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Translational regulation and endosomal trafficking in synaptic adaptation to stress

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Translational regulation and endosomal trafficking in synaptic adaptation to stress

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Translational regulation and endosomal trafficking in synaptic
adaptation to stress

by

Xun Chen

A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(NEUROSCIENCE)

August 2018

Copyright 2018 Xun Chen
ii

DEDICATION

To the memory of my grandfather, Daoquan Xiong, who taught me the meaning of life

iii

ACKNOWLEDGEMENTS

I gratefully acknowledge my graduate advisor, Dr. Dion Dickman, who has been supportive of
my scientific pursuit and guided me throughout my graduate school, I have benefited so much
from all the valuable lessons he taught me on being a good scientist.
I would like to sincerely thank my committee members, Dr. Emily Liman and Dr. Karen Chang
for their continued support of my scientific development and their valuable advice on my project.
I would also like to thank Dr. Don Arnold for being supportive of my career development.
I would like to thank all past and present members of the Dickman lab for making graduate
school an enjoyable experience. I want to especially thank Dr. Wenpei Ma, Beril Kiragasi and
Xiling Li for their contribution to my projects.
Most importantly, I would like to thank my parents, my sister and my other relatives and friends
for supporting me throughout the years.

iv

TABLE OF CONTENTS

DEDICATION.............................................................................................................................. ii
ACKNOWLEDGEMENTS .......................................................................................................... iii
LIST OF FIGURES ..................................................................................................................... v
ABSTRACT .............................................................................................................................. vii

CHAPTER ONE: Introduction .................................................................................................. 1
CHAPTER TWO: The BLOC-1 Subunit Pallidin Facilitates Activity-Dependent Synaptic Vesicle
Recycling ..................................................................................................................................... 8
2.1 ABSTRACT ......................................................................................................................................... 9
2.2 INTRODUCTION ............................................................................................................................... 10
2.3 MATERIALS AND METHODS .......................................................................................................... 12
2.4 RESULTS .......................................................................................................................................... 18
2.5 DISCUSSION .................................................................................................................................... 29
CHAPTER THREE: Development of a tissue-specific ribosome profiling approach in Drosophila
enables genome-wide evaluation of translational adaptations ...........................................................50
3.1 ABSTRACT ....................................................................................................................................... 51
3.2 INTRODUCTION ............................................................................................................................... 52
3.3 MATERIALS AND METHODS .......................................................................................................... 55
3.4 RESULTS .......................................................................................................................................... 61
3.5 DISCUSSION .................................................................................................................................... 78
CHAPTER FOUR: Preliminary investigation of GluClα’s role in presynaptic homeostatic depression
............................................................................................................................................... 108
4.1 ABSTRACT ..................................................................................................................................... 109
4.2 INTRODUCTION ............................................................................................................................. 110
4.3 MATERIALS AND METHODS ........................................................................................................ 111
4.4 RESULTS ........................................................................................................................................ 113
4.5 DISCUSSION .................................................................................................................................. 116
CHAPTER FIVE: Conclusions .............................................................................................. 128
REFERENCES ....................................................................................................................... 132

v

LIST OF FIGURES

Figure 2.1: Generation of pallidin null mutations and synaptic localization of Pldn protein. .......34
Figure 2.2: Synaptic growth and structure is unperturbed in pallidin mutants. ..........................36
Figure 2.3: Pallidin stability is dependent on dysbindin and blos1. ...........................................38
Figure 2.4: Baseline synaptic transmission is normal in pallidin mutants. .................................40
Figure 2.5: pallidin mutants retain the capacity to express presynaptic homeostatic potentiation.
.................................................................................................................................................42
Figure 2.6: BLOC-1 mutants fail to sustain neurotransmitter release during high frequency
stimulation. ................................................................................................................................44
Figure 2.7: Activity-dependent loss of FYVE-positive synaptic endosomes in pallidin mutants. 46
Figure 2.8: Activity-dependent accumulation of tubular endosomal structures in pallidin mutants.
.................................................................................................................................................48
Figure 3.1: Schematic detailing transcriptional and translational profiling of retrograde
homeostatic signaling at the Drosophila NMJ. ...........................................................................84
Figure 3.2: Development and validation of an optimized tissue specific ribosome profiling
protocol in Drosophila. ..............................................................................................................86
Figure 3.3: Comparison of translational and ribosome profiling from Drosophila larval muscle. 88
Figure 3.4: Analysis of the transcriptome and translatome reveals dynamic translational
regulation in Drosophila muscle. ...............................................................................................90
Figure 3.5: Analysis of transcriptional and translational profiling of GluRIIA expression in
GluRIIA mutants and Tor overexpression in Tor-OE. ................................................................92
Figure 3.6: Few changes in postsynaptic transcription or translation are observed in GluRIIA
mutants. ....................................................................................................................................94
Figure 3.7: Increased cellular translation triggers adaptive responses in both transcription and
translation. ................................................................................................................................96
vi

Figure 3.S1: RpL3-Flag can restore viability to RpL3 mutants and does not perturb synaptic
growth or function when overexpressed. ...................................................................................98
Figure 3.S2: Comparative metagene analysis of genome-wide averaged reads distribution
around the start and stop codons from transcriptional, translational (TRAP) and ribosome
profiling. .................................................................................................................................. 100
Figure 3.S3: Functional classes for the 100 genes with the lowest and highest translational
efficiency. ................................................................................................................................ 102
Figure 3.S4: GO analysis and validation of transcriptional and translational upregulation of heat
shock proteins in Tor-OE using quantitative PCR. ................................................................... 104
Figure 3.S5: Effects of number of mapped reads on transcriptional and ribosome profiling
measurement variability. ......................................................................................................... 106
Figure 4.1: An electrophysiological screen for glutamate receptors required for PHD signaling.
............................................................................................................................................... 118
Figure 4.2: Generation of new GluClα mutants using CRISPR and UAS transgenic lines. ..... 120
Figure 4.3: GluClα is expressed in the nervous system. ......................................................... 122
Figure 4.4: GluClα co-localize with synaptic vesicle. .............................................................. 124
Figure 4.5: PHD modulates glutamate concentration in larvae hemolymph. ........................... 126

vii

ABSTRACT

Efficient and orderly communications between cells in multicellular organisms are crucial for
survival. The nervous system contains one of the most elaborate cell to cell communication
network, much of these communications happen in specialized cellular structure called synapse.
Electrical and biochemical signals are transmitted between cells through synapses, how are
these signals transmitted faithfully and stably over time and in ever changing internal and
external environments remains poorly understood. This study focuses on synaptic adaptations
to stress that enable stable but also adaptive communication between neurons. First, stress
induced by high synaptic activities was examined and a role of the BLOC-1 complex subunit,
Pallidin, in facilitating synaptic vesicle recycling through endosomal structures was revealed.
This molecular pathway maintains strength of synaptic transmission during high activity stress.
Second, synapses adapt to postsynaptic receptor perturbation by increasing presynaptic
release, a stress response called presynaptic homeostatic potentiation (PHP). And translational
control in the postsynaptic compartment is implicated in PHP signaling. A tissue specific
genome wide translational profiling approach was developed to profile PHP signaling and
showed that translational control is unlikely to act on specific target genes to enable PHP
signaling but rather, global elevation of translation maybe what is mediating the signal. Third,
the role of GluClα, a glutamate gated chloride channel, in mediating a signaling pathway that
senses and adapt to excess glutamate release that could potentially trigger cellular stress and
damage was investigated. Together, these work add to our understanding of molecular
mechanisms that guard synaptic transmission against stress and may help uncover therapeutic
targets for diseases that arise from abnormal synaptic adaptation to stress.
1

CHAPTER ONE
Introduction
It is estimated that 37 trillion cells make up the human body (Bianconi et al., 2013), and these
37 trillion building units work together to enable the human to behave as one individual. This
remarkable feature of cells requires elaborate communication systems between cells. In the
nervous system, cell-cell communications are especially important, where both electrical and
biochemical signals transmit between neurons in highly controlled ways. These communications
enable the stable functions of the nervous system throughout the life of the organism.
Malfunctions of these communications are detrimental to survival, thus specialization in cellular
structure, process and molecular signaling systems have evolved to ensure optimal
communication between neurons both in basal conditions and stress.
Synapse, a specialized cellular structure of neurons, is the hot spot for communication
between neurons (Sudhof and Malenka, 2008). The synapse has two compartments, one
belongs to the neuron where signal originates and is called the presynaptic compartment, the
other belongs to the neuron that receives the signal and is called the postsynaptic compartment.
The presynaptic compartment contains molecular machinery required for controlled release of
neurotransmitters to the synaptic cleft (Sudhof, 2004). Abundant synaptic vesicles containing
neurotransmitters are present in the presynaptic compartment, the identity and abundance of
proteins localized on synaptic vesicles have been defined (Sudhof and Jahn, 1991), cooperative
actions of these synaptic vesicle proteins with proteins localized on the presynaptic membrane
allows successful release and recycle of synaptic vesicles. In the postsynaptic compartment,
neurotransmitter receptors play crucial roles in receiving neuronal signal from the presynaptic
side. In addition, structural scaffolding proteins are also present at the postsynaptic
compartment to control maintenance of neurotransmitter receptors (Sanes and Lichtman, 2001).
2

The specialized structures in the pre and postsynaptic compartments work together to permit
efficient synaptic transmission.
During synaptic transmission, electrical signals, or changes in membrane voltage
potential, is transmitted from the presynaptic neuron to the postsynaptic neuron. Presynaptic
electrical signals, called action potentials, arrive at the presynaptic terminal and triggers calcium
influx through voltage gated calcium channels, locally increased calcium binds synaptic vesicle
protein, synaptotagmin, which further trigger fusion of synaptic vesicle with the presynaptic
membrane through the work of the SNARE complex (Sudhof, 2004). Neurotransmitter in
synaptic vesicle is then released into the synaptic cleft and binds postsynaptic receptors which
are usually ion channels, this leads to ions flowing through the receptors and changes the
membrane potential of the postsynaptic cell. After synaptic vesicle fusion with the presynaptic
membrane, membrane materials are endocytosed to regenerate synaptic vesicles, this
completes the synaptic transmission cycle and prepares the synapse for future synaptic
transmissions. Additional factors fine tune and install robustness in the process of synaptic
transmission so that it happens in a highly controlled and efficient manner (Sudhof, 2004).
Synaptic transmission faces both internal and external stress. Mechanisms that adapt to
those stresses ensure consistent performance of the synapse over time. This study focused on
three types of stress to synapse and the molecular mechanisms mobilized by the synapse to
adapt to these stresses. First, Synapses are known to fire at a frequency up to 400Hz (Wang et
al., 2016), under such high activity, synaptic vesicles go through release cycles rapidly and this
challenges the vesicle recycling and rejuvenation pathways. Second, the key player for synaptic
transmission in the postsynaptic compartment, neurotransmitter receptors, are subject to
internal drives that lead to abnormal levels (Caporale and Dan, 2008) or functional blockage as
a result of external toxins (Frank et al., 2006). These circumstances threaten the efficacy of
synaptic transmission if no compensatory mechanisms are present. Third, under abnormal,
3

chronically increased presynaptic release condition, neurotransmitters are released in excess
and results in excessive postsynaptic activity, which is associated with cytotoxicity (Lau and
Tymianski, 2010). Mechanisms that prevents excessive neurotransmitter release are thus
important for maintaining a healthy nervous system.
Drosophila neuromuscular junction (NMJ) is a great model synapse to study synaptic
adaptation to stress. The powerful drosophila genetics combined with ease of maintenance of
drosophila culture makes drosophila a great model organism for studying the details of
molecular mechanisms. The drosophila NMJ is easily accessible and rich collections of genetic
mutants, RNAi lines and other genetic toolkits that are relevant to synaptic structure and
physiology research are available (Bellen et al., 2010; Groth et al., 2004; Jenett et al., 2012;
Nagarkar-Jaiswal et al., 2015; Spradling et al., 1999). Using drosophila NMJ as a model, I
studied the molecular mechanisms utilized by synapses to adapt to stresses caused by high
synaptic activity, postsynaptic receptor perturbation and excitotoxicity.
Chapter two concerns the stress to synapse induced by high synaptic activity. The cycle
of synaptic vesicle release and recycle is under exquisite control, when any component of this
cycle is perturbed, efficacy of synaptic transmission will be affected. Synaptic vesicle release
and recycle not only consumes a lot of energy, this process will also wear out protein machinery
implicated in the process. It has been demonstrated that synaptic vesicles go through early
endosomal structures for rejuvenating protein components located on synaptic vesicles,
rejuvenated vesicles improve efficacy of future vesicle release (Uytterhoeven et al., 2011).
However, much of the molecular mechanisms controlling such vesicle renewal pathways
remains unclear.
The biogenesis of lysosome-related organelles complex 1 (BLOC-1) is a promising
candidate player in the early endosome-dependent vesicle renewal pathway. BLOC-1 is a
4

stable complex made up of eight subunits (Lee et al., 2012). BLOC-1 is broadly expressed and
is shown to be required for the biogenesis of organelles such as melanosomes (Falcon-Perez et
al., 2002), in addition, BLOC-1 also regulates trafficking of endosomal structures (Delevoye et
al., 2016; Di Pietro et al., 2006; John Peter et al., 2013). The BLOC-1 subunits, Dysbindin and
Snapin localizes on synaptic vesicles (Dickman and Davis, 2009; Dickman et al., 2012) and
Dysbindin is implicated in the psychiatric disorder, schizophrenia (Straub et al., 2002).
Furthermore, BLOC-1 subunits Dysbindin and Snapin were shown to be required for synaptic
plasticity (Dickman and Davis, 2009; Dickman et al., 2012). Taken together, these data imply a
neuronal function of BLOC-1 and warrant a thorough investigation into the molecular
mechanisms of BLOC-1 and BLOC-1 subunits’ role in synaptic transmission. In chapter two, by
focusing on a new mutant in the BLOC-1 subunit Pallidin, a role of BLOC-1/BLOC-1 subunit in
early endosome-dependent vesicle recycling is uncovered (Chen et al., 2017b).
Chapter three concerns the stress conferred on synaptic transmission by perturbation of
postsynaptic receptors. Many toxins found in the animal kingdom target the postsynaptic
neurotransmitter receptors, such as the bungarotoxin and cobratoxin found in venomous snakes,
and philanthotoxin found in wasps. As a survival mechanism, animals have evolved molecular
pathways to ameliorate the damaging effects of postsynaptic receptor blockade (Davis and
Muller, 2015a). At the drosophila NMJ, when postsynaptic receptor function is perturbed by
genetic or pharmacological approaches, the postsynaptic compartment becomes less sensitive
to neurotransmitter, the presynaptic compartment then compensates for loss of postsynaptic
sensitivity by releasing more synaptic vesicles, a phenomenon called homeostatic synaptic
plasticity (Frank et al., 2006). This capability is mediated by a signaling pathway that originates
from the postsynaptic side, travels in a retrograde direction to the presynaptic side and then
eventually instructs more synaptic vesicle release. Many molecules have been shown to be
required in the presynaptic compartment to enable this signaling pathway. Such as Dysbindin,
5

Snapin, MCTP, Rbp and DKaiR1D (Dickman and Davis, 2009; Dickman et al., 2012; Genc et al.,
2017; Kiragasi et al., 2017; Muller et al., 2015).
Little is known about the molecular events in the postsynaptic compartment that
constitute the postsynaptic portion of the homeostatic signaling pathway. Work in both
vertebrate and invertebrate systems revealed that postsynaptic translation regulation
retrogradely modulates presynaptic vesicle release (Henry et al., 2012; Penney et al., 2012). At
the Drosophila NMJ, translation in the postsynaptic compartment is up-regulated, blocking the
activity of target of rapamycin (Tor), a largely general positive regulator of translation, blocks the
homeostatic signaling, moreover, overexpression of Tor in the postsynaptic compartment
increases presynaptic release independent of postsynaptic receptor perturbation (Penney et al.,
2012). All these observations strongly implicate a role of postsynaptic translation regulation in
the homeostatic signaling.
Translational regulation can be globally applied to nearly all mRNA transcripts in the cell
or specifically influence certain target genes (Kong and Lasko, 2012). Global translation
regulation can happen through regulation of cap-dependent translation initiation, a mode of
translation initiation utilized by most endogenous cellular mRNAs, by modulating binding of the
5’ Cap structure by initiation factors (Kong and Lasko, 2012). eIF4E binding to the 5’ Cap
structure can be regulated through mTOR1 signaling pathway which could impact many mRNAs
in a largely non-specific manner (Saxton and Sabatini, 2017). Modulation of mRNA polyA tail
length could also impact large sets of mRNA without much specificity (Park et al., 2016). Gene
specific translation regulation can happen through multiple mechanisms, such as translation
activation or suppression mediated by sequence motif found on specific mRNAs that allow cap-
independent translation initiation, uORF translation, mRNA stability modulation or binding by
RNA binding proteins (Colak et al., 2013; Mauer et al., 2017; Meyer et al., 2015; Tushev et al.,
2018). Although Tor signaling has been implicated in homeostatic signaling, it is unclear
6

whether Tor plays a permissive or instructive role in the signaling pathway. Nor is it known
whether other general or gene specific translational regulation mechanisms are also required for
the retrograde signal in homeostatic synaptic plasticity.
How is regulation of translation transformed into the retrograde signaling? One
hypothesis is that specific gene or genes are up-regulated in translation and the elevated
protein levels of those genes lead to the retrograde signal. In this scenario, Tor signaling is likely
permissive to homeostatic signaling and other gene specific translational regulator might also be
involved. The other possibility takes into consideration that Tor is a largely non-specific
translation regulator, thus it may not target specific genes in homeostatic conditions and instead
increase overall translation non-specifically which could be what contributes to the retrograde
signal. In chapter three, a genome wide translational profiling approach is developed and
utilized to differentiate these two possibilities (Chen and Dickman, 2017).
Chapter four concerns neuronal stress elicited by excessive neurotransmitter release.
Postsynaptic compartment undergoes significant changes when neurotransmitter binds
postsynaptic receptors. Ionotropic receptors open their pore and allow ions influx into the cell,
including Ca
2+
. Ca
2+
is an important second messenger and cytosolic Ca
2+
concentration is
tightly controlled in the cytosolic space and elevated Ca
2+
activates many enzymes and triggers
multiple signaling pathways (Brini et al., 2014). When these effects of Ca
2+
are not controlled
within the physiological level, they start to damage the cell and could even lead to apoptosis
(Mehta et al., 2013).
The Drosophila NMJ can be induced to chronically release excess glutamate. When the
glutamate transporter, VGlut is overexpressed in drosophila motor neuron, synaptic vesicle size
increases through an unknown mechanism (Daniels et al., 2004). This lead to each synaptic
vesicle containing more neurotransmitter. In this condition, excessive glutamate is released
7

during each vesicle fusion. However, the synapse turns down the number of released vesicles
to maintain normal level of postsynaptic excitation (Daniels et al., 2004; Gavino et al., 2015; Li
et al., 2018). This process is termed presynaptic homeostatic depression (PHD). Previous work
suggested a presynaptically localized inhibitory receptor to be a key player in the PHD signaling
(Li et al., 2018). In chapter four, candidate receptors were characterized with an emphasis on
glutamate-gated chloride channel alpha, GluClα, the only known glutamate gated chloride
channel in the fly genome. This initial characterization will provide preliminary results to support
the development of mechanistic model for PHD signaling.

8

CHAPTER TWO

The BLOC-1 Subunit Pallidin Facilitates Activity-Dependent Synaptic Vesicle Recycling

This work was first published as:
Xun Chen*, Wenpei Ma*, Shixing Zhang, Jeremy Paluch, Wanlin Guo, and Dion K. Dickman.
The BLOC-1 subunit pallidin facilitates activity-dependent synaptic vesicle recycling. eNeuro.
2017 Feb 8;4(1). *These authors contributed equally to this work.

Author contributions:
Xun Chen, Wenpei Ma and Dion K. Dickman designed research;
Wanlin Guo contributed unpublished reagents/analytic tools;
Dion K. Dickman wrote the paper;
Xun Chen performed experiments and analysis presented in Figure 2.1; 2.2; 2.3; 2.4 F,G; 2.6 C;
Wenpei Ma performed experiments and analysis presented in Figure 2.4 A-E, H-I; 2.5; 2.6 A,B;
2.7; 2.8;
Shixing Zhang contributed to analysis presented in Figure 2.8
Jeremy Paluch performed experiments and analysis presented in Figure 2.6 D,E.

9

2.1 ABSTRACT
Membrane trafficking pathways must be exquisitely coordinated at synaptic terminals to
maintain functionality, particularly during conditions of high activity. We have generated null
mutations in the Drosophila homolog of pallidin, a central subunit of the Biogenesis of
Lysosome-related Organelles Complex 1 (BLOC-1), to determine its role in synaptic
development and physiology. We find that Pallidin localizes to presynaptic microtubules and
cytoskeletal structures, and that the stability of Pallidin protein is highly dependent on the
BLOC-1 components Dysbindin and Blos1. We demonstrate that the rapidly recycling vesicle
pool is not sustained during high synaptic activity in pallidin mutants, leading to accelerated
rundown and slowed recovery. Following intense activity, we observe a loss of early endosomes
and a concomitant increase in tubular endosomal structures in synapses without Pallidin.
Together, our data reveals that Pallidin subserves a key role in promoting efficient synaptic
vesicle recycling and re-formation through early endosomes during sustained activity.

10

2.2 INTRODUCTION
Synaptic vesicle trafficking must be tightly coordinated to ensure rapid, efficient, and reliable
replenishment following exocytosis, particularly during intense levels of neuronal activity. To
accomplish this, synapses are endowed with a variety of mechanisms to ensure fast and robust
synaptic vesicle recycling. It is clear that there are slow and fast forms of synaptic vesicle
endocytosis (Kononenko and Haucke, 2015; Smith et al., 2008; Sudhof, 2004), and that
dynamin is necessary for vesicular fission (Ferguson and De Camilli, 2012; Rizzoli, 2014).
There are at least three fundamental trafficking pathways for synaptic vesicles at the synapse.
These include Clathrin-mediated endocytosis and direct re-formation of synaptic vesicles to the
fusion competent pool (McMahon and Boucrot, 2011; Murthy and De Camilli, 2003), trafficking
of synaptic vesicles through endosomal intermediates (Brown et al., 2009; de Hoop et al., 1994;
Hoopmann et al., 2010; Kononenko and Haucke, 2015; Salem et al., 1998; Uytterhoeven et al.,
2011; Voglmaier et al., 2006; Wucherpfennig et al., 2003), and bulk endocytosis pathways
(Clayton and Cousin, 2009a; Murthy and De Camilli, 2003; Soykan et al., 2016; Wu et al.,
2014). Further, following re-formation of functional synaptic vesicles through these routes, these
vesicles must be organized into pools of varying locations, releasable states, and biochemical
interactions that ultimately maintain neurotransmission and determine synaptic strength (Rizzoli,
2014). Although several of the key molecules involved in the essential steps of membrane
trafficking have been identified, much less is known about how synaptic vesicle recycling is
dynamically modulated to meet the changing demands of synapses during sustained
neurotransmission. More specifically, how the demands of synapses during intense stimulation
are transduced to enable adaptive modulations in the speed and destinations of synaptic vesicle
transport remains enigmatic.
Endosomes are key nodes of signaling and trafficking during synaptic vesicle recycling.
Although Rab5 and other factors are known be involved in synaptic endosomal function
(Kononenko and Haucke, 2015; Uytterhoeven et al., 2011; Wucherpfennig et al., 2003), our
11

understanding of how synaptic vesicle trafficking through endosomes remains incomplete. The
Biogenesis of Lysosome-related Organelles Complex-1 (BLOC-1) complex has been implicated
in endosomal sorting in a variety of tissues, but its role at synapses is not established. The
BLOC-1 is composed of eight subunits, Blos1, Blos2, Blos3, Snapin, Dysbindin, Pallidin, Muted,
and Cappuccino (Ciciotte et al., 2003; Falcon-Perez et al., 2002; Lee et al., 2012; Li et al., 2003;
Starcevic and Dell'Angelica, 2004). Mutations in human pallidin and other BLOC-1 components
are associated with Hermansky-Pudlak Syndrome, a disease in which trafficking and biogenesis
of platelets, lysosomes, and other endosomal organelles are impaired (Wei, 2006). In addition,
mutations in the BLOC-1 components dysbindin, muted, and blos3 have been associated with
schizophrenia (Ghiani and Dell'Angelica, 2011; Morris et al., 2008; Ryder and Faundez, 2009;
Straub et al., 2002). The central component of this complex is Pallidin, which biochemically
interacts with Dysbindin, Blos1, and Cappuccino (Lee et al., 2012; Starcevic and Dell'Angelica,
2004). However, the role of Pallidin and BLOC-1 in the nervous system in general, and at
synapses in particular, is not understood.
The fruit fly Drosophila melanogaster is an attractive model system to elucidate the role
of pallidin at synapses. The fly genome encodes single orthologs of each vertebrate BLOC-1
subunit, including pallidin (Cheli et al., 2010; Mullin et al., 2015), and there is evidence for
similar interactions between subunits (Cheli et al., 2010). Drosophila blos1 has been implicated
in pigmentation trafficking in photoreceptors (Cheli et al., 2010), while dysbindin and snapin
were found to be necessary for presynaptic homeostatic plasticity (Dickman and Davis, 2009;
Dickman et al., 2012), an adaptive form of synaptic plasticity that leads to an increase in
presynaptic release in response to perturbation of postsynaptic neurotransmitter receptors,
maintaining stable levels of synaptic strength (Davis and Muller, 2015b; Frank, 2014). Finally, in
addition to the sophisticated genetic approaches available in Drosophila, the fly neuromuscular
junction (NMJ) permits powerful electrophysiological, imaging, and cell biological tools with the
12

potential to reveal the functions of pallidin and other BLOC-1 subunits in synaptic structure and
function.
To gain insight into the role of BLOC-1 at synapses, we have generated mutations in the
Drosophila homolog of pallidin. Our and characterization of synaptic development and
physiology in these mutants has revealed that Pallidin is localized to cytoskeletal structures at
synaptic terminals, but is surprisingly dispensable for synaptic growth, structure, baseline
function, and homeostatic plasticity. However, pallidin is necessary to replenish depleted
synaptic vesicles during high levels of activity by promoting the rapid trafficking and re-formation
of vesicles through endosomal intermediates. Thus, Pallidin has an important function in
maintaining and replenishing the activity-dependent synaptic vesicle pool.

2.3 MATERIALS AND METHODS
Drosophila Genetics and molecular biology
Drosophila stocks were raised at 25°C on standard molasses media containing 12g inactive
yeast, 60g cornmeal, 6g agar, 74ml molasses, 5.5ml propionic acid, and 11ml tegosept (10%
w/v) per liter. It was communicated to us that raising pldn mutants in an alternative food source,
containing 36g inactive yeast, 89g cornmeal, 6.6g agar, 89ml molasses, 6.6ml propionic acid,
17.8ml tegosept (10% w/v) per liter results in changes in synaptic growth in pldn and other
BLOC-1 mutants that were not observed using the recipe used in this study (Victor Faundez,
Emory University, personal communication). The reasons for this distinction are under active
investigation by Dr. Faundez.
The precise deletion of the pallidin locus was generated using FLP-mediated
recombination between pairs of transposon-based FRT sites, as described in the DrosDel
Collection (Parks et al., 2004). Specifically, two transposons flanking the pallidin locus,
pBac
f05716
and pBac
f05753
(Thibault et al., 2004), were obtained from the Bloomington Drosophila
13

Stock Center. Each contained FRT sites in the correct orientation to permit a precise deletion.
Following FLP-mediated recombination and excision of the remaining hybrid transposon, we
confirmed the deletion by PCR using the following primers: forward primer 5’
GTCATTGGGTGCAAAGTGCTC; reverse primer 5’ CTCCCGAGCTGCATGTTGAATC. This
revealed that bases 11,689,565 to 11,691,527 on chromosome 3L were deleted. The transcript
of the gene located 5’ to pallidin is not perturbed, while an estimated 591 bases of the 3’UTR of
sugb, the gene located 3’ to the pldn locus, is deleted in pldn
∆1
. This region overlaps with the
predicted pallidin 3’UTR, making it difficult to conclude whether the neighboring gene is
impacted. The w
1118
strain was used as the wild-type control unless otherwise noted, as this was
the genetic background in which all genotypes were bred. UAS-GFP-myc-2xFYVE was obtained
from Marcos Gonzalez-Gaitan (Wucherpfennig et al., 2003), and blos1
ex2
(Cheli et al., 2010)
was a gift from Esteban Dell’Angelica (UCLA). Df(3L)BSC675 (pldn deficiency) was obtained
from Bloomington Drosophila Stock Center (Bloomington, IN, USA), as were all others unless
otherwise noted. Standard second and third chromosome balancers and genetic strategies were
used for all crosses and for maintaining mutant lines. For all experiments, animals of either sex
were used unless otherwise specified.
We obtained cDNA of the entire pallidin open reading frame from the Berkeley
Drosophila Genome Project (IP05492). We cloned the pallidin cDNA into the pACU2 vector
(Han et al., 2011b) using standard cloning methods (5’ EcoR1 restriction enzyme; 3’ Xba1
restriction enzyme) to generate UAS-pallidin. For generation of UAS-pallidin-3xflag we inserted
synthesized 3xflag coding sequence:
GCATGGATTACAAGGATCACGACGGCGATTACAAGGATCACGACATCGATTACAAGGATGA
CGATGATAAGTAA and cloned the sequence into the pACU2 vector using standard cloning
methods (5’ Nde1 restriction enzyme; 3’ Spe1 restriction enzyme), we then cloned the pallidin
cDNA into this pACU2 vector that contains the 3xflag sequence using standard cloning methods
(5’ EcoR1 restriction enzyme; 3’ Xba1 restriction enzyme). These constructs were sequenced
14

and sent to BestGene Inc. (Chino Hills, CA) for recombination-mediated insertion into the VK18
(Venken et al., 2006) recombination site on the second chromosome.

Immunochemistry and Immunoblot analysis
Wandering third-instar larvae were dissected with pins on a Sylgard dish, fixed in Bouin’s
fixative (Sigma, HT10132-1L) or 4% paraformaldehyde in PBS (Sigma, F8775), and
immunostained with primary antibodies diluted in PBST at 4 degree overnight or room
temperature for 30min, followed by 3 times washing with PBST and secondary antibody
incubation at room temperature for 2 hours, samples were then washed three times with PBST
and mounted on a glass slide for imaging. To generate the Pallidin antibody used in this study,
we synthesized a peptide consisting of amino acids GRQNKTYIDLSKEKYK of the Pallidin
amino acid sequence (amino acids 80-95). This peptide was conjugated to KLH and injected
into rabbits to obtain immunosera that was subsequenctly affinity purified (Yenzym LLC, CA,
USA). The following primary antibodies were used at the indicated dilutions: mouse nc82 anti-
BRP 1:100 (Developmental Studies Hybridoma Bank; DSHB, RRID:AB_2314867); rabbit anti-
DLG 1:10,000 (Koh et al., 1999), mouse anti-SYN 1:20 (3C11; DSHB, RRID:AB_2313867),
guinea pig anti-vGlut (1:200), goat anti-HRP 1:200 (directly conjugated to Alexa-647; Jackson
Immunoresearch), mouse anti-Futsch 22C10 1:50 (Developmental Studies Hybridoma Bank;
DSHB, RRID:AB_528403) and rabbit anti-PLDN 1:200. Alexa-fluor 488- and Cy3-conjugated
donkey secondary antibodies (Jackson Immunoresearch) were used at 1:400. Images were
acquired with a Nikon A1R Resonant Scanning Confocal microscope equipped with NIS
Elements software and a 100x APO 1.4NA oil immersion objective. Settings were optimized for
detection without saturation of the signal. Z-stacks were obtained using identical settings within
each experiment and maximal intensity projection of each Z-stack were used for analysis.
Bouton numbers were quantified directly from preparations stained for Synapsin under a
confocal microscope on muscle 6 and 7 of segment A3. BRP number and density and Pldn
15

intensity were quantified using NIKON NIS-Elements Advanced Research software, intensity
values were measured as mean intensity. BRP number, density and HRP area were quantified
from images of muscle 4 segment A3. Pldn intensity were quantified from images of muscle 6
and 7 of segment A2.
For GFP-2XFYVE live imaging, animals were dissected in 0.4 mM Ca
2+
HL-3 and
incubated in modified HL-3 with 2 mM Ca
2+
, 90mM K
+
(high K
+
) HL-3 for 5 min for high K
+

stimulation. GFP-2XFYVE and Cy3-HRP (Jackson Immunoresearch, 1:400) were imaged using
a Zeiss LSM700 confocal microscope equipped with Zen software using a 63X 1.0 NA water
immersion objective. Analysis of GFP-2XFYVE puncta, including the density (number of GFP-
2XFYVE puncta/HRP area), size, and intensity, was performed using ImageJ (NIH). For
analysis of size and intensity, the number of puncta after high K
+
was normalized to each
genotype’s puncta number at rest and after high K
+
.
For immunoblot analysis, 50 adult heads were collected and homogenized in 100ul lysis
buffer (10mM HEPES PH 7.4, 150mM NaCl, Protease inhibitors (Roche), 1% Triton X-100 ).
10ul of protein lysate was separated by SDS-PAGE and transferred to PVDF membranes.
Western blot analysis were performed according to manufacturer’s protocols. SuperSignal West
Femto Maximum sensitivity substrate (Thermo Scientific) were used for X-ray film based band
visualization. The film were scanned and band intensities were quantified with ImageJ (NIH).
The following antibodies were used: Rabbit anti-PLDN 1:1000, mouse anti-α-Tubulin 1:2000
(T6199, Sigma-Aldrich).

Electrophysiology
Electrophysiology was performed using a Zeiss Axioscope AX10 fixed stage microscope
equipped with a 40x 0.8 NA water-dipping objective. Third-instar larvae were dissected and
bathed in a modified HL-3 saline (in mM): NaCl 70, KCl 5, MgCl2 10, NaHCO3 10, sucrose 115,
trehelose 5, HEPES 5 (pH 7.2), and a calcium concentration of 0.4 mM unless otherwise
16

specified. Sharp electrode current-clamp recordings were performed on muscles 6 and 7 in
abdominal segments A2 or A3. Severed ventral nerves were stimulated using a 5V command
pulse at 3 msec stimulus duration through pClamp software to an Isoflex stimulation unit
(A.M.P.I., Israel). Data was acquired using an Axoclamp 900A amplifier, digitized using a
Digidata 1440A, and controlled using pClamp 10.5 software (Molecular Devices, CA, USA).
Electrophysiological sweeps were sampled at a rate of 10 kHz and filtered at 400 Hz. Data was
analyzed using MiniAnalysis (Synaptosoft), SigmaPlot (Systat Software), Graphpad Prism,
Microsoft Excel, and SPSS 13.0. Quantal content was calculated for each individual recording
by calculating the average EPSP, average mEPSP and corrected for non-linear summation for
the calcium-cooperativity analysis, Using equation QCcorrected = (EPSP/mEPSP)(1-EPSP/V0)
-1

where V0 = (reversal potential – resting potential) (Martin, 1955). A recording electrode (15-30
MΩ resistance) filled with 3M KCl was used and data was only analyzed from cells with a resting
potential more hyperpolarized than -60 mV, input resistance of at least 5 MΩ, and resting
potentials that did not deviate by more than 5% for the duration of the recording. For acute
pharmacological homeostatic challenge, semi-intact preparations, with the CNS, and gut left
intact, were perfused with Philanthatoxin-433 (20 µM in HL3, Sigma) for 10 min followed by full
dissection and electrophysiological recording as described (Frank et al., 2006). For failure
analysis, recordings were performed in HL3 saline with 0.1mM Ca
2+
and percent failure of EPSP
were calculated from 40 stimulation trials in each recording. For asynchronous release
measurement, mEPSP frequency were calculated for the 2 second period immediately following
EPSPs from 30 EPSP trials in each recording.
Two electrode voltage clamp recordings were used to determine the readily releasable
pool (RRP) size. Recordings were made from cells with input resistances ≥5 MΩ and membrane
potentials between −55 and −70 mV in modified HL-3 saline containing 3 mM extracellular
calcium. Intracellular electrodes with resistances of 10–30 MΩ filled with 3 mM KCl were used.
The holding potential was −70 mV. EPSC amplitudes during a stimulus train (60 Hz, 60 stimuli)
17

were calculated as the difference between the peak and baseline before stimulus onset of a
given EPSC. The number of release-ready vesicles was obtained by back-extrapolating a line fit
to the linear phase of the cumulative EPSC plot (the last 30 stimuli) to time 0 and dividing the
cumulative EPSC amplitude at time 0 by the mean mEPSC amplitude recorded in the same cell,
as described (Muller et al., 2012).

Electron microscopy
EM analysis was performed as described (Kaufmann et al., 2002). Wandering third-instar larvae
were dissected in Ca
2+
-free HL-3 saline (rest) or 2 mM Ca
2+
, 90 mM K
+
(high K
+
) modified HL-3
saline, then fixed in 2.5% paraformaldehyde/5.0% glutaraldehyde/0.06% Picric Acid/0.1M
cacodylate buffer for ~18 hours at room temperature. Fillets were rinsed three times for twenty
minutes in 0.1M cacodylate buffer. The larval pelts were then placed in 1% osmium
tetroxide/potassium ferrocyanide mix buffer (1% OsO4,1.5% K4[Fe(CN)6] in water) for 1 hour at
room temperature. After rinsing and dehydration in an Ethanol series, samples were cleared in
propylene oxide and infiltration with half propylene oxide and half TAAB resin overnight at 4C.
The following day, samples were embedded in fresh TAAB resin. EM sections were obtained on
a JEOL 1200EX microscope at the EM Facility of Harvard Medical School. The 6/7 muscle
region was located by taking 0.5 μm sections and the bouton regions were located by taking 90
nm sections until boutons were identified. The blocks were then trimmed and serial sectioned at
a 60 nm thickness for approximately 240 sections. The sections were mounted on Formvar
coated single slot grids and viewed at a 25,000x magnification. Measurements were taken to
scale with 10x lupe/micrometer. Images were analyzed blind to genotype using ImageJ (NIH)
and Adobe Photoshop (Adobe Systems) software. The 3D reconstruction model was generated
using IMOD software (Kremer et al., 1996).

18

Statistical Analyses
Statistical analyses were performed using SPSS 13.0 software (IBM, USA). Student’s t test was
used to compare two groups. The one-way ANOVA plus post-hoc LSD (with equal variances) or
Tamhane’s T2 (with unequal variances) tests were used to compare three or more groups.
Statistical significance was defined as p < 0.05 levels. All data are presented as group
means ± SEM.

2.4 RESULTS
Generation and analysis of mutations in the Drosophila homolog of pallidin
Pallidin is a core component of the BLOC-1 complex (Lee et al., 2012; Li et al., 2003; Starcevic
and Dell'Angelica, 2004) but has not been studied in Drosophila. We identified independent
transposon insertions flanking the Drosophila pallidin locus (CG14133; hereafter abbreviated
pldn). These piggyBac transposons fortuitously carried FRT sequences in the same orientation,
enabling FLP-mediated recombination and excision of the intervening sequence (Parks et al.,
2004; Ryder et al., 2007). Following recombination and precise excision of the remaining hybrid
transposon, the entire open reading frame of the pldn locus was removed (Fig. 2.1A; this allele
referred to as pldn
Δ1
), and the genetic lesion was confirmed by PCR (Fig. 2.1B). pldn
Δ1
mutants
were viable and fertile, and could be maintained as healthy homozygous stocks.
Immunoblot analysis revealed that Pldn is expressed as a single band at 19 kDa (Fig.
2.1C), consistent with the predicted molecular weight of the lone isoform in the Drosophila
genome. This 19 kDa band was absent in heads of pldn
Δ1
mutants, confirming the specificity of
the Pldn antibody, and overexpression of a UAS-pldn-3xflag transgene revealed increased Pldn
protein running at a slightly larger size, as expected with the 3xflag tag (Fig. 2.1C). Pldn was
expressed in all stages examined (embryos through adults), and was present in larval and adult
brain and body extracts (data not shown), consistent with Drosophila Pldn, like the vertebrate
19

homolog, being broadly expressed. Together, this demonstrates that pldn is broadly expressed
and that pldn
Δ1
is a null mutation.

Pallidin localizes to presynaptic microtubules and is not required for synaptic growth or
structure
In vertebrates, Pldn is associated with the AP-3 complex, F-actin and Syntaxin 13 (Bang et al.,
2001; Delevoye et al., 2016; Di Pietro et al., 2006; Falcon-Perez et al., 2002; Huang et al., 1999;
Parast and Otey, 2000), suggesting Pldn may interact with both the cytoskeleton and
endosomal structures, but whether this association holds at synapses is not known. We
therefore examined the synaptic expression and localization of Pldn at the Drosophila
neuromuscular junction (NMJ). Pldn immunostaining revealed a specific signal in both
presynaptic terminals of motor neurons as well as in postsynaptic muscles. In presynaptic
terminals, Pldn immunostaining appeared to label neuronal microtubule structures, significantly
overlapping with Futsch, a marker for neuronal microtubules (Hummel et al., 2000) (Fig. 2.1D,
E, F). This signal was absent in pldn
Δ1
mutant synapses (Fig. 2.1D), confirming the specificity of
this antibody and that is a pldn
Δ1
null mutation. In the postsynaptic muscle, Pldn appeared to
localize to Z-disc structures (Fig. 2.1G), consistent with observations of Pldn association in
vertebrate striated muscle (Bang et al., 2001; Parast and Otey, 2000). These Z-discs are
cytoskeletal anchors for muscle filaments as well as signal transduction centers (Clark et al.,
2002; Collin et al., 2012; Delevoye et al., 2016; Granger et al., 2014). We also noted that the
normally organized F-actin bundles in the muscle, labeled with phalloidin, appeared
disorganized in pldn mutants (Fig. 2.1G). Thus, Pldn is present in both pre- and post-synaptic
compartments at the Drosophila NMJ, where it is associated with cytoskeletal structures.
Recent studies have suggested that pldn and other components of the BLOC-1 complex
have roles in synaptic development (Ghiani et al., 2010; Mullin et al., 2015; Wu et al., 2011;
Zhou et al., 2012). We therefore examined synaptic growth and structure in pldn
Δ1
mutants as
20

well as in pldn
Δ1
in trans with a deficiency (pldn
Δ1/Df
). We immunolabeled the NMJ with
antibodies specific to the presynaptic neuronal membrane (HRP), the postsynaptic density
(Discs Large; DLG), presynaptic active zones (Bruchpilot; BRP), synaptic vesicles (Synapsin;
SYN; vesicular glutamate transporter; vGlut), and postsynaptic glutamate receptors (GluRIII and
GluRIIA). No major differences in synapse morphology or structure were observed in pldn
Δ1

mutants or pldn
Δ1/Df
, nor did we note any changes in axon guidance or targeting of motor
neurons to their proper target muscle (Fig. 2.2A,B and data not shown). To measure synaptic
growth and structure, we quantified membrane surface area (HRP area), the number of synaptic
boutons, and the density and numbers of active zones (Fig. 2.2C,D,E,F). We found no
significant difference in any of these values in pldn
Δ1
mutants compared to wild type, nor did we
observe any changes in the organization of postsynaptic glutamate receptors (Fig. 2.2B and
data not shown). Thus, we conclude that pldn is not required for proper synaptic
morphogenesis, growth, or architecture.

Pallidin stability depends on the BLOC-1 components Dysbindin and Blos1
Biochemical studies of the BLOC-1 complex have demonstrated that the stability of some
BLOC-1 components depend on the presence of other components, and Pldn protein levels are
reduced in dysbindin, cappuccino, muted, and blos3 mutants (Ciciotte et al., 2003; Falcon-Perez
et al., 2002; Li et al., 2003; Starcevic and Dell'Angelica, 2004). We therefore examined Pldn
expression in the two other genetic mutations in BLOC-1 subunits that exist in Drosophila,
dysbindin (dysb) (Dickman and Davis, 2009) and blos1 (Cheli et al., 2010). We observed a
reduction in Pldn immunolabeling at NMJ synapses in both mutants, with an almost complete
loss of Pldn in dysb
1
mutants (92.4% reduction), and a more moderate reduction in blos1
ex2

mutants (31.5% reduction; Fig. 2.3A,B). Further, we examined Pldn protein stability by
immunoblot of lysates from dysb
1
and blos1
ex2
mutant heads. Similarly, we observed a drastic
loss of Pldn in dysb
1
mutants (71.6% reduction), with an even larger reduction in blos1
ex2

21

mutants (96.2% reduction) (Fig. 2.3C,D). Given the large reduction of Pldn in dysb mutants, we
asked whether overexpression of pldn in dysb mutants could overcome the dependency of Pldn
stability on endogenous dysb. Surprisingly, we observed no significant difference in Pldn
immunostaining when pldn was neuronally overexpressed in dysb mutants (Fig. 2.3A,B). Finally,
we asked whether the dependence of Pldn stability on dysb was reciprocal. To address this
question, we overexpressed a UAS-Venus-Dysbindin transgene in neurons in a wild-type and
pldn mutant background and immunostained NMJs for Venus-Dysbindin. We found no reduction
in Venus-Dysbindin levels in pldn mutants compared to wild type (Fig. 2.3E). In fact, we
observed a significant increase in Venus-Dysbindin expression in pldn mutants (1.91 fold
increase; p<0.001; Student’s t test), suggesting that Pldn may actually limit the stability of
Dysbindin. Thus, Dysbindin and Pallidin protein stability are not reciprocally dependent on each
other.
Together, this data demonstrates three important points about the dependency of Pldn
stability on other BLOC-1 subunits. First, while Pldn stability is dependent on other BLOC-1
components, as observed biochemically (Ciciotte et al., 2003; Falcon-Perez et al., 2002; Gwynn
et al., 2004; Li et al., 2003), this dependence is not uniform, with Pldn at synaptic terminals
appearing to be more sensitive to levels of dysb than to blos1. Second, Pldn levels at synaptic
terminals, as determined by immunostaining, did not quantitatively correspond to the reduction
in levels observed in whole head lysates by immunoblot, suggesting there may be differential
dependency for Pldn stability in neuronal compartments and/or cell types. Third, at least in the
case of Dysb stability, pldn does not exert the same control of stability on Dysb compared to the
dependency of Pldn on dysb. Rather, Dysb levels are not reduced when overexpressed in pldn
mutants, and appears to actually increase at synaptic terminals.

22

Pallidin is dispensable for baseline neurotransmission and presynaptic homeostatic
potentiation
Synaptic physiology has not been examined in pallidin mutants in any system, although
changes in basal synaptic transmission have been observed in other BLOC-1 mutants (Cheli et
al., 2010; Chen et al., 2008; Dickman and Davis, 2009; Numakawa et al., 2004; Pan et al.,
2009; Tang et al., 2009). We therefore characterized synaptic physiology at the NMJ in pldn
Δ1

mutants and pldn
Δ1/Df
, comparing values of miniature EPSP (mEPSP) frequency, mEPSP
amplitude, evoked EPSP amplitude, and quantal content across a range of extracellular calcium
conditions. We observed no major differences in mEPSP frequency, amplitude, or EPSP
amplitudes in standard saline (0.4 mM extracellular Ca
2+
) in pldn mutants compared with
controls (Fig. 2.4A,C,D,E). In addition, there was no change in the apparent calcium
cooperativity of synaptic transmission, with pldn
Δ1
mutants releasing similar quantal content
across a range of extracellular calcium concentrations compared with wild type (Fig. 2.4B). We
went on to test the state of presynaptic function in further detail, examining asynchronous
release, presynaptic release probability (failure analysis), and the size of the readily releasable
synaptic vesicle pool. We observed no significant difference in asynchronous release (Fig.
2.4F), failure analysis (Fig. 2.4G), synaptic transmission at elevated extracellular calcium using
two electrode voltage clamp (2 mM; Fig. 2.4H), or the estimated size of the readily releasable
vesicle pool (Fig. 2.4I). Thus, surprisingly, pldn null mutants have no major defects in synaptic
growth, structure, or baseline function, while changes in these processes have been reported in
mutations in other BLOC-1 subunits.
The Drosophila NMJ has been established as a powerful model synapse to characterize
presynaptic homeostatic plasticity (Davis and Muller, 2015b; Frank et al., 2013). Using an acute
pharmacological assay in which sub-blocking concentrations of the postsynaptic glutamate
receptor antagonist philanthotoxin (PhTx) is applied to the dissected larval NMJ, mEPSP
amplitude is reduced due to the irreversible binding of the toxin (Frank et al., 2006). However,
23

EPSP amplitude is restored to baseline levels due to a rapid, homeostatic increase in
presynaptic release (quantal content). The BLOC-1 components dysbindin and snapin have
previously been shown to be required for this homeostatic increase in presynaptic release,
where quantal content remains unchanged in these mutants after application of PhTx, leading to
a reduced EPSP amplitude (Dickman and Davis, 2009). Given that PHP is blocked in two
BLOC-1 components, we sought to determine whether PHP could be expressed over acute and
chronic time scales in pldn mutants.
We applied PhTx to pldn
Δ1
NMJs and measured mEPSP amplitude, EPSP amplitude,
and calculated quantal content. Although mEPSP amplitudes were reduced to similar levels in
wild type and pldn mutants due to the acute blockade of postsynaptic glutamate receptors by
PhTx (Fig. 2.5E), EPSP amplitudes were maintained and PHP was robustly expressed (Fig.
2.5A,B,F). We then tested whether synaptic homeostasis was expressed normally in pldn
Δ1

mutations when chronically induced due to genetic loss of the postsynaptic glutamate receptor
GluRIIA throughout development, which normally triggers a homeostatic increase in presynaptic
release (Petersen et al., 1997). Similar to the acute induction and expression of PHP, PHP is
robustly expressed in GluRIIA;pldn mutants (Fig. 2.5C,D). Thus pldn is dispensable for both the
acute induction and chronic expression of PHP.

pallidin is necessary to maintain and recover the synaptic pool during high activity
Although we did not observe any major changes in synaptic function under basal conditions in
pldn mutants, we asked whether an important function of pldn may be revealed under conditions
of synaptic stress. BLOC-1 components have been implicated in endosomal sorting in a variety
of tissues, and we considered that during high levels of activity, the importance of pldn at
synapses may be revealed. During these conditions, membrane trafficking at synapses must be
rapidly and accurately orchestrated to ensure proper endocytosis, sorting, regeneration, and
mobilization of synaptic vesicles to maintain the functional vesicle pool and sustain
24

neurotransmission. Indeed, the importance of trafficking and sorting at synaptic endosomes
would be highlighted in this condition, and we reasoned that by stressing the synapse through
high intensity stimulation, we may reveal a role for pldn that would not be apparent at rest.
Under conditions of high extracellular calcium, we first stimulated NMJs at 10 Hz for 10
min to deplete the synaptic vesicle pool, then measured recovery of the pool for an additional 10
mins, taking a test pulse every 5 sec. Following an initial rapid depletion typically observed in
the initial seconds of stimulation, wild-type synapses largely maintained synaptic vesicle release
for the duration of the stimulus, ending at ~80% of the starting EPSP amplitude and rapidly
recovering to above 90% of pre-stimulus amplitudes (Fig. 2.6A). In contrast, this stimulation
protocol revealed a more rapid rundown of the synaptic vesicle pool in pldn mutants, ending at
~45% of the starting EPSP amplitude (Fig. 2.6A). Further, pldn mutants failed to fully replenish
the depleted synaptic vesicle pool over the course of the next 10 min of recovery following high
frequency stimulation, only recovering to ~60% of pre-stimulus amplitudes (Fig. 2.6A). This level
of depletion was significantly reduced when a pldn transgene was expressed in motorneurons in
a pldn mutant background, demonstrating that pldn is required presynaptically to maintain the
rapidly recycling synaptic vesicle pool.
Given the reduction in Pldn protein levels in both dysb
1
and blos1
ex2
mutants, we
considered whether these mutants share a similar deficit in maintaining the rapidly recycling
synaptic vesicle pool. Using the same stimulation paradigm, we observed a similar rundown in
both dysb
1
and blos1
ex2
mutants, as well as a delayed recovery of the depleted synaptic vesicle
pool (Fig. 2.6B). Thus, pldn is necessary for both the sustainment and recovery of the synaptic
vesicle pool during and following high levels of activity, a phenotype shared in dysb
1
and
blos1
ex2
mutants, which also exhibits a marked reduction in Pldn expression.
Next, we sought to determine whether the deficit in maintaining the functional vesicle
pool during high activity in dysb mutants was due to loss of Pldn itself, or rather whether Dysb
may have a direct role in maintaining the recycling vesicle pool. To distinguish between these
25

possibilities, we neuronally overexpressed pldn in dysb mutants and overexpressed dysb in pldn
mutants. As anticipated by the failure to restore Pldn protein levels at synapses in dysb mutants
(Fig. 2.3), the rapid rundown of the vesicle pool failed to be rescued in either of these conditions
(Fig. 2.6C). However, we were able to restore the recycling vesicle pool by overexpressing dysb
in dysb mutants (Fig. 2.6C). As we have demonstrated that Dysb levels were not reduced in
pldn mutants (Fig. 2.3E), this indicates that loss of Pldn itself, and not other BLOC-1
components, likely explains the failure to maintain the recycling vesicle pool in dysb mutants.
Finally, we considered the possibility that pldn may not be necessary for synaptic vesicle
recycling per se, but rather for establishing the full size of the starting synaptic vesicle pool. In
principle, this pool might be reduced in pldn mutants, given the roles of pldn and BLOC-1 in
vesicle biogenesis (Delevoye et al., 2016; Di Pietro et al., 2006). Indeed, a reduction in the
starting vesicle pool could explain the more rapid rundown of the synaptic vesicle pool without
necessitating any additional role for Pldn in synaptic vesicle recycling. To determine the size of
the entire releasable synaptic vesicle pool, we took advantage of the temperature-sensitive
mutation in the Drosophila dynamin gene, shibire

(shi). Although synaptic transmission is normal
at room temperature in these mutants, all synaptic vesicle endocytosis ceases at restrictive
temperatures (32°C) due to a disruption in the ability of Dynamin to drive fission of synaptic
vesicles and replenish the vesicle pool (Delgado et al., 2000; Kuromi and Kidokoro, 1998). We
measured synaptic transmission at the restrictive temperature in shi mutants alone, as well as in
shi;pldn and shi;dysb double mutants (Fig. 2.6D). In shi mutants alone, full depletion of the
entire synaptic vesicle pool was achieved after ~400 sec of stimulation at 15 Hz in 2 mM
extracellular calcium. Transmission ceases because every releasable vesicle is lost without the
replenishment of new synaptic vesicles due to this complete block of endocytosis. Importantly,
both shi;pldn and shi;dysb mutants also showed similar rates of depletion of the releasable
synaptic vesicle pool (Fig. 2.6D). We calculated the total quanta released in each mutant until
full depletion, finding that all genotypes had ~75,000 total quanta, with no significant differences
26

between the genotypes (Fig. 2.6E). Thus, pldn is not required for the biogenesis or
establishment of a full initial synaptic vesicle pool size, but rather is necessary to rapidly
replenish depleted synaptic vesicles during and following high levels of activity.

High synaptic activity depletes FYVE-positive endosomes in pallidin mutants
A variety of dynamic endosomal structures are known to exist at the presynaptic terminal, where
they are involved in modulating diverse aspects of synaptic growth signaling, membrane
trafficking and exchange, and synaptic vesicle recycling (Deshpande and Rodal, 2016;
Kononenko and Haucke, 2015; Rodal and Littleton, 2008; Saheki and De Camilli, 2012;
Uytterhoeven et al., 2011; Wucherpfennig et al., 2003). Given the inability of pldn mutants to
sustain the synaptic vesicle pool during high frequency stimulation, and the associations of
BLOC-1 in controlling endosomal sorting and trafficking, we considered whether endosomal
dysfunction may contribute to the failure to sustain the vesicle pool in pldn mutants. In particular,
we focused on endosomal structures known to participate in synaptic vesicle recycling. One key
endosome at the synapse that has been characterized in significant detail at the Drosophila
NMJ are Rab5-positive early endosomes. These distinct structures are defined by specific
labeling with the small GTPase Rab5 (Rodal et al., 2011; Wucherpfennig et al., 2003), and are
enriched in the phospholipid phosphatidylinositol-3-phosphate (PI[3]P). PI[3]P specifically binds
to the FYVE zinc-finger domain of endosomal factors such as the Rab5 effectors EEA1 and
Rabeenosyn-5 (Lawe et al., 2000; Nielsen et al., 2000; Stenmark et al., 1995; Wucherpfennig et
al., 2003). These endosomes serve as sorting stations for synaptic vesicles, directing proteins
and membrane to distinct intracellular compartments, including pathways for degradation or the
genesis of new synaptic vesicle pools (Uytterhoeven et al., 2011; Wucherpfennig et al., 2003).
Ultimately, these key endosomal sorting stations help to maintain the synaptic vesicle pool
during high activity, even disappearing when endocytosis from the plasma membrane is blocked
due to acute inactivation of shibire (Wucherpfennig et al., 2003).
27

To determine the dynamics and functionality of Rab5-positive endosomal structures in
the absence of Pldn, we characterized the number and maintenance of these endosomes. First,
we expressed GFP-2xFYVE in motor neurons and examined the punctate endosomal structures
in synaptic boutons at the NMJ that has been observed by others (Rodal et al., 2011;
Wucherpfennig et al., 2003). At rest, FYVE-positive endosomes in pldn mutants showed similar
size and density compared with wild type, although pldn mutants displayed a slight reduction in
the density of these structures (Fig. 2.7A,B). We then subjected the NMJ to stimulation with 90
mM KCl for 5 min, and found that FYVE-positive endosomes in wild-type terminals maintained
their integrity, showing no significant changes in density or size, and a small but significant
reduction in fluorescence intensity (Fig. 2.7). In contrast, GFP-2xFYVE labeled endosomes
were greatly reduced following high K
+
stimulation in pldn mutants, exhibiting large reductions in
the density, size, and intensity of GFP-2xFYVE puncta (Fig. 2.7), with a significant fraction
disappearing altogether. Similar results were observed in dysb mutants, consistent with the loss
of Pldn and reduced capacity to maintain the recycling vesicle pool in these mutants (Fig.
2.7B,C,D). Together, these experiments demonstrate that the activity-dependent maintenance
of FYVE-positive early endosomal structures depends on Pldn.

Tubular endosomal structures emerge in pallidin mutant synapses following high activity
Given the inability to sustain neurotransmission during high frequency stimulation in pldn
mutants, as well as the deficits in maintaining FYVE-positive endosomal structures following
activity, we considered whether visualization of synaptic ultrastructure may reveal details about
the state of endosomal structures during activity that could not be discerned through confocal
imaging alone. At rest, overall synaptic ultrastructure appeared relatively consistent between
wild type, pldn, and dysb genotypes, each showing similar levels of active zones and T bars,
and no significant differences in the size, number, and density of synaptic vesicles at NMJ
boutons (Fig. 2.8A and data not shown). However, cisternal endosomal structures, defined as
28

clear vesicles >80 nm in diameter, were increased in both pldn, and dysb mutants at rest (Fig.
2.8A,C), suggesting an accumulation of newly formed vesicular structures (Heuser and Reese,
1973; Korber et al., 2012). These are typically transient structures, and cisternal endosomes
were observed to accumulate in wild-type synapses following activity (Fig. 2.8B,D). Interestingly,
a similar accumulation of cisternal endosomes at rest were reported in Rab5 mutants
(Wucherpfennig et al., 2003), consistent with defects in synaptic vesicle endocytosis and,
perhaps, Rab5-dependent endosomal trafficking of recycling synaptic vesicles.
To determine how endosomal structures are altered following high levels of activity and
synaptic vesicle recycling, we subjected control, pldn, and dysb genotypes to depolarization in
90 mM KCl for 5 min, followed by immediate fixation and preparation for electron microscopy
(see methods). Following stimulation, wild-type NMJs exhibited no significant change in synaptic
vesicle density and an increase in cisternal endosomal structures (Fig. 2.8B,C,D), consistent
with increased rates of endocytosis, as observed in other studies (Akbergenova and
Bykhovskaia, 2009). In contrast, analysis of NMJs in both pldn, and dysb mutants revealed a
significant decrease in synaptic vesicle density and cisternal endosomal structures, consistent
with reduced recycling rates (Fig. 2.8B,C,D). However, the most striking change observed in
pldn and dysb synapses following activity was the emergence of tubular endosomal structures
(Fig. 2.8B,E). These structures are rarely observed in wild type, but have been observed at the
Drosophila NMJ when Rab5 activity is perturbed (Wucherpfennig et al., 2003). Considering that
elevated activity leads to a reduction in FYVE-positive endosomes in pldn and dysb mutants,
these tubular structures may be related to a diminishment of PI[3]P-enriched synaptic
endosomes and a concomitant expansion of the endosomal system.
Finally, we considered the possibility that these tubular endosomal structures were not
actually intracellular endosomes, but rather were invaginations from the plasma membrane.
These invaginations are indicative of an “activity-dependent bulk endocytosis” pathway that can
be triggered when other forms of endocytosis are disrupted (Heerssen et al., 2008; Kasprowicz
29

et al., 2008; Verstreken et al., 2009; Wu et al., 2014), and have also been observed in dynamin
mutants (Wu et al., 2014). To test whether the tubular endosomes we observed were
continuous with the plasma membrane, we performed two experiments. First, a bulk
endocytosis assay utilizes dextran uptake to reveal synaptic uptake through the activity-
dependent bulk endocytic pathway (Clayton and Cousin, 2009b; Uytterhoeven et al., 2011). We
performed this assay but did not observe any change in bulk endocytosis levels between wild
type, pldn, and dysb mutants (data not shown). In addition, we performed a 3D serial
reconstruction using electron microscopy of the tubular endosomal structures that emerged
following high activity in pldn mutants. This reconstruction revealed that these structures are
entirely cytosolic and discontinuous with the plasma membrane (Fig. 2.8F). Together, these
results demonstrate that Pldn is necessary during conditions of high synaptic activity to rapidly
transition membrane trafficking of synaptic vesicles through key endosomal intermediaries. In
the absence of Pldn, tubular endosomal compartments emerge with a concomitant reduction in
FYVE-positive early endosomes, leading to a decrease in the rapid and efficient recycling and
recovery of the synaptic vesicle pool.

2.5 DISCUSSION
We have generated null mutations in pallidin, a central component of BLOC-1, and
characterized synaptic structure and physiology in these mutants. Pldn is present at presynaptic
terminals, where it localizes to synaptic microtubules and the cytoskeleton. We find that while
pallidin does not have major roles in synaptic growth, structure, or function under basal
conditions, pallidin is crucial to maintain the releasable synaptic vesicle pool during conditions of
high activity. During these conditions, tubular endosomal structures accumulate with loss of
Pldn, while FYVE-positive endosomes are reduced. We also find that the stability of Pldn
depends crucially on the BLOC-1 subunits dysbindin and blos1, and that mutations in these
30

subunits phenocopy pallidin mutants, as expected due to destabilization of the protein.
Together, our data demonstrate that while pallidin has no obvious roles in basal synaptic
development and function, pallidin has a critical role during adaptive responses to synaptic
activity by promoting the efficient trafficking and re-formation of synaptic vesicles through FYVE-
positive endosomes.

Synaptic functions of Pallidin at the Drosophila NMJ
We find no major alterations in synaptic development or transmission in pallidin mutants.
Further, pallidin mutants are viable and healthy, and although we cannot rule out more subtle
phenotypes or differences between species or systems, it is surprising how unperturbed
synapses are in pallidin null mutants during basal conditions. In contrast, previous studies have
reported moderate changes in synaptic growth, baseline function, and homeostatic plasticity in
genetic mutations of other BLOC-1 subunits (Dickman and Davis, 2009; Dickman et al., 2012;
Ghiani et al., 2010; Mullin et al., 2015; Shao et al., 2011; Wu et al., 2011; Zhou et al., 2012). For
example, baseline synaptic transmission at lowered extracellular calcium is reduced in
dysbindin mutants (Dickman and Davis, 2009), yet no such effect is observed in pldn mutants
(Fig. 2.4). In addition, although both dysbindin and snapin are required for acute and chronic
forms of synaptic homeostasis in Drosophila (Dickman and Davis, 2009; Dickman et al., 2012),
no defects in presynaptic homeostatic plasticity were found in pldn mutants (Fig. 2.5). Similarly,
blos1 mutants were also found to robustly express homeostatic plasticity (Dickman et al., 2012),
in contrast to dysbindin and snapin mutants. This suggests that despite being a central part of
the BLOC-1 complex, genetic distinctions in synaptic function exist between pallidin and other
components in Drosophila. This may be due to partial redundancy and complex gene dosage
interactions between BLOC-1 components, as were recently reported (Larimore et al., 2014;
Mullin et al., 2015).
31

Our data suggest that a core function of Pallidin at synapses is to promote the rapid and
efficient maintenance of the functional synaptic vesicle pool under conditions of high activity. We
found no evidence that pldn controls synaptic vesicle biogenesis, as may have been anticipated
for the BLOC-1 complex, as we did not observe any change in the total releasable synaptic
vesicle pool (Fig. 2.6). Instead, pldn is necessary for efficient synaptic vesicle trafficking during
conditions of high activity, when at least a subset of synaptic vesicles are guided through
endosomal intermediates for sorting, maintenance, and re-formation of critical functional
constituents (Fernandes et al., 2014; Jovic et al., 2010; Uytterhoeven et al., 2011;
Wucherpfennig et al., 2003). In addition, we observed a striking increase in tubular endosomal
structures following activity. These tubular endosomes are rarely if ever observed in wild-type
synapses, and do not appear at rest in pldn mutants. Interestingly, similar structures have been
observed when Rab5 activity is manipulated, when overexpression or dominant negative forms
of Rab5 lead to the appearance of similar tubular endosomal structures (Shimizu et al., 2003;
Wucherpfennig et al., 2003). These tubular structures are likely the result of an expanded and
defective synaptic endosomal system due to abnormal regulation of Rab5 activity; such
activities have been reported for other BLOC-1 components (John Peter et al., 2013; Rana et
al., 2015). Indeed, there appears to be an intimate relationship between neuronal activity, Pldn,
and Rab5 in synaptic vesicle trafficking, as loss of pldn leads to a reduction of FYVE/Rab5-
positive endosomes following activity (Fig. 2.7). More generally, these findings point to a role for
pldn and other BLOC-1 components having important and yet distinct functions at synapses
during adaptive responses to neuronal stress, such as intense stimulation and homeostatic
challenge to neurotransmission.

Pallidin localization, stability, and the BLOC-1 complex
Pldn is ubiquitously expressed and localizes to cytoskeletal and endosomal structures (Bang et
al., 2001; Di Pietro et al., 2006; Falcon-Perez et al., 2002; Huang et al., 1999; Parast and Otey,
32

2000). At the Drosophila NMJ, Pldn localized to Z-bands in the postsynaptic muscle (Fig. 2.1G).
These structures are a major cytoskeletal component of muscles that anchors actin filaments to
enable muscle contraction (Clark et al., 2002; Collin et al., 2012; Delevoye et al., 2016; Granger
et al., 2014). It is worthwhile to note that these muscle Z-bands appear disorganized in pldn
mutants, suggesting that Pldn is required for the integrity or organization of these structures. In
the presynaptic terminal, Pldn exhibits a high degree of co-localization with the neuronal
microtubule marker Futsch (Fig. 2.1D). Given the role of pldn in promoting synaptic vesicle
trafficking, this suggests that Pldn may coordinate interactions between synaptic vesicles and
the cytoskeleton. Indeed, proper coordination between adaptors and the cytoskeleton is
particularly important at synaptic terminals during synaptic vesicle recycling (Delevoye et al.,
2016; Ferguson and De Camilli, 2012; Kononenko and Haucke, 2015). Interestingly, two other
BLOC-1 subunits, Dysbindin and Snapin, co-localize with synaptic vesicle markers, not
cytoskeletal structures (Dickman and Davis, 2009; Dickman et al., 2012). Given the disparate
roles in transmission, homeostatic plasticity, and synaptic vesicle endocytosis observed
between BLOC-1 subunits, it is tempting to speculate that these distinctions in localization are
related to their different functions at synapses.
A variety of studies have noted the apparent unitary nature of the BLOC-1 complex, with
significant biochemical evidence that the entire complex associates together as a single entity
(Delevoye et al., 2016; Falcon-Perez et al., 2002; Larimore et al., 2014; Setty et al., 2007;
Starcevic and Dell'Angelica, 2004). Further, genetic reductions of some BLOC-1 subunits lead
to the biochemical destabilization of other subunits (Ciciotte et al., 2003; Delevoye et al., 2016;
Falcon-Perez et al., 2002; Feng et al., 2008; Ghiani and Dell'Angelica, 2011; Gwynn et al.,
2004; Larimore et al., 2014; Li et al., 2003; Setty et al., 2007; Starcevic and Dell'Angelica, 2004;
Zhang et al., 2002). For example, Dysbindin and Muted are reduced by up to 60% and 90% in
brain lysates from pldn mutants in mice (Li et al., 2003). However, complex interactions between
BLOC-1 subunits have been reported (Larimore et al., 2014; Mullin et al., 2015), suggesting
33

simple loss-of-function analysis may not always explain important functional properties of the
BLOC-1 complex. Further, there is evidence for biochemical sub-complexes of the BLOC-1, in
which Pallidin/Blos1/Cappucino and Dysbindin/Snapin/Blos2 exhibit differential association from
the entire BLOC-1 complex (Lee et al., 2012). Consistent with these observations, we have
found that protein levels of Pldn appear highly dependent on Dysb, where Pallidin is almost
completely absent in dysb mutants, and even overexpression of pldn in dysb mutants fails to
restore levels Pldn (Fig. 2.3). However, this dependence is not reciprocal: dysbindin
overexpression in pldn mutants led to no reduction in Dysb levels; if anything, an increase in
Dysb was observed (Fig. 2.3). Finally, it is loss of Pldn at synapses, and not a function for Dysb
itself, which was necessary for rapid endocytosis, since overexpression of dysb in pldn mutants
failed to rescue this phenotype (Fig. 2.6).
Interestingly, although Pldn is also reduced in blos1
ex2
mutants, this reduction is not as
severe as observed in dysb mutants at presynaptic NMJ terminals. Indeed, this distinction in
stability may also be influenced by subcellular expression and/or trafficking of Pldn, as overall
levels of Pldn, assessed through immunoblots of whole head lysates, were quantitatively
different from levels assessed by immunostaining at the NMJ (Fig. 2.3). Notably, there is some
precedence for distinct genetic roles for BLOC-1 components in rodents. The changes in coat
color observed in pallidin, dysbindin, and other BLOC-1 mutants are not exactly the same, and
have not been reported in snapin mutants (Tian et al., 2005), nor have the neurodegenerative
defects reported in snapin mutants (Cai et al., 2010; Tian et al., 2005) been observed for other
BLOC-1 mutants. Ultimately, BLOC-1 components at synapses appear to be involved in
membrane trafficking, with complex inter-relationships between individual components in
regulating protein stability and functions in adaptive responses to synaptic stress.

34

Figure 2. 1
35

Figure 2.1: Generation of pallidin null mutations and synaptic localization of Pldn protein.
(A) Schematic of the workflow utilized to generate the excision of the pallidin locus. Black
arrows indicate primers used for PCR confirmation of the excision. (B) PCR confirmation of the
deletion of the pldn locus. (C) Immunoblot analysis of adult heads lysates from wild type (w
1118
),
pldn
Δ1
mutants (w
1118
;pldn
Δ1
), and neuronal pldn overexpression (pldn-OE; c155-Gal4/Y;UAS-
pldn-3xflag/+), which reveals a band running at 19 kDa, the predicted molecular weight of
Drosophila Pldn. This band (indicated by arrowhead) is absent in pldn
Δ1
, and increased in pldn-
OE. Anti-α-tubulin immunoblot was used as loading control. (D) Representative images of third-
instar larval NMJs from wild type and pldn
Δ1
mutants immunostained for Pldn (green) and the
neuronal microtubule marker Futsch (magenta). The neuronal membrane is immunolabeled with
anti-HRP (blue). (E) Magnified images of area 1 and area 2 (F) marked in (D), exhibiting a high
degree of colocalization between Pldn and Futsch. (G) Representative images of third-instar
larval muscle immunostained for Pldn (green) and F-actin (phalloidin; magenta), showing Pldn
localization to the muscle Z band.

36

Figure 2. 2
37

Figure 2.2: Synaptic growth and structure is unperturbed in pallidin mutants.
Representative images of muscle 6/7 NMJs from wild type (w
1118
) (A) and pldn
Δ1
mutants (B)
immunostained with anti-vGlut (synaptic vesicle marker; green), anti-HRP (white). Below: wild
type and pldn
Δ1
NMJs on muscle 4 immunostained with anti-BRP (active zone marker; green)
and GluRIII (postsynaptic glutamate receptor marker; magenta). No significant differences are
observed in bouton number (C), BRP density (D), BRP number/NMJ (E) or HRP area (F) in wild
type (n=12), pldn
Δ1
(n=12) and pldn
Δ1/Df
(w
1118
;pldn
Δ1
/Df(3L)BSC675; n=10). p>0.05; one-way
ANOVA for all parameters.

38

Figure 2. 3
39

Figure 2.3: Pallidin stability is dependent on dysbindin and blos1. (A) Representative
images of Pldn immunostaining (green) in NMJs of wild type, pldn
Δ1
, dysb
1
(w
1118
;dysb
1
),
dysb+pldn-OE (w
1118
;OK6/UAS-pldn-3xflag;dysb
1
), and blos1
ex2
(w
1118
;blos1
ex2
) mutants. Inset:
neuronal membrane (HRP; white). (B) Quantification of Pldn signal intensity, normalized to HRP
intensity, for the indicated genotypes (n=13-20). (C) Immunoblot analysis of adult head lysates
probed for Pldn in wild type, pldn
Δ1
, dysb
1
, blos1
ex2
. (D) Quantification of Pldn immunoblot
intensity normalized to α-tubulin for the genotypes indicated (n=3). (E) Representative images of
Venus-Dysb immunostaining (green) in NMJs of wt+venus-dysb-OE (w
1118
;OK6/+;UAS-venus-
dysb/+) and pldn+venus-dysb-OE (w
1118
;OK6/+;UAS-venus-dysb, pldn
Δ1
/pldn
Δ1
). The intensity
of Venus-Dysb in pldn+venus-dysb-OE (1.74±0.11 a.u. n=15) is significantly increased
compared to that of wt+venus-dysb-OE (0.91±0.09 a.u. n=9); p<0.001; Student’s t-test.

40

Figure 2. 4
41

Figure 2.4: Baseline synaptic transmission is normal in pallidin mutants. (A)
Representative electrophysiological traces (EPSP and mEPSP traces) from wild type and pldn
Δ1

mutant synapses. (B) Quantal content was determined across a range of extracellular calcium
concentrations for wild type and pldn
Δ1
synapses. No significant difference in the slope of the
line, indicating the apparent calcium cooperativity of synaptic transmission, was observed. No
significant differences were observed in the EPSP amplitude (C), mEPSP frequency (D),
mEPSP amplitude (E), asynchronous release (assayed by determining the mEPSP frequency
within the 2s immediately following EPSP stimulation) (F), or probability of release measured by
failure analysis (assayed by % EPSP failure in 0.1mM Ca
2+
) (G) in wild type (n=10), pldn
Δ1

(n=10) and pldn
Δ1/Df
(n=10) mutant synapses. (H) Representative EPSC traces (top) and
cumulative EPSC amplitudes (bottom) using two electrode voltage clamp evoked by 60-Hz
stimulation (60 stimuli) in wild type and pldn
Δ1
mutant synapses. No significant differences were
observed in the estimated readily releasable synaptic vesicle pool (RRP) between wild type
(n=7) and pldn
Δ1
(n=9) (I). p>0.05; one-way ANOVA for all parameters.

42

Figure 2. 5
43

Figure 2.5: pallidin mutants retain the capacity to express presynaptic homeostatic
potentiation. (A) Representative EPSP and mEPSP traces from wild type and pldn
Δ1
mutant
synapses after PhTx application (20 µM). (B) Normalized mEPSP amplitude and quantal
content values of wild type (n=10) and pldn
Δ1
(n=12) mutants following PhTx application. Data
are normalized to values of each genotype in the absence of PhTx. No deficit in acute
presynaptic homeostatic potentiation was observed in pldn
Δ1
mutants. (C) Representative EPSP
and mEPSP traces from GluRIIA (w
1118
;GluRIIA
sp16
), and GluRIIA;pldn
Δ1

(w
1118
;GluRIIA
sp16
;pldn
Δ1
) mutant synapses. (D) Normalized mEPSP amplitude and quantal
content values of GluRIIA (n=18) and GluRIIA;pldn
Δ1
(n=8). Data are normalized to wild type
values. No deficit in chronic presynaptic homeostatic potentiation was observed in pldn
Δ1

mutants. (E) Absolute values of mEPSP amplitude, EPSP amplitude (F) and quantal content (G)
from the normalized data in (B) and (D) for the indicated genotypes.

44

Figure 2. 6
45

Figure 2.6: BLOC-1 mutants fail to sustain neurotransmitter release during high
frequency stimulation. (A) Increased rate of depletion and slowed recovery of the synaptic
vesicle pool is observed under high frequency stimulation in pldn
Δ1
mutants (n=15; p<0.01;
Student’s t-test) compared with wild type (n=11). Presynaptic overexpression of pallidin in pldn
Δ1

mutants (pldn+pldn-OE: w
1118
;OK6-Gal4/UAS-pldn;pldn
Δ1
; n=12) significantly slows the rate of
depletion and increases the rate of recovery. Synapses were stimulated at 10 Hz in 2 mM
extracellular calcium for 10 mins, then allowed to recover, taking a test pulse at 0.2 Hz for the
following 10 mins. EPSP amplitudes for each time point were binned for 2 sec, normalized to
pre-stimulus amplitudes, and plotted as a function of time. (B) A similar increase in depletion
and slowing of recovery is observed in dysb
1
(n=9; p<0.05; Student’s t-test) and blos1
ex2

mutants (n=13; p<0.05; Student’s t-test). (C) Both overexpression of dysb in pldn
Δ1
mutants
(pldn+dysb-OE: w
1118
;OK6-Gal4/UAS-3xflag-dysb;pldn
Δ1
) and overexpression of pldn in dysb
1

mutants (dysb+pldn-OE: w
1118
;OK6-Gal4/UAS-pldn-3xflag;dysb
1
) fail to rescue the increased
rundown during high frequency stimulation. (D) Determination of the total releasable synaptic
vesicle pool. Control (shi
ts1
, n=6), pldn
Δ1
(shi
ts1
;pldn
Δ1
, n=5) and dysb
1
(shi
ts1
;dysb
1
, n=5) mutants
were stimulated at 10 Hz in 2 mM calcium at 32°C to deplete the total releasable synaptic
vesicle pool. EPSP amplitudes at each time point were plotted as a percentage of the starting
EPSP amplitude. (E) No significant difference was observed in the total quanta released
between the three genotypes. p>0.05; one-way ANOVA.

46

Figure 2. 7
47

Figure 2.7: Activity-dependent loss of FYVE-positive synaptic endosomes in pallidin
mutants. (A) GFP-2xFYVE puncta are observed in synapses from control (w
1118
;OK6-
Gal4/UAS-GFP-myc-2xFYVE) and pldn
Δ1
mutants (w
1118
;OK6-Gal4/UAS-GFP-myc-2xFYVE;
pldn
Δ1
) at rest and following 5 min incubation in 90 mM KCl (high K
+
). Similar GFP-2xFYVE
density and intensity are observed in controls before and after stimulation, while pldn
Δ1
NMJs
have reduced GFP-2xFYVE density and intensity following stimulation. Quantification of GFP-
2xFYVE density (B), size (C), and intensity (D) in wild type (n=13 rest and high K+), pldn
Δ1
(n=8
rest, n=9 high K
+
), and dysb
1
mutants (w
1118
;OK6-Gal4/UAS-GFP-myc-2xFYVE; dysb
1
; n=9 rest
and high K
+
) at rest and following high K
+
stimulation. *=p<0.05, **=p<0.01, ***=p<0.001; paired
Student’s t-test.

48

Figure 2. 8
49

Figure 2.8: Activity-dependent accumulation of tubular endosomal structures in pallidin
mutants. (A) Representative electron micrographs of NMJs at rest in wild type, pldn
Δ1
, and
dysb
1
mutants. (B) Increased tubular endosomal structures are observed in BLOC-1 mutants
following incubation in high K
+
(90 mM K
+
, 5 min), while rarely observed in controls. Tubular
endosomes (white arrows) and cisternal endosomes (black arrows) are noted. Quantification of
the density of synaptic vesicles (C), cisternal endosomes (D), and tubular endosomes (E) at rest
and following high K
+
stimulation in wild type (n=16 rest and high K
+
), pldn
Δ1
(n=13 rest and
n=28 high K
+
), and dysb
1
(n=7 rest and n=28 high K
+
). Note that synaptic vesicle and cisternal
endosome densities are reduced, while the tubular endosome density is increased in pldn
Δ1
and
dysb
1
mutants following stimulation. (F) 3D serial EM reconstruction of tubular endosomal
structures (red) near synaptic vesicles (yellow), demonstrating they are not continuous with the
plasma membrane (green). *=p<0.05, **=p<0.01, ***=p<0.001; Student’s t-test.

50

CHAPTER THREE

Development of a tissue-specific ribosome profiling approach in Drosophila enables
genome-wide evaluation of translational adaptations

This work was first published as:
Xun Chen, Dion K. Dickman. Development of a tissue-specific ribosome profiling approach in
Drosophila enables genome-wide evaluation of translational adaptations. PLoS Genet. 2017
Dec 1;13(12).

Author contributions:
Xun Chen, and Dion K. Dickman designed research;
Xun Chen performed experiments and analysis;
Xun Chen, and Dion K. Dickman wrote the paper.

51

3.1 ABSTRACT
Recent advances in next-generation sequencing approaches have revolutionized our
understanding of transcriptional expression in diverse systems. However, measurements of
transcription do not necessarily reflect gene translation, the process of ultimate importance in
understanding cellular function. To circumvent this limitation, biochemical tagging of ribosome
subunits to isolate ribosome-associated mRNA has been developed. However, this approach,
called TRAP, lacks quantitative resolution compared to a superior technology, ribosome
profiling. Here, we report the development of an optimized ribosome profiling approach in
Drosophila. We first demonstrate successful ribosome profiling from a specific tissue, larval
muscle, with enhanced resolution compared to conventional TRAP approaches. We next
validate the ability of this technology to define genome-wide translational regulation. This
technology is leveraged to test the relative contributions of transcriptional and translational
mechanisms in the postsynaptic muscle that orchestrate the retrograde control of presynaptic
function at the neuromuscular junction. Surprisingly, we find no evidence that significant
changes in the transcription or translation of specific genes are necessary to enable retrograde
homeostatic signaling, implying that post-translational mechanisms ultimately gate instructive
retrograde communication. Finally, we show that a global increase in translation induces
adaptive responses in both transcription and translation of protein chaperones and degradation
factors to promote cellular proteostasis. Together, this development and validation of tissue-
specific ribosome profiling enables sensitive and specific analysis of translation in Drosophila.

52

3.2 INTRODUCTION
Recent advances in next-generation sequencing such as RNA-seq have revolutionized the
measurement and quantification of genome-wide changes in transcriptional expression, without
pre-existing knowledge of gene identity, at unprecedented resolution (Chen et al., 2016;
Ozsolak and Milos, 2011; Wang et al., 2009). In addition, biochemical tagging of ribosomes has
emerged as a powerful way to provide insight into gene translation by separating the actively
translating mRNA pool from overall mRNA abundance (Chen and Rosbash, 2017; Heiman et
al., 2008; Huang et al., 2013; Sanz et al., 2009; Thomas et al., 2012; Yang et al., 2005; Zhang
et al., 2016), a technique termed TRAP (Translating Ribosome Affinity Purification). Although
this approach provides important insights into translational regulation, it lacks the resolution to
differentiate between mRNA populations associated with few or high numbers of ribosomes, a
distinction that can have major consequences for accurately defining translational rates
(Chekulaeva and Landthaler, 2016; Heiman et al., 2014). This limitation was recently overcome
through the development of a technique called “ribosome profiling”, which quantifies only mRNA
fragments that are protected by ribosomes (“ribosome footprints”). This enables the quantitative
analysis of the number of ribosomes associated with each mRNA transcript, and is even
capable of defining regions within RNA transcripts of ribosome association (Ingolia, 2016;
Ingolia et al., 2009; Li et al., 2014). This technology has been used to reveal genome-wide
adaptations to translation that would not have been apparent from transcriptional or translational
profiling (TRAP) approaches alone (Cho et al., 2015; Dunn et al., 2013; Gonzalez et al., 2014;
Ingolia et al., 2009; Ingolia et al., 2011; Jeong et al., 2016). However, despite the potential of
ribosome profiling, this approach has not been developed for tissue-specific applications in
Drosophila.
The Drosophila neuromuscular junction (NMJ) is an attractive system to test the power
of ribosome profiling. At this model synapse, sophisticated genetic approaches have revealed
fundamental genes and mechanisms involved in synaptic growth, structure, function, and
53

plasticity (Bellen et al., 2010; Collins and DiAntonio, 2007; Harris and Littleton, 2015; Menon et
al., 2013). In particular, translational mechanisms contribute to synaptic growth (Menon et al.,
2015; Menon et al., 2004), function (Mee et al., 2004), and plasticity (Penney et al., 2012;
Penney et al., 2016) at this synapse. Indeed, a key role for translation has recently been
implicated in mediating a form of synaptic plasticity intensively studied at the Drosophila NMJ
referred to as Presynaptic Homeostatic Plasticity (PHP). At this glutamatergic synapse, genetic
loss of the postsynaptic receptor subunit GluRIIA leads to a reduction in the amplitude of
miniature excitatory postsynaptic potentials (mEPSPs; Fig. 3.1A; (Petersen et al., 1997)).
However, the amplitude of evoked excitatory postsynaptic potentials (EPSPs) are maintained at
wild-type levels due to an enhancement in the number of synaptic vesicles released (quantal
content). Thus, a retrograde signaling system is induced by loss of GluRIIA that ultimately
potentiates presynaptic release, restoring baseline levels of synaptic transmission (Davis and
Muller, 2015a; Frank, 2014). Recent forward genetic screening and candidate approaches have
revealed the identity of several genes and effector mechanisms in the presynaptic neuron
required for the homeostatic potentiation of presynaptic release (Dickman and Davis, 2009;
Frank, 2014; Kiragasi et al., 2017). However, very little is known about the postsynaptic
signaling system that transduces a reduction in glutamate receptor function into a retrograde
signal that instructs an adaptive increase in presynaptic release. Thus, ribosome profiling of the
postsynaptic muscle may reveal the nature of the retrograde signaling system mediating PHP.
There is emerging evidence to strongly suggest that translational modulations in the
postsynaptic muscle plays a key role in retrograde PHP signaling. In particular, pharmacologic
or genetic inhibition of postsynaptic protein synthesis through the Target of Rapamycin (Tor)
pathway and associated translational modulators disrupts the expression of PHP in GluRIIA
mutants (Kauwe et al., 2016; Penney et al., 2012; Penney et al., 2016). Interestingly, a
constitutive increase in muscle protein synthesis through postsynaptic overexpression of Tor
was also shown to be sufficient to trigger the retrograde enhancement of presynaptic release
54

without any perturbation to glutamate receptors (Penney et al., 2012; Penney et al., 2016).
Further, ongoing and sustained postsynaptic protein synthesis is necessary to maintain PHP
expression, as acute inhibition of protein synthesis in late larval stages is sufficient to block PHP
expression in GluRIIA mutants (Kauwe et al., 2016; Penney et al., 2012). While these results
establish some of the first insights into the postsynaptic signal transduction system controlling
retrograde PHP signaling, the putative translational targets involved, and to what extent
transcriptional and/or post-translational mechanisms contribute to PHP signaling, remain
unknown. Thus, ribosome profiling has the potential to illuminate the translational targets
necessary for postsynaptic PHP transduction.
We have developed and optimized a streamlined system that enables ribosome profiling
from specific tissues in Drosophila. We first validate the success of this approach in defining
translational regulation in the larval muscle, and reveal dynamics in translation that are distinct
from overall transcriptional expression. Next, we highlight the superior sensitivity of ribosome
profiling in reporting translational regulation over the conventional TRAP method. Finally, we
utilize this ribosome profiling approach to assess translational changes during two cellular
processes. First, we evaluate the contributions of transcriptional, translational, and post-
translational mechanisms in the postsynaptic muscle that drive the retrograde signaling system
underlying presynaptic homeostatic potentiation. Second, we distinguish adaptive changes in
transcription and translation that are triggered following a chronic elevation in muscle protein
synthesis. This effort has highlighted the unanticipated importance of post-translational
mechanisms in ultimately driving retrograde PHP signaling and illuminated the dynamic
interplay between modulations in gene transcription and translation as cells acclimate to
elevated metabolic activity while maintaining cellular homeostasis.

55

3.3 MATERIALS AND METHODS
Fly stocks and molecular biology
Drosophila stocks were raised at 25°C on standard molasses food. The w
1118
strain is used as
the wild type control unless otherwise noted, as this is the genetic background of the transgenic
lines and other genotypes used in this study. The following fly stocks were used: GluRIIA
SP16

(Petersen et al., 1997), UAS-Tor-myc (Wang et al., 2012), RpL3
G13893
(Bloomington Drosophila
Stock Center, BDSC, Bloomington, IN, USA), RpL3
KG05440
(BDSC). All other Drosophila stocks
were obtained from the BDSC. To control for the effects of genetic background on next
generation sequencing data, we generated an isogenic stock and bred the genetic elements
used in this study, (BG57-Gal4, UAS-RpL3-Flag, GluRIIA
SP16
, and UAS-Tor-myc) into this
isogenic line by outcrossing for five generations to minimize differences in the genetic
background.
During initial testing phases to determine the optimal ribosome subunit and biochemical
tag to use, we generated several constructs and systematically compared purification efficiency.
In particular, we inserted 1xFlag-6XHis tags to the C-terminals of the ribosome subunits RpL3,
RpL36, RpS12, and RpS13. We engineered expression with each subunit’s genomic promotor
into the pattB vector (Groth et al., 2004). Transgenic stocks were made and tested for affinity
purification of intact ribosomes using cobalt ion-coupled beads (Clontech, 635501). These
biochemical tags were found to be inferior when compared to a single 3xFlag tag, which was
used for the design of all subsequent constructs. To generate the UAS-RpL3-3xFlag and UAS-
RpS13-3xFlag transgenic lines, we obtained cDNA containing the entire coding sequences of
RpL3 (FBcl0179489) and RpS13 (FBcl0171161). RpL3 and RpS13 coding sequence were PCR
amplified and sub-cloned into the pACU2 vector (Han et al., 2011a) with C-terminal 3xflag tag
using a standard T4 DNA ligase based cloning strategy. To generate the genomic RpL3-3xflag
construct, a 6.5kb sequence containing the entire RpL3 genomic locus was PCR amplified from
a genomic DNA preparation of w
1118
using the following primers 5’-
56

ATCGGTACCACTTACTCCCTTGTTG-3’ and 5’-
CAGCTGCAGGGTTTGTGACTGATCTAAAAG-3’. The same linker-3xflag sequence used in
UAS-RpL3-3xflag was inserted before the stop codon of RpL3 using extension PCR. This
sequence was cloned into the pattB vector (Groth et al., 2004). Constructs were sequence
verified and sent to BestGene Inc. (Chino Hills, CA) for transgenic integration.

Affinity purification of ribosomes and library generation
Tissue collection, lysis, and library generation for transcriptional profiling: 18 female third-instar
larvae for each preparation were controlled for developmental stage by selection within 3 hours
of emerging from media (Arbeitman et al., 2002). Larvae were dissected in HL-3 saline as
previously described (Chen et al., 2017a), with all internal organs and the central nervous
system removed, leaving only the body wall and attached muscle tissue. Following dissection,
the tissue was immediately frozen on dry ice. The frozen tissue was then homogenized in 540 µl
lysis solution (10 mM HEPES, PH 7.4, 150 mM KCl, 5 mM MgCl 2, 100 µg/ml Cycloheximide)
supplemented with 0.5% Triton-X100, 1U/µl ANTI-RNase (ThermoFisher scientific, AM2690)
and protease inhibitor (EDTA-free, Sigma, COEDTAF-RO). 120 µl of the lysate was used for
total RNA extraction by TRIzol LS Reagent (ThermoFisher scientific, 10296010). 2.5 µg of total
RNA was used for isolation of mRNA with the Dynabeads mRNA DIRECT Purification Kit
(ThermoFisher scientific, 61011). The entire isolated mRNA sample was used for library
generation with the NEBNext Ultra Directional RNA Library Prep Kit for Illumina sequencing
(NEB, E7420S). 15 PCR cycles were used in the amplification step of all library generation
protocols, as no significant PCR duplicates were observed in the sequencing results of the
quality control step. We used whole body wall tissue as an approximation for the total muscle
RNA preparation used for transcriptional profiling, as muscle is the dominant tissue in this
preparation.

57

Purification of ribosome associated mRNA and library generation (TRAP): 180 µl of the lysate
described above was incubated with anti-flag antibody coated magnetic beads to purify
ribosomes with associated mRNA. 75 µl of Dynabeads protein G (ThermoFisher scientific,
10004D) was used to coat 3 µg anti-Flag antibody (Sigma, F1804). The lysate-beads mixture
was incubated at 4°C with rotation for 2 hours, then washed in buffer (10 mM HEPES, PH 7.4,
150 mM KCl, 5 mM MgCl2, 100 µg/ml Cycloheximide), supplemented with 0.1% Triton-X100
(0.1 U/µl SUPERase in RNase Inhibitor (ThermoFisher scientific, AM2696). RNA was extracted
from ribosomes bound to the beads by TRIzol Reagent, and the co-precipitant linear acrylamide
(ThermoFisher scientific, AM9520) was used to increase the RNA recovery rate. mRNA
isolation and library generation were performed as described above.

Library generation for ribosome profiling: 240 µl of lysate was incubated with anti-Flag antibody
coated magnetic beads and 10000 units of RNase T1 (ThermoFisher scientific, EN0541) to
perform digestion of exposed mRNA and ribosome purification simultaneously. 100 µl of
Dynabeads protein G coated with 4 µg anti-Flag antibody was used. The lysate-beads-RNase
T1 mixture was incubated at 4°C for 6 hours and washed; RNA was extracted as described
above.
To perform size selection, the extracted RNA sample was separated on a denaturing
15% polyacrylamide urea gel. The gel region corresponding to 30-45 nt, as estimated by oligo
markers, was excised. The gel slice was homogenized in 500 µl elution buffer (10 mM Tris-HCl,
PH 7.5, 250 mM NaCl, 1 mM EDTA) supplemented with 0.2% SDS and RNAsecure reagent
(ThermoFisher scientific, AM7005). The gel slurry was heated at 60°C for 10 min to allow
inactivation of contaminating RNase by RNAsecure reagent and transferred to 4°C for overnight
elution of RNA from the gel. The eluate was collected by centrifuging the gel slurry through a
Spin-X column (Sigma, CLS8162), and RNA was precipitated by adding an equal volume of
isopropanol and 25 µg linear acrylamide, incubated at room temperature for 30 min, and
58

centrifuged for 15 min at 17000Xg, 4°C. The pellet was air dried and dissolved in 15 µl RNase-
free water.
Library generation for ribosome profiling was performed using NEBNext Small RNA
Library Prep Set for Illumina (NEB, E7330S) with minor modifications. The entire size selected
mRNA fragments sample were first treated by phosphatase, rSAP (NEB, M0371S), to remove
the 3’-phosphate. The samples were then incubated and denatured according to manufacturer’s
instructions. RNA was precipitated from the reaction as described above, and the 3’ adaptor
ligation was performed using NEBNext Small RNA Library Prep Set. The 5’-phosphate was then
added to the mRNA fragments by supplying 2.5 µl 10 mM ATP, 1.5 µl 50 mM DTT and 0.5 µl T4
Polynucleotide Kinase (NEB, M0201S) to the 20 µl 3’ adaptor ligation reaction and incubating at
37°C for 30 min. 1 µl SR RT primer of the NEBNext Small RNA Library Prep Set was then
added to the T4 polynucleotide kinase reaction and RT primer hybridization was performed. 5’
adaptor ligation, reverse transcription, PCR amplification and size selection of the PCR
amplified library were performed using the NEBNext Small RNA Library Prep Set.

High-throughput sequencing and data analysis
All libraries were sequenced on the Illumina NextSeq platform (single read, 75 cycles), and
three replicates were performed for each genotype. All sequencing datasets are deposited in
the NCBI GEO datasets, accession number: GSE99920. Sequencing data analysis was
performed using CLC genomics Workbench 8.0 software (Qiagen). Raw reads were trimmed
based on quality scores, and adaptor sequences were removed from reads. Trimmed high
quality reads were then mapped to the Drosophila genome (Drosophila melanogaster, NCBI
genome release 5_48). Only genes with more than 10 reads uniquely mapped to their exons
were considered to be reliably detected and further analyzed, as the variability was sharply
higher for genes with less than 10 mapped reads compared to genes with mapped reads above
10 (Fig. 3.S5). We excluded genes from further analysis that were only found to be
59

transcriptionally expressed, which were likely to result from non-muscle RNA. Relative mRNA
expression levels were quantified by calculating RPKM (Reads Per Kilobase of exon per Million
mapped reads) using mapping results from transcriptional profiling. Relative translation levels
were quantified by calculating RPKM (Reads Per Kilobase of exon per Million mapped reads)
using mapping results from ribosome profiling. Translation efficiency was calculated by dividing
ribosome profiling (or translational profiling TRAP) RPKM by transcriptional profiling RPKM.
To determine differentially transcribed or translated genes, a weighted t-type test
(Baggerly et al., 2003) was performed based on three replicate expression values for each gene
between GluRIIA mutants and wild type, and Tor-OE and wild type using the statistical analysis
tool of CLC genomics workbench. The analysis was performed on expression values obtained
by transcriptional profiling to determine differentially transcribed genes, and on expression
values obtained by ribosome profiling to determine differentially translated genes. Genes with a
p-value less than 0.05 and fold change higher than 3-fold were considered differentially
transcribed or translated. We also determined differentially transcribed or translated genes
using R package DESeq2 analysis (Love et al., 2014), considering genes with adjusted p-values
less than 0.05 as differentially expressed. The Baggerly’s t test method and DESeq2 method
produced highly similar lists of differentially expressed genes. To determine gene targets
undergoing translational regulation in GluRIIA mutants and Tor-OE compared to wild type, two
criteria were used. First, the gene must have at least a 2-fold significant increase (p<0.05,
Student’s t test) in translation efficiency compared to wild type. Second, a significant increase in
ribosome profiling expression value (p<0.05, Baggerly’s t test) must also exist for the same
gene. These two criteria ensure identification of genes that have true translational up-regulation
that are not due to transcriptional changes. Metagene analysis was performed using Plastid
analysis software (Dunn and Weissman, 2016) using default settings.

60

Immunocytochemistry and confocal imaging
Third-instar larvae were dissected in ice cold 0 Ca
2+
HL-3 and fixed in Bouin's fixative for 2 min
as described (Kikuma et al., 2017). Mouse anti-Flag (Sigma, F1804) was used at 1:500, while
donkey anti-mouse Alexa Fluor 488-conjugated secondary antibody (Jackson Immunoresearch)
was used at 1:400. Alexa Fluor 647-conjugated goat anti-HRP (Jackson ImmunoResearch) was
used at 1:200. Samples were imaged using a Nikon A1R Resonant Scanning Confocal
microscope equipped with NIS Elements software and a 100x APO 1.4NA oil immersion
objective, using separate channels with two laser lines (488 nm and 561 nm). Images were
obtained using settings optimized for detection without saturation of the signal.

Quantitative PCR
Quantitative PCR (qPCR) was performed using Luna® Universal One-Step RT-qPCR Kit (NEB,
E3005S) according to manufacturer’s instructions. RNA was isolated and prepared from body
wall tissue as described above. 5 ng of total RNA was used as template in each reaction. Three
biological replicates were performed for each sample and the 2^
-∆∆Ct
method was used for qPCR
data analysis. The primers used for assaying each target are as follows (fwd/rev, 5’-3’):
Hsp83: GCAGCGTCTGAAAAGTTTTGTG; AATCTCAGCCTGGAATGCA.
Hsp68: ACCATCAAGAACGACAAGGG; ATAGGTCTCCAGTTGATTGCG.
Hsp26: CTCACCGTCAGTATTCCCAAG; CCTTCACCTCGCTTTCATTTG.
αTub84D (control): CTACAACTCCATCCTAACCACG; CAGGTTAGTGTAAGTGGGTCG.
RpS6: ATGAAGCAGGGTGTCTTGAC; AGAGCCAGCACAGACATG.

Electrophysiology
All recordings were performed in modified HL-3 saline with 0.3 mM Ca
2+
as described (Perry et
al., 2017).

61

Statistical Analysis
All data are presented as mean +/-SEM. A Student’s t test was used to compare two groups. A
one-way ANOVA followed by a post-hoc Bonferroni’s test was used to compare three or more
groups. All data was analyzed using Graphpad Prism or Microsoft Excel software, with varying
levels of significance assessed as p<0.05 (*), p<0.01 (**), p<0.001 (***), N.S.=not significant.
Statistical analysis on next generation sequencing data was described in the High-throughput
sequencing and data analysis section.

3.4 RESULTS
A strategy to profile adaptations in postsynaptic transcription and translation that may
drive retrograde PHP signaling
To assess the postsynaptic retrograde signaling systems that drive presynaptic homeostatic
potentiation (PHP) at the Drosophila NMJ, we focused on three genetic conditions (Fig. 3.1A).
First is the wild-type control genotype (w
1118
;BG57-Gal4/UAS-RpL3-3xflag), which serves as the
control condition in which PHP is not induced or expressed. Second, null mutations in the
postsynaptic glutamate receptor subunit GluRIIA (GluRIIA
SP16
;BG57-Gal4/UAS-RpL3-3xflag)
lead to a chronic reduction in mEPSP amplitude (Petersen et al., 1997). However, EPSP
amplitudes are maintained at wild-type levels due to a homeostatic increase in presynaptic
release (quantal content) following retrograde signaling from the muscle (Fig. 3.1A-D). This
serves as one condition in which we hypothesized that gene transcription, translation, and/or
post-translational changes may have occurred in response to loss of GluRIIA, triggering the
induction of retrograde signaling that drives PHP. Indeed, in GluRIIA mutants, genetic disruption
of the translational regulator Target of rapamycin (Tor) blocks PHP expression, resulting in no
change in quantal content and a concomitant reduction in EPSP amplitude (Penney et al., 2012).
Finally, postsynaptic overexpression of Tor in an otherwise wild-type muscle (Tor-OE: UAS-Tor-
62

myc/+;BG57-Gal4/UAS-RpL3-3xflag) is sufficient to trigger PHP signaling, leading to increased
presynaptic release and EPSP amplitude with no change in mEPSP or glutamate receptors (Fig.
3.1A-D; (Penney et al., 2012)). Tor-OE therefore served as the final genotype in which PHP
signaling was induced through Tor overexpression without any perturbation of postsynaptic
glutamate receptors. We hypothesized that changes in translation, and perhaps even
transcription, in GluRIIA mutants, which may also be apparent in Tor-OE, would illuminate the
nature of the postsynaptic transduction system underlying homeostatic retrograde signaling at
the Drosophila NMJ.
To define genome-wide changes in mRNA transcription and translation in the
postsynaptic muscle that may be necessary for PHP signaling, we sought to purify RNA from
third instar larvae muscle in wild type, GluRIIA mutants, and Tor-OE (Fig. 3.1E). We then sought
to define mRNA expression through three methods: Transcriptional profiling, translational
profiling using translating ribosome affinity purification (TRAP), and ribosome profiling (Fig.
3.1F). First, we approximated the muscle transcriptome using transcriptional profiling of total
mRNA expression by isolating RNA from the dissected third instar body wall preparation. This is
primarily composed of muscle tissue, but also contains non-muscle cells including epithelia (Fig.
3.1F). Following extraction of RNA from this preparation, we generated RNA-seq libraries using
standard methods (Brown et al., 2014; Mortazavi et al., 2008). Next, to define translational
changes specifically in muscle cells, we engineered an affinity tag on a ribosome subunit under
control of the upstream activating sequence (UAS), which enables tissue-specific expression
(Fig. 3.1E). This biochemically tagged ribosome could therefore be expressed in muscle to
purify ribosomes, then processed to sequence only mRNA sequences associated with or
protected by ribosomes (Fig. 3.1F). Affinity tagging of ribosomes enabled us to perform
translational profiling (TRAP: Translating Ribosome Affinity Purification), an established
technique capable of detecting ribosome-associated mRNA (Chen and Rosbash, 2017; Heiman
63

et al., 2014; Heiman et al., 2008; Huang et al., 2013; Zhang et al., 2016). Finally, we reasoned
that this approach could be optimized to enable ribosome profiling, which has been used
successfully to determine changes in translational rates, with superior sensitivity over TRAP, in
a variety of systems (Dunn et al., 2013; Ingolia et al., 2009; Ingolia et al., 2011; Jeong et al.,
2016). However, ribosome profiling has not been developed for use in specific Drosophila
tissues. Our next objective was to optimize a tissue-specific ribosome profiling approach.

Optimization of a tissue-specific ribosome profiling approach in Drosophila
Ribosome profiling is a powerful approach for measuring genome-wide changes in mRNA
translation. However, high quantities of starting material is necessary to obtain sufficient
amounts of ribosome protected mRNA fragments for the subsequent processing steps involved
(Brar and Weissman, 2015). Since this approach has not been developed for Drosophila tissues,
we first engineered and optimized the processing steps necessary to enable highly efficient
affinity purification of ribosomes and ribosome protected mRNA fragments by incorporating
ribosome affinity purification into the ribosome profiling protocol.
Although tissue-specific ribosome affinity purification strategies have been developed
before in Drosophila (Huang et al., 2013; Thomas et al., 2012; Zhang et al., 2016), these
strategies have not been optimized to meet the unique demand necessary for ribosome profiling.
Previous approaches tagged the same ribosome subunit (RpL10A) with GFP and 3xflag tags
(Huang et al., 2013; Thomas et al., 2012; Zhang et al., 2016), however, we found that these
strategies lacked the efficiency necessary for ribosome profiling during pilot experiments. We
thus set out to develop and optimize a new ribosome affinity purification strategy that enables
the efficient purification and processing of ribosome-protected mRNA. First, we generated
transgenic animals that express a core ribosome subunit in frame with a biochemical tag (3xflag)
64

under UAS control to enable expression of this transgene in specific Drosophila tissues (Fig.
3.1E and 2A). Therefore, based on high resolution crystal structures of eukaryotic ribosomes
(Ben-Shem et al., 2011; Khatter et al., 2015), we selected alternative ribosomal proteins from
the large and small subunits expected to have C terminals exposed on the ribosome surface.
We cloned the Drosophila homologs of these subunits, RpL3 and RpS13, in frame with a C-
terminal 3xflag tag and inserted this sequence into the pACU2 vector for high expression under
UAS control (Han et al., 2011a). We then determined whether intact ribosomes could be
isolated in muscle tissue following expression of the tagged ribosome subunit. We drove
expression of UAS-RpL3-Flag or UAS-RpS13-Flag with a muscle-specific Gal4 driver (BG57-
Gal4) and performed anti-Flag immunoprecipitations (Fig. 3.2A). An array of specific bands
were detected in a Commassie stained gel from the RpL3-Flag and RpS13-Flag
immunoprecipitations, but no such bands were observed in lysates from wild type (Fig. 3.2B).
Importantly, identical sized bands were observed in immunoprecipitates from both RpL3-Flag
and RpS13-Flag animals, matching the expected distribution of ribosomal proteins (Anger et al.,
2013). The RPL3-Flag immunoprecipitation showed the same distribution as RpS13 but higher
band intensity, indicating higher purification efficiency, so we used RpL3-Flag transgenic
animals for the remaining experiments. In addition to ribosomal proteins, the other major
constituent of intact ribosomes is ribosomal RNA. Significant amounts of ribosomal RNA were
detected in an agarose gel from RpL3-Flag immunoprecipitates (Fig. 3.2C), providing additional
independent evidence that this affinity purification strategy was efficient at purifying intact
ribosomes.
Next, we tested the ability of RpL3-Flag to functionally integrate into intact ribosomes.
We generated an RpL3-Flag transgene under control of the endogenous promotor (genomic-
RpL3-Flag; Fig. 3.S1A). This transgene was able to rescue the lethality of homozygous RpL3
mutations (Fig. 3.S1A), demonstrating that this tagged ribosomal subunit can integrate and
65

function in intact endogenous ribosomes, effectively replacing the endogenous untagged RpL3
protein. Further, anti-Flag immunostaining of UAS-RpL3-Flag expressed in larval muscle
showed a pattern consistent with expected ribosome distribution and localization (Fig. 3.S1B).
Next, we verified that muscle overexpression of RpL3-Flag did not lead to perturbations in
viability, synaptic growth, structure, or function (Fig. 3.S1C-L), nor did muscle overexpression of
RpL3-Flag disrupt the expression of PHP in GluRIIA mutants or Tor-OE (Fig. 3.1A-D). Finally,
we confirmed that ubiquitous or muscle overexpression of RpL3-Flag did not induce phenotypes
characteristic of flies with perturbed ribosome function such as the “minute” phenotype of
inhibited growth ((Marygold et al., 2007); Fig. 3.S1A). Thus, biochemical tagging of RpL3 does
not disrupt its localization or ability to functionally integrate into endogenous ribosomes.
Finally, we developed and optimized a method to process the isolated ribosomes to
generate only ribosome protected mRNA fragments for RNA-seq analysis. First, we digested
the tissue lysate with RNaseT1, an enzyme that cuts single stranded RNA at G residues, while
performing anti-Flag affinity purification at the same time (Fig. 3.2D). Following digestion, we ran
RNA on a high percentage PAGE gel, excising the mRNA fragments protected from digestion
by ribosome binding (30-45 nucleotides in length; Fig. 3.2D). Sequencing of this pool of RNA
demonstrated that the vast majority of reads mapped to the 5’UTR and coding regions of mRNA
transcripts, with very few reads mapping to the 3’UTR of mRNA transcripts (Fig. 3.2E), where
ribosomes are not expected to be associated. This coverage map also revealed heterogeneous
distributions on mRNA transcripts with irregular and prominent peaks, as expected, which are
indicative of ribosome pause sites on mRNA (Fig. 3.2E; (Li et al., 2012)). In contrast, RNA-seq
reads for transcriptional and translational profiling using TRAP mapped to the entire mRNA
transcript with relatively even coverage (Fig. 3.2E). Extensive metagene analysis confirmed
similar distributions around start and stop codons for genome-wide averaged RNA-seq reads
(Fig. 3.S2). Importantly, replicate experiments demonstrated that this protocol generated highly
66

reproducible measures of relative protein synthesis rates, defined by mRNA ribosome density,
or the number of ribosome profiling Reads Per Kilobase of exon per Million mapped reads
(RPKM, also referred to as ribosome profiling expression value; Fig. 3.2F). Thus, expression of
RpL3-Flag enables the purification of ribosomes from specific tissues in Drosophila, and further
processing reproducibly generates and quantifies ribosome protected mRNA fragments, which
have been demonstrated to correlate with protein synthesis rates (Li et al., 2014).

Ribosome profiling is more sensitive in detecting translational regulation compared to
translational profiling (TRAP)
Translation can differ in significant ways from overall transcriptional expression through
modulations in the degree of ribosome association with each mRNA transcript, in turn
suppressing or enhancing protein synthesis rates (Chekulaeva and Landthaler, 2016; Kong and
Lasko, 2012). Translation efficiency is a measure of these differences, defined as the ratio of
translational to transcriptional expression (Ingolia et al., 2009). Hence, translation efficiency (TE)
reflects the enhancement or suppression of translation relative to transcriptional expression due
to various translational control mechanisms (Jackson et al., 2010; Kong and Lasko, 2012).
Although both translational (TRAP) and ribosome profiling approaches can report TE, ribosome
profiling should, in principle, exhibit superior sensitivity in revealing translational dynamics. We
therefore compared translational and ribosome profiling directly to test this prediction.
We compared TRAP and ribosome profiling to transcriptional profiling in wild-type
muscle. In particular, we tested whether differences were apparent in the number of genes
revealed to be translationally suppressed or enhanced through ribosome profiling compared to
TRAP. We first analyzed the extent to which ribosome profiling and TRAP measurements
correlate with transcriptional profiling by plotting the ribosome profiling and TRAP expression
values as a function of transcriptional profiling (Fig. 3.3A,B; see materials and methods). A low
67

correlation would indicate more translational regulation is detected, while a high correlation is
indicative of less translational regulation. This analysis revealed a low correlation between
ribosome profiling and transcriptional profiling (correlation of determination r
2
=0.100; Fig. 3.3A),
while a relatively high correlation was observed between TRAP and transcriptional profiling
(r
2
=0.617; Fig. 3.3B). Further, we subdivided all measured genes into three categories: high TE,
medium TE, and low TE. These groups were based on translation efficiency as measured by
ribosome profiling or TRAP, with high TE genes having a TE value >2, low TE genes having a
TE value <0.5, and medium TE genes having a TE between 0.5 and 2. This division revealed a
higher number of genes in the high and low TE groups detected by ribosome profiling compared
to TRAP (Fig. 3.3C). Together, these results are consistent with ribosome profiling detecting
more genes under translational regulation compared to TRAP.
We next investigated the genes under significant translational regulation (genes with
high TE or low TE), detected through either ribosome profiling or TRAP, to determine whether
differences exist in the amplitude of translational regulation detected. Specifically, genes were
divided into the three categories mentioned above based on the average translation efficiency
measured by ribosome profiling and TRAP. We then determined the TE value ratio of ribosome
profiling to TRAP within the three categories. A ratio above 0 (log2 transformed) in the high TE
group indicates a more sensitive reporting of translation for ribosomal profiling, while a ratio
below 0 in the low TE group would also indicate superior sensitivity for the ribosomal profiling
approach. This investigation revealed an average ratio of 0.28 within the high TE group, -0.15
within the medium TE group, and -1.25 within the low TE group (Fig. 3.3D). This analysis
demonstrates that ribosome profiling is at least 22% more sensitive in detecting high TE, and
138% more sensitive in detecting low TE in comparison to TRAP. Thus, this characterization
demonstrates that ribosome profiling provides a more sensitive and quantitative measurement
of translational regulation in comparison to TRAP, validating this approach.
68

Transcriptional and ribosome profiling reveals dynamic translational regulation in
Drosophila muscle
Both subtle and dramatic differences have been observed in rates of mRNA translation relative
to transcription, particularly during cellular responses to stress (Dunn et al., 2013; Halbeisen
and Gerber, 2009; Spriggs et al., 2010). Having optimized and validated our approach, we went
on to perform transcriptional and ribosome profiling in GluRIIA

mutants and Tor-OE in addition
to wild type (Table S1). To minimize genetic variation, the three genotypes were bred into an
isogenic background, and three replicate experiments were performed for each genotype (see
Materials and Methods). We first determined the total number of genes expressed in Drosophila
muscle, as assessed through both transcription and ribosome profiling. The fly genome is
predicted to encode 15,583 genes (NCBI genome release 5_48). We found 6,835 genes to be
expressed in wild-type larval muscle through transcriptional profiling, and a similar number
(6,656) through ribosome profiling (Fig. 3.4A), with ~90% of transcripts being shared between
the two lists (Table S2), indicating that the vast majority of transcribed genes are also translated.
A subset of genes that appeared to be transcribed but not translated likely result from non-
muscle RNA transcripts derived from the body preparation (see Materials and Methods).
Therefore, these transcripts were not analyzed further. We observed no significant differences
in the size of the transcriptome and translatome between wild type, GluRIIA mutants, and Tor-
OE. We then compared the muscle transcriptome to a published transcriptome from the central
nervous system (CNS) of third-instar larvae (Brown et al., 2014). This analysis revealed
dramatic differences in gene expression between the two tissues (Fig. 3.4B). In particular, we
found several genes known to be enriched in muscle, including myosin heavy chain, α actinin,
and zasp52, to be significantly transcribed and translated in muscle, as expected. In contrast,
neural-specific genes such as the active zone scaffold bruchpilot, the post-mitotic neuronal
69

transcription factor elav, and the microtubule associated protein tau, were highly expressed in
the CNS but not detected in muscle (Table S2). Together, this demonstrates that the muscle
transcriptome and translatome can be defined by the transcriptional and ribosome profiling
strategy we developed with high fidelity.
Next, we investigated genome wide translation efficiency distribution in larval muscle,
and compared this with gene expression as assessed through transcriptional and ribosome
profiling. We first calculated translation efficiency for all genes expressed in larval muscle and
compared heat maps of TE to heat maps of the transcription and translation level (Fig. 3.4C).
This revealed a dynamic range of translation efficiency, and a surprising trend of genes with
high TE displaying relatively low transcriptional expression levels, while genes with low TE
exhibited high transcriptional expression levels (Fig. 3.4C). We then analyzed the genes
categorized as high TE, medium TE and low TE (described above) in more detail, comparing
the relative distribution in transcriptional expression. We found this trend to be maintained, in
that high TE genes exhibited significantly lower transcriptional expression, while low TE genes
were significantly higher in transcriptional expression (Fig. 3.4D). Together, this implies a
general inverse correlation between translational and transcriptional expression.
Finally, we examined the genes with the most extreme enhancement or suppression of
translation efficiency to gain insight into the functional classes of genes that exhibit strong
translational control under basal conditions. Interestingly, amongst the genes with the most
suppressed translation (100 genes with the lowest translation efficiency), we found a
surprisingly high enrichment of genes encoding ribosome subunits and translation elongation
factors (Fig. 3.4E-F; Fig. 3.S3A and Table S3). Indeed, 73 of the 100 genes with the lowest
translation efficiency were ribosome subunits, with all subunits exhibiting a consistently low TE,
averaging 0.091. Importantly, we confirmed that overexpression of RpL3-Flag does not change
transcription of other ribosomal subunits, as quantitative PCR analysis of RpS6 transcript levels
70

were not significantly different between wild type and RpL3-Flag overexpression animals
(1.03±0.05 fold compared to wild type, n=3, p>0.05, Student’s t test). In contrast, RpL3, the
subunit we overexpressed (UAS-RpL3-Flag), was a clear outlier compared with the other
ribosome subunits, showing a translation efficiency of 2.85. This was expected due to the RpL3-
Flag transcript containing artificial 5’ and 3’ UTRs optimized to promote high levels of protein
synthesis (Han et al., 2011a). This overall suppression in TE of ribosome subunits may enable a
high dynamic regulatory range, enabling a rapid increase in production of ribosomal proteins
under conditions of elevated protein synthesis. Consistent with this idea, we observed a
coordinated upregulation of translation efficiency for ribosomal subunits when overall muscle
translation is elevated in Tor-OE (Fig. 3.4H). This is in agreement with previous findings
showing ribosome subunits and translation elongation factors as targets for translational
regulation by Tor (Jefferies et al., 1994; Thoreen et al., 2012). In contrast to the enrichment of
ribosome subunits observed in the low TE group, diverse genes were found among the most
translationally enhanced group, with genes involved in cellular structure being the most
abundant (Fig. 3.4E,G; Fig. 3.S3B and Table S4). These genes may encode proteins with high
cellular demands, being translated at high efficiency. Indeed, counter to what was observed in
genes with low TE, genes with high TE showed no significant change in TE following Tor-OE
(Fig. 3.4H). Together, this analysis reveals that translation differs in dramatic ways from overall
transcriptional expression, reflecting a highly dynamic translational landscape in the muscle.

Transcriptional and ribosome profiling defines genomic deletion in GluRIIA mutants and
enhanced translation in Tor-OE
We confirmed the fidelity of our transcriptional and ribosome profiling approach by examining in
molecular genetic detail the two manipulations we utilized to trigger postsynaptic retrograde
71

signaling. The GluRIIA
SP16
mutation harbors a 9 kb deletion that removes the first half of the
GluRIIA locus as well as the adjacent gene, oscillin (Fig. 3.5A; (Petersen et al., 1997)). Analysis
of both transcriptional and ribosome profiling of GluRIIA
SP16
mutants revealed no transcription or
translation of the deleted region, as expected (Fig. 3.5A). Transcription and translation of the
adjacent gene, oscillin, was also negligible (wild type vs. GluRIIA: transcription=15.9 vs. 0.08
RPKM; translation=9.8 vs. 0.4 RPKM). However, the 3’ portion of the GluRIIA coding region was
still transcriptionally expressed in GluRIIA mutants, while a significant reduction in translation
was observed by ribosome profiling (Fig. 3.5A). Together, this confirms that although the
residual 3’ region of the GluRIIA locus was transcribed, likely through an adjacent promoter, this
transcript was not efficiently translated. Indeed, the peak ribosome profiling signals, which
represent ribosome pause sites on the mRNA transcript, is known to be conserved for specific
open reading frames (Li et al., 2012). However, this pattern was altered in GluRIIA mutants
compared to wild type (Fig. 3.5A), suggesting the translation of the residual 3’ region of GluRIIA
in GluRIIA
SP16
mutants was not in the same reading frame as the intact transcript. Thus, both
transcriptional and ribosome profiling confirms that GluRIIA expression is abolished in
GluRIIA
SP16
mutants.
Next, we examined the expression of endogenous (genomic) and transgenically
overexpressed (UAS) Tor through transcriptional and ribosome profiling. While both
endogenous Tor and UAS-Tor mRNA share the same coding region, the 5’UTR and 3’UTR
regions differ between these transcripts (Fig. 3.5B), enabling us to distinguish expression
between these transcripts. We first confirmed a large increase in Tor coding region expression
through both transcriptional profiling (68-fold) and ribosome profiling (1200 fold) (Fig. 3.5B,
black). In contrast, analysis of the 5’ and 3’ UTRs revealed very little difference in endogenous
Tor expression in UAS-Tor compared to wild type (Fig. 3.5B, grey), while a dramatic increase in
both transcription (125-fold) and translation (1200-fold) was observed (Fig. 3.5B, red). Indeed,
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the translation efficiency of Tor was increased 14 fold in Tor-OE, consistent with the known
influences of engineered 5’UTR and 3’UTR sequences in promoting translation in UAS
constructs (Brand and Perrimon, 1993). This analysis defines the levels at which Tor
transcription and translation are enhanced when UAS-Tor is overexpressed in the Drosophila
larval muscle, and further serve to validate the sensitivity of ribosome profiling.
Finally, we sought to define whether Tor-OE induced a global elevation in translation and
to determine whether a similar global shift may have also occurred in GluRIIA mutants. Indeed,
most if not all mRNAs are capable of being translationally modulated by Tor, with Terminal
OligoPyrimidine tract (TOP) mRNAs being the most sensitive to Tor regulation (Hsieh et al.,
2012; Thoreen et al., 2012). First, we confirmed a global shift in translation in Tor-OE compared
to wild type, as expected given the role of Tor as a general regulator of Cap-dependent
translation initiation (Saxton and Sabatini, 2017). We plotted a gene count histogram of Tor-OE
versus wild type fold change measured by ribosome profiling, and overlaid the graph over a wild
type over wild type ribosome profiling fold change histogram. This analysis should indicate the
relative degree of variation in translation between these groups. Indeed, a shift in global
translation was observed in Tor-OE, with an average of 1.6 fold change in translation compared
to 1.09 for wild type (Fig. 3.5C). This shift is significant when tested by Kolmogorov–Smirnov
test (p<0.001) (Fig. 3.5D). We then performed this same analysis for GluRIIA vs WT. However,
we observed no significant shift in translation in GluRIIA (0.97 fold change compared to 1.09;
Fig. 3.5C). Thus, while Tor-OE induces a global increase in translation, loss of the GluRIIA
receptor subunit in muscle does not measurably change overall translation.

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Transcriptional and ribosome profiling suggest no major differences in transcription or
translation contribute to retrograde signaling
Given the substantial evidence that Tor-mediated control of new protein synthesis in the
postsynaptic cell is necessary for retrograde PHP signaling (Penney et al., 2012), we compared
transcriptional and translational changes in muscle between wild type, GluRIIA mutants, and
Tor-OE. We anticipated a relatively small number of transcriptional changes, if any, between
these genotypes, while we hypothesized substantial differences in translation would be
observed in both GluRIIA mutants and Tor-OE. The elevated translation of this exceptional
subset of targets would, we anticipated, initiate postsynaptic PHP signaling and lead to an
instructive signal that drives the retrograde enhancement in presynaptic release. Alternatively,
we also considered the possibility that Tor-mediated protein synthesis may act in a non-specific
manner, increasing overall protein synthesis in the postsynaptic cell, while there would be no
overlap in translational changes between GluRIIA mutants and Tor-OE. In this case, post-
translational mechanisms would operate on a global elevation in protein expression in Tor-OE,
sculpting the proteome into an instructive retrograde signal. Indeed, the acute pharmacological
induction and expression of PHP does not require new protein synthesis (Frank et al., 2006),
providing some support for this model. We therefore compared transcription and translation in
wild type, GluRIIA mutants, and Tor-OE.
We first compared transcription and translation in GluRIIA mutants and Tor-OE relative
to wild type by plotting the measured expression values for each condition and determining the
coefficient of determination, r
2
. An r
2
value equal to 1 indicates no difference between the two
conditions, while a value of 0 implies all genes are differentially expressed. This analysis
revealed a high degree of similarity between wild type and GluRIIA mutants in both transcription
and translation, with r
2
values above 0.98 (Fig. 3.6A, left). In contrast, a slightly larger difference
exists in transcription between Tor-OE and wild type, with r
2
=0.920 (Fig. 3.6B, left). Although
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transcription should not be directly affected by Tor-OE, this implies that perhaps an adaptation
in transcription was induced in the muscle in response to chronically elevated translation. Finally,
translational differences were the largest between Tor-OE and wild type, with r
2
values equal to
0.363 (Fig. 3.6B). Indeed, 2,352 genes showed changes greater than 1.5 fold in their measured
RPKM compared to wild type in this condition (Table S6). This global analysis demonstrates
there are very few transcriptional and translational changes in GluRIIA compared to wild type,
while moderate transcriptional and robust translational changes exist in Tor-OE.
Unexpectedly, in depth analysis of the transcriptome and translatome in GluRIIA muscle
revealed that no genes were significantly altered. In particular, we eliminated genes that were
up- or down-regulated due to known or expected influences in the genetic background (GluRIIA
and oscillin expression, and closely linked genes to this locus; see Materials and Methods).
Using a standard cut off for expression, we found no gene to have a significant up-regulation in
TE more than 2 fold in GluRIIA mutants (Fig. 3.6A, right). Even with a lowered threshold for
significant expression changes (>1.5 fold change), we observed only 5 genes transcriptionally
upregulated and 1 gene translationally upregulated in GluRIIA versus WT (Fig. 3.6A, right.
Table S5). Given this small number at such a lowered threshold, we considered the possibility
that the genetic background may influence expression of these genes. Consistent with this idea,
all 6 upregulated genes are closely linked to the GluRIIA locus or were located on the X
chromosome, areas we could not fully outcross to the isogenic line (Materials and Methods and
Table S5). Although we cannot rule out transcripts with more subtle differences in translation
(below 1.5 fold) or genes with very low and/or highly variable expression that may nonetheless
contribute to translational regulation in GluRIIA mutants, the sensitivity of ribosome profiling
enables us to conclude that no major changes in transcription or translation are present in the
postsynaptic muscle of GluRIIA mutants.
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While no specific translational targets were identified to significantly change in GluRIIA
mutants, we did identify 47 genes (including Tor itself) that exhibited significant increases in
translation efficiency in Tor-OE (>2 fold; Fig. 3.6B, right and Table S6). Among these 47 genes,
7 encode TOP RNAs (Jefferies et al., 1994; Meyuhas, 2000), including ribosome subunits (Fig.
3.6C). Tor-dependent translational control directly regulates TOP RNAs (Hsieh et al., 2012;
Jefferies et al., 1994; Thoreen et al., 2012), ribosome profiling was successful in identifying
genes in this class. Given the striking finding that very few genes appear to be under
transcriptional or translational control in the postsynaptic muscle of GluRIIA mutants, we
considered the possibility that the 47 genes we identified to be translationally upregulated in
Tor-OE may also show a parallel trend in GluRIIA mutants but below statistical significance. We
therefore generated a heat map of these 47 genes in Tor-OE vs WT and compared this to the
same 47 genes in GluRIIA vs WT (Fig. 3.6C). This analysis revealed no particular trend or
correlation in GluRIIA among the 47 genes with increased translation efficiency in Tor-OE (Fig.
3.6C). Together, these results suggest that retrograde signaling in the postsynaptic muscle,
induced through loss of GluRIIA, does not alter translation of a specific subset of targets, while
Tor-OE induces a global, non-specific increase in translation. Thus, post-translational
mechanisms are likely to confer the specific signaling processes that ultimately instruct
retrograde PHP communication.

Chronic elevation in muscle protein synthesis leads to adaptive cellular responses
Although Tor -OE is not expected to directly impact transcription, our analysis above indicated
that transcriptional changes are induced following the global increase in translation by Tor-OE
(Fig. 3.6B). This suggested that adaptations in transcription, and perhaps also translation, may
have been triggered in Tor-OE in response to the cellular stress imparted by the chronic, global
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increase in muscle protein synthesis. Indeed, proteome homeostasis (proteostasis) is under
exquisite control (Kong and Lasko, 2012; Vogel and Marcotte, 2012), and sustained
perturbations in Tor activity induces transcriptional programs that adaptively compensate to
maintain proteostasis (Tiebe et al., 2015; Wullschleger et al., 2006; Zhang et al., 2014). We
therefore reasoned that by examining the changes in transcription and translation induced by
Tor-OE, we may gain insight into how a cell adapts to the stress of chronically elevated
translation.
Transcriptional and ribosome profiling revealed 11 genes with significantly upregulated
transcription (fold change>3 and adjusted p-value<0.05; Fig. 3.7A and Table S7), and 75 genes
with significantly upregulated translation (fold change>3 and adjusted p-value<0.05; Fig. 3.7A
and Table S7) in Tor-OE compared to wild type. Interestingly, 8 of these genes exhibited shared
increases in both transcription and translation (Fig. 3.7A), with their translational fold change
(revealed by ribosome profiling) being larger than would be expected by their transcriptional fold
change alone. This suggests a coordinated cellular signaling system that adaptively modulates
both transcription and translation in response to the global elevation in translation following
overexpression of Tor in the muscle. Further analysis revealed these upregulated genes to
belong to diverse functional classes (Fig. 3.7B). Notably, we observed a striking enrichment in
genes encoding heat shock proteins and chaperones (GO term fold enrichment of 45.13, p-
value=0.006; GO enrichment test; Fig. 3.S4), factors known to assist with protein folding and to
participate in the unfolded protein response, particularly during cellular stress (Bukau et al.,
2006; Hetz, 2012; Hohfeld et al., 2001; Romisch, 2005; Taipale et al., 2010). Indeed, among the
7 heat shock protein genes with significant expression in the muscle (Table S7), 5 were
significantly upregulated in translation and 3 were significantly upregulated in transcription, with
the remaining 2 showing a strong trend towards upregulation (Fig. 3.7C,D and Table S7). We
performed quantitative PCR as an independent approach to verify the upregulation of heat
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shock proteins, which confirmed upregulation in the level of total mRNA and ribosome-
associated mRNA (Fig. 3.S4B,C). Given the well documented role for heat shock proteins in
regulating protein folding, stability, and degradation in conjunction with the proteasome system
(Hohfeld et al., 2001; Romisch, 2005; Taipale et al., 2010), this adaptation likely contributes to
the stabilization of elevated cellular protein levels resulting from Tor-OE. Thus, the coordinated
upregulation of heat shock proteins is one major adaptive response in transcription and
translation following Tor-OE.
In addition to heat shock proteins, we also identified genes involved in other cellular
functions that are upregulated in Tor-OE and appear to enable adaptive responses to elevated
cellular protein synthesis. For example, the E3 ubiquitin ligase subunit APC4, involved in
proteasome-dependent protein degradation (Glickman and Ciechanover, 2002; Huang and
Bonni, 2016), was upregulated in Tor-OE (Fig. 3.7D). Interestingly, proteasome subunits were
reported to be upregulated in cells with increased Tor activity (Zhang et al., 2014). We also
identified the RNA polymerase subunit rpl1 and transcription factor myc to be upregulated
following Tor-OE (Fig. 3.7D). These genes promote ribosome biogenesis, with Rpl1 necessary
to synthesize ribosomal RNA and Myc involved in promoting the expression of ribosome
assembly factors (van Riggelen et al., 2010; White, 2005). Together, Rpl1 and Myc likely
promote the generation of additional ribosomes to meet the increased demands of protein
synthesis induced by Tor-OE, consistent with previous studies showing Tor inhibition leads to
decreased RpI1 transcription (Mayer et al., 2004). Hence, transcriptional and ribosome profiling
defined adaptations in gene expression and protein synthesis that maintain proteostasis
following chronic elevation in protein synthesis.

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3.5 DISCUSSION
We have developed a tissue-specific ribosome profiling strategy in Drosophila and used this
approach to reveal the transcriptional and translational landscapes in larval muscle. Our
analysis revealed significant differences between overall transcriptional and translational
expression, and illuminated specific classes of genes with suppressed or elevated levels of
translation relative to transcription. We went on to leverage this technology to define the
transcriptional, translational, and post-translational influences in the postsynaptic muscle that
drive the retrograde control of presynaptic efficacy. Unexpectedly, we found no evidence that
specific changes in transcription or translation are necessary for retrograde signaling, indicating
that post-translational mechanisms may ultimately transform the loss of postsynaptic receptors
and enhanced protein synthesis into instructive retrograde cues. Finally, we identified adaptive
cellular responses, in both transcription and translation, to chronically elevated protein synthesis
that promote protein stability. Together, this study demonstrates the potential to combine the
sophisticated genetic approaches in Drosophila with the power of ribosome profiling to
illuminate the complex interplay of transcriptional and translational mechanisms that adaptively
modulate cellular proteome stability and trans-synaptic retrograde signaling.

Ribosome profiling and translational regulation in Drosophila
We have developed a highly efficient affinity tagging strategy and optimized RNA processing to
enable tissue-specific ribosome profiling in Drosophila. Ribosome profiling has major
advantages over measuring total mRNA expression and ribosome-associated mRNA
(translational profiling using TRAP). Profound differences can exist between transcriptional
expression and actual protein synthesis of genes expressed in a tissue. RNA-seq of total mRNA
(transcriptional profiling) does not capture translational dynamics (Liu et al., 2016; Mortazavi et
al., 2008). Translational profiling using TRAP does provide insights into translation (Heiman et
al., 2014), but is less sensitive in detecting translational dynamics compared to ribosome
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profiling, which accurately quantifies the number of ribosomes associated with mRNA
transcripts (Fig. 3.3; (Ingolia et al., 2012)). One major obstacle that limited the development of
tissue-specific ribosome profiling is the relatively large amount of starting material necessary to
generate the library for next generation sequencing. Because only ~30 nucleotides of mRNA
are protected from digestion in each ribosome (Ingolia et al., 2009), ribosome profiling requires
much more input material compared to standard RNA-seq (Brar and Weissman, 2015). Thus,
the purification efficiency of the ribosome affinity tagging strategy and subsequent processing
steps are very important to enabling successful profiling of ribosome protected mRNA fragments
in Drosophila tissues. We achieved this high purification efficiency by systematically testing and
optimizing multiple ribosome subunits (RpL3, RpL36, RpS12, RpS13) and affinity tags (6xHis,
1xFlag, 3xFlag), finally settling on the RpL3-3xflag combination to enable the highest purification
efficiency (Fig. 3.2B and Materials and Methods). Collectively, this effort differentiates our
strategy from previous approaches in Drosophila that achieved ribosome profiling but lacked
tissue specificity (Dunn et al., 2013) or purified ribosome-associated RNA from specific tissues
but lacked the ability to quantify ribosome association with mRNA transcripts (Huang et al.,
2013; Thomas et al., 2012; Yang et al., 2005; Zhang et al., 2016).
This optimized ribosome profiling approach has illuminated genome-wide translational
dynamics in Drosophila muscle tissue and demonstrated two opposing protein production
strategies utilized in these cells: elevated transcriptional expression coupled with low translation
efficiency, which was apparent for genes encoding ribosomal subunits (Fig. 3.4F), and low
transcriptional expression coupled with high translation efficiency, which was observed for
genes encoding proteins belonging to diverse functional classes (Fig. 3.4G). These
complementary strategies are likely tailored towards different cellular needs, enabling
modulatory control of nuclear gene transcription and cytosolic protein synthesis (Chekulaeva
and Landthaler, 2016; Kong and Lasko, 2012). Thus, transcriptional and ribosome profiling of
80

muscle tissue has revealed that translational control of ribosomal protein synthesis may be a
strategy tailored to the unique metabolic needs of this tissue.

Transcriptional, translational, and post-translational mechanisms during retrograde
synaptic signaling
We have used transcriptional and translational profiling to determine the contributions of
transcription and translation in the postsynaptic signaling system that drives the retrograde
enhancement of presynaptic efficacy. Strong evidence has suggested that protein synthesis is
modulated during homeostatic signaling at the Drosophila NMJ, with genetic disruption of Tor-
mediated protein synthesis blocking expression and activation of the Tor pathway triggering
expression (Kauwe et al., 2016; Penney et al., 2012; Penney et al., 2016). We had expected
that translational profiling would discover targets with increased translation efficiency in the
muscles of GluRIIA mutants and/or following postsynaptic Tor overexpression, genetic
conditions in which presynaptic homeostatic plasticity is chronically activated. However, no
specific changes in transcription or translation were observed in GluRIIA mutants, while a large
percentage of muscle genes increased in translation following Tor-OE (Fig. 3.5C,D).
Furthermore, an apparent global increase in translation also appears sufficient to instruct
enhanced presynaptic release, consistent with the nature of the translational regulators
implicated in PHP: Tor, S6 Kinase, eIF4E, and LRRK2 (Jackson et al., 2010; Penney et al.,
2012; Penney et al., 2016). These factors are cap-dependent translational regulators that act on
nearly all mRNAs, although there is some degree of differential sensitivity of mRNAs to cap-
dependent translational regulation (Hsieh et al., 2012; Thoreen et al., 2012). Although we
cannot rule out very subtle changes in translation, nor can we accurately measure levels of
transcription or translation in genes with very low or highly variable expression, the sensitivity of
the ribosome profiling approach rules out major changes in the translation of specific genes
being necessary to promote PHP transduction. Thus, a global enhancement of translation may
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initiate post-translational mechanisms that are likely to ultimately drive PHP signaling in Tor-OE.
Indeed, a recent study demonstrated that GluRIIA- and Tor-OE-mediated PHP ultimately
converge at a post-translational mechanism to mediate the same retrograde signaling pathway
(Goel et al., 2017). We consider several possible explanations and implications of these
findings.
There are three conditions that trigger homeostatic retrograde signaling in the
postsynaptic muscle: Acute pharmacological blockade of GluRIIA-containing postsynaptic
receptors (Frank et al., 2006), genetic mutations in GluRIIA (Petersen et al., 1997), and chronic
overexpression of Tor (Penney et al., 2012). First, all three manipulations lead to a similar
enhancement in presynaptic release and converge to drive the same unitary retrograde
signaling system (Goel et al., 2017). Further, the acute pharmacological induction of PHP does
not require new protein synthesis (Frank et al., 2006; Goel et al., 2017). This implies that while
distinct pathways mediate PHP signaling, they all ultimately converge on the same pathway that
utilizes post-translational mechanisms. Indeed, there is evidence for post-translational
mechanisms in the induction of PHP signaling in GluRIIA mutants, as changes in CamKII
phosphorylation and activity have been observed (Goel et al., 2017; Haghighi et al., 2003;
Newman et al., 2017). In addition, other post-translational mechanisms, such as protein
degradation or ubiquitination, could contribute to homeostatic signaling in the muscle. However,
while all three manipulations appear to ultimately utilize the same retrograde signal transduction
system, it is quite intriguing that somehow the global increase in translation observed in Tor-OE
is sculpted, perhaps by shared post-translational mechanisms, into a specific retrograde signal
that instructs enhanced presynaptic release.
Second, it is possible that pharmacological, genetic, or Tor-OE-mediated inductions of
PHP signaling are all mechanistically distinct, in which case no common transcriptional,
translational, or post-translational mechanisms would be expected. Indeed, forward genetic
screening approaches to discover genes necessary for PHP expression have failed to identify
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any genes needed for PHP induction in the postsynaptic muscle (Dickman and Davis, 2009;
Muller et al., 2011), suggesting possible redundancy in these signaling systems. Further, it is
possible that very small, local changes in translation are necessary to drive retrograde signaling
in GluRIIA mutants and Tor-OE, in which case our ribosome profiling approach may have
lacked sufficient resolution to detect these changes, as tagged ribosomes were purified from
whole cell muscle lysates. Indeed, a recent report demonstrated synapse-specific PHP
expression (Newman et al., 2017). Future studies utilizing genetic, electrophysiological,
biochemical, and imaging approaches will be necessary to identify the specific post-translational
mechanisms that drive PHP signaling, and to what extent shared or distinct mechanisms are
common between pharmacologic, genetic, and Tor-OE mediated PHP signaling.

Proteostasis and adaptive cellular responses to elevated protein synthesis
Cells possess a remarkable ability to homeostatically control protein expression and stability, a
process called proteostasis (Kaushik and Cuervo, 2015). This requires a robust and highly
orchestrated balance between gene transcription, mRNA translation, and protein degradation
(Sala et al., 2017; Vogel and Marcotte, 2012), while disruption of this process contributes to
aging and disease (Hipp et al., 2014; Labbadia and Morimoto, 2015). Further, proteostatic
mechanisms are not only customized to the unique demands of specific cells and tissues, but
are adjusted throughout developmental stages and even tuned over hours according to diurnal
metabolic and feeding cycles (Atger et al., 2015; Khapre et al., 2014; Sinturel et al., 2017;
Wullschleger et al., 2006). The homeostatic nature of proteostasis is highlighted by the
adaptations triggered in response to perturbations that threaten stable cellular protein levels,
such as starvation and inhibitions of protein degradation (Bush et al., 1997; Fleming et al., 2002;
Shang et al., 2011). We have used transcriptional and ribosome profiling to reveal new
homeostatic adaptations triggered by proteostatic mechanisms that stabilize the proteome
following chronic elevations in protein synthesis. In particular, genes that promote protein
83

stability (chaperones), protein degradation, and ribosome biogenesis were transcriptionally
and/or translationally upregulated following Tor overexpression in muscle (Fig. 3.7), modulations
in complementary pathways that synergistically prevent inappropriate protein interactions,
promote protein removal, and increase the machinery necessary to maintain elevated protein
synthesis (Claypool et al., 2004; Mayer et al., 2004; Zhang et al., 2014). Interestingly, many of
these pathways are also targeted following other homeostatic perturbations to proteome
stability, including heat shock, starvation, and inhibitions in protein degradation (Bar-Peled and
Sabatini, 2014; Bush et al., 1997; Richter et al., 2010). This may suggest that proteostatic
signaling involves a core program orchestrating adaptive modulations to transcription and
translation in response to a diverse set of challenges to protein stability. Thus, ribosome
profiling enabled the definition of transcriptional and translational mechanisms that respond to
chronic elevations of protein synthesis, revealing changes in translation that would not be
apparent through profiling of total RNA expression alone.
Recent developments in next-generation sequencing have greatly expanded our ability
to investigate complex biological phenomena on genome-wide scales. The power and variety of
sophisticated genetic approaches are well-known in Drosophila. These include tissue-specific
expression with a broad array of Gal4 and LexA drivers, transposable element manipulations,
CRISPR/Cas-9 gene editing, and extensive collections of genetic mutations and RNAi lines
(Gratz et al., 2015; Nagarkar-Jaiswal et al., 2015; Spradling et al., 1999; Venken and Bellen,
2014). Although some approaches have emerged that permit the analysis of RNA from entire
organs as well as ribosome-associated RNA from specific tissues (Brown et al., 2014; Daines et
al., 2011; Heiman et al., 2014; Huang et al., 2013; Sanz et al., 2009; White et al., 1999; Zhang
et al., 2016), the technology described here now adds ribosome profiling to join this powerful
toolkit to enable the characterization of translational regulation in specific cells with
unprecedented sensitivity.

84

Figure 3. 1 Figure 2. 9
85

Figure 3.1: Schematic detailing transcriptional and translational profiling of retrograde
homeostatic signaling at the Drosophila NMJ. (A) Schematic illustrating synaptic
transmission at the Drosophila NMJ. Representative EPSP and mEPSP electrophysiological
traces in wild type (w
1118
; BG57-Gal4/UAS-RpL3-3xflag, n=6), GluRIIA mutants (w; GluRIIA
SP16
;
BG57-Gal4/UAS-RpL3-3xflag, n=6), and overexpression of Tor in the postsynaptic muscle (Tor-
OE: w;UAS-Tor-myc/+;BG57-Gal4/UAS-RpL3-3xflag; n=6). Note that while mEPSP amplitudes
are reduced in GluRIIA mutants, EPSP amplitudes remain the same as wild type because of a
homeostatic increase in presynaptic release (quantal content). Tor-OE does not change mEPSP
amplitude, but retrograde homeostatic signaling is induced, leading to increased EPSP
amplitude and quantal content. Quantification of mEPSP amplitude (B), EPSP amplitude (C),
and quantal content (D) for the indicated genotypes. (E) Schematic illustrating the putative role
of protein synthesis in retrograde homeostatic signaling and the design of ribosome tagging to
isolate postsynaptic RNA. (F) Schematic representing the workflow for transcriptional profiling,
translational profiling using TRAP (translating ribosome affinity purification), and ribosome
profiling. Student’s t test was used to compare GluRIIA and Tor-OE to wild type; **=p<0.01.

86

Figure 3. 2 Figure 2. 10
87

Figure 3.2: Development and validation of an optimized tissue specific ribosome profiling
protocol in Drosophila. (A) Schematic illustrating the ribosome affinity purification strategy. A
tagged ribosome subunit (RpL3-Flag) is expressed and incorporated into ribosomes. Magnetic
beads coated with anti-flag antibodies are used to immunoprecipitate ribosomes along with
associated mRNA. (B) Anti-flag immunoprecipitation from wild type (control), postsynaptic
expression of RpL3-Flag (w;BG57-Gal4/UAS-RpL3-3xflag), and postsynaptic expression of
RpS13-Flag (w;BG57-Gal4/UAS-RpS13-3xflag) in third-instar larval muscle. Samples were run
on an SDS-PAGE gel and Commassie stained. The expected distribution of ribosomal proteins
are present in RpL3-Flag and RpS13-Flag samples (noted by arrowheads), but not observed in
wild-type controls. (C) Total RNA was extracted from anti-flag immunoprecipitations from wild
type and RpL3-Flag larval muscle tissue and run on an agarose gel. Ribosomal RNA is present
in RpL3-Flag RNA samples but absent in wild type samples. Total RNA extracted from wild type
whole larvae was loaded to show the position of ribosomal RNA. (D) Workflow for the ribosome
profiling strategy. (E) Representative RNA-seq mapping of the actin57B locus from
transcriptional, translational (TRAP), and ribosome profiling. Note that ribosome profiling reads
predominantly map to 5’UTR and coding regions, and are absent from the 3’UTR. RPM: reads
per million total mapped reads. (F) Replicate ribosome profiling sequencing demonstrates highly
reproducible results.

88

Figure 3. 3 Figure 2. 11
89

Figure 3.3: Comparison of translational and ribosome profiling from Drosophila larval
muscle. (A) Plot of ribosome profiling RPKM as a function of transcriptional profiling RPKM for
all muscle genes in wild type. Genes with high translation efficiency (TE; TE>2) or low TE
(TE<0.5) are labeled in red and blue respectively. Genes with medium TE (TE between 0.5 and
2, indicated by the two dash lines) are labeled in grey. (B) Plot of translational profiling (TRAP)
RPKM as a function of transcriptional profiling RPKM for all muscle genes in wild type. The
same color coding scheme is used as in (A). (C) Graph showing percentage of total muscle
genes that are in the high TE, medium TE or low TE group based on ribosome profiling or
TRAP. Note that a lower percentage of genes are revealed to have high or low TE with TRAP
compared to ribosome profiling. (D) Plot of translational efficiency defined by ribosome profiling
as a ratio of TRAP of all genes in the three categories: high TE (ribosome profiling and TRAP
TE average>2), medium TE (TE average between 0.5 and 2), and low TE (TE average<0.5).
Note that ribosome profiling reveals higher TE for high TE genes, and lower TE for low TE
genes compared to TRAP. ***=p<0.001; one-way ANOVA with post hoc Bonferroni’s test.

90

Figure 3. 4 Figure 2. 12
91

Figure 3.4: Analysis of the transcriptome and translatome reveals dynamic translational
regulation in Drosophila muscle. (A) Definition of number of genes encoded in the Drosophila
genome and those expressed in the muscle transcriptome and translatome. (B) Heat map
showing transcriptional levels of all annotated genes in the Drosophila larval muscle compared
to those expressed in the central nervous system (CNS; (Brown et al., 2014)). These genes are
grouped into four sections according to their expression status in muscle and CNS; the
percentage of total genes is indicated above each section. (C) Heat map defining translation
efficiency (TE) and transcription and translation expression levels (RPKM) of genes expressed
in muscle. Genes are ordered according to translation efficiency, with a trend observed for
genes with high translation efficiency having low transcriptional expression levels and vice
versa. (D) Transcriptional expression levels of genes with low TE (TE<0.5, blue), medium TE
(TE between 0.5 and 2, grey), and high TE (TE>2, red). The transcriptional expression levels of
genes in the low TE group is significantly higher than that of the medium TE group, while
transcriptional expression of the high TE group is significantly lower than the that of the medium
TE group (***=p<0.001; one-way ANOVA with post hoc Bonferroni’s test). (E) Histogram of
translation efficiency across all genes expressed in the muscle. The 100 genes with the lowest
translation efficiency (blue) and highest translation efficiency (red) are indicated. (F) Histogram
of translation efficiency for the 100 genes with the lowest translation efficiency. An enrichment in
ribosomal proteins, indicated in blue, is observed. (G) Histogram of translation efficiency for the
100 genes with the highest translation efficiency. Genes in the most abundant functional class,
encoding proteins involved in cellular structure, are indicated in red. (H) Graph showing the TE
of the 100 genes with the highest or lowest TE in Tor-OE as a ratio of wild type. Note that the
translational efficiency of ribosomal proteins in Tor-OE are significantly increased compared to
wild type. ***=p<0.001; paired Student’s t-test. Additional details can be found in Table S3,
Table S4, and Figure S3.

92

Figure 3. 5 Figure 2. 13
93

Figure 3.5: Analysis of transcriptional and translational profiling of GluRIIA expression in
GluRIIA mutants and Tor overexpression in Tor-OE. (A) Schematic illustrating the genomic
GluRIIA locus in wild type and GluRIIA
SP16
mutants. Note that the 5’ region of GluRIIA is deleted
in the GluRIIA
SP16
mutant, as well as the adjacent oscillin gene. Below: RNA-seq reads mapping
to the GluRIIA locus from transcriptional and ribosome profiling in wild type and GluRIIA
SP16

mutants. The coverage graphs were divided into four sections corresponding to the regions
indicated in the GluRIIA transcript. The numbers in each graph indicates the expression value of
that region normalized to wild type transcriptional or ribosome profiling expression value. Note
that no expression was detected by transcriptional or ribosome profiling in the deleted region in
GluRIIA mutants, as expected. (B) Schematic illustrating the endogenous Tor mRNA transcript
and the mRNA transcript transgenically expressed in Tor-OE (UAS-Tor-myc). Both transcripts
share the same coding sequence, but differ in their 5’UTR and 3’UTR sequences. Below are
reads mapping to the indicated regions, divided into the three indicated sections. Note that both
transcriptional and translational expression of UAS-Tor mRNA are significantly increased in Tor-
OE, while transcription and translation of endogenous Tor mRNA is largely unchanged in Tor-
OE. (C) Histogram of the distribution of gene translation changes in wild type versus wild type
(black), which represents intrinsic variability, that of Tor-OE versus wild type (Red), and that of
GluRIIA mutants versus wild type (blue). Note the shift in distribution observed in Tor-OE,
suggesting a global increase in translation. (D) Cumulative percentage plot of distributions
shown in (C), showing significant difference between Tor-OE versus wild type distribution
compared to wild type versus wild type distribution. (p<0.001, Kolmogorov–Smirnov test).

94

Figure 3. 6 Figure 2. 14
95

Figure 3.6: Few changes in postsynaptic transcription or translation are observed in
GluRIIA mutants. (A) Plot of transcriptional and translational expression levels of all genes in
GluRIIA mutants (w
1118
;GluRIIA
SP16
; BG57-Gal4/UAS-RpL3-3xflag) compared to wild type (w
1118
;
BG57-Gal4/UAS-RpL3-3xflag), with near perfect correlations observed (indicated by r
2
values).
Right: Table showing the number of genes with significantly up-regulated (up arrow) or down-
regulated (down arrow) transcription or translation efficiency (TE) in GluRIIA compared to wild
type using the indicated cut off values. (B) Plot of transcriptional and translational expression
levels of all genes in Tor-OE (w
1118
;UAS-Tor-myc/+;BG57-Gal4/ UAS-RpL3-3xflag) compared to
wild type. Note that while moderate changes in transcription are observed, large differences in
translation are found (indicated by r
2
values). Right: Table showing the number of genes with
significantly up-regulated (up arrow) or down-regulated (down arrow) transcription or TE in Tor-
OE compared to wild type using the indicated cut off values. (C) Heat map indicating the 47
genes with significant increase in TE in Tor-OE, with the corresponding genes in GluRIIA
mutants shown below. Note that no trend is observed in translational expression of these genes
in GluRIIA mutants. TOP mRNAs are highlighted in red. Additional details can be found in
Tables S5 and S6.

96

Figure 3. 7 Figure 2. 15
97

Figure 3.7: Increased cellular translation triggers adaptive responses in both
transcription and translation. (A) Diagram showing the number of significantly upregulated
genes in transcription and translation in Tor-OE compared to wild type (p<0.05, fold change>3).
(B) Pie chart illustrating the classes of differentially upregulated genes in Tor-OE compared to
wild type. (C) Comparative fold changes for chaperones differentially upregulated in
transcription and translation in Tor-OE compared to wild type. Note that all but one exhibit
higher translational changes compared to transcriptional changes, implying an additional layer
of regulation in translation efficiency in addition to the increased transcriptional expression. (D)
Graphs showing RPKM values measured by transcriptional and ribosome profiling in wild type
and Tor-OE for the representative heat shock protein Hsp23, the ubiquitin E3 ligase APC4, the
RNA polymerase Rpl1, and the transcription factor myc. Read mapping of the indicated genes
are illustrated below. **=p<0.01, ***=p<0.001; Student’s t-test. Additional details can be found in
Table S7.

98

Figure 3.S 1 Figure 2. 16
99

Figure 3.S1: RpL3-Flag can restore viability to RpL3 mutants and does not perturb
synaptic growth or function when overexpressed. (A) Schematic illustrating the genomic
RpL3-Flag rescue construct and table showing the lethal phase of RpL3 mutants
(w;RpL3
G13893/KG05440
) compared to RpL3-Flag rescue (w;genomic-RpL3-3xflag/+;
RpL3
G13893/KG05440
). (B) Representative images of larval muscles of BG57>RpL3-Flag (muscle
overexpression of RpL3-Flag; w;BG57-Gal4/UAS-RpL3-3xflag) immunostained with anti-Flag
(green) and anti-HRP (neuronal membrane marker; magenta) antibodies. (C) Representative
images of muscle 6/7 NMJs from wild type (w
1118
) and BG57>RpL3-Flag immunostained with
antibodies against vGlut (synaptic vesicle marker; magenta) and HRP (white). (D) Quantification
of bouton number in the indicated genotypes. (E) Individual boutons of wild type and
BG57>RpL3-Flag NMJs immunostained with antibodies against BRP (active zone marker;
green), GluRIID (postsynaptic glutamate receptor marker; magenta), and DLG (postsynaptic
density marker, white). No significant differences are observed in BRP number per NMJ (F), Dlg
intensity (G), or GluRIID intensity (H) in wild type (n=12) and BG57>RpL3-Flag (n=12). (I)
Representative traces of EPSP and miniature EPSP recordings from wild type and
BG57>RpL3-Flag third-instar larval NMJs. No significant differences are observed in miniature
EPSP frequency (J), miniature EPSP amplitude (K), or EPSP amplitude (L) in wild type (n=11)
and BG57>RpL3-Flag (n=15). N.S=p>0.05; Student’s t test.

100

Figure 3.S 2 Figure 2. 17
101

Figure 3.S2: Comparative metagene analysis of genome-wide averaged reads distribution
around the start and stop codons from transcriptional, translational (TRAP) and
ribosome profiling. (A) Metagene analysis plot of averaged read density around the start
codon for transcriptional, translational (TRAP) and ribosome profiling. (B) Metagene analysis
plot of averaged read density around the stop codon for transcriptional, translational (TRAP)
and ribosome profiling. Note the highly reduced density of ribosome profiling reads in the
3’UTR. UTR: untranslated region.

102

Figure 3.S 3 Figure 2. 18
103

Figure 3.S3: Functional classes for the 100 genes with the lowest and highest
translational efficiency (TE). (A) Functional classes for the 100 genes with the lowest TE.
Note that ribosomal proteins represent the largest class, with 73 of the 100 genes encoding
ribosomal proteins. (B) Functional classes for the 100 genes with the highest TE. Diverse
functional classes are present, with genes encoding proteins involved in cellular structure being
the most abundant class.

104

Figure 3.S 4 Figure 2. 19
105

Figure 3.S4: GO analysis and validation of transcriptional and translational upregulation
of heat shock proteins in Tor-OE using quantitative PCR. (A) GO term enrichment analysis
of genes up-regulated in Tor-OE; a GO enrichment test is used to generate the p-values
indicated. (B) Quantitative PCR (qPCR) analysis of three heat shock proteins (Hsp83, Hsp68,
Hsp26) from wild type (w
1118
; BG57-Gal4/UAS-RpL3-3xflag) and Tor-OE (w
1118
;UAS-Tor-
myc/+;BG57-Gal4/ UAS-RpL3-3xflag) larvae. Total RNA from preparations of each genotype
was used as the template for the qPCR assay to assess transcriptional changes. (C) qPCR
analysis of the indicated heat shock proteins and genotypes using ribosome associated RNA
from the preparations as the qPCR template to assay translational changes. ***=p<0.001;
Student’s t-test.

106

Figure 3.S 5 Figure 2. 20
107

Figure 3.S5: Effects of number of mapped reads on transcriptional and ribosome
profiling measurement variability. (A) Number of genes (of 15583 total genes encoded in the
Drosophila genome) that fall into each indicated read number range for transcriptional profiling
and ribosome profiling. (B) Transcriptional profiling variability, defined as the standard error of
the mean (SEM) normalized to the mean RPKM value of each gene, as a function of number of
mapped reads. Mapped reads for each gene were grouped and binned at indicated values. (C)
The same plot as in B for reads obtained through ribosome profiling. Note that variability is
sharply increased at read numbers below 10 in both transcriptional and ribosome profiling.

108

CHAPTER FOUR

Preliminary investigation of GluClα’s role in presynaptic homeostatic depression

Contributions:
Xun Chen, Xiling Li, Beril Kiragasi and Dion K. Dickman designed research;
Xiling Li and Beril Kiragasi performed experiments and analysis presented in Figure 4.1;
Xun Chen performed experiments and analysis presented in Figure 4.2; 4.3; 4.4; 4.5.

109

4.1 ABSTRACT
Neurotransmitter release during synaptic transmission is tightly controlled to ensure signal
transmission efficacy while preventing excitotoxicity induced by excessive release. The
excitatory neurotransmitter, glutamate, is a major neurotransmitter type in multiple organisms.
Both acute and chronically release of excessive glutamate are associated with neuronal
damage and are implicated in multiple neurological diseases. At the drosophila NMJ, when
glutamate transporter, vGlut, is overexpressed in motor neurons, synaptic vesicles become
bigger and contain more glutamate, leading to excess glutamate being released per synaptic
vesicle fusion. However, presynaptic terminals tune down the number of released vesicles to
keep postsynaptic excitation to the normal level. This process, called presynaptic homeostatic
depression or PHD, prevents overexcitation of postsynaptic muscle and possible excitotoxicity.
The molecular mechanism of this process is not fully understood. Previous studies have
suggested a presynaptic receptor that initiates the signaling pathway. Here, we screened
candidate receptor genes and identified GluClα, the only glutamate gated chloride channel in
the fly genome, and showed that loss of this receptor leads to failure in PHD signaling.
Furthermore, GluClα is expressed in motor neuron where it colocalizes with synaptic vesicles
and it is not expressed in muscle. Thus, GluClα may be a key player in the PHD signaling and
its mammalian counterpart might be part of an intrinsic mechanism to suppress excitotoxicity.

110

4.2 INTRODUCTION
The efficacy of synaptic transmission in the nervous system is crucial for survival. Excitatory
synapses maintain proper excitation of the postsynaptic neuron through molecular pathways
such as presynaptic homeostatic potentiation and depression (Daniels et al., 2004; Davis and
Muller, 2015a; Gavino et al., 2015; Li et al., 2018). Overexcitation of the postsynaptic neuron
cause abnormally high level of cytosolic Ca
2+
which then over activates enzymes and signaling
pathways that could eventually lead to neuronal death through apoptotic pathways (Brini et al.,
2014; Lau and Tymianski, 2010; Mehta et al., 2013). Excitotoxicity is implicated in neurological
diseases that arise from both acute causes such as stroke (Lai et al., 2014) and chronic
conditions such as Parkinson’s disease (Caudle and Zhang, 2009). Understanding how synapse
avoid excess glutamate release under normal conditions could reveal strategies to intervene the
progression of those neurological diseases.
The Drosophila NMJ is a great model synapse to study mechanisms that counteract
forces driving excitotoxicity. In addition to powerful genetic tools available for drosophila,
drosophila NMJ is easily accessible to electrophysiological and imaging approaches.
Furthermore, glia at the Drosophila NMJ do not express the glutamate uptake transporter,
EAAT1 (Rival et al., 2006). Thus, the major mechanism that controls glutamate level is not
present, making the Drosophila NMJ more sensitive to glutamate level changes. This sensitized
condition offers an opportunity to uncover subtle contributions by mechanisms that stabilize
glutamate release.
At the Drosophila NMJ, neuronal overexpression of glutamate transporter, VGlut
increases synaptic vesicle size. These enlarged synaptic vesicles contain more glutamate and
lead to excess glutamate release. However, the excitatory postsynaptic potential (EPSP) remain
at the normal level indicating normal excitation of the postsynaptic cell. This is achieved by
reducing the number of vesicles released, a process called presynaptic homeostatic depression
111

(PHD) (Daniels et al., 2004; Gavino et al., 2015; Li et al., 2018). This condition presents a model
of synaptic adaptation to chronically increased glutamate release. Previous study has implied an
autocrine mechanism in the initiation of this adaptive signaling system and predicted a
presynaptic glutamate receptor that functions in sensing elevated glutamate release per
synaptic vesicle (Li et al., 2018). This receptor would then exert an inhibitory effect on the
presynaptic terminal thorough either an ionotropic or metabotropic mechanism or both.
Here, we conducted a candidate screen on glutamate receptors and identified GluClα as
being required for PHD signaling. Additional mutations in GluClα are generated using CRISPR
to confirm the phenotype. We show that GluClα is broadly expressed in the nervous system
including motor neurons. Moreover, GluClα colocalize with synaptic vesicles. These data lay the
foundation for a thorough characterization of GluClα’s role in PHD signaling and contribute to a
more detailed understanding of the molecular mechanisms of PHD.

4.3 MATERIALS AND METHODS
Drosophila stocks
Drosophila stocks are raised on standard molasses food at 25 °C. The w
1118
strain is used as
control. The following stocks are ordered from Bloomington Drosophila Stock Center, BDSC,
Bloomington, IN, USA: GluClα
MI02890
(GluClα
m25
), Df(3R)BSC809 (GluClα
DF
). UAS-CD4-tdGFP is a
gift from Chun Han (Cornell University) (Han et al., 2011a). All other stocks are obtained from
Bloomington Drosophila Stock Center. Drosophila transgenic lines are generated by PhiC31
integrase-mediated transgenesis (BestGene Inc. Chino Hills, CA).

Molecular Biology
GluClα isoform RM cDNA is obtained by PCR using Drosophila adult head cDNA as template.
GluClα RM cDNA is then inserted into pACU2 vector or pACU2-C-10XFlag smFP vector by
112

DNA ligase based cloning strategy to generate UAS-GluClα untagged or UAS-GluClα C-
terminal 10XFlag smFP tagged construct. GluClα with internal 10XFlag smFP tag is generated
by extension PCR and then inserted into pACU2 vector to generate UAS-GluClα internal
10XFlag smFP tag.
To generate single gRNA constructs of GluClα gRNA1, 2 and 3. gRNA sequences are
designed using the online tool, flycrispr target finder
(http://tools.flycrispr.molbio.wisc.edu/targetFinder/). DNA oligos encoding the gRNAs are
synthesized by IDT (http://www.idtdna.com/pages), then annealed and inserted into pattB vector
containing U6 promoter. To generated the construct containing gRNA 4, 5 and 6. A strategy
described previously was used, briefly, PCR products containing gRNA and tRNA sequences
are assembled with a pattB vector containing U6 promoter in a NEBuilder assembly reaction
(NEB, E5520S). The gRNA sequences are as follows:
gRNA1: GGGCAGCGGACACTATTTCT; gRNA2: GACGCCCGAATACGACCATC;
gRNA3: CAGTGAAGCACTGCACAGGC; gRNA4: AGTTAACCTTCCGTGAACAG;
gRNA5: GAGCAGATCTGTCTATCCAG; gRNA6: CCAACGACTTGGTCTTCCTG.
To generate mRNA for expression of GluClα in Xenopus oocytes. GluClα isoform RM
cDNA was cloned into pcDNA3 containing 5’ and 3’ UTR of Xenopus β-globulin (Liman et al.,
1992; Tu et al., 2018), the sequence of the 5’ and 3’ UTR of Xenopus β-globulin is PCR
amplified from the pGEMHE vector (a gift from Emily Liman, USC) (Liman et al., 1992) and
inserted into pcDNA3. In vitro transcription was performed using the HiScribe™ T7 Quick High
Yield RNA Synthesis Kit (NEB, E2050S) using PCR amplified DNA containing the whole mRNA
coding region on the plasmid generated above as input. In vitro transcribed mRNA are purified
using column purification and diluted to 400 ng/µl. 20 nl of this diluted mRNA was injected into
each oocyte for expression of GluClα.
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Hemolymph glutamate concentration measurement
To collect hemolymph sample, two wondering third instar larvae were pinned down on a glass
slide and 40 µl of PBS were dropped on the slide covering both larvae. The larvae body wall
was then cut open using micro dissection scissors while avoiding cutting open internal organs of
the larvae. The preparation was gently agitated for 3 mins to allow hemolymph to completely
diffuse into PBS and then the PBS solution containing hemolymph was drew up using a pipette
tip and kept on ice. The hemolymph sample were then centrifuged at 3000Xg for 15 mins and
30 µl supernatant were used for glutamate concentration measurement. The glutamate assays
were performed using the Glutamate Assay Kit (Sigma Aldrich, MAK004). Calculations were
done according to manufacturer’s instructions and the volume of hemolymph in each larva is
assumed to be 1.5 µl.

4.4 RESULTS
An electrophysiological screen for glutamate receptors required for PHD signaling
We first compiled a list of genes known or are predicted to be glutamate receptors based on
information curated on FlyBase (www.flybase.org). Available mutation lines for these genes are
collected. This collection of fly lines are then assayed by electrophysiology. The mutations are
introduced into ok371 driving VGlut overexpression (ok371>VGlut OE) background, a condition
in which all Pertussis toxin sensitive G-protein coupled receptor signaling is blocked in motor
neurons (MN Pertussis toxin +vGlut-OE) is also included in the screen to test for a role of G-
protein coupled receptor signaling. Under normal condition, quantal content (the number of
vesicles released per action potential, defined by dividing EPSP amplitude by miniature EPSP
amplitude) is reduced in ok371>VGlut OE. We expect normal quantal content and increased
EPSP when a gene required for PHD signaling is mutated. We found that all screened mutants
114

have reduced quantal content and normal EPSP except for a GluClα mutant (we termed this
mutant GluClα
m25
) (data contributed by Beril Kiragasi and Xiling Li) (Fig. 4.1A). To control for
genetic background, we further tested GluClα
m25
over a GluClα deficiency chromosome, in this
genetic condition, PHD signaling is also blocked (Fig. 4.1B,C), supporting a role of GluClα in
PHD signaling.

Characterization of existing GluClα mutants and generation of new GluClα mutants using
CRISPR.
GluClα is predicted to have 11 isoforms, 9 out of the 11 isoforms encode for proteins with 447 to
463 amino acids (long isoforms), the other 2 isoform encode for proteins with 263 and 349
amino acids (short isoforms). The GluClα
m25
mutation is a Minos based element inserted in the
intron before the last coding exon of the long isoforms RM, RO, RI and RJ. Because the
insertion site is after the transcription termination of two short isoforms, this mutation is
predicted to mainly affect the 9 long isoforms and leave the two short isoforms largely
unaffected (Fig. 4.2A).
We further used CRISPR to generate additional alleles that are unambiguous null alleles.
Constructs containing a total of 6 gRNAs are made, gRNA 1-3 are inserted into three individual
constructs and gRNA 4-6 are inserted into one construct using a tRNA-gRNA architecture. By
crossing individual gRNA1, 2 and 3 to Drosophila line that express Cas9 in germline cells and
then screening the offspring. A total of 5 mutation lines are established and verified by
sequencing. Three GluClα isoform RM overexpression line under Gal4/UAS control are
generated, they are GluClα untagged, GluClα tagged on the C-terminal end by 10XFlag smFP
(GluClα-smFP), and GluClα tagged on the internal loop between transmembrane domain 3 and
4 by 10XFlag smFP (GluClα-smFPin). Moreover, to perform channel properties characterization
of GluClα, we generated construct for expression of GluClα in xenopus oocytes.
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GluClα is expressed in the nervous system and co-localize with synaptic vesicle.
According to our prediction, the glutamate receptor functioning in PHD signaling should be
present at the presynaptic terminal. To test whether GluClα is expressed in the nervous system.
The predicted promoter region of GluClα gene is cloned to drive expression of Gal4. This
GluClα promoter>Gal4 line is then used to drive UAS-CD4-tdGFP. In the third instar larvae of
these animals, GFP signal broadly distribute over the nervous system, including in motor
neurons, and no GFP signal is observed in muscle tissue (Fig. 4.3). Thus these experiments
indicate that GluClα is expressed in the nervous system. However, the predicted promoter
region may not faithfully reflect the expression pattern of endogenous GluClα, and Gal4/UAS
based reporter system typically do not reflect the real expression level of genes. To address
these caveats, we are in the process of generating GluClα alleles with a 10XFlag smFP tag
inserted at the C-terminal end of the endogenous genomic site using CRISPR.
To observe the sub-cellular localization of GluClα protein, we overexpressed the two
tagged versions of GluClα in both muscle and motor neurons and stained against Flag. In
muscle cells, GluClα-smFP localize uniformly throughout the cell (Fig. 4.4A) while in motor
neuron, GluClα-smFP co-localize with synaptic vesicle marker, synaptotagmin (Syt) (Fig. 4.4B).
GluClα-smFPin appears to be trapped in the perinuclear zone in muscle cells (Fig. 4.4A), while
in motor neuron GluClα-smFPin does not co-localize with synaptic vesicle marker (Fig. 4.4B).
Given that the localization pattern of GluClα-smFPin in muscle suggest internal tagging of
GluClα causes trafficking problems, GluClα-smFP localization pattern in motor neurons more
likely represent the localization pattern of endogenous GluClα. Further rescue experiments will
help differentiate which of the two tagged strategy represent true localization of GluClα.

116

PHD condition modulate glutamate level in Drosophila larvae hemolymph.
Previous characterization of Drosophila GluClα showed GluClα being a high affinity glutamate
gated channel that is capable of responding to 10 µM glutamate. To characterize how GluClα
behave in the native NMJ environment. We first set out to measure the concentration of
glutamate in hemolymph, where NMJ is exposed in, and test whether PHD condition modulates
glutamate level in hemolymph. Using an enzyme based reporter assay, we determined the
glutamate concentration in wild type hemolymph to be 374.5±11.9 µM (Fig. 4.5B). Remarkably,
ok371>VGlut OE reduced glutamate concentration by 49% and normal glutamate level is
restored when GluClα
m25
mutation is introduced into the PHD background. Furthermore,
overexpressing the glutamate uptake transporter, EAAT1, either through a muscle driver, BG57-
Gal4 or a glia driver, Repo-Gal4, reduces glutamate concentration in hemolymph (Fig. 4.5B).
Taken together, these data suggests glutamate concentration is subject to modulation by PHD
and at least some of the modulation pathways go through GluClα.

4.5 DISCUSSION
Through an electrophysiology based screen, we identified GluClα as being required for PHD
signaling. We showed GluClα expresses broadly in the nervous system and co-localize with
synaptic vesicles. Our preliminary characterization of GluClα suggest a working model in which
GluClα responds to changes in glutamate at the Drosophila NMJ by opening up Cl
-
conductance,
equilibrium potential of Cl
-
is lower than the gating voltage of typical voltage-gated sodium and
calcium channels, thus a Cl
-
current would suppress Ca
2+
influx and inhibit synaptic vesicle
release.
Localization of GluClα on synaptic vesicles may not be directly associated with GluClα’s
function in PHD signaling, as only GluClα that is on the cell membrane can sense released
glutamate. Rather, being localized on synaptic vesicles is likely a mean of delivering GluClα to
117

the synaptic terminal and mobilizing GluClα to the cell membrane in an activity dependent
manner.
GluClα in C. elegans is shown to form homopentamers, and expressing GluClα alone in
Xenopus oocytes leads to functional channels. Both findings suggest Drosophila GluClα form
homomultimeric channels. Thus it is highly likely that GluClα form functional channels as
homomultimers in vivo and do not require other subunits. It would be interesting to test whether
all downstream events of PHD signaling originates from the function of GluClα alone.
GluClα is the only gene so far the loss of which blocks PHD signaling. Its suggested
function lies in the induction phase of the PHD signaling, a phase very little is known about
previously. Characterization of GluClα’s role would provide novel insight into how synapses
avoid and adapt to excess glutamate release and ultimately help understand excitotoxicity.

118

Figure 2. 21
119

Figure 4.1: An electrophysiological screen for glutamate receptors required for PHD
signaling. (A) Mutants in glutamate receptors are screened in PHD background using
electrophysiology, PHD signaling leads to reduced quantal content. A mutation in GluClα
showed normal quantal content, implying blocked PHD signaling. (B) miniEPSP, EPSP and
quantal content measurement of vGlut-OE (w
1118
; ok371/UAS-vGlut;), vGlut-OE+GluClα
m25

(w
1118
; ok371/UAS-vGlut; GluClα
MI02890
), and vGlut-OE+GluClα
m25/DF
(w
1118
; ok371/UAS-vGlut;
GluClα
MI02890
/ Df(3R)BSC809) showing increased EPSP and quantal content in PHD with GluClα
mutation compare to PHD alone. (C) Representative electrophysiology traces for the indicated
genotypes. (Data contributed by Beril Kiragasi and Xiling Li)

120

Figure 2. 22
121

Figure 4.2: Generation of new GluClα mutants using CRISPR and UAS transgenic lines.
(A) Genomic annotation of the GluClα locus (schematic retrieved from FlyBase). The GluClα
m25

allele insertion and the 6 gRNA target sites are indicatied. (B) Schematic showing the topology
of GluClα with four transmembrane domains and a N-terminal ligand binding domain. The
topology of the two differently 10XFlag smFP tagged GluClα is also shown.

122

Figure 2. 23
123

Figure 4.3: GluClα is expressed in the nervous system. Representative images of third
instar larvae with GluClα promoter driving CD4-tdGFP (w
1118
; GluClα promoter_Gal4/+; UAS-
CD4-tdGFP/+) at low magnification (A) and high magnification (B) of indicated regions, stained
by anti-HRP and Phalloidin. Note that GluClα promoter activity is broadly present in the nervous
system but is undetectable in muscle.

124

Figure 2. 24
125

Figure 4.4: GluClα co-localize with synaptic vesicle. (A) Representative images of larvae
with muscle overexpression of GluClα-smFP (BG57>GluClα-smFP: w
1118
;UAS-GluClα-smFP/+;
BG57/+) and GluClα-smFPin (BG57>GluClα-smFPin: w
1118
;UAS-GluClα-smFPin/+; BG57/+)
stained by anti-Flag antibody. GluClα-smFP distribute evenly in the cell while GluClα-smFPin
appears to be mainly trapped in the perinuclear zone. (B) Representative images of larvae with
neuronal overexpression of GluClα-smFP (OK371>GluClα-smFP: w
1118
;UAS-GluClα-
smFP/OK371) and GluClα-smFPin (OK371>GluClα-smFPin: w
1118
;UAS-GluClα-smFPin/OK371;)
stained by anti-Flag, anti-synaptotagmin and anti-HRP antibodies. GluClα-smFP showed high
degree of co-localization with the synaptic vesicle marker synaptotagmin.

126

Figure 2. 25
127

Figure 4.5: PHD modulates glutamate concentration in larvae hemolymph. (A) Schematic
of hemolymph sample preparation for glutamate concentration measurement. (B) Measured
glutamate concentration in indicated genotypes. Wild type (w
1118
), ok371 vGlut OE (w
1118
;
ok371/UAS-vGlut), ok6 vGlut OE (w
1118
; ok6/UAS-vGlut), BG57 EAAT1 OE (w
1118
;; BG57/UAS-
EAAT1), repo EAAT1 OE (w
1118
;; repo-Gal4/UAS-EAAT1), ok371 vGlut OE; GluClα
m25
(w
1118
;
ok371/UAS-vGlut; GluClα
MI02890
), ok371 vGlut+EAAT1 OE (w
1118
; ok371/UAS-vGlut; +/UAS-
EAAT1), ok371 repo vGlut+EAAT1 OE (w
1118
; ok371/UAS-vGlut; repo-Gal4/UAS-EAAT1), ok6
BG57 vGlut+EAAT OE (w
1118
; ok6/UAS-vGlut; BG57/UAS-EAAT1). N.S. p>0.05; * P<0.05; **
P<0.01; one-way ANOVA for all parameters.

128

CHAPTER FIVE
Conclusions
BLOC-1 complex and synaptic vesicle recycling
Previous work has identified two BLOC-1 complex subunits, Dysbindin and Snapin, as being
required in the presynaptic compartment for presynaptic homeostatic potentiation (Dickman and
Davis, 2009; Dickman et al., 2012). However, the detailed nature of their role in PHP remains
unclear. The gain a better understanding of the underlying molecular mechanism, we sought to
characterize additional BLOC-1 subunits. As biochemical work in other systems revealed that
BLOC-1 exist as a stable complex composed of eight subunits and they are mutually dependent
on each other for stability (Falcon-Perez et al., 2002; Lee et al., 2012), we initially hypothesized
that other BLOC-1 complex subunits would also be required for PHP signaling. To test this idea,
we generated mutation in Pallidin, a central subunit of BLOC-1 complex, by Flipase mediated
excision. To our surprise, Pallidin is dispensable for PHP signaling. Both Dysbindin and Snapin
localize on synaptic vesicles (Dickman and Davis, 2009; Dickman et al., 2012), we then went on
to test whether Pallidin share the same localization pattern.
Antibody staining of endogenous Pallidin at the Drosophila NMJ revealed that Pallidin
co-localize with neuronal microtubules and not with synaptic vesicles. This suggest that at the
neuronal terminals, BLOC-1 complex exist as smaller sub-complexes, with at least one
subcomplex localize on synaptic vesicles containing Dysbindin and Snapin. The distinct
localization of Pallidin compared to that of Dysbindin and Snapin is consistent with their
differential roles in PHP signaling.
Synaptic vesicles are shown to traffic through endosomal structures, a process thought
to rejuvenate the synaptic vesicles and improve their performance in future release cycles
(Uytterhoeven et al., 2011). While BLOC-1 localize to presynaptic terminals and has been
129

implicated in membranous structure trafficking and modulation (Delevoye et al., 2016; Di Pietro
et al., 2006; John Peter et al., 2013). We thus hypothesized that BLOC-1 may influence vesicle
recycling through an endosome dependent pathway. Indeed, all BLOC-1 complex mutants
tested, including Pallidin, Dysbindin and Blos1 failed to maintain synaptic transmission under
high synaptic activity. This phenotype is not due to differences in synaptic vesicle pool as both
Pallidin and Dysbindin mutants have the same synaptic vesicle pool size as wild type. We then
looked into whether synaptic vesicle recycling is perturbed in BLOC-1 mutants. First, we
observed an activity dependent loss of 2XFYVE-GFP labelled synaptic early endosome
structures in Pallidin and Dysbindin mutants. And second, we observed the ultrastructure of
synaptic terminals with electron micrograph and found a dramatic accumulation of tubular
membrane structure in BLOC-1 mutants after high activity. These findings support our
hypothesis that Pallidin and likely the whole BLOC-1 complex mediates synaptic vesicle
recycling during high activity.
BLOC-1 mutants show normal basal synaptic transmission at lower synaptic activity and
defects were only revealed when the synapse is stressed by high activity. This implies that the
endosome independent synaptic vesicle recycling pathway is the dominant pathway under low
synaptic activity. While endosome-dependent vesicle recycling pathway only have a more
prominent role during high activity. The synapse’s increased dependency on endosome-
dependent vesicle recycling during high activity could be explained by that high activity wear out
more synaptic vesicles that need to go through endosomes for repair and rejuvenation, thus a
higher percentage of recycled synaptic vesicles go through endosomes for recycling during high
synaptic activity.

130

Postsynaptic translational control and homeostatic synaptic plasticity
The presynaptic and postsynaptic compartment of a synapse function in a highly cooperative
manner to ensure proper synaptic function. Communication between the two compartments are
thus critical for optimized synaptic transmission in both basal condition and under stress.
Translational control has been strongly implicated in the communications between synaptic
compartments. Several translational regulators and components of translational machinery were
shown to modulate synaptic transmission or be responsive the changes in synaptic transmission
(Henry et al., 2012; Penney et al., 2012; Penney et al., 2016; Sutton et al., 2007; Sutton et al.,
2004). At the Drosophila NMJ, activity of the positive translational regulator, Tor, has been
demonstrated to be required in the postsynaptic compartment for PHP signaling (Penney et al.,
2012). However, how does Tor activity get transformed into the retrograde homeostatic signal
remains unclear. One hypothesis suggests Tor selectively regulate specific target genes to
induce the PHP signal, while it is also possible that global elevated translation is what triggers
the PHP signal because Tor is a largely non-specific translation regulator that typically up-
regulates global translation when activated. To test whether specific target genes are under
translational control, we took a genome wide profiling approach to quantitatively compare
translation efficiency of all genes expressed in Drosophila larvae muscle between wild type
control and homeostatic conditions.
We developed a tissue specific ribosome profiling (Ingolia et al., 2009) approach with
simplified protocol and showed that this modified approach is more sensitive compared to a
previous approach that measure translation in a tissue specific manner, TRAP (translating
ribosome affinity purification) (Heiman et al., 2008). Using our approach, we observed robust
translational regulation under basal conditions and showed a surprising negative correlation
between transcription level and translation efficiency.
131

We then applied our approach to characterize genome wide translation and transcription.
We found no genes under translational regulation in homeostatic condition. Suggesting that
elevated global translation might be what is connecting perturbed postsynaptic receptors and
downstream PHP signaling. However, we could not rule out subtle changes in translation
efficiency that falls below the detection limit of our approach. Since we showed that our
approach is the most sensitive method for measuring translation in a tissue specific manner, we
are confident that no major changes in translation happened as a result of PHP signaling.
Alternatively, it is possible that changes in translation happened locally at the synapse.
Local translation has been implicated in a variety of neuronal functions (Colak et al., 2013; Cox
et al., 2008; Tushev et al., 2018; Wu et al., 2005). In this scenario, translation of specific mRNA
happens locally at the postsynaptic density while translation of other cellular mRNA are
unchanged. Because of the much higher abundance of mRNA outside of the local environment,
changes in locally translated mRNA will be masked in ribosome profiling results which measure
the whole cell. A modified version of ribosome profiling called proximity-specific ribosome
profiling (Jan et al., 2014) could be used to directly test possible roles of local translation in PHP
signaling.
In conclusion, we have developed a simplified tissue specific ribosome profiling
approach in Drosophila and demonstrated its superior sensitivity. We believe the resources and
protocols generated in this work will benefit broad research projects and encourage more
comprehensive investigation of translational regulation.

132

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Abstract (if available)

Abstract Efficient and orderly communications between cells in multicellular organisms are crucial for survival. The nervous system contains one of the most elaborate cell to cell communication network, much of these communications happen in specialized cellular structure called synapse. Electrical and biochemical signals are transmitted between cells through synapses, how are these signals transmitted faithfully and stably over time and in ever changing internal and external environments remains poorly understood. This study focuses on synaptic adaptations to stress that enable stable but also adaptive communication between neurons. First, stress induced by high synaptic activities was examined and a role of the BLOC-1 complex subunit, Pallidin, in facilitating synaptic vesicle recycling through endosomal structures was revealed. This molecular pathway maintains strength of synaptic transmission during high activity stress. Second, synapses adapt to postsynaptic receptor perturbation by increasing presynaptic release, a stress response called presynaptic homeostatic potentiation (PHP). And translational control in the postsynaptic compartment is implicated in PHP signaling. A tissue specific genome wide translational profiling approach was developed to profile PHP signaling and showed that translational control is unlikely to act on specific target genes to enable PHP signaling but rather, global elevation of translation maybe what is mediating the signal. Third, the role of GluClα, a glutamate gated chloride channel, in mediating a signaling pathway that senses and adapt to excess glutamate release that could potentially trigger cellular stress and damage was investigated. Together, these work add to our understanding of molecular mechanisms that guard synaptic transmission against stress and may help uncover therapeutic targets for diseases that arise from abnormal synaptic adaptation to stress.

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Asset Metadata

Creator Chen, Xun (author)

Core Title Translational regulation and endosomal trafficking in synaptic adaptation to stress

Contributor Electronically uploaded by the author (provenance)

School College of Letters, Arts and Sciences

Degree Doctor of Philosophy

Degree Program Neuroscience

Publication Date 07/30/2020

Defense Date 06/19/2018

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag BLOC-1 complex,Drosophila,neuromuscular junction,OAI-PMH Harvest,presynaptic homeostatic potentiation,ribosome profiling,RNA-seq,synaptic plasticity,synaptic vesicle recycling,translating ribosome affinity purification

Format application/pdf (imt)

Language English

Advisor Liman, Emily (committee chair), Chang, Karen (committee member), Dickman, Dion (committee member)

Creator Email xunchen@usc.edu,xunchen1988@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c89-41122

Unique identifier UC11670357

Identifier etd-ChenXun-6568.pdf (filename),usctheses-c89-41122 (legacy record id)

Legacy Identifier etd-ChenXun-6568.pdf

Dmrecord 41122

Document Type Dissertation

Format application/pdf (imt)

Rights Chen, Xun

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA