Medicine, Faculty of
Biochemistry and Molecular Biology, Department of
DSpace
UBCV
Chapman, Abigail
Doctor of Philosophy - PhD
University of British Columbia
As the sole female capable of laying fertilized eggs within a colony of thousands, honey bee (Apis mellifera) queens are vital to their colonies. However, the factors contributing to “poor quality queens,” a primary cause of colony loss, remain unclear. Although agrichemical exposure and temperature stress are recognized as detrimental, the impact of viral infections on queen health is just beginning to be investigated. In this work, we initially conducted surveys of honey bee queens from beekeeping operations throughout British Columbia, finding that higher levels of viral infection correlate with decreased ovary mass and beekeeper-identified poor quality queens. We then showed that queens experimentally infected with Israeli acute paralysis virus had significantly smaller ovary masses compared to controls, establishing a causal link between viral infection and ovary size. Next, we performed experiments utilizing a combination of deformed wing virus and black queen cell virus, and an inactivated version of the inoculum to explore a potential trade-off between reproduction and immunity, a concept prevalent in many organisms but not yet fully investigated in honey bees. Infected queens displayed smaller ovaries and a reduced likelihood of egg-laying post-injection relative to controls. In the field, heavily infected queens had smaller ovaries, and infection was a predictor for the presence of supersedure cells in the colony. Immune responses in queens receiving infectious virus were comparable to those receiving inactivated virus, and several of the same immune proteins were negatively associated with ovary mass in the field. Lastly, we examined how maternal viral infections affected the proteomic, metabolomic, and lipidomic composition of eggs laid in controlled experiments and collected from beekeeping operations. Experimentally immune-challenged queens upregulated immune effectors in their eggs and ovaries. However, naturally infected queens showed no significant differences in egg composition linked to viral load or ovary size. Notably, the date of egg collection significantly impacted egg composition, suggesting that transgenerational effects of maternal viral infection on egg proteomes can be overshadowed by seasonal and nutritional factors. This work collectively demonstrates that viral infections impair queen reproductive success, and a reproductive-immunity trade-off likely partially, but not entirely, accounts for these effects.
FERTILITY COSTS OF COVERT VIRAL INFECTIONS IN HONEY BEE QUEENS by Abigail Chapman B.Sc., The University of British Columbia, 2019 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Biochemistry and Molecular Biology) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) December 2024 © Abigail Chapman, 2024 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Fertility costs of covert viral infections in honey bee queens submitted by Abigail Chapman in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Biochemistry and Molecular Biology Examining Committee: Leonard Foster, Professor, Biochemistry and Molecular Biology, UBC Supervisor Thibault Mayor, Professor, Biochemistry and Molecular Biology, UBC Supervisory Committee Member Katie Marshall, Assistant Professor, Zoology, UBC Supervisory Committee Member Risa Sargent, Associate Professor, Land and Food Systems, UBC University Examiner Kayla King, Professor, Zoology, UBC University Examiner iii Abstract As the sole female capable of laying fertilized eggs within a colony of thousands, honey bee (Apis mellifera) queens are vital to their colonies. However, the factors contributing to “poor quality queens,” a primary cause of colony loss, remain unclear. Although agrichemical exposure and temperature stress are recognized as detrimental, the impact of viral infections on queen health is just beginning to be investigated. In this work, we initially conducted surveys of honey bee queens from beekeeping operations throughout British Columbia, finding that higher levels of viral infection correlate with decreased ovary mass and beekeeper-identified poor quality queens. We then showed that queens experimentally infected with Israeli acute paralysis virus had significantly smaller ovary masses compared to controls, establishing a causal link between viral infection and ovary size. Next, we performed experiments utilizing a combination of deformed wing virus and black queen cell virus, and an inactivated version of the inoculum to explore a potential trade-off between reproduction and immunity, a concept prevalent in many organisms but not yet fully investigated in honey bees. Infected queens displayed smaller ovaries and a reduced likelihood of egg-laying post-injection relative to controls. In the field, heavily infected queens had smaller ovaries, and infection was a predictor for the presence of supersedure cells in the colony. Immune responses in queens receiving infectious virus were comparable to those receiving inactivated virus, and several of the same immune proteins were negatively associated with ovary mass in the field. iv Lastly, we examined how maternal viral infections affected the proteomic, metabolomic, and lipidomic composition of eggs laid in controlled experiments and collected from beekeeping operations. Experimentally immune-challenged queens upregulated immune effectors in their eggs and ovaries. However, naturally infected queens showed no significant differences in egg composition linked to viral load or ovary size. Notably, the date of egg collection significantly impacted egg composition, suggesting that transgenerational effects of maternal viral infection on egg proteomes can be overshadowed by seasonal and nutritional factors. This work collectively demonstrates that viral infections impair queen reproductive success, and a reproductive-immunity trade-off likely partially, but not entirely, accounts for these effects. v Lay Summary The health of a honey bee queen is paramount to the health of her colony because she is the only female in a colony of thousands capable of laying fertilized eggs. Queens are frequently reported by beekeepers as being of “poor quality,” whereby they exhibit signs of being incapable of producing enough offspring to sustain a vigorous colony. In this work, we investigated if and how virus infections could be responsible for these outcomes. We first collected queens from beekeeping operations and found that queens identified by beekeepers as poor quality had higher levels of virus infection. We then performed multiple experiments in which we injected queens with viruses and observed that viral infections caused some of the hallmarks of failure, including decreased ovary size. Overall, this work establishes virus infection as one underappreciated cause of queen failure, which is a significant problem for the beekeeping industry. vi Preface I conceived the hypotheses and questions guiding this thesis in collaboration with Dr. Alison McAfee, who provided direction, ideas, and feedback. I wrote all parts of this thesis, except for Chapter 2 and short portions of the Introduction, which I co-wrote with Dr. McAfee. Dr. McAfee assisted with data interpretation and provided feedback and revisions on all other chapters. For all proteomics experiments in this thesis, the UBC proteomics core facility team—Jason Rogalski, Renata Moravcova, and Jeanne Yuan—acquired the mass spectrometry data, conducted instrument maintenance, and provided technical expertise. Dr. Armando Alcazar maintains the mass spectrometry instruments for lipidomic and metabolic analysis, acquired and processed this data, and provided the methods and guidance for the preparation of the samples. Dr. Scott Covey helped extensively with developing the qPCR methods, which were originally based on protocols from Dr. David Tarpy’s laboratory at North Carolina State University. For the experiments presented in Chapters 2 and 3, Emily Huxter (Wild Antho) and Liz Huxter (Kettle Valley Queens) provided the honey bee queens. Kenzie Wright helped prepare the survey samples presented in Chapter 3 for proteomic, lipidomic, and metabolomic analyses, as part of her undergraduate honors thesis. A version of Chapter 2 has been published. Chapman, A., Amiri, E., Han, B., McDermott, E., Rueppell, O., Tarpy, D. R., Foster, L. J., & McAfee, A. (2022). Fertility costs of cryptic viral infections in a model social insect. Scientific Reports, 12(1), 15857. vii With A.M., I co-wrote the first draft of the manuscript, made the figures, and interpreted the data with editing assistance from L.J.F. and D.R.T. A.M. and I conducted queen survey 1, and A.M. conducted queen surveys 2 and 3 with assistance from acknowledged collaborators. I conducted the ovary proteomics and data analysis. A.M. and I conducted the IAPV infection experiment for ovary mass. E.M. conducted the qPCR analysis of viruses in queens from survey 3. E.A. and B.H. conducted the infections, associated qPCRs and data analysis. O.R., E.A., and B.H. conceptualized the experimental queen infections. A.M. and I conceptualized the queen survey study and associated proteomics analyses. Grants supplied to A.M., L.J.F., D.R.T., and me funded the queen survey and proteomics work. Grants to E.A., O.R., and B.H. funded the experimental infection and associated qPCR analysis. A version of Chapter 3 has been published. Chapman, A., McAfee, A., Tarpy, D. R., Fine, J., Rempel, Z., Peters, K., Currie, R., & Foster, L. J. (2024). Common viral infections inhibit egg laying in honey bee queens and are linked to premature supersedure. Scientific Reports, 14(1), 17285. A.M. and I conceptualized the experiments and analyses with input from L.J.F. and D.R.T. I wrote the first draft of the manuscript, made the figures, and interpreted the data with assistance from A.M. L.J.F., D.R.T., R.W.C., J.F., A.M., Z.R. and K.P. provided editing assistance. I conducted the cage experiment and A.M. conducted the field experiment with assistance from me. J.F. provided the queen monitoring cages and provided guidance on their use. R.W.C., K.P., and Z.R. produced the purified virus for use in experimental infections. I conducted the proteomics and RT-qPCR virus analysis. Grants to A.M., L.J.F., D.R.T., and me funded the work. viii A version of Chapter 4 has been posted as a pre-print and submitted for publication. Chapman, A., McAfee, A., Kenzie, L. C. C., Magaña, A. A., Tarpy, D. R., Fine, J. D., Rempel, Z., Peters, K., Currie, R. W., Hoover, S. E. R., & Foster, L. J. (2024). Honey bee egg composition changes seasonally and after acute maternal virus infection. bioRxiv. A.M. and I conceptualized the experiments and analyses with input from L.F.J. and D.R.T.. I wrote the first draft of the manuscript, made the figures, and interpreted the data with assistance from A.M. L.F.J., D.R.T, A.M., R.W.C., J.D.F., A.M., S.E.R.H., K.L.C.W., Z.R. and K.P. provided editing assistance. I conducted the cage experiment. A.M. and I collected the queens and eggs from the field, with help from S.E.R.H. J.F. provided the queen monitoring cages and provided guidance on their use. R.W.C., K.P., and Z.R. produced the purified virus inoculum for use in the immune challenge injections. I conducted RT-qPCR virus analysis and prepared the ovary samples for proteomic analysis. K.W. prepared the egg samples for proteomic analysis and assisted me in preparing the egg samples for lipidomic and metabolomic analysis. A.A.M. conducted the metabolomic and lipidomic analysis. Grants supplied to me, A.M., L.J.F., and D.R.T. funded the work. Research on honey bees does not currently require ethics approval at the University of British Columbia. ix Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ......................................................................................................................... ix List of Tables ................................................................................................................................xv List of Figures ............................................................................................................................. xvi List of Abbreviations ............................................................................................................... xviii Glossary ........................................................................................................................................xx Acknowledgments ...................................................................................................................... xxi Dedication ................................................................................................................................. xxiii Chapter 1: Introduction ................................................................................................................1 1.1 The history of honey bees and humans ........................................................................... 1 1.2 Honey bee health challenges ........................................................................................... 2 1.2.1 Varroa mites ............................................................................................................ 2 1.2.2 Viruses .................................................................................................................... 3 1.2.3 Bacterial, fungal, and microsporidian pathogens .................................................... 6 1.2.4 Other environmental stressors ................................................................................. 7 1.3 Honey bee queens ........................................................................................................... 8 1.3.1 Specialized reproductive physiology ...................................................................... 9 1.3.1.1 Reproductive plasticity ..................................................................................... 10 1.3.2 Queen problems .................................................................................................... 10 x 1.3.3 Differentiating “queen problems” from “colony problems” ................................. 11 1.3.3.1 Innate, physiological causes .............................................................................. 12 1.3.3.2 Abiotic causes ................................................................................................... 12 1.3.3.3 Biotic causes ..................................................................................................... 13 1.4 Honey bee immunity ..................................................................................................... 14 1.4.1 Social immunity .................................................................................................... 14 1.4.2 Innate immunity .................................................................................................... 16 1.4.3 Transgenerational immune priming ...................................................................... 17 1.5 A reproduction-immunity trade-off .............................................................................. 18 1.6 Goals and hypotheses .................................................................................................... 20 1.6.1 Research goals ...................................................................................................... 20 1.6.2 Hypothesis 1.......................................................................................................... 21 1.6.3 Hypothesis 2.......................................................................................................... 21 1.6.4 Hypothesis 3.......................................................................................................... 22 Chapter 2: Virus infections cause symptoms of queen failure ................................................23 2.1 Rationale and research goals ......................................................................................... 23 2.2 Quantifying queen failure ............................................................................................. 24 2.2.1 Failed queens have smaller ovaries and reduced sperm viability ......................... 24 2.3 Investigating the relationship between virus infection and symptoms of queen failure 27 2.3.1 Ovary size is inversely linked to viral abundance in naturally infected queens ... 27 2.3.2 Experimental infection with IAPV causes a reduction in ovary mass .................. 30 2.4 Molecular changes associated with failure and infection ............................................. 31 2.4.1 Expression of vitellogenin is reduced after injection with IAPV ......................... 31 xi 2.4.2 The ovaries of failed queens have altered protein composition compared to healthy queens ....................................................................................................................... 32 2.4.3 Heat-shock proteins are upregulated in virus-infected queens ............................. 35 2.5 Conclusion .................................................................................................................... 38 2.6 Methods......................................................................................................................... 39 2.6.1 Queen surveys ....................................................................................................... 39 2.6.2 Sperm viability, sperm counts, and ovary mass .................................................... 40 2.6.3 Viral quantification for queen surveys .................................................................. 41 2.6.4 Statistical analyses of phenotypic data ................................................................. 42 2.6.5 Ovary proteomic sample preparation .................................................................... 43 2.6.6 Ovary proteomic data acquisition and analysis .................................................... 44 2.6.7 Statistical analysis of spermathecal proteomic data .............................................. 45 2.6.8 IAPV experimental infections for measuring ovary mass .................................... 46 2.6.9 IAPV experimental infections for expression analysis ......................................... 46 2.6.10 RNA isolation, cDNA synthesis, and quantitative PCR (qPCR) on experimentally infected queens ...................................................................................................................... 48 Chapter 3: Common viral infections inhibit egg laying in honey bee queens and are linked to premature supersedure ...........................................................................................................51 3.1 Rationale and research goals ......................................................................................... 51 3.2 Cage experiment ........................................................................................................... 52 3.2.1 Confirming levels of virus copy numbers ............................................................. 52 3.2.2 Fertility analysis .................................................................................................... 54 3.2.3 Hemolymph proteome analysis ............................................................................. 56 xii 3.2.4 Perturbation caused by the cage environment ...................................................... 57 3.3 Field experiment ........................................................................................................... 59 3.3.1 Virus RNA copies and mite levels ........................................................................ 59 3.3.2 Supersedure cells, ovary mass, and brood area ..................................................... 61 3.3.3 Hemolymph proteome changes ............................................................................. 65 3.4 Discussion ..................................................................................................................... 66 3.4.1 Discrepancy between outcomes of the cage vs. field experiments ....................... 67 3.4.2 Evidence for a partial escape of trade-off between reproduction and immunity .. 68 3.4.3 Non-canonical antiviral immune response ............................................................ 70 3.4.4 Unexplained presence of Varimorpha (Nosema) apis protein .............................. 71 3.4.5 Influence of colony environment on queen infection levels ................................. 71 3.5 Conclusion .................................................................................................................... 72 3.6 Methods......................................................................................................................... 73 3.6.1 Cage experiment ................................................................................................... 73 3.6.2 Field experiment ................................................................................................... 74 3.6.3 Virus isolation and propagation ............................................................................ 77 3.6.4 Virus Purification .................................................................................................. 78 3.6.5 Injection of queens ................................................................................................ 79 3.6.6 Queen dissection ................................................................................................... 80 3.6.7 Proteomics sample preparation ............................................................................. 80 3.6.8 Mass spectrometry data acquisition ...................................................................... 81 3.6.9 Mass spectrometry data processing and analysis .................................................. 82 3.6.10 Sample preparation for viral quantification .......................................................... 83 xiii 3.6.11 RT-qPCR for viral quantification ......................................................................... 84 3.6.12 Statistical analysis ................................................................................................. 86 Chapter 4: Egg composition changes seasonally and after acute maternal virus infection ..87 4.1 Rationale and research goals ......................................................................................... 87 4.2 Immune effectors are upregulated in eggs after immune activation in queens ............. 89 4.3 Egg composition is unchanged among queens with differing levels of natural viral infections ................................................................................................................................... 91 4.4 The lipidome and proteome of eggs collected from the field are strongly influenced by date of collection ....................................................................................................................... 94 4.5 Conclusion .................................................................................................................... 97 4.6 Methods......................................................................................................................... 98 4.6.1 Immune challenge cage experiment ...................................................................... 98 4.6.2 Queen and egg collection from field sites ............................................................. 98 4.6.3 Statistical analysis ................................................................................................. 99 4.6.4 Proteomic sample preparation and data acquisition .............................................. 99 4.6.5 Proteomic data processing and analysis .............................................................. 100 4.6.6 Lipidomic and metabolomic sample preparation ................................................ 102 4.6.7 Metabolomics LC-MS/MS analysis .................................................................... 103 4.6.8 Lipidomics LC-MS/MS analysis ........................................................................ 103 4.6.9 Lipidomics and metabolomics data processing .................................................. 105 4.6.10 Lipidomic and metabolomic data analysis .......................................................... 105 4.6.11 Viral quantification on queens collected from the field ...................................... 106 Chapter 5: Conclusion ...............................................................................................................107 xiv 5.1 Viral infections impact honey bee queens' reproductive success ............................... 108 5.2 A reproduction-immunity trade-off partially explains the link between virus infection and reduced queen fertility ...................................................................................................... 110 5.3 The effects of maternal infection on eggs are context-dependent .............................. 111 5.4 Caveats and limitation ................................................................................................. 111 5.4.1 Ovary mass as a proxy metric for fertility .......................................................... 111 5.4.2 Relevant routes of transmission for experimental infections .............................. 112 5.5 Future directions and applications .............................................................................. 113 5.5.1 Interactive effect of nutrition .............................................................................. 113 5.5.2 Can queens recover from viral infections? ......................................................... 114 5.5.3 Potential for queen “vaccines” ............................................................................ 115 5.5.4 Applications for beekeeping operations .............................................................. 116 5.6 Conclusion .................................................................................................................. 117 Bibliography ...............................................................................................................................118 xv List of Tables Table 2.1 Summary of queen surveys ........................................................................................... 24 Table 2.2 Detailed parameters for statistical models of field surveys .......................................... 26 Table 2.3 Statistical summaries of proteins correlating with total viral loads. ............................ 36 Table 3.1 Statistical parameters for cage experiment* ................................................................. 53 Table 3.2 Statistical parameters for field experiment* ................................................................. 64 xvi List of Figures Figure 1.1 Overview of innate immune pathways. ....................................................................... 17 Figure 2.1 Fertility metrics of failed and healthy queens. ............................................................ 25 Figure 2.2 Relationships between viral RNA copies and ovary mass .......................................... 29 Figure 2.3 Ovary mass and vitellogenin transcription are significantly decreased after IAPV injection. ........................................................................................................................................ 31 Figure 2.4 Changes to ovary protein expression in failed honey bee queens. .............................. 34 Figure 2.5 Heat-shock proteins associated with natural and experimental viral infections in honey bee queens. .................................................................................................................................... 37 Figure 3.1 Virus RNA copy numbers of cage experiment queens. .............................................. 53 Figure 3.2 Viral infections reduce ovary mass and egg laying in cage experiment queens ......... 56 Figure 3.3 Hemolymph immune proteins were differentially expressed between groups in cage experiment queens ........................................................................................................................ 57 Figure 3.4 Effects of cage environment on ovary mass and protein expression ........................... 59 Figure 3.5 Virus RNA copies in field experiment queens and workers ....................................... 61 Figure 3.6 Relationships between ovary mass, virus RNA copies, brood, and supersedure cells in field experiment queens ................................................................................................................ 62 Figure 3.7 Proteins associated with ovary mass in the hemolymph of the field experiment queens....................................................................................................................................................... 66 Figure 4.1 Immune proteins are upregulated in both the ovaries and eggs of queens injected with inactivated and infectious virus. ................................................................................................... 91 Figure 4.2 Variation among queens collected from the field ........................................................ 94 xvii Figure 4.3 The composition of eggs changes with the season ...................................................... 96 xviii List of Abbreviations AFB American foulbrood BQCV black queen cell virus Cq quantification cycle/cycle threshold dsRNA double-stranded RNA DWV deformed wing virus EFB European foulbrood HSP heat shock protein IIS insulin/insulin-like growth factor IAPV Israeli acute paralysis virus IPM integrated pest management JH juvenile hormone LC liquid chromatography MS mass spectrometry PAMP pathogen associated molecular pattern PCA principle component analysis QC quality control QMC queen monitoring cages QMP queen mandibular pheromone qPCR quantitative polymerase chain reaction RCF relative centrifugal force RPM rotations per minute SBV sacbrood virus xix sHSP small heat shock protein TGIP transgenerational immune priming Vg vitellogenin xx Glossary Fecundity: the lifetime potential for an organism to produce offspring, measured by the number of gametes produced in a lifetime Fertility: the ability of an organism to produce offspring, measured by the actual number of offspring produced in a period of time Plasticity: the capacity of an organism to alter its behavior, physiology, and/or morphology in response to the environment xxi Acknowledgments My immense gratitude goes to Dr. Leonard Foster, who has been not only an incredibly supportive, patient, and generous supervisor but also a friend who has changed the course of my life and helped shape my identity as I metamorphosed into the person I am today. Thank you for introducing me to a love of moving through the mountains and the realization that I am far more capable than I had ever dreamed. Thank you for fostering a fascination and love of honey bees which has now set the course for my life. To Dr. Alison McAfee, I offer my deep appreciation for all the years of mentorship, collaboration, and, above all, friendship. To have had a mentor who was not only willing to spend hundreds of hours over the years guiding me through everything from big-picture brainstorming to the most minute, detailed revisions of final drafts, but is also a wonderful friend with whom I can talk and laugh with for hours is one of the greatest gifts I will have ever been given. I am thankful beyond what words can express for my parents, Jeff and Carla, who have never had any agenda or expectations for my path through life and who have always had nothing but the most intense enthusiasm for any and every pursuit I’ve chosen. Your enduring support, pride, and love have made up the raft that has already carried me so far in life. Thank you for being the most amazing parents. Thank you, Rhys, for being the most wonderful, loving, steadfast, and supportive partner in everything that I do, and all the things I dream of doing in the future. xxii I will be forever grateful to Heather Higo, Julia Common, and Emily Huxter for the years of patient mentorship in the bee yard, which helped shape me into a beekeeper, not just a bee-researcher. My thanks to the entire Foster Lab, for all the years of friendship, laughter, inspiration, community, mentorship, adventures, and patience with escaped bees flying around the lab. Special thanks to Jenny Moon, Lab Manager Extraordinaire: an absolute superhero of a human whose abilities to find things, answer questions, and generally be helpful are rivaled by none. Thank you to Rob for walking with me through all the challenges of the early years. I am grateful to my committee members, Dr. Thibault Mayor and Dr. Katie Marshall, as well as Dr. David Tarpy for the support, guidance, and feedback over the years and to Dr. Scott Covey for helping me develop my scientific writing and for all his efforts in creating a course that prepares students for scientific research. And finally, an abundance of gratitude to all the funding sources that supported me and this research over the years: Project Apis m., the Canadian Bee Research Fund, the Boone Hodgson Wilkinson Trust Fund, the Canadian Association of Professional Apiculturists, the SH Zbarsky Fellowship, the American Association of Professional Apiculturists, the Eastern Apiculture Society, the AS Atwal Graduate Scholarship, the Foundation for the Preservation of Honey Bees, and the Vail Resorts EpicPromise Scholarship program. xxiii Dedication To all the incredible teachers I’ve had throughout my life. Thank you for instilling in me a love of learning, creating opportunities for me to excel, introducing me to new worlds, and supporting my passions. 1 Chapter 1: Introduction1 1.1 The history of honey bees and humans Humans have benefited from honey bees since pre-historic times: cave drawings from ~7000 B.C. depict ancient humans gathering honey from bee nests in trees1. Over the centuries, we have progressed from merely robbing honey from wild colonies to housing swarms of bees in earthenware jars and woven baskets to ultimately managing and manipulating honey bees as livestock on a large scale, predominantly in wooden hives with removable frames2. The Western honey bee (Apis mellifera) originated in the Middle East and North Africa, but has spread through natural movement, and later by human transport, across the entire globe3. Modern commercial agriculture now relies heavily on honey bees – nearly half of the major global food commodities depend on commercial honey bee pollination4,5. This pollination service is estimated to be worth at least tens of billions annually6–9, in addition to the value of commodities from the hive, including honey, wax, and pollen5. In recent years, there have been significant concerns about increasing colony loss, particularly over the winter – in the early 2000’s in the US and Canada the overwintering losses doubled from the previously deemed acceptable 15% to 30% and have remained elevated ever since10,11. 1 Content in this chapter has been published: Chapman, A., et al. (2022). Fertility costs of cryptic viral infections in a model social insect. Scientific Reports, 12(1), 15857; Chapman, A., et al. (2024). Common viral infections inhibit egg laying in honey bee queens and are linked to premature supersedure. Scientific Reports, 14(1), 17285; and posted as a pre-print: Chapman, A., et al. (2024). Honey bee egg composition changes seasonally and after acute maternal virus infection. BioRxiv. 2 1.2 Honey bee health challenges Honey bees face an increasing number of interacting stressors, which are made more complex by the fact that they a) live in teeming colonies with thousands of individuals through which disease can rapidly spread12, b) are transported in huge numbers inter- and intra-continentally, allowing further spread of novel diseases and pests13–15, c) experience nutritional stress as a result of monoculture agricultural practices and having more colonies that a given area can support16, and d) are often kept in proximity to agricultural landscapes, which results in regular exposure to agrichemicals17. All of this is in addition to other large-scale factors including climate change, habitat loss, and the intensification of agricultural land use that have been suggested as responsible for declines in global insect populations as a whole18–21, although some researchers have suggested that some of the most alarming reports of insect decline may be overstated or misleading due to biased methodologies22,23. 1.2.1 Varroa mites Among many stressors, one parasite is now regarded as the most significant global threat to honey bee health: the Varroa destructor mite24, hereon referred to as varroa. Varroa was originally endemic to north-east Asia, where it co-evolved with its host, the Asian honey bee (Apis cerana)25. With the movement of Western honey bees in and out of Asia in the 1950s, varroa jumped hosts to A. mellifera and was then introduced to North America in the 1980s15. Varroa feeds primarily on the hemolymph of pupae and on the fat body of adult bees26,27, which is a multi-functional organ that appears as a thin membrane lining the abdominal cavity and is the main site for both the storage and utilization of nutrient reserves, as well as the synthesis of circulating hemolymph proteins and metabolites28. The varroa mite is an obligate parasite of the 3 honey bee, requiring feeding on larval tissue to initiate reproduction, which occurs only in the capped cells of developing pupae24. Parasitism by varroa has numerous detrimental effects on bees, including reduction of body weight, interference with organ development, and suppression of the immune system29, which is particularly problematic as varroa are also efficient vectors of honey bee-associated viruses, which can dramatically impact the health of colonies24. As a result, controlling varroa infestation is one of the most important aspects of modern beekeeping. A number of synthetic miticides are available for commercial use, although widespread resistance has developed over the years30,31. As a result, there is a recent focus on encouraging beekeepers to employ integrated pest management (IPM) principles involving a combination of cultural methods (i.e. selective breeding), mechanical methods (i.e. using screened bottom boards), and the use of organic chemicals (i.e. formic acid, oxalic acid, and essential oil-based treatments) in addition to, or replacement of, synthetic miticides30. 1.2.2 Viruses Honey bees are known to be infected by more than 20 different viruses, with more being discovered regularly. These viruses are primarily positive-sense single-stranded RNA viruses in the order Picornavirales32–34. Additionally, viruses that affect managed species like honey bees (Apis mellifera) can infect a broad range of insect host species, creating the potential for pathogen spillover to native species pollinating plants in the same regions35–42. Several of the most common viruses are regularly (and for some, almost ubiquitously) detected in honey bee populations across the globe. These include deformed wing virus (DWV) and sacbrood virus (SBV), which are Iflaviruses, and black queen cell virus (BQCV), Israeli acute paralysis virus (IAPV), acute bee paralysis virus (ABPV), and Kashmir bee virus (KBV), which are 4 Dicistroviruses32. DWV and BQCV are the most prevalent viruses in North America, detected in 90-100%, and 70-100% of samples, respectively, in surveys across the United States and Canada43–45. IAPV is the next most prevalent virus, detected in 40-70% of samples in some provinces in Canada43 and 5-20% of samples in a survey in the United States45. Most viral infections persist at levels that do not result in overt symptoms, but can nonetheless cause a reduction in lifespan and increase in mortality46–48. Viruses, and other pathogens, are transmitted in numerous ways through and between colonies (reviewed in Yañez et al.49). Horizontal transmission is argued as the most common route of transmission for most viruses49 and takes place via oral contact such as during trophallaxis (where food is exchanged orally between two individuals) and through oral contact with contaminated feces. Direct evidence for sexual transmission during mating between queens and drones exists for DWV and many other viruses (including BQCV and IAPV) have been detected in reproductive tissues in both queens and drones49. Vector-mediated transmission occurs predominantly via the varroa mite, which has been shown to be a biological vector (where the virus multiplies inside the host) and a mechanical vector (transmitted during feeding activities) for DWV and IAPV and a purely mechanical vector for many other viruses49. Other parasites and pests including the tracheal mite (Acarapis woodi), the tropilaelaps mite (Tropilaelaps mercedesae), and the small hive beetle (Aethina tumida) have also been shown to be reservoirs for and transmit DWV49. Additionally, viruses can be transmitted vertically, whereby queens transmit viruses to their offspring either within the egg, on the surface of the egg, or via infected sperm49, for which there is direct evidence for DWV and BQCV, and indirect evidence for numerous other viruses in the detection of virus in surface-sterilized eggs49. 5 Deformed wing virus appears to be the single most prevalent virus and is one of the two viruses which is clearly and regularly vectored by varroa32,47,50,51. Although DWV mainly persists in the honey bee population by causing a low-level covert infection, in some conditions it causes clear symptoms in adult bees, which emerge from pupation with shriveled wings, reduced body size, and a blackened appearance32. DWV has two common genotypes, A and B, the latter of which was detected more recently, is also called Varroa destructor virus-1 after it was isolated from varroa52, and is potentially replacing genotype A53. Some data derived from worker infection experiments suggest that DWV-B is also more virulent than DWV-A54,55, but Tehel et al. (2019)56 found no difference, and Norton et al. (2020)57 found that DWV-A was more virulent than DWV-B. ABPV is the other main virus to be vectored by varroa24 and is closely related to KBV and IAPV, the three together forming a complex (the “Acute–Kashmir–Israeli or AKI complex”) of related species which are often analyzed and discussed together58. These viruses are also widely prevalent at low levels, without obvious symptoms, but at elevated titres or when injected into pupae or adults are extremely virulent, causing trembling, rapid paralysis, the inability to fly, and hairlessness58. SBV also infects all life stages of honey bees, but is primarily symptomatic in larvae, which are the most susceptible to infection. It was the first virus to be reported as being widely distributed throughout the United States in 191359. Larvae infected with SBV fail to pupate, then turn yellow and brown as fluid containing millions of viral particles accumulates in their saclike cuticle32,48. Adults infected with SBV may have a reduced lifespan60. 6 BQCV is another extremely widespread virus, most prevalent in the spring, and is the only virus whose most visible effects are specific to the queen caste rather than appearing primarily in workers. Infected queen pupae die and turn dark-brown or black, which eventually causes the outer wax walls of the queen cell to turn black32. BQCV is known to be associated with the microsporidian parasite Nosema apis61. 1.2.3 Bacterial, fungal, and microsporidian pathogens Honey bees also suffer infections from a wide range of other pathogens and parasites. The common bacterial and fungal diseases present in honey bees predominantly impact the brood (developing larvae and pupae), while the microsporidian parasites Nosema sp. mainly impact adult bees. There are two bacterial “foulbrood” diseases: European foulbrood (EFB), caused by Melissococcus plutonius, and American foulbrood (AFB), caused by Paenibacillus larvae. Both bacteria infect the midgut of larvae, cause death and decomposition, and ultimately lead to characteristic foul-smelling brood62. While both infections are economically damaging, AFB is considered to be the most destructive brood disease of honey bees: the spores, of which only less than 100 are necessary for infection, can persist in the environment and on equipment for decades, and a colony experiencing clinical symptoms will likely die without treatment63. Chalkbrood results from infection by the fungal pathogen Ascosphaera apis, which is fatal to individual larvae but rarely responsible for the mortality of a whole colony, though it reduces colony productivity through a reduction in the worker population64. After infection via ingestion, the fungal mycelium takes over the entire body cavity of a larva, ultimately leaving “mummies” ― white or black hardened larval cadavers, which appear as small pieces of chalk, characteristic 7 of the name. The obligate intracellular microsporidian parasites N. apis and N. ceranae (recently suggested to be reclassified as Vairimorpha65, with dissenting proposals that both genera should be synonymized66) infect the epithelial cells of the midgut67, are transmitted mainly via the fecal-oral pathway, and have been associated with colony losses68. N. apis causes dysentery10 and both species can lead to a reduction in the lifespan of adults69. 1.2.4 Other environmental stressors The abiotic stressors to honey bee health, which often synergistically worsen the outcomes of infection, include agrochemical exposure, extreme weather, habitat loss, and nutritional stress. Pesticide exposure and climate change are also often cited as drivers of broader insect decline70–74 and extreme temperatures can have immunomodulatory effects in some insects75,76; therefore, climate change may have interactive effects with other stressors77. Honey bees encounter pesticides and herbicides while out foraging in the environment on crops that have been sprayed, or adjacent forage which has been unintentionally contaminated, as well as in their own hives. Crops that have been treated with pesticides as seed coating reduce this environmental drift, but the pesticide remains active in the plant tissue. Returning foragers bring back contaminated pollen and nectar to the hive, exposing the rest of the colony to the chemical. Beekeepers also intentionally expose their colonies to agrochemicals in the form of synthetic miticides, which are lipid-soluble and accumulate in the wax. The lethal dose at which half the population dies (LD50) is a widely accepted way of assessing chemical toxicity, but it varies hugely based on study design and fails to capture the plentiful 8 sublethal effects of agrochemical exposure that can significantly damage the health of a colony78. These effects include decreased nutritional stores in workers79, increased worker mortality and reduced hygienic behavior80, impacts on the motor function of adults81, and impaired olfactory learning and memory storage82, among many other effects. Additionally, pesticide exposure also acts synergistically with other stressors, including varroa, to reduce flight capacity of foragers83, and virus infection reduce antiviral defense mechanisms resulting in increased susceptibility to infection84. Interestingly, some of these interactions may be dependent on nutritional stress85, highlighting the complex, context-dependent nature of honey bee health challenges. 1.3 Honey bee queens Honey bees and other social insects exhibit a reproductive division of labour whereby the queen is the sole producer of offspring sustaining the population, while bees in the worker caste carry out all other tasks necessary for colony survival. Queens develop from female larvae that have been fed royal jelly, a nutritious secretion from the hypopharyngeal glands of nurse bees, during their entire larval development, while larvae that are destined to become workers are fed a mixture of royal jelly, pollen, and honey as older larvae86. Queens emerge as adults 16 days after being laid as an egg ― faster than workers (21 days) and drones (26 days), which are male bees that are haploid and develop from unfertilized eggs87. The presence of the queen, and more specifically her queen mandibular pheromone (QMP), is responsible for influencing much of the social organization in a colony, including stimulating pollen foraging and brood rearing, guiding the timing of behavioural transitions in workers, inhibiting the activation of worker ovaries, and stimulating the attraction of young bees to care for the queen88,89. The queen, in turn, depends on the worker bees to care for and feed her, as she is primarily fed glandular secretions from the 9 hypopharyngeal glands (HPGs) of nurse bees, the young female worker bees whose main task in the hive is to feed the queen and larvae90. 1.3.1 Specialized reproductive physiology A queen can live for up to 5 years, compared to worker bees, which live only six to eight weeks in the summer and several months in the winter91. Honey bee queens mate over a short period of time with multiple drones at a few weeks of age and store the sperm they acquire for the rest of their lives in a specialized organ called the spermatheca87. Their physiology is tailored for laying eggs and little else: their ovaries take up the vast majority of their abdominal cavity, allowing them to lay up to 2000 eggs a day, which is roughly equivalent to their own body weight92. Queens do not have pollen baskets, barbed stingers, or honey crops that workers possess87. While workers possess rudimentary ovaries, each ovary only contains a handful of ovarioles (the conveyor belt-like tubes through which developing oocytes travel), while queen ovaries contain ~200 ovarioles per ovary93–95. During the process of oogenesis, oocytes (developing eggs) start as differentiating germline cells and progress through the ovarioles accompanied by nurse cells which provide nutrients and maternal RNA before passing through the oviduct to be laid93,95. One of the main egg yolk proteins required for the maturation of oocytes is vitellogenin, which is mainly synthesized in the fat body of the queen93. During this process, a series of regulated cell-death “checkpoints” governs whether the oocyte continues to develop or dies and is reabsorbed93,96. 10 1.3.1.1 Reproductive plasticity These cell-death checkpoints allow honey bee queens to exhibit a high degree of reproductive plasticity with their ability to rapidly start and stop laying eggs in response to environmental cues, which mainly depends on the frequency and abundance of nutrition provided by attendants. For example, in the days before swarming (during which about three quarters of the colony leaves with the existing queen to establish a new colony), a queen’s egg output declines, causing her ovaries to shrink, allowing her to fly more easily when she leaves the hive with the swarm. In this period, the frequency and duration of feedings by worker attendants decreases97. In temperate climates, colonies are mostly broodless for a period over winter, during which a queen greatly or entirely reduces her egg laying. The cues triggering this transition are likely the lack of environmental pollen resources in combination with declining day length98. Another circumstance under which queens cease laying is when they are confined to a cage for beekeeping practices, during which they lack space to lay eggs. In each of these circumstances, the oogenesis checkpoints regulate the queens’ production of eggs, allowing her to avoid wasting resources when laying eggs would not be beneficial, but also allowing her to quickly recommence laying eggs once circumstances change93. 1.3.2 Queen problems Stressors that reduce fertility are particularly worrisome, especially for social insects, as damage to a single individual (the queen) can weaken the fitness of the entire colony. “Poor queens” are regularly and increasingly reported by beekeepers as one of the top contributing factors to annual (usually overwintering) colony losses99–102. While honey bee queens can live for five years or more 87, beekeepers regularly replace queens at much younger ages, often after less than one 11 year103. Queens that beekeepers deem poor or failing can have a wide variety of symptoms, including decreased or poor patterns of egg laying, laying only unfertilized eggs (which develop into drones), the prevalence of brood disease, or rejection by workers104. These compromised queens have been shown to have reduced sperm viability105. This has obvious consequences for beekeepers, who then either have to buy queens from queen producers or invest their own time and resources into producing replacement queens themselves. This regular replacement of queens is particularly problematic in Canada – Canadian beekeepers have the greatest need for replacement queens in early spring, before the weather conditions are suitable for Canadian queen producers to supply queens. This results in the large-scale importation of queens from warmer climates and, therefore, the constant introduction of new genetic traits which are maladapted for the colder climate and the prevalence of diseases (e.g. chalkbrood) in Canada106. 1.3.3 Differentiating “queen problems” from “colony problems” A major challenge to studying the causes of poor queens is that many of the symptoms that beekeepers typically attribute to problems with the queen could also be caused by other non-queen related issues in the colony. For instance, “poor brood pattern” is an often-cited queen problem, wherein the pattern of the brood is spotty rather than appearing as a full comb, with nearly every cell occupied by brood of approximately the same age. But recent work by Lee et al.107 has shown that when queens with poor brood patterns are switched into colonies with a good brood pattern, their pattern often improves, and when the opposite switch is made, the pattern of “good” queens often declines, indicating that some other colony effect is at play. 12 1.3.3.1 Innate, physiological causes Queen producers artificially induce colonies to raise queens on a large scale by manually moving just-hatched larvae from worker brood cells into plastic or wax vertically hanging “cups” (which mimic natural, bee-made queen cups) in a process called grafting. The bees, sensing the orientation of these cells, feed these queen-destined larvae an altered diet which induces the physiological differentiation into a queen. The age of larvae that beekeepers graft when rearing new queens can impact the physiology and size of queens108, with older larvae developing into worker-like queens, impacting their future reproductive potential109–112. How well a queen is mated is also a significant determining factor to her quality or reproductive potential. During her early-life matings, a queen must acquire enough sperm, approximately 5-7 million,113,114 to last her lifetime. Honey bees are highly polyandrous – a queen will mate with an average of 12 drones115 – and so an additional determining factor of her mating quality is the diversity of sperm she obtains. A colony benefits from this genetic diversity in numerous ways (reviewed in Delaney et al.113), including increased resistance to pathogens, increased diversity in the behavior of the workforce, and reduced impacts of the single-locus sex-determination system. This in system fertilized eggs which are homozygous at the complementary sex determiner (csd) gene develop into diploid drones which are promptly cannibalized by the workers116. 1.3.3.2 Abiotic causes Honey bee queens can be exposed to extreme temperature fluctuations in the hive and especially during shipping, which can damage the viability of their stored sperm105,117, causing their productivity to decline105,118. Meanwhile, a large number of studies have shown that exposure to pesticides, fungicides, and miticides negatively affects queens in a myriad of ways including 13 suppressing their immune system119, causing a decrease in metabolic and detoxification enzymes, leading to increased mortality, especially when combined with infection120, disrupting nuptial flights and causing a decrease in stored sperm quantity121, reducing sperm viability122, altering the chemical composition of QMP, resulting in reduced worker attractiveness123, and impacting reproductive physiology124. 1.3.3.3 Biotic causes Many of the honey bee pathogens commonly found in colonies can infect queens ― including Nosema, DWV, SBV, BQCV, ABPV, IAPV, and others ― but their pathological effects are not yet fully defined, particularly when it comes to the effects on their reproductive potential. Both Nosema species have been shown to infect the gut of queens, and N. ceranae has been detected in the ovaries125. Both can be transmitted to queens horizontally from workers126,127 or sexually during mating128, but not vertically to their offspring126. A few studies done on small numbers of queens have identified some pathological effects of Nosema in queens, including similar gut lesions as seen in workers, reduced lifespan127, and, in extreme cases, degeneration of oocytes103. N. ceranae also caused altered pheromone production and a seemingly compensatory increase in vitellogenin titers and total antioxidant capacity69. DWV is commonly vectored by varroa, but is also transmitted horizontally, vertically, and sexually 129–131. Queens rarely show any overt symptoms of infection, and the deformed wing phenotype seen in workers has only been reported a single time in a queen132. However, the virus has been detected in several queen tissues, including the head, thorax, gut, fat body, ovaries, and 14 spermatheca133,134. High levels of DWV infection have been associated with ovarian degeneration and egg-laying deficiencies135, but few other studies have examined the pathological effects of DWV infection on queens, with most focusing solely on detection of the virus in various organs. Other viruses, including SBV, BQCV, ABPV, and IAPV have also been detected in various queen tissues including the ovaries, but generally only in low titers, and the effects on queen health have been even less well-studied103,134,136,137. Infection with IAPV and KBV, which infect both Apis and non-Apis pollinators, results in slower colony start-up and a reduction in egg laying in Bombus terrestris138. A recent study on the prevalence of a newly identified honey bee virus, Solinvivirus-1, found that apiaries testing positive for it were nearly twice as likely to contain queenless colonies139. Given the high and increasing prevalence of viruses in colonies, and that viruses make up most of only a handful of pathogens known to infect adult queens, a better understanding of the effects of virus infection will be instrumental to better understanding queen health as a whole. 1.4 Honey bee immunity 1.4.1 Social immunity While living in a social group begets numerous benefits arising from cooperative nest-building, brood care, foraging, and predator defenses, it also brings with it the disadvantage of increased risk for the transmission of disease12,140. As a result, honey bees (and other social insects like termites, ants, and wasps) have developed a diverse set of group-level behaviors to defend the colony against disease spread. Some behaviors are constitutive and preventative, such as the collection of antimicrobial plant resins for mixing with wax to create propolis for use as a nest-building material, which has been shown to reduce bacterial loads in the colony141. The age-15 based division of labour in a colony is also prophylactic. Younger bees are mainly responsible for performing tasks in the hive, starting with cleaning cells and, as they age, moving to feeding larvae and the queen, and finally to handling nectar142. Typically, only older bees commence foraging, which protects the young workers in the nest from exposure to outside pathogens. Other behaviors are in response to pathogens or parasites. Examples include allogrooming, where nestmates groom each other to remove parasites such as varroa and tracheal mites143. One of the most quintessential examples of social immunity in honey bees is hygienic behavior. Bees can detect dead, diseased, or parasitized brood, in addition to infected and immune-stimulated adult individuals, via odorant cues and remove them from the hive12,140,142,144–147. Honey bees also exhibit “altruistic behavior” in which infected individuals voluntarily leave the hive and do not return, thereby preventing the further spread of disease145. Workers infected with Nosema have also been shown to be less likely to feed the queen148,149. When viewing the colony as a superorganism in which the queen could be viewed analogously to germline cells, this particular special protection of the queen from infection is paramount to the survival of the colony as a whole140. The colony can even produce a “social fever” in which the bees elevate the temperature of the nest in response to infection by the heat-sensitive bacterium A. apis (the causative agents of chalkbrood)150. These behaviors taken together are valuable because mounting an induced, molecular immune response is energetically costly to individuals151, the effects of which can be observed at the colony level with a reduction in productivity152. 16 1.4.2 Innate immunity Honey bees also possess individual-level immune defenses, of which the first line of constitutive defense is the physical barrier of their cuticle and gut epithelium151. When pathogens do surmount these social and physical barriers, individuals can mount an innate, inducible immune response involving both cellular and humoral immunity. Searches for honey bee genes orthologous to fruit flies, mosquitoes, and moths have identified four main signaling pathways responsible for directing inducible immune responses to pathogen-associated molecular patterns (PAMPs): the Toll, Imd, JAK/STAT, and JNK pathways153. There is likely significant overlap and cross-talk between these pathways and their effectors, which include the phenoloxidase cascade responsible for the melanization response, antimicrobial peptides, lysozymes, and the proliferation of hemocytes153. The hallmark antiviral immune response in honey bees is the RNAi response, which uses the double-stranded RNA (dsRNA) made by viruses to produce small interfering RNAs (siRNA) that target the virus for degradation153,154. Honey bees also possess non-specific responses in recognition of dsRNA as a PAMP, which activates all four of the above-mentioned major immune pathways, resulting in the activation of responses canonically associated with bacterial or fungal infection, such as the transcription of antimicrobial peptides (Figure 1.1)154. 17 Figure 1.1 Overview of innate immune pathways. A brief overview of which pathogens and PAMPs activate the various innate immune pathways and the major immune responses that they are responsible for activating153,154. 1.4.3 Transgenerational immune priming While insects lack the lymphocyte and antibody-based adaptive, or acquired, immunity that vertebrates possess, there is growing evidence that they are capable of inducing immune responses that involve immunological memory and are specific155–157. This “acquired” immune response in invertebrates is referred to as “immunological priming.”155,157 One intriguing function of this priming in many invertebrates is transgenerational immune priming (TGIP), whereby parents transfer either immune effectors or immunological triggers to their offspring, helping them prepare for a potential pathological encounter (reviewed in Tetreau et al.157). The mechanism for the transfer of bacterial immunological triggers has been elucidated in honey bees: the yolk protein vitellogenin binds and transports bacterial fragments containing pathogen-associated molecular patterns from queen to offspring (as well as horizontally from workers to the queen in glandular secretions)158,159 conferring resistance to bacterial infection in the 18 larvae160. In contrast, the evidence for similar immune priming of offspring after maternal virus exposure in honey bees is less clear, appearing to be idiosyncratic and dependent on context, including the route of maternal exposure and genetics136,161,162. 1.5 A reproduction-immunity trade-off Beyond the symptomatic impacts on insect health, infections also have the potential to indirectly reduce fecundity, even in the absence of overt symptoms, through a trade-off between reproductive ability and immune activation. A compromise between reproduction and immunity has been well studied in many insects163, but not previously in honey bees. Across multiple orders, mated female insects tend to have both reduced constitutive and induced immunity compared to their unmated counterparts. Reproductively active insects including crickets (Allonemobius socius), mealworm beetles (Tenebrio molitor) and wood ants (Formica paralugubris) have been shown to have reduced hemocyte number and bacterial lytic capacity of the hemolymph164, reduced phenoloxidase activity165,166, reduced encapsulation response164,167–169, and reduced survivorship after infection170. Correspondingly, immune challenged individuals frequently showed a reduction in reproductive output measured as reduced overall fecundity171, reduced protein in both ovaries and eggs172,173, reduced oviposition rate173, and reduced viability of stored sperm174. This compromise, which is generally considered to be driven by the allocation of limited resources since both reproduction and immune activation are energetically costly163, could be particularly relevant for honey bee queens, who can sustain an enormous reproductive output92. It is reasonable to assume that sustaining such an energetically intensive process would come with costs, not only to immune function but to longevity and somatic maintenance in general, as is the case in many insects, many of which are not as specialized for 19 reproduction as the honey bee queen. Additionally, a queen’s resource investment in ovary size is highly plastic and responds dramatically to external stressors like nutrient availability87. Thus, infections could negatively impact fertility either through direct effects of infecting reproductive tissue, or compromised allocation of resources associated with systemic infections. Honey bees and other highly social insects have escaped the reproductive trade-off with lifespan, where the reproductive castes exhibit both enormous reproductive output and increased longevity compared to the non-reproductive castes91,92,175,176. This uncoupling of fecundity and longevity appears to be due in part to a rewiring of the endocrine network that signals resources to be directed towards one process or the other in solitary insects91,177,178 and involves insulin/insulin-like growth factor (IIS), juvenile hormone (JH), and vitellogenin (Vg). Given that social insects have escaped the reproduction-lifespan compromise, it therefore calls into question whether they may have also evaded the trade-off of reproduction with immunocompetence. However, there is conflicting evidence for other social insects. Black garden ant (Lasius niger) queens appear to have removed the main mediator of a trade-off by uncoupling JH from its role as stimulator of reproduction (as is the case in solitary insects) while maintaining its role as a suppressor of immune activation179. Conversely, a different study in ant queens found evidence for a trade-off after wounding, in which the queens exhibited reduced egg laying with an increase in immune expression and a decrease in germ cell development genes180. In social bee species, the limited evidence so far is similarly conflicting. We have recently suggested that in bumble bees, what appears as evidence for a trade-off between immunity and reproduction in the protein expression patterns of queens may actually be due instead to differing selection pressures at different stages of a queen’s life cycle, which cannot be de-coupled from her reproductive activation181. In honey 20 bees, it has been shown that reproductive potential does not reduce the heat-shock response (a response that includes antiviral immune effectors182), which is unique among the invertebrates that have been studied to date183. While surviving infection is clearly tied to being long-lived, it is unclear from these studies the extent to which honey bees and other social insects may have escaped the reproductive trade-off with immunity, and the ways in which this and other signaling pathways might be mediating an interaction between immune activation and reproduction. 1.6 Goals and hypotheses 1.6.1 Research goals The primary aim of this work is to further the understanding of the impact viral infections have on honey bee queens' health and reproductive success. Beekeepers have identified the health and longevity of queens as one of the more significant concerns for the sustainability of their operations, but we have a yet limited understanding of the many factors that lead to undesirable outcomes for honey bee queens, and only a few studies thus far have delved into the relationship between queen failure and viral infections. We therefore ask the following questions to guide this investigation: 1) Can we quantify reproductive physiological measures related to beekeeper-identified queen failure and associate them with patterns of natural viral infection? 2) Do experimental viral infections cause the symptoms of queen failure? 3) What are the molecular changes underlying this relationship and is there evidence that the effects of virus infection on queens are due, at least in part, to a trade-off between reproduction and immunity? 4) Does the molecular composition of eggs reflect the infection status of the queen that laid them? 21 1.6.2 Hypothesis 1 We hypothesize that viral infections measurably impact honey bee queens' reproductive success, even though infections in queens are usually covert and lack the symptoms displayed in workers. To test this hypothesis, we first conduct several surveys of queens from beekeeping operations to establish that 1) ovary mass and sperm viability are measurable proxies for fertility that are linked to beekeeper-identified queen problems and, 2) failing queens have higher levels of natural virus infections. To investigate the molecular changes accompanying queen failure and virus infection in the field we perform proteomic analysis on these queens. Then, to establish that the relationship between virus infection and queen failure is causal, rather than correlative, we perform an initial experimental virus infection in the laboratory to see if we can recapitulate the symptoms of queen failure observed from the field. 1.6.3 Hypothesis 2 Our second hypothesis is that the effects of virus infection on queen reproductive output are due in part to a trade-off between reproduction and immunity. While this phenomenon of resource compromise has been documented in a huge number of other species163, it has never been specifically investigated in honey bee queens. If this trade-off is at play in honey bee queens, it means that even in the absence of direct pathogenic effects, the immune activation resulting from the ubiquitous levels of virus infection present in colonies could be diverting resources away from reproductive output and contributing to symptoms of queen failure. To address this, we perform a second, more complex experimental infection including a group of queens injected with inactivated virus to uncouple the effects of infectious virus from the effects of viral immune activation. To validate the findings of these laboratory-based infection experiments, we 22 simultaneously conducted a parallel field-trial experiment to observe how the environment of colony vs. cage contextualizes the effects we observe. 1.6.4 Hypothesis 3 Finally, we hypothesize that the effects of maternal infections will be evident in the eggs of infected queens. To investigate this, we perform proteomics, metabolomics, and lipidomics on eggs collected from the queens in the above-described experimental infection and also conduct a third field survey, this time collecting eggs in addition to queens from beekeeping operations. Understanding if the effects of maternal infection extend beyond the queen to her offspring will help to connect the symptoms of viral infection in individual queens to symptoms observed at the colony level related to brood viability and population. 23 Chapter 2: Virus infections cause symptoms of queen failure2 2.1 Rationale and research goals We aim here to address our first two questions: 1) Can we quantify reproductive physiological measures related to beekeeper-identified queen failure and associate them with patterns of natural viral infection? and 2) do experimental viral infections cause the symptoms of queen failure? Addressing the former question allows us to establish that ovary mass and sperm viability are valid proxy measures for fertility, which are meaningfully linked to real-world symptoms of queen failure by beekeepers. This groundwork is integral to addressing the latter question by setting us up to use the same quantifiable measures in future laboratory-based experiments. To do so, we measured the ovary masses of queens rated as “failed” and “healthy” by beekeepers and which had natural variations in viral infection. In two independent populations, we identified and validated a significant negative relationship between viral infection and ovary size. We measured ovarian protein investment associated with viral infection using quantitative proteomics and confirmed the upregulation of antiviral proteins in virus infected queens. Finally, we confirmed the causal, rather than correlative, effect of virus infection on ovaries by experimentally infecting queens with IAPV. This work lays the foundation for the idea pathogenic infections negatively impact reproductive quality and fitness in honey bee queens, a model social insect. 2 A version of this chapter has been published: Chapman, A., et al. (2022). Fertility costs of cryptic viral infections in a model social insect. Scientific Reports, 12(1), 15857 24 2.2 Quantifying queen failure 2.2.1 Failed queens have smaller ovaries and reduced sperm viability To investigate if small ovary size is a general feature of queens with poor reproductive output (described by beekeepers as “failing”), we compared the ovary masses of failing and healthy queens collected from beekeeping operations in three independent queen surveys (denoted as Field Surveys 1-3) conducted in multiple geographical locations (described in Table 2.1). We found a highly significant reduction in ovary mass (linear mixed model; t = -4.37, df = 104, p < 0.0001) in failed queens across surveys (Figure 2.1a), indicating that small ovaries are a widespread phenotype associated with poor fecundity, as described by beekeepers. Whether this is a causal factor or merely a symptom remains unclear, but underscores the relevance of ovary mass as an important queen quality metric. Table 2.1 Summary of queen surveys Survey Year collected Location Queens age-matched? Quality metrics acquired Survey 1 2019 Throughout British Columbia, subset including only queens from south-central BC1 No Ovary mass, viral abundance, sperm viability, absolute sperm counts Survey 2 2018 Pennsylvania Yes Ovary mass Survey 3 2020 Greater Vancouver, BC Yes Ovary mass, viral abundance, sperm viability, relative sperm counts 1 These queens were chosen from a previously published dataset104,184 because failed and healthy queens were included in the same shipment, eliminating potential extraneous variables associated with shipping. In our previous queen survey (Survey 1), we also found that queen failure was associated with reduced stored sperm viability and sperm counts (not recorded for Survey 2)104. However, the ages of failed queens were unknown, leading to questions over whether differences in queen quality metrics were linked to queen failure or old age. We therefore collected an additional n = 25 54 queens (19 failed and 35 healthy), comprising Survey 3, with known ages, the oldest of which were 2 years old, all from our local region in British Columbia to avoid extraneous effects of shipping. We found that sperm viability (generalized linear mixed model, family = binomial; z = -2.65, p = 0.008) and relative sperm counts (linear mixed model; t = -2.32, df = 44, p = 0.025) were again significantly lower in failed queens, even when accounting for age as a fixed factor and queen source as a random effect (Figure 2.1b-f), corroborating our previous studies117,184. See Table 2.2 for complete statistical reporting. In this survey, failed queens also had significantly smaller ovaries (linear mixed model, t = -2.33, df = 43, p < 0.023) (Figure 2.1g). Figure 2.1 Fertility metrics of failed and healthy queens. a) Ovary masses of failed and healthy queens collected across three different surveys conducted in British Columbia and Pennsylvania (analyzed using a linear mixed model with status as a fixed effect and source location as a random effect). Because the average ovary size differed between surveys, data were mean-centered by survey prior to analysis to better highlight the effect of status. Boxes represent the interquartile 26 range, bars indicate the median, and whiskers span 1.5 times the interquartile range. Red indicates failed queens and blue indicates healthy queens. b) Queens rated as ‘failed’ (spotty brood pattern, drone layer, or dwindling adult population) or c) ‘healthy’ (contiguous worker brood patterns, medium-strong adult population) by local beekeepers in British Columbia were collected in the summer of 2020. Sperm viability and sperm counts were determined by fluorescent imaging, and wet ovary weight was measured on an analytical balance. Years indicate the year the queen was reared. d) Statistical analyses on data presented in b) and c) were conducted using either a linear mixed model (ovary mass and sperm counts) or a generalized linear mixed model fitted by maximum likelihood (sperm viability; see Table 2.2 for details). In the statistical models, queen age (0, 1, or 2 years, which corresponds to queens reared in 2020, 2019, and 2018, respectively) and health status (healthy or failed) were included as fixed effects and source location was included as a random effect. Asterisks indicate statistical significance (p < 0.05), with exact p values given in panels e-g. e-g) Same data as in d) but separated by the year in which the queen was reared (i.e., a 2018 queen was 2 years old). Table 2.2 Detailed parameters for statistical models of field surveys Data set Response Model Fixed effect Estimate df |t/z/F| P Ovary masses of queens in Field Surveys 1-31,2 Ovary mass Linear mixed model (lme) Health status -12.36 104 -4.37 < 0.0001 *** Field Survey 1 queens (n = 93)1 Ovary mass Linear mixed model (lme) DWV-A -1.61 88 -2.15 0.035 * BQCV -1.28 -1.70 0.093 SBV -0.32 -0.34 0.73 Health status -5.22 -1.51 0.13 Linear mixed model (lme) Total viral load -2.26 90 -3.53 < 0.001 *** Health status -3.30 -1.22 0.23 Field Survey 3 queens (n = 54)1,3 Ovary mass Linear mixed model (lme) DWV-B -0.61 49 -0.35 0.73 DWV-A -5.17 49 -1.81 0.077 LSV 2.62 48 0.49 0.63 Health status -12.43 49 -2.33 0.024 * Linear mixed model (lme) Total viral load -2.07 49 -1.37 0.18 Health status -12.65 43 -2.34 0.023 * Sperm viability Generalized linear mixed Age 0.04 n/a 2.65 0.06 Health status -0.066 -1.78 0.008 ** 27 model (glmer) Total viral load 0.0074 1.15 0.25 Sperm counts Linear mixed model (lme) Age -17.87 44 -1.21 0.23 Health status -49.07 -2.32 0.02 * Total viral load 9.28 1.49 0.14 1Models included queen source location as a random effect. 2Survey 2 contributed only ovary mass data, and not sperm metrics. Survey 2 data were analyzed in conjunction with ovary mass data from Surveys 1 and 3. 3Generalized linear mixed models, used for the sperm viability model for Survey 3, do not report degrees of freedom. For this model there were 54 observations and 7 groups. 2.3 Investigating the relationship between virus infection and symptoms of queen failure 2.3.1 Ovary size is inversely linked to viral abundance in naturally infected queens Honey bee queens are readily infected by many pathogenic agents103,135,185,186, but are most commonly infected with sacbrood virus (SBV), black queen cell virus (BQCV), and deformed-wing virus (DWV)104,185. We previously found that the failed queens from Survey 1 have higher copy numbers of SBV and BQCV RNA104. We therefore hypothesized that ovary size is linked to viral load, with the rationale that viral infection could either directly impact ovary function by infecting the tissue, or indirectly impact ovaries by shunting resources into immune activation while depleting resources available to invest in ovarioles. Using the previously published viral analysis of queens from Survey 1 (n = 93)104, which was acquired by absolute quantification using qPCR and reported as copies per ng total RNA, we tested if viral load was a predictor of ovary mass. The presence of 7 of the most common honey bee viruses was previously measured in a subset of these queens, but only DWV-A (DWV-B was not specifically included), SBV, and BQCV were detected, so the remaining queens were 28 analyzed for the presence of just these three viruses. We used two models for analysis: the first with the copies of each individual virus as fixed effects, and the second using a “total viral load” in which the counts of each virus were summed together. We were interested in the effects of an additive total viral load because we hypothesized that the changes in ovary mass are caused by viral infection generally, and not driven by the effects of one particular virus. For both, we used a linear mixed effect model (lme package in R) with queen source (breeder) as a random effect, and queen health status (failing or healthy) and virus counts (separately or added) as fixed effects. Viral abundance was not significantly associated with the fertility metrics associated with sperm quality including sperm viability and sperm counts (Table 2.2). In contrast, some association was noted between viral abundance and ovary mass that prompted us to examine this relation more closely. 29 Figure 2.2 Relationships between viral RNA copies and ovary mass See Table 2.2 for complete statistical details. a) Comparison of virus load in the head vs the mass of ovaries in Survey 1 (previously published; see Table 2.1 for details). Includes viral RNA copies for deformed-wing virus A (DWV-A), sacbrood virus (SBV), and black queen cell virus (BQCV). b) Viral RNA copies for each virus were added to produce the variable “Total viral load” and analyzed as a fixed effect instead of individual virus copies. c) Proportion of each virus in each queen, ordered by increasing ovary mass. Gaps represent queens for which the total virus quantified was zero. d) Comparison of virus load in the thorax vs the mass of ovaries in a validation data set (Survey 3) of n = 54 queens analyzed for 8 different viruses using a different laboratory service. Queens were mainly infected with DWV-B (non-detected viruses not shown), with sporadic DWV-A and Lake Sinai virus (LSV). For A) and D) data were analyzed using a linear mixed effect model with queen source as a random effect, and the copies of each virus and health status (healthy vs. failing) as fixed effects. Regression lines shown only for viruses with > 1 non-zero point. e) The summed total viral load was analyzed as a fixed effect instead of the individual virus copies. When the counts of each of the three viruses were used as predictors individually (Figure 2.2a), we found that only DWV was statistically significant as a predictor of ovary mass (estimate = -1.6111, df = 88, t = -2.146, p = 0.03), although BQCV had a similar effect size (estimate = -1.2779, df = 88, t= -1.698, p = 0.09). In the combined model (Figure 2.2b), total viral load was significant with a greater effect on ovary size (estimate = -2.2598, df = 90.0000, t = -3.530, p = 0.0007). Figure 2.2c illustrates the relative proportion of each virus in each queen, which did not reveal any discernable pattern between each virus and ovary size. Combined, these illustrate that increased viral load is significantly associated with decreased ovary mass, and that this is not obviously due to the effects of one specific virus, but more likely due to viral infection in general. We then investigated this relationship with the n = 54 queens from Survey 3, which we sampled independently from different apiaries, and whose viral abundances were analyzed by a different laboratory and reported as copies per ng total RNA. Despite a much smaller sample size, identifying an overall lower range of viral abundance, and not identifying two of the three most 30 common viruses present in queens (BQCV and SBV) - all of which may be due to different laboratory methods - we observed the same trends as for Survey 1. Though none of these relationships were significant, observing the same correlation in an independent set of samples, with different viruses quantified from a different tissue, provides further evidence that this is a biologically relevant phenomenon. Future studies with larger sample sizes and uniform viral quantitation methods could serve to illuminate this relationship between natural infections. 2.3.2 Experimental infection with IAPV causes a reduction in ovary mass To confirm that viral infection is causally associated with the reduced ovary size of failed queens and not merely a result of failed queens potentially being more susceptible to infection, we injected age-matched, mated queens with IAPV. We chose IAPV because it naturally infects honey bee queens, but has a low frequency of infection185, so experimental infection is unlikely to confound with natural abundance. After just 65 hours, we found that queens with higher levels of IAPV had significantly smaller ovaries (Figure 2.3a) (simple linear model: estimate = -1.36, t = -2.51, df = 15, p = 0.024), echoing our results from the field. There appeared to be contamination of the mock-injected queens with IAPV so we have analyzed the data as a continuous relationship, rather than as a comparison between groups for transparency. The number of IAPV copies was confirmed by absolute quantification using qPCR, and the mock-injection queens were additionally checked for DWV, SBV, and BQCV, of which there was no detectable infection in any of the queens. 31 Figure 2.3 Ovary mass and vitellogenin transcription are significantly decreased after IAPV injection. a) Mated, age-matched queens were injected with IAPV or a mock (buffer only; n = 10 each) and the ovary mass measured after 65 hours. Data was modelled using a simple linear model, estimate = -1.36, t = -2.51, df = 15, p = 0.024. b) The transcript for vitellogenin was measured using RTqPCR in the abdomens of queens experimentally infected with IAPV at two days before emergence and two weeks post-emergence (after CO2 ovary activation). The fold-change in gene expression was calculated using the 2–∆∆Ct method. Pairwise comparisons of gene expression were evaluated using the Wilcoxon Rank Sum test (n.s. = not significant, *** = p < 0.0001). We assessed the mock-injected queens for DWV, SBV, and BQCV, allowing us to broadly conclude that the levels of these viruses in this population of queens are low. However, we cannot rule out the possibility that these (or other) viruses may have been present in one or more queens in the IAPV injection group, which could potentially lead to interactive effects between viruses136. 2.4 Molecular changes associated with failure and infection 2.4.1 Expression of vitellogenin is reduced after injection with IAPV In a separate experiment, we investigated vitellogenin gene expression in the abdomens of IAPV-injected queens relative to mock-injected queens at three different ages: two days before 32 emergence (pupae), two days after emergence, and two weeks after emergence (with CO2 treatment to stimulate ovary activation and egg laying187. The success of the infections was confirmed by measuring IAPV copies using qPCR. In pupal queens and queens two weeks after emergence (after CO2 ovarian stimulation), we found that expression of vitellogenin was significantly reduced by IAPV infection (Figure 2.3b). Vitellogenin, the major egg yolk protein precursor, is one of the most significant sources of nutrition for developing oocytes, where it is deposited in massive quantities. Much of the mass of a queen’s ovary is made up of developing eggs, therefore a decrease in vitellogenin expression is consistent with a reduction in ovary mass and together represents a reduction in the resources being invested into producing or provisioning eggs. Vitellogenin is also responsible for the binding and transport of immune elicitors (i.e., bacterial cell wall components) during transgenerational immune priming158,159. So, this reduction in vitellogenin could have knock-on effects for the success of the queen’s progeny by reducing her capacity to provide them with immune priming. 2.4.2 The ovaries of failed queens have altered protein composition compared to healthy queens To determine if the changes we observed in the ovaries of failing queens were associated with altered patterns of protein expression consistent with a disruption in egg production, we performed quantitative proteomics on the ovaries of n = 88 queens collected in British Columbia from Survey 1. We found that 415 proteins, around 20% of the total number of quantified proteins, were differentially expressed between failing and healthy queens at a false discovery rate of 5% (Benjamini Hochberg method; Figure 2.4a). Many of the proteins that were downregulated in failing queens are associated with metabolic processes, but no gene ontology 33 (GO) terms were significantly enriched among the differentially expressed proteins. This could be due to the poor GO characterization of many honey bee proteins, as we have previously described104. However, among these differentially expressed proteins are several indicators of an increase in immune activity and decrease in egg development (Figure 2.4b). One of the top proteins decreased in failed queens was prohibitin (XP_624330.3), a highly multifunctional protein associated with cellular proliferation188, the regulation of follicular development in mammalian ovaries189, and a mediator of viral entry into insect cells190. Furthermore, heat-shock proteins have antiviral activity in insects182,191, and our data are consistent with that role in queens. We found that two small heat shock proteins both called protein lethal(2) essential for life (XP_001119884.1 and XP_001120194.1) as well as dnaJ homolog shv (XP_006569897.2) were upregulated in failed queens. Despite sharing the same name “protein lethal(2) essential for life”, these proteins do not share tryptic peptides > 6 residues long; therefore, their quantification was not influenced by shared peptide sequences. DnaJ homolog shv was also previously reported to be associated with the antiviral heat-shock response in workers182. Studies in Lepidopterans and Orthopterans have shown that the protein apolipophorin III (apoLp-III) is involved in both the immune system and metabolism192. When in its monomeric form, it acts as an immune surveillance pattern-recognition receptor with the ability to activate the immune response. We previously found that apoLp-III is significantly co-expressed with a cluster of predominantly innate immune proteins in the spermathecal fluid104, lending confidence to its involvement in immune processes in queens. But, when combined with high-density apolipophorin (apoLp-I/II), it acts as a lipid transporter, shuttling lipids liberated from the fat body and from the digestive system around the body193. One of the most significant destinations 34 of these lipids in reproductively active female insects are the developing oocytes, where they will make up 30-40% of each egg’s dry weight28. These lipophorins could be a molecular switch governing both the movement of resources and immune function, and we predicted that if increased viral loads are a feature of failed queens, then they should have increased levels of apoLp-III to promote immune function. Additionally, we predicted that apoLp-I/II, which is constitutively involved in lipid transport and juvenile hormone signalling, would be reduced in the smaller ovaries of failing queens which are likely producing fewer eggs. We found that apoLp-III was indeed upregulated in failed queens, and apoLp-I/II was downregulated (Figure 2.4c), suggesting that lipid transport to developing eggs is reduced in failing queens. Figure 2.4 Changes to ovary protein expression in failed honey bee queens. 35 Protein abundance was measured via LC-MS/MS using label-free quantitation (LFQ). Analysis was done on proteins identified in at least 10 samples and expression is reported as log2-transformed LFQ intensity. We used limma with status (failed vs. healthy), ovary mass, and total viral counts as fixed effects for n = 88 queens (41 healthy and 47 failed) to identify the differentially expressed proteins shown in a, b, and c. Exact sample sizes may differ due to missing values for some proteins. The false-discovery rate (FDR) was controlled using the Benjamini-Hochberg method (5% FDR). a) Protein expression patterns in ovaries of healthy and failed queens. Only proteins differentially expressed and quantified in 75% of samples are shown (387 proteins). b) Proteins associated with the antiviral response are upregulated in failed queens (sHSP (XP_001119884.1): t = 3.8, adjusted p = 0.003; sHSP (XP_001120194.1): t = 3.3, adjusted p = 0.01; DnaJ: t = 4.9, adjusted p = 0.0003). c) Apolipophorins I/II are involved in lipid transport and are downregulated in failed queens (t = -2.59, adjusted p = 0.048). Apolipophorin III is an important protein for immune function and lipid transport and is upregulated in failed queens (t = 5.25, adjusted p < 0.0001). 2.4.3 Heat-shock proteins are upregulated in virus-infected queens To gauge whether the levels of viral infection in the field were sufficient for the queens to launch an antiviral response, we investigated expression of putative antiviral proteins. Virus-infected workers express elevated levels of antimicrobial peptides (e.g., defensin, hymenoptaecin); however, these peptides’ molecular properties are not favourable for quantification by shot-gun proteomics without specialized enrichment strategies. Additionally, we have not been able to identify or quantify several of the other proteins classically involved in the antiviral response, including Dicer and Argonaute-2, in these or similar samples. Previous work has demonstrated that in fruit flies and worker honey bees, heat-shock proteins, which are more easily quantified by proteomics, are an important part of the antiviral defense182,191,194. Virus-infected workers also upregulate heat-shock protein mRNA (pl(2)el, hsp70-3, hsp70-4, hsp83-like, and hsp90), and workers whose heat-shock proteins were experimentally induced by temperature stress shortly after infection had 74-90% lower viral titers compared to non-induced controls182. We therefore hypothesize that antiviral heat-shock protein expression is also correlated with viral load in queens. 36 We first analyzed previously published quantitative proteomics data obtained from spermathecal fluid samples of the queens from Survey 1 with varying degrees of viral infection104,195. Honey bee viruses can be transmitted sexually, so spermathecal fluid is a reasonable tissue in which to look for an antiviral response133. Our statistical model included sperm counts, health status, and log-transformed total viral RNA copies as fixed effects. We found that six proteins were non-significantly correlated with log-transformed total viral RNA copy numbers (adjusted p-values between 0.054 and 0.082) (Table 2.3), half of which are heat-shock proteins: heat-shock protein 70 cognate 4 (HSP70-4) and two other small heat-shock proteins (sHSPs) XP_001120006.2 and XP_001119884.1. As we predicted, all three HSPs positively correlate with viral abundance (HSP70-4 and sHSP XP_001120006.2, which have been previously associated with the worker bee antiviral response182, are depicted in Figure 2.5a-b). The other three proteins—a transmembrane protease, zinc carboxypeptidase, and arylsulfatase-B—have not been previously linked to viral infection in honey bees. Notably, the two sHSPs were previously proposed as candidate diagnostic markers for heat-stress and were significantly upregulated in failed queens relative to healthy queens184; here, we show that they are linked to viral abundance even when health status is included as a fixed effect. This observation, unfortunately, negates their utility as heat stress biomarkers due to confounding virus infection. Table 2.3 Statistical summaries of proteins correlating with total viral loads. Protein Common name |t| P Adjusted p* XP_016771183.1 Transmembrane protease serine 11B like 4.48 0.0000355 0.054 XP_001120006.2 Protein lethal(2) essential for life (small HSP) 4.17 0.0000778 0.054 NP_001153522.1 Heat shock protein 70 cognate 4 4.04 0.000114 0.054 XP_623922.2 Zinc carboxypeptidase 4.05 0.000136 0.054 XP_006569022.1 Arylsulfatase-B 4.81 0.000101 0.054 XP_001119884.1 Protein lethal(2) essential for life (small HSP) 3.82 0.000246 0.082 *Benjamini Hochberg method 37 To validate that these HSPs are part of the antiviral immune defense in queens we further examined the queens experimentally infected with IAPV. We selected the two heat shock proteins that we identified as upregulated in naturally virus infected queens that overlapped with the previously reported list of HSPs associated with the antiviral response in workers: protein lethal(2) essential for life (XP_001120006.2, Pl(2)el) and heat-shock protein 70 cognate 4 (Hsp70-4)182. Pl(2)el and HSP70-4 were significantly upregulated after viral infection in queens of all three ages, with the exception of two-week-old queens, in which HSP70-4 was not differentially expressed (Figure 2.5c-d). The fact that HSP70-4 was not upregulated in queens whose ovaries had been stimulated to develop may be because insects which have mated or begun to reproduce often demonstrate a reduced ability to mount an immune response relative to their unmated counterparts163, and trade-offs involving the immune response often involve a complicated reconfiguration in which different components of the response might become up or downregulated196. Figure 2.5 Heat-shock proteins associated with natural and experimental viral infections in honey bee queens. a and b) We analyzed previously published proteomics data using limma, including health status, sperm counts, and log transformed total viral loads as fixed effects (n = 88 queens had complete proteomics with no missing values in continuous covariates). Individual virus types are shown, but only total titers were statistically analyzed. P values were corrected for multiple hypothesis testing by Benjamini-Hochberg 38 correction (see Table 2.3 for statistical summaries of these and other significant proteins). c and d) The transcripts of two heat-shock proteins found in the spermathecal fluid of naturally infected queens and previously identified in the antiviral response in workers are upregulated in the abdomens of queens experimentally infected with IAPV. Fold gene expression was calculated using the 2–∆∆Ct method. Pairwise comparisons of gene expression were evaluated using the Wilcoxon Rank Sum test. ns: not significant; ** = p < 10-7; *** = p < 10-10. 2.5 Conclusion These data show that viral infection is impacting the fertility, and therefore success, of honey bee queens. Queens collected from the field showed a clear negative relationship between viral counts and ovary mass ― a trend which was observed in a second, independent set of queens. Furthermore, experimental infections resulted in a reduction in ovary mass and reduced vitellogenin expression, which suggests a causal, rather than merely correlative role of virus infection. Queens deemed to be failing by beekeepers have increased viral loads and smaller ovaries relative to queens perceived to be healthy. This finding, combined with protein expression patterns indicating a decrease in oocyte development and increased antiviral response, suggests that this is an economically relevant phenomenon. The facts that each virus individually showed a similar effect size on ovary mass from the first survey queens, that an additive total viral count model better predicted ovary mass than each virus separately, and that experimental infections done with a different virus from those which we identified in the field provide evidence that this upregulation of the antiviral response and reduction in reproductive activity are general responses to viral immune activation, and not a specific response to a particular virus. 39 2.6 Methods 2.6.1 Queen surveys For all surveys, beekeepers considered the queens to have ‘failed’ if they either displayed spotty brood patterns, were drone layers, or if the adult population rapidly dwindled, including the presence of chalkbrood infections that the colony could not resolve on their own. Beekeepers considered the queens to be ‘healthy’ if they produced robust brood patterns, the colony did not have signs of appreciable brood infections, and the adult population was strong. Additionally, queens were collected during the summer and beekeepers determined that the colonies were not in the process of gearing up to swarm. For Survey 1, we utilized previously published data104, which included N = 105 queens (excluding imported queens, which were biased to smaller ovary sizes due to a longer caging duration). N = 93 of these queens had associated viral data. For the ovary mass comparison across surveys, we retained only queens from producers that met the following criteria: the producer collected both failed and healthy queens (n>2 of each) and queens were delivered to the laboratory in one shipment. These criteria were applied because in the 2019 survey, producers from many different locations shipped queens to the laboratory by different methods and with collection schedules; therefore, queens spent different lengths of time in transit and in cages prior to transit, which we know from previous work impacts ovary mass. Applying these criteria reduced our sample size for the ovary mass comparison but removed these extraneous effects. For Survey 2, N = 22 queens were collected from colonies in 2018 from three different apiaries in southeastern Pennsylvania. Healthy and failed queens were age- and apiary-matched in order 40 to eliminate sampling bias. The queens were frozen on dry ice immediately in the field and shipped frozen to the University of British Columbia, at which time the queens were thawed on ice and dissected. For Survey 3, queens were collected from local collaborating beekeepers in British Columbia as they became available during the summer of 2020. Apiary location, colony symptoms (if failed), genetic background (i.e., local or imported stock), and age were recorded. Failed and healthy queens were caged with candy and attendants then kept in a queen bank with plentiful nurse bees if they could not be immediately analyzed in the laboratory. The queens were transported to the University of British Columbia, where they were anesthetized with carbon dioxide, decapitated, and their spermathecae were removed for sperm viability analysis. The remaining queen bodies were stored at -70°C until further dissection to determine ovary mass. 2.6.2 Sperm viability, sperm counts, and ovary mass Sperm viability measurements for Survey 3 queens were obtained essentially as previously described35, following the methods of Collins and Donaghue197. Briefly, we burst the spermatheca with tweezers in 100 µl of Buffer D (17 mM D-glucose, 54 mM KCl, 25 mM NaHCO3, 83 mM Na3C6H5O7) and gently agitated the solution until sperm cells were homogeneously dispersed. We then stained the sperm using SYBR-14/propidium iodide dual fluorescent staining following the manufacturer’s protocol (Live/Dead sperm viability kit, Thermo). We dispensed 5 µl of the stained sperm into a well of a 24-well plate, over which we placed a round glass cover slip. We imaged the sperm (three fields of view per queen, average of ~100 sperm cells per image) using a Cellomics ArrayScan XTI (Thermo) and automatically 41 counted live (green) and dead (red) channels using ImageJ v1.52a. Relative sperm abundances were obtained by counting the cumulative number of sperm across three fields of view. Every sample was analyzed in the same style of plate and with the same sized cover slip, so measuring the total sperm in a given area is proportional to total sperm in the sample. We later dissected the ovaries from the queen remains and weighed them using an analytical balance. Data for Survey 1 is already published and we direct the reader to McAfee et al.104 for the methods regarding the collection and processing of those queens. 2.6.3 Viral quantification for queen surveys Viral data (SBV, DWV-A, and BQCV) for Survey 1 were obtained by RT-qPCR exactly as previously described and are previously published data104. Briefly, samples were submitted to the National Bee Diagnostics Center, where total RNA was extracted from the head using a guanidinium isothiocyanate extraction buffer, cDNA was synthesized using the iScript cDNA synthesis kit (Bio-Rad Laboratories, Hercules, USA), and virus RNA was quantified by qPCR with previously published primers104. Standard curves for each virus amplicon were generated using serially diluted plasmids containing the target sequence. RP49 was used as the honey bee reference gene, enabling the viral RNA copy number per 30 ng of total RNA to be calculated. Copy numbers per 30 ng of RNA of all three viruses were added together to generate the ‘Total counts’ variable. All viral count data was then rescaled as counts per µg of extracted RNA in order to have data from both surveys depicted on the same scale, and log10 transformed. Queens from Survey 2 were not analyzed for viral infections because the sample size (22 queens) is expected to be too small to yield sufficient statistical power linking viral abundance to ovary size. 42 Queens from Survey 3 were processed by the Honey Bee Queen and Disease Clinic at North Carolina State University using methods as previously described107 with minor adjustments. For this sample set, we analyzed thoraxes because we have observed that other components of the head can sometimes interfere with PCR. We also tested for a wider range of viruses, including DWV-A, DWV-B, ABPV, SBV, IAPV, LSV, BQCV, and CBPV. Thoraxes were pulverized by bead beating and total RNA was extracted via the Zymo Research DirectZol RNA miniprep kit and Trizol reagent. RNA was quantified and verified for quality by NanoDrop Spectrophotometry and diluted to 200 ng/µL in RNAse/DNAse free water. cDNA was synthesized using the BioBasic All-In-One RT MasterMix. qPCR was performed on a QuantStudio 5 thermocycler using PowerUp Sybr Green chemistry. Standard curves were generated using serial dilutions of known quantities of custom plasmids containing the target sequences. Actin, 28s ribosomal subunit, and GapDH were used as reference genes. Final copy numbers were normalized to reference gene values using GeNorm software198 and calculated per 3.33 ng of total RNA, then rescaled to counts per µg total RNA and log10 transformed. 2.6.4 Statistical analyses of phenotypic data All statistical analyses were performed in R v3.5.1. For Survey 3 data, we first checked if ovary mass, sperm count, and sperm viability were normally distributed and had equal variance between failed and healthy queens using the Shapiro and Levene test, respectively. The ovary mass and sperm viability passed these tests (p > 0.05); therefore, a linear mixed model (lme) was used for both, including health status (failed vs. healthy) and age (0, 1, or 2 years) as fixed effects and location (9 levels) as a random effect. The sperm viability data, however, was not 43 normally distributed, so we analyzed these data with a generalized linear mixed model with a logit link function (glmer, family = binomial) as previously described199, constructed using the same fixed and random factors, but where sperm viability was represented as counts of ‘successes’ (live) and ‘failures’ (dead). Pooled ovary mass data from the three independent queen surveys were analyzed using a linear mixed model (lme) with health status as a fixed effect and queen source location as a random effect. To determine the relationship between ovary mass and virus infection, we used a linear mixed effect model (R package lme4) fit by restricted maximum likelihood (REML) with queen source (apiary) as a random effect and viral counts (either individually, or as summed total viral load) and health status (healthy vs. failing) as fixed effects. We used the ‘DHARMa’ package for a simulation-based approach to assess the goodness of fit of these models by evaluating the scaled (quantile) residuals and tested the model for typical misspecification problems, such as over/underdispersion and residual spatial and temporal autocorrelation. To compare the individual virus vs. total viral load models we refit them using maximum likelihood (ML) in order to compare Akaike Information Criterion scores. Including source (breeder) as a random effect in the models for Survey 1 produced a singular fit with the breeder variance estimated as zero, but we chose to retain this part of the model because it is still a relevant, structural variable in the data and the differences between ovaries masses from each breeder are nontrivial. 2.6.5 Ovary proteomic sample preparation Ovary samples from Survey 1 (N = 88, 5 samples from the original 93 queens were lost during processing) were prepared for mass spectrometry essentially as previously described117. One 44 ovary from each queen was homogenized in a separate 2-ml screw cap tube containing 300 µl of lysis buffer (6 M guanidinium chloride, 100 mM Tris, pH 8.5) and four ceramic beads. The homogenizer (Precellys 24, Bertin Instruments) was set to 6,500 s-1 for 30 s: samples were homogenized three times and placed on ice for 1 minute between each. Then samples were centrifuged (16,000 relative centrifugal force, 10 min, 4 °C) to remove debris. 100 µL of supernatant was transferred to a new tube and diluted 1:1 with distilled H2O. Protein was precipitated by adding four volumes of ice-cold acetone and incubating overnight at −20 °C. The precipitated protein was pelleted by centrifugation (6,000 relative centrifugal force, 15 min, 4 °C) and the supernatant was discarded. The protein pellet was washed twice with 500 µl of 80% acetone, then the pellet was allowed to air dry (~5 min) before solubilization in 100 µl of digestion buffer (6 M urea, 2 M thiourea). Approximately 20 µg of protein was reduced (0.4 µg dithiothreitol, 20 min), alkylated (2 µg iodoacetamide, 30 min, dark), and digested (0.4 µg Lys-C for 2.5 h, then 0.5 µg trypsin overnight). Digested peptides were acidified with one volume of 1% trifluoroacetic acid and desalted with high-capacity STAGE tips as previously described200. Eluted samples were dried (SpeedVac, Eppendorf, 45 min) and resuspended in Buffer A (0.1% formic acid, 2% acetonitrile), then peptide concentrations were determined using a NanoDrop (Thermo, 280 nm). 2.6.6 Ovary proteomic data acquisition and analysis LC–MS/MS data acquisition was performed as previously described117 whereby 0.5 ug of peptides were injected into an Impact II Q-TOF mass spectrometer. MS data was searched using MaxQuant (v1.6.8.0) with the parameters and database as previously described117. In Perseus (v 1.5.5.3), the resulting protein groups were filtered to remove reverse hits, contaminants, proteins 45 identified only by site, and proteins identified in fewer than 10 samples. Normalized LFQ intensity was then log2 transformed. Subsequent analyses were performed in R (v3.6.3). Differential expression analysis was performed using the limma package in R201 with ovary mass, status (failed vs. healthy), and total viral counts (log10 transformed of combined RT-qPCR counts, +1 to avoid undefined terms) included as fixed effects. Empirical Bayes moderation of the standard errors was then performed on the resulting linear model, and finally the false discovery rate was controlled to 5% using the Benjamini-Hochberg correction. GO term enrichment analysis was performed on the proteins differentially expressed among status groups using Ermine J as previously described184, though no terms were significant. We used a linear model with ovary mass, status, and total viral counts as fixed effects to analyze the expression of ApoLp-I/II and ApoLp-III. 2.6.7 Statistical analysis of spermathecal proteomic data We analyzed previously published proteomics data104 using the limma R package essentially as previously described for ovaries. Proteomics data were first groomed to remove samples that did not have associated viral abundance data. We included log10 transformed total viral load (+1 to avoid undefined terms), health status (failed or healthy), and sperm counts as fixed effects in the statistical model. We used a Benjamini-Hochberg correction to control the false discovery rate 5%. We direct the reader to McAfee et al.104 for details on proteomics sample preparation and upstream data processing. 46 2.6.8 IAPV experimental infections for measuring ovary mass Mated queens of approximately the same age (<1 year old, roughly ~1-2 months) were obtained in a single shipment from California. Queens were anesthetized with CO2 for 6 minutes before being injected between the abdominal tergites with approximately 4nL of IAPV-inoculum containing ~1,400 copies of IAPV (determined by absolute quantification using qPCR) (n=10) or phosphate buffer saline (PBS) as mock (n=10). Injections were performed using the FemtoJet Microinjector (Eppendorf) with hand-pulled glass needles. The queens were then kept in wooden cages with 5 attendant workers and provided with sugar candy in the incubator at 34°C with a water pan for humidity. After 65 hours the queens were sacrificed, and the wet ovary masses measured on an analytical balance. IAPV levels were quantified in the heads of all queens by the National Bee Diagnostic Center using the same methods as described above for the viral analysis of the queen surveys. The mock-injected queens were additionally analyzed for DWV, SBV, and BQCV. 2.6.9 IAPV experimental infections for expression analysis Three large groups of sister queens were produced from a healthy mother queen to study the direct effect of virus infection on queens of three different ages following honey bee queen-rearing technique202. Briefly, young larvae (12 – 24 hours old) were grafted into artificial queen cups and placed in a populous but queenless nurse colony to develop. Prior to grafting, the donor colony was visually determined to be free of symptomatic diseases (Nosemosis, Varroosis, European foulbrood, American foulbrood, and Chalkbrood). After 6 days, sealed queen cells were transferred to an incubator (35 °C, 65% humidity) and treated as follow: The first group was used for pupal infections. Two days before emergence, queen pupae were gently removed 47 from their capped cells. These pupae were either injected with 2 µL of IAPV-inoculum containing 5.24x103 virus particles (n= 34), or phosphate buffered saline (PBS) as a mock control (n= 31). Injections were performed using a Nanojet™ syringe pump (Chemix, USA) with an infusion flow rate of 0.1 µL/s, where the needle was inserted into the first abdominal segment located immediately behind the metathorax. Each queen then was placed individually in a 15 mm well in the 24 well glass bottom cell culture plate and kept in the incubator (35 °C, 65% humidity). While in the incubator, development of the virus injected queens was compared with the PBS injected queens. Our observations confirmed a complete cessation of development in IAPV injected queens 20 hours post injection, which was not observed in the control group. Therefore, the experiment was terminated, and the abdomen of each queen was then separated, placed in an Eppendorf tube, and stored in a freezer at -80 °C until RNA extraction. The second group of queens was used for infecting newly emerged queen in the pre-reproductive stage. Two days after emergence, queens were either injected with the IAPV-inoculum (n= 30), or PBS as mock control (n= 30) as explained above. Each queen was accompanied with 10-15 worker honey bees from the donor colony in a single-use plastic cup, provided with water and sugar candy. Cups of IAPV-inoculated and PBS-injected groups were maintained separately in the incubator (35 °C, 65% humidity). The IAPV-inoculated queens started showing paralysis symptoms around 20-24 hours post injection. The experiment was terminated at the onset of symptoms, and after anesthetization using CO2 the queens’ abdomens were sampled and stored as described above. 48 For infection of the last group of reproductive queens, right after emergence, queens were caged and banked in a donor colony for one week. Then, they were taken out and treated with CO2 two times a week later and returned to the donor colony for one more week to stimulate ovary development without mating187. Thereafter, queens were either injected with the IAPV-inoculum (n= 34), or PBS as mock control (n=31) and maintained with attendant workers in the incubator (35 °C, 65% humidity), as described above. Paralysis symptoms started in the IAPV-inoculated queens 40-44 hours post injection, when the experiment was terminated. Abdomens were sampled as described above. 2.6.10 RNA isolation, cDNA synthesis, and quantitative PCR (qPCR) on experimentally infected queens Each abdomen was transferred into a 2 mL Ruptor homogenizing tube containing 5 ceramic beads. The samples were then homogenized using the automated Bead Ruptor Elite (Omni International, Kennesaw, GA, USA) at a speed of 5 meter per second (5 m/s) for 20 s. Followed by homogenization, total RNA was extracted using an established TRIzol™ (Invitrogen, Carlsbad, CA, USA) protocol. The concentration and purity of the extracted RNA samples were measured using a Nanodrop OneC Microvolume UV-Vis Spectrophotometer (Thermo Fisher Scientific, MA, USA). The total RNA concentration was adjusted to 20 ng/µL in molecular grade water (Fisher Scientific, Fair Lawn, NJ, USA). Then, a two-step quantitative qPCR assay was carried out to quantify the target genes. For each sample, cDNA was synthesized using the High-Capacity cDNA Reverse-Transcription Kit (Applied Biosystems, Foster City, CA, USA). Ten microliters of the RNA template (total, 200 ng) were added to 10 µL of the provided cDNA master mix, followed by an incubation period as recommended by the manufacturer; 10 min at 49 25 °C, 120 min at 37 °C, and 5 min at 85 °C. The resulting cDNA solution then diluted 10-fold in molecular grade water to serve as template in subsequent qPCR. The qPCR was conducted in duplicate using 384-well plates on a QuantStudio™ 6 cycler (Thermo Fisher Scientific). The reactions were performed using unlabeled primers and SYBR Green DNA binding dye (Applied Biosystems) with a volume of 12 µL with the final primer concentrations of 0.4 µM. We added RNase-free water as template for a No Target Control (NTC) and a No Reverse Transcriptase (NRT) control as an additional negative control203. The thermal cycling conditions were 10 min at 95 °C, followed by 40 cycles consisting of a denaturing stage at 95 °C for 15 s and an annealing/extension stage at 60 °C for 1 min. This procedure was followed by a final melt-curve dissociation analysis to confirm the specificity of the products. For IAPV, the Cq values were determined at the same fluorescence threshold (0.05) for all plates, and a Cq value of 35 or lower was recorded as positive amplification. For three genes of interest and three housekeeping genes we collected Cq values based on the default threshold as determined by the QuantStudio™ 6 for each target gene. Fluoresence measurements were taken at the end of each cycle. We quantified the IAPV intensity and the expression level of three genes of interest including vitellogenin, protein lethal(2)essential for life-like, and heat shock protein 70 cognate. Three housekeeping genes including actin, glyceraldehyde 3-phosphate dehydrogenase (GapDH) and ribosomal protein S5 (RPS5) were also amplified as internal control and for relative quantification. The primers used in this experiment have previously been validated to detect the intended targets and are commonly used in honey bees204,205. We quantified immune-gene expression for each sample using relative quantification206. We calculated geometric mean of the Cq value from three reference genes (RPS5, actin, and GapDH) to confirm amplification. We 50 subtracted the Cq value of geometric mean from target gene for each sample (∆Cq= Cq(gene of interest) – Cq(GMean of reference genes)). To calculate the ∆∆Cq for each target gene, we subtracted the average of the ∆Cq values across the control samples at each age from the ∆Cq for each target gene. The fold-change in gene expression was then calculated using the 2-(∆∆Cq) method. Pairwise comparisons of gene expression were evaluated using the wilcox.test function in the ggpubr package (v0.4.0). Data are plotted in the form of log10(fold change in gene expression) for clarity. 51 Chapter 3: Common viral infections inhibit egg laying in honey bee queens and are linked to premature supersedure3 3.1 Rationale and research goals We aim here to further investigate our second question: Do experimental viral infections cause the symptoms of queen failure? Our previous experimental infections with IAPV were conducted over a short time frame (3 days) and measured only ovary mass to quantify the impact on reproduction. However, poor queen performance is a chronic symptom displayed over time, not an acute effect. In this study, we therefore aimed to determine if the effects of experimental virus infection extend to an observable effect on actual reproductive output beyond just smaller ovaries by employing more realistic experimental settings and a longer time frame. We also aim to address our third question: What are the molecular changes underlying this relationship and is there evidence that the effects of virus infection on queens are due, at least in part, to a trade-off between reproduction and immunity? With their long lives and extreme reproductive output, social insect queens have escaped the classic trade-off between fecundity and lifespan, but evidence for a trade-off between fecundity and immunity has been inconclusive. This is in part because pathogenic effects are seldom decoupled from the effects of immune induction. 3 A version of this chapter has been published: Chapman, A., et al. (2024). Common viral infections inhibit egg laying in honey bee queens and are linked to premature supersedure. Scientific Reports, 14(1), 17285 52 We therefore conducted two separate experiments in parallel in which we observed queens experimentally infected with a combination of BQCV and DWV-B either in laboratory cages (queen monitoring cages, or QMCs)207, which allowed queens to continue to lay eggs for one week following infection, or in field colonies for 6 weeks following infection. To address the potential presence of a reproductive-immunity trade-off, we also injected a group of queens in the cage experiment with a UV-inactivated version of the same viral inoculum to decouple the role of immune activation caused by wounding from viral pathogenesis. 3.2 Cage experiment Young, mated honey bee queens were injected with a) a combination of infectious BQCV and DWV-B, b) the same mixture of viruses that had been inactivated with ultraviolet light, or c) saline (injection control) and subsequently kept in incubated cages which enabled them to lay eggs for 7 days following injection. 3.2.1 Confirming levels of virus copy numbers Because queens are likely to have background infections, we assessed the RNA copy numbers of common viruses (BQCV, DWV-A, DWV-B, and SBV) in all groups using RT-qPCR. As expected, there were no differences in viral copy numbers in the inactivated group compared to the saline-injected control, and in the infected group DWV-B was increased and BQCV was marginally increased (Figure 3.1a). Correspondingly, the infected group had a significantly higher level of “total virus”, a metric representing the sum of natural logarithm-transformed copy numbers of the individual viruses (Figure 3.1b), which we have previously determined to be a more important predictor of ovary mass in queens collected from the field than the levels of 53 any singular virus 208. Levels of DWV-A and SBV (which were not present in the inoculum) were similar between groups, as expected, but were difficult to robustly evaluate as few samples fell above the limit of detection. See Table 3.1 for complete statistical details. Figure 3.1 Virus RNA copy numbers of cage experiment queens. a and b) Bars show mean values and error bars represent standard deviation; p-values are shown relative to the control group. Control queens were injected with saline, infected queens were injected with a combination of BQCV and DWV-B, and queens in the inactivated group were injected with the same combination of viruses that were UV-inactivated (n = 9 for all groups). Queens were kept in queen monitoring cages in an incubator with attendants and sacrificed 7 days after injection. a. Comparison of RNA copy numbers of BQCV, DWV-B, DWV-A, and SBV between groups. The dashed horizontal lines represent the RT-qPCR quantification threshold. b. Comparison of the summed total virus copy numbers between groups. Table 3.1 Statistical parameters for cage experiment* Response Fixed effects Model Estimate dfb |t/z| p BQCV Group (control group as reference) Linear mixed model (lmer)a Live: 0.81 23 1.97 0.061 Inactive: -0.16 -0.40 0.69 SBV Live: 0.88 23 0.29 0.77 Inactive: 0.51 0.17 0.86 DWV-B Live: 5.12 21 3.59 0.0017 Inactive: -0.37 -0.27 0.79 DWV-A Live: 1.75 21 1.045 0.31 Inactive: 0.071 0.044 0.97 54 Total virus Live: 8.75 21 2.45 0.023 Inactive: 0.37 0.11 0.92 Ovary mass Group Linear mixed model (lmer)a Live: -6.74 22 -3.31 0.0032 Inactive: -3.61 -1.77 0.090 Ovary mass BQCV Linear mixed model (lmer)a -2.32 22 -2.28 0.033 DWV-B -0.56 24 -2.72 0.012 DWV-A -0.062 10 -0.24 0.82 SBV -0.21 22 -1.22 0.23 Total virus -0.26 15 -2.62 0.020 Recommence laying eggs after injection? (Y/N) Group (control group as reference) Generalized linear model, binomial (GLM) Live: -3.33 n/ac -2.51 0.012 Inactive: -0.83 -0.62 0.53 # eggs laid Total virus Linear mixed model (lmer)a -2.51 24 -2.64 0.014 *N = 27; n = 9 per group. All virus values (copies per ng RNA) were transformed by taking the natural logarithm aExperimental batch was included as a random effect bFor linear mixed models, degrees of freedom were estimated using Satterthwaite’s method. cGeneralized linear models do not report degrees of freedom; for this model there were 27 observations and 3 groups. 3.2.2 Fertility analysis The ovaries of queens injected with live virus were significantly smaller 7 days post infection relative to the saline-injected queens (linear mixed model with batch as a random effect, t = -3.31, p = 0.0032; Figure 3.2a), which is consistent with what we previously observed after injection with IAPV208. The ovary masses of queens injected with the inactivated virus also tended to be non-significantly reduced relative to the controls, though the effect size was about half that of the live virus group (linear mixed model with batch as random effect, t = -1.77, p = 0.090). Regardless of the group, ovary mass was negatively associated with levels of BQCV, DWV-B, and total virus load (Figure 3.2b, see Table 3.1 for statistical reporting). Queens were kept in specialized queen monitoring cages (QMCs) which enabled us to record their egg laying. All queens were confirmed to be laying consistently before being injected, but 55 queens injected with live virus were 75% less likely to recommence laying in the 7 days following injection relative to the control queens (binomial generalized linear model, estimate = -3.33, z = -2.51, p = 0.012), with only 2/9 (22.2%) queens in the infected group recommencing laying compared to 8/9 (88.9%) control queens (Figure 3.2c). Before injections, queens laid an average of 165 eggs per day (± 46), whereas after injection, queens that did recommence laying produced an average of 65 eggs per day (± 51) (both within the range of what has been observed previously in QMCs)207. Mirroring the results of ovary mass, regardless of group, for every additional unit of total virus infection queens laid approximately 2.5 fewer eggs per day over the final 3 days before being sacrificed (Figure 3.2d, linear model, t = -2.64, p = 0.014). 56 Figure 3.2 Viral infections reduce ovary mass and egg laying in cage experiment queens a. Comparison of ovary mass between queens injected with saline (control), live virus, or inactivated virus. b. The relationship between ovary mass and levels of BQCV, DWV-B, and total virus load. The dashed vertical lines represent the RT-qPCR quantification threshold. c. The number of queens in each group which recommenced egg laying 7 days after injection. d. The relationship between the number of eggs laid per day, averaged over days 5 to 7 after injection, and the total virus RNA copies across all queens. See Table 3.1 for detailed statistical reporting. 3.2.3 Hemolymph proteome analysis To investigate how the immune response of queens injected with UV-inactivated virus differed from those infected with live virus, we performed quantitative proteomics on the hemolymph of queens. Of the 1,071 identified protein groups (1% FDR) and 933 protein groups quantified in at least 75% of samples, 23 proteins were differentially expressed in the live infected group, and 15 were differentially expressed in the UV-inactivated group relative to the control (Figure 3.3, 5% FDR, Benjamini-Hochberg correction). Between these two groups, 12 proteins are shared, including many which are known to be associated with immunity. IRP30, for example, is a secreted immune protein found in a wide variety of hymenopteran species and is co-expressed with carboxylesterase 209,210, expression of which was also significantly elevated in both groups compared to the controls. Transferrin mediates iron-sequestration as an innate immune strategy to limit the availability of this element for acquisition by pathogens211. Beta-1,3-glucan binding protein is a pattern-recognition receptor in invertebrates for the glucans found on fungal cell walls, which triggers the phenoloxidase cascade, possibly by interacting with a serine protease 212,213. Additionally, phenoloxidase activating factor, a modular serine protease, and several of the antimicrobial peptides including defensin, hymenoptaecin, and apidaecin, were all expressed more strongly in queens receiving UV-inactivated virus and live virus groups compared to 57 controls. The only immune effector not shared between groups, lysozyme, was significantly upregulated in only the group receiving the inactivated inoculum. A channel protein from Vairimorpha apis (formerly Nosema apis 65 was also differentially expressed, with higher levels in both the inactivated and infected groups. Figure 3.3 Hemolymph immune proteins were differentially expressed between groups in cage experiment queens Each row represents a protein, and each column represents a sample, showing the Uniprot accession number. Proteins that were not quantified in a sample (“NA”) are shown as grey tiles. Proteins in bold were significant in both control-inactivated and control-infected contrasts (5% FDR, Benjamini-Hochberg correction). 3.2.4 Perturbation caused by the cage environment For experiments in which bees are kept in the laboratory, it is often unknown exactly how disruptive the laboratory system is to their natural functioning. To address this, we sacrificed three “baseline” queens from each of the three batches we received for the cage experiment immediately upon arrival for comparison to the control queens that were subsequently kept in the 58 QMCs. The ovaries of the baseline queens were significantly larger than the ovaries of control queens that were sacrificed 2 weeks later (Figure 3.4a). This is unsurprising, given that the baseline queens had been actively laying eggs in colonies only ~24 hours before being sacrificed, while the control queens had been kept in cages which, while allowing them to lay, did restrict their laying compared to a colony environment. To assess the impact of the QMCs more thoroughly, we also performed proteomics on the hemolymph of the baseline queens and found that even though 39 proteins in the hemolymph were differentially expressed in the baseline queens compared to the controls (5% FDR, Benjamini-Hochberg correction), only 3 proteins were overlapping with the proteins differentially expressed in either of the experimental inactivated or infected groups (Figure 3.4b). This indicates that, even though the cage environment clearly had an impact on the physiology of queens over this two-week period, those effects did not appear to overlap substantially with the effects we observed because of our experimental injections. 59 Figure 3.4 Effects of cage environment on ovary mass and protein expression a. Each row represents a protein differentially expressed between the baseline queens and the control queens, showing the Uniprot accession number, and each column represents a sample. Proteins that were not quantified in a sample are shown as grey tiles. Proteins in bold were also differentially expressed between the control and live virus group queens (5% FDR, Benjamini-Hochberg correction). Proteins are clustered via Euclidean distance. b. Comparison between the ovaries of control queens and baseline queens (linear model est. = -12.86, t = -5.99, p = <0.001). 3.3 Field experiment 3.3.1 Virus RNA copies and mite levels For the field experiment, queens were injected with either saline or a combination of BQCV and DWV-B in the same manner as the cage experiment, placed in colonies, and assessed weekly for 7 weeks following injection. We again assessed the virus loads of the queens at the end of the experiment using RT-qPCR. As shown in Figure 3.5a, BQCV was higher in the infected group while DWV-B was actually lower in the infected group (see Table 3.2 for statistical details). DWV-A was marginally higher in the infected group and SBV was only detected in two queens. 60 Correspondingly, there was no significant difference in the total virus level between the two groups. We also randomly sampled workers from brood frames at both the beginning and the end of the experiment to assess the levels of viruses present in the colony. The viral load in workers at the beginning of the experiment was a significant predictor of the total weight gained by the colony over the course of the experiment (Figure 3.5b) while the queen viral load was not. Interestingly, we found that the level of DWV-B in queens was significantly linked to the RNA copies of DWV-B present in the workers at the end of the experiment, though there was no correlation between queen virus RNA copies and worker virus RNA copies for the other viruses after controlling for the effect of site (Figure 3.5c). This trend with DWV-B appears to be mainly driven by colonies in the control group. We also examined the correlations of worker viral RNA copies, queen viral RNA copies, and the levels of mites in the colonies at the end of the experiment to gain a better understanding of how these metrics covaried. While mite load is positively correlated with levels of both variants of DWV in both queens and workers, the correlation is not significant after adjusting for multiple hypothesis testing (Bonferroni) (Figure 3.5d). There were no differences in mite loads between the experimental groups and colonies generally had low mite loads. 61 Figure 3.5 Virus RNA copies in field experiment queens and workers Control queens (n = 15) were injected with saline and infected queens (n = 14) were injected with a combination of BQCV and DWV-B. Queens were kept in nucleus colonies at two field sites in the lower mainland of British Columbia and sacrificed 7 weeks after injection. See Table 3.2 for detailed statistical reporting. a. RNA copy numbers of BQCV, DWV-A, and DWV-B between groups. There were no differences in the RNA copies of SBV between the two groups (not-shown). The dashed horizontal lines represent the RT-qPCR quantification threshold. b. The relationship between total weight gained by a colony over the 7 weeks and the levels of virus present in the workers at the start of the experiment. c. Correlations between infection in queens and levels of infection present in the workers of the colony at the end of the experiment. d. Correlation matrix showing the Spearman correlation between worker virus RNA copies, queen virus RNA copies, and colony mite counts. Asterisks represent relationships that have p < ɑ/n (Bonferroni correction for multiple hypothesis testing). 3.3.2 Supersedure cells, ovary mass, and brood area There was no difference in the ovary masses of the queens between the two groups, which was unsurprising given the similar levels of infection, but as we observed in the cage trial, there was a negative correlation between ovary mass and the levels of DWV-A and BQCV after controlling 62 for the effect of site (Figure 3.6a, see Table 3.2 for statistical details). To investigate whether ovary mass, as a proxy-metric for queen fertility, could be directly linked to colony population, we assessed the total brood and adult bee area present in the colonies over the course of the experiment. We found that at the termination of the experiment (the time point closest to when queens were sampled), the ratio of brood to adult bees was positively associated with ovary mass (Figure 3.6b). This relationship appears to be driven by the correlation between ovary mass and increasing brood area, as there was no significant relationship between ovary mass and adult bee population after controlling for the number of adult bees present at the start of the experiment. Premature supersedure is a common symptom associated with beekeeper-identified poor or failing queens, and we found that colonies with more heavily infected queens were indeed more likely to build at least one supersedure cell during the experiment, though the statistical significance of this effect was marginal (Figure 3.6c; binomial logistic regression/GLM, est. = 0.070, z = 1.93, p = 0.053). Figure 3.6 Relationships between ovary mass, virus RNA copies, brood, and supersedure cells in field experiment queens 63 See Table 3.2 for complete statistical details. a. Relationships between queen ovary mass and levels of DWV-B, DWV-A, and total virus load. The dashed vertical lines represent the RT-qPCR quantification threshold. b. The associations between ratio of brood to adult bees, measured as number of frame equivalents, present in the colony and the ovary mass of the queens after controlling for the effect of the site and the number of adult bees present at the start of the experiment. c. The probability of supersedure cells being observed at some point during the experiment related to levels of virus in the queens. The green points along the curve represent the fitted values of the binomial generalized linear model. 64 Table 3.2 Statistical parameters for field experiment* Response Model Fixed effectsa Estimate df |t/z| p Queen BQCV Linear model (lm) Group 2.16 26 2.52 0.018 Site -1.19 -1.39 0.18 Queen DWV-B Group -2.93 26 -3.18 0.0038 Site 10.17 11.02 2.7e-11 Queen DWV-A Group 2.91 26 2.0 0.055 Site 11.20 7.83 3.34e-8 Queen total virus Group 0.59 26 0.23 0.82 Site 19.7 7.56 5e-8 Queen BQCV Linear model (lm) Worker BQCV 0.17 26 1.66 0.11 Site -1.74 -1.88 0.07 Queen DWV-B Worker DWV-B 0.49 26 4.91 4.3e-5 Site 7.24 7.09 1.6e-7 Queen DWV-A Worker DWV-A -0.021 26 -0.12 0.91 Site 10.9 7.02 1.9e-7 Queen total virus Worker total virus -0.01 26 -0.065 0.95 Site 19.7 6.5 6.8e-7 Ovary mass Linear model (lm) Group 7.16 26 -1.55 0.13 Site 22.3 -4.8 5.4e-5 Ovary mass Linear model (lm) Queen BQCV -2.54 26 -2.98 0.0061 Site -25.1 -5.82 3.9e-6 Queen DWV-B -0.05 26 -0.054 0.96 Site -21.02 -2.04 0.052 Queen DWV-A -1.14 26 -2.01 0.055 Site -9.13 -1.2 0.24 Queen total virus -0.53 26 -1.52 0.14 Site -11.1 -1.35 0.19 Brood:bees ratio Linear model (lm) Ovary mass 0.0091 25 4.69 8.4-5 Site 0.32 4.77 6.8e-6 Initial adult bees -0.025 -0.95 0.35 Supersedure cells Generalized linear model (glm), binomial Queen total virus 0.071 n/ab 1.93 0.053 Colony weight gain Linear model (lm) Worker starting virus -0.21 29 -1.93 0.063 Site -3.55 -2.43 0.022 *N = 29: control n = 15, infected n = 14. All virus values (copies per ng RNA) were transformed by taking the natural logarithm aFor group, shown as control as reference group. bGeneralized linear models do not report degrees of freedom, for this model there were 28 observations. 65 3.3.3 Hemolymph proteome changes While the experimental infection in the field queens did not yield significantly different viral RNA copies between the two groups, we were nonetheless interested in investigating what proteomic changes corresponded to actual virus infection level and ovary size. Quantitative proteomics analysis of hemolymph showed that, of the 1,408 protein groups quantified (1% FDR) in at least 75% of samples, 28 proteins were significantly associated with ovary mass (Figure 3.7, 5% FDR, Benjamini-Hochberg correction) several of which overlap with proteins differentially expressed among the groups in the cage experiment. These overlapping proteins clustered together (Euclidean distance) and are immune-related. Only 4 proteins were significantly associated with total viral load: an uncharacterized protein (A0A7M7TFJ8), DWV genome polyprotein (G3F401), and the V. apis channel protein (T0LDL0) were elevated with total viral load and phospholipase A1 (A0A7M7IQ52) was downregulated. However, when both ovary mass and viral load were included in the same model, only one protein (beta glucuronidase, A0A7M7MM50, upregulated) was significantly associated with ovary mass and none with virus, reflecting the high level of collinearity between the two variables. 66 Figure 3.7 Proteins associated with ovary mass in the hemolymph of the field experiment queens Each row represents a protein significantly associated with ovary mass (5% FDR, Benjamini-Hochberg method), showing the Uniprot accession number, and each column represents a sample. Total virus RNA copies were not included in this limma model but are shown here to illustrate the correlation between the two variables. Proteins that were not quantified in a sample are shown as grey tiles. Proteins in bold were also differentially expressed between groups in the hemolymph of the cage experiment queens (5% FDR, Benjamini-Hochberg correction). Proteins are clustered via Euclidean distance. 3.4 Discussion In this work, we further substantiated the relationship between viral infection in honey bee queens and the symptoms associated with beekeeper-identified poor queen quality while also investigating a potential reproductive immunity compromise using two parallel infection experiments. In the cage experiment, we found that queens injected with infectious viruses had significantly smaller ovaries and were significantly less likely to begin laying eggs again after infection, while queens that were injected with inactivated viruses had marginally smaller ovaries and had no difference in their likelihood to recommence egg laying. However, both experimental groups exhibited a similar immune response to infection, as determined by the 67 elevated expression of immune proteins in the hemolymph. And, across all three groups, higher levels of viruses were associated with smaller ovaries and fewer eggs laid. The field experiment became a correlational rather than manipulative study, as our experimental infection surprisingly did not alter the overall virus load of the queens (as measured at the end of the experiment). However, the total virus load of the queens, regardless of the group, was indeed negatively associated with ovary mass and positively associated with the presence of supersedure cells, suggesting that there are real impacts of viral infections on queen productivity and possibly worker perception of the queen’s quality. 3.4.1 Discrepancy between outcomes of the cage vs. field experiments We were surprised to find that the queens we attempted to infect in the field trial did not actually have higher viral titers than the control group. This contrasts with the cage experiment, where infected queens did have elevated viral loads using the same inoculum; therefore, we know our technique is not fully to blame. This could be due to the lengthened time frame, which could have allowed queens to either recover from the acute effects of infection or acquire new infections from contact with workers. However, it is likely that queens in the cage trials were also nutritionally stressed to some degree compared to queens in the field trial. This could have exacerbated the effects of the experimental infection given that the egg laying output of queens has been shown to be directly related to the nutrition available to her attendants 207. Moreover, oogenesis is more severely disrupted in queens kept in cages outside of a colony compared to queens caged inside of a colony, likely due to improved nutrition provided to queens in the colony setting 93. Cages had fewer (~100) workers present to act as attendants and were supplied with pollen patty supplement, which is less nutritious than the bee bread present in colonies 214. 68 Moreover, the attendants that were present in the cages aged beyond the optimal age of nurse bees during the experiment 215. While the nutritional stress that queens experience in cages is somewhat artificial, queens could in theory also become nutritionally stressed in colonies during periods of dearth, if worker populations have dwindled due to disease or other stressors, or if mismanagement leads to suboptimal age structure for brood and queen care. We therefore argue that our findings from the cage trial still have the practical utility of highlighting the range of effects that are theoretically possible to occur in colonies. 3.4.2 Evidence for a partial escape of trade-off between reproduction and immunity We endeavoured to further investigate whether the impact of virus exposure on reproductive output was due entirely to the pathogenic effects of viral infection or if it was the result of a reproduction immunity trade-off, which assumes nutritional resources are limited to some extent. Such a trade-off is therefore expected to be exacerbated by the reduced nutrition present in the cage environment. Simply comparing the reproductive output of queens challenged with live pathogens fails to fully address this question, given that it is impossible to decouple the effects of the pathogen itself from the effects of the immune response that it has induced. However, by injecting queens with inactivated viruses, we were able to isolate immune effects from pathogenicity. The moderated reduction in ovary size in the queens receiving the inactivated inoculum suggests that honey bees have not totally escaped the reproduction-immunocompetence trade-off. This partial escape is likely due in part to social insect queens’ access to far more, but not infinitely more, nutritional resources than solitary reproductive individuals. Additionally, the presence of multi-functional proteins that are involved in both reproduction and immunity (e.g., IRP30; see below) are a tantalizing potential mechanism for 69 reducing the overall cost to sustaining both tasks simultaneously, since resources become effectively shared via common pathways. Honey bees have relatively few immune-related genes, possessing only about one-third of those found in Drosophila melanogaster153. However, D. melanogaster is also far better studied and there could be unrecognized immune proteins which are either uniquely multi-functional or are yet uncharacterized in social insects. One such protein is IRP30 (immune response protein 30), which was the top significant protein expressed abundantly in both the live virus and inactivated groups in the cage experiment and was negatively associated with ovary mass in hemolymph of the field experiment queens. IRP30 is characterized as an immune effector unique to Hymenoptera and is upregulated in response to a variety of immune challenges including bacteria 216, viruses 217, and dsRNA218. More recently, it has robustly been shown to also be tied to reproduction: in both workers and queens in bumble bees and honey bees, the expression of IRP30 increases between 20- to 90-fold in egg-laying individuals 210. Vitellogenin is another hallmark multi-functional protein, which is not only the major egg yolk protein precursor responsible for provisioning nutrients to developing oocytes, but is also a well-established player in innate immunity in a wide variety of organisms. It functions as a pathogen-associated molecular pattern (PAMP) recognition protein219–221, acts as a carrier of pathogen fragments in transgenerational priming158, and possesses antioxidant functions177,222. Vitellogenin expression levels have also been shown to rise in response to Varimorpha ceranae infection, perhaps contributing to a compensatory increase in antioxidant capacity69. The dual role of these proteins in both reproduction and immunity means that a resource allocation conflict should be mitigated, since resources invested in producing these proteins can support both processes. 70 3.4.3 Non-canonical antiviral immune response If the intermediate phenotype of queens receiving inactivated virus was caused by an immune response to the viral particles alone, we would expect queens receiving the live virus to have a stronger response since the number of live viral particles proliferated far beyond the number contained in the single dose of inactivated virus. This is the response we observed for ovary masses, but not immune proteins. The patterns of immune protein expression indicate that both treatments appear to stimulate a very similar immune response, both in the identity of the effectors and the magnitude of expression. Even though this was a viral infection experiment, all the immune-related proteins we identified as significant are canonically associated with the immune response to bacterial and fungal infection. This is unsurprising, given that it has been repeatedly shown there is significant cross-talk among the various immune response pathways, and so-called “antimicrobial” effectors are robustly upregulated in response to viral infection in honey bees 182,223. Interestingly, while proteins were differentially expressed between groups in the cage experiment, there were no proteins significantly associated with the total viral load. While this is likely due at least in part to easier statistical ability to detect differences between discrete groups rather than among variable, continuous data with somewhat limited sample numbers, it could also indicate that a perturbation from an individual’s background virus infection in an acute time frame is more impactful to the immune system and ovary mass than absolute virus load (which includes some level of pre-existing infections). Several of these proteins were associated with ovary mass in the field experiment, highlighting how interconnected ovary mass and responses to viral infections are. It is not clear why the immune response in the queens receiving live virus did not have a larger magnitude than the inactivated 71 group, but it is possible that there is an upper limit to immune inducibility that we surpassed with our treatments. 3.4.4 Unexplained presence of Varimorpha (Nosema) apis protein The V. apis protein that was identified in both experiments is intriguing. V. apis has been known to frequently co-infect both colonies and individuals with BQCV, and has been shown to act synergistically to increase mortality of adults bees beyond infection with either pathogen individually61, although the way in which these pathogens interact is still unknown. This protein was the only one identified in its protein group, and several other proteins from this pathogen were quantified (although not differentially abundant), which suggests that this is a legitimate finding. The V. ceranae proteome was included in the search database, indicating these are not mis-identified microsporidia proteins either. It is unlikely that the viral inoculum was contaminated with V. apis spores after ultra-centrifugation, given the large size difference between virus particles and spores. Because we also saw this protein in the inactivated group, this suggests that perhaps there is something about stimulating the immune system, either through natural BQCV infection or another stimulant like inactivated virus, which may actually help V. apis invade and proliferate. More research about the relationship between these pathogens and immune stimulation is needed to address this possibility. 3.4.5 Influence of colony environment on queen infection levels Queen “failure” in the field has previously been associated with higher levels of viruses, and we were able to recapitulate symptoms of failure with experimental infections; however, the results of our study also suggest that queen health is intrinsically tied to the circumstances of her colony 72 environment. For instance, levels of virus infection in the colony and in the queen likely have a two-way interaction. Here, we show that in the colony context, it appears that the levels of DWV-B in the workers have a stronger influence on the queen’s DWV-B RNA copies than actual injection with the virus, despite the layers of social immunity that should conceivably shield her from exposure. This is in contrast to previous work from Kevill et al.224 showing that generally the pathogen profile of a queen is not influenced by the pathogens in her colony, at least in the first 24 days after introduction. The presence of varroa in the colony adds an additional influencing factor, given that DWV is transmitted by mites130 and we found that levels of DWV and mite load were positively correlated, although not significantly. Given that the colonies with the highest levels of DWV-B in the workers and queens were control colonies, it seems unlikely that the relationship between queen and worker infections is driven by infected queens increasing the virus RNA copies in their colonies (though it has previously been shown that virus infection can be vertically transmitted from queen to progeny131,135,225. Many of the common queen failure symptoms can actually be attributed to factors external to the queen, as illustrated by Lee et al. (2019)107, who found that switching queens with poor or “failing” brood patterns into colonies with “good” brood patterns significantly improved the brood pattern of the “bad” queen. 3.5 Conclusion While viral infection is clearly tied to queen reproductive output, it seems likely that adequate care from her colony can at least partially ameliorate these effects. This is consistent with our hypothesis that honey bee queens may have an increased threshold beyond which a trade-off manifests, rather than a simple presence or absence of a reproduction-immunity trade-off. This 73 could be mediated by multiple factors which are not mutually exclusive: (1) a decreased cost to activating both processes due to the presence of Hymenoptera-specific, multi-functional proteins that function in both reproduction and immunity; (2) the rewiring of specific hormones, which act as a discrete switch between the two processes; or (3) access to an unusually high amount of energetic resources among invertebrates due to social provisioning. Given that the latter element is highly dependent on the health status of the colony, and we found that a compromise between reproduction and immune activation may be partially responsible for some of these effects, this represents a further way in which what we observe as symptoms associated with a queen could be tied to the conditions of the colony. Aside from several issues with queen reproductive health that can be directly attributed to poor mating, aging, or reduced sperm quality, laying blame solely on the queen in cases of queen “failure” belies a significant part of the story involving the health, infection, and nutrition status of a honey bee colony. 3.6 Methods 3.6.1 Cage experiment Young, naturally-mated queens (reared from closely-related mothers) that had commenced laying within the previous week were couriered overnight in JZ’s BZ’s battery boxes filled with nurses from a British Columbia queen producer (Wild Antho). Queens were received in three batches, each 2 weeks apart, starting in the middle of July. Queens were placed in queen monitoring cages (QMCs)207 along with approximately 11 grams of worker bees (~100 individuals) collected from a brood frame of a single colony maintained at the University of British Columbia (UBC). The cages were kept in an incubator at 34 °C, with a large water pan to increase humidity, and were supplied ad libitum with fondant (Ambrosia), protein patty (Global 74 Patties), and tap drinking water. A total of n = 9 “baseline” queens, three from each of the three batches received, were anesthetized and frozen immediately upon receipt. Each QMC contained two “egg laying plates” that are made up of 264 hexagonal cells in the dimensions of natural brood comb 207. These plates were cleaned and/or replaced every 48-72 hours to provide queens with continuous space to lay. Queens were allowed to acclimate to the cages and confirmed to be consistently laying for 6 days before injection, which ensured their ovaries had time to reactivate after caging during shipment. Queens were then injected on Day 6 and observed for 7 more days before sampling. For each batch, queens were randomly assigned to each experimental group (saline injected control, live virus infected, and UV-inactivated virus injected) such that the experimenter (Chapman) was blind to group identifiers. To ensure equal inoculum integrity, the master inoculum was first aliquoted and stored at -70 °C, with a fresh aliquot used for each batch of queens. In total, n = 9 queens were tested in each group, spread equally across the three experimental batches (3 in each batch). Blinding was maintained until all data had been collected. 3.6.2 Field experiment Young, age-matched, naturally mated queens (n = 32) were obtained as a single batch of sister queens (grafted from the same donor colony) from the same producer as for the cage experiment and introduced into small, five-frame colonies (referred to by beekeepers as nucleus colonies or “nucs”). The nucs were established on June 7, 2023 almost exclusively from a commercial source of bees (packages obtained from Kintail and Arataki suppliers in New Zealand) that were previously hived in March and allowed to grow until June. Stock imported from New Zealand into Canada is required to have less than 1% varroa mites (mites per 100 bees), but an oxalic acid 75 mite treatment (3.5% in 50% syrup) was still applied at hiving as a precautionary measure. Some additional stock was pulled from overwintered colonies maintained at the University of British Columbia that had sufficiently low (<1%) mite loads and an absence of brood disease symptoms. Nucs were produced such that each colony had one frame of honey, two frames of brood, one frame with space for laying (drawn, irradiated comb), and one frame of plastic foundation. Adult bee populations were equalized as much as possible during nuc production but they were further equalized 3 days after queen introduction by adding young adult bees to smaller colonies. The experiment was conducted at two sites 2 km apart (16 nucs at each site) such that no nuc remained at the same site as its parent colony. Nucs were provided with protein supplement (Global Patty) and fondant (Hive Alive) and replenished weekly throughout the experiment to minimize potential nutritional differences among colonies. On July 7, European foulbrood disease was detected in a subset of colonies, therefore all colonies at both sites were treated with Oxytet-25 (oxytetracycline antibiotic) according to the label rate to minimize impact of the disease on the experiment while still managing all colonies equally. Two weeks after the nucs were produced, baseline colony weights were recorded (supplemental feed was removed prior to weighing), a sample of 15 randomly selected workers from a brood frame was taken for initial worker viral analysis, and both sides of each frame were photographed for subsequent brood area and adult bee coverage calculations. The same day, queens were retrieved then randomly and blindly assigned (with the help of a colleague not otherwise involved in the experiment) to either the saline injected control or live virus infected group (using an aliquot of the same virus mix used for the cage experiments), with balanced groups within each site. Once injected, the queens were returned to their respective colonies 76 within 1-2 hours. From that point on, colony weights were recorded weekly and frames were photographed weekly or biweekly (June 22, June 29, July 6, July 19, and Aug 2). During photographing, incidence of brood disease (number of affected cells) was noted and the presence or absence of supersedure cells was recorded before the cells were destroyed to prevent queen loss. During the course of the experiment, 11 colonies grew sufficiently to require an additional box of frames (consisting of a mixture of foundation and irradiated drawn comb), which were weighed and subtracted from the subsequently measured colony weights. At the end of the experiment (Aug 10), we sampled the experimental queens, 15 randomly selected workers from a brood frame (for final worker viral analysis), and ½ cup of bees (~300 adults) for varroa mite counts using alcohol wash method (Jong et al., 1982). A total of 29 queens persisted to the end of the experiment, as in the final 2 weeks one queen was lost by accident and two were superseded. The operators (Chapman and McAfee) remained blind to experimental groups until all data were collected, including image analysis. Photographs of frames were analyzed using ImageJ to calculate the total area of adult bee coverage and brood coverage. In cases where adult bee coverage was too dense to adequately assess the brood, each side of the frame was photographed, then the bees were gently agitated to expose the brood for a second set of photographs. Top bars were labelled with the colony ID and frame number to associate photos with colonies. In ImageJ, bee area and brood area were traced and calculated, then expressed as a fraction of total frame area, which were summed for each colony. 77 3.6.3 Virus isolation and propagation Samples of homogenized honey bees from individual colonies that had been stored at -80oC from previous projects were tested by RT-qPCR to identify samples with the highest concentration of the target virus. Those with the highest virus purity relative to other commonly detected viruses in our samples (DWV-B, DWV-A, IAPV, BQCV and SBV) were used for the primary extracts as described by de Miranda et al., 2013226. These extracts were passed through a Corning syringe filter (0.45 µm) to remove bacteria. Primary extracts were used to make 100 µL serial dilutions; DWV primary extracts were diluted with 90 µL of 0.5M potassium phosphate buffer solution (pH 7.5), BQCV primary extracts were diluted with 0.01 M potassium phosphate buffer (pH 7.0), and three concentrations (105, 104, and 103 gene copies per µL) were used to inoculate pupae. Pupae that contained low background virus levels were obtained from honey bees that had been initiated from Australian package bees (Aussie bees® sourced from Tasmania) hived on new plastic foundation and held in isolated apiaries in Canada (Star Lake Manitoba 49°45'08"N 95°15'24"W and Red Deer Lake Ontario 50°00'47"N 94°11'26"W) in areas where no other managed honey bees occur. Colonies were sampled using sticky boards and alcohol washes to confirm the absence of varroa mites. Honey bee pupae collected at the white-eyed to pink-eyed stage were then used to propagate the viruses. A Hamilton® syringe with a 30GxK gauge sterile needle was mounted on a Schley Compact MDL II® insemination stand and then the target virus solution was inoculated into the pupae through the lateral side of the abdomen between the 2nd and 3rd tergites. One µL of virus solution was injected per bee. Inoculated pupae were then placed in a covered Petri dish with paper towel dampened with distilled water, with 20 pupae per 78 dish and incubated at 31.5 oC for 5 days. Pupae were checked regularly, and distilled water was added as required with any dead pupae removed from the dishes. Any pupae that reached the adult stage prior to the end of 5 days were stored at -80oC for later processing. After incubation, all healthy pupae (or adults) from each dose (105, 104, or 103 virus particles) were collected in three 50 mL VWR centrifuge tubes. The pupae were stored at -80 oC until further processing or homogenized immediately after collection. Pupae were placed in 50 mL Spex SamplePrep polycarbonate cryovials with two 9 mm steel beads and homogenized at 1750 rpm for 3 minutes in a Spex SamplePrep Geno/Grinder® 2010. The homogenate was then used for virus purification. 3.6.4 Virus Purification Extraction buffer, which varied with type of virus, was first added to the propagated virus samples. DWV extraction buffer consisted of 0.5 M potassium phosphate buffer (pH 7.5) and 0.2% ascorbic acid. BQCV buffer consisted of 0.01 M potassium phosphate buffer (pH 7.0) and 0.02% ascorbic acid. To make primary extracts, 2 mL of extraction buffer and 0.5 mL chloroform were added per 1 g of homogenate and samples were kept on ice in a fume hood. Each sample was vortexed for 30 seconds, then centrifuged at 8,000 g (8,700 rpm, Eppendorf Centrifuge 5430, placed in the fridge) for 10 min at 15oC. From each tube, the supernatant was collected into a 5 mL centrifuge tube based on sample origin. Samples were stored at -80oC until further purification. High speed centrifugation was done to further purify the virus samples. The primary extracts were thawed and added to 35 mL Thermo Scientific™ PA Ultracrimp Tubes, with the rest of the 79 volume filled with more extraction buffer. The tubes were centrifuged at 15 oC for 3 hours, at 75,000 g (27,200 rpm, Sorval Discovery 100SE, Sorval T-865 Rotor). The supernatants were discarded, and the pellets were placed in new 5 mL centrifuge tubes. DWV was resuspended in 5 mL of 0.5 M potassium phosphate buffer (pH 7.5), and BQCV in 0.01 M potassium phosphate (pH 7.0). The solutions were mixed using a spatula and via vortex. The samples were left overnight at 4oC and then mixed gently in the morning. The samples were split into 2 mL microcentrifuge tubes to be centrifuged for 15 min at 4 oC, at 8,000 g (8,700 rpm, Eppendorf Centrifuge 5430, placed in the fridge). The supernatants were collected into a new 5 mL centrifuge tube based on sample. The samples were then passed through a 0.45 µm syringe filter into new 5 mL centrifuge tubes, and 20 µL of each sample was taken for viral analysis to determine the final concentrations and purities. The final extracts had the following purities: DWV-B was 99.99% pure (with other components consisting of 0.000006% DWV-A, 0.00% IAPV, 0.00% BQCV, and 0.000006% SBV), and BQCV was 99.88% pure (with other components consisting of 0.02% DWV-B, 0.02% DWV-A, 0.05% IAPV and 0.02% SBV). 3.6.5 Injection of queens Queens were anesthetized with carbon dioxide for 3 minutes (starting when the queen ceased all movement) before being injected on the dorsal side between the 2nd and 3rd abdominal tergites with ~100 nL containing either sterile phosphate-buffered saline, a combination of infectious BQCV (~1 x 108 copies) and DWV-B (~2 x 106 copies), or the same combination and quantity of 80 virus which had been UV-inactivated (cage experiment only). These doses are in line with other experimental infections in honey bees in the literature227–229. Injections were performed using the FemtoJet Microinjector (Eppendorf) with pulled-capillary glass needles. Injection volumes were calibrated by estimating the droplet volume using a hemocytometer grid under a compound microscope. Everything associated with the injection apparatus was wiped down or soaked in 70% ethanol before and after injections. The saline injected queens were injected first and the live virus group was injected last. To inactivate the virus mixture, a 50 µL aliquot of virus on parafilm (kept on ice) was exposed to 4 consecutive doses of 120,000 mJ of ultraviolet light using a Stratalinker UV Crosslinker (Stratagene) in a manner similar to previously published protocols230,231. 3.6.6 Queen dissection Queens were sacrificed by anesthetization with carbon dioxide for ~5 min. To collect hemolymph for proteomics while avoiding potential contamination from the gut, a leg was removed from the thorax of each queen and a glass capillary was used to collect the exuded hemolymph. After hemolymph was collected, queens were decapitated and the ovaries were removed and weighed on an analytical balance. The thoraxes were stored for viral qPCR analysis. All samples were stored at -70 °C until subsequent preparation. 3.6.7 Proteomics sample preparation Hemolymph samples (n = 36 for the cage experiment and n = 29 for the field experiment) were prepared for mass spectrometry essentially as previously described 208. Between ~5 and ~15 µL of hemolymph which had been collected from the queens was directly precipitated by the 81 addition of 500 µL of ice-cold 80% acetone and incubation overnight at -20 °C. The precipitated protein was pelleted by centrifugation (6,000 g, 15 min, 4°C) and the supernatant was discarded. The protein pellet was washed twice with 500 μL of 80% acetone, then the pellet was allowed to air dry (~5 min) before solubilization in 15 μL of digestion buffer (6 M urea, 2 M thiourea, 100 mM Tris, pH 8). The resuspended protein was quantified using a Bradford assay (PIERCE) and then 20 μg of protein was reduced (0.4 μg dithiothreitol, 30 min) and alkylated (2 μg iodoacetamide, 20 min, in the dark). The samples were then digested overnight with 0.8 μg trypsin/LysC mixture (Promega) and diluted with 5 volumes (150 µL) of digestion buffer (50 mM ammonium bicarbonate) after the first 3 h. Digested peptides were acidified with 30 µL of 20% formic acid (to pH < 2.0) and desalted with high-capacity STAGE tips as previously described (Rappsilber et al., 2003). Eluted samples were dried (SpeedVac, Eppendorf), resuspended in Buffer A (0.1% formic acid, 2% acetonitrile), and then the peptide concentrations were determined using a NanoDrop (Thermo, 205 nm). 3.6.8 Mass spectrometry data acquisition LC–MS/MS data acquisition was performed exactly as previously described181. A total of 50 ng of digested peptides were injected onto the LC system in randomized order. The digest was separated using NanoElute UHPLC system (Bruker Daltonics) with Aurora Series Gen2 (CSI) analytical column (Ion Opticks, Parkville, Victoria, Australia) heated to 50°C and coupled to timsTOF Pro (Bruker Daltonics) operated in DIA-PASEF mode. A standard 30 min gradient was run from 2% B to 12% B over 15 min, then to 33% B from 15 to 30 min, then to 95% B over 0.5 82 min, and held at 95% B for 7.72 min, where buffer B consisted of 0.1% formic acid in 99.4% acetonitrile and buffer A consisted of 0.1% aqueous formic acid and 0.5 % acetonitrile in water. Before each run, the analytical column was conditioned with 4 column volumes of buffer A. The NanoElute thermostat temperature was set at 7°C. The analysis was performed at 0.3 μL/min flow rate. The trapped ion mobility time-of-flight mass spectrometer (TimsTOF Pro; Bruker Daltonics, Germany) was set to parallel accumulation-serial fragmentation (PASEF) scan mode for data-independent acquisition scanning (100 – 1700 m/z). The capillary voltage was set to 1800 V, drying gas to 3 L/min, and drying temperature to 180 °C. The MS1 scan was followed by 17 consecutive PASEF ramps containing 22 non-overlapping 35 m/z isolation windows, covering the 319.5 – 1089.5 m/z range. As for TIMS setting, ion mobility range (1/k0) was set to 0.70 – 1.35 V·s/cm2, 100 ms ramp time and accumulation time (100% duty cycle), and ramp rate of 9.42 Hz; this resulted in 1.91 s of total cycle time. The collision energy was ramped linearly as a function of mobility from 27 eV at 1/k0 = 0.7 V·s/cm2 to 55 eV at 1/k0 = 1.35 V·s/cm2. Mass accuracy was typically within 3 ppm and was not allowed to exceed 7 ppm. For calibration of ion mobility dimension, the ions of Agilent ESI-Low Tuning Mix ions were selected. 3.6.9 Mass spectrometry data processing and analysis MS data was searched using DIA-NN (v1.8.1)232 using “FASTA digest for library-free search” with “Deep-learning based spectra, RTs and IMs prediction”, allowing for 2 missed cleavages, selecting “Unrelated runs”, setting protein inference to “Protein names from FASTA”, and setting the neural-network classifier to “Double-pass mode”. “N-term M excision” and “C 83 carbamidomethylation” were selected as modifications, and “Use isotopologues”, “MBR”, and “No shared spectra” were all left checked for the algorithm. All other parameters were left set as default. The protein database was the UniProt proteome for Apis mellifera (based on the Amel_HAv3.1 genome assembly), plus common honey bee virus and pathogen proteins from UniProt downloaded on February 6, 2023. To this, a comprehensive list of potential protein contaminants was added 233. All subsequent analyses were performed in R (v 4.3.0). The identified and quantified protein groups from the DIA-NN search were filtered to remove contaminants and proteins identified in fewer than 75% of samples. Normalized LFQ intensity was then log2 transformed. Differential expression analysis was performed using the limma package in R201. For the cage experiment, experimental batch was included as a blocking factor and group (all 4 groups including the baseline queens) was included as a fixed effect. Contrasts were defined with the control queens as reference. For the field experiment, three models were used: just ovary mass as a fixed effect, just total virus RNA copies as a fixed effect, or both. We checked if site should be included as a blocking factor, but the consensus correlation was negative, so it was not included. Empirical Bayes moderation of the standard errors was performed on the resulting outputs, and the false discovery rate was controlled to 5% using the Benjamini–Hochberg correction. 3.6.10 Sample preparation for viral quantification The thoraces from each queen were placed in separate 2 mL screw-cap homogenizer tubes containing 500 µL of Trizol reagent and three ceramic beads. The samples were homogenized (Precellys 24 homogenizer, Bertin Instruments) 2 times for 30 seconds at 6,500 rpm and placed 84 on ice for 5 minutes between runs. The homogenate was then centrifuged for 30 seconds at 14,000 g and all 500 µL of supernatant was transferred to a new tube containing 500 µL of 100% ethanol for nucleic acid precipitation. For the worker samples, 5 thoraces from workers were pooled with 1 mL of Trizol reagent and 4 ceramic beads in three tubes for a total of 15 workers sampled from each colony. 250 µL of centrifuged homogenate from each of the 3 tubes was pooled with 500 µL of 100% ethanol for precipitation. The rest of the RNA extraction was performed using the Direct-zol RNA Miniprep spin-column kit (Zymo Research), including the DNase I treatment, according to manufacturer instructions. The RNA was eluted in 50 µL of RNase-free water and quantified using a Qubit Fluorometer with the Qubit RNA High Sensitivity assay kit (see Supplementary Data 1 for total RNA extract from each thorax). A subset of samples was assessed for contaminants using a NanoDrop One spectrophotometer. The extracted RNA was stored at -70 °C and thawed only once. RNA was diluted further in RNase-free water and 200 ng was used to produce cDNA. Reverse transcription was carried out using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems), which utilizes random primers, according to the manufacturer’s instructions. cDNA was kept after synthesis at 4 °C for a maximum of 2 weeks. 3.6.11 RT-qPCR for viral quantification Quantitative real-time PCR was performed on a C1000 Touch Thermal Cycler with a CFX96 Touch Real-Time PCR Detection System (Bio-Rad) using SsoAdvanced™ Universal SYBR® Green Supermix (Bio-Rad). Reactions (10 µL total volume) were set in 96-well plates with 2.5 µL of cDNA at 2 ng/µL (for a total of 5 ng per reaction). Primers were included at a final 85 concentration of 250 nM for both the forward and reverse primers, except for the SBV amplicon for which both primers were at a final concentration of 500 nM. A standard curve in triplicate was included on every plate using a single plasmid containing all the amplicons of interest. The standard curve spanned 6 orders of magnitude, from 960 copies/µL to 9.6 x 108 copies/µL. The plasmid was dissolved in RNase-free water and the concentration was determined spectrophotometrically using a NanoDrop One. For every plate, the efficiency of the standard curve was between 90-110% and the R2 was ≥ 0.99. The qPCR thermocycling conditions were as suggested by the Supermix manufacturer: polymerase activation and initial denaturation for 30 sec at 95 °C, followed by denaturation for 10 seconds at 95 °C, then extension for 20 seconds at 60°C for 40 cycles. A melt-curve analysis was performed after every assay following a 65-95°C gradient at 0.5 °C increments every 2 seconds, and no unintended amplicons were ever observed. A “no transcript control” was included in triplicate on every plate and was never amplified. During the development of this method, we assessed 3 housekeeping genes (actin, S18, S5) on a subset of 25 samples to verify our sample processing and handling consistency and the standard deviation of the Cqs were under 0.5 for all three genes. Cqs were determined by the regression mode in the CFX Manager Software (v 2.1) rather than by a single threshold. Starting quantity (SQ) in copies was calculated within the software based on the standard curve. These values were then further processed in R (v 4.3.0) for statistical analysis. Samples with a Cq greater than the average Cq of the lowest point on the standard curve for that plate were removed. The SQ’s of the remaining samples were then averaged across replicates for each sample. Viral quantity is reported as copies/ng of starting RNA. 86 3.6.12 Statistical analysis All data analysis was performed in R (v 4.3.0). To analyze the differences in virus RNA copies and ovary mass between groups, analyzing the relationship between ovary mass and virus RNA copies, and assessing the relationship between the number of eggs laid and the total virus RNA copies of queens in the cage experiment, we used a linear mixed model (lmer in the lme4 package234) in addition to lmerTest235 to obtain p-values using Satterthwaite's degrees of freedom method, with experimental batch as a random effect. To assess the differences in the likelihood of queens beginning to lay eggs again after infection in the cage experiment, we used a generalized linear model with a binomial family function (glm in the base R “stats” package) and group as a factor (3 levels). For the field experiment, all models (see Table 3.2 for detailed model structures), except for the model examining the presence of supersedure cells, were fit using a simple linear model including site as a fixed effect (2 levels). The supersedure cell model was fit with a generalized linear model with a binomial family function (glm in base R “stats” package) and site as a fixed effect. All models were assessed for goodness-of-fit using the DHARMa package 236, which utilizes simulation-based residuals to check for typical model misspecification problems such as over/underdispersion and residual autocorrelation. 87 Chapter 4: Egg composition changes seasonally and after acute maternal virus infection 4 4.1 Rationale and research goals We finally aim to address our fourth question: Does the molecular composition of eggs reflect the infection status of the queen that laid them? The composition and size of honey bee eggs is plastic ― several recent studies have shown that honey bee queens predictably alter the size of their eggs in response to the size of the colony in which they reside237,238and that this change in size is accompanied by molecular changes, and changes in survival and adult bee size239. These changes are examples of maternal effects, where the phenotype of the offspring is influenced by the mother through altered deposition of maternal proteins or mRNA240. Changes to eggs associated with maternal infection could conceivably be maladaptive, as in the case of a trade-off, whereby one might expect the resources intended for developing oocytes in the ovaries to be diverted towards an immune response. This has been demonstrated in several Dipteran species, where the eggs laid by immune-challenged individuals have reduced protein content (reviewed in Schwenke et al.163). In honey bees, trans-generational costs of maternal viral immune activation were observed by Leponiemi et al.162: queens were orally inoculated with either inactivated DWV or phosphate-buffered saline (PBS). Their pupae were then challenged with injection of either DWV or PBS to see if the offspring benefited from the 4 A version of this chapter has been posted online as a pre-print article and submitted for publication: Chapman, A., et al. (2024). Honey bee egg composition changes seasonally and after acute maternal virus infection. BioRxiv. 88 priming of the queens. The authors found that, paradoxically, the control, PBS-injected offspring of queens that had been primed had higher levels of deformed wings compared to the offspring of unprimed (PBS-fed) queens. This suggests that when combined with another stressor (injection), maternal viral immune activation potentially induced a trans-generational cost resulting in developmental defects (which may or may not have been due to existing DWV infections). Conversely, there could also be adaptive changes after maternal immune challenge, which benefit the embryo (which, in honey bees, develops rapidly over ~72 hours after egg-laying) in the form of TGIP. While viral TGIP in honey bees may not involve the transfer of immunological triggers using the same mechanism as for bacterial immune priming, if the queen can prime her offspring to resist viral infections in some contexts, we theorize that this may involve the transfer of an increased number of immune effectors or other alterations in the composition of eggs she lays, influencing embryonic or larval virus resistance downstream. Highlighting the potential for this, recent work by Amiri et al. 136 found that, although very few genes were differentially expressed, the transcriptome of eggs containing either DWV or sacbrood virus (SBV) from natural, existing maternal infections, functionally differed (determined by GO enrichments) from eggs with no virus detected. Here we first investigate if the anatomical differences in honey bee queen ovary mass resulting from a viral immune challenge translate to differences in egg protein composition. To further investigate if these findings are replicated in a field setting, and expanding on the transcriptional work done by Amiri et al.136, we then examine eggs sampled from a field survey to see if the 89 protein, lipid, and metabolite composition differs as a results of naturally-occurring queen virus infections. Given that both reproduction and immune activation are dependent on adequate nutrition207,241–243, and that nutritional availability varies greatly across the season, we collected these samples at two different timepoints across the spring and summer. 4.2 Immune effectors are upregulated in eggs after immune activation in queens We further analyze samples collected from the cage experiment described in Chapter 3, where we collected eggs and the ovaries from queens 7 days after they had been immune-challenged with an injection of either 1) a combination of infectious black queen cell virus (BQCV) and DWV-B (live), 2) a UV-inactivated version of the same inoculum (inactivated) or, 3) saline (control). To see if the anatomical differences in the ovaries we previously observed translate to differences in ovary and egg protein composition, we conduct proteomic analyses of the ovaries (n=9 in each group) and eggs of the queens after being sacrificed. We were only able to perform proteomics on the eggs collected from n=7 control queens and n=6 inactivated virus-injected queens and could not obtain meaningful proteomic information from the eggs collected from the live virus group, given that only two of these queens resumed laying eggs after injection. Of the 5,296 protein groups we quantified (1% FDR), 16 protein groups were differentially expressed (5% FDR, Figure 4.1a) in the ovaries of the live virus-injected queens compared to the control queens. Five of these were immune-related proteins (IRP30, transferrin, hymenoptaecin, beta-1,3-glucan binding protein, and carboxyesterase) that we also previously reported to be upregulated in the hemolymph of these queens244. For the queens injected with inactivated virus, only one protein was differentially expressed (5% FDR) in the ovaries: the 90 immune effector IRP30 (Figure 4.1b). In the eggs of these inactivated group queens, only two proteins were differentially expressed: IRP30 and another immune effector, transferrin (Figure 4.1c). These two proteins were among the top proteins we previously reported as being upregulated in the hemolymph of these queens, among various other immune effectors244. IRP30 is a uniquely hymenopteran, multi-functional protein that is upregulated in response to viral, bacterial, and fungal immune challenges209,210,216–218 while also being expressed more highly in reproductively active, egg-laying individuals in both honey bees and bumble bees210. Transferrin is a protein associated with innate immunity across many organisms, including both insects and mammals and functions to sequester iron away from invading pathogens211. Given that the proteomic changes between the ovaries and eggs of the queens injected with inactivated virus were nearly identical, and that much of the mass of the ovaries is made up of developing oocytes, one might infer that changes to the proteins expressed in the eggs of the live virus injected queens reasonably mirror the differentially expressed proteins we observed in the ovaries. The increased differentially expressed proteins in the ovaries of the live virus-injected queens relative to the inactivated virus-injected queens support a previous suggestion by McMenamin et al.182 that an interaction between the virus and host is likely responsible for eliciting a complete immune response. These authors observed that injection with dsRNA elicited only part of the immune response compared to injection with active virus. This effect also mirrors the intermediate phenotype we previously observed, where queens injected with inactivated virus had marginally smaller ovaries relative to the control queens244. 91 Figure 4.1 Immune proteins are upregulated in both the ovaries and eggs of queens injected with inactivated and infectious virus. For all differential expression analyses, the false-discovery rate was controlled to 5% using the Benjamini-Hochberg method. a) The proteins differentially expressed in the ovaries of queens injected with infectious virus, compared to control queens (n = 9 for each group). Queens injected with inactivated virus are also shown (n = 9), but only IRP30 (bolded) was significantly differentially expressed in this group. Each row represents a protein showing the Uniprot accession number, and each column represents a sample. Proteins are clustered via Euclidean distance. b) The only protein significantly upregulated in the ovaries of queens injected with inactivated virus compared to the control queens (n = 9 for each group). c) The two proteins upregulated in the eggs of queens injected with inactivated virus (n = 6) compared to the control queens (n = 7). 4.3 Egg composition is unchanged among queens with differing levels of natural viral infections Our cage-based experiment demonstrated the maternal effects of an acute immune challenge with a combination of two common virus infections. To investigate how the effects of natural maternal viral infections compare, we collected queens and eggs from colonies managed by three 92 beekeepers in BC and Alberta. The ovary mass and total virus copies (the logarithm-transformed sum of the RNA copy numbers of DWV-A, DWV-B, SBV, and BQCV) of these queens varied both within and among beekeepers - across 83 queens collected, both ovary mass and total virus copies varied three-fold (Figure 4.2a). Surprisingly, we found no correlation between these two metrics (simple linear model including beekeeper and total virus copies as fixed effects with ovary mass as the response variable; F = 0.4855, p = 0.488, dfbeekeeper = 2, dftotal virus = 1), despite previously finding that ovary mass is negatively correlated with total virus copies in queens collected from the field208. Given that potential maternal effects in the form of altered egg composition could extend beyond changes to the proteome, we performed lipidomics and metabolomics in addition to proteomics on the eggs from these queens. However, we found no differences (5% FDR, Benjamini-Hochberg correction) in egg proteins (5,574 identified), lipids (367 detected), or metabolites (997 detected) associated with maternal ovary mass or total virus copies. This lack of relationship is further reflected by no discernable clustering of samples by principal component analysis (Figures 4.2b, 4.2c, and 4.2d). While we only collected eggs standing upright in each cell, which is indicative of newly laid eggs, the age of the eggs collected likely still varied considerably. Over the 72 hours of embryonic development between when an egg is laid and hatches into a larva, significant proteome changes occur245,246 which may have partially obscured our ability to detect maternal effects. We also cannot rule out the possibility that some eggs laid by queens in these colonies differ in their protein, lipid, or metabolite profile but were removed by workers by some external 93 quality control mechanism. Honey bees demonstrate an ability to detect infected and immune-stimulated nestmates across all life stages from larvae to adults, and remove the individual from the hive in a form of social immunity144,147. Honey bees can also detect and remove diploid drone larvae247. Such behaviour could have been at play here, where eggs exhibiting some altered signal were removed. The consistency in egg composition observed here could instead indicate the presence of some protective “quality control” mechanisms preserving the composition of eggs that queens lay despite changes to ovary activation and immune status due to the fact that the transcriptome of early embryonic development is stabilized by homeostatic feedback loops136,248. 94 Figure 4.2 Variation among queens collected from the field a) No significant relationship between ovary mass and the total virus RNA copies in the ovaries of queens (F = 0.4855, p = 0.488, n = 87) collected from three beekeepers in British Columbia and Alberta. b) Principle component analysis (PCA) of 5,574 unique quantified protein groups (5% FDR, Benjamini-Hochberg correction) with respect to ovary mass and virus abundance. c) PCA of 367 unique identified compounds from lipidomic analysis in negative mode with respect to ovary mass and virus abundance. d) PCA of the 977 unique compounds identified across both positive and negative ion modes during metabolomic analysis with respect to ovary mass and virus abundance. 4.4 The lipidome and proteome of eggs collected from the field are strongly influenced by date of collection While we detected no changes in the eggs related to queen health metrics, there was a strong effect of the date of collection on the protein, lipid, and metabolite composition of the eggs collected from the two BC beekeepers on two different dates in mid-May and mid-June (n = 37 for each date). We found 691 protein groups (Figure 4.3a), 220 lipids (of which 34 are annotated, Figure 4.3b), and 126 metabolites (of which 18 are annotated, Figure 4.3c), to be differentially expressed with regards to the date of collection. Honey bees are known to exhibit seasonal variations in protein and gene expression, although most studies documenting this phenomenon compare extreme phenotypes (e.g., summer bees to winter bees), which experience very different life histories249,250. Even though the samples taken in this study are temporally more proximate, one might still reasonably expect the changing environment over a single season to result in proteomic changes. We found that the gene ontology terms “large ribosomal subunit”, “unfolded protein binding”, “ribosomal subunit”, and “calcium ion binding” were significantly enriched (adjusted p < 0.05) in the proteins that were upregulated later in the season. These GO terms are related to some of the functional groups Han et al. found to be enriched in large eggs compared to smaller eggs including “structural 95 constituent of ribosome” and “protein binding”238. Surprisingly, almost all of the differentially expressed proteins were upregulated later in the season, and as a result, there were no GO terms significantly enriched in the proteins which were downregulated later in the season. The 18 annotated metabolites that were significantly different were all upregulated at the second time point (Figure 4.3c). While the roles that most of these compounds play in honey bee eggs have not been previously described, some are involved in the same pathways that Han et al. found to be enriched in larger eggs238, similar to the protein changes we observed. These authors also noted distinct changes in egg metabolites and proteins associated with sampling date but the precise temporal effect is unclear. There is evidence for many taxa, including mammals, amphibians, reptiles, and insects, that females shift to laying fewer, larger eggs later in the reproductive season251. This contextualizes our finding that the proteins and metabolites we quantified as upregulated later in the season corresponded with similar markers in larger eggs. Many of the annotated lipids that changed significantly over time in the eggs (Figure 4.3) are also found in pollen collected by honey bees252–254. It is well-established that the protein and lipid content of collected pollen varies over the season253,255 and that young worker bees convert the pollen they consume into nutrient-rich jelly to feed the queen and developing larvae256. It is unsurprising, then, that the lipid composition of eggs varies seasonally as well, given the available resources to a colony. 96 Figure 4.3 The composition of eggs changes with the season The results of differential expression analysis were controlled to 5% FDR using the Benjamini-Hochberg method. Each row represents a protein or compound with rows clustered via Euclidean distance, and each column represents a sample. a) The 691 protein groups that were differentially expressed between May and June out of 5,574 identified protein groups. b) The 34 annotated lipids (from a total of 220 differentially expressed compounds) out of 367 total identified compounds, with differential abundance between May and June. c) The 18 annotated metabolites (from a total of 126 differentially expressed compounds) out of 997 compounds identified, with differential abundance between May and June. 97 4.5 Conclusion Our results showed that immune-challenged queens upregulated immune effectors in their eggs and ovaries. In contrast, naturally infected queens from field surveys did not; there were no significant differences in egg protein, lipid, or metabolite composition related to maternal viral load or ovary size. But, the lack of relationship between maternal virus level and ovary mass in this dataset indicates that perhaps the potentially chronic viral infections in this collection of queens are mild enough (the viral copies present in the field-collected queens were generally lower than the viral copies in all groups of the cage experiment) that they are not impacting the queen’s reproductive output or stimulating a transgenerational response. Given that we did see an effect of immune activation on the protein composition of ovaries and eggs in the laboratory cage experiment, this suggests that either 1) the effects of immune activation are magnified in a laboratory setting, especially given that the nutrition provided to a queen in a cage is likely reduced relative to a queen in a full colony environment, or 2) maternal effects are present in eggs only in the early phase of immune activation, whereas queens with infections in the field have reached some internal equilibrium where the activity of their immune systems is no longer elevated. However, the date of egg collection strongly influenced egg composition, likely reflecting seasonal variations in pollen resources. These findings highlight that while viral infections can induce transgenerational effects on egg proteomes under short-term experimental conditions, such effects are less apparent in natural settings and can be overshadowed by seasonal and other ecological factors. 98 4.6 Methods 4.6.1 Immune challenge cage experiment As previously published (see Chapman et al., 2024 244 for full details), young, naturally mated queens were kept in an incubator with approximately 100 attendants in specialized “queen monitoring cages” 207 containing plates that enabled them to lay eggs. Queens were allowed to acclimate to the cages and were confirmed to be consistently laying before experimental injection. Queens were then injected on Day 6 after introduction to the cages, and observed for 7 more days before sampling. Queens were randomly assigned to each experimental injection group (n = 9 for each group) with the experimenter (Chapman) conducting the experiment blind to group identifiers. Queens were anesthetized with CO2 and then injected with a combination of either: (1) infectious BQCV (~ 1 × 108 copies) and DWV-B (~ 2 × 106 copies; live virus infected), or (2) the same combination and quantity of virus which had been UV-inactivated (UV-inactivated), or (3) the same volume of saline (saline control). Eggs were collected on Day 7 after injection and were no more than 48 hours old, although they were likely mostly younger than this based on anecdotal observations that the queens tended to take 12-24 hours to lay eggs in newly provided, clean egg-laying plates. Blinding was maintained until all data had been collected. Ovaries were dissected immediately upon queen sacrifice and all samples were stored at -70 °C until further processing. 4.6.2 Queen and egg collection from field sites Honey bee queens and their eggs were sampled from colonies in the field in 2021. Samples were collected twice from the two British Columbia beekeepers (in Grand Forks and Vernon), first on May 14 and 15 and then again on June 15 and 16, and collected from a Lethbridge, AB 99 beekeeper on June 27 and 28. At each time point, 10 eggs still standing on end (not fallen onto their side, which is indicative of older eggs close to hatching) were collected using a Chinese grafting tool and transferred to Eppendorf tubes. All samples were collected directly onto dry-ice and subsequently stored at -70 °C until processing. 4.6.3 Statistical analysis The relationship between ovary mass and virus RNA copies in the field queens was analyzed in R (v 4.3.0) using a simple linear model (lm) that was assessed for goodness-of-fit using tools within the DHARMa package 236, which utilizes simulation-based residuals to check for typical model misspecification problems such as over- or under-dispersion and residual autocorrelation. 4.6.4 Proteomic sample preparation and data acquisition For the ovary samples, one ovary from each queen was homogenized in a 2 mL screw cap tube containing 300 μL of lysis buffer (6 M guanidinium chloride, 100 mM Tris, pH 8.5) and four ceramic beads. For the egg samples, a microcentrifuge tube containing 10 eggs was centrifuged for 1 min at 14,000 rcf before 100 μL of lysis buffer was added. A plastic micro pestle was used to grind the eggs until any tissue was no longer visible. The mixture was then transferred to a 2-mL screw cap tube containing 2 ceramic beads along with an additional 50 μL of lysis buffer. All samples were then homogenized three times for 30 seconds at 6500 rpm using a Precellys 24 homogenizer (Bertin Instruments) and placed on ice for 5 minutes between each homogenization run. 100 μL of the homogenate was then transferred to a new tube and diluted 1:1 with distilled H2O. The protein was precipitated by adding four volumes (800 µL) of ice-cold acetone and 100 incubating overnight at 20 °C. All subsequent sample preparation and mass spectrometry data acquisition was conducted exactly as previously described 244. Briefly, the protein was resuspended, quantified using a Bradford assay (PIERCE), and then reduced (dithiothreitol) and alkylated (iodoacetamide). The samples were then digested overnight with trypsin/LysC mixture (Promega) before being acidified with 20% formic acid (to pH < 2.0) and desalted with high-capacity STAGE tips as previously described (Rappsilber et al., 2003). A total of 50 ng of digested peptides were injected onto the LC system in randomized order. The digest was separated using NanoElute UHPLC system (Bruker Daltonics) with Aurora Series Gen2 (CSI) analytical column (Ion Opticks, Parkville, Victoria, Australia) coupled to timsTOF Pro (Bruker Daltonics) operated in DIA-PASEF mode for data-independent acquisition scanning. 4.6.5 Proteomic data processing and analysis Mass spectrometry data were searched using DIA-NN (v1.8.1) 232 as previously described 244 using “FASTA digest for library-free search” with “Deep-learning based spectra, RTs and IMs prediction”, allowing for 2 missed cleavages, selecting “Unrelated runs”, setting protein inference to “Protein names from FASTA”, and setting the neural-network classifier to “Double-pass mode”. “N-term M excision” and “C carbamidomethylation” were selected as modifications, and “Use isotopologues”, “MBR”, and “No shared spectra” were all left checked for the algorithm. All other parameters were left set as default. The protein database was the UniProt proteome for Apis mellifera (based on the Amel_Hav3.1 genome assembly), plus common honey bee virus and pathogen proteins from UniProt downloaded on February 6, 2023. To this, a comprehensive list of potential protein contaminants was added233. 101 In R (v 4.3.0), the identified and quantified protein groups from the DIA-NN search were filtered to remove contaminants and proteins identified in fewer than 75% of samples. There were 5,999 protein groups identified before filtering for the eggs from the field survey and 5,574 after. For the eggs from the cage experiment, there were 5,965 protein groups and 5,296 after. For the cage experiment ovaries, there were 5,965 and 5,414 after. The normalized label-free quantification (LFQ) intensity data were then log2 transformed. Differential expression analysis was performed using the limma package in R 201. For both the ovaries and the eggs from the cage experiment, group was included as a fixed effect. Contrasts were defined with the control (saline-injected) queens as reference. For assessing the effect of date on the eggs collected from the field survey, the day of collection, the beekeeper, and the mass spectrometry sample injection order were included as fixed effects. The models used to investigate proteins associated with ovary mass or total virus copies (which yielded no significant proteins) included beekeeper and injection order as fixed effects. Empirical Bayes moderation of the standard errors was performed on the resulting outputs, and the false discovery rate was controlled to 5% using the Benjamini–Hochberg correction. GO term enrichment analysis was performed in ErmineJ 257 using the gene score resampling (GSR) method, which tests for enrichment along the continuum of raw p-values as scores rather than comparing enrichments in an arbitrary group of “significant” proteins against all “background” proteins. The false discovery rate for enrichment analysis was controlled to 5% using the Benjamini-Hochberg method. 102 4.6.6 Lipidomic and metabolomic sample preparation Eppendorf tubes containing 10 eggs each were centrifuged for 1 min at 14,000 rcf immediately upon removal from the freezer. 100 μL of methanol extraction solvent (75% methanol:water, made with 0.01% BHT-methanol and containing standards of methionine-d3, caffeine-13C3, and ferulic acid-d3 each at 1 ppm and SPLASH LIPIDOMIX (Avanti Polar Lipids, Alabama, USA) at 5 ppm) was added. The eggs were then ground with a mini-pestle to lyse the eggs. The tubes were thoroughly vortexed, and samples were transferred to a 2 mL screw cap tube homogenizer tube. Another 100 μL of extraction solvent was used to rinse the original tube and was also transferred to the homogenization tube to increase yields. Two ceramic beads were then added and samples were homogenized three times for 30 seconds at 6500 rpm as described for proteomics sample processing. 500 μL of methyl tert-butyl ether was then added and samples were incubated for 1 hour at room temperature while shaking at 1000 rpm. The mixture was transferred to a new tube and centrifuged at 14,000 rcf for 10 min. 600 μL was transferred to a new tube to which 107 μL of water was added (to a final additional concentration of 15%) before incubating again at room temperature for 10 minutes while shaking at 700 rpm. The samples were then centrifuged for 15 min at 14000 rcf at 4 °C. 300 μL of the upper fraction containing the non-polar (lipid) compounds was transferred to a new tube while 400 μL of the lower fraction containing the polar (metabolite) compounds was transferred to a different tube. The polar fraction was dried (SpeedVac, Eppendorf) immediately and stored at -70 °C until mass spectrometry analysis. The non-polar fraction was stored in solution at -70 °C and dried immediately before analysis to prevent oxidation. 103 4.6.7 Metabolomics LC-MS/MS analysis Samples were analyzed following previously established protocols in our group258,259, with some modifications. The polar fraction was resuspended with 250 µL of 50% aqueous methanol (v/v) and then spun at 16,000 rcf for 10 minutes. After that, 200 µL were transferred into LC vials for LC-MS/MS analysis. A quality control (QC) sample was made by mixing 20 µL from each sample. QCs and blanks were injected approximately every 12 samples. Samples were randomized before injecting 3 µL. The metabolites were separated using an Inertsil Ph-3 UHPLC column (2 µm, 150 x 2.1 mm, GL Sciences) on a Vanquish Horizon UHPLC system (Thermo) connected to an Impact™ II high-resolution mass spectrometer (Bruker Daltonics). The mobile phase consisted of water (A) with 0.1% (v/v) formic acid, and methanol (B) with 0.1% (v/v) formic acid. A multi-step gradient from 5% to 99% of mobile phase B over 18 minutes was used. The column temperature was set at 55 °C, the autosampler at 4 °C, and the flow rate at 0.3 mL/min. Data-dependent acquisitions were conducted in positive (ESI+) and negative (ESI-) ionization modes. For ESI+, the settings were as follows: a capillary voltage of 4,500 V, nebulizer pressure of 2.0 bar, dry gas flow of 9 L/min, dry gas temperature of 220°C, and a mass scan range of 60–1,300 m/z with a 0.6-second cycle time. Collision energy was 20 V ramped from 100 to 250% in MS/MS scans. ESI- used a capillary voltage of -3,500 V. Internal calibration was performed using sodium formate (10 µL of 10 mM injected at 0–0.15 min) to ensure high mass accuracy. 4.6.8 Lipidomics LC-MS/MS analysis Untargeted lipidomics analysis was conducted as previously described260,261 with some modifications. Shortly, analysis was conducted using an Impact IITM high-resolution mass 104 spectrometer (Bruker Daltonics, Bremen, Germany) coupled with an Elute UHPLC system (Bruker Daltonics). Compounds were separated using a multigradient method on an Acquity CSH column (130Å, 1.7 µm, 100 x 2.1 mm, Waters, Milford, MA) equipped with a CSH C18 VanGuard FIT Cartridge (1.7 µm, 2.1 x 5 mm). Mobile phase A consisted of acetonitrile: water (60:40, v/v), and mobile phase B was isopropanol: acetonitrile (90:10, v/v), both containing 0.1% formic acid (v/v) and 10 mM ammonium formate. Separation was achieved using a gradient ranging from 15% to 99% mobile phase B over 17 minutes as follows: 0 min (15% B), 0–2 min (30% B), 2–2.5 min (50% B), 2.5–12 min (80% B), 12–12.5 min (99% B), 12.5–13.5 min (99% B), 13.5–13.7 min (15% B), and 13.7–17 min (15% B). The column was maintained at 65 °C with a flow rate of 0.5 mL/min. The injection volume was 2 µL, and the autosampler was kept at 4°C. A quality control sample was prepared to track instrument performance by pooling 20 µL aliquots from each sample. QC sample was injected every twelve analytical runs. Data-dependent acquisitions were performed in positive ionization mode (ESI+) to obtain precursor and fragment ion information for compound annotation. The mass spectrometer settings were as follows: capillary voltage of 4,500 V, nebulizer gas pressure of 2.0 bar, dry gas flow rate of 9 L/min, dry gas temperature of 220°C, mass scan range of 65–1,700 m/z, spectral acquisition rate of 3 Hz, and cycle time of 0.7 s. Collision energy of 20 V was ramped through each MS/MS scan from 100% to 250%. Calibration was carried out by injecting 10 µL of 10 mM sodium formate at the beginning of each run via the 6-port diverter valve. 105 4.6.9 Lipidomics and metabolomics data processing The raw data was processed using Progenesis QI™ software (version V3.0.7600.27622) with METLIN™ plugin (version V1.0.7642.33805, NonLinear Dynamics). This involved peak picking, alignment, deconvolution, normalization, and database searching. Annotations were carried out following established protocols258,262 with additional stringency applied. Briefly, only molecular features containing MS/MS data were used to query and match against libraries. Ions detected in quality control (QC) samples were retained if their coefficient of variation (CV) was below 25%. In cases where compounds were detected in both positive and negative ionization modes, the one with the lower CV in QC samples was retained. For level one (L1) annotations, features were matched against the UBC Life Sciences Institute's in-house spectral library258 (Mass Spectrometry Metabolite Library of Standards, MSMLS, IROA Technologies), containing over 500 standards representative of primary metabolism and injected under identical analytical conditions. Level two (L2) annotations were based on screening features against METLIN™263, GNPS264, HMDB265, and LipidBlast266. Relative metabolite quantities were calculated by measuring the corresponding peak areas, and normalization was performed using the "all compounds normalization" method, a robust built-in feature of Progenesis QI™ designed for untargeted metabolomics studies. 4.6.10 Lipidomic and metabolomic data analysis Differential expression analysis was performed using the limma package in R 201 including date of collection, beekeeper and injection order as fixed effects. Metabolite data collected using positive and negative modes was analysed separately to allow for controlling for injection order. 106 The models used to investigate compounds associated with ovary mass or total virus copies (which yielded no significant proteins) included beekeeper and injection order as fixed effects. 4.6.11 Viral quantification on queens collected from the field One ovary from each queen was placed in a separate 2 mL screw-cap homogenizer tube containing 500 μL of Trizol reagent and three ceramic beads. The samples were homogenized (Precellys 24 homogenizer, Bertin Instruments) 2 times for 30 s at 6500 rpm and placed on ice for 5 min between runs. The homogenate was then centrifuged for 30 s at 14,000 rcf and all (500 μL) supernatant was transferred to a new tube containing 500 μL of 100% ethanol for nucleic acid precipitation. The rest of the RNA extraction was performed using the Direct-zol RNA Miniprep spin-column kit (Zymo Research), including the DNase I treatment, according to manufacturer instructions. The RNA was eluted in 50 μL of RNase-free water and quantified using a Qubit Fluorometer with the Qubit RNA High Sensitivity assay kit (see Supplementary Data 1 for total RNA extracted from each ovary). A subset of samples were assessed for contaminants using a NanoDrop One spectrophotometer and assessed for RNA integrity using a formaldehyde gel (prepared according to Mansour & Pestov 267). The extracted RNA was stored at -70 °C. RNA was diluted further in RNase-free water and 200 ng was used to produce cDNA. Reverse transcription was carried out using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems), which utilizes random primers, according to the manufacturer’s instructions. cDNA was kept after synthesis at 4 °C for a maximum of 4 weeks. The RT-qPCR was performed exactly as previously described in Chapman et al. 244. 107 Chapter 5: Conclusion5 Amidst a backdrop of widespread insect declines17–21,268 and fluctuating populations18,23, it is vitally important to better understand the impacts of interacting biotic and abiotic stressors on insect physiology72. For managed honey bee colonies, managing these stressors is becoming increasingly challenging for beekeepers: in recent years some Canadian provinces have reported nearly 50% overwinter losses269. Given that problems with queens are consistently ranked as one of the main perceived causes of honey bee colony loss on surveys, understanding the factors leading to queen failure is of great importance to the beekeeping industry. Elucidating why honey bee queens’ reproductive activity declines also has the potential to offer valuable insights into social insect biology and inform our understanding of what is causing declines in insect populations more generally. Honey bee queen failure is likely not explained by any one factor in isolation – much like colony failure as whole, multiple stressors probably interact to cause a reduction in reproductive fitness. The field is beginning to outline the ways which pesticide exposure119,120,123,124, temperature stress117,184, and poor nutrition contribute to poor queen quality. Other research is forming a more complete picture of all the pathogens which infect honey bee queens, their routes of transmission, and what symptoms – visible and hidden – those infections cause in queens103,185. 5 Content in this chapter has been published: Chapman, A., et al. (2024). Common viral infections inhibit egg laying in honey bee queens and are linked to premature supersedure. Scientific Reports, 14(1), 17285; and posted as a pre-print: Chapman, A., et al. (2024). Honey bee egg composition changes seasonally and after acute maternal virus infection. BioRxiv. 108 However, the knowledge gap this thesis aims to address is the specific ways in which virus infection in general – not just the effect of one specific virus – impacts honey bee queen fertility and how that can be related to field-relevant observations. Using proteomics to identify molecular changes associated with queen failure and virus infections in different contexts, I begin to piece together a story that involves canonical and non-canonical immune pathways and highly-context dependent effects of infection. I also offer some of the first specific evidence for a reproductive immunity trade-off in honey bee queens. 5.1 Viral infections impact honey bee queens' reproductive success We first present evidence supporting my first hypothesis that natural virus infections cause the symptoms of queen failure: in Chapter 2, we show that beekeeper-identified failing queens have measurably smaller ovaries and reduced sperm viability, that failing queens collected from three independent surveys have higher levels of virus infection, and that natural virus infection is associated with smaller ovaries, regardless of health status. we also highlight that the ovaries of failing queens have altered ovarian protein composition compared to their healthy counterparts. In the 2021 survey we present in Chapter 4, though, we did not find any significant relationship between ovary mass, virus infection, and the beekeeper’s impressions of queen quality, highlighting the context-dependent nature of the relationship between these variables. We then present evidence for the causal nature of the relationship between virus infections and the symptoms of queen failure. In Chapter 2, we first conduct a short (~72 hours) experimental infection with IAPV, a common but highly virulent virus, and show for the first time that ovary mass is significantly reduced after injection with a virus. We also validate that the heat-shock 109 response is a part of the non-canonical antiviral response, as has been reported previously in workers182, with proteomic evidence correlated with the natural viral infection levels of queens collected from the field and in the transcriptional response of young queens injected with IAPV. In Chapter 3, we then conduct a longer experimental infection in specialized queen monitoring cages (QMCs)207 using a combination of BQCV and DWV-B, two very common viruses that tend to have less dramatic symptoms in adult bees and are almost always asymptomatic in adult queens, We found that queens injected with the infectious virus had smaller ovaries and were less likely to recommence egg-laying than the control queens, validating our previous findings with IAPV and suggesting that these effects are likely the result of virus infection in general and not the pathogenic effect of one specific virus. Finally, we connect this causality to field-relevant observations by performing a field experiment in Chapter 3. Despite this field trial becoming correlational rather than manipulative (as the experimental groups displayed no differences in virus level or ovary mass 6 weeks after injection), we again observed that more heavily infected queens have smaller ovaries and found that infection is a meaningful predictor of whether supersedure cells were observed in the colony. This latter point is particularly interesting because supersedure is an often-cited indicator of queen failure, reflecting the colony’s instinctive assessment of queen quality, which cannot be addressed in laboratory experiments. 110 5.2 A reproduction-immunity trade-off partially explains the link between virus infection and reduced queen fertility Next, we address our second hypothesis that the effects of virus infection on queen reproductive output are due in part to a trade-off between reproduction and immunity. In Chapter 2, we present initial evidence for a trade-off, showing that after injection with IAPV, the expression of vitellogenin, an egg yolk protein, is reduced in the abdomens of young queens while heat-shock proteins associated with the antiviral immune response are upregulated. In Chapter 3, we show that queens injected with UV-inactivated virus displayed an intermediate reduced-ovary mass phenotype between control queens and queens injected with infectious virus. Additionally, we demonstrate that the immune response is similar between queens receiving inactivated virus and live virus, and that several of the same immune proteins are negatively associated with ovary mass in the field. We propose that this moderated reduction in ovary size suggests that honey bees are subject to a trade-off between reproduction and immune activation which is tempered by a complex interplay of multiple factors. These include the presence of multifunctional proteins that reduce the energetic cost of simultaneously sustaining both reproduction and immune activation, hormone rewiring, and the unusually abundant nutritional provisioning in a social environment. This latter point highlights the fact that queen failure is not solely due to reproductive health issues but is also closely linked to the overall health, infection, and nutritional status of the colony and that adequate colony care may mitigate some of the effects of virus infection. 111 5.3 The effects of maternal infection on eggs are context-dependent In Chapter 4, we then address our final hypothesis that the effects of maternal infections will be evident in the eggs of infected queens. We found that, following an experimental immune challenge with a combination of two common viruses (BQCV and DWV-B), queens laid eggs that were primed with an increased number of immune effectors relative to control-injected queens. However, eggs collected from queens in field settings showed no protein, lipid, or metabolite composition changes associated with natural variations in maternal ovary size and virus abundance. It is possible that variations in the ages of the eggs we collected obscured some changes associated with these variables. The composition of eggs collected from the field, though, does appear to be strongly affected by date, as others have reported238, which is consistent with previous reports of seasonal shifts in the honey bee proteome249,250 as well as the seasonal variation in the protein and lipid contained in pollen252,253,255. This work underscores that maternal viral infections in honey bees can indeed trigger a transgenerational effect on the egg proteome, but these effects are likely dependent on the context of the infection, potentially including the route of exposure, timing of exposure, degree of infection, type of virus, the season, nutritional availability, and genetics. 5.4 Caveats and limitation 5.4.1 Ovary mass as a proxy metric for fertility The field surveys presented in Chapter 2 show that failed queens have smaller ovaries and the work presented in Chapter 3 connects ovary mass (a proxy-metric for fertility) to effects observable at the level of reproductive output (i.e., egg laying) and at the colony level (i.e., relative amount of brood). This validates the use of ovary mass as a metric for current 112 reproductive health, if compared among similar queens in the same environment. Importantly, though, the ovary masses of queens of different ages, sampled at different times, from different locations, and especially those that have been caged and/or transported for different lengths of time, should not be directly compared. Ovary mass is a highly plastic parameter ― for example, it tends to decline when queens are confined to small cages with only a few attendants during shipping, but it rebounds again after being placed in a queen bank colony for several days or when she reinitiates oviposition184. Therefore, the effects that we observe on ovary size are likely temporary and could conceivably be reversed if, for instance, the virus infection was cleared. 5.4.2 Relevant routes of transmission for experimental infections For this work, we chose injection as the route of experimental infection for ease of accurate dosing and because previous work has shown that oral infection/propagation generally requires much larger doses of virus226. However, the route of infection does influence the effects of infection. Infection with BQCV by injection has previously been shown to increase mortality, viral titer, and expression of immune effectors relative to the oral infection route in worker honey bees227 and injection with DWV leads to much higher viral titers compared to oral infection270. Other work has shown that the maternal effects observed in eggs of queens infected with DWV via sexual transmission differed from queens infected orally161. This means that the responses we observed here are likely to represent a more extreme response than from the other routes of transmission (oral, vertical, or sexual). Currently, evidence for which of these represents the most common “natural” route of infection for adult queens is lacking. However, queens are less likely to be hosts to varroa due to the intense attention and grooming they receive in addition to the natural aversion of varroa to parasitize queen pupae (due in part to the deterrent chemical 113 composition of royal jelly271 and their shortened pupation period which does not allow varroa to reproduce). Additionally, mated queens tend to have higher rates of infection than virgins133,135 and DWV has been detected in sperm133. Together, this means that that oral and venereal routes of transmission are probably more relevant for honey bee queens than the vector-mediated transmission which injection stimulates. 5.5 Future directions and applications 5.5.1 Interactive effect of nutrition A valuable future direction for this work would be to formally examine the potentially moderating effects of nutritional availability on the interaction between virus infection and reduced queen fecundity. The link between nutrition and ovary development and reproductive output has already been established: workers fed a high-protein diet show higher levels of ovary development compared to workers fed low-protein272 and the number of eggs queens laid in cages was influenced by the quality of the diet fed to her attendants207. This is unsurprising, given that the quality and content of the diet of nurse bees impact the development of their hypopharyngeal glands273, which is the main site of synthesis for the jelly diet fed to the queen. Nutrition as a mediating factor on the effects of stressors has also been established: a recent study by Hsieh et al.85 found that in workers, agrichemical exposure worsened the mortality associated with viral infection only when bees were provided with an artificial pollen-substitute. When the bees were fed naturally-collected polyfloral pollen, the effect was reversed. Given that the trade-off between reproduction and immunity is assumed to be mediated by the allocation of limited resources, investigating the role of nutrition in this dynamic system is crucial. 114 We observed that acute effects of experimental virus infection were magnified in our laboratory trials, where nutritional stress was likely an exacerbating factor, compared to the field trial where colonies with excess food availability could provide queens with more adequate nutrition. Forage dearth (and, therefore, nutritional stress) can, however, also occur in the field, and our data suggest that the effects of virus infection on queens may be worsened under such conditions. The nutrition available to a colony depends on the resources available in the environment, which vary seasonally as different flowering plants are in bloom in the surrounding ecology. Acquisition of those resources depends upon the colony possessing a population of workers strong enough to retrieve those resources, which reciprocally depends on the queen’s ability to produce that workforce and workers’ ability to rear larvae to adulthood. Colony dynamics therefore depend on both resource availability and the colony’s ability to capitalize on those resources. Given the dynamic system of a colony, it is possible that attendants help the queen to compensate for infection by increasing the nutrition they provide the queen, potentially at their own expense. 5.5.2 Can queens recover from viral infections? An obvious question that arises from this work is whether queens can recover from viral infections and their effects, as we currently do not know if viral infections permanently or only temporarily impact a queen’s quality. Little is known about the dynamics of infection in queens, particularly if and how quickly they can clear infections. The literature states that most viruses exist as persistent, latent infections throughout the population32, indicating that individuals may never fully clear an infection from their system. But understanding what circumstances may allow a queen’s immune system to at least bring the level of viral infection down below the 115 threshold beyond which negative effects manifest would be a key piece of information for contextualizing our findings and may lead to therapeutic applications. 5.5.3 Potential for queen “vaccines” Having established that virus infections have the potential to impact queen quality, the logical next step for applying these findings would be to develop ways to prevent infections in the first place. There is potential for a vaccination strategy that could both protect queens as individuals, and protect their offspring through TGIP. Previous work has demonstrated the efficacy of using dsRNA as a “vaccine,” to stimulate an RNAi response specific to a particular virus and reduce the infection levels, symptoms, and mortality associated with subsequent infection in workers. Desai et al.274 demonstrated this with DWV-dsRNA in the laboratory in both larva and adults and Hunter et al.275 showed that IAPV-dsRNA prevented mortality in a large-scale field trial. Over a decade has passed since this work, though, and this technology has not yet been commercialized. But, a new “vaccine” for AFB has recently been brought to market by Dalan Animal Health©276 which markets both “vaccinated” queens, marketed to provide the whole hive with resistance to AFB, and the inactivated AFB bacterium itself with which beekeepers can vaccinate their own queens. In one study, the larvae from vaccinated queens showed increase resistance to AFB infection in the laboratory 277. While this vitellogenin-mediated strategy, apparently specific to bacterial fragments, would be unlikely to work for a virus vaccine in the absence of bacterial PAMPs, this product certainly showcases the industry's desire for such technologies. 116 A potential risk of these immune priming strategies, though, is the fitness cost of unnecessary immune activation. While enhanced immunity for either a queen or her offspring would obviously be beneficial in the presence of those pathogens, in their absence immune activation could be unnecessarily costly. This is illustrated by the fact that selection has not favored an elevated basal level of immune activation157. Our findings neatly illustrate this risk, that while infection could be contributing to reduced reproductive output in queens, so could merely activating their immune systems to prevent those infections. An additional concern with such vaccine strategies that appears to be currently unaddressed is that it unknown how long the “vaccine” bacterial fragments remain present and available in the system of the queen to pass on her to offspring, but it is unlikely to be for her lifetime. 5.5.4 Applications for beekeeping operations Considering the work that still needs to be done on potential vaccination strategies to prevent infection in queens, the best strategies for beekeepers to mitigate the effects of virus infection on queen health overlap with current best practices for colony management in general. The effective management of varroa will help reduce the overall viral load of colonies, reducing the risk of queen infection. Given the circular relationship in which a healthy colony is required to obtain adequate nutrition with which to feed the queen, and a healthy queen is required to provide a colony with the necessary workforce to do so, managing the single largest stressor of colony health (varroa) will likely have a significant positive impact on queen health by extension. Queen producers could also consider using virus diagnostic tools (such as those available at the National Bee Diagnostic Center) to select colonies with the lowest levels of virus to be cell builder colonies to minimize the exposure of developing queens to infection. Additionally, continuing to 117 focus on selectively breeding varroa -resistant stock may concomitantly result in improving the general levels of virus resistance in the population278. 5.6 Conclusion In the context of widespread insect declines broadly, and honey bee health challenges specifically, this work provides insight into the complex interactions between viral infections and honey bee queen fertility. By using proteomics to explore the molecular changes associated with queen failure, we offer novel evidence that viral infections significantly impact queen reproductive success, highlighting non-canonical immune pathways and the context-dependent nature of these effects. Moreover, this study is among the first to provide specific evidence for a reproduction-immunity trade-off in honey bee queens, though it may be moderated by the vast amount of nutritional resources available to social insect queens, and possibly multi-functional proteins. 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Thesis/Dissertation
eng
Biochemistry and Molecular Biology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Foster, Leonard J.
Fertility costs of covert viral infections in honey bee queens
Text