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Fluorescent protein turnover in free-moving Drosophila melanogaster

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Fluorescent protein turnover in free-moving Drosophila melanogaster

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Content Copyright 2024 Hans Bell
Fluorescent protein turnover in free-moving Drosophila melanogaster
by
Hans Bell
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
(MOLECULAR BIOLOGY)
August 2024

ii
Acknowledgements
I am immensely grateful to my adviser, John Tower, for his mentorship and guidance throughout
my project, and for his continual support through every challenge. My gratitude also extends to
the other members of my committee. Sean Curran, Fabien Pinaud, and Kelvin Davies: Thank
you for your thoughtful feedback on my research and your support.
I would also like to acknowledge the members of the Tower Lab, past and present, who
have helped me grow as a researcher. In particular, I would like to thank Dr. Gary Landis, whose
expertise and assistance have been invaluable, and whose company is always appreciated.
In addition I would like to thank my family for their unwavering love and support,
particularly my parents, Wendy and Steve. Thank you for visiting, for listening, and always
being there for me. Finally I thank my fiancé, Miles, whose love makes everything worthwhile.

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Table of Contents
Acknowledgements ......................................................................................................................... ii
List of Tables ...................................................................................................................................v
List of Figures ................................................................................................................................ vi
Abstract ......................................................................................................................................... vii
Chapter 1: Introduction ....................................................................................................................1
Pathways of Protein Degradation .........................................................................................1
Roles for Protein Degradation in Aging and Disease ..........................................................3
Measuring Protein Turnover ................................................................................................5
Sex Differences in Aging and Protein Turnover ..................................................................8
Explanations for Sex Differences in Aging .......................................................................10
Drosophila melanogaster Sex Determination Pathway .....................................................11
Chapter 2: A fluorescent protein assay of in vivo protein turnover ...............................................13
Materials and Methods .......................................................................................................13
Drosophila Strains and Fly Culture .......................................................................13
Generation of Multi-Copy Strains ........................................................................14
Video Assay ...........................................................................................................16
Cameras and Filters ................................................................................................17
Bortezomib and Cycloheximide Video Experiments ............................................18
Video Software ......................................................................................................19
Microscope Assay ..................................................................................................20
In Vitro Proteasome Assay ....................................................................................21
Generation of Partially Purified eGFP Extract ......................................................22
Results ................................................................................................................................22
Description of Video Assay ...................................................................................22
Validation with Bortezomib ...................................................................................25
Validation with Cycloheximide .............................................................................28
Half-life of Whole-Body eGFP ..............................................................................29
Half-life of Tissue-Specific eGFP .........................................................................31
Half-life of MitoGFP .............................................................................................33
Half-life of Red Fluorescent Proteins ....................................................................35
Discussion ..........................................................................................................................38
Chapter 3: Effects of Transformer on lifespan ..............................................................................48
Materials and Methods .......................................................................................................48
Drosophila Strains .................................................................................................48
Lifespan Assay .......................................................................................................48
Results ................................................................................................................................50
Lifespan of Pseudo-females: UAS-TraF ................................................................50
Lifespan of Pseudo-females: TraFD ......................................................................52

iv
Lifespan of Pseudo-males ......................................................................................55
Discussion ..........................................................................................................................56
References ......................................................................................................................................59

v
List of Tables
Table 2.1 Whole body eGFP half-life ...........................................................................................30
Table 2.2 Tissue-specific expression of eGFP ..............................................................................35
Table 2.3 Half-life of mitoGFP .....................................................................................................36
Table 2.4 Half-life of DsRED .......................................................................................................40
Table 2.5 mCherry and MitoTimer ...............................................................................................41
Table 3.1 Lifespans of UAS-TraF pseudo-females ......................................................................51
Table 3.2 Lifespans of TraFD pseudo-females .............................................................................53
Table 3.3 Lifespans of Tra-2 RNAi pseudo-males .......................................................................55

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List of Figures
Figure 1.1 Drosophila sex determination pathway ........................................................................12
Figure 2.1 Schematic of video assay and example plots ...............................................................24
Figure 2.2 Validation with bortezomib and cycloheximide ...........................................................26
Figure 2.3 Half-lives of different fluorescent proteins ..................................................................29
Figure 2.4 eGFP half-life variation in young and old flies ............................................................32
Figure 2.5 Tissue-specific eGFP degradation rates .......................................................................34
Figure 2.6 DsRED degradation rate changes with age ..................................................................39
Figure 3.1 Pseudo-female transformation ......................................................................................49
Figure 3.2 Pseudo-male transformation .........................................................................................49
Figure 3.3 Lifespan of UAS-TraF pseudo-females ........................................................................51
Figure 3.4 Lifespan of TraFD pseudo-females ..............................................................................52
Figure 3.5 Lifespan of Tra-2 RNAi pseudo-males ........................................................................54

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Abstract
Protein turnover is an essential function of the cell, and increasing evidence indicates that protein
degradation is disrupted during aging. With the aim of assaying protein turnover under different
conditions in living flies, we have developed a method to calculate the half-life of fluorescent
proteins in free-moving adult flies. Conditional transgenic systems are used to transiently express
a protein of interest, and the decay in fluorescence is used to calculate half-life. This assay's
utility was validated using inhibitors of synthesis and proteasomal degradation, and half-life
values were calculated for several fluorescent proteins in male and female flies of varying ages.
Additionally, half-lives of proteins expressed to specific tissues were calculated, revealing
different degradation rates of eGFP in different tissue types. Fluorescent proteins targeted to the
mitochondria rather than the cytoplasm were also used. Taken together, the data indicate that this
assay is a promising tool for studying changes in protein turnover under different conditions,
such as age, sex, and exposure to drugs and small molecules. Finally, some experiments
investigating the effects of the Drosophila sex determination gene Transformer on lifespan are
discussed, and the utility of sex transformation for studying differences in the aging trajectories
of male and female animals is explored.

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Chapter 1: Introduction
Pathways of Protein Degradation
Protein degradation is an essential function of the cell. Damaged, misfolded, and denatured
proteins must be removed to avoid toxicity, but regular turnover of proteins is also necessary
even in the absence of damage. Some proteins must be short-lived to fulfill their function, such
as cyclins and some transcription factors, while others are long-lived and less prone to
degradation (Hochstrasser & Kornitzer, 1998). The degradation rate of a protein is partially
determined by sequence elements called degrons (Varshavsky 2019, Ravid and Hochstrasser
2008). There are many other factors that can influence protein half-life, however, including cell
type, aging, and cellular conditions such as starvation and stress (Goldberg and Dice 1974).
Proteins with an abnormal conformation, indicating damage or misfolding, can be specifically
targeted due to the exposure of normally hidden elements (Giulivi 1994). Oxidized proteins are
also recognized and degraded more rapidly (Grune 2003).
Deficiencies in protein degradation lead to the accumulation of abnormal proteins, which
can form large aggregates that resist proteolysis and cause toxicity by physically damaging
membranes and disregulating essential cellular processes (Mogk 2018). Such aggregates can also
overwhelm the capacity of protein degradation networks, leading to a cascade of reduced
proteolytic capacity (Hipp 2014). As discussed in the following section, these effects can
contribute to age-related disease.
There are two major protein degradation pathways: the proteasome and autophagy. The
proteasome is a large, multi-subunit protein complex which degrades specific proteins targeted to
it in an ATP-dependent manner. Proteasomal degradation begins with the addition of polyubiquitin tags to specific proteins to target them for degradation. Three different families of

2
enzymes carry out this function: the ubiquitin E1, E2, and E3 ubiquitin ligases (Hershko and
Ciechanover 1998). E3 ubiquitin ligases recognize specific sequences, known as degrons, which
are determinants of a particular protein's half-life. The E3 ligase then attaches a ubiquitin
molecule to the protein, generally at a lysine, to begin the polyubiquitin chain. The 19S
regulatory particles of the 26S proteasome contain ubiquitin receptors, which recognize ubiquitin
chains and begin to unfold the tagged protein, recycling their ubiquitin molecules in the process.
The protein is then translocated to the 20S catalytic core, where cleavage takes place (Finley
2009).
In autophagy, proteins are engulfed by the lysosome, a vesicle containing a variety of
hydrolases, including proteases, which operate efficiently inside its acidic interior. Autophagy
can be divided into subtypes depending on the exact mechanism of delivery to the lysosome. In
macroautophagy, a portion of the cytosol is first engulfed in a structure called the
autophagosome, which then delivers its contents to the lysosome by fusing with it. In
microautophagy, part of the cytosol is taken up directly by a portion of the lysosomal membrane
(Wong and Cuervo 2010, Bell and Tower 2021). Autophagy is generally associated with bulk
degradation of proteins and whole organelles as opposed to the more selective proteasome.
However, chaperone-mediated autophagy is a third type which is highly specific. In this form,
specific proteins are recognized by cytosolic chaperones and delivered into the lysosome
(Cuervo 2010).
The removal of whole mitochondria by autophagy is called mitophagy. This is a subtype
of macroautophagy in which damaged mitochondria are engulfed in autophagosomes and
disassembled in the lysosome. Mitophagy is vital for mitochondrial quality control, removing
those mitochondria that no longer function effectively. Continuous exposure to oxidative damage

3
means that mitochondria inevitably accumulate damage and must be replaced, and mitophagy is
also upregulated under stressful conditions (Chen 2020). Finally, mitochondrial proteases can
also degrade proteins within the mitochondria, such that not all mitochondrial proteins turn over
at the same rate (Vincow 2019, Augustin 2005).
Roles for Protein Degradation in Aging and Disease
Changes in protein turnover have been associated with aging, so much so that loss of proteostasis
was named one of the hallmarks of aging in a widely cited 2013 paper (López-Otín 2013); in
2023, disabled macroautophagy was added as its own hallmark (López-Otín, 2023). Older
individuals accumulate damaged proteins, including as toxic protein aggregates, which are
implicated in age-related neurodegenerative disease in particular (Walther 2015, David 2010).
Accumulation of ubiquitinated proteins, or proteins that would normally be ubiquitinated,
indicates normal clearance mechanisms are not functioning at sufficient levels to maintain
cellular health (Nezis 2008, Srinivasan 2022, Koyuncu 2021).
These changes have been attributed to declining activity and efficacy of protein
degradation pathways. Decreases in the activity and abundance of proteasome components with
age have been observed in different tissues across multiple species, including humans (Landis
2004, Tonoki 2009, Vernace 2007, Chondrogianni and Gonos 2005, Zeng 2005). One study
reported higher proteasome activity in healthy centenarians compared to older subjects in worse
health (Chondrogianni 2000). Across species, higher proteasome activity is associated with
longer lifespan (Pérez 2009). Upregulating the proteasome can have positive effects on aging; for
example, increased expression of proteasome subunits can increase lifespan in Drosophila, as
does constitutive activation of proteasome in C. elegans (Chondrogianni 2015, Munkácsy 2019,

4
Anderson 2022) and in yeast (Kruegel 2011). However, decreased activity with age is not
uniformly observed, and the full picture may be more complex. Some studies actually report
increased proteasomal activity or expression with age, and actual function may not be accurately
represented by total expression level or particular measures of activity. Some more complex
proteasomal changes that have been reported include increased expression with reduced activity,
increases in both expression and activity but with a loss of stress adaptation, and modifications to
particular subunits (Ferrington 2005, Walther 2015, Raynes 2017, Carrard 2003). Changes may
also vary by tissue type and disease state (Dahlmann 2007, Price 2003).
Reduced autophagy has also been observed with aging. Expression of several autophagyrelated genes decline with age in Drosophila, and increased expression of these same genes in
neurons extends lifespan and reduces accumulation of damaged proteins (Simonsen 2008).
Likewise, increasing autophagy via overexpression of Atg5 extends lifespan in mice (Pyo 2013),
while inhibition of autophagy appears to accelerate aging (Cassidy 2020). Decreased autophagy
has also been observed in aging human brains (Lipinski 2010). The highly conserved target of
rapamycin (TOR) downregulates autophagy, and inhibiting TOR via mutation or the drug
rapamycin increases autophagy and extends lifespan in multiple species (Dennis 1999, Kapahi
2004, Bjedov 2010, Harrison 2009).
Protein degradation increases in response to some anti-aging interventions, such as
dietary restriction and inhibition of the insulin/insulin-like growth factor 1 signaling (IIS)
pathway. Furthermore, some interventions for extending lifespan depend on protein degradation
pathways, suggesting these changes may play a causal role in aging. For example, autophagy is
required for the life extension induced by dietary restriction, entry into dauer stage, or
pharmacological inhibition of TOR in C. elegans (Hansen 2008, Meléndez 2003). The life-

5
extending effects of rapamycin in yeast and spermidine in multiple invertebrates also depend on
autophagy (Alvers 2009, Eisenberg 2009).
Morphologically abnormal mitochondria accumulate with age in Drosophila and other
species (Brandt et al., 2017; Oda et al., 2012). These would normally be removed by mitophagy,
so their accumulation suggests an impairment of mitophagy with age. Likewise, a decline in
mitochondrial function with age has been reported, including decreased respiration rates and
increased ROS production (Ferguson et al., 2005; Navarro and Boveris, 2004). Defective
mitochondrial turnover and quality control have been associated with age-related
neurodegenerative diseases such as Parkinson's and Alzheimer's (Hu 2016, Reddy and Oliver
2019, Vives-Bauza 2010), and induction of mitophagy leads to improvement in animal models of
these diseases (Lin 2019, Fang 2019). Additionally, mitophagy induction has been shown to
improve mitochondrial function with age and even extend lifespan (Rana 2013, Ryu 2016).
In summary, turnover and quality control of proteins and mitochondria decline with age,
leading to accumulation of damaged and dysfunctional proteins which contribute to aging and
disease.
Measuring Protein Turnover
Protein turnover has been measured with a variety of techniques, with varying levels of
specificity. For example, measurements of total ubiquitin protein or specific ubiquitin-protein
conjugates have been used to estimate proteasome activity (Mimnaugh 1999, Weaver 2023). A
problem with this approach is that ubiquitinated proteins normally persist for only a short time
before proteasomal degradation, and in fact high levels of ubiquitination may even indicate
reduced proteasomal activity (Nezis 2008). There are also assays that use the cleavage of

6
fluorogenic peptides such as Suc-LLVY-AMC to detect proteasome activity. The peptides do not
fluoresce in their whole state, but after cleavage at a specific site, they fluoresce and indicate a
particular catalytic activity (Liggett 2010). This can be a useful technique to measure overall
proteasome activity, but does not provide information about the half-life of any particular
protein.
Measuring the rates at which proteins are degraded is more complicated than simply
measuring protein abundance. A method is needed to create a temporary pulse of either protein
expression or protein labeling, in order to measure degradation without the confounding effects
of new synthesis. Protein half-life can be estimated given several measurements of declining
concentration over time, by fitting these measurements to an exponential decay curve.
One strategy for measuring degradation without the influence of synthesis is to simply
halt synthesis altogether; this can be accomplished with cycloheximide, a drug derived from
bacteria which blocks the translocation step of protein translation (Dai 2013, Kellerman 1990). A
weakness of this method is that it is non-specific. Halting synthesis of all proteins may affect
proteins involved in the regulation of protein degradation itself, thereby affecting measurements.
Cycloheximide-induced synthesis inhibition may also have other effects on protein degradation
(Dai 2013). Pulse labeling is a common strategy, utilizing a short exposure to a large
concentration of a heavy or radioactive isotope in order to mark a population of proteins so that
their degradation kinetics can be followed (Swovick 2016). One weakness of this method is the
potential for the label to be reincorporated after the degradation of the initially labeled protein,
which may result in a bias towards overestimating a protein's half-life in the cell. A pulse-chase
approach, in which the pulse of the label is followed by the addition of unlabelled precursors,

7
makes this event less likely, and this is a popular method for measuring protein half-life (Pratt
2002).
Fluorescent proteins (FPs) provide a reliable means of detecting protein levels and
changes in their expression level. The brightness of proteins such as GFP are proportional to
their expression level, providing a visual method to track protein abundance (Soboleski 2005).
Fluorescent proteins expressed at high levels can be visualized in living flies, preventing the
need for sacrifice of the animal. Strategies using fluorescent proteins have been employed to
track protein turnover. Analogous to pulse-chase methods, a bleach-chase approach can be used
to visualize a subset of FP-labeled proteins (Eden 2011). Photoactivatable and photoconvertible
proteins can be used, with the change in ratio of different fluorescence colors providing readouts
of protein synthesis and degradation (Babatz 2018). Similarly, change in color ratio of the Timer
protein, which shifts from green to red fluorescence over time, can be used to compare protein
turnover across conditions (Terskikh 2000). A mitochondria-targeted version of this protein,
MitoTimer, can be used to evaluate mitochondrial turnover (Hernandez 2013).
Conditional transgenic systems can also be used to transiently express a fluorescent
protein, after which its degradation kinetics can be followed in the absence of further synthesis.
These systems include Gene-Switch and Tet-ON. In these systems, the gene sequence encoding
the fluorescent protein of interest is placed under the control of a promoter which can only be
activated by a transcription factor in the presence of a particular drug. In Gene-Switch, the
eponymous transcription factor is active only in the presence of mifepristone (RU486). Upon
administration of the drug to flies via feeding, the Gene-Switch transcription factor enters the
active conformation and binds to the UAS sequence to drive transcription of the fluorescent
protein. In Tet-ON, similarly, the rtTA transcription factor is active only in the presence of

8
doxycycline, whereupon it binds to tet-operator (Teto) sites which act as a promoter for the gene
of interest. After removal of the activating drug, transcription stops, allowing for a transient
pulse of protein expression via a limited period of drug administration. Another advantage of the
conditional transgenic systems described is that the transcription factor can be expressed under a
tissue-specific promoter, allowing for the assay of protein half-life in a specific tissue type.
These systems therefore grant control over the expression of a protein in both time and space.
An advantage of approaches based on fluorescent proteins is that they need not be
destructive; protein concentration can be quantified in a living animal. This allows for the assay
of the same animals over time, at different ages and under different conditions. The
measurements could also be correlated with other variables like behavior.
Sex Differences in Aging and Protein Turnover
In many species, there is a difference in average lifespan between the sexes. For example, there
is a female longevity advantage in humans, great apes, and many other wild vertebrates (Hägg &
Jylhävä 2021, Bronikowski 2011, Clutton-Brock and Isvaran 2007). In Drosophila, females
often live longer than males (Yoon 1989, Lehtovaara 2013), although this varies with genetic
background (Malick and Kidwell 1966, Arya 2010). The female lifespan advantage is also
somewhat reduced in mated females compared to virgins (Fowler and Partridge 1989).
In addition to lifespan, sex differences exist in the course of aging, specific age-related
pathologies, and response to anti-aging interventions. For example, Drosophila females
experience a greater degree of life extension in response to rapamycin treatment (Regan 2022)
and dietary restriction (Magwere 2004). Similar results are also seen in mice, suggesting a

9
general pattern of sex-specific regulation of lifespan (Miller 2014). Conversely, mild heat stress
preferentially extends lifespan in Drosophila males (Sørensen 2007).
In Drosophila, some of these differences are related to a greater degree of gut pathology
in aging females. Specifically, aging females experience alterations in epithelial structure and
loss of gut barrier function, while males maintain normal gut function and morphology. This
female-specific gut dysfunction improves with dietary restriction, perhaps explaining the greater
life extension observed in females undergoing dietary restriction (Regan 2016). This is one
example of a sex-specific aging pathology which can alter response to anti-aging interventions.
Interestingly, protein degradation pathways have been implicated in some of these
differences. Basal levels of autophagy were found to be higher in male enterocytes, and did not
increase upon treatment with rapamycin, whereas low basal autophagy increased significantly in
response to rapamycin in female enterocytes (Regan 2022). This suggests that intestinal
autophagy is a limiting factor for lifespan in females, but not males. This is an important result,
because it shows that successful life-extending interventions may differ between the sexes, and
underlines the importance of understanding how aging affects individuals in sex-specific ways.
Female and male flies also show different degrees of resistance to different forms of oxidative
stress. Males adapt to paraquat stress while females do not, and females adapt to hydrogen
peroxide stress while males do not (Pomatto 2017). Here, too, protein degradation plays a role.
The female-specific response to hydrogen peroxide stress depends on proteasomal activity, and
the β5 subunit of the 20S proteasome is necessary for its induction (Pomatto 2020).
Finally, mating has significant effects on the physiology of female flies. As stated above,
mated females experience shorter lifespans relative to virgins, as well as midgut hypertrophy,
increased inflammation, and changes in metabolism (Landis 2024). Mating is therefore a variable

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which affects fly aging in females specifically. It is unknown if protein turnover is involved in
the lifespan difference between virgin and mated females.
In summary, lifespan and aging pathology can differ between males and females, in ways
that sometimes depend on protein degradation pathways. However, it remains to be understood
exactly how differences between males and females can lead to different courses of aging, and
ultimately different lifespans.
Explanations for Sex Differences in Aging
There are multiple proposed mechanisms for sex differences in longevity. One possibility is
fundamental genetic differences—for example, being XY rather than XX may be
disadvantageous, perhaps because of susceptibility to X-linked mutations. Another similar male
disadvantage which has been discovered in Drosophila is the de-repression of heterochromatic
repeats on the Y chromosome with age (Brown 2020). There is some evidence for the role of
chromosomal differences: Comparative analysis has shown that where sexual dimorphism in
lifespan exists, it tends to favor the homogametic sex—e.g. female mammals with the karyotype
XX live longer than XY males, while ZZ male birds outlive ZW females, with some exceptions
(Xirocostas 2020). Perhaps, therefore, the vulnerability of heterogametic males to mutations on
the X chromosome explains their longevity disadvantage. This explanation is appealing in its
simplicity, but likely to be incomplete. This same analysis found that the lifespan advantage of
homogametic females tends to be greater than that of homogametic males, suggesting that other
factors may contribute to the frequency of greater lifespan in females.
Differential gene expression between the sexes almost certainly plays a role. The theory
of antagonistic pleiotropy posits that genetic variants which have advantageous effects early in

11
an organism's life can have deleterious effects later; in particular, genes involved in growth and
reproduction may incur costs in the postreproductive phase of life (Williams 1957). Different
strategies and levels of investment in reproduction between males and females suggests that each
sex may be subject to different instances of antagonistic pleiotropy. In addition, the concept of
sexual antagonistic pleiotropy refers to alleles that may benefit one sex to the detriment of the
other, due to the different reproductive strategies employed by each (Tower 2017). Differences
in mitochondrial function have also been proposed as an explanation for female longevity
advantage, since mitochondria are passed on only through the egg, therefore selection for
mitochondrial function can only occur in the female line (Tower 2017).
Drosophila melanogaster Sex Determination Pathway
In Drosophila, the gene transformer (Tra) is an early step in the sex determination pathway.
In females, the presence of two X chromosomes triggers the expression of the female-specific
Sex-lethal (Sxl) protein (SxlF), which in turn directs the production of a functional Transformer
transcript (TraF). In males, lacking SxlF, default tra splicing leads to a nonfunctional protein.
In females, TraF, along with its cofactor Tra-2, then promotes splicing of doublesex into the
female specific isoform, dsx-F. In males, the absence of TraF leads to splicing of doublesex into
the male-specific dsx-M (Salz & Erickson 2010). The doublesex proteins are zinc-finger proteins
that control the expression of many genes, influencing such sexually dimorphic traits as
pigmentation, genital development, and courtship behaviors (Christiansen 2011, Williams 2008).
However, not all sexually dimorphic traits depend on the Tra-dsx pathway. For example,
female flies are larger on average than males. This size dimorphism only partially depends on
Tra; in particular, it results from an escape from dosage compensation in which the X-linked

12
gene Myc is expressed at higher levels in females (Mathews 2017). Several other X-linked genes
are known to escape dosage compensation, and there are other genes differentially expressed
between males and females which are not regulated by Tra (Roberts and Evans-Roberts 1979,
Legube 2006, Chang 2011). It is therefore possible that other sexually dimorphic traits, such as
those relevant to aging, may not wholly depend on Tra.
Figure 1.1 Drosophila sex
determination pathway.
Simplified diagram
showing the relationship
between Sxl, Tra, and dsx
in males and females.
In investigating sex differences, it is possible to tease apart the effects of Tra from other
mechanisms by manipulating Tra itself. Expression of TraF in genetically male flies results in
pseudo-females, flies which develop to look morphologically female, although they are sterile
(Pomatto 2017). Likewise, the inhibition of either TraF or Tra-2 in genetic females results in
pseudo-males. The resulting flies appear male, including the presence of male genitalia and sex
combs on the foreleg, and exhibit male courtship behaviors (Belote and Baker 1987). They are
also sterile (Rideout 2010). These transformed flies enable us to ask whether a given sexually
dimorphic trait, such as lifespan, depends on the Tra-dsx pathway or not.

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Chapter 2: A fluorescent protein assay of in vivo protein turnover
Materials and Methods
Drosophila Strains and Fly Culture
Flies were maintained on a standard agar/dextrose/corn meal/yeast media in 25°C incubators
(Ren 2009).
Actin-GS-255B expresses eGFP throughout the body: it contains the Actin5C promoter
driving whole-body expression of the Gene-Switch transcription factor (Ford 2007). For
mitoGFP, the following strains were acquired from Bloomington Drosophila Stock Center:
P{UAS-mito-HA-GFP.AP}2 (BDSC#8442) and P{UAS-mito-HA-GFP.AP}3 (BDSC#8443).
Generation of multi-copy mitoGFP strain is described below.
For tissue-specific eGFP expression, Gene-Switch strains were as follows. Repo-GS
drives Gene-Switch expression in glial cells, and was generously provided by Amita Sehgal
(Jacobs 2020). The 5966-GS strain expresses Gene-Switch in midgut enterocytes, and was
generously provided by Pankaj Kapahi (Luis 2016). Actin88F-GS drives expression of GeneSwitch in flight muscle, and was generously provided by Jason Karpac (Mlih 2018). Mhc-GS
drives expression of Gene-Switch in muscle, and the specific strain y[1], w[*]; wg[Sp-1]/CyO;
P{Mhc-Switch.O}GSG314-2 (BDSC#43641) was obtained from Bloomington Drosophila Stock
Center.
The w[1118]; 3xP3-GFP[M1] strain transgenic construct includes a synthetic promoter
containing three binding sites for the Eyeless/PAX6 transcription factor, and was generously
provided by Ernst Wimmer (Horn 2000). This construct drives high-level expression of eGFP
specifically in retinal tissue, and is abbreviated here as eyeless-GFP (Tower 2019). The
following strains were obtained from Bloomington Drosophila Stock Center: Strain y[1] w[*];

14
P{w[+mC]=elav-Switch.O}GSG301 (BDSC#43642), abbreviated here as y; Elav-GS; strain
y[1],w[*]; P{tubP-GAL4}LL7/TM3 (BDSC#5138), abbreviated here as tub-GAL4; strain y[1]
w[*]; wg[Sp-1]/CyO, P{Wee-P.ph0}Bacc[Wee-P20]; P{y[+t7.7]w[+mC]=20XUAS6XmCherry-HA}attP2 (BDSC#52268), abbreviated here as UAS-mCherry; and strain w[1118];
P{UAS-MitoTimer}3 (BDSC#57323), abbreviated here as UAS-MT (Laker 2014, Xu 2019). The
w[1118] strain is the isogenized version (w[1118]- iso; 2-iso; 3-iso) which was previously cured
of Wolbachia (Ren 2007).
Generation of Multi-Copy Strains
Ultra-GFP: The strain we refer to as ultra-GFP contains multiple copies of a UAS-2xeGFP
construct on the second and third chromosomes (previously described in Yang 2009).
Multi-copy mitoGFP strain. The P{UAS-mito-HA-GFP.AP}2 and P{UAS-mito-HAGFP.AP}3 strains were obtained from BDSC. Each strain was crossed to P(ry+,Δ2-3)99B
transposase strain and chromosomes were isolated that had increased dosage of the mini-white+
marker strain, as indicated by a more red eye color, and named UAS-mitoGFP[AP2-4] and UASmitoGFP[AP3-7], respectively. The increased copy number of the constructs was confirmed by
crossing to the tub-GAL4 strain and assay of progeny to confirm two-fold increase in GFP signal
using fluorescence microscopy. UAS-mitoGFP[AP2-4] and UAS-mitoGFP[AP3-7] were then
combined into the same strain using double-balancer crosses, to yield strain y[*], w[*]; UASmitoGFP[AP2-4]; UAS-mitoGFP[AP3-7]/TM3 Ser.
Tet-ON strain REDA: The Tet-ON driver construct rtTA, which consists of the Actin5C
promoter driving the reverse tetracycline trans-activator protein, was inserted on the third
chromosome. This insertion strain was named rtTA(3)E2. The target construct contains a

15
synthetic promoter consisting of seven Tet-operator (TetO) sequences fused to the core promoter
sequences of the hsp70 gene, driving expression of DsRED (Hoe 2011). Two independent strains
bearing insertions of this TetO-DsRED construct on the third chromosome were used, TetODsRED[6] and TetO-DsRED[26B] (Hoe 2011), each in the w[1118] genetic background. To
increase the copy number of the target constructs, each strain was crossed to P(ry+,Δ2-3)99B
transposase strain (Robertson 1998), and third chromosomes were isolated that had increased
dosage of the mini-white+ marker strain, as indicated by a more red eye color, and named TetODsRED[6-7] and TetO-DsRED[26B-6], respectively. The increased copy number of the
constructs was confirmed by crossing to the rtTA(3)E2 strain, assay of progeny -/+ doxycycline
treatment, and confirming two-fold increase in DsRED signal using fluorescence microscopy.
The TetO-DsRED[6-7] and TetO-DsRED[26B-6] insertions were then recombined onto the same
third chromosome, and the increased copy number was confirmed by again crossing to the
rtTA(3)E2 strain, assay of progeny -/+ doxycycline, and confirming total four-fold increase in
DsRED signal using fluorescence microscopy. Finally, the chromosome bearing the TetODsRED[6-7] and TetO-DsRED[26B-6] insertions was recombined with rtTA(3)E2 to generate a
chromosome bearing all three insertions on the same third chromosome, balanced over TM3 Sb
balancer, and in a y[*] w[*] genetic background. The resulting strain y[*], w[*];TetO-DsRED[6-
7], TetO-DsRED[26B-6], rtTA(3)E2/TM3 Sb is abbreviated as REDA. The yellow mutant body
color allows for increased detection of fluorescence from internal tissues relative to a wild type
body color.
Multi-copy MitoTimer strain: The UAS-MT strain was crossed to P(ry+,Δ2-3)99B
transposase strain. A third chromosome was isolated with increased dosage of the mini-white+
marker, as indicated by a more red eye color, and named UAS-MT[1]. The increased copy

16
number of the construct was confirmed by crossing to the tub-GAL4 strain and assay of progeny
to confirm two-fold increase in green and red fluorescence signals using fluorescence
microscopy. UAS-MT[1] was crossed to the P(ry+,Δ2-3)99B transposase strain, and again a
third chromosome was isolated that had increased dosage of the mini-white+ marker strain, as
indicated by a more red eye color, and named UAS-MT[1-1]. The increased copy number of the
construct was confirmed by crossing to the tub-GAL4 strain and assay of progeny to confirm
three-fold increase in green and red fluorescence signals using fluorescence microscopy.
Video Assay
Assay of tissue-general eGFP in parallel in young and old, male and female flies: Flies were
maintained on grape agar media (Genesee Scientific 47-102) with added casein (0.5%,
Sigma/Aldrich) to reduce background gut fluorescence that occurs with standard media. For
videos, flies were knocked into empty 4 dram glass shell vials (Kimble 60965-4) without
anesthetization. The bottom of the vial was covered with a round piece of black filter paper to
reduce glare, and the top of the vial was covered with a glass cover slip.
Assay of eGFP, mitoGFP and DsRED in various tissue patterns: For videos, flies were
placed into small glass vials (4ml, ThermoFisher), each containing 500ul grape agar media with
added casein (0.5%) and blue dye (0.25% weight/volume blue dye #1), along with activating
drug. The purpose of the blue dye is to greatly reduce background fluorescence of the food in the
videos. Just before the first day of videos, 4 flies (or 6, in earlier experiments) at a time are added
to each vial after being lightly anesthetized with CO2, and each vial was labeled with removable
tape tags. Vials were covered with transparent tape to prevent flies from escaping without
compromising visibility within the vial. Flies were moved to fresh vials every 48 hours for the

17
duration of the experiment. When not being assayed, all flies were kept in an incubator at 25°C,
with exception of mitoGFP flies, which were maintained at 22(+/-0.5)°C.
For the Gene-Switch system, fluorescence was activated with 24 (earliest experiments) or
48 hours (later experiments) in a small vial as above with 25 μl of 4 mg/ml mifepristone added,
for a final concentration of 200 μg/ml mifepristone in the fly food. Drug was allowed to sink into
the food until vials were dry before adding flies. For Tet-ON, the same procedure was followed,
but 50 μl of 6400 µg/ml doxycycline was added to vials for a final concentration of 640 µg/ml
and allowed to dry. Flies were exposed to doxycycline for 48 hours, after which they were
removed into plain blue grape agar vials as above.
Cameras and Filters
The 3D tracking and quantification of GFP fluorescence was carried out essentially as previously
described (Ardekani 2012). Two video cameras (Grasshopper type GRAS-20S4C, Point Grey
Research, Scottsdale, AZ) were placed on either side of the vial containing the flies. The vial was
illuminated by two LED lights, and video was recorded at 30fps. For GFP fluorescence tracking,
blue LEDs were used as follows: MF469-35 filters (Thorlabs, Inc.) were fixed in front of the
blue LEDs to limit the light to a range of approximately 452 nm to 486 nm, which overlaps the
eGFP absorption peak at approximately 488 nm. The MF525-39 filters were placed in front of
the camera lenses to limit the light to the range of approximately 510 nm to 548 nm, which
overlaps the emission peak for eGFP at approximately 509 nm. For DsRED tracking, green
LEDs were used as follows: TRITC excitation filters (MF542-20; Thorlabs, Inc.) were fixed in
front of the green LEDs to limit the light to a range of approximately 532 nm to 552 nm, which
overlaps the DsRED absorption peak at approximately 555nm. The TRITC/CY3.5 emission

18
filters (Thorlabs MF620-52) were placed in front of the camera lenses to limit the light to the
range of approximately 595 nm to 645 nm, which overlaps the emission peak for DsRED at
approximately 590 nm.
Bortezomib and Cycloheximide Video Experiments
Virgin female progeny of actin-GS-255B x ultra-GFP cross were placed in vials with media
adjusted to a final concentration of 20µM bortezomib. The control group media received an
equal volume of ethanol vehicle. All flies were also exposed to 200µg/ml mifepristone in the
media for 48 hours starting from the first day of videos. Videos were taken every day for 11
days. Flies were switched to fresh +/- drug vials every 48 hours through the end of the
experiment, so that flies in the drug group were continuously exposed to bortezomib throughout
the experiment.
For cycloheximide experiments, flies were pre-treated with cycloheximide starting 7 days
before the first video recording, at an initial age of 1-3 days. Cycloheximide treatment continued
through the end of the video recordings. Flies in the low concentration group were maintained on
media adjusted to a final concentration of 5µM cycloheximide, and the high concentration group
was maintained on media adjusted to a final concentration of 10µM cycloheximide. The control
group was maintained on media with an equal volume of water, which was used as the vehicle
for the cycloheximide. Beginning with the initial video recording, all flies were transferred to
vials adjusted to 200µg/ml mifepristone, in addition to any cycloheximide. Videos were taken
every day for 7 days. After 48 hours flies were removed from mifepristone and transferred to
new +/- cycloheximide vials; flies continued to be switched to fresh drug vials every 48 hours
through the end of the experiment.

19
Video Software
Point Grey FlyCap2 is used to configure the camera settings and check camera views prior to
starting videos. The script SaveImageToAviEx_v142.exe is used to record videos at 30 frames
per second. The desired number of frames to capture is input by the user, and the resulting video
from each camera is recorded and saved as .avi video files. The FluoreScore suite is next used to
process these videos, yielding quantitative measurements of fluorescence intensity over time.
The software FluoreScoreGUI is used to analyze representative videos, and visualize the result of
selecting different fluorescence thresholds for fly detection, to aid in selecting the appropriate
value. The FluoreScoreGUI program can also be used to isolate the area to analyze, by selecting
a region of interest (ROI) which excludes any area outside the vial, or by creating a mask if
needed, to omit any areas with background fluorescence from the analysis. Next, the template.bat
file is used, and can be edited for each experiment to indicate the number of flies used, set the
chosen threshold for fluorescence detection, and input the coordinates for the ROI, and which
mask files to utilize, if any. The template.bat file is the only file the user edits and runs directly.
It calls FluoreScoreCMDV2.exe, which analyzes the videos and derives fluorescence values for
the flies in each frame of video, and also calls SqueezeData_V1.1.exe, which combines the data
from the two cameras, and filters out signal below the input threshold. The final output of the
FluoreScore suite is .csv spreadsheet files with fly fluorescence values for each frame of video.
To arrange this data into user-interpretable form, the R script Cymito (cymito_v5.R) is used.
Cymito uses the data from the .csv files to create plots of average fluorescence versus time for
each group in the experiment. This software also generates a summary .csv file, with the data in
an easy to read format, including columns for the average daily fluorescence for every vial,

20
average across all vials, and standard deviation. The data is then plotted as average
ln(fluorescence) versus time. Either R or Prism 9 can be used to conduct linear regression and
generate half-life values from the resulting data.
Average fluorescence values for each category of fly (e.g. young virgin female) are used
to construct these models. Linear models were also constructed using values from every replicate
vial, rather than a single average for each time point, but demonstrated no advantage in
identifying differences between groups or constructing lines with a good fit. To conduct
ANOVA, the half-life values along with information about age and sex of the flies were
organized into a table, which was then read into R [52]. The R lm function was used to construct
a linear model relating half-life to age and sex (including interaction), and then the ANOVA
function was used to run the statistical test. Both functions are available in base R.
(Ardekani 2012)
Microscope Assay
For microscope image capture assay, 3 flies at a time were continuously anesthetized on a CO2
pad (Genesee Scientific). Flies were positioned on their side. Images were taken with a Leica
MZFLIII fluorescence microscope and Spot imaging system.
Fluorescence was quantified with ImageJ. Relevant fly body regions were selected as ROI using
the free-hand drawing tool. For each time point, >10 flies per sample were quantified, and data
average +/- standard deviation was calculated. The ln(fluorescence) versus time was plotted and
analyzed using Prism 9 to conduct linear regression and generate half-life values.

21
In Vitro Proteasome Assay
Young (3-4 day old) virgin female flies from the M1 eyeless-GFP strain were used to generate
extracts containing abundant eGFP. Whole flies were homogenized using a motorized pestle in
extraction buffer composed of 50 mM Tris, 25 mM KCl, 10 mM NaCl, 1 mM MgCl2, and 1 mM
DTT at pH 7.5. Three groups of 20 flies each were homogenized in 500µl of buffer. After
homogenization, each sample was subjected to 3 rounds of a freeze/thaw cycle consisting of 5
minutes in dry ice followed by 5 minutes in a room temperature water bath. After 3 cycles the
samples were centrifuged for 2 minutes at 10,000 g in a 4°C cold room. The supernatents from
the 3 replicates were pooled, and then diluted 1:4 in the buffer solution. Next, 180µl aliquots of
the diluted extract were added to black 96-well plates (Greiner Bio-One 82050-728) and 20µl of
either pure ethanol (control) or 250µM Bortezomib (experimental group) was added to each
sample. Purified recombinant eGFP (Cell Biolabs STA-201) at 1mg/ml in PBS was diluted 1:5,
1:10 and 1:15 with PBS, and 200µl each sample was loaded into the plate in duplicate as
controls. This plate was then introduced to the plate reader pre-warmed to 37°C. GFP
fluorescence was quantified using the BioTek Synergy H4 Hybrid Multi-Mode Microplate
Reader, located in the USC Dornsife NanoBiophysics Core facility. Reads were taken at 30
second intervals at 395 nm excitation/510 nm emission to detect GFP fluorescence.
A slight inhibition of eGFP fluorescence was noticed in the presence of bortezomib
compared to the ethanol controls. To investigate the magnitude of this effect, fluorescence
readings from different dilutions of eGFP extract from eyeless-GFP flies with and without
bortezomib were plotted side by side, and the absolute and percent difference between the
control and bortezomib groups at each dilution was calculated. Additionally, dilutions of purified
recombinant eGFP with and without bortezomib were measured and the differences calculated.

22
Generation of Partially Purified eGFP Extract
Young virgin female flies of the eyeless-GFP strain were anesthetized with CO2 on the fly pad,
and their heads removed using a razor blade. 80 heads were combined with 1 ml PBS,
homogenized, subjected to 3 rounds of freeze/thaw, and centrifuged as described above. The
resulting supernatant was used as extract. This extract was diluted 1:2, 1:4, 1:8, and 1:16 in PBS,
and added to the plate at 180µl per well. 3 replicates of each concentration were supplemented
with 20µl of 250µM bortezomib, and controls were supplemented with 20µl ethanol vehicle.
Reads were conducted using the plate reader as described above, at room temperature (23.5 +/-
0.5°C).
Results
Description of Video Assay
An in vivo assay of protein turnover was developed to measure the half-life of fluorescent
proteins in living adult flies, which are free to move around a small glass vial during
measurements. Measurements of declining fluorescence of a transiently expressed FP are used to
estimate a rate of degradation and a half-life, which can be compared between groups to detect
changes in protein turnover under different conditions. Transient expression of fluorescent
proteins was achieved using the conditional transgenic systems Gene-Switch and Tet-ON. In
both these systems, it is important for our purposes that gene expression can be turned on
transiently through feeding the drug to flies for only a short period of time. When the activating
drug is removed, transcription and protein synthesis halts once the remaining drug leaves the
fly's system. This transient expression is vital for our assay because in order for fluorescence to

23
reflect the kinetics of protein degradation, we need to observe a decline in protein levels in the
absence of new synthesis.
For fluorescence measurements, flies (4 or 6 at a time) are placed into a small glass vial
in the center of a setup consisting of 2 video cameras and 3 lights, all enclosed in a darkened
chamber (Fig. 2.1a). The 2 camera views ensure more complete coverage of the vial; when a fly
is further away from one camera and therefore dimmer, it will appear brighter from the second
camera, thereby minimizing the impact of fly movement on fluorescence readings (Fig. 2.1b).
Lights emit the appropriate activating wavelength to visualize the protein of interest (452-486
nm for GFP, 510-548 nm for red fluorescent proteins). Short 4 minute videos are recorded using
FlyCap software. During this time, flies are free to move around inside the small vial. Videos are
taken 24 hours apart for several days (typical lengths range from 6 days to 2 weeks, depending
on the experiment), beginning from either the first exposure to the activating drug or 24 hours
after. Flies are fed the drug for 24 or 48 hours, and then removed into drug-free vials until the
end of the experiment. Fluorescence initially increases after the addition of the drug and then, in
the several days following drug removal, visibly declines.
Fluorescence is quantified using Fluorescore software, then plotted and printed into a
readable format by the R program cymito. The declining portion of the resulting fluorescence
curve is used to estimate degradation rate in the following manner: The average fluorescence
values from this decreasing portion are log transformed and plotted against time. A linear model
is fit to this log-transformed data, and the slope of the line and the R2
value are calculated using
R or Prism. Experiments compared multiple groups of flies, for example male and female, old
and young, or virgin and mated.

24
Half-life is calculated from this slope according to standard assumptions (Swovick 2018).
Protein degradation is assumed to follow first order kinetics with respect to protein
concentration. The relationship between half-life and degradation rate is described by the
following equation:
t1/2 = ln(2)/k
Here, k is the decay constant. In our experiments, k represents the slope of the line fitted to the
log-transformed fluorescence data, giving the rate of change of ln(fluorescence) over time in
days. The natural log of 2 is equal to approximately 0.693. A half-life value in days is the output.
To compare degradation rates between groups, ANCOVA is used to ask how age, sex, mating
status, or drug exposure affect the change in fluorescence with time. Trends in half-life values
among large numbers of experiments can also be determined using ANOVA, looking at the
effects of variables such as sex and age on half-life values.
Figure 2.1 Setup of
video assay and
example plots.
(a) Schematic of
experimental setup for
video assay. Two video
cameras (camera 0 and
camera 1), and two
LED lights are placed
on opposite sides of a
glass vial containing
flies and media. A third
LED light is placed
directly above the vial.
(b) Example of a single
video frame captured
by camera 0 (view 0)
and by camera 1 (view
1). (c) Fluorescence
decay of eGFP in
young (Y; 6 days old)
and old (O; 36 days old) virgin males (VM), and virgin females (VF). ID#092619. (d) Fluorescence decay of eGFP
in virgin females (VF) and mated females (MF). ID#121720. Statistical test is linear regression and ANCOVA;
Statistical summary presented in Table 2.1.

25
For additional measurements of half-life, a microscope image capture assay was used. In
this assay, flies are anesthetized with CO2 and arranged on their side on a CO2 pad under a
microscope. Still images are taken 24 hours apart, and fluorescence intensity is quantified using
ImageJ. Decay rate and half-life are calculated as above. The microscope assay is an alternative
method for capturing fluorescence measurements, and provides additional data for comparison to
the video assay.
Validation with Bortezomib
To ensure this video assay is capable of detecting changes in protein degradation, the proteasome
inhibitor drug bortezomib was employed. Inhibition of the proteasome was expected to result in
a significantly increased half-life if a) eGFP is degraded by the proteasome and b) our video
assay is sensitive enough to detect the resulting change. Bortezomib was fed to flies continuously
at a final concentration of 20µM in the food, alongside mifepristone for 48 hours to induce eGFP
expression throughout the body. The maximum eGFP fluorescence level reached in the
bortezomib group was approximately 3 times higher than in the control group (Fig 2.2a-b).
Quantification of area under the curve, a measure of total eGFP accumulation, likewise revealed
greater total eGFP fluorescence over time compared to controls (Fig 2.2c). Finally, the half-life
calculated for eGFP in the bortezomib-treated group was 17 days vs. 3.5 days in the control
group, with a significantly slower rate of degradation (p=0.049), showing that this increased
accumulation is attributable to slower degradation (Table 2.1, Fig 2.2d). Two additional replicate
video assays were performed; both showed significantly higher eGFP accumulation in the drug
group.

26
bd
eacg
hfbd
eac

27
Figure 2.2 Validation with bortezomib and cycloheximide. (a) Fluorescence assay of pulsed eGFP expression in
free-moving virgin females (Control; blue symbols), and in virgin females treated with 20µM bortezomib (Btz; red
symbols). ID#070722. (b) Peak expression level for control and bortezomib-treated flies, from (a). p=0.00315. (c)
Area under the curve (AUC) quantification of eGFP fluorescence for control and bortezomib-treated flies in (a).
p=0.00239. (d) Fluorescence decay from peak for control and bortezomib-treated flies in (a). Control half-life 3.5
days, Btz half-life 17 days. p=0.0489. (e) In vitro proteasomal degradation assay. Fluorescence decay of eGFP in fly
extract (Control; blue symbols) and in presence of 20µM bortezomib (Btz; red symbols). Control half-life 78 min,
Btz half-life 165 min. p=0.0004. ID#072922. Statistical test is linear regression and ANCOVA; Statistical summary
presented in Table 2.1. (f) Fluorescence assay of pulsed eGFP expression in free-moving virgin females, and in
females pre-treated with low concentration (5µM) or high concentration (10µM) cycloheximide. The peak levels of
eGFP fluorescence for control and cycloheximide-treated flies are presented (Experiment ID#081323). Peak levels
in the high drug concentration group significantly lower than the control group (p=0.003284). Low concentration
group did not significantly differ from control (p=0.09583) or high drug concentration (p=0.1586). (g) Area under
the curve (AUC) quantification of eGFP fluorescence for control and cycloheximide-treated flies (Experiment
ID#081323). Control AUC significantly different from low drug concentration (5µM; p=0.02091) and high drug
concentration (10µM; p=0.001728). (h) Fluorescence decay of eGFP in control and cycloheximide-treated flies in
(Experiment ID#081323). Slope for control group was -0.2558 (half-life 2.71 days), slope for low concentration was
-0.3648 (half-life 1.90 days), and for high concentration was -0.3801 (half-life 1.82 days). Slopes did not
significantly differ (p=0.4281776) Slopes compared with ANCOVA.
For further confirmation that proteasomal eGFP degradation occurs and is blocked by
bortezomib, in vitro experiments were also run. Extracts from eyeless-GFP flies were prepared
and incubated in the presence or absence of 20µM bortezomib, and the resulting fluorescence
over time was measured by a plate reader. Here, as in the video experiments, increased eGFP
half-life in the presence of bortezomib was observed (Fig 2.2e). These results demonstrate that
inhibition of proteasomal eGFP degradation is measurable either in vitro or using the in vivo
video assay, thereby confirming that the video assay can detect changes in protein half-life as a
result of changes in proteasomal activity.
It was observed that in the plate reader assay, absolute eGFP fluorescence was slightly
lower in the presence of bortezomib. The absolute magnitude of this effect was approximately
equal across different concentrations of eGFP, with a larger percent change at lower
concentrations. This indicates that the observed effect, a decrease in half-life in the presence of
bortezomib, was unlikely to be an artifact of this inhibition—since a preferential decrease in

28
eGFP fluorescence at lower concentrations would tend to produce a steeper rate of decay, and the
effect observed with bortezomib is in the opposite direction. Thus, the presence of this inhibitory
effect should not affect our conclusions. The inhibitory effect was not noticeable in the video
experiments.
Validation with Cycloheximide
To additionally confirm our video assay could detect changes in protein synthesis, the translation
inhibitor cycloheximide was used. Final concentrations of 5µM and 10µM cycloheximide in the
media were used, alongside temporary administration of mifepristone as above to activate eGFP
expression. Maximum eGFP fluorescence was significantly lower in the 10µM cycloheximide
group compared to the control group, suggesting successful inhibition of synthesis at this
concentration. Maximum fluorescence values in the 5µM group were intermediate between the
control and high conditions, but did not significantly differ from either (Fig 2.2f). Quantification
of area under the curve likewise showed lower accumulation of eGFP in the treatment groups
(Fig 2.2g). Finally, eGFP half-life did not differ between the groups, indicating that the changes
in fluorescence were driven by reduced synthesis rather than faster degradation (Fig 2.2h).
Together with the results of the bortezomib experiments, these results support the ability of the
video assay to detect changes in synthesis and degradation of fluorescent proteins, at least to the
degree provoked by these two drugs.

29
Figure 2.3 Half-lives of different fluorescent proteins. (a) Fluorescence decay of eGFP in young (Y; 6 days old)
and old (O; 36 days old) virgin males (VM), and virgin females (VF). Half-life values in days: 2.9 (VM Y), 3.1 (VM
O), 4.1 (VF Y), 2.9 (VF O). ID#092619. (b) Fluorescence decay in virgin females (VF) and mated females (MF).
ID#121720. Half-life values in days: 6.2 (VF), 7.6 (MF). Statistical summary for eGFP experiments presented in
Table 2.1. (c) Fluorescence decay of DsRED in free-moving flies, using Tet-ON system. Virgin males (VM) and
virgin females (VF). p=0.2552. ID#111920. Half-life in days: 6.7 (VM), 5.8 (VF). (d) Fluorescence decay of
mCherry in flies anesthetized during microscope image capture, using Gene-Switch system. Virgin females (VF),
and mated females (MF). p=0.0319. ID#072221. Half-life in days: s51 (VF), 32 (MF). Statistical summary for
mCherry presented in Table 2.5.
Half-life of Whole-Body eGFP
The ultra-GFP strain, which contains multiple copies of UAS-2xeGFP, was crossed to flies
expressing actin-GS-255B. This results in eGFP expressed throughout the body under the actinGS-255B driver, which contains the Gene-Switch transcription factor placed under a ubiquitous
actin promoter. Flies of both sexes and varying ages were compared in a series of five video

30
experiments. Young (5-6 days old at start of experiment) males and females were compared to
old (35-55 days old) males and females. For these experiments, all females were unmated. An
additional experiment was also conducted to compare males and females of a more middle-range
age (19 days old).
Experiment ID# Age
(Days)
Type Slope R sq. t1/2 p
080719 Ultra/255B 4 VM -0.2781 0.9846 2.5 days 0.0077
38 VM -0.1559 0.963 4.4 days
4 VF -0.149 0.9248 4.7 days
38 VF -0.1551 0.9762 4.5 days
082119 Ultra/255B 5 VM -0.1128 0.9703 6.1 days < 0.0001
35 VM -0.1987 0.9979 3.5 days
5 VF -0.198 0.9907 3.5 days
35 VF -0.2317 0.9937 3.0 days
111219 Ultra/255B 6 VM -0.1198 0.9257 5.8 days 0.0168
55 VM -0.1634 0.9738 4.2 days
6 VF -0.1513 0.991 4.6 days
55 VF -0.2203 0.9902 3.1 days
092619 Ultra/255B
(Fig 2.1c)
6 VM -0.239 0.9683 2.9 days 0.0414
36 VM -0.2226 0.9897 3.1 days
6 VF -0.1701 0.9688 4.1 days
36 VF -0.2363 0.9949 2.9 days
090919 Ultra/255B 5 VM -0.3136 0.9687 2.2 days 0.0014
44 VM -0.1432 0.9893 4.8 days
5 VF -0.2589 0.9844 2.7 days
43 VF -0.2005 0.9523 3.5 days
070722 Ultra/255B
(Fig 2.2a-d)
10 VF (-)Btz -0.1971 0.6404 3.5 days 0.0489
10 VF (+)Btz -0.04159 0.1706 17 days
072922
Plate reader
(Fig 2.2e)
4 VF (-)Btz -0.00883 0.7987 78 mins 0.0004
4 VF (+)Btz -0.00418 0.7366 165 mins
120119 Ultra/255B 19 VM -0.1884 0.966 3.7 days 0.6473
19 VF -0.1458 0.972 4.8 days
121720 Ultra/255B
(Fig 2.1d)
13 VF -0.1121 0.9993 6.2 days 0.1286
13 MF -0.09095 0.9721 7.6 days
081323 Ultra/255B (Fig 2.2f-h) 9 VF (-)Chx -0.2558 0.9271 2.71 days 0.4282
9 VF (+)Chx (5µM) -0.3648 0.9036 1.90 days
9 VF (+)Chx (10µM) -0.3801 0.9425 1.82 days
Table 2.1 Whole body eGFP half-life. Ultra: Ultra-GFP (multi-copy UAS-eGFP strain containing an estimated 8
copies of a bistronic UAS-2xeGFP insertion). 255B: Actin-GS-255B (Gene-Switch driven by ubiquitous Actin5C
promoter). VF = virgin female, MF = mated female, VM = virgin male. Btz = bortezomib, Chx = cycloheximide.

31
Half-life was generally similar across the different groups. Average eGFP half-life was
3.9 days in young virgin females, 3.9 days in young males, 3.4 days in old virgin females, and 4
days in old males (Table 1). In 4 of the 5 experiments, half-life was lower in old than young
females, however the differences were small and not statistically significant. There was also no
relationship between age and half-life in males, or in both sexes combined (Fig 2.3a). However,
while the complete dataset did not suggest any trend of half-life with age, it is notable that eGFP
half-life was more variable in younger flies (Fig 2.4a). At later ages, this variability was lower,
and half-life values generally increased with age past a certain point. Indeed, when limiting
analysis to only flies over 30 days of age, a significant effect of age on half-life was detected by
ANOVA. (Fig 2.4b). No significant relationship was found between half-life and sex. Only one
experiment compared mated to virgin females, but no significant effect of mating on half-life
was found (Fig 2.3b). Fit to the linear model was generally good, with R2
values typically >0.95,
indicating the protein degradation process is well described by first-order exponential decay.
Half-life values calculated with this method were close to other values for eGFP half-life
reported in the literature. Average whole-body eGFP half-life across all our experiments was 4.1
days (range 2.2-7.6, SD 1.3), while another group found an eGFP half-life of 3.8 days in cell
culture (Verkhusha 2003).
Half-life of Tissue-Specific eGFP
Because half-life can vary by tissue type, several tissue-specific drivers were employed with the
Gene-Switch system and the resulting eGFP half-life values compared. The drivers used
expressed eGFP in the muscle (Mhc-GS), flight muscle (88F-GS), midgut enterocytes (5966-
GS), or glia (REPO-GS). Half-life of eGFP targeted to the midgut enterocytes exhibited an

32
average half-life of 2.6 days (range 1.3-4.8), and eGFP targeted to glial cells with REPO-GS
driver exhibited a half-life of 3.1 days, both in young virgin females. In muscle, eGFP exhibited
a generally longer half-life, both using the general Mhc-GS driver and the flight muscle specific
88F-GS driver. The former yielded an average half-life of 5.6 days (range 4.4-7.5), while the
latter yielded a half-life of 8.5 days with video assay and 12.4 days with microscope image
capture (Table 2.2). Mated females were also assayed, but mating had no significant effect on
half-life for any driver.
Figure 2.4 eGFP half-life variation in young and old flies. (a) Summary of eGFP half-life values for experiments
listed in Table 1. M, males, F, females. (A) All flies. No significant relationship was found between half-life and age
(p=0.6134) or between half-life and sex (p=0.419). (b) Flies >30 days of age. There is a significant relationship
between half-life and age (p=0.03907), and no significant relationship between half-life and sex (p=0.2356).
To enable direct comparison of half-life between tissues, data from experiments using
different drivers were compared, selected for similarity in age of the flies and length of
experiment. Datasets with the largest number of data points showing fluorescence decline were
preferred for better comparison. Time was normalized such that the maximum fluorescence
value for each experiment was considered to fall on day 1. Experiments using different drivers
were compared separately for virgin females and mated females. The effect of tissue on rate of

33
fluorescence decay was analyzed with ANCOVA, and the longer eGFP half-life in muscle (using
either muscle-specific driver) compared to midgut enterocytes was found to be statistically
significant in both virgin and mated females (Fig 2.5). Glia was not compared to the other tissues
because there was only one experiment using the REPO-GS driver and its length was shorter
than the others.
Previous work in mice found slower protein turnover in muscle compared to other tissues
(Rolfs 2021). Muscle tissue also shows greater accumulation of protein aggregates during aging,
suggesting muscle-specific protein turnover may be a promising subject of future study. Only
relatively young flies (8-17 days) were used in the experiments described here, but in the future
it may be of interest to ask if tissue-specific protein degradation rates change with age.
Expanding experiments to other tissue types would also be desirable. The specific drivers here
were chosen on the basis of producing a strong enough signal to enable detection and half-life
calculations, and providing a range of diverse tissue types that may display different properties.
The midgut is a metabolically active tissue that displays age-related sexual dimorphism and
changes with mating in females, although we did not detect a mating effect in eGFP turnover
here; this tissue type may be of particular interest in future studies to look for effects of age.
Half-life of MitoGFP
eGFP can be targeted to the mitochondria by fusing the COX8 mitochondrial targeting sequence
to its N-terminus; the resulting construct is called mitoGFP (Cox 2003). UAS-mitoGFP construct
insertions were mobilized to increase copy number and then combined into the same strain. This
strain was then crossed to actin-GS-255B to produce flies which express mitoGFP in the whole
body when fed mifepristone. The average half-life of mitoGFP was 2.6 days over 11 video

34
experiments (range 1.0-6.2, SD 1.6). While the range overlaps with cytoplasmic eGFP, mitoGFP
half-life was significantly lower when comparing all video experiments (p=0.00096). This is an
interesting example of the same protein having a different degradation rate when targeted to
different subcellular compartments. Flies of various ages were assayed, with starting ages
ranging from 4-52 days, and virgin females were compared to males as well as to mated females.
No significant effects of age, sex, or mating status were observed (Table 2.3). Overall,
fluorescence intensity of mitoGFP was lower than cytoplasmic GFP.
Figure 2.5 Tissue-specific eGFP
degradation rates. Fluorescence decay
of eGFP targeted to muscle and midgut.
eGFP fluorescence decay is plotted for
flight muscle (88F driver, ID#100221),
total muscle (Mhc-GS driver,
ID#092021), and midgut (5966-GS
driver, ID#111021). (a) Virgin females.
The slope of eGFP decay in midgut
differed from flight muscle
(p=0.0112766) and total muscle
(p=0.03111), whereas no significant
difference was detected between flight
muscle and total muscle (p=0.255553).
(b) Mated females. The slope of eGFP
decay in midgut differed from flight
muscle (p=0.003159) and muscle
(p=0.0006211), whereas no significant
difference was detected between flight
muscle and total muscle (p=0.9671537).

35
Experiment ID# Tissue Age Type Slope R sq. t1/2 p
083021 Ultra/5966 microscope assay Midgut 12 VF -0.234 0.9991 3.0 days 0.0105
12 MF -0.1298 0.9377 5.3 days
082921
Ultra/88F
microscope assay
Flight muscle 12 VF -0.0560 0.9425 12.4 days 0.5709
12 MF -0.047 0.8545 14.7 days
091921 Ultra/255B All 13 VF -0.4488 0.8472 1.5 days 0.4645
13 MF -0.3486 0.707 2.0 days
091721 Ultra/255B All 5 VM -0.5641 0.9906 1.2 days 0.1257
5 VF -0.7312 0.937 1.0 days
090721
Ultra/5966
Midgut 17 VF -0.2403 0.6383 2.9 days 0.9955
17 MF -0.2408 0.8045 2.9 days
111021 Ultra/5966 Midgut 8 VF -0.2605 0.7811 2.7 days 0.6038
8 MF -0.2282 0.8832 3.0 days
090321
Ultra/5966
Midgut 8 VF -0.5259 0.9754 1.3 days 0.2472
8 MF -0.4349 0.9892 1.6 days
091121 Ultra/5966 Midgut 17 VF -0.1448 0.4631 4.8 days 0.1364
17 MF -0.2927 0.759 2.4 days
092021
Ultra/Mhc
Muscle 13 VF -0.1373 0.7553 5.0 days 0.1048
13 MF -0.0918 0.7655 7.5 days
090521
Ultra/Mhc
Muscle 17 VF -0.1316 0.8458 5.3 days 0.4538
17 MF -0.1569 0.8476 4.4 days
100221
Ultra/88F
Flight muscle 12 VF -0.0817 0.877 8.5 days 0.6526
12 MF -0.0905 0.8657 7.6 days
100621 Ultra/REPO Glia 10 VF -0.2233 0.5732 3.1 days 0.5498
10 MF -0.2768 0.9014 2.5 days
Table 2.2 Tissue-specific expression of eGFP. Ultra = Ultra-GFP (multi-copy UAS-eGFP strain containing an
estimated 8 copies of a bistronic UAS-2xeGFP insertion). 255B = Actin-GS-255B (Gene-Switch driven by
ubiquitous Actin5C promoter). 5966 = 5966-GS (Gene-Switch driven by midgut enterocyte-specific 5966-GS). 88F
= Actin88F-GS (Gene-Switch driven by flight muscle-specific Actin88F promoter). Mhc = Mhc-GS (Gene-Switch
driven by muscle-specific Mhc promoter). REPO = REPO-GS (Gene-Switch driven by glial REPO promoter). VF =
virgin female, MF = mated female, VM = virgin male.
Half-life of Red Fluorescent Proteins
In addition to eGFP, several other fluorescent proteins were utilized. The red fluorescent protein
DsRED was assayed using the Tet-ON conditional gene expression system, and found to have an
average half-life of 7.8 days across 16 video assays (Table 2.4). This is in line with a previous

36
study using cell culture, which found a half-life for DsRED of 8 days (Verkhusha 2003).
However, here there was a change in half-life with age. In the youngest flies assayed (2-6 days
old), DsRED half-life in virgin females ranged from 2-5.8 days, and in young virgin males from
6.7-6.9 days. In slightly older flies (10-12 days old), larger half-life was observed: 8.6-13.1 days
in virgin females, and 16.3 days in males. To compare DsRED degradation rates in flies of
different ages, log-transformed data was normalized with respect to time, so that the initial
(maximum) fluorescence values were considered to occur on day 1. The rate of decline in
DsRED fluorescence decreased with age for virgin females, mated females, and males (Fig 2.6).
However, for males, the increased half-life with age was not significant in every experiment.
Experiment ID# Age Type Slope R sq. t1/2 p
4D1 4 VM -0.3653 0.8824 1.9 days 0.3526
4 VF -0.3653 0.9423 1.9 days
4D3 4 VM -0.1121 0.807 6.2 days 0.5246
4 VF -0.137 0.7676 5.1 days
35D5 35 VM -0.4493 0.9733 1.5 days 0.5395
35 VF -0.5694 0.8183 1.2 days
52D10 52 VM -0.4397 0.9254 1.6 days 0.5715
52 VF -0.4859 0.9906 1.4 days
52D11 52 VM -0.3145 0.6717 2.2 days 0.9982
52 VF -0.3142 0.7831 2.2 days
52D13 52 VM -0.2051 0.8758 3.4 days 0.792
52 VF -0.1937 0.8119 3.6 days
52D14 52 VM -0.3634 0.8887 1.9 days 0.8198
52 VF -0.3919 0.8532 1.8 days
52D2 52 VM -0.5039 0.8677 1.4 days 0.0818
52 VF -0.7142 0.936 1.0 days
M4 6 VF -0.4163 0.9115 1.7 days 0.016
6 MF -0.1367 0.8783 5.1 days
M2 6 VF -0.4604 0.7391 1.5 days 0.5666
6 MF -0.5733 0.7553 1.2 days
M3 6 VF -0.2106 0.8406 3.3 days 0.3572
6 MF -0.1311 0.7405 5.3 days
Table 2.3 Half-life of mitoGFP. VF = virgin female, MF = mated female, VM = virgin male.

37
DsRED half-life was also assayed with microscope image capture. In these assays, halflife was generally longer than in the video experiments, with some experiments yielding half-life
values of over 30 days. These experiments showing larger values used 12 day old flies. Other
microscope experiments used flies at the youngest adult age, immediately after eclosion. In two
of these experiments, no doxycycline was used, as there was enough DsRED expression in these
very young flies to be visible even without induction by drug. In another experiment,
doxycycline was fed to larvae during development. All three experiments produced much shorter
half-life values than in the 12 day old flies, ranging from 1.4-3.5 days. This may support the idea
of increasing DsRED half-life with age, although additional microscope data with a larger
variety of fly ages would provide stronger confirmation of this. No significant effect of either sex
or mating status was found on DsRED half-life using either assay (Fig 2.3c).
Another red fluorescent protein, mCherry, was found to have a particularly long half-life.
The Gene-Switch system and the tissue general actin-GS-255B driver were used to drive
expression of mCherry, using a high copy number UAS-mCherry strain. The resulting red
fluorescence signal was very high, and assays were complicated by high levels of background
expression in the absence of mifepristone. Using microscope image capture assay, mCherry halflife was found to be 51 days in virgin females and 32 days in mated females, a significant
difference in degradation rate (Table 2.5, Fig 2.3d). In the case of the video assay, however, no
significant differences were found, and the log-transformed data had very poor fit to the linear
model, with R squared values < 0.3. Calculated half-life values were even larger, at 131 days in
the neurons of virgin females and 129 days in the whole body of mated females. Video assays
suffered from high background fluorescence, and in some experiments no decay at all in red

38
fluorescence could be detected. The mCherry protein is therefore not a good fit for the video
assay, and DsRED is preferred.
Finally, the fluorescent protein MitoTimer was assayed. Targeted to the mitochondria by
a COX8 targeting sequence, this protein is a mutant form of DsRED that initially fluoresces
green after synthesis and then irreversibly shifts to red with time due to dehydrogenization of its
Tyr-67 residue (Terskikh 2000). MitoTimer red fluorescence was assayed in neurons of young
adult flies using the Elav-GS driver. In video assays, the average half-life of MitoTimer was
found to be 6.3 days in males and 5.27 days in virgin females; the sex difference was not
significant.
Discussion
The results demonstrate the utility of the video assay to measure changes in protein turnover
across different conditions in living, free-moving flies. A variety of fluorescent proteins can be
employed successfully, and they can be targeted to distinct tissue types to investigate tissuespecific turnover rates, and even to the mitochondria within certain cell types. The different
fluorescent proteins tested varied in their average degradation rates, though typical half-life
values were in the range of 2-10 days, allowing for considerable degradation to occur over the
course of a several day long experiment. Changes in turnover due to pharmacological inhibition
of synthesis or proteasomal degradation were reflected in the calculated half-life of eGFP. The
assay is a promising method for investigating changes in protein turnover, and there are many
directions for future research to expand upon the results shown here.

39
Figure 2.6 DsRED degradation
rate changes with age. To
facilitate comparisons, the yintercepts of all lines are set to 1,
and time is normalized so that
maximum fluorescence of all
experiments occurs at time 0.5
days. (a) Virgin females. Half-life
values for DsRED were
calculated as 8.6 days (dark red,
experiment ID#080921, 12 days
old), 3.7 days (orange,
ID#120121, 6 days old), and 2.0
days (pink, ID#112920, 3 days
old). The half-life for 12 day old
flies (dark red) was significantly
different from both 6 day old flies
(orange, p=0.01244) and 3 day
old flies (pink, p=0.01798). (b)
Mated females. Half-life values
were calculated as 11.0 days (dark
red, ID#081021, 12 days old), 2.4
days (orange, ID#112920, 3 days
old), and 3.6 days (pink,
ID#122020, 2 days old). The halflife of 12 day old flies (dark red)
was significantly different from 3
day old flies (orange,
p=0.007627); the other half-life
values did not significantly differ.
(c) Males. Half-life values were
calculated as 13.1 days (dark red,
ID#091621, 34 days old), 16.3
days (orange, ID#070721, 10
days old), 6.9 days (pink,
ID#091621, 4 days old), and 6.7
days (bright red, ID#111920, 2
days old). The half-life of 2 day
old flies (bright red) was
significantly different from 10
day old flies (orange,
p=0.000922) and also
significantly different than 34 day
old flies (dark red, p=0.04675); the other half-life values did not significantly differ. Statistical tests are linear
regression and ANCOVA.

40
Experiment ID# Age Fly type Slope R sq. t1/2 p
081121W Whole fly
Microscope assay
12 MF (-)Mif -0.02321 0.9715 30 days 0.0082
12 MF (+)Mif -0.03785 0.927 18.3 days
081121A Abdomen
Microscope assay
12 MF (-)Mif -0.02115 0.9583 33 days 0.0169
12 MF (+)Mif -0.0396 0.8929 19 days
081121T Thorax
Microscope assay
12 MF (-)Mif -0.02907 0.8726 24 days 0.9807
12 MF (+)Mif -0.02829 0.8876 24 days
080821W Whole fly
Microscope assay
12 VF -0.02173 0.7532 32 days 0.1272
12 MF -0.04081 0.7679 17 days
080821T Thorax
Microscope assay
12 VF -0.02278 0.6071 30 days 0.5229
12 MF -0.03087 0.6991 22 days
100120W Whole fly
Microscope assay
12 VM -0.03409 0.6606 20 days 0.21
12 VF -0.05358 0.8919 13 days
080921 12 VF -0.081 0.707 8.6 days 0.7162
12 MF -0.06695 0.2307 10.4 days
081021 12 VF -0.06781 0.5343 10.2 days 0.8425
12 MF -0.06273 0.6238 11.0 days
122020 2 VF -0.1151 0.896 6.0 days 0.0619
2 MF -0.195 0.9839 3.6 days
112920 3 VF -0.3485 0.5914 2.0 days 0.7196
3 MF -0.2823 0.9016 2.4 days
120121 6 VF (-)Mif -0.1883 0.817 3.7 days 0.6605
6 VF (+)Mif -0.2085 0.9124 3.3 days
070721 10 VM -0.04249 0.7636 16.3 days 0.6278
10 VF -0.05278 0.5937 13.1 days
111920 2 VM -0.1032 0.9664 6.7 days 0.2552
2 VF -0.1195 0.9723 5.8 days
091621 4 VM -0.1003 0.8449 6.9 days 0.3226
34 VM -0.05267 0.5723 13.1 days
071423 Microscope assay Newly eclosed VM (No Dox) -0.396 0.8644 1.75 days 0.4619
Newly eclosed VF (No Dox) -0.5049 0.8851 1.37 days
071623 Microscope assay Newly eclosed VM (No Dox) -0.2562 0.962 2.7 days 0.3295
Newly eclosed VF (No Dox) -0.3414 0.9269 2.03 days
071623 +drug Microscope assay Newly eclosed VM (Dev. Dox) -0.3017 0.9991 2.3 days 0.0911
Newly eclosed VF (Dev. Dox) -0.1976 0.9735 3.5 days
Table 2.4 Half-life of DsRED. VF = virgin female, MF = mated female, VM = virgin male. Dox = doxycycline.

41
Experiment ID# Age Type Slope R sq. t1/2 p
072221 mCherry/255B
Microscope assay
(Fig 2.3d)
5 VF -0.01369 0.9023 51 days 0.0319
5 MF -0.0217 0.9802 32 days
072421E
mCherry/ElavGS
6 VF -0.00527 0.00186 131 days 0.6151
6 MF -0.03718 0.2568 19 days
072421B
mCherry/255B
6 VF +0.04266 0.209 ND 0.355
6 MF -0.005947 0.005366 129 days
072421R mCherry/REPO 6 VF +0.01788 0.103 ND 0.84
6 MF +0.00954 0.0128 ND
030123 MT/ElavGS 3 VM -0.10333 0.589 6.71 days 0.8026
3 VF -0.12052 0.9285 5.75 days
033023 MT/ElavGS 6 VM -0.11737 0.6701 5.9 days 0.7042
6 VF -0.14484 0.6225 4.79 days
Table 2.5 mCherry and MitoTimer. 255B = Actin-GS-255B (Gene-Switch driven by ubiquitous Actin5C
promoter). REPO = REPO-GS (Gene-Switch driven by glial REPO promoter). MT = MitoTimer. ElavGS = GeneSwitch driven by neuronal Elav promoter. VF = virgin female, MF = mated female, VM = virgin male. ND = no
data; half-life could not be calculated for these cohorts.
No effects of age, sex, or female mating status were found on cytoplasmic whole-body
eGFP turnover, despite previous evidence that protein turnover may decrease during aging. One
explanation for the lack of age effect in eGFP is that such an effect may not be visible until later
ages, as we did see a significant effect of age when flies younger than 30 days were excluded.
This finding may be related to the larger variability of half-life measurements in younger flies.
That greater variability may in turn reflect the tendency of younger flies to be more active,
moving around to different parts of the vial, which may cause more divergent fluorescence
measurements, or it may reflect a real change with age—e.g. perhaps flies with very high or low
proteasome activity die sooner, or perhaps proteasome activity simply stabilizes with age. In any
case, more data is needed to support the conclusion that eGFP half-life increases with age after
midlife. There was no effect of age, sex, or mating on the half-life of mitoGFP, though flies of

42
widely varying ages (4-52 days) were assayed. MitoGFP fluorescence was generally less bright
than cytoplasmic eGFP. Stronger signal may improve comparisons between groups, and might
be achieved in the future with further increases in transgene copy number or changes in lighting.
The DsRED protein did exhibit increased half-life with age, indicative of slower
degradation. Further work is needed to confirm this trend persists at later ages. The oldest flies
assayed were 34 days old on the first day of videos, and this was only one group in a single
experiment; besides this one group, the oldest flies assayed were only 12 days of age. Given the
small difference in ages among the flies, it is striking that a significant relationship exists.
Interestingly, the DsRED decay in the 34 day old males closely resembled the 12 day old group,
while both were significantly slower than the decay rate in the youngest (2 day old) male flies
(Figure 2.6c). It may therefore be the case that the half-life stops increasing after a certain point
and the relationship is reduced at later ages, but more data is needed to confirm this.
Additionally, except for one experiment comparing young and old males, each experiment
compared REDA flies of the same ages and looked for effects of sex or mating. Therefore, the
comparisons shown in Figure 2.6 are mostly between flies assayed at different times. While
conditions were kept the same between experiments as much as possible, it would be ideal to
also assay flies of different ages side by side in the same experiment.
Effects of tissue type and subcellular compartment on eGFP half-life were discovered.
eGFP targeted to the mitochondria displayed a shorter half-life than eGFP in the cytoplasm,
perhaps due to the activity of mitochondrial proteases. Decay rate of eGFP expressed only in the
muscle or flight muscle was significantly slower than eGFP expressed only in the midgut
enterocytes, consistent with previous work showing slower turnover in muscle (Rolfs 2021). The

43
half-life of eGFP in midgut and glia was similar to that of eGFP expressed in a tissue-general
manner.
The mitochondria-targeted, color-shifting protein MitoTimer was expressed in neurons,
and the decay in its red fluorescence was used to calculate half-life in young (3-6 day old) males
and females. No relationship between half-life and sex was found, though more replicates would
help to confirm this conclusion. In the future it would also be of interest to express MitoTimer in
other tissues and in older flies, as well as to measure changes in the red/green ratio to make full
use of this protein.
Our results show that treatment with bortezomib slows proteasomal degradation of GFP.
Consistent with this, previous work in cultured human carcinoma cells has also found
proteasomal degradation of GFP, which is inhibited by bortezomib (Neznanov 2011). There are
many potential future directions to further characterize this assay's sensitivity to changes in
protein turnover. For example, we have demonstrated that inhibition of the proteasome results in
a slower decline in fluorescence, but it would also be of interest to see if inhibition of autophagy
has the same effect, especially given the importance of autophagy to life extension. Inversely, it
would be interesting to confirm that increasing the activity of either the proteasome or autophagy
has the effect of shortening half-life. Rapamycin promotes autophagy and extends lifespan, but
when tested, displayed fluorescence in the video tracker that would interfere with half-life
calculations. Other regulators of autophagy may be tested in order to confirm that changes in
autophagy are reflected in the rate of fluorescence decline. Starvation—either reduction of total
calories or protein starvation—is known to induce autophagy, and a simple next step would be to
ask if starving flies increases degradation rate of FPs (Nilangekar 2019). Chloroquine is an
inhibitor of autophagy which can be fed to flies; reduced fluorescence decay rate in response

44
would indicate that autophagy contributes to removal of FPs and that the video assay can detect
major changes in autophagic activity (Lőrincz 2017, Bargiela 2019). Additionally, it would be
valuable to repeat the inhibition of synthesis and degradation with other fluorescent proteins,
such as DsRED, to ensure such changes are visible in the fluorescence readings of proteins other
than eGFP. One minor issue with the bortezomib experiments was that eGFP extracts exposed to
bortezomib displayed slightly reduced brightness compared to controls. The mechanism for this
is unknown, and unlikely to significantly affect our results for the reasons outlined previously,
but nonetheless verification with a different proteasome inhibitor may avoid this inhibitory effect
(Tsakiri 2017).
In addition to the video assay, data from a microscope image capture assay were also
presented. Measured half-life values frequently differed between video and microscope assays,
with the microscope experiments producing longer half-life values for the same protein in the
same genotype of flies. While measuring fluorescence in stationary vs. moving flies may play a
role, part of the reason for this may also be the longer CO2 exposure in the microscope assay.
Flies are anesthetized for several minutes in this assay while images are taken, while in the video
assay flies are only knocked out for a couple seconds for transfer into new vials. CO2
anesthetization is known to have physiological effects on flies including changes in metabolite
levels, so it is conceivable that protein turnover may be altered due to the anesthesia (Colinet
2012). For this reason, video assay may have an advantage, although it introduces the variable of
fly movement. Video assay also allows for a less labor-intensive approach to data collection,
since multiple flies can be recorded at once in the video tracker, and fluorescence is quantified
by FluoreScore software without the need to select regions by hand for each fly. While fly
movement may introduce more variability into the video assay results, flies moving around in

45
view of 2 cameras ensures that fluorescence readings are captured from multiple angles, whereas
differences in fly positioning may affect the results of the microscope assay. An advantage of the
microscope assay is increased sensitivity; there were some cases in which fluorescence was too
dim to measure using video cameras, but was more visible on the microscope. This assay may
therefore be a better choice for certain drivers which do not produce very bright fluorescence.
Limitations of the video assay include effects of fly movement. While the two cameras
were set up to provide the best coverage possible, and the lights positioned so that the vial was
evenly illuminated, some slight differences in illumination and visibility across the vial volume
likely still exist. Fly movement within the vial may affect measurements. Young flies in
particular tend to be more active than older flies (Tower 2019), perhaps contributing to the
greater variability seen in experiments using young flies (Fig 2.4a).
Some intrinsic limitations of fluorescent proteins also exist. Fluorescent proteins must be
expressed at high levels to be visible. Multi-copy strains were generated to produce very bright
and clearly visible fluorescence, where the decline in fluorescence over time is obvious and easy
to measure, but some tissue-specific drivers produced fluorescence too dim to be viable for halflife calculation, which limits the cell types that can be targeted with this assay. Background
fluorescence, where FPs are synthesized in the absence of the activating drug, can be an issue.
Further synthesis of FPs after removal of the drug could potentially affect half-life calculations.
Also, it is difficult to pinpoint the time at which new synthesis has halted, in order to select a
starting fluorescence value for degradation rate calculations. After the flies are removed from
food containing the drug, for synthesis to cease the drug must fully leave their system, and all
ongoing transcription and translation must halt. Indeed, after removal from the drug we
sometimes see fluorescence continue to rise for a day or more, particularly with DsRED. Here,

46
the maximum fluorescence value was used as the starting point for half-life calculations, but it is
possible some synthesis is still taking place at this time point. We experimented with using the
data point immediately following the maximum as the initial value, but this did not improve
either fit to the linear model or consistency of half-life measurements, and so the maximum was
used for simplicity.
Different conditional transgenic systems were used to express eGFP and DsRED,
meaning effects of the system or drug may complicate interpretation of differences between
these proteins. One reason for this asymmetry is a lack of reagents for Tet-ON. Expression of
eGFP using Tet-ON is possible and our lab has reported it in the past, but currently available
strains did not produce detectable fluorescence (Hoe 2011). It would certainly be of interest to
generate strains to express eGFP and MitoTimer using the Tet-ON system for better comparisons
between proteins. The Tet-ON system also enables assay of the effects of mifepristone, the
activating drug for the Gene-Switch system. Mifepristone can have significant effects on the
physiology of flies, including increasing their lifespan, particularly in mated females (Landis
2015). The mifepristone treatment used to activate Gene-Switch is brief; limiting exposure to 48
hours should limit confounding effects. Still, the Tet-ON system avoids this issue, which is of
particular importance when comparing virgin and mated females due to mifepristone's ability to
attenuate the effects of mating.
It was somewhat surprising that stronger relationships of half-life with age were not
found for more proteins, such as eGFP. However, as discussed previously, protein turnover
likely changes in complex ways with aging and may not appear as a uniform decline with age.
Previous studies have not always found an absolute decline in proteasomal activity; for example,
one study in mice found that age-related proteasome changes depended on tissue type (Martínez

47
2021). It may be that a functional decline is not reflected by eGFP degradation rate, or that loss
of activity occurs preferentially in specific tissues. Examining tissue-specific eGFP turnover at
later ages could help to investigate this further. DsRED did show an increase of half-life with
age, but proteasomal degradation was not confirmed with DsRED, and it may be that different
fluorescent proteins reflect activities of different degradation systems. On this note, it must also
be said that the fluorescent proteins used here are not native to the fly, and changes in their rate
of turnover may not fully reflect what is happening to the proteins encoded by the fly's own
genome. Adding fluorescent tags to a naturally expressed protein could address this issue and
complement the present approach. In addition, to improve the chance of identifying age effects
where they exist, experiments should be done with a wider range of ages for every protein. As
discussed previously, there was a narrow age range used for experiments with DsRED in
particular. It is challenging to conduct experiments with very old flies, since a high number of
deaths necessitates constant replacement of flies used in the assays, but flies of at least 30 days of
age should be assayed for each protein.
Finally, further study of mitochondrial turnover in particular is also desirable. The
MitoTimer protein, which shifts from green to red fluorescence after synthesis, has been used to
track mitochondrial synthesis and degradation (Laker 2014, Hernandez 2013, Gottlieb &
Stotland 2015). We have successfully tracked the decline in the red fluorescence of transiently
expressed MitoTimer, but could also track changes in green fluorescence as well as the red/green
ratio to incorporate information about synthesis.

48
Chapter 3: Effects of Transformer on lifespan
Materials and Methods
Drosophila Strains
UAS-TraF: The strain w[1118]; P{w[+mC]=UAS-tra.F}20J7 (BDSC#4590) was obtained from
Bloomington Drosophila Stock Center, and crossed to the daughterless-GAL4 strain, w[*];
Kr[If-1]/CyO; P{w[+mW.hs]=GAL4-da.G32}UH1 (BDSC#55850), to drive TraF expression
throughout the body. Daughterless-GAL4 was also crossed to a w[1118] strain to provide control
males and females.
TraFD: The strain w[*]/Dp(1;Y)Bar[S]; P{w[+mW.hs]=U2af50-tra[F]}2B/+; tra[5]
(BDSC#58745) was backcrossed 9 times to a y[1] w[1] strain; the resulting strain is referred to
here as TraFD. TraF is expressed in half of male and female progeny without using the GAL4-
UAS system; each generation, female flies carrying the transgene are selected using eye color
and crossed to males. TraFD was backcrossed into a y[1] w[1] f[1] background to generate strain
ywf; TraFD/+, in order to assay the effect of transformation in a second genetic background.
Tra-2 RNAi: The strain y[1] v[1]; P{y[+t7.7] v[+t1.8]=TRiP.JF02852}attP2
(BDSC#28018) was acquired from Bloomington Drosophila Stock Center. Males of this strain
were crossed to a backcrossed strain carrying Tubulin-GAL4 to drive Tra-2 RNAi under a
ubiquitous tubulin promoter.
Lifespan Assay
For lifespan, newly eclosed flies were placed into labeled vials with standard media at a density
of 20 flies per vial, and tossed to fresh vials every other day, at which time any deaths in each
vial were recorded. One cohort typically contained 5 vials per category (e.g., pseudo-female), for

49
a total of 100 flies per category, but this sometimes varied. All females were unmated.
Experiments continued until all flies were dead. Analysis was carried out in R. From the raw
data, the median and 90th percentile lifespan was calculated for each group, and log-rank
analysis was carried out to compare lifespans between groups. To correct for multiple
comparisons, the Bonferroni correction was used to set the significance threshold to 0.05 divided
by the total number of comparisons for each experiment.
Figure 3.1 Pseudo-female
transformation. Comparison of
males, females, and pseudofemales from the TraFD strain.
(a) Comparison of full-body
morphology between males
(left), females (center), and
pseudo-females (right). Pseudofemales are smaller than
females, but have female
coloration and genitalia. (b)
Close-up images of forelegs,
showing sex combs are present in the male (top) but absent in both females (center) and pseudo-females (bottom).
Figure 3.2 Pseudo-male
transformation. Comparison of
males, females, and pseudomales transformed with Tra-2
RNAi. (a) Comparison of fullbody morphology between
females (left), males (center),
and pseudo-males (right). The
darker pigmentation and
narrower abdomen typical of
males is seen in the pseudomale, which is larger than the
male. (b) Ventral view showing
the presence of male genitalia in
the pseudo-male (right). (c)
Close-up image of foreleg,
showing the presence of sex
combs in the pseudo-male.

50
Results
Lifespan of Pseudo-females: UAS-TraF
Pseudo-females can be generated by expressing TraF protein in male flies. First, UAS-TraF flies
were crossed to daughterless-GAL4 to express TraF throughout the body under the ubiquitous
daughterless promoter. Females of this cross express additional TraF, while males are
transformed into pseudo-females. These flies were compared to control males and females,
offspring of a w[1118] strain and the daughterless-GAL4 strain.
Control females lived longer than control males in both cohorts. Pseudo-females had
significantly shorter lifespans than both control males and control females (Table 3.1). However,
in one cohort, their lifespans did not differ from females with the TraF transgene, and the cohort
which did show a difference included an unusual number of early deaths in the pseudo-female
group. Females with the transgene did have significantly lower lifespans than control females; in
other words, the UAS-TraF transgene reduced lifespan in both sexes (Figure 3.3). It is therefore
difficult to say if transformation from male to pseudo-female itself reduced lifespan, or if there is
a non-sex-specific factor causing this effect in both genetic females and pseudo-females.
However, we can be certain that transformation did not grant pseudo-females the female
longevity advantage, since it actually reduced their lifespan below that of the control males.

51
Replicate Genotype Sex n Med 90% Mort M vs F M vs PF FCtrl vs PF FTraF vs PF FCtrl vs FTraF
1 Control M 90 58 70 ΔMed:
+25.64%
p=7.91E26
ΔMed:
-55.17%
p=2.68E27
ΔMed:
-66.67%
p=1.55E-44
ΔMed:
-18.75%
p=0.351
ΔMed:
-58.97%
Control p=3.15E-43 F 94 78 86
TraF F 101 32 52
TraF PF 115 26 55
2 Control M 86 64 74 ΔMed:
+12.5%
p=7.75E05
ΔMed:
-84.38%
p=1.07E-37
ΔMed:
-86.11%
p=4.14E-44
ΔMed:
-70.59%
p=1.29E-10
ΔMed:
-52.78%
Control F 100 72 82 p=5.70E-32
TraF F 95 34 56
TraF PF 96 10 43
Table 3.1 Lifespans of UAS-TraF pseudo-females. Lifespans of males (M), females (FCtrl), females with the UASTraF transgene (FTraF), and transformed pseudo-females (PF). 'Control' flies are w[1118]; da-GAL4/+. 'TraF' flies
are w[1118]; UAS-TraF/+; da-GAL4/+. All females are virgins. 2 cohorts are shown. Experiments 1 and 2 were
performed concurrently. Med = median lifespan (days); 90% Mort = 90% mortality (days). Statistical comparisons
show the percent change in median lifespan between the first group and the second, as well as the log rank p-value
for the comparison. Significance threshold after correction for multiple comparisons = 0.01.
Figure 3.3 Lifespan of UASTraF pseudo-females. Results of
lifespan assay with one cohort,
~100 flies per group. Median
lifespans were 60 days for males,
78 days for control females, 44
days for females expressing
additional TraF, and 36 days for
pseudo-females. Males lived
longer than females (p=9.53E-12)
and pseudo-females (p=9.20E16). Control females also lived
longer than both pseudo-females
(p=5.26E-18) and females with
the TraF transgene (p=1.94E-16);
pseudo-females and females +
TraF did not differ (p=0.336).

52
Lifespan of Pseudo-females: TraFD
In a second set of experiments, TraF was expressed under the promoter of ubiquitous
splicing factor U2af50 to achieve constitutive expression (described in Evans and Cline 2007).
We call this strain TraFD, because it produces a dominant transformation of males to pseudofemales from one copy. When crossed, males and females from this strain produce the following
mix of progeny in equal proportions: males, females, females with additional expression of TraF,
and males expressing TraF, i.e. pseudo-females. Presence of the transgene is marked by eye
color, so that flies without the TraF transgene have white eyes, females with extra TraF have
orange eyes, and pseudo-females have dark red eyes. This ability to compare TraF flies to sibling
controls is ideal for our purposes because the genetic differences between the groups are
minimized as much as possible. Transformation of males to pseudo-females was confirmed by
the presence of morphologically female flies with dark red eyes. The darker eyes and slightly
smaller body size distinguish them from the TraF females (Figure 3.1). Another set of lifespan
experiments was set up in a second genetic background, the TraFD forked strain.
Figure 3.4 Lifespan of TraFD
pseudo-females. Results of lifespan
assay with one cohort, ~100 flies per
group. Median lifespans were 38 days
for males, 30 days for control females,
30 days for females expressing
additional TraF, and 38 days for
pseudo-females. Males lived longer
than females (p=1.15E-11) but male
and pseudo-female lifespans did not
differ (p=0.19).

53
Experiment Replicate Sex n Med 90% Mort M vs F M vs PF F vs PF F vs FTraF
1 1 M 85 29 50 ΔMed:
+6.89%
p=1.39E-03
ΔMed:
+20.69%
p=0.342
ΔMed:
+12.90%
p=4.51E-05
ΔMed:
+6.45%
F 95 31 35 p=0.125
FTraF 98 33 37
PF 97 35 43
2 1 M 89 34 51 ΔMed:
-11.76%
p=4.70E-04
ΔMed:
0%
p=0.112
ΔMed:
+13.33%
p=0.0103
ΔMed:
+6.67%
F 96 30 38 p=0.148
FTraF 96 32 42
PF 93 34 42
2 M 87 45 62 ΔMed:
-26.67%
p=1.03E-05
ΔMed:
-4.44%
p=0.185
ΔMed:
+30.30%
p=1.66E-04
ΔMed:
+6.06%
F 98 33 48 p=0.0800
FTraF 106 35 55
PF 90 43 61
3 1 M 91 38 54 ΔMed:
-21.05%
p=1.15E-11
ΔMed:
0%
p=0.192
ΔMed:
+26.67%
p=6.93E-10
ΔMed:
0%
F 100 30 36 p=0.0104
FTraF 100 30 46
PF 104 38 52
2 M 86 39 54 ΔMed:
-17.94%
p=1.41E-05
ΔMed:
+2.56%
p=0.338
ΔMed:
+25%
p=2.49E-05
ΔMed:
+6.25%
F 98 32 40 p=0.0334
FTraF 98 34 46
PF 96 40 52
4
(forked)
1 M 88 46 56 ΔMed:
-26.09%
p=2.42E-07
ΔMed:
0%
p=0.335
ΔMed:
+35.29%
p=2.59E-11
ΔMed:
+5.88%
F 98 34 46 p=0.946
FTraF 93 36 46
PF 96 46 56
2 M 99 46 56 ΔMed:
-21.74%
p=1.32E-07
ΔMed:
0%
p=0.666
ΔMed:
+27.78%
p=9.29E-09
ΔMed:
0%
F 96 36 49 p=0.685
FTraF 99 36 48
PF 98 46 60
Table 3.2 Lifespans of TraFD pseudo-females. Lifespans of males (M), females (F), females with the TraF
transgene (FTraF), and transformed pseudo-females (PF). All females are virgins. Med = median lifespan (days); 90%
Mort = 90% mortality (days). Statistical comparisons show the percent change in median lifespan between the first
group and the second, as well as the log rank p-value for the comparison. Significance threshold after correction for
multiple comparisons = 0.0125.

54
Lifespan experiments were designed comparing pseudo-females to their male, female,
and female + TraF siblings. Groups of flies were kept separate and females were unmated. The
result was that the lifespan of pseudo-females more closely resembled males (Figure 3.4).
Averaged across 5 cohorts, the median lifespans were 37 days for males, 31 days for females,
and 38 days for pseudo-females. In every cohort, male and pseudo-female lifespans significantly
differed from the female, but did not differ from each other (Table 2.2).
Similar results were seen with the forked strain. Averaged across 2 cohorts, the median
lifespans were 46 days for males, 35 days for females, and 46 days for pseudo-females. Once
again, the lifespan advantage of males and pseudo-females over females was statistically
significant, whereas the male and pseudo-female lifespans were indistinguishable from one
another. There was no significant difference between the control females and females with extra
TraF (Table 2.2).
Figure 3.5 Lifespan of Tra-2
RNAi pseudo-males. Results of
lifespan assay with one cohort,
~100 flies per group. Median
lifespans were 74 days for control
males, 76 days for females, 82 days
for males with Tra-2 RNAi, and 44
days for pseudo-males. Females
lived longer than males
(p=0.00039) and pseudo-males
(p=5.71E-43). Males also outlived
pseudo-males (p=1.08E-34), and
males expressing Tra-2 RNAi
outlived control males (p=2.02e17).

55
Replicate Sex/Group n Med 90% Mort M vs F M vs PM F vs PM M vs MTra-2
RNAi
1 M 174 70 80 ΔMed:
+14.29%
p=7.66E-21
ΔMed:
-40%
p=7.40E-53
ΔMed: -
90.48%
p=2.85E-93
ΔMed:
+17.14%
p=8.23E-26
F 197 80 84
MTra-2 RNAi 163 82 86
PM 190 42 58
2 M 90 74 80 ΔMed:
+2.70%
p=3.90E-04
ΔMed: -
40.54%
p=1.08E-34
ΔMed: -
72.73%
p=5.71E-43
ΔMed:
+10.81%
p=2.02E-17 F 94 76 84
MTra-2 RNAi 88 82 89
PM 86 44 55
3 M 90 76 80 ΔMed:
0%
p=0.0223
ΔMed: -
31.58%
p=1.21E-27
ΔMed: -
46.15%
p=1.46E-45
ΔMed:
+3.95%
p=5.33E-06 F 99 76 82
MTra-2 RNAi 90 79 88
PM 91 52 64
Table 3.3 Lifespans of Tra-2 RNAi pseudo-males. Lifespans of males (M), females (F), females with Tra-2 RNAi
(FTra-2 RNAi), and transformed pseudo-females (PF). All females are virgins. Med = median lifespan (days); 90% Mort
= 90% mortality (days). Statistical comparisons show the percent change in median lifespan between the first group
and the second, as well as the log rank p-value for the comparison. Significance threshold after correction for
multiple comparisons = 0.0125.
Lifespan of Pseudo-males
Pseudo-males were generated by expressing RNAi against Tra-2 in female flies. While Tra-2 is
normally expressed in both sexes, in females it is essential for the effect of TraF on doublesex
splicing; therefore without Tra-2, females develop as pseudo-males despite the presence of TraF
(Rideout 2011). Tra-2 RNAi was driven in the whole body using a Tubulin-GAL4 driver.
Similar to the above experiments, flies were scored based on eye color: control males had white
eyes, males with Tra-2 RNAi had orange eyes, and both control females and pseudo-males had
red eyes. Pseudo-males were larger than typical males but otherwise male in appearance (Figure
3.2). Lifespan experiments compared these 4 groups.
Females lived significantly longer than males in 2 of the 3 cohorts; in the last cohort,
there was no difference between them (Table 3.3). The lifespan of pseudo-males was
significantly shorter than both males and females in every cohort (Figure 3.5). It is therefore

56
difficult to draw any conclusions about the role of the Tra-dsx pathway in longevity sex
differences from this data, as transformed flies differed from both sexes. Surprisingly, males
expressing Tra-2 RNAi lived longer than control males in all 3 cohorts.
Discussion
The lifespan experiments yielded mixed and somewhat difficult to interpret results. When males
were transformed into pseudo-females with UAS-TraF, their lifespans were decreased relative to
both males and females. On the other hand, pseudo-female transformation with TraFD did not
change male lifespan at all. The latter results suggest that the Tra pathway is not responsible for
sexual dimorphism in lifespan, since the addition of TraF did not alter this dimorphism.
However, additional TraF expression depressed lifespans in both sexes in the UAS-TraF
experiments. This is unlikely to be due to TraF itself, or transformation from male to pseudofemale per se, since it was not recapitulated in the TraFD experiments. It is possible that
something about this specific transgene, or its pattern of expression by the daughterless-GAL4
driver, reduced health of flies in a way that either native female expression of TraF or transgenic
TraFD did not.
Considering only the TraFD results, the correlation of lifespan with chromosomal sex
rather than the presence or absence of TraF suggests that lifespan may be independent of Tra.
This result is somewhat surprising given that several traits relevant to aging are known to depend
on Tra. For example, response to rapamycin and level of basal autophagy in the intestine are both
altered with Tra (Regan 2022), as well as multiple forms of stress resistance (Pomatto 2017) and
gut pathology (Regan 2016). Genetic background of the flies may be important here. While
female flies often outlive males, in the strains used here, males lived longer. Additionally, the

57
median lifespans for all groups in the TraFD strains were also low, even in those flies which did
not inherit the TraF transgene, with the longest lived cohorts having a median lifespan of only 46
days and the majority of median lifespans falling below 40 days. The unusual features of these
strains—higher male lifespan and low longevity overall—should be taken into consideration
before assuming these results apply universally.
The results of the Tra-2 RNAi experiments are also difficult to interpret in a way that
provides a clear answer about the impact of Tra on lifespan. In contrast to the UAS-TraF
experiments described above, in these experiments the Tra-2 RNAi transgene had opposite
effects in males and females—female lifespan was reduced, while male lifespan increased.
Because pseudo-males had shorter lifespans even than control males, this result does not indicate
anything about the Tra pathway's role in regulating sex differences in lifespan. In the future it
may be worthwhile to attempt this transformation using Tra itself rather than Tra-2, to see if that
has the same effect on pseudo-males.
It was an interesting and unexpected result that Tra-2 suppression in males increased
lifespan. The reason for this is not immediately clear, and further investigation to determine the
mechanism behind this life extension is warranted, especially as it may point to a male-specific
longevity mechanism. In males, Tra-2 is essential for normal spermatogenesis, and this may be
the mechanism for their comparatively greater lifespan (Belote and Baker 1983). Germline
depletion or removal have been shown to increase lifespan in male C. elegans and turquoise
killifish (Lind 2023, Moses 2024). Some previous data show increased male lifespan with
ablation of the germ cells in Drosophila (Flatt 2008, Shen 2009), perhaps due to altered insulin
signaling; however, not all studies report this result (Barnes 2006). Loss of Tra-2 does not result
in complete loss of sperm; rather, sperm are produced with abnormal morphology, resulting in

58
loss of motility (Belote and Baker 1983). Further study is needed to determine if altered
spermatogenesis is the mechanism behind the longevity of males with Tra-2 RNAi. This data
does provide an interesting example of a case where the same intervention, reduction of Tra-2
protein levels via RNAi, causes opposite effects on lifespan in males and females. It is
illustrative of the fact that anti-aging interventions are not always universal, and can be
beneficial or detrimental depending on factors such as sex.
Given the inconsistent results, further study is needed to determine the impact of the Tra
pathway on lifespan. It would be ideal to gather more data on longevity in a variety of genetic
backgrounds, using a variety of methods to transform flies, to determine any observed effects are
the result of Tra itself. Finally, it would be of interest to combine the transformations described
here with the fluorescent protein video assay described in Chapter 2. In particular, if any sex
differences in protein turnover are uncovered in the future using this assay, it would be of great
interest to ask if transformation alters those differences.

59
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Abstract (if available)

Abstract Protein turnover is an essential function of the cell, and increasing evidence indicates that protein degradation is disrupted during aging. With the aim of assaying protein turnover under different conditions in living flies, we have developed a method to calculate the half-life of fluorescent proteins in free-moving adult flies. Conditional transgenic systems are used to transiently express a protein of interest, and the decay in fluorescence is used to calculate half-life. This assay's utility was validated using inhibitors of synthesis and proteasomal degradation, and half-life values were calculated for several fluorescent proteins in male and female flies of varying ages. Additionally, half-lives of proteins expressed to specific tissues were calculated, revealing different degradation rates of eGFP in different tissue types. Fluorescent proteins targeted to the mitochondria rather than the cytoplasm were also used. Taken together, the data indicate that this assay is a promising tool for studying changes in protein turnover under different conditions, such as age, sex, and exposure to drugs and small molecules. Finally, some experiments investigating the effects of the Drosophila sex determination gene Transformer on lifespan are discussed, and the utility of sex transformation for studying differences in the aging trajectories of male and female animals is explored.

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Creator Bell, Hans (author)

Core Title Fluorescent protein turnover in free-moving Drosophila melanogaster

School College of Letters, Arts and Sciences

Degree Doctor of Philosophy

Degree Program Molecular Biology

Degree Conferral Date 2024-08

Publication Date 02/27/2025

Defense Date 08/19/2024

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

Tag aging,fluorescent proteins,OAI-PMH Harvest,proteasome,protein degradation,protein turnover,sex differences

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Language English

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Advisor Tower, John (committee chair), Curran, Sean (committee member), Davies, Kelvin (committee member), Pinaud, Fabien (committee member)

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