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Computational and experimental approaches for the identification of genes and gene networks in the Drosophila sex-determination hierarchy

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Computational and experimental approaches for the identification of genes and gene networks in the Drosophila sex-determination hierarchy

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COMPUTATIONAL AND EXPERIMENTAL APPROACHES FOR THE

IDENTIFICATION OF GENES AND GENE NETWORKS IN THE DROSOPHILA

SEX-DETERMINATION HIERARCHY

by

Matthew Stewart Lebo

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

May 2008

Copyright 2008 Matthew Stewart Lebo

ii

Dedication
To my wife Meredith, whose love and support is always with me in whatever I am doing.
She keeps me happy and grounded, and I could not ask for anything more.

To my parents, Stewart and Emily, whose guidance has helped me become the person
and scientist I am today. They have pushed and inspired me, while forever encouraging
me to succeed.

iii

Acknowledgements
I would first like to thank Dr. Fengzhu Sun for his guidance, support and mentorship
during my time at USC. I am grateful for the experience of learning how to be a scientist
from him. He has encouraged me to pursue the research in which I am interested, while
guiding me when things proved difficult. He taught me to explore the interesting
questions and think critically of what I am doing and why it is important.

I would also like to thank my co-advisor Michelle Arbeitman. She accepted me into her
lab and allowed me to experience the world of a molecular biologist. Her guidance has
taught me the process and value of data generation, which drives all computational
analysis, and allowed me to develop as a scientific researcher.

Laura Sanders, my collaborator throughout most of my graduate career, has been
invaluable as a colleague and as a valued friend. Without her incredible effort and input,
this research project would have been much more difficult and far less fun. I want to also
acknowledge Tom Goldman for his partnership in the development of SUPRfly, the best
CRM search program around. I would like to thank Dr. Tim Chen for his assistance early
in my graduate career in developing my ideas and skills. A special thanks to Dr. Kim
Siegmund for graciously agreeing to serve as my outside committee member.

I would like to thank Dr. Xiaotu Ma and Li Wang for their collaborations and discussions
about research not presented in this dissertation. I would additionally like to thank
iv

numerous lab members for their various forms of help throughout the years: Justin Dalton
for his laughs and ribs; JP Masly for his help with editing and for his numerous
arguments; and Saori Lobbia, Zhidong Tu, Anat Sommelet, and Emma Peebles.

Finally I would like to thank all of my friends and family for their support throughout my
graduate career, including Bill and Laura Lebo, Chris and Jen Lebo, and my nephews
Tommy and Henry. And my friends who have braved graduate school along side me:
Shawn Szyjka, Kim Rapp, Chris Viggiani, Daniel Ford, and John McCrow. A final thank
you to anyone who I may have mistakenly overlooked.

v

Table of Contents
Dedication ii

Acknowledgements iii

List of Tables vii

List of Figures ix

Abstract x

Chapter 1: Introduction – Metamorphosis and the sex-determination hierarchy of
Drosophila melanogaster 1
1.1 Introduction 1
1.2 Overview of dissertation work 13

Chapter 2: Gene expression profiles of the whole D. melanogaster genome in
males and females during metamorphosis 20
2.1 Introduction 20
2.2 Results and Discussion 21
2.3 Conclusion 45
2.4 Materials and Methods 46

Chapter 3: Identification of genes expressed downstream of doublesex at the 48 hr
pupal stage of D. melanogaster 52
3.1 Introduction 52
3.2 Results and Discussion 53
3.3 Conclusions 76
3.4 Materials and Methods 77

Chapter 4: Identification of genes expressed downstream of fruitless at the 48 hr
pupal stage of D. melanogaster 80
4.1 Introduction 80
4.2 Results and Discussion 82
4.3 Conclusion 99
4.4 Materials and Methods 100

Chapter 5: Computational prediction of cis-regulatory modules in Drosophila and
an application to the sex-determination hierarchy 104
5.1 Introduction 104
5.2 Materials and Methods 106
5.3 Results and Discussion 116
5.4 Conclusions 127

vi

Chapter 6: Conclusions 129

References 132

Appendices 151
Appendix A: Supplemental tables for Chapter 2 151
Appendix B: Supplemental tables for Chapter 3 175
Appendix C: Supplemental tables for Chapter 4 187
Appendix D: Supplemental tables for Chapter 5 192
Appendix E: SUPRfly – A web-based program for predicting cis-regulatory
modules in Drosophila 212

vii

List of Tables
Table 3.1 Genotypes used to find sex-hierarchy regulated gene expression 55
Table 3.1 Genes downstream of the sex-specifically spliced transcription
factor DSX

67
Table 4.1 Microarray design to identify genes downstream of FRU
M
at 48 hr
APF

83
Table 4.2 Genes downstream of FRU
M
at 48 hr APF that are regulated by
ecdysone-regulatory pathway

93
Table 5.1 Our CRM searching method outperforms previous web-based
methods in identifying true CRMs

121
Table 5.2 Microarray experiments analyzing gene expression in flies over-
expressing DSX or FRU isoforms identifies candidate direct targets
of these two transcription factors

125
Table A1 Sex-differentially expressed somatic genes during metamorphosis 151
Table A2 Sex-differentially expressed somatic genes at 0 hr APF 152
Table A3 Sex-differentially expressed somatic genes at 24 hr APF 153
Table A4 Sex-differentially expressed somatic genes at 48 hr APF 158
Table A5 Sex-differentially expressed somatic genes at 72 hr APF 159
Table A6 Sex-differentially expressed somatic genes at 96 hr APF 160
Table A7 Correlation values among microarray replicates for time course
study

160
Table A8 Genes expressed in the male-germline during metamorphosis 161
Table A9 Genes expressed in the female-germline during metamorphosis 171
Table A10 Oligo sequences of sex-determination hierarchy genes for control
spots on microarrays

174
Table B1 Somatic sex-differentially expressed genes and their gene
expression level changes in sex-determination hierarchy mutants

175
viii

Table B2 Adult somatic sex-differentially expressed genes from Arbeitman
et al (2004)

182
Table B3 Sex-differentially expressed genes in the CNS during
metamorphosis

184
Table B4 Expression levels of DSX-regulated genes in comparisons of dsx
null flies to wild type flies

186
Table C1 Genes significantly differentially expressed between fru P1 males
and wild type males

187
Table C2 Genes significantly differentially expressed in the CNS between
fru P1 males and wild type males

189
Table C3 Genes involved in the ecdysone-regulatory pathway

191
Table D1 CRMs annotated as having more than one TFBS

192
Table D2 Position weight matrices (PWMs) for searched transcription factors 194
Table D3 Comparison of out CRM search method with Ahab and cis-Analyst

197
Table D4 Genes with differential expression when over-expressing DSX
F
in
females and identification of DSX CRMs

199
Table D5 Genes with differential expression when over-expressing DSX
M
in
males and identification of DSX CRMs

202
Table D6 Genes with differential expression when over-expressing FRU
A

isoforms in males and identification of FRU CRMs

205
Table D7 Genes with differential expression when over-expressing FRU
B

isoforms in males and identification of FRU CRMs

207
Table D8 Genes with differential expression when over-expressing FRU
C

isoforms in males and identification of FRU CRMs
210

ix

List of Figures
Figure 1.1 The Drosophila melanogaster sex-determination hierarchy 7
Figure 2.1 Number of somatic sex-differentially expressed genes during five
time points in metamorphosis

27
Figure 2.2 Expression profiles of somatic gene clusters with similar patterns of
expression

29
Figure 2.3 Cluster of global expression profiles for all Drosophila transcripts
across metamorphosis

44
Figure 3.1 Proposed model for DSX modes of regulation 74
Figure 4.1 Transcript of a putative FRU
M
target, CG8213, appears to have
higher expression in fru P1 null males compared to wild type males

89
Figure 4.2 EcR isoforms A and B1 are co-expressed with FRU
M
in an isoform
specific manner in the central nervous system at 0 hr APF

96
Figure 4.3 EcRA shows high levels of co-expression with FRU
M
in the central
nervous system at 48 hr APF

96
Figure 4.4 Decreasing levels of EcR isoforms A or B1 in fru P1-expressing
cells does not significantly alter levels of male-courtship ritual

98
Figure 4.5 Decreasing levels of EcRA isoform in fru P1-expressing cells
causes increased levels of male-male courtship

99
Figure 5.1 Prediction accuracy when searching for cis-regulatory modules
(CRMs)

119
Figure E1 Sample input layout for SUPRfly 214
Figure E2 Sample output layout for SUPRfly 217
x

Abstract
Sexual reproduction in most multi-cellular species requires the development of sex-
specific adult structures and the potential to perform sex-specific behaviors. These adult
sex-specific phenotypes are typically patterned during development. Therefore, a more
thorough understanding of how the genome is deployed in a sex-differential manner
during development provides insight into how gene networks give rise to sex-specific
traits. The fruit fly, Drosophila melanogaster, is an excellent model system for studying
how sex-specific differences are established at the molecular-genetic level. The
Drosophila sex determination hierarchy is responsible for establishing adult sex-specific
morphologies and behaviors through an alternative pre-mRNA splicing cascade that
culminates in the production of sex-specific transcription factors encoded by doublesex
(dsx) and fruitless (fru). Little is known, however, about the genes regulated by these
transcription factors during development.

Metamorphosis is the developmental stage during which the largely asexual larval form is
transformed into the sexually dimorphic adult form. We, therefore, focus our analyses on
this developmental stage. Using a microarray-based approach, we analyzed gene
expression during metamorphosis of all predicted Drosophila genes, for males and
females separately, in both wild type animals and animals that lack germline tissues. We
then narrowed our focus to the developmental stage 48-hours-after-puparium-formation
to identify genes that are regulated downstream of DSX and/or FRU. We identified
several genes regulated by DSX and/or FRU during metamorphosis and our results
allowed us to propose an extension of the canonical models for how DSX regulates gene
xi

expression. In addition, we show that the sex-determination hierarchy and the ecdysone-
regulatory hierarchy regulate expression of similar gene sets. We also show that
abrogating ecdysone receptor function in the fru neural circuit leads to defects in male
courtship behaviors. Finally, we developed a computational approach to search for
genomic regions called cis-regulatory modules that contain clusters of transcription factor
binding sites. We used this computational method, in combination with our expression
data analyses, to predict direct targets of DSX and FRU. This computational method is
publically available as a web-based program (SUPRfly; Significant Upstream Regions of
the fly).
1

Chapter 1
Introduction – Metamorphosis and the sex-determination hierarchy of
Drosophila melanogaster

1.1 Introduction
Developmental biology is a field of study that addresses how an organism grows and
develops from an embryo to an adult. In particular, it is a study of how adult
morphologies, physiologies, and behaviors are patterned and established through genetic
and ceullular processes. This includes many areas of research: the patterning and
differentiation of multiple cell lineages into distinct yet interacting systems; the
determination of tissue boundaries and the interconnection of these tissues; and the
incorporation and integration of environmental, signaling, and genetic cues to produce
complex morphologies and behaviors. Further identification at the level of genes and
proteins that underlie development provides insight into how the morphology,
physiology, and behaviors of an organism are patterned and related.

The fruit fly, Drosophila melanogaster, is a well studied multicellular model organism
with ~14,000 known and predicted genes, many of which have begun to be characterized
for their functional role in the fly. Although Drosophila is an invertebrate, research in
Drosophila has shed light into the studies of humans and mammals in general due to
conservation of gene, proteins, and pathways and has provided important insight into
2

biological processes, including neurogenerative disease (reviewed in Bilen & Bonini,
2005), cancer (reviewed in Vidal & Cagan, 2006), and aging (reviewed in Lim et al,
2006). There is a wealth of molecular genetic techniques available for the study of
Drosophila that enables both whole genome and individual gene analyses, which can be
used to further characterize the genes and proteins directly involved in developmental
processes. Combining molecular genetic analyses with computational analyses can
further our understanding of the processes in developmental biology and allow
researchers to focus on specific genes with high confidence.

1.1.1 The life cycle of Drosophila melanogaster
There are four different broad developmental stages during the Drosophila life cycle:
embryonic, larval, pupal, and adult (reviewed in Ashburner et al, 2005). The embryonic
and pupal stages are stationary stages in which the fly undergoes dramatic patterning and
morphological changes. During the larval and adult stages, the fly is motile and feeding,
with the adult stage geared towards many aspects of sexual reproduction. Many aspects
of the morphology, physiology, and behaviors of the larva and adult are established at a
genetic level during the embryonic and pupal stages, respectively. In addition, during the
larval stages, patterning of imaginal discs – physically distinct primordia that were
formed in the embryonic stage – is occuring (reviewed in Ashburner et al, 2005).

Metamorphosis consists of several pupal stages that culminate in the production of the
adult fly. During the ~five days the fly takes to complete metamorphosis, most larval
tissues and structures are degraded and tissues that give rise to the adult undergo
3

morphogenesis (reviewed in Buszczak & Segraves, 2000; Postlethwait & Schneiderman,
1973). The start of metamorphosis – as with molting during the larval stages – is
triggered by a pulse of the steroid hormone 20-hydroxy-ecdysone (referred to as
ecdysone for the rest of this dissertation) (reviewed in Buszczak & Segraves, 2000). The
start of metamorphosis occurs at the end of the third instar larval stage, with this
ecdysone pulse causing the pupal case, a hard chitinous exterior shell, to form from the
larval epidermis (reviewed in Ashburner et al, 2005). Another ecdysone pulse triggers
the start of the pupal stages – referred to as 0 hours after puparium formation (0 hr APF)
for the remainder of this dissertation – the pupae detaches from the pupal case (reviewed
in Ashburner et al, 2005). At this point some larval tissues, including the epidermis,
musculature, and organs of the digestive tract, begin to be degraded to allow for the
formation of the adult tissue (reviewed in Buszczak & Segraves, 2000). Other larval
tissues persist into the adult stage, including larval fat cells (Butterworth, 1972) and some
larval neurons (reviewed in Truman, 1990). In addition, the adult tissue and structures
begin to arise from the imaginal discs, including the early stages of morphogenesis of the
eyes, antennae, wings, legs, and genitalia (reviewed in Postlethwait & Schneiderman,
1973). During metamorphosis, histoblast nests – groups of cells formed early in
embryogenesis – proliferate into adult structures, including the abdominal epidermis
(Madhavan & Madhavan, 1980).

There is an additional pulse of ecdysone at 12 hr APF followed by the largest ecdysone
pulse that peaks just after 24 hr APF, both of which continue to drive the dramatic
changes in the fly at this stage (reviewed in Riddiford et al, 2000). The imaginal discs
4

continue to undergo morphogenesis into their corresponding adult structures, including
wings, legs, and genitalia, during the first half of metamorphosis and are close to the final
adult form by 72 hr APF (Fristrom and Fristrom, 1993). The adult tissues are then further
refined and start to develop additional structures required during the adult stage. By 96
hr APF the pupae is within a few hours of eclosion – the process of emerging from the
pupal case as an adult fly.

In addition to adult specific morphologies and physiologies, fruit flies also have innate,
adult-specific behaviors. It is during metamorphosis that the potential for some of these
innate behaviors are thought to be established through the patterning of the neural
circuitry. The central nervous system (CNS) of Drosophila is comprised of the brain and
the ventral nerve cord (VNC). The larval form and function of the CNS are substantially
different at a morphological and functional level from the adult CNS (reviewed in
Truman, 1990). The CNS is repatterned during metamorphosis by 1) degradation of
some larval nervous system tissue, 2) development of adult nervous system tissue, and 3)
receding of larval arborization and production of adult arborization. Establishment of the
adult CNS is similar in time frame to the production of adult structures from the imaginal
discs, with the adult form largely present by 72 hr APF and further fine-scale connections
established during the end of metamorphosis (reviewed in Truman, 1990).

Male and female Drosophila display little morphological differences at the onset of
metamorphosis, with the exception of cells fated to become the gonads. However, by the
adult stage male and female flies display many morphological and physiological
5

differences in somatic and germline tissues as well as differences in their behaviors. Like
other adult-specific phenotypes, these sex-specific traits are largely established during
metamorphosis (reviewed in Christiansen et al, 2002); thus, during metamorphosis there
must be different male and female genetic developmental programs. While much is
known about the morphogenesis of specific tissues during metamorphosis, as well as the
regulatory hierarchy required for the male and female developmental programs (referred
to as the sex determination hierarchy, see 1.1.2), almost nothing is known about the
specific genes of the sex determination hierarchy and their roles in the establishment of
male- and female-specific morphologies and behaviors.

The current annotation and assembly of the Drosophila genomic sequence is in its fifth
stage and contains ~14,000 known and predicted genes. Detection of the expression
levels of mRNA from all Drosophila genes and how these levels change during
metamorphosis could reveal novel insight into how individual genes and sets of genes are
regulated in the fly. In addition, studying gene expression in the somatic tissues and how
gene expression differs between males and females could identify genes and proteins that
are required for the establishment of adult- and sex-specific morphologies and behaviors.

1.1.2 Drosophila sex determination hierarchy
In Drosophila, one genetic hierarchy that controls all aspects of sex-specific morphology
and behavior is the sex determination hierarchy. This gene network has been studied for
over 50 years, leading to a wealth of information about its roles in the development of the
adult fruit fly (reviewed in Casper & Van Doren, 2006; Christiansen et al, 2002). The
6

sex determination hierarchy is known to control all aspects of somatic sex-differential
morphology (reviewed in Casper & Van Doren, 2006; Christiansen et al, 2002) as well as
playing an important role in the production of both the male and the female germlines
through both direct control and regulation by the somatic gonads (reviewed in Casper &
Van Doren, 2006; Christiansen et al, 2002). In addition, the hierarchy has been shown to
control the innate and robust male-courtship ritual (reviewed in Billeter et al, 2006).

In addition to the known roles of the sex determination hierarchy in the development and
maintenance of sex-specific phenotypes, the specific genes and proteins in the hierarchy
are well-studied at a molecular genetic level (Figure 1.1; reviewed in Christiansen et al,
2002). This includes knowledge of how these proteins are produced in a sex-specific
manner. In particular, the hierarchy contains genes whose pre-mRNA transcripts are
alternatively spliced in a sex-specific manner. This culminates in the production of sex-
pecific transcription factors. At the top of the hierarchy is the master regulatory gene Sex
lethal (Sxl), of which an active protein is only produced in flies with an X:autosomal
chromosome ratio of 1:1 (XX, females). In females, SXL protein regulates the splicing
of its own pre-mRNA (Bell et al, 1991) as well as the pre-mRNA of the gene transformer
(tra), producing functional TRA protein (McKeown et al, 1988; Nagoshi et al, 1988).
TRA then binds to the protein product of the constitutively expressed transformer-2 (tra-
2), and the TRA:TRA-2 complex regulates the splicing of both doublesex (dsx) (Burtis
and Baker, 1989) and fruitless P1 (fru P1) (Ryner et al, 1996). The fru locus is a
complex locus, with transcription being driving from multiple promoters. The transcript
produced from the P1 promoter is the pre-mRNA that is sex-specifically spliced due to
7

Figure 1.1. The Drosophila melanogaster sex determination hierarchy. The sex of a fruit fly is
determined by its X chromosome to autosomal chromosome (A) ratio. In females (X:A =1), Sex lethal
(SXL) is produced and regulates the splicing of its own pre-mRNA as well the pre-mRNA of transformer
(tra). This results in the production of TRA, which, in conjunction with the constitutively expressed TRA-
2, alternatively splices the pre-mRNAs of doublesex (dsx) and fruitless P1 (fru P1), leading to the
production of the female-specific protein DSX
F
and non sex-specific isoforms of FRU. In males (X:A =
0.5), SXL is not produced, leading to the default splicing of tra, resulting in a non-functioning TRA
isoform. In the absence of TRA, dsx and fru P1 pre-mRNAs undergo default splicing, which results in the
production of the male sex-specific proteins DSX
M
, male-specific FRU isoforms (FRU
M
), and non sex-
specific isoforms of FRU.

the function of TRA. This then culminates in the production of female-specific DSX
isoform (DSX
F
). In flies whose X:autosomal chromosome ratio is 1:2 (XY, males), no
functional SXL protein is produced, causing the tra pre-mRNA to be spliced in its default
manner, leading to a transcript with an early stop codon and thus no functional TRA. The
dsx and fru P1 pre-mRNAs are then spliced by the default pathway, leading to the
production of male-specific DSX isoform (DSX
M
) (Burtis and Baker, 1989) and male-
specific FRU isoforms (FRU
M
) (Ryner et al, 1996).

8

Molecular-genetic analysis has shown that the two terminal effectors of the Drosophila
sex determination hierarchy, DSX and FRU, are responsible for directing sex-specific
development in somatic tissues and for the potential for male-specific courtship behaviors
(reviewed in Casper & Van Doren, 2006; Christiansen et al, 2002). DSX specifies nearly
all aspects of somatic sex-differentiation through its male- and female-specific isoforms
(Duncan and Kaufman, 1975; Hildreth, 1965), while FRU
M
is necessary for the potential
for the male-courtship ritual (Gailey & Hall, 1989; Hall, 1978). Interestingly, despite all
of the research done in this field, only the hierarchy itself – up to DSX and FRU – has
been well investigated. Although the regulation of and the effects of mutations in Sxl,
tra, dsx, and fru are well-understood, thegenes that function downstream of the hierarcyh
are relatively unknown. It is therefore important to identify the genes that lie
downstream of the sex determination hierarchy and how these downstream genes are
involved in establishing and maintaining sex-specific morphology and behaviors.

1.1.3 Transcription regulatory pathways
Two of the major effectors of the sex determination hierarchy, DSX and FRU, are
transcription factors (TF). TFs are proteins containing DNA binding domains that bind
DNA in a sequence dependent manner to enhancer regions that reside cis to genes. They
also contain additional domains through which they function to regulate the rates of
transcription through both activating and inhibitory mechanisms. TFs play an important
role in almost every biological process by regulating when a gene should be turned on,
for how long, and at what levels. A TF controls the expression pattern of a downstream
target gene by binding to the enhancer regions at specific nucleotide sequences
9

(Davidson, 2001). The binding of a TF is sequence specific and determining genomic
regions that contain these specific sequences can provide information on which genes’
expression patterns might be regulated by this TF.

There are, however, limitations when searching genomic sequences for occurrences of a
transcription factor binding site (TFBS). One limitation with this method is that currently
not all TFs are known, and for those that are known, many do not have well-characterized
binding site information. However, it still important to accurately identify the
downstream targets of TFs to further delineate transcriptional regulatory networks, as this
provides insight into both genetic regulatory networks and when and where a gene is
expressed. Searching for binding sites of known transcription factors in the regulatory
regions of potential target genes is a useful method, but can often lead to the
identification of binding sites which are false. There are, however, features of regulatory
regions which can be used in the prediction of these genomic sequences.

In experimentally verified gene regulatory regions of different organisms, including yeast
and Drosophila, there are often multiple TFBSs clustered into a small region, either from
one TF (homotypic) or multiple TFs (heterotypic) (Davidson, 2001). These small regions
containing many TFBSs are termed cis-regulatory modules, or CRMs. Searching for
clusters of TFBSs enhances the prediction accuracy biologically verified regulatory
regions (Berman et al, 2002; Rajewsky et al, 2002). In addition, as regulatory regions are
of functional importance in the genome, they are often more conserved than non-
regulatory intergenic DNA sequences (Dermitzakis et al, 2003). Thus, computational
10

algorithms that incorporate sequence analysis using alignments of orthologous sequences
from multiple species into the prediction of regulatory regions is can enhance the
identification of CRMs. Furthermore, there is evidence that the prediction accuracy
increases when using this type of comparative genomic approach (Gertz et al, 2005;
Kellis et al, 2003; Sinha et al, 2004; Wong & Nielsen, 2007). By using the genomic
sequences of multiple species, we can get a better idea of which sequences are conserved
and therefore more likely to be regulatory elements.

In addition to D. melanogaster, there are now eleven different Drosophila species for
which the genome has been sequenced (Clark et al, 2007; for details see
http://rana.lbl.gov/drosophila/). These species vary in divergence time from D.
melanogaster from less than five million years to almost 50 million years. This provides
a good dataset to search for CRMs and genes regulated by the same TFs in Drosophila.
Using these additional genomes and an improved approach for identifying CRMs provide
a useful tool for the Drosophila community in identifying transcriptional regulatory
modules.

1.1.4 Regulation of downstream targets of the sex determination hierarchy
As mentioned above, much is known about the sex determination hierarchy at a
molecular-genetic level. The genes regulated downstream of the transcription factors
encoded by dsx and fru are relatively unknown, and there are only two known direct
targets of dsx and no known direct targets of fru.

11

The sex-specific isoforms of DSX (DSX
F
and DSX
M
) share a common DNA-binding
domain, but differ in their C-terminal ends (Burtis & Baker, 1989). Thus, both DSX
F
and
DSX
M
are thought to act through the same sequences to regulate gene expression.
However, direct regulation of gene expression by DSX has only been shown for two
genes, Yolk protein 1 and Yolk protein 2 (Burtis et al, 1991; Coschigano & Wensink,
1993a). Yolk protein 1 and Yolk protein 2 share a common regulatory region to which
DSX binds, so only one regulatory region for DSX has been characterized. Additional
studies have identified a handful of other genes whose expression levels are regulated by
the activity of DSX (Arbeitman et al, 2004; Dauwalder et al, 2002), but none of these
have been shown to be direct targets. Based upon these studies (Arbeitman et al, 2004;
Dauwalder et al, 2002), it is believed that DSX
F
and DSX
M
function in diverse ways to
regulate the majority of their downstream targets, including both gene expression being
regulated by both isoforms or gene expression being regulated only in one sex by a single
DSX isoform. When DSX
F
and DSX
M
both act to regulate gene expression of
downstream targets, they are believed to do so in opposite manners; i.e., for female-
biased genes DSX
F
activates transcription in females, while DSX
M
represses transcription
in males. Furthermore, previous research has defined the binding site motif bound by
DSX using in vitro techniques (Erdman et al, 1996), and has shown that DSX binds to
DNA as a dimer.

There is considerably less known about how FRU
M
regulates its downstream targets and
to date no direct targets have been identified. Dauwalder et al (2002) showed that FRU
M

works in concert with DSX
M
to repress the levels of takeout in males. Additional studies
12

have posited that FRU
M
controls the expression patterns of the genes yellow and male-
specific neuropeptide f (Drapeau et al, 2003; Lee et al, 2006). However, FRU
M
binding
to the regulatory regions of these genes or any other gene has yet to be demonstrated.
Due to alternative splicing at the 3’ end of the transcript, FRU
M
protein can contain one
of potentially four zinc-finger DNA-binding domains (A, B, C, and D; Ito et al, 1996;
Ryner et al, 1996) and the isoforms are believed to bind to DNA either as homo- or
heterodimers due to the presence of the BTB domain (Perez-Torrado et al, 2006; Zollman
et al, 1994). Characterization of FRU’s downstream targets is inherently more difficult,
as there are numerous combinations of potential binding sequences. However,
preliminary work has identified sequence motifs bound by each DNA binding domain
(Arbeitman and Baker, personal communication), making characterization of downstream
targets more promising.

Finally, both dsx and fru are well-conserved genes (Gailey et al, 2006; reviewed in
Zarkower, 2002). Proteins containing amino acids similar to the DM domain of
Drosophila DSX are found in many invertebrates and vertebrates and play a role in sex
determination and sex-differentiation (reviewed in Hong et al, 2007). dsx is largely
conserved at the sequence level throughout the Drosophila species, and homologs that are
sex-specifically spliced have been found in distant insect species (reviewed by
(Zarkower, 2002). For fru, a recent study (Gailey et al, 2006) showed that the three
DNA-binding domains are all highly conserved at the sequence level in mosquitoes
(Anopheles gambiae), which diverged from Drosophila approximately 250 million years
ago. Additionally, using transgeneic approaches to express the mosquito fru in a fru P1-
13

null Drosophila male, some fru P1-specific phenotypes are rescued (Gailey et al, 2006).
This suggests that the binding domains of FRU are conserved as well as the DNA
sequence motifs that they recognize.

1.2 Overview of dissertation work
In this dissertation, I sought to determine how adult somatic sex-specific morphology and
behaviors are established during metamorphosis. I started with a global view of how the
D. melanogaster genome is deployed during metamorphosis in both males and females,
with particular interest in the somatic tissues. I then focused on the downstream effectors
of the sex determination hierarchy in Drosophila, the transcription factors doublesex and
fruitless. As outlined above, there is a wealth of knowledge on these genes, however not
much is known about the genes whose expression levels they regulate. Furthermore, the
sex determination hierarchy is involved in establishing the somatic sex-specific
morphology and some sex-specific behaviors of the fruit fly. Thus, insight into how dsx
and fru P1 regulate their downstream genes, as well as the identification of these genes,
can give insight into how the body plan and nervous system are patterned. For this work,
I focused on the middle of metamorphosis (48 hours after puparium formation [APF]) as
this stage shows high levels of both fru and dsx expression (Lee et al, 2000; Lee et al,
2002; Sanders and Arbeitman, personal communication). Collaboratively, I initially
identified genes that are regulated downstream of DSX and posit a role for how DSX
might regulate its downstream targets. Similarly, I collaboratively identified candidates
regulated downstream of FRU
M
in both the whole body and the central nervous system at
48 hr APF. Collaboratively, I also developed a computational approach to identify
14

potential regulatory regions of any Drosophila transcription factor, created a web-based
program for the fly community to implement this approach, and used it to identify
potential direct targets of both DSX and FRU
M
.

1.2.1 Sex-differential gene expression during metamorphosis
In Chapter 2, we describe our work examining the expression profiles of every predicted
Drosophila gene during metamorphosis in both male and female flies. This work was
done in collaboration with Laura Sanders, Fengzhu Sun, and Michelle Arbeitman
(University of Southern California). We used both wild type Drosophila and Drosophila
with genetically ablated germlines to identify genes with sex-differential expression
during metamorphosis and distinguish between somatic tissues and germline tissues. We
identified large sets of genes that have similar patterns of expression during
metamorphosis. We found that the predicted functions of genes in these co-regulated sets
are consistent with the known morphological events occurring while these genes show
high levels of expression. Using whole animals that lack germlines, we found small
levels of sex-differential gene expression in the somatic tissues during metamorphosis
(<50 genes for four time points, and 291 for the fifth), with numbers similar to what has
previously been seen in adults (Arbeitman et al, 2004; Parisi et al, 2004; Parisi et al,
2003). Furthermore, we defined sets of genes with high expression in the male and
female germlines, showing that genes expressed in the adult male germline are also
present in the male germline during metamorphosis, and that the female germline has low
levels of expression during metamorphosis but is not highly expressed until the end of
this stage of development, as previously shown (Arbeitman et al, 2004).
15

1.2.2 Identification of downstream targets of doublesex during metamorphosis
In Chapter 3, we describe our identification of genes regulated downstream of dsx during
metamorphosis. This work was done in collaboration with Laura Sanders, Fengzhu Sun,
and Michelle Arbeitman (University of Southern California). We examined gene
expression levels for all predicted Drosophila genes during the middle of metamorphosis
(48 hr APF) using both wild type flies and flies with mutations in sex determination
hierarchy genes. According to the microarray analysis of gene expression at multiple
time points during metamorphosis described in Chapter 2, dsx transcripts have high
expression between 24 and 48 hr APF, while fru transcripts have high expression levels
between 48 and 72 hr APF. Thus, 48 hr APF is a time when these two major effectors of
the sex determination hierarchy are likely functioning. As we were particularly interested
in which genes lie downstream of DSX to effect sex-specific development in the somatic
tissues, we initially looked at sex-differential expression in all somatic tissues as well as
in one particular somatic tissue, the central nervous system (CNS). We chose to look at
the CNS in particular, as examination of this tissue can give insight into how the potential
for sex-specific behaviors are patterned during metamorphosis. We identified 421 genes
with sex-differential expression in the somatic tissues and 105 that are sex-differentially
expressed in the CNS.

We next determined if somatic sex-differential expression was downstream of DSX.
Using microarrays probed with every predicted Drosophila gene and flies with mutations
in tra, we compared gene expression levels between flies that produce TRA and flies
with no functional TRA in the same chromosomally XX background. This allowed us to
16

determine if sex-differential gene is downstream of TRA, and thus potentially
downstream of DSX or FRU
M
. Again using microarrays, we compared gene expression
levels of wild type females to XX flies that produce the DSX
M
isoform, but not the DSX
F

isoform. This allowed us to conclude that 66 of the 421 sex-differentially expressed
somatic genes are expressed downstream of dsx at 48 hr APF.

We further examined the expression levels of the all predicted Drosophila genes in
comparisons of wild type females and XX dsx null flies and in comparisons of wild type
males to XY dsx null flies, allowing us to see the effects on gene expression of DSX
F
in
female flies and DSX
M
in male flies. Based upon these experiments and an analysis of
the expression data of our set of 66 DSX-regulated genes, we proposed a model
extending how the DSX isoforms regulate their downstream target genes. In this model
the majority of DSX downstream targets are regulated by DSX
F
and DSX
M
in the same
manner. DSX
F
in females and DSX
M
in males will both activate or repress gene
expression of downstream target genes. Sex-differential expression results from one of
the isoforms being a more potent regulator.

1.2.3 Identification of downstream targets of fruitless during metamorphosis
In Chapter 4, we discuss our research identifying downstream targets of FRU
M
at 48 hr
APF using a microarray approach to find genes differentially expressed between wild
type males and fru P1 null males. This work was done in collaboration with Laura
Sanders, Fengzhu Sun, and Michelle Arbeitman (University of Southern California). We
examined differential gene expression for the all predicted Drosophila genes between
17

wild type males and fru null males for both whole body pupae and dissected central
nervous system (CNS) tissue. We identified 236 genes regulated downstream of FRU
M

when we analyzed gene expression in whole flies and 94 genes regulated downstream of
FRU
M
in the CNS. We identified many genes that may function in the establishment of
male-specific neural circuitry.

We saw that for the sets of genes downstream of FRU
M
identified in gene expression
analysis of both the whole body and CNS were over-represented with genes that are in
the ecdysone-regulatory hierarchy. Ecdysone is a steroid hormone that regulates molting
and metamorphosis in Drosophila by binding to the Ecdysone Receptor:Ultraspiricle
(EcR:USP) heterodimeric transcription factor (reviewed in Kozlova & Thummel, 2000).
EcR encodes three different isoforms (EcR-A, B1, and B2), all of which have the same
DNA- and hormone-binding domains, but distinct trans-activation domains (Talbot et al,
1993). We sought to determine if EcR functions in concert with FRU
M
in establishing
neural circuitry required for sex-specific behviors – possibly through co-regulation of
downstream targets – and to determine if any possible co-regulation is isoform specific
for EcR. EcR-A and EcR-B1 co-localize with FRU
M
in temporally and spatially different
manners in the CNS (Sanders and Arbeitman, personal communication), giving evidence
for potential co-regulation of downstream targets. When we decreased the expression
levels of EcR-A and EcR-B1 in the fru P1 circuitry, we found defects in the male
courtship ritual suggesting an effect of both EcR and FRU
M
in co-regulating downstream
targets required for the potential of this behavior.

18

1.2.4 Prediction of cis-regulatory modules in Drosophila
In Chapter 5, we describe the development and analysis of a cis-regulatory module
(CRM) search engine for Drosophila and our use of this program to identify potential
direct targets of DSX and FRU. This work was done in collaboration with Thomas
Goldman, Laura Sanders, Fengzhu Sun, and Michelle Arbeitman (University of Southern
California). Making use of the fact that transcription factor binding sites (TFBSs) are
located close to each other in the genome we wrote a program to identify TFBSs and
statistically determine the significance of the distance between adjacent TFBSs. This
method avoided the problem of having to define a window size in which to search for
CRMs, a technique more commonly used. As functional CRMs are known to be more
highly conserved between orthologous species than other non-functional intergenic DNA
sequences (Gertz et al, 2005; Kellis et al, 2003; Sinha et al, 2004; Wong & Nielsen,
2007), we developed a conservation score for our discovered CRMs using the genomic
sequences of 9 additional Drosophila species.

We tested our CRM search engine on experimentally verified CMRs identified from two
recently developed, hand-curated databases (Bergman et al, 2005; Gallo et al, 2006).
Using these test CRMs, we showed that our method predicts CRMs with similar accuracy
as using a window-size based method, but with a considerable increase in speed. In
addition, our method identified more of the true CRMs than previously developed web-
based CRM searching tools (Berman et al, 2002; Rajewsky et al, 2002).

19

As a major focus of this thesis is to find downstream effectors of DSX and FRU, we next
sought to identify genes that are directly regulated by these two TFs. We again used
microarrays for whole-genome analyses of gene expression to find genes whose
expression levels change significantly after briefly over-expressing isoforms of DSX or
FRU. We thus identified 496 and 473 potential direct targets of DSX and FRU,
respectively. Using our CRM search method, we found 123 and 98 of these potential
direct targets have significant DSX or FRU CRMs, respectively.

In Appendix E, we present the development of a web-based server (SUPRfly [Significant
Upstream Regulation in the fly], http://suprfly.cmb.usc.edu) that uses our CRM search
method to find significant CRMs in Drosophila. SUPRfly also has the ability to perform
additional searches in the regulatory regions of candidate genes. In Appendix E we
discuss these additional features of SUPRfly, including searching for CRMs using de
novo predicted motifs and identifying significance for single occurrences of a TFBS.
20

Chapter 2
Gene expression profiles of the whole D. melanogaster genome in males
and females during metamorphosis

2.1 Introduction
In many multicellular species, males and females undergo different developmental
programs to reach their final sexually dimorphic state (reviewed in Western & Sinclair,
2001). Morphological and behavioral differences between males and females have been
extensively studied in the fruit fly Drosophila melanogaster, particularly in adult flies.
However, little is known about the alternate developmental programs that underlie
differences between adult males and females. During metamorphosis, dramatic changes
occur that result in sexual dimorphic adult flies that differ at morphological,
physiological, and behavioral levels. This requires a wholesale set of changes to occur in
the five days the fly takes to undergo metamorphosis: many larval tissue and structures
are degraded, imaginal discs evert and form their corresponding adult tissues, the nervous
system is expanded and repatterned, and adult structures and pigmentations are
established (reviewed in Ashburner et al, 2005).

Microarray studies have characterized sex-specific transcriptional differences between
males and females at the adult stage (Arbeitman et al, 2004; Arbeitman et al, 2002;
Meiklejohn et al, 2003; Parisi et al, 2004; Parisi et al, 2003; Ranz et al, 2003; Zhang et al,
21

2004), as well as transcriptional changes that occur across metamorphosis (Arbeitman et
al, 2002; White et al, 1999). No studies, to date, have examined the regulation of sex-
biased transcripts during metamorphosis. Of particular interest here are genes that
function in somatic tissues to establish sex-specific morphologies and/or the potential for
sex-specific behaviors. In addition, an analysis of temporal expression profiles of all
known and predicted Drosophila genes during metamorphosis provides novel insight into
patterning during development.

In this chapter, we discuss our work generating and analyzing gene expression profiles
across metamorphosis for all predicted Drosophila genes. As we were most interested in
the development of somatic sex-specific traits, we analyzed the relative transcript
abundance of every Drosophila gene separately for male and female flies with
genetically-ablated germlines. In addition, we similarly analyzed wild type male and
female flies, which allowed us to identify genes that show high expression in the male
and female germlines during metamorphosis by comparing gene expression in flies with
germline tissues to those containing only somatic tissues.

2.2 Results and Discussion
One of the major foci of the research presented in this thesis was to identify the genes
that direct the changes involved in establishing adult sex-specific morphology,
physiology, and behavior during metamorphosis, the stage where larvae transition to
adults. Towards this aim, we conducted two-color microarray-based analyses and
assayed gene expression of all predicted Drosophila genes (14454 transcripts representing
22

13820 unique genes), in male and female wild type and mutant animals with genetically
ablated germlines. We analyzed 5 time points during metamorphosis: every 24 hours,
ranging from 0 hours after puparium formation (APF; 0 hr APF is the white pre-pupal
stage) to 96 hr APF (pharate adults). Animals lacking germline tissue are the progeny of
female flies homozygous for the maternal effect mutation tudor (tud); referred to as tud
progeny. All samples were compared to a common reference sample consisting of RNA
derived from male and female pupae at all stages of metamorphosis to facilitate
comparisons. This study differs from previous studies in two important aspects. First,
previous studies only considered approximately a third of the Drosophila open reading
frames (Arbeitman et al, 2002; White et al, 1999). Second, this is the first study to
examine male and female transcriptional profiles separately, in both wild type flies and
tud progeny, throughout metamorphosis.

2.2.1 Gene expression in somatic tissues across metamorphosis
First we present our analyses of tud progeny expression data, which enabled us identify
genes showing sex-differential expression in somatic tissues during metamorphosis. We
chose to analyze the expression data from male and female tud progeny using three
separate techniques: analysis of variance (ANOVA), hierarchical clustering, and t-tests.
Using two-way ANOVA with sex and time as the independent factors, we identified
genes that show changes in gene expression levels across metamorphosis in both sexes
and genes that show sex-differential expression during metamorphosis. We additionally
sought to identify sets of genes that have similar patterns of gene expression throughout
metamorphosis. Therefore, we next used hierarchical clustering to identify genes that are
23

co-regulated during metamorphosis (see 2.4.5; Figure 2.2). Finally, to identify genes that
are sex-differentially regulated in the somatic tissues at each of the five time points
examined in this study, we employed t-tests comparing the expression data from male
and female tud progeny (2.2.4-2.2.8).

Hierarchical clustering was used to examine how temporal regulation of gene expression
profiles underlies the morphological changes that are occurring during metamorphosis.
For our hierarchical clustering, we analyzed only clusters whose members showed an
average correlation of greater than 0.80, as these genes are highly co-regulated during
metamorphosis. For easier visualization of each cluster, we averaged the logarithm of the
expression ratio of every member gene of each cluster at each of the time points for males
and females separately and plotted the averages for each sex (see 2.4.5). Although our
cluster analyses used only the expression data from tud progeny – as we were interested
in somatic gene regulation and did not want to have the influence of germline gene
expression – in our visualization we also plotted the average wild type male and female
expression levels to give us additional validation and insight into these clusters. Below
we describe those clusters whose gene members are involved in bringing about adult
morphology and behaviors at different time points.

We identified genes with similar expression patterns through both hierarchical clustering
and statistics. It is important to understand the functions co-regulated gene sets underlie,
and as such we identified over-represented features – including functional annotation,
sequence location, and protein interactions – in co-regulated sets. To do this we analyzed
24

sets of co-regulated genes using the program DAVID (Database for Annotation,
Visualization and Integrated Discovery; (Dennis et al, 2003). We additionally searched
co-regulated sets using our own program that analyzes the following features for each
gene: chromosome location, sequence location, physical protein-protein interactions,
genetic interactions, and if a genes exons are among the 2500 most highly conserved
sequences across three Drosophila species and one mosquito (see 2.4.6).

2.2.2 Identification of temporally and sex differentially expressed genes in somatic
tissue across metamorphosis using ANOVA
To find genes that showed significant expression differences in somatic tissues either
between the two sexes or between the five time points, we performed a two-way analysis
of variance (ANOVA) on the tud expression data with sex and time as the independent
factors. For this analysis and all subsequent analyses with multiple testing, we converted
our lists of p-values to q-values. The q-value (Storey, 2003) is an estimate of the false
discovery rate, or the proportion of genes declared to be significant that are actually false
positives. We identified 7182 genes (50% of genes represented on array) with significant
changes in expression levels between the five metamorphosis time points examined
(q<0.05). This fraction that is similar to what was previously seen when one third of the
predicted Drosophila genes were examined (1929/4028, q < 0.05; (Arbeitman et al,
2002). The previous study, however, used wild type flies and as such included gene
expression in the germline. It should be noted that in our microarray experiment, we
found that 7388 of the 13820 genes examined were expressed in the somatic tissues for at
25

least one time point during metamorphosis, suggesting 53% of the Drosophila genome is
expressed in the somatic tissues during metamorphosis.

Using the two-way ANOVA results, we additionally identified 35 genes whose
expression levels were significantly different between males and females in the somatic
tissues (q<0.05; Table A1): 22 genes showed higher expression in females (female-biased
genes) and 13 showed higher expression in males (male-biased genes). The 22 female-
biased somatic genes included a gene, cut, known to function specifically in adult
somatic tissues to maintain the female germline (Jackson & Blochlinger, 1997). cut is
also expressed in the chemosensory bristles of the developing wing during
metamorphosis (Ludlow et al, 1996); chemosensory bristles respond to fly pheromones
(reviewed in Amrein, 2004), and sex-differential expression of a gene involved in bristle
development may underlie sex-specific responses to pheromones in the adult. Another
gene in the female-biased set, Larval serum protein 1 alpha, has been shown to be
expressed in the fat body and is known to be female-biased in third instar larvae, the
stage just prior to the onset of metamorphosis (Roberts & Evans-Roberts, 1979).

The 13 male-biased somatic genes include the non-coding RNA roX1, which is known to
function in dosage compensation in males, a process by which transcription of many
genes on the X chromosome is up-regulated on the single X chromosome in males, so
they are expressed at the as seen in females with two X chromosomes (reviewed
(Gilfillan et al, 2004; Lucchesi, 1999). Interestingly, this male-biased set also includes
CG3056, which has recently been shown to be a paralog of the gene at the top of the sex
26

determination hierarchy, Sex-lethal (Sxl) (see Figure 1.1; (Traut et al, 2006). SXL is only
produced in females and functions as a sex-specific splicing factor. Therefore higher
expression of its paralog, CG3056, in males could result in sex-biased splicing of pre-
mRNAs in males, and thus sex-differential production of proteins.

2.2.3 Identification of sex-differential gene expression in somatic tissues at specific
stages of metamorphosis
As mentioned, we only found a small number of genes showing significant sex-
differences in expression in somatic tissues when comparing gene expression across all of
metamorphosis using ANOVA. We hypothesized that most somatic sex-differences may
show sex-differential expression only at a specific time point and we therefore performed
paired t-tests between male and female expression data at each of the five time points
assayed (t-test of means on the logarithm of the ratio data; q < 0.05). We found
substantially more genes that show somatic sex-differential expression (414 unique
genes) from this approach. For each time point, however, we identified fairly small
numbers of genes with sex-differential expression in somatic tissues, with the numbers
being similar to previous studies that examined somatic sex-differential expression in
adult flies (287 in Arbeitman et al (2002), 273 in Parisi et al (2003), and 152 in Parisi et
al (2004)). Three time points in our pupal study (0, 48, and 72 hr APF) all have similar
number of genes with sex-biased expression levels (39, 41, and 36, respectively), while a
fourth (96 hr APF) has about half this number (19) (Figure 2.1, Tables A2-A6).
However, the 24 hr APF time point contains substantially more genes (291) with somatic
sex-differential expression. The higher number of sex-biased genes at 24 hr APF does
27

not seem to be due to experimental issues. At all five time points during metamorphosis
we found expression data for a similar number of genes in both sexes and, when
comparing microarray replicates within each experiment, we saw high correlation among
the microarray replicates (see 2.4.7, Table A7).

Figure 2.1. Number of somatic sex-differentially expressed genes during five time points in
metamorphosis. Plotted is the number of genes declared to be sex-differentially expressed at five time
points during metamorphosis (0, 24, 48, 72, and 96 hr APF). At 24 hrs, APF we see a substantial increase
in the number of sex-differentially expressed genes. Sex-differentially expressed genes were identified
using a t-test of the means on the gene expression data of male and female flies with genetically ablated
germlines (progeny of tudor females; q<0.05). Female-biased genes are shown in red and male biased
genes are shown in blue.

Overall, for the 414 genes with somatic sex-differential expression, eight genes are sex-
differentially expressed at two time points, and one gene, roX1, is sex-differentially
expressed at every time point. Only a small percentage of the sex-differentially
expressed genes (9/414, 2%) were sex-biased at multiple time points, suggesting somatic
sex-differential expression is specific for the five different time points. Below, we
28

describe in more detail the sex-differentially expressed gene sets and compare how their
annotated functions relate to the morphological events occurring at that stage of
development. We additionally describe our results clustering the expression data of all
predicted Drosophila genes during metamorphosis.

2.2.4 Gene expression at the onset of metamorphosis
At the earliest stage examined, the white pre-pupal stage (0 hr APF), the fly has
transitioned from a wandering larva, which scavenges for food, into an immobile pupa.
While a pupa, the fly undergoes a major transition while protected within the pupal case
as tissues are being transformed from their larval forms into adult tissues. During
metamorphosis, this occurs in discrete ways: 1) larval tissues – including the epidermis,
musculature, and organs of the digestive tract – are largely destroyed and replaced by
corresponding adult tissues (reviewed in Buszczak & Segraves, 2000), 2) imaginal discs
– physically distinct primordia that have been formed during the embryonic stage and
proliferate during the larval stages – begin to undergo metamorphosis to give rise to adult
structures including eyes, antennae, wings, legs, and genitalia (reviewed in Postlethwait
& Schneiderman, 1973), 3) histoblast nests – groups of cells fated early in embryogenesis
– proliferate into adult structures, including the epidermis (Madhavan & Madhavan,
1980), and 4) the central nervous system is remodeled through destruction of larval
neurons, proliferation of adult neurons, and rearborization of projections (reviewed in
Truman, 1990). At the start of metamorphosis massive changes are occurring in a fly’s
morphology and physiology and as such, we expected this dramatic developmental shift
to be reflected in the transcriptional profiles of the fly at the beginning of the pupal stage.
29

Accordingly, 608 genes showed a peak in expression levels at the 0 hr APF stage, but
their expression levels remained lower during the remainder of metamorphosis (Figure
2.2, Cluster 1). This set was comprised of and over-represented by functional groups that
likely act in degrading the larval tissues and proteins, including proteolysis (101 genes),

Figure 2.2. Expression profiles of somatic gene clusters with similar patterns of expression. Clusters
were generated using hierarchical clustering of expression data from five time points of metamorphosis
(see 2.4.5 for details). Each cluster has an average correlation of at least 0.80. Plotted are the averages of
the logarithm ratio expression data of every gene in the cluster, with tud progeny females in red and tud
progeny males in blue. Also plotted are the average logarithm ratio expression data for wild type females
(in maroon) and wild type males (in green).

30

histolysis (17 genes), programmed cell death (35 genes), and 34 of the 54 proteosome
complex genes. These functional categories fit well with the corresponding destruction
of larval tissues in the fly at the start of metamorphosis.

At 0 hr APF, we found a similar number of male- and female-biased genes in the somatic
tissue of the pupa (23 and 16, respectively, Table A2). Aside from the roX1 gene
(described above), the transcript with the highest fold-change between males and females
was the female-biased Larval serum protein 1 alpha (Lsp1 α; female to male fold change
is 2.34). Lsp1 α has been shown to be fat body specific and is female-biased in third
instar larvae (Roberts & Evans-Roberts, 1979; Wolfe et al, 1977). The only known direct
targets of the sex determination hierarchy transcription factor DSX, Yolk protein 1 and
Yolk protein 2, are expressed only in the female fat body of adults (reviewed in Bownes,
1994). It would be interesting if the female-biased expression of Lsp1 α is regulated in
part by the sex determination hierarchy.

As mentioned above, when clustering all predicted Drosophila genes, the set of 608 with
peak levels of expression at 0 hr APF contained 101 genes annotated as being involved in
proteolysis, possibly to transition from proteins needed for the larval stages to those
needed for the adult. Of the 39 sex-biased genes we found at this stage, four are
annotated as being involved in protein degradation, suggesting possible sex-specific
decrease in the levels of larval proteins.

31

2.2.5 Gene expression during the early stages of metamorphosis
Twelve hours after puparium formation ecdysone levels are peaking for the pre-
pupal/pupal transition (reviewed in Riddiford, 1993). Shortly after this ecdysis is another
large ecdysone pulse that stimulates the pupal to adult transition. At this time, larval
imaginal discs are everting, or unfolding, and differentiating into corresponding adult
structures (reviewed in Postlethwait & Schneiderman, 1973). The nervous system
undergoes a dramatic repatterning into an adult form (reviewed in Truman, 1990) and
larval-specific tissues are still being degraded (reviewed in Buszczak & Segraves, 2000).
The largest cluster we identified, Cluster 2, contained 1193 transcripts and showed its
peak of expression at 24 hr APF (Figure 2.2). We found that this set is over-represented
with genes annotated as functioning in imaginal disc morphogenesis (73 genes),
neurogenesis (58 genes), and programmed cell death (62 genes), processes occurring at
the early stages of metamorphosis.

We found the greatest number of sex-biased somatic genes at 24 hr APF, with more
male- than female-biased genes at this stage (185 and 106 genes, respectively; Table A3).
Interestingly, the 24 hr APF sex-biased somatic set was over-represented with genes that
are the most highly conserved among Drosophila species (p<0.05), with 59 genes
overlapping with the most evolutionarily conserved Drosophila sequential elements.
Evolutionarily conserved elements represent highly conserved Drosophila sequences
ranging from hundreds to thousands of base pairs in length (Siepel et al, 2005). In their
study, they used multiple alignments of the genomic sequences of four insect species; we
used the set of genes whose exons overlap with the 2500 most conserved sequences. This
32

conservation of DNA sequences in genes that are somatic sex-differentially expressed is
in interesting contrast to genes with high expression in the male germline, which has been
shown to be highly divergent on the DNA sequence level (Jagadeeshan & Singh, 2005).

When further examining this set of somatic sex-biased genes at 24 hr APF using DAVID,
we found the set to be significantly over-represented (p<0.05) with 25 genes that are
annotated in the Swiss-Prot database as having alternative splice forms (Bairoch et al,
2005). A recent study suggested that up to 22% of genes with alternative splice forms
show different sex-biased expression between the alternate transcripts in adults (McIntyre
et al, 2006). This study, however, analyzed transcripts from a wild type strain, and thus
the sex-differential expression is most likely due to the gene expression in the germline.
Using our data set, we wanted to determine if some alternative transcripts are sex-
differentially expressed in the somatic tissues at this stage. Twelve of the 25 genes
annotated as having alternative transcripts had array elements that detected different
splice forms of transcripts. Only two genes showed significant sex-differential
expression for multiple transcripts; in both instances the alternative transcripts had higher
expression in the same sex. Thus, no evidence for sex-specific splicing was detected,
although the sample size is very small.

The set of 291 genes showing somatic sex-differential expression at 24 hr APF is also
significantly over-represented with genes whose protein products function in
programmed cell death and endopeptidase inhibition (17 and nine, respectively, p<0.05).
The two terminal effectors of the sex determination hierarchy, DSX and FRU
M
, have
33

been shown to play a role in sex-specific cell death in the nervous system during
development to produce a sexually dimorphic central nervous system (Sanders and
Arbeitman, personal communication; Kimura et al, 2005). We show that genes involved
in cell death show high expression at 24 hr APF; further studies are warranted to
determine if these genes with somatic sex-differential expression are involved in
regulating sex-specific cell death, and if their expression levels are under control of the
sex determination hierarchy.

Many studies have shown a role for cuticular hydrocarbons acting as sex-pheremones in
adult Drosophila to affect sex-specific behaviors, including the male courtship ritual
(Chertemps et al, 2006; Chertemps et al, 2007; Ejima et al, 2007; Ferveur et al, 1997;
Grillet et al, 2006; Marcillac & Ferveur, 2004; Marcillac et al, 2005; Siwicki et al, 2005).
We identified nine genes from our set of 291 whose protein products contain the insect
cuticle domain – an insect-specific domain by which some cuticular proteins bind chitin
(Rebers & Willis, 2001). Cuticulin – the outer layer of an insect’s cuticle – starts to be
deposited in Drosophila wings shortly after this stage (34-39 hr APF; (Mitchell et al,
1983) and Cluster 2, with peak expression at 24 hr APF, is over-represented with genes
involved in wing morphogenesis. Sex-specific regulation of cuticular proteins,
particularly in the wing, may be important in establishing sex-specific morphologies and
behaviors associated with these cuticular structures, such as the male wing-song and
pheromone binding.

34

This set of 291 sex-biased genes also included Ecdysone receptor and 7 additional genes
that are regulated in response to the steroid hormone ecdysone, suggesting a possible
coordination of the ecdysone regulatory pathway and sex-biased development during
metamorphosis. This is intriguing as there is a large ecdysone pulse that peaks just after
24 hr APF; ecdysone is present in large, but decreasing levels for the remainder of
metamorphosis (Riddiford, 1993). Also, since sex-specific development of adult
structures is occurring at the same time as this ecdysone pulse, there may be co-
regulation between the ecdysone-regulatory hierarchy and the somatic sex-specific
development necessary for establishment of sex-specific morphologies or behaviors.
This possible co-regulation is explored in more detail in Chapter 4.

2.2.6 Gene expression halfway through metamorphosis (48 hr APF)
As the fly transitions from 24 to 48 hr APF, the imaginal discs that are progenitors of
adult tissues and structures are still undergoing morphogenesis, but are close to their final
adult form. We found a cluster of 141 genes that show very high expression levels only
at this stage (Figure 2.2, Cluster 3). Cell organization and biogenesis (21 genes) was an
over-represented functional category (p<0.05) for the 141 genes with high expression
levels at this stage, which suggests that although the rudimentary adult structures are
formed, there are still many changes taking place. At this stage, there are still high levels
of the steroid hormone ecdysone from the pulse that peaks at 24 hr APF (Riddiford,
1993), and we found three genes annotated to be downstream of steroid hormone
receptors in this set. Additionally, four of the 18 Drosophila genes containing the zona
pellucida (ZP) domain – a domain found in a subset of both invertebrate and vertebrate
35

extracellular proteins and thought to be involved in the polymerization of extracellular
proteins (Jovine et al, 2005) – show high expression levels at 48 hr APF. Two of these
four genes, dusky and miniature, are involved in cell organization and cuticle secretion in
the developing wing (Roch et al, 2003).

This set of 141 genes whose expression levels peak at 48 hr APF also contained 10 genes
from the Osiris gene family. This family is a group of 23 insect-specific, highly similar
genes, of which 20 are found in within 175kb on chromosome arm 3R in D. melanogaster
(Dorer et al, 2003). An examination of gene expression during wing morphogenesis
found that 10 Osiris genes show an increase in expression from 24 to 40 hr APF in the
pupal wing (Ren et al, 2005), seven of which are in Cluster 3. Cluster 3 shows elevated
expression levels only at 48 hr APF, suggesting tight regulation of ZP domain and the
Osiris gene family

When we looked for sex-biased gene expression at the mid-point of metamorphosis (48
hr APF), unlike the 0 and 24 hr time points, we identify many more female-biased genes
(35) than male-biased genes (6) (Table A4). The female-biased genes included seven
known to function in the mitochondria. As described below, a large number of
mitochondrial genes showed their peak in expression at 72 hr APF; there is thus earlier
activation of some mitochondrial genes in Drosophila females.

36

2.2.7 Gene expression during late morphogenesis
When the fly reaches the later stages of metamorphosis, many of the tissues and
structures developing in the pupae are close to their final adult form (reviewed in
Truman, 1990; Williams & Carroll, 1993). In Cluster 4 (Figure 2.2), which contains
genes showing low expression levels at 0 hr APF and had a dramatic rise to ultimately
peak at 72 hr APF, we found an over-represented group of mitochondrion-related genes
(153 genes out of the 762 genes in this set). This is consistent with what is known about
respiration during insect metamorphosis – respiration is at its lowest levels for most of
metamorphosis and then sharply increases prior to eclosion (Gilbert & Schneiderman,
1961). Genes involved in muscle development are also over-represented at this stage (12
genes; p<0.05). This is consistent with the known rapid increase in myfibrils in the flight
muscles of Drosophila from 60-76 hr APF (Iwamoto et al, 2007). Genes that encode
proteins containing the insect cuticle domain (28 genes) are also over-represented, as
expected since it has been shown that adult cuticle synthesis occurs between 53 and 90 hr
APF (Ashburner et al, 2005; Fristrom & Fristrom, 1993).

Similarly to 48 hr APF, the 72 hr APF sex-biased somatic set also contained more
female-biased genes (23) than male-biased genes (13) (Table A5). By 72 hr APF, the
structure of the nervous system largely resembles the adult tissue, and even contains the
same neurotransmitter release sites (Truman, 1990). We found that the female-biased
genes at the 72 hr APF time point include PAK-kinase, snap-25, and vegetable, all of
which have been shown to function in nervous system development or maintenance
(Albin & Davis, 2004; Prokopenko et al, 2000; Rao et al, 2001). In addition, the female-
37

biased set contained rutabaga, whose functions include enabling the adult female fly to
remember male wing song during the courtship ritual (Kyriacou & Hall, 1984) and also
memory of the olfactory system (Zars et al, 2000). rutabaga is an adenylate cyclase that
functions in the mushroom body in the G-protein signaling response for learning
(Margulies et al, 2005), and higher expression in females at 72 hr APF may help establish
the potential for adult female-specific memory. In addition, rutabaga mutants have been
shown to cause a loss of bristle number, suggesting a possible role for this gene during
development (Norga et al, 2003). The sex-differential production of genes involved in
the nervous system at 72 hr APF may be integral in establishing the neural circuitry
required for complex sex-specific adult behaviors.

2.2.8 Gene expression at the end of metamorphosis
By 96 hr APF, the pupa is within a few hours of eclosion resulting in emergence of the
adult fly (Ashburner et al, 2005). There are further changes occurring shortly after
eclosion and genes expressed at 96 hr APF may produce proteins whose main functions
are post-eclosion. We found a cluster that contained genes that showed a sharp rise in
expression levels at 96 hr APF, Cluster 5 (286 genes; Figure 2.2). This cluster contained
seven genes that are annotated as functioning in the response to light stimulus and in
visual perception, including six that are present in the rhabdomere. In addition, this
cluster contained six genes annotated as being a structural component of the cuticle due
to similarity with another cuticle protein. Four of these genes come from a group of
seven such genes on chromosome arm 3R, while the other two come from a group of four
38

such genes on chromosome arm 3L. The coordinated expression of these cuticle structure
genes suggests a concerted role, possibly in formation of the final adult cuticle.

When we identified genes with somatic sex-differential expression at this stage of
metamorphosis, we found two female-biased genes, much less than the 17 male-biased
genes in this set (Table A6). The 17 male-biased genes from the 96 hr APF set included
five whose protein products bind to DNA, including daughterless (da), a gene required in
the somatic tissue of both males and females for proper neural development (Caudy et al,
1988). Additionally, carmine, which encodes a product that functions in synaptic vesicle
coating, and His1, which encodes a protein that shares high sequence similarity with a
chromatin-remodeling protein, are male-biased at 96 hr APF. This suggests potential
sex-biased regulation of nervous system processes, as well as a means of sex-biased
expression of additional genes through sex-specific chromatin remodeling.

2.2.9 Sex-biased gene expression in germline tissue during metamorphosis
We next set out to identify genes expressed in the male and female germlines during the
pupal stages by comparing gene expression data from the wild type and tud progeny
microarray experiments. In doing so, we noticed that when using the same total RNA
amounts in the microarray experiments, the male germline – due to its high expression
levels (Parisi et al, 2004) – represented a large fraction of the wild type male RNA pool,
thus diluting the levels of somatic genes in this pool. This caused false positives to
appear in defining sex-biased gene expression, particularly for female-biased genes, an
39

observation that needs to be taken into consideration when analyzing previous sets
comparing male and female expression and when designing new experiments.

Use of the wild type and tud progeny expression data still allowed us to identify genes
expressed in the male and female germlines. There has been extensive research in
defining male and female germline-biased genes in Drosophila (Arbeitman et al, 2002;
Parisi et al, 2004; Parisi et al, 2003). However, aside from one previous study examining
gene expression levels in approximately 1/3 of the Drosophila genome (Arbeitman et al,
2002), the Drosophila genomic studies of germline gene expression has focused on adult
flies. We expect a gene that is sex-differentially expressed due to its presence in either
the male or female germline will show a significant difference in expression between
wild type males and females (two-way ANOVA, sex and time as independent factors,
q<0.05). For genes expressed in the male or female germline, they should show
significantly higher expression in wild type flies than in the tud progeny flies of the same
sex (two-way ANOVA, genotype and time as independent factors, q<0.05). We included
in our germline sets any gene that is significantly sex-biased in the wild type ANOVA
(q<0.05) but for which there is no expression data in the tud experiments to avoid false
negatives, as it is expected that the tud progeny data will lack germline expressed genes.
Thus defined, the male-biased germline set contained 1281 genes and the female-biased
set 391 genes (Tables A8 and A9).

The pupal germline sets are smaller than what was previously seen when dissected adult
gonads were examined; this study identified 1951 male-germline genes and 1113 female
40

biased genes (Parisi et al, 2003). As this set used dissected gonads it contains somatic
tissues, possibly contributing to the differences in the number of genes found to be
expressed in the germline tissues. We still use these sets for further comparison as Parisi
et al also analyzed gene expression of all predicted Drosophila genes. It should be noted
that a second set of genes expressed in the adult germlines has previously been identified
(Arbeitman et al, 2002) using 1/3 of the Drosophila genome and found 410 and 485 genes
with male- and female-germline expression, respectively. This study is of interest,
because it analyzed gene expression data from both pupae and adults to define germline
expression andsaw no peak of expression for genes expressed in the female germline
during metamorphosis. The Arbeitman et al (2002) study, however, found genes
expressed in the male germline had peak levels of expression in both pupae and adults.
In this study, where we have examined gene expression of all predicted Drosophila
genes, we found genes with increased expression in the female germline as compared to
female somatic tissues.

The male-biased pupal germline set we identified had significant overlap with the Parisi
et al (2003) male-biased germline set (817 of 1281 genes; p<0.05, hypergeometric test),
as did our female-biased pupal germline set with the Parisi et al adult female-biased
germline set (48 of 391, p<0.05). A high percentage of overlap among the male-biased
germline sets suggests that genes expressed in the male-germline are similar during
metamorphosis and the adult stages, as previously shown (Arbeitman et al, 2002). We
also found that the pupal female germline set contained many fewer genes than both the
pupal male germline set and the adult female germline set, suggesting that during
41

metamorphosis genes expressed in the female germline are not yet expressed at their peak
levels.

We were also interested in the classes of genes that are present in the male and female
germline tissues during metamorphosis. Towards this aim we searched our germline sets
using the program DAVID to find genes with functions that are significantly over-
represented (p<0.05). Of the 1281 genes in the male germline set, 98 genes are known to
function in the mitochondria. Mitochondria play important roles in spermatid
development, where mitochondria fuse to form two large mitochondria that then interact
to form a large mitochondrial derivative (Fuller, 1993). This mitochondrial derivative is
associated with the sperm’s flagellum and is required for sperm mobilization (Fuller,
1993). Production of mitochondrial specific proteins in the male germline is essential for
proper male gamete development. The male-germline set also includes 30 genes that
function in spermatogenesis, 64 whose protein products are in the cytoskeleton, and 12
that function in the proteosome. As has been seen with the adult male germline (Parisi et
al, 2003), genes expressed in the pupal male germline are underrepresented on the X
chromosome (p<0.01, hypergeometric test), and interestingly are over-represented on the
right arm of the third chromosome (p<0.01, hypergeometric test).

The female germline set of 391 genes was over-represented with 7 genes whose protein
products functioning in oocyte axis determination and 12 that function in mRNA splicing
(p<0.05). Interestingly, one of the mRNA splicing genes in the female germline set is
tra. Although Sxl is expressed in female germ cells (Bopp et al, 1993), there is no known
42

role for expression of TRA in the female germline (Marsh & Wieschaus, 1978). In fact,
it is believed that TRA functions to feminize female germ cells through its function in the
soma (Evans and Cline, 2007; Steinmann-Zwicky, 1994; Waterbury et al, 2000), but
there is no evidence TRA functioning within the female germline. Transplating XX pole
cells – the precursors of germ cells – into XY animals causes these pole cells to develop
into spermatocytes (Steinmann-Zwicky, 1994); however, if tra is expressed in the XY
somatic tissues, the XX pole cells have the ability to develop into oocytes, demonstrating
the function of tra in the somatic tissues and not in the germline. Previous studies have
shown tra to have increased expression in the ovaries, which contain both germline and
somatic tissues (FlyAtlas; Chintapalli et al, 2007). High levels of tra expression in wild
type females as compared to germline minus females may represent a positive feedback
loop for increased expression of tra in the female somatic gonads.

The female-biased germline set was also over-represented with the most evolutionarily
conserved Drosophila genes (p<0.05, hypergeometric test); 77 genes from this set have
their coding regions overlapping the most conserved Drosophila sequential elements
(Siepel et al, 2005), indicating genes expressed in the female germline during
metamorphosis tend to be conserved compared all predicted Drosophila genes.

2.2.10 Global transcriptional profiles during metamorphosis
Our microarray study examining gene expression of all predicted Drosophila genes
allowed us to determine similar gene expression on a global scale at the five pupal time
points analyzed. We clustered our male and female tud progeny data by all array data,
43

rather than for individual genes (Figure 2.3a), as this compares global gene expression
profiles between the five time points of metamorphosis examined. We determined that at
all time points, the gene expression data from males and females were most highly
similar to one another; this is as expected, since there is very little sex-biased expression
in the somatic tissues during metamorphosis. Interestingly, we found a clear separation
in global transcriptional profiles between early and late metamorphosis: male and female
expression data from 0-48 hr APF are most similar to one another and separate out
distinctly from the expression data for the 72 and 96 hr APF time points.

This suggests a substantial change in the global expression profile of Drosophila in the
middle of metamorphosis. This is likely due to the fact that early on in metamorphosis,
imaginal discs are everting and forming their adult structures, larval tissue are being
degraded, and the nervous system is being repatterned (Buszczak & Segraves, 2000;
Postlethwait & Schneiderman, 1973; Truman, 1990). By late metamorphosis the adult
structures are close to their final form and it is then necessary to increase the expression
of genes required for proper adult function of somatic tissues (reviewed in Truman, 1990;
Williams & Carroll, 1993). Thus, the different morphological events occurring from 0-
48 hr APF and those occurring from 72-96 hr APF may lead to different global patterns
for how the Drosophila genome is deployed.

We additionally included the global transcriptional profiles of wild type males and
females with tud progeny in our clustering (Figure 2.3b). Interestingly, for the wild type
data which includes gene expression in the germline, we still find a clear distinction
44

Figure 2.3. Cluster of global expression profiles for all Drosophila transcripts across metamorphosis.
We used hierarchical clustering and correlation distance measure to group experiments based on their
global expression profile using every known and predicted Drosophila transcript. A) Global expression
profile clustering of male and female tud progeny. tud progeny females are colored in red and tud progeny
males are colored in blue. B) Global expression profile clustering of male and female tud progeny and
male and female wild type flies. Wild type females are colored in violet, wild type males in green, tud
progeny females in red and tud progeny males in blue.

between early and late metamorphosis. As expected, we also find the male-germline to
have high effect in the global transcriptional profiles, with wild type male expression data
always clustering separately from wild type females and from the male and female tud
45

progeny. This effect appears to be less dramatic at 0 hr APF, suggesting that at the start
of metamorphosis the male germline has yet to reach its peak of expression. We also find
evidence of the effect of the female germline during metamorphosis, as the wild type
females tend to cluster closely to, but separately from, the tud progeny. Wild type female
gene expression is less and less similar to the tud progeny expression levels as the fly
progresses through metamorphosis, suggesting an increased effect of female germline
gene expression at 72 and 96 hr APF. The previous study looking that looked at female
germline expression during metamorphosis (Arbeitman et al, 2002) found female-biased
genes at the adult stage and looked examined their expression levels in combined samples
of male and female pupae. The high expression of the male gemline may have masked
the expression of these genes in the female germline. This explains why the previous
study (Arbeitman et al, 2002) found no peak of expression of the female germline during
metamorphosis, as we have only found it to have low levels of expression at this stage of
development.

2.3 Conclusion
In this chapter, we presented our work exploring the expression patterns of all Drosophila
genes in the somatic and germline tissues of males and females throughout
metamorphosis, providing a valuable dataset for understanding temporal and sex-specific
regulation of gene expression during development. We found that co-regulated genes
whose expression peaks at a specific stage of metamorphosis are annotated as functioning
in processes occurring at that stage of development. We also found very low levels of
somatic sex-differential expression during metamorphosis, with most genes having sex-
46

differential expression only showing sex-biased transcript levels at one time point. We
identified sets of genes with high expression in the male and female germlines, showing
female germline expression during metamorphosis. Finally, analysis of this dataset
revealed a clear distinction between global gene expression profiles of early and late
metamorphosis.

We would like to further explore sex-differential expression during metamorphosis to
find genes that function in establishing specific morphological and behavioral differences
between the two sexes. In particular, using more sophisticated techniques to examine the
time-series data to identify smaller interacting sets of genes that are co-regulated will
hopefully give insight into specific complexes and networks underlying the establishment
of sex-specific adult phenotypes.

2.4 Materials and Methods
2.4.1 Drosophila Strains
The following D. melanogaster stocks were used: Canton-S Hogness (CS); tud
1
bw
1
sp
1
;
y
1
w
67c23
P{Ubi-GFP.D}ID-1 P{FRT(w
hs
)}101. All flies were kept at 25
◦
C in a cycle of
12 hours light/12 hours dark.

2.4.2 Fly collections
All time course experiments were conducted with three biological replicates. We used
Canton-S Hogness (CS) and germline-minus flies (progeny of XX tud
1
bw
1
sp
1
and XY
y
1
w
67c23
P{Ubi-GFP.D}ID-1 P{FRT(w
hs
)}101). We collected male and female samples
47

of both genotypes at the following times APF: 0, 24, 48, 72, and 96 hours APF. Samples
collected were compared to a common reference of CS flies from across the pupal stages.

2.4.3 RNA and cDNA preparation and microarray hybridization
All flies were collected between Zeitgeber time (Nilsson et al) 1 and ZT 4 as white pre-
pupae, aged to the appropriate time point at 25
◦
C, and then snap frozen in liquid nitrogen.
RNA was isolated from 30 whole pupal by homogenization and extraction using TRIzol
®

(Invitrogen, Carlsbad, CA). Phases were separated using 0.2mL per 1mL TRIzol
®
and
centrifugation at 12000g for 15 min at 4
◦
C, and RNA was precipitated using 0.5mL
isopropanol per 1mL TRIzol
®
and centrifugation at 12000g for 10 min at 4
◦
C.
Precipitated RNA was washed with 1mL 75% ethanol, allowed to dry, and redissolved in
20µL DEPC-treated H
2
O.

We directly labeled our cDNAs with 1µL Cy5 or Cy3 during reverse transcription with
2µL Superscript II (Invitrogen, Carlsbad, CA) and 30µg of total RNA. The reverse
transcription reaction was run for two hours at 42
◦
C, and then stopped using 10M NaOH
and .5M EDTA for 20 minutes at 65
◦
C. After adding 1M HEPES and sodium acetate
(pH5.2) to buffer the solution, cDNA samples were purified with Qiagen Gel-Purification
kit (Valencia, CA).

For the following, values in parenthathes are final molarity or final concentration.
Cleaned cDNA samples were then dried and resuspended in formamide (25 μM), 3 M
NaCl, and 0.3 M sodium citrate buffer (SSC, 3.3×), SDS (1.1%), Denhardts (5.56×), and
48

Polyadenylic acid potassium salt (8.88 μM; Sigma-Aldrich, St. Louis, MO). Samples
were boiled for 2 minutes and then placed onto microarray slides. Microarrays were kept
at 42
◦
C for 14-18 hours, then washed in a solution of 1.5% SDS, 1X SSC for 5 minutes, a
solution of 0.20X SSC for 5 minutes, and two solutions of 0.05X SSC for 10 minutes
each.

2.4.4 Microarrays production and analysis
Each array consisted of 15,158 70-mers representing the full predicted set of transcribed
regions of the D. melanogaster genome, including 14,456 known and predicted open
reading frames in the D. melanogaster genome, and an additional 702 general control
spots. The oligomer set was designed by the International Drosophila Array Consortium
(INDAC; http://www.indac.net/) from release 4.1 of the D. melanogaster genome using a
custom implementation of OligoArray2 (Rouillard et al, 2003). The oligonucleotides
were designed with sizes ranging between 65–69 nucleotides, a minimal Tm window,
bias towards the 3 ′-ends of transcripts, and minimal sequence similarity to other genes
(Cherbas et al, 2006). The oligonucleotides were synthesized by Illumina (San Diego,
CA) and the sequences can be downloaded from Flymine:
http://www.flymine.org/release-5.0/aspect.do?name=INDAC. Additionally, these
microarrays contained control spots for Sxl, tra, the female-specific splice form of dsx
(dsx
F
), the male-specific splice form of dsx (dsx
M
), the fru transcript that is sex-
specifically spliced to produce FRU
M
(fru P1), and three of the fru DNA-binding
domains (fru
A
, fru
B
, and fru
C
). Sequences for control spots can be found in Table A10.
All microarrays were printed in the laboratory of Dr. Eric Johnson at the University of
49

Oregon (Eugene, OR) using slides coated with aldehyde chemistry and were
postprocessed using the Nunc SuperChip Aldehyde protocol (Thermo Fisher Scientific,
Waltham, MA).

All arrays were scanned using the GenePix 4100A scanner and GenePix Pro 5.0 software
from Axon Instruments (Molecular Diagnostics, Sunnyvale, CA). Visual inspection of
the microarray images filtered out florescence most likely not due to labeled cDNA
binding. Spots were only considered for further analysis if at least one channel (Cy3 or
Cy5) had greater than 75% of the pixels with intensity values one standard deviation
above background levels. All normalization and analyses were done using the limma
package of BioConductor in the statistical program R (Gentleman et al, 2004; Smyth,
2004; Smyth, 2005; Smyth & Speed, 2003). Global-loess normalization was used for all
arrays, and significant genes were found using a t-test and John Storey’s q-value
application for R (Storey, 2003).

Two-way ANOVA analyses on the wild type and tudor time course arrays used the
logarithm of the ratio data to find genes that varied significantly sex and time or with
genotype and time.

2.4.5 Clustering of microarray expression data
Clusters were generated using the program Cluster (Eisen et al, 1998). For a gene to be
clustered using the tudor expression data it must have had expression values for at least
three of the five time points for both females and males. The logarithms of the ratio
50

values for each sex were then median-centered for each gene and clustered using
hierarchical clustering. Clustering parameters used with average linkage and correlation
as the distance measure. The cluster files outputted from Cluster were then imaged and
analyzed using Java TreeView (Saldanha, 2004). We kept as co-regulated clusters any
group of gene for which the correlation was at least 0.80 and contained at least 15
members.

The graphical images in Figure 2.1 were generated by averaging the logarithm of the
ratio data for all member genes of the cluster at each time point. This was done for tud
progeny males and females separately as well as for wild type males and females.

2.4.6 Analysis of over-represented features
Significant over-representation of functional annotations were generated with the
program DAVID (Dennis et al, 2003). We used unique GenBank accession number
identifiers for the gene list and the whole set of unique GenBank accession numbers for
all possible transcripts in the full array set (13,614 genes total). We additionally wrote
our own program to analyze over-representation of chromosomal location, conserved
genes, protein interactions, and genetic interactions. Significance was declared using the
binomial approximation of the hypergeometric test on the list of unique identifiers, with
all possible unique identifiers in the full array set as background. For analyzing the over-
representation of conserved genes, we used genes whose coding regions overlap with the
2500 most conserved elements, as generated by PhastCons (Siepel et al, 2005). The 2500
most evolutionarily conserved elements represent highly conserved Drosophila sequences
51

ranging from hundreds to thousands of base pairs in length. In their study, they used
multiple alignments of the genomic sequences of four insect species to determine
conservation.

To analyze chromosomal location we looked both at each chromosomal arm individually,
as well as overlapping 250kb regions along each chromosome. To analyze significant
numbers of pairwise genetic and physical interactions in our gene sets, we used the
genetic interaction list downloaded from Flybase (http://flybase.bio.indiana.edu/) and the
physical interaction list parsed by Wang and Sun (personal communication). We
analyzed interactions of genes in our sets with all genes annotated as having interactions
in the Drosophila genome. Significant over-representation was then determined using a
hypergeometric test comparing the number of genes in our set annotated as interacting
with a particular gene to the total number of genes in the Drosophila genome that interact
with the particular gene.

2.4.7 Correlation among microarray replicates
Correlation values were calculated for each experiment using Pearson’s correlation on the
logarithm of the ratio values for every oligonucleotide on the microarray. For each
experiment, we conducted pairwise correlation comparisons among the three microarray
replicates. See Table A7 for values.
52

Chapter 3
Identification of genes expressed downstream of doublesex at the 48 hr
pupal stage of D. melanogaster

3.1 Introduction
In Chapter 2, we analyzed how the D. melanogaster genome is deployed throughout
metamorphosis in both males and females. We next chose to focus our efforts on a single
time point during metamorphosis to understand in greater detail how sex-differential
expression is regulated and how this differential expression might be involved in
establishing adult sex-specific morphology and the potential for sex-specific behaviors.
We sought to identify genes that are expressed downstream of two of the major effectors
of the sex-determination hierarchy, doublesex (dsx) and fruitless (fru). Sex-biased
expression of genes important for aspects of somatic sex-differential morphology and
sex-specific behaviors are thought to be patterned just prior to or during metamorphosis
(Arbeitman et al, 2004; Arthur et al, 1998; Chapman & Wolfner, 1988; DiBenedetto et al,
1987), and identification of genes regulated by DSX and FRU
M
gives insight into how
these traits are patterned. We focused on 48 hours after puparium formation (APF),
because this stage has been shown to have high expression levels of both DSX and FRU
M

(Lee et al, 2000; Lee et al, 2002; Sanders and Arbeitman, personal communication). In
addition, according to our microarray analysis examining gene expression at multiple
time points during metamorphosis described in Chapter 2, dsx transcripts have high
53

expression between 24 and 48 hr APF, while fru transcripts have high expression levels
between 48 and 72 hr APF.

This chapter focuses on identifying genes with somatic sex-differential expression
regulated by the activity of DSX. Currently there are only two known direct targets of
DSX, Yolk protein 1 (Yp1) and Yolk protein 2 (Yp2). DSX’s role in regulating the
expression of the Yp1 and Yp2 is only characterized for adults, since these genes are not
expressed until the adult stage (Kambysellis, 1977). In addition, two other microarray-
based approaches have identified downstream targets of DSX in adults (Arbeitman et al,
2004; Goldman & Arbeitman, 2007). To date, however, there has been no thorough
examination of genes that are downstream of DSX during metamorphosis. It is of
interest to determine downstream targets of DSX in pupae, as they may be responsible for
affecting sex-differential development. The experiments presented in this chapter
allowed us to identify genes regulated by DSX at 48 hr APF. From these results we
proposed a novel model for how the sex-specific isoforms of DSX regulate their
downstream targets.

3.2 Results and Discussion
3.2.1 Sex-differentially expressed genes in somatic tissues of 48-hour APF pupae
We completed a microarray study to assay expression differences in all predicted
Drosophila genes at 48 hr APF and directly compared the mRNA levelsof two genotpyes
on the microarrays; i.e. the mRNA from the two experimental genotypes were each
independently converted to cDNA and labeled with a different florophore and then
54

hybridized to the same microarray (Table 3.1). For this analysis and all subsequent
analyses with multiple testing in this chapter, we converted our lists of p-values to q-
values. The q-value (Storey, 2003) is an estimate of the false discovery rate, or the
proportion of genes declared to be significant that are actually false positives.

Our objective was to identify a high-confidence set of somatic sex-differentially
expressed genes. We looked for genes that have sex-biased expression when comparing
both wild type males to females and when comparing males and females with genetically
ablated germlines (tud progeny). We first compared gene expression between males and
females of two wild type strains, Canton S (CS) and Berlin. The use of two strains gave
us a higher number of replicates to analyze gene expression. In addition, previous
research has shown that Drosophila strains have strain-specific expression patterns (Jin et
al, 2001; Ranz et al, 2003), and use of two strains allows us to identify genes that may be
strain specific, and which are thus less important to establishment of sex-specific
phenotypes. To find genes that show sex-differential expression in somatic tissues we
compared gene expression between male and female tud progeny and performed a one-
tailed t-test on the resulting expression data, assuming that the direction of change should
be the same in both wild type and tud progeny. This resulted in a set of 420 genes (320
female-biased and 100 male-biased) which we hereafter refer to as our somatic sex-
biased set (Table B1). This is substantially more than we identified in the previous
chapter for this time point (41). This discrepancy may be do to the increased number of
replicates in this chapter (four replicates versus three replicates in Chapter 2) and the
55

decreased error by directly comparing male and female gene expression on the same
arrays.

As in Chapter 2, we aimed to determine if genes showing similar expression patterns are
involved in similar functions or if they are annotated with similar features, including
chromosomal location, sequence location, physical protein interactions, and genetic

Table 3.1. Genotypes used to find sex-hierarchy regulated gene expression.
Microarray experimental design

Experiment Rationale Number of Genes
Wild type male and female
whole pupae
Find sex-biased expression
differences in whole pupae
7972
Wild type male and female
CNS
Find sex-biased expression
differences in CNS
105*
Germline minus male and
female (tud progeny)
Find sex-biased expression
differences in somatic tissue
421
tra pseudomales and wild
type females
Find sex-biased expression
differences regulated by TRA
95
dsx
D
pseudomales and
wild type females
Find sex-biased expression
differences regulated by DSX
M

and/or DSX
F

66
dsx null and wild type
males and females
Find genes regulated by DSX
M
in
males and by DSX
F
in females
57**

* Set was not used to define subsequent gene sets
** These genes were differentially expressed in both XX dsx null and XY dsx null experiments

interactions. We examined the set of 421 somatic sex-biased genes identified from the 48
hr APF array experiments using DAVID, as described in 2.4.6. The somatic sex-biased
set at 48 hr APF is over-represented with female-biased genes (18/320, p<0.05)
functioning in the mitochondria. Female-biased genes at 48 hr APF were also found to
be over-represented with mitochondrial proteins in our study examining sex-differential
expression at multiple time points during metamorphosis (see Chapter 2). The 320 genes
56

female-biased in the somatic tissues at 48 hr APF contained 19 genes annotated with a
function in chitin binding. The protein products of these genes are therefore likely to be
present in the cuticle since chitin is a major component of the fly’s exoskeleton. This is
similar to what was seen when analyzing sex-differential expression during multiple time
points of metamorphosis (see Chapter 2), where we found at 24 hr APF sex-differentially
expressed genes are over-represented with genes whose protein products are components
of the cuticle. The importance of expressing cuticular genes in a sex-biased manner
during the middle of metamorphosis is likely to be involved in the development of sex-
specific cuticular structure.

The 100 genes showing higher expression in the somatic tissues of male pupae at 48 hr
APF included seven genes whose protein products are known to contain the zona
pellucida (ZP) domain, a domain first found in proteins expressed in the mammalian ova
for binding of sperm (Bork & Sander, 1992). When looking at our gene expression
analysis for multiple time points during metamorphosis in Chapter 2, we found that genes
encoding proteins with this domain have dramatically increased expression at 48 hr APF
in both sexes. In addition, some of these genes encoding ZP proteins have been
identified as playing a role in wing morphogenesis (Roch et al, 2003), which is occurring
at this period of development (Fristrom et al, 1993; Mitchell et al, 1983). ZP domain
containing proteins are usually transmembrane proteins and are thought to act in
polymerizing external proteins (reviewed in Jovine et al, 2005), and the Drosophila ZP
proteins have been shown to be present in epithelial tissue (Ja źwi ńska and Affolter 2004).
Because seven of the ZP proteins are male-biased in the somatic tissues at 48 hr APF,
57

these proteins may have a role in male-specific development, specifically in epithelial
tissue and potentially in the wing.

This set of 100 somatic male-biased genes at 48 hr APF also included two genes that
function in dopamine biosynthesis, courtship behaviors, circadian rhythms, and pigment
formation (ebony and Dopa decarboxylase), along with a third neuronal and circadian
gene, vrille. Dopamine is known to function in male flies in their ability to learn not to
court immature males (Neckameyer, 1998) and also the precursor to a cuticular
hydrocarbon involved in cross-linking of the adult cuticle (reviewed in Andersen, 1979).
Increased production of dopamine in male pupae during metamorphosis may be
necessary for the role of dopamine in setting up male-specific cuticle after eclosion.
Also, the production of dopamine in male CNS may to help to establish the potential for
male-specific learning, including the courtship response to immature males (Gailey et al,
1982).

3.2.2 Comparison of somatic sex-differential expression in pupae and adult
Drosophila
As we identified a high-confidence set of somatic sex-differentially expressed genes in
pupae, we were interested in how our set compared to somatic sex-biased sets previously
defined in adults (Arbeitman et al, 2002; Parisi et al, 2004; Parisi et al, 2003). The two
Parisi et al reports defined two somatic sex-biased sets: one generated using Drosophila
from which the gonads were removed and the other generated using the progeny of tud
mutants. We defined male- and female-biased somatic sets for (Arbeitman et al, 2004)
58

for this comparison, similar to how our somatic sex-biased set was defined: we required a
gene to be sex-differentially expressed in the same direction for both 0-24 hr adults and
0-24 hr tud progeny microarray experiments (q<0.15; Table B2). The full Parisi et al
experimental set has 11658 genes that are in common with genes present in our arrays:
131 in the female-biased gonad-removed set, 142 in the male-biased gonad-removed set,
52 in the female-biased tud progeny set, and 100 in male-biased tud progeny set. The
smaller Arbeitman et al. set has 3817 genes in common with our array: 164 in the female-
biased set and 123 in the male-biased set.

We first looked for overlap between the pupal sex-biased somatic sets and adult sex-
biased somatic sets in order to find genes that show sex-biased expression during both
metamorphosis and adult life. Interestingly, we saw very little overlap between the sets
of genes sex-differentially expressed at 48 hr APF and the sets of genes sex-differentially
expressed in the adult. Of the 100 male-biased genes in our 48 hr APF set, none were
present in either of the Parisi et al male-biased sets and only four were present in the
Arbeitman et al male-biased set. For our female-biased genes at 48 hr APF we saw
similar results: three genes from our set were present in both Parisi et al adult female-
biased sets – with no other genes showing overlap – and four genes were found in the
Arbeitman et al female-biased set. There is a clear distinction between sex-differentially
expressed somatic genes at different times during development, something we also saw
when looking at multiple time points during metamorphosis (see Chapter 2). This is
expected between pupae and adult. In pupae, sex-biased genes are responsible for
establishing sex-specific morphology and the potential for sex-specific behaviors,
59

whereas in the adult stage sex-biased genes are responsible for phyisiology and
behaviors. Differences in the results of the pupal and adult experiments could also be due
to the types of probes used – our arrays were spotted with 70mer-oligos, the Arbeitman et
al arrays with ESTs, and the Parisi et al arrays with 150-600bp PCR products – leading to
potential differences in sensitivity. However, we don’t expect these technical issues to
bias the results substantially.

Previous research has examined additional characteristics of adult male- and female-
biased somatic sets. Parisi et al (2003) found that although male-biased genes have
higher expression than female-biased genes on average, male and female somatic genes
have similar expression levels (somatic genes from adults with gonads removed). In a
later study where somatic genes were defined by tud progeny, male-biased somatic genes
had significantly higher expression levels than female-biased somatic genes (Parisi et al,
2004). However, our pupae female-biased somatic genes showed significantly higher
expression levels than our set of male-biased somatic genes (2.48 and 1.85 average fold-
change, respectively; t-test of means, p<0.001). It has also been shown that adult male-
biased somatic genes are underrepresented on the X-chromosome (Parisi et al, 2003).
We did not find the 100 genes identifies as being male-biased in the pupal somatic tissue
to be biased towards any chromosome but rather saw an equal distribution across all
chromosomes (hypergeometric test, p<0.001). For female-biased somatic genes, (Parisi
et al, 2003) did not find any significant under- or over-representation on any of the
chromosomes; however, our pupae female-biased somatic set was over-represented with
genes located on the X chromosome (hypergeometric test, p<0.001). Overall, we found
60

substantial differences between pupal sex-biased genes and adult sex-biased genes,
including the genes that are sex-differentially expressed, the chromosomal location, and
the average expression values. This is not surprising, as even across metamorphosis we
have found that genes with sex-differential expression are sex-biased at specific time
points (see Chapter 2).

3.2.3 Gene expression differences in the central nervous system of male and female
pupae
During metamorphosis, aspects of the central nervous system (CNS) develop differently
in males and females (Kimura et al, 2005; Sanders and Arbeitman, personal
communication; Taylor and Truman, 1992). FRU has been shown to establish a sex-
specific number of neurons and projection patterns in a specific region of the brain
(Kimura et al, 2007) and DSX has been shown control sex-speicifc neurogenesis in the
abdominal ganglion (Taylor and Truman, 1992), as well as DSX controlling sex-specific
cell death of another set of neurons in the ventral nerve cord (Sanders and Arbeitman,
personal communication). Thus, these two terminal transcription factors influence sex-
specific patterning of the CNS, which may underlie sex-specific behavior. We sought to
identify genes with sex-differential expression in the CNS, as they may play a role in the
establishment of sex-specific neural circuitry, potentially due to regulation by the sex-
determination hierarchy. To find genes with sex-differential expression in the CNS
during metamorphosis, we compared the expression profiles of dissected CNS tissue
from male and female pupae at 48 hr APF. We found 104 sex-differentially expressed
genes that differ in expression between male and female CNS tissue (q-value<0.15, Table
61

B3). Interestingly, 97 of these 104 genes were more highly expressed in the male CNS
than in the female CNS, and only seven genes with higher expression in the female CNS.

We analyzed the functional annotations of the 97 male-biased genes expressed in the
CNS using DAVID and found ten of these genes to be expressed in the mitochondria,
again suggesting the important role of mitochondria in sex-differential development (see
Chapter 2). Mitochondria are known to play important roles in fly neurons: mitochondria
are required at synapses for proper utilization of a reserve pool of vesicles (Verstreken et
al, 2005) and are involved Drosophila models of human neurodegenerative diseases
(reviewed in Bossy-Wetzel et al, 2003; Dodson & Guo, 2007). In addition, in the CNS
during metamorphosis, but not in adults, electron-dense mitochondria (EDMIT) are seen
(Singh & Singh, 1999); EDMITs are thought to be dying mitochondria and this suggests
a possible turnover of larval and pupal mitochondria (Singh & Singh, 1999).
Understanding how mitochondria might play a role in sex-specific neural development is
worth further consideration, since increased energy production in the CNS of one sex
may lead to sex-specific neurogeneration.

The protein products of four genes (Male-specific RNA 84Dc [Mst84Dc], CG4959,
CG17376, and CG17377) from this male-biased CNS set have been shown to have a
physical interaction with DSX in yeast-2 hybrid screens, three of which also have an
interaction with DISCO (data compiled by Wang and Sun, personal communication).
DISCO is involved in a fly’s visual system and has been shown to function to help
determine circadian and eclosion rhythm (Dushay et al, 1989). All four of the genes
62

whose protein products physically interact with DSX have no known function with the
exception of Mst84Dc, which is annotated in GO as functioning in electron transport due
to sequence homology with other electron transport genes. Two of the remaining genes
(CG17376 and CG17377) are located adjacently in the genome (500bp apart), perhaps
indicating co-regulated expression.

We found some genes that were thought to be testes-specific, including Mst84Dc, don
juan, and don juan like – show high expression in both larval and adult testes (Hempel et
al, 2006; Kuhn et al, 1991; Santel et al, 1997) – are more highly expressed in the pupal
male CNS than in the female CNS. We do not think the presence of these genes in our
dissected CNS set is caused by contamination from germline tissues; as noted in Chapter
2, gene expression in the male germline is at high levels at this stage, and thus if the
dissected CNS were contaminated we would expect to see a much greater number of
germline genes found to be sex-differentially expressed in the CNS. One possibility is
that these genes have been previously missed as being expressed outside of the testes,
because of the high expression in the male germline and the relatively small size of the
Drosophila CNS, as gene expression in the CNS will not have a large contribution to the
overall mRNA pool when analyzing whole flies. This suggestion is supported by
examining at the expression profiles of these genes in FlyAtlas (Chintapalli et al, 2007),
where each has very high expression in the adult testes but also have high expression in
the larval fat body. However, expression in the larval fat body is not declared to be
significantly enriched, as expression levels were compared to expression levels in whole
body males and females that contain the highly expressed male germline. A recent
63

microarray study using a whole-genome EST library found 61 genes which had high
expression in both the larval fat body and the adult testes (Jiang et al, 2005), further
suggesting expression of “testes-specific” genes outside of the male germline. A possible
function in the brain of the testes-specific proteins would not be unprecedented. In mice,
the gene TB-RBP performs translational gene regulation during spermatogenesis, and
was thought to be testes-specific (Kwon & Hecht, 1991; Kwon & Hecht, 1993). Recently
TB-RBP, however, has also been shown to regulate translation of gene products in mouse
brains (Han et al, 1995), an interesting observation that warrants future consideration for
Drosophila male-biased CNS genes.

As mentioned, we only found seven genes with higher expression in the female CNS as
compared to the male CNS. This set of seven female-biased CNS genes included both
Sxl and the female-specific transcript of dsx. The expression of the female-specific
transcript of dsx in female CNS is further confirmed with three DSX
F
control spots
showing female-biased expression. Apart from the genes encoding DSX
F
and SXL, the
remaining five female-specific CNS genes are not annotated. Further analyses into their
functions could reveal novel female-biased genes important for specifying female
behavior.

3.2.4 Comparison of sex-differential expression in all somatic tissues to that in the
CNS
We compared our results from the CNS tissue and tud progeny to find genes significantly
sex-differentially expressed in multiple tissues. We found only 12 genes that are sex-
64

biased in both experiments; one of these is Sxl, suggesting that its transcript is abundant
in female whole animal tissue and female CNS tissue. This finding is interesting; it has
previously been thought that the Sxl transcript is expressed in similar levels in males and
females. This suggests that mRNA stability may play an important role in the regulation
of Sxl, potentially through the mRNA surveillance system, which degrades mRNA whose
translation would produce a truncated protein (reviewed in Hentze & Kulozik, 1999).
This set of 12 genes also includes roX1 and roX2, both of which are non-protein coding
genes important in male dosage compensation in somatic tissues (Amrein & Axel, 1997;
Franke & Baker, 1999), providing a validation of the microarray experiments.

With the few key exceptions mentioned above, the paucity of genes that are found to be
differentially expressed in whole animal tud experiments and CNS tissue suggests that
the majority of genes that are differentially-expressed in male and female CNS tissue
may have gone undetected in our whole body tud experiments. One likely explanation is
that genes that are tissue-specific may have such low overall expression values in whole
flies that they do not appear to be expressed, or the sex-differential expression is masked.
Alternatively, these genes may be expressed in all tissues, but only have sex-differential
expression in a particular tissue. Looking for sex-differential expression in specific
tissues will be an important tool for understanding the genetic regulation that underlies
sex-specific morphologies and behaviors.

65

3.2.5 Genes differentially expressed as a consequence of the sex hierarchy
We next identified genes that are downstream of TRA, a critical gene in the somatic sex-
determination hierarchy. We compared gene expression in chromosomally XX flies
mutant for tra (hereafter called tra pseudomales) to wild type female flies. Given our
previous comparisons of wild type males and females, and tud progeny males and
females, we could predict the direction of change in our tra comparisons and thus
performed one-tailed t-tests. To find a refined set of genes that are downstream of TRA,
we assumed female-biased genes in the somatic set that are downstream of TRA will also
be more highly expressed in wild type females than tra pseudomales, and vice versa for
male-biased genes, due to the absence of TRA in both wild type males and tra
pseudomales. We identified 95 genes (72 female-biased and 23 male-biased) that are
significantly differentially expressed between wild type females and males, between tud
progeny females and males, and between XX tra pseudomales and wild type females (q
<0.15 for each test, Table B1). As a validation of this set, Sxl, tra, roX1, and roX2 are all
sex-differentially expressed in the somatic tissues, but only tra is found in the set refined
by the tra mutant comparisons.

We found only a small number of the somatic sex-differentially expressed genes are
regulated by TRA activity (23%). However, of the 326 genes that are not regulated by
TRA, a large portion (169) may be false negatives as they are significant or close to
significant (q<0.30) in microarray experiments identifying DSX regulation (see 3.2.6)
and genes expressed downstream of DSX should also be expressed downstream of TRA.
Another 19 have a q-value close to the cutoff for significance in the tra microarray
66

expression data (q<0.30). Removing these 187 genes still leaves a large portion of genes
(139) that are sex-differentially expressed independently of TRA. A significant number
of these genes (45; p<0.05, hypergeometric test) are located on the X chromosome,
including Sxl, roX1, and roX2. It is possible that alternative methods exist for generating
large numbers of sex-differentially expressed genes. This could particularly be true for
genes located on the X chromosome, as males and females have a different number of X
chromosomes. Local differences in the effect of the male dosage compensation pathway
for genes on the X chromosome isone method for potentially having sex-differential
expression that is not downstream of TRA activity.

3.2.6 DSX regulates a wide range of genes at the 48 hour APF stage
dsx is one of the major effectors of the sex-determination hierarchy and encodes both
male- and female-specific transcription factors (DSX
F
and DSX
M
). Here, we sought to
identify genes regulated by DSX in both males and females. We compared gene
expression in flies that are transheterozygous for a dsx
D
allele (Duncan & Kaufman,
1975) – dsx
D
only produces the male-specific isoform (DSX
M
) – and a dsx null deletion
allele (dsx
M+R15
) to wild type females. These chromosomally XX dsx
D
/dsx
M+R15
flies are
somatically male at the level of DSX and, with the exception of body size, look like
phenotypic males. We used a one-tailed t-test and assumed that genes more highly
expressed in the XX dsx
D
/dsx
M+R15
flies compared to wild type females are also more
highly expressed in wild type males than wild type females. Genes in this set had to be
differentially expressed in the 48 hr APF wild type, tud, and tra microarray comparisons.
We identified 66 genes in total (referred to as our DSX-regulated set), 46 genes more
67

highly expressed in wild type females and 20 genes more highly expressed in the XX
dsx
D
/dsx
M+R15
pseudomales (Table 3.2, Table B1).

This experiment allowed us to identify genes that may be sex-differentially expressed
downstream of TRA but which are regulated independently of DSX in the somatic sex-
determination hierarchy. An additional gene encoding a transcription factor

Table 3.2. Genes downstream of the sex-specifically spliced transcription factor DSX and their
biological functions.
Nervous system Chitin/Cuticle
Tropomyosin 1 Gasp
brother of iHog LDLa domain containing chitin binding protein 1
C901 CG12009
CG14304
Transcription CG31878
abdominal A dusky-like
cactus miniature
Limpet CG15020
vrille
Signalling
Proteolysis windbeutel
omega CG11438
CG6337 CG14030
CG9850
CG11771 Unknown
CG4386 dpr13
HDC15381
Ion Binding Msr-110
Aconitase yellow-h
Peroxidase CG1561
Sarcoplasmic calcium-binding protein
1 CG5506
CG30437 CG6739
CG11458
Metabolism CG12164
lethal (1) G0334 CG13062
lethal (2) k05713 CG13616
polypeptide GalNAc transferase 2 CG13931
Sialic acid phosphate synthase CG14534
CG4484 CG16820
ade5 CG16884
CG31637 CG16885
CG17032
Other CG30101
SRY interacting protein 1 CG31781
flightin CG32512
CG1702 CG1172
CG11267 CG10249
Eb1 CG13059
Ecdysone-inducible gene E1 CG13078
CG13728
CG15589
68

(dissatisfaction; dsf) has been identified as being expressed in a tra-dependent, but dsx-
independent manner, and has been shown to be important for aspects of sex-specific
behaviors. Genes sex-differentially expressed downstream of TRA, but not DSX could
be regulated by FRU
M
, DSF, or by an additional unknown sex-specific factor. Aside
from the tra gene itself, 26 genes were in the gene set defined as regulated downstream of
tra but were not differentially expressed between dsx
D
and wild type females, even
though 25 of these genes have data from three of the four dsx
D
comparisons. If we
remove the genes that are close to the significance level (q<0.30; 6 genes) and the gene
with expression data in only two dsx
D
comparisons, 19 genes remain that are downstream
of TRA, but not DSX. Interestingly, none of these genes are significantly differentially
expressed in experiments looking at FRU
M
regulation at this stage (see Chapter 4). This
suggests either an alternate branch of the sex-hierarchy downstream of TRA, possibly
through the actions of DSF, or an increase in the number of genes on which TRA acts to
sex-specifically splice their pre-mRNAs, leading to differential expression through
changes in mRNA stability.

Although the 19 genes that are downstream of TRA but independent of DSX form a very
interesting set, this study focused on identifying genes regulated by DSX. We, thus,
further characterized the 66 genes found in the DSX-regulated set to identify what classes
of genes DSX regulates at 48 hr APF. Nine of the genes regulated by DSX and with
higher expression in females were found to function in carbohydrate metabolism and four
of these nine specifically function in chitin metabolism and binding. This suggests that
69

DSX regulates sex-specific cuticular development. The DSX-regulated set was also
over-represented with three male-biased genes containing a ZP domain, including
miniature and dusky-like. miniature is highly expressed in the developing wing and is
required for apical membrane reorganization during wing development (Roch et al,
2003). It should be noted that the other ZP protein involved in wing morphogenesis,
dusky, is a close paralog of dusky-like and would be a male-biased gene in our DSX-
regulated set, except for being slightly above our significance cutoff in the tud progeny
experiments (q=0.159). A female-biased gene downstream of DSX, CG14534, has also
been shown to be expressed in the developing wing (Butler et al, 2003; Jacobsen et al,
2006). It is known that dsx null males perform courtship in a quantitatively subnormal
manner, and they cannot perform one aspect of the wing song (Villella & Hall, 1996).
DSX may play a role in the developing wing to help establish the sex-specific behaviors
associated with this tissue in the adult, possibly through the action of the genes listed
above.

3.2.7 Characterization of the modes of DSX regulation
DSX
F
and DSX
M
have the same DNA binding domain, but are able to specify different
morphologies in males and females, presumably through differences in the regulation of
their downstream target genes. The only known direct targets of DSX are Yolk protein 1
(Yp1) and Yolk protein 2 (Yp2), in which DSX
F
drives expression in the female fat body
(Burtis et al, 1991). In females, DSX
F
binds directly to the common promoter region of
Yp1 and Yp2 and activates transcription, whereas in males, DSX
M
binds and represses
Yp1 and Yp2 transcription (Coschigano & Wensink, 1993b). Thus, DSX
F
and DSX
M

70

have opposite effects on Yp1 and Yp2 transcription. Previous studies have looked more
closely at the DSX modes of regulation for seven genes in the adult, which were
identified as being regulated as a consequence of DSX using a microarray-based
approach (Arbeitman et al, 2004). Of these genes, two appear to be activated by DSX
M

and repressed by DSX
F
, consistent with the idea that DSX
F
and DSX
M
have opposite
functions. Five additional genes are regulated downstream of DSX in only one sex. A
second study identified the fat body gene takeout (to) as being activated by DSX
M
in
males (in concert with FRU
M
) and repressed by DSX
F
in females (Dauwalder et al,
2002). Thus, the regulation of to follows a mode similar to Yp1 and Yp2, except this gene
is activated, and not repressed, in males. The set of genes known to be downstream of
DSX is small and examination of the modes of DSX regulation during metamorphosis
could help explain how sex-biased expression is specified by the sex-determination
hierarchy.

Because DSX modes of regulation have only been examined on a fairly small scale, we
sought to examine at DSX
M
and DSX
F
regulation of their downstream targets using a
microarray approach examining gene expression of all predicted Drosophila genes. We
compared gene expression in chromosomally XX and XY dsx flies to wild type females
and males, respectively, to determine the effects of having no active DSX on individual
gene expression at 48 hr APF (Table 2.1). We were specifically interested in analyzing
the effects of having no DSX on the 66 genes we identified as being downstream of DSX
(Table B4). Five genes either do not have enough data for statistical analyses, or their q-
value is close to the significance cutoff (q<0.30) for at least one of the dsx null-wild type
71

comparisons. These five genes were therefore not used for further comparisons. Of the
61 genes that comprise the DSX-regulated set, 57 show significant differential expression
(q<0.15) between XX dsx and wild type females and between XY dsx and wild type
males, demonstrating regulation by both DSX
F
and DSX
M
.

The remaining four genes only showed significantly different expression in one of the dsx
null experiments: three genes show differential expression only between XY dsx and wild
type males and one gene showed differential expression only between XX dsx and wild
type females. These four genes may possibly be regulated by one isoform of DSX, a
method of DSX regulation that was previously posited for some genes with sex-
differential expression in the adult (Arbeitman et al, 2004). One of these genes, cactus, is
female-biased and appears to be repressed by DSX
M
activity, while the other two,
CG13059 and CG14030, are male-biased and appear to be activated by DSX
M
activity.
Finally, one gene, lethal (1) G0334, is female-biased and appears to be up-regulated by
DSX
F
, but is unaffected by DSX
M
, as it is only significantly differentially expressed in
the comparisons of wild type females and XX dsx flies.

We observe the canonical DSX mode of regulation for only one gene in our set,
abdominal-A (abdA), which appears to be activated by DSX
F
in females and repressed by
DSX
M
in males. abdA is a well-characterized HOX gene required for many aspects of
proper Drosophila development; this includes conferring the correct segment identity
onto the abdominal segments A2-A8 – one of which (A8) develops into the female
genitalia (Foronda et al, 2006; Sanchez-Herrero et al, 1985; Tiong et al, 1985) – as well
72

as regulation of target genes in the CNS (Jijakli & Ghysen, 1992; Prokop et al, 1998).
Along with Abdominal B and Ultrabithorax, abdA makes up the bithorax complex of
genes, a gene cluster that controls segmental identity in the posterior two-thirds of the fly
(reviewed in Maeda & Karch, 2006). In some instances these genes affect the same
downstream targets and even bind to the same regulatory regions and play both similar
and antagonistic roles in the regulation of these downstream genes (reviewed in Hughes
& Kaufman, 2002; Maeda & Karch, 2006).

Here, we found abdA mRNA expression to be regulated as a consequence of DSX
activity at 48 hr APF. Previous research looking at a stage of pupal development that is
close in time to our analyses (40-45 hr APF) found similar ABD-A expression in the
abdominal segments of males, females, and dsx nulls using immunohistochemistry (Kopp
et al, 2000). These authors suggest that ABD-A and DSX, along with ABD-B, act to
regulate the expression level of a downstream target, bric-a-brac, but that DSX does not
regulate ABD-A levels. However, as they only used immnuohistochemical assays, they
may have missed DSX regulation of abd-A transcript levels that we found in our
experiments, since they could not accurately quantitate protein levels. In addition, (Kopp
et al, 2000) only looked at expression in the abdomen, and, thus, the differences we see in
DSX regulation of abd-A levels could be due to expression in other tissues, as abdA has
been shown to be expressed and functional outside of the abdomen (Bello et al, 2003;
Miguel-Aliaga & Thor, 2004; Sprecher et al, 2004). ABD-A also induces apoptosis in
abdominal neuroblasts at the end of the third instar stage (Bello et al, 2003). It would be
73

interesting to test if DSX activity regulates the expression of abdA in other neuroblasts at
48 hr APF, potentially inducing apoptosis of these neuroblasts only in females.

3.2.8 Mode of regulation for the transcription factor, DSX
We propose here a novel mode of regulation that can explain the expression results
observed for the majority of genes downstream of DSX during metamorphosis. As
demonstrated in Figure 3.1, 41 of the 44 genes in our female-biased DSX-regulated set
were more highly expressed in wild type females than XX dsx flies and were more highly
expressed in wild type males than XY dsx flies. This suggests that these genes are
activated by DSX
F
in females and by DSX
M
in males. However, because these genes
were female-biased, they were more highly expressed in flies that produce the DSX
F

protein (females) than in flies that produce the DSX
M
protein (males). Thus, DSX
F
and
DSX
M
both act to increase the expression levels of these genes, but DSX
F
must act as the
more potent activator. Similarly, when we examined the male-biased genes in our DSX-
regulated set, 15 of the 17 were more highly expressed in XY dsx flies than in wild type
males and were more highly expressed in XX dsx flies than in wild type females. These
genes are repressed as a consequence of both DSX
F
and DSX
M
activity. Since both
DSX
F
and DSX
M
act to decrease the expression levels of these genes, there must be
mechanisms to activate these genes independently of DSX. DSX
F
activity in females
serves as a more potent repressor than DSX
M
activity in males, leading to these genes
being more highly expressed when DSX
M
is present. Our data therefore suggests that for
the majority of genes downstream of DSX, DSX
F
and DSX
M
act to affect their expression
levels in a similar manner.
74

Figure 3.1. Proposed model for DSX modes of regulation. A) Previously suggested model generated
from the expression pattern of Yolk protein 1 & Yolk protein 2 (Yp1 and Yp2). In females, DSX
F
binds to
the upstream region of Yp1 and activates its expression, while in males DSX
M
binds to the same region and
represses Yp1 expression. This was seen for one gene in our DSX-regulated set, abdominal-A (abd-A). B)
Proposed model for female-biased genes suggests that in females DSX
F
acts as an activator and in males
DSX
M
acts as an activator, but that DSX
F
is the more potent activator. This was seen for 43 genes in our
data set. C) Proposed model for male-biased genes suggests that there is a basal expression level
independent of DSX. In females when DSX
F
is expressed, it acts on these genes as a repressor, as is the
case with DSX
M
in males. However, DSX
F
is the more potent repressor, leading to these genes having
male-biased expression. This was seen for 18 genes in our DSX-regulated set.

75

This proposed model was validated in microarray experiments in which the male and
female isoforms of DSX were over-expressed in order to find direct targets of DSX (see
Chapter 5). Since these experiments aimed to find direct targets, we did not expect all
genes in our DSX-regulated set to have significant differential expression. Of the 61
genes of the DSX-regulated set for which we explored DSX modes of regulation, 22 did
not show significant differential expression in the experiments when we either over-
expressed DSX
F
in females or DSX
M
in males. Of the remaining 39 genes, 13 were
male-biased; these genes showed decreased expression when DSX was over-expressed,
either in one or both of the DSX isoform over-expression experiments. Similarly, of the
remaining 27 female biased genes, 26 showed increased expression levels when DSX
was over-expressed, either in one or both of the DSX isoform over-expression
experiments. Only one female-biased gene (CG4484) showed decreased expression
levels when DSX
F
was over expressed in females compared to control females, opposite
of the predicted effect from our model.

We were interested in how the previously reported downstream targets of DSX activity
were regulated in our pupal dataset. We note that the Yolk protein 1 and Yolk protein 2
are expressed only at the adult stage and are therefore not included in our defined DSX-
regulated set. This appears to be similarly true for to as it has no expression data in either
the time course or the 48 hr APF microarrays. In addition, the seven genes found to be
regulated by DSX activity in (Arbeitman et al, 2004) were also previously shown not to
be expressed in pupae. This was verified in our own experiments looking at gene
expression throughout metamorphosis (see Chapter 2). It is possible that this proposed
76

mode of regulation is specific for metamorphosis. However, a recent study using the
same microarray comparisons presented here confirmed that DSX
F
and DSX
M
regulate
their downstream targets in the same manner in adult heads (Goldman & Arbeitman,
2007).

3.3 Conclusions
In this chapter, we presented our detailed analysis of somatic sex-differential expression
at one particular time point during the middle of metamorphosis (48 hr APF). We
additionally identified a set of genes with sex-differential expression in the central
nervous system (CNS) at a time while the CNS is being repatterned for its adult form and
function. A focus of this dissertation is to identify genes that are sex-differentially
expressed during metamorphosis, specifically those downstream of the sex-determination
hierarchy. In this chapter, we also identified a gene set regulated at 48 hr APF by one
main downstream effector of the sex-hierarchy, doublesex (dsx), and further expression
analysis enabled us to present a novel mode of regulation for DSX. In this model, DSX
F

and DSX
M
regulate the majority of their downstream targets in the same manner, i.e.,
both activate or both repress transcription, with one of the two isoforms being a more
potent regulator. This is in contrast to the traditional model of DSX regulation in which
DSX
F
and DSX
M
have antagonistic modes of regulation.

As a further analysis, we would like to examine in more detail the role of specific DSX-
regulated genes in controlling male and female modes of development. We also want to
determine how the specific modes of DSX regulation function in individual genes and
77

how the new model of regulation is chosen over the traditional model. Finally, it would
be valuable to further characterize the interaction between DSX and ABD-A, since both
are important regulatory proteins in development and DSX regulates the production of
abd-A transcript differently than it does for the majority of its downstream targets.

3.4 Materials and Methods
3.4.1 Drosophila Strains
The following D. melanogaster stocks were used: Canton-S Hogness (CS); Berlin; w
118
,
Berlin; st
1
βTub85D
D
ss
1
e
s
/TM2 & dsx
D
Sb
1
e
1
/TM2; B
S
Y, In(3R)dsx
D+R3
e/TM6b
(Tb,Hu, e); B
S
Y, dsx
M+R15
/TM6b{Tb,Hu, e); y
1
w
67c23
P{Ubi-GFP.D}ID-1; w
a
, tra
1
/TM2;
f
1
/Dp(1;Y)B
S
; Df(3L)st-j7, Ki
1
/TM6B, Tb
1
; tud
1
bw
1
sp
1
; y
1
w
67c23
P{Ubi-GFP.D}ID-1
P{FRT(w
hs
)}101). All flies were kept at 25
◦
C in a cycle of 12 hours light/12 hours dark.

3.4.2 Drosophila collections for microarray analysis
All samples were collected between ZT 1 and ZT 4 as white pre-pupae and aged to 48 hr
APF. Wild type collections were XX vs. XY CS and XX vs. XY Berlin. Germline-minus
flies were the progeny of tud
1
bw
1
sp
1
(XX) and y
1
w
67c23
P{Ubi-GFP.D}ID-1
P{FRT(w
hs
)}101 (XY) (called tud progeny) and these microarray experiments compared
XY and XX tud progeny. Chromosomally XX tra pseudomales were of the genotype XX
y
1
w
67c23
P{Ubi-GFP.D}ID-1/ w
a
, tra
1
/ Df (3L) st-j7 and were compared to CS females.
For the chromosomally XX dsx pseudomales, we compared XX y
1
w
67c23
P{Ubi-
GFP.D}ID-1/w, dsx
D
Sb
1
e
1
/ dsx
M+R15
to CS females. For dsx-null analysis, XX y
1
w
67c23

78

P{Ubi-GFP.D}ID-1, In(3R)dsx
D+R3
e / dsx
M+R15
flies were compared to CS females and
XY In(3R)dsx
D+R3
e / dsx
M+R15
flies were compared to CS males.

3.4.3 RNA and cDNA preparation and microarray hybridization
All flies were collected between ZT 1 and ZT 4 as white pre-pupae and then aged to 48 hr
APF at 25
◦
C at which point they were snap frozen with liquid nitrogen. RNA was
isolated and cDNA prepared according to 2.4.3, except in the case of the dissected CNS
experiments. For the dissected CNS experiments, male and female CNSs were dissected
from 48 hr APF pupae and immediately placed into TRIzol
®
(Invitrogen, Carlsbad, CA).
We used ~10-20 brains and ventral nerve cords for RNA isolation (described in 2.4.3).
We then amplified and labeled 1 μg of the resulting RNA pool using the Amino Allyl
MessageAmp II aRNA Amplification Kit (Ambion, Austin, TX), and cleaned the
resulting cRNA using the Qiagen Gel-Purification kit (Valencia, CA). Each experiment
was conducted with four biological replicates.

All microarrays were hybridized according to 2.4.3.

3.4.4 Microarrays production and analysis
All microarrays were generated and analyzed according to 2.4.4. Additionally, only the
dsx null and dissected CNS microarray experiments contained control spots for Sxl, tra,
the female-specific splice form of dsx (dsx
F
), the male-specific splice form of dsx (dsx
M
),
the fru transcript that is sex-specifically spliced to produce FRU
M
(fru P1), and three of
the fru DNA-binding domains (fru
A
, fru
B
, and fru
C
) (see Table A10 for sequences).
79

3.4.5 Analysis of over-represented features
All analyses of over-represented features were conducted according to 2.4.6.
80

Chapter 4
Identification of genes expressed downstream of fruitless at the 48 hr
pupal stage of D. melanogaster

4.1 Introduction
In the previous two chapters, we emphasized the identification of genes with sex-
differential expression during metamorphosis that may function to establish adult sex-
specific morphology and behavior. In Chapter 3 we discussed our experiments
identifying genes that are expressed downstream of DSX at 48 hours after puparium
formation (APF), a time during metamorphosis when both DSX and FRU
M
are expressed
at high levels (Sanders and Arbeitman, personal communication; Lee et al, 2000; Lee et
al, 2002). In addition, we showed this stage to be a point when both dsx and fru
transcripts show high levels of expression (see Chapter 2). In this chapter we discuss our
experiments identifying downstream targets of one of the other major branches of the
sex-determination hierarchy, fruitless (fru).

As presented in 1.1.2, the fru locus is a complex locus, with transcription being driven
from four different promoters. It is the transcript produced from the P1 promoter (fru P1)
that is sex-specifically spliced, leading to the production of male-specific FRU isoforms
(FRU
M
). Drosophila males perform an elaborate, innate courtship ritual that is under the
genetic control of the sex-determination hierarchy (reviewed in Greenspan & Ferveur,
81

2000). The sex-specific transcription factor FRU
M
specifies the neural substrates in the
central nervous system (CNS) that establish the potential for this male courtship behavior
(reviewed in (Manoli et al, 2006). During metamorphosis, the CNS is being patterned for
its adult form and function (see 1.1.1; reviewd in Truman, 1990) and, as FRU
M
is highly
expressed at this stage, it is likely that FRU
M
is functioning to establish the potential for
the male-courtship ritual. Although a few downstream targets of FRU
M
have been
characterized (Dauwalder et al, 2002; Drapeau et al, 2003; Lee et al, 2006), there is still
limited information about genes that function downstream of FRU
M
and how they might
act to establish the potential for and maintain the male courtship ritual.

In this chapter we discuss our research performing microarray experiments examining
gene expression levels of all predicted Drosophila genes using fru P1 mutants in both
whole-body pupae and dissected CNS. We identified a subset of genes downstream of
fru that are also regulated by the ecdysone-regulatory pathway. From these results, we
next examined if the transcription factor at the top of the ecdysone-regulatory pathway
(the Ecdysone Receptor:Ultraspiricle complex [EcR:USP]) acts in the fru P1 neuronal
circuitry to help establish the phenotypically wild type male-courtship ritual. We found
that EcR acts in an isoform dependent manner in the fru P1 neuronal circuitry to regulate
specific components of the male-courtship ritual.

82

4.2 Results and Discussion
4.2.1 Expression differences between fru P1 mutant males and wild type males
The goal of this study was to identify genes that function downstream of FRU
M
during
metamorphosis and that may therefore be directly involved in helping to establish a
sexually dimorphic central nervous system. We conducted a microarray-based study in
which the probes represented all known and predicted Drosophila genes (for details see
2.4.4). We first sought to identify genes that show differential expression between fru P1
mutant males and wild type males by examining the expression profiles of pupae at 48 hr
APF. fru P1 is the fru transcript derived from the P1 promoter, which is then sex-
specifically spliced to produced FRU
M
in males. Thus, fru P1 mutants do not produce
functional FRU
M
, but still produce non-sex-specific FRU isoforms. Our microarray
study was conducted at 48 hr APF, as previous results have shown this to be time of high
expression of both FRU
M
protein and fru P1 mRNA expression levels during
metamorphosis (Lee et al, 2000; Chapter 2), and so examining this stage may identify the
largest number of genes that respond to FRU
M
expression. We compared two distinct fru
P1 transheterozygotes to two strains of wild type males (fru
440/P14
vs. Canton S and w;
fru
w12/ChaM5
vs. w; Berlin, Table 4.1). Different hypomorphic combinations of fru P1
alleles have different phenotypic effects (Gailey & Hall, 1989; Ito et al, 1996; Ryner et al,
1996; Villella et al, 1997) and thus may affect different subsets of genes. In addition,
Drosophila strains show strain-specific expression patterns (Jin et al, 2001; Ranz et al,
2003). Combining the data from two experimental fru P1 mutants and two wild type
strains controls for genes that show strain-specific expression or genes that are regulated
only by distinct hypomorphic combinations of fru P1 alleles. We report 236 genes
83

Table 4.1. Microarray design to identify genes downstream of FRU
M
at 48 hr APF.
Microarray experimental design

Experiment Rationale Number of Genes
fru P1 null male and
wild type male

Identify genes downstream of
FRU
M
in male pupae
236
fru P1 null male and
wild type male CNS
Identify genes downstream of
FRU
M
in male CNS
94

showing a significant difference between wild type males and fru P1 mutant males in the
combined gene expression data from these microarray experiments, with 122 having
higher expression in wild type males, and 114 with higher expression in fru P1 mutant
males (q-value<0.15). We refer to this set as our FRU
M
-regulated set (Table C1).

We were interested in analyzing our FRU
M
-regulated set in order to determine what types
of genes are downstream of FRU
M
at this time in development. To characterize the 236
genes in our FRU
M
-regulated set, we looked for the presence of over-represented features
by using both the program DAVID (Database for Annotation, Visualization and
Integrated Discovery; (Dennis et al, 2003) and our own analysis program (for details see
2.4.6); features include functional annotation, chromosome location, sequence location,
genetic interactions, and physical protein interactiosn. Among the over-represented
functional annotations in the FRU
M
-regulated set, we identified 12 genes with
oxoreductase activity, including four Cytochrome P450s and five genes that also have
peroxidase activity. An additional five genes are known to function in glutathione-
transferase activity; these genes also function to protect against oxidative stress but are
84

additionally believed to have a role in insecticide resistance (reviewed in Tu & Akgul,
2005). One of these genes with gluatathione-transferase activity, microsomal glutathione
S-transferase-like, shows higher expression in fru P1 mutants as compared to males and
has previously been shown to have high expression in the larval fat body, but not in the
larval CNS (Toba & Aigaki, 2000). Thus, FRU
M
activity could be involved in regulating
gene expression in the fat body, a hypothesis which has previously been proposed for
adult flies (Goldman & Arbeitman, 2007).

The FRU
M
-regulated set is also significantly over-represented with three proteins
containing the Calycin domain: CG6783, Neural lazarillo, and the unannotated Karl.
The Calycin domain is found across various phyla and is defined by a common structural
pattern and not by sequence similarity (Flower 1995); it is thought this domain may be
involved in either ligand binding or protein-protein binding. Both Neural lazarillo and
CG6783 have been shown to be expressed in the developing nervous system and to be
circadian-regulated (Ceriani et al, 2002; Kearney et al, 2004; Sanchez et al, 2000). This
suggests a potential co-regulation of nervous system genes by FRU
M
and by a circadian
clock. Previous reports examining adults have also identified downstream targets of fru
P1 that are also regulated by the circadian clock (Dauwalder et al, 2002; Goldman &
Arbeitman, 2007), further suggesting coordination of the circadian clock and the sex-
determination hierarchy.

The FRU
M
-regulated set also contains two of the three Drosophila genes annotated with
the ninjurin domain (NijA and CG14394); the third appears to have no expression in
85

pupae at 48 hr APF. In mammals, ninjurin proteins are up-regulated in response to nerve
injury and are known to function in cell-adhesion (Araki & Milbrandt, 1996). A role for
NIJA in cell-adhesion in Drosophila has been shown (Zhang et al, 2006) and suggests
potential regulation of these genes by FRU
M
in developing neurons. In addition, seven
genes in the FRU
M
-regulated set are annotated as being insect cuticle proteins. These
genes may be regulated in the cuticle downstream of FRU
M
and may function in the
production and maintenance of the sex-specific pheromones which act as cuticular
hydrocarbons. There could also be additional roles for these genes in the central nervous
system. Two of the seven genes (Cuticular protein 51A [Cpr51A] and Ecdysone-
dependent gene 78E [Edg78E]) appear to be regulated in neurons as a consequence of the
transcription factor escargot (Hekmat-Scafe et al, 2005). FRU
M
may therefore directly
regulate Cpr51A and Edg78E expression levels in the CNS.

4.2.2 Further analysis of genes expressed downstream of FRU
M
at 48 hr APF
Other interesting FRU
M
-regulated genes can be observed when looking at the genes
individually. For instance, Odorant-binding protein 56a (Obp56a) shows significantly
higher expression in the somatic tissues of males than females (see Chapter 3) and also
appears to be positively regulated as a consequence of FRU
M
activity. This gene has also
previously been shown to be up-regulated in females in response to the presence of sperm
(McGraw et al, 2004) and to have higher expression in female lines that mate more
slowly (Mackay et al, 2005). One rational for Obp56a having higher expression in
males, but also having increased expression in mated females, could be that Obp56a
binds male pheromones and causes these individuals to reject male courtship. As it has
86

already been shown that FRU
M
is responsible for regulating other odorant binding
proteins (Keleman et al, 2007), Obp56a is a potential target for further study.

Another candidate gene for future study is astray (aay), which has been identified in
high-throughput screens as functioning in the peripheral nervous system, specifically in
the regulation of the number of sensory bristles (Nuzhdin et al, 1999; Prokopenko et al,
2000). In addition, aay is differentially expressed between male flies that are fast to
copulate and male flies that are slow to copulate (Mackay et al, 2005), suggesting a role
of aay in males in courtship. Since FRU
M
is expressed in neurons innervating the male
sensory bristles (Manoli et al, 2005), exploring a role for FRU
M
regulating aay in
determining the number of bristles could provide novel insight into the genetic regulation
of the male-courtship ritual.

When we examined the chromosomal location of genes in the FRU
M
-regulated set, we
found three groups of 5-6 genes located within 125kb of one another on three different
chromosomes. All three chromosomal regions were significantly over-represented with
genes from this FRU
M
-regulated set (hypergeometric test, p<0.05). It is possible that
FRU
M
binds to enhancers in these regions and may coordinately regulate the expression
of nearby genes. The first region is located on chromosome arm 3L starting at position
4250000 (coordinates from D. melanogaster genome release 4.1) and our set contained
six of the 47 predicted genes in this region. The second region, located on the X
chromosome starting at position 9625000, contains 20 genes, five of which are in our
FRU
M
-regulated set. The third region starts at position 14375000 on chromosome arm
87

3R, and 5 of the 18 genes were in our set. Many of the genes in each of these regions
have no annotated functions. From the genes which are annotated, no clear overall
function could be ascertained for any of the regions. It should also be noted that the third
gene region is located upstream of the fru locus itself and that both the fru
440
and fru
ChaM5

alleles are large deletions overlapping with this region (Anand et al, 2001; Gailey & Hall,
1989). Thus, these five genes’ expression changes could be due to the large deletions and
inversion used to generate the fru P1mutant flies, and not a true biological reason.
However, this only represents five genes from our set of 236 FRU
M
-regulated genes, and
only five of the 18 genes in the third region described above.

4.2.3 in situ analysis of a gene expressed downstream of FRU
M
at 48 hr APF
To confirm the differential expression of a gene from our FRU
M
microarray study, we
performed in situ hybridizations on wild type males and fru P1 mutant males 48 hr APF.
We were interested in possible overlapping functions of the terminal effectors of the sex-
determination hierarchy, DSX and FRU
M
. We chose to focus on CG8213, an unnamed
serine-type endopeptidase that shows higher expression in fru P1 null males as compared
to wild type males. Additionally CG8213 appears to be regulated by DSX, with higher
expression when DSX
M
is present (see Chapter 3). CG8213 is significant in every
microarray experiment used to define our DSX-regulated set presented in Chapter 3,
except for wild type male versus female, where it has a q-value of 0.166, just outside the
significance cutoff.

88

As predicted from our microarray experiments, CG8213 appears to have higher pupae
(Figure 4.1), which suggests that CG8213 is down-regulated as a consequence of FRU
M

activity. CG8213 is present in similar locations in fru P1 null males and wild type males,
with transcript levels appearing to be greater when looking at similar proximal sections
for the two genotypes. Endopeptidases have been shown to be crucial signaling
molecules for diverse cellular functions including cell death, immunity, mitochondrial
fusion, and cell fate determination (Kuranaga & Miura, 2007; Urban, 2006) We also find
expression of CG8213 in the developing wing (data not shown), which is consistent with
another microarray study that confirmed its presence by RT-PCR in this tissue (Ren et al,
2005). CG8213 may play a role in establishing neural connections required sex-specific
phenotypes associated with a sexually dimorphic wing though regulation by FRU
M
and
possibly DSX, although further studies are required to conclusively determine its
function.

4.2.4 Expression differences in the CNS between fru P1 mutant males and wild type
males
FRU
M
is expressed in the male CNS and is essential for the formation of male-specific
neural circuitry (reviewed in Manoli et al, 2006) and male-specific behaviors, including
courtship and aggression (reviewed in Greenspan & Ferveur, 2000; Vrontou et al, 2006).
Recently, it has been shown that the presence of FRU
M
in homologously positioned
neurons in a female CNS is sufficient to elicit male-specific courtship behavior (Manoli
et al, 2005; Stockinger et al, 2005). We were interested in discovering genes that are
genetically downstream of FRU
M
specifically in the CNS, since these genes may function
89

Figure 4.1. Transcript of a putative FRU
M
target, CG8213, appears to have higher expression in fru
P1 null males compared to wild type males. The spatial pattern of transcript abundance of a putative
FRU
M
target, CG8213 in the heads of 48 hr APF pupae by in situ hybridization. Transcript abundance in
wild type males is shown for three sections of the head in A, C, and E, with the sense control in wild type
males shown in G. Similarly, transcript abundance in fru P1 null males is shown for three sections of the
head in B, D, and F, with the sense control in fru P1 null males shown in H. Transcript levels appear
higher in fru P1 null males when comparing similar sections in wild type males (A-B, C-D, and E-F). No
staining is seen for the sense controls in G and H.

90

in the male-specific patterning and connectivity of the CNS at 48 hr APF, leading to the
establishment of the adult neural circuitry needed to perform male-specific courtship
behavior. To this end, we compared the expression profiles of mRNA from dissected
CNS tissue between wild type males and the same two fru P1 mutants described in 4.2.1
(Table 4.1).

We identified 94 genes with significant differential expression in these experiments
(q<0.15, Table C2): 23 were more highly expressed in wild type males and 71 were more
highly expressed in fru P1 mutants (referred to as the FRU
M
-regulated CNS set). Thus,
at 48 hr APF in the CNS, the expression levels of more genes are down-regulated by the
activity of FRU
M
than are up-regulated, whereas in the entire pupae, FRU
M
appears to be
up- and down-regulating gene expression for similar numbers of genes (122 and 114,
respectively). In order to further characterize the set of genes regulated by FRU
M
in the
CNS of developing pupae, we again utilized both our own program and the program
DAVID to find over-represented functional annotations. We found no Gene Ontology
(GO) functional groups to be over-represented in this set. In fact, 55 of the 94 genes in
this set have no assigned annotation or are annotated as unknown in GO.

The set of 94 genes downstream of FRU
M
in the CNS set contained many interesting
candidate genes, including four genes previously annotated as functioning in the CNS:
Resistant to dieldrin (Rdl), capability (capa), snapin, and CG10617, with both snapin and
CG10617 annotated as functioning in synaptic vesicles. Both Rdl and capa were also
identified as being regulated by FRU
M
in the adult CNS (Goldman & Arbeitman, 2007).
91

The CAPA peptide is cleaved to produce two similar neuropeptides (capa-1 and capa-2),
and a third neuropeptide with different cleavage sites (capa-3) (Kean et al, 2002). capa-1
and capa-2 regulate fluid production in the Malpighian (renal) tubules, but there are no
known roles for capa-3. capa-3, also called Drm-PK-1, is a pyrokinin and members of
this family have varied roles in insects, including sex-pheromone production (reviewed in
Altstein, 2004). We would like to determine if capa-3 plays a role in sex-pheromone
production in Drosophila through its regulation by FRU
M
in future studies.

4.2.5 Comparison of genes downstream of FRU
M
in the CNS with previously
reported CNS and FRU
M
-regulated gene sets
Fifteen of the 94 genes downstream of FRU
M
in the CNS were also present in our
previously reported wild type sex-biased CNS gene set at 48 hr APF (see Chapter 3.2.2).
Thirteen of the 15 show higher expression in wild type males than in wild type females
and also higher expression in fru P1mutants than in wild type males. This implies a
possible secondary male-biased activator of these genes that is independent of FRU
M
.
Also, of the 15 overlapping genes, 11 have gene expression data in FlyAtlas (Chintapalli
et al, 2007), 10 of which show up-regulation in the testes and possible up-regulation in
the larval fat body, as compared to other tissues. This suggests a possible connection
between regulation of the testes, larval fat body, and pupal CNS in males. The remaining
gene, CG8709, is expressed in the developing CNS during embryogenesis (Kearney et al,
2004); as it has already been shown to be expressed in both the germline and the nervous
system, it could be a good candidate to further explore FRU
M
-regulated expression in the
CNS.
92

We also compared the set of genes found to be downstream of FRU
M
in the CNS set with
those found to be regulated by FRU
M
in the whole pupae. Four genes were present in
both sets, including three that appear to be repressed by the activity of FRU
M
(CG9508,
CG12896, and CG13641), and one that is up-regulated downstream of FRU
M
(ATP-
dependent chromatin assembly factor large subunit, Atf1). Atf1 has been shown to be
expressed in the developing CNS midline cells during embryogenesis (Kearney et al,
2004) and it warrants further studies to explore if it plays a role in FRU
M
-regulated
neuron development during metamorphosis.

4.2.6 Ecdysone hierarchy genes are regulated by fru P1 during metamorphosis
When we examined the list of genes that were differentially expressed between wild type
males and fru P1 mutant males, we noticed that there were several genes annotated as
functioning in the ecdysone-regulatory pathway (Table 4.2). Ecdysone is a steroid
hormone that triggers molting and metamorphosis during the Drosophila life cycle.
Based on this observation, we compiled a list of 57 genes that were previously
characterized as functioning in the ecdysone-regulatory pathway (Table C3), and found
that these genes are significantly over-represented in both the FRU
M
-regulated whole
body and FRU
M
-regulated CNS gene sets (p<0.05, hypergeometric test). In addition,
genes from this list of 57 ecdysone regulated genes were over-represented (p<0.05,
hypergeometric test) among genes whose expression levels significantly change when
FRU isoforms are over-expressed (see Chapter 5). We therefore hypothesized that the
ecdysone regulatory hierarchy may function in concert with FRU
M
to establish the neural
circuitry that underlies male courtship behaviors.
93

Table 4.2. Genes downstream of FRU
M
at 48 hr APF that are regulated by ecdysone-regulatory
pathway.
Whole-body pupae microarray experiment
Gene Name Gene Symbol Genotype with
higher expression
Fold Change
Cytochrome P450-18a1 Cyp18a1 fru P1 male 1.82
Ecdysone-inducible gene L2 ImpL2 Wild type male 1.45
Ecdysone-dependent gene 78E Edg78E Wild type male 2.65
Ecdysone-induced gene 71Ej Eig71Ej Wild type male 2.56

Dissected CNS microarray experiment
Gene Name Gene Symbol Genotype with
higher expression
Fold Change
Inhibitor of apoptosis 2 Iap2 fru P1 male 1.80
Ecdysone-induced gene 71Ef Eig71Ef Wild type male 3.97
Ecdysone-induced gene 71Eg Eig71Eg Wild type male 3.15

The response to ecdysone in Drosophila is mediated through the ecdysone receptor, a
heterodimeric receptor containing the nuclear hormone receptors Ecdysone Receptor
(EcR) and Ultraspiracle (USP) (reviewed in Kozlova & Thummel, 2000). The ecdysone
receptor is a steroid responsive transcription factor that regulates gene expression. It has
been shown that the ecdysone-bound-receptor turns on expression of a set of genes called
“early genes” and represses expression of a set of genes called “late genes” (reviewed in
Ashburner et al, 1974; Thummel, 2002). Several of the early genes encode transcription
factors, which then turn on late gene expression and repress their own early gene
expression (reviewed in King-Jones & Thummel, 2005; Riddiford et al, 2000). Tissue
specific ecdysone regulatory hierarchies are thought to mediate the diverse responses to
the single steroid hormone (Talbot et al, 1993). Thus, a nervous system-specific
ecdysone regulatory hierarchy would trigger the remodeling of the larval nervous system
for adult functions in response to pulses of ecdysone that are present at the end of third
instar larval and pupal stages.

94

EcR encodes three isoforms: EcR-A, EcR-B1, and EcR-B2, which share both common
DNA- and hormone-binding domains, but differ in their amino-terminal regions (Talbot
et al, 1993). EcR-A and EcR-B1 have distinct temporal and spatial expression patterns
(Talbot et al, 1993; Truman et al, 1994). Additionally, the EcR-A and EcR-B1 isoforms
have been shown to be functionally distinct; they show differences in their transcription
factor activities and their abilities to regulate downstream target genes (Mouillet et al,
2001; Schubiger et al, 2003). Much less is known about the expression pattern of the
EcR-B2 protein, as there are no reagents available due to the small size of the B2-specific
region. It has previously been shown that among EcR-A and EcR-B1, EcR-B1 is the
predominant isoform expressed during the early stages of metamorphosis, while EcR-A is
the predominant isoform expressed by the middle of metamorphosis (Truman et al,
1994). Also, these two time points closely follow the two peaks of ecdysone release
during metamorphosis (reviewed in Riddiford, 1993). Binding of ecdysone activates the
EcR-USP heterodimer, which in turn up-regulates the transcription of EcR itself
(Varghese & Cohen, 2007). Thus, EcR is expressed and active at these two stages.

We hypothesized that the EcR:USP complex and FRU
M
may be acting in concert to
regulate the expression of a common set of downstream targets. We were interested in
determining if EcR acts in an isoform dependent manner in any potential co-regulation
with FRU
M
and so we looked for isoform specific phenotypes of EcR in the fru P1
circuit. It has been shown that EcR requires USP to function as a transcription factor
(reviewed in Kozlova & Thummel, 2000), and so we did not additionally look at USP.
Recent immunohistochemistry results have shown that FRU
M
and EcR co-localize during
95

metamorphosis (Figures 4.2 & 4.3, Sanders and Arbeitman, personal communication).
This co-localization is isoform dependent: EcR-B1 and FRU
M
co-localize in several cells
in each hemisphere of the developing brain at 0 hr APF. However, at 48 hr APF there is
no co-localization observed between EcR-B1 and FRU
M
. A different pattern of co-
localization with FRU
M
is seen for EcR-A, where at 0 hr APF limited overlap is observed
among EcR-A- and FRU
M
-expressing cells. At 48 hr APF, however, nearly all FRU
M
-
expressing cells express EcR-A. Given that EcR-B1 and EcR-A overlap with FRU
M
at
distinct times in development and in different patterns, if EcR plays a role in remodeling
the cells in the fru P1 circuit for adult functions, the isoforms likely have distinct roles.

4.2.7 EcR isoforms required in fru neural circuitry for male-courtship ritual
To determine if there are EcR isoform-specific roles in the fru P1 circuit, we utilized uas-
RNAi transgenic strains that specifically abrogate either EcR-B1 or EcR-A levels
(Roignant et al, 2003). We drove expression of the RNAi’s in fru P1-expressing cells
using a fru P1-GAL4 transgene, and assayed male courtship behaviors. Previous reports
have shown that the two RNAi transgenic strains (EcR-B1 and EcR-A) abrogate the
transcript levels of their respective isoforms to a similar extent when the daughterless-
GAL4 driver was utilized (Roignant et al, 2003); this suggests that the two RNAi
transgenes have similar efficacy. In our experiments, control males contain the fruP1-
GAL4 transgene in combination with a uas-GFP RNAi transgene to control for non-
specific RNAi effects. All transgenic strains were out-crossed to a common background
for five generations, to eliminate potential strain specific modifiers (see 4.4.6).

96

Figure 4.2. EcR isoforms A and B1 are co-expressed with FRU
M
in an isoform specific manner in the
central nervous system at 0 hr APF. All images show EcR isoform expressing cells in green and FRU
M

expressing cells in red. A) EcR-A co-localizes with a limited number of FRUM-expressing cells at 0 hr
APF. B). EcR-B1 overlaps with more FRUM-expressing cells than does EcR-A at this stage, although both
may overlap with the same cluster of FRUM-expressing cells. Note that EcR-B1 is more highly expressed
at this stage than EcR-A. Images adapted from Sanders and Arbeitman (personal communication).

Figure 4.3. EcRA shows high levels of co-expression with FRU
M
in the central nervous system at 48
hr APF. All images show EcRA expressing cells in green and FRU
M
expressing cells in red. A) In the
brain, nearly every FRU
M
expressing cell is also expressing the EcRA isoform. B) In the ventral nerve
cord, most FRU
M
expressing cells are also expressing the EcRA isoform. Notable exceptions are present in
the abdominal ganglion (white box). Images adapted from Sanders and Arbeitman (personal
communication).

97

Both fruP1-GAL4; uas-EcR-A RNAi males and fruP1-GAL4; uas-EcR-B1 RNAi males
showed control levels of courtship; they courted virgin females at similar levels as
control males and as the uas-EcR-A RNAi and uas-EcR-B1 RNAi single transgenes
(Figure 4.4). All genotypes containing a uas-RNAi transgene (uas-EcR-A RNAi, uas-
EcR-B1 RNAi, or uas-GFP RNAi) showed significantly lower levels of courtship than the
fruP1-GAL4 single transgene genotype. This suggests a non-specific RNAi effect on
decreasing the levels of courtship. When further examining fruP1-GAL4; EcR-B1 RNAi
courtship, we noticed an unexpected phenotype. Male courtship behavior is a sequence
dependent action series, where progression through late steps requires completion of
early steps. fruP1-GAL4; EcR-B1 RNAi males, however, perform aspects of the male-
courtship ritual while they are not following the female. This phenotype may be due to
an inability to discern location of sensory cues or may be due to a failure to successfully
terminate the courtship sequence and suggests that EcR-B1 may be required in portions
of the neural circuitry necessary for phenotypically normal progression through the male
courtship ritual.

Some hypomorphic combinations of fru P1 mutant alleles cause the mutant males to fail
in discriminating between males and females as appropriate mate choices, and will
therefore court other males. We next set to determine if EcR activity in the fru P1 circuit
is required for mate discrimination, by pairing fruP1-GAL4; uas-EcR RNAi males with
w, CS Heberlein males. fruP1-GAL4; uas-EcR-A RNAi males showed significantly
increased male-male courtship (CI of 0.36) as compared to fruP1-GAL4; uas-EcR-B1
RNAi males (CI of 0.12), control males (CI of 0.11), and fruP1-GAL4 and uas-EcR-A
98

Figure 4.4. Decreasing levels of EcR isoforms A or B1 in fru P1-expressing cells does not significantly
alter levels of male-courtship ritual Courtship indexes (CIs) of control and experimental males towards
w; Canton-S virgins. Males carrying the uas-EcRa RNAi or uas-EcRB1 RNAi transgenes do not show
significantly different CIs whether present alone or present along with a fru P1-GAL4 transgene. Males
carrying the uas-EcRa RNAi or uas-EcRB1 RNAi transgenes also do not show significantly different CIs as
compared to fru P1-GAL4; uas-GFP RNAi males. Males carrying the fru P1-GAL4 transgene alone show
significantly higher CIs (**, p<0.05) than all other genotypes, suggesting a general RNAi effect on the
male-courtship ritual.

RNAi single transgenes (CI of 0.13 and 0.06, respectively; Figure 4.5). This is similar to
previous studies using a temperature sensitive EcR allele, which contains a mutation in
the ligand binding domain and affects all three isoforms (Ganter et al, 2007). When male
flies were raised at the non-permissive temperature they displayed male-male courtship
behavior. We cannot rule out the possibility that fruP1-GAL4; uas-EcR-A RNAi males
can no longer discriminate between males and females. However, the previous study
with the temperature sensitive EcR allele have shown that in preference-assays, EcR-
deficient males prefer to court females over males, which suggests that their ability to
discriminate between males and females remains intact (Ganter et al, 2007).
99

Figure 4.5. Decreasing levels of EcRA isoform in fru P1-expressing cells causes increased levels of
male-male courtship. Courtship indexes (CIs) of control and experimental males towards w; Canton-S
males. fru P1-GAL4/uas-EcRa RNAi males show significantly higher CIs (**, p<0.05) towards w; Canton-
S males than every other genotype. Also, fru P1-GAL4/uas-EcRB1 RNAi males and fru P1-GAL4/+ males
show significantly higher CIs than uas-EcRa RNAi/+ males and uas-EcRB1 RNAi/+ males, suggesting a
slight increase in male-male courtship due to the fru P1-GAL4 transgene.

4.3 Conclusion
In this chapter we presented our experiments finding genes that are regulated downstream
of fru P1 during the middle of metamorphosis. This includes genes whose expression
levels are regulated in the whole body of the pupae as well as genes that are regulated by
fru P1 specifically in the central nervous system. fru is necessary for all aspects of an
innate, male-specific behavior – the male courtship ritual – the potential for which is
established during metamorphosis (Arthur et al, 1998). As such we found many potential
targets of fru that have been shown to function in the nervous system; we would like to
further characterize these downstream genes to determine what effect they might have in
the fru neuronal circuitry.

100

We also found that during metamorphosis, FRU
M
controls the expression levels of genes
that are also regulated by the ecdysone-regulatory hierarchy. It has been shown that
FRU
M
is co-localized to the same cells in the pupal nervous system as one of the top
components of the ecdysone-regulatory hierarchy in an isoform specific manner
(Ecdysone Receptor [EcR]; Sanders and Arbeitman, personal communication). In this
chapter we presented our results showing that decreased expression levels of EcR
isoforms in the fru circuitry caused isoform specific defects in the male courtship ritual.
From these studies, we posit co-regulation of downstream targets by both the ecdysone
regulatory pathway and the sex-determination hierarchy. We are interested in further
validating these results by examining the effects of decreasing EcR isoform levels using
another mechanism. In addition, we would like to determine if there are any genes that
are direct targets of both FRU
M
and the EcR:USP complex; the method for finding cis-
regulatory modules presented in Chapter 5 gives us a great tool for identifying these
potential direct targets

4.4 Materials and Methods
4.4.1 Drosophila Strains
The following D. melanogaster stocks were used: Canton-S Hogness (CS); w
118
, Berlin;
w, CS-Heberlein; T(3;het)fru
w12
(called fru
w12a
); Df(3R)fru
4-40
(called fru
4-40
); Df(3R)P14
(called fru
p14
); Df(3R)Cha
M5
(called fru
ChM5
); fruP1-GAL4; w
1118
; P{UAS-
Avic\GFP.dsRNA.R}142 (called uas-GFP RNAi); w
1118
, P{UAS-EcR.A.dsRNA}91/TM3,
P{ActGFP}JMR2, Ser
1
(called uas-EcR-A RNAi); w
1118
, P{UAS-EcR.B1.dsRNA}168
101

(called uas-EcR-B1 RNAi). Unless otherwise noted, all flies were kept at 25
◦
C in a cycle
of 12 hours light/12 hours dark.

4.4.2 fru P1 mutant collections
All flies were collected between ZT 1 and ZT 4 as white pre-pupae and then aged to 48 hr
APF at 25
◦
C. For the whole-body microarray experiments, we used two different fru P1
mutant combination in comparison with wild type males, both microarray expereiments
conducted with four biological replicates and a dye-swap design. XY fru
p14/4-40
flies were
compared to CS males and XY w; fru
w12a/ChM5
flies were compared to w; Berlin males.
For the dissected CNS experiments, we used three replicates of XY fru
p14/4-40
flies labeled
with Cy-3 vs. CS males labeled with Cy-5 and three replicates of XY w; fru
w12a/ChM5

labeled with Cy-5 vs. w; Berlin males labeled with Cy-3.

4.4.3 RNA and cDNA preparations and microarray hybridization
RNA and cDNA samples for all whole-body fruit fly productions were prepared
according to 2.4.3. All RNA and cDNA samples for the dissected CNS experiments were
prepared according to 3.4.3. All microarray hybridization was conducted according to
2.4.3.

4.4.4 Microarrays production and analysis
All microarrays were produced and analyzed according to 2.4.4. Only the dissected CNS
microarray experiments contained control spots for Sxl, tra, the female-specific splice
form of dsx (dsx
F
), the male-specific splice form of dsx (dsx
M
), the fru transcript that is
102

sex-specifically spliced to produce FRU
M
(fru P1), and three of the fru DNA-binding
domains (fru
A
, fru
B
, and fru
C
) (see Table A10 for sequences).

4.4.5 Analysis of over-represented features
All over-represented features were analyzed according to 2.6.4.

4.4.6 In situ hybridizations
Frozen section in situs were performed as described in (Goodwin et al, 2000), with the
exception that the tissue was not fixed with paraformaldehyde. Probes were synthesized
using the DIG RNA kit (Roche, Indianapolis, IN), and hydrolyzed to roughly 200 bp
fragments. Anti-digoxigenin FAB fragments coupled to alkaline phosphatase (Roche,
Indianapolis, IN) were visualized using BCIP/NBT developing solution. Genotypes used
were 1) XY; fru
440
/fru
P14
2) XX Canton S and 3) XY Canton S.

4.4.7 Courtship assays
All flies used for behavioral assays were raised at 29
◦
C. All genotypes were backcrossed
for five generations to w; Canton-S Heberlein to reduce any strain-specific biases on
courtship activity. Behavioral assays were performed by collecting 0-24 hour old males,
aging them separately for 4-7 days in a 12 hours light/ 12 hours dark cycle, then placing
them with either a 4-7 day old w; Canton-S Heberlein virgin female or male in a
courtship chamber at 29° C. All assays were performed between ZT5 and ZT9. Flies
were videotaped for 10 minutes and then their behavior was analyzed using the Noldus
software (Wageningen, Netherlands). Courtship indices (CIs) were calculated as the time
103

the fly spent courting divided by the total time of the assay. Whitney-Mann
(nonparametric) rank-sum tests were calculated to determine the significance of
differences and significant differences were declared as a p<0.05. With the exception of
the fly carrying the fru P1-GAL4 transgene, all flies were in the w; CS Heberlein
background.
104

Chapter 5
Computational prediction of cis-regulatory modules in Drosophila and
an application to the sex-determination hierarchy

5.1 Introduction
Drosophila is an valuable model organism for understanding and elucidating concepts of
molecular biology, including transcriptional regulatory pathways. These pathways, as
detailed in 1.1.3, involve one or multiple transcription factors that act to regulate the
expression of downstream targets, some of which may also be transcription factors.
Understanding how transcriptional regulation works is a major question in molecular
biology, as this allows researchers to determine how mRNA expression levels, and thus
protein levels, are controlled in the cell. Often multiple transcription factors bind to the
genomic region surrounding the gene that is regulated and interact with the
transcriptional machinery to control the expression level of the target gene (Davidson,
2001). This may include the intergenic regions both upstream and downstream of the
coding region, as well as intron sequences.

It is believed that multiple transcription factors binding sites (TFBSs) often are clustered
together in small genomic regions (Davidson, 2001). These clusters, called cis-regulatory
modules (CRMs), may consist of TFBSs for one or many transcription factors – termed
homotypic or heterotypic CRMs, respectively. There is a wealth of information for
Drosophila on experimentally verified CRMs (Halfon et al, 2007) as well as
105

experimentally verified TFBSs (Bergman et al, 2005). Previous research has focused on
predicting CRMs in Drosophila using various features, including the clustering of TFBSs
and the conservation of functional CRMs among the Drosophila clade (Rajewsky et al,
2002; Sinha et al, 2004). However, these methods used similar sets of well-defined set of
CRMs for genes expressed during the blastoderm stage of Drosophila (refered to as
blastoderm CRMs for the remainder of this chapter) to determine prediction accuracy.
Recent studies have suggested that these blastoderm CRMs, including those of
hunchback, hairy, and knirps, are a distinct subset of CRMs as they are more likely to
have clusters of homotypic TFBSs and are more likely to contain a large number of
distinct TFBSs (Li et al, 2007); therefore, the may not be the best set to test CRM
prediction methods. In addition, a recent initiative has led to the completion of
sequencing the genomes of 12 Drosophila species (Clark et al, 2007; see
http://rana.lbl.gov/drosophila/), allowing for conservation of predicted CRMs to be well
examined.

In the previous chapters we identified genes whose expression levels are regulated
downstream of DSX and FRU
M
during metamorphosis. We were next interested in
identifying genes that are direct targets of these two transcription factors; i.e., DSX or
FRU
M
bind to the regulatory regions of these genes to directly affect their transcription
rates. In this chapter, we present our development of a CRM searching method
specifically for Drosophila. We present evidence of our method having similar
prediction accuracy as previous methods with improvements in speed complexity. We
finally show how our method improves upon previous web-based CRM searching
106

methods developed for Drosophila. We also present a conservation score for predicted
CRMs to determine if predicted CRMs are present in orthologous species. In Appendix
E, we detail our web-based program for predicting CRMs based upon the method
described in this chapter (SUPRfly; Significant Upstream Regulation for the fly) and
present in detail additional components and features of SUPRfly.
Because a major focus of this dissertation is to determine which Drosophila genes are
expressed downstream of DSX and FRU, we next aimed to use our developed method to
find direct targets of DSX and FRU. In this chapter we additionally describe our
experiments using a microarray-based approach and flies that over-express isoforms of
DSX and FRU to find potential direct targets of these two transcription factors. We
identified potential CRMs of DSX and FRU using our searching method that may
respond to DSX or FRU activity.

5.2 Materials and Methods
5.2.1 Definition of Positive and Negative cis-regulatory modules
To define a set of known cis-regulatory modules with which to test our CRM-searching
algorithm, we used two recently developed hand-curated databases in a manner similar to
that of (Narang et al, 2006). The first database, REDFly (Regulatory Element Database
for Drosophila), contains experimentally validated genomic regulatory regions from
Drosophila (Gallo et al, 2006). The second, FlyReg, is a Drosophila DNAse I footprint
database that contains experimentally verified transcription factor binding sites (TFBSs;
(Bergman et al, 2005). We then took as our positive set of CRMs all regulatory regions
107

from REDFly that contain at least two experimentally verified TFBSs from FlyReg. This
left us with a set of 78 true CMRs, listed in Table D1.

We defined a negative set of CRMs to test our CRM prediction method, with the set
containing one negative CRM for each positive CRM. The negative set consists of
random sequences of the same size as the known CRMs. These random sequences are
generated for each CRM independently using the 2
nd
-order Markov probabilities from the
regulatory region of the target gene. The regulatory region is defined as the genomic
sequence upstream from the transcription start site of the target gene, spanning 10kb or to
the nearest gene, as well as the intron sequences of the target gene. We then used the
Markov probabilities to generate a random sequence of the same size as the known CRM
corresponding to this target gene. This negative set gave us a CRM of the same length
and DNA sequence probabilities as the positive CRM. In determining the prediction
accuracy of our method, we defined 10 such negative CRM sets independently

5.2.2 Transcription factor binding site representations
When searching for transcription factor binding sites (TFBSs) in the genomic sequences,
we used position weight matrix (PWM) representations of the TFBSs. PWMs are
generated from the total number of occurrences for each nucleotide at each position for
the length of the motif. They are then transformed into their log-odds probabilities for
each nucleotide at each position using the nucleotide content of the Drosophila genome.
For nucleotide i at position j, the positional score is:
⎟
⎟
⎠
⎞
⎜
⎜
⎝
⎛
+
+
=
i j
i j i
j i
p N
p n
s
) 1 (
log
,
,
, where
j i
n
,
is
108

the number of occurrences of nucleotide i at position j,
j
N is the total nucleotide count of
position j, and
i
p is the frequency of nucleotide i in the genome. Since many PWMs are
generated from only a few known TFBSs, we added a pseudocount of 1 to each
nucleotide at each position in the PWM.

To provide the most independence from the TFBSs found in the positive CRM set, the
sequence information of the PWMs that we used for searching was gathered in the
following manner. A) If a transcription factor had an associated PWM in Transfac
(Matys et al, 2003), we used this motif. B) Otherwise, if there were a list of binding sites
in Transfac for this TF, we used the sequence information from these binding sites and
the de novo motif finding program MEME (Bailey & Elkan, 1994) to generate PWMs.
MEME is a program which takes in a list of sequences and statistically defines PWMs
present in these seqeunces. We input the sequences of the defined TFBSs and searched
for PWMs from 6-12 nucleotides in length identifying one TFBS per sequence. Although
the footprint sequences may contain additional binding sites for this TF, we only searched
for one TFBS per sequence to minimize effects of TFBSs for other TFs that may be
present in these sequences. C) Finally, if no binding information was available for this
TF in Transfac and if there were at least three TFBSs annotated in the DNAse I footprint
database FlyReg (Bergman et al, 2005) for this transcription factor, we used these sites
and MEME to define the PWM for this transcription factor. A list of all PWMs used in
this chapter is presented in Table D2.

109

When searching for TFBSs in the genomic sequences, we identified sites by the
following manner. For a given genomic sequence Q with length l equal to the PWM
length, the information content score for the PWM occurring within Q
∑
=
=
l
j
j i
s Q S
1
,
) ( ,
where i is the nucleotide in position j of Q. This sequence, Q, is defined to be a TFBS for
this PWM if the information content score S(Q) is greater than a given threshold t, with a
higher value for t representing a more stringent cutoff for the PWM.

5.2.3 cis-regulatory module searching method
To find CRMs, we incorporated the approach developed by (Wagner, 1999), which uses
r-scans to search for potential CRMs. An r-scan is defined as the nucleotide distance
between r consecutive motifs. Many CRM searching methods require the user to input a
window-size in which to search for TFBSs (Berman et al, 2002; Rajewsky et al, 2002).
The use of r-scans provides increased flexibility over set-length windows to scan across
the regulatory region since it allows for CRMs of varying size to be captured
simultaneously, as well as allowing the CRM to define its own size. It is important to
note that this method can be applied to both homotypic and heterotypic CRMs. Although
the window length parameter of other methods is removed, r-scans still require the value
r to be pre-determined. We only searched for r-scans of size r = 2, 3, 4, or 5 to reduce
the searching complexity. Additionally, we required 2, 3, 4, and 5-scans to be less than
200, 300, 400, and 500bp, respectively, a restriction defined by observing the occurrences
of TFBSs in the positive set of CRMs. Every CRM in the positive set had at least one r-
scan that followed these restrictions, the exception being a CRM with only two defined
110

TFBS. In addition, every CRM with at least three known TFBSs had a 3-scan of size 300
or smaller, only one CRM with at least four TFBSs did not have a 4-scan of less than
400bp, and every CRM but four with at least five TFBS had a 5-scan of size 500 or
smaller.

To define significance of an r-scan, we assumed that occurrences of transcription factor
binding sites follow a Poisson distribution, as described in (Wagner, 1999). This is a
common assumption, but one which can be violated, especially with repetitive motifs. To
control for this violation, our method only keeps one TFBS if there are overlapping
TFBSs for the same input motif. Given a homotypic r-scan of distance x starting at motif
occurrence i, the r-scan significance is defined as the probability of finding a shorter r-
scan and is calculated as follows:
∫
− −
− +
− Γ
= <
x
z r
r i i
dz e z
r
x d P
0
2
1 ,
) (
) 1 (
) (
λ
λ
λ
, where λ is the
probability of the TFBS occurring in the genomic region. Heterotypic r-scans can
similarly be calculated by assuming independence between TFBS occurrences of two
different motifs. With this assumption, the probability of a TFBS occurring in the
genomic sequence for any motifs in then
∑
=
j
j
0
λ λ where
j
λ is the probability of a TFBS
occurring for motif j.

Calculating the probability of a motif occurrence, λ, is an additional task. A common
measure is to scan the entire genome using the PWM and to take λ as the number of motif
occurrences over the total number of nucleotides searched. (Wagner, 1999) used a
111

combined global and local measure for λ, where the local measure uses the dinucleotide
content of the r-scan. As our method searches for CRMs on a gene-by-gene basis, we
proposed a gene-based measure for λ. We generate 100000 random l-mers using the GC
content of the regulatory region of each gene to be searched and take λ as the number of
random l-mers that would be declared a motif divided by 100000. This method is defined
as method 1). This method produces λ values in the range of those obtained by the whole
genome scan. We also tested defining λ using a 2
nd
-order Markov model to generate the
random l-mers and found no substantial difference to using only the GC content (data not
shown).

For comparison with previous methods, we included in our testing an additional method
(method 3) to search for cis-regulatory modules by finding clusters of binding sites
within a defined length. We chose to search for CRMs of size 300bp, as this is the
middle-range for the r-scan cutoffs defined above. To declare significance of these
clusters of TFBSs within 300bp, we generated 1000 random sequence permutations of
length 10kb using the 2
nd
-order Markov probabilities for the target gene, as described
above. Significance was then defined as the number of random sequences out of 1000
that contained as many TFBSs in 300bp as the discovered CRM. As an additional test of
the r-scan method, we also used 1000 permuted sequences to define significance of the r-
scans, with significance defined as the number of random sequences out of 1000 that
contained an r-scan of the same size or shorter. This significance method is referred to as
method 2).

112

5.2.4 Conservation of cis-regulatory modules
It is known that a CRM is more likely to be functional if it is conserved across multiple
species (Gertz et al, 2005; Kellis et al, 2003; Sinha et al, 2004; Wong & Nielsen, 2007).
We took advantage of the recently sequenced Drosophila genomes (see
http://rana.lbl.gov/drosophila/) to provide additional evidence of our predicted CRMs
through the use of a conservation score. We used the genomic sequence information
from nine additional Drosophila genomes: D. pseudoobscura, D. simulans, D.
anannassae, D. grimshawi, D. virilis, D. erecta, D .mojavensis, D. sechellia, and D.
yakuba. We used only nine of the additional 11 species, as the remaining two genomes
were not annotated at the time of this study. Finding correct homologs is an essential part
of any cross-species comparison, because inaccurate gene descriptions will lead to
incorrect results. The upstream regions that are being compared must be correctly
aligned, or else unrelated sequences will be used to find predicted CRMs resulting in
misleading results. Thus, we need to use a strict alignment and homology detection
method for finding D. melanogaster genes in the remaining genomes. To find
orthologous genes in each of the additional species, we used the results from a previously
generated alignment and annotation method (Pollard et al, 2006), which has a rigorous
criteria for annotating orthologous genomes.

To define a conservation score of our discovered CRM, we first search the orthologous
gene in each of the additional species for significant CRMs. We define a conservation
score for each reported significant CRM of the target gene in D. melanogaster by doing
pair-wise comparisons with significant CRMs from the orthologous gene in each of the
113

additional species. The conservation score depends on both the sequence similarity
(defined by an alignment score) and the TFBS content of the CRM. For each gene the
conservation score is calculated as follows:
1. For D. melanogaster, all significant CRMs, A, are found for the regulatory
region of the target gene;
2. For the orthologous gene in species k, all significant CRMs, B
k
, are found;
3. For each significant CRM from the primary species, a
i
Є A, we find its
orthologous conserved CRM in species k by
⎟
⎟
⎠
⎞
⎜
⎜
⎝
⎛
+ = ) , (
) (
) , (
max ) , (
,
,
j k i
i
j k i
j k i
b a C
a l
b a G
B a S , where G(a
i
,b
k,j
) is a score for the
alignment of a
i
and b
k,j
, l(a
i
) is the length of sequence a
i
, and C(a
i
,b
k,j
) is a
score for the TFBS content of a
i
and b
k,j
. G(a
i
,b
k,j
) and C(a
i
,b
k,j
) are defined
below.
4. Repeat steps 2 and 3 for each of the additional species k = (1-9);
5. The overall conservation score for a CRM a
i
Є A is
∑
=
=
9
1
) , ( ) (
k
k i i
B a S a S ;

The use of a CRM content score (C(a
i
,b
k,j
)) is a necessary component for analyzing CRM
conservation. If we assume that binding sites are necessary for a functional CRM, then
these binding sites must be present in the orthologous CRM for it to be functional as well.
However, additional motif occurrences in the CRM may be present in the primary or
orthologous CRM due to the use of the same consensus motif or PWM. It may be the
case that a binding site motif may diverge slightly between species due to divergence of
114

the corresponding transcription factor’s DNA binding domain, such that a motif hit for
one species may not perfectly align with another species. We take these into
consideration when we define the conservation score:
) ( * 1 . 0 ) ( * 2 . 0 ) , (
2
1
) , (
, , , j k i j k i j k i
b u a u b a m b a C − − = , where m is the number of matching
motifs between a and b, and u is the number of unmatched motifs. For example, given
two input motifs (1 and 2), assume a
i
= (1,2,1,1) and b
k,j
= (2,2,1). Then C(a
i
,b
k,j
) = 0.5*2
– 0.2*2 – 0.1*1 = 0.5. The order of binding sites is irrelevant in this scoring scheme.

The alignment score is based upon the idea that conserved CRMs contain similar patterns
and thus have similar alignments. Much of this may be contained by the content score;
however, most likely there are additional TFBSs within the CRM for which we do not
search. Using an alignment score will help account for these additional motifs. Since we
define CRMs using an r-scan, our motifs are at the boundaries of the CRM. We therefore
extend our CRM by 50bp on both ends of the CRM before doing the alignment. We use
the Needleman-Wunsch global alignment algorithm, allowing for seqeunce overhang on
either end. This is done so that CRMs that are extended due to a possible non-functional
motif are not penalized. For our parameters, we use a match score of 4, a mismatch score
of -3, a gap-open penalty of 10, and a gap-extension penalty of 0.5. The use of a large
gap-open penalty and small gap-extension penalty allow for larger gaps without a
substantial increase in penalty. This is important so that the method does not heavily
penalize for indels and allows for blocks of aligned sequences, where blocks represent
conserved and possibly functional motifs. To determine the score of the alignment,
115

G(a
i
,b
k,j
), we use the program Needle (Sarachu & Colet, 2005). Once we generate an
alignment score, we scale it using the length of the D. melanogaster CRM, l(a
i
), so longer
CRMs are not over-weighted. Therefore, for each pair-wise comparison of a D.
melanogaster CRM and an orthologous species, we define the conservation score as:
⎟
⎟
⎠
⎞
⎜
⎜
⎝
⎛
+ = ) , (
) (
) , (
max ) , (
,
,
j k i
i
j k i
j k i
b a C
a l
b a G
B a S
.

5.2.5 Drosophila Strains
The following Drosophila melanogaster genotypes were used: w; P{GAL4-
Hsp70.PB}/CyO on II and w; P{GAL4-Hsp70.PB} on III allow us to drive expression of
the yeast transcription factor GAL4 by increasing the temperature of the flies
environment. The genotypes uas-DSX
M
/CyO, uas-DSX
F
/Tm3,Sb, yw; uas-FRU
A
on II;
uas-FRU
B
/CyO, yw; and yw; uas-FRU
C
on II express, under the presence of GAL4, either
the male isoform of DSX, the female isoform of DSX, or one of the three isoforms of
FRU with the A, B, or C DNA-binding domains, respectively. All flies were kept at 25
◦
C
in a cycle of 12 hours light/12 hours dark.

5.2.6 Drosophila collections for microarray analysis
For microarray experiments in which we over-expressed DSX
F
, we compared XX w;
P{GAL4-Hsp70.PB} ; uas-DSX
F
to w; P{GAL4-Hsp70.PB} females. For microarray
experiments in which we over-expressed DSX
M
, we compared XY w; P{GAL4-
Hsp70.PB} ; uas-DSX
M
to w; P{GAL4-Hsp70.PB} males. For microarray experiments in
which we over-expressed FRU
A
, we compared XY w; P{GAL4-Hsp70.PB} ; uas-FRU
A
to
116

w; P{GAL4-Hsp70.PB} males. For microarray experiments in which we over-expressed
FRU
B
, we compared XY w; P{GAL4-Hsp70.PB} ; uas-FRU
B
to w; P{GAL4-Hsp70.PB}
males. For microarray experiments in which we over-expressed FRU
C
, we compared XY
w; P{GAL4-Hsp70.PB} ; uas-FRU
C
to w; P{GAL4-Hsp70.PB} males. Microarrays
contained control spots for Sxl, tra, the female-specific splice form of dsx (dsx
F
), the
male-specific splice form of dsx (dsx
M
), the fru transcript that is sex-specifically spliced
to produce FRU
M
(fru P1), and three of the fru DNA-binding domains (fru
A
, fru
B
, and
fru
C
) (see Table A10 for sequences).

5.2.7 Microarray analysis for the identification of genes regulated by increases in the
expression of DSX and FRU
All samples were collected between ZT 1 and ZT 4 (one hour to four hours after lights
on) as white pre-pupae and were aged to 48 hr APF at 25°C. At 48 hr APF, flies used in
collections for microarray analysis were heat-shocked at 37°C for ½ hour and then
allowed to recover at 25°C for 1 hour at which point they were snap frozen in liquid
nitrogen. mRNA was then extracted, reverse transcribed into cDNA, and the cDNA
prepared for the microarray experiments according to 2.4.3. Microarrays were hybridized
according to 2.4.3. Microarrays were generated and analyzed according to 2.4.4. Each
experiment was conducted with four biological replicates and a dye-swap design.

5.3 Results and Discussion
We wanted to test the prediction accuracy of our cis-regulatory module prediction
method and see how it compares to other methods to define CRMs by determining the
117

ability of the methods to identify the known CRMs. Previous methods have been aimed
at identifying and tested using CRMs containing many TFBSs within small window
sizes. Our method has the ability to predict small CRMs, and so we next compared our
method to two previously defined methods, paying particular interest to CRMs only
bound by one or two transcription factors. We finally used our developed method in
concert with a microarray-based approach to identify potential direct targets of both DSX
and FRU.

5.3.1 Prediction accuracy of cis-regulatory module searching method
We first aimed to determine the accuracy of our method described in 5.2.3 in predicting
cis-regulatory modules (CRMs). To do this, we used the positive and negative CRMs
described in 5.2.1. We limited our search region to find CRMs that occur within 10kb of
the transcriptional start and stop sites. In addition, we also limited our set of known
CRMs by requiring them to be adjacent to the regulated gene; i.e., no other annotated
gene can occur between the CRM and the target gene. When searching for novel CRMs,
it is more difficult to annotate which gene the CRM is regulating if we allow CRMs to be
identified more closely to the transcriptional start sites of other genes. This left us with
61 CRMs on which to test our method.

We first tested the accuracy of our method using r-scans to find CRMs as compared to
identifying CRMs using a window-size based approach. We also tested defining the
significance of r-scans using Poisson statistics versus using permutation-based statistics.
We searched both the positive and negative CRM sets using the methods presented in
118

5.3.2: 1) r-scans with Poisson-based p-value, 2) r-scans with permutation-based p-value,
and 3) CRMs within a window size of 300bp with permutation-based p-values. We
searched for CRMs using all of the PWMs for the transcription factors that are known to
bind to this CRM. We declared a CRM to be present if at least one TFBS in the predicted
CRM was found to overlap with the known regulatory region. To determine the
prediction accuracy, we first needed to define thresholds for the information content
scores when identifying TFBSs in the genomic sequences. We chose to define default
threshold values when searching for PWMs, rather than declaring a unique threshold for
each TF, to provide the most independence when comparing our method to previous
CRM prediction methods. The following thresholds were used when searching for
TFBSs: if one or two TFs were searched, the PWM threshold t = 0.8*l(PWM), where l is
the length of the PWM; and if three of more TFs were searched, t = 0.9*l(PWM).

To determine the prediction accuracy, we used the receiver operating characteristic
(ROC) – ROC curve. The ROC curve was generated by using –log(p) as the threshold
for declaring presence of a CRM, where p is the significance of the CRM. The area
under the ROC curve (ROC score) determines the prediction accuracy of the method,
with values closer to one representing better predictors. ROC curves were generated for
all three CRM prediction methods defined in 5.2.3 using each of the 10 negative sets of
randomly permuted CRMs. The ROC curves using one of the negative sets is shown in
Figure 5.1. We identified similar ROC scores for each of the three methods: Method 1)
using r-scans and Poisson statistics to define the CRM had an average ROC score of
0.7657; Method 2) using r-scans and permutation statistics had an ROC score of 0.7727;
119

Figure 5.1. Prediction accuracy when searching for cis-regulatory modules (CRMs). We analyzed
performance of three methods on predicting CRMs using the receiver operating characteristic (ROC) curve.
Method 1) uses r-scans and Poisson statistics to compute significance (solid black line). Method 2) uses r-
scans and permutations to compute significance (dashed red line). Method 3) uses a 300bp window and
permutations to compute significance (dotted green line). This is a representation of the prediction
accuracy using the positive set of CRMs and one of the 10 sets of negative CRMs. We found similar
prediction accuracies for all three methods, with average ROC scores of 0.7657, 0.7727, and 0.7841, for
Method 1), 2), and 3), respectively.

and Method 3) using a window of 300bp and permutation statistics had an average ROC
score of 0.7841. Although all three methods have similar prediction accuracies, Method
1) takes substantially less computational time to determine the p-values. For instance,
when searching a 5kb regulatory region that contains one CRM, Method 1) takes on the
120

order of 1-2 seconds. However, Methods 2) and 3) both take on the order of 4-5 minutes.
As Method 1) had similar prediction levels as Methods 2) and 3), but at much faster
speeds, we chose to use Method 1) for the remainder of this chapter.

5.3.2 Comparison of our method with previous methods
We next compared our method to two previous methods that incorporate a web-based
approach to identify CRMs in Drosophila: Ahab and cis-Analyst (Berman et al, 2002;
Rajewsky et al, 2002). To test our method against the previous methods, we used the
same information content score thresholds as defined in the previous section and used a
p-value cutoff of 0.05 to predict CRMs. We used the default thresholds of both Ahab and
cis-Analyst to predict CRMs with these methods.

Using our defined threshold values of 0.8*l(PWM) for CRMs bound by one or two TFs
and 0.9*l(PWM) for CRMs bound by three or more TFs we correctly predicted 51 of the
78 known CRMs and incorrectly predicted on average 15.5 of the 78 negative CRMs
(Table 5.1). Let the sensitivity of our prediction be TP/(TP+FN) and specificity be
TN/(TN+FP), where TP is the number of false positives, TN is the number of true
negatives, FN is the number of false negatives, and FP is the number of false positives.
At this threshold we find a sensitivity of 0.6538 and an average specificity of 0.8012. For
CRMs annotated as having one TF binding, we correctly predicted 13 of 28 true CRMs
and incorrectly predicted on average 2.8 of 28 CRMs in the negative CRM set. Similarly,
for CRMs annotated as having two TFs binding, we correctly predicted 14 of 21 true
CRMs and incorrectly predicted on average 3.9 of 21 negative CRMs. Finally, for CRMs
121

containing 3 or more TFs binding, we correctly predicted 24 of 29 true CRMs and
incorrectly predicted on average 8.8 of 29 negative CRMs.

Table 5.1. Our CRM searching method outperforms previous web-based methods in identifying true
CRMs.
Number of Correctly Predicted CRMs
Number of bound TF Our Method Ahab cis-analyst 1* cis-analyst 2
+
Total CRMs
One TF 13 1 0 1 28
Two TFs 14 3 1 4 21
Three or more TFs 24 15 4 11 29

* Searched cis-Analyst using same PWMs as our method and cis-Analyst default threshold
+
Searched cis-Analyst using PWMs and thresholds it provides

As mentioned, when searching for CRMs using Ahab and cis-Analyst, we used their
default parameters. cis-Analyst only searches for a given number of binding sites in a
given window size. To make the results more similar to the search criteria we used in our
method, we searched for clusters of two TFs in 200bp when using cis-Analyst. We used
their default parameters for finding TFBSs. We found that our method has the ability to
identify substantially more CRMs than the previous methods. Ahab was only able to
correctly find 1, 3, and 15 true CRMs bound by one, two, and three or more TFs,
respectively (Table 5.1). cis-Analyst was a poor predictor, with the program only finding
CRMs for 0, 1, and 4 true CRMs bound by one, two, and three or more TFs, respectively,
when we used the same PWMs we used in our predictions (Table 5.1). However, cis-
Analyst provides PWMs with unique thresholds defined for some motifs. When we
searched for CRMs using these provided PWMs, as a user might do, the prediction rate of
cis-Analyst was slightly improved, identifying 1, 4, and 11 true CRMs bound by one,
two, and three or more TFs, respectively (Table 5.1).

122

Our program was able to identify substantially more true CRMs than previous methods,
while still having high sensitivity. This is especially true for CRMs bound by only one or
two transcription factors, where we correctly predicted 20 true CRMs with only four false
positives and Ahab and cis-Analyst only predicted four and five true CRMs, respectively.
This is an important finding, as CRMs containing many TFBSs from multiple TFs,
particularly the blastoderm CRMs, are only a small subset of all known CRMs (Li et al,
2007) and CRMs bound by only one or two TFs represent 69% (42 of 61) of the analyzed
CRMs. Increased prediction of CRMs bound by only one or two transcription factors is
important for discovery of biologically relevant CRMs.

5.3.3 Identification of Potential Direct Targets of DSX and FRU
As previously mentioned, a major focus of the research presented in this dissertation is
the identification of genes whose expression levels are controlled by DSX and FRU
activity. This includes determination of direct targets of DSX and FRU. Currently, there
are only two known direct targets of DSX, Yolk protein 1 and Yolk protein 2, and no
known direct targets of FRU (Burtis et al, 1991; Coschigano & Wensink, 1993a). To
identify additional direct targets we sought to use an approach that incorporates both our
CRM searching program and microarray analysis.

To generate a candidate list of direct targets, we used the conditional uas-GAL4
expression system. For this purpose we over-expressed constructs of DSX and FRU
using the heat shock-GAL4 (hs-GAL4) transgene, with the hs promoter used as a
conditional switch activating the yeast GAL4 transcription factor in response to increased
123

temperatures. We aged flies to the middle of metamorphosis (48 hr APF), at which point
we heated them to 37°C for one half an hour. We then allowed them to recover for an
hour before collection. This time frame was chosen to maximize the expression
differences of direct targets of DSX and FRU, while keeping expression changes of
indirect targets unperturbed. We looked for direct targets of DSX or FRU by driving the
following over-expression constructs in the appropriate sex: uas-DSX
F
in XX flies, uas-
DSX
M
in XY flies, and uas-FRU
A
, uas-FRU
B
, and uas-FRU
C
each in XY flies. FRU
A
,
FRU
B
, and FRU
C
, represent the FRU
M
isoform and non-sex-specific FRU isoforms with
the A, B, and C binding domains, respectively. We used our microarray platform
assaying gene expression in all predicted Drosophila genes to determine expression
changes in response to DSX or FRU by comparing the over-expression constructs with
flies only containing the hs-GAL4 transgene of the appropriate sex, but treated similarly.

To search for DSX and FRU CRMs we the CRM prediction method described in this
chapter. To find DSX CRMs, we used the DSX PWM generated in (Erdman et al, 1996;
Table D2). We used similarly generated PWMs for both FRU
A
and FRU
C
(Arbeitman
and Baker, personal communication; Table D2) and searched for CRMs using both FRU
PWMs together. Although a PWM was generated for FRU
B
by Arbeitman and Baker,
this PWM showed limited complexity and was not used in this study. In comparison of
our datasets of genes regulated by DSX and FRU (see Chapters 3 and 4, respectively), we
found only four genes that are present in both gene lists. Therefore, we do not expect
DSX and FRU co-regulation and as such we did not search for CRMs containing TFBSs
for both transcription factors.
124

In Table 5.2, we present the number of genes that are significantly differentially
expressed in response to increased levels of DSX or FRU (q-value<0.05 and fold-
change>1.5, Tables D5-D9). When we over-expressed DSX
F
in females or DSX
M
in
males, we identified 333 and 213 genes, respectively, whose expression was significantly
different from that of control flies, with 50 genes being found in both lists (Tables D5 &
D6). As a validation that we over-expressed the correct transgenes in our microarray
analyses, the female-specific splice variant of dsx increased in expression when we over-
expressed DSX
F
and the male-specific splice variant of dsx increased in expression when
we over-expressed DSX
M
. We searched for significant DSX CRMs in the regulatory
regions of these genes by defining an information content score threshold, t, as above,
with t = 0.8*l(PWM) = 5.6. We found significant CRMs for 72 genes whose expression
levels change in females in response to increase levels of DSX
F
and 65 whose expression
levels change in males in response to increased levels of DSX
M
(Table 5.2, Tables D5 &
D6). We found 14 genes with significant DSX CRMs that respond to increased levels of
DSX
F
in females and of DSX
M
in males. We found a significant DSX PWM within the
regulatory region of dsx itself with a high conservation score (39.08), suggesting possible
auto-regulation of this transcription factor.

When we over-expressed different isoforms of FRU in males we found the expression
levels for 159, 298, and 187 genes respond to increases in the FRU
A
, FRU
B
, and FRU
C

isoforms, respectively (Table 5.2, Tables D7-D9). As a validation that we over-expressed
the fru transgenes, the fru transcript increased in expression when we over-expressed
each of the FRU isoforms. Although the three FRU binding domains bind to different
125

Table 5.2. Microarray experiments analyzing gene expression in flies over-expressing DSX or FRU
isoforms identifies candidate direct targets of these two transcription factors.
Microarray experimental design
Experiment Number of Genes Number of CRMs
Over-express DSX
F
in females

333 72 (DSX)
Over-express DSX
M
in males

213 65 (DSX)
Over-express FRU
A
in males 159 32 (FRU
A
/FRU
C
)
Over-express FRU
B
in males

298 70 (FRU
A
/FRU
C
)
Over-express FRU
C
in males

187 30 (FRU
A
/FRU
C
)

sequences, we found high overlap among the genes whose expression levels change in
response to increased expression levels of the FRU isoforms. In particular we found 57
genes are downstream of FRU
A
and FRU
B
, 82 are downstream of FRU
A
and FRU
C
, and
71 are downstream of both FRU
B
and FRU
C
. In total, we identified 39 genes whose
expression levels significantly change in response to over-expression of all three
isoforms.

We searched for significant FRU CRMs in the sets of genes that respond to increases in
the FRU isoforms by identifying either homotypic FRU
A
or FRU
C
CRMs or heterotypic
FRU
A
-FRU
C
CRMs. We searched for significant FRU CRMs in the regulatory regions of
these genes by defining an information content score threshold, t, as above, with t =
0.8*l(PWM) (t = 4.8 and 5.6, for FRU
A
and FRU
C
, respectively). Although we did not
search for FRU
B
CRMs, we still aimed to identify FRU CRMs containing the other
isoforms in the set of genes whose expression levels change as a result of over-expressing
FRU
B
. We identified 32, 70, and 30 significant FRU CRMs among the list of genes
whose expression values change in response to over-expression of FRU
A
, FRU
B
, and
126

FRU
C
, respectively (Table 5.2, Tables D7-D9). We found the highest percentage of FRU
CRMs among the genes responding to increases in FRU
B
levels, even though we did not
search for FRU
B
CRMs. This finding, along with the high number of genes found to
have significant expression changes in response to increases in multiple FRU isoforms,
suggests substantial co-regulation of downstream targets for the different FRU isoforms.

We also wanted to identify which genes with significant FRU PWMs were found to have
differential expression when different FRU isoforms were over-expressed. We identified
15 genes with significant FRU CRMs that are downstream of both FRU
A
and FRU
B
, 16
genes that are downstream of both FRU
A
and FRU
C
, and 13 genes that are downstream of
both FRU
B
and FRU
C
. Many of the genes with significant CRMs that were found to be
downstream of two FRU isoforms were also found to be downstream of the third isoform
(10 of 24). This again suggests overlap among the genes that are targets of the different
FRU isoforms. One finding that is of particular interest is that the fru locus itself has a
significant CRM with the second highest conservation score (32.67) among all predicted
FRU CRMs. Thus, like DSX, FRU potentially auto-regulates the expression of its own
pre-mRNA.

In this section we have identified many potential genes that may be directly regulated by
either DSX or FRU at 48 hr APF. We have also predicted cis-regulatory modules to
which DSX and FRU might bind to regulate the expression levels of some of these
downstream target genes. We are interested in biologically validating the CRM
predictions made in this section. We are using transgenes in which the predicted CRMs
127

drive expression of a downstream reporter gene (GFP). Changing the levels of DSX and
FRU in the flies allows us to determine the response of these CRMs to DSX and FRU
activity on gene expression. Mutating the predicted DSX or FRU binding sites enables
us to determine if these are in fact direct targets to which DSX and FRU actively bind.

5.4 Conclusions
In this chapter, we have presented our approach for identifying cis-regulatory modules
(CRMs) in Drosophila. We showed that our method of allowing the transcription factor
binding sites (TFBSs) to determine the length of the regulatory region allows for similar
prediction accuracy with much quicker speeds than when using a window-sized based
measure common in previous efforts. In addition, we showed that our method
outperforms two previous methods in finding known CRMs (Berman et al, 2002;
Rajewsky et al, 2002). In particular, our method is able to find functional CRMs that are
bound by only one or two transcription factors (TF), while the previous methods fail in
finding these CRMs. The previous methods can find CRMs bound by many different
TFs with numerous TFBSs in a small window, such as the blastoderm CRMs. However,
a recent study has shown that these CRMs are unique among known Drosophila CRMs
(Li et al, 2007), and thus the ability to only predict CRMs of this type is of limited use.

Our method also incorporates information from the genomic sequences of nine additional
Drosophila species. Conservation of CRMs has been shown to be a good predictor of a
CRM’s functionality (Gertz et al, 2005; Kellis et al, 2003; Sinha et al, 2004; Wong &
Nielsen, 2007), and use of this multitude of genomes should increase the accuracy of our
128

predictions. Indeed when we incorporate a conservation score into our prediction
method, we find modest increases in our prediction accuracy.

Our approach is amenable for incorporation into more sophisticated methods. Drosophila
CRMs have features that can be used to help distinguish them from other intergenic DNA
sequences, including clustering of binding sites, conservation, a higher GC content, and
an increased number of repeated k-mers (Li et al, 2007). Using a method such as logistic
regression to integrate all of these features could improve accuracy when searching for
Drosophila CRMs. We are interested in using these more sophisticated methods to
further develop our CRM-searching method and to make prediction of CRMs easier and
more accurate.

We also set out to find direct targets of the two main effectors of the sex-determination
hierarchy, doublesex (dsx) and fruitless (fru). Using a microarray-based approach we
identified a list of candidate genes to whose upstream regions DSX and FRU might bind
and regulate the expression level of the gene. We ran our CRM searching method on
these lists to further refine our candidate list of direct targets. We identified many
potential DSX and FRU CRMs and have begun to determine if they are indeed direct
targets. We are actively pursuing a subset of these CRMs to see if they are regulated by
FRU or DSX. Of particular interest is the finding that both DSX and FRU potentially
bind to their own regulatory regions to control the expression levels of their own pre-
mRNAs.
129

Chapter 6
Conclusions

In the research presented in this dissertation, my collaborators and I undertook a
systematic to approach understand how somatic sex-specific morphology and behaviors
of adult D. melanogaster are patterned during metamorphosis. We started with by
examining how the Drosophila genome is deployed during metamorphosis in whole flies.
This study included examining gene expression for all known and predicted D.
melanogaster genes in males and females, with specific emphasis placed on gene
expression in the somatic tissues. Based on these and previous results, we chose to focus
on one time point during metamorphosis (48 hours after puparium formation) to examine
sex-differential expression and how it is regulated by the sex-determination hierarchy, in
particular by the hierarchy’s two terminal effectors, doublesex (dsx) and fruitless (fru).
We were able to identify genes that are regulated downstream of dsx and fru during this
stage of metamorphosis that may be involved in establishing sex-specific phenotypes. Of
particular interest are two results: 1) DSX regulates the majority of its downstream
targets differently than previously thought, with the female isoform in females and the
male isoform in males both activating or both repressing the expression levels of the
same downstream targets; and 2) FRU
M
acts in concert with the ecdysone-regulatory
pathway to establish the potential for the male-courtship ritual. Finally, we used both
molecular and computational techniques to identify possible direct targets of DSX and
130

FRU at 48 hr APF. Of particular interest from this study is that DSX and FRU might act
to regulate the expression levels of their own transcripts.

From this work we have identified many candidate genes that are regulated by the
activity of DSX and FRU during metamorphosis. In-depth analysis of these candidate
genes may reveal novel genes and proteins required for the establishment of sex-specific
morphology and behaviors. Future research will likely include the exploration of the
specific function of these candidate genes and how their expression levels change in
response to DSX and FRU
M
activity. In particular, we plan on examining how the sex-
specific DSX isoforms act to regulate the same set of downstream targets and how FRU
M

acts in concert with the ecdysone-regulatory pathway to establish the potential for the
male-courtship ritual.

We are also extremely interested in further developing our computational method
(SUPRfly) to predict cis-regulatory modules (CRMs). A recent study has shown that
functional CRMs have a set of common features – including an elevated GC content,
over-representation of ultraconserved elements, and a tendency to be transcribed – and
that can distinguish them from other intergenic sequences (Li et al, 2007). Incorporating
these additional features into SUPRfly may increase the accuracy in predicting functional
CRMs. We are currently validating our method by using molecular techniques to
determine if predicted DSX and FRU CRMs have the ability to drive expression of a
reporter gene in response to expression of DSX or FRU
M
.

131

The research presented in this dissertation lays a foundation for understanding what is
occurring in establishing sex-specific traits during metamorphosis. We plan to further
synthesize our results by designing a method that can incorporate the findings of these
studies. In particular, we will use the sex-specific temporal gene expression profiles from
Chapter 2 in conjunction with SUPRfly, presented in Chapter 5, and ChIP-chip assays.
ChIP-chip assays – chromatin immunoprecipitation combined with microarray analysis –
enable an investigator to identify regions of the genome to which a transcription factor in
binding. Sampling DSX or FRU
M
binding at multiple time points during metamorphosis
gives us an idea of their temporal binding to direct targets. By incorporating these results
with temporal gene expression in the two sexes, we can begin to understand how the
activity of DSX and FRU
M
act to regulate their downstream targets to establish sex-
specific morphology and behaviors.

We believe that these results provide a valuable resource for those studying how somatic
sex-specific phenotypes are established during metamorphosis. In general, this research
provides a foundation for analyzing and understanding gene regulation, gene networks,
and transcription factor pathways in multicellular organisms, including humans. As
presented here, the intersection of computational and molecular techniques will continue
to enable researchers to thoroughly explore developmental processes from the genomic
level to the level of integrated systems.
132

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Appendices
Appendix A: Supplemental tables for Chapter 2
Table A1. Sex-differentially expressed somatic genes during metamorphosis
Female-biased
Gene Name Gene Symbol q-value Female/Male FC
Larval serum protein 1 alpha Lsp1alpha 0.0020263 2.25073573
CG15369 CG15369 0.00281022.151156935
CG15347 CG15347 0.00676231.709563404
CG32695 CG32695 0.01037261.70543061
white w 0.00050661.655707761
CG13360 CG13360 0.02222361.655519974
Ilp6 Ilp6 0.0078881.53239224
CG14629 CG14629 0.02136161.509637833
CG31636 CG31636 0.04302861.445543191
CG9009 CG9009 0.00855631.42532074
CG6961 CG6961 0.00796031.410445222
CG1702 CG1702 0.00491191.404846049
CG15247 CG15247 0.03216961.401417523
CG32687 CG32687 0.02222361.379343975
CG32827 CG32827 0.01277271.342327803
CG1440 CG1440 0.0084911.309924887
CG34346 CG34346 0.04302861.300990223
cut ct 0.02092051.280948372
technical knockout tko 0.0213616 1.234648281
CG30184 CG30184 0.02222361.223393186
lethal (1) G0156 l(1)G0156 0.0111978 1.200611085
lethal (1) G0334 l(1)G0334 0.0322355 1.163792334

Male-biased
Gene Name Gene Symbol q-value Male/Female FC
RNA on the X 1 roX1 2.64E-17 119.733169
CG9914 CG9914 0.00491191.523242956
CG3056 CG3056 0.02222361.457320781
Microsomal glutathione S-transferase-like Mgstl 0.0028102 1.445326175
Glutathione Synthetase GS 0.0085031 1.44490511
CG9509 CG9509 0.00124471.41838466
Transport and Golgi organization 4 Tango4 0.0212066 1.331928002
CG2082 CG2082 0.02092051.266342407
CG1172 CG1172 0.02590161.266342407
CG33713 CG33713 0.00796031.230647327
CG13298 CG13298 0.02136161.211805489
astray aay 0.03671681.210756483
real-time retm 0.02136161.192961644

FC: average fold change of gene expression data across all 5 time points.
q-values are from 2-way ANOVA with sex and time as the independent factors

Table A2. Sex-differentially expressed somatic genes at 0 hr APF.
152

Female-biased
Gene Name Gene Symbol q-value Female/Male FC
Larval serum protein 1 alpha Lsp1alpha 0.0174336 2.33934388
Transferrin 1 Tsf1 0.0469903 2.028293419
CG15369 CG15369 0.03046961.899066336
CG3699 CG3699 0.0094451.832467756
CG14629 CG14629 0.01787961.669804763
CG31806 CG31806 0.04750981.649605787
CG33254 CG33254 0.03046961.603876964
Chitinase-like Chit 0.0027751.517447009
Alcohol dehydrogenase Adh 0.0304696 1.442895592
Larval cuticle protein 1 Lcp1 0.0304696 1.392772446
CG30269 CG30269 0.03076941.32398669
GUK-holder gukh 0.01743361.313426348
CG1837 CG1837 0.03046961.294552078
Chromosome associated protein H2 Cap-H2 0.0323178 1.279866873
Jonah 65Aiv Jon65Aiv 0.0323178 1.271813545
CG5261 CG5261 0.01248841.171515665

Male-biased
Gene Name Gene Symbol q-value Male/Female FC
RNA on the X 1 roX1 0.0005325 24.13076613
Eig71Ej Eig71Ej 0.00155591.986791655
CG12290 CG12290 0.0094451.907273526
Punch Pu 0.03046961.8363425
CG9509 CG9509 0.0094451.647045487
metabotropic GABA-B receptor subtype 3 GABA-B-R3 0.0304696 1.603333701
Ard1 Ard1 0.02772481.567767137
CG7888 CG7888 0.02983141.515552438
CG14406 CG14406 0.01248841.510587204
CG15343 CG15343 0.01001221.508230466
Sh3beta Sh3beta 0.03076941.365715258
CG7778 CG7778 0.0094451.363256283
CG30104 CG30104 0.01248841.346847384
Vps20 Vps20 0.03681711.330884344
CG30035 CG30035 0.04391581.316919394
CG11697 CG11697 0.04652011.289746369
CG6885 CG6885 0.01743361.261515461
CG12000 CG12000 0.02983141.241730405
CG8211 CG8211 0.03046961.222755137
Proteasome 29kD subunit Pros29 0.0465201 1.18616703
TBP-associated factor 6 Taf6 0.045461 1.15866894
mob as tumor suppressor mats 0.032876 1.136114473
CG31357 CG31357 0.03046961.129686597

FC: average fold change of gene expression data.

q-value determined by a t-test of means of the logarithm of the ratio data

153

Table A3. Sex-differentially expressed somatic genes at 24 hr APF.
Female-biased
Gene Name Gene Symbol q-value Female/Male FC
CG14244 CG14244 0.0450007 5.696221653
CG1342 CG1342 0.0021357 3.12508559
Myosin heavy chain Mhc 0.0365756 2.610320196
white w 0.0269253 2.320121755
26-29kD-proteinase 26-29-p 0.0102837 2.305535015
CG32695 CG32695 0.0343196 2.281767775
ion transport peptide itp 0.0053966 2.161616209
CG16865 CG16865 0.0424184 2.036061843
Myosin alkali light chain 1 Mlc1 0.0093544 2.033252596
CG32159 CG32159 0.0158344 1.981272118
beta galactosidase Gal 0.0212725 1.871198755
Serpin-27A Spn27A 0.0274159 1.86050973
globin 1 glob1 0.0372933 1.85074484
SP1029 SP1029 0.036343 1.820846904
Karl Karl 0.0214497 1.820285217
CG1092 CG1092 0.0389701 1.798247629
CG11835 CG11835 0.011666 1.796017228
CG3513 CG3513 0.0329911 1.786492791
Cytochrome P450-4e2 Cyp4e2 0.0031931 1.783217315
CG1120 CG1120 0.0246227 1.753873239
CG7802 CG7802 0.0031931 1.734164717
CG6234 CG6234 0.0269253 1.723306356
CG7920 CG7920 0.0154013 1.703817712
CG15735 CG15735 0.0174608 1.658351282
CG6357 CG6357 0.0031931 1.646331145
Organic cation transporter Orct 0.0031931 1.637335015
CG5690 CG5690 0.0246227 1.627901373
Ecdysone-induced protein 78C Eip78C 0.0403947 1.624823239
mushroom-body expressed mub 0.0002541 1.614203794
yellow-f yellow-f 0.0281824 1.609161143
ERp60 ERp60 0.027692 1.601158289
CG4408 CG4408 0.0066482 1.599701833
CG11919 CG11919 0.0269253 1.594541128
beta-Tubulin at 60D betaTub60D 0.0340052 1.581727291
bent bt 0.0093544 1.569526353
Lipid storage droplet-2 Lsd-2 0.0164804 1.567415731
CG32687 CG32687 0.0343196 1.523165477
CG30359 CG30359 0.0081755 1.522092784
smell impaired 35A smi35A 0.0421122 1.51423916
Papilin Ppn 0.039644 1.511960513
Eukaryotic initiation factor 4a eIF-4a 0.0400225 1.499474874
CG32050 CG32050 0.005945 1.491742021
CG7763 CG7763 0.0049562 1.480004726
Troponin C at 73F TpnC73F 0.0151423 1.477778983
CG11658 CG11658 0.0136073 1.4740496
CG33224 CG33224 0.0214497 1.472269049
Transferrin 2 Tsf2 0.0345061 1.462011713
G protein alpha49B Galpha49B 0.0164804 1.456649509
Thiolester containing protein II TepII 0.0495201 1.443204368
tramtrack ttk 0.0407722 1.44024375
ovo ovo 0.0490352 1.437991723
CG17224 CG17224 0.0269253 1.428685479
CG8036 CG8036 0.0155283 1.428283604
CG9009 CG9009 0.0155283 1.428117876
CG16758 CG16758 0.0372933 1.427167639
Ilp6 Ilp6 0.0035567 1.42440744
terribly reduced optic lobes trol 0.0203132 1.414025802
Phosphogluconate mutase Pgm 0.027692 1.408747293
CG4250 CG4250 0.0354607 1.405340166
CG10026 CG10026 0.0356836 1.402997935
CG16876 CG16876 0.0115873 1.401412196
CG32982 CG32982 0.0090424 1.396757794
charybde chrb 0.0157198 1.379607281
CG32350 CG32350 0.0392871 1.372014798
Serine protease inhibitor 43Aa Spn43Aa 0.0185656 1.369492659
technical knockout tko 0.0364288 1.362120673
154

Table A3, Continued
Arginine kinase Argk 0.0176886 1.360939491
pumilio pum 0.0492234 1.359841986
I'm not dead yet Indy 0.0246227 1.339211784
Enolase Eno 0.0078598 1.335145351
CG4845 CG4845 0.0354607 1.324517501
Src oncogene at 64B Src64B 0.0414932 1.323480312
CG4300 CG4300 0.0318834 1.307760453
Cyclophilin 1 Cyp1 0.007334 1.305221642
BM-40-SPARC BM-40-SPARC 0.0279344 1.303524642
CG9338 CG9338 0.0342421 1.301649643
CG18624 CG18624 0.0392871 1.281139098
CG14207 CG14207 0.0451806 1.271346681
CG11134 CG11134 0.0174608 1.266188697
Lamin B receptor LBR 0.0026815 1.26495545
Hexosaminidase 1 Hexo1 0.0157198 1.252492219
Oscillin Oscillin 0.0190804 1.247360589
Casein kinase II beta subunit CkIIbeta 0.0436622 1.244949467
CG17019 CG17019 0.0277461 1.243235654
CG3415 CG3415 0.0070996 1.235696142
TH1 TH1 0.0350142 1.231602856
CG31781 CG31781 0.0326085 1.229843601
mitochondrial carnitine palmitoyltransferase I CPTI 0.0164804 1.229474155
CG1236 CG1236 0.0338865 1.226133274
CG5431 CG5431 0.0155283 1.223926867
polyA-binding protein pAbp 0.0235394 1.22133639
14-3-3zeta 14-3-3zeta 0.0090424 1.220381823
CG12523 CG12523 0.0364288 1.217261735
CG6783 CG6783 0.0115873 1.207438256
Ecdysone receptor EcR 0.0279344 1.204383826
CG6907 CG6907 0.0155283 1.197470801
G protein gamma 1 Ggamma1 0.0093157 1.195405217
taranis tara 0.0414932 1.188565057
CG17002 CG17002 0.0372933 1.175351384
Glucose transporter 1 Glut1 0.0235394 1.168543775
walrus wal 0.0090424 1.13573645
polychaetoid pyd 0.0190804 1.132943382
crooked legs crol 0.0068683 1.129711655
RhoGDI RhoGDI 0.0343196 1.115182753
Ribosomal protein L7 RpL7 0.0200077 1.108655952
CG4729 CG4729 0.0154013 1.064367887

Male-biased
Gene Name Gene Symbol q-value Male/Female FC
RNA on the X 1 roX1 0.0001477 150.5844909
dusky dy 0.0067317 16.05418832
CG12998 CG12998 0.0174279 6.07404928
CG1368 CG1368 0.0028511 5.00536794
RNA on the X 2 roX2 0.0364288 4.231539604
CG3604 CG3604 0.0454017 3.638126391
CG15202 CG15202 0.0183768 3.504274541
CG5597 CG5597 0.0246227 3.375742239
CG2444 CG2444 0.0424184 3.165335462
CG11350 CG11350 0.0095348 3.157911798
Eig71Ej Eig71Ej 0.0001477 2.876413523
CG9314 CG9314 0.0421122 2.662909956
CG13722 CG13722 0.0136073 2.60532191
CG15422 CG15422 0.0281824 2.57815805
Attacin-B AttB 0.0155283 2.548407388
Cuticular protein 100A Cpr100A 0.0354607 2.475773111
CG9134 CG9134 0.0155283 2.468776294
Cuticular protein 97Eb Cpr97Eb 0.0204493 2.423381789
Cuticular protein 64Ad Cpr64Ad 0.0212178 2.229211653
Moesin Moe 0.0155283 2.190343815
CG2016 CG2016 0.0020329 2.188322883
CG2082 CG2082 0.0111624 2.182024059
CG11905 CG11905 0.0454017 2.150132067
CG3153 CG3153 0.0121139 2.086543831
Cuticular protein 12A Cpr12A 0.0049562 2.015659786
155

Table A3, Continued
Pherokine 3 Phk-3 0.0051746 2.011083815
CG8986 CG8986 0.0183768 1.98594285
CG15212 CG15212 0.0320503 1.967212824
Moca-cyp Moca-cyp 0.0155283 1.935597792
charlatan chn 0.0199399 1.919135199
CG13188 CG13188 0.037003 1.912000805
CG2471 CG2471 0.0031931 1.901263273
Gasp Gasp 0.0190804 1.866914081
CG15893 CG15893 0.0190804 1.858041677
CG14534 CG14534 0.0035567 1.856918241
Downstream of kinase Dok 0.02519 1.855518511
CG4702 CG4702 0.0102837 1.838240612
Dopamine transporter DAT 0.0078598 1.801182659
CG17777 CG17777 0.0424184 1.798961225
Phosphoenolpyruvate carboxykinase Pepck 0.0020358 1.784386992
CG5177 CG5177 0.0354607 1.779922861
Cuticular protein 30F Cpr30F 0.0246227 1.776913181
Transmembrane 4 superfamily TM4SF 0.0289777 1.755460418
CG9914 CG9914 0.0078598 1.748798547
CG2091 CG2091 0.0364288 1.728827223
CG7224 CG7224 0.0027762 1.723102356
CG2145 CG2145 0.0067236 1.722294035
Cuticular protein 97Ea Cpr97Ea 0.0291288 1.717569926
astray aay 0.0424184 1.716828106
CG15213 CG15213 0.0371922 1.71627504
Structure specific recognition protein Ssrp 0.0238682 1.71529948
CG15313 CG15313 0.0020358 1.711155753
optic atrophy 1-like opa1-like 0.0028282 1.708298769
Cuticular protein 51A Cpr51A 0.0078598 1.698402042
825-Oak 825-Oak 0.0424184 1.69486619
Jupiter Jupiter 0.0017696 1.688109825
Odorant-binding protein 83g Obp83g 0.0164725 1.682488829
CG33528 CG33528 0.0097377 1.679744095
NC2beta NC2beta 0.0279344 1.674019847
Glutathione Synthetase GS 0.0291288 1.672524501
CG4091 CG4091 0.0236529 1.66476463
CG11880 CG11880 0.0177301 1.651359288
CG14073 CG14073 0.0246227 1.65121329
Defensin Def 0.0442313 1.636729299
CG1172 CG1172 0.0105888 1.633142625
Dorsocross1 Doc1 0.0017696 1.632950536
suppressor of white-apricot su(w[a]) 0.0079202 1.62589126
CG6426 CG6426 0.0029688 1.625422015
CG11825 CG11825 0.0051746 1.619391839
Ejaculatory bulb protein III PebIII 0.0228461 1.618275036
lethal (2) 08717 l(2)08717 0.0338865 1.605813021
CG31029 CG31029 0.0177301 1.603348633
CG15030 CG15030 0.0068452 1.594225604
CG13069 CG13069 0.0351717 1.58731521
CG9406 CG9406 0.0035567 1.583760884
CG2269 CG2269 0.0155283 1.577446691
hook-like hkl 0.0269253 1.577421301
CG13047 CG13047 0.0057955 1.563660987
CG33115 CG33115 0.0020329 1.55251254
Painting of fourth Pof 0.0411513 1.551214984
CG3777 CG3777 0.0164804 1.549152452
Syntaxin 6 Syx6 0.0079202 1.548225746
Centrosomal protein 190kD Cp190 0.0085845 1.532467662
Protein phosphatase 4 regulatory subunit 2-related protein PPP4R2r 0.0155283 1.530823921
CG18294 CG18294 0.0354607 1.526961034
Cuticular protein 49Ag Cpr49Ag 0.0166244 1.525151018
CG8740 CG8740 0.0440006 1.52500183
Paramyosin Prm 0.0164804 1.524474705
CG1648 CG1648 0.0267275 1.518207241
Zeelin1 Zeelin1 0.0407722 1.515133224
CG33993 CG33993 0.0407722 1.512336341
Darkener of apricot Doa 0.0386777 1.498284231
yuri gagarin yuri 0.0204493 1.491883993
CG8209 CG8209 0.0020329 1.491233004
156

Table A3, Continued
CG34380 CG34380 0.0053966 1.481087869
CG4038 CG4038 0.0246227 1.478984203
CG14096 CG14096 0.0155283 1.472456688
CG6231 CG6231 0.0343196 1.471708494
CG11984 CG11984 0.0239911 1.469640691
CG30193 CG30193 0.0154013 1.468380584
kekkon-1 kek1 0.0031931 1.466169697
CG13551 CG13551 0.0419734 1.461831858
Diphthamide methyltransferase Dph5 0.0291288 1.460293709
CG30197 CG30197 0.0155918 1.45266195
CLIP-190 CLIP-190 0.0176886 1.446373971
CG7532 CG7532 0.0036857 1.431111385
Synaptobrevin Syb 0.0473159 1.43069651
Krueppel target at 95D KrT95D 0.0068683 1.430293465
Ecdysone-induced protein 28/29kD Eip71CD 0.0216149 1.428458481
CG17723 CG17723 0.0473159 1.427211002
CG7714 CG7714 0.0154013 1.418356896
CG14989 CG14989 0.0026815 1.418337316
Glutamine:fructose-6-phosphate aminotransferase 1 Gfat1 0.0183768 1.416818525
CG15136 CG15136 0.0492883 1.414642253
CG30190 CG30190 0.0131002 1.408197203
CG33111 CG33111 0.0020358 1.407371354
A kinase anchor protein 200 Akap200 0.0332378 1.40564133
CG1969 CG1969 0.0074303 1.405426199
CG31313 CG31313 0.0356269 1.405392279
Hsc70Cb Hsc70Cb 0.0166244 1.404811894
CG1837 CG1837 0.0438764 1.401704624
mitochondrial ribosomal protein L16 mRpL16 0.0364288 1.400386382
CG5455 CG5455 0.0340052 1.398411768
FK506-binding protein 1 FK506-bp1 0.0021357 1.396795072
Ribosomal protein S12 RpS12 0.0238682 1.388581352
Cellular Repressor of E1A-stimulated Genes CREG 0.0028511 1.383703778
CG9894 CG9894 0.0292624 1.379816167
no ocelli noc 0.0017696 1.376085375
CG5059 CG5059 0.0224984 1.363878276
stathmin stai 0.0246227 1.359857053
CG17746 CG17746 0.0001477 1.358254128
Copper transporter 1A Ctr1A 0.0200077 1.349312822
CG5653 CG5653 0.0115873 1.347647316
CG34350 CG34350 0.0492883 1.34740669
CG9686 CG9686 0.0183768 1.34708859
CG6393 CG6393 0.0214313 1.341697941
Cuticular protein 92F Cpr92F 0.0176886 1.338453438
BBS1 BBS1 0.0056242 1.337740732
Heme oxygenase Ho 0.0343196 1.335281768
CG10254 CG10254 0.0205567 1.334617045
CG6718 CG6718 0.0367481 1.329511373
CG1646 CG1646 0.0414932 1.328642476
Kinesin heavy chain Khc 0.0228461 1.325808755
discs lost dlt 0.0407722 1.320026902
adenosine 3 ade3 0.0392871 1.317037726
Xe7 Xe7 0.0117647 1.300548261
tsunagi tsu 0.0427864 1.300094877
CTP:phosphocholine cytidylyltransferase 1 Cct1 0.0389456 1.298854401
CG31749 CG31749 0.0424184 1.296508744
Actin-related protein 14D Arp14D 0.0289777 1.293671225
scribbler sbb 0.0155283 1.278784521
broad br 0.0177301 1.277180802
domeless dome 0.0489997 1.272529801
Proteasome 26S subunit subunit 4 ATPase Pros26.4 0.0100334 1.266484459
CG5169 CG5169 0.0365756 1.261971035
staufen stau 0.0079202 1.25979293
Phosphofructokinase Pfk 0.0145003 1.25959855
CG1021 CG1021 0.0338865 1.256192168
Akt1 Akt1 0.0115873 1.252483529
igloo igl 0.0206386 1.250648922
Kinesin-like protein at 98A Klp98A 0.0051746 1.245612567
CG2095 CG2095 0.0336472 1.242584681
CG12384 CG12384 0.0340052 1.242149078
157

Table A3, Continued
Succinate dehydrogenase B SdhB 0.0461848 1.237054977
CG34413 CG34413 0.0392871 1.207060238
CG1746 CG1746 0.0300007 1.203615346
CG11857 CG11857 0.0354607 1.20338809
CG11727 CG11727 0.0033408 1.202329845
CG9691 CG9691 0.0279344 1.193491289
CG5850 CG5850 0.0078598 1.19122498
CG8830 CG8830 0.0155283 1.188808856
Translocase of outer membrane 20 Tom20 0.0115873 1.171232174
obstructor-A obst-A 0.0407722 1.161484198
CG6512 CG6512 0.0451806 1.16021604
Ataxin-2 Atx2 0.0329911 1.154328918
Chmp1 Chmp1 0.0440006 1.149139562
Rab-protein 2 Rab2 0.0204493 1.146855165
CG5933 CG5933 0.0289777 1.129331228
CG8963 CG8963 0.0160249 1.116635785
CG11275 CG11275 0.0020358 1.11438465
Ribosomal protein S9 RpS9 0.0155283 1.107949199
CG9300 CG9300 0.0492883 1.107788259
Myosin light chain cytoplasmic Mlc-c 0.0345061 1.09572207
CG7033 CG7033 0.0364288 1.093704675
hibris hbs 0.0320176 1.089301551

FC: average fold change of gene expression data.
q-value determined by a t-test of means of the logarithm of the ratio data

158

Table A4. Sex-differentially expressed somatic genes at 48 hr APF.
Female-biased
Gene Name Gene Symbol q-value Female/Male FC
CG13721 CG13721 0.0365401 3.615116004
sugar transporter 4 sut4 0.0045735 3.408142076
CG10881 CG10881 0.027431 2.700350597
CG17261 CG17261 0.0460356 2.584180619
CG12009 CG12009 0.0063 2.486279161
CG30285 CG30285 0.0029019 2.319509613
CG15369 CG15369 0.0065153 2.210715403
CG14613 CG14613 0.02561 2.130468928
mitochondrial ribosomal protein S18A mRpS18A 0.0049343 2.04318733
CG1702 CG1702 0.0404153 1.89191312
CG5023 CG5023 0.0091255 1.84318998
CG2147 CG2147 0.0060746 1.828622087
CG8142 CG8142 0.0252334 1.796543266
mitochondrial ribosomal protein L43 mRpL43 0.0030921 1.729592811
CG11178 CG11178 0.0063 1.723958235
CG9357 CG9357 0.0404153 1.703731907
PGRP-SC1b PGRP-SC1b 0.0275244 1.623995784
CG14407 CG14407 0.0275932 1.60299511
CG6630 CG6630 0.0388749 1.581355442
Sucb Sucb 0.027431 1.542084915
CG15099 CG15099 0.0490646 1.48732945
CG30184 CG30184 0.0441295 1.447570577
CG5112 CG5112 0.0275244 1.426964469
CG30269 CG30269 0.027431 1.42302909
CG12736 CG12736 0.0365401 1.409314644
CG8791 CG8791 0.0275932 1.389562258
NP15.6 NP15.6 0.0091255 1.348655304
CG5188 CG5188 0.0002305 1.319946571
CG6512 CG6512 0.0063 1.314396551
bitesize btsz 0.0195243 1.281584925
CG13192 CG13192 0.0300139 1.253853217
CG4766 CG4766 0.0460356 1.248995315
daughter of sevenless dos 0.0404153 1.224526545
CG10664 CG10664 0.0195243 1.223102592
ATP synthase-beta ATPsyn-beta 0.0365401 1.221778329

Male-biased
Gene Name Gene Symbol q-value Male/Female FC
RNA on the X 1 roX1 9.296E-05 232.752176
CG3355 CG3355 0.038935 1.583572497
G protein alpha49B Galpha49B 0.0252334 1.505115018
CG13298 CG13298 0.027431 1.320860782
tankyrase tankyrase 0.0027576 1.209671298
CG13907 CG13907 0.0301648 1.183308496

FC: average fold change of gene expression data.
q-value determined by a t-test of means of the logarithm of the ratio data

159

Table A5. Sex-differentially expressed somatic genes at 72 hr APF.
Female-biased
Gene Name Gene Symbol q-value Female/Male FC
CG12481 CG12481 0.00288215.155503079
CG3734 CG3734 0.00066872.779576008
CG10809 CG10809 0.01191282.409511395
vegetable veg 0.04888311.980287095
CG14043 CG14043 0.04888311.908738775
Synapse protein 25 Snap25 0.0488831 1.705906092
CG32827 CG32827 0.04888311.647972337
IGF-II mRNA-binding protein Imp 0.0488831 1.553284074
CG11807 CG11807 0.01928741.511514817
rutabaga rut 0.00066871.473071173
tomboy40 tomboy40 0.04888311.415931643
CG6181 CG6181 0.01881041.385621899
CG7288 CG7288 0.01351961.380862819
CG33980 CG33980 0.02454121.354028727
CG4841 CG4841 0.04888311.319641874
CG14757 CG14757 0.00288211.318937792
PAK-kinase Pak 0.00695411.314989167
CG15247 CG15247 0.01283191.295774263
CG3754 CG3754 0.04908071.269156842
Bicaudal D BicD 0.0245817 1.268279318
CG15609 CG15609 0.02458171.257998945
CG12099 CG12099 0.02090141.167327548
CG32556 CG32556 0.03632551.165215817

Male-biased
Gene Name Gene Symbol q-value Male/Female FC
RNA on the X 1 roX1 0.0003232 147.6307041
CG6465 CG6465 0.02454127.054766393
unplugged unpg 0.00169482.527645367
dalao dalao 0.04888311.631129827
Retinoid- and fatty-acid binding protein RfaBp 0.0493989 1.567261632
Signal recognition particle protein 19 Srp19 0.0019211 1.530741738
pointed pnt 0.00056341.519214214
CG9391 CG9391 0.04308291.492313239
Toll-7 Toll-7 0.03632551.456104339
CG13917 CG13917 0.04888311.356119128
CG8950 CG8950 0.00066871.255091764
GTPase-activating protein 1 Gap1 0.0069541 1.197964333
Eukaryotic initiation factor 4a eIF-4a 0.026515 1.133420979

FC: average fold change of gene expression data.
q-value determined by a t-test of means of the logarithm of the ratio data

160

Table A6. Sex-differentially expressed somatic genes at 96 hr APF.
Female-biased
Gene Name Gene Symbol q-value Female/Male FC
CG32695 CG32695 0.0055388 2.434059479
Actin-related protein 14D Arp14D 0.0433927 1.323670404

Male-biased
Gene Name Gene Symbol q-value Male/Female FC
RNA on the X 1 roX1 0.0055388 325.5124153
carmine cm 0.0015534 3.103847001
CG1079 CG1079 0.0404639 2.599251081
CG10830 CG10830 0.0055388 2.428765966
CG31917 CG31917 0.0136454 2.094436135
CG4281 CG4281 0.0292098 1.844571175
CG7506 CG7506 0.0433927 1.723340258
His1:CG31617 His1:CG31617 0.0211609 1.553836192
pleiohomeotic pho 0.0001774 1.398684899
Phosphoribosylamidotransferase Prat 0.0433927 1.309204947
daughterless da 0.018472 1.281031978
CG8397 CG8397 0.0292098 1.277618194
quaking related 58E-1 qkr58E-1 0.0433927 1.249520868
Srp14 Srp14 0.0053288 1.230884659
Eukaryotic initiation factor 4a eIF-4a 0.0292098 1.17676537
CG7375 CG7375 0.0404639 1.175891487
Calreticulin Crc 0.0292098 1.094458459

FC: average fold change of gene expression data.
q-value determined by a t-test of means of the logarithm of the ratio data

Table A7. Correlation values among microarray replicates for time course study.
Experiment Slide 1-2 Slide 2-3 Slide 1-3
tud F 0hr 0.9585249 0.926813 0.9451924
tud F 24hr 0.9295115 0.9297422 0.9157704
tud F 48hr 0.899306 0.8630575 0.7853708
tud F 72hr 0.8206264 0.8242274 0.8947079
tud F 96hr 0.8677953 0.9013578 0.8561389

tud M 0hr 0.9437278 0.946911 0.9536745
tud M 24hr 0.9462758 0.9431974 0.9363783
tud M 48hr 0.8853002 0.8279348 0.8544052
tud M 72hr 0.8194281 0.8898785 0.8069947
tud M 96hr 0.8293697 0.7841103 0.7537272

wt F 0hr 0.8995975 0.9429379 0.9227235
wt F 24hr 0.9079941 0.8943825 0.8625713
wt F 48hr 0.8557799 0.9051788 0.8525104
wt F 72hr 0.8390648 0.9043213 0.8035565
wt F 96hr 0.9225718 0.9331362 0.9191251

wt M 0hr 0.9611165 0.8864775 0.860469
wt M 24hr 0.7358822 0.9126831 0.6858983
wt M 48hr 0.9143322 0.9169455 0.9485165
wt M 72hr 0.9068894 0.8196776 0.8506622
wt M 96hr 0.8925447 0.8367983 0.9107053

Values are Pearson correlations. See 2.4.7 for details.
Slide 1-2 is correlation between 1st and 2nd microarray replicate.
Slide 2-3 is correlation between 2nd and 3rd microarray
replicate.
Slide 1-3 is correlation between 1st and 3rd microarray replicate.

161

Table A8. Genes expressed in the male-germline during metamorphosis.
Gene Symbol wtM/wtF FC wtM/tudM FC Gene Symbol wtM/wtF FC wtM/tudM FC
Acer 1.483304092 1.408669224 CG32652 68.87312577 40.35096753
Ack 1.356688738 1.46883715 CG32655 2.613992509 NA
Act87E 3.258911323 3.027469425 CG32690 15.07227086 13.54015292
Actn3 4.006253255 2.059521328 CG32713 2.129483467 1.445813723
Adk1 1.516684866 2.441074792 CG32718 2.072983351 NA
Adk3 1.186575331 1.246402274 CG32806 1.311639765 1.364922062
Aldh 1.483344563 1.554662221 CG32820 16.34772084 18.7654193
alpha-Est3 2.219811463 2.101896438 CG32832 5.272032488 2.732594863
alphaTub84D 1.818945085 1.822560976 CG32835 2.809539311 NA
And 9.439140938 6.311597776 CG32847 2.139064665 NA
Ant2 5.984437004 6.386500271 CG32944 1.149813782 1.127756863
aret 9.071518818 6.040282657 CG33054 1.188330886 NA
Arf84F 1.02289237 1.055266385 CG33060 1.736563999 NA
Arp53D 2.81080104 2.253119824 CG33125 1.352149818 NA
As 1.522343076 2.006262458 CG33129 1.542129455 1.520949103
Atg8b 2.521738332 1.918370068 CG33140 2.055404957 NA
ATP7 1.201216622 1.149633046 CG33170 1.628162975 1.412452692
att-ORFA 1.267245482 1.211581625 CG33182 1.68244679 1.584930616
aur 1.465252437 NA CG33189 3.071176708 3.812729855
ball 1.218068997 1.37819308 CG33218 2.961385029 1.775225123
barr 1.329671045 1.239997763 CG3323 4.377004943 5.496117161
betaTub85D 171.4161907 167.9446876 CG33287 2.283504498 NA
B-H2 1.041335846 NA CG33293 6.537117186 5.71347169
BicC 3.444757751 2.427748891 CG3330 8.995429314 7.32685651
blue 1.1963151 1.491388755 CG33317 3.392287791 3.433296343
bol 5.203499059 4.372537953 CG33340 77.88276964 86.51250059
bor 2.389188581 1.785592474 CG3345 3.164997393 3.244545919
br 2.091173971 3.037697393 CG33523 1.529383296 1.713481004
Cam 1.090657437 1.216324638 CG33937 3.999208121 3.314236731
Cap-G 1.629382677 1.840772928 CG3408 1.43233095 1.8077555
capu 2.280988904 2.633107381 CG34110 4.680321717 NA
Cchl 1.281652922 1.242615602 CG34124 1.45895704 1.777125241
Cct2 6.082760387 6.603647102 CG34133 1.439116475 1.59745558
c-cup 1.611106743 1.904947564 CG34345 1.018069544 NA
cdc14 1.975741916 1.886016551 CG34400 1.092278693 0.586434616
Cdc27 1.434464307 1.240146362 CG34409 9.569919671 17.54422023
Cdc37 1.153528447 1.266991354 CG3492 3.372384558 5.393775291
Cdlc2 8.741561627 5.62621762 CG3494 3.799572526 4.122623898
CdsA 1.209329285 1.17588422 CG3499 1.760723227 1.769003704
CG10019 10.01810467 6.381878392 CG3517 3.836683054 3.355637813
CG10053 1.380378428 0.400931168 CG3528 1.019933965 NA
CG10055 1.941673613 1.952521919 CG3552 1.828998333 1.587040728
CG10063 1.588771899 1.756458069 CG3565 3.868631167 1.591111339
CG10089 3.050554323 2.320654193 CG3581 3.22447689 6.404832432
CG10126 2.033651468 1.452805552 CG3687 4.563020276 3.093338078
CG10158 1.856108932 1.810964356 CG3698 1.246258952 NA
CG10164 2.139806892 3.493740295 CG3702 1.078790517 1.213572361
CG10171 1.328008027 1.41237077 CG3748 1.670097269 NA
CG10177 4.382051617 3.372599324 CG3788 2.500039914 2.219882603
CG10191 2.045714402 2.550199268 CG3795 1.310906235 1.192731882
CG10219 1.273125127 1.22213549 CG3809 1.264217782 2.1152263
CG10221 1.418070656 1.318905013 CG3875 3.786980473 3.347736567
CG10225 1.479196522 1.582617468 CG3894 1.384536407 2.166925603
CG10249 1.465880827 1.166496239 CG3927 2.433485771 2.437721489
CG10252 89.56162953 91.17457904 CG3942 1.48268648 NA
CG10254 1.973047265 2.144670506 CG3964 2.90185546 2.913876889
CG10317 1.993805503 1.794950144 CG3967 1.903901992 1.72200353
CG10326 1.136008582 1.147259345 CG3982 6.053022083 6.366460388
CG10343 1.196990031 NA CG3994 1.505536595 1.376805833
CG10365 1.911350231 2.296566836 CG40042 1.303705056 1.286693114
CG10396 7.138037134 3.371961626 CG40137 2.42895266 2.734459709
CG10459 1.606809016 NA CG4021 2.918838055 2.873702289
CG10510 7.960542359 NA CG40367 1.370176631 1.818167145
CG10561 1.664244047 1.775828565 CG40368 2.855838498 1.666220385
CG10565 1.091665579 1.078065899 CG40381 1.268093344 2.007136514
CG10566 1.692814674 NA CG4068 3.132820142 3.217220484
162

Table A8, Continued
CG10589 3.834286732 3.976313234 CG41065 2.266895101 2.079255672
CG10600 1.303696456 1.325585319 CG41106 3.083877487 NA
CG10616 2.599882684 2.567793434 CG4161 2.186006005 1.688145298
CG10732 1.491439821 1.513959692 CG4198 5.977541775 2.757775811
CG10734 20.68585305 20.21191644 CG4218 4.536046762 3.33726925
CG10749 2.999679069 3.307988187 CG4230 1.374217383 1.264699897
CG10750 4.643760007 3.608250491 CG4270 1.886807483 NA
CG10809 1.26481988 1.257447827 CG4286 3.309567713 2.16538339
CG10834 1.803581387 1.624656718 CG4323 3.892077909 5.017900572
CG10839 1.527227957 NA CG4375 13.33240053 8.287398525
CG10845 1.68347488 2.258535158 CG4390 1.662594586 1.841289437
CG10855 5.293509274 4.018209678 CG4434 2.448155884 3.65522552
CG10859 2.818948463 5.613489605 CG4438 2.735838611 1.93030671
CG10862 7.418615497 3.325650578 CG4439 44.84246018 37.31159852
CG10869 1.858017035 6.979748631 CG4449 2.05245255 1.789288749
CG10881 2.332701569 2.320054945 CG4477 2.333536996 NA
CG10899 1.115094746 2.663690686 CG4502 1.451466305 1.767334492
CG10908 1.504475197 1.317017142 CG4538 1.54974478 1.518841717
CG10920 2.617997029 NA CG4546 11.63351807 14.33234181
CG10934 8.952396084 2.414497069 CG4552 1.122072999 1.150993007
CG10958 2.779796869 NA CG4661 3.737244552 1.780295419
CG10993 5.354516605 3.781434074 CG4669 2.662814883 6.757648388
CG10999 3.616432099 3.211805545 CG4681 3.135817834 2.258725485
CG11009 1.09483962 1.15091286 CG4686 3.031368606 1.8113399
CG11043 3.246503181 2.437705307 CG4691 11.01775837 15.40815279
CG11068 2.261515036 2.701179695 CG4692 1.258328798 1.380013472
CG11106 1.56195948 1.664350581 CG4701 3.542134373 2.90532404
CG11110 1.268548624 1.254353416 CG4707 1.486194594 1.24209344
CG11125 5.010288217 4.704736957 CG4712 1.319627736 2.453379345
CG11145 2.737559728 2.56509226 CG4714 3.229660286 2.751481303
CG11165 5.40034792 NA CG4744 3.360300317 1.727273511
CG11196 3.226995367 NA CG4750 491.3249167 231.2617328
CG11201 3.224580179 2.926665508 CG4774 1.482958662 1.496776129
CG11226 4.766046318 1.997544822 CG4836 143.5964695 85.13936026
CG11249 3.050519263 NA CG4845 1.137005629 1.174752269
CG11251 3.279965024 NA CG4880 1.204023858 1.60761503
CG11253 1.896017605 1.707292885 CG4907 2.712318415 2.984722702
CG11291 2.693207256 NA CG4955 1.348109713 1.30043742
CG11294 1.326576812 2.274846797 CG4959 48.68476843 28.90463734
CG11298 1.477278836 NA CG4968 1.808773703 1.864575109
CG11320 1.418496958 1.22147663 CG4983 1.77312192 2.20293142
CG11327 3.420119059 3.474244481 CG4988 3.285520986 2.040463649
CG1134 2.619507217 1.963222549 CG4995 4.281851787 1.489633357
CG11362 1.672628725 NA CG5017 13.52887226 10.69722884
CG1137 4.685351405 1.805585454 CG5024 2.270592813 1.711979541
CG11373 4.211200926 3.949356221 CG5043 1.27874908 1.590899361
CG11379 1.364560359 NA CG5045 2.968505683 2.746989129
CG11404 4.568907182 1.871423675 CG5048 4.801050343 4.497026638
CG11423 1.667791356 1.418022501 CG5050 13.71855311 9.305174634
CG11455 1.02157974 1.768286176 CG5075 6.096037476 6.393733174
CG1146 1.324820771 1.340865948 CG5089 31.65849334 32.74654716
CG11474 1.390620438 1.286411759 CG5103 2.092863866 2.014766769
CG11489 1.210506285 1.338381941 CG5122 2.492941877 NA
CG11526 1.266279897 1.78950379 CG5139 3.074523512 2.506240484
CG11537 1.546314961 1.291639745 CG5144 5.44869187 2.912179655
CG11562 2.45148673 2.066833605 CG5155 2.772487365 1.375130909
CG11588 2.400247466 2.268864792 CG5197 1.43487637 1.450129174
CG11591 31.6055006 17.05438467 CG5213 3.901410933 1.862918416
CG11634 2.669934604 2.78947399 CG5217 10.12084245 6.137865781
CG11656 2.217349811 1.679684064 CG5261 1.307877173 1.214712995
CG11663 1.32370212 1.741217738 CG5276 1.836971033 1.158185608
CG11694 1.371249759 1.240541945 CG5280 5.690419036 4.968296084
CG11722 1.920339161 1.643061183 CG5343 2.240635898 2.067948682
CG11778 7.760878916 NA CG5366 1.225459713 1.264644926
CG11779 1.632229113 1.795140875 CG5388 2.968329484 2.320402456
CG11896 2.507250422 2.496620225 CG5389 1.858386153 NA
CG11913 1.409257006 2.351551792 CG5398 1.7830046 1.801488279
CG11927 1.049263107 1.413602558 CG5432 3.389354694 2.967716357
CG11929 3.473003118 3.165413347 CG5435 2.961083288 1.891607607
163

Table A8, Continued
CG1193 2.356983636 2.624380375 CG5458 2.537648969 1.954695335
CG11982 1.587345551 1.65699377 CG5509 2.453550719 2.111140892
CG12000 1.185087119 1.178223977 CG5524 1.994387536 1.800508157
CG12027 3.628358537 1.247502947 CG5538 42.56909329 40.03525315
CG12035 3.011482118 2.081248591 CG5539 2.157249244 3.20381951
CG12118 1.30741132 1.5908302 CG5555 2.599168988 2.249150407
CG12147 2.871810466 2.284911323 CG5556 2.956533671 NA
CG12159 1.090645948 1.211910594 CG5561 5.10960572 3.63401722
CG12169 3.934552869 2.569274795 CG5565 5.164842183 1.329292144
CG12179 2.214370893 2.247799363 CG5614 6.268778399 5.336357311
CG12201 4.666956021 NA CG5660 1.56634179 1.611301039
CG12209 1.639286608 1.47385086 CG5718 1.50173525 1.912728084
CG12214 1.144488104 1.41139196 CG5732 3.516026261 2.600300666
CG12229 18.71504703 3.934276754 CG5741 1.877125563 2.212652047
CG12250 2.192896201 2.36181426 CG5762 4.445748408 3.416475253
CG12289 1.817183495 NA CG5781 1.463478499 1.32287049
CG12307 1.473888724 2.257015743 CG5823 1.696564303 1.593135864
CG12309 2.876091999 2.549330169 CG5844 1.168097475 1.246473387
CG12313 11.43264295 7.128847355 CG5886 1.608944354 1.493060725
CG12360 1.980480306 1.812919503 CG5948 2.973845687 NA
CG12362 3.344177435 2.171993219 CG5987 3.040458346 3.003846475
CG12376 1.136671668 1.478478378 CG6036 1.884426097 2.42704464
CG12464 1.564536604 NA CG6059 3.597278193 2.251065479
CG12470 4.286083682 3.392825478 CG6083 1.68623897 1.820427404
CG12493 3.21354668 2.578109432 CG6091 2.464176122 2.656874373
CG12498 1.105275767 0.526657537 CG6129 2.086545578 2.60955858
CG12516 1.742515947 1.964652904 CG6130 3.027496107 2.844564946
CG12521 2.612040887 NA CG6138 6.899286851 3.309574648
CG12534 1.46613182 1.21215265 CG6140 5.370039531 3.927928985
CG12592 2.670352618 2.528317535 CG6209 2.500112565 1.533328359
CG12617 1.372897374 1.445523294 CG6218 1.75817839 1.544722441
CG12620 4.637322487 3.088417071 CG6230 2.450407696 1.968533207
CG12677 2.00726244 1.823323453 CG6255 5.040203909 4.53043237
CG12679 1.207228868 NA CG6259 1.268091934 1.153867374
CG12681 3.535263558 1.817821533 CG6262 2.967562852 5.14934115
CG12684 4.032743923 2.627674278 CG6279 8.734337573 6.130779539
CG12689 1.908059238 1.519440197 CG6304 4.094404135 2.200415552
CG12692 1.941217704 2.790149867 CG6321 1.413350991 NA
CG12693 2.288945216 2.574988544 CG6332 8.040173857 4.568021952
CG12698 1.181409772 1.845620306 CG6333 2.772474714 2.875784234
CG12699 157.1584736 71.77030449 CG6372 421.6070971 210.2020396
CG12725 9.714015217 8.501086238 CG6380 2.700001011 1.674012267
CG12736 2.020232754 2.176326814 CG6404 1.965286941 1.828324273
CG12780 1.114381529 NA CG6409 1.283732949 1.887235104
CG12817 1.733359694 NA CG6439 1.342769964 1.323755572
CG12853 29.80137424 6.295779175 CG6441 3.012299775 2.58086251
CG12857 2.306803206 1.446713961 CG6443 1.283907762 1.252061991
CG12860 20.92724814 20.12470924 CG6455 1.27318993 1.38308026
CG12861 24.79279734 25.03404688 CG6470 2.332782889 1.898518484
CG1287 1.991884728 4.018916992 CG6481 2.087638352 1.633026045
CG1288 18.121945 20.47580884 CG6485 1.700104949 2.118157965
CG12901 3.880053602 NA CG6497 3.568034168 NA
CG12902 22.44285113 22.74045614 CG6512 1.37000022 1.280485605
CG12907 5.983896073 5.923056331 CG6527 4.874466228 3.382124525
CG12923 2.491844734 NA CG6569 4.864773666 5.527206463
CG12963 1.352059857 1.498170306 CG6576 2.694406932 2.169064951
CG12983 4.18494011 2.53694833 CG6597 1.203464779 1.557292363
CG12992 2.224900056 2.113131146 CG6629 4.418288264 2.125995458
CG13004 1.193479769 1.280827438 CG6652 7.164127425 6.221846699
CG13008 2.062783037 2.682527493 CG6661 10.53376826 NA
CG13030 5.814977581 7.740748607 CG6662 1.899389527 1.500596228
CG13032 2.279306663 3.087903773 CG6664 1.539637888 1.509743238
CG1309 1.424432226 1.311032753 CG6693 1.226897688 NA
CG13110 3.75097377 NA CG6697 1.252962208 1.30075252
CG1314 2.518555551 1.540679317 CG6709 1.346911524 NA
CG13165 1.471145448 0.746120939 CG6752 2.642657678 2.750619833
CG13167 7.158901971 3.691989168 CG6761 2.317122063 2.323462355
CG13168 3.309709481 3.318657255 CG6790 1.758263098 2.377102344
CG13176 2.069989144 1.979396293 CG6859 1.471436529 1.414156004
164

Table A8, Continued
CG13186 6.391241514 4.337287902 CG6878 1.861318369 1.700901735
CG1324 6.363635565 8.623592717 CG6888 3.189812256 2.651966499
CG13243 2.195541179 2.682875514 CG6907 1.322658042 1.180218174
CG13245 34.17388744 33.18345004 CG6914 1.483980942 NA
CG13272 1.132672156 2.218897457 CG6921 2.514651917 2.569745574
CG13280 3.954522286 3.532016106 CG6923 1.126193146 1.707586124
CG13322 1.292924178 1.328091049 CG6961 1.210704704 1.353107196
CG13340 111.1167603 105.9330172 CG6971 1.205643025 NA
CG13353 1.959764941 0.930715761 CG7024 3.319527023 NA
CG13359 2.025892209 NA CG7045 40.62166599 19.71754331
CG13394 4.718389899 3.161425373 CG7046 6.545007073 4.478337031
CG1340 2.489098109 2.687600136 CG7048 1.255747747 1.198001662
CG13430 1.044841149 NA CG7051 1.753805117 NA
CG13436 2.934628246 2.306018809 CG7131 9.17559896 12.59178571
CG13442 1.282369128 3.347824728 CG7140 2.353986781 3.366722883
CG13455 4.755336263 2.205375322 CG7164 2.446366103 2.724318334
CG13457 1.250465544 2.560564774 CG7182 2.077922776 1.953572817
CG13477 9.445350733 5.111497157 CG7188 1.694264256 1.691108302
CG13481 1.540211316 NA CG7196 2.62416743 2.307713884
CG13484 1.187446434 NA CG7200 1.112959003 NA
CG13527 1.071605116 NA CG7202 4.266290149 3.889855437
CG13539 1.377619121 1.420734226 CG7251 5.533161463 3.246550082
CG13540 1.354374499 NA CG7263 1.783659824 1.58744747
CG13544 4.224719087 2.955803335 CG7276 2.635729097 1.256281215
CG13564 1.865053665 NA CG7294 1.084472181 1.40843413
CG13569 4.765822687 1.823836664 CG7309 12.66594422 6.439844228
CG13577 2.231558745 1.799848175 CG7311 3.638725314 3.133317365
CG13588 2.490622412 1.826669016 CG7335 2.669150778 NA
CG13667 3.063596703 1.92297195 CG7349 16.92813622 11.23683827
CG13700 3.863281529 3.323500088 CG7366 3.348716518 3.029522048
CG13733 1.069483054 NA CG7387 1.788215165 7.039677983
CG1387 1.805750404 2.898173508 CG7441 5.111661147 3.971897425
CG13871 2.237948003 2.629909116 CG7557 4.536455138 2.725790398
CG13872 1.29185109 1.536396278 CG7616 1.157161656 1.769875804
CG13884 2.749494882 NA CG7656 1.457034015 1.523345099
CG13889 2.189437816 2.032848005 CG7669 8.44959866 2.288629029
CG13898 4.30822389 3.986368075 CG7694 1.449992043 1.331645116
CG13901 1.471108477 NA CG7707 1.59697415 3.107372923
CG1394 17.46963549 16.59406871 CG7716 2.209556883 1.710983507
CG13978 2.895449075 2.840061068 CG7742 3.20495725 3.272378225
CG13989 1.269932766 1.992488838 CG7755 2.822772684 1.828711677
CG13991 3.33410426 3.435826834 CG7768 14.36021352 14.47517521
CG13994 1.513370627 1.460005839 CG7794 2.008082664 1.663558874
CG14011 1.694136401 NA CG7804 1.629882212 NA
CG14043 2.123880528 1.779951431 CG7816 1.679925439 2.062441622
CG14077 2.867868197 NA CG7828 2.027844114 2.034004254
CG14098 5.451465001 8.374300381 CG7841 1.740186735 1.655209328
CG14101 2.176066283 3.412074188 CG7848 3.18708008 NA
CG14113 1.832428438 1.679580242 CG7866 1.373588392 1.14990234
CG14128 3.832112617 NA CG7886 13.21591862 9.820800753
CG14154 1.247533117 NA CG7927 1.321949484 1.206196485
CG14164 1.627244931 1.535411498 CG7946 1.317250864 1.180679534
CG1418 2.877604703 2.568429488 CG8001 1.938463634 1.920294387
CG14183 7.877823174 4.825905882 CG8042 3.293647699 4.119845752
CG14215 1.447996711 1.31487078 CG8043 5.806673353 6.001985528
CG14262 2.357889865 NA CG8058 1.917862682 2.02899121
CG14282 1.505278984 1.234455935 CG8086 3.885242128 2.6949528
CG14290 2.556837757 2.241392036 CG8097 2.855410528 2.64186127
CG14297 2.210336708 3.193097896 CG8102 2.43789187 1.669084408
CG14305 3.400891371 4.332518874 CG8136 13.07529032 8.144976275
CG14316 3.287687512 2.104901556 CG8138 2.00460228 2.106567232
CG14346 2.562462962 1.694403989 CG8197 1.497497121 1.548704494
CG14354 1.235233382 NA CG8204 1.446273027 1.46030855
CG14355 13.09786265 18.74517616 CG8237 1.071446907 1.722259431
CG14384 1.521869889 0.843554134 CG8245 1.661201931 1.529469819
CG14448 2.219034431 1.545644599 CG8257 3.851228131 3.310695217
CG14480 1.510753442 1.711495853 CG8292 2.771216331 1.759944549
CG14488 4.069341161 2.002034381 CG8312 1.251148503 1.202713753
CG14508 2.041890216 1.868779615 CG8335 1.142260414 NA
165

Table A8, Continued
CG14540 2.794123087 4.802413052 CG8349 1.963113301 1.862175674
CG14556 1.408131807 1.317321992 CG8368 4.231788409 4.341758064
CG14579 4.734290084 5.793150609 CG8378 1.237089569 1.170820927
CG14589 2.77123023 3.404940911 CG8397 1.308016713 1.185018922
CG14605 1.744396366 2.263920571 CG8407 1.947523693 1.511033663
CG14609 3.926883462 2.361584982 CG8417 1.136909836 1.181379648
CG14617 1.48548406 NA CG8420 2.377450061 2.021356908
CG14644 4.701983301 1.705603173 CG8476 1.650706212 2.83549919
CG14658 3.130626568 2.779327617 CG8478 1.923957194 2.2876813
CG14689 1.442007355 2.098722767 CG8489 3.009120278 2.926020099
CG14691 2.136191775 2.668988735 CG8493 1.414497409 1.285572387
CG14708 4.394675574 2.382146965 CG8494 1.511468897 1.337189379
CG14712 3.268291222 3.68131086 CG8508 6.502662838 1.841050907
CG14718 14.56810355 17.83727809 CG8517 10.08615887 7.175220691
CG14721 1.731603306 2.143891706 CG8525 3.906055965 3.660793945
CG14735 3.06431379 2.254275434 CG8526 3.015678589 4.51535609
CG14739 1.961923362 1.725241311 CG8531 1.930726056 1.87303508
CG14740 2.567001633 3.154121824 CG8549 1.277457885 1.27111611
CG14757 2.089619947 1.681273514 CG8564 12.03651602 12.16145255
CG14763 4.845400105 2.007630457 CG8565 4.533260782 5.699461012
CG14785 3.588674719 2.907969305 CG8680 1.516114361 1.633163826
CG14801 1.400211628 1.474880009 CG8701 15.50694461 9.525349463
CG14835 7.660113949 3.087895704 CG8712 2.796013178 NA
CG14840 1.024344535 NA CG8728 1.703836419 1.56765322
CG14841 1.115610587 NA CG8735 1.278624929 1.280088459
CG14861 4.311081492 2.7885093 CG8746 4.062019015 3.336314898
CG14864 2.501973218 1.417112946 CG8750 2.013714599 1.175994313
CG14876 1.231976278 NA CG8798 1.660967929 1.497728003
CG14896 1.73136269 1.702280266 CG8813 11.16079771 9.307896049
CG14899 1.551195277 1.173837209 CG8830 1.668359142 1.564270675
CG14926 34.8228153 29.14461608 CG8831 1.584417114 1.373888333
CG14968 1.39675926 1.443713343 CG8838 4.050589918 1.90649806
CG14995 8.328522083 8.451567417 CG8840 1.28604199 1.378203692
CG15025 2.910526063 1.729642743 CG8851 3.004770686 NA
CG1503 2.115323963 NA CG8863 1.29172061 1.530228079
CG15109 22.13844164 21.23886228 CG8959 2.449984862 NA
CG15111 1.334788521 1.363162579 CG8979 1.788352644 1.491044836
CG15124 2.842817363 NA CG9001 3.269749278 NA
CG15128 1.719480384 6.232149874 CG9008 1.309334321 1.312847579
CG15136 1.797324198 1.41870573 CG9010 1.979052069 NA
CG15172 1.800666111 1.875546526 CG9014 1.832090382 2.912988196
CG15177 2.123314341 2.07273065 CG9016 101.8557373 61.58008087
CG15178 7.738876169 3.82042943 CG9030 2.61641112 1.692996715
CG15180 2.366117073 NA CG9106 3.312409699 1.972889785
CG15198 2.447680162 1.784452952 CG9129 12.00118202 14.46626288
CG15200 2.945023317 3.417328081 CG9130 44.20977351 33.19682566
CG15219 39.26990671 10.94364381 CG9147 1.543119897 NA
CG15256 1.646656621 NA CG9173 2.893732034 4.05521941
CG15258 1.956048758 NA CG9222 1.859279241 1.50918181
CG15260 4.591283833 NA CG9254 6.162829672 6.933028389
CG15262 2.372568148 NA CG9263 1.79102207 2.191244212
CG15283 2.589704624 1.708151404 CG9279 2.182232117 1.962416902
CG15286 3.95086694 4.23492394 CG9284 22.55357377 23.71930241
CG15306 3.506777207 3.843386659 CG9288 1.33691223 1.237769104
CG15330 1.062229765 NA CG9313 2.465598446 4.06567357
CG15337 2.400567657 NA CG9314 3.587441236 4.884423053
CG15344 1.418904846 1.22954976 CG9316 3.464032044 1.31108754
CG15357 1.139300528 2.089131173 CG9323 2.269213624 2.318641157
CG15400 1.360033414 1.334353977 CG9389 5.157707922 6.497550611
CG15403 3.320585265 2.019563114 CG9392 1.199751339 2.543835905
CG15450 2.169605384 NA CG9406 1.78919644 1.259454943
CG15452 4.363790405 3.398170919 CG9410 1.610250881 1.66053627
CG15459 1.500030356 2.280511144 CG9437 1.391095387 1.894562913
CG15461 1.78094325 NA CG9445 1.251034731 1.299846087
CG15472 1.806708237 NA CG9483 1.730985651 1.656800687
CG15475 3.361368125 2.039427995 CG9531 1.886780959 1.770904796
CG15498 4.210398925 4.365475715 CG9570 4.515367691 4.38631617
CG1553 1.250123845 1.694151839 CG9602 4.635390073 2.588715451
CG15530 2.841670841 2.167289857 CG9611 5.564921829 2.341354587
166

Table A8, Continued
CG15541 1.43764386 NA CG9617 1.450072954 1.443719376
CG15547 1.465026162 1.903017161 CG9624 2.855092869 NA
CG15572 1.544271135 NA CG9632 1.732153279 3.035165978
CG15577 5.570882803 2.717659728 CG9641 1.443336639 1.414810985
CG15597 1.054408321 NA CG9723 1.387842759 1.43248988
CG15602 1.121672625 0.715922063 CG9777 1.465526553 NA
CG15605 2.22403563 NA CG9853 1.040647266 1.125810927
CG15625 4.76108322 NA CG9855 1.853544179 1.75214047
CG15631 1.992296371 4.049762332 CG9861 3.379969555 3.27287569
CG15638 2.368829543 2.011620841 CG9875 3.637079789 2.521118714
CG15676 1.344842133 NA CG9876 1.020267005 NA
CG15695 1.438128315 NA CG9895 2.004680569 2.288341033
CG15705 1.603545424 NA CG9920 74.0856985 43.45446471
CG15708 1.706480601 1.682307743 CG9922 1.852088381 2.068265925
CG15742 3.196570347 2.884522984 CG9970 2.108372014 4.198431292
CG15764 3.503483791 2.127411217 CG9975 21.68795651 14.61592608
CG15800 5.372342061 NA chb 2.208282266 2.512337522
CG15892 8.12352907 4.689835368 CkIIbeta2 2.059250871 2.179267048
CG1631 2.544355044 NA CLIP-190 1.441186791 1.552686415
CG1662 1.096708727 1.627979278 cmet 1.663719994 2.605296512
CG16716 3.048022201 2.645343205 Cnx99A 1.214643291 1.311094655
CG16719 1.760781413 NA comr 1.487791903 NA
CG16739 4.650568163 2.37546532 Coprox 1.471373633 1.220194404
CG16758 1.380441258 1.483994243 CR32657 5.492920677 3.526406966
CG16781 2.14521799 NA CR32658 10.93409758 10.76391284
CG16782 3.175025992 3.914602361 CR32660 10.92165187 9.189998408
CG16825 3.787814353 2.990064074 CR32661 6.621345197 2.507918386
CG16849 3.219372288 NA CR33318 3.478833832 3.387924854
CG16852 2.434311483 NA CR33319 2.825610925 1.604353477
CG16894 2.004420982 1.847643582 Crtp 2.427885285 2.053299032
CG1690 7.718433339 9.507604748 CSN8 1.02396269 1.133412126
CG16940 1.32342088 1.19396263 cup 3.298341887 3.468176709
CG16957 4.136732159 1.915426684 CYLD 1.929991332 1.923848534
CG16964 1.129832289 NA Cyp312a1 5.775622027 3.618357652
CG16972 5.995818702 6.418462842 Cyt-c-d 11.35634554 8.279675107
CG16979 2.310244507 2.191128805 Dgk 1.702484388 NA
CG16984 2.95337646 3.394770681 Dhc98D 1.309521195 1.533444561
CG17010 3.778647363 2.998523548 Dhh1 1.706914626 1.709843259
CG17030 1.884343649 2.097347456 Dhod 2.316758191 1.729038773
CG17083 2.349408411 2.248869229 dj 4.78942327 4.172446672
CG17098 1.676310842 1.977488922 djl 3.990464248 1.787201359
CG17118 2.137857816 2.822911086 Dlc90F 1.311801509 1.150119122
CG17122 1.662155346 2.482495448 DnaJ-H 1.511876635 2.231167706
CG17140 1.54356127 NA DppIII 1.769140824 1.670526104
CG17195 2.299179819 NA dpr17 2.56711064 2.820096342
CG17198 1.550574836 2.243150388 Drp1 1.275834371 1.251321286
CG1722 1.68023761 1.923382064 dtr 1.543586205 1.278026763
CG17230 4.27285367 3.832097952 eIF-1A 1.03888016 1.142484528
CG17237 3.660222445 4.172120713 eIF-4a 1.350708743 1.800796986
CG17260 3.031146397 NA eIF4E-3 5.804846638 6.611936541
CG17261 5.360849615 3.425388877 eIF4E-4 4.388937844 4.270444632
CG17294 1.015959768 NA eIF4E-5 3.692632437 4.029959571
CG17300 1.99764818 2.372663432 eIF4E-7 1.723575112 1.875133596
CG17302 1.76984367 1.959188974 eIF5B 1.177661776 1.192681336
CG17329 4.080277035 2.788693563 endos 1.825988208 1.755379101
CG17343 1.378471841 1.328804202 exu 51.91982349 52.76282303
CG17344 4.545809579 3.309600779 fan 6.794505341 3.74754506
CG17349 2.615632852 4.112572754 fig 1.084079676 5.757677848
CG17376 93.17249383 60.19457409 FucTB 3.779513179 3.744618533
CG17377 36.43830786 27.68031469 FucTD 1.51278345 3.477621161
CG17380 3.849461129 NA fzo 3.175651418 1.442547228
CG17387 2.160105232 2.428963012 Galpha49B 1.34867666 1.099478683
CG17438 5.235179138 4.091503657 Gapdh1 1.059389714 1.113394582
CG17470 10.08620151 10.5385478 Gas8 1.179220501 NA
CG17494 1.579334537 1.73353904 GC 3.594541681 2.811762924
CG17564 2.566310649 1.932679052 Gclm 1.268690773 1.296354327
CG17567 28.02266909 25.58582122 gdl 2.780827739 3.108790308
CG17648 1.172599128 NA gft 1.995770726 1.994483033
CG17666 6.888222577 5.667858836 Gint3 1.4760884 1.568963634
167

Table A8, Continued
CG17717 3.440299344 NA gish 1.361304353 1.574835361
CG17735 1.550295964 1.662474721 gk 1.399091855 1.152127193
CG1774 1.308228869 1.456546877 Golgin84 2.073176047 1.887786785
CG17763 3.255403274 2.628103966 gom 2.412280181 1.836400592
CG17764 2.484689089 NA Got1 1.58652023 1.678197302
CG17770 3.202231213 3.541796616 granny-smith 1.684629015 1.670781871
CG17838 3.570971056 3.239901597 Grx-1 1.201154522 1.879392737
CG17931 1.265777335 1.296677672 gskt 5.234831242 3.282457475
CG17991 5.104292842 5.714128749 GstD9 1.978842601 1.744781901
CG18063 1.850645152 1.430679343 HBS1 1.441241466 1.200991303
CG18131 2.14146724 3.63982104 HDC02577 3.976901104 NA
CG18155 1.992995034 1.402361307 heph 1.596563815 1.466215289
CG18157 1.759902434 NA Hex-t1 1.559878645 NA
CG18170 6.357083406 5.450226216 Hex-t2 1.650299991 2.083097596
CG18193 7.1741389 4.082242776 His2A:CG31618 1.904316429 1.419973475
CG18259 1.411168088 2.253119482 His2B:CG17949 1.644345765 1.614498306
CG1826 1.343898178 1.620166786 Hmx 1.262632053 1.201515504
CG18266 2.787313338 4.382207954 Hrb98DE 1.359402027 1.365716431
CG18335 3.103236497 4.07081011 Hsc70-1 2.162990528 1.928370553
CG18336 4.266770807 NA Hsc70-2 5.686377481 4.821787991
CG1835 6.465152164 1.764701902 Hsp60B 6.7804035 8.375451137
CG18369 3.495861332 2.622413818 Hsp60C 111.2055439 100.870849
CG18371 3.151563341 NA Hsp60D 1.680536074 NA
CG18397 1.185435519 1.214800952 Hsp83 1.453344242 1.620803049
CG18449 27.87411504 14.30607464 HtrA2 1.427604678 1.506654026
CG18568 3.307944503 4.729780263 hyd 1.195204226 1.146215593
CG18662 9.185620355 7.257123581 hydra 1.900454186 4.184805428
CG18675 2.538711112 2.368750632 Imp 1.424157 1.780267409
CG18787 2.050868391 2.121086048 Irk2 4.530859797 NA
CG1882 1.526426303 1.286250941 isopeptidase-T-3 3.238564969 1.795645607
CG18869 3.050901689 2.186830699 Iswi 1.559494859 1.419801479
CG1902 1.187029143 1.193186791 I-t 1.121584909 0.827692862
CG1958 3.951784603 3.153715644 Jafrac2 2.627092955 2.639396449
CG1979 1.129307418 4.978651061 janA 1.621565371 1.515768272
CG1988 2.012887455 2.321805285 janB 5.051760004 5.042718329
CG1999 3.412459552 2.59019371 jar 1.263248659 1.564582143
CG2051 1.45455721 2.497745773 Jupiter 7.389895572 5.608177057
CG2053 4.64031688 NA Kap-alpha1 1.565750453 1.431321054
CG2061 1.585690862 1.612664333 kermit 2.162059934 2.298905803
CG2113 2.892485819 1.691967958 Klp10A 1.408404332 1.505647097
CG2127 22.95213473 41.8813532 Klp3A 2.016604306 2.53943998
CG2147 1.106174503 1.151558484 Klp59D 2.562475557 3.288678056
CG2241 2.410156218 1.775937954 l(1)G0095 1.011639656 NA
CG2267 2.551952301 4.500139317 l(1)G0148 2.032485521 2.250765161
CG2277 2.304676706 1.914206122 l(2)03709 1.29228638 1.219054729
CG2291 5.07100954 3.834639196 l(2)05070 1.192455044 1.257804838
CG2336 1.722213644 2.054141328 l(2)37Bb 1.399779163 1.164276683
CG2533 2.824939287 1.90973611 l(2)tid 1.304536404 1.14808699
CG2574 1.028480684 1.576145778 laf 2.069613121 NA
CG2750 4.577419117 2.083507784 Lasp 2.454517542 2.467118794
CG2871 2.776598746 2.233760348 lat 3.714609853 2.038587016
CG2921 2.931734793 2.920248945 lkb1 1.273091747 1.322692735
CG2955 10.27433164 10.79660265 loqs 1.666893019 1.496427751
CG2964 3.453910364 3.196778149 MAN1 1.297705603 1.346112061
CG30038 3.963631088 4.521988847 mbf1 1.298139627 1.158932655
CG30039 22.72876355 7.565035797 MCPH1 2.928084401 4.027539499
CG30056 2.723851445 2.185942936 mei-S332 1.469944773 NA
CG30058 8.137250492 8.423449595 Men 1.783861896 1.22293411
CG30065 2.114295529 NA Menl-2 2.995863661 2.30436577
CG30093 2.432078859 1.885542987 Mes2 1.169887554 1.15921323
CG30094 1.188689558 NA mfr 1.413376909 NA
CG30109 1.638526257 1.388428139 mge 1.909129346 1.744322023
CG30157 1.597324402 1.278407703 mib2 1.507763605 1.571560152
CG3016 1.327680205 1.435957373 MICAL 1.270025881 1.365690598
CG30177 1.121782682 NA milt 1.408787769 1.520345038
CG30182 2.672225057 1.990629614 Miro 1.497302253 1.44005106
CG30183 1.12759586 1.183789524 Mkrn1 1.434591841 1.124424823
CG30184 1.330844507 1.353886135 Mlf 1.760944907 1.576506406
CG30192 2.033116255 NA mrj 1.650091533 1.737473364
168

Table A8, Continued
CG30222 4.9658639 NA Msi 5.846867666 3.765822277
CG30270 3.820763033 1.508108265 Mst33A 5.402766809 2.963993885
CG30271 2.833071672 2.880902903 Mst35Ba 28.8689315 16.88368754
CG30273 1.011420868 NA Mst77F 120.2051127 55.72864789
CG30278 2.194055343 1.724683446 Mst84Da 31.72221639 20.22910049
CG30324 3.297671811 2.972401036 Mst84Db 33.09298711 28.85318553
CG30334 1.371654885 2.306662189 Mst84Dc 24.80018382 26.03573563
CG30350 1.19625763 2.984142545 Mst84Dd 21.62102952 18.70242188
CG30355 3.03113388 1.58421151 Mst87F 47.39528869 23.29514964
CG30356 1.793465762 1.468318717 Mst89B 2.875025774 2.132625938
CG30362 5.947697991 3.441304492 Mst98Ca 138.4304418 130.9629747
CG30363 3.009340402 NA Mst98Cb 26.73584923 31.04059915
CG30365 4.901508711 5.086071444 NA 1.543223734 NA
CG30366 2.329290288 2.148749763 neb 1.264094287 1.32439605
CG30376 4.626003745 4.055742602 Nep4 1.996387701 1.229965284
CG30378 2.296274905 2.360170455 nmdyn-D7 1.951733863 2.726926179
CG30384 1.418460621 NA Noa36 1.403465252 1.466856805
CG30393 4.325432533 3.85851934 nrv2 1.523527617 1.438664867
CG30412 2.776807979 3.092802399 Ntl 2.440884697 2.198452332
CG30416 2.45175731 2.011236829 nudC 1.6686873 1.713965416
CG30429 3.455687985 1.806075035 nudE 1.378098958 1.425678052
CG30430 37.24088246 25.38873367 Nup58 2.115869078 1.919936281
CG30432 1.468615584 NA Nup62 1.198778271 1.175275096
CG30447 1.816220851 2.080704363 oaf 1.705085619 1.219049593
CG30459 1.430729118 1.727400829 ocn 39.53053719 22.93418772
CG30460 2.618497471 2.613435068 ofs 1.945654108 1.95873963
CG30471 2.731275378 2.56712439 orb2 5.113163711 3.016720269
CG30487 1.978520993 1.673768063 osm-6 1.198705244 NA
CG3061 1.178189053 1.175807421 Paip2 1.358605582 1.482006921
CG3085 21.03173812 20.03800834 Pect 1.270548664 1.30536451
CG3092 7.434557856 6.251051444 Pen 12.21562939 14.94785699
CG31007 4.223822942 4.449747091 Pfk 1.335977316 1.86306149
CG31008 6.322630849 NA Pgi 1.432287932 1.376523075
CG31010 1.63361052 2.101112567 Pglym87 1.694960343 2.231099668
CG31025 10.92837985 16.02908923 phtf 1.748039076 2.92201857
CG31029 2.690843633 3.134680514 Pif1A 3.311447732 2.660779473
CG31050 1.989049836 NA Pif2 2.581086881 2.357269039
CG31055 3.47227637 3.631101252 pim 1.235348417 NA
CG31088 1.460350748 NA pita 1.007406771 1.22607421
CG31093 1.289683143 NA Pka-C3 1.264401246 1.445225614
CG31108 1.67953676 2.148644604 Pmm45A 2.829560058 2.680432566
CG31128 2.192669069 0.781737227 Pof 9.883161933 12.50153342
CG31141 1.478937903 2.079888551 polo 1.267933804 1.68661023
CG31161 7.335126205 2.333669319 poly 1.447791873 1.387736498
CG31169 2.806006688 4.247598649 pont 1.397619813 1.811090908
CG31174 2.614790467 NA Porin2 2.544546295 3.291649413
CG31178 3.122932119 2.648245242 Pp1-13C 3.612040804 1.596073098
CG31204 1.686054951 2.611277034 PpD3 1.124345174 1.304323188
CG31206 2.632269905 2.604099866 PpD5 1.507799365 0.918621858
CG3121 1.07185542 1.601647521 PpD6 1.366334195 1.256484089
CG31226 14.76624408 12.00485471 ppl 1.793476406 1.525164884
CG31230 1.72038165 NA Ppox 2.555966859 2.140528854
CG31231 7.84542766 7.258278387 PQBP-1 1.104630038 0.639523894
CG3124 96.51681661 101.8290658 primo-2 2.7729868 1.80156906
CG31244 5.256361634 2.906068534 prom 1.137153052 NA
CG31245 2.23433995 NA Pros28.1A 2.903508136 1.66733395
CG31275 1.818262351 2.287176211 Pros28.1B 2.132074778 1.693682677
CG31281 19.07133504 6.195007895 Prosalpha6T 1.569915846 1.780956987
CG31286 2.025310682 1.339271432 Prosalpha7 1.1476442 1.254668917
CG31287 3.051173108 2.419060417 Prosbeta3 1.220298171 1.263379903
CG31294 1.304647141 1.61211471 Prosbeta5R1 2.760860676 2.460940742
CG31320 2.901124046 1.630549628 Prx5037 1.423214456 1.547018916
CG31327 2.775379043 2.620700318 Psa 1.538702096 1.69727024
CG31347 2.451101084 2.820170492 pygo 1.562328103 1.504587054
CG31360 1.235706988 1.161740781 qtc 1.580279058 1.522519287
CG31406 2.133387032 1.590282384 qua 3.044515188 2.92375617
CG31407 2.113791163 2.731319696 Rab30 1.540740671 1.259941101
CG31415 1.224924735 NA Ranbp16 1.160871801 1.313580472
CG31459 3.354208704 2.22205202 Ranbp9 1.694706155 1.55177736
169

Table A8, Continued
CG31467 1.164618169 NA ran-like 4.150762421 3.95901602
CG31468 82.22794006 52.81072197 r-cup 1.537065048 1.434775004
CG31473 1.736520834 2.692153365 rept 1.346459621 1.60134639
CG31523 1.75913629 1.907535718 rha 2.836715286 1.136475275
CG31528 1.033033365 2.012628912 rho-6 1.626405281 1.551620065
CG31533 16.19309623 8.310515575 rho-7 1.378517587 1.460661945
CG31538 22.88516852 24.00989339 robl62A 36.81601656 12.30129431
CG31542 5.360601015 4.489350005 Roe1 2.100119608 1.658167772
CG31546 3.505854916 2.577130883 RpII18 2.025132253 1.890293814
CG31556 1.327928765 1.350492325 RpL10Aa 1.340272537 NA
CG31601 1.089322879 NA RpL22-like 2.536151796 1.979536552
CG31624 57.31012128 62.94600975 Rpn6 1.158985712 1.295296778
CG31639 29.03684028 21.21631095 RpS19b 4.283595504 2.801075663
CG31644 2.151615652 1.94481741 RpS29 1.033851669 0.563688352
CG31679 1.196874177 1.812345369 RpS5b 1.399575826 2.240334646
CG31682 1.489978178 2.627761911 Rpt3R 2.932254584 2.075546553
CG31694 1.532524026 1.391131105 Rrp6 2.218072109 3.049360935
CG31697 1.692082763 NA Rtnl2 1.040003684 2.60237191
CG31709 25.25065876 20.69716411 Sac1 1.408218142 1.480700152
CG31737 5.646897596 1.961518211 sbr 1.239767806 1.198719245
CG31740 63.89243367 56.21157527 schuy 1.957773543 8.084429827
CG31752 1.18321609 0.552726788 scpr-A 2.092070143 NA
CG31765 4.965077452 NA scpr-C 3.518353727 NA
CG31773 2.674270429 2.848764309 Sdic1 1.740064303 1.869403942
CG31788 23.94458216 2.808275328 Sdic2 3.446341781 3.452773541
CG31798 1.697785468 NA Sdic3 5.070572926 5.718853967
CG31802 12.83023416 4.283141512 sec24 1.352767702 1.366883984
CG31818 1.661719622 1.828769254 sec71 1.502156535 1.678597086
CG31820 26.27814611 21.39196249 Sgt 1.454739963 1.224316561
CG31835 3.87290807 NA Sip1 1.173162276 NA
CG31858 2.685392665 2.445181977 SIP2 13.30015334 17.94879856
CG31861 2.113959075 1.931576816 sip3 1.593153448 1.253491373
CG31870 4.941412805 4.11034605 Sirt2 1.973984124 1.880042344
CG31874 5.341264475 2.524940899 sle 1.217454645 1.991946243
CG31875 1.284727343 0.500099787 sli 1.455667024 1.255654793
CG31882 3.453249642 3.337784266 sm 3.814807389 2.718453809
CG31907 2.975885838 2.986655571 smg 1.300844538 1.515217617
CG31909 6.878319005 2.641814374 Smg6 1.244246567 1.334822845
CG31910 2.420447255 2.143615509 SMSr 1.633499867 2.005049031
CG31913 4.643317925 4.659263615 sno 1.62240879 1.848051473
CG31920 11.22807863 9.151589945 SP555 1.362532254 1.258376513
CG31921 2.993476867 2.92599146 spag 1.243902764 1.532165859
CG31948 11.13972657 16.4409612 sra 1.256833036 1.379248234
CG31949 2.560425358 1.576903093 ssh 1.604101709 1.692237695
CG31960 1.583883539 NA sut4 7.154699701 6.692394329
CG31988 98.11551491 22.97960829 Taf12L 3.007194447 NA
CG3199 1.736303859 NA tafazzin 1.228949298 1.780273744
CG32026 6.19885518 5.349660689 tan 1.439813703 1.408498682
CG32061 1.874758214 0.951284055 Tango11 1.174960408 1.186841606
CG32063 16.64815374 29.33047289 Tektin-A 1.607783382 1.655420563
CG32064 77.09358938 72.15774025 Tektin-C 7.352070921 5.548067313
CG32081 8.145250515 4.32975762 TfIIB 1.249814155 1.143342553
CG32106 1.865833849 2.662335114 TfIIS 1.236045623 1.227132967
CG32112 1.959862418 1.812809451 tilB 1.499639998 1.270847905
CG32119 2.503887193 3.388500423 Tim10 1.4307738 1.365923688
CG3213 56.06845702 38.67850464 Tim17a1 2.899742318 2.291342879
CG32137 1.488261778 1.527085423 Tim17a2 1.838807062 1.318081703
CG32141 2.697962811 3.133939407 Tim17b1 2.946562564 1.73990142
CG32148 3.626877925 NA Tim17b2 2.78095529 2.968898723
CG3215 5.04057664 2.030114214 Tim9a 1.47316796 1.307407797
CG32163 1.512953651 NA Tim9b 1.549886618 1.352930661
CG32164 1.894715138 1.951159944 Tob 2.202751804 1.220679151
CG32192 4.675291071 5.423941452 Tom20 1.262122654 1.148421173
CG3222 5.290446238 4.70949501 Tom70 2.605474866 2.833551676
CG32236 2.44002371 1.74574115 tomboy20 11.67043388 5.950012661
CG32238 2.374619588 3.375101864 tomboy40 3.128095275 2.918752288
CG32240 5.074944943 4.528181906 Tpi 3.096047373 2.322786288
CG32262 2.461338152 1.874893389 trk 1.145414779 1.606905564
CG32263 2.085210007 1.579183955 Trxr-2 1.700769839 NA
170

Table A8, Continued
CG32299 2.521522895 1.945440902 TrxT 9.530767462 8.885425426
CG32306 2.546915438 1.893916176 TSG101 2.041028607 1.66309007
CG32308 2.078804189 1.478323578 Tsp66A 3.294777475 2.630970755
CG32351 99.53918975 75.40622926 twe 4.345543669 3.107640907
CG32371 1.915989523 2.839540173 twin 2.14464043 2.134974941
CG32388 6.664013688 7.352215927 Tyler 2.539928007 2.462181081
CG32392 3.662103561 3.345355128 Ubc84D 3.570422889 1.925172849
CG32396 4.829551086 NA Ubi-p63E 2.523750948 3.548112705
CG32436 3.355331884 4.823575764 Uch 1.396421788 1.640235853
CG32437 2.825598613 3.407865647 Uch-L3 1.61101356 1.499734966
CG32440 4.310722518 0.900301235 und 1.252369215 1.464073841
CG32445 5.157942789 8.480968758 veg 1.736774654 1.645892059
CG32450 1.583887162 3.579842154 Vha100-3 3.712298752 1.609201962
CG32509 1.471359927 2.292904668 vih 1.512180917 1.48493401
CG32548 10.12197553 6.297904262 vis 2.966784589 3.273374045
CG32572 2.988027397 1.837591208 Vps28 1.560113592 1.803493983
CG32574 1.893529999 2.265662695 w-cup 12.59729328 5.35462876
CG32588 2.326895309 NA wee 1.932226274 1.468611463
CG32591 3.490023675 2.932868277 Xpac 1.258869244 0.556594901
CG32598 3.242909776 1.588197629 yellow-k 5.08376941 NA
CG32627 1.203163311 1.490963707 yuri 1.888759725 1.972988324
CG32628 5.643768769 5.592776871 Zw 1.139613392 1.279399999
CG32650 1.257228642 NA

171

Table A9. Genes expressed in the female-germline during metamorphosis.
Gene Symbol wtF/wtM FC wtF/tudF FC Gene Symbol wtF/wtM FC wtF/tudF FC
Aats-his 1.574386013 1.343471895 CG8219 1.075927272 NA
aay 1.294943874 1.362963965 CG8500 1.274489194 NA
Acox57D-p 1.328459619 NA CG8636 1.297306957 1.181981973
Acp76A 1.005038009 NA CG8788 1.28423382 1.527904183
Acph-1 1.368618589 NA CG8791 1.372252911 1.392248278
Adam 1.302285306 1.593486102 CG8932 2.095265095 NA
Adgf-A 1.551649816 NA CG8949 2.102170464 NA
Akap200 1.212186957 1.369997337 CG9018 1.387738324 NA
alpha-Adaptin 1.433421298 1.098381526 CG9025 1.476157649 1.529937934
AP-1gamma 1.472177004 NA CG9027 1.132318769 1.29719374
Ark 1.540902231 NA CG9144 1.372884929 NA
aru 1.341545285 1.248267844 CG9307 1.604086593 2.417163271
bif 1.465534218 1.343218357 CG9331 1.315261365 1.441157281
bowl 1.445005468 1.304552221 CG9686 2.42898618 2.646677759
brat 1.523135092 1.271731726 CG9740 1.089152599 NA
brp 1.059625762 NA CG9784 1.118837309 NA
bwa 1.445096004 1.258050424 CG9812 1.068984651 1.418467044
CaMKI 1.476526082 1.288562457 CG9894 1.227736162 1.282468591
Cct1 1.072397068 1.131472893 CG9917 1.537646326 1.217447218
CG10144 1.188169111 NA CG9940 1.372090217 NA
CG10268 1.70406357 NA CG9961 1.282816059 NA
CG10286 1.332160451 NA chrw 1.16901274 1.551536723
CG10663 1.231058627 NA Cht2 1.397204434 1.394886712
CG10984 1.214519535 1.27317568 Cip4 1.292918885 1.160596237
CG11000 1.916733585 1.733274334 Cirl 1.31557554 1.608159581
CG11155 1.3430881 1.498167745 cln3 2.361935151 NA
CG11403 1.505600102 NA Corin 1.729426802 NA
CG11448 1.522933471 1.275868447 Cp190 1.497108381 1.308817475
CG11753 1.164949824 NA CR32205 1.375826118 NA
CG11791 1.30207714 1.277976362 CR32477 1.117605633 1.290645832
CG11852 2.263148938 3.285651172 CR32745 1.160950127 NA
CG11964 2.128600491 NA Ctr1A 1.230812741 1.405825568
CG12355 1.527286174 NA cv-2 2.159991823 NA
CG12576 1.454417772 1.158191282 Cyp6d5 1.462499732 1.949524118
CG12701 1.737928981 NA D2R 1.021444727 NA
CG12936 1.285220383 NA DAT 1.476748392 2.508287612
CG12990 1.06275077 NA Df31 1.401917705 1.244492575
CG1304 1.707113097 NA Dg 1.036291899 1.169933958
CG13213 1.685816872 1.646320756 dmt 1.427956487 NA
CG13344 1.434898363 NA Doa 1.528720878 1.52786937
CG13538 1.024470982 NA dro6 1.3261616 2.674890302
CG13617 1.008528226 NA drongo 1.331001822 1.474606875
CG13784 1.662968086 1.332961728 Dsk 2.399873015 NA
CG13875 1.452104535 NA ecd 1.326211146 1.137275539
CG13907 1.209769093 1.465587429 Eh 3.440755372 NA
CG14054 1.204941511 NA eIF-4a 1.264180759 1.330209273
CG14057 1.377927928 NA Eip63E 1.356581809 1.229534336
CG14270 1.052418938 NA ex 1.552986793 1.397860901
CG14341 2.008633334 NA Fas1 1.28099864 1.185712849
CG14442 1.433582822 1.270818459 flw 1.376272211 1.196693821
CG14567 1.23191897 1.647444303 fng 1.409692036 1.287880206
CG14621 1.575481096 NA fok 1.15419941 1.151841221
CG14676 1.951832312 NA Gadd45 2.113479407 2.486182754
CG14852 1.962614243 NA Gbp 1.768658083 NA
CG14854 1.581938181 1.441522279 Gclc 2.216162071 NA
CG14998 1.364003513 1.623453442 glo 1.314235257 1.337424463
CG15012 1.253314585 1.260715201 glob1 1.313718176 1.711141709
CG15065 1.601003301 NA gol 2.193858423 NA
CG15309 1.763567085 1.584581703 Grip 1.170186943 NA
CG15374 1.105174835 NA Gs1l 1.135396694 1.198201332
CG15589 2.522069654 NA GstD7 1.40872455 NA
CG15735 1.353448628 1.384950338 Gug 1.603167812 1.199186169
CG15878 2.643952147 NA gw 1.377886463 1.180266235
CG15893 1.606906495 1.53418345 Gycalpha99B 2.447186513 NA
CG1600 1.28317139 1.315999807 His2Av 1.298461288 1.203867685
CG1602 1.371026835 NA hkl 1.449340116 1.274456747
CG16721 1.23862178 1.449168128 HmgD 1.393698981 1.459185213
172

Table A9, Continued
CG1677 1.399524313 1.211203738 Ho 1.233886935 1.323545289
CG17028 1.37941555 NA HP1b 1.462282453 1.210665781
CG17196 1.407594803 NA ImpL2 1.425255336 1.51522228
CG17211 1.872846489 NA Inos 1.152252984 1.613007814
CG17262 1.392113899 NA InR 1.587189591 1.057166141
CG17322 1.113962085 NA IP3K1 1.166790048 1.298704601
CG17549 1.534605272 1.715611008 Ipk2 2.46312555 NA
CG17598 1.67953586 NA Jafrac1 1.573647607 1.311023808
CG17657 1.306985094 NA katanin-60 1.361411376 1.299894477
CG17754 1.869669491 1.415514022 Khc 1.301417819 1.283076892
CG1792 1.448873259 NA kkv 1.664539581 1.253110442
CG18317 1.110832632 1.26326723 klar 1.223562741 1.180022065
CG1832 1.185683567 1.295460474 Klp59C 1.248603755 NA
CG18472 1.144947328 NA Krn 1.61397576 NA
CG18516 1.44406064 NA kuk 1.076315898 1.265829872
CG18542 1.281450323 1.358233795 l(1)G0003 1.077411801 1.354756316
CG18549 1.369154526 1.241019966 l(1)G0193 1.926236383 1.678652534
CG18766 1.31089221 1.204345378 l(2)05510 1.244006045 1.397704569
CG18812 1.319147276 1.131101705 l(2)08717 1.554576762 1.397380347
CG18854 1.118450165 1.172941283 l(2)35Bd 1.025141629 NA
CG1965 1.636601009 NA l(3)73Ah 1.164604026 NA
CG2082 1.131514362 1.379153074 l(3)82Fd 1.371263078 1.280774314
CG2446 1.412119323 1.208621223 laza 3.665332783 NA
CG2680 3.587583377 NA Lectin-galC1 1.440394714 1.335178062
CG2781 2.162159558 1.175224311 Mad 1.445861771 1.247478716
CG2924 1.341132891 1.317946637 mad2 1.135646139 NA
CG30084 1.212104025 1.173872144 MED14 1.679780992 NA
CG30197 1.289102372 1.575566816 MED17 1.23101304 NA
CG30466 1.092088299 NA MED23 1.968756793 1.702347836
CG3077 1.643646053 1.334170657 Mekk1 1.396941282 1.258868726
CG31012 1.213338174 1.238011041 mio 1.699059859 1.081725603
CG31200 3.153465767 NA mip40 1.056708175 NA
CG31374 1.268430132 1.435882347 Mipp2 1.667798522 NA
CG31524 1.014482512 NA mir-100 1.499106201 4.609413242
CG31627 1.008700217 NA mld 1.301689971 1.311715211
CG3168 1.323594997 1.533120655 mod(mdg4) 1.441275938 NA
CG3191 1.695106523 NA Moe 1.167090096 1.446484578
CG32039 1.232743414 1.308952614 mri 1.279234338 1.303539968
CG32091 1.599497702 1.389029676 msps 1.184712719 1.131452436
CG32133 2.749999317 NA mth 1.281526396 1.352558892
CG32181 1.163280562 NA mthl1 2.11004636 NA
CG32412 1.379407853 1.390884197 mus308 1.169815835 NA
CG32446 1.306767242 1.315387478 mxc 1.65043196 NA
CG32479 1.240244306 1.312151862 nAcRbeta-64B 1.572281752 NA
CG32495 1.008350452 NA nemy 1.8741815 NA
CG32543 1.904943993 NA Nlp 1.331193843 1.308952041
CG32580 1.681564645 NA Nmd3 1.596405198 NA
CG32662 1.321665067 1.243733607 Not1 1.0543251 1.170586572
CG32676 1.606725156 1.561752363 oaf 1.186856712 NA
CG32772 1.591347276 1.451800913 Oat 1.714072337 1.861418376
CG33087 1.498989152 1.324953853 Obp56a 1.235191924 1.830489001
CG3309 1.530228745 2.378207286 odd 1.17508998 NA
CG33171 1.26882846 1.238070319 Os9 1.670378995 1.445177987
CG33249 1.348168665 1.285391608 osk 3.555952029 NA
CG33285 1.101581889 NA Pde11 1.470737112 1.456527226
CG33467 1.316504847 NA Pepck 1.368479031 3.285848691
CG33474 1.240641805 NA PFE 1.39362279 1.218516947
CG33490 1.338375164 NA pgant5 1.389528892 1.350559487
CG33510 2.235270296 NA Pi3K59F 1.808981959 1.754697953
CG33980 1.072091834 1.180080532 Pi4KIIalpha 1.21444522 NA
CG34379 1.041246568 NA pio 1.424813889 1.423093069
CG3609 1.295329457 1.314782174 Plip 1.911399664 NA
CG3630 1.249371709 1.97803505 PpN58A 1.000472941 NA
CG3649 1.335385542 0.413775522 Pros45 1.094421383 1.213121481
CG3704 1.387483262 NA Prosbeta2 1.227217328 1.229351262
CG3829 1.281819678 1.180827838 Prosbeta5 1.229439963 1.275631854
CG3940 1.371295361 1.450598883 pUf68 1.246238152 1.207260838
CG40006 2.636071662 NA Pvf1 1.204171082 NA
CG40100 1.309365668 NA pyr 1.481532399 NA
173

Table A9, Continued
CG40263 2.287637563 NA qm 1.312514101 1.399300335
CG40275 1.470587579 0.87253993 Rab2 1.09463036 1.176805341
CG4073 1.062813386 NA Rbp1 1.829729071 1.498663078
CG4090 1.954324445 1.427105016 Rel 1.698559664 1.567240345
CG4091 1.282878129 1.434623822 retm 1.357065376 1.271662919
CG41127 1.474373378 NA RhoGAP88C 1.330845161 1.232977389
CG4221 1.344341531 1.432624038 RpII140 1.297139577 1.162989945
CG4389 1.14201079 1.092797248 RpL27 1.389867725 1.294188311
CG4446 1.202313097 1.490869948 RpL28 1.424003288 1.742111459
CG4705 1.081075752 NA Rpn2 1.014716277 1.447067751
CG4860 1.401222709 1.276111023 sano 1.520254426 1.457961052
CG4875 1.567976605 NA scrt 1.282225801 1.469925254
CG4928 1.190466634 1.265817814 Sh3beta 1.306461852 1.170666539
CG4942 1.002379802 NA shark 1.619454665 NA
CG4960 1.208466563 NA simj 1.146228315 1.227189814
CG5028 1.153462014 1.135591101 skpE 1.8754486 NA
CG5080 1.266390861 1.386836891 skpF 1.667019269 NA
CG5157 1.130158942 NA SmD3 1.096920957 1.254971277
CG5171 1.30767814 2.029792468 Spn7 1.181479121 NA
CG5181 1.670771558 NA spn-E 1.523865381 NA
CG5250 1.30544029 NA spn-F 1.02238596 NA
CG5254 1.809355973 1.197280461 sprt 1.747227676 NA
CG5326 1.342014346 1.522270395 Sps2 1.502165134 NA
CG5357 1.193998871 NA Spt6 1.474780521 1.233244447
CG5522 1.123079043 NA stai 1.029063392 1.055641108
CG5548 1.09500054 1.121777883 stl 1.990153649 NA
CG5618 1.531978498 1.214828717 Su(dx) 1.563469802 1.243521479
CG5651 1.172936685 1.182832267 Su(var)2-10 1.489304093 NA
CG5694 1.151810528 NA su(w[a]) 1.316683241 1.271317021
CG5765 1.40494214 NA Su(z)2 1.812100496 NA
CG6049 1.154074869 1.305458034 Suv4-20 1.888959846 1.226733869
CG6051 1.753511312 1.550248662 sw 1.228449236 1.189014834
CG6191 1.151873369 4.773840877 Syb 1.126240168 1.205649164
CG6231 1.39042639 1.577723633 tacc 1.297164341 1.2440952
CG6299 1.670439087 1.180905559 Taf4 1.36415135 1.236470596
CG6426 1.262303948 1.436730969 TBPH 1.147251442 1.226301734
CG6522 1.289030808 1.240197598 Thor 1.562587509 1.753227082
CG6617 1.256663602 1.1645433 Tim13 1.068137745 NA
CG6680 1.481041691 1.348688869 tmod 1.262471049 1.33064178
CG6763 2.084083131 NA tok 1.375185575 1.431597004
CG6770 1.183864266 1.227184747 TppII 1.491016428 1.494121164
CG6784 1.179596684 NA tra 2.96383308 NA
CG6865 1.277281066 NA Treh 1.257000123 1.355821923
CG6873 1.107121543 NA Trh 1.1385149 NA
CG6904 1.426395616 1.350233492 Tsc1 1.720472219 NA
CG6912 1.439888446 2.005374759 tud 1.193092973 1.3067385
CG6951 1.449799489 NA UbcD2 1.152115361 1.29565431
CG7039 1.346184628 NA Ubc-E2H 1.481639929 1.143730854
CG7154 1.663877495 NA Ubi-p5E 1.137956178 1.537179341
CG7168 1.063185867 NA Ugt37a1 1.453202268 NA
CG7255 1.562189224 NA Uhg1 1.633302961 NA
CG7337 1.376250139 1.431097335 unc-115 1.180446733 1.522453779
CG7369 1.769776023 NA usnp 1.152187222 1.434533955
CG7429 1.103066247 NA vimar 1.588023403 1.40323259
CG7589 2.214417265 NA vir-1 1.104500539 1.293186221
CG7818 1.254354558 NA WASp 1.316021212 NA
CG7856 1.127016022 NA wrapper 2.096792244 NA
CG7874 1.519708949 2.130794793 wun 1.757511022 1.386723587
CG7912 1.462124346 NA X11Lbeta 1.71653628 1.29157214
CG8108 1.304340465 1.434158661

174

Table A10. Oligo sequences of sex-determination hierarchy genes for control spots on microarrays.
Gene Oligo Sequence
Sxl agacactcactgactcttaagatagtatgtagtttttatttgcacgggggggcaaacgcgccacgtccgc
tra gagaagctaggacaataggactctcaactgcgcattacgtggattccgtctccgacgatgcgccaaacac
dsx
F
gccaaatatgtcgatgtgtgacagccgttctacgcgtcagctttcttcaatcaacattaccccgtgctga
dsx
M
ctcgagtggaaataaatcgcactgtagcccagatctactacaactactacaccccgatggccctggtgaa
fru p1 tgttttagtcggtcctttcgcgcttgacttgttttgcaactgtgtgcgtacgtttgagtgtgcgagtgcc
fru
A
atgcagtcgccagcagcacatgatgtcccactattcgccgcatcatccgcaccatcgatccctcatagat
fru
B
ccacctatacgcgcagcgacaatttgcgcacccactgcaagttcaagcatcccatgtacaatccggatac
fru
C
agcaactggagcagctggccataattcgcatcacaccatgtcgtaccacaacatgttcacgccgtcccgc

175

Appendix B: Supplemental tables for Chapter 3
Table B1. Somatic sex-differentially expressed genes and their gene expression level changes in sex-
determination hierarchy mutants

Female-biased somatic genes
Significantly differentially expressed for wild type, tud progeny, tra null and dsx null experiments
Gene Symbol wtF/wtM FC tudF/tudM FC wtF/tra FC wtF/dsxD FC
CG1561 1.502823574 1.805791745 1.459968466 3.142286197
Sip1 1.528080388 1.367449416 1.36219054 2.53036414
CG31878 2.19388397 78.89207641 3.076031016 11.93797833
CG16885 2.137963951 6.506019573 4.063906217 10.19163611
Pxd 1.458530767 1.95107646 1.857587356 2.658767285
CG6337 3.211246052 8.942324815 4.668270768 15.19744184
CG14534 1.815629071 2.569503233 1.645773987 3.52904422
CG17032 1.825822661 2.361836992 2.254948573 2.904532908
CG1702 1.406905475 1.795640845 1.924794282 2.985285348
fln 1.636751593 1.62066439 1.603416103 2.485625958
CG6739 1.611343777 1.468208206 1.765739733 2.469275931
CG4484 1.30314193 1.366122101 1.431105316 2.093104165
ome 1.610209334 1.617741457 1.572129186 2.35364406
pgant2 1.359907313 1.474116061 1.411369446 2.161815353
Tm1 1.243151191 1.454081323 1.635428998 1.714349316
CG16884 2.782252482 7.184082856 4.178712172 11.00951097
Lmpt 1.349663925 1.791140957 2.205195601 1.997770872
CG32512 1.296056799 1.452402699 1.268972451 1.610203032
CG13931 1.937509977 6.405711842 2.580281722 3.626202433
Sas 1.193886362 2.326599092 1.458278526 1.845536553
abd-A 1.694715121 1.553007429 1.396210892 1.550548182
CG31781 1.265187254 1.34560679 1.665316552 1.864427603
CG11267 1.121574896 1.374527754 1.471414685 1.71755288
CG5506 1.83642974 2.663515175 2.386807525 2.415620894
Gasp 1.434215998 1.449440736 1.790290115 1.54282746
CG13616 1.35889223 2.022389616 2.078631968 2.923233339
LCBP1 1.745191365 3.086345741 2.342639456 2.568001557
CG9850 1.24938273 2.335052931 2.773218816 2.378218922
CG12164 6.366356796 17.71444255 5.573371936 6.069600116
l(2)k05713 1.480524077 1.403141819 1.315768141 1.468122373
CG16820 1.828621491 2.097033572 1.66508761 2.076774369
CG13062 1.925508822 13.91275356 2.101728387 2.465005615
HDC15381 1.557071555 2.146593059 3.021032467 2.807144795
Acon 1.227174317 1.334407448 1.337779971 1.348439364
cact 1.198514819 1.454446821 1.30110472 1.237970148
yellow-h 1.332857047 1.738251406 1.640564621 1.494780838
l(1)G0334 1.455068191 1.480596839 1.25882256 1.286421065
CG14304 1.940068182 4.510493749 1.995524387 1.701208261
Scp1 1.37965761 2.126689383 1.797719716 1.860627133
dpr13 2.319951804 9.463320252 2.982459118 2.407010937
Msr-110 1.370674329 2.141550618 1.66755333 1.444935744
CG12009 1.926256922 2.111625447 2.268752031 1.649456343
CG30437 1.338628042 1.488255261 1.713728468 1.4332487
CG11771 1.176715056 1.388894513 1.439249467 1.322905135
CG11458 1.866211608 1.80680383 1.851482956 1.679250186
CG30101 2.572354489 14.00942079 3.054822752 2.367798385

Significantly differentially expressed for wild type, tud progeny and tra null experiments only
Gene Symbol wtF/wtM FC tudF/tudM FC wtF/tra FC wtF/dsxD FC
Cpr49Af 1.209346669 1.442003828 1.381451213 1.306017723
Cpr72Ec 1.464680418 26.36243014 2.732771917 1.701085756
CG30076 2.350029706 6.716587482 2.11097168 1.838072583
CG14997 1.250620057 1.671943872 1.31722623 1.219198615
CG11382 2.752791656 5.093317738 2.414053487 1.673040112
Wnt6 1.871468445 6.863336842 2.716506321 1.487622332
CG13377 1.240239902 1.544827836 1.464972375 1.151568615
coro 1.238544343 1.285294313 1.579854569 1.139216875
AnnIX 1.510427093 2.303609815 1.462694555 1.232525964
176

Table B1, Continued
Spn 1.418798533 1.40833895 1.395841195 1.136112852
CG5026 1.315339139 1.532045874 1.919736624 1.176135267
CG5273 1.346675482 1.622794107 1.342631698 1.025732305
CG14636 2.240658421 3.553324508 1.921983151 1.048650358
CG7432 1.189504195 2.785831478 1.619027841 1.031451955
Spn5 1.377229885 1.764146335 1.44880038 1.007804559
CG13720 1.572810605 3.234354921 2.43557268 0.924779093
tra 1.569371168 1.498219282 1.86094181 0.937243193
CG13458 1.363446165 1.572527965 1.461900256 0.947574452
Osi6 1.457333362 1.659326404 1.648902868 0.866694169
Nmdmc 1.404883716 2.013620709 1.667463272 0.528443295
CG13027 1.617078776 2.657099383 1.80274974 0.334956091
CG14624 6.036696754 14.31104169 2.247863524 0.452554716
ChLD3 1.344622536 1.703820194 1.468153635 0.89733039
CG12206 1.815473851 2.255119207 1.381777333 0.805825601
CG8568 2.321829544 2.805473555 2.940415485 NA
CG9517 2.25821529 2.71361847 1.74206692 NA

Significantly differentially expressed for wild type, tud progeny and tra null experiments only
Gene Symbol wtF/wtM FC tudF/tudM FC wtF/tra FC wtF/dsxD FC
CG10664 1.089968671 1.30812448 1.277079038 1.548470934
CG6393 1.148460544 1.50912823 1.322821083 1.49695282
l(2)efl 1.661101253 1.926407331 1.438560095 2.280613082
CG12964 1.377984697 1.698731633 1.306165706 1.210908376
CG17042 1.504270179 1.527028327 1.391506117 1.161944346
CG17025 1.70439824 3.610970921 1.847965472 1.939896825
CG30179 1.228940274 1.37369725 1.234837026 1.179596494
CG34382 2.097767397 4.09570883 1.835236873 2.110205734
CG14191 1.763838242 2.320367079 1.64830883 2.254172681
CG6043 1.666541975 2.024166775 1.481136085 1.137429886
tau 1.241844878 1.417596477 1.242048925 1.354705085
CG14866 1.326193694 1.973448036 1.657849743 0.558866336
CG10641 1.118004337 1.33765598 1.146998256 1.101666123
CG13847 1.853631068 2.180476709 1.474979108 1.9785637
Dl 1.873929875 1.395116853 1.179513353 1.107902868
CG7031 2.020682696 4.006385229 1.579790038 3.309961602
obst-E 1.888063767 4.929277837 1.686589401 1.640606011
Cpr97Ea 1.846579245 2.12205738 1.450696441 3.309932451
CG9009 1.432775179 1.646441337 1.335820176 1.023666155
CG9083 3.126649904 8.242943306 2.439676495 2.234242342
CG11380 2.938645063 7.527428932 1.815828331 4.83949568
b6 2.446316089 7.403603742 1.657455121 5.245138304
CG31973 1.463979136 2.411093534 1.413904282 1.668730958
TpnC4 1.306153563 1.530937349 1.257195227 1.345709506
CG11175 1.209441987 1.647759717 1.311797543 0.98793145
CG5873 1.734571531 2.39153723 1.417426638 2.728280937
CG32082 1.326691199 2.138532401 1.336798685 1.753542493
CG17777 1.563311092 2.498408577 1.468719397 3.207187929
CG16886 3.09431549 10.3226133 2.352250151 5.302984793
CG7294 1.452803597 2.30586455 1.513152975 1.045477135
serp 2.07618025 4.30230525 1.50679184 2.111889929
melt 1.5660504 1.702452959 1.287053714 2.124614378
mRpL49 1.46835661 1.506113958 1.317708755 1.39257243
Fkbp13 1.220215422 1.431386907 1.170101936 0.994971925
mei-P26 1.184250275 1.516514423 1.254229466 1.112262198
CG11284 1.270135705 1.406529161 1.262722461 1.088241959
bsf 1.274111226 1.274875767 1.164277915 1.067196863
CG10264 2.974523799 21.05257635 2.005509261 5.974853142
CG3244 1.813856012 3.613068157 1.612961984 4.04868215
CG8791 1.358788382 1.349257002 1.17985274 1.979597067
CG8172 2.060421025 3.163034553 1.559245033 0.55742938
CG33299 2.359102165 4.861897047 1.602653116 3.269344659
CG33256 1.623581038 1.426370167 1.230188552 1.024181724
Peritrophin-A 2.188082649 16.87964289 1.626040685 5.233066024
CG15105 1.366736307 1.447824695 1.173778482 1.200313954
CG8192 1.783012345 3.256382913 1.457852703 2.780013447
CG33224 2.087447771 2.010226302 1.309255851 0.855829777
CG9411 1.922927421 2.326141457 1.589603872 2.350740602
177

Table B1, Continued
CG6280 2.17004733 6.339288333 1.643593873 1.62213905
CG31758 1.498762469 1.395499298 1.223618338 0.691578021
Tis11 1.390163149 1.603179376 1.314163404 1.325378156
CG11693 2.488198725 8.352767131 1.765403513 2.668396978
CG9796 1.16083646 1.431945257 1.176008488 1.290114891
Oatp74D 1.32305659 1.49339716 1.188198157 1.30632868
CG10211 1.360565645 1.915193794 1.263513072 1.512560059
CG14946 1.34717432 3.782367484 1.378173488 2.643939565
Cpr50Ca 2.80905111 5.660645507 1.543475481 3.517264347
CG8170 2.248933994 5.436671227 1.595210139 0.93522936
CG8503 1.614750709 1.376701077 1.133748577 1.240173671
CG9021 2.361860098 5.305713772 1.774504215 3.446601501
Cpr66Cb 2.710779338 7.321180463 1.685229497 3.033680773
CG3777 1.724108161 2.525496595 1.180504362 1.581317531
CG11658 1.358513574 1.450923418 1.196470552 1.747870078
CG33521 1.358928434 1.567808535 1.23962977 1.091205198
CG32682 1.980747107 1.78061918 1.21266994 0.935657109
CG15005 1.737586105 2.89192209 1.400818061 1.085234857
CG14244 1.671870418 8.884083171 2.203217828 0.464336686
CG32816 1.690371162 2.268244205 1.237462842 2.259249513
CG2150 1.989274565 3.510042534 1.439290733 1.070381323
CG4666 1.300598823 1.725279227 1.176769129 0.667435226
CG17829 1.746421403 2.659900794 1.30470368 4.5299708
CG14257 2.108650059 4.384176307 1.391862924 0.532817275
CG4404 1.788817173 1.434467863 1.173023352 2.350866887
yellow-e 1.359726316 1.896059404 1.279847424 1.876045697
CG32499 1.623083104 2.381742614 1.294231735 3.783399583
CG11699 1.5237607 1.262001964 1.11329426 1.189876339
l(1)G0156 1.180878486 1.344431432 1.163116875 1.275713832
CG31176 1.536328763 1.520693052 1.165729276 0.782275704
CG14625 2.372420125 4.48545341 1.384971477 0.485307796
Fim 1.369600497 1.372645794 1.155565308 0.688021906
CG10657 1.98398323 3.601515398 1.301028388 1.899326995
CG7135 1.25792536 1.483011874 1.152634649 2.183968587
ken 1.45805175 1.581513013 1.172853279 1.390282792
CG14830 1.337589742 1.559613401 1.151480525 1.078278437
obst-B 1.417848935 1.542294791 1.127857611 0.72777817
Nox 1.37436796 1.44460993 1.131285196 1.121857803
CG12057 1.736308292 6.33460764 1.230146414 2.942414089
Cpr66D 1.524996927 2.503364702 1.384030911 1.890224771
CG31005 1.66330333 2.84314134 1.260108241 0.984224013
CG8927 1.842325176 19.31627396 1.222920913 7.477061233
Paps 1.111728775 1.403785178 1.100189393 1.531647828
CG13188 1.45956562 1.611758392 1.188439506 2.058095303
CG4998 2.556238974 8.074086629 1.372030116 1.380792967
mol 1.686192685 5.433820319 1.281465221 1.680231825
CG6592 2.298867659 9.806152533 1.301951593 2.877588484
CG16733 1.483275689 2.937562125 1.206398952 1.008781158
CG14770 2.773561559 8.999692735 1.382885105 3.371256536
CG15615 3.349312436 15.96297502 1.547395543 3.101978098
Gapdh2 1.253551401 1.293429673 1.067147514 1.185849389
CG17707 2.051247323 3.24571273 1.186452327 3.542499699
CG8854 1.483294903 2.491171646 1.167757756 1.317076493
mRpL13 1.397544628 1.480861624 1.111200886 1.250267838
CG14752 2.52138462 4.935106394 1.286559103 8.967912937
GCR(ich) 1.833963786 3.199024567 1.247190071 1.270530422
CG32036 1.338130268 2.423182803 1.146990188 4.199029106
CG13138 3.024230728 8.928120781 1.310969513 3.470131705
CG14892 4.560759045 8.157113244 1.427421052 1.384834474
Gip 1.263053077 1.422481016 1.079396068 1.128630798
CG6329 1.459796771 1.465996903 1.10347603 1.499708968
yellow-d2 1.285995197 1.847816612 1.146314567 1.008422861
CG2247 1.218518759 1.605929724 1.075698946 1.085271708
CG10005 2.884946995 12.32851638 1.389496246 3.174438539
CG15766 1.46917103 1.898990515 1.093594815 0.950970224
CG3754 1.147567001 1.337598277 1.061344973 1.265726745
CG13154 2.552441841 14.70862286 1.295624338 2.716005612
CG1136 1.718783568 6.298760306 1.148482282 1.838041117
CG17127 2.173499829 6.103747588 1.268662682 2.584224404
178

Table B1, Continued
CG10051 1.582728787 3.166791564 1.195058822 3.697759067
CG17600 1.616373003 2.074082685 1.099630964 1.34906904
CG9990 2.017192895 3.666775478 1.111314527 2.062975174
mRpS35 1.234057231 1.403427604 1.06266596 1.084449565
CG5254 1.669550859 1.339098419 1.060214758 0.706024805
msta 1.524938786 1.317437856 1.060561978 0.844220474
CG32645 1.922777811 11.89481557 1.175533813 4.28017234
Cyp303a1 3.11394514 15.62430281 1.241947765 4.069387985
CG14052 3.236568243 4.674521324 1.206690103 4.112929781
CG9357 1.960888721 2.097752429 1.226531608 0.239116952
Mlc2 1.192009596 1.465012674 1.052744279 1.277451196
CG5157 1.41934047 1.556604262 1.057226249 0.946445151
CG15545 4.7557534 25.09898962 1.315572908 2.62831947
Nep2 1.317739327 1.620214607 1.054623247 1.0578707
CG13606 2.769523091 5.84182968 1.147606654 2.174975813
Dhpr 1.353970254 1.657445804 1.072778118 1.382029989
Cpr51A 1.565110284 2.519983252 1.10219405 2.788823342
ImpL2 1.688364273 1.442685495 1.058128508 0.968096753
CG34422 1.294852467 1.363648342 1.035849129 1.010421855
CG3793 1.237387681 1.239928089 1.035098411 1.228989007
CG4115 1.722391207 2.89329965 1.084705365 1.907913276
CG17219 1.184566444 1.896982581 1.067482314 1.332677845
CG15322 2.924257853 8.552058118 1.16841824 3.331885008
Gfat1 1.469420866 1.899647248 1.059638704 2.475083049
CG7047 1.540894161 2.270467879 1.059391933 2.394675992
mtacp1 1.244687176 1.36534738 1.042662248 1.605053689
CG13674 1.3634027 1.486115046 1.057111035 0.803482691
Pk61C 1.20499325 1.514909407 1.037621533 0.886552352
GV1 1.455867028 2.023577323 1.04549853 3.403831339
CG14627 2.545625864 5.750538356 1.100028993 2.114607204
Gld 1.499405317 1.5248429 1.039209569 1.261416623
CG30427 1.347226155 3.15373623 1.046037489 4.868718098
CG7330 1.734327996 2.386474269 1.057709217 6.732006751
RpL17 2.354368326 2.537812184 1.029818525 1.066474967
tko 1.551367589 1.581721308 1.024891963 1.402006185
y 5.045691601 44.14830903 1.083580773 5.553241745
CG13258 3.133588545 8.398112496 1.030904823 3.192294883
obst-A 1.488656226 1.901938912 1.00633554 2.497208573
CG12523 1.302653748 3.631298748 0.994995079 2.147367369
CG15251 2.137431672 2.69352947 0.987387275 6.476623276
Ccap 1.175484773 1.715528067 0.990978378 1.438338114
CG9328 1.526434079 2.110920548 0.98652737 2.119444067
CG10802 1.194257706 1.220613998 0.990498391 1.383604582
Mlp84B 1.46251612 1.476684142 0.983800434 1.364265362
CG14218 1.366902908 2.471597476 0.963588277 3.293713252
CG7860 1.370046815 1.375845792 0.984735676 0.841424274
Lcp65Ad 1.834280121 2.850030201 0.923230864 2.044198687
CG12003 6.729243162 37.85134954 0.844871422 5.163259931
CG15778 3.576881073 18.31766634 0.910080202 4.700368413
Cyp4d1 3.059271839 3.519987791 0.258779252 0.514079769
Sxl 2.764438546 4.224103013 0.944707709 1.086179225
Cry 2.456563192 12.09195894 0.111210018 1.945425528
CG15369 2.375656384 2.601509278 0.313064055 0.548635482
CG10175 2.365822727 7.153952128 0.752049184 4.908134211
CG15055 2.206590768 2.301043811 0.703827757 3.198110903
sprt 1.935201893 1.301990287 0.933079449 0.972032442
CG32550 1.921330338 2.987606656 0.764044045 3.728619681
CG31636 1.861346849 1.655086809 0.569470873 0.929343795
CG15364 1.725023527 1.879569582 0.886454415 NA
CG15210 1.665208042 1.339136703 0.684011758 1.017392837
CG9360 1.589489213 1.53997855 0.940263214 0.858291691
resilin 1.581884485 1.3546197 0.755909761 1.080819109
rg 1.552602513 1.434896176 0.942970623 1.92026197
Ilp6 1.544769824 1.330967625 0.793816432 0.904979159
Hex-A 1.50827168 1.359261995 0.958369763 1.0413396
CG32850 1.505474299 1.802733065 0.203907544 1.036484684
itp 1.501258037 1.470667937 0.951098221 1.356494288
CG9485 1.490437259 1.275073242 0.921922152 1.454543885
CG4749 1.464807019 1.365167219 0.947480027 0.968180385
179

Table B1, Continued
CG3655 1.459006233 1.335134329 0.963306678 0.678762169
CG7695 1.458675619 3.233402457 0.783266414 0.721418296
TotB 1.42832103 1.682268009 0.648964353 0.457909678
Tal 1.417804346 1.427409688 0.93923669 0.695680974
CG14959 1.390690644 1.516833992 0.727443649 0.858453804
CG11752 1.385613859 1.649455214 0.888904003 1.297376649
CG5116 1.38301587 1.310021054 0.786822153 0.932554194
CG30095 1.373263246 1.304651041 0.517266989 0.632208814
CG3823 1.372010254 1.851554636 0.527104516 0.806972719
Toll-9 1.371407651 2.626704908 0.78229453 0.64137265
Pdp1 1.368435094 1.721599993 0.498180657 0.997856498
Rbp1 1.364628314 2.057382562 0.64510512 1.288854172
CG2989 1.353420294 1.65775648 0.703494872 0.731410394
CG12278 1.350845808 1.522096354 0.901947879 1.013598259
CG9134 1.349836466 1.655714472 0.859167313 2.121698472
LysB 1.34442857 1.719875288 0.959127432 1.686323939
CG11089 1.338713088 1.369701912 0.56335122 0.569325769
CG13360 1.323979131 1.605354541 0.567762291 0.76229591
CG15347 1.313993666 1.814049913 0.692960942 1.057852753
l(2)01289 1.294678188 1.398491367 0.93765221 NA
CG2145 1.29283941 1.348712668 0.803056262 0.797579283
CG1397 1.287597012 1.344799272 0.826033396 0.939398452
CG4679 1.284276333 1.365958897 0.927164851 0.954212384
Sptr 1.280133861 1.375204464 0.84524395 0.554007021
CG15314 1.276653255 1.506909661 0.846968564 0.6262026
CG17041 1.268935464 1.570435109 0.962446374 1.161755065
CG40224 1.266812334 1.643921534 0.40575537 NA
CG4103 1.256401377 1.470661807 0.865207328 1.488093163
CG18563 1.241004032 1.566392843 0.455364312 0.549847952
Phk-3 1.240519207 2.418904763 0.881406814 3.618674742
CG17781 1.236727278 1.668798757 0.851580652 NA
CG17177 1.189823421 2.168907137 0.730408388 0.969267481
pll 1.181717289 1.310395913 0.776603649 0.737756585
825-Oak 1.17370966 2.877186258 0.915608078 1.055665407
dnk 1.17192134 1.241170042 0.914202175 0.906281192
GATAe 1.169809106 1.392071772 0.955075363 1.274661814
CG40274 1.163984523 1.46922021 0.81976837 0.888809658
CG5548 1.158030507 1.366967338 0.931141747 1.310430486
Mlc1 1.156107022 1.47354077 0.944540841 1.296133117
CG8206 1.150372209 1.356522433 0.970207392 1.069952424
Nc 1.1483657 1.256507237 0.919461603 0.603367025
Cas 1.139007588 1.685500241 0.866629149 0.878548747
CG1840 1.132796284 1.463265422 0.934327276 1.410275286
CG10748 1.129696552 1.51212771 0.937566074 0.813956716
LpR1 1.104906008 1.28029743 0.745128962 1.671647251
beta4GalNAcTA 1.102146307 1.305157925 0.856097433 1.184620364
CG33502 1.096445646 1.510788006 0.803442021 0.963283626
CG8323 1.095604217 1.246388114 0.886729935 0.82726914
CG3939 1.088665984 1.765528679 0.807954428 1.051343441
CG5010 1.071501221 1.245946192 0.876381536 1.194525397
CG13403 2.732196776 1.695978376 NA 0.486540926
dro2 2.520351765 10.12840016 NA NA
CG1441 2.025065944 2.767115495 NA 4.338821791
CG6812 1.596214618 1.716839576 NA NA
CG17298 1.532560423 2.073344497 NA 1.16843865
Cpr97Eb 1.433624488 2.453676129 NA 5.927257153
CG5321 1.408548733 1.368089656 NA 0.779771753
CG32694 1.379204804 25.7506769 NA 5.645740895
CG8358 1.339126299 1.512704756 NA 2.149588271
CG10948 1.277128377 1.705068679 NA NA
CG17184 1.221904645 2.110251757 NA NA
CG40378 1.164479245 1.350375614 NA NA

180

Table B1, Continued
Male-biased somatic genes
Significantly differentially expressed for wild type, tud progeny, tra null and dsx null experiments
Gene Symbol wtM/wtF FC tudM/tudF FC tra/wtF FC dsxD/wtF FC
CG4386 1.134844677 1.82762623 1.396933055 4.544903882
ImpE1 1.148628996 1.988399708 1.453978012 4.769450757
m 1.162198778 1.55493507 1.593284856 3.762802004
CG13728 1.136177014 1.407460906 1.427341972 2.949151647
CG15020 1.122545274 1.57851095 1.464333284 2.81917529
CG13078 1.195240164 1.663663106 1.724630446 2.673074064
dyl 1.202616774 1.71618278 1.550526431 3.110301435
CG10249 2.069760749 1.562276796 1.300877006 2.007819708
Eb1 1.102534033 1.47360754 1.328840381 1.726796879
vri 1.237605663 2.734618284 2.060197251 2.961553007
CG31637 1.132661375 1.561604923 1.411894474 2.150957684
CG15589 1.36959338 1.702792406 1.887872825 2.658881728
boi 1.302500585 1.771309397 1.85674839 2.03002917
wbl 1.218489312 1.847396505 1.468982331 1.792716906
CG11438 1.173381718 1.717839894 1.922241686 2.118629042
CG13059 1.358736155 2.003002312 1.835408962 2.287302952
CG1172 1.111412044 1.305300404 1.620113672 1.64698171
ade5 1.106907901 1.687199438 1.547231705 1.407682941
C901 1.139694331 2.481010545 1.463144401 1.70306099
CG14030 1.137558789 1.290620001 1.262939699 1.384683531

Significantly differentially expressed for wild type, tud progeny and tra null experiments
Gene Symbol wtM/wtF FC tudM/tudF FC tra/wtF FC dsxD/wtF FC
CG32447 1.133349181 1.716653006 1.297068391 1.267406332
CG4753 1.293867588 1.552700725 1.401040211 0.932343323
CG14411 1.137046828 1.315998582 1.244991948 0.95391401

Significantly differentially expressed for wild type and tud progeny experiments only
Gene Symbol wtM/wtF FC tudM/tudF FC tra/wtF FC dsxD/wtF FC
CG2082 1.149900101 1.512937856 1.313823065 1.765186253
CG1309 1.575934397 1.329222532 1.216885305 1.278187882
CG10914 1.172082445 1.3164167 1.223724615 1.172706274
Cont 1.382127871 1.416832966 1.318753777 1.030014297
kermit 2.32552254 1.650926506 1.286598429 1.66700447
CG15643 1.22928105 1.604049817 1.573633077 2.819194559
rols 1.081190943 1.548773996 1.184179387 1.559085278
Lac 1.097049732 1.409963503 1.19440815 1.300723136
CG1342 1.296158707 3.206478656 1.485636233 5.964097943
CG32159 1.223508356 2.163773427 1.431831466 6.531107913
Osi11 1.519527188 2.931580249 1.437904189 4.930900064
SP71 1.210105935 2.396034363 1.342136547 4.0003164
Ance-2 1.275269374 2.24798348 1.375931334 NA
CG10585 1.077549292 1.314752372 1.136042812 1.384608339
CG10862 1.157458924 1.459393659 1.156480869 1.225115794
olf186-F 1.426545599 1.678269211 1.123117479 1.160459917
CG1499 1.362827566 2.576267141 1.280140798 3.10074765
Cyp4p3 2.140206698 1.489130222 1.162083158 0.648510943
ImpL1 1.254242178 2.184775878 1.27238986 3.084749364
CG14995 3.703374692 1.956045162 1.200940028 1.846747192
CG4702 1.270918623 1.94762329 1.231841268 3.320530079
CG12948 1.11258085 1.427978667 1.106969015 1.660482318
pio 1.15857955 1.986279637 1.204988098 1.744451645
Osi21 1.356840578 1.957996018 1.158675241 3.439765956
CG8420 1.146743254 2.091628379 1.140484471 3.244851474
CG13535 1.262362265 1.702869408 1.192928422 4.168120377
Tsp 1.090159769 1.36593597 1.094246088 1.017928748
CG12063 1.338204816 2.479724607 1.158108517 4.799389737
CG32354 1.181056547 1.399764934 1.108252943 2.707938956
sha 1.224511779 1.969312493 1.203230336 2.658726854
Obp56a 1.297535903 2.117768261 1.157211256 1.663487646
SP1070 1.26226165 1.905804387 1.131596325 1.462422433
r 1.288714616 1.407886251 1.053398722 1.085265443
Pdi 1.108702933 1.36628816 1.048852273 1.798481794
Act42A 1.382519988 1.257007809 1.027700253 1.433109592
181

Cht3 1.122705483 1.857710609 1.037767538 1.820870053
CG10898 1.333211813 2.330474448 1.054409132 2.500993717
CG13082 1.253655715 2.387759548 1.042865845 1.735668863
lgs 1.222096184 1.350296151 1.023153606 1.109438962
sdk 1.928481993 1.488548787 1.023336506 1.171380722
bnb 1.416242964 2.427140546 1.042784613 1.593933332
dalao 2.546539935 1.497570626 1.019364348 0.848514439
roX1 35.29985692 226.7168646 1.006182964 1.033187325
CG15029 1.102278623 1.439922947 0.867004367 0.81044879
Cka 1.118613469 1.34509339 0.946905168 1.229712003
CG6359 1.128217054 1.439285926 0.78679359 1.103241293
CG11857 1.13133236 1.411964865 0.842152929 1.194109412
CG12880 1.133626046 1.505483225 0.837578462 1.296537461
Mapmodulin 1.135924164 1.356053248 0.913628093 0.947297533
CG9316 1.144274573 1.74815636 0.762832319 1.199208861
Pop2 1.180489962 1.346818946 0.944507773 0.88071519
CG5484 1.190137062 1.435568915 0.937419961 1.084869685
dre4 1.200484098 1.408433898 0.659721374 0.710155838
CG5174 1.218620355 1.333338308 0.970638579 1.261616697
CG5508 1.237972599 1.537464547 0.721827873 1.141292622
ect 1.239743049 1.957279961 0.807503616 2.980663042
CG16721 1.248304239 1.258019101 0.90366723 0.861817239
CG4844 1.254544949 2.910702001 0.89205394 2.255330902
CR11700 1.303330464 1.329275438 0.77298215 1.014670196
MESK2 1.411309792 1.271838153 0.772184133 1.016078159
CG40354 1.51737423 1.451541678 0.839191447 1.512821484
Hrb98DE 1.777599482 1.312567242 0.916851504 1.340112203
nrv2 1.96735618 1.872615659 0.971249271 1.181709396
As 2.190844917 1.421627685 0.918520223 0.985418369
ssh 2.235485629 1.252100051 0.809419393 1.141846402
alphaTub85E 1.102963532 1.310226182 NA 1.370159536
CG31687 1.227005649 1.89820947 NA 1.096410578
CG11425 1.314558704 1.594091366 NA NA
CG32817 1.377375725 2.217083928 NA NA
CG40303 1.442266631 3.85402082 NA NA
CG5180 1.889041308 2.43952496 NA NA
e 1.930287942 1.851929631 NA 0.753845905
snRNA:U4:25F 2.031399988 5.878509495 NA NA
Ddc 4.355770305 1.84439797 NA 2.04239743
CG8086 4.410791017 1.459340017 NA 1.817358329
CG30049 4.5544452 1.986216099 NA NA
roX2 7.533009989 13.20082269 NA NA

q: q-value for differential expression
wt: wild type females vs wt males tud: tud progeny females vs tud progeny males
tra: tra pseudomales vs wild type females dsx
D
: dsx
D
pseudomales vs wild type females

182

Table B2. Adult somatic sex-differentially expressed genes from Arbeitman et al (2004)
Female-biased somatic genes Male-biased somatic genes
Gene Symbol Gene Symbol Gene Symbol Gene Symbol
Acox57D-d epsin-like Ac3 CG7607
Acph-1 fl(2)d Acer CG7675
alpha4GT1 Flo-2 Acp36DE CG8237
Art3 fs(1)Ya alpha-Man-II CG8420
Atg9 gbb And CG8525
atms G-ialpha65A caup CG8565
az2 Gr28a Cdlc2 CG8654
bgm hep CG10096 CG8776
CG10283 how CG10383 CG8909
CG10566 l(1)G0469 CG10553 CG9313
CG10632 l(2)03709 CG10561 CG9510
CG10702 l(2)37Cc CG10590 CG9519
CG1090 Lis-1 CG10631 CG9520
CG11033 micr CG10639 CG9523
CG11089 Mitf CG10657 CG9581
CG1109 mld CG11146 CG9746
CG11255 mRpL18 CG12268 CG9795
CG11473 msl-2 CG12558 Cyp4d21
CG11892 Nacalpha CG12907 Cyp4d8
CG12269 Ngp CG12918 Cyp4g15
CG12785 Nplp1 CG14024 Dgp-1
CG13298 Orc2 CG14290 DnaJ-H
CG13630 osa CG14681 dpr14
CG14235 p120ctn CG14995 endoA
CG14630 Paps CG15097 Ent3
CG14641 Pgk CG1516 eyc
CG15093 PGRP-SC1b CG15772 Gel
CG15312 Pka-C3 CG16719 Got1
CG17012 ppan CG16820 Gs2
CG17806 Prx5037 CG17010 HisCl1
CG18490 qkr58E-2 CG17843 ia2
CG2246 Qm CG18284 Ipod
CG2260 Rack1 CG2267 kermit
CG2493 Rbf CG2663 KrT95D
CG30015 RfC40 CG30427 l(1)G0168
CG30069 RpL10Ab CG31140 laza
CG31705 RpL12 CG31149 Leucokinin
CG32635 RpL17 CG31374 Men
CG33096 RpL18 CG3168 Nep5
CG3342 RpL23A CG31757 Oatp74D
CG34352 RpL30 CG31883 Obp99a
CG3868 RpL37a CG32425 ofs
CG3902 RpL4 CG32833 OstStt3
CG4186 RpL7 CG34130 Pdi
CG4300 RpL7A CG3529 poe
CG4446 RpL8 CG3967 prd
CG4497 RpL9 CG4375 qua
CG5013 RpLP0 CG4662 Rbp9
CG6283 RpLP1 CG4679 RhoGEF3
CG6287 RpLP2 CG4750 schuy
CG6543 Rpn11 CG4847 SMSr
CG6686 RpS14a CG4995 spir
CG7011 RpS15 CG5001 sprt
CG7275 RpS24 CG5112 syd
CG7456 RpS29 CG5390 tafazzin
CG7628 RpS3 CG5506 tna
CG7777 RpS6 CG5740 tomosyn
CG7834 RpS7 CG5802 Tsf1
CG8135 Rtc1 CG6230 twin
CG8312 sba CG6355 Ubp64E
CG8314 scra CG6761 Ugt35b
CG8351 sgg CG6923 wupA
CG8370 shf CG7188 yellow-d
CG8677 Slob CG7342
CG8765 sop
CG8944 SP2353
183

Table B2, Continued
CG9104 SpdS
CG9119 Spn5
CG9144 spri
CG9238 spt4
CG9547 sta
CG9699 svr
CG9715 Syb
CG9754 synj
Csk TfIIA-L
Cyp1 TfIIFalpha
Cyp6g1 Traf2
dco tral
Ddx1 UGP
dec-1 wdn
Dox-A3 wus
eIF4AIII Yp1
eIF6 yrt
epsilonCOP

184

Table B3. Sex-differentially expressed genes in the CNS during metamorphosis
Male-
biased Female-biased
Gene Symbol q-value M/F FC Gene Symbol q-value F/M FC
CG14391 0.041756 8.059698 Sxl 0.012404 3.382461
CG31870 0.035907 7.0003 dsxF-control-60µM 0.058723 3.161911
CG32192 0.00568 6.924119 dsxF-control-20µM 0.094081 2.913351
roX2 0.000966 6.631525 CG12003 0.14842 2.861203
CG6372 0.013251 5.881794 dsx 0.028494 2.68157
CG31644 0.009555 5.801417 CG13138 0.028494 2.552078
CG32351 0.00568 5.389184 dsxF-control-40µM 0.123645 2.525566
CG4750 0.027867 5.268905
snoRNA:Me18S-
G1620 0.040847 2.022633
Mst98Ca 0.012404 5.165368 CG32816 0.065058 1.714187
CG32371 0.00568 4.941675 CG13258 0.059098 1.653894
CG13476 0.00568 4.939959
CG9130 0.023976 4.763371 F/M FC: Female/Male Fold Change
CG10396 0.022755 4.745026
Arp53D 0.01731 4.741105
CG15498 0.140574 4.49775
CG31286 0.014769 4.459586
CG31226 0.03794 4.419135
CG8840 0.013251 4.415006
CR32661 0.059098 4.367969
Mst33A 0.027867 4.188563
CG4959 0.007588 4.129037
CG17377 0.05017 3.95442
CG10252 0.035943 3.95255
Prx5037 0.070719 3.852072
CG30039 0.06407 3.835495
CG16849 0.029502 3.743619
CG15657 0.027867 3.7135
CG30182 0.013602 3.600971
CG13841 0.00568 3.57374
CG9920 0.028494 3.568183
HDC02577 0.059098 3.532687
CG10822 0.028494 3.451811
CG32236 0.130135 3.436616
CG11588 0.131065 3.333615
CG12902 0.028494 3.28721
CG18449 0.028494 3.262773
CG17140 0.007317 3.245523
CG32295 0.010531 3.193874
CG3492 0.11871 3.016796
snRNA:U4:25F 0.041756 2.995249
CG11591 0.041756 2.970036
dj 0.051227 2.892138
CG12689 0.039698 2.877171
CG31735 0.072122 2.858964
CG31820 0.115009 2.837359
roX1 0.00722 2.825075
CG32652 0.124574 2.811007
Tim17b2 0.05571 2.799435
CG31788 0.140409 2.778426
CG8498 0.057947 2.73523
CG17744 0.05062 2.695408
CG40303 0.00568 2.674553
CG30270 0.079497 2.671478
CG12309 0.131065 2.644248
Hsp60C 0.140409 2.643295
CG15450 0.042878 2.62998
CG7295 0.065058 2.539453
Mst84Dc 0.05062 2.533597
Cdlc2 0.028494 2.528982
CG17376 0.029808 2.500232
CG8709 0.113091 2.494625
CG11145 0.047082 2.493799
CG7251 0.039698 2.478217
CG31174 0.042878 2.469491
185

Table B3, Continued
CG1999 0.123645 2.463756
CG32625 0.041756 2.448472
CG17198 0.128754 2.395783
CG9284 0.140409 2.373165
CG5538 0.061953 2.372606
CG1288 0.107341 2.363684
CG13110 0.055958 2.348537
djl 0.044563 2.347625
CG5213 0.064135 2.343786
CG33189 0.127917 2.285474
CG1409 0.127917 2.260399
CG5280 0.115009 2.22523
CG7126 0.064135 2.20359
CG6888 0.097202 2.196008
CG2127 0.064135 2.195033
CG17994 0.027867 2.162966
CG15256 0.124574 2.149902
CR33319 0.140409 2.125865
CG32832 0.047734 2.124994
CG33218 0.129276 2.110823
CG8701 0.140409 2.103676
CG5762 0.130135 2.096501
CG33060 0.140574 2.078223
ocn 0.147158 2.056146
Pnn 0.088963 2.044976
CG15200 0.11871 2.021529
CG12831 0.147158 1.924865
CG5398 0.027867 1.918221
CkIIbeta2 0.140409 1.890791
CG11043 0.110843 1.882264
CG6687 0.129753 1.837812
CG7712 0.140409 1.769631
CG14470 0.107341 1.762056

M/F FC: Male/Female Fold Change

186

Table B4. Expression levels of DSX-regulated genes in comparisons of dsx null flies to wild type flies.
Gene Symbol
wtF/XX dsx
FC
wtM/XY dsx
FC Gene Symbol
wtF/XX dsx
FC
wtM/XY dsx
FC
abd-A 1.283643442 0.779722905 CG4386 0.325000748 0.231771237
CG6337 10.36787106 9.063321406 m 0.344070172 0.268745886
CG14534 4.03904214 4.229352056 wbl 0.357151414 0.204382334
CG1561 3.08998302 1.337003077 vri 0.377184284 0.206126656
CG31878 37.78824873 71.58101026 ImpE1 0.409710093 0.338486663
CG16885 28.20890916 26.63319613 dyl 0.425723566 0.229594382
CG16884 29.59676477 21.1922452 CG10249 0.431803568 0.762379489
CG12164 24.21073283 76.90273741 C901 0.437934238 0.254745257
CG16820 3.04206999 3.896027376 CG11438 0.482756195 0.416387747
LCBP1 3.126146839 8.36362274 CG13728 0.49164714 0.501676012
fln 2.266297658 1.549237795 CG31637 0.548360977 0.423289669
CG9850 3.028796366 5.002777719 CG15589 0.594714071 0.213305962
CG13931 5.61164539 10.81020018 boi 0.674949592 0.496053074
CG17032 2.972035009 3.951102497 Eb1 0.670554959 0.531257632
CG13062 3.298270711 12.72983262 CG14030 1.016209927 0.870487403
CG30101 17.63749062 50.63417712 CG13059 1.031623786 0.730313781
Gasp 1.854892852 2.057131426 ade5 0.750902562 0.907276633
pgant2 2.181220845 2.162791351 CG1172 0.994611161 0.937149735
Scp1 2.044610446 2.354137926 CG15020 NA NA
dpr13 12.87592228 27.5227666 CG13078 NA NA
CG1702 2.608062057 1.828905472
Sas 1.772401904 2.512034932
Sip1 2.235696168 1.850145546
CG31781 1.955035437 1.508703438
CG12009 1.809011201 1.666344081
Msr-110 1.820830748 2.207914301
Lmpt 1.770239522 1.993274978
CG6739 1.896671904 2.334016223
CG30437 1.51005728 1.533996166
ome 2.120092386 1.407508835
CG32512 1.624989998 1.547863604
CG11267 1.511074913 1.198777225
CG4484 2.0177829 2.202649493
yellow-h 1.41846518 1.329810375
CG13616 1.857958682 1.589855133
l(2)k05713 1.400682812 1.234558389
Pxd 1.65144785 1.198260663
CG5506 3.044250951 3.563875635
Tm1 1.510922815 1.443203023
HDC15381 1.882516461 1.595354123
CG14304 1.549613894 6.462299775
Acon 1.319323504 1.142575227
l(1)G0334 1.24087569 1.040512276
cact 1.070688405 1.206534332
CG11771 1.1690742 1.046919737
CG11458 1.175453528 NA

FC: Fold change of gene expression data.
Significant q-value (<0.15) for this experiment
NA No expresssion data

187

Appendix C: Supplemental tables for Chapter 4
Table C1. Genes significantly differentially expressed between fru P1 males and wild type males.
Higher expression in XY fru null Higher expression in wild type males
Gene
Symbol q-value
XY fru/wtM
FC Gene Symbol q-value
wtM/XY fru
FC
Aats-ala 0.070652 1.317125281 aay 0.002812 2.141332447
Ama 0.097818 1.353687402 Acf1 0.002368 1.823090091
CG10814 0.003145 2.636831173
alphagamma-
element:CR32865 0.01427 1.659757752
CG10863 0.039663 1.361087099 Art3 0.075662 1.474401772
CG10882 0.094563 1.21206812 Cbp20 0.032036 1.471792956
CG10962 0.00696 1.562840765 CG10508 0.004779 1.75823603
CG1103 0.034702 1.255435099 CG10516 0.018655 1.381119398
CG11347 0.109198 1.330858808 CG10625 0.08597 1.920322436
CG11459 0.002368 2.280328093 CG10680 0.143037 1.72896888
CG12116 0.070267 1.471383391 CG11314 0.072274 1.646909046
CG1236 0.077462 1.309644125 CG11350 0.041242 2.762143542
CG12428 0.108669 1.566934706 CG11380 0.138945 2.111309819
CG12896 0.054138 1.597605822 CG11814 0.017527 1.949636518
CG1311 0.000903 1.706558755 CG11873 0.037813 1.537669551
CG13120 0.054138 1.585242561 CG12104 0.143367 1.204034503
CG13627 0.095348 1.411248128 CG12111 0.0194 1.633452803
CG13641 0.100424 1.60503354 CG12231 0.129764 2.53342874
CG13822 0.106659 1.354603266 CG12519 1.73E-05 2.034530334
CG13962 0.143037 1.66742882 CG12521 0.096851 1.694098319
CG14118 0.062588 1.687966937 CG12725 0.143037 1.718991088
CG14148 0.059804 1.581010954 CG12917 0.130965 2.096469574
CG14291 0.002264 1.903934133 CG13117 0.009638 1.702436035
CG14394 0.034338 1.95890883 CG13643 0.126673 1.81551319
CG14715 0.034704 2.089289947 CG14096 0.12114 1.364531934
CG14928 0.029143 1.544506641 CG14262 0.053331 1.617467193
CG15239 0.054676 1.308471705 CG14292 0.143037 2.017987496
CG15506 0.018655 1.592397971 CG14304 0.129764 1.624642266
CG17107 0.076463 1.786546281 CG14534 0.098796 1.636016287
CG17683 0.032036 1.462657332 CG14566 0.023525 1.972347614
CG17691 0.047286 1.752934403 CG14591 0.134961 1.430910627
CG18343 0.118123 1.638442771 CG15251 0.087207 2.093637987
CG1968 0.143623 1.377263209 CG15531 0.143037 1.545632518
CG2082 0.006648 1.488064472 CG15911 0.129764 1.635481124
CG30083 0.077055 1.419131009 CG16712 0.021059 2.555157078
CG30095 0.109198 1.316062893 CG16857 0.010914 1.660759699
CG30104 0.096789 1.672955172 CG1702 7.51E-05 2.594194374
CG30148 0.032036 1.717342878 CG17036 0.077055 1.694872245
CG31344 0.077055 1.354375334 CG17105 0.050522 2.070446863
CG31352 7.51E-05 1.686906908 CG17278 0.034702 1.337610696
CG31406 0.054676 1.713323663 CG17352 0.000411 3.528906654
CG31559 0.086888 1.326352041 CG17556 0.055253 1.298339507
CG32006 0.095681 1.400709812 CG18067 0.091706 1.53881305
CG32242 0.097818 1.322066596 CG18156 0.097142 1.573649672
CG32354 0.109198 1.381133616 CG18223 0.126673 1.823812281
CG32495 0.132083 1.235453778 CG2233 0.02138 3.008168177
CG32521 0.050522 1.509419105 CG2249 0.070652 2.290396832
CG32698 0.123273 1.27243813 CG30080 0.097818 2.911577341
CG33225 0.114037 1.394676767 CG30291 0.143037 1.588685961
CG3397 0.063374 1.392022009 CG30428 0.041189 1.786244839
CG33980 0.027258 2.349835132 CG31078 0.101522 2.073385164
CG3831 0.143037 1.554932033 CG31224 0.094563 1.43508524
CG40002 0.042888 1.462189187 CG31229 0.100723 1.535244426
CG4335 0.083277 1.593828954 CG31281 0.048044 1.917210181
CG4580 0.009839 1.688706671 CG31633 0.101522 2.146920426
CG4686 0.139675 1.421343704 CG31665 0.058041 1.379841741
CG5104 0.117934 1.284683186 CG31769 0.000146 2.256968017
CG5629 0.054138 1.58743498 CG32244 0.140075 1.421091265
188

Table C1, Continued
CG7016 0.133802 1.316117316 CG4091 0.104996 1.284749385
CG5966 0.143037 1.597239671 CG3244 0.066205 2.329442275
CG6026 0.109198 1.384790072 CG32462 0.011856 2.033751457
CG6199 0.129764 1.621231314 CG32694 0.027258 2.099715317
CG6485 0.129764 1.674042111 CG32850 0.011856 1.967299929
CG6579 0.129764 2.263966461 CG40115 0.042888 1.554118141
CG6726 0.113219 1.681783852 CG40343 0.094563 1.662502798
CG6870 0.108868 1.292959699 CG40381 0.083277 1.410120885
CG7518 0.095038 1.214873015 CG41136 0.000262 2.569421475
CG8213 0.064353 1.453944454 CG4480 0.032245 1.470530089
CG8997 0.037813 2.865211039 CG4860 0.023556 1.513425195
CG9095 0.071899 1.321683171 CG5399 0.108461 1.658082778
CG9508 0.117934 1.387472499 CG5618 0.050522 1.345279866
CG9509 0.029421 1.547181879 CG6191 0.042888 2.226844211
CG9629 0.123936 1.28111977 CG6660 0.002368 2.051978268
CG9689 0.100723 1.627007996 CG6783 0.133802 1.300098325
CG9691 0.118716 1.256472746 CG6897 0.065564 1.3247956
CG9837 0.034702 1.73445125 CG7215 0.000378 1.818728971
colt 0.133802 1.663312467 CG7224 0.133802 1.333371683
CREG 0.027258 1.334644063 CG7328 0.086888 1.772903954
Cyp18a1 0.056364 1.819636293 CG7532 0.02138 2.154391439
Cyp6a8 0.037813 1.825857614 CG7694 7.51E-05 1.806923414
Cyp9b2 0.017862 2.338992757 CG7738 0.018655 3.423973378
D 0.144185 1.341211529 CG8209 0.064353 1.233019785
debcl 0.091706 1.324202818 CG8713 0.063374 1.752632928
fng 0.123936 1.363025195 CG8927 0.002862 2.309160094
Gel 0.10911 1.246142606 CG9990 0.096851 1.748342579
gfzf 0.054676 2.643814974 Corp 0.011856 1.68421322
GS 0.035252 1.656518704 Cpr100A 0.070652 2.188427109
GstD3 0.086383 3.896755094 Cpr50Ca 0.094563 2.214461391
HDAC4 0.055663 2.184862249 Cpr51A 0.075662 1.829348962
Hexo1 0.086021 1.306636807 Cpr72Eb 0.011856 3.532397684
Idgf2 0.111032 1.682388063 Cpr92F 0.064353 1.625219662
IM23 0.118674 4.427269635 Cyp4g1 0.02138 1.75005069
Indy 0.129764 1.262536579 Dg 0.002157 1.87215326
Karl 0.087207 1.644483379 Dlc90F 0.037813 1.365726344
kek5 0.133802 1.493522371 Doa 0.042888 1.367551896
ksr 0.111654 2.984009512 dpr2 0.143037 1.474741009
l(3)02640 0.123936 1.261661074 DptB 0.123936 2.308981368
Mgstl 0.001153 2.071056375 dro6 0.002543 8.551038522
Nc 0.059661 1.285009832 Edg78E 0.086383 2.648983568
neur 0.143037 1.325962678 Eig71Ej 0.130754 2.558027035
NijA 0.137181 1.345310383 fray 0.002588 1.57286792
Nplp3 0.054676 2.244088774 Grip84 0.123936 1.353535707
Past1 0.076463 1.583337674 GstD5 0.019908 2.318129413
Pu 0.149046 1.708419459 Hmx 0.101522 1.496752324
rad 0.048219 1.470355943 Hsp67Bb 0.032339 1.711331896
rho-4 0.116102 1.319465188 ImpL2 0.02824 1.447236456
RpS12 0.109198 1.474957855 l(1)G0060 0.018313 1.662722418
RpS26 0.064353 1.248698516 lama 0.041432 1.415510213
SelR 0.002353 1.46712762 Lcp65Ag2 0.109198 2.159025861
Sema-5c 0.129764 1.212902086 Lsp1gamma 0.129764 1.467453434
Spn1 0.062676 1.387582866 Mlp60A 0.129764 1.428278895
Spn27A 0.133944 1.358264 NLaz 0.023525 1.430464836
ssh 0.062209 1.280449425 Oat 0.017527 1.800048532
Tsp42Ek 0.064353 1.598588887 Obp56a 0.000378 2.60498116
yellow-f 0.143037 1.254853321 Pal 0.064353 1.49771868
PEK 0.029421 2.576336892
Pepck 7.51E-05 2.440114726
PGRP-SB1 0.130419 1.591767866
Ptth 0.123551 1.714291726
Pxd 0.1058 1.389678463
pyd3 0.054138 1.472532618
rhea 0.111278 1.226868808
RpL10Aa 0.097818 1.483918506

FC: Gene expresion fold change copmaring XY fru null and wild type males

189

Table C2. Genes significantly differentially expressed in the CNS between fru P1 males and wild type
males.
Higher expression in XY fru null
Higher expression in wild type
males
Gene
Symbol
q-
value
XY fru/wtM
FC
Gene
Symbol
q-
value
wtM/XY fru
FC
CG2177 0.0221 3.162137935 Cpr47Ee 0.0221 4.483677359
Uhg2 0.13093 2.789857317 Eig71Ef 0.12059 3.968037065
CG30447 0.04309 2.571869738 CG13841 0.0221 3.342875371
CG15281 0.05395 2.570389788 Eig71Eg 0.12218 3.153359045
CG10617 0.11856 2.442733177 CG33468 0.1166 3.152445198
CG7955 0.0521 2.43526025 CG8564 0.11714 3.093412106
CG17625 0.06631 2.372395118 Apf 0.05395 3.026654774
CG15734 0.06631 2.324073678 LysX 0.02279 2.753019074
Mst35Ba 0.13012 2.305973569 CG30339 0.06631 2.67444141
CG7742 0.13936 2.285603968 mRpL2 0.06631 2.495550795
CG5509 0.06631 2.274811703 yellow-e 0.06631 2.199976577
CG8840 0.1166 2.26189804 CG33262 0.06631 2.136273548
CG5213 0.08821 2.232681127 CG13299 0.13936 2.103152253
CG14628 0.06631 2.210924934 CG9960 0.13936 2.042489259
CG16741 0.13936 2.209409023 CG34353 0.06631 1.967328992
CG30270 0.14203 2.193454824 CG33120 0.08821 1.934092162
CG31286 0.10257 2.177398721 mir-100 0.11959 1.904135111
CG11362 0.06631 2.165504022 tex 0.14795 1.896158736
CG12691 0.11959 2.15008151 CG13087 0.0901 1.830433673
Cyp4d1 0.11762 2.148015928 Acf1 0.0521 1.745371699
CG4770 0.13093 2.118399825 capa 0.06631 1.735277039
CG16849 0.12059 2.107829382 CG7638 0.14664 1.534689032
CG10822 0.10791 2.091133971 CG8408 0.13771 1.415842846
CG17010 0.12639 2.078673825
CG10750 0.13012 2.074956634
CG5781 0.14341 2.023292219
CG2150 0.13012 2.021322913
CG32718 0.11959 2.017547983
CG31528 0.14664 2.012046851
CG8709 0.12059 2.003685042
CG31128 0.14078 2.003412339
CG12896 0.06631 1.995590131
CG31644 0.13936 1.99426075
djl 0.1166 1.989433837
CG17666 0.13936 1.982173136
CG32945 0.12059 1.971016058
CG12689 0.13936 1.92016556
CG6059 0.14341 1.918019114
CG18472 0.13012 1.90910878
CG4749 0.06631 1.898345658
CG31742 0.13936 1.897094782
CG33278 0.13093 1.860851737
CG31878 0.10257 1.843577584
CG33060 0.12054 1.839776464
CG9875 0.13771 1.826054919
CG31775 0.05499 1.814265074
CG15800 0.12218 1.80958817
Iap2 0.06631 1.797256282
Pp1-13C 0.11856 1.772107105
CG16704 0.06631 1.770020442
CG17127 0.13517 1.764404344
CG7330 0.12059 1.763508515
CG33218 0.13093 1.760066664
CG13641 0.07503 1.750193793
Tsp42Eb 0.08821 1.749832031
CG31206 0.13936 1.749713759
CG11552 0.14341 1.740198219
CG15296 0.14203 1.725776322
CG32295 0.14698 1.697957873
CG10063 0.13771 1.697070015
CG13258 0.06631 1.673037108
CG4842 0.08821 1.629578457
Ugt35b 0.12218 1.620366054
190

Table C2, Continued
CG12164 0.1166 1.619130623
CG9150 0.12054 1.593166117
Rdl 0.12218 1.590959942
CG4306 0.14436 1.575746282
CG13154 0.13936 1.527927704
CG9508 0.12054 1.505085916
Aldh 0.13012 1.4524124
l(2)01810 0.13936 1.372065932

FC: Gene expresion fold change copmaring XY fru null and wild type males

191

Table C3. Genes involved in the ecdysone-regulatory pathway
Gene Symbol fru whole body fru CNS
betaTub60D
br
crol
Cyp18a1
Ddc
E23
Edg78E
Edg84A
Edg91
Eig71Ea
Eig71Eb
Eig71Ec
Eig71Ed
Eig71Ee
Eig71Ef
Eig71Eg
Eig71Eh
Eig71Ei
Eig71Ej
Eig71Ek
Eip55E
Eip63E
Eip63F-1
Eip63F-2
Eip71CD
Eip74EF
Eip75B
Eip78C
Eip93F
Fbp1
ftz-f1
how
Hr39
Hr46
Hsp26
Hsp27
Iap2
Ilp5
ImpE1
ImpE2
ImpE3
ImpL1
ImpL2
ImpL3
l(3)82Fd
Lsp2
ng1
ng2
rig
rpr
Sgs1
Sgs3
Sgs4
Sgs5
Sgs7
Sgs8
Spn

represents if a gene is significantly differentially expressed in a particular
experiment
192

Appendix D: Supplemental tables for Chapter 5
Table D1. CRMs annotated as having more than one TFBS
Associated CRM
Regulated
Gene Bound TFs
1.28_DRE jing Deaf1, Dfd
abd-A_iab-2(1.7) adb-A eve, gt, hb, Kr
alphaTub84B_alpha1-tubulin_promoter alphaTub84B Trl
Ance_race_533 Ance Zen, Mad, Med
ap_ApME680 ap Antp
bap_baplac4.5 bap Mad, Med, tin
betaTub60D_b3-lac333 betaTub60D tin
betaTub60D_beta3-14/vm1 betaTub60D Ubx, bin, tin
btl_P ctl slbo
Cp15_s15_promoter Cp15 Cf2, usp
ct_wingmargin_Guss ct sd
Ddc_5'del-126 Ddc grh, Adf1
Dfd_EAE-D Dfd Dfd
Dfd_EAE-E Dfd exd, Dfd, Deaf1
Dfd_EAE-F9 Dfd Dfd, Deaf1
Dll_304 Dll Ubx
dpp_BS1.1 dpp en
dpp_dpp813 dpp bin, Ubx, exd
dpp_intron2 dpp dp, grh
ems_ARFE ems Abd-B, ems
ems_elementIV ems bcd, tll
en_stripe_enhancer_intron_1 en en, ftz, ftz-f1
en_upstream_enhancer en en, eve, ftz, hb, Kr, zen
eve_EME-B eve pan, Med, tin
eve_mas eve eve, ttk
eve_proximal_promoter_inc._TATA eve eve, prd
eve_stripe_3+7 eve kni, hb
eve_stripe2 eve bcd, gt, hb, Kr
ftz_neurogenic_enhancer ftz grh
ftz_upstream_enhancer ftz ftz, ttk
ftz_zebra_element ftz cad, ftz-f1, ttk
gsb_fragIV gsb prd, eve, ftz
h_h7_element h bcd, hb, kni, Kr, tll
h_stripe_6 h bcd, hb, kni, Kr, tll
h_stripe3_ET38 h h, hb
h_stripe5 h Kr
hb_anterior_activator hb bcd, hb
hb_HZ1.4 hb tll
hb_P1_promoter hb hb
ind_moduleC+ ind vnd
kni_223 kni cad, hb
kni_KD kni bcd, cad, gt, hb, His2B, Kr, tll
kni_L2_enhancer kni sd
Kr_CD1 Kr bcd, gt, hb, kni, tll
Kr_H/I Kr Trl
lab_lab550 lab brk
Mef2_IIA237 Mef2 tin
193

Table D1, Continued
ninaE_distal_enhancer1 ninaE gl
otp_C otp byn
otp_P oyp byn
otu_otu:lacZ out ovo
ovo_lacZdel-ap-del-6 ovo ovo
rho_NEE rho dl, sna, twi
salm_sal242S/P salm bcd, cad, Kr
salm_sal272P/P salm bcd, cad, hb, hkb, Kr
salm_salE/Pv salm Ubx, sd
Scr_6.5KS Scr cad, ftz, ftz-f1, grh, Hr39, slp1, ttk
Ser_minimal_wing_enhancer Ser ap, pan, Su(H)
sim_mesectoderm sim twi, sna
slp1_5-2 slp1 pan
sna_0.9 sna twi
sna_VA sna dl
so_so10 so et, toy
tin_tinB-374 tin twi, eve
tin_tinD tin tin, Mad, Med
tll_P3 tll bcd, grh, Trl, ttk
tsh_enhancer tsh Antp, Ubx, ftz
twi_dl_mel twi dl
Ubx_basal_promoter Ubx eve, grh, trl, Ubx, z, zen
Ubx_BRE Ubx en, ftz, hb, tll, twi
Ubx_pbxAS Ubx en, ftz, hb, kni, tll
Ubx_pbxPB Ubx en, ftz, hb, Kr, tll, twi
Ubx_PRE_polycomb_response_element Ubx z, Trl, hb, pho
vg_boundary_enhancer vg Su(H), nub
vg_quadrant_enhancer vg sd, vvl, Mad
vvl_dfr_autoregulatory vvl vvl
w_Bmdel-W w z
w_H-del-BgRVdel-W w z
Yp1_fat_body_enhancer Yp1 Aef1, dsx, slbo
zen_0.7 zen Mad, brk
zen_dorsal_ectoderm zen dl, grh, brk, Mad

194

Table D2. Position weight matrices (PWMs) for searched transcription factors
Gene nt Gene nt
abd-
A A 12 16 37 0 11 29 gl A 0 4 5 4 0 0 2 1
C 16 21 0 0 2 3 C 0 1 0 1 2 0 0 4
G 5 0 0 0 0 0 G 0 0 0 0 2 1 0 0
T 4 0 0 37 24 5 T 5 0 0 0 1 4 3 0
Abd-
B A 0 4 11 36 6 2 10 1 grh A 0 0 0 1 0 1 1
C 1 2 2 2 7 2 4 32 C 0 0 0 0 0 1 0
G 7 4 8 0 1 30 24 6 G 4 5 0 1 0 0 3
T 28 29 24 7 31 9 4 1 T 1 0 5 3 5 3 1
Adf1 A 0 1 0 0 1 1 0 1 2 0 gt A 5 7 1 8 3 0 1 4 5 3
C 0 8 5 0 7 4 0 6 5 0 C 0 0 0 0 0 2 5 0 3 0
G 11 0 2 11 0 1 11 0 2 11 G 0 1 2 0 5 0 2 4 0 0
T 0 2 4 0 3 5 0 4 2 0 T 3 0 5 0 0 6 0 0 0 5
Aef1 A 0 3 3 0 2 3 0 hb A 11 0 0 0 2 1 1
C 3 0 0 3 0 0 3 C 2 0 0 0 1 1 8
G 0 0 0 0 0 0 0 G 1 2 0 0 0 0 4
T 0 0 0 0 1 0 0 T 12 24 26 26 23 24 13
Antp A 0 0 0 5 5 0 2 0
His-
2b A 0 0 0 3 2 0
C 1 1 0 0 0 0 0 0 C 1 0 4 1 0 0
G 0 2 0 0 0 0 3 4 G 0 4 0 1 1 1
T 5 3 6 1 1 6 1 2 T 4 1 1 0 2 4
ap A 12 5 0 14 14 1 hkb A 0 0 0 0 0 0
C 0 4 1 0 0 4 C 0 0 2 0 0 0
G 1 1 1 0 0 2 G 2 2 0 2 0 0
T 1 4 12 0 0 7 T 0 0 0 0 2 2
bcd A 0 2 0 12 0 0 12 6 kni A 3 8 18 0 22 7 5 19
C 4 1 0 0 0 0 0 1 C 2 7 6 33 4 7 8 2
G 7 8 12 0 0 0 0 2 G 22 0 2 0 4 4 10 2
T 1 1 0 0 12 12 0 3 T 6 18 7 0 3 15 10 10
bin A 0 0 0 0 0 0 0 1 1 5 Kr A 8 1 0 3 0 0 2 7 10
C 1 5 1 2 0 0 0 0 0 0 C 1 0 0 8 11 10 3 0 1
G 0 0 2 0 0 5 1 0 0 0 G 0 0 2 0 0 0 4 3 0
T 4 0 2 3 5 0 4 4 4 0 T 2 10 9 0 0 1 2 1 0
br-z1 A 1 5 7 3 6 4 7 1 9 8 Mad A 1 2 0 0 0 0
C 1 2 0 0 0 1 0 8 0 0 C 10 15 0 19 4 1
G 1 1 1 0 0 4 1 0 0 1 G 7 2 11 0 0 17
T 6 1 1 6 3 0 1 0 0 0 T 1 0 8 0 15 1
br-z2 A 3 5 0 0 12 1 2 1 2 1 Med A 0 0 0 0 1 1
C 1 2 10 1 0 1 0 2 2 0 C 0 8 0 0 3 0
G 1 1 0 0 0 2 1 1 3 3 G 1 0 4 8 0 4
T 7 4 2 11 0 8 9 8 5 8 T 7 0 4 0 4 3
br-z3 A 3 9 10 12 0 4 10 nub A 0 3 0 1 0 4 2 0
C 1 2 0 0 10 0 0 C 0 0 0 1 3 0 0 0
G 1 1 0 0 1 0 0 G 0 0 0 2 0 0 1 4
T 7 0 2 0 1 8 2 T 4 1 4 0 1 0 1 0

195

Table D2, Continued
br-z4 A 0 6 6 6 0 4 3 5 4 ovo A 1 4 21 21 0 8 0
C 0 0 0 0 3 0 1 0 0 C 5 2 0 0 21 5 0
G 2 0 0 0 2 1 0 0 0 G 13 2 0 0 0 3 21
T 4 0 0 0 1 1 2 1 2 T 2 13 0 0 0 5 0
brk A 1 0 0 1 0 1 1 pan A 0 0 0 0 0 0 36 0
C 7 1 0 0 10 0 6 C 36 36 0 0 0 0 0 0
G 2 0 10 9 0 9 2 G 0 0 0 0 0 36 0 0
T 0 9 0 0 0 0 1 T 0 0 36 36 36 0 0 36
byn A 1 2 1 0 8 0 3 1 pho A 0 0 0 0 1 4 2 0
C 0 4 0 9 0 8 3 0 C 0 0 5 5 0 0 0 0
G 0 0 7 0 1 0 0 1 G 2 5 0 0 0 0 1 0
T 8 3 1 0 0 1 3 7 T 3 0 0 0 4 1 2 5
cad A 6 1 0 7 0 0 prd A 0 0 2 7 5 0
C 3 7 6 0 0 0 C 7 0 0 1 1 1
G 3 4 1 0 0 0 G 1 6 0 0 0 0
T 1 1 6 6 13 13 T 0 2 6 0 2 7
CF1 A 15 25 1 2 1 0 80 sd A 0 10 14 18 0 4
C 4 6 3 4 0 84 5 C 3 1 5 0 0 0
G 80 66 93 90 7 10 14 G 12 8 0 1 0 10
T 1 3 3 4 92 6 1 T 4 0 0 0 19 5
CF2 A 26 0 94 0 96 0 70 3 81 slbo A 1 2 3 1 0 1
C 4 7 0 9 1 4 3 7 0 C 4 0 0 0 0 0
G 67 0 6 1 3 0 26 0 16 G 0 5 5 0 7 10
T 0 93 0 90 0 96 1 90 3 T 7 5 4 11 5 1
Deaf1 A 7 10 0 0 5 2 sna A 27 0 39 3 2 0 0
C 3 0 10 0 0 3 C 0 39 0 0 0 0 0
G 0 0 0 10 5 4 G 8 1 0 37 38 0 37
T 0 0 0 0 0 1 T 3 0 1 0 0 40 3
Dfd A 11 14 0 1 16 9 3 8 Su(H) A 0 0 0 3 0 10 10
C 3 0 1 1 0 0 0 6 C 0 0 0 0 0 0 0
G 2 0 0 0 0 5 5 2 G 10 0 10 7 10 0 0
T 0 2 15 14 0 2 8 0 T 0 10 0 0 0 0 0

dl A 0 0 0 4 4 1 0 0 1 0 tin A 1 9 0 0 0 3 9
C 2 0 0 2 1 0 0 0 21 22 C 10 2 10 0 1 0 1
G 18 22 22 0 0 0 0 0 0 0 G 0 0 0 0 0 8 1
T 2 0 0 16 17 21 22 22 0 0 T 0 0 1 11 10 0 0
dsx A 27 1 29 33 6 1 0 tll A 0 2 0 8 6 2 0
C 0 29 0 0 0 2 1 C 0 0 0 1 4 1 3
G 0 1 0 0 1 28 0 G 0 1 7 0 0 0 0
T 0 2 4 0 26 2 31 T 11 8 4 2 1 8 8
ems A 3 0 0 3 0 3 toy A 0 0 0 0 5 0 0
C 0 0 0 0 2 0 C 5 4 2 4 0 5 0
G 0 0 2 0 1 0 G 0 0 1 0 0 0 0
T 0 3 1 0 0 0 T 0 1 2 1 0 0 5

196

Table D2, Continued
en A 0 16 15 2 1 15 9 Trl A 16 1 24 0 20 2
C 12 0 0 0 1 1 1 C 4 0 1 0 8 0
G 0 0 0 0 0 0 6 G 1 27 0 28 0 26
T 4 0 1 14 14 0 0 T 7 0 3 0 0 0
espl A 0 0 0 0 1 0 1 0 0 0 0 ttk A 0 0 0 0 0 0 0 0
C 1 0 0 0 2 4 1 2 1 0 0 C 0 1 0 4 4 0 0 3
G 0 3 0 4 1 0 1 1 3 0 4 G 4 3 0 0 0 0 4 1
T 3 1 4 0 0 0 1 1 0 4 0 T 0 0 4 0 0 4 0 0
eve A 0 0 6 6 3 0 0 twi A 1 10 0 5 0 0
C 0 1 3 1 2 0 0 C 14 2 0 1 0 0
G 10 0 1 3 1 5 3 G 0 3 3 7 0 14
T 0 9 0 0 4 5 7 T 0 0 12 2 15 1
exd A 5 1 0 4 6 2 Ubx A 9 3 74 85 0 8 13
C 0 0 5 0 0 0 C 13 0 3 0 3 4 3
G 1 2 0 2 0 0 G 8 0 3 2 3 41 41
T 0 3 1 0 0 4 T 32 76 6 1 82 21 2
ey A 0 1 2 0 0 0 3 0 0 0 vnd A 0 3 0 0 0 0 3
C 3 2 1 3 0 3 0 2 0 2 C 3 0 3 0 0 0 0
G 0 0 0 0 0 0 0 0 0 1 G 0 0 0 0 0 3 0
T 0 0 0 0 3 0 0 1 3 0 T 0 0 0 3 3 0 0
fruA A 13 0 1 14 15 0 vvl A 5 7 0 11 3 0
C 2 0 1 2 0 12 C 0 2 0 0 0 9
G 1 16 1 1 2 3 G 2 0 0 0 7 0
T 1 1 14 0 0 2 T 4 2 11 0 1 2
fruC A 14 0 0 0 14 14 0 zen A 9 0 2 12 9 2 0 5
C 0 0 0 0 0 0 14 C 1 0 2 0 0 4 4 5
G 0 0 14 0 0 0 0 G 0 0 0 0 3 0 8 1
T 0 14 0 14 0 0 0 T 2 12 8 0 0 6 0 1
ftz A 0 7 9 0 0 9 9 0 z A 4 0 0 26 0 1 0
C 6 0 0 0 0 0 0 0 C 3 5 0 0 0 4 1
G 0 1 0 0 0 0 0 9 G 1 1 26 0 26 0 24
T 3 1 0 9 9 0 0 0 T 18 20 0 0 0 21 1
ftz-
1f A 0 0 0 2 0 0 0 0
C 0 1 0 0 3 3 0 0
G 3 0 3 0 0 0 0 0
T 0 2 0 1 0 0 3 3
nt is the nucleotide and the numbers represent the number of occurences
of that nucleotide in that position of the PWM
197

Table D3. Comparison of out CRM search method with Ahab and cis-Analyst
One Bound TF Our Method Ahab
cis-Analyst
1*
cis-Analyst
2**
alphaTub84B_alpha1-tubulin_promoter
Ance_race_533
ap_ApME680
betaTub60D_b3-lac333
btl_P
Dfd_EAE-D
ftz_neurogenic_enhancer
h_stripe5
hb_P1_promoter
Kr_H/I
lab_lab550
Mef2_IIA237
ninaE_distal_enhancer1
otp_C
otp_P
otu_otu:lacZ
ovo_lacZdel-ap-del-6
slp1_5-2
sna_0.9
sna_VA
twi_dl_mel
vvl_dfr_autoregulatory
w_Bmdel-W
w_H-del-BgRVdel-W

Two Bound TFs Our Method Ahab
cis-Analyst
1*
cis-Analyst
2**
1.28_DRE
Cp15_s15_promoter
Ddc_5'del-126
Dfd_EAE-F9
dpp_intron2
h_stripe3_ET38
ems_ARFE
ems_elementIV
eve_mas
eve_proximal_promoter_inc._TATA
eve_stripe_3+7
ftz_upstream_enhancer
hb_anterior_activator
kni_223
salm_salE/Pv
sim_mesectoderm
so_so10
tin_tinB-374
vg_boundary_enhancer

3 Or More Bound TFs Our Method Ahab
cis-Analyst
1*
cis-Analyst
2**
abd-A_iab-2(1.7)
bap_baplac4.5
betaTub60D_beta3-14/vm1
Dfd_EAE-E
198

Table D3, Continued
dpp_dpp813
en_stripe_enhancer_intron_1
eve_EME-B
eve_stripe2
ftz_zebra_element
gsb_fragIV
h_h7_element
h_stripe_6
Kr_CD1
rho_NEE
salm_sal242S/P
salm_sal272P/P
tin_tinD
tll_P3
ubx_BRE
vg_quadrant_enhancer
Yp1_fat_body_enhancer

* Searched cis-Analyst using same PWMs as our method and cis-Analyst default threshold
** Searched cis-Analyst using PWMs and thresholds it provides

199

Table D4. Genes with differential expression when over-expressing DSX
F
in females and
identification of DSX CRMs
Higher expression levels in control females Higher expression levels in hs-DSX
F
females
Gene Symbol
q-
value
F/hs-DSX
F

FC
DSX
CRM
Gene
Symbol
q-
value
hs-DSX
F
/F
FC
DSX
CRM
825-Oak 0.01812 2.242633739 Acp1 0.04503 1.984460095
asparagine-
synthetase 0.01701 2.403562051 Adk3 0.03878 1.635084371
AttB 0.02374 2.569381726 Ady43A 0.0199 2.215953907
bmm 0.02312 1.669862699 Ance-5 0.01846 3.113668377
bnb 0.04227 1.553850947 aPKC 0.00174 4.353171677
br 0.03291 2.281119615 Arp5 0.02324 1.771022609
brat 0.02716 1.511494004 CG10639 0.03097 1.573161052
CG10200 0.0296 1.74979612 CG10962 0.03814 1.847973542
CG10625 0.04299 2.327869814 CG11044 0.04213 1.695974277
CG11029 0.01701 3.615192079 CG11255 0.03139 1.610469385
CG11350 0.00881 3.474104386 CG11314 0.03816 1.987840189
CG11370 0.00889 2.402199714 CG11347 0.03139 1.526954208
CG11498 0.04628 1.531827809 CG11395 0.03097 1.500803296
CG11570 0.04878 4.315173651 CG12116 0.02192 1.564844681
CG12017 0.04577 2.364314378 CG12848 0.04338 1.562388948
CG12063 0.04479 1.682831973 CG12859 0.03045 1.545545408
CG12505 0.01701 1.749089703 CG12926 0.03877 1.771447037
CG12835 0.03239 1.582668649 CG12986 0.02527 2.706946984
CG1299 0.01681 1.733675557 CG13085 0.03991 2.002816118
CG12996 0.02236 2.72457081 CG13315 0.03097 1.707996386
CG12998 0.02149 4.937487402 CG13641 0.03855 2.362823654
CG13044 0.02361 2.143766449 CG13850 0.02267 1.506140819
CG13067 0.02236 1.74091785 CG14218 0.04342 1.744051973
CG13068 0.0145 2.634451922 CG14286 0.02192 1.70314182
CG13284 0.03984 2.172306526 CG14301 0.04859 2.026493607
CG13321 0.04941 1.681017342 CG14375 0.03097 1.664305461
CG1342 0.04338 2.163477427 CG14482 0.03046 1.625985416
CG1368 0.00493 5.371424744 CG14625 0.01685 2.091104403
CG13722 0.00742 3.869234303 CG14757 0.02138 1.582273466
CG13731 0.01618 3.971674965 CG14817 0.03097 1.5058969
CG13905 0.01701 2.552073729 CG15210 0.01618 1.714267008
CG13913 0.03097 1.519323813 CG15251 0.04506 2.038344444
CG14207 0.03239 1.693802076 CG15261 0.01618 2.116718606
CG14244 0.01039 4.387416495 CG15281 0.0043 3.873379238
CG14291 0.02192 1.920565495 CG15369 0.02149 2.420354022
CG14431 0.04628 2.168820824 CG15563 0.02149 2.683623411
CG14566 0.02192 1.846717625 CG15641 0.02264 2.046038804
CG14598 0.02649 3.102560241 CG15877 0.02697 2.816723164
CG14872 0.0203 2.306739772 CG15911 0.04314 1.981115116
CG1499 0.01701 1.661567138 CG15917 0.03097 1.704955388
CG14995 0.02236 1.525262711 CG16704 0.0025 3.847806559
CG15169 0.02149 3.028003981 CG16713 0.02955 3.347638593
CG15202 0.03822 2.505760366 CG1675 0.02649 2.338365799
CG15213 0.01342 4.053056871 CG16772 0.0108 4.683841176
CG15678 0.04939 3.738801357 CG16799 0.03984 2.013617331
CG1623 0.03239 1.602536437 CG16885 0.0349 2.606314559
CG1850 0.04275 2.785225336 CG16896 0.0148 3.696949723
CG18735 0.02324 2.030993505 CG16926 0.02649 1.544960641
CG2083 0.03239 1.698749302 CG1702 0.03816 1.642219295
CG2493 0.04731 1.503728421 CG17081 0.04338 1.933335899
CG2915 0.04338 1.599417642 CG17086 0.02006 1.749051328
CG2930 0.03628 1.767528771 CG17105 0.03628 3.654290075
CG3036 0.01254 1.899883898 CG17127 0.02958 1.583024048
CG30375 0.04338 1.610563342 CG17145 0.02324 2.502762871
CG31149 0.04628 1.619169905 CG17154 0.04057 1.959428426
CG31624 0.02132 1.991459964 CG17173 0.01958 1.741098992
CG3209 0.04995 1.508647112 CG17198 0.04507 1.676470564
CG32212 0.02006 2.558215689 CG17244 0.00924 2.6556472
CG32495 0.0229 1.582524518 CG1774 0.02149 1.599172272
CG32850 0.02006 1.641278873 CG17777 0.01561 1.670239522
CG34350 0.01742 1.5880034 CG17829 0.03952 1.955961734
CG34380 0.02693 2.030485965 CG18067 0.01429 2.702129241
CG3604 0.00174 3.224519269 CG18557 0.03388 1.634421601
200

Table D4, Continued
CG3842 0.01618 1.924273569 CG18563 0.02149 4.201965249
CG40271 0.04367 1.88037125 CG2249 0.03097 1.587569209
CG4151 0.00397 4.404916662 CG30010 0.02843 1.537912672
CG4702 0.03952 1.830215677 CG30174 0.01055 2.571964916
CG4844 0.02578 1.678199965 CG30179 0.00229 3.009219065
CG5399 0.02149 2.519311952 CG30287 0.04637 1.832721385
CG5830 0.02963 1.64214704 CG30355 0.04619 1.5345611
CG5953 0.02367 1.867306995 CG31076 0.04791 1.544438092
CG6426 0.01701 1.682044325 CG31636 0.00828 2.49284503
CG7219 0.01701 2.020863747 CG31775 0.01398 2.33820476
CG7448 0.04878 2.545547547 CG31778 0.00832 1.923787458
CG7802 0.04367 1.62194801 CG31806 0.01053 2.476242245
CG7874 0.01701 1.992231446 CG31839 0.01701 1.743735188
CG8736 0.04338 3.303844723 CG31898 0.04628 1.838675995
CG9021 0.04878 3.378867424 CG32068 0.03952 1.690922217
CG9135 0.03004 1.544177461 CG32267 0.01701 1.646133968
CG9192 0.02955 1.895388661 CG32598 0.02209 2.46021159
CG9196 0.0199 1.628234806 CG32695 0.01618 1.707104944
CG9307 0.02192 1.642909733 CG33170 0.03207 1.526267676
CG9357 0.02006 2.033334066 CG3348 0.04878 1.59199455
CG9867 0.02006 2.573786681 CG33493 0.03555 2.023754831
CG9977 0.04458 1.610342398 CG34411 0.03239 2.394131364
Cp1 0.03952 1.918541312 CG3560 0.01701 1.662448742
Cpr49Ab 0.00742 3.015101278 CG3699 0.01701 1.583677794
Cpr64Ad 0.02367 2.538665097 CG40216 0.03093 3.223510079
Cpr65Ax2 0.01661 3.66731144 CG40323 0.04628 2.181023489
Cpr65Ec 0.01295 11.12588721 CG4269 0.02802 1.731127345
Cpr72Eb 0.01701 18.56924 CG4306 0.02693 1.54185742
CS-2 0.03174 2.155248789 CG4408 0.00488 4.106923582
Cyp4d14 0.03141 5.22371 CG4646 0.03752 2.168855687
Def 0.01701 3.286356531 CG4692 0.01618 1.668526535
DnaJ-1 0.0051 2.896798645 CG4716 0.01701 2.519770731
drpr 0.03675 1.590511277 CG5287 0.02327 1.576732904
dyl 0.03253 1.793609944 CG5621 0.0379 1.647393559
ect 0.04036 1.739066492 CG5644 0.04059 1.533964799
Ect3 0.02345 2.021734872 CG5793 0.03096 1.771441637
Edg78E 0.00229 9.798677282 CG6138 0.04744 1.868462878
Edg84A 0.04423 4.955641325 CG6144 0.03529 1.943718845
eIF-4a 0.03496 2.365961844 CG6463 0.01618 1.636826479
Eig71Eb 0.03366 7.000506504 CG6767 0.04338 1.518826783
Eig71Ej 0.02132 9.717985757 CG7047 0.04453 1.695426899
Eig71Ek 0.03899 3.963225718 CG7181 0.0484 1.525446666
esn 0.02972 1.598146866 CG7275 0.04577 1.591767018
Fatp 0.04628 1.661746468 CG7498 0.02802 1.967018094
Gal 0.04452 1.893935202 CG7638 0.01398 1.904096725
Galpha49B 0.03097 1.615876099 CG7712 0.02267 1.517078771
Gr64b 0.01701 2.754270212 CG7763 0.02132 2.325052844
GS 0.03816 1.737848706 CG7795 0.02728 2.546044865
GstE1 0.01618 2.024948418 CG8012 0.0144 2.422831499
His1:CG31617 0.03097 1.52555809 CG8317 0.02267 2.209271871
His2B:CG17949 0.01701 2.135484265 CG8369 0.00488 2.277473802
Hmu 0.03892 1.628294576 CG9186 0.03296 1.618516269
Hph 0.02324 1.660657316 CG9249 0.03059 1.623191235
Hsp67Ba 0.02312 1.809338356 CG9297 0.02192 2.023575141
Hsp67Bc 0.02025 1.831612961 CG9508 0.0144 2.202457119
Hsp68 0.00397 5.350745516 CG9577 0.04338 2.108713676
Hsp70Ab 0.00789 10.44941865 CG9642 0.03952 1.601745045
Hsp70Ba 0.01295 10.95478581 CG9914 0.00923 3.171535624
Hsp70Bbb 0.01701 9.86610478 Chit 0.02488 1.546532142
ImpL1 0.02192 2.272467165 Cpr30F 0.03952 1.719028052
inx5 0.02901 2.317761679 CREG 0.03284 1.906989921
Iswi 0.01959 1.627566608 Cry 0.02138 2.582387673
Lcp65Ad 0.01701 6.019433067 Cyp4d1 0.03952 2.672471587
Lcp65Ae 0.03855 3.970402951 Cyp4e2 0.02312 1.603444197
Lcp65Ag2 0.00789 5.228425659 Cyp9b2 0.02236 1.85399149
LysB 0.04069 3.705147247 Cyt-c-p 0.01685 1.705916249
LysS 0.00397 6.30125718 dro4 0.0266 3.270208527
LysX 0.00397 6.037923309 dro5 0.02236 5.190917625
Myd88 0.04613 1.530732905 dsx 0.00924 3.330177085
201

Table D4, Continued
NUCB1 0.0455 1.517620119 Eig71Ea 0.01701 3.24970145
Obp56d 0.0043 6.736023236 Fbp1 0.01393 2.907350022
Osi1 0.0484 1.809711493 Fbp2 0.00397 8.328749681
Osi11 0.03097 1.80981 fln 0.04251 1.618960801
Osi21 0.03388 2.069643449 fon 0.01295 1.927337258
Osi8 0.03816 2.458527594 Gld 0.02367 9.686816877
Osi9 0.04338 1.825307682 GstE3 0.03253 1.872960131
Pcaf 0.02433 1.503308193 hbs 0.03621 1.609435208
Pcp 0.02361 1.906196819 Hn 0.01295 1.782083406
Pepck 0.03621 1.592932822 l(2)k05713 0.02649 1.518118906
PGRP-LA 0.02578 1.636989967 Lsp1alpha 0.01701 3.726975246
pio 0.03529 1.540085876 Lsp1beta 0.00845 3.067274627
pwn 0.0145 1.701006342 Mlp60A 0.04955 1.746154479
ref(2)P 0.0203 2.168310405 mRpL27 0.02236 1.586282185
snRNA:U2:34ABa 0.01655 1.73729204 mRpL32 0.02312 1.647067483
snRNA:U5:23D 0.03628 2.0507412 mRpL33 0.02525 1.517394648
spirit 0.03984 1.577658927 mRpL4 0.01701 1.697715887
Spn27A 0.03628 1.631503061 mRpL52 0.02312 1.539358069
Spn43Aa 0.04161 1.554144822 mRpS10 0.03984 1.534789692
stv 0.01701 1.870303423 mRpS14 0.04923 1.543915876
tok 0.02312 1.612616644 mRpS16 0.04791 1.526365719
Tsf1 0.02236 1.574181229 mRpS18A 0.02643 1.524598922
Vha100-2 0.02312 1.861177407 mRpS2 0.04338 1.575832353
wmd 0.02192 1.538300456 mRpS23 0.02324 1.506208244
wus 0.02236 1.579559069 mRpS6 0.03899 1.674520056
zormin 0.01391 1.750116509 msta 0.02236 1.524983084
mtacp1 0.02312 1.684164269
nec 0.03555 1.621070537
Paps 0.02192 1.524836958
pgant8 0.03628 1.569858042
ppk14 0.03952 1.958743818
ppl 0.0229 1.588380561
Rala 0.00174 3.499682777
Rh50 0.02361 1.53814935
RhoL 0.01701 1.674937972
RpL22-like 0.03555 1.673845814
Tig 0.02312 1.572318944
Tkr 0.02766 2.48337783
Tm2 0.04046 1.881873031
TotA 0.00174 7.512382895
TpnC4 0.01295 1.780163148
TpnC41C 0.02821 1.504789806
Unc-89 0.02006 1.66115107
Vm32E 0.0442 1.8779125
w 0.01053 2.824669784
wibg 0.04791 1.625587199

F/hs-DSXF FC: Fold change of gene expression data between control females and females over expressing DSX
F

202

Table D5. Genes with differential expression when over-expressing DSX
M
in males and identification
of DSX CRMs
Higher expression levels in control males Higher expression levels in hs-DSX
M
males
Gene Symbol q-value M/hs-DSX
M
FC DSX CRM Gene Symbol q-value hs-DSX
M
/M FC DSX CRM
Adh 2.44231 0.043970027 Ady43A 1.56910.045121794
C901 2.66458 0.013602338 aPKC 3.457440.011434616
CG10268 1.53767 0.043041627 AQP 2.222750.043970027
CG10898 1.85388 0.04756139 AttA 1.730420.048523969
CG11370 2.20093 0.033332943 AttC 5.013260.003699108
CG11836 1.80685 0.03867674 CecA2 2.668580.005096325
CG11852 2.38166 0.029841201 cenG1A 1.688190.026744927
CG11905 1.94955 0.031760065 CG10175 3.22788 0.031760065
CG11966 2.22038 0.023958053 CG10332 5.44926 0.003783929
CG11997 8.44402 0.007438657 CG10853 6.43404 0.046861151
CG12063 2.93134 0.025618543 CG12164 6.47085 0.048114508
CG12505 2.9383 0.003699108 CG12947 2.857660.0236383
CG1273 1.74826 0.026269804 CG12995 2.023790.023958053
CG13023 3.09464 0.011263353 CG13042 1.991860.031760065
CG13027 2.26028 0.020611979 CG13982 2.651440.046089673
CG13053 3.53144 0.01209256 CG14509 1.744670.02786869
CG13081 3.24154 0.026832767 CG14608 3.00880.037122664
CG13082 2.08785 0.031760065 CG14964 1.637110.044768141
CG13117 1.64041 0.048114508 CG15250 1.52119 0.046089673
CG1342 2.48409 0.021658125 CG16884 4.67210.040936122
CG13504 1.78099 0.035339009 CG16885 4.197910.046089673
CG13627 1.95363 0.031165066 CG17380 6.664380.01209256
CG13840 5.16174 0.001677289 CG2196 2.266160.042236026
CG13905 4.50573 0.048114508 CG30174 2.736370.005996372
CG14107 2.51322 0.023958053 CG30179 2.662630.011800473
CG14470 2.04731 0.021065075 CG30427 2.145330.023958053
CG14566 3.10027 0.043740077 CG30492 1.864690.048114508
CG14567 2.37968 0.042591569 CG31038 1.537810.043041627
CG14625 1.66304 0.031376236 CG31858 1.530440.043970027
CG14928 1.64643 0.043041627 CG32185 2.761080.007438657
CG14960 1.96395 0.029841201 CG32550 3.285370.049528804
CG1499 2.63504 0.017422898 CG32694 2.84011 0.042740619
CG15213 10.6364 0.048114508 CG34355 2.301920.032267921
CG15335 2.03734 0.046089673 CG3544 1.666830.023958053
CG15888 3.69478 0.012496043 CG3823 1.6666 0.049780586
CG1681 2.2055 0.02786869 CG4757 1.936940.012320264
CG17272 1.7561 0.048114508 CG5928 2.188370.023958053
CG18735 4.38911 0.005828327 CG6785 2.339930.028079887
CG1998 1.60351 0.046089673 CG6912 4.230940.017672043
CG2016 2.48748 0.007438657 CG7214 4.148760.048114508
CG2444 3.28127 0.023958053 CG7638 2.176610.021065075
CG3036 1.86276 0.048114508 CG7709 3.015010.04756139
CG30463 1.69166 0.048114508 CG8927 2.91684 0.036655984
CG3108 2.49213 0.043970027 CG9297 1.850460.033332943
CG31344 2.18093 0.043041627 CG9411 2.30390.023958053
CG31559 1.69742 0.046089673 CG9444 2.407910.009590939
CG32159 2.12938 0.017485909 CG9850 1.78639 0.038124583
CG33458 2.45238 0.04756139 CG9990 2.415610.048114508
CG34350 2.30687 0.02085677 Cpr47Ea 1.764480.043041627
CG4250 2.03112 0.048114508 Cpr62Bc 4.272120.044824904
CG4382 3.32577 0.011263353 Cpr92A 2.089930.02786869
CG4386 2.3312 0.023958053 Cpr92F 2.119360.01930781
CG4702 2.27541 0.026269804 Cyp309a1 1.868460.032340188
CG4726 3.38368 0.031376236 Cyp4e2 3.023130.031376236
CG4822 1.56983 0.039131013 Cyp4p2 4.047320.013495244
CG4844 2.62535 0.029841201 Cyp6a16Psi 2.097590.042079481
CG5399 2.83109 0.046174744 dock 4.789120.005096325
CG7224 1.66104 0.023958053 dpr13 12.6580.016111933
203

Table D6, Continued
CG7802 3.71533 0.013495244 Dpt 3.757120.003863079
CG8172 1.96131 0.024800618 DptB 3.959980.003863079
CG8239 1.74763 0.023958053 dro5 3.266540.029885108
CG8317 2.20116 0.026744927 dsx 2.85274 0.003863079
CG8420 2.14356 0.01930781 Fbp2 2.469640.023958053
CG8776 1.63454 0.038718421 Fur2 1.69214 0.046089673
CG9192 6.83789 0.043948996 GV1 1.851550.039399818
CG9196 2.64353 0.0236383 hbs 1.551670.042591569
CG9294 2.48537 0.023958053 Idgf3 2.41737 0.005096325
CG9307 1.78794 0.034380353 Lsp1gamma 2.058470.042079481
CG9384 2.10313 0.018146549 Lsp2 1.855940.031904976
CG9503 2.18715 0.013495244 Mst36Fa 1.697240.029841201
CG9664 1.8097 0.03790092 mth 3.336380.032340188
CG9689 2.33586 0.031760065 Mtk 2.978670.011263353
Cht3 1.78663 0.023958053 ninaC 1.648340.033332943
Cpr47Ec 2.61695 0.042980426 Pdp1 2.297920.046861151
Cpr49Ab 4.95156 0.003699108 PGRP-SB1 1.654120.029841201
Cpr72Ea 2.89967 0.013495244 PGRP-SC1b 5.646630.012108099
Cpr76Bc 2.11354 0.011800473 PGRP-SC2 3.72180.005436398
Cpr78Cc 3.81332 0.048114508 phr 1.60260.03867674
Cpr78E 1.99573 0.026269804 prc 1.833550.021027659
Def 1.79057 0.046089673 Ptth 2.68154 0.005828327
Ect3 2.86536 0.007438657 RFeSP 3.108180.017672043
edl 1.97459 0.04756139 Rgk1 1.654970.03467883
Eig71Ej 16.0877 0.001769906 Rya-r44F 1.677960.042591569
Eig71Ek 18.612 0.001769906 TotA 1.673510.046089673
esn 2.01529 0.012496043 Unc-89 1.76294 0.044064093
His1:CG31617 2.14756 0.018201097 yellow-d2 4.73694 0.00093044
His2B:CG17949 1.60474 0.033332943
His3:CG31613 1.83203 0.032267921
His-Psi:CR31614 1.8208 0.02509226
His-Psi:CR31615 1.58592 0.043333041
HLHmbeta 1.63421 0.042980426
HmgD 1.72001 0.04756139
Hsp67Bb 1.63448 0.043041627
IM3 3.63751 0.048114508
ImpL3 2.30301 0.04756139
LysS 4.73115 0.018073529
LysX 5.12596 0.013495244
mas 1.93746 0.033073644
MtnA 1.7059 0.033518226
MtnB 1.71666 0.027964048
Nmdmc 1.58267 0.047082221
nrv1 1.5894 0.035208858
Obp56a 2.33449 0.012496043
Obp99d 15.2065 0.034410128
oho23B 1.68936 0.042231344
Osi11 2.29563 0.021727516
Osi12 1.58778 0.048114508
Osi21 2.44308 0.046607187
Osi22 5.56506 0.011800473
Osi3 1.85732 0.035208858
Osi4 3.18386 0.022706529
Osi7 1.61367 0.046089673
Osi8 2.45592 0.038124583
Osi9 2.62605 0.024800618
PGRP-LA 1.78228 0.031376236
Rab23 1.52417 0.04756139
rad 2.23212 0.043740077
RpL27 1.50446 0.047084639
RpS12 2.22378 0.023224196
204

Table D5, Continued
Snap24 1.5297 0.048114508
SP1029 2.32357 0.039672486
Spn27A 1.91887 0.048114508
stg 2.16644 0.042236026
vri 1.92755 0.04756139
wbl 2.09821 0.026832767
wun 1.75157 0.021658125
wus 2.25646 0.029499252

M/hs-DSXM FC: Fold change of gene expression data between control males and males over expressing DSX
M

205

Table D6. Genes with differential expression when over-expressing FRU
A
isoforms in males and
identification of FRU CRMs
Higher expression levels in control males Higher expression levels in hs-FRU
A
males
Gene Symbol q-value M/hs-FRU
A
FC FRU CRM Gene Symbol q-value hs-FRU
A
/M FC FRU CRM
Adh 0.03581 2.734930436 26-29-p 0.013241.945213142
Ag5r 0.02485 11.70102614 alphaTub85E 0.047391.500176049
Ance-4 0.00966 1.903397705 aPKC 0.003353.885856071
AttA 0.00541 4.095249676 cenG1A 0.030982.295123363
AttB 0.00461 6.23439422 CG15911 0.007272.186854253
AttD 0.0032 3.569254972 CG17380 0.030985.973552288
br 0.02706 3.380668793 CG2177 0.0033511.53435145
CecA2 0.00288 2.907460634 CG2641 0.030982.026120605
CecB 0.02625 2.794380809 CG30174 0.008932.656954082
CecC 0.00163 3.922993597 CG30179 0.003352.476740221
CG11370 0.03226 1.697548872 CG31839 0.007452.092621508
CG11619 0.03214 1.799226072 CG31858 0.02811.560295833
CG12998 0.02988 6.228788057 CG32244 0.023791.793695291
CG13026 0.01036 2.322504736 CG32795 0.02811.638675248
CG13044 0.01575 1.991261806 CG3492 0.001957.046174171
CG13047 0.01887 3.086868035 CG3513 0.005482.545712403
CG13067 0.007 2.212399263 CG3940 0.004652.215786205
CG13068 0.04265 3.252382635 CG6026 0.044751.52286461
CG13070 0.00732 4.167139463 CG6783 0.044581.539028052
CG13248 0.007 3.360426424 CG7077 0.033781.596479701
CG13287 0.00637 3.499330074 CG7763 0.012832.697779496
CG13323 0.01283 2.176694443 CG8602 0.029351.954775449
CG13365 0.04135 1.502094809 CG9342 0.041311.542359653
CG13641 0.01106 2.778725115 Cyp4p2 0.0309811.76149278
CG13722 0.00541 5.971719763 Cyp6a8 0.005482.674044419
CG13731 0.00335 8.980856532 dock 0.006949.114305098
CG13926 0.0281 1.651484798 fru 0.009182.180513724
CG14096 0.03202 2.265879661 Gld 0.0248517.31001861
CG14375 0.00568 2.133151659 glob1 0.012832.556432362
CG14529 0.00952 4.159552809 GstE9 0.042631.790531333
CG14566 0.02706 1.929010619 hbs 0.047591.779852627
CG14568 0.007 4.507812569 ImpE1 0.042631.560947903
CG14598 0.00893 8.042899281 lectin-28C 0.024732.010714543
CG14610 0.04458 2.385313256 mth 0.004984.664936917
CG14752 0.01695 7.482767467 PH4alphaEFB 0.027061.748044804
CG15105 0.04887 1.622521636 Tps1 0.04121 1.526069229
CG15202 0.0072 5.258622914 unc-115 0.035561.56385557
CG15212 0.00335 8.514805625 yellow-d2 0.003353.840730543
CG15213 0.01106 5.82604385
CG15281 0.00288 6.545075012
CG15544 0.03441 1.626908634
CG16713 0.00752 4.162511613
CG17834 0.03214 1.665920476
CG2444 0.00335 7.729207524
CG2789 0.01528 1.692870748
CG30197 0.00254 3.185486465
CG30375 0.00732 4.59811018
CG30457 0.01199 2.499936983
CG31076 0.01677 1.753547917
CG31775 0.00541 5.102384967
CG31901 0.0072 2.570072797
CG31955 0.01138 1.93125107
CG32185 0.00541 5.709150089
CG32499 0.03378 1.76914861
CG32550 0.01106 1.765044838
CG33056 0.00335 2.704986457
CG33254 0.02706 2.18182789
CG33300 0.04233 1.588511678
CG34369 0.007 2.339406422
206

Table D6, Continued
CG34380 0.02586 1.731992358
CG3604 0.00541 7.592256585
CG3650 0.00548 7.497128881
CG40271 0.00839 2.546044327
CG4151 0.02777 8.077689473
CG4250 0.03214 1.67638033
CG4726 0.00862 2.081093881
CG4860 0.04148 1.761589491
CG7532 0.02632 2.189100394
CG8736 0.01936 5.810922856
CG8740 0.03202 1.572889013
CG8785 0.03202 2.750898905
CG9192 0.00239 5.09020575
CG9357 0.00966 3.807091346
CG9867 0.01436 3.617491734
Cks30A 0.01335 1.702214688
Cpr100A 0.00195 6.379088918
Cpr12A 0.03226 3.016553579
Cpr51A 0.04608 4.670081355
Cpr64Ad 0.0281 3.210425902
Cpr65Ec 0.00195 35.37332727
Cpr78Cc 0.007 7.664204998
Cpr97Eb 0.02624 7.408899944
CycB 0.02632 1.81557107
Cyp28d1 0.04263 1.770913591
Def 0.00058 15.86903224
Dpt 0.02097 2.694585845
DptB 0.03984 4.848573639
dro4 0.03226 1.932392284
Drs 0.00581 4.229386226
Drs-l 0.00771 4.505374103
Edg78E 0.04458 20.93760172
Edg84A 0.01263 39.66811119
Eig71Eb 0.00631 15.16385577
Eig71Ef 0.00727 2.039700835
Eig71Ej 0.00694 25.20607479
fon 0.0086 2.608889375
His1:CG31617 0.01695 1.871622616
His2B:CG17949 0.01106 1.970705102
Hmx 0.01324 3.630407497
Hph 0.04441 1.504306296
Hsp67Bc 0.03873 1.950255838
Ilp7 0.02154 2.386001979
IM1 0.00893 2.114703461
IM3 0.0072 2.685109438
Lcp1 0.02379 3.049928972
Lcp65Ad 0.00237 25.40628331
Lcp65Ae 0.03214 8.860440024
Lcp65Ag2 0.02485 12.04223346
Lsp1beta 0.03098 2.769268707
LysB 0.00335 3.552590705
Mmp1 0.02154 1.853995699
Mtk 0.01138 5.814600904
Nplp4 0.04441 1.8034599
pall 0.02924 1.795301945
PGRP-LA 0.01858 1.756079682
PGRP-SB1 0.00237 2.987071622
snRNA:U5:23D 0.04751 2.257579295
snRNA:U5:34A 0.03483 2.108312639
swaPsi 0.01301 1.797370206
Tsp42Eb 0.03202 2.311569783
Vha100-2 0.04812 1.838227684

207

Table D7. Genes with differential expression when over-expressing FRU
B
isoforms in males and
identification of FRU CRMs
Higher expression levels in control males Higher expression levels in hs-FRU
B
males
Gene Symbol
q-
value
M/hs-FRU
B

FC
FRU
CRM
Gene
Symbol
q-
value
hs-FRU
B
/M
FC
FRU
CRM
Ag5r 0.00438 4.161832532 Ady43A 0.00724 1.817707656
asparagine-
synthetase 0.04114 1.639870894 aPKC 0.00041 3.833284305
AttD 0.02905 1.919886001 bgm 0.0466 1.5283427
CG10005 0.04551 4.891943429 boi 0.02244 1.632024766
CG10508 0.00962 1.736016253 C901 0.03946 1.676870933
CG10527 0.02296 1.744627978 Cad74A 0.01953 1.513337632
CG10931 0.03443 1.558960361 Cad89D 0.02371 2.013486472
CG11023 0.0247 1.512905057 caps 0.03946 1.668482489
CG11029 0.02221 1.811504399 CecB 0.03117 1.509978753
CG11089 0.02201 1.726560469 cenG1A 0.00679 3.203058056
CG11380 0.04159 3.861963651 CG10650 0.02296 2.065611555
CG11455 0.04525 1.639727223 CG10898 0.03569 1.747279793
CG11459 0.00615 2.924786847 CG11498 0.0125 2.084638642
CG11619 0.03196 1.95906925 CG11966 0.03946 2.217225835
CG11693 0.04808 4.316757532 CG12063 0.01755 1.998346928
CG12505 0.00392 2.836612389 CG12111 0.03355 1.649311668
CG12986 0.00613 4.817241318 CG12116 0.0138 2.422745416
CG12996 0.00613 2.980054209 CG12432 0.03804 1.90229383
CG13068 0.02371 1.727839519 CG12551 0.02914 1.985582393
CG13070 0.03212 1.684559231 CG1273 0.04551 1.896566194
CG13138 0.03548 6.684183078 CG12730 0.04551 1.943823238
CG13154 0.04894 5.240582046 CG12769 0.03355 1.827709275
CG13641 0.02889 1.719065394 CG12780 0.00724 1.977245634
CG13670 0.04353 1.522472298 CG12814 0.00967 1.7275475
CG13723 0.00317 2.34949338 CG12947 0.02244 1.615159378
CG13732 0.00317 1.932165261 CG13081 0.03946 2.000613982
CG13840 0.03117 2.448726701 CG13082 0.01656 2.027164674
CG13847 0.04204 2.452712938 CG1342 0.03465 1.764617873
CG14174 0.04938 1.560290144 CG13744 0.04284 1.758324208
CG14244 0.0028 2.333572622 CG14395 0.03577 1.560994398
CG14257 0.04996 2.961897838 CG14515 0.03465 1.633611226
CG1428 0.02662 1.775971667 CG14681 0.03946 1.510708576
CG14313 0.01705 2.040282574 CG1499 0.04551 1.631628118
CG14375 0.0005 2.816039804 CG15028 0.03355 1.601754909
CG14529 0.01265 1.871213178 CG15293 0.01684 2.59413836
CG14624 0.01478 6.564232168 CG15690 0.02254 1.529841536
CG14752 0.04726 3.135537337 CG15822 0.04317 1.670328498
CG14770 0.04749 4.580493172 CG15888 0.03117 1.74740831
CG14946 0.00679 4.418990688 CG1598 0.0138 1.592206383
CG15005 0.04353 1.708759222 CG16712 0.00235 3.181828153
CG15202 0.03602 3.417802952 CG16798 0.00679 2.216448903
CG15251 0.04726 2.073142829 CG17121 0.00868 1.708512085
CG15281 0.00133 9.580209858 CG17380 0.00297 19.93766269
CG15369 0.00724 2.012686821 CG17549 0.01598 1.567026879
CG15545 0.04087 9.564316713 CG18317 0.01727 1.745943411
CG1561 0.04353 1.513356967 CG18542 0.01684 1.598783282
CG15678 0.02275 2.369671912 CG18673 0.00711 1.815802869
CG15784 0.01755 1.592744855 CG18735 0.02914 2.219179191
CG16733 0.04996 2.46725072 CG2177 0.00317 9.955034782
CG16799 0.0329 2.02020025 Cg25C 0.03082 1.846782235
CG1698 0.04114 1.847134862 CG2641 0.00154 3.050423474
CG17127 0.04726 4.092337918 CG30174 0.00317 2.171387751
CG17298 0.01608 2.826972623 CG30179 0.01605 1.756006179
CG17600 0.04996 1.840464023 CG30344 0.0466 1.631993005
CG18063 0.03118 1.575703093 CG31004 0.03531 2.184972717
CG18641 0.01047 2.559170213 CG31203 0.01608 1.723434961
CG2444 0.00799 2.705015197 CG31431 0.04651 1.559587903
CG30026 0.04551 1.764102555 CG31728 0.01322 1.668334288
CG30076 0.03661 4.962166242 CG31839 0.00184 2.398635051
CG30160 0.00756 1.816031615 CG31886 0.0138 1.776447895
CG30197 0.00317 1.973545042 CG32021 0.02027 1.889857597
CG30375 0.03661 1.669777744 CG32244 0.03117 1.937551151
CG30457 0.02546 1.550958718 CG32939 0.01598 1.696209378
208

Table D7, Continued
CG31005 0.03661 1.926340782 CG33267 0.03988 1.643768114
CG31742 0.00847 1.756902272 CG34346 0.0466 1.529181106
CG31775 0.00026 11.24533796 CG34350 0.01608 1.83271634
CG31806 0.04308 1.649822814 CG34380 0.04551 1.526781172
CG31878 0.00724 28.30854858 CG34383 0.01782 2.157595841
CG31933 0.04308 1.533610188 CG3492 0.00186 8.760930309
CG31955 0.01652 1.799865074 CG3513 0.01188 2.878773796
CG31973 0.02186 1.675527841 CG4090 0.01824 1.7755654
CG3244 0.04493 1.911404485 CG4374 0.03608 2.340777725
CG3285 0.04149 1.82286406 CG4382 0.0428 1.870735294
CG33459 0.04644 1.621315132 CG4646 0.0212 1.582928969
CG3397 0.02071 2.611272407 CG4747 0.04308 1.600231286
CG3434 0.03355 1.506221537 CG4757 0.00317 2.504835612
CG34382 0.04551 2.959473314 CG4844 0.0398 1.730787883
CG34409 0.04308 1.545742902 CG6928 0.04149 1.738449325
CG3604 0.00395 2.231771067 CG7201 0.03355 1.901877329
CG40159 0.0088 1.682435717 CG8112 0.02296 2.242033164
CG40271 0.00962 1.981321693 CG8420 0.03066 1.746476844
CG4151 0.0141 3.022274924 CG9027 0.0304 1.656805688
CG4408 0.00679 1.855561313 CG9196 0.03624 1.719618832
CG4998 0.02218 3.774248952 CG9360 0.02617 1.73247308
CG5693 0.02071 1.513402917 CG9460 0.00724 1.907396168
CG5697 0.02505 1.862701868 CG9509 0.01026 2.062289644
CG5873 0.04969 1.951860777 cher 0.03842 1.545328431
CG6231 0.02609 1.537611043 comm2 0.04726 1.559091235
CG6337 0.03815 2.695208134 Cpr73D 0.00711 2.102396803
CG6608 0.03476 2.024116994 Cyp18a1 0.02296 1.72009263
CG6753 0.04996 1.873238915 Cyp4e2 0.01656 1.61596924
CG7322 0.04284 1.820494157 Cyp4p2 0.00026 9.572011801
CG7448 0.01335 3.328470483 Cyp6a16Psi 0.01932 1.587027845
CG7532 0.01852 1.89351128 Cyt-b5 0.02175 1.662678335
CG7795 0.00598 2.024619677 dei 0.0212 1.773572506
CG7852 0.00879 1.757647024 dock 0.00133 7.603156285
CG8192 0.02218 2.958441214 dro5 0.01981 1.666363184
CG8568 0.03988 1.674190662 dro6 0.0138 1.858833028
CG8927 0.03842 2.428554229 dy 0.02241 1.790716456
CG9021 0.02222 2.309634906 fau 0.03066 1.515630312
CG9083 0.00392 5.362297785 fru 6.6E-05 4.755973922
CG9192 0.03577 1.890000721 Gld 0.00055 6.465989867
CG9357 0.02201 1.68609832 hbs 0.01642 1.557852443
CG9914 0.00456 2.35714245 hts 0.04937 1.654125312
CG9972 0.01608 2.811014539 Idgf3 0.0136 2.092107463
Cpr47Ec 0.02201 1.75200545 IM1 0.03707 1.564298473
Cpr49Ab 0.01608 1.585376972 IM2 0.00724 3.176123193
Cpr49Ac 0.01426 2.564777195 ImpE1 0.0136 2.591758694
Cpr50Ca 0.04308 3.483137706 ImpE2 0.0453 1.514335171
Cpr51A 0.03569 2.525888733 l(3)neo38 0.0421 1.616859383
Cpr72Ec 0.00727 11.29456669 mas 0.00756 1.817189094
Cpr76Bc 0.02371 1.732358517 Mec2 0.04726 1.834061416
Cpr97Ea 0.03465 1.574688254 mfas 0.04996 1.513267292
Cpr97Eb 0.01047 2.387146686 mRpL37 0.01642 1.540736861
Cry 0.01782 10.40455042 mth 0.00026 8.026118247
Cyp28d1 0.04353 1.809762407 MYPT-75D 0.04317 1.65474239
Cyp309a1 0.00369 2.277333567 Orct 0.04227 2.112225365
Cyp4d14 0.04551 2.593924285 Osi11 0.01727 1.985057263
Def 0.00235 6.760739955 Osi22 0.0398 1.840818194
Edg91 0.04284 1.846340979 pio 0.03465 1.704165026
Eig71Ej 0.00486 11.6459795 pip 0.04317 1.500436971
Eig71Ek 0.00724 5.327527953 Pis 0.01684 2.891990369
Est-P 0.00679 1.937835168 prom 0.03528 1.659592134
Fbp1 0.00317 4.113008161 pwn 0.03661 1.568467312
fon 0.00112 3.555023793 Rbp1 0.01792 1.577915705
Gr64b 0.04444 1.988019918 rumi 0.04317 1.531288818
GV1 0.02154 1.53337144 scpr-A 0.03196 1.681210628
hdc 0.02222 1.510809191 scpr-C 0.0136 1.68912014
His1:CG31617 0.00481 1.864232479 Sema-1a 0.04149 1.551237037
His2B:CG17949 0.00456 2.06738489 slbo 0.02239 1.722478263
His-Psi:CR31614 0.01782 1.774212351 SP1029 0.02789 2.127291192
His-Psi:CR31615 0.01306 1.715194501 SP1070 0.03661 1.777827466
209

Table D7, Continued
Hsp68 0.04308 2.940517049 Src64B 0.027 1.61037641
Hsp70Ab 0.04644 2.989043583 TepII 0.04366 1.855412593
JTBR 0.02027 1.738259675 tnc 0.0159 2.356524226
Lsp1alpha 0.00041 2.84616365 TotA 0.00967 3.148263793
Lsp1beta 0.00055 3.651533514 Trp1 0.04519 1.560767948
Lsp1gamma 0.00027 4.594168303 tutl 0.0095 1.879028332
Mp20 0.01947 1.58560334 unc-115 0.02201 1.680510805
Nmdmc 0.04214 2.165562034 wit 0.0179 1.749456402
Nox 0.0466 1.6779152 wus 0.02727 1.771234329
Obp99b 0.00045 5.571941928 yellow-d2 0.00055 5.036588931
Obp99d 0.00459 4.587800302
obst-E 0.01332 3.061629237
PGRP-LA 0.00679 1.772481416
Phk-3 0.03569 1.958423694
regucalcin 0.04726 1.730511002
SA-2 0.04081 1.870633974
Spat 0.03355 1.590418819
SpdS 0.01608 1.796769008
Thor 0.02175 1.861090957
Tim17b1 0.02914 1.566057211
Tsp42Eb 0.00317 2.308486473
Tsp42Ek 0.0095 1.93558497
Tsp42Ep 0.01755 1.581111326
Tsp42Er 0.04308 1.804857251

M/hs-FRU
B
FC: Fold change of gene expression data between control males and males over expressing FRU
B

210

Table D8. Genes with differential expression when over-expressing FRU
C
isoforms in males and
identification of FRU CRMs
Higher expression levels in control males Higher expression levels in hs-FRU
C
males
Gene Symbol
q-
value
M/hs-FRU
C

FC
FRU
CRM
Gene
Symbol
q-
value
hs-FRU
C
/M
FC
FRU
CRM
Adh 0.00217 2.682012283 aPKC 0.00849 3.777864727
Ag5r 0.00404 4.5359422 cenG1A 0.00707 3.096315292
asparagine-
synthetase 0.0137 3.462749966 CG10799 0.04846 1.679520239
AttA 0.00166 5.814882213 CG11395 0.0403 1.583441771
AttB 0.00255 5.080191793 CG12780 0.01307 1.808106888
AttC 0.0089 4.126126102 CG13062 0.02892 1.967353407
AttD 0.00217 3.229213178 CG13231 0.0195 3.75016712
br 0.01163 2.543944854 CG13982 0.01789 2.251746776
CAH2 0.02352 2.020172669 CG14275 0.00165 4.072833426
Ccp84Ag 0.01065 2.552003053 CG15615 0.02868 2.634137443
CecA2 0.00264 3.830001786 CG16712 0.00255 5.296997867
CecB 0.00035 5.47229088 CG18477 0.01048 1.841090761
CecC 0.00023 6.543207722 CG2543 0.00742 2.531112231
CG10359 0.04022 2.750297432 CG2641 0.00721 2.251509802
CG10625 0.01237 4.340154317 CG30174 0.00404 2.766481377
CG11350 0.00361 4.861471967 CG30179 0.00552 2.309158504
CG11825 0.01174 2.369603487 CG31839 0.00279 2.474330206
CG11852 0.03675 2.194699332 CG31878 0.04281 2.357132385
CG12268 0.01332 2.765269886 CG3492 5.5E-05 13.71902117
CG12996 0.0306 2.817395032 CG3513 0.01365 2.25344625
CG12998 0.00166 8.219740695 CG6280 0.04588 2.207328264
CG13026 0.00552 2.980710422 CG6639 0.03282 2.152207281
CG13068 0.01875 2.356303516 CG6912 0.00552 4.801924183
CG13081 0.00707 2.972295628 CG8586 0.04046 1.652042397
CG13248 0.02141 3.128317571 CG8665 0.03022 2.236537751
CG13287 0.00758 3.648322865 CG9989 0.02352 1.866506388
CG13335 0.01399 14.70967399 Cpr72Ec 0.02352 3.60041886
CG13618 0.03356 2.629569576 Cry 0.04107 1.88951227
CG1368 0.00199 7.446078755 Cyp4p1 0.04588 2.2868458
CG13722 0.00707 4.860453488 Cyp4p2 0.00343 7.960118426
CG13731 0.00552 6.981376898 Cyp6d2 0.0089 3.415280486
CG14244 0.02753 6.802117551 DNaseII 0.00742 2.170959004
CG14529 0.00707 3.779891262 dock 5.5E-05 13.51927243
CG14566 0.0294 2.06454929 fit 0.02209 10.56908066
CG14568 0.04373 4.747321803 fru 0.0001 10.90587518
CG14598 0.0089 6.687670276 Gld 5.5E-05 10.67091477
CG14752 0.01875 4.563087998 GRHR 0.04588 2.637624933
CG15212 0.00999 6.940342991 GstD5 0.00328 3.235218469
CG15213 0.00404 7.805318193 hbs 0.03633 1.723182383
CG15281 0.03993 5.015030297 Idgf3 0.00213 3.0995634
CG15422 0.00807 2.852272048 IM2 0.00404 3.023356567
CG15517 0.02378 8.483836954 lectin-28C 0.02862 2.10163147
CG15678 0.01332 6.277802701 Mlp60A 0.04285 1.868147357
CG1773 0.00552 2.190833604 mth 0.01057 3.983543021
CG18067 0.03843 1.743014471 Nplp2 0.00165 7.019876116
CG1850 0.00213 4.903008442 Orct 0.0195 2.635407654
CG2444 0.00092 6.320516501 Pdh 0.02402 2.80931296
CG2781 0.03022 1.712148349 PGRP-SC1b 0.00275 4.832065735
CG30375 0.01006 2.595529717 PGRP-SC2 5.5E-05 7.522756941
CG30457 0.02594 2.557422413 prc 0.04894 1.658079609
CG31775 0.02229 5.702705149 scpr-A 0.03981 3.098350704
CG31955 0.02573 2.069038626 scpr-C 0.04335 2.337745662
CG32185 0.00275 10.23903058 smp-30 0.0195 2.746397073
CG32241 0.04075 4.4097971 unc-115 0.03682 1.645799025
CG3244 0.00278 4.482698056 yellow-d2 0.00213 5.477109752
CG33056 0.04054 2.037735238
CG33229 0.0389 1.613621332
CG33254 0.0115 1.98513682
CG34380 0.03682 2.453569112
CG3604 0.00707 5.83781406
CG40271 0.00736 2.656541983
CG4151 0.02566 12.79160707
CG4726 0.00344 2.395938589
211

Table D8., Continued
CG5326 0.04107 1.573209122
CG5697 0.04713 1.642604736
CG6426 0.03981 2.400047824
CG6429 0.0389 4.177374688
CG7214 0.0187 1.797210082
CG7532 0.0173 2.243046466
CG8213 0.02629 1.943975316
CG8483 0.0283 2.616271597
CG8736 0.00404 7.705616477
CG8927 0.00328 3.828917283
CG9021 0.04548 4.851780625
CG9192 0.00742 3.622067953
CG9357 0.02892 2.579009716
CG9747 0.01042 3.811444676
CG9867 0.02082 3.213684455
CG9914 0.04335 1.956282742
Cpr100A 0.002 6.30958715
Cpr12A 0.03022 2.784950464
Cpr49Ab 0.02167 1.797291145
Cpr49Ac 0.00217 4.853987823
Cpr51A 0.00707 3.16844359
Cpr65Ec 0.00333 25.30120195
Cpr72Eb 0.00255 86.04556112
Cpr97Eb 0.00217 8.545433631
Cyp18a1 0.02923 1.861837826
Cyp4d14 0.03992 3.693333016
Def 0.00023 15.26272561
Dpt 0.00879 3.088108548
DptB 0.01158 5.09979147
Drs 0.00125 3.943855914
Drs-l 0.00101 4.814872417
e 0.0115 2.846955669
Edg78E 0.0047 22.73107398
Edg84A 0.0283 12.61541114
Eig71Ek 0.02082 10.75306152
Gr64b 0.03276 2.538357475
Gr64c 0.02352 2.086363115
Hmx 0.04028 3.05741129
Hsp68 0.03981 4.202031489
Hsp70Ab 0.00807 6.665108675
Hsp70Ba 0.00859 6.435275814
Hsp70Bbb 0.01006 6.526638763
ImpL3 0.02419 1.667047637
Lcp65Ad 0.0119 12.82625833
Lcp65Ae 0.00707 8.519158617
Lcp65Ag2 0.00707 9.492968947
LysB 0.00995 2.245949788
LysS 0.02753 1.682995898
LysX 0.02352 1.735726587
malpha 0.02862 2.358679878
Msp-300 0.03901 2.181833064
Nplp4 0.02352 1.667151269
Oat 0.01678 1.948181155
Obp99d 0.0137 7.517515906
Pcp 0.0283 3.277107508
PGRP-SB1 0.00349 3.75356051
PGRP-SD 0.01451 1.900242188
Phk-3 0.0089 2.810489637
pie 0.04846 1.585474585
ref(2)P 0.0283 2.242151406
snRNA:U5:23D 0.01065 2.752421917
snRNA:U5:34A 0.01291 2.458737716
snRNA:U5:38ABa 0.02862 2.454778355
snRNA:U5:38ABb 0.00404 2.665831344
Spat 0.04588 2.185832254
swaPsi 0.0306 1.991043411
Sxl 0.03503 3.281485803
Thor 0.02525 2.464230469
Tsp42Eb 0.02097 2.501187927
212

Appendix E: SUPRfly – A web-based program for predicting cis-
regulatory modules in Drosophila
Introduction
Determination of cis-regulatory modules (CRMs) in Drosophila is an important, ongoing
question. Much progress has been made in generating methods to search for CMRs;
however, it is still a difficult problem because known Drosophila CRMs do not have
clear-cut features that allow for easy detection from non-functional genomic DNA (Li et
al, 2007). In addition, although CRM searching methods for the Drosophila exist, there
are limited methods available in a web-based format that allow for easy use by the
Drosophila community. Two features that have proved useful in predicting CRMs are the
likelihood of CRMs to contain clusters of multiple transcription factor binding sites
(TFBSs) and the likelihood of functional CRMs to be conserved in orthologous genes of
closely related species (Berman et al, 2002; Gertz et al, 2005; Kellis et al, 2003;
Rajewsky et al, 2002; Sinha et al, 2004; Wong & Nielsen, 2007). Incorporation of these
features is important for any method aimed at finding Drosophila CRMs.

In this appendix, we describe the development of a web-based CRM search method
specific for Drosophila (SUPRfly; Significant Upstream Regions of the fly;
http://suprfly.cmb.usc.edu). The main function of SUPRfly is to take three user inputs: 1)
a gene or list of genes, 2) transcription factor binding site representations, and 3) a
regulatory search region. From these inputs, SUPRfly outputs significant cis-regulatory
modules. The methodology and prediction accuracy of this method are described in
213

Chapter 5. SUPRfly also has the ability to take a gene list input and the regulatory search
region and use de novo prediction of TFBSs, which are then inputted into the SUPRfly
pipeline to find CRMs in the set of co-regulated genes. Additional features of SUPRfly
are also discussed in this appendix.

Program Feature Summary
Here we describe the various searching features of SUPRfly, including the different
statistical methods available. See Figure E1 for a sample of the layout of the main search
page.

Searching for transcription factor binding sites in Drosophila
SUPRfly has the ability to search for binding sites for up to five transcription factors at a
time. The representative motifs for these transcription factor binding sites (TFBSs) can
either be entered as consensus sequences or as position weight matrices (PWMs). When
PWMs are entered by the user, the user has the ability to change the stringency of what is
declared a TFBS by changing the PWM cutoff. The PWM cutoff refers to the threshold
of the information content score for the TFBS, as described in 5.2.2. PWMs for common
transcription factors along with their suggested threshold values are also available. The
user then chooses the region in which to search for CRMs. The possible upstream and
downstream regions may extend up to 10,000bp or to the boundary of the next closest
gene. Also, the user has the ability to search in all introns or only in the first n user
defined introns, a useful feature as some Drosophila CRMs are located in the first few
introns (Halfon et al, 2007).
214

Figure E1. Sample input layout for SUPRfly. On this page is where the user selects which genes to
search, inputs PWMs or consensus sequences, and chooses the search region. The user also has the ability
to search multiple Drosophila species for CRMs, as well as generate an alignment of the search regions.
The main significance test is to search for CRMs using an r-scan that allows for determination of a
conservation score. The paremeters for additional significance tests are hidden. Not shown are the input
areas for additional motifs (up to five).
215

A recent initiative has led to the sequencing of 12 Drosophila genomes (see
http://rana.lbl.gov/drosophila), which can greatly aid in determining the conservation of
Drosophila CRMs. Currently SUPRfly has the ability to search for TFBSs and CRMs in
10 of these Drosophila species, as annotations for two species were not available at the
time of development. The gene annotations are based upon the D. melanogaster genome,
and the orthologous genes from the additional species were gathered from the annotation
developed by (Pollard et al, 2006). The user has the ability to choose one of the 10
species to be the target species to search, as well as the ability to search in all species. As
an additional component, SUPRfly can generate a multiple alignment against the
regulatory region for the gene in the target species using ClustalW (Chenna et al, 2003).

Searching for cis-regulatory modules
The default CRM searching method for SUPRfly is the method using r-scans described in
detail in Chapter 5. If the user chooses to search using this method, he/she also has the
ability to find the conservation score of significant CRMs, as described in 5.2.4. The
conservation score returned will be for the significant CRMs discovered in the target
species chosen by the user.

If the user would like to search for CRMs using a more traditional method, SUPRfly has
the ability to search for clusters of TFBSs in a given window size. With this method,
significance is determined by permuting a user-defined number of random search regions
using the 2
nd
-order Markov probabilities of the regulatory region, as described in 5.2.3.

216

Searching for significant number of occurrences of transcription factor binding sites
An additional feature of SUPRfly is to provide significance for the number of TFBSs, as
opposed to a cluster of TFBSs. When this significance test is chosen, SUPRfly generates
a user-defined random number of search regions, again using the 2
nd
-order Markov
probabilities of the regulatory region. Significance is determined from the number of
random sequences that have as many or more TFBSs as the regulatory region of the
target gene.

Searching for over-representation of TFBSs in an input list of genes
A user of SUPRfly also has the ability to determine if its input list of genes is over-
represented with genes containing TFBSs as compared to the entire Drosophila genome.
Given user-inputted consensus sequences or PWMs, SUPRfly searches the gene list for
occurrences of these TFBSs and additionally searches the entire Drosophila genome.
SUPRfly then determines significance of the motif being found in the input gene list by
using the binomial approximation of the hypergeometric test. The user also has the
ability to further constrain which genes are considered to have the motif by requiring the
TFBS to be present at least a certain number of times or in a certain number of
orthologous species.

Program Output
The initial results page for SUPRfly contains a table displaying all genes in the input list,
with links to the specific results of each gene (see Figure E2 for an example of the
output). If a significance test was chosen, the most significant p-value for each motif (or
217

A

B

Figure E2. Sample output layout for SUPRfly. A) The first results page that the user sees displays the
most significant CRMs found and the maximum CRM conservation score for every gene in the input list.
B) When the user selects an individual gene, the information of where the motifs and the CRMs are found
appears on this screen. There is a coordinate representation as well as a sequence representation.

218

all motifs) for each gene is included on this page. In addition, if the conservation score is
selected, the maximum conservation score for CRMs in each gene is also displayed on
this page. This page also displays a link to the multiple alignment for each gene if the
alignment option is selected.

Once the user selects the link for a particular gene, a second results page is displayed.
This results page for the selected gene contains all the information for each input motif,
including motif positions and significance values. It also includes all significant cis-
regulatory modules along with their p-value and their location. If the user selected to
search for the conservation score, this results page presents the conservation of the CRM
along with a link to see which significant CRM in the additional species showed the
highest conservation with this particular CRM of the target species. Additionally, the
regulatory region searched is displayed in this output, with clear delineation of the
transcribed regions as well as the location of the TFBSs. If all species are searched or if
the conservation score is determined, this page also presents the results in a similar
format as the target species for each of the additional nine species.

Conclusions
In this appendix, we present SUPRfly, the web-based cis-regulatory module and
transcription factor binding site search tool that uses the method described in Chapter 5.
SUPRfly is specific for the Drosophila community, and provides additional features not
present in the previous web-based Drosophila CRM searching programs (Berman et al,
2004; Rajewsky et al, 2002). As an additional feature of this program, we will include a
219

graphical output of the discovered CRMs in the Drosophila genome, using a program like
the UCSC Genome Browser or Gbrowse (Kent et al, 2002; Stein et al, 2002). Finally,
this program is scalable to other organisms with closely related species containing
annotated orthologous genes, including humans, plants, and yeast.

Abstract (if available)

Abstract Sexual reproduction in most multi-cellular species requires the development of sex-specific adult structures and the potential to perform sex-specific behaviors. These adult sex-specific phenotypes are typically patterned during development. Therefore, a more thorough understanding of how the genome is deployed in a sex-differential manner during development provides insight into how gene networks give rise to sex-specific traits. The fruit fly, Drosophila melanogaster, is an excellent model system for studying how sex-specific differences are established at the molecular-genetic level. The Drosophila sex determination hierarchy is responsible for establishing adult sex-specific morphologies and behaviors through an alternative pre-mRNA splicing cascade that culminates in the production of sex-specific transcription factors encoded by doublesex (dsx) and fruitless (fru). Little is known, however, about the genes regulated by these transcription factors during development.

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Asset Metadata

Creator Lebo, Matthew Stewart (author)

Core Title Computational and experimental approaches for the identification of genes and gene networks in the Drosophila sex-determination hierarchy

School College of Letters, Arts and Sciences

Degree Doctor of Philosophy

Degree Program Computational Biology

Publication Date 02/11/2008

Defense Date 01/14/2008

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

Tag cis-regulatory module,doublesex,Drosophila,fruitless,metamorphosis,OAI-PMH Harvest,sex-determination

Language English

Advisor Sun, Fengzhu (committee chair), Arbeitman, Michelle N. (committee member), Siegmund, Kimberly D. (committee member)

Creator Email lebo@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-m1013

Unique identifier UC1322522

Identifier etd-Lebo-20080211 (filename),usctheses-m40 (legacy collection record id),usctheses-c127-48093 (legacy record id),usctheses-m1013 (legacy record id)

Legacy Identifier etd-Lebo-20080211.pdf

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Document Type Dissertation

Rights Lebo, Matthew Stewart

Type texts

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

Repository Name Libraries, University of Southern California

Repository Location Los Angeles, California

Repository Email cisadmin@lib.usc.edu