INSECT MICROBE INTERACTIONS: HONEY BEE ANTIVIRAL DEFENSE MECHANISMS AND CHARACTERIZATION OF SPIROPLASMA COLONIZING WHEAT STEM SAWFLY by Laura Marie Brutscher A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Microbiology and Immunology MONTANA STATE UNIVERSITY Bozeman, Montana July 2017 ©COPYRIGHT by Laura Marie Brutscher 2017 All Rights Reserved ii ACKNOWLEDGEMENTS First of all, I would like to thank my mentors Dr. Michelle Flenniken and Dr. Carl Yeoman. Specifically, I would like to thank Michelle for being a strong role model and working hard to help me grow as a scientist by always holding me to high standards. I would like to thank Carl for his ever-positive and encouraging mentorship. I am also grateful for all of my lab mates, both current and former, from the Flenniken and Yeoman labs for their friendship and support. I would also like to thank Project Apis m and Costco for the PhD in Honey Bee Biology Fellowship that helped supported my research efforts. Finally, I would also like to thank all of my friends and family both here in Bozeman and from afar for their love and encouragement. iii TABLE OF CONTENTS 1. HONEY BEE ANTIVIRAL DEFENSE ................................................................ 1 Background ....................................................................................................... 1 Understanding of Antiviral Defense Mechanisms in Fruit-Flies and Mosquitoes ....................................................... 4 Bioinformatic Comparison of Honey Bees, Fruit-Flies, and Mosquitoes ......................................................... 7 Honey bee Innate Immune Signaling in Antiviral Immune ............................................................................ 8 RNA Interference and Honey bee Antiviral Defense ....................................... 11 Non-Specific dsRNA-Triggered Virus Reduction ............................................. 16 Honey Bee Viruses and Other Stressors ........................................................ 18 Two Sides to The Story – Host Vs. Virus Genetics ......................................... 20 Conclusion ....................................................................................................... 22 Project Summary and Hypothesis Statement .................................................. 23 References Cited ............................................................................................. 29 2. VIRUS AND DSRNA-TRIGGERED TRANSCRIPTIONAL RESPONSES REVEAL KEY COMPONENTS OF HONEY BEE ANTIVIRAL DEFENSE ........................................................ 49 Contribution of Authors .................................................................................... 49 Manuscript Information .................................................................................... 50 Abstract ........................................................................................................... 51 Background ..................................................................................................... 52 Results and Discussion ................................................................................... 55 Virus Abundance is Reduced in dsRNA-Treated Honey Bees .................................................................... 57 Transcriptional Level Evaluation of Virus and dsRNA Induced Immune Responses in Honey Bees ............................... 59 Genes Differentially Expressed in Virus-Infected Bees and dsRNA-Treated Bees ........................................ 60 qPCR Validation of RNASeq Results ....................................................... 65 Differentially Expressed Genes in a Cellular Context ............................... 66 Reduced Expression Of dicer and cyclin-dependent kinase Enhanced Virus Abundance in Vivo and Confirmed Their Role in Limiting Virus Infection in Honey Bees ................................ 75 Synthesis of Honey Bee Transcriptional Response to Virus Infection ...................................................................... 77 Summary ......................................................................................................... 79 Methods ........................................................................................................... 81 iv TABLE OF CONTENTS CONTINUED Honey bees .............................................................................................. 81 Sindbis Virus (SINV-GFP) Infection Trials ................................................ 81 dsRNA Preparation ................................................................................... 83 dsRNA-Mediated Gene Knockdown ......................................................... 84 Fluorescence Microscopy ......................................................................... 84 Honey Bee Protein Lysate Preparation and Analysis ............................... 85 Honey Bee RNA Isolation and Purification ............................................... 86 Reverse Transcription / cDNA Synthesis ................................................. 86 Quantitative PCR (qPCR) ......................................................................... 86 RNASeq Library Preparation and Sequencing ......................................... 89 Comparative Analysis of DEGs in Virus-Infected Bees .............................................................................. 93 Identification of a Previously Unrecognized Transcript ............................. 94 Supplementary Figures ................................................................................... 96 References Cited ........................................................................................... 116 3.WHEAT STEM SAWFLY-MICROBIAL INTERACTIONS ............................... 128 Project Summary and Hypothesis Statement ................................................ 134 References Cited ........................................................................................... 137 4. IDENTIFICATION AND CHARACTERIZATION OF A SPIROPLASMA SP. (IXODETIS) ASSOCIATED WITH THE WHEAT STEM SAWFLY (CEPHUS CINCTUS) ................................... 142 Contribution of Authors .................................................................................. 142 Manuscript Information .................................................................................. 143 Abstract ......................................................................................................... 144 Main Text ....................................................................................................... 145 Supplementary Methods ............................................................................... 154 Sample Collection ................................................................................... 154 Sample Preparation and DNA Extraction ............................................... 154 16S rRNA Sequencing ........................................................................... 154 Sequencing Processing and Analysis .................................................... 155 Metagenomic Sequencing ...................................................................... 157 Metagenomic Sequencing Analysis ........................................................ 158 Phylogenetic Analysis ............................................................................. 159 SEM Sample Preparation ....................................................................... 160 Spiroplasma Culturing Attempts ............................................................. 161 GC/MS Analysis ...................................................................................... 162 Supplementary Figures ................................................................................. 165 v TABLE OF CONTENTS CONTINUED References Cited ........................................................................................... 183 APPENDICES ............................................................................................... 189 APPENDIX A: The Honey Bee Microbiome .................................................. 190 APPENDIX B: Assement Of The Relationship Between Honey Bee Microbiome And Virus Abundance In Individual Bees .................................................................. 199 APPENDIX C: Assessing The Relationship Between Honey Bee Microbiome, Virus Abundance, And Colony Health ................................... 208 APPENDIX D: Assessing The Effect Of Tylosin On Honey Bee Microbiome And Pathogen Profile .................................................................... 218 APPENDIX E: Honey Bee Microbiome Methods ........................................... 223 APPENDIX F: Table Of Live Bee Experiments From 2/5/12-9/1/16 ................................................................ 233 APPENDIX G: Transcriptome Analysis Pipeline ........................................... 236 APPENDIX H: Bacteriome/16S rRNA Sequencing Analysis ............................................................. 244 APPENDIX I: Metagenomic Sequencing And Genome Assembly Analysis Pipeline .................................................... 251 ALL REFERENCES CITED ............................................................................... 257 vi LIST OF TABLES Table Page 3.1. Currently available complete and draft genome sequences from arthropod-borne Spiroplasma strains ......................................................................... 135 3.2. Currently available 16S rRNA sequences from Ixodetis-Spiroplasma strains ........................................................... 136 4.1. 16S rRNA OTU composition of wheat stem sawflies collected from grass and wheat populations in Montana and at both female adult and larval stages ..................................................................... 166 4.2. PATRIC annotated feature table of coding sequences from draft WSS-associated Spiroplasma ......................................................... 167 4.3. Genes used for phylogenetic analysis of 16S rRNA sequences .................................................................. 176 4.4. Table of metabolic pathways associated with PATRIC annotated gene features of draft WSS-associated Spiroplasma gene set .......................................... 177 4.5. Primers used to amplify and prepare V3-V4 of 16S rRNA libraries in WSS samples ........................................... 178 4.6. MiSeq read yields from 16S rRNA sequencing ............................... 179 4.7. MiSeq read yields from metagenomic sequencing .......................... 180 4.8. List of Spiroplasma genomes used for Blastn comparison and phylogenetic tree comparison ............................................................................... 181 4.9. Relative metabolite concentrations in cultures over four time points and in three replicates .................................... 182 C.1. Colony sample dates and 16S sequencing read yields ............................................................ 211 vii LIST OF TABLES CONTINUED Table Page E.1. Primers used for PCR pathogen screening and qPCR quantification ................................................. 230 viii LIST OF FIGURES Figure Page 1.1 Honey Bee Immune Pathways - Highlighting Genes Implicated in Antiviral Immune Responses ......................................... 26 2.1 Schematic of honey bee virus infection trials. .................................... 57 2.2 Relative virus RNA abundance was reduced in dsRNA-treated bees as compared to virus-infected bees.. ...................................................... 59 2.3 Honey bee transcriptional response to virus-infection and dsRNA-treatment is time-dependent ............................................................................... 62 2.4 Differentially expressed genes in response to virus and/or dsRNA treatment in a cellular context. ............................................................................ 67 2.5 Reduced expression of two honey bee resulted in increased virus abundance ............................................................. 76 2.6 Venn diagram of shared and unique DEGs in bees infected with viruses from this and other studies. ................................................................ 79 2.7 Supplementary. Fluorescence microscopy of virus-infected bees indicates that dsRNA reduced virus abundance. ....................... 96 2.8 Supplementary. Viral GFP production was reduced in dsRNA treated honey bees... ............................................ 97 2.9 Supplementary. Virus abundance in honey bees increased with time post-infection. ..................................................... 98 2.10 Supplementary. Relative virus RNA abundance was reduced in dsRNA-treated bees, as compared to virus-infected bees, in three biological replicates. ............................ 99 2.11 Supplementary. Venn diagram of shared and unique DEGs in virus-infected and/or dsRNA-treated bees ....................... 101 ix LIST OF FIGURES CONTINUED Figure Page 2.12 Supplementary. Gene ontology enrichment analysis of DEGs determined that biological processes including phosphorylation, morphogenesis, transcription, and development were differentially regulated in virus-infected bees 6, 48, and 72 hpi .......................... 103 2.13 Supplementary. qPCR analysis of a subset of DEGs confirms RNAseq results in bees at 48 and 72 hpi .............................................................................. 105 2.14 Supplementary. qPCR analyses confirmed differentially expression of putative antiviral genes in bees from different genetic backgrounds ......................... 107 2.15 Supplementary. Identification of previously unrecognized honey bee transcript, A. mellifera probable cyclin-dependent serine/threonine-protein kinase transcript (MF116383) .................................................................... 112 2.16 Supplementary. RNAi-mediated gene knockdown of dicer and cyclin-dependent kinase was efficient at 48 and 72 hpi and resulted in increased virus abundance ............................................................. 114 4.1 Wheat stem sawflies are primarily colonized by Spiroplasma ................................................................. 148 4.2 Spiroplasma 16S rRNA UPGMA Phylogentic Tree. ......................... 151 4.3 SEM Images of WSS-associated Spiroplasma Indicate it is Non-Helical .............................................. 153 4.4 Supplementary. UPGMA Phylogentic Tree of concatenated 16S rRNA, DNA-directed RNA polymerase, RecA, and FtsZ ................................................... 165 x LIST OF FIGURES CONTINUED Figure Page B.1 Percent relative 16S rRNA OTU abundance averaged across mock-infected and virus-infected newly emerged workers ............................................. 202 B.2 16S rRNA profiles do not correlate with virus load in newly emerged workers 72 hpi ............................................ 202 B.3 Percent relative 16S rRNA OTU abundance averaged across mock-infected and virus-infected adult bees ................................................................... 203 B.4 Percent relative 16S rRNA OTU abundance of individual mock-infected and virus-infected adult bees ................................................................... 203 C.1 Lake Sinai virus 2 abundance trends with colony health .................................................................. 210 C.2 Relative abundance of OTUS (16S rRNA profiles) of pooled samples (five bees per sample) collected from weak and strong colonies ........................................................ 212 C.3 The 16S rRNA profiles of samples collected from weak and strong colonies clusters by time point ............................. 214 C.4 The 16S rRNA profiles of samples collected from weak and strong colonies averaged by time point .................................................................................... 215 xi ABSTRACT Insects play important roles in ecosystems throughout the world. There are many beneficial insects, including those that pollinate plants in diverse landscapes, while other insects are considered agricultural pests. Regardless of ecological role, insects are hosts for microbial symbionts and pathogens. Some microorganisms (e.g., viruses) are harmful to insect health, but many microbial symbionts aid in host biological processes. The projects herein describe the interplay between insects and microbes; specifically (1) honey bee host – virus interactions and (2) identification and characterization of a wheat stem sawfly- associated Spiroplasma. Honey bees (Apis mellifera) are pollinators of numerous agricultural crops and other plant species. Since 2006, there have been high annual losses of honey bee colonies in the U.S. (~33%) and throughout the world. Colony deaths are influenced by multiple factors including RNA virus infections. Honey bee antiviral defense involves several immune pathways, including dsRNA mediated responses, (i.e., RNA interference and non-sequence-specific dsRNA-triggered responses), but their relative importance in antiviral defense is not well understood. To investigate honey bee antiviral defense, bees were infected with model virus in the absence or presence of dsRNA, which reduced virus abundance. Transcriptome-level analysis determined hundreds of genes were differentially expressed in response to co-treatment of dsRNA and virus, including immune-related genes. RNAi-mediated gene knockdown of two putative antiviral genes increased virus abundance and supported their antiviral role. Additional investigation of these and other genes will improve our understanding of dsRNA-mediated antiviral defense in honey bees. In contrast, wheat stem sawflies (Cephus cintus) are major wheat pests in the Northwest United States. Strategies that target endosymbionts of sawflies could reduce wheat crop losses. Hereunto, the microbes that colonize wheat stem sawfly have not been explored. Targeted DNA sequencing determined sawflies were colonized by a Spiroplasma species that has greatest 16S rRNA sequence identity with Ixodetis clade species. Metagenomic sequencing identified several Spiroplasma encoded genes involved in metabolism, which may be important to the sawfly host. Further characterization of honey bee-virus interactions and the role of Spiroplasma in sawfly health may contribute to limiting threats to global crop production and will further scientific understanding of non-model insect-microbe interactions. 1 HONEY BEE ANTIVIRAL DEFENSE Background Honey bees (Apis mellifera) are fascinating insects that play a critical role in agriculture as pollinators of crops (U.S. value over $14.6 billion/year) and plant species that enhance the biodiversity of both agricultural and non-agricultural landscapes1. Since 2006, honey bee populations in the U.S., Canada, and Europe have experienced high annual losses2–4. An average of 33% of U.S. honey bee colonies die each year, and a fraction of these losses are attributed to Colony Collapse Disorder (CCD)5–9. Multiple biotic and abiotic factors contribute to colony health and survival (i.e., viruses, mites, microbes, bee genetics, weather, forage quality and availability, management practices, and agrochemical exposure)9–12. Understanding the most influential factors and potential synergistic effects on honey bee health is critical to developing pollinator management and conservation strategies that limit bee colony losses13. Several epidemiologic and temporal monitoring studies indicate the important role of pathogens in colony loss including viruses, bacteria, fungi, trypanosomatids, and mites2,4,9,11,14–27. Pathogen incidence and abundance have been positively correlated with Colony Collapse Disorder (CCD)-affected colonies in the U.S.9,11,14 and colony losses in different regions of North America, South America, and Europe2,15–17,22–27. The majority of honey bee infecting pathogens are single-stranded positive sense RNA viruses, including the dicistroviruses: 2 Israeli acute paralysis virus28, Kashmir bee virus29, Acute bee paralysis virus30, Black queen cell virus31 and Big Sioux River virus16,19, and Aphid lethal virus (strain Brookings)16,19 and the Iflaviruses: Deformed wing virus (strains A and B)32,33, Kakugo virus34, Varroa destructor virus-1, Sacbrood virus35, and Slow bee paralysis virus36, as well as taxonomically unclassified viruses such as Chronic bee paralysis virus37, Cloudy Wing virus38, and the Lake Sinai virus group11,16,19,22,25. Honey bee virus infections may cause deformities, paralysis, death, or remain asymptomatic39. Honey bee viruses are transmitted both and vertically (i.e., from queen bees to offspring) and horizontally40–43. Virus transmission within a colony is enhanced by crowded conditions and the routine transfer of nectar, pollen, and bee bread between colony members via mouth to mouth feeding (trophallaxis)39. Several viruses have been detected in pollen and honey, and virus infections have been transferred from these substrates to uninfected queen bees and subsequently detected in eggs and progeny41. While bee- infecting viruses are primarily studied in honey bees, and thus upon discovery are called “honey bee viruses”, many of these viruses infect other bee species41,44,45. Intra-species and inter-species transmission of bee-associated viruses occurs in natural settings where viruses are spread via floral resources41,46. The ectoparasitic mite Varroa destructor serves as a major vector for several honey bee viruses40,47,48 and causes colony loss by parasitism of bee 3 brood and adults49. Several studies indicate that combinatorial effects of mites and viruses result in colony loss (reviewed in50–53). The relationship between colony health and pathogen prevalence and abundance is complex and dependent upon season, geographic location, pathogen strain, and both individual and colony level bee immune responses. Honey bees, like all other organisms, have evolved mechanisms to detect and limit virus infection. Knowledge of honey bee immune mechanisms is largely derived via comparison to the better-characterized immune responses in fruit- flies (i.e., Drosophila melanogaster) and mosquitoes (i.e., Aedes spp.). While comparative genomics is a useful approach for evaluating honey bee immune gene function, it is important to note that Western honey bees (Apis mellifera) are eusocial Hymenopteran insects, an order that diverged from the solitary Dipteran insects including fruit-flies and mosquitos approximately 300 million years ago54– 57. General aspects of immunity, including detection of pathogen associated molecular patterns (PAMPs) and production of effector molecules are conserved in mammals, plants, and insects, and both plants and insects employ RNA interference (RNAi) as a major mechanism of antiviral defense58–60. These immune pathways provide a framework for understanding honey bee host – virus interactions. Further characterization of honey bee antiviral defense may lead to the development of strategies that reduce virus infection in honey bees and virus- associated colony losses. 4 Understanding of Antiviral Defense Mechanisms in Fruit-Flies and Mosquitoes Insect immune responses include melanization, encapsulation, reactive oxygen species production, RNA interference, and activation of signal transduction cascades that result in the production of antimicrobial peptides (AMPs) and other effector proteins (Figure 1.1). These pathways include the Toll, Imd (Immune Deficiency), JNK (c-Jun N-terminal kinase), and Jak-STAT (Janus kinase and Signal Transducer and Activator of Transcription) innate immune response pathways (Figure 1.1) (reviewed in59,61–65). There are numerous orthologous proteins utilized in plant, insect, and mammalian immune defense mechanisms (reviewed in58,66), and discovery of the Drosophila Toll pathway led to the identification of a repertoire of mammalian Toll-like receptors (TLRs) (reviewed in66,67). The importance of the Toll, Imd, Jak-STAT, and other innate immune pathways in antiviral defense is variable and specific to individual virus-host interactions62,64. For example, the Toll pathway is involved in D. melanogaster and Aedes aegypti defense against Drosophila X virus68 and Dengue69, respectively, as dif loss of function mutants were more susceptible to virus infection. The Drosophila Imd pathway plays a larger role than the Toll pathway in limiting Sindbis virus70 and Cricket paralysis virus (CrPV)71, and the Jak-STAT pathway is critical to combating Drosophila C virus infection72. Antimicrobial peptides (AMPs) are small cationic peptides that penetrate microbial membranes and play additional uncharacterized functions (reviewed in63). While the role of 5 AMPs in virus infection is not known, changes in AMP expression are used as indicators of immune pathway regulation. Induced expression of AMPs in D. melanogaster varies, as some viruses induce expression (i.e., DXV and SINV) and others do not (i.e., CrPV and Rhabidovirus73). Numerous studies suggest the role of additional pathways in insect antiviral defense65,72–76. RNA interference is a mechanism of post-transcriptional gene silencing conserved across several phyla that encompasses three distinct pathways (reviewed in60,77), including the short interfering RNA (siRNA), microRNA (miRNA) pathway, and piwi-interacting RNA (piRNA) pathways. Each of these pathways is characterized by its unique biological function and involvement of distinct proteins. The siRNA pathway is involved in antiviral defense in plants and in the primary antiviral defense mechanisms in insects, but the function of the siRNA pathway in mammalian immunology is debated (reviewed in78–84). This pathway is triggered by cytosolic dsRNA that is either produced by replicating viruses or experimentally introduced. Double-stranded RNA is recognized and cleaved by the RNAse III enzyme, Dicer (Dicer-2 in Drosophila74,85 and Dicer in Apis mellifera54) into 21-22 bp short interfering RNAs (siRNAs) (reviewed in86) (Figure 1.1). Short interfering RNAs are short dsRNAs with 5'-monophosphate ends and two nucleotide overhangs at their 3' hydroxyl-termini (reviewed in87). The siRNAs are subsequently bound by Argonaute 2 (AGO2), an endoribonuclease and catalytic component of the multi-protein RNA-inducing silencing complex (RISC). One strand of the siRNA, the passenger strand, is 6 then released, leaving the other strand, the guide strand, to target complementary viral and transposon sequences for cleavage (reviewed in87). The Piwi-interacting RNA- and Micro-RNA- mediated RNAi pathways also aid in antiviral defense, but likely to a lesser extent. Micro-RNAs are derived from endogenous nuclear-encoded short-hairpin RNAs that are processed into shorter hairpin RNAs (pre-miRNA), cleaved by Dicer into 21-22 bp segments in the cytosol, and incorporated into RISC (reviewed in87). The miRNA-containing RISC primarily targets complementary host-encoded mRNA transcripts for degradation or translational inhibition. Conversely, miRNAs can also serve to induce transcription and translation of mRNA, reduce nonsense-mediated RNA decay, and improve mRNA stability (reviewed in88). MicroRNAs can function in antiviral response via targeting of viral nucleic acid and host gene regulation (reviewed in88). Piwi-interacting RNAs, which are larger than siRNAs and miRNAs (24-32 nucleotides (nts)), are generated in a Dicer-independent manner from single- stranded RNA precursors transcribed from genomic regions (reviewed in86,89). Piwi-interacting RNAs are involved in transposon silencing, epigenome regulation, and antiviral defense (reviewed in86,89). Direct evidence of the antiviral role of siRNA-mediated RNAi in insects has predominantly come from studies in Drosophila melanogaster, Aedes aegypti, and Anopheles gambiae, which involved experimental infections via injections with pure virus inocula, mutant-flies, or gene knock-down in mosquitos85,90–93. Likewise, field and laboratory based studies in Apis mellifera (Western honey 7 bee)94–99 and Apis cerana (Eastern honey bee)100 indicate that RNAi-mediated antiviral immunity is important in honey bees (reviewed in101). In addition, dsRNA may serve as a non-sequence-specific virus associated molecular pattern (VAMP) that triggers innate antiviral immune pathways in fruit-flies74 and honey bees102,103, similar to the mammalian interferon response104 (Figure 1.1). These studies in fruit-flies and mosquitoes, describing both the roles of RNAi and innate immune signaling in response to virus infection, serve as important platforms for understanding antiviral defense in the less well-characterized honey bee. Bioinformatic Comparison of Honey Bees, Fruit-Flies, and Mosquitoes Bioinformatic analysis of the honey bee genome identified A. mellifera orthologs of insect immune genes and suggests that bees have fewer immune genes55. Specifically honey bees have fewer genes encoding fibrogen domain- containing proteins, serpins, antimicrobial peptides, and other known classes of immune signaling proteins as compared to D. melanogaster or Ae. anopheles55. The honey bee genome encodes for all the main components of the Toll, Imd, JNK, Tor, and Jak-STAT immune pathways (except upd), as well as immune effector proteins including AMPs (i.e., abaecin, hymenoptaecin, apidaecin family members, and defensin) and prophenoloxidases55,105 (Figure 1.1). Likewise, honey bees encode a functional suite of genes required for RNAi including dicer- 1, ago-2, r2d2, and dicer-like, which shares 30% nucleotide identity with Dm dicer-254,57,106. The RNAi, Toll, Imd, endocytosis, MAPK, and non-specific 8 dsRNA-mediated immune pathways have been implicated in honey bee antiviral defense (Figure 1.1). Honey Bee Innate Immune Signaling in Antiviral Defense Several studies in honey bees implicate the involvement of uncharacterized genes/pathways in antiviral responses17,26,99,102,107. However, the regulation of genes in the Toll, Imd, Jak-STAT, JNK, and RNAi pathways are the best characterized. Central players in honey bee immune signal transduction cascades include insect orthologs of a well-characterized mammalian transcription factor NF-κB, including Dorsal-1A, Dorsal-1B, and Relish (Figure 1.1). Nazzi et al. determined that dorsal-1A expression is key in limiting DWV infection26. Activation of NF-κB-family transcription factors results in the production of AMPs, which have unknown roles in antiviral immunity, and numerous other less well-characterized genes18,55,108–110. Symptomatic young bees experimentally infected with IAPV via feeding exhibited increased expression of Toll pathway members (i.e., toll-6, cactus, and hymenoptaecin)107, whereas transcriptional profiling of IAPV positive bees from naturally infected colonies did not implicate either the Toll or Imd pathways in antiviral defense17. Young bees experimentally infected with Sindbis virus via injection and harboring very low levels of other bee pathogens expressed less apidaecin and hymenoptaecin than mock-infected controls102. Similarly, neither ABPV-challenge nor ABPV and E. coli co-challenge via injection resulted in AMP production (i.e., 9 Defensin-1, Abaecin, and Hymenoptaecin) at the protein level in adults or larvae, indicating that ABPV may suppress bee immune responses111. There are few general trends in the transcriptional response of honey bees to viruses due to the small number of studies performed to date and experimental differences including: virus-challenge methodologies (e.g., infection via injection, oral infection), experimental vs. natural infections, tissues examined, post-infection assay time, and developmental stage of the bee17,51,99,100,102,107,112–115. Furthermore, variability between experimentally infected-bees may be attributed to differences in immune gene regulation of individual bees (both within and between colonies), purity and strain of virus inoculum (i.e, IAPV17,107, DWV99,115, SBV115, Sindbis virus102), varied microbiomes, and prevalence of pre-existing pathogens (reviewed in105). In addition, there are relatively few predicted genes (~25%) that are involved in well-annotated pathways; 33% of the DEGs in naturally IAPV-infected adults had Drosophila orthogs and could be assigned putative function17. That said, differential expression of genes in immune, endocytic, and metabolic pathways are common to several data sets, but the directionality of regulation varies between studies and bee developmental stage17,102,107. Several investigations have focused on IAPV due to its association with colony health and the development of methods to produce IAPV-augmented infectious stocks via passaging bee viruses in pupae113. In adult bees, IAPV abundance is highest in the gut and hypopharyngeal gland and low in hemocytes (insect blood/immune 10 cells) and the fat body, a tissue involved in metabolic activities (insect liver)17,116– 118. Transcriptional profiling of IAPV-infected adults revealed differential expression of over 3,000 genes17. Functional analysis determined that genes involved in signal transduction and immune responses exhibited increased expression and that genes involved in metabolism and mitochondrial dysfunction had reduced expression17. In addition, IAPV-infection resulted in increased expression of genes involved in the TCA cycle II, protein ubiquitination, and eIF2 signaling, and that IAPV-infection reduced expression of genes in the γ-glutamyl cycle17. Chen et al. determined that IAPV-infection also perturbed expression of genes involved in insect immune pathways (i.e., oxidative phosphorylation, ABC transporter function, endocytosis, phagocytosis, TGF-beta signaling, Tor signaling, MAPK signaling, Jak-STAT signaling, and lysosomal degradation)17. Specific immune genes with increased expression in IAPV-infected adult honey bees include Jak/STAT pathway members (i.e., cbl, stat, pias, and hopscotch), Tor pathway members (i.e., gbl, mo25, dmel, and eIF4B), MAPK members (i.e., pointed, phi, and corkscrew), and genes involved in endocytosis (i.e., egfr, pastI, rabenosysn, and vacuolar protein sorting-associated protein 37B-like)17 (Figure 1.1). It is noteworthy that IAPV-infected larvae had a different suite of DEGs with little overlap in the adult dataset17. Pupae infected with IAPV exhibited variable expression of ribosomal RNAs and increased expression of ribosomal protein S5a (RPS5), and glutathione S-transferase 1113; bees from CCD-affected colonies also had increased rRNA expression114. The transcriptional profiles of 11 the fat bodies from young, IAPV-infected worker bees107 shared the most genes with IAPV-infected adult bees17, and had little overlap with DEGs in bees infected with either E. coli bacteria119 or microsporidia (Nosema spp.)120, indicating that honey bee antiviral responses are distinct from immune responses mounted against other parasites. Increased expression of argonaute-2 and dicer-like in response to IAPV-infection also supports the role of a distinct antiviral response involving RNAi, Toll, and Jak-STAT pathways107. More recently, Doublet et al. reanalyzed 19 gene expression data sets of Varroa destructor-parasitized and virus-infected honey bees and produced a ranked list of commonly differentially expressed genes. This meta-analysis determined that genes from the Imd (iap2 and rel) and Toll pathways (tube and def-2) were consistently differentially expressed in the majority of honey bee transcriptome studies121. The research performed to date is informative, but additional studies are needed to better understand honey bee antiviral immune mechanisms at the transcriptional level (e.g., mechanisms of regulation of gene expression and the role of splice variants) and beyond. RNA Interference in Honey Bee Antiviral Defense A distinguishing feature of virus infection is the presence of long, double- stranded RNA molecules in the cytosol of the infected cell. Importantly, long dsRNAs also serve as the substrate for siRNA-RNAi-mediated antiviral responses. RNA interference-mediated gene knockdown has been used to study 12 gene function in different honey bee developmental stages, including embryos122–127, larvae124,128–135, pupae124,136,137, and fully-developed adult honey bees138–152 (reviewed in 153), demonstrating that the RNAi machinery is functional in honey bees. Initial studies implicating the role of RNAi in honey bee antiviral defense demonstrated that feeding sucrose solutions containing IAPV-specific dsRNA resulted in increased bee survival, lower levels of IAPV94, larger colony size, and increased honey yields97. This also sparked commercial interest in dsRNA / RNAi-mediated antiviral treatments97 and raised concerns regarding potential off-target effects and the use of RNAi-based crop treatments, including genetically engineered insecticidal crops154. A subsequent laboratory-based study demonstrated that pre-treatment of larvae and adults with DWV-specific dsRNA prior to DWV-infection via feeding resulted in increased survival and decreased virus titers95. Likewise, Apis cerana larvae pre-treated with virus- specific dsRNA had reduced levels of Chinese Sacbrood virus following infection via feeding100. One of the hallmarks of RNAi-mediated antiviral responses in insects is siRNA production. The first molecular evidence of virus-specific siRNAs in honey bee samples was obtained by Northern blot analysis94. Recently, Chejanovsky et al. evaluated siRNA populations isolated from bees obtained from either CCD- affected or unaffected colonies using high throughput sequencing and determined that there were more virus-specific (i.e., IAPV, KBV, and DWV) siRNA reads in CCD-affected samples96. These siRNAs were predominantly 22- 13 nt long and distributed throughout the virus genomes96, indicating that the dsRNA replicative intermediate forms of the viral genomes were the Dicer substrate (reviewed in86). Further analysis of the IAPV-siRNAs from CCD-affected samples determined that most were negative-sense, and may thus serve as guide sequences that target the (+)ssRNA IAPV genome96. High throughput sequencing of small RNAs obtained from Varroa-infested, DWV-like, and VDV-1- infected bees identified a greater number of positive sense virus-specific siRNAs than negative sense siRNAs, and showed that DWV-like virus and siRNA abundance were proportional99. Interestingly, pupae with low virus levels that were exposed to few Varroa mites had five-times more siRNAs than viral genomes, suggesting that when mite-pressure was low, the honey bee RNAi- mediated defense system was able to overcome virus replication99. The dsRNA uptake mechanisms in insects and their relationship to systemic RNAi and antiviral defense are not completely understood. Current studies suggest that there are at least two mechanisms of dsRNA uptake in insects: transmembrane channel-mediated uptake (reviewed in155) and endocytosis-mediated uptake93,156,157. SID-1 (systemic RNA defective), a dsRNA- transporting transmembrane protein originally identified in C. elegans158,159, has been implicated in facilitating systemic RNAi in honey bees; bees injected with dsRNA had over three times greater expression of SID-1 than controls160. C. elegans also encodes additional SID proteins, SID-2, -3, and -5, which have also been implicated in dsRNA uptake but have not been identified in the honey bee 14 genome161–163. Honey bees encode for one SID-1 ortholog with two protein isoforms (XP_006565236.1 and XP_006565237.1), which both share ~ 25% amino acid identity with the C. elegans SID-1 (NP_504372.2)164. In addition, transgenic Drosophila S2 cells expressing the C. elegans SID-1 protein had improved dsRNA uptake159. Interestingly, SID-1 is not present in all insect genomes (reviewed in155), including Drosophila77, and is not required for systemic RNAi in locusts165, indicating that additional molecules are likely involved in this process. Proteins involved in phagocytosis and endocytosis may function in dsRNA uptake, as the scavenger receptors SR-CI and Eater and the endocytosis-associated proteins Clathrin heavy chain and H+ ATPase are important for dsRNA uptake in Drosophila S2 cells156,157. Investigating dsRNA uptake and systemic RNAi will be an important step towards further characterizing honey bee antiviral response. Double-stranded RNA treatment of honey bees has also been employed to reduce gene expression in honey bee-associated parasites including the microsporidia Nosema ceranae166 and the ectoparasitic mite Varroa destructor167. Honey bees inoculated with N. ceranae spores and fed dsRNA targeting Nosema-specific ADP/ATP genes had reduced Nosema spore count, and Nosema had lower expression of the targeted genes166. Likewise, when bees were fed dsRNA targeting mite sequence-specific housekeeping genes, mites had lower levels of the targeted-transcripts167. Interestingly, long, unprocessed dsRNA were detected in bee hemolymph three days post-feeding 15 dsRNA167. The biological relevance of dsRNA as a systemically active molecule in naturally infected honey bees is unknown, but it is remarkable that orally introduced dsRNA remains stable enough to spread throughout the honey bee host and into associated parasites166,167. It is interesting that several studies have demonstrated that dsRNA or siRNA feeding is an effective strategy to reduce virus loads in both larval-stage and adult bees, while achieving effective in vivo gene silencing is difficult in mammalian model systems (reviewed in168). Tail-vein injections of siRNA in post- natal mice have been an effective strategy for gene knockdown169, but overall systemic siRNA delivery into mammalian systems often requires siRNAs with chemical modifications such as lipophilic conjugates or nanoparticle mediated delivery (reviewed168,170). Preliminary results on the effects of RNAi-mediated treatment of honey bee viruses and parasites are promising, but additional investigation is required to better understand the feasibility, effectiveness, and risk of off-target effects. Additionally, it will be important to develop methods to functionally test the role of the RNAi machinery via gene knockout/knockdown. Genome integration of IAPV also requires further examination171. Both genome- integrated RNA viral sequences, putatively encoding for target nucleic acid or reverse-transcriptase, and RNAi are involved in limiting and maintaining persistent virus infections in D. melanogaster77,93. These and other studies will reveal the role of RNAi in honey bee antiviral defense. 16 Non-Specific dsRNA Triggered Virus Reduction in Honey Bees Since long dsRNAs are not typical products of eukaryotic gene expression, these molecules are recognized as pathogen-associated molecular patterns (PAMP) in hosts including plants, arthropods, insects, and mammals172. Mammals have several receptors (e.g., TLR3, PKR, RIG-I, MDA-5) that upon binding dsRNA — in a non-sequence-specific fashion — activate signal transduction cascades, resulting in the transcriptional activation of genes involved in generating an “antiviral state” including thousands of interferon stimulated genes (reviewed in104,173). Similarly, in addition to inducing RNAi, dsRNA may also engage a previously uncharacterized non-sequence-specific immune pathway in honey bees102. Bees co-injected with Sindbis virus (SINV) and virus sequence-specific dsRNA or non-sequence-specific dsRNA had similarly decreased viral titers as compared to bees injected with virus only102. Likewise, adult bees treated with non-virus specific dsRNA (i.e., GFP (green fluorescent protein)-targeting) and infected with DWV had a greater rate of survival as compared to DWV-infected bees that received no dsRNA-treatment95. In addition, experimental introduction of non-specific dsRNA alone in honey bees perturbs honey bee gene expression102,103,174. Transcriptome analysis of honey bee larvae fed GFP-targeting dsRNA revealed ~1,400 differentially regulated genes (DEG)103. Nine genes had sequence similarity with 21 nt regions of GFP, indicating off-target RNAi103. However, most DEGs did not share sequence similarity with dsRNA-GFP, and were reported to function in oxidoreductase 17 activity, aging, cell homeostasis, morphogenesis, response to external stimulus and stress, and immune response103. Also, bees injected with non-sequence- specific dsRNA had differential expression, including decreased expression of several apidaecin AMP family members102. In a recent study that examined the role of RNAi-mediated antiviral defense in bumblebees (i.e., Bombus terrestris), adults fed non-sequence-specific dsRNA had increased survival when infected with IAPV and similar virus titers as compared to bees fed dsRNA targeting IAPV175. Together these results suggest that honey bees and other members of the Apidae family may have an alternative dsRNA-stimulated immune pathway akin to the interferon response in mammals. Mammals have dsRNA recognition receptors such as Toll-like receptor 3 (TLR3), Protein kinase R (PKR), Retinoic acid-inducible gene 1 (RIG-I), and Melanoma-differentiation-associated gene 5 (MDA-5), that when activated, induce expression of numerous genes that contribute to an antiviral state (reviewed in104). Analogously, non-sequence- specific dsRNA-mediated immune pathways may be important for antiviral defense of honey bees. Non-specific dsRNA-triggered antiviral immunity has also been observed in other arthropods including, Chinese oak silk moth pupae176, shrimp177–181, Bombyx mori larvae182, and sand-fly cells183, implicating dsRNA as a viral pathogen associated molecular pattern (PAMP or VAMP). In addition, there is evidence that Dicer-2 serves as a dsRNA pathogen recognition receptor (PRR) in both D. melanogaster74 and Culex pipiens f. molestus mosquito cells184. When 18 bound with dsRNA, Dicer-2 stimulates a signal-transduction cascade that results in increased expression of vago and Jak-Stat pathway genes (reviewed in59,64). Intriguingly, larvae orally infected with DWV from Varroa destructor mite infested colonies had significantly greater expression of the honey bee ortholog of vago as compared to control larvae from colonies with lower mite pressure99. Though application of non-sequence specific dsRNA does not always improve survival or reduce viral titer in virus infected bees94,100, it is important to further examine the mechanisms involved in non-sequence-specific dsRNA-mediated antiviral immunity in honey bees. Honey Bee Viruses and Other Stressors Honey bees live in a complex environment, so the effects of viruses on bees, and the functionality of the bee immune responses, may be co-infection by other pathogens11,18,19, the microbial context of infection (microbiome185–189), environmental factors including agrochemical exposure109,190–193 and adequate nutrition194–196. Several studies indicate that bees infected with multiple pathogens have increased mortality and CCD-affected samples have a greater number of pathogens than control colonies9,11,14. While it is widely accepted that mite infestation is detrimental to honey bee colonies and that mites also serve as virus vectors26,47,51,197,198, the mechanism(s) of synergistic detrimental interactions have not been fully elucidated26,50–52,110. 19 Nazzi et al. investigated the combinatorial effects of mites and virus in both field and laboratory settings from the colony to the molecular level26. They determined that high mite infestation coupled with increasing levels of DWV from June to October resulted in increased colony mortality26. Transcriptome (RNASeq) analysis of adult bees in these colonies revealed lower expression of 19 immune genes including dorsal-1A, pathogen recognition receptors (AmSCR, B5 and B7 scavenger receptors, and C-type lectin 8), and immune signaling pathway members including hem, tak1, and socs26 (Figure 1.1). Bees from colonies with both high mite and DWV levels exhibited increased expression of other immune genes including genes involved in pathogen recognition (PGRP- S2, nimC2, eater-like) and serine proteases26. Laboratory experiments confirmed that a combination of mites and DWV, but not mites alone, reduced dorsal-1A expression in adult bees26. Also, larvae in which dorsal-1A expression was reduced by RNAi-mediated knock-down harbored a greater number of DWV genome copies26. Recent studies by Kuster et al. demonstrated that DWV virus abundance increased up to 72 hours post experimental wounding or Varroa mite exposure51. Assessment of the transcriptional responses to wounding and mite exposure at times ranging from 24 – 240 hours post-capping demonstrated increased expression of immune genes (i.e., abaecin, apidaecin, defensin, hymenoptaecin, PGRPs, PPOact, and relish) and DWV infection (up to 72 hours) and reduction of mite numbers in conjunction with immune activation51. Cluster analysis suggested co-regulation of defensin and relish, and apidaecin and 20 hymenoptaecin, whereas abaecin and PPOact were not associated with other immune gene regulation51. Interestingly, results to date indicate that mite- mediated transmission of DWV results in increased levels of DWV-like virus strains with a VDV-1 CP coding region99. The interactions between the honey bee host, Varroa destructor, and viruses are not fully understood and require further investigation. Since honey bee colonies located in Newfoundland and Labrador, Canada199, and several Hawaiian islands lack V. destructor200, these populations provide unique opportunities to examine the effects of viruses on colony health and immune regulation. Two Sides to The Story – Host Vs. Virus Genetics The genetic background of the host has implications on susceptibility to virus infection and disease severity. This is particularly relevant for honey bees as they live in colonies of ~ 30,000, the majority of which are sterile, genetic-half sisters, since queens typically mate with 12 drones201. Colony level diversity due to queen polyandry reduces the prevalence of honey bee diseases113,201–205, and may result in varying transcriptional responses, variation between individual hemocyte populations, and differences in social immune mechanisms (e.g., grooming behavior, propolis production)116,206. Moreover, genetic diversity is not limited to the host, as the majority of honey bee viruses are RNA viruses with error prone polymerases that generate virus quasispecies over the course of infection207. Different virus variants within particular quasispecies populations 21 may have greater or lesser pathogenicity in a particular host organism. In addition, different strains of honey bee viruses exhibit differential pathogenicity (i.e., DWV and IAPV)17,99,200. Recent studies determined that DWV strain prevalence was reduced in the presence of mites200 and the recombinant strain of DWV, DWVv, is more virulent than other DWV-like viruses99. Furthermore, like many insect-infecting viruses, some honey bee viruses have likely evolved specific mechanisms to counteract RNAi-mediated antiviral defense, including virus-encoded suppressors of RNAi (VSR). For example, the B2 protein dimer encoded by Flock house virus binds dsRNA, subsequently preventing Dicer-2 from cleaving long dsRNA208,209 and siRNA from loading into RISC209. Dicistroviruses encode protein 1A, a VSR whose mode of action (e.g., binds to Dicer-2 or AGO2) and efficacy in inhibiting the RNAi pathway varies by virus (reviewed in59). Based on analysis of VSR-expressing viruses (i.e., Drosophila C virus and Cricket paralysis virus), the presence of the highly conserved DvExNPGP motif and the coding sequences upstream of it in a viral genome are indicative of the ability to express VSR proteins210,211. Sequence analysis revealed that the honey bee dicistroviruses IAPV, KBV, and Acute bee paralysis virus (ABPV) contain a DvExNPGP motif at the 5’ terminus of their genomes, suggesting these honey bee-infecting viruses may encode a VSR17. Experimental feeding of naturally IAPV-infected bees with siRNAs targeting the putative IAPV-encoded RNAi suppressor decreased IAPV loads at least three times more than treatment with siRNAs targeting the IAPV IRES17,212. Better 22 understanding of the importance of RNAi in honey bee antiviral defense and the means by which viruses may evade the honey bee antiviral response will facilitate the manipulation of these mechanisms in the lab as well as their potential application in the field setting. A greater appreciation of the existing virus genomic diversity across the globe is needed to better evaluate the effects of distinct virus strains on colony health. The development of infectious virus clones that are amenable to mutation (reverse genetic systems) are needed to verify strain-specific virulence and determine mechanism(s) of enhanced virulence or increased tolerance. Honey bees may vary in their degree of virus tolerance64,213. This should be explored at both the individual and colony levels, since the information gained may guide the use of virus susceptibility as an additional selectable trait in honey bee breeding programs206,214,215. In addition, further use and development of immortalized honey bee lines (i.e., AmE-711)216,217, long-term cell cultures218, and primary cell cultures219,220 are required to further the field of honey bee virology. Future use of immortalized cell lines and infectious honey bee virus clones will serve to normalize future studies and lead to a better understanding of honey bee antiviral defense mechanisms. Conclusion Investigating virus-host interactions throughout all domains of life has led to a greater biological understanding of fundamental cellular processes and host- 23 virus coevolution. Honey bee host – virus interactions likely depend upon bee age or developmental stage, additional biotic and abiotic variables, and genetics of both host and pathogen. Only with additional research in laboratory and field settings at both the individual bee and colony level, will the mechanisms of honey bee antiviral defense be understood. Undoubtedly, continued investigation of honey bee host-virus pairs will lead to the discovery of evolutionarily conserved immune defense strategies, as well as reveal numerous unique co-evolved relationships that are specific to each host-virus combination. It is a critical and exciting time to investigate honey bee antiviral response mechanisms. Hypothesis Statement and Project Summary Intellectual Merit Recent high annual losses of these important pollinators in North America and parts of Europe2–4 have been associated with several factors abiotic (e. and biotic factors including the prevalence and abundance of viruses2,9,11,14–17,22–27. Virus infection of honey bees results in a variety of symptoms including deformities, paralysis, and mortality39. The severity of virus infections are largely governed by the honey bee immune response to virus infection, including the activation of innate immune pathways and RNA interference59,106,222. In addition to the sequence-specific RNAi pathways, treatment with non-sequence-specific dsRNA also reduces virus infection in bees95,102,175. To further investigate the 24 mechanisms of honey bee antiviral defense, particularly dsRNA-mediate responses, we carried out the research described in Chapter Two of this dissertation. Better understanding of honey bee antiviral defense may result in the development of strategies that reduce honey bee colony losses that are attributed to virus infection. Hypothesis Honey bee antiviral defense is mediated by dsRNA and many of the genes involved in antiviral defense mechanisms (i.e., innate immune and RNAi pathways) will be transcriptionally regulated. RNAi-mediated gene knock-down of select genes that exhibited increased expression will result in increased virus abundance. Research Aims Aim 1. Determine the mechanism(s) of RNA-mediated antiviral responses in honey bees and examine the effects of exogenous dsRNA on honey bee gene expression. To examine the mechanisms of dsRNA-triggered antiviral immune responses, honey bees were infected with Sindbis-GFP, a model virus, in the absence or presence of dsRNA that was either sequence- specific or non-specific to the virus. Transcriptome sequencing of bees collected 6, 48, and 72 hours post-infection (hpi) identified genes involved in immediate, mid, and late stages of immune response. Genes that were differentially regulated in response to virus infection and dsRNA treatment as compared to 25 mock-injected controls were identified via high-throughput sequencing (RNAseq) using the Illumina HiSeq. Aim 2. Determine the effect of reducing the expression of putative antiviral gene expression on virus abundance. The antiviral role of two genes that exhibited increased expression (i.e., dicer and cyclin-dependent serine/threonine kinase) was confirmed in vivo using RNAi-mediated gene knockdown in conjunction with virus-infection trials. 26 Figure 1.1 Honey Bee Immune Pathways - Highlighting Genes Implicated in Antiviral Immune Responses. The honey bee genome encodes major members of insect immune pathways including: RNAi (RNA interference); Jak-STAT (Janus kinase/Signal Transducer and Activator of Transcription); Toll; NF-kB (Nuclear Factor kB); JNK (c-Jun N-terminal kinase); and MAPK (Mitogen- Activated Protein Kinases), as well as orthologs of genes involved in autophagy, eicosanoid biosynthesis, endocytosis, and melanization. Bold text indicates genes and proteins differentially expressed in virus-infected honey bees. Additional information including Apis mellifera (Am) gene accession numbers is provided in Tables 1 and S1. The first step in immune activation is host recognition of pathogen associated molecular patterns (PAMPs) including viral dsRNA, bacterial peptidoglycans, and fungal b-glucans. In general, the Toll pathway is involved in defense against Gram(+) bacteria and fungi and the Imd pathway is activated by Gram(-) bacteria, but specific host-pathogen interactions are unique. This is particularly true for host – virus interactions since data from fruit-flies, mosquitoes, and honey bees indicate differential activation of immune genes and pathways. The Jak/STAT pathway is activated via ligand binding to the Domeless receptor; while Drosophila melanogaster (Dm) express Domeless ligands (unpaired, upd, upd2, and upd3), a honey bee upd ortholog has not been identified. Following Domeless-ligand binding, Hopscotch Janus kinases are transphosphorylated, leading to phosphorylation and dimerization of STAT92E- like proteins. Activated STATs transcriptionally regulate antimicrobial effectors TEP7 (Thioester-containing protein 7), TEPA, TEPB, and the Jak-STAT inhibitor SOCS (Suppressor of Cytokine Signaling). 27 Figure 1.1 continued. The honey bee genome also encodes for D-PIAS (Protein Inhibitor of Activated STAT), another inhibitor of the Jak/STAT pathway. The RNAi-pathway is initiated by Dm Dicer-2 cleavage of viral dsRNA into 21-22 bp siRNAs; Am Dicer-like share ~30% aa identity with Dm Dicer-2. The siRNAs are then loaded into AGO2 (Argonaute-2), the catalytic component of the RISC (RNA Induced Silencing Complex). A single strand of the siRNA is retained in the RISC and used to specifically target cognate viral genome sequences for cleavage. In addition, Dm Dicer-2 serves as a dsRNA sensor that mediates a signal transduction cascade resulting in increased expression of Dm Vago, suppresses viral replication. Am Dicer-like may serve as a dsRNA sensor, and honey bees have a vago ortholog (Table S1), but the mechanism(s) of honey bee non- specific dsRNA-mediated antiviral responses require additional characterization. The Toll pathway is activated by a family of pathogen recognition receptors (PRRs) (e.g., peptidoglycan receptor proteins and Gram(-) binding proteins) that bind fungal and bacterial PAMPs. The Toll pathway is activated in some insect host-virus combinations, although the activation mechanism is unknown. Following PAMP binding, a serine protease cascade results in cleavage of pro- Spaetzle into mature Spaetzle. The honey bee genome encodes two putative spaetzle orthologs, which bind the membrane-anchored Toll receptor. Toll dimerization results in the recruitment of dMyD88, Tube, and Pelle. Pelle is likely involved in degradation of NF-kB inhibitors (e.g., Cactus-1, Cactus-2, Cactus-3), resulting in the release of transcription factors Dorsal-1A and Dorsal-1B. Nuclear translocation of Dorsal results in increased expression of antimicrobial peptides (AMPs). The Imd pathway is activated by Peptidoglycan recognition protein LC (PGRP-LC) binding to diaminopimelic-containing peptidoglycan of Gram(-) bacteria, followed by activation of the adaptor protein Immune deficiency (IMD), Relish phosphorylation by the IKK complex (IkB kinase), and cleavage of Relish by the caspase Dredd (Death-related ced-3/Nedd2-like). Relish transcriptionally regulates expression of AMPs and other genes involved in antimicrobial defense. The JNK pathway is also activated by TAB (Transforming growth factor-activated kinase 1) and TAK1 (Transforming growth factor-activated kinase 1 binding protein), resulting in AMP expression and/or apoptosis. In Drosophila, binding of vesicular stomatitis virus to the Toll-7 receptor promotes autophagy, likely by inhibiting the PI3/Akt/Tor ( phosphatidylinositol 3-kinase / Protein kinase B / Target of rapamycin) pathway which suppresses autophagy. The honey bee genome encodes for one gene of the Toll-7/2 clade, 18-wheeler (am18w), which shares ~49% aa identity with Dm Toll-7 and ~45% aa identity with Dm Toll-2. The role of Am18w protein in antiviral defense and autophagy in honey bees is unknown. In insects, Eicosanoid biosynthesis begins with the induction of PLA2 (Phospholipase 2) from signal cascades downstream of viral, fungal, or bacterial PAMP recognition. Activated PLA2 hydrolyzes arachidonic acid (AA) from cellular phospholipids. Eicosonoid production likely occurs via oxidation of AA by an unidentified enzyme. 28 Figure 1.1 continued. Eicosanoids are critical for nodulation and aid in phagocytosis, micro-aggregation, adhesion, and release of prophenoloxidase (PPO) from hemocytes. 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Kreissl, S. & Bicker, G. Dissociated neurons of the pupal honeybee brain in cell culture. J. Neurocytol. 21, 545–56 (1992). 221. Brutscher, L. M. & Flenniken, M. L. RNAi and Antiviral Defense in the Honey Bee. J. Immunol. Res. 2015, 1–10 (2015). 49 VIRUS AND DSRNA-TRIGGERED TRANSCRIPTIONAL RESPONSES REVEAL KEY COMPONENTS OF HONEY BEE ANTIVIRAL DEFENSE Contribution of Authors and Co-Authors Manuscript in Chapter 2 Author: Laura M. Brutscher Contributions: Conceived the study; designed, performed and analyzed experiments, wrote the manuscript. Co-Author: Katie F. Daughenbaugh Contributions: Assisted in experiments and manuscript critique. Co-Author: Michelle L. Flenniken Contributions: Conceived the study; edited/critiqued manuscript in every stage of preparation. 50 Manuscript Information Page Laura M. Brutscher, Katie F. Daughenbaugh, Michelle L. Flenniken Scientific Reports Status of Manuscript: ____ Prepared for submission to a peer-reviewed journal ____ Officially submitted to a peer-review journal __X_ Accepted by a peer-reviewed journal ____ Published in a peer-reviewed journal *Note: All supplementary tables will be available online upon publishing. 51 Abstract Recent high annual losses of honey bee colonies are associated with many factors, including RNA virus infections. Honey bee antiviral responses include RNA interference and immune pathway activation, but their relative roles in antiviral defense are not well understood. To better characterize the mechanism(s) of honey bee antiviral defense, bees were infected with a model virus in the presence or absence of dsRNA, a virus associated molecular pattern. Regardless of sequence specificity, dsRNA reduced virus abundance. We utilized next generation sequencing to examine transcriptional responses triggered by virus and dsRNA at three time-points post-infection. Hundreds of genes exhibited differential expression in response to co-treatment of dsRNA and virus. Virus-infected bees had greater expression of genes involved in RNAi, Toll, Imd, and JAK-STAT pathways, but the majority of differentially expressed genes are not well characterized. To confirm the virus limiting role of two genes, including the well-characterized gene, dicer, and a probable uncharacterized cyclin dependent kinase in honey bees, we utilized RNAi to reduce their expression in vivo and determined that virus abundance increased, supporting their involvement in antiviral defense. Together, these results further our understanding of honey bee antiviral defense, particularly the role of a non- sequence specific dsRNA-mediated antiviral pathway. 52 Background Globally, honey bees (Apis mellifera) and other insects are important pollinators of plants in both natural and agricultural landscapes. Insect pollination services are valued worldwide at $175 billion annually1, and in the United States honey bee pollination is valued at $14.6 billion annually2. Commercially managed honey bee colonies, which are the primary pollinators of numerous agricultural crops, have experienced high annual mortality in the U.S. (i.e., 33% average annual loss since 2006) and parts of Europe3–5. Multiple abiotic and biotic factors, including pathogens, contribute colony losses6,7. Pathogen incidence and abundance have been positively correlated with Colony Collapse Disorder (CCD)-affected colonies in the U.S.3,6,8 and colony losses in different regions of North America, South America, and Europe9,10,7,11–17. Honey bees are eusocial insects that live in colonies comprising approximately 40,000 sterile female workers, hundreds of male bees, and a single reproductive queen bee. Honey bees are often infected with pathogens including viruses, fungi, bacteria, and trypanosomatids, and they are typically parasitized by the Varroa destructor mite (reviewed in18). The largest group of honey bee infecting pathogens are positive sense single-stranded viruses, including several Dicistroviruses (e.g., Israeli acute paralysis virus, Kashmir bee virus, Acute bee paralysis virus, and Black queen cell virus), Iflaviruses (e.g., Deformed wing virus, Sacbrood virus, and Slow bee paralysis virus), as well as taxonomically unclassified viruses (e.g., Chronic bee 53 paralysis virus and the Lake Sinai virus group (reviewed in19,20)). Honey bee- associated viruses exhibit variable pathogenicity and may cause deformity, paralysis, death, or remain asymptomatic (reviewed in19,20). However, asymptomatic infections are commonly reported at levels of over 107 virus equivalents (i.e., genomes and transcripts) per bee21, thus they likely affect bee physiology and health. Like other insects, honey bee antiviral responses include autophagy, apoptosis, eicosanoid biosynthesis, endocytosis, melanization, the JAK/STAT (Janus Kinase/Signal Transducer and Activator of Transcription), Toll, NF-κB (Nuclear Factor κB), JNK (c-Jun N-terminal kinase), and MAPK (Mitogen- Activated Protein Kinases) pathways, and RNA interference (RNAi)11,16,22–26. RNAi is a post-transcriptional, sequence-specific, gene silencing mechanism and the small interfering RNA (siRNA)-mediated pathway is one of the primary insect antiviral defense mechanisms27–37. Correspondingly, several studies have shown that administration of virus-specific dsRNA or siRNA reduced viral load in honey bees36,38–40. Furthermore, CCD-affected colonies had higher amounts of virus- specific 22 nt siRNAs as compared to non-CCD-affected colonies37 and early field studies suggested that honey bees fed IAPV-specific dsRNA had increased honey production and larger colony size41. While experimental introduction of virus-specific dsRNA reduced honey bee virus infections, likely via RNAi36,38–40, non-sequence-specific dsRNA (ns- dsRNA) has also been shown to reduce virus abundance and affect gene 54 expression in honey bees and bumble bees22,42–45. The more prominent role of ns-dsRNA mediated reduction in virus abundance in eusocial hymenopteran insects (e.g., honey bees and bumble bees) as compared to solitary insects (e.g., fruit flies and mosquitos) that do not exhibit this response, may reflect an evolutionary adaptation to limit virus transmission within colonies using general non-virus specific antiviral responses. In mammals, dsRNA serves as a virus- associated molecular pattern (VAMP) that is recognized by pathogen recognition receptors (PRRs), such as Toll-like receptor 3 (TLR3), Protein kinase R (PKR), Retinoic acid-inducible gene 1 (RIG-I), and Melanoma differentiation-associated gene 5 (MDA-5), and results in induction of the antiviral interferon response46. Similarly, Dicer, which is the endoribonuclease involved in RNAi, also serves as a dsRNA sensor that induces expression of antiviral defense genes (e.g., vago) in fruit flies, mosquitoes, and bumble bees44,47–49. However, the role of specific genes in honey bee antiviral defense, particularly nonspecific dsRNA-mediated antiviral responses, are not well characterized. Previously, we determined that treating honey bees with either virus sequence-specific-dsRNA (sp-dsRNA), or non-sequence specific dsRNA (ns- dsRNA), decreased virus abundance at 72 hours post-infection (hpi)22. To further investigate the mechanisms of dsRNA triggered antiviral defense and the dynamics of virus infection and corresponding immune responses in honey bees, we performed a time series experiment (i.e., 6, 48, and 72 hpi) that included transcriptional profiling of individual virus-infected and dsRNA-treated bees. We 55 determined that honey bee gene expression varied with the progression of virus infection and included genes involved in endocytosis, development, transcriptional regulation, RNAi, and the Toll, Imd, JAK-STAT, and JNK pathways. Interestingly, bees that exhibited decreased virus abundance in the context of dsRNA treatment exhibited increased expression of two RNA helicases, one JNK pathway member, and several genes involved in dsRNA transport. Furthermore, we performed in vivo studies that confirmed the importance of the genes dicer and cyclin-dependent serine/threonine kinase (MF116383), which exhibited increased expression in virus-infected bees, in honey bee antiviral defense. Together these results further our understanding of honey bee antiviral defense mechanisms and the effects of dsRNA on honey bee gene expression which may lead to the development of strategies that limit virus infection in honey bees. Development and increased use of siRNAs and dsRNAs to reduce pathogens and pests (i.e., fungi, nematodes, and insects) in crops that are frequently visited by pollinators50,51 also underscores the need to further examine the effects of these molecules on bee health. Results and Discussion Honey bees are commonly infected by positive sense single-stranded RNA (+ssRNA) viruses, which replicate via a dsRNA intermediate. RNAi mediated by virus-specific dsRNA is an important antiviral defense mechanism in honey bees11,22,36,38–41, but nonspecific dsRNA-triggered antiviral response 56 pathway(s) also play a role in honey bee antiviral defense22. To further investigate honey bee antiviral defense mechanisms, including RNAi and nonspecific dsRNA-mediated mechanisms22, we infected bees with a model virus in the presence or absence of dsRNA (Figure 2.1). There are currently no honey bee virus isolates or infectious clones, so we utilized a recombinant Sindbis virus that expresses GFP (SINV-GFP) as a tractable model of virus infection in honey bees22. SINV-GFP inoculations were performed via intrathoracic injection in the absence or presence of multiple species and lengths of dsRNA, including virus- specific dsRNA (sp-dsRNA, 928 bp), nonspecific dsRNA corresponding to Drosophila C virus sequence (ns-dsRNA, 1,017 bp), and luciferase sequence (dsRNA-short, 355 bp). In addition, co-administration of polyinosinic-polycytidylic acid (poly(I:C)), a synthetic mimic of dsRNA, or nucleoside triphosphates (NTPs), served as positive and negative controls, respectively; mock-infected controls were also performed. Bees from each experimental treatment group were collected at 6, 48, and 72 hpi, a time course that allowed for assessment of both early and late antiviral responses. Relative virus abundance was examined via fluorescence microscopy and quantified based on relative protein and RNA abundance using Western blot analyses and quantitative PCR (qPCR), respectively (Figures 2.1 and 2.2, Supplementary Figures 2.7 and 2.8). 57 Figure 2.1 Schematic of honey bee virus infection trials. Schematic representation of the experiments performed to investigate honey bee antiviral immune mechanisms, including dsRNA triggered immune responses. Honey bees were infected with virus, Sindbis-GFP, and/or treated with dsRNA, which was composed of either virus-specific dsRNA (sp-dsRNA) or nonspecific dsRNA (ns-dsRNA). Bees were collected at 6, 48, and 72 hours post infection (hpi); virus abundance in individual bees was assessed by fluorescence microscopy, Western blot analysis, and qPCR, and honey bee gene expression was assessed using RNASeq. Honey bee image courtesy of Kathy Keatley Garvey; used with permission. Virus Abundance Reduced in dsRNA-Treated Honey Bees Honey bees treated with sp-dsRNA or ns-dsRNA had reduced virus abundance. Fluorescent microscopy of bees that were infected with SINV-GFP in the absence or presence of dsRNA provided qualitative evidence that dsRNA treatment reduced SINV-GFP at 72 hpi (Supplementary Figure 2.7). To more quantitatively examine the reduction of SINV-GFP, we performed Western blot analyses of individual bee lysates at 72 hpi, which determined that dsRNA, regardless of sequence composition, reduced virus abundance at the protein level (Supplementary Figure 2.8). Virus abundance was most accurately measured and compared by assessing relative RNA abundance via qPCR (Supplementary Table S1 and Supplementary Figure 2.9). At 48 hpi and 72 hpi, bees treated with sp-dsRNA, ns-dsRNA, and poly(I:C) had decreased relative 58 SINV-GFP abundance as compared to bees infected with virus only and bees simultaneously treated with virus and NTPs (Figure 2.2 and Supplementary Figure 2.10). At 48 hpi, the relative virus abundances of sp-dsRNA and ns- dsRNA treated bees were reduced by 64% (p < 0.005) and 44% (p < 0.05), respectively, as compared to bees infected with virus only (Figure 2.2 A). At 72 hpi, the relative virus abundances of sp-dsRNA- and ns-dsRNA-treated bees were reduced by 54% (p < 0.005) and 56% (p < 0.005) as compared to bees infected with virus only (Figure 2.2 B). Bees treated with poly(I:C) at 72 hpi had reduced virus abundance by 63% (p< 0.005). Reduced relative virus abundance in dsRNA-treated bees was also observed in additional biological replicates, which included virus-infected bees at 48 and 72 hpi from two additional honey bee colonies for a total of n=30 per treatment (Supplementary Figure 2.10). 59 Figure 2.2 Relative virus RNA abundance was reduced in dsRNA-treated bees as compared to virus-infected bees. Relative abundance of SINV-GFP in individual bees (n=10) was assessed by qPCR. (A) At 48 hours post-infection (hpi), bees treated with dsRNA (1 kb) had reduced relative virus RNA abundance (includes both virus genomes and transcripts) by 64% for sp-dsRNA-treated bees (dotted purple, **p< 0.005) and 44% for ns-dsRNA-treated bees (checkered blue, *p< 0.05), as compared to bees infected with virus only (green stripes). Likewise, bees treated with short dsRNA (0.5 kb) had 24% less virus (dotted red, *p< 0.05) than virus-infected bees. (B) At 72 hpi bees treated with dsRNA had reduced relative virus abundance by 54% (**p< 0.005) and 56% (**p< 0.005) for sp- dsRNA and ns-dsRNA, respectively. Similarly, bees treated with poly(I:C) (yellow-stripes), a structural analog of dsRNA, had 63% less virus than virus- infected bees (**p< 0.005). Bees treated with short dsRNA also had 43% less virus (dotted red, *p< 0.05) than bees infected with virus alone. The virus abundance in bees treated with NTPs (orange wavy lines) was not significantly different from virus-infected bees at either 48 hpi (A) or 72 hpi (B). Percent relative virus abundance for each sample was determined via ΔΔCT analysis (using Am rpl8 as the house keeping gene); statistical differences between treatment and virus-infected bees were performed using one-sided Student’s t- tests, *p< 0.05, **p< 0.005. The bars represent the standard error of the mean. Transcriptional Level Evaluation of Virus and dsRNA Induced Immune Responses in Honey Bees The transcriptional profiles of virus-infected honey bees are indicative of the cellular pathways and mechanisms that are regulated in response to virus infection. Likewise, we hypothesized that a subset of the differentially expressed 60 genes would also be regulated in response to dsRNA, a VAMP. To further elucidate the honey bee transcriptional response to virus infection and the mechanisms of dsRNA triggered antiviral defense, we performed transcriptome profiling (RNASeq) of individual virus-infected bees, bees infected with virus in the presence of sp-dsRNA or ns-dsRNA, dsRNA-treated bees in the absence of virus, and mock-infected bees at 6, 48, and 72 hpi (Figure 2.1). Forty-seven individual bee RNASeq libraries were prepared using the Illumina TruSeq Stranded RNA Sample Prep kit and paired-end sequenced (2x100 nt) on an Illumina HiSeq 2500, resulting in an average of 12 million reads per individual bee sample (Supplementary Table S2). On average, 77% of reads mapped to the A. mellifera genome assembly 4.5 from NCBI52. Prior to sequencing, bees were screened for confounding pre-existing infections via pathogen-specific PCR and qPCR in order to identify individuals with little to no preexisting infections (Supplementary Tables S1 and S3). Genes Differentially Expressed in Virus-Infected Bees and dsRNA-Treated Bees Transcriptome analysis of virus-infected bees over the course of infection (i.e., 6, 48, and 72 hpi) determined that virus-infection altered the expression of hundreds of genes as compared to mock-infected bees (Figure 2.3A, Supplementary Figure 2.11). The majority of differentially expressed genes in virus-infected and dsRNA-treated bees are not well characterized or do not have known roles in antiviral defense (Supplementary Table S5). Genes that exhibited 61 increased expression at 6 hpi were functionally enriched for the biological processes phosphorylation and transcriptional regulation (Supplementary Figure 2.12). The genes with increased expression at 48 hpi were enriched in transcriptional regulation, cell adhesion, immune responses, and cellular migration (Supplementary Figure 2.12 and Supplementary Table S5). Similarly, virus-infected bees 72 hpi also exhibited increased expression of genes enriched for transcriptional regulation and gene silencing (Supplementary Figure 2.12). Genes involved in morphogenesis were differentially expressed throughout all time points (Supplementary Figure 2.12)53. 62 Figure 2.3 Honey bee transcriptional response to virus-infection and dsRNA- treatment is time-dependent. (A) There were hundred of differentially expressed genes (DEGs) in virus-infected bees, as compared to mock-infected bees. Venn diagram analysis identified shared and unique DEGs of virus-infected bees 6, 48, and 72 hours post-infection (hpi). Twenty-three genes were differentially expressed at all three time points post-infection. Six of these genes consistently exhibited increased expression (highlighted in green and listed from highest average log2 fold change to lowest) and two genes consistently exhibited decreased expression (red). 63 Figure 2.3 continued. Interestingly, ten genes exhibited increased expression at 6 hpi and decreased expression at 48 and 72 hpi (yellow). Five genes (purple) displayed increased expression at 48 hpi, but decreased expression at 6 and 72 hpi. Arrows pointing up denote number of genes that exhibited increased expression for each time point and arrows pointing down denote number of genes that exhibited decreased expression. (B) There were hundred of differentially expressed genes (DEGs) in dsRNA-treated bees compared to mock-infected bees. Venn diagram analysis identified shared and unique DEGs of dsRNA-treated bees at 6, 48, and 72 hours post treatment demonstrated that 14 genes were differentially expressed at all time points. Interestingly, there were four genes (green) that exhibited increased expression at 6 and 48 hpi and decreased expression at 72 hpi, three of which are antimicrobial peptides (AMPs). One gene, crzr, exhibited increased expression at 6 hpi, but decreased expression in bees 48 and 72 hpi (blue). Three genes displayed consistently decreased expression (red). Five genes had decreased expression at 6 hpi and increased expression at 48 and 72 hpi (gray), including heat shock protein 90. One gene, nyctalopin-like, exhibited decreased expression in bees 6 and 72 hpi, but increased expression in bees 48 hpi (orange). DEGs with Benjamini- Hochberg corrected q-values < 0.05 were included in Venn diagram analyses. Full lists of DEGs and their fold changes from all contrasts in each Venn diagram are provided in Supplementary Tables S7 and S8. Venn diagram analysis demonstrated that the honey bee transcriptional response to virus infection varies with time post-infection (Figure 2.3A). As time post-infection increased, so did the number of DEGs, from 236 DEGs to 773 DEGs (Figure 2.3A). Twenty-three genes were commonly differentially expressed throughout the course of the infection (Figure 2.3A), though only eight of these genes were differentially expressed in a uniform direction (Figure 2.3A, Supplementary Tables S4, S5, S7). Six of these genes exhibited increased expression including an uncharacterized transcript encoding a probable cyclin- dependent serine/threonine kinase (MF116383), apid1, dna n6-methyl adenine demethylase (loc412878), orb2-like, solute carrier organic anion transporter family member 3a1-like (sloc3a1), and titin-like (Figure 2.3A, Supplementary 64 Tables S4, S5, S7). Two genes had lower expression in all virus-infected bees: obp16 and zinc finger protein 431-like. Many viruses generate long dsRNA molecules during their replication cycle. Long dsRNA molecules are not a typical product of eukaryotic gene expression, so they serve as triggers of eukaryotic antiviral immune responses (e.g., RNAi and interferon responses)46,54. To further investigate the role of dsRNA stimulation in honey bee antiviral defense, we examined changes in gene expression over time. The genes that exhibited increased expression in dsRNA treated bees 48 hours after treatment were enriched for functions including oxidation-reduction, cellular morphogenesis, and immune response (Supplementary Table S5). Bees 72 hours post-treatment exhibited increased expression of genes enriched for cellular morphogenesis, transcriptional regulation, vesicle-mediated transport, and RNA interference (Supplementary Table S5), paralleling the results of previous study that examined the effects of nonspecific dsRNA (GFP-dsRNA) on honey bee gene expression43. Venn diagram analysis of dsRNA-treated bees 6, 48, and 72 hours post- treatment identified 14 shared DEGs, three of which exhibited decreased expression: carbonic anhydrase 1, venom acid phosphatase acph-1-like, and odorant binding protein 16, which also exhibited decreased expression in virus- infected bees (Figure 2.3B, Supplementary Tables S5 and S8). Also similar to virus-infected bees, heat shock protein 90 (hsp90) was also differentially expressed throughout all dsRNA-treated bees. In dsRNA-treated bees 6 and 48 65 hpi, several genes encoding antimicrobial peptides (i.e., apidaecin, apidaecins type 73-like, abaecin and hymenoptaecin) exhibited increased expression (Figure 2.3B), most of which also exhibited increased expression in virus-infected bees (Supplementary Table S6). In addition, both virus-infected and dsRNA-treated bees 48 and 72 hpi exhibited increased expression of scavenger receptor class c (scr-c), which plays a role in dsRNA uptake in D. melanogaster55 and may play an analogous role in honey bees. Together, our analyses indicate that dsRNA- treatment alters gene expression in honey bees, and that there are common and unique aspects between differential gene expression in virus-infected bees and virus-infection in the context of dsRNA. qPCR Validation of RNASeq Results In order to validate RNAseq results, we examined the relative expression of fourteen genes (i.e., cyclin-dependent kinase, orb2-like, titin-like, DNA n6- methyl adenine demethylase, slco31-like, hsp90, abaecin, ago2, dicer, igfn3-10, mfs-transporter, jra, fam102b) that exhibited increased expression in virus and/or dsRNA treated bees at 48 and/or 72 hpi via qPCR of sequenced bees 72 hpi (Supplementary Figure 2.13). The expression of ten of those genes was also examined by qPCR in sequenced bees at 48 hpi (Supplementary Figure 2.13). All but two of the fourteen genes assayed (igfn3-10 and titin-like) were confirmed to have increased expression in virus-infected and/or dsRNA-treated bees via qPCR (Supplementary Figure 2.13). Several genes (e.g., hsp90, cyclin- dependent kinase, ago2, dicer, mfs-transporter, formin-j) were also confirmed to 66 have increased expression in biological replicate experiments that utilized pooled virus-infected honey bee samples (72 hpi) from two different colonies, likely with different genetic backgrounds (Supplementary Figure 2.14). ed confirm RNASeq results and provided further evidence to their importance in honey bee antiviral defense. Differentially Expressed Genes in a Cellular Context To compare our results with what is currently known about insect immunity, we surveyed the DEGs of virus-infected and dsRNA-treated bees for genes involved in previously characterized insect immune pathways. This analysis determined that many genes encoding extracellular receptors and proteins involved in endocytosis, signal transduction, as well as immune effector proteins (e.g., antimicrobial peptides) exhibited increased expression (Figure 2.4 and Supplementary Table S6). Some of the genes identified herein are illustrated in a cellular context in order to illustrate their potential functions in antiviral defense (Figure 2.4). 67 Figure 2.4 Differentially expressed genes in response to virus and/or dsRNA treatment in a cellular context. The DEGs of virus-infected and dsRNA-treated honey bees were surveyed for differential expression of genes involved in previously characterized insect immune pathways: RNAi, Toll, JAK/STAT (Janus Kinase and Signal Transducer and Activator of Transcription), Imd (Immune Deficiency), and JNK (c-Jun N-terminal kinases). This analysis determined that many genes encoding I. extracellular receptors, II. proteins involved in endocytosis, III. proteins involved in signal transduction cascades, and IV. Immune effector proteins exhibited increased expression; these DEGs (denoted by green font) are depicted in a cellular context. Many genes exhibited higher fold change in bees treated with both virus and dsRNA as compared bees infected with virus only (denoted with dsRNA by the gene label), suggesting their involvement in dsRNA-triggered immune responses. I. Extracellular Receptors and Transporters In the context of virus infection, extracellular receptors can serve in pathogen recognition and host defense or be co-opted by the virus to facilitate entry. The membrane localized solute carrier organic anion transporter family member 3a1-like (sloc3a1) consistently exhibited increased expression in virus- 68 infected bees (i.e., 1.6 – 2.3 fold increase) (Figures 2.3A and 2.4, Supplementary Tables S4, S5, and S7). Interestingly, slco3a1 also exhibited increased expression in SBV and DWV infected bees25. The JAK/STAT receptor, hopscotch, exhibited increased expression in virus-infected bees (Figure 2.4, Supplementary Table S6). The JAK/STAT pathway is involved in both insect development and antiviral defense29,56. Additionally, two pathogen recognition receptors of the Toll pathway, peptidoglycan receptor s2 (pgrp-s2) and peptidoglycan receptor s3 (pgrp-s3), and the toll-like receptor toll-10 exhibited increased expression in virus-infected bees (Figure 2.4, Supplementary Table S6). Similar to virus-infected bees, bees treated with only dsRNA also exhibited greater expression of toll-10 and pgrp-s3. The Toll pathway is primarily involved in defense against Gram-positive bacteria and fungi, but has also been implicated in antiviral defense in D. melanogaster and Aedes aegypti56. Similar to the Toll pathway, activation of the Imd pathway results in antimicrobial peptide production, but it is typically activated by Peptidoglycan recognition protein LC (PGRP-LC) binding to the diaminopimelic-containing peptidoglycan of Gram- negative bacteria29. The Imd pathway is also important for fruit fly antiviral defense against some viruses, including Sindbis virus56–58. Lastly, three genes encoding immunoglobulin domain-containing proteins (i.e., igfn3-5, igfn3-10, and igfn3-11) exhibited increased expression in virus- infected bees (Figure 2.4, Supplementary Table S6). In insects, immunoglobulin domain-containing proteins aid a variety of functions, including cell to cell 69 adhesion, pathogen recognition, and phagocytosis59. Similarly, hemolin, an immunoglobulin-domain containing protein exclusively expressed in lepidopterans, exhibits increased expression in Chinese Oak Silk moths treated with dsRNA or infected by Baculovirus60. II. Endocytosis Endocytosis, including phagocytosis, is an immune effector function carried out by hemocytes61 and may also be important for systemic RNAi55,62. In addition, many viruses exploit endocytocitic pathways for entry63. SINV and other alphaviruses typically enter cells via receptor binding followed by clathrin- mediated endocytosis64. Several genes involved in phagocytosis, including nimc1, nimc2, dhc64c-like, and laminin a, exhibited increased expression in virus-infected bees (Figure 2.4, Supplementary Table S6). Laminins aid the cell in cellular adhesion, migration, differentiation, and morphology65. Interestingly, in mammalian cells, SINV utilizes a laminin receptor for viral entry65. Likewise, virus-infected honey bees had greater expression of a JAK/STAT effector molecule thioester protein 7 (tep7). In mosquitoes, thioester proteins bind to invading bacteria which promotes phagocytosis of these pathogens66, but thioesters are also associated with improved defense against Dengue and West Nile viruses via unknown mechanisms67. In D. melanogaster S2 cells, genes involved in receptor-mediated endocytosis are important for dsRNA uptake, including the genes scavenger receptor c (scr-c), fam102b, and sap-r55. Bees treated with virus, dsRNA, or both 70 exhibited increased expression of scr-c, but bees treated with both virus and dsRNA exhibited the greatest increase (Figure 2.4, Supplementary Table S5). Additionally, the expression of fam102b was significantly increased in bees that were treated with both virus and dsRNA. In contrast, sap-r, which is a protease associated with late stage endosomes, exhibited decreased expression in bees treated with both virus and dsRNA and may be indicative of virus-specific dsRNA triggered modification of endosomal development68, although future investigation is required. III. Signal Transduction Cascades Signal transduction cascades are the means by which a chemical or physical signal is transmitted through a cell resulting in a response; for example detection of pathogen associated molecular patterns (PAMPs), including dsRNA, results in activation of cellular transduction cascades that result in activation of particular immune responses69. In our data set, reads aligning to Apis mellifera LOC25387 and encoding a previously uncharacterized transcript (MF116383), that had greatest sequence homology to an Eastern honey bee (Apis cerana) probable cyclin-dependent serine/threonine kinase (XM_017051141.1), exhibited the greatest increase in expression in virus-infected bees as compared to mock- infected controls (i.e., 5.7 – 13 fold increase) (Figures 2.3A and 2.4, Supplementary Figure 2.15, Supplementary Tables S4, S5, and S7)11. In general, cyclin-dependent serine/threonine kinases are activated by cyclins and phosphorylate serine and threonine residues of substrate proteins, resulting in 71 regulation of cell cycle progression and transcription. Nevertheless, the specific proteins that interact with this cyclin-dependent serine/threonine kinase are unknown. Likewise the expression of Am LOC25387 transcripts were increased in DWV and SBV co-infected bees and IAPV-infected bees11,25. Genes involved in Toll pathway signal transduction also exhibited increased expression in virus-infected bees including cactus 1 and cactus 2, which suppress NF-κB signaling, and tube, an adaptor protein that promotes NF- κB signaling (Figure 2.4, Supplementary Table S5). In Drosophila, immune pathways are tightly regulated in order to balance immune responses, thus increased expression of pathway inhibitors (e.g., cactus 2) does not necessarily indicate complete or continuous repression of the pathway (e.g., Toll)70. Likewise, we determined that pirk, which represses Imd pathway signaling71,72, exhibited increased expression in virus-infected bees (Figure 2.4, Supplementary Table S5). JNK pathway activation is often linked with Imd pathway activation56. The transcriptional effector of the JNK pathway, jun-related antigen (jra), had greater expression in bees that were both virus-infected and treated with dsRNA at 6 and 48 hpi, and increased expression in all virus-infected groups at 72 hpi (Supplementary Figure 2.13, Supplementary Table S5). Bees treated with only dsRNA followed similar, but lower, expression patterns as compared to mock- infected bees, suggesting that the JNK signaling may be involved in dsRNA- triggered responses. 72 The Wnt/beta-catenin signaling pathway, which is involved in cellular proliferation and differentiation, has also been implicated in insect host-virus interactions, though its role in immune function is less well characterized73,74. Several genes involved in Wnt signaling (e.g., osa) exhibited increased expression in virus-infected bees (Figure 2.4, Supplementary Table S5). The involvement of the Wnt signaling pathway in honey bee antiviral defense has also been implicated in the context of IAPV infection11, thus it is likely that Wnt signaling is important to honey bee antiviral defense. IV. Immune Effector Proteins Several antimicrobial peptides, which are effector molecules of Toll, Imd, and JNK pathways, exhibited increased expression in virus-infected and dsRNA- treated bees. Importantly, apidaecin 1 exhibited increased expression in all virus- infected bees and bees treated with dsRNA alone (Figures 2.3 and 2.4, Supplementary Tables S4-S8)56,75. Apidaecins are proline-rich antimicrobial peptides (AMPs) that have bactericidal activity against Gram-negative bacteria75,76. Virus-infected and/or dsRNA-treated bees also exhibited increased expression of abaecin and hymentoptaecin (Supplementary Table S5), indicating activation the Imd and/or JNK pathways56,75,77. Increased AMP expression in virus-infected honey bees and other insects has previously been reported, though their role in antiviral defense is not yet understood24–26,78. It may be that AMPs do not have a direct role in antiviral defense and that increased transcript levels of AMPs and genes involved in pathogen recognition and signal 73 transduction (e.g., pgrp-s3) simply indicate activation of these pathways56,75,79. The activation of Toll, Imd, and JNK signal transduction cascades likely stimulate transcription of hundreds of genes, including antiviral effectors that await further characterization. Heat shock proteins (Hsps) are involved in general stress responses and protein degradation and stabilization. In fruit flies, these ubiquitously expressed proteins are important for defense against some viruses80,81. Our transcriptional level analysis identified several genes encoding heat shock and accessory proteins that exhibited increased expression in virus-infected bees including, hsp90, activator of hsp90, 60 kda hsp, 10 kda hsp, hsp83-like, and hsf5 (Supplementary Table S5). Hsp90 expression was also increased in dsRNA- treated bees. In Drosophila, Hsp90 binds to and stabilizes the RNA-induced silencing complex (RISC) as part of the RNAi response82,83, but Hsp90 can also be exploited by both insect and human viruses (e.g., Flock House virus and Polio virus) in order to stabilize RNA replication84,85. Future studies aimed at better understanding the functions of heat shock proteins, particularly Hsp90, in virus- infected honey bees will be exciting since these proteins may either be antagonistic or beneficial to specific viruses. RNA interference is an important antiviral and post-transcriptional gene regulatory mechanism in honey bees that is initiated by Dicer recognition of dsRNA22,36,37,39. Notably, there was greater expression of genes involved in RNAi (i.e., argonaute-2 (ago2), dicer, tudor-sn, hsc70-4, and tarbp2) in virus-infected 74 bees (Figure 2.4, Supplementary Table S6). Interestingly, enhanced expression of dicer and ago-2 in virus-infected honey bees was observed in another study24, whereas increased expression of genes involved in RNAi has not been observed in virus-infected fruit flies49. In our studies, administration of dsRNA, in the absence of virus infection, did not induce Apis mellifera dicer or ago2 expression, indicating that VAMP immune triggering does not completely recapitulate the immune response to virus infection. Additional studies are required to better understand the mechanisms of transcriptional activation of genes involved in honey bee RNAi86. The role of DExD box RNA helicases in honey bee antiviral defense is particularly interesting because, in mammals, many of these proteins function as nonspecific cytosolic sensors of dsRNA (e.g., MDA-5 and RIG-I), which activate the antiviral interferon response46,49. In Culex pipiens f. molestus mosquitoes, D. melanogaster, and Bombus terrestris, Dicer-2 serves as a dsRNA pathogen recognition receptor (PRR), that after binding dsRNA, results in the increased activation of antiviral immune effectors (e.g., vago)29,42,44,47,49. DWV-infected honey bees exhibited increased expression of Apis mellifera vago (loc503505), but differential expression vago was not observed in our data set; many host factors (e.g., age/life stage) may be involved, but perhaps vago expression is only increased in response to specific honey bee-infecting viruses (Supplementary Table S5). The expression of two RNA helicases (i.e., rna helicase ddx33 and rna helicase dhx52) was increased in virus-infected and 75 dsRNA-treated honey bees (Figure 2.4, Supplementary Table S5). RNA helicase DHX33 has been identified as a dsRNA receptor in mammals that when bound to dsRNA or bacterial RNA, activates NLRP3 inflammasome-mediated interferon stimulation87, but RNA Helicase DDX33 has not been implicated in dsRNA- immunostimulation in insects. Future exploration of the role of these important dsRNA sensors in activating antiviral response in honey bees will likely lead to the discovery of analogous pathways in other organisms. Reduced Expression of dicer and cyclin-dependent kinase Enhanced Virus Abundance in vivo And Confirmed their Role in Limiting Virus Infection in Honey Bees In order to further investigate the biological importance of two putative antiviral genes, dicer and a probable cyclin-dependent kinase (MF116383), we utilized RNAi-mediated gene knock down to reduce their expression and investigate the impact on virus abundance. We expected that reduced expression of these antiviral genes would result in increased virus abundance, as compared to the virus abundance in bees treated with ns-dsRNA. Virus-infected bees treated with cyclin-dependent kinase-specific dsRNA exhibited decreased expression by 48 hpi (40%) and 72 hpi (30%) compared to respective controls (i.e., virus-infected and ns-dsRNA treated bees) (Supplementary Figure 2.16). At 72 hpi, virus abundance in bees with reduced cyclin-dependent kinase levels was higher compared to the virus abundance in bees treated with ns-dsRNA, 77% versus 43% relative virus abundance (p<0.05) (Figure 2.5). Bees 48 hpi followed similar trends (Supplementary Figure 2.16). Bees treated with dicer-specific 76 dsRNA in the context of virus infection exhibited reduced expression of dicer at 48 hpi, but not 72 hpi (Supplementary Figure 2.16). Though the kinetics of dicer knock-down differed from the probable cyclin-dependent kinase, bees treated with dicer-specific dsRNA had a greater abundance of virus compared to bees treated with ns-dsRNA, 90% versus 43% (Figure 2.5). These results confirm the role of dicer in limiting virus infections in honey bees and highlight the importance of a previously uncharacterized transcript encoding a probable kinase, cyclin- dependent kinase (MF116383), in limiting virus infection. Further investigation of this probable cyclin-dependent kinase and the proteins with which it interacts may lead to the discovery of novel honey bee antiviral pathways or aid in further characterization of known immune pathways. Figure 2.5 Reduced expression of two honey bee genes resulted in increased virus abundance. To further investigate the role of dicer and cyclin-dependent kinase in honey bee antiviral defense, we used RNAi-mediated gene knock down to reduce their expression and qPCR to determine the impact on virus abundance. SINV-GFP abundance in kinase and dicer specific dsRNA-treated bees at 72 hpi was increased by 48% (*p< 0.05) and 44% (*p< 0.05), respectively, compared to the ns-dsRNA control, which is the most relevant comparison since administration of dsRNA, including the dsRNAs used to reduce the expression of dicer and cyclin-dependent kinase reduces virus abundance. 77 Figure 2.5 continued. Percent relative virus abundance for each sample was determined via qPCR and ΔΔCT analysis using Am rpl8 as the house keeping gene. Statistical differences between treatment and virus-infected and ns-dsRNA bees were determined using one-sided Student’s t-tests, *p< 0.05, **p< 0.005. The bars are standard error of the mean. Synthesis of Honey Bee Transcriptional Response to Virus Infection This is the first study to examine individual honey bee antiviral responses to infection with controlled inoculum of model virus at multiple time points. Other studies have examined honey bee responses to virus infection at the transcriptional level, but they vary by virus-challenge methodologies (e.g., mite- vectored, infection via injection, and oral infection), purity and strain of virus inoculum, tissues examined, post-infection assay time, and bee developmental stage, which reduce commonalities in transcriptional results between studies11,16,22,24–26,88–91. In spite of the methodological differences between this and other honey bee transcriptional level analyses, we identified common DEGs associated with virus infected honey bees. Using Venn diagram analysis, we compared our DEG lists to DEGs of symptomatic IAPV-fed bees24, SBV and DWV-infected bees25, adult honey bees naturally infected with IAPV11, and a synthesis of common DEGs that was recently generated from 19 gene expression data sets from Varroa destructor-parasitized and virus-infected bees92 (Figure 2.6, Supplementary Tables S14 and S15). There was one DEG that was shared in all five DEG lists: protein lethal(2)essential for life-like, which encodes a protein in the small heat shock protein (Hsp 20) family (Supplementary Table S15)93. 78 Additional comparisons, between this study and the other transcriptomes, indicated that there were many shared DEGs involved in the Toll, Imd, JAK/STAT, JNK, and RNAi pathways, as well numerous uncharacterized pathways (Supplementary Table S14 and S15)11,22,24,26,94. There were 87 shared DEGs between virus-infected bees at 72 hpi (this work) and symptomatic IAPV- fed bees (Figure 2.6 and Supplementary Table S15), including increased expression three genes involved in RNAi (i.e., ago2, dicer, and tar rna-binding protein 2)24. A few AMPs also exhibited differential expression in many of the DEG lists. For example, hymenoptaecin exhibited differential expression in SINV- GFP-infected bees, DWV and SBV co-infected bees, and in the virus and Varroa destructor DEG synthesis25,92 (Figure 2.6 and Supplementary Table S15). The gene encoding for Apidaecin exhibited differential expression in SINV-GFP- infected bees, symptomatic IAPV-fed bees, SBV and DWV-infected bees, and adult honey bees naturally infected with IAPV11,24,25. Notably, one of the top ranked genes with decreased expression in the transcriptome synthesis was zinc finger protein 431-like92 (Figure 2.6). Our study also determined that this gene had decreased expression in most virus-infected and dsRNA co-treated bees at all time points (Figure 2.3). The members of the Pit-Oct-Unc (POU) family have a wide variety of functions primarily involved in the neuroendocrine system95. This may correspond with work showing that pathogen infections induce neuronal and behavioral changes (e.g., premature foraging behavior) in honey bees, which may function as a form of social immunity in insect societies96. 79 Figure 2.6 Venn diagram of shared and unique DEGs in bees infected with viruses from this and other studies. The DEGs identified in SINV-GFP-infected bees 72 hpi were compared to those identified in naturally IAPV-infected bees (Chen et al. 2014, orange)9, bees infected with IAPV via oral inoculation (Galbraith et al., 2015, green)24, SBV and DWV-infected bees (Ryabov et al., 2016, pink)25, and a common DEG list that was compiled from 19 gene expression data sets including Varroa destructor-parasitized and virus-infected bees (Doublet et al., 2017)93 (purple). The Venn diagram was generated using an online tool131. Lists of DEGs from all studies that were used to generate the Venn diagram are provided in Supplementary Tables S14 and Venn diagram results are listed in S15. Summary Managed honey bee colonies in the US and parts of Europe have experienced high annual mortality levels (i.e, 33% average in US since 2006)4,5,9. In addition to other factors, several studies indicate that colony losses correlate with high pathogen incidence and abundance7,9–17, including infection by (+)ssRNA viruses20,56. The outcome of virus infection is influenced by many factors97, including virus strain98, agrochemical exposure97, nutritional status99, 80 genetic diversity of the colony88,100–104, colony management, mite parasitism16,89,105, co-infections106,107, and immune responses at both the colony and individual levels. Honey bee antiviral responses include canonical immune pathways (e.g., Toll, JAK/STAT, Imd, JNK), RNAi, and nonspecific dsRNA- mediated immune pathways, though the relative roles of these pathways and the mechanistic details of honey bee antiviral immune responses are not thoroughly understood. This is the first study to examine both individual and temporal honey bee transcriptional response to virus infection. Our results further indicate that honey bee antiviral defense includes canonical insect immune pathways, RNAi, and a nonspecific dsRNA-mediated antiviral defense mechanism. Transcriptional analysis of dsRNA-treated bees showed that dsRNA results in increased expression of genes involved in the JNK pathway, RNA helicases, and dsRNA transport, which parallels dsRNA transport and response mechanisms in fruit flies and mammals. While the results described herein implicate the role of numerous genes, several biological processes, and the involvement of Dicer and a probable cyclin-dependent kinase (MF116383), which had greatest sequence similarity with an Apis cerana gene (XM_017051141.1) in honey bee antiviral defense, future studies are required to further elucidate the honey bee antiviral defense network. Better understanding of honey bee antiviral defense mechanisms may aid in the development of strategies that reduce honey bee colony losses and furthers our knowledge of antiviral immune responses in 81 insects, which may ultimately reveal evolutionary conserved pathways in other organisms. Methods Honey Bees Frames of newly emerging bees were obtained from honey bee colonies maintained at Montana State University in Bozeman, MT, USA. Young (~ 24 hours post-emergence) female worker bees were utilized for experiments. The bees were housed in modified deli-containers at 32ºC and provided water and bee candy (i.e., powder sugar and corn syrup) for the duration of the experiment22,108. Sindbis Virus (SINV-GFP) Infection Trials Currently, there are no infectious honey bee virus clones, and though studies with semi-purified honey bee virus preparations have provided valuable information24,25,56, we utilized a recombinant model virus, Sindbis virus expressing green fluorescent protein (SINV-GFP)22,109. There are several advantages to utilizing this virus including the ability to control the dose of virus inoculum, monitor the progression of virus infection using GFP, and the assurance that the honey bees were not previously infected with, nor exposed to, SINV-GFP. In addition, Sindbis virus does not encode a suppressor of RNAi (VSR)110. We and others have used SINV-GFP to investigate honey bee22, fruit fly109, and mosquito111 antiviral defense mechanisms, thus facilitating comparison 82 of immune responses in both natural mosquito hosts and non-native hosts (i.e., honey bee and fruit fly) that have not co-evolved with this virus. In brief, honey bees were immobilized via incubation at 4ºC for 20 minutes and injected in the thorax with 3,750 plaque forming units (PFUs) of SINV-GFP22 diluted in 2 µl of 10 mM Tris buffer (pH 7.5) using a Harbo large capacity syringe equipped with disposable needles (Honey Bee Insemination Service; http://www.honeybeeinsemination.com/equipment2.html). The needles were prepared from borosilicate capillary tubes (0.8-1.10 x 100 mm) with a micropipette puller (Narishige Model PC-10, East Meadow, New York, USA). To investigate the role of dsRNA in honey bee antiviral defense, SINV-GFP was inoculated with multiple species and lengths of dsRNA (1 µg each), including virus-specific dsRNA (sp-dsRNA, 928 bp), nonspecific dsRNA corresponding to Drosophila C virus sequence (ns-dsRNA, 1,017 bp), and luciferase sequence (LUC dsRNA, 355 bp) (Supplementary Table S1). Bees were also co-injected with 1 µg high molecular weight polyinosinic-polycytidylic acid (poly(I:C)), InvivoGen, San Diego, California, USA), a synthetic mimic of dsRNA, or 1 µg nucleoside triphosphates (NTP), the positive and negative controls, respectively. After injection, bees typically recovered after 5 minutes at room temperature. Bees rarely died after injection (i.e., < 6%) and if so death was attributed to poor injection technique, as it was not associated with the substance injected (e.g., buffer, dsRNA, virus) and those bees were removed from the study. Mock- infection controls were also performed. Bees were collected at 6, 48, or 72 hours 83 post-infection (hpi). This time course was established to examine both early and late immune responses within a time frame that allowed for virus dissemination and infection, while maintaining optimal conditions for bees housed within the laboratory setting22. For each experimental treatment two additional biological repetitions that utilized bees from different colonies were evaluated at 48 and 72 hpi. dsRNA Preparation dsRNA was generated by in vitro transcription with T7 RNA polymerase109,112. T7 promoter containing dsDNA PCR-products (1-10 µg) were amplified using the primers listed in Supplementary Table S1, with the following thermocycler program: pre-incubation of 95ºC (5 min), 35 cycles of 95ºC (30 s), 60ºC (30 s), and 72ºC (1 min) followed by a final incubation at 72ºC (5 min), and sequence verified using Sanger sequencing. The PCR products served as templates for T7 polymerase transcription (100 µl reactions: NTPs (each 7.5 mM final), RNase OUT (40 units) (Invitrogen, Carlsbad, CA, USA), buffer (400 mM HEPES pH 7.5, 120 mM MgCl2, 10 mM Spermidine, 200 mM DTT); reactions were carried out at 37ºC overnight (8-10 hours). DNA templates were removed by incubating with RQ1 DNAse (1 unit; Promega, Madison, WI, USA) for 15 minutes at 37ºC. ssRNA products were ethanol precipitated, suspended in 200 µl Rnase-free water, and annealed by heating the reaction to 100ºC for 5 minutes and then slowly cooling to room temperature. dsRNA products were purified by phenol:chloroform extraction followed by ethanol precipitation; dsRNA for 84 injection was suspended in 10 mM Tris pH 7.5. dsRNA quality was assessed by agarose gel electrophoresis and spectrophotometer (NanoDrop 2000c, ThermoScientific, Waltham, MA, USA).The dsRNA quantity based on agarose gel band intensity was assessed using ImageJ113. dsRNA-Mediated Gene Knockdown The expression of two candidate antiviral genes: dicer (XM_016917734.1) and a novel transcript (MF116383) with 91% sequence identity with the Apis cerana probable cyclin-dependent serine/threonine-protein kinase DDB_G0292550 (XM_017051141.1), was reduced by RNAi-mediated gene knockdown (i.e., bees were injected with 1 µg of gene-specific dsRNA) (Supplementary Table S1). In order to assess the effects of gene knockdown on virus abundance, bees were infected with SINV-GFP (using methods as above) and co-injected with either gene-specific or nonspecific (DCV-specific) dsRNA (control). Fluorescence Microscopy The abdomens of individual honey bee bees were imaged using a Zeiss Stereoscope Stemi SV11 Apo, equipped with a Jenoptik Camera ProgResC14 Plus (Optical Systems GmBH P-07739 Jena). Fluorescence images were taken under fluorescent light with a GFP filter using standardized camera and exposure settings (i.e, 20x magnification, gain 60 and 150 ms exposure time). White light images were also taken 20x magnification (50 ms exposure). 85 Honey Bee Protein Lysate Preparation and Analysis Bees were dissected into head, thorax, and abdomen. The thoraxes and abdomens of bees collected at 72 hours post-infection were individually homogenized in 400 µl sterile H2O with two sterile glass beads (5 mm) via bead beating for 1.5 minutes (BioSpec Products 1001 Mini-BeadBeater-96 Homogenizer, Bartlesville, Oklahoma, USA). The thorax and abdomens of bees collected at 6 and 48 hours post-infection were individually homogenized in 400 µl sterile H2O with one sterile 4.5 mm steel ball bearing using a Tissue Lyzer II (Qiagen, Hilden, Germany) for 1 minute. Lysates were clarified by spinning for 12 minutes at 12,000 x g. For Western blot analysis, protein-containing honey bee lysates were combined with Laemmli buffer (95ºC for 3 min), electrophoresed on 12% acrylamide gels (Mini- PROTEAN TGX, BioRad, Hercules, CA), transferred to Immobilon-P PVDF membranes (EMD Millipore Corporation, Billerica, MA, USA), blocked with 5% milk in TBS buffer containing 0.1% Tween-20, incubated at 4ºC overnight with primary antibodies: either α-GFP (sc-8334; Santa Cruz Biotech, Santa Cruz, CA, USA) or α-β-actin (#4967L; Cell Signaling Technology, Danvers, MA, USA), washed, and then incubated with Horseradish peroxidase-conjugated anti-rabbit secondary antibody (ECL, GE Healthcare) for one hour at room temperature. The Western blots were developed with SuperSignal West Femto Maximum Sensitivity Substrate (Pierce, ThermoScientific, Waltham, MA, USA) and imaged with the Syngene G:Box F3 (software: G:Box Chemi-XR5). 86 ImageJ113 was used to quantify the sum of the pixels within β-actin and GFP regions in order to calculate and compare SINV-GFP levels in samples. The number of pixels within GFP regions were normalized based on the number of β- actin pixels as a loading control. Treatment groups in each Western blot set were assessed for statistical differences using Wilcoxon ranked-sum test. Honey Bee RNA Isolation and Purification TRizol reagent (Invitrogen), 400 µl was added to 400 µl of individual bee thorax and abdomen homogenate, and RNA was isolated according to manufacturer’s instructions. Prior to gene expression analysis by RNASeq or qPCR, RNA was further purified using Qiagen RNAeasy columns including on column DNase Treatment (Qiagen) to remove DNA from samples. RNA was quantified using a Thermo Scientific NanoDrop 2000 Spectrophotometer (Waltham, MA, USA). Reverse Transcription / cDNA Synthesis Reverse transcription reactions (25 µl) were performed using 500 ng of total RNA and random hexamer primers (500 ng) (IDT, Coralville, IA) incubated with Maloney murine leukemia virus (M-MLV) reverse transcriptase (Promega, Madison, WI) for 1 hour at 37 °C, according to the manufacturer’s instructions. Quantitative PCR (qPCR) Quantitative PCR was utilized to examine the relative abundance of virus and honey bee host gene expression in each sample using previously described 87 methods that are in accordance with published guidelines114. All qPCR reactions were performed in triplicate using 2 µL of cDNA as template. Each 20 µl reaction was composed of cDNA template, 1X SYBR Green (Invitrogen, Cat.57563), 1X Choice Taq Master Mix (Denville Scientific Inc., Holliston, MA), 3 mM MgCl2, and forward and reverse primers (600 nM each). A CFX Connect Real Time instrument (BioRad, Hercules, CA) was utilized for qPCR, the thermo-profile for virus (e.g., SINV-GFP and BQCV) and Apis mellifera rpl8 analyses consisted of a single pre-incubation 95ºC (3 min), 40 cycles of 95ºC (5 s), and 60ºC (20 s); primers listed in Supplementary Table S1. Positive and negative controls, including the use of RNA templates from no RT enzyme cDNA reactions, were included for all qPCR analyses and exhibited the expected results. To quantify the viral RNA (i.e., genome and transcript) abundance in each sample target SINV-GFP qPCR amplicons were cloned into the pGEM-T (Promega) vector, as described in Flenniken and Andino et al. 201322. Plasmid standards, containing from 109 to 103 copies per reaction, were used as qPCR templates to assess primer efficiency and generate the SINV-specific standard curve used to quantify the viral RNA copy numbers within this range of detection22. The qPCR primers for RNAseq validation were designed using Primer3Plus and typically designed to have 60ºC annealing temperatures115 (Supplementary Table S1). Melt point analysis and 2% agarose gel electrophoresis ensured qPCR specificity116. Primer efficiencies were evaluated using qPCR assays of cDNA and plasmid dilution series, and calculated by 88 plotting log10 of the concentration versus the crossing point threshold (C(t)) values and using the primer efficiency equation, (10(1/Slope)-1) x 100) (Supplementary Table S16). The ΔΔC(t) method was used to calculate relative abundance of Sindbis- GFP in individual bees because it was most accurate since this method ensures that results are not skewed by inadvertent differences in RNA reverse- transcription efficiencies and starting cDNA template abundance114,116,117. Specifically, the ΔC(t) for each sample was calculated by subtracting the Am rpl8 C(t) from the SINV-GFP C(t). The honey bee gene encoding ribosomal protein 8, Am rpl8, was selected as an appropriate housekeeping gene for qPCR, since it has been utilized in several other studies118–121. Analysis of the RNASeq data presented herein confirmed that rpl8 expression was not significantly different in all sequenced libraries. The ΔΔC(t) was calculated by subtracting the average virus-infected ΔC(t) values from the ΔCt values for each treatment group. For host gene expression analyses and RNAseq validation, the percent gene expression for each gene of interest (GOI) was calculated using the following formula: 2^- ΔΔC(t) x100 = % gene expression, in which ΔC(t)=GOI C(t)- rpl8 C(t), and ΔΔC(t)= sample ΔC(t) – mock-infected control ΔC(t). To statistically examine the differences between the relative virus abundance of each treatment group for each time point, the SINV-GFP copy numbers and calculated relative abundance of each sample and was imported into R122. Based on previous work22,44, we hypothesized that bees (n=10) co- 89 injected with dsRNA or poly(I:C) would have decreased relative virus abundance as compared to the virus-only treated group. To examine relative virus abundance between treatment groups (e.g., virus-infected bees an dsRNA or poly(I:C) co-treated bees) that had equal variance and normal distribution we performed one-tailed students t-tests. Analysis of honey bee host gene expression revealed unequal variance between treatments groups and thus Welch’s t-tests were used to identify statistical differences in host gene expression. RNAseq Library Preparation and Sequencing Bees were obtained from honey bee colonies which are subject to naturally occurring infections that may confound transcriptional results123, so individual bee cDNA was screened for pre-existing infections via PCR for several honey bee pathogens (Supplementary Table S3) using the following PCR thermocycler protocol: 95 °C (5 min); 35 cycles of 95 °C (30 s), 57 °C (30 s), and 72 °C (30 s), followed by final elongation at 72 °C for 4 minutes. If the sample was positive for a pathogen, the quantity was then assessed using qPCR. The RNA isolated from the abdomens of at least three representative bees with low (< 2,000 DWV and/or BQCV virus genome copies versus 7 x 104 - 7 x 106 SINV- GFP copies) to no pre-existing infections for each treatment group and time point were selected for transcriptome sequencing for a total of 47 individual bees (Supplementary Table S3). The abdomen was selected as the tissue of interest for RNASeq analysis of honey bee immune responses since virus infections 90 would have naturally disseminated into this site post inoculation via intra-thoracic injection and it houses the fat body, which is an important tissue for immune function. Prior to RNASeq library preparation, RNA from each sample was further purified using Qiagen RNeasy columns, including on-column DNase Treatment (Qiagen). The RNA quality was assessed using an Agilent 2200 Bioanalyzer (Santa Clara, CA, USA) and quantified with a Thermo Scientific NanoDrop 2000 Spectrophotometer (Waltham, MA, USA). The RNA was sent to Roy J. Carver Biotechnology Center at the University of Illinois at Urbana–Champaign for library preparation (Illumina TruSeq Stranded RNA Sample Prep kit). The libraries were prepared and pooled by experimental time point (15-17 samples per pool) in equimolar concentration and was quantitated using an Illumina Library quantification kit (Kapa). Each pool was sequenced on one lane for 101 cycles from each end of the fragments on a HiSeq2500 using a TruSeq SBS sequencing kit version 4. Sequencing yielded ~12 million reads per sample, which corresponding to at least 9.7 fold coverage using the standard equation that is used for genome / RNASeq coverage124 (i.e., coverage = number reads (12 x 106 reads) x average read length (200bp) / length of genome (246.927 million bp for honey bee125)) (Supplementary Table S2), which is in the range of coverage reported in other honey bee transcriptome studies24,25,126. Previous work has shown that sequencing as few as 1 million reads can provide significant and valuable information of differential expression patterns in honey bees126. 91 Sequence data was deposited into the NCBI Sequence Read Archive under accession number SRP101337 and is also linked with NCBI BioProject #PRJNA377749. Specific code used for RNAseq analysis is listed in Appendix G. Briefly, the programs FastQC and fastx-toolkit were used to remove low quality reads (2,000x coverage) (Supplementary Table S1 and Fig. S9). Together nucleotide and amino acid alignments indicate the RNAseq reads aligning to LOC725387 are most similar to a computationally predicted A. cerana cyclin-dependent serine/threonine-protein kinase DDB_G0292550 (Supplementary Figure 2.15), therefore, we refer to the gene identified herein as A. mellifera probable cyclin-dependent serine/threonine-protein kinase (Supplementary Figure 2.15) and submitted the sequence of this transcript to NCBI (MF116383). 96 Supplementary Figures Figure 2.7 Fluorescence microscopy of virus-infected bees indicates that dsRNA reduced virus abundance. Fluorescence microscope images of the abdomens of virus-infected bees indicate that dsRNA reduced virus abundance. Pictured are representative images of mock-infected bees, virus-infected bees, virus-infected and virus-specific dsRNA (sp-dsRNA)-treated bees, and virus-infected and non- specific dsRNA (ns-dsRNA)-treated bees at 72 hpi using fluorescence (top row) and light microscopy (bottom row). Virus-infected bees that were treated with either sp-dsRNA or ns-dsRNA exhibited decreased fluorescence as compared to virus-infected bees that were not treated with dsRNA. virus mock-infected virus and sp-dsRNA virus and ns-dsRNA 97 Figure 2.8 Viral GFP production was reduced in dsRNA treated honey bees. Western blot analysis of individual bee lysates 72 hours post infection (hpi) using α-GFP and α-β-actin antibodies. (A) Bees treated with dsRNA, sp-dsRNA (lanes 8-12) and (B) ns-dsRNA (lanes 13-17), exhibited reduced Sindbis-GFP as compared to (A) virus infected bees (lanes 1-5) and (B) virus and NTP-treated bees (lanes 20-24); (-) denotes pooled samples from mock-infected bees and (+) denotes pooled samples from virus-infected positive control samples. ImageJ was utilized to quantify the pixel count in each band, and the GFP: β-actin ratio was calculated for each sample and plotted on a box-and-whisker; p-values were assessed via Wilcoxon rank-sum tests. The median GFP to actin ratio was 1.0 for virus-infected bees, whereas the median ratio in bees that were co-injected with sp-dsRNA was 0.3 (p = 0.03). Similarly, co-injection of virus and ns-dsRNA reduced the relative production of SINV-GFP as compared to co-injected with virus and NTPs, with median ratios of 0.5 versus 1.1 (p = 0.01). The sample size for each treatment group in this representative Western blot was five, although numerous other samples were analyzed via Western blot. ȕDFWLQ SINV*)P   YLUXV YLUXVVSGV51$   ȕDFWLQ SINV*)P   YLUXVQVGV51$ YLUXV173V   0.4 0.6 0.8 1.0 *)3VLJQDOQRUPDOL]HGWRȕDFWLQVLJQDO S  YLUXVQVGV51$ YLUXV173V *)3VLJQDOQRUPDOL]HGWRȕDFWLQVLJQDO S  0.2 0.4 0.6 0.8 1.0 YLUXV YLUXVVSGV51$1 2  4  6 7 8 9 10 11 12  14  16 17 18 19 20 21 22  24 $ B 98 Figure 2.9 Virus abundance in honey bees increased with time post-infection. Relative virus RNA abundance (including both virus genomes and transcripts) was assessed in the abdomens of virus-infected bees 6, 48, and 72 hpi (n=10). Little to no virus was detected in virus-infected bees 6 hpi. Relative virus abundance increased as time post-infection increased. Relative virus abundance was assessed using qPCR and ΔΔCT analysis and Am rpl8 was used as the house keeping gene and ΔΔCT data was normalized to virus-infected bees at 72 hpi. The bars represent standard error of the mean. 99 Figure 2.10 Relative virus RNA abundance was reduced in dsRNA-treated bees, as compared to virus-infected bees, in three biological replicates. The abdomens of bees (A) 48 and (B) 72 hours post-infection (hpi) have reduced relative virus abundance when treated with dsRNA, whether it is sequence-specific to the virus (sp-dsRNA, dotted purple) or nonspecific (ns-dsRNA, checkered blue). 100 Figure 2.10 continued This result was consistent in three different biological replicates, including bees from different colonies. Treatment with NTPs (wavy orange lines) did not result in different relative virus abundance. Biological replicate 1 (rep 1) includes bees that were utilized for RNAseq analysis for which their relative virus abundance is also shown in Fig 2. Biological replicates 2 and 3 were bees collected from different colonies and likely represent bees with different genetic compositions. Relative virus abundance was assessed using qPCR and ΔΔCT analysis using Am rpl8 as the house keeping gene; statistical differences between treatment and virus-infected bees (n=10) were performed using student’s t-test, *p< 0.05, **p< 0.005. The bars represent standard error of the mean. 101 102 Figure 2.11 Venn diagram of shared and unique DEGs in virus-infected and/or dsRNA-treated bees. Venn diagrams were utilized to identify shared and unique differentially expressed genes (DEGs) among treatment groups within each time points post-infection. By identifying DEGs that were shared by virus-infected bees (green), dsRNA-treated bees (orange), bees treated with both virus and virus-specific dsRNA (sp-dsRNA, blue), and bees treated with virus and nonspecific dsRNA (ns-dsRNA, purple), we could identify the number of genes that are induced by biologically relevant levels of dsRNA at (A) 6 hpi, (B) 48 hpi, and (C) 72 hpi, as well as identify DEGs specific to each treatment or each specific comparison of treatments. (D) The shared DEGs in all virus-infected bees at 48 hpi and 72 hpi were too large to visualize in Venn diagrams, therefore the number of shared DEGs were expressed in table format. In order to identify genes that may contribute to enhanced antiviral defense in dsRNA-treated bees (Fig 2), the shared genes with increased expression in all virus-infected and dsRNA treated bees, but not shared with the bees that were only infected with virus (five genes), were assessed. Five genes had increased expression in virus- infected and dsRNA treated bees, but not in bees only infected with virus. There were two genes with decreased expression in all virus and dsRNA treated bees, but not in virus treated bees. 103 104 Figure 2.12 Gene ontology enrichment analysis of DEGs determined that biological processes including phosphorylation morphogenesis, transcription, and development were differentially regulated in virus-infected bees 6, 48, and 72 hpi. Gene ontology (GO) enrichment analysis was performed using DAVID to identify the top biological processes (BP) perturbed by virus infection at 6 hpi, 48, and 72 hpi. (A) DEGs at 6 hpi were enriched for several GO terms, including phosphorylation (14 genes), pattern specification (12 genes), imaginal disc development (12 genes), and cell death (5 genes). (B) Virus-infected bees 48 hpi exhibited DEGs enriched for functions including morphogenesis (20 genes), cell adhesion (10 genes), and immune response (8 genes). (C) Virus-infected bees 72 hpi exhibited DEGs enriched for functions including transcriptional regulation, cell morphogenesis, gene silencing, and protein folding. *Similar to 48 and 72 hpi, several genes involved in transcriptional regulation were also differentially expressed at 6 hpi and were worth noting, but was not significantly enriched. DAVID gene enrichment p-values are listed to the right of each graph. 105 Figure 2.13 qPCR analysis of a subset of DEGs confirms RNAseq results in bees at 48 and 72 hpi. Transcriptome level sequencing (RNAseq) identified hundreds of honey bee (Apis mellifera) genes that exhibited differential expression in the context of virus-infection and/or dsRNA-treatment, and qPCR was used to validate RNAseq results of 14 genes that exhibited increased expression in bees at 48 and 72 hpi (q < 0.05 after BH correction, Supplementary Table S6). A B 106 Fig. 2.13 continued. (A) The nine DEGs exhibited increased expression in virus- infected bees (green stripes) and virus and dsRNA treated bees 48 hpi (sp- and ns dsRNA, dotted purple and checkered blue) via qPCR analysis including hsp90, abaecin, cyclin-dependent kinase, ago2, dicer, igfn3-10, orb-2-like, mfs- type transporter, and fam102b; whereas jra did not, which was expected based on RNAseq analysis. Two DEGs, jra, and fam102b, exhibited increased expression in the dsRNA (dotted orange) bees, which was expected. (B) The majority of the 11 DEGs displayed increased expression in virus-infected bees (green stripes) and virus and dsRNA treated bees 72 hpi (sp- and ns-dsRNA, dotted purple and checkered blue) via qPCR analysis including hsp90, abaecin, cyclin-dependent kinase, ago2, dicer, orb-2-like, igfn3-10, mfs-type transporter, DNA n6-methyl adenine demethylase, formin-j, 3a1-transporter-like; whereas titin-like did not. Two DEGs, jra, and fam102b, exhibited increased expression in the dsRNA bees (dotted orange). Relative gene transcript abundance was assessed using qPCR and ΔΔCT analysis using Am rpl8 as the house keeping gene; expression in each treatment group (n=5) was compared to mock-infected controls. Statistical differences in gene expression between mock-infected and treatment bees were performed using Welch’s two sample t-tests, *p< 0.05, **p< 0.005. The bars are standard error of the mean. 107 e xp re ss io n re la tiv e to m oc k- in fe ct ed c on tro ls 0 2 4 6 hsp90 expression A rep 1 0 1 2 3 rep 2 rep 3 mock virus virus + sp-dsRNA virus + ns-dsRNA individual bees pooled bee samples 0 1 2 3 cyclin dependent kinase expression B rep 1 e xp re ss io n re la tiv e to m oc k- in fe ct ed c on tro ls 0 1 2 rep 2 rep 3 mock virus virus + sp-dsRNA virus + ns-dsRNA individual bees pooled bee samples S7 Fig. qPCR analyses confirmed differentially expression of putative antiviral genes in bees from differ- ent genetic backgrounds Transcriptome analysis (RNAseq) of virus-infected and/or dsRNA-treated bees identified numerous candidate honey bee antiviral genes that exhibited greater expression as compared to mock-infected controls (q-value < 0.05 after BH correction), including the genes (A) hsp90, (B) cyclin-dependent serine/threonine kinase, (C) ago2, (D) dicer, (E) mfs-transporter, (F) formin-j, (G) tet-2, (H) orb-2, (I) 3a1-like, (J) and igfn 3-10. qPCR analysis was performed for these genes on the bees that were sequenced (rep 1). (A-J) In order to confirm that these genes consistently exhibit increased expression in response to virus infection in honey bees with different genetic backgrounds, qPCR was performed on pooled samples (5 bees per pool) from virus-infected bees that were collected from different honey bee colonies (reps 2 and 3). (A-F) Several of these genes displayed increased expression via qPCR in all three biological replicates (A-D, G, J), but not as robustly, perhaps due to the fact that the samples were pooled as opposed to individually analyzed as in rep 1. (E, H, I) Other genes, displayed increased expression in 2 of the 3 biological replicates. (F) One gene, igfn 3-10, only exhibited increased expression in the sequenced bees (rep 1) and not in the other biological replicates (rep 2 and 3). Normalized gene transcript abundance was DVVHVVHGXVLQJT3&5DQGǻǻ&7DQDO\VLVXVLQJ$PUSODVWKHKRXVHNHHSLQJJHQH7KHEDUVDUHVWDQGDUGHUURURIWKH mean. 108 0 4  12 16 20 ago2 expression C rep 1 e xp re ss io n re la tiv e to m oc k- in fe ct ed c on tro ls 0 1 2 3 4 rep 2 rep 3 mock virus virus + sp-dsRNA virus + ns-dsRNA individual bees pooled bee samples 0 2 4 6  10 dicer expression D rep 1 e xp re ss io n re la tiv e to m oc k- in fe ct ed c on tro ls 0 3 2 1 rep 2 rep 3 individual bees pooled bee samples mock virus virus + sp-dsRNA virus + ns-dsRNA E 0 2 4 6 orb2-like expression rep 1 e xp re ss io n re la tiv e to m oc k- in fe ct ed c on tro ls 0 1 2 3 rep 2 rep 3 individual bees pooled bee samples mock virus virus + sp-dsRNA virus + ns-dsRNA 109 0 4  12 16 igfn 3-10 expression F rep 1 e xp re ss io n re la tiv e to m oc k- in fe ct ed c on tro ls 0 1 2 3 4 rep 2 rep 3 individual bees pooled bee samples mock virus virus + sp-dsRNA virus + ns-dsRNA 0 1 2 3 4 mfs transporter expression G rep 1 e xp re ss io n re la tiv e to m oc k- in fe ct ed c on tro ls 0 1 2 rep 2 rep 3 individual bees pooled bee samples mock virus virus + sp-dsRNA virus + ns-dsRNA 0 1 2 rep 1 DNA n6-methyl adenine demethylase expression e xp re ss io n re la tiv e to m oc k- in fe ct ed c on tro ls 0 1 2 rep 2 rep 3 H individual bees pooled bee samples mock virus virus + sp-dsRNA virus + ns-dsRNA 110 Figure 2.14 qPCR analyses confirmed differentially expression of putative antiviral genes in bees from different genetic backgrounds. Transcriptome analysis (RNAseq) of virus-infected and/or dsRNA-treated bees identified numerous candidate honey bee antiviral genes that exhibited greater expression as compared to mock-infected controls (q-value < 0.05 after BH correction), including the genes (A) hsp90, (B) cyclin-dependent serine/threonine kinase, (C) ago2, (D) dicer, (E) mfs-transporter, (F) formin-j, (G) tet-2, (H) orb-2, (I) 3a1-like, (J) and igfn 3-10. qPCR analysis was performed for these genes on the bees that were sequenced (rep 1). (A-J) In order to confirm that these genes consistently exhibit increased expression in response to virus infection in honey bees with different genetic backgrounds, qPCR was performed on pooled samples (5 bees per pool) from virus-infected bees that were collected from different honey bee colonies (reps 2 and 3). 0 0.2 0.6 1.0 1.4  slco31-like expressionJ rep 1 e xp re ss io n re la tiv e to m oc k- in fe ct ed c on tro ls 0 0.2 0.6 1.4  rep 2 rep 3 1.0 individual bees pooled bee samples mock virus virus + sp-dsRNA virus + ns-dsRNA 0 2 4 6 formin-j expression I rep 1 e xp re ss io n re la tiv e to m oc k- in fe ct ed c on tro ls 0 2 4 6 rep 2 rep 3 individual bees pooled bee samples mock virus virus + sp-dsRNA virus + ns-dsRNA 111 Figure 2.14 continued. (A-F) Several of these genes displayed increased expression via qPCR in all three biological replicates (A-D, G, J), but not as robustly, perhaps due to the fact that the samples were pooled as opposed to individually analyzed as in rep 1. (E, H, I) Other genes, displayed increased expression in 2 of the 3 biological replicates. (F) One gene, igfn 3-10, only exhibited increased expression in the sequenced bees (rep 1) and not in the other biological replicates (rep 2 and 3). Normalized gene transcript abundance was assessed using qPCR and ΔΔCT analysis using Am rpl8 as the house keeping gene. The bars are standard error of the mean. 112 Figure 2.15 Identification of previously unrecognized honey bee transcript, A. mellifera probable cyclin-dependent serine/threonine-protein kinase transcript (MF116383). RNASeq analysis determined that reads aligning to LOC725387 were more abundant in virus-infected bees. To identify the gene or genes encoded by these differentially expressed reads, the consensus nucleotide sequence was used to query the NCBI Nucleotide collection (nr/nt) and A. mellifera databases using blastn, Sanger sequencing was performed to verify transcript sequence and length, and the results were evaluated using Geneious. (A) The updated LOC725387 A. mellifera probable cyclin-dependent serine/threonine-protein kinase transcript (5,158 nt, MF116383), A. cerana probable cyclin-dependent serine/threonine-protein kinase transcript (XM_017051141.1), A. dorsata GATA zinc finger domain-containing protein 14- like, transcript variant X1 (XM_006614476.1), and the original A. mellifera probable serine/threonine-protein kinase clkA transcript (XM_001121241.4) were aligned in Geneious (65% similarity Cost Matrix, 12 nt gap open penalty, 3 nt gap extension penalty, 2 refinement iterations). The updated A. mellifera transcript shared 85.2% nucleotide identity (4,271/5,021 nt) with entire A. cerana transcript. The previously annotated A. mellifera transcript (XM_001121241.4) is 1,587 nucleotides in length and 99.8% identical to the 5’ end of the longer updated A. mellifera transcript (MF116383). 113 Figure 2.15 continued. There is mismatch between the updated A. mellifera transcript (MF116383) and the previously annotated A. mellifera transcript (XM_001121241.4) at position 544 nt, and the updated transcript contains an additional nucleotide at two sites (i.e., 1,303 nt and 1,342 nt). Gray regions indicate conserved nt sequence and black regions indicate nucleotide mismatches. (B) The amino acid sequences of A. mellifera probable cyclin- dependent serine/threonine-protein kinase (MF116383), A. cerana (XP_016906623.1), A. dorsata (XP_006616974.1), and A. mellifera (XP_001121241.2) were aligned using Geneious (Blosum62 parameters). The translated A. mellifera transcript described herein aligned the best with the A. cerana probable cyclin-dependent serine/threonine-protein kinase sequence with 1,234 identical amino acids (81.5% identity with gaps totaling 174 aa). Together nucleotide and amino acid alignments indicate that the LOC725387 RNASeq consensus is most similar to A. cerana probable cyclin- dependent serine/threonine-protein kinase (XM_017051141.1), therefore, we refer to this gene as A. mellifera probable cyclin-dependent serine/threonine- protein kinase and deposited the sequence in GenBank Accession MF116383. 114 re la tiv e ki na se e xp re ss io n 0 2 3 1 vir us + ns -ds RN A vir us ns -ds RN A ki na se ds RN A 4 mo ck ns -ds RN A mo ck vir us + ns -ds RN A vir us ns -ds RN A mo ck * *** * ki na se ds RN A vi rus + kin as e d sR NA vir us + ns -ds RN A ki na se ds RN A A 24 hpi 48 hpi 72 hpi re la tiv e di ce r e xp re ss io n 0 2 3 1 vir us + ns -ds RN A vir us + di ce r d sR NA vir us ns -ds RN A dic er ds RN A 4 mo ck ns -ds RN A dic er ds RN A mo ck vir us + ns -ds RN A vir us ns -ds RN A mo ck vir us + di ce r d sR NA dic er ds RN A * * * B 24 hpi 48 hpi 72 hpi S10 Fig. RNAi-mediated gene knockdown of dicer and cyclin-dependent kinase was efficient at 48 and 72 hpi and resulted in increased virus abundance To further investigate the role of dicer and cyclin-dependent kinase in honey bee antiviral defense, we utilized RNAi-mediated gene knock down to reduce their expression and qPCR to confirm reduced target gene expression and determine the impact on virus abundance (Fig 7). (A) In bees treated with dsRNA in the absence of virus at 48 and 72 hpi, cyclin dependent kinase expression was decreased in kinase specific dsRNA treated bees (spotted orange) by 46% (*p<0.05) and 50% (**p< 0.005) as compared to the ns-dsRNA controls (red horizontal lines). In virus and dsRNA-treated bees at 48 and 72 hpi, cyclin dependent kinase expression was decreased in kinase specific dsRNA treated bees (black diagonal lines) by 40% (*p<0.05) and 30% (*p< 0.05) as compared to the ns-dsRNA controls (checkered blue). (B) In bees treated with dsRNA in the absence of virus at 48 and 72 hpi, dicer expression was decreased in dicer specific dsRNA treated bees (vertical purple lines) by 56% (*p<0.05) and 31% (*p< 0.05) as compared to the ns-dsRNA controls (red horizontal lines). In virus-infected bees at 48 hpi, dicer expression was decreased in dicer specific dsRNA treated bees (spotted blue) by 32% (*p< 0.05), but it was not reduced at 72 hpi as compared to the ns-dsRNA controls. (C) The virus abundance of kinase and dicer specific dsRNA-treated bees had a trend of increased virus abundance compared to the ns-dsRNA controls, but this was not significant at 48 hpi. Virus abundance in kinase and dicer specific dsRNA-treated bees at 72 hpi was increased by 48% (*p< 0.05) and 44% (*p< 0.05) compared to the ns-dsRNA control. 5HODWLYHJHQHWUDQVFULSWDEXQGDQFHZDVDVVHVVHGXVLQJT3&5DQGǻǻ&7DQDO\VLVXVLQJ$PUSODVWKHKRXVHNHHSLQJJHQH expression in each treatment group was compared to mock-infected controls. Percent relative virus abundance for each sample ZDVGHWHUPLQHGYLDT3&5DQGǻǻ&7DQDO\VLVXVLQJ$PUSODVWKHKRXVHNHHSLQJJHQH6WDWLVWLFDOGLIIHUHQFHVEHWZHHQWUHDW- PHQW Q  DQGYLUXVLQIHFWHGDQGQVGV51$EHHVZHUHGHWHUPLQHGXVLQJW\SHWZRRQHVLGHG6WXGHQW¶VWWHVWV S S 0.005. The bars are standard error of the mean. 40 80 120 no rm al iz ed v iru s ab un da nc e 160 * * virus virus + ns-dsRNA virus + dicer- dsRNA virus + kinase- dsRNA virus virus + ns-dsRNA virus + dicer- dsRNA virus + kinase- dsRNA 0 C 48 hpi 72 hpi * 115 Figure 2.16 RNAi-mediated gene knockdown of dicer and cyclin-dependent kinase was efficient at 48 and 72 hpi and resulted in increased virus abundance. To further investigate the role of dicer and cyclin-dependent kinase in honey bee antiviral defense, we utilized RNAi-mediated gene knock down to reduce their expression and qPCR to confirm reduced target gene expression and determine the impact on virus abundance (Fig 5). (A) In bees treated with dsRNA in the absence of virus at 48 and 72 hpi, cyclin dependent kinase expression was decreased in kinase specific dsRNA treated bees (spotted orange) by 46% (*p<0.05) and 50% (**p< 0.005) as compared to the ns-dsRNA controls (red horizontal lines). In virus and dsRNA-treated bees at 48 and 72 hpi, cyclin dependent kinase expression was decreased in kinase specific dsRNA treated bees (black diagonal lines) by 40% (*p<0.05) and 30% (*p< 0.05) as compared to the ns-dsRNA controls (checkered blue). (B) In bees treated with dsRNA in the absence of virus at 48 and 72 hpi, dicer expression was decreased in dicer specific dsRNA treated bees (vertical purple lines) by 56% (*p<0.05) and 31% (*p< 0.05) as compared to the ns-dsRNA controls (red horizontal lines). In virus- infected bees at 48 hpi, dicer expression was decreased in dicer specific dsRNA treated bees (spotted blue) by 32% (*p< 0.05), but it was not reduced at 72 hpi as compared to the ns-dsRNA controls. (C) The virus abundance of kinase and dicer specific dsRNA-treated bees had a trend of increased virus abundance compared to the ns-dsRNA controls, but this was not significant at 48 hpi. Virus abundance in kinase and dicer specific dsRNA-treated bees at 72 hpi was increased by 48% (*p< 0.05) and 44% (*p< 0.05) compared to the ns-dsRNA control. Relative gene transcript abundance was assessed using qPCR and ΔΔCT analysis, using Am rpl8 as the house keeping gene; expression in each treatment group was compared to mock-infected controls. Percent relative virus abundance for each sample was determined via qPCR and ΔΔCT analysis; using Am rpl8 as the house keeping gene. Statistical differences between treatment (n=10) and virus-infected and ns-dsRNA bees were determined using type two, one-sided Student’s t-tests, *p< 0.05, **p< 0.005. The bars are standard error of the mean. 116 References Cited 1. Gallai, N., Salles, J.-M., Settele, J. & Vaissière, B. E. Economic valuation of the vulnerability of world agriculture confronted with pollinator decline. Ecol. Econ. 68, 810–821 (2009). 2. Calderone, N. W. 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Geneious Basic: An integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28, 1647–1649 (2012). 128 WHEAT STEM SAWFLY – MICROBIAL INTERACTIONS The wheat stem sawfly (Cephus cinctus) is also a hymenopteran insect and is native to the Western U.S. and Canada. The wheat stem sawfly (WSS) is a major wheat pest, causing $100–350 million USD in wheat yield losses annually1–3. In native landscapes, WSS utilize natural grasses such as large stem prairie grasses including Western wheat grass and basin wildrye and other wheatgrasses of the genus Agropyron as reproductive habitat4. In agricultural areas, WSS have adapted to using wheat as a reproductive habitat since its introduction into the Northwest, with the first report of WSS infestation in Manitoba spring wheat crops in 18955 and the first reports of WSS in winter wheat in the 1980’s5. The adaptation of WSS to reproduce in wheat required synchronization of its life cycle to the phenological development of the wheat; ovideposition, larval growth, and pupation occurs within the host plant4. The females oviposit their eggs into wheat stems, preferring wider and more hollowed stems4. After WSS eggs hatch, the larvae consume and deplete the internal stem vasculature, working their way towards the base of the stem. This makes the stem more susceptible to bending or breaking, also known as lodging6–8. Growers can employ several different methods to control WSS-associated crop losses, including operational control measures such as crop rotation, tillage, and swathing, application of insecticides, and crop introduction of wasps (i.e., Bracon cephi and Bracon lissogaster) that parasitize WSS9. Most research efforts aimed 129 at curbing this problem have been to develop WSS-resistant wheat cultivars, including solid-stemmed varieties that provide less reproductive habitat for the WSS1,6,10, but these wheat varieties have often lower yield potentials and reduced resistance to disease3. Alternatively, targeting the endosymbiotic microbes of WSS may serve as a feasible biocontrol solution. Metabolic contributions of endogenous microbes are often critical to nutritive, physiological, immunological, and developmental health in animals4. Insects, in particular, have many well-described long-term relationships with endosymbiotic microbes, including both obligate and facultative microbial interactions. Insect host-obligate microbe interactions are longstanding relationships in which the host has become dependent on the microbe for digestion or providing necessary nutrients that may be lacking in the host diet11,12. These microbes are typically transmitted vertically to offspring11, which can greatly effect the evolution and ecology of both host and parasite13. Because plants are low in nitrogen, many plant-feeding insects have developed mutualistic relationships with obligate bacteria to meet their nutritional needs14. A classic example is the pea aphid (Acyrthosiphon pisum) and its intracellular symbiont Buchnera aphidicola. The aphid’s primary diet consists of plant sap, which provides the host with carbohydrates, but not nitrogen, which is important for amino acid synthesis. Consequently, the aphid has developed specialized cells called bacteriocytes that house Buchnera. Buchnera in turn, produces nitrogen that supplement the aphid’s sap diet14,15. 130 In contrast, facultative endosymbionts are not necessary for host survival10. Colonization of the insect host by facultative endosymbiotic bacteria can result in reduced or improved host fitness (i.e., be mutualistic or parasitic) or have no effect (i.e, commensal)16. Wolbachia and Spiroplasma are two of the most prevalent and characterized insect-infecting facultative endosymbiotic genera. Wolbachia are Gram-negative, intracellular, members of the Rickettsial family that widely colonize and are maternally transmitted in at least 40% of arthropod species16 and various nematode species18. While Wolbachia- nematode interactions are typically mutualistic18, most Wolbachia-insect interactions are parasitic, causing reproductive phenotypes such as feminization, male killing, parthenogenesis, and cytoplasmic incompatibility (CI)19. The specific reproductive phenotype caused by Wolbachia varies by insect host and Wolbachia strain19. In addition to affecting host reproduction, Wolbachia can shorten host lifespan20–23. Interestingly, many of the same Wolbachia strains that cause CI also inhibit replication of RNA viruses such as Dengue24, West Nile25, Chikungunya24, Zika26, and other arboviruses27,28 in both fruit flies and mosquitoes, so Wolbachia has been implemented as biocontrol method for reducing virus-vectoring insect populations. Spiroplasma are Gram-positive, small, helical, motile bacteria lacking cell walls29. In contrast to Wolbachia, most species do not require host cells for replication, and they can also be found in the gut or hemolymph of insect hosts and/or plant phloem13. Different strains of Spiroplasma are heritable in 5-10% of 131 insects and present and non-heritable in >50% of insect species30. Many species have also been reported to be pathogenic or induce male-killing in fruit flies, ladybird beetles, and butterflies13,30, but most reported Spiroplasma species are commensal or do not induce obvious phenotypes. Some species have also been reported to be mutualistic by improving host defense against fungal pathogens31 and parasitoid wasps32–34. The genus Spiroplasma belong to the Entomoplasmatales order, which contains four clades: Citri-Chrysopicola-Mirum (CCM), Apis, Ixodetis, and Mycoides–Entomoplasmataceae. The CCM, Apis clade, and Ixodetis clades only include Spiroplasma species, while the Mycoides–Entomoplasmataceae clade consists of Mycoplasma, Mesoplasma and Entomoplasma species12. There is little correlation between Spiroplasma phylogeny and host range. Rather, each Spiroplasma clade has a wide variety of host-pathogen relationships, including both pathogenic and commensal Spiroplasma species. Of the known fruit fly- associated Spiroplasma species, there a few strains from both the Ixodetis and CCM clades, with the primary male-killing strains in the CCM clade30. Additionally, the Apis clade contains both the pathogenic mosquito-associated species S. culicicola and S. taiwanense and two non-pathogenic mosquito- associated species, S. sabaudiense and S. diminutum36–38. There are two known Spiroplasma species known to cause disease in honey bees which belong in two separate clades: S. melliferum in the CCM clade and S. Apis in the Apis clade39– 41. Furthermore, there are also two closely related commensal deerfly-associated 132 strains, S. chrysopicola and S. syrphydicola, that belong to the Citri- Chrysopicola-Mirum clade37. Other Spiroplasma species are major plant pathogens that are vectored by insects. Spiroplasma citri is a major pathogen in citrus fruit (i.e., causes citrus stubborn) and is transmitted by leaf hoppers that feed on the plants42. Similarly, Spiroplasma kunkelii, which is also vectored by leaf hoppers, causes corn stunt in maize43. To date, roughly 24 Spiroplasma genomes have been sequenced and deposited as either draft or complete assemblies in the NCBI genome database. Similar to other insect endosymbiotic bacterial genomes44,45, Spiroplasma genomes are small in size, ranging from 0.8-2.2 Mb and are low in GC content35 (Table 3.1). Spiroplasma genomes are difficult to assemble because they contain large amounts of repetitive mobile gene elements, and Citri and Mirum genomes tend to be laden with plectovirus-related sequences related to plectoviruses, a single-stranded DNA bacteriophage that commonly infects bacteria lacking cell walls35,46. While many Ixodetis clade members of Spiroplasma strains have been identified31,47–56, there is currently 16S rRNA (Table 3.2) and RNA polymerase beta unit (rpoB) sequence data available but no full genome sequences available. 133 Hypothesis Statement And Project Summary Intellectual Merit Wheat stem sawflies (Cephus cinctus) are a major wheat pest in North West regions of North America. Wheat growers often utilize solid-stem varieties of wheat to mitigate sawfly-associated losses, but many of these varieties have reduced yields. An additional solution to reduce sawfly-associated losses may be to alter or disturb the microbes colonizing the wheat stem sawfly, as there are many examples of insect-endosymbiont relationships and a few examples in which microbes have been used to reduce pest populations (e.g., introduction of virus- and host lifespan- limiting Wolbachia into Dengue virus-vectoring mosquito populations). Hypothesis The sawfly microbiome is colonized by endosymbiotic bacteria important to host metabolism. Research Aims Aim 1. Determine what bacteria colonize the wheat stem sawfly (WSS) using 16S rRNA sequencing. 16S rRNA sequencing determined that both adult and larvae sawflies harbor Spiroplasma, a common insect endosymbiont. 134 Aim 2. Capture genomic sequence of the WSS-associated Spiroplasma assess its metabolic contributions to the WSS host using metagenomic sequencing. Aim 3. Characterize the morphology of sawfly-associated Spiroplasma using Scanning electron microscopy (SEM). 135 Table 3.1 Currently available complete and draft genome sequences from arthropod-borne Spiroplasma strains. Table adapted from Bolaños et al., 2014. species/strain genome ID host common name host scientific name relationship genome (Mb) G+C (%) rRNA operon tRNA operon CDS genes S. sp. TU-14 18308 NA NA NA 1.2 28.7 3 32 1017 S. sp. NBRC 100390 18308 NA NA NA 1.2 28.7 3 32 1018 S. melliferum IPMB4A 11130 honey bee Apis mellifera pathogenic 1.1 27.5 1 29 920 S. melliferum KC3 11130 honey bee Apis mellifera pathogenic 1.3 27 1 29 1046 S. apis B31T 15951 honey bee Apis mellifera pathogenic 1.2 28.3 1 29 997 S. mirum, strain SMCA 11332 rodents/ rabbit tick Haemaphysalis leporispalustris pathogenic/ commensal 1.1 29.4 3 33 1098 S. mirum 11332 rodents/ rabbit tick Haemaphysalis leporispalustris pathogenic/ commensal 1.1 29.4 3 33 1101 S. eriocheiris, strain DSM 21848 11290 Chinese mitten crab Eriocheir sinensis pathogenic 1.4 29.8 3 31 1191 S. eriocheiris, strain CCTCC M207170 11290 Chinese mitten crab Eriocheir sinensis pathogenic 1.4 29.8 3 32 1190 S. sabaudiense Ar-1343 15950 mosquito Aedes sp. commensal 1.1 30.2 2 30 924 S. culicicola AES-1 15949 mosquito Aedes sollicitans pathogenic 1.2 26.4 1 29 1071 S. syrphidicola EA-1 15948 hoverfly Syrphidae commensal 1.1 29.2 1 29 1006 S. chrysopicola DF-1 15947 horse fly Tabanidae commensal 1.1 28.8 1 29 1009 S. diminutum CUAS-1 15945 mosquito Culex annulus commensal 0.9 25.5 1 29 858 S. taiwanense CT-1 15944 mosquito Culex, Anopheles, Aedes sp. pathogenic 1.1 23.9 1 29 991 S. kunkelii CR2-3x 1043 corn/corn leafhopper Zea mays/Dalbulus maidis pathogenic/ commensal 1.5 25 3 32 1375 S. citri, strain R8-A2 1228 citrus/ leafhopper Citrus sp./ Cicadellidae pathogenic/ commensal 1.5 25.5 3 32 1611 S. citri 1228 citrus/ leafhopper Citrus sp./ Cicadellidae pathogenic/ commensal 1.6 25.9 1 29 1170 S. helicoides 45881 horse flies Tabanus abactor commensal 1.3 26.8 3 29 1137 S. cantharicola 40089 soldier beetle Cantharidae commensal 1.2 25 3 29 998 S. litorale 39890 horse fly Tabanidae family commensal 1.2 24.9 3 29 1086 S. turonicum 39852 horse fly Haematopota sp. commensal 1.3 24.2 3 29 1070 S. atrichopogonis 38472 biting midge Atrichopogon sp. commensal 1.2 29.3 3 33 1138 S. poulsonii, strain MSRO 256019 fruitfly Drosophila sp. male-killing 1.8 26.5 1 31 2146 136 Table 3.2 Currently available 16S rRNA sequences from Ixodetis-Spiroplasma strains. species/strain gene accession # host common name host scientific name relationship Spiroplasma sp. JX943565.1 pea aphid Acyrthosiphon pisum multualistic Spiroplasma sp. JX943566.1 pea aphid Acyrthosiphon pisum multualistic Spiroplasma sp. JX943567.1 pea aphid Acyrthosiphon pisum multualistic Spiroplasma sp. SM AB048263.1 pea aphid Acyrthosiphon pisum commensal Spiroplasma sp. FJ657241.1 fruitfly Drosophila sp. commensal Spiroplasma sp. FJ657246.1 fruitfly Drosophila sp. commensal Spiroplasma endosymbiont of Curculio elephas JN100091.1 weevil Curculio elephas commensal Spiroplasma sp. JQ692307.1 weevil Curculio elephas commensal Spiroplasma sp. Bratislava KP967685.1 tick Ixodes ricinus ticks commensal Spiroplasma sp. NR_104852.1 tick Ixodes ricinus ticks commensal Spiroplasma platyhelix PALS-1 NR_104857.1 dragonfly Pachydiplax longipennis commensal Spiroplasma sp. JQ768460.1 cricket Gryllus bimaculatus pathogenic Spiroplasma sp. 'Gent' AY569829.1 little housefly Fannia manicata male-killing Spiroplasma sp. AB542740.1 moth Ostrinia zaguliaevi male-killing Spiroplasma sp. 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(IXODETIS CLADE) ASSOCIATED WITH THE WHEAT STEM SAWFLY (CEPHUS CINCTUS) Contribution of Authors and Co-Authors Manuscript in Chapter 4 Author: Laura M. Brutscher Contributions: Designed, performed and analyzed experiments, wrote the manuscript. Co-Author: Curtis Fowler Contributions: Assisted in sample preparation for SEM imaging. Co-Author: David K. Weaver Contributions: Provided scientific insights in initial stages of planning the study; Provided wheat stem sawflies for experiments. Co-Author: Carl J. Yeoman Contributions: Conceived the study; edited/critiqued manuscript in final stages of preparation. 143 Manuscript Information Page Laura M. Brutscher, Curtis Fowler, David K. Weaver, and Carl J. Yeoman Microbial Ecology (submitted as a note) Status of Manuscript: ____ Prepared for submission to a peer-reviewed journal _ X _ Officially submitted to a peer-review journal ____ Accepted by a peer-reviewed journal ____ Published in a peer-reviewed journal *Note: Manuscript was submitted as a note to Microbial Ecology. 144 Abstract The wheat stem sawfly (Cephus cinctus) is one the greatest pests of wheat crops in the Northwest United States, causing millions of dollars in wheat yield losses annually. Endosymbiotic microbes are important to animal health, and those associated with insects often have obligate relationships. Other insect endosymbionts can impact their insect host population sex ratios (i.e., male- killing) and/or affect host reproductive dynamics (e.g., cytoplasmic incompatibility). These attributes may be exploited to control pest and disease- vectoring insect populations. Hereunto, neither the microbes that colonize the wheat stem sawfly, nor their metabolic and phenotypic contributions have been explored. High throughput 16S sequencing determined that sawflies of multiple haplotypes and life stages were colonized by a Spiroplasma species. Metagenomic sequencing yielded a partial genomic sequence (220 Kb) of the sawfly-associated Spiroplasma. Based on 16S rRNA, DNA-directed RNA polymerase subunit beta, RecA, and FtsZ sequence analyses, the sawfly associated-Spiroplasma is likely a part of the Ixodetis clade. The 268 protein- encoding Spiroplasma genes identified were involved in several metabolic pathways typical to Spiroplasma species, including glycolysis, fructose and mannose metabolism, purine, pymidine, and glycerophospholipid metabolism. We also identified two putative Spiroplasma virulence genes: cardiolipin and chitinase. The sawfly-associated Spiroplasma may contribute to its host 145 metabolism, but further studies investigating the effects of eliminating Spiroplasma in the sawfly host may clarify its contribution to host metabolism. To date, this is the first report of extensive genomic sequence from a Spiroplasma Ixodetis clade member. Main Text The wheat stem sawfly (Cephus cinctus) is a hymenopteran insect native to the Western U.S. and Canada. Currently, wheat stem sawflies (WSS) are one of the greatest wheat pests, causing $100–350 million USD in wheat yield losses annually1–3. Females oviposit into wheat stems, and after the eggs hatch, the larvae consume the internal structure of the stem, making it more susceptible to lodging4–6. Current methods used by growers to reduce WSS-associated crop losses include operational control measures (e.g., crop rotation, tillage, and swathing), and chemical (e.g., insecticides) and biological control agents (e.g., parasitic wasps). Most research efforts aimed at curbing this problem are to develop WSS-resistant wheat cultivars, including solid-stemmed varieties that provide less reproductive habitat for the WSS1,4,7. An additional option is to exploit the endosymbiotic microbes of WSS to negatively affect their fitness. Metabolic contributions of endosymbiotic microbes are often critical to nutritive, physiological, immunological and developmental health in animals4, while others may be deleterious to host health. Insects, in particular, have many well-described long-term relationships with endosymbiotic 146 microbes. Wolbachia and Spiroplasma are the most prevalent and characterized genera of facultative insect-infecting endosymbionts. Wolbachia are Gram- negative, intracellular, members of the Rickettsial family that are maternally transmitted in at least 40% of arthropod species8. Many Wolbachia-insect interactions are parasitic, reducing host lifespan and causing reproductive phenotypes such as feminization, male killing, parthenogenesis, and cytoplasmic incompatibility9. Because Wolbachia have also been reported to reduce viral replication in their hosts, they have been exploited as a bio-control method for virus-vectoring mosquito and fruit fly populations10–12. Spiroplasma are Gram- positive, small, helical, motile bacteria lacking cell walls13. Many species are pathogenic or induce male-killing in insects14,15, but most identified strains are commensal. Some Spiroplasmas have also been reported to improve host defense against fungal pathogens16 and parasitoid wasps17–19. Spiroplasma belong to three phylogenetic clades: Citri-Chrysopicola-Mirum (CCM), Apis, and Ixodetis, each containing a variety of pathogenic and commensal species14. Herein, we sought to determine the microbe(s) that colonize the WSS. Identification and characterization of the microbe(s) that colonize the WSS may facilitate the development of measures to control WSS infestation of crops. Midseason haplodiploid larval and early-adult WSS representatives were collected from highland grass (Flesher pass, Lincoln, MT) and lowland wheat crops (Three Forks, MT), representing the two major haplotype clades in Montana20. The DNA of five whole adult females and larvae collected from both 147 highland grass and lowland wheat sample locations were extracted and 16S rRNA libraries were prepared and sequenced on the Illumina MiSeq (Supplementary Methods). OTUs were assessed and classified by genera via Mothur and RDP classifier (Supplementary methods)21. Individuals harbored 30- 50 different OTUs, although Chao1 estimates of total OTU diversity predicted as many as 113-198 OTUs may be associated with WSS (Table S1). The most common and abundant identified genera were Spiroplasma, which represented and 93.6% +/- 4.8% of all reads (Figure 4.1). Less abundant genera were all from the family Pasteurellaceae and included Nicoletella, Histophilus, Lonepinella, Actinobacillus, Basfia, Haemophilus, and Aggregatibacter genera (Figure 4.1). In order to obtain additional genomic sequence of the WSS Spiroplasma, metagenomic libraries were prepared and sequenced on an Illumina MiSeq from DNA of representative WSS specimen and larval cell lysates enriched for Spiroplasma (Supplementary Methods). Reads from all libraries were assembled together using Soapdenovo222. Contigs containing sequences with >60% nucleotide identity with Spiroplasma genomes were identified using BLAST and BLAST+ 23. The resultant 177 contigs (224,315 bp total) were annotated via PATRIC 24, which identified 387 coding DNA sequences, including 66 hypothetical proteins, 3 rRNA-encoding genes, and 17 tRNA encoding genes (Table S2). Similar to other insect endosymbiotic bacterial genomes25,26, described Spiroplasma genomes are small in size, ranging from 0.8-2.2 Mb27. Spiroplasma genome assembly is difficult because they often contain repetitive 148 transposon-like elements and Citri sub-clade genomes contain plectiviral sequences27. Conservatively, the contigs assembled likely represent approximately one tenth of the genome. Figure 4.1 Wheat stem sawflies are primarily colonized by Spiroplasma. Female adult and haplo-diploid larval WSS were collected from highland native grasses and lowland wheat crops and were 16S rRNA sequenced. Across all groups, OTUs classified as Spiroplasma were the most prominent (93.6% +/- 4.8 of all reads). The other genera Nicoletella, Histophilus, Lonepinella, Actinobacillus, Basfia, Haemophilus, and Aggregatibacter are from the family Pasteurellaceae. A 1,535 bp fragment of the 16S rRNA gene was identified and compared to the 16S rRNA sequences of 48 other Spiroplasma strains (Table S3). The full- length WSS-associated Spiroplasma 16S rRNA sequence had 99.5% identity (1,509/1,518 nt) with a male-killing strain that infects ladybird beetles, Spiroplasma Anisosticta 28 and 99.8% identity (1,471/1,474 nt) with Spiroplasma Aggregatibacter Haemophilus Basfia Actinobacillus Lonepinella Histophilus Nicoletella Spiroplasma adults larvae adults larvae wheat natural grasses %16S OTUs 1000 149 sp. ‘Gent’, a commensal Spiroplasma isolated from Ixodetis ricinus ticks29, indicating WSS-associated Spiroplasma is part of the Ixodetis clade. After aligning and trimming to 1,050 bp, the length of the shortest sequence being aligned, the 16S rRNA genes were mapped in an UPGMA phylogenetic tree using Geneious (Figure 4.2). While many Ixodetis clade members have been identified16,28–37, little genomic information beyond 16S rRNA and RNA polymerase beta unit (rpoB) sequences are available. Phylogenetic comparisons with the WSS-associated Spiroplasma DNA-directed RNA polymerase subunit beta, RecA, and FtsZ sequences also suggest that the WSS-associated Spiroplasma is distinct from CCM and Apis clade members (Supplementary Figure 4.4). Genes involved in purine metabolism, glycolysis, pyrimidine metabolism, aminoacyl-tRNA biosynthesis, methane metabolism, oxidative phosphorylation, fructose and mannose metabolism, and other metabolic pathways were identified in the draft Spiroplasma gene set (Table S4). Spiroplasma are fastidious to culture, as they encode a limited number of genes involved in biosynthetic pathways. However, Spiroplasma typically encode for all 17 proteins involved in glycolysis27, whereas we identified nine glycolysis genes, four genes involved in mannose and fructose metabolism, and the gene encoding for a fructose transporter, PTS system, fructose-specific IIA component. In addition, Spiroplasma typically encode for genes involved in arginine and proline 150 hydrolysis27; agmatine deiminase and ornithine transcarbamylase were identified in the gene set. The WSS-Spiroplasma probable sequence set was then screened for putative virulence factors, and cardiolipin synthetase and chitinase were identified. Cardiolipin is a component of the Spiroplasma membrane and is also produced in eukaryotic mitochrondria38. When produced in excess in eukaryotic cells, cardiolipin can promote apoptosis39, thus Spiroplasma production of cardiolipin may also be toxic to their hosts. Cardiolipin has also been implicated in defense against nematodes and parasitic wasp due to its toxic nature40 and Spiroplasma can limit lipid availability via cardiolipin production41,42. Chitinase may also defend against parasitic wasps by degrading their chitin-based cuticles38. The putative roles of cardiolipin and chitinase in protecting against parasitic wasps is intriguing because the WSS is commonly parasitized by two species of parasitic wasps: Bracon cephi and Bracon lissogaster43. Future studies investigating the role of the WSS-Spiroplasma in host resistance to wasp parasitism would be interesting. In order to obtain morphological information about the WSS associated- Spiroplasma, cells were enriched from WSS larvae lysates via filtration and prepared for scanning electron microscope imaging (Supplementary Methods). Cells were non-helical and filamentous, ranging 1-4 µM in length (Figure 4.3). Some cells exhibited a y-shaped morphology (Figure 4.3D), suggesting that they divide via longitudinal scission44. 151 152 Figure 4.2. Spiroplasma 16S rRNA UPGMA clustering. The WSS-associated 16S rRNA gene was aligned with 16S rRNA sequences of 49 other Spiroplasma strains spanning species from the three Spiroplasma phylogentic clades: Apis, Citri-Chrysopicola-Mirum (CCM), and Ixodetis (Supplementary Table 4.3). Aligned sequences were mapped in an UPGMA hierarchical clustering tree using a Tamura-Nei distance model in Geneious. The genus Spiroplasma is named for its predominantly helical shape, but in addition to our observation of the WSS-associated Spiroplasma, several cases of non-helical Spiroplasma have been reported, including a strain of Spiroplasma citri45,46 and Spiroplasma culilicola, isolated from Aedes sollicitans47. Interestingly, Spiroplasma platyhelix exhibits a “kinked” morphology45. Alternatively, Spiroplasma morphology may vary based on age and nutrient status of culture48, which may have affected our observations since we isolated Spiroplasma from larval cell lysates and not from pure bacterial culture. This is the first work to characterize the microbiota of the wheat pest, wheat stem sawfly (Cephus Cinctus). WSS is endosymbiotically colonized by a novel Spiroplasma species. Metagenomic sequencing and assembly yielded new insights into the WSS-associated Spiroplasma and the broader Ixodetis clade; a clade hereinto lacking genomic data. Further attempts to culture and obtain full genome information on the WSS-associated Spiroplasma are on-going and may lead to a greater understanding of the endosymbiont’s potential role in host metabolism and health, which in the future may contribute to reducing WSS- associated wheat crop losses. 153 Figure 4.3 SEM Images of WSS-associated Spiroplasma Indicate it is Non- Helical. In order to obtain morphological information about the WSS-associated Spiroplasma, cells were enriched from sawfly larvae lysates via filtration and prepared for scanning electron microscope imaging. (A-C) Cells were non-helical and filamentous, ranging 1-4 µM in length. (D) Some cells exhibited a y-shaped morphology, which may be cells replicating via longitudinal fission. The scale bar is 1 µm. bar= 1 um A C D B 154 Supplementary Methods Sample Collection Midseason haplodiploid larval and early-adult wheat stem sawfly (Cephus cinctus) representatives were collected from highland grass (Flesher pass, Lincoln, MT) and lowland wheat crops (Three Forks, MT) over the 2013 and 2015 growing seasons by the D.K. Weaver lab (Montana State University). Both the highland grass and lowland wheat populations represent the two major haplotype clades in Montana20. Sample Preparation and DNA Extraction Protocols Five whole adult females and whole larvae collected from both highland grass and lowland wheat sample locations were surfaced sterilized by washing samples in 1% bleach and rinsing with sterile water under aseptic conditions. Each sample was individually processed through the Mo-Bio Power-Soil kit (Mo- Bio, Carlsbad, CA) following the manufacturers protocol except a 2 min bead- beating step was used instead of a 10 min vortex. DNA concentration was assayed using a Qubit® 2.0 Fluorometer (Invitrogen, Carlsbad, CA). 16S rRNA Sequencing In order to prepare the DNA samples for high throughput 16S rRNA gene sequencing, a 464 bp portion of the 16S rRNA gene spanning the V3 and V4 hypervariable regions was PCR amplified with barcoded universal bacterial primers 341F and 806R49. The primers were modified by adding ligation 155 adaptors, indexes for dual-indexing, and distinct 8 bp barcodes for identifying individual samples50 (Tables S5 and S6). The PCR was performed using KAPA HiFi DNA Polymerase (Kapa Biosystems, Wilmington, MA, USA) and a PCR protocol with initial denaturation of 98°C for 45 s, followed by 25 cycles of 15 s denaturation at 98°C, 30 s annealing step at 60°C, and 30 s elongation step at 72°C, and then a final extension at 72°C for 2 min. The PCR reaction was limited to 25 cycles to minimize the potential for chimera formation and error51. Resultant amplicons were purified using the AxyPrep MagTM PCR Clean-up Kit (Corning, NY, USA) and quantified with an Agilent 2200 Tapestation (Santa Clara, CA, USA). The 16S rRNA libraries were then pooled in equimolar concentration, and pools were quantified using the KAPA Library Quantification Kit – Illumina (Kapa Biosystems) and sequenced with the Illumina Miseq sequencing Platform using custom sequencing primers (Table S5) and 2 x 250 V2 sequencing kit (Illumina, San Diego, USA). On average, each sample yielded 4,125 reads with one sample only yielding 151 reads (Table S6). Sequences were submitted to Sequence Read Archive under the submission ID #SUB2735725. Sequence Processing and Analysis The raw FASTQ generated from sequencing were filtered and trimmed by a minimum phred score of 30 and minimum nucleotide length of 250 bp using the fastq_quality_trimmer tool from FastX-Toolkit52. Filtered reads were then assembled into contigs using the “make.contigs” command from the Mothur software platform21. The deltaq, or difference in Q-Scores, necessary for one 156 base in one read to be called over a disagreement from the other read, was set to 1. The dataset was condensed by using the Mothur ‘‘unique.seqs’’ command to generate a non-redundant set of sequences. The unique sequences were aligned to an adaptation of the Bacterial SILVA SEED database as a template using the Mothur “align.seqs” command21. Sequences that started after 6,500 nt or ended before 25,000 nt, had homopolymers longer than 10nt, more than 2 ambiguous nt, and had lengths shorter than 400 nt were filtered using the Mothur “screen.seqs” command. The sequences were than filtered for any common gaps and extraneous overhangs using the Mothur “filter.seqs” command. To reduce the effect of sequencing error, the sequence were then preclustered based on 1- 2 nt differences using the “pre.cluster” command. Chimeric sequences were detected and removed with the “chimera.uchime” (Mothur’s implementation of UCHIME53) and “remove.seqs” commands. Sequences that were of chloroplast or mitochondrial origin or could not classified with less than 60% confidence at the phylum level were removed from analysis using the “classify.seqs” (with RDP-II Naive Bayesian Classifier54 as a reference) and “remove.lineage” commands. OTUs were clustered based on taxonomic classification using the “cluster.split” command (splitmethod=classify). Using the remove.rare command, singleton OTUs were removed. The “make.shared” command was used to generate an OTU table based on 95% sequence similarity, resulting in 497 unique OTUs. Next, representative OTU’s were classified by RDP Naive Bayesian Classifier54. Mothur was then used to estimate community diversity, 157 species/OTU richness, and sequencing coverage (e.g., Shannon’s index of diversity, Chao richness, Good’s coverage) of each sample’s microbiome. Each sample was then normalized to the sample with the second smallest number of reads (1,353 reads). One lowland adult sample only had low number of reads reads, so it was omitted from further analysis. Additionally, microbial composition was compared among samples using multivariate statistical approaches provided in the Vegan package of R55,56. Community-level inter-individual and inter-species similarities were determined by pairwise measurements of Bray-Curtis dissimilarity and analysis of similarities (ANOSIM). A heat map was generated using gplots in R. Metagenomic Sequencing In order to obtain additional genomic sequence of the sawfly-associated Spiroplasma, the DNA of three representative wheat stem sawfly samples from both larvae and adults of the highland and lowland populations (12 samples total) was prepared for metagenomic sequencing. Metagenomic libraries were prepared using the Nextera XT kit (Ilumina) per the manufacturer’s instructions, resulting in ~250 bp insert sizes. Metagenomic libraries were pooled in equimolar concentrations with three libraries per pool and sequenced on Illumina Miseq sequencing Platform using a 2x150 V2 sequencing kit. Sequences were submitted to Sequence Read Archive under the submission ID #SUB2735720. 158 Metagenomic Sequencing Analysis On average sequencing, yielded ~2.9 million reads per sample (Table S7). As an initial assessment of Spiroplasma genome coverage, raw reads were filtered and trimmed by a minimum phred score of 30 and minimum nucleotide length of 100 bp using the fastq quality trimmer tool from FastX-Toolkit52. Reads were then arranged into paired and singleton files using a custom script and individually assembled using SOAPdenovo222. Open reading frames (ORF) were defined using MetaGeneMark57. ORF sequences were then uploaded to MG- Rast58. 96-98% of each sample’s reads were classified as being eukaryotic, and included host reads (Cephus cinctus) and reads belonging to wheat (Triticum aestivum). In order to further enrich samples for Spiroplasma, lowland Sawfly larvae were collected from wheat stems from near Amsterdam, MT in the summer of 2015. Based on a previous protocol59, 50 larvae washed in 70% ethanol were lightly crushed with a mortar and pestle in 3 ml sterile PBS under aseptic conditions. The entire solution was filtered through 450 nm nitrocellulose filters in order enrich the bacteria in the cell lysate for Spiroplasma and remove other bacterial species larger than 450 nm60. The filtrate was then centrifuged at 16,000 x g for 30 minutes at 4°C in order to pellet the cells that passed through the filter. The pellet was then resuspended, and filtered and centrifuged again. DNA from the pellet was then extracted using the Mo-Bio Power-Soil kit (Mo-Bio, Carlsbad, CA) following the manufacturers protocol except a 1 min bead-beating 159 step was used instead of a 10 min vortex. DNA extraction yielded 15 ng total. The DNA prepared and sequenced for metagenomic sequencing with the Nextera XT kit as before and yielded ~20 million reads (Table S7). Raw reads were filtered and trimmed by a minimum phred score of 30 and minimum nucleotide length of 100 bp using the fastq quality trimmer tool from FastX-Toolkit52. In order to improve the Spiroplasma assemblies, the quality filtered reads from all samples were concantentated into groups of three, and Illumina adaptors were removed using Trimmomatic61, which arranged the reads into paired and unpaired read files. The reads were then assembled using Soapdenovo222. The contigs with similar nucleotide sequence identity (at least 25% identity) with other Spiroplasma genomes available on NCBI (Table S4) were identified using Blast+23. The contigs with at least 10X coverage containing sequences greater than 250 bp that had at least 60% sequence identity with other Spiroplasma genomes (Table S8) or whose top hits in Blast were Spiroplasma were then annotated using the online program PATRIC24. Genes were then mapped to metabolic maps using iPATH 262. Phylogenetic Analysis The 16S rRNA, Ftsz, DNA-directed RNA polymerase, and RecA genes of the wheat stem sawfly-associated Spiroplasma and 15 additional species belonging to the CCM and Apis clades (Table S8) were downloaded from NCBI and imported into Geneious63. Each gene was individually aligned with the same gene from all available species and trimmed to the shortest sequence to reduce 160 bias from missing sequence. The genes were concatenated in the following order: 16S rRNA (1,535 bp), DNA-directed RNA polymerase subunit beta (1,356 nt), RecA (513bp), and FtsZ (1,027 nt). Then, concatenated gene sets for each species were aligned. A UPGMA tree (Tamura-Nei distance model) was generated in Geneious. Notably the concatenated sequence of the WSS- associated Spiroplasma was distinct from the other species (Supplementary Figure 4.4). Because there is 16S rRNA sequence data available for many more strains, the 16S rRNA of 50 Spiroplasma strains (Table S3) were aligned, trimmed, and a UPGMA tree (Tamura-Nei distance model) was generated (Figure 4.2). SEM Sample Preparation Twenty-five larvae washed in 70% ethanol were lightly crushed with a mortar and pestle in 3 ml sterile PBS under aseptic conditions. The entire solution was filtered through a disposable 450 nm nitrocellulose filter60, and pelleted by centrifugation at 16,000 x g for 30 minutes at 4°C as before. The pellet was then eluted in 300 µl PBS. A volume of 40 µl of the pellet solution was pipetted onto a coverslip and left for one hour for bacteria to attach. For fixation, a modified protocol from Stadtländer 200764 was used: the cover slip was immersed in a 1.5% glutaraldehyde solution prepared in 0.1 M cacodylic acid buffer (pH 7.3) and incubated at 4°C overnight. The coverslip was then rinsed in water for 20 minutes four times. The sample was then progressively dehydrated by placing the slip in solutions increasing by relative ethanol: 25% EtOH 30 161 minutes, 50% EtOH 30 minutes, 75% EtOH 30 minutes, 95% EtOH 30 minutes, 100% 60 minutes (three times to rinse coverslip off). Critical point drying, specimen mounting, and SEM imaging were performed at the Imaging and Chemical Analysis Laboratory (ICAL) at Montana State University. The coverslip was critically point dried in a Tousimis SAMDRI®-795 (Tousimis, Rockville, MD, USA) for thirty minutes, and mounted. The coverslip was then imaged on a Zeiss SUPRA 55VP (Carl Zeiss Oberkochen, Germany) field emission scanning electron microscope. Spiroplasma Culturing Attempts In order to isolate the sawfly associated Spiroplasma for culturing, one hundred larval specimens were washed in 70% ethanol followed by rinsing with sterile water. Based on the protocol used by Tully et al., 198159, larvae were then lightly ground with a mortar and pestle in 5 ml sterile PBS containing 7.5 percent bovine plasma albumin (pH 7.5) under a flow hood. The entire solution was filtrated through a disposable 450 nm nitrocellulose filter in order enrich the bacteria in the cell lysate for Spiroplasma and remove other bacterial species60. Immediately following filtration, 100 µl of the cell lysate filtrate was inoculated into 5 ml each of three different media types (prepared as per ATCC instructions) previously used to culture Spiroplasma species: M1D media (ATCC1541)65, Singh's media (ATCC 798)66, and SP-4 (ATCC 2611)67. The media was then incubated at 30°C and monitored daily for a change in pH as indicated visually by a change in media color from red to yellow (via phenol red) 162 that would indicate that Spiroplasma was actively replicating. Small wriggling bacteria (<5 nm) were observed via microscope at 100x magnification in the first 2-3 days of culturing in both Singh's and SP-4 media, but usually not after four days, indicating that the both media types are not optimized for the isolated bacteria. Addition of cholesterol (10µg/ml) to Singh’s media improved growth of bacteria and extended the culturing time to ~4-5 days. GC/MS Analysis In order to discern potential nutrient deficits in the culturing media, samples of the three replicate cultures in Singh’s media supplemented with cholesterol (10µg/ml) were collected at 0, 1, 2, and 4 days post-inoculation for metabolomic analysis. Samples were dried and derivatized with 50 mL methoxyamine hydrochloride (Sigma-Aldrich, MO, USA) (40 µg mL-1 in pyridine) for 60 min at 400°C, then with 50 mL MSTFA+1%TMCS (Thermo, MA, USA) at 500°C for 120 min, and following 2-hour incubation at room temperature. Then, 5 µL of the internal standard (hentriacontanoic acid, 10 µg mL-1) was added to each sample prior to derivatization. Metabolite profiles were acquired using a GC-MS system (Agilent Inc, CA, USA) consisting of an Agilent 7890 gas chromatograph, an Agilent 5975 MSD and a HP 7683B autosampler. Gas chromatography was performed on a ZB-5MS (60m×0.32mm I.D. and 0.25mm film thickness) capillary column (Phenomenex, CA, USA). The inlet and MS interface temperatures were 2500°C, and the ion source temperature was adjusted to 2300°C. An aliquot of 1 µL was injected with the split ratio of 15:1. 163 The helium carrier gas was kept at a constant flow rate of 2 ml min-1. The temperature program was: 5-min isothermal heating at 700°C, followed by an oven temperature increase of 50C min-1 to 3100°C and a final 10 min at 3100°C. The mass spectrometer was operated in positive electron impact mode (EI) at 69.9 eV ionization energy at m/z 30-800 scan range. The spectra of all chromatogram peaks were evaluated using the AMDIS 2.71 (NIST, MD, USA) using a custom-built database (460 unique metabolites). All known artificial peaks were identified and removed prior data mining. To allow comparison between samples, all data were normalized to the internal standard in each chromatogram and the sample volume. The instrument variability was within the standard acceptance limit (5%). Although we were unable to culture the bacterium to concentrations high enough for DNA isolation and species confirmation, we obtained metabolic profiles of the cholesterol-supplemented cultures over five days using mass spectrometry. This would further elucidate the metabolic capabilities of the cultured strain. Eighteen metabolites either consistently decreased or consistently increased in the cultures over the five-day sampling period (Table S9). Notably, pyruvate acid, trehalose, serine, and cysteine exhibited decreases in concentration over time. In contrast, several amino acids, uridine, xanthine, allantoin, glycolic acid, 2-aminoethylglycerophosphate, 3,4-dihydroxybutanoic acid, galactose exhibited an increase in concentration. Interestingly, trehalose is the most abundant sugar in insect hemolymph, followed by glucose and 164 mannose68, so the culture metabolomics data may suggest that WSS-associated Spiroplasma may utilize host-derived trehalose as a major energy source. 165 Supplementary Figures Supplementary Figure 4.4 UPGMA Phylogentic Tree of concatenated 16S rRNA, DNA-directed RNA polymerase, RecA, and FtsZ. The 16S rRNA, Ftsz, DNA- directed RNA polymerase, and RecA genes of the wheat stem sawfly-associated Spiroplasma and 15 additional species belonging to the CCM and Apis clades (Table S8) were downloaded from NCBI, concatenated and mapped onto a UPGMA hierarchical clustering tree (Tamura-Nei distance model) using Geneious21. The gene were concatenated in the following order: 16S rRNA (1,535 bp), DNA-directed RNA polymerase subunit beta (1,356 nt), RecA (513bp), and FtsZ (1,027 nt). Importantly, the wheat stem sawfly-associated Spiroplasma, which is likely a member of the Ixodetis clade, formed a distinct branch from the other CCM and Apis clade Spiroplasma species. 0.122 0.0281 0.0044 0.0382 0.024 0.0233 0.0381 0.0399 0.0131 0.0152 0.0318 0.0336 0.0288 0.0982 0.0099 0.0244 0.0575 0.0562 0.0024 0.0031 0.0043 0.0563 0.0147 0.0329 0.0362 0.0046 0.0672 0.0554 0.0821 Spiroplasma apis B31 Spiroplasma culicicola Spiroplasma helicoides Spiroplasma cantharicola strain CC-1 Spiroplasma diminutum Spiroplasma taiwanense CT-1 Spiroplasma litorale strain TN-1 Spiroplasma turonicum strain Tab4c Spiroplasma sabaudiense Spiroplasma melliferum KC3 Spiroplasma poulsonii MSRO Spiroplasma atrichopogonis strain GNAT3597 Spiroplasma eriocheiris CCTCC Spiroplasma chrysopicola Spiroplasma syrphidicola Wheat Stem Sawfly-associated Spiroplasma Apis CCM 166 Supplementary Table 4.1 16S rRNA OTU composition of wheat stem sawflies collected from grass and wheat populations in Montana and at both female adult and larval stages. Sample Group N 16S rRNA Sequences Obtained Coverage estimate (%) Observed OTUs Predicted species (Chao1) Diversity (Shannon’s index) Grass (Larvae) 5 5844 +/- 3128 46 +/- 13 42 +/- 10 198 +/- 182 3.0 +/- 0.9 Grass (Adult) 5 4096 +/- 1175 70 +/- 22 49.2 +/- 24 127 +/- 74 3.5 +/- 0.2 Wheat stem (Larvae) 5 3910 +/- 2944 40 +/- 29 35.2 +/- 25 113 +/- 68 2.9 +/- 0.8 Wheat stem (Adult) 4 2117 +/- 1448 51 +/- 16 29.8 +/- 14 114 +/- 94 2.8 +/- 0.5 167 Supplementary Table 4.2 PATRIC annotated feature table of coding sequences from draft WSS-associated Spiroplasma. Product Length FIGfam ID PATRIC genus- specific families (PLfams) PATRIC cross- genus families (PGfams) AA Length 16S rRNA (uracil(1498)-N(3))- methyltransferase (EC 2.1.1.193) 582 193 3'->5' exoribonuclease Bsu YhaM 924 FIG01114325 308 3'-to-5' exoribonuclease RNase R 1227 FIG00000308 PLF_2132_00000367 PGF_01085368 409 3'-to-5' oligoribonuclease A, Bacillus type 1026 FIG00002194 341 6-phosphofructokinase (EC 2.7.1.11) 1053 FIG00058830 PGF_00427201 350 ABC transporter ATP-binding protein 786 FIG00638284 262 ABC transporter, ATP-binding protein 1014 FIG00744535 PLF_2132_00000069 PGF_00378721 337 ABC transporter, ATP-binding protein 816 PLF_2132_00000292 PGF_00378721 271 ABC transporter, ATP-binding protein 399 PLF_2132_00003014 PGF_03444768 132 Adenylate kinase (EC 2.7.4.3) 666 FIG00000089 221 Adenylosuccinate lyase (EC 4.3.2.2) @ SAICAR lyase (EC 4.3.2.2) 1098 FIG00000117 PLF_2132_00000052 PGF_00035720 366 Adenylosuccinate synthetase (EC 6.3.4.4) 687 FIG00000107 PLF_2132_00000365 PGF_00035752 228 Agmatine deiminase (EC 3.5.3.12) 111 36 Agmatine deiminase (EC 3.5.3.12) 825 FIG00001062 PLF_2132_00000619 PGF_00037006 274 Agmatine deiminase (EC 3.5.3.12) 138 FIG00001062 46 Alanyl-tRNA synthetase (EC 6.1.1.7) 375 FIG00000139 PGF_00037392 124 Alanyl-tRNA synthetase (EC 6.1.1.7) 1917 FIG00000139 PLF_2132_00000066 PGF_00037392 639 Aminopeptidase YpdF (MP-, MA-, MS-, AP-, NP- specific) 870 FIG00135469 PLF_2132_00000814 PGF_00054031 289 Aspartyl-tRNA synthetase (EC 6.1.1.12) @ Aspartyl-tRNA(Asn) synthetase (EC 6.1.1.23) 879 FIG00000750 293 Aspartyl-tRNA(Asn) amidotransferase subunit A (EC 6.3.5.6) @ Glutamyl-tRNA(Gln) amidotransferase subunit A (EC 6.3.5.7) 819 FIG00000359 PLF_2132_00000375 PGF_00067524 273 Aspartyl-tRNA(Asn) amidotransferase subunit A (EC 6.3.5.6) @ Glutamyl-tRNA(Gln) amidotransferase subunit A (EC 6.3.5.7) 651 216 Aspartyl-tRNA(Asn) amidotransferase subunit B (EC 6.3.5.6) @ Glutamyl-tRNA(Gln) amidotransferase subunit B (EC 6.3.5.7) 750 FIG00000399 PLF_2132_00000141 PGF_00067554 250 ATP synthase alpha chain (EC 3.6.3.14) 1584 FIG00000082 PLF_2132_00000048 PGF_00015154 527 ATP synthase beta chain (EC 3.6.3.14) 753 FIG00040241 PGF_00015179 251 ATP synthase F0 sector subunit a 408 FIG00000138 PLF_2132_00000053 PGF_00015039 135 168 (EC 3.6.3.14) ATP synthase F0 sector subunit c (EC 3.6.3.14) 267 FIG00017607 PGF_00015142 89 ATP-dependent protease La (EC 3.4.21.53) Type I 2355 FIG00132617 PLF_2132_00000071 PGF_01170648 785 ATPase component of general energizing module of ECF transporters 843 FIG00013638 PLF_2132_00000073 PGF_00021840 280 ATPase component of general energizing module of ECF transporters 609 FIG00013638 PGF_00021840 202 Bis-ABC ATPase SPy1206 1494 FIG00638284 PLF_2132_00000061 PGF_03244749 497 Cardiolipin synthetase (EC 2.7.8.-) 675 FIG00052605 225 CDP-diacylglycerol--glycerol-3- phosphate 3- phosphatidyltransferase (EC 2.7.8.5) 393 FIG00001550 PLF_2132_00000060 PGF_01900719 131 Cell division protein FtsH (EC 3.4.24.-) 1443 FIG00018369 PLF_2132_00000063 PGF_01181185 481 Cell division protein FtsZ (EC 3.4.24.-) 1155 FIG00000145 PLF_2132_00000142 PGF_03520151 384 Chaperone protein DnaK 1683 FIG00023369 PLF_2132_00000065 PGF_00417433 561 Chitinase (EC 3.2.1.14) 249 PGF_00956478 83 Chromosome (plasmid) partitioning protein ParA 693 PLF_2132_00003355 PGF_02627106 230 Chromosome (plasmid) partitioning protein ParA 669 FIG00006461 PLF_2132_00000369 PGF_01296193 223 Chromosome (plasmid) partitioning protein ParA 363 FIG00006461 PLF_2132_00000369 PGF_01296193 121 Chromosome (plasmid) partitioning protein ParA 234 PLF_2132_00000369 PGF_01296193 78 Chromosome (plasmid) partitioning protein ParA 312 PLF_2132_00000369 PGF_01296193 104 Chromosome (plasmid) partitioning protein ParA / Sporulation initiation inhibitor protein Soj 576 FIG00006461 192 Chromosome partition protein smc 708 236 ClpB protein 2142 FIG00025216 PLF_2132_00000025 PGF_01178419 713 COG2740: Predicted nucleic-acid- binding protein implicated in transcription termination 291 FIG00001133 96 CTP synthase (EC 6.3.4.2) 1605 FIG00000176 PLF_2132_00000062 PGF_00416129 534 Cyclic-di-AMP phosphodiesterase GdpP 321 106 Cysteinyl-tRNA synthetase (EC 6.1.1.16) 621 FIG00000086 PGF_02797400 207 Cytidylate kinase (EC 2.7.4.14) 321 106 Cytosol aminopeptidase PepA (EC 3.4.11.1) 975 FIG00000336 PLF_2132_00000017 PGF_00420731 325 DEAD-box ATP-dependent RNA helicase CshA (EC 3.6.4.13) 1023 FIG00001381 341 Deoxyribose-phosphate aldolase (EC 4.1.2.4) 345 FIG00000450 PGF_01604929 115 Dihydrofolate reductase (EC 1.5.1.3) 357 PGF_00144272 119 Dihydrolipoamide acetyltransferase component of pyruvate dehydrogenase complex (EC 2.3.1.12) 1302 PLF_2132_00000299 PGF_03456505 433 169 Dihydrolipoamide dehydrogenase of pyruvate dehydrogenase complex (EC 1.8.1.4) 1497 FIG01303880 PLF_2132_00000300 PGF_03154751 498 DNA gyrase subunit A (EC 5.99.1.3) 1479 FIG00000425 PGF_03272313 493 DNA gyrase subunit B (EC 5.99.1.3) 1041 FIG00028203 PGF_00421264 346 DNA gyrase subunit B (EC 5.99.1.3) 375 FIG00028203 125 DNA gyrase subunit B (EC 5.99.1.3) 384 FIG00028203 PGF_00421264 128 DNA ligase (NAD(+)) (EC 6.5.1.2) 1215 FIG00051439 PLF_2132_00000051 PGF_00421366 404 DNA polymerase I (EC 2.7.7.7) 921 FIG00000404 PGF_00421471 307 DNA polymerase I (EC 2.7.7.7) 738 FIG00000404 PLF_2132_00000389 PGF_00421471 246 DNA polymerase I (EC 2.7.7.7) 321 FIG00000404 PLF_2132_00000389 PGF_00421471 107 DNA polymerase III polC-type (EC 2.7.7.7) 2583 FIG00041038 PLF_2132_00000864 PGF_03783629 860 DNA polymerase III subunits gamma and tau (EC 2.7.7.7) 744 FIG00000414 PGF_01286532 248 DNA-directed RNA polymerase alpha subunit (EC 2.7.7.6) 648 FIG00000140 PGF_00422271 216 DNA-directed RNA polymerase beta subunit (EC 2.7.7.6) 3711 FIG00000156 PLF_2132_00000081 PGF_02797402 1236 DNA-directed RNA polymerase beta' subunit (EC 2.7.7.6) 3759 FIG00000242 PLF_2132_00000036 PGF_03501796 1252 DNA/RNA helicases, SNF2 family 2325 FIG01320115 774 Endonuclease IV (EC 3.1.21.2) 900 FIG00000899 PLF_2132_00000301 PGF_00424337 299 Enolase (EC 4.2.1.11) 1305 FIG00000118 PLF_2132_00000195 PGF_02516909 434 Excinuclease ABC subunit A 1536 FIG00000147 PLF_2132_00000085 PGF_00424963 512 Excinuclease ABC subunit A 900 FIG00000147 PLF_2132_00000085 PGF_00424963 300 Excinuclease ABC subunit B 969 FIG00000146 PLF_2132_00000086 PGF_00950554 323 Ferritin and Dps 522 173 FIG000859: hypothetical protein YebC 630 FIG00000859 PGF_00425684 210 FIG00833898: hypothetical protein 843 280 Fructose-1,6-bisphosphatase, GlpX type (EC 3.1.3.11) 798 265 Fructose-bisphosphate aldolase class II (EC 4.1.2.13) 870 FIG00000370 PGF_00006393 289 Glutamyl-tRNA synthetase (EC 6.1.1.17) @ Glutamyl-tRNA(Gln) synthetase (EC 6.1.1.24) 1446 FIG00000686 PLF_2132_00000096 PGF_00008337 481 Glycyl-tRNA synthetase (EC 6.1.1.14) 309 FIG00001189 PLF_2132_00000099 PGF_00009966 102 Glycyl-tRNA synthetase (EC 6.1.1.14) 222 FIG00001189 PGF_00009966 74 GMP synthase [glutamine- hydrolyzing], amidotransferase subunit (EC 6.3.5.2) / ATP pyrophosphatase subunit (EC 6.3.5.2) 768 FIG00042267 PLF_2132_00000823 PGF_00006935 255 GTP-binding protein EngA 1320 FIG00129616 PGF_00007024 440 GTP-binding protein Era 492 FIG01304818 PGF_00007027 164 GTP-binding protein TypA/BipA 1845 FIG00000268 PLF_2132_00000640 PGF_00007041 614 170 Guanosine-3',5'-bis(diphosphate) 3'-pyrophosphohydrolase (EC 3.1.7.2) / GTP pyrophosphokinase (EC 2.7.6.5), (p)ppGpp synthetase II 969 FIG00047414 PLF_2132_00000038 PGF_00010349 323 Heat-inducible transcription repressor HrcA 1014 FIG00000544 PLF_2132_00000101 PGF_00011014 337 Helicase 444 148 Helicase 312 104 Hemolysins and related proteins containing CBS domains 762 253 hypothetical protein 342 113 hypothetical protein 129 42 hypothetical protein 306 101 hypothetical protein 273 PLF_2132_00003356 PGF_03445763 90 hypothetical protein 315 104 hypothetical protein 2073 FIG00638284 691 hypothetical protein 393 130 hypothetical protein 162 53 hypothetical protein 207 69 hypothetical protein 459 152 hypothetical protein 408 135 hypothetical protein 111 37 hypothetical protein 330 FIG00638284 109 hypothetical protein 102 33 hypothetical protein 240 PLF_2132_00001773 PGF_03444306 79 hypothetical protein 645 214 hypothetical protein 114 38 hypothetical protein 117 39 hypothetical protein 150 49 hypothetical protein 444 148 hypothetical protein 222 73 hypothetical protein 354 118 hypothetical protein 678 PLF_2132_00000212 PGF_00207558 225 hypothetical protein 150 49 hypothetical protein 1290 FIG00638284 429 hypothetical protein 129 43 hypothetical protein 87 28 hypothetical protein 165 54 hypothetical protein 261 86 hypothetical protein 528 175 hypothetical protein 417 138 hypothetical protein 1026 341 171 hypothetical protein 645 214 hypothetical protein 408 135 hypothetical protein 531 FIG00834986 PLF_2132_00001129 PGF_00327047 176 hypothetical protein 393 131 hypothetical protein 1260 FIG00638284 419 hypothetical protein 282 FIG00638284 94 hypothetical protein 312 103 hypothetical protein 651 216 hypothetical protein 471 156 hypothetical protein 732 243 hypothetical protein 93 30 hypothetical protein 117 39 hypothetical protein 264 87 hypothetical protein 81 26 hypothetical protein 93 30 hypothetical protein 354 118 hypothetical protein 348 116 hypothetical protein 303 101 hypothetical protein 114 37 hypothetical protein 225 74 hypothetical protein 339 113 hypothetical protein 366 122 hypothetical protein 306 102 hypothetical protein 276 91 hypothetical protein 204 68 hypothetical protein 534 177 hypothetical protein 423 140 hypothetical protein 252 83 hypothetical protein 225 75 hypothetical protein 144 48 hypothetical protein 450 150 hypothetical protein 162 53 Inner membrane protein translocase component YidC, long form 1077 359 Inorganic pyrophosphatase (EC 3.6.1.1) 507 FIG00000429 PLF_2132_00000104 PGF_00014306 169 Inosine-5'-monophosphate dehydrogenase (EC 1.1.1.205) / CBS domain 1467 FIG00020613 PLF_2132_00000199 PGF_00014311 488 Isoleucyl-tRNA synthetase (EC 6.1.1.5) 1128 FIG00000085 PGF_00015718 376 KtrCD potassium uptake system, integral membrane component KtrD 960 PLF_2132_00000132 PGF_00234864 319 172 KtrCD potassium uptake system, integral membrane component KtrD 507 169 Leucyl-tRNA synthetase (EC 6.1.1.4) 2439 FIG00000114 PLF_2132_00000115 PGF_00016918 812 Lipid A export permease/ATP- binding protein MsbA 717 FIG00017059 PLF_2132_00000005 PGF_01555403 239 LSU ribosomal protein L14p (L23e) 369 FIG00090781 PGF_00016340 122 LSU ribosomal protein L15p (L27Ae) 270 FIG00000166 89 LSU ribosomal protein L16p (L10e) 486 FIG00001132 PGF_00016343 161 LSU ribosomal protein L18p (L5e) 351 FIG00000200 PLF_2132_00000421 PGF_00016353 116 LSU ribosomal protein L21p 606 FIG00000276 PGF_00016365 201 LSU ribosomal protein L22p (L17e) 333 FIG00000219 PLF_2132_00000109 PGF_00016368 110 LSU ribosomal protein L24p (L26e) 348 115 LSU ribosomal protein L27p 288 FIG00000189 PGF_00016385 95 LSU ribosomal protein L29p (L35e) 495 164 LSU ribosomal protein L2p (L8e) 768 FIG00000223 PLF_2132_00000428 PGF_00016393 255 LSU ribosomal protein L36p @ LSU ribosomal protein L36p, zinc- dependent 114 37 LSU ribosomal protein L5p (L11e) 558 FIG00000174 PGF_00016443 185 LSU ribosomal protein L6p (L9e) 555 FIG00001577 PLF_2132_00000659 PGF_00016444 184 LSU ribosomal protein L9p 444 147 MATE efflux family protein 861 287 MATE efflux family protein 240 80 Methenyltetrahydrofolate cyclohydrolase (EC 3.5.4.9) / Methylenetetrahydrofolate dehydrogenase (NADP+) (EC 1.5.1.5) 852 FIG00000105 284 Methenyltetrahydrofolate cyclohydrolase (EC 3.5.4.9) / Methylenetetrahydrofolate dehydrogenase (NADP+) (EC 1.5.1.5) 288 96 Methionine aminopeptidase (EC 3.4.11.18) 783 FIG00000036 PLF_2132_00000117 PGF_00020735 260 Methionyl-tRNA formyltransferase (EC 2.1.2.9) 513 FIG00000112 PLF_2132_00000118 PGF_00020768 171 Mg(2+) transport ATPase, P-type (EC 3.6.3.2) 1137 FIG01123715 PLF_2132_00000018 PGF_01580528 378 Mg(2+) transport ATPase, P-type (EC 3.6.3.2) 915 FIG01123715 PLF_2132_00000018 PGF_01580528 305 Mg(2+) transport ATPase, P-type (EC 3.6.3.2) 216 FIG01123715 PGF_01580528 72 MOB-like protein 147 PLF_2132_00000509 PGF_03447422 49 MOB-like protein 270 90 MOB-like protein 282 PLF_2132_00000509 PGF_03447422 94 MOB-like protein 186 62 N(6)-L-threonylcarbamoyladenine synthase (EC 2.3.1.234) 939 FIG00134348 PLF_2132_00000121 PGF_00023591 312 NAD synthetase (EC 6.3.1.5) 546 FIG00000590 181 173 NAD-dependent glyceraldehyde-3- phosphate dehydrogenase (EC 1.2.1.12) 1008 FIG00132586 PLF_2132_00000318 PGF_00024478 335 Nicotinate-nucleotide adenylyltransferase (EC 2.7.7.18) 756 252 Ornithine carbamoyltransferase (EC 2.1.3.3) 1005 FIG00000084 PLF_2132_00000265 PGF_02346669 334 oxidoreductase of aldo/keto reductase family, subgroup 1 696 FIG01239241 232 Peptide chain release factor 1 1047 FIG00000188 PLF_2132_00000126 PGF_00030640 349 Peptide deformylase (EC 3.5.1.88) 237 PLF_2132_00000127 PGF_03205916 78 Phage tail length tape-measure protein 396 131 Phenylalanyl-tRNA synthetase alpha chain (EC 6.1.1.20) 930 FIG00000098 PLF_2132_00000129 PGF_00033076 309 Phosphate transport ATP-binding protein PstB (TC 3.A.1.7.1) 726 FIG00011114 PGF_00033244 241 Phosphate transport system permease protein PstA (TC 3.A.1.7.1) 984 FIG00135264 327 Phosphate:acyl-ACP acyltransferase PlsX (EC 2.3.1.n2) 768 FIG00000446 PGF_00033289 256 Phosphoenolpyruvate-protein phosphotransferase of PTS system (EC 2.7.3.9) 1734 FIG00028694 PLF_2132_00000019 PGF_00033423 577 Phosphomannomutase (EC 5.4.2.8) 1683 560 Polyribonucleotide nucleotidyltransferase (EC 2.7.7.8) 1200 FIG00000391 PLF_2132_00000443 PGF_00034776 400 Possible oxidoreductase 633 PGF_03143411 210 Possible oxidoreductase 351 117 Predicted cell-wall-anchored protein SasA (LPXTG motif) 678 FIG00001347 226 Prolyl-tRNA synthetase (EC 6.1.1.15), archaeal/eukaryal type 594 FIG00000069 PGF_00037586 198 Prolyl-tRNA synthetase (EC 6.1.1.15), archaeal/eukaryal type 411 PGF_00037586 137 Protein translocase subunit SecA 1974 FIG00034293 PLF_2132_00000134 PGF_00038646 658 Protein translocase subunit SecY 1359 FIG00937056 PLF_2132_00000135 PGF_00038676 452 PTS system, fructose-specific IIA component / PTS system, fructose- specific IIB component / PTS system, fructose-specific IIC component 1452 FIG00001234 484 PTS system, N-acetylglucosamine- specific IIB component / PTS system, N-acetylglucosamine- specific IIC component 123 41 Purine nucleoside phosphorylase (EC 2.4.2.1) 828 FIG00000659 PLF_2132_00000022 PGF_00038987 275 Putative deoxyribonuclease YcfH 846 FIG00000184 PLF_2132_00000446 PGF_00040578 281 Putative hydrolase 468 PLF_2132_00000448 PGF_00041476 156 Pyruvate dehydrogenase E1 component alpha subunit (EC 1.2.4.1) 882 FIG00053889 PLF_2132_00000325 PGF_01825890 293 Pyruvate dehydrogenase E1 component beta subunit (EC 1041 FIG00025527 PLF_2132_00000326 PGF_02152246 346 174 1.2.4.1) Pyruvate kinase (EC 2.7.1.40) 1116 FIG00000043 PGF_00045991 372 RecA protein 696 FIG00000234 PGF_00047078 231 Recombinational DNA repair protein RecT (prophage associated) 354 FIG00002410 PGF_03919051 118 Replicative DNA helicase (DnaB) (EC 3.6.4.12) 855 FIG00061313 PGF_00047630 284 Replicative DNA helicase (EC 3.6.1.-) 462 154 Ribonuclease J1 (endonuclease and 5' exonuclease) 1656 FIG01300409 PLF_2132_00000147 PGF_00048564 552 Ribonuclease Y 834 FIG00002344 PLF_2132_00000087 PGF_00425786 277 Ribonucleotide reductase of class Ib (aerobic), alpha subunit (EC 1.17.4.1) 1605 FIG00001069 PLF_2132_00000455 PGF_00048640 535 Ribonucleotide reductase of class Ib (aerobic), alpha subunit (EC 1.17.4.1) 474 FIG00001069 PLF_2132_00000455 PGF_00048640 157 Ribonucleotide reductase of class II (coenzyme B12-dependent) (EC 1.17.4.1) 2199 FIG00010285 PLF_2132_00000028 PGF_00048617 732 RidA/YER057c/UK114 superfamily protein 303 FIG00002122 100 RNA polymerase sigma factor RpoD 1326 FIG00038814 PLF_2132_00000144 PGF_03717544 442 S-adenosylmethionine synthetase (EC 2.5.1.6) 1227 FIG00000329 PGF_00049330 408 Seryl-tRNA synthetase (EC 6.1.1.11) 576 FIG00000121 PLF_2132_00000041 PGF_00051525 192 Signal recognition particle protein Ffh 1272 FIG00000113 PGF_00052238 424 Spermidine/putrescine import ABC transporter ATP-binding protein PotA (TC 3.A.1.11.1) 1053 FIG00007514 PLF_2132_00000333 PGF_02025114 350 SSU ribosomal protein S11p (S14e) 393 FIG00055673 PGF_00049837 130 SSU ribosomal protein S13p (S18e) 399 FIG00000160 PGF_00049840 132 SSU ribosomal protein S14p (S29e) @ SSU ribosomal protein S14p (S29e), zinc-dependent 186 61 SSU ribosomal protein S17p (S11e) 279 FIG00000181 92 SSU ribosomal protein S19p (S15e) 273 FIG00000199 PGF_00049860 90 SSU ribosomal protein S3p (S3e) 744 FIG00000218 247 SSU ribosomal protein S5p (S2e) 705 FIG00000155 234 SSU ribosomal protein S7p (S5e) 432 FIG00000209 PGF_00049904 143 SSU ribosomal protein S8p (S15Ae) 405 FIG00000153 134 Threonine dehydratase, catabolic (EC 4.3.1.19) @ L-serine dehydratase, (PLP)-dependent (EC 4.3.1.17) 282 PGF_01557745 94 Thymidine kinase (EC 2.7.1.21) 591 FIG00000655 PLF_2132_00000167 PGF_00056871 196 Thymidylate synthase (EC 2.1.1.45) 870 FIG00000398 PLF_2132_00000169 PGF_00056897 289 Topoisomerase IV subunit B (EC 1545 FIG00000439 PLF_2132_00000704 PGF_00057272 514 175 5.99.1.-) Transcription termination protein NusA 1176 FIG00000168 391 Transcriptional regulator, MerR family, near polyamine transporter 534 FIG00103661 PLF_2132_00000483 PGF_00059725 177 Translation elongation factor G 1965 FIG00063189 PLF_2132_00000173 PGF_00060409 655 Translation elongation factor LepA 1815 FIG00000165 PLF_2132_00000043 PGF_00060414 604 Translation elongation factor P 564 FIG00000177 PLF_2132_00000484 PGF_00060421 187 Translation elongation factor Tu 1257 FIG00000039 PGF_00060428 418 Translation initiation factor 1 321 106 Translation initiation factor 2 1527 FIG00000102 PLF_2132_00000044 PGF_02704712 509 Translation initiation factor 2 258 FIG00000102 86 Transmembrane component of general energizing module of ECF transporters 849 FIG00024851 PLF_2132_00000176 PGF_00060545 282 tRNA (guanine(37)-N(1))- methyltransferase (EC 2.1.1.228) 684 FIG00000230 PGF_00413208 228 tRNA-5- carboxymethylaminomethyl-2- thiouridine(34) synthesis protein MnmG 1359 FIG00000576 PLF_2132_00000047 PGF_00413300 453 tRNA-specific 2-thiouridylase MnmA (EC 2.8.1.13) 1116 FIG00000626 PLF_2132_00000248 PGF_00261173 371 tRNA(Ile)-lysidine synthetase (EC 6.3.4.19) 426 FIG00638284 141 Tyrosyl-tRNA synthetase (EC 6.1.1.1) 657 FIG00346039 PGF_00063916 219 Ubiquinol--cytochrome c reductase, cytochrome B subunit (EC 1.10.2.2) 177 59 Ubiquinol--cytochrome c reductase, cytochrome B subunit (EC 1.10.2.2) 345 114 Valyl-tRNA synthetase (EC 6.1.1.9) 1902 FIG00000129 PLF_2132_00000180 PGF_00066453 634 Xanthine/uracil/thiamine/ascorbate permease family protein 954 FIG00016110 PLF_2132_00000273 PGF_00066964 318 176 Supplementary Table 4.3 Genes used for phylogenetic analysis of 16S rRNA sequences. Species/strain Gene Accession # Wheat Stem Sawfly-associated Spiroplasma NA Spiroplasma turonicum strain Tab4c NR_025712.1 Spiroplasma sp. SM of Acyrthosiphon pisum AB048263.1 Spiroplasma sp. Ozg typeA AB542740.1 Spiroplasma sp. Ozg typeB AB542741.1 Spiroplasma sp. of Adalia bipunctata AJ006775.1 Spiroplasma sp. of Harmonia axyridis AJ132412.1 Spiroplasma sp. of Danaus chrysippus AJ245996.1 Spiroplasma sp. 'Gent' of Fannia manicata AY569829.1 Spiroplasma melliferum KC3 NR_025756.1 Spiroplasma endosymbiont of Drosophila tenebrosa isolate SaCat-47 FJ657241.1 Spiroplasma endosymbiont of Drosophila atripex isolate Malay-30 FJ657246.1 Spiroplasma chrysopicola AY189127.1 Spiroplasma corruscae AY189128.1 Spiroplasma culicicola AY189129.1 Spiroplasma diminutum AY189130.1 Spiroplasma floricola AY189131.1 Spiroplasma helicoides AY189132.1 Spiroplasma insolitum AY189133.1 Spiroplasma leptinotarsae AY189305.1 Spiroplasma sabaudiense AY189308.1 Spiroplasma syrphidicola AY189309.1 Spiroplasma velocicrescens AY189311.1 Spiroplasma chrysopicola strain DF-1 NR_025699.1 Spiroplasma helicoides strain TABS-2 NR_025704.1 Spiroplasma insolitum strain M55 NR_025705.1 Spiroplasma penaei strain SHRIMP NR_043177.1 Spiroplasma kunkelii strain E275 NR_104847.1 Spiroplasma ixodetis strain Y32 NR_104852.1 Spiroplasma monobiae strain MQ-1 NR_118705.1 Spiroplasma diabroticae strain DU-1 NR_118706.1 Spiroplasma citri strain JK KP145008.1 Spiroplasma endosymbiont of Curculio elephas strain J73E JN100091.1 Spiroplasma endosymbiont of Curculio glandium strain M1_H2 JQ692307.1 Spiroplasma sp. crk JQ768460.1 Spiroplasma endosymbiont of Acyrthosiphon pisum strain 161 JX943565.1 Spiroplasma endosymbiont of Acyrthosiphon pisum strain 185 JX943566.1 Spiroplasma endosymbiont of Acyrthosiphon pisum strain 333 JX943567.1 Spiroplasma sp. Bratislava 1 KP967685.1 Spiroplasma anisosticta AM087471.1 Spiroplasma platyhelix strain PALS-1 NR_104857.1 Spiroplasma apis B31 NR_121708.1 Spiroplasma atrichopogonis strain GNAT3597 NR_104720.1 Spiroplasma cantharicola strain CC-1 NR_125516.1 Spiroplasma eriocheiris CCTCC M 207170 strain DSM 21848 NR_125517.1 Spiroplasma litorale strain TN-1 NR_025708.1 Spiroplasma poulsonii MSRO NZ_JTLV00000000.1 Spiroplasma taiwanense CT-1 NR_121701.1 Spiroplasma alleghenense AY189125.1 177 Supplementary Table 4.4 Table of metabolic pathways associated with PATRIC annotated gene features of draft WSS-associated Spiroplasma gene set. Pathway ID Pathway Name Unique Gene Count Unique EC Count EC Conservation Gene Conservation 230 Purine metabolism 21 14 100 1.5 970 Aminoacyl-tRNA biosynthesis 19 17 100 1.12 240 Pyrimidine metabolism 17 9 100 1.89 10 Glycolysis / Gluconeogenesis 10 9 100 1.11 680 Methane metabolism 8 5 100 1.6 190 Oxidative phosphorylation 7 3 100 2.33 670 One carbon pool by folate 5 5 100 1 620 Pyruvate metabolism 5 4 100 1.25 480 Glutathione metabolism 4 2 100 2 30 Pentose phosphate pathway 4 4 100 1 51 Fructose and mannose metabolism 4 4 100 1 20 Citrate cycle (TCA cycle) 4 3 100 1.33 195 Photosynthesis 4 1 100 4 330 Arginine and proline metabolism 4 2 100 2 983 Drug metabolism - other enzymes 3 3 100 1 710 Carbon fixation in photosynthetic organisms 3 3 100 1 760 Nicotinate and nicotinamide metabolism 3 3 100 1 790 Folate biosynthesis 2 2 100 1 260 Glycine, serine and threonine metabolism 2 3 100 0.67 650 Butanoate metabolism 2 1 100 2 564 Glycerophospholipid metabolism 2 2 100 1 520 Amino sugar and nucleotide sugar metabolism 2 2 100 1 720 Reductive carboxylate cycle (CO2 fixation) 2 2 100 1 270 Cysteine and methionine metabolism 2 2 100 1 250 Alanine, aspartate and glutamate metabolism 2 2 100 1 910 Nitrogen metabolism 2 1 100 2 860 Porphyrin and chlorophyll metabolism 1 1 100 1 280 Valine, leucine and isoleucine degradation 1 1 100 1 290 Valine, leucine and isoleucine biosynthesis 1 1 100 1 52 Galactose metabolism 1 1 100 1 440 Phosphonate and phosphinate metabolism 1 1 100 1 513 High-mannose type N-glycan biosynthesis 1 1 100 1 600 Sphingolipid metabolism 1 1 100 1 770 Pantothenate and CoA biosynthesis 1 1 100 1 178 Supplementary Table 4.5 Primers used to amplify and prepare V3-V4 of 16S rRNA libraries in WSS samples. Primer Name Adaptor Index Primer Pad Linker Primer (341F/806R) MiSeq F1 aatgatacggcgaccaccgagatctacac tagatcgc tatggtaatt gt cctacgggaggcagcag MiSeq F2 aatgatacggcgaccaccgagatctacac ctctctat tatggtaatt gt cctacgggaggcagcag MiSeq F3 aatgatacggcgaccaccgagatctacac tatcctct tatggtaatt gt cctacgggaggcagcag MiSeq F4 aatgatacggcgaccaccgagatctacac agagtaga tatggtaatt gt cctacgggaggcagcag MiSeq F5 aatgatacggcgaccaccgagatctacac gtaaggag tatggtaatt gt cctacgggaggcagcag MiSeq F6 aatgatacggcgaccaccgagatctacac actgcata tatggtaatt gt cctacgggaggcagcag MiSeq F7 aatgatacggcgaccaccgagatctacac aaggagta tatggtaatt gt cctacgggaggcagcag MiSeq F8 aatgatacggcgaccaccgagatctacac ctaagcct tatggtaatt gt cctacgggaggcagcag MiSeq R1 caagcagaagacggcatacgagat tcgcctta agtcagtcag cc gctgcgttcttcatcgatgc MiSeq R2 caagcagaagacggcatacgagat ctagtacg agtcagtcag cc gctgcgttcttcatcgatgc MiSeq R3 caagcagaagacggcatacgagat ttctgcct agtcagtcag cc gctgcgttcttcatcgatgc MiSeq R4 caagcagaagacggcatacgagat gctcagga agtcagtcag cc gctgcgttcttcatcgatgc MiSeq R5 caagcagaagacggcatacgagat aggagtcc agtcagtcag cc gctgcgttcttcatcgatgc MiSeq R6 caagcagaagacggcatacgagat catgccta agtcagtcag cc gctgcgttcttcatcgatgc MiSeq R7 caagcagaagacggcatacgagat gtagagag agtcagtcag cc gctgcgttcttcatcgatgc MiSeq R8 caagcagaagacggcatacgagat cctctctg agtcagtcag cc gctgcgttcttcatcgatgc MiSeq R9 caagcagaagacggcatacgagat agcgtagc agtcagtcag cc gctgcgttcttcatcgatgc MiSeq R10 caagcagaagacggcatacgagat cagcctcg agtcagtcag cc gctgcgttcttcatcgatgc MiSeq R11 caagcagaagacggcatacgagat tgcctctt agtcagtcag cc gctgcgttcttcatcgatgc MiSeq R12 caagcagaagacggcatacgagat tcctctac agtcagtcag cc gctgcgttcttcatcgatgc MiSeq R13 caagcagaagacggcatacgagat accaggtt agtcagtcag cc gctgcgttcttcatcgatgc MiSeq R14 caagcagaagacggcatacgagat cgttgagt agtcagtcag cc gctgcgttcttcatcgatgc MiSeq R15 caagcagaagacggcatacgagat gacgacat agtcagtcag cc gctgcgttcttcatcgatgc MiSeq R16 caagcagaagacggcatacgagat tggtactg agtcagtcag cc gctgcgttcttcatcgatgc MiSeq R17 caagcagaagacggcatacgagat aattgggg agtcagtcag cc gctgcgttcttcatcgatgc MiSeq R18 caagcagaagacggcatacgagat ttgaaagg agtcagtcag cc gctgcgttcttcatcgatgc MiSeq R19 caagcagaagacggcatacgagat gagtgatc agtcagtcag cc gctgcgttcttcatcgatgc MiSeq R20 caagcagaagacggcatacgagat ggacaatc agtcagtcag cc gctgcgttcttcatcgatgc MiSeq R21 caagcagaagacggcatacgagat atctcacc agtcagtcag cc gctgcgttcttcatcgatgc MiSeq R22 caagcagaagacggcatacgagat gcccatac agtcagtcag cc gctgcgttcttcatcgatgc MiSeq R23 caagcagaagacggcatacgagat ggtattca agtcagtcag cc gctgcgttcttcatcgatgc MiSeq R24 caagcagaagacggcatacgagat ccaatgaa agtcagtcag cc gctgcgttcttcatcgatgc Entire Primer Sequence Sequencing Primer 1 tatggtaattacctacgggaggcagc Sequencing Primer 2 agtcagtcagccggactachvgggtw Index Sequencing Primer tagawacccbdgtagtccggctgact 179 Supplementary Table 4.6 MiSeq read yields from 16S rRNA sequencing. Sample Index 2 Index 1 # reads pass filter Highland/Native Grass Adult 6 tcctgagc ctctctat 5636 Highland/Native Grass Adult 7 taggcatg ctctctat 3669 Highland/Native Grass Adult 8 ctctctac ctctctat 4592 Highland/Native Grass Adult 9 cagagagg ctctctat 4705 Highland/Native Grass Adult 10 taaggcga tatcctct 2468 Lowland/Wheat Adult 6 cgtactag tatcctct 4065 Lowland/Wheat Adult 7 aggcagaa tatcctct 2956 Lowland/Wheat Adult 8 tcctgagc tatcctct 2414 Lowland/Wheat Adult 9 ggactcct tatcctct 151 Lowland/Wheat Adult 10 taggcatg tatcctct 1353 Highland/Native Grass Larva 6 ctctctac tatcctct 2223 Highland/Native Grass Larva 7 cagagagg tatcctct 8237 Highland/Native Grass Larva 8 taaggcga agagtaga 2912 Highland/Native Grass Larva 9 cgtactag agagtaga 7495 Highland/Native Grass Larva 10 aggcagaa agagtaga 9324 Lowland/Wheat Larva 6 tcctgagc agagtaga 4624 Lowland/Wheat Larva 7 ggactcct agagtaga 9053 Lowland/Wheat Larva 8 taggcatg agagtaga 1456 Lowland/Wheat Larva 9 ctctctac agagtaga 3513 Lowland/Wheat Larva 10 cagagagg agagtaga 1668 180 Supplementary Table 4.7 MiSeq read yields from Nextera XT metagenomic sequencing. Sample Index 1 (I7) Index 2 (I5) # of reads PF Highland/Native grass Adult 6 tcctgagc ctctctat 3,614,067 Highland/Native grass Adult 7 ggactcct ctctctat 3,346,786 Highland/Native grass Adult 8 taggcatg ctctctat 3,103,714 Lowland/wheat Adult 7 tcctgagc tagatcgc 2,039,503 Lowland/wheat Adult 8 ggactcct tagatcgc 4,218,306 Lowland/wheat Adult 10 taggcatg tagatcgc 2,542,697 Highland/Native grass Larva 6 taaggcga ctctctat 3,150,786 Highland/Native grass Larva 7 cgtactag ctctctat 3,364,979 Highland/Native grass Larva 8 aggcagaa ctctctat 3,617,154 Lowland/wheat Larva 6 taaggcga tagatcgc 1,304,180 Lowland/wheat Larva 8 cgtactag tagatcgc 1,726,005 Lowland/wheat Larva 10 aggcagaa tagatcgc 2,964,583 Spiroplasma pellet aggcagaa tatcctct 19,319,981 181 Supplementary Table 4.8 List of Spiroplasma genomes used for Blastn comparison and phylogenetic tree comparison. species/strain name genome ID Spiroplasma sp. TU-14 18308 Spiroplasma sp. NBRC 100390 18308 Spiroplasma melliferum IPMB4A 11130 Spiroplasma melliferum KC3 11130 Spiroplasma apis B31T 15951 Spiroplasma mirum ATCC 29335, strain SMCA 11332 Spiroplasma mirum ATCC 29335 11332 Spiroplasma eriocheiris, strain DSM 21848 11290 Spiroplasma eriocheiris, strain CCTCC M207170 11290 Spiroplasma sabaudiense, Ar-1343 15950 Spiroplasma culicicola AES-1 15949 Spiroplasma syrphidicola EA-1 15948 Spiroplasma chrysopicola DF-1 15947 Spiroplasma diminutum CUAS-1 15945 Spiroplasma taiwanense CT-1 15944 Spiroplasma kunkelii CR2-3x 1043 Spiroplasma citri, strain R8-A2 1228 Spiroplasma citri 1228 Spiroplasma helicoides 45881 Spiroplasma cantharicola 40089 Spiroplasma litorale 39890 Spiroplasma turonicum 39852 Spiroplasma atrichopogonis 38472 182 Supplementary Table 4.9 Relative metabolite concentrations in cultures over four time points and in three replicates. relative concentration/200ul media metabolite rep1 day 1 rep1 day 2 rep1 day 3 rep1 _ day 5 rep 2_ day 1 rep 2_ day 2 rep2 day 3 rep2 day 5 rep3 _ day 1 rep 3_ day 2 rep3 _ day 3 rep3 day 5 CYSTEINE 73.9 53.6 65.2 69.4 72. 6 68. 9 80.4 63.1 84.1 59. 7 63.4 63.7 pyruvic acid 20.8 11.3 9.6 6.7 16. 9 14. 1 10.2 7.6 19.4 9.5 7.9 6.0 serine 2730. 1 2572. 8 2686 .6 3043 .6 248 3.1 267 7.7 2728. 9 3522 .6 2564. 4 236 9.5 2691 .1 3217. 8 trehalose 5794. 2 5096. 6 4635 .6 4572 .0 566 1.2 540 4.0 4634. 4 6124 .7 5477. 4 490 7.4 4709 .8 4797. 0 2- aminoethylglyce rophosphate 85.1 97.8 104. 3 112. 3 89. 9 92. 0 95.1 113. 7 112.3 113 .4 98.9 128.5 3,4- Dihydroxybutan oic acid 31.5 30.6 33.3 37.5 26. 3 31. 8 31.5 33.0 27.6 27. 0 30.5 42.4 Alanine 3676. 3 3242. 7 3504 .5 3826 .6 277 2.9 348 4.2 3537. 5 4246 .3 3376. 9 280 5.2 3584 .9 4149. 5 Allantoin 11.4 14.7 39.9 22.3 10. 4 18. 8 18.0 7.7 14.5 18. 8 40.8 25.5 Arginine 83.1 96.5 142. 7 140. 1 100 .8 94. 1 136.6 116. 9 128.9 95. 4 123. 0 155.0 aspartic acid 2505. 2 3567. 8 3501 .3 1358 .2 317 2.2 219 3.8 3506. 3 4664 .5 3323. 4 283 9.9 3569 .0 4234. 8 galactose 1088. 4 6789. 1 4829 .9 2264 .6 257 6.1 215 9.0 3161. 6 9395 .7 1268. 3 752 7.5 6926 .5 8962. 3 Glycolic acid 25.4 26.4 29.0 43.3 24. 4 25. 5 29.4 42.6 22.9 24. 0 29.2 44.6 proline 1404. 0 1976. 1 2454 .1 3100 .2 123 3.9 199 1.0 2504. 3 3285 .5 1335. 5 175 9.0 2423 .8 3399. 4 pyroglutamic acid 4593. 5 5948. 8 5657 .1 7375 .1 439 2.6 605 2.9 5350. 3 6822 .4 3462. 0 546 0.9 5957 .7 6633. 0 serine 2730. 1 2572. 8 2686 .6 3043 .6 248 3.1 267 7.7 2728. 9 3522 .6 2564. 4 236 9.5 2691 .1 3217. 8 Tyrosine 1184. 0 1291. 3 1389 .6 1661 .6 108 7.1 134 8.2 1391. 6 1827 .1 1055. 7 120 1.1 1427 .1 1717. 9 Uridine 55.6 80.7 87.4 113. 2 59. 8 85. 9 87.3 110. 7 67.5 65. 8 83.4 104.9 valine 3033. 0 3072. 2 3323 .8 3764 .8 281 3.5 326 9.5 3340. 0 4037 .3 3044. 2 281 5.6 3283 .3 3979. 0 Xanthine 27.1 41.7 41.4 70.2 27. 7 37. 4 47.6 66.8 28.4 32. 6 46.0 66.7 183 References Cited 1. 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The best characterized commensal microbes of bees are honey bee gut associated bacteria, including eight bacterial phylotypes/bacterial clusters predominantly in the phyla Proteobacteria, Firmicutes, and Actinobacteria (4,5 and reviewed in3), which comprise greater than 95% of the honey bee gut microbiome3,6,7. These microbes have been consistently detected in honey bee workers collected throughout North America, Europe, Asia, and Australia, and comprise the “core” gut microbiota of worker honey bees 8–14. In addition, the Gram-negative Proteobacteria Gilliamella apicola and Snodgrassella alvi bacteria have also been detected in the guts of other Apis spp. (i.e., Apis florea and Apis cerana) and bumble bee species6,15–19, suggesting coevolutionary relationships between gut bacteria and social bees. The honey bee crop/honey stomach also contains bacteria5,20, primarily consisting of Lactobacillus kunkeei, other environmental Lactobacillus, and Alpha2.2, a member of the Alpha2 cluster (reviewed in3). These bacteria are also present in hive components, nectar, and beebread21,22. The crop has been proposed as important source of bacterial inoculation for younger bees as well as fermentative bacteria in beebread1. However, the crop microbial community contains low absolute bacterial cell quantity5,11,23. In addition, worker bees develop their microbiomes over the course of 3-5 days11,24 via interaction with nurse bees, hive components, fecal material, and food stores24,25; trophallaxis alone does not lead to full colonization of the core microbiome in the gut of young 192 bees24. Metatranscriptomic, metagenomic, and genomic analyses have implicated the role of honey bee-associated microbes in host nutrition and health. Several, but not all G. apicola strains encode for multiple carbohydrate-processing genes, including, pectate lyase, which is involved in the breakdown of pectin breakdown pollen grain cell walls26. A metatranscriptomic study also detected multiple several genes involved in breakdown of complex polysaccharides, including Beta-glucosidases27. Furthermore, single-cell sequencing indicated that some G. apicola strains are enriched for carbohydrate metabolism28. Lactobacillus and Bifidobacterium spp. also encode for genes that likely contribute to carbohydrate processing27. Pollen is an important source of amino acids, lipids, vitamins, and minerals for the honey bee, whereas nectar and honey are the primary sources of carbohydrates29. The bacteria inoculated into beebread by nurse bees has been proposed as being important for fermenting and degrading complex polysaccharides in pollen, making the nutrients are more easily digestible1,30–32, but others have proposed that the beebread-associated bacteria may just have a role preserving but not in fermenting or digesting these stored products21. The relationship between the gut microbiome and viruses has been characterized in mammals (reviewed in33) and in solitary insects, but less is known about their relationship in honey bees. In fruit flies and mosquito spp., several strains of the bacteria Wolbachia reduce RNA virus replication and plasmodium infection34,35. Wolbachia 16S rRNA sequences have been detected 193 in different subspecies of Apis mellifera samples from southern Africa36 and Germany37, and in five species of European bumble bees38, but the potential influence of Wolbachia on virus infections in bees has not been investigated. Honey bee-associated microbes may also be involved in pathogen defense via in pathogen exclusion1,20,39 or immune activation40; some bee- associated bacteria may have antimicrobial properties and encode for toxins, which may act in excluding pathogenic species41–48. Also, recent findings suggest that Parasaccharibacter apiumin may improve larval survival49, and enhance defense against Nosema50, but the potential effects of this bacteria on virus replication is not known. Bee microbiome research has primarily focused on the benefits of these microbes to bee health, but not all bee-associated bacteria are beneficial; some may be opportunistic pathogens (e.g., F. perrara)41,51, whereas others (i.e., Paenibacillus larvae and Melissococcus plutonius) are pathogenic. The relationship between the bee bacteriome and virome, as well as the effects of both on bee health require further characterization. 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Detection and identification of a novel lactic acid bacterial flora within the honey stomach of the honeybee Apis mellifera. Curr. Microbiol. 57, 356–63 (2008). 21. Anderson, K. E. et al. Hive-stored pollen of honey bees: Many lines of evidence are consistent with pollen preservation, not nutrient conversion. Mol. Ecol. 23, 5904–5917 (2014). 196 22. Endo, A. & Salminen, S. Honeybees and beehives are rich sources for fructophilic lactic acid bacteria. Syst. Appl. Microbiol. 36, 444–8 (2013). 23. Anderson, K. E. et al. Microbial ecology of the hive and pollination landscape: bacterial associates from floral nectar, the alimentary tract and stored food of honey bees (Apis mellifera). PLoS One 8, e83125 (2013). 24. Powell, J. E., Martinson, V. G., Urban-Mead, K. & Moran, N. a. Routes of acquisition of the gut microbiota of Apis mellifera. Appl. Environ. Microbiol. (2014). doi:10.1128/AEM.01861-14 25. Vojvodic, S., Rehan, S. M. & Anderson, K. E. 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K., Engel, P., Koch, H. & Moran, N. a. Genomics and host specialization of honey bee and bumble bee gut symbionts. Proc. Natl. Acad. Sci. 111, 11509–11514 (2014). 43. Evans, J. D. & Armstrong, T.-N. Antagonistic interactions between honey bee bacterial symbionts and implications for disease. BMC Ecol. 6, 4 (2006). 44. Wu, M. et al. Characterization of bifidobacteria in the digestive tract of the Japanese honeybee, Apis cerana japonica. J. Invertebr. Pathol. 112, 88– 93 (2013). 198 45. Butler, È. et al. Proteins of novel lactic acid bacteria from Apis mellifera mellifera: an insight into the production of known extra-cellular proteins during microbial stress. BMC Microbiol. 13, 235 (2013). 46. Vásquez, A. et al. Symbionts as major modulators of insect health: lactic acid bacteria and honeybees. PLoS One 7, e33188 (2012). 47. Wu, M. et al. Inhibitory effect of gut bacteria from the Japanese honey bee, Apis cerana japonica, against Melissococcus plutonius, the causal agent of European foulbrood disease. J. Insect Sci. 14, 1–13 (2014). 48. Killer, J., Dubna, S., Sedlacek, I. & Svec, P. Lactobacillus apis sp. nov., from the stomach of honeybees (Apis mellifera), having an in vitro inhibitory effect on the causative agents of American and European foulbrood. Int. J. Syst. Evol. Microbiol. 64, 152–157 (2014). 49. Corby-Harris, V. et al. Origin and effect of Acetobacteraceae Alpha 2.2 in honey bee larvae and description of Parasaccharibacter apium, gen. nov., sp. nov. Appl. Environ. Microbiol. (2014). doi:10.1128/AEM.02043-14 50. Corby-Harris, V. et al. Parasaccharibacter apium, gen. Nov., sp. Nov., Improves Honey Bee (Hymenoptera: Apidae) resistance to Nosema. J. Econ. Entomol. 109, 537–543 (2016). 51. Engel, P., Bartlett, K. D. & Moran, A. The bacterium Frischella perrara causes scab formation in the gut of its honeybee host. MBio 6, 1–8 (2015). 199 APPENDIX B Assessment Of The Relationship Between Honey Bee Microbiome And Virus Abundance In Individual Bees 200 OVERVIEW As discussed in Appendix A, the insect microbiome can influence outcome of virus infection for which a classic example is that Wolbachia can reduce RNA virus replication In fruit flies and mosquito spp.1,2. Honey bee-associated microbes (e.g., Parasaccharibacter apiumin3) may also be involved in pathogen defense via in pathogen exclusion4-6 or immune activation7, but the relationship between the bee bacteriome and virus load in bees is not well-characterized. In order to investigate honey bee-host virus interactions, we infected newly emerged workers with model Sindbis virus tagged with GFP (SINV-GFP) and observed that virus loads, assayed via qPCR, are variable between individual bees 72 hours post-injection (hpi)8. While there are likely multiple factors that influence virus infection outcome (e.g., individual immune response, genetics, etc.), we hypothesized that the honey bee microbiome may also have an effect on virus loads in honey bees. OBJECTIVE To characterize the bacterial community in the digestive tract of individual honey bees, via 16S rRNA profiling, and examine the relationship of the bacterial community with abundance of an experimentally introduced model virus (i.e., Sindbis-GFP) using the methods outlined in Appendix E and Appendix H. HYPOTHESIS Virus-infected bees that exhibit different virus loads will have also exhibit different 16S rRNA profiles. 201 RESULTS Honey workers from two different age groups (i.e, newly emerged workers and older adult bees) were injected with Sindbis virus and collected 72 hours post-injection (hpi). The newly emerged workers were collected within 24 hours after emergence and older adult bees were collected from the inner frame of honey bee colonies using a modified vacuum (Appendix E). At least five bees from each condition and age group were dissected into head thorax and abdomen. For each bee, Sindbis was qPCR quantified and the gut was 16S rRNA sequenced (Appendix E). The 16S rRNA profiles of virus-infected newly emerged workers and adult bees were assessed at the family taxonomic level. Virus-infected and mock-infected newly emerged workers exhibited similar 16S rRNA profiles (Appendix B Figure 1). The majority of OTUs in each newly emerged worker were Orbus species. The 16S rRNA profiles of individual virus- infected newly emerged workers did not exhibit any discernable trends with virus loads (Appendix B Figure 2). Interestingly, virus-infected adults had greater relative abundance of OTUs classified as Orbus and lower relative abundance of Neisseriaceae as compared to mock-injected bees (Appendix B Figures 3 and 4). It is important to note that Sindbis-GFP was not detectable via PCR in virus-infected adult bees 72 hpi, so this difference 16S rRNA profiles may be due to cage effect and not virus infection. 202 Appendix B Figure 1. Percent relative 16S rRNA OTU abundance (classified to family taxonomic level) averaged across mock-infected and virus-infected newly emerged workers 72 hpi. Bees were predominantly colonized by bacteria belonging to the Orbus family. 16S rRNA profiles did not vary widely between mock-infected and virus-infected bees. Appendix B Figure 2. 16S rRNA profiles do not appear to correlate with virus load in newly emerged workers 72 hpi. (A) OTUs were classified to family taxonomic level and graphed by relative abundance in individual mock-infected and virus- infected newly emerged workers. While there was some variation in 16S rRNA profiles; mock-infected control 1 and virus-infected bee 2 had ~30% OTUs classified as Lactobacillaceae, while all other bees had >95% OTUs classified as Orbus. (B) virus-infected bees 72 hpi exhibited virus loads ranging from 1.3 x 107 to 5.41 x 107 virus copies (per 500 ng of RNA). (A & B) While Virus 1 and Virus 2 bees displayed disparate virus loads (1.3 x 107 versus 5.41 x 107 virus copies), their 16S rRNA profiles were similar. 203 Appendix B Figure 3. Percent relative 16S rRNA OTU abundance (classified to family taxonomic level) averaged across mock-infected and virus-infected adult bees 72 hpi. On average, adult virus-infected bees exhibited lower relative abundance of Neisseriaceae and higher relative abundance of Orbus species. Appendix B Figure 4. Percent relative 16S rRNA OTU abundance (classified to family taxonomic level) of individual mock-infected and virus-infected adult bees 72 hpi. DISCUSSION These preliminary results and more recent reports9-12 suggest that newly emerged workers (NEWs) are not an ideal model for investigating the relationship between honey bee microbiome and virus abundance. After larval 204 eclosion, honey bees typically shed any resident bacteria from their guts, and workers then emerge essentially “germ-free”9. Young bees typically develop their microbiomes 3-5 days post-emergence10,11 via interaction with nurse bees and hive components11,12. The bees utilized in the virus infection trials were collected within 24 hours of emergence in a laboratory setting (i.e., brood comb incubated at 30°C), injected, and then placed in relatively “clean” deli containers. Thus, the newly emerged workers likely do not get the chance to be optimally inoculated with biologically relevant bacteria that honey bees typically are exposed to in the hive. The adult/nurse bees, which were collected directly from honey bee colonies located outside the laboratory, are more ideal for examining the honey bee microbiome. However, injection with 3,750 PFU of Sindbis, which has been optimized for newly emerged workers, did not result in infection and/or detectable virus 72 hpi. Perhaps injecting the adult bees with an increased virus inoculum would allow infection, but it has not been tested. Additionally, adult bees collected from the hive are more likely to have pre-existing infections that would confound the results of the Sindbis-GFP infection trials. FUTURE DIRECTIONS AND PROJECT IDEAS The results presented herein suggest that the current set-up for virus- infection trials is not optimal for investigating the relationship between the honey bee microbiome and virus load. Other microbiome studies have cultured honey bee-associated strains for use in inoculation of newly emerged workers and 205 larvae for investigating their role in pathogen defense3,6,7, which may serve as a viable solution for future studies examining the effect of honey bee-associated microbes on virus abundance. 206 References Cited 1. Martinez, J. et al. Symbionts Commonly Provide Broad Spectrum Resistance to Viruses in Insects: A Comparative Analysis of Wolbachia Strains. PLoS Pathog. 10, e1004369 (2014). 2. Teixeira, L., Ferreira, A. & Ashburner, M. The bacterial symbiont Wolbachia induces resistance to RNA viral infections in Drosophila melanogaster. PLoS Biol. 6, e2 (2008). 3. Corby-Harris, V. et al. Origin and effect of Acetobacteraceae Alpha 2.2 in honey bee larvae and description of Parasaccharibacter apium, gen. nov., sp. nov. Appl. Environ. Microbiol. (2014). doi:10.1128/AEM.02043-14 4. Mattila, H. R., Rios, D., Walker-Sperling, V. E., Roeselers, G. & Newton, I. L. G. Characterization of the active microbiotas associated with honey bees reveals healthier and broader communities when colonies are genetically diverse. PLoS One 7, e32962 (2012). 5. Olofsson, T. C. & Vásquez, A. Detection and identification of a novel lactic acid bacterial flora within the honey stomach of the honeybee Apis mellifera. Curr. Microbiol. 57, 356–63 (2008). 6. Forsgren, E., Olofsson, T. C., Vásquez, A. & Fries, I. Novel lactic acid bacteria inhibiting Paenibacillus larvae in honey bee larvae. Apidologie 41, 99–108 (2009). 7. Evans, J. D. & Lopez, D. L. Bacterial probiotics induce an immune response in the honey bee (Hymenoptera: Apidae). J. Econ. Entomol. 97, 752–6 (2004). 8. Flenniken, M. L. & Andino, R. Non-specific dsRNA-mediated antiviral response in the honey bee. PLoS One 8, e77263 (2013). 9. Moran, N. a. Genomics of the honey bee microbiome. Curr. Opin. Insect Sci. 10, 22–28 (2015). 10. Martinson, V. G., Moy, J. & Moran, N. a. Establishment of characteristic gut bacteria during development of the honeybee worker. Appl. Environ. Microbiol. 78, 2830–40 (2012). 207 11. Powell, J. E., Martinson, V. G., Urban-Mead, K. & Moran, N. a. Routes of acquisition of the gut microbiota of Apis mellifera. Appl. Environ. Microbiol. (2014). doi:10.1128/AEM.01861-14 12. Vojvodic, S., Rehan, S. M. & Anderson, K. E. Microbial gut diversity of Africanized and European honey bee larval instars. PLoS One 8, e72106 (2013). 208 APPENDIX C Assessing The Relationship Between Honey Bee Microbiome, Virus Abundance, And Colony Health 209 OVERVIEW Honey bee colony losses are associated with elevated pathogen incidence and abundance (reviewed in1). The pathogens that infect honey bees are primarily positive sense single-stranded RNA viruses, including the Lake Sinai viruses (LSVs), a recently discovered and abundant group of viruses. In a previous study, Daughenbaugh et al. Flenniken, 20152, performed longitudinal pathogen monitoring of three weak and three strong colonies (using colony population as a proxy for health) maintained in California. Samples were collected from each colony at January 7th, January 22nd , February 10th, and March 8th in 2013 (Appendix C Table 1). Incidence and abundance of honey bee pathogens were ascertained via pathogen-specific PCR and quantitative PCR (Appendix E). Weak colonies had greater abundance of Lake Sinai virus 1 and Lake Sinai virus 2 (Appendix C Figure 1), and Black queen cell virus (BQCV) as compared to colonies of greater population size. While several studies have examined the pathogen profile of longitudinally collected samples, few, if any, studies have characterized the potential relationship between the microbiome and colony health over multiple time points. As a pilot project, the 16S rRNA profiles of samples collected from the weak and strong colonies (described in Daughenbaugh et al., 20152) were characterized. 210 Appendix C Figure 1. Lake Sinai virus 2 abundance trends with colony health. Honey bee colony health and pathogen prevalence and abundance (n = 6) were monitored from January–March 2013. Honey bee colonies that were weak (<5 frames, n = 3) at the onset of the study are labeled W1, W2, and W3 and colonies that were strong (>9 frames, n = 3) are labeled S1, S2, and S3. Overall, weak colonies had higher relative number of LSV2 genomes than strong colonies, as did colonies that died during the monitoring study. Recovery of one weak colony (W1) coincided with lower levels of LSV2. Figure from Daughenbaugh et al., 20152. OBJECTIVE To perform 16S rRNA sequencing on all samples collected from strong and weak colonies using methods described in Appendix E and 16S sequencing analysis (Mothur) methods in Appendix H. HYPOTHESIS The honey bee microbiome, assessed by 16S rRNA profiling, will correlate with colony health, using colony population size as a proxy for colony health. 211 RESULTS The 16S rRNA profiles of samples collected from three weak and three strong colonies over two months were examined (Appendix C Table 1), and OTUs were classified to the family level. Most samples were predominantly colonized by Orbus species, except for four samples collected in January 7th, 2013, which had greater OTU diversity and lower levels of Orbus (Appendix B Figure 2). Appendix C Table 1. Colony sample dates and 16S sequencing read yields. Colony Date Sampled # Sequencing Reads Strong 1 1/7/13 37,710 Strong 2 1/7/13 61,808 Strong 3 1/7/13 47,023 Weak 1 1/7/13 58,109 Weak 2 1/7/13 25,722 Weak 3 1/7/13 33,012 Strong 1 1/22/13 39,787 Strong 2 1/22/13 13,303 Strong 3 1/22/13 47,629 Weak 1 1/22/13 19,513 Weak 2 1/22/13 16,858 Weak 3 1/22/13 59,751 Strong 1 2/10/13 27,178 Strong 2 2/10/13 40,911 Strong 3 2/10/13 67,276 Weak 1 2/10/13 57,833 Weak 2 2/10/13 28,389 Weak 3 2/10/13 53,035 Strong 1 3/8/13 25,064 Weak 1 (recovered) 3/8/13 64,743 212 Appendix C Figure 2. Relative abundance of OTUs (16S rRNA profiles) of pooled samples (five bees per sample) collected from weak and strong colonies during almond pollination season. The 16S rRNA profiles were further examined for dissimilarities by plotting them onto a Nonmetric Multidimensional Scaling (NMDS) Ordination plot using the Vegan package in R3 (Appendix Figure 3A). Most samples collected at the first time point, January 7th, had distinctly different 16S rRNA profiles from all other samples collected at different time points. Additionally, samples collected at time point 2, January 22nd, and time point 3, February 10th, mostly clustered separately by time point. Within each time point, there was not distinct clustering by colony strength (weak versus strong). Because the samples tended to cluster by time point in the NMDS plot, the 16S rRNA profiles were averaged across each time point (Appendix Figure 3B). Interestingly, the relative abundance of Orbus sp. increased throughout the sampling time course. 213 Because sample date is likely a confounding factor in examining the relationship between the honey bee microbiome and colony strength, 16S rRNA profiles were averaged by weak and strong colonies within by sample collection date (i.e., day of year) (Appendix C Figure 4). When comparing the microbiomes of strong colonies to weak colonies within time points 2 and 3 (January 22nd and February 10th), OTUs classified as Brucellaceae and Bifidobacteriaceae appeared to have higher relative abundance in strong colonies as compared to weak colonies within the same time point. However, on closer examination, Brucellaceae was not consistently higher within individual colonies, rather Strong colony 3 was likely skewing the average relative abundance (Appendix C Figure 2). In addition, Bifidobacteriaceae was consistently higher in relative abundances in strong colonies as compared to weak. 214 Appendix C Figure 3. The 16S rRNA profiles of samples collected from weak and strong colonies clusters by time point. (A) 16S rRNA profiles from each colony and time point were analyzed for Bray-Curtis dissimilarities and graphed onto an NMDS plot. Samples mostly clustered by time point. Green symbols are from time point 1, blue are time point 2, red are time point 3, and yellow are time point 4. (B) In order to further visualize how samples may vary by time point the relative abundances of OTUs were averaged across each time point. Most strikingly, the relative amount of Orbus OTUs increased over time. 215 Appendix C Figure 4. The 16S rRNA profiles of samples collected from weak and strong colonies averaged within each time point. In order to further assess the microbiomes of weak vs strong colonies by time point, the relative abundances of OTUs were averaged within each time point. On average, strong colonies in time points 2 and 3 had higher relative levels of Brucellaceae and Bifidobacteriaceae. DISCUSSION The 16S rRNA profiles of samples collected from three weak and three strong colonies over late winter (January 7th- March 8th, 2013) were assessed. The results presented herein suggest that the microbiomes associated with bees collected from honey bee colonies differ highly by sampling date. This is interesting because recent work by Glenny et al Flenniken., 2017 (in review,), which assessed the relationship between pathogen profiles and colony health over time, showed that the pathogen profiles of honey bee colonies also vary widely by sampling date. However, the sampling time points within this study are within the span of two months and the time points examined by Glenny et al., 216 span seven months. Also, the fact that many of the samples from the first sample date (January 7th, 2013) are highly disparate from the other samples suggests that there was an inadvertent error while preparing the samples for sequencing. Examining more samples from additional time points, potentially those with characterized pathogen profiles (i.e., Glenny et al. 2017, in review), will provide greater understanding on the relationship between sample date and honey bee microbome. Interestingly, Bifidobacteriaceae consistently exhibited higher relative abundance in strong colonies as compared to weak colonies within time points 2 and 3. A previous study also observed a negative correlation between Bifidobacterium and pathogen abundance in samples collected from honey bee colonies4. However, higher Bifidobacteriaceae was not observed the first time point, and the relative abundance of Bifidobacteriaceae was less than 2-fold higher in strong as compared to weak colonies, so the difference is not striking. FUTURE DIRECTIONS AND PROJECT IDEAS Although the results presented herein do not strongly support that colony microbiome relates to health, it would be prudent to examine the 16S rRNA profiles of additional longitudinally collected samples from weak, average, and strong colonies. The sampling size for this study was small (n=3). Perhaps, increasing sample size will provide further support that Bifidobacteriaceae abundance is related to colony health. Also, perturbing the microbiome via antibiotic treatment (as presented in Appendix D) may lead to interesting results. 217 References Cited 1. McMenamin, A. J. & Genersch, E. Honey bee losses and associated viruses. Current Opinion in Insect Science 8, 121–129 (2015). 2. Daughenbaugh, K. F. et al. Honey Bee Infecting Lake Sinai Viruses. Viruses 7, 3285–3309 (2015). 3. Oksanen, J. Multivariate analysis of ecological communities in R: vegan tutorial. R Packag. version (2011). 4. Mattila, H. R., Rios, D., Walker-Sperling, V. E., Roeselers, G. & Newton, I. L. G. Characterization of the active microbiotas associated with honey bees reveals healthier and broader communities when colonies are genetically diverse. PLoS One 7, e32962 (2012). 218 APPENDIX D Assessing The Effect Of Tylosin On Honey Bee Microbiome And Pathogen Profile 219 OVERVIEW Tylan (tylosin) is an antibiotic that targets Gram-positive bacteria and is commonly utilized as a management tool to prevent to colony loss associated with infection by American Foulbrood (AFB)(Paenibacillus larvae ssp. larvae). Once a colony has AFB, beekeepers must destroy the colony or shake the colony onto new equipment, burn the old frames, and burn or scorch the hive boxes. Recent work has shown that tylosin application perturbs the honey bee microbiome and makes them more susceptible to infection by Serratia kz111, an opportunistic pathogen of honeybees. However, few other studies have examined how tylosin treatment affects the prevalence and abundance of other honey bee pathogens, such as viruses. To address how tylosin affects the honey bee microbiome and pathogen abundance, we performed colony level studies in collaboration with a California- based beekeeper who applied treatments and collected samples. Ten colonies of moderate strength were treated with the label-recommended dose of Tylan, which is mixed in with divert sugar, and ten control colonies of moderate strength were treated with sugar alone three times over a three week period. Bees were sampled near the brood frames on the day of treatment 0 days post-treatment (dpt), 3 dpt, 7 dpt, and three weeks post-treatment (21 dpt), resulting in a total of 80 samples. Five bees from selected sample were dissected (i.e., head, thorax, abdomen, and gut) and PCR was utilized to screen for 16 common honey bee pathogens (i.e., LSV1, LSV2, BQCV, DWV, SBV, Crithidia, American 220 Foulbrood/Paenibacillus sp., ABPV, CBPV, IAPV, KBV, Nosema sp., Melissococcus, LSV3, LSV4, and LSV5) (See Appendix E for more details on methods). OBJECTIVE To examine the relationship between the pathogen profiles and bacteriomes of naturally infected honey bees at the colony level by experimentally manipulating the abundance of Gram-positive bacteria via antibiotic (i.e., tylosin) treatment. HYPOTHESIS Tylosin-treated bees will exhibit different bacteriome profiles and increased pathogen loads as compared to untreated controls. RESULTS AND RESEARCH IN PROGRESS To test the effects of tylsosin on honey bee microbiome and pathogen profiles, ten colonies of moderate strength were treated with Tylan and ten control colonies of moderate strength were treated with sugar alone three times over a three week period. Bees were sampled near the brood frames on the day of treatment 0 days post-treatment (dpt), 3 dpt, 7 dpt, and three weeks post- treatment (21 dpt). As a pilot assessment of the samples that were collected, we prioritized processing and assessing the pathogen profiles of bees collected from three treatment and control colonies at 0 days post-treatment (dpt) and 21 dpt. Currently, three control samples 0 and 21 dpt and two treatment samples 221 0 and 21 dpt have been screened for common honey bee pathogens (i.e., LSV1, LSV2, BQCV, DWV, SBV, Crithidia, American Foulbrood/Paenibacillus sp., ABPV, CBPV, IAPV, KBV, Nosema sp., Melissococcus, LSV3, LSV4, and LSV5). The most updated information is provided in an Excel file labeled “Pathogen Screening Results for T.R.O. 5-18-15” and it is located in the Flenniken lab Dropbox -> /Dropbox/Flenniken Lab/DATA Oct 2012-/Randy Oliver 2015 Tylosin Field Trials. In light of the fascinating study recently published showing the effects of tylosin treatment on honey bee microbiome and defense against Serratia1, it will be exciting to further examine the samples from the tylosin project described herein. Specifically, the next steps of the project are to process and to assess the pathogen profiles more samples to obtain data from a total of five treatment and five control colonies at 0 and 21 dpt. It will be interesting to see if there is a difference in pathogen profiles between treatment and control colonies at 21 dpt. Also, these samples and potentially additional samples collected between 0 and 21 dpt will then be 16S rRNA sequenced. 222 References Cited 1. Raymann, K. et al. Antibiotic exposure perturbs the gut microbiota and elevates mortality in honeybees. PLos Biology 15, 1–22 (2017). 223 APPENDIX E Honey Bee Microbiome Methods 224 Sindbis virus (SINV-GFP) infection trials Frames of newly emerging bees were obtained from honey bee colonies maintained at Montana State University in Bozeman, MT, USA. Young (~ 24 hours post-emergence) female worker bees were utilized for experiments. Older nurse bees were collected from across the top of the upper most box of the same colony using a modified “bee collecting vacuum”. The bees were housed in modified deli-containers at 32ºC and provided water and bee candy (i.e., powder sugar and corn syrup) for the duration of the experiment. Honey bees were immobilized via incubation at 4ºC for 20 minutes and injected in the thorax with 3,750 plaque forming units (PFUs) of SINV-GFP diluted in 2 µl of 10 mM Tris buffer (pH 7.5) using a Harbo large capacity syringe equipped with disposable needles (Honey Bee Insemination Service; http://www.honeybeeinsemination.com/equipment2.html). The needles were prepared from borosilicate capillary tubes (0.8-1.10 x 100 mm) with a micropipette puller (Narishige Model PC-10, East Meadow, New York, USA). After injection, bees typically recovered after 5 minutes at room temperature. Mock-infection controls were also performed. Bees were collected at 24, 48, or 72 hours post-infection (hpi). 2015 Tylosin Field Treatment The recommended dosage of Tylan (tylosin) is to mix 200mg Tylan to 20g powdered sugar and apply this mixture to the brood combs weekly for three weeks. Ten colonies of moderate strength were treated with the Tylan mixture 225 and ten control colonies of moderate strength were treated with sugar alone. Bees (~50-100) were sampled from the frames near the brood area on the day of treatment (0 days post-treatment), three days post-treatment (3 dpt), seven days post-treatment (7 dpt) and three weeks post-treatment (21 dpt), resulting in 80 sample collections total. Samples were collected into plastic bags or tubes, immediately put on dry ice, and stored at −80 ◦C until RNA extraction. Samples were obtained from a frame containing brood, which likely contained bees from all different castes, including nurse, mid-age, or field bees. Nurse bees are best identified by the presence of yellow colored material (pollen) in their guts, whereas the guts of mid-age house bees are duller in color. Foragers are more easily detectable by the presence of pollen in the pollen baskets located on their legs. Presence of any of these traits during dissection was recorded. Five bees from each sample were then dissected and homogenized following the protocol for processing individual bees below. PCR-based pathogen screening (i.e., LSV1, LSV2, BQCV, DWV, SBV, Crithidia, American Foulbrood/Paenibacillus sp., ABPV, CBPV, IAPV, KBV, Nosema sp., Melissococcus, LSV3, LSV4, and LSV5) was performed for each bee following methods below. 2013 Colony-level and field collection of honey bees during almond pollination In order to investigate the relationship between colony health and pathogen prevalence and abundance, honey bees (Apis mellifera) were collected from privately owned, commercially managed honey bee colonies in California. Three weak and three strong colonies were sampled three to four times: January 226 7th, January 22nd, February 10th, and March 8th in 2013. Samples were collected into plastic bags or tubes, immediately put on dry ice, and stored at −80 ◦C until RNA extraction. Five bees per colony were combined and processed together for pathogen screening following the methods below. The data describing these samples are located in Appendix C and further information is provided in Daughenbaugh et al., 20151. Honey bee homogenization Individual honey bee analysis. Individual bees were dissected into head, thorax, abdomen, and whole gut (crop, small and large intestines, and hindgut). Guts were individually homogenized in 200-400 uL sterile H2O with sterile glass beads and beating for 1.5 min (BioSpec Products 1001 Mini-BeadBeater-96 Homogenizer, Bartlesville, Oklahoma, USA). For future studies, a Qiagen TissueLyser II (Hilden, Germany) could be used to homogenize samples, which requires one sterile 4.5 mm steel ball instead of two glass beads. Combined honey bee sample analysis. Whole bee samples were prepared by combining five bees from each hive into one tube and homogenizing in 800 ul sterile H2O. Each body part, excluding heads, were individually homogenized in 200-400 µL sterile H2O with sterile glass beads and beating for 1.5 min. RNA isolation and cDNA Synthesis TRizol reagent (Invitrogen) was added to each homogenized sample and RNA was isolated according to manufacturer’s instructions. The RNA was further 227 purified using Qiagen RNeasy columns, including on-column DNase Treatment (Qiagen). RNA quality was assessed using a NanoSpec. cDNA synthesis reactions were performed with 2000 ng RNA, Moloney Murine Leukemia Virus Reverse Transcriptase (M-MLV RT) (Promega), and random hexamer primers. 16S rRNA Amplification and Sequencing A 464 bp portion of the 16S rRNA gene spanning the V3 and V4 hypervariable regions was PCR amplified with barcoded universal bacterial primers 341F and 806R from honey bee cDNA (Table 4.5). The primers were modified by adding ligation adaptors, indexes for dual-indexing, and distinct 8 bp barcodes for identifying individual samples. The PCR was performed using KAPA HiFi DNA Polymerase (Kapa Biosystems, Wilmington, MA, USA) and a PCR protocol with initial denaturation of 98°C for 45 s, followed by 25 cycles of 15 s denaturation at 98°C, 30 s annealing step at 60°C, and 30 s elongation step at 72°C, and then a final extension at 72°C for 2 min. The number of cycles was kept at 25 cycles to reduce the risk of PCR bias and error. Resultant amplicons were purified using the AxyPrep MagTM PCR Clean-up Kit (Corning, NY, USA) and quantified with an Agilent 2200 Tapestation (Santa Clara, CA, USA). The 16S rRNA libraries were then pooled in equimolar concentration, and pools were quantified using the KAPA Library Quantification Kit – Illumina (Kapa Biosystems) and sequenced with the Illumina Miseq sequencing Platform using custom sequencing primers (Table 4.5). 228 PCR pathogen screening Pathogen-specific PCR was performed according to standard methods using the primers listed in Appendix E Table 1. In brief, 1 µL cDNA template was combined with 10 pmol of each forward and reverse primer and amplified with ChoiceTaq polymerase (Denville) according to the manufacturer’s instructions using the following cycling conditions: 95 °C for 5 min; 35 cycles of 95 °C for 30 s, 57 °C for 30 s, and 72 °C for 30 s, followed by final elongation at 72 °C for 4 min. The PCR products were visualized by gel electrophoresis/fluorescence imaging. Quantitative PCR (qPCR) Quantitative PCR was utilized to examine the relative abundance of virus in each sample using previously described methods that are in accordance with published guidelines. All qPCR reactions were performed in triplicate using 2 µL of cDNA as template. Each 20 µl reaction was composed of cDNA template, 1X SYBR Green (Invitrogen, Cat.57563), 1X Choice Taq Master Mix (Denville Scientific Inc., Holliston, MA), 3 mM MgCl2, and forward and reverse primers (600 nM each). A CFX Connect Real Time instrument (BioRad, Hercules, CA) was utilized for qPCR, the thermo-profile for virus (e.g., SINV-GFP and BQCV) and Apis mellifera rpl8 analyses consisted of a single pre-incubation 95ºC (3 min), 40 cycles of 95ºC (5 s), and 60ºC (20 s). Positive and negative controls, including the use of RNA templates from no RT enzyme cDNA reactions, were included for all qPCR analyses and exhibited the expected results. 229 To quantify the viral RNA (i.e., genome and transcript) abundance in each sample, target qPCR amplicons for all target honey bee pathogens and Sindbis were cloned into the pGEM-T (Promega) vectors, as described in Flenniken and Andino et al. 20132 and Cavigli et al., 20153. Plasmid standards, containing from 109 to 103 copies per reaction, were used as qPCR templates to assess primer efficiency and generate the SINV-specific standard curve used to quantify the viral RNA copy numbers within this range of detection. The qPCR primers for RNAseq validation were designed using Primer3Plus and typically designed to have 60ºC annealing temperatures. Melt point analysis and 2% agarose gel electrophoresis ensured qPCR specificity. Primer efficiencies were evaluated using qPCR assays of cDNA and plasmid dilution series, and calculated by plotting log10 of the concentration versus the crossing point threshold (C(t)) values and using the primer efficiency equation, (10(1/Slope)-1) x 100. The honey bee gene encoding ribosomal protein 8, Am rpl8, was selected as an appropriate housekeeping gene for qPCR, since it has been utilized in several other studies. 230 Appendix E Table 1. Primers used for PCR pathogen screening and qPCR quantification. Genome NCBI Accession No. Primer name Sequence (5'-3') Product Size (bp) Reference Acute bee paralysis virus (ABPV) NC_002548.1 qABPV F5305 (5457) GGATGAGAGAAGACC AATTG 169 Cavigli et al 2015 qAPBV R5473 GGAATAAACATTAG TTCCTATGG Black queen cell virus (BQCV) NC_003784.1 qBQCVorf2F_6664 TCCTCAAATCTGGA GCGAAC 141 Cavigli et al 2015 qBQCVorf2R_6805 GTATTCGCTGGCCG TAAAAC Chronic bee paralysis virus (CBPV) NC_010711.1 CBPV1 AGTTGTCATGGTTA ACAGGATACGAG 455 Ribiere 2002 CBPV2 TCTAATCTTAGCAC GAAAGCCGAG Deformed wing virus (DWV) AY292384.1 DWV-F (DWV-F-1170) CTTACTCTGCCGTC GCCCA 194 Cavigli et al 2015 DWV-R (DWV-R-1364) CCGTTAGGAACTCA TTATCGCG Israeli acute paralysis virus (IAPV) NC_009025.1 IAPV_F_7762 GCAGCTATTTTTGG CTGGTC Cavigli et al 2015 IAPV_R_7876 CCAATGGTACGCTC ATATCG Kashmir bee virus (KBV) NC_004807.1 KBV_F1_3603 CCGGACATTTGACT GGATTC 132 Cavigli et al 2015 KBV_R1_3735 TCCTTGGATTCACC AAGAGC Sacbrood virus (SBV) AF092924.1 SBV_F2_5120 AATGTCACCCACGA GTGTTG 125 Cavigli et al 2015 SBV_R2_5243 GCGATGCAACCATA CAACTG Nosema ceranae DQ235446.1 NosCeranFW4186chen CGGATAAAAGAGTC CGTTACC 250 Cavigli et al 2015 NosCeranREV4435che n TGAGCAGGGTTCTA GGGAT Crithidia mellificae/ Lotmaria passim PRJNA61967 mCrFw1 TCCACTCTGCAAAC GATGAC 153 Cavigli et al 2015 mCrRev1 GGGCCGAATGGAA AAGATAC Lake Sinai virus 1 (LSV1) qLSV1-F-2569 qLSV1-F-2569 AGAGGTTGCACGGCAGCATG 174 Runckel, Flenniken (2011) qLSV1-R- 2743** qLSV1-R-2743** GGGACGCAGCACG ATGCTCA Lake Sinai virus 2 (LSV2) HQ888865 qLSV2-F-1722 CGTGCTGAGGCCA CGGTTGT 225 Runckel, Flenniken (2011) GI:335057589 qLSV2-R-1947** GCGGTGTCGATCTC GCGGAC Lake Sinai Virus 3 (LSV3) JQ480620 LSV3-F-2186 CGTGAGCACGATGA GTCAGT 243 Daughenbaugh, et al. (2015) GI:386289721 LSV3-R-2429 TGGAGGTGCTTGTT GCATAA Lake Sinai virus JX878492 LSV4-F-1896 CCATCTCCTCATCC 379 Daughenbaugh, 231 4 (LSV4) ACGTTT et al. (2015) GI:512134519 LSV4-R-2278 GATTCCCAAATCAG GCTCAA Lake Sinai virus 5 (LSV5) KC880124 LSV5-F-2081 TCCGATACTCACGA CGAACA 190 Daughenbaugh, et al. (2015) GI:537367126 LSV5-R-2270 GACACGCGTCAATA TCATGG P. larvae PRJNA30269 PL2-Fw CGGGAGACGCCAGGTTAG 380 Marinez et al 2010; PL2-Rev TTCTTCCTTGGCAA CAGAGC Marinez et al 2011 Nosema spp. DQ235446.1 Nos pan rRNA F-322 GGCAGTTATGGGAAGTAACA 207 Chen et al. (2008) Nos pan rRNA R-529 GGTCGTCACATTTC ATCTCT M. plutonius PRJDA73165 MelissoF CAGCTAGTCGGTTTGGTTCC 796 Roetschi et al 2007; MelissoR TTGGCTGTAGATAG AATTGACAAT Roetschi et al 2008 Sindbis quantification and house keeping gene qPCR primers Genome/ Gene NCBI Acession No. Primer name Sequence (5'-3') Product Size (bp) Reference Ribosomal Protein L8 XM_393671.3 Rpl8Fw** TGGATGTTCAACAG GGTTCATA 100 Evans et al. (2006) Insect Mol Bio Rpl8Rev** CTGGTGGTGGACGT ATTGATAA Sindbis virus NC_001547.1 SINV-FW TAGACAGAACTGAC GCGGACGT 110 Flenniken and Andino 2013 SINV-REV TCCATACTAACTCAT CGTCGATCTC 232 References Cited 1. Daughenbaugh, K. F. et al. Honey Bee Infecting Lake Sinai Viruses. Viruses 7, 3285–3309 (2015). 2. Flenniken, M.L., Andino, R. Non-specific dsRNA-mediated antiviral response in the honey bee. PLos One 8, e77263 (2013). 3. Cavigli, I. et al. Pathogen prevalence and abundance in honey bee colonies involved in almond pollination. Apidologie, 251-266 (2015 233 APPENDIX F Table Of Live Bee Experiments From 2/5/12-9/1/16 234 Appendix F Table 1. Live honey bee experiments using newly emerged workers (NEWs) approximately 24 hours post-emergence and older, field collect house/nurse bees. All Experiments were performed at Montana State University, except for Experiment #26, which was performed at University of California, San Francisco. *sp-dsRNA is sequence-specific to Sindbis (SINV) and ns-dsRNA is nonspecific to SINV and/or the honey bee genome. Exp. Date performed Age group Treatments Time points (hpi) Hypotheses Examined General Conclusions Freezer # and Location of samples (i.e., whole bees, dissected bees, cell lysate, RNA, cDNA, etc.) Exp. #26 2/5/12- 2/6/12 (MLF at UCSF) NEWs SINV 72 Does microbiome correlate to virus load? It does not in NEWs. (n=10) (-80°C) freezer #3, shelf 1 Exp. #5 6/6/13- 6/11/13 NEWs SINV, tylosin (antibiotic) 72 Does microbiome correlate to virus load? It does not in NEWs. (n=10) freezer #1, shelf 2 Does tylosin affect virus load? not accessed Exp. #6 7/7/13- 7/10/13 NEWs, nurses SINV, tylosin (antibiotic) 72 Does microbiome correlate to virus load? In NEWs (n=5), it does not. Nurse bees infected with SINV did not have detectable virus at 72 hpi, but did have altered 16S rRNA profiles. (-80°C) freezer #1, shelf 1 Does tylosin affect virus load? NEWs given tylsosin had reduced virus, (n=6) Exp. #8 10/2/13 - 10/5/13 NEWs, nurses SINV, sp- and ns- dsRNA 6 , 72 Does dsRNA reduce virus load in bees? Both sp- and ns-dsRNA reduce virus in bees 48 and 72 hpi (n=10) (-80°C) freezer #1, shelf 3 Exp. #9 6/15/14 - 6/21/14 NEWs SINV, sp- and ns- dsRNA 6, 48, 72 Does dsRNA reduce virus load in bees? Both sp- and ns-dsRNA reduce virus in bees 48 and 72 hpi (n=10) (-80°C) freezer #3, shelf 1 Exp. #10 7/8/14- 7/10/14 NEWs SINV, sp- and ns- dsRNA 6, 48, 72 Does dsRNA reduce virus load in bees? Both sp- and ns-dsRNA reduce virus in bees 48 and 72 hpi (n=10) (-80°C) freezer #3, shelf 1 235 Exp. #11 8/5/14- 8/8/14 NEWs SINV, sp- and ns- dsRNA 6, 48, 72 Does dsRNA reduce virus load in bees? Both sp- and ns-dsRNA reduce virus in bees 48 and 72 hpi (n=10) (-80°C) freezer #3, shelf 1 Exp. #12 8/19/14 - 8/22/14 NEWs, nurses SINV, tylosin (antibiotic) 0,48, 72,1 20 Does tylosin affect virus load? not accessed (-80°C) freezer #3, shelf 1 Does tylosin affect 16S profiles in nurse bees? not accessed Exp. #15 9/8/15- 9/9/15 NEWs SINV; V. destructor proteins 6, 72 Do Varroa excretions affect virus load in bees? not accessed (-80°C) freezer #2, shelf 3 Exp. #16 6/12/16 - 6/15/16 NEWs SINV; hsp90, dcr, CDK dsRNA 24, 48, 72 Does gene KD of hsp90, dcr, and CDK affect virus loads? dsRNA KD dcr and CDK, virus went up (n=10) (-80°C) freezer #2, shelf 3 Exp. #17 7/8/16- 7/11/16 NEWs SINV; hsp90, dcr, CDK dsRNA 72 Does gene KD of hsp90, dcr, and CDK affect virus loads? dsRNA did not KD (n=10) (-80°C) freezer #2, shelf 3 Exp. #18 8/7/16- 8/10/16 NEWs SINV; hsp90, dcr, CDK dsRNA 24, 48, 72 Does gene KD and 17- AAG affect virus loads? dsRNA did not KD, 17-AAG- treated bees did not survive past 12 hours. (-80°C) freezer #3, shelf 1 Exp. #19 8/30/16 -9/1/16 NEWs SINV; dcr siRNA 48, 72 Does siRNA gene KD of dcr affect virus loads. siRNA did not KD dcr (n=10) (-80°C) freezer #3, shelf 1 236 APPENDIX G Transcriptome Analysis Pipeline 237 HONEY BEE RNASEQ ANALYSIS PIPELINE Sequencing and raw data information • At least 1 ug of total DNAsed RNA in a volume of 20-50ul of RNAse-free water was required for sequencing. • The stranded RNAseq libraries were prepared with Illumina's 'TruSeq Stranded RNA Sample Prep kit'. • Average cDNA fragment size: 230nt (range from 80nt - 700nt) • 100 nt paired-end reads sequenced on the Illumina Hiseq • The libraries were pooled in equimolar concentration and the pool was quantitated by qPCR and sequenced on one lane for 101 cycles from each end of the fragments. Programs in order of use 1. FastQC a. Can be used to assess these things in your raw fastq files: i. Number of sequences ii. Sequences flagged as poor quality iii. Sequence length iv. %GC v. Per base sequence quality vi. Per tile sequence quality vii. Per sequence quality scores viii. Per base sequence content ix. Per sequence GC content x. Per base N content xi. Sequence Length Distribution xii. Sequence Duplication Levels xiii. Overrepresented sequences 1. gives sequences that you can identify by using BLAST: http://blast.ncbi.nlm.nih.gov/Blast.cgi xiv. Sequencing Adapter Content xv. Kmer Content Example code fastqc 2b_A_72hr_CGATGT_L008_R1_001.fastq 238 2. Trimmomatic a. Used to trim Illumina adaptor sequences from reads b. See manual for more details: i. http://www.usadellab.org/cms/uploads/supplementary/Trim momatic/TrimmomaticManual_V0.32.pdf c. Uses both paired-end reads and creates four fastq files that pass trimming, Forward paired reads, Reverse paired reads, Forward unpaired reads, Reverse unpaired reads d. Use the Trimmomatic provided TruSeq3-PE-2.fa to trim the HiSeq2500 adaptor sequence. Example code trimmomatic PE -phred33 -threads 4 –trimlog 2b_48hr_trimlog 2b_48hr_ATCACG_L005_R1_001.fastq 2b_48hr_ATCACG_L005_R2_001.fastq 2b_48hr_F_paired.fastq 2b_48hr_F_unpaired.fastq 2b_48hr_R_paired.fastq 2b_48hr_R_unpaired.fastq ILLUMINACLIP:TruSeq3-PE-2.fa:2:10:10:6 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36 TOPHRED33 e. In “ILLUMINACLIP:TruSeq3-PE-2.fa:2:10:10:6”, “Illumina clip” is the program, “TruSeq3-PE-2.fa” is the file with the HiSeq2500 adaptor sequence, “2” is the seed mismatches, “10” is the palindrome clip threshold, “10” is the simple clip threshold, and “6” is the minimum length of adaptor sequence that needs to be detected in order to trim it out. i. Or, as in the manual: 1. ILLUMINACLIP::::: ii. I have had to adjust the minimum adaptor length so that it was small enough so that the adaptor was actually being trimmed (a lot of reads only have pieces of the adaptor sequence on the ends), but not so small and ambiguous that honey bee sequences were being trimmed off. f. Specifying a trimlog file (by adding “-trimlog trimlog_name“ in the code) creates a log of all read trimmings, indicating the following details: i. the read name ii. the surviving sequence length iii. the location of the first surviving base, aka. the amount trimmed from the start the location of the last surviving base in the original read iv. the amount trimmed from the end 239 3. You can use FastQC again and to look at the “Adapter Content” statistic for remnant adaptors in file. 4. Bowtie2 build a. Builds a bowtie-indexed files from a genome of choice that is in fasta format. b. Apis mellifera genome from NCBI: “GCF_000002195.4_Amel_4.5_genomic.fna” http://www.ncbi.nlm.nih.gov/genome/?term=apis%20mellifera Example code bowtie2-build ./GCF_000002195.4_Amel_4.5.fna ./ GCF_000002195.4_Amel_4.5.scaffolds bowtie2-inspect --summary ./ GCF_000002195.4_Amel_4.5.scaffolds > GCF_000002195.4_Amel_4.5.scaffolds.summary 5. Tophat2 a. Aligns reads to bowtie-indexed files b. Can simultaneously align both paired and unpaired reads if desired c. May want to choose option to create sam files instead of bam (default) depending on programs being used downstream, but Samtools can be used to convert bam to sam or vice versa later on. Example code tophat2 -p 6 --mate-inner-dist 200 --library-type fr-firststrand --max-multihits 1 -- mate-std-dev 100 -o ./tophat2_out_2b_48hr GCF_000002195.4_Amel_4.5.scaffolds 2b_48hr_F_paired.fastq 2b_48hr_R_paired.fastq, 2b_48hr_R_unpaired.fastq, 2b_48hr_F_unpaired.fastq d. Parameters used (look at Tophat2 manual for more details) i. –p= number of threads/processors being used ii. mate-inner-dist= size of insert iii. library type= indicate is library is stranded or not iv. max-multihits= limit to this many alignments to the reference for a given read v. –o name you want to give output folder vi. Follow end of code with files being aligned. Put commas between file names, unless they’re paired reads. 240 6. Genome Browsers a. Can and register for download the genome browser IGV (http://www.broadinstitute.org/software/igv/log-in) i. Is already installed on iMac by Laura ii. IGB (http://bioviz.org/igb/index.html) is also downloaded b. Allows you to visualize where your reads align to the genome. c. It requires the genome in gff format and the tophat alignment file in sam format. IGV provides some genomes but only the old 2004 honey bee genome, so I input the NCBI honey bee genome manually. d. Use it to confirm: i. library type choice looks correct. ii. Lack of widespread exact read duplicates (i.e. probably library PCR depth issues). iii. 5' / 3' density of reads, possible library or RNA quality biases. 7. Samtools a. Can be used to convert bam to sam or sam to bam if desired. IGV requires the file to be in sam format. Example code samtools view accepted_hits.bam > 3_a_accepted_hits.sam b. Bam files are often to big to process on the galaxy website 8. Cuffdiff a. http://cole-trapnell-lab.github.io/cufflinks/manual/ b. Input: two or more groups/treatments of bam files i. Use it to assess differential expression between groups/treatments in a pairwise fashion. ii. ALLOWS comparisons of two or more samples at same time c. Outputs FPKM data of reads, as well as also which genes are differentially spliced or are undergoing other types of isoform-level regulation. Example code /Users/michelleflenniken/Documents/Terminal_Programs/cufflinks- 2.2.1.OSX_x86_64/cuffdiff --num-threads 2 --library-type fr-firststrand GCF_000002195.4_Amel_4.5_genomic.gff 2b_48hr.bam,2d_48hr.bam,2g_48hr.bam 3a_48hr.bam,3h_48hr.bam,3f_48hr.bam -o cuffdiff_48hr_buffer_vs_virus 241 d. List of output files: i. bias_params.info ii. cds_exp.diff iii. cds.count_tracking iv. cds.diff v. cds.fpkm_tracking vi. cds.read_group_tracking vii. gene_exp.diff 1. Gene-level differential expression. Tests differences in the summed FPKM of transcripts sharing each gene_id viii. genes.count_tracking 1. Gene read group tracking. Tracks the summed expression and counts of transcripts sharing each gene_id in each replicate ix. genes.fpkm_tracking x. genes.read_group_tracking xi. isoform_exp.diff 1. Transcript-level differential expression. xii. isoforms.count_tracking xiii. isoforms.fpkm_tracking xiv. isoforms.read_group_tracking xv. promoters.diff xvi. read_groups.info xvii. run.info xviii. splicing.diff xix. tss_group_exp.diff xx. tss_groups.count_tracking xxi. tss_groups.fpkm_tracking xxii. tss_groups.read_group_tracking xxiii. var_model.info e. The link to how to decipher some of the terms in the sheet is in this link: http://cole-trapnell- lab.github.io/cufflinks/cuffdiff/index.html#fpkm-tracking-files f. Cuffdiff uses FPKM statistics while other programs may use RPKM, what’s the difference? (cuffdiff Q&A) i. RPKM is Reads Per Kilobase of transcript per Million mapped reads. FPKM is Fragments Per Kilobase of transcript per Million mapped reads. In RNA-Seq, the relative expression of a transcript is proportional to the number of cDNA fragments that originate from it. Paired- end RNA-Seq experiments produce two reads per fragment, but that doesn't necessarily mean that both reads will be mappable. For example, if the second read is of poor 242 quality. If we were to count reads rather than fragments, we might double-count some fragments but not others, leading to a skewed expression value. Thus, FPKM is calculated by counting fragments, not reads. 9. CummeRbund a. Graphically interpret Cuffdiff results in R. b. CummeRbund takes the various output files from a cuffdiff run and creates a SQLite database of the results describing appropriate relationships between genes, transcripts, transcription start sites, and CDS regions. Once stored and indexed, data for these features, even across multiple samples or conditions, can be retrieved very efficiently and allows the user to explore sub- features of individual genes, or gene sets as the analysis requires. We have implemented numerous plotting functions as well for commonly used visualizations. c. From manual (http://bioconductor.org/packages/2.11/bioc/vignettes/cummeRbun d/inst/doc/cummeRbund-manual.pdf ) i. CummeRbund begins by re-organizing output files of a cuffdiff analysis, and storing these data in a local SQLite database. CummeRbund indexes the data to speed up access to specific feature data (genes, isoforms, TSS, CDS, etc.), and preserves the various relationships between these features. Access to data elements is managed via the RSQLite package and data are presented in appropriately structured R classes with various convenience functions designed to streamline your workflow. This persistent database storage means that inter-connected expression values are rapidly accessible and quickly searchable in future analyses. d. http://compbio.mit.edu/cummeRbund/ e. http://cole-trapnell-lab.github.io/pdfs/papers/trapnell-tuxedo- protocol.pdf 10. DAVID (http://david.abcc.ncifcrf. gov) and AmiGO (http://www. geneontology.org) a. Nature protocol: http://www.nature.com/nprot/journal/v4/n1/pdf/nprot.2008.211.pdf 243 ***In addition to the above pipeline if you want to do more strenuous statistical analysis 11. HTseq a. Another way to use .bam files to create a raw (not normalized) count gene table b. HTSeq only works with just paired files or just unpaired files, so if we really want to move onto HTSeq, we may have to align paired and unpaired reads separately or just focus on paired reads. c. Also the bam files need to be sorted by name with samtools. Example code samtools sort -n accepted_hits.bam name_sorted_3_a_accepted_hits.bam python -m HTSeq.scripts.count -f bam -s reverse -o SAMOUT.2 -r name -i ID name_sorted_3_a_accepted_hits.bam.bam GCF_000002195.4_Amel_4.5_genomic.gff>sub.set.counts.2 12. DESeq (download as library in R) a. http://bioconductor.org/packages/release/bioc/manuals/DESeq/ma n/DESeq.pdf b. May be used for more statistically complex analysis of differential expression. c. Less sensitive than Cuffdiff 244 APPENDIX H Bacteriome/16S rRNA Sequencing Analysis Pipeline 245 WHEAT STEM SAWFLY 16S rRNA SEQUENCING ANALYSIS PROTOCOL Sequencing prep and raw data information • Sequencing libraries were prepared using PCR amplification with barcoded 16S rRNA primers. • PCR products should be ~563 bp. • 16S rRNA libraries pooled in equimolar concentration. Up to 184 libraries can be pooled based on number of index combinations possible (see Table 4.5). • 2 x 250 nt paired-end reads were sequenced on the Illumina MiSeq (located in Animal BioSciences) with V2 500-cycle kits (yields up to 15 million reads). Sequencing pools were loaded at 8 pm concentration, spikining in 5% PhiX. Programs in order of use 13. Hannon Fastx toolkit a. http://hannonlab.cshl.edu/fastx_toolkit/commandline.html b. Suite of tools that can be used to modify raw fastq and fasta files. c. For example, it is important to screen the raw fastq for reads that are short or low in sequencing quality, so the quality trimmer can be used for this: Example code fastq_quality_trimmer -Q33 -t 30 -l 150 -i [raw sequences] -o [new file] Parameters for quality trimmer [-h] = This helpful help screen. [-t N] = Quality threshold - nucleotides with lower quality will be trimmed (from the end of the sequence). [-l N] = Minimum length - sequences shorter than this (after trimming) will be discarded. Default = 0 = no minimum length. [-z] = Compress output with GZIP. [-i INFILE] = FASTQ input file. default is STDIN. [-o OUTFILE] = FASTQ output file. default is STDOUT. [-v] = Verbose - report number of sequences. d. Downstream analyses may require that the sequence files are in fasta format. To convert the fastq to fasta the tool Example code fastq_to_fasta [-h] [-r] [-n] [-v] [-z] [-i INFILE] [-o OUTFILE] Parameters [-h] = This helpful help screen. [-r] = Rename sequence identifiers to numbers. [-n] = keep sequences with unknown (N) nucleotides. [-v] = Verbose - report number of sequences. 246 2. RDP classifier a. You can do preliminary taxonomic analyses of sequencing reads prior to OTU clustering. (And/or classify OTUs after clustering) b. RDP is a naive Bayesian classifier that can rapidly and accurately provide taxonomic assignments from domain to genus, with confidence estimates for each assignment. Output files can be opened in Excel. c. Requires fasta files, so fastq files need to be converted to fasta with Hannon Fastx toolkit prior to RDP classification. Example code java -Xmx1g -jar rdp_classifier-2.12.jar –q sample.fasta -o rdp_otput.txt -f fixrank Steps 3-22 will performed in Mothur https://mothur.org/wiki/MiSeq_SOP 3. Assemble contigs with forward and reverse sequences. a. There are a few options for contig assembly: i. Mothur “make.contigs” command. ii. PANDAseq iii. FLASh b. The Mothur command is reported to be not very good compared to other programs because it doesn’t properly assemble forward and reverse sequences with an overlap (for which my 16S F&R sequences should overlap by ~47 nt), resulting in many more contigs that are filtered out later. I wasn’t aware of this when performing the pipelines, so I used Mothur to assemble contigs. c. It requires an association file. See website for directions. Example code ./mothur make.contigs(file=[association file], processors=4, deltaq=1) d. The deltaq is the difference in Q-Scores that are necessary for one base in one read to be called over a disagreement from the other read. 4. Next, look in the summary of the file to see the total number of sequences there are, their size distributions, number of ambiguous characters, and number of homopolymers (typically a problem with 454 sequencing). Example code summary.seqs(fasta=[sample set].contigs.fasta) 247 5. Condense the data set using the mothur ‘‘unique.seqs’’ command to generate a non-redundant set of sequences. Example code unique.seqs(fasta=[sample set].contigs.fasta)) 6. Next, create a counts file. Example code count.seqs(name=[.names file], group=[groups file]) 7. Align the unique sequences to an adaptation of the Bacterial SILVA SEED database using the mother “align.seqs” command. Example code align.seqs(fasta=[.unique.fasta file] , reference=silva.bacteria.fasta) 8. Quality check alignment Example code summary.seqs(fasta=current, count=current) 9. Remove sequences that started after 6,500 nt or end before 25,000 nt, have homopolymers longer than 10nt, more than 2 ambiguous nt, and/or lengths shorter than 400 nt. The start and end parameters may need to be adjusted based on samples. Example code screen.seqs(fasta=[.unique.align.fasta] count=[count_table table], start=6500, end=25000, maxhomop=10, maxambig=2, minlength=400) 10. Look at what sequences are left after screening. Example code summary.seqs(fasta=[. good.align file], count=[.good.count_table file], diffs=2) 11. Filtered the sequences for any common gaps and extraneous overhangs using the mother “filter.seqs” command. Example code filter.seqs(fasta=[.good.align file) 12. To reduce the effect of sequencing error, the sequence can be preclustered based on 1-2 nt differences using the “pre.cluster” command. 248 Example code pre.cluster(fasta=[.good.filter.fasta file], count=[.good.count_table file], diffs=2) 13. Detect and remove chimeric sequences with the “chimera.uchime” (mothur’s implementation of UCHIME) and “remove.seqs” commands. Example code chimera.uchime(fasta=[.precluster.fasta], count=[.precluster.count_table], dereplicate=t) remove.seqs(fasta=[.precluster.fasta], accnos=[.accnos file]) 14. Remove sequences of mitochondrial or chloroplast origin or sequences that cannot be taxonomically classified. Example code classify.seqs(fasta=[.pick.fasta], count=[. pick.count_table], template=[rdp classification file], taxonomy=[.taxonomy file], cutoff=60) remove.lineage(fasta=[.pick.fasta], count=[.pick.count_table], taxonomy=[.taxonomy], taxon=unknown) 15. Operational taxonomic unit (OTU) clustering a. There are two options in Mothur for OTU clustering: i. Dist.seqs 1. Calculates uncorrected pairwise distances between aligned DNA sequences. 2. Needs to be followed by cluster command, which assigns sequences to OTUs. Example code dist.seqs(fasta=[.good.filter.fasta file], calc=onegap, cutoff=.10, output=lt) cluster(phylip=[.dist file], method=nearest, cutoff=.10) ii. Cluster.split 1. Bins sequences based on taxonomy (as assigned by the classify.seqs command) and creates OTUs within each bin. Example code cluster.split(fasta=final.fasta, count=final.count_table, taxonomy=final.taxonomy, taxlevel=6, method=average, cutoff=0.1) b. Dist.seqs is good for small data sets and produces OTUs with higher quality/Matthews correlation coefficients (MCC). Cluster.split is less computationally intense and requires less memory because it clusters OTUs within each taxonomic bin. 249 16. Generate an OTU table based on 95% sequence similarity using the “make.shared” command. Example code make.shared(list=[.list file], count=[.pick.count_table], taxonomy=[.taxonomy file], label=0.05) 17. Using the remove.rare command, you can remove singleton OTUs. Example code mothur > remove.rare(shared=[.shared file], nseqs=2) 18. Estimate community diversity, species/OTU richness, and sequencing coverage (e.g., Shannon’s index of diversity, Chao richness, Good’s coverage) of each sample’s microbiome. Example code summary.single(shared=[.shared file], calc=Shannon-sobs-chao-coverage) 19. Count number of resultant reads in sample using Mothur. Example code count.groups(shared=[.shared file]) 20. At this point, you will probably want to normalize the reads to the sample with the fewest number of reads or exclude samples that have too few reads. You can do both by performing the “sub.sample” command in Mothur. Samples that do not have the required # of reads are eliminated from the shared output file. You can than follow that again with the summary.single command. Example code sub.sample(shared= [.shared file] , size=[# of reads you want to normalize to] ) 21. You can check what the rarefaction curve looks like after sample for a certain number of reads. Example code rarefaction.single(shared=[.shared file], calc=sobs, freq=[read sample #]) 22. Next, create a fasta file containing only single representative sequences for each OTU. You can then taxonomically classify each OTU by running the fasta through RDP classifer (Step 2). Example code dist.shared(shared=association_sheet.trim.contigs.unique.good.filter.precluster .pick.an.unique_list.shared.0.05.subsample.txt, groups=all) 250 get.oturep(column=[distance file from dist.shared], name=[names file], fasta=amazon.unique.fasta, list=98_sq_phylip_amazon.fn.list) 23. You can then use the Vegan package in R. a. See site for instructions. cc.oulu.fi/~jarioksa/opetus/metodi/vegantutor.pdf 251 APPENDIX I Metagenomic Sequencing And Genome Assembly Analysis Pipeline 252 WHEAT STEM SAWFLY METAGENOME ANALYSIS PROTOCOL Sequencing prep and raw data information • Sequencing libraries were prepared with Illumina's Nextera XT kit. • Average insert size: 200nt • The libraries were pooled in equimolar concentration, with three libraries per pool. • 2 x 150 nt paired-end reads were sequenced on the Illumina MiSeq (located in Animal BioSciences) with V2 300-cycle kits (yields up to 15 million reads). Sequencing pools were loaded at 14 pm concentration. • Main goal is to assemble reads belonging to Spiroplasma genomes Programs in order of use 14. FastQC a. Can be used to assess these things in your raw fastq files: i. Number of sequences ii. Sequences flagged as poor quality iii. Sequence length iv. %GC v. Per base sequence quality vi. Per tile sequence quality vii. Per sequence quality scores viii. Per base sequence content ix. Per sequence GC content x. Per base N content xi. Sequence Length Distribution xii. Sequence Duplication Levels xiii. Overrepresented sequences xiv. Sequencing Adapter Content xv. Kmer Content Example code fastqc 2b_A_72hr_CGATGT_L008_R1_001.fastq 15. Trimmomatic a. Used to trim Illumina adaptor sequences from reads b. See manual for more details: i. http://www.usadellab.org/cms/uploads/supplementary/Trim momatic/TrimmomaticManual_V0.32.pdf c. Uses paired-end reads and arrange reads into to paired and singleton files (one each for forward and reverse reads for total of four files). d. Use the Trimmomatic provided NexteraPE-PE.fa to trim the Nextera kit adaptor sequence. 253 Example Trimmomatic code trimmomatic PE -phred33 Spiro-pellet_S1_L001_R1_001.fastq Spiro- pellet_S1_L001_R2_001.fastq paired_Spiro-pellet_S1_R1.fq unpaired_Spiro- pellet_S1_R1.fq paired_Spiro-pellet_S1_R2.fq unpaired_Spiro-pellet_S1_R2.fq ILLUMINACLIP:./NexteraPE-PE.fa:2:10:10:6 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:30 e. ILLUMINACLIP::::: f. Specifying a trimlog file (by adding “-trimlog trimlog_name“ in the code) creates a log of all read trimmings. 16. You can use FastQC again and to look at the “Adapter Content” statistic for remnant adaptors in file. 17. Bowtie2 build a. Builds a bowtie-indexed files from a genome of choice that is in fasta format. b. Wheat stem sawfly (Cephus cinctus) genome from NCBI: “GCA_000341935.1” https://www.ncbi.nlm.nih.gov/genome/?term=cephus+cinctus Example code bowtie2-build ./ GCA_000341935.1_Ccin1_genomic.fna ./ WSS.scaffolds bowtie2-inspect --summary ./ WSS.scaffolds > WSS.scaffolds.summary 18. Bowtie2 (aligns reads to bowtie-indexed files) a. Simultaneously align both paired and unpaired reads if desired b. Allows filtering out of host sequence or selection of target sequence. c. The current wheat stem sawfly genome contains many sequences that are likely Spiroplasma because they have the highest sequence similarity on NCBI with genes from other Spiroplasma genomes, including “PREDICTED: Cephus cinctus protein RecA- like (LOC107274328), mRNA, XM_015753352.1”, so this step should be skipped until a new genome version is available. Example code bowtie2 -x WSS.scaffolds -f -1 HighlandAdult8_S3_L001_R1_001.fasta -2 HighlandAdult8_S3_L001_R2_001.fasta -S HLA8.sam -L 20 --un-conc HLA8.fasta$ --al- conc-gz HLA8hostreads 19. Concantenate sequencing files prior to assembly a. Improve chances of assembling longer Spiroplasma reads 254 Example code cat unpaired_HighlandAdult6_R1.fq unpaired_HighlandAdult7_R1.fq unpaired_HighlandAdult8_R1.fq unpaired_TRY1_HLA8_R1.fq unpaired_TRY2_HLA8_R1.fq unpaired_TRY3_HLA8_R1.fq > unpaired_HLA678_R1.fq 20. SOAPdenovo a. Assemble reads de novo (without template). Example code SOAPdenovo-63mer all -s ./HLA678.config.txt -K 21 -f -F -R -N 1500000 -o soap_HLA678_output Examples of parameters that can be adjusted within the Configuration files: • max_rd_len=maximal read length • avg_ins=average insert size • reverse_seq=Reverse sequences • asm_flags=3, produce scaffolds and contigs • pair_num_cutoff=cutoff of pair number for a reliable connection • map_len=minimum aligned length to contigs for a reliable read locatio Example Configuration file: “HLA678.config.txt” [LIB] max_rd_len=300 avg_ins=200 reverse_seq=0 asm_flags=3 rank=1 pair_num_cutoff=3 map_len=32 rank=2 q1=/home/laura/2014_nextera_WSS_fastq/paired_HLA678_R1.fq q2=/home/laura/2014_nextera_WSS_fastq/paired_HLA678_R2.fq [LIB] max_rd_len=300 avg_ins=200 reverse_seq=0 asm_flags=3 rank=1 pair_num_cutoff=3 map_len=32 rank=2 q=/home/laura/2014_nextera_WSS_fastq/unpaired_HLA678_R1.fq [LIB] max_rd_len=300 avg_ins=200 reverse_seq=0 asm_flags=3 rank=1 pair_num_cutoff=3 map_len=32 rank=2 q=/home/laura/2014_nextera_WSS_fastq/unpaired_HLA678_R2.fq 255 21. Blastn from the blast+ package from the NCBI a. Select for reads that have high sequence similarity with other Spiroplasma genomes that have been concatenated into one fasta file “Spiroplasma_genomes.fasta”. b. Can output information in several different formats. I selected “6” because it can be opened as an excel and will give you a list of sequences that have sequence similarity with the database file (In this case the Spiroplasma genomes file). Example code makeblastdb -dbtype nucl -in spiroplasma_genomes.fasta –out spiroplasma blastn -db spiroplasma -query soap_HLA678_output.contigs -outfmt 6 -perc_identity 60 -out soap_HLA678_blastn c. The output only contains the list of sequences that have the specified sequence similarity, so more code will need to be used in order to isolate these sequences into another file. d. Here is the link to some Perl code to extract sequences by their ID from a FASTA file i. https://edwards.sdsu.edu/research/perl-one-liner-to-extract- sequences-by-their-identifer-from-a-fasta-file/ Example code perl -ne 'if(/^>(\S+)/){$c=grep{/^$1$/}qw(id1 id2)}print if $c' fasta.file e. This will extract the two sequences with the sequence idenfiers id1 and id2. One only has to change the identifiers within the parentheses and separate them by space to extract the sequences you need. f. Just copy and paste the sequence names from the Blastn output into the area between the parantheses and enter the code into Terminal. g. This will result in the output on Terminal for which you will have to copy and paste the results into a new text file. 22. Concatenate all resultant files that passed Blastn analysis and do another SOAPdenovo assembly. 256 23. Annotating resultant SOAPdenovo contigs a. There are couple options for annotating: b. 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