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Synaptic transmission, nutrient sensors, and aging in Drosophila melanogaster

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Synaptic transmission, nutrient sensors, and aging in Drosophila melanogaster

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Content SYNAPTIC TRANSMISSION, NUTRIENT SENSORS, AND AGING IN DROSOPHILA
MELANOGASTER
by
Megumi Mori
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
(BIOLOGY OF AGING)
May 2021
Copyright 2021 Megumi Mori

ii
Acknowledgements
My journey to completing this dissertation has been full of unknowns and discoveries.
First, I would like to thank Dr. Pejmun Haghighi for his continued support throughout my PhD.
My dissertation took unexpected twists and turns, and I appreciate your trust in me to take control of the
steering wheel. The freedom allowed me to learn so many valuable skills to become an independent
researcher.
I would like to thank Drs. Henri Jasper, Lisa Ellerby, Sean Curran, and Gordon Lithgow for being
on my dissertation committee. Thank you for your invaluable inputs along the way, and for your time and
support throughout my progress.
Many thanks to members of the Haghighi lab, past and present for your support. My sincere thanks
to all the students who have worked with me on my project: Neeku Amnat, Alexandra Bassett, Cameron
Du Bose, and Jeri Martensen. I hope you’ll share my joy in seeing our efforts come to fruition in this
dissertation.
The successes of the MiniPy project are thanks to the continued feedbacks and encouragements
from Jill Farnsworth. Your enthusiasm in the project has pushed it to the Beta-testing phase.
Thank you to all the friends who believed in me and supported me throughout this journey. You’ve
made this a fun journey, and I’m sure to remember the times fondly in the future.
This dissertation would not have been possible without all the mentors I had over the years. I’ve
learned so much from so many people. Special mentions go to Grant Kauwe and Lindsay Gray for
electrophysiology; Mario Calderon for RNA and protein work; Elie Maksoud for fly aging and RNA work;
Gary Scott for polysome profiling; Helen Cifuentes for cell culture; Tal Oron for Python.
I would also like to thank my partner in crime, Stephen Scheeler. You have been instrumental to
my development as a scientist and as an individual. Cheers to our shared adventures in the past, the present,
and the future, both in real life and in worlds filled with dungeons, dragons, etc.

iii
The criminal mind who inspired me to pursue a career in science was my mother, Sumiko. Thank
you for the endless hours of phone calls during the best of times and the worst of times.
I have greatly benefited from the relentless efforts by the GSS and the PDA to better the experience
of the trainees at the Buck. Thank you to everyone who has dedicated their time to bettering the PhD
experience at the Buck.
I would also like to thank the P.E.O. for the Scholar’s Award, and the beautiful people at the P.E.O.
Novato Chapter for their enthusiasm and support for my research. Thank you for pushing me to the finish
line.
This is already getting too long, but I would also like to take some space to acknowledge the
treasure-troves of knowledge known as StackOverflow and Biostars. Thank you to every user who had
already asked my questions years ago, and to those who answered them so informatively.
The wonderful tutorial for RNAseq analysis put together by the Griffith lab was instrumental in my
learning of bioinformatics. It was an exciting journey to take my FASTQ data and unearth the treasures
hidden in the data.
This dissertation was funded by:
USC Provost’s PhD Fellowship
USC Leonard Davis School of Gerontology Graduate Student Award
P.E.O. Scholar’s Award
Navigage Travel Award
DoubleX Foundation Award
The following are acknowledgements of authorship for data presented in this dissertation:
Chapter 3: Grant Kauwe performed the electrophysiology recordings. The data included in this
chapter has been published as part of (Kauwe et al., 2016).

iv
Chapter 4: Grant Kauwe performed the electrophysiology recordings. Jeri Martensen performed
gel electrophoresis for the RT-PCR experiment. The data included in this chapter is part of an article in
preparation for submission (Kauwe et al., 2021).
Chapter 5: The data included in this chapter is part of an article in preparation for submission
(Calderon et al., 2021).
Chapter 6: Suzana Benitez performed the blood brain barrier integrity assay. Mario Caldron
performed the adult D. melanogaster Western blots. Cameron Du Bose performed the adult D.
melanogaster qPCR for stRNAi efficiency confirmation. Myriam Moujahidine performed the adult D.
melanogaster qPCR for white, Atg8a, and antimicrobial peptide expression in wRNAi. Rishi Sharma
performed the targeted metabolomics assay on adult D. melanogaster lysates. Analysis of polyubiquitinsignal in adult D. melanogaster was performed with the help of Neeku Amnat, Alexandra Bassett, Cameron
Du Bose, and Jeri Martensen. The holidic media was adopted and optimized by Cameron DuBose.
Chapter 7: Grant Kauwe performed the electrophysiology recording at the adult D. melanogaster
NMJ.

v
Table of Contents
Acknowledgements.......................................................................................................................................ii
List of Tables.............................................................................................................................................viii
List of Figures..............................................................................................................................................ix
Abbreviations...............................................................................................................................................xi
Abstract....................................................................................................................................................xviii
Chapter 1. Introduction .................................................................................................................................1
1.1 Hallmarks of aging ....................................................................................................................1
1.2 Nutritional effects on aging .......................................................................................................3
1.3 Nutrient sensing pathways and aging ........................................................................................4
1.3.1 mTOR signaling.........................................................................................................6
1.3.2 Insulin/IGF-1 signaling..............................................................................................8
1.3.3 GCN2.........................................................................................................................9
1.4 Translational mechanisms and synaptic plasticity...................................................................11
1.4.1 Postsynaptic Compensation in Vertebrate Central Nervous System .......................12
1.4.2 Presynaptic Compensation in Vertebrate Central Nervous System.........................12
1.4.3 Presynaptic Homeostatic Plasticity at the Neuromuscular Junction........................13
1.4.4 Postsynaptic Translation Drives Presynaptic Release .............................................14
1.5 D. melanogaster model............................................................................................................15
1.6 Concluding remarks.................................................................................................................16
Chapter 2. Methods.....................................................................................................................................18
2.1 Reagent Recipes ......................................................................................................................18
2.2 Fly experiments .......................................................................................................................21
2.2.1 Adult aging ..............................................................................................................21
2.2.2 Glutamine supplementation .....................................................................................22
2.2.3 Acute nutrient restriction .........................................................................................22
2.2.4 Rapamycin treatment ...............................................................................................22
2.2.5 Holidic media treatment...........................................................................................22
2.2.6 Lifespan assay..........................................................................................................24
2.2.7 Climbing assay.........................................................................................................24
2.2.8 Eye color assay ........................................................................................................25
2.2.9 Fly foods..................................................................................................................25
2.3 Morphology .............................................................................................................................28
2.3.1 Immunohistochemistry ............................................................................................28
2.3.2 Blood brain barrier penetration assay ......................................................................29
2.3.3 Antibody List (Immunohistochemistry) ..................................................................29
2.3.4 Image analysis..........................................................................................................30
2.4 Biochemistry............................................................................................................................30
2.4.1 Western blot.............................................................................................................30
2.4.2 Antibody List (Western blot)...................................................................................32
2.4.3 Quantitative PCR.....................................................................................................32

vi
2.4.4 Polysome Profiling ..................................................................................................33
2.4.5 RT-PCR ...................................................................................................................34
2.4.6 Proteasome activity assay ........................................................................................34
2.4.7 Mass Spectrometry ..................................................................................................35
2.5 Electrophysiology....................................................................................................................36
2.6 Bioinformatics.........................................................................................................................37
2.6.1 5’-UTR screen..........................................................................................................37
2.6.2 RNAseq analysis of white deficient thoraces...........................................................38
2.7 MiniPy Development...............................................................................................................38
2.8 Statistical Analysis ..................................................................................................................39
Chapter 3. 4E-BP and Foxo impede synaptic homeostasis during acute starvation ...................................40
3.1 Abstract....................................................................................................................................40
3.2 Background..............................................................................................................................40
3.3.1 Acute fasting does not change synaptic morphology...............................................41
3.3.2 4E-BP expression is induced in muscles during acute starvation............................44
3.3.3 FOXO is required to induce 4E-BP expression during acute starvation..................45
3.4 Discussion................................................................................................................................47
3.5 Conclusion...............................................................................................................................49
Chapter 4. Maintenance of synaptic strength during amino acid starvation depends on translational
upregulation of still life (sif) through a GCN2/eIF2α dependent pathway.......................................50
4.1 Abstract....................................................................................................................................50
4.2 Background..............................................................................................................................50
4.3 Results .....................................................................................................................................52
4.3.1 eIF2α is phosphorylated during amino acid restriction ...........................................52
4.3.2 Bioinformatics analysis of 5’-UTR reveals candidate transcripts under
translational control by eIF2α phosphorylation .....................................................53
4.3.3 still life (sif) is translationally regulated in response to amino acid restriction........58
4.3.4 sif is required in the muscle to maintain baseline synaptic transmission during
amino acid restriction .............................................................................................59
4.4 Discussion................................................................................................................................60
4.5 Conclusion...............................................................................................................................62
Chapter 5. Nutritional glutamine protects synaptic strength against Gs2 deficiency .................................63
5.1 Abstract....................................................................................................................................63
5.2 Background..............................................................................................................................63
5.3 Results .....................................................................................................................................64
5.3.1 Lack of Delta in glial cells reduce synaptic strength ...............................................64
5.3.2 Knockdown of Gs2 in glia is sufficient to reduce synaptic strength........................66
5.3.3 Glutamine supplementation rescues synaptic strength in delta deficiency..............67
5.3.4 Lack of Delta in glial cells does not change readily releasable pool availability ....67
5.4 Discussion................................................................................................................................69
5.5 Conclusion...............................................................................................................................69
Chapter 6. Tryptophan transporter, synaptic transmission, proteostasis and aging ....................................71
6.1 Abstract....................................................................................................................................71
6.2 Background..............................................................................................................................71
6.3 Results .....................................................................................................................................72
6.3.1 Characterizing aging in D. melanogaster muscles...................................................72
6.3.2 Presynaptic marker, Brp, increases at the adult NMJ during aging.........................76

vii
6.3.3 Reduction in pre- and post-synaptic components reduce polyubiquitin
aggregation with age ..............................................................................................76
6.3.4 Synaptic mutants show increased lifespan and healthspan......................................79
6.3.5 Calcium sensing as a candidate regulator of muscle aging......................................79
6.3.6 CaMKII is required to maintain muscle proteostasis during aging..........................83
6.3.7 white is required in the muscle to maintain muscle proteostasis during aging ........85
6.3.8 Transgenic knockdown of white in the muscle is sufficient to reduce lifespan.......89
6.3.9 White functions with its partner st to modulate proteostasis...................................90
6.3.10 white knockdown in the muscle marginally decreases tryptophan and its
metabolites..............................................................................................................91
6.3.11 Lack of white during the larval stage accelerates protein aggregation in the
adult muscle............................................................................................................93
6.3.12 Lack of white drives mTOR activity in larval muscle ...........................................95
6.3.13 Lack of white suppresses autophagy......................................................................99
6.3.14 RNAseq reveals increased AMP expression in w deficient flies. ........................103
6.4 Discussion..............................................................................................................................106
6.4.1 Deficiency of white in the muscle increases protein aggregate accumulation
and reduces lifespan .............................................................................................106
6.4.2 mTOR signaling during larval development influence proteostasis in adult.........106
6.4.3 Autophagy, but not proteasome function, is reduced in w deficiency ...................107
6.4.4 white deficiency leads to BBB disruption during aging ........................................108
6.5 Conclusion.............................................................................................................................109
Chapter 7. Open-Source Software to Analyze Electrophysiological Recordings of Synaptic Activity....110
7.1 Abstract..................................................................................................................................110
7.2 Background............................................................................................................................110
7.3 Results ...................................................................................................................................111
7.3.1 Algorithm development and design.......................................................................111
7.3.2 Methods for using MiniPy .....................................................................................126
7.3.3 Analysis of adult mEJC recordings reveals changes in channel kinetics...............138
7.4 Discussion..............................................................................................................................143
7.5 Conclusion.............................................................................................................................144
Chapter 8. Discussion ...............................................................................................................................145
8.1 Setpoint of neurotransmitter release, homeostasis, and dietary restriction............................145
8.2 Protein restriction and the maintenance of the setpoint of neurotransmitter release .............147
8.3 Clash between release and energy: a losing battle or harmonious dance? ............................148
8.4 Age-dependent proteostasis decline in the muscle: a new role for a classic gene.................150
8.5 Fast and easy-to-use open-source software for electrophysiologists.....................................152
8.6 Summary................................................................................................................................153
References.................................................................................................................................................154
Appendix...................................................................................................................................................188
Appendix A..................................................................................................................................188

viii
List of Tables
Table 2.1 Fly lines.......................................................................................................................................23
Table 2.2 Holidic Media ingredients...........................................................................................................27
Table 2.3 Primers........................................................................................................................................33
Table 4.1 Top 50 synaptic transcripts ranked by ΔG (lowest to highest)...................................................56
Table 6.1 Genotypes and sources of white transcripts................................................................................83
Table 7.1 Comparison of mEJCs detected from larval D. melanogaster NMJ recordings.......................138
Table 7.2 Comparison of mEJCs detected from larval D. melanogaster NMJ recordings.......................140
Supplemental Table 1. Genes significantly regulated by the knockdown of white in the muscle ............188

ix
List of Figures
Figure 1.1 Major Nutrient sensing pathways that regulate protein translation in D. melanogaster .............5
Figure 1.2 Example of postsynaptic homeostatic plasticity (PHP) in GluRIIA-/- mutants..........................13
Figure 3.1 Starvation represses PHP in GluRIIA-/- mutants......................................................................41
Figure 3.2 Acute starvation does not change NMJ growth.........................................................................43
Figure 3.3 Induction of 4E-BP during acute starvation is required to suppress PHP .................................45
Figure 3.4 Acute starvation induces 4E-BP expression in the muscle and suppresses PHP in a Foxo
dependent manner.....................................................................................................................46
Figure 3.5 Model: Acute starvation inhibits PHP by Foxo-dependent expression of 4E-BP .....................48
Figure 4.1 GCN2 is required to maintain synaptic transmission during acute amino acid restriction........51
Figure 4.2 Phosphorylation of eIF2α increases in the larval muscle on amino acid restriction diet ..........52
Figure 4.3 uORF classifications in uORFlight............................................................................................54
Figure 4.4 Free energy, but not the number of Type 2 uORF, correlates with 5’-UTR length...................55
Figure 4.5 sif transcript exhibits highly complex 5’-UTR secondary structure ..........................................57
Figure 4.6 Sif translation is increased during amino acid restriction..........................................................59
Figure 4.7 sif is required in the muscle to maintain synaptic strength during acute amino acid
restriction..................................................................................................................................60
Figure 4.8 Model figure: GCN2/eIF2α maintains baseline synaptic strength during amino acid
restriction through translational upregulation of Sif.................................................................61
Figure 5.1 Cross-sectional model of third instar larval peripheral nerve in D. melanogaster.....................63
Figure 5.2 Lack of Delta in glial cells reduce synaptic strength.................................................................65
Figure 5.3 SPG knockdown of Gs2 is sufficient to reduce synaptic strength.............................................66
Figure 5.4 Dietary L-glutamine rescues synaptic strength defect in Delta knockdown .............................68
Figure 6.1 Amount of polyubiquitinated aggregation during aging differs between muscle groups..........73
Figure 6.2 Characterization of polyubiquitin aggregation with age ...........................................................74
Figure 6.3 Expression of Brp increases at the DLM NMJ with age ...........................................................75
Figure 6.4 Reduction in pre- and post-synaptic proteins reduce polyubiquitinated protein aggregates.....78
Figure 6.5 Disruption of pre- and post-synaptic proteins extends lifespan and healthspan in fruit flies....80
Figure 6.6 Manipulation of CaMKII show conflicting effects on muscle proteostasis ..............................81
Figure 6.7 Transgenic disruption of CamKII increases polyubiquitinated protein aggregate
accumulation when compared against transgenic control lines................................................84
Figure 6.8 white expression in the muscle is required to maintain proteostasis during aging ....................86
Figure 6.9 white is required in the muscle to maintain muscle proteostasis during aging..........................88
Figure 6.10 Muscle-specific knockdown of white shortens lifespan but does not affect healthspan..........89
Figure 6.11 ABC transporter partner st is also required in the muscle to maintain proteostasis. ...............90
Figure 6.12 Lack of white marginally decreases tryptophan and tryptophan metabolite concentration
in the adult muscle....................................................................................................................92
Figure 6.13 white is required in the muscle during larval development to maintain muscle
proteostasis during aging. .........................................................................................................94
Figure 6.14 Larval white deficiency activates mTOR and disrupt proteostasis later in life .......................97
Figure 6.15 Activation of TOR during larval development disrupts muscle aging ....................................98
Figure 6.16 Autophagy is reduced in white deficient muscles..................................................................100
Figure 6.17 Ref(2)P colocalizes with the polyubiquitinated protein aggregates ......................................101
Figure 6.18 Chymotrypsin-like proteolytic activity of 20S and 26S proteosomes are not reduced by
white knockdown....................................................................................................................102
Figure 6.19 RNAseq of white deficient thoraces show increase in AMP expression...............................105
Figure 7.1 Challenges to automated analysis of electrophysiology recordings........................................113
Figure 7.2 Brakel’s method for signal peak detection can isolate synaptic events...................................116
Figure 7.3 Finding local extrema detects synaptic event peaks but also selects noise .............................118

x
Figure 7.4 Average curve intersects with raw trace to estimate start of a synaptic event.........................119
Figure 7.5 Thresholding the amplitude of the synaptic events successfully rejects noise and identifies
synaptic events........................................................................................................................122
Figure 7.6 Estimating the decay time constant .........................................................................................125
Figure 7.7 MiniPy GUI.............................................................................................................................126
Figure 7.8 Navigating electrophysiological recording data ......................................................................128
Figure 7.9 Automated detection and analysis of synaptic events .............................................................130
Figure 7.10 Sorting mEJC quantification data in MiniPy.........................................................................131
Figure 7.11 Saving analyzed data in MiniPy ............................................................................................133
Figure 7.12 Using MiniPy to select representative traces for evoked synaptic release ............................135
Figure 7.13 Changing trace style in MiniPy .............................................................................................137
Figure 7.14 Comparison of larval Drosophila NMJ mEJC analysis ........................................................139
Figure 7.15 Comparison of adult Drosophila NMJ mEJC analysis..........................................................141
Figure 7.16 mEJC kinetics are shorter in adult NMJ compared to larval NMJ........................................142

xi
Abbreviations
4E-BP 4E-binding proteins
5'-UTR 5’ untranslated region
°C Degrees Celsius
β-gal Beta galactosidase
µL Microliter
µm Micrometer
µM Micromolar
µs Microsecond
AA Amino acid
ABC ATP-Binding Cassette
ABF Axon Binary File
ACN Acetonitrile
AD Alzheimer’s disease
AMC 7-amino-4-methylcoumarin
AMP Antimicrobial peptides
AMPA α-Amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid
ANOVA Analysis of variance
ATF4 Activating transcription factor 4
ATP Adenosine triphosphate
BBB Blood brain barrier
BDGP Berkeley Drosophila Genome Project
BDNF Brain-derived neurotrophic factor
BDSC Bloomington Drosophila Stock Center
bp Base pair

xii
Brp Bruchpilot
BSA Bovine serum albumin
CaMKII Ca2+/calmodulin-dependent protein kinase II
cDNA Complementary DNA
CR Caloric restriction
CRAN Comprehensive R Archive Network
CSV Comma separated values
DEPC Diethyl pyrocarbonate
D. melanogaster Drosophila melanogaster
Dlg Discs-large
DLM Dorsolateral muscle
DNA Deoxyribonucleic acid
DR Dietary restriction
Drs Drosomycin
DTT Dithiothreitol
DVM Dorsoventral muscle
EDTA Ethylenediaminetetraacetic acid
eIF2 Eukaryotic initiation factor 2
eIF2α Alpha subunit of eif2
eIF2B Eukaryotic initiation factor 2B
eIF4A Eukaryotic initiation factor 4A
eIF4E Eukaryotic translation initiation factor 4E
eiF4F Eukaryotic translation initiation factor 4F
EJC Excitatory junctional current
EPSC Excitatory postsynaptic current

xiii
FIJI FIJI is just ImageJ
FOXO Forkhead box protein O
g Gram
GABA γ-aminobutyric acid
Gal80ts Temperature sensitive gal80
GDP Guanosine diphosphate
GEF Guanine nucleotide exchange factor
GS Glutamine synthetase
Gs2 Glutamine synthetase 2
GTP Guanosine triphosphate
GUI Graphical user interface
HD Huntington’s disease
HEX Hexadecimal
HL3 Hemolymph-like saline 3
hr Hour
Hrp Horseradish peroxidase
Htt Huntingtin
Hz Hertz
IFM Indirect flight muscle
IGF-1 Insulin-like growth factor 1
IgG Immunoglobulin G
IgM Immunoglobulin M
IM14 Immune inducible molecule 14
IM4 Immune inducible molecule 4
IRS Integrated stress response

xiv
kDa Kilodalton
kHz Kilohertz
L Liter
LC-MS Liquid chromatography-mass spectrometry
LFC Log2 fold change
LRRK2 Leucine rich repeat kinase 2
LTP Long term potentiation
LTD Long term depression
M Molar
m/z Mass-to-charge ratio
mEJC Miniature excitatory junctional current
mEPSC Miniature excitatory synaptic current
mg Milligram
Mimic Minos-mediated integration cassette
mL Milliliter
mm Millimeter
mM Millimolar
mORF Main open reading frame
mRNA Messenger RNA
ms Millisecond
mTOR Mechanistic target of rapamycin
mTORC1 mTOR complex 1
mTORC2 mTOR complex 2
mV Millivolt
N Nolar

xv
nA Nano ampere
NAD+ Nicotinamide adenine dinucleotide
ng Nanogram
NGS Normal goat serum
NIH National Institute of Health
nL Nanoliter
NL Neural lamina
NMJ Neuromuscular junction
n.s. Not significant
NS Not significant
ORF Open reading frame
PBS Phosphate buffer saline
PBT PBS + triton-x
PBTN PBT + normal goat serum
PCA Principal component analysis
PCR Polymerase chain reaction
PD Parkinson’s disease
PFA Paraformaldehyde
PG Perineurial glia
pH Potential hydrogen
PHP Presynaptic homeostatic plasticity
PolyUb Polyubiquitin
psig Pounds per square inch gauge
PVDF Polyvinylidene difluoride
QC Quantal content

xvi
qPCR Quantitative polymerase chain reaction
QToF Quadruple time of flight
RIPA Radio-immunoprecipitation assay
rlog Regularized logarithm
RNA Ribonucleic acid
RNAi RNA interference
RNAseq RNA sequencing
ROS Reactive oxygen species
RRP Readily releasable pool
RT Room temperature
RT-PCR Reverse transcription polymerase chain reaction
s Second
SASP Senescence-associated secretory phenotype
S6K p70 S6 Kinase
S6K1 S6K p70 S6 kinase 1
SDS Sodium dodecyl sulfate
SEM Standard Error of the Mean
sif Still life
SPG Subperineurial glia
st scarlet
Suc-LLVY-AMC N-Succinyl-Leucine-Leucine-Valine-Tryptamine-7-animo-4-
methylcoumarin
TBS Tris-buffered saline
TBST TBS + tween-20
TDT Tergal depressor of trochanter

xvii
TEMED Tetramethylethylenediamine
TEVC Two-electrode voltage-clamp
TOR Target of Rapamycin
TRiP Transgenic RNAi Project
tRNA Transfer RNA
TSC Tuberculosis sclerosis complex
TTX Tetrodotoxin
UAS Upstream activation sequence
ULK1 Unc-51-like autophagy-activating kinase 1
uORF Upstream open reading frames
UV Ultraviolet
UVRAG UV radiation resistance-associated gene product
V Volt
W Tryptophan
WG Wrapping glia
w/v Weight per volume
w white

xviii
Abstract
The relationship between the nervous system and aging has, for the most part, focused on the role of aging
in driving neurodegeneration and cognitive decline. However, accumulating experimental data suggest that
changes in synaptic function and neural circuitry occur early in neurodegenerative diseases, potentially in
turn driving neurotoxicity and disease progression. We have therefore postulated that aberrant excitatory
synaptic transmission functions as a mediator of aging.
In my dissertation I have approached this idea from two perspectives: understanding the role of
synaptic activity in mediating the benefits of dietary restriction; and to examine the role of synaptic activity
in muscle aging and proteostasis maintenance. I have found that synaptic activity sits at a tight balance
dictated by various nutrient sensors that respond to dietary restriction. This work will describe the role of
nutrient sensors in the muscle, as well as nutrient maintenance by the glia to regulate synaptic strength.
Furthermore, I describe a novel role for a common gene in the fruit fly in regulating muscle proteostasis.
Finally, the development and usage of a new toolset for analyzing electrophysiological data is described in
detail.

1
Chapter 1. Introduction
1.1 Hallmarks of aging
Contrary to the classical belief that aging is a process of inevitable deterioration, evidence indicates
that aging is a multifaceted biological process that influences the onset and progress of age-related diseases.
Understanding the biology of aging is thus key to tackling a multitude of age-dependent diseases.
In order to unravel the intricate molecular underpinning of aging, it is critical to understand the
phenotypes and underlying drivers of aging. To this regard, several hallmarks of aging have been proposed
to characterize the phenotypes associated with the aging process (López-Otín et al., 2013). The hallmarks
proposed by López-Otín et al. were chosen based on three criteria: (1) the hallmarks are present during
normal aging, (2) manipulations that aggravate the hallmarks accelerate the aging process, and (3)
manipulations that ameliorate the hallmarks slow the aging process. Nine hallmarks were proposed in total:
genomic instability, telomere attrition, epigenetic alterations, loss of proteostasis, deregulated nutrient
sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, and altered intercellular
communications (López-Otín et al., 2013).
A slightly modified set of hallmarks was suggested for the brain due to the non-proliferative nature
of neurons and its unique synaptic structures. Telomere attrition and cellular senescence, two hallmarks of
aging in proliferating cells, have yet to be demonstrated as hallmarks of aging in the brain (Mattson &
Arumugam, 2018). These hallmarks may be present in glial cells, the proliferative components of the
nervous system. More importantly, abnormalities in neuronal network activity and maintenance of calcium
homeostasis have been proposed as unique hallmarks for brain aging (Mattson & Arumugam, 2018). Brain
aging is associated with changes in connectivity and activation patterns within and between brain regions.
For example, a reduction in inhibitory γ-aminobutyric acid (GABA) signaling has been reported in the
thalamus and the hippocampus. As GABAergic neurons provide inhibitory modulation on network activity,
reduction in GABAergic inputs results in aberrant excitatory/inhibitory ratio, and disrupts auditory
processing and memory formation (McQuail et al., 2015; Tang et al., 2014).

2
Several studies indicate that aberrant excitatory synaptic transmission is a common phenotype
found in various age-dependent neurodegenerative diseases. For example, Alzheimer’s disease (AD)
patients are expected to have 2- to 6-fold increase in risk of experiencing seizures, indicating a disruption
in the excitatory/inhibitory ratio (Nicastro et al., 2016). Rodent models of AD also exhibit network
hyperactivity and epileptiform activity (Palop et al., 2007; Verret et al., 2012). Other familial alleles for
age-dependent neurodegenerative diseases, such as mutant Huntingtin (Htt) allele in Huntington’s Disease
(HD) and leucine rich repeat kinase 2 (LRRK2) alleles in Parkinson’s Disease (PD), induce increased
presynaptic neurotransmitter release when examined in model synapses in transgenic animals (Penney et
al., 2016; Romero et al., 2008). These findings indicate that abnormal excitatory synaptic transmission is
commonly associated with neurodegenerative diseases.
Some findings also support the notion that aberrant excitatory synaptic transmission is a driver of
neurodegenerative disease progression. For example, application of anti-epilepsy drug levetiracetam has
shown promise to ameliorate cognitive impairment in rodent models of AD (Sanchez et al., 2012). Similarly,
genetic mutations that reduce synaptic transmission suppress retinal degeneration in fly model of HD
(Romero et al., 2008). At the cellular level, primary hippocampal and neocortical neurons from AD model
mice exhibit increased DNA damage in response to excitatory stimulation (Suberbielle et al., 2013),
indicating that neurons may become sensitized to damage caused by excitatory synaptic transmission. Thus,
these findings support the idea that aberrantly high excitatory synaptic transmission is a modulator of aging
and disease progression.
We commonly think of nervous system dysfunction as a consequence of aging. However, more and
more experimental data suggest that disruption in neural circuit function as well as abnormalities in synaptic
activity could occur prior to the onset of any detectible phenotypes associated with disease. The overarching
theme that I have pursued during my PhD has been to understand how nervous system dysfunction and
specifically synaptic dysfunction contribute to the process of aging. We argue that synaptic dysfunction,
albeit difficult to quantify at times, should be considered as a mediator of aging.

3
Here I describe my work aimed at (1) understanding the role of synaptic activity in mediating the
beneficial effects of dietary restriction, and (2) examining the role of synaptic activity in the maintenance
of muscle proteostasis during aging. During the course of my investigation, I have contributed to the
discovery of novel mechanisms for synaptic transmission regulation and discovered a novel role for a
common gene in Drosophila for the regulation of muscle proteostasis. In addition, I have developed a userfriendly software for efficient analysis of electrophysiological data for distribution as an open-source
software.
1.2 Nutritional effects on aging
Diet is one of the major external factors that influences the aging process. The so-called Western
style diet, characterized by a nutrient-poor and energy-dense food profile, has been linked to increased
chronic diseases, including obesity, diabetes, cardiovascular diseases (Mozaffarian, 2016), cancer (Kerr et
al., 2017), and neurodegenerative diseases (Martins et al., 2006; Puglielli et al., 2003). The impact of diet
on aging is particularly underscored by the fact that obesity, which is thought to be largely due to overly
high caloric intake (Aronne et al., 2009), itself is a common risk factor for many of the age-dependent
diseases, such as AD and cancer (Alford et al., 2018; Avgerinos et al., 2019; Haslam & James, 2005). In
the United States alone, the prevalence of obesity has been on a steady rise, reaching just under 20% of the
total population in 2018 (Fryar et al., 2020), suggesting that the risk for age-related diseases is likely to be
increasing with the obesity epidemic.
While unbalanced and caloric rich diet has been linked to many age-dependent diseases, dietary
restriction (DR) has been associated with multiple health benefits and improving healthspan (Golbidi et al.,
2017). This is particularly highlighted by accumulating experimental evidence that DR can promote
longevity in a number of model organisms from yeast to primates (Fontana & Klein, 2007; C. J. Kenyon,
2010; Mattison et al., 2017). Data from animal models indicate that DR has diverse and widespread benefits
in multiple tissues that contribute to the extension of lifespan. Some examples include the following: DR
promotes protein clearance and folding, both of which are critical for maintaining proteostasis (Levine &
Klionsky, 2004; Yang et al., 2016); DR improves DNA repair mechanisms that decline with age and

4
contribute to DNA instability (Cabelof et al., 2003; Guo et al., 1998; Lee et al., 2011); and senescent cells
are reduced in response to DR (Fontana et al., 2018; C. Wang et al., 2010). These findings indicate that DR
activates pathways that are critical to combatting the hallmarks of aging and promote longevity.
Although data from human subjects are still limited, benefits of DR on human aging has been
observed (Brandhorst & Longo, 2016; Van Cauwenberghe et al., 2016). The diet commonly used by the
people of Okinawa, the southern part of Japan, has served as epidemiological evidence of the benefits of
DR. The original reports of the Okinawan diet indicated that the Okinawan adults consumed reduced caloric
intake while recording fewer age-dependent disease cases and more centenarians compared to the rest of
the Japanese population (Chan et al., 1997; Kagawa, 1978). More recently, a 2-year randomized controlled
trial confirmed that caloric restriction (CR), a subset of DR regimens, has no adverse effects on non-obese
individuals and demonstrated benefits such as reduced inflammation and reduced cardiometabolic risk
factors (Ravussin et al., 2015). Some clinical data also exist in the context of age-dependent diseases. For
example, CR improves prognosis after cancer treatment (Brandhorst & Longo, 2016), and CR was shown
to improve cognitive function in seniors with mild-cognitive impairment (Horie et al., 2016; Van
Cauwenberghe et al., 2016). These data confirm that CR is effective in combating and preventing agedependent diseases.
1.3 Nutrient sensing pathways and aging
The wide-ranging benefits of DR in humans as well as in model organisms indicate that reduced
nutritional intake without starvation triggers a series of cellular responses opposing the aging process.
Therefore, it should be of no surprise that nutrient sensing pathways act as major modulators of aging.
Indeed, several nutrient sensing pathways have been found to be responsible for modulating lifespan in
response to DR. Genetic manipulation of these pathways has led to enhanced lifespan and produced health
benefits in various model organisms, further highlighting the benefits of DR (C. J. Kenyon, 2010). These
pathways can work together or independently to slow the aging process. Interestingly, many of these
pathways converge on proteostasis by regulating protein translation.

5
The balance between protein translation and clearance is critical for maintaining proteostasis and
therefore slowing the aging process. Several lines of evidence indicate that inhibition of protein translation
and activation of protein clearance pathways are required for benefits of DR (Jia & Levine, 2007; Zid et al.,
2009), indicating that maintenance of proteostasis is a major mechanism by which DR confers benefits to
lifespan. Furthermore, genetic manipulations that decrease protein translation (Hansen et al., 2007),
increase protein clearance (Lapierre et al., 2013; Simonsen et al., 2008), or promote proper protein folding
(Demontis & Perrimon, 2010; Schumpert et al., 2014; Vos et al., 2016) all extend lifespan in multiple
organisms.
Several major nutrient sensing pathways associated with aging and their role in regulating
proteostasis are discussed below (Figure 1.1).
Figure 1.1 Major Nutrient sensing pathways that regulate protein translation in D. melanogaster
DR inhibits TOR activity, resulting in disinhibition of 4E-BP. 4E-BP acts as a break to cap-dependent protein translation by
sequestering eIF4E, a critical component of the cap-binding complex. Inhibition of TOR also reduces the positive effect of
S6K on protein translation
DR also reduces insulin/IGF-1 signaling, which disinhibits FOXO transcription factor. FOXO enhances the transcription of
4E-BP, thereby suppressing cap-dependent translation.
GCN2 responds to deficiency in amino acids by phosphorylating its target, eIF2α. This inhibits global protein translation by
limiting the availability of the ternary complex.

6
1.3.1 mTOR signaling
A major cellular nutrient sensor that has the task of integrating nutritional signals to cellular growth
and differentiation is the mechanistic target of rapamycin (mTOR), a serine/threonine protein kinase that
has been identified as a major regulator of aging (C. J. Kenyon, 2010; G. Y. Liu & Sabatini, 2020). mTOR
exists in two protein complexes within the cell: mTOR complex 1 (mTORC1) and mTOR complex 2
(mTORC2). The two complexes have distinct cellular functions: mTORC1 promotes anabolic pathways
and inhibits breakdown of cellular components in response to protein abundance, while mTORC2 regulates
cytoskeletal dynamics and inhibits apoptosis (G. Y. Liu & Sabatini, 2020). DR is thought to extend lifespan
mainly through inhibition of mTORC1 pathway (Kaeberlein et al., 2005; Kapahi et al., 2004; G. Y. Liu &
Sabatini, 2020).
The importance of mTOR in modulating aging is demonstrated by its robust effect on lifespan in
various model organisms. Genetic and pharmacological inhibition of mTOR significantly and consistently
increases lifespan in various model organisms, including yeast (Kaeberlein et al., 2005; Powers et al., 2006),
C. elegans (Robida-Stubbs et al., 2012; Vellai et al., 2003), fruit flies (Bjedov et al., 2010; Kapahi et al.,
2004), and mice (Harrison et al., 2009; J. J. Wu et al., 2013). These observations have fueled the search for
rapalogs, analogs of the mTOR inhibitor rapamycin, for anti-aging therapy (Lamming et al., 2013).
mTOR is also implicated in many diseases of aging, most notably in cancer. Biopsy samples from
various cancer indicate a high occurrence of mTOR hyperactivity (G. Y. Liu & Sabatini, 2020; Menon &
Manning, 2008). Several mTOR inhibitors are thus under development as chemotherapeutic agents, some
of which have been approved for treatment (Hua et al., 2019). mTORC1 hyperactivation is also at the center
of obesity and type 2 diabetes pathophysiology. Hyperactivation of mTORC1 decouples insulin receptor
with the downstream effectors, resulting in insulin resistance (Shah et al., 2004; Y. Yu et al., 2011). Lack
of the mTORC1 downstream effector p70 S6 kinase 1 (S6K1) is protective against diet-induced obesity in
mice (Um et al., 2004). Furthermore, metformin, the first-line medication for type 2 diabetes, suppresses
mTORC1 activity (Howell et al., 2017). These observations suggest that mTOR is at the core of multiple
age-dependent diseases.

7
mTORC1 is well known for its role in promoting protein synthesis. It achieves this effect by
phosphorylation of its substrates, eukaryotic initiation factor 4E-binding proteins (4E-BPs) and S6K1
(Burnett et al., 1998). Unphosphorylated 4E-BP inhibits protein translation initiation by sequestering its
target eukaryotic translation initiation factor 4E (eIF4E), while phosphorylation of 4E-BP removes the
inhibitory pressure and promotes protein translation (Haghighat et al., 1995). eIF4E is part of the eukaryotic
translation initiation factor 4F (eIF4F) complex that recruits the ribosome to the 5’ end of mRNA at the cap
structure (Sonenberg & Hinnebusch, 2009). eIF4E exists in the cell in limiting quantities (Hiremath et al.,
1985); thus, the binding of 4E-BP to eIF4E reduces the availability of the eIF4F complex, thereby inhibiting
cap-dependent protein translation initiation (Haghighat et al., 1995). Phosphorylation of 4E-BP by
mTORC1 prevents its binding to eIF4E, thereby releasing the inhibition on cap-dependent protein
translation (Pause et al., 1994).
The mechanism by which phosphorylation of S6K1 by mTORC1 promotes protein translation is
less clear; nevertheless, phosphorylation of S6K1 at T389 promotes its kinase activity and is used as a
readout of mTORC1 and S6K1 activity (Pullen et al., 1998). It has been suggested that S6K1 promotes
ribogenesis by phosphorylating its target, ribosomal protein S6 (Burnett et al., 1998; Chauvin et al., 2014).
S6K1 also enhances cap-dependent protein translation initiation by activating the eukaryotic initiation
factor 2B (eIF2B) (Holz et al., 2005) and promoting the activity of the eukaryotic initiation factor 4A
(eIF4A) (Dorrello et al., 2006). mTORC1 thus promotes protein translation by two separate pathways.
In addition to the regulation of cap-dependent translation, mTORC1 also regulates protein
clearance through autophagy at both the early and late phases. mTORC1 inhibits initiation of autophagy by
phosphorylating unc-51-like autophagy-activating kinase 1 (ULK1) and Atg13, both members of the ULK1
complex (Hosokawa et al., 2009; J. Kim et al., 2011). ULK1 complex is required to initiate the formation
of autophagosomes, which are cytoplasmic vesicles that sequester cytosolic proteins and organelles for
eventual degradation (Kaur & Debnath, 2015; Lamb et al., 2013). mTORC1 also inhibits autophagy by
phosphorylating and inhibiting the function of UV radiation resistance-associated gene product (UVRAG)
(Y.-M. Kim et al., 2015). UVRAG promotes the degradation of autophagosome cargo by stimulating

8
lysosomal fusion with the autophagosome (Liang et al., 2008). Through these mechanisms mTORC1 acts
as a brake to the early and late steps of autophagy.
mTOR is thus a nutrient sensor that is activated during resource abundance. mTORC1 promotes
cellular growth by limiting resource recycling and promoting protein synthesis. The benefits of mTORC1
inhibition can be found in normal aging as well as diseases of aging, indicating that mechanisms that
promote growth are detrimental during the aging process.
1.3.2 Insulin/IGF-1 signaling
The first gene and nutrient sensor found to regulate aging was daf-2, the C. elegans gene belonging
to the family of insulin and insulin-like growth factor (IGF-1) receptors (C. Kenyon et al., 1993). Since
then, mutations in the insulin/IGF-1 signaling pathway has been confirmed to extend lifespan in fruit flies
(Grönke et al., 2010; Tatar et al., 2001) and mice (Bartke, 2008; Junnila et al., 2013; Kappeler et al., 2008).
DR inhibits insulin/IGF-1 signaling, and certain mutations in the insulin/IGF-1 pathway seem to mimic the
conditions of DR, indicating that the benefits conferred by the two pathways overlap (C. J. Kenyon, 2010).
Variants of the insulin receptor gene have been identified among the centenarians in a Japanese cohort and
an Ashkenazi Jewish cohort, indicating that this pathway is relevant to human longevity (Kojima et al.,
2004; Suh et al., 2008).
Inhibition of the insulin/IGF-1 pathway leads to the disinhibition of the Forkhead box protein O
(FOXO) transcription factor. FOXO is required for the longevity effects of insulin/IGF-1 receptor mutants
(Tullet et al., 2008). Furthermore, overexpression of FOXO is sufficient to increase lifespan in fruit flies
(Demontis & Perrimon, 2010; Giannakou et al., 2004; Hwangbo et al., 2004). FOXO seems to promote
longevity by inducing the transcription of various stress-response genes that promote cellular repair,
including protein folding, protein clearance, and removal of reactive oxygen species (ROS) (Murphy et al.,
2003). The relevance of FOXO to human longevity is supported by the fact that a variant of the human
FOXO3A appears consistently in candidate gene association studies of long-lived individuals (Flachsbart
et al., 2009; Pawlikowska et al., 2009; Soerensen et al., 2010; Willcox et al., 2008).

9
The role of FOXO in regulating protein translation has come to light in the recent years. In D.
melanogaster, FOXO was shown to activate 4E-BP transcription (Demontis & Perrimon, 2010). This
provides a similar, yet distinct regulation of protein translation initiation to mTORC1. Overexpression of
FOXO or 4E-BP in fly muscles decreases the accumulation of protein aggregates and extends lifespan,
underscoring the role of FOXO in longevity (Demontis & Perrimon, 2010).
Reduced insulin/IGF-1 signaling also encourages proteostasis by promoting protein clearance. For
example, in C. elegans, DR requires the induction of autophagy-associated genes bec-1 and vps-34 to
achieve lifespan extension (Hansen et al., 2008; Meléndez et al., 2003). The induction of autophagy seems
to be independent of daf-16/FOXO (Hansen et al., 2008). Instead, transcription factor pha-4/FOXA is
required for induction of autophagy during DR (Hansen et al., 2008).
Insulin/IGF-1 pathway therefore responds to nutrient restriction to promote protein clearance and
inhibit protein translation.
1.3.3 GCN2
In addition to regulating translation at the level of cap-binding complex via mTOR, cells can
regulate translation by controlling the availability of the ternary complex, which is formed by association
of the eukaryotic initiation factor 2 (eIF2) in complex with the methionine tRNA and GTP. The main
function of the ternary complex is to deliver the tRNA to the 40S ribosomal subunit (Kimball, 1999). The
alpha subunit of eIF2 (eIF2α) is a phosphorylation target of four kinases, PERK, GCN2, PKR and HRI,
which respond to accumulation of unfolded proteins, nutrient deprivation, viral infection, and oxidative
stress, respectively (Taniuchi et al., 2016). Phosphorylation of eIF2α under these stressful conditions and
the subsequent cellular changes are collectively known as the integrated stress response (IRS) (CostaMattioli & Walter, 2020). Of the four kinases mentioned above, GCN2 is the main nutrient sensor, which
is thought to be activated by uncharged tRNAs during amino acid deficiency (Zhu & Wek, 1998).
Phosphorylation of eIF2α inhibits protein translation initiation by reducing the availability of the
ternary complex. During protein translation initiation, the GTP on eIF2 is hydrolyzed to GDP, and eIF2 is

10
released from the mRNA. Phosphorylation of eIF2 at the alpha subunit prevents its GDP from being
exchanged to GTP, inhibiting the incorporation into a new ternary complex (Kimball, 1999). Consequently,
the lack of ternary complex reduces global translation.
Counterintuitively, certain transcripts from stress response genes experience increased protein
translation during conditions of global translation repression thanks to their unique 5’ untranslated region
(5’-UTR) sequence. The best studied of such stress response genes is activating transcription factor 4
(ATF4) (Vattem & Wek, 2004). During normal conditions, the upstream open reading frames (uORFs) of
ATF4 repress its translation; however, during conditions of reduced ternary complex availability, ribosomes
have a higher likelihood of successfully finding the main reading frame of ATF4, thereby successfully
translating the protein product (Vattem & Wek, 2004). ATF4 promotes transcription of various stress
response genes to carry out the IRS, including several chaperones (Han et al., 2013). Amino acid restriction
thus facilitates proteostasis by dampening new protein translation and inducing stress response genes.
Recently, GCN2 has gained attention as a regulator of aging (Falcón et al., 2019). GCN2 is required
for lifespan extension of DR or amino acid restriction in yeast (Z. Wu et al., 2013), C. elegans (Rousakis
et al., 2013), and fruit flies (M.-J. Kang et al., 2016). In fact, lack of GCN2 causes lifespan to shorten under
DR and amino acid conditions (M.-J. Kang et al., 2016; Rousakis et al., 2013), indicating that GCN2 acts
in a protective manner when the animal is faced with amino acid restriction.
Interestingly, GCN2 is also required for lifespan extension in mTOR mutants, indicating that GCN2
and mTOR pathways converge (Rousakis et al., 2013). One point of convergence is 4E-BP, a common
target among the GCN2, the mTOR, and the insulin/FOXO pathways. GCN2 has been shown to induce 4EBP transcription during stress, including during amino acid restriction conditions (M.-J. Kang et al., 2016;
Vasudevan et al., 2017).
GCN2 thus is a nutrient sensor whose role in modulating aging is gaining interest in the recent
years. The major consequence of GCN2 activation is inhibition of protein translation and induction of stress
response genes.

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1.4 Translational mechanisms and synaptic plasticity
Over the past two decades, a large body of experimental evidence has pointed to a critical role for
translational mechanisms in the regulation of synaptic function and plasticity. Synapses maintain a unique
ability to undergo functional and structural modifications during development and throughout the adult life
of the organism in response to environmental cues and experience. These modifications that are known as
synaptic plasticity and are essential for many higher brain functions such as learning and memory (Citri &
Malenka, 2008). Synaptic changes can be short or long lasting in the forms of potentiation or depression.
Long-term potentiation (LTP) and long-term depression (LTD) have been studied extensively, and
mounting evidence connects these aspects of synaptic plasticity to learning and memory (Lüscher &
Malenka, 2012; Nabavi et al., 2014). Translational mechanisms play a prominent role in triggering and
maintaining these forms of synaptic plasticity in many parts of the nervous system (Mori et al., 2019;
Pfeiffer & Huber, 2006). On the other hand, a different category of synaptic plasticity that is known as
homeostatic synaptic plasticity is tasked with ensuring stability and the maintenance of the setpoint of
synaptic strength (G. Turrigiano, 2012). We believe that understanding the regulation of homeostatic
synaptic plasticity and the maintenance of neurotransmitter setpoint is critical to understanding the
relationship between neuronal function and aging. Therefore, I have focused on aspects of homeostatic
synaptic plasticity for my dissertation.
Synaptic transmission in the central nervous system and at the neuromuscular junction (NMJ) are
tightly regulated by homeostatic mechanisms. Disruptions in synaptic release and neurotransmitter receptor
functions induce compensatory mechanisms at pre- and post-synaptic terminals. Recent evidence indicates
that protein translation plays a key role in maintaining synaptic homeostasis, and aberrant protein translation
regulation has consequences to synaptic activity (Mori et al., 2019). Together, these findings indicate that
nutrient availability could have a major impact on synaptic activity. The role of protein translation in various
forms of synaptic homeostasis is discussed below.

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1.4.1 Postsynaptic Compensation in Vertebrate Central Nervous System
One of the most studied form of homeostatic plasticity occurs at excitatory glutamatergic synapses
on hippocampal or cortical neurons in response to inhibition of evoked synaptic activity by tetrodotoxin
(TTX). In response to the absence of presynaptic evoked release, the synapse experiences a compensatory
increase in miniature excitatory synaptic current (mEPSC) amplitude (G. G. Turrigiano et al., 1998). This
synaptic compensation is achieved via the insertion of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic
acid (AMPA) receptors into the postsynaptic membrane (Schanzenbächer et al., 2016; Wierenga et al.,
2005). Insertion of new AMPA receptors is dependent on increase in protein translation via mTOR, and
upregulation of mTOR activity can increase mEPSC amplitude in the absence of TTX (C.-C. Wang et al.,
2013). Together, these findings suggest that postsynaptic mTOR activity is critical for increased
sensitization of the postsynaptic terminal in response to the absence of evoked synaptic activity.
1.4.2 Presynaptic Compensation in Vertebrate Central Nervous System
In hippocampal synapses, the presynaptic neurons also participate in compensation by increasing
the frequency of mEPSC in response to activity blockade (Henry et al., 2012, 2018; Jakawich et al., 2010;
Wierenga et al., 2005). The morphology of the presynaptic terminal indicates that the change in release
frequency is accompanied by increased number of docked vesicles, size of readily releasable pool, and size
of vesicles (Murthy et al., 2001). Interestingly, these presynaptic changes appear to be dependent on protein
translation (Jakawich et al., 2010) and mTOR activity (Henry et al., 2012, 2018) in the postsynaptic dendrite,
suggesting a retrograde regulatory mechanism. Activation of mTOR is sufficient to increase presynaptic
release probability in the absence of activity blockers (Henry et al., 2018). Additionally, BDNF has been
identified as the retrograde signaling molecule downstream of mTOR and protein synthesis that drives
presynaptic release (Henry et al., 2012; Jakawich et al., 2010). These findings indicate that mTOR-mediated
protein translation in the postsynaptic cell can retrogradely influence synaptic vesicle release probabilities
in the presynaptic membrane.

13
1.4.3 Presynaptic Homeostatic Plasticity at the Neuromuscular Junction
Synaptic compensation occurs at the neuromuscular junction NMJ as well. A clinical example of
this phenomenon is seen in the NMJ of patients with Myasthenia Gravis, an autoimmune disease that
reduces the acetylcholine receptors and receptor sensitivity at the NMJ (Gilhus & Verschuuren, 2015).
Motoneurons show a compensatory increase in neurotransmitter release, allowing for patients to carry on
normal motor function at the early phases of the disease (Albuquerque et al., 1976).
The phenomenon of presynaptic compensation has been well characterized at the NMJ of D.
melanogaster glutamate receptor subunit mutants. Removal of both copies of the glutamate receptor subunit
GluRIIA result in a decrease in single channel mean open time and reduction in miniature excitatory
junctional current (mEJC) amplitudes (DiAntonio et al., 1999). The presynaptic motoneuron compensate
by enhancing neurotransmitter release, thereby maintaining a wildtype level of excitatory junctional current
(EJC) (Figure 1.2) (Petersen et al., 1997). The compensatory increase in presynaptic release in response to
postsynaptic neurotransmitter receptor disruption is termed presynaptic homeostatic plasticity (PHP).
Figure 1.2 Example of postsynaptic homeostatic plasticity (PHP) in GluRIIA-/- mutants
(A) Scheme of two-electrode volage clamp (TEVC) at the D. melanogaster larval NMJ. Two electrodes are used to
voltage-clamp muscle 7 at abdominal segment 3. Muscle numbers are indicated in the diagram. The stimulating
electrode is used to depolarize the motoneuron to evoke neurotransmitter release.
(B) Representative EJC and mEJC traces from wildtype and GluRIIA-/- mutant larvae. While GluRIIA-/- mutant shows
reduced mEJC amplitudes, the EJC amplitudes are identical to wildtype NMJ due to homeostatic compensation.
EJCs contain 10 superimposed traces evoked at 0.5 Hz; mEJCs are samples of continuous recording in the absence
of presynaptic stimulation. Holding potential was -80mV.

14
Similar to the role of mTOR in retrograde signaling at the vertebrate synapses, the fly homolog,
TOR, is required in the postsynaptic muscle to maintain PHP at the larval NMJ (Penney et al., 2012).
Interestingly, acute inhibition of TOR by rapamycin (under 6 hours) does not affect synaptic strength;
however, PHP is compromised after 6 – 12 hours of TOR inhibition via rapamycin (Penney et al., 2012).
This indicates that increased protein synthesis is required for the long-term maintenance of synaptic
homeostasis. Consistent with the observation that TOR is required for retrograde signaling, overexpression
of TOR in the postsynaptic muscle is sufficient to drive presynaptic release in an otherwise wildtype
background. Consequently, EJC amplitude and the quantal content (QC) of a larvae overexpressing TOR
in the muscle surpasses the wildtype EJC amplitude (Penney et al., 2012).
1.4.4 Postsynaptic Translation Drives Presynaptic Release
In vertebrate and invertebrate model synapses, postsynaptic mTOR is required for presynaptic
compensation, either through increased mEPC frequency or increased evoked release. These observations
indicate that mTOR is upstream of a retrograde signal that communicates with the presynaptic neuron to
increase vesicular release. Consistent with this interpretation, overactivation of mTOR in the postsynaptic
cell in an otherwise wildtype condition is sufficient to highjack the retrogrades signaling and results in
aberrant increase in presynaptic release.
Similarly, other methods to increase protein translation in the postsynaptic terminal commonly
drive retrograde signaling and increase presynaptic release. For example, 4E-BP-/- mutant mice show
increased mEPSC amplitude and frequency in hippocampal slices (Bidinosti et al., 2010). In the larval NMJ
model, overexpression of leucine rich repeat kinase 2 (LRRK2) drives cap-dependent protein translation
and increases EJC amplitude and QC (Penney et al., 2016). These observations indicate that regulation of
presynaptic release is sensitive to postsynaptic protein translation.
Overall, findings from vertebrate and invertebrate model synapses demonstrate a common role of
protein translation in achieving synaptic homeostasis. Disruption in the synapse result in compensatory
changes at the pre- and post-synaptic terminals that require protein translation to accomplish. Furthermore,
activation of protein translation is sufficient to highjack the synaptic regulation mechanisms which results

15
in overcompensation. Together, these findings converge on a common theme: increased protein translation
leads to increased excitatory synaptic activity.
1.5 D. melanogaster model
In order to investigate the role of dietary restriction on excitatory synaptic transmission and aging,
we decided to take advantage of the D. melanogaster model. The wide array of genetic tools available, the
short life cycle (~ 2 weeks), and the short lifespan (~ 3 months) make the fruit fly an ideal model for
identifying genes and mechanisms involved in synaptic regulation and aging. The larval NMJ model has
been well characterized for studying genetic and pharmacological regulation of synaptic homeostasis and
synaptic strength (Broadie & Bate, 1995). Importantly, the glutamatergic larval NMJ synapses share many
presynaptic and postsynaptic molecules and structures with vertebrate central nervous system synapses, and
many gene discovered at the larval NMJ model have been confirmed in vertebrate systems.
One of the greatest advantages for studying the genetics of aging and synaptic regulation in D.
melanogaster is the availability of transgenic tools that allow for tissue and temporal specific manipulation
of gene expression. The Gal4/UAS system is based on the yeast transcriptional activator Gal4 and the
corresponding yeast promoter, upstream activation sequence (UAS). The combination of UAS-transgenes
with tissue- or cell-specific Gal4 allow for spatial control of transgene expression within the fly (Brand &
Perrimon, 1993). Thus, at the fly NMJ, it is possible to dissect the roles of the pre- and post-synaptic inputs
in regulating synaptic strength by using the Gal4/UAS system. As the postsynaptic tissue is muscle and the
presynaptic tissue is neuronal, different drivers can be used to target expression at one of the synaptic
components.
Temporal regulation of transgenic expression can be achieved by using the temperature sensitive
Gal80 (Gal80ts) in conjunction with the Gal4/UAS system or by using the Gene-Switch system (McGuire
et al., 2004; Nicholson et al., 2008; Roman et al., 2001). Gal80 is a yeast repressor of Gal4 that has been
shown to inhibit Gal4 function in the fly as well. A temperature sensitive mutation of the Gal80 renders it
inactive at high temperatures (29°C), allowing for temporal control of transgene expression using
temperature. In combination with Gal4/UAS, Gal80ts is inactive and UAS-transgene is expressed at

16
permissive temperatures (29°C), while Gal80 is active and UAS-transgene is repressed at non-permissive
temperatures (18°C) (McGuire et al., 2004). The advantage of this system is that the Gal80ts can be
combined with Gal4 lines that already exist, which can be done quickly. The high temperature for transgenic
expression will need to be taken into account when performing aging experiments, as high temperature has
been shown to accelerate aging in flies (Miquel et al., 1976).
In contrast, the Gene-Switch system uses a chimeric protein that combines the DNA-binding
domain of Gal4, ligand-binding domain of the human progesterone receptor, and the transcription activation
domain of human p65 (Roman et al., 2001). The resulting chimeric protein is a ligand-inducible
transcription factor that responds to the presence of antiprogestin, mifepristone (RU486). The advantage of
this system is the ability to compare isogenic flies that have been treated with mifepristone and those that
have not been treated with the drug. However, some Gene-Switch lines exhibit leakiness, in which the flies
express the UAS-transgene in the absence of the inducer drug (Osterwalder et al., 2001). Furthermore, not
all drivers available in the Gal4 system have been generated for the Gene-Switch system, and thus a new
fly line may need to be generated depending on the promoter desired for the experiment.
Overall, the D. melanogaster provides a well-established model with a powerful genetic toolset to
study synaptic regulation and aging.
1.6 Concluding remarks
In many neurodegenerative diseases, decline in synaptic activity and loss of synaptic structures are
commonly considered a late-stage disease phenotype that manifest in behavioral defects. However, recent
findings suggest abnormalities in neural circuit function and synaptic transmission are apparent in early
phases of the disease, even before the onset of neurodegeneration (Bookheimer et al., 2000; Frere & Slutsky,
2018a; Iovino et al., 2020; Mondadori et al., 2006; Pini et al., 2020). Interestingly, it appears that in many
cases, neurodegenerative disease genes can cause an increase in neurotransmission when tested in model
organisms (Hall et al., 2015; Matikainen-Ankney et al., 2016; Penney et al., 2016; Romero et al., 2008),
suggesting that they may be responsible for causing excitotoxicity in neurons. Based on these observations,
we propose aberrant excitatory neural activity as a hallmark of aging.

17
During my PhD I have pursued the idea that aberrant excitatory synaptic transmission has
detrimental effects on the periphery, focusing on the NMJ as the model synapse. Previous findings have
indicated that synaptic strength increases at the NMJ during aging in various organisms. Similar to the
central nervous system, we suspected that excitatory synaptic transmission can be toxic to postsynaptic
tissues in the periphery. We therefore propose that not only is aberrant synaptic activity a canary in the
coalmine for aging, but also a driver of systemic aging.
I have approached this idea from two perspectives. First, since DR's beneficial effects relay on
suppression of protein translation, and that translational mechanisms play a critical role in regulating
synaptic strength and plasticity, I therefore sought to study how nutrients and nutrient sensing pathways
affect excitatory synaptic transmission, using the larval NMJ as a model synapse. Second, I examined the
role of synaptic activity in modulating aging in the postsynaptic tissue. To this effect, I have used the adult
NMJ to take advantage of the knowledge accrued in the larval NMJ model, and I have chosen proteostasis
as an indicator of tissue aging.
This dissertation will describe findings of several nutrient-related pathways that regulate synaptic
strength at the larval NMJ, a novel role for a classic Drosophila gene in regulation of muscle proteostasis
and aging, and a toolset developed for the neurophysiology community to study changes in synaptic
transmission.

18
Chapter 2. Methods
2.1 Reagent Recipes
PBS
Phosphate Buffer Saline. 1x PBS was diluted from a 10x PBS stock solution (5493, Sigma) in water.
PBT
PBS + Triton-X. 50µL of Triton X-100 (0.1%) (21568-2500, Acros) was added to 50mL of PBS.
The mixture was rocked for 30 minutes to ensure complete dissolution.
5% PBTN
PBT + Normal Goat Serum (NGS). 50µL of NGS (5%) (005-000-121, Jackson ImmunoResearch)
was added to 950µL PBT. Made fresh on the day of the experiment.
4% PFA in PBT
10mL of 20% PFA stock (15713, Electron Microscopy Sciences) was diluted in 40mL of PBT
solution. The mixture was aliquoted and kept in -20°C. The aliquots were thawed on ice fresh on the day
of the experiment.
4% PFA in PBS
10mL of 20% PFA stock (15713, Electron Microscopy Sciences) was diluted in 40mL of 1x PBS.
The mixture was aliquoted and kept in -20°C. The aliquots were thawed on ice fresh on the day of the
experiment.
TBS
Tris-Buffered Saline. 1x TBS was diluted from 10x TBS stock (1706435, Bio-Rad) in distilled
water.
TBST
TBS + Tween-20. 1mL Tween-20 (P1379, Sigma) was diluted in 1L 1xTBS.

19
HL3
Hemolymph-like saline. HL3 stock solution was made according to (Stewart et al., 1994). 2.045g
sodium chloride (70mM) (S7653, Sigma-Aldrich), 0.1867g potassium chloride (5mM) (P9333, SigmaAldrich), 2.033g magnesium chloride hexahydrate (20mM) (M2670, Sigma-Aldrich), 0.420g sodium
bicarbonate (10mM), 0.945g trehalose (5mM) (T9531, Sigma), 19.6823g sucrose (115mM), and 0.595g
HEPES (5mM) (H4034, Sigma) were dissolved in a total of 500mL of distilled water. The pH was adjusted
to 7.2 using sodium hydroxide (72068, Sigma) and hydrochloric acid (E447, Ameresco). The solution was
filtered using 0.22µm vacuum filter system (25-225, Genessee Scientific) and stored at RT.
Calcium stock solution
54.77g calcium chloride hexahydrate (1mM) (21108, Sigma) was dissolved in 250mL of distilled
water. The solution was filtered using 0.22µm vacuum filter system (25-225, Genessee Scientific) and
stored at RT for a maximum of 1 year.
Potassium chloride solution
55.91 g potassium chloride (3M) (P9333, Sigma-Aldrich) was dissolved in 250mL of distilled water.
The solution was stored at RT.
Proteasome lysis buffer
536.125mg magnesium acetate tetrahydrate (5mM) (M5661, Sigma), 745.50mg potassium chloride
(20mM) (P9333, Sigma) and 25mL Tris/HCl diluted from 1M stock solution (25mM) (T1076, TEKnova)
were diluted in distilled water. pH was adjusted to 7.5. Final volume was adjusted to 500mL. The buffer
was stored in RT. 1mM dithiothreitol (DTT) (43816, Sigma) was added fresh on the day of the experiment.
Suc-LLVY-AMC (10mM) Stock
5mg Suc-LLVY-AMC (BML-P802-0005, Enzo Life Sciences) was diluted in 655µL of Dimethyl
sulfoxide (DMSO) (D4540, Sigma) for a final concentration of 10mM. 15µL aliquots were stored in -20°C
until use. The buffer was diluted to 5x concentration of 200µM in proteasome lysis buffer fresh on the day
of the experiment. Substrate was added to the proteasome lysate to a final concentration of 40µM.

20
AMC Standard (5mM) Stock
1mg of 7-amino-4-methylcoumarin (5mM) (AMC, BML-KI144-0001, Enzo Life Sciences) was
diluted in 1140µL of dimethyl sulfoxide (DMSO) (D4540, Sigma). Diluted to working concentrations fresh
on the day of the experiment using the proteasome lysis buffer.
10x MgSO4 (1M) stock
24.074g magnesium sulfate (1M) (M7506, Sigma) was diluted in 200mL distilled water. The stock
was stored at RT.
Mg ATP (90mM) stock
551mg adenosine triphosphate (ATP) (90mM) (A20209, Sigma) and 1mL 10x MgSO4 stock
solution were dissolved in 8mL of distilled water. pH adjusted to 7.3 with 1N potassium hydroxide (319376,
Fluka). Final volume was adjusted to 11mL. The stock was stored as aliquots in -20°C.
PS-341 (1mM) stock
2mg of PS-341 (B31092, APExBIO) was diluted in 5.2mL of DMSO (D4540, Sigma). Aliquots
were stored in -80°C for long-term storage, and in -20°C for short-term storage. PS-341 was diluted to 5µM
5x solution using proteasome lysis buffer fresh on the day of the experiment. The final concentration of PS341 in the lysate was 1µM.
Cycloheximide (100mg/mL) stock
100mg of cycloheximide (Sigma) was dissolved in 1mL of 100% ethanol (V1001, Koptec). The
solution was stored in -20°C.
10% NP40
1mL Nonidet® P 40 Substitute (74385, Fluka) was dissolved in 9mL distilled water. The stock was
stored at RT.
Polysome lysis buffer
0.726g Trizma® Base (T1503, Sigma) (20mM), 0.6g magnesium chloride hexahydrate (M9272,
Sigma) (M9772, 10mM) and 4.42g sodium chloride (S7653, Sigma) (250mM) were dissolved in 300mL of

21
distilled water. The pH was adjusted to 7.2-7.5 with hydrochloric acid (E447, Ameresco). Filtered using
0.22µm vacuum filter system (25-225, Genessee Scientific) and kept at RT. This stock solution was used
to make sucrose gradients.
For tissue lysis, 1x Ethylenediaminetetraacetic acid (EDTA) free cOmplete Tablet (04 693 159 001,
Roche), 100µg/mL cycloheximide, and 0.4% NP40 were added from stock solution to the buffer on the day
of the experiment and kept on ice.
15% Hand-cast Western blot gel
The gels were cast using the Mini-PROTEAN® Tetra Cell casting stand and clamps system
(1568050, Bio-Rad). 15-well 1.5mm Mini-PROTEAN® Combs (1653366, Bio-Rad) and Mini-PROTEAN®
spacer plates with 1.5mm integrated spacers (1653312, Bio-Rad) were used.
8µL Tetramethylethylenediamine (TEMED) (161-0801, Bio-Rad), 200µL 10% ammonium
persulfate solution, 10mL 30% Acrylamide/Bis Solution 29:1 (1610156, Bio-Rad), 1.5M Tris (pH8.8),
200µL 10% SDS (1610416, Bio-Rad), and 4.6 mL of distilled water was mixed. Approximately 5.5mL of
the solution was poured into each cast. The solution was set at RT.
Stacking gel was composed of 5.5mL distilled water, 1.3mL 30% Acrymaide/Bis Solution 29:1,
1.0 mL 1.0M Tris (pH6.8), 80 µL 10% SDS solution (1610416, Bio-Rad), 80µL 10% ammonium persulfate
solution, and 8µL TEMED (161-0801, Bio-Rad). Approximately 2 mL of the solution was poured into each
cast. The solution was set at RT.
The 10% ammonium persulfate solution was made fresh by dissolving 0.1g of ammonium
persulfate (1610700, Bio-Rad) in 1mL of distilled water.
2.2 Fly experiments
2.2.1 Adult aging
Fly progenies eclosed for 2 days, after which 20 females and 10 males were collected into vials.
After 2 days of mating, female flies were collected to new vials. The flies were considered 0-days-old at
this point. Flies were transferred to new food every 2 to 3 days until indicated ages for analysis.

22
2.2.2 Glutamine supplementation
Adults were mated for 48-hour period after which they were transferred to a new vial for egg-lay.
Eggs were laid on standard fly food or standard fly food supplemented with 50mM of L-glutamine (see Fly
Foods).
2.2.3 Acute nutrient restriction
Feeding larvae (~110 hours after egg-lay) were picked out of the food and transferred to new food
conditions (starvation, amino acid restriction, or control foods, depending on the nature of the experiment)
(see Fly Foods). Larvae were dissected after 6 hours of exposure to the new food unless otherwise indicated.
2.2.4 Rapamycin treatment
Adults were mated for 24-hour period, after which they were transferred to a new vial for egg-lay.
Eggs were laid on standard fly food for 24 hours. 60 hours after egg-lay, the larval progenies were picked
out of the food and transferred to 10mM rapamycin food (see Fly Foods) or standard food. The progenies
were allowed to eclose in the same vial, after which they were raised on standard food until dissection.
2.2.5 Holidic media treatment
Adults were mated for a 24-hour period, after which they were transferred to holidic media
containing 100% or 50% of the total L-tryptophan (see Table 2.2). The progenies were raised and aged in
their respective diet until dissection.

23
Table 2.1 Fly lines
Fly line Source Identifier
D. melanogaster. GluRIIA deficiency: Df(2L)clh4/Cyo-GRP
C. Goodman Flybase: FBab0001759
D. melanogaster. GluRIIA mutant w[*];
GluRIIA[SP16]/Cyo-GFP
C. Goodman Flybase: FBal0085982
D. melanogaster. RNAi for Foxo. y[1] sc[*] v[1]
sev[21]; P{y[+t7.7] v[+t1.8]=TRiP.HMS00793}attP2
Bloomington Drosophila Stock
Center
BDSC: 32993
D. melanogaster. repo-GAL4: w[1118];
P{w[+m*]=GAL4}repo
Bloomington Drosophila Stock
Center
BDSC:7415
D. melanogaster. RNAi for mCherry: y[1] sc[*] v[1]
sev[21]; P{y[+t7.7] v[+t1.8]=VALIUM20-
mCherry}attP2
Bloomington Drosophila Stock
Center
BDSC:35785
D. melanogaster. RNAi for Delta: y[1] sc[*] v[1]
sev[21];
P{y[+t7.7] v[+t1.8]=TRiP.HMS01309}attP2
Bloomington Drosophila Stock
Center
BDSC:34322
D. melanogaster. SPG driver Moody-GAL4 (3rd chr.):
P{moody-GAL4}
R.Ordway (Schwabe et al., 2005)
D. melanogaster. SPG driver Moody-GAL4 (2nd
chr.): P{moody-GAL4.SPG}
V. Auld (Schwabe et al., 2005)
D. melanogaster. tub-GAL80ts 2nd chr.). w[*];
P{w[+mC]=tubP-GAL80[ts]}20
Bloomington Drosophila Stock
Center
BDSC: 7019
D. melanogaster. Repo-GAL4 (X chr.) Karla Kaun
D. melanogaster. DeltaRF: Dl[RF] Bloomington
Drosophila Stock
Center
BDSC: 5603
D. melanogaster. DeltaRevF10: Dl[RevF10] e[*]
P{ry[+t7.2]=neoFRT}82B
Bloomington
Drosophila Stock
Center
BDSC: 6300
D. melanogaster. RNAi for Gs2. y[1] v[1]; P{y[+t7.7]
v[+t1.8]=TRiP.HMS02197}attP40
Bloomington
Drosophila Stock
Center
BDSC: 40949
D. melanogaster. RNAi against GCN2. y[1] sc[*] v[1]
sev[21]; P{y[+t7.7] v[+t1.8]=TRiP.GL00267}attP2
Bloomington
Drosophila Stock
Center
BDSC: 35355
D. melanogaster. RNAi against sif. y[1] v[1];
P{y[+t7.7] v[+t1.8]=TRiP.HMJ23517}attP40
Bloomington
Drosophila Stock
Center
BDSC: 61934
D. melanogaster. brp Mimic mutant.
y[1] w[*];
Mi{y[+mDint2]=MIC}brp[MI08003]/SM6a
Bloomington
Drosophila Stock
Center
BDSC: 44730
D. melanogaster. Dominant negative GluRIIAM/R.
w[*]; P{w[+mC]=UAS-GluRIIA.M614R}2
Bloomington
Drosophila Stock
Center
BDSC: 64256
D. melanogaster. Muscle driver MHC-Gal4 (Schuster et al., 1996)
D. melanogaster. Muscle driver. 24B-Gal4 (Brand & Perrimon, 1993)
D. melanogaster. Ubiquitous driver Da-Gal4.
w[*]; P{w[+mW.hs]=GAL4-da.G32}UH1,
Sb[1]/TM6B, Tb[1]
Bloomington
Drosophila Stock
Center
BDSC: 55851
D. melanogaster. Neuronal driver. elav-Gal4.
P{w[+mW.hs]=GawB}elav[C155]
Bloomington
Drosophila Stock
Center
BDSC: 458
D. melanogaster. RNAi against w (#1). y[1] v[1];
P{y[+t7.7] v[+t1.8]=TRiP.HMS00017}attP2
Bloomington
Drosophila Stock
Center
BDSC: 33623
D. melanogaster. RNAi against w (#2). y[1] sc[*] v[1]
sev[21]; P{y[+t7.7] v[+t1.8]=TRiP.GL00094}attP2
Bloomington
Drosophila Stock
Center
BDSC: 35573

24
D. melanogaster. RNAi against w (#3). y[1] sc[*] v[1]
sev[21]; P{y[+t7.7] v[+t1.8]=TRiP.GLV21005}attP2
Bloomington
Drosophila Stock
Center
BDSC: 35641
D. melanogaster. RNAi against st. y[1] sc[*] v[1]
sev[21]; P{y[+t7.7]
v[+t1.8]=TRiP.HMC05128}attP40
Bloomington
Drosophila Stock
Center
BDSC: 60134
D. melanogaster. RNAi against Atg8a. y[1] sc[*]
v[1] sev[21]; P{y[+t7.7]
v[+t1.8]=TRiP.HMS01328}attP2
Bloomington
Drosophila Stock
Center
BDSC: 34340
D. melanogaster. w1118
D. melanogaster. Red-eyed wildtype Bloomington
Drosophila Stock
Center
BDSC: 64349
D. melanogaster. RNAi against CaMKII. y[1] v[1];
P{y[+t7.7] v[+t1.8]=TRiP.JF03336}attP2
Bloomington
Drosophila Stock
Center
BDSC: 29401
D. melanogaster. CaMKII inhibitory peptide UASAla. w[*]; P{w[+mC]=UAS-CamKII-I.Ala}2
Bloomington
Drosophila Stock
Center
BDSC: 29666
D. melanogaster. UAS-LacZ. w[*]; P{w[+mC]=UASlacZ.Exel}2
Bloomington
Drosophila Stock
Center
BDSC: 8529
D. melanogaster. UAS-S6KSTDE. w[1118];
P{w[+mC]=UAS-S6k.STDE}3
Bloomington
Drosophila Stock
Center
BDSC: 6913
2.2.6 Lifespan assay
Flies were incubated at 25°C on a 12-hour day/night cycle on standard fly food after 4 days of egglay. Adult flies were collected over the course of 2 days, after which 20 female flies were housed with 10
male flies for 2 more days to ensure mating. 20 female flies were collected into individual vials for further
analysis and were considered 0 days old. Flies were transferred to new food every 2 to 3 days, at which
point the number of dead flies were recorded. For wRNAi lifespan, the experiment was repeated three times
from independent genetic crosses.
2.2.7 Climbing assay
Every 7 days, flies in the lifespan cohort were tested for standard negative geotaxis test. Briefly,
the flies were transferred to an empty polypropylene fly vials (32-113, Genessee Scientific) marked at 5cm
from the bottom. The vials were tapped 3 times to ensure the flies start at the bottom of the vial. The number
of flies that crossed the line within 15s was recorded. 3 technical replicates were recorded per vial. For
synaptic mutant climbing assays, each vial of flies were considered biological replicates. For wRNAi climbing
assays, results from three genetically independent crosses were averaged. The percentage of climbing

25
activity was determined by dividing the average number of flies that reached the designated height during
3 tests by the total number of flies in a tube at the start of the day.
2.2.8 Eye color assay
Quantitation of eye pigments was performed according the method in (Ephrussi & Herold, 1944).
Female flies were aged at 25°C with 12-hour light/dark cycle for 6 days prior to freezing in liquid nitrogen
and storage at –80°C. Heads were separated from bodies by briefly vortexing the frozen flies. Red pigment
from aliquots of ten heads per genotype was extracted in 1.5 ml of acidified ethanol (AEA; 30% ethanol
(V1001, Koptec) acidified to pH 2 with hydrochloric acid (E447, Ameresco)) by homogenizing the heads
with a pestle and incubating at 25°C for 48 hours in the dark. Each sample was split into two tubes, for
duplicate processing through the remaining steps. The particulate matter was removed through two
centrifugation steps, and the optical absorbance at 480 nm was measured using the cleared supernatants.
The duplicate readings for each sample were averaged.
2.2.9 Fly foods
Fly foods were prepared using the recipe below. All foods were poured into polypropylene fly vials
(32-113, Genessee Scientific).
Standard fly food
1.8g brewer’s yeast (62-107, Genessee Scientific), 1.38g Drosophila Agar Type II (66-104, Apex),
2.2g molasses (62-117, Genessee Scientific), 7.5g malt (62-110, Genessee Scientific), 8g corn flour (003-
331-0001, Honeyville Grain), and 1g soy flour (62-115, Genesee Scientific) were mixed in 100mL of water.
The mixture was heated to 95°C. The food was cooled while stirring to 75°C before adding 0.2g/0.72mL
methyl paraben (IC10234101, VWR) in ethanol (V1001, Koptec) and 0.625mL propionic acid (14930-0025,
Acros).
Electrophysiology fly food
5.4g agar (Gelidium, Mooragar), 8.8g malt, 7.2g brewer’s yeast (62-107, Genessee Scientific), 32g
corn flour (Bob’s Red Mill), 4g soy flour (62-115, Genesee Scientific) were mixed in 400mL of water. The

26
mixture was heated to 95°C using a hotplate with a stir bar. The food was cool down to 75°C while stirring,
after which 0.8g/2.88 mL methyl paraben (H6643, Sigma-Aldrich) in ethanol (V1001, Koptec) and 2.5mL
propionic acid (402907, Sigma-Aldrich) were added to the food. The recipe was scaled between 100mL
and 400mL based on experimental needs.
Acute starvation food
7.5g bacteriological agar (A5360, Sigma-Aldrich) was mixed in 500mL water and heated to 95°C
using a hotplate with a stir bar. The mixture was cooled down to 75°C while stirring, after which 1.5mL
propionic acid (402907, Sigma-Aldrich) and 0.25g methyl paraben (H6643, Sigma-Aldrich) dissolved in
2.5mL of ethanol were added to the mixture.
Acute amino acid restriction food
2.76g bacteriological agar (A5360, Sigma-Aldrich), 10g sucrose (S7903, Sigma), and 16g corn
flour (Bob’s Red Mill) were mixed in 200mL of water. The mixture was heated to 95°C using a hotplate
with a stir bar. The mixture was cooled down to 75°C while stirring, after which 0.1g/1mL methyl paraben
(H6643, Sigma-Aldrich) in ethanol (V1001, Koptec) and 0.6mL propionic acid (402907, Sigma-Aldrich)
were added to the food.
Acute starvation and amino acid restriction control food
2.76g (1.38%) bacteriological agar (A5360, Sigma-Aldrich), 10g sucrose (S7903, Sigma), 16g corn
flour (Bob’s Red Mill) and 4.8g brewer’s yeast (62-107, Genessee Scientific) were mixed in 200mL of
water. The mixture was heated to 95°C using a hotplate with a stir bar. The mixture was cooled down to
75°C while stirring, after which 0.1g/1mL methyl paraben (H6643, Sigma-Aldrich) in ethanol (V1001,
Koptec) and 0.6mL propionic acid (402907, Sigma-Aldrich) were added to the food.
10mM Rapamycin food
18g brewer’s yeast (62-107, Genessee Scientific), 1.3g Drosophila Agar Type II (66-104, Apex),
22g molasses (62-117, Genessee Scientific), 65g malt (62-110, Genessee Scientific), 80g corn flour (003-
331-0001, Honeyville Grain), and 10g soy flour (62-115, Genesee Scientific) were mixed in 1L of water.

27
The mixture was heated to 95°C while stirring using Hotmix Pro (Advanced Gourmet) and cooled while
stirring to 75°C before adding 2g methyl paraben (H6643, Sigma-Aldrich) in 7.3mL (28.75%) and 6.2mL
of propionic acid (402907, Sigma-Aldrich). 10mg Rapamycin (R-5000, LC Laboratories) was dissolved in
1mL ethanol (V1001-Koptec) and mixed into the food.
Glutamine supplemented food
730.7mg L-glutamine (G3126, Sigma) was added to 100mL of standard fly food (final
concentration 50mM).
Holidic Media
Holidic media was made using previously published recipe (Piper et al., 2014). A brief protocol is
summarized in table 2.2
Table 2.2 Holidic Media ingredients
Ingredient Amount per 1L food
Sugar Sucrose (S7963, Sigma) 17.12g
Gel Agar (Gelidium, Mooragar) 20.0g
Amino acids L-isoleucine (I2752, Sigma-Aldrich) 1.82g
L-leucine (L8912, Sigma) 1.21g
L-tyrosine (T3754, Sigma-Aldrich) 0.42g
Buffer stock 30mL/L acetic acid (124040025, Acros) 100mL
30g/L potassium phosphate monobasic (P9791, Sigma)
10g/L sodium bicarbonate (S6297, Sigma-Aldrich)
Metal ions stocks 12.5g/50mL calcium chloride hexahydrate (21108, Sigma) 1mL
2.5g/L copper (II) sulfate pentahydrate (C7631, SigmaAldrich)
1mL
1.25g/50mL Iron (II) sulfate heptahydrate (F7002, SigmaAldrich)
1mL
12.54g/50mL magnesium sulfate anhydrous (MX0075-1,
Millipore)
1mL
1g/L manganese (II) chloride tetrahydrate (M3634, SigmaAldrich)
1mL
1.25g/50mL zinc sulfate heptahydrate (Z0251, Sigma) 1mL
Cholesterol stock solution 1.0g/50mL cholesterol (C8667, Sigma-Aldrich) in ethanol
(V1001, Koptec)
5.0mL
Water 750mL
Microwaved at 30-second intervals until boiling.
Essential amino acids 3.03g/200mL L-phenylalanine (102623, MP Biomedicals) 60.51mL
2.24g/200mL L-histidine (H8000, Sigma-Aldrich)
5.74g/200mL L-lysine monohydrochloride (L5626,
Sigma-Aldrich)
1.12g/200mL L-methionine (M9625, Sigma-Aldrich)
4.70g/200mL L-arginine monohydrochloride (A5131,
Sigma-Aldrich)
4.28g/200mL L-threonine (T8625, Sigma-Aldrich)
4.42g/200mL L-valine (V0500, Sigma-Aldrich)
2.25g/200mL L-alanine (05129, Sigma) 60.51mL

28
Non-essential amino acid stock
solution
2.78g/200mL L-aspartic acid (100809, MP Biomedicals)
3.58g/200mL L-glycine (G7126, Sigma)
2.78g/200mL L-asparagine anhydrous (100794, MP
Biomedicals)
1.86g/200mL L-proline (102730, MP Biomedicals)
5.98g/200mL L-glutamine (194678, MP Biomedicals)
2.51g/200mL L-serine (102873, MP Biomedicals)
Tryptophan stock solution 1.45g/200mL L-tryptophan (103151, MP Biomedicals) 60.51mL (100% tryptophan),
30.25mL (50% tryptophan)
L-cysteine 0.05g L-cysteine (C1276, Sigma) 5.28mL
L-glutamic acid stock solution 0.364g L-glutamic acid (G5889, Sigma) 18.21mL
Vitamin stock solution (23.6x) 0.033g/L thiamine (T4625, Sigma-Aldrich) 28mL
0.017g/L riboflavin (R4500, Sigma)
0.199g/L nicotinic acid (N4126, Sigma-Aldrich)
0.258g/L calcium pantothenate (21210-Sigma)
0.042g/L pyridoxine (P9755, Sigma-Aldrich)
0.004g/L biotin (22582, Cayman Chemical)
Folic acid stock solution (1000x) 0.5g/L folic acid (20515, Cayman Chemical) 1mL
Nucleic acid and lipid related
metabolites stock solution (125x)
6.25g/L choline chloride (26978, Sigma) 8mL
0.63g/L myo-inositol (I7508, Sigma)
8.13g/L inosine (I4125, Sigma)
7.50g/L uridine (20300, Cayman Chemical)
Preservatives 1.5g/15mL 4-Hydroxybenzoate (H6643, Sigma-Aldrich)
in ethanol (V1001, Koptec)
15mL
6mL propionic acid (402907, Sigma-Aldrich) 6mL
Mixed and poured into fly vials
2.3 Morphology
2.3.1 Immunohistochemistry
For thoracic dissections, adult female flies were dissected and mounted as previously described
(Hunt & Demontis, 2013). Briefly, up to 20 thoracic segments were pre-fixed using 4% PFA in PBT (see
Reagent Recipes) for 10 minutes on the nutator. The thoracic segments were washed once with PBT (see
Reagent Recipes) and dissected to expose the dorsolateral muscles (DLM). The dissected muscles were refixed using 4% PFA in PBT for 20 minutes on the nutator. Muscles were washed 3 times in PBT for a total
of 15minutes, after which they were blocked in 5% PBTN (see Reagent Recipes) for at least 30minutes on
the nutator.
For NMJ imaging, larval muscles were dissected as previously described (Schuster et al., 1996).
Larvae were fixed for 10 minutes with ice-cold 4% PFA in PBS solution, washed briefly with PBT and
PBS. The tissues were blocked in 5% PBTN for at least 30 minutes on the nutator.

29
The samples were incubated with appropriate primary antibodies (see Antibody List) in 5% PBTN
for overnight at 4°C on the nutator. Samples were washed 3 times with 0.1% PBT for a total of 45 minutes
on the nutator, after which they were incubated with appropriate secondary antibodies (see Antibody List)
in 5% PBTN on the nutator. The samples were washed 3 times with 0.1% PBT for a total of 45 minutes on
the nutator, after which the tissues were mounted using Vectashield (H-1000, Vector).
The samples were imaged using LSM780 confocal microscope (Zeiss).
2.3.2 Blood brain barrier penetration assay
Adult flies were injected with approximately 150nL of 10mg/mL Dextran Red 3kDa (D3329,
Sigma) in PBS using microinjector (Nanoinject II, Drummond Scientific) at the thoracic cavity. Flies were
kept in standard fly food protected from light for 24 hours after injection. Brains of surviving flies were
dissected according to (J. S. Wu & Luo, 2006). Briefly, the heads were pre-fixed using 4% PFA in PBS
(see Reagent Recipes) for 15 minutes, shielded from light. The exoskeleton was removed to extract the
brain in 4% PFA in PBS. The dissected brains were washed 3 times in 0.1% PBT for 30 minutes at RT.
Brains were mounted on coverslips using Vectashield (H-1000, Vector).
The samples were imaged using LSM700 confocal microscope (Zeiss).
Images were analyzed using Fiji (ImageJ, NIH). The mean signal intensity of the background was
subtracted from the mean signal intensity of the brain.
2.3.3 Antibody List (Immunohistochemistry)
Primary antibodies were used in the following concentrations: mouse anti-mono- and polyubiquitin 1:100 (FK2, BML-PW0755-0025, Enzo) (Fujimuro et al., 1994), rabbit anti-Ref(2)P 1:100
(ab178440, Abcam), mouse anti Dlg 1:500 (4F3E3E9, Developmental Studies Hybrodima Bank) (Budnik
et al., 1996), mouse anti-Brp 1:250 (Nc82, Developmental Studies Hybridoma Bank) (Hofbauer et al.,
2009), anti-βGal 1:100 (40-1a-s, Developmental Studies Hybridoma Bank), rabbit anti-phosho-S6K 1:250
(T398, 9209S, Cell Signaling).
Secondary antibodies were used in the following concentrations: phalloidin fluorescene 1:1000
(F432, Life Technologies), Cy3-conjugated goat anti-Hrp 1:250 (123-165-021, Jackson ImmunoResearch),

30
Cy3-conjugated goat anti-mouse 1:500 (115-165-166, Jackson ImmunoResearch), Alexa Fluor® 488-
conjugated goat anti-rabbit 1:500 (111-545-003, Jackson ImmunoResearch Laboratories Inc), Alexa Fluor®
488-conjugated goat anti-mouse 1:500 (115-545-166, Life Technologies).
2.3.4 Image analysis
Larval NMJ boutons were counted manually using FIJI (ImageJ, NIH), using the cell counter plugin.
The muscle size was estimated by measuring the length and width of muscles 6/7 using FIJI (ImageJ, NIH).
The average number of boutons per muscle surface area is reported.
Larval NMJ active zone densities were measured using Imaris (Bitplane). Briefly, the confocal
images were compressed using maximum intensity projection in the Zen software (Zeiss). The compressed
images were loaded in Imaris (Bitplane). The number of presynaptic active zones counted by using the
points feature on the Brp signal and by masking the image using the Hrp signal. The Hrp area was estimated
using FIJI (ImageJ, NIH). The number of active zones per Hrp area is reported.
Larval NMJ ghost boutons were counted manually using FIJI (ImageJ, NIH) using the cell counter
plugin. A bouton-like structure that us positive for Hrp signal but negative for Dlg signal is counted as a
single ghost bouton. The average number of ghost boutons per NMJ is reported.
Polyubiquitin aggregate volume was measured using Imaris (Bitplane). The surface feature was
used to estimate the volume of polyubiquitin-positive signals. The data from each image were consolidated
using Python to a file format readable by Microsoft Excel (Microsoft). The total volume of aggregates was
divided by the muscle area analyzed for each muscle. The average polyubiquitin volume per muscle area is
reported.
Colocalization of Ref(2)P and polyubiquitin signal was calculated using Imaris (Bitplane).
2.4 Biochemistry
2.4.1 Western blot
Samples were dissected and lysed in radio-immunoprecipitation assay (RIPA) buffer (89901,
Thermo Scientific) supplemented with PhoSTOP phosphatase inhibitor (04 906 845 001, Roche) and
cOmplete mini protease inhibitor cocktail (04 693 124 001, Roche). The samples were sonicated for 30s in

31
1s pulses on ice (Vibra cellTM, Sonics). The lysate was centrifuged for 10 minutes at 15,000g, and the
supernatant was collected for further analysis. Protein concentration was measured using PierceTM BCA
Protein Assay Kit (23225, Thermo Scientific) according to the manufacturer’s protocols. Equal amounts of
protein were loaded into each well mixed with 2x Laemmli sample buffer (1310737 Bio-Rad) and 2.5%
Beta-Mercaptoethanol (1610710, Bio-Rad) according to the manufacturer’s protocols. Mini-PROTEAN®
TGXTM stain free 5-15% gradient precast gels were used (4568084, Bio-Rad) for most experiments. For
Atg8a, a hand-cast 15% linear gel was used (see Reagent Recipes). Electrophoresis was performed in MiniPROTEAN® Tetra system (Bio-Rad). Tris/Glycine/SDS buffer (1610772, Bio-Rad) was used for running
buffer. The protein was then transferred to Polyvinylidene difluoride (PVDF) membrane (1620177, BioRad) using Tris/Glycine transfer buffer (1910771, Bio-Rad) in a Mini Trans-Blot® Electrophoretic Transfer
Cell system (Bio-Rad). The transfer was confirmed using Ponceau staining (K793, Amresco).
The membranes were blocked using 5% blotting-grade blocker (170-6404, Bio-Rad) for nonphospho-antibodies and 5% bovine serum albumin (BSA) (A2153, Sigma) for phospho-antibodies diluted
in TBST (see Reagent Recipes) for 1 hour at RT. Membranes were incubated with the appropriate primary
antibody (see Antibody List) overnight at 4°C in 4% blotting-grade blocker for non-phospho-antibodies
and 4% BSA for phospho-antibodies. The membranes were washed 3 times with TBST for a total of 15
minutes. The membranes were incubated in appropriate secondary antibodies (see Antibody List) in 4%
blotting-grade blocker in TBST for 2 hours RT. The membranes were washed 3 times for a total of 15
minutes before imaging.
The following chemiluminescence reagents were used to visualize the stains: AmershamTM ECLTM
start Western blotting detecting reagent (RPN3243, Cytiva) and SuperSignalTM West Pico PLUS
Chemiluminescent Substrate (34577, Thermo Fisher Scientific).
Chemiluminescence signals were visualized on PrometheusTM ProSignalTM Blotting Film (30-810L,
Genesee). For the short exposure on Atg8a-I, Azure Biosystems c600 imaging system (Azure) was used.
Membranes were stripped using RestoreTM PLUS Western Blot Stripping Buffer (46430, Thermo
Scientific) for reprobing.

32
2.4.2 Antibody List (Western blot)
The membranes were stained with the following antibodies at the indicated concentrations: anti
phosho-eIF2α 1:1000 (S371, 119A11, Cell Signaling), anti-non-phospho eIFα 1:1000 (EIF2S1, ab26197,
Abcam), anti-Actin 1:5000 (C4, MAB1501, Sigma-Aldrich), anti-Atg8a 1:000 (GABARAP +
GABARAPL1 + GABARAPL2, ab109364, Abcam), anti phospho-S6K 1:1000 (T398, 9209S, Cell
Signaling), anti-Ref(2)P 1:500 (ab178440, Abcam).
The following secondary antibodies were used at the indicated concentrations: HRP-conjugated
anti-mouse IgG, IgM 1:2000 (A-10677, Invitrogen), HRP-conjugated anti-rabbit IgG, IgM 1:2000 (G21234,
Invitrogen).
For exposure on film, the film was scanned using HP Scanjet 8300. All Western blot images were
analyzed in FIJI (ImageJ, NIH).
2.4.3 Quantitative PCR
Quantitative PCR (qPCR) was performed as previously described (Maksoud et al., 2019). Briefly,
20 adult females were used for qPCR analysis per genotype and condition. For basal expression level
analysis, 1-week-old flies were used. For antimicrobial peptide (AMP) expression analysis, 4-week-old
flies were used. Adult female flies were frozen on dry ice and stored until extraction. Heads were removed
from the body by vortexing. Thoracic segments were manually dissected with the exoskeleton intact.
RNA was extracted using Tri Reagent® (RT-111, Molecular Research Center) and BAN Phase
Separation Reagent (BN-191, Molecular Research Center) according to the manufacture’s protocols. 200
proof pure ethanol (V1001, Koptec) and 2-Propanol (I9516, Sigma) were used for RNA precipitation. RNA
was reconstituted in non DEPC treated nuclease-free water (AM9938, Life Technologies). RNA
concentration was measured using NanoDrop 2000 (Thermo Fisher Scientific).
cDNA was synthesized from total RNA with iScript cDNA synthesis kit (1708891, Bio-Rad)
according to the manufacturer’s protocols.
mRNA expression was measured by quantitative PCR (qPCR) with iTaqTM universal SYBR green
supermix (1725121, Bio-Rad) on a Bio-Rad CFX96 thermocycler.

33
See table 2.3 for a list of primers.
Table 2.3 Primers
target F R Source
sif (RTPCR)
TGGTAAAGGATCTGCGGCAA GCCCACAACCTGAACGAACT
actin5C
(RTPCR)
TGTGTGGATACTCCTCCCGA CACATTTTGTAAGATTTGGTGTGTT
white CTCAAGAACGTTTGCGGCG GAAAGGCAAGGGCATTCAGC
scarlet TACCAATGTCGGTGGTTCCG AGAGCCACTCGAGCCCATTA
drs CGTGAGAACCTTTTCCAATATGATG TCCCAGGACCACCAGCAT (Sudmeier
droA CACCCATGGCAAAAACGC TGAAGTTCACCATCGTTTTCCTG et al., 2015)
attC TGGGCTACAACAATCATGGA GCGTATGGGTTTTGGTCAGT
dpt GCTGCGCAATCGCTTCTACT TGGTGGAGTGGGCTTCATG
cec ACGCGTTGGTCAGCACACT ACATTGGCGGCTTGTTGAG
Mtk CGTCACCAGGGACCCATTT CCGGTCTTGGTTGGTTAGGA
Atg8a TCGCAAATATCCAGACCGTGTGCCCGTC GCCGATGTTGGTGGAATGACGTTGTTCAC (Demontis
& Perrimon,
2010)
2.4.4 Polysome Profiling
Sucrose gradient was created by dissolving 20%, 30%, 40%, 50%, and 60% w/v sucrose (S7903,
Sigma) in polysome lysis buffer (see Reagent Recipes) in ultracentrifuge tubes. The gradients were frozen
at -80°C for at least 16 hours. On the day of the experiment, the gradients were thawed at RT.
Early 3rd instar larvae muscles were dissected. A total of 80 larvae were used per genotype per
condition. The muscle preps were lysed in polysome lysis buffer (see Reagent Recipes) using a 10mL
dounce (Wheaton) on ice. The lysate was centrifuged at 4500rpm for 5 minutes and supernatant collected
for further analysis.
Relative RNA contration was measured using NanoDrop 2000 (Thermo Fisher Scientific). Volume
of the lysates were normalized using the absorbance at 260 nm. The lysates were loaded to the top of the
sucrose gradient, and centrifuged at 38,000 rpm at 4°C for 1 hour 50 minutes using the SW41-Ti rotor in
Beckman OptimaTM XL-100k Ultracentrifuge (Beckman).
Fractions were collected using Biocomp Gradient MasterTM (Model 108, Biocomp), Piston
Gradient Fractionator (Model 152, Biocomp), Gradient Station (Model 153, Biocomp), and Model EM-1

34
Econo UV Monitor (731-8160, Bio-Rad). The data was recorded using UV gradient profiling software
version 7.18 (Biocomp). The fractions were stored at -80°C until further analysis.
The polysome profile was plotted using RStudio for presentation in the figures.
2.4.5 RT-PCR
RNA was extracted using Trizol® LS Reagent (10296028, Invitrogen) and chloroform (472476,
Sigma-Aldrich) according to the manufacture’s protocols. 200 proof pure ethanol (V1001, Koptec) and 2-
Propanol (I9516, Sigma) was used for RNA precipitation. RNA was reconstituted in non DEPC treated
nuclease-free water (AM9938, Life Technologies). RNA concentration was measured using NanoDrop
2000 (Thermo Fisher Scientific).
1000ng of total RNA and 120ng of RNA from each fraction was used for cDNA was synthesis
from each fraction. cDNA synthesis was performed with iScript cDNA synthesis kit (1708891, Bio-Rad)
according to the manufacturer’s protocols using C1000 Touch Thermal Cycler (Bio-Rad). PCR was
performed using GoTaq Green 2x Supermix (M7823, Promega). 50ng of total cDNA or 5ng of cDNA from
each fraction was used per PCR reaction. Stepdown PCR protocol was used with blocks of 4 cycles and
steps of 2°C (Korbie & Mattick, 2008). PCR products were visualized on 1.5% agarose gel (20-101,
Genesee) in TAE stained with GelRed (41003, Biotium) imaged with Bio-Rad ChemiDoc system (BioRad)
2.4.6 Proteasome activity assay
The proteosome kinetics were measured using a protocol modified from (Pomatto et al., 2017) and
(Tsakiri et al., 2013). Proteasomes were extracted from thoracic segments from 20 female flies. Tissues
were homogenized in 160µl proteasome extraction buffer (see Reagent Recipes). The extracts were
centrifuged at 10,000g for 5 minutes, and the supernatant was collected for further analysis. The protein
concentration was measured from 2µl of the sample using DC Protein Assay kit (Bio-Rad) according to the
manufacturer’s instructions. 10mg protein was used for further analysis.
For the 20S proteasome function, the lysates were incubated with 1µM of proteasome inhibitor
PS341(see Reagent Recipes) to estimate background activity and 0.5% DTT (43816, Sigma) for control.

35
For the 26S proteasome function, the lysates were incubated with 5mM MgATP (see Reagent Recipes) for
active 26S and 10mM MgSO4 (see Reagent Recipes) for background activity. 40µM Suc-LLVY-AMC
substrate (see Reagent Recipes) was added directly to the samples. The fluorescence was measured every
2 minutes for 2.5 hours on VICTOR Multilabel plate reader (PerkinElmer). The fluorescence was compared
against the fluorescence of the AMC standards (0µM, 1.25µM, 2.5µM, 5µM, 10µM, 20µM, 40µM) (see
Reagent Recipes) and converted to µM units. The background substrate processing was subtracted from the
active substrate cleavage curve. The kinetics of the enzymes were estimated by performing a linear fit to
the linear phase of the substrate processing curve. The calculations were done on Microsoft Excel
(Microsoft).
2.4.7 Mass Spectrometry
Metabolites were extracted from fly thoracic segments according to (L. Wang et al., 2019). Briefly,
20 thoracic segments were homogenized in 50µL methanol (A452, Fisher Scientific). The tissue was
sonicated for 1 minute on ice. 75µL of chloroform (472476, Sigma-Aldrich) was added and mixed for 1
minute. The lysate was centrifuged at 10,000g for 20 minutes at 4°C to separate the organic and aqueous
layers. Aqueous layer was collected for further analysis.
The LC-MS analysis was performed using Agilent 6520 accurate mass quadruple time of flight (QToF) mass spectrometer coupled with Agilent 1260 Infinity Pump (Agilent Technologies). Ionization was
performed in negative mode. Nitrogen was used as a desolvation and collision gas. The separation was
carried out using HILIC Kinetex 2.1 x 100 mm, 1.5 micron from Phenomenex. The autosampler was set at
50C. The solvent system was composed of Solvent A- 0.1% acetic acid in 95:5 (water: ACN) containing
20 mM ammonium acetate and Solvent B- 0.1% acetic acid in 95:5 (ACN: water) containing 20 mM
ammonium acetate. The flow rate was maintained at 0.3 mL/min, and the initial solvent conditions started
with 95% solvent B. At 2 min, the percentage of B was decreased to 50% B over 6 minutes, followed by
further decrease of % B to 10% for 3 minutes. At 11 minutes % B was increased 95% B for 3 minutes and
then maintained at starting conditions of 95% B for additional 2 minutes. The run time was 16 minutes.
Drying gas flow and temperature was set at 9.0 L/min and 350 0C, respectively and nebulizer gas pressure

36
was set at 35 psig. The applied capillary voltage was 3000 V. Full scan spectra was acquired from 100-
1000 m/z. The instrument was operated with Agilent MassHunter Work station LC/MS Data Acquisition
version 05.01, and chromatograms were processed with MassHunter Workstation qualitative software
version B.08.00.
The following standards were used to identify target peaks: L-tryptophan (93659, Sigma) in 0.1%
formic acid, L-kynurenine (K8625, Sigma-Aldrich) in 10% ACN, 3-hydroxy-DL-kynurenine (H1771,
Sigma) in 0.1% formic acid, kynurenic acid (K3375, Sigma) in DMSO, 5-hydroxy-L-tryptophan (107751,
Aldrich) in 10% ACN.
2.5 Electrophysiology
Wandering third instar larvae were dissected in cold HL3 solution (see Reagent Recipes) without
Ca2+ following standard protocol (Schuster et al., 1996). The spontaneous (mEJC) and evoked (EJC)
membrane currents were recorded from muscle 6 in abdominal segment A3 with standard two-electrode
voltage-clamp (TEVC) technique (Kauwe et al., 2016). All recordings were performed at room temperature
in HL3 solution containing 0.5mM Ca2+ for standard recording (see Reagent Recipes). The calcium chloride
solution was added to the HL3 solution fresh on the day of the experiment. The current recordings were
performed using AxoClamp 900A amplifier and Axon Digidata 1550 (Molecular Devices). The suction
electrode and recording electrodes were made from borosilicate glass capillaries (1B100F-4, World
Precision Instruments). The recording electrodes were filled with 3M potassium chloride solutions (see
Reagent Recipes). Nerve stimulation was delivered through a suction electrode connected to ISO-Flex
(A.M.P.I., Israel). All records were subjected to 1kHz low-pass filtering during acquisition, and a Boxcar
Lowpass Filter with a kernel of 11 was applied before analysis. For each NMJ, mEJC was recorded
continuously for 3 minutes and EJC was recorded with 40 pulses at 1Hz for Chapter 5, and at 0.5Hz for
Chapters 3 and 4. For both mEJC and EJC recordings, the holding potential was -80mV.
Amplitudes of mEJCs were measured using Mini Analysis 6.0.7 software (Synaptosoft) or MiniPy
(Chapter 7) and those of EJCs were measured using Clampfit 11.1 (pClamp). QC was calculated by dividing
the mean EJC amplitude by the mean mEJC amplitude for a given NMJ.

37
For high-frequency stimulation recording, 3mM Ca2+ was used and stimulation for EJC was
performed at 60Hz. The cumulative EJC at time zero was estimated by back-extrapolating the linear phase
of the cumulative EJC curve to time zero.
For adult NMJ recording, membrane current was recorded from proboscis muscles as previously
described (Mahoney et al., 2014). Recordings were performed on AxoClamp 2B amplifier (Molecular
Devices). Muscles were kept in HL3 solution with 0.5mM Ca2+. EJC was recorded with 40 pulses at 0.5Hz.
Figures of representative traces were made using MiniPy (Chapter 7). EJCs contain 10
superimposed traces evoked at 0.5 Hz or 1 Hz; mEJCs are samples of recording in the absence of
presynaptic stimulation. A single set of representative EJC and mEJC traces is comprised of data from the
same genotype and condition but not necessarily the same NMJ.
2.6 Bioinformatics
2.6.1 5’-UTR screen
The total length of the 5’-UTRs and the genomic region of the 5’-UTRs for each transcript were
extracted from the gene annotation data using R. The gene ID, gene name, and GO-terms for the parent
gene of each transcript were extracted in R using the biomaRt package from Bioconductor (Durinck et al.,
2009) connected to the Ensembl database. The transcripts were annotated as “synaptic” if the associated
GO term included the regular expression “synap”, such that terms such as “synapse” and “synaptic” were
included.
The sequence for 5’-UTR regions were extracted from BDGP 6.28 (Flybase). Thermodynamic
stability of the 5’-UTR was calculated using RNAfold software from the ViennaRNA package (Lorenz et
al., 2011). Centroid secondary structure prediction of 5’-UTR were generated using the RNAfold web
server (http://rna.tbi.univie.ac.at/cgi-bin/RNAWebSuite/RNAfold.cgi).
uORF information was extracted from the uORFlight database (Niu et al., 2020). Selenium package
on Python was used to automate the interaction with the uORFlight website and extraction of the relevant
text data.

38
2.6.2 RNAseq analysis of white deficient thoraces
RNA was extracted from 1-week old fly thoraces (see RNA extraction from quantitative PCR).
Library preparation and sequencing was performed by Genewiz. Sequencing was performed on Illumina
HiSeq platform (Illumina) by Genewiz.
FastQC (Babraham Institute) was used for quality check of RNAseq sequencing and adapter
trimming.
Ubuntu terminal on Windows (Linux) was used to run Salmon for alignment and quantification
(Patro et al., 2017).
DESeq2 (Love et al., 2014) and tximport (Soneson et al., 2015) were used for differential
expression analysis and visualization. Regularized logarithm (rlog) was used for transformation in DESeq2.
MA-plot and PCA plot were plotted using the built-in function of DESeq2. Volcano plot was plotted using
the Enhanced Volcano package (Blighe et al., 2020) using the results after applying LFC shrinkage.
Heatmap of significantly regulated genes were generated using the Pretty Heatmaps (pheatmap) package
from Comprehensive R Archive Network (CRAN). ClusterProfiler (G. Yu et al., 2012) was used for
enrichment analysis of the GO terms among significantly up and regulated genes, with q-value cutoff of
0.1 and q-value cutoff of 0.2. The GO terms were accessed from Ensembl database using biomaRt.
2.7 MiniPy Development
Anaconda (Anaconda) distribution of Python 3.8 was used for bioinformatic analysis in Chapter 3
and the development and the testing of MiniPy in Chapter 5. Code was written using Atom text editor
(Atom) and PyCharm (Jet Brains). Development and testing were performed on a Windows10 computer
(Microsoft). Results were compared against analysis from MiniAnalysis software (Synaptosoft).
Scatterplots and density plots were generated using ggplot2 package from CRAN (Pedersen, 2020).
Venn diagrams were produced using the limma package (Ritchie et al., 2015).
The following packages were used for the development of MiniPy:
tkinter – for graphical user interface (GUI) development
matplotlib – for data graphical display

39
NumPy – for mathematical operations and data interactions
Pyabf – for importing Axon Binary File formats (ABF) (Harden, n.d.)
Axographio – for importing Axograph file formats (Gill, n.d.)
2.8 Statistical Analysis
Pair-wise comparisons were analyzed with Student’s t-test on Microsoft Excel (Microsoft). Oneway ANOVA was performed using the base package on R. For comparison with significant variance,
Games-Howell post-hoc test was performed on R using the userfriendlyscience package (Peters, 2018).
Otherwise, Tukey’s post-hoc test was performed on R using the base package.
Error bars indicate Standard Error of the Mean (SEM) unless otherwise indicated.
Survival assays were compared using the Mantel-Cox (log-rank) test using the survival package
from R (Therneau, 2020).

40
Chapter 3. 4E-BP and Foxo impede synaptic homeostasis during
acute starvation
3.1 Abstract
While the benefits of DR to health and longevity are widely recognized in various models of disease
and aging, little is known about how nutrient deprivation influences synaptic function and plasticity. We
had previously reported that protein translation is required to induce retrograde signaling in response to a
reduction in postsynaptic receptor function at the D. melanogaster larval NMJ. Consequentially, acute
starvation is sufficient to suppress presynaptic homeostatic plasticity (PHP); however, we were surprised
to find that the suppression of PHP by acute starvation is independent of mTOR. I have undertaken to
identify the molecular mechanism by which acute starvation inhibits retrograde signaling and disrupts PHP.
3.2 Background
Evidence from vertebrate and invertebrate model synapses indicates that homeostatic plasticity is
highly dependent on protein translation at the postsynaptic cell (Mori et al., 2019). Limiting postsynaptic
translation as a result of transgenic knockdown of mTOR results in a failure in synaptic compensation at
pre- and post-synaptic terminals (Henry et al., 2012, 2018; Penney et al., 2012; C.-C. Wang et al., 2013).
At the Drosophila NMJ, GluRIIA-/- mutants provide an excellent genetic model to study PHP
(Petersen et al., 1997). We previously reported that mTOR is critical to achieving PHP in GluRIIA-/-
mutants
(Penney et al., 2012). Postsynaptic knockdown of mTOR results in failure to induce presynaptic
compensation, resulting in reduced EJC and QC in GluRIIA-/-
mutants (Penney et al., 2012). It was therefore
logical to suspect that starvation, which is known to inhibit mTOR activity, would impede on the synapse’s
ability to maintain homeostasis. As expected, acute starvation disrupts the NMJ’s ability to achieve PHP,
mimicking mTOR inhibition while having no effect on wildtype synaptic strength (Figure 3.1 A and B)
(Kauwe et al., 2016).
What we did not expect, however, was that the disruption in PHP during acute starvation was
independent of suppression of mTOR. For example, removal of amino acids from the diet, which effectively

41
reduces S6K phosphorylation, did not affect the NMJ’s ability to achieve PHP. Furthermore, larvae
expressing a constitutively active form of S6K still underwent reduction in EJC and QC during starvation,
indicating that starvation suppresses presynaptic release through a pathway independent of S6K (Kauwe et
al., 2016). This was particularly puzzling, as activation of mTOR or S6K in the postsynaptic muscle in an
otherwise wildtype larva is sufficient to induce retrograde signaling and drive presynaptic release (Penney
et al., 2012). Thus, acute starvation seemed to compete against S6K to suppress retrograde signaling and
PHP.
The aim for this chapter was to determine the mechanism by which starvation inhibits PHP.
3.3.1 Acute fasting does not change synaptic morphology
The growth of synaptic structures, known as boutons, at the Drosophila larval NMJ is tightly
regulated, and a number of genes have been identified that contribute to this intricate regulation (Ball et al.,
2010; Broadie & Bate, 1995). A wealth of experimental evidence suggests that often changes in synaptic
Figure 3.1 Starvation represses PHP in GluRIIA-/- mutants
Figure modified from (Kauwe et al., 2016) with permission from the author
(A) Representative traces for mEJC and EJC in wildtype (Df(2L)Cl-h4/+) and GluRIIA-/- mutant larvae
(GluRIIASP16/Df(2L)Cl-h4) treated with control food (6hr) or starvation food (6hr) prior to recording.
(B) Quantification of mEJC, EJC, and QC from genotypes and conditions in (A). N = 18, 19, 17, 20 (NMJ) One-way
ANOVA, followed by Tuckey post-hoc test.
Error bars represent SEM. **p<0.01, ***p<0.001

42
markers and pre and postsynaptic structures can affect presynaptic vesicular release and postsynaptic
neurotransmitter receptor localization, resulting in in electrophysiological phenotypes (Aberle et al., 2002;
Kittel et al., 2006; McCabe et al., 2004). I thus asked whether changes in NMJ morphology as a result of
acute starvation underlie the associated failure in achieving PHP in GluRIIA-/- mutant model.
Bouton number
To assess the number of synaptic boutons, I dissected GluRIIA-/- mutant larvae (Df(2L)Cl
-
h4/GluRIIASp16) raised on standard food or under starvation condition (agar and water) for 6 hourrs. The
NMJs were stained with antibodies against Discs-large (Dlg, fly ortholog of PSD-95) and Horseradish
peroxidase (Hrp). Dlg is associated with specialized postsynaptic structure and is commonly used as a
postsynaptic marker for the NMJ (Lahey et al., 1994). Hrp staining has been shown to selectively stain all
neuronal membranes in insects (Jan & Jan, 1982). My imaging and follow-up analysis found no significant
difference in the total number of glutamatergic boutons normalized by muscle surface area at NMJs between
fed and starved GluRIIA-/- mutant larvae (Figure 3.2 A and B).
Presynaptic active zones
I next assessed the density of the presynaptic vesicle release sites by visualizing Bruchpilot (Brp).
Brp is a key protein in assembly and maintenance of presynaptic active zones, and reduction in Brp
expression at the NMJ can significantly dampen EJC amplitudes (Kittel et al., 2006). The density of Brp
punctate quantity per NMJ area is routinely reported as an indicator of active zone density (Smith & Taylor,
2011). After 6 hours of starvation, I determined that acute starvation does not affect active zone density
(Figure 3.2 C and D).
Ghost boutons
Finally, as part of the morphological assessments, I examined the presence of ghost boutons in fed
and starved GluRIIA-/- mutant NMJ. Ghost boutons are thought to be immature synaptic structures that
appear bouton-like in Hrp signal and contain synaptic vesicle release machinery, but do not colocalize with
postsynaptic structures such as Dlg (Ataman et al., 2008). As such, ghost boutons can be identified as Hrp-

43
positive bouton-like structures that lack signal for Dlg (Figure 3.2 E). Ghost boutons appear spontaneously
in wildtype NMJ and their appearance is increased upon pulsed depolarization (Ataman et al., 2008). As
previously reported, the wildtype level of ghost bouton was low (Ataman et al., 2006). Additionally, there
was no difference in average ghost bouton numbers between fed and starved larvae (Figure 3.2 E and F).
Figure 3.2 Acute starvation does not change NMJ growth
(A) Representative images of GluRIIA-/- mutant larvae (GluRIIASP16/Df(2L)Cl-h4) from fed (6hr) or starved (6hr)
conditions stained with antibodies against Dlg (green) and HRP (magenta) at muscle 6/7. Scale bar = 20µm
(B) Quantification of the total number of type 1 synaptic boutons per muscle area in conditions from (A). N = 22, 19
(NMJ) Student’s t-test p = 0.7432
(C) Representative images of GluRIIA-/- mutant larvae (GluRIIASP16/Df(2L)Cl-h4) from fed (6hr) or starved (6hr)
conditions stained with antibodies against Brp (green) and HRP (magenta) at muscle 6/7. Scale bar = 20µm
(D) Quantification of the active zone density per synaptic area. N = 11, 13 (NMJ) Student’s t-test p = 0.260
(E) Representative images of GluRIIA-/- mutant larvae (GluRIIASP16/Df(2L)Cl-h4) from fed (6hr) or starved (6hr)
conditions stained with antibodies against Dlg (magenta) and HRP (green) at muscle 6/7. Arrow heads indicate
ghost boutons. Scale bar = 20µm
(F) Quantification of the number of ghost boutons per NMJ for the conditions in (E). N = 22, 19 (NMJ) Student’s t-test
p = 0.8138
Error bars represent SEM.

44
These results ruled out the possibility that the structural changes take place during acute fasting.
This suggested that a separate mechanism underlies the disruption in PHP in response to acute fasting.
3.3.2 4E-BP expression is induced in muscles during acute starvation
We next decided to investigate the role of a nutrient sensory different from TOR that could respond
to starvation. One such candidate was FOXO, which is activated in response to reduced insulin/IGF-1
signaling (Brunet et al., 1999; Greer et al., 2007). Recent evidence indicated that Foxo, the fly homolog of
the FOXO transcription factor family, induces the transcription of cap-dependent protein translation
inhibitor, 4E-BP (Demontis & Perrimon, 2010). As we had previously demonstrated that postsynaptic
protein translation is critical to the maintenance of PHP, and as 4E-BP is a well-known inhibitor of protein
translation, increased expression of 4E-BP was a likely cause for the disruption of PHP during acute
starvation. Furthermore, we had previously observed that heterozygosity for eIF4E, the target of 4E-BP
inhibition, is sufficient to suppress PHP in GluRIIA-/-
mutants (Penney et al., 2012). Together, these findings
strengthened the case for investigating if 4E-BP was involved in the disruption of PHP during acute
starvation.
In order to determine if acute fasting drives 4E-BP transcription in the muscle, I took advantage of
the publicly available fly line that contains an enhancer trap LacZ reporter inserted in the 4E-BP genetic
locus (Thor-LacZ) (Rodriguez et al., 1996). Activation of 4E-BP promoter can be visualized by staining for
the LacZ translation product, beta galactosidase (β-gal) (Cooper & Zhou, 2013). Supporting involvement
of 4E-BP, the LacZ signal in the muscle nuclei doubles in intensity in response to acute fasting, indicating
that muscle cells induce 4E-BP transcription during nutrient deprivation (Figure 3.3 A and B). This
observation was confirmed by qPCR and Western blot of muscle tissue (Kauwe et al., 2016).
Based on these observations, we examined if 4E-BP was required for the suppression of PHP in
GluRIIA-/- mutants during acute starvation. We performed two-electrode voltage clamp (TEVC) (see
Methods) on GluRIIA-/- larvae combined with a heterozygous mutation for 4E-BP. In the absence of one
gene copy of 4E-BP, the GluRIIA-/- mutants were able to maintain PHP during acute starvation (Figure 3.3
C and D), indicating that the disruption in PHP is dependent on 4E-BP (Kauwe et al., 2016).

45
3.3.3 FOXO is required to induce 4E-BP expression during acute starvation
Next, I questioned if activation of Foxo during acute starvation is responsible for induction of 4EBP expression. I took advantage of the UAS/Gal4 system to knockdown Foxo in a muscle-specific manner
while examining the level of Thor-LacZ reporter expression. Enhancement of the LacZ reporter expression
was no longer observed during acute starvation in combination with the RNAi against Foxo (Thor-LacZ/+;
MHC-Gal4/UAS-FoxoRNAi) (Figure 3.4 A and B). This confirmed previous observations that Foxo promotes
transcription of 4E-BP during starvation.
Figure 3.3 Induction of 4E-BP during acute starvation is required to suppress PHP
Panels C and D of this figure were reproduced and modified from (Kauwe et al., 2016) with the author’s permission.
(A) Representative images of Thor-LacZ larvae (Thor-LacZ/+;MHC-Gal4/+) from fed (6hr) or starved (6hr)
conditions stained with antibody against β-gal. Scalebar = 20µm.
(B) Quantification for conditions in (A). N = 3 (experiments) Student’s t-test p = 0.024
(C) Representative traces of mEJCs and EJCs from GluRIIA-/- mutants heterozygous for 4E-BP (GluRIIASP16,
Thor1/Df(2L)Clh4) treated with control food or starvation food (6hr).
(D) Quantification of mEJC, EJC, and QC from the genotype and conditions in (A). N = 19, 20 (NMJ) The data was
normalized to data from wildtype larvae (w1118) treated with control food. (N = 20) Student’s t-test.
Error bars represent SEM. *p<0.05

46
Figure 3.4 Acute starvation induces 4E-BP expression in the muscle and suppresses PHP in a
Foxo dependent manner
Panels C and D of this figure were reproduced and modified from (Kauwe et al., 2016) with the author’s permission.
(A) Representative images of Thor-LacZ larvae expressing RNAi against Foxo in the muscle (Thor-LacZ/+;MHCGal4/UAS-FoxoRNAi) from fed (6h4) or starved (6hr) conditions stained with antibody against β-gal. Scalebar =
20µm.
(B) Quantification for conditions in (C). N = 5 (experiments) Student’s t-test p = 0.843
(C) Representative traces of mEJCs and EJCs from control larvae expressing FoxoRNAi in the muscle (MHCGal4/UAS-FoxoRNAi), GluRIIA-/- mutant larvae expressing FoxoRNAi in the muscle (GluRIIASP16/Df(2L)Cl−h4;
MHC-Gal4/UAS-FoxoRNAi) fed or fasted (6hr), and GluRIIA-/- larvae with a heterozygous mutation for eIF4E
expressing FoxoRNAi in the muscle (GluRIIASP16/Df(2L)Cl−h4; MHC-Gal4, eIF4Es058911/UAS-FoxoRNAi)
fasted (6hr)
(D) Quantification of genotypes and conditions in (A). N = 25, 15, 18, 16 (NMJ) One-way ANOVA. Tukey post-hoc
test for mEJC and EJC, Games-Howell post-hoc test for QC.
Error bars represent SEM. *p<0.05, **p<0.01, ***p<0.001

47
Based on the observations that 4E-BP is required for disruption of PHP during acute starvation and
that Foxo is required to induce 4E-BP transcription during acute starvation, we examined if the disruption
in PHP during acute starvation is also dependent on Foxo. The combination of transgenic knockdown of
Foxo in the muscle with GluRIIA-/- indeed protected the PHP phenotype during acute starvation (Figure 3.4
C and D). However, we noted that the transgenic knockdown of Foxo in the muscle reduced the baseline
synaptic strength (Figure 3.4 C and D). To confirm that the reduced baseline synaptic transmission in Foxo
knockdown larvae did not affect the nature of synaptic compensation, we removed one copy of eIF4E in
the FoxoRNAi background. eIF4E is the target of 4E-BP inhibition, and we had previously demonstrated that
heterozygosity for eIF4E suppresses PHP in GluRIIA-/- mutants without affecting baseline synaptic
transmission. Indeed, the addition of heterozygocity for eIF4E in GluRIIA-/- mutants expressing FoxoRNAi in
the muscle was sufficient to suppress PHP in response to acute starvation (Figure 3.4 C and D) (Kauwe et
al., 2016).
These observations confirmed that the suppression of PHP during acute starvation is dependent on
Foxo and its ability to upregulate 4E-BP expression.
3.4 Discussion
In this chapter I investigated the mechanism behind disruption of PHP during acute starvation in
GluRIIA-/- mutants. I demonstrated that acute starvation does not alter NMJ morphology, but rather induces
the expression of 4E-BP in the muscle in a Foxo-dependent manner. Further electrophysiological
examinations revealed that 4E-BP and Foxo are responsible for disrupting PHP in GluRIIA-/-
mutants during
acute starvation. Overall, these results demonstrate that acute starvation inhibits PHP through upregulation
of 4E-BP via Foxo (Figure 3.5).
The scope of this chapter has been focused on molecular changes in the muscle; however, as Foxo
acts downstream of insulin signaling pathway (Barthel et al., 2005), it is likely that regulation of PHP during
acute starvation involves non-cell autonomous signaling via insulin signaling. More work will be needed
to determine if insulin signaling underlies the disruption of PHP during acute fasting.

48
Overall, 4E-BP seems to inhibit suppression of presynaptic release, keeping presynaptic release in
check. The ability of starvation to compete against increased synaptic transmission is particularly exciting
in the context of epilepsy treatment. Ketogenic diet and fasting have shown success in suppressing seizures
especially in patients that do not respond to pharmacological treatment (Bailey et al., 2005). Dysregulation
of mTOR through Tuberculosis Sclerosis Complex (TSC) is thought to underly abnormal circuitry in some
cases of epilepsy (Curatolo et al., 2018). The ability of 4E-BP to compete against TOR in regulating
synaptic release may underly the benefits of ketogenic diet and fasting in suppressing seizures.
Additionally, age-dependent increase in synaptic strength has been observed at the NMJ (Mahoney
et al., 2014). It is currently unknown what mechanisms drive this increase in presynaptic release. More
experiments will be needed to determine if such changes in the presynaptic release are responsive to
postsynaptic 4E-BP and acute starvation as seen in GluRIIA-/- mutants.
Figure 3.5 Model: Acute starvation inhibits PHP by Foxo-dependent expression of 4E-BP
This image was modified from (Kauwe et al., 2016) with the author’s permission.
During normal nutrient availability, lack of GluRIIA-/- induces PHP in a TOR dependent manner. During acute starvation,
activation of FOXO and subsequent upregulation of 4E-BP inhibits protein translation independently of TOR/S6K. This
results in the suppression of retrograde signaling required for PHP.

49
3.5 Conclusion
In this chapter I have described the discovery of a novel mechanism of PHP regulation at the D.
melanogaster larval NMJ. During acute starvation, FOXO-dependent transcription of 4E-BP in the muscle
suppresses retrograde signaling and inhibits PHP. These results demonstrate the impact of DR on synaptic
strength.

50
Chapter 4. Maintenance of synaptic strength during amino acid
starvation depends on translational upregulation of still life (sif)
through a GCN2/eIF2α dependent pathway.
4.1 Abstract
We had observed that baseline synaptic transmission is maintained during conditions of complete
starvation, amino acid restriction, or genetic knockdown of nutrient sensors and downstream pathways.
These observations led us to question the molecular pathways involved to maintain synaptic strength during
conditions of nutrient scarcity. We found that GCN2, a classic amino acid sensor, is required to maintain
synaptic strength during amino acid deprivation; however, the canonical translational target of
GCN2/eIF2α, ATF4, was dispensable. Therefore, I have set out to determine a novel translational target of
GCN2 involved in synaptic strength maintenance.
4.2 Background
While there is ample evidence that translational mechanisms are central in ensuring normal synaptic
plasticity at a variety of synapses, how these mechanisms influence baseline synaptic strength remains less
explored. Similarly unclear is whether and how specific translational mechanisms mediate the effect of DR
as it pertains to the regulation of synaptic strength and the adjustment of the setpoint of neurotransmitter
release. The powerful genetic tools in Drosophila together with our wealth of knowledge of the
electrophysiological properties of larval NMJ presented a unique opportunity for me to seek mechanistic
insights into this unknown area.
Our published and unpublished findings have previously demonstrated that baseline synaptic
transmission at the NMJ is maintained with high fidelity under complete starvation, amino acid restriction,
or genetic knockdown of translational machinery (Kauwe et al., 2016; Penney et al., 2012). These results
indicate that, while additional demand on the synapse in the case of homeostatic potentiation is highly
sensitive to these manipulations, a separate mechanism most likely exists to maintain basal synaptic

51
transmission during times of nutrient scarcity. From an evolutionary point of view, such a protective
mechanism would be adaptive, as animals would need to maintain basal synaptic transmission to search for
food and replenish nutrient resources.
A series of preliminary experiments initially suggested that the amino acid sensor GCN2 may be
playing a role in this process. GCN2 is normally activated when cells are faced with amino acid scarcity
(Dever & Hinnebusch, 2005). Activation of GCN2 is thought to be part of a complex cellular response that
is known as the integrated stress response (IRS) (Pakos‐Zebrucka et al., 2016; Taniuchi et al., 2016).
Additional electrophysiological assessment suggested that while GCN2 is dispensable for synaptic function
at the NMJ under normal conditions, GCN2 becomes critical for the maintenance of synaptic strength under
amino acid restriction (Figure 4.1 A and B) (Kauwe et al., 2021). As predicted, GCN2 functions through
phosphorylation of eIF2α and inhibition of translation initiation; however, the classical effector of IRS, the
transcription factor ATF4, does not participate in this response (Kauwe et al., 2021). Therefore, I set out to
search for a novel translational target of GCN2/eIF2α responsible for sustaining synaptic transmission
during amino acid scarcity.
Figure 4.1 GCN2 is required to maintain synaptic transmission during acute amino acid
restriction
This image was modified from (Kauwe et al., 2021) with the author’s permission.
(A) Representative traces of mEJCs and EJCs from larvae with transgenic knockdown of GCN2 in the muscle (MHCGal4/UAS-GCN2-RNAi), treated with control food or amino acid restriction food (6 hr).
(B) Quantification of mEJC, EJC, and QC from genotype and conditions in (A). N = 10, 15 (NMJ) Student’s t-test. p =
0.1741 (mEJC), 0.0040 (EJC), 0.0230 (QC)
Error bars represent SEM. *p<0.05, **p<0.01

52
4.3 Results
4.3.1 eIF2α is phosphorylated during amino acid restriction
To ensure that our amino acid restriction diet induces the phosphorylation of eIF2α in the larval
muscle, I performed Western blot on muscle extracts from wildtype (MHC-Gal4/+) larvae treated with
standard food (fed) or with amino acid restriction food (AA restriction) for 6 hours. The main source of
protein in the standard fly food is the yeast. As such, the amino acid restriction diet was created by
completely removing yeast from the ingredients of the standard fly food (see Methods). As expected from
the established role of GCN2 during amino acid restriction, I detected a robust increase in phosphorylatedeIF2α signal in muscle extracts from larvae fed on yeast-deprived diet compared to their sister larvae
(Figure 4.2 A and B). The total eIF2α level was unchanged between the two groups (Figure 4.2 A and C).
This indicated that our amino acid restriction regimen does induce GCN2 activation and IRS as expected.
Figure 4.2 Phosphorylation of eIF2α increases in the larval muscle on amino acid restriction diet
(A) Representative western blot of muscle lysates from wildtype larvae (MHC-Gal4/w1118) from fed (6hr) or starved
(6hr) conditions immunoblotted with antibodies against phosphorylated eIF2α, total-eIF2α, and actin.
(B) Quantification of the protein levels of phosphorylated eIF2α with respect to total eIF2α in conditions in (A),
normalized to fed. N = 3 (experiments). Student’ t-test p<0.001
(C) Quantification of the protein levels of total eIF2α with respect to actin in conditions in (A). N = 3 (experiments).
Student’s t-test p = 0.983
Error bars represent SEM. ***p<0.001

53
4.3.2 Bioinformatics analysis of 5’-UTR reveals candidate transcripts under translational control by
eIF2α phosphorylation
Phosphorylation of eIF2α suppresses global protein translation initiation by preventing the
formation of the ternary complex (Sonenberg & Hinnebusch, 2009). During this time, as the rate of
translation initiation is severely reduced, a select number of mRNAs experience a counterintuitive increase
in translation (Vattem & Wek, 2004). ATF4 is the archetype of this group of mRNAs, whose translation is
increased in response to eIF2α phosphorylation (Hinnebusch, 1993; Lu et al., 2004). As we had observed
that ATF4 is not required to maintain synaptic strength during acute amino acid restriction, we sought for
a novel ATF4-like target of GCN2/eIF2α that mediated the protective effects of GCN2.
Ranking of fly transcripts based on 5'-UTR complexity:
The critical element in mRNAs that show alternative response to the inhibitory consequence of
phosphorylation of eIF2α is features in their 5' untranslated region (5-'UTR) (Hinnebusch et al., 2016). I,
therefore, obtained the newest Drosophila melanogaster genome data from Ensembl (BDGP6.28) to
perform bioinformatics analysis on the the 5’-UTR structure of all known fly transcripts.
One of the major mechanisms by which the 5’-UTR regulates protein translation is the thermal
stability of the 5’-UTR sequence (Sonenberg & Hinnebusch, 2009). mRNAs with highly complex 5’-UTR
require unwinding by the cap-binding complex before the ribosome can start scanning for the start codon
(Sonenberg & Hinnebusch, 2009). We have previously reported that genes with potential relevance to
synaptic signaling at the NMJ show negative correlation between 5’-UTR complexity and translation of the
downstream open reading frame (ORF) (Penney et al., 2016). Screening for mRNA species with complex
5’-UTR has previously led to a successful identification of fur1 as the target of LRRK2 translational
regulation (Penney et al., 2016).
Following the same strategy, I calculated the thermal stability (ΔG) of each 5’-UTR using RNAfold
(ViennaRNA) (see Methods). The transcripts were ranked in the order of decreasing thermal stability
(increasing ΔG).

54
Evaluating the presence of upstream Open Reading Frames:
Another major mechanism by which the 5’-UTR regulates translation initiation is the presence of
upstream ORFs (uORFs). uORFs can inhibit translation of the main ORF (mORF) by forcing the ribosome
to miss the start codon in the mORF (Hinnebusch et al., 2016; K. Kang et al., 2015; Lu et al., 2004). During
amino acid restriction conditions, phosphorylation of eIF2α slows the reassembly of the 43S ribosomal
complex, increasing the chances that the ribosome misses the detracting uORF and successfully initiates
translation at the mORF (Vattem & Wek, 2004). This phenomenon is known as leaky scanning and is
known to regulate ATF4 translation in response to eIF2α phosphorylation (Vattem & Wek, 2004).
Based on these observations, I extracted the number of uORF in each transcript from the uORFlight
database (Niu et al., 2020) (see Methods). uORFlight categorizes uORFs into 3 groups based on the position
of the uORF stop codon: Type 1 located before the mORF, Type 2 located within the mORF, and Type 3
shared with the mORF (Figure 4.3) (Niu et al., 2020). Type 2 uORFs are thought to restrict translation via
the leaky scanning strategy, making it the most relevant to the search for ATF4-like transcripts (Niu et al.,
2020).
Figure 4.3 uORF classifications in uORFlight
Schematic representation of uORF categories found in uORFlight database. A Type 1 uORF has its stop codon in the 5’-
UTR, upstream of the main ORF. A Type 2 uORF has its stop codon within the main ORF, downstream of the start codon of
the main ORF. A Type 3 uORF shares its stop codon with the main ORF.

55
Relationship between 5’-UTR length, thermodynamic stability, and uORF presence:
Comparison of 5’-UTR length and thermodynamic stability indicated that longer 5’-UTR tended
to have higher thermodynamic stability and more uORFs, as would be expected (Figure 4.4 A and B).
However, the number of Type 2 uORF did not correlate with the length of 5’-UTR (Figure 4.4 C).
Identification of translational target candidates of GCN2/eIF2α:
In order to increase the chance of finding a gene relevant to synaptic transmission regulation, I
filtered transcripts based on their GO terms. Only transcripts whose parent genes had a known function at
the synapse were considered for further analysis (see Methods).
Among top 50 most thermodynamically stable 5’-UTRs, I found only 11 transcripts that contain at
least one Type 2 uORF in their 5’-UTR (Table 4.1). still life (sif) is one of the genes identified through this
screen. Prediction of sif 5’-UTR secondary structure confirms its highly complex form (Figure 4.5).
Figure 4.4 Free energy, but not the number of Type 2 uORF, correlates with 5’-UTR length
(A) Scatterplot comparing the 5’-UTR length of each transcript within the D. melanogaster genome against the
predicted free energy calculation. Multiple R-squared value for the correlation is indicated.
(B) Scatterplot comparing the 5’-UTR length of each transcript within the D. melanogaster genome against the number
of known uORFs. Multiple R-squared value for the correlation is indicated.
(C) Scatterplot comparing the 5’-UTR length of each transcript within the D. melanogaster genome against the number
of known Type 2 uORFs. Multiple R-squared value for the correlation is indicated.

56
Table 4.1 Top 50 synaptic transcripts ranked by ΔG (lowest to highest)
50 transcripts with the lowest predicted free energy for their 5’-UTR obtained from bioinformatic analysis of D. melanogaster
genome. Flybase gene ID, Flybase transcript ID, gene name, length of 5’-UTR, estimated free energy, and the number of Type 1,
Type 2, and Type 3 uORFs are listed. Transcripts for sif are indicated in bold.
Gene ID Transcript ID Gene Symbol 5’-UTR
length (bp)
ΔG
(kcal/mol)
type1 type2 type3
FBgn0000163 FBtr0111000 baz 4957 -1236.24 93 0 0
FBgn0000163 FBtr0343764 baz 4957 -1236.24 93 0 0
FBgn0000479 FBtr0333312 dnc 3636 -1061.5 49 1 0
FBgn0000479 FBtr0070519 dnc 3582 -1019.1 49 0 0
FBgn0025726 FBtr0344971 unc-13 3735 -999.2 98 0 0
FBgn0266757 FBtr0273291 mfr 3036 -945.5 54 5 0
FBgn0261556 FBtr0302712 CG42674 3326 -936.9 36 0 0
FBgn0261556 FBtr0302714 CG42674 3326 -936.9 36 0 0
FBgn0261556 FBtr0302713 CG42674 3326 -936.9 36 0 0
FBgn0267001 FBtr0300203 Ten-a 3038 -931.97 39 0 0
FBgn0261556 FBtr0302715 CG42674 3217 -901.3 35 0 0
FBgn0016975 FBtr0344111 stnB 2859 -890.8 5 0 0
FBgn0267001 FBtr0299860 Ten-a 2767 -889.8 37 0 0
FBgn0016975 FBtr0302020 stnB 2797 -880.6 5 0 0
FBgn0016975 FBtr0344466 stnB 2797 -880.6 5 0 0
FBgn0016975 FBtr0302021 stnB 2671 -853.2 5 0 0
FBgn0000479 FBtr0333317 dnc 2714 -834.2 14 0 0
FBgn0085447 FBtr0340656 sif 2908 -792.07 19 1 0
FBgn0000479 FBtr0333316 dnc 1949 -675 2 0 0
FBgn0038063 FBtr0347149 Octbeta2R 2760 -672.47 41 0 0
FBgn0038063 FBtr0304846 Octbeta2R 2681 -668.8 41 0 0
FBgn0085447 FBtr0330126 sif 1626 -609.4 16 1 0
FBgn0085447 FBtr0112709 sif 1626 -609.4 16 1 0
FBgn0085447 FBtr0112710 sif 1626 -609.4 16 1 0
FBgn0004595 FBtr0304604 pros 2448 -531.67 17 0 0
FBgn0003380 FBtr0302902 Sh 2388 -530.8 18 0 0
FBgn0003380 FBtr0089661 Sh 2388 -530.8 18 0 0
FBgn0003380 FBtr0332299 Sh 2388 -530.8 18 0 0
FBgn0266757 FBtr0273285 mfr 1732 -506.9 36 0 0
FBgn0085414 FBtr0300553 dpr12 1534 -479.4 14 0 0
FBgn0086913 FBtr0331794 Rab26 1898 -473.5 25 6 0
FBgn0030230 FBtr0301558 Rph 1421 -471.4 13 0 0
FBgn0085414 FBtr0335273 dpr12 1503 -470.4 14 0 0
FBgn0085414 FBtr0335272 dpr12 1503 -470.4 14 0 0
FBgn0032151 FBtr0079913 nAChRalpha6 2371 -468.06 18 0 0
FBgn0032151 FBtr0079915 nAChRalpha6 2371 -468.06 18 0 0
FBgn0052791 FBtr0333794 DIP-alpha 1575 -449.7 38 0 0
FBgn0003380 FBtr0308200 Sh 1995 -449.6 15 0 0
FBgn0003380 FBtr0302903 Sh 1995 -449.6 15 0 0
FBgn0259108 FBtr0307598 futsch 1897 -447 7 0 0
FBgn0050361 FBtr0330608 mtt 1621 -445.9 11 9 0
FBgn0050361 FBtr0273325 mtt 1621 -445.9 11 3 0
FBgn0050361 FBtr0273326 mtt 1621 -445.9 11 3 0
FBgn0030230 FBtr0332296 Rph 1253 -440.9 10 0 0
FBgn0039617 FBtr0085347 DIP-gamma 1380 -440.5 13 0 0
FBgn0030230 FBtr0332295 Rph 1283 -434.5 19 1 0
FBgn0065108 FBtr0100832 ppk16 1616 -434.1 37 0 0
FBgn0261617 FBtr0071402 nej 1710 -432.35 8 0 0
FBgn0040823 FBtr0331536 dpr6 1642 -431.1 20 0 0
FBgn0040823 FBtr0332666 dpr6 1642 -431.1 20 0 0

57
Figure 4.5 sif transcript exhibits highly complex 5’-UTR secondary structure
Centroid secondary structure predictions of 5’-UTRs from sif-RP transcript and Tor-RB transcript as predicted by the
RNAfold web server (see Methods). The free energy and the length of the 5’-UTRs are indicated next to the respective
secondary structures.

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4.3.3 still life (sif) is translationally regulated in response to amino acid restriction
Sif is a Rac-GTPase specific guanine nucleotide exchange factor (GEF) with orthologs in mammals
(Sone et al., 2000). At the fly NMJ, Sif has been reported to primarily localize to the periactive zones in the
presynaptic terminal and is reported to participate in the regulation of synaptic growth and the larval NMJ
(Sone et al., 1997, 2000).
To determine if Sif is translationally regulated during amino acid scarcity, I performed polysome
profiling on muscle lysates from wildtype 3rd instar larvae (MHC-Gal4/+) raised on with standard food or
no-yeast food (6hr). Polysome profiling allows the visualization of global translation efficiency and
separation of mRNAs based on their association with ribosomes. A typical polysome profile reveals
distinctive peaks corresponding to free mRNA, as well as single- and poly- ribosome bound transcripts.
Performing RT-PCR on the RNA from each fraction can identify transcripts that are being actively
translated: a shift to the heavier polysome fractions suggests an increase in translation, while a shift to the
lighter fractions suggests the opposite.
The polysome profile of amino acid deprived muscle showed significantly reduced polysome peaks
compared to fed larvae, indicating a decrease in global translation (Figure 4.6 A), as it would be expected
in response to activation of GCN2 and phosphorylation of eIF2α (see Figure 4.2 A-C). I extracted the RNA
from each fraction and performed RT-PCR using primers against sif and actin5C (Figure 4.6 B). Amino
acid restriction significantly increased the enrichment of sif transcript in the heavy polysome fractions
compared to the sister larvae that were fed with the standard diet, while not changing the enrichment of the
actin5c transcripts (Figure 4.6 C and D). These results indicated that translation of sif transcripts increase
in the muscle during amino acid restriction, making it a candidate transcript responsible for protecting
synaptic strength during amino acid deficiency downstream of GCN2.

59
4.3.4 sif is required in the muscle to maintain baseline synaptic transmission during amino acid
restriction
To confirm the findings that sif is a likely target of GCN2 that maintains synaptic strength during
amino acid restriction, we took advantage of the UAS/Gal4 system to transgenically knockdown sif in the
muscle and exposed the larvae to acute amino acid restriction. TEVC recordings demonstrated that sif is
indeed required in the muscle to maintain synaptic strength during acute amino acid deprivation, similar to
Figure 4.6 Sif translation is increased during amino acid restriction
(A) Representative polysome profile of muscle lysates from wildtype (MHC-Gal4 x w1118) larvae in fed (6hr) or
amino acid restricted (6hr) conditions. Gray box indicates fractions assayed for RT-PCR.
(B) Representative RT-PCR gel from conditions in (A) probed with primers against sif and actin5C.
(C) Quantifications of the RT-PCR using primers against sif from conditions in (A), normalized to the pooled
monoribosome fractions. Fractions were pooled for analysis. N = 3 (experiments) Student’s t-test.
(D) Quantifications of the RT-PCR using primes against actin5C from conditions in (A), normalized to the pooled
monoribosome fractions. Fractions were pooled for analysis. N = 3 (experiments) Student’s t-test.
Error bars represent SEM. *p<0.05, **p<0.01

60
the requirement of GCN2. During amino acid restriction, transgenic knockdown of sif in the muscle
compromised the NMJ’s ability to maintain synaptic strength, resulting in suppression of EJC amplitude
and QC (Figure 4.7 A and B).
Together, these experiments demonstrate that increase in sif translation in the muscle during amino
acid restriction is required to maintain synaptic strength.
4.4 Discussion
In this chapter, I evaluated the mechanism by which baseline synaptic activity is protected during
amino acid restriction. We had previously observed that GCN2 is indispensable in the maintenance of
baseline synaptic strength during acute amino acid deprivation. As expected from the classical role of
GCN2, maintenance of baseline synaptic strength is achieved via the phosphorylation of eIF2α. However,
the classical effector of IRS, ATF4, was not necessary for protection of synaptic strength. This prompted a
search to a novel translational target of GCN2 that underlies the protection against loss in synaptic strength
during amino acid deprivation.
Figure 4.7 sif is required in the muscle to maintain synaptic strength during acute amino acid
restriction
This image was modified from (Kauwe et al., 2021) with the author’s permission.
(A) Representative EJC and mEJC traces from larvae expressing RNAi against sif in the muscle (UAS-sif RNAi/+;
MHC-Gal4/+) in fed (6hr) or amino acid restricted (6hr) conditions.
(B) Quantifications of mEJC, EJC, and QC from genotype and conditions in (A). N = 9, 9 (NMJ). Student’s t-test. p =
0.170 (mEJC), p<0.001 (EJC), p<0.001(QC)
Error bars represent SEM. ***p<0.001

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Bioinformatic analysis of synaptic transcripts indicated that multiple isoforms of sif matched the
5’-UTR features that make it likely to be a target of GCN2 translational regulation. Polysome analysis
confirmed that the translational efficiency of sif increases in response to acute amino acid restriction.
Follow-up electrophysiological analyses confirmed that postsynaptic sif is required to maintain synaptic
strength during acute amino acid deficiency. These data demonstrate that GCN2 and its novel translational
target, Sif, are required to maintain synaptic transmission during acute amino acid restriction (Figure 4.8).
The bioinformatic screen has identified several other candidate transcripts that could also respond
to eIF2α phosphorylation in an ATF4-like manner. More experiments will be needed to determine if sif is
the only translational target of GCN2 that is required to maintain synaptic strength during amino acid
restriction or if other ATF4-like transcripts also participate in this pathway.
Figure 4.8 Model figure: GCN2/eIF2α maintains baseline synaptic strength during amino acid
restriction through translational upregulation of Sif
During acute amino acid deprivation, eIF2α is phosphorylated by GCN2. Reduction in global protein translation induces
translation of Sif in the postsynaptic muscle, which triggers retrograde signaling to maintain baseline synaptic strength.

62
We have previously observed that synaptic compensation is maintained in GluRIIA-/- mutants
during amino acid restriction, despite mTOR activity being reduced (Kauwe et al., 2016). It is possible that
activated GCN2/eIF2α pathway sustains the retrograde signaling in the absence of mTOR during amino
acid restriction. More experiments will be needed to determine if GCN2 plays a role in maintaining PHP
during acute amino acid restriction.
4.5 Conclusion
In this chapter, I have investigated the molecular mechanism by which GCN2 provides necessary
support to maintain baseline synaptic strength during conditions of amino acid restriction. Through
bioinformatic analysis and biochemical experiments, I’ve discovered a novel translational target of
GCN2/p-eIF2α, sif. Electrophysiological analysis confirmed that sif is indispensable during amino acid
restriction to maintain baseline synaptic activity. These results further highlight the orchestration of nutrient
sensors in regulating synaptic strength during condition of nutrient scarcity.

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Chapter 5. Nutritional glutamine protects synaptic strength against
Gs2 deficiency
5.1 Abstract
Glial cells provide critical support to the peripheral neurons via axonal ensheathment. One of the
critical roles of glial cells is to provide necessary nutrients to the neurons. We have found that disruption
of the Delta/Notch signaling in subperineurial glia (SPG) reduces their expression of Glutamine synthetase
2 (Gs2). I took advantage of this system to investigate the role of glutamine imbalance in regulating synaptic
transmission. These results provide insight into the regulation of synaptic transmission through dietary Lglutamine through the function of glia.
5.2 Background
Glial ensheathment of peripheral axon provides critical support for neural function, including
maintenance of ion balance, nutrient transport, and trophic support (Salzer & Zalc, 2016). In Drosophila,
ensheathment of the motoneuron axons is provided by three layers of glia: peripheral wrapping glia (WG)
which forms the first layer of ensheathment around a bundle of axons; SPG and perineurial glia (PG) which
form concentric glial layers around the WG; and neural lamina (NL) which form the outer most layer
(Figure 5.1) (Rodrigues et al., 2011).
Figure 5.1 Cross-sectional model of third instar larval peripheral nerve in D. melanogaster
Peripheral axons are ensheathed by the wrapping glia (WG), subperineurial glia (SPG), perineurial glia (PG) and the neural
lamella (NL).

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At glutamatergic synapses the neurotransmitter glutamate released by the presynaptic neuron is
recaptured and taken up by the surrounding glia to prevent unwanted receptor activation (McKenna et al.,
1996). In order to replenish the neuronal glutamate resource, glial cells metabolize glutamate to glutamine
via Gs2 (Waniewski & Martin, 1986). Glutamine can then be shuttled back to the neuron, where it is
reverted to glutamate. This cycle is known as the glutamine-glutamate cycle (Sonnewald & Schousboe,
2016). Gs2 is integral to this pathway, and its insufficiency causes synaptic dysfunction and behavioral
defects (Son et al., 2019).
We had found that glial knockdown of delta, the ligand for the notch signaling pathway, reduces
the glial expression of Gs2 protein (Calderon et al., 2021). This therefore provided a perfect opportunity to
test if nutritional availability of a single amino acid, glutamine, can influence synaptic strength.
Based on the effect of delta knockdown on Gs2 expression, we hypothesized that knockdown of
delta would compromise synaptic transmission. Using the Gal4/UAS system in combination with
transgenic RNAi technology, I demonstrate that knockdown of Delta in glial cells have defects in synaptic
strength which can be rescued by L-glutamine supplementation in the food.
5.3 Results
5.3.1 Lack of Delta in glial cells reduce synaptic strength
I first examined the consequence of knockdown of Delta in glial cells affected synaptic strength.
Gal4/UAS system (Brand & Perrimon, 1993) was used to knockdown delta in the glial cells. To avoid
disrupting Delta/Notch signaling during the embryonic stages, the Gal80ts system was used to temporally
restrict the transgenic knockdown (McGuire et al., 2004).
I performed TECV on the delta knockdown (Repo-Gal4/+; tub-Gal80ts/UAS-DeltaRNAi) and
control (Repo-Gal4/+;tub-Ga80ts/UAS-mCherryRNAi) larvae and found that the EJC and QC are reduced in
delta deficient larvae without changes to the mEJC amplitudes (Figure 5.2 A and B). To confirm this
phenotype, I used temperature-sensitive delta mutants to temporally control Delta activity. The deltaRF
allele has normal activities at permissive temperatures (18°C) but reduced activity at non-permissive
temperatures (29°C). Combination of deltaRF with the amorphic allele deltaRevF10 allow for temporal control

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of Delta function. I found that the trans-heterozygote combination (deltaRF/deltaRevF10) also significantly
compromised synaptic strength compared to heterozygous control (deltaRF/+) (Figure 5.2 C and D),
confirming the synaptic phenotype seen in response to transgenic knockdown of delta.
These observations demonstrated that lack of delta, which reduces Gs2 expression, does have an
electrophysiological phenotype.
Figure 5.2 Lack of Delta in glial cells reduce synaptic strength
(A) Representative traces of mEJCs and EJCs from larvae expressing RNAi against mCherry (Repo-Gal4/+;tubGa80ts/UAS-mCherryRNAi) or Delta (Repo-Gal4/+; tub-Gal80ts/UAS-DeltaRNAi) in glial cells during last 60
hours of larval development.
(B) Quantifications of mEJC, EJC, and QC from the gneotypes in (A), normalized to mCherryRNAi. N = 11, 11
(NMJ) Student’s t-test. p = 0.245 (mEJC), 0.012 (EJC), 0.025 (QC)
(C) Representative traces of mEJCs and EJCs from deltaRF/+ or deltaRF/RevF10 larvae.
(D) Quantifications of mEJC, EJC, and QC from genotypes in (C), normalized to deltaRF/+/. N = 12, 10 (NMJ)
Student’s t-test. p = 0.464 (mEJC), 0.037 (EJC), 0.075 (QC)
Error bars represent SEM. *p<0.05

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5.3.2 Knockdown of Gs2 in glia is sufficient to reduce synaptic strength
I next asked whether the transgenic reduction of Gs2 would be sufficient to phenocopy the
reduction in synaptic strength found in delta deficient larvae. We had observed that transgenic knockdown
of delta in a subset of glial cells, known as the Sub-Perineural Glia (SPG) is sufficient to reduce Gs2 protein
expression in the neural sheath (Calderon et al., 2021). I therefore used the SPG-specific Gal4 line to
transgenically knockdown Gs2 and test if this is sufficient to reproduce the electrophysiological phenotype
of delta deficiency.
As expected, I found that SPG-specific knockdown of Gs2 (SPG-Gal4/UAS-Gs2RNAi) reduces the
EJC amplitude and QC without affecting mEJC amplitude compared to control (SPG-Gal4/UASmCherryRNAi) (Figure 5.3 A and B), confirming that knockdown of Gs2 in SPG cells recapitulates the
synaptic defects of glial knockdown of delta. These results indicate that when Delta/Notch signaling is
compromised, a reduction in Gs2 expression may underlie the reduction in synaptic strength.
Figure 5.3 SPG knockdown of Gs2 is sufficient to reduce synaptic strength
(A) Representative traces for mEJC and EJC from larvae expressing RNAi against mCherry (SPG-Gal4/UASmCherryRNAi and SPG-Gal4/+; UAS-mCherryRNAi) or Gs2 (SPG-Gal4/UAS-Gs2RNAi and SPG-Gal4/+; UASDeltaRNAi) in glial subset population of SPG.
(B) Quantifications of mEJC, EJC, and QC for genotypes in (A). N = 26, 19 (NMJ) Student’s t-test. p = 0.741 (mEJC),
0.012 (EJC), 0.021 (QC)
Error bars represent SEM. *p<0.05

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5.3.3 Glutamine supplementation rescues synaptic strength in delta deficiency
At vertebrate glutamatergic synapses, glial cells play an important role in glutamate reuptake and
cycling (Eid et al., 2013). Glial cells metabolize glutamate to glutamine via Gs2 and shuttle the glutamine
to the neuron (Sonnewald & Schousboe, 2016; Waniewski & Martin, 1986).
The reduction in Gs2 protein in glial Delta deficiency indicated that the level of glutamine maybe
negatively affected by the lack of Delta in glial cells. Since lack of Gs2 is sufficient to reduce synaptic
strength at the larval NMJ, lack of glutamine may be underlying the synaptic dysfunction in delta
knockdown and mutants. This provided an opportunity to test if nutritional glutamine has an impact on the
synaptic strength.
I assessed the electrophysiological properties of pan-glial delta knockdown supplemented with
50mM L-glutamine and compared them against their sister larvae raised on standard fly food, as well as
control larvae raised on standard fly food. The 50mM glutamine supplementation was indeed sufficient to
bring the EJC amplitude and QC of Delta knockdown back to control levels (Figure 5.4 A and B),
demonstrating that Delta insufficiency reduces synaptic strength by limiting the availability of glutamine
at the synapse.
5.3.4 Lack of Delta in glial cells does not change readily releasable pool availability
Glutamine shuttled from glial cells can be converted to glutamate at the neuron for vesicular
refilling (Sonnewald & Schousboe, 2016). We suspected that the lack of Gs2 affects the vesicular filling of
glutamate at the presynaptic terminal, especially since nutritional glutamine supplementation is sufficient
to rescue the synaptic defect. I therefore sought to determine if Delta deficiency is affecting the vesicle
availability in the motoneurons.
The availability of synaptic vesicles can be determined as the size of the readily releasable pool
(RRP), which represents the fraction of synaptic docked for immediate release from the presynaptic
terminal (Kaeser & Regehr, 2017). If glutamate availability is changing vesicle filling and availability, the
size of the RRP should be affected by delta deficiency.

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There are several methods to estimate RRP, one of which is to record postsynaptic current during
high-frequency stimulation (Schneggenburger et al., 1999). The high frequency evoked synaptic currents
were recorded while larvae were bathed in 3mM external calcium (see Methods). Surprisingly, analysis of
the cumulative EJC showed that there is no difference in the size of RRP as a result of temporally restricted
knockdown of delta in glial cells (Figure 5.5 C and D). This result indicates that synaptic phenotype arising
from delta deficiency is independent of synaptic vesicle availability.
Overall, these results indicate that synaptic defects induced by Gs2 deficiency can be overcome by
glutamine supplementation, but the mechanism is independent of vesicular refilling.
Figure 5.4 Dietary L-glutamine rescues synaptic strength defect in Delta knockdown
(A) Representative traces for mEJC and EJC from larvae expressing RNAi against mCherry (Repo-Gal4/+;tubGa80ts/UAS-mCherryRNAi) or Delta (Repo-Gal4/+; tub-Gal80ts/UAS-DeltaRNAi) in glial cells during last 60
hours of larval development and grown in either control food or food supplemented with 50mM L-glutamine.
(B) Quantifications of mEJC, EJC, and QC from genotypes and conditions in (A). N = 7, 15, 16 (NMJ) One-way
ANOVA, p = 0.289 (mEJC), <0.001 (EJC), <0.001 (QC), followed by Game’s-Howell post-hoc test.
(C) Mean cumulative EJC of high stimulation recording from larvae expressing RNAi against mCherry (RepoGal4/+;tub-Ga80ts/UAS-mCherryRNAi) or Delta (Repo-Gal4/+; tub-Gal80ts/UAS-DeltaRNAi) in glial cells
during last 60 hours of larval development
(D) Quantification of cumulative EJC at time zero in (C) by back extrapolating from the linear fit of the last ten stimuli
to time zero. N = 10, 8 (NMJ) Student’s t-test p = 0.685
Error bars represent SEM. *p<0.05, **p<0.01, ***p<0.001

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5.4 Discussion
We have observed that temporally controlled inhibition of Delta/Notch signaling in glial cells
significantly curtails the expression of Gs2 protein in the neural sheath without any morphological defects
at the NMJ (Calderon et al., 2021). Lack of Gs2 has been shown to have synaptic and behavioral defects,
suggesting that disruption in Delta/Notch signaling could also exhibit synaptic defects in a Gs2- and
glutamine-dependent manner (Son et al., 2019).
I have demonstrated that lack of delta in glial cells reduces synaptic strength, and this is
recapitulated by transgenic knockdown of Gs2 in the SPG subset population of glial cells. Supplementation
of L-glutamine through the food rescues that synaptic strength in delta knockdown, indicating that
disruption in glutamine maintenance underlies the synaptic strength defects when delta is deficient in glial
cells.
The classical role of Gs2 in the glutamine-glutamate cycle predicts that lack of glial Gs2 should
slowdown the replenishment of vesicular glutamate. However, I found that lack of Delta in glial cells does
not change the size of the readily releasable pool. Lack of glial glutamine production therefore seemed to
be compromising the presynaptic release via a different mechanism.
Glutamine and glutamate can both be metabolized for energy production (DeBerardinis & Cheng,
2010). Neurons have been shown to divert their pool of glutamine and glutamate between vesicular refilling
and energy demands (Divakaruni et al., 2017). It is therefore possible that under Gs2 deficiency, neurons
prioritize the need for vesicular refilling at the cost of fulfilling energetic demands. Metabolomic
assessment of the NMJ in Delta deficiency or Gs2 deficiency would be needed to determine if a shift in
energetic equilibrium is indeed taking place.
5.5 Conclusion
In this chapter I have demonstrated that disruption in Delta/Notch signaling in glial cells
significantly disrupts baseline synaptic strength in the NMJ of D. melanogaster larvae. Knockdown in delta
in SPG cells reduces the expression of Gs2, resulting in reduced glutamine availability. I demonstrated that
L-glutamine supplementation is sufficient to rescue the loss in synaptic strength in delta knockdown;

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however, the lack of change in RRP indicates that Delta/Notch deficiency disrupts synaptic transmission
independent of vesicle refiling. Overall, this chapter demonstrates the role of glia and glutamine balance in
regulating synaptic strength.

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Chapter 6. Tryptophan transporter, synaptic transmission,
proteostasis and aging
6.1 Abstract
Aberrant excitatory synaptic function has emerged in the recent years as a potential hallmark of
brain aging, as more findings point to excitation/inhibition imbalance and aberrantly high presynaptic
release as early markers and drivers of various neurodegenerative diseases. Increase in presynaptic release
has also been observed at the peripheral nervous system at the NMJ. These observations have prompted us
to investigate if excitatory synaptic activity can aggravate the postsynaptic aging process in the peripheral
tissue. Currently, there is little experimental evidence for how nervous system activity might influence
aging and age-related decline in peripheral tissue homeostasis. I have undertaken to shed light on the role
of synaptic transmission on systemic aging.
6.2 Background
The previous chapters have covered the effects of nutrient intake and nutrient sensing on synaptic
transmission. I next asked if it is possible that synaptic transmission, in turn, has effects on tissue aging.
Increased excitatory neurotransmission has been associated with several mutations related to agedependent neurovegetative diseases, including Parkinson’s disease (PD), Huntington’s disease (HD), and
Alzheimer’s disease (AD) (Palop et al., 2007; Penney et al., 2016; Romero et al., 2008). In the context of
natural aging, evoked neurotransmitter release has been shown to increase both in the central nervous
system (Barnes, 1994; Potier et al., 1992) and in the periphery (Willadt et al., 2016). Similarly, in the adult
Drosophila NMJ model, the synaptic setpoint has been shown to increase with age, both in the context of
baseline wildtype synaptic transmission and in the context of presynaptic homeostatic plasticity (PHP)
(Mahoney et al., 2014).
While maintenance of baseline synaptic transmission is necessary for organismal survival,
abnormal increase in neurotransmission is potentially damaging to the tissues. For example, sudden spike
in glutamatergic excitatory transmission can cause the postsynaptic neuron to overload with calcium, a

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phenomenon termed “excitotoxicity”. Excitotoxicity is the cause of neuronal death during ischemia
reperfusion, caused by the increase in neurotransmission during reperfusion (Belov Kirdajova et al., 2020).
Furthermore, increased calcium influx has been linked with slow excitotoxic damage in AD patients (Hynd
et al., 2004). In line with these observations, interventions to protect against excitotoxicity have shown
promise in mammalian models of AD (Roberson et al., 2007). Additionally, limiting neurotransmitter
release through mutations in the presynaptic machinery resulted in reduction of Htt aggregates in fly models
of HD (Romero et al., 2008). Together, these observations indicate that increased synaptic transmission
during aging and age-related diseases may further stress the system and drive the aging process.
I was therefore interested in investigating the link between neurotransmission and aging in the
peripheral nervous system. In order to investigate this question, I continued to take advantage of the D.
melanogaster system and the NMJ model synapses. If abnormally high synaptic input is detrimental to
cellular aging, we should be able to manipulate synaptic activity at the NMJ to regulate aging in
postsynaptic muscles. I thus began exploring ways in which I could manipulate synaptic activity and assess
the effects on muscle aging, using proteostasis as a marker of tissue aging (Hunt & Demontis, 2013).
While investigating this question, we faced an obstacle – I found that the white gene commonly
mutated in various transgenic fly strains was interfering with lifespan and proteostasis. The white gene is
known as a determinant of the fly eye color, but it’s role in muscle is unknown (Susan M. Mackenzie et al.,
1999). The project thus pivoted as I decided to determine how white affects age-dependent regulation of
proteostasis in muscle tissues.
6.3 Results
6.3.1 Characterizing aging in D. melanogaster muscles
As a proof of concept that aberrantly high excitatory synaptic transmission is toxic to peripheral
tissues, I focused on assessing postsynaptic muscle aging in response to modulation of the NMJ. The
accumulation of polyubiquitinated protein aggregates in muscle have been shown to negatively correlate
with lifespan in flies (Demontis & Perrimon, 2010). Therefore, proteostasis was chosen as a readout to
screen for manipulations that slow the aging process.

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In the thorax of fruit flies, the indirect flight muscles (IFMs) are formed by groups of large muscles.
These muscles can be divided into two groups, the dorsoventral muscles (DVMs) and the dorsal
longitudinal muscles (DLMs). As opposed to the tubular nature of the other muscles in the fruit fly, IFMs
have a fibrillar structure, similar to the vertebrae somatic muscles (Gunage et al., 2017).
Polyubiquitin aggregate accumulation in the DLM is commonly used in aging studies (Hunt &
Demontis, 2013). Assessment of various thoracic muscles confirmed that the fibrous DLM and DVM show
abundant aggregation of polyubiquitinated proteins by 5 weeks after eclosion (Figure 6.1 A and B).
However, the tubular muscles in the direct flight muscle showed different patterns of polyubiquitin staining
with age. The large aggregates of polyubiquitin signal could not be seen in tergal depressor of trochanter
(TDT) even in old age (Figure 6.1 C). These observations confirmed that the DLM and DVM are
appropriate muscle groups to study age-dependent protein aggregation compared to the TDT muscles.
Further studies were carried out in the DLM.
Figure 6.1 Amount of polyubiquitinated aggregation during aging differs between muscle groups
(A) Representative images of DLM in wildtype (MHC-Gal4 x w1118) adult flies at 0-week and 5-week of adulthood,
stained against polyubiquitin (PolyUb) and Actin. Scalebar = 20µm
(B) Representative images of DVM in wildtype (MHC-Gal4 x w1118) adult flies at 0-week and 5-week of adulthood,
stained against polyubiquitin (PolyUb) and Actin. Scalebar = 20µm
(C) Representative images of TDT in wildtype (MHC-Gal4 x w1118) adult flies at 0-week and 5-week of adulthood,
stained against polyubiquitin (PolyUb) and Actin. Scalebar = 20µm

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In order to determine an ideal timepoint to perform cross-sectional studies on proteostasis
maintenance in the DLM, I dissected wildtype animals at different time points and compared the level of
polyubiquitinated protein aggregate accumulation. Protein aggregate accumulation increases sharply at the
first three weeks of adulthood and plateaus thereafter (Figure 6.2 A and B). The increase in
polyubiquitinated aggregate accumulation is accompanied by a decrease in the average polyubiquitin signal
intensity (Figure 6.2 C). To capture the changes in protein aggregate accumulation early, the time point of
2 to 3 weeks was chosen for assessing age-dependent protein aggregate accumulation.
Figure 6.2 Characterization of polyubiquitin aggregation with age
(A) Representative images of DLM from wildtype (MHC-Gal4 x w1118) stained against polyubiquitin (PolyUb) and
actin at the indicated ages after ecolosion. Scale bar = 20 µm
(B) Quantification of polyubiquitin volume per muscle area from age groups in (A). N = 10, 13, 12, 12, 17, 11 (flies).
One-way ANOVA. p<0.001. Games-Howell post-hoc test.
(C) Quantification of polyubiquitin signal intensity from age groups in (A). N = 10, 13, 12, 12, 17, 11 (flies). One-way
ANOVA. p<0.001. Games-Howell post-hoc test. p-values for post-hoc tests against 0-week-old group is indicated.
Error bars represent SEM. *p<0.05, **p<0.01, ***p<0.001

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Figure 6.3 Expression of Brp increases at the DLM NMJ with age
(A) Representative images of DLM in young (<1 week after ecolosion) wildtype (MHC x w1118) adult flies stained
against Hrp and Brp. Scale bar = 20µm
(B) Representative images of DLM in wildtype (MHC x w1118) adult flies stained against Brp at various ages. Scale
bar = 20 µm.
(C) Quantification of mean signal intensity from age groups in (B). N = 4, 12, 8, 6, 7 (flies). One-way ANOVA. p =
0.01. Games-Howell post-hoc test.
Error bars represent SEM. *p<0.05,

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6.3.2 Presynaptic marker, Brp, increases at the adult NMJ during aging
The fibrous muscles of the DLMs are highly innervated by both motor and sensory neurons (Figure
6.3 A). Individual muscle groups are more densely innervated compared to the larval abdominal muscles,
and each synaptic bouton is smaller in size. Co-staining with the presynaptic marker Brp (Kittel et al., 2006)
indicated that the bouton-like structures do correspond to presynaptic terminals (Figure 6.3 A).
I questioned if there are any morphological changes to the NMJ during aging. Using Brp as the
presynaptic marker, I assessed if the innervation of the DLM changes with age. Quantification of the Brp
signal from immunohistochemistry revealed that the expression of Brp at the NMJ increases with age
(Figure 6.3 B and C). Age-dependent increase in Brp level has been previously reported in the central
nervous system of fruit flies, and suppression of synaptic vesicle release was shown to improve memory
function (Gupta et al., 2016). The increase in Brp staining also is in line with the observation that synaptic
strength increases with age (Mahoney et al., 2014). These observations support the hypothesis that aberrant
increase in synaptic strength may be detrimental to the aging process in postsynaptic muscle.
6.3.3 Reduction in pre- and post-synaptic components reduce polyubiquitin aggregation with age
To demonstrate that abnormally high synaptic input is detrimental to tissue aging, I tested various
methods to reduce synaptic strength and assess whether this is sufficient to improve muscle aging as well
as lifespan and healthspan.
Heterozygosity for brp reduces polyubiquitin aggregate accumulation in the DLM
I took advantages of the genetic mutations that have been generated in the fly genome to reduce
the expression of synaptic proteins. As reduction in Brp in the central nervous system has been shown to
improve brain aging in flies, I decided to test if reduction in Brp also improves muscle aging. The Minosmediated integration cassette (Mimic) library is comprised of fly strains that have been randomly inserted
with the Mimic cassette throughout the genome. The Mimic vector contains a splice acceptor site and stop
codons in all three reading frames, resulting in truncated protein if inserted within a protein coding gene in
the correct orientation (Venken et al., 2011). Using the Mimic insertion line in the brp locus, I found that
heterozygosity for brp (brpMimic/+) significantly reduced the accumulation of polyubiquitin in adult thoracic

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muscle compared to the wildtype strain (w1118) (Figure 6.4 A and B). This result indicated that reduced
presynaptic vesicle release is protective to the postsynaptic tissue.
GluRIIA-/- mutants exhibit reduced polyubiquitin aggregate accumulation in the DLM
I took advantage of our knowledge of the glutamate receptors at the larval NMJ to further tease out
the role of mEJC and EJC on postsynaptic health. We have known that larvae that lack the GluRIIA
glutamate receptor subunit show reduced signal channel mean open time, resulting in reduced amplitude of
mEJCs (Petersen et al., 1997). Due to synaptic homeostasis, the presynaptic neuron increase synaptic
release, allowing the animals that lack GluRIIA subunit maintain a wildtype level of EJCs (Petersen et al.,
1997). The persistence of homeostatic compensation in the adult NMJ has been shown previously
(Mahoney et al., 2014), indicating that I could take advantage of our knowledge of PHP in GluRIIA mutants
to dissect the role of mEJCs and EJCs in tissue aging. If EJC amplitudes are critical for driving aging,
GluRIIA-/- mutants should age at the same rate as the wildtype flies. In contrast, if mEJC amplitudes, but
not EJC amplitudes, are responsible for driving aging, mutations at the GluRIIA locus should confer
protection on muscle aging.
I found that GluRIIA-/- mutants (Df(2L)clh4/sp16;MHC-Gal4/+) exhibit decreased level of
polyubiquitinated aggregates when compared to heterozygous controls (Df(2L)clh4/+;MHC-Gal4/+)
(Figure 6.4. C and D). This phenomenon was recapitulated when we expressed the dominant negative form
of GluRIIA (GluRIIAM/R) (DiAntonio et al., 1999) in a muscle-specific manner (Figure 6.4 E and F). These
results suggested that reduction in mEJC is sufficient to slow muscle aging compared to wildtype flies.

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Figure 6.4 Reduction in pre- and post-synaptic proteins reduce polyubiquitinated protein
aggregates
(A) Representative images of DLM from control (w1118) and brp+/- (w1118 x brpMimic) adult 3-week-old flies stained
against polyubiquitin and actin. Scale bar = 20µm
(B) Quantification of polyubiquitin volume per muscle area in genotypes in (A). N = 16, 15 (flies) Student’s t-test p <
0.001
(C) Representative images of DLM from control (Df(2L)clh4 x w1118) and GluRIIA-/- (Df(2L)clh4 x GluRIIAsp16)
adult 3-week-old flies stained against polyubiquitin and actin. Scale bar = 20µm
(D) Quantification of polyubiquitin volume per muscle area in genotypes in (C). N = 26, 33 (flies) Student’s t-test. p <
0.001
(E) Representative images od DLM from control (MHC-Gal4 x w1118) and transgenic expression of dominant
negative GluRIIA (MHC-Gal4/UAS-GluRIIAM/R) in adult 3-week-old flies stained against polyubiquitin and actin.
Scale bar = 20µm
(F) Quantification of polyubiquitin volume per muscle area in genotypes in (E). N =75, 76 (flies) Student’s t-test. p
<0.001
Error bars represent SEM. ***p<0.001

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6.3.4 Synaptic mutants show increased lifespan and healthspan
Disruption in proteostasis is one of the hallmarks of aging (López-Otín et al., 2013) and has been
shown to associate with age-dependent diseases and shortening of life span in a number of species (BenZvi et al., 2009; Demontis & Perrimon, 2010; Mattson & Arumugam, 2018; Min et al., 2008). The
significant improvements in proteostasis in response to disruption of pre- and post-synaptic machineries
suggested that they should have a positive impact on lifespan. Indeed, I found that heterozygosity for brp
and GluRIIA-/- mutation extends lifespan compared to their respective control population (Figure 6.5 A and
C).
Healthspan was measured as the ability of the flies to exhibit negative geotaxis, measured by the
ability of the flies to climb the side of a fly vial (see Methods) (Ganetzky & Flanagan, 1978). Both brp
heterozygous mutants and GluRIIA-/- mutants exhibited a robust improvement to their healthspan (Figure
6.5 B and D). Muscle-specific expression of dominant negative GluRIIA recapitulated the benefits of
GluRIIA-/- mutation to healthspan and, to a lesser extent, also showed benefits to lifespan (Figure 6.5 E-F).
Together, these results demonstrate that reducing the synaptic activity at the adult NMJ can slow down
muscle aging and prolong lifespan and healthspan of the animals.
6.3.5 Calcium sensing as a candidate regulator of muscle aging
Given the beneficial effects of reducing synaptic inputs to the muscle, I investigated the mechanism
by which synaptic activity promotes aging in muscle tissues. The glutamate receptors at the fly NMJ contain
four subunits, one of which can be GluRIIA or GluRIIB. The interchangeable GluRIIA and GluRIIB
subunits differ in their ion conductance properties; in particular, calcium conductance is reduced in GluRIIB
containing receptors (Gray, 2016; Petersen et al., 1997). We suspected that the reduction of calcium influx
may be responsible for the reduced polyubiquitin aggregate accumulation.
Ca2+/calmodulin-dependent protein kinase II (CaMKII) is a calcium sensor that is implicated in a
number of signal transduction cascades in response to elevated calcium signals. Experimental evidence
indicates that a reduction in CaMKII kinase activity in postsynaptic muscles is responsible for inducing
homeostatic compensation in GluRIIA-/- mutants (Haghighi et al., 2003). Therefore, I examined if reduction

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Figure 6.5 Disruption of pre- and post-synaptic proteins extends lifespan and healthspan in fruit
flies
(A) Lifespan of brp+/+ (w1118) and brp+/- (brpMimic/-) populations. N = 203, 150 (flies) Log-rank test. p << 0.001
(B) Percentage of flies passing the climbing test from the populations in (A). N = 12, 9 (vials)
(C) Lifespan of control (w1118) and GluRIIA-/- (Df(2L)clh4/sp16;MHC-Gal4/+) and control (Df(2L)clh4/+;MHCGal4/+) populations. N = 164, 87 (flies). Log-rank test. p << 0.001
(D) Percentage of flies passing the climbing test from the populations in (B). N = 9, 8 (vials)
(E) Lifespan of flies transgenically expressing a dominant form of GluRIIA in the muscle (MHC-Gal4/UASGluRIIAM/R) and control (MHC-Gal4/+). N = 296, 346 (flies) Log-rank test. p = 0.0012
(F) Percentage of flies passing the c5limbing test from the populations in (E). N = 9, 11 (vials)
Error bars represent SEM. *p<0.05, **p<0.01, ***p<0.001

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in calcium sensing by CaMKII is responsible for benefits conferred by synaptic alterations. To test this idea,
I transgenically expressed an RNAi against CamKII in the muscle (24B-Gal4/UAS-CaMKIIRNAi). I found
that knockdown of CamKII in the muscle significantly decreased the volume of polyubiquitinated protein
aggregates in the thoracic muscles compared to control (Figure 6.6 A and B). This was surprising, as
increased CamKII activity has been previously reported to promote autophagy and therefore promote
protein clearance (Li et al., 2017).
Figure 6.6 Manipulation of CaMKII show conflicting effects on muscle proteostasis
(A) Representative images of DLM from 3-week-old control flies (24B-Gal4/+) and flies expressing RNAi against
CaMKII in muscle tissues (24B-Gal4/UAS-CaMKIIRNAi) stained against polyubiquitin and actin. Scale bar = 20µm
(B) Quantification of volume of polyubiquitinated aggregates per muscle area in genotypes from (A). N = 30, 31.
(flies) Student’s t-test. p << 0.001
(C) Representative images of DLM from 3-week-old control flies (24B-Gal4/+) and flies expressing CaMKII
inhibitory peptide (24B-Gal4/UAS-Ala) stained against polyubiquitin and actin. Scale bar = 20µm
(D) Quantification of volume of polyubiquitinated aggregates per muscle area in genotypes from (B). N = 12, 13 (flies)
Student’s t-test. p = 0.520
Error bars represent SEM. ***p<0.001

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I examined if the beneficial effects of CaMKII knockdown can be reproduced by expressing an
inhibitory peptide against CaMKII (Ala) in a muscle-specific manner (Griffith et al., 1993). Contrary to the
observation with CaMKIIRNAi, muscle-specific transgenic expression of the inhibitory peptide (24BGal4/UAS-Ala) exhibited no benefit to the level of polyubiquitin aggregation compared to control (24BGal4/+) (Figure 6.6 C and D). These conflicting results with CamKII prompted me to carefully re-examine
the experimental design up to this point in the project.
The w1118 strain as a potential confound
In examining the genetic background of the strains tested, I noticed that I had been using w1118 as
the control strain, as it is commonly used, to compare against other mutants and transgenic combinations.
The w1118 strain is commonly used for making transgenic insertion lines and transposon-based genedisruptions, therefore, usually it is an appropriate control for the genetic backbone. However, the w1118
strain has a mutation on the white gene, which is responsible for conferring the red eye to wild type flies:
white homozygous mutant flies have white eyes due to their inability to make the wildtype eye pigmentation
(Susan M. Mackenzie et al., 1999). The eye color makes a convenient selection marker for making
transgenic lines. Plasmids to be inserted into the fly genome are engineered with a version of white, termed
mini-white. When transgenes are successfully inserted into the genome the copy of white encoded in the
mini-white cassette confers an eye color, ranging from yellow to red depending on the expression level, to
the emerging adults. This provides a rapid method to screen for successful transgenic fly lines (Johnston,
2013). The w1118 strain has thus been commonly used as the background control strain in the fly
community.
In many of our experiments, differences in polyubiquitin accumulation corresponded to differences
in copy numbers of the white gene (Table 6.1). Several articles have been published where assessment of
age-dependent changes in muscle proteostasis using polyubiquitin as a readout was conducted without
acknowledgement and careful consideration of the potential role of the white gene. This was a significant
turning point in my scientific training, where I had to decide to go on and ignore the white problem or
change my immediate focus and study the role of the white gene in the muscle and establish a new standard

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in the field for future investigators. I opted for the latter option and started on a journey of discovery with
many new challenges along the way.
Table 6.1 Genotypes and sources of white transcripts
Genotype Cross Female
chromosomes
Source of
white
Male chromosomes Source of white
brp +/+ w1118 x w1118 w1118 w1118
brp +/- w1118 x brp
MI09003
w1118 brpMI9003 Mimic cassette insertion
Mini-white
GluRIIA +/- Df(2L)cl-h4 x
w1118
Df(2L)cl-h4 Genomic
copy of white
w1118
GluRIIA -/- Df(2L)cl-h4 x
GluRIIA[sp16]
Df(2L)cl-h4 Genomic
copy of white
w[*]; GluRIIA[SP16] P-element insertion
P{LacW}
Mini-white
Muscle driver >
+/+
MHC-Gal4 x
w1118
MHC-Gal4 Genomic
copy of white
w1118
Muscle driver >
UASGluRIIA[M/R]
MHC-Gal4 x
UASGluRIA[M/R]
MHC-Gal4 Genomic
copy of white
w[*]; P{w[+mC]=
UASGluRIIA.M614R}2
P-element insertion
P{UASGluRIIA.M614R}
Mini-white
Muscle driver>
UAS-CaMKIIRNAi
MHC-Gal4 x
CaMKII-RNAi
MHC-Gal4 Genomic
copy of white
y[1] v[1]; P{y[+t7.7]
v[+t1.8]=TRiP.
JF03336} attP2
Genomic copy of white
Muscle driver >
UAS-Ala
MHC-Gal4 x
UAS-Ala
MHC-Gal4 Genomic
copy of white
w[*]; P{w[+mC]=UASCamKII-I.Ala}2
P-element insertion
P{UAS-CamKII-I.Ala}
Mini-white
Details of the genetic crosses made to obtain each genotype used in the adult synaptic modulation study is described. The control
strain used for experiments are bolded, and transgenic or mutant lines compared against the control lines are listed immediately
after. Source of the white transcripts are indicated for each genotype if present.
6.3.6 CaMKII is required to maintain muscle proteostasis during aging
To determine if the genetic background was responsible for the conflicting results from CaMKII
experiments, I repeated the two CaMKII manipulations using new controls. For CaMKIIRNAi, a transgenic
RNAi against mCherry from the Transgenic RNAi Project (TRiP) library was chosen. Since the CaMKIIRNAi
transgenic line was made as part of the same library as the mCherryRNAi line, and the plasmids used in TRiP
lines do not use mini-white as the selectable marker, this ensured that the white dosage is controlled in the
comparison (Perkins et al., 2015). For the CaMKII inhibitory peptide Ala, transgenic overexpression of
LacZ was chosen. As both transgenic lines contain a copy of mini-white in the plasmid, white dosage is
also maintained between the two strains. In both comparisons, I found that reduction in CaMKII activity
leads to increased polyubiquitinated protein aggregate accumulation (Figure 6.7 A-C), confirming previous
reports that CaMKII plays a critical role in protein clearance.

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These results supported my suspicions that that white may be involved in regulating age-dependent
proteostasis. At this point, I pivoted my project to investigating the role of white in regulating adult protein
aggregate accumulation.
Figure 6.7 Transgenic disruption of CamKII increases polyubiquitinated protein aggregate
accumulation when compared against transgenic control lines
(A) Representative images of DLM from 3-week-old flies expressing RNAi against mCherry (24B-Gal4/UASmCherryRNAi) or CaMKII (24B-Gal4/UAS-CaMKIIRNAi) in muscle tissues stained against polyubiquitin and actin.
Scale bar = 20µm
(B) Quantification of volume of polyubiquitinated aggregates per muscle area in genotypes from (E). N = 12, 16 (flies)
Student’s t-test. p = 0.0003
(C) Representative images of DLM from 3-week-old flies overexpressing LacZ (MHC-Gal4/UAS-LacZ) or inhibitory
peptide against CaMKII (MHC-Gal4/UAS-Ala) in muscle tissues stained against polyubiquitin and actin. Scale bar
= 20µm
(D) Quantification of volume of polyubiquitinated aggregates per muscle area in genotypes from (G). N = 12, 16 (flies)
Student’s t-test. p << 0.001
Error bars represent SEM. ***p<0.001

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6.3.7 white is required in the muscle to maintain muscle proteostasis during aging
Genetic dosage of white negatively correlates with protein aggregate accumulation in adult thoraces
In order to confirm whether the dosage levels of white directly correlated with polyubiquitinated
aggregate accumulation with age, I first assessed polyubiquitinated protein aggregate accumulation in flies
with varying number of the white gene. w1118 was used as the source of whitemutation, and CantonS was
used as the source of wildtype copy of white. At 3 weeks of age, the number of genomic copies of white
negatively correlates with the level of protein aggregate accumulation (Figure 6.8 A and B).
In order to rule out the possibility that w1118 is simply a vulnerable strain for age-dependent loss
of proteostasis, I transgenically expressed an RNAi against white ubiquitously using the daughterless-Gal4
(Da-Gal4) driver (Wodarz et al., 1995). The RNAi line was chosen from the TRiP library (Perkins et al.,
2015), and the RNAi against mCherry was used as control. I found that ubiquitous knockdown of white
(Da-Gal4/UAS-wRNAi) increases the polyubiquitin accumulation in the DLM compared to the mCherryRNAi
control (Da-Gal4/UAS-mCherryRNAi) (Figure 6.8 C and D), recapitulating the effect of genomic white
dosage on muscle proteostasis. This observation further confirmed that white is responsible for loss of
proteostasis in adult muscles as they age and ruled out the possibility that our observations were biased by
other genetic elements/mutations in our w1118 strain.
Expression of white in the muscle is required to maintain muscle proteostasis during aging
According to the FlyAtlas RNAseq data, expression of white is highest in the eye, as expected from
its classical role in eye pigment production. In contrast, white expression in the adult carcass (including the
muscle) is much lower (Chintapalli et al., 2007). This prompted me to question if white regulates muscle
proteostasis because of a systemic influence or whether it function specifically in the muscle. To address
this, I took advantage of specific muscle and neuronal Gal4 drivers to express the RNAi against white in a
tissue-specific manner. Muscle-expression of wRNAi (MHC-Gal4/UAS-wRNAi) was sufficient to drastically

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increase protein aggregates in 2-week-old flies in contrast to the control mCherryRNAi (MHC-Gal4/UASmCherryRNAi), recapitulating the effect of ubiquitous wRNAi expression (Figure 6.9 A and B).
Figure 6.8 white expression in the muscle is required to maintain proteostasis during aging
(A) Representative images of DLM from 2-week-old flies with no copies of white (w1118), one copy of whtie
(CantonS x w1118), and both copies of white (CantonS) at 2 weeks of age stained against polyubiquitin and actin.
Scale bar = 20µm
(B) Quantification of polyubiquitin aggregate volume per muscle area for genotypes in (A). N = 13, 14, 12 (flies) Oneway ANOVA p <<0.001 followed by Games-Howell post-hoc tests.
(C) Representative images of DLM from 3-week-old flies with a ubiquitous Gal4 driver expressing RNAi against
mCherry (Da-Gal4/UAS-mCherryRNAi) or against white (Da-Gal4/UAS-wRNAi) stained against polyubiquitin and
actin. Scale bar = 20µm
(D) Quantification of polyubiquitin aggregate volume per muscle area for genotypes in (C). N = 16, 28 (flies)
Student’s t-test. p = 0.0006
Error bars represent SEM. ***p<0.01, ***p<0.001

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In contrast, the same wRNAi line expressed using a neuronal driver (elav-Gal4/+;;UAS-wRNAi/+) did not
increase polyubiquitin in the muscle compared to mCherryRNAi control (elav-Gal4/+;;UAS-mCherryRNAi/+).
Instead, the wRNAi expression in the neuron slightly lowered the accumulation of protein aggregates in the
muscle (Figure 6.9 C and D). Based on these observations, I did not further study the potential effect of
white knockdown in neurons.
The protein aggregation phenotype of white knockdown in the muscle was reproduced using two
other wRNAi lines from the TRiP library (Figure 6.9 B). I confirmed that the white transcript was reduced in
the thorax in all three RNAi lines by qPCR (Figure 6.9 E). Additionally, the same three RNAi lines against
white expressed in neurons changed the eye color from red to white. The extent of red-pigment loss was
quantified by extracting the pigments from head lysates and measuring their absorbance at 480nm (Figure
6.9 F). As these three independent RNAi transgenes all phenocopied the w1118 mutant flies, these results
demonstrate that white expression functions cell-autonomously in the muscle to maintain muscle
proteostasis during aging.

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Figure 6.9 white is required in the muscle to maintain muscle proteostasis during aging
(A) Representative images of DLM from 2-week-old flies expressing RNAi against mCherry (MHC-Gal4/UASmCherryRNAi) or white (MHC-Gal4/UAS-wRNAi) in the muscle stained against polyubiquitin and actin. Scale bar =
20µm
(B) Quantification of polyubiquitin aggregate volume per muscle area from genotypes in (A) as well as two other
strains of wRNAi expressed with the muscle driver. N = 63, 45, 47, 50 (flies). One-way ANOVA p <<0.001 followed
by Games-Howell post-hoc test.
(C) Representative images of DLM from 2.5-week-old flies expressing RNAi against mCherry (elav-Gal4/+;;UASmCherryRNAi/+) or w (elav-Gal4/+;;UAS-wRNAi/+) under a neuronal driver stained against polyubiquitin and actin.
Scale bar = 20µm
(D) Quantification of polyubiquitin aggregate volume per muscle area from genotypes in (C). N = 33, 35 (flies).
Student’s t-test. p = 0.0478
(E) The effectiveness of wRNAi transgenes measured by qPCR. N = 3 (experiments) Student’s t-test against
mCherryRNAi. p = 0.0002 (wRMAi#1), 0.0033 (wRMAi#2), 0.0027 (wRMAi#3).
(F) Absorbance of eye pigment extracts at 480nm from young adults expressing mCherryRNAi (elav-Gal4/+;;UASmCherryRNAi/+) or one of three wRNAi transgenes (elav-Gal4/+;;UAS-wRNAi/+). N = 2 (experiments) Student’s t-test
against mCherryRNAi. p = 0.0005 (wRMAi#1), 0.0023 (wRMAi#2), 0.0020 (wRMAi#3)
Error bars represent SEM. *p<0.05, **p<0.01, ***p<0.001

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6.3.8 Transgenic knockdown of white in the muscle is sufficient to reduce lifespan
As described earlier, disruption in proteostasis is associated with age-dependent diseases and
shortening of life span in a number of species (Ben-Zvi et al., 2009; Demontis & Perrimon, 2010; Mattson
& Arumugam, 2018; Min et al., 2008). Therefore, I set out to examine the effect of muscle-specific white
knockdown on lifespan in otherwise genetically identical flies. My examination of transgenic knockdown
of white in the muscle showed negative impact on lifespan (p = 8e-10) with reduction of median lifespan
by 5 days (Figure 6.10 A). These results highlight the critical role of white in muscle for the organismalwide longevity and is consistent with previous reports that have established a link between muscle
proteostasis and lifespan.
Disruption in muscle proteostasis could have affected muscle function and subsequently
compromised locomotion in flies, a defect that could be causal for the observed shortening of lifespan.
Therefore, I investigated whether transgenic knockdown of white in the muscle has detrimental effects on
the stereotypical locomotion behavior in flies, known as negative geotaxis (see Methods). I found that the
control and white knockdown populations showed similar decline in their climbing abilities with age
(Figure 6.10 B). This indicates that the negative effect of white insufficiency in muscle on lifespan is not
merely due to muscle damage and locomotion defects (see section 6.3.14).
Figure 6.10 Muscle-specific knockdown of white shortens lifespan but does not affect healthspan
(A) Lifespan curves of female flies expressing RNAi against mCherry (MHC-Gal4/UAS-mCherryRNAi) or white (MHCGal4/UAS-wRNAi) in the muscle. n = 340, 380 (flies), N = 3 (experiments) Log-rank test. p << 0.001
(B) Healthspan measured by negative geotaxis for populations in (A). Student’s t-test for each age-matched pair.
Error bars represent SEM. **p<0.01

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Overall, these results indicate that disruption of muscle proteostasis by muscle-specific white
knockdown is sufficient to shorten lifespan.
6.3.9 White functions with its partner st to modulate proteostasis
White is a member of the ATP-Binding Cassette (ABC) transporter family of proteins that transport
molecules required for eye pigment production. One of the co-transporters that works with white is scarlet
(st). The mature transporter formed by white and st transport tryptophan into pigment granules, where
pigment molecules are synthesized (Figure 6.11 A) (S. M. Mackenzie et al., 2000; Tearle et al., 1989).
Figure 6.11 ABC transporter partner st is also required in the muscle to maintain proteostasis.
(A) Schematic of white and st function in the eye for pigment production
(B) Representative images of DLM of 3-week-old flies expressing RNAi against mCherry (MHC-Gal4/UASmCherryRNAi) or st (MHC-Gal4/UAS-stRNAi) in the muscle stained against polyubiquitin and actin. Scale bar =
20µm
(C) Quantification of polyubiquitin aggregate volume per surface area. N = 31, 35 (flies) Student’s t-test. p <<0.001
(D) Representative images of 1-day old flies expressing RNAi against mCherry (elav-Gal4/UAS-mCherryRNAi) or st
(MHC-Gal4/UAS-stRNAi) in the neurons.
(E) The efficacy of RNAi against st assayed by qPCR. N = 3 (experiments) Student’s t-test. p = 0.0018
Error bars represent SEM. **p<0.01, ***p<0.001

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If white functions in the muscle similarly to its classical role in the eye, lack of its co-transporter
should mimic the effects of white knockdown to aggravate the loss of proteostasis with age. Indeed,
expression of RNAi against st in a muscle-specific manner (UAS-stRNAi/+;MHC-Gal4/+) increased the
accumulation of polyubiquitinated protein aggregates in adult thoracic muscles (Figure 6.11 B and C). The
effectiveness of the RNAi in reducing the st transcript expression was confirmed by qPCR and by changes
in the eye pigment (Figure 6.11 D and E).
6.3.10 white knockdown in the muscle marginally decreases tryptophan and its metabolites
While the classical role of white and st is to transport tryptophan into subcellular compartments in
the eye, previous publications indicated that white mutants exhibit reduced levels of tryptophan metabolites
in the brain, including dopamine and serotonin (Borycz et al., 2008). Tryptophan is also a major source of
NAD+ in mammals (L. Liu et al., 2018), and changes to tryptophan transport can have consequences in the
energetic balance. Recently, the tryptophan metabolism pathway has gained attention as a modulator of
aging (van der Goot & Nollen, 2013; Wirthgen et al., 2018). I therefore asked whether white also modulated
tryptophan or tryptophan metabolism in the muscles.
In collaboration with the Ramanathan lab, we used targeted metabolomic analysis to measure the
quantity of tryptophan and its metabolites from thoracic extracts (see Methods). We found that the
knockdown of white in muscles reduced tryptophan and some of its metabolites only marginally (Figure
6.12 A and B). We found that the NAD levels in the muscles were unaltered by knockdown of white, ruling
out lack of NAD+ availability as the cause of protein aggregation in white knockdown muscles (Figure 6.12
A).
I next investigated whether I could reproduce the effects of white knockdown by reducing the level
of tryptophan intake from the food. As tryptophan is an essential amino acid, the only source of tryptophan
is from the diet. In order to remove tryptophan from the recipe, a previously published protocol was used
to recreate the standard fly food by only using defined chemical ingredients (Piper et al., 2014). CantonS
flies were used as wildtype strains. I found that fly larvae did not develop to pupal stages when they were
maintained on a diet that contained only 10% of the tryptophan found in the control diet. I therefore

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conducted the experiment using 50% reduction of tryptophan compared to the full-nutrient recipe.
Treatment of flies on tryptophan deficient food during development and 2 weeks of adulthood did not
reproduce the effect of white knockdown (Figure 6.12 C and D). I therefore decided not to pursue this line
of investigation. It would be necessary to test if tryptophan deficient food indeed reduced tryptophan and
tryptophan metabolite levels in the muscle, and any feeding behavior that compensates for the lack of
tryptophan in the diet will need to be controlled for.
Figure 6.12 Lack of white marginally decreases tryptophan and tryptophan metabolite
concentration in the adult muscle.
(A) Comparison of metabolite concentrations from 1-week-old thoraces of flies expressing mCherryRNAi (MHCGal4/UAS-mCherryRNAi) or wRNAi (MHC-Gal4/UAS-wRNAi) in the muscle measured by targeted metabolomics
analysis. N = 3 (experiments) Student’s t-test for each metabolite.
(B) Schematic of tryptophan metabolism via the kynurenine metabolism pathway, highlighting the metabolites assayed
in (A).
(C) Representative images of DLM from 2-week-old wildtype flies (CantonS) fed on food with full nutrients (nutrientrich) or with 50% less tryptophan (50% W) stained against polyubiquitin and actin. Scale bar = 20µm
(D) Quantification of polyubiquitin volume per muscle area from conditions in (C). N = 26, 28 (flies). Student’s t-test.
p = 0.137
Error bars represent SEM. *p<0.05, ***p<0.001

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6.3.11 Lack of white during the larval stage accelerates protein aggregation in the adult muscle
All of our transgenic experiments thus far relied on knockdown of white throughout life either
globally or in tissue specific manner, pointing to the critical role of White in muscle; however, these
experiments shed no light on the temporal requirement of White in the muscle. Therefore, Isought to dissect
the temporal requirement for white in the regulation of normal muscle proteostasis during aging to answer
the following questions: is White required throughout life, or does it have a developmental role? Would
knockdown of white only in the adult muscle be sufficient to disrupt proteostasis, or is the knockdown of
white during development required to disrupt proteostasis in adulthood?
To address these questions, I used the inhibitory Gal80ts system in combination with the Gal4/UAS
system to temporally restrict the expression of wRNAi in muscle at different stages of development (McGuire
et al., 2004).
I first wanted to ensure that the genetic background of the wRNAi is not modulating proteostasis in
adults. I kept the flies in 18°C for the entirety of the experiment, so that the wRNAi was never expressed in
the muscle. As we expected, without the Gal4 expression of the wRNAi, there is no difference in polyubiquitin
aggregation (Figure 6.13 A and B). I next expressed the wRNAi during adulthood to reproduce the effects of
wknockdown. Surprisingly, I observed that limiting the expression of wRNAi to adulthood had negligible on
the adult proteostasis (Figure 6.13 C and D). The fact that proteostasis was not affected by knockdown of
Figure 6.13 (see next page for the figure)
(A) Representative images of DLM from 2-week-old flies whose expression of mCherry (tubulin-Gal80ts/+;MHCGal4/UAS-mCherryRNAi) or white (tubulin-Gal80ts/+;MHC-Gal4/UAS-wRNAi) were kept off during development
and adulthood stained against polyubiquitin and actin. Scale bar = 20µm
(B) Quantification of volume of polyubiquitin per muscle area for genotypes and condition\s in (A). N = 20, 22 (flies)
Student’s t-test. p = 0.349
(C) Representative images of DLM from 2-week-old flies whose expression of mCherry (tubulin-Gal80ts/+;MHCGal4/UAS-mCherryRNAi) or white (tubulin-Gal80ts/+;MHC-Gal4/UAS-wRNAi) were restricted to adulthood stained
against polyubiquitin and actin. Scale bar = 20µm
(D) Quantification of volume of polyubiquitin aggregate per muscle are for genotypes and condition in (C). N = 39, 39
(flies) Student’s t-test. p = 0.3297
(E) Representative images of DLM from 2-week-old flies whose expression of mCherry (tubulin-Gal80ts/+;MHCGal4/UAS-mCherryRNAi) or w (tubulin-Gal80ts/+;MHC-Gal4/UAS-wRNAi) were restricted to larval development
stained against polyubiquitin and actin. Scale bar = 20µm
(F) Quantification of volume of polyubiquitin aggregate per muscle area for genotypes and condition in (E). N = 49,
45 (flies) Student’s t-test. p << 0.001
Error bars represent SEM. ***p<0.001

94
Figure 6.13 white is required in the muscle during larval development to maintain muscle
proteostasis during aging.

95
white when the transgenic RNAi expression was limited to adult-phase indicated that larval knockdown of
white may be critical for regulating adult proteostasis.
To test this hypothesis, I next limited the expression of wRNAi to larval stages by exposing the flies
to permissive temperatures for transgene expression during the last 60 hours of larval development, making
sure that embryos were not affected. I found that driving the expression of wRNAi during larval stages is
sufficient to increase protein aggregation in adult muscle (Figure 6.13 E and F).
Overall, these results from temporal restriction experiments indicate that white is necessary during
the larval stages to maintain proteostasis during adulthood. This is of particular importance as a growing
body of evidence suggests that early exposures and experiences in life can have serious consequences later
in life. An intriguing possibility is that the reduction in white in larvae has changed the transcriptomic or
epitranscriptomic landscape such that the complex motor interactions required for normal proteostasis will
not function appropriately in the adult muscle.
6.3.12 Lack of white drives mTOR activity in larval muscle
The temporal requirement for white expression pointed to the existence of a biological pathway
whose alteration during development has consequences for adult aging. Based on the observations that
white is required during larval stages, I examined the molecular mechanisms by which larval knockdown
of white influences proteostasis in the adult. I suspected TOR, the fly homolog of mTOR, as a potential
target.
During adulthood, suppression of mTOR is associated with longevity and protection from various
age-related diseases (Harrison et al., 2009; Kaeberlein et al., 2005; Kapahi et al., 2004; Laplante & Sabatini,
2012; Vellai et al., 2003). mTOR is a major regulator of cell growth and proliferation, and increased mTOR
function is associated with increased protein translation and reduced autophagy (Laplante & Sabatini, 2012)
– either of which can be detrimental to proteostasis. While the benefits of inhibiting mTOR during aging
has been widely observed, whether there is a relationship between mTOR activity during development to
aging later in life is not known.

96
One of the targets of mTOR kinase activity is S6K (Chung et al., 1992; Ma & Blenis, 2009). Flies
have only one p70 S6K (S6K), instead of the S6K1 and S6K2 found in mammals. The level of S6K
phosphorylation can thus be assayed by using Western blot using an antibody against phospho-S6K. As
suspected, I found that knockdown of white in the muscle drastically increases the level of phosphorylatedS6K in the larval muscle (Figure 6.14 A and B). In contrast, the level of phospho-S6K was indistinguishable
in adult tissues expressing mCherryRNAi or wRNAi throughout life (Figure 6.14 C and D). These results
indicated that w regulates TOR activity in the muscle during larval stages but not in the adult tissue.
Upregulation of mTOR and S6K activity during adulthood is associated with disease and shortening
of lifespan (Johnson et al., 2013); however, mTOR is required during development to promote cell growth
and proliferation (Jia et al., 2004). In order to determine if larval hyperactivation of S6K is sufficient to
disrupt proteostasis later in life, I overexpressed a constitutively active form of S6K (S6KSTDE) (Barcelo &
Stewart, 2002) in the muscle and used Gal80ts to limit the expression to 2nd and 3rd instar larval stages.
Similar to wRNAi, restricted expression of S6KSTDE in the larval muscle (tubulin-Gal80ts/+;MHCGal4/UAS-S6KSTDE) was sufficient to increase polyubiquitinated protein aggregates in adult muscles
compared to control flies expressing LacZ (tubulin-Gal80ts/+;MHC-Gal4/UAS-S6KSTDE) (Figure 6.15 A
and B).
While the dynamic and the degree of S6K activation may be different in the case of direct transgenic
expression of S6KSTDE and as a result of knockdown of white, these results provide support for the idea that
the increased S6K activity may be at least partially responsible for the loss of proteostasis downstream of
white knockdown. In addition, these results provide genetic evidence for the first time that early disruptions
in translational regulation can have serious consequences for the ability of the muscle to maintain its
proteostasis later in life.

97
To test the hypothesis that larval activation of TOR is responsible for muscle aging in w deficient
flies, I took advantage of rapamycin to reduce the activity of TOR (Loewith et al., 2002). Larvae expressing
RNAi against mCherry or white in the muscle were treated with standard fly food or food supplemented
with 10mM rapamycin (see Methods). Exposure to rapamycin was started from 2nd instar larval stage, as
exposure to rapamycin at earlier time in development was found to be lethal. The flies were taken off the
rapamycin diet at the beginning of the adult stage, but the RNAi expression was sustained. While rapamycin
had no effect on flies expressing the control mCherryRNAi, larval feeding of rapamycin significantly reduced
Figure 6.14 Larval white deficiency activates mTOR and disrupt proteostasis later in life
(A) Representative western blot membranes of lysates from 3rd instar larvae muscle expressing mCherryRNAi (MHCGal4/UAS-mCherryRNAi) or wRNAi (MHC-Gal4/UAS-wRNAi) immunoblotted against phosphorylated form of S6K
(pS6K T398) and actin.
(B) Quantification of relative phospho-S6K signal normalized to actin signal from genotypes in (A). N = 3
(experiments) Student’s t-test. p = 0.2939
(C) Representative western blot membranes of lysates from 1-week-old adult thoraces from flies expressing
mCherryRNAi (MHC-Gal4/UAS-mCherryRNAi) or wRNAi (MHC-Gal4/UAS-wRNAi) immunoblotted against
phosphorylated form of S6K (pS6K T398) and actin.
(D) Quantification of relative phospho-S6K signal normalized to actin signal from genotypes in (A). N = 2
(experiments). Student’s t-test. p = 0.7409
Error bars represent SEM. *p<0.05

98
polyubiquitin aggregates in flies expressing wRNAi (Figure 6.15 C and D). These results support the
hypothesis that larval activation of mTOR has a negative impact on age-dependent proteostasis.
Overall, these data demonstrate that lack of white transcripts in the muscle during larval
development significantly increases TOR activity, which in turn negatively impacts muscle aging during
adulthood.
Figure 6.15 Activation of TOR during larval development disrupts muscle aging
(A) Representative images of DLM from 3-week-old adult flies after expressing LacZ or S6KSTDE during larval
development stained against polyubiquitin and actin. Scale bar = 20µm
(B) Quantification of polyubiquitin aggregate volume per muscle area from genotypes and condition in (C). N = 27, 18
(flies) Student’s t-test. p << 0.001
(C) Representative images of DLM from 2-week-old adult flies. Flies expressing mCherryRNAi (MHC-Gal4/UASmCherryRNAi) or wRNAi (MHC-Gal4/UAS-wRNAi) were treated with standard fly food or 10mM rapamycin food
during larval development stained against polyubiquitin and actin. Scale bar = 20µm
(D) Quantifications of polyubiquitin aggregate volume per muscle area for genotypes and conditions in (E). N = 9, 6,
12 ,14 (flies) Two-way ANOVA. p (interaction) = 0.0710. Tukey’s post-hoc test.
Error bars represent SEM. *p<0.05, ***p<0.001

99
6.3.13 Lack of white suppresses autophagy
To understand how lack of white disrupts proteostasis in the adult, I decided to investigate if protein
clearance pathways are affected in white deficient muscles. Protein clearance is achieved via two major
pathways: misfolded proteins and other damaged proteins can be degraded by the ubiquitin-proteasome
system (Nandi et al., 2006); protein aggregates and membrane proteins are instead degraded by the
autophagy pathway (Kaur & Debnath, 2015).
LC3 protein expression is reduced in white knockdown muscle
One of the ways to assay autophagy levels is through the lipidation of LC3 (Atg8a in flies).
Posttranslational modifications causes LC3 to change from cytosolic (LC3-i) to membrane-bound (LC3-ii),
allowing incorporation of LC3 into autophagosomal membranes (Kabeya et al., 2000). The addition of
phosphatidylethanolamine to LC3 changes the electromobility shift in SDS-PAGE gels, resulting in two
separate bands (Kabeya et al., 2000). Fly orthologs Atg8a-i and Atg8a-ii behave similarly to LC3-i and
LC3-ii, producing two distinct protein bands in Western blot (Nagy et al., 2015). I found that in the adult
fly lysates from thoracic tissue, Atg8a-ii bands were much fainter than Atg8a-i, requiring two separate
exposure times (Figure 6.16 A). Comparison of tissue lysates from flies expressing wRNAi or mCherryRNAi in
the muscle revealed that both the levels of LC3-i and LC3-ii were reduced in white deficient samples(Figure
6.16 A-C). Measurement of LC3 mRNA levels with qPCR revealed that the transcript levels were also
slightly reduced in white knockdown muscles (Figure 6.16 D). The reduction of LC3 protein and transcript
levels indicated that autophagy is dampened in response to white knockdown.
It would be expected that a reduction in a key component of autophagy would be detrimental to
proteostasis. To confirm this hypothesis, I expressed an RNAi against Atg8a in the adult muscle and
assessed the level of polyubiquitinated protein aggregate accumulation. As would be expected from limiting
the expression of a major autophagy machinery, polyubiquitin level was increased in response to Atg8a
knockdown (tubulin-Gal80ts/+;MHC-Gal4/UAS-Atg8aRNAi) compared to mCherryRNAi control (tubulinGal80ts/+;MHC-Gal4/UAS-mCherryRNAi) (Figure 6.16 E and F). Suppression of Atg8a expression in adult

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muscle is thus sufficient to disrupt proteostasis and likely to underly the mechanism by which white
deficiency disrupts adult proteostasis.
Figure 6.16 Autophagy is reduced in white deficient muscles
(A) Representative Western blots of thoraces of 1-week old flies expressing mCherryRNAi (MHC-Gal4/UASmCherryRNAi) and wRNAi (MHC0Gal4/UAS-wRNAi) in the muscle immunoblotted against Atg8a-i and Atg8a-ii and
actin.
(B) Quantification of Atg8a-i signal normalized to actin for genotypes in (A). N = 3 (experiments) Student’s t-test. p =
0.0548
(C) Quantification of Atg8a-ii signal normalized to actin for genotypes in (A). N = 3 (experiments) Student’s t-test. p =
0.0464
(D) Expression of Atg8a in thoraces of 1-week-old flies expressing mCherryRNAi (MHC-Gal4/UAS-mCherryRNAi) and
wRNAi (MHC0Gal4/UAS-wRNAi) in the muscle measured by qPCR. N = 3 (experiments) Student’s t-test. p = 0.0446
(E) Representative images of DLM from 5-week-old flies expressing mCherryRNAi (tubulin-Gal80ts/+;MHCGal4/UAS-mCherryRNAi) or Atg8aRNAi (tubulin-Gal80ts/+;MHC-Gal4/UAS-Atg8aRNAi) in the muscle during
adulthood. Scale bar = 20µm
(F) Quantification of polyubiquitinated aggregate volume per muscle area for genotypes and condition in (E). N = 5, 5
(flies) Student’s t-test. p = 0.0159
Error bars represent SEM. *p<0.05

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p62 tagged aggregate accumulation is increased in white knockdown muscle
I next wanted to determine if the polyubiquitinated aggregates are substrates of autophagy that are
accumulating due to the lack of LC3. While polyubiquitin proteins to be degraded by both autophagy and
proteasome, p62 is a tag that links ubiquitinated cargo to LC3 for eventual degradation by
autophagolysosomes (Pankiv et al., 2007). Presence of the p62 tag on the protein aggregates would suggest
that the accumulated proteins were targeted for clearance by autophagy. Previous reports indicated that
polyubiquitinated aggregates in the aged fly muscle do colocalize with Ref(2)P, the fly ortholog for p62
(Demontis & Perrimon, 2010). I confirmed that in both the control and white deficient muscles, many of
the polyubiquitinated aggregates were also positive for Ref(2)P (Figure 6.17 A-C). Colocalization analysis
indicated that Ref(2)P and polyubiquitin had weak to moderate positive relationship (r = 0.4809 for
mCherryRNAi, r = 0.3388 for wRNAi). The volume of Ref(2)P positive aggregates in white deficient muscles
were more than double that of control muscles (Figure 6.17 C). Accumulation of Ref(2)P positive
aggregates indicate that insufficient autophagic activity is clogging the protein clearance necessary to
remove the polyubiquitinated aggregates.
Figure 6.17 Ref(2)P colocalizes with the polyubiquitinated protein aggregates
(A) Representative images of DLM from 3-week old flies expressing mCherryRNAi (MHC-Gal4/UAS-mCherryRNAi) and
wRNAi (MHC-Gal4/UAS-wRNAi) in the muscle stained against polyubiquitin, Ref(2)P, and actin. Arrowheads
indicate colocalization of polyubiquitin and Ref(2)P. Scale bar = 20µm. Box indicates area shown in (B).
(B) Zoomed in images of (A).
(C) Quantification of Ref(2)P-positive aggregate volume per muscle area for genotypes in (A). N = 14, 18 (flies)
Student’s t-test. p = 0.00094
Error bars represent SEM. **p<0.01

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20S and 26S chymotrypsin-like proteolytic activity is not reduced in white knockdown muscle
To determine if autophagy is the only protein clearance pathway affected by white deficiency, I
assessed the proteolytic function of 20S and 26S proteasomes by fluorometry using Suc-LLVY-AMC. The
fluorescent signal of the 7-Amino-4-MethylCoumarin (AMC) becomes detectable after cleavage of the
peptide substrate by the proteasome. Suc-LLVY-AMC and other substrate-conjugates have been classically
used to measure the rate of proteolysis be proteasomes and other proteolytic enzymes (Pickering & Davies,
2012; Stein et al., 1996). I found that knockdown of white did not affect the ATP-dependent 26S proteasome
function but slightly increased the activity of ATP-independent 20S proteasome function (Figure 6.18 A -
C).
The reduction in expression of autophagic protein LC3, the colocalization and accumulation of
p62-positive aggregates, as well as the lack of evidence for reduction in proteasome kinetics, demonstrate
that lack of white leads to a repression of autophagy that ultimately results in protein aggregation with age.
Figure 6.18 Chymotrypsin-like proteolytic activity of 20S and 26S proteosomes are not reduced
by white knockdown
(A) Enzyme kinetic curve for 20S proteasome in 1-week-old thoraces expressing mCherryRNAi (MHC-Gal4/UASmCherryRNAi) and wRNAi (MHC-Gal4/UAS-wRNAi) in the muscle.
(B) Enzyme kinetic curve for 26S proteasome in 1-week-old thoraces expressing mCherryRNAi (MHC-Gal4/UASmCherryRNAi) and wRNAi (MHC-Gal4/UAS-wRNAi) in the muscle.
(C) Quantification of the linear phases of 20S proteasome kinetic curves in (A) and the linear phases of 26S
proteasome kinetic curves in (B). N = 3 (experiments) Student’s t-test. p = 0.00487 (20S), p = 0.2756 (26S)
Error bars represent SEM. **p<0.01

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6.3.14 RNAseq reveals increased AMP expression in w deficient flies.
To better understand the molecular mechanism by which knockdown of white in muscle
compromises lifespan in flies, I performed RNAseq on RNA extracted from young (1-week-old) adult
thoraces. I compared the transcriptome of flies expressing control mCherryRNAi against and wRNAi in a muscle
specific manner. Using Salmon and DESeq2 (see Methods), I identified 217 transcripts that showed
differential expression (adjusted p value < 0.1) in white knockdown thoraces compared to control (Figure
6.19 A-E). PCA analysis confirmed that the transcriptome profiles of the two genotypes are distinctly
separated (Figure 6.19 D). Among the transcripts with the lowest p values was white, as expected from the
experimental design (Figure 6.19 A and C). GO enrichment analysis of the significantly upregulated
transcripts indicated that the GO-term “response to bacterium” was enriched (Figure 6.19 E). Among the
significantly upregulated genes, I noted the presence of antimicrobial peptides (AMP) such as Drosomycin
(Drs) and other immune response genes (Immune induced Molecule 4 (IM4) and Immune induced Molecule
14 (IM14)) (Appendix A: Supplemental Table 1).
Increased inflammatory response is a hallmark of aging as part of a phenomenon known as
“inflammaging” (López-Otín et al., 2013; Salminen et al., 2012). Secretion of proinflammatory cytokines
increases during cellular senescence as part of senescence-associated secretory phenotype (SASP) (Rodier
& Campisi, 2011), while adaptive immunity declines (Deeks, 2011). AMPs such as Drs are part of the
innate immune response regulated by NF-κB proteins and comprise the main immune defense against
infection in fruit flies (Hoffmann, 2003).
AMPs have been shown to drive aging in the fly, and suppression of AMP expression late in life
extends lifespan (Badinloo et al., 2018). Increased expression of inflammatory cytokine can also be
disruptive to the blood brain barrier (BBB), a protective layer of blood vessels that surround the brain
(Varatharaj & Galea, 2017). Systemic increase in AMP expression could reach the BBB, resulting in
breakdown of the BBB. To determine if increases in AMP expression reaches the brain, I assayed a panel
of AMPs and performed qPCR using head lysates. I found that there was high variability in the AMP

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expression between experiments, but I did detect increased AMP expression in the brain of white deficient
flies (Figure 6.19 F).
I then asked if wRNAi expression led to disruptions in the BBB. Dextran penetration in the brain
indicates a leakier barrier between the periphery and the central nervous system. As expected from increased
inflammation, knockdown of white in the muscle resulted in increased dextran penetration into the brain,
indicating a disruption in the BBB (Figure 6.19 G and H). These results suggest that muscle deficiency of
white leads to proteostatic imbalance and increased brain inflammation during aging.
Figure 6.19 (see next page for the figure)
(A) Heatmap of most significantly differentially expressed genes in thoracic extracts of 1-week-old adult flies
expressing mCherryRNAi (MHC-Gal4/UAS-mCherryRNAi) or wRNAi (MHC-Gal4/UAS-wRNAi) in the muscle.
(B) MA plot of all transcripts identified in RNAseq from genotypes and conditions in (A).
(C) Volcano plot of transcripts identified in RNAseq from genotypes and conditions in (A). Log 2 foldchange cutoff =
±0.5, p-value cutoff = 10e-6.
(D) PCA analysis of RNAseq data from genotypes and conditions in (A).
(E) GO enrichment analysis of significantly updaregulated transcripts from genotypes and conditions in (A)
(F) qPCR analysis of AMP transcripts in head extracts of 3-week-old adult flies expressing mCherryRNAi (MHCGal4/UAS-mCherryRNAi) or wRNAi (MHC-Gal4/UAS-wRNAi) in the muscle. N = 3 (experiments). Student’s ttest.
(G) Representative images of brains from 4-week-old adult flies expressing mCherryRNAi (MHC-Gal4/UASmCherryRNAi) or wRNAi (MHC-Gal4/UAS-wRNAi) in the muscle treated with peripheral injection of dextran.
Scale bar = 50 µm
(H) Quantification of mean dextran signal intensity for genotypes and conditions in (G). N = 16, 18 (flies). Student’s ttest. p = 0.0433
Error bars represent SEM. *p<0.05

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Figure 6.19 RNAseq of white deficient thoraces show increase in AMP expression

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6.4 Discussion
6.4.1 Deficiency of white in the muscle increases protein aggregate accumulation and reduces
lifespan
In this chapter, I demonstrated that the knockdown of white in the muscle is sufficient to increase
polyubiquitinated aggregate accumulation in the adult fruit flies and shortening of lifespan. Classically,
white has been known to function in the eye to produce eye color pigments and in the brain to alter
tryptophan metabolite availability (Borycz et al., 2008); however, its role in the muscle in larval
development or adult aging has not been reported. As such, white deficient strains such as w1118 and wRNAi
has been used as control strains in many experiments. The results presented in this chapter, therefore, will
be of great relevance to fly biologists in the aging field for using white deficient strains as controls for
studying proteostasis and lifespan.
Comparing the impact of transgenic white knockdown using muscle- and neuron-specific drivers
demonstrated that expression of white in the muscle is required to maintain proteostasis during aging. It is
possible that neural knockdown of white aggravates protein aggregate accumulation in the brain similar to
muscle knockdown of white; this was not pursued in this dissertation to focus on muscle proteostasis.
The initial study described in this chapter aimed to find ways to manipulate synaptic activity to
slow down muscle and systemic aging. I have started my investigation by using w1118 as the control strain,
which eventually led me to question the role of white in maintaining muscle proteostasis during aging. After
observing that white expression does alter muscle proteostasis and lifespan, I revisited my original data
with the effects of white on aging in mind. The apparent decrease in protein aggregation and extension of
lifespan by synaptic mutants and transgenes may be explained by the difference in white dosage. Knowing
the role of white in muscle, future experiments will have to be designed to minimize the role of White and
to isolate the effect of synaptic activity on organismal health and lifespan.
6.4.2 mTOR signaling during larval development influence proteostasis in adult
The temporal restriction experiments demonstrate the role of developmental mTOR signaling on
adult aging. I have shown that white is required during larval development to maintain proteostasis later in

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life. Lack of white during larval development is sufficient to worsen proteostastic imbalance during
adulthood. Larvae that lack white show an increased level of TOR activity, and treatment with rapamycin
during larval development is sufficient to rescue polyubiquitin aggregation in white deficient flies. While
mTOR activity in the adult is accepted as driver of aging, the consequences of mTOR activity during
development on aging is not known. The data presented in this chapter indicate that TOR activity during
larval stages has consequences during adulthood in the fly.
TOR phosphorylates S6K and promotes protein translation via its function in the TOR complex 1
(TORC1 or mTORC1 in mammals). While rapamycin is thought mainly inhibit the activity of mTORC1,
it is also known to inhibit mTORC2 after prolonged exposure (Lamming, 2016). Increased phosphorylation
of S6K in the muscles of white deficient larvae suggest that increase in mTORC1 activity is detrimental to
tissue aging later in life; however, to separate the roles of mTORC1 and mTORC2, genetic interaction
studies will need to be performed. Reduction of mTORC1 components in combination with wRNAi should
be sufficient to rescue the wRNAi phenotype if mTORC1 is indeed responsible for decline in proteostasis in
response to white deficiency.
6.4.3 Autophagy, but not proteasome function, is reduced in w deficiency
Assessment of autophagic protein Atg8a, accumulation of Ref(2)P, and proteasome kinetics
revealed that autophagy activity is reduced in white deficient flies, but the proteosome function remain at
wildtype levels. In this study, change in autophagy level was assessed by the levels of Atg8a-i and -ii. Since
Atg8a is processed during autophagic proteolysis, it has been suggested that the measurement of Atg8a-i
and -ii flux is a more accurate way to assess autophagy function (Nagy et al., 2015). The assessment of the
autophagic flux can be assayed, for example, but looking at the level of Atg8a in autophagosome versus
autolysosomes. This can be achieved by detecting the fluorescent signal of a tandem tagged version of
Atg8a; the GFP tag is quenched in the autolysosome while mCherry fluorescence remain intact (Nagy et
al., 2015). Another approach is to compare the accumulation of Atg8a-ii in response to lysosomal protease
inhibitor treatment (Mizushima & Yoshimori, 2007). This method can dissect whether low level if Atg8ai during steady-state is caused by active clearance of autolysosomes or by defective initiation of autophagy

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(Mizushima & Yoshimori, 2007). In these ways, analysis of autophagic flux will further support how
autophagy is affected by white deficiency.
Additionally, changes in proteasome function was ruled out by assessing the proteasome kinetics
using Suc-LLVY-AMC as the fluorogenic substrate. Suc-LLVY-AMC is a substrate of the β5 subunit
within the 20S proteasome. There are two other types of proteolytic sites within the 20S proteasome, named
trypsin-like and caspase-like based on their cleavage sites (Liggett et al., 2010). Comparison of the
chymotrypsin-like proteolytic activity alone may not be sufficient to rule out changes in overall proteasome
function. The enzymatic function of the other two types of cleavage sites in the 20S proteasomes can also
be assayed using site-specific substrates (Liggett et al., 2010).
6.4.4 white deficiency leads to BBB disruption during aging
RNAseq data and follow-up qPCR analysis of aged flies indicated that AMP expression is increased
in flies expressing wRNAi in the muscle. This was followed up by demonstration of BBB disruption in white
deficient flies. These results demonstrate that peripheral proteostasis has impacts on the central nervous
system health during aging.
It is still unclear if the increased inflammation in white deficient flies is dependent on the
proteostatic stress. Recent studies connecting peripheral tissue health to brain health would suggest that this
is entirely possible. For example, a “muscle-gut-brain axis” has been suggested recently to describe the
relationship between muscle health, gut health, and neurodegeneration (Schlegel et al., 2019). Through this
paradigm, for example, proteostatic stress on the muscle could affect the integrity of the gut, which in turn
affects inflammation response in the organism.
If there exists a relationship between general muscle proteostasis and BBB integrity, other methods
to reduce protein clearance should also result in leaky BBB. This hypothesis can be tested using musclespecific disruption key protein clearance pathways, such as components of autophagy, as well as protein
folding pathways that contributes to proteostatic health.

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6.5 Conclusion
In this chapter I have presented evidence for muscle w expression in the larva having consequences
on protein clearance and proteostasis in the adult. The data indicate that developmental mTOR is
responsible for changes in adult proteostasis, which is a new aspect by which mTOR influences the aging
process. Furthermore, the relationship between white deficiency and BBB leakiness indicate that loss of
muscle proteostasis may have consequences to brain aging.

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Chapter 7. Open-Source Software to Analyze Electrophysiological
Recordings of Synaptic Activity
7.1 Abstract
Electrophysiology experiments require analysis of spontaneous and evoked synaptic events.
Several programs exist to assist in the analysis; however, I noticed that some of the popular algorithms have
been discontinued, too complex for an ordinary user, or sometimes insufficient for accurate measurement
of the fast kinetics of synaptic events. Furthermore, the algorithms for many of the software are hidden
from the user. I recognized the need for an open-source analysis software that supports fast and accurate
automated detection and analysis of synaptic events, and I have decided to build such a package.
7.2 Background
Electrophysiological recordings of synaptic activity provide key insights into how neurons
communicate with each other and with their target tissues in the periphery. We routinely measure mEPSC
and EPSC amplitudes to assess changes in synaptic strength. Additionally, changes in kinetics of activation
and decay of synaptic events can reveal differences in the function and composition of individual receptor
complexes (Epsztein et al., 2005). Efficient analysis of electrophysiological events is therefore crucial to
studies in neurophysiology.
Various models of aging and age-dependent disease in the nervous system have demonstrated that
synaptic properties change with age and disease. Of prominent interest to us was the increase in synaptic
setpoint with age reported in Drosophila NMJ, which others and we have observed (Mahoney et al., 2014).
In addition to the increased amplitude of synaptic currents, we observed a marked change in the kinetics of
evoked and spontaneous synaptic events. I sought to characterize these age-dependent changes in the
electrophysiological properties at the Drosophila NMJ.
While studying the mechanisms of synaptic strength regulation, I noticed that several of the analysis
software used for mEJC analysis were proprietary or outdated and no longer supported. Furthermore, while
these platforms allowed customizations for complex analysis of electrophysiological recording data, the

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complexity was unnecessary to answer my question. I therefore aimed to build a user-friendly and opensource software for analyzing electrophysiology recordings. The aim of the project was to develop an
algorithm that can detect and evaluate synaptic events in a fast, accurate and automatic manner, equipped
with a graphical user interface (GUI) that is simple to learn, with the ability to manually verify synaptic
events detected by the automated software.
Python was chosen as the program language for this software for its wide array of scientific and
GUI packages. The software was developed using sample recordings from Drosophila larval NMJ; however,
in theory, the algorithm is compatible for analysis of synaptic events in other model synapses.
In this chapter, I describe the design and algorithm for the open-source software that I have called
MiniPy. I will then present the analysis obtained using this software. The methods to perform automated
detection and analysis of synaptic events will be described in detail.
7.3 Results
7.3.1 Algorithm development and design
Intuition to event detection algorithm
Recording of spontaneous vesicular release, termed miniature synaptic events, at the Drosophila
larval NMJ and mammalian cell culture models involve recording the electrical signals from a cell without
stimulation to the presynaptic motoneuron (Imlach & McCabe, 2009). In our routine recording of the larval
NMJ system, a typical recording results in baseline noise of about 0.5nA, which can be filtered and reduced
to about 0.2nA (Figure 7.1 A and B). An average mEJC from wildtype larvae are only about 0.7nA. The
task during mEJC analysis is to pick out data points that belong to spontaneous synaptic evens out of the
noise and extract relevant information from the data points.
In our routine analysis of mEJCs, we typically manually pick portions of the trace that conform to
a typical shape of an mEJC event. Having an automated system to pick the relevant data points as synaptic
events will greatly speed up this process. For simplicity, this section will focus on automated detection and
analysis of mEJCs from Drosophila NMJ.

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While there are many ways to analyze the properties of a synaptic event, for the purpose of this
software development I first focused on extracting the following information: baseline, event amplitude,
rise time, and decay constant (Figure 7.1 C).
At first glance, applying some threshold to distinguish datapoints beyond the noise seems like an
intuitive approach. Within a small window, setting the average y-value as a threshold detects most of the
data points in the mEJC event; however, some baseline noise is also detected as significant (Figure 7.1 D).
Adjusting the threshold by requiring data points to be a certain amplitude beyond the average value
successfully rejects noise and selects data points belonging to the synaptic event (Figure 7.1 E).
Unfortunately, baseline can shift during the recording, and applying a single value as a threshold to a longer
recording period is not appropriate (Figure 7.1 F).

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Figure 7.1 Challenges to automated analysis of electrophysiology recordings
(A) A snippet of a typical mEJC recording from Drosophila larval NMJ.
(B) The data from (A) after Lowpass Boxcar filtering has been applied.
(C) A typical mEJC. Terminology of properties and phases of synaptic events are labelled.
(D) Result of applying a threshold to detect data points belonging to a synaptic event. While majority of the data points
belonging to a synaptic event are identified, data points of baseline noise are also annotated as events.
(E) Approach in (D) combined with an amplitude threshold of 0.3nA. The baseline noise is successfully filtered out of
the event selection
(F) Approach in (E) applied to a wider time window. A single threshold value is no longer appropriate to select
synaptic events.

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Brakel method
A popular algorithm for signal detection overcomes the problem of shifting baseline by calculating
the average and standard deviation of previous data points (Brakel, 2014). The algorithm uses lag, the set
of values trailing behind the data value being evaluated, to applying smoothing and determining the
standard deviation. lag of 100 was chosen for this test because mEPCS rise time was reported to be 0.2-
10ms, and 100 data point represents 10ms in a 10kHz recording.
In this algorithm, data points that satisfy equation (1) are marked as signals, and the rest are marked
as baseline.
Equation (1) ( − ) > ∗ ℎℎ
Where
yt = y value at time t
averaget = average of data points from time (t-lag) to (t)
stdevt = standard deviation of data points from time (t-lag) to (t)
threshold = multiplier to the standard deviation that needs to be satisfied for a datapoint to be
marked as a signal
Applying this method to a synaptic recording data, it is possible to discern that the average and the
zone within 1 standard deviation from the average closely follow the data points (Figure 7.2 A). By
adjusting the threshold value of this algorithm, it is possible to filter out many of the baseline noise (Figure
7.2 B and C). However, high threshold also filters out the majority of synaptic event (Figure 7.2 D).
The algorithm also allows applying weight to a data point based on whether it was annotated as a
signal or noise. The benefit of lowering the weight of the signal is so that the value of a signal does not
corrupt the threshold for proceeding signal detection. More influence given to a signal pulls the average
closer to the raw data, while giving no weight the signals will keep the calculated averages stabilized at the
baseline. When the influence is set to 0, the algorithm successfully detects the majority of the mEJC event
as signals, while avoiding most of the baseline noise (Figure 7.2 E).

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The main downside of applying this algorithm is the computational time. This algorithm was made
to detect sudden signal peaks during real-time signal acquisition, and therefore is optimized to work with
data in real time. Furthermore, the algorithm is better suited for types of recording that show a sudden spike
in signal strength (i.e. one data point that stands above the noise) rather than the relatively slow rise and
decay of synaptic events, comprised of hundreds of data points that diverge from the baseline noise. As
such, the suggested lag parameter is set at 5, much lower than the 100 that I found works with our
electrophysiology data.
Comparison of data length (in length or electrophysiological recording) and time it takes for the
algorithm to complete the analysis (threshold = 3, influence = 0) shows a linear trend (Figure 7.2 F), which
is expected, as the algorithm has a complexity of O(n). Based on the linear trend, a typical mEJC recording
lasting 3 minutes will take about 110 seconds to analyze which is undoubtedly too long.
A different approach was thus needed to analyze synaptic events.

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Figure 7.2 Brakel’s method for signal peak detection can isolate synaptic events
(A) Representation of the raw trace, average, and standard deviation calculated by the Brakel’s method of peak signal
detection.
(B) Brakel’s method using threshold of 1 and lag of 100. Brakel’s method selects data points outside of the standard
deviation as signals. Only the signals below the average are marked in this example, as mEJCs peak in the
negative direction by definition.
(C) Brakel’s method using threshold of 2 and lag of 100. Increasing the threshold parameter makes the algorithm more
stringent.
(D) Brakel’s method using threshold of 3 and lag of 100. Most of the noise are rejected at the cost of also rejecting
most of the data points belonging to a synaptic event.
(E) Brakel’s method using threshold of 3, lag of 100, and influence of 0. The majority of the synaptic event is isolated
as desired.
(F) Time required for analysis vs. the length of the electrophysiological recording.

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Sliding window algorithm for event peak detection
If a single synaptic event is isolated for consideration, the peak of the event can be detected by
looking for the local extremum (Figure 7.3A). The question is how to isolate such an event, without
knowing where the synaptic events are a priori. A crude approach is to create a small window of time to
be considered by the algorithm and look for the local extremum within such a window.
I have created a parameter named search range, which determines the radius of such a search
window (Figure 7.3 A). The search range can be adjusted by the user to optimize the event search algorithm.
The search window is shifted sequentially to cover the entire trace and extract a single local extremum
within each window (Figure 7.3 B).
while xi is less than xmax {
ypeak = max(abs(y)) within range xi to xi + 2*search range
xpeak = x-value corresponding to ypeak
If xpeak is not xi or xi + 2*search range: save xpeak, ypeak as event extremum
xi = xi + search range
}
This is obviously a crude algorithm, as many local minima within the baseline noise are also
selected as candidate events (Figure 7.3 C).

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Moving average algorithm
The algorithm requires a way to filter out the noise and only select peaks that stand out from the
noisy baseline. Theoretically, filtration of noise can be achieved by setting a minimum amplitude required
for candidate peaks to be accepted as synaptic events. In order to determine the amplitude of a candidate of
a synaptic event, the software needs to compare the y-value of the peak against the baseline.
I took notice of the fact that using the Brakel’s method, the curve of the average intersected always
intersected with the synaptic evet at the beginning of the rise phase (Figure 7.4 A). This makes sense, as
most of the signal during the rise of a will be further from the baseline than the preceding data point, whereas
the data points within the baseline oscillate around a relatively stable value of y.
Figure 7.3 Finding local extrema detects synaptic event peaks but also selects noise
(A) Schematic for algorithm strategy. Within some search range, the peak of the synaptic event will be the
minimum/maximum.
(B) Representation of the algorithm sliding a search window for the entirety of the trace.
(C) Using the strategy in (A) successfully selects synaptic peaks but also selects data points within noise.

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Figure 7.4 Average curve intersects with raw trace to estimate start of a synaptic event
(A) Two representative mEJC traces overlayed with the average and standard deviation zones defined by Brakel’s
method. The average curve in Brakel’s method intersects the raw trace at the start of a synaptic event. Arrowhead
indicates the intersection which corresponds to the start of a synaptic event.
(B) Raw trace overlayed with its central moving average, with a kernel of 101.
(C) Raw trace overlayed with its moving average, with a kernel of 100. The average curve is identical to the central
moving average but shifted to the right by 50 data points.

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Based on this observation, I applied the lag parameter from Brakel’s method to identify the start of
a synaptic event. At a given time x, the trailing average is defined as:
Equation (2)
= � � ()

=− �()−1
Where
averagei = the trailing average calculated at time of i
lag = the number of trailing datapoints from i
y(x) = function to be evaluated
In other words, the trailing average for a given data point is the average of n = lag data points
trailing behind it.
The trailing average effectively applies a smoothing effect to the trace, which makes sense,
considering its similarity of the trailing average formula to the Lowpass Boxcar filtering, also known as
central moving average smoothing.
Equation (3)
′(, ) =
⎝
⎛ � ()
+−1
2
=−−1
2 ⎠
⎞ ()−1
Where
N ∈ {odd integer}
p(x) = function to be filtered
p’(i,N) = filtered function p at i, using a kernel of N
The trailing average effectively applies a Central Moving Average filtering with a kernel equal to
the lag parameter and translates the smoothed curve to the right by lag/2 (Figure 7.4 B and C).

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The intersection of the trailing average and the raw data immediately before a candidate peak can
be used to estimate the start of a synaptic event, providing an estimate of the baseline for the synaptic event.
It follows that the amplitude of the synaptic event can be estimated as follows:
If:
Equation (4.1) ≈
It follows that:
Equation (4.1) = ( − )
Equation (4.2) ≈ � − �
Where y is the current for the corresponding data points for mEJC traces.
ypeak = y value of the mEJC extremum
ybaseline = y value of the baseline before the start of mEJC
ystart = y value of the data point at the start of the mEJC
If the amplitude of a candidate peak is below a specified threshold, the candidate is rejected. A
threshold of 0.3nA amplitude was chosen for the demonstration because the noise of the baseline was
estimated to be around 0.2nA by eye (Figure 7.1 A). This approach successfully rejects noise and selects
appropriate peaks as mEJC extremum (Figure 7.5 A and B).
Both lag and threshold parameters can be adjusted by the user to optimize the analysis.
The processing speed for this algorithm was much faster than the original Brakel’s method (Figure
7.5 C). The initial implementation of this algorithm was able to complete a search of a 90 seconds recording
file in 10.6s.

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Figure 7.5 Thresholding the amplitude of the synaptic events successfully rejects noise and
identifies synaptic events.
(A) Applying event amplitude threshold successfully selects the starting point of an mEJC event and rejects noise.
(B) Zoomed out view of the signal detection result. The algorithm successfully detects mEJC events from the entire
trace.
(C) Comparison of the original Brakel’s method and the moving average with respect to processing time versus the
length of the electrophysiological recording.

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Rise time and decay time constant
In addition to identifying a synaptic event and calculating its amplitude, I wanted to implement a
way to calculate the rise time and decay constant for the synaptic event.
Rise time can be calculated using the equation:
Equation (5) = −
Where
xpeak = time (x-value) at peak of mEJC
xstart = time (x-value) at the start of mEJC
Finally, the decay constant is a number that describes the curve of decay phase of synaptic events.
The decay phase of synaptic events occur after the neurotransmitter channels are closed, and it represents
the repolarization phase of synaptic membrane.
The repolarization of excitable membranes, such as those found in neurons, can be modelled by
exponential decay (Kandel et al., 2000). The decay time constant (or τ) represents the time it takes for the
signal (current in the case of mEJC) to reduce to 1/e ≈ 0.386 of the peak amplitude. Thus, the decay of the
mEJC can be modelled as:
Equation (6) () ∝ �
−�−�
� (Kandel et al., 2000)
Where
τdecay = time constant for the decay
x = time (x-value) for a given data point
tstart = time at the peak of the mEJC
To estimate the decay constant for a given synaptic event, I decided to evaluate the fit of multiple
values of tau and select the best candidate. Given a mEJC whose peak has been identified, the decay can be
modelled using Equation (6). The algorithm iterates through 800 possible values of tau, ranging from
0.0001s to 0.08s, at increments of 0.0001s. At the hippocampal pyramidal cells, decay constant of 5 to 6ms

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has been suggested for modeling EPSCs (Jonas et al., 1993), and thus we can safely assume that the bounds
of 0.1ms to 80ms should be more than sufficient to analyze most mEJCs.
The algorithm models the decay using each candidate values of tau, using the equation:
Equation (7) () = � − �
−(−)
+
Where
pi = model of the mEJC decay using the i-th candidate τ
ybaseline = y-value of the baseline
ypeak = y-value at the event peak
x = time (x value)
tpeak = time of the event peak
τi = i-th candidate of τ
The algorithm initially calculates the exponential decay for all 800 candidate values of the decay
time constant (Figure 7.6 A). The best fitting model is chosen using the least square fitting algorithm, which
calculates the vertical deviations R2 of each model and the raw data as follows:
Equation (8) 2 = ∑[ − ()]2
Where
yk = y-value (current) at time k
pi(k) = decay model for the i-th candidate for τ, as defined in Equation (7), at time k
k spans from the peak of mEJC to 800 data points after the peak.
In summary, the algorithm calculates the model for every candidate value of τ and chooses the
model with the least vertical deviation (Figure 7.6 B and C).
The algorithms presented so far can be generalized to voltage recordings by replacing current for
potential. Furthermore, the parameters can be adjusted to analyze evoked synaptic events, such as EJC
(Figure 7.6 D).

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With other optimization based on Python’s coding language structure and NumPy library properties,
the program was able to analyze a 90 seconds-long recording in an impressive speed of 2.34653 seconds,
in which the software detected 58 mEJC events.
Figure 7.6 Estimating the decay time constant
(A) Exponential curves produced by all 800 of the possible tau values overlayed on the raw trace.
(B) The curve with the least square distance plotted as overlay on the raw trace
(C) The data point at one decay constant away from the peak is indicated by the purple dot.
(D) The algorithm successfully detects and analyzes evoked synaptic events (EJC)

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7.3.2 Methods for using MiniPy
The above algorithm for synaptic event detection was incorporated into a Python-based GUI
software. The GUI was developed using the tkinter package for Python (Tkinter — Python Interface to
Tcl/Tk — Python 3.9.1 Documentation, n.d.). Axographio (Gill, n.d.) and pyabf (Harden, n.d.) packages are
used to read data from axograph and pClamp software, respectively. Matplotlib package (Matplotlib · PyPI,
n.d.) is used to display the traces within the GUI.
Figure 7.7 MiniPy GUI
(A) Screenshot of the MiniPy GUI,
labelled for key components
(B) Examples of instructions that
appear in the help display
(C) Screenshot of the MiniPy GUI
after analysis of an mEJC
recording

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The GUI is laid out in 5 major components: Menu Bar, Control Panel, Help Display, Trace Viewer,
and Data Display (Figure 7.7 A). Hovering over functional components of the software prompts a short
description to appear within the Help Display. This feature was added to help with user-friendliness (Figure
7.7 B). The Trace Viewer displays the electrophysiology recording along with markers for any detected
synaptic events, and the Data Display lists the numerical details of detect synaptic events (Figure 7.7 C).
Automated or manual analysis of spontaneous and evoked synaptic events
1. Import recording trace on MiniPy
a. New traces can be opened by clicking the “Open” prompt in the Trace Viewer, or from the
Menu  File  Open. Currently MiniPy can open Axon Binary File (ABF) format 1 and 2
(pCalmp, .abf) and axograph file format (.axgx). The trace will be displayed in Trace Viewer.
It is recommended to apply filtration on the trace before importing to MiniPy.
b. Select desired channels for analysis under “channels” drop down menu in the Control Panel
under the Plot tab (Figure 7.8 A). MiniPy automatically detects channels stored in the recording
data and populates the dropdown menu.
2. Adjust traces within Trace Viewer for desired presentation
a. Click on the “Show All” in the Control Panel under the Plot tab to fit the entire recording trace
within the display.
b. Adjust the scroll bars to move the trace to the desired position within the graph (Figure 7.8 B).
c. Adjust the zoom of the trace to desired level within the Trace Viewer by using the “+” and “˗”
buttons on each axis (Figure 7.8 B)
d. x- and y- limits can be defined manually in the Control Panel under the Plot tab (Figure 7.8 C).
e. Areas within the Trace Viewer can be zoomed by holding “Shift” key while clicking and
dragging on the Trace Viewer (Figure 7.8 D).
f. While the cursor is located within the Trace Viewer, scroll the mouse wheel to adjust the xaxis zoom, and scroll the mouse wheel while holding the “Shift” key to adjust the y-axis zoom.

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g. The trace can also be navigated horizontally and vertically using the arrow keys, “WASD”
keys, or [2,4,6,8] keys (recommended for number pad).
Figure 7.8 Navigating electrophysiological recording data
(A) Desired channels can be displayed by the dropdown menu under the display tab. Voltage channel and current
channel of a typical mEJC recording is displayed.
(B) Screenshot of the Trace Viewer. Arrowheads represent buttons that can be used for navigation of traces. “+” and
“-” buttons control zoom of respective axes, while the scroll bar and “<” “>” buttons control the position of the
respective axes.
(C) Screen shot of the Plot tab in the control panel. x and y limits can be specified to display desired regions of the
trace.
(D) Before and after screenshots of zooming using the mouse. Traces can be zoomed by dragging the mouse while
holding the “Shift” key.

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3. Adjust the search algorithm parameters
a. Open the Mini tab in the Control Panel (Figure 7.9 A)
b. Use the “direction” dropdown menu to indicate which direction the peaks are directed. Select
positive for potentials and select negative for currents.
c. Enter the minimum amplitude of synaptic events in “threshold”. The unit is based on the
recording data (see y-axis in the Trace Viewer). Any potential synaptic events with lesser
amplitudes will be rejected.
d. Enter the number of data points used to calculate the trailing average in “lag”.
e. Enter the radius of x-data points used to search for a synaptic event in “range”
f. Enter the minimum decay time constant (tau) required in “tau”. Any synaptic event with
smaller tau will be rejected
g. To revert to default parameters, click “Default Parameters”.
4. Automatically search synaptic events
a. All detected synaptic events will be marked as overlays on the trace within the Trace Viewer.
Extracted data will be displayed in the Data Viewer.
b. Click on “Auto find (all)” to allow MiniPy to search for all synaptic events within the recording
file (Figure 7.9 B).
c. Click on “Auto find (window)” to limit the automated search to within the x-limit visible within
the Trace Viewer. y-value that is cropped in the Trace Viewer will still be considered.
5. Manual selection of synaptic events
a. Click on or near the on the desired synaptic event within the Trace Viewer (Figure 7.9 C). If a
synaptic event that matches the criteria in (3) exists within the search window centered around
the cursor (defined by “range”), the event will be marked.
6. Manual rejection of synaptic events
a. Events marked by the software can be individually selected and deleted. To select a synaptic
event from the Trace Viewer, right click on or near the synaptic marker to reject. The marker

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overlay indicating the event peak will turn red. The data pertaining to the selected synaptic
event will be highlighted in the Data Viewer (Figure 7.9 D).
b. To select a synaptic event from the list within Data Viewer, click on the row containing the
data of the target event. The Trace Viewer will attempt to display the selected synaptic event.
c. Hit the “Delete” key to reject the event from the dataset.
Figure 7.9 Automated detection and analysis of synaptic events
(A) A screenshot of the Mini tab in the control panel. Search parameters can be adjusted here.
(B) A screenshot of MiniPy after automatically analyzing the data in the entire trace.
(C) A screenshot after selecting a single mEJC event manually. Synaptic events can be manually picked by clicking
near or on the event within the Trace Viewer.
(D) A screenshot after interacting with a single mEJC event. The peak marker turns red and the relevant information in
the Data Viewer is highlighted.

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7. Exploring the event data
a. View the numerical data for all synaptic events in the Data Viewer. The time of synaptic event
peak, amplitude, rise time, baseline value, and the lag parameter are displayed.
b. To sort the data by each category, click on the column header (Figure 7.10)
Figure 7.10 Sorting mEJC quantification data in MiniPy
Screenshots of the Data Display after detecting synaptic events. Each data column can be sorted by ascending or descending
order.

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8. Exporting analysis data
a. Extract the calculated summary of the synaptic events from Menu  “Analysis”  “Summary”
(Figure 7.11 A). This produces a popup window that lists the average, standard deviation, and
SEM of amplitude, decay constant, and rise time.
b. Frequency plot of synaptic events can be obtained from Menu  “Analysis”  “Frequency
Plot” (Figure 7.11 B). This produces a popup window with the frequency plot of the events.
c. The information in the data frame can be exported to a Comma Separated Value (CSV) format
file by Menu  “File”  “Save Minis” or “Save Minis As”. Choose the CSV format. CSV
files can be opened in other programs, such as any text-reader programs and Microsoft Excel
(Figure 7.11 C).
9. Save .minipy file
a. Save the event file by Menu  “File”  “Save Minis” or “Save Minis As” and choose
the .minipy format. The .minipy format is a text file formatted to be read by MiniPy software
to reproduce the event analysis for a given trace.
10. Load previously analyzed data
a. Open previously saved event file for a corresponding trace by Menu  “File”  “Open Minis”
(Figure 7.11 D). Opening an event data that does not match the trace will prompt a warning.
The method described in this section can analyze both spontaneous synaptic events and evoked
synaptic events by adjusting the minimum amplitude threshold.

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Figure 7.11 Saving analyzed data in MiniPy
(A) A screenshot of the analysis summary given by MiniPy
(B) An example of event frequency plot generated by Minipy
(C) A screenshot of the CSV file containing analyzed data on each synaptic event generated by MiniPy
(D) Screenshots of MiniPy before and after loading a saved MiniPy event file.

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Display evoked synaptic event sweeps as overlay
When presenting data on evoked synaptic events, it is necessary to select representative traces from
the recording data (for examples, see electrophysiology data in Chapters 3-5). MiniPy has been added with
a feature to streamline the representative trace selection. This feature is only supported for ABF file formats.
1. Import recording trace on Minipy as described in the previous section (Figure 7.12 A)
2. Overlay sweeps in Trace Viewer
a. Change from continuous data display to sweep overlay display by Menu  “View” 
“Overlay”
b. MiniPy will automatically display the first sweep of the recording data (Figure 7.12 B and
C)
c. To display all sweeps, go to the Sweeps tab in the Control Panel. Click “Show all” (Figure
7.12 C and D)
d. To clear all sweeps from Trace Viewer, click “Hide all”
3. Display select sweeps in Trace Viewer
a. Click on the sweep name to hide or display the select sweep in the Trace Viewer overlay.
All currently displayed sweeps will have a check box next to the sweep name (Figure 7.12
E).
b. Sweeps can be selected by clicking in the Trace Viewer near or on the trace. The selected
trace will appear red (Figure 7.12 F). Press the “Delete” key or the “Backspace” key to
hide the trace from view.
The traces displayed can be exported as images for use in other programs. The steps to export the
images is described below.

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Figure 7.12 Using MiniPy to select representative traces for evoked synaptic release
(A) A screenshot of MiniPy opening a typical EJC recording in “continuous” mode
(B) The same recording in (A) displayed as a “sweep” mode
(C) A screenshot of MiniPy displaying the Sweep tab in the control panel.
(D) A screenshot of MiniPy displaying all sweeps within the recording
(E) A screenshot of MiniPy displaying a select number of sweeps
(F) A screenshot of MiniPy selecting a trace in the Trace Viewer. The selected sweep is colored in red. The trace can
be hidden by pressing the “Delete” or “Backspace” keys.

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Export traces as image files
The displayed traces in the Trace Viewer can be exported as images to be incorporated into
scientific figures. Using the navigation features in the Trace Viewer (described above) and additional
stylization features described in this section, the user can adjust the trace as desired for presentation and
export images.
1. Alter trace display styles
a. Display options can be adjusted in the Style Tab within the Control Panel.
b. Adjust the thickness of the trace by increasing or decreasing the “linewidth” parameter (Figure
7.13 A and B).
c. Change the color of the trace in the “color” parameter (Figure 5.13 A and B). MiniPy accepts
standard Python color names in the matplotlib library
(https://matplotlib.org/3.1.0/gallery/color/named_colors.html) and HEX color codes (starting
with “#”).
d. Change the major tick of the x-axis through the “major tick” parameter. This changes the
interval at which the labels for the x-axis appears.
e. Change the minor tick of the x-axis through the “minor tick” parameter.
2. Export images
a. Export the trace image by Menu  “File”  “Export plot”. Several image file types are
available for selection.
An example of a trace exported as a Scalable Vector Graphics format (.svg) is shown in Figure 7.13
C.

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Figure 7.13 Changing trace style in MiniPy
(A) A screenshot of the Style tab in the control panel. Parameters can be adjusted to change how the traces are
displayed.
(B) Screenshot of before and after changing the line color and line width of the trace.
(C) An example of a SVG file exported from MiniPy.

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7.3.3 Analysis of adult mEJC recordings reveals changes in channel kinetics
Software Comparison
To assess the performance of MiniPy, I compared its results from mEJC with a popular proprietary
software, Mini Analysis (Synaptosoft).
To compare the analysis of mEJCs, I extracted mEJC peak time, amplitude, rise time, and decay
time constant from a recording file of Drosophila adult NMJ using MiniPy and Mini Analysis. Automated
analysis was used for both programs. Comparison of the detected mEJC extrema indicated that the majority
of the mEJCs identified by Mini Analysis was also detected by MiniPy (Figure 7.14 A).
The detected events were matched by the event time. I compared the amplitudes for each of the
mEJCs detected by MiniPy and Mini Analysis, and I found that the MiniPy software closely resembles the
data from Mini Analysis (Figure 7.14 B). The average absolute difference between the amplitudes
calculated by the two algorithms was less than 3% of the average mEJC amplitude (Table 7.1).
Next, I compared the kinetic features for each of the mEJCs detected by MiniPy and Mini Analysis.
The estimation for the decay constants were similar from the two programs except for one occasion in
which Mini Analysis detected an mEJC with a decay > 20µs (Figure 7.14 C). Such an event would normally
be rejected during manual confirmation of the automated analysis. The estimation for the rise time was less
correlated between the two algorithms compared to the amplitudes or the decay constants (Figure 7.14 D).
Overall, the average decay constant and rise time differed between the two algorithms by less than 0.1ms
(Table 7.1); however, when comparing the kinetics of matching mEJCs, the average absolute difference
between the two programs was wider, at 0.24 ms and 0.711 ms, respectively.
Table 7.1 Comparison of mEJCs detected from larval D. melanogaster NMJ recordings
Property Mini Analysis MiniPy Average absolute difference
Amplitude 0.558 ± 0.018 nA 0.563 ± 0.018 nA 0.014 ±0.001 nA
Decay Constant 1.734 ± 0.124ms 1.646 ± 0.000 ms 0.24 ± 0.095 ms.
Rise Time 2.584 ± 0.109 ms 2.512 ± 0.000 ms 0.711 ± 0.075 ms
Quantifications of mEJCs from adult D. melanogaster NMJ detected by Mini Analysis and MiniPy. Errors are SEM.

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Figure 7.14 Comparison of larval Drosophila NMJ mEJC analysis
(A) Venn diagram of the mEJCs detected by MiniPy and Mini Analysis
(B) Scatterplot of mEJC amplitudes for adult Drosophila NMJ estimated by MiniPy and Mini Analysis. Multiple Rsquared for the correlation is indicated. y = x line is indicated in blue.
(C) Scatterplot of mEJC decay time constants for adult Drosophila NMJ estimated by MiniPy and Mini Analysis.
Multiple R-squared for the correlation is indicated. y = x line is indicated in blue. Decay above 20 ms was
excluded from the correlation analysis.
(D) Scatter plot of mEJC rise time for adult Drosophila NMJ estimated by MiniPy and Mini Analysis. Multiple Rsquared for the correlation is indicated. y = x line is indicated in blue.

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Since MiniPy was developed using recordings from larval recordings for algorithm optimization, I
also compared analyses performed on larval recordings (Figure 7.15 A-C, Table 7.2). Similar to adult mEJC,
the majority of mEJC events were detected by both algorithms (Figure 7.15 A), and the amplitude
estimations were nearly identical (Figure 7.15 B). The average absolute difference of the amplitudes
estimated for each mEJC by the two algorithms was at around 3% of the average (Table 7.2). The decay
estimates were less correlated in the larva compared to the adult, but the rise correlated better in the larval
analysis than the adult recording (Figure 7.15 C and D). The average decay constant and rise time calculated
the two algorithms were both within 0.1 ms of each other (Table 7.2).
Table 7.2 Comparison of mEJCs detected from larval D. melanogaster NMJ recordings
Property Mini Analysis MiniPy Average absolute difference
Amplitude 0.5844 ± 0.021 nA 0.5959 ± 0.021 nA 0.0173 ±0.0024 nA
Decay Constant 5.874 ± 0.212ms 5.812 ± 0.157ms 1.184 ± 0.127 ms.
Rise Time 5.381 ± 0.184 ms 5.281 ± 0.161 ms 0.703 ± 0.099 ms
Quantifications of mEJCs from larval D. melanogaster NMJ detected by Mini Analysis and MiniPy. Errors are SEM.
These results indicate that the algorithms for peak detection and amplitude calculation for MiniPy
is comparable to Mini Analysis. On average, the mEJC amplitudes, decay constant, and rise time estimated
by two algorithms for the recordings were within 3% of each other; however, the decay constant and rise
time calculated for each matching mEJCs had wider differences between the two algorithms. Individual
events will need to be visually checked to determine how the MiniPy algorithm can be improved.

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Figure 7.15 Comparison of adult Drosophila NMJ mEJC analysis
(A) Venn diagram of the mEJCs detected by MiniPy and Mini Analysis
(B) Scatterplot of mEJC amplitudes for larva Drosophila NMJ estimated by MiniPy and Mini Analysis. Multiple Rsquared for the correlation is indicated. y = x line is indicated in blue.
(C) Scatterplot of mEJC decay time constants for larval Drosophila NMJ estimated by MiniPy and Mini Analysis.
Multiple R-squared for the correlation is indicated. y = x line is indicated in blue. Decay above 15 µs was excluded
for correlation analysis.
(D) Scatter plot of mEJC rise time for larval Drosophila NMJ estimated by MiniPy and Mini Analysis. Multiple Rsquared for the correlation is indicated. y = x line is indicated in blue.

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mEJC kinetics are altered in the adult compared to larva
Previously, it has been reported that adult NMJ increases its synaptic setpoint with age (Figure 7.16
A) (Mahoney et al., 2014). We had also noticed that the kinetics of the synaptic events looked faster in the
adult recordings compared to larval recordings (Figure 7.16 B). Analysis using the MiniPy software
indicated that while the average mEJC amplitude did not differ greatly with age (Figure 7.16 C), both the
decay time constants and rise time was much shorter at the adult NMJ compared to the larval NMJ (Figure
7.16 D and E). These results indicate that as we had suspected, there are changes at the Drosophila NMJ
that shorten the rise and decay time of mEJCs.
Figure 7.16 mEJC kinetics are shorter in adult NMJ compared to larval NMJ
(A) Representative traces for mEJC for larval, young, and old (50 days) w1118 NMJ.
(B) Density curve for mEJC amplitudes for groups in (A). N (mEJC) = 202 (larva), 367 (young), 1350 (old).
(C) Density curves for mEJC decay time constants for groups in (A). N (mEJC) = 202 (larva), 367 (young), 1350
(old). One-way ANOVA p <<0.001.
(D) Density curves for mEJC rise time for groups in (A). N (mEJC) = 202 (larva), 367 (young), 1350 (old). One-way
ANOVA p<<0.001

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7.4 Discussion
The development of MiniPy was driven by the need for fast, user-friendly analysis software for
electrophysiological recordings. In this chapter I have presented the algorithm used to detect and analyze
synaptic events, described the step-by-step method for using the MiniPy software, and demonstrated that
MiniPy analysis is comparable to proprietary software currently available for electrophysiological analysis.
The MiniPy algorithm for calculating the decay time constant uses a simplification of the synaptic
decay process. The algorithm assumes that the rise and decay are modelled by separate exponential curves,
and thus only considers the decay time constant to model the trace. However, it has been suggested that the
rise and decay should be considered together to better model the shape of spontaneous synaptic events.
Jonas et al. suggests the model:
Equation (9) () ∝ �1 −
−(−)
��
−(−)
� (Jonas et al., 1993)
Where
τstart = time constant for the rise
τdecay = time constant for the decay
tstart = time at the start of the mEJC event
Thus, the decay phase is affected by the time constants of both the rise and the decay phases. This
approximation may underlie the difference in decay and rise time estimation between MiniPy and Mini
Analysis software. In the future algorithm for MiniPy can be updated to incorporate this equation.
Currently, MiniPy does not offer filtration methods for noise reduction and require the user to preprocess the traces before importing the recording to MiniPy. Acquisition software, such as Clampex and
others, do offer filtering, but making the step available in MiniPy would streamline the user experience.
The AstroPy library (SciPy — SciPy v1.6.0 Reference Guide, n.d.) is part of the Anaconda distribution of
Python that includes deconvolution methods such as Boxcar Lowpass filtering and Guasiann Lowpass
filtering. These methods can be incorporated in the future to MiniPy to offer filtering within the software.

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MiniPy’s memory usage depends on the size of the recording file. For a 90-second recording with
2 channels worth of data, MiniPy uses approximately 116MB of memory, while using about 91MB at the
startup state without importing any data. In comparison, Mini Analysis uses approximately 5MB of RAM
while displaying the same data file. Stimfit, another open-source electrophysiology analysis software, uses
a down-sampling method reduce the number of data points displayed in the GUI (Guzman et al., 2014).
This may reduce the strain on the memory that arise from displaying millions of data points in a single
graph. Current attempts to recreate such an algorithm resulted in computational strain that impeded the
advantage of smooth graphic manipulation offered by MiniPy. More consideration will be needed to further
optimize the memory used by MiniPy.
Furthermore, while the algorithm theoretically should hold for recordings from other synaptic
models, the software has not yet been tested on such samples. This issue can be addressed by obtaining
recordings from other model systems and testing the performance of MiniPy.
The analysis of larval, young adult, and old adult mEJC recordings by MiniPy indicated that the
mEJC kinetics dramatically decreases during the transition from larvae to adult flies. My preliminary
analysis also indicated that the decay constant also increases with age. More experiments will be needed to
confirm this analysis and determine what changes underly the shift in mEJC kinetics from larva to adult,
and from young adult to aged adult.
7.5 Conclusion
In this chapter I described my contribution to the neurophysiology community through an easy-touse open source software for electrophysiology data analysis. MiniPy provides a simple algorithm for
detecting and analyzing minis. The simplicity of the GUI provides a fast learning process for users. Analysis
of D. melanogaster NMJ recordings successfully demonstrated that kinetics of mEJCs undergo a change
between larval and adult phases.

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Chapter 8. Discussion
We have begun to interrogate the idea that aging and neuronal function mutually influence one
another. Accumulating experimental evidence supports this idea; however, very little mechanistic insight
exists into how aging or age-dependent changes in basic cellular function influence neuronal function and
how aberrant neuronal activity exerts its action as a driver of cellular aging. To address this unmet need, I
set out to answer two key questions: How does dietary restriction, as a major regulator of lifespan and
healthspan, influence synaptic activity; and can tuning synaptic activity during aging improve healthspan
and enhance longevity?
My results demonstrate that synaptic activity responds swiftly to changes in nutrient availability,
and multiple mechanisms work in concert to ensure the appropriate adjustments are made to synaptic
function. On the other hand, environmental or genetic disturbances can push the system to the point of
breakdown revealing the key players and showing the limit of its resilience, an important notion in
understanding the relationship between neuronal function and aging.
8.1 Setpoint of neurotransmitter release, homeostasis, and dietary restriction
My work described in chapter 3 demonstrates that acute fasting can block the ability of the NMJ to
undergo presynaptic homeostatic potentiation (PHP) through a FOXO/4E-BP dependent mechanism.
Briefly, when GluRIIA, one of the five GluR subunits that form the postsynaptic receptor complexes at the
NMJ, is genetically removed, the glutamate-induced receptor conductance is greatly reduced. The synapse
compensates for this reduction in receptor conductance by inducing a retrograde signaling cascade that
ultimately results in a compensatory increase in the amount of presynaptic neurotransmitter release per
single action potential triggered in the motoneuron. This robust homeostatic response is completely blocked
when larvae are starved for as little as one to two hours. Previously, the Haghighi lab had discovered that a
muscle specific, mTOR-dependent translational mechanism was critical for the ability of the NMJ to show
retrograde synaptic compensation (Penney et al., 2012); however, our genetic experiments indicated that
mere suppression of mTOR activity due to acute starvation could not account for the robust block of PHP.

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My in vivo analysis of the effect of acute starvation showed that it activated a strong increase in Foxodependent 4E-BP transcription in postsynaptic muscles. Subsequent genetic/electrophysiological analysis
confirmed that indeed this increase in 4E-BP was the main culprit for the block of PHP. We propose that
the increase in 4E-BP in postsynaptic muscles as a result of starvation suppresses cap-dependent translation
and thereby blocks the retrograde signaling required for the maintenance of PHP in GluRIIA-/-
mutant larvae.
While explaining how acute starvation can interfere with synaptic homeostasis, our findings present
a dilemma: how can suppression of synaptic homeostasis be beneficial for the organism? Afterall, our initial
compulsion for studying the effect of dietary restriction on synaptic function was based on the hope of
finding traces of those beneficial effects by examining changes in synaptic function. It is true that
homeostatic mechanisms are thought to bring stability into neural circuits by adjusting synaptic output
within a desirable range, but we now know that in many instances the normal set point for neurotransmitter
release is surpassed due to disease-related mutations or age (Penney et al., 2016; Romero et al., 2008).
One could imagine this abnormal increase in release as a form of overcompensation or a broken homeostat,
in which case suppression of such overcompensation could be beneficial for the organism. For example,
gain-of function of Parkinson's disease (PD) related gene Leucine rich repeat kinase 2 (LRRK2) in
postsynaptic muscles leads to a retrograde enhancement of presynaptic release in a similar manner as in
GluRIIA-/- mutant larvae, resulting in an abnormally large evoked postsynaptic potentials and large quantal
content (QC) (Penney et al., 2016). Interestingly, treatment with rapamycin for a few hours completely
restores normal synaptic release in these mutants, suggesting that perhaps intermittent fasting or acute
starvation might have a similar beneficial effect. Future experiments will have to test this idea that dietary
restriction or intermittent starvation in adult flies would counteract the increase in synaptic release seen in
old flies, and whether manipulating synaptic activity in older flies would lead to an extension of healthspan
and lifespan.

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8.2 Protein restriction and the maintenance of the setpoint of neurotransmitter release
As indicated above, previous findings have suggested a prominent role for mTOR-dependent
translational mechanisms in the postsynaptic muscles for the regulation of synaptic function (Henry et al.,
2012, 2018; Penney et al., 2012; C.-C. Wang et al., 2013). Interestingly, however, we observed that despite
its great reliance of mTOR, synaptic homeostasis at the NMJ was maintained when larvae were raised for
a few hours under amino acid (AA) restricted conditions. This prompted us to explore other mechanisms
that might be at play. Perhaps the most prominent cellular response, in addition to the suppression of mTOR,
to AA restriction is the activation of GCN2 and the subsequent phosphorylation of eIF2α and suppression
of translation (Dever & Hinnebusch, 2005). My findings showed that indeed a few hours of protein
restriction (removal or yeast from food) was sufficient to suppress mTOR and enhance GCN2 activity based
on the increase in eIF2a phosphorylation. Therefore, we tested the role of GCN2 and eIF2α in the regulation
of synaptic release under normal and aa restricted conditions. While GCN2 appeared dispensable under
normal food conditions, we found that removal of GCN2 under AA restricted conditions greatly
compromised the ability of NMJ to reach its normal set-point of release. These results were supported by
another set of experiments, where we rescued a loss of function mutant combination of eIF2α with a
genomic copy of eIF2α that was mutated at its phosphorylation site (Kauwe et al., 2021). These results
suggested that phosphorylation of eIF2α by GCN2 is critical for the maintenance of synaptic release when
the NMJ is faced with a shortage of amino acids.
On the face of it, these finding might appear counterintuitive, as several lines of evidence indicate
that enhancement of postsynaptic translation leads to an enhancement of presynaptic release. However, now
we were observing the opposite: GCN2 activity, that is normally associated with suppression of translation,
was boosting synaptic release. Here, the rich literature describing the role of GCN2/eIF2α in the integrated
stress response (ISR) came to our aid. While general translation is suppressed in response to
phosphorylation of eIF2α, there are a few mRNAs that tend to undergo a relative enhancement in their
translation as a result of slowing of the rate of translation, among them most prominently ATF4

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(Hinnebusch, 1993). We were disappointed, however, when we found that transgenic knockdown of
ATF4 did not phenocopy the knockdown of GCN2 (Kauwe et al., 2021). At this juncture, I postulated that
perhaps other translational targets of GCN2/eIF2α are at play here. Through a series of bioinformatic
analysis and biochemical experiments, I set out for a search for a candidate. My experiments led me to the
identification of Sif, a GEF that had been previously implicated at the NMJ (Sone et al., 1997), mediating
the effect of GCN2 under AA restriction. Subsequent electrophysiological analysis showed that like GCN2,
Sif was dispensable under normal food conditions, but became critical for the maintenance of synaptic
activity once larvae were raised under AA restricted conditions.
My discovery of the role of Sif in the retrograde regulation of synaptic function not only identifies
a novel target of GCN2/eIF2α, but also indicates that an alternative to the canonical ISR maybe at play at
the synapse when faced with food scarcity. Identifying and understanding the novel targets of the pathway
and their specific mechanism of action will open up new avenues for designing novel therapies aimed at
tackling aberrant synaptic activity during aging or related to age-dependent diseases. In addition, my
findings suggest the intriguing idea that other ATF4-independent mechanisms might exist in neurons and
other tissues to mediate the effect of GCN2/eIF2α in response to AA scarcity.
8.3 Clash between release and energy: a losing battle or harmonious dance?
My work on the relationship between nutrient access in the immediate surrounding of the organism
demonstrated that synaptic activity and signaling cues that adjust synaptic activity can be among the first
to respond to such environmental changes. But an important question remained: what are the key molecular
players and cellular pathways that deliver nutrients to neurons? To address this question, I set out to study
the glutamine/glutamate cycle at the NMJ. The glutamatergic synapses at the NMJ are dependent on a
reliable source of glutamate to ensure availability of glutamate packed synaptic vesicles for release. The
current model at the Drosophila NMJ postulates that upon release glutamate is swiftly taken up by the glial
processes that surround the synaptic areas or via the hemolymph. Glutamate in glia is converted to
glutamine, and it is glutamine that is then shuttled back to the motoneuron to be converted into glutamate

149
needed for refilling of synaptic vesicles. This glutamate-glutamine cycle is conserved at mammalian
glutamatergic synapses, where astrocytes are in charge of uptake and conversion of glutamate to glutamine
(Eid et al., 2013; Tani et al., 2014). One of the critical molecular players in this process is the enzyme
glutamine synthase (GS) (Eid et al., 2013). Interestingly, the expression of Gs2 (the Drosophila GS
expressed at the NMJ) appears to depend on close interaction between glia and motoneurons and thus
sensitive to small changes in extracellular matrix, a topic that the Haghighi lab is currently studying. I set
out to test how limiting GS2 expression would influence synaptic function. My electrophysiological
analysis demonstrated that Gs2 is critical for the maintenance of the normal set-point of neurotransmitter
release: knockdown of Gs2, transgenically in glia alone, led to a substantial reduction in QC. Furthermore,
I found that supplementing the food with glutamine was sufficient to restore normal synaptic function in
larvae partially deficient for Gs2. These findings brought a deeper insight into the reliance of the synapse
on individual amino acids through translation-independent pathways.
There are at least two theories for why the response of the synapse to glutamate release is reduced.
One possibility is that the disruption of Gs2 and inability to convert glutamate to glutamine would create
congestion in the cycle, leading to accumulation of glutamate at the synapse and subsequently
desensitization of the receptors. We ruled out this possibility, as we did not see any change in the size or
kinetics of miniature synaptic events. The second possibility is that because of the reduction in delivery of
glutamine to the motoneuron there would be a smaller pool of glutamate filled vesicles as a result of
knockdown of Gs2. To our surprise this was not the case either. My analysis of the size of the readily
releasable pool (RRP) of vesicle showed that it was not changed in larvae with lower Gs2 levels. Then why
do these synapses not match the wild type setpoint? Glutamine transported from glia to neurons is converted
back to glutamate, which is then used either to fill synaptic vesicles or to be routed into the TCA cycle to
generate energy (Divakaruni et al., 2017). We favour the possibility that the reduction in glutamine might
have caused an energetic inadequacy. It is well accepted that neurons use glutamine/glutamate almost as
effectively as glucose for their energy requirements. Our findings, therefore, point to an intriguing

150
possibility that glutamine provided by glia contributes to achieving an energetic balance that is essential for
regulating the set point for quantal release at the synapse. Future experiments using a combination of genetic
and metabolomic analysis will have to test this hypothesis.
8.4 Age-dependent proteostasis decline in the muscle: a new role for a classic gene
My work in Chapter 6 was initially aimed at determining if excitatory synaptic activity can be tuned
to improve healthspan and extend lifespan. The project was spurred by the mounting evidence pointing to
high excitatory synaptic transmission underlying many neurodegenerative diseases (Frere & Slutsky,
2018b; Hynd et al., 2004; Iovino et al., 2020), as well as evidence that peripheral nervous system
experiences increased neurotransmission with age (Banker et al., 1983; Mahoney et al., 2014). We
hypothesized that excitatory synaptic transmission is not only toxic to the central nervous system but may
also have negative impacts on other synaptic targets in the periphery. To demonstrate this concept, I took
advantage of our knowledge of the D. melanogaster NMJ and investigated the effects of synaptic
modulation on muscle aging.
Observations of muscle aging demonstrated that the presynaptic active zone marker, Brp, increased
in expression with age at the thoracic NMJ. This was reminiscent of a previous report indicating that Brp
expression increases in the fly central nervous system (Gupta et al., 2016). Furthermore, the increase in Brp
expression in the periphery may underly the increase in synaptic transmission seen at the adult fly NMJ
during aging (Mahoney et al., 2014). Together, these observations indicated that age-dependent increase in
Brp and synaptic setpoint may be a target for modulating aging. Indeed, my initial assessment of mutations
in pre- and post-synaptic machineries seemed to successfully prolong healthspan and lifespan, and improve
proteostasis in the muscle, providing a launching pad for the investigation of excitatory synaptic
transmission and its effect on aging.
However, I was soon faced with a roadblock. The benefits conferred by manipulations at the
synapse correlated with increased genetic doses of the classic fly gene, white. This realization brought me
to perhaps the most important juncture of my PhD. I was presented with two options: either to continue

151
with the ongoing experiments and ignore the potential of white interfering with the interpretation of the
data; or further investigate the role of white in regulating muscle proteostasis. I chose the latter, with the
hopes that the investigation will shed light on novel mechanisms that regulate age-dependent proteostasis
and to detail the role of white in aging to serve as a reference for the fly community.
Dissection of temporal as well as spatial requirement of white revealed that white expression is
required in the muscle during larval stages for the proper maintenance of proteostasis during aging. The
tissue-specificity of white was surprising, as white’s known function is limited to the eye and the neurons.
The function of white in the muscle had not been discussed in the fly community, and white mutant and
RNAi strains were used as popular control strains in many genetic experiments, including many aging
studies.
The temporal aspect of white requirement was even more surprising, as white seemed to be
dispensable in the adult, but critical during the larval stages to promote healthy muscle aging. Thus, white
seemed to function at the nexus of developmental signaling and adult aging. Other examples of molecular
pathways during development having impacts on lifespan has been reported thus far, indicating that signals
during early life can alter the trajectory of aging later in life, both positively and negatively (Borch Jensen
et al., 2017; Dillin et al., 2002; Finch & Crimmins, 2004). In particular, I demonstrated that
developmental activation of mTOR has negative consequences to tissue and organismal aging. As mTOR
is a major nutrient sensor activated during overnutrition, this raises an important question of whether high
energy diet during early life has lasting consequences during aging.
A particularly intriguing consequence of white deficiency was the increase in inflammatory gene
expression. Upregulation of antimicrobial peptides (AMPs) has been linked to shortened lifespan and
increased blood brain barrier (BBB) defects. Indeed, knockdown of white in the muscle was sufficient to
increase BBB leakiness in old age. These results seem to indicate that muscle proteostasis has consequences
to systemic inflammation and BBB defects. The idea of a peripheral-central axis for regulation of brain
aging has been suggested, most notably in the form of the gut-brain axis. Previously, muscle proteostasis

152
through Foxo was reported to influence lifespan via altered insulin signaling (Demontis & Perrimon,
2010). It is therefore plausible that muscle aging can lead to systemic consequences that affect brain
function. More research will be needed to determine if disruption of muscle proteostasis in general has
impacts on BBB integrity and brain aging, or if the brain inflammation phenotype is unique to white
knockdown.
8.5 Fast and easy-to-use open-source software for electrophysiologists
The development of MiniPy described in Chapter 7 was a rather unorthodox and possibly the most
unexpected part of my dissertation. I recognized the need for an easy-to-use, open-source software for the
neuroscience community to streamline the analysis of electrophysiological recordings. The needs in our
routine analysis required very simple tasks: label synaptic events and extract quantitative data from each
synaptic event. One of the most popular tools in the community was no longer being updated, and other
available options were powerful but perhaps too complex and cumbersome to use in our routine analysis. I
thus set out to develop a simple, easy-to-use software with the intension of publishing the code as an opensource software, a process that introduced me to a whole set of new skills and challenges that will be
invaluable in my future scientific endeavors.
Through relatively simple algorithms, I was able to automate the detection of mEJCs from TEVC
recordings from D. melanogaster larvae and adults. Comparison with a proprietary software indicated that
nearly all of the synaptic events detected by MiniPy matched those detected by the proprietary software.
The average calculation of mEJC amplitudes from larval and adult recordings indicated that the calculated
values from two programs differed by about 2-3%, highlighting the success of MiniPy. The degree of error
in the calculated kinetics of individual mEJCs seemed to be wider, indicating that careful examination of
each mEJCs may be necessary to increase the accuracy of the software. Of course, in the future, MiniPy
should be compared against other programs used by the neuroscience community to avoid bias towards a
single existing software.

153
Using MiniPy, I have demonstrated that the kinetics of mEJCs change from larva to adult,
indicating changes in the electrophysiological properties of the adult NMJ. Our initial evaluation indicated
that the change is specific to the transition between the larva and the adult phases, and young and old adult
flies do not show a difference in kinetics. More experiments will be needed to confirm this observation.
MiniPy has also proven useful in generating scientific figures for electrophysiological data. Most
of the representative mEJC and EJC traces presented in this dissertation were generated by using the image
export function of MiniPy. MiniPy thus aims to aid the neuroscientist community through multiple aspects
of interaction with electrophysiological data.
As an open-source software, MiniPy will grow with the help of the community to meet the unmet
needs in electrophysiology research.
8.6 Summary
The work presented in this dissertation demonstrates the role of nutrient signaling and nutrient
resource allocation for regulating synaptic transmission. Furthermore, I have demonstrated the classic fly
gene white, plays a critical role in the nexus of development and aging and in muscle-brain axis of aging.
Finally, the MiniPy software provides a much-needed tool for the neurophysiology community.

154
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Appendix
Appendix A
Supplemental Table 1. Genes significantly regulated by the knockdown of white in the muscle
Genes identified to be significantly up- or down-regulated through RNAseq are indicated in the table. Adjusted p-value cutoff
was set to 0.1.
baseMean log2FoldChange p-value Adjusted p-value Symbol
FBgn0000473 2699.869 1.881924 1.17E-104 1.28E-100 Abl
FBgn0015037 2908.141 -1.25786 9.74E-78 5.33E-74 Amy-d
FBgn0003082 358.7327 2.719754 5.81E-76 2.12E-72 Amy-p
FBgn0041243 146.2574 4.697969 1.33E-52 3.63E-49 Cyp6a2
FBgn0033458 152.7656 -3.1204 2.46E-40 5.37E-37 exu
FBgn0013771 2006.434 1.028957 1.21E-38 2.21E-35 Hsp22
FBgn0033395 122.7856 -5.72964 7.12E-37 1.11E-33 phr
FBgn0031910 1883.192 -7.50669 8.02E-35 1.10E-31 sesB
FBgn0085452 3080.415 1.005432 6.54E-29 7.95E-26 sev
FBgn0025866 2112.843 -0.80941 7.58E-26 8.30E-23 Sgs1
FBgn0051809 125.3605 -2.56015 1.30E-25 1.29E-22 betaTub60D
FBgn0039932 165.6895 -2.02621 3.69E-25 3.36E-22 w
FBgn0040260 2426.49 0.848853 2.36E-24 1.99E-21 LysP
FBgn0042173 343.855 11.9372 6.85E-24 5.35E-21 deltaTry
FBgn0014417 764.3113 0.964738 1.53E-22 1.12E-19 gammaTry
FBgn0003360 81486.15 0.675599 6.21E-20 4.24E-17 Obp19d
FBgn0027070 1134.269 -0.82586 3.47E-19 2.23E-16 Ret
FBgn0013772 1144.466 1.397785 9.16E-16 5.57E-13 Aldh
FBgn0032699 2635.55 0.82282 1.10E-15 6.32E-13 AttA
FBgn0033683 1148.854 -0.66771 1.34E-14 7.34E-12 Odc1
FBgn0050360 7258.816 0.710367 1.69E-14 8.79E-12 Odc2
FBgn0032900 3485.674 -0.74478 2.19E-14 1.09E-11 Ptth
FBgn0051664 592.4269 1.370061 9.96E-14 4.74E-11 Cyp6a9
FBgn0010359 6636.693 2.857508 1.25E-13 5.70E-11 Cyp6a8
FBgn0052984 4254.787 1.079699 1.62E-13 7.11E-11 CG13397
FBgn0262565 89.25861 9.030027 1.86E-13 7.81E-11 Hydr2
FBgn0013307 4274.288 0.529603 3.07E-13 1.24E-10 Cyp4p1
FBgn0033742 195.2398 1.248824 1.58E-12 6.19E-10 Fer1HCH
FBgn0032618 3078.422 0.511758 1.05E-11 3.97E-09 phr6-4
FBgn0040653 7841.398 0.780811 1.30E-11 4.73E-09 mGluR
FBgn0032131 274.9063 4.900899 4.11E-11 1.45E-08 Jon25Bi
FBgn0265487 15709.61 0.735315 6.28E-11 2.15E-08 rnh1
FBgn0053301 1925.314 0.944895 6.65E-11 2.20E-08 CG3857
FBgn0034534 2481.817 -0.55037 8.88E-11 2.86E-08 bbx
FBgn0260767 52.71176 -2.16867 1.24E-10 3.89E-08 Rilpl
FBgn0050148 527.7877 0.846714 3.19E-10 9.68E-08 CG17636
FBgn0032187 2635.941 -0.49269 4.09E-10 1.21E-07 CalpB
FBgn0051704 355.907 0.75444 7.21E-10 2.08E-07 Ugt36E1
FBgn0023520 20018.63 -0.57266 9.31E-10 2.61E-07 CG6495
FBgn0261508 394.898 -0.76827 2.33E-09 6.37E-07 CG4757
FBgn0033115 70.04591 -1.61653 3.04E-09 8.10E-07 GstE12
FBgn0038180 724.1527 0.701182 4.73E-09 1.23E-06 blot
FBgn0069354 199.2301 1.000075 7.22E-09 1.84E-06 sut3
FBgn0025837 132.4218 1.928293 8.62E-09 2.14E-06 SmydA-9
FBgn0003996 243.8225 -1.31404 2.82E-08 6.84E-06 CG6294
FBgn0012036 45529.98 0.403459 2.88E-08 6.84E-06 CG16700
FBgn0034913 1221.35 0.42671 5.69E-08 1.32E-05 CG14191

189
FBgn0003372 36.97007 2.026775 6.13E
-08 1.40E
-05 Obp18a
FBgn0032614 2622.427
-0.46007 1.24E
-07 2.77E
-05 Cyp6t1
FBgn0267347 36.35131
-1.95369 1.38E
-07 3.02E
-05 Tango14
FBgn0000078 73160.8 0.812982 1.83E
-07 3.92E
-05 CG18131
FBgn0261975 987.3952
-0.66201 2.02E
-07 4.26E
-05 Cyp309a1
FBgn0259145 598.1558 0.765188 2.07E
-07 4.28E
-05 insv
FBgn0031343 11.49259
-6.89551 3.57E
-07 7.23E
-05 Elba2
FBgn0267828 6956.134 0.31094 4.17E
-07 8.30E
-05 CG3117
FBgn0044810 1601.178 1.670481 4.29E
-07 8.38E
-05 CG17224
FBgn0033204 393.7365
-0.65292 4.44E
-07 8.42E
-05 CG17264
FBgn0034225 1542.152 0.775759 4.46E
-07 8.42E
-05 CG9663
FBgn0030985 669.5156 0.718098 4.78E
-07 8.86E
-05 Jon25Biii
FBgn0031489 4898.364 0.536159 5.78E
-07 0.000105 Jon25Bii
FBgn0031653 15.12424 3.665616 9.35E
-07 0.000168 Cyp28d1
FBgn0264979 1462.212
-0.43446 1.00E
-06 0.000176 CG13784
FBgn0263200 4884.971 0.445086 1.03E
-06 0.000179 CG15818
FBgn0262815 15.7652 5.608082 1.09E
-06 0.000187 pes
FBgn0034517 371.5919 0.672828 1.70E
-06 0.000286 CG13101
FBgn0032601 1058.363 0.499953 1.99E
-06 0.00033 CG9555
FBgn0035280 1481.424 0.413865 2.51E
-06 0.000409 CG3841
FBgn0035083 1652.021 0.457899 3.57E
-06 0.000574 CG17633
FBgn0032084 565.8604 0.569758 4.34E
-06 0.000687 CG4594
FBgn0033593 385.9477
-0.69501 4.44E
-06 0.000694 CG5853
FBgn0053508 14.05241 3.139035 5.70E
-06 0.000872 CG4839
FBgn0041712 12606.96 0.313002 5.74E
-06 0.000872 CG4908
FBgn0034219 233.7193
-0.69636 6.69E
-06 0.001002 CG5676
FBgn0034140 3203.791
-0.47518 7.40E
-06 0.001094 CG7456
FBgn0032161 540.5886
-0.46653 7.80E
-06 0.001138 CG7296
FBgn0033387 413.5007
-0.51982 8.20E
-06 0.001181 CG7300
FBgn0020906 685.5762 2.523423 9.97E
-06 0.001417 CG6287
FBgn0033302 203.9658 0.741196 1.03E
-05 0.00144 Ppt2
FBgn0263235 2983.97
-0.55991 1.21E
-05 0.001653 Mal
-B1
FBgn0031471 55.92876 5.389156 1.21E
-05 0.001653 CG16965
FBgn0033134 1414.482 0.372244 1.31E
-05 0.001773 CG9377
FBgn0039685 38.77029 1.841884 1.58E
-05 0.002113 yellow
-
b
FBgn0051708 128.1238
-0.90189 1.64E
-05 0.002167 CG13284
FBgn0033372 131 0.884336 1.77E
-05 0.002303 CG31743
FBgn0034784 31.71288 2.472876 1.88E
-05 0.00242 CG17681
FBgn0039927 4355.102 0.335831 1.91E
-05 0.002424 CG10383
FBgn0032200 4691.137
-0.31237 2.16E
-05 0.002699 CG10341
FBgn0036362 664.0512
-0.55418 2.17E
-05 0.002699 CG17597
FBgn0085453 1607.277 0.458797 2.27E
-05 0.002794 CG14401
FBgn0035557 61.97754
-1.59715 2.34E
-05 0.002848 CG15237
FBgn0261675 78.52963 2.257362 2.47E
-05 0.00297 Spn42De
FBgn0032701 638.839
-0.40344 2.95E
-05 0.003514 Tsp42El
FBgn0019985 651.7814
-0.63584 3.13E
-05 0.003686 CG12164
FBgn0024985 3249.26
-0.4597 3.17E
-05 0.003686 CG2065
FBgn0033980 4307.156 0.346257 3.31E
-05 0.003808 CG1946
FBgn0035619 256.8897 0.944976 3.39E
-05 0.003862 Cyp6a14
FBgn0031435 193.7254
-0.66407 3.49E
-05 0.003942 PPO2
FBgn0034761 8147.105 0.315961 3.76E
-05 0.004199 CG13742
FBgn0013323 56.46389 1.381199 4.08E
-05 0.004514 CG8008
FBgn0032144 23922.97
-0.50518 4.35E
-05 0.004703 Cyp4p2
FBgn0032358 1784.324 0.326485 4.38E
-05 0.004703 Cyp4p3
FBgn0033978 4951.04 0.747519 4.38E
-05 0.004703 CG18446
FBgn0028561 8.137048 5.548694 4.85E
-05 0.005153 CG12896
FBgn0003888 1111.777 0.608073 5.00E
-05 0.005262 Listericin
FBgn0031312 1294.617
-0.35622 5.31E
-05 0.005537 CG18343
FBgn0033104 641.2389
-0.40278 5.42E
-05 0.005593 Cyp6t3

190
FBgn0032350 12069.64 0.275268 5.96E
-05 0.006093 CG8834
FBgn0262964 13.11097 3.043941 6.06E
-05 0.006136 Nepl10
FBgn0031654 597.8139 2.722827 7.43E
-05 0.007457 Cyp9h1
FBgn0000615 120.3483 0.820316 7.73E
-05 0.007636 CG8067
FBgn0038095 74.33708 1.555242 7.75E
-05 0.007636 CG18327
FBgn0032085 207.5179 0.735336 8.67E
-05 0.008473 Achl
FBgn0015222 36197.49 0.230797 9.99E
-05 0.00964 Ciao1
FBgn0083945 81.63593 0.924331 0.0001 0.00964 Cyp6a23
FBgn0053470 8684.27 0.587884 0.000104 0.009938 Cyp6a20
FBgn0036321 297.086 0.862304 0.000106 0.009983 Cyp6a21
FBgn0034804 355.2703 0.480669 0.000108 0.010118 ADPS
FBgn0000017 9321.109
-0.26358 0.000114 0.010599 CG8401
FBgn0011280 1277.746
-0.65151 0.000123 0.011315 Lst
FBgn0013308 138.5472
-0.99467 0.00013 0.011847 mthl4
FBgn0033158 53.85474
-1.09495 0.000132 0.011979 veil
FBgn0035620 378.0982 0.754495 0.000138 0.01241 CG9416
FBgn0051675 859.1434
-0.40158 0.00014 0.012498 CG10073
FBgn0024251 5411.443 0.282459 0.000172 0.015155 Cpr57A
FBgn0032715 2329.529
-0.36524 0.000189 0.016457 mafS
FBgn0039666 18.1941 2.084554 0.00019 0.016457 Mes4
FBgn0016054 49.90838
-1.14682 0.000219 0.018874 gas
FBgn0027584 2878.771 0.554042 0.000243 0.020802 CG4250
FBgn0259226 258.1111
-0.52888 0.000264 0.022388 CG9826
FBgn0032668 35.27737
-1.44739 0.00027 0.022592 CG3831
FBgn0027590 13452.42 0.246632 0.00027 0.022592 Snap29
FBgn0031490 1136.27 0.636494 0.000274 0.022691 CG5569
FBgn0033397 265.5084 0.645581 0.000302 0.024873 Tina
-
1
FBgn0033216 76.25691 1.166641 0.000317 0.025908 Cpr62Bb
FBgn0033775 748.4824 0.497848 0.000321 0.025984 CG16758
FBgn0030816 3893.482
-0.26124 0.000344 0.027665 CG11353
FBgn0052985 55.16068 1.079059 0.000353 0.028176 Alp10
FBgn0067905 976.5907 0.432902 0.000355 0.028176 Alp9
FBgn0043578 621.7069 0.882891 0.000369 0.02907 CG8539
FBgn0035791 1567.324 0.572238 0.000383 0.029918 CG14120
FBgn0051955 130.4948 0.747542 0.000414 0.03213 CG10725
FBgn0261446 547.0829
-0.38819 0.000418 0.032234 CG10516
FBgn0023171 616.1301
-0.35236 0.000424 0.032348 CG13059
FBgn0033521 656.0398
-0.64146 0.000426 0.032348 CG6034
FBgn0032283 236.0584 0.703489 0.000441 0.03316 CG6933
FBgn0001223 13150.24
-0.53689 0.000442 0.03316 CG6908
FBgn0032167 904.5125 0.399265 0.000465 0.034601 CG17738
FBgn0036549 1336.591 0.562017 0.000484 0.035819 CG10097
FBgn0032507 803.8944
-0.53069 0.000489 0.035892 Cyp304a1
FBgn0038033 10342.42
-0.26174 0.000492 0.035912 CCHa2
FBgn0032286 918.6088 0.39995 0.000502 0.036364 Cht5
FBgn0012042 103.7582
-0.92074 0.000505 0.036364 CG5246
FBgn0031969 3795.658 0.28271 0.000532 0.03808 CG4465
FBgn0033981 919.6897
-0.43018 0.000539 0.038263 CG9988
FBgn0031516 870.556 0.35438 0.000564 0.0398 Diedel
FBgn0038484 236.639 0.808393 0.000567 0.0398 Obp99b
FBgn0031432 1331.463 0.469091 0.000574 0.040014 CG11155
FBgn0030640 31.12511
-1.42577 0.000619 0.04284 fuss
FBgn0085240 105.8358
-0.81967 0.000644 0.04434 yip2
FBgn0033367 1640.813 0.415547 0.000649 0.044362 Ugt37D1
FBgn0033972 1807.897 0.340612 0.000667 0.045362 IM4
FBgn0286929 1296.018
-0.2737 0.000688 0.046485 Gr43a
FBgn0032195 383.9986
-0.42172 0.000708 0.047405 yellow
-
d
FBgn0038009 327.4015
-0.56798 0.000711 0.047405 CG18853
FBgn0003366 3170.816 0.385658 0.000782 0.051871 PGRP
-SB1

191
FBgn0010358 5350.213 1.059475 0.000813 0.053602 TotX
FBgn0034736 255.1048 0.692656 0.000825 0.054026 CG30031
FBgn0000079 133556.9
-0.44946 0.000858 0.055616 CG30054
FBgn0052425 5456.6 0.319446 0.000859 0.055616 CG30148
FBgn0034919 1101.507
-0.40469 0.00087 0.055999 Mal
-A6
FBgn0267408 16248.99 0.281721 0.000915 0.058571 CG31259
FBgn0039591 2178.673
-0.43712 0.000937 0.059415 CG31664
FBgn0051259 181.516 0.611263 0.000939 0.059415 CG31675
FBgn0027660 1129.983
-0.2933 0.000999 0.062295 CG31704
FBgn0027550 1360.829 0.430292 0.001 0.062295 DIP
-zeta
FBgn0054054 283.9262
-0.69182 0.001008 0.062295 CG31809
FBgn0050054 114.4484
-0.87118 0.001008 0.062295 CG31955
FBgn0034440 311.0807 0.618595 0.001014 0.062337 CG32425
FBgn0036952 18.20114 1.933972 0.001074 0.065652 CG32984
FBgn0262366 738.2218 0.799545 0.001084 0.065874 CG32985
FBgn0034069 160.6962 0.571405 0.001098 0.066372 CG33296
FBgn0085237 1052.023
-0.45346 0.001105 0.066462 CG33301
FBgn0038147 2254.315 0.309249 0.00112 0.066948 CG33470
FBgn0033697 122.3729 0.610965 0.001153 0.068078 ppk13
FBgn0032387 587.4978 0.50641 0.001155 0.068078 CG34054
FBgn0050031 581.1768 12.69417 0.001157 0.068078 IM14
FBgn0053296 784.6401 0.38278 0.001165 0.068153 Porin2
FBgn0033891 1193.246
-0.36843 0.001187 0.069095 CG34109
FBgn0034438 2666.693 0.291871 0.001202 0.06959 CG34208
FBgn0031689 16998.87
-0.42956 0.00121 0.069679 CG34211
FBgn0033733 1607.361 1.437273 0.00128 0.073344 CG34215
FBgn0030981 68.71492
-1.03451 0.001294 0.073754 Shrm
FBgn0031897 6206.338 0.294261 0.001314 0.074484 CG34423
FBgn0011829 176.1907
-0.55112 0.001358 0.076577 Mthfs
FBgn0033904 1270.046
-0.3441 0.00137 0.076854 CG42260
FBgn0036750 59.98712 0.937611 0.001383 0.076999 CG42326
FBgn0040064 7889.803 0.233219 0.001386 0.076999 CG42486
FBgn0038750 62.63489
-0.97139 0.001413 0.078067 CG42565
FBgn0032258 1502.18 0.266503 0.001468 0.080714 CG13377
FBgn0036607 119.9971
-0.73771 0.001496 0.081828 CG42656
FBgn0031182 946.4294 0.327515 0.001541 0.083899 Npc1b
FBgn0014906 3476.044
-0.31014 0.00155 0.083947 CG42806
FBgn0283461 1352.696 0.577197 0.00158 0.085176 CG43064
FBgn0033936 475.0156 0.455901 0.001618 0.08678 CR43105
FBgn0037936 745.8406 0.312676 0.001682 0.089771 CR43186
FBgn0259989 890.7597 0.37677 0.001692 0.089863 CR43275
FBgn0085408 1863.873
-0.29402 0.001704 0.090096 Galt
FBgn0085244 541.9135
-0.36306 0.001771 0.092867 Phae2
FBgn0032381 5832.329
-0.93156 0.001774 0.092867 CG4267
FBgn0030102 4136.252
-0.25038 0.001798 0.093348 mbl
FBgn0031434 313.7451
-0.47318 0.001807 0.093348 squ
FBgn0283480 646.4071
-1.21941 0.001809 0.093348 AOX1
FBgn0033983 2496.122
-0.29274 0.001847 0.094875 Fatp1
FBgn0004429 23366.82 0.195795 0.001926 0.098471 Drs
FBgn0035348 12982.63
-0.31199 0.001948 0.099106 Alp2
FBgn0034726 439.197 0.366452 0.00196 0.099106 Fuca
FBgn0285958 535.9207
-0.37936 0.001965 0.099106 CG46426

Abstract (if available)

Abstract The relationship between the nervous system and aging has, for the most part, focused on the role of aging in driving neurodegeneration and cognitive decline. However, accumulating experimental data suggest that changes in synaptic function and neural circuitry occur early in neurodegenerative diseases, potentially in turn driving neurotoxicity and disease progression. We have therefore postulated that aberrant excitatory synaptic transmission functions as a mediator of aging. ❧ In my dissertation I have approached this idea from two perspectives: understanding the role of synaptic activity in mediating the benefits of dietary restriction; and to examine the role of synaptic activity in muscle aging and proteostasis maintenance. I have found that synaptic activity sits at a tight balance dictated by various nutrient sensors that respond to dietary restriction. This work will describe the role of nutrient sensors in the muscle, as well as nutrient maintenance by the glia to regulate synaptic strength. Furthermore, I describe a novel role for a common gene in the fruit fly in regulating muscle proteostasis. Finally, the development and usage of a new toolset for analyzing electrophysiological data is described in detail.

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Asset Metadata

Creator Mori, Megumi (author)

Core Title Synaptic transmission, nutrient sensors, and aging in Drosophila melanogaster

School Leonard Davis School of Gerontology

Degree Doctor of Philosophy

Degree Program Biology of Aging

Degree Conferral Date 2021-05

Publication Date 04/30/2023

Defense Date 03/17/2021

Publisher University of Southern California. Libraries (digital)

Tag aging,Drosophila melanogaster,electrophysiology,integrated stress response,neuromuscular junction,neuroscience,OAI-PMH Harvest,protein translation,proteostasis,starvation,synapse,synaptic homeostasis,synaptic plasticity

Format theses (aat)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Haghighi, Pejmun (committee chair), Curran, Sean (committee member), Ellerby, Lisa (committee member), Jasper, Henri (committee member), Lithgow, Gordon (committee member)

Creator Email m.mori.usc@gmail.com,megumimo@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC113752380

Unique identifier UC113752380

Identifier etd-MoriMegumi-9555.pdf (filename)

Legacy Identifier etd-MoriMegumi-9555

Dmrecord 458404

Document Type Dissertation

Format theses (aat)

Rights Mori, Megumi

Internet Media Type application/pdf

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 author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright. The original signature page accompanying the original submission of the work to the USC Libraries is retained by the USC Libraries and a copy of it may be obtained by authorized requesters contacting the repository e-mail address given.

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