The Pennsylvania State University. The Graduate School. College of Agricultural Sciences GENOMIC, PHYSIOLOGICAL, AND CHEMICAL ANALYSIS OF THE

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1 The Pennsylvania State University The Graduate School College of Agricultural Sciences GENOMIC, PHYSIOLOGICAL, AND CHEMICAL ANALYSIS OF THE IMPACTS OF ENVIRONMENTAL STRESSORS ON HONEY BEE (APIS MELLIFERA L.) WORKERS AND QUEENS A Dissertation in Entomology by Daniel R. Schmehl 2013 Daniel R. Schmehl Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy May 2013

2 ii The dissertation of Daniel R. Schmehl was reviewed and approved* by the following: James Tumlinson Ralph O. Mumma Professor of Entomology Director, Center for Chemical Ecology Co-dissertation Advisor Chair of Committee James Frazier Professor of Entomology Co-dissertation Advisor Christina Grozinger Associate Professor of Entomology Director, Center for Pollinator Research Christopher Mullin Professor of Entomology Tracy Langkilde Associate Professor of Biology Gary Felton Professor of Entomology Head of the Department of Entomology *Signatures are on file in the Graduate School

3 iii ABSTRACT Honey bee (Apis mellifera L.) populations are experiencing worldwide declines, averaging over 30% mortality each winter. In the US alone, honey bees account for over $11 billion in pollination services. Honey bees are exposed to numerous stressors that are contributing to the decline of honey bee health including pesticides, pathogens, parasites, malnutrition, environment, and management practices. The presence of these stressors and their potentially synergistic interactions can have large implications for the survival of honey bee colonies. There is only a limited understanding regarding the impacts of nutritional and pesticide stress on the behavior and physiology of individual bees, which may impact communication systems and thereby the organization of the colony. In this dissertation, I explore impacts of pesticide exposure and nutrition on honey bee queen and worker gene expression, physiology, and chemical communication. First, I examined the impact of coumaphos (organophosphate) and fluvalinate (pyrethroid), common in-hive miticides used for Varroa mite control, on the queen pheromone production and queen-worker pheromone-mediated communication. My analysis revealed that pesticide exposure had a minor impact. Next, I examined the impact of feeding single source (almond) and multisource (wildflower) pollen on colony growth, juvenile hormone (JH) endocrine pathways, protein levels, queen chemistry, and queen-worker pheromone-mediated communication. I found no impacts of pollen source on colony growth, endocrine pathways, protein levels, or queen-worker interactions. However queens fed wildflower pollen had significantly increased homovanillyl alcohol (HVA, queen pheromone component) content of the queen mandibular glands. It is

4 iv uncertain what contribution increased levels of HVA is having on the colony; however previous findings have linked HVA with learning and the prevention of aversive behaviors in young workers. Lastly, I conducted a whole-genome microarray study to investigate the impact of coumaphos and fluvalinate on the genomic phenotype and physiology of workers. My analysis revealed a significant change in expression patterns in response to pesticide exposure, including the upregulation of several detoxification genes. Furthermore, pesticide exposure significantly impacted expression of genes associated with behavioral maturation. Subsequent analysis on JH pathways (known to impact behavioral maturation) revealed no change in circulating levels of JH, although I found a significant decrease in the JH precursor methyl farnesoate (MF) in response to pesticide exposure, but the role of MF is unclear. Furthermore, pesticide exposure significantly altered expression of genes associated with honey and pollen consumption. Subsequent nutritional bioassays and molecular analysis found that bees fed pollen had had a significant upregulation of genes whose expression was upregulated by pesticide exposure. Additionally, pollen was found to increase survival when challenged with pesticides compared to bees fed only sucrose. These research findings strengthen our understanding of pesticide and nutritional stress on honey bee health and impact current honey bee management practices. Our understanding of the physiological and behavioral mechanisms of pollen consumption and pesticide exposure will aid in our preservation of further declines in the honey bee population.

5 v TABLE OF CONTENTS List of Figures....vii List of Tables.....xi Acknowledgements... xiii Chapter 1 Introduction... 1 Background... 1 Nutritional Stress... 3 Pesticide Stress... 5 Pesticide Frequency... 7 Impact of stress on queen physiology... 9 Impact of stress on worker physiology Dissertation Objectives Chapter 2 Examining the impacts of pesticides on queen pheromone production and queen-worker chemical communication Abstract Introduction Materials and Methods Chemicals Treatments, collections, and behavioral bioassays Experiment Experiment Experiment Chemical Analysis Results Behavioral assays Chemical analysis Discussion Chapter 3 Effects of almond and wildflower pollen on colony health and communication in honey bees Abstract Introduction Materials and Methods Colony Health Worker Physiology Queen-worker chemical communication Chemical analysis of the queen mandibular gland Results... 60

6 vi Colony Health Worker Physiology Queen-worker chemical communication QMG Chemistry Discussion Chapter 4 Genomic analysis of the interactions between pesticide exposure and nutrition in honey bees Abstract Introduction Materials and Methods General bee rearing Microarray analysis Comparative Analyses Characterization of the juvenile hormone and methyl farnesoate hemolymph titers qrt-pcr Results Effects of pesticide on global gene expression patterns Pesticide qrt-pcr Comparative Analyses Effect of pesticide exposure on hormone levels Nutrition qrt-pcr Impact of diet on pesticide tolerance Discussion Appendix Literature Cited

7 vii LIST OF FIGURES Figure 1-1 Contributing factors which impact honey bee health (adapted from Le Conte et al. 2010) Figure 1-2 Structures of candidate pesticides Figure 1-3 Honey bee queen mandibular pheromone (QMP) components Figure 1-4 Example derivitization reaction of queen mandibular pheromone components using bistrimethylsilyl-trifluoroacetamide (BSTFA). Example reaction is adapted from Knapp (1979) Figure 2-1 Retinue responses in treated cages of workers and queens, Cages of 30 workers and one queen were fed sucrose solutions containing methanol, 100 ppm coumaphos, or 100 ppm fluvalinate for 7 days. On day 7, the number of workers performing the retinue response was observed at three time points over a 40 minute period. 12 cages were observed for each treatment. Significantly fewer workers performed the retinue response in the fluvalinate treatment (repeated measures ANOVA, F 2,33 =4.84, p=0.0143). Tukey-HSD revealed fluvalinate was lower than control (p = ) with coumaphos being intermediate between the two treatments Figure 2-2 Retinue responses in treated cages of workers and queens, Cages of 30 workers and one queen were fed sucrose solutions containing methanol, coumaphos, and fluvalinate for 7 days. On day 7, the number of workers responding to the queen was observed at four time points over 60 minutes.10 cages for methanol, 8 for coumaphos, and 11 for fluvalinate were observed for each treatment. There was no significant difference in retinue response across the treatments (repeated measures ANOVA, F 2,26 =1.45, p=0.2530) Figure 2-3 Responses of untreated workers to the queen mandibular gland extracts of 2010 treated queens. 30 workers were placed in a cage, reared in the presence of synthetic QMP, and fed 50% sucrose solution and water daily for a period of 7 days. On day 7, a choice test of two QMG extracts was presented to the caged workers. The number of workers responding to each of the extracts was recorded at five time points over 20 minutes. 9 replicates were performed for each paired QMG comparison. There was no significant difference of worker retinue towards the QMG extracts between our control and the two pesticide treatments coumaphos (ANOVA, repeated measures: F 1,16 =0.2073, p= ) and fluvalinate (F 1,16 =0.5896, p=0.4537). There was a significant difference of worker retinue towards our control and the non-solvent blank (F 1,16 =86.04, p<0.0001)

8 Figure 2-4 Responses of untreated workers to the queen mandibular gland extracts of 2011 treated queens. 30 workers were placed in a cage, reared in the presence of synthetic QMP, and fed 50% sucrose solution and water daily for a period of 7 days. On day 7, a choice test of two QMG extracts was presented to the caged workers. The number of workers responding to each of the extracts was recorded at five time points over 20 min. 10 replicates for each paired QMG comparison were performed. There was a significant difference of worker retinue response to the QMG extracts of methanol control versus coumaphos treated queens (ANOVA, repeated measures: F 1,18 =5.95, p<0.0253), but no significant differences in response to the QMG extracts of methanol control vs fluvalinate treated queens (F 1,18 =0.9064, p=0.3537). There was a significant difference of worker retinue towards our control and the non-solvent blank (F 1,18 =556.91, p<0.0001) Figure 2-5 Effect of treatment on worker retinue responses to synthetic queen mandibular pheromone (QMP). 30 workers were placed in a cage and were fed sucrose solutions containing methanol, coumaphos, and fluvalinate for 7 days. On day 7, the number of workers responding to the glass cover slip containing synthetic QMP was observed at 5 time points over 20 min. Each treatment contained 10 replicates. There was no significant difference of worker retinue towards the queen (repeated measures ANOVA, F 3,36 =1.32, p=0.2835) Figure 2-6 Effects of treatment on the quantities of compounds in the mandibular glands. A Linear Discriminant Analysis of the quantity of QMG compounds from three treatment groups did not reveal any significant treatment effect Figure 2-7 Effects of treatment on the relative proportions of compounds in the mandibular glands. A Linear Discriminant Analysis of the quantity of QMG compounds from three treatment groups did not reveal any significant treatment effect Figure 2-8 The effect of treatment on the total quantity of queen mandibular gland (QMG) compounds. The total quantity of compounds was summed for each queen. The number of queens used in each analysis is noted at the base of the bar for each treatment. There was no significant effect of treatment on the total amount of compounds produced in the QMG (ANOVA, F=0.6502, p=0.5265) Figure 3-1. Protein quantity in the honey bee when fed almond or wildflower pollen. 10-day old marked bees from colonies fed either almond or wildflower pollen were collected and analyzed for protein content in the head. The heads for 5 bees from each colony for almond-fed (6 replicates) and wildflower-fed (5 replicates) were dissected and homogenized, and viii

9 ix analyzed using a BCA protein assay kit. No significant differences were observed between treatments (ANOVA, F 1 =0.0538, p=0.8208) Figure 3-2. Worker retinue response to live queens from wildflower or almond fed colonies day old workers were placed in a cage, reared in the presence of synthetic QMP, and fed 50% sucrose solution and water daily for a period of 7 days. On day 7, queens were collected from 6 replications for each treatment and one queen was placed in each cage with the workers. After a 1 hour period of acclimation, the number of workers responding to the queen was recorded at 10 time points over a 45 minute period for each cage. The graph represents the mean number or workers responding to the queen for each treatment ± S.E. There was no significant difference in the worker retinue between our almond pollen-fed and wildflower pollen-fed queens (ANOVA, repeated measures: F 1,12 =1.2604, p=0.2835) Figure 3-3. Worker retinue response to the QMG extracts of queens from either almond fed or wildflower fed colonies day old workers were placed in a cage, reared in the presence of synthetic QMP, and fed 50% sucrose solution and water daily for a period of 7 days. On day 7, a choice test was presented to the caged workers. The number of workers responding to each of the extracts was recorded at five time points. The graph represents the mean number of workers responding ± SE. 12 replicates for each paired comparison were performed. There was no significant difference in the number of workers attending the QMG extracts when giving the bees a choice of almond or wildflower (F 1,22 = , p=0.6272) Figure 3-4. Linear Discriminant Analysis of the quantity of QMG compounds from colonies of almond-fed or wildflower-fed pollen. Of the 13 samples for each treatment, no samples were misrepresented in the wrong group Figure 3-5. Linear Discriminant Analysis of the proportion of QMG compounds from colonies of almond-fed or wildflower-fed pollen. Of the 13 samples for each treatment, no samples were misrepresented in the wrong group Figure 4-1 Hierarchical clustering analysis of significantly regulated genes. Overall gene expression patterns of coumaphos and fluvalinate treated bees clearly clustered separately from methanol and sucrose. This grouping is supported by an approximately-unbiased p-value of 100 and a bootstrap value of Figure 4-2 Venn diagram of significantly regulated transcripts in the three treatment groups relative to the control. GO analysis of the different sets of transcripts identified several over-represented (p < 0.05) functional categories (listed in black)

10 x Figure 4-3 Relative expression levels of selected candidate genes in response to pesticide exposure. Cages of 30 newly-emerged bees were chronically exposed (orally) to pesticides for a seven day period. Abdomens were homogenized and RNA extracted. Samples were analyzed using qrt-pcr and relative amounts were calculated using the ΔΔCt method. CYP9S1 (Kruskal-Wallis, chi-squared (χ 2 ) = 16.14; degrees of freedom (DF) = 4; p = ), CYP9Q3 (χ 2 = 15.29; DF = 4; p = ), and CYP306A1 (χ 2 = 10.76; DF = 4; p = ) were found to be significantly upregulated relative to our methanol control Figure 4-4 Pesticide exposure reduces levels of methyl farnesoate in worker bees. Titers were measured in pooled samples of methanol (8), fluvalinate (8), and coumaphos (7) treated bees. The total amount of MF was significantly lower in workers treated with coumaphos and fluvalinate than our methanol treatments (ANOVA-Tukey HSD; F 2 = 9.96, p= ) Figure 4-5 Relative expression levels of selected candidate genes in response to diet. Cages of 30 newly-emerged bees were fed one of six diets (sucrose, sucrose/pollen (wildflower), sucrose/protein (soy), honey, honey/pollen, honey/protein) for a seven day period. Abdomens were homogenized and RNA extracted. Samples were analyzed using qrt-pcr and relative amounts were calculated using the ΔΔCt method. Kruskal Wallis revealed significant effects of treatment in CYP9S1 (chi-squared (χ 2 ) = 34.10; degrees of freedom (DF) = 5; p < ), CYP9Q3 (χ 2 = 45.42; DF = 5; p < ), CYP305D1 (χ 2 = ; DF = 5; p < ), and SODH2 (χ 2 = 44.91; DF = 5; p < ). Subsequent posthoc pairwise comparisons were conducted to identify differentially regulated treatment groups and statistical differences are denoted by different letters Figure 4-6 Contribution of diet on honey bee survival. Cages were established with one of four diets for a period of 16 days. Mean survival for each of the diets was found to be greater than 15.5 days. We found a significant decrease in cages fed a long-term protein diet relative to cages receiving a sucrose (Chi-squared (χ 2 ) = 5.52, p = 0.02) or short-term pollen (χ 2 = 3.87, p = 0.05) Figure 4-7 Diet impacts honey bee pesticide tolerance. Honey bees were challenged with a daily chronic feeding of 3 ppm chlorpyrifos beginning on day five of adult development. Cages were established with one of four diets for a period of 16 days. We found a significant increase in longevity in relation to diet (long-term pollen > long-term protein = short-term pollen > sucrose only)

11 xi LIST OF TABLES Table 2-1 Number of misrepresented samples in QMG linear discriminant analysis. Of the 18 Methanol samples, 16 coumaphos samples, and 20 fluvalinate samples, there was no significant separation between the treatment groups Table 2-2 Analysis of the derivitized QMG compound quantity (mean ± S.E.) in ng from queens treated with methanol, coumaphos, or fluvalinate. There were no significant differences in any of the 15 compounds analyzed in the QMG, including any of the QMP components (listed in bold) (One-way ANOVAsignificant values are highlighted in yellow) Table 2-3 Analysis of the derivitized QMG compound proportion (mean ± S.E.) in ng from queens treated with methanol, coumaphos, or fluvalinate. There were no significant differences in any of the 15 compounds analyzed in the QMG, including any of the QMP components (listed in bold) (significant values are highlighted in yellow) Table 2-4 Analysis of the queen cuticular hydrocarbon (CHC) compound quantity (mean ± S.E.) in µg from queens treated with methanol, coumaphos, or fluvalinate. There were no significant differences in any of the top 10 analyzed compounds Table 2-5. Analysis of the queen cuticular hydrocarbon (CHC) compound proportion (mean ± S.E.) in ng from queens treated with methanol, coumaphos, or fluvalinate. There were no significant differences in any of the top 10 analyzed compounds Table 3-1 Analysis of the QMG compound quantity (mean ± S.E.) in ng from colonies fed wildflower or almond pollen. There was a significant increase in the amount of HVA (F 1 =6.0009, p=0.0220) and Unknown 1 (F 1 =4.7481, p=0.0394) produced in the QMG in the wildflower Table 3-2 Analysis of the QMG compound quantity (mean ± S.E.) in ng from colonies fed wildflower or almond pollen. There was a significant increase in the amount of HVA (F 1 = , p=0.0036) and Unknown 1 (F 1 = , p<0.0001) produced in the QMG in the wildflower Table 4-1 Primer sequences for qrt-pcr analyses. It is noted in the table whether the primer was used for nutrition, pesticide, or both (pest./nut.) studies Table 4-2 Variation between microarray and qrt-pcr gene expression... 92

12 xii Table 4-3 The 1118 significantly regulated transcripts associated with pesticide exposure were compared to four previously performed studies Table 4-4 Analysis of directional expression overlap among pesticide, physiological, and behavioral associated transcripts Table 4-5 Effect of diet on honey bee survival when challenged (pesticide exposed) or not challenged (sucrose) Table A Overrepresented GO categories (p<0.05) regulated by coumaphos, fluvalinate, or both (FDR <0.01) Table B Overrepresented GO categories (p<0.05) upregulated by either coumaphos or fluvalinate and rich (pollen/honey) diet, or downregulated by either coumaphos or fluvalinate and poor (sucrose) diet (Pesticide: FDR < 0.01, Nutrition FDR <

13 xiii ACKNOWLEDGEMENTS It is a pleasure and honor to thank those who have made this dissertation possible. I first thank my primary research advisors Dr. Jim Tumlinson and Dr. Jim Frazier for research instruction, guidance, and friendship. I am incredibly grateful for the research mentorship of Dr. Christina Grozinger and her incredible investment in my scientific development. I also thank Christina, as well as Dr. Christopher Mullin and Dr. Tracy Langkilde for serving on my committee and providing me with guidance and support. I also want to thank the department head of entomology, Dr. Gary Felton, who has given me support through guidance, leadership training, and departmental support. I am very grateful for many collaborative research relationships throughout graduate school. I thank Sam Droege for native pollinator ecology instruction. I give much thanks to the amazing researchers at icipe (Nairobi, Kenya), including Dr. Baldwyn Torto, Dr. Elliud Muli, James, and Kilonzo for aiding in my earlier graduate school research projects. I would like to thank Dr. Ramesh Sagili and his lab technician Carolyn Breece (Oregon State University, Corvallis, OR) for their collaboration on our honey bee nutrition project which contributed to my dissertation research. I thank Dr. Peter Teal (USDA-ARS, Gainesville, FL) for honey bee juvenile hormone analysis and incredible research advice which also contributed to my dissertation research. It is an honor for me to thank the numerous lab groups that I have had the pleasure to work with at Penn State. I thank the entire Frazier/Mullin lab and all of their guidance and assistance. Specifically, I thank Maryann Frazier who has been fundamental and influential in my honey bee biology training. I always tell people I am

14 xiv grateful to have learned apiculture from the best! Sara Ashcraft, Lauren Russert, and Stephanie Mellott have helped my research efficiency by aiding my research projects in countless ways. I also thank Wanyi Zhu and Tim Ciarlo and their expertise and friendship in our collaborative research projects. I thank the entire Tumlinson lab for their incredible support. Specifically I thank Nate McCartney for all of his assistance around the lab, Dr. Tracy Conklin for her friendship and guidance in analytical chemistry, and Dr. Irmgard Seidl-Adams for challenging research inquiries and molecular guidance. I thank the entire Grozinger lab for their incredible support. Specifically, I thank Bernardo Niño for placing countless orders and managing my research colonies, Tracey Baumgarten for molecular training and countless answers to my many questions, Dr. Elina Lastro-Niño for statistical and molecular advice, and Holly Holt for molecular assistance. I am forever grateful to Mariam Khraibani for her summer research assistance and her numerous hours spent in the dark room taking care of my honey bees as well as Janet Teeple for helping to set up several queen biosassys. I also thank the Statistical Counseling Center (specifically Joyce Jiang and Xi Tian) for their short-term statistical counseling. There have been several really special fellow graduate students who have helped me throughout graduate school through many life and research conversations. Jason Smith has been an incredible friend to me and has always been there with encouraging conversation and beautiful statistical assistance. I am really thankful for the statistical help from Eric Bohnenblust and his friendship since day one of graduate school. I am grateful for Beth Johnson for her friendship and her time coordinating departmental outreach events. I thank Dr. Andy Myrick for help building circuits for my automated

15 xv proboscis extension response research. I also thank Craig Cella for the entertaining conversations and the annual transportation of honey bee packages. This dissertation would not have been possible without the exemplary assistance of the Penn State department of entomology office staff. I thank LuAnn Weatherholtz, Roxie Smith, Thelma Brodzina, Karen Dreibelbis, Ellen Johnson, Dave Love, Pamela Murray, Nick Sloff, Marcia Kerschner, and Kylie Rogers. They have an incredible willingness to serve the department of which I extremely appreciative. I am especially thankful for the incredible undergraduate entomology professor, Dr. Joseph Sheldon (Messiah College), who helped shape my passion for entomology and my career aspirations. It is a special honor for me to thank Ash Holleman for serving as my life mentor during my time at Penn State. He has helped me mature as a person and a leader in more ways than I can count. I owe my deepest gratitude to my family. I will be eternally grateful for the love and never-ending support of my parents Dennis and Kay. My sisters, Alison and Amy, as well as their families have been incredible and are always there for a supportive conversation. Last, but certainly not least, I am the luckiest and most blessed man in the world to have such a loving and supportive wife. Kerrie, you have been amazing and I am so excited to see what our future holds as we continue on this incredible journey.

16 1 Chapter 1 Introduction Background There has been a significant decline in the worldwide number of pollinators, despite the increased demand for pollination services (Council 2007). The economic value of pollination is immense, with 80% of angiosperms dependent on animals for pollination (Council 2007). In 2009, the estimated worldwide value of pollination, primarily for vegetables, fruits, and edible oil crops, was $198.4 billion (Gallai et al. 2009). In the US, the total value attributed to insect pollination was $15.12 billion, with honey bees accounting for $11.68 billion (Calderone 2012). Honey bees (Apis mellifera L.) are arguably the most important pollinators due their capacity to be easily managed and transported for pollination services. During the past decade, much of the recent honey bee decline has gained awareness due to losses from Colony Collapse Disorder (CCD), diagnosed by the absence of worker honey bees in the colony (Cox-Foster et al. 2007, Johnson et al. 2009a, VanEngelsdorp et al. 2009). However despite the occurrence of CCD in the US and Europe, the honey bee decline is clearly not attributed to a single origin of stress (VanEngelsdorp et al. 2009, Dainat et al. 2012, Rennich et al. 2012). Honey bees are exposed to numerous stressors that contribute to the decline of honey bee health (Figure 1-1), including hive management techniques, pesticide exposure, parasites, pathogens, global climate change, and fluctuations of the microbial

17 2 community. The presence of these stressors and their potentially synergistic interactions can have large implications for the survival of honey bee colonies (VanEngelsdorp et al. 2009, Le Conte et al. 2010). Despite a strong understanding on the impact of pesticide exposure, and to a lesser extent nutrition, on the behavior and physiology on individual bees, no research has examined how these changes in individual honey bees may impact communication systems and thereby the organization of the colony. The goal of our study is to characterize effects of pesticide and nutritional stress on queen pheromone communication and the physiology of female workers, and how these stressors affect the social dynamics and health of the colony. Figure 1-1 Contributing factors which impact honey bee health (adapted from Le Conte et al. 2010).

18 3 Nutritional Stress Honey bees require a pollen and nectar diet which can vary considerably depending on plant source, (reviewed in Haydak 1970). As noted by Brodschneider and Crailsheim (2010) the health of honey bee colonies is not only defined by the absence of diseases, but also by the presence of many well-nourished individuals capable of producing progeny and resisting stressors such as parasites, infections, insecticides and periods of dearth. Receiving adequate nutrition is fundamental to the health of the colony. Nectar and honey are primarily a source of carbohydrates and provide an energy source for flight and metabolic processes, while pollen provides proteins, lipids, vitamins, and minerals (Haydak 1970, Brodschneider and Crailsheim 2010, Nicolson 2011). Pollen is the most important requirement for colony growth, especially for brood development (Stanley and Linskens 1974). Pollen can vary drastically in protein content, as well as essential vitamins and amino acids (Roulston et al. 2000, Nicolson 2011). While protein is necessary for colony growth, adult bees can cannibalize brood for the protein needs of the colony during times of pollen deficiencies (reviewed in Brodschneider and Crailsheim 2010). However as expected, if pollen is not available, brood production will cease. For proper adult function, protein is necessary and consumed at the highest amounts between the ages of 4 and 9 days (Morton 1951, Crailsheim et al. 1992). The high consumption of pollen by young adults is necessary for muscle, hypopharyngeal gland, ovary development (reviewed in Brodschneider and Crailsheim 2010). In particular, the pair of hypopharyngeal glands is the site for converting pollen to the food

19 4 fed to developing brood (Hrassnigg and Crailsheim 1998) and their size is directly correlated with the amount of protein consumed (Sagili et al. 2005, Sagili and Pankiw 2007, DeGrandi-Hoffman et al. 2010). Inadequate nutrition can lead to precocious foraging (Toth et al. 2005, Toth and Robinson 2005, Tofilski 2009, Woyciechowski and Moroń 2009), which contributes to a shorter lifespan for workers (Janmaat and Winston 2000, DeGrandi-Hoffman et al. 2010). Poor nutrition is known to exacerbate other routes of stress, such as parasitism and pesticides (Wahl and Ulm 1983, Janmaat and Winston 2000). Furthermore, nutritionallystressed colonies are subject to a compromised immune response (Alaux et al. 2010b). Deformed Wing Virus (DWV) is significantly more prevalent in nutritionally stressed colonies (DeGrandi-Hoffman et al. 2010). Nutrition-deprived colonies have increased sensitivity to pesticide exposure (Wahl and Ulm 1983). Bees fed either no protein or a poor protein source, such as dandelion pollen, have a significantly higher rate of mortality than bees fed a mixed protein source (Wahl and Ulm 1983). Lastly, workers reared in a colony with low pollen stores were found to exhibit precocious foraging behavior and when compromised with Varroa as pupae, the onset of foraging behavior was even further accelerated (Janmaat and Winston 2000). From these studies, it is clear that nutritionally stressed honey bees are more susceptible to other forms of stress affecting the colony. A primary concern among beekeepers is the impact of monoculture pollination on honey bee health. Honey bees are well adapted to monoculture pollination due to their ease of maintenance and transportation. They are often necessary to pollinate angiosperm monocultures because the numbers of resident native pollinators are not adequate for

20 5 successful crop pollination. The biggest monoculture in the United States dependent on honey bees for pollination services are almonds, requiring over 1 million colonies annually (Ratnieks and Carreck 2010). However feeding on monocrop landscapes can result in poor nutrition because very few monocultures are adequate in providing all the essential nutrients needed for honey bee health (reviewed in Brodschneider and Crailsheim 2010). Willard et al. (2011) observed that colonies fed a poor diet produced less brood. Additionally, a diverse diet may protect the colony from infection (Alaux et al. 2010b). But broad impacts on bee physiology and behavior have not been well characterized. Pesticide Stress Historically, large, acute pesticide doses may kill bees and colonies outright (reviewed in Atkins 1992, Johnson et al. 2010), but long term exposure to low doses lead to sublethal effects (reviewed in Thompson and Maus 2007, reviewed in Johnson et al. 2010) which may not impact mortality or population size (Chauzat et al. 2009), but may overall affect colony performance and function. Pesticides have been implicated in part in recent declines of world-wide honey bee populations (VanEngelsdorp et al. 2009, Henry et al. 2012). Pesticide exposure can reduce learning, memory, and orientation in adult female worker bees (Decourtye et al. 2004, Decourtye et al. 2005, Aliouane et al. 2009, Decourtye et al. 2011, Ciarlo et al. 2012, Henry et al. 2012), alter adult worker locomotion and feeding behavior (Teeters et al. 2012), and modify larval development (Wu et al. 2011, Zhu et al. 2013). In male bees (drones), pesticides have been shown to

21 6 reduce body weight and longevity (Rinderer et al. 1999), as well as reduce sperm viability (Burley et al. 2008) which likely contributes to poor queen mating quality. In queens, pesticide exposure during development reduced adult queen weight (Haarmann et al. 2002, Pettis et al. 2004), the number of stored sperm, (Haarmann et al. 2002), and egg laying (Haarmann et al. 2002, Collins et al. 2004), and also disrupted ovary activation (Haarmann et al. 2002). At very high rates of coumaphos exposure, queen rearing is found to be greatly inhibited (Collins et al. 2004, Pettis et al. 2004). In addition, pesticide exposure, similar to reduced nutrition, may have large effects on behavioral maturation pathways (as described above). Pesticides can also act synergistically with other stressors. These competing stressors can greatly influence our understanding of pesticide exposure beyond just the direct pesticide impacts. Viruses, such as DWV, are very prevalent in the colony (Cox- Foster et al. 2007) and are directly correlated with the presence of Varroa mite (Shen et al. 2005). In colonies treated with fluvalinate, DWV prevalence increases in the honey bee pupae despite whether or not Varroa mites are present (Locke et al. 2012). Additionally, when mites are present, there is a significant increase in the initial DWV titers in both pupae and adults treated with fluvalinate relative to the untreated colonies suggesting an increased vulnerability to viral infections in the presence of fluvalinate (Locke et al. 2012). Similarly, Nosema, a microsporidian parasite, was found at higher amounts in the honey bee when exposed to the neonicotinoid insecticide imidacloprid (Alaux et al. 2010a). And when imidacloprid and Nosema were presented in combination, higher rates of mortality were observed than with either treatment individually (Alaux et al. 2010a).

22 7 Pesticide Frequency In our pesticide study, we examine two of the most common pesticides detected in the colony (Frazier et al. 2008, Mullin et al. 2010). Coumaphos and tau-fluvalinate are the most frequent and most abundant pesticides in hives across the United States and Europe (Chauzat et al. 2006, Mullin et al. 2010). These two pesticides became widely used in the 1990s due to hive infestations of the Varroa mite (De Jong 1997). Varroa mite infestations are likely a contributing factor to honey bee colony losses (VanEngelsdorp et al. 2009, Le Conte et al. 2010). Tau-fluvalinate (Figure 1-2) was registered in 1990 under the trade name Apistan for Varroa mite control. Though this pyrethroid miticide was effective initially, the efficacy quickly faded due to wide spread resistance in mite populations (Lodesani et al. 1995, Elzen et al. 1999). To address its ineffective control of Varroa mite populations, the U.S. approved the organophosphate coumaphos (Figure 1-2) for mite control in 1998 under the trade name Checkmite+. Even though coumaphos has a different mode of action than tau-fluvalinate, widespread resistance formed very rapidly in mite populations as early as 2001 (Elzen and Westervelt 2002, Johnson et al. 2009b, Johnson et al. 2010). Despite being used at low concentrations in the hive, both of these compounds can accumulate to unsafe levels to the bees in the hive (Haarmann et al. 2002). The residuals of both compounds in the wax are slow to break down and have a half life of five years; therefore they are routinely found in colonies (Bogdanov 2004). And since these two pesticides are still being found at such high amounts in the colony (Mullin et al. 2010), evidence suggests that beekeepers are still commonly using both pesticides to protect against Varroa

23 8 infestations. Though having different modes of action, the combination of fluvalinate and coumaphos has demonstrated synergistic toxicity in adult honey bees attributable to a shared method of detoxification (Johnson et al. 2009b). Though coumaphos and fluvalinate were our main focus of this study, we also examined the impact of chlorpyrifos (organophosphate), permethrin (pyrethroid), and amitraz (Figure 1-2) on honey bee female worker physiology. These three pesticides, though not as abundant in the hive as coumaphos and fluvalinate, are very frequently used. Chlorpyrifos (eg. Dursban) and permethrin (eg. Ambush) are commonly used in the agricultural setting for pest control, while amitraz (Taktic, unregistered) is used for inhive mite control (Johnson et al. 2010, Mullin et al. 2010). Chlorpyrifos is the 3 rd most frequently detected pesticide in the hive, where permethrin is 6 th most detected and DMPF (amitraz metabolite) is the 7 th most detected (Mullin et al. 2010).

24 9 Figure 1-2 Structures of candidate pesticides. Impact of stress on queen physiology The behavior and physiology of worker honey bees are highly dependent on pheromone communication (Free 1987, Slessor et al. 1998, Slessor et al. 2005). These chemical signals are extremely specific in releaser (evokes immediate response) and primer (evokes changes over long periods of time) behavioral and physiological changes (Bossert and Wilson 1963, Kocher and Grozinger 2011) and commonly involve complex synergistic interactions between components (Slessor et al. 2005, Grozinger et al. 2007). Pesticide exposure and nutritional stress may lead to a disruption of queen pheromone

25 10 production which is fundamental for the social organization and proper function of the colony (Pankiw et al. 1998, Slessor et al. 1998, Le Conte and Hefetz 2008). Queens produce a multi-component pheromone from multiple glands to communicate information to the colony (Plettner et al. 1997, Wossler and Crewe 1999, Katzav-Gozansky et al. 2001, Katzav-Gozansky et al. 2004, Alaux et al. 2010c, Kocher and Grozinger 2011). The best characterized queen pheromone component is the queen mandibular pheromone (QMP) (Keeling et al. 2003, Slessor et al. 2005). First fully characterized in 1988, QMP is produced in the queen mandibular glands (QMGs) and consist of a five component chemical blend (Figure 1-3): 9-oxo-(E)-2 decenoic acid (9- ODA), (R)- and (S)- 9-hydroxy-(E)-2-decenoic acid (9-HDA), methyl p-hydroxybenzoate (HOB), and 4-hydroxy-3-methyoxyphenylethanol (HVA) (Slessor et al. 1988). QMP has low volatility and is distributed through the hive primarily by contact between the queen with workers and wax (Naumann et al. 1991, Slessor et al. 2005). QMP plays a primary role in worker retinue attraction (Keeling et al. 2003), inhibits the activation of worker ovaries (Hoover et al. 2003), inhibits aggression of young workers to queen (Vergoz et al. 2007), orients comb building (Ledoux et al. 2001), and modulates the biosynthesis of juvenile hormone (JH) in workers (Kaatz et al. 1992, Pankiw et al. 1998). Despite the attraction of worker honey bees to the 5-component QMP blend, it is still not as attractive as a whole live queen. Some of the QMP components elicit as strong a primer response as QMP, but fail to elicit a releaser response (such as worker attraction) of any magnitude comparable to QMP (Grozinger et al. 2007). Subsequent discovery of four additional queen produced chemical components in methyl oleate, coniferyl alcohol (produced in the QMGs), 1-hexadecanol, and linolenic acid (Keeling et al. 2003) -

26 11 provided a more comprehensive understanding of worker attraction to the queen. Individually, they have no effect on worker attraction, but when added in combination with QMP, this 9-component blend termed queen retinue pheromone (QRP) is found to be more attractive to workers than QMP alone. Despite this increased worker retinue to QRP over QMP alone, this attraction still falls short in comparison to whole queens (Keeling et al. 2003). Figure 1-3 Honey bee queen mandibular pheromone (QMP) components. The chemical profile of the QMGs can be analyzed using gas-chromatography/ mass-spectrometry, as described in (Richard et al. 2007). The QMG extract was silylated using bistrimethylsilyltri-fluoroacetamide (BSTFA). Treatment of these glandular extract

27 12 components to form trimethylsilyl (TMS) derivatives allows for improved compound volatility, GC resolution and quantification (Figure 1-4) (Keeling et al. 2003). Figure 1-4 Example derivitization reaction of queen mandibular pheromone components using bistrimethylsilyl-trifluoroacetamide (BSTFA). Example reaction is adapted from Knapp (1979) In addition to QMP, cuticular hydrocarbons (CHCs) are also known to signal information between organisms. CHCs are composed of lipids found on the epicuticle of both solitary and social insects (Singer 1998, Kocher and Grozinger 2011). In social insects, they can have a role in nest mate recognition and are important for sustaining eusociality (Singer 1998). CHCs in honey bee queens and workers are very sensitive to changes in environment, diet, genotype, and physiology (Breed et al. 1988, Singer 1998, Howard and Blomquist 2005, Fan et al. 2010). The CHCs are similar among related colonies and can be transferred between individuals within the colony (Breed et al. 1988, Page et al. 1991). Even though CHCs are frequently used in nest mate recognition and reproductive status (Sledge et al. 2001, Châline et al. 2005, Dani et al. 2005), CHCs in honey bee queens do not elicit a retinue attraction from the workers (Keeling et al. 2003). Queen pheromone production is extremely plastic and can be impacted by multiple factors. Mating has been shown to influence the queen pheromone production. (Richard et al. 2007, Kocher et al. 2009). Differences in the chemical profile of the QMGs are clearly evident between naturally and artificially inseminated queens (Kocher

28 13 et al. 2009), as well as between virgin, single-mated (SDI), and multiple-mated (MDI) queens in both quantity and proportion of compounds (Richard et al. 2007). In addition, workers showed an increase in retinue response towards naturally versus artificially mated queens (Kocher et al. 2009), and MDI versus SDI or virgin queens (Richard et al. 2007). Interestingly enough, the five QMP components did not differ between SDI and MDI queens, which suggests that there is more involved in attraction than the QMP components alone. Insemination volume during queen mating has also been shown to impact worker attraction (Niño et al. 2012). Queens which were differentially inseminated with different amounts of semen produced different pheromones, which in turn resulted in different worker responses (Niño et al. 2012). Pathogen infection has also been shown to alter queen pheromone production (Alaux et al. 2011). Infection with Nosema cerane (a microsporidian parasite of the honey bee) is correlated with increases in the QMP components 9-ODA and 9-HDA in infected queens (Alaux et al. 2011), suggesting that the production of queen pheromone compounds may increase in response to stress in the colony. Impact of stress on worker physiology At the physiological level, exposure to stressors, such as reduced nutrition and pesticides, may impact endocrine pathways. The primary hormonal regulator of adult worker behavior is juvenile hormone III (JH), which is synthesized from methyl farnesoate (MF) (Robinson 1987, Huang et al. 1991, Sullivan et al. 2000). Rising titers of JH accelerates behavioral maturation, or the transition from nursing (brood care) to

29 14 foraging in honey bee workers (Robinson 1987, Huang et al. 1991, Sullivan et al. 2000). Bees experiencing Nosema infection, Varroa mites, sackbrood virus, Kakugo virus, insecticide poisioning, injury, wax deprivation, and starvation have all been observed to induce precocious foraging behavior (reviewed in Tofilski 2009). Honey bees facing varying forms of stress exhibit changes in gene expression patterns. Colonies experiencing CCD-like symptoms reveal a distinct gene expression pattern relative to non-ccd colonies (Johnson et al. 2009a). In addition, nutrition (Ament et al. 2011), pests and pathogens (Navajas et al. 2008, Chaimanee et al. 2012, Richard et al. 2012), queen reproductive success (Kocher et al. 2008, Kocher et al. 2010, Niño et al. 2011), and pesticide exposure (Mao et al. 2011, Boncristiani et al. 2012, Gregorc et al. 2012, Johnson et al. 2012) were found to modify the genetic phenotype of the honey bee. Dissertation Objectives This dissertation focuses on three main objectives concerning pesticide and nutritional stress on honey bee health. First, I investigate the impact of coumaphos and fluvalinate pesticide exposure on queen physiology and worker behavior. Second, I explore the impact of single source and multisource pollen on colony growth, queen physiology, and worker hormone levels. Lastly, I explore the impact of pesticides on worker gene expression and behavioral maturation, worker hormone levels and the interaction between pesticide exposure and nutrition. From my results, I found coumaphos and fluvalinate exposure to have a minor impact on queen pheromone production and queen-worker communication. Next, I

30 15 similarly found single source almond pollen to have a negligible effect on queen-worker communication when compared to multisource wildflower pollen, however I did observe an increase in HVA production, a QMP component, in queens from multisource colonies. Finally, I found pesticide exposure to significantly alter the genomic phenotype of workers and upregulate several detoxification pathways. Methyl farnesoate (JH precursor) titers were reduced in pesticide-exposed bees, but JH was unchanged. Pollen was also found to regulate similar gene expression patterns as pesticide exposure and pollen consumption decreased pesticide sensitivity when compared with soy-protein, honey, or sucrose consumption.

31 16 Chapter 2 Examining the impacts of pesticides on queen pheromone production and queen-worker chemical communication Authors: Daniel R. Schmehl, Christina Grozinger, and James Tumlinson Abstract Honey bee populations have been in decline in the US for decades, with average overwintering losses of 30% in the US and Europe (vanengelsdorp et al. 2008b). One likely factor contributing to this decline is exposure to pesticide and pesticide metabolites. There has been mounting evidence revealing the sublethal impact of pesticide exposure on honey bee longevity, development, reproductive fitness, learning, and behavior; however little is known on the impacts of pesticides on the social dynamics of the colony. Here, we examine the impact of coumaphos and tau-fluvalinate, two of the most commonly and highly concentrated pesticides found in US honey bee colonies, on queen pheromone production and worker behavioral responses to the queen. Our results indicate that coumaphos and tau-fluvalinate have a minor impact on queen pheromone composition and quantity. In addition, worker attraction to the queen is not strongly affected by pesticide exposure. Our findings suggest that despite the impact of pesticide exposure on honey bee colony health, chemical communication of the hive may not be largely affected by these two chemicals.

32 17 Introduction Honey bees (Apis mellifera L.) are key pollinators of many agricultural crops and provide pollination services accounting for over $11 billion annually in the US alone (Calderone 2012). Over several decades, a steady decline of honey bee populations has occurred with no clear causative agent (Council 2007, VanEngelsdorp et al. 2009, Johnson et al. 2010). Many factors are likely involved in this decline including pesticides exposure. Residues from over 120 different pesticides have been found in honey bee colonies in the US, with levels and prevalence of fluvalinate and coumaphos being the highest(mullin et al. 2010). These two chemicals are miticides routinely used by beekeepers to treat a devastating parasite of honey bee colonies, the Varroa mite. Honey bees live in large social groups, and the interactions between individuals and the overall organization of the group can play a key role in the survival and productivity of the colony. These interactions are largely mediated by pheromone communication systems (Free 1987, Slessor et al. 1998, Slessor et al. 2005, Alaux et al. 2010c). Here, we examine the effects of these two pesticides on one of the most important chemical communication systems in the colony, between the queen and her workers. Queens produce a multi-component pheromone from multiple glands to communicate information to the colony (Plettner et al. 1997, Wossler and Crewe 1999, Katzav-Gozansky et al. 2001, Katzav-Gozansky et al. 2004, Kocher and Grozinger 2011). It acts as a both releaser (elicits instant behavioral response) and primer (slow-acting, long-term physiological/behavioral effect) pheromone (Kocher and Grozinger 2011). Queen pheromone production is critical for regulating key elements of colony

33 18 organization, including reproductive division of labor and worker division of labor (behavioral maturation from nursing/brood care to foraging). Queen pheromone elicits the retinue response : worker attraction over short distances, followed by licking and antennation (Keeling et al. 2003). It inhibits the rearing of new queens (Winston et al. 1990), orients comb building (Ledoux et al. 2001), and inhibits aggression of young workers to queen (Vergoz et al. 2007). As a primer pheromone, it inhibits the activation of worker ovaries (Hoover et al. 2003), and slows down worker behavioral maturation from nursing to foraging (Pankiw et al. 1998). Its modulation of behavioral maturation has been correlated with its effects on juvenile hormone synthesis global gene expression patterns (Kaatz et al. 1992, Pankiw et al. 1998). Queen pheromone has not been fully characterized (Keeling et al. 2003, Slessor et al. 2005). However, the chemicals produced by the queen mandibular gland (QMG) can recapitulate many of the effects of the live queen. The QMG produces a wellcharacterized behaviorally active five component blend, queen mandibular pheromone (QMP), which includes 9-ODA, ± 9-HDA, HOB, and HVA. Additional minor components that synergize with QMP are produced in the QMG and other glands, but these have been less well studied (Keeling et al. 2003, Slessor et al. 2005, Richard et al. 2007, Kocher et al. 2009, Niño et al. 2012). In addition to QMG, all bees produce cuticular hydrocarbons (CHCs) which can mediate nestmate recognition and are modulated by physiology, environmental cues, and nestmate interactions (Breed et al. 1988, Singer 1998, Howard and Blomquist 2005, Fan et al. 2010). However, CHCs have not been shown to stimulate worker retinue responses to the queen (Keeling et al. 2003).

34 19 There has been mounting concern about the effects of pesticides on honey bees. Bees are exposed to a high number and amount of pesticide residues, particularly from agricultural crops. Colony surveys demonstrated that around 90% of colonies have pesticide residues, with approximately six pesticides on average and detections of over 120 different types of pesticides including insecticides, fungicides, herbicides, and insect growth regulators (Frazier et al. 2008, Mullin et al. 2010). Large, acute pesticide doses may kill bees and colonies outright (Atkins 1992), but long term exposure to low doses also lead to sublethal effects. Pesticide exposure can reduce learning, memory, and orientation (Decourtye et al. 2004, Decourtye et al. 2005, Aliouane et al. 2009, Ciarlo et al. 2012, Henry et al. 2012), alter worker locomotion and feeding behavior (Teeters et al. 2012), and negatively impact larval development (Wu et al. 2011, Zhu et al. 2013). However sublethal effects on social interactions, colony structure, and organization have not been well studied. The most common pesticide residues found in hive are coumaphos and fluvalinate (Chauzat et al. 2006, Mullin et al. 2010). These pesticides are intentionally introduced by beekeepers to control harmful mites within the hive. Tau-fluvalinate is a pyrethroid miticide introduced for mite control in the 1990s, but it quickly lost efficacy due to widespread resistance (Lodesani et al. 1995, Elzen et al. 1999). Coumaphos is an organophosphate miticide introduced in 1998, but despite having a different mode of action, it triggered widespread mite resistance as early as three years after introduction (Elzen and Westervelt 2002, Johnson et al. 2009b, Johnson et al. 2010). Both miticides have a long half-life (Bogdanov 2004), build up to toxic levels in colonies (Haarmann et al. 2002), and are routinely found in the colony even when miticides are not used due to

35 20 the recycling of wax to create the foundations for new frames, creating an increased risk of exposure. Interestingly, coumaphos and fluvalinate can have synergistic toxicity in the honey bee due to a common method of detoxification (Johnson et al. 2009b). Studies of sublethal effects of coumaphos on workers demonstrated no adverse effects on learning (Weick and Thorn 2002). Studies of miticide effects on queens showed reductions in queen weight (Haarmann et al. 2002, Pettis et al. 2004), sperm number in storage (Haarmann et al. 2002), egg laying (Haarmann et al. 2002, Collins et al. 2004), and in ovary development (Haarmann et al. 2002). At very high rates of coumaphos exposure, queen rearing is found to be greatly inhibited (Collins et al. 2004, Pettis et al. 2004). Pesticide exposure may lead to a disruption of queen pheromone production which is fundamental for the social organization and proper function of the colony (Pankiw et al. 1998, Slessor et al. 1998, Le Conte and Hefetz 2008). Here, we examined the effects of exposure to coumaphos and fluvalinate on queen-worker chemical communication. Previous studies have demonstrated that the chemical profiles of the mandibular glands are exquisitely sensitive to queen mating status and quality, resulting in altered worker attraction to these modified blends (Grozinger et al. 2007, Richard et al. 2007, Kocher et al. 2009, Niño et al. 2012). Furthermore, infection with the microspordian parasite Nosema sp. also altered the chemical profiles of the mandibular glands (Alaux et al. 2011). In our study, we examined the impact of chronic exposure to sublethal doses of coumaphos and fluvalinate on the chemical profiles of the mandibular glands and cuticular hydrocarbons of queens. In addition, we examined the response of pesticide-treated workers to live, treated queens, the responses of untreated workers to the mandibular gland extracts of treated

36 21 and control queens, and the responses of treated workers to synthetic queen pheromone lures. Materials and Methods Chemicals Coumaphos (Chemservice, #PS-656, 99.5%) and tau-fluvalinate (Chemservice, #PS-1071, 95% mix of isomers) were mixed in 1 ml of 1:1 w:v sucrose:water at a pesticide concentration of 100 parts per million (ppm). Widespread colony surveys documented a maximum dose of 91.9 ppm and 204 ppm for coumaphos and fluvalinate, respectively (Mullin et al. 2010). Furthermore, a pilot bioassay demonstrated that feeding bees a 100 ppm dose resulted in zero deaths over a 96 hour period. In order to dissolve pesticides into the feeding solution, each pesticide was dissolved in methanol at a concentration of 3000 ppm to create a stock solution before diluting in the 50% sucrose solution. Thus, the control treatments contained 3% methanol (equivalent to that found in our pesticide solutions). Treatments, collections, and behavioral bioassays To determine the effects of pesticide exposure on queen-worker chemical communication, we performed three different experiments examining the retinue response of workers. The retinue response is defined as attraction over short distances to the queen or a pheromone lure, and subsequent licking and antennating of the queen/lure

37 22 (Slessor et al. 1988, Pankiw et al. 1994, 1995, Richard et al. 2007). Previous studies have demonstrated that worker s retinue response is very sensitive to both changes in the chemical profiles of the mandibular glands (Richard et al. 2007, Kocher et al. 2008, Niño et al. 2012), and the physiological state of the worker (Kocher et al. 2010, Fussnecker et al. 2011). In experiment 1, both queens and workers were fed 1 ml/day of the treatment diet and the retinue responses of the workers to the queens were recorded (see below for experimental details). Upon completion of the experiment, queens were collected onto dry ice, the queen mandibular glands (QMGs) were dissected, and the QMGs and CHCs were extracted and chemically analyzed (see below). In experiment 2, we examined the responses of untreated workers to QMG extracts of the control and treated queens. In experiment 3, we examined the responses of control and treated workers to synthetic queen mandibular pheromone (QMP) (Pherotech, Canada). All bioassays were conducted in a controlled walk-in environmental chamber set at 50% R.H. and 34.5 C and equipped with red-lights. Experiment 1 Live queen retinue response bioassays were performed in June 2010 and June Frames of late-stage pupating workers were collected from colonies of honey bees (purchased from Spell Bee Company, Baxley, GA) maintained in the apiaries at Penn State University according to standard commercial practices. Workers emerged in incubators under controlled humidity and temperature (50% R.H., 34 C). Cages were

38 23 established containing 30 newly-emerged (<24 hours old) workers and one queen (purchased from BeeWeaver Apiaries, Lynn Grove, TX). Workers from two colonies were combined and randomly distributed among the cages. Cages were constructed using two paired 100mm (width) x 20mm (height) plastic Petri dish tops or bottoms (VWR, Radnor, PA) with a 15 cm (height) x 30 cm piece of metal screen formed into a cylinder. Holes for the pesticide feeders and cage maintenance were punched into the Petri dish tops using a hot metal cork borer. Twelve cages for each of the three treatments were established and maintained for a seven day period. Each cage was fed 50% sucrose on day one, followed by its respective treatment for days 2-7. On day seven, cages were observed for retinue response. In 2010, 12 cages for each treatment were observed, and in 2011, 10 cages for methanol, 8 for coumaphos, and 11 for fluvalinate were observed. The number of workers attending the queen (licking and/or antennating) was recorded a total of seven times over two years (three times in 2010 and four times in 2011 at 20 minute intervals). Data were log2 transformed and analyzed using a repeated measures ANOVA with time as the repeated variable (JMP 9, SAS, Cary, NC). At the end of the seven days, the queens were collected directly on dry ice and stored at -80 C for chemical analysis of the cuticular hydrocarbons (CHC) and queen mandibular glands (QMG). Experiment 2 QMGs from the live retinue response bioassay queens were dissected and extracted. Queen heads were dissected on dry ice and placed in an ultralow freeze dryer

39 24 (Labconco Freezone 2.5 plus, Kansas City, MO) for 60 minutes under vacuum at -86 C. The heads were removed and the QMG glands dissected. If the glands could not be cleanly dissected, they were removed from further analysis. Dissected glands (two per queen) were immediately placed in a 250 µl pulled point glass insert (Agilent, Santa Clara, CA) in an amber two ml vial (12 mm x 32 mm, Fisher) and capped with a PTFE/red silicone septa (Agilent). 50 µl GC-grade diethyl ether was placed in the microvial with QMGs for 24 hours at 4 C. A five µl portion was removed for later chemical analysis. The remaining 45 µl was evaporated and redissolved in 90 µl GCgrade hexane (0.9 queen equivalent). The day before the retinue response bioassay, the QMG extracts of seven queens for each of the three treatments were pooled together in their respective treatment. The assay was replicated for the 2010 and 2011 queens. Retinue response bioassays of untreated workers to the extracts of the queen mandibular glands (QMG) of control and treated queens were performed in July 2011 and June Cages were established containing 30 newly-emerged workers (<24 hours old) according to Niño et al. (2012). Workers (purchased from Spell Bee Company, Baxley, GA) were placed in individual Plexiglas cages (10 x 10 x 7 cm) with a small portion of MegaBee pollen supplement (S.A.F.E. R&D, Tucson, AZ), a 1.7 ml Eppendorf tube with 50% sucrose solution, and a 1.7mL Eppendorf tube with distilled water. Workers were reared in the presence of 0.1 queen equivalents (Qeq) synthetic QMP, (dissolved in isopropanol and placed on a glass cover slips) to prevent abnormal changes to worker physiology (Grozinger et al. 2003, Fischer and Grozinger 2008, Niño et al. 2012). The sucrose, water, and synthetic QMP were replaced daily at approximately the same time in the morning.

40 25 On day seven, retinue response assays were performed at 10 AM. The synthetic QMP was removed and discarded. A choice test was performed to determine the preference of unexposed workers to either the gland extracts of the methanol control or pesticide treated queens. The experiment included three paired choice tests: methanol control vs hexane (solvent blank), methanol control vs coumaphos, and methanol control vs fluvalinate. A total of nine cages/comparison for 2011 (2010 QMG extracts) and ten cages/comparison for 2012 (2011 QMG extracts) were set up according to Richard et al. (2007). Five µl of pooled extract (0.05 Qeq) was placed on each cover slip and allowed to evaporate before being placed in the cages. Once the two-slip choice test was placed in the cages, there was a five minute acclimation period before any observations were recorded. Following the acclimation period, observations were recorded for each cage over a 20 minute period totaling five observations. Data was log2 transformed and statistical analysis was performed using a repeated measures ANOVA, Tukey-HSD with time as the repeated variable (JMP 9, SAS, Cary, NC). Experiment 3 In order to determine whether worker exposure to pesticides affect attraction to the queen, 30 newly emerged (<24 hours old) workers (purchased from Spell Bee Company, Baxley, GA) were fed pesticides and tested for responses to synthetic QMP in June Cages were constructed using a 150 mm (width) x 20 mm (height) Petri dishes. Each cage was designed with a mesh-screened hole for ventilation, a hole for the pesticide feeder, and two slits at the base of the side for the cover slips. The treatments

41 26 were fed to the cages and replaced daily, as described above in experiment 1. Workers were reared with synthetic QMP at 0.1 Qeq, which was replaced daily. On day seven, retinue response bioassays were performed. Each treatment was presented with either synthetic QMP at 0.05 Qeq, or isopropanol (solvent blank). A total of 10 cages for each comparison were established. Once the two-slip choice test was placed in the cages, there was a five minute acclimation period before any observations were recorded. Following the acclimation period, observations were recorded for each cage over a 20 minute period totaling 5 observations. Worker responses to the synthetic QMP were log2 transformed and analyzed using a repeated measures ANOVA, Tukey- HSD with time as the repeated variable (JMP 9, SAS, Cary, NC). Chemical Analysis Queen mandibular gland analysis Chemical analysis of the QMGs was adapted from the protocol described in Richard et al. (2007). Five µl (0.1 Qeq) of QMG extract for each queen was placed in a new 250 µl glass insert and inserted into a 2 ml amber gas chromatography (GC) vial. For each vial, the sample was gently evaporated in a hood. A new 50 ul of diethyl ether containing undecanoic acid (99.0%, Fluka Analytical) as an internal standard at a concentration of 151 ng/µl was placed in each vial. Each sample was gently evaporated in a hood. Once evaporated, the residue was silylated overnight using five µl neat bistrimethylsilyltri-fluoroacetamide (BSTFA, Sigma-Aldrich), to form trimethylsilyl

42 27 (TMS) derivatives for improved compound volatility, resolution and quantification by GC (Keeling et al. 2003). Once fully silylated, each sample was diluted with 100µL GCgrade hexanes. A total of 18 methanol queens (2010-8, ), 16 coumaphos queens (2010-9, ), and 20 fluvalinate queens (2010-9, ) were used in our analysis. Following gland dissection, extraction, and silylation in 2010, a one µl of derivatized sample was injected onto GC-FID (Gas Chromatography-Flame Ionization Detection, Agilent 6890N) using an HP-1 column (15 m x 250 µm x 250 nm) in splitless mode. Helium was used as the carrier gas at a head pressure of 9.75 psi and a flow rate of 0.8 ml/minute. The GC temperature was held at 45 C for one minute and increased 10 C/min. to 300 C (held for 5 minutes). The injector was set at 260 C. Samples were also injected onto a GC-MS (Gas Chromatography-Mass Spectrometry, Agilent 5973 Mass Selective Detector) for peak identification. Similar parameters were used for GC-MS as with the GC-FID with the exception of the column (HP-1MS- 30 m x 250 µm x 250 nm). After the results were examined, we noticed that two of the peaks in our samples did not separate using an HP-1 column on the GC-FID. Thus, we chose a different column in 2011 using a HP-5MS column (30 m x 250 µm x 250 nm) with identical parameters as Compound identities between the two years of samples were confirmed by matching the mass spectra between years. Fifteen confirmed compounds representing at least 94% of the total volume of QMG compounds for 2010 and at least 89% for 2011 were included in our peak quantification and identification. Each individual compound quantity and proportion (relative to the 15 selected compounds) across the three

43 28 treatments was transformed and analyzed using an ANOVA. A linear discriminant analysis (LDA), used to calculate the Mahalanobis distance from each compound to form a multivariate mean for each sample, was performed to determine differences between our treatment groups. All statistical analyses were performed in (JMP 9, SAS, Cary, NC). Queen cuticular hydrocarbon analysis The queens dissected for QMG chemical analysis were also used for cuticular hydrocarbon (CHC) analysis. A total of 21 methanol queens ( , ), 20 coumaphos queens ( , ), and 21 fluvalinate queens ( , ) were processed and analyzed in the summer of The decapitated bodies were placed in a 2 ml glass vial in 1mL pentane for five minutes. The queens were removed and the pentane was evaporated using nitrogen gas. After the sample was completely evaporated, the CHC residue was re-dissolved in 100 µl hexane containing C36 hydrocarbon (99% purity, source unknown) as an internal standard at a concentration of 50 ng/ul. The sample was transferred into a micro-vial and placed into a new 2 ml vial prior to being injected into the GC. For GC-FID analysis, one µl of sample was injected onto a HP-5MS column (15 m x 250 µm x 250 nm) in splitless mode. Helium was used as the carrier gas at a head pressure of 10.1 psi and a flow rate of 0.8 ml/minute. The GC temperature was held at 50 C for one minute. The first ramp in temperature increased the oven 30 C/min to 150 C, followed by a second ramp of 10 C/min to 300 C and held for 20 minutes. The injector was set at 260 C. Samples were also injected onto the GC-MS for compound

44 29 identification using the identical column and method as used on the GC-FID. GC compound quantification and identification focused on the ten most abundant compounds (relative to the total quantity of compounds for each sample) to determine whether pesticide exposure altered queen CHC profiles. The same ten compounds were found to be most abundant in both years of analysis. Each individual compound quantity and proportion across the three treatments over both years of samples was transformed and analyzed using a two-way ANOVA, with treatment and year as a variable (JMP 9, SAS, Cary, NC). Results Behavioral assays Experiment 1 Cages of queens and workers were treated with methanol (solvent control), 100 ppm fluvalinate, or 100 ppm coumaphos. Retinue responses of the workers to the queens were monitored in these cages in two separate trials, conducted in 2010 and Since there was a significant treatment x year interaction (ANOVA, repeated measures: F 2,194 =4.22, p= ), data from the two years were analyzed separately. In 2010, there were significant differences in the retinue response among the three treatment groups (Figure 2-1, ANOVA, repeated measures: F 2,33 =4.84, p=0.0143). Posthoc Tukey-HSD pairwise comparisons revealed that the response in the fluvalinate treated cages was

45 Number of workers in retinue mean ± SE 30 significantly lower than responses in methanol treated cages (p = ), while responses in coumaphos treated cages were intermediate to methanol (p = ) and fluvalinate (p = ). In 2011 (one additional time point was observed) there was no significant difference among treatments (Figure 2-2, ANOVA, repeated measures: F 2,26 =1.45, p=0.2530). 3 A AB 2 B 1 0 Methanol Coumaphos Fluvalinate Figure 2-1 Retinue responses in treated cages of workers and queens, Cages of 30 workers and one queen were fed sucrose solutions containing methanol, 100 ppm coumaphos, or 100 ppm fluvalinate for 7 days. On day 7, the number of workers performing the retinue response was observed at three time points over a 40 minute period. 12 cages were observed for each treatment. Significantly fewer workers performed the retinue response in the fluvalinate treatment (repeated measures ANOVA, F 2,33 =4.84, p=0.0143). Tukey-HSD revealed fluvalinate was lower than control (p = ) with coumaphos being intermediate between the two treatments.

46 Number of workers in retinue mean ± S.E Methanol Coumaphos Fluvalinate Figure 2-2 Retinue responses in treated cages of workers and queens, Cages of 30 workers and one queen were fed sucrose solutions containing methanol, coumaphos, and fluvalinate for 7 days. On day 7, the number of workers responding to the queen was observed at four time points over 60 minutes.10 cages for methanol, 8 for coumaphos, and 11 for fluvalinate were observed for each treatment. There was no significant difference in retinue response across the treatments (repeated measures ANOVA, F 2,26 =1.45, p=0.2530) Experiment 2 We examined the responses of untreated workers to the mandibular gland extracts of the methanol, coumaphos, and fluvalinate treated queens collected in 2010 and Since there was a significant treatment x year interaction, data from each year was analyzed separately. In 2011 (using extracts derived from queens collected in 2010), workers were significantly more attracted to the QMG extract of methanol treated queens compared to the hexane blank (Figure 2-3; ANOVA, repeated measures: F 1,16 =86.04, p<0.0001). There were no significant differences in the retinue responses to QMP extracts from the methanol control versus coumaphos treated queens (F 1,16 =0.2073, p=

47 Number of workers in retinue mean ± S.E ) or the methanol control versus fluvalinate treated queens (F 1,16 = , p=0.4537). In 2012 (using extracts derived from queens collected in 2011), workers were again significantly more attracted to the QMG extract of methanol treated queens compared to the hexane blank (Figure 2-4; F 1,18 =556.91, p<0.0001). There was no significant difference in the retinue response to the QMG extracts of the methanol compared to fluvalinate treated queens (F 1,18 =0.9064, p=0.3537). However, workers were significantly less attracted to the QMG extracts of the coumaphos treated versus methanol queens (F 1,18 =5.95, p<0.0253). 6 A B 0 Methanol Hexane Methanol Coumaphos Methanol Fluvalinate Figure 2-3 Responses of untreated workers to the queen mandibular gland extracts of 2010 treated queens. 30 workers were placed in a cage, reared in the presence of synthetic QMP, and fed 50% sucrose solution and water daily for a period of 7 days. On day 7, a choice test of two QMG extracts was presented to the caged workers. The number of workers responding to each of the extracts was recorded at five time points over 20 minutes. 9 replicates were performed for each paired QMG comparison. There was no significant difference of worker retinue towards the QMG extracts between our control and the two pesticide treatments coumaphos (ANOVA, repeated measures: F 1,16 =0.2073, p= ) and fluvalinate (F 1,16 =0.5896, p=0.4537). There was a significant difference of worker retinue towards our control and the non-solvent blank (F 1,16 =86.04, p<0.0001).

48 Number of workers in retinue mean ± S.E A A B 1 0 B Methanol Hexane Methanol Coumaphos MethanolFluvalinate Figure 2-4 Responses of untreated workers to the queen mandibular gland extracts of 2011 treated queens. 30 workers were placed in a cage, reared in the presence of synthetic QMP, and fed 50% sucrose solution and water daily for a period of 7 days. On day 7, a choice test of two QMG extracts was presented to the caged workers. The number of workers responding to each of the extracts was recorded at five time points over 20 min. 10 replicates for each paired QMG comparison were performed. There was a significant difference of worker retinue response to the QMG extracts of methanol control versus coumaphos treated queens (ANOVA, repeated measures: F 1,18 =5.95, p<0.0253), but no significant differences in response to the QMG extracts of methanol control vs fluvalinate treated queens (F 1,18 =0.9064, p=0.3537). There was a significant difference of worker retinue towards our control and the non-solvent blank (F 1,18 =556.91, p<0.0001). Experiment 3 We next examined whether treatment altered the responses of workers to synthetic QMP. Though there was a reduced response in the coumaphos and fluvalinate treated groups relative to the controls (~20% reduction), the number of workers responding to the synthetic QMP was not significantly different among the treatment groups (Figure 2-5, ANOVA, repeated measures: F 3,36 =1.32, p= ). Note that

49 Number of workers in retinue mean ± S.E. 34 worker attraction to isopropanol was negligible (less than 0.25 bees responding on average, data not shown) Sucrose Methanol Coumaphos Fluvalinate Figure 2-5 Effect of treatment on worker retinue responses to synthetic queen mandibular pheromone (QMP). 30 workers were placed in a cage and were fed sucrose solutions containing methanol, coumaphos, and fluvalinate for 7 days. On day 7, the number of workers responding to the glass cover slip containing synthetic QMP was observed at 5 time points over 20 min. Each treatment contained 10 replicates. There was no significant difference of worker retinue towards the queen (repeated measures ANOVA, F 3,36 =1.32, p=0.2835) Chemical analysis Queen mandibular gland analysis The QMGs of queens from the three treatment groups in both 2010 and 2011 were characterized by GC and GC-MS. 15 compounds (making up at least 89% of each sample) were used in the subsequent analyses. A linear discriminant analysis (LDA) using either quantities (Figure 2-6) or relative proportions (Figure 2-7) of these 15

50 35 compounds did not reveal any significant differences and prevented the assignment of samples to their correct treatments (Table 2-1). Figure 2-6 Effects of treatment on the quantities of compounds in the mandibular glands. A Linear Discriminant Analysis of the quantity of QMG compounds from three treatment groups did not reveal any significant treatment effect.

51 Figure 2-7 Effects of treatment on the relative proportions of compounds in the mandibular glands. A Linear Discriminant Analysis of the quantity of QMG compounds from three treatment groups did not reveal any significant treatment effect. 36

52 37 Table 2-1 Number of misrepresented samples in QMG linear discriminant analysis. Of the 18 Methanol samples, 16 coumaphos samples, and 20 fluvalinate samples, there was no significant separation between the treatment groups. Actual rows by predicted columns in QMG analysis Quantity Proportion Treatment Methanol Coumaphos Fluvalinate Methanol Coumaphos Fluvalinate Methanol Coumaphos Fluvalinate While we observe a trend for higher total quantities of compounds in the glands of treated queens vs controls, this difference was not significant (Figure 2-8, ANOVA, F=0.6502, p=0.5265). A LDA grouping the QMG samples by year ( samples, samples) found significant separation in both quantity and proportion with no samples being misrepresented (data not shown). Queens in 2010 produced significantly higher quantities than queens in 2011 (F=4.2113, p=0.0456), but there was no interaction effect between year and treatment (F=0.4703, p=0.6276).

53 Total QMG compounds (µg) mean ± S.E Methanol Coumaphos Fluvalinate Figure 2-8 The effect of treatment on the total quantity of queen mandibular gland (QMG) compounds. The total quantity of compounds was summed for each queen. The number of queens used in each analysis is noted at the base of the bar for each treatment. There was no significant effect of treatment on the total amount of compounds produced in the QMG (ANOVA, F=0.6502, p=0.5265). Next, we examined quantities and relative proportions of the 15 compounds individually across the treatment groups. In terms of compound quantity, there was a significant effect of year for many compounds (Table 2-2). However, while quantities of many compounds tended to be higher in the pesticide treated queens, there were no significant effects of treatment (Table 2-2) including each of the five QMP components (9-ODA, ±9-HDA, HOB, and HVA). In terms of relative proportions, we similarly found a significant effect of year on many compounds, but no significant effect of treatment (Table 2-3).

54 Table 2-2 Analysis of the derivitized QMG compound quantity (mean ± S.E.) in ng from queens treated with methanol, coumaphos, or fluvalinate. There were no significant differences in any of the 15 compounds analyzed in the QMG, including any of the QMP components (listed in bold) (One-way ANOVA- significant values are highlighted in yellow). R.T. Methanol n=18 Coumaphos n=16 Fluvalinate n=20 ANOVA: p-value Mean ±S.E. Mean ±S.E Mean ±S.E year tmt year x tmt HOB HOAA hydroxybenzoic acid ODA HVA < oxodecanoic acid hydroxy-3- methoxybenzoic acid HDA HDAA HDA Unknown < Unknown Unknown Unknown Unknown HOB: Methyl-p-hydroxybenzoate, HOAA: 8-hydroxy octanoic acid, 9-ODA: 9-oxo-2-decenoic acid, HVA: Homovanillyl alcohol, 9-HDA: 9-hydroxy-2-decenoic acid, 10-HDAA: 10-hydroxy decanoic acid, 10-HDA: 10-hydroxy-2-decenoic acid

55 Table 2-3 Analysis of the derivitized QMG compound proportion (mean ± S.E.) in ng from queens treated with methanol, coumaphos, or fluvalinate. There were no significant differences in any of the 15 compounds analyzed in the QMG, including any of the QMP components (listed in bold) (significant values are highlighted in yellow). R.T. Methanol n=18 Coumaphos n=16 Fluvalinate n=20 ANOVA: p-value Mean ±S.E Mean ±S.E Mean ±S.E. year tmt year x tmt HOB % 0.2% 1.2% 0.2% 1.2% 0.2% HOAA % 0.7% 10.0% 0.9% 8.5% 0.7% < hydroxybenzoic acid % 0.2% 1.0% 0.2% 0.8% 0.2% < ODA % 2.3% 39.2% 1.8% 45.0% 2.3% HVA % 0.1% 0.3% 0.1% 0.6% 0.2% oxodecanoic acid % 0.1% 0.8% 0.0% 0.8% 0.1% hydroxy-3- methoxybenzoic % 0.0% 0.6% 0.0% 0.7% 0.0% < acid 9-HDA % 2.4% 38.0% 1.8% 33.7% 1.8% HDAA % 0.1% 0.7% 0.1% 0.7% 0.1% HDA % 0.5% 5.8% 0.6% 5.3% 0.6% < Unknown % 0.1% 0.3% 0.1% 0.4% 0.1% < Unknown % 0.0% 0.4% 0.1% 0.4% 0.0% Unknown % 0.0% 0.4% 0.0% 0.4% 0.0% Unknown % 0.1% 0.7% 0.1% 0.8% 0.1% Unknown % 0.1% 0.5% 0.1% 0.6% 0.1% < Queen cuticular hydrocarbon analysis The total quantity of the top ten most abundant CHC compounds revealed no significant difference between our three treatments (ANOVA, F2=0.8893, p=0.4167), however the coumaphos treatment does trend downwards in total quantity (data not shown). When analyzing the top 10 compounds individually across the treatment groups, there were no significant differences in terms of quantities (Table 2-4) or proportions

56 (Table 2-5) of compounds, though again, many compounds exhibited a significant effect of year. 41 Table 2-4 Analysis of the queen cuticular hydrocarbon (CHC) compound quantity (mean ± S.E.) in µg from queens treated with methanol, coumaphos, or fluvalinate. There were no significant differences in any of the top 10 analyzed compounds. R.T. Methanol n=21 Coumaphos n=20 Fluvalinate n=21 ANOVA: p-value Mean ± S.E. Mean ± S.E. Mean ± S.E. year tmt year x tmt Tricosane Pentacosane < Heptacosane Nonacosane Tetradecanoic acid Unknown Hentriacontane < Unknown Unknown Unknown Table 2-5. Analysis of the queen cuticular hydrocarbon (CHC) compound proportion (mean ± S.E.) in ng from queens treated with methanol, coumaphos, or fluvalinate. There were no significant differences in any of the top 10 analyzed compounds.

57 42 R.T. Methanol n=21 Coumaphos n=20 Fluvalinate n=21 ANOVA: p-value Mean ± S.E. Mean ± S.E. Mean ± S.E. year tmt year x tmt Tricosane % 1.1% 5.4% 0.6% 5.4% 0.8% Pentacosane % 1.0% 12.2% 1.2% 11.2% 1.2% < Heptacosane % 1.3% 30.3% 1.1% 30.9% 1.3% Nonacosane % 0.9% 17.6% 1.3% 18.3% 1.3% < Tetradecanoic acid % 0.4% 2.9% 0.3% 3.1% 0.5% Unknown % 0.2% 2.6% 0.2% 2.8% 0.3% Hentriacontane % 1.5% 16.6% 1.6% 14.7% 1.4% < Unknown % 0.6% 4.0% 0.4% 5.0% 0.9% Unknown % 0.5% 6.4% 0.3% 6.3% 0.5% Unknown % 0.7% 1.9% 0.2% 2.4% 0.5% Discussion We examined the effects of coumaphos and fluvalinate exposure on queen pheromone production and worker behavior in a series of behavioral bioassays and chemical analyses. This is the first time to our knowledge that the effects of pesticides on queen pheromone production and worker behavioral responses to pheromones have been explored. Overall, our results suggest that the impacts of coumaphos and fluvalinate on the queen-worker chemical communication system are relatively minor. However, it is important to note that our assays were conducted in cages, and thus there may be differential effects in full colonies. Furthermore, there have been several studies that demonstrated negative impacts of exposure to these pesticides during queen development (Haarmann et al. 2002, Collins et al. 2004, Pettis et al. 2004) and thus the effects of preadult exposure on queen pheromone production should also be explored.

58 43 Previous studies have found that the retinue response of workers can be quite variable, and is affected by genetic background, season, behavioral state (nurse vs forager), worker ovary size, and treatment with physiological factors that are associated with behavioral maturation (Pankiw et al. 1994, Pankiw et al. 1998, Keeling et al. 2003, reviewed in Slessor et al. 2005, Grozinger and Robinson 2007). However, in our study, chronic feeding with coumaphos and fluvalinate only slightly decreased response to synthetic QMP. Similarly, treated workers exposed to treated queens also did not have consistently different responses across the two trials than control workers and queens. Studies with other pesticides (imidacloprid, deltamethrin, fipronil, fluvalinate, imidacloprid, endosulfan, prochloraz, and thiamethoxam) significantly impact multiple aspects of worker behavior learning and memory, orientation, locomotion, and feeding (Decourtye et al. 2004, Decourtye et al. 2005, Aliouane et al. 2009, Henry et al. 2012, Teeters et al. 2012). However, the impacts of chronic adult feeding of sublethal doses of coumaphos and fluvalinate on complex behaviors have not been examined. Thus, it is possible that at the dosages we used, there was no discernable effect on behavioral parameters, or, more unlikely, the retinue response is a highly robust behavior that is less likely to be perturbed than other behaviors, such as olfactory learning, foraging, and feeding. It is important to note that microarray studies of gene expression patterns in the workers from the pesticide-treated cages revealed coumaphos and fluvalinate exposure significantly altered expression of more than 1000 genes, and thus these treatments clearly had large scale molecular and physiological impacts (Schmehl, Teal, Tumlinson and Grozinger, unpublished data).

59 44 Similarly, the chemical profiles of the queen mandibular glands have also been previously found to be quite variable, and significantly impacted by mating, ovary activation, insemination volume and substance, and infection with parasites ((Richard et al. 2007, Kocher et al. 2009, Alaux et al. 2011, Niño et al. 2012) However, there were no significant impacts of coumaphos and fluvalinate on chemical composition, in terms of quantities and relative proportions. There was a non-significant increase in the quantities of many individual chemicals in pesticide exposed queens, but the effects of year on both quantities and relative proportions were much more pronounced. Similarly, responses of untreated workers to the QMG extracts of treated and control queens were also not consistently significantly different, though there was a trend for slightly reduced responses to the extracts from pesticide treated queens. Cuticular hydrocarbon profiles are exquisitely sensitive to environment, genotype, physiology, diet, and even social state (Breed et al. 1988, Singer 1998, Howard and Blomquist 2005, Fan et al. 2010). However, again CHCs do not appear to be affected by treatments both in terms of quantity or proportion in our studies. It is important to explore this question further, as pesticide contaminated hives may alter the CHC profiles of the whole colony. Whether this has an impact on nest mate recognition is unlikely, since worker CHCs would probably all change according to their altered environment. Coumaphos and fluvalinate have little effect, both on worker retinue and queen chemistry. Even though we did not find significant differences in our treatments, increases in pesticide dose may in fact impact queen health and pheromone communication. However this is improbable in nature since the concentration of coumaphos and fluvalinate (100 ppm) were worst-case scenario level doses. Most

60 45 colonies do not experience doses this high. Our minor observed pesticide effect may suggest that pesticide resistance may be occurring through activation of detoxification pathways. Through rapid pesticide detoxification, changes to queen physiology and worker retinue may be inhibited, yet this has to be tested to further understand the validity of this connection. Further, coumaphos and fluvalinate may have a greater impact at the colony level than in a controlled cage experiment. In the colony, the bees are bombarded by stressors ranging from inadequate nutrition and pathogens/parasites to additional pesticides. We know that coumaphos and fluvalinate can interact synergistically with one another (Johnson et al. 2009b), as well as interact both synergistically and antagonistically with other pesticides across pesticide classes (Zhu et al. 2013). Even if the individual pesticide effect on queens is minor, exposure to over 120 pesticides (Mullin et al. 2010) may have a profound effect. The next steps to further examine the impact of pesticides on the colony social dynamics will require field experiments at the colony level. A colony level approach, though difficult to control for variables, will provide a more thorough understanding of the interactions between pesticides and other pesticide, nutritional, and environmental, and biological stressors.

61 46 Chapter 3 Effects of almond and wildflower pollen on colony health and communication in honey bees Authors: Daniel R. Schmehl, Ramesh Sagili, Peter Teal, Christina Grozinger, and James Tumlinson Abstract Honey bee pollination services are vital for key economic crops such as almonds, which require over one million colonies for pollination annually. Among several other factors, poor nutrition has been associated with colony losses. In addition to decreased abundance of flowering plants, decreased diversity may also lead to poor nutrition. Here we examined the effect of feeding on a single source (almond) vs multi-source (wildflower) pollen on honey bee colony growth, worker behavioral maturation, queen pheromone production, and worker responses to the queen. While no significant impact on colony size, queen-worker pheromone-mediated interactions, or juvenile hormone pathways, pollen source did significantly impact homovanillyl alcohol (HVA) production, a key component of queen pheromone. This increase of HVA production in wildflower pollen-fed colonies may greatly influence long-term queen success and colony survival. A better understanding of the contribution of pollen source on honey bee

62 47 fitness is crucial for further understanding the recent decline of honey bee health in the U.S. Introduction Honey bees (Apis mellifera L.) account for over $11 billion annually (Calderone 2012), with most of these pollination services requiring extensive transportation of colonies to the specific crop in need of pollinators. Over several decades a steady decline of honey bee populations has occurred with no clear causative agent (Council 2007, VanEngelsdorp et al. 2009, Johnson et al. 2010). Poor nutrition has recently been associated with colony losses (vanengelsdorp et al. 2008a), and suggest that adequate and diverse floral resources may be the key element in mediation of honey bee resistance to stressors, including parasites, diseases, and pesticide exposure (Brodschneider and Crailsheim 2010). Here, we examine the relationship of nutrition to honey bee health by examining the effects of feeding on pollen from a single source (almond) versus multiple sources (wildflower) on colony health, worker physiology, and queen-worker chemical communication. Honey bees require a pollen and nectar diet which can vary considerably depending on plant source (reviewed in Haydak 1970). Adequate nutrition is essential to maintaining the health of the colony. Nectar is primarily a source of carbohydrates and provides energy for flight and metabolic processes, while pollen provides proteins, lipids, vitamins, and minerals (Haydak 1970, Brodschneider and Crailsheim 2010, Nicolson 2011). Pollen is the most important requirement for colony growth because it provides

63 48 the critical proteins needed, especially for brood development (Stanley and Linskens 1974). Pollen can vary drastically in protein content, as well as other essential vitamins and amino acids (Roulston et al. 2000, Nicolson 2011). Pollen provides the critical proteins needed for brood development (Stanley and Linskens 1974), and thus is a critical factor for colony growth. Indeed, during times of pollen deficiencies in the hive, adult bees can cannibalize brood for the protein needs of the colony and cease brood production (Schmickl and Crailsheim 2001, Brodschneider and Crailsheim 2010). In adult honey bees, pollen is necessary for flight muscle development, hypopharyngeal gland secretion production, and ovary activation (Hersch et al. 1978, Alqarni 2006, Hoover 2006, Brodschneider and Crailsheim 2010). Hypopharyngeal glands convert the protein from consumed pollen into brood food (Hrassnigg and Crailsheim 1998) and hypopharyngeal size is directly correlated with the amount of protein consumed (Sagili et al. 2005, Sagili and Pankiw 2007, DeGrandi-Hoffman et al. 2010). However, pollen can vary drastically in overall protein content and concentrations of essential vitamins and amino acids (Roulston et al. 2000, Nicolson 2011), suggesting that bees need to collect a variety of pollen types to meet their nutritional needs. Willard et al. (2011) observed that colonies fed a poor diet (1:1 pollen:cellulose) food source produced less brood, however no changes to adult longevity were observed. Similarly when comparing pollen substitute diets, poor nutrition led to a reduced number of brood (Degrandi-Hoffman et al. 2008). Inadequate nutrition can lead to precocious foraging, which contributes to a shorter lifespan for workers (Janmaat and Winston 2000, DeGrandi-Hoffman et al. 2010). Colonies that were pollen-deprived have workers with lower lipid stores, which accelerates behavioral maturation and leading to earlier foraging behavior by young bees

64 49 (Janmaat and Winston 2000, Toth et al. 2005). Titers of juvenile hormone are positively correlated with the onset of foraging behavior (Pankiw et al. 1998, Sullivan et al. 2000), and accelerated behavioral maturation is associated with increased amounts of juvenile hormone III (JH) in the hemolymph (Sullivan et al. 2000). Thus, nutritionally deprived workers are expected to have higher levels of JH. Poor nutrition also reduces the ability of worker bees to defend against pathogens, parasites, and pesticides (Wahl and Ulm 1983, Janmaat and Winston 2000, Alaux et al. 2010b). Deformed Wing Virus (DWV) was found to be significantly more prevalent in nutritionally stressed colonies (DeGrandi-Hoffman et al. 2010). Nutrition-deprived colonies increase the sensitivity to pesticide exposure (Wahl and Ulm 1983). Bees fed either no protein or a poor protein source, such as dandelion pollen, have a significantly higher rate of mortality than bees fed a mixed protein source (Wahl and Ulm 1983). Lastly, workers reared in a colony with low pollen stores were found to begin foraging sooner in the presence of Varroa mites, than in the absence of Varroa mites (Janmaat and Winston 2000). The health and physiology of queen honey bees may also be impacted by poor nutrition. Queens produce a multi-component pheromone from multiple glands that communicates information to the colony (Plettner et al. 1997, Wossler and Crewe 1999, Katzav-Gozansky et al. 2001, Katzav-Gozansky et al. 2004, Kocher and Grozinger 2011). Queen pheromone is critical for colony organization, as it regulates key elements of colony organization, including reproductive and worker division of labor (behavioral maturation). While not as active as a live queen (Pankiw et al. 1995), the extracts of the mandibular glands can elicit many of the same behavioral and physiological responses in

65 50 worker bees (Keeling et al. 2003). In particular, a five component blend produced in these glands, termed queen mandibular pheromone (QMP), has been shown to be behaviorally active (Slessor et al. 1988) Queen pheromone and/or QMP stimulates attraction over short distances (termed the retinue response ) (Keeling et al. 2003), inhibits the activation of worker ovaries (Hoover et al. 2003), orients comb building (Ledoux et al. 2001), and reduces the biosynthesis of juvenile hormone (JH) in workers (Kaatz et al. 1992, Pankiw et al. 1998), thereby slowing down the transition to foraging (Pankiw et al. 1998). Furthermore, workers reared in the presence of QMP have significantly greater lipid stores and survival during starvation periods than control workers, suggesting that QMP modulates nutrition-responsive pathways (Fischer and Grozinger 2008). It is unclear whether colonies fed monofloral pollen, rather than a multifloral wildflower diet, is harmful to honey bee health. Glucose oxidase, which helps sterilize food within the colony and may protect the colony from infection, was found at significantly higher levels in polyfloral pollen diets compared to monofloral pollen diets (Alaux et al. 2010b), but studies of general impacts on the health of individual bees and colonies are lacking. In a natural foraging setting, honey bees forage on a diverse variety of floral resources. However, with the development of large monocropping systems, farmers have become increasingly dependent of pollinator services of managed honey bees (Morse and Calderone 2000, Ratnieks and Carreck 2010, Calderone 2012). Beekeepers will transport colonies large distances and install them temporarily in these agricultural fields, where the bees can only primarily forage on one type of flowering plant. However, very few monocultures can provide all the essential nutrients needed for

66 51 honey bee health (reviewed in Brodschneider and Crailsheim 2010). Indeed, it is possible that one of the most critical honey bee pollinated crops, almonds, may actually have negative impacts on bees: almond pollen contains the cyanogenic glycoside amygdalin (London-Shafir et al. 2003) which is moderately toxic to honey bees (Detzel and Wink 1993). Here we examined the impact of feeding almond- versus wildflower-pollen on colony growth, worker physiology (protein and hormone levels), pheromone production in queen mandibular glands, and pheromone-mediated queen-worker interactions. Materials and Methods Nucleus colonies were established in the summer of 2011 at Oregon State University (Corvalis, OR) from naturally mated colonies. Two splits were made from one parent colony for each treatment replicate. Each pair of newly-split nucleus colonies was established with a new mated queen, five drawn frames (two pollen, two nectar/honey, one open), and approximately 10,000 workers. Each colony was treated with either single-source almond pollen or multi-source wildflower pollen. Pollen was gathered by foraging honey bees from the respective floral resource and collected from pollen traps at the hive entrance. Colonies were established in a shaded hoop house with partitions for each nucleus colony. The respective pollen was packed into a full frame comb and replaced in the colony at weekly intervals. A 50:50 sucrose:water (w:v) solution and water were provided in feeders outside the colonies. No foraging outside of the partition

67 52 was permitted for the colonies. A total of 14 replicates were performed during the summer. In six of the replicates for each treatment, 200 bees ( focal bees ) from a single colony and single genetic line (carniolan queens, Glenn Apiaries, Fallbrook, CA) were paint-marked on the thorax to be collected at day ten for hormone analysis (hemolymph) and protein/molecular analysis. However, after focal bees were paint-marked and added to colonies, it became apparent that drift between partitions/colonies was possible. Since the focal bees were marked for age and not for treatment, it is not possible to be certain that these focal bees received only one treatment they may have drifted between colonies in the two treatment groups. Queens were collected 45 days after colony establishment. For our study, we analyzed 14 replicates for colony growth, seven for worker retinue response to live queen, six for worker hormone analysis, five (wildflower)/six (almond) for protein content and 14 for queen mandibular gland chemical analysis. Experimental details for each sample group are provided below. Colony Health Colony growth was calculated for 14 colonies for each treatment by measuring the number of bees, brood area, nectar area, honey area, and pollen area 2-4 times over a four week span (corresponding to approximately 11 to 35 days after start of treatments). Three blocks of colonies were established throughout the season with the first block beginning May 21 st (seven colonies per treatment), the second on June 21 st (five colonies

68 53 per treatment), and the third on August 1 st (two colonies per treatment). Nectar, honey and pollen area were not analyzed. Colony size was determined by counting both the number of frames filled with adult bees, and the number of brood in the colony. The number of brood was determined using a 6.45 cm 2 grid. The data werelog 2 transformed and a repeated measures ANOVA was performed to examine significance between treatments (JMP 9, SAS, Cary, NC). Worker Physiology Juvenile Hormone and Methyl Farnesoate levels in hemolymph Two-hundred one-day old (<24 hours) workers were collected from a singledrone inseminated colony and marked on the thorax with model paint and placed in each nucleus colony for six replicates. Bees remained in the colonies for a total of ten days. On day 11, bees were collected from the colonies and placed in a container with ice. Once returned to the lab, the hemolymph was collected from under the 4 th abdominal segment using a ten µl pulled glass capillary tube. Hemolymph was obtained from pooled sets of bees until ten µl of hemolymph was collected (corresponding to approximately 3-5 workers). Hemolymph was placed in a 1.7 ml Eppendorf tube and combined with 90 µl of methanol (HPLC grade). The tube and cap were wrapped with Teflon tape to prevent leaking pooled samples per hive (from our 6 replicates) were collected from the colonies. The number of samples/colony was dependent on the number of marked workers that we were able to be recovered from the colonies. Across the six colony

69 54 replications, 47 samples were collected from the almond treatment and 51 from the wildflower treatment and sent immediately to Peter Teal (USDA-ARS,Gainsville, FL) for processing using Chemical Ionization Gas Chromatography-Mass Spectrometry according to the protocols described in (Teal et al. 2000, Teal and Proveaux 2006, Jones et al. 2010, Niño et al. 2012). Of the collected samples, a total of 69 samples were analyzed, with 64/69 being analyzed successfully (34 samples were analyzed for the wildflower treatment and 30 for the almond treatment). Methyl (Z)-9-tetradecenoic methyl ester (Z9-14:ME) was used as an internal standard. Data were log2 transformed and statistical analysis was performed using a nested ANOVA (JMP 9, SAS, Cary, NC), with colony as the nested variable. Protein levels in pooled worker heads To determine whether there were differences in the hypopharyngeal gland protein content in workers between treatments, we performed a protein assay on pooled heads of focal bees collected from each of the colonies using a Pierce Bicinchoninic Acid (BCA) Protein Assay Kit (Smith et al. 1985). The protocol for protein analysis was modified from DeGrandi-Hoffman et al. (2010). 10-day old marked focal bees were collected from treated colonies, as described above. Pools of 5 heads of bees from 5 of the wildflower fed colonies and from 6 of the almond fed colonieswere homogenized in 200 µl of 75 mm, 7.4 ph phosphate buffer solution (PBS) using the FastPrep (3 cycles, 30 seconds/cycle). Samples were centrifuged at 15,000 RCF for 5 minutes at 4 C. Supernatant was removed and diluted 10 fold for analysis. Each sample was run in

70 55 triplicate beside a series of bovine serum albumin (BSA) standards ranging from 0 (blank) to 2000 µg/ml. 25 µl of each standard or unknown and 200 µl of Working Reagent (50:1 ratio of BCA Reagent A (sodium carbonate, sodium bicarbonate, bicinchoninic acid, and sodium tartrate in 0.1M sodium hydroxide) and BCA Reagent B (4% cupric sulfate)) was placed in a well of a 96-well plate. The plate was placed on a shaker for 30 seconds before covering and incubating at 37 C for 30 minutes. The plate was removed from the incubator and allowed to cool to room temperature. Once cooled, plate was shaken for 10 seconds and immediately measured at 560 nm using a plate reader (Vmax Microplate Reader, Molecular Devices, Sunnyvale, CA) Concentrations were calculated by comparing each sample to the standard curve. Statistical analysis was performed using one-way ANOVA (JMP 9, SAS, Cary, NC). Queen-worker chemical communication To determine the effects of single- or multi-source pollen on queen-worker chemical communication, we performed two types of retinue response bioassays. Experiment 1examined the retinue response of cage-reared, untreated workers to the live queens of colonies fed either almond or wildflower pollen. Experiment 2 examined the retinue response of cage reared, untreated workers to the QMG extracts of the queens from either almond- or wildflower-fed colonies to determine whether changes in the QMG chemistry affected worker retinue. All bioassays occurred in a temperaturecontrolled environmental chamber set at 50% R.H. and 34.5 C and equipped with redlights.

71 56 Experiment 1 In order to measure worker attraction to queens from almond-fed or wildflowerfed colonies, we performed a worker retinue response bioassay. Cages were established according to Nino et al. (2012). Newly emerged workers (<24 hours old) from a single drone inseminated (SDI) colony (Glenn Apiaries) were collected at emergence on May 26 th, placed in individual Plexiglas cages (10 x 10 x 7 cm)cages with 30 workers/cage, and provisioned with a small portion of MegaBee pollen supplement (S.A.F.E. R&D, Tucson, AZ), a 1.7 ml Eppendorf tube with 50% sucrose solution, and a 1.7 ml Eppendorf tube with distilled water. Workers were reared in the presence of 0.1 queen equivalents (Qeq) synthetic QMP for seven days, (dissolved in isopropanol and placed on a glass cover slip) to prevent abnormal changes to worker physiology (Grozinger et al. 2003, Grozinger et al. 2007) The sucrose, water, and synthetic QMP were replaced daily at approximately the same time in the morning. All cages were labeled according to colony number and retinue response was recorded blind. On June 2 nd, queens from all seven replications from their respective colonies were gathered at 8 9 am and placed in each of the cages at 9:45 am. The queens were allowed to acclimate to the cages for one hour. At 10:45 am, we began recording the worker retinue response to the queens every five minutes for a total of ten observations. Halfway through the observations (after the fifth observation), cages on the top shelf of the incubator were moved to the bottom incubator, and the cages on the bottom shelf were shifted to the top shelf. At the end of the bioassay, queens were removed from the cages and promptly returned to their respective colonies. Data were log2 transformed and statistical analysis was performed

72 57 using a repeated measures ANOVA with time as the repeated variable (JMP 9, SAS, Cary, NC). Experiment 2 A series of retinue response bioassays to observe responses of untreated workers to the queen mandibular glands (QMG) of queens from either almond-fed or wildflower pollen-fed colonies were performed in July Bioassay cages were established according to Nino et al. (2012). Newly emerged (<24 hours old) workers (purchased from Spell Bee Company, Baxley, GA) were placed in individual Plexiglas cages (10 x 10 x 7 cm) with 30 workers/cage and provisioned with a small portion of MegaBee pollen supplement, a 1.7 ml Eppendorf tube with 50% sucrose solution, and a 1.7 ml Eppendorf tube with distilled water. Workers were reared in the presence of 0.1 queen equivalents (Qeq) synthetic QMP. The sucrose, water, and synthetic QMP were replaced daily at approximately the same time in the morning. QMGs from the queens used in the experiment 1 bioassay were dissected and extracted. Queen heads were dissected on dry ice and placed in an ultralow Labconco Freezone 2.5 plus (Kansas City, MO) freeze dryer for 60 minutes under vacuum at -86 C. The heads were removed and the QMG glands dissected. If the glands were unsatisfactory due to dissection error, they were removed from further analysis. Dissected glands were immediately placed in a micro 250 µl pulled point glass insert (Agilent Technologies, Santa Clara, CA) in an amber two ml vial (12 mm x 32 mm, ThermoFisher Scientific, Waltham, MA) and capped with a PTFE/red silicone septa

73 58 (Agilent). 50 µl GC-grade diethyl ether was placed in microvial with QMGs for 24 hours. A five µl portion was removed for later chemical analysis. The remaining 45 µl was evaporated and redissolved in 90 µl GC-grade hexane (0.9 queen equivalent). The retinue response bioassay choice tests were performed when workers reached seven days of age. The synthetic QMP was removed and discarded. A choice test was performed to determine the preference of unexposed workers to either the gland extracts of our almond queens or wildflower queens. Our experiment included three paired choice tests: synthetic QMP vs hexane (solvent blank), synthetic QMP vs pollen (pooled almond/wildflower extracts), and almond vs wildflower. The day before the retinue response bioassay, the QMG extracts of each queen for the two treatments were pooled together in their respective treatment. The pollen sample included the QMG extract of two queens for both colonies. This treatment was established to determine whether our QMG extract was more attractive to workers than a synthetic QMP blend. A total of 12 cages for each of the three comparisons were performed. Five µl of pooled extract (0.05 Qeq) was placed on each cover slip and allowed to evaporate before presenting the QMG extracts to the cages. Once the two-slip choice test was placed in the cages, there was a five minute acclimation period before any observations were recorded. Following the acclimation period, observations were recorded for each cage over a 20 minute period totaling five observations. Data were log2 transformed and statistical analysis was performed using a repeated measures ANOVA with time as the repeated variable (JMP 9, SAS, Cary, NC).

74 59 Chemical analysis of the queen mandibular gland Queens from 14 replicates between our two treatment groups were collected on dry ice and placed immediately in the -80 C freezer. Since three separate blocks of colonies were established during the summer, queens were collected at different dates (dates listed above). Chemical analysis of the QMGs was modified from the protocol described in Richard et al (2007). The five µl of QMG extract that was set aside for each queen following our QMG retinue response bioassay was placed in a new 250 µl glass insert and inserted into a 2 ml amber GC vial. For each vial, the sample was gently evaporated in a hood. A new 50 ul of diethyl ether containing undecanoic acid (99.0%, Fluka Analytical, St. Louis, MO) as an internal standard at a concentration of 151 ng/µl was placed in each vial. Each sample was gently evaporated in a hood. Once evaporated, the residue was silylated overnight using five µl neat bistrimethylsilyltri-fluoroacetamide (BSTFA) to form TMS derivatives of honey bee glandular compounds for their resolution and quantification on the GC (Keeling et al. 2003). Once fully silylated, each sample was diluted with 100 µl GC-grade hexanes. Due to technical errors, only a total of 13 almond queens and 13 wildflower queens were included in our analysis. Following gland dissection and extraction in 2010, one µl of sample was injected onto GC-FID (Gas Chromatography-Flame Ionization Detection, Agilent 6890N) using HP-5 column (30 m x 250 µm x 250 nm) in splitless mode. Helium was used as the carrier gas at a head pressure of 9.75 psi and a flow rate of 0.8 ml/minute. The GC temperature was held at 45 C for one minute and increased 10 C/min. to 300 C (held for five minutes). The injector was set at 260 C. Samples were later injected onto a GC-

75 60 MS (Gas Chromatography-Mass Spectrometry, Agilent 5973 Mass Selective Detector) for peak identification. Similar parameters were used for GC-MS. We selected 15 individual compounds representing at least 90% of the total quantity of QMG compounds in each sample in our analysis of peak quantification, proportion and identification. Each individual compound quantity and proportion across the two treatments was transformed and analyzed using an ANOVA. A linear discriminant analysis (LDA), used to calculate the Mahalanobis distance from each compound to form a multivariate mean for the sample, was performed to determine differences between our treatment groups (JMP 9, SAS, Cary, NC). Results Colony Health We measured the number of brood and adult bees to determine whether pollen source affects colony growth. A significant difference was between the three blocks of colonies, likely due to seasonal differences (F 2 = , p=0.0002). However, no significant interaction was found between treatment and block (F 2 =0532, p=0.9483) therefore treatments across blocks were analyzed together. There was no significant difference in the number of adult bees (F 1 =0.6420, p=0.4291) or brood (F 1 =0.1246, p=0.7270) between treatments (data not shown).

76 Total Protein mean ± S.E. (mg/ml 61 Worker Physiology JH/MF levels in hemolymph There were no significant differences in the JH levels (nested ANOVA- F 1,62 =0.4580, p=0.5011) or MF levels (F 1,62 =0.3288, p=0.5684). In addition, when examining the ratio of JH to MF, we also found no significant differences (F 1,62 =0.4103, p=0.5242, data not shown). Protein levels in worker heads In our protein assays, there were no significant differences between treatments (Figure 3-1) (F 1 =0.0538, p=0.8208) Almond Wildflower Figure 3-1. Protein quantity in the honey bee when fed almond or wildflower pollen. 10-day old marked bees from colonies fed either almond or wildflower pollen were collected and analyzed for protein content in the head. The heads for 5 bees from each colony for almond-fed (6 replicates) and wildflower-fed (5

77 Number of workers in retinue mean ± S.E. replicates) were dissected and homogenized, and analyzed using a BCA protein assay kit. No significant differences were observed between treatments (ANOVA, F 1 =0.0538, p=0.8208). 62 Queen-worker chemical communication Experiment 1 Queens from almond or wildflower fed colonies were placed in cages with untreated workers. The retinue response of the workers was observed in five minute intervals for a total span of 20 minutes. There was no significant difference in the number of workers responding to the almond or wildflower queens (Figure 3-2) (F 1,12 = , p= ) Almond Wildflower Figure 3-2. Worker retinue response to live queens from wildflower or almond fed colonies day old workers were placed in a cage, reared in the presence of synthetic QMP, and fed 50% sucrose solution and water daily for a period of 7 days. On day 7, queens were collected from 6 replications for each treatment and one queen was placed in each cage with the workers. After a 1 hour period of acclimation, the number of workers responding to the queen was recorded at 10 time points over a 45 minute period for each cage. The graph represents the mean number or workers responding to the queen for each treatment ± S.E. There was no significant difference in the worker retinue between our almond pollen-fed and wildflower pollenfed queens (ANOVA, repeated measures: F 1,12 =1.2604, p=0.2835).

78 Number workers in retinue mean ± S.E. 63 Experiment 2 We examined the responses of untreated workers to the mandibular gland extracts of queens from almond- and wildflower-fed colonies. No significant differences were found in the worker retinue response towards QMG extracts between our almond and wildflower treatments (Figure 3-3) (F 1,22 =0.2426, p=0.6272). There was clearly a retinue attraction to QMP relative to a solvent control (F 1,22 = 16.96, p=0.0005), and the combined almond/wildflower QMG extract relative to synthetic QMP (F 1,22 = 60.12, p<0.0001). 4 A 3 2 A 1 0 B QMP Hexane Pollen Hexane AlmondWildflower B Figure 3-3. Worker retinue response to the QMG extracts of queens from either almond fed or wildflower fed colonies day old workers were placed in a cage, reared in the presence of synthetic QMP, and fed 50% sucrose solution and water daily for a period of 7 days. On day 7, a choice test was presented to the caged workers. The number of workers responding to each of the extracts was recorded at five time points. The graph represents the mean number of workers responding ± SE. 12 replicates for each paired comparison were performed. There was no significant difference in the number of workers attending the QMG extracts when giving the bees a choice of almond or wildflower (F 1,22 = , p=0.6272).

79 64 QMG Chemistry From the GC analyses, 15 compounds (making up at least 90% of the total quantity of chemicals for each sample) from the QMG extract of queens reared in almond- or wildflower-fed colonies were analyzed across each treatment group. There was no significant difference in the quantity of total compounds produced in the QMGs between the two treatments (F 1 =0.6693, p=0.4214, data not shown). There was a significant separation between treatments when performing a linear discriminant analysis (LDA) of the quantity of the QMG chemicals (Figure 3-4). Similarly, an LDA of the relative proportions of the QMG chemicals reveals a significant separation among the two treatments (Figure 3-5). For both quantity and proportion, no samples were misrepresented in the treatment groups. Figure 3-4. Linear Discriminant Analysis of the quantity of QMG compounds from colonies of almond-fed or wildflower-fed pollen. Of the 13 samples for each treatment, no samples were misrepresented in the wrong group.

80 65 Figure 3-5. Linear Discriminant Analysis of the proportion of QMG compounds from colonies of almondfed or wildflower-fed pollen. Of the 13 samples for each treatment, no samples were misrepresented in the wrong group. When we examined the 15 compounds individually, two are significantly different in quantity (Table 3-1) and proportion (Table 3-2). Homovanillyl alcohol (HVA), one of the components of the queen mandibular pheromone (QMG), is produced in significantly greater amounts in queens from wildflower pollen-fed colonies (F 1 =6.0009, p=0.0220) and also makes up a greater proportion of the total quantity of compounds (F 1 = , p=0.0036). In addition to HVA, Unknown 1 was found to be significantly higher in both quantity (F 1 =4.7481, p=0.0394) and proportion (F 1 = , p<0.0001) in our wildflower treatment.

81 Table 3-1 Analysis of the QMG compound quantity (mean ± S.E.) in ng from colonies fed wildflower or almond pollen. There was a significant increase in the amount of HVA (F 1 =6.0009, p=0.0220) and Unknown 1 (F 1 =4.7481, p=0.0394) produced in the QMG in the wildflower. 66 Name Kovats R.T. Almond Wildflower Treatment Mean ± S.E. Mean ± S.E. p-value HOB HOAA hydroxybenzoic acid ODA HVA oxodecanoic acid hydroxy-3-methoxy benzoic acid HDA HDAA HDA Unknown Unknown Unknown Unknown Unknown Total HOB: Methyl-p-hydroxybenzoate, HOAA: 8-hydroxy octanoic acid, 9-ODA: 9-oxo-2-decenoic acid, HVA: Homovanillyl alcohol, 9-HDA: 9-hydroxy-2-decenoic acid, 10-HDAA: 10-hydroxy decanoic acid, 10-HDA: 10-hydroxy-2-decenoic acid

82 67 Table 3-2 Analysis of the QMG compound quantity (mean ± S.E.) in ng from colonies fed wildflower or almond pollen. There was a significant increase in the amount of HVA (F 1 = , p=0.0036) and Unknown 1 (F 1 = , p<0.0001) produced in the QMG in the wildflower. Name Kovats R.T. Almond Wildflower Treatment Mean ± S.E. Mean ± S.E. p-value HOB % 0.3% 3.2% 0.3% HOAA % 1.1% 12.6% 0.6% hydroxybenzoic acid % 0.0% 0.5% 0.1% ODA % 2.1% 34.5% 1.6% HVA % 0.2% 1.4% 0.1% oxodecanoic acid % 0.1% 0.7% 0.1% hydroxy-3-methoxybenzoic acid % 0.0% 0.6% 0.0% HDA % 1.8% 31.2% 1.9% HDAA % 0.3% 3.1% 0.4% HDA % 0.8% 8.5% 1.0% Unknown % 0.1% 1.4% 0.1% < Unknown % 0.1% 0.9% 0.1% Unknown % 0.0% 0.4% 0.0% Unknown % 0.1% 0.6% 0.0% Unknown % 0.0% 0.6% 0.0% Discussion In this study, we examined the effects of single source vs. multisource pollen on colony growth, worker physiology, pheromone-mediated queen-worker behavioral interactions, and the chemical composition of pheromone-producing glands in the queen. We observed little impact of pollen source on colony size, JH and MF hemolymph titers in workers, worker protein content in the head (which includes the hypopharyngeal

83 68 glands) or worker retinue towards the queen. However pollen source did effect the QMG chemical composition, with an increased quantity and proportion of HVA and an unknown compound in queens from the wildflower-fed colonies. Despite being limited to the consumption of single source almond pollen, there was no difference in colony growth. Almond pollen contains all the essential amino acids necessary for successful honey bee brood rearing development (Loper and Berdel 1980, Loper et al. 1980). Past studies observed a decline in brood production in colonies with reduced nutrition (Degrandi-Hoffman et al. 2008, Willard et al. 2011), but we saw no negative effect of a single source diet on colony size. Our findings suggest that almond pollen alone may not negatively affect honey bee health. However, it is possible that under conditions of stress (ie, overwintering, parasitization, pesticide exposure) the impacts of poor nutrition would be more observable. Indeed, colonies fed multisource pollen vs single source pollen has been shown to assist colonies in resisting infections (Alaux et al. 2010b, Brodschneider and Crailsheim 2010) Single- vs multi-source pollen diets also did not significantly impact two measurements of worker physiology. First, there was no significant difference in protein titers in the heads of bees collected from each treatment group. The worker head contains a pair of hypopharyngeal glands, which is the site for the conversion of collected pollen to protein rich worker jelly (Hrassnigg and Crailsheim 1998). A protein analysis of the head is a strong indicator for the nutritional status of the adult honey bee (DeGrandi- Hoffman et al. 2010). Secondly, there were no significant differences in JH or MF (precursor to JH) hemolymph titers between workers from almond- and wildflower-fed colonies. Levels of JH are regulated by multiple factors, including pheromonal cues. Both

84 69 QMP and brood pheromone have been shown to suppress JH titers (Kaatz et al. 1992, Pankiw et al. 1998, Le Conte et al. 2001). Diet is having a significant effect on on QMG production, but it is possible that any effects of diet on QMG chemistry were buffered by the presence of common pheromone signals in the two treatment groups. Similarly, Niño et al. (2012) found that queen insemination volume did not impact worker JH and MF hemolymph titers, despite significant increase of worker retinue towards queens receiving a high insemination volume; this lack of response was attributed to the equal numbers of brood in the two treatment colonies. Notably, however, the bees were found to drift between treatments within the flight cage, which may mitigate any observable affects to JH and MF. Intriguingly, queens from almond- vs wildflower-fed colonies had significantly different chemical profiles in their mandibular glands. The mandibular glands are exquisitely sensitive to mating status, ovary activation, insemination substance and volume, and infection status (Richard et al. 2007, Kocher et al. 2009, Alaux et al. 2011). However, we found significant changes in HVA levels, and this compound was unaffected in these previous studies (Richard et al. 2007, Alaux et al. 2011). Since we did not see any differences in colony growth or worker physiology between colonies provisioned with almond or wildflower pollen, it is unlikely that the nutritional value of the pollen is impacting this change. HVA is found to synergize with other QMP components to evoke worker retinue (Slessor et al. 1988), but the ~50% increase in HVA quantity and 100% proportional increase in wildflower treated queens had no effect on worker retinue. However HVA has been shown to have a large impact on learning in young workers (Vergoz et al. 2007,

85 70 Jarriault and Mercer 2012). The neurohormone dopamine has several functions in worker behavior, including aversive learning (reviewed in Jarriault and Mercer 2012). HVA activates a dopamine receptor in the brain and antennae, AmDOP3, and this appears to inhibit young nurse honey bee from forming any type of aversive learning towards the queen (Beggs et al. 2007, Vergoz et al. 2007, Beggs and Mercer 2009). By preventing learning aversion towards the queen, the workers fail to associate the queen with any negative effects of her pheromone (Jarriault and Mercer 2012), ensuring that workers will continue to attend the queen with low aggression. In addition, HVA has the highest positive correlation of any QMG pheromone components with high sperm counts and queen survival (Rhodes and Somerville 2003), indicating that long-term queen fecundity and survival may be rooted in HVA production. It is uncertain what in the pollen may be contributing to increases in HVA. There is no evidence of pheromone production being linked to diet in honey bees, however the reduction of HVA through fermentation of vanillin in the presence of yeast (common component of pollen) can occur (Loscos et al. 2007). If vanillin is present in pollen (unknown), the conversion of vanillin to HVA in the colony may be feasible. Vanillin is a key component of grapes (Loscos et al. 2007) and our wildflower pollen was collected in close proximity to a high density of vineyards in the Wallamette Valley, OR. This may be an unlikely scenario, but certainly one for further investigation. Our findings suggest that a diverse diet could be an important factor for long-term success and survival of the colony. Despite no immediate physiological or behavioral changes of colony health or worker behavior being observed in our study, pollen source is clearly having an impact on queen pheromone production. Since queen pheromone

86 71 mediates many key elements of worker behavior and physiology, disruptions to this communication system may impact overall colony function, performance and health, though the effects of HVA specifically on the colony level remain to be determined. This work serves as a foundation for future work to determine how different pollen sources impact queen physiology and long-term colony survival.

87 72 Chapter 4 Genomic analysis of the interactions between pesticide exposure and nutrition in honey bees Authors: Daniel R. Schmehl, Peter Teal, James Tumlinson, and Christina Grozinger Abstract The decline of honey bee populations during the past decade has been alarming. This decline is likely due to a combination of stressors. Pesticides in particular have been linked to declines in bee longevity and performance; however the molecular and physiological pathways mediating sensitivity and resistance to pesticides have not been well characterized. In this study, we explore the physiological impact of coumaphos and fluvalinate, the most abundant and frequently detected pesticides in the hive, on workers. We analyzed the genome-wide expression patterns of the worker honey bee and found changes in detoxification genes, behavioral maturation pathways, and nutrition genes. From these results, we examined the physiological indicators of behavioral maturation and found significant decreases in methyl farnesoate (juvenile hormone precursor) as a result of pesticide exposure. We then looked at nutrition in more detail and saw an upregulation of detoxification genes when consuming pollen. We followed this with a survival analysis and found that pollen-fed honey bees have a reduced sensitivity to pesticide exposure. Lastly we looked to see if pesticide expression patterns were specific to a particular chemical class and found that class does not appear to specifically affect gene expression. In summary, pesticide exposure transforms the genomic phenotype of

88 73 the honey bee through upregulation of detoxification genes and modification of behavioral maturation. Pollen consumption and pesticide exposure activate similar detoxification pathways which may be involved in reducing pesticide sensitivity. Introduction Honey bees (Apis mellifera L.) are key pollinators of many agricultural crops, (Calderone 2012). Yet over the last several decades, a steady decline of honey bee populations has occurred with no clear causative agent (Council 2007, VanEngelsdorp et al. 2009, Johnson et al. 2010). Many factors are likely involved in this decline, including exposure to pesticides. Residues from over 120 different pesticides, with an average of over six pesticides per colony, have been found in honey bee colonies in the US, with levels and prevalence of two chemicals, fluvalinate and coumaphos, being the highest (Mullin et al. 2010). Fluvalinate and coumaphos are commonly applied by placing an impregnated strip between the frames of the hive. The bees contact the strip and spread the pesticide throughout the colony for mite control. However, while there have been many studies examining the impacts of pesticides exposure on the behavior and longevity of individual honey bees, our understanding of the molecular and physiological mechanism mediating these impacts, and the related pathways that convey resistance to these chemicals, remains limited. Large, acute pesticide doses may kill bees and colonies outright (reviewed in Atkins 1992, Johnson et al. 2010), but long term exposure to low doses lead to sublethal effects (reviewed in Thompson and Maus 2007, reviewed in Johnson et al. 2010).

89 74 Pesticide exposure can reduce learning, memory, and orientation in adult female worker bees (Decourtye et al. 2004, Decourtye et al. 2005, Aliouane et al. 2009, Decourtye et al. 2011, Ciarlo et al. 2012, Henry et al. 2012), alter adult worker locomotion and feeding behavior (Teeters et al. 2012), and modify larval development (Wu et al. 2011, Zhu et al. 2013). In male bees (drones), pesticides have been shown to reduce body weight and longevity (Rinderer et al. 1999), as well as reduce sperm viability (Burley et al. 2008) which likely contributes to poor queen mating quality. In queens, pesticide exposure during development reduced adult queen weight (Haarmann et al. 2002, Pettis et al. 2004), the number of stored sperm, (Haarmann et al. 2002), and egg laying (Haarmann et al. 2002, Collins et al. 2004), and also disrupted ovary activation (Haarmann et al. 2002). At very high rates of coumaphos exposure, queen rearing is found to be greatly inhibited (Collins et al. 2004, Pettis et al. 2004). At the physiological level, exposure to pesticides may impact endocrine pathways. The primary hormonal regulator of adult worker behavior is juvenile hormone III (JH), which is synthesized from methyl farnesoate (MF) (Robinson 1987, Huang et al. 1991, Sullivan et al. 2000). Rising titers of JH regulate behavioral maturation, or the transition from nursing (brood care) to foraging in honey bee workers (Robinson 1987, Huang et al. 1991, Sullivan et al. 2000). Bees exhibiting stresses from Nosema infection, Varroa mites, sackbrood virus, Kakugo virus, insecticide poisioning, injury, wax deprivation, and starvation have all been observed to induce precocious foraging behavior (reviewed in Tofilski 2009). In a modeling experiment based upon pesticide field data, pesticide exposure was implicated in precocious foraging (Thompson et al. 2007). Precocious foraging contributes towards a shorter lifespan for workers (Janmaat and

90 75 Winston 2000, Toth et al. 2005, Toth and Robinson 2005, DeGrandi-Hoffman et al. 2010) and may interfere with the social dynamics of the colony. If pesticide exposure is inducing stress and accelerating behavioral maturation, we would expect to see increases in the levels of JH. A mechanism for pesticide resistance in honey bees is vital due to this high degree of exposure and the rate at which pesticides are sequestered in the hive in bee products (Mullin et al. 2010). Previous studies have shown that pesticides interact with detoxification pathways (Johnson et al. 2006, Johnson et al. 2009b, Mao et al. 2011, Johnson et al. 2012). Cytochrome P450 monooxygenases (P450s) are involved in xenobiotic detoxification (Claudianos et al. 2006, Johnson et al. 2006, Johnson et al. 2009b), as well as hormone synthesis and metabolism (Helvig et al. 2004, Claudianos et al. 2006). Coumaphos and fluvalinate were found to activate P450 enzymes both individually (Johnson et al. 2006) and synergistically in combination (Johnson et al. 2009b). A recent study examining five different miticides found changes in gene expression to thymol and coumaphos (Boncristiani et al. 2012). Cyp6a14 was upregulated by thymol, while several other detoxification, immune, and developmental genes were either up or downregulated (Boncristiani et al. 2012). Another study examined the effects of pesticides on larval development and did not detect P450 activity, but did find an effect on several genes involved in possible immune function and behavioral maturation (Gregorc et al. 2012). Here, we used whole genome microarray analysis to monitor global gene expression responses to two pesticides, coumaphos and fluvalinate, which have been found to have the highest prevalence and concentrations in US hives. We identified

91 76 significantly impacted genes and pathways, and determined if common or unique processes were affected by the two pesticides. We performed comparative analyses with previously published studies of gene expression patterns in honey bees, to determine if pesticide exposure preferentially impacted expression of genes associated with nutrition (Ament et al. 2011), immune function (Evans et al. 2006, Richard et al. 2012), and behavioral maturation (Ament et al. 2011). Since expression of behavioral maturation genes were significantly altered, we examined the effects of pesticide exposure on hormone pathways associated with behavioral maturation in honey bee workers. Similarly, since expression of genes associated with nutrition and diet were preferentially affected, we examined the impacts of nutrition on resistance to pesticides. Finally, we monitored expression of candidate genes using quantitative real-time PCR to both confirm our microarray results and examine the effects of different pesticides classes and the interactions between diet and pesticide exposure in greater detail. This explorative study on honey bee physiology provides a comprehensive foundation for understanding genetic expression patterns of pesticide-exposed honey bees, as well as further insight into the evolutionary relationship between pollen and honey consumption and pesticide resistance.

92 77 Materials and Methods General bee rearing Collections for microarray and hormonal assays Worker bees were derived from one colony headed by asingle-drone inseminated (SDI) carniolan queen (Glenn Apiaries, Fallbrook, CA) and maintained using standard commercial apicultural practices at a Penn State University apiary. Honeycomb frames containing emerging workers were removed from the colony and placed in an incubator overnight. Individual cages were constructed using two paired 100 mm x 20 mm Petri dish tops or bottoms (VWR, Radnor, PA) with a 15 cm x 30 cm piece of metal screen formed into a cylinder. Holes for the pesticide feeders and cage maintenance were punched using a hot metal cork borer. Each cage was established using 30 newly emerged workers (<24 hours old), along with one naturally mated Italian queen (BeeWeaver Apiaries, Austin, TX) and 1 ml 1:1 sucrose:water (w/v), and was placed in a dark environmental chamber at 35 C and 50% humidity. The following day (when the bees were <2 days old) cages were fed sublethal doses of pesticides or controls. 100 ppm fluvalinate (Chemservice- PS-1071, 95% purity) and 100 ppm coumaphos (Chemservice- PS-656, 99.5% purity) were dissolved in a 3% methanol/50% sucrose/water solution. Each cage received one ml of the following four treatments daily, to simulate a chronic, sublethal dose: fluvalinate, coumaphos, methanol (3 % solvent control), and sucrose (non-solvent control). Pilot studies were performed to ensure these doses did not cause significant mortality (data not shown). The

93 78 selected doses are also relevant to levels found in the wax (coumaphos ppm, fluvalinate- 204 ppm) of honey bee colonies, (Mullin et al 2010). Each treatment group had 12 replicates, for a total of 48 cages. There was no difference in the volume of diet consumed between treatments (data not shown). The diet was replaced and mortality recorded daily during a course of seven days. After seven days, five workers were removed from each cage and placed on ice to immobilize them for hemolymph extraction. The remaining workers were placed directly on dry ice and stored at -80 C. Cages experiencing more than 10% mortality were not selected for hemolymph collections or microarray analysis. There was no significant difference in mortality across treatments (Kruskal-Wallis, chi-squared = 2.36; degrees of freedom = 3; p = ). Collections for quantitative real-time PCR of pesticide treatments To verify our microarray results and determine whether pesticides within and across different classes (coumaphos (organophosphate) and fluvalinate (pyrethroid)) generate a different genetic response, we performed a series of quantitative real time polymerase chain reaction (qrt-pcr) experiments in July Cages were established as described for the microarray experiments, however instead of a live queen, workers were reared in the presence of synthetic queen mandibular pheromone (QMP) at a concentration of 0.1 queen equivalents (Qeq) to simulate being in the presence of the queen (Grozinger et al. 2003, Fischer and Grozinger 2008, Niño et al. 2012). QMP was replaced daily. In addition, the cage design was modified, using 150 mm x 20 mm Petri

94 79 dishes. Each cage was designed with a mesh-screened opening for ventilation, an opening for a pesticide feeder, and two slits at the base of the side for the cover slips. The day following cage establishment, the bees were fed coumaphos, fluvalinate, chlorpyrifos, permethrin, amitraz, methanol, or sucrose. Feeding was performed as above, with each treatment dissolved in 3% methanol/50% sucrose/water and each cage provided with 1 ml of diet daily. Doses of coumaphos (100 ppm) and fluvalinate (100 ppm) were as before. A pilot toxicity study on chlorpyrifos, permethrin, amitraz was performed to determine appropriate sublethal doses. Concentrations were used that were similar to levels founds in colonies (Mullin, personal communication). Two cages were established for each dose for chlorpyrifos (organophosphate, Chemservice- PS-674, 95.7% purity) at 1, 3, and 10 ppm, permethrin (pyrethroid, Chemservice- PS-758, 99.5% purity) at 1, 3, and 10 ppm, and amitraz (interacts with octopamine receptors, Chemservice- PS-1005, 99.5% purity) at 3, 10, 30, and 100 ppm. Mortality was recorded every 24 hours for a total of 96 hours to determine the sublethal dose for each pesticide (under 10% mortality by the end of the experiment). Chlopyrifos was determined to have a sublethal concentration of 1 ppm, while permethrin was 3 ppm, and amitraz was 30 ppm. Each treatment had six replicates, for a total of 36 cages. The diet was replaced and mortality recorded daily during a course of seven days. There was no difference in the volume of diet consumed between treatments. At the end of the seven days, five workers were removed from each cage and temporarily placed on ice for hemolymph extraction (samples not yet analyzed). The remaining workers were placed directly on dry ice and stored at -80 C until qrt-pcr analysis. Cages experiencing more than 10% mortality were not selected for hemolymph

95 80 collections or microarray analysis. Average mortality was less than 5% and was not significantly different across treatments (Kruskal-Wallis, chi-squared = 5.60; degrees of freedom = 5; p= ). Collections for quantitative real-time PCR of nutritional effects We performed a series of qrt-pcr experiments in July 2012 to determine the impact of honey and pollen on expression of candidate genes identified in the microarray study. Cages were established as for the pesticide qrt-pcr study, but were modified to include an opening for the pollen or soy protein diet. Cages received one of six treatment diets: sucrose only, honey only, sucrose/pollen, honey/pollen, sucrose/protein, and honey/protein. The sucrose (1:1 sucrose/water- w/v) and honey (wildflower, YS Bee Farms, Illinois) treatments received no protein source during the course of the experiment. The pollen diet consisted of bee-collected trapped wildflower pollen from an organic farm in Oregon (undisclosed) mixed with sucrose solution (1:1) at a at 1:1 w/v pollen/sucrose ratio to create a smooth, creamy texture. The protein diet consisted of soy protein isolate (NOW, 90% protein) (Roulston and Cane 2002) mixed at a 4:1 w/v protein/sucrose ratio to produce a consistency comparable to that of the pollen mixture. Sucrose and honey (~1.7 ml at each feeding) were replaced every three days, whereas the pollen and protein (~0.5 g at each feeding) were replaced every two days. Feeders were weighed to determine the amount consumed throughout the course of the experiment. Each treatment had six replicates, for a total of 36 cages. After seven days,

96 81 the workers were placed directly on dry ice and stored at -80 C until qrt-pcr analysis. There was no mortality during the course of the experiment. Since pollen and honey are known to contain large numbers of pesticides (Mullin et al. 2010), the honey and pollen were analyzed using LC/MS-MS to confirm they were not contaminated. Honey was analyzed for pesticide residues by the USDA-AMS-NSL at Gastonia, NC according to the protocol in Mullin et al. (2010) and was found to be absent of all pesticides except trace amounts of coumaphos (0.001ppm). Pollen was analyzed similarly for pesticide contamination and found to be free of all pesticides except for a negligible amount of carbaryl (0.005ppm) and pendimethalin (0.002ppm). Effect of diet on pesticide tolerance We examined the impact of diet on the longevity of pesticide-exposed honey bees. Cages were established as in the nutrition qrt-pcr studies. Cages were fed 1:1 sucrose solution daily in addition to one of four diets: pollen long-term, pollen shortterm, protein long-term, or sucrose only (diets described above). The long-term diets were fed throughout the experiment and replaced and weighed every two days. The short-term diet was fed 24 hours prior to initial pesticide exposure. Half of the cages in each treatment group were fed pesticides while the other half received sucrose beginning on day five. Cages receiving the pesticide treatment were chronically fed chlorpyrifos in 1:1 sucrose solution at 3 ppm, a dose known from earlier pilot studies to have an LD50 of approximately three days. Mortality was recorded daily for 16 days.

97 82 To determine differences in survival among our treatment groups, we conducted a Kaplan-Meier survival log-rank test (Kleinbaum and Klein 2012) using diet and pesticide treatment as variables. Before performing our Kaplan-Meier analysis, we conducted a Cox regression proportional hazards model to confirm that the assumption of a linear hazard ratio between diets was met. Statistical analysis was performed using SPSS (v.21, IBM, Armonk, NY). Microarray analysis Six cages per treatment group were selected for microarray analysis to characterize pesticide-induced changes in gene expression in the honey bee. Sample preparation and microarray analysis were performed as in Niño et al. (2011) with slight modifications. Whole abdomens from a pooled sample of five workers/cage were extracted using QIAshredder (Qiagen, Valencia, CA) and an RNeasy RNA extraction kit (Qiagen). 750 ng of RNA/sample were amplified using the Ambion MessageAMP II arna kit (Life Technologies, Grand Island, NY). Four µg of amplified RNA from each sample were labeled independently with Cy3 and Cy5 dyes (Kreatech, Amsterdam, Netherlands). Samples were hybridized to 24 microarrays (two samples/array) in a loop design with dye swaps incorporated. Whole genome microarrays containing 28,800 spotted oligos (13,400 paired honey bee oligos) were purchased from the W.M. Keck Center for Functional Genomics at the University of Illinois, Urbana-Champaign, hybridized using the Maui mixer (BioMicro Systems, Salt Lake City, Utah) and scanned

98 83 on an Axon Genepix 4000B scanner (Molecular Devices, Sunnyvale, CA) using GENEPIX software (Agilent Technologies, Santa Clara, CA). Analysis of the array data followed the protocol described in Richard et al. (2012). Spots with an intensity of less than 100 (the average array background for both dyes) were removed from the analysis. Transcripts present on less than 7 of the 24 arrays were excluded from further analysis. Expression data was log-transformed and normalized using a mixed-model ANOVA (proc MIXED, SAS, Cary, NC) with the following model: Y= µ + dye + block + array + array*dye + array*block + ϵ where Y is expression, dye, and block are a fixed effect, and array, array*dye and array*block are random effects. Transcripts with significant expression differences between groups were detected by using a mixed-model ANOVA with the model: Y= µ + treatment + spot + dye + array + ϵ where Y represents the residual from the previous model; treatment, spot, and dye are fixed effects; and array is a random effect. P-values were corrected for multiple testing using a false discovery rate of < 0.01 (proc MULTTEST, SAS). The expression levels of all significantly regulated genes were normalized by calculating the average value across the treatment groups and subtracting this average from the normalized residual. Two-way hierarchical clustering analysis was performed

99 84 using JMP 9 (SAS, Cary, NC). Approximately unbiased P values, bootstrap values, and Euclidean distances were calculated using R version with 100,000 bootstrap replicates ( All significantly regulated transcripts were annotated according to their Drosophila orthologs in Flybase (< when available. Gene ontology (GO) analysis was performed using DAVID version 6.7 (Dennis et al. 2003, Huang et al. 2008). Comparative Analyses To determine which genes may be associated with a possible stress response, we compared our significantly regulated lists of genes with genes whose expression levels were significantly associated with behavioral maturation (Ament et al. 2011), immune function (Evans et al. 2006, Richard et al. 2012), and nutrition (Ament et al. 2011). In addition, we performed directional analyses with gene lists from Ament et al. (2011), to determine if genes were similarly up- or down-regulated by diet and pesticide exposure. Comparisons between gene lists were performed using Venny (Oliveros 2007; < Significance overlap in the gene lists was determined using a Fisher s exact test, using all the genes present on the microarray as a background list (Jim Lund, University of Kentucky, <

100 85 Characterization of the juvenile hormone and methyl farnesoate hemolymph titers Hemolymph was collected from under the 4 th abdominal segment of individual bees using a ten µl pulled glass capillary tube. Hemolymph was pooled from 3-5 bees for each cage and placed in a 1.7 ml Eppendorf tube and combined with 90 µl of methanol (HPLC grade). The tube and cap were wrapped with Teflon tape to prevent leaking. Samples were shipped to the USDA-ARS lab in Gainsville, FL for processing according to the protocols described in (Teal et al. 2000, Teal and Proveaux 2006, Jones et al. 2010, Niño et al. 2012) Across the four treatment groups collected for the microarray studies, eight samples were collected from sucrose, eight from methanol, seven from coumaphos, and eight from fluvalinate. Data were log2 transformed and analyzed using an ANOVA- Tukey HSD with treatment as a variable (JMP 9, SAS, Cary, NC). qrt-pcr For each study (see below for details on numbers of replicates for each study), whole abdomens from three bees/cage were homogenized using a FastPrep FP120 (Thermo Scientific, Rockford, IL) for two-30 second cycles at a speed of 6.5 m/sec. Samples were cooled on ice between cycles for 12 minutes. Each sample was transferred to a QIAshredder column (Qiagen) and centrifuged at G for 1.5 minutes. Lysate was removed and RNA was extracted with RNeasy RNA extraction kit (Qiagen). DNA was removed from the product using a Turbo DNA-free kit (Life Technologies). RNA was quantified using a Nanodrop 1000 (Thermo Scientific) and cdna was synthesized

101 86 from 200 ng RNA using SuperScript II Reverse Transcriptase (Life Technologies). Expression levels of the selected candidate genes were determined using quantitative real-time polymerase chain reaction (qrt-pcr) on an ABI Prism 7900 sequence detector with the SYBR Green detection method (Life Technologies). Duplicate reactions were performed for each of the samples and averaged together. The expression of each candidate gene was normalized to the geometric mean (Vandesompele et al. 2002) of the two housekeeping genes actin and eif-s8 (Grozinger et al. 2003, Huising and Flik 2005)(Figure 4-1). The ΔΔCT method was used for relative quantification of gene expression. A water and no- enzyme control was included for each primer to ensure no contamination from DNA or primer dimers. For the pesticide qrt-pcr study, a total of 12 pooled samples (two samples/cage) from each of our six pesticide treatments were analyzed to determine the relative expression of six candidate genes (Figure 4-1). CYP305D1, CYP9S1, and Glutathion S-transferase D1 (GSTD1) were significantly regulated in the microarray study and are thought to play a role in insecticide metabolism and possibly hormone biosynthesis (Collins et al. 2004, Corona et al. 2005, Claudianos et al. 2006). CYP9Q3 was previously shown to be upregulated in fluvalinate-exposed bees (Mao et al. 2011), and CYP306A1 was downregulated in response to thymol and fluvalinate exposure (Boncristiani et al. 2012).Neither CYP9Q3 nor CYP306A1 were significantly regulated in our microarray study. For the nutrition qrt-pcr study, a total of 12 pooled samples (2 samples/cage) from each of our six nutrition treatments were analyzed to determine the relative expression of five candidate genes (Figure 4-1). Here, we again monitored expression of

102 87 CYP305D1, CYP9S1, GSTD1, and notably CYP9Q3, which was previously shown to be upregulated by honey (Mao et al. 2011). We also examined superoxide dismutase (SODH2); this gene was significantly upregulated by pesticide exposure in our microarray study, and in response to a rich diet (Ament et al. 2011), and may function in immunity (Luque et al. 1998, Richard et al. 2012). Table 4-1 Primer sequences for qrt-pcr analyses. It is noted in the table whether the primer was used for nutrition, pesticide, or both (pest./nut.) studies Name qrt-pcr GB AM Sequence (Forward 5'-3') Sequence (Reverse 5'-3') eif-s8 Pest./Nut. TGAGTGTCTGCTATGGATTGCAA TCGCGGCTCGTGGTAAA Actin Pest./Nut. CCTAGCACCATCCACCATGAA GAAGCAAGAATTGACCCACCAA Glutathione S-transferase D1 Pest./Nut. GB18045 AM10617 GCCGCTTCAAAAGAAGTACG GTGGCGAAAACAAGGATGAT SODH2 Nutrition GB14284 AM06884 CAGTGCATGGTAGCCTGAGA ACAGTGCTCCTTCAGCCAAT CYP305D1 Pest./Nut. GB11943 AM04560 TCGATCTTTTTCTCGCTGGT TTGCTTTGTCCTCCATGTTG CYP306A1 Pesticide GB12311 AM04930 ACGAGGAGGAAGATCGGATT TGCGTTTTATCCTTCCCATC CYP9S1 Pest./Nut. GB13748 AM06347 CTAATTTTCGCGTTCCCAAA CTCCCGTTACGTTTGTCGAT CYP9Q3 Pest./Nut. GB19967 AM12514 GTTCCGGGAAAATGACTAC GGTCAAAATGGTGGTGAC A nonparametric Kruskal-Wallis one-way ANOVA was performed for all multiple comparison statistics using JMP 9 (SAS, Cary, NC). Ordered letter differences were determined using a paired-difference Wilcoxon t-test. In addition, a Wilcoxon t-test was also used to determine significant differences between sucrose and methanol. Results Effects of pesticide on global gene expression patterns We used microarrays to monitor genome-wide gene expression patterns in the abdomens from honey bees from the four treatment groups (sucrose, methanol, coumaphos, and fluvalinate). A total of 1118 distinct transcripts (of the 13,439 transcripts printed on our arrays) were significantly regulated at a False Discovery Rate (FDR) of p

103 88 <0.01. A hierarchical clustering analysis of the 1118 significantly regulated genes demonstrates that the pesticide-treated groups have distinct gene expression patterns relative to the sucrose and methanol groups (Figure 4-1). This clustering is supported by an approximately-unbiased p-value of 100 and a bootstrap value of 100 (R, v )

104 Figure 4-1 Hierarchical clustering analysis of significantly regulated genes. Overall gene expression patterns of coumaphos and fluvalinate treated bees clearly clustered separately from methanol and sucrose. This grouping is supported by an approximately-unbiased p-value of 100 and a bootstrap value of

105 90 Of these 1118 transcripts, 814 transcripts are regulated by coumaphos and/or fluvalinate relative to our control group. 566 and 131 transcripts are significantly regulated by only coumaphos and only fluvalinate, respectively, while 117 transcripts are significantly regulated by both pesticides (Figure 4-2).The observed overlap of significantly regulated genes between treatments was significantly greater than chance (Fisher s exact test; p < 0.001). Figure 4-2 Venn diagram of significantly regulated transcripts in the three treatment groups relative to the control. GO analysis of the different sets of transcripts identified several over-represented (p < 0.05) functional categories (listed in black). Gene ontology analysis (Appendix, table A) identified several functional groups of genes whose expression was significantly altered by pesticide exposure. Of the 814 transcripts significantly affected by exposure to either or both pesticides, 576 had unique

106 91 Drosphila orthologs with Flybase annotations and were used in this analysis. Genes were in 16 distinct categories, including transport, metabolism, cellular respiration, and developmental pathways (Figure 4-2, p < 0.05). Of these 16 categories, the Citrate Cycle (coumaphos) and Lysine Degradation (coumaphos/fluvalinate) survived the Benjamini correction (p < 0.05). Expression of several genes involved in detoxification was significantly altered in response to pesticide exposure. Indeed, the highest overexpressed gene was CYP305D1, whose expression increased 5.35 fold in response to coumaphos relative to sucrose. CYP305D1 is found in the CYP2 clade of cytochrome P450s (P450s) and is traditionally associated with hormone function (Claudianos et al. 2006). Other P450s that were upregulated by both coumaphos and fluvalinate were CYP6AS3, CYP6AS4, and CYP9S1, all within the CYP3 clade with known functions including insecticide metabolism and resistance (Berenbaum 2002, Feyereisen 2005). In addition to P450s, GB10854, a carboxyl/cholinesterase (CCE), and GSTD1, a glutathione-s-transferase (GST), were upregulated in response to coumaphos exposure. GB10854 may function in organophosphate detoxification (Claudianos et al. 2006, Johnson et al. 2009a), while GSTD1 is found in the Delta class, which is a well-known class for insecticide detoxification (Claudianos et al. 2006). In other insect systems, delta class GSTs have the capability to metabolize organochlorines, such as DDT, and organophosphate insecticides (reviewed in Claudianos et al. 2006). Interestingly, GSTD1 is the only Delta class GST identified in honey bees.

107 92 Pesticide qrt-pcr The microarray study revealed substantial overlap in transcriptional responses to coumaphos and fluvalinate, but each pesticide also elicited expressional changes in unique sets of genes. In order to validate the microarray results and examine responses to other pesticides in similar or distinct activity classes, we examined expression of candidate genes in sucrose fed bees and bees that were chronically fed methanol, coumaphos (organophosphate), and fluvalinate (pyrethroid). Since there was no significant difference between our sucrose and methanol treatments for any of our five candidate genes, all pesticide treatments were compared relative to methanol expression levels. Amitraz was not shown to significantly regulate any of the candidate genes relative to our control (p > 0.05) and was removed from our chi-square analysis. Differences in gene expression were evident between the microarray and qrt-pcr results (Table 4-2), with only 2/5 genes being differentially regulated in both. Table 4-2 Variation between microarray and qrt-pcr gene expression Genes regulated in both CYP9S1 CYP305D1 Candidate Gene Regulation in Pesticide Microarray and qrt-pcr Genes regulated in arrays, but not PCR GSTD1 Genes regulated in PCR, but not arrays CYP306A1 CYP9Q3 Expression of CYP9S1 was upregulated by coumaphos and fluvalinate in our microarray study, but was not found to be upregulated by fluvalinate in a previous study (Mao et al. 2011). In our qrt-pcr analysis, expression of CYP9S1 was significantly

108 93 affected by treatment (Figure 4-3; Kruskal-Wallis, chi-squared (χ 2 )= 16.14; degrees of freedom (DF) = 4; p = ). Post-hoc pairwise comparisons revealed that expression of CYP9S1 was significantly upregulated by chlorpyrifos (p = ) and fluvalinate (p = ), and non-significantly upregulated by coumaphos (p = ) relative to methanol. Permethrin had no impact on expression levels (p = ). Expression of CYP9Q3, a cytochrome P450 that is closely related to CYP9S1, was upregulated by fluvalinate in previous studies (Mao et al. 2011), but was not significantly regulated in our microarray study. In our qrt-pcr analysis, expression of CYP9Q3 was significantly affected by treatment (Figure 4-3; Kruskal-Wallis, χ 2 = 15.29; DF = 4; p = ). Expression was significantly upregulated by coumaphos (p = ) and fluvalinate (p = ), non-significantly upregulated by chlorpyrifos (p = ), and unaffected by permethrin (p = ) relative to methanol. Expression of CYP305D1 was upregulated by 5.35 fold by coumaphos in our microarray study. In our qrt-pcr analysis, however, there was no significant effect (Figure 4-3; Kruskal-Wallis, χ 2 = 4.43; DF = 4; p = ), though expression tended to be higher in pesticide treated groups. There was also considerable variation within sample groups. Expression of CYP306A1 was previously found to be down-regulated in bees from colonies treated with thymol and coumaphos (Boncristiani et al. 2012), but was not differentially regulated in our microarray study. In our qrt-pcr assay, CYP306A1 expression was significantly affected by treatment (Figure 4-3; Kruskal-Wallis, χ 2 = 10.76; DF = 4; p = ). It was significantly upregulated by chlopyrifos (p = )

109 94 and non-significantly upregulated by coumaphos (p = ) and fluvalinate (p = , and unaffected by permethrin (p = ). There was no indication that expression of this gene was downregulated in any of the treatment groups, as observed by Boncristiani et al. (2012). Expression of GSTD1 was upregulated by coumaphos in our microarray study, was not significantly differentially regulated in our qrt-pcr study (Figure 4-3; Kruskal- Wallis, χ 2 = 0.85; DF = 4; p = ).

110 Relative RNA levels (mean ± SE) Methanol Chlorpyrifos Coumaphos Fluvalinate Permethrin 2 1 B B B AB B AB A A A B A AB AB A A 0 CYP9S1 CYP9Q3 CYP305D1 CYP306A1 GSTD1 Figure 4-3 Relative expression levels of selected candidate genes in response to pesticide exposure. Cages of 30 newly-emerged bees were chronically exposed (orally) to pesticides for a seven day period. Abdomens were homogenized and RNA extracted. Samples were analyzed using qrt-pcr and relative amounts were calculated using the ΔΔCt method. CYP9S1 (Kruskal-Wallis, chi-squared (χ 2 ) = 16.14; degrees of freedom (DF) = 4; p = ), CYP9Q3 (χ 2 = 15.29; DF = 4; p = ), and CYP306A1 (χ 2 = 10.76; DF = 4; p = ) were found to be significantly upregulated relative to our methanol control.

111 96 Comparative Analyses We examined the overlap between the 1118 significantly regulated transcripts and those identified in previous studies of genes associated with behavioral and physiological processes in honey bees (Table 4-3). A set of canonical immune genes were identified during annotation of the honey bee genome (Evans et al. (2006); 22 of these were also present in our significantly regulated transcript list, which is not greater than expected by chance (p = 0.70). However a recent study examining genome-wide transcriptional responses to immunostimulation (Richard et al. 2012) identified 302 significantly regulated transcripts ; 44 of these were also significantly regulated in our study, which was significantly greater than chance (p < 0.001). Of the genes differentially expressed in the fat bodies of nurses and foragers (Ament et al. (2011) 429 transcripts overlapped with those from our study, which was significantly greater than expected by chance (p < 0.001). Of the genes differentially expressed in the fat bodies of bees fed a rich (pollen/honey) or poor (sugar syrup) diet, 527 transcripts overlapped with those from our study, which was significantly greater than expected by chance (p < 0.001).

112 97 Table 4-3 The 1118 significantly regulated transcripts associated with pesticide exposure were compared to four previously performed studies Gene List Immune Function canonical # Sig Transcripts All regulated genes (1118) p-value Immune Function <0.001 Nurse/Foragerassociated Rich/Poor dietassociated < <0.001 Reference Evans et al Richard et al Ament et al Ament et al Of the 814 transcripts that were significantly regulated in coumaphos and/or fluvalinate treated bees relative to sucrose, 500 transcripts were up-regulated by coumaphos and/or fluvalinate and 314 transcripts were down-regulated by coumaphos and/or fluvalinate. We compared these directional transcript lists with lists of transcripts that were upregulated in nurses relative to foragers ( nursing associated ), upregulated in foragers relative to nurses ( foraging associated ), upregulated in rich diet relative to poor diet ( rich diet associated ), upregulated in poor diet relative to rich diet ( poor diet associated ) and up- or down- regulated in bacteria-injected bees relative to controls (Table 4-4). We found transcripts upregulated by pesticide exposure overlapped significantly with both nursing and foraging associated transcripts, while transcripts downregulated by pesticides only significantly overlapped with foraging associated transcripts (p < 0.05). The significant overlap with both nursing and foraging associated genes suggests that this may simply represent a general effect on multiple physiological processes associated with

113 98 behavioral maturation. We also found transcripts upregulated by pesticide exposure overlapped significantly with upregulated immune transcripts (p < 0.001), suggesting that pesticide exposure upregulates immune function. There was significant overlap in pesticide-upregulated and rich diet associated transcripts, as well as in pesticide-downregulated and poor diet associated transcripts (p < 0.05). A GO analysis of the 181 rich diet associated/pesticide upregulated transcripts (165 of which had unique Drosphila orthologs with Flybase annotations and were used in the analysis) revealed a significant overrepresentation of 10 categories (Appendix, table B), including transport and metabolism. A GO analysis of the 165 transcripts poor diet associated/pesticide downregulated transcripts (47 of which had unique Drosphila orthologs with Flybase annotations and were used in the analysis) revealed a significant overrepresentation of 4 categories (Appendix, table B), including respiratory system development, regulation of developmental process, anatomical structure morphogenesis, and metamorphosis.

114 99 Table 4-4 Analysis of directional expression overlap among pesticide, physiological, and behavioral associated transcripts. Gene List Nursingassociated Foragingassociated Up-regulated immune function Down-regulated immune function Rich dietassociated Poor dietassociated # Sig Transcripts Coumaphos and/or Fluvalinate Upregulated (500) p-value Coumaphos and/or Fluvalinate Downregulated (314) p-value < < < <0.001 Reference Ament et al Ament et al Richard et al Richard et al Ament et al Ament et al Effect of pesticide exposure on hormone levels Hemolymph titers of juvenile hormone (JH) and methyl farnesoate (MF)) were analyzed in control (sucrose), methanol, coumaphos and fluvalinate treated workers after seven days of exposure. The total amount of JH did not differ between treatments (ANOVA, F 3 =0.4759, p=0.7018; data not shown). However, MF quantity (Figure 4-4) was significantly lower in both coumaphos and fluvalinate treated groups relative to methanol. There was no significant difference between methanol (14.14 pg ± 2.70) and sucrose (13.05 pg ± 3.89); ANOVA, F 1 = , p = , data not shown). Our coumaphos (3.11 pg ± 1.07) and fluvalinate treatment (5.02 pg ± 0.70) were significantly lower relative to methanol (ANOVA-Tukey HSD; F 2 = 9.96, p= ).

115 pg/µl ± S.E A B B 0 Methanol Coumaphos Fluvalinate Figure 4-4 Pesticide exposure reduces levels of methyl farnesoate in worker bees. Titers were measured in pooled samples of methanol (8), fluvalinate (8), and coumaphos (7) treated bees. The total amount of MF was significantly lower in workers treated with coumaphos and fluvalinate than our methanol treatments (ANOVA-Tukey HSD; F 2 = 9.96, p= ). Nutrition qrt-pcr Our microarray study revealed a significant overlap between genes upregulated by pesticide treatment and those upregulated in bees fed a rich diet (honey and pollen) versus a poor diet (sucrose). To examine the relationship between diet and pesticide exposure on gene expression further, we used qrt-pcr to measure expression of five candidate genes (CYP9S1, CYP9Q3, CYP305D1, GSTD1, and SODH2) in response to diets consisting of sucrose, sucrose/pollen, sucrose/soy protein, and honey, honey/pollen, honey/soy protein alone (Figure 4-5). Expression of four of the five genes were upregulated in response to coumaphos and/or fluvalinate in our microarray study excluding Cyp9Q3 (Mao et al. 2011). Previous studies found that expression of CYP9S1

116 101 and CYP9Q3 were upregulated by honey feeding (Mao et al. 2011), while expression of SODH2 was upregulated in rich diet vs poor diet fed bees (Ament et al. 2011) Expression of Cyp9S1 and Cyp9Q3 were upregulated while expression of Cyp305D1 was downregulated in bees fed a pollen diet (regardless of whether the carbohydrate source was sucrose or honey) relative to all other treatment groups (see Figure 4-5 and statistics reported therein). Expression of SODH2 was significantly upregulated in bees fed both pollen and soy protein diets, regardless of the carbohydrate source (Figure 4-5). Expression of GST was not significantly affected by diet (Figure 4-5).

117 Relative RNA levels (mean ± SE) BC 3 B C BC B B Sucrose Sucrose / Pollen 2 1 ABC D A C D BC A A A A AC B AC A B C A A Sucrose / Protein Honey Honey / Pollen Honey / Protein 0 CYP9S1 CYP9Q3 CYP305D1 GSTD1 SODH2 Figure 4-5 Relative expression levels of selected candidate genes in response to diet. Cages of 30 newly-emerged bees were fed one of six diets (sucrose, sucrose/pollen (wildflower), sucrose/protein (soy), honey, honey/pollen, honey/protein) for a seven day period. Abdomens were homogenized and RNA extracted. Samples were analyzed using qrt-pcr and relative amounts were calculated using the ΔΔCt method. Kruskal Wallis revealed significant effects of treatment in CYP9S1 (chi-squared (χ 2 ) = 34.10; degrees of freedom (DF) = 5; p < ), CYP9Q3 (χ 2 = 45.42; DF = 5; p < ), CYP305D1 (χ 2 = ; DF = 5; p < ), and SODH2 (χ 2 = 44.91; DF = 5; p < ). Subsequent posthoc pairwise comparisons were conducted to identify differentially regulated treatment groups and statistical differences are denoted by different letters.

118 103 Impact of diet on pesticide tolerance We examined the effects of diet on survival of honey bees challenged with a chronic feeding of chlorpyrifos. During a 16 day time period, honey bees not challenged with pesticides had a mean survival time of greater than 15.5 days regardless of diet (Table 4-5). In the absence of pesticide exposure (Figure 4-6), we found a significant decrease in life span when fed a long-term protein diet relative to cages receiving a sucrose (χ 2 = 5.52, p = 0.02) or short-term pollen (χ 2 = 3.87, p = 0.05). Table 4-5 Effect of diet on honey bee survival when challenged (pesticide exposed) or not challenged (sucrose). Treatment Diet Mean Survival (Days) Pollen long-term χ 2 p- value Protein long-term χ 2 p- value Sucrose χ 2 p- value Pollen short-term χ 2 p- value Pollen, long-term Pesticide Protein, long-term Sucrose Pollen, short-term Pollen, long-term Sucrose Protein, long-term Sucrose Pollen, short-term

119 104 Figure 4-6 Contribution of diet on honey bee survival. Cages were established with one of four diets for a period of 16 days. Mean survival for each of the diets was found to be greater than 15.5 days. We found a significant decrease in cages fed a long-term protein diet relative to cages receiving a sucrose (Chi-squared (χ 2 ) = 5.52, p = 0.02) or short-term pollen (χ 2 = 3.87, p = 0.05). In groups challenged with pesticides beginning on day five, diet significantly impacted mean survival time (Table 4-5, Figure 4-7). Bees fed a long-term pollen diet (10.71 ± 0.25 days) had significant reduction in mortality than those fed long-term protein (χ 2 = 42.15, p < 0.01), short-term pollen (χ 2 = 73.25, p < 0.01), or sucrose only (χ 2 = , p < 0.01). Bees fed a long-term protein diet (8.81 ± 0.22) had a reduction in mortality than for a sucrose only diet (χ 2 = 20.97, p < 0.01), but were not significantly different from bees fed a short-term pollen diet (χ 2 = 2.76, p < 0.10). A short-term pollen diet (8.28 ± 0.16) had a reduction in mortality than for a sucrose only diet (χ 2 = 10.89, p < 0.01). Our sucrose diet had the shortest longevity when exposed to pesticides (7.63 ±

120 ). Therefore diet impacts survival during pesticide exposure as follows: long-term pollen > long-term protein = short-term pollen > sucrose only. Figure 4-7 Diet impacts honey bee pesticide tolerance. Honey bees were challenged with a daily chronic feeding of 3 ppm chlorpyrifos beginning on day five of adult development. Cages were established with one of four diets for a period of 16 days. We found a significant increase in longevity in relation to diet (long-term pollen > long-term protein = short-term pollen > sucrose only). Discussion Our microarray analysis revealed significant expression changes in hundreds of genes. The two pesticides tested had overlapping and unique effects on gene expression. Subsequently qrt-pcr studies using candidate genes and a wider panel of pesticides revealed that even pesticides in similar chemical classes (eg, pyrethroids) had distinct effects on gene expression. Array analysis also revealed significant impacts on expression