Microbial Modulation of Host Locomotion Citation Schretter, Catherine Elizabeth (2019) Microbial Modulation of Host Locomotion. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/Z1DX-4J03. https://resolver.caltech.edu/CaltechTHESIS:08222018-105935103 Abstract Coordinated locomotor behavior is critical for the survival and propagation of an individual and is modulated by internal and external sensory inputs. The microbiota regulates host metabolism, which is closely intertwined with motor behavior. However, little is known regarding influences by the gut microbiome on host locomotion, or the pathways involved. The work presented in this thesis examines microbial regulation of locomotor behavior from both bacterial and host perspectives. Removal of the microbiota results in hyperactivity in female D. melanogaster, which is reversible through colonization with specific bacteria or administration of bacterial-derived products, including xylose isomerase (Xi) from Lactobacillus brevis. We found that Xi modulates host speed via sugar metabolism and octopamine signaling in flies. Additionally, aspects of microbial regulation of host locomotion appear to be conserved in mice. This work suggests that microbial modulation of host physiology extends beyond local intestinal effects to locomotor behavior through alterations in energy-related pathways. Item Type: Thesis (Dissertation (Ph.D.)) Subject Keywords: Microbiota; Locomotion; Behavior; Drosophila Degree Grantor: California Institute of Technology Division: Biology and Biological Engineering Major Option: Biology Awards: Everhart Distinguished Graduate Student Lecturer Award, 2016. Thesis Availability: Public (worldwide access) Research Advisor(s): Mazmanian, Sarkis K. Thesis Committee: Lester, Henry A. (chair) Anderson, David J. Gradinaru, Viviana Mazmanian, Sarkis K. Defense Date: 14 August 2018 Funders: Funding Agency Grant Number NIH Training Grant 5T32 GM07616 NIH NS085910 Center for Environmental Microbiology Interactions UNSPECIFIED Heritage Medical Foundation UNSPECIFIED Record Number: CaltechTHESIS:08222018-105935103 Persistent URL: https://resolver.caltech.edu/CaltechTHESIS:08222018-105935103 DOI: 10.7907/Z1DX-4J03 Related URLs: URL URL Type Description https://doi.org/10.1016/j.cell.2016.11.018 DOI Article adapted for Ch. 5 ORCID: Author ORCID Schretter, Catherine Elizabeth 0000-0002-3957-6838 Default Usage Policy: No commercial reproduction, distribution, display or performance rights in this work are provided. ID Code: 11154 Collection: CaltechTHESIS Deposited By: Catherine Schretter Deposited On: 27 Aug 2018 21:18 Last Modified: 04 Oct 2019 00:23 Thesis Files Preview PDF - Final Version See Usage Policy. 3MB Repository Staff Only: item control page

Microbial Modulation of Host Locomotion Thesis by Catherine Elizabeth Schretter In Partial Fulfillment of the Requirements for the degree of Doctor of Philosophy CALIFORNIA INSTITUTE OF TECHNOLOGY Pasadena, California 2019 (Defended August 14, 2018) ii  2019 Catherine Elizabeth Schretter All rights reserved ORCID: 0000-0002-3957-6838 iii ACKNOWLEDGEMENTS Much of the basis for this work was grounded in one question, which I have been very fortunate to be able to explore. A large group aided me in this process and I am very grateful for all of their support and help. Firstly, I would like to thank my advisor, Sarkis Mazmanian, for supporting me and for taking a chance on me and this project. The entire Mazmanian lab has been a terrific source of knowledge throughout the project and I am very grateful for their advice. Special thanks goes to Hiu, who contributed to my development as a scientist and supported me throughout my graduate career. Additional thanks goes to Arya, Gil, and Wei-Li for their continued help and advice. I would also like to thank my committee, David Anderson, Henry Lester, Viviana Grandinaru, Marie-Françoise Chesselet, and Paul Patterson, each of whom contributed both to my development as a scientist and to this work. I would like to thank Aurora for the fly media preparation and the Aravin and Fejes Tόth labs for allowing me to use part of their fly room for this work. Outside of the lab, my friends (including Eyrun, Ali, Sofi, Grant, Riley, Matteo, and Zsofi) have made this time fly by and improved my work. All of my family has provided me with a large amount of support while I have been in California and my only regret is not being able to be closer to them during this time. I am especially grateful for all of the support and encouragement from my parents and my sister, Jenny. I would also like to thank my aunt, Dr. Barbara A. Chotiner. My grandparents had an intellectual curiosity that was inspiring and I am glad that they were at least able to see the initial conception of this work. Finally, a large amount of thanks goes to my partner, Dave, for his patience, positivity, and support throughout. iv ABSTRACT Coordinated locomotor behavior is critical for the survival and propagation of an individual and is modulated by internal and external sensory inputs. The microbiota regulates host metabolism, which is closely intertwined with motor behavior. However, little is known regarding influences by the gut microbiome on host locomotion, or the pathways involved. The work presented in this thesis examines microbial regulation of locomotor behavior from both bacterial and host perspectives. Removal of the microbiota results in hyperactivity in female D. melanogaster, which is reversible through colonization with specific bacteria or administration of bacterial-derived products, including xylose isomerase (Xi) from Lactobacillus brevis. We found that Xi modulates host speed via sugar metabolism and octopamine signaling in flies. Additionally, aspects of microbial regulation of host locomotion appear to be conserved in mice. This work suggests that microbial modulation of host physiology extends beyond local intestinal effects to locomotor behavior through alterations in energy-related pathways. v PUBLISHED CONTENT AND CONTRIBUTIONS Schretter, C. E., J. Vielmetter, I. Bartos, Z. Marka, S. Marka, S. Argade, and S. K. Mazmanian. “A gut microbial factor modulates locomotor behavior in Drosophila.” [Manuscript Accepted Nature] C.E.S conceived of the project, developed, executed, analyzed the data for this work and wrote the manuscript with input from S.K.M. and the other authors. Sampson, T.R., J.W. Debelius, T. Thron, S. Janssen, G.G. Shastri, Z.E. Ilhan, C. Challis, C. E. Schretter, S. Rocha, V. Gradinaru, M.-F. Chesselet, A. Keshavarzian, K.M. Shannon, R. Krajmalnik-Brown, P. Wittung-Stafshede, R. Knight, S. K. Mazmanian (2016). “Gut Microbiota Regulate Motor Deficits and Neuroinflammation in a Model of Parkinson’s Disease.” Cell. 167(6):1469-1480. DOI: 10.1016/j.cell.2016.11.018. C.E.S. was involved in the initial conception and the preliminary findings for this project. vi For my grandparents, Dr. Allan I. Chotiner (1915 – 2012) and Ann B. Chotiner (1919 – 2016). vii TABLE OF CONTENTS Acknowledgements…………………………………………………………...iii Abstract………….……………...…………………………………………….iv Published Content and Contributions………....……………………………….v Table of Contents…………………………………………………………….vii List of Figures.......……………...…………………………………………….ix Chapter 1: Overview of Drosophila melanogaster ............................................ 1 1.1 Introduction ............................................................................................. 2 1.2 Brief description of D. melanogaster ..................................................... 2 1.3 Links between the intestine and central brain in arthropods .................. 3 1.4 The microbiome of Drosophila .............................................................. 4 1.5 Host-microbe interactions in D. melanogaster ....................................... 5 Chapter 2: Delving into locomotion in D. melanogaster ................................. 12 2.1 Introduction to locomotion and its neuronal correlates ........................ 13 2.2 Overview of the neurotransmitters involved in regulating locomotion ............................................................................. 14 2.3 Regulation by internal stimuli ............................................................... 15 Chapter 3: A gut microbial factor modulates locomotion in Drosophila ........ 22 3.1 Abstract .................................................................................................. 23 3.2 Introduction ........................................................................................... 24 3.3 The microbiota influences host locomotion ......................................... 24 3.4 Select bacteria modulate locomotor behavior ...................................... 25 3.5 Bacterial-derived products affect host speed and activity.................... 26 3.6 Xylose isomerase from L. brevis alters host locomotion ..................... 27 3.7 Summary................................................................................................ 28 3.8 Figures ................................................................................................... 34 3.9 Methods ................................................................................................. 49 Chapter 4: Host pathways mediating microbial modulation of locomotion .... 56 4.1 Introduction ........................................................................................... 57 4.2 Post-eclosion microbial signals decrease host locomotion .................. 57 4.3 Interaction between locomotor phenotypes and other aspects of host physiology ................................................................. 58 4.4 Octopamine signaling mediates xylose isomerase-induced changes in locomotion ................................................................................. 59 4.5 Conclusion ............................................................................................. 60 4.6 Figures ................................................................................................... 66 4.7 Methods ................................................................................................. 81 Chapter 5: Examining conservation of microbial effects on locomotion ........ 87 5.1 Introduction ........................................................................................... 88 5.2 Microbial modulation of motor-related brain regions and behavior in mice .......................................................................................... 88 viii 5.3 Conclusion ............................................................................................. 89 5.4 Figures ................................................................................................... 94 5.5 Methods ............................................................................................... 103 Chapter 6: Conclusion ..................................................................................... 105 ix LIST OF FIGURES Chapter 3 Number Page 1. Select gut bacteria modulate locomotor behavior in flies .................... 34 2. Effects of colonization level, bacterial strain, and host diet on L. brevis-modulation of locomotion. ................................ 36 3. L. brevis modulates host locomotion under conventional and mono-associated conditions. .......................................................... 38 4. Select bacteria modulate host speed. .................................................... 39 5. Xylose isomerase (Xi) from L. brevis alters host locomotion. ............ 40 6. Bacterial-derived products from L. brevis alter locomotion. ............... 42 7. The role of timing and testing environment in microbial effects on locomotion. .......................................................... 43 8. Modulation of locomotion by the bacterial enzyme, xylose isomerase. ..................................................... 44 9. Fractionation of L. brevis-derived products. ........................................ 46 10. Xylose isomerase activity and key carbohydrates are involved in Xi-mediated changes in locomotion.................................. 47 Chapter 4 Number Page 1. Post-eclosion microbial signals decrease host locomotion. ................. 66 2. Mass and protein levels are not significantly altered by treatment with microbial metabolites .................................................. 68 3. Colonization with L. plantarum or L. brevis alone does not alter excretion number ................................................. 69 4. The role of food intake, anti-microbial peptides, as well as the Immune Deficiency (IMD) and Toll pathways in locomotor phenotypes. ..................................................................... 70 5. Lifespan and percentage of intestinal apoptotic cells x under various microbial conditions. .................................................... 72 6. Sleep analysis for mono-colonized flies and flies administered with bacteria factors ............................................... 73 7. Octopamine mediates xylose isomerase-induced changes in locomotion .......................................................................... 74 8. Thermogenetic activation of neuromodulator-GAL4 lines ................ 76 9. Activation of octopaminergic neurons in flies carrying a null allele for Tβh (TβHM18). ............................................................. 78 10. Octopamine mediates L. brevis- and xylose isomerase-induced changes in locomotion. ......................................................................... 79 Chapter 5 Number 1. GF mice display increased speed compared to SPF and Page antibiotic-treated mice. .......................................................................... 94 2. GF mice have reduced TH+ cells in the SNc compared to SPF controls .................................................................... 95 3. Lower TH+ cells and cell counts in the SNc of GF mice compared to SPF controls .................................................................... 96 4. Reduced TH+ cell counts are reversed through colonization .............. 97 5. TH+ optical density in the dorsomedial striatum of GF mice is decreased compared to SPF animals................................................ 98 6. Experimental design for examining microbial involvement in a mouse model of Parkinson’s .................. 99 7. Weight of mice over the course of the experiment ............................ 100 8. General locomotion in mice with varying microbial status and rotenone treatment............................................ 101 9. Staining for TH and ChAT expression is decreased in the proximal small intestine of GF mice ........................................... 102 1 Chapter 1 OVERVIEW OF DROSOPHILA MELANOGASTER 2 1.1 INTRODUCTION In order to understand host-microbe interactions, it is important to consider the microbial species and metabolites that influence the host in addition to the corresponding pathways that mediate these effects. A limited microbial community and the ability to perform high-throughput genetic screens facilitate this type of examination into both sides of host-microbe interactions. The microbiome of vinegar fruit fly Drosophila melanogaster contains 5-20 bacterial species in laboratory strains. Therefore, along with its genetic tractability and ease of behavioral methodology1,2, it is uniquely positioned for examining host-microbe interactions. 1.2 A BRIEF DESCRIPTION OF D. MELANOGASTER Drosophila melanogaster originated in sub-Saharan Africa and is commonly associated with rotting fruit and fermented beverages3,4. This ability to survive and reproduce on sources commonly produced by humans is thought to contribute to the dispersal of D. melanogaster4. The developmental period of D. melanogaster is relatively quick compared to mice and humans, with only 9-10 days needed to go from an egg to a sexually mature adult4. During its life cycle, D. melanogaster metamorphoses from a larval stage into its adult form, undergoing eclosion, or the emergence of a pupa to an adult, in this time period. Once in its adult stage, its average life span is between 34 to 53 days5. Although D. melanogaster was first described by Johann Wilhelm Meigen in 1830, the first reported cultivation in the laboratory was in the 1900s by Harvard entomologist Charles Woodworth3,4. In the laboratory, flies are grown on standardized media that usually contain mold inhibitors along with antibiotics3. However, there is a large variability in the specific ingredients used in fly media recipes leading recent researchers to develop chemically defined media that have produced more consistent experimental results6,7. In addition to this difference in food from the natural environment, D. melanogaster is also reared at a constant temperature and humidity in the laboratory. In part due to its adaptability, short life cycle, and widely available genetic toolkits, D. melanogaster has become a model organism used throughout biology. The utilization of D. 3 melanogaster has led to major breakthroughs, from the work of Thomas Hunt Morgan in genetics to studies by Jeffrey C. Hall, Michael Rosbash, and Michael W. Young in circadian rhythms. 1.3 LINKS BETWEEN THE INTESTINE AND CENTRAL BRAIN IN ARTHROPODS Arthropods have an open circulatory system, which contains hemolymph8. Separating the circulatory system and the lumen of the gastrointestinal tract are epithelial cells, the peritrophic matrix in the midgut, and the cuticle in the foregut and hindgut9. Molecules that pass through or are transported across these barriers can enter the body cavity, or hemocoel8. As invertebrates lack a true endothelium with intercellular junctions, the hemocoel only restricts the hemolymph through the extracellular matrix lining the vessels8. Changes in the gastrointestinal tract can also impact neurological function through their effects on the enteroendocrine cells (EECs) and neurons that innervate the gut. Similar to vertebrates, stem cells in the adult fly gastrointestinal tract give rise to EECs in addition to enterocytes10,11. Gustatory receptors are expressed by EECs in the midgut, which may relay information about changes in nutrient levels to the central nervous system12. In addition to signaling through EECs, information can be relayed from the gastrointestinal tract through immune pathways in the epithelial layer, such as the IMD pathway13,14. Neuronal innervation of the adult fly gastrointestinal tract comes from the stomatogastric nervous system (SGS), the corpus cardiacum, and the central nervous system9. Sensory neurons innervate predominantly the anterior and posterior regions of the intestine with their cell bodies predicted to reside primarily in the stomatogastric ganglia9. The functions of these intestinal innervating neurons span from facilitating peristalsis to sensing nutrient and fluid levels15–18. Recently, two neuroendocrine peptides and their receptors have been described as gut-brain peptides with expression in both the brain and gastrointestinal tract: CCHamide-1, CCHamide-2, and Diuretic hormone 44 (Dh44) along with its receptors17,19. Although the exact function of CCHamide-1 has not been fully enumerated, CCHamide-2 has been found to affect food intake and locomotor behavior19. Neurons that produce Dh44 act as nutritive sugar sensors, responding to sugar levels in the hemolymph independent of taste17. The hemolymph, EECs, and enteric neurons link the complex milieu within the gastrointestinal tract to other body sites throughout the organism, including the central nervous system. 4 1.4 THE MICROBIOME OF DROSOPHILA A mixture of bacteria, viruses, and fungi reside inside of the gastrointestinal tract, and collectively constitute the gut microbiota. The microbiota of laboratory-reared D. melanogaster contains between 5 – 20 different bacterial species, with the most common commensals from the Lactobacillus and Acetobacter genera1. When compared to that of mice and humans, the bacterial community of D. melanogaster is relatively limited; however, many of these core commensals are found in mammals, including Lactobacillus plantarum and Lactobacillus brevis1,20. The four dominant bacterial species identified in laboratory strains of D. melanogaster are: L. plantarum, L. brevis, Enterococcus faecalis, and Acetobacter pomorum20. Multiple factors influence the microbial composition within Drosophila. In the wild, Drosophila populations associate with a wider diversity of bacterial species, which is proposed to be due in part to their diet21. Additionally, shifts in the carbohydrate:protein ratio of laboratory fly media dramatically influence the proportions of specific bacterial genera21–23. In addition to dietary influences, the microbiota also changes throughout the life cycle of D. melanogaster. During the larval stage, L. fructivorans and L. plantarum are the two dominant species, with the former retaining this status in young adults1,24. Microbial dysbiosis can occur during old age (30 days post-eclosion) with an expansion in Gammaproteobacteria, similar to that found in elderly humans25–27. Within adult flies, seasonal changes in the microbiota do occur, and L. brevis is a species that particularly displays this temporal variation23. Furthermore, the composition of the microbiota differs between males and female flies, which is another feature also found in mammals1,24,28. In addition to bacteria, Drosophila harbor fungal commensals and endosymbionts, some of which are removed in common fly media preparations. The connection between the diet and the microbiota in flies extends to fungi, as diet outweighs host species in its contribution to fungal diversity29. In addition to fungi, the endosymbiont Wolbachia is an Alphaproteobacteria that has a range of effects on host physiology, including cytoplasmic incompatibility30. Furthermore, roughly 30% of all stocks at the Bloomington Drosophila Stock Center harbor Wolbachia31. Multiple factors influence the microbial composition within Drosophila. The use of flies lacking a 5 microbiota, also known as axenic flies, facilitates the examination of the aspects of host physiology that microbes modulate. 1.5 HOST-MICROBE INTERACTIONS IN D. MELANOGASTER Due to the genetic tools available and its limited microbial community D. melanogaster represents an ideal model organism to unravel the complexities of host-microbial interactions. The primary focus of such studies previously centered on bacterial activation of immune pathways, such as immune deficiency (IMD) and Toll. However, more recent research has uncovered commensal regulation of various aspects of host physiology, including growth and development32– 35 . Axenic larvae exhibit a significant delay in development under nutrient-poor conditions compared to conventional controls bearing a complete microbiota36. Metabolic products from the commensal A. pomorum activate systemic insulin/insulin-like growth factor signaling in D. melanogaster, resulting in changes in larval growth and energy metabolism37. Additionally, another commensal, L. plantarum, increases growth and development in Drosophila under conditions of chronic undernutrition. Association with L. plantarum causes these effects through enhancing the expression of larval peptidases that alter protein assimilation, and subsequently stimulate the TOR and insulin-like peptide pathways36,38,39. Interestingly, L. plantarum-mediated effects on growth are conserved in mammals, further demonstrating the importance of this bacterium and the beneficial impact of its symbiosis on various hosts40. The microbiota also regulates additional aspects of host physiology and function, including metabolism and behavior. Depending on the protein level within the diet, axenic female flies display significantly increased levels of triglycerides (TAG) and carbohydrates, including glucose and trehalose, compared to conventional controls41,42. While mono-colonization with five different commensal bacteria (A. pomorum, A. tropicalis, L. brevis, L. fructivorans, or L. plantarum) was able to restore glucose to levels similar to conventional controls, only A. pomorum, A. tropicalis, or to a lesser extent L. plantarum mono-colonization were able to lower TAG levels42. Related to metabolism, food consumption is reduced in axenic flies compared to conventional counterparts. However, mono-colonization with only either A. pomorum or A. tropicalis increases feeding to conventional levels in female flies43. No differences in food intake are found between male axenic and conventional flies41. 6 Recently, commensal bacteria have been implicated in food preference, reproduction, and oviposition behaviors44,45. After the removal of essential amino acids from the diet, flies had an increased preference for amino-acid rich media and decreased egg laying that was diminished by A. pomorum and Lactobacilli species (L. planatrum and L. brevis), demonstrating the importance of the microbiota under nutrient-poor conditions44. Microbial metabolism of specific nutrients is also implicated in oviposition preference with Enterococci catabolism of carbohydrates and the receptors Gr5a and Gr64 mediating this behavior45. Different compounds are produced in microbial communities depending on their composition, as bacterial and fungal members of the microbiome can catabolize products from each other. Products of one such co-culture, acetate and its metabolites, drive host preference for a mixture of Saccharomyces and Acetobacter species, partially through the olfactory receptor, Or42b46. While the microbiota and microbial-derived products are implicated in regulating host behavior, very little is known about the underlying host mechanisms facilitating these changes. Feedback from the host is another important and only recently examined aspect of hostmicrobe interactions. One study using Drosophila Genetic Reference Panel (DGRP) lines found links between specific genes, such as those in insulin-like signaling and TOR, and microbial effects on metabolism in flies47,48. Furthermore, a genome-wide association study (GWAS) revealed that neuronal-related genes alter the levels of particular bacterial species, including Acetobacter tropicalis48,49. However, more research is needed in order to understand this aspect of hostmicrobial interactions. The microbial community within D. melanogaster is relatively limited compared to that of mammals and has known effects on host physiology, making it an excellent candidate for exploring microbial contributions to conserved behaviors. As there is a close relationship between specific species and host metabolism, we chose a metabolically-related behavior, locomotion, for examination in the context of host-microbe interactions. Previous literature in mice suggested that host walking speed is regulated by the microbiota50,51; however, this had not been shown in Drosophila nor had the underlying pathways been explored. 7 REFERENCES 1. Lee, W.-J. & Brey, P. T. How microbiomes influence metazoan development: insights from history and Drosophila modeling of gut-microbe interactions. Annu. Rev. Cell Dev. Biol. 29, 571–92 (2013). 2. Venken, K. J. T., Simpson, J. H. & Bellen, H. J. Genetic manipulation of genes and cells in the nervous system of the fruit fly. Neuron 72, 202–30 (2011). 3. Markow, T. A. The secret lives of Drosophila flies. Elife 4, 1–9 (2015). 4. Keller, A. Drosophila melanogaster’s history as a human commensal. Curr. Biol. 17, 77– 81 (2007). 5. Partridge, L., Piper, M. D. W. & Mair, W. Dietary restriction in Drosophila. Mech. Ageing Dev. 126, 938–950 (2005). 6. Lee, W. C. & Micchelli, C. A. Development and Characterization of a Chemically Defined Food for Drosophila. PLoS One 8, 1–10 (2013). 7. Piper, M. D. W. et al. A holidic medium for Drosophila melanogaster. Nature Methods 11, 100–105 (2014). 8. Monahan-Earley, R., Dvorak, A. M. & Aird, W. C. Evolutionary origins of the blood vascular system and endothelium. J. Thromb. Haemost. 11, 46–66 (2013). 9. Lemaitre, B. & Miguel-Aliaga, I. The Digestive Tract of Drosophila melanogaster. Annu. Rev. Genet. 47, 377–404 (2013). 10. Ohlstein, B. & Spradling, A. The adult Drosophila posterior midgut is maintained by pluripotent stem cells. Nature 439, 470–4 (2006). 11. Micchelli, C. A. & Perrimon, N. Evidence that stem cells reside in the adult Drosophila midgut epithelium. Nature 439, 475–9 (2006). 8 12. Park, J.-H. & Kwon, J. Y. Heterogeneous expression of Drosophila gustatory receptors in enteroendocrine cells. PLoS One 6, e29022 (2011). 13. Chu, H. & Mazmanian, S. K. Innate immune recognition of the microbiota promotes hostmicrobial symbiosis. 14, (2013). 14. You, H., Lee, W. J. & Lee, W.-J. Homeostasis between gut-associated microorganisms and the immune system in Drosophila. Curr. Opin. Immunol. 30C, 48–53 (2014). 15. Cognigni, P., Bailey, A. P. & Miguel-Aliaga, I. Enteric neurons and systemic signals couple nutritional and reproductive status with intestinal homeostasis. Cell Metab. 13, 92– 104 (2011). 16. Palmer, G. C., Tran, T., Duttlinger, A. & Nichols, R. The drosulfakinin (DSK) peptide encoded in the conserved Dsk gene affects adult Drosophila melanogaster crop contractions. J. Insect Physiol. 53, 1125–33 (2007). 17. Dus, M. et al. Nutrient Sensor in the Brain Directs the Action of the Brain-Gut Axis in Drosophila. Neuron 87, 139–151 (2015). 18. Kaminski, S., Orlowski, E., Berry, K., & Nichols, R. The effects of three Drosophila melanogaster myotropins on the frequency of foregut contractions differ. J. Neurogenet. 16, 125–134 (2002). 19. Ren, G. R. et al. CCHamide-2 Is an Orexigenic Brain-Gut Peptide in Drosophila. PLoS One 10, e0133017 (2015). 20. Erkosar, B., Storelli, G., Defaye, A. & Leulier, F. Host-intestinal microbiota mutualism: ‘learning on the fly.’ Cell Host Microbe 13, 8–14 (2013). 21. Chandler, J. A., Lang, J. M., Bhatnagar, S., Eisen, J. A. & Kopp, A. Bacterial communities of diverse Drosophila species: ecological context of a host-microbe model system. PLoS Genet. 7, e1002272 (2011). 9 22. Ridley, E. V, Wong, A. C. N. & Douglas, A. E. Microbe-dependent and nonspecific effects of procedures to eliminate the resident microbiota from Drosophila melanogaster. Appl. Environ. Microbiol. 79, 3209–14 (2013). 23. Wong, C. N. A., Ng, P. & Douglas, A. E. Low-diversity bacterial community in the gut of the fruitfly Drosophila melanogaster. Environ. Microbiol. 13, 1889–900 (2011). 24. Bakula, M. The persistence of a microbial flora during postembryogenesis of Drosophila melanogaster. J. Invertebr. Pathol. 14, 365–374 (1969). 25. Clark, R. I. et al. Distinct Shifts in Microbiota Composition during Drosophila Aging Impair Intestinal Function and Drive Mortality. Cell Rep. 12, 1656–1667 (2015). 26. Biagi, E. et al. 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Rev. 10 Immunol. 25, 697–743 (2007). 33. Kuraishi, T., Hori, A. & Kurata, S. Host-microbe interactions in the gut of Drosophila melanogaster. Front. Physiol. 4, 375 (2013). 34. Martino, M. E., Ma, D. & Leulier, F. Microbial influence on Drosophila biology. Curr. Opin. Microbiol. 38, 165–170 (2017). 35. Ma, D., Leulier, F., Storelli, G. & Mitchell, M. Studying host-microbiota mutualism in Drosophila: Harnessing the power of gnotobiotic flies. Biomed. J. 38, 285 (2015). 36. Storelli, G. et al. Lactobacillus plantarum promotes Drosophila systemic growth by modulating hormonal signals through TOR-dependent nutrient sensing. Cell Metab. 14, 403–14 (2011). 37. Shin, S. C. et al. Drosophila Microbiome Modulates Host Developmental and Metabolic Homeostasis via Insulin Signaling. Science 334, 670–4 (2011). 38. Erkosar, B. et al. Pathogen Virulence Impedes Mutualist-Mediated Enhancement of Host Juvenile Growth via Inhibition of Protein Digestion. Cell Host Microbe 4, 445-55 (2015). 39. Matos, R. C. et al. D-Alanylation of teichoic acids contributes to Lactobacillus plantarummediated Drosophila growth during chronic undernutrition. Nat. Microbiol. 2, 1635–1647 (2017). 40. Schwarzer, M. et al. Lactobacillus plantarum strain maintains growth of infant mice during chronic undernutrition. Science 351, 854–857 (2016). 41. Wong, A. C.-N., Dobson, A. J. & Douglas, A. E. Gut microbiota dictates the metabolic response of Drosophila to diet. J. Exp. Biol. 217, 1894–901 (2014). 42. Newell, P. D. & Douglas, A. E. Interspecies interactions determine the impact of the gut microbiota on nutrient allocation in Drosophila melanogaster. Appl. Environ. Microbiol. 80, 788–96 (2014). 11 43. Newell, P. D. et al. In vivo function and comparative genomic analyses of the Drosophila gut microbiota identify candidate symbiosis factors. Front. Microbiol. 5, 1–15 (2014). 44. Leitão- Goncalves, R., Carvalho-Santos, Z., Francisco, A. P., Fioreze1, G. T., Anjos, M., Baltazar, C., Elias, A. P., Itskov, P. M., Piper, M. D. W. & Ribeiro, C. Commensal bacteria and essential amino acids control food choice behavior and reproduction. PLoS Biol. 15, 1–29 (2016). 45. Liu, W. et al. Enterococci Mediate the Oviposition Preference of Drosophila melanogaster through Sucrose Catabolism. Sci. Rep. 7, 13420 (2017). 46. Fischer, C. et al. Metabolite exchange between microbiome members produces compounds that influence Drosophila behavior. Elife 6, 1–25 (2017). 47. Dobson, A. J., Chaston, J. M. & Douglas, A. E. The Drosophila transcriptional network is structured by microbiota. BMC Genomics 1, 975 (2016). 48. Martino, M. E. et al. Bacterial adaptation to host diet is a key evolutionary force shaping host-microbe symbiosis. Cell Host Microbe 24, 109-119 (2017). 49. Chaston, J. M., Newell, P. D. & Douglas, A. E. Metagenome-Wide Association of Microbial Determinants of Host Phenotype in Drosophila melanogaster. mBio 5, e0163114 (2014). 50. Diaz Heijtz, R. et al. Normal gut microbiota modulates brain development and behavior. Proc. Natl. Acad. Sci. U. S. A. 108, 3047–52 (2011). 51. Nishino, R. et al. Commensal microbiota modulate murine behaviors in a strictly contamination-free environment confirmed by culture-based methods. Neurogastroenterol. Motil. 25, 521–528 (2013). 12 Chapter 2 DELVING INTO LOCOMOTION IN D. MELANOGASTER 13 2.1 INTRODUCTION TO LOCOMOTION AND ITS NEURONAL CORRELATES At first glance, coordinated movement through an environment appears simple; however, it requires the complex integration of multiple stimuli and coordination of various organ systems throughout the body1. Locomotion has been studied across organisms spanning from humans and cats to ants and flies. While the number of appendages varies, there is a multitude of consistent features, including energy conservation and feedback from peripheral sensors1. Locomotor behavior in insects has been relatively well studied and provides a good model for examining contributions from internal and external stimuli. As larvae, Drosophila crawl along a surface through coordinated peristaltic waves of muscle contractions2. Patterned movements are locally generated through central pattern generators (CPGs) with additional control from descending neurons in the subesophageal zone (SEZ) and central brain2. Within the ventral nerve cord (VNC), excitatory and inhibitory interneurons coordinate the activity of motor neurons. Nevertheless, the exact mechanisms by which sensory information is processed by VNC and integrated into motor behavior in larvae are not currently known. Adult Drosophila have six legs along a body plan consisting of a head, thorax, and abdomen. Multi-joined legs are widely used among terrestrial animals for locomotion. Furthermore, as in many other invertebrates and vertebrates, CPGs constitute a critical component for the generation of rhythmic movements in adult flies3. For example, if the central brain is removed, CPGs can be stimulated through exogenous application of biogenic amines4. Regions within the central brain, including the central complex, provide inputs to CPGs that significantly alter walking behavior. Analysis of walking behavior in mutant Drosophila lines revealed that the central complex regulates walking initiation, step length, swing speed, and leg placement5. Furthermore, the ellipsoid body in the central complex is necessary for maintenance of the temporal walking pattern of adult flies6. In addition to the central complex, removal or inhibition of the mushroom body results in increased walking speed7. Such experiments in Drosophila demonstrate the use of both central and peripheral neuronal circuits in the execution of motor behaviors. 14 2.2 OVERVIEW OF THE NEUROTRANSMITTERS INVOLVED IN REGULATING LOCOMOTION A variety of biogenic amines and small molecules have been implicated in modulating locomotor behavior in the central brain and the periphery, including dopamine, serotonin, octopamine, and tyramine. Dopamine (DA) is a catecholamine synthesized from tyrosine through the use of the enzymes tyrosine hydroxylase (TH) and DOPA decarboxylase (DDC). In both vertebrates and invertebrates, dopamine signaling is necessary for coordinated movement4,8–11. For example, dopamine deficient flies display decreased walking speeds and climbing behavior, both of which exhibit age-dependent declines8,12. Interestingly, these age-related changes in walking are associated with exploratory behavior, rather than centrophobism12. Similar to DA, exogenous application of serotonin to the spinal cord of cats or the VNC in insects stimulates walking behavior, implicating its involvement in locomotor regulation4,13,14. Serotonin (5HT) is derived from tryptophan through the use of tryptophan hydrolase (TPH) and DDC. Inactivation of serotonergic pathways in the central brain and RNAi knockdown of 5HT receptors further demonstrate the involvement of serotonin in motor behavior, specifically that of the 5HT receptors 1B, 2 and 7 in the mushroom body15,16. Two other monoamines involved in locomotion, octopamine (OA) and tyramine (TA), are synthesized from tyrosine and lie along the same pathway, with tyramine produced as an intermediate17. Tyramine was previously assumed to only act as a precursor of octopamine; however, it is now known as an independent neurotransmitter that modulates behavior and physiology through G-coupled protein receptors18–26. Octopamine and tyramine share homology with the adrenergic system both in terms of structure and due to their involvement in regulating metabolism and similar behaviors17,20. Octopamine is widely expressed within the nervous system, particularly in the ventromedial (VM) neuropil cluster along the SEZ, the AL2 cluster of the antennal lobes, and the VNC27,28. Additionally, it can reach other body sites through circulation in the hemolymph17. Tyraminergic neurons have also been identified in the central nervous system and are thought to be a distinct population as they lack octopamine expression29. In terms of their involvement in locomotor regulation, octopamine and tyramine have opposite effects with higher levels of octopamine driving elevated muscle contractions and locomotion, and corresponding decreases in both features associated with increased tyramine20. 15 However, others have proposed a dependence on a proper balance of both neurotransmitters for normal locomotion18. In addition to aminergic signaling, neuropeptides and small molecules, such as GABA, also regulate locomotion in Drosophila. Experiments knocking down inhibitory GABAergic inputs to leg motorneurons impaired adult locomotion in flies, demonstrating their importance in motor behavior30. Additionally, cholinergic interneurons are involved in the regulation of locomotor behavior31. Other neuropeptides, including tachykinin (DTK) and short neuropeptide F (sNPF) expressed in the central complex, modulate locomotor activity32. Contributions from such a diverse array of neuronal subsets and neurotransmitters demonstrates the complex circuitry underling this multifaceted behavior. 2.3 REGULATION BY INTERNAL STIMULI In order to successfully navigate a complex environment, feedback is needed from multiple sensory systems detecting internal and external stimuli33–35. Sensory receptors detect changes in internal metabolic state that alter various behaviors, including locomotion33, which are mediated by certain neuromodulators36. Under periods of starvation, flies will execute foraging behaviors, altering their trajectory and speed37–42. Local search behaviors vary as the starvation period increases, with the highest activity taking place in the first three hours after starvation41. Foraging is mediated in part by different sets of dopaminergic neurons depending on the satiation state of the fly, with dopaminergic neurons in the PAM cluster mediating behavior in starved flies43. However, both of the neuronal populations employed in the two satiation states converge on the dopamine D1 receptor expressed in Kenyon cells in the mushroom body43. Deficits in specific nutrients, such as amino acids, also shift exploration away from a global strategy to a focused search for proteinaceous patches within defined arena, illustrating the importance of nutritional need in foraging strategies38. Furthermore, the bacterial populations within Drosophila can alter foraging patterns and preferences through olfactory cues, with an innate preference towards exploring their associated Lactobacilli species44. 16 In addition to foraging trajectories, starvation increases the activity and speed of flies39,40,42. Drosophila originating from various locations with climates ranging from temperate to tropical all exhibit increased activity in response to starvation42. Recently, the involvement of octopaminergic neurons within the SEZ in starvation-induced hyperactivity was identified39,40. Yu et al. (2016) also found that adipokinetic hormone (Akh), similar to mammalian glucagon, mediates these effects independent of food intake. However, whether these same circuits are influenced by changes in the microbial community is currently unknown. 17 REFERENCES 1. Dickinson, M. H. et al. How animals move: An integrative view. Science 288, 100–106 (2000). 2. Clark, M. Q., Zarin, A. A., Carreira-Rosario, A. & Doe, C. Q. Neural circuits driving larval locomotion in Drosophila. Neural Development 13, 1–10 (2018). 3. Marder, E. & Bucher, D. Central pattern generators and the control of rythmic movements. Curr. Biol. 11, R986–R996 (2001). 4. Yellman, C., Tao, H., He, B. & Hirsh, J. Conserved and sexually dimorphic behavioral responses to biogenic amines in decapitated Drosophila. Proc. Natl. Acad. Sci. U. S. A. 94, 4131–4136 (1997). 5. Strauss, R. The central complex and the genetic dissection of locomotor behaviour. Curr. Opin. Neurobiol. 12, 633–638 (2002). 6. Martin, J.-R., Faure, P. & Ernst, R. The Power Law Distribution for Walking-Time Intervals Correlates with the Ellipsoid-Body in Drosophila. J. Neurogenet. 15, 205–219 (2001). 7. Martin, J. R., Ernst, R. & Heisenberg, M. Mushroom bodies suppress locomotor activity in Drosophila melanogaster. Learn. Mem. 5, 179–91 (1998). 8. Riemensperger, T., Isabel, G., Coulom, H., Neuser, K., Seugnet, L., Kume, K., IchéTorres, M., Cassar, M., Strauss, R., Preat, T., Hirsh, J. & Birman, S. Behavioral consequences of dopamine defciency in the Drosophila central nervous system. Proc. Natl. Acad. Sci. U. S. A. 108, 834-839 (2011). 9. Giros, B., Jaber, M., Jones, S. R., Wightman, R. M. & Caron, M. G. Hyperlocomotion and indifference to cocaine and amphetamine in mice lacking the dopamine transporter. Nature 379, 606–612 (1996). 18 10. Zhou, Q. Y. & Palmiter, R. D. Dopamine-deficient mice are severely hypoactive, adipsic, and aphagic. Cell 83, 1197–1209 (1995). 11. Beninger, R. J. The role of dopamine in locomotor activity and learning. Brain Res. Rev. 6, 173–196 (1983). 12. White, K. E., Humphrey, D. M. & Hirth, F. The dopaminergic system in the aging brain of Drosophila. Front. Neurosci. 4, 205 (2010). 13. Grillner, S. The motor infrastructure: From ion channels to neuronal networks. Nat. Rev. Neurosci. 4, 573–586 (2003). 14. Grillner, S. Neurobiological bases of rhythmic motor acts in vertebrates. Science 228, 143–149 (1985). 15. Rodriguez-Oroz, M. C. et al. Initial clinical manifestations of Parkinson’s disease: features and pathophysiological mechanisms. Lancet Neurol. 8, 1128–39 (2009). 16. Silva, B., Goles, N. I., Varas, R. & Campusano, J. M. Serotonin receptors expressed in Drosophila mushroom bodies differentially modulate larval locomotion. PLoS One 9, (2014). 17. Roeder, T. Octopamine in invertebrates. Prog. Neurobiol. 59, 533–561 (1999). 18. Saraswati, S., Fox, L. E., Soll, D. R. & Wu, C. F. Tyramine and Octopamine Have Opposite Effects on the Locomotion of Drosophila Larvae. J. Neurobiol. 58, 425–441 (2004). 19. Selcho, M., Pauls, D., el Jundi, B., Stocker, R. F. & Thum, A. S. The role of octopamine and tyramine in Drosophila larval locomotion. J. Comp. Neurol. 520, 3764–3785 (2012). 20. Roeder, T. Tyramine and octopamine: ruling behavior and metabolism. Annu. Rev. Entomol. 50, 447–77 (2005). 21. Damrau, C., Toshima, N., Tanimura, T., Brembs, B. & Colomb, J. Octopamine and 19 Tyramine Contribute Separately to the Counter-Regulatory Response to Sugar Deficit in Drosophila. Front. Syst. Neurosci. 11, 1–11 (2018). 22. Blenau, W. & Baumann, A. Molecular and pharmacological properties of insect biogenic amine receptors: Lessons from Drosophila melanogaster and Apis mellifera. Arch. Insect Biochem. Physiol. 48, 13–38 (2001). 23. Saudou, F., Amlaiky, N., Plassat, J. L., Borrelli, E. & Hen, R. Cloning and characterization of a Drosophila tyramine receptor. EMBO J. 9, 3611–7 (1990). 24. Blumenthal, E. M. Regulation of chloride permeability by endogenously produced tyramine in the Drosophila Malpighian tubule. AJP Cell Physiol. 284, C718–C728 (2003). 25. Alkema, M. J., Hunter-Ensor, M., Ringstad, N. & Horvitz, H. R. Tyramine functions independently of octopamine in the Caenorhabditis elegans nervous system. Neuron 46, 247–260 (2005). 26. Huang, J., Liu, W., Qi, Y. xiang, Luo, J. & Montell, C. Neuromodulation of Courtship Drive through Tyramine-Responsive Neurons in the Drosophila Brain. Curr. Biol. 26, 2246–2256 (2016). 27. Busch, S., Selcho, M., Ito, K. & Tanimoto, H. A map of octopaminergic neurons in the Drosophila brain. J. Comp. Neurol. 513, 643–667 (2009). 28. Monastirioti, M., Linn, C. E. & White, K. Characterization of Drosophila tyramine betahydroxylase gene and isolation of mutant flies lacking octopamine. J. Neurosci. 16, 3900– 3911 (1996). 29. Nagaya, Y., Kutsukake, M., Chigusa, S. I. & Komatsu, A. A trace amine, tyramine, functions as a neuromodulator in Drosophila melanogaster. Neurosci. Lett. 329, 324–328 (2002). 30. Gowda, S. B. M. et al. GABAergic inhibition of leg motoneurons is required for normal walking behavior in freely moving Drosophila. Proc. Natl. Acad. Sci. 115, E2115–E2124 20 (2018). 31. Imlach, W. L. et al. SMN is required for sensory-motor circuit function in Drosophila. Cell 151, 427–439 (2012). 32. Kahsai, L., Martin, J.-R. & Winther, A. M. E. Neuropeptides in the Drosophila central complex in modulation of locomotor behavior. J. Exp. Biol. 213, 2256–65 (2010). 33. Huston, S. J. & Jayaraman, V. Studying sensorimotor integration in insects. Curr. Opin. Neurobiol. 21, 527–34 (2011). 34. Tuthill, J. C. & Wilson, R. I. Mechanosensation and Adaptive Motor Control in Insects. Curr. Biol. 26, R1022–R1038 (2016). 35. Tuthill, J. C. & Azim, E. Proprioception. Curr. Biol. 28, R193–R194 (2018). 36. Hultborn, H. State-dependant modulation of sensory feedback. J. Physiol. 533, 5–13 (2001). 37. Kim, I. S., Dickinson, M. H., Kim, I. S. & Dickinson, M. H. Idiothetic Path Integration in the Fruit Fly Drosophila melanogaster. Curr. Biol. 1–12 (2017). doi:10.1016/j.cub.2017.06.026 38. Corrales-Carvajal, V. M., Faisal, A. A. & Ribeiro, C. Internal states drive nutrient homeostasis by modulating exploration-exploitation trade-off. Elife 5, 1–29 (2016). 39. Yu, Y. et al. Regulation of starvation-induced hyperactivity by insulin and glucagon signaling in adult Drosophila. Elife 5, e15693 (2016). 40. Yang, Z. et al. Octopamine mediates starvation-induced hyperactivity in adult Drosophila. Proc. Natl. Acad. Sci. 201417838 (2015). doi:10.1073/pnas.1417838112 41. Bell, W. J., Cathy, T., Roggero, R. J., Kipp, L. R. & Tobin, T. R. Sucrose-stimulated searching behaviour of Drosophila melanogaster in a uniform habitat: modulation by period of deprivation. Anim. Behav. 33, 436–448 (1985). 21 42. Knoppien, P., van Der Pers, J. & van Delden, W. Quantification of locomotion and the effect of food deprivation on locomotor activity in Drosophila. J. Insect Behav. 13, 27–43 (2000). 43. Landayan, D., Feldman, D. S. & Wolf, F. W. Satiation state-dependent dopaminergic control of foraging in Drosophila. Sci. Rep. 8, 1–9 (2018). 44. Wong, A. C. N. et al. Gut Microbiota Modifies Olfactory-Guided Microbial Preferences and Foraging Decisions in Drosophila. Curr. Biol. 27, 2397–2404.e4 (2017). 22 Chapter 3 A GUT MICROBIAL FACTOR MODULATES LOCOMOTION IN DROSOPHILA Schretter, C. E., J. Vielmetter, I. Bartos, Z. Marka, S. Marka, S. Argade, and S. K. Mazmanian A modified version of this chapter along with Chapter 4 was submitted in “A gut microbial factor modulates locomotor behavior in Drosophila.” 23 3.1 ABSTRACT While research into the biology of animal behavior has primarily focused on the central nervous system, cues from peripheral tissues and the environment have been implicated in brain development and function1–6. Emerging data suggest bidirectional communication between the gut and the brain affects behaviors including anxiety, cognition, nociception, and social interaction, among others4,7–17. Coordinated locomotor behavior is critical for the survival and propagation of animals, and is regulated by internal and external sensory inputs18–20. However, little is known regarding influences by the gut microbiome on host locomotion, or the pathways involved. Here we report that germ-free status or antibiotic treatment result in hyperactive locomotor behavior in female Drosophila melanogaster. Increased walking speed and daily activity found in the absence of a gut microbiome are rescued by mono-colonization with specific bacteria, including the fly commensal Lactobacillus brevis. The bacterial enzyme xylose isomerase (Xi) from L. brevis is sufficient to recapitulate the locomotor effects of microbial colonization, likely via modulation of sugar metabolism in flies. Additionally, thermogenetic activation of octopaminergic neurons or exogenous administration of octopamine, the invertebrate counterpart of noradrenaline, abrogates Xi-induced effects on Drosophila locomotion. These findings reveal a previously unappreciated role for the gut microbiome in modulating locomotion, and identify octopaminergic neurons as mediators of peripheral microbial cues that regulate motor behavior in animals. 24 3.2 INTRODUCTION Coordinated locomotion is required for fundamental activities of life such as foraging, social interaction, and mating, and involves the integration of multiple contextual factors, including the internal state of the animal and external sensory stimuli18,19. The intestine represents a major conduit for exposure to environmental signals that influence host physiology and is connected to the brain through both neuronal and humoral pathways. Recently, seminal studies have uncovered that the intestinal microbiome regulates developmental and functional features of the nervous system4–7, though gut bacterial effects on the neuromodulators and neuronal circuits involved in locomotion remain poorly understood. Since central mechanisms of locomotion, including sensory feedback and neuronal circuits integrating these modalities, are shared in lineages spanning arthropods and vertebrates19,21,22, we employed the fruit fly Drosophila melanogaster to explore host-microbiome interactions that contribute to locomotor behavior. 3.3 THE MICROBIOTA INFLUENCES HOST LOCOMOTION Locomotion was examined in the presence (conventional; Conv) and absence (axenic; Ax) of commensal bacteria23. In comparison to conventionally-reared animals, axenic female adult flies exhibit increased walking speed and daily activity (Fig. 1a – b, and 1g). Axenic adult flies also display corresponding changes in locomotor coordination (Fig. 1h) that were previously correlated with elevated average walking speeds24. Drosophila locomotion is characterized by a pattern of intermittent periods of pauses and activity bouts19,25,26, in which the average speed of the fly is above a set threshold of 0.25 mm/second. An increased average speed may be related to changes in temporal patterns, including the number and/or duration of walking bouts25. We discovered that axenic flies display an increased average walking bout length in addition to a decreased average pause length, while remaining indistinguishable in the number of bouts compared to animals harboring a microbial community (Fig. 1c – f). These data reveal that the microbiota modulates walking speed and temporal patterns of locomotion in Drosophila. 25 3.4 SELECT BACTERIA MODULATE LOCOMOTOR BEHAVIOR The microbial community of Drosophila melanogaster contains 5 – 20 bacterial species27– 31 . In laboratory-raised flies, two of the dominant species are Lactobacillus brevis and Lactobacillus plantarum28. Specific bacteria in this community affect distinct features of Drosophila physiology, and even closely related microbial taxa can exhibit unique biological influences on the host28,32–35. Accordingly, we examined whether locomotor performance was impacted differentially by individual bacterial species. Despite similar levels of colonization (Fig. 2a), mono-association with L. brevis, but not L. plantarum, starting at eclosion is sufficient to correct speed and daily activity deficits in axenic flies (Fig. 1a – b, 1g, and 2b – e). However, these locomotor effects appear to be sex-specific as male flies did not exhibit significant changes in speed depending on microbial status (Fig. 2b), consistent with recent reports36. Varying the strain of L. brevis or host diet did not alter bacterial influences on host speed (Fig. 2c – e), and L. brevis is able to reverse changes in average pause length (Fig. 1c – f and 2f). Detailed gait analysis reveals that L. brevis-associated flies display comparable locomotor coordination to that of conventionally-reared flies (Fig. 1h and 2g). As flies are usually associated with more than one bacterial species, we evaluated L. brevisdependent changes in locomotion under different microbial conditions. Axenic flies co-colonized with a 1:1 mixture of L. brevis and L. plantarum display similar changes in speed to flies monoassociated with L. brevis (Fig. 2h). Conventional and mono-associated flies with lower speeds also appear to exhibit increased levels of L. brevis (Fig. 3a – b). However, a similar trend was not found in flies mono-associated with L. plantarum (Fig. 3c). In order to examine the locomotor effects of L. brevis in a complex microbial background, we supplemented conventional flies with either L. brevis or L. plantarum. Administration of L. brevis, and not L. plantarum, significantly reduced walking speeds compared to naïve conventional animals (Fig. 3d). Due to the diversity of bacterial species within the microbiota of Drosophila, we tested if other commensal or non-commensal bacteria also lower host locomotion upon colonization. Of the four remaining bacteria tested (Acetobacter pomorum, Acetobacter tropicalis, Escherichia coli, Enterococcus faecalis), only A. tropicalis and E. coli significantly reduced host walking speed (Fig. 4). The strain of A. tropicalis tested was isolated from the flies used in this study; however, it is not fully sequenced. Therefore, the sequenced Drosophila commensal L. brevis and the non- 26 commensal E. coli were subsequently used to examine the involvement of specific bacterial factors. 3.5 BACTERIAL-DERIVED PRODUCTS AFFECT HOST SPEED AND ACTIVITY Gut bacteria secrete molecular products that regulate aspects of host physiology, including immunity and feeding behavior37,38. To explore how microbes influence locomotion, we administered either cell-free supernatant (CFS) harvested from bacterial cultures or heat-killed bacteria to axenic flies. CFS alone from L. brevis (L.b CFS) reduces hyperactivity in axenic flies, while heat-killing bacteria ablates modulation of locomotion (Fig. 5a and 6a – e), demonstrating a requirement for metabolically active L. brevis. Treatment with MRS media alone or L.p CFS did not significantly lower host walking speed compared to axenic flies (Fig. 6a – f). The time course of administration is also important, as L.b CFS does not alter host locomotion if supplied less than 24 hours prior to testing (Fig. 7a). However, varying the concentration of sucrose present in the testing chamber did not alter L.b CFS ability to reduce host walking speed (Fig. 7b). While previous studies have revealed that L. brevis produces uracil35, a molecule that affects the host immune response and may impact locomotion37,39, administration of physiologic levels of uracil to axenic flies did not alter walking speed (Fig. 6g). As CFS contains complex mixture of molecules, we employed a series of biochemical approaches to narrow down the bacterial factors involved. While amylase treatment did not alter the locomotor effects of L.b CFS, protease- or heat-treated L.b CFS no longer lowered host walking speed (Fig. 8a – d). CFS from L. brevis grown in minimal media decreased locomotion in axenic flies (Fig. 9a), limiting the complexity of the molecules potentially involved. Subsequent sizedependent fractionation of L.b CFS using either dialysis or fast protein liquid chromatography (FPLC) revealed eluted fractions containing molecules greater than 30 kDa reduced locomotion (Fig. 9b – c). Multiple attempts to further identify the bacterial factors in this fraction using mass spectrometry proved unsuccessful. 27 3.6 XYLOSE ISOMERASE FROM L. BREVIS ALTERS HOST LOCOMOTION Bacterial metabolism of amino acids and carbohydrates is associated with changes in host behavior8,16; however, it is not known whether metabolic enzymes from bacteria influence host locomotion. Biochemical analysis of L.b CFS and comparative functional analysis of bacterial strains40–42 determined that bacterial locomotor effects are mediated via proteinaceous molecule(s) present in select bacteria, including L. brevis and E. coli (Fig. 8a – e). Subsequently, a screen of E. coli strains containing single gene mutations related to amino acid and carbohydrate metabolism identified xylose isomerase (Xi) as a candidate factor modulating locomotor behavior (Fig. 8f). Xi is an enzyme with four 43 kDa subunits that catalyzes the reversible isomerization of certain sugars, including the conversion of D-glucose to D-fructose43–45. Xi is also present only in L. brevis and E. coli of the sequenced bacterial strains tested (Fig. 8e). Administration of His-tagged Xi from L. brevis (Xi*) reduces locomotor behavior in axenic flies to levels similar to L.b CFS and conventional flies (Fig. 5b – c and 8g – h). The addition of His-tagged L-arabinose isomerase (Ai*), an enzyme that is not differentially expressed among the bacteria tested, is not sufficient to influence host speed in axenic flies (Fig. 5c). Furthermore, we generated a chromosomal deletion of the xylose isomerase gene xylA in L. brevis, and demonstrate the mutant strain lacks the ability to modulate host speed and daily activity (Fig. 5d and 8g). Neither the addition of the predicted products of Xi (D-fructose, D-glucose, D-xylose, and D-xylulose) alone, nor Xi inactivated by EDTA43 or paraformaldehyde treatment, reduces walking speed in axenic flies (Fig. 10a – d). We next sought to explore Xi activity through carbohydrate analysis of whole flies, which revealed those given Xi* exhibit increased ribose and reduced trehalose levels compared to axenic controls (Fig. 5e), with no differences in these sugars in the fly media (Fig. 10e). While EDTA-treated Xi* did not significantly alter trehalose levels, these flies still display heightened levels of ribose compared to axenic controls (Fig. 10f). Additionally, similar to previous findings46, conventional and L. brevis-colonized flies show reduced levels of trehalose compared to axenic groups (Fig. 10g – h). Administration of trehalose alone abrogates microbial effects on host speed, while supplementation with arabinose or ribose did not (Fig. 5f and 10i – l). Collectively, these results demonstrate that xylose isomerase from L. brevis alters locomotion in Drosophila, likely via modulation of key carbohydrates, such as trehalose. 28 3.7 SUMMARY Herein, we found that the gut microbiota modulates locomotion in female D. melanogaster. Removal of the microbiota results in increased locomotion, and colonization with select bacterial species, including L. brevis, A. tropicalis, and E. coli, decreases host speed. 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Drosophila lifespan enhancement by exogenous bacteria. Proc. Natl. Acad. Sci. 101, 12974–9 (2004). 24. Mendes, C. S., Bartos, I., Akay, T., Márka, S. & Mann, R. S. Quantification of gait parameters in freely walking wild type and sensory deprived Drosophila melanogaster. Elife 2013, 1–24 (2013). 25. Martin, J. R., Ernst, R. & Heisenberg, M. Temporal pattern of locomotor activity in Drosophila melanogaster. J. Comp. Physiol. 184, 73–84 (1999). 26. Wolf, F. W., Rodan, A. R., Tsai, L. T.-Y. & Heberlein, U. High-resolution analysis of ethanol-induced locomotor stimulation in Drosophila. J. Neurosci. 22, 11035–44 (2002). 27. Lee, W.-J. & Brey, P. T. How microbiomes influence metazoan development: insights from history and Drosophila modeling of gut-microbe interactions. Annu. Rev. Cell Dev. Biol. 29, 571–92 (2013). 28. Erkosar, B., Storelli, G., Defaye, A. & Leulier, F. Host-intestinal microbiota mutualism: ‘learning on the fly’. Cell Host Microbe 13, 8–14 (2013). 29. Ren, C., Webster, P., Finkel, S. E. & Tower, J. Increased internal and external bacterial load during Drosophila aging without life-span trade-off. Cell Metab. 6, 144–52 (2007). 30. Ryu, J.-H. et al. Innate immune homeostasis by the homeobox gene caudal and commensal-gut mutualism in Drosophila. Science. 319, 777–82 (2008). 31. Wong, C. N. A., Ng, P. & Douglas, A. E. Low-diversity bacterial community in the gut of the fruitfly Drosophila melanogaster. Environ. Microbiol. 13, 1889–900 (2011). 32. Storelli, G. et al. Lactobacillus plantarum promotes Drosophila systemic growth by modulating hormonal signals through TOR-dependent nutrient sensing. Cell Metab. 14, 32 403–14 (2011). 33. Schwarzer, M. et al. Lactobacillus plantarum strain maintains growth of infant mice during chronic undernutrition. Science. 351, 854–857 (2016). 34. Chu, H. et al. Gene-microbiota interactions contribute to the pathogenesis of inflammatory bowel disease. Science. 352, 1116–1120 (2016). 35. Lee, K.-A. et al. Bacterial-derived uracil as a modulator of mucosal immunity and gutmicrobe homeostasis in Drosophila. Cell. 153, 797–811 (2013). 36. Selkrig, J. et al. The Drosophila microbiome has a limited influence on sleep, activity, and courtship behaviors. Sci Rep 8, 10646 (2018). 37. Blacher, E., Levy, M., Tatirovsky, E. & Elinav, E. Microbiome-modulated metabolites at the interface of host immunity. J. Immunol. 198, 572–580 (2017). 38. Breton, J. et al. Gut commensal E. coli proteins activate host satiety pathways following nutrient- induced bacterial growth. Cell Metab. 23, 1–11 (2016). 39. Aidy, S. El, Dinan, T. G. & Cryan, J. F. Immune modulation of the brain-gut-microbe axis. Front. Microbiol. 5, 3–6 (2014). 40. Kim, E.-K., Park, Y. M., Lee, O. Y., & Lee, W.-J. Draft Genome Sequence of Lactobacillus plantarum Strain WJL, a Drosophila Gut Symbiont. Genome Announc. 1, e01011-13 (2013). 41. Martino, M. E. et al. Resequencing of the Lactobacillus plantarum Strain WJL Genome. 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H., Yildirim, E. & Edery, I. Assaying locomotor activity to study circadian rhythms and sleep parameters in Drosophila. J. Vis. Exp., 1–8 (2010). 49. Schmid, B, Helfrich-Förster, C, & Yoshii, T. A new ImageJ plugin "ActogramJ" for chronobiological analyses. J Biol Rhythms, 26, 464-467 (2011). 50. Simon, J. C. & Dickinson, M. H. A new chamber for studying the behavior of Drosophila. PLoS One. 5, e8793 (2010). 51. White, K. E., Humphrey, D. M. & Hirth, F. The dopaminergic system in the aging brain of Drosophila. Front. Neurosci. 4, 1-12 (2010). 34 3.8 FIGURES Fig. 1. Select gut bacteria modulate locomotor behavior in flies. a, Experimental design unless otherwise stated. Flies were either left untreated or administered a single bacterial species or bacterial-derived factors. All treatments were supplied daily through application to the fly media (40 µL) for 6 days following eclosion and all assays were performed on day 7. b, Average speed of conventional (Conv), axenic (Ax), and L. plantarum (L.p) or L. brevis (L.b) mono-associated flies. Traces below are representative of individuals from each group and all groups were tested over a 10-min. period. Conv, n = 36; Ax, n = 36; L.p, n = 35; L.b, n = 36. c, Average instantaneous speed of Conv, Ax, and L.p or L.b mono-associated flies. All groups were tested over a 10-min. period and dashes below represent 5-min. mark for each group. Conv, n = 23; Ax, n = 35; L.p, n = 23; L.b, n = 21. d – f, Analysis of the average bout length (d), average pause length (e), and number of bouts (f) over a 10- min. testing period for Conv, Ax, L.p, and L.b groups. Conv, n = 32; Ax, n = 36; L.p, n = 22; L.b, n = 20. g, Daily activity of Conv, Ax, L.p, and L.b groups (virgin female OregonR flies) over a 2-day light-dark cycle period each lasting 12 hrs., starting at time 0. White boxes represent lights on and gray boxes represent lights off. n = 8/condition. h, Stance linearity index calculated for Conv, Ax, L.p, and L.b groups. Conv, n = 6; Ax, n = 7; L.p, n = 5; L.b, n = 5. Boxplots: lower and upper whiskers represent 1.5 interquartile range of the lower and upper quartiles, respectively; boxes indicate lower quartile, median, and 35 upper quartile, from bottom to top. * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001. Kruskal-Wallis and Dunn’s post-hoc test was applied for statistical analysis. Data are representative of at least 3 independent trials for each experiment. 36 Figure 2. Effects of colonization level, bacterial strain, and host diet on L. brevis-modulation of locomotion. a, Colony forming units (CFU) per individual fly for L.p or L.b mono-associated flies. Error bars represent mean +/- S.E.M. n = 16/condition. b, Average speed of Conv, Ax, and L.b monoassociated female or male flies. Females: Conv, n = 90; Ax, n = 92; L.b, n = 89; Males: Conv, n = 100; Ax, n = 100; L.b, n = 95. c – d, Average speed of Ax or flies mono-associated with L.b strains EW, Bb14, or P-2. n = 24/condition. e, Average speed of Ax or L.b mono-associated flies raised on different diet compositions from eclosion until day 7. n = 18/condition. f, Average speed during 37 walking bouts for Conv, Ax, L.p, and L.b groups. Conv, n = 32; Ax, n = 36; L.p, n = 22; L.b, n = 20. g, Tripod index for Conv, Ax, L.p, and L.b groups. Conv, n = 6; Ax, n = 7; L.p, n = 5; L.b, n = 5. h, Average speed of Ax flies or flies mono-associated with L.p or L.b alone or in combination (1:1). n = 24/condition. Boxplots: lower and upper whiskers represent 1.5 interquartile range of the lower and upper quartiles, respectively; boxes indicate lower quartile, median, and upper quartile, from bottom to top. * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001. Unpaired Student’s t-test (a), Kruskal-Wallis and Dunn’s (b – d and f – h), or Mann-Whitney U (e) post-hoc test was applied for statistical analysis. Data are representative of at least 3 independent trials for each experiment. 38 Fig. 3. L. brevis modulates host locomotion under conventional and mono-associated conditions. a, Average speed of two groups of Conv flies with corresponding levels of L.b in each group. n = 6/condition. b – c, Correlation analysis for average speed of individual mono-associated flies with corresponding colony forming units of either L.b (b) or L.p (c). L.b, n = 35 (two-tailed Spearman correlation, * P = 0.02, line of best fit with 95% CI); L.p, n = 32 (two-tailed Spearman correlation, * P = 0.02, line of best fit with 95% CI). d, Average speed of naïve Conv flies or Conv flies supplemented with either L.p or L.b. n = 36/condition. Kruskal-Wallis and Dunn’s post-hoc test was applied for statistical analysis. Boxplots: lower and upper whiskers represent 1.5 interquartile range of the lower and upper quartiles, respectively; boxes indicate lower quartile, median, and upper quartile, from bottom to top. **** P < 0.0001. 39 Fig. 4. Select bacteria modulate host speed. Average speed of Ax flies and flies treated with L.b, L.p, A. pomorum (A.p), A. tropicalis (A.t), E. faecalis (E.f), or E. coli (E.c). n = 30/condition. Boxplots: lower and upper whiskers represent 1.5 interquartile range of the lower and upper quartiles, respectively; boxes indicate lower quartile, median, and upper quartile, from bottom to top. *** P < 0.001, **** P < 0.0001. Kruskal-Wallis and Dunn’s post-hoc test was applied for statistical analysis. Data are representative of at least 2 independent trials for the experiment. 40 Fig. 5. Xylose isomerase (Xi) from L. brevis alters host locomotion. a, Average speed of Ax, L.b mono-associated, and Ax flies treated with cell-free supernatant (CFS) from L.b or heat-killed (HK) L.b alone. Ax, n = 57; L.b, n = 42; L.b CFS, n = 36; L.b HK, n = 24. b, Average speed of Conv, Ax, and Ax flies treated with His-tagged xylose isomerase from L.b (Xi*, 100 µg/mL). Conv, n = 17; Ax, n = 45; Xi*, n = 29. c, Average speed of Ax and Ax flies treated with L.b CFS, Xi*, or His-tagged L-arabinose isomerase from L.b (Ai*,100 µg/mL). Ax, n = 31; L.b CFS, n = 12; Xi*, n = 28; Ai*, n = 13. d, Average speed of Ax and Ax flies treated with CFS from either WT L.b or xylA mutant L.b (L.b∆xylA) bacterial strains. Ax, n = 28; L.b CFS, n = 29; L.b∆xylA CFS, n = 18. e, Carbohydrate levels in Ax and Xi*-treated flies. Each sample contains 5 flies. Error bars represent mean +/- S.E.M. n = 5 samples/condition. f, Average speed of Ax flies and Xi*-treated Ax flies either left untreated or supplemented with trehalose (10 mg/mL) for 3 days before testing. Ax, n = 16; Xi*, n = 18; Ax+Treh, n = 16; Xi*+Treh, n = 17. Boxplots: lower and upper whiskers represent 1.5 interquartile range of the lower and upper quartiles, respectively; boxes indicate lower quartile, median, and upper quartile, from bottom to top. * P < 0.05, ** P < 41 0.01, *** P < 0.001, **** P < 0.0001. Kruskal-Wallis and Dunn’s (a – d and f) or Mann-Whitney U (e) post-hoc test was applied for statistical analysis. Data are representative of at least 2 independent trials. Gluc, glucose; Fruc, fructose; Ribo, ribose; Treh, trehalose. 42 Figure 6. Bacterial-derived products from L. brevis alter locomotion. a, Average speed of Ax, L.p or L.b mono-associated, and Ax flies treated with cell-free supernatant (CFS) from L.p or L.b. Ax, n = 45; L.p, n = 17; L.b, n = 42; L.p CFS, n = 17; L.b CFS, n = 16. b – e, Average speed (b), average bout length (c), average speed during walking bouts (d), and daily activity (e) of Ax and Ax virgin female OregonR flies treated with CFS from either L.p or L.b. White boxes represent lights on and gray boxes represent lights off. Speed: Ax, n = 23; L.p CFS, n = 20; L.b CFS, n = 19. Activity: Ax, n = 8; L.p CFS, n = 8; L.b CFS, n = 4. f, Average speed of Ax, L.b, L.b CFS-, and MRS-treated flies. n = 48/condition. g, Average speed of Ax, L.b monoassociated, and Ax uracil-treated flies. n = 24/condition. Boxplots: lower and upper whiskers represent 1.5 interquartile range of the lower and upper quartiles, respectively; boxes indicate lower quartile, median, and upper quartile, from bottom to top. * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001. Kruskal-Wallis and Dunn’s post-hoc tests was applied for statistical analysis. Data are representative of at least 2 independent trials for each experiment. 43 Fig. 7. The role of timing and testing environment in microbial effects on locomotion. a, Average speed of Ax flies and flies treated with L.b CFS 1 min., 1 hr., or 1 day prior to testing. n = 18/condition. b, Average speed of Ax and L.p or L.b CFS-treated flies tested in arenas with 0, 1, or 5% sucrose and 1% agar. n = 24/conditions. Boxplots: lower and upper whiskers represent 1.5 interquartile range of the lower and upper quartiles, respectively; boxes indicate lower quartile, median, and upper quartile, from bottom to top. * P < 0.05, ** P < 0.01. Kruskal-Wallis and Dunn’s post-hoc tests was applied for statistical analysis. Data are representative of at least 2 independent trials for each experiment. 44 Fig. 8. Modulation of locomotion by the bacterial enzyme, xylose isomerase. a – c, Average speed of Ax or Ax flies treated with unaltered, protease- (Typ, Trypsin; PK, Proteinase-K), or heat-treated (100°C) L.b CFS. n = 18/condition. d, Average speed of Ax flies administered with amylase-treated PBS (Ax), amylase-treated L.b CFS (+amyl L.b CFS), or unaltered L.b CFS (-amyl L.b CFS). n = 18/condition. e, Average speed of Ax flies or flies mono-associated with L.b, L.p, A. pomorum (A.p), or E. coli (E.c). n = 30/condition. f, Average speed of Ax and flies mono-associated with either WT E.c or a strain of E.c carrying a knockout of a single gene (∆tyrA, ∆trpC, ∆manX, ∆treA, ∆xylA). Ax, n = 60; E.c; n = 60; E.c∆tyrA, n = 24; 45 E.c∆trpC, n = 24; E.c∆manX, n = 48; E.c∆treA, n = 48; E.c∆xylA, n = 24. g, Daily activity of Conv, Ax, and Ax virgin female OregonR flies treated with L.b CFS, L.b∆xylA CFS, or Xi* over a 2-day lightdark cycle period each lasting 12 hrs., starting at time 0. White boxes represent lights on and gray boxes represent lights off. Conv, n = 16; Ax, n = 23; L.b CFS, n = 18; L.b∆xylA CFS, n = 19; Xi*, n = 8. h, Average speed of Ax and Ax flies treated with L.b CFS or Xi*. Ax, n = 16; L.b CFS, n = 11; 10 µg/mL Xi*, n = 12; 100 µg/mL Xi*, n = 14. Boxplots: lower and upper whiskers represent 1.5 interquartile range of the lower and upper quartiles, respectively; boxes indicate lower quartile, median, and upper quartile, from bottom to top. * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001. Kruskal-Wallis and Dunn’s post-hoc test was applied for statistical analysis. Data are representative of at least 2 independent trials for each experiment. 46 Fig. 9. Fractionation of L. brevis-derived products. a, Average speed of Ax flies and flies treated with L.b CFS grown in MRS or minimal media. n = 24/condition. b, Average speed of Ax, L.p or L.b mono-associated flies, and flies treated with either the fraction of L.b CFS above or below 3.5 kDa. n = 48/condition. c, Average speed of Ax and flies treated with L.b CFS or the fraction of L.b CFS between approximately 30 to 100 kDa or less than 30 kDa. n = 96/condition. Boxplots: lower and upper whiskers represent 1.5 interquartile range of the lower and upper quartiles, respectively; boxes indicate lower quartile, median, and upper quartile, from bottom to top. * P < 0.05, ** P < 0.01, **** P < 0.0001. Kruskal-Wallis and Dunn’s post-hoc test was applied for statistical analysis. Data are representative of at least 2 independent trials for each experiment. 47 Figure 10. Xylose isomerase activity and key carbohydrates are involved in Xi-mediated changes in locomotion. a – b, Average speed of Ax and Ax flies treated with Xi* or 100 µg of D-fructose, D-glucose, Dxylose, or D-xylulose. (a) Ax, n = 16; Xi*, n = 13; D-fructose, n = 13; D-glucose, n = 15. (b) Ax, n = 26; Xi*, n = 21; D-xylose, n = 22; D-xylulose, n = 18. c, Average speed of Ax and Ax flies treated with either Xi* or Xi* inactivated through treatment with 5 mM EDTA. Ax, n = 21; Xi*, n = 16; Xi*+EDTA, n = 18. d, Average speed of Ax and Ax flies treated with Xi*, 1.6% PFA, or Xi* treated with 1.6% PFA. Ax, n = 27; Xi*, n = 19; Xi*+PFA, n = 21; PFA, n = 21. e, 48 Carbohydrate levels in Ax and Xi*-treated fly media. Each sample is from 0.1 g of fly media and represents a separate vial. Error bars represent mean +/- S.E.M. n = 3 samples/condition. f, Carbohydrate levels in Ax, Xi*, and EDTA-treated Xi* flies. Each sample contains 5 flies. Error bars represent mean +/- S.E.M. n = 5 samples/condition. g, Trehalose levels in Conv, Ax, and Xi*treated flies. Error bars represent mean +/- S.E.M. Conv, n = 9 samples; Ax, n = 6 samples; Xi*, n = 3 samples. h, Trehalose levels in Ax and L.b colonized flies. Error bars represent mean +/S.E.M. n = 15 samples/condition. i, Average speed of Ax and Xi*-treated flies supplemented with either trehalose (Treh, 10 mg/mL) or arabinose (Ara, 10 mg/mL) for 3 days before testing. Ax, n = 40; Xi*, n = 40; Xi*+Treh, n = 39; Xi*+Ara, n = 18. j, Average speed of Ax, Xi*-, or ribose(Ribo, 10 mg/mL) treated flies. Ax, n = 29; Xi*, n = 25; Ribo, n = 12. k, Average speed of Conv and Ax flies supplemented with trehalose (Treh, 10 mg/mL) for 3 days before testing. n = 30/condition. l, Average speed of Ax and Xi* or EDTA-treated Xi* Ax flies subsequently left untreated or supplemented with trehalose (Treh, 10 mg/mL) for 3 days before testing. n = 30/condition. Boxplots: lower and upper whiskers represent 1.5 interquartile range of the lower and upper quartiles, respectively; boxes indicate lower quartile, median, and upper quartile, from bottom to top. * P < 0.05, ** P < 0.01, *** P < 0.001. Kruskal-Wallis and Dunn’s (a – d, f – g, i – l) or a Mann-Whitney U (e and h) post-hoc test was applied for statistical analysis. Data are representative of at least 2 independent trials for each experiment. Gluc, glucose; Fruc, fructose; Mann, mannose; Xylu, xylulose; Treh, trehalose; Ribo, ribose. 49 3.9 METHODS Fly Stocks and Rearing We obtained the Canton-S (#64349) line from Bloomington Drosophila Stock Center at Indiana University. Other fly stocks used were OregonR (kindly provided by A. A. Aravin and K. Fejes Tόth). Flies were cultured at 25˚C and 60% humidity on a 12-hr. light:12-hr. dark cycle and kept in vials containing fresh fly media made at California Institute of Technology consisting of cornmeal, yeast, molasses, agar, and p-hydroxy-benzoic acid methyl ester. Other dietary compositions used were created through altering this standard diet or the Nutri-Fly “German Food” Formula (Genesee Scientific) and were calculated using previously published nutritional data47. Axenic flies were generated using standard methods23,30,32,35. Briefly, embryos from conventional flies were washed in bleach, ethanol, and sterile PBS before being cultivated on fresh irradiated media23. Axenic stocks were maintained through the application of an irradiated diet supplemented with antibiotics (500 μg/ml ampicillin, Putney; 50 μg/ml tetracycline, Sigma; 200 μg/ml rifamycin, Sigma) for at least one generation. For experiments, virgin female flies were collected shortly after eclosion and placed at random into vials (10 – 15 flies per vial) containing irradiated media without antibiotics. Vials were changed every 3 – 4 days using sterile methods. The antibioticsupplemented diet was applied to conventional flies shortly after eclosion to generate antibiotictreated (ABX) flies. Both antibiotic-treated and axenic flies were tested for contaminants through plating animal lysates on Man, Rogosa, and Sharpe (MRS, BD Biosciences); Mannitol (25 g/L Mannitol, Sigma; 5 g/L Yeast extract, BD Biosciences; 3 g/L Peptone, BD Biosciences); and Luria-Bertani (LB, BD Biosciences) nutrient agar plates. Bacterial Strains Lactobacillus brevis EW, Lactobacillus plantarum WJL, and Acetobacter pomorum were obtained from laboratory-reared flies in the laboratory of Won-Jae Lee (Seoul National University)35,40,41. Lactobacillus brevisBb14 (ATCC, #14869) and Lactobacillus brevisP-2 (ATCC, #27305) were isolated from human feces and fermented beverages, respectively. Acetobacter tropicalis was isolated from conventional flies in the Mazmanian laboratory and Enterococcus 50 faecalis was isolated from mice. Escherichia coliK12 (CGSC, #7636) was grown in LB broth and Escherichia coli∆tyrA (CGSC, #9131), Escherichia coli∆trpC (CGSC, #10049), Escherichia coli∆manX (CGSC, #9511), Escherichia coli∆treA (CGSC, #9090), and Escherichia coli∆xylA (CGSC, #10610)42 were grown in LB broth supplemented with kanamycin (50 μg/mL). Lactobacillus brevis and Lactobacillus plantarum cultures were grown overnight in a standing 37˚C incubator in MRS broth (BD Biosciences). For mono-associations, fresh stationary phase bacterial cultures (OD600 = 1.0, 40 μl) were added directly to fly vials. Associations with 2 bacterial species were performed in a 1:1 mixture. For heat-killed experiments, fresh cultures of Lactobacillus brevis (OD600 = 1.0) were washed 3 times in sterile PBS, incubated at 100˚C for 30 min., and cooled to room temperature before administering to flies. All treatments were supplied daily through application to the fly media (40 µL) for 6 days following eclosion. Bacterial Supernatant Preparations Cell-free supernatants (CFS) of specified bacterial strains were harvested from bacterial cultures (OD600 = 1.0) by centrifuging at 13,000 x g for 10 min. and subsequent filtration through a 0.22-µm sterile filter (Millipore). CFS was dialyzed in MilliQ water with a 3.5 kDa membrane (Thermo Scientific) overnight at 4˚C to generate L.b CFS and L.p CFS samples. Each of these treatments was supplied daily through application to the fly media (40 µL) for 6 days following eclosion. Heat and Enzymatic Treatment of L.b CFS For heat-inactivation experiments, freshly prepared L.b CFS samples were incubated at 100˚C for 30 min. and cooled to room temperature before administering to flies. For proteinase K (PK) and trypsin (Typ) treatment, overnight dialysis of CFS was performed in Tris-HCl (pH 8 for PK and pH 8.5 for Typ) after which samples were treated with either PK (100 μg/mL, Invitrogen) or Typ (0.05 μg/mL, Sigma) at 37˚C for 24 or 7 hrs., respectively. A proteinase inhibitor cocktail (Sigma) was added to stop the reaction and subsequently removed through overnight dialysis (Thermo Scientific) at 4˚C in MilliQ water. Aliquots of the samples were run on a 4-20% Trisglycine gel (Invitrogen) to confirm protein cleavage. Controls followed the same protocol except for the addition of proteinase K or trypsin. For amylase digests, 20 μl of 100 mU/mL amylase 51 (Sigma) was added to either freshly prepared L.b CFS or a PBS control for 30 min. and inhibited through lowering the pH to 4.5. Each of these treatments was supplied daily through application to the fly media (40 µL) for 6 days following eclosion. Production of His-tagged proteins (Xi* and Ai*) An expression plasmid for the production of His-tagged xylose isomerase from L.b, here termed as Xi*, was constructed by amplification of its gene and cloning the resulting PCR product in the pQE30 cloning vector (Qiagen) using SLIC ligation. The following primer sequences were used for the construct: 5’- CGCATCACCATCACCATCACGGATCTTACTTGCTCAACGTATCGATGATGTAA-3’ and 5’-GGGGTACCGAGCTCGCATGCGGATCATGACTGAAGAATACTGGAAAGGC-3’. Conformation of the resulting plasmid was verified and transformed into E. coli (Turbo, NEB). This strain was then grown in LB containing ampicillin (100 µg/mL) and chloramphenicol (25 µg/mL) with shaking at 220 rpm at 37˚C for 1 hr. before the addition of 0.1 mM IPTG. After 4 hrs. of shaking at 220 rpm at 37˚C, cells were pelleted and lysed using lysozyme (Sigma) and bead beating with matrix B beads (MP Biomedicals) for 45 sec. Supernatant was collected after centrifugation and the Xi* protein purified through metal affinity purification under native conditions using HisPur™ Ni-NTA Spin Columns (Thermo Scientific). Protein purification was verified through western blot using an Anti-6X His tag® antibody (Abcam) and quantified using a Pierce BCA Protein Assay kit (Thermo Scientific) after which protein was stored at -20˚C. Expression and purification of His-tagged L-arabinose isomerase from L.b, here termed as Ai*, was performed under the exact same conditions and the following primer sequences were used for the construct: 5’- GGGGTACCGAGCTCGCATGCGGATCATGTTATCAGTTCCAGATTATGAATTTTGG-3’ and 5’-CGCATCACCATCACCATCACGGATCCTTACTTGATGAACGCCTTTGTCAT-3’. For EDTA treatment, purified Xi* was combined with 5 mM EDTA for 44 hrs. at 4°C and subsequently dialyzed prior to administering to flies through application to the fly media (40 µL) for 6 days following eclosion. For paraformaldehyde (PFA) treatment, 1.6% PFA was added to Xi* at room temperature for 2 hrs. and subsequently dialyzed prior to administering to flies through application to the fly media (40 µL) for 6 days following eclosion. 52 Generation of xylA deletion mutant (∆xylA) ~1-kb DNA segments flanking the region to be deleted were PCR amplified using the following primers: 5’-ATTCCAATACTACCACTAGCAACGACATCCGTAAAGT-3’; 5’AATTCGAGCTCGGTACCCGGGGATCCACAATCAGAATTGATCGCGGCAAC-3’; 5’- TCGTTGCTAGTGGTAGTATTGGAATCCTAAACCAGATTTCTTATCTTGATG-3’; 5’- GCCTGCAGGTCGACTCTAGAGGATCCCGCAAGTCTAGTGCGGCT-3’. The forward primers were designed using to be partially complementary at their 5’ ends by 25 bp. The fused PCR product was cloned into the BamHI site of the Lactobacilli vector pGID023 and mobilized into L.b. Colonies selected for the erythromycin (Erm) resistance, indicating integration of the vector into the host chromosome were re-plated onto MRS+Erm and subsequently passaged over 5 days and plated onto MRS+Erm. Colonies selected for Erm resistance were passaged again in MRS alone over 3 days and plated on MRS. Resulting colonies were plated in replica on MRS and MRS+Erm. Erm sensitive colonies were screened by PCR to distinguish wild-type revertants from strains with the desired mutation. Bacterial Load Quantification Intestines dissected from surface sterilized 7-day-old adult female flies were homogenized in sterile PBS with ~100 µl matrix D beads using a bead beater. Lysate dilutions in PBS were plated on MRS agar plates and enumerated after 24 hrs. at 37˚C. Locomotion Assays Locomotor behavior was assayed through three previously established methods: the Drosophila Activity Monitoring System (DAMS, Trikinetics)48,49, video-assisted tracking26,50,51, and gait analysis24. Activity measurements 7-day-old individual female flies were cooled on ice for 1 min. and transferred into individual vials (25 x 95 mm) containing standard irradiated media. Tubes were then inserted and secured into Drosophila activity monitors (DAMS, Trikinetics) and kept in a fly incubator held at 53 25˚C. Flies were allowed to acclimate to the new environment for 1 day before testing and midline crossing was sampled every min. Average daily activity was calculated from the 2 days tested and actograms were generated using ActogramJ49. Video-assisted tracking Individual female flies were cooled on ice for 1-2 min. before being introduced under sterile conditions into autoclaved arenas (3.5 cm diameter wells), which allowed free movement but restricted flight. After a 1 hr. acclimation period, arenas were placed onto a light box and recorded from above for a period of 10 min. at 30 frames per sec. All testing took place between ZT 0 and ZT 3 (ZT, Zeitgeber time; lights are turned on at ZT 0 and turned off at ZT 12) and both acclimation and testing occurred at 25˚C unless otherwise stated. Videos were processed using Ethovision software or the Caltech FlyTracker (http://www.vision.caltech.edu/Tools/FlyTracker/). Bout analysis was performed using custom python scripts (available upon request). The velocity curve was smoothed from the acquired video at 30 frames per sec. using a 15 sec. moving average window. A minimum walking speed of 0.25 mm/s was given below which flies were moving but not walking (‘pause bouts’) and above which they were designated as walking (‘walking bouts’). Lengths were measured as time between bout onset and offset. Gait analysis Experiments used an internally illuminated glass surface with frustrated total internal refraction (fTIR) to mark the flies' contact with the glass24. The movement of the flies and their contact was recorded with a high-framerate camera, and videos were quantified using the FlyWalker software package. For further details of the parameters see 24. All groups consisted of 7-day-old female flies and were tested at room temperature. Measurement of carbohydrate levels Fly (5 flies per sample) and fly media (0.1 g per sample) were homogenized in TE Buffer (10 mM Tris, pH = 8, 1 mM EDTA) using a bead beater for 45 sec. followed by centrifugation at 54 7,000 x g for 3 min. The supernatant was heat treated for 30 min. at 72°C before being stored at 80°C before subsequent clean-up steps prior to running on HPAEC-PAD. 100 µL of fly or fly media homogenate sample in TE buffer was diluted with 200 µL of UltraPure distilled water (Invitrogen) and sonicated to get uniform solution. Samples were centrifuged at 2,000 rpm for 15 sec. to precipitate insoluble material. 100 µL of the sample were filtered through pre-washed Pall Nanosep® 3K Omega centrifugal device (MWCO 3KDa, SigmaAldrich) for 15 min. at 14,000 rpm, 7°C. The filtrate was dried on Speed Vac. The dry sample was reconstituted in 300 µL of UltraPure water and loaded onto pre-washed Dionex OnGuard® IIH 1cc cartridge. The flow through and 2x1 mL elution with Ultrapure water was collected in the same tube and lyophilized. Monosaccharide analysis was done using Dionex CarboPac™ PA1 column (4 x 250 mm) with PA1 guard column (4 x 50 mm). Flow rate 1 mL/min. Pulsed amperometric detection with gold electrode. The elution gradient was as follows: 0 min - 20 min, 19mM sodium hydroxide; 20 min. - 50 min., 0 mM - 212.5 mM sodium acetate gradient with 19 mM sodium hydroxide; 50 min. - 65 min., 212.5 mM sodium acetate with 19 mM sodium hydroxide; 65 min. - 68 min., 212.5 mM - 0 mM sodium acetate with 19 mM sodium hydroxide; 68 min. - 85 min., 19 mM sodium hydroxide Trehalose, arabinose, galactose, glucose, mannose, xylose, fructose, ribose, sucrose and xylulose were used as standards. The monosaccharides were assigned based on the retention time and quantified using Chromeleon™ 6.8 chromatography data system software. In Figure 10g – h, measurements of trehalose levels were performed following the same isolation procedure and subsequently processed using a Trehalose Assay Kit (Megazyme) according to the manufacturer’s instructions. For experiments treating flies with trehalose, arabinose or ribose, groups of conventional, axenic, or axenic flies previously treated with Xi* were administered with trehalose, arabinose, or ribose (10 mg/mL, Sigma) through application to the fly media (40 µL) for every day for 3 days before testing. 55 Statistical Analysis All statistical analysis was performed using Prism Software (GraphPad, version 7). Sample size was based on data from pilot experiments and experimenters were not blinded as almost all data acquisition and analysis was automated. To analyze two sets of data following a normal distribution, we used an unpaired two-sided Student’s t-test. If the data did not follow a normal distribution, a non-parametric two-sided Mann-Whitney U test was used. Comparisons among 3 or more data sets and comparisons with more than one variant were analyzed using One-way ANOVA or Two-way ANOVA, respectively. If statistical significance was identified for the variables tested, then a Dunn’s post-hoc test was performed. ANOVAs on normally distributed data were followed by a Bonferroni post-hoc test to determine significant differences between genotypes. Boxplots: lower and upper whiskers represent 1.5 interquartile range of the lower and upper quartiles, respectively; boxes indicate lower quartile, median, and upper quartile, from bottom to top. Bar graphs are presented as mean values +/- S.E.M. 56 Chapter 4 HOST PATHWAYS MEDIATING MICROBIAL MODULATION OF LOCOMOTION Schretter, C. E., J. Vielmetter, I. Bartos, Z. Marka, S. Marka, S. Argade, and S. K. Mazmanian A modified version of this chapter along with Chapter 3 was submitted in “A gut microbial factor modulates locomotor behavior in Drosophila.” 57 4.1 INTRODUCTION The microbiota is an important regulator of host physiology, multiple aspects of which directly or indirectly alter locomotion. Microbial factors, such as xylose isomerase (Xi), may signal through these host pathways to subsequently alter motor behavior. For example, a reduction in host speed is also found in instances of ill health and could indicate recruitment of immune pathways involved in sickness behavior1. The gut microbiota also regulates host metabolism; therefore, increased locomotion upon removal of the gut microbiota may suggest a starvation-like state regulated by similar neuromodulators2. In order to investigate the host pathways involved, we broadly examined the importance of development, immunity, feeding behavior, and neuromodulators in mediating microbial effects on motor behavior. 4.2 POST-ECLOSION MICROBIAL SIGNALS DECREASE HOST LOCOMOTION To investigate whether the effects of microbial exposure are dependent on host developmental stage, we mono-colonized flies at 3 – 5 days post-eclosion (Fig. 1a), a time point in which the development of the gastrointestinal tract and remodeling of the nervous system are complete3–5. Colonization with L. brevis in fully developed female flies decreases locomotor speed and average walking bout length to levels similar in flies treated immediately following eclosion (Fig. 1b – e). Changes in locomotion are likely independent of bacterial effects on host development, as conventionally-reared flies treated after eclosion with broad spectrum antibiotics exhibit similar walking speeds to animals born under axenic conditions (Fig. 1f). Administration of antibiotics increases fly locomotion in two different wild-type lines (Fig. 1g). Furthermore, colonization with L. brevis, but not L. plantarum, after the removal of antibiotics reduces locomotor behavior to levels similar to conventional flies (Fig. 1h – m). From these data, we conclude that locomotion is modulated by select bacterial species of the Drosophila microbiome and is mediated by active signaling, rather than developmental influences. 58 4.3 INTERACTION BETWEEN LOCOMOTOR PHENOTYPES AND OTHER ASPECTS OF HOST PHYSIOLOGY Gut bacteria secrete molecular products that regulate multiple aspects of host physiology, including immunity and feeding behavior6,7. As colonization with L. brevis and treatment with either cell-free supernatant (CFS) or Xi from L. brevis reduced host locomotion in flies (Chapter 3), we subsequently used this bacterium and its products to examine the host pathways involved. Changes in mass can alter motor behavior; however, we did not find any significant differences in weight between L.b CFS-treated flies and axenic or L.p CFS-treated groups (Fig. 2a). Total protein levels are also similar between these groups (Fig. 2b). While the number of excreta was increased in axenic flies, it did not alter upon colonization with either L. brevis or L. plantarum (Fig. 3). We next explored the involvement of immunity and feeding behavior in microbial-mediated locomotion. Depletion of the microbiome in Immune Deficiency (IMD) and Toll knockout flies using antibiotics results in similar increases in walking speed compared to wild-type flies (Fig. 4a – b). There are no differences in the expression of anti-microbial peptides or the dual oxidase gene, Duox, in L.b CFS-treated axenic flies (Fig. 4c). Moreover, while food intake may be influenced by bacterial species and can inhibit locomotor behavior7–9, there is no significant change in the amount of food ingested by L.b CFS-treated flies compared to controls (Fig. 4d – e). Although L.b CFS did not cause changes in antimicrobial peptides or food intake, xylose isomerase (Xi) could alter aspects of host health that contribute to changes in locomotion. While L.b CFS did increase the number of apoptotic cells similar to previous reports10, no changes in survival or intestinal cellular apoptosis occur at the time of motor testing in Xi*-treated flies compared to controls (Fig. 5a – b). However, administration of Xi* throughout life did lead to an overall reduction in survival compared to axenic flies starting at day 42, suggesting Xi negatively affects host health at later time points (Fig. 5a). The microbiota is known to affect circadian oscillations of host metabolites and transcription of certain genes in the epithelium11,12. Although recent work has revealed conflicting findings on changes in sleep in mice and Drosophila13,14, alterations in sleep:wake behavior could contribute to our previous results. Axenic female flies do exhibit a significant reduction in total sleep compared to conventional and L. brevis mono-colonized flies during the light phase (Fig. 59 6a). However, Xi*-treatment did not significantly alter sleep in axenic flies (Fig. 6b), indicating that other microbial factor(s) could contribute to changes in sleep:wake behavior in female flies. 4.4 OCTOPAMINE SIGNALING MEDIATES XYLOSE ISOMERASE-INDUCED CHANGES IN LOCOMOTION Specific neuronal pathways regulate complex behaviors in animals2,15–17, and can be modulated by peripheral inputs, including intestinal and circulating carbohydrate levels18,19. To explore the involvement of various neuronal subsets in bacterial-induced motor behavior, we used the thermosensitive cation channel Drosophila TRPA1 (dTRPA1) to activate neuronal populations previously implicated in locomotion15,20 via a repertoire of GAL4-driver lines. In combination with UAS-dTrpA1 at the activity-inducing temperature (27˚C), we observed that activation of only two GAL4 lines that both label octopaminergic neurons, tyrosine decarboxylase (Tdc2) and tyramine beta-hydroxylase (Tβh), override L. brevis modulation of locomotion (Fig. 7a and 8). Accordingly, activation of Tdc-expressing cells abrogated the effects of Xi*-treatment and differences between conventional and antibiotic-treated groups (Fig. 7b – c and 9). The ability of L. brevis to decrease locomotion, however, is not changed by the activation of dopaminergic, serotoninergic, GABAergic, or cholinergic neurons (Fig 7a and 8e – h). The administration of octopamine to conventional, Xi*-, or L.b CFS-treated flies increases host walking speed to levels similar to that of axenic flies (Fig. 7d – e and 10a). Further, Tdc2 and Tβh transcript levels are reduced in RNA extracted from the heads of Xi*- and L.b CFS-treated flies (Fig. 10b – c). As Tdc and Tβh are important for octopamine synthesis, these results further link octopamine to Xi-induced locomotor effects. Octopamine and tyramine are involved in multiple aspects of host physiology, including metabolism and behavior, and display opposite roles in regulating certain motor behaviors21–29. While administration of tyramine did not influence walking speed in Xi* and L.b CFS conditions (Fig. 7e and 10d), antibiotic-treated flies carrying a null allele for Tdc (Tdc2RO54) no longer display differences in locomotion upon supplementation with Xi* (Fig. 7f), suggesting an indirect role for tyramine. Limiting the expression of a transgene for diphtheria toxin (DTI) to octopaminergic and tyraminergic neurons outside of the ventral nerve cord24,30 results in equivalent speeds between antibiotic- and Xi*-treated flies (Fig. 10e), implicating the involvement of neurons in the supraesophageal and the subesophageal zones in microbial effects on motor behavior. Octopamine 60 signaling is necessary for locomotor changes, as axenic flies administered with mianserin, an octopamine receptor antagonist, and antibiotic-treated flies carrying a null allele for Tβh (TβHM18) or expressing Tβh RNAi no longer respond to Xi* or L.b CFS treatment (Fig. 7g and 10f – h). Similar results are also found under conventional conditions compared to antibiotic-treated groups (Fig. 10i – k). Collectively, we conclude that defined products of the microbiome, and specifically Xi, negatively regulate octopamine signaling to control Drosophila locomotion (Fig. 10l). 4.5 CONCLUSION The microbiome influences neurodevelopment, regulates behavior, and contributes to various neurologic and neuropsychiatric disorders. Herein, we demonstrate that gut bacteria modulate locomotion in female Drosophila. The depletion of the gut microbiota increases host exploratory behavior, and the commensal bacterium L. brevis is sufficient to regulate locomotion. In addition, we establish that xylose isomerase from L. brevis corrects the locomotor phenotypes of axenic flies, a process that is mediated by trehalose and octopamine signaling in the host. However, further work is needed to identify the exact neurons and neuronal mechanisms involved, including potential changes in firing patterns. It would also be important to clarify the sex-specific aspects of these microbial effects on locomotion14. It is intriguing that germ-free mice display hyperactivity similar to axenic Drosophila, and specific bacteria have been shown to decrease locomotor activity in mice13,31,32, although the neuronal pathways implicated in mammalian systems have yet to be identified. The mammalian counterpart of octopamine, noradrenaline, modulates locomotion2,33,34, potentially implicating adrenergic circuitry as a conserved pathway that is co-opted by the microbiome in flies and mammals. In addition to motor behavior, octopamine signaling is linked to sugar metabolism, and trehalose serves as a major energy source for Drosophila21,35. Xylose isomerase may therefore facilitate adrenergic regulation of host physiology through orchestrating metabolic homeostasis, such as via altering internal energy storage, although additional work is needed to define how the microbiome mediates interactions between sugar metabolism and octopamine signaling. The link between metabolic state and locomotion suggests that peripheral influences on metabolism, including the microbiota, may signal via neuronal pathways that modify physical activity. 61 REFERENCES 1. Ayres, J. S. & Schneider, D. S. The role of anorexia in resistance and tolerance to infections in Drosophila. PLoS Biol. 7, (2009). 2. Yang, Z. et al. Octopamine mediates starvation-induced hyperactivity in adult Drosophila. Proc. Natl. Acad. Sci. 112, 5219-5224 (2015). 3. Lemaitre, B. & Miguel-Aliaga, I. 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Post-eclosion microbial signals decrease host locomotion. a, Experimental design (b – e) in which Ax flies were associated with L.b either directly after (day 0, dark green arrows) or 3 – 5 days (light green arrows) following eclosion. b – d, Average speed (b), average bout length (c), and average speed during walking bouts (d) of Ax and flies monoassociated with L.b at either day 0 or day 3 – 5. n = 46/condition. e, Average speed of Conv, Ax, and flies mono-associated with L.b at either day 0 or day 3 – 5. n = 46/condition f, Average speed of Conv, Ax, and Conv flies treated with antibiotics for 3 days after eclosion (ABX). n = 30/condition. g, Average speed of OregonR (OR) and Canton S (CS) Conv flies and Conv flies treated with antibiotics for 3 days after eclosion (ABX). n = 15/condition. h, Experimental design (i – m) in which conventionally-reared flies were treated with antibiotics (ABX, black arrow) for 3 days following eclosion. All flies were subsequently placed on irradiated media either without supplementation (ABX) or associated with L.p (blue arrows) or L.b (green arrows) for the 3 days 67 prior to testing. i, Average speed of Conv and Conv flies treated with antibiotics (ABX) for 3 days, after which flies were either left naïve or colonized with L.p or L.b. Conv, n = 25; ABX, n = 29; L.p, n = 24; L.b, n = 35. j – l, Average speed (j), average bout length (k), and average speed during walking bouts (l) calculated for ABX, L.p-, and L.b-associated flies. n = 36/condition. m, Daily activity of ABX, L.p and, L.b groups (virgin female OregonR flies) over a 2-day light-dark cycle period each lasting 12 hrs., starting at time 0. White boxes represent lights on and gray boxes represent lights off. n = 6/condition. Boxplots: lower and upper whiskers represent 1.5 interquartile range of the lower and upper quartiles, respectively; boxes indicate lower quartile, median, and upper quartile, from bottom to top. * P < 0.05, ** P < 0.01. Kruskal-Wallis and Dunn’s (b – f and i – m) or Mann-Whitney U (g) post-hoc test was applied for statistical analysis. Data are representative of at least 2 independent trials for each experiment. 68 Figure 2. Mass and protein levels are not significantly altered by treatment with microbial metabolites. a, Mass of Ax and L.p CFS- or L.b CFS-treated Ax flies. Error bars represent mean +/- S.E.M. Ax, n = 9; L.p CFS, n = 9; L.b CFS, n = 8. b, Protein levels in whole Ax and L.p CFS- or L.b CFS-treated Ax flies. n = 6 samples/condition. Error bars represent mean +/- S.E.M. KruskalWallis and Dunn’s post-hoc test was applied for statistical analysis. Data are representative of at least 2 independent trials for each experiment. 69 Figure 3. Colonization with L. plantarum or L. brevis alone does not alter excretion number. Number of excreta per fly for Conv, Ax, and L.p or L.b colonized flies. Conv, n = 6; Ax, n = 10; L.p, n = 13; L.b, n = 10. Boxplots: lower and upper whiskers represent 1.5 interquartile range of the lower and upper quartiles, respectively; boxes indicate lower quartile, median, and upper quartile, from bottom to top. * P < 0.05. Kruskal-Wallis and Dunn’s post-hoc test was applied for statistical analysis. Data are representative of at least 2 independent trials for each experiment. 70 Figure 4. The role of food intake, anti-microbial peptides, as well as the Immune Deficiency (IMD) and Toll pathways in locomotor phenotypes. a, Average speed of wild-type background (OregonR, Wt) and Imd-/- flies placed on either media alone or media supplemented with antibiotics (ABX) following eclosion. n = 24/condition. b, Average speed of wild-type background (Canton S, Wt) and Ti-/- flies placed on either media alone or media supplemented with antibiotics (ABX) following eclosion. n = 18/condition. c, qRT-PCR of immune-related transcripts in Ax and Ax L.p- or L.b-CFS treated flies. Error bars represent mean +/- S.E.M. n = 6 samples/condition. d, Amount ingested by Ax and Ax L.p- or L.b-CFS treated flies over 10 trials during MAFE assay. n = 6/condition. e, Intestinal content measured through supplementing the diet of Conv, Ax, and L.p- or L.b-CFS treated Ax flies with blue food dye. Conv, n = 7; Ax, n = 13; L.p CFS, n = 7; L.b CFS, n = 10. Boxplots: lower and upper whiskers represent 1.5 interquartile range of the lower and upper quartiles, respectively; boxes indicate lower quartile, median, and upper quartile, from bottom to top. * P < 0.05, ** P < 0.01, **** P < 71 0.0001. Mann-Whitney U (a – b), One-way ANOVA and Bonferroni (c), or Kruskal-Wallis and Dunn’s (d – e) post-hoc test was applied for statistical analysis. Data are representative of at least 2 independent trials for each experiment. Dpt, Diptericin; Drs, Drosomycin; Cec, Cecropin; AttA, Attacin-A; Duox, Dual Oxidase. 72 Figure 5. Lifespan and percentage of intestinal apoptotic cells under various microbial conditions. a, Lifespan measurements for Ax and Ax treated with L.p CFS, L.b CFS, or Xi*. Asterisks above represent significance at the time point measured by Kruskal-Wallis and Dunn’s post-hoc test. Inset image shows survival at day 7. Ax, n = 4 groups; L.p CFS, n = 5 groups; L.b CFS, n = 5 groups; Xi*, n = 4 groups. b, Percentage of apopotic cells in the intestine of Conv, Ax, and Ax treated with L.p CFS, L.b CFS, or Xi*. Error bars represent mean +/- S.E.M. Conv, n = 7; Ax, n = 5; L.p CFS, n = 4; L.b CFS, n = 6; Xi*, n = 6. * P < 0.05. Kruskal-Wallis and Dunn’s post-hoc test was applied for statistical analysis. Data are representative of at least 2 independent trials for each experiment. 73 Figure 6. Sleep analysis for mono-colonized flies and flies administered with bacteria factors. a, 24-hour sleep profiles of Conv, Ax, L.p-, and L.b-colonized virgin female OregonR flies with the number of sleep bouts in 60 min. time window and total sleep in the light or dark phase. n = 8/condition b, 24-hour sleep profiles of Conv, Ax, L.b CFS-, L.b∆xylA CFS-, and Xi* treated Ax virgin female OregonR flies with the number of sleep bouts in 60 min. time window and total sleep in the light or dark phase. Conv, n = 17; Ax, n = 25; L.b CFS-, n = 19; L.b∆xylA CFS-, n = 21; Xi*, n = 8. Boxplots: lower and upper whiskers represent 1.5 interquartile range of the lower and upper quartiles, respectively; boxes indicate lower quartile, median, and upper quartile, from bottom to top. * P < 0.05. Kruskal-Wallis and Dunn’s post-hoc test for multiple comparisons was applied for statistical analysis. Data are representative of at least 2 independent trials for each experiment. 74 Figure 7. Octopamine mediates xylose isomerase-induced changes in locomotion. a, Difference in average speed between flies previously treated with antibiotics and subsequently left untreated (ABX) or administered with L.b CFS calculated for each GAL4 line crossed with UAS-dTRPA1 tested at 27˚C (for further details and graphs of each GAL4 line, see Fig. 8). Asterisks above bars represent statistical significance (Two-way ANOVA, Mann-Whitney U posthoc tests) between untreated and L.b CFS-treated flies within each GAL4 line. Error bars represent mean +/- S.E.M. b, Average speed of flies previously treated with antibiotics and subsequently left untreated (ABX) or administered with L.b CFS with and without thermogenetic activation of the Tdc2-GAL4 line. GAL4: ABX, n = 15; Xi*, n = 12; UAS: ABX, n = 23; Xi*, n = 23; GAL4>UAS (27°C): ABX, n = 14; Xi*, n = 12; GAL4>UAS (20°C): ABX, n = 12; Xi*, n = 15. c, Average speed of Conv flies and flies previously treated with antibiotics and subsequently left untreated (ABX) with and without thermogenetic activation of the Tdc2-GAL4 line. GAL4: Conv, n = 57; ABX, n = 30; UAS: Conv, n = 59; ABX, n = 39; GAL4>UAS (27°C): Conv, n = 59; ABX, n = 39; GAL4>UAS (20°C): Conv, n = 15; ABX, n = 18. d, Average speed of Conv, Ax, and Xi*treated Ax flies left untreated or supplemented with octopamine (OA, 10 mg/mL) daily for 3 days. 75 Conv, n = 13; Ax, n = 33; Xi*, n = 21; Conv+OA, n = 29; Ax+OA, n = 27; Xi*+OA, n = 32. Kruskal-Wallis and Dunn’s post-hoc test for multiple comparisons was applied for statistical analysis. e, Average speed of Ax flies and Xi*-treated Ax flies either left untreated or supplemented with octopamine (OA, 10 mg/mL) or tyramine (TA, 10 mg/mL) daily for 3 days after the removal of Xi*. Ax, n = 58; Ax+OA, n = 13; Ax+TA, n = 10; Xi*, n = 54; Xi*+OA, n = 46; Xi*+TA, n = 27. Kruskal-Wallis and Dunn’s post-hoc test for multiple comparisons was applied for statistical analysis. f, Average speed of wild-type background (w+, Wt) and Tdc2 null mutants (Tdc2RO54) after treatment with antibiotics for 3 days following eclosion. All flies were subsequently placed on irradiated media without supplementation (ABX) or treated with Xi* daily for 3 days. Wt ABX, n = 12; Wt Xi*, n = 17; TdcRO54 ABX, n = 19; TdcRO54 Xi*, n = 17. MannWhitney U post-hoc test was applied for statistical analysis. g, Average speed of wild-type background (Canton-S, Wt) and Tβh null mutants (TβHM18) after treatment with antibiotics for 3 days following eclosion. All flies were subsequently placed on irradiated media without supplementation (ABX) or treated with Xi* daily for 3 days. Wt ABX, n = 15; Wt Xi*, n = 15; TβHM18 ABX, n = 11; TβHM18 Xi*, n = 12. Mann-Whitney U post-hoc test was applied for statistical analysis. Boxplots: lower and upper whiskers represent 1.5 interquartile range of the lower and upper quartiles, respectively; boxes indicate lower quartile, median, and upper quartile, from bottom to top. * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001. For experiments containing two independent variables (a – c), Two-way ANOVA was applied, and statistical significance was assessed. Mann-Whitney U post-hoc tests were subsequently performed. Data are representative of at least 2 independent trials for each experiment. Tdc, Tyrosine decarboxylase; Tβh, Tyramine beta-hydroxlyase; Ddc, DOPA decarboxylase; Th, Tyrosine hydroxylase; Gad1, Glutamate decarboxylase 1; ChAT, Choline acetyltransferase. 76 Figure 8. Thermogenetic activation of neuromodulator-GAL4 lines. a, Experimental design in which Conv flies (Canton-S) were treated with antibiotics (ABX, black arrow) for 3 days following eclosion. All flies were subsequently placed on irradiated media either without supplementation or treated with L.b CFS (green arrows) for 3 days. 1 hr. prior to and during testing flies were either exposed to 27˚C (red line) to facilitate thermogenetic activation or kept at 20˚C (blue line). b – h, Average speed of flies previously treated with antibiotics and subsequently left untreated (ABX) or administered with L.b CFS for 3 days prior to testing. n = 24/condition. Boxplots: lower and upper whiskers represent 1.5 interquartile range of the lower and upper quartiles, respectively; boxes indicate lower quartile, median, and upper quartile, from 77 bottom to top. * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001. Two-way ANOVA was applied to test for the effect of two independent variables, and statistical significance was assessed for both variables. Mann-Whitney U post hoc tests were subsequently performed. Data are representative of at least 2 independent trials for each experiment. 78 Figure 9. Activation of octopaminergic neurons in flies carrying a null allele for Tβh (TβHM18). Average speed of flies previously treated with antibiotics and subsequently left untreated (ABX) or administered with L.b CFS for 3 days prior to testing. n = 24/condition. Boxplots: lower and upper whiskers represent 1.5 interquartile range of the lower and upper quartiles, respectively; boxes indicate lower quartile, median, and upper quartile, from bottom to top. Two-way ANOVA was applied to test for the effect of two independent variables, and statistical significance was assessed for both variables. Mann-Whitney U post hoc tests were subsequently performed. Data are representative of at least 2 independent trials. 79 Figure 10. Octopamine mediates L. brevis- and xylose isomerase-induced changes in locomotion. a, Average speed of Ax and L.b CFS-treated Ax flies left untreated or supplemented with octopamine (OA, 10 mg/mL) or L-dopa (1 mg/mL) for 3 days. n = 36/condition. b, qRT-PCR for transcripts from heads of Ax and L.b CFS-treated Ax flies. Error bars represent mean +/- S.E.M. n = 6 samples/condition. c, qRT-PCR for transcripts from heads of Ax or Ax Xi*-treated flies. Error bars represent mean +/- S.E.M. Ax, n = 5 samples; Xi*, n = 6 samples. d, Average speed of Ax and L.b CFS-treated Ax flies left untreated or supplemented with tyramine (TA, 10 mg/mL) for 3 days. n = 12/condition. e, Average speed of control lines and flies expressing DTI in octopaminergic and tyraminergic neurons outside of the ventral nerve cord. All flies were previously treated with antibiotics and subsequently left untreated (ABX) or administered with Xi* for 3 days prior to testing. n = 24/condition. f, Average speed of control lines and flies expressing Tβh RNAi in all neurons. All flies were previously treated with antibiotics and 80 subsequently left untreated (ABX) or administered with L.b CFS for 3 days prior to testing. n = 12/condition. g, Tβh mRNA measured from heads of flies previously treated with antibiotics. Error bars represent range. n = 2 samples/condition. h, Average speed of Ax and Xi*-treated Ax flies left untreated or supplemented with mianserin (2 mg/mL) for 3 days. n =14/condition. i, Average speed of Conv, Ax, and Xi*-treated Ax flies left untreated or supplemented with mianserin (2 mg/mL) for 3 days. n = 30/condition. k, Average speed of wild-type background (w+, Wt) and Tdc2 null mutants (Tdc2RO54) either left untreated or after treatment with antibiotics for 3 days following eclosion. Wt Conv, n = 13; Wt ABX, n = 21; TdcRO54 Conv, n = 28; TdcRO54 ABX, n = 34. Mann-Whitney U post-hoc test was applied for statistical analysis. g, Average speed of wildtype background (Canton-S, Wt) and Tβh null mutants (TβHM18) either left untreated or after treatment with antibiotics for 3 days following eclosion. Wt Conv, n = 38; Wt ABX, n = 42; TβHM18 Conv, n = 26; TβHM18 ABX, n = 33. Mann-Whitney U post-hoc test was applied for statistical analysis. l, Model of bacterial modulation of host locomotion. Boxplots: lower and upper whiskers represent 1.5 interquartile range of the lower and upper quartiles, respectively; boxes indicate lower quartile, median, and upper quartile, from bottom to top. * P < 0.05, ** P < 0.01, *** P < 0.001. Kruskal-Wallis and Dunn’s (a, d, h – i), unpaired Student’s t-test (b – c and g), or MannWhitney U (e – f and j – k) post-hoc test was applied for statistical analysis. Data are representative of at least 2 independent trials for each experiment. Tdc, Tyrosine decarboxylase; Tβh, Tyramine beta-hydroxlyase; Ddc, DOPA decarboxylase; Tph, Tryptophan hydroxylase. 81 4.7 METHODS Fly Stocks and Rearing We obtained Canton-S (#64349), Imd-/- (#55711), Ti-/- (#30652), UAS-dTrpA1 (#26264), Tdc2-GAL4 (#52243), Tβh-GAL4 (#48332), Th-GAL4 (#8488), Ddc-GAL4 (#7009), Gad1-GAL4 (#51630), ChAT-GAL4 (#60317), Elav-GAL4 (#46655), UAS-TβhRNAi (#27667), UAS-DTI (#25039), and pBDPG4U-GAL4 (#68384) lines from Bloomington Drosophila Stock Center at Indiana University. Other fly stocks used were OregonR (kindly provided by A. A. Aravin and K. Fejes Tόth), TβHM18 (kindly provided by M. H. Dickinson)36, Tdc2R054, and tsh-GAL80 (both kindly provided by D. J. Anderson)37,38. To minimize the effect of genetic background on behaviors, mutant fly lines were outcrossed for at least three generations onto a wild-type background. Flies were cultured at 25˚C and 60% humidity on a 12-hr. light:12-hr. dark cycle and kept in vials containing fresh fly media made at California Institute of Technology consisting of cornmeal, yeast, molasses, agar, and p-hydroxy-benzoic acid methyl ester. Other dietary compositions used were created through altering this standard diet or the Nutri-Fly “German Food” Formula (Genesee Scientific) and were calculated using previously published nutritional data39. Axenic flies were generated using standard methods10,40–42. Briefly, embryos from conventional flies were washed in bleach, ethanol, and sterile PBS before being cultivated on fresh irradiated media40. Axenic stocks were maintained through the application of an irradiated diet supplemented with antibiotics (500 μg/ml ampicillin, Putney; 50 μg/ml tetracycline, Sigma; 200 μg/ml rifamycin, Sigma) for at least one generation. For experiments, virgin female flies were collected shortly after eclosion and placed at random into vials (10 – 15 flies per vial) containing irradiated media without antibiotics. Vials were changed every 3 – 4 days using sterile methods. The antibiotic supplemented diet was applied to conventional flies shortly after eclosion to generate antibiotictreated (ABX) flies. Both antibiotic-treated and axenic flies were tested for contaminants through plating animal lysates on Man, Rogosa, and Sharpe (MRS, BD Biosciences); Mannitol (25 g/L Mannitol, Sigma; 5 g/L Yeast extract, BD Biosciences; 3 g/L Peptone, BD Biosciences); and Luria-Bertani (LB, BD Biosciences) nutrient agar plates. 82 Bacterial Supernatant Preparations Cell-free supernatants (CFS) of specified bacterial strains were harvested from bacterial cultures (OD600 = 1.0) by centrifuging at 13,000 x g for 10 min. and subsequent filtration through a 0.22µm sterile filter (Millipore). CFS was dialyzed in MilliQ water with a 3.5 kDa membrane (Thermo Scientific) overnight at 4˚C to generate L.b CFS and L.p CFS samples. Each of these treatments were supplied daily through application to the fly media (40 µL) for 6 days following eclosion. Production of His-tagged proteins (Xi* and Ai*) An expression plasmid for the production of His-tagged xylose isomerase from L.b, here termed as Xi*, was constructed by amplification of its gene and cloning the resulting PCR product in the pQE30 cloning vector (Qiagen) using SLIC ligation. The following primer sequences were used for the construct: 5’- CGCATCACCATCACCATCACGGATCTTACTTGCTCAACGTATCGATGATGTAA-3’ and 5’-GGGGTACCGAGCTCGCATGCGGATCATGACTGAAGAATACTGGAAAGGC-3’. Conformation of the resulting plasmid was verified and transformed into E. coli (Turbo, NEB). This strain was then grown in LB containing ampicillin (100 µg/mL) and chloramphenicol (25 µg/mL) with shaking at 220 rpm at 37˚C for 1 hr. before the addition of 0.1 mM IPTG. After 4 hrs. of shaking at 220 rpm at 37˚C, cells were pelleted and lysed using lysozyme (Sigma) and bead beating with matrix B beads (MP Biomedicals) for 45 sec. Supernatant was collected after centrifugation and the Xi* protein purified through metal affinity purification under native conditions using HisPur™ Ni-NTA Spin Columns (Thermo Scientific). Protein purification was verified through western blot using an Anti-6X His tag® antibody (Abcam) and quantified using a Pierce BCA Protein Assay kit (Thermo Scientific) after which protein was stored at -20˚C. Expression and purification of His-tagged L-arabinose isomerase from L.b, here termed as Ai*, was performed under the exact same conditions and the following primer sequences were used for the construct: 5’- GGGGTACCGAGCTCGCATGCGGATCATGTTATCAGTTCCAGATTATGAATTTTGG-3’ and 5’-CGCATCACCATCACCATCACGGATCCTTACTTGATGAACGCCTTTGTCAT-3’. 83 Drug treatments Axenic flies were either left untreated or administered with L.b CFS or Xi* for 3 days after eclosion. After switching to new irradiated fly media, groups of axenic flies were treated through application to the fly media (40 µL) with octopamine (OA, 10 mg/mL, Sigma), tyramine (TA, 10 mg/mL, Sigma), L-dopa (1 mg/mL, Sigma), or mianserin (2 mg/mL) every day for 3 days before testing, similar to previously published methods16,17,22. Locomotion Assays Locomotor behavior was assayed through three previously established methods: the Drosophila Activity Monitoring System (DAMS, Trikinetics)43,44, video-assisted tracking45–47, and gait analysis48. Activity measurements 7-day-old individual female flies were cooled on ice for 1 min. and transferred into individual vials (25 x 95 mm) containing standard irradiated media. Tubes were then inserted and secured into Drosophila activity monitors (DAMS, Trikinetics) and kept in a fly incubator held at 25˚C. Flies were allowed to acclimate to the new environment for 1 day before testing and midline crossing was sampled every minute. Average daily activity was calculated from the 2 days tested and actograms were generated using ActogramJ44. Sleep was defined as a 5 min. bout of inactivity as previously described49. Video-assisted tracking Individual female flies were cooled on ice for 1-2 min. before being introduced under sterile conditions into autoclaved arenas (3.5 cm diameter wells), which allowed free movement but restricted flight. After a 1 hr. acclimation period, arenas were placed onto a light box and recorded from above for a period of 10 min. at 30 frames per sec. All testing took place between ZT 0 and ZT 3 (ZT, Zeitgeber time; lights are turned on at ZT 0 and turned off at ZT 12) and both acclimation and testing occurred at 25˚C unless otherwise stated. Videos were processed using Ethovision software or the Caltech FlyTracker (http://www.vision.caltech.edu/Tools/FlyTracker/). 84 Bout analysis was performed using custom python scripts (available upon request). The velocity curve was smoothed from the acquired video at 30 frames per sec. using a 15 sec. moving average window. A minimum walking speed of 0.25 mm/s was given below which flies were moving but not walking (‘pause bouts’) and above which they were designated as walking (‘walking bouts’). Lengths were measured as time between bout onset and offset. Feeding Assays Female flies were collected at the same time as described for Locomotor Assays. Flies were transferred regularly onto fresh food until day 7, upon which the flies were starved for 2 hrs. and subsequently transferred for 30 min. to an irradiated standard fly media dyed with FD&C Blue no. 1 (Sigma) at a final concentration of 0.5 g dye per 100 g food. Flies were allowed to feed on the food (3-4 biological replicates and 7 flies per replicate) at 25˚C after which they were decapitated and their bodies collected. Each replicate was homogenized in 150 µL of PBS/0.05% Triton X100 and centrifuged at 5,000 x g for 1 min. to remove debris. Absorbance for all groups was measured together at 630 nm and the amount of food consumed was estimated from a standard curve of the same dye solution. The MAFE assay was performed as described previously34,50. Briefly, individual flies were introduced into a 200 μL pipette tip, which was cut to expose the proboscis. Flies were first water satiated and presented with 100 mM sucrose delivered in a fine graduated capillary (VWR). After flies were unresponsive to 10 food stimuli, the assay was terminated and the total volume of food was calculated. Measurement of life span Adult female flies were transferred under sterile conditions to irradiated fly media every 4 – 5 days. Survival in 3 or more independent cohorts containing 15 – 25 flies each was monitored over time. Apoptosis assay Midguts from 7-day-old female flies were dissected in PBS containing 0.1% Triton X-100 and the apoptosis assay was performed as previously described10,41. The percentage of apoptotic 85 cells was determined by dividing the number of apoptotic cells by the total number of cells in each section and multiplying by 100. RNA isolation and quantitative real-time PCR Heads (20 flies per sample) or decapitated bodies (5 flies per sample) were dissected on ice and immediately processed using an ArcturusTM PicoPureTM RNA isolation kit (Applied Biosystems) or a standard TRIzolTM-Chloroform protocol (ThermoFisher). 1 μg of RNA was reverse transcribed using iScript cDNA Synthesis Kit, according to manufacturer’s protocol (BioRad) and diluted to 10 ng/μl based on the input concentration of total RNA. Previously published primer pairs were used to target immune-related gene transcripts10,51. Other primer sequences used include Tdc (F: GGTCTGCCGGACCACTTTC, R: CACTCCGATGCGGAAGTCTG), Tβh (F: GCTTATCCGACACAAAGCTGC, R: GAAAGCATTCTGCAAGTGGAA), Ddc (F: TGGGATGAGCACACCATCTTG, R: GTAGAAGGGAATCAAACCCTCG), Tph (F: TGTTTTCGCCCAAGGATTCGT, R: CACCAGGTTTATGTCATGCTTCT). All primers were synthesized by Integrated DNA Technologies. Real-time PCR for the house-keeping genes Rp49 and RpL32 were used to ensure that input RNA was equal among all samples. Real-time PCR was performed on cDNA using an ABI PRISM 7900 HT system (ThermoFisher) according to the manufacturer’s instructions. Statistical Analysis All statistical analysis was performed using Prism Software (GraphPad, version 7). Sample size was based on data from pilot experiments and experimenters were not blinded as almost all data acquisition and analysis was automated. To analyze two sets of data following a normal distribution, we used an unpaired two-sided Student’s t-test. If the data did not follow a normal distribution, a non-parametric two-sided Mann-Whitney U test was used. Comparisons among 3 or more data sets and comparisons with more than one variant were analyzed using One-way ANOVA or Two-way ANOVA, respectively. If statistical significance was identified for the variables tested, then a Dunn’s post-hoc test was performed. ANOVAs on normally distributed data were followed by a Bonferroni post-hoc test to determine significant differences between genotypes. Boxplots: lower and upper whiskers represent 1.5 interquartile range of the lower and 86 upper quartiles, respectively; boxes indicate lower quartile, median, and upper quartile, from bottom to top. Bar graphs are presented as mean values +/- S.E.M. 87 Chapter 5 EXAMINING CONSERVATION OF MICROBIAL EFFECTS ON LOCOMOTION Schretter, C. E. and S. K. Mazmanian. This chapter was used as preliminary data for “Gut Microbiota Regulate Motor Deficits and Neuroinflammation in a Model of Parkinson’s Disease.” 88 5.1 INTRODUCTION The nervous system and behavior of mammals are also influenced by their gut microbiome1–4. Similar to our experiments in D. melanogaster, mice that lack a microbiota (germfree, GF) exhibit increased speed compared to conventional or specific pathogen free (SPF) controls5. Further, GF mice exhibit elevated locomotion under both western and low-fat diets6. Colonization with specific bacterial species can also decrease locomotor behavior in mice7. In addition to behavior, higher striatal turnover of norepinephrine, dopamine, and serotonin are found in GF animals compared to their SPF counterparts5. Many of these studies thereby suggest that the microbial-dependent locomotor phenotypes found in Drosophila could be conserved in mice. However, differences in their development and the stability of the microbiota between the two organisms could lead to divergent effects on host behavior and mediation through dissimilar neuronal pathways. 5.2 MICROBIAL MODULATION OF MOTOR-RELATED BRAIN REGIONS AND BEHAVIOR IN MICE As previous research identified changes in locomotion depending on microbial status, we examined SPF, GF, and antibiotic-treated SPF mice (ABX) in an open field test. While GF mice exhibited increased speed compared to SPF mice, ABX animals displayed lower speeds than GF (Fig. 1). Similar decreases in locomotion have been reported in C57BL/6 mice upon antibiotic treatment; however, this appears to differ based on the antibiotics used and host genetics 8,9. Due to this difference in locomotion between SPF and GF animals, we next explored if there were any changes in neuronal populations related to motor behavior, including the substantia nigra pars compacta (SNc) and the striatum. Compared to SPF mice, GF animals had a 60% and 35.8% reduction in the number of cells positive for a marker of dopaminergic neurons, tyrosine hydroxylase (TH), and total neurons in the SNc, respectively (Fig. 2 – 3). Bacterial colonization of GF mice (EX-GF) increased the number of TH-positive cells to levels similar to SPF mice (Fig. 4). In the striatum, GF mice exhibit a decrease in the optical density of TH staining in the dorsomedial region, which is a target for projections from the SNc (Fig. 5). 89 A dramatic loss of dopaminergic neurons in the SNc as well as a resulting loss of dopamine in the striatum has been implicated in the cognitive motor deficits seen in Parkinson’s Disease (PD)10. Due to microbial involvement in motor behavior and enteric contributions to the progression of PD11,12, we examined the potential role of the gut microbiota in a mouse model of PD. Epidemiological studies have correlated pesticide exposure with the development of certain cases of PD13,14. Rotenone is common pesticide that inhibits mitochondrial Complex I and produces a loss of striatal dopaminergic terminals followed by progressive degeneration of dopaminergic neurons in the SNc15–17. While rotenone has traditionally been given systemically, oral treatment in mice results in specific nigrostriatal dopaminergic neurodegeneration, motor deficits, and the up-regulation of α-synuclein in the surviving dopaminergic neurons18,19. Moreover, oral treatment prompts α-synuclein inclusions to form in the ENS and follow a retrograde progression to the brain, as well as colonic motility deficits 18–20. Due to these connections and our previous data, we employed this model of PD to examine if removal of the gut microbiota alters its progression. There were no significant changes in weight across the groups, and rotenone-treated SPF and GF animals exhibited no differences in speed (Fig. 6 – 8). Specific populations of neurons in the myenteric plexus of the ENS, such as dopaminergic and cholinergic neurons, have been linked to the regulation of gut motility and PD12. While we found no differences in the total number of neurons in the proximal small intestine, GF mice display significantly lower numbers of TH-positive cells in this region (Fig. 9a – b). Additionally, mRNA expression of choline actyltransferase (ChAT), an enzyme involved in acetylcholine synthesis, is reduced in the proximal small intestine of GF animals (Fig. 9c). This data indicates that the gut microbiota may affect these neurotransmitters in the gastrointestinal tract, which have been implicated in the pathogenesis of PD. 5.3 CONCLUSION Herein, we found that mice lacking a microbiota exhibit increased locomotor behavior along with decreases in TH-positive neurons in the SNc and TH staining in the dorsomedial striatum. TH-positive cells and ChAT expression is also reduced in the proximal small intestine of GF mice. Further, administration of rotenone appeared to diminish behavioral differences between GF and SPF animals. While the gut microbiota may play a role in locomotor behavior as well as 90 related regions and disorders in mammals, more research needs to be performed in order to evaluate these changes in the context of our previous results in Drosophila. Additional recent research has shown that the microbiota contributes to the progression of certain mouse models of PD. In the rotenone model, changes in the microbiota precede intestinal and motor deficits21. Mice overexpressing wild-type human α-synuclein under the Thy1 promoter develop many features of sporadic PD, including: progressive changes in dopamine release and striatal content, α-synuclein pathology, deficits in motor and non-motor functions, inflammation, in addition to biochemical and molecular alterations similar to those observed in PD17,22,23. Research conducted in the Mazmanian laboratory after our experiments found that the microbiota is required for motor deficits in this mouse model of PD24. 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Signals from the gut microbiota to distant organs in physiology and disease. Nat. Med. 22, 1079–1089 (2016). 4. De Vadder, F. et al. Gut microbiota regulates maturation of the adult enteric nervous system via enteric serotonin networks. Proc. Natl. Acad. Sci. U. S. A. 115, 6458-6463 (2018). 5. Diaz Heijtz, R. et al. Normal gut microbiota modulates brain development and behavior. Proc. Natl. Acad. Sci. U. S. A. 108, 3047–52 (2011). 6. Bäckhed, F., Manchester, J. K., Semenkovich, C. F. & Gordon, J. I. Mechanisms underlying the resistance to diet-induced obesity in germ-free mice. Proc. Natl. Acad. Sci. U. S. A. 104, 979–84 (2007). 7. Nishino, R. et al. Commensal microbiota modulate murine behaviors in a strictly contamination-free environment confirmed by culture-based methods. Neurogastroenterol. Motil. 25, 521–528 (2013). 8. Gacias, M. et al. Microbiota-driven transcriptional changes in prefrontal cortex override genetic differences in social behavior. Elife 5, e13442 (2016). 9. Kiraly, D. D. et al. Alterations of the Host Microbiome Affect Behavioral Responses to Cocaine. Sci. Rep. 6, 35455 (2016). 10. Beal, M. F. Experimental models of Parkinson's disease. Nat. Rev. Neurosci. 2, 325-334 (2001). 92 11. Braak, H., Rüb, U., Gai, W. P. & Del Tredici, K. Idiopathic Parkinson’s disease: possible routes by which vulnerable neuronal types may be subject to neuroinvasion by an unknown pathogen. J. Neural Transm. 110, 517–36 (2003). 12. Cersosimo, M. G. & Benarroch, E. E. Neural control of the gastrointestinal tract: implications for Parkinson disease. Mov. Disord. 23, 1065–75 (2008). 13. Ascherio, A. et al. Pesticide Exposure and Risk for Parkinson’s Disease. Ann. Neurol. 60, 197–203 (2006). 14. Ritz, B. R. et al. Of Pesticides and Men : a California Story of Genes and Environment in Parkinson’s Disease. Curr. Environ. Health. Rep. 3, 40–52 (2016). 15. Betarbet, R. et al. Chronic systemic pesticide exposure reproduces features of Parkinson ’ s disease. Nat. Neurosci. 26, 1301–1306 (2000). 16. Hoglinger, G. U. et al. Chronic systemic complex I inhibition induces a hypokinetic multisystem degeneration in rats. J. Neurochem. 84, 491–502 (2003). 17. Rockenstein, E. et al. Differential Neuropathological Alterations in Transgenic Mice Expressing α-synuclein From The Platelet-derived Growth Factor and Thy-1 Promoters. J. Neurosci. Res. 68, 568–578 (2002). 18. Inden, M. et al. Neurodegeneration of mouse nigrostriatal dopaminergic system induced by repeated oral administration of rotenone is prevented by 4-phenylbutyrate, a chemical chaperone. J. Neurochem. 101, 1491–1504 (2007). 19. Tasselli, M. et al. Effects of oral administration of rotenone on gastrointestinal functions in mice. Neurogastroenterol. Motil. 25, e183-93 (2013). 20. Pan-Montojo, F. et al. Environmental toxins trigger PD-like progression via increased alpha-synuclein release from enteric neurons in mice. Sci. Rep. 2, 898 (2012). 21. Yang, X., Qian, Y., Xu, S., Song, Y. & Xiao, Q. Longitudinal Analysis of Fecal 93 Microbiome and Pathologic Processes in a Rotenone Induced Mice Model of Parkinson’s Disease. 9, 1–12 (2018). 22. Chesselet, M. et al. A Progressive Mouse Model of Parkinson’s Disease : The Thy1-aSyn (“ Line 61 ”) Mice. Neurotherapeutics 9, 297–314 (2012). 23. Fleming, S. M. et al. Early and progressive sensorimotor anomalies in mice overexpressing wild-type human alpha-synuclein. J. Neurosci. 24, 9434–40 (2004). 24. Sampson, T. R. et al. Gut Microbiota Regulate Motor Deficits and Neuroinflammation in a Model of Parkinson’s Disease. Cell. 167, 1469–1480 (2016). 94 5.4 FIGURES Figure 1. GF mice display increased speed compared to SPF and antibiotic-treated mice. 4-month-old SPF, GF, and antibiotic-treated SPF (ABX) mice were recorded over 10 min. and average speed was calculated by Ethovision software. n = 8/condition. Kruskal-Wallis and Dunn’s post-hoc test was applied for statistical analysis. 95 Figure 2. GF mice have reduced TH+ cells in the SNc compared to SPF controls. Representative images at 4x of TH+ (green) staining in the SNc of SPF (6-month-old female) and GF (6-month-old female) C57Bl/6 mice. 96 Figure 3. Lower TH+ cells and cell counts in the SNc of GF mice compared to SPF controls. Brains from 3 – 6 month-old female SPF (black, n=4) and GF (gray, n=7) were stained using antiTH and goat anti-rabbit Alexa Fluor 488 antibodies. Images were taken using a fluorescent microscope and analyzed using Image-J. Cell counts were performed after Nissl staining and were subsequently analyzed with Image-J. There were no significant differences between counts taken from 3-month versus 6-month-old animals in GF or SPF animals (data not shown). A two-tailed ttest test was applied for statistical analysis. 97 Figure 4. Reduced TH+ cell counts are reversed through colonization. Brains from 4-month-old female SPF (n=6), EX-GF (n=4), and GF (n=4) were stained using antiTH and goat anti-rabbit Alexa Fluor 488 antibodies. Images were taken using a fluorescent microscope and counts were performed by a blinded experimenter using Image-J. Kruskal-Wallis and Dunn’s post-hoc test was applied for statistical analysis. 98 Figure 5. TH+ optical density in the dorsomedial striatum of GF mice is decreased compared to SPF animals. Analysis was performed on slices from 4-month-old male SPF (gray, n=4) and GF (black, n=4) using Image-J. No differences were observed in the ventral striatum. A two-tailed t-test was applied for statistical analysis. 99 Figure 6. Experimental design for examining microbial involvement in a mouse model of Parkinson’s. 4-month-old female specific pathogen free (SPF) or germ free (GF) mice were orally treated via gavage with rotenone (10 mg/kg) for 28 days under sterile conditions. Following treatment, open field tests were performed. 100 Figure 7. Weight of mice over the course of the experiment. 101 Figure 8. General locomotion in mice with varying microbial status and rotenone treatment. Naïve and rotenone-treated SPF and GF mice were recorded for 10 min. and average velocity was calculated by Ethovision software. SPF vehicle-treated (SPF-V), n = 6; GF vehicle-treated (GFV), n = 3; SPF rotenone-treated (SPF-T), n = 6; GF rotenone-treated (GF-T), n = 7. Kruskal-Wallis and Dunn’s post-hoc test was applied for statistical analysis. 102 Figure 9. Staining for TH and ChAT expression is decreased in the proximal small intestine of GF mice. A – B, Small intestine of 3-month-old male SPF (gray, n = 3) and GF (black, n = 3) mice stained with anti-TH or PGP 9.5. Fluorescence intensity and cell counts were measured using Image J. C, Whole small intestinal tissue from 3-month-old male SPF (gray) and GF (black) animals (n = 3-4 for proximal and medial; n = 2 for distal) were dissected, luminal contents flushed, and mRNA extracted using the Qiagen RNeasy mini kit. Expression was tested in triplicates and normalized using the housekeeping gene, GAPDH. A two-tailed t-test was applied for statistical analysis. 103 5.5 METHODS Animals C57BL/6 mice were purchased from Taconic Farms. 3- to 6-month-old age- and sexmatched mice were used. All mice were group housed (2–5 mice per cage) with a 13 hr. light/11 hr. dark cycle (lights on at 06:00) at 21–23 °C and 45% relative humidity within a range of 30– 70% in ventilated cages. All procedures were performed in accordance with the guidelines and approved protocols from the Institutional Animal Care and Use Committee at the California Institute of Technology. Immunohistochemistry Brains were placed in 4% PFA for 48 hr. and subsequently sectioned at 50 µm in cold PBS using a vibratome. Sections were stained using anti-TH (1:400) and goat anti-rabbit Alexa Fluor 488 (1:400) antibodies. Images were taken using a fluorescent microscope and analyzed using Image-J. Nissl staining was performed on corresponding slices and subsequent analysis was performed with Image-J. For staining in the small intestine, the gastrointestinal tract was dissected and luminal contents flushed with 4% PFA and OCT and subsequently frozen on dry ice. Sections were taken using a cryostat and stained with anti-TH (1:400) or PGP 9.5 (1:400). Fluorescence intensity and cell counts were measured using Image J. RNA extraction and qRT-PCR RNA extraction was based on the manufacturer’s protocol (Trizol, Life Technologies). Before reverse transcription, RNA was treated with DNase I to eliminate genomic DNA contamination. 1 μg RNA from each sample was reverse transcribed by using the iScript cDNA synthesis kit (Bio-Rad). The gene expression in the small intestine was measured using Power SYBR Green PCR master mix (Life Technologies). Gene expression was normalized using the housekeeping gene, GAPDH. 104 Open Field Testing Mice were placed in a novel behavior testing room in groups of 4. Animals were then individually placed in plastic open top arenas (50 X 50 X 30cm) and allowed to freely move over a 10-min. period. Video was captured using an overhead mounted video camera. Ethovision software was used to generate trajectory maps and analyze average velocity. 105 Chapter 6 CONCLUSION 106 In the wild, D. melanogaster is associated with a range of environments with different nutritional compositions1,2. Therefore, there is a need to maintain homeostasis under changing external conditions and to quickly identify beneficial situations. As the microbiota both acts as a food source and increases the amount of readily available nutrients3,4, it could buffer against large changes in nutritional composition and indicate a favorable environment. Specific species within the microbiota significantly alter host metabolism5–8, thus helping to maintain homeostasis within an individual. Given the close ties between metabolism and certain behaviors, including locomotion, we hypothesized that the microbiota also contributes to this aspect of host physiology. In this work, we found that removal of the microbiota in female Drosophila results in hyperactivity compared to conventional counterparts, independent of developmental influences. Starvation is also reported to cause similar increases in fly locomotion9. Mono-colonization with select bacteria, including Lactobacillus brevis, restored host speed and daily activity to conventional levels and exhibited similar effects under more complex microbial conditions. The addition of a microbial-derived enzyme, xylose isomerase (Xi), was also sufficient to increase host speed and daily activity in axenic flies. Furthermore, we found that octopamine signaling is involved in mediating microbial effects on locomotion, which is similarly implicated in starvationinduced hyperactivity9–11. These overlapping phenotypes and pathways suggest that the removal of the microbiota may result in a starvation-like state reversed by certain bacterial species and their products, including Xi. However, a more in-depth analysis of the neuronal circuits involved in our phenotypes is needed to facilitate this comparison. As male flies did not exhibit the same degree of changes in host speed upon removal of the microbiota or mono-colonization with L. brevis, these effects on host locomotion appear sexspecific. A previous report also found that increased levels of trehalose in axenic flies is sexspecific8. Further, sexually dimorphic locomotor behavior was previously linked to trehalose and insulin signaling12,13. Future work should examine potential ties between sex-specific microbial effects on locomotion and the differences in metabolic requirements and neuronal circuitry between males and females. Microbial effects on locomotion may also be conserved as our work and that of others similarly reported hyperactivity in germ-free mice14,15. However, as antibiotic-treated mice 107 displayed decreased locomotion, there are likely distinct differences in the microbial effects and the underlying pathways mediating changes in locomotion in Drosophila and mice. It would be interesting to examine if the same bacterial species and products identified in Drosophila are able to alter locomotion in mice. However, researchers should carefully consider the ecological and developmental dissimilarities between the two model organisms. The work presented in this thesis demonstrates that the microbiota acts as a source of stimuli that can alter host behavior. We have identified a novel role for Xi in modulating host locomotion and octopamine signaling in mediating microbial-induced changes in motor behavior. Additionally, our data lays the groundwork for future analysis into the mechanisms that underlie microbiota-related changes in locomotion. As we did not pinpoint the exact host molecular and neuronal pathways by which Xi signals, this is one important avenue for future research. The behavioral results from ablating Tdc-expressing neuronal populations within the central brain suggest their involvement; yet, central pattern generators within the ventral nerve cord could still contribute. Furthermore, parallel pathways by which Xi and the microbiota influence host locomotion may also exist. It is important to note that while we did not see any significant changes in the carbohydrates tested in the diet upon Xi* treatment, Xi could still act on carbohydrate ratios within the diet or the intestine of the fly. As select bacterial species and their products restore locomotion to conventional levels, our work also demonstrates the importance of examining contributions from individual components of the microbiota. The microbiome differs by laboratory, individual, temperature, and genotype; therefore, this reductionist approach helps to minimize variability due to changes in the microbial community. As with most experimental designs, there are limitations to these types of experiments that can be complemented by additional work. For example, results from monoassociation experiments may not directly correspond with effects under conventional conditions where there is increased microbial complexity. Combining experiments with mono-colonization or single metabolites with those using whole microbial communities provides insight into both the relative contributions of species and the natural environment. Furthermore, this thesis illustrates the importance of considering the whole organism, including its microbial counterparts, in behavioral analysis. In Brummel et al. (2002), the authors state: “Drosophila geneticists have paid relatively little attention to the bacterial flora within fly stocks, yet such flora may increase 108 phenotypic variation.” The tools for rigorous examinations of microbial communities and neuronal circuits have rapidly progressed, enabling studies revealing both a deeper understanding of the stimuli emanating from the microbiota and the corresponding host pathways mediating their potential behavioral and physiological effects. 109 REFERENCES 1. Markow, T. A. The secret lives of Drosophila flies. Elife 4, 1–9 (2015). 2. Keller, A. Drosophila melanogaster’s history as a human commensal. Curr. Biol. 17, 77– 81 (2007). 3. Engel, P. & Moran, N. A. The gut microbiota of insects - diversity in structure and function. FEMS Microbiol. Rev. 37, 699–735 (2013). 4. Fetissov, S. O. Role of the gut microbiota in host appetite control: bacterial growth to animal feeding behaviour. Nat Rev Endocrinol 13, 11-25 (2016). 5. Wong, A. 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