Optogenetic investigation of nervous system functions using walking behavior and genome wide transcript analysis of synapsin and Sap47 mutants of Drosophila [Elektronische Ressource] / vorgelegt von Nidhi Nuwal

Nidhi

Sujets

• Biologie

Informations

• Publié par: julius-maximilians-universitat_wurzburg
• Publié le: 01 janvier 2010
• Nombre de lectures: 15
• Langue: English
• Poids de l'ouvrage: 27 Mo

Extrait

Optogenetic investigation of nervous system
functions using walking behavior
and
genome wide transcript analysis of Synapsin and
Sap47 mutants of Drosophila



Dissertation zur Erlangung des
naturwissenschaftlichen Doktorgrades der
Bayerischen Julius-Maximilians-Universität Würzburg



vorgelegt von





Nidhi Nuwal
aus Gangali, India




Würzburg, 2010






Eingereicht am: ……………………………………






Mitglieder der Promotionskommission

Vorsitzender: ……………………………………


Erster Gutachter: Prof. Dr. Erich Buchner


Zweiter Gutachter: Prof. Dr. André Fiala






Tag des Promotionskolloqiums: ……………………………………






Doktorurkunde ausgehändigt am: ……………………………………







Index
Part I
1. Introduction
1.1 Drosophila a geneticist’s tool 1
1.2 Gene expression tools 2
1.2.1 The Gal4 UAS system 2
1.2.2 LexA-LexAop 3
1.3 Optophysiological and optogenetic tools 4
1.3.1 Tools to visualize and monitor neuronal activity 4
1.3.2 Tools to manipulate neuronal activity 6
1.3.2.1 Caged biogenic compounds 7
1.3.2.2 Natural photosensitive proteins 9
1.3.2.3 Small molecule photoswitches 15
1.4 Miscellaneous neuronal manipulation tools 16
1.4.1 Temperature induced neuronal manipulation 16
1.4.2 Sodium activated bacterial channel 17
1.5 Experience dependent modification of behavior 17
1.5.1 Classical conditioning 18
1.5.2 Spatial orientation memory 19
1.5.3 Operant conditioning 20
1.6 Brain Stimulation Reward 23
1.7 Biogenic amines 24
1.7.1 Dopamine 25
1.7.2 Tyramine and octopamine 27
1.7.3 Serotonin 29
1.8 Aim of this study 31

2. Materials and Methods
2.1 Walking ball paradigm 32
2.1.1 Description of electronics 33
2.1.1.1 Electronic measuring system 33 2.1.1.2 Electronic devices and accessories with specifications 34
2.1.2 Description of experimental procedure and setup 35
2.1.3 Description of computer software 37
2.1.3.1 Measuring software 37
2.1.3.2 Features of measuring and evaluation software 38
2.1.3.3 Data analysis 41
2.2 Light elicitation of proboscis extension reflex 42
2.3 Molecular techniques used 42
2.4 Fly strains used 44

3. Results
3.1 Standardization of walking ball paradigm 46
3.1.1 Verification of effect of lasers 46
3.1.2 Choosing appropriate laser intensity for training 48
3.1.3 Choosing of time window best suited for training 50
3.1.4 Avoidance effect specific to laser training 52
3.2 Threshold dependent avoidance 54
3.3 Dual training of animals to analyze memory for laser training 60
3.4 Laser training effect on mutants 64
3.4.1 rutabaga mutant 64
3.4.2 Synapsin mutant 68
3.4.3 Tßh and TDC-2 mutant 71
3.5 Silencing of aminergic neurons 75
3.5.1 Silencing of dopaminergic neurons 75
3.5.2 Silencing of dopaminergic and serotonergic neurons 77
3.5.3 Silencing of tyraminergic and octopaminergic neurons 79
3.5.4 Silencing of neurons labeled by TPH gal-4 81
3.6 Neuronal activation studies 83
3.6.1 Standardization of parameters for neuronal activation studies 83
3.6.1.1 Bringing flies into blind background 83
3.6.1.2 Verification of genotype by inverse PCR 84
3.6.1.3 Quantification of secondary effects of blue light 85
3.6.2 Proof of principle: Training animals by selective activation of
neurons using channelrhodopsin-2 87
3.6.3 Activation of modulatory aminergic neurons 90
3.6.3.1 Activation of dopaminergic neurons labeled by Tyrosine
Hydroxylase Gal4 90
3.6.3.2 Activation of dopaminergic and serotonergic neurons labeled by
Dopa Decarboxylase Gal4 94
3.6.3.3 Activation of tyraminergic and octopaminergic neurons
labeled by Tyrosine Decarboxylase Gal4 97
3.6.3.4 Activation of putative serotonergic neurons 100
3.6.4 Summary of master slave neuronal activation studies 104

4. Discussion
4.1 Establishment of new paradigm 109
4.2 Molecular players in operant conditioning 110
4.3 Neuronal activation studies 111
4.3.1 Activation of gustatory neurons 111
4.3.2 Activation of aminergic neurons 112
4.3.2.1 Dopamine 112
4.3.2.2 Octopamine 114
4.4 Outlook 116
4.4.1 Our proposed model 117
5. Bibliography 119

Part-II
1. Introduction
1.1 Background about fly lines 131
1.2 Questions addressed in this part of thesis 132

2. Materials and Methods
2.1 Fly Samples 132
2.1.1 Primers 133 2.2 Overview of gene chip hybridization procedure 134
2.3 RNA extraction 134
2.4 Verification of RNA quality on gel 135
2.5 Reverse transcription 136
2.5.1 Quantitative real time PCR 137
2.6 Processing steps for gene chip hybridization 137
2.6.1 Extraction of RNA and quality verification 137
2.6.2 Reverse transcription 138
2.6.3 Stopping the reaction and RNA degradation 139
2.6.4 Purification with GFX columns 139
2.6.5 Hybridization 139
2.7 Analysis of gene chip expression data 140
2.7.1 Normalization of intensities between different arrays 142
2.7.2 Selection of candidates based on fold change 144
2.7.3 Selection of candidate genes according to functional clustering
by DAVID software 145
2.7.4 Verification of candidates from chip experiments 148

3. Results
3.1 Standardization experiments prior to microarray experiments 150
3.2 Microarray experiments 152
3.2.1 Sap47 mutant 155
3.2.2 Synapsin mutant 158
3.2.3 Sap-Synapsin double mutant 161
3.3 Verification of microarray data by quantitative Polymerase
Chain Reaction (qPCR) 166

4. Discussion 175
5. Bibliography 178
6. Summary 182
7. Zusammenfassung 184
Erklärung 186
Curriculum vitae 187
Publications 188
Acknowledgements 189


Part I Introduction
1. Introduction

“The brain is a world consisting of a number of unexplored continents and great
stretches of unknown territory.”

Santiago Ramón y Cajal

The brain comprises different regions which are attributed to have different
functions, and these regions communicate with each other to form a larger functional
complex. The wiring between different regions determines the behavioral output of an
individual. Although the basic functions of most of the individual regions in the brain
have been known for years, the underlying mechanisms and significance of the
connections made remain largely unknown. The human brain is estimated to have 100
billion neurons with 100 trillion synapses which pose a daunting task to unravel the
mechanisms underlying neuronal communication and functional significance of neuronal
subtypes comprising the brain. Thus, model organisms like Drosophila melanogaster
with smaller neuronal complexity serve as an elementary model to study nervous system
functions which could then be applied to vertebrates.

1.1 Drosophila: a geneticist’s tool

Drosophila has a brain with ~ 100000 neurons which is small compared to the
large numbers of nerve cells in vertebrates, but it still displays a huge repertoire of
behavioral phenomena. The small genome size, short life cycle and ease of genetic
manipulation make Drosophila an apt model organism for neurobiological research. The
genetic simplicity of Drosophila was one of the reasons to select it for sequencing of an
entire eukaryotic genome (Adams et al., 2000), which provides a unique resource for
genetic information that is also valuable for neurobiological areas of research.

A variety of behaviors have been well investigated in Drosophila like olfactory
behavior (Carlson, 1996), aggression (Chen et al., 2002), flight behavior (Wolf and
Heisenberg, 1991), courtship (Siegel and Hall, 1979), gustatory behavior (Chyb et al.,
1 Part I Introduction

2003), learning and memory (Wustmann et al., 1996), or locomotion (Strauss et al.,
1992). It is interesting that despite the small brain these behaviors can be rather complex
and often resemble in their organization and their principles to behavioral actions exerted
by higher vertebrates.

The strongest advantage of the fruit fly as a model organism is the ease by which
germ.line cells can be transformed, allowing for a cell.type specific and stable
manipulation of the genetic material. Germ.line transformations are fairly simple and can
be done by P element or PiggBac vector