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Behavioral effects of amyloid precursor protein beta mutation in zebrafish

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Behavioral effects of amyloid precursor

protein beta mutation in zebrafish

Contents

Popular Scientific Summary ... 3

Abstract ... 4

1. Introduction ... 4

1.1. Alzheimer´s Disease: ... 4

1.2. βAPP (Amyloid peptide precursor beta) ... 4

1.3. How βAPP contributes to Alzheimer’s disease. ... 5

1.4. appb mutant and fish boldness ... 5

1.5. Aim ... 6

2. Methods and Materials: ... 6

2.1. Animal Care: ... 6

2.2. Behaviour analysis ... 6

2.2.1. The Gothenburg protocol for NTDT ... 6

2.2.2. The Uppsala protocol for NTDT ... 7

2.2.3. Ethovision ... 7

2.3. Statistical analysis ... 7

3. Results: ... 9

3.1 Total velocity ... 9

3.2 Duration in top zone ... 10

3.3 Duration in the bottom zone ... 11

3.4 Frequency in top zone... 12

3.5 Frequency in bottom zone ... 13

4.Discussion: ... 14

5. Conclusion: ... 15

6. Acknowledgement: ... 15

References ... 16

Appendix ... 19

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Popular Scientific Summary

In life sciences, animal studies and experiments have become increasingly relevant for explaining the pathogenic functions associated with different human diseases. The use of animal models in development of biology has been used for many years, because the morphology of humans and animals are practically identical, particularly in mammals. The most specific and well-established animal models for neuroscience are rats and mice, but Zebrafish (Danio rerio) seems to be a very influential model organism for researching brain disorders. Alzheimer´s disease is a common type dementia, which can affect a person’s ability to carry out daily activities because it involves parts of the brain that control thought, memory, and language. Plaques are deposits of a protein fragment known as beta-amyloid protein (βAPP) which build up spaces between nerve cells and it blocks communication among nerve cells and disrupts processes that cells need to survive. The plaques are formed mainly by fragments of βAPP. Our appb mutant Zebrafish carries a mutation of the βAPP gene which stops expression of the protein. An earlier unpublished study hinted that these mutants are bolder than regular Zebrafish. The aim of the report is to compare the boldness of different strains of Zebrafish and the protocols used by two Swedish testing institutions. The strains were a regular zebrafish (called wild type) and an appb mutant.

The two forms of stress management described by the zebrafish are proactive (bold) and reactive (shy). In this project we use Novel Tank Diving Test, which is the most widely used behavioral test to see the fishes reaction to stress; it’s cheap, fast, easy for both us and the fishes, accurate and reproductible. We record the fish swimming and note their speed, time spent in different areas of the tank and other parameters to determine their preferred form of stress management. When a fish has anxiety, it prefers to be at the bottom of the tank and in the safety of the dark. The proactive fish will spend more time on the upper, bright part of the tank, boldly exploring close to the surface of the water.

The major findings of this study showed the differences in boldness depending on the strain of Zebrafish and protocol used by the two testing institutions: appb mutant fishes show more boldness compared with wild control strains. Future work involves checking exactly how stressed the fish are by dissecting their brains and measuring the amounts of cortisol, serotonin, dopamine, and others.

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Abstract

Amyloid precursor protein beta (βAPP) plays an important role in the pathogenesis of Alzheimer’s disease. An appb mutant strain of zebrafish has been previously generated and has shown increased boldness. Here we tested boldness by Novel Tank Diving Test and compared the results between the wildtype AB strain controls (WT) (N=16) and appb mutant strain (N=28), as well as between two Swedish testing institutions that use different protocols. Fish were tracked by automated video tracking in Ethovision. Compared with the wild type fish, using both the Uppsala and Gothenburg protocols, the mutant fish have a higher cumulative duration in the top area suggesting increased boldness. Greater boldness in mutants appears specifically context dependent and only expressed when the test fish is taken from a larger group of fish.

1. Introduction

1.1. Alzheimer´s Disease:

Alzheimer’s disease is a chronic progressive brain condition that gradually impairs learning abilities, memory and ultimately the capability to perform even the easiest tasks. The main pathological features of AD are neurofibrillary tangles, dystrophic neurites, extensive neuronal cell loss and neuritic plaque (amyloid plaque) (8).

1.2. βAPP (Amyloid peptide precursor beta)

Beta precursor protein family contains APP (amyloid precursor protein) and the amyloid beta precursor light proteins (APLP 1 and 2), which contain a large amino extracellular domain, a small transmembrane protein and a short cytoplasmic tail (9) (Figure 1). This gene is located on the long arm of the human chromosome 21 (2). APP is cleaved by other secretases as well but these are considered as the major ones: alpha, beta and gamma secretase.

According to Figure 1, cleavage by alpha secretase is known as non-amyloidogenic pathway, which creates AICD (APP intracellular domain) and APPsα, which is the soluble extracellularly secreted fragment. The functions of APPsα is neuroprotective and neurotrophic. On the other hand, cleavage by beta secretase is known as amyloidogenic pathway, it results in AICD and APPsβ. It can be noticed that, the small peptide amyloid beta peptide exists in different monomeric or multimeric soluble forms, which results in aggregation to fibrils and plaques.

The extracellular accumulation of amyloid plaques is believed to cause the intracellular deposition of neurofibrils, which is a noticeable initial pathophysiological sign of AD (5).

Moreover, it has also been found that A beta amyloid is normally secreted from cultured cells and that it is found in cerebrospinal fluid (CSF) of patients with AD and as well as in healthy individuals (3).

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Figure 1. APP´s proteolytic processing by the different secretases. (A) Full length Amyloid Precursor Protein (APP). Green parts represent C-terminal domain, Red part indicates the amyloid sequence and the longer N-terminal extracellular chain.

Arrowheads point to where the secretases cleave. (B, C and D) Cleavage by different secretases. (C) APPβ is the result of APP being cleaved by β-secretase and then by γ-secretase (5).

1.3. How βAPP contributes to Alzheimer’s disease.

The biological functions of βAPP are still unclear, even though it appears to have distinct functions during embryonic brain development such as regulation of synaptic transmission, plasticity, dendritic sprouting, neuronal migration and calcium homeostasis (5). Moreover, one study suggests that βAPP and its metabolites play an important role in the pathogenesis of Alzheimer’s disease (AD) and Down’s syndrome (2). Neuritic plaques largely consist of the 39 to 43 residue of beta amyloid peptide (βAPP), which is a 4kDa Aβ proteolytic cleavage product of a larger βAPP (3). Because of APPs structure and its metabolites, it can be said that Aβ at high concentration can aggregate and form plaques which impairs several functions such as inhibiting synaptic transmission and plasticity, impairments in network function, while APPsα influences network activity and promotes learning and memory formation (5).

1.4. appb mutant and fish boldness

Zebrafish carrying a mutation on the APP homologue appb which induces a premature stop on exon 2. Mutants are significantly smaller until 2 day post fertilization (DPf) but by 6DPf they become fully healthy. As adults they are fertile and display no obvious behavioural defects (10). However, one study states that single knockdown of APP homologue appb causes death in the embryonic stage (9).

Boldness and shyness relate mainly to an individual’s willingness to take risks, particularly in novel unfamiliar environments. Bold individuals are exploratory and aggressive, while shyness is characterized by low exploration and passive responses (19). All animals face problems which can be overcome in two main ways, each described as a 'style of coping.' Proactive animals respond

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aggressively by fleeing or fighting threats and are considered bold. Reactive animals tend to be more passive and are thus known as shy (20,21). These ways of coping are closely associated with social status. Proactive animals are more aggressive and have a competitive advantage when interacting with reactive animals (22,23). Various tests for boldness in zebrafish exist, such as the open field test and the scototaxis (dark/light preferernce) test. In the open field test, in which the animal explores a novel environment, parameters such as time in or visits to the center zone, time in the periphery, distance traveled and average velocity are quantified (15). According to this novelty paradigm, boldness is indicated by more entries, longer cumulative duration and longer distance travelled in the center zone (16,17,18). In the scototaxis test bold fish tend to spend less time in the dark zone than shy fish. The novel tank diving test (NTDT) is an often used test to study anxiety-like behavior in zebrafish. In the NTDT the upper zone, where the fish is closer to the water surface, is considered more risky, and bold fish tend to spend more time in this zoon than shy fish (24). There is no clear conclusion on which of these tests to use to characterize boldness in zebrafish, and results may differ depending on the test selected (25).

1.5. Aim

To screen and quantify boldness in appb mutant and wild type zebrafish using the NTDT. Moreover, since boldness may be context dependent, two different protocols for testing was compared. The protocol used by Alexandra Abrahamsson, Sahlgrenska Academy, Gothenburg (Alexandra and the preotocol used by Winberg lab, Dept. of Neuroscience, Uppsala University.

2. Methods and Materials:

2.1. Animal Care:

Adult zebrafish (N=44) were used for this project; wildtype AB strain controls (WT) (N=16) and appb mutants (N=28). For this project, we have used the Gothenburg protocol with only minor differences:

Behavioral analysis (Novel Tank Diving Tests, NTDT) were performed at Sahlgrenska Academy, Gothenburg (NWT=4, Nmutant=16) and at Uppsala Universities Biomedical Center (Nwt=12,Nmutant=12). We have used automated video tracking in Ethovision (Noldus, The Netherlands): the changes we made to the maintenance protocol are as follows: system water was kept at a pH of 7.2-7.5 and conductivity of 600μS. The tank contained small coral pieces, heater and air-pump. The room and water temperature was on a 14h10h light:dark cycle maintained at 27°C. System water was kept at a pH of 7.4-7.6 and conductivity was 900 µs. The fish were fed dry food twice per day.

2.2. Behaviour analysis

2.2.1. The Gothenburg protocol for NTDT

The NTDT is one of the most commonly used behavioral tests for quantifying anxiety behavioral responses in Zebrafish (1). The protocol of the test is as follows: zebrafish are transferred from a holding tank (via net) into a novel tank for behavioral observation and the movement of fish is recorded by a video camera. The novel tank design normally consists of a narrow tank divided horizontally into three zones: top, middle and bottom (fig. 2). Usually, when a zebrafish is introduced to a new tank system it enters a state of anxiety (fear, increased discomfort) by showing preference for the bottom of the tank (11)(Figure 2). In addition, this model causes stress related physiological responses such as raised cortisol levels and higher frequency of breathing (12).

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Figure 1. Typical NTDT setup. The fish are moved from the pretank to the NTDT tank, where their movement is recorded by video camera.

Recordings were started before the fish were netted so that the movements of the fish would all be recorded. One fish was scooped out from 50 L aquarium by netting very gently and rapidly transferred to the 1.5L aquaneering tank filled with water from quarantine as the same level used in the previous tanks. The first fish was tested at 10:00 and the last fish at 16:00. The sequence of recording was one WT, one mutant, one WT, etc. The water in the testing arena was changed twice a day and the recorded temperature of testing water was 27-29°C for every trial. Because of an error in the video recordings from Gothenburg, I was unable to videotrack all of the fish properly. In addition, we excluded the data from one mutant fish from Gothenburg since the duration in the arena was longer than 6 minutes whereas the duration of other trials were for 6 minutes. That is why only 4 trials of wild control fishes and 16 trials of mutant fishes were analyzed.

2.2.2. The Uppsala protocol for NTDT

When testing according to the Uppsala protocol the fish were kept isolated in 1.5 litre tanks (identical to the ones used as test arenas) for one week prior to testing. The NTDT and behavioural recording were performed as described above for the Gothenburg protocol.

2.2.3. Ethovision

The recording videos are analysed by Ethovision (Noldus Netherlands), a video tracking software used to analyse animal behaviour.

The fish is identified from the background and its movement is tracked by video camera. The video recording can be seen in a frame camera on the screen. The advanced program analyses each frame and recognizes the animal from its setting. After that, appropriate data can be obtained from the software. Additional information on how to use Ethovision (ver. 15) can be found in the annex.

2.3. Statistical analysis

Statistical analysis was performed using jamovi 1.1.9.0 debut (Sydney, Australia) which is based on the R programming language for the analysis of the Ethovision performance parameters. The selected parameters were observed from Ethovision and those are total velocity in the tank (known as arena in the software), duration time moving at the top, duration time moving at the bottom, frequency in top zone and frequency in bottom zone. During the NTDT test the middle zone was included but we did not use the data from it because it is not relevant in this case. For velocity, top duration and bottom duration, I have performed a two-way ANOVA with Strain and Testing Institution as fixed factors.

Frequency at the top and bottom were analyzed with a Generalized Linear Mixed Model with

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quasibinomial error distribution. Full analyses can be found in Appendix 1. Data were presented as means and standard errors of the mean (±SEM).

All strains and their parameters were assessed by one-way analysis of variance (ANOVA). Group analysis was contacted with ANOVA followed by Post Hoc comparison test. The p_value system used in jamovi with significant level 0.05 and confidence level 95%. Jamovi program applies p<0.0001 for all p values less than 0.0001.

For statistical analysis, jamovi software was used. For the variables Velocity, Top duration and Bottom duration I performed a two-way ANOVA with Strain and Testing institution as fixed factors. However, because Velocity the residuals were not normally distributed, I used a non-parametric one-way ANOVA Kruskal Wallis test with Strain as a fixed factor. Frequency in the top and bottom were analyzed with a Generalized Linear Mixed Model with quasibinomial error distribution. Full analyses can be found in Appendix 1.

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3. Results:

3.1 Total velocity

Table 1. Parametric ANOVA table showing velocity differences between strains and testing institutions and strains across testing institutions.

Figure 23. Total velocity, mean and standard error mean value

There was no difference between the strains or between the testing institutions in velocity (ANOVA, F(1,41)=0.167, p=0.685, Table 1). Whether or not there was a difference between the mutant and the wildtype was not affected by Testing institution (ANOVA, F(1,41)=0.105, p=0.747). However the

residuals of this ANOVA were not normally distributed (Shapiro-Wilk, p>0.001). Because of this, we have used the one-way ANOVA non-parametric table, Table 2 (Kruskal-Wallis, χ²=1.19, p=0.275). Kruskal-Wallis

Table 2. Non-parametric ANOVA (Kruskal-Wallis) showing the normally distributed residual value Sum of Squares df Mean Square F p

Strain 0.657 1 0.657 0.167 0.685

Testing_institution 6.279 1 6.279 1.599 0.213

Strain ✻ Testing_institution 0.413 1 0.413 0.105 0.747

Residuals 160.992 41 3.927

χ² df p

arena_v 1.19 1 0.275

Total velocity

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3.2 Duration in top zone

Table 3. Parametric ANOVA table showing duration in top zone between strains and testing institutions and strains across testing institutions.

Table 4. Post Hoc comparisons between strains across the institutions (p values lower than 0.05 marked with *)

There was no difference between the strains or between the testing institutions in top zone duration (ANOVA, F (1, 40) =2.626, p=0.113, Table 3) and (ANOVA, F(1,40)=0.802 ,p=0.376 Table 3). The mutant and the wild type were affected by the Testing Institution (ANOVA, F (1,40)=12.720, p <.001, table 3). According to Post Hoc comparison, the difference can be seen between mutant Gothenburg and Wild Type Gothenburg (p=0.013, table 4).

Strain Testing_institution Strain Testing_institution Mean

Difference SE df t ptukey

mutant Gothenburg - mutant Uppsala 58.3 24.1 40.0 2.421 0.089

- wildtype Gothenburg 113.3 35.3 40.0 3.211 0.013*

- wildtype Uppsala 15.8 24.1 40.0 0.657 0.912

Uppsala - wildtype Gothenburg 55.0 36.4 40.0 1.508 0.442

- wildtype Uppsala -42.5 25.8 40.0 -1.650 0.363

wildtype Gothenburg - wildtype Uppsala -97.5 36.4 40.0 -2.675 0.051

Sum of Squares df Mean Square F p

Strain 10460 1 10460 2.626 0.113

Testing_institution 3193 1 3193 0.802 0.376

Strain Testing_institution 50664 1 50664 12.720 < .001

Residuals 159327 40 3983

Duration in top zone

Figure 34. Duration in top zone, mean and standard error mean.

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3.3 Duration in the bottom zone

Table 5. Parametric ANOVA table showing duration in the bottom zone between strains and testing institutions and strains across testing institutions.

Sum of Squares df Mean Square F p

Strain 32062 1 32062 5.070 0.030

Testing_institution 2068 1 2068 0.327 0.571

Strain Testing_institution 133557 1 133557 21.120 < .001

Residuals 252944 40 6324

There was no significant difference between the testing institutions in bottom zone durations (ANOVA, F(1,40)=0,327, p=0,571)). Significant difference can be seen between all strains (ANOVA, F(1.40)=5.070, p=0.030) and also between the strains across testing institutions (ANOVA, F(1.40)=21.120, p=(<.001)).

Table 6. Post Hoc comparisons between strains across the institutions (p values lower than 0.05 marked with *, p values lower than 0.01 marked with **, p values lower than 0.001 marked with ***)

Strain Testing_institution Strain Testing_institution Mean

Difference SE df t ptukey

mutant Gothenburg - mutant Uppsala -110.7 30.4 40.0 -3.65 0.004**

- wildtype Gothenburg -188.5 44.5 40.0 -4.24 < .001***

- wildtype Uppsala -46.2 30.4 40.0 -1.52 0.434

Uppsala - wildtype Gothenburg -77.7 45.9 40.0 -1.69 0.341

- wildtype Uppsala 64.5 32.5 40.0 1.99 0.210

wildtype Gothenburg - wildtype Uppsala 142.2 45.9 40.0 3.10 0.018*

Duration in Bottom zone

Figure 45. Duration in bottom zone, mean and standard error mean.

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Post Hoc comparison shows the specific difference between mutant Gothenburg and mutant Uppsala (p=0.004), mutant Gothenburg and wild type Gothenburg (p=<.001), wild type Gothenburg and wild type Uppsala (p=0.018).

3.4 Frequency in top zone

Table 7. Generalized Linear Model. Frequency in top zone

X² df p

Strain 1.588 1 0.208

Testing_institution 0.156 1 0.693 Strain

Testing_institution

9.366 1 0.002**

There was no effect between strains (GLM X²=1.588, p=0.208) or testing institution (GLM X²=0.156, p=0.693). There was a significant difference between strain across testing institutions (GLM, X²=

9.366, p=0.002).

Table 8. Post Hoc comparison of frequency in the top zone between strains across testing institutions.

Strain Testing_institution Strain Testing_institution exp(B) SE z pbonferroni

mutant Gothenburg - mutant Uppsala 2.283 0.571 3.302 0.006*

mutant Gothenburg - wildtype Gothenburg 2.817 1.227 2.378 0.104 mutant Gothenburg - wildtype Uppsala 1.518 0.328 1.931 0.321

mutant Uppsala - wildtype Uppsala 0.665 0.186 -1.458 0.869

wildtype Gothenburg - mutant Uppsala 0.810 0.381 -0.447 1.000 wildtype Gothenburg - wildtype Uppsala 0.539 0.244 -1.364 1.000

The frequency of the mutant Gothenburg strain was higher in the top zone than the mutant Uppsala strain (p=0.006).

Frequency in top zone

Figure 56. Frequency in top zone, mean and standard error mean.

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3.5 Frequency in bottom zone

Table 9. Generalized Linear Model. Frequency in bottom zone.

There is no effect of frequency in bottom zone between all strains (X²=2.27e-4, p=0.988), strains across testing institutions (X²= 0.101), p=0.750). There was an effect between the testing institutions (X²=4.003, p=0.045).

Table 10. Post Hoc comparison of frequency in bottom zone between the testing institutions.

Testing_institution Testing_institution exp(B) SE z pbonferroni

Gothenburg - Uppsala 1.63 0.392 2.04 0.041

Uppsala strains had higher bottom zone frequencies compared to the Gothenburg strains (p=0.041).

X² df p

Strain 2.27e-4 1 0.988

Testing_institution 4.003 1 0.045*

Strain

Testing_institution

0.101 1 0.750

Frequency in bottom zone

Figure 67. Frequency in bottom zone, mean and standard error mean.

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4.Discussion:

The results of this study demonstrate that appb mutant and wild type zebrafish differ in boldness.

Moreover, the results show that this difference is context dependent. Compared with the wild type fish, mutant fish show a higher cumulative duration in the top zone of the NTDT arena than wild type fish. This difference in boldness is evident using both testing protocols, even though the difference is larger when using the Gothenburg protocol. The top zone is considered a more risky environment.

Thus, in accordance with our hypothesis appb mutants appear bolder (table 4). Duration in the bottom zone as well as frequency in bottom and top zones, show the same pattern with mutant fish visiting the top zone more frequently and spending more time in this zone than wild type fish.

By contrast, velocity and total distance traveled did not differ between mutants and wild type fish (table 1). Thus, appb mutants and wild type fish do not differ in activity, another often studied personality trait (13). We hope that the lack of APPβ would result in no or reduced formation of amyloid plaques.

The Uppsala and Gothenburg protocols for NTDT were highly similar except that in the Uppsala protocol the fish were reared in isolation for one week prior to the NTDT. When using the Gothenburg protocol the fish were group reared prior to testing. It is likely that this isolation period affected the behavior of both appb mutants and wild type fish in the NTDT. Interestingly mutants and wild type fish appear to show opposite effects in response to this isolation. The wild type fish spent more time in the top zone of the NTDT arena when tested using the Uppsala protocol whereas appb mutant spent less time in this zone when tested using the Uppsala protocol. Thus, possibly isolation made wild type fish bolder but mutants shyer. Social isolation may affect boldness through several mechanisms.

Zebrafish is a social species and isolation can be stressful (14).

Stress is known to have anxiogenic effects resulting in behavioral inhibition and making animals less explorative (26) However, living in groups may also be stressful. When kept in small groups zebrafish often develop strong dominance hierarchies (27,28) The position of a fish in the dominance hierarchy largely affects its physiology and behavior. Social subordination results in chronic stress, behavioural inhibition and a general shift towards more of a reactive behavioral profile whereas experience of being dominant has the opposite effect, making the fish more explorative and aggressive (26). If wildtype zebrafish develop stronger dominance hierarchies than appb mutants social isolation may have opposite effects on behavior, relieving the the wildtype fish from chronic social stress while inducing stress in the mutants. This is of course highly speculative. The full behavioral profile of appb mutant zebrafish has not been studied. Thus, we do not know how aggressive these fish are.

We know from previous studies that boldness is a behavioral trait related to brain monoaminergic functions (for example serotonin and dopamine) (28,26). Behavioral effects of stress and social interaction are to a large degree mediated by the brain monoaminergic systems. Social subordination results in a chronic activation of the brain serotonergic system, an effect which has been shown to mediated behavioral inhibition. Dominant fish on the other hand show higher brain dopaminergic activity which could mediate elevated aggressive behavior and a more proactive stress coping style (26). In this project brains were sampled for analyses of monoamines and monoamine metabolites.

Unfortunately, the COVID-19 pandemic made it impossible to perform these analyses.

Except for the lack of brain monoamine data another weakness of the present study is the fact that we got very few wildtype controls tested by the Gothenburg protocol. This was due to tracking difficulties.

The behavioral difference between appb mutants and wildtype zebrafish is very interesting and these mutant fish may be used to study neuroendocrine mechanisms controlling personality traits. Future

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work should include analysis of the cortisol levels as well as brain monoamines of the fish to quantify their stress reactions.

It would also be important to obtain a more complete behavioral profile of the appb mutant and to compare that with the behavioral profile of wildtype fish. This type of behavioral screening should include behavioral tests like open field and scototaxis (29). It would also be interesting to study aggressive behavior and the development of dominance hierarchies in the mutant fish. Aggression in zebrafish is usually studied by a mirror test where the test fish is fighting its own mirror image. The development of dominance hierarchies can be studied by staged dyadic interactions (30)

5. Conclusion:

In conclusion the results of the present study confirm that zebrafish carrying a mutation in the appb gene are bolder than wildtype zebrafish. Moreover, this behavioural divergence appears to be affected by stress experienced. The difference in boldness between appb mutants and wildtype zebrafish were more obvious if the test fish were kept in groups prior to behavioural testing. Isolation prior to testing had different behavioural effects in appb mutants and wildtype fish, making the wildtype fish bolder but mutants shyer.

6. Acknowledgement:

We would like to acknowledge Dr. Alexandra Abrahamsson, Sahgrenska Academy, Gothenburg University for providing the video recordings of Zebrafish. I would like to thank my thesis supervisor.

The door to Prof. Winbergs office was always open whenever I ran into trouble or had questions about my research or writing. He consistently allowed this paper to be my own work and pushed me in the right the direction whenever he thought I needed it. I would also like to thank the experts who were involved in the validation survey for this research project: Johanna Axling, Laura E. Vossen, Oly Sen Sarma, Per-ove Thörnqvist, Åsa Konradsson and Arshi Mustafa . Without their passionate participation and input, the validation survey could not have been successfully conducted. Finally, I must express my very profound gratitude to my friends Razvan Nicolae Radu and Christopher Brennan.

This accomplishment would not have been possible without them. Thank you.

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Appendix

On using Ethovision: When multiple tests are performed at a time, each area needs to be measured before beginning the analysis in order to calibrate. Without changing the default settings, the program cannot identify unknown areas in the arena. Slightly modifying the arena areas when duplicating helps in reducing analytical conflict. The signal from the video camera allows the arena on the computer screen to be viewed, allowing the user to identify the experimental setup and procedures to be integrated in the video track. The arenas can also be obtained by calculating a clear video image. After all the trials, the necessary data can be acquired from the video files. The object tracking will appear on the screen. Any mistakes made in the tracking of the object can be identified and corrected manually.

Total velocity data:

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Duration in top zone data

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Duration in bottom zone data

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Frequency in top zone data

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Frequency in bottom zone data

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References

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