The Effects of Population Density and Knock-Downs of Lipid Metabolism Genes on the Expression of Cuticular Hydrocarbons in Droso

The effects of population density and knock-downs of lipid metabolism

genes on the expression of cuticular hydrocarbons in Drosophila

melanogaster

Adrienne Chu

A thesis submitted in conformity with the requirements for the degree of

Master of Science

Graduate Department of Ecology and Evolutionary Biology

University of Toronto

2009

Adrienne Chu

Graduate Department of Ecology and Evolutionary Biology

University of Toronto

Abstract

In Drosophila melanogaster, chemical cues in the form of cuticular hydrocarbons play an important role in reproductive behavior. The social and genetic processes that regulate their expression, however, are poorly understood. The social environment has been shown to influence hydrocarbon display. In this study, the effect of population density on the expression of hydrocarbons was evaluated. I demonstrate that the production of certain hydrocarbons depends on the population density in which the animal is reared. Individual hydrocarbons fluctuate in quantity independently from one another but the peaks during a light-dark cycle are static depending mostly on chain length. The regulation of fly hydrocarbons which are density-dependent is shown to be sexually dimorphic. The RNAi knockdown of various putative lipid metabolism genes was also used to study hydrocarbon expression. This study reveals that lipid metabolism genes which are not obvious mediators of HC synthesis influence cuticular hydrocarbon profiles.

ii Acknowledgments

I would like to gratefully acknowledge all those who helped me with this project as it would not have been possible without them. I would like to thank Joel Levine, Richard Dunbar-Yaffe, and Clement Kent for help with preparation of this manuscript. Throughout my project,

Joshua Krupp, Jean-Christophe Billeter, Tony So, Amanda Formosa, and Jade Atallah have been helpful with the gas chromatography work and various other aspects of biology. I thank

Dr. Barry Dickson for his gracious provision of RNAi lines. The entire Levine and Sokolowski lab were also extremely helpful throughout my project and defense. Finally, I thank Joel Levine and Marla Sokolowski for their supervision and guidance.

iii Table of contents Abstract...... ii Acknowledgments...... iii Table of contents ...... iv Introduction ...... 1 Hydrocarbons (HCs)...... 1 Courtship in Drosophila melanogaster...... 2 Hydrocarbon affects during courtship ...... 3 Behaviors shaped by gene and environment ...... 4 Genetic control of CH profiles ...... 5 Environmental influences in hydrocarbon profiles...... 6 Summary ...... 7 Chapter 1 ...... 9 Introduction ...... 9 Density influences courtship and mating behaviors...... 10 Genetic and behavioral changes due to environment...... 12 Genes and density influences behaviors ...... 13 Summary ...... 14 Material and Methods...... 14 Rearing conditions...... 14 Extraction of cuticular hydrocarbons...... 15 GC Chromatography ...... 16 Statistical analysis ...... 16 Results...... 17 Hydrocarbon expression changes with density ...... 17 Female hydrocarbons have diurnal variations...... 25 Desat1 mutants males and females differ in density regulation ...... 29 Discussion...... 29 Chapter Two ...... 35 Introduction ...... 35 Genetic Screens...... 35 The mechanism of RNA interference (RNAi)...... 36 Creating RNAi screening in Drosophila ...... 38

iv Hydrocarbon synthesis ...... 38 RNAi screening...... 40 Summary...... 41 Material and Methods...... 41 Rearing conditions...... 41 Extraction of cuticular hydrocarbons...... 42 GC Chromatography ...... 43 Courtship Experiment ...... 43 Statistical analysis ...... 44 Results...... 44 Hydrocarbon profile of male and female RNAi lines...... 44 Courtship of RNAi lines ...... 49 Discussion...... 49 General Discussion ...... 54 Appendix A ...... 58 Appendix B...... 63 References...... 76

v 1

Introduction

Endogenous chemical signals contribute to reproduction, survival and group activities

in insects and other animals. I am interested in the social and genetic processes that regulate

these chemical signals in Drosophila melanogaster. These chemical displays are not simply

determined by gene expression or driven by environmental conditions but instead they define

a response to gene-environment interactions and contribute to the social experience of each

fly. My thesis consists of two studies that addressed the regulation of hydrocarbons in the fruit

fly. The first study emphasized effects of environmental conditions. In particular, I evaluated

the effects of density on the expression of chemical signals. In addition, I examined how group

density interacted with light-dark cycles to control hydrocarbon levels on the cuticle of

females. In the second study I evaluated cuticular hydrocarbon expression in courtship

mutants. These mutants were identified by the Dickson laboratory in an RNA interference

screen for genetic loci that affected courtship.

It is important to appreciate the past and recent research into hydrocarbons as it

pertains directly to the work presented here. In addition, a cursory understanding of courtship

behavior of D. melanogaster is needed.

Hydrocarbons (HCs)

There is variability in the number and type of compounds used by animals for chemical communication (Wyatt, 2003). Chapter one focuses on cuticular hydrocarbons (CHs) found on

Drosophila melanogaster. Recent studies have proposed as many as 31 and 53 hydrocarbon

compounds present on the male and female fly, respectively (Foley et al., 2007). Other studies

2 identified a variety of hydrocarbons but the present study will focus on 22 male and 30 female

CHs because they are well-defined. Also, each is expressed throughout the day in a sufficient amount for circadian study (Levine lab, unpublished). These CHs plays an important role in waterproofing, preventing environmental stress and chemical communication. Since the discovery and pioneering studies of courtship behavior among Drosophila species during the early 20th century, there has been an increasing appreciation of chemical communication

between flies. While the communication must involve an interaction, sensing, processing, and

responding to chemical signals it is clearly amenable to genetic dissection because there are

enzymes, peptides and transcription factors that control Drosophila surface chemistry.

Courtship in Drosophila melanogaster

The reproduction of Drosophila melanogaster involves a well-defined behavioral

sequence of events during courtship (reviewed in Greenspan and Ferveur, 2000). A

heterosexual male orients toward a potential mate and taps her with his front leg. He then

follows her if she moves. Secondly, when she remains still, he circles her and licks her genitalia with his proboscis. Thirdly, the male tries to copulate by bending his abdomen and aiming his genitalia towards hers. The male repeats this sequence of actions until successful copulation has occurred. Although the courtship process appears simple, both partners exchange cues that rely on several sensory pathways including those which are visual, acoustic, tactile and chemical. The chemical signaling that occurs during courtship is of particular interest to the current study and therefore deserves substantial attention.

Species and mate recognition occurs during courtship involving a normal male D. melanogaster. This process is facilitated by CHs of mature flies (Legendre et al., 2008). Both

3 male and female D. melanogaster can produce alkanes (straight chain hydrocarbon). With

regard to unsaturated compounds, males can synthesize only cis-vaccenyl acetate (cVA) and unsaturated monoenes (hydrocarbon which contain one double bond) whereas females can produce both monoenes and dienes (Antony and Jallon, 1982a; Butterworth, 1969). This sexually dimorphic system allows mate recognition and aids in species recognition. Females of closely related species show differences in the length of the HC chain as well as both the number and position of double bonds (reviewed in Ferveur, 2005).

These sex specific unsaturated hydrocarbons including cVA, 5-tricosene (5-T), 7-

tricosene (7-T), 9-pentacosene (9-P), heptacosadiene (7,11-HD), and nonacosadiene (7,11-ND)

have been shown to influence behaviors more than their alkane counterparts (reviewed in

Ferveur, 2005).

Hydrocarbon effects during courtship

Jean-Marc Jallon in collaboration with his students and co-workers began to elucidate

the role of CHs in Drosophila courtship nearly 40 years ago. The body of work they produced

suggests that specific compounds influence distinct features of the courtship ritual (Greenspan

and Ferveur, 2000; Sokolowski, 2001).

The female-specific compound heptacosadiene is believed to act as the main

aphrodisiac pheromone because it produced the longest enduring wing vibration response

when perceived by males. However, large quantities of monoenes or dienes with 25, 27 and

29 carbons also produced sustained wing vibration (Antony et al., 1985; Antony and Jallon,

1982a). Higher concentrations of 7,11-HD also appear to entice females to mate more

frequently and rapidly than their untreated counterparts (Ferveur et al., 1996). At higher than

4 normal concentrations, similar HCs (7,11ND and 7P) can also induce male wing vibrations

(Antony et al., 1985). CHs may not work independently of one another. Flies might assess the overall HC profile before taking action. For example, with decreased 7,11-ND and increased

7,11-PD copulation latency increased while the number of copulations decreased (Chertemps et al., 2007).

During mating, physical contact allows the exchange of male specific HCs, such as cVA and 7T. The male fly synthesizes cVA in the ejaculatory bulb which can produce two effects

after transfer to the female (Butterworth, 1969). When male-derived cVA leaves the ovipositor,

along with eggs, it can cause flies to aggregate to food (Bartelt et al., 1985). Another function

of cVA is to act as an inhibitory pheromone for a few days after copulation or to prevent

homosexual courtship (Ferveur, 2005). 7T works by a similar mechanism. Courtship decreases with time after successful copulation. The difference between the two is that after copulation, male transferred cVA is undetectable after 4-6 hours but 7T synthesis is increased in females.

This could mimick the effect of male-derived cVA once it is depleted (Scott, 1986). It has been suggested that 7T and bitter stimuli (avoided by most animals) are processed by the same neurons allowing it to work as an inhibitory pheromone (Lacaille et al., 2007). Large doses of

7P and 9P also increase the duration of attempted copulation (Ferveur and Sureau, 1996).

Behaviors shaped by gene and environment

The distinct behavioral components of courtship and the action of chemical cues

provide a partial account of the mechanism underlying reproductive behavior. Genes and the

environment also influence this process. Much research has been devoted to the

understanding of behavioral genetics within specific environments. This includes different

5 types of social experience, time, food, temperature and other factors. To understand this complex interplay we must first understand the genes and pathways involved in D. melanogaster hydrocarbon synthesis (reviewed in Tillman et al., 1999).

Genetic control of CH profiles

Flies housed in constant or dynamic laboratory conditions have similar HC profiles

between generations (Ferveur, 1991). This implies that HCs are determined genetically rather

than (or in addition to) environmentally. The biosynthesis of Drosophila CHs is performed

within fat body-associated cells called oenocytes located along the dorsal abdominal wall (Pho et al., 1996). Cuticle-destined HCs originate from the reduction of long chain acyl-CoA molecules to aldehydes which are then elongated to form long, unsaturated fatty acids (Reed et al., 1994). An enzymatic decarboxlyation step removes the acid group at one end of the chain, leaving an essentially bare hydrocarbon suitable for desaturation or methylation if necessary (Pennanech et al., 1991). Like most other molecular pathways, each step involves many enzymes for regulation and expression.

The desaturation step (which produces double bonds) has been a large focus on hydrocarbon synthesis because the number of double bonds influences courtship behavior. For example 7 monoenes and the first double bond in 7,11 dienes are produced by desat1

(Dallerac et al., 2000; Marcillac et al., 2005b). Regulation of desat1 is complex as evidenced by

the multiple transcripts present in D. melanogaster (Legendre et al., 2008). Five transcripts

have been described with different expression levels in different tissues (Marcillac et al.,

2005a). In D. melanogaster, females have a separate gene called desatF which is involved in the second desaturation step of 7,11-dienes (Chertemps et al., 2006).

Another gene that has been of interest is the elongase gene for the reason that females produce more HCs with longer carbon chains. These include the pheromone dienes

7,11-HD and 7,11-ND. Knockdown of eloF (elongase-F) dramatically decreased the amount of

chains of 27 and 29 carbons while increasing molecules with 25 carbons. eloF is negligible in

males (Chertemps et al., 2007).

The manipulation of both desaturation and elongation genes proved to have

hydrocarbon consequences which are important for courtship behavior. However additional

genes and regulatory proteins are involved in each metabolic step and can be susceptible to

environmental influences.

Environmental influences in hydrocarbon profiles

Neural circuitry and hydrocarbon genes are not the only participants influencing

mating behaviors. The environmental influences on hydrocarbon profiles can be complicated;

climate, humidity, food, vapor pressure, day, age, time, and social experience all act to impact hydrocarbon expression (Cook and Cook, 1975; Kent et al., 2008; Krupp et al., 2008; Rouault

et al., 2000; Savarit and Ferveur, 2002; Tillman et al., 1999). Experiments must be kept in

controlled environments to avoid allowing extraneous factors to influence results.

Initial hydrocarbon research focused on how each hydrocarbon influenced mating

behavior in simple settings. Now the direction is to learn a more complex system in which

social experience modifies pheromone expression and thus the behaviors seen in the past.

These studies depict more realistically that which happens in natural surroundings where flies

come together and socialize.

The Levine lab has contributed a number of findings that link social influences to temporal chemical cues. I will only briefly discuss this topic, since it will be the focus of chapter one. Social experience in D. melanogaster has been shown to reset circadian rhythms of

locomotor activity, most likely through chemosensory mechanisms (Levine et al., 2002).

Although the social resetting cue is still unknown, hydrocarbons may reset circadian rhythms

making it possible that the expression of hydrocarbon is itself rhythmic. In fact, male flies

express a circadian HC pattern that depends on time and lighting conditions – this has yet to

be proven in females (Kent et al., 2007). Hydrocarbon profiles change not only under different

social settings (genotypic group or population density) but also by the gene expression of both

the intrinsic clock and desat1 genes (Kent et al., 2008; Krupp et al., 2008). Altering chemical

cues is possible due to a 24 hour turnover rate of hydrocarbons but this comes at a cost (Kent

et al., 2007). And we must wonder why natural selection has allowed such energy use. The

possibility arises that these simple molecules of hydrogen and carbon have the communication power that helps survival and reproduction to such an extent that it outweighs their energy cost.

Summary

The relationship between social experience and chemical cue is interesting in the fruit

fly because humans are social animals who also use chemical communication (Grosser et al.,

2000; Stern and McClintock, 1998). Thus, examining the effect of social experience on

communication in flies will provide testable hypotheses. Chapter 1 will analyze the interplay

between social interaction and the expression of CHs. Determining whether or not population

density influence the expression of these compounds in males and females will be the main

8 focus. In addition, diurnal variation of CHs in females maintained at different densities will be investigated. This will not tell us whether the expression of hydrocarbons is regulated by a circadian clock in the female, but it will characterize the temporal baseline of female hydrocarbon expression for the first time in D. melanogaster. These results will advance our

understanding of how population density shapes behavior in social animals.

Previous work on Drosophila hydrocarbons has focused entirely on those of the male

but the female has a vital role in courtship as well by having the last say. For these reasons,

the investigation of population density effects on flies will be examined because it also

incorporates the effects of females. Female HC profiles will be analyzed to determine whether

or not group density changes the quantity of and/or temporal release as seen in males (Kent et

al., 2008; Krupp et al., 2008).

To study the phenotype of CHs, many have manipulated genes by creating knock outs

or knock downs. Past studies have created knock downs by using RNA interference but this

explains few direct effects on hydrocarbon production. In reality many enzymes are needed for

proper production of hydrocarbons and as yet, high-throughput manipulation of the genes

involved has not been accomplished. With this in mind, RNAi lines involved in various steps in

fat metabolism have been generated by the Dickson Lab (Chapter 2); the current study

examines the effects of hydrocarbon expression and subsequently improves the understanding

of fly behavior.

Chapter 1

The effects of population density on the expression of cuticular

hydrocarbons in Drosophila melanogaster

Introduction

Chemical signals produced by Drosophila melanogaster have a variety of functions

that are involved in survival, fitness and communication. Production of these chemical cues

involves the interplay of genetic and environmental factors. The objective of this chapter is to

explore how the expression of chemical signals adjusts in response to genetic background and

population density (one of several environmental factors that may affect chemical signaling).

Further, female hydrocarbon expression is evaluated in a 24 hour light-dark cycle under

different population densities. This will contribute to our understanding of how social animals

respond to one another in the form of hydrocarbon (HC) communication. The widespread use of D. melanogaster in social experiments necessitates such an understanding, especially

considering the sparse nature of similar studies.

Flies gather hydrocarbon information by contact or airborne from short distances which often influences behavioral responses (reviewed in Ferveur, 2005). These behavioral

responses are due to the perception and interpretation of signals from other flies. Given their

volatile nature, hydrocarbons displayed on one fly can induce behavioral responses in multiple

others. With this in mind, the social experience of a fly can bring about a ripple of behavioral

responses; the interwoven nature of behavioral responses in flies makes social experience a

10 difficult topic to study. Nevertheless, the study of social experience communication allows a

more realistic view of what happens in nature.

One phenomenon occurring in nature is aggregation. The male-specific hydrocarbon,

cis-vacenyl acetate (cVA) is transferred to the ovipositor of a female during copulation and has

two effects. First cVA seems to be an inhibitory pheromone that prevents remating or male-

male courtship (Ferveur, 2005). Secondly, and more relevant to this chapter, cVA released with

laid eggs promotes fly aggregation on food (Butterworth, 1969). This raises interesting

questions; why would natural selection bring about such a phenomenon? Is there some

advantage to grouping flies together? Is communication occurring within the group? What

regulates the expression of these communicatory hydrocarbons? Do communication cues

change with an increase in group density? The effect of social experience on the secretion of

HCs has not been widely studied in D. melanogaster; nevertheless the research generated thus

far is interesting.

Density influences courtship and mating behaviors

Past research revealed how the population density can influence courtship components of many Drosophila species. Several behavioral changes have been observed with an increase

in the population density of D. pseudoobscura. Among them are a faster rate of mating

initiation, increased average time for the first mating, and increased proportion of mating flies

(Eckstrand and Seiger, 1975). Similar results were found in D. persimilis as well; in addition,

flies which did not mate when housed in pairs where found to mate when housed in larger

groups (Spiess and Spiess, 1969).

Other mating behaviors, such as the remating phenomenon (multiple matings of a single female) may be density-dependent. The frequency of remating of both D. ananassae and

D. persimilis females has been linked to the population density (Richmond, 1976; Singh and

Singh, 2001). However, not all Drosophila species followed this trend. The relationship between remating and density-dependence in D. melanogaster is still controversial. Some research has shown a positive relationship between density and remating (Marks et al., 1988), yet other studies provide evidence that the relationship may depend on other factors. For example, D. melanogaster subpopulations had varied remating behavior due to genotypic

variation (Harshman et al., 1988). Also a diminished population might force females to remate

due to sparsity of mates (Aspi and Lankinen, 1992).

Socially isolated and communal flies are two extreme forms of density which had HC and behavioral consequences in various Drosophila species. All six semispecies of D. paulistorum showed density-dependent changes CH, morphology, and behavior. Isolated D. paulistorum expressed a greater amount of HCs than communal flies. This trend was also true

for sexually dimorphic hydrocarbons (Kim et al., 2004). Additionally, flies which were isolated

from one another had an increase in wing length (Kim et al., 2004). Isolated males showed

higher success in mating, more courtship towards females and a higher frequency of same sex

courtship (Kim and Ehrman, 1998; Ueda and Kidokoro, 2002). Under different population

densities isolated males also increased male-male courtship attempts but the courtship profiles

did not differ between group density (Svetec and Ferveur, 2005). This behavior was dependent

on the age and not duration of isolation. These examples are all courtship related but other

behaviors such as aggression are influenced by social experience. Aggressive behaviors

increased in D. melanogaster when separated from society (Ueda and Kidokoro, 2002).

Genetic and behavioral changes due to environment

It is not surprising that differing social environments influence behavior. Interestingly,

rapid genome-wide response has been shown after social interaction involving courtship.

Using microarray technology, up to 43 loci had significantly different gene expressions after

five minutes of social exposure without copulation (Carney, 2007). This rapid mRNA change

included a number of down and up regulated genes. For example the Obp99b (odorant

binding protein) gene was upregulated during this short period of experience. Obps usually

binds and transports odorants to their receptors for downstream signaling (Rutzler and

Zwiebel, 2005). Interestingly Obps in Drosophila are involved in the detection of pheromones

(Benton et al., 2007). Carney’s (2007) genome wide study is only a glimpse of what happened to genes after five minutes of courtship exposure. These microarray studies can be used to test

many different social situations making this an exciting area for future studies.

Genome wide changes may directly or indirectly influence hydrocarbon metabolism

and hence communication which can lead to behavioral changes. Model based analysis of CH

patterns in male D. melanogaster has shown hydrocarbons with similar chain length and other

chemical characteristics followed similar trends (Kent et al., 2007). These HC clusters persist

despite changes in time of day and lighting conditions. The patterns seen were most likely due

to an overall change in genes which control the display of HCs. Such genes, including the

desaturase gene (desat1), have been confirmed to be regulated by time keeping genes at the site of HC synthesis (Krupp et al., 2008).

Genes and density influences behaviors

Circadian rhythm is a biological process in which patterns with a period of approximately 24 hours are observed. This rhythm is influenced by genotype variation and population density. Flies housed in groups were more synchronized in locomotor activity than isolated flies. Mixed genotypes housed in the same group also showed an increase in phase coherence (Levine et al., 2002). Olfactory mutants did not show this pattern which suggested a chemosensory pathway (Levine et al., 2002).

Social experience may not only aid in resetting the internal clocks but also help heighten olfactory sensation. In an olfactory plate test, grouped D. melanogaster larva are

found with less dispersion and spent more time in scented areas than lone larvae. This pattern

was proportional to the number of larvae in test plates (Kaiser and Cobb, 2008).

Although experience is important, genes also contribute to the overall behavior. To illustrate how genes impact courtship behavior Svetec and coworkers used hybrid fly strains with high and low homosexual tendencies. This produced different genotypes of varying degrees of courtship pattern. When isolated and then exposed to siblings for 1-4 days, heterosexual courtship index gradually decreased with experience but remained relatively high compared to controls. However if males spent five days with siblings, same sex courtship significantly decreased (Svetec et al., 2005). In these experiments it is not known exactly which genes are involved but it does illustrate the importance of experience and how the degree of behavior will change with respect to the genotype.

Summary

There is very little research on how social experience changes hydrocarbon accumulation in D. melanogaster. As previously discussed a mixed genotype group and varying environments have exposed such an effect but population density has not. Density could cause as yet unknown phenotypic variation in hydrocarbon. This study will hopefully give an insight into how a population density gradient changes hydrocarbon output and what types of genes are involved to allow such a change. As more flies are lured to a scent of volatile molecules, density grows to produce different group dynamics. This is why females with different densities will be studied to determine whether or not there is a circadian shift in HC expression.

Behavior is influenced by how the environment is observed, interpreted and acted upon. Since pheromones have been shown to have influences on communication, a critical HC gene, desat1 will be tested to see how mutants of this gene react when placed under different densities.

Material and Methods

Rearing conditions

Canton-S wild-type flies of D. melanogaster were used in the LD 12:12 cycle. Adult flies were allowed to mate within bottles of a sucrose-yeast medium for 24-48 hours to produce eggs.

Bottles were maintained in LD 12:12 cycle at 24ºC for optimal mating conditions. Adult flies were removed after the mating period and bottles were stored in LD 12:12 cycle at 24ºC for

15 nine to ten days. Flies were separated by sex within eight hours of eclosion while anaesthetized with carbon dioxide.

Different fly densities were housed in plastic vials (92mm X 25mm diameter) containing 8mL of medium. Females were housed in groups of two, four, eight, forty and eighty. Males were housed in additional groups of one and sixteen. After collection, vials were returned to LD 12:12 cycle at 24ºC for six days. On the sixth day extraction of cuticular

hydrocarbons was done at ZT13

Strains used for desaturase density experiments were cantonized Desat11573 and

Canton-S. Males and females of both strains were housed in groups of two and eighty. After collection, vials were returned to LD 12:12 cycle at 24ºC for six days. On the sixth day extraction of cuticular hydrocarbons were done at ZT13.

Culturing conditions for hourly experiments were either reared with 16 or 40 flies in a vial. After collection, vials were returned to LD 12:12 cycle at 24ºC for six days.

Extraction of cuticular hydrocarbons

Fly density experiments sampled 8-10 flies per density at ZT13 for three trials. While hourly

experiments sampled either 5 out of 40 or 15 out of 16 flies and done in 24 hours sampling

every two hours starting from ZT 0 for three trials. Extraction of cuticular hydrocarbons has

been previously described (Ferveur, 1991). Flies, 6 days old, were anaesthetized with diethyl ether. Extractions were done by placing each fly in a 2mL conical glass vial with a 200μL insert. The inserts contained 50μL of a standard mixture (10 μg/mL of octadecane

(C18)[Aldrich chemical company Inc.] and 10 μg/mL hexacosane (C26)[Aldrich chemical

company Inc.] in hexane solvent [Aldrich chemical company Inc. >99.0% purity]. Flies were

16 then shaken at low speed for 2 minutes and removed from the standard mix using a thin wire probe. The extracts were stored at -20ºC prior to analysis.

GC Chromatography

Samples were analyzed using a gas chromatograph joined to a flame-ionization detector

(Varian CP-3800 with autosampler CP-8400). The column employed was a DB-

1 custom capillary column with a 5m X 0.25mm guard column (J&W Scientific; length

20m, diameter 0.18mm and film 0.18 μm; model 100-2000). Programming conditions are

shown below in table 1.

Table 1: Programming conditions of Varian CP-3800 (autosampler CP-8400) for this study Oven Temperature Profiles Injector Temperature Profiles Temperature Rate Hold Total Temperature Rate Hold Total (ºC) (ºC/min) (min) (min) (ºC) (ºC/min) (min) (min) 50 1 1 50 0.1 0.1 150 36.6 0 3.73 200 200 36.45 37.70 200 5 8 37.73

Chromatograms were processed with Varian GC Workstation (Version 6.30) software.

Peaks were identified by comparing retention times to n-alkane standards [10μL/mL,

Aldrich Chemical Company Inc.] and using strains whose main hydrocarbons had been

identified by mass spectroscopy. All peaks were normalized with hexacosane [Aldrich

chemical company Inc.] from the standard solution.

Statistical analysis

Statistical analysis for this study was done by Clement Kent. Analysis of variance,

general linear model fits, Student t and F tests, multidimensional scaling, and hierarchical

clustering were performed in version 2.8.1 of the R computing environment (Lund and

Agostinelli, 2007). False Discovery Rate tests were implemented in R by the authors according

to Theorem 1 of (Benjamini and Hochberg, 1995). Estimates and 95% confidence limits for

peak times were obtained using a Maximum Likelihood Estimator of the von Mises distribution

location parameter mu bootstrapped 1000 times (Lund and Agostinelli, 2007).

Analysis of total CH versus density – the experiments for both males and females were

replicated 3 times. All flies within a replicate came from the same stock bottles and were kept

in the same incubator. For females 8 flies were sampled at each of 5 densities in each replicate

for a total of 24 samples at each density; for males 3-4 flies were sampled at each of 7 densities for a total of 9-10 flies at each density. Linear regression analysis of mean values

against log10 density (as number of flies/vial) was performed using the R linear regression

program lm(Lund and Agostinelli, 2007) for females, whose distribution of total CH at each

density was approximately normal. The distribution of total CH for males was closer to

lognormal; the geometric mean values at each density were regressed on log10 density for

males, and error bars use the geometric standard error of the mean (Zar, 1999).

Each sample contains known spiked-in amounts of an internal C18 and C26 standards.

Compounds abundances are normalized to the C26 GC reading; as a quality control samples

where C18 was below 85% or above 115% (C26=100%) were discarded.

Results

Hydrocarbon expression changes with density

A semi-log regression model was applied to find whether or not interactions between

flies affected CH signals. In addition, the average CH levels were plotted as a function of

density to investigate whether or not there was a critical density at which the group

18 responded. All the semi-log regressions were done by Clement Kent and detailed methods can be seen in Appendix A.

The results revealed that total hydrocarbon expression did change with population

density when log10 transformed. The mean HC signal of both male and female data

significantly decreased in total CH as population density increased. The semi-log regression

line had p=0.0007 for males and p=0.007 for females. Density had a greater effect on females

with a seven fold decrease in HC expression compared to males (p=0.014).

Figure 1: The average cuticular hydrocarbon expression of Drosophila melanogaster as a 2 function of log10 density for a) virgin males and b) virgin females. For males β=-0.17±0.023ug/fly, R =0.92 with p=0.0007. For females: β=-1.3±0.2ug/fly, R2=0.94 with p=0.007. β represents the slope of the semi-log regression.

Further analyses of individual hydrocarbons were used to determine whether or not

log decreasing trends were static across all hydrocarbons. Although both males and females decreased total hydrocarbon amount with an increased population density, the trends of

individual hydrocarbon varied between the sexes. 10 out of 24 compounds in males and 16

19 out of 30 compounds in females were successfully modeled by a semi log regression (p<0.05).

Hydrocarbons which associated significantly with population density were further investigated.

Individual male compounds which demonstrated a density-dependence had a pattern which varied according to carbon chain length. Longer carbon chains increased in both absolute and proportional amounts under high density, while shorter carbon chains decreased

similarly (figure 2). Methlyated compounds and alkanes generally conformed to this pattern

but monoenes did not.

The average quantity of short chains decreased relative to the total CH (figure 2a) but

individual compounds such as nC23 (figure 2e) and 2MeC24 (figure 2c) were more sensitive

(having a steeper regression slope) to population density. For example 2MeC24 had a log density slope of -0.31ug/log density while the slope for total CH was log density of -0.09 ug/log density. Various long chains including alkanes and methyl-branched compounds had a very strong population density-dependence compared to the sum of long chains. Straight chain

C28 (figure 2f) and 2MeC30 (figure 2d) had a regression slope of 0.45 ug/log density

(p=0.0002) and 0.35 ug/log density (p=0.0004), respectively. The sum of compounds with

chain length above 26 (figure 2b) had an average slope of 0.12 ug/log density (p=0.006).

Thus compounds like C28 and 2MeC30 had twice the density effect compared to the average

long chain compound. Interestingly, the most abundant and studied male 7-monoene had no

evidence of density-dependence.

Figure 2: Male Drosophila melanogaster hydrocarbons are population-dependent. a) Sum of compounds with chain length below 25 as a function of log10 density (β1=-0.09, p=0.001) b) Sum of compounds with chain length above carbon 26 as a function of log10 density (β 1=0.12, p=0.006) c) total 2MeC24 expression

( 1=-0.31, p=0.002) d) total cuticular 2MeC30 expression (β 1=0.35, p=0.0004) e) and f) illustrate alkane C23 and

C28 respectively with (β1=-0.13, p=0.0009) and (β 1=0.45, p=0.0002). Full statistics and fits for all compounds are in Appendix A supplementary table 1. β represents the slope of the semi-log regression.

In females, the quantity of all 16 significant compounds (p<0.004) decreased in proportion to density (figure 3). However each compound had a different degree of population density-dependence as indicated by the slopes. The average log density rate of decrease was -

0.15ug/log density but some methylalkanes and 9,13-dienes had a larger density-dependence.

For example, the regression slope of 2MeC26 (figure 3a) and 9,13C27 (figure 3d) were -0.26 and -0.22 respectively. 7,11HD has been shown to have pheromonal effects throughout courtship but followed the average rate of density decrease at the slope of -0.15ug/log density

(figure 3b).

Figure 3: Virgin female Drosophila melanogaster hydrocarbon all decreased with proportion to

density. p<0.004 for all cases shown (a) Total 2MeC26 compound with β1=-0.26 (b) Sum of 7,11C27 expression

had a slope of β1=-0.15 (c) straight chain C25 β1=-0.22 and (d) 9,13C27 had a decreasing slope of β1=-0.24. Full statistics and fits for all compounds are in Appendix A supplementary table 1. β represents the slope of the semi- log regression.

Notwithstanding each hydrocarbon having a different log density slope, the cuticular hydrocarbon mix was different among densities. To illustrate this, the percent of total CH levels was plotted against log density (Figure 4). The log density of eleven compounds out of

30 conformed to a semi log regression with a p-value of less than 0.05. Examples of these compounds are found in figure 4. Methylated compounds and straight chains were found to be significant, as well as desaturated hydrocarbons such as 7,11C25, 7:C23:1 and 7C25:1. HCs

23 which decreased in proportion to density included 9,13C27:2 and all short chain methyl compounds. 7,11HD maintained an almost steady expression in different densities when examining absolute and relative values.

Figure 4: Percent of various female hydrocarbons in different groups as a function of log10 density. Expression of (a) 7,11C27 (β0=0.002, p=0.94) and (b) nC27 (β0=0.005, p=0.83) did not change with density. Figure 4c-h shows various increases or decreases in hydrocarbons. Full statistics for each of these compounds and others are in Appendix A Supplementary table 2. β represents the slope of the semi-log regression.

These results illustrated how male and female flies independently change hydrocarbon expression when housed in different group densities. Thus, flies can smell and taste differently from one density to another. However, it is unclear whether these density-dependent results are due to circadian regulation of hydrocarbons since samples were taken at one time point.

Female hydrocarbons have diurnal variations

Females were further investigated for diurnal hydrocarbon variations. Virgin female

flies housed in populations of 40 were tested in a 24 hour light-dark cycle and a sinusoidal 24

hour regression was fitted to each hydrocarbon. 14 of 30 compounds showed a significant

sinusoidal regression (p<0.05). The peak time of each fitted curve is plotted on a clock

diagram (figure 5). Compounds tended to peak near subjective noon (ZT6) and subjective

midnight (ZT18). Peak times largely depended on the carbon length. Figure 6 illustrates peak times of each hydrocarbon, shorter chains peaked near midnight while longer chains peaked towards noon.

Figure 5: Peak times for cycling female Drosophila melanogaster cuticular hydrocarbons housed in groups of 40. Compounds of each chemical type are color coded while carbon lengths are symbolized. Short chains with less than 27 carbons (triangles) and long chain compounds (squares) are plotted around the clock to indicate the peak time. Methyl branched hydrocarbons, alkanes, dienes and monoenes are red, green, blue and black, respectively. Filled symbols are significant with p<0.05 while open symbols indicates a failure to achieve statistical significance. Hydrocarbons tend to peak near subjective noon or midnight. Tests of location using circular statistic show that day-peaking compounds had a mean peak time at ZT 6.2 (95%CI 4.9-7.6). Night peaking compounds peaked around ZT17.4 (95%CI 16.5-18.4). The radial distance between the centre of the circle and each point has no significance. Full statistics for each compound are in Appendix A Supplementary table 3.

Figure 6: Assorted significant female Drosophila melanogaster cuticular hydrocarbons with diurnal expressions over a light-dark cycle. Six day old virgin females were housed in groups of 40 and the percent of hydrocarbon was calculated over a period of 24 hours.

Due to the density-dependent effects on hydrocarbon seen in figures 1-4, different

populations of flies (16 and 40) were housed and sampled for HC for 24 hours to investigate

whether or not a sustained rhythm was present. The patterns seen for groups of 40 were also

seen in the lower population density (figure 5). Figure 7 illustrates how both densities cycled

around their respective averages in a light-dark period. Overall amplitudes and phases were

not significantly different. Despite a non-significant regression, comparable troughs and peaks

were often found between the two population densities.

Figure 7: Assorted sinusoidal regression for female Drosophila melanogaster cuticular hydrocarbons with diurnal expressions over a light-dark cycle. Virgin females were housed in groups of 40 (red) or 16 (blue) and the percent of hydrocarbon was calculated over a period of 24 hours. Solid lines indicate significant sinusoidal regression whereas dotted line indicates a failure to achieve statistical significance. Full statistics for each compound are in Appendix A Supplementary table 3.

Desat1 mutants males and females differ in density regulation

Desaturase 1 is an enzyme which removes hydrogen at carbon 7, producing 7-

monoenes and the first desaturation to 7,11dienes (Marcillac et al., 2005a). A recent series of

p-element mutants in desat1 has been developed. From these, the effect of ablation of the

gene appears to be a reduction in the quantities of desaturated hydrocarbons in the cuticular wax. In addition, an increase in the production of alkanes was observed (Marcillac et al.,

2005a). Hydrocarbon expression in desat1 mutant males has been shown to vary depending

on the surrounding social group (Krupp et al., 2008). Desat1 mutants were used to determine

whether or not hydrocarbon expression followed the same trend as wild type flies. Flies were

housed in densities of two or 80 and sampled for hydrocarbon. Female desat1 mutants had the

same proportional decline as CS controls. Conversely, desat1 males showed a density-

dependence with a slope of only 5.4% of that of the wild-type.

Discussion

A semi-log regression illustrated how hydrocarbon quantity could reflect population

density in D. melanogaster. Wild-type flies of both sexes displayed a decrease in hydrocarbon

expression inversely proportional to the logarithm of density (figure 1). Instead of synthesizing

large HC quantities to communicate with one another, flies communicate in a more intimate

manner. This might save metabolic energy and does not confuse neighboring communication

(in which CHs are detected at short distances or by physical contact) (Ferveur, 2005). These

results agreed with those found in D. paulistorum flies. When raised in isolation, D.

paulistorum had double the quantity of HCs compared to flies raised in groups of 20 (Kim et al., 2004).

The population density affected females more than males. The reason for this difference is unclear. Hydrocarbons are neither expressed in the same proportion nor are the same compounds expressed in both sexes (Ferveur, 2005). Females also have additional

hydrocarbon regulation. Genes such as desatF and eloF are not expressed in males (Chertemps

et al., 2007; Chertemps et al., 2006). Sexually dimorphic gene expressions could be the reason

underlying these results.

The average group hydrocarbon quantity changed logarithmically with density but the

rate of change was different for individual compounds. It is evident that flies smell and taste

differently depending on population density because there were differing proportions of

compounds at a given density. Other studies provided similar results for D. paulistorum (Kim et

al., 2004). This is most likely due to the different metabolic pathways which contribute to the overall hydrocarbon profile. Regulation of pathways must be impacted by population density to a varying degree. It is unknown as to why gene expression changed with density experience.

Although individual compounds reacted differently to population density, the alkanes and methylated compounds responded to population density differently according to chain length. Shorter compounds decreased in absolute amount as the population density increased while longer compounds behaved oppositely. 7-monoenes which are involved in communication during courtship did not show any significant population density regulation. A possible explanation could be density effects on gene regulation involved in early hydrocarbon synthesis. The synthesis of HCs can follow one of three pathways to produce alkanes, alkenes

31 or methylated compounds (reviewed in Howard and Blomquist, 2005). All three pathways begin with fatty acyl-CoA undergoing beta-oxidation to produce acetyl-CoA. From here either amino acids can be incorporated to produce methylated HCs or elongase can extend the HC to produce alkanes or alkenes. If alkenes do not follow the same trend as the other two hydrocarbon classes then the desat1 (which is involved in the production of 7-monoenes) must

not adjust to the changing environment leaving a constant expression of products.

Intermediate chain lengths of 26 and 27 did not show significant density-dependence (data

not shown). The observed chain length segregation could be due to two different elongase

enzymes. Potentially, one elongase may synthesize short fatty acid chains and another

synthesizes long fatty acid chains. Although it is unclear why elongase would have this effect,

more experiments would be needed.

Similarly to males, females also kept a constant amount of 7-monoenes. 7,11-HD

alternatively did not maintain a constant level. It declined at the same rate as the total CH

levels (figure1) but maintained relatively constant compared to other hydrocarbons. Both 7-

monoenes in males and 7,11HD in females are the most potent pheromone known to date.

This mutually indicates that there are underlying regulations to the amount of hydrocarbon

expressed within changing population densities. Flies may need to keep a steady quantity of

pheromones in changing environments to attract the opposite sex. Sexual selection has been shown to operate on CHs more strongly than physical phenotypes such as wing length

(Chenoweth et al., 2007). Perhaps flies prefer or need their partners to have a certain range of

pheromone concentration. Dienes not only entice males but also communicate species and

gender information (Antony et al., 1985). There could also be a maximum concentration in

which to create a pheromonal reaction. Different studies have proven that flies act in response

32 to absolute amounts, percentages and ratio of hydrocarbons (Antony and Jallon, 1982b;

Ferveur, 1997). For example, the ratio of 7-T/7-P influenced male response in courtship

(Ferveur, 1997). Perhaps flies use all these to perceive hydrocarbon before acting but whatever the reason, both sexes managed to regulate their sex pheromone within changing populations.

It would be interesting to investigate the outcomes of other social environments on pheromone expression.

The rate of change was disparate between the sexes but tended to follow a logarithmic scale. In theory, when a fly is incorporated into a group it can interact and

influence another fly. However as the crowd increases one fly cannot easily interact with all the flies and the effect becomes “diluted” producing the observed log pattern. This effect is present not only within the overall hydrocarbon profile but also individual hydrocarbons.

From this study it is unclear what produced these observations. Genes are probably involved; hydrocarbons can change depending on the genomic makeup of the fly as seen from the hydrocarbons of desat1 mutants. But there are different theories as to why this might

happen. Firstly, lipophorin (an insect haemolymph lipid carrier) could be used for removal of

CHs as well for their deposition, physical removal of hydrocarbon through grooming or rubbing against objects (Coyne et al., 1999) or finally enzymes may be present on the cuticle for degradation. High density has been observed to promote disturbance while feeding. If a fly is feeding and is disturbed it stops feeding and starts to groom and rests before it starts to feed once more (Pearl, 1932). This could be a source of HC removal but when flies were perfumed

with excess hydrocarbons, a natural HC decay was still observed in both freely moving (Kent et al., 2007) or artificially paralyzed flies (Levine lab, unpublished). This suggests physical grooming or rubbing is at most not a main source of decay. In addition, the absolute increase

33 in long chain compounds in males and selective HC changes makes grooming an unlikely explanation of HC removal.

Kaiser and Cobb (2008) also proposed a density disturbance which resulted in larva D. melanogaster finding scents more rapidly. Crawling disturbances from other larva allows a

period of olfactory reflection before continuation towards odor. These experiences also allow

time for gene expressions to change as shown by Carney (2007) where five minutes of

interaction were sufficient for hundreds of mRNA expressions to change. These mRNA changes

could have produced the hydrocarbon expressions seen in the current study.

Recent studies revealed that at high densities, female desat1 and desatF mRNA levels

were reduced by 25.7% and 17.8% respectively (unpublished results, Levine lab). These results

are congruent with desat1 patterns seen in the current study. Male desat1 mutants only

showed 5.4% of the wild-type decline. Thus alkenes are required for the density effect in

males but females must control density-dependence by other means. In theory desatF, eloF or

any sexually dimorphic component could be responsible for the response. This confirms that

males and females have different hydrocarbon-density regulation. The reasons for these

observations are not apparent from the data presented here. It is possible that desaturase 1 is

critical for the density-dependent regulation of male hydrocarbon production, while it is not

required in females. Further experiments with different HC synthesis genes are needed to

investigate whether or not other genes are involved in this regulation and the degree of their

involvement.

Neither phase nor amplitude was affected by density in females (figure 7). These data

neither confirm nor refute the presence of an internal circadian clock to control female

hydrocarbons because constant darkness experiments were not performed. However, under

34 different densities there was a diurnal variation in HC expression. During a light-dark period, density does not shift phase and amplitude, the hydrocarbon changes seen in figure 1-4 could not have been due to a circadian regulation.

The hydrocarbon peaks and troughs throughout the 24 light-dark period were not surprising. Insects must adapt to their environment to prevent desiccation or other environmental stresses. Both population density 16 and 40 had short chains peak near subjective midnight and long chains peak near subjective noon. This reflects their response to climate change. As the carbon chain increases more energy is required to synthesize these

hydrocarbons and hence reduce desiccation. Past experiments which increased the

temperature from 20oC to 25oC showed a decreased ratio of short unsaturated HCs and longer

chains (Savarit and Ferveur, 2002).

In summary D. melanogaster males and females responded to changes in density by

changing HC expression. The population density-dependence can be depicted in a log-linear

pattern. A density threshold does not seem to be present to produce hydrocarbon variation.

The strength of the density-dependence varied by hydrocarbon class and length. Active

hydrocarbons such as 7-monoenes did not show density regulation and were maintained over

a steady range. Desat1 mutants showed a different density regulation in males and females.

The desat1 gene is required for density effects in males. However more experiments are

needed to determine the density regulation of females. Female hydrocarbons showed a diurnal variation where short chains peaked at subjective midnight and long chains peaked near subjective noon. Different densities do not disrupt this diurnal pattern, having similar phases and amplitudes.

Chapter Two

The effects RNAi lipid metabolism genes on the expression of cuticular

hydrocarbons in Drosophila melanogaster

Introduction

Courtship behavior in Drosophila is known to be heavily influenced by the cuticular

hydrocarbons displayed on both the male and female (Antony et al., 1985; Ferveur et al.,

1996). These hydrocarbons are produced by a concert of enzymes involved in lipid metabolism.

As yet, these genes have not been manipulated to gauge their direct effects on hydrocarbon

production and courtship behavior. To that end, Dr. Barry Dickson has genetically screened 25

RNA interference (RNAi) lines related to annotated lipid metabolism genes. This chapter

focuses on the examination of oenocyte-targeted GAL4 heterozygotes with RNAi insertions

(generated through crosses). The direct effect of these gene knockdowns on both hydrocarbon

profile as well as behavior has been identified.

Genetic Screens

Traditionally genetic screens depended on feeding flies with ethyl methane sulphonate

(EMS) to generate genetic mutations in germ lines for analysis (reviewed in St Johnston, 2002).

Although random mutagenesis has contributed to many findings, this method has drawbacks.

These drawbacks include painstaking determination of where mutations reside, lack of specificity of point mutations and the unsafe nature of mutagens. Modern molecular techniques have allowed geneticists to use EMS in a productive manner with the use of single nucleotide polymorphism (SNP) to map mutations to regions of less than 50kb. However,

36 when using EMS the probability of finding all the genes that affect a specific phenotype is very

low. Instead of generating random mutations, it is possible to manipulate genes more

specifically through our knowledge of the genome sequence.

The genome sequence of the fly has allowed identification of almost every expressed

gene. The complete genome library allowed new methods of gene manipulation such as

selective gene knockdown techniques with RNA interference (RNAi) (see review in Boutros and

Ahringer, 2008). RNAi can be used to produce systematic loss-of-function to reveal novel gene

functions (Simmer et al., 2003), confirm the identity of known mutants from traditional screens

and most importantly to synthesize a global understanding of gene relationships (Kamath et

al., 2003).

The advantages and disadvantages of RNAi screens compared to other traditional

genetic screens are reviewed in Boutros and Ahringer, 2008. Various advantages include:

sequences manipulated are identified genes with known sequences, gene knockdowns can be

targeted to either the entire organism or individual cells/tissues using specific GAL4 drivers,

lethal knockdowns can be activated in a time-dependent manner. Since RNAi often leads to

partial knockdowns, a range of phenotypes may be seen. This is also a disadvantage in that

knockdowns are incomplete and the efficiency can vary for different targets (Kamath et al.,

2003). Both the advantages and disadvantages in the use of RNAi stem from the physiological

processes by which it works.

The mechanism of RNA interference (RNAi)

In Drosophila, RNAi can be triggered by the expression of double stranded RNA

(dsRNA) with an inverted repeat to the specific target gene. When dsRNAs are recognized by

37 dicer proteins in the cytoplasm, cleavage by dicer produces a 3’overhang called small interfering RNA (siRNA). siRNA can form a ribonucleoprotein complex called RISC (RNAi silencing complex) that possess endonucleases activity but is only activated when siRNA unwinds into single stranded RNA. The activated RISC can bind to a target RNA in a sequence- specific manner and cleave the bound mRNA. The cleaved mRNA is recognized by the cell as waste and destroyed such that the target can no longer be translated into a functional protein.

The expression of the dsRNA must be incorporated into the fly genome to produce a transgenic fly.

Figure 1: Mechanism of Drosophila RNAi: dsRNAs are recognized and cleaved by dicer proteins and produce a 3’overhang called small interfering RNA (siRNA). siRNA can form RISC that possess endonucleases activity but is only activated when siRNA unwinds into single stranded RNA. The activated RISC can bind to a target RNA in a sequence-specific manner and cleave the bound mRNA. The cleaved mRNA is recognized by the cell and degraded.

Creating RNAi screening in Drosophila

The biosynthesis of CHs is mainly performed within cells known as oenocytes. With

this in mind, lipid metabolism genes expressed in the oenocyte will be targeted for gene

knockdown. To flexibly target gene knockout within these cells, a binary oenocyte-

GAL4/upstream activation sequence (UAS) expression system was used. The specific GAL4 line

used was the m484 line and it was provided by the Dickson lab. Although green florescent protein (GFP) expressed under the control of this Gal4 line showed expressions in other parts of the fly, GFP expression is clearly visible in the oenocytes (Appendix B fig 1). For the purposes of this introduction, I will focus on the methodologies of the Dickson Lab in which

RNAi lines were tested (detailed methods can be found in Dietzl et al., 2007).

The first step to creating RNAi libraries begins with the UAS-driven inverted repeat

(UAS-IR) construct. Short fragments of the target gene are amplified with polymerase chain reaction (PCR) and cloned as inverted repeats into a vector containing multiple GAL4- responsive UAS signals, polyadenylation signal and mini-white gene. RNAi strains can then be created by germline transformation of isogenic w1118 host (Rubin and Spradling, 1982; Ryder et

al., 2004). Confirmation of valid UAS-IR insertion and noise quantification can then be done

using PCR. To activate UAS-IR transcription flies must be crossed to flies that carry GAL4 with

an oenocyte targeted promoter.

Hydrocarbon synthesis

The knock down of one gene can ultimately describe the purpose of the functional

version; this is the principle of reverse genetics. Until now the systematic RNAi knockdown to

assess its phenotypic HC profile has not yet been accomplished. There are many enzymatic

39 steps to produce the final HC product which is displayed on the cuticle (Giot et al., 2003).

Detailed steps are reviewed in Howard and Blomquist, 2005.

Briefly, cuticle-destined HCs are synthesized from the beta-oxidation of long chain

acyl-CoA molecules to produce acetyl-CoA molecules (Reed et al., 1994). Beta-oxidation of

fatty acids occurs in four tightly regulated enzymatic steps. From acetyl-CoA, HCs can follow

one of three pathways. The simplest path is the production of alkanes. Elongation is performed

by the addition of two carbons to the growing fatty acid chain. After elongation of acetyl-CoA,

decarboxylase hydrolizes the carboxyl group leaving alkanes of varying lengths. Monoene

hydrocarbons are subjected to a desaturation step after elongation. Desaturation of acetyl-CoA

involves desat genes (desat1 and desatF) which have stearoyl-CoA 9 desaturase activity

(Marcillac et al., 2005a). Decarboxylase hydrolizes the carboxyl group of the compound to

generate the monoenes which are displayed on the cuticle. Methylated alkanes follow a

similar mechanism but involve the addition of amino acids to methylate the HC.

The genes and regulations involved in understanding the complete hydrocarbon

synthesis pathway are unclear. Research has focused on desaturase genes, which are involved

in the production of alkene molecules. The double bond at the seventh carbon is produced by

desat1 (Dallerac et al., 2000; Marcillac et al., 2005b). Five transcripts have been found in D. melanogaster (Marcillac et al., 2005a) but there may be as many as twelve desaturate genes

transcripts (personal communication, Mike Ritchie). In D. melanogaster, females have a

separate gene called desatF which is involved in the second desaturation step of 7,11-dienes

(Chertemps et al., 2006). The extension of the carbon chain is likely performed by elongase

genes. The RNAi knockdown of elongase-F (eloF) has been shown to decrease the quantity of

hydrocarbons having chain lengths of 27 and 29 while increasing HCs of 25 carbons.

Each step likely entails as yet unidentified regulatory enzymes that could affect the production of any individual HC displayed on the cuticle. Hence RNAi screens were used to knockdown single genes in isolation to see the overall phenotypic consequences through hydrocarbon analysis. This could demonstrate a change in chemical communication which may lead to behavioral changes.

RNAi screening

RNAi screening for behavioral effects has been performed previously. Specifically, the

knockdown of a female specific desaturase (desatF) has been examined (Chertemps et al.,

2006). desatF is responsible for 7,11-diene production in D. melanogaster and is responsible

for species and mate recognition (Antony and Jallon, 1982a; Dallerac et al., 2000). The RNAi

knockdown of this gene significantly decreased female dienes while increasing monoene

production. This also changed courtship behavior with an increase in copulation latency while

courtship index and copulation attempts by males decreased (Chertemps et al., 2006).

Removal of other female-specific hydrocarbon synthesis genes also causes departures

from the normal HC profile. The generation of transgenic strains for elongaseF (eloF)

knockdown through RNAi decreased C29 dienes and increased C25 dienes. The consequences

of these changes were increased copulation latency and decreased copulation attempts

(Chertemps et al., 2007).

Gene knockdowns not involved in HC expression also show behavioral changes.

SIFamide is a neuropeptide in Drosophila. When transcription was lowered by RNAi, males

became indiscriminant when courting while females appeared sexually hyper-receptive

(Terhzaz et al., 2007). Individual knockdown of accessory gland proteins (Acps) changed

41 female post-mating responses including receptivity to remating and storage of sperm (Ram and Wolfner, 2007).

Summary

Several examples of discoveries found using RNAi were described which influence

behavior, but as yet a high throughput screen of behavioral changes due to HC profiles is

lacking. Obvious hydrocarbon synthesis genes have been tested but there is a wide range of other genes that influence HC expressions.

The Dickson Lab did the initial RNAi screening for courtship behavior. 23 RNAi lines related to lipid metabolism had significant courtship behaviors and I identified a direct effect of the knockdown of these genes on both hydrocarbon profile as well as behavior. Depending on preliminary behavior results from the Dickson lab; male, female or both sexes were analyzed for HC. Six of the 23 RNAi lines showed interesting HC data and were furthered investigated for courtship effects.

The synthesis of cuticular hydrocarbon is complex, comprising several regulated reactions. These results will improve our understanding of how lipid metabolism genes affect the overall hydrocarbon profile. The HC profile from the RNAi line may also induce behaviors in other flies which would give us another of few examples of how genes contribute directly to behavior.

Material and Methods

Rearing conditions

Canton-S wild-type flies of D. melanogaster were used in the LD 12:12 cycle.

Eggs were obtained by crossing UAS virgin females to GAL4-m484 virgin males within vials

(92mm X 25mm diameter) of a sucrose-yeast medium for 24-48 hours. In the case of females with an attached-X, the cross was reversed. Vials were maintained in LD 12:12 cycle at 24ºC

for optimal mating conditions. Adult flies were removed after the mating period and stored in

LD 12:12 cycle at 24ºC for nine to ten days. Flies were separated by sex within eight hours of

eclosion while anaesthetized with carbon dioxide.

RNAi virgin females were housed in plastic vials containing 8mL of sucrose-yeast

medium in groups of ten. Males were housed in individual 10mL glass culture tubes with 1mL

of sucrose-yeast medium. After collection, flies were returned to LD 12:12 cycle at 24ºC for six

days. On the sixth day extraction of cuticular hydrocarbons or courtship experiments were

done at ZT0.

Extraction of cuticular hydrocarbons

Fly density experiments sampled 8-10 flies per density at ZT13 for three trials. While

hourly experiments sampled either 5 out of 40 or 15 out of 16 flies and done in 24 hours

sampling every two hours starting from ZT0 for three trials. Extraction of cuticular

hydrocarbons has been previously described (Ferveur, 1991). Flies, 6 days old, were

anaesthetized with diethyl ether. Extractions were done by placing each fly in a 2mL conical

glass vial with a 200μL insert. The inserts contained 50μL of a standard mixture (10 μg/mL of

octadecane (C18)[Aldrich chemical company Inc.] and 10 μg/mL hexacosane (C26)[Aldrich

chemical company Inc.] in hexane solvent [Aldrich chemical company Inc. >99.0% purity]. Flies

were then shaken at low speed for 2 minutes and removed from the standard mix using a thin

wire probe. The extracts were stored at -20ºC prior to analysis.

GC Chromatography

Samples were analyzed using a gas chromatograph joined to a flame-ionization

detector (Varian CP-3800 with autosampler CP-8400). The column employed was a DB-

1 custom capillary column with a 5m X 0.25mm guard column (J&W Scientific; length

20m, diameter 0.18mm and film 0.18 μm; model 100-2000). Programming conditions are shown below in table 1.

Chromatograms were processed with Varian GC Workstation (Version 6.30) software.

Peaks were identified by comparing retention times to n-alkane standards [10μL/mL,

Aldrich Chemical Company Inc.] and using strains whose main hydrocarbons had been

identified by mass spectroscopy. All peaks were normalized with hexacosane [Aldrich

chemical company Inc.] from the standard solution.

Courtship Experiment

Courtship experiments were tested with the RNAi line of interest and a Canton-S

partner. Five courtship parameters were measured. These included courtship latency (time from the introduction of the male into the female containing observation chamber until courting begins), number of attempted copulations (measured during a ten minute observation period), copulation latency (time in minutes from the introduction of the male into the

44 chamber until copulation), courtship index and wing extension index. These measurements were done in a courtship chamber (1cm wide).

Statistical analysis

One-way analysis of variance was used with a response statistic of mean hydrocarbon

quantity for each genotype level (RNAi lines). The significance threshold was considered to be

p<0.05 for all tests. Significant ANOVA’s were followed by Tukey’s Honestly Significant

Difference (HSD) post-hoc analyses. All analyses were performed using SPSS Version 15.0 for

Windows.

Results

Hydrocarbon profile of male and female RNAi lines

The hydrocarbon profiles of all 23 RNAi lines were examined after being crossed with

the m484-Gal4 drivers. A range of significance was detected from no change to almost all CHs

affected. Male and female HC profiles are shown in table 1a and 1b, respectively.

The female RNAi lines which had the most significant HC change from the controls are

shown in table 1. These lines included CG5887 (desat1), CG5326 (function is unknown),

CG6984 (enoyl-CoA hydratase activity), CG31523 (acyltransferase activity) and CG8522

(transcription factor activity).

With female RNAi-CG5887 almost all hydrocarbons changed in their expression levels compared to their control counterparts. The trend was an increase in alkanes and methylated compounds while there was a decrease in monoene and diene expression. The uas-gal4 line had at least a four-fold increase in alkanes and a seven-fold decrease in monoenes compared

45 to controls. The dienes were more affected than the monoenes, having a 25-fold decrease compared to the monoene decrease of only three-fold.

The knockdown of CG5326 showed 19 (out of 28 total) hydrocarbons that deviated from controls. Short hydrocarbons increased while long hydrocarbons decreased in absolute amount. Although this pattern was not seen for each hydrocarbon, it is still seen when all hydrocarbon groups are considered together. Both long and short hydrocarbons had a two fold change when compared to their controls. Medium length hydrocarbons (with 25 or 26 carbons) did not show any significant change in expression.

Regarding the knockdown of enoyl-CoA hydratase (CG6984), most of the affected hydrocarbons were dienes and methylated compounds. Additionally, the only significant

changes from baseline were increases and not reductions. Reduced acyltransferase activity

using RNAi-CG31523, increased most hydrocarbons that possessed a double bond at carbon-9.

Other affected hydrocarbons decreased in signal compared to their control groups. All of the

alkanes that were affected decreased in total amount. CG8522 which has transcription factor

activity mostly decreased dienes and increased methylated compounds by 30 and 20 percent,

respectively. Line CG9577 influenced mainly methylated compounds. Other RNAi lines had

various influences on hydrocarbon expression but did not follow any obvious patterns. Table 1

summarizes the lines mentioned above. Only hydrocarbons which are significant are shown.

Complete data can be seen in Appendix B Table T1A, T1B, T1C, T1D and T2.

Similarly to females, the male desat1 RNAi (CG5887) line had the greatest number of

significant deviations from control hydrocarbon signals (18 out of 20); all monoenes decreased while alkanes increased compared to their controls. Both changes were two-fold. According to

FlyBase the molecular function of CG30008 is unknown. The HC profile produced, however,

46 was similar to that of the desat1 RNAi line wherein most of the monoenes had decreased

signal but alkanes did not follow a specific trend. Both CG6984 and CG8522 showed 8 out of

20 significant HC changes but the pattern was not obvious. Table 2 summarizes the lines

mentioned above. Only hydrocarbons which are significant are shown. Complete data can be seen in Appendix B Table T3A, T3B and T4.

Table 1: Amounts of CHs on D. melanogaster females. Mean ± standard error of m484-Gal4 control, IR- UAS control and RNAi cross strains are shown. All values are relative quantities. Only significant data are shown. All RNAi lines are shown in Appendix B Table 1A, 1B and 1C

Table 2 (continued): Amounts of CHs on D. melanogaster females. Mean ± standard error of m484-Gal4 control, IR-UAS control and RNAi cross strains are shown. All values are relative quantities. Only significant data are shown. All RNAi lines are shown in Appendix B Table 1A, 1B and 1C

Table 3: Amounts of CHs on D. melanogaster males. Mean ± standard error of m484-Gal4 control, IR- UAS control and RNAi cross strains are shown. All values are relative quantities. Only significant data are shown. All RNAi lines are shown in Appendix B Table 1A, 1B and 1C

Courtship of RNAi lines

Courtship data of female lines CG6984, CG8522, CG5326 and CG5887/desat1 were performed in both white and red light. Male line CG30008 and CG5887/desat1 were also

tested for courtship significance. Although hydrocarbon profiles of these lines were most

significant, all courtship behaviors tested (including latency to copulate, courtship index, wing

extension index and attempted copulation) were not significant compared to control lines.

Complete data is shown in the appendix, table 5.

Discussion

The knockdown of lipid metabolism genes showed a wide range of hydrocarbon

profiles. Table 3 shows the putative functions of all the annotated genes tested, as well as

molecular and biological processes attributed to each gene.

Table 4: The putative functions of all the annotated genes studied.

Both males and females with desat1-RNAi (CG5887) knock down displayed the largest

hydrocarbon effect; alkenes all decreased while alkanes increased in expression. These results have been seen in the past using mutant desat1 lines (Marcillac et al., 2005a; Marcillac et al.,

2005b). Since the desaturation pathway was knocked down, there was a buildup of alkanes

(Ueyama et al., 2005). These results demonstrate that the RNAi knockdowns were indeed

functioning confirming the accuracy of the gal4-UAS expression system used. Given the

random nature of the RNAi insertion, however, this may not be true for all RNAi lines used.

The second-largest significant response to RNAi knockdown occurred in line CG5326.

Flybase indicates an unknown molecular function but from the HC results it is reasonable to

conclude that this gene may be an elongase. Since short chains (<25 carbons in length)

increased in quantity while long carbon chains decreased, it is possible that CG5326 is an

orthologue of eloF. Past RNAi knockdown of eloF has resulted in outcomes similar to those

observed here. These included a dramatic decrease in both C27 and C29 quantity with

concomitant increase in both C23 and C25 expressions (Chertemps et al., 2007). Although HC

data for eloF and CG5326 are similar, published eloF knockdowns had several courtship

parameter changes which were not observed in the current study (data not shown). Further

investigation will be needed to indicate whether or not CG5326 has elongase function and to

definitively determine whether or not behavioral effects result from its knockdown.

Enzymes which have enoyl-CoA hydratase activity can be involved in one of the four metabolic steps to fatty acid beta-oxidation. This enzyme prepares the “building blocks” of hydrocarbon biosynthesis. Paradoxically, the knockdown of CG6984 caused an increase in the amount of dienes and methylated hydrocarbons. It is unclear how the knockdown of a biosynthetic gene could accomplish this unless the pathways of hydrocarbon production are

52 only incompletely understood in D. melanogaster. One possibility is that the knocked down

protein is involved in HC inhibition; it follows that its reduction would therefore lead to an

increase in overall hydrocarbon synthesis. The results indicate that the inhibition probably involves the diene and methylated pathway. It is further possible that CG6984 is pleiotropic in nature, potentially working with other inhibition proteins. Alternatively the insertion of the

UAS-IR could have disrupted an inhibition protein. Further experiments would be needed to test these theories. In addition, the specific quantity increase of diene and methylated compounds would be interesting to explore.

The transcription factor RNAi knockdown of CG8522 showed changes in dienes and methylated compounds. Flybase indicates that its putative function is as a positive regulator of fatty acid biosynthesis. The situation is probably not as simple because hydrocarbon quantities were found to both increase and decrease. Since dienes decreased by 30 percent and methylated compounds increased by 20 percent, the two chemical groups might have interrelated synthetic or regulatory pathways. The current theory proposes alkanes and alkenes in one pathway and methlatated compounds having their own pathway. The results of

CG8522 knockdown suggest that alkene and methylated hydrocarbon production may share simlar roots with respect to the transcription factor encoded by CG8522.

The knockdown of acyltransferase activity (CG31523) increased various 9-alkenes and decreased alkane quantities. Flybase indicates that this protein may be involved in very long chain fatty acid metabolic processes. However, the results from this study do not indicate such a role. The reduction in total HC content is logical given the putative function of the gene. This protein may also have multiple functions involved in the inhibition of 9-alkenes and synthesizes of alkanes.

One of two RNAi-CG9577 lines decreased methylated compound quantity. According to Flybase CG9577 has numerous activities including delta5-delta2,4-dienoyl-CoA isomerase activity, hydro-lyase activity and oxidoreductase activity. It is unclear how methylated compounds could have decreased with the known CG9577 abilities. The other RNAi insertion of the same gene did not produce the same effects. Thus it is possible that the insertion is the reason for these results.

The highly significant results of the RNAi lines mentioned above give an estimated phenotype of the gene. Most of the genes in this experiment have unknown phenotypes.

Although this work is only preliminary data, these RNAi lines should be further studied to

provide a detailed phenotypic picture.

Transgenic RNAi can reduce endogenous gene expression but the degree to which it

accomplishes this is unpredictable. There are various explanations as to why not all RNAi

knockdowns changed HC expression significantly. Due to the mechanism of RNAi knockdown,

the range of RNAi efficacy is variable. Examples include the gene targeted, insertion position,

helper proteins available for RNAi knockdown (for example, dicer protein), single or multiple

RNAi insertions and other factors (Boutros and Ahringer, 2008; Kamath et al., 2003).

General Discussion

Chemical communication contributes to the reproduction, fitness and social activities

of Drosophila melanogaster. This thesis focused on the regulation of these chemical cues

under different social and genetic influences. Chapter one revealed an interesting, as yet

undescribed, population density-dependence phenomenon. The work accomplished in chapter two was the first gene-centric screen in which hydrocarbon expression was determined from

the knockdown of lipid metabolism genes. These results serve to link population experience,

lipid metabolism and hydrocarbon expression – an area lacking in experimental data. One

caveat is that no direct causal link between any of the genes used in the RNAi knockdowns

and hydrocarbon expression can be made. The work suggests that genetics can provide a

substantial base from which to evaluate the expression of hydrocarbons.

The results of the first chapter support the hypothesis that social experience affects the

production of certain hydrocarbons in D. melanogaster. It is logical to surmise that population

density influences courtship behavior at least partially through cuticular hydrocarbon display. If

this were not the case, it would be unlikely that a difference between wild-type and desat1-

null mutants would have been observed. The courtship behavior of a fly partly relies on a

response to sensing odor. It is thought that this sensation can occur either auto-olfactorily or

via the hydrocarbon scent of potential partners or mates. For example, flies raised in different

populations could behave differently since the expression of certain species-specific

hydrocarbons differs. In conjunction, other flies may respond to these expression levels by

adjusting their behavior or HC expression.

Interesting future experiments would determine courtship behavior of individuals in different population densities and their respective partners. Further, experiments using olfactory mutants should be performed to investigate whether or not HC expression is truly the cause of these responses by excluding the possibility that population density affects behavior by other mechanisms. Perhaps the visual and auditory information that is gathered while housed in different population densities indirectly causes a change in HC production. However, if this were true then the metabolic changes in HC expression would be of a wasteful nature.

Further experiments are required to understand why there is density-dependent HC change because the energy use involved in such a change indicates a vital evolutionary function.

The changes in hydrocarbon expression resulting from population density could also have implications on speciation. If a group of flies were to become isolated and live in a constant density, a change in hydrocarbon preference could occur simply as a result of constant exposure to a specific subset of hydrocarbons. Segregation from a different density community for many generations could lead to reproductive isolation and speciation. However this would require that hydrocarbon synthesis also adjust for the hydrocarbon preference.

The hydrocarbon production results from the actions of several genes and enzyme- regulated biosynthetic pathways. To gain a full and proper comprehension of the population density-dependence, the genes which are involved in HC synthesis should be further characterized. Mutant strains could prove to be a viable tool in studying the effects of these genes as shown with desat1 males. Major CH genes including those with desaturase and

elongase functions should be tested as well as other lipid metabolism genes similar to those

studied in chapter two.

The use of microarrays can also be used to denote which genes are being influenced when a fly is subjected to differing population densities. Specifically, a repeat of the current

study could be performed and the mRNA expression changes of known genes could be

evaluated using microarrays. If genes are found to be correlated with population density experience, then further examination of knockout or knockdown effects could be confirmatory.

In addition, the circadian rhythmicity of flies within different population densities can be

investigated to determine whether or not there is a density-dependence on biological rhythms.

Chapter two demonstrated HC expression of lipid metabolism gene knockdowns. The

RNAi screens allowed a more gene-centric analysis of lipid metabolism on HC expression.

Although phenotype data for most of the lines tested is unavailable, much work needs to be

done to further examine the mechanism of such results.

The RNAi lines did not show a significant change in courtship behavior when

compared to controls. This does not necessarily suggest that these gene knockdowns have no

effect on behavior. Past studies revealed that the zygosity of the gene influences courtship

behavior. Heterozygous desat1 mutant females were as attractive for wild-type males however female homozygous mutants were diminished in attractiveness (Ueyama et al.,

2005). Another possibility is that the RNA interference was incomplete or inefficient; perhaps the addition of dicer proteins may help improve the knockdown of RNAi lines.

The understanding of chemical communication has been an interest to me because it has direct implications on human social behavior. It is indeed possible that density-dependent communication could also apply to humans. Neither of these possibilities has been exhaustively explored. Although chemical communication is not commonly thought to relate to humans, (perhaps because more sophisticated communication methods like speech have

57 been developed,) it is possible chemical communication still influences human behavior in a

more subliminal way. Studies have shown pheromones transmitted through social interactions among female friends could cause synchronization of the menstrual cycle (Mcclintock, 1971).

Additionally, circadian rhythms are a fascinating and ubiquitous but often overlooked aspect of behavior. The study of the fly is also fascinating. The depth and complexity of the vast research possible from these tiny creatures is surprising.

I am pleased to be a part of such innovative work. Although much work remains to be done, from this project it is known that the production of certain hydrocarbons depends on the density in which the animal is aged. Individual hydrocarbons change independently from one another but the hydrocarbon peaks during a light-dark cycle are static depending mostly on the chain length. The regulation of male and female HCs which are density-dependent is sexually dimorphic. Density and genes influence the regulation of HC expression as shown with desat1 mutant HC expressions. Finally, lipid metabolism genes which are not directly

obvious to HC synthesis do influence the expression of hydrocarbon profiles. This study will be useful to all researchers who study insects and are interested in social experience effects.

Appendix A

Supplementary Table T1: Reaction norm model fits show strong effects of carbon chain length. Total CH and some short chain compounds within each class decrease as density increases, while some longer chain compounds increase significantly for males. By contrast all compounds with significant responses decrease with density in females. Sums of shorter and longer chain compounds (“low” and “high”) have significant but opposite responses to density. Equation 3 is fit to the reaction norm for male and female CH compounds and compound classes, with the significance of the scale-independent slope β1 shown in bold if it passes a False

Discovery Rate test with q=1/15. β1 values are shown in bold italic if (negative (compound declines with increasing density) or italic if positive (compound increases with density). Males: compound p t beta1 C21 0.015 -3.633 -0.193 C22 0.683 -0.433 -0.025 C23 0.009 -4.160 -0.131 C24 0.943 -0.076 -0.005 C25 0.222 -1.395 -0.046 C27 0.007 4.432 0.261 C28 0.000 9.496 0.454 C29 0.009 4.168 0.855 5C23 0.082 -2.173 -0.086 5C24 0.883 -0.155 -0.030 5C25 0.978 0.029 0.009 7C22 0.726 -0.371 -0.094 7C23 0.342 -1.048 -0.046 7C24 0.033 2.919 0.583 7C25 0.645 -0.490 -0.028 7C27 0.760 0.323 0.101 9C23 0.001 -7.556 -0.125 9C24 0.099 -2.025 -0.306 9C25 0.517 0.696 0.029 MeC24 0.002 -6.127 -0.314 MeC26 0.016 -3.559 -0.229 MeC28 0.888 -0.148 -0.008 MeC30 0.000 8.299 0.349 Sum 0.001 -7.477 -0.092 alkanes 0.038 -2.791 -0.091 5-monoenes 0.227 -1.376 -0.055 7-monoenes 0.222 -1.394 -0.049 9-monoenes 0.006 -4.515 -0.082 methyls 0.035 -2.875 -0.159 low 0.001 -6.481 -0.102 high 0.006 4.586 0.120

Females: compound p F beta1 nC21 0.426 0.843 -0.051 nC22 0.661 0.236 0.038 nC23 0.038 12.537 -0.122 nC24 0.422 0.861 -0.044 nC25 0.001 169.545 -0.217 nC27 0.009 35.887 -0.151 nC28 0.347 1.240 0.090 nC29 0.819 0.062 0.016 7-11-C23:2 0.784 0.090 -0.026 7-11-C25:2 0.418 0.877 -0.048 7-11-C27:2 0.004 63.049 -0.150 7-11-C29:2 0.047 10.717 -0.123 9-13-C27:2 0.004 64.691 -0.241 9-13-C29:2 0.023 18.683 -0.215 5-C23:1 0.048 10.474 -0.147 5-C25:1 0.028 16.151 -0.108 5-C27:1 0.008 41.169 -0.109 7-C23:1 0.725 0.149 0.021 7-C25:1 0.668 0.225 -0.018 7-C27:1 0.196 2.751 -0.098 7-C29:1 0.789 0.085 0.038 9-C23:1 0.208 2.562 -0.072 9-C25:1 0.165 3.352 -0.070 9-C27:1 0.017 23.485 -0.179 9-C29:1 0.123 4.530 0.495 2-MeC22 0.001 148.273 -0.268 2-MeC24 0.002 116.580 -0.216 2-MeC26 0.003 88.169 -0.262 2-MeC28 0.012 30.689 -0.188 2-MeC30 0.170 3.243 -0.097 Sum 0.007 44.986 -0.154 AlkaneSum 0.011 32.772 -0.143 DieneSum 0.016 23.793 -0.136 MonoeneSum 0.123 4.546 -0.091 MethylSum 0.005 58.067 -0.224 low 0.095 5.794 -0.095 high 0.027 16.629 -0.132

Supplementary Table T 5: Reaction norm model fits to percent hydrocarbons. As in Table T1 (q.v.), Equation 3 is fit to the responses of males and females to density, but in this case using the percent composition of each compound. The strong increase (as a percent of total CH) of longer chain compounds is evident for males. Females however exhibit a U-shaped percent CH response, with compounds in both short and longer chain ends of the spectrum increasing. Notably, the behaviorally important compounds 7-tricosene (for males; 7-nC23:1) and 7-11 heptacosadiene (for females; 7-11-C27:2) have almost flat responses to density, suggesting flies may be regulating these to make up a constant percentage of the pheromone mix even as other compounds and total CH change. 1 values are shown in bold italic if (negative (compound declines with increasing density) or italic if positive (compound increases with density).See Table T1 for explanation of color coding. Males: compound p t beta1 C21 0.1160 -1.899 -0.115 C22 0.3016 1.152 0.088 C23 0.2474 -1.309 -0.045 C24 0.2893 1.185 0.111 C25 0.1740 1.584 0.058 C27 0.0035 5.184 0.449 C28 0.0003 9.125 0.717 C29 0.0074 4.339 1.307 5C23 0.8039 0.262 0.011 5C24 0.6473 0.486 0.122 5C25 0.6961 0.414 0.144 7C22 0.9381 0.082 0.023 7C23 0.2052 1.456 0.055 7C24 0.0195 3.388 0.858 7C25 0.2600 1.270 0.083 7C27 0.4569 0.806 0.319 9C23 0.1118 -1.928 -0.040 9C24 0.1675 -1.614 -0.262 9C25 0.0317 2.956 0.153 MeC24 0.0042 -4.969 -0.269 MeC26 0.0547 -2.497 -0.170 MeC28 0.1464 1.718 0.095 MeC30 0.0002 10.189 0.540 Sum NA NA NA alkanes 0.9108 0.118 0.004 monoOdd5 0.2570 1.279 0.049 monoOdd7 0.1185 1.883 0.053 monoOdd9 0.5809 0.590 0.014 methyls 0.1910 -1.512 -0.086 low 0.3971 -0.926 -0.011 high 0.0001 10.851 0.259

Females: compounds p F beta1 nC21 0.010 33.972 0.211 nC22 0.056 9.263 0.344 nC23 0.074 7.233 0.057 nC24 0.030 15.017 0.186 nC25 0.002 110.785 -0.095 nC27 0.830 0.054 0.005 nC28 0.076 7.069 0.458 nC29 0.075 7.163 0.294 7-11-C23:2 0.083 6.587 0.200 7-11-C25:2 0.019 21.768 0.175 7-11-C27:2 0.937 0.007 0.002 7-11-C29:2 0.100 5.538 0.041 9-13-C27:2 0.003 89.423 -0.133 9-13-C29:2 0.059 8.782 -0.098 5-C23:1 0.927 0.010 0.004 5-C25:1 0.217 2.432 0.081 5-C27:1 0.214 2.477 0.058 7-C23:1 0.012 30.293 0.310 7-C25:1 0.016 24.280 0.223 7-C27:1 0.125 4.455 0.106 7-C29:1 0.144 3.861 0.386 9-C23:1 0.062 8.505 0.154 9-C25:1 0.064 8.289 0.142 9-C27:1 0.584 0.374 -0.023 9-C29:1 0.047 10.722 1.388 2-MeC22 0.002 97.306 -0.180 2-MeC24 0.016 24.215 -0.102 2-MeC26 0.013 28.914 -0.165 2-MeC28 0.197 2.738 -0.053 2-MeC30 0.030 15.264 0.091 Sum 0.007 44.986 -0.154 AlkaneSum 0.372 1.097 0.022 DieneSum 0.151 3.679 0.023 MonoeneSum 0.094 5.842 0.115 MethylSum 0.030 14.997 -0.108 low 0.036 13.184 0.103 high 0.041 11.973 0.033

Supplementary Table T6: Diurnal cycling for female hydrocarbons. The strong effect of carbon chain length on time of peak expression can be seen for several classes of compounds. For instance, alkanes C21 and C23 peak near midnight while longer chain C25,C27, and C29 peak near noon. Similar patterns are seen in dienes, 7-monoenes, and methylalkanes. A 24-hour sinusoid is fit to CH levels over 24 hours for females at densities of 16/vial and 40/vial (compare to Figures 5,6, S1). p values are for the significance of the sinusoid and are shown in bold if they pass a False Discovery Rate test with q=1/15. Maxima of the fitted curve (in CT hours) are shown in bold if significant by the FDR test; italic highlights peaks near midnight and bold italic those near noon. max max compound p d=16 d=16 p d=40 d=40 nC21 0.0078 17.8 0.0110 18.2 nC22 0.0509 19.1 0.0200 19 nC23 0.0210 18 0.0430 19.6 nC24 0.3296 3 0.1900 3.9 nC25 0.0196 5.5 0.2300 5.5 nC27 0.0003 5.5 0.0032 5.9 nC28 0.0011 4.7 0.1500 3.3 nC29 0.0014 5.4 0.0026 5.6 7-11-C23:2 0.0013 18 0.0064 19.4 7-11-C25:2 0.0007 18.2 0.0024 19.9 7-11-C27:2 0.0004 18.4 0.0001 19.4 7-11-C29:2 0.0144 5.5 0.0005 7.1 9-13-C27:2 0.1349 19 0.8300 11.3 9-13-C29:2 0.1048 6.4 0.0120 6.9 5-C23:1 0.1083 20 0.3500 21.4 5-C25:1 0.1617 17.2 0.5900 3.6 5-C27:1 0.0786 5.1 0.2300 6.5 7-C23:1 0.0014 17.7 0.0024 17 7-C25:1 0.0006 17.7 0.0001 18 7-C27:1 0.7826 15.2 0.0290 9.5 7-C29:1 0.0247 5 0.0340 6.7 9-C23:1 0.0050 17.8 0.0095 17.5 9-C25:1 0.0005 17.8 0.1600 16.5 9-C27:1 0.0604 6 0.3300 10.8 9-C29:1 0.0410 6.3 0.5100 8.4 2-MeC22 0.4923 12.9 0.0200 16.7 2-MeC24 0.0136 16.6 0.0014 18 2-MeC26 0.0121 7.4 0.6300 13.5 2-MeC28 0.0009 6.9 0.0180 6.5 2-MeC30 0.3405 9.3 0.1200 6.7

Appendix B

Figure 1: Green florescent protein (GFP) expressed under the control of m484-Gal4 line. A) internal ventral surface view of female abdomen GFP expression. The oenocytes are located in the posterior region of each abdominal segment. B) Adult female GFP expression. C) internal ventral surface view of male abdomen GFP expression. D) Adult male GFP expression.

Table T1A: Amounts of CHs on D. melanogaster females. Mean ± standard error of m484-Gal4 control, IR-UAS control and RNAi cross strains are shown. All values are relative quantities.

Table T1A (continued) : Amounts of CHs on D. melanogaster females. Mean ± standard error of m484- Gal4 control, IR-UAS control and RNAi cross strains are shown. All values are relative quantities.

Table T1B: Amounts of CHs on D. melanogaster females. Mean ± standard error of m484-Gal4 control, IR-UAS control and RNAi cross strains are shown. All values are relative quantities.

Table T1B (continued): Amounts of CHs on D. melanogaster females. Mean ± standard error of m484- Gal4 control, IR-UAS control and RNAi cross strains are shown. All values are relative quantities.

Table T1C: Amounts of CHs on D. melanogaster females. Mean ± standard error of m484-Gal4 control, IR-UAS control and RNAi cross strains are shown. All values are relative quantities.

Table T1C (continued) : Amounts of CHs on D. melanogaster females. Mean ± standard error of m484- Gal4 control, IR-UAS control and RNAi cross strains are shown. All values are relative quantities.

Table T1D: Amounts of CHs on D. melanogaster females. Mean ± standard error of m484-Gal4 control, IR-UAS control and RNAi cross strains are shown. All values are relative quantities.

Table T1D (continued): Amounts of CHs on D. melanogaster females. Mean ± standard error of m484- Gal4 control, IR-UAS control and RNAi cross strains are shown. All values are relative quantities.

Table T3A: Amounts of CHs on D. melanogaster males. Mean ± standard error of m484-Gal4 control, IR- UAS control and RNAi cross strains are shown. All values are relative quantities.

Table T3B: Amounts of CHs on D. melanogaster males. Mean ± standard error of m484-Gal4 control, IR- UAS control and RNAi cross strains are shown. All values are relative quantities.

Table 4: ANOVA results of CH on D. melanogaster males. Only significant p-values are shown

Table 5: Tukey post-hoc test of courtship parameters (Initiation, courtship index, wing extension index and number of attempter copulation)

Wing Number of Lighting Initiation Courtship extension attempted Condition (seconds) index index copulation CG5887/ light 0.163249 0.6583937 0.8471811 0.4053313 male dark 0.005406 0.8303118 0.5964847 0.3198995 light 0.167746 0.4720071 0.7516068 0.9332849 CG6984 dark 0.855249 0.8218004 0.2862726 0.9220912 light 0.429744 0.2488414 0.3442608 0.8612474 CG8522 dark 0.511679 0.9381648 0.8327461 0.1922556 light 0.254365 0.112142 0.1118013 0.4549021 CG5326 dark 0.488103 0.6612265 0.6314663 0.1772312 CG5887/ light 0.351955 0.4538302 0.8906911 0.1247071 female dark 0.810315 0.1757817 0.2068536 0.3100766

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