal net expression have been

observed in the amygdala, entorhinal cortex, and prefrontal cortex (Berretta, Pantazopoulos,

Markota, Brown, & Batzianouli, 2015). Together, these data emphasize the relevance of

perineuronal nets in maintaining proper physiological function.

1.3 Hippocampus

The hippocampus is a seahorse shaped structure found in the medial temporal lobes of humans,

and a cashew shaped structure found below the neocortex in rodents (Knierim, 2015). It receives

cortical inputs through the entorhinal cortex, which projects into the dentate gyrus, which in turn

projects to the CA3 region, followed by the CA1 region, which outputs through the entorhinal

cortex. This simplified circuit defines the trisynaptic loop (Knierim, 2015). This circuit has been

extensively studied in its role in spatial memory processing. This section will briefly review the

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three regions of the trisynaptic loop and their associated functions, with an emphasis on the

hippocampal CA1 region and its contributions to memory.

1.3.1 Memory Function

The hippocampus is thought to function in the encoding of episodic memories, and in the

retrieval of recent memories (Knierim, 2015). Insights into the hippocampal function in memory

processing arise from human and animal lesion studies, and neurophysiological studies. For

example, patient H.M. exhibited deficits in the formation of new declarative memories

(anterograde amnesia) but not in the formation of new procedural memories, and could retrieve

temporally-remote memories but not temporally-recent memories (temporally-graded retrograde

amnesia) (Squire, 2009). These effects were observed following a surgical procedure to remove

epileptogenic tissue, which in this case included the hippocampus within bilateral medial

temporal lobe lesions (Squire, 2009). Animal studies have corroborated a role for the

hippocampus in the formation of spatial memories. These animal studies often examine spatial

learning through tasks such as the Morris water maze, radial arm maze, and contextual fear

conditioning (Knierim, 2015). These lesion studies suggest that the hippocampus functions in

contextual memory processing.

The identification of long-term potentiation, and of space and time cells in the hippocampus,

further support a role for the hippocampus in the translation of experiences into memories.

Although not exclusive to the hippocampus, synaptic strengthening through long-term

potentiation was first identified in perforant projections to the dentate gyrus (Bliss & Lomo,

1973). Classic long-term potentiation (that is NMDA-receptor dependent) is presumed to be the

neurobiological basis by which memories are formed. This is because long-term potentiation is

persistent, input (synapse) specific, associative, and cooperative (Malenka, 2003). Memories

parallel these same properties. They are persistent, they are distinguished via specific sets of

information (input specificity), different sources of information can be associated, and

insignificant information is forgotten (non-cooperative input). Therefore, long-term potentiation

is thought to equal memory (Stevens, 1998), and the presence of synaptic plasticity in the

hippocampus provides a mechanism by which memories can be formed in the hippocampus. The

presence of space and time cells suggests that these memories would be of a contextual basis.

Place cells are cells that fire exclusively to a specific location in an environment (Knierim,

13

2015). Time cells are cells that fire when a specific amount of time has passed (Eichenbaum,

2014). The same neurons can encode a place or a time (Eichenbaum, 2014). These neurons

include granule cells of the dentate gyrus, and pyramidal cells of the CA1, CA2, and CA3 areas

(Knierim, 2015). Neurophysiological data provide a basis by which the hippocampus may

function in translating experiences, defined by space and time, into memories through synaptic

plasticity.

1.3.2 The Trisynaptic Loop

1.3.2.1 Dentate Gyrus

The dentate gyrus is the input structure of the hippocampus (within the trisynaptic loop). The

dentate gyrus has a presumed function in pattern separation. The dentate gyrus circuitry is

distinct in that it is characterized by an extensive number of granule cells (Amaral, Scharfman, &

Lavenex, 2007), and by the presence of neurogenesis (Drew, Fusi, & Hen, 2013). These

characteristics, in addition to the firing activity patterns of the cells, is thought to facilitate the

function of pattern separation, wherein similar experiences can be distinguished through sparse

coding in the circuit (orthogonalization of similar inputs).

1.3.2.2 CA3

Projections from the dentate gyrus arrive at the CA3 region, which is characterized by an auto-

associative network formed by multiple recurrent collaterals (Drew et al., 2013). This circuit is

thought to facilitate the function of pattern completion, wherein a cue is able to reactivate an

encoded memory pattern and that allows for different cues to associate and activate an encoded

memory pattern (Rolls, 2013).

1.3.2.3 CA1

The CA1 outputs from the hippocampus. Studies have been performed to dissociate the role of

the CA1 region in memory processing from that of the CA3 region and the dentate gyrus. For

example, whereas both the CA1 and CA3 areas appear necessary for the context dependent

extinction of an auditory conditioned fear memory, CA1 is required for the retrieval of this

context dependency (Ji & Maren, 2008). The CA1 region may also play an important role in the

encoding of memories with a temporal component, including the encoding of temporally distant

events, the encoding of temporally distant events at long intervals, and temporal pattern

14

separation (Rolls & Kesner, 2006). For example, knocking out NMDA receptors in the

hippocampal CA1 pyramidal cells is associated with impaired auditory fear memory acquired

under the trace (time delay between conditioned stimulus and unconditioned stimulus)

conditioning paradigm but not delayed (no delay between conditioned stimulus and

unconditioned stimulus) (Huerta, Sun, Wilson, & Tonegawa, 2000). Similarly, in a trial-unique

odor pair conditioning task, CA1 lesions impaired the association of odor pairs when the delay

between the presentations of these odors was long, whereas CA3 lesions led to impairments at

brief interval delays (Farovik, Dupont, & Eichenbaum, 2010). In addition to the role of

associating temporally separate events, the CA1 functions in temporal pattern separation. Gilbert,

Kesner, and Lee (2001) demonstrate that rats with lesions to the CA1 area are impaired in their

ability to recall the earliest arm visited in a radial arm maze (Gilbert, Kesner, & Lee, 2001).

1.4 Engram

The term engram was coined by Richard Semon to refer to a persistent physical change in the

brain that encodes a memory (Tonegawa, Liu, Ramirez, & Redondo, 2015). Engram cells are

thus a population of cells that are activated during learning, undergo changes as a consequence of

this learning, and can be reactivated to recall the memory formed as a product of learning

(Tonegawa et al., 2015). Presumably, this population of engram cells form a neuronal ensemble

through (Hebbian) synaptic plasticity (Tonegawa et al., 2015).

There are three basic methods applied to identifying an engram: observational approach, loss-of-

function approach, and gain-of-function approach (Tonegawa et al., 2015).

1.4.1 Observational Studies

Observational studies can be used to identify cells that are active during a learning episode. For

example, cells active during a learning episode can be tagged by labelling immediate early genes,

such as cFos and Arc. These genes are expressed following sufficient activation of a neuron

(Guzowski et al., 2005). Another form of observational study that can be used to assess whether

the cells that were activated during learning underwent cellular changes as a consequence of this

learning include electrophysiological and imaging studies. For example, one can examine

whether cells have undergone changes in synaptic strength using in vivo electrophysiology

(Nabavi et al., 2014), or changes in spine dynamics using in vivo imaging of spine dynamics

15

(Holtmaat, Wilbrecht, Knott, Welker, & Svoboda, 2006). Finally, observational studies can be

used to examine whether cells active during retrieval of a memory overlap with those cells active

during learning. For example, Reijmers et al., (2007) used TetTag transgenic mice to label cells

active during learning through induced LAC expression, and immunohistochemistry to label

cells active during retrieval by staining for ZIF, an immediate early gene. Overlap between LAC

(learning) and ZIF (retrieval) was then examined.

1.4.2 Loss-of-Function and Gain-of-Function Studies

Cells tagged during learning can further be manipulated in loss-of-function or gain-of-function

studies. This describes the tag-and-manipulate strategy (Josselyn, Kohler, & Frankland, 2015).

For example, TetTag and TRAP transgenic animals can be used to label cells active during a

learning episode, and these labelled cells can be induced to express various effectors, such as

opsins or DREADDs. Inhibition of these cells or activation of these cells through these effector

molecules has been associated with deficits in memory expression, and expression of the

memory, respectively (Josselyn et al., 2015).

Therefore, engram cells can be identified through observational studies, and tagged and

manipulated to examine the behavioural effects of presumed engram cell manipulation.

1.5 Summary and Research Questions

1.5.1 Summary Points

Balance between stability and plasticity determines optimal learning. Too much plasticity

is associated with forgetting. Too much stability is associated with a limited learning

capacity.

The interaction between stability and plasticity may result in memory erasure, memory

updating (flexibility), or memory generalization.

Perineuronal nets appear to promote stability within circuits.

The hippocampus plays an important role in the processing of contextual memories.

Engrams are memory traces.

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1.5.2 Research Questions

Our research project wanted to investigate the effects of shifting the stability-plasticity balance in

the brain on learning and memory. This shift was achieved by manipulating the levels of

perineuronal nets in the brain. Specifically, we enzymatically degraded perineuronal nets using

chondroitinase ABC, which degrades chondroitin-sulfate proteoglycans, a primary component of

perineuronal nets. These manipulations were performed in the hippocampal CA1 region,

associated with contextual memory processing. We additionally examined how memory

representations were altered following perineuronal net disruptions.

The following are points that were addressed in this research project:

What is the effect of increasing plasticity, through perineuronal net disruption, in the

hippocampal CA1 region on learning and memory?

o Does perineuronal net disruption facilitate contextual fear memory erasure?

o Does perineuronal net disruption promote behavioural flexibility of a contextual

memory?

o Does perineuronal net disruption facilitate contextual fear memory

generalization?

How do interactions between perineuronal nets and parvalbumin-positive cells translate

to the observed behavioural effects?

o How are engram properties influenced by perineuronal net disruption in the

hippocampal CA1 region?

By addressing these research questions, we establish a (speculative) model that explains how

perineuronal net disruptions cause alterations in engram representations that lead to the observed

behavioural effects.

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Chapter 2 Methods

Methods

2.1 Animals

129SvEV x C57BL6/N F1 hybrid male and female mice of at least 8 weeks of age were used in

each experiment. These mice were housed in groups of 4 per cage, had continuous access to food

and water, and were exposed to a 12-hour light/dark cycle at the Lab Animal Services facility at

the Peter Gilgan Centre for Research and Learning.

2.2 Stereotaxic Surgeries

Mice were intraperitoneally injected with atropine sulfate (0.1 mg/kg) and chloral hydrate (400

mg/kg), before being placed in a stereotaxic frame. Once placed, the skull was exposed and holes

were drilled at the hippocampal CA1 coordinates (AP: -1.8 mm, ML: ±1.5mm, DV: -1.5mm

from bregma). Penicillinase or chondroitinase ABC was infused bilaterally into the CA1 through

glass micropipettes. A volume of 1µl at a rate of 0.1µl/min was infused, followed by a 10min

wait period to ensure full transfer before removing the pipette. Mice were subsequently

subcutaneously injected with 1ml of saline and either ketoprofen or Metacam (2mg/kg). Mice

were allowed to recover from surgeries for 3 days before behavioural testing.

2.3 Behavioural Assays

2.3.1 Water Maze

2.3.1.1 Apparatus

A large circular white tub was filled with water, adjusted to a temperature of approximately

26ºC, and with nontoxic white paint to render the water opaque. During training sessions, a small

circular platform was positioned in the pool as specified by the WaterMaze software, and

submerged by at least 5mm of water. The pool was encircled by a white curtain marked by red or

black shapes that could be used for spatial orientation. The room was dimly lit, with ventilation.

A camera was positioned directly above the pool that would track the mouse’s movements.

These tracked movements were analyzed using the WaterMaze software.

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2.3.1.2 Habituation, Training, and Testing

Mice were handled for three consecutive days (2min/mouse) prior to water maze training. On

training and test days, mice were brought to the water maze room at least 1 hour before

commencement of the training or testing sessions. On training days, mice were trained to find the

location of the hidden platform. Each training day consisted of 2-blocks of 3-trials, with each

block separated by at least 1 hour. Each trial was at most 60 seconds long (the length of which

was determined by the time it took the mouse to reach the platform), followed by a 15 second

wait period during which the mouse waited on the platform before removal. There were 3

consecutive training days. The start position (or drop-off point) per trial was pseudorandomly

determined by the WaterMaze software from 4 possible locations (North, South, East, or West),

but was the same between mice. On test day, the platform was removed and mice were dropped-

off at the same start point between mice. Test day consisted of a single 60 second trial per

mouse.

2.3.1.3 Reversal Training

Before reversal training, forward training and testing took place as described above. In the

reversal training sessions, the platform location was moved to the opposite quadrant, as

determined by the WaterMaze software. Similar to forward training, mice underwent 2-blocks of

3 trials, with each block separated by at least 1 hour. Similar to forward training, these trials were

at most 60 seconds long, with a 15 second wait period when the mouse has reached the platform.

On the third consecutive day of reversal training, a test session was conducted as described

previously. At least an hour after this test session, the third reversal training session took place.

Two additional consecutive reversal training days took place before. After five consecutive

reversal training days, a second test session was conducted.

2.3.2 Contextual Fear Conditioning

2.3.2.1 Contextual Fear Training and Testing Apparatus

The contextual fear training and testing chamber consisted of a grid floor, two opposing

plexiglass walls, and two opposing aluminum walls. Below the grid floor was a tray that was

wiped with 70% EtOH, to provide the chamber with a distinct smell.

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2.3.2.2 Novel Context Testing Apparatus

To produce a novel context, a white mat wiped with distilled water was placed above the grid

floor in the same chambers as above. A folded white board was additionally placed, such that

now the chamber appeared to have 3 walls forming a triangular context, with 2 white walls and 1

plexiglass wall.

2.3.2.3 Training and Testing

Mice were brought into a holding room at least 1 hour before contextual fear training and testing

sessions. On training day, mice were placed in the contextual fear conditioning chamber. Mice

were allowed to explore the chamber for 2 minutes, before 3 shock deliveries at 0.5mA separated

by 1 minute. After these 3 shock deliveries, mice were allowed to explore the chamber for an

additional minute before being retrieved. On test days, mice were placed in the contextual fear

training chamber or the novel context chamber for 5 minutes. The order in which either of these

testing chambers was presented was altered between mice. Mouse movements were tracked

using cameras mounted on top of the chambers. Freezing levels during training and testing

sessions were analyzed using FreezeFrame software.

2.4 Histology

2.4.1 Tissue Preparation

Mice were anaesthetized with chloral hydrate, prior to transcardial perfusion with 40mL of 0.1M

phosphate-buffered solution, followed by 40mL of 4% paraformaldehyde solution, at a rate of

13-15mL/min. Brains were removed and incubated overnight in 4% paraformaldehyde solution

in 4ºC. Brains were then coronally sectioned in 50µm thick slices using a vibratome. Slices were

stored in 0.1M phosphate-buffered solution with 0.05% sodium azide in 4ºC.

2.4.2 Immunohistochemistry

Slices were washed with 0.1M phosphate-buffered solution, followed by a 1 hour incubation in

blocking solution (4% goat serum and 0.5% Triton-X diluted in 0.1M phosphate-buffered

solution). The following primary antibodies were used: wisteria floribunda agglutinin to

visualize perineuronal nets and anti-parvalbumin. Primary antibodies were diluted in blocking

solution, and slices were incubated overnight with the primary antibodies in 4ºC. After primary

20

antibody incubation, slices were rinsed with 0.1% Triton-X diluted in 0.1M phosphate-buffered

solution, followed by a 2-hour secondary antibody incubation at room temperature. The

following secondary antibodies were used: streptavidin conjugated to Alexa Fluor 488 to

visualize perineuronal nets and goat anti-mouse conjugated to Alexa Fluor 568 to visualize

parvalbumin. Slices were counterstained with DAPI for 10 minutes at room temperature, rinsed

with 0.1M phosphate-buffered solution, and mounted on slides using PermaFluor mounting

medium (Thermo Scientific).

2.4.3 Imaging and Analysis

Slides were imaged using a laser confocal microscope (Zeiss LSM 710). Serial z-stack images

were obtained at 20x magnification. The number of perineuronal nets and parvalbumin positive

cells was manually quantified within a single plane.

2.5 Engram Experiment

2.5.1 Mice

For the engram experiment, ArcTRAP-TdTomato transgenic mice of a C57Bl6 background were

used (male and female, at least 8 weeks of age).

2.5.2 Contextual Fear Conditioning

Prior to contextual fear conditioning, these mice were handled for 3 consecutive days (2

min/day) and placed in a holding room for at least 6 hours. On training day, mice were brought

to the holding room 1 hour before fear training. Fear training was conducted as described

previously for wildtype mice, except that 0.7mA shocks were delivered since these mice are

poorer learners. Immediately after fear training, mice were intraperitoneally injected with 4-

hydrotamoxifen to induce expression of TdTomato in Arc expressing cells, and placed in the

holding room for 6 hours before returning them to the housing facility. 3 days after training,

these mice were tested for fear memory expression as described above. 90 minutes post testing,

mice were perfused and tissue was prepared for immunohistochemistry as descried previously in

order to label cFos expression.

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2.5.3 Immunohistochemistry

Immunohistochemistry was conducted as described previously with some changes. First, the

following primary antibodies were used: wisteria floribunda agglutinin to visualize perineuronal

nets and anti-cFos. Second, the primary antibodies were incubated for 3 overnights in order to

optimize cFos labelling. Third, the following seconday antibodies were used: streptavidin

conjugated to Alexa Fluor 488 to visualize perineuronal nets and anti-rabbit conjugated to Alex

Fluor 647 to visualize cFos. Fourth, the secondary antibodies were incubated overnight to

optimize cFos labelling.

2.5.4 Imaging and Analysis

Slides were imaged as described previously, except that a single image plane was taken at 10x

magnification. Image conditions were identical for all slides. The number of perineuronal nets,

TdTomato expressing cells, and cFos expressing cells were quantified using a semi-automatic

method on ImageJ.

2.6 Statistical Analysis

Statistical analysis was performed using SPSS (IBM Corporation, Armonk, NY). Normality

assumptions were validated using the Kolmogorov-Smirnov test. One-way ANOVA (with post-

hoc Tukey HSD comparisons) was used to assess histological differences, two-way ANOVA

was used to assess generalization differences, and paired student’s t-test or Wilcoxon signed-

rank test was used in all other comparisons. Significance level was set to 0.05. Data are

expressed as mean ± standard error mean.

22

Chapter 3 Results

Results

3.1 Histology

To validate chABC mediated disruption of perineuronal nets in the hippocampal CA1 region, we

performed immunohistochemistry and quantified the number of perineuronal nets in the CA1

area at 3, 14, 28, and 56 days post-enzymatic infusion (FIG1A). One-way ANOVA analyses

reveal a statistically significant decrease in perineuronal net density at 3 days (2.91 ± 1.4

counts/mm2, n = 4 mice), 14 days (20.4 ± 3.3 counts/mm2, n = 4 mice), and 28 days (25.2 ± 3.2

counts/mm2, n = 3 mice) post-chABC infusion compared to control mice treated with PEN (38.2

± 2.3 counts/mm2, n = 4 mice), but not at 56 days (40.1 ± 3.7 counts/mm2, n = 2 mice) post-

chABC infusion, (F(4,12)=30.499, p<0.001), (FIG1B). Enzymatic disruption of perineuronal

nets using chABC was therefore effective, and was associated with a gradual recovery in

perineuronal net density over two months post-infusion. This recovery period is in line with data

observed in the perirhinal cortex, with approximately 50%, 70%, and full recovery at 2 weeks, 6

weeks, and 8 weeks post-chABC infusion, respectively (Romberg et al., 2013).

23

Figure 1 Histological perineuronal net quantification in the hippocampal CA1 region.

A. Examples of perineuronal nets stained with Wisteria floribunda agglutinin in the hippocampal

CA1 region of mice treated with PEN or chABC at 3, 14, 28, and 56 days post-treatment. B.

Quantifications of perineuronal net densities in counts/mm2. N=4 mice per condition, except

chABC-28d (3) and chABC-56d (2). Statistical comparisons to PEN-3d group, using One-Way

ANOVA and Tukey’s HSD test.

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3.2 Behavioural Flexibility

To examine the effects of perineuronal net disruption in the hippocampal CA1 region on

behavioural flexibility, mice were trained in a spatial water maze task (FIG2A). In this task,

mice had to locate a hidden platform in a pool of water using spatially guided cues. PEN and

chABC mice were able to acquire the location of the hidden platform equally well, as assessed

by the percent time spent within the target zone (PEN: 19.5 ± 2.7%, n = 10 mice; chABC: 21.8 ±

2.6%, n = 9 mice; t(17) = 0.624, p = 0.541) (FIG2B,C). However, when the platform was

relocated to the opposite quadrant, chABC mice displayed faster acquisition of this new platform

location compared to PEN mice, as assessed by the percent time spent within the new target zone

(PEN: 6.0 ± 1.4%; chABC: 12.9 ± 3.0%; t(17) = 0.107, p = 0.048) and the cumulative proximity

to the new platform location (PEN: 2719.1 ± 68.3 cm; chABC: 1991.8 ± 86.0 cm; t(17) = -6.687,

p<0.001) (FIG2B,C,D). With further reversal training, both groups of mice displayed similar

performance levels, as assessed by percent time spent within the new target zone (PEN: 16.9 ±

2.4%; chABC: 16.9 ± 3.4%; t(17) = -0.003, p = 0.998) and the cumulative proximity to the new

platform location (PEN: 1920.1 ± 125.1 cm; chABC: 1774.5 ± 115.6 cm; t(17) = -0.849, p =

0.408) (FIG2B,C,D). Together, these results demonstrate that perineuronal net disruption in

CA1 is associated with enhanced behavioural flexibility, as measured by facilitated reversal

learning of a new platform location in a spatial water maze task. These effects are supported by

previous data demonstrating faster water maze and odor associated reward reversal learning tasks

in tenascin-R knockout mice (Morellini et al., 2010).

25

Figure 2 chABC treated mice show facilitated water maze reversal learning

A. Schematic of spatial water maze training. B. Sample movement traces of a single PEN treated

mice (top) and a single chABC treated mice (bottom) in the water maze pool at forward probe,

reversal probe 1, and reversal probe 2. C. Percent time spent in each zone (target, left, right, and

opposite) quantified for the forward probe, reversal probe 1, and reversal probe 2. chABC mice

spend significantly more time within the new target zone compared to PEN mice at reversal

probe 1. D. Cumulative proximity to target measured for the reversal probe 1 and reversal probe

2. chABC mice are significantly closer to the new target platform location compared to PEN

mice at reversal probe 1. N=10 PEN, 9 chABC mice.

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3.3 Generalization

To examine the effects of perineuronal net disruption on memory generalization, mice were

contextually fear conditioned and the specificity of the contextual fear memory was assessed by

comparing freezing levels between the training context and a novel context (FIG3A). In the

training context, two-way ANOVA analyses reveal no statistically significant difference in

freezing levels between PEN and chABC mice (F(1,27) = 1.740, p = 0.198), in the order of

training context presentation (F(1,27) = 0.006, p = 0.938), or in the interaction between the

mouse condition and order of context presentation (F(1, 27) = 0.311, p = 0.582) (FIG3B). In the

novel context, there is a significant effect of condition (F(1,27) = 6.785, p = 0.015), order of

context presentation (F(1,27) = 17.104, p<0.001), but no interaction effect (F(1, 27) = 0.186, p =

0.669) (FIG3C). This is associated with higher freezing levels in chABC treated mice (37.0 ±

5.1%, n = 15 mice) compared to PEN mice (22.1 ± 4.1%, n = 16 mice); and higher freezing

levels when the novel context is presented prior to the training context for both conditions

(training context before novel (PEN: 12.3 ± 3.4%, n = 8 mice; chABC: 23.8 ± 6.2%, n = 7 mice);

novel context before training (PEN: 31.8 ± 5.8%, n = 8 mice; chABC: 47.8 ± 5.4%, n = 8 mice))

(FIG3C). Together, the results suggest that chABC treatment in the CA1 region is associated

with enhanced contextual fear memory generalization (FIG3D).

27

28

Figure 3 chABC treated mice show enhanced generalization of a contextual fear memory

A. Schematic of contextual fear training and test sessions. B. Percent freezing in the training

context only. C. Percent freezing in the novel context only. chABC mice freeze more to PEN

mice in the novel context. D. Percent freezing in either the training or novel context, independent

of context order presentation. chABC mice freeze more within the novel context compared to

PEN mice. N= 16 PEN, 15 chABC mice.

3.4 ArcTRAP

To examine how perineuronal net disruption influences memory representation within CA1

circuits, we contextually fear conditioned mice and tagged active neurons at training and testing

sessions using ArcTRAP-TdTomato transgenic mice and cFos immunohistochemistry,

respectively (FIG4A,B). We found an increased but non-significant Arc-positive cell density in

the CA1 region at training in chABC mice (12.2 ± 3.0 counts/mm2, n = 7 mice) compared to

PEN mice (6.8 ± 1.2 counts/mm2, n = 8 mice), (t(13) = -1.768, p = 0.101) (FIG4C). In addition,

we found a significantly increased cFos-positive cell density at testing in chABC mice (9.8 ± 1.6

counts/mm2) compared to PEN mice (4.5 ± 0.9 counts/mm2), (t(13) = -3.024, p = 0.010)

(FIG4C). Behaviourally, both PEN and chABC ArcTRAP-TdTomato mice displayed similar

freezing levels during the test session (PEN: 30.0 ± 3.6%; chABC: 35.7 ± 8.0%; t(13) = -0.675, p

= 0.511) (FIG4D). Therefore, an increase in active CA1 cell population is observed at contextual

fear testing following perineuronal net disruption.

29

Figure 4 Perineuronal Net Disruption is Associated with Increased Active Cell Population

Counts

A. Schematic of ArcTRAP and cFos labeling in contextually fear conditioned mice. B. Sample

images of Arc-expression (red) and cFos-expression (purple), additionally immunostained for

perineuonal nets (green) and counterstained with DAPI (blue) in the hippocampal CA1 region of

PEN or chABC treated mice. C. Quantifications of perineuronal net density, Arc density, and

cFos density in counts/mm2. chABC mice show higher Arc-positive (non-significant) and cFos-

positive cells. D. Percent freezing levels at test between PEN and chABC mice. N=8 PEN, 7

chABC.

30

Chapter 4 Discussion

Discussion

4.1 Summary of Results

4.1.1 Behavioural Effects

We have shown that perineuronal net disruption in the hippocampal CA1 region is associated

with enhanced behavioural flexibility, as measured by faster water maze reversal learning, and

increased contextual fear memory generalization. These behavioural results are in line with

previous data showing faster water maze reversal learning in tenascin-R knockout mice

(Morellini et al., 2010), and decreased discrimination between a novel versus a familiar arm in a

spatial Y maze task in brevican knockout mice (Favuzzi et al., 2017). The novelty of our

behavioural results arises from the fact that we have shown hippocampal specific effects

achieved through chondroitin sulfate proteoglycan degradations (1. Hippocampal CA1 region

specific; 2. CSPG component specific). Combined, these results demonstrate the importance of

perineuronal nets in learning and memory, wherein disrupting these structures is associated with

increased behavioural flexibility and generalization.

4.1.2 Neural Representation Effects

We further attempted to identify how perineuronal net disruptions might translate to the observed

behavioural effects by examining neural representations of an experience (which has not been

done previously). To do this we had labelled different immediate early genes, Arc and cFos,

during contextual fear conditioning (Arc) and contextual fear memory testing (cFos). We had

predicted an increase in active cell density representing the contextual fear experience given that

(1) perineuronal net disruption is associated with decreased parvalbumin-positive cell activity

(Balmer, 2016) and (2) inhibition of parvalbumin-cells is associated with an increase in auditory

fear memory engram size in the lateral amygdala (though this altered engram size does not

correlate to freezing levels) (Morrison et al., 2016). Our results show a trend towards increased

Arc-positive cell density following perineuronal net disruption at training, and a statistically

significant increase in cFos-positive cell density at testing. Our inability to statistically capture a

significant increase in Arc expression may be due to the low counts (5-10 cell counts/mm2) or

31

due to the leakiness of Arc expression, which may influence the signal: noise ratio (Guenthner,

Miyamichi, Yang, Heller, & Luo, 2013). Alternatively, the differences in active cell density

count may reflect the differences in experiences Whereas Arc is capturing cells active at the

encoding of the contextual fear memory, cFos is labeling cells active at the retrieval of the

contextual fear memory. These differences may reveal differences in hippocampal CA1 function

between the encoding and retrieval of the memory (Ji & Maren, 2008), and differences in how

the CA1 circuit is activated between these two processes (Manns, Zilli, Ong, Hasselmo, &

Eichenbaum, 2007).

Given the observed increase in active cell density of a contextual fear memory, and the increased

reversal learning and generalization, how might these two observed effects be related?

4.2 Model

The suggested increase in Arc positive cell density and cFos positive cell density suggests an

increased neural representation of the memory. This larger active cell population may be more

likely to overlap with other engrams or more likely to undergo interference through reactivation,

resulting in altered behavioural expression.

4.2.1 Facilitated Extinction

An increased active cell population is more likely to include (or activate) cells belonging to the

original memory engram. An increased likelihood of activating this engram, and repeated

activation of this engram may facilitate memory extinction, as observed with an auditory fear

memory in amygdala (Gogolla et al., 2009) and contextual fear memory in the retrosplenial

cortex (data not shown).

4.2.2 Increased Behavioural Flexibility

An increased active cell population is likewise more likely to include (or activate) cells

belonging to the original engram, which opens up a labile period for updating of information

encoded in that engram. This means that novel or conflicting information, such as a change in the

location of a hidden platform in a spatial water maze, is more easily updated into the original

engram. This paradigm is similar to reconsolidation, where reexposure to a cue opens up a labile

period during which a memory can be updated (Lee, Nader, & Schiller, 2017). In contrast,

32

control mice may have two representations of say platform location, that of the old and that of

the novel, that may conflict with each other, resulting in slower acquisition of the new platform

location.

4.2.3 Increased Generalization

Increased active cell populations may also explain generalization, where a novel context may

contain features sufficient to activate the original engram resulting in fear memory expression in

the novel context. In our results, we observed an effect of order and of condition on freezing

levels within the novel context. The recency (temporal feature) of the event may explain

increased freezing levels, wherein chABC mice displayed even higher freezing levels than PEN

mice due to increased active cell populations during novel context presentation. chABC mice

still showed higher freezing levels when the novel context was presented after the training

context, supporting a contextual discrimination impairment or enhanced generalization.

4.2.4 Overlap between Arc and cFos

We mentioned how an increased active cell population at retrieval is more likely to include (or

activate) cells belonging to the original engram, resulting in the observed behavioural effects.

Presumably, this inclusion may be reflected in the Arc and cFos labelling experiments, where we

would predict an increase in overlap between the two labels in chABC treated mice compared to

the PEN treated mice. However, there was limited overlap (0-2 cells) within the CA1 region

between Arc and cFos and therefore this measure could not be compared between chABC and

PEN mice. An alternative explanation to this interference based mechanism is that the increased

active cell population at retrieval has a greater capacity to encode additional features, and/or may

overempower the original memory. Or, the original engram was unstable due to perineuronal net

disruption, and the new information may appear as novel. Further experimentation is required to

better understand how perineuronal net disruption may translate mechanistically to observed

behavioural effects (elaborated in Future Directions section).

4.3 Results Summary

To summarize, we observed an increase in active cell population of a memory that was

associated with increased behavioural flexibility and generalization. The novelty of our results is

a demonstration of a hippocampal CA1 specific effect through CSPG component degradation

33

specific effect on behavioural flexibility and generalization, and a first attempt at examining

neural representations of an experience in a circuit state with disrupted perineuronal nets.

4.4 Future Directions

We have demonstrated the behavioural effects of perineuronal net disruption. It would be of

interest to further investigate:

1. The behavioural effects of increasing perineuronal net density. This could be done by

increasing activity of the genetic programs relevant to perineuronal net synthesis (ex. the

synthesis of hyaluronan synthase) or by inhibiting degradation of perineuronal nets (ex.

by infusing tissue inhibitors of metalloproteinases) (Brew et al., 2000; Wang & Fawcett,

2012). This increase in perineuronal net density can be performed prior to the

endogenous development of perineuronal nets, thereby presumably enclosing the critical

period at an earlier time, or in a region with normally low levels of perineuronal nets.

This would allow us to bidirectionally examine the effects of perineuronal net

manipulations, thereby providing a clearer picture of its function.

2. The effects of perineuronal net disruption within different regions of the hippocampal

region. This may be of interest given that the endogenous perineuronal net density largely

differs between these regions, with particularly high expression in the CA3 region

compared to the CA1 (Bruckner et al., 1994). Differential effects of perineuronal net

disruption in regions differentially expressing these structures may allow us to identify

(1) why certain regions express perineuronal nets moreso than others and (2) how these

structures influence computations within circuits between regions that translate to

behavioural effects.

3. To further investigate how perineuronal nets influence circuit activity patterns, it may be

of interest to perform calcium imaging experiments. This provides a spatially and

temporally dynamic in vivo approach to examining circuit activity patterns, compared to

the histology based assessments performed in this study.

4. Parvalbumin-positive cells and perineuronal nets are tightly coupled in their expression

patterns, and may have similar behavioural effects. How then do these two features

dynamically interact with each other during a learning episode? For example, contextual

fear conditioning causes an increase in parvalbumin expression, and this increase is

associated with impaired object recognition memory (Allen & Monyer, 2013; Donato,

34

Rompani, & Caroni, 2013). How does altered parvalbumin expression (possibly)

influence perineuronal net expression (or vice-versa) and in turn result in changes in

circuit activity patterns to result in the observed behavioural effects.

5. An important question of interest is how perineuronal net expression is modulated

endogenously. One may presume that in an error prone environment, perineuronal net

synthesis may decrease. In contrast, in a highly stable environment, perineuronal net

synthesis may increase. This idea may be supported by the fact that delaying visual inputs

can delay the development of perineuronal nets, thereby delaying the closure of the

critical period (Hockfield et al., 1990; Ye & Miao, 2013). In regards to learning and

memory in the hippocampus, this relationship between error-prone versus stable

environment on perineuronal net development may be investigated in the following

manner. For example, a mouse that is constantly subjugated to an error prone

environment, such as through water maze learning where the platform would be

continuously changed, might see an active decrease in perineuronal net density.

4.5 Conclusion: Perineuronal Nets and the Stability-Plasticity Dilemma

Perineuronal nets are considered an important stability factor. Perineuronal net disruption in the

hippocampal CA1 region was associated with enhanced behavioural flexibility and

generalization, and an increased active cell population . This balance between stability and

plasticity is critical to our ability to learn. Whereas stability may be advantageous in stable

environments, allowing for the persistence of originally formed memories; plasticity may be

behaviourally advantageous in dynamic environments. This is reflected through the observed

increased behavioural flexibility and generalization effects. In dynamic environments,

information must be rapidly updated. In the water maze context, this means irrelevant

information on platform position is rapidly updated with incoming novel information. In the

generalization context, this means a particular piece of information learnt under one context can

now be readily adapted to other contexts. Therefore plasticity in the brain may be important for

behavioural adaptation in dynamic environments. The optimal balance between stability and

plasticity may in turn be determined by features within the environment. Altering this balance

can influence neural representations of the memory, and in turn result in differences in

35

behavioural outputs. Our results shed light on fundamental mechanisms underlying our ability to

learn.

36

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